Pardee Wiki - User contributions [en]

Change Selected Functions

2026-02-16T21:57:18Z

Taylor.Hanna:

Variables are forecast based on mathematical relationships that are represented by functions within IFs. These functions can be changed based on different understandings of relationships between variables. Changing functions gives you a powerful tool for using IFs to investigate possible futures and, to an extent, to change the model itself. Change the relationships between two variables or multiple variables using the ''Bivariate'' and ''Multivariate function'' pages.

= Bivariate Functions =

To open the ''Bivariate Functions'' page from the Main Menu of IFs: choose '''Scenario Analysis''', '''Change Selected Functions''', and click '''''Bivariate Function.''''' Once on the ''Bivariate Function'' page, search for a particular function in the '''Filter Functions (press enter)''' field or scroll through the '''Function (click to see/edit)''' dropdown to view a relationship that is already in IFs. Use the filter options ('''Used in Run''', '''Used in Historical Run,''' etc.) to the right of the dropdown to filter for functions that are used for particular model tasks, or not used at all.
[[File:Example of Bivariate Function page.png|center|thumb|950x950px|An example of the Bivariate Function page used to change functions in IFs.]]
When a function is clicked, the relationship of the function will be shown in graph form at the bottom of the screen. Depending on the type of function, an R-squared and standard error of the relationship may also be shown below the graph. Export the graph by clicking on the three lines in the upper right corner of the graph, then select the desired format to export.

To change a chosen function, use the '''Table Functions Points''' frame option or the '''Analytic Function''' button option to specify a new relationship.

'''Table Functions Points''': A table function relationship is based upon lines that connect given points. In the '''Table''' '''Function Points''' frame, there are boxes for specifying the X-axis and Y-axis values. Specify a desired point by entering the X and Y axis values, then click '''Add'''. Add as many points as desired, and click '''Previous''' and '''Next''' to move between already created points. Click '''Delete''' to remove a point from the graph. When the points desired to create a function with are all added, click '''Alter''' to alter the function to now be based on this new relationship.

'''Analytic Function''': Click to open the ''Specify Analytic Function'' page. Use this page to change the mathematical formula of a function by adjusting its function terms. A function based on a linear regression will only have a and b1 terms, and altering other bs will change the whole function type. More complex functions will use different b terms, for example, an exponential will use b2 and a logarithmic b4.
[[File:Example of the Specify Analytic Function page.png|center|thumb|950x950px|An example of the Specify Analytic Function page, for GDP and car sales.]]
Change the '''Display Figure from X Value''' and '''To X Value''' fields to change the graph in the ''Bivariate Functions'' screen. Once the function is changed as desired, click '''Save and Continue''' to save and return to the ''Bivariate Functions'' page or '''Exit - Abandon Changes''' to return without saving the change.

Click '''Reverse Changes''' to return to the default for the selected function, '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Exit''' to return to the ''Main Menu''. After clicking '''Exit,''' IFs gives a very important informational warning on how any function changes will be active only in the current session of IFs. Starting IFs again resets all functions to standard values. Moreover, although runs of the model that you make with altered functions will reflect the changes in functions, no information about the changed functions is saved with the model Run Files. Keep track of these changes somewhere for replicability.

Now [[Running the Model|run the model]] using the [[Quick Scenario Analysis with Tree]] option from the Main Menu. Once the model has run, a [[Understand Scenarios in IFs#Scenario Files and Run Files|new working file]] is loaded that reflects the changed functions and their impact on all computations. Compare this working file (or a saved run file version of it) with the base case or with other scenarios.

= Multivariate Functions =

To open the ''Multivariate Functions'' page from the Main Menu of IFs: choose '''Scenario Analysis''', '''Change Selected Functions''', and click '''''Multivariate Functions'''.'' This page displays a table with multivariate function names, their intercept, R-squared, and standard errors. Search for a particular function using the search bar or scroll through the table to find the desired function. Use the filter options ('''Used in Run''', '''Used in Historical Run,''' etc.) to the right of the table to filter for functions that are used for particular model tasks, or not used at all.
[[File:Example of Multivariate Functions page.png|center|thumb|950x950px|An example of the Multivariate Functions page.]]
Click on any intercept to change the intercept term or on any function name to alter the other terms used in the function. A new page will open, where changes to function terms can be made.
[[File:Example of Change Multivariate Function Sreen.png|center|thumb|950x950px|An example of the Change Multivariate Function screen for Bilateral Aid Exports.]]
Click on the desired b coefficient to change the value for that coefficient. The b coefficients are based upon the standard function shown above the table; for example, b4 is for logarithmic functions. Once clicked, enter the desired value, then click '''Continue''' to save that term and repeat for each coefficient term desired to change. Then click '''Continue''' to save and update the function. Remember, starting IFs again resets all functions to standard values, and although runs of the model that you make with altered functions will reflect the changes in functions, no information about the changed functions is saved with the model Run Files. Keep track of these changes somewhere for replicability.

Again, [[Running the Model|run the model]] using the [[Quick Scenario Analysis with Tree]] option from the Main Menu to see the results of the altered functions. Once the model has run, a [[Understand Scenarios in IFs#Scenario Files and Run Files|new working file]] is loaded that reflects the changed functions and their impact on all computations. Compare this working file (or a saved run file version of it) with the base case or with other scenarios.

Quick Scenario Analysis with Tree

2026-02-16T21:55:51Z

Taylor.Hanna: /* Parameter Search */

The ''Quick Scenario Analysis with Tree'' page can be accessed from the Main Menu by selecting '''Scenario Analysis''' and then '''''Quick Scenario Analysis with Tree'''.'' Use this feature to develop, review, and edit scenarios in IFs. This feature allows for building and editing a Scenario File (.sce) which loads interventions to be run in the IFs model to produce a Run File (.run), which displays scenario results.
[[File:Example of the Quick Scenario Guide.png|center|thumb|950x950px|An example of the Quick Scenario Guide page in IFs when first opened.]]
This page has a number of features and options to create, load, adjust, and run scenarios. These options and features include:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Scenario Files''':
** '''Clear Tree''': Clear any interventions currently loaded to the scenario tree.
** '''Open''': Load any set of saved or preloaded Scenario Files (.sce). Hover over folder names to find and select the desired scenario. The '''Open''' drop-down folders reflect those in \IFs\Scenario file path on the current device. The scenare tree will update with the interventions in the chosen scenario file.
** '''Name and Save''': Open a pop-up to save the scenario currently reflected in the scenario tree. The pop-up includes two fields to enter text into; type the desired scenario file name and folder name in the fields, then click '''Save''' or '''Cancel'''. The two fields are:
*** '''File Name''': Enter a file name for the created scenario, which will create a scenario file named ''FileName.sce.''
*** '''Folder Name''': Enter a new or already existing folder name to save the scenario file to that location. This folder will show up in \IFs\Scenario\User Defined Scenarios\ on the current device.
** '''Export to IFs Standard CSV Format''': Save a CSV version of the scenario to the same folder that the currently displayed scenario is stored in. For newly created scenarios, click '''Name and Save''' first before clicking this option. CSV versions of scenarios can be easier to manage and understand, as all years and dimensions are aligned in titled columns, unlike Scenario Files (.sce) that are written in comma-separated text format.
** '''Export Working File''': Download a Scenario File (.sce) of the current scenario tree.
** '''Import IFs Standard CSV Format files''': Load a scenario that has been saved using the '''Export to IFs Standard CSV Format''' in the scenario tree. This requires that a scenario has previously been saved using the IFs standard CSV format and located in a folder within \IFs\Scenario\. Follow the appropriate file path and click on the desired scenario CSV to import.
* '''Add Scenario Component''': Similar to '''Open''' from the '''Scenario Files''' options. Layer multiple scenario files or interventions by adding components from another scenario once one has loaded.
* '''[[Running the Model|Run Scenario]]''': Open the '''[[Running the Model|Run]]''' page for the current scenario in the scenario tree. Adjust the horizon to the desired end year and click '''Start Run'''.
* '''Delete Selection''': When an intervention is displayed on the screen next to the scenario tree from either clicking on a parameter directly in the tree or from the '''Parameter Search''' option, click '''Delete''' to remove that intervention from the current scenario.
* '''[[Country/Region, Group or G-List|Set Group or Country]]''': Click '''Groups''' to make interventions for particular groups, or '''Countries''' for a particular country or countries. The current selection will have a checkmark next to it.
* '''Annotate Scenario''': Click Annotate to open a description of the scenario interventions based on the current Scenario Tree. An annotation is generated by default, which can be edited or supplemented with explanatory or narrative information describing the scenario. To edit the annotation, click in the text box and change or add text as desired. Below the text box click '''Save''' to save edits, '''Clear''' to remove all text in the annotation, '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Exit''' to return to the ''Quick Scenario Analysis with Tree.''
* '''Parameter Search''': Click '''Search''' to open the ''Parameter Search'' page to search to find and select specific parameters. The Parameter Search feature is described below.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

= Parameter Search =

Use the ''Parameter Search'' feature to search, explore, and choose parameters to adjust for scenario analysis. Use this feature with the ''[[Guide to Scenario Analysis in International Futures (IFs)|Guide to Scenario Analysis in IFs]]'' to find the parameters most useful for the intended intervention or scenario goal. Type the desired parameter name in the search bar, then click '''Search'''. Click on the parameter, which will bring up a few options described below. Click '''Load''' to select this parameter in the scenario tree. Click '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Continue''' to go back to the previous menu or to the Main Menu of IFs.
[[File:Example of Parameter Search Option.png|center|thumb|950x950px|An example of the Parameter Search option for the total fertility rate multiplier tfrm.]]
The options, once a parameter is clicked, include:
* '''Define''': See a brief definition of the parameter and how it works, where available.
* '''Block Diagram (Pop-Up)''': Open a new tab with a wiki page displaying a diagram and text explanation that broadly explains how drivers interact for the area of inquiry (agriculture, economics, etc.), where available.
* '''Equations (Pop-Up)''': Open a new tab with a wiki page of the mathematical equations that are used to determine this parameter, where available.

= Using the Scenario Tree =
The easiest way to create scenario interventions is to use the ''[[Guide to Scenario Analysis in International Futures (IFs)|Guide to Scenario Analysis in IFs]]'' along with the Parameter Search option, described above. The Scenario Tree can also be used to find and select a parameter. Click on any of the categories in the Scenario Tree (ex., Technological change. to open sub -categories and eventually to open up a parameter choices box. Once this box appears to the right of the scenario tree with parameter names and descriptions, click on any parameter to reveal a few options shown below.
[[File:Example of Scenario Tree usage.png|center|thumb|950x950px|An example of the scenario tree being used for Technological Change, Energy, and with the QEM parameter selected; shows the options that selecting a parameter will reveal.]]
The options include:

* '''Select''': Choose the parameter in order to load and edit it.
* '''Drivers''': Click to see what variables are affecting the chosen parameter, where available.
* '''Explain (Pop-Up)''': Open a new tab with a wiki page displaying a diagram and text explanation that broadly explains how drivers interact for the area of inquiry (agriculture, economics, etc.), where available.
* '''View Equations (Pop-Up)''': Open a new tab with a wiki page of the mathematical equations that are used to determine this parameter, where available.
* '''Define''': See a brief definition of the parameter and how it works, where available.

= Adjusting Parameters =
Once a parameter is chosen by clicking '''Select''', as described above, a drop-down will appear to choose either a particular [[Country/Region, Group or G-List|country or group]]. Choose the desired country or group by clicking on it. Another drop-down will appear if the parameter has dimensions (e.g., Male, Female, Total), click on the desired dimension choice, and repeat if the parameter has multiple dimension choices. After this, the selected parameter will appear to the right of the tree with its base value or scenario value (if opened within an already made scenario) shown. An example of this screen is shown below.
[[File:Example of Parameter Adjustment on Scenario Tree.png|center|thumb|950x950px|An example of a parameter selected to adjust on the Quick Scenario Analysis Tree page. Example of the Government expenditures by destination multiplier (Health) parameter for Argentina.]]
This screen offers a few basic options for adjusting parameters. Adjust the initial parameter value to make interventions using default changes by clicking the bubble for '''High''' or '''Low''', or to return to the base, click '''Base'''. Alternately, use the slider or enter the desired number into the field above the slider and click '''Apply'''. Using this slider or field option will apply that value to the whole time horizon to the year 2150, with an initial shift period based upon the choice from the '''Shift Years''' dropdown. In the example, the value is 5.177, and the shift years are 10, so the parameter interpolates from 1 to 5.177 from 2020 to 2030 and then stays at that value for the whole horizon.

For developing high-quality scenarios, full parameter customization is recommended. Click '''Fully Customize''' for more options and to load the '''Change Values''' screen'''.'''
[[File:Example of the Fully Customize Parameter option.png|center|thumb|950x950px|An example of the Fully Customize option for parameter changes. Example of the Government expenditures by destination multiplier (Health) parameter for Argentina.]]
The '''Fully Customize''' option allows more control over parameter behavior. The '''Information''' table displays information on parameter value for a given year, and the minimum and maximum values that IFs recommends for intervention. If above or below these, a warning message will appear. Use the '''Year''' field in this table to jump to a particular year to view the value or to start an intervention from.

The '''Change Numerically''' table is where the changes to a parameter are made. Use the '''Next Year''' or '''Previous Year''' buttons to change the '''Year''' field by one. Once at the desired year to start an intervention shown in the '''Year''' field, enter in the '''Desired Value''' field the value to change the parameter to, use the '''Years to Repeat or Interpolate''' field to set the number of years for the intervention to take place (this includes the current year so to increase the value for the next 10 years, enter 11). Click '''Change/Repeat''' to apply the desired value for the number of years entered (for example, an increase to 8 for 61 years from 2090 to 2150, shown above) or click '''Interpolate''' to smoothly shift the value over the number of years entered from the current value to the desired value. Continue to use the '''Change Numerically''' table until the parameter is adjusted as desired.

To ensure that the parameter is adjusted as desired, hover over the graph on the right side of the page to see the parameter value in any given year. Click '''Register Change''' to apply this intervention to the current scenario, '''Cancel All Changes''' to clear the current parameter adjustments, '''Exit to Scenario Tree''' to return to the scenario tree, or '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]].

Running the Model

2026-02-13T21:55:24Z

Taylor.Hanna:

= Single Scenario Run =
To run the current selected or created scenario in the ''Scenario Tree'' from the ''Main Menu,'' choose '''Scenario Analysis''' and click '''Run'''.
[[File:Example of the Run option.png|center|thumb|950x950px|An example of the Run option screen.]]
Enter the desired end year for the scenario run into the text box, then click '''Start Run''' to run the model or '''Cancel''' to return to the previous page. Click '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]]. When the model begins to run, the cursor will change to the loading circle, four graphs will appear, and a message at the bottom of the screen will state "Running the Model, Please wait for the initialization to finish...." or "Running the Model...."
[[File:Example of Running the Model.png|center|thumb|950x950px|An example of the Run option while running a model.]]
While the model is running, click the '''Interrupt''' button to stop the run. Once the model has finished running, the '''Interrupt''' and '''Start Run''' buttons will be replaced by a '''Run Successful - Click to Continue''' button, and the message at the bottom of the screen will state "The Model was completed successfully". Click '''Run Successful - Click to Continue''' to return to the Mani Menu.
[[File:Example of Successful Scenario Run.png|center|thumb|950x950px|An example of the Run page when a scenario is successfully run.]]
The run file is still not yet saved, though the results can be viewed using the "Working file" scenario until starting a new session of IFs. Follow the directions in '''[[File Management]]''' to save the run file.

= Batch Run =
To run multiple saved scenarios in one process, use the ''Batch Run'' option. From the Main Menu, choose ''Scenario Analysis'' and click '''''Batch Run'''''. A screen with a list of scenario folders saved in \IFs\Scenario\ on the current device will appear. Click on the folder that houses the scenarios desired to run. Enter the desired end year for the scenario run into the text box labeled '''Run Year'''. Then click '''Select''' to run the scenarios. Click '''Continue''' to cancel the run and return to the ''Main Menu''.
[[File:Example of Batch Run page.png|center|thumb|950x950px|An example of the Batch Run option.]]The screen will begin to appear as it is loading. After a few to several minutes, a new screen with a count of which scenario has run and what year of run within that scenario will be shown. Depending on the size and number of scenarios, this could take 30 minutes or longer. When a message appears saying the runs were successful once complete, click '''Continue'''. The scenarios run using ''Batch Run'' will be automatically saved as run files and available to pull results from.

Change Selected Functions

2026-02-13T21:43:03Z

Taylor.Hanna:

Variables are forecast based on mathematical relationships that are represented by functions within IFs. These functions can be changed based on different understandings of relationships between variables. Changing functions gives you a powerful tool for using IFs to investigate possible futures and, to an extent, to change the model itself. Change the relationships between two variables or multiple variables using the ''Bivariate'' and ''Multivariate function'' pages.

= Bivariate Functions =

To open the ''Bivariate Functions'' page from the Main Menu of IFs: choose '''Scenario Analysis''', '''Change Selected Functions''', and click '''''Bivariate Function.''''' Once on the ''Bivariate Function'' page, search for a particular function in the '''Filter Functions (press enter)''' field or scroll through the '''Function (click to see/edit)''' dropdown to view a relationship that is already in IFs. Use the filter options ('''Used in Run''', '''Used in Historical Run,''' etc.) to the right of the dropdown to filter for functions that are used for particular model tasks, or not used at all.
[[File:Example of Bivariate Function page.png|center|thumb|950x950px|An example of the Bivariate Function page used to change functions in IFs.]]
When a function is clicked, the relationship of the function will be shown in graph form at the bottom of the screen. Depending on the type of function, an R-squared and standard error of the relationship may also be shown below the graph. Export the graph by clicking on the three lines in the upper right corner of the graph, then select the desired format to export.

To change a chosen function, use the '''Table Functions Points''' frame option or the '''Analytic Function''' button option to specify a new relationship.

'''Table Functions Points''': A table function relationship is based upon lines that connect given points. In the '''Table''' '''Function Points''' frame, there are boxes for specifying the X-axis and Y-axis values. Specify a desired point by entering the X and Y axis values, then click '''Add'''. Add as many points as desired, and click '''Previous''' and '''Next''' to move between already created points. Click '''Delete''' to remove a point from the graph. When the points desired to create a function with are all added, click '''Alter''' to alter the function to now be based on this new relationship.

'''Analytic Function''': Click to open the ''Specify Analytic Function'' page. Use this page to change the mathematical formula of a function by adjusting its function terms. A function based on a linear regression will only have a and b1 terms, and altering other bs will change the whole function type. More complex functions will use different b terms, for example, an exponential will use b2 and a logarithmic b4.
[[File:Example of the Specify Analytic Function page.png|center|thumb|950x950px|An example of the Specify Analytic Function page, for GDP and car sales.]]
Change the '''Display Figure from X Value''' and '''To X Value''' fields to change the graph in the ''Bivariate Functions'' screen. Once the function is changed as desired, click '''Save and Continue''' to save and return to the ''Bivariate Functions'' page or '''Exit - Abandon Changes''' to return without saving the change.

Click '''Reverse Changes''' to return to the default for the selected function, '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Exit''' to return to the ''Main Menu''. After clicking '''Exit,''' IFs gives a very important informational warning on how any function changes will be active only in the current session of IFs. Starting IFs again resets all functions to standard values. Moreover, although runs of the model that you make with altered functions will reflect the changes in functions, no information about the changed functions is saved with the model Run Files. Keep track of these changes somewhere for replicability.

Now run the model and create a new working file that reflects the changed functions and their impact on all computations in IFs. Compare this run file (or a saved version of it) with the base case or with other scenarios.

= Multivariate Functions =

To open the ''Multivariate Functions'' page from the Main Menu of IFs: choose '''Scenario Analysis''', '''Change Selected Functions''', and click '''''Multivariate Functions'''.'' This page displays a table with multivariate function names, their intercept, R-squared, and standard errors. Search for a particular function using the search bar or scroll through the table to find the desired function. Use the filter options ('''Used in Run''', '''Used in Historical Run,''' etc.) to the right of the table to filter for functions that are used for particular model tasks, or not used at all.
[[File:Example of Multivariate Functions page.png|center|thumb|950x950px|An example of the Multivariate Functions page.]]
Click on any intercept to change the intercept term or on any function name to alter the other terms used in the function. A new page will open, where changes to function terms can be made.
[[File:Example of Change Multivariate Function Sreen.png|center|thumb|950x950px|An example of the Change Multivariate Function screen for Bilateral Aid Exports.]]
Click on the desired b coefficient to change the value for that coefficient. The b coefficients are based upon the standard function shown above the table; for example, b4 is for logarithmic functions. Once clicked, enter the desired value, then click '''Continue''' to save that term and repeat for each coefficient term desired to change. Then click '''Continue''' to save and update the function.

Quick Scenario Analysis with Tree

2026-02-13T21:08:47Z

Taylor.Hanna:

The ''Quick Scenario Analysis with Tree'' page can be accessed from the Main Menu by selecting '''Scenario Analysis''' and then '''''Quick Scenario Analysis with Tree'''.'' Use this feature to develop, review, and edit scenarios in IFs. This feature allows for building and editing a Scenario File (.sce) which loads interventions to be run in the IFs model to produce a Run File (.run), which displays scenario results.
[[File:Example of the Quick Scenario Guide.png|center|thumb|950x950px|An example of the Quick Scenario Guide page in IFs when first opened.]]
This page has a number of features and options to create, load, adjust, and run scenarios. These options and features include:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Scenario Files''':
** '''Clear Tree''': Clear any interventions currently loaded to the scenario tree.
** '''Open''': Load any set of saved or preloaded Scenario-Load-Files (.sce). Hover over folder names to find and select the desired scenario. The '''Open''' drop-down folders reflect those in \IFs\Scenario file path on the current device. The scenare tree will update with the interventions in the chosen scenario file.
** '''Name and Save''': Open a pop-up to save the scenario currently reflected in the scenario tree. The pop-up includes two fields to enter text into; type the desired scenario file name and folder name in the fields, then click '''Save''' or '''Cancel'''. The two fields are:
*** '''File Name''': Enter a file name for the created scenario, which will create a scenario file named ''FileName.sce.''
*** '''Folder Name''': Enter a new or already existing folder name to save the scenario file to that location. This folder will show up in \IFs\Scenario\User Defined Scenarios\ on the current device.
** '''Export to IFs Standard CSV Format''': Save a CSV version of the scenario to the same folder that the currently displayed scenario is stored in. For newly created scenarios, click '''Name and Save''' first before clicking this option. CSV versions of scenarios can be easier to manage and understand, as all years and dimensions are aligned in titled columns, unlike Scenario-Load-Files (.sce) that are written in comma-separated text format.
** '''Export Working File''': Download a Scenario-Load-File (.sce) of the current scenario tree.
** '''Import IFs Standard CSV Format files''': Load a scenario that has been saved using the '''Export to IFs Standard CSV Format''' in the scenario tree. This requires that a scenario has previously been saved using the IFs standard CSV format and located in a folder within \IFs\Scenario\. Follow the appropriate file path and click on the desired scenario CSV to import.
* '''Add Scenario Component''': Similar to '''Open''' from the '''Scenario Files''' options. Layer multiple scenario files or interventions by adding components from another scenario once one has loaded.
* '''Run Scenario''': Open the '''[[Running the Model|Run]]''' page for the current scenario in the scenario tree. Adjust the horizon to the desired end year and click '''Start Run'''.
* '''Delete Selection''': When an intervention is displayed on the screen next to the scenario tree from either clicking on a parameter directly in the tree or from the '''Parameter Search''' option, click '''Delete''' to remove that intervention from the current scenario.
* '''Set Group or Country''': Click '''Groups''' to make interventions for particular groups, or '''Countries''' for a particular country or countries. The current selection will have a checkmark next to it.
* '''Annotate Scenario''': Click Annotate to open a description of the scenario interventions based on the current Scenario Tree. An annotation is generated by default, which can be edited or supplemented with explanatory or narrative information describing the scenario. To edit the annotation, click in the text box and change or add text as desired. Below the text box click '''Save''' to save edits, '''Clear''' to remove all text in the annotation, '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Exit''' to return to the ''Quick Scenario Analysis with Tree.''
* '''Parameter Search''': Click '''Search''' to open the ''Parameter Search'' page to search to find and select specific parameters. The Parameter Search feature is described below.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

= Parameter Search =

Use the ''Parameter Search'' feature to search, explore, and choose parameters to adjust for scenario analysis. Use this feature with the ''[[Guide to Scenario Analysis in International Futures (IFs)|Guide to Scenario Analysis in IFs]]'' to find the parameters most useful for the intended intervention or scenario goal. Type the desired parameter name in the search bar, then click '''Search'''. Click on the parameter, which will bring up a few options described below. Click '''Load''' to select this parameter in the scenario tree. Click '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]], or '''Continue''' to go back to the previous menu or to the Main Menu of IFs.
[[File:Example of Parameter Search Option.png|center|thumb|950x950px|An example of the Parameter Search option for the total fertility rate multiplier tfrm.]]
The options, once a parameter is clicked, include: These options are not available for all parameters.
* '''Define''': See a brief definition of the parameter and how it works.
* '''Block Diagram (Pop-Up)''': Open a new tab with a wiki page displaying a diagram and text explanation that broadly explains how drivers interact for the area of inquiry (agriculture, economics, etc.).
* '''Equations (Pop-Up)''': Open a new tab with a wiki page of the mathematical equations that are used to determine this parameter.

= Using the Scenario Tree =
The easiest way to create scenario interventions is to use the ''[[Guide to Scenario Analysis in International Futures (IFs)|Guide to Scenario Analysis in IFs]]'' along with the Parameter Search option, described above. The Scenario Tree can also be used to find and select a parameter. Click on any of the categories in the Scenario Tree (ex., Technological change. to open sub -categories and eventually to open up a parameter choices box. Once this box appears to the right of the scenario tree with parameter names and descriptions, click on any parameter to reveal a few options shown below.
[[File:Example of Scenario Tree usage.png|center|thumb|950x950px|An example of the scenario tree being used for Technological Change, Energy, and with the QEM parameter selected; shows the options that selecting a parameter will reveal.]]
The options include: These options are not available for all parameters.

* '''Select''': Choose the parameter in order to load and edit it.
* '''Drivers''': Click to see what variables are affecting the chosen parameter.
* '''Explain (Pop-Up)''': Open a new tab with a wiki page displaying a diagram and text explanation that broadly explains how drivers interact for the area of inquiry (agriculture, economics, etc.).
* '''View Equations (Pop-Up)''': Open a new tab with a wiki page of the mathematical equations that are used to determine this parameter.
* '''Define''': See a brief definition of the parameter and how it works.
Once a parameter is chosen by clicking '''Select''', a drop-down will appear to choose either a particular country or group. Choose the desired country or group by clicking on it. Another drop-down will appear if the parameter has dimensions (e.g., Male, Female, Total), click on the desired dimension choice, and repeat if the parameter has multiple dimension choices. After this, the selected parameter will appear to the right of the tree with its base value or scenario value (if opened within an already made scenario) shown. An example of this screen is shown below.
[[File:Example of Parameter Adjustment on Scenario Tree.png|center|thumb|950x950px|An example of a parameter selected to adjust on the Quick Scenario Analysis Tree page. Example of the Government expenditures by destination multiplier (Health) parameter for Argentina.]]
This screen offers a few basic options for adjusting parameters. Adjust the initial parameter value to make interventions using default changes by clicking the bubble for '''High''' or '''Low''', or to return to the base, click '''Base'''. Alternately, use the slider or enter the desired number into the field above the slider and click '''Apply'''. Using this slider or field option will apply that value to the whole time horizon to the year 2150, with an initial shift period based upon the choice from the '''Shift Years''' dropdown. In the example, the value is 5.177, and the shift years are 10, so the parameter interpolates from 1 to 5.177 from 2020 to 2030 and then stays at that value for the whole horizon.

For developing high-quality scenarios, full parameter customization is recommended. Click '''Fully Customize''' for more options and to load the '''Change Values''' screen'''.'''
[[File:Example of the Fully Customize Parameter option.png|center|thumb|950x950px|An example of the Fully Customize option for parameter changes. Example of the Government expenditures by destination multiplier (Health) parameter for Argentina.]]
The '''Fully Customize''' option allows more control over parameter behavior. The '''Information''' table displays information on parameter value for a given year, and the minimum and maximum values that IFs recommends for intervention. If above or below these, a warning message will appear. Use the '''Year''' field in this table to jump to a particular year to view the value or to start an intervention from.

The '''Change Numerically''' table is where the changes to a parameter are made. Use the '''Next Year''' or '''Previous Year''' buttons to change the '''Year''' field by one. Once at the desired year to start an intervention shown in the '''Year''' field, enter in the '''Desired Value''' field the value to change the parameter to, use the '''Years to Repeat or Interpolate''' field to set the number of years for the intervention to take place (this includes the current year so to increase the value for the next 10 years, enter 11). Click '''Change/Repeat''' to apply the desired value for the number of years entered (for example, an increase to 8 for 61 years from 2090 to 2150, shown above) or click '''Interpolate''' to smoothly shift the value over the number of years entered from the current value to the desired value. Continue to use the '''Change Numerically''' table until the parameter is adjusted as desired.

To ensure that the parameter is adjusted as desired, hover over the graph on the right side of the page to see the parameter value in any given year. Click '''Register Change''' to apply this intervention to the current scenario, '''Cancel All Changes''' to clear the current parameter adjustments, '''Exit to Scenario Tree''' to return to the scenario tree, or '''Help''' to open the corresponding page in the [[Main Page|Pardee Wiki]].

Self-Managed Display (Download)

2025-08-06T20:51:35Z

Taylor.Hanna:

The ''Self-Managed Display'' can be accessed from the Main Menu: click Display then '''''Self-Managed Display (Complete Flexibility)'''''. This display first opens to a page titled: ''Full Variable/Parameter Selection.''

= Full Variable/Parameter Selection =
On this page use the display option fields and other options in the menu bar to select particular scenarios and variables or parameters to display, or create new variable lists and new G-lists using the '''Variable Selection Options'''.

In the center of the page there is a search bar and a dropdown. First, choose whether you would like to view variables by Country/Region, by Group, by Decomposed Groups, or by Geography-Lists by selecting the respective option. Second, use the ''lower'' selection box, Files Available for Display, to select one or more scenarios from the [[Use IFs (Download) Scenario Analysis|scenario]] list. Then choose the variable or parameter to display: use the search bar to select particular variables or parameters to display and click the desired choice or scroll and click on a desired choice from the dropdown. Once a parameter or variable is clicked, a dropdown with countries or groups will appear; choose from the dropdown or select '''All''' to choose all countries or groups. If the chosen variable has dimensions (ex. Male, Female, and Total) a dropdown with these will appear choose from the dropdown or select '''All''' to choose all dimensions.

After choosing the desired countries or groups, and dimensions the original ''Full Variable/Parameter Selection'' page will open again but with a selection box showing the current choices at the bottom of the screen. Select another variable or parameter from the search bar or dropdown to add more to the selection box. Below this box are four display option buttons; click on a particular button depending on the intended use of ''Self Managed Display.''
[[File:Example of the Self-Managed Display.png|center|thumb|950x950px|An example of the Self-Managed Display when first opened.]]
Display Option Buttons:

* '''Clear Select''': Clear current variable or parameter choices from the selection box.
* '''Computations''': Create a unique variable from a computation of existing variables. See the section below for more information.
* '''Go to Display''': Open ''Display Menu'' page and from here choose desired display format.

Other Display Options:

* Choose [[Introduction to IFs#Geographic Representation of the World|'''Countries/Regions''', '''Groups''', '''Decomposed Groups''', or '''Geography Lists''']] (G-lists) to display desired groupings.
* '''Type name or click on name; then touch Enter key''': Select desired variable or parameter from search results or scroll in drop down.
* '''Files Available for Display (the active file is in the Text Box)''': Select the desired scenario(s).
* '''To Show a Subset of Names''': Choose a particular display category from the dropdown, to only show variables and parameters within that category.
The ''Full Variable/Parameter Selection'' page has a few other options and features from the menu bar:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.
* '''Scenario Output Files''':
** '''Explain File Numbering''': Open a pop-up with explanation to scenario numbering.
** '''Explain Scenario Analysis''': Open a pop-up with an overview of scenarios and their use in IFs.
* '''Variable Selection Options''':
** '''Explain Names''': View an explanation of the difference between variable and parameter naming.
** '''Show Variables''': Only show variables and no parameters. Will have checkmark next to it if selected.
** '''Show Variables and Parameters''': Show both variables and parameters and is chosen by default. Will have checkmark next to it if selected.
** '''Show Variable and Display Lists''': Show both variable and variable-lists (a pre-set list of certain variable(s) to display). Will have checkmark next to it if selected.
** '''History plus Forecast''': Display the historical data for variables when available. Will have checkmark next to it if selected.
** '''Create variable list from selection status box''': Create a unique variable list. See the section below for more information.
** '''Create G-list from selection status box''': Create a new G-list based upon the chosen group(s) and country(s) in the selection box. Enter a name when prompted, and the Edit G-List page will open.
** '''Edit G-list''': Edit, import, and export different G-lists. Same process as the '''Edit Group or Country/Region Members''' page.
** '''Use estimation to complete data''': Extrapolate the historical data to fill in any holes that may exist. Will have checkmark next to it if already being used.
** '''Use estimation to complete data (only holes no extrapolation)''': Fill any holes that may exist with estimations but does not extrapolate between holes.
** '''Use all available Historical Data''': provide the most up to date historical data available. Will have checkmark next to it if selected.
** '''Exclude Total when using All Dimensions''': When choosing '''All''' dimensions the total dimension will not be chosen.
** '''Group Aggregation''': Choose how to aggregate from the country to group level for variables that have an aggregation rule.
* '''Open IFsBaseHist''' or '''Open IFsBase''': Click '''Switch''' to access historical empirical data and the historical forecasts. Use this feature to see historical forecasts using IFs from 1960 to 2020 for variables to fill in missing data. Or use this feature to validate past forecasts against empirical historical data. If '''IFsBaseHist''' is currently selected then '''Open IFsBase''' will be shown and vice versa.
* '''History Variable Options''': Only available when '''Open IFsBaseHist''' is clicked first.
** '''Select from Forecasted Historical Variables''': Display variables that are forecasted from 1960-2020.
** '''Select from Empirical Historical Variables''': Display all empirical data from 1960-2020 where available.
** '''Select for Validation''': Feature is no longer available or outdated in the model.
** '''Open for Validation''': Feature is no longer available or outdated in the model.
*'''Variable Search''': Click '''Search''' to open a ''Variable/Parameter Search'' page to search for a certain variable or parameter.
*'''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

= Computations =
Click on the '''Computations''' button at the bottom of the page. A new screen will appear as displayed below. In the box, type in a formula for IFs to calculate. For example, the number of literate people within a country will be a*b/100; "a" represents the percent of literate people variable LIT, and b represents population in millions variable POP (the use of "a" and "b" are arbitrary and other letters can be used). Use standard symbols in order to distinguish different mathematical functions. Use * for multiplication, / for division, + for addition, - for subtraction, () to group terms, etc. After typing in this formula, click '''SelectVars''' to select input variables or '''Exit''' to return to the previous page.
[[File:Example of the Computations 1.png|thumb|457x457px|An example of the Computations page for creating a number of literate people variable.]]
[[File:Example of Computations Formula name prompt.png|left|thumb|410x410px|An example of the Computations page prompt to enter and formula for the number of literate people.]]

'''Formula Name, Dimensions Name, and Units Name:''' These three naming options are available but only the Formula Name is required. The Dimensions Name could be used to describe, for example, the geographic bounds of the formula. The Units Name could be used to describe the units being displayed in the formula (e.g. Millions of People). Once the preferred names are entered click '''OK'''. The original ''Computations'' page will open but with a small table added at the bottom. The variables used in the formula will be displayed in this table.

Click on a letter in the table, then select the desired variable to be used for that letter in the formula from the search and dropdown box. When prompted to select dimension, click the desired dimension or '''All''' to create a variable with these multiple dimension. When prompted to select a Country or Group choose whichever, but ensure the same choice across all variables. Do this for each letter/variable.
[[File:Example of the Computations for variable seleciton1.png|center|thumb|420x420px|An example of the Computations page with the variables selected for the number of literate people.]]

Once each letter has a variable next to it, click '''Exit''' to return to the original ''Self-Managed Display'' page. The new variable based upon the newly created formula will now be in the section box. Click '''Go to Display''' to visualize this new variable or click '''Create variable list from selection status box''' to create a new variable list from the '''Variable Selection Options''' dropdown.

Click '''Help''' on any page to be brought to the corresponding wiki.

= Create Variable List from Selection Status Box =
Use the '''Create variable list from selection status box''' option to create new variable lists, which can be accessed from other parts of IFs such as in ''[[Flexible Displays]].'' Variable lists can be sets of variables for displaying together such population and percent of literate people, or new '''Computations''' made from multiple variables, such as number of literate people.

First, ensure the desired variables for a new list from the variable drop down or from the '''Computations''' option are in the selection box. Then click Variable '''Selection Options''' and '''Create Variable list from selection status box'''. Enter the desired variable list name then click '''Save List''' or click '''Cancel''' to be brought back to the Self-Managed Display screen. The ''Edit List'' page opens once '''Save List''' is clicked; shown below.
[[File:Example of Edit List page to create to variable.png|center|thumb|950x950px|An example of the Edit List page when creating a new variable for the number of literate people.]]
Choose the desired category for the new variable list from '''Category''' dropdown, then click '''Update''' to ensure this category is updated with the new variable list. Finally click '''Save Changes''' and '''OK''' on the warning prompt. A message should appear saying the variable list was successfully saved.

Country Profile

2025-08-06T18:09:41Z

Taylor.Hanna:

The ''Country Profile'' page is unique to the  [[Main_Menu_Map_Pop-up|Main Menu Map]] and can only be accessed from the main menu: click on the desired country then click '''''Country Profile''''' from the drop down. This option shows a brief portrait of various important variables for the selected country. It displays a map of the country at the top of the form followed by a list of various current values and forecasts, organized into the following categories: Population, Health, Education, Social/Human, Economy, Energy, Environment, International Politics, and Domestic Government.

The Population variables will be opened by default. To see other variables scroll and click '''Choose''' next to the desired set of variables or click the name of the desired set from the list at the top right of the screen. To open a graph or table of a particular variable forecast click on the graph or table icon next to the variable name.
[[File:Example of Country Profile.png|center|thumb|950x950px|Example of the Country Profile page, accessed from the Main Menu pop-up. ]]
There are a few features and options to access from the menu bar at the top of the screen:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Change Country''': Choose a country to explore from the drop down.
*'''Display Options''':
**'''Horizon''': Choose from the drop down to display '''Forecast''', '''History plus Forecast''', or '''History (empirical)'''.
**'''Scenarios''': Choose the desired saved scenario to display from the drop down.
**'''Regions/Countries''': For countries with regions, choose '''All Regions/Countries''' or '''Single Region/Country''' to display the whole country or one single region. Current choice will have a checkmark next to it.
**'''Use all available Historical Data''': Provide the most up to date historical data available. Will have checkmark next to it if already being used.
*'''Map Source''': Display information on source of the display map.
*'''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

Use IFs (Download): Environment

2025-08-06T17:41:19Z

Taylor.Hanna:

Information on the data and equations used in the Environment displays can be found in the wiki page on the [[Environment]] sub-module.

= Advanced Sustainability Analysis =
The ''Advanced Sustainability Analysis'' specialized display can be reached from the Main Menu: click the '''Display''' option, the '''Specialized Display''' sub-option and then the '''''Advanced Sustainability Analysis''''' sub-sub-option. It is also located under the [[Main Menu Map Pop-up|Main Menu Map]] options.

This display shows a matrix of varying human systems use of material inputs and production of emissions. The top of the matrix is material inputs that relate most to environmental systems (fossil fuel use, water use, and deforestation) and carbon emissions. The side of the matrix is three specific human systems: GDP, population, and labor. Each of these systems shows the impact or intensity in the chosen year, the cumulative impact or intensity, and the raw values associated with each human systems growth. Below this matrix there is a table that shows the total value and percent change for GDP, population, and labor.
[[File:Advanced Sustainability Analysis Example.png|center|thumb|950x950px|Example of the Advanced Sustainability Analysis specialized display for Kenya in 2030.]]

There are a few options and features to adjust this display:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''View''': Click '''Normal''' or '''Percent Change From Base Case''' to change the way numbers are displayed. The current selection will have a checkmark next to it.
* '''Switch''': To change between countries/regions or groups.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

Display Option Fields:

* Click on a numeric cell to graph its value over time.
* '''Countries or Regions''': Choose the desired country, region, or group.
* '''Select Year''': Choose the desired year to display.
* '''Select File''': Choose the desired scenario to display.

Main Menu Map Pop-up

2025-07-18T22:05:34Z

Taylor.Hanna:

The Main Menu Map is the first thing that appears after starting and loading IFs. It shows a world map with easy access to a range of information related to the countries covered by IFs. Zoom in or out in the map by clicking on the plus or minus signs at the right of map.
[[File:Main Menu Popup example.png|center|thumb|950x950px|Example of the Main Menu Popup once a country on the Main Menu map is clicked.]]

Click on a country to open a pop-up menu with options to explore country specific information and displays. The options on the pop menu include:

POPULATION

'''Population by Age and Sex''': Open the [[Use IFs (Download): Population#Population by Age and Sex|''Population by Age and Sex'']] specialized display for the chosen country.

'''Birth Cohort Information''': Open the [[Use IFs (Download): Population#Birth Cohort Information|''Birth Cohort Information'']] specialized display for the chosen country.

EDUCATION

'''Education by Age, Sex, and Level''': Open the [[Use IFs (Download): Education#Education by Age, Sex, and Level|''Education by Age, Sex, and Level'']] specialized display for the chosen country.

HEALTH

'''Mortality by Age, Sex, and Cause''': Open the [[Use IFs (Download): Health#Mortality and Morbidity by Age, Sex, and Cause|''Mortality by Age, Sex, and Cause'']] specialized display for the chosen country.

'''Morbidity by Age, Sex, and Cause''': Open the [[Use IFs (Download): Health#Mortality and Morbidity by Age, Sex, and Cause|''Morbidity by Age, Sex, and Cause'']] specialized display for the chosen country.

DEVELOPMENT GOALS

'''SDG Overview Table''': Open the [[Use IFs (Download): Development Goals#SDG Overview Table|''SDG Overview Table'']] specialized display for the chosen country.

'''SDG Graph''': Open the [[Use IFs (Download): Development Goals#SDG Graph|''SDG Graph'']] specialized display for the chosen country.

GENERAL PERFORMANCE ANALYSIS

[[Country_Profile|'''Country Profile''']]: Display a map of the selected country and a list of current and forecasted values for a set of key variables. This is an option unique to the Main Menu Map.

'''Basic Report for Countries/Regions or Groupings''': Open the [[Basic Report (Download)|''Basic Report for Countries/Regions or Groupings'']] display for the chosen country.

'''Performance Risk Evaluation''': Open the [[Use IFs (Download): General Performance Analysis|''Performance Risk Evaluation'']] specialized display for the chosen country.

ECONOMY

'''Development Profile''': Open the [[Use IFs (Download): Economy#Development Profile|''Development Profile'']] specialized display for the chosen country.

'''Social Accounting Matrix''': Open the [[Use IFs (Download): Economy#Social Accounting Matrix|''Social Accounting Matrix'']] specialized display for the chosen country.

'''World Bank Financial Flows''': Open the [[Use IFs (Download): Economy#World Bank Financial Flows|''World Bank Financial Flows'']] specialized display for the chosen country.

WORLD VALUES

'''World Value Survey by Age''': Open the [[Use IFs (Download): World Values|''World Value Survey by Age'']] specialized display for the chosen country.

ENVIRONMENT

'''Advanced Sustainability Analysis''': Open the [[Use IFs (Download): Environment#Advanced Sustainability Analysis|''Advanced Sustainability Analysis'']] specialized display for the chosen country.

Use IFs (Download): Health

2025-07-18T21:56:37Z

Taylor.Hanna: Minor grammatical edits and approval.

Information on the data and equations used in the Health displays can be found in the wiki page on the [[Health]] sub-module.

= Mortality and Morbidity by Age, Sex, and Cause =

The ''Mortality by Age, Sex, and Cause'' and the ''Morbidity by Age, Sex, and Cause'' pages can be found from the Main Menu: choose the '''Display''' option, the '''Specialized Displays''' sub-option, and the '''''Mortality by Age, Sex, and Cause''' or'' '''''Morbidity by Age, Sex, and Cause''''' sub-sub-option. Both options are also found in the [[Main_Menu_Map|Main Menu Map]] options. The ''Mortality by Age, Sex, and Cause'' pages show the distribution of the number of deaths per 1000 people between males and females and between age groups for varying causes. The ''Morbidity by Age, Sex, and Cause'' pages show the distribution of the number of people with diseases or illnesses per 1000 people between males and females and between age groups for varying ailments.
[[File:Mortality by Age, Sex, and Cause Display Example.png|center|thumb|950x950px|An example of the Mortality by Age, Sex, and Cause Display when it is first opened, for Kenya in the year 2030.]]
There are several options and features that can adjust the display to explore the causes and distributions of mortality and morbidity on a deeper level. The ''Morbidity by Age, Sex, and Cause'' display does not have all the options featured below:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Distribution Type''': Select other types of mortality/morbidity charts to be displayed. Click on one of the following sub-options.
** '''All types of Mortality/Morbidity''': Display a distribution of the three main categories: communicable disease, non communicable disease, and injury.
** '''Communicable Disease''': Display a distribution of only communicable disease.
** '''Non communicable Disease''': Display a distribution of only non communicable disease.
** '''Injuries''': Display a distribution of only injuries.
** '''Communicable Disease Subtypes''': Display a distribution of communicable disease broken down into subtypes: AIDS, diarrhea, malaria, respiratory infections, and other communicable diseases.
** '''Non Communicable Disease Subtypes''': Display a distribution of non communicable disease broken down into subtypes: malignant neoplasms, cardio vascular, digestive, respiratory, diabetes, mental health, and other non communicable diseases.
** '''Injuries Subtypes''': Display a distribution of injuries broken down into subtypes: road traffic accidents, intentional, other unintentional.
** '''Compare Countries''': Compare four charts for different countries or groups, years, or scenarios; or any combination of the three. This option will initially bring up four charts for the country and distribution type most recently displayed for the next four 5-year increments, for example 2030, 2035, 2040, and 2045. An example of this display option is shown below for Kenya.
* '''Show Numbers''':
** '''Show Numbers''': Display the numbers that were used to create the current chart being viewed.
** '''Show Factors''': Display the scaling and normalization factors that were used to create the current chart being viewed.
* '''Set Group or Country/Region''': Click '''Countries/Regions''' or '''Groups''' to change between the two or click '''Hide Non Core Groups''' to hide or display them. The current selections will have check mark next to it.
* '''Change Display''': Display a mortality or morbidity chart for any country or group, scenario, or year in IFs by hovering over any of the three sub-options.
** '''[[Country/Region, Group or G-List|Change Countries/Regions]]''' [[Country/Region, Group or G-List|or '''Group''']]: A list will appear; scroll down and then click on the country or group of interest.
** '''Set Year''': A list of available years to display will appear; click on the desired year.
** '''Change Scenario''': Display the results from the base case or any other saved scenario runs by clicking the desired scenario.
** '''Using Deaths''': Choose '''On''' to display in total number of deaths or '''Off''' display deaths per thousand.

* '''Time''': Hover over to bring up two sub-options to click.
** '''Advance:''' Move the chart forward 5 years.
** '''Regress''': Move the chart backwards 5 years.
* '''Include oldest age group''': Choose '''On''' or '''Off.'''
* '''Change Scale''': Hover over to bring up four sub-options for different ways to adjust the scaling of Mortality or Morbidity charts.
** '''Fix Constant Scale across Time and Regions''': Set a constant scale for the number of deaths or number of people (X) axis across all years and across all countries or groups.
** '''Fix Constant Scale across Time''': Set a constant scale for the number of deaths or number of people (X) axis across all years for the currently selected country or group.
** '''Fix Constant Scale Manually''': Manually set the maximum value to be displayed for Mortality or Morbidity.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].

[[File:Example Compare Countries Mortality Display.png|center|thumb|950x950px|Example of the Compare Countries Mortality Display for Kenya.]]
At the bottom of the compare countries screen, select which country, year, and scenario desired to be displayed for each of the four graphs; after adjusting then click '''Refresh''' to change the displays. To switch to a different type of population chart to compare, click '''Distribution Type''' from the menu bar and click the desired distribution.

= Mortality Display: J-Curve =
The ''Mortality Display: J-Curve'' option can be accessed from the Main Menu: choose the '''Display''' option, the '''Specialized Display''' sub-option, and then the '''''Mortality Display: J-Curve''''' sub-sub-option. This option displays the distribution of types of mortality across various dimensions, which include grouping options, gender, years, and different scenarios. The J-Curve refers to the shape that most of the graphs produced in this display option have.
[[File:Example of the Mortality Display J-curve.png|center|thumb|950x950px|Example of the Mortality Display J-curve when first opened.]]

There are several options and features to adjust the display:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.

* '''[[Country/Region, Group or G-List|Grouping Options]]''': Change the display level by clicking '''Using''' '''Countries/Regions''', '''Using Groups''', '''Using Decomposed Groups''', or '''Using G-Lists''' sub-options. The selection for the current chart being displayed will have a checkmark next to it.
* '''Age Options''': Current selections will have a check next them.
** '''Include 100+''': Click to include age groups above 100 years.
** '''Include Infants''': Click to include the 0-1 year infant age category.
** '''Adults Only 25+''': Click to only display 25+ age groups.
** '''Use GDB Display Categories''': Use the age categories from the Global Burden of Disease Project.
* '''Display Type Options''': Choose between different display options. The current selection will have a checkmark next to it.
** '''J-Curve''': Click to display the typical mortality display for different age groups. This is the default selection.
** '''Probability of Dying as a Child 0-4 (Longitudinal)''': Click to display this specific probability over time to 2100.
** '''Child Mortality Rate per Year 0-4 (Longitudinal)''':Click to display this specific mortality rate over time to 2100.
** '''Probability of Dying as an Adult (Longitudinal)''':Click to display this specific probability over time to 2100.
** '''Adult Mortality Rate per Year (Longitudinal)''': Click to display this specific mortality rate over time to 2100.
** '''Define Adult Age (15 to 59)''': Click to adjust the starting and ending age for the ages in the adult age cohort.
** '''Life Expectancy by Age''': Click to display life expectancy for different ages.
* '''Other Display Type Options''': Click '''Use Deaths''' to display selections in the number of deaths in thousands. '''Use Deaths''' will have a checkmark next to it. Otherwise the default of deaths per thousand will be used.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].
Display Option Fields:

* '''Regions''': Select the desired country or countries, group(s), g-list(s), or decomposed group(s) from the drop down.
* '''Mortality Type:''' Select the desired mortality type(s) from the drop down.
* '''Files''': Select the desired scenario(s) from the drop down.
* '''Gender''': Select the desired gender(s) or total from the drop down.
* '''Years''': Select the desired year(s) to display from the drop down.

After adjusting the options and selecting the desired choices from the display option fields click '''Graph''' or '''Table''' to produce the desired display, or click '''Clear''' to revert back to the default selections.

= Mortality and Disability Years =
The ''Mortality and Disability Years'' display can be accessed from the Main Menu: choose the '''Display''' option, the '''Specialized Display''' sub-option, and then the '''''Mortality and Disability Years''''' sub-sub-option. This options displays years of life lost, years of living with a disability, and disability-adjusted life years for different mortality types.
[[File:Mortality and Disability Specialized Display.png|center|thumb|950x950px|An Example of the Mortality and Disability Years Specialized Display page.]]
There are several options and features to adjust the display:

*[[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.

* '''[[Country/Region, Group or G-List|Grouping Options]]''': Change the display level by clicking '''Using''' '''Countries/Regions''', '''Using Groups''', '''Using Decomposed Groups''', or '''Using G-Lists''' sub-options. The selection for the current chart being displayed will have a checkmark next to it.
* '''Change Discount Rate''': Change the discount rate on people's preferences for current over future health benefits; lower weight brings more preference to future benefits and vice versa.
* '''Age Weighting''': Choose '''On''' or '''Off''' to use age weighting, which gives more value to working age years of life lost or with a disease. Choose '''Change Beta''' to adjust the age weighting.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]].
Display Option Fields:
* '''Regions''': Select the desired country or countries, group(s), g-list(s), or decomposed group(s) from the drop down.
* '''Mortality Type:''' Select the desired mortality type(s) from the drop down.
* '''Scenarios''': Select the desired scenario(s) from the drop down.
* '''Gender''': Select the desired gender(s) or total from the drop down.
* '''Variables''': Choose '''HLYLL''' to display years of life lost, '''HLYLD''' to display years living with a disability, '''HLDALY''' to display disability-adjusted life years, and any of the three options with '''WORK''' on the end to display the given option for the working age population only.

After adjusting the options and selecting the desired choices from the display option fields click '''Graph''' or '''Table''' to produce the desired display, or click '''Clear''' to revert back to the default selections.

= Health Proximate Driver Display =

<div style="text-align: center; color: red; font-weight: bold; font-size: 200%;">
Feature is no longer available or outdated in the model.
</div>

Health Proximate Driver Display is a sub-sub-option of Specialized Display, which is a sub-option of Display, which is in turn an option on the main menu of IFs. The Health Proximate Driver Display allows the user to view the level of a given driver and the effect of that driver on the level of mortality in a [[Country/Region,_Group_or_G-List|country/group or region]] over time.

To use this application, first select one or more proximate drivers. Next, choose a country/group or region to display. Toggle through the different selections under the Grouping Options heading. After selecting a driver, other boxes may appear which offer ways to disaggregate the data represented by the driver according to gender, age, and types of access to safe water and sanitation. If displayed, the user selects the desired 2nd and 3rd dimensions, and then selects one or more files that display the trends of the data over time. The user then opts to view the data in a [[General_Display_Options#Table_Use|table]] or as a [[General_Display_Options#Graph_Use|graph]]. In Display Options, choose to view the level of the drivers, the effect on mortality of the driver, or both.

[[File:Mort1.gif|frame|center|Example of settings to display the effect of a driver on the level of mortality]]

Use IFs (Download): General Performance Analysis

2025-06-01T21:47:50Z

Taylor.Hanna: /* Performance and Risk Analysis */

= Performance and Risk Analysis =

'''''Performance and Risk Analysis''''' is located under the Specialized Display sub-option, as well as on the [[Main_Menu_Map|Main Menu Map]]. This feature is a tool to forecast the level of risk and performance of a given country or group over a time horizon of nearly one hundred years.

The first step in using this feature is to decide whether to examine a [[Country/Region,_Group_or_G-List|country or a group]]. For this example, the selected country is Kenya. Next, decide which year to examine. Select any year up to 2100 using the “Select Year” drop-down at the top of the screen. Next, decide which file to use.
[[File:Performance Risk.png|center|thumb|900x900px|Example of a performance and risk evaluation for Kenya 2030]]

The menu bar at the top of the screen includes the following tools:
* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.

* '''Using Country/Regions:''' Switch

* '''Help:''' Go to the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.
* '''Display Options''': Customize how performance and risk are evaluated and displayed. Options include:
** '''Recalculate and update with new weights''': Refresh the evaluation by recalculating values based on any updated weightings entered by the user in the table.
** '''Using absolute evaluation''' (checked by default): Compare each variable against fixed, global goal and alert thresholds for a universal standard across countries and regions.
** '''Using relative evaluation with income level''': Adjust evaluation thresholds (goal and alert levels) based on the average values within a country’s income group. This mode doesn't display the group names but updates the threshold values in the table to reflect peer-relative performance.
** '''Select multiple scenarios''': Compare performances across several different scenario run files.
** '''Select multiple Country/Regions or Groups''': Evaluate and compare multiple countries or predefined regional groupings simultaneously.

The calculation of the performance and risk is based on a number of variables, such as Corruption, Youth Bulge, and State fragility, divided into categories, such as Governance, Population, and Social/Political. The variables are highlighted in green, yellow or red. The color indicates whether or not the variable has met the goal level set for that variable. If it has, the variable will be highlighted in green. If it has not, it will either be highlighted in yellow, if the value for the variable is between the alert level and the goal level, or in red, if the value for the variable has crossed the threshold for the alert level.

These goal and alert levels can be evaluated in two ways:

- Absolute evaluation compares each variable to fixed global targets.

- Relative evaluation with income level adjusts the thresholds based on a country’s income group, although income groups are not displayed explicitly; only the goal and alert values change accordingly.

Additional columns include Global Rank, Scaled Level, and Weighting for deeper analysis and the ability to customize a summary score. A GDP per capita value is also displayed at the top and can be edited.

To look at a variable in greater detail, click on the variable name. A box appears that allows the user to select from three options:

'''- Cross-Sectional Relationship''' displays the selected variable in relation to GDP per capita.

'''- Provide Information''' gives additional context about the variable.

'''- Show Count''' presents a pop-up summary of how many countries fall into each risk category—green (goal met), yellow (moderate risk), and red (alert level)—for the selected variable.

Click on a number in the table (such as the Value or Scaled Level columns) to get the variable over time or across countries. This brings up a box with two options:

- '''Show Over Time''' opens a table showing the selected variable’s forecast across years for the chosen country.

- '''Display All Years and All Countries Ranked''' opens a comparative view with a matrix of values across time and multiple countries to explore rankings and trends globally.

For more information on how to use the available table tools, see the [[General Display Options|Table Use]] page.

= Evaluate Priorities =
<div style="text-align: center; color: red; font-weight: bold; font-size: 200%;">

Feature is no longer available or outdated in the model.

</div>
Evaluate Priorities can be found under the Specialized Display sub-option under the Display option on the Main Menu. It is also located under the [[Main_Menu_Map|Main Menu Map]] options.

This display feature of IFs allows users to analyze and evaluate any Run-Result-File normatively. Users can set evaluative criterion and assign them numerical weights. The model is then run and each criterion is evaluated with respect to what variables most heavily affected the resulting number.

[[File:Evalprior.gif|frame|right|Example of evaluated priorities for a particular country/region or group]]When this option is selected from the Display option on the Main Menu and the Specialized Display sub-option, a window opens that asks for your name. You are then guided to a menu that allows you to flexibly evaluate your Run-Result-File to your needs and goals.

On the Evaluate Priorities menu, you are able to toggle between countries/regions or groups. The next option is a Display option. This allows you to access some of the display features of IFs. The next option on the menu of Evaluate Priorities is the Reset Priorities option. This allows users to clear all priority changes that were previously made.

The final option allows users to Change Priorities. Choosing this option opens up a secondary window where users can individualize their evaluative criteria. For example, if you were interested in emphasizing GDP per capita, double click on the number found in the column labeled “Weight”. This will bring up a smaller window that allows you to increase or decrease the numeric priority assigned to GDP per capita. The average priority for all variables is 5. From this screen, users can choose Define Priorities. This brings up a new window that allows for many different variables to be added to the main priorities menu.

If a user is interested in agricultural yield figures but it is currently not displayed on the Main Menu of Evaluate Priorities, they can click on the “No” that is found under the Guided Use column on the Define Priorities menu. Users can also delete any changes made to this list by selecting the Reset G.U. option found at the bottom of the Define Priorities menu.

Returning to the Evaluate Priorities form, users are able to see and change the weight allotted to certain variables by double clicking on the number located under the column labeled “Prior”. Also, if you would like to display the result of one variable over time, double-click on the number located under the column labeled Run-File. This will bring up a new [[General_Display_Options#Table_Use|table]].

From the scroll-down lists located at the top of the Evaluate Priorities screen just below the menu, users can change the countries/regions or groups, the year that is being displayed and the Run-Result-File that is used as the source of results.

Use IFs (Download): Development Goals

2025-05-12T15:53:23Z

Taylor.Hanna:

The Development Goals specialized display options, explore how far the world and countries are from attaining the Sustainable Development Goals. The Sustainable Development Goals (SDGs) are a set of 17 goals adopted by all United Nations Member states in 2015, that focus on collaboration between countries toward better sustainable development. Each goal has several targets that help to quantify their attainment. Each of the display options described below can help to assess SDG targets over time and explore their potential for attainment in the future.

== SDG Overview Table ==
The ''SDG Overview Table'' display can be found from the Main Menu: choose the '''Display''' option, the '''Specialized Display''' sub-option, and then the '''''SDG Overview Table''''' sub-sub-option.

This display shows a table of the SDGs and their targets that IFs has corresponding variables to measure. This table displays forecasts and historical data on SDG targets for a chosen country and scenario. The table has five columns: the names of indicators that correspond with a certain target and SDG, the most recent year of historical data, forecast for the chosen year and reference scenario, forecast for the chosen intervention scenario, and if available the target value. When a target value is available the forecast columns will be filled red if the forecast is not reaching the target, and green if it is reaching the target.
[[File:SDG overview 2.png|center|thumb|950x950px|An example of the SDG Overview Table display for Kenya with the year 2040 and goal 1 selected.]]
There are several options and features to adjust and explore the display that can be found in the menu bar or the display field options at the top of the screen:
* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* [[Country/Region, Group or G-List|'''Using Groups''' or '''Using Countries/Regions''']]: Click '''Switch''' to change between displaying groups or countries and regions. If groups are currently selected then '''Using Groups''' will be displayed on the tool bar and vice versa.
* '''Use History Only Indicators''' or '''Back to Forecasted Indicators''': Click '''Switch2''' to change between only displaying the most recent historical data that IFs has with no forecasts or to displaying forecast and all the variables with data. If forecasts are currently displayed then '''Use History Only Indicators''' will be displayed on the tool bar and vice versa.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.

Display Option Fields:

* '''Groups''' or '''Countries or Regions''': Choose the desired country or region, or group form the drop down.
* '''Select Year''': Choose the desired year to display from the drop down.
* '''Select Reference Scenario''': Choose the desired saved scenario to display from the drop down.
* '''Select Intervention Scenario''': Choose the desired saved scenario to display from the drop down.
* '''Goal''': Choose the desired SDG from the drop down.

== SDG Graph ==
The ''SDG Graph'' display can be found from the Main Menu: choose the '''Display''' option, the '''Specialized Display''' sub-option, and then the '''''SDG Graph''''' sub-sub-option.

This display shows the history and forecast of an IFs variable that corresponds with the chosen SDG goal and target for a given country and scenario. Along with this forecast, the graph also shows the target value if available.
[[File:SDG Graph.png|center|thumb|950x950px|An example of the SDG Graph display for Kenya with goal 1 and target 1.1 chosen.]]
There are several options and features to adjust and explore the graph that can be found in the menu bar or the display field options:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Global Picture''': Click '''Situation Map''' to open a [[Use IFs (Download): World Map and All-Variable Displays#Map|display map]] comparing all countries for the chosen goal and target.
*'''Causality''': Open a display of the drivers of the currently displayed variable. This display shows what variables drive the currently displayed variable in the IFs model.
*'''Switch''':
**[[Country/Region, Group or G-List|'''Use Groups or Use Countries''']]: Change between displaying groups or countries and regions. If groups are currently selected then '''Use Countries''' will be displayed on the tool bar and vice versa.
**'''Compare''': Compare two countries or groups. First Select the desired countries or groups; one at the drop down at the top of the screen and and the other at the drop down below the graph. Then click '''Compare'''.
*'''Display Options''':
**'''Earliest Year to Display''': Select the desired earliest year to display on the graph from the drop down that appears.
**'''Latest Year to Display''': Select the desired latest year to display on the graph from the drop down that appears.
**'''Not filling holes in group data''' or '''Using estimation to fill holes in group data''': Click this to switch between either using estimation techniques to fill in missing data for countries in the selected group or not filling in data at all. '''Not filing holes in group data''' is displayed if the display is not using estimations to fill in missing data and vice versa.
**'''Using Cross-Sectionally enhanced Longitudinal Regression to fill holes''': Click to use this estimation technique.
**'''Estimation with Regression''': Hover over to choose the desired regression technique from the drop down. This regression is used to estimate the base year and most recent year when the chosen country or group does not have data available. The current technique will have a check mark next to in the drop down.
**'''Show Regression Values''': Show the regression line that is used to estimate the base year and most recent year when the chosen country or group does not have data available.
*'''Edit Target''': Click '''Edit''' to manually set the target year and value.
*'''About SDG''':
**'''Goals''': Open a page that provides more detailed information about the SDGs.
**'''Explain''': Open a page that explains the estimation process for missing data, and how IFs sets target values based upon the SDG targets.
*'''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display being used.
Display Option Fields; after each selection the graph will automatically refresh and reflect the change:

* Goal drop down: Select desired goal.
* Target drop down: Select the desired target.
* Indicator drop down: Select the desired indicator.
* Country or Group drop down (top of screen): Select the desired country or group.
* Scenario drop down: Select the desired scenario.
* Country or Group drop down (bottom of screen): Select the desired country or group prior to using the '''Compare''' option.
* '''Display''': Select '''Chart''' or '''Data''' to change the display format between a graph or table.

== Poverty Level Display ==
The ''Poverty Level Display'' can be found from the Main Menu: choose the '''Display''' option, the '''Specialized Display''' sub-option, and then the '''''Poverty Level Display''''' sub-sub-option.

This display feature is useful for generating and displaying forecasts of populations who live under a certain monetary constraint, between two chosen income levels, or the income of a chosen percentile within a geographically bound region. Change selections and the display option fields then choose to display the generated forecasts as either a table, line graph, or bar graph.
[[File:Poverty level.png|center|thumb|950x950px|An example of the Poverty Level Display.]]
There are several options and features to adjust and explore the displays that can be found in the menu bar or the display field options:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Display Forma'''t:
** '''Set Title, Display Interval, or Year''': Adjust the display year, the title, and the titles of axes.
** '''Currency''': Change the currency being displayed. The default currency is USD.
** '''Horizon''':
*** '''Display/Run Horizon''': Select the desired forecast horizon year from the drop down that appears.
*** '''Run File Horizon Choice (With Multiple File Use)''': When multiple scenario files are selected from the '''Scenarios''' drop down, choose to set the forecast horizon to the '''Shortest Run File Horizon''' or the '''Longest Run File Horizon'''.
* [[Country/Region, Group or G-List|'''Group Options''']]: Select an option from the drop down to change between '''Using Countries/Regions''', '''Using Groups''', '''Using G-Lists''', or '''Using Decomposed Groups''' as the geographic level of display'''.''' The selection for the current chart being displayed will have a checkmark next to it.
* '''Using Percent of Population''' or '''Using Millions of People''': Click '''Change''' to either display forecasts in millions or percent. '''Using Percent of Population''' will be displayed to reflect the current choice and vice versa.
* '''Using 2017 PPP''' or '''Using 2011 PPP''': Click '''Switch''' to either display forecasts in in 2011 or 2017 USD PPP. '''Using 2017 PPP''' will be displayed to reflect the current choice and vice versa.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.
Display Option Fields:

* '''Regions''': Select the desired country, group, g-list, or decomposed group from the drop down.
* '''Scenarios''': Select the desired scenario from the drop down.
* '''Poverty Level''': Choose to generate forecasts and displays based upon the income level. Set the income level in the corresponding field.
* '''Income Range''': Choose to generate forecasts and displays based upon the income range. Set the range in the two corresponding fields.
* '''Poverty Percentile''': Choose to generate forecasts and displays based upon the income percentile. Set the percentile in the corresponding field.
* '''Add''': Click to add the current selections to the display or table. Once clicked, the selections will then show up in a box at the bottom of the screen. Use this multiple times to compare between countries or scenarios.
* '''Table''': Generate a table based upon selections.
* '''Line Graph''': Generate a line graph based upon selections.
* '''Bar Graph''': Generate a bar graph based upon selections.
* '''Clear''': Clear the current selections that are in the box at the bottom of the screen.

Use IFs (Download): Economy

2025-05-06T20:11:06Z

Taylor.Hanna: /* Development Profile */

= Development Profile =

The '''''Development Profile''''' display can be reached from the Display option of the Main Menu, Specialized Display sub-option and Development Profile sub-sub-option. It is also located under the [[Main_Menu_Map|Main Menu Map]] options.

The purpose of the '''''Development Profile''''' display feature is to track how Human Capital, Social Capital, Physical Capital and Knowledge contribute to the annual growth of a country/region or a group. For purposes of development, it is helpful to understand what aspects of a country/region or group are helping or harming the growth of a country/region or group.

There are three dropdown menus at the top of the screen. Use the first toggle in the top-left corner to switch between regions or global groups (depending on the selection made in the Main Menu). To the right of the country/region or group scroll-down list, choose a year to display. Finally, use the third menu below to select the appropriate Scenario Run File.
[[File:Development profile screen.png|center|thumb|889x889px|Development Profile screen]]
The menu bar at the top of the screen includes the following tools:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.

* '''Using Country/Regions:''' Switch

* '''Compare Country/Regions:''' Select "Show" to display dropdown menus at the top of the screen: Select File is a selection of the scenario run for comparison, and Compare Groups or Countries specify options for selecting a comparison region or country. Select "Hide" to remove the comparison view and restore the display to a single country.

* '''Help:''' Go to the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.

The left column of the table represents different variables calculated by IFs organized into categories. The Computed Value column includes the actual numbers calculated for the selected year (top right scroll-down list). These values are then used to calculate the Expected Value Predicted from GDP (Gross Domestic Product) Per Capita at PPP (Purchasing Power Parity). A Standard Error is then displayed, followed by a Standard Error of the Value from the Prediction. The final two values display how the categories Contributed to Annual Growth (as a percentage) and how their contribution can be displayed as a parameter.

Click any variable displayed on the left to graph the Computed Value of that variable for the selected year, as well as a logarithmic graph of GDP at PPP compared to the variable. See an example below:
[[File:Display function.png|center|thumb|900x900px|Display Functions screen]]

To see forecast over time for the selected variable, click on the number displayed in the Computed Value column. For more information on how to use the available table tools, see the [[General Display Options|Table Use]] page.

To visually represent the data, use a [[General_Display_Options#Line_Graph,_Bar_Graph,_Scatter,_Etc.|line graph or a bar graph]], save the information, toggle between whole number representation or representation as a percentage of the base year, toggle between displaying results as cumulative totals, turn the interval average on or off, add or design a filter or set the time horizon for your information.

= Social Accounting Matrix =

The '''''Social Accounting Matrix''''' sub-sub-option can be reached from the Display option on the Main Menu, the Specialized Display sub-option and then the Social Accounting Matrix sub-sub-option. It is also located under the [[Main_Menu_Map|Main Menu Map]] options.

A '''''Social Accounting Matrix (SAM)''''' is an extension of an input-output table that economists use to show the inter-sectoral flows of goods and services within an economy (see the upper left-hand cell of the SAM). The SAM more generally displays flows among actor-classes in the socio-economic system. For instance, the cell in the government column and household row shows flows from the government to households (such as pension payments). The cell in the firms' column and government row similarly shows flows from firms to the government (such as tax payments). Double-click on certain numbers within the SAM table and this will open up a small window with options to Expand Cell, Show Over time, Explain Cell Content or Cancel the operations. When expanded, the SAM shows additional sectors and categories such as FDI outflows, worker remittances, aid donations, and debt repayments.

Move across [[Country/Region,_Group_or_G-List|countries/regions or groupings]] and across years with the drop-down boxes at the top of the screen.[[File:SAM.png|center|frame|Example of a Social Accounting Matrix page, Flows]]
There are several features and options that can be accessed using the menu bar at the top of the screen. These include:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.

* '''Switch:''' Modify the display settings of the SAM with the following options:
** '''Use Groups:''' Click to switch the display from individual countries/regions to predefined groups. Click again (the menu will now say Use Country/Regions) to switch back to viewing countries/regions individually.
** '''Percent Change From Base Case:''' This option toggles the display to show the percent change from the Base Case (Current Path) instead of absolute values. It helps to quickly identify relative differences between the selected run and the Base Case.
** '''Show Stocks or Switch to Show Flows:''' This option toggles between showing stocks (e.g., accumulated levels) and flows (e.g., yearly transactions) within the SAM.
** '''Expand SAM:''' Expand the SAM table to display a more detailed view of flows between sectors, households, firms, capital accounts, government sectors, and the rest of the world (ROW). Additional categories such as FDI outflows, worker remittances, aid donations, and debt repayments are included in the expanded view.

* '''Help:''' Go to the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.
* '''About:''' Click to display short description of the feature being used.

''Note: When toggling between stocks and flows, users may note that some of the stocks are not present. This is because this feature of IFs is currently under development.''

= <big>Growth Accounting</big> =
The '''''Growth Accounting''''' sub-option can be reached from the Display option on the Main Menu, the Specialized Display sub-option, and then the Growth Accounting sub-option. It is also located under the [[Main Menu Map]] options.

This display provides historical and forecasted data on the sources of economic growth, decomposing GDP growth into contributions from factors such as capital accumulation, labor force growth, multifactor productivity (MFP), capacity utilization, and climate impacts. Analyze these contributions across different countries, sectors, and scenarios.

Use the drop-down boxes at the top of the screen to select different Regions (countries), Economic Sectors, Scenarios, and Variables.

[[File:Growth Acc.png|center|thumb|900x900px|Example of Growth Accounting Page]]
There are several features and options that can be accessed using the menu bar at the top of the screen. These options include:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.
* '''Print:''' Print the current display or export it to a printable format.
* '''Display Format:''' Modify how the data is displayed:
** '''Change to Bar Chart:''' Display the selected variables in a bar chart format.
** '''Change to Line Graph:''' Display the selected variables in a line graph format.
** '''Set Title, Display Interval, or Year:''' Modify of the title, time interval, year labeling, and X/Y axis for graphs.
** '''Display/Run Horizon:''' Adjust the horizon for the data display.
** '''Annual Growth Rate:''' Pull up annualized growth rates. When unselected, it presents full interval growth rates.
** '''Adjustment to Actual Growth:''' Adjust modeled outputs to better align with historical actual growth data.
** '''Decompose MFP:''' Break down multifactor productivity into its detailed components.
* '''Grouping Options:''' Select between viewing [[Index.php?title=Country/Region, Group or G-List&action=edit&redlink=1|Country/Regions, Groups or G-lists]].
** '''Using Countries/Regions:''' Display individual countries or regions.
** '''Using Groups:''' Display groups.
** '''Using Decomposed Groups:''' Select a group but display data separately for each country within the selected group.
** '''Using G-Lists:''' Customized group lists for analysis.

* '''Label Options:''' Adjust labeling settings for graphs or tables to customize the appearance of outputs.
** '''Use First Dimension Only:''' Label based on the first selected dimension.
** '''Use Sector Only:''' Display only the sector names.
** '''Use Scenario Names Only:''' Display only the scenario names.
** '''Use Components Only:''' Display only component names.
** '''Use Alias for Group Names:''' Replace group names with user-defined aliases.
** '''Reset Labels:''' Return all labels to default settings.

* '''Go to Development Profile:''' Go directly to a country or region’s detailed Development Profile.
* '''Explanatory Note:''' View a brief description of the Growth Accounting display and its underlying methodology.
* '''Help:''' Go to the corresponding in the [[Main Page|Pardee Wiki]] for the current feature or display.

The Variables available in the '''''Growth Accounting''''' display include:

* '''Capital:''' Contribution of physical capital accumulation to GDP growth.
* '''Labor:''' Contribution of labor force growth to GDP growth.
* '''Multifactor Productivity (MFP):''' Contribution of improvements in efficiency and technology to GDP growth. MFP is further disaggregated into several components (see below).
* '''Capacity Use:''' Impact of variations in the utilization rate of available capital and labor.
* '''Climate Impact:''' Effect of climate change on productive capacity.
* '''GDP Growth (for comparison):''' Overall GDP growth rate combining all components.
* '''Technology Base MFP:''' Impact of advancements in technological infrastructure and innovation.
* '''Knowledge MFP:''' Effect of accumulated knowledge and research on productivity.
* '''Human Capital MFP:''' Contribution of improvements in education, skills, and workforce capabilities.
* '''Social Capital MFP:''' Impact of societal institutions, trust, and networks on economic productivity.
* '''Physical Capital MFP:''' Productivity gains linked to better utilization or quality of physical assets.
* '''Correction MFP:''' Adjustments made to account for modeling or measurement inconsistencies.

After making selections, choose to '''Graph''' the results, view them in a '''Table''', or '''Clear''' the selections to start over by clicking one of these options at the bottom of the page.
[[File:Gr Acc graph.png|center|thumb|900x900px|Example of Growth Accounting Graph for Kenya]]

For example, the graph above shows the contribution of labor to agricultural growth in Kenya under the IFsBase scenario. The x-axis represents years from 2020 to 2100, while the y-axis displays the growth rate contribution of the labor factor. For more information on how to use the available table tools, see the [[General Display Options|Table Use]] page.

= Infrastructure Profile =

<div style="text-align: center; color: red; font-weight: bold; font-size: 200%;">
Feature is no longer available or outdated in the model.
</div>
Infrastructure Profile is a sub-sub option, located under Specialized Display, which in turn is a sub-option of the Display option on the main menu of IFs. Infrastructure Profile is also located under the [[Main_Menu_Map|Main Menu Map]] options.

Clicking on Infrastructure Profile allows the user to view multiple components of the infrastructure for a [[Country/Region,_Group_or_G-List|country/region or group]], including: Water and Sanitation, Transportation, Energy, Information-Communications Technology (ICT), and Knowledge Systems. Most of these components are a composite of different variables. The value of each of these variables is displayed in four ways: Computed Value, Expected Value Predicted from GDP per Capita at Purchasing Power Parity (PPP), Standard Error of Estimate, and Standard Errors of Value from Prediction. The user can change the year displayed in the Infrastructure Profile up to 2100 by clicking on the ‘Select Year’ dropdown box at the top-center of the screen

The user can display the computed values for the variables over time in a [[General_Display_Options#Table_Use|table]] by clicking on a desired value. The table that appears displays the computed values over time according to several different [[Scenario_Description|scenarios]]. The user can also view a variable displayed in a [[General_Display_Options#Line_Graph,_Bar_Graph,_Scatter,_Etc.|graph]] by clicking on a given variable. After viewing the graph, the user can return to the Infrastructure Profile by clicking on continue.
[[File:SAM.png|center|frame|Example of an infrastructure profile]]

= Financial Profile =

<div style="text-align: center; color: red; font-weight: bold; font-size: 200%;">
Feature is no longer available or outdated in the model.
</div>

The Financial Profile sub-sub-option can be reached from the Display option on the Main Menu, the Specialized Display sub-option and the Financial Profile sub-sub-option. It is also located under the [[Main_Menu_Map|Main Menu Map]] options.

When you select the Financial Profile from the Specialized Display menu, you are presented with a chart that divides financial information for any given country/region or group into Domestic and International data. The chart then organizes this data into absolute numbers, percentages of GDP and percentages of exports.

Experiment with the Financial Profile. Assume you would like to know what trends you can see in global reserves held by all countries except for the United States. From the top of the Financial Profile menu first click on the [[Country/Region,_Group_or_G-List|Using Countries/Regions - Using Groups]] toggle. Scroll down the groups until you find the option for World Except USA.

Choose a cell that you would like to explore more fully. If you would like to look at the total global reserves held by all countries except for the USA, click no the table where the Reserves row intersects with the Absolute column. This will bring up a prompt called Show Over Time. If you select this option, a [[General_Display_Options#Table_Use|table]] will open showing this value forecast using different Run-Result-Files.

Another Financial Profile option is the Show Top Countries/Regions. Selecting this will bring up a new screen called the Financial Profile Filter. This feature of IFs allows users to quickly access countries who have the highest levels of certain financial data filtered as absolute numbers, percentages of GDP or percentages of exports. Selecting any one of the filters located at the top of the Financial Profile Filter page will organize the table into the 5 countries with the highest value for the selected filter. Click Continue to return to the Financial Profile menu.

[[File:Dev3.gif|frame|center|Example of financial profile]]

= World Bank Financial Flows =

The '''''World Bank Financial Flows''''' sub-sub-option can be reached from the Display option on the Main Menu, the Specialized Display sub-option and then the World Bank Financial Flows sub-sub-option. It is also located under the [[Main_Menu_Map|Main Menu Map]] options.

This display provides historical and forecasted data on loans, repayments, and sectoral financial interactions between countries and the World Bank. Click on any variable name to open a table displaying the values for that variable over time and different scenarios. From the table menu options, create a graph, map the data, save it, or adjust display settings. For more information on how to use the available table tools, see the [[General Display Options|Table Use]] page.

Use the drop-down boxes at the top of the screen to select different countries/regions and scenario files.

There are several features and options that can be accessed using the menu bar at the top of the screen. These options and features include:
* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.

* '''View:''' Modify options for adjusting how data is displayed in the table:
** '''Normal:''' Display data in absolute values.
** '''Percent Change from the First Year:''' Show the percent change of the variable compared to its value in the first year displayed in the table.
** '''Percent Change from the Base Case:''' Show the percent change of the variable compared to its value in Base Case scenario in the same year.

* '''Switch:''' Click to switch the display from individual countries/regions to predefined groups. Click again (the menu will now say Use Country/Regions) to switch back to viewing countries/regions individually.
* '''Help:''' Go to the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.
[[File:WB flows.png|thumb|600x600px|Example of World Bank financial flows]]

The variables in the '''''World Bank Financial Flows''''' page table are organized into three categories. Annual Flows show the year-by-year financial transactions between countries and the World Bank, including loans disbursed ("From Bank") and repayments made ("To Bank"). Cumulative Position tracks the accumulated financial relationship over time, reflects total loans received and total repayments made, which provide a view of outstanding obligations or net balances. Finally, Sector of Flow disaggregates financial flows by major sectors, such as education, health, and water and sanitation, showing how World Bank resources are allocated across development priorities.

Use IFs (Download): Education

2025-05-06T18:35:23Z

Taylor.Hanna: /* Education by Age, Sex, and Level */

= Education by Age, Sex, and Level =

Learn more about the [https://pardeewiki.du.edu/index.php?title=Education Education] model on IFs.

To access the '''''Education by Age, Sex and Level''''' option, select Display from the Main Menu, then select sub-option Specialized Display and then '''''Education by Age, Sex and Level''.''' It is also accessible through the [[Main Menu Map]] options.

This display is broken down into levels of educational attainment with each level of attainment being distinguished by a different color. Educational attainment is broken down as follows: no formal education, primary education, secondary education, tertiary education and those too young to achieve formal education. Check with the legend at the bottom of the graph to see what colors represent each level of educational attainment.

From this display, there are several features and options that can be accessed using the menu bar at the top of the screen.
[[File:Tanzania 2030 Education by Sex.jpg|center|thumb|990x990px|Projected educational attainment in Tanzania in 2030]]
These options and features include:

* '''[[Repeated Features#General%20Display%20Options|Continue]]:''' Go back to the previous menu or to the Main Menu of IFs.
* '''Distribution Type:''' Select other types of Education charts to be displayed through clicking on one of the following sub-options.
** All levels of education for all age cohorts
** No completed formal education
** Primary, Secondary or Tertiary education levels
** Labor cohorts by level of education disaggregated by male, female or both
** '''Compare Countries:''' Compare four charts for different countries, years, scenarios, or any combination of the three. This option will initially bring up four charts for the country and distribution type most recently displayed for the next four 5-year increments for example 2030, 2035, 2040 and 2045. At the bottom of the compare countries screen, select which country, year and scenario desired to be displayed for each of the four graphs; after adjusting click Refresh to change the displays. To switch to a different type of education chart to compare, click Distribution Type from the menu bar and click the desired distribution.

* '''Show Numbers:''' Display the underlying data for the graph in the table format, broken down by educational level, gender, and age cohort. This gives a detailed view of the exact values represented visually. Export or print the data through Print Numbers and Save Numbers features.
* '''Set Group or Country/Regions:''' Select one of the following categories:
** Countries/Regions
** Groups
** Hide Non Core Groups

* '''Change Display:''' Select type of display through clicking on one of the following sub-options.
** Countries/Regions: A list will appear; scroll down and then click on the country of interest.
** Set Year: A list will appear; scroll down and then click on the year of interest.
** Change Scenario: Analyze how alternative pathways impact educational attainment and select different scenarios, including Base Case, Working File, and pre-defined scenarios such as SSP1 to SSP5 or SDG Push.

* '''Time''': Project graphs through time using the Advance and Regress options.
* '''Change Scale:''' Customize the graph’s scale with following options:
** Fix Constant Scale across Time and Regions to standardize comparisons
** Fix Constant Scale across Time to track changes within a single region over time
** Fix Constant Scale Manually for user-defined scale adjustment
** Use Percent of Population

* '''Help:''' Go to the corresponding page in the [[Main Page|Pardee Wiki]] for the current feature or display.
* '''About:''' Not functioning in current version of IFs

= Costs of Education Goals =

This Specialized Display feature estimates the cost of achieving selected educational attainment goals for different units of countries or groups.

First, choose a scenario. Next, define an educational attainment goal by specifying the percentage of total enrollment for primary, secondary or tertiary education and set the Start and Target years.

The '''''Use Constant Cos''t''' option at the top of the page fixes the per-student cost at the base year level throughout the analysis period and prevents cost changes over time from influencing the results. In the same way, the '''Use Constant Enrollment''' option holds enrollment levels constant, regardless of other scenario adjustments. These two features help to isolate specific variables without the results being affected by cost or enrollment fluctuations. [[File:Scale-up Cost Display Window.png|center|thumb|973x973px|Scale-up Cost Display Window]]There are several other options that can be accessed through the menu bar at the top of the screen. These options include:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]''':''' Go back to the previous menu or to the Main Menu of IFs.
* '''Display Format:''' This tool on the menu offers further customization of the following features:
** '''Set Title, Display Interval, or Year'''. Edit specific display features for the graph, including axis titles and ranges, and the display year for a pie chart, scatterplot, or map.
** '''Currency'''. Specify a display currency and exchange rate.
** '''Horizon.''' A toggle will appear at the right side of the window with list to Change Horizon year. Run File Horizon Choice (With Multiple File Use). Shortest Run File Horizon, or Longest Run File Horizon

* '''Group Options:''' Select one of the following categories below. After that, a box with the list of [[Index.php?title=Country/Region, Group or G-List|Country/Regions, Groups or G-lists]] will appear on the left hand side of the display.
** Using Countries/Regions
** Using Groups
** Using G-Lists
** Using Decomposed groups
* '''Help:''' Got to the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are on.

Once parameters are set, click any of the boxes at the bottom of this display option and these variables will be loaded into the variable display box. Finally, choose an output—a table, line graph or bar graph—to visualize results.

Use IFs (Download): Infrastructure

2025-05-01T22:09:40Z

Taylor.Hanna:

The ''Infrastructure Profile'' specialized display can be found from the '''Main Menu''' by hovering over the display option, then over the '''Specialized Display for Issues''' sub-option, and clicking on the '''''Infrastructure Profile''''' sub-sub-option.

The ''Infrastructure Profile'' page shows the computed value for a number of infrastructure related variables for a specific year, country or region, and scenario. It also shows the expected value based upon the selected country or regions GDP per capita at PPP which is shown near the top of the screen. This expected value is calculated from a regression function (line of best fit) of GDP per capita at PPP and the selected variable for all countries. This page also displays the standard error of the regression function (Estimate) and the standard error of the specific computed value (Value from Prediction).
[[File:Infrastructure Specialized Display.png|center|thumb|950x950px|This shows an example of the Infrastructure Profile specialized display page for Kenya in 2030.]]
There are several options and features on this page:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* [[Country/Region, Group or G-List|'''Using Groups''' or '''Using Countries/Regions''']]: Click '''Switch''' to change between displaying groups or countries and regions. If groups are currently selected then '''Using Groups''' will be displayed on the tool bar and vice versa.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.

Display Option Fields:

* '''Groups''' or '''Countries or Regions''': Choose the desired country or region, or group form the drop down.
* '''Select Year''': Choose the desired year to display form the drop down.
* '''Select File''': Choose the desired saved scenario to display from the drop down.
Click on one of the computed values to open a table of that variable forecasted through 2050 for all the scenarios loaded onto the current version of IFs. Click on the variable name to open a graph that displays the current GDP per capital and the computed value for the selected variable and country or group; and a line graph of the expected value of the variable for varying levels of GDP per capita.

Use IFs (Download): World Values

2025-05-01T22:07:37Z

Taylor.Hanna:

To access the World Value Survey by Age display, choose Display from the Main Menu followed by the Specialized Display sub-option and the World Value Survey sub-sub-option. The World Value Survey is also found as an option on the [[Main_Menu_Map|Main Menu Map]].

The World Value Survey by age display, shows forecasts of different values based on a global project begun at the University of Michigan. To learn more about the World Value Survey, click on '''About''' '''WVS''' on the Main Menu.
[[File:World Value Survey Example.png|center|thumb|950x950px|Example of the World Value Survey specialized display for Kenya in the year 2030 for the Materialism/Postmaterialism value.]]
Options and features in the tool bar:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.

* '''Time''':
** '''Advance''': Click to move the display year forward by 5 years.
** '''Regress''': Click to move the display year back by 5 years.
* '''[[Country/Region, Group or G-List|Set Group or Country]]''': Change between displaying either country/region or groups by clicking on either '''Group''' or '''Country/Region''' sub-options. The selection for the current chart being displayed will have a checkmark next to it.
* '''About WVS''': Provides more information on the World Value Survey.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are on.
Display Option Fields:

*'''[[Country/Region, Group or G-List|Countries or Regions]]''': Choose the desired country or region form the drop down.
* '''Variables''': Select which dimension of values they would like to display: Materialism/Postmaterialism ('''MATPOSTR'''), Survival/Self-Expression ('''SURSE''') or Traditionalism/Secular-Rationalism ('''TRADSRAT''').
* '''Select File''': Choose the desired saved scenario to display from the drop down.

Use IFs (Download): World Map and All-Variable Displays

2025-05-01T22:05:01Z

Taylor.Hanna: typo

= World Map, Lorenz Curve, Gini, Histogram =

''The World Map, Lorenz Curve, Gini, Histogram'' specialized display can be reached from the '''Display''' option on the Main Menu, the '''Specialized Displa'''y sub-option and then the '''''World Map, Lorenz Curve, Gini sub-sub-option'''''.
[[File:World Map Specialized Display Page.png|alt=The world map display page is shown as users see it when first opened. There is list of variables that takes up most of the screen. A drop down to select a scenario and buttons to toggle to which display type is desired are at the bottom of the image.|thumb|530x530px|Displaying World Map, Lorenz Curve, or Gini Index]]
The main screen of World Map, Lorenz Curve, Gini is shown on the right.

There are a few features and options in the tool bar:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''History plus Forecast''': Select either '''On''' or '''Off''' from this drop down. '''On''' displays historical data along with the forecast, and '''Off''' only displays the forecast.
* '''Help''': Click to open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are on.

At the bottom of the screen select the desired scenario from the scenario drop down. Click the type of display to view the results as. The display options are: Map, Lorenz curve, Gini, or Histogram.

After selecting the desired display and scenario, select the desired variable to explore from the list of variables that takes up most of the screen. Scroll to a variable or click in the text box and search for a variable. The chosen display for the selected scenario and variable will open. Sometimes, before the desired display is presented, select a dimension for certain variables, for example whether to display Male, Female, or Total.

Each of the display options are explained below using GDP MER (2017) as the selected variable:

=== Map ===
[[File:All variable map 2030.png|alt=This image shows an example of what the map display option looks like for the Map, Lorenz, and Gini specialized display.|center|thumb|950x950px|Map display page example for GDP (2017)]]
The options and features that can adjust the map:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Refresh Map''': After making changes to the display option fields, click this to update those changes in the map.
* '''Define Categories''': Open a screen to adjust the cutoffs for the display categories.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.
* '''Adv Years''': Move the display year forward by 5 years.
* '''Reg Years''': Move the display year back 5 years.
* '''First Year''': Display the base year.
* '''Last Year''': Display the last year of forecasting: 2100.

Display option fields:

* '''Display Type''': Select one of the two sub options:
** '''Equal Interval''': Set the display categories to have the same range of variables (ex. 0 to 400 and 400 to 800).
** '''Equal Count''': Set the display categories to each hold the same count of countries.
* '''Number of Categories''': Change the number of display categories using this drop down from 2 to 7.
* '''Color''': Choose the display color of the map from this dropdown: sandy brown, green, blue, red, or gray.
* '''Projection''': Change the type of map projection from the dropdown.

=== Lorenz ===
The Lorenz display option on the Menu of World Map leads to a display called a Lorenz curve. Lorenz curves and Gini indices are standard measures of distribution of any variable across any population. Lorenz curves show the portion of the variable being examined (such as GDP) that accrues to different portions of the population (such as the world population). Reading across the bottom of the curve one can see the portions of the population, beginning with those that receive/control the smallest portion of the variable. Thus a 0.1 portion of the population (10%) will normally receive considerably less than a 0.1 (10%) share of the resource. And a 0.5 portion will receive less than 50%. But as one moves across the graph, ultimately a 1.0 (100%) portion of the population will receive or control 100%.

It is theoretically possible that the "poorest" 10% of the population could obtain a 10% share, that the "poorest" 50% portion could obtain 50%, and so on. If so, the Lorenz curve would be equivalent to the diagonal line that rises from the lower left to the upper right. That line is called the "Line of Equality," and it is almost an invariant rule that the actual Lorenz curve will fall well below it. The area between the Line of Equality and the actual Lorenz curve is the area of inequality.[[File:Lorenz Curve for Map specialized.png|center|thumb|950x950px|Example of Lorenz curve for global population on GDP MER (2017)]]
The Lorenz curve window has several options:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Open the previous menu page.
* '''Main Menu''': Open to the Main Menu of IFs.
* '''Options''':
** '''Save''': *Not functioning in current version of IFs.*
** '''Refresh''': Update the display to the desired chosen year.
** '''Reset''': Remove any curves except the chosen year's.
** '''Add Curve''': Forecast changing inequality over time by adding multiple curves. Once Add curve is clicked, enter the desired year for an additional curve, then click '''OK.'''
* '''Variables''': Open World Map, Lorenz Curve, Gini, and Histogram menu page.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.

=== Gini ===
The Gini index is calculated as the area of inequality divided by the entire area under the Line of Equality. Thus, larger numbers indicate greater inequality. Gini can range from 0 (perfect equality) to 1 (perfect inequality), but for most economic distributions, the value of Gini will fall between 0.2 (quite high equality or low inequality) and 0.8 (very high inequality).

Choosing the Gini option, opens the [[Display Menu Options#Display_Options_for_Self-Managed_Display|'''Display Menu''']] with the Gini index of their chosen variable loaded into the status box. Select '''Graph''' and then '''Line''' to see a line graph of the Gini index they just created.

=== Histogram ===
[[File:Hiotagram Display.png|alt=Image of Histogram for world GDP MER 2017 takes up most of screen. Most countries are in the first bin. Display options for users to adjust the display are at the top of the screen.|center|thumb|950x950px|Example of Histogram Display Page for GDP MER (2017)]]
The Histogram Display window has several options:
* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Open the previous menu page.
* '''Main Menu''': Open the Main Menu of IFs.
* '''Category Numbers''': Change the number of bins or categories in the histogram. Adjust number and then hit submit.
* '''Display Options''' :Choose from the drop down options:
** '''X Values''': Display the x axis as values or percentage of the variable being displayed.
** '''Y Values''': Display the y axis values in percent of countries or percent of global population.
** '''Order Detai'''l: Organize the order of histograms by selecting one of the following options:
*** '''Alphabetical for Regions Name'''
*** '''Value for Chosen Variable'''
** '''Set Title, Display Interval, or Year''': Adjust the display year, the title, and the titles of axes.
** '''Display/Run Horizon''': Choose from the drop down or type the maximum year that this histogram can be displayed for, which will limit the '''Year Options.'''
* '''Year Options''':
** '''Advance''': Move the display year forward by 5 years.
** '''Regress''': Move the display year back 5 years.
** '''Go To Year''': Enter the desired year to display.
World Map Movie (Forecast) <div style="color: red; font-weight: bold; font-size: 200%;">

Feature is no longer available or outdated in the model.

</div>

The World Map Movie is accessible in two locations in IFs. The first is under the Data Analysis heading on the main IFs screen. The second is under Specialized Display, which is a sub-heading of the Display heading on the Main Menu. The World Map Movie found under the Data Analysis heading deals with historic data, while the World Map Movie found under the Specialized Display heading deals with forecasted data. Selecting the World Map Movie allows the user to display on a map of the world the changes in selected variables over time in all of the countries for which IFs has data. This section describes how to use the various options to tailor the movie to the user’s preferences.

After selecting World Map Movie, the dialog box pictured below appears on the user’s screen.

[[File:Map1.gif|frame|right|World Map Movie Settings]]'''Choose Variables and Years:'''

'''Variables''': allows user to select from the entire database of variables in IFs for the Historic World Map Movie. The forecasted World Map Movie includes a more limited database of variables from which to choose.

'''Start Year''': Enter the year from which the movie will begin.

'''End Year''': Enter in the year with which the movie will end.

'''Filter''': User can set the filter from 1% to 100%. This feature sets a minimum value for a portion of the data to be displayed in a given year.

'''Category''': Can be set from 1 to 16. Determines the number of categories to divide the values into.

'''Constant/Variable Legend''': Selecting the constant function will keep the numbers and measurements in the individual categories constant. The numbers and measurements will change over time if the variable legend is selected.

'''Display Type''': When Constant Legend is selected, two options will appear under display type: equal interval and equal count. The equal interval function divides the values equally between categories, with the interval calculated by the equation (highest value-lowest value/number of categories= interval width). The equal count function distributes the number of countries equally between the selected of number of categories. When Variable Legend is selected, a third option becomes available: equal match. If equal match is selected, legend values are assigned to each value uniquely. If there are not enough categories to represent each value, the countries that do not have the selected values are represented as blank.

'''Controlling Options:'''

'''Automatic''': When deselected, the user scrolls through the years of the movie manually. When selected, the movie will automatically play the start year to the end year.

'''World/Country''': This toggle switch is available only under the Historic World Map Movie. It allows the user to view a movie of changes of the selected variable across the world or, when country is selected, to view a movie of the change in a variable across regions in a country. Three countries are available to the user to select: China, India, and Mexico.

'''Create Movie File''': This toggle switch allows the user to save the movie created. When the playing of the movie is complete, a prompt appears which allows the user to name and save the movie file. The saved movie file is available in [[#Stored_Map_Movie|Stored Map Movie]].

After clicking ‘Start’, the dialog box will close, and the World Map as pictured below (or similar to it) will appear. This section describes what the user is viewing, and how to manipulate the functions at the bottom of the screen.

[[File:Map3.gif|frame|right|World Map as typically displayed by World Map Movies]]'''Playing Movie for Historic Data:'''

The upper left portion of the screen lists whether the movie is showing a variable or constant legend and across which period of time. Underneath this information is the number of categories and the corresponding color for each category, and the numerical range for each category.

The main portion of the screen is the world map, divided into all countries for which IFs has data. The various shadings of the countries correspond to the various shading of the categories listed in the map legend. By right-clicking on the world map, the user is able to copy the map, zoom in on a particular location of the map, zoom out, or to reset the map to its original point of view.

The bottom section of the screen, from left to right, lists the variable displayed by the map, with the start year shown and the level of the filter. Next is the selected display type. The four buttons to the right of the display type allow the user to zoom in, zoom out, pan, and show the full extent of the world map. The first two buttons allow the user to zoom in on or zoom out from the area under which the cursor is located, while the full extent option resets the view of the world to the original perspective. The pan button allows the user to pan across the world by clicking on different parts of the map, which effectively pans across the world by re-centering the map on the area selected, after having zoomed in or zoomed, without zooming in or out any further. The next set of buttons allows the user to play or pause the movie, and to adjust the speed at which the movie will play. Next is a toggle switch which, when selected, makes the movie play automatically; when the switch is not selected, the user is able to advance the year displayed on the map or to go over previous years.

If the user selected “Create Movie File” during the setup phase, a dialog box will appear after the movie is finished playing that prompts the user to provide a name and a place to save the movie file. The program will alert the user if the .avi file is successfully created and saved. To review the saved movie file, go to [[#Stored_Map_Movie|Stored Map Movie]].

Stored Map Movie <div style="color: red; font-weight: bold; font-size: 200%;">

Feature is no longer available or outdated in the model.

</div>

This feature allows the user to review world map movies that were created and saved. The types of world map movies fall under three categories: Historic, Forecast, and Historic Analysis. The location of a given stored map movie depends on the feature that was used to create it: movie files created in the [[#World_Map_Movie_.28Forecast.29|World Map Movie]] in Data Analysis are found in the Historic category; movie files created in the World Map Movie in Specialized Display are found in the Forecast category; and movies created in [[Use_IFs_(Download)_Data_Analysis#Analyze_Across_Countries_(Cross-Sectional_Analysis)|Analyze Across Countries]] are found in the Historic Analysis category.

To play a movie, simply double click on the desired movie or highlight it and press Play.

Use IFs (Download): World Map and All-Variable Displays

2025-04-15T16:40:21Z

Taylor.Hanna: /* World Map, Lorenz Curve, Gini, Histogram */ Made some minor grammatical and spelling edits.

= World Map, Lorenz Curve, Gini, Histogram =

The World Map, Lorenz Curve, Gini specialized display can be reached from the Display option on the Main Menu, the Specialized Display sub-option and then the World Map, Lorenz Curve, Gini sub-sub-option.
[[File:World Map Specialized Display Page.png|alt=The world map display page is shown as users see it when first opened. There is list of variables that takes up most of the screen. A drop down to select a scenario and buttons to toggle to which display type is desired are at the bottom of the image.|thumb|530x530px|Displaying World Map, Lorenz Curve, or Gini Index]]
The main screen of World Map, Lorenz Curve, Gini is sown on the right.

There are a few features and options in the tool bar:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: To go back to the previous menu or to the Main Menu of IFs.
* '''History plus Forecast''': Select either '''On''' or '''Off''' from this drop down. '''On''' displays historical data along with the forecast, and '''Off''' only displays the forecast.
* '''Help''': Opens the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are on.

At the bottom of the screen select the desired scenario from the scenario drop down. Click the type of display to view the results as. The display options are: Map, Lorenz curve, Gini, or Histogram.

After selecting the desired display and scenario, select the desired variable to explore from the list of variables that takes up most of the screen. Scroll to a variable or click in the text box and search for a variable. The chosen display for the selected scenario and variable will open. Sometimes, before the desired display is presented, select a dimension for certain variables, for example whether to display Male, Female, or Total.

Each of the display options are explained below using GDP MER (2017) as the selected variable:

=== Map ===
[[File:All variable map 2030.png|alt=This image shows an example of what the map display option looks like for the Map, Lorenz, and Gini specialized display.|center|thumb|950x950px|Map display page example for GDP (2017)]]
The options and features that can adjust the map:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu or to the Main Menu of IFs.
* '''Refresh Map''': After making changes to the display option fields, click this to update those changes in the map.
* '''Define Categories''': Open a screen to adjust the cutoffs for the display categories.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.
* '''Adv Years''': Click to move the display year forward by 5 years.
* '''Reg Years''': Click to move the display year back 5 years.
* '''First Year''': Click to display the base year.
* '''Last Year''': Click to display the last year of forecasting: 2100.

Display option fields

* '''Display Type''': Has two sub options:
** '''Equal Interval''': Set the display categories to have the same range of variables (ex. 0 to 400 and 400 to 800).
** '''Equal Count''': Set the display categories to each hold the same count of countries.
* '''Number of Categories''': Change the number of display categories using this drop down from 2 to 7.
* '''Color''': Choose the display color of the map: sandy brown, green, blue, red, or gray.
* '''Projection''': Change the type of map projection.

=== Lorenz ===
The Lorenz display option on the Menu of World Map leads to a display called a Lorenz curve. Lorenz curves and Gini indices are standard measures of distribution of any variable across any population. Lorenz curves show the portion of the variable being examined (such as GDP) that accrues to different portions of the population (such as the world population). Reading across the bottom of the curve one can see the portions of the population, beginning with those that receive/control the smallest portion of the variable. Thus a 0.1 portion of the population (10%) will normally receive considerably less than a 0.1 (10%) share of the resource. And a 0.5 portion will receive less than 50%. But as one moves across the graph, ultimately a 1.0 (100%) portion of the population will receive or control 100%.

It is theoretically possible that the "poorest" 10% of the population could obtain a 10% share, that the "poorest" 50% portion could obtain 50%, and so on. If so, the Lorenz curve would be equivalent to the diagonal line that rises from the lower left to the upper right. That line is called the "Line of Equality," and it is almost an invariant rule that the actual Lorenz curve will fall well below it. The area between the Line of Equality and the actual Lorenz curve is the area of inequality.[[File:Lorenz Curve for Map specialized.png|center|thumb|950x950px|Example of Lorenz curve for global population on GDP MER (2017)]]
The Lorenz curve window has several options:

* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu
* '''Main Menu''': Go back to the Main Menu of IFs
* '''Options''':
** '''Save''': *Not functioning in current version of IFs.*
** '''Refresh''': Update the display to the chosen year.
** '''Reset''': Remove any curves except the chosen year's.
** '''Add Curve''': Forecast changing inequality over time by adding multiple curves. Once Add curve is clicked, enter the desired year for an additional curve, then click '''OK.'''
* '''Variables''': Go back to the World Map, Lorenz Curve, Gini, and Histogram menu page.
* '''Help''': Open the corresponding page in the [[Main Page|Pardee Wiki]] with the current feature or display they are using.

=== Gini ===
The Gini index is calculated as the area of inequality divided by the entire area under the Line of Equality. Thus, larger numbers indicate greater inequality. Gini can range from 0 (perfect equality) to 1 (perfect inequality), but for most economic distributions, the value of Gini will fall between 0.2 (quite high equality or low inequality) and 0.8 (very high inequality).

Choosing the Gini option, opens the [[Display Menu Options#Display_Options_for_Self-Managed_Display|'''Display Menu''']] with the Gini index of their chosen variable loaded into the status box. Select '''Graph''' and then '''Line''' to see a line graph of the Gini index they just created.

=== Histogram ===
[[File:Hiotagram Display.png|alt=Image of Histogram for world GDP MER 2017 takes up most of screen. Most countries are in the first bin. Display options for users to adjust the display are at the top of the screen.|center|thumb|950x950px|Example of Histogram Display Page for GDP MER (2017)]]
The Histogram Display window has several options:
* [[Repeated Features#General%20Display%20Options|'''Continue''']]: Go back to the previous menu.
* '''Main Menu''': Go back to the Main Menu of IFs.
* '''Category Numbers''': Change the number of bins or categories in the histogram. Adjust number and then hit submit.
* '''Display Options''' :
** '''X Values''': Choose to display the x axis as values or percentage of the variable being displayed.
** '''Y Values''': Choose to display the y axis values in percent of countries or percent of global population.
** '''Order Detai'''l: Organize the order of histograms by selecting one of the following options:
*** '''Alphabetical for Regions Name'''
*** '''Value for Chosen Variable'''
** '''Set Title, Display Interval, or Year''': Adjust the display year, the title, and the titles of axes.
** '''Display/Run Horizon''': Choose from the drop down or type the maximum year that this histogram can be displayed for, which will limit the '''Year Options.'''
* '''Year Options''':
** '''Advance''': Click to move the display year forward by 5 years.
** '''Regress''': Click to move the display year back 5 years.
** '''Go To Year''': Enter the desired year to display.
World Map Movie (Forecast) <div style="color: red; font-weight: bold; font-size: 200%;">

Feature is no longer available or outdated in the model.

</div>

The World Map Movie is accessible in two locations in IFs. The first is under the Data Analysis heading on the main IFs screen. The second is under Specialized Display, which is a sub-heading of the Display heading on the Main Menu. The World Map Movie found under the Data Analysis heading deals with historic data, while the World Map Movie found under the Specialized Display heading deals with forecasted data. Selecting the World Map Movie allows the user to display on a map of the world the changes in selected variables over time in all of the countries for which IFs has data. This section describes how to use the various options to tailor the movie to the user’s preferences.

After selecting World Map Movie, the dialog box pictured below appears on the user’s screen.

[[File:Map1.gif|frame|right|World Map Movie Settings]]'''Choose Variables and Years:'''

'''Variables''': allows user to select from the entire database of variables in IFs for the Historic World Map Movie. The forecasted World Map Movie includes a more limited database of variables from which to choose.

'''Start Year''': Enter the year from which the movie will begin.

'''End Year''': Enter in the year with which the movie will end.

'''Filter''': User can set the filter from 1% to 100%. This feature sets a minimum value for a portion of the data to be displayed in a given year.

'''Category''': Can be set from 1 to 16. Determines the number of categories to divide the values into.

'''Constant/Variable Legend''': Selecting the constant function will keep the numbers and measurements in the individual categories constant. The numbers and measurements will change over time if the variable legend is selected.

'''Display Type''': When Constant Legend is selected, two options will appear under display type: equal interval and equal count. The equal interval function divides the values equally between categories, with the interval calculated by the equation (highest value-lowest value/number of categories= interval width). The equal count function distributes the number of countries equally between the selected of number of categories. When Variable Legend is selected, a third option becomes available: equal match. If equal match is selected, legend values are assigned to each value uniquely. If there are not enough categories to represent each value, the countries that do not have the selected values are represented as blank.

'''Controlling Options:'''

'''Automatic''': When deselected, the user scrolls through the years of the movie manually. When selected, the movie will automatically play the start year to the end year.

'''World/Country''': This toggle switch is available only under the Historic World Map Movie. It allows the user to view a movie of changes of the selected variable across the world or, when country is selected, to view a movie of the change in a variable across regions in a country. Three countries are available to the user to select: China, India, and Mexico.

'''Create Movie File''': This toggle switch allows the user to save the movie created. When the playing of the movie is complete, a prompt appears which allows the user to name and save the movie file. The saved movie file is available in [[#Stored_Map_Movie|Stored Map Movie]].

After clicking ‘Start’, the dialog box will close, and the World Map as pictured below (or similar to it) will appear. This section describes what the user is viewing, and how to manipulate the functions at the bottom of the screen.

[[File:Map3.gif|frame|right|World Map as typically displayed by World Map Movies]]'''Playing Movie for Historic Data:'''

The upper left portion of the screen lists whether the movie is showing a variable or constant legend and across which period of time. Underneath this information is the number of categories and the corresponding color for each category, and the numerical range for each category.

The main portion of the screen is the world map, divided into all countries for which IFs has data. The various shadings of the countries correspond to the various shading of the categories listed in the map legend. By right-clicking on the world map, the user is able to copy the map, zoom in on a particular location of the map, zoom out, or to reset the map to its original point of view.

The bottom section of the screen, from left to right, lists the variable displayed by the map, with the start year shown and the level of the filter. Next is the selected display type. The four buttons to the right of the display type allow the user to zoom in, zoom out, pan, and show the full extent of the world map. The first two buttons allow the user to zoom in on or zoom out from the area under which the cursor is located, while the full extent option resets the view of the world to the original perspective. The pan button allows the user to pan across the world by clicking on different parts of the map, which effectively pans across the world by re-centering the map on the area selected, after having zoomed in or zoomed, without zooming in or out any further. The next set of buttons allows the user to play or pause the movie, and to adjust the speed at which the movie will play. Next is a toggle switch which, when selected, makes the movie play automatically; when the switch is not selected, the user is able to advance the year displayed on the map or to go over previous years.

If the user selected “Create Movie File” during the setup phase, a dialog box will appear after the movie is finished playing that prompts the user to provide a name and a place to save the movie file. The program will alert the user if the .avi file is successfully created and saved. To review the saved movie file, go to [[#Stored_Map_Movie|Stored Map Movie]].

Stored Map Movie <div style="color: red; font-weight: bold; font-size: 200%;">

Feature is no longer available or outdated in the model.

</div>

This feature allows the user to review world map movies that were created and saved. The types of world map movies fall under three categories: Historic, Forecast, and Historic Analysis. The location of a given stored map movie depends on the feature that was used to create it: movie files created in the [[#World_Map_Movie_.28Forecast.29|World Map Movie]] in Data Analysis are found in the Historic category; movie files created in the World Map Movie in Specialized Display are found in the Forecast category; and movies created in [[Use_IFs_(Download)_Data_Analysis#Analyze_Across_Countries_(Cross-Sectional_Analysis)|Analyze Across Countries]] are found in the Historic Analysis category.

To play a movie, simply double click on the desired movie or highlight it and press Play.

Use IFs (Download): Millennium Development Goals

2025-01-28T21:24:25Z

Taylor.Hanna: /* Millennium Development Goals */

{{DISPLAYTITLE:Millennium Development Goals}}

= Millennium Development Goals =

The Millennium Development Goals (MDGs) option is located under the Specialized Display option, which is in turn located under the Display option on the main menu. The MDGs option is also located under the [[Main_Menu_Map|Main Menu Map]] options.

The MDGs are a set of 8 goals which were adopted in 2000 and are supposed to be met by participating countries by 2015. The goals are the following:

*Eradicate extreme poverty and hunger
*Achieve universal primary education
*Promote gender equality and empower women
*Reduce child mortality
*Improve maternal health
*Combat HIV/AIDS, malaria and other diseases
*Ensure environmental sustainability
*Develop a global partnership for development

Each goal has a target that is in turn measured by one or more indicators. For instance, the target of the goal to eradicate extreme poverty is to halve the proportion of people whose income is less than $1 per day, which is measured in by the indicator of purchasing power parity (PPP).

To use the MDGs option, first select the goal to display. Next, select the target, and then the indicator. Experimenting with the various targets and indicators will help familiarize the user to the relationships between the goal, targets, and indicators. The user should then select the [[Country/Region,_Group_or_G-List|country/region or group]] to display. Click the Use Groups option in the heading to switch from countries to groups. Finally, select the run file with which to forecast the selected indicator. The one to use first is probably the Working File, but the user can begin with any of the available run files.

At this point, a chart should appear like the one below.

[[File:Devgoals.gif|frame|right|Example of the current trajectory of a particular indicator, the goal for the indicator, and the forecast]]The box at the top left of the picture shows the selected goal, target, and indicator. The top right box in this case is green, which means that the country/region or group is making fast progress towards meeting the MDG. This color of this box will change according to whether the country is making progress towards meeting the MDG and the speed of that progress (green for fast progress, blue for moderate, yellow for slow), whether the country is regressing from the MDG (red for regression), or whether no change is made in relation to the goal (purple for no progress). Next, the user can select to display the data as a chart (the default setting) or to simply view the data.

The chart will usually display blue bars, which represent historic data, and a series of lines. These lines represent the current trajectory of the particular indicator (red), the goal for the indicator (green) and then the forecast, which will come in a variety of colors. The user can choose to display the chart monochromatically by selecting the Monochrome option under the Graphic heading at the top of the screen. The user can also choose to view the earliest available data for the selected indicator or to view the latest, options which are both located under the Display Options heading. By selecting Using estimated historic line that may not meet forecast line, which is also located under the Display Options heading, the user can view an unadjusted historical trajectory; selecting the option Overriding the base year value historic line with forecast value will force the historic trajectory and forecasted data to meet.

Several options are located in the heading. Clicking on Continue returns the user to the previous screen. The File option allows the user to export or print the file. Clicking on Global Picture causes a dialog box to appear, which contains an option titled Situation Map. The Situation Map is similar to the [[Use_IFs_(Download)_Data_Analysis#World_Map:_Historic_Data|World Map]]. The next option is Causality, that, when selected, will display the particular drivers that affect and are affected by the variable in question. To learn more about the MDGs, select one of the options under the About MDG heading.

= Poverty Level Display =

This feature can be accessed from the main menu of IFs by clicking on Display, then Specialized Display and then Poverty Level Display.

This display feature is useful for visually displaying forecasts of populations who live under a certain monetary constraint within a geographically bound region. For example, if you would like to see a forecast of people living in China who live under $1 a day, simply use the default settings of the "Population with income less than $ ___ per day, log normal computation," box, then select China from the Regions box and click the Add button to place it into the display box at the bottom of this display.

Then, choose to visually display this information as either a Table, a Line Graph or a Bar Graph.

It is also possible to change certain features of the display by selecting the Display Format button at the top of this menu. By selecting these options, it is possible to format your chart or graph for presentation and also change the time horizon of the display to fit your forecasting needs.

Next, it may be of interest to change the Using Countries/Regions and Using Groups toggle. This allows you to see a forecast of how many people live under certain monetary constraints in differently constructed geographically bound regions. The last toggle on this screen allows displays to be either in millions of people or in a percentage of population.

[[File:Devgoals1.gif|frame|right|Example of display settings for poverty levels]]

However, while $1 per day has been a gold standard for poverty measurement for many years, rising incomes have made it necessary to look at populations who live under a higher set of monetary constraints. For example, it is possible to change the default $1 setting to a higher setting, like $5 per day. Additionally, it is possible to display the nth poorest percentile in a country by selecting the Poverty Percentile button and then changing the percentage located in the box.

Guide to Scenario Analysis in International Futures (IFs)

2025-01-13T22:36:30Z

Taylor.Hanna: Merged newer edits (citation and Institute) with the bigger edit.

Please cite as: Sara Turner, Carey Neill, Barry B. Hughes, Kanishka Narayan, Deva Sahadevan, and Jonathan D. Moyer. 2024. "Guide to Scenario Analysis in International Futures (IFs)." Working paper 2024.06.06. Pardee Institute for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY.

The purpose of this document is to facilitate the development of scenarios with the International Futures (IFs) system. This document supplements the IFs Training Manual. That manual provides a general introduction to IFs and assistance with the use of the interface (e.g., how do I create a graphic?). In turn, the broader Help system of IFs supplements this manual. It provides detailed information on the structure of IFs, including the underlying equations in the model (e.g., what does the economic production function look like?).

This document should help users understand the leverage points that are available to change parameters (and in a few cases even equations) and create alternative scenarios relative to the Base Case scenario of IFs (e.g., how do I decrease fertility rates or increase agricultural production?). It proceeds across the modules of IFs, such as demographic, economic, energy, health, and infrastructure, to (1) identify some of the key variables that you might want to influence to build scenarios and (2) the parameters that you will want to manipulate to affect your variables of interest. The Training Manual will help you actually make the parameter changes in the computer program and the Help system will facilitate your understanding of the structures, equations and algorithms that constitute the model. We begin by introducing the types of parameters within IFs and then proceed to a discussion of variables and parameters within each of the IFs modules.

== A Note on Parameter Names ==

In this manual we will provide the internal computer program names of variables and parameters, as well as their descriptions. Those names are especially important for use of the Self-Managed Display form, which provides model users with complete access to all variables and parameters in the system. Most model use, however, employs the Scenario Tree form to build scenarios and the Flexible Display form to show scenario-specific forecasts, and both of those forms rely primarily on natural language descriptions of variables and parameters. To match the names provided here with the options in those forms, you can use the Search feature from the menu. The Training Manual describes how to use features such as the Flexible Display form to see computed forecast variables in natural language. And it also describes how to use the Scenario Tree form to access parameters in something close to natural language. Nonetheless, it helps very much in the use of those features and the model generally to know the actual variable and parameter names.

== Types of Parameters in IFs ==

Equations in IFs have the general form of a dependent or computed variable, as a function of one or more driving or independent variables. ''Variables'', like population and GDP, are the dynamic elements of forecasts in which you are ultimately interested. For instance, total fertility rate or TFR (the number of children a woman has in her lifetime) is a function of GDP per capita at purchasing power parity ('''GDPPCP'''), education of adults 15 or more years of age ('''EDYRSAG15'''), the use of contraception within a country ('''CONTRUSE'''), and the level of infant mortality ('''INFMORT'''). In the most general terms the equation is

:<math>TFR=F(GDPPCP,EDYRSAG15,CONTRUSE,INFMORT)</math>

Parameters of several kinds can alter the details of such a relationship. That is, ''parameters'' are numbers (also represented by names in IFs), that help specify the exact relationship between independent and dependent variables in equations or other formulations (including logical procedures called algorithms). For instance, the model may contains different parameters that tell us how much '''TFR''' rises or falls per unit change in GDP per capita, education levels, contraception use, and infant mortality1; and it contains still others to set bounds on the lower and/or upper values of '''TFR''' over the long run (obviously '''TFR''' should never go negative and probably we will not even want it to go, at least for a long time, to a very low level such as an average of 0.5 children per woman). Some of these parameters are more technical than others in the sense that they may significantly affect the overall stability of the model if users are not very careful with the magnitude or direction of the changes they make; we will focus heavily in this manual on parameters that are easiest to interpret and modify.

In many cases, we are more interested in using a parameter to make a direct change to a variable, rather than indirectly affecting a variable like '''TFR''' through one of its drivers. We often refer to this as the "brute force" method of changing a variable, and this can be done by multiplying the entire result of a basic equation like that above by a number, adding something to that result, or simply over-riding the result with an exogenously (externally) specified series of values. In the case of '''TFR''' we use the multiplier approach, which is described below. The strengths of this approach should be obvious: it preserves model stability, and makes the model more accessible for users. However, the weakness is that in many instances it is more realistic to affect one of the drivers of '''TFR''' rather than '''TFR''' directly.

Beyond multipliers, there are many other types of parameters that IFs uses, although we are forced to abandon '''TFR''' to provide examples. For instance, a switch parameter may turn on or off a particular formulation in preference to another. A target may specify a value towards which we want a variable to move gradually (we would need to specify both the target level and the years of convergence to it).

Overall, key parameter types are:

1. '''Equation Result Parameters'''. Most users will use these parameter types far more often than any other. The three types are:

:a. '''Multipliers'''. This most common of all parameter types in scenario analysis comes into play after an equation has been calculated. They multiply the result by the value of the parameter. The default value, i.e. the value for which the parameter has no effect and to which multipliers almost invariably are set in the Base Case, is 1.0. These parameters are usually denoted with the suffix '''-m''' at the end of the parameter name.

:b. '''Additive factors'''. Like multipliers, these change the results after an equation computation, but add to the result rather than multiplying. The default value is normally 0.0. These are usually denoted with the suffix '''-add''' at the end of the parameter name.

:c. '''Exogenous Specification'''. Sometimes these parameters override the computation of an equation. In other cases, they are actually substitutes for having an equation; that is, they are actually equivalent to specifying the values of a variable over time for which the model has no equation. This typically means establishing a new exogenous series. They typically will have the name of the variable that they over-ride within their own name.

2. '''Targets'''. Especially for the purposes of policy analysis, we often want to force the result of an equation toward a particular value over time (e.g. to achieve the elimination of indoor use of solid fuels). Target parameters are generally paired, one for the target level and one for the number of years to reach the target (from the initial year of the model forecast, 2010). Targets have different types:

:a. '''Absolute targets'''. In this case the target value and year define the absolute value the variable should move toward and the number of years after the first model year over which the goal should be achieved. Together they determine a path in which the value for the variable moves quite directly2 from the value in first year to the target value in the target year. '''Trgtval''' and '''trgtyr''' are the parameter suffixes used for this parameter type. The first of these changes the target itself, and the second alters the number of years to the target. The default value of '''*trgtyr''' parameters should normally be 10 years, but in some cases it is 0, meaning that users must set the number of years to target as well as the target value in order to use these parameters. 
:b. '''Relative (standard error) targets'''. In this case, the target value and year define a relative value towards which the variable should move and the number of years that will pass before the target is reached. The relative value is defined as the number of standard errors above or below the “predicted” value of the variable of interest (a prediction usually based on the country's GDP per capita). Target values less than 0 set the target below the typical or predicted (as indicated by cross-sectional estimations) value of the variable. Target values above 0 set the target above the predicted value. As with the absolute targets, the value calculated using relative targeting is compared to the default value estimated in the model. The computed value then gradually moves from the normal or default-equation based value to the target value. If, however, the computed value already is at or beyond the target (that could be above or below depending on whether the target is above or below the default or predicted value), the model will not move it toward the target. Two different parameter suffixes direct relative targeting: '''setar''' and '''seyrtar'''. The first of these changes the target itself and the second alters the number of years to the target. The default value of the *'''seyrtar''' parameters varies based on the module and even variable. Governance parameters are set to a default of 10 years from the year of model initialization, while infrastructure parameters are set to a default of 20 years. These defaults mean that users do not have to change *'''seyrtar''' as well as *'''setar''' in order to build standard error target scenarios. Changing *'''setar''' should be enough. 

3. '''Rates of change'''. Some parameters specify an annual percentage rate of change. Unfortunately, IFs does not consistently use percentage rates (5 percent per year) versus proportional rates (0.05 increase rate per year, which is equivalent to 5 percent), so the user should be attentive to definitions. There are multiple suffixes that may apply to these, including -'''r''' (changes in the rate) and -'''gr''' (changes the rate of change, growth or decline).

4. '''Limits'''. As indicated for the TFR example, long-term national rates are unlikely to fall and stay below a minimum value. Limits can be minimum or maximum values. These are typically denoted by the suffixes '''-min''', '''-max''', or '''-lim'''.

5. '''Switches'''. These turn off and on elements in the model. These most often affect linkages between modules, but can also change relationships within modules. They are typically denoted by the suffix '''-sw'''.

6. '''Other parameters''' in equations and algorithms. Equations within IFs can become quite complicated. The parameter types discussed to this point provide the easiest control over them for most model users. Relatively few users will proceed further with parameters, and to do so will typically require attention to the specific nature of the equation (e.g. whether independent variables are related to dependent ones via linear, logarithmic, exponential or other relationship forms). That is, one would normally need to understand the model via the Help system or other project documentation in order to use them meaningfully and without causing substantial risk of bad model behavior. The sections of this manual will provide very little information about these technical parameters.

:a. '''Elasticities''': These are relatively common within IFs and specify the percentage change in the dependent variable associate with a percentage change in the independent variable. They are typically prefixed '''el'''- or '''elas'''-.

:b. Equilibration '''control parameters'''. IFs balances supply and demand for goods and services via prices, savings and investment with interest rates, and so on. These processes typically use an algorithmic controller system that responds to both the magnitude of imbalance or disequilibrium and the direction and extent of its change over time (see the Help system descriptions of the model). Although they are not typical elasticities, the two parameters that control each such process usually have the prefix '''el'''- and the suffixes -'''1''' or -'''2'''. Parameters ending with '''1''' relate to disequilibrium magnitude; and parameters end with '''2''' relate to the direction of change.

:c. '''Other coefficients in equations'''. Beyond elasticities, many other forms of parameter can manipulate an equation. When analysts in many fields think of parameters, this is what they mean. In IFs, most users will use them quite rarely because, in the absence of knowledge concerning equation forms and reasonable ranges, the parameters often have little transparent meaning—experts in a field may use them more often. Many analysts think of such parameters as having a constant value over time, and some are unchangeable over time in IFs. IFs allows almost all, however, to be entered as time series and vary with great flexibility across time. Some can be changed for each country and/or sub-dimensions of the associated variable, such as energy types, but others can only be changed globally.

:d. '''Equation forms'''. Although most users will change parameters using the Scenario Tree (see again the Training Manual), the IFs model has made it possible over time to change an increasing number of functions directly (both bivariate and multivariate ones). The advantage this confers is the ability to alter the nature of the formulation (e.g. going from linear to logarithmic) and even, to a very limited degree, the independent or driver variables in the equation. Although some module discussions will occasionally suggest this option, most users will not avail themselves of it. Users who wish to make such changes can do so via the Change Selected Functions options, which can be accessed from the Scenario Analysis Menu on the main page. 

7. '''Initial conditions''' for endogenous variables and convergence of initial discrepancies

:a. '''Initial conditions '''are not, strictly speaking, true parameters, but should reflect data. Yet some users will believe that they have data superior to that in IFs, and the system allows the user to change most initial conditions. After the first year, the model will compute subsequent values internally (endogenously). Initial conditions don’t have a suffix; their names are, in fact, those of the variable itself (e.g., '''POP''' for population).

:b. '''Convergence speed''' of initial-condition based discrepancies to forecasting functions. Because initial conditions taken from empirical data often vary from the values that are computed in the estimated equation used for forecasting, the model protects the empirically-based initial condition by computing shift factors that represent that initial discrepancy (they can be additive or multiplicative). For many variables, values rooted in initial conditions in the first model year should converge to the value of the estimated equation over time; convergence parameters control the speed of such convergence. Most model users will never change the convergence speed. These are denoted by the suffixes '''-cf''' or '''-conv'''.

In the use of all parameters, especially those other than equation result parameters, users will often be uncertain how much it is reasonable to move them—as are often even the model developers. The Scenario Tree form provides some support for judgments on this by indicating high and low alternatives to that of that Base Case. This manual will sometimes provide some additional information.

----

1Because the nature of the equation or formulation will vary (sometimes a driving variable is linearly linked to the dependent variable, sometimes the equation uses a logarithmic, exponential, or other formulation), the coefficients in the equation cannot invariably or even regularly be interpreted as units of change linked to units of change. You may need to explore the Help system and specific equations to fully understand the relationship. This is one of the key reasons we very often turn to the multipliers and additive factors explained in the next paragraph.

2The movement normally will be linear, except that it is possible to set moving targets that create non-linear progression patterns. In some cases, the model explicitly uses non-linear convergence; e.g. to accelerate movement in early years and then to slow it as the target is approached.

== Manipulating Parameters in IFs ==

You will typically manipulate parameters to create scenarios or internally coherent stories about the future. You may create scenarios because you wish to represent and explore the possible impact of policy interventions. Or your stories may represent views of the dynamics of global systems alternative to that in the IFs Base Case scenario. Most of the time, you will be interested in tracking the possible futures of selected variables having particular interest to you. The following sections, each covering a module of the IFs system, begin by identifying some of the variables of potentially greatest interest to you. They then provide suggestions on which parameters are likely to be of most useful in building alternative scenarios for those variables. Each section includes tables listing the most effective parameters with which to target certain outcomes. While these suggestions are intended to help you start to think about which parameters you might use to build your scenarios, it is essential that you consider seriously what the policy-based, empirical-knowledge-rooted, or theoretically informed foundations are for your changes.

== Keys to Successfully Modifying Parameters in IFs ==

*Test all parameter changes individually before building combinations, in order to be able to identify which parameters are having specific impacts
*After changing a parameter value and running a scenario, check the impact on the most proximate or closely related variables (identified in the tables of each module section), before checking the secondary impacts of your selected parameter on more distally related variables
*Tie parameter changes to policy options, empirical knowledge, or theoretical insight identified in literature
*Bear in mind the relevant geographical level at which a parameter operates; some parameters function directly at a global level (e.g., global migration rates), while others will be most relevant at the regional, or national level
*Some parameters are only effective when used in combination with one another (such as target values and years to reach a target)
*Some parameters cancel one another out; for example, '''trgtval''' and '''setar''' parameters cannot be used together except under very limited circumstances that we attempt to note in the subsequent text
*In many cases, variables affected by certain parameters have natural maximums (e.g. 100 percent) or minimums (e.g. fertility rate), so that changes to the parameters affecting them, where countries may already be approaching such a limit, will not have a significant impact
*The IFs systems contains many equilibrating processes, such as those around prices; interventions meant to affect one side of such an equilibration (such as efforts to reduce energy demand) may have offsetting effects (such as lower prices for energy and resultant demand increase) that make it harder than you expect to push the system in the desired direction; real-world policy makers often face such difficulties and may need to push harder than anticipated 
== Prepackaged Scenarios ==

A number of alternative scenarios come prepackaged with the model. To access them, select Scenario Analysis from the main menu, and then the option labeled Quick Scenario Analysis with Tree. Once in the scenario display, select Add Scenario Component to view all of the .sce (scenario) files that are stored on your computer normally at the path C:/Users/Public/IFs/Scenario. Exploring several simple interventions contained in the folder structure should give users an overview of some of the leverage points in that they may wish to use in each module.

= Demographic Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width: 343px;"
|-
| style="width: 57px;" | '''Variable'''
| style="width: 235px;" | '''Description'''
|-
| style="width: 57px;" | '''POP'''
| style="width: 235px;" | Total population
|-
| style="width: 57px;" | '''POPLE15'''
| style="width: 235px;" | Population, age 15 or less 
|-
| style="width: 57px;" | '''POP15TO65'''
| style="width: 235px;" | Population, age 15 to 65 
|-
| style="width: 57px;" | '''POPGT65 '''
| style="width: 235px;" | Population, greater than 65 
|-
| style="width: 57px;" | '''POPPREWORK '''
| style="width: 235px;" | Population, pre-working years 
|-
| style="width: 57px;" | '''POPWORKING '''
| style="width: 235px;" | Population, working years 
|-
| style="width: 57px;" | '''POPRETIRED '''
| style="width: 235px;" | Population, retired 
|-
| style="width: 57px;" | '''YTHBULGE '''
| style="width: 235px;" |  % of the population between 15 and 29 
|-
| style="width: 57px;" | '''POPMEDAGE '''
| style="width: 235px;" | Population, median age 
|-
| style="width: 57px;" | '''LAB '''
| style="width: 235px;" | Labor force size 
|-
| style="width: 57px;" | '''BIRTHS '''
| style="width: 235px;" | Births 
|-
| style="width: 57px;" | '''DEATHS '''
| style="width: 235px;" | Deaths 
|-
| style="width: 57px;" | '''MIGRANTS '''
| style="width: 235px;" | Net migration (inward) 
|-
| style="width: 57px;" | '''CBR '''
| style="width: 235px;" | Crude birth rate 
|-
| style="width: 57px;" | '''CDR'''
| style="width: 235px;" | Crude death rate 
|-
| style="width: 57px;" | '''TFR'''
| style="width: 235px;" | Total fertility rate 
|-
| style="width: 57px;" | '''CONTRUSE'''
| style="width: 235px;" | Contraceptive usage 
|-
| style="width: 57px;" | '''LIFEXP'''
| style="width: 235px;" | Life expectancy 
|-
| style="width: 57px;" | '''MIGRATE'''
| style="width: 235px;" | Net migration rate (inward) 
|}

The IFs demographic module breaks country populations down into 21 five-year age groups, each one subdivided by gender. This allows the model to create an age-sex cohort structure that responds to changes in the three fundamental drivers of population: fertility, mortality, and migration. Births are calculated as a function of each country’s fertility distribution and age distribution. As children are born, they enter the lowest band of the age-sex structure, the layer representing people aged 0 through 5. Each country’s population growth is reduced by deaths at each age level; like births, deaths are calculated as a function of the mortality distribution and the age distribution. Finally, migration patterns either add to, or subtract from, each country’s population, depending on the balance of immigration and emigration3 . Each of the three proximate drivers of population is influenced by deeper social processes: births are a product of fertility patterns; deaths are linked to life expectancy; and net migrants are determined by an overall global migration rate.

Total population is represented in millions of people via '''POP''', but users may also choose to explore the age structure within society. Three variables break population down into broad age groups: '''POPLE15''', people age 15 or younger, '''POP15TO65''', people age 15 to age 65, and '''POPGT65''', people older than age 65. Three additional variables provide a similar disaggregation of population: '''POPPREWORK''', '''POPWORKING''', '''POPRETIRED'''—as the names suggest, they measure the number of people who have yet to enter their working years, the number of people currently in their working years, and the number of people who have completed their working years. The years comprising an adult’s working life may vary from country to country, depending on education systems and retirement ages. Users can explore additional population characteristics via the variables '''YTHBULGE''', the percent of all adults (15 and older) between the ages 15 and 29; '''POPMEDAGE''', the median age of a country’s population; and '''LAB''', the size of the labor force, recorded in millions of people. For any country, the complete age and sex breakdown is available under the Specialized Displays for Issues option under the Display sub-menu. From the Specialized Displays menu, select Population by Age and Sex, and click the button labeled Show Numbers. This will bring up detailed population figures for any of the countries in the IFs system. To view a population pyramid display, toggle the Distribution Type setting on the menu bar.

The three immediate drivers of population change—births, deaths, and migration—are captured in the model as flows. Every year babies are born ('''BIRTHS'''), people die ('''DEATHS''') and people leave countries to live elsewhere ('''MIGRANTS'''). These processes alter the stock of population in countries, regions and the world as a whole. The speed at which a population will grow or decline, and the attendant shift in a population’s age structure, depend on crude birth rates ('''CBR''') and crude death rates ('''CDR''')—the number of births and deaths per 1,000 people.

Each of the immediate drivers is linked to deeper determinants of population. For instance, fertility rates are responsive to income, education and infant mortality rates, offering points of access elsewhere in the model. Total Fertility Rate ('''TFR''') is a variable that is essential to our understanding of populations’ reproductive behavior. '''TFR''' is, essentially, the number of children the average woman in a country can expect to have over the course of her lifetime. In order for the overall population size to remain roughly stable, '''TFR''' must meet the replacement rate for that country. For developed countries this is approximately 2.1 children per woman, but the figure may be higher in countries with high mortality rates, and is lower in many. While '''TFR''' largely determines future population growth, it is not the only behavioral variable of note: '''CONTRUSE''' captures the percent of fertile women who routinely use some method of contraception.

For a complete discussion of mortality see the [[Health#Health|Health module]], where deaths are computed. They are responsive to deep or distal factors such as income, education and technological advance, as well as to more proximate ones such as levels of undernutrition and smoking. A key indicator for the population model, linked to deaths, is '''LIFEXP''', or life expectancy, which provides a measure of the median life expectancy of a newborn in a particular year given the current mortality distribution. Although life expectancy can be calculated for any age, IFs focuses on life expectancy at birth. This variable is key to the functioning of the IFs system because many of the parameters that affect mortality do so by changing life expectancy.

The final proximate driver of population growth is migration. '''MIGRANTS''' measures net migrants in raw figures, reported in millions of people; but this variable is determined by '''MIGRATE''', the net migration rate, reported as percent of the total population. The basic forecasts of migration in IFs are one of the very few variables that are exogenous. Nonetheless, there is parametric control of it.

The demographic module features an array of parameters that allow users to create alternative demographic scenarios by exploring uncertainty surrounding: fertility, mortality and migration, as well as the years making up people’s working lives.

----

3In IFs, the age distribution of migrants is controlled by an internal vector across age categories, not available for manipulation through the model’s front-end.

== Parameters to Affect Fertility ==

{| border="1" cellspacing="1" cellpadding="1" style="width: 443px;"
|-
| style="width: 68px;" | '''Parameter'''
| style="width: 115px;" | '''Variable of Interest'''
| style="width: 151px;" | '''Description'''
| style="width: 93px;" | '''Type'''
|-
| style="width: 68px;" | '''tfrm'''
| style="width: 115px;" | TFR, CBR
| style="width: 151px;" | Total fertility multiplier 
| style="width: 93px;" | Multiplier 
|-
| style="width: 68px;" | '''contrusm'''
| style="width: 115px;" | CONTRUSE
| style="width: 151px;" | Contraceptive use multiplier 
| style="width: 93px;" | Multiplier 
|-
| style="width: 68px;" | '''eltfrcon'''
| style="width: 115px;" | TFR
| style="width: 151px;" | Elasticity of total fertility rate to contraception use 
| style="width: 93px;" | Elasticity
|-
| style="width: 68px;" | '''tfrmin'''
| style="width: 115px;" | TFR
| style="width: 151px;" | Long term TFR convergence value 
| style="width: 93px;" | Limit
|}

The single most powerful way for users to modify fertility rates is to manipulate '''tfrm''', a parameter that directly alters the total fertility rate within a country or region. This parameter serves as a multiplier on the fertility rate calculated by the model—a 20% increase or decrease in the value of the parameter will result in a similar magnitude of change in the value of the associated variable, '''TFR'''. Because it is a brute force multiplier, users should justify their modifications to the parameter. When used thoughtfully, '''tfrm''' can be a powerful tool for scenario analysis. It can be used to model the impact of fertility control initiatives that extend beyond simple contraceptive use. An example would be the implementation of a program to offer public seminars on the benefits of having fewer children, which could lower the fertility rate even when overall contraceptive usage rates are low. Health care programs for women are a major contributor to fertility decline.

Users can also directly change the percentage of the population that uses contraceptives via '''contrusm''', a parameter that indirectly affects the total fertility rate via '''CONTRUSE'''. As this is a multiplier, it works the same way as '''tfrm'''. It can be used to model the impact of an increase in the availability of family planning education, a campaign to promote the use of condoms, or any other intervention that would likely increase (or decrease) the percentage of a population using contraceptives. Additionally, the parameter '''eltfrcon''' allows users to control the elasticity of total fertility to contraceptive use. For example, a weaker relationship between the two variables might be justified if the contraceptive methods in use in a country or region are widely known to have high failure rates.

When creating alternative scenarios that span long time horizons, users may wish to modify fertility assumptions built into the demographic module. As countries grow richer and reach higher levels of educational attainment, total fertility rates tend to decrease. However, in forecast years, a minimum value prevents countries from dipping too far below replacement rate. As a default setting, the minimum parameter, '''tfrmin''', is set to 1.9. Thus, in the Base Case, '''TFR''' in highly developed countries will converge to just below 2 children per woman. By increasing or decreasing the parameter, users can experiment with different long-term fertility patterns.

== Parameters to Affect Mortality ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''mortm'''
| DEATHS
| Mortality multiplier (not cause specific) 
| Multiplier 
|-
| '''hlmortm'''
| DEATHS
| Mortality multiplier by cause 
| Multiplier 
|}

The health module write-up includes a full description of the drivers of mortality in the IFs system, and explains how to manipulate each one. However, one parameter affecting mortality, '''mortm''', is worth discussing separately. This parameter functions similarly to the '''hlmortm''' parameter available in the health module, but does not disaggregate by cause of death. Similar to '''tfrm''', '''mortm''' can be used to model the impact of events that have broad impacts across the population, such as the end of an armed conflict or the implications of a plague. Usually however, if a user is building a scenario analyzing health trends, using the '''hlmortm''' multiplier will be more useful because it disaggregates mortality on the basis of cause. Because morbidity rates in IFs are linked normally to mortality rates, these parameters will affect them also.

== Parameters to Affect Migration ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''wmigrm'''
| MIGRATE, MIGRANTS
| World migration rate multiplier 
| Multiplier
|-
| '''migrater'''
| MIGRATE, MIGRANTS
| Net migration rate (inward) as a percent total population 
| Rate of change
|-
|'''migrateinm'''
|MIGRANTS, MIGRANTSIN
|Inward migration rate multiplier
|Multiplier
|-
|'''migrateoutm'''
|MIGRANTS, MIGRANTSOUT
|Outward migration rate multiplier
|Multiplier
|}

Users interested in modifying migration patterns should bear in mind that migrant flows are subject to an accounting system that keeps the global number of net migrants equal to zero. In other words, a person leaving one country will be accounted for when they enter another country. Changing the world migration rate, '''wmigrm''', is the easiest way to affect migration patterns in IFs. Altering this parameter will allow users to increase the overall rate at which migration occurs at a global level, enabling users to simulate large scale increases (or decreases) in migration generated by, say, reductions in visa fees, or the opening of borders as is the case in the EU’s Schengen area. The parameter '''migrater''', on the other hand, allows users to affect the rate of migration into individual countries or regions (values can range from positive, indicating net inward migration, to negative, indicating net outward migration). Alternatively, '''migrateinm''' and '''migrateoutm''' can also be used to change in-migration and out-migration rates for a country or region.

== Parameters to Affect Working Age ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''workageentry'''
| POPPREWORK, POPWORKING 
| Working age determinant 
| Exogenous specification 
|-
| '''workageretire'''
| POPWORKING, POPRETIRED 
| Retirement age determinant 
| Exogenous specification 
|-
|'''workageretirendpctuse'''
|WORKAGERETIREND
|Percent of needed retirement age to deal with aging
|Exogenous specification
|-
|'''workageretiresv'''
|CPCTND
|Age of retirement to compute savings rate
|Exogenous specification
|}

In addition to manipulating the rate at which populations grow, users can experiment with the effects of changing a country’s working age, something that will be fiscally important in many countries as populations age. The variables '''POPPREWORK''', '''POPWORKING''' and '''POPRETIRE''' map the typical age structure of a country or region’s work force. Two parameters, '''workageentry''' and '''workageretire''', control the age at which a person is considered eligible for work and the age at which a person is eligible for retirement. Changes in the workforce’s age configuration link forward to economic production via the size of the labor force ('''LAB'''). Raising or lowering the retirement age will additionally affect government finances via the size of population of retirement age and the level of pension support provided to households ('''GOVHHPENT''').

== Prepackaged Scenarios ==

An installation of IFs includes high and low population-framing scenarios. Originally created for the poverty volume of the Pardee Institute’s Potential Patterns of Human Progress (PPHP) series, the two files are located in the Framing Scenarios folder under Population. Both scenarios feature the direct total fertility rate multiplier. '''Tfrm''' in the high fertility scenario is set to 1.5 globally. In the low fertility scenario, '''tfrm''' is set to .6 in non-OECD nations, and the limit parameter '''tfrmin''' is set to 1.6 globally. Although the two scenarios only feature a few interventions, the effects of such a large change in human reproductive behavior would have significant forward linkages throughout each of the model’s systems.

Four of the prepackaged scenarios located in the folder Interventions and Agent Behavior contain additional examples of the demographic module’s parameters: Non OECD Contraception Use Slowed, Non OECD Contraception Use Accelerated, World Migration High, and World Migration Low. The pair of scenarios focusing on contraceptive usage rates both utilize '''contrusm'''. In the accelerated scenario, the multiplier takes the value 1.2 in non-OECD nations; and the value 0.8 in the slowed scenario for all non-OECD nations. The two alternate migration scenarios similarly feature interventions on a single parameter: the global migration multiplier '''wmigrm'''. In the high scenario the parameter takes on a value of 2, doubling global migration flows; and in the low scenarios flows are halved, with '''wmigrm''' declining to a value of 0.5.

= Health Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''LIFEXP/LIFEXPHLM '''
| Life Expectancy 
|-
| '''CDR'''
| Crude Death Rate 
|-
| '''DEATHCAT'''
| Deaths by Mortality Type 
|-
| '''HLYLL'''
| Years of Life Lost 
|-
| '''HLYLLWORK'''
| Years of Working Life Lost 
|-
| '''HLYLD'''
| Years Lived with Disability 
|-
| '''HLDALY'''
| Disability Adjusted Life Years Lost 
|-
| '''INFMOR'''
| Infant mortality rate 
|-
| '''HLSTUNT'''
| Percentage of population stunted 
|-
| '''MALNCHP'''
| Percentage of children malnourished 
|-
| '''MALNPOPP'''
| Percentage of population malnourished 
|-
| '''HLBMI'''
| Body Mass Index 
|-
| '''HLOBESITY'''
| Percentage of population obese 
|-
| '''HLSMOKING'''
| Percentage of population that smokes 
|}

The primary variables of interest in the IFs health module are those that pertain to mortality and morbidity due to a variety of causes. '''LIFEXP''' and '''CDR''', discussed in the population module, provide basic measures of population health. '''DEATHCAT''' provides a measure of the number of deaths (in thousands) due to different categories of mortality. IFs can display health variables in the following categories of disease: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other Non-Communicable Diseases, Unintentional Injuries, Intentional Injuries, Diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Using the Flexible Display form, it is also possible to see many of these variables in the rolled-up categories of Communicable Disease, Non-Communicable Disease, and Injuries or Accidents. Because different health conditions affect age cohorts differentially, the above measure is insufficient in understanding the full impact of ill health. For this reason, it is also possible to break down the actual number of deaths accruing to each cohort, sex, and cause via the Specialized Display menu under the health heading. For example, both the Mortality by Age, Sex, and Cause and the J-Curve displays provide useful information about the health status of a country.

Three other measures help to enrich the picture: '''HLYLL''', '''HLYLD''' and '''HLDALY'''. Like '''DEATHCAT''', these aggregate (across age-cohort) measures are available by cause and country. '''HLYLL''' is a measure of the number of life years lost due to premature death. It differs from the '''DEATHCAT''' variable because it represents the burden of premature mortality in terms of life years lost, which allows us to account for the fact that some diseases, like HIV/AIDS, primarily affect younger people, while others, like cardiovascular disease, are primarily fatal in older adults. Although the total number of deaths may be the same between two countries for each cause, there may be significant differences between two countries’ health profiles in terms of YLLs.

'''HLYLD''' is another measure that represents the burden of ill health in terms of life years of impact. It indicates the burden of years lived with disability or disease. In calculating YLD, IFs uses the disability weights that WHO created to rank the relative severity of different conditions and their impact on productivity.

Finally, Disability Adjusted Life Years (DALYs) are a measure of morbidity (disability or infirmity due to ill health). '''HLDALY''' sums YLLs and YLDs to create a measure of the number of years of life lost to both premature mortality and morbidity due to ill health. Like the other measures discussed above, DALYs can be broken down by different disease categories within IFs. The DALY is probably the most expansive measure of ill-health within a population because it includes mortality burden by age of death and the lost quality of life for those who did not die from health events, but who are disabled by them in some way.

Other measures provide indicators of health in regard to certain specific risk factors for disease or among certain segments of the population. Infant mortality, '''INFMOR''', can be used to assess the burden of ill health among children under one year of age. '''HLSTUNT''', displays the percentage of the population who are stunted (have low height for age),while '''MALNCHP''' and '''MALNPOPP''', provide information on the percentage of the child and adult population who are undernourished respectively. The variables '''INFMOR,''' '''HLSTUNT''' and '''MALNCHP''' are especially useful for assessing the burden of ill health due to communicable diseases and other conditions that primarily affect children. By contrast, the variables '''HLBMI''', '''HLOBESITY''', and '''HLSMOKING''' provide risk factor information on diseases that affect primarily adults. '''HLBMI''' represents the body mass index in a country while '''HLOBESITY''' and '''HLSMOKING''' provide information on the percentage of the population that is obese or smokes.

Other variables that will be useful to users interested in specific conditions or subpopulations include indicators on stunting and BMI, as well as smoking and obesity. Variables for HIV/AIDS are also available and discussed separately below in the subsection on the HIV/AIDS sub-module.

== Parameters to Affect Overall Health and Burden of Disease ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hlmortm'''
| DEATHCAT/HLYLL/HLDALY
| Multiplier on Mortality (by cause) 
| Multiplier 
|-
| '''hlmorbm'''
| YLD
| Multiplier on morbidity 
| Multiplier 
|-
| '''hlstddthsw'''
| DEATHCAT
| Switches DEATHCAT from absolute numbers to deaths/1000 
| Switch
|}

The above parameters provide simple ways to directly affect the burden of disease within a country. The most important parameter for modifying mortality rates is '''hlmortm''', a parameter that allows users to increase or decrease the prevalence of deaths in any particular category of illness. IFs modifies mortality in the following categories: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other Non-Communicable Diseases, Unintentional Injuries, Intentional Injuries, diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Altering the mortality burden will affect the variables '''DEATHCAT''', '''HLYLL''', and '''HLDALYs'''. The parameter will indirectly affect morbidity because of its direct link to mortality. In the case of Mental Health Diseases, the parameter will not have much impact on '''DEATHCAT''', but may have a significant impact on the number of DALY’s experienced by a population. Because '''hlmortm''' is a multiplier, increasing its value from 1 to 1.2 represents a 20% increase in the burden of mortality from a particular cause. A similar parameter, '''hlmorbm''', allows users to affect morbidity directly through a brute force multiplicative parameter. This allows users to affect the years lost to disability in a working life and by extension multifactor productivity due to human capital ('''MFPHC'''). The '''hlstddthsw''' allows users to switch between displaying '''DEATHCAT''' in absolute numbers to deaths per thousand people.

== Parameters that Affect Communicable Diseases ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''watsafem'''
| WATSAFE, INFMOR 
| Percentage of population with access to safe water 
| Multiplier 
|-
| '''sanitationm'''
| SANITATION, INFMOR 
| Percentage of population with access to improved sanitation 
| Multiplier 
|-
|'''malnelimstartyr'''
|MALNPOPP
|Undernourishment elimination start year
|Standard error
|-
|'''malnelimtargetyr'''
|MALNPOPP
|Undernourishment elimination target year
|Standard error
|-
| '''malelimprecisesw'''
| MALNPOPP
| Undernourishment elimination
| Multiplier 
|-
| '''ylm'''
| YL
| Yield multiplier on agriculture 
| Multiplier 
|-
| '''hivm'''
| HIVCASES 
| Rate of HIV infection 
| Multiplier 
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|}

Above are a number of the parameters that users may wish to use to manipulate communicable diseases (which predominantly affect children). '''Ylm''' is a multiplicative parameter in the agriculture module that can be used to change the yield of agricultural lands within a country, affecting the number of calories available for consumption, and thereby altering the rates of malnutrition and obesity. '''Clpccvm''' is another parameter in the agriculture module that impacts the calories per capita available, and also alters the rate of malnutrition by changing the distribution of caloric consumption for a country/region. '''Watsafem''' and '''sanitationm''', in the infrastructure module, influence the percentage of the population that has access to safe water and sanitation respectively, thus decreasing childhood exposure to diarrheal disease, malnutrition and premature death. Other parameters to control safe water and sanitation access are discussed in the infrastructure section of the model.

Finally, although HIV is more thoroughly discussed in the HIV/AIDs submodule, one brute force parameter is worth noting here. '''Hivm''' allows users to directly affect the rate of infection with the HIV virus.

== Parameters that Affect Non-Communicable Disease ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''envpm2pt5m '''
| ENVPM2PT5 
| Increases levels of environmental pollution 
| Multiplier 
|-
| '''hlsmokingm '''
| HLSMOKING 
| Increases rate of smoking 
| Multiplier 
|-
| '''hlobesitym '''
| HLOBESITY 
| Prevalence of obesity 
| Multiplier 
|-
| '''hlbmim '''
| HLBMI 
| Multiplier on body mass index 
| Multiplier 
|}

'''Hlsmokingm''' is a multiplicative parameter that will change the rate of smoking, which will affect the prevalence of respiratory diseases. '''Envpm2pt5m''' is a multiplicative parameter that will change the level of ambient environmental pollution in terms of parts per million; higher levels of environmental pollution are a risk factor for both communicable and non-communicable respiratory diseases.

'''Hlobesitym''' works similarly to affect the prevalence of obesity within a society in the absence of overall caloric intake changes. This parameter can be used to model the impact of changing levels of physical activity within a society. Both of the above parameters work similarly to other multiplicative parameters: increasing the value of the parameter to 1.2 from 1, represents a 20% increase in the value of the parameter over the base case. By the same token, users can use '''hlbmim''' to affect the body mass index in a country, a major risk factor for cardiovascular diseases, diabetes, and overall morbidity. Please note: '''hlobesitym''' affects only obesity rates and has no affect on health; in contrast, '''hlbmim''' will affect body mass index, obesity, and deaths from heart disease and diabetes.

== Parameters that Affect Injuries and Accidents ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''deathtrpvm '''
| DEATHTRPV 
| Multiplier on traffic deaths per vehicle 
| Multiplier 
|-
| '''deathtrpvsetar, deathtrpseyrtar '''
| DEATHTRPV 
| Standard error target for traffic deaths per vehicle 
| Relative target Value/Year 
|}

Only a small set of parameters allow users to affect injuries and accidents, and these primarily revolve around reducing traffic deaths. Users may reduce traffic deaths as a ratio of the number of vehicles in a country using either a multiplier, '''deathtrpvm''', or a pair of standard error targeting parameters, '''deathtrpvsetar''' and '''deathtrpseyrtar'''. Standard error targeting is discussed in detail in the [[Infrastructure#Infrastructure|infrastructure module]]. These parameters allow users to model the impact of road safety on mortality and, by extension, on economic productivity.

== Parameters to Affect Technology ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hlmortmodsw '''
| CDR 
| Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific 
| Switch
|-
| '''hltechshift '''
| CDR 
| Rate of change in health technology 
| Additive factor 
|-
| '''hltechlinc '''
| CDR 
| Rate of change in health technology in low income countries 
| Additive factor 
|-
| '''hltechssa '''
| CDR 
| Rate of change in health technology in Sub-Saharan Africa 
| Additive factor 
|-
| '''hltechbase '''
| CDR 
| Rate of change in health technology at base 
| Exogenous specification 
|}

Aside from the direct and indirect parameters affecting health, the distal drivers of health include per capita GDP, years of education, and technology. Per capita GDP is an element of the [[Economics#Economics|economic module]] and can be changed in a number of ways, but especially by changing the elements that make up multifactor productivity. Years of education is an element of the [[Education#Education|education module]] and can be changed by altering the duration of schooling, and the completion rate.

Moving to the third distal driver of health, there are a number of parameters built into the health module that can be used to alter the rate of technological change. '''Hlmortmodsw''' is a master switch that, when set to 1 as in the Base Case default, reduces technological progress for low-income countries of Africa, Europe, Southeast Asia, and West Pacific based on geographic and income categories. There are parameters available to alter these assumptions about differentials in mortality declines in these regions, but they only have an effect in the base case; when '''hlmortmodsw''' is set to 0 these parameters have no impact.

Once '''hlmortmodsw''' is set to 1, users can manipulate mortality patterns through several parameters. '''Hltechshift''', alters the rate of change for health technology impacts relative to GDP. The '''hltechshift''' parameter allows users to change the mortality rate using a shift parameter that alters the technology factor affecting mortality decline relative to initial GDP. '''Hltechlinc''' and '''hltechssa''' can be used to change the rate of technological advance resulting in mortality decline in low-income countries ('''hltechlinc''') and sub-Saharan Africa ('''hltechssa''') specifically. Meanwhile, the '''hltechbase''' parameter allows users to change the base level of technological change across the 20 world, rather than country by country as you can do using the '''hltechshift''' parameter. All of these parameters pertain to all causes of mortality except cardiovascular mortality, which uses a different regression equation.

== Prepackaged Scenarios ==

Three major integrated scenarios on health were developed by the Pardee Center for the health volume of the Patterns of Potential Human Progress series (Hughes et al., 2011). The World Integrated Scenario Sets folder contains the scenarios that were built for this volume, of which three are worth an extended discussion. The first is the Proximate Drivers Excluding Environment folder, which contains parameters to individually alter four of the major risk factors for several causes of mortality. These are Body Mass Index which is a risk factor for cardiovascular disease; under nutrition, which is a risk factor for communicable diseases; smoking which is a risk factor for respiratory disease; and large increases in the number of cars per person coupled with poor pedestrian safety, which is a major risk factor for accidental death. This scenario also includes increased to improved water sources and piped sanitation taken from the infrastructure module, and parameters to reduce environmental exposure to poor air quality. This scenario reduces these risk factors to their theoretical minima, to simulate aggressive efforts to reduce, high BMI, the obesity rate, childhood malnutrition, smoking, and traffic mortality. Malnutrition is set to 0.01, smoking and obesity multipliers are set to 0, BMI multiplier to 0.8, vehicle fleets to 0.5, and traffic mortality to 0.

Another important pair of prepackaged scenarios contains the optimistic Luck and Enlightenment scenario, and a scenario that considers what happens when Things Go Wrong. The Luck and Enlightenment scenario includes improvements to HIV/AIDS, sanitation access, improved air quality, and reduced smoking rates which help lower the burden of NCDs. It also features changes to the burden of communicable disease designed to increase the levels of these. A variation to Luck and Enlightenment has add-ins that also increase foreign aid donations and agricultural yields, effectively modeling a situation in which increased global cooperation supports these efforts. Things Go Wrong models a world in which air quality worsens, smoking and obesity rates increase and there is little international cooperation on addressing these challenges.

= HIV/AIDS Submodule =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''HIVCASES'''
| Number of HIV cases 
|-
| '''HIVRATE'''
| HIV infection rate 
|-
| '''HIVTECCNTL'''
| Rate of technical control of infection, cumulative reduction in infection rate 
|-
| '''AIDSDTHS'''
| Number of AIDS deaths 
|-
| '''AIDSDRATE'''
| Death rate from AIDS 
|-
| '''AIDSDTHSCM'''
| Cumulative Number of AIDS deaths since first year of model 
|}

HIV and AIDS have attracted significant interest among policy makers because of the tremendous toll that these diseases have on populations in both human and economic terms. Because of this interest, it is worth discussing the HIV/AIDS submodule separately from the rest of the health module. That submodule represents both the extent of HIV prevalence in a population (a stock variable) and the annual deaths from AIDS (a flow variable driven in substantial part by the prevalence rate, but also responsive to technological advance in the fight against AIDS). A number of key variables are available to represent the burden of HIV and AIDS within a country.

Three variables are key to understanding the progression of infection within a country. '''HIVCASES''' provides the total number of HIV cases, '''HIVRATE''' represents a flow variable showing the rate at which people are being infected with HIV, and '''HIVTECCNTL''' indicates the progress being made in reducing the rate of infection within a country.

Three other variables assess mortality due to HIV and AIDs within a country. Similar to HIV, the variables '''AIDSDTHS''' and '''AIDSDRATE''' indicate the number of AIDs deaths and the rate of mortality from AIDs respectively, while '''AIDSDTHSCM''' represents the cumulative number of deaths due to the disease.

== Parameters to Affect Prevalence ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hivm'''
| HIVRATE 
| HIV infection rate, multiplier of percent of population infected 
| Multiplier
|-
| '''hivtadvr '''
| HIV CASES/ HIVRATE 
| Technical advance rate in of control of infection 
| Rate of change 
|-
| '''hivpeakr '''
| HIVCASES/ HIVRATE 
| HIV infection rate at year of peak 
| Target value
|-
| '''hivpeakyr '''
| HIVRATE 
| Sets year of epidemic peak 
| Target year
|-
| '''hivincr '''
| HIVCASES 
| HIV increase rate, only used prior to 2000 
| Rate of change 
|}

Modifying the infection rate with '''hivm''' is probably the easiest way to adjust the burden of HIV infection within a country. Like '''hlmortm''', '''hivm''' is a multiplicative parameter. In other words, increasing the value of the parameter in scenario analysis from 1 to 1.2 represents a 20% increase in the rate of infection relative to the base case. '''Hivtadvr''' allows users to change the prevalence of HIV, once the epidemic has peaked, by a certain percent annually to model different assumptions about the rate at which control technologies will improve, reducing the prevalence of the disease over time. Unlike the mortality multiplier, which takes effect once the model has calculated the base case, this parameter will affect the actual calculations the model makes while running. This parameter functions as additive factor to a growth rate within IFs. In other words, a 0.01 increase in the parameter represents a 0.01 increase in the growth rate for the technical advance rate in HIV infection control ('''hivtadvr''').

The HIV submodule is designed to allow users to affect the course of the epidemic across countries and across time. One way to alter the course of the epidemic is by manipulating the coefficient on '''hivpeakr''', which is an additive parameter that will increase the peak rate of infection over the course of the epidemic. Thus a 0.01 increase in the value of the coefficient represents a 0.01 increase in the peak infection rate. An associated parameter, '''hivpeakyr''' sets the date at which the epidemic will peak before the infection rate begins to decline. Changing this parameter in the Scenario Analysis page will allow users to set any year between 2010 and 2100 as the year of peak infection rate depending on their assumptions regarding the technical rate of advance in controlling the disease. Finally, the parameter '''hivincr''' controls the increased rate in infection prior to 2000, when our knowledge of the epidemic was much less complete and control efforts were far less effective. It is important to note that the parameter '''hivpeakr''' must be used in combination with parameters, '''hivpeakyr''' and '''hivincr'''.

== Parameters to Affect Mortality ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''aidsdrtadvr '''
| AIDSDTHS/AIDSRATE 
| AIDs death rate, technical annual advance rate in control 
| Rate of change 
|-
| '''aidsdratem '''
| AIDSRATE 
| AIDs death rate as % of HIV infection rate, multiplier 
| Multiplier
|}

Just as there are a variety of parameters available to control the prevalence of HIV within a population, there are also a number of parameters that allow users to control the lethality of the epidemic. The first of these parameters allow user to change the death rate as a percentage of the infection rate via the parameter '''aidsdratem'''. This parameter directly alters the lethality of the disease; it serves as a proxy for the presence or absence of control measures within a country since the availability of anti-retroviral medications will affect the rate at which people who are HIV positive die from AIDs. Of course, new research strongly suggests that ART therapies may also significantly reduce the HIV infection rate as well, but because these are not yet linked in the model, users should be aware that a more realistic use of this parameter would alter not only the AIDs mortality rate, but the infection rate as well. The other parameter available to users to control mortality from AIDs is '''aidsdrtadvr''', a parameter which changes the technical annual advance rate in control. This parameter simulates the annual advance in technologies to control AIDs mortality, altering the lethality of the disease.

== Prepackaged Scenarios ==

There are several prepackaged scenarios that deal with the HIV/AIDS epidemic. The first, under the heading HIV/AIDS in the Technological Advance section of the Framing Scenarios folder, models two scenarios around technological advance to control the epidemic. One models rapid technical advances to control HIV infection, while the other presents a scenario in which technological progress slows, slowing the resulting decline in infections.

A second set of prepackaged scenarios are available to affect HIV/AIDS are focused on altering the course of the epidemic in key countries, rather than at a global level. They are called: Intermediate HIV/AIDS, Intermediate for New School Paper, Severe HIV Aids and Total Failure to Control HIV AIDs and are located in the Surprises and Wildcards folder, under the heading AIDs. These scenarios modify the course of the AIDS epidemic in Russia, China, India, and the world at large. Each one affects parameters controlling the infection rate at the peak year of the epidemic, the peak infection rate, the initial rate of infection, the rate of advance in the infection, and the elasticity of multifactor productivity to life expectancy. They give a good example of how to modify combinations of parameters in specific countries to create different trajectories for the epidemic.

= Sever Acute Malnutrition (SAM) Submodule =
In addition to the prevalence and incidence of the above mentioned diseases, IFs also calculates the prevalence and incidence of severe acute malnutrition (SAM) and its impact on productivity. Severe Acute Malnutrition (SAM) (also known as severe wasting) is a deficiency of protein or other minerals and vitamins leading to a loss of body fats and muscle tissues. SAM can be defined by a Weight-for-Height Z-score <-3 compared to the median WHO growth standards; by visible severe wasting; or by the presence of nutritional oedema (Prudhon, Briend, Prinzo, Daelmans, & Mason, 2006). It has been identified as a significant cause of death and disability globally (Black et al., 2008, 2013).

The key variables to understanding the impact of the disease are the prevalence of SAM (MALNCHPSAM), the number of cases of SAM that occur in a particular year or the incidence of the disease (MALNCHPSAMINC), the number of people who are admitted for treatment for the disease in a particular year (SAMADMISSION) and the number of cases where SAM is averted (SAMAVERTED). Since the disease itself shares a relationship with stunting, the model also allows the user to explore the extent to which SAM affects stunting (HLSTUNT) and malnutrition (MALNCHP).

The table below explains the key variables in this sub-module,
{| class="wikitable"
|'''Variable Name'''
|'''Description'''
|-
|'''MALNCHPSAM'''
|Prevalence of SAM
|-
|'''MALNCHPSAMINC'''
|Annual new number of cases of SAM
|-
|'''SAMADMISSION'''
|Total national number of admissions for treatment
|-
|'''SAMAVERTED'''
|Success of treatment as a percent of total admission
|}

== Parameters to affect prevalence and incidence ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpsamadj'''
|MALNCHPSAMINC
|Used to adjust the incidence correction factor
|Adjustment factor
|-
|'''malnchpsamm'''
|MALNCHPSAM
|Used to change prevalence of SAM
|Multiplier
|}

The easiest lever a user can pull to influence prevalence of SAM is the multiplier '''''malnchpsamadj''''' which can directly increase or reduce the number of prevalent cases of the disease. Incidence of SAM is calculated by applying a correction factor on the prevalence rate. The user can adjust the correction rate thereby influencing incidence of the disease using the adjustment factor '''malnchpsamadj.'''

== Parameters to affect admissions and treatment ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''samadmissionadd'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Additive factor
|-
|'''samadmissionm'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Multiplier
|-
|'''samadmissionsw'''
|SAMADMISSION
|Allows users to forecast SAM admissions in three ways (0) forecasts all countries according to the relationship with GDPPCP, (1) holds all initial zero values constant over time (default value), and (2) holds all initial values constant.
|Switch
|-
|'''samavertedconvyr'''
|SAMAVERTED
|Allows users to change the years to converge to 100
|Convergence factor
|-
|'''samavertedm'''
|SAMAVERTED
|Allows users to change the number of averted cases
|Multiplier
|}

There are two parameters available for changing the number of admissions for treatment, '''''samadmissionadd''''' which is an additive factor and '''''samadmissionm''''' which is a multiplicative factor. Further, the user can forecast the number of admissions using the GDP per capita or by holding all initial zero values constant over time or by holding all initial values constant through the manipulation of the switch '''''samadmissionsw.'''''

When it comes to the number of cases that are cured, the model assumes a success rate to converge to 100 percent over a 100-year time horizon. The user can change the speed of convergence through the parameter '''''samavertedconvyr.''''' Finally, the user can change the number of cases averted using a brute force multiplier '''''samavertedm.'''''

== Relationship between malnutrition, stunting and SAM ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpsamtostunt'''
|HLSTUNTCHL and HLSTUNT
|Controls the degree to which MALNCHPSAM affects HLSTUNT
|Adjustment factor
|-
|'''samrrsw'''
|HLSTUNT and DEATHS
|Used to substitute the severity of MALNCHP with that of MALNCHPSAM
|Switch
|-
|'''samtomalnchpsw'''
|MALNCHP
|allows users to specify scenarios in which successful treatment does not result in full weight
|Switch
|}

The adjustment factor '''''malnchpsamtostunt''''' allows the user to change the extent to which SAM affects stunting in the population. IFs calculates rates of stunting using a slow moving average using the number of undernourished children, the prevalence of SAM, the number of years for which stunting is expected to occur and the relevant height for age. The activation of the switch '''''samrrsw''''' substitutes the severity of undernourishment for undernourished children (MALNCHP) with the severity of SAM (MALNCHPSAM). Severity here refers to the relative risk of mortality and morbidity. This switch allows the user to explore the possible severity of the disease. For a more detailed description of this parameter, refer section 6.1.1 of the Health Model documentation.

By default, it is assumed that all successfully averted SAM cases are rehabilitated to normal weight for age. Changing the parameter '''''samtomalnchpsw''''' allows users to specify scenarios in which successful treatment does not result in full weight (for age) rehabilitation, with a value of 1 resulting in all averted SAM cases remaining malnourished (by default this parameter is set to 0).

= Education Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
|
'''EDYRSAG15'''

'''EDYRSAG25'''

'''EDYRSAG15TO24'''

| Educational attainment, adults by age group
|-
|
'''EDPRIPER'''

'''EDSECPER'''

'''EDTERPER'''

| Percent of the population completing primary, secondary and tertiary
|-
|
'''EDPRIENRG'''

'''EDSECENRG'''

'''EDSECLOWRENRG'''

'''EDSECUPPRENRG'''

'''EDTERENRG'''

| Gross enrollment rate in primary, secondary, lower secondary, upper secondary and tertary
|-
|
'''EDPRIENRN'''

'''EDSECENRN'''

| Net enrollment rate in primary, secondary
|-
|
'''EDPRIINT'''

'''EDPRIINTN'''

'''EDTERINT'''

| Primary intake rates, gross and net, and tertiary intake rates, gross
|-
|
'''EDSECLOWRTRAN'''

'''EDSECUPPRTRAN'''

| Transition rates from primary to lower secondary and from lowers secondary to upper secondary
|-
|
'''EDPRISUR'''

'''EDSECLOWRSUR'''

'''EDSECUPPRSUR'''

| Survival rates for primary, lower secondary, upper secondary
|-
|
'''EDPRICR'''

'''EDSECLOWRGRATE'''

'''EDSECUPPRGRATE'''

'''EDTERGRATE'''

| Graduation rates in primary, lower, secondary, upper secondary and tertiary
|-
|
'''EDSECLOWRVOC'''

'''EDSECUPPRVOC'''

| Vocational enrollment rates, lower and upper secondary
|-
|
'''EDEXPERPRI'''

'''EDEXPERSEC'''

'''EDEXPERTER'''

| Government spending per student as a percentage of GDP for primary, secondary and tertiary
|-
| '''GDSED'''
| Spending shares by level of education
|}

Like all of IFs, the education module is amenable to systems thinking, or conceptualizing elements as stocks and flows. Students flow through primary, lower-secondary, upper-secondary and tertiary education levels. Each time an age cohort completes a grade level, a year is added to that group’s stock of educational attainment; as adults (aged either 15 and older or 25 and older) they will have attained some number of years of formal education based on grades completed. For any given grade, the number of students enrolled is determined by the intake rate—or, at the secondary level, the transition rate from the previous level of education—and by the percentage of students passing through the last grade. Government spending influences the system by restraining the number of students that can be sustained at a given level of education.

Most analysis of the international education system focuses on educational attainment. IFs operationalizes this stock as the average years of education successfully completed by adults in all countries, following a typology designed by Harvard economists Robert Barro and Jong-Wha Lee (2000, 2001). '''EDYRSAG15''' captures the mean years of education attained for all adults older than 15; this important variable links forward to several other sectors of the model, including economics through multifactor productivity, health through fertility rate, and even governance through state fragility indicators. Users may wish to view mean years of education by other age breakdowns, available through '''EDYRSAG25''' and '''EDYRSAG15TO24''' though these variables do not have the forward linkages in the model that '''EDYRSAG15''' does. As an alternative to average years of education, '''EDPRIPER, EDSECPER and EDTERPER''' track attainment as a percentage of the population to successfully complete each level of education.

In addition to attainment measures, most users will focus their attention on enrolment rates, the most common measure of student flows. Enrolment rates answer the simple question: at a given time, how many children are in school?

Enrollment is captured in the variables '''EDPRIENRG''' and '''EDPRIENRN''' for primary, '''EDSECENRG''', '''EDSECENRN''', '''EDSECLOWERENRG''' and '''EDSECUPPRENRG''' for secondary and '''EDTERENRG''' for the tertiary level'''.''' Many of the indicators used to measure education systems are either gross rates or net rates. This important distinction has to do with student age. Gross Enrollment Rates (GER), for example, are calculated by dividing the total student body by the official school-age population; Angola’s primary GER would be the number of primary students enrolled in school divided by the primary age population of the country and has often exceeded 100 percent because of primary enrollment by over-aged students who enter or return to school for various reasons. Net Enrollment Rates (NER), on the other hand, measure only the students of official school age. A country’s NER is the enrolled age-appropriate students divided by only the school-age population and should not exceed 100 percent. Net enrolment data are often more difficult to find than gross, simply because it requires the age of enrollees to calculate. Intake rates are also presented in both net and gross formulations. For a more detailed description of commonly used education indicators see UNESCO’s technical guidelines: <nowiki>http://www.uis.unesco.org/Library/Documents/eiguide09-en.pdf</nowiki>.

For each of the countries in IFs, two factors ultimately control enrolment rates: intake rates and survival rates. Drawing on drivers such as GDP per capita and educational spending, the model forecasts intake rates for primary and tertiary levels ('''EDPRIINT, EDPRIINTN, and EDTERINT''') and transition rates from the previous level of schooling for secondary education ('''EDECLOWRTRAN EDSECUPPRTRAN'''). The model also forecasts a student persistence rate for all levels (except tertiary), often referred to as survival rate, captured in the variables '''EDPRISUR''', '''EDSECLOWRSUR''', and '''EDSECUPPRSUR'''. Enrolment rates at each level are calculated as the combined result of these student flows. Or thought of more simply, enrolment rates track the number of students who enter school and stay in school. Graduation rates at all levels are also forecast and the corresponding variables are: '''EDPRICR''', '''EDSECLOWRGRATE''', '''EDSECUPPRGRATE''' and '''EDTERGRATE'''. The model also forecasts vocational shares in lower secondary and upper secondary and the variables are '''EDSEECLOWRVOC''' and '''EDSECUPPRVOC'''.

All indicators in the model that measure the stock of educational attainment or student flows can be disaggregated by gender. Thus, the user is able to evaluate each country’s progress toward eliminating gender-based disparity in the classroom. In order to disaggregate performance by gender, a second dimension of almost all education variables allows the selection of display by male, female or total. In addition to setting normative goals, separate treatment of female education allows the model to establish powerful forward linkages. For example, female educational attainment and fertility rates are negatively related, because schooling increases the opportunity cost of having and raising a child.

In addition to student flows, the model captures financial flows tied to each country’s education system. Educational finance is the result of interplay between supply-side and demand-side forces. Demand for education can be conceptualized as the product of spending per student and enrollment rates at each level of schooling—'''EDEXPERPRI, EDEXPERSEC''' and '''EDEXPERTER''' track spending per student as a percent of GDP per capita for primary, secondary and tertiary education. However, such a formulation of demand is crude, as the number of children already in the system is constrained by the preexisting structure of government spending. Financial supply, captured as a percent of GDP by the variable '''GDSED''', is generated by the government finance submodule. Due to a lack of reliable data, IFs does not capture private educational funding, though it is important in some countries.

== Parameters to Affect Intake Rates and Survival Rates: Annual Growth ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:135px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:234px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:145px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edpriintngr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edterintgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edseclowrtrangr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edsecupprtrangr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edprisurgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edtergradgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary graduation rates

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|}

In the IFs model, improved education outcomes can be achieved most directly through changes to intake and survival rates. The number of students entering the first grade of each schooling level can be pushed up or down using annual (percentage point) growth parameters: '''edpriintngr''' for primary school''', edseclowrtrangr''' for transfer rates between primary and secondary''', edsecupprtrangr''' for transition between lower and upper secondary and '''edterintgr''' for intake into the tertiary level'''.''' However, any improvements to enrolment stemming from intake rate boosts are restrained when coupled with high dropout or repetition rates. Like intake rates, survival rates can be directly manipulated through annual growth parameters. '''Edprisurgr''' controls survival growth (in percentage points) at the primary level. And finally, '''edtergradgr''' controls tertiary survival rates (also referred to as graduation rates).

Though intake rates and survival rates can be manipulated separately, users will often wish to manipulate them in conjunction due to the presence of interaction effects between the two sets of variables. For example, a rapid increase in intake/transition rates can result in a decline in country’s survival rate. The educational system may simply not have the resources to cope with an influx of students. Furthermore, the incoming cohort, which has spent years outside the system, may represent individuals from disadvantaged backgrounds—a group prone to dropping out. On the other hand, rising intake rates may have a positive threshold effect on survival rates. In many countries, once intake rates reach a certain level, dropout and repetition have declined. At this time such relationships are not captured in IFs. Thus, the user may want to build interaction and threshold effects into education policy interventions.

== Parameters to Affect Intake Rates and Survival Rates: Target Year for Universal Education ==

{| border="1" cellspacing="1" cellpadding="1" width="638" style="width:638px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:126px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:192px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:193px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edpriintntrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:192px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edseclowrtrantrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:192px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edsecupprtrantrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:192px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edterinttrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:192px;height:22px;" |
Tertiary Intake Rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%    

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edprisurtrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:192px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edtergradtrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:192px;height:22px;" |
Tertiary graduation rates

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|}

Development goals like universal access to education with a specific target date can be explored through target year parameters on intake (transition) or survival (graduation) rates. These parameters ramp up the base year value of the variable of interest to one hundred percent within the number of years marked by the parameter. For countries with limited availability of government resources, these target parameters might need an education budget set aside through the '''edbudgon''' parameter described later in the budget sub-section.

== Parameters to Affect Intake Rates and Survival Rates: Multiplier ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:135px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:234px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:145px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edpriintnm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edterintm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edseclowrtranm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edsecupprtranm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edprisurm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edtergradm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary survival rates

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|}

In addition to the growth and target parameters described above, intake (or transition) and survival (or graduation) rates can also be modified using a set of multipliers listed in the table above. Like other multipliers in the model these work by ramping up (or down) the base case forecast over a horizon chosen in the scenario design.

== Parameters to Affect Education Spending ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:190px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:195px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''gdsm (education)'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Government spending on education

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edbudgon'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Education funding impact and priority

| nowrap="nowrap" style="width:121px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''gdsedm'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDSED

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending distribution

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexppconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERPRI

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, primary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexpslconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERSEC

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, secondary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexpsuconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERSEC

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, secondary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexptconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERTER

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, tertiary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edqtqltrm'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Education quantity-quality balance

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|}

Attainment levels also respond indirectly to changes in the educational spending structure. The number of students enrolled at all grade levels is constrained by total public spending on education. In IFs, public finance allocations are distributed between transfer payments, the military, education, health and infrastructure in the government budget submodule. Bottom-up factors like demographic changes and policies targeting intake or survival will pressure the government to increase education spending. But the model features somewhat rigid top-down control of the budget—spending on education competes with other government spending and IFs maintains accounting of both total government revenues and expenditures. Most countries spend something close to the global average of 5 percent of GDP on education each year. Thus demand-side shifts may secure an increase in resources, but it is difficult to increase total education spending much in excess of historically observed values. Users can intervene in this accounting process by manipulating '''gdsm (education)''', a multiplier that directly increases the share of government expenditure accounted for by education, at the expense of spending in other arenas such as military or health. Alternatively, users can prioritize education spending over other government expenditure targets. The priority parameter '''edbudgon''', takes on values from zero to one, with lower values representing lower priority to education funding demands and one representing maximum priority. Any non-zero value pushes education allocations away from those determined by historical spending patterns and level of economic development—as calculated in the economic module—and toward the bottom-up demand for expenditures calculated in the education model. The parameter '''edbudgon''' can also be used to completely turn off the budget impacts on student flow rates through a zero value of the parameter. The turning off of budget impacts is helpful in projecting the demand for funds for certain types of education intervention, e.g., universal primary education. However, fund demand in such a scenario is calculated in the EDTOTCOST and not in the GDS(Education) variable, which is determined, in this case, completely through the government budget allocation algorithm without using any input from the education model.

The government-budget submodule automatically allocates resources to the educational level most in need (although that interacts with historical patterns of educational preference), starting with primary education. As enrolment rates in a country’s primary education system reach a very high level, the model reallocates funding up the chain to secondary. Users may wish to change this pattern and create normative scenarios that prioritize higher levels of education, or balance spending between primary, secondary and tertiary. The '''gdsedm''' parameter allows for such experimentation via a second dimension that allots the spending increase to a specific level of schooling.

When developing education budgets, planners must account for trade-offs between equity and efficiency. What is more important, increasing total spending on the system in an effort to support more students, or increasing spending per student in an effort to improve the experience of being in school? In a crude way this can be thought of as the difference between increasing intake and increasing survival rates. IFs includes parameters that shift the spending focus in the direction of system efficiency. '''Edexppconv''' can be used to decrease the time it takes a country’s initial spending per primary student to converge with the model’s expected function. '''Edexppslconv''' and '''edexppuconv''' control the student expenditure function for lower and upper secondary, and '''edexpptconv''' does the same for the tertiary level'''.''' Increasing the target value used in a convergence parameter will increase per student spending for countries below the expected value, but the opposite is true in countries exhibiting higher than expected values. If a user wishes to boost per student expenditure in a high performing country, he or she should make use of the '''edqtqltrm''' parameter, a multiplier that shifts emphasis away from education quantity and towards education quality—with values less than one indicating a shift towards quality.

== Parameters to Affect Gender Parity ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:122px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:239px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:116px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edprigndreqintn'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for primary intake

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edprigndreqsur'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for primary survival

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edseclowrgndreqtran'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDSECLWRTRAN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for lower secondary transition

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edsecupprgndreqtran'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for upper secondary transition

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edtergndreqint'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for tertiary intake

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edtergndreqgrad'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDTERSUR

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for tertiary survival

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|}

Some development projects focus on gender disparity in education, rather than aggregate enrolment. In recent decades male and female enrolment rates have converged rapidly across all regions of the world. In fact, primary, secondary and tertiary enrolment taken as a global aggregate has already surpassed a female-male ratio of 0.97—commonly considered an indicator of parity. However<ins>,</ins> this abstracted view obscures struggles facing women in low-performing areas, especially sub-Saharan Africa. Users may explore effects linked to improvements in female-specific enrolment rates via the intake and survival parameters described above; each parameter has a dimension that applies the annual increase to males, females, or the total student population. Alternatively, users can experiment with closing the gender attainment gap existing in some countries by setting time sensitive goals. Goals around intake can be set through '''edprigndreqintn''' for primary, '''edsecupprgndreqtran''' for secondary and '''edtergndreqint''' for the tertiary level. Gender conversion goals may also be set for the other direct driver of enrolment, survival rates: '''edprigndreqsur''' controls female survival rates at the primary level''',''' and '''edtergndreqgrad''' controls tertiary rates. Each of the gender-focused parameters are convergence speed goals, meaning the user sets the number of years that will elapse before the country’s gender parity in the corresponding measure converges to the expected function.

== Parameters to Affect Education Quality ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edqualpriallm'''
|EDQUALPRIALL and EDQUALAG15PRI
|Multiplier on primary education learning quality
|Multiplier
|-
|'''edqualsecallm'''
|EDQUALSECALL and EDQUALAG15SEC
|Multiplier on secondary education learning quality
|Multiplier
|-
|'''edqualag15m'''
|EDQUALAG15
|Multiplier on total learning quality for adults
|Multiplier
|-
|'''mfpedqual'''
|MFPHC
|Elasticity of multifactor productivity to quality of education
|Elasticity
|}

In addition to the education model described above IFs also has a representation of learning quality. Learning quality is represented at the primary, secondary levels<ins>,</ins> and for the adult population as a whole, for both sexes for four categories (math, science, reading and total). There is also a linkage from education quality to multifactor productivity.

Users can build scenarios with increased or decreased levels of learning quality through the multipliers '''edqualpriallm''' (learning quality at the primary level), '''edqualsecallm'''(learning quality at the secondary level) and '''edqualag15m''' (learning quality for the adult population as a whole). Users can change the elasticity of learning quality to multifactor productivity using the parameter '''mfpedqual'''.

== Parameters to Affect Education Grade-Level Model ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edmodelsw'''
|N/A
|Education switch between old and new model (0=old model; 1=new model)
|Switch
|-
|'''edpridrpgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edprienrgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|-
|'''edseclowrdrpgrdm'''
|EDSECLOWRDRPGRD/ EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edseclowrenrgrdm'''
|EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|}

== Prepackaged Scenarios ==

An installation of IFs includes scenarios used in each entry in the Pardee Institute’s Potential Patterns of Human Progress (PPHP) series. Scenarios created for the education volume include: a best and worst case framing scenario; case study interventions in several countries and regions; high spending and low spending scenarios; and over twenty normative scenarios (Dickson et al., 2010). This section will briefly describe the parameters used to construct a single normative scenario titled, Norm Mar 1 2009.

Norm Mar 1 2009 simulates a worldwide improvement in educational performance through interventions to: intake rates, student persistence, gender parity and educational spending, including boosts to foreign aid. From 2010 to 2100, the parameter '''edpriintngr''' is set to 2.2, and '''edprisurgr''' is set to 1.2, setting up annual increases to primary school intake and survival rates. The scenario includes similar annual growth increases for intake and student persistence across lower secondary and upper secondary schooling. '''Edseclowrtrangr''' is set to one percent, '''edsecupprtrangr''' to 0.5 percent. The corresponding parameters for tertiary education are not assigned a value other than the default of zero. Additional interventions speed up the narrowing of persisting gender gaps in intake, survival and transition rates. The parameters '''edprigndreqintn and edprigndreqsur''' set a ten year time goal for gender parity at the primary level; '''edseclowrgndreqtran''' sets a thirteen year goal for parity at the lower secondary level; and, '''edsecupprgndreqtran''' sets a 20 year time goal for parity at the upper secondary level. In order to simulate improved education quality, across levels, '''edexppconv, edexpslconv, edexpsuconv and edexptconv''' all take the value of 20, signifying a convergence to the expected function after 20 forecast years. Because this scenario was created as a tool to explore the potential impacts of a world in which educational goals are achieved by all nations, the budget switch, '''edbudgon''', is set to 0, the highest prioritization of education funding.

= Economic Module =

== Variables of Interest ==

The treatment of economics in IFs draws on both the classical tradition’s focus on economic growth (with great attention in IFs to the newer work on endogenous growth theory) and the neoclassical perspective's general equilibrium approach.

The economic module is a core component of the IFs system for multiple reasons, in particular for its close interactions with all other modules. On the input side, variables from almost all other modules affect production levels. On the output side, the magnitude of GDP and the level of GDP per capita are critical, in turn, for essentially all other modules. Mostly closely linked to the economic module are the energy and agriculture modules, both of which use a partial equilibrium structure that echoes the one in the economic module, and both of which provide physical values that fully determine the currency value-based representations of their respective sectors in the economic model.

Basic economic variables include: GDP at market exchange rates ('''GDP'''), GDP at purchasing power parity ('''GDPP'''), GDP per capita at market exchange rates ('''GDPPC'''), and GDP per capita at purchasing power parity ('''GDPPCP'''). The model represents all of these in constant 2011 dollars (the interface allows the user to convert to other currencies). The model also includes a representation of the portion of the economy that is informal.

=== Multifactor Productivity ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''GDP'''
| Gross domestic product
|-
| '''GDPP'''
| GDP at purchasing power parity
|-
| '''GDPPCP'''
| Per capita GDP at purchasing power parity
|-
| '''GDPPC''' 
| Per capita GDP
|-
| '''LAB''' 
| Labor 
|-
| '''KS''' 
| Capital stock 
|-
| '''I''' 
| Investment 
|-
| '''POPRETIRED''' 
| Retired Population 
|-
| '''MFP (HC,SC,PC,KN)''' 
| Multifactor productivity 
|}

The supply side of the economic module is based on the Cobb-Douglas Production Function and uses labor ('''LAB'''), capital ('''KS'''), and multifactor productivity ('''MFP''') as the primary drivers of economic growth. Capital stock ('''KS''') is a function of investment ('''I''') and depreciation rates. Labor supply ('''LAB''') is determined from population and endogenously derived labor force participation rates.

While the treatment of capital and labor in the IFs system will be familiar to users with an understanding of neoclassical economics, the treatment of productivity within IFs deserves greater explanation. Unlike most neoclassical models, which primarily focus on technology as the determining factor of productivity in their equations, the IFs system uses a broader definition of productivity called multifactor productivity (MFP). This multi-factor productivity term in IFs has four basic components: human ('''MFPHC'''), social ('''MFPSC'''), physical ('''MFPPC'''), and knowledge capital productivity ('''MFPKN'''). Each of these components can take on a positive or negative value depending on whether the calculated value of the component is providing a positive or negative impact to economic growth rates relative to what would be expected based on the country’s level of development. (See the Development Profile form in IFs to display the magnitude and direction of the four productivity elements.)

Drivers of multifactor productivity vary by component. '''MFPHC''' is driven by years of education, education expenditures, life expectancy and health expenditure. '''MFPSC''' is driven by Freedom House’s measure of political freedom (a variable describing democracy), governance effectiveness, corruption perceptions, and economic freedom. '''MFPPC''' is driven by two separate indices of infrastructure: traditional (roads, electricity, and water and sanitation), and information and communications technology (ICT). Finally, '''MFPKN''' is driven by R&D expenditures and economic integration (in the model, trade serves as a proxy for trade). This final component of MFP represents a measure of connectedness to the global economy. Altering any of these using the appropriate parameters will result in changes to the relevant component of multifactor productivity and therefore to economic growth.

=== The Social Accounting Matrix and Domestic Finance ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''C'''
| Private consumption
|-
| '''SAVINGS'''
| Net national savings
|-
| '''HHINC'''
| Household income
|-
| '''HHSAV'''
| Household savings
|-
| '''FIRMINC'''
| Firm income
|-
| '''FIRMSAV'''
| Firm savings
|-
| '''IDS'''
| Investment by destination sector
|-
| '''GOVREV'''
| Government revenue
|-
| '''GOVEXP'''
| Government expenditures
|-
| '''GOVHHTRN'''
| Government household transfers
|-
| '''GOVCON'''
| Government consumption
|-
| '''GDS'''
| Government spending by destination
|-
| '''GOVBAL'''
| Government balance
|-
| '''GOVDEBT'''
| Government debt
|}

The production function is embedded in a six sector model of the economy featuring agriculture, raw materials, energy, manufactures, services, and ICT that balances domestic demand and trade in a general equilibrium seeking structure. Production and consumption of goods and services are in turn incorporated into a larger social accounting matrix (SAM) which represents the behavior and financial interaction of households, firms and government. A social accounting matrix traditionally represents ''flows'' among different economic sectors and agent categories (i.e., households, firms, and government). For instance, it represents private consumption ('''C''') and net national savings ('''SAVINGS'''), as well as household income ('''HHINC''') and savings ('''HHSAV'''); firm income ('''FIRMINC'''), investment by sector ('''IDS'''), and savings ('''FIRMSAV'''); government revenues ('''GOVREV'''), total expenditures with transfers ('''GOVEXP'''), transfers to households ('''GOVHHTRN'''), directed consumption in total ('''GOVCON'''), and by sector ('''GDS'''), and balance ('''GOVBAL'''). IFs builds a full and balanced social accounting matrix of these and many other inter-agent flows. It also creates a second matrix which represents financial ''stocks'' (assets and liabilities) of different agent categories for all countries in the system, including for instance, government debt ('''GOVDEBT'''). The representation of stocks in this fashion provides the foundation upon which the system adjusts flows of finance among different agents and among countries over time (see, for instance, the net foreign debt or '''XDEBT''' of countries), maintaining consistency with the liability-asset approach used in standard accounting systems. The behavior of agents within this system is not fixed, like it is in many computable general equilibrium models (which use SAMs commonly). Instead, agent behavior is partially endogenized using algorithms that allow the behavior of agents to shift depending on the levels of stocks of relevant variables within the SAM. So, for example, different levels of government debt trigger different patterns of government spending in IFs.

Because of its centrality to the IFs system, users should understand the basic character of the social accounting matrix. Both the stock and flow matrices are available in the specialized displays menu in the display section of the main menu. The social accounting matrix loads the flow matrix by default, but the stock matrix can be accessed via the Show Stocks button on the bar at the top. Full breakouts of the SAM into its component parts is also available on this screen by clicking the button Expand SAM on the same top bar.

=== International Trade and Finance ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''CURACT''' 
| Current account balance
|-
| '''CAPACT'''
| Capital account
|-
| '''TRADEBAL'''
| Trade balance
|-
| '''XWORKREMIT'''
| Worker remittances from abroad 
|-
| '''AID''' 
| Net foreign aid receipts 
|-
| '''X or XS''' 
| Exports and exports by sector 
|-
| '''M or MS''' 
| Imports and Imports by sector 
|-
| '''ENX/ENM''' 
| Energy exports/imports 
|-
| '''AGX/AGM''' 
| Agricultural exports/imports 
|-
| '''XFDIFIN''' 
| Inward flow of FDI 
|-
| '''XFDIFOUT''' 
| Outward flow of FDI 
|-
| '''XFDISTOCK''' 
| Inward stocks of FDI 
|-
| '''XFDISTOUT''' 
| Outward stocks of FDI 
|-
| '''XPORTFIN''' 
| Inward flows of portfolio investment 
|-
| '''XPORTFOUT''' 
| Outward flows of portfolio investment 
|-
| '''XPORTFOLIO''' 
| Inward stocks of portfolio investment 
|-
| '''XPORTSTOUT''' 
| Outward stocks of portfolio investment 
|-
| '''XDEBTRPA''' 
| External debt, relative price adjusted 
|-
| '''EXRATE''' 
| Exchange rate 
|}

The international financial position of a country is typically represented by the balance of payments, which is equal to zero if all flows of goods and finance into and out of a country are included. The balance of payments is determined by the status of three indicators, the current account ('''CURACT'''), the capital account ('''CAPACT'''), and the foreign reserve account (not explicitly modeled in IFs). Imbalances can exist in any of these if imports of goods and services outweigh exports for example, or if governments spend down foreign reserves.

In IFs, the status of the current account balance ('''CURACT'''), which reflects the many flows into and out of a country, is a function of the trade balance ('''TRADEBAL'''), net foreign worker remittances ('''XWORKREMIT'''), net foreign aid ('''AID'''), and net interest on foreign debt. Worker remittances are calculated on the basis of the size of the population of a country that is living and working abroad.

Of these the trade balance and treatment of trade within IFs more generally is worth further discussion. Trade in the IFs system is part of the social accounting matrix structure and is modeled using a pooled rather than bilateral approach. This means that IFs tracks information on gross exports and imports of countries by sector and in total. An algorithm sums price-adjusted import demand and export capacity across all countries that trade in a given sector and defines world trade as the average of those two values. Demand and capacity are then normalized to the total of world trade to determine total and sectoral exports ('''X''' or XS '''by''' sector) and imports ('''M''' or '''MS''' by sector) by country. As already noted, the agricultural and energy modules each represent production, consumption and trade that supersede the results of the economic module’s production function and SAM. For instance, exports and imports in these modules are represented separately within the IFs system via '''ENX'''/'''ENM''' and '''AGX'''/'''AGM'''.

Interacting with the current account is the capital account ('''CAPACT'''). It captures the flows of foreign direct and portfolio investment into ('''XFDIFIN''' and '''XPORTFIN''') and out of countries ('''XFDIFOUT''' and '''XPORTFOUT'''). The system also represents the stocks of inward and outward FDI and portfolio investment ('''XFDISTOCK'''/ '''XPORTFOLIO''' and '''XFDISTOUT'''/'''XPORTOUT'''). Together the current and capital account flows shape the stock of relative-price-adjusted net foreign indebtedness ('''XDEBTRPA'''), which in turn via an equilibration process changes the exchange rate ('''EXRATE'''). The exchange rate, in interaction with local relative prices, affects trade and financial flows over time as a key part of that equilibration process.

=== Informal Economy ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''LABINFORMSHR'''
| Informal share of the labor force 
|-
| '''LABINFORMPCNTINF''' 
| Portion of informal labor employed inside informal sector 
|-
| '''LABINFORMPCNTNONINF''' 
| Portion of informal labor employed outside the informal sector (in formal enterprises or households) 
|-
| '''GDPINFORMSHR''' 
| Informal share of GDP 
|-
| '''GDPSHADOWSHR''' 
| Shadow economy share of GDP 
|-
| '''EDYRSAG15''' 
| Educational attainment of adults 15 and older 
|-
| '''GOVBUSREGIND''' 
| Government business regulation index (higher is better) 
|-
| '''GOVCORRUPT''' 
| Government corruption (higher is less corrupt) 
|-
| '''GOVHHTRN''' 
| Government transfers to households for welfare and pensions 
|-
| '''GDS, R&D''' 
| Government spending on research and development 
|-
| '''FIRMTAXR''' 
| Tax rate (direct) on firms 
|-
| '''HHTAXR''' 
| Tax rate (direct) on households 
|-
| '''SSWELTAXR ''' 
| Tax rate (direct) on households and firms for social welfare 
|-
| '''RANDDEXP''' 
| Total (public and private) spending on research and development 
|-
| '''TEFF ''' 
| Stock of total (multifactor)productivity 
|}

The economic module represents the informal economy as the share of the total labor force employed informally ('''LABINFORMSHR'''), the share of total GDP generated by informal activities ('''GDPINFORMSHR'''), and the share of total GDP generated by the shadow economy ('''GDPSHADOWSHR'''). The informal labor share is driven by four main variables: the educational attainment of adults 15 years of age and older ('''EDYSAG15''', see the education model for parameters to affect this variable), the government business index ('''GOVBUSREGIND'''), the ratio of government transfers to households as a portion of GDP ('''GOVHHTRN'''), and the tax rate on firms ('''FIRMTAXR''').

The informal labor share is divided into two sectors, the portion of informal labor employed inside the informal sector ('''LABINFORMPCNTINF''', informal) and the portion of informal labor employed in the formal sector (represented by a simple residual '''LABINFORMPCNTNONINF'''). Each sector is further divided by sex.

The share of informal labor in total labor and the portion of informal labor employed inside the informal sector both help drive informal and shadow GDP shares. Other drivers include the level of corruption ('''GOVCORRUPT'''), public expenditure on research and development as a percentage of its GDP ('''GDS, R&D'''), and total (public and private) spending on research and development ('''RANDDEXP).'''

While informal ''and'' shadow GDP shares are initialized in the Base Case, ''only'' informal ('''GDPINFORMSHR''') has active forward linkages. To drive the forward linkages with the shadow economy share instead, you must change the switch, '''gdpshadowon''', from its default, Base Case setting of 0 to 1 (see Parameters for Affecting the Informal Economy section below).

The informal GDP shares, in turn, have forward linkages to total or multifactor productivity ('''TEFF'''), effective household ('''HHTAXR)''' and firm tax rates, as well as to the effective rate for social welfare ('''SSWELTAXR''').

== Parameters to Affect Production and Growth ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
| colspan="4" |''Labor''
|-
|'''labparm'''
|LAB
|Labor participation rate multiplier
|Multiplier
|-
|'''labfemshrm'''
|FEMSHRLAB
|Female labor force participation rate multiplier
|Multiplier
|-
|'''labretagem'''
|LAB
|Retirement age, labor force participation multiplier
|Multiplier
|-
|'''labunemprm'''
|LABUNEMPR
|Labor unemployment rate multiplier
|Multiplier
|-
|'''labmarketsw'''
|LABS
|Labor demand to economic production switch (1=on)
|Switch
|-
|'''labdemsm'''
|LABDEMS
|Labor force demand by sector multiplier
|Multiplier
|-
|'''labcoeffsm'''
|LABCOEFFS
|Labor demand, multiplier on technology coefficients
|Multiplier
|-
|'''workageentry'''
|POPRETIRED
|Age at which individuals enter the labor force
|Exogenous specification
|-
|'''workageretire'''
|POPRETIRED
|Age at which individuals leave the labor force
|Exogenous specification
|-
| colspan="4" |''Capital''
|-
|'''invm'''
|IDS
|Multiplier on all types of investments
|Multiplier
|-
| colspan="4" |''Productivity''
|-
|'''mfpadd'''
|MFP components
|Multifactor productivity, additive
|Additive factor
|-
|'''mfpleadr'''
|MFP
|MFP growth rate of the technological lead country
|Growth Rate[HL1]
|-
|'''mfpedspn'''
|GDS-educ
|Elasticity of multifactor productivity to education spending
|Elasticity
|-
| '''malnelimstartyr'''
|MALNPOPP
|Undernourishment elimination start year
|Standard error target
|-
| '''malnelimtargetyr'''
|MALNPOPP
|Undernourishment elimination target year
|Standard error target
|-
| '''malelimprecisesw'''
|MALNPOPP
|Undernourishment elimination
|Switch
|-
|'''ylm'''
|YL
|Agricultural yield
|Multiplier
|-
|'''aginvm'''
|INAG and IDS
|Investment in agriculture
|Multiplier
|-
|'''econfreem'''
|ECONFREE
|Economic freedom
|Multiplier
|-
|'''goveffectm'''
|GOVEFFECT
|Government effectiveness
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption
|Multiplier
|-
|'''freedomm'''
|FREEDOM
|Freedom/democracy
|Multiplier
|-
|'''mfpinfrindtrad'''
|MFPPC
|Elasticity of multifactor productivity to traditional infrastructure index
|Elasticity
|-
|'''mfpinfrindict'''
|MFPPC
|Elasticity of multifactor productivity to ICT infrastructure
|Elasticity
|-
|'''mfpenpri'''
|WEP
|Elasticity of multifactor productivity with energy prices
|Elasticity
|-
|'''gdsm, R&D'''
|GDS, R&D
|Government expenditures towards R & D
|Multiplier
|}

Based upon the discussion of key variables, the areas of likely interest to users are clear: parameters that affect production and growth most directly, parameters that affect domestic finance (including government) and the flows of the social accounting system, and parameters that affect trade and finance. We start with production and growth and the key parameters affect labor, capital and multifactor productivity. Parameters exist in the IFs system that allows users to modify any of the component parts of the Cobb Douglas Production function. Parameters affecting labor supply include '''labparm,''' '''labfemshrm,''' and '''labretagem'''. The first of these, '''labparm''' modifies the labor force participation rate, while the second changes the female share of the existing labor force, and the last, '''labretagem,''' changes the number of retired persons who participate in the labor market. Other parameters, discussed in the demographic section, also impact the size of the labor force. The parameters '''workageentry''' and '''workageretire''', will change when people enter and leave the labor force, thus altering the length of time they are employed and the size of the labor pool. In addition to these, the user can influence the unemployment rate in the population through the multiplier '''labunemprm.''' The activation of the switch '''labmarketsw''' allows a user to observe the impact of changes in labor demand on production.

For the demand side of labor, the parameter '''labcoeffsm''' helps a user influence the labor demand co-effcicients by sector and skill level (which are measured in terms of millions of people per dollar). The multiplier '''labdemsm''' influences total labor demand by sector and skill level. ''' '''

The labor market demand and supply equilibrate on the basis of the variable '''LABWAGEIND.''' This equilibration occurs through four co-efficients that equilibrate demand and supply and demand through a PID (Proportional, Integral and Differential) formulation. '''ellabwage1''' and '''ellabwage2''' equilibrate demand and supply for labor while '''elwageunemp1''' and '''elwageunemp2''' account for the impact of unemployment on the equilibration. ''' '''

Users can affect capital accumulation by changing the parameter '''invm''', which alters investment in the economy using a multiplicative approach. Unlike spending multipliers discussed later under Domestic Finance, this multiplier cannot be broken out by destination. It will also affect savings because domestic savings rates are directly tied to investment rates. In addition to altering savings, investment rate changes affect the rate of capital accumulation.

Two brute force multipliers on productivity are available to users. First, '''mfpadd''' allows users to increase productivity growth in an additive manner on a country-by-country basis. Second, the parameter '''mfpleadr''' allows users to set the growth rate of the world’s technological leader, the United States. Since all other country’s productivity is tied to the leader, changes to this parameter model assumptions about global economic growth rates, which will tend to change productivity growth rates across a number of countries. Although both these parameters represent powerful ways to affect GDP and growth, users should note that they do not carry a cost. Like other brute force multipliers, there is no cost accounting system that might limit the impact of changes introduced, so users must be extremely careful to justify any changes made with these parameters.

Users can change any one of the components of multifactor productivity by affecting the drivers of the component. Any of the parameters that impact life expectancy, education levels, or health and education spending, as discussed in the health or education modules, will impact MFPHC. Examples include but are not limited to: '''mfpedspn''', '''ylm''', and '''aginvm.'''

Similarly, anything that affects drivers of social capital will affect MFPSC. These include many of the governance parameters, such as: '''econfreem''', '''goveffectm''', '''govcorruptm''', and '''freedomm.'''

Physical capital is primarily impacted by two indices of physical infrastructure development, one for traditional infrastructure, which includes electricity, transportation, and water and sanitation, and a separate index for ICT. Users can affect the elasticity of MFP to these indices using '''mfpinfrindtrad''', and '''mfpinfrindict'''. By changing these parameters users can make the value of MFPPC more or less responsive to the speed of growth in the two areas of infrastructure discussed above. They can also alter the elasticity of MFP to the world energy prices using '''mfpenpri''', which performs a similar function for changing the relationship of MFPPC to energy prices globally. See the discussion of the infrastructure module to see the large number of parameters that affect the specific elements of the two general infrastructure indices.

Knowledge capital will respond to changes in the parameter '''gdsm,''' targeted at changing the amount of government spending directed towards research and development. It will also respond to the level of trade interconnectedness, which the level of protectionism ('''protecm''') can affect—to be discussed again below.

== Parameters to Affect Domestic Financial Flows and the Social Accounting System ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:81px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:316px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:109px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govrevm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:316px;height:22px;" |
Government revenue multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdsm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDS

| nowrap="nowrap" style="width:316px;height:22px;" |
Government spending by destination sector

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdsedm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDS

| nowrap="nowrap" style="width:316px;height:22px;" |
Education spending distribution multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govhhtrnpenm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GOVHHTRN

| nowrap="nowrap" style="width:316px;height:22px;" |
Government pension spending multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''firmtaxrm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on firms, households, and indirect taxation

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''hhtaxrm (skilled, unskilled)'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on households, broken down by skilled and unskilled

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''indirecttaxrm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on firms, households, and indirect taxation

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''ginidomr'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GINIDOM

| nowrap="nowrap" style="width:316px;height:22px;" |
Domestic Gini Index growth rate

| nowrap="nowrap" style="width:109px;height:22px;" |
Growth Rate

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''ginidomm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GINIDOM

| nowrap="nowrap" style="width:316px;height:22px;" |
Domestic Gini Index multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdprext'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDP

| nowrap="nowrap" style="width:316px;height:22px;" |
GDP growth rate

| nowrap="nowrap" style="width:109px;height:22px;" |
Growth Rate

|}

The leverage points within the area of domestic financial flows and the social accounting matrix of greatest interest for most analyses and government revenues and expenditures. The brute force multiplier on government revenues ('''govrevm''') will increase or decrease those and have wide secondary effects as processes play out to adjust revenue raising and expenditure patterns to the new level. On the outflow side, the government spending by destination multiplier ('''gdsm''') will change patterns of government consumption using a multiplier that acts on military, health, education, R&D, infrastructure, other infrastructure, and other (the residual). Education spending can also be modified using '''gdsedm,''' which directly modifies spending on education by level (as discussed in the education module). Unless total government consumption ('''GOVON''') is altered, however, changes in spending patterns by target will create shifts from one category to another rather than changes in the total, because the model will normalize the sum of spending by sector to that total. Similarly, the user can use multipliers on government pension ('''govhhtrnpenm''') spending to change levels in either of those categories, but subject along with direct consumption expenditure to the constraint of total government expenditures ('''GOVEXP'''). Hence, parameters that affect government revenues are very important.

Changes in the tax rates can have profound implications for government capacity, and for this reason IFs contains a number of parameters to alter taxation rates for different actors, in addition to the direct multiplier on government revenues, which will cause the model to endogenously adjust such tax rates. Of these, three stand out. '''Hhtaxrm''' affects household savings rates, '''firmtaxrm''' modifies firm taxation rates, and '''indirecttaxrm''' changes the indirect taxation rate on goods and services. The household taxation rate multiplier is subdivided according to whether households are skilled or unskilled. Users who are interested in scenarios that change domestic income inequality may want to change '''ginidomr''', which alters the ratio of the domestic Gini ('''GINIDOM''') to the initial condition. Domestic Gini ('''GINIDOM''') can also be altered using the parameter '''ginidomm''', which acts as a multiplier on the calculated value of '''GINIDOM''' in the model. Finally, '''gdprext''' offers users a power way to speed up, or slow down, total GDP growth.

== Parameters to Affect Trade and International Finance ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:94px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:304px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:137px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xshift'''

| nowrap="nowrap" style="width:94px;height:22px;" |
X, XS

| nowrap="nowrap" style="width:304px;height:22px;" |
Export shift as a result of trade promotion

| nowrap="nowrap" style="width:137px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''protecm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
M, MS

| nowrap="nowrap" style="width:304px;height:22px;" |
Protectionism in trade, multiplier on import prices

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''termtrm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
TERMTR

| nowrap="nowrap" style="width:304px;height:22px;" |
Terms of trade balance, higher favors global south

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdistockm''' 

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDISTOCK

| nowrap="nowrap" style="width:304px;height:22px;" |
Inward stocks of FDI multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdistoutm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDISTOUT

| nowrap="nowrap" style="width:304px;height:22px;" |
Outward stocks FDI multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdiwgrm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDI STOCK, XFDIOUT

| nowrap="nowrap" style="width:304px;height:22px;" |
World FDI growth rate multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xportfoliom'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XPORTFOLIO

| nowrap="nowrap" style="width:304px;height:22px;" |
Inward portfolio investment multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xportstoutm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XPORTFOLIO

| nowrap="nowrap" style="width:304px;height:22px;" |
Outward portfolio investment stock multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aiddon'''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid foreign loan donations as % GDP

| nowrap="nowrap" style="width:137px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aidrecm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid receipts as % GDP

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier 

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aidlpm '''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid foreign loan percentage multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|}

Users can alter imports and exports via two major channels. First, they can force a shift towards export promotion using the additive '''xshift''' parameter. Increasing the parameter value to .05 would result in an additional 5% being added to the value of exports for that year and all subsequent years until the dynamics of the model dampen the effect. Protectionism in imports can be modeled by changing the value of '''protecm''', which is a multiplicative parameter that increases the cost of imports and thereby proxies an increase in tariff rates or of non-tariff barriers to trade.

Finally the distribution of the global terms of trade can be impacted by '''termtrm'''. Raising this multiplicative parameter will alter the balance of global trade patterns to favor southern developing countries (colloquially known as the Global South). This means that an increase in this parameter will increase the financial value of the exports from these developing nations, while reducing the relative financial value of exports from developed nations.

Affecting global financial flows is a relatively complex process in the IFs economic system. Multiple channels exist to modify the different major sources of international financial flows. Users can alter foreign direct investment flows using the '''xfdistockm''' parameter, which increases the stocks of FDI within a country and adjusts flows accordingly. Conversely, '''xfdistoutm''' will increase the size of the stock of FDI slated to exit a country. Users may also wish to alter the growth rate for FDI at a global level via the parameter '''xfdiwgrm'''.

Portfolio investment can be modified using a similar set of parameters. '''Xportfoliom''' allows users to increase the stocks of portfolio investment within a country, while '''xportstout''' controls the amount of portfolio investment that a country’s citizens hold abroad.

Aid parameters differ somewhat from those available for FDI and portfolio investment. Users can change the amount of foreign loan donations as a percent of GDP via '''aiddon''', which specifies the portion of donor country GDP given in the form of aid. They may change the amount of aid a country receives using the '''aidrecm''' parameter, which determine the portion of the global aid pool created by donors that any individual country will receive (the total claims on that pool determined by '''aidrecm''' will be normalized to the total size of the pool). Finally, users may alter the portion of aid that comes in the form of loans that accrue interest and must be repaid within a certain time frame using the multiplicative parameter '''aidlpm'''.

== Parameters to Affect the Informal Economy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''labinformshrm'''
|LABINFORMSHR
|Informal labor force share multiplier
|Multiplier
|-
|'''gdpinformshrm'''
|GDPINFORMSHR
|Informal GDP share multiplier
|Multiplier
|-
|'''gdpshadowshrm'''
|GDPSHADOWSHR
|Shadow economy GDP share multiplier
|Multiplier
|-
|'''gdpshadowon'''
|GDPSHADOWSHR
|Switch to turn on GDPSHADOWSHR instead of GDPINFORMSHR. Default is 0 (GDPINFORMSHR). 1 turns on GDPSHADOWSHR instead.
|Switch
|-
|'''edyrsagm'''
|EDYRSAG15
|Years of education ages 15 and up multiplier
|Multiplier
|-
|'''firmtaxrm'''
|FIRMTAX
|Tax rate on firm’s multiplier
|Multiplier
|-
|'''gdsm'''
|GDS
|Government R&D (and education) spending distribution multiplier
|Multiplier
|-
|'''govbusregindm'''
|GOVBUSREGIND
|Government business regulation index multiplier
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption multiplier
|Multiplier
|-
|'''govhhtrnpenm'''
|GOVHHTRN
|Government pension spending multiplier
|Multiplier
|-
|'''govhhtrnwelm'''
|GOVHHTRN
|Government welfare spending multiplier
|Multiplier
|-
|'''randdexpm'''
|RANDEXP
|Total (public and private) expenditure on research and development multiplier
|Multiplier
|-
|'''taxinfadjm'''
|HHTAXR, FIRMTAXR, SSWELTAXR
|Multiplier of impact of informal GDP share on tax rates
|Multiplier
|-
|'''labinformcoeffbus'''
|LABINFORMSHR
|Informal labor share, coefficient of business index
|Coefficient
|-
|'''labinformcoeffed'''
|LABINFORMSHR
|Informal labor share, coefficient of education years
|Coefficient
|-
|'''labinformcoeffhhtrn'''
|LABINFORMSHR
|Informal labor share, coefficient of household transfers
|Coefficient
|-
|'''labinformcoefftax'''
|LABINFORMSHR
|Informal labor share, coefficient of tax rate
|Coefficient
|-
|'''gdpinformcoeffcorrupt'''
|GDPINFORMSHR
|Informal GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpinformcoeffintercept'''
|GDPINFORMSHR
|Informal GDP share, intercept of model
|Coefficient
|-
|'''gdpinformcoefflabinf'''
|GDPINFORMSHR
|Informal GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpinformcoeffrandd'''
|GDPINFORMSHR
|Informal GDP share, coefficient of R&D spending
|Coefficient
|-
|'''gdpshadowcoeffintercept'''
|GDPSHADOWSHR
|Shadow economy GDP share, intercept of model
|Coefficient
|-
|'''gdpshadowcoefflabinf'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpshadowcoeffcorrupt'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpshadowcoeffrandd'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of R&D spending
|Coefficient
|}

Users can affect the labor informal share of the total labor force directly ('''labinformshrm'''). In most cases they will do so indirectly by affecting the driving variables of that share. One of the driving variables is educational attainment of adults, so any parameter affecting that attainment will have a potential impact (see the parameters for education including the prepackaged scenarios). Another is the government business index ('''govbusregindm'''). A third is government to household transfers for either pensions or welfare ('''govhhtrnpenm''' and '''govhhtrnwelm'''). A fourth is the tax rate on firms ('''firmtaxm''').

The forward linkage from informal labor share to informal GDP share is affected by a multiplier ('''gdpinformshrm''') that can totally turn off that linkage if set to 0. The informal GDP share can be directly affected via '''gdpinformshrm''', or indirectly by affecting its drivers, including informal labor share. Other drivers include: the level of government corruption ('''govcorruptm'''), government spending on research and development (gdsm, R&D) and total (public and private) spending on research and development ('''randdexpm'''). Manipulating '''randdexpm''' itself will increase private sector spending while changing '''gdsm''' will also impact the public portion of '''RANDDEXP'''.

A set of parameters ( '''labinformcoeffbus, labinformcoeffed, labinformcoeffhhtrn, labinformcoefftax''') control the calculation of informal labor share from its assorted drivers.

A similar set of parameters ('''gdpinformcoeffintercept''', '''gdpinformcoeffcorrupt''', '''gdpinformcoefflabinf''', '''gdpinformcoeffRandD''') control the calculation of informal GDP from its assorted drivers.

The model includes the shadow economy share of GDP ('''GDPSHADOWSHR''') as an additional measure of informality in the economy. Under the Base Case, '''GDPSHADOWSHR''' is initialized but does not have active forward linkages (the Base Case drives all informality-related forward linkages with GDPINFORMSHR instead). The shadow economy GDP share can be affected directly by changing '''gdpshardowshrm''' or indirectly by changing its drivers, which as with the informal economy share, are: informal labor, government corruption, and spending on R&D. The shadow economy uses a similar set of parameters to the informal economy ('''gdpshadowcoeffintercept''', '''gdpshadowcoeffcorrupt''', '''gdpshadowcoefflabinf''', '''gdpshadowcoeffRandD''') to control the calculation of the shadow economy from its drivers.

Users wanting to drive the forward linkages from informality with the shadow economy instead of the informal economy can do so by changing the switch '''gdpshadowon''' from its default, Base Case setting of 0 (which tells the model to drive the forward linkages with '''GDPINFORMSHR''') to 1 (which tells the model to drive them with '''GDPSHADOWSHR''' instead).

The informal and shadow GDP shares (whichever is activated—see above) use the same multipliers for their linkages to multifactor productivity ('''tefinfadjm''') and tax rate ('''taxinfadjm''')'''.'''

== Prepackaged Scenarios ==

A large number of prepackaged scenarios that revolve around economic development and poverty alleviation are available for user access, located under the PPHP Poverty heading in the World Integrated Scenario Sets. The best and worst case Framing Scenarios include changes to the growth in MFP for the world leader, the additive growth rate factor for MFP, TFR, and the Gini index.

More integrated scenario sets are available as well to model the effects of combinations of interventions. These are labeled Combined Dom Intl Interventions (Rev, Rev2). This scenario is built from two different series of interventions: Leverage Testing Intl, and Leverage Testing Domestic, which user can explore if they wish to look more closely at individual interventions from the combined scenario. Users should also use these scenarios and the Combined Dom Intl Interventions (Rev, Rev2) to get a sense of how to scale interventions to different regions of the world or even globally, rather than altering a single country at a time.

Revision 2 models a number of parameter changes including: growth in multifactor productivity ('''mfpadd'''), [[#_The_Health_Module|declines in TFR ('''tfrm'''),]] increases in the female share of the labor force('''labfemshrm''') and [[#_The_Health|world migration]]('''wmigrm'''), increases in investment ('''invm)''', foreign aid, FDI and portfolio investment ('''xfdistockm''' and '''xportfoliom'''), declines in protectionism and resulting increase in exports ('''protecm''' and '''xshift'''), increased government expenditures on education, health, and R&D ('''gdsm'''), greater welfare transfers to unskilled labor('''govhhtrnwelm'''),[[#_Governance_Module|improvements to governance]] (declines in corruption-'''govcorruptm''', increases in effectiveness- '''goveffectm''', and economic freedom- '''econfreem'''), [[#_Infrastructure_2|improved infrastructure]] in roads, ICT, and telephone network density ('''infranetm''', '''infraroadm''','''infratelem'''), and [[#_Energy_Module|increased energy production]] from renewable resources ('''enpm''').

Of these, the economic changes deserve special attention. This scenario includes an improvement in multifactor productivity in the World Bank developing economies to 0.002 over a ten year time horizon, growth in the female share of the labor force to 1.5 time the base case over a 50 year time frame and increases in world migration multiplier to 1.5 over a 15 year time frame. It also increases investment multiplier to between 1.3 and 2 over a 15 to 25 year time frame depending on the region triggering increased capital accumulation and savings.

Government expenditure is also affected. Expenditure to education increases to between 1.2 and 1.8 times the base case implemented over ten years. Expenditure to health and research and development also increase by similar amounts over approximately the same time frame. Government to household transfers increase in different regions by 50 to 100% (multiplier values of 1.5 to 2) over a 20-year time frame. Trade and export promotion increase as well. Levels of protectionism decline by 20% over 20 years (a multiplier value of .8) and the ratio of exports to imports grows by .04.

And finally, FDI and portfolio investment are affected. FDI doubles over 25 years in the World Bank developing economies while portfolio investment increases by 50% over the same time frame. Foreign aid by donor nations increases to .5% of GDP over ten years. There are also increases in the availability of IMF funding that go beyond the scope of this guide, but which may be of interest to specialists in international finance.

With respect to the informal economy, eight scenarios have been developed for Peru. Six of them each manipulate one of the drivers of either informal labor or informal GDP share.  In each case an "aggressive but reasonable" value of the driver works to reduce informality.  The other two involve more general impacts on information.  The first (Comb Lab GDP 1.5 drivers.sce) combines all of the six individual drivers into a single scenario to reduce informality. The second or counterfactual scenario (Informal GDP Share Total Decline Peru 15 years.sce) use a brute force multiplier on the informal GDP share to reduce it to the minimum value allowed (1 percent) between 2016 and 2030.

= Infrastructure Module =

== Variables of Interest ==

The Infrastructure module in the IFs system forecasts infrastructure development and its consequences via a five stage process that is driven primarily by the expected/demanded levels of infrastructure and the funding available to meet these expectations or demands.

The model first estimates the expected/demanded level of infrastructure within a country in relation to key drivers like GDP per capita and population.4 Second, it translates these expectations into financial requirements, accounting for both new construction and maintenance. Third, the system balances these desired funding levels with the actual resources available for infrastructure construction and maintenance. Fourth, it forecasts the actual attained levels of infrastructure (both in raw physical terms and in terms of population access rates). Fifth, these levels of infrastructure have specific and direct social, economic and environmental impacts related to these attained levels (processes discussed in connection with other modules).

The distinction between expected and demanded levels of infrastructure is a function of the scenario being modeled. In general scenarios, e.g., the Base Case, we use the term expected because the underlying equations are based on historical data reflecting both underlying demands and supply constraints. In scenarios with targets, however, these equations are overridden by equations reflecting the target path, when the expected values lag behind the values defined by the target path. In such instances, therefore, we are more clearly identifying demands for infrastructure, rather than expectations.

Infrastructure variables can be divided into three major types: physical infrastructure, access, and funding.5 Road density, percentage of road paved, electricity generation capacity, and the amount of land equipped for irrigation all represent key physical infrastructure variables that indicate actual infrastructure stocks within a country. Key access variables, which indicate the degree to which people are able to benefit from existing infrastructure, include: rural population living within 2 kilometers of an all-season road (the road access index); population with access to electricity; reliance on solid fuels for energy; population with access to improved safe water, sanitation, and wastewater treatment; and subscriptions per 100 people to fixed line telephones, mobile phones, and fixed or mobile broadband.
<div>
----
<div id="ftn1">
4This calculation treats physical stocks differently from access indicators depending on the type of infrastructure being considered. For roads, expected access is calculated as a function of the physical stocks, while for electricity, water and sanitation, and ICT, physical stocks are calculated on the basis of the expected levels of access. This has implications for the functioning of the parameters discussed below.
</div><div id="ftn2">
5IFs also distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and future types of infrastructure yet to be envisioned. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid under-representing the total demand for infrastructure. The choice of what to include as core infrastructure reflects the availability of historical data and our determination of what can be modelled within IFs at this time.
</div></div>

=== Physical Infrastructure ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| style="width: 246px;" | '''Variable Name'''
| style="width: 241px;" | '''Description'''
|-
| style="width: 246px;" colspan="2" | ''Transportation'' 
|-
| style="width: 246px;" | '''INFRAROAD*''' 
| style="width: 241px;" | Total road density 
|-
| style="width: 246px;" | '''INFRAROADPAVEDPCNT*''' 
| style="width: 241px;" | Percentage of roads paved 
|-
| style="width: 246px;" colspan="2" | ''Electricity'' 
|-
| style="width: 246px;" | '''INFRAELECGENCAP*''' 
| style="width: 241px;" | Electricity generation capacity per capita 
|-
| style="width: 246px;" | '''INFRAELECTRANSLOSS''' 
| style="width: 241px;" | Electricity transmission loss 
|-
| style="width: 246px;" | '''INFRAELECADJFACT''' 
| style="width: 241px;" | Electricity adjustment factor 
|-
| style="width: 246px;" | '''ENELECSHRENDEM''' 
| style="width: 241px;" | Ratio of electricity use to total primary energy use 
|-
| style="width: 246px;" colspan="2" | ''Water and Sanitation'' 
|-
| style="width: 246px;" | '''LANDIRAREAEQUIP''' 
| style="width: 241px;" | Area equipped with irrigation 
|-
| style="width: 246px;" colspan="2" | * each variable marked with an asterisk has a companion variable with the suffix DEM, which indicates the expected/demanded level of the variable in the absence of funding constraints 
|}
Key physical Infrastructure variables for transportation are '''INFRAROAD''', and '''INFRAROADPAVEDPCNT'''. The variable '''INFRAROAD''' represents a measure of total road density (paved and unpaved), while the variable '''INFRAROADPAVEDPCNT''' offers a measure of the quality of the transportation infrastructure within a country using paved percentage as a proxy.

Key physical Infrastructure variables for transportation are '''INFRAROAD''', and '''INFRAROADPAVEDPCNT'''. The variable '''INFRAROAD''' represents a measure of total road density (paved and unpaved), while the variable '''INFRAROADPAVEDPCNT''' offers a measure of the quality of the transportation infrastructure within a country using paved percentage as a proxy.

Key variables for electricity are the amount of electricity generation capacity within a country, denoted by '''INFRAELECGENCAP''' and expressed in kilowatts per person. Other variables, which represent quality indicators, are '''INFRAELECTRANSLOSS'''—the transmission and distribution loss in percent—and '''INFRAELECADJFACT'''—the capacity factor for electricity production, expressed as a fraction, and '''ENELECSHRENDEM''', the ratio of electricity use to total primary energy use. It is also possible to calculate electricity connections using the results on access to electricity, discussed below, and population.

The only physical infrastructure variable for water and sanitation is the area equipped for irrigation, '''LANDIRAREAEQUIP''', which represents the amount of land in thousands of hectares that is equipped with irrigation. There are no key physical infrastructure variables associated with ICT, only access rates. Users wishing to affect ICT infrastructure should modify access, which, along with population, can be used to calculate actual physical infrastructure levels.

=== Access to Infrastructure ===

{| border="1" cellspacing="1" cellpadding="1" width="589"
|-
| style="width:151px;" |
'''Variable Name'''

| style="width:192px;" |
'''Sub Categories'''

| style="width:246px;" |
'''Description'''

|-
| style="width:151px;" colspan="3" |
''Transportation''

|-
| style="width:151px;" |
'''INFRAROADRAI*'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to rural roads

|-
| style="width:151px;" colspan="3" |
''Electricity''

|-
| style="width:151px;" |
'''INFRAELECACC*'''

| style="width:192px;" |
Rural, urban, total

| style="width:246px;" |
Access to electricity

|-
| style="width:151px;" |
'''ENSOLFUEL'''

| style="width:192px;" |
 

| style="width:246px;" |
Solid fuel use

|-
| style="width:151px;" colspan="3" |
''Water and Sanitation''

|-
| style="width:151px;" |
'''WATSAFE*'''

| style="width:192px;" |
None, other improved, piped

| style="width:246px;" |
Access to improved water

|-
| style="width:151px;" |
'''SANITATION*'''

| style="width:192px;" |
Other unimproved, shared, improved

| style="width:246px;" |
Access to improved sanitation

|-
| style="width:151px;" |
'''WATWASTE'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to wastewater collection

|-
| style="width:151px;" |
'''WATWASTETREAT*'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to wastewater treatment

|-
| style="width:151px;" colspan="3" |
''ICT''

|-
| style="width:151px;" |
'''INFRATELE*'''

| style="width:192px;" |
 

| style="width:246px;" |
Fixed telephone lines

|-
| style="width:151px;" |
'''ICTBROAD*'''

| style="width:192px;" |
 

| style="width:246px;" |
Fixed broadband subscriptions

|-
| style="width:151px;" |
'''ICTMOBIL*'''

| style="width:192px;" |
 

| style="width:246px;" |
Mobile telephone subscriptions

 

|-
| style="width:151px;" |
'''ICTBROADMOBIL*'''

| style="width:192px;" |
 

| style="width:246px;" |
Mobile broadband subscriptions

|-
| colspan="3" style="width:589px;" |* each variable marked with an asterisk has a companion variable with the suffix DEM, which indicates the expected/demanded level of the variable in the absence of funding constraints. Please see the note below for a more comprehensive description of the difference6

|}

Physical infrastructure may be essential, but unless people have access to it, they cannot benefit. Because of this, access variables are included wherever possible in the IFs system. Transportation contains only one key access variable. This variable, '''INFRAROADRAI''', provides a measure of degree of access people have to the existing road infrastructure. It is defined as the percent of the rural population living within 2km of an all season road.

'''INFRAELECACC''' provides a measure of access to electricity, disaggregated by rural and urban users. Another measure of access to electricity that users may be interested in from a health perspective is '''ENSOLFUEL''' which represents the percentage of the population relying on solid fuels for energy, and a significant factor in both environmental change and certain health conditions.

The two fundamental access variables of interest for water and sanitation are '''WATSAFE''' and '''SANITATION''', which measure the percentage of the population with access to improved water sources and sanitation, respectively, by type of access. Water access is subdivided into unimproved, other improved, and piped. Sanitation access is subdivided into other unimproved, shared, and improved (note that shared access is not considered improved). '''WATWASTE''' and '''WATWASTETREAT''' represent the percentage of the population whose wastewater is collected and treated, respectively.

Four ICT variables represent access to the major components of information and communication technologies. '''INFRATELE''' represents the number of fixed line telephones per 100 people while the other three indicators, '''ICTBROAD''', '''ICTMOBIL''', and '''ICTBROADMOBIL''', provide the number of subscriptions per 100 people for fixed broadband, mobile telephones, and mobile broadband, respectively. The dynamics of the model are structured in such a way that the long term trend is for fixed line telephones to decline, while use of mobile and mobile broadband increases.

----
<div id="ftn1">
6The vast majority of Infrastructure variables have both a standard variable, representing the actual achieved level of infrastructure stock or access, and a demand/expectations related variable suffixed by *DEM. The interpretation of the *DEM variables should change subtly depending on the analytical context. When discussing the Base Case or when applying any of the multiplicative parameters, it is more useful to think of the *DEM variables as expected levels; when building scenarios that manipulate the '''*setar/*seyrtar''' parameters, it is more accurate to think of them as demand variables.

</div>
=== Infrastructure Funding ===

{| border="1" cellspacing="1" cellpadding="1" width="637" style="width:637px;"
|-
| style="width:211px;" |
'''Variable Name'''

| style="width:426px;" |
'''Description'''

|-
| style="width:211px;" |
'''GDS (infrastructure, infraother)'''

| style="width:426px;" |
Government consumption, by category

|-
| style="width:211px;" |
'''INFRAINVESTMAINT'''

| style="width:426px;" |
Total (public plus private) investment for core infrastructure maintenance, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTMAINTPUB*'''

| style="width:426px;" |
Public investment for core infrastructure maintenance, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTNEW'''

| style="width:426px;" |
Total (public plus private) investment for construction of new core infrastructure, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTNEWPUB*'''

| style="width:426px;" |
Public investment for construction of new core infrastructure, by type of infrastructure

|-
| colspan="2" style="width:637px;" |* the companion variables, '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''', are the amounts of public spending originally requested prior to the government budget allocation process

|}

The infrastructure module in IFs incorporates a cost accounting system that ensures that all infrastructure improvements are funded prior to construction, which allows for a gap between the amount of infrastructure that is expected/demanded and the amount that is actually achieved.

As in other modules, GDS provides a good overview of total public spending on infrastructure, but this cannot be broken down by type of infrastructure any more specifically than core infrastructure and other infrastructure. '''INFRAINVESTMAINT''' provides the measure of the total investment for infrastructure maintenance, while '''INFRAINVESTMAINTPUB''' provides infrastructure investment for maintenance provided by public sector funds. '''INFRAINVESTNEW''' provides an indicator of the total spending on construction of new infrastructure, while '''INFRAINVESTNEWPUB''' provides the spending that came from public sources for the purposes of new investment. For the purposes of parameter manipulation, we will discuss the types of infrastructure separately because their dynamics vary widely with regards to how the model calculates physical infrastructure versus access. Absolute and relative target parameters to affect infrastructure access are discussed together because all share similarities in terms of structure and function within the model. Finally, there is some discussion of how to combine the two different types of parameter. These variables are subdivided based on whether the spending is for maintenance purposes or for the building of new infrastructure, and whether funding comes from public or private sources. Private funding is related to public funding in some ratio in all cases but ICT, where private funding has historically been the driver of infrastructure investment.

The two public spending variables, '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''', have companion variables like many of the physical infrastructure and access variables discussed above. Specifically, the variables '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''' represent the amounts of public spending originally requested prior to the government budget allocation process. When there are shortages, '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''' will be less than '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW'''.7 In addition to producing a shortfall of public investment, this will also reduce private spending, except for the case of ICT infrastructure. The treatment of ICT differs because private funding has historically been the driver of investment in ICT infrastructure, whereas the public sector has been the driver of investment in other infrastructure.
<div>
----
<div id="ftn1">
7It is also possible for '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''' to be less than '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''' for another reason. This occurs when countries try to increase infrastructure spending more rapidly than is considered feasible. In this case, some of the funds allocated are held back and released over several years. Please refer to the technical documentation on the infrastructure model for more details on this process.
</div></div>
== Parameters to Affect Infrastructure ==

Four basic types of parameter are used in this module. They are, multiplicative parameters, technological shift factors, absolute, and relative targets. In general, multiplicative and technological shift factors operate in the same way; likewise, absolute and relative targets have similar functionalities.

There are some general rules that govern the interaction among the different types of parameters. 8The first rule states that, if multiplicative or tech shift factor parameters are used in conjunction with absolute or relative targets, the multiplicative/technological shift parameters are applied first and then the relative or absolute targets are calculated. The second rule states that absolute and relative targets cannot be applied together. If they are, the model interprets this as an inconsistent application of parameters and will not implement either. The exception to both these rules are ICT fixed and mobile broadband, where the multipliers '''ictbroadm '''(for fixed broadband) and'''ictbroadmobilm '''(for mobile broadband), are applied after the absolute or relative target parameters are applied. Fixed broadband is also the exception to the rule that absolute and relative targets cannot be applied together. Instead, they are both calculated within the model and then the larger of the two values is taken.

In analyzing scenarios involving changes to the majority of the parameters described below, users should first check variables with the suffix DEM, where available, because many of the parameters modify expected/demanded levels of access. Assuming that the model has modified the expected/demanded level as desired, the user should next check the actual achieved levels of infrastructure. The reason is that if a parameter change increased the level of demand, but the impact did not raise demand above achieved levels otherwise computed in the model, there would likely be no apparent impact on the achieved level. For instance, users wishing to affect access to sanitation via '''sanitationm '''should first check '''SANITATIONDEM''' to examine the impact on expected/demanded levels, before checking '''SANITATION''' to determine the impact on the actual level fuels of access to sanitation within the population.
<div>
----
<div id="ftn1">
8There are some exceptions to the rules laid out below, primarily as pertains to the treatment of fixed broadband within ICT.
</div></div>
=== Roads ===

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:154px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:228px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadm'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROAD

| nowrap="nowrap" style="width:228px;height:22px;" |
Multiplier on road density expected/demanded

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadpavedpcntm'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADPAVED

PCNT

| nowrap="nowrap" style="width:228px;height:22px;" |
Multiplier on percentage of roads paved

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''Infraroadraitrgtval, Infraroadraitrgtyr'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADRAI

| nowrap="nowrap" style="width:228px;height:22px;" |
Target value/years after 2010 for achievement for population percentage living w/in 2km of an all-weather road

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadraisetar, infraroadraiseyrtar'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADRAI

| nowrap="nowrap" style="width:228px;height:22px;" |
Standard error target and years after 2010 for achievement for rural road access index

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}

All the multiplicative

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:235px;height:22px;" |
eneration capacity

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''Infraelecaccm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on electricity access

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''enelecshrendemm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECDEM

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on ratio of electricity use to total primary energy use

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelectranlossm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECTRANLOSS

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on the loss of electricity in transmission and distribution

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfuelm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on the reliance on solid fuel as a source of energy

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelecacctrgtval/ infraelecacctrgtyr'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Target value and years after 2010 for target achievement for population percentage w/ access to electricity

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelecaccsetar/ infraelecaccseyrtar'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Standard error target and years after 2010 for population percentage w/ access to electricity

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfueltrgtval/ ensolfueltrgtyr'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Target value and years after 2010 for target achievement for percentage of households reliant on solid fuels

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfuelsetar/ ensolfuelseyrtar'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Standard error target and years after 2010 for target achievement for percentage of households reliant on solid fuels

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}

Electricity in the infrastructure model contains parameters that alter both physical infrastructure and access levels using multiplicative parameters. The parameter '''infraelecgencapm''' directly affects '''INFRAELECGEN''', which represents the physical capacity to generate electricity and is a physical infrastructure variable.

Access to electricity, which is represented by '''INFRAELECACC''', can be directly affected by '''infraelecaccm''', a parameter that directly impacts access to electricity. This parameter is subdivided to allow users to affect urban and rural access separately.

Three other multiplicative parameters are relevant to discussion of electricity access in the infrastructure module. These are '''ensolfuelm''', '''enelecshrendemm,''' and '''infraelectranlossm'''. '''Ensolfuelm''' changes solid fuel reliance as a primary energy source for domestic cooking and heating (ENSOLFUEL), which is also driven by '''INFRAELECACC'''. '''Enelecshrendemm''', changes the ratio of electricity use to total primary energy use (this is a variable of relevance to the [[#_Energy_Module|energy]] module, but is not yet linked to it). It allows users to change how reliant a country is on electricity relative to other sources of final energy use. It indirectly affects the expected/demanded amount of electricity generation capacity as represented by the variable '''INFRAELECACCDEM'''. '''Infraelectranlossm''' affects the loss of electricity during transmission and distribution. It is a parameter that allows users to affect the efficiency and quality of the electricity infrastructure. Like '''enelecshrendemm''', this parameter indirectly affects '''INFRAELECACCDEM'''.

There are also target access parameters to allow users to set targets for desired levels of infrastructure access. These include '''infraelecacctrgtval/infraelecacctrgtyr''', '''infraelecaccsetar/infraelecaccseyrtar''', '''ensolfueltrgtval/ensolfueltrgtyr''', and '''ensolfuelsetar/ensolfuelseyrtar'''. These set absolute and relative targets and target years for desired levels of electricity access and solid fuel use respectively.

=== Water and Sanitation ===
In IFs, water and sanitation sub-model comprises of two levels of disaggregation. The first one is an older 3-tier breakdown that categorizes water access into: (i) Unimproved, (ii) Other Improved and (iii) Piped and sanitation access into: (i) Improved, (ii) Other Unimproved and (iii) Shared.

The second one, reflects the new update from JMP (WHO-UNICEF) that categorizes water and sanitation into a 5-tier breakdown. In this new 5-tier system, water access is classified as (i) Surface, (ii) Unimproved, (iii) Limited, (iv) Basic and (v) Safely Managed and sanitation access is classified as (i) Open Defecation, (ii) Unimproved, (iii) Limited, (iv) Basic and (v) Safely Managed.

In order to default to the older 3-tier model, parameter switches namely, '''watsafenewsw''' and '''sanithhmodel2sw''' must be set to 0 before rebuilding the base. Alternatively, upon setting their values to 1 and rebuilding the base allows the user to switch to the newer 5-tier water and sanitation model. The first table below lists all relevant parameters associated with the 3-tier model while the second table lists all relevant parameters associated with the 5-tier model.

'''For the 3-tier model:'''
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''landirareaequipm'''
|LANDIRAREAEQUIP
|Multiplier on land area equipped for irrigation
|Multiplier
|-
|'''sanitationm'''
|SANITATION
|Multiplier on percentage of people with access to sanitation
|Multiplier
|-
|'''watsafem'''
|WATSAFE
|Multiplier on percentage of people with access to safe water
|Multiplier
|-
|'''watwastem'''
|WATWASTE
|Multiplier on percentage of people connected to wastewater collection system,
|Multiplier
|-
|'''watwastetreatm'''
|WATWASTETREAT
|Multiplier on percentage of people connected to wastewater treatment
|Multiplier
|-
|'''sanitationtrgtval/ sanitationtrgtyr'''
|SANITATION
|Target value and years after 2010 for target achievement for population percentage with improved access to improved sanitation, by category of
|Relative target
|-
|'''watsafetrgtval/ watsafetrgtyr'''
|WATSAFE
|Target value and years after 2010 for target achievement for population percentage without access to improved sources of water
|Relative target
|-
|'''sanitnoconsetar/ sanitimpconsetar/ sanithhconsetar/ sanitnoconseyrtar'''
|SANITATION
|Standard error targets and years after 2010 for target achievement for population percentage with varying access to sanitation
|Relative target
|-
| colspan="4" |'''Note: Using both''' *trgtval/trgtyr '''and the''' *setar/*seyrtar '''parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.'''
|}

'''For the 5-tier model:'''
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhm'''
|SANITHH
|Multiplier on percentage of people with access to sanitation by type
|Multiplier
|-
|'''sanithhbasictrgtval'''

'''sanithhbasictrgtyr'''
|SANITHH
|Target value and years from the base to reach at least basic sanitation services
|Relative target
|-
|'''sanithhsafetrgtval'''

'''sanithhsafetrgtyr'''
|SANITHH
|Target value and years from the base to reach safely managed sanitation services
|Relative target
|-
|'''waterhhm'''
|WATERHH
|Multiplier on percentage of people with access to water by type
|Multiplier
|-
|'''waterhhbasictrgtval'''

'''waterhhbasictrgtyr'''
|WATERHH
|Target value and years from the base to reach at least basic water services
|Relative target
|-
|'''waterhhsafetrgtval'''

'''waterhhsafetrgtyr'''
|WATERHH
|Target value and years from the base to reach safely managed water services
|Relative target
|}

Water and sanitation only contains one parameter which directly affects physical infrastructure levels. This parameter is '''landirareaequipm''', a parameter that affects the land area equipped for irrigation. All other parameters affect access to water, wastewater collection and treatment, and sanitation; the access rate then determines the expected/demanded level of the physical stock. There are no direct multipliers on physical stocks for these types of infrastructure. '''Sanitationm''', '''watsafem''', '''watwastem''', and '''watwastetreatm''' are access parameters that affect access to sanitation, safe water, waste water collection, and waste water treatment, respectively.

Targeting parameters for water and sanitation are: '''sanitationtrgtval/sanitationtrgtyr''', '''watsafetrgtval/watsafetrgtyr''', '''sanitnoconsetar/sanitimpconsetar/sanihhconsetar/sanitnoconseyrtar''', '''watsafenoconsetar/watsafimpconsetart/watsafhhconsetar/watsafenoconseyrtar''', and '''watwastetreatsetar/watwastetreatseyrtar'''. The first two pairs provide absolute target values for sanitation and safe water access. The second two pairs define relative target values and years for reducing the number of people with no access to improved sanitation, safe water, or wastewater treatment.

Water and sanitation only contains one parameter which directly affects physical infrastructure levels. This parameter is '''landirareaequipm''', a parameter that affects the land area equipped for irrigation. All other parameters affect access to water, wastewater collection and treatment, and sanitation; the access rate then determines the expected/demanded level of the physical stock. There are no direct multipliers on physical stocks for these types of infrastructure. '''Sanitationm''', '''watsafem''', '''watwastem''', and '''watwastetreatm''' are access parameters that affect access to sanitation, safe water, waste water collection, and waste water treatment, respectively.

Targeting parameters for water and sanitation are:'''sanitationtrgtval/sanitationtrgtyr''', '''watsafetrgtval/watsafetrgtyr''', '''sanitnoconsetar/sanitimpconsetar/sanihhconsetar/sanitnoconseyrtar''', '''watsafenoconsetar/watsafimpconsetart/watsafhhconsetar/watsafenoconseyrtar''', and '''watwastetreatsetar/watwastetreatseyrtar'''. The first two pairs provide absolute target values for sanitation and safe water access. The second two pairs define relative target values and years for reducing the number of people with no access to improved sanitation, safe water, or wastewater treatment.

=== ICT ===

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:134px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:245px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''infratelem'''

| nowrap="nowrap" style="width:134px;height:22px;" |
INFRATELE

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on fixed-line telephone lines per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadm'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on fixed-line broadband subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobilm'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on mobile broadband subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadtrgtval, ictbroadtrgtyr'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Target value and years after 2010 for target achievement for number of subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobiltrgtval, ictbroadmobiltrgtyr'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Target value and years after 2010 for target achievement for number of subscriptions per 100 persons; varies between 0 and 150

| nowrap="nowrap" style="width:104px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobilsetar, ictbroadmobilseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for ICT mobile broadband access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadsetar, ictbroadseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for fixed broadband access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictmobilsetar, ictmobilseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for mobile phone access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}
'''Infratelem''', '''ictbroadm''' and '''ictbroadmobilm''' are the only three multiplicative parameters that apply to ICT, and they serve to modify expected or demanded levels of access to telephones, fixed broadband, and mobile broadband respectively. IFs calculates broadband ICT slightly differently than other infrastructure types. Rather than being mutually exclusive, both absolute target values and relative targets (discussed in the paragraph below) apply to the calculation of ICTBROAD. Rather than calculating the value of the parameter adding the multiplier effect and then applying the relevant absolute and relative targets, which is what occurs in the rest of the infrastructure module, the calculation process differs markedly in regards to broadband. In this case, the value of ICTBROAD is calculated twice, once with and once without the application of the target access levels, and the larger of these two values is used by the model for calculations going forward. Then the multiplier '''ictbroadm''' is applied.

'''Infratelem''', '''ictbroadm''' and '''ictbroadmobilm''' are the only three multiplicative parameters that apply to ICT, and they serve to modify expected or demanded levels of access to telephones, fixed broadband, and mobile broadband respectively. IFs calculates broadband ICT slightly differently than other infrastructure types. Rather than being mutually exclusive, both absolute target values and relative targets (discussed in the paragraph below) apply to the calculation of ICTBROAD. Rather than calculating the value of the parameter adding the multiplier effect and then applying the relevant absolute and relative targets, which is what occurs in the rest of the infrastructure module, the calculation process differs markedly in regards to broadband. In this case, the value of ICTBROAD is calculated twice, once with and once without the application of the target access levels, and the larger of these two values is used by the model for calculations going forward. Then the multiplier '''ictbroadm''' is applied.

'''Ictbroadtrgtval/ictbroadtrgtyr '''and '''ictbroadmobiltrgtval/ictbroadmobiltrgtvr '''set absolute target values and years for fixed and mobile broadband access. '''Ictbroadsetar/ictbroadseyrtar''', '''ictbroadmobilsetar/ictbroadmobilseyrtar''', and '''ictmobilsetar/ictmobilseyrtar '''establish relative target access values for desired broadband access, mobile broadband, and mobile access respectively. Absolute and relative target parameters behave differently when used in combination with one another in regards to fixed broadband than in other infrastructure areas. Instead of canceling one another out, combining'''ictbroadtrgtval '''and '''ictbroadsetar/ictbroadseyrtar''', causes the model to calculate both and then take the larger of the two values and use that.

== Parameters to Affect Funding ==

{| border="1" cellspacing="1" cellpadding="1" width="618" style="width:618px;"
|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:150px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:222px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:89px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''gdsm (infrastructure)'''

| style="width:150px;height:22px;" |
GDS

| nowrap="nowrap" style="width:222px;height:22px;" |
Gross domestic spending multiplier (infrastructure)

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infrainvnewpubshrm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Portion of maintenance funding coming from public sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''Infrainvmaintpubshrm'''

| style="width:150px;height:22px;" |
INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Portion of new infrastructure funding coming from public sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''hhsizem'''

| style="width:150px;height:22px;" |
GDS

| nowrap="nowrap" style="width:222px;height:22px;" |
Household size multiplier

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraroadpavedcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of paved roads

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraroadunpavedcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of unpaved roads

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecgencostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of electricity generation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecaccruralcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of rural electricity access

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecaccurbancostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of urban electricity access

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watsafecostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of safe water

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watsafeimpcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of improved safe water sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''sanitationcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of basic sanitation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''sanitationimpcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of improved sanitation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watwastetreatcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of waste water treatment

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''landircostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of equipping land for irrigation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infratelecostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of telephone

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictmobilcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of mobile

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictbroadcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of broadband

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictbroadmobilcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on mobile broadband

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|}
A number of major parameters for affecting the funding process in the infrastructure module exist. The first, more completely discussed in the [[#_The_Economic_Module|economic module]] (in association with the social accounting matrix and government expenditures), is the government spending by destination multiplier '''gdsm'''. By manipulating the infrastructure component of '''gdsm''' users can change the proportion of government spending that goes towards infrastructure thus altering the amount of funding available to achieved the expected or demanded levels of core and other infrastructure; it can also be used to increase or decrease the spending on other infrastructure. 

A number of major parameters for affecting the funding process in the infrastructure module exist. The first, more completely discussed in the [[#_The_Economic_Module|economic module]] (in association with the social accounting matrix and government expenditures), is the government spending by destination multiplier '''gdsm'''. By manipulating the infrastructure component of '''gdsm''' users can change the proportion of government spending that goes towards infrastructure thus altering the amount of funding available to achieved the expected or demanded levels of core and other infrastructure; it can also be used to increase or decrease the spending on other infrastructure. 

Public spending on infrastructure, both for new infrastructure and maintenance purposes, can be increased using the parameters '''infrainvnewpubshrm''' and '''infrainvmaintpubshrm''' respectively. Each of these parameters can be manipulated separately for different types of infrastructure, and changes to the public share will also affect private sector in a positive relationship (as public funding goes up so does private). Users can change the average household size using the parameter '''hhsizem''', which has an indirect effect on budget by altering the total number of infrastructural connections required for a population. 9Since budget availability affects attainment of infrastructure, as explained above, this parameter can ultimately change access rates.

Finally, users can also directly alter the unit cost of certain kinds of infrastructure, using one of a series of cost multipliers. The parameters '''infraroadpavedcostm''', and '''infraroadunpavedcostm '''alter the unit cost of paved and unpaved roads respectively. '''Infraelecgencostm''' alters the cost of electricity generation, while '''infraelecaccruralcostm''' and '''infraelecaccurbancostm''' alter the cost of electricity access in rural and urban areas. The parameters '''watsafecostm''' and '''sanitationcostm''', alter the cost of basic water and sanitation access while '''watsafeimpcostm''' and '''sanitationimpcostm''' change the cost of providing improved access to each. '''Landircostm''' changes the unit cost of equipping land for irrigation. '''Infratelecostm''', '''ictmobilcostm''', '''ictbroadcostm''', and '''ictbroadmobilcostm''' all change the cost of different types of ICT.
<div> 
----
<div id="ftn1">
9Household size parameters are applicable only for those types of infrastructure that are delivered at a household level, e.g., water, sanitation and electricity connections.
</div></div>
== Prepackaged Scenarios ==

Because of the complexity of the infrastructure module, and the sheer number of parameters it contains, users may wish to rely more heavily on prepackaged scenarios in order to find effective combinations of multipliers than they do in other IFs modules. One package of scenarios that was developed to model extensions of ICT broadband connectivity and resulting potential gains in efficiency of energy use was developed for the EU and includes modifications to a number of parameters. '''Ictbroadm''' is used to increase broadband penetration rates while '''lke''' (lifetime of capital in energy) increases the efficiency of energy capital investments, making energy production more efficient. This prepackaged scenario set can be accessed from the Scenario Development form of IFs, using the Add Scenario Component menu option. The set can be found within the World Integrated Scenario Sets grouping where it is labeled ICT Scenarios for the EU. See Acknowledging Limits, Betting on Silver Bullets, Broadband Stupidity, and Networked Solutions (Moyer and Hughes, 2012).

Other Prepackaged Scenarios were developed for Chapter 6 of the Infrastructure volume of the ''Patterns of Potential Human Progress ''series (Rothman et al., 2013). This set of scenarios can be located in the PPHP InfrastructureChapter6 grouping of the World Integrated Scenario Sets. It contains a large number of scenarios using infrastructure targets. The scenario TarAllNNoBudgPriority, referred to as Universal Targets Pursuit in the volume, represents a future in which efforts are made to achieve universal access to water and sanitation, electricity, rural roads, and mobile broadband, as well as the elimination of solid fuel use, by 2030; no budget priority is given to infrastructure in this scenario. The scenario TarSENoBudgPriority has the same target date of 2030, but instead of universal access, countries only aim to achieve levels of access that exceed the expected value for their level of development by one standard deviation; again no budget priority is given to infrastructure. The other scenarios, TarAll2050NoBudgPriority, TarElecNoBudgPriority, TarICTNoBudgPriority, TarSE0NoBudgPriority, TarSENoP2050, TarTranNoBudgPriority, and TarWatNoBudgPriority, build on these two scenarios, adjusting the target level, the target date, or the categories of infrastructure with targets.

= Agriculture Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''AGDEM''' 
| Agricultural demand 
|-
| '''FDEM''' 
| Food demand for consumption 
|-
| '''FEDDEM''' 
| Feed demand for livestock 
|-
| '''INDEM''' 
| Industrial demand for food 
|-
| '''AGP''' 
| Agricultural production 
|-
| '''YL''' 
| Yield per hectare of land 
|-
| '''LD''' 
| Land area 
|-
| '''AGM''' 
| Agriculture Imports 
|-
| '''AQUACUL''' 
| Million metric tons of fish produced via aquaculture 
|-
| '''AGX''' 
| Agriculture Exports 
|-
| '''LOSS''' 
| Portion of food produced that never reaches markets 
|-
| '''CLPC''' 
| Calories per capita 
|-
| '''MALNCHP''' 
| Percent of children malnourished 
|-
| '''MALNPOPP''' 
| Percent of population malnourished 
|}

The IFs system models agriculture in terms of supply and demand. Consumption, from the demand side, links forward into food availability and nutrition. Both supply and demand dynamics in the agricultural module are closely linked to dynamics in the population and economic modules. For simplicity of representation, IFs models three primary foodstuffs: meat, crops and fish. Stocks (inventories) of food are driven by interaction between production and demand. Stocks affect prices and prices generate movement to supply and demand equilibration, a process that also involves trade.

The agriculture model represents demand for food ('''AGDEM''') as a function of population size, population income, the share of income spent on food, and agricultural prices. A number of other variables also help to illustrate different aspects of the demand side of the equation. The first is food demand '''FDEM''', which represents the amount of food demanded for human consumption. The second is '''FEDDEM''', which represents the amount of food demanded for use as feed by livestock and the third is INDEM which represents the amount of food demanded for use in industrial processes. As a country develops, the balance between these types of demand change; in general, nations with more industry will have a higher level of industrial demand for food, and major meat producing nations will have a higher demand for crops to use as feed.

At a basic level, the supply of food produced within a country ('''AGP''') is a product of the amount of land available for cultivation ('''LD''') and the productivity or yield of that land represented by YL in the IFs model. A number of factors affect yield ('''YL''') including: the level of technology, the amount of labor available for agriculture, the investment of capital into agriculture, and the propensity for yields to saturate at some point even in the face of progressively increased input levels. Investment in agriculture is driven by product prices relative to the additional capital investment required and therefore profit levels. Given the common cycles in agriculture and long lead times in response to investment, however, farmers cannot always make decisions based on current conditions. Investment is also directly responsive to global inventory or stock levels and has an inertial component.

IFs also represents losses to the agricultural production system, and they can have a powerful impact on the availability of food in a country. Not all of the food produced within a country makes it to a market, because of poor infrastructure, such as a lack of refrigeration or other inefficiencies in the production system. In IFs this process is represented via the '''LOSS''' variable. For higher income countries, a larger portion of the loss is actually on the demand or consumption side. Because agricultural products are globally traded commodities, imports and exports of food, discussed in the [[#_The_Economic_Module|economic module]] as well, play an important role in moderating the availability of food within a country. In IFs these are represented by the variables'''AGX''', agricultural exports, and '''AGM''', agricultural imports.

The dynamics of the above processes ultimately affect the ability of people in a country to avoid malnutrition in the form of either obesity or starvation. In many developing nations, meeting daily caloric needs is a struggle and large portions of the population in these countries may be malnourished, leaving them vulnerable to death by preventable diseases. This is especially true of children, for whom the consequences of both short and long term under nutrition are especially severe. To help users keep track of this essential developmental indicator, IFs includes a number of variables that represent the nutritional challenges faced within a country. First, calories per capita ('''CLPC''') provides a rough measure of the total number of calories available to individuals. It provides a rough guide to the kinds of nutritional challenges a nation may face. Populations getting less than approximately 2100 calories a day may experience higher levels of malnutrition, while those that are getting significantly above 3000 calories a day may experience increasing levels of overweight and obesity. However, more important than a crude measure of calorie availability is the impact that chronic malnourishment poses from a development standpoint. To illustrate this burden, IFs includes variables that provide the percentage of the population that is malnourished ('''MALNPOPP'''). Data tend to be better for child undernutrition and we typically have more confidence in and pay more attention to the variable that indicates the percentage of a country’s children (under five) who are malnourished ('''MALNCHP''').

== Parameters to Affect Demand ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''clpcm'''
|AGDEM
|Multiplier on all per capita caloric demand
|Multiplier
|-
|'''elascd'''
|AGDEM
|Elasticity of crop demand to changes in food price
|Elasticity
|-
|'''elasmd'''
|AGDEM
|Elasticity of meat demand to changes in food price
|Elasticity
|-
|'''elasfd'''
|AGDEM
|Elasticity of fish demand to changes in food price
|Elasticity
|-
|'''indemm'''
|INDEM
|Industrial demand for crops
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|}

A brute force multiplier on agricultural demand ('''clpcm''') allows direct manipulation of demand and can be used to represent a wide range of scenarios relative to the Base Case, including changing tastes among consumers, changing responses to prices, and changes in incomes or the movement of individuals into the middle class. Changes in consumer desires for certain foods could also be modeled using the parameter '''clpcm '''which also adjusts the portion of calories obtained from meat. It is a multiplicative parameter, so a 0.2 increase in the value of the parameter from its base value of 1.0 would generate a 20% increase in the value of the variable relative to the base case. However, this is a relatively difficult parameter for which to identify reasonable values for such changes. Similarly, changes to '''elascd''' (elasticity of crop demand to changes in price) ,'''elasmd''' (elasticity of meat demand to changes in price) and '''elasfd''' (elasticity of fish demand to changes in price) should be made with caution because they involve changing the responsiveness of demand to changes in price. Such parametric interventions might be appropriate in cases where you want to model the impact of a mass movement to change diet patterns, such as in response to a national campaign for healthy eating or decreased meat consumption due to preference changes. In all three cases the relevant outcome variable is '''AGDEM'''. Also, the parameter '''indemm '''can be used to manipulate levels of industrial demand for crops.

== Parameters to Affect Supply (Production) ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''tgrld'''
|AGP
|Target growth rate in cultivated land
|Initial Condition
|-
|'''ylm'''
|YL
|Crop yield (agricultural production)
|Multiplier
|-
|'''ylmax'''
|YL
|Maximum crop yield
|Exogenous specification
|-
|'''envylchgadd'''
|ENVYLCHG
|Agricultural yield change
|Additive factor
|-
|'''slr'''
|AGP
|Livestock slaughter rate
|Exogenous specification
|-
|'''livhdpro'''
|AGP
|Livestock herd productivity gain with grain feeding
|Exogenous specification
|-
|'''aquaculm'''
|AGP
|Fish production through aquaculture, multiplier
|Multiplier
|-
|'''aquaculgr'''
|AQUACUL
|Growth in the use of aquaculture
|Growth Rate
|-
|'''aquaculconv'''
|AQUACUL
|The number years in which aquaculture growth rate converges to 0
|Convergence rate
|-
| '''fishcatchm '''
|AGP
|Fish production through wild catch
|Multiplier
|-
|'''aglosstransm'''
|AGLOSSTRANS
|Agricultural wastage
|Multiplier
|-
|'''aglossprodm'''
|AGLOSPROD
|Agricultural production loss
|Multiplier
|-
|'''aglossconsm'''
|AGLOSSCONS
|Food consumption loss
|Multiplier
|-
|'''aginvm'''
|YL, IDS (agriculture)
|Investment in agriculture
|Multiplier
|-
|'''elagind'''
|AGP
|Elasticity of industrial use of crops with energy price
|Elasticity
|-
|'''elinag1'''
|IDS (agriculture)
|Elasticity of investment in agriculture to profit levels
|Elasticity
|-
|'''elinag2'''
|IDS (agriculture)
|Elasticity of investment in agriculture to changes in profit level
|Elasticity
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|}

There are two primary channels to think about changing the supply of agricultural production. The first is via increases in the amount of resources devoted to agricultural production such as by increasing the amount of land under cultivation. This can be done via the '''tgrld '''parameter. The earth’s arable land is relatively finite, however, and land under cultivation has not been a major driver of increased production for decades in most countries.

A more appropriate channel to think about is technological change that increases the efficiency of food production systems thereby increasing yield. The yield multiplier ('''ylm'''), which affects the yield of land being used for crop production, could be used, for example, to model the effect of a second green revolution. This parameter does not carry an intrinsic cost to use, making it both a powerful way of affecting the agricultural model, but also making it essential to justify the reason for any yield change. For example, this multiplier could be effective in representing the impact of improvements to climate or technology that increase the output of a particular unit of land in the absence of increased labor or investment. Users can also alter the maximum yield that can be harvested from a plot of land using '''ylmax''', an exogenous parameter which sets a maximum level for yield. Changes to this parameter can model extreme changes to land yield, such as a second green revolution. Moreover, the parameter '''ldcropm '''can be used to change the share of crop land in the total land.

Another parameter that can be used to affect yield is '''envylchgadd''', also discussed in the [[#_Environment_Module|environmental module]]. It can be used to model the effect global warming is having on agricultural yields. Modifying this parameter allows users to test different assumptions about the effect that increased CO2 and increased temperatures will have on crop yield.

Changes in the patterns of production of meat and fish can be modeled in the IFs system through a number of parameters. Meat production can be affected by changing the rate at which livestock are slaughtered via '''slr'''. This parameter could be altered to model the transition from subsistence to factory farming, and the increasing use of modern slaughterhouses in developing countries. The parameter '''livhdpro''' is also useful for modeling the impact of increased meat production from livestock as a result of the transition to grain feeding, which allows producers to extract more meat from each animal they own.

Similarly, the parameters '''aquaculm '''and '''aquaculgr''' can be altered to model the impact of increasing reliance on aquaculture for fish production because of technological improvements to this method of food production. The variable of interest will be FISH. The parameter '''aquaculm''' works like other multiplicative parameters, while the parameter '''aquaculgr''' alters the rate at which the use of aquaculture increases. The first parameter targets the actual amount of aquaculture a country uses, and is easiest to manipulate for the average user. The second, '''aquaculgr''', is set within the model and alters the rate at which countries begin to develop aquaculture resources. Finally the parameter '''aquaculconv''' can be used to determine the number years in which aquaculture growth rate converges to 0.

Other parameters that affect the level of fish production include '''fishcatchm''' and '''aquaculm. '''The first, modifies the amount of fish catch while the second''', '''changes the rate of catch from aquaculture.

Changing the loss rate of agricultural output to waste (rotting in fields, lost/spoiled during transportation, or not used in the home) via the '''Aglosstransm''' parameter is a powerful way to model the impact of changing agricultural distribution loss of food. It is a multiplicative parameter. Moreover production losses and consumption losses can be manipulated through the parameters, '''aglossprodperc''' and '''aglossconsperc''' which allow the user to specify the rates of production losses and consumption losses respectively.

Technological improvement is not the only pathway through which yield ('''YL''') may be affected within IFs. Financial pathways can change the amount of capital being invested in agriculture, resulting in higher yields. For example, parameter '''aginvm''', directly changes the investment in agriculture. This parameter can be used to model the effects of investment in improved agricultural technologies like genetically modified crops or new methods of aquaculture. Because of the financial accounting systems within IFs, changes in agricultural investment will affect investment in other sectors. '''Elagind''' is a parameter that may be useful to those who wish to adjust the percentage of crop production that is diverted for industrial uses such as ethanol, or beer.

== Parameters to Affect Nutrition ==

{| border="1" cellspacing="1" cellpadding="1" width="0" style="width:623px;"
|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malnelimstartyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination start year

| nowrap="nowrap" style="width:104px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malnelimtargetyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination target year

| nowrap="nowrap" style="width:104px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malelimprecisesw'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination

| nowrap="nowrap" style="width:104px;height:22px;" |
Switch

|}

Finally, model users may wish to use parameters to explore the health and economic impacts of different levels of nutritional deficiencies. In the health model, nutritional deficiency manifests itself in terms of mortality and morbidity related to communicable diseases. It can have especially large effects on children, who if undernourished for a significant period of their childhood, suffer developmental and cognitive delays that can impact their future productivity. The most important of these parameters are '''malnelimstartyr '''and '''malnelimtargetyr, '''which are standard error target parameters that affects the number of undernourished people in a country as a percent of the population. These parameters could also be used to represent the impact of school based nutrition programs or other targeted feeding programs. The parameter '''malelimprecisesw''' is a switch that when activated calculates the required number of calories to eliminate hunger for the most undernourished part of the population.

== Prepackaged Scenarios ==

One prepackaged scenario that makes significant improvements to agricultural yield in the developing world was developed as a policy brief, and models the impact of a green revolution in Africa. The scenario combines multipliers and other parameters, including '''ylm, ylmax, aginvm, '''and'''tgrld'''. It was designed to model the impact of a green revolution type of event occurring in the developing world and involving improved yield of crops through improvements in cultivation techniques and new technologies. Users will find it saved under the African Policy Briefs section of the World Integrated Scenario Sets, under the heading Green Revolution, which is also the title of the scenario. It increases agricultural yields by 76 percent over a 32-year period (increasing the parameter '''ylm''' to 1.76). It also increases the maximum yield in all African countries, as well as modeling 20 percent growth in agricultural investment and an initial target for growth in cultivated land of 8 percent.

Another prepackaged scenario that makes significant changes to agriculture is the Politics of Belly scenario developed for the African Futures 2050 project. This scenario is located in the Afp 2050 folder in the World Integrated Scenario Sets heading. This posits a much less positive outlook for African agriculture, simulating a 20 percent decline in yields that could represent the negative impact of climate change, much more than offsetting the mitigating effect that plant responsiveness to C02 increases might have on yields.

= Energy Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" width="366" style="width:366px;"
|-
| nowrap="nowrap" style="width:135px;height:24px;" |
'''Variable Name'''

| nowrap="nowrap" style="width:231px;height:24px;" |
'''Description'''

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENDEM'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy demand

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WEP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
World energy prices

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WEPBYEAR'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Translates global energy prices into dollars per barrel of oil equivalent

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENPRI'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy prices

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENST'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy stock

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENRGDP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ratio of energy demand to GDP

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy production

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WENP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Global energy production

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''KS'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Capital stock

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''IDS'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Investment in energy

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''QE'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Capital output ratio

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESER'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Accessible energy reserves

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESOR'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ultimate conventional energy resources

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESORUNCON'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ultimate unconventional energy resources

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENM'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy imports

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENX'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy exports

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENSHO'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy shortage

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''VADD'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Value added, energy

|}
Rapid development of unconventional oil and gas production—particularly from shale sources—and high rates of growth in energy from renewable sources make the future of energy highly uncertain. Several research organizations have forecasting projects that provide long-term analysis. In the United States, the Department of Energy's Energy information Agency produces a yearly report titled, ''International Energy Outlook; ''the International Energy Agency produces similar forecasts for the world as a whole, as well as for many individual countries, in its annual ''World Energy Outlook.'' The IFs energy module contributes to this existing body of research on international energy futures. A selection of variables and parameters allows users to explore alternative patterns of energy demand, production, resource discovery and technological developments.

Rapid development of unconventional oil and gas production—particularly from shale sources—and high rates of growth in energy from renewable sources make the future of energy highly uncertain. Several research organizations have forecasting projects that provide long-term analysis. In the United States, the Department of Energy's Energy information Agency produces a yearly report titled, ''International Energy Outlook; ''the International Energy Agency produces similar forecasts for the world as a whole, as well as for many individual countries, in its annual ''World Energy Outlook.'' The IFs energy module contributes to this existing body of research on international energy futures. A selection of variables and parameters allows users to explore alternative patterns of energy demand, production, resource discovery and technological developments.

The structure of the IFs energy model is similar to that of the agriculture module, [[#_Agriculture_Module|discussed in the preceding section]]. Both systems are driven by demand and supply dynamics within an equilibrium-seeking system that involves prices, trade, and investment. In both cases, the model translates forecasts of the physical system into value terms for the economic module and interaction with other sectors. The size of the economy and population shape demand, which IFs represents in the aggregate (not within sub-categories such as transportation, residential/commercial and industrial). The supply side differentiates production dynamics for oil, gas, coal, hydroelectric, nuclear, and other renewable energy and separately represents conventional and unconventional energy resources, such as shale deposits.

Beginning our elaboration with the demand side first, IFs derives each country’s energy demand from the overall size of the economy, the level of development as measured by GDP per capita, and world energy prices. The variable '''ENDEM,''' displayed as a default in billion barrels of oil equivalent (BBOE), captures demand from all sectors, and for all types of energy. The energy intensity of an economy—the usage of energy per unit of GDP ('''ENRGDP''')—is a very important measure, influenced by the development level, the advance of technology, and energy prices. Reductions in that, or improvements of efficiency over time, have been occurring in most countries. Users may explore several different formulations of energy price, which is a key factor used to derive demand and affect the energy intensity. '''WEP''' is a single composite measure of the global price of energy, expressed as an index with a base of 100 in the first model year ('''WEPBYEAR''' translates global energy prices into dollars per barrel of oil equivalent). Like '''WEP''', '''ENPRI''' displays country-level energy price as an index, base 100. Each country’s price is a function of global capital costs of energy production and a regional markup factor that reflects the level of country-specific energy stocks ('''ENST''').

Key indicators for the other side of the energy system, the supply side, include production, capital/output ratio and available natural resources. Production is measured in the variable '''ENP'''; production can be displayed as a total, or disaggregated through the energy type dimension to produce individual displays of oil, natural gas, coal, hydroelectric power, nuclear, and other renewables. '''WENP '''is energy production for the world as a whole, disaggregated by production type. For each country, energy production is partially determined by relationships around capital. The portion of the initial capital stock dedicated to the energy sector, '''KS''', limits the scale of possible production. And the capital-output ratio, '''QE''', translates capital stock ('''KS''') into production level. Capital stock itself is augmented over time by investment, '''IDS''', dependent on the profitability of investment in energy.

The concept of capital-output ratio ('''QE''') warrants clarification because it is important to the energy module's equilibrating dynamics. When production requires relatively little invested capital to produce a unit of output, the capital-output ratio is low. Conversely, a high ratio would mean a large amount of capital is required to produce a unit of output. All things equal, the lower the capital-output ratio, the greater the opportunity for profits. Thus, in the model, a low '''QE '''will generally trigger an increase in production.

Available natural resources are the final factor constraining supply. Levels of proven reserves, '''RESER''', drive the price structure component of production—by affecting '''QE'''. Proven reserves are in turn bounded by the availability of ultimately recoverable resources, displayed in the variable '''RESOR '''for conventional resources, and the variable '''RESORUNCON '''for unconventional resources, both of which must be discovered ('''RD''') to become reserves. As we shall see in the discussion of parameters, '''QE''' is affected not just by resource constraints, but also by the advance of technology.

A set of trade variables measures countries’ external reliance on energy. While high levels of energy exports can enrich public finances through taxation, an overreliance on either energy imports or exports increases vulnerability to external price shocks. Energy imports and exports are measured in billion barrels of oil equivalent by '''ENM '''and '''ENX''' and represent aggregate trade in energy without distinguishing energy types. A more direct indication of vulnerability to shock is located in Flexible Display, under the heading Energy Imports as a Percent of Total Demand.

Disruptive price swings are inherent to any commodity market. In energy markets they can result from either political disruption in energy producers such as Iraq or from collusion among producers—evidenced by the cartel behavior of The Organization of Petroleum Exporting Countries (OPEC). As economists point out, prices can theoretically always rise or fall to balance demand and supply—to clear the market. But as history has shown, severe energy shocks, sometimes in environments where leaderships do not allow prices to fully adjust, can result in actual physical shortages ('''ENSHO'''). In normal years the level of energy stocks in a country is visible through the variable '''ENST'''.

A final set of indicators, measured not in physical but in monetary value terms, represents the overlap between the energy and economic modules in IFs. The variable '''VADD '''displays the total value added that is contributed to a country's GDP by various sectors, including energy; this gives a quick look at how much a country relies on energy production as a source of wealth. Investment ('''IDS''') and capital stock ('''KS''') present additional measures of the size of the energy sector in absolute terms and relative to other sectors.

== Parameters to Affect Demand ==

{| border="1" cellspacing="1" cellpadding="1" width="578" style="width:578px;"
|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:91px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''endemm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENDEM

| nowrap="nowrap" style="width:305px;height:22px;" |
Energy demand

| nowrap="nowrap" style="width:91px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''elasde'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP, WEP

| nowrap="nowrap" style="width:305px;height:22px;" |
Price elasticity of energy demand

| nowrap="nowrap" style="width:91px;height:22px;" |
Elasticity

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''carbtax'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Carbon tax

| nowrap="nowrap" style="width:91px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:79px;height:24px;" |
'''enrgdpgr'''

| nowrap="nowrap" style="width:103px;height:24px;" |
ENP, QE

| nowrap="nowrap" style="width:305px;height:24px;" |
Growth in energy efficiency

| nowrap="nowrap" style="width:91px;height:24px;" |
Rate of change

|}
The demand side of the energy system features several leverage points that can be used to create alternative scenarios. The multiplier '''endemm''' directly manipulates aggregate energy demand in a country or region relative to its underlying calculation from economic size and other driving variables. A second key demand-side parameter, '''enrgdpgr''', allows the user to change the default rate of change in energy intensity (the inverse of energy efficiency). Turning from the more brute-force parametric intervention approaches to a more policy specific one, country-level energy prices and therefore energy demand can also be manipulated directly through the additive parameter '''carbtax'''. As the name implies, this parameter simulates the effects of a tax on carbon emissions—even employed at a reasonable level, a carbon tax can have substantial impacts on energy demand.

The demand side of the energy system features several leverage points that can be used to create alternative scenarios. The multiplier '''endemm''' directly manipulates aggregate energy demand in a country or region relative to its underlying calculation from economic size and other driving variables. A second key demand-side parameter, '''enrgdpgr''', allows the user to change the default rate of change in energy intensity (the inverse of energy efficiency). Turning from the more brute-force parametric intervention approaches to a more policy specific one, country-level energy prices and therefore energy demand can also be manipulated directly through the additive parameter '''carbtax'''. As the name implies, this parameter simulates the effects of a tax on carbon emissions—even employed at a reasonable level, a carbon tax can have substantial impacts on energy demand.

In the energy analysis community there is always some uncertainty and debate concerning the strength of the relationship between energy price and demand. The parameter '''elasde''' allows users to change the long-term price elasticity of demand. This elasticity is negative in the model, corresponding to traditional formulations of supply and demand—as prices rise demand for energy falls. Users building scenarios may wish to experiment with different values of '''elasde''', while bearing in mind the possibility that long-term and short-term elasticities may differ.

These demand-side leverage points offer a rich area for experimentation. Work by groups such as the International Energy Agency (IEA) and the International Panel on Climate Change (IPCC), have simulated several alternative future forecasts for energy efficiency development.

== Parameters to Affect Supply ==

{| border="1" cellspacing="1" cellpadding="1" width="602" style="width:602px;"
|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:96px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enpm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Production multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''eprodr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Production growth rate

| nowrap="nowrap" style="width:96px;height:22px;" |
Growth rate

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''resorm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESOR 

| nowrap="nowrap" style="width:305px;height:22px;" |
Conventional resource multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''resorunconm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESORUNCON

| nowrap="nowrap" style="width:305px;height:22px;" |
Unconventional resource multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''rdm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESER

| nowrap="nowrap" style="width:305px;height:22px;" |
Discovery multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''etechadv'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Rate of technical advance in energy

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''etechadvuncon'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Rate of technical advance in unconventional energy

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''qem'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Capital output multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''eninvm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENIV

| nowrap="nowrap" style="width:305px;height:22px;" |
Energy investment

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enprix'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENPRI

| nowrap="nowrap" style="width:305px;height:22px;" |
Regional energy price

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enml, enxl'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENM, ENX

| nowrap="nowrap" style="width:305px;height:22px;" |
Limits on energy imports and exports

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|}

Turning to the supply side of the IFs energy module, two parameters in the model directly alter the relative production of oil, gas, coal, hydroelectric, nuclear, and other renewable energy. '''Enpm''', a multiplier, scales up or down existing production of a specified energy type; '''eprodr''', a growth rate parameter, forces a specified yearly percentage increase in production on the desired energy type. Both types of direct production modification can be used to trace the effects that a shift in energy production profile may have on life in a country, region, or the entire global community. For some observers, renewables represent the most important source of uncertainty surrounding the character global energy production. The IFs Base Case forecast assumes that renewable energy will grow rapidly in importance in the decades to come – see '''WENP''' across all energy types for a clear depiction. What if the model’s baseline assumptions are too optimistic, or too pessimistic? Scenarios created with '''enpm '''and'''eprodr''' can explore variations on the Base Case renewable energy story.

Moving below the direct interventions on production to parameters that affect the drivers of that production, both conventional and unconventional deposits of oil, gas and coal resources offer possible points of leverage for supply-side energy scenarios. As mentioned above, IFs divides raw materials used in energy production into resources and reserves. Scenario interventions are most effective when they include simultaneous changes to the reserves that are very closely tied to production, and to the more distil element of total resources (resources only become reserves when they are discovered and deemed to be economically exploitable with current technology and prices). An increase to the '''resorm '''or '''resorunconm '''parameters will expand the amount of materials that can potentially transition to reserves and become economically viable. As a second step, users can speed up this transition by manipulating '''rdm''', a parameter that represents the rate of geologic discovery (or improved economic exploitability) in a country. An increase in the stock of fossil fuel reserves creates the possibility that they will be produced and that they will have forward impacts on energy price ('''ENPRI''') and '''GDP''' of energy producing countries.

A key source of uncertainty around the future of energy supply concerns production costs. Technological progress could dramatically increase the amount of energy output possible at current levels of investment. The parameters '''etechadv''' and '''etechadvuncon''' directly control the rate of technological advance and cost reduction by energy type, whether based on fossil fuels or renewable energy. For fossil-based energy in particular, several other parameters enable users to explore the consequences of various energy production cost structures. '''Qem''', a capital-output multiplier, directly scales up—or down—the capital cost per output unit of each energy type at the country level.

Government policies could additionally affect the future of energy production. Actions such as tax increases or direct subsidies can be simulated using '''eninvm''', an investment multiplier. The parameter either increases or decreases overall investment in energy—the model still controls which energy-type is affected, based on price signals. Remember that the equilibrating mechanisms of the model will respond to any change in investment patterns. For example, an increase in investment flowing to oil, would increase production, eventually reducing oil prices, and leading to a subsequent decrease in investment. Remember also that the accounting mechanisms of the model will assure that any additional investment in energy will come from somewhere, such as investment in other sectors, other government expenditures, or household consumption.

Government decisions can also affect energy prices quickly, through intentional price shocks similar to those initiated by OPEC in the 1970s. The '''enprix''' parameter can simulate a concerted effort on the part of energy exporters to fix prices; '''enprix '''exogenously sets the type-specific energy price—'''ENPRI'''— for a region. As in real life, any price fixing scenario will have wide-ranging, and potentially perverse economic consequences. Two additional parameters exist that can simulate short-term supply disruptions via trade restrictions: '''enml '''exogenously imposes a limit to energy imports and '''enxl''' imposes a limit to energy exports.

== Prepackaged Scenarios ==

Currently, none of the prepackaged scenario files demonstrate the use of parameters linked to the energy module.

= Environment Module =

== Variables of Interest ==
{| class="wikitable"
|'''Variable Name'''
|'''Description'''
|-
|'''CO2PPM'''
|CO2 parts per million
|-
|'''CO2PER'''
|Percentage increase in atmospheric CO2
|-
|'''CARANN'''
|Annual carbon emissions from fossil fuels
|-
|'''CARFOROTH'''
|Annual net carbon release from forests
|-
|'''WTEMP'''
|Average annual world temperature
|-
|'''ENVTPCHG'''
|Annual average temperature change from 1990
|-
|'''ENVPRCHG'''
|Annual average precipitation change from 1990
|-
|'''WFORST'''
|World forest area
|-
|'''LD'''
|Land area by type
|-
|'''WATUSE'''

|Annual water usage
|-
|'''WATUSEPC'''
|Annual water usage per capita
|-
|'''ENVPM2PT5'''
|PM2.5 levels in residential areas
|-
|'''ENSOLFUEL'''
|Solid fuel use
|-
|'''CLIMECONIMP '''
|Climate change to economy impact
|}

As the global population has grown, and more and more countries have industrialized, sustainable development has come to occupy a permanent place on national, regional and global agendas. Growing concern for the environment was brought to the fore at the 1992 UN Conference on the Environment in Rio de Janiero. More than 2 decades have passed since the Rio conference, and the global community’s dedication to environmental concerns (if not always to action) has remained strong. In 2012 a follow up conference was held in Rio—the United Nation’s Conference on Sustainable Development, or Rio + 20—at which leaders discussed strategies to end poverty, address environmental destruction and build a bridge to the future. Other forward-looking environmental initiatives include the United Nations Environment Program’s (UNEP) Global Environmental Outlook Series. Since the first entry in the series, published in 1997, the UNEP has followed up with four additional reports (the International Futures model contributed to the most recent report). Despite the creation of dozens of internationally agreed upon goals, 2012’s ''Global Environmental Outlook 5'' warned that the world remains on an unsustainable path. Around the world, forestland is decreasing, centuries-old glaciers are retreating, aquifers are being depleted and the amount of atmospheric carbon dioxide is on the rise. Not all environmental trends are negative, however. Developed countries have made progress on urban air pollution. Developing countries have experienced improved drinking water quality and safe sanitation access. And globally, the volume of CFCs damaging the ozone layer has dropped dramatically over the course of recent decades.

Environmental issues have potential or actual connections to all of the modules in IFs. For instance, processes associated with energy, agriculture, infrastructure, and the broader economy directly impact the earth’s forests, water supply, and atmosphere, as does governance more indirectly. The environmental module elaborates a subset of these potential connections in four specific areas: the global carbon cycle, land use (notably forest area), water use (agricultural), and air quality (urban and indoor).

The numerous ways in which human actions influence the environment are evident throughout the carbon cycle. Carbon is continuously moving within and across the boundaries of the Earth’s atmosphere, hydrosphere and biosphere. In systems dynamics terminology, the elements in the cycle are known as stocks and flows. Put simply, stocks are long-term repositories of carbon such as the world's fossil fuels, biomass and oceans, and flows are movements from one stock to another. This stock-flow dichotomy serves as a useful heuristic for users who are interested in creating scenarios in the IFs environment model more generally, but especially with respect to carbon. Interventions can target carbon stocks through processes that affect flows, such as energy production and reforestation. Key carbon stock indicators are '''CO2PPM''', the level of atmospheric carbon dioxide measured in parts per million, and '''CO2PER''', atmospheric carbon dioxide, measured as a percent relative to pre-industrial levels. On the flow side, key indicators available in the model include '''CARANN''', the annual flow of carbon emissions from fossil fuel use, and '''CARFOROTH''', global net carbon released annually from forests and other sources (discussed below). As we shall see, the model also allows parametric control of the flow of carbon from the atmospheric stock to that dissolved in the oceans, another stock.

The atmospheric carbon stocks have important forward linkages. One of the most important, temperature change, can be tracked via '''WTEMP''', the global average annual temperature in degrees centigrade. A derivative temperature variable but at the country level, '''ENVTPCHG''', captures the average annual temperature change from the year 1990, and '''ENVPRCHG''' similarly tracks the change in precipitation. These have further forward linkages to agriculture.

Several variables in the model measure the configuration of the world’s land area. Two variables capture trends in forest coverage: '''WFORST''' is an aggregate measure of the world’s land area covered by forest, reported in millions of hectares; and the land variable '''LD''' is disaggregated by land type, enabling country- or region-level displays of the following categories: crop, grazing, forest, urban and other. (See the agricultural module for a complete discussion of land use variables and parameters). Land use links back to the carbon representation because trees and other plants making up the world’s forests absorb carbon dioxide through photosynthesis and their carbon content is still another important global stock. As indicated above, change in forest area, such as clearing of land for agricultural use, generates one of the carbon flow terms of the model.

The environment submodule includes water indicators. The annual use of freshwater resources is reported in cubic centimeters via '''WATUSE,''' tied in IFs to the major source of water use, agricultural production. Users can additionally choose to explore water use per capita, expressed in thousands of cubic meters, via the variable '''WATUSEPC'''. Currently, the model’s treatment of water does not feature the stock and flow elements seen in the carbon cycle. Although '''WATUSE''' is a flow of water used each year, no corresponding variable captures the total stock of water—say in nonrenewable (or at least very slowly recharging) fossil water aquifers. This is a shortcoming that we hope to address in the near future. However, a parameter, '''watres''' (see the discussion below), does provide an estimate of the level of annually renewable water resources in countries and regions. The ratio of use to renewable resources serves as a measure of relative water stress. With respect to forward linkages, water stress is one of the drivers of state fragility (see the government module).

A set of environmental indicators pertains to air pollution. The amount of solid matter suspended in the Earth’s atmosphere serves as a measure of relative air quality; high levels of atmospheric particulate matter (PM) can have adverse effects on human health. Depending on the data source, PM concentration readings may include particles that are either 10 or 2.5 micrometers in diameter. In the model, the variable '''ENVPM2PT5''' captures levels of fine particulate matter, PM2.5, found in residential areas (although our current data source is actually for 10 micrometer particulates). The prevalence of solid fuel use is a second indicator of air pollution. The variable '''ENSOLFUEL''' captures the percent of a population relying on solid fuel as the primary source of domestic energy. Burning solid fuel, such as wood, peat, coal, dung, and certain grains, produces a toxic environment indoors (see the infrastructure module).

Finally, one indicator pertains to the impact of climate change on the economy. '''CLIMECONIMP''' feeds forward the impact of climate change to the GDP. This is done through a quadratic formulation that was originally developed by Nordhaus.

Alternative scenarios, built in any IFs module, are likely to have bearing on environmental indicators. Users exploring the consequences of scenario interventions in economics, energy or agriculture are likely to see the greatest environmental effects. Interventions in other modules will still affect the environment, though through weaker, more distal, drivers. The environmental sub-module, however, allows users to directly explore the effects of environmental change. Environmental parameters can be categorized as those that affect carbon stocks and flows, land use, water resources, and air quality.

== Parameters Affecting Carbon ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:129px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:283px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''enpm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy production by type

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''endemm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENDEM, WEP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy demand multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carbtax'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP, CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon tax

| nowrap="nowrap" style="width:121px;height:22px;" |
Exogenous Specification

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''qem'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Capital-output ratio multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''resorm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
RESOR

| nowrap="nowrap" style="width:283px;height:22px;" |
Resource multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''enrgdpgr'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy efficiency

| nowrap="nowrap" style="width:121px;height:22px;" |
Growth rate

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel1'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by gas

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel2'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by natural gas

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel3'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by coal

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envclimsens'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
Time sensitivity of global temperature to carbon dioxide

| nowrap="nowrap" style="width:121px;height:22px;" |
Limit

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carabr'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon absorbed by oceans

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carabrchng'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
annual change in this absorption rate of carbon by oceans

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envco2fert'''

| nowrap="nowrap" style="width:129px;height:22px;" |
AGP

| nowrap="nowrap" style="width:283px;height:22px;" |
Crop sensitivity to environmental change

| nowrap="nowrap" style="width:121px;height:22px;" |
Elasticity

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envylchgadd'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENVYLDCHNG

| nowrap="nowrap" style="width:283px;height:22px;" |
Agricultural yield change

| nowrap="nowrap" style="width:121px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envylchgm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENVYLDCHNG

| nowrap="nowrap" style="width:283px;height:22px;" |
Agricultural yield change

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|}
There is no way to directly target atmospheric carbon stocks in the model. Instead, points of intervention involve flows, the most important of which is the burning of fossil fuels and therefore the rate of flow of carbon from its stock in those fuels to the stock in the atmosphere With respect to fossil fuel use, there are leverage points on both the production and consumption side—see the [[#_Energy_1|energy model]] discussion. '''Enpm''', the energy production multiplier, will alter the flow of carbon into the atmosphere and can serve as a powerful leverage point in energy-driven environmental scenarios. Users can alter the mix of energy types produced within a country, thereby reducing or increasing the use of fossil fuels, and trace the environmental effects.

There is no way to directly target atmospheric carbon stocks in the model. Instead, points of intervention involve flows, the most important of which is the burning of fossil fuels and therefore the rate of flow of carbon from its stock in those fuels to the stock in the atmosphere With respect to fossil fuel use, there are leverage points on both the production and consumption side—see the [[#_Energy_1|energy model]] discussion. '''Enpm''', the energy production multiplier, will alter the flow of carbon into the atmosphere and can serve as a powerful leverage point in energy-driven environmental scenarios. Users can alter the mix of energy types produced within a country, thereby reducing or increasing the use of fossil fuels, and trace the environmental effects.

On the demand side, a reduction in '''endemm''', the energy demand multiplier, can likewise simulate conservation/efficiency enhancement programs that alter the flow of carbon into the atmosphere. It is important to note that reductions in energy use triggered by '''endemm''' are free; that is to say, they are not accompanied in the model by economic costs. In reality, conservation or efficiency efforts occasionally do reduce energy use without a large cost—as evidenced by voluntary behavioral shifts on the part of citizens, or regulations enforced by the government. However, because it may be unrealistic to assume costless energy demand changes, users may wish to include financial considerations in their scenarios. The parameter '''carbtax '''simulates a reduction in demand triggered by tax; users would pay an additional fee on fossil fuels proportional to carbon content. This would encourage conservation or a transition to less expensive forms of energy. Users experimenting with carbon taxes should observe the effects on the energy production system '''(ENP)''', the level of annual carbon emissions '''(CARANN'''), world temperature ('''WTEMP''') and the level of economic output ('''GDP)'''.

Users can also simulate government interventions designed to encourage research and development focused on renewable energy or energy efficiency. Each energy type’s cost profile is linked to a capital-output ratio multiplier, '''qem'''. Users can simulate a decrease in the cost of renewables by reducing the multiplier’s initial value from 1.0 to 0.8—this would reduce all production costs by 20 percent (on top of whatever cost changes the model endogenously computes). Energy prices can also be indirectly controlled by changes to natural resources needed to produce fossil fuels. As oil and gas resources are exploited, the remainder becomes more expensive to extract. Users can use '''resorm''' to enforce alternative assumptions about the scale of ultimate resources available for discovery, and eventually, production. Mechanisms involving resources, reserves, and energy price operate over a long time horizon. Any scenario using '''resorm''' should be run out to at least the year 2050 in order to display significant forward linkages.

Changes in the efficiency of energy use from oil and gas sources would have bearing on carbon emissions as well. Changes in energy efficiency are controlled by the growth rate parameter '''enrgdpgr '''in addition or instead of the use of '''endemm. '''Changes to this parameter simulate the impact of successful research and development programs in the public or private sphere. Three parameters offer an additional way to simulate improvements to energy efficiency: '''carfuel1, carfuel2 '''and '''carfuel3 '''set, respectively, the amount of carbon emitted by burning oil, natural gas and coal. Normally these should be considered physical constants and not changed, but the model does not contain a representation of carbon sequestration and '''carfuel3''' could be used to represent its impact, although not its cost. For a complete discussion on parameters pertaining to energy production see the [[#_Energy_Module|energy section]].

Returning to the issue of stock-to-stock flows of carbon, so far we have focused only on the movement of carbon from fossil fuels to the atmosphere. Not all carbon emitted into the atmosphere remains there, and the model contains parameters to control the absorption of atmospheric carbon by the oceans. Two parameters control this process in the model. The first is '''carabr''', an exogenous specification of the billion tons of carbon that annually moves to or is absorbed by the oceans. The second is '''carabrchng''', which sets the annual change in the absorption rate (a measure of flow). At this point the parameter is not functional, and the user should be aware that change in it will have no effect. The final stock-to-stock flow represented in the model is between the atmosphere and forests (to be discussed below). Because the model does not explicitly represent the flow between the atmosphere and other non-forest vegetation (or organic matter in the soil), users with interest in this area could turn to the parameter '''carabr''' to implicitly capture, not just absorption into oceans, but into other matter as well.

Turning to forward linkages, future trends in the carbon system will affect the world’s agricultural sector. The stock of atmospheric carbon dioxide ('''CO2PPM''') and the related level of global temperature ('''WTEMP''') are the variables that transmit the carbon system's effects. The model represents two major paths of impact, one related to each variable. The first path is the so-called carbon-fertilization effect. Because plants need carbon, an increase in '''CO2PPM''' can actually increase agricultural yields, and the parameter that controls that is '''envco2fert''', crop sensitivity to atmospheric carbon dioxide. A value of zero would turn off this linkage.

The second path is from global temperature ('''WTEMP''') to country-specific temperature and precipitation changes ('''ENVTPCHG '''and'''ENVPRCHG) '''and then to agricultural yield. The translation in the model of changes in global temperature to changes in country-specific temperature and precipitation uses information from climate-specific models and is not subject to parametric control by users (in modeling terminology, they are “hard-coded” within the model). The user can, however, affect the further impact of country-specific values to yield. While the model's formulation for that impact ('''ENVYLCHNG''') on yield is quite complex, two parameters exist for influencing it, the multiplicative parameter '''envylchgadd '''and the additive parameter '''envylchgm. '''Their effects are sequential, so it is best that the user rely on one or the other, not both.

One additional parameter is important in this consideration of forward linkages, namely that which controls the translation of atmospheric carbon levels ('''CO2PPM)''' into global temperature ('''WTEMP'''). That parameter is '''envclimsens''', the time sensitivity of global temperature to carbon dioxide. The very large-scale global circulation models of climate change struggle to make the connection between these two variables and IFs uses this simple parametric linkage to summarize some of what we have learned from them. But the relationship is uncertain, so a scenario that would raise or lower the parameter and the impact on global temperature might be of interest. Moreover, the impact of carbon on temperature is likely to be one that involves lags (increasing temperature slowly even at a given fixed level of raised carbon dioxide), so one could consider a dynamic value of the parameter over time in order to capture that.

==== Land Use ====

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:129px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:283px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''forestm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
WFOREST

| nowrap="nowrap" style="width:283px;height:22px;" |
World forest multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''tgrld'''

| nowrap="nowrap" style="width:129px;height:22px;" |
WFOREST, LD

| nowrap="nowrap" style="width:283px;height:22px;" |
Land growth by type

| nowrap="nowrap" style="width:121px;height:22px;" |
Initial condition

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carforst'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM

| nowrap="nowrap" style="width:283px;height:22px;" |
Level of carbon sequestered by forests

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|}
The importance of land extends beyond its role in carbon cycle. We are interested in forest area, for instance, due to its involvement in species preservation, in water cycle control, and for its own intrinsic value. A direct multiplier, '''forestm''', imposes an increase or a decrease on a country’s forest area. Any change in land area is constrained by a country’s geographic size because an increase in forest area requires a corresponding decrease in the land available for other types of use, such as crop production. As another way to affect land use, the parameter '''tgrld''' sets an initial target for annual growth in cropland. This second parameter achieves a change in land devoted to crops primarily as a trade off with forest area. For a complete discussion of parameters concerning patterns of land use, see the [[#_Agriculture_Module|agricultural section]]. Changes in land use will, of course, have secondary consequences visible in '''GDP''', '''AGP''', '''AGX''', and '''AGM''', among other variables

The importance of land extends beyond its role in carbon cycle. We are interested in forest area, for instance, due to its involvement in species preservation, in water cycle control, and for its own intrinsic value. A direct multiplier, '''forestm''', imposes an increase or a decrease on a country’s forest area. Any change in land area is constrained by a country’s geographic size because an increase in forest area requires a corresponding decrease in the land available for other types of use, such as crop production. As another way to affect land use, the parameter '''tgrld''' sets an initial target for annual growth in cropland. This second parameter achieves a change in land devoted to crops primarily as a trade off with forest area. For a complete discussion of parameters concerning patterns of land use, see the [[#_Agriculture_Module|agricultural section]]. Changes in land use will, of course, have secondary consequences visible in '''GDP''', '''AGP''', '''AGX''', and '''AGM''', among other variables

Returning to the role of forests in the carbon cycle, the key parameter is the amount of carbon sequestered by each million hectares of the earth’s forests, '''carforst'''. By default, this coefficient is set to 0.12—meaning that every million hectares of forest sequesters 0.12 billion tons of carbon. By increasing or decreasing the value, users can explore futures in which forests are more or less effective at removing carbon from the atmosphere.

== Parameters Affecting Water Resources ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watres'''
|GOVRISK, THREAT
|Renewable water resources
|Exogenous specification
|-
|'''ylm'''
|YL
|Agricultural yield multiplier
|Multiplier
|-
|'''watdemandelas'''
|WATDEMAND
|Elasticity from water price to water demand
|Elasticity
|-
|'''landirareaequipelas'''
|LANDIRAREAACTUAL
|Elasticity from water price to land equipped for irrigation
|Elasticity
|-
|'''desali'''
|WATERDESALINATED
|Desalination responsiveness to change in stock levels of price
|PID factor
|-
|'''desald'''
|WATERDESALINATED
|Desalination responsiveness to change in price
|PID factor
|}

'''Watres''' allows users to set the renewable resources located in each of the countries in IFs. Changes to this parameter or exogenous variable can either be instantaneous or gradual, as specified by the user. Though each country’s renewable water resources, and associated water stress, has bearing on state fragility, it is important to note that the '''watres''' parameter does not constrain water use. Users can manipulate the elasticity of water demand to water prices through the parameter '''watdemandelas.''' Similarly, users can change the elasticity of water price to land equipped for irrigation using the parameter '''landirareaequipelas.''' Moreover, user can change the responsiveness of desalination to water prices through two parameters '''desali''' and '''desald''' that affect the integral and the differential factors on the PID formulation.

Users can affect water use via deeper drivers. Any increase (or decrease) in crop land, and subsequent increases (or decreases) in production, will require a corresponding change in water usage ('''WATUSE'''). Water usage per capita is linked to crop production per capita by a function in the model (Crop Production/Capita Versus Water Use/Capita). Thus, changing that function or manipulating the land growth by type parameter, '''tgrld''', can trigger patterns of water uses differing from those featured in the Base Case. The yield multiplier '''ylm''' provides a very direct method of indirect water use modification; increases in production will require more water, often (implicitly in the model) through irrigation.

== Parameters Affecting Air Pollution ==

{| border="1" cellspacing="1" cellpadding="1" width="626" style="width:626px;"
|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:96px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''envpm2pt5m'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENVPM2PT5

| nowrap="nowrap" style="width:305px;height:22px;" |
PM2.5 levels multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelsetar'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use SE target

| nowrap="nowrap" style="width:96px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelseyrtar'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use SE target

| nowrap="nowrap" style="width:96px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfueltrgtval'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOL FUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel elimination target

| nowrap="nowrap" style="width:96px;height:22px;" |
Target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfueltrgtyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel elimination target

| nowrap="nowrap" style="width:96px;height:22px;" |
Target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''hlsolfuelsw'''

| nowrap="nowrap" style="width:103px;height:22px;" |
DEATHCAT

| nowrap="nowrap" style="width:305px;height:22px;" |
Relationship between solid fuel and respiratory diseases

| nowrap="nowrap" style="width:96px;height:22px;" |
Switch

|}

Several environmental parameters allow users to directly manipulate levels of air pollution. Fine grain aerial particulate matter levels in residential areas ('''ENVPM2PT5''') are controlled by a multiplicative parameter, '''envpm2pt5m'''. Like any multiplier, assigning higher or lower values will result in a proportional change in the associated variable—in this case '''ENVPM2PT5'''. As an alternative, users can explore variations of solid fuel use in a specific country, region, or the world as a whole. The percentage of a population using solid fuel as their primary source of electricity ('''ENSOLFUEL)''' is connected to a multiplier, '''ensolfuelm'''. The model also features a pair of standard error target parameters: one that establishes the target, '''ensolfuelsetar''', and one that establishes the year by which the target will be reached, '''ensolfuelseyrtar'''. Users can alternatively (not simultaneously) utilize a second pair of parameters to set a year by which solid fuel use will be either reduced, or eliminated completely. '''Ensolfueltrgtval''' sets the desired reduction, and '''ensolfueltrgtyr''' sets the target year. Finally, and related to forward linkages, users can control whether there is a relationship between the burning of solid fuel and health. When the switch parameter '''hlsolfuelsw''' is set to 1 (the default), a positive relationship between solid fuel use and respiratory disease is active; when the switch is set to 0, the relationship is deactivated.

== Parameters Affecting climate change impact on the economy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''climeconimpsw '''
|'''CLIMECONIMP'''
|Climate change to economy impact switch (1=on)
|Switch
|-
|'''climeconimplin '''
|'''CLIMECONIMP'''
|Climate change to economy impact, linear term coefficient
|Co-efficient
|-
|'''climeconimpsq '''
|'''CLIMECONIMP'''
|Climate change to economy impact, squared term coefficient
|Co-efficient
|-
|'''climeconimpstk'''
|'''VADD'''
|Climate change to econ impact, portion affecting productivity or capital stocks
|Multiplier
|}

Climate change impacts are fed forward to the economy through three parameters. '''climeconimpsw''' is a switch when turned initiates the computation of the impact of climate change on the economy. The impact is calculated through a quadratic formulation which was initially developed by Nordhaus. Accordingly, two co-efficients, '''climeconimplin''' and '''climeconimpsq''' compute the linear and the quadratic impact of climate change on the economy through an impact on Value added (VADD). '''climeconimpstk''' calculates the economic impact of climate change on capital stocks.

== Prepackaged Scenarios ==

Exploring an alternative scenario conceptualized by the Netherlands Environmental Assessment Agency (PBL) can give users a quick overview of some of the powerful leverage points in the environment module. Using their integrated assessment model, called IMAGE, PBL constructed a set of forecasts representing a future in which carbon emissions drop dramatically. A replication of this scenario comes pre-packaged with the IFs installation, in the World Integrated Scenario Sets folder and the NCAR IPCC subfolder, under the title IMAGE_hirenew_lowcarbon. The folder contains, in total, five scenarios, all of which were developed during the course of a joint project between the Pardee Institute and the National Center for Atmospheric Research (NCAR), which is located in Boulder, Colorado. Each alternative scenario uses IFs parameters to approximate work done by another integrated assessment model: BPL’s Integrated Model to Assess the Global Environment (IMAGE), Asia-Pacific Environment Innovation Strategy’s AIM, the International Atomic Energy Agency’s MESSAGE, and Pacific Northwest National Laboratory’s Mini-Climate Assessment Model, or MiniCAM,

In order to simulate a significant reduction in global carbon emissions, the IMAGE_hirenew_lowcarbon scenario includes interventions in capital-output ratio, fossil fuel burning behavior, and carbon tax policy. For oil, gas, coal and nuclear production in every country, the parameter '''qem '''is set to rise steadily, reaching a value of four by mid-century and remaining there until 2100. Conversely, the capital cost of each unit of renewable energy produced in the world is set to decline, with '''qem''' approaching 0.3, and remaining at that value until 2100. As fossil fuel production becomes expensive and green energy becomes relatively cheaper, price signals will push global energy producers to reduce carbon emissions. The use of the '''carbtax''' parameter augments the economic incentives attached to renewables in the scenario. By 2100 a carbon tax of 400 billion U.S. dollars is in place in every country in the world. Finally, the scenario adds an element of environmentally friendly technological change, through a reduction in the parameters '''carfuel1''', '''carfuel2''', and '''carfuel3'''. This would simulate worldwide discoveries allowing for the cleaner burning of oil, natural gas, and coal.

= Governance Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''GOVINDSECUR''' 
| Governance security index 
|-
| '''GOVINDCAPAC''' 
| Governance capacity index 
|-
| '''GOVINDINCLUS''' 
| Government inclusiveness index 
|-
| '''GOVINDTOTAL''' 
| Total governance index 
|-
| '''SFINTLWARALL''' 
| Internal war, event occurrence 
|-
| '''SFINTLWARMAG''' 
| Internal war, magnitude 
|-
| '''GOVRISK''' 
| Government risk index 
|-
| '''GOVREV''' 
| Government revenues 
|-
| '''AID''' 
| Foreign Aid 
|-
| '''GOVCORRUPT''' 
| Government Corruption 
|-
| '''REGQUALITY''' 
| Regulatory quality 
|-
| '''ECONFREE''' 
| Economic freedom measure 
|-
| '''GOVEFFECT''' 
| Government effectiveness 
|-
| '''DEMOCPOLITY''' 
| Level of democracy 
|-
| '''FREEDOM''' 
| Freedom measure 
|-
| '''GEM''' 
| Gender empowerment measure 
|}
The IFs governance module contributes to a growing body of work dedicated to quantifying the performance of governments around the world. The IFs model adds to that literature especially by facilitating forecasting and scenario analysis. The module represents governance as a composite system, made up of three interconnected dimensions: security, capacity and inclusiveness. The modelling process builds on a large number of data series and governance indices constructed by various research groups, mostly in academia, non-profit organizations, and international organizations. Variables capturing regulatory quality and government effectiveness, for example, come from the World Bank’s World Governance Indicator project; Freedom House and Transparency International provide measures of economic freedom and corruption; and the Center for Systemic Peace provides state fragility and democracy indicators. Based on such data and series, the IFs system builds up to aggregate indices for its three dimensions of governance: '''GOVINDSECUR '''for security, '''GOVINDCAPAC '''for capacity, and '''GOVINDINCLUS '''for inclusivity. These three indices are then combined into an overall governance index,'''GOVINDTOTAL.'''

The IFs governance module contributes to a growing body of work dedicated to quantifying the performance of governments around the world. The IFs model adds to that literature especially by facilitating forecasting and scenario analysis. The module represents governance as a composite system, made up of three interconnected dimensions: security, capacity and inclusiveness. The modelling process builds on a large number of data series and governance indices constructed by various research groups, mostly in academia, non-profit organizations, and international organizations. Variables capturing regulatory quality and government effectiveness, for example, come from the World Bank’s World Governance Indicator project; Freedom House and Transparency International provide measures of economic freedom and corruption; and the Center for Systemic Peace provides state fragility and democracy indicators. Based on such data and series, the IFs system builds up to aggregate indices for its three dimensions of governance: '''GOVINDSECUR '''for security, '''GOVINDCAPAC '''for capacity, and '''GOVINDINCLUS '''for inclusivity. These three indices are then combined into an overall governance index,'''GOVINDTOTAL.'''

The building blocks of the three indices, however, are sub dimensions and component variables. Security’s sub-dimensions are the probability of intrastate conflict ('''SFINTLWARALL''') and a general level of government performance with associated risk of instability ('''GOVRISK'''). Although not used in the security index, '''SFINTLWARMAG''' supplements '''SFINTLWARALL''' by forecasting the average magnitude of internal conflict.

Forecasts of intrastate war probability ('''SFINTLWARALL''') are heavily path dependent, driven by a moving average of countries’ past experiences with violence. Over time, however, other factors—infant mortality, level of democracy, economic growth rate, and trade openness, among others—affect the probability of conflict as well. IFs uses an index, '''GOVRISK''', to measure the second sub-dimension of security, government performance and associated risk. For the 186 countries included in the model, the overall index value ranges roughly from 15 to 60, with a higher number signifying increased levels of risk. Low risk countries such as Sweden will generally fall at the bottom of the range, while high risk ones such as Somalia will be at the top. Like the probability of war, each country’s performance risk profile is driven by an array of factors across demographic, environmental, economic, health and education subsystems. A comprehensive look at each country’s performance and risk profile is available through Specialized Display under the option Performance Risk Evaluation. The display includes raw values and a global ranking, for each of the index’s nine components. Weighting and relative risk levels associated with each component are also listed.

The sub-dimensions of the second dimension of governance, capacity, are government revenue and government quality. Each country’s ability to raise revenue is captured by a simple ratio of '''GOVREV '''to '''GDP'''—net of foreign aid ('''AID''') received by governments, which the index does not include. The more revenue a government is able to raise relative to GDP, the higher the capacity score will be—for a discussion of the equilibrating processes used to calculate government revenue, expenditure and transfer payments see the [[#_The_Economic_Module|economic module]]. Governance quality, capacity’s second sub-dimension, is represented by corruption. In the model, '''GOVCORRUPT''' measures relative levels of political corruption, with 0 representing extremely corrupt practices, and 10 representing extreme transparency, or lack of corruption. Indices tracking regulatory quality ('''REGQUALITY)''', economic freedom ('''ECONFREE)''', and government effectiveness ('''GOVEFFECT)''' offer alternative formulations of governance quality. Although the '''GOVCORRUPT''' score is the only element used to generate the composite index '''GOVINDCAP''', the other indicators link forward to economic and infrastructure development processes.

The third dimension of governance, inclusiveness, is made up of the level of formal democratization and an element that measures gender empowerment in society. The '''DEMOCPOLITY '''index represents the strength of democratic institutions; in IFs, values range from 0 to 20, higher numbers signifying greater levels of democracy. In forecast years, several factors—the age structure of society, dependence on raw materials, energy exports and energy price—determine the strength of democracy. The model uses the gender empowerment measure ('''GEM''') as another index of inclusiveness. The measure takes its historical values—and its name—from the United Nations Development Program’s Gender Empowerment Measure. Forecasts of gender empowerment are driven by a country’s youth population size, GDP per capita at purchasing power parity, and years of formal education attained by female adults. The index '''FREEDOM''', created by Freedom House, offers a final formulation of civil participation in society. This measure is not used to generate the aggregate inclusiveness index, but it links forward to the social capital component of multifactor productivity.

While the three dimensions represent distinct facets of governance, they are tightly interrelated. Indeed, the drivers of security, capacity and inclusion often overlap. For example, gender empowerment, a sub-dimension of inclusion, drives security’s vulnerability to conflict index and capacity’s corruption index. And the three composite indices—'''GOVINDSECUR, GOVINDCAPAC '''and '''GOVINDINCLUS'''—interact bi-directionally with each other. Beyond the sub-module, strong relationships link the key governance indices and other measures of human well-being, represented by GDP per capita and the Human Development Index (HDI). For instance, economic freedom ('''ECONFREE''') links forward to multifactor productivity ('''MFP''') and thus on to broader economic growth. And conversely, other systems affect governance forecasts, as evidenced by the relationship between levels of per capita income and '''HDI''' and '''DEMOCPOLITY'''—higher levels of well-being lead to increased pressure for democracy.

The tri-part conceptualization of governance originated in the Pardee Institute's analysis of historical trends, as discussed in ''Potential Patterns of Human Progress'': ''Strengthening Governance Globally (''Hughes et al. (2014)). Over time, the story of governance has unfolded differently in countries across the world—some countries have developed security, capacity and inclusiveness sequentially, and some countries have developed all three dimensions simultaneously. A suite of parameters attached to the sub-dimensions of each dimension allows users to explore a range of potential patterns of global governance.

== Parameters to Affect Security ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''sfusehist'''
|N/A
|State failure switch to use history for base values(0=off; 1=on; 2=converge)
|Switch
|-
|'''sfintlwaradd'''
|SFINTLWARALL
|State failure/internal war addition
|Additive
|-
|'''sfconv'''
|SFINTLWARALL
|State failure convergence
|Convergence speed (years)
|-
|'''sfmassrep'''
|SFINTLWARALL
|State failure – Internal war repression by government
|Switch
|-
|'''wpextinterv'''
|SFINTLWARALL
|External intervention factor
|Switch
|-
|'''govriskm'''
|GOVRISK
|Government risk multiplier
|Multiplier
|-
|'''govriskweight'''
|GOVRISK
|Government risk weight
|Weight
|}

The most direct way in which to change the probability of internal war, and thus its broader consequences, is to manipulate the additive parameter affecting the variable, '''sfintlwaradd'''. Other parameters connected to internal war and the country performance risk evaluation allow users to explore alternate scenarios around security. Over time, a country’s probability of internal war will converge to the model’s expected function, a function driven by demographic and economic factors. However, users can control the speed of convergence through the parameter '''sfconv'''—accelerating this convergence would be an effective way to improve the fortunes of countries with especially bad histories of intrastate violence, because path dependency is a driver of internal war probability. Though set to 0.0 in the Base Case, two additional parameters exist that can directly affect the likelihood of internal war. '''Wpextinterv '''is a parameter that, when activated, simulates a conflict-creating external intervention; when the parameter is set to 1.0 the probability of intrastate conflict rises to 1.0 also. Another parameter, '''sfmassrep''', works in a similar way but with a reverse valence. Assigning it a value other than 0.0 simulates a campaign of mass repression by the central government that lowers the probability of overt intra-state violence.

Turning to government performance and risk evaluation, the multiplier '''govriskm '''directly controls the overall risk index, '''GOVRISK'''. Changes to the risk profile link forward to the higher order '''GOVINDVSECUR''' index, which in turn links to the overall governance score captured in '''GOVINDTOTAL'''. Users can also experiment with different risk configurations of a country or region via '''govriskweight'''; as the name implies, this parameter sets the weighting of '''GOVRISK'''’s component parts. As a final option, users can tailor their interventions to the components’ values, rather than simply the weighting. Any of the government risk index’s determining factors can be manipulated. The list of potential intervention targets includes: [[#_Variables_of_Interest_1|youth bulge]], [[#_Variables_of_Interest_2|the ratio of water use to renewable resources]], [[#_Variables_of_Interest_2|climate change]], [[#_Variables_of_Interest_3|resource export dependence]], [[#_Variables_of_Interest_4|rate of per capita GDP growth]], [[#_Variables_of_Interest|infant mortality]], [[#_Variables_of_Interest_1|life expectancy]], [[#_Variables_of_Interest|malnutrition]], [[#_HIV.2FAIDS_Submodule_1|HIV prevalence rate]], [[#_Education|primary net enrolment]] and [[#_Variables_of_Interest_5|adult education years]]. Scenario interventions could target one of these elements or several, cutting across IFs modules.

== Parameters to Affect Capacity ==

{| border="1" cellspacing="1" cellpadding="1" width="618" style="width:618px;"
|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:110px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:240px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:138px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govrevm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Government Revenue Multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''firmtaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Firm tax multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''hhtaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Household tax rate multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''ssweltaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Social Security multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''indirecttaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Indirect tax multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption Multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''econfreem'''

| nowrap="nowrap" style="width:110px;height:22px;" |
ECONFREE

| nowrap="nowrap" style="width:240px;height:22px;" |
Economic freedom multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|}
The governance module includes parameters connected to both sub-dimensions of capacity: government revenue and government effectiveness. '''Govrevm''' is a direct multiplier of government revenue, '''GOVREV'''; assigning values to this parameter would signify either growing or diminishing government capacity to gather revenue. However, various, more nuanced, government revenue collecting programs can be simulated via several taxation parameters. The module includes multipliers to the tax rate paid by firms ('''firmtaxrm''') and households ('''hhtaxrm'''), taxes paid on social security ('''ssweltaxrm''') and indirect taxes such as sales tax or value added tax ('''indirecttaxrm'''). For a full discussion of government revenue interventions see the [[#_The_Economic_Module|economic module]] discussion.

The governance module includes parameters connected to both sub-dimensions of capacity: government revenue and government effectiveness. '''Govrevm''' is a direct multiplier of government revenue, '''GOVREV'''; assigning values to this parameter would signify either growing or diminishing government capacity to gather revenue. However, various, more nuanced, government revenue collecting programs can be simulated via several taxation parameters. The module includes multipliers to the tax rate paid by firms ('''firmtaxrm''') and households ('''hhtaxrm'''), taxes paid on social security ('''ssweltaxrm''') and indirect taxes such as sales tax or value added tax ('''indirecttaxrm'''). For a full discussion of government revenue interventions see the [[#_The_Economic_Module|economic module]] discussion.

Each of the components of government effectiveness represents a potential target for scenario intervention. The variable '''GOVCORRUPT''', measuring corruption levels, is connected to a multiplicative parameter, '''govcorruptm'''. Although corruption is the only representation of effectiveness tied to the '''GOVINDCAPAC''', users may wish to explore the consequences of changes to other measures. Multipliers '''govregqualm '''and'''econfreem''' directly control the level of a country’s regulatory quality ('''REGQUALITY''') and economic freedom ('''ECONFREE'''). Increases to regulatory quality link forward to broadband connectivity and other information and communication technology (ICT) indicators; and changes in economic freedom affect multifactor productivity through social capital. Finally, users can manipulate the '''GOVEFFECT''' variable, a formulation of effectiveness taken from the World Bank’s World Governance Indicators project. '''Goveffectm '''is a straightforward multiplier, with assigned values acting to scale effectiveness up or down.

Users can alternatively choose to manipulate governance quality indicators via standard error targeting. Two parameters are linked to each corruption, regulatory quality and effectiveness. The first parameter sets the target increase or decrease in the index, expressed in standard errors away from the expected function, and the second parameter sets the number of years that will pass before the target is reached. These parameters are '''govcorruptsetar '''and '''govcorruptseyrtar '''for corruption, '''govregqualsetar '''and '''govreqqualseyrtar''' for regulatory quality, and '''goveffectsetar''' and '''goveffectseyrtar '''for government effectiveness.

== Parameters to Affect Inclusiveness ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''democm'''
|DEMOCPOLITY
|Democracy level multiplier
|Multiplier
|-
|'''swseffects'''
|DEMOCPOLITY
|Swing state effect
|Switch
|-
|'''democimpus'''
|DEMOCPOLITY
|Democracy impetus by leading power
|Coefficient
|-
|'''democimpoth'''
|DEMOCPOLITY
|Democracy impetus by other powers
|Coefficient
|-
|'''DEMOCWDIR'''
|N/A
|Democracy wave direction (-1=down; 0=off; 1=up)
|Switch
|-
|'''democwvlen'''
|DEMOCPOLITY
|Democracy wave length in years
|Limit
|-
|'''democwvmax'''
|DEMOCPOLITY
|Democracy wave upper limit
|Limit
|-
|'''democpolitysetar'''
|DEMOCPOLITY
|Democracy SE target
|Standard error target
|-
|'''democpolityseyrtar'''
|DEMOCPOLITY
|Democracy SE year
|Standard error target
|-
|'''freedomm'''
|FREEDOM
|Freedom multiplier
|Multiplier
|-
|'''gemm'''
|GEM
|Gender empowerment multiplier
|Multiplier
|-
|'''gemsetar'''
|GEM
|Gender empowerment target
|Standard error target
|-
|'''gemseyrtar'''
|GEM
|Gender empowerment year target
|Standard error target
|}

The two sub-dimensions of government inclusiveness are democracy and gender empowerment. Users wishing to explore alternative scenarios around levels of democracy can choose from an array of leverage points. First, the multiplier '''democm''' allows direct manipulation of the variable '''DEMOCPOLITY''' for a country or region. Users choosing this brute force method should direct their attention to linkages in multifactor productivity, state fragility, and the overall '''GOVINDINCLUS''' index. Scenario interventions could also target any of the deeper drivers of democracy. In the model, forecasts of democracy levels are a function of gender empowerment, the youth bulge, dependency on raw material exports and energy prices. Two other parameters—disabled in the base case—can affect '''DEMOCPOLITY'''. The first simulates a neighborhood affect: if '''swseffects''' is set to a value of 1, swing states in a geographic region will be pressured to conform to their neighbors’ democratic practices—or lack thereof. The second additional method is rather complicated and will not be of interest to most users. The variable '''DEMOCWAVE''' either adds to or subtracts from the democracy levels determined by the model, simulating a wider neighborhood, in fact global, effect known as a wave of democracy. The magnitude of upswings or downswings is specified exogenously through several parameters: '''democimpus, '''the impetus provided to democracy by the leading power; '''democimpoth''', the impetus provided to democracy by other powers; '''democwvlen''', the wave of democracy’s duration in years; and, '''democwvmax''', the maximum rise in democracy levels. When a democracy wave is active, two variables track the process: '''DEMOCWVCOUNT''' measures the duration of the wave’s run and recession, and '''DEMOCWVDIR '''contains the direction of the wave, whether it supports or hinders the consolidation of democracy. As an alternative to other types of intervention, users may override the polity function through the use of external targeting. '''Democpolitysetar '''specifies a standard error target, and '''democpolityseyrtar''' specifies the year in which this target will be achieved.

 The degree of civilian participation – on the part of all citizens – is another driver of inclusiveness. Whereas the polity measure of democracy ('''DEMOCPOLITY''') measures formal, electoral inclusion, the variable '''FREEDOM '''is a more comprehensive measure of civil participation. '''FREEDOM''' is computed from levels of GDP and general educational attainment—among both men and women; and like the other elements inclusion, it is linked to a brute force multiplier, '''freedomm'''. Unlike the other parameters, however, standard error targeting is not available for the civil society freedom variable.

The second dimension of government inclusiveness, gender empowerment, offers a final target for scenario analysis. The three primary drivers of the gender empowerment measure ('''GEM''') are youth bulge size, GDP per capita at purchasing power parity, and years of formal education obtained by females over age fifteen. Users can build alternate scenarios around a combination of driving factors, or, more directly, through a multiplicative parameter. The multiplier '''gemm '''allows users to directly control the progression/regression of gender empowerment in a country or region. Additional parameters control standard error targeting around '''GEM''': used together, '''gemsetar '''and'''gemseyrtar '''establish a target value, measured in distance from the expected, cross-sectional function, and the time required to reach the target.

== Prepackaged Scenarios ==

An installation of IFs includes scenarios used in each entry in the Pardee Institute's Potential Patterns of Human Progress (PPHP) series. Scenarios created for the governance volume include: a high conflict scenario; a scenario representing challenges to the world’s governments; a set of scenarios that speed up economic growth via multifactor productivity; and, finally, a set of government strengthening scenarios. This section will briefly describe the parameter interventions used to construct several of the enhanced government scenarios, located in the folder Strengthened Governance.

The collection of governance scenarios includes one scenario for each of the three dimensions: Strengthened Security, Strengthened Capacity SE1 and Strengthened Inclusiveness SE1. Each scenario includes a single parameter shift that targets one sub-dimension of security, capacity, or inclusion. Users wishing to load the three interventions simultaneously can select the combined scenario, Strengthened Governance SE1. The boost to capacity operates through '''govrevm''', the multiplier tied to the revenue sub-dimension of capacity; the parameter is set to 1.1 for the majority of the forecast horizon, raising revenue collection each year. The intervention in inclusion targets the democracy sub-dimension. For the entire forecast horizon, from 2010 to 2100, '''democpolitysetar''' is set to 1, establishing an improvement target in democracy levels of one standard error. As the corresponding year parameter, '''democpolityseyrtar''', is not changed, it takes on its default value of 10.0. This means that the target of one standard error will be reached in 10 years. The final intervention simulates a decline in intra-state violence the world over. The additive factor '''sfintlwaradd''' interpolates from 0 to -1 over thirty years; after reaching negative 1, it remains at that value through 2100.

The final scenario in the governance set, Strengthened Policies, features interventions to deeper drivers of each governance dimension, including: educational improvements at primary, lower secondary, upper secondary and tertiary schooling levels; reductions in childhood malnutrition; increased government transfers and international development assistance; steady decline in total fertility rates across western, central and eastern Africa as well as Oceania; rising agricultural yields; less solid fuel use, and a corresponding reduction in atmospheric particulate matter.

The two comprehensive scenarios contained in the PPHP Governance folder represent distinct methods of alternative forecast analysis. Strengthened Governance SE1 explores the effects of directly manipulating governance indicators. Strengthened Policies, on the other hand, explores the forward linkages of governance drivers.

= International Politics Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''POWER''' 
| Total power index 
|-
| '''CPOW''' 
| Conventional power 
|-
| '''NPOW''' 
| Nuclear power 
|-
| '''WCPOW''' 
| World nuclear power 
|-
|'''WNPOW'''
|World nuclear power
|-
| '''PCONGREAT''' 
| Power concentration of great powers 
|-
| '''PCONGREATEU''' 
| Power concentration of great powers, single Europe 
|-
| '''PCONGREATEUF''' 
| Power concentration of great powers, flexibly defined, with single Europe 
|-
| '''THREAT''' 
| Probability of militarized conflict 
|-
| '''THREATREC''' 
| Threat received 
|-
| '''THREATSENT''' 
| Threat received 
|-
| '''THREATSENT''' 
| Threat sent 
|-
| '''WGDP''' 
| World GDP 
|-
| '''WTRADE''' 
| World trade 
|-
| '''GOVEMBED''' 
| Government embeddedness 
|-
| '''CWAR''' 
| Conventional war 
|-
| '''CWARPB''' 
| Conventional war probability 
|-
| '''NWAR''' 
| Nuclear war 
|-
| '''NWARPB''' 
| Nuclear war probability 
|-
| '''CIVDM''' 
| Civilian damage (multiplier) 
|-
| '''DEMOCPOLITY''' 
| Level of democracy 
|-
| '''DIPLOEMBASSY10''' 
| Number of embassies held in other countries 
|-
| '''DIPLOIGO''' 
| International organization membership 
|-
| '''DIPLOTREATY''' 
| Treaty participation
|}
<div><div id="ftn1">
10For '''DIPLOEMBASSY, DIPLOIGO '''and '''DIPLOTREATY''', there exist three corresponding variables with the suffix '''MAX''' that cap forecast values.
</div></div>
Unlike other systems, such as demographics or economics, there is no standard organizational structure used to represent international politics. In order to establish a theoretical structure that describes interactions between states, IFs draws on in-house expertise, as well as work conducted by other research organizations, academics and governments and, of course, international relations theory and empirical work. The two foundational elements in the model are power and threat levels, which trigger alliances, conflicts and military spending. IFs takes conceptual and theoretical inputs from projects that have been dedicated to quantifying power and threat dynamics, including J. David Singer’s Correlates of War project, initiated at the University of Michigan, which produced the Composite Index of National Capability (CINC) –a measure of hard power, the CINC formulation includes demographic, economic and military components. IFs reaches out for data to a variety of sources including the Stockholm International Peace Research Institute’s (SIPRI) datasets on military expenditures, arms trade, and international conflicts. The IFs international politics module contributes to existing work in the field by forecasting measures of hard power and of threat levels between states. It is increasingly also developing data and forecasting capability around diplomacy.

IFs includes several formulations of power. The variable '''POWER''' is an index, computed in the model, that measures each country’s share of world power. The index is comprised of weighted elements for: GDP, population, GDP per capita (as a proxy for technological sophistication), government size, public military spending, conventional power, and nuclear power. Levels of conventional ('''CPOW''') and nuclear ('''NPOW''') power have their own dynamics, but both are primarily dependent on military spending patterns, as captured in the variable '''GDS''' and an associated parameter that determines the amount of funding available for nuclear weapons research. (For a full discussion of government spending flowing to the military and to other destinations, see the economic section.) '''NPOW''' is not an index – like '''POWER''' or '''CPOW''' – but rather the number of nuclear warheads owned by each country. Two additional variables measure global power. The indicator of the world’s stock of nuclear power, '''WNPOW''', is measured in megatons. And '''WCPOW''' is an aggregate value of all countries’ conventional power, expressed as an index.

Depending on the configuration of power at a given point in time, the international system may be multipolar, bipolar, or unipolar. A set of variables in the model capture the degree to which power is concentrated in the hands of a few nations. The index '''PCONGREAT''' measures the percentage of global power held by dominant, or great powers— as values approach zero, smaller shares of power would be held by so-called great powers. '''PCONGREATEU''' measures the percentage of global power held by great powers, considering the European Union to be a single actor. '''PCONGREATEUF''' measures the percentage of global power held by great powers, with the European Union considered a single actor and the category “great power” flexibly defined; the flexible definition refers to the phasing in of great power status above a threshold share of global power up to full great power status upon attaining a higher share. And finally, the variable '''PCONSYS''' is an index indicating the power concentration of the global system, regardless of the number of great powers.

In IFs, the balance of power between actors in the international system links forward to threat ('''THREAT''') of militarized interstate disputes (MIDs). The model calculates the threat that any state poses to any other state as a product of historical conflict data and a predictive function. Three especially important factors drive the dyadic function: contiguity, whether or not either of the countries is a great power, and the existence of a territorial dispute between the countries. Additional drivers of international threat include world trade ('''WTRADE'''), energy production ('''WENP'''), and strength of democracy ('''DEMOCPOLITY'''). We also compute a level of government embeddedness ('''GOVEMBED''') that may interact with power structures in future model development. (Government embeddedness is a concept taken from international political economy; a government is considered embedded if its actions are constrained by domestic institutions, firms and households). The results of computations are stored in an index, '''THREAT''', which can be interpreted very roughly as the probability that a pair of countries will experience a militarized dispute in a given year (but expressed in percentage terms of 0-100 instead of probabilistic ones of 0-1). Countries can be theorized as both recipients and deliverers of threat: '''THREATREC''' is of the total of all threat received from all senders; '''THREATSENT''' is, as the name indicates, the total threat sent by a country. And the variable '''WTHREAT''' is an average of threat levels between all country pairs, thus offering an indication of global military tension.

The model includes a specialized display for threat dynamics and other dyadic (country-to-country) interaction. To access the display select the Data Analysis option from the IFs main menu, then select Dyadic Interaction Display. On the resulting form, users can explore the relationship between any two countries via the list boxes labeled CountryA and CountryB. In addition to '''THREAT''', a slew of international political variables are available for dyadic display in the Variable box: Midpresent, FatalityBinary, StandAlliance, StandDiplo, StandGO, Stand UN, StandTrade, StandCOI, StandLiberalisHigherMoreLib, StandRealismLowerMoreTension.

As an additional feature, the international politics module includes an optional war simulation. When the war option is activated, the model will stochastically (randomly) introduce a war, based on dyadic threat levels described above. The variables '''CWARPB''' and '''NWARPB''' measure the probability that a conventional or nuclear war will break out in a given year, whether or not the switch for the war option is activated. When the switch is on, a second pair of variables identifies the occurrence or absence of conventional war and nuclear war: '''CWAR''' and '''NWAR''' take on binary values, 1 indicating the presence of war, and 0 indicating the absence of war. Once a war event takes place, the countries involved will experience fatalities and material damage. '''CIVDM''' tracks the human costs to overall population and the capital lost in the fighting.

In addition to forecasting power, threat and conflict, the IFs model forecasts several diplomatic measures. The model currently includes variables containing the number of embassies each country has abroad ('''DIPLOEMBASSY'''), international organization membership ('''DIPLOIGO'''), and treaty membership ('''DIPLOTREATY'''). Like the other variables in IFs, these measures of soft power are forecast out to 2100 for each country.

Parameters in the international politics model allow users to create alternative scenarios that explore uncertainty around the international power structure, dyadic threat levels, the outbreak of war, and diplomacy.

== Parameters Affecting Power ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:281px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:120px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Factor controlling military spending conversion

| nowrap="nowrap" style="width:120px;height:22px;" |
Convergence factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowldcf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Higher impact of military spending in LDCs

| nowrap="nowrap" style="width:120px;height:22px;" |
Convergence factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''drcpow'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Depreciation rate of conventional power

| nowrap="nowrap" style="width:120px;height:22px;" |
Rate of change

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowdf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:281px;height:22px;" |
Conventional power, damage done by war

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govnucwpadd'''

| nowrap="nowrap" style="width:104px;height:22px;" |
NPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Nuclear weapons stockpile change

| nowrap="nowrap" style="width:120px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpwghtpow'''

| nowrap="nowrap" style="width:104px;height:22px;" |
POWER

| nowrap="nowrap" style="width:281px;height:22px;" |
Contribution of various components to power index

| nowrap="nowrap" style="width:120px;height:22px;" |
Index weight

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpgreatthresh'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONGREAT

| nowrap="nowrap" style="width:281px;height:22px;" |
Systemic power share required for threshold of great power status

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpgreatlev'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONGREAT

| nowrap="nowrap" style="width:281px;height:22px;" |
Systemic power share required for full great power status

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpconmax'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONSYS

| nowrap="nowrap" style="width:281px;height:22px;" |
Maximum power concentration

| nowrap="nowrap" style="width:120px;height:22px;" |
Limit

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpconmin'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONSYS

| nowrap="nowrap" style="width:281px;height:22px;" |
Minimum power concentration

| nowrap="nowrap" style="width:120px;height:22px;" |
Limit

|}

A suite of parameters is available to users who wish to explore power dynamics in the IFs model. Conventional power ('''CPOW''') can be manipulated using the parameters '''cpowf''', '''cpowldcf''', and '''drcpow'''. The first controls the elasticity between military spending and conventional power accrual; higher values of '''cpowf''' increase the impact that military spending has on conventional power measures. '''Cpowldcf''' controls the impact of military spending within LDCs, where military spending has historically had a greater impact on measures of power, largely because the costs of military personnel are lower. And '''drcpow''' controls the depreciation rate of conventional power. This parameter controls the rate at which past investments in conventional power decline as a contribution to power in successive years. The destructiveness of interstate conflict has a negative impact on conventional power measures. The impact that conventional war has on combatant nations, as measured by '''CIVDM''', can be controlled by '''cpowdf'''—the higher the parameter value, the greater the civilian deaths and capital destruction. Users may also increase or decrease a country’s stock of nuclear weapons via the parameter '''govnucwpadd'''. Nuclear weapons capacity influences total power through the nuclear power component, '''NPOW'''.

The aggregate measurement of power is similarly connected to parameter levers. The parameter '''wpwghtpow '''allows users to customize the set of weights used in the calculation of the power index ('''POWER'''). Individual weights are attached to each of the index’s components: population, GDPatPPP, GDP, GDPxGDPPCatPPP, GDPxGDPPC, Govt, MilSpend, ConvPow, NucPow, NucWarHeads, EnergyNetExport, TradeTotal, FDIInflows, AidNet, HumCapWkPopHDI, GovRev, InnovationR&D, and ConnectivityICTProd.

Users can also alter the role of great powers in the international political system. '''Wpgreatthresh''' controls the threshold level of the power index that, when achieved, signals the basic level of a nation’s transition into (or out of) being a great power. '''Wpgreatlev''' controls the power share held by full great powers, allowing users to model greater or lower levels of power diffusion. And, '''wpconeusw''' is a switch parameter that controls whether the EU is considered a single great power in the global power structure.

Two final parameters place limits on the levels of power that any country can experience. '''Wpconmax''' and '''wpconmin '''set the maximum and minimum level of power that a country can reach in forecast years.

== Parameters Affecting Threat Levels ==

{| border="1" cellspacing="1" cellpadding="1" width="637" style="width:637px;"
|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''Parameter'''

| style="width:86px;height:20px;" |
'''Variable of Interest'''

| style="width:252px;height:20px;" |
'''Description'''

| style="width:156px;height:20px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpgreat1'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Dyadic dispute probability, one great power

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpgreat2'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Dyadic dispute probability, two great powers

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wppowtran1'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Power transition threshold

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wppowtran2'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Increase in dispute probability with 10% greater power parity

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcon'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with power concentration

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpconsq'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with power concentration

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcontiguity'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Threat associated with geographic proximity

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpdemdist'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to political distance

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpdemmin'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to the minimum democracy level in the dyad

| style="width:156px;height:20px;" |
Limit

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''glocom'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Global community multiplier

| style="width:156px;height:20px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcommun'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to global community level

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wptrade'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with systemic trade

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''ally'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Alliance membership switch

| style="width:156px;height:20px;" |
Switch

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpally'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Reduction in threat with allied dyad

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''terdispute'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Territorial dispute or absence

| style="width:156px;height:20px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpterdisp'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Conflict arising from territorial disputes

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpthrconv'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Threat level convergence

| style="width:156px;height:20px;" |
Convergence Speed

|}

All parameters modifying threat affect the same variable, probability of dyadic conflict ('''THREAT'''). '''Wpgreat1 '''and '''wpgreat2''' specify the likelihood of disputes involving one and two great powers, respectively. The reason these parameters exist is that great powers have historically been much more likely to experience conflict than have other states. A subset of great powers, namely the global leader in power and any power challenging that leadership and transition itself into system leadership, is especially likely to be engaged in conflict. '''Wppowertran1''' sets the dyadic power ratio for those two countries that identifies the beginning of a power transition and thereby triggers an increase in conflict threat levels. For example, a '''wppowertran1''' value of 0.7 means that a threat increase would begin once the weaker dyad member achieve a power score ('''POWER''') equal to 70 percent of the more powerful member’s score. '''Wppowertran2 '''then establishes increase in the likelihood of conflict associated with increased power parity. More specifically, a '''wppowertran2''' value of 0.05 means that, once a power transition has begun, each further reduction in a dyad’s power differential of 10 percent leads to an increase in threat levels of 5 percent.

Regardless of the number of great powers or the occurrence of a power transition, the concentration of power in the global system of great powers ('''PCONGREAT'''), as described earlier, can affect the level of threat of conflict (THREAT). Users may wish to take note of, '''wpcon '''and '''wpconsq''', the two coefficients that control the relationship between power concentration and conflict likelihood. '''Wpcon''' directly links concentration to threat and '''wpconsq''' links the square of power concentration to threat.

Several additional parameters relate to the probability or threat of conflict in a dyad . '''Wpcontiguity''' controls the threat of dispute generated by geographic proximity, one of the strongest determinants of conflict. '''Wpdemdist '''alters threat generated in response to political incompatibility—i.e., divergent levels of democracy—between two members in a dyad (the greater the distance, the greater the threat). '''Wpdemmin '''relates conflict to the democracy level of the less democratic member of a dyad (if democracy of that member rises, the default value of that parameter would lower threat). A pair of parameters together establish the relationship between the global community and threat. '''Glocom '''represents an exogenous specification of the cohesiveness of the global community, and'''wpcommun '''is an elasticity between international cohesion and the probability of conflict. '''Wptrade''' modifies the response of threat to the level of systemic trade. The parameter '''ally '''is a switch parameter that turns on or off alliance memberships, while the parameter '''wpally '''reduces the probability of dispute if two dyadic members are allied. And finally, '''terdispute''' defines the presence or absence of a territorial dispute within a dyad, while '''wpterdisp''' changes the likelihood of conflict arising from such disputes.

One other parameter is worth noting. '''Wpthrconv''' defines the convergence speed of historical threat levels within a dyad (which in initialization for the first model year reflect the reality of past conflicts) to the level of dyadic threat as calculated using all of the drivers and relationships indicated above. If that convergence speed, expressed in terms of the number of years for full convergence, is very slow (a high value for years needed), then the path dependency of past conflict will continue much more strongly to affect future likelihood than if the convergence speed is very fast (a low value for years).

== Parameters Affecting War Simulation ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:100px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:295px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:120px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''wpthrwar'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Portion of disputes that escalate to war

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''waron'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWAR, NWAR

| nowrap="nowrap" style="width:295px;height:22px;" |
War switch

| nowrap="nowrap" style="width:120px;height:22px;" |
Switch

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarbase'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war probability addition to base computation

| nowrap="nowrap" style="width:120px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war forcing

| nowrap="nowrap" style="width:120px;height:22px;" |
Switch

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarsv'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war severity

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''nwarf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
NWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Probability that conventional war will escalate to nuclear

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cdmf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Civilian war damage factor

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''nwarsv'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Nuclear war severity

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''elndpow'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Elasticity of nuclear damage with nuclear power

| nowrap="nowrap" style="width:120px;height:22px;" |
Elasticity

|}
Threat ('''THREAT''') within a dyad is, as indicated above, related to the probability of conflict but not identical to it, because scaling of the various contributions to the threat variable is complicated and uncertain. The parameter '''wpthrwar''' translates threat within a dyad into a probability of conventional conflict ('''CWARPB''') in that same dyad. There will never be an actual conventional war, however, unless the war switch is turned on ('''waron'''=1). When that switch is on and '''CWARPB''' for a dyad is non-zero, conventional war in any dyad-year occurs randomly in relationship to that war probability. For any given dyad-year a user can change the probability of conventional war with the additive parameter '''cwarbase'''. Or wars can be forced with the parameter switch, '''cwarf''', when it is set to 100 or above.

Threat ('''THREAT''') within a dyad is, as indicated above, related to the probability of conflict but not identical to it, because scaling of the various contributions to the threat variable is complicated and uncertain. The parameter '''wpthrwar''' translates threat within a dyad into a probability of conventional conflict ('''CWARPB''') in that same dyad. There will never be an actual conventional war, however, unless the war switch is turned on ('''waron'''=1). When that switch is on and '''CWARPB''' for a dyad is non-zero, conventional war in any dyad-year occurs randomly in relationship to that war probability. For any given dyad-year a user can change the probability of conventional war with the additive parameter '''cwarbase'''. Or wars can be forced with the parameter switch, '''cwarf''', when it is set to 100 or above.

The probability of a nuclear war ('''NWARPB''') is zero unless there is a conventional war (CWAR=1). If there is a conventional war, the probability of a nuclear war is determined by the severity of conventional war ('''cwarsv''') and the probability of escalation ('''nwarf'''). If '''nwarf''' is set to 100 or above, it will force a nuclear war when a conventional one occurs between nuclear powers.

Once a conventional or nuclear war takes place, several parameters determine how damaging the effects will be. '''Cdmf''' is a multiplier that serves as a civilian war damage factor–the higher the parameter’s value, the greater the casualties and physical destruction. Two related parameters, '''cwarsv '''and '''nwarsv''', control the severity of conventional and nuclear war. '''Elndpow''' is an elasticity of nuclear war severity and nuclear weapon numbers.

== Parameters Affecting Diplomacy ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:148px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:257px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:92px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diploembassymaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOEMBASSYMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum embassies abroad

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diploigomaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOIGOMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum international organization membership

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diplotreatymaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOTREATYMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum bilateral treaty participation

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|}

The diplomacy portion of the IFs international politics module is under development. Currently, three multiplicative parameters exist that allow users to manipulate forecast values of embassies abroad ('''DIPLOEMBASSY'''), international organization membership ('''DIPLOIGO)''' and bilateral treaty agreements ('''DIPLOTREATY'''). Forecast values of each measure of soft power are limited by cap variables— '''DIPLOEMBASSYMAX''', '''DIPLOIGOMAX''', and '''DIPLOTREATYMAX'''. Users can raise or lower these caps via the parameters '''diploembassymaxm''', '''diploigomaxm''' and '''diplotreatymaxm'''.

== Prepackaged Scenarios ==

There are few prepackaged scenarios which make use of the international politics parameters. Two sets featuring limited interventions are located under the Understandings of Causality heading in the Prepackaged Scenarios folder. Under the heading Power Capabilities are a number of different formulations of global power which modify the parameter '''wpwghtpow'''. These modifications are designed to reproduce the distributions of global power that have been discussed by academic researchers. For instance, the scenario COW-Like 3-Element at PPP increases the weighting for population, GDP at PPP, and military spending in the calculation of power. Three other scenarios in this folder (Brecke Formulation, Hillebrand-Herman Base and COW-Like 3-Element at Market) create alternative structuring of the POWER variable. Two other scenarios in this folder, Slow China Formulation, and Slow China Formulation with growth, model a slow growth in power for China by underweighting the value of military spending, GDP, GDPPPC, and population, while overweighting GDPPC.

In the folder labeled Threat International are two scenarios that model the threat of international war. These two scenarios modify the parameter '''wpthrconv''' in order to frame various assumptions about the dynamics of threats at the international level.

Finally, one scenario in the World Integrated Scenario Sets demonstrates the use of power parameters. This scenario is located in the MENA Scenarios and is called Iran Nukes + Sanctions. The scenario models the possible effect of Iran acquiring nuclear weapons and the potential US response. This scenario increases '''govnucwpadd '''to simulate the effect of Iran acquiring nukes, and then alters the calculation of the power index to underweight population, GDPPP, GDPxGDPPCatPPP, and military spending, while overweighting the effect of nuclear warheads, net exports of energy, trade flows, FDI inflows, net aid, government revenue, human capital, innovation and R&D, and connectivity of ICT. It also decreases exports ('''xshift'''), protectionism ('''protecm'''), oil and gas production ('''enpm'''), to demonstrate the possible impact of an international response that favored increasing sanctions against the country.

= Parameter Dictionary =

== Population ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''tfrm'''
|TFR, CBR
|Total fertility multiplier
|Multiplier
|-
|'''contrusm'''
|CONTRUSE
|Contraceptive use multiplier
|Multiplier
|-
|'''eltfrcon'''
|TFR
|Elasticity of total fertility rate to contraception use
|Elasticity
|-
|'''tfrmin'''
|TFR
|Long term TFR convergence value
|Limit
|-
|'''mortm'''
|DEATHS
|Mortality multiplier (not cause specific)
|Multiplier
|-
|'''hlmortm'''
|DEATHS
|Mortality multiplier by cause
|Multiplier
|-
|'''wmigrm'''
|MIGRATE, MIGRANTS
|World migration rate multiplier
|Multiplier
|-
|'''migrater'''
|MIGRATE, MIGRANTS
|Net migration rate (inward)
|Rate of change
|-
|'''migrateoutm'''
|MIGRANTSOUT, MIGRANTS
|Migration rate outward multiplier
|Multiplier
|-
|'''migrateinm'''
|MIGRANTSIN, MIGRANTS
|Migration rate inward multiplier
|Multiplier
|-
| '''workageentry'''
|POPPREWORK, POPWORKING
|Working age determinant
|Exogenous specification
|-
| '''workageretire'''
|POPWORKING, POPRETIRED
|Retirement age determinant
|Exogenous specification
|}

== Health ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''hlmortm'''
|DEATHCAT, HLYLL, HLDALY
|Multiplier on Mortality (by cause)
|Multiplier
|-
|'''hlmorbm'''
|YLD
|Multiplier on morbidity
|Multiplier
|-
|'''hlstddthsw'''
|DEATHCAT
|Switches DEATHCAT from absolute numbers to deaths/1000
|Switch
|-
|'''watsafem'''
|WATSAFE, INFMOR
|Percentage of population with access to safe water
|Multiplier
|-
|'''sanitationm'''
|SANITATION, INFMOR
|Percentage of population with access to improved sanitation
|Multiplier
|-
|'''malnelimstartyr'''
|MALNPOP
|Prevalence of undernutrition
|Standard error target start year
|-
|'''malnelimtargetyr'''
|MALNPOP
|Prevalence of undernutrition
|Standard error target year
|-
|'''malelimprecisesw'''
|MALNPOP
|Prevalence of undernutrition
|Switch
|-
|'''ylm'''
|YL
|Yield multiplier on agriculture
|Multiplier
|-
|'''hivm'''
|HIVCASES
|Rate of HIV infection
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|-
|'''envpm2pt5m'''
|ENVPM2PT5
|Increases levels of environmental pollution
|Multiplier
|-
|'''hlsmokingm'''
|HLSMOKING
|Increases rate of smoking
|Multiplier
|-
|'''hlobesitym'''
|HLOBESITY
|Prevalence of obesity
|Multiplier
|-
|'''hlbmim'''
|HLBMI
|Multiplier on body mass index
|Multiplier
|-
|'''deathtrpvm'''
|DEATHTRPV
|Multiplier on traffic deaths per vehicle
|Multiplier
|-
|'''deathtrpvsetar, deathtrpseyrtar'''
|DEATHTRPV
|Standard error target for traffic deaths per vehicle
|Relative target Value/Year
|-
|'''hlmortmodsw'''
|CDR
|Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific
|Switch
|-
|'''hltechshift'''
|CDR
|Rate of change in health technology
|Additive factor
|-
|'''hltechlinc'''
|CDR
|Rate of change in health technology in low income countries
|Additive factor
|-
|'''hltechssa'''
|CDR
|Rate of change in health technology in Sub-Saharan Africa
|Additive factor
|-
|'''hltechbase'''
|CDR
|Rate of change in health technology at base
|Exogenous specification
|}

== HIV/AIDS ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''hivm'''
|HIVRATE
|HIV infection rate, multiplier of percent of population infected
|Multiplier
|-
|'''hivtadvr'''
|HIV CASES, HIVRATE
|Technical advance rate in of control of infection
|Rate of change
|-
|'''hivpeakr'''
|HIVCASES, HIVRATE
|HIV infection rate at year of peak
|Target value
|-
|'''hivpeakyr'''
|HIVRATE
|Sets year of epidemic peak
|Target year
|-
|'''hivincr'''
|HIVCASES
|HIV increase rate, only used prior to 2000
|Rate of change
|-
|'''aidsdrtadvr'''
|AIDSDTHS, AIDSRATE
|AIDs death rate, technical annual advance rate in control
|Rate of change
|-
|'''aidsdratem'''
|AIDSRATE
|AIDs death rate as % of HIV infection rate, multiplier
|Multiplier
|}

=== Severe Acute Malnutrition (SAM) Submodule ===
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpadj'''
|MALNCHPSAMINC
|Used to adjust the incidence correction factor
|Adjustment factor
|-
|'''malnchpsamm'''
|MALNCHPSAM
|Used to change prevalence of SAM
|Multiplier
|-
|'''samadmissionadd'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Additive factor
|-
|'''samadmissionm'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Multiplier
|-
|'''samadmissionsw'''
|SAMADMISSION
|Allows users to forecast SAM admissions in three ways (0) forecasts all countries according to the relationship with GDPPCP, (1) holds all initial zero values constant over time (default value), and (2) holds all initial values constant.
|Switch
|-
|'''samavertedconvyr'''
|SAMAVERTED
|Allows users to change the years to converge to 100
|Convergence factor
|-
|'''samavertedm'''
|SAMAVERTED
|Allows users to change the number of averted cases
|Multiplier
|-
|'''malnchpsamtostunt'''
|MALNCHPSAM and HLSTUNT
|Controls the degree to which MALNCHPSAM affects HLSTUNT
|Adjustment factor
|-
|'''samrrsw'''
|MALNCHPSAM and MALNCHP
|Used to substitute the severity of MALNCHP with that of MALNCHPSAM
|Switch
|-
|'''samtomalnchpsw'''
|MALNCHP
|allows users to specify scenarios in which successful treatment does not result in full weight
|Switch
|}

== Education ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edpriintngr'''
|EDPRIINTN
|Primary net intake rate
|Annual growth rate
|-
|'''edterintgr'''
|EDTERINT
|Tertiary intake rate
|Annual growth rate
|-
|'''edseclowrtrangr'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Annual growth rate
|-
|'''edsecupprtrangr'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Annual growth rate
|-
|'''edprisurgr'''
|EDPRISUR
|Primary survival rate
|Annual growth rate
|-
|'''edtergradgr'''
|EDTERSUR
|Tertiary survival rates
|Annual growth rate
|-
|'''edpriintntrgtyr'''
|EDPRIINTN
|Primary net intake rate
|Target
|-
|'''edseclowrtrantrgtyr'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Target
|-
|'''edsecupprtrantrgtyr'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Target
|-
|'''edterinttrgtyr'''
|EDTERINT
|Tertiary Intake Rate
|Target
|-
|'''edprisurtrgtyr'''
|EDPRISUR
|Primary survival rate
|Target
|-
|'''edtergradtrgtyr'''
|EDTERGRAD
|Tertiary graduation rates
|Target
|-
|'''edpriintnm'''
|EDPRIINTN
|Primary net intake rate
|Multiplier
|-
|'''edterintm'''
|EDTERINT
|Tertiary intake rate
|Multiplier
|-
|'''edseclowrtranm'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Multiplier
|-
|'''edsecupprtranm'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Multiplier
|-
|'''edprisurm'''
|EDPRISUR
|Primary survival rate
|Multiplier
|-
|'''edtergradm'''
|EDTERGRAD
|Tertiary survival rates
|Multiplier
|-
|'''gdsm (education)'''
|GDS
|Government spending on education
|Multiplier
|-
|'''edbudgon'''
|GDS
|Education funding priority
|Exogenous specification
|-
|'''gdsedm'''
|GDS
|Education spending distribution
|Multiplier
|-
|'''edexppconv'''
|EDEXPERPRI
|Education spending per student, primary
|Convergence speed
|-
|'''edexpslconv'''
|EDEXPERSEC
|Education spending per student, secondary
|Convergence speed
|-
|'''edexpsuconv'''
|EDEXPERSEC
|Education spending per student, secondary
|Convergence speed
|-
|'''edexptconv'''
|EDEXPERTER
|Education spending per student, tertiary
|Convergence speed
|-
|'''edqtqltrm'''
|EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS
|Education quantity-quality balance
|Multiplier
|-
|'''edprigndreqintn'''
|EDPRIINTN
|Gender parity for primary intake
|Convergence speed
|-
|'''edprigndreqsur'''
|EDPRISUR
|Gender parity for primary survival
|Convergence speed
|-
|'''edseclowrgndreqtran'''
|EDSECLWRTRAN
|Gender parity for lower secondary transition
|Convergence speed
|-
|'''edsecupprgndreqtran'''
|EDSECUPPRTRAN
|Gender parity for upper secondary transition
|Convergence speed
|-
|'''edtergndreqint'''
|EDTERINT
|Gender parity for tertiary intake
|Convergence speed
|-
|'''edtergndreqgrad'''
|EDTERSUR
|Gender parity for tertiary survival
|Convergence speed
|-
|'''edqualpriallm'''
|EDQUALAG15PRI
|Multiplier on primary education learning quality
|Multiplier
|-
|'''edqualsecallm'''
|EDQUALAG15SEC
|Multiplier on secondary education learning quality
|Multiplier
|-
|'''edqualag15m'''
|EDQUALAG15
|Multiplier on total learning quality for adults
|Multiplier
|-
|'''mfpedqual'''
|TFP
|Elasticity of education quality to multifactor productivity
|Elasticity
|-
|'''edmodelsw'''
|N/A
|Education switch between old and new model (0=old model; 1=new model)
|Switch
|-
|'''edpridrpgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edprienrgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|-
|'''edseclowrdrpgrdm'''
|EDSECLOWRDRPGRD/ EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edseclowrenrgrdm'''
|EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|}

== Economics ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''labparm'''
|LAB
|Labor participation rate multiplier
|Multiplier
|-
|'''labfemshrm'''
|LAB
|Female labor force participation rate multiplier
|Multiplier
|-
|'''labretagem'''
|LAB
|Retirement age, labor force participation multiplier
|Multiplier
|-
|'''labunemprm'''
|LABUNEMPR
|Labor unemployment rate multiplier
|Multiplier
|-
|'''labmarketsw'''
|LABS
|Labor demand to economic production switch (1=on)
|Switch
|-
|'''labdemsm'''
|LABDEMS
|Labor force, multiplier on demand
|Multiplier
|-
|'''labcoeffsm'''
|LABCOEFFS
|Labor demand, multiplier on technology coefficients
|Multiplier
|-
|'''workageentry'''
|POPRETIRED
|Age at which individuals enter the labor force
|Exogenous specification
|-
|'''workageretire'''
|POPRETIRED
|Age at which individuals leave the labor force
|Exogenous specification
|-
|'''invm'''
|IDS
|Investment by destination sector
|Multiplier
|-
|'''mfpadd'''
|MFP components
|Multifactor productivity, additive
|Additive factor
|-
|'''mfpleadr'''
|MFP
|MFP growth rate of the technological lead country
|Growth Rate
|-
|'''mfpedspn'''
|GDS-educ
|Increase portion of spending going to education
|Elasticity
|-
|'''malnelimstartyr'''
|MALNPOP
|Undernutrition elimination standard error target
|Standard error target
|-
|'''malnelimtargetyr'''
|MALNPOP
|Undernutrition elimination standard error target
|Standard error target
|-
| '''malelimprecisesw '''
|MALNPOP
|Undernourishment elimination
|Switch
|-
|'''ylm'''
|YL
|Land yield
|Multiplier
|-
|'''aginvm'''
|AGINV
|Investment in agriculture
|Multiplier
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|-
|'''econfreem'''
|ECONFREE
|Economic freedom
|Multiplier
|-
|'''goveffectm'''
|GOVEFFECT
|Government effectiveness
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption
|Multiplier
|-
|'''freedomm'''
|FREEDOM
|Freedom/democracy
|Multiplier
|-
|'''mfpinfrindtrad'''
|INFRAINDTRAD
|Traditional infrastructure
|Elasticity
|-
|'''mfpinfrindict'''
|INFRAINDICT
|ICT infrastructure
|Elasticity
|-
|'''mfpenpri'''
|WEP
|World energy price
|Elasticity
|-
|'''gdsm, R&D'''
|GDS, R&D
|Connectivity
|Multiplier
|-
|'''govrevm'''
|GOVREV
|Government revenue multiplier
|Multiplier
|-
|'''gdsm'''
|GDS
|Government spending by destination sector
|Multiplier
|-
|'''gdsedm'''
|GDS
|Education spending distribution multiplier
|Multiplier
|-
|'''govhhtrnpenm'''
|GOVHHTRN
|Government pension spending multiplier
|Multiplier
|-
|'''firmtaxrm,'''
|SAVINGS
|National tax rates on firms, households, and indirect taxation
|Multiplier
|-
|'''hhtaxrm (skilled, unskilled)'''
|SAVINGS
|National tax rates on households, broken down by skilled and unskilled
|Multiplier
|-
|'''indirecttaxrm'''
|SAVINGS
|National tax rates on firms, households, and indirect taxation
|Multiplier
|-
|'''ginidomr'''
|GINIDOM
|Domestic Gini Index
|Growth Rate
|-
|'''ginidomm'''
|GINIDOM
|Domestic Gini Index multiplier
|Multiplier
|-
|'''gdprext'''
|GDP
|GDP growth rate
|Growth Rate
|-
|'''xshift'''
|X, XS
|Export shift as a result of trade promotion
|Additive factor
|-
|'''protecm'''
|M, MS
|Protectionism in trade, multiplier on import prices
|Multiplier
|-
|'''termtrm'''
|TERMTR
|Terms of trade balance, higher favors global south
|Multiplier
|-
|'''xfdistockm'''
|XFDISTOCK
|Inward stocks of FDI multiplier
|Multiplier
|-
|'''xfdistoutm'''
|XFDISTOUT
|Outward stocks FDI multiplier
|Multiplier
|-
|'''xfdiwgrm'''
|XFDI STOCK, XFDIOUT
|World FDI growth rate multiplier
|Multiplier
|-
|'''xportfoliom'''
|XPORTFOLIO
|Inward portfolio investment multiplier
|Multiplier
|-
|'''xportstoutm'''
|XPORTFOLIO
|Outward portfolio investment stock multiplier
|Multiplier
|-
|'''aiddon'''
|AID
|Aid foreign loan donations as % GDP
|Additive factor
|-
|'''aidrecm'''
|AID
|Aid receipts as % GDP
|Multiplier
|-
|'''aidlpm'''
|AID
|Aid foreign loan percentage multiplier
|Multiplier
|-
|'''labinformshrm'''
|LABINFORMSHR
|Labor informal share multiplier
|Multiplier
|-
|'''gdpnformshrm'''
|GDPINFORMSHR
|GDP informal share multiplier
|Multiplier
|-
|'''govbusindxm'''
|GOVBUSINDEX
|Government business index multiplier
|Multiplier
|-
|'''tefinfadjm'''
|TEFF
|Multiplier of impact of informal GDP share on productivity
|Multiplier
|-
|'''taxinfadjm'''
|HHTAXR, FIRMTAXR, SSWELTAXR
|Multiplier of impact of informal GDP share on tax rates
|Multiplier
|-
|'''randdexpm'''
|RANDEXP
|Total (public and private) expenditure on research and development multiplier
|Multiplier
|-
|'''firmtaxrm'''
|FIRMTAX
|Tax rate on firm’s multiplier
|Multiplier
|-
|'''labinformcoeffbus'''
|LABINFORMSHR
|Informal labor share, coefficient of business index
|Coefficient
|-
|'''labinformcoeffed'''
|LABINFORMSHR
|Informal labor share, coefficient of education years
|Coefficient
|-
|'''labinformcoeffhhtrn'''
|LABINFORMSHR
|Informal labor share, coefficient of household transfers
|Coefficient
|-
|'''labinformcoefftax'''
|LABINFORMSHR
|Informal labor share, coefficient of tax rate
|Coefficient
|-
|'''gdpinformcoeffcorrupt'''
|GDPINFORMSHR
|Informal GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpinformcoeffintercept'''
|GDPINFORMSHR
|Informal GDP share, intercept of model
|Coefficient
|-
|'''gdpinformcoefflabinf'''
|GDPINFORMSHR
|Informal GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpinformcoeffrandd'''
|GDPINFORMSHR
|Informal GDP share, coefficient of R&D spending
|Coefficient
|-
|'''gdpshadowcoeffintercept'''
|GDPSHADOWSHR
|Shadow economy GDP share, intercept of model
|Coefficient
|-
|'''gdpshadowcoefflabinf'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpshadowcoeffcorrupt'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpshadowcoeffrandd'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of R&D spending
|Coefficient
|}

== Infrastructure ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''infraroadm'''
|INFRAROAD
|Multiplier on road density expected/demanded
|Multiplier
|-
|'''infraroadpavedpcntm'''
|INFRAROADPAVEDPCNT
|Multiplier on percentage of roads paved
|Multiplier
|-
|'''Infraroadraitrgtval, Infraroadraitrgtyr'''
|INFRAROADRAI
|Target value/years after 2010 for achievement for population percentage living w/in 2km of an all-weather road
|Absolute target
|-
|'''infraroadraisetar, infraroadraiseyrtar'''
|INFRAROADRAI
|Standard error target and years after 2010 for achievement for rural road access index
|Relative target
|-
|'''infraelecgencapm'''
|INFRAELECGENCAP
|Multiplier on electricity generation capacity
|Multiplier
|-
|'''infraelecaccm'''
|INFRAELECACC
|Multiplier on electricity access
|Multiplier
|-
|'''enelecshrendemm'''
|INFRAELECDEM
|Multiplier on ratio of electricity use to total primary energy use
|Multiplier
|-
|'''infraelectranlossm'''
|INFRAELECTRANLOSS
|Multiplier on the loss of electricity in transmission and distribution
|Multiplier
|-
|'''ensolfuelm'''
|ENSOLFUEL
|Multiplier on the reliance on solid fuel as a source of energy
|Multiplier
|-
|'''infraelecacctrgtval/ infraelecacctrgtyr'''
|INFRAELECACC
|Target value and years after 2010 for target achievement for population percentage w/ access to electricity
|Absolute target
|-
|'''infraelecaccsetar/ infraelecaccseyrtar'''
|INFRAELECACC
|Standard error target and years after 2010 for population percentage w/ access to electricity
|Relative target
|-
|'''ensolfueltrgtval/ ensolfueltrgtyr'''
|ENSOLFUEL
|Target value and years after 2010 for target achievement for percentage of households reliant on solid fuels
|Absolute target
|-
|'''ensolfuelsetar/ ensolfuelseyrtar'''
|ENSOLFUEL
|Standard error target and years after 2010 for target achievement for percentage of households reliant on solid fuels
|Relative target
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''landirareaequipm'''
|LANDIRAREAEQUIP
|Multiplier on land area equipped for irrigation
|Multiplier
|-
|'''sanitationm'''
|SANITATION
|Multiplier on percentage of people with access to sanitation
|Multiplier
|-
|'''watsafem'''
|WATSAFE
|Multiplier on percentage of people with access to safe water
|Multiplier
|-
|'''watwastem'''
|WATWASTE
|Multiplier on percentage of people connected to wastewater collection system,
|Multiplier
|-
|'''watwastetreatm'''
|WATWASTETREAT
|Multiplier on percentage of people connected to wastewater treatment
|Multiplier
|-
|'''sanitationtrgtval/ sanitationtrgtyr'''
|SANITATION
|Target value and years after 2010 for target achievement for population percentage with improved access to improved sanitation, by category of
|Relative target
|-
|'''watsafetrgtval/ watsafetrgtyr'''
|WATSAFE
|Target value and years after 2010 for target achievement for population percentage without access to improved sources of water
|Relative target
|-
|'''sanitnoconsetar/ sanitimpconsetar/ sanithhconsetar/ sanitnoconseyrtar'''
|SANITATION
|Standard error targets and years after 2010 for target achievement for population percentage with varying access to sanitation
|Relative target
|-
|'''sanithhm'''
|SANITHH
|Multiplier on percentage of people with access to sanitation by type
|Multiplier
|-
|'''sanithhbasictrgtval'''

'''sanithhbasictrgtyr'''
|SANITHH
|Target value and years from the base to reach at least basic sanitation services
|Relative target
|-
|'''sanithhsafetrgtval'''

'''sanithhsafetrgtyr'''
|SANITHH
|Target value and years from the base to reach safely managed sanitation services
|Relative target
|-
|'''waterhhm'''
|WATERHH
|Multiplier on percentage of people with access to water by type
|Multiplier
|-
|'''waterhhbasictrgtval'''

'''waterhhbasictrgtyr'''
|WATERHH
|Target value and years from the base to reach at least basic water services
|Relative target
|-
|'''waterhhsafetrgtval'''

'''waterhhsafetrgtyr'''
|WATERHH
|Target value and years from the base to reach safely managed water services
|Relative target
|-
|'''infratelem'''
|INFRATELE
|Multiplier on fixed-line telephone lines per 100 persons
|Multiplier
|-
|'''ictbroadm'''
|ICTBROAD
|Multiplier on fixed-line broadband subscriptions per 100 persons
|Multiplier
|-
|'''ictbroadmobilm'''
|ICTBROADMOBIL
|Multiplier on mobile broadband subscriptions per 100 persons
|Multiplier
|-
|'''ictbroadtrgtval, ictbroadtrgtyr'''
|ICTBROAD
|Target value and years after 2010 for target achievement for number of subscriptions per 100 persons
|Absolute target
|-
|'''ictbroadmobiltrgtval, ictbroadmobiltrgtyr'''
|ICTBROADMOBIL
|Target value and years after 2010 for target achievement for number of subscriptions per 100 persons; varies between 0 and 150
|Absolute target
|-
|'''ictbroadmobilsetar, ictbroadmobilseyrtar'''
|ICTBROADMOBIL
|Standard error target and years after 2010 for target achievement for ICT mobile broadband access
|Relative target
|-
|'''ictbroadsetar, ictbroadseyrtar'''
|ICTBROAD
|Standard error target and years after 2010 for target achievement for fixed broadband access
|Relative target
|-
|'''ictmobilsetar, ictmobilseyrtar'''
|ICTMOBIL
|Standard error target and years after 2010 for target achievement for mobile phone access
|Relative target
|-
|'''gdsm (infrastructure)'''
|GDS
|Gross domestic spending multiplier (infrastructure)
|Multiplier
|-
|'''infrainvnewpubshrm'''
|INFRAINVESTNEWPUB
|Portion of maintenance funding coming from public sources
|Multiplier
|-
|'''Infrainvmaintpubshrm'''
|INFRAINVESTMAINTPUB
|Portion of new infrastructure funding coming from public sources
|Multiplier
|-
|'''hhsizem'''
|GDS
|Household size multiplier
|Multiplier
|-
|'''infraroadpavedcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of paved roads
|Multiplier
|-
|'''infraroadunpavedcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of unpaved roads
|Multiplier
|-
|'''infraelecgencostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of electricity generation
|Multiplier
|-
|'''infraelecaccruralcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of rural electricity access
|Multiplier
|-
|'''infraeleaccurbancostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of urban electricity access
|Multiplier
|-
|'''watsafecostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of safe water
|Multiplier
|-
|'''watsafeimpcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of improved safe water sources
|Multiplier
|-
|'''sanitationcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of basic sanitation
|Multiplier
|-
|'''sanitationimpcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of improved sanitation
|Multiplier
|-
|'''watwastetreatcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of waste water treatment
|Multiplier
|-
|'''landircostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of equipping land for irrigation
|Multiplier
|-
|'''infratelecostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of telephone
|Multiplier
|-
|'''ictmobilcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of mobile
|Multiplier
|-
|'''ictbroadcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of broadband
|Multiplier
|-
|'''ictbroadmobilcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on mobile broadband
|Multiplier
|}

== Agriculture ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''clpcm'''
|AGDEM
|Caloric demand for agricultural production
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|-
|'''elascd'''
|AGDEM
|Elasticity of crop demand to changes in food price
|Elasticity
|-
|'''elasmd'''
|AGDEM
|Elasticity of meat demand to changes in food price
|Elasticity
|-
|'''elasfd'''
|AGDEM
|Elasticity of fish demand to changes in food price
|Elasticity
|-
|'''indemm'''
|INDEM
|Industrial demand for crops
|Multiplier
|-
|'''tgrld'''
|AGP
|Target growth rate in cultivated land
|Initial
|-
|'''ylm'''
|YL
|Crop yield (agricultural production)
|Multiplier
|-
|'''ylmax'''
|YL
|Maximum crop yield
|Exogenous specification
|-
|'''envylchgadd'''
|ENVYLDCHNG
|Agricultural yield change
|Additive factor
|-
|'''slr'''
|AGP
|Livestock slaughter rate
|Exogenous specification
|-
|'''livhdpro'''
|AGP
|Livestock herd productivity gain with grain feeding
|Exogenous specification
|-
|'''aquaculm'''
|FISH
|Fish production through aquaculture, multiplier
|Multiplier
|-
| '''fishcatchm'''
|FISH
|Fish production through catch
|Multiplier
|-
|'''aquaculgr'''
|AQUACUL
|Growth in the use of aquaculture
|Growth Rate
|-
|'''aquaculconv'''
|AQUACUL
|The number years in which aquaculture growth rate converges to 0
|Convergence rate
|-
|'''aglosstransm'''
|LOSS
|Agricultural wastage
|Multiplier
|-
|'''aglossprodm'''
|LOSS
|Agricultural Production loss
|Exogenous specification
|-
|'''aglossconsm'''
|LOSS
|Agricultural Consumption loss
|Exogenous specification
|-
|'''aginvm'''
|YL, IDS (agriculture)
|Investment in agriculture
|Multiplier
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|-
|'''Elagind'''
|AGP
|Elasticity of industrial use of crops with price
|Elasticity
|-
|'''elinag1'''
|IDS (agriculture)
|Elasticity of investment in agriculture to profit levels
|Elasticity
|-
|'''elinag2'''
|IDS (agriculture)
|Elasticity of investment in agriculture to changes in profit level
|Elasticity
|-
| '''malnelimstartyr'''
|MALNPOP
|Undernourishment elimination target start year
|Standard error target start year
|-
| '''malnelimtargetyr'''
|MALNPOP
|Undernourishment elimination target year
|Standard error target year
|-
| '''malelimprecisesw '''
|MALNPOP
|Undernourishment elimination
|Switch
|}

== Energy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''endemm'''
|ENDEM
|Energy demand
|Multiplier
|-
|'''elasde'''
|ENP, WEP
|Price elasticity of energy demand
|Elasticity
|-
|'''carbtax'''
|ENP
|Carbon tax
|Exogenous specification
|-
|'''enrgdpgr'''
|ENP, QE
|Growth in energy efficiency
|Rate of change
|-
|'''enpm'''
|ENP
|Production multiplier
|Multiplier
|-
|'''eprodr'''
|ENP
|Production growth rate
|Growth rate
|-
|'''resorm'''
|RESOR
|Conventional resource multiplier
|Multiplier
|-
|'''resorunconm'''
|RESORUNCON
|Unconventional resource multiplier
|Multiplier
|-
|'''rdm'''
|RESER
|Discovery multiplier
|Multiplier
|-
|'''etechadv'''
|QE
|Rate of technical advance in energy
|Exogenous specification
|-
|'''etechadvuncon'''
|QE
|Rate of technical advance in unconventional energy
|Exogenous specification
|-
|'''qem'''
|QE
|Capital output multiplier
|Multiplier
|-
|'''eninvm'''
|ENIV
|Energy investment
|Multiplier
|-
|'''enprix'''
|ENPRI
|Regional energy price
|Exogenous specification
|-
|'''enml, enxl'''
|ENM, ENX
|Limits on energy imports and exports
|Exogenous specification
|}

== Environment ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''enpm'''
|ENP
|Energy production by type
|Multiplicative
|-
|'''endemm'''
|ENDEM, WEP
|Energy demand multiplier
|Multiplicative
|-
|'''carbtax'''
|ENP, CARANN
|Carbon tax
|Exogenous Specification
|-
|'''qem'''
|ENP
|Capital-output ratio multiplier
|Multiplicative
|-
|'''resorm'''
|RESOR
|Resource multiplier
|Multiplicative
|-
|'''enrgdpgr'''
|ENP
|Energy efficiency
|Growth rate
|-
|'''carfuel1'''
|CARANN
|Carbon generated by gas
|Coefficient
|-
|'''carfuel2'''
|CARANN
|Carbon generated by natural gas
|Coefficient
|-
|'''carfuel3'''
|CARANN
|Carbon generated by coal
|Coefficient
|-
|'''envclimsens'''
|CO2PPM, WTEMP
|Time sensitivity of global temperature to carbon dioxide
|Limit
|-
|'''carabr'''
|CO2PPM, WTEMP
|Carbon absorbed by oceans
|Coefficient
|-
|'''carabrchng'''
|CO2PPM, WTEMP
|annual change in this absorption rate of carbon by oceans
|Coefficient
|-
|'''envco2fert'''
|AGP
|Crop sensitivity to environmental change
|Elasticity
|-
|'''envylchgadd'''
|ENVYLDCHNG
|Agricultural yield change
|Additive factor
|-
|'''envylchgm'''
|ENVYLDCHNG
|Agricultural yield change
|Multiplicative
|-
|'''forestm'''
|WFOREST
|World forest multiplier
|Multiplicative
|-
|'''tgrld'''
|WFOREST, LD
|Land growth by type
|Initial condition
|-
|'''carforst'''
|CO2PPM
|Level of carbon sequestered by forests
|Coefficient
|-
|'''watres'''
|Unknown
|Renewable water resources
|Exogenous specification
|-
|'''tgrld'''
|AGP
|Land growth by type
|Initial condition
|-
|'''ylm'''
|YL
|Agricultural yield multiplier
|Multiplier
|-
|'''envpm2pt5m'''
|ENVPM2PT5
|PM2.5 levels multiplier
|Multiplier
|-
|'''ensolfuelm'''
|ENSOLFUEL
|Solid fuel use multiplier
|Multiplier
|-
|'''ensolfuelsetar'''
|ENSOLFUEL
|Solid fuel use SE target
|Standard error target
|-
|'''ensolfuelseyrtar'''
|ENSOLFUEL
|Solid fuel use SE target
|Standard error target
|-
|'''ensolfueltrgtval'''
|ENSOL FUEL
|Solid fuel elimination target
|Target
|-
|'''ensolfueltrgtyr'''
|ENSOLFUEL
|Solid fuel elimination target
|Target
|-
|'''hlsolfuelsw'''
|DEATHCAT
|Relationship between solid fuel and respiratory diseases
|Switch
|-
|'''climeconimpsw '''
|CLIMECONIMP
|Climate change to economy impact switch (1=on)
|Switch
|-
|'''climeconimplin '''
|CLIMECONIMP
|Climate change to economy impact, linear term coefficient
|Co-efficient
|-
|'''climeconimpsq '''
|CLIMECONIMP
|Climate change to economy impact, squared term coefficient
|Co-efficient
|-
|'''climeconimpstk'''
|VADD
|Climate chnge to econ impact, portion affecting productivity or capital stocks
|Multiplier

|-
|'''watdemandelas'''
|WATDEM
|Elasticity from water price to water demand
|Elasticity
|-
|'''landirareaequipelas'''
|LANDIRAREAACTUAL
|Elasticity from water price to land equipped for irrigation
|Elasticity
|-
|'''desali'''
|WATERDESALINATED
|desalination responsiveness to change in stock levels of price
|PID factor
|-
|'''desald'''
|WATERDESALINATED
|desalination responsiveness to change in price
|PID factor
|}

== Governance ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''sfusehist'''
|N/A
|State failure switch to use history for base values(0=off; 1=on; 2=converge)
|Switch
|-
|'''sfintlwaradd'''
|SFINTLWAR
|State failure/internal war addition
|Additive
|-
|'''sfconv'''
|SFINTLWAR
|State failure convergence
|Convergence speed (years)
|-
|'''sfmassrep'''
|SFINTLWAR
|Mass repression factor
|Additive
|-
|'''wpextinterv'''
|SFINTLWAR
|External intervention factor
|Additive
|-
|'''govriskm'''
|GOVRISK
|Government risk multiplier
|Multiplicative
|-
|'''govriskweight'''
|GOVRISK
|Government risk weight
|Weight
|-
|'''govrevm'''
|GOVREV
|Government Revenue Multiplier
|Multiplier
|-
|'''firmtaxrm'''
|GOVREV
|Firm tax multiplier
|Multiplier
|-
|'''hhtaxrm'''
|GOVREV
|Household tax rate multiplier
|Multiplier
|-
|'''ssweltaxrm'''
|GOVREV
|Social Security multiplier
|Multiplier
|-
|'''indirecttaxrm'''
|GOVREV
|Indirect tax multiplier
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Corruption Multiplier
|Multiplier
|-
|'''govcorruptsetar'''
|GOVCORRUPT
|Corruption target
|Standard error target
|-
|'''govcorruptseyrtar'''
|GOVCORRUPT
|Corruption target
|Standard error target
|-
|'''goveffectm'''
|GOVEFFECT
|Effectiveness multiplier
|Multiplier
|-
|'''goveffectsetar'''
|GOVEFFECT
|Effectiveness target
|Standard error target
|-
|'''goveffectseyrtar'''
|GOVEFFECT
|Effectiveness target
|Standard error target
|-
|'''govregqualm'''
|REGQUALITY
|Regulatory quality multiplier
|Multiplier
|-
|'''govregqualsetar'''
|REGQUALITY
|Regulatory quality target
|Standard error target
|-
|'''govregqualseyrtar'''
|REGQUALITY
|Regulatory quality target
|Standard error target
|-
|'''econfreem'''
|ECONFREE
|Economic freedom multiplier
|Multiplier
|-
|'''democm'''
|DEMOCPOLITY
|Democracy multiplier
|Coefficient
|-
|'''swseffects'''
|DEMOCPOLITY
|Swing state effect
|Additive factor
|-
|'''democimpus'''
|DEMOCPOLITY
|Democracy impetus by leading power
|Coefficient
|-
|'''democimpoth'''
|DEMOCPOLITY
|Democracy impetus by other powers
|Coefficient
|-
|'''DEMOCWDIR'''
|N/A
|Democracy wave direction (-1=down; 0=off; 1=up)
|Switch
|-
|'''democwvlen'''
|DEMOCPOLITY
|Democracy wave length
|Limit
|-
|'''democwvmax'''
|DEMOCPOLITY
|Democracy wave upper limit
|Limit
|-
|'''democpolitysetar'''
|DEMOCPOLITY
|Democracy SE target
|Standard error target
|-
|'''democpolityseyrtar'''
|DEMOCPOLITY
|Democracy SE year
|Standard error target
|-
|'''freedomm'''
|FREEDOM
|Freedom multiplier
|Multiplier
|-
|'''gemm'''
|GEM
|Gender empowerment multiplier
|Multiplier
|-
|'''gemsetar'''
|GEM
|Gender empowerment target
|Standard error target
|-
|'''gemseyrtar'''
|GEM
|Gender empowerment year target
|Standard error target
|}

== International Relations ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''cpowf'''
|CPOW
|Factor controlling military spending conversion
|Convergence factor
|-
|'''cpowldcf'''
|CPOW
|Higher impact of military spending in LDCs
|Convergence factor
|-
|'''drcpow'''
|CPOW
|Depreciation rate of conventional power
|Rate of change
|-
|'''cpowdf'''
|CIVDM
|Conventional power, damage done by war
|Coefficient
|-
|'''govnucwpadd'''
|NPOW
|Nuclear weapons stockpile change
|Additive factor
|-
|'''wpwghtpow'''
|POWER
|Contribution of various components to power index
|Index weight
|-
|'''wpgreatthresh'''
|PCONGREAT
|Systemic power share required for threshold of great power status
|Coefficient
|-
|'''wpgreatlev'''
|PCONGREAT
|Systemic power share required for full great power status
|Coefficient
|-
|'''wpconeusw'''
|PCONGREAT
|EU great power switch
|Switch
|-
|'''wpconmax'''
|PCONSYS
|Maximum power concentration
|Limit
|-
|'''wpconmin'''
|PCONSYS
|Minimum power concentration
|Limit
|-
|'''wpgreat1'''
|THREAT
|Dyadic dispute probability, one great power
|Coefficient
|-
|'''wpgreat2'''
|THREAT
|Dyadic dispute probability, two great powers
|Coefficient
|-
|'''wppowtran1'''
|THREAT
|Power transition threshold
|Coefficient
|-
|'''wppowtran2'''
|THREAT
|Increase in dispute probability with 10% greater power parity
|Elasticity
|-
|'''wpcon'''
|THREAT
|Change in threat with power concentration
|Coefficient
|-
|'''wpconsq'''
|THREAT
|Change in threat with power concentration
|Coefficient
|-
|'''wpcontiguity'''
|THREAT
|Threat associated with geographic proximity
|Elasticity
|-
|'''wpdemdist'''
|THREAT
|Response of threat to political distance
|Elasticity
|-
|'''wpdemmin'''
|THREAT
|Response of threat to the minimum democracy level in the dyad
|Limit
|-
|'''glocom'''
|THREAT
|Global community multiplier
|Exogenous specification
|-
|'''wpcommun'''
|THREAT
|Response of threat to global community level
|Elasticity
|-
|'''wptrade'''
|THREAT
|Change in threat with systemic trade
|Elasticity
|-
|'''ally'''
|THREAT
|Alliance membership switch
|Switch
|-
|'''wpally'''
|THREAT
|Reduction in threat with allied dyad
|Elasticity
|-
|'''terdispute'''
|THREAT
|Territorial dispute or absence
|Exogenous specification
|-
|'''wpterdisp'''
|THREAT
|Conflict arising from territorial disputes
|Elasticity
|-
|'''wpthrconv'''
|THREAT
|Threat level convergence
|Convergence Speed
|-
|'''wpthrwar'''
|CWARPB
|Portion of disputes that escalate to war
|Coefficient
|-
|'''waron'''
|CWAR, NWAR
|War switch
|Switch
|-
|'''cwarbase'''
|CWARPB
|Conventional war probability addition to base computation
|Additive factor
|-
|'''cwarf'''
|CWARPB
|Conventional war forcing
|Switch
|-
|'''cwarsv'''
|CIVDM
|Conventional war severity
|Multiplier
|-
|'''nwarf'''
|NWARPB
|Probability that conventional war will escalate to nuclear
|Coefficient
|-
|'''cdmf'''
|CIVDM
|Civilian war damage factor
|Multiplier
|-
|'''nwarsv'''
|CIVDM
|Nuclear war severity
|Multiplier
|-
|'''elndpow'''
|CIVDM
|Elasticity of nuclear damage with nuclear power
|Elasticity
|-
|'''diploembassymaxm'''
|DIPLOEMBASSYMAX
|Maximum embassies abroad
|Multiplier
|-
|'''diploigomaxm'''
|DIPLOIGOMAX
|Maximum international organization membership
|Multiplier
|-
|'''diplotreatymaxm'''
|DIPLOTREATYMAX
|Maximum bilateral treaty participation
|Multiplier
|}

Guide to Scenario Analysis in International Futures (IFs)

2025-01-13T22:29:45Z

Taylor.Hanna: Changed Pardee Center to Pardee Institute

Please cite as: Sara Turner, Carey Neill, Barry B. Hughes, Kanishka Narayan, Deva Sahadevan, and Jonathan D. Moyer. 2024. "Guide to Scenario Analysis in International Futures (IFs)." Working paper 2024.06.06. Pardee Institute for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY.

The purpose of this document is to facilitate the development of scenarios with the International Futures (IFs) system. This document supplements the IFs Training Manual. That manual provides a general introduction to IFs and assistance with the use of the interface (e.g., how do I create a graphic?). In turn, the broader Help system of IFs supplements this manual. It provides detailed information on the structure of IFs, including the underlying equations in the model (e.g., what does the economic production function look like?).

This document should help users understand the leverage points that are available to change parameters (and in a few cases even equations) and create alternative scenarios relative to the Base Case scenario of IFs (e.g., how do I decrease fertility rates or increase agricultural production?). It proceeds across the modules of IFs, such as demographic, economic, energy, health, and infrastructure, to (1) identify some of the key variables that you might want to influence to build scenarios and (2) the parameters that you will want to manipulate to affect your variables of interest. The Training Manual will help you actually make the parameter changes in the computer program and the Help system will facilitate your understanding of the structures, equations and algorithms that constitute the model. We begin by introducing the types of parameters within IFs and then proceed to a discussion of variables and parameters within each of the IFs modules.

== A Note on Parameter Names ==

In this manual we will provide the internal computer program names of variables and parameters, as well as their descriptions. Those names are especially important for use of the Self-Managed Display form, which provides model users with complete access to all variables and parameters in the system. Most model use, however, employs the Scenario Tree form to build scenarios and the Flexible Display form to show scenario-specific forecasts, and both of those forms rely primarily on natural language descriptions of variables and parameters. To match the names provided here with the options in those forms, you can use the Search feature from the menu. The Training Manual describes how to use features such as the Flexible Display form to see computed forecast variables in natural language. And it also describes how to use the Scenario Tree form to access parameters in something close to natural language. Nonetheless, it helps very much in the use of those features and the model generally to know the actual variable and parameter names.

== Types of Parameters in IFs ==

Equations in IFs have the general form of a dependent or computed variable, as a function of one or more driving or independent variables. ''Variables'', like population and GDP, are the dynamic elements of forecasts in which you are ultimately interested. For instance, total fertility rate or TFR (the number of children a woman has in her lifetime) is a function of GDP per capita at purchasing power parity ('''GDPPCP'''), education of adults 15 or more years of age ('''EDYRSAG15'''), the use of contraception within a country ('''CONTRUSE'''), and the level of infant mortality ('''INFMORT'''). In the most general terms the equation is

:<math>TFR=F(GDPPCP,EDYRSAG15,CONTRUSE,INFMORT)</math>

Parameters of several kinds can alter the details of such a relationship. That is, ''parameters'' are numbers (also represented by names in IFs), that help specify the exact relationship between independent and dependent variables in equations or other formulations (including logical procedures called algorithms). For instance, the model may contains different parameters that tell us how much '''TFR''' rises or falls per unit change in GDP per capita, education levels, contraception use, and infant mortality1; and it contains still others to set bounds on the lower and/or upper values of '''TFR''' over the long run (obviously '''TFR''' should never go negative and probably we will not even want it to go, at least for a long time, to a very low level such as an average of 0.5 children per woman). Some of these parameters are more technical than others in the sense that they may significantly affect the overall stability of the model if users are not very careful with the magnitude or direction of the changes they make; we will focus heavily in this manual on parameters that are easiest to interpret and modify.

In many cases, we are more interested in using a parameter to make a direct change to a variable, rather than indirectly affecting a variable like '''TFR''' through one of its drivers. We often refer to this as the "brute force" method of changing a variable, and this can be done by multiplying the entire result of a basic equation like that above by a number, adding something to that result, or simply over-riding the result with an exogenously (externally) specified series of values. In the case of '''TFR''' we use the multiplier approach, which is described below. The strengths of this approach should be obvious: it preserves model stability, and makes the model more accessible for users. However, the weakness is that in many instances it is more realistic to affect one of the drivers of '''TFR''' rather than '''TFR''' directly.

Beyond multipliers, there are many other types of parameters that IFs uses, although we are forced to abandon '''TFR''' to provide examples. For instance, a switch parameter may turn on or off a particular formulation in preference to another. A target may specify a value towards which we want a variable to move gradually (we would need to specify both the target level and the years of convergence to it).

Overall, key parameter types are:

1. '''Equation Result Parameters'''. Most users will use these parameter types far more often than any other. The three types are:

:a. '''Multipliers'''. This most common of all parameter types in scenario analysis comes into play after an equation has been calculated. They multiply the result by the value of the parameter. The default value, i.e. the value for which the parameter has no effect and to which multipliers almost invariably are set in the Base Case, is 1.0. These parameters are usually denoted with the suffix '''-m''' at the end of the parameter name.

:b. '''Additive factors'''. Like multipliers, these change the results after an equation computation, but add to the result rather than multiplying. The default value is normally 0.0. These are usually denoted with the suffix '''-add''' at the end of the parameter name.

:c. '''Exogenous Specification'''. Sometimes these parameters override the computation of an equation. In other cases, they are actually substitutes for having an equation; that is, they are actually equivalent to specifying the values of a variable over time for which the model has no equation. This typically means establishing a new exogenous series. They typically will have the name of the variable that they over-ride within their own name.

2. '''Targets'''. Especially for the purposes of policy analysis, we often want to force the result of an equation toward a particular value over time (e.g. to achieve the elimination of indoor use of solid fuels). Target parameters are generally paired, one for the target level and one for the number of years to reach the target (from the initial year of the model forecast, 2010). Targets have different types:

:a. '''Absolute targets'''. In this case the target value and year define the absolute value the variable should move toward and the number of years after the first model year over which the goal should be achieved. Together they determine a path in which the value for the variable moves quite directly2 from the value in first year to the target value in the target year. '''Trgtval''' and '''trgtyr''' are the parameter suffixes used for this parameter type. The first of these changes the target itself, and the second alters the number of years to the target. The default value of '''*trgtyr''' parameters should normally be 10 years, but in some cases it is 0, meaning that users must set the number of years to target as well as the target value in order to use these parameters. 
:b. '''Relative (standard error) targets'''. In this case, the target value and year define a relative value towards which the variable should move and the number of years that will pass before the target is reached. The relative value is defined as the number of standard errors above or below the “predicted” value of the variable of interest (a prediction usually based on the country's GDP per capita). Target values less than 0 set the target below the typical or predicted (as indicated by cross-sectional estimations) value of the variable. Target values above 0 set the target above the predicted value. As with the absolute targets, the value calculated using relative targeting is compared to the default value estimated in the model. The computed value then gradually moves from the normal or default-equation based value to the target value. If, however, the computed value already is at or beyond the target (that could be above or below depending on whether the target is above or below the default or predicted value), the model will not move it toward the target. Two different parameter suffixes direct relative targeting: '''setar''' and '''seyrtar'''. The first of these changes the target itself and the second alters the number of years to the target. The default value of the *'''seyrtar''' parameters varies based on the module and even variable. Governance parameters are set to a default of 10 years from the year of model initialization, while infrastructure parameters are set to a default of 20 years. These defaults mean that users do not have to change *'''seyrtar''' as well as *'''setar''' in order to build standard error target scenarios. Changing *'''setar''' should be enough. 

3. '''Rates of change'''. Some parameters specify an annual percentage rate of change. Unfortunately, IFs does not consistently use percentage rates (5 percent per year) versus proportional rates (0.05 increase rate per year, which is equivalent to 5 percent), so the user should be attentive to definitions. There are multiple suffixes that may apply to these, including -'''r''' (changes in the rate) and -'''gr''' (changes the rate of change, growth or decline).

4. '''Limits'''. As indicated for the TFR example, long-term national rates are unlikely to fall and stay below a minimum value. Limits can be minimum or maximum values. These are typically denoted by the suffixes '''-min''', '''-max''', or '''-lim'''.

5. '''Switches'''. These turn off and on elements in the model. These most often affect linkages between modules, but can also change relationships within modules. They are typically denoted by the suffix '''-sw'''.

6. '''Other parameters''' in equations and algorithms. Equations within IFs can become quite complicated. The parameter types discussed to this point provide the easiest control over them for most model users. Relatively few users will proceed further with parameters, and to do so will typically require attention to the specific nature of the equation (e.g. whether independent variables are related to dependent ones via linear, logarithmic, exponential or other relationship forms). That is, one would normally need to understand the model via the Help system or other project documentation in order to use them meaningfully and without causing substantial risk of bad model behavior. The sections of this manual will provide very little information about these technical parameters.

:a. '''Elasticities''': These are relatively common within IFs and specify the percentage change in the dependent variable associate with a percentage change in the independent variable. They are typically prefixed '''el'''- or '''elas'''-.

:b. Equilibration '''control parameters'''. IFs balances supply and demand for goods and services via prices, savings and investment with interest rates, and so on. These processes typically use an algorithmic controller system that responds to both the magnitude of imbalance or disequilibrium and the direction and extent of its change over time (see the Help system descriptions of the model). Although they are not typical elasticities, the two parameters that control each such process usually have the prefix '''el'''- and the suffixes -'''1''' or -'''2'''. Parameters ending with '''1''' relate to disequilibrium magnitude; and parameters end with '''2''' relate to the direction of change.

:c. '''Other coefficients in equations'''. Beyond elasticities, many other forms of parameter can manipulate an equation. When analysts in many fields think of parameters, this is what they mean. In IFs, most users will use them quite rarely because, in the absence of knowledge concerning equation forms and reasonable ranges, the parameters often have little transparent meaning—experts in a field may use them more often. Many analysts think of such parameters as having a constant value over time, and some are unchangeable over time in IFs. IFs allows almost all, however, to be entered as time series and vary with great flexibility across time. Some can be changed for each country and/or sub-dimensions of the associated variable, such as energy types, but others can only be changed globally.

:d. '''Equation forms'''. Although most users will change parameters using the Scenario Tree (see again the Training Manual), the IFs model has made it possible over time to change an increasing number of functions directly (both bivariate and multivariate ones). The advantage this confers is the ability to alter the nature of the formulation (e.g. going from linear to logarithmic) and even, to a very limited degree, the independent or driver variables in the equation. Although some module discussions will occasionally suggest this option, most users will not avail themselves of it. Users who wish to make such changes can do so via the Change Selected Functions options, which can be accessed from the Scenario Analysis Menu on the main page. 

7. '''Initial conditions''' for endogenous variables and convergence of initial discrepancies

:a. '''Initial conditions '''are not, strictly speaking, true parameters, but should reflect data. Yet some users will believe that they have data superior to that in IFs, and the system allows the user to change most initial conditions. After the first year, the model will compute subsequent values internally (endogenously). Initial conditions don’t have a suffix; their names are, in fact, those of the variable itself (e.g., '''POP''' for population).

:b. '''Convergence speed''' of initial-condition based discrepancies to forecasting functions. Because initial conditions taken from empirical data often vary from the values that are computed in the estimated equation used for forecasting, the model protects the empirically-based initial condition by computing shift factors that represent that initial discrepancy (they can be additive or multiplicative). For many variables, values rooted in initial conditions in the first model year should converge to the value of the estimated equation over time; convergence parameters control the speed of such convergence. Most model users will never change the convergence speed. These are denoted by the suffixes '''-cf''' or '''-conv'''.

In the use of all parameters, especially those other than equation result parameters, users will often be uncertain how much it is reasonable to move them—as are often even the model developers. The Scenario Tree form provides some support for judgments on this by indicating high and low alternatives to that of that Base Case. This manual will sometimes provide some additional information.

----

1Because the nature of the equation or formulation will vary (sometimes a driving variable is linearly linked to the dependent variable, sometimes the equation uses a logarithmic, exponential, or other formulation), the coefficients in the equation cannot invariably or even regularly be interpreted as units of change linked to units of change. You may need to explore the Help system and specific equations to fully understand the relationship. This is one of the key reasons we very often turn to the multipliers and additive factors explained in the next paragraph.

2The movement normally will be linear, except that it is possible to set moving targets that create non-linear progression patterns. In some cases, the model explicitly uses non-linear convergence; e.g. to accelerate movement in early years and then to slow it as the target is approached.

== Manipulating Parameters in IFs ==

You will typically manipulate parameters to create scenarios or internally coherent stories about the future. You may create scenarios because you wish to represent and explore the possible impact of policy interventions. Or your stories may represent views of the dynamics of global systems alternative to that in the IFs Base Case scenario. Most of the time, you will be interested in tracking the possible futures of selected variables having particular interest to you. The following sections, each covering a module of the IFs system, begin by identifying some of the variables of potentially greatest interest to you. They then provide suggestions on which parameters are likely to be of most useful in building alternative scenarios for those variables. Each section includes tables listing the most effective parameters with which to target certain outcomes. While these suggestions are intended to help you start to think about which parameters you might use to build your scenarios, it is essential that you consider seriously what the policy-based, empirical-knowledge-rooted, or theoretically informed foundations are for your changes.

== Keys to Successfully Modifying Parameters in IFs ==

*Test all parameter changes individually before building combinations, in order to be able to identify which parameters are having specific impacts
*After changing a parameter value and running a scenario, check the impact on the most proximate or closely related variables (identified in the tables of each module section), before checking the secondary impacts of your selected parameter on more distally related variables
*Tie parameter changes to policy options, empirical knowledge, or theoretical insight identified in literature
*Bear in mind the relevant geographical level at which a parameter operates; some parameters function directly at a global level (e.g., global migration rates), while others will be most relevant at the regional, or national level
*Some parameters are only effective when used in combination with one another (such as target values and years to reach a target)
*Some parameters cancel one another out; for example, '''trgtval''' and '''setar''' parameters cannot be used together except under very limited circumstances that we attempt to note in the subsequent text
*In many cases, variables affected by certain parameters have natural maximums (e.g. 100 percent) or minimums (e.g. fertility rate), so that changes to the parameters affecting them, where countries may already be approaching such a limit, will not have a significant impact
*The IFs systems contains many equilibrating processes, such as those around prices; interventions meant to affect one side of such an equilibration (such as efforts to reduce energy demand) may have offsetting effects (such as lower prices for energy and resultant demand increase) that make it harder than you expect to push the system in the desired direction; real-world policy makers often face such difficulties and may need to push harder than anticipated 
== Prepackaged Scenarios ==

A number of alternative scenarios come prepackaged with the model. To access them, select Scenario Analysis from the main menu, and then the option labeled Quick Scenario Analysis with Tree. Once in the scenario display, select Add Scenario Component to view all of the .sce (scenario) files that are stored on your computer normally at the path C:/Users/Public/IFs/Scenario. Exploring several simple interventions contained in the folder structure should give users an overview of some of the leverage points in that they may wish to use in each module.

= Demographic Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width: 343px;"
|-
| style="width: 57px;" | '''Variable'''
| style="width: 235px;" | '''Description'''
|-
| style="width: 57px;" | '''POP'''
| style="width: 235px;" | Total population
|-
| style="width: 57px;" | '''POPLE15'''
| style="width: 235px;" | Population, age 15 or less 
|-
| style="width: 57px;" | '''POP15TO65'''
| style="width: 235px;" | Population, age 15 to 65 
|-
| style="width: 57px;" | '''POPGT65 '''
| style="width: 235px;" | Population, greater than 65 
|-
| style="width: 57px;" | '''POPPREWORK '''
| style="width: 235px;" | Population, pre-working years 
|-
| style="width: 57px;" | '''POPWORKING '''
| style="width: 235px;" | Population, working years 
|-
| style="width: 57px;" | '''POPRETIRED '''
| style="width: 235px;" | Population, retired 
|-
| style="width: 57px;" | '''YTHBULGE '''
| style="width: 235px;" |  % of the population between 15 and 29 
|-
| style="width: 57px;" | '''POPMEDAGE '''
| style="width: 235px;" | Population, median age 
|-
| style="width: 57px;" | '''LAB '''
| style="width: 235px;" | Labor force size 
|-
| style="width: 57px;" | '''BIRTHS '''
| style="width: 235px;" | Births 
|-
| style="width: 57px;" | '''DEATHS '''
| style="width: 235px;" | Deaths 
|-
| style="width: 57px;" | '''MIGRANTS '''
| style="width: 235px;" | Net migration (inward) 
|-
| style="width: 57px;" | '''CBR '''
| style="width: 235px;" | Crude birth rate 
|-
| style="width: 57px;" | '''CDR'''
| style="width: 235px;" | Crude death rate 
|-
| style="width: 57px;" | '''TFR'''
| style="width: 235px;" | Total fertility rate 
|-
| style="width: 57px;" | '''CONTRUSE'''
| style="width: 235px;" | Contraceptive usage 
|-
| style="width: 57px;" | '''LIFEXP'''
| style="width: 235px;" | Life expectancy 
|-
| style="width: 57px;" | '''MIGRATE'''
| style="width: 235px;" | Net migration rate (inward) 
|}

The IFs demographic module breaks country populations down into 21 five-year age groups, each one subdivided by gender. This allows the model to create an age-sex cohort structure that responds to changes in the three fundamental drivers of population: fertility, mortality, and migration. Births are calculated as a function of each country’s fertility distribution and age distribution. As children are born, they enter the lowest band of the age-sex structure, the layer representing people aged 0 through 5. Each country’s population growth is reduced by deaths at each age level; like births, deaths are calculated as a function of the mortality distribution and the age distribution. Finally, migration patterns either add to, or subtract from, each country’s population, depending on the balance of immigration and emigration3 . Each of the three proximate drivers of population is influenced by deeper social processes: births are a product of fertility patterns; deaths are linked to life expectancy; and net migrants are determined by an overall global migration rate.

Total population is represented in millions of people via '''POP''', but users may also choose to explore the age structure within society. Three variables break population down into broad age groups: '''POPLE15''', people age 15 or younger, '''POP15TO65''', people age 15 to age 65, and '''POPGT65''', people older than age 65. Three additional variables provide a similar disaggregation of population: '''POPPREWORK''', '''POPWORKING''', '''POPRETIRED'''—as the names suggest, they measure the number of people who have yet to enter their working years, the number of people currently in their working years, and the number of people who have completed their working years. The years comprising an adult’s working life may vary from country to country, depending on education systems and retirement ages. Users can explore additional population characteristics via the variables '''YTHBULGE''', the percent of all adults (15 and older) between the ages 15 and 29; '''POPMEDAGE''', the median age of a country’s population; and '''LAB''', the size of the labor force, recorded in millions of people. For any country, the complete age and sex breakdown is available under the Specialized Displays for Issues option under the Display sub-menu. From the Specialized Displays menu, select Population by Age and Sex, and click the button labeled Show Numbers. This will bring up detailed population figures for any of the countries in the IFs system. To view a population pyramid display, toggle the Distribution Type setting on the menu bar.

The three immediate drivers of population change—births, deaths, and migration—are captured in the model as flows. Every year babies are born ('''BIRTHS'''), people die ('''DEATHS''') and people leave countries to live elsewhere ('''MIGRANTS'''). These processes alter the stock of population in countries, regions and the world as a whole. The speed at which a population will grow or decline, and the attendant shift in a population’s age structure, depend on crude birth rates ('''CBR''') and crude death rates ('''CDR''')—the number of births and deaths per 1,000 people.

Each of the immediate drivers is linked to deeper determinants of population. For instance, fertility rates are responsive to income, education and infant mortality rates, offering points of access elsewhere in the model. Total Fertility Rate ('''TFR''') is a variable that is essential to our understanding of populations’ reproductive behavior. '''TFR''' is, essentially, the number of children the average woman in a country can expect to have over the course of her lifetime. In order for the overall population size to remain roughly stable, '''TFR''' must meet the replacement rate for that country. For developed countries this is approximately 2.1 children per woman, but the figure may be higher in countries with high mortality rates, and is lower in many. While '''TFR''' largely determines future population growth, it is not the only behavioral variable of note: '''CONTRUSE''' captures the percent of fertile women who routinely use some method of contraception.

For a complete discussion of mortality see the [[Health#Health|Health module]], where deaths are computed. They are responsive to deep or distal factors such as income, education and technological advance, as well as to more proximate ones such as levels of undernutrition and smoking. A key indicator for the population model, linked to deaths, is '''LIFEXP''', or life expectancy, which provides a measure of the median life expectancy of a newborn in a particular year given the current mortality distribution. Although life expectancy can be calculated for any age, IFs focuses on life expectancy at birth. This variable is key to the functioning of the IFs system because many of the parameters that affect mortality do so by changing life expectancy.

The final proximate driver of population growth is migration. '''MIGRANTS''' measures net migrants in raw figures, reported in millions of people; but this variable is determined by '''MIGRATE''', the net migration rate, reported as percent of the total population. The basic forecasts of migration in IFs are one of the very few variables that are exogenous. Nonetheless, there is parametric control of it.

The demographic module features an array of parameters that allow users to create alternative demographic scenarios by exploring uncertainty surrounding: fertility, mortality and migration, as well as the years making up people’s working lives.

----

3In IFs, the age distribution of migrants is controlled by an internal vector across age categories, not available for manipulation through the model’s front-end.

== Parameters to Affect Fertility ==

{| border="1" cellspacing="1" cellpadding="1" style="width: 443px;"
|-
| style="width: 68px;" | '''Parameter'''
| style="width: 115px;" | '''Variable of Interest'''
| style="width: 151px;" | '''Description'''
| style="width: 93px;" | '''Type'''
|-
| style="width: 68px;" | '''tfrm'''
| style="width: 115px;" | TFR, CBR
| style="width: 151px;" | Total fertility multiplier 
| style="width: 93px;" | Multiplier 
|-
| style="width: 68px;" | '''contrusm'''
| style="width: 115px;" | CONTRUSE
| style="width: 151px;" | Contraceptive use multiplier 
| style="width: 93px;" | Multiplier 
|-
| style="width: 68px;" | '''eltfrcon'''
| style="width: 115px;" | TFR
| style="width: 151px;" | Elasticity of total fertility rate to contraception use 
| style="width: 93px;" | Elasticity
|-
| style="width: 68px;" | '''tfrmin'''
| style="width: 115px;" | TFR
| style="width: 151px;" | Long term TFR convergence value 
| style="width: 93px;" | Limit
|}

The single most powerful way for users to modify fertility rates is to manipulate '''tfrm''', a parameter that directly alters the total fertility rate within a country or region. This parameter serves as a multiplier on the fertility rate calculated by the model—a 20% increase or decrease in the value of the parameter will result in a similar magnitude of change in the value of the associated variable, '''TFR'''. Because it is a brute force multiplier, users should justify their modifications to the parameter. When used thoughtfully, '''tfrm''' can be a powerful tool for scenario analysis. It can be used to model the impact of fertility control initiatives that extend beyond simple contraceptive use. An example would be the implementation of a program to offer public seminars on the benefits of having fewer children, which could lower the fertility rate even when overall contraceptive usage rates are low. Health care programs for women are a major contributor to fertility decline.

Users can also directly change the percentage of the population that uses contraceptives via '''contrusm''', a parameter that indirectly affects the total fertility rate via '''CONTRUSE'''. As this is a multiplier, it works the same way as '''tfrm'''. It can be used to model the impact of an increase in the availability of family planning education, a campaign to promote the use of condoms, or any other intervention that would likely increase (or decrease) the percentage of a population using contraceptives. Additionally, the parameter '''eltfrcon''' allows users to control the elasticity of total fertility to contraceptive use. For example, a weaker relationship between the two variables might be justified if the contraceptive methods in use in a country or region are widely known to have high failure rates.

When creating alternative scenarios that span long time horizons, users may wish to modify fertility assumptions built into the demographic module. As countries grow richer and reach higher levels of educational attainment, total fertility rates tend to decrease. However, in forecast years, a minimum value prevents countries from dipping too far below replacement rate. As a default setting, the minimum parameter, '''tfrmin''', is set to 1.9. Thus, in the Base Case, '''TFR''' in highly developed countries will converge to just below 2 children per woman. By increasing or decreasing the parameter, users can experiment with different long-term fertility patterns.

== Parameters to Affect Mortality ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''mortm'''
| DEATHS
| Mortality multiplier (not cause specific) 
| Multiplier 
|-
| '''hlmortm'''
| DEATHS
| Mortality multiplier by cause 
| Multiplier 
|}

The health module write-up includes a full description of the drivers of mortality in the IFs system, and explains how to manipulate each one. However, one parameter affecting mortality, '''mortm''', is worth discussing separately. This parameter functions similarly to the '''hlmortm''' parameter available in the health module, but does not disaggregate by cause of death. Similar to '''tfrm''', '''mortm''' can be used to model the impact of events that have broad impacts across the population, such as the end of an armed conflict or the implications of a plague. Usually however, if a user is building a scenario analyzing health trends, using the '''hlmortm''' multiplier will be more useful because it disaggregates mortality on the basis of cause. Because morbidity rates in IFs are linked normally to mortality rates, these parameters will affect them also.

== Parameters to Affect Migration ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''wmigrm'''
| MIGRATE, MIGRANTS
| World migration rate multiplier 
| Multiplier
|-
| '''migrater'''
| MIGRATE, MIGRANTS
| Net migration rate (inward) as a percent total population 
| Rate of change
|-
|'''migrateinm'''
|MIGRANTS, MIGRANTSIN
|Inward migration rate multiplier
|Multiplier
|-
|'''migrateoutm'''
|MIGRANTS, MIGRANTSOUT
|Outward migration rate multiplier
|Multiplier
|}

Users interested in modifying migration patterns should bear in mind that migrant flows are subject to an accounting system that keeps the global number of net migrants equal to zero. In other words, a person leaving one country will be accounted for when they enter another country. Changing the world migration rate, '''wmigrm''', is the easiest way to affect migration patterns in IFs. Altering this parameter will allow users to increase the overall rate at which migration occurs at a global level, enabling users to simulate large scale increases (or decreases) in migration generated by, say, reductions in visa fees, or the opening of borders as is the case in the EU’s Schengen area. The parameter '''migrater''', on the other hand, allows users to affect the rate of migration into individual countries or regions (values can range from positive, indicating net inward migration, to negative, indicating net outward migration). Alternatively, '''migrateinm''' and '''migrateoutm''' can also be used to change in-migration and out-migration rates for a country or region.

== Parameters to Affect Working Age ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''workageentry'''
| POPPREWORK, POPWORKING 
| Working age determinant 
| Exogenous specification 
|-
| '''workageretire'''
| POPWORKING, POPRETIRED 
| Retirement age determinant 
| Exogenous specification 
|-
|'''workageretirendpctuse'''
|WORKAGERETIREND
|Percent of needed retirement age to deal with aging
|Exogenous specification
|-
|'''workageretiresv'''
|CPCTND
|Age of retirement to compute savings rate
|Exogenous specification
|}

In addition to manipulating the rate at which populations grow, users can experiment with the effects of changing a country’s working age, something that will be fiscally important in many countries as populations age. The variables '''POPPREWORK''', '''POPWORKING''' and '''POPRETIRE''' map the typical age structure of a country or region’s work force. Two parameters, '''workageentry''' and '''workageretire''', control the age at which a person is considered eligible for work and the age at which a person is eligible for retirement. Changes in the workforce’s age configuration link forward to economic production via the size of the labor force ('''LAB'''). Raising or lowering the retirement age will additionally affect government finances via the size of population of retirement age and the level of pension support provided to households ('''GOVHHPENT''').

== Prepackaged Scenarios ==

An installation of IFs includes high and low population-framing scenarios. Originally created for the poverty volume of the Pardee Institute’s Potential Patterns of Human Progress (PPHP) series, the two files are located in the Framing Scenarios folder under Population. Both scenarios feature the direct total fertility rate multiplier. '''Tfrm''' in the high fertility scenario is set to 1.5 globally. In the low fertility scenario, '''tfrm''' is set to .6 in non-OECD nations, and the limit parameter '''tfrmin''' is set to 1.6 globally. Although the two scenarios only feature a few interventions, the effects of such a large change in human reproductive behavior would have significant forward linkages throughout each of the model’s systems.

Four of the prepackaged scenarios located in the folder Interventions and Agent Behavior contain additional examples of the demographic module’s parameters: Non OECD Contraception Use Slowed, Non OECD Contraception Use Accelerated, World Migration High, and World Migration Low. The pair of scenarios focusing on contraceptive usage rates both utilize '''contrusm'''. In the accelerated scenario, the multiplier takes the value 1.2 in non-OECD nations; and the value 0.8 in the slowed scenario for all non-OECD nations. The two alternate migration scenarios similarly feature interventions on a single parameter: the global migration multiplier '''wmigrm'''. In the high scenario the parameter takes on a value of 2, doubling global migration flows; and in the low scenarios flows are halved, with '''wmigrm''' declining to a value of 0.5.

= Health Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''LIFEXP/LIFEXPHLM '''
| Life Expectancy 
|-
| '''CDR'''
| Crude Death Rate 
|-
| '''DEATHCAT'''
| Deaths by Mortality Type 
|-
| '''HLYLL'''
| Years of Life Lost 
|-
| '''HLYLLWORK'''
| Years of Working Life Lost 
|-
| '''HLYLD'''
| Years Lived with Disability 
|-
| '''HLDALY'''
| Disability Adjusted Life Years Lost 
|-
| '''INFMOR'''
| Infant mortality rate 
|-
| '''HLSTUNT'''
| Percentage of population stunted 
|-
| '''MALNCHP'''
| Percentage of children malnourished 
|-
| '''MALNPOPP'''
| Percentage of population malnourished 
|-
| '''HLBMI'''
| Body Mass Index 
|-
| '''HLOBESITY'''
| Percentage of population obese 
|-
| '''HLSMOKING'''
| Percentage of population that smokes 
|}

The primary variables of interest in the IFs health module are those that pertain to mortality and morbidity due to a variety of causes. '''LIFEXP''' and '''CDR''', discussed in the population module, provide basic measures of population health. '''DEATHCAT''' provides a measure of the number of deaths (in thousands) due to different categories of mortality. IFs can display health variables in the following categories of disease: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other Non-Communicable Diseases, Unintentional Injuries, Intentional Injuries, Diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Using the Flexible Display form, it is also possible to see many of these variables in the rolled-up categories of Communicable Disease, Non-Communicable Disease, and Injuries or Accidents. Because different health conditions affect age cohorts differentially, the above measure is insufficient in understanding the full impact of ill health. For this reason, it is also possible to break down the actual number of deaths accruing to each cohort, sex, and cause via the Specialized Display menu under the health heading. For example, both the Mortality by Age, Sex, and Cause and the J-Curve displays provide useful information about the health status of a country.

Three other measures help to enrich the picture: '''HLYLL''', '''HLYLD''' and '''HLDALY'''. Like '''DEATHCAT''', these aggregate (across age-cohort) measures are available by cause and country. '''HLYLL''' is a measure of the number of life years lost due to premature death. It differs from the '''DEATHCAT''' variable because it represents the burden of premature mortality in terms of life years lost, which allows us to account for the fact that some diseases, like HIV/AIDS, primarily affect younger people, while others, like cardiovascular disease, are primarily fatal in older adults. Although the total number of deaths may be the same between two countries for each cause, there may be significant differences between two countries’ health profiles in terms of YLLs.

'''HLYLD''' is another measure that represents the burden of ill health in terms of life years of impact. It indicates the burden of years lived with disability or disease. In calculating YLD, IFs uses the disability weights that WHO created to rank the relative severity of different conditions and their impact on productivity.

Finally, Disability Adjusted Life Years (DALYs) are a measure of morbidity (disability or infirmity due to ill health). '''HLDALY''' sums YLLs and YLDs to create a measure of the number of years of life lost to both premature mortality and morbidity due to ill health. Like the other measures discussed above, DALYs can be broken down by different disease categories within IFs. The DALY is probably the most expansive measure of ill-health within a population because it includes mortality burden by age of death and the lost quality of life for those who did not die from health events, but who are disabled by them in some way.

Other measures provide indicators of health in regard to certain specific risk factors for disease or among certain segments of the population. Infant mortality, '''INFMOR''', can be used to assess the burden of ill health among children under one year of age. '''HLSTUNT''', displays the percentage of the population who are stunted (have low height for age),while '''MALNCHP''' and '''MALNPOPP''', provide information on the percentage of the child and adult population who are undernourished respectively. The variables '''INFMOR,''' '''HLSTUNT''' and '''MALNCHP''' are especially useful for assessing the burden of ill health due to communicable diseases and other conditions that primarily affect children. By contrast, the variables '''HLBMI''', '''HLOBESITY''', and '''HLSMOKING''' provide risk factor information on diseases that affect primarily adults. '''HLBMI''' represents the body mass index in a country while '''HLOBESITY''' and '''HLSMOKING''' provide information on the percentage of the population that is obese or smokes.

Other variables that will be useful to users interested in specific conditions or subpopulations include indicators on stunting and BMI, as well as smoking and obesity. Variables for HIV/AIDS are also available and discussed separately below in the subsection on the HIV/AIDS sub-module.

== Parameters to Affect Overall Health and Burden of Disease ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hlmortm'''
| DEATHCAT/HLYLL/HLDALY
| Multiplier on Mortality (by cause) 
| Multiplier 
|-
| '''hlmorbm'''
| YLD
| Multiplier on morbidity 
| Multiplier 
|-
| '''hlstddthsw'''
| DEATHCAT
| Switches DEATHCAT from absolute numbers to deaths/1000 
| Switch
|}

The above parameters provide simple ways to directly affect the burden of disease within a country. The most important parameter for modifying mortality rates is '''hlmortm''', a parameter that allows users to increase or decrease the prevalence of deaths in any particular category of illness. IFs modifies mortality in the following categories: Other Communicable Disease, Malignant Neoplasm, Cardiovascular, Digestive, Respiratory, Other Non-Communicable Diseases, Unintentional Injuries, Intentional Injuries, diabetes, AIDs, Diarrhea, Malaria, Respiratory Infections, and Mental Health. Altering the mortality burden will affect the variables '''DEATHCAT''', '''HLYLL''', and '''HLDALYs'''. The parameter will indirectly affect morbidity because of its direct link to mortality. In the case of Mental Health Diseases, the parameter will not have much impact on '''DEATHCAT''', but may have a significant impact on the number of DALY’s experienced by a population. Because '''hlmortm''' is a multiplier, increasing its value from 1 to 1.2 represents a 20% increase in the burden of mortality from a particular cause. A similar parameter, '''hlmorbm''', allows users to affect morbidity directly through a brute force multiplicative parameter. This allows users to affect the years lost to disability in a working life and by extension multifactor productivity due to human capital ('''MFPHC'''). The '''hlstddthsw''' allows users to switch between displaying '''DEATHCAT''' in absolute numbers to deaths per thousand people.

== Parameters that Affect Communicable Diseases ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''watsafem'''
| WATSAFE, INFMOR 
| Percentage of population with access to safe water 
| Multiplier 
|-
| '''sanitationm'''
| SANITATION, INFMOR 
| Percentage of population with access to improved sanitation 
| Multiplier 
|-
|'''malnelimstartyr'''
|MALNPOPP
|Undernourishment elimination start year
|Standard error
|-
|'''malnelimtargetyr'''
|MALNPOPP
|Undernourishment elimination target year
|Standard error
|-
| '''malelimprecisesw'''
| MALNPOPP
| Undernourishment elimination
| Multiplier 
|-
| '''ylm'''
| YL
| Yield multiplier on agriculture 
| Multiplier 
|-
| '''hivm'''
| HIVCASES 
| Rate of HIV infection 
| Multiplier 
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|}

Above are a number of the parameters that users may wish to use to manipulate communicable diseases (which predominantly affect children). '''Ylm''' is a multiplicative parameter in the agriculture module that can be used to change the yield of agricultural lands within a country, affecting the number of calories available for consumption, and thereby altering the rates of malnutrition and obesity. '''Clpccvm''' is another parameter in the agriculture module that impacts the calories per capita available, and also alters the rate of malnutrition by changing the distribution of caloric consumption for a country/region. '''Watsafem''' and '''sanitationm''', in the infrastructure module, influence the percentage of the population that has access to safe water and sanitation respectively, thus decreasing childhood exposure to diarrheal disease, malnutrition and premature death. Other parameters to control safe water and sanitation access are discussed in the infrastructure section of the model.

Finally, although HIV is more thoroughly discussed in the HIV/AIDs submodule, one brute force parameter is worth noting here. '''Hivm''' allows users to directly affect the rate of infection with the HIV virus.

== Parameters that Affect Non-Communicable Disease ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''envpm2pt5m '''
| ENVPM2PT5 
| Increases levels of environmental pollution 
| Multiplier 
|-
| '''hlsmokingm '''
| HLSMOKING 
| Increases rate of smoking 
| Multiplier 
|-
| '''hlobesitym '''
| HLOBESITY 
| Prevalence of obesity 
| Multiplier 
|-
| '''hlbmim '''
| HLBMI 
| Multiplier on body mass index 
| Multiplier 
|}

'''Hlsmokingm''' is a multiplicative parameter that will change the rate of smoking, which will affect the prevalence of respiratory diseases. '''Envpm2pt5m''' is a multiplicative parameter that will change the level of ambient environmental pollution in terms of parts per million; higher levels of environmental pollution are a risk factor for both communicable and non-communicable respiratory diseases.

'''Hlobesitym''' works similarly to affect the prevalence of obesity within a society in the absence of overall caloric intake changes. This parameter can be used to model the impact of changing levels of physical activity within a society. Both of the above parameters work similarly to other multiplicative parameters: increasing the value of the parameter to 1.2 from 1, represents a 20% increase in the value of the parameter over the base case. By the same token, users can use '''hlbmim''' to affect the body mass index in a country, a major risk factor for cardiovascular diseases, diabetes, and overall morbidity. Please note: '''hlobesitym''' affects only obesity rates and has no affect on health; in contrast, '''hlbmim''' will affect body mass index, obesity, and deaths from heart disease and diabetes.

== Parameters that Affect Injuries and Accidents ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''deathtrpvm '''
| DEATHTRPV 
| Multiplier on traffic deaths per vehicle 
| Multiplier 
|-
| '''deathtrpvsetar, deathtrpseyrtar '''
| DEATHTRPV 
| Standard error target for traffic deaths per vehicle 
| Relative target Value/Year 
|}

Only a small set of parameters allow users to affect injuries and accidents, and these primarily revolve around reducing traffic deaths. Users may reduce traffic deaths as a ratio of the number of vehicles in a country using either a multiplier, '''deathtrpvm''', or a pair of standard error targeting parameters, '''deathtrpvsetar''' and '''deathtrpseyrtar'''. Standard error targeting is discussed in detail in the [[Infrastructure#Infrastructure|infrastructure module]]. These parameters allow users to model the impact of road safety on mortality and, by extension, on economic productivity.

== Parameters to Affect Technology ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hlmortmodsw '''
| CDR 
| Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific 
| Switch
|-
| '''hltechshift '''
| CDR 
| Rate of change in health technology 
| Additive factor 
|-
| '''hltechlinc '''
| CDR 
| Rate of change in health technology in low income countries 
| Additive factor 
|-
| '''hltechssa '''
| CDR 
| Rate of change in health technology in Sub-Saharan Africa 
| Additive factor 
|-
| '''hltechbase '''
| CDR 
| Rate of change in health technology at base 
| Exogenous specification 
|}

Aside from the direct and indirect parameters affecting health, the distal drivers of health include per capita GDP, years of education, and technology. Per capita GDP is an element of the [[Economics#Economics|economic module]] and can be changed in a number of ways, but especially by changing the elements that make up multifactor productivity. Years of education is an element of the [[Education#Education|education module]] and can be changed by altering the duration of schooling, and the completion rate.

Moving to the third distal driver of health, there are a number of parameters built into the health module that can be used to alter the rate of technological change. '''Hlmortmodsw''' is a master switch that, when set to 1 as in the Base Case default, reduces technological progress for low-income countries of Africa, Europe, Southeast Asia, and West Pacific based on geographic and income categories. There are parameters available to alter these assumptions about differentials in mortality declines in these regions, but they only have an effect in the base case; when '''hlmortmodsw''' is set to 0 these parameters have no impact.

Once '''hlmortmodsw''' is set to 1, users can manipulate mortality patterns through several parameters. '''Hltechshift''', alters the rate of change for health technology impacts relative to GDP. The '''hltechshift''' parameter allows users to change the mortality rate using a shift parameter that alters the technology factor affecting mortality decline relative to initial GDP. '''Hltechlinc''' and '''hltechssa''' can be used to change the rate of technological advance resulting in mortality decline in low-income countries ('''hltechlinc''') and sub-Saharan Africa ('''hltechssa''') specifically. Meanwhile, the '''hltechbase''' parameter allows users to change the base level of technological change across the 20 world, rather than country by country as you can do using the '''hltechshift''' parameter. All of these parameters pertain to all causes of mortality except cardiovascular mortality, which uses a different regression equation.

== Prepackaged Scenarios ==

Three major integrated scenarios on health were developed by the Pardee Institute for the health volume of the Patterns of Potential Human Progress series (Hughes et al., 2011). The World Integrated Scenario Sets folder contains the scenarios that were built for this volume, of which three are worth an extended discussion. The first is the Proximate Drivers Excluding Environment folder, which contains parameters to individually alter four of the major risk factors for several causes of mortality. These are Body Mass Index which is a risk factor for cardiovascular disease; under nutrition, which is a risk factor for communicable diseases; smoking which is a risk factor for respiratory disease; and large increases in the number of cars per person coupled with poor pedestrian safety, which is a major risk factor for accidental death. This scenario also includes increased to improved water sources and piped sanitation taken from the infrastructure module, and parameters to reduce environmental exposure to poor air quality. This scenario reduces these risk factors to their theoretical minima, to simulate aggressive efforts to reduce, high BMI, the obesity rate, childhood malnutrition, smoking, and traffic mortality. Malnutrition is set to 0.01, smoking and obesity multipliers are set to 0, BMI multiplier to 0.8, vehicle fleets to 0.5, and traffic mortality to 0.

Another important pair of prepackaged scenarios contains the optimistic Luck and Enlightenment scenario, and a scenario that considers what happens when Things Go Wrong. The Luck and Enlightenment scenario includes improvements to HIV/AIDS, sanitation access, improved air quality, and reduced smoking rates which help lower the burden of NCDs. It also features changes to the burden of communicable disease designed to increase the levels of these. A variation to Luck and Enlightenment has add-ins that also increase foreign aid donations and agricultural yields, effectively modeling a situation in which increased global cooperation supports these efforts. Things Go Wrong models a world in which air quality worsens, smoking and obesity rates increase and there is little international cooperation on addressing these challenges.

= HIV/AIDS Submodule =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''HIVCASES'''
| Number of HIV cases 
|-
| '''HIVRATE'''
| HIV infection rate 
|-
| '''HIVTECCNTL'''
| Rate of technical control of infection, cumulative reduction in infection rate 
|-
| '''AIDSDTHS'''
| Number of AIDS deaths 
|-
| '''AIDSDRATE'''
| Death rate from AIDS 
|-
| '''AIDSDTHSCM'''
| Cumulative Number of AIDS deaths since first year of model 
|}

HIV and AIDS have attracted significant interest among policy makers because of the tremendous toll that these diseases have on populations in both human and economic terms. Because of this interest, it is worth discussing the HIV/AIDS submodule separately from the rest of the health module. That submodule represents both the extent of HIV prevalence in a population (a stock variable) and the annual deaths from AIDS (a flow variable driven in substantial part by the prevalence rate, but also responsive to technological advance in the fight against AIDS). A number of key variables are available to represent the burden of HIV and AIDS within a country.

Three variables are key to understanding the progression of infection within a country. '''HIVCASES''' provides the total number of HIV cases, '''HIVRATE''' represents a flow variable showing the rate at which people are being infected with HIV, and '''HIVTECCNTL''' indicates the progress being made in reducing the rate of infection within a country.

Three other variables assess mortality due to HIV and AIDs within a country. Similar to HIV, the variables '''AIDSDTHS''' and '''AIDSDRATE''' indicate the number of AIDs deaths and the rate of mortality from AIDs respectively, while '''AIDSDTHSCM''' represents the cumulative number of deaths due to the disease.

== Parameters to Affect Prevalence ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''hivm'''
| HIVRATE 
| HIV infection rate, multiplier of percent of population infected 
| Multiplier
|-
| '''hivtadvr '''
| HIV CASES/ HIVRATE 
| Technical advance rate in of control of infection 
| Rate of change 
|-
| '''hivpeakr '''
| HIVCASES/ HIVRATE 
| HIV infection rate at year of peak 
| Target value
|-
| '''hivpeakyr '''
| HIVRATE 
| Sets year of epidemic peak 
| Target year
|-
| '''hivincr '''
| HIVCASES 
| HIV increase rate, only used prior to 2000 
| Rate of change 
|}

Modifying the infection rate with '''hivm''' is probably the easiest way to adjust the burden of HIV infection within a country. Like '''hlmortm''', '''hivm''' is a multiplicative parameter. In other words, increasing the value of the parameter in scenario analysis from 1 to 1.2 represents a 20% increase in the rate of infection relative to the base case. '''Hivtadvr''' allows users to change the prevalence of HIV, once the epidemic has peaked, by a certain percent annually to model different assumptions about the rate at which control technologies will improve, reducing the prevalence of the disease over time. Unlike the mortality multiplier, which takes effect once the model has calculated the base case, this parameter will affect the actual calculations the model makes while running. This parameter functions as additive factor to a growth rate within IFs. In other words, a 0.01 increase in the parameter represents a 0.01 increase in the growth rate for the technical advance rate in HIV infection control ('''hivtadvr''').

The HIV submodule is designed to allow users to affect the course of the epidemic across countries and across time. One way to alter the course of the epidemic is by manipulating the coefficient on '''hivpeakr''', which is an additive parameter that will increase the peak rate of infection over the course of the epidemic. Thus a 0.01 increase in the value of the coefficient represents a 0.01 increase in the peak infection rate. An associated parameter, '''hivpeakyr''' sets the date at which the epidemic will peak before the infection rate begins to decline. Changing this parameter in the Scenario Analysis page will allow users to set any year between 2010 and 2100 as the year of peak infection rate depending on their assumptions regarding the technical rate of advance in controlling the disease. Finally, the parameter '''hivincr''' controls the increased rate in infection prior to 2000, when our knowledge of the epidemic was much less complete and control efforts were far less effective. It is important to note that the parameter '''hivpeakr''' must be used in combination with parameters, '''hivpeakyr''' and '''hivincr'''.

== Parameters to Affect Mortality ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Parameter'''
| '''Variable of Interest'''
| '''Description'''
| '''Type'''
|-
| '''aidsdrtadvr '''
| AIDSDTHS/AIDSRATE 
| AIDs death rate, technical annual advance rate in control 
| Rate of change 
|-
| '''aidsdratem '''
| AIDSRATE 
| AIDs death rate as % of HIV infection rate, multiplier 
| Multiplier
|}

Just as there are a variety of parameters available to control the prevalence of HIV within a population, there are also a number of parameters that allow users to control the lethality of the epidemic. The first of these parameters allow user to change the death rate as a percentage of the infection rate via the parameter '''aidsdratem'''. This parameter directly alters the lethality of the disease; it serves as a proxy for the presence or absence of control measures within a country since the availability of anti-retroviral medications will affect the rate at which people who are HIV positive die from AIDs. Of course, new research strongly suggests that ART therapies may also significantly reduce the HIV infection rate as well, but because these are not yet linked in the model, users should be aware that a more realistic use of this parameter would alter not only the AIDs mortality rate, but the infection rate as well. The other parameter available to users to control mortality from AIDs is '''aidsdrtadvr''', a parameter which changes the technical annual advance rate in control. This parameter simulates the annual advance in technologies to control AIDs mortality, altering the lethality of the disease.

== Prepackaged Scenarios ==

There are several prepackaged scenarios that deal with the HIV/AIDS epidemic. The first, under the heading HIV/AIDS in the Technological Advance section of the Framing Scenarios folder, models two scenarios around technological advance to control the epidemic. One models rapid technical advances to control HIV infection, while the other presents a scenario in which technological progress slows, slowing the resulting decline in infections.

A second set of prepackaged scenarios are available to affect HIV/AIDS are focused on altering the course of the epidemic in key countries, rather than at a global level. They are called: Intermediate HIV/AIDS, Intermediate for New School Paper, Severe HIV Aids and Total Failure to Control HIV AIDs and are located in the Surprises and Wildcards folder, under the heading AIDs. These scenarios modify the course of the AIDS epidemic in Russia, China, India, and the world at large. Each one affects parameters controlling the infection rate at the peak year of the epidemic, the peak infection rate, the initial rate of infection, the rate of advance in the infection, and the elasticity of multifactor productivity to life expectancy. They give a good example of how to modify combinations of parameters in specific countries to create different trajectories for the epidemic.

= Sever Acute Malnutrition (SAM) Submodule =
In addition to the prevalence and incidence of the above mentioned diseases, IFs also calculates the prevalence and incidence of severe acute malnutrition (SAM) and its impact on productivity. Severe Acute Malnutrition (SAM) (also known as severe wasting) is a deficiency of protein or other minerals and vitamins leading to a loss of body fats and muscle tissues. SAM can be defined by a Weight-for-Height Z-score <-3 compared to the median WHO growth standards; by visible severe wasting; or by the presence of nutritional oedema (Prudhon, Briend, Prinzo, Daelmans, & Mason, 2006). It has been identified as a significant cause of death and disability globally (Black et al., 2008, 2013).

The key variables to understanding the impact of the disease are the prevalence of SAM (MALNCHPSAM), the number of cases of SAM that occur in a particular year or the incidence of the disease (MALNCHPSAMINC), the number of people who are admitted for treatment for the disease in a particular year (SAMADMISSION) and the number of cases where SAM is averted (SAMAVERTED). Since the disease itself shares a relationship with stunting, the model also allows the user to explore the extent to which SAM affects stunting (HLSTUNT) and malnutrition (MALNCHP).

The table below explains the key variables in this sub-module,
{| class="wikitable"
|'''Variable Name'''
|'''Description'''
|-
|'''MALNCHPSAM'''
|Prevalence of SAM
|-
|'''MALNCHPSAMINC'''
|Annual new number of cases of SAM
|-
|'''SAMADMISSION'''
|Total national number of admissions for treatment
|-
|'''SAMAVERTED'''
|Success of treatment as a percent of total admission
|}

== Parameters to affect prevalence and incidence ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpsamadj'''
|MALNCHPSAMINC
|Used to adjust the incidence correction factor
|Adjustment factor
|-
|'''malnchpsamm'''
|MALNCHPSAM
|Used to change prevalence of SAM
|Multiplier
|}

The easiest lever a user can pull to influence prevalence of SAM is the multiplier '''''malnchpsamadj''''' which can directly increase or reduce the number of prevalent cases of the disease. Incidence of SAM is calculated by applying a correction factor on the prevalence rate. The user can adjust the correction rate thereby influencing incidence of the disease using the adjustment factor '''malnchpsamadj.'''

== Parameters to affect admissions and treatment ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''samadmissionadd'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Additive factor
|-
|'''samadmissionm'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Multiplier
|-
|'''samadmissionsw'''
|SAMADMISSION
|Allows users to forecast SAM admissions in three ways (0) forecasts all countries according to the relationship with GDPPCP, (1) holds all initial zero values constant over time (default value), and (2) holds all initial values constant.
|Switch
|-
|'''samavertedconvyr'''
|SAMAVERTED
|Allows users to change the years to converge to 100
|Convergence factor
|-
|'''samavertedm'''
|SAMAVERTED
|Allows users to change the number of averted cases
|Multiplier
|}

There are two parameters available for changing the number of admissions for treatment, '''''samadmissionadd''''' which is an additive factor and '''''samadmissionm''''' which is a multiplicative factor. Further, the user can forecast the number of admissions using the GDP per capita or by holding all initial zero values constant over time or by holding all initial values constant through the manipulation of the switch '''''samadmissionsw.'''''

When it comes to the number of cases that are cured, the model assumes a success rate to converge to 100 percent over a 100-year time horizon. The user can change the speed of convergence through the parameter '''''samavertedconvyr.''''' Finally, the user can change the number of cases averted using a brute force multiplier '''''samavertedm.'''''

== Relationship between malnutrition, stunting and SAM ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpsamtostunt'''
|HLSTUNTCHL and HLSTUNT
|Controls the degree to which MALNCHPSAM affects HLSTUNT
|Adjustment factor
|-
|'''samrrsw'''
|HLSTUNT and DEATHS
|Used to substitute the severity of MALNCHP with that of MALNCHPSAM
|Switch
|-
|'''samtomalnchpsw'''
|MALNCHP
|allows users to specify scenarios in which successful treatment does not result in full weight
|Switch
|}

The adjustment factor '''''malnchpsamtostunt''''' allows the user to change the extent to which SAM affects stunting in the population. IFs calculates rates of stunting using a slow moving average using the number of undernourished children, the prevalence of SAM, the number of years for which stunting is expected to occur and the relevant height for age. The activation of the switch '''''samrrsw''''' substitutes the severity of undernourishment for undernourished children (MALNCHP) with the severity of SAM (MALNCHPSAM). Severity here refers to the relative risk of mortality and morbidity. This switch allows the user to explore the possible severity of the disease. For a more detailed description of this parameter, refer section 6.1.1 of the Health Model documentation.

By default, it is assumed that all successfully averted SAM cases are rehabilitated to normal weight for age. Changing the parameter '''''samtomalnchpsw''''' allows users to specify scenarios in which successful treatment does not result in full weight (for age) rehabilitation, with a value of 1 resulting in all averted SAM cases remaining malnourished (by default this parameter is set to 0).

= Education Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
|
'''EDYRSAG15'''

'''EDYRSAG25'''

'''EDYRSAG15TO24'''

| Educational attainment, adults by age group
|-
|
'''EDPRIPER'''

'''EDSECPER'''

'''EDTERPER'''

| Percent of the population completing primary, secondary and tertiary
|-
|
'''EDPRIENRG'''

'''EDSECENRG'''

'''EDSECLOWRENRG'''

'''EDSECUPPRENRG'''

'''EDTERENRG'''

| Gross enrollment rate in primary, secondary, lower secondary, upper secondary and tertary
|-
|
'''EDPRIENRN'''

'''EDSECENRN'''

| Net enrollment rate in primary, secondary
|-
|
'''EDPRIINT'''

'''EDPRIINTN'''

'''EDTERINT'''

| Primary intake rates, gross and net, and tertiary intake rates, gross
|-
|
'''EDSECLOWRTRAN'''

'''EDSECUPPRTRAN'''

| Transition rates from primary to lower secondary and from lowers secondary to upper secondary
|-
|
'''EDPRISUR'''

'''EDSECLOWRSUR'''

'''EDSECUPPRSUR'''

| Survival rates for primary, lower secondary, upper secondary
|-
|
'''EDPRICR'''

'''EDSECLOWRGRATE'''

'''EDSECUPPRGRATE'''

'''EDTERGRATE'''

| Graduation rates in primary, lower, secondary, upper secondary and tertiary
|-
|
'''EDSECLOWRVOC'''

'''EDSECUPPRVOC'''

| Vocational enrollment rates, lower and upper secondary
|-
|
'''EDEXPERPRI'''

'''EDEXPERSEC'''

'''EDEXPERTER'''

| Government spending per student as a percentage of GDP for primary, secondary and tertiary
|-
| '''GDSED'''
| Spending shares by level of education
|}

Like all of IFs, the education module is amenable to systems thinking, or conceptualizing elements as stocks and flows. Students flow through primary, lower-secondary, upper-secondary and tertiary education levels. Each time an age cohort completes a grade level, a year is added to that group’s stock of educational attainment; as adults (aged either 15 and older or 25 and older) they will have attained some number of years of formal education based on grades completed. For any given grade, the number of students enrolled is determined by the intake rate—or, at the secondary level, the transition rate from the previous level of education—and by the percentage of students passing through the last grade. Government spending influences the system by restraining the number of students that can be sustained at a given level of education.

Most analysis of the international education system focuses on educational attainment. IFs operationalizes this stock as the average years of education successfully completed by adults in all countries, following a typology designed by Harvard economists Robert Barro and Jong-Wha Lee (2000, 2001). '''EDYRSAG15''' captures the mean years of education attained for all adults older than 15; this important variable links forward to several other sectors of the model, including economics through multifactor productivity, health through fertility rate, and even governance through state fragility indicators. Users may wish to view mean years of education by other age breakdowns, available through '''EDYRSAG25''' and '''EDYRSAG15TO24''' though these variables do not have the forward linkages in the model that '''EDYRSAG15''' does. As an alternative to average years of education, '''EDPRIPER, EDSECPER and EDTERPER''' track attainment as a percentage of the population to successfully complete each level of education.

In addition to attainment measures, most users will focus their attention on enrolment rates, the most common measure of student flows. Enrolment rates answer the simple question: at a given time, how many children are in school?

Enrollment is captured in the variables '''EDPRIENRG''' and '''EDPRIENRN''' for primary, '''EDSECENRG''', '''EDSECENRN''', '''EDSECLOWERENRG''' and '''EDSECUPPRENRG''' for secondary and '''EDTERENRG''' for the tertiary level'''.''' Many of the indicators used to measure education systems are either gross rates or net rates. This important distinction has to do with student age. Gross Enrollment Rates (GER), for example, are calculated by dividing the total student body by the official school-age population; Angola’s primary GER would be the number of primary students enrolled in school divided by the primary age population of the country and has often exceeded 100 percent because of primary enrollment by over-aged students who enter or return to school for various reasons. Net Enrollment Rates (NER), on the other hand, measure only the students of official school age. A country’s NER is the enrolled age-appropriate students divided by only the school-age population and should not exceed 100 percent. Net enrolment data are often more difficult to find than gross, simply because it requires the age of enrollees to calculate. Intake rates are also presented in both net and gross formulations. For a more detailed description of commonly used education indicators see UNESCO’s technical guidelines: <nowiki>http://www.uis.unesco.org/Library/Documents/eiguide09-en.pdf</nowiki>.

For each of the countries in IFs, two factors ultimately control enrolment rates: intake rates and survival rates. Drawing on drivers such as GDP per capita and educational spending, the model forecasts intake rates for primary and tertiary levels ('''EDPRIINT, EDPRIINTN, and EDTERINT''') and transition rates from the previous level of schooling for secondary education ('''EDECLOWRTRAN EDSECUPPRTRAN'''). The model also forecasts a student persistence rate for all levels (except tertiary), often referred to as survival rate, captured in the variables '''EDPRISUR''', '''EDSECLOWRSUR''', and '''EDSECUPPRSUR'''. Enrolment rates at each level are calculated as the combined result of these student flows. Or thought of more simply, enrolment rates track the number of students who enter school and stay in school. Graduation rates at all levels are also forecast and the corresponding variables are: '''EDPRICR''', '''EDSECLOWRGRATE''', '''EDSECUPPRGRATE''' and '''EDTERGRATE'''. The model also forecasts vocational shares in lower secondary and upper secondary and the variables are '''EDSEECLOWRVOC''' and '''EDSECUPPRVOC'''.

All indicators in the model that measure the stock of educational attainment or student flows can be disaggregated by gender. Thus, the user is able to evaluate each country’s progress toward eliminating gender-based disparity in the classroom. In order to disaggregate performance by gender, a second dimension of almost all education variables allows the selection of display by male, female or total. In addition to setting normative goals, separate treatment of female education allows the model to establish powerful forward linkages. For example, female educational attainment and fertility rates are negatively related, because schooling increases the opportunity cost of having and raising a child.

In addition to student flows, the model captures financial flows tied to each country’s education system. Educational finance is the result of interplay between supply-side and demand-side forces. Demand for education can be conceptualized as the product of spending per student and enrollment rates at each level of schooling—'''EDEXPERPRI, EDEXPERSEC''' and '''EDEXPERTER''' track spending per student as a percent of GDP per capita for primary, secondary and tertiary education. However, such a formulation of demand is crude, as the number of children already in the system is constrained by the preexisting structure of government spending. Financial supply, captured as a percent of GDP by the variable '''GDSED''', is generated by the government finance submodule. Due to a lack of reliable data, IFs does not capture private educational funding, though it is important in some countries.

== Parameters to Affect Intake Rates and Survival Rates: Annual Growth ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:135px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:234px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:145px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edpriintngr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edterintgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edseclowrtrangr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edsecupprtrangr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edprisurgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edtergradgr'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary graduation rates

| nowrap="nowrap" style="width:145px;height:22px;" |
Annual growth

|}

In the IFs model, improved education outcomes can be achieved most directly through changes to intake and survival rates. The number of students entering the first grade of each schooling level can be pushed up or down using annual (percentage point) growth parameters: '''edpriintngr''' for primary school''', edseclowrtrangr''' for transfer rates between primary and secondary''', edsecupprtrangr''' for transition between lower and upper secondary and '''edterintgr''' for intake into the tertiary level'''.''' However, any improvements to enrolment stemming from intake rate boosts are restrained when coupled with high dropout or repetition rates. Like intake rates, survival rates can be directly manipulated through annual growth parameters. '''Edprisurgr''' controls survival growth (in percentage points) at the primary level. And finally, '''edtergradgr''' controls tertiary survival rates (also referred to as graduation rates).

Though intake rates and survival rates can be manipulated separately, users will often wish to manipulate them in conjunction due to the presence of interaction effects between the two sets of variables. For example, a rapid increase in intake/transition rates can result in a decline in country’s survival rate. The educational system may simply not have the resources to cope with an influx of students. Furthermore, the incoming cohort, which has spent years outside the system, may represent individuals from disadvantaged backgrounds—a group prone to dropping out. On the other hand, rising intake rates may have a positive threshold effect on survival rates. In many countries, once intake rates reach a certain level, dropout and repetition have declined. At this time such relationships are not captured in IFs. Thus, the user may want to build interaction and threshold effects into education policy interventions.

== Parameters to Affect Intake Rates and Survival Rates: Target Year for Universal Education ==

{| border="1" cellspacing="1" cellpadding="1" width="638" style="width:638px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:126px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:192px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:193px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edpriintntrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:192px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edseclowrtrantrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:192px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edsecupprtrantrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:192px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edterinttrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:192px;height:22px;" |
Tertiary Intake Rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%    

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edprisurtrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:192px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''edtergradtrgtyr'''

| nowrap="nowrap" style="width:126px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:192px;height:22px;" |
Tertiary graduation rates

| nowrap="nowrap" style="width:193px;height:22px;" |
No. of years to reach 100%  

|}

Development goals like universal access to education with a specific target date can be explored through target year parameters on intake (transition) or survival (graduation) rates. These parameters ramp up the base year value of the variable of interest to one hundred percent within the number of years marked by the parameter. For countries with limited availability of government resources, these target parameters might need an education budget set aside through the '''edbudgon''' parameter described later in the budget sub-section.

== Parameters to Affect Intake Rates and Survival Rates: Multiplier ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:135px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:234px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:145px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edpriintnm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary net intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edterintm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary intake rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edseclowrtranm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECLOWRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Lower secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edsecupprtranm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:234px;height:22px;" |
Upper secondary transition rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edprisurm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:234px;height:22px;" |
Primary survival rate

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''edtergradm'''

| nowrap="nowrap" style="width:135px;height:22px;" |
EDTERGRAD

| nowrap="nowrap" style="width:234px;height:22px;" |
Tertiary survival rates

| nowrap="nowrap" style="width:145px;height:22px;" |
Multiplier on base case

|}

In addition to the growth and target parameters described above, intake (or transition) and survival (or graduation) rates can also be modified using a set of multipliers listed in the table above. Like other multipliers in the model these work by ramping up (or down) the base case forecast over a horizon chosen in the scenario design.

== Parameters to Affect Education Spending ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:190px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:195px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''gdsm (education)'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Government spending on education

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edbudgon'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Education funding impact and priority

| nowrap="nowrap" style="width:121px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''gdsedm'''

| nowrap="nowrap" style="width:190px;height:22px;" |
GDSED

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending distribution

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexppconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERPRI

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, primary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexpslconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERSEC

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, secondary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexpsuconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERSEC

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, secondary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edexptconv'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERTER

| nowrap="nowrap" style="width:195px;height:22px;" |
Education spending per student, tertiary

| nowrap="nowrap" style="width:121px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:126px;height:22px;" |
'''edqtqltrm'''

| nowrap="nowrap" style="width:190px;height:22px;" |
EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS

| nowrap="nowrap" style="width:195px;height:22px;" |
Education quantity-quality balance

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplier

|}

Attainment levels also respond indirectly to changes in the educational spending structure. The number of students enrolled at all grade levels is constrained by total public spending on education. In IFs, public finance allocations are distributed between transfer payments, the military, education, health and infrastructure in the government budget submodule. Bottom-up factors like demographic changes and policies targeting intake or survival will pressure the government to increase education spending. But the model features somewhat rigid top-down control of the budget—spending on education competes with other government spending and IFs maintains accounting of both total government revenues and expenditures. Most countries spend something close to the global average of 5 percent of GDP on education each year. Thus demand-side shifts may secure an increase in resources, but it is difficult to increase total education spending much in excess of historically observed values. Users can intervene in this accounting process by manipulating '''gdsm (education)''', a multiplier that directly increases the share of government expenditure accounted for by education, at the expense of spending in other arenas such as military or health. Alternatively, users can prioritize education spending over other government expenditure targets. The priority parameter '''edbudgon''', takes on values from zero to one, with lower values representing lower priority to education funding demands and one representing maximum priority. Any non-zero value pushes education allocations away from those determined by historical spending patterns and level of economic development—as calculated in the economic module—and toward the bottom-up demand for expenditures calculated in the education model. The parameter '''edbudgon''' can also be used to completely turn off the budget impacts on student flow rates through a zero value of the parameter. The turning off of budget impacts is helpful in projecting the demand for funds for certain types of education intervention, e.g., universal primary education. However, fund demand in such a scenario is calculated in the EDTOTCOST and not in the GDS(Education) variable, which is determined, in this case, completely through the government budget allocation algorithm without using any input from the education model.

The government-budget submodule automatically allocates resources to the educational level most in need (although that interacts with historical patterns of educational preference), starting with primary education. As enrolment rates in a country’s primary education system reach a very high level, the model reallocates funding up the chain to secondary. Users may wish to change this pattern and create normative scenarios that prioritize higher levels of education, or balance spending between primary, secondary and tertiary. The '''gdsedm''' parameter allows for such experimentation via a second dimension that allots the spending increase to a specific level of schooling.

When developing education budgets, planners must account for trade-offs between equity and efficiency. What is more important, increasing total spending on the system in an effort to support more students, or increasing spending per student in an effort to improve the experience of being in school? In a crude way this can be thought of as the difference between increasing intake and increasing survival rates. IFs includes parameters that shift the spending focus in the direction of system efficiency. '''Edexppconv''' can be used to decrease the time it takes a country’s initial spending per primary student to converge with the model’s expected function. '''Edexppslconv''' and '''edexppuconv''' control the student expenditure function for lower and upper secondary, and '''edexpptconv''' does the same for the tertiary level'''.''' Increasing the target value used in a convergence parameter will increase per student spending for countries below the expected value, but the opposite is true in countries exhibiting higher than expected values. If a user wishes to boost per student expenditure in a high performing country, he or she should make use of the '''edqtqltrm''' parameter, a multiplier that shifts emphasis away from education quantity and towards education quality—with values less than one indicating a shift towards quality.

== Parameters to Affect Gender Parity ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:122px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:239px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:116px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edprigndreqintn'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDPRIINTN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for primary intake

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edprigndreqsur'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDPRISUR

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for primary survival

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edseclowrgndreqtran'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDSECLWRTRAN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for lower secondary transition

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edsecupprgndreqtran'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDSECUPPRTRAN

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for upper secondary transition

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edtergndreqint'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDTERINT

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for tertiary intake

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|-
| nowrap="nowrap" style="width:155px;height:22px;" |
'''edtergndreqgrad'''

| nowrap="nowrap" style="width:122px;height:22px;" |
EDTERSUR

| nowrap="nowrap" style="width:239px;height:22px;" |
Gender parity for tertiary survival

| nowrap="nowrap" style="width:116px;height:22px;" |
Convergence speed

|}

Some development projects focus on gender disparity in education, rather than aggregate enrolment. In recent decades male and female enrolment rates have converged rapidly across all regions of the world. In fact, primary, secondary and tertiary enrolment taken as a global aggregate has already surpassed a female-male ratio of 0.97—commonly considered an indicator of parity. However<ins>,</ins> this abstracted view obscures struggles facing women in low-performing areas, especially sub-Saharan Africa. Users may explore effects linked to improvements in female-specific enrolment rates via the intake and survival parameters described above; each parameter has a dimension that applies the annual increase to males, females, or the total student population. Alternatively, users can experiment with closing the gender attainment gap existing in some countries by setting time sensitive goals. Goals around intake can be set through '''edprigndreqintn''' for primary, '''edsecupprgndreqtran''' for secondary and '''edtergndreqint''' for the tertiary level. Gender conversion goals may also be set for the other direct driver of enrolment, survival rates: '''edprigndreqsur''' controls female survival rates at the primary level''',''' and '''edtergndreqgrad''' controls tertiary rates. Each of the gender-focused parameters are convergence speed goals, meaning the user sets the number of years that will elapse before the country’s gender parity in the corresponding measure converges to the expected function.

== Parameters to Affect Education Quality ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edqualpriallm'''
|EDQUALPRIALL and EDQUALAG15PRI
|Multiplier on primary education learning quality
|Multiplier
|-
|'''edqualsecallm'''
|EDQUALSECALL and EDQUALAG15SEC
|Multiplier on secondary education learning quality
|Multiplier
|-
|'''edqualag15m'''
|EDQUALAG15
|Multiplier on total learning quality for adults
|Multiplier
|-
|'''mfpedqual'''
|MFPHC
|Elasticity of multifactor productivity to quality of education
|Elasticity
|}

In addition to the education model described above IFs also has a representation of learning quality. Learning quality is represented at the primary, secondary levels<ins>,</ins> and for the adult population as a whole, for both sexes for four categories (math, science, reading and total). There is also a linkage from education quality to multifactor productivity.

Users can build scenarios with increased or decreased levels of learning quality through the multipliers '''edqualpriallm''' (learning quality at the primary level), '''edqualsecallm'''(learning quality at the secondary level) and '''edqualag15m''' (learning quality for the adult population as a whole). Users can change the elasticity of learning quality to multifactor productivity using the parameter '''mfpedqual'''.

== Parameters to Affect Education Grade-Level Model ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edmodelsw'''
|N/A
|Education switch between old and new model (0=old model; 1=new model)
|Switch
|-
|'''edpridrpgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edprienrgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|-
|'''edseclowrdrpgrdm'''
|EDSECLOWRDRPGRD/ EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edseclowrenrgrdm'''
|EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|}

== Prepackaged Scenarios ==

An installation of IFs includes scenarios used in each entry in the Pardee Institute’s Potential Patterns of Human Progress (PPHP) series. Scenarios created for the education volume include: a best and worst case framing scenario; case study interventions in several countries and regions; high spending and low spending scenarios; and over twenty normative scenarios (Dickson et al., 2010). This section will briefly describe the parameters used to construct a single normative scenario titled, Norm Mar 1 2009.

Norm Mar 1 2009 simulates a worldwide improvement in educational performance through interventions to: intake rates, student persistence, gender parity and educational spending, including boosts to foreign aid. From 2010 to 2100, the parameter '''edpriintngr''' is set to 2.2, and '''edprisurgr''' is set to 1.2, setting up annual increases to primary school intake and survival rates. The scenario includes similar annual growth increases for intake and student persistence across lower secondary and upper secondary schooling. '''Edseclowrtrangr''' is set to one percent, '''edsecupprtrangr''' to 0.5 percent. The corresponding parameters for tertiary education are not assigned a value other than the default of zero. Additional interventions speed up the narrowing of persisting gender gaps in intake, survival and transition rates. The parameters '''edprigndreqintn and edprigndreqsur''' set a ten year time goal for gender parity at the primary level; '''edseclowrgndreqtran''' sets a thirteen year goal for parity at the lower secondary level; and, '''edsecupprgndreqtran''' sets a 20 year time goal for parity at the upper secondary level. In order to simulate improved education quality, across levels, '''edexppconv, edexpslconv, edexpsuconv and edexptconv''' all take the value of 20, signifying a convergence to the expected function after 20 forecast years. Because this scenario was created as a tool to explore the potential impacts of a world in which educational goals are achieved by all nations, the budget switch, '''edbudgon''', is set to 0, the highest prioritization of education funding.

= Economic Module =

== Variables of Interest ==

The treatment of economics in IFs draws on both the classical tradition’s focus on economic growth (with great attention in IFs to the newer work on endogenous growth theory) and the neoclassical perspective's general equilibrium approach.

The economic module is a core component of the IFs system for multiple reasons, in particular for its close interactions with all other modules. On the input side, variables from almost all other modules affect production levels. On the output side, the magnitude of GDP and the level of GDP per capita are critical, in turn, for essentially all other modules. Mostly closely linked to the economic module are the energy and agriculture modules, both of which use a partial equilibrium structure that echoes the one in the economic module, and both of which provide physical values that fully determine the currency value-based representations of their respective sectors in the economic model.

Basic economic variables include: GDP at market exchange rates ('''GDP'''), GDP at purchasing power parity ('''GDPP'''), GDP per capita at market exchange rates ('''GDPPC'''), and GDP per capita at purchasing power parity ('''GDPPCP'''). The model represents all of these in constant 2011 dollars (the interface allows the user to convert to other currencies). The model also includes a representation of the portion of the economy that is informal.

=== Multifactor Productivity ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''GDP'''
| Gross domestic product
|-
| '''GDPP'''
| GDP at purchasing power parity
|-
| '''GDPPCP'''
| Per capita GDP at purchasing power parity
|-
| '''GDPPC''' 
| Per capita GDP
|-
| '''LAB''' 
| Labor 
|-
| '''KS''' 
| Capital stock 
|-
| '''I''' 
| Investment 
|-
| '''POPRETIRED''' 
| Retired Population 
|-
| '''MFP (HC,SC,PC,KN)''' 
| Multifactor productivity 
|}

The supply side of the economic module is based on the Cobb-Douglas Production Function and uses labor ('''LAB'''), capital ('''KS'''), and multifactor productivity ('''MFP''') as the primary drivers of economic growth. Capital stock ('''KS''') is a function of investment ('''I''') and depreciation rates. Labor supply ('''LAB''') is determined from population and endogenously derived labor force participation rates.

While the treatment of capital and labor in the IFs system will be familiar to users with an understanding of neoclassical economics, the treatment of productivity within IFs deserves greater explanation. Unlike most neoclassical models, which primarily focus on technology as the determining factor of productivity in their equations, the IFs system uses a broader definition of productivity called multifactor productivity (MFP). This multi-factor productivity term in IFs has four basic components: human ('''MFPHC'''), social ('''MFPSC'''), physical ('''MFPPC'''), and knowledge capital productivity ('''MFPKN'''). Each of these components can take on a positive or negative value depending on whether the calculated value of the component is providing a positive or negative impact to economic growth rates relative to what would be expected based on the country’s level of development. (See the Development Profile form in IFs to display the magnitude and direction of the four productivity elements.)

Drivers of multifactor productivity vary by component. '''MFPHC''' is driven by years of education, education expenditures, life expectancy and health expenditure. '''MFPSC''' is driven by Freedom House’s measure of political freedom (a variable describing democracy), governance effectiveness, corruption perceptions, and economic freedom. '''MFPPC''' is driven by two separate indices of infrastructure: traditional (roads, electricity, and water and sanitation), and information and communications technology (ICT). Finally, '''MFPKN''' is driven by R&D expenditures and economic integration (in the model, trade serves as a proxy for trade). This final component of MFP represents a measure of connectedness to the global economy. Altering any of these using the appropriate parameters will result in changes to the relevant component of multifactor productivity and therefore to economic growth.

=== The Social Accounting Matrix and Domestic Finance ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''C'''
| Private consumption
|-
| '''SAVINGS'''
| Net national savings
|-
| '''HHINC'''
| Household income
|-
| '''HHSAV'''
| Household savings
|-
| '''FIRMINC'''
| Firm income
|-
| '''FIRMSAV'''
| Firm savings
|-
| '''IDS'''
| Investment by destination sector
|-
| '''GOVREV'''
| Government revenue
|-
| '''GOVEXP'''
| Government expenditures
|-
| '''GOVHHTRN'''
| Government household transfers
|-
| '''GOVCON'''
| Government consumption
|-
| '''GDS'''
| Government spending by destination
|-
| '''GOVBAL'''
| Government balance
|-
| '''GOVDEBT'''
| Government debt
|}

The production function is embedded in a six sector model of the economy featuring agriculture, raw materials, energy, manufactures, services, and ICT that balances domestic demand and trade in a general equilibrium seeking structure. Production and consumption of goods and services are in turn incorporated into a larger social accounting matrix (SAM) which represents the behavior and financial interaction of households, firms and government. A social accounting matrix traditionally represents ''flows'' among different economic sectors and agent categories (i.e., households, firms, and government). For instance, it represents private consumption ('''C''') and net national savings ('''SAVINGS'''), as well as household income ('''HHINC''') and savings ('''HHSAV'''); firm income ('''FIRMINC'''), investment by sector ('''IDS'''), and savings ('''FIRMSAV'''); government revenues ('''GOVREV'''), total expenditures with transfers ('''GOVEXP'''), transfers to households ('''GOVHHTRN'''), directed consumption in total ('''GOVCON'''), and by sector ('''GDS'''), and balance ('''GOVBAL'''). IFs builds a full and balanced social accounting matrix of these and many other inter-agent flows. It also creates a second matrix which represents financial ''stocks'' (assets and liabilities) of different agent categories for all countries in the system, including for instance, government debt ('''GOVDEBT'''). The representation of stocks in this fashion provides the foundation upon which the system adjusts flows of finance among different agents and among countries over time (see, for instance, the net foreign debt or '''XDEBT''' of countries), maintaining consistency with the liability-asset approach used in standard accounting systems. The behavior of agents within this system is not fixed, like it is in many computable general equilibrium models (which use SAMs commonly). Instead, agent behavior is partially endogenized using algorithms that allow the behavior of agents to shift depending on the levels of stocks of relevant variables within the SAM. So, for example, different levels of government debt trigger different patterns of government spending in IFs.

Because of its centrality to the IFs system, users should understand the basic character of the social accounting matrix. Both the stock and flow matrices are available in the specialized displays menu in the display section of the main menu. The social accounting matrix loads the flow matrix by default, but the stock matrix can be accessed via the Show Stocks button on the bar at the top. Full breakouts of the SAM into its component parts is also available on this screen by clicking the button Expand SAM on the same top bar.

=== International Trade and Finance ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''CURACT''' 
| Current account balance
|-
| '''CAPACT'''
| Capital account
|-
| '''TRADEBAL'''
| Trade balance
|-
| '''XWORKREMIT'''
| Worker remittances from abroad 
|-
| '''AID''' 
| Net foreign aid receipts 
|-
| '''X or XS''' 
| Exports and exports by sector 
|-
| '''M or MS''' 
| Imports and Imports by sector 
|-
| '''ENX/ENM''' 
| Energy exports/imports 
|-
| '''AGX/AGM''' 
| Agricultural exports/imports 
|-
| '''XFDIFIN''' 
| Inward flow of FDI 
|-
| '''XFDIFOUT''' 
| Outward flow of FDI 
|-
| '''XFDISTOCK''' 
| Inward stocks of FDI 
|-
| '''XFDISTOUT''' 
| Outward stocks of FDI 
|-
| '''XPORTFIN''' 
| Inward flows of portfolio investment 
|-
| '''XPORTFOUT''' 
| Outward flows of portfolio investment 
|-
| '''XPORTFOLIO''' 
| Inward stocks of portfolio investment 
|-
| '''XPORTSTOUT''' 
| Outward stocks of portfolio investment 
|-
| '''XDEBTRPA''' 
| External debt, relative price adjusted 
|-
| '''EXRATE''' 
| Exchange rate 
|}

The international financial position of a country is typically represented by the balance of payments, which is equal to zero if all flows of goods and finance into and out of a country are included. The balance of payments is determined by the status of three indicators, the current account ('''CURACT'''), the capital account ('''CAPACT'''), and the foreign reserve account (not explicitly modeled in IFs). Imbalances can exist in any of these if imports of goods and services outweigh exports for example, or if governments spend down foreign reserves.

In IFs, the status of the current account balance ('''CURACT'''), which reflects the many flows into and out of a country, is a function of the trade balance ('''TRADEBAL'''), net foreign worker remittances ('''XWORKREMIT'''), net foreign aid ('''AID'''), and net interest on foreign debt. Worker remittances are calculated on the basis of the size of the population of a country that is living and working abroad.

Of these the trade balance and treatment of trade within IFs more generally is worth further discussion. Trade in the IFs system is part of the social accounting matrix structure and is modeled using a pooled rather than bilateral approach. This means that IFs tracks information on gross exports and imports of countries by sector and in total. An algorithm sums price-adjusted import demand and export capacity across all countries that trade in a given sector and defines world trade as the average of those two values. Demand and capacity are then normalized to the total of world trade to determine total and sectoral exports ('''X''' or XS '''by''' sector) and imports ('''M''' or '''MS''' by sector) by country. As already noted, the agricultural and energy modules each represent production, consumption and trade that supersede the results of the economic module’s production function and SAM. For instance, exports and imports in these modules are represented separately within the IFs system via '''ENX'''/'''ENM''' and '''AGX'''/'''AGM'''.

Interacting with the current account is the capital account ('''CAPACT'''). It captures the flows of foreign direct and portfolio investment into ('''XFDIFIN''' and '''XPORTFIN''') and out of countries ('''XFDIFOUT''' and '''XPORTFOUT'''). The system also represents the stocks of inward and outward FDI and portfolio investment ('''XFDISTOCK'''/ '''XPORTFOLIO''' and '''XFDISTOUT'''/'''XPORTOUT'''). Together the current and capital account flows shape the stock of relative-price-adjusted net foreign indebtedness ('''XDEBTRPA'''), which in turn via an equilibration process changes the exchange rate ('''EXRATE'''). The exchange rate, in interaction with local relative prices, affects trade and financial flows over time as a key part of that equilibration process.

=== Informal Economy ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''LABINFORMSHR'''
| Informal share of the labor force 
|-
| '''LABINFORMPCNTINF''' 
| Portion of informal labor employed inside informal sector 
|-
| '''LABINFORMPCNTNONINF''' 
| Portion of informal labor employed outside the informal sector (in formal enterprises or households) 
|-
| '''GDPINFORMSHR''' 
| Informal share of GDP 
|-
| '''GDPSHADOWSHR''' 
| Shadow economy share of GDP 
|-
| '''EDYRSAG15''' 
| Educational attainment of adults 15 and older 
|-
| '''GOVBUSREGIND''' 
| Government business regulation index (higher is better) 
|-
| '''GOVCORRUPT''' 
| Government corruption (higher is less corrupt) 
|-
| '''GOVHHTRN''' 
| Government transfers to households for welfare and pensions 
|-
| '''GDS, R&D''' 
| Government spending on research and development 
|-
| '''FIRMTAXR''' 
| Tax rate (direct) on firms 
|-
| '''HHTAXR''' 
| Tax rate (direct) on households 
|-
| '''SSWELTAXR ''' 
| Tax rate (direct) on households and firms for social welfare 
|-
| '''RANDDEXP''' 
| Total (public and private) spending on research and development 
|-
| '''TEFF ''' 
| Stock of total (multifactor)productivity 
|}

The economic module represents the informal economy as the share of the total labor force employed informally ('''LABINFORMSHR'''), the share of total GDP generated by informal activities ('''GDPINFORMSHR'''), and the share of total GDP generated by the shadow economy ('''GDPSHADOWSHR'''). The informal labor share is driven by four main variables: the educational attainment of adults 15 years of age and older ('''EDYSAG15''', see the education model for parameters to affect this variable), the government business index ('''GOVBUSREGIND'''), the ratio of government transfers to households as a portion of GDP ('''GOVHHTRN'''), and the tax rate on firms ('''FIRMTAXR''').

The informal labor share is divided into two sectors, the portion of informal labor employed inside the informal sector ('''LABINFORMPCNTINF''', informal) and the portion of informal labor employed in the formal sector (represented by a simple residual '''LABINFORMPCNTNONINF'''). Each sector is further divided by sex.

The share of informal labor in total labor and the portion of informal labor employed inside the informal sector both help drive informal and shadow GDP shares. Other drivers include the level of corruption ('''GOVCORRUPT'''), public expenditure on research and development as a percentage of its GDP ('''GDS, R&D'''), and total (public and private) spending on research and development ('''RANDDEXP).'''

While informal ''and'' shadow GDP shares are initialized in the Base Case, ''only'' informal ('''GDPINFORMSHR''') has active forward linkages. To drive the forward linkages with the shadow economy share instead, you must change the switch, '''gdpshadowon''', from its default, Base Case setting of 0 to 1 (see Parameters for Affecting the Informal Economy section below).

The informal GDP shares, in turn, have forward linkages to total or multifactor productivity ('''TEFF'''), effective household ('''HHTAXR)''' and firm tax rates, as well as to the effective rate for social welfare ('''SSWELTAXR''').

== Parameters to Affect Production and Growth ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
| colspan="4" |''Labor''
|-
|'''labparm'''
|LAB
|Labor participation rate multiplier
|Multiplier
|-
|'''labfemshrm'''
|FEMSHRLAB
|Female labor force participation rate multiplier
|Multiplier
|-
|'''labretagem'''
|LAB
|Retirement age, labor force participation multiplier
|Multiplier
|-
|'''labunemprm'''
|LABUNEMPR
|Labor unemployment rate multiplier
|Multiplier
|-
|'''labmarketsw'''
|LABS
|Labor demand to economic production switch (1=on)
|Switch
|-
|'''labdemsm'''
|LABDEMS
|Labor force demand by sector multiplier
|Multiplier
|-
|'''labcoeffsm'''
|LABCOEFFS
|Labor demand, multiplier on technology coefficients
|Multiplier
|-
|'''workageentry'''
|POPRETIRED
|Age at which individuals enter the labor force
|Exogenous specification
|-
|'''workageretire'''
|POPRETIRED
|Age at which individuals leave the labor force
|Exogenous specification
|-
| colspan="4" |''Capital''
|-
|'''invm'''
|IDS
|Multiplier on all types of investments
|Multiplier
|-
| colspan="4" |''Productivity''
|-
|'''mfpadd'''
|MFP components
|Multifactor productivity, additive
|Additive factor
|-
|'''mfpleadr'''
|MFP
|MFP growth rate of the technological lead country
|Growth Rate[HL1]
|-
|'''mfpedspn'''
|GDS-educ
|Elasticity of multifactor productivity to education spending
|Elasticity
|-
| '''malnelimstartyr'''
|MALNPOPP
|Undernourishment elimination start year
|Standard error target
|-
| '''malnelimtargetyr'''
|MALNPOPP
|Undernourishment elimination target year
|Standard error target
|-
| '''malelimprecisesw'''
|MALNPOPP
|Undernourishment elimination
|Switch
|-
|'''ylm'''
|YL
|Agricultural yield
|Multiplier
|-
|'''aginvm'''
|INAG and IDS
|Investment in agriculture
|Multiplier
|-
|'''econfreem'''
|ECONFREE
|Economic freedom
|Multiplier
|-
|'''goveffectm'''
|GOVEFFECT
|Government effectiveness
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption
|Multiplier
|-
|'''freedomm'''
|FREEDOM
|Freedom/democracy
|Multiplier
|-
|'''mfpinfrindtrad'''
|MFPPC
|Elasticity of multifactor productivity to traditional infrastructure index
|Elasticity
|-
|'''mfpinfrindict'''
|MFPPC
|Elasticity of multifactor productivity to ICT infrastructure
|Elasticity
|-
|'''mfpenpri'''
|WEP
|Elasticity of multifactor productivity with energy prices
|Elasticity
|-
|'''gdsm, R&D'''
|GDS, R&D
|Government expenditures towards R & D
|Multiplier
|}

Based upon the discussion of key variables, the areas of likely interest to users are clear: parameters that affect production and growth most directly, parameters that affect domestic finance (including government) and the flows of the social accounting system, and parameters that affect trade and finance. We start with production and growth and the key parameters affect labor, capital and multifactor productivity. Parameters exist in the IFs system that allows users to modify any of the component parts of the Cobb Douglas Production function. Parameters affecting labor supply include '''labparm,''' '''labfemshrm,''' and '''labretagem'''. The first of these, '''labparm''' modifies the labor force participation rate, while the second changes the female share of the existing labor force, and the last, '''labretagem,''' changes the number of retired persons who participate in the labor market. Other parameters, discussed in the demographic section, also impact the size of the labor force. The parameters '''workageentry''' and '''workageretire''', will change when people enter and leave the labor force, thus altering the length of time they are employed and the size of the labor pool. In addition to these, the user can influence the unemployment rate in the population through the multiplier '''labunemprm.''' The activation of the switch '''labmarketsw''' allows a user to observe the impact of changes in labor demand on production.

For the demand side of labor, the parameter '''labcoeffsm''' helps a user influence the labor demand co-effcicients by sector and skill level (which are measured in terms of millions of people per dollar). The multiplier '''labdemsm''' influences total labor demand by sector and skill level. ''' '''

The labor market demand and supply equilibrate on the basis of the variable '''LABWAGEIND.''' This equilibration occurs through four co-efficients that equilibrate demand and supply and demand through a PID (Proportional, Integral and Differential) formulation. '''ellabwage1''' and '''ellabwage2''' equilibrate demand and supply for labor while '''elwageunemp1''' and '''elwageunemp2''' account for the impact of unemployment on the equilibration. ''' '''

Users can affect capital accumulation by changing the parameter '''invm''', which alters investment in the economy using a multiplicative approach. Unlike spending multipliers discussed later under Domestic Finance, this multiplier cannot be broken out by destination. It will also affect savings because domestic savings rates are directly tied to investment rates. In addition to altering savings, investment rate changes affect the rate of capital accumulation.

Two brute force multipliers on productivity are available to users. First, '''mfpadd''' allows users to increase productivity growth in an additive manner on a country-by-country basis. Second, the parameter '''mfpleadr''' allows users to set the growth rate of the world’s technological leader, the United States. Since all other country’s productivity is tied to the leader, changes to this parameter model assumptions about global economic growth rates, which will tend to change productivity growth rates across a number of countries. Although both these parameters represent powerful ways to affect GDP and growth, users should note that they do not carry a cost. Like other brute force multipliers, there is no cost accounting system that might limit the impact of changes introduced, so users must be extremely careful to justify any changes made with these parameters.

Users can change any one of the components of multifactor productivity by affecting the drivers of the component. Any of the parameters that impact life expectancy, education levels, or health and education spending, as discussed in the health or education modules, will impact MFPHC. Examples include but are not limited to: '''mfpedspn''', '''ylm''', and '''aginvm.'''

Similarly, anything that affects drivers of social capital will affect MFPSC. These include many of the governance parameters, such as: '''econfreem''', '''goveffectm''', '''govcorruptm''', and '''freedomm.'''

Physical capital is primarily impacted by two indices of physical infrastructure development, one for traditional infrastructure, which includes electricity, transportation, and water and sanitation, and a separate index for ICT. Users can affect the elasticity of MFP to these indices using '''mfpinfrindtrad''', and '''mfpinfrindict'''. By changing these parameters users can make the value of MFPPC more or less responsive to the speed of growth in the two areas of infrastructure discussed above. They can also alter the elasticity of MFP to the world energy prices using '''mfpenpri''', which performs a similar function for changing the relationship of MFPPC to energy prices globally. See the discussion of the infrastructure module to see the large number of parameters that affect the specific elements of the two general infrastructure indices.

Knowledge capital will respond to changes in the parameter '''gdsm,''' targeted at changing the amount of government spending directed towards research and development. It will also respond to the level of trade interconnectedness, which the level of protectionism ('''protecm''') can affect—to be discussed again below.

== Parameters to Affect Domestic Financial Flows and the Social Accounting System ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:81px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:316px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:109px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govrevm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:316px;height:22px;" |
Government revenue multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdsm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDS

| nowrap="nowrap" style="width:316px;height:22px;" |
Government spending by destination sector

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdsedm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDS

| nowrap="nowrap" style="width:316px;height:22px;" |
Education spending distribution multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govhhtrnpenm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GOVHHTRN

| nowrap="nowrap" style="width:316px;height:22px;" |
Government pension spending multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''firmtaxrm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on firms, households, and indirect taxation

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''hhtaxrm (skilled, unskilled)'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on households, broken down by skilled and unskilled

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''indirecttaxrm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
SAVINGS

| nowrap="nowrap" style="width:316px;height:22px;" |
National tax rates on firms, households, and indirect taxation

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''ginidomr'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GINIDOM

| nowrap="nowrap" style="width:316px;height:22px;" |
Domestic Gini Index growth rate

| nowrap="nowrap" style="width:109px;height:22px;" |
Growth Rate

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''ginidomm'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GINIDOM

| nowrap="nowrap" style="width:316px;height:22px;" |
Domestic Gini Index multiplier

| nowrap="nowrap" style="width:109px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''gdprext'''

| nowrap="nowrap" style="width:81px;height:22px;" |
GDP

| nowrap="nowrap" style="width:316px;height:22px;" |
GDP growth rate

| nowrap="nowrap" style="width:109px;height:22px;" |
Growth Rate

|}

The leverage points within the area of domestic financial flows and the social accounting matrix of greatest interest for most analyses and government revenues and expenditures. The brute force multiplier on government revenues ('''govrevm''') will increase or decrease those and have wide secondary effects as processes play out to adjust revenue raising and expenditure patterns to the new level. On the outflow side, the government spending by destination multiplier ('''gdsm''') will change patterns of government consumption using a multiplier that acts on military, health, education, R&D, infrastructure, other infrastructure, and other (the residual). Education spending can also be modified using '''gdsedm,''' which directly modifies spending on education by level (as discussed in the education module). Unless total government consumption ('''GOVON''') is altered, however, changes in spending patterns by target will create shifts from one category to another rather than changes in the total, because the model will normalize the sum of spending by sector to that total. Similarly, the user can use multipliers on government pension ('''govhhtrnpenm''') spending to change levels in either of those categories, but subject along with direct consumption expenditure to the constraint of total government expenditures ('''GOVEXP'''). Hence, parameters that affect government revenues are very important.

Changes in the tax rates can have profound implications for government capacity, and for this reason IFs contains a number of parameters to alter taxation rates for different actors, in addition to the direct multiplier on government revenues, which will cause the model to endogenously adjust such tax rates. Of these, three stand out. '''Hhtaxrm''' affects household savings rates, '''firmtaxrm''' modifies firm taxation rates, and '''indirecttaxrm''' changes the indirect taxation rate on goods and services. The household taxation rate multiplier is subdivided according to whether households are skilled or unskilled. Users who are interested in scenarios that change domestic income inequality may want to change '''ginidomr''', which alters the ratio of the domestic Gini ('''GINIDOM''') to the initial condition. Domestic Gini ('''GINIDOM''') can also be altered using the parameter '''ginidomm''', which acts as a multiplier on the calculated value of '''GINIDOM''' in the model. Finally, '''gdprext''' offers users a power way to speed up, or slow down, total GDP growth.

== Parameters to Affect Trade and International Finance ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:94px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:304px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:137px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xshift'''

| nowrap="nowrap" style="width:94px;height:22px;" |
X, XS

| nowrap="nowrap" style="width:304px;height:22px;" |
Export shift as a result of trade promotion

| nowrap="nowrap" style="width:137px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''protecm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
M, MS

| nowrap="nowrap" style="width:304px;height:22px;" |
Protectionism in trade, multiplier on import prices

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''termtrm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
TERMTR

| nowrap="nowrap" style="width:304px;height:22px;" |
Terms of trade balance, higher favors global south

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdistockm''' 

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDISTOCK

| nowrap="nowrap" style="width:304px;height:22px;" |
Inward stocks of FDI multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdistoutm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDISTOUT

| nowrap="nowrap" style="width:304px;height:22px;" |
Outward stocks FDI multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xfdiwgrm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XFDI STOCK, XFDIOUT

| nowrap="nowrap" style="width:304px;height:22px;" |
World FDI growth rate multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xportfoliom'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XPORTFOLIO

| nowrap="nowrap" style="width:304px;height:22px;" |
Inward portfolio investment multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''xportstoutm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
XPORTFOLIO

| nowrap="nowrap" style="width:304px;height:22px;" |
Outward portfolio investment stock multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aiddon'''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid foreign loan donations as % GDP

| nowrap="nowrap" style="width:137px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aidrecm'''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid receipts as % GDP

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier 

|-
| nowrap="nowrap" style="width:97px;height:22px;" |
'''aidlpm '''

| nowrap="nowrap" style="width:94px;height:22px;" |
AID

| nowrap="nowrap" style="width:304px;height:22px;" |
Aid foreign loan percentage multiplier

| nowrap="nowrap" style="width:137px;height:22px;" |
Multiplier

|}

Users can alter imports and exports via two major channels. First, they can force a shift towards export promotion using the additive '''xshift''' parameter. Increasing the parameter value to .05 would result in an additional 5% being added to the value of exports for that year and all subsequent years until the dynamics of the model dampen the effect. Protectionism in imports can be modeled by changing the value of '''protecm''', which is a multiplicative parameter that increases the cost of imports and thereby proxies an increase in tariff rates or of non-tariff barriers to trade.

Finally the distribution of the global terms of trade can be impacted by '''termtrm'''. Raising this multiplicative parameter will alter the balance of global trade patterns to favor southern developing countries (colloquially known as the Global South). This means that an increase in this parameter will increase the financial value of the exports from these developing nations, while reducing the relative financial value of exports from developed nations.

Affecting global financial flows is a relatively complex process in the IFs economic system. Multiple channels exist to modify the different major sources of international financial flows. Users can alter foreign direct investment flows using the '''xfdistockm''' parameter, which increases the stocks of FDI within a country and adjusts flows accordingly. Conversely, '''xfdistoutm''' will increase the size of the stock of FDI slated to exit a country. Users may also wish to alter the growth rate for FDI at a global level via the parameter '''xfdiwgrm'''.

Portfolio investment can be modified using a similar set of parameters. '''Xportfoliom''' allows users to increase the stocks of portfolio investment within a country, while '''xportstout''' controls the amount of portfolio investment that a country’s citizens hold abroad.

Aid parameters differ somewhat from those available for FDI and portfolio investment. Users can change the amount of foreign loan donations as a percent of GDP via '''aiddon''', which specifies the portion of donor country GDP given in the form of aid. They may change the amount of aid a country receives using the '''aidrecm''' parameter, which determine the portion of the global aid pool created by donors that any individual country will receive (the total claims on that pool determined by '''aidrecm''' will be normalized to the total size of the pool). Finally, users may alter the portion of aid that comes in the form of loans that accrue interest and must be repaid within a certain time frame using the multiplicative parameter '''aidlpm'''.

== Parameters to Affect the Informal Economy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''labinformshrm'''
|LABINFORMSHR
|Informal labor force share multiplier
|Multiplier
|-
|'''gdpinformshrm'''
|GDPINFORMSHR
|Informal GDP share multiplier
|Multiplier
|-
|'''gdpshadowshrm'''
|GDPSHADOWSHR
|Shadow economy GDP share multiplier
|Multiplier
|-
|'''gdpshadowon'''
|GDPSHADOWSHR
|Switch to turn on GDPSHADOWSHR instead of GDPINFORMSHR. Default is 0 (GDPINFORMSHR). 1 turns on GDPSHADOWSHR instead.
|Switch
|-
|'''edyrsagm'''
|EDYRSAG15
|Years of education ages 15 and up multiplier
|Multiplier
|-
|'''firmtaxrm'''
|FIRMTAX
|Tax rate on firm’s multiplier
|Multiplier
|-
|'''gdsm'''
|GDS
|Government R&D (and education) spending distribution multiplier
|Multiplier
|-
|'''govbusregindm'''
|GOVBUSREGIND
|Government business regulation index multiplier
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption multiplier
|Multiplier
|-
|'''govhhtrnpenm'''
|GOVHHTRN
|Government pension spending multiplier
|Multiplier
|-
|'''govhhtrnwelm'''
|GOVHHTRN
|Government welfare spending multiplier
|Multiplier
|-
|'''randdexpm'''
|RANDEXP
|Total (public and private) expenditure on research and development multiplier
|Multiplier
|-
|'''taxinfadjm'''
|HHTAXR, FIRMTAXR, SSWELTAXR
|Multiplier of impact of informal GDP share on tax rates
|Multiplier
|-
|'''labinformcoeffbus'''
|LABINFORMSHR
|Informal labor share, coefficient of business index
|Coefficient
|-
|'''labinformcoeffed'''
|LABINFORMSHR
|Informal labor share, coefficient of education years
|Coefficient
|-
|'''labinformcoeffhhtrn'''
|LABINFORMSHR
|Informal labor share, coefficient of household transfers
|Coefficient
|-
|'''labinformcoefftax'''
|LABINFORMSHR
|Informal labor share, coefficient of tax rate
|Coefficient
|-
|'''gdpinformcoeffcorrupt'''
|GDPINFORMSHR
|Informal GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpinformcoeffintercept'''
|GDPINFORMSHR
|Informal GDP share, intercept of model
|Coefficient
|-
|'''gdpinformcoefflabinf'''
|GDPINFORMSHR
|Informal GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpinformcoeffrandd'''
|GDPINFORMSHR
|Informal GDP share, coefficient of R&D spending
|Coefficient
|-
|'''gdpshadowcoeffintercept'''
|GDPSHADOWSHR
|Shadow economy GDP share, intercept of model
|Coefficient
|-
|'''gdpshadowcoefflabinf'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpshadowcoeffcorrupt'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpshadowcoeffrandd'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of R&D spending
|Coefficient
|}

Users can affect the labor informal share of the total labor force directly ('''labinformshrm'''). In most cases they will do so indirectly by affecting the driving variables of that share. One of the driving variables is educational attainment of adults, so any parameter affecting that attainment will have a potential impact (see the parameters for education including the prepackaged scenarios). Another is the government business index ('''govbusregindm'''). A third is government to household transfers for either pensions or welfare ('''govhhtrnpenm''' and '''govhhtrnwelm'''). A fourth is the tax rate on firms ('''firmtaxm''').

The forward linkage from informal labor share to informal GDP share is affected by a multiplier ('''gdpinformshrm''') that can totally turn off that linkage if set to 0. The informal GDP share can be directly affected via '''gdpinformshrm''', or indirectly by affecting its drivers, including informal labor share. Other drivers include: the level of government corruption ('''govcorruptm'''), government spending on research and development (gdsm, R&D) and total (public and private) spending on research and development ('''randdexpm'''). Manipulating '''randdexpm''' itself will increase private sector spending while changing '''gdsm''' will also impact the public portion of '''RANDDEXP'''.

A set of parameters ( '''labinformcoeffbus, labinformcoeffed, labinformcoeffhhtrn, labinformcoefftax''') control the calculation of informal labor share from its assorted drivers.

A similar set of parameters ('''gdpinformcoeffintercept''', '''gdpinformcoeffcorrupt''', '''gdpinformcoefflabinf''', '''gdpinformcoeffRandD''') control the calculation of informal GDP from its assorted drivers.

The model includes the shadow economy share of GDP ('''GDPSHADOWSHR''') as an additional measure of informality in the economy. Under the Base Case, '''GDPSHADOWSHR''' is initialized but does not have active forward linkages (the Base Case drives all informality-related forward linkages with GDPINFORMSHR instead). The shadow economy GDP share can be affected directly by changing '''gdpshardowshrm''' or indirectly by changing its drivers, which as with the informal economy share, are: informal labor, government corruption, and spending on R&D. The shadow economy uses a similar set of parameters to the informal economy ('''gdpshadowcoeffintercept''', '''gdpshadowcoeffcorrupt''', '''gdpshadowcoefflabinf''', '''gdpshadowcoeffRandD''') to control the calculation of the shadow economy from its drivers.

Users wanting to drive the forward linkages from informality with the shadow economy instead of the informal economy can do so by changing the switch '''gdpshadowon''' from its default, Base Case setting of 0 (which tells the model to drive the forward linkages with '''GDPINFORMSHR''') to 1 (which tells the model to drive them with '''GDPSHADOWSHR''' instead).

The informal and shadow GDP shares (whichever is activated—see above) use the same multipliers for their linkages to multifactor productivity ('''tefinfadjm''') and tax rate ('''taxinfadjm''')'''.'''

== Prepackaged Scenarios ==

A large number of prepackaged scenarios that revolve around economic development and poverty alleviation are available for user access, located under the PPHP Poverty heading in the World Integrated Scenario Sets. The best and worst case Framing Scenarios include changes to the growth in MFP for the world leader, the additive growth rate factor for MFP, TFR, and the Gini index.

More integrated scenario sets are available as well to model the effects of combinations of interventions. These are labeled Combined Dom Intl Interventions (Rev, Rev2). This scenario is built from two different series of interventions: Leverage Testing Intl, and Leverage Testing Domestic, which user can explore if they wish to look more closely at individual interventions from the combined scenario. Users should also use these scenarios and the Combined Dom Intl Interventions (Rev, Rev2) to get a sense of how to scale interventions to different regions of the world or even globally, rather than altering a single country at a time.

Revision 2 models a number of parameter changes including: growth in multifactor productivity ('''mfpadd'''), [[#_The_Health_Module|declines in TFR ('''tfrm'''),]] increases in the female share of the labor force('''labfemshrm''') and [[#_The_Health|world migration]]('''wmigrm'''), increases in investment ('''invm)''', foreign aid, FDI and portfolio investment ('''xfdistockm''' and '''xportfoliom'''), declines in protectionism and resulting increase in exports ('''protecm''' and '''xshift'''), increased government expenditures on education, health, and R&D ('''gdsm'''), greater welfare transfers to unskilled labor('''govhhtrnwelm'''),[[#_Governance_Module|improvements to governance]] (declines in corruption-'''govcorruptm''', increases in effectiveness- '''goveffectm''', and economic freedom- '''econfreem'''), [[#_Infrastructure_2|improved infrastructure]] in roads, ICT, and telephone network density ('''infranetm''', '''infraroadm''','''infratelem'''), and [[#_Energy_Module|increased energy production]] from renewable resources ('''enpm''').

Of these, the economic changes deserve special attention. This scenario includes an improvement in multifactor productivity in the World Bank developing economies to 0.002 over a ten year time horizon, growth in the female share of the labor force to 1.5 time the base case over a 50 year time frame and increases in world migration multiplier to 1.5 over a 15 year time frame. It also increases investment multiplier to between 1.3 and 2 over a 15 to 25 year time frame depending on the region triggering increased capital accumulation and savings.

Government expenditure is also affected. Expenditure to education increases to between 1.2 and 1.8 times the base case implemented over ten years. Expenditure to health and research and development also increase by similar amounts over approximately the same time frame. Government to household transfers increase in different regions by 50 to 100% (multiplier values of 1.5 to 2) over a 20-year time frame. Trade and export promotion increase as well. Levels of protectionism decline by 20% over 20 years (a multiplier value of .8) and the ratio of exports to imports grows by .04.

And finally, FDI and portfolio investment are affected. FDI doubles over 25 years in the World Bank developing economies while portfolio investment increases by 50% over the same time frame. Foreign aid by donor nations increases to .5% of GDP over ten years. There are also increases in the availability of IMF funding that go beyond the scope of this guide, but which may be of interest to specialists in international finance.

With respect to the informal economy, eight scenarios have been developed for Peru. Six of them each manipulate one of the drivers of either informal labor or informal GDP share.  In each case an "aggressive but reasonable" value of the driver works to reduce informality.  The other two involve more general impacts on information.  The first (Comb Lab GDP 1.5 drivers.sce) combines all of the six individual drivers into a single scenario to reduce informality. The second or counterfactual scenario (Informal GDP Share Total Decline Peru 15 years.sce) use a brute force multiplier on the informal GDP share to reduce it to the minimum value allowed (1 percent) between 2016 and 2030.

= Infrastructure Module =

== Variables of Interest ==

The Infrastructure module in the IFs system forecasts infrastructure development and its consequences via a five stage process that is driven primarily by the expected/demanded levels of infrastructure and the funding available to meet these expectations or demands.

The model first estimates the expected/demanded level of infrastructure within a country in relation to key drivers like GDP per capita and population.4 Second, it translates these expectations into financial requirements, accounting for both new construction and maintenance. Third, the system balances these desired funding levels with the actual resources available for infrastructure construction and maintenance. Fourth, it forecasts the actual attained levels of infrastructure (both in raw physical terms and in terms of population access rates). Fifth, these levels of infrastructure have specific and direct social, economic and environmental impacts related to these attained levels (processes discussed in connection with other modules).

The distinction between expected and demanded levels of infrastructure is a function of the scenario being modeled. In general scenarios, e.g., the Base Case, we use the term expected because the underlying equations are based on historical data reflecting both underlying demands and supply constraints. In scenarios with targets, however, these equations are overridden by equations reflecting the target path, when the expected values lag behind the values defined by the target path. In such instances, therefore, we are more clearly identifying demands for infrastructure, rather than expectations.

Infrastructure variables can be divided into three major types: physical infrastructure, access, and funding.5 Road density, percentage of road paved, electricity generation capacity, and the amount of land equipped for irrigation all represent key physical infrastructure variables that indicate actual infrastructure stocks within a country. Key access variables, which indicate the degree to which people are able to benefit from existing infrastructure, include: rural population living within 2 kilometers of an all-season road (the road access index); population with access to electricity; reliance on solid fuels for energy; population with access to improved safe water, sanitation, and wastewater treatment; and subscriptions per 100 people to fixed line telephones, mobile phones, and fixed or mobile broadband.
<div>
----
<div id="ftn1">
4This calculation treats physical stocks differently from access indicators depending on the type of infrastructure being considered. For roads, expected access is calculated as a function of the physical stocks, while for electricity, water and sanitation, and ICT, physical stocks are calculated on the basis of the expected levels of access. This has implications for the functioning of the parameters discussed below.
</div><div id="ftn2">
5IFs also distinguishes between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to those types of infrastructure that we represent explicitly in IFs—roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to those types that we do not represent explicitly—e.g., railroads, ports, airports, and future types of infrastructure yet to be envisioned. Please note that although we do not represent these other forms of infrastructure explicitly, we do estimate spending on them in order to avoid under-representing the total demand for infrastructure. The choice of what to include as core infrastructure reflects the availability of historical data and our determination of what can be modelled within IFs at this time.
</div></div>

=== Physical Infrastructure ===
{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| style="width: 246px;" | '''Variable Name'''
| style="width: 241px;" | '''Description'''
|-
| style="width: 246px;" colspan="2" | ''Transportation'' 
|-
| style="width: 246px;" | '''INFRAROAD*''' 
| style="width: 241px;" | Total road density 
|-
| style="width: 246px;" | '''INFRAROADPAVEDPCNT*''' 
| style="width: 241px;" | Percentage of roads paved 
|-
| style="width: 246px;" colspan="2" | ''Electricity'' 
|-
| style="width: 246px;" | '''INFRAELECGENCAP*''' 
| style="width: 241px;" | Electricity generation capacity per capita 
|-
| style="width: 246px;" | '''INFRAELECTRANSLOSS''' 
| style="width: 241px;" | Electricity transmission loss 
|-
| style="width: 246px;" | '''INFRAELECADJFACT''' 
| style="width: 241px;" | Electricity adjustment factor 
|-
| style="width: 246px;" | '''ENELECSHRENDEM''' 
| style="width: 241px;" | Ratio of electricity use to total primary energy use 
|-
| style="width: 246px;" colspan="2" | ''Water and Sanitation'' 
|-
| style="width: 246px;" | '''LANDIRAREAEQUIP''' 
| style="width: 241px;" | Area equipped with irrigation 
|-
| style="width: 246px;" colspan="2" | * each variable marked with an asterisk has a companion variable with the suffix DEM, which indicates the expected/demanded level of the variable in the absence of funding constraints 
|}
Key physical Infrastructure variables for transportation are '''INFRAROAD''', and '''INFRAROADPAVEDPCNT'''. The variable '''INFRAROAD''' represents a measure of total road density (paved and unpaved), while the variable '''INFRAROADPAVEDPCNT''' offers a measure of the quality of the transportation infrastructure within a country using paved percentage as a proxy.

Key physical Infrastructure variables for transportation are '''INFRAROAD''', and '''INFRAROADPAVEDPCNT'''. The variable '''INFRAROAD''' represents a measure of total road density (paved and unpaved), while the variable '''INFRAROADPAVEDPCNT''' offers a measure of the quality of the transportation infrastructure within a country using paved percentage as a proxy.

Key variables for electricity are the amount of electricity generation capacity within a country, denoted by '''INFRAELECGENCAP''' and expressed in kilowatts per person. Other variables, which represent quality indicators, are '''INFRAELECTRANSLOSS'''—the transmission and distribution loss in percent—and '''INFRAELECADJFACT'''—the capacity factor for electricity production, expressed as a fraction, and '''ENELECSHRENDEM''', the ratio of electricity use to total primary energy use. It is also possible to calculate electricity connections using the results on access to electricity, discussed below, and population.

The only physical infrastructure variable for water and sanitation is the area equipped for irrigation, '''LANDIRAREAEQUIP''', which represents the amount of land in thousands of hectares that is equipped with irrigation. There are no key physical infrastructure variables associated with ICT, only access rates. Users wishing to affect ICT infrastructure should modify access, which, along with population, can be used to calculate actual physical infrastructure levels.

=== Access to Infrastructure ===

{| border="1" cellspacing="1" cellpadding="1" width="589"
|-
| style="width:151px;" |
'''Variable Name'''

| style="width:192px;" |
'''Sub Categories'''

| style="width:246px;" |
'''Description'''

|-
| style="width:151px;" colspan="3" |
''Transportation''

|-
| style="width:151px;" |
'''INFRAROADRAI*'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to rural roads

|-
| style="width:151px;" colspan="3" |
''Electricity''

|-
| style="width:151px;" |
'''INFRAELECACC*'''

| style="width:192px;" |
Rural, urban, total

| style="width:246px;" |
Access to electricity

|-
| style="width:151px;" |
'''ENSOLFUEL'''

| style="width:192px;" |
 

| style="width:246px;" |
Solid fuel use

|-
| style="width:151px;" colspan="3" |
''Water and Sanitation''

|-
| style="width:151px;" |
'''WATSAFE*'''

| style="width:192px;" |
None, other improved, piped

| style="width:246px;" |
Access to improved water

|-
| style="width:151px;" |
'''SANITATION*'''

| style="width:192px;" |
Other unimproved, shared, improved

| style="width:246px;" |
Access to improved sanitation

|-
| style="width:151px;" |
'''WATWASTE'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to wastewater collection

|-
| style="width:151px;" |
'''WATWASTETREAT*'''

| style="width:192px;" |
 

| style="width:246px;" |
Access to wastewater treatment

|-
| style="width:151px;" colspan="3" |
''ICT''

|-
| style="width:151px;" |
'''INFRATELE*'''

| style="width:192px;" |
 

| style="width:246px;" |
Fixed telephone lines

|-
| style="width:151px;" |
'''ICTBROAD*'''

| style="width:192px;" |
 

| style="width:246px;" |
Fixed broadband subscriptions

|-
| style="width:151px;" |
'''ICTMOBIL*'''

| style="width:192px;" |
 

| style="width:246px;" |
Mobile telephone subscriptions

 

|-
| style="width:151px;" |
'''ICTBROADMOBIL*'''

| style="width:192px;" |
 

| style="width:246px;" |
Mobile broadband subscriptions

|-
| colspan="3" style="width:589px;" |* each variable marked with an asterisk has a companion variable with the suffix DEM, which indicates the expected/demanded level of the variable in the absence of funding constraints. Please see the note below for a more comprehensive description of the difference6

|}

Physical infrastructure may be essential, but unless people have access to it, they cannot benefit. Because of this, access variables are included wherever possible in the IFs system. Transportation contains only one key access variable. This variable, '''INFRAROADRAI''', provides a measure of degree of access people have to the existing road infrastructure. It is defined as the percent of the rural population living within 2km of an all season road.

'''INFRAELECACC''' provides a measure of access to electricity, disaggregated by rural and urban users. Another measure of access to electricity that users may be interested in from a health perspective is '''ENSOLFUEL''' which represents the percentage of the population relying on solid fuels for energy, and a significant factor in both environmental change and certain health conditions.

The two fundamental access variables of interest for water and sanitation are '''WATSAFE''' and '''SANITATION''', which measure the percentage of the population with access to improved water sources and sanitation, respectively, by type of access. Water access is subdivided into unimproved, other improved, and piped. Sanitation access is subdivided into other unimproved, shared, and improved (note that shared access is not considered improved). '''WATWASTE''' and '''WATWASTETREAT''' represent the percentage of the population whose wastewater is collected and treated, respectively.

Four ICT variables represent access to the major components of information and communication technologies. '''INFRATELE''' represents the number of fixed line telephones per 100 people while the other three indicators, '''ICTBROAD''', '''ICTMOBIL''', and '''ICTBROADMOBIL''', provide the number of subscriptions per 100 people for fixed broadband, mobile telephones, and mobile broadband, respectively. The dynamics of the model are structured in such a way that the long term trend is for fixed line telephones to decline, while use of mobile and mobile broadband increases.

----
<div id="ftn1">
6The vast majority of Infrastructure variables have both a standard variable, representing the actual achieved level of infrastructure stock or access, and a demand/expectations related variable suffixed by *DEM. The interpretation of the *DEM variables should change subtly depending on the analytical context. When discussing the Base Case or when applying any of the multiplicative parameters, it is more useful to think of the *DEM variables as expected levels; when building scenarios that manipulate the '''*setar/*seyrtar''' parameters, it is more accurate to think of them as demand variables.

</div>
=== Infrastructure Funding ===

{| border="1" cellspacing="1" cellpadding="1" width="637" style="width:637px;"
|-
| style="width:211px;" |
'''Variable Name'''

| style="width:426px;" |
'''Description'''

|-
| style="width:211px;" |
'''GDS (infrastructure, infraother)'''

| style="width:426px;" |
Government consumption, by category

|-
| style="width:211px;" |
'''INFRAINVESTMAINT'''

| style="width:426px;" |
Total (public plus private) investment for core infrastructure maintenance, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTMAINTPUB*'''

| style="width:426px;" |
Public investment for core infrastructure maintenance, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTNEW'''

| style="width:426px;" |
Total (public plus private) investment for construction of new core infrastructure, by type of infrastructure

|-
| style="width:211px;" |
'''INFRAINVESTNEWPUB*'''

| style="width:426px;" |
Public investment for construction of new core infrastructure, by type of infrastructure

|-
| colspan="2" style="width:637px;" |* the companion variables, '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''', are the amounts of public spending originally requested prior to the government budget allocation process

|}

The infrastructure module in IFs incorporates a cost accounting system that ensures that all infrastructure improvements are funded prior to construction, which allows for a gap between the amount of infrastructure that is expected/demanded and the amount that is actually achieved.

As in other modules, GDS provides a good overview of total public spending on infrastructure, but this cannot be broken down by type of infrastructure any more specifically than core infrastructure and other infrastructure. '''INFRAINVESTMAINT''' provides the measure of the total investment for infrastructure maintenance, while '''INFRAINVESTMAINTPUB''' provides infrastructure investment for maintenance provided by public sector funds. '''INFRAINVESTNEW''' provides an indicator of the total spending on construction of new infrastructure, while '''INFRAINVESTNEWPUB''' provides the spending that came from public sources for the purposes of new investment. For the purposes of parameter manipulation, we will discuss the types of infrastructure separately because their dynamics vary widely with regards to how the model calculates physical infrastructure versus access. Absolute and relative target parameters to affect infrastructure access are discussed together because all share similarities in terms of structure and function within the model. Finally, there is some discussion of how to combine the two different types of parameter. These variables are subdivided based on whether the spending is for maintenance purposes or for the building of new infrastructure, and whether funding comes from public or private sources. Private funding is related to public funding in some ratio in all cases but ICT, where private funding has historically been the driver of infrastructure investment.

The two public spending variables, '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''', have companion variables like many of the physical infrastructure and access variables discussed above. Specifically, the variables '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''' represent the amounts of public spending originally requested prior to the government budget allocation process. When there are shortages, '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''' will be less than '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW'''.7 In addition to producing a shortfall of public investment, this will also reduce private spending, except for the case of ICT infrastructure. The treatment of ICT differs because private funding has historically been the driver of investment in ICT infrastructure, whereas the public sector has been the driver of investment in other infrastructure.
<div>
----
<div id="ftn1">
7It is also possible for '''INFRAINVESTMAINTPUB''' and '''INFRAINVESTNEWPUB''' to be less than '''INFRABUDDEMMNT''' and '''INFRABUDDEMNEW''' for another reason. This occurs when countries try to increase infrastructure spending more rapidly than is considered feasible. In this case, some of the funds allocated are held back and released over several years. Please refer to the technical documentation on the infrastructure model for more details on this process.
</div></div>
== Parameters to Affect Infrastructure ==

Four basic types of parameter are used in this module. They are, multiplicative parameters, technological shift factors, absolute, and relative targets. In general, multiplicative and technological shift factors operate in the same way; likewise, absolute and relative targets have similar functionalities.

There are some general rules that govern the interaction among the different types of parameters. 8The first rule states that, if multiplicative or tech shift factor parameters are used in conjunction with absolute or relative targets, the multiplicative/technological shift parameters are applied first and then the relative or absolute targets are calculated. The second rule states that absolute and relative targets cannot be applied together. If they are, the model interprets this as an inconsistent application of parameters and will not implement either. The exception to both these rules are ICT fixed and mobile broadband, where the multipliers '''ictbroadm '''(for fixed broadband) and'''ictbroadmobilm '''(for mobile broadband), are applied after the absolute or relative target parameters are applied. Fixed broadband is also the exception to the rule that absolute and relative targets cannot be applied together. Instead, they are both calculated within the model and then the larger of the two values is taken.

In analyzing scenarios involving changes to the majority of the parameters described below, users should first check variables with the suffix DEM, where available, because many of the parameters modify expected/demanded levels of access. Assuming that the model has modified the expected/demanded level as desired, the user should next check the actual achieved levels of infrastructure. The reason is that if a parameter change increased the level of demand, but the impact did not raise demand above achieved levels otherwise computed in the model, there would likely be no apparent impact on the achieved level. For instance, users wishing to affect access to sanitation via '''sanitationm '''should first check '''SANITATIONDEM''' to examine the impact on expected/demanded levels, before checking '''SANITATION''' to determine the impact on the actual level fuels of access to sanitation within the population.
<div>
----
<div id="ftn1">
8There are some exceptions to the rules laid out below, primarily as pertains to the treatment of fixed broadband within ICT.
</div></div>
=== Roads ===

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:154px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:228px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadm'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROAD

| nowrap="nowrap" style="width:228px;height:22px;" |
Multiplier on road density expected/demanded

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadpavedpcntm'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADPAVED

PCNT

| nowrap="nowrap" style="width:228px;height:22px;" |
Multiplier on percentage of roads paved

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''Infraroadraitrgtval, Infraroadraitrgtyr'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADRAI

| nowrap="nowrap" style="width:228px;height:22px;" |
Target value/years after 2010 for achievement for population percentage living w/in 2km of an all-weather road

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:147px;height:22px;" |
'''infraroadraisetar, infraroadraiseyrtar'''

| nowrap="nowrap" style="width:154px;height:22px;" |
INFRAROADRAI

| nowrap="nowrap" style="width:228px;height:22px;" |
Standard error target and years after 2010 for achievement for rural road access index

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}

All the multiplicative

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:235px;height:22px;" |
eneration capacity

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''Infraelecaccm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on electricity access

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''enelecshrendemm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECDEM

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on ratio of electricity use to total primary energy use

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelectranlossm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECTRANLOSS

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on the loss of electricity in transmission and distribution

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfuelm'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Multiplier on the reliance on solid fuel as a source of energy

| nowrap="nowrap" style="width:103px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelecacctrgtval/ infraelecacctrgtyr'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Target value and years after 2010 for target achievement for population percentage w/ access to electricity

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''infraelecaccsetar/ infraelecaccseyrtar'''

| nowrap="nowrap" style="width:147px;height:22px;" |
INFRAELECACC

| nowrap="nowrap" style="width:235px;height:22px;" |
Standard error target and years after 2010 for population percentage w/ access to electricity

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfueltrgtval/ ensolfueltrgtyr'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Target value and years after 2010 for target achievement for percentage of households reliant on solid fuels

| nowrap="nowrap" style="width:103px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:148px;height:22px;" |
'''ensolfuelsetar/ ensolfuelseyrtar'''

| nowrap="nowrap" style="width:147px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:235px;height:22px;" |
Standard error target and years after 2010 for target achievement for percentage of households reliant on solid fuels

| nowrap="nowrap" style="width:103px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}

Electricity in the infrastructure model contains parameters that alter both physical infrastructure and access levels using multiplicative parameters. The parameter '''infraelecgencapm''' directly affects '''INFRAELECGEN''', which represents the physical capacity to generate electricity and is a physical infrastructure variable.

Access to electricity, which is represented by '''INFRAELECACC''', can be directly affected by '''infraelecaccm''', a parameter that directly impacts access to electricity. This parameter is subdivided to allow users to affect urban and rural access separately.

Three other multiplicative parameters are relevant to discussion of electricity access in the infrastructure module. These are '''ensolfuelm''', '''enelecshrendemm,''' and '''infraelectranlossm'''. '''Ensolfuelm''' changes solid fuel reliance as a primary energy source for domestic cooking and heating (ENSOLFUEL), which is also driven by '''INFRAELECACC'''. '''Enelecshrendemm''', changes the ratio of electricity use to total primary energy use (this is a variable of relevance to the [[#_Energy_Module|energy]] module, but is not yet linked to it). It allows users to change how reliant a country is on electricity relative to other sources of final energy use. It indirectly affects the expected/demanded amount of electricity generation capacity as represented by the variable '''INFRAELECACCDEM'''. '''Infraelectranlossm''' affects the loss of electricity during transmission and distribution. It is a parameter that allows users to affect the efficiency and quality of the electricity infrastructure. Like '''enelecshrendemm''', this parameter indirectly affects '''INFRAELECACCDEM'''.

There are also target access parameters to allow users to set targets for desired levels of infrastructure access. These include '''infraelecacctrgtval/infraelecacctrgtyr''', '''infraelecaccsetar/infraelecaccseyrtar''', '''ensolfueltrgtval/ensolfueltrgtyr''', and '''ensolfuelsetar/ensolfuelseyrtar'''. These set absolute and relative targets and target years for desired levels of electricity access and solid fuel use respectively.

=== Water and Sanitation ===
In IFs, water and sanitation sub-model comprises of two levels of disaggregation. The first one is an older 3-tier breakdown that categorizes water access into: (i) Unimproved, (ii) Other Improved and (iii) Piped and sanitation access into: (i) Improved, (ii) Other Unimproved and (iii) Shared.

The second one, reflects the new update from JMP (WHO-UNICEF) that categorizes water and sanitation into a 5-tier breakdown. In this new 5-tier system, water access is classified as (i) Surface, (ii) Unimproved, (iii) Limited, (iv) Basic and (v) Safely Managed and sanitation access is classified as (i) Open Defecation, (ii) Unimproved, (iii) Limited, (iv) Basic and (v) Safely Managed.

In order to default to the older 3-tier model, parameter switches namely, '''watsafenewsw''' and '''sanithhmodel2sw''' must be set to 0 before rebuilding the base. Alternatively, upon setting their values to 1 and rebuilding the base allows the user to switch to the newer 5-tier water and sanitation model. The first table below lists all relevant parameters associated with the 3-tier model while the second table lists all relevant parameters associated with the 5-tier model.

'''For the 3-tier model:'''
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''landirareaequipm'''
|LANDIRAREAEQUIP
|Multiplier on land area equipped for irrigation
|Multiplier
|-
|'''sanitationm'''
|SANITATION
|Multiplier on percentage of people with access to sanitation
|Multiplier
|-
|'''watsafem'''
|WATSAFE
|Multiplier on percentage of people with access to safe water
|Multiplier
|-
|'''watwastem'''
|WATWASTE
|Multiplier on percentage of people connected to wastewater collection system,
|Multiplier
|-
|'''watwastetreatm'''
|WATWASTETREAT
|Multiplier on percentage of people connected to wastewater treatment
|Multiplier
|-
|'''sanitationtrgtval/ sanitationtrgtyr'''
|SANITATION
|Target value and years after 2010 for target achievement for population percentage with improved access to improved sanitation, by category of
|Relative target
|-
|'''watsafetrgtval/ watsafetrgtyr'''
|WATSAFE
|Target value and years after 2010 for target achievement for population percentage without access to improved sources of water
|Relative target
|-
|'''sanitnoconsetar/ sanitimpconsetar/ sanithhconsetar/ sanitnoconseyrtar'''
|SANITATION
|Standard error targets and years after 2010 for target achievement for population percentage with varying access to sanitation
|Relative target
|-
| colspan="4" |'''Note: Using both''' *trgtval/trgtyr '''and the''' *setar/*seyrtar '''parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.'''
|}

'''For the 5-tier model:'''
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhm'''
|SANITHH
|Multiplier on percentage of people with access to sanitation by type
|Multiplier
|-
|'''sanithhbasictrgtval'''

'''sanithhbasictrgtyr'''
|SANITHH
|Target value and years from the base to reach at least basic sanitation services
|Relative target
|-
|'''sanithhsafetrgtval'''

'''sanithhsafetrgtyr'''
|SANITHH
|Target value and years from the base to reach safely managed sanitation services
|Relative target
|-
|'''waterhhm'''
|WATERHH
|Multiplier on percentage of people with access to water by type
|Multiplier
|-
|'''waterhhbasictrgtval'''

'''waterhhbasictrgtyr'''
|WATERHH
|Target value and years from the base to reach at least basic water services
|Relative target
|-
|'''waterhhsafetrgtval'''

'''waterhhsafetrgtyr'''
|WATERHH
|Target value and years from the base to reach safely managed water services
|Relative target
|}

Water and sanitation only contains one parameter which directly affects physical infrastructure levels. This parameter is '''landirareaequipm''', a parameter that affects the land area equipped for irrigation. All other parameters affect access to water, wastewater collection and treatment, and sanitation; the access rate then determines the expected/demanded level of the physical stock. There are no direct multipliers on physical stocks for these types of infrastructure. '''Sanitationm''', '''watsafem''', '''watwastem''', and '''watwastetreatm''' are access parameters that affect access to sanitation, safe water, waste water collection, and waste water treatment, respectively.

Targeting parameters for water and sanitation are: '''sanitationtrgtval/sanitationtrgtyr''', '''watsafetrgtval/watsafetrgtyr''', '''sanitnoconsetar/sanitimpconsetar/sanihhconsetar/sanitnoconseyrtar''', '''watsafenoconsetar/watsafimpconsetart/watsafhhconsetar/watsafenoconseyrtar''', and '''watwastetreatsetar/watwastetreatseyrtar'''. The first two pairs provide absolute target values for sanitation and safe water access. The second two pairs define relative target values and years for reducing the number of people with no access to improved sanitation, safe water, or wastewater treatment.

Water and sanitation only contains one parameter which directly affects physical infrastructure levels. This parameter is '''landirareaequipm''', a parameter that affects the land area equipped for irrigation. All other parameters affect access to water, wastewater collection and treatment, and sanitation; the access rate then determines the expected/demanded level of the physical stock. There are no direct multipliers on physical stocks for these types of infrastructure. '''Sanitationm''', '''watsafem''', '''watwastem''', and '''watwastetreatm''' are access parameters that affect access to sanitation, safe water, waste water collection, and waste water treatment, respectively.

Targeting parameters for water and sanitation are:'''sanitationtrgtval/sanitationtrgtyr''', '''watsafetrgtval/watsafetrgtyr''', '''sanitnoconsetar/sanitimpconsetar/sanihhconsetar/sanitnoconseyrtar''', '''watsafenoconsetar/watsafimpconsetart/watsafhhconsetar/watsafenoconseyrtar''', and '''watwastetreatsetar/watwastetreatseyrtar'''. The first two pairs provide absolute target values for sanitation and safe water access. The second two pairs define relative target values and years for reducing the number of people with no access to improved sanitation, safe water, or wastewater treatment.

=== ICT ===

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:134px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:245px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''infratelem'''

| nowrap="nowrap" style="width:134px;height:22px;" |
INFRATELE

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on fixed-line telephone lines per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadm'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on fixed-line broadband subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobilm'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Multiplier on mobile broadband subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadtrgtval, ictbroadtrgtyr'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Target value and years after 2010 for target achievement for number of subscriptions per 100 persons

| nowrap="nowrap" style="width:104px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobiltrgtval, ictbroadmobiltrgtyr'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Target value and years after 2010 for target achievement for number of subscriptions per 100 persons; varies between 0 and 150

| nowrap="nowrap" style="width:104px;height:22px;" |
Absolute target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadmobilsetar, ictbroadmobilseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROADMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for ICT mobile broadband access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictbroadsetar, ictbroadseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTBROAD

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for fixed broadband access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" style="width:149px;height:22px;" |
'''ictmobilsetar, ictmobilseyrtar'''

| nowrap="nowrap" style="width:134px;height:22px;" |
ICTMOBIL

| nowrap="nowrap" style="width:245px;height:22px;" |
Standard error target and years after 2010 for target achievement for mobile phone access

| nowrap="nowrap" style="width:104px;height:22px;" |
Relative target

|-
| nowrap="nowrap" colspan="4" style="width:632px;height:22px;" |
Note: Using both *'''trgtval/trgtyr''' and the *'''setar/*seyrtar''' parameters to affect the same outcome variable will block the impact of both, so users should be careful to only use one or the other of these types at a time, except in the case of fixed broadband, where the larger of the two effects will have an impact.

|}
'''Infratelem''', '''ictbroadm''' and '''ictbroadmobilm''' are the only three multiplicative parameters that apply to ICT, and they serve to modify expected or demanded levels of access to telephones, fixed broadband, and mobile broadband respectively. IFs calculates broadband ICT slightly differently than other infrastructure types. Rather than being mutually exclusive, both absolute target values and relative targets (discussed in the paragraph below) apply to the calculation of ICTBROAD. Rather than calculating the value of the parameter adding the multiplier effect and then applying the relevant absolute and relative targets, which is what occurs in the rest of the infrastructure module, the calculation process differs markedly in regards to broadband. In this case, the value of ICTBROAD is calculated twice, once with and once without the application of the target access levels, and the larger of these two values is used by the model for calculations going forward. Then the multiplier '''ictbroadm''' is applied.

'''Infratelem''', '''ictbroadm''' and '''ictbroadmobilm''' are the only three multiplicative parameters that apply to ICT, and they serve to modify expected or demanded levels of access to telephones, fixed broadband, and mobile broadband respectively. IFs calculates broadband ICT slightly differently than other infrastructure types. Rather than being mutually exclusive, both absolute target values and relative targets (discussed in the paragraph below) apply to the calculation of ICTBROAD. Rather than calculating the value of the parameter adding the multiplier effect and then applying the relevant absolute and relative targets, which is what occurs in the rest of the infrastructure module, the calculation process differs markedly in regards to broadband. In this case, the value of ICTBROAD is calculated twice, once with and once without the application of the target access levels, and the larger of these two values is used by the model for calculations going forward. Then the multiplier '''ictbroadm''' is applied.

'''Ictbroadtrgtval/ictbroadtrgtyr '''and '''ictbroadmobiltrgtval/ictbroadmobiltrgtvr '''set absolute target values and years for fixed and mobile broadband access. '''Ictbroadsetar/ictbroadseyrtar''', '''ictbroadmobilsetar/ictbroadmobilseyrtar''', and '''ictmobilsetar/ictmobilseyrtar '''establish relative target access values for desired broadband access, mobile broadband, and mobile access respectively. Absolute and relative target parameters behave differently when used in combination with one another in regards to fixed broadband than in other infrastructure areas. Instead of canceling one another out, combining'''ictbroadtrgtval '''and '''ictbroadsetar/ictbroadseyrtar''', causes the model to calculate both and then take the larger of the two values and use that.

== Parameters to Affect Funding ==

{| border="1" cellspacing="1" cellpadding="1" width="618" style="width:618px;"
|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:150px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:222px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:89px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''gdsm (infrastructure)'''

| style="width:150px;height:22px;" |
GDS

| nowrap="nowrap" style="width:222px;height:22px;" |
Gross domestic spending multiplier (infrastructure)

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infrainvnewpubshrm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Portion of maintenance funding coming from public sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''Infrainvmaintpubshrm'''

| style="width:150px;height:22px;" |
INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Portion of new infrastructure funding coming from public sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''hhsizem'''

| style="width:150px;height:22px;" |
GDS

| nowrap="nowrap" style="width:222px;height:22px;" |
Household size multiplier

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraroadpavedcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of paved roads

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraroadunpavedcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of unpaved roads

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecgencostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of electricity generation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecaccruralcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of rural electricity access

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infraelecaccurbancostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of urban electricity access

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watsafecostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of safe water

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watsafeimpcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of improved safe water sources

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''sanitationcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of basic sanitation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''sanitationimpcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of improved sanitation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''watwastetreatcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of waste water treatment

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''landircostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of equipping land for irrigation

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''infratelecostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of telephone

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictmobilcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of mobile

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictbroadcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on unit cost of broadband

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:157px;height:22px;" |
'''ictbroadmobilcostm'''

| style="width:150px;height:22px;" |
INFRAINVESTNEWPUB,

INFRAINVESTMAINTPUB

| nowrap="nowrap" style="width:222px;height:22px;" |
Multiplier on mobile broadband

| nowrap="nowrap" style="width:89px;height:22px;" |
Multiplier

|}
A number of major parameters for affecting the funding process in the infrastructure module exist. The first, more completely discussed in the [[#_The_Economic_Module|economic module]] (in association with the social accounting matrix and government expenditures), is the government spending by destination multiplier '''gdsm'''. By manipulating the infrastructure component of '''gdsm''' users can change the proportion of government spending that goes towards infrastructure thus altering the amount of funding available to achieved the expected or demanded levels of core and other infrastructure; it can also be used to increase or decrease the spending on other infrastructure. 

A number of major parameters for affecting the funding process in the infrastructure module exist. The first, more completely discussed in the [[#_The_Economic_Module|economic module]] (in association with the social accounting matrix and government expenditures), is the government spending by destination multiplier '''gdsm'''. By manipulating the infrastructure component of '''gdsm''' users can change the proportion of government spending that goes towards infrastructure thus altering the amount of funding available to achieved the expected or demanded levels of core and other infrastructure; it can also be used to increase or decrease the spending on other infrastructure. 

Public spending on infrastructure, both for new infrastructure and maintenance purposes, can be increased using the parameters '''infrainvnewpubshrm''' and '''infrainvmaintpubshrm''' respectively. Each of these parameters can be manipulated separately for different types of infrastructure, and changes to the public share will also affect private sector in a positive relationship (as public funding goes up so does private). Users can change the average household size using the parameter '''hhsizem''', which has an indirect effect on budget by altering the total number of infrastructural connections required for a population. 9Since budget availability affects attainment of infrastructure, as explained above, this parameter can ultimately change access rates.

Finally, users can also directly alter the unit cost of certain kinds of infrastructure, using one of a series of cost multipliers. The parameters '''infraroadpavedcostm''', and '''infraroadunpavedcostm '''alter the unit cost of paved and unpaved roads respectively. '''Infraelecgencostm''' alters the cost of electricity generation, while '''infraelecaccruralcostm''' and '''infraelecaccurbancostm''' alter the cost of electricity access in rural and urban areas. The parameters '''watsafecostm''' and '''sanitationcostm''', alter the cost of basic water and sanitation access while '''watsafeimpcostm''' and '''sanitationimpcostm''' change the cost of providing improved access to each. '''Landircostm''' changes the unit cost of equipping land for irrigation. '''Infratelecostm''', '''ictmobilcostm''', '''ictbroadcostm''', and '''ictbroadmobilcostm''' all change the cost of different types of ICT.
<div> 
----
<div id="ftn1">
9Household size parameters are applicable only for those types of infrastructure that are delivered at a household level, e.g., water, sanitation and electricity connections.
</div></div>
== Prepackaged Scenarios ==

Because of the complexity of the infrastructure module, and the sheer number of parameters it contains, users may wish to rely more heavily on prepackaged scenarios in order to find effective combinations of multipliers than they do in other IFs modules. One package of scenarios that was developed to model extensions of ICT broadband connectivity and resulting potential gains in efficiency of energy use was developed for the EU and includes modifications to a number of parameters. '''Ictbroadm''' is used to increase broadband penetration rates while '''lke''' (lifetime of capital in energy) increases the efficiency of energy capital investments, making energy production more efficient. This prepackaged scenario set can be accessed from the Scenario Development form of IFs, using the Add Scenario Component menu option. The set can be found within the World Integrated Scenario Sets grouping where it is labeled ICT Scenarios for the EU. See Acknowledging Limits, Betting on Silver Bullets, Broadband Stupidity, and Networked Solutions (Moyer and Hughes, 2012).

Other Prepackaged Scenarios were developed for Chapter 6 of the Infrastructure volume of the ''Patterns of Potential Human Progress ''series (Rothman et al., 2013). This set of scenarios can be located in the PPHP InfrastructureChapter6 grouping of the World Integrated Scenario Sets. It contains a large number of scenarios using infrastructure targets. The scenario TarAllNNoBudgPriority, referred to as Universal Targets Pursuit in the volume, represents a future in which efforts are made to achieve universal access to water and sanitation, electricity, rural roads, and mobile broadband, as well as the elimination of solid fuel use, by 2030; no budget priority is given to infrastructure in this scenario. The scenario TarSENoBudgPriority has the same target date of 2030, but instead of universal access, countries only aim to achieve levels of access that exceed the expected value for their level of development by one standard deviation; again no budget priority is given to infrastructure. The other scenarios, TarAll2050NoBudgPriority, TarElecNoBudgPriority, TarICTNoBudgPriority, TarSE0NoBudgPriority, TarSENoP2050, TarTranNoBudgPriority, and TarWatNoBudgPriority, build on these two scenarios, adjusting the target level, the target date, or the categories of infrastructure with targets.

= Agriculture Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''AGDEM''' 
| Agricultural demand 
|-
| '''FDEM''' 
| Food demand for consumption 
|-
| '''FEDDEM''' 
| Feed demand for livestock 
|-
| '''INDEM''' 
| Industrial demand for food 
|-
| '''AGP''' 
| Agricultural production 
|-
| '''YL''' 
| Yield per hectare of land 
|-
| '''LD''' 
| Land area 
|-
| '''AGM''' 
| Agriculture Imports 
|-
| '''AQUACUL''' 
| Million metric tons of fish produced via aquaculture 
|-
| '''AGX''' 
| Agriculture Exports 
|-
| '''LOSS''' 
| Portion of food produced that never reaches markets 
|-
| '''CLPC''' 
| Calories per capita 
|-
| '''MALNCHP''' 
| Percent of children malnourished 
|-
| '''MALNPOPP''' 
| Percent of population malnourished 
|}

The IFs system models agriculture in terms of supply and demand. Consumption, from the demand side, links forward into food availability and nutrition. Both supply and demand dynamics in the agricultural module are closely linked to dynamics in the population and economic modules. For simplicity of representation, IFs models three primary foodstuffs: meat, crops and fish. Stocks (inventories) of food are driven by interaction between production and demand. Stocks affect prices and prices generate movement to supply and demand equilibration, a process that also involves trade.

The agriculture model represents demand for food ('''AGDEM''') as a function of population size, population income, the share of income spent on food, and agricultural prices. A number of other variables also help to illustrate different aspects of the demand side of the equation. The first is food demand '''FDEM''', which represents the amount of food demanded for human consumption. The second is '''FEDDEM''', which represents the amount of food demanded for use as feed by livestock and the third is INDEM which represents the amount of food demanded for use in industrial processes. As a country develops, the balance between these types of demand change; in general, nations with more industry will have a higher level of industrial demand for food, and major meat producing nations will have a higher demand for crops to use as feed.

At a basic level, the supply of food produced within a country ('''AGP''') is a product of the amount of land available for cultivation ('''LD''') and the productivity or yield of that land represented by YL in the IFs model. A number of factors affect yield ('''YL''') including: the level of technology, the amount of labor available for agriculture, the investment of capital into agriculture, and the propensity for yields to saturate at some point even in the face of progressively increased input levels. Investment in agriculture is driven by product prices relative to the additional capital investment required and therefore profit levels. Given the common cycles in agriculture and long lead times in response to investment, however, farmers cannot always make decisions based on current conditions. Investment is also directly responsive to global inventory or stock levels and has an inertial component.

IFs also represents losses to the agricultural production system, and they can have a powerful impact on the availability of food in a country. Not all of the food produced within a country makes it to a market, because of poor infrastructure, such as a lack of refrigeration or other inefficiencies in the production system. In IFs this process is represented via the '''LOSS''' variable. For higher income countries, a larger portion of the loss is actually on the demand or consumption side. Because agricultural products are globally traded commodities, imports and exports of food, discussed in the [[#_The_Economic_Module|economic module]] as well, play an important role in moderating the availability of food within a country. In IFs these are represented by the variables'''AGX''', agricultural exports, and '''AGM''', agricultural imports.

The dynamics of the above processes ultimately affect the ability of people in a country to avoid malnutrition in the form of either obesity or starvation. In many developing nations, meeting daily caloric needs is a struggle and large portions of the population in these countries may be malnourished, leaving them vulnerable to death by preventable diseases. This is especially true of children, for whom the consequences of both short and long term under nutrition are especially severe. To help users keep track of this essential developmental indicator, IFs includes a number of variables that represent the nutritional challenges faced within a country. First, calories per capita ('''CLPC''') provides a rough measure of the total number of calories available to individuals. It provides a rough guide to the kinds of nutritional challenges a nation may face. Populations getting less than approximately 2100 calories a day may experience higher levels of malnutrition, while those that are getting significantly above 3000 calories a day may experience increasing levels of overweight and obesity. However, more important than a crude measure of calorie availability is the impact that chronic malnourishment poses from a development standpoint. To illustrate this burden, IFs includes variables that provide the percentage of the population that is malnourished ('''MALNPOPP'''). Data tend to be better for child undernutrition and we typically have more confidence in and pay more attention to the variable that indicates the percentage of a country’s children (under five) who are malnourished ('''MALNCHP''').

== Parameters to Affect Demand ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''clpcm'''
|AGDEM
|Multiplier on all per capita caloric demand
|Multiplier
|-
|'''elascd'''
|AGDEM
|Elasticity of crop demand to changes in food price
|Elasticity
|-
|'''elasmd'''
|AGDEM
|Elasticity of meat demand to changes in food price
|Elasticity
|-
|'''elasfd'''
|AGDEM
|Elasticity of fish demand to changes in food price
|Elasticity
|-
|'''indemm'''
|INDEM
|Industrial demand for crops
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|}

A brute force multiplier on agricultural demand ('''clpcm''') allows direct manipulation of demand and can be used to represent a wide range of scenarios relative to the Base Case, including changing tastes among consumers, changing responses to prices, and changes in incomes or the movement of individuals into the middle class. Changes in consumer desires for certain foods could also be modeled using the parameter '''clpcm '''which also adjusts the portion of calories obtained from meat. It is a multiplicative parameter, so a 0.2 increase in the value of the parameter from its base value of 1.0 would generate a 20% increase in the value of the variable relative to the base case. However, this is a relatively difficult parameter for which to identify reasonable values for such changes. Similarly, changes to '''elascd''' (elasticity of crop demand to changes in price) ,'''elasmd''' (elasticity of meat demand to changes in price) and '''elasfd''' (elasticity of fish demand to changes in price) should be made with caution because they involve changing the responsiveness of demand to changes in price. Such parametric interventions might be appropriate in cases where you want to model the impact of a mass movement to change diet patterns, such as in response to a national campaign for healthy eating or decreased meat consumption due to preference changes. In all three cases the relevant outcome variable is '''AGDEM'''. Also, the parameter '''indemm '''can be used to manipulate levels of industrial demand for crops.

== Parameters to Affect Supply (Production) ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''tgrld'''
|AGP
|Target growth rate in cultivated land
|Initial Condition
|-
|'''ylm'''
|YL
|Crop yield (agricultural production)
|Multiplier
|-
|'''ylmax'''
|YL
|Maximum crop yield
|Exogenous specification
|-
|'''envylchgadd'''
|ENVYLCHG
|Agricultural yield change
|Additive factor
|-
|'''slr'''
|AGP
|Livestock slaughter rate
|Exogenous specification
|-
|'''livhdpro'''
|AGP
|Livestock herd productivity gain with grain feeding
|Exogenous specification
|-
|'''aquaculm'''
|AGP
|Fish production through aquaculture, multiplier
|Multiplier
|-
|'''aquaculgr'''
|AQUACUL
|Growth in the use of aquaculture
|Growth Rate
|-
|'''aquaculconv'''
|AQUACUL
|The number years in which aquaculture growth rate converges to 0
|Convergence rate
|-
| '''fishcatchm '''
|AGP
|Fish production through wild catch
|Multiplier
|-
|'''aglosstransm'''
|AGLOSSTRANS
|Agricultural wastage
|Multiplier
|-
|'''aglossprodm'''
|AGLOSPROD
|Agricultural production loss
|Multiplier
|-
|'''aglossconsm'''
|AGLOSSCONS
|Food consumption loss
|Multiplier
|-
|'''aginvm'''
|YL, IDS (agriculture)
|Investment in agriculture
|Multiplier
|-
|'''elagind'''
|AGP
|Elasticity of industrial use of crops with energy price
|Elasticity
|-
|'''elinag1'''
|IDS (agriculture)
|Elasticity of investment in agriculture to profit levels
|Elasticity
|-
|'''elinag2'''
|IDS (agriculture)
|Elasticity of investment in agriculture to changes in profit level
|Elasticity
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|}

There are two primary channels to think about changing the supply of agricultural production. The first is via increases in the amount of resources devoted to agricultural production such as by increasing the amount of land under cultivation. This can be done via the '''tgrld '''parameter. The earth’s arable land is relatively finite, however, and land under cultivation has not been a major driver of increased production for decades in most countries.

A more appropriate channel to think about is technological change that increases the efficiency of food production systems thereby increasing yield. The yield multiplier ('''ylm'''), which affects the yield of land being used for crop production, could be used, for example, to model the effect of a second green revolution. This parameter does not carry an intrinsic cost to use, making it both a powerful way of affecting the agricultural model, but also making it essential to justify the reason for any yield change. For example, this multiplier could be effective in representing the impact of improvements to climate or technology that increase the output of a particular unit of land in the absence of increased labor or investment. Users can also alter the maximum yield that can be harvested from a plot of land using '''ylmax''', an exogenous parameter which sets a maximum level for yield. Changes to this parameter can model extreme changes to land yield, such as a second green revolution. Moreover, the parameter '''ldcropm '''can be used to change the share of crop land in the total land.

Another parameter that can be used to affect yield is '''envylchgadd''', also discussed in the [[#_Environment_Module|environmental module]]. It can be used to model the effect global warming is having on agricultural yields. Modifying this parameter allows users to test different assumptions about the effect that increased CO2 and increased temperatures will have on crop yield.

Changes in the patterns of production of meat and fish can be modeled in the IFs system through a number of parameters. Meat production can be affected by changing the rate at which livestock are slaughtered via '''slr'''. This parameter could be altered to model the transition from subsistence to factory farming, and the increasing use of modern slaughterhouses in developing countries. The parameter '''livhdpro''' is also useful for modeling the impact of increased meat production from livestock as a result of the transition to grain feeding, which allows producers to extract more meat from each animal they own.

Similarly, the parameters '''aquaculm '''and '''aquaculgr''' can be altered to model the impact of increasing reliance on aquaculture for fish production because of technological improvements to this method of food production. The variable of interest will be FISH. The parameter '''aquaculm''' works like other multiplicative parameters, while the parameter '''aquaculgr''' alters the rate at which the use of aquaculture increases. The first parameter targets the actual amount of aquaculture a country uses, and is easiest to manipulate for the average user. The second, '''aquaculgr''', is set within the model and alters the rate at which countries begin to develop aquaculture resources. Finally the parameter '''aquaculconv''' can be used to determine the number years in which aquaculture growth rate converges to 0.

Other parameters that affect the level of fish production include '''fishcatchm''' and '''aquaculm. '''The first, modifies the amount of fish catch while the second''', '''changes the rate of catch from aquaculture.

Changing the loss rate of agricultural output to waste (rotting in fields, lost/spoiled during transportation, or not used in the home) via the '''Aglosstransm''' parameter is a powerful way to model the impact of changing agricultural distribution loss of food. It is a multiplicative parameter. Moreover production losses and consumption losses can be manipulated through the parameters, '''aglossprodperc''' and '''aglossconsperc''' which allow the user to specify the rates of production losses and consumption losses respectively.

Technological improvement is not the only pathway through which yield ('''YL''') may be affected within IFs. Financial pathways can change the amount of capital being invested in agriculture, resulting in higher yields. For example, parameter '''aginvm''', directly changes the investment in agriculture. This parameter can be used to model the effects of investment in improved agricultural technologies like genetically modified crops or new methods of aquaculture. Because of the financial accounting systems within IFs, changes in agricultural investment will affect investment in other sectors. '''Elagind''' is a parameter that may be useful to those who wish to adjust the percentage of crop production that is diverted for industrial uses such as ethanol, or beer.

== Parameters to Affect Nutrition ==

{| border="1" cellspacing="1" cellpadding="1" width="0" style="width:623px;"
|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malnelimstartyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination start year

| nowrap="nowrap" style="width:104px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malnelimtargetyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination target year

| nowrap="nowrap" style="width:104px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:111px;height:22px;" |
'''malelimprecisesw'''

| nowrap="nowrap" style="width:103px;height:22px;" |
MALNPOP

| nowrap="nowrap" style="width:305px;height:22px;" |
Undernourishment elimination

| nowrap="nowrap" style="width:104px;height:22px;" |
Switch

|}

Finally, model users may wish to use parameters to explore the health and economic impacts of different levels of nutritional deficiencies. In the health model, nutritional deficiency manifests itself in terms of mortality and morbidity related to communicable diseases. It can have especially large effects on children, who if undernourished for a significant period of their childhood, suffer developmental and cognitive delays that can impact their future productivity. The most important of these parameters are '''malnelimstartyr '''and '''malnelimtargetyr, '''which are standard error target parameters that affects the number of undernourished people in a country as a percent of the population. These parameters could also be used to represent the impact of school based nutrition programs or other targeted feeding programs. The parameter '''malelimprecisesw''' is a switch that when activated calculates the required number of calories to eliminate hunger for the most undernourished part of the population.

== Prepackaged Scenarios ==

One prepackaged scenario that makes significant improvements to agricultural yield in the developing world was developed as a policy brief, and models the impact of a green revolution in Africa. The scenario combines multipliers and other parameters, including '''ylm, ylmax, aginvm, '''and'''tgrld'''. It was designed to model the impact of a green revolution type of event occurring in the developing world and involving improved yield of crops through improvements in cultivation techniques and new technologies. Users will find it saved under the African Policy Briefs section of the World Integrated Scenario Sets, under the heading Green Revolution, which is also the title of the scenario. It increases agricultural yields by 76 percent over a 32-year period (increasing the parameter '''ylm''' to 1.76). It also increases the maximum yield in all African countries, as well as modeling 20 percent growth in agricultural investment and an initial target for growth in cultivated land of 8 percent.

Another prepackaged scenario that makes significant changes to agriculture is the Politics of Belly scenario developed for the African Futures 2050 project. This scenario is located in the Afp 2050 folder in the World Integrated Scenario Sets heading. This posits a much less positive outlook for African agriculture, simulating a 20 percent decline in yields that could represent the negative impact of climate change, much more than offsetting the mitigating effect that plant responsiveness to C02 increases might have on yields.

= Energy Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" width="366" style="width:366px;"
|-
| nowrap="nowrap" style="width:135px;height:24px;" |
'''Variable Name'''

| nowrap="nowrap" style="width:231px;height:24px;" |
'''Description'''

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENDEM'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy demand

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WEP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
World energy prices

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WEPBYEAR'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Translates global energy prices into dollars per barrel of oil equivalent

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENPRI'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy prices

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENST'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy stock

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENRGDP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ratio of energy demand to GDP

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy production

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''WENP'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Global energy production

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''KS'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Capital stock

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''IDS'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Investment in energy

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''QE'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Capital output ratio

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESER'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Accessible energy reserves

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESOR'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ultimate conventional energy resources

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''RESORUNCON'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Ultimate unconventional energy resources

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENM'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy imports

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENX'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy exports

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''ENSHO'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Energy shortage

|-
| nowrap="nowrap" style="width:135px;height:23px;" |
'''VADD'''

| nowrap="nowrap" style="width:231px;height:23px;" |
Value added, energy

|}
Rapid development of unconventional oil and gas production—particularly from shale sources—and high rates of growth in energy from renewable sources make the future of energy highly uncertain. Several research organizations have forecasting projects that provide long-term analysis. In the United States, the Department of Energy's Energy information Agency produces a yearly report titled, ''International Energy Outlook; ''the International Energy Agency produces similar forecasts for the world as a whole, as well as for many individual countries, in its annual ''World Energy Outlook.'' The IFs energy module contributes to this existing body of research on international energy futures. A selection of variables and parameters allows users to explore alternative patterns of energy demand, production, resource discovery and technological developments.

Rapid development of unconventional oil and gas production—particularly from shale sources—and high rates of growth in energy from renewable sources make the future of energy highly uncertain. Several research organizations have forecasting projects that provide long-term analysis. In the United States, the Department of Energy's Energy information Agency produces a yearly report titled, ''International Energy Outlook; ''the International Energy Agency produces similar forecasts for the world as a whole, as well as for many individual countries, in its annual ''World Energy Outlook.'' The IFs energy module contributes to this existing body of research on international energy futures. A selection of variables and parameters allows users to explore alternative patterns of energy demand, production, resource discovery and technological developments.

The structure of the IFs energy model is similar to that of the agriculture module, [[#_Agriculture_Module|discussed in the preceding section]]. Both systems are driven by demand and supply dynamics within an equilibrium-seeking system that involves prices, trade, and investment. In both cases, the model translates forecasts of the physical system into value terms for the economic module and interaction with other sectors. The size of the economy and population shape demand, which IFs represents in the aggregate (not within sub-categories such as transportation, residential/commercial and industrial). The supply side differentiates production dynamics for oil, gas, coal, hydroelectric, nuclear, and other renewable energy and separately represents conventional and unconventional energy resources, such as shale deposits.

Beginning our elaboration with the demand side first, IFs derives each country’s energy demand from the overall size of the economy, the level of development as measured by GDP per capita, and world energy prices. The variable '''ENDEM,''' displayed as a default in billion barrels of oil equivalent (BBOE), captures demand from all sectors, and for all types of energy. The energy intensity of an economy—the usage of energy per unit of GDP ('''ENRGDP''')—is a very important measure, influenced by the development level, the advance of technology, and energy prices. Reductions in that, or improvements of efficiency over time, have been occurring in most countries. Users may explore several different formulations of energy price, which is a key factor used to derive demand and affect the energy intensity. '''WEP''' is a single composite measure of the global price of energy, expressed as an index with a base of 100 in the first model year ('''WEPBYEAR''' translates global energy prices into dollars per barrel of oil equivalent). Like '''WEP''', '''ENPRI''' displays country-level energy price as an index, base 100. Each country’s price is a function of global capital costs of energy production and a regional markup factor that reflects the level of country-specific energy stocks ('''ENST''').

Key indicators for the other side of the energy system, the supply side, include production, capital/output ratio and available natural resources. Production is measured in the variable '''ENP'''; production can be displayed as a total, or disaggregated through the energy type dimension to produce individual displays of oil, natural gas, coal, hydroelectric power, nuclear, and other renewables. '''WENP '''is energy production for the world as a whole, disaggregated by production type. For each country, energy production is partially determined by relationships around capital. The portion of the initial capital stock dedicated to the energy sector, '''KS''', limits the scale of possible production. And the capital-output ratio, '''QE''', translates capital stock ('''KS''') into production level. Capital stock itself is augmented over time by investment, '''IDS''', dependent on the profitability of investment in energy.

The concept of capital-output ratio ('''QE''') warrants clarification because it is important to the energy module's equilibrating dynamics. When production requires relatively little invested capital to produce a unit of output, the capital-output ratio is low. Conversely, a high ratio would mean a large amount of capital is required to produce a unit of output. All things equal, the lower the capital-output ratio, the greater the opportunity for profits. Thus, in the model, a low '''QE '''will generally trigger an increase in production.

Available natural resources are the final factor constraining supply. Levels of proven reserves, '''RESER''', drive the price structure component of production—by affecting '''QE'''. Proven reserves are in turn bounded by the availability of ultimately recoverable resources, displayed in the variable '''RESOR '''for conventional resources, and the variable '''RESORUNCON '''for unconventional resources, both of which must be discovered ('''RD''') to become reserves. As we shall see in the discussion of parameters, '''QE''' is affected not just by resource constraints, but also by the advance of technology.

A set of trade variables measures countries’ external reliance on energy. While high levels of energy exports can enrich public finances through taxation, an overreliance on either energy imports or exports increases vulnerability to external price shocks. Energy imports and exports are measured in billion barrels of oil equivalent by '''ENM '''and '''ENX''' and represent aggregate trade in energy without distinguishing energy types. A more direct indication of vulnerability to shock is located in Flexible Display, under the heading Energy Imports as a Percent of Total Demand.

Disruptive price swings are inherent to any commodity market. In energy markets they can result from either political disruption in energy producers such as Iraq or from collusion among producers—evidenced by the cartel behavior of The Organization of Petroleum Exporting Countries (OPEC). As economists point out, prices can theoretically always rise or fall to balance demand and supply—to clear the market. But as history has shown, severe energy shocks, sometimes in environments where leaderships do not allow prices to fully adjust, can result in actual physical shortages ('''ENSHO'''). In normal years the level of energy stocks in a country is visible through the variable '''ENST'''.

A final set of indicators, measured not in physical but in monetary value terms, represents the overlap between the energy and economic modules in IFs. The variable '''VADD '''displays the total value added that is contributed to a country's GDP by various sectors, including energy; this gives a quick look at how much a country relies on energy production as a source of wealth. Investment ('''IDS''') and capital stock ('''KS''') present additional measures of the size of the energy sector in absolute terms and relative to other sectors.

== Parameters to Affect Demand ==

{| border="1" cellspacing="1" cellpadding="1" width="578" style="width:578px;"
|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:91px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''endemm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENDEM

| nowrap="nowrap" style="width:305px;height:22px;" |
Energy demand

| nowrap="nowrap" style="width:91px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''elasde'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP, WEP

| nowrap="nowrap" style="width:305px;height:22px;" |
Price elasticity of energy demand

| nowrap="nowrap" style="width:91px;height:22px;" |
Elasticity

|-
| nowrap="nowrap" style="width:79px;height:22px;" |
'''carbtax'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Carbon tax

| nowrap="nowrap" style="width:91px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:79px;height:24px;" |
'''enrgdpgr'''

| nowrap="nowrap" style="width:103px;height:24px;" |
ENP, QE

| nowrap="nowrap" style="width:305px;height:24px;" |
Growth in energy efficiency

| nowrap="nowrap" style="width:91px;height:24px;" |
Rate of change

|}
The demand side of the energy system features several leverage points that can be used to create alternative scenarios. The multiplier '''endemm''' directly manipulates aggregate energy demand in a country or region relative to its underlying calculation from economic size and other driving variables. A second key demand-side parameter, '''enrgdpgr''', allows the user to change the default rate of change in energy intensity (the inverse of energy efficiency). Turning from the more brute-force parametric intervention approaches to a more policy specific one, country-level energy prices and therefore energy demand can also be manipulated directly through the additive parameter '''carbtax'''. As the name implies, this parameter simulates the effects of a tax on carbon emissions—even employed at a reasonable level, a carbon tax can have substantial impacts on energy demand.

The demand side of the energy system features several leverage points that can be used to create alternative scenarios. The multiplier '''endemm''' directly manipulates aggregate energy demand in a country or region relative to its underlying calculation from economic size and other driving variables. A second key demand-side parameter, '''enrgdpgr''', allows the user to change the default rate of change in energy intensity (the inverse of energy efficiency). Turning from the more brute-force parametric intervention approaches to a more policy specific one, country-level energy prices and therefore energy demand can also be manipulated directly through the additive parameter '''carbtax'''. As the name implies, this parameter simulates the effects of a tax on carbon emissions—even employed at a reasonable level, a carbon tax can have substantial impacts on energy demand.

In the energy analysis community there is always some uncertainty and debate concerning the strength of the relationship between energy price and demand. The parameter '''elasde''' allows users to change the long-term price elasticity of demand. This elasticity is negative in the model, corresponding to traditional formulations of supply and demand—as prices rise demand for energy falls. Users building scenarios may wish to experiment with different values of '''elasde''', while bearing in mind the possibility that long-term and short-term elasticities may differ.

These demand-side leverage points offer a rich area for experimentation. Work by groups such as the International Energy Agency (IEA) and the International Panel on Climate Change (IPCC), have simulated several alternative future forecasts for energy efficiency development.

== Parameters to Affect Supply ==

{| border="1" cellspacing="1" cellpadding="1" width="602" style="width:602px;"
|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:96px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enpm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Production multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''eprodr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENP

| nowrap="nowrap" style="width:305px;height:22px;" |
Production growth rate

| nowrap="nowrap" style="width:96px;height:22px;" |
Growth rate

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''resorm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESOR 

| nowrap="nowrap" style="width:305px;height:22px;" |
Conventional resource multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''resorunconm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESORUNCON

| nowrap="nowrap" style="width:305px;height:22px;" |
Unconventional resource multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''rdm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
RESER

| nowrap="nowrap" style="width:305px;height:22px;" |
Discovery multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''etechadv'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Rate of technical advance in energy

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''etechadvuncon'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Rate of technical advance in unconventional energy

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''qem'''

| nowrap="nowrap" style="width:103px;height:22px;" |
QE

| nowrap="nowrap" style="width:305px;height:22px;" |
Capital output multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''eninvm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENIV

| nowrap="nowrap" style="width:305px;height:22px;" |
Energy investment

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enprix'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENPRI

| nowrap="nowrap" style="width:305px;height:22px;" |
Regional energy price

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:98px;height:22px;" |
'''enml, enxl'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENM, ENX

| nowrap="nowrap" style="width:305px;height:22px;" |
Limits on energy imports and exports

| nowrap="nowrap" style="width:96px;height:22px;" |
Exogenous specification

|}

Turning to the supply side of the IFs energy module, two parameters in the model directly alter the relative production of oil, gas, coal, hydroelectric, nuclear, and other renewable energy. '''Enpm''', a multiplier, scales up or down existing production of a specified energy type; '''eprodr''', a growth rate parameter, forces a specified yearly percentage increase in production on the desired energy type. Both types of direct production modification can be used to trace the effects that a shift in energy production profile may have on life in a country, region, or the entire global community. For some observers, renewables represent the most important source of uncertainty surrounding the character global energy production. The IFs Base Case forecast assumes that renewable energy will grow rapidly in importance in the decades to come – see '''WENP''' across all energy types for a clear depiction. What if the model’s baseline assumptions are too optimistic, or too pessimistic? Scenarios created with '''enpm '''and'''eprodr''' can explore variations on the Base Case renewable energy story.

Moving below the direct interventions on production to parameters that affect the drivers of that production, both conventional and unconventional deposits of oil, gas and coal resources offer possible points of leverage for supply-side energy scenarios. As mentioned above, IFs divides raw materials used in energy production into resources and reserves. Scenario interventions are most effective when they include simultaneous changes to the reserves that are very closely tied to production, and to the more distil element of total resources (resources only become reserves when they are discovered and deemed to be economically exploitable with current technology and prices). An increase to the '''resorm '''or '''resorunconm '''parameters will expand the amount of materials that can potentially transition to reserves and become economically viable. As a second step, users can speed up this transition by manipulating '''rdm''', a parameter that represents the rate of geologic discovery (or improved economic exploitability) in a country. An increase in the stock of fossil fuel reserves creates the possibility that they will be produced and that they will have forward impacts on energy price ('''ENPRI''') and '''GDP''' of energy producing countries.

A key source of uncertainty around the future of energy supply concerns production costs. Technological progress could dramatically increase the amount of energy output possible at current levels of investment. The parameters '''etechadv''' and '''etechadvuncon''' directly control the rate of technological advance and cost reduction by energy type, whether based on fossil fuels or renewable energy. For fossil-based energy in particular, several other parameters enable users to explore the consequences of various energy production cost structures. '''Qem''', a capital-output multiplier, directly scales up—or down—the capital cost per output unit of each energy type at the country level.

Government policies could additionally affect the future of energy production. Actions such as tax increases or direct subsidies can be simulated using '''eninvm''', an investment multiplier. The parameter either increases or decreases overall investment in energy—the model still controls which energy-type is affected, based on price signals. Remember that the equilibrating mechanisms of the model will respond to any change in investment patterns. For example, an increase in investment flowing to oil, would increase production, eventually reducing oil prices, and leading to a subsequent decrease in investment. Remember also that the accounting mechanisms of the model will assure that any additional investment in energy will come from somewhere, such as investment in other sectors, other government expenditures, or household consumption.

Government decisions can also affect energy prices quickly, through intentional price shocks similar to those initiated by OPEC in the 1970s. The '''enprix''' parameter can simulate a concerted effort on the part of energy exporters to fix prices; '''enprix '''exogenously sets the type-specific energy price—'''ENPRI'''— for a region. As in real life, any price fixing scenario will have wide-ranging, and potentially perverse economic consequences. Two additional parameters exist that can simulate short-term supply disruptions via trade restrictions: '''enml '''exogenously imposes a limit to energy imports and '''enxl''' imposes a limit to energy exports.

== Prepackaged Scenarios ==

Currently, none of the prepackaged scenario files demonstrate the use of parameters linked to the energy module.

= Environment Module =

== Variables of Interest ==
{| class="wikitable"
|'''Variable Name'''
|'''Description'''
|-
|'''CO2PPM'''
|CO2 parts per million
|-
|'''CO2PER'''
|Percentage increase in atmospheric CO2
|-
|'''CARANN'''
|Annual carbon emissions from fossil fuels
|-
|'''CARFOROTH'''
|Annual net carbon release from forests
|-
|'''WTEMP'''
|Average annual world temperature
|-
|'''ENVTPCHG'''
|Annual average temperature change from 1990
|-
|'''ENVPRCHG'''
|Annual average precipitation change from 1990
|-
|'''WFORST'''
|World forest area
|-
|'''LD'''
|Land area by type
|-
|'''WATUSE'''

|Annual water usage
|-
|'''WATUSEPC'''
|Annual water usage per capita
|-
|'''ENVPM2PT5'''
|PM2.5 levels in residential areas
|-
|'''ENSOLFUEL'''
|Solid fuel use
|-
|'''CLIMECONIMP '''
|Climate change to economy impact
|}

As the global population has grown, and more and more countries have industrialized, sustainable development has come to occupy a permanent place on national, regional and global agendas. Growing concern for the environment was brought to the fore at the 1992 UN Conference on the Environment in Rio de Janiero. More than 2 decades have passed since the Rio conference, and the global community’s dedication to environmental concerns (if not always to action) has remained strong. In 2012 a follow up conference was held in Rio—the United Nation’s Conference on Sustainable Development, or Rio + 20—at which leaders discussed strategies to end poverty, address environmental destruction and build a bridge to the future. Other forward-looking environmental initiatives include the United Nations Environment Program’s (UNEP) Global Environmental Outlook Series. Since the first entry in the series, published in 1997, the UNEP has followed up with four additional reports (the International Futures model contributed to the most recent report). Despite the creation of dozens of internationally agreed upon goals, 2012’s ''Global Environmental Outlook 5'' warned that the world remains on an unsustainable path. Around the world, forestland is decreasing, centuries-old glaciers are retreating, aquifers are being depleted and the amount of atmospheric carbon dioxide is on the rise. Not all environmental trends are negative, however. Developed countries have made progress on urban air pollution. Developing countries have experienced improved drinking water quality and safe sanitation access. And globally, the volume of CFCs damaging the ozone layer has dropped dramatically over the course of recent decades.

Environmental issues have potential or actual connections to all of the modules in IFs. For instance, processes associated with energy, agriculture, infrastructure, and the broader economy directly impact the earth’s forests, water supply, and atmosphere, as does governance more indirectly. The environmental module elaborates a subset of these potential connections in four specific areas: the global carbon cycle, land use (notably forest area), water use (agricultural), and air quality (urban and indoor).

The numerous ways in which human actions influence the environment are evident throughout the carbon cycle. Carbon is continuously moving within and across the boundaries of the Earth’s atmosphere, hydrosphere and biosphere. In systems dynamics terminology, the elements in the cycle are known as stocks and flows. Put simply, stocks are long-term repositories of carbon such as the world's fossil fuels, biomass and oceans, and flows are movements from one stock to another. This stock-flow dichotomy serves as a useful heuristic for users who are interested in creating scenarios in the IFs environment model more generally, but especially with respect to carbon. Interventions can target carbon stocks through processes that affect flows, such as energy production and reforestation. Key carbon stock indicators are '''CO2PPM''', the level of atmospheric carbon dioxide measured in parts per million, and '''CO2PER''', atmospheric carbon dioxide, measured as a percent relative to pre-industrial levels. On the flow side, key indicators available in the model include '''CARANN''', the annual flow of carbon emissions from fossil fuel use, and '''CARFOROTH''', global net carbon released annually from forests and other sources (discussed below). As we shall see, the model also allows parametric control of the flow of carbon from the atmospheric stock to that dissolved in the oceans, another stock.

The atmospheric carbon stocks have important forward linkages. One of the most important, temperature change, can be tracked via '''WTEMP''', the global average annual temperature in degrees centigrade. A derivative temperature variable but at the country level, '''ENVTPCHG''', captures the average annual temperature change from the year 1990, and '''ENVPRCHG''' similarly tracks the change in precipitation. These have further forward linkages to agriculture.

Several variables in the model measure the configuration of the world’s land area. Two variables capture trends in forest coverage: '''WFORST''' is an aggregate measure of the world’s land area covered by forest, reported in millions of hectares; and the land variable '''LD''' is disaggregated by land type, enabling country- or region-level displays of the following categories: crop, grazing, forest, urban and other. (See the agricultural module for a complete discussion of land use variables and parameters). Land use links back to the carbon representation because trees and other plants making up the world’s forests absorb carbon dioxide through photosynthesis and their carbon content is still another important global stock. As indicated above, change in forest area, such as clearing of land for agricultural use, generates one of the carbon flow terms of the model.

The environment submodule includes water indicators. The annual use of freshwater resources is reported in cubic centimeters via '''WATUSE,''' tied in IFs to the major source of water use, agricultural production. Users can additionally choose to explore water use per capita, expressed in thousands of cubic meters, via the variable '''WATUSEPC'''. Currently, the model’s treatment of water does not feature the stock and flow elements seen in the carbon cycle. Although '''WATUSE''' is a flow of water used each year, no corresponding variable captures the total stock of water—say in nonrenewable (or at least very slowly recharging) fossil water aquifers. This is a shortcoming that we hope to address in the near future. However, a parameter, '''watres''' (see the discussion below), does provide an estimate of the level of annually renewable water resources in countries and regions. The ratio of use to renewable resources serves as a measure of relative water stress. With respect to forward linkages, water stress is one of the drivers of state fragility (see the government module).

A set of environmental indicators pertains to air pollution. The amount of solid matter suspended in the Earth’s atmosphere serves as a measure of relative air quality; high levels of atmospheric particulate matter (PM) can have adverse effects on human health. Depending on the data source, PM concentration readings may include particles that are either 10 or 2.5 micrometers in diameter. In the model, the variable '''ENVPM2PT5''' captures levels of fine particulate matter, PM2.5, found in residential areas (although our current data source is actually for 10 micrometer particulates). The prevalence of solid fuel use is a second indicator of air pollution. The variable '''ENSOLFUEL''' captures the percent of a population relying on solid fuel as the primary source of domestic energy. Burning solid fuel, such as wood, peat, coal, dung, and certain grains, produces a toxic environment indoors (see the infrastructure module).

Finally, one indicator pertains to the impact of climate change on the economy. '''CLIMECONIMP''' feeds forward the impact of climate change to the GDP. This is done through a quadratic formulation that was originally developed by Nordhaus.

Alternative scenarios, built in any IFs module, are likely to have bearing on environmental indicators. Users exploring the consequences of scenario interventions in economics, energy or agriculture are likely to see the greatest environmental effects. Interventions in other modules will still affect the environment, though through weaker, more distal, drivers. The environmental sub-module, however, allows users to directly explore the effects of environmental change. Environmental parameters can be categorized as those that affect carbon stocks and flows, land use, water resources, and air quality.

== Parameters Affecting Carbon ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:129px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:283px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''enpm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy production by type

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''endemm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENDEM, WEP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy demand multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carbtax'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP, CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon tax

| nowrap="nowrap" style="width:121px;height:22px;" |
Exogenous Specification

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''qem'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Capital-output ratio multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''resorm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
RESOR

| nowrap="nowrap" style="width:283px;height:22px;" |
Resource multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''enrgdpgr'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENP

| nowrap="nowrap" style="width:283px;height:22px;" |
Energy efficiency

| nowrap="nowrap" style="width:121px;height:22px;" |
Growth rate

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel1'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by gas

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel2'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by natural gas

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carfuel3'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CARANN

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon generated by coal

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envclimsens'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
Time sensitivity of global temperature to carbon dioxide

| nowrap="nowrap" style="width:121px;height:22px;" |
Limit

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carabr'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
Carbon absorbed by oceans

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carabrchng'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM, WTEMP

| nowrap="nowrap" style="width:283px;height:22px;" |
annual change in this absorption rate of carbon by oceans

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envco2fert'''

| nowrap="nowrap" style="width:129px;height:22px;" |
AGP

| nowrap="nowrap" style="width:283px;height:22px;" |
Crop sensitivity to environmental change

| nowrap="nowrap" style="width:121px;height:22px;" |
Elasticity

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envylchgadd'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENVYLDCHNG

| nowrap="nowrap" style="width:283px;height:22px;" |
Agricultural yield change

| nowrap="nowrap" style="width:121px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''envylchgm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
ENVYLDCHNG

| nowrap="nowrap" style="width:283px;height:22px;" |
Agricultural yield change

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|}
There is no way to directly target atmospheric carbon stocks in the model. Instead, points of intervention involve flows, the most important of which is the burning of fossil fuels and therefore the rate of flow of carbon from its stock in those fuels to the stock in the atmosphere With respect to fossil fuel use, there are leverage points on both the production and consumption side—see the [[#_Energy_1|energy model]] discussion. '''Enpm''', the energy production multiplier, will alter the flow of carbon into the atmosphere and can serve as a powerful leverage point in energy-driven environmental scenarios. Users can alter the mix of energy types produced within a country, thereby reducing or increasing the use of fossil fuels, and trace the environmental effects.

There is no way to directly target atmospheric carbon stocks in the model. Instead, points of intervention involve flows, the most important of which is the burning of fossil fuels and therefore the rate of flow of carbon from its stock in those fuels to the stock in the atmosphere With respect to fossil fuel use, there are leverage points on both the production and consumption side—see the [[#_Energy_1|energy model]] discussion. '''Enpm''', the energy production multiplier, will alter the flow of carbon into the atmosphere and can serve as a powerful leverage point in energy-driven environmental scenarios. Users can alter the mix of energy types produced within a country, thereby reducing or increasing the use of fossil fuels, and trace the environmental effects.

On the demand side, a reduction in '''endemm''', the energy demand multiplier, can likewise simulate conservation/efficiency enhancement programs that alter the flow of carbon into the atmosphere. It is important to note that reductions in energy use triggered by '''endemm''' are free; that is to say, they are not accompanied in the model by economic costs. In reality, conservation or efficiency efforts occasionally do reduce energy use without a large cost—as evidenced by voluntary behavioral shifts on the part of citizens, or regulations enforced by the government. However, because it may be unrealistic to assume costless energy demand changes, users may wish to include financial considerations in their scenarios. The parameter '''carbtax '''simulates a reduction in demand triggered by tax; users would pay an additional fee on fossil fuels proportional to carbon content. This would encourage conservation or a transition to less expensive forms of energy. Users experimenting with carbon taxes should observe the effects on the energy production system '''(ENP)''', the level of annual carbon emissions '''(CARANN'''), world temperature ('''WTEMP''') and the level of economic output ('''GDP)'''.

Users can also simulate government interventions designed to encourage research and development focused on renewable energy or energy efficiency. Each energy type’s cost profile is linked to a capital-output ratio multiplier, '''qem'''. Users can simulate a decrease in the cost of renewables by reducing the multiplier’s initial value from 1.0 to 0.8—this would reduce all production costs by 20 percent (on top of whatever cost changes the model endogenously computes). Energy prices can also be indirectly controlled by changes to natural resources needed to produce fossil fuels. As oil and gas resources are exploited, the remainder becomes more expensive to extract. Users can use '''resorm''' to enforce alternative assumptions about the scale of ultimate resources available for discovery, and eventually, production. Mechanisms involving resources, reserves, and energy price operate over a long time horizon. Any scenario using '''resorm''' should be run out to at least the year 2050 in order to display significant forward linkages.

Changes in the efficiency of energy use from oil and gas sources would have bearing on carbon emissions as well. Changes in energy efficiency are controlled by the growth rate parameter '''enrgdpgr '''in addition or instead of the use of '''endemm. '''Changes to this parameter simulate the impact of successful research and development programs in the public or private sphere. Three parameters offer an additional way to simulate improvements to energy efficiency: '''carfuel1, carfuel2 '''and '''carfuel3 '''set, respectively, the amount of carbon emitted by burning oil, natural gas and coal. Normally these should be considered physical constants and not changed, but the model does not contain a representation of carbon sequestration and '''carfuel3''' could be used to represent its impact, although not its cost. For a complete discussion on parameters pertaining to energy production see the [[#_Energy_Module|energy section]].

Returning to the issue of stock-to-stock flows of carbon, so far we have focused only on the movement of carbon from fossil fuels to the atmosphere. Not all carbon emitted into the atmosphere remains there, and the model contains parameters to control the absorption of atmospheric carbon by the oceans. Two parameters control this process in the model. The first is '''carabr''', an exogenous specification of the billion tons of carbon that annually moves to or is absorbed by the oceans. The second is '''carabrchng''', which sets the annual change in the absorption rate (a measure of flow). At this point the parameter is not functional, and the user should be aware that change in it will have no effect. The final stock-to-stock flow represented in the model is between the atmosphere and forests (to be discussed below). Because the model does not explicitly represent the flow between the atmosphere and other non-forest vegetation (or organic matter in the soil), users with interest in this area could turn to the parameter '''carabr''' to implicitly capture, not just absorption into oceans, but into other matter as well.

Turning to forward linkages, future trends in the carbon system will affect the world’s agricultural sector. The stock of atmospheric carbon dioxide ('''CO2PPM''') and the related level of global temperature ('''WTEMP''') are the variables that transmit the carbon system's effects. The model represents two major paths of impact, one related to each variable. The first path is the so-called carbon-fertilization effect. Because plants need carbon, an increase in '''CO2PPM''' can actually increase agricultural yields, and the parameter that controls that is '''envco2fert''', crop sensitivity to atmospheric carbon dioxide. A value of zero would turn off this linkage.

The second path is from global temperature ('''WTEMP''') to country-specific temperature and precipitation changes ('''ENVTPCHG '''and'''ENVPRCHG) '''and then to agricultural yield. The translation in the model of changes in global temperature to changes in country-specific temperature and precipitation uses information from climate-specific models and is not subject to parametric control by users (in modeling terminology, they are “hard-coded” within the model). The user can, however, affect the further impact of country-specific values to yield. While the model's formulation for that impact ('''ENVYLCHNG''') on yield is quite complex, two parameters exist for influencing it, the multiplicative parameter '''envylchgadd '''and the additive parameter '''envylchgm. '''Their effects are sequential, so it is best that the user rely on one or the other, not both.

One additional parameter is important in this consideration of forward linkages, namely that which controls the translation of atmospheric carbon levels ('''CO2PPM)''' into global temperature ('''WTEMP'''). That parameter is '''envclimsens''', the time sensitivity of global temperature to carbon dioxide. The very large-scale global circulation models of climate change struggle to make the connection between these two variables and IFs uses this simple parametric linkage to summarize some of what we have learned from them. But the relationship is uncertain, so a scenario that would raise or lower the parameter and the impact on global temperature might be of interest. Moreover, the impact of carbon on temperature is likely to be one that involves lags (increasing temperature slowly even at a given fixed level of raised carbon dioxide), so one could consider a dynamic value of the parameter over time in order to capture that.

==== Land Use ====

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:129px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:283px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:121px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''forestm'''

| nowrap="nowrap" style="width:129px;height:22px;" |
WFOREST

| nowrap="nowrap" style="width:283px;height:22px;" |
World forest multiplier

| nowrap="nowrap" style="width:121px;height:22px;" |
Multiplicative

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''tgrld'''

| nowrap="nowrap" style="width:129px;height:22px;" |
WFOREST, LD

| nowrap="nowrap" style="width:283px;height:22px;" |
Land growth by type

| nowrap="nowrap" style="width:121px;height:22px;" |
Initial condition

|-
| nowrap="nowrap" style="width:99px;height:22px;" |
'''carforst'''

| nowrap="nowrap" style="width:129px;height:22px;" |
CO2PPM

| nowrap="nowrap" style="width:283px;height:22px;" |
Level of carbon sequestered by forests

| nowrap="nowrap" style="width:121px;height:22px;" |
Coefficient

|}
The importance of land extends beyond its role in carbon cycle. We are interested in forest area, for instance, due to its involvement in species preservation, in water cycle control, and for its own intrinsic value. A direct multiplier, '''forestm''', imposes an increase or a decrease on a country’s forest area. Any change in land area is constrained by a country’s geographic size because an increase in forest area requires a corresponding decrease in the land available for other types of use, such as crop production. As another way to affect land use, the parameter '''tgrld''' sets an initial target for annual growth in cropland. This second parameter achieves a change in land devoted to crops primarily as a trade off with forest area. For a complete discussion of parameters concerning patterns of land use, see the [[#_Agriculture_Module|agricultural section]]. Changes in land use will, of course, have secondary consequences visible in '''GDP''', '''AGP''', '''AGX''', and '''AGM''', among other variables

The importance of land extends beyond its role in carbon cycle. We are interested in forest area, for instance, due to its involvement in species preservation, in water cycle control, and for its own intrinsic value. A direct multiplier, '''forestm''', imposes an increase or a decrease on a country’s forest area. Any change in land area is constrained by a country’s geographic size because an increase in forest area requires a corresponding decrease in the land available for other types of use, such as crop production. As another way to affect land use, the parameter '''tgrld''' sets an initial target for annual growth in cropland. This second parameter achieves a change in land devoted to crops primarily as a trade off with forest area. For a complete discussion of parameters concerning patterns of land use, see the [[#_Agriculture_Module|agricultural section]]. Changes in land use will, of course, have secondary consequences visible in '''GDP''', '''AGP''', '''AGX''', and '''AGM''', among other variables

Returning to the role of forests in the carbon cycle, the key parameter is the amount of carbon sequestered by each million hectares of the earth’s forests, '''carforst'''. By default, this coefficient is set to 0.12—meaning that every million hectares of forest sequesters 0.12 billion tons of carbon. By increasing or decreasing the value, users can explore futures in which forests are more or less effective at removing carbon from the atmosphere.

== Parameters Affecting Water Resources ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''watres'''
|GOVRISK, THREAT
|Renewable water resources
|Exogenous specification
|-
|'''ylm'''
|YL
|Agricultural yield multiplier
|Multiplier
|-
|'''watdemandelas'''
|WATDEMAND
|Elasticity from water price to water demand
|Elasticity
|-
|'''landirareaequipelas'''
|LANDIRAREAACTUAL
|Elasticity from water price to land equipped for irrigation
|Elasticity
|-
|'''desali'''
|WATERDESALINATED
|Desalination responsiveness to change in stock levels of price
|PID factor
|-
|'''desald'''
|WATERDESALINATED
|Desalination responsiveness to change in price
|PID factor
|}

'''Watres''' allows users to set the renewable resources located in each of the countries in IFs. Changes to this parameter or exogenous variable can either be instantaneous or gradual, as specified by the user. Though each country’s renewable water resources, and associated water stress, has bearing on state fragility, it is important to note that the '''watres''' parameter does not constrain water use. Users can manipulate the elasticity of water demand to water prices through the parameter '''watdemandelas.''' Similarly, users can change the elasticity of water price to land equipped for irrigation using the parameter '''landirareaequipelas.''' Moreover, user can change the responsiveness of desalination to water prices through two parameters '''desali''' and '''desald''' that affect the integral and the differential factors on the PID formulation.

Users can affect water use via deeper drivers. Any increase (or decrease) in crop land, and subsequent increases (or decreases) in production, will require a corresponding change in water usage ('''WATUSE'''). Water usage per capita is linked to crop production per capita by a function in the model (Crop Production/Capita Versus Water Use/Capita). Thus, changing that function or manipulating the land growth by type parameter, '''tgrld''', can trigger patterns of water uses differing from those featured in the Base Case. The yield multiplier '''ylm''' provides a very direct method of indirect water use modification; increases in production will require more water, often (implicitly in the model) through irrigation.

== Parameters Affecting Air Pollution ==

{| border="1" cellspacing="1" cellpadding="1" width="626" style="width:626px;"
|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:103px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:305px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:96px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''envpm2pt5m'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENVPM2PT5

| nowrap="nowrap" style="width:305px;height:22px;" |
PM2.5 levels multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelm'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use multiplier

| nowrap="nowrap" style="width:96px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelsetar'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use SE target

| nowrap="nowrap" style="width:96px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfuelseyrtar'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel use SE target

| nowrap="nowrap" style="width:96px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfueltrgtval'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOL FUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel elimination target

| nowrap="nowrap" style="width:96px;height:22px;" |
Target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''ensolfueltrgtyr'''

| nowrap="nowrap" style="width:103px;height:22px;" |
ENSOLFUEL

| nowrap="nowrap" style="width:305px;height:22px;" |
Solid fuel elimination target

| nowrap="nowrap" style="width:96px;height:22px;" |
Target

|-
| nowrap="nowrap" style="width:122px;height:22px;" |
'''hlsolfuelsw'''

| nowrap="nowrap" style="width:103px;height:22px;" |
DEATHCAT

| nowrap="nowrap" style="width:305px;height:22px;" |
Relationship between solid fuel and respiratory diseases

| nowrap="nowrap" style="width:96px;height:22px;" |
Switch

|}

Several environmental parameters allow users to directly manipulate levels of air pollution. Fine grain aerial particulate matter levels in residential areas ('''ENVPM2PT5''') are controlled by a multiplicative parameter, '''envpm2pt5m'''. Like any multiplier, assigning higher or lower values will result in a proportional change in the associated variable—in this case '''ENVPM2PT5'''. As an alternative, users can explore variations of solid fuel use in a specific country, region, or the world as a whole. The percentage of a population using solid fuel as their primary source of electricity ('''ENSOLFUEL)''' is connected to a multiplier, '''ensolfuelm'''. The model also features a pair of standard error target parameters: one that establishes the target, '''ensolfuelsetar''', and one that establishes the year by which the target will be reached, '''ensolfuelseyrtar'''. Users can alternatively (not simultaneously) utilize a second pair of parameters to set a year by which solid fuel use will be either reduced, or eliminated completely. '''Ensolfueltrgtval''' sets the desired reduction, and '''ensolfueltrgtyr''' sets the target year. Finally, and related to forward linkages, users can control whether there is a relationship between the burning of solid fuel and health. When the switch parameter '''hlsolfuelsw''' is set to 1 (the default), a positive relationship between solid fuel use and respiratory disease is active; when the switch is set to 0, the relationship is deactivated.

== Parameters Affecting climate change impact on the economy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''climeconimpsw '''
|'''CLIMECONIMP'''
|Climate change to economy impact switch (1=on)
|Switch
|-
|'''climeconimplin '''
|'''CLIMECONIMP'''
|Climate change to economy impact, linear term coefficient
|Co-efficient
|-
|'''climeconimpsq '''
|'''CLIMECONIMP'''
|Climate change to economy impact, squared term coefficient
|Co-efficient
|-
|'''climeconimpstk'''
|'''VADD'''
|Climate change to econ impact, portion affecting productivity or capital stocks
|Multiplier
|}

Climate change impacts are fed forward to the economy through three parameters. '''climeconimpsw''' is a switch when turned initiates the computation of the impact of climate change on the economy. The impact is calculated through a quadratic formulation which was initially developed by Nordhaus. Accordingly, two co-efficients, '''climeconimplin''' and '''climeconimpsq''' compute the linear and the quadratic impact of climate change on the economy through an impact on Value added (VADD). '''climeconimpstk''' calculates the economic impact of climate change on capital stocks.

== Prepackaged Scenarios ==

Exploring an alternative scenario conceptualized by the Netherlands Environmental Assessment Agency (PBL) can give users a quick overview of some of the powerful leverage points in the environment module. Using their integrated assessment model, called IMAGE, PBL constructed a set of forecasts representing a future in which carbon emissions drop dramatically. A replication of this scenario comes pre-packaged with the IFs installation, in the World Integrated Scenario Sets folder and the NCAR IPCC subfolder, under the title IMAGE_hirenew_lowcarbon. The folder contains, in total, five scenarios, all of which were developed during the course of a joint project between the Pardee Institute and the National Center for Atmospheric Research (NCAR), which is located in Boulder, Colorado. Each alternative scenario uses IFs parameters to approximate work done by another integrated assessment model: BPL’s Integrated Model to Assess the Global Environment (IMAGE), Asia-Pacific Environment Innovation Strategy’s AIM, the International Atomic Energy Agency’s MESSAGE, and Pacific Northwest National Laboratory’s Mini-Climate Assessment Model, or MiniCAM,

In order to simulate a significant reduction in global carbon emissions, the IMAGE_hirenew_lowcarbon scenario includes interventions in capital-output ratio, fossil fuel burning behavior, and carbon tax policy. For oil, gas, coal and nuclear production in every country, the parameter '''qem '''is set to rise steadily, reaching a value of four by mid-century and remaining there until 2100. Conversely, the capital cost of each unit of renewable energy produced in the world is set to decline, with '''qem''' approaching 0.3, and remaining at that value until 2100. As fossil fuel production becomes expensive and green energy becomes relatively cheaper, price signals will push global energy producers to reduce carbon emissions. The use of the '''carbtax''' parameter augments the economic incentives attached to renewables in the scenario. By 2100 a carbon tax of 400 billion U.S. dollars is in place in every country in the world. Finally, the scenario adds an element of environmentally friendly technological change, through a reduction in the parameters '''carfuel1''', '''carfuel2''', and '''carfuel3'''. This would simulate worldwide discoveries allowing for the cleaner burning of oil, natural gas, and coal.

= Governance Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''GOVINDSECUR''' 
| Governance security index 
|-
| '''GOVINDCAPAC''' 
| Governance capacity index 
|-
| '''GOVINDINCLUS''' 
| Government inclusiveness index 
|-
| '''GOVINDTOTAL''' 
| Total governance index 
|-
| '''SFINTLWARALL''' 
| Internal war, event occurrence 
|-
| '''SFINTLWARMAG''' 
| Internal war, magnitude 
|-
| '''GOVRISK''' 
| Government risk index 
|-
| '''GOVREV''' 
| Government revenues 
|-
| '''AID''' 
| Foreign Aid 
|-
| '''GOVCORRUPT''' 
| Government Corruption 
|-
| '''REGQUALITY''' 
| Regulatory quality 
|-
| '''ECONFREE''' 
| Economic freedom measure 
|-
| '''GOVEFFECT''' 
| Government effectiveness 
|-
| '''DEMOCPOLITY''' 
| Level of democracy 
|-
| '''FREEDOM''' 
| Freedom measure 
|-
| '''GEM''' 
| Gender empowerment measure 
|}
The IFs governance module contributes to a growing body of work dedicated to quantifying the performance of governments around the world. The IFs model adds to that literature especially by facilitating forecasting and scenario analysis. The module represents governance as a composite system, made up of three interconnected dimensions: security, capacity and inclusiveness. The modelling process builds on a large number of data series and governance indices constructed by various research groups, mostly in academia, non-profit organizations, and international organizations. Variables capturing regulatory quality and government effectiveness, for example, come from the World Bank’s World Governance Indicator project; Freedom House and Transparency International provide measures of economic freedom and corruption; and the Center for Systemic Peace provides state fragility and democracy indicators. Based on such data and series, the IFs system builds up to aggregate indices for its three dimensions of governance: '''GOVINDSECUR '''for security, '''GOVINDCAPAC '''for capacity, and '''GOVINDINCLUS '''for inclusivity. These three indices are then combined into an overall governance index,'''GOVINDTOTAL.'''

The IFs governance module contributes to a growing body of work dedicated to quantifying the performance of governments around the world. The IFs model adds to that literature especially by facilitating forecasting and scenario analysis. The module represents governance as a composite system, made up of three interconnected dimensions: security, capacity and inclusiveness. The modelling process builds on a large number of data series and governance indices constructed by various research groups, mostly in academia, non-profit organizations, and international organizations. Variables capturing regulatory quality and government effectiveness, for example, come from the World Bank’s World Governance Indicator project; Freedom House and Transparency International provide measures of economic freedom and corruption; and the Center for Systemic Peace provides state fragility and democracy indicators. Based on such data and series, the IFs system builds up to aggregate indices for its three dimensions of governance: '''GOVINDSECUR '''for security, '''GOVINDCAPAC '''for capacity, and '''GOVINDINCLUS '''for inclusivity. These three indices are then combined into an overall governance index,'''GOVINDTOTAL.'''

The building blocks of the three indices, however, are sub dimensions and component variables. Security’s sub-dimensions are the probability of intrastate conflict ('''SFINTLWARALL''') and a general level of government performance with associated risk of instability ('''GOVRISK'''). Although not used in the security index, '''SFINTLWARMAG''' supplements '''SFINTLWARALL''' by forecasting the average magnitude of internal conflict.

Forecasts of intrastate war probability ('''SFINTLWARALL''') are heavily path dependent, driven by a moving average of countries’ past experiences with violence. Over time, however, other factors—infant mortality, level of democracy, economic growth rate, and trade openness, among others—affect the probability of conflict as well. IFs uses an index, '''GOVRISK''', to measure the second sub-dimension of security, government performance and associated risk. For the 186 countries included in the model, the overall index value ranges roughly from 15 to 60, with a higher number signifying increased levels of risk. Low risk countries such as Sweden will generally fall at the bottom of the range, while high risk ones such as Somalia will be at the top. Like the probability of war, each country’s performance risk profile is driven by an array of factors across demographic, environmental, economic, health and education subsystems. A comprehensive look at each country’s performance and risk profile is available through Specialized Display under the option Performance Risk Evaluation. The display includes raw values and a global ranking, for each of the index’s nine components. Weighting and relative risk levels associated with each component are also listed.

The sub-dimensions of the second dimension of governance, capacity, are government revenue and government quality. Each country’s ability to raise revenue is captured by a simple ratio of '''GOVREV '''to '''GDP'''—net of foreign aid ('''AID''') received by governments, which the index does not include. The more revenue a government is able to raise relative to GDP, the higher the capacity score will be—for a discussion of the equilibrating processes used to calculate government revenue, expenditure and transfer payments see the [[#_The_Economic_Module|economic module]]. Governance quality, capacity’s second sub-dimension, is represented by corruption. In the model, '''GOVCORRUPT''' measures relative levels of political corruption, with 0 representing extremely corrupt practices, and 10 representing extreme transparency, or lack of corruption. Indices tracking regulatory quality ('''REGQUALITY)''', economic freedom ('''ECONFREE)''', and government effectiveness ('''GOVEFFECT)''' offer alternative formulations of governance quality. Although the '''GOVCORRUPT''' score is the only element used to generate the composite index '''GOVINDCAP''', the other indicators link forward to economic and infrastructure development processes.

The third dimension of governance, inclusiveness, is made up of the level of formal democratization and an element that measures gender empowerment in society. The '''DEMOCPOLITY '''index represents the strength of democratic institutions; in IFs, values range from 0 to 20, higher numbers signifying greater levels of democracy. In forecast years, several factors—the age structure of society, dependence on raw materials, energy exports and energy price—determine the strength of democracy. The model uses the gender empowerment measure ('''GEM''') as another index of inclusiveness. The measure takes its historical values—and its name—from the United Nations Development Program’s Gender Empowerment Measure. Forecasts of gender empowerment are driven by a country’s youth population size, GDP per capita at purchasing power parity, and years of formal education attained by female adults. The index '''FREEDOM''', created by Freedom House, offers a final formulation of civil participation in society. This measure is not used to generate the aggregate inclusiveness index, but it links forward to the social capital component of multifactor productivity.

While the three dimensions represent distinct facets of governance, they are tightly interrelated. Indeed, the drivers of security, capacity and inclusion often overlap. For example, gender empowerment, a sub-dimension of inclusion, drives security’s vulnerability to conflict index and capacity’s corruption index. And the three composite indices—'''GOVINDSECUR, GOVINDCAPAC '''and '''GOVINDINCLUS'''—interact bi-directionally with each other. Beyond the sub-module, strong relationships link the key governance indices and other measures of human well-being, represented by GDP per capita and the Human Development Index (HDI). For instance, economic freedom ('''ECONFREE''') links forward to multifactor productivity ('''MFP''') and thus on to broader economic growth. And conversely, other systems affect governance forecasts, as evidenced by the relationship between levels of per capita income and '''HDI''' and '''DEMOCPOLITY'''—higher levels of well-being lead to increased pressure for democracy.

The tri-part conceptualization of governance originated in the Pardee Institute’s analysis of historical trends, as discussed in ''Potential Patterns of Human Progress'': ''Strengthening Governance Globally (''Hughes et al. (2014)). Over time, the story of governance has unfolded differently in countries across the world—some countries have developed security, capacity and inclusiveness sequentially, and some countries have developed all three dimensions simultaneously. A suite of parameters attached to the sub-dimensions of each dimension allows users to explore a range of potential patterns of global governance.

== Parameters to Affect Security ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''sfusehist'''
|N/A
|State failure switch to use history for base values(0=off; 1=on; 2=converge)
|Switch
|-
|'''sfintlwaradd'''
|SFINTLWARALL
|State failure/internal war addition
|Additive
|-
|'''sfconv'''
|SFINTLWARALL
|State failure convergence
|Convergence speed (years)
|-
|'''sfmassrep'''
|SFINTLWARALL
|State failure – Internal war repression by government
|Switch
|-
|'''wpextinterv'''
|SFINTLWARALL
|External intervention factor
|Switch
|-
|'''govriskm'''
|GOVRISK
|Government risk multiplier
|Multiplier
|-
|'''govriskweight'''
|GOVRISK
|Government risk weight
|Weight
|}

The most direct way in which to change the probability of internal war, and thus its broader consequences, is to manipulate the additive parameter affecting the variable, '''sfintlwaradd'''. Other parameters connected to internal war and the country performance risk evaluation allow users to explore alternate scenarios around security. Over time, a country’s probability of internal war will converge to the model’s expected function, a function driven by demographic and economic factors. However, users can control the speed of convergence through the parameter '''sfconv'''—accelerating this convergence would be an effective way to improve the fortunes of countries with especially bad histories of intrastate violence, because path dependency is a driver of internal war probability. Though set to 0.0 in the Base Case, two additional parameters exist that can directly affect the likelihood of internal war. '''Wpextinterv '''is a parameter that, when activated, simulates a conflict-creating external intervention; when the parameter is set to 1.0 the probability of intrastate conflict rises to 1.0 also. Another parameter, '''sfmassrep''', works in a similar way but with a reverse valence. Assigning it a value other than 0.0 simulates a campaign of mass repression by the central government that lowers the probability of overt intra-state violence.

Turning to government performance and risk evaluation, the multiplier '''govriskm '''directly controls the overall risk index, '''GOVRISK'''. Changes to the risk profile link forward to the higher order '''GOVINDVSECUR''' index, which in turn links to the overall governance score captured in '''GOVINDTOTAL'''. Users can also experiment with different risk configurations of a country or region via '''govriskweight'''; as the name implies, this parameter sets the weighting of '''GOVRISK'''’s component parts. As a final option, users can tailor their interventions to the components’ values, rather than simply the weighting. Any of the government risk index’s determining factors can be manipulated. The list of potential intervention targets includes: [[#_Variables_of_Interest_1|youth bulge]], [[#_Variables_of_Interest_2|the ratio of water use to renewable resources]], [[#_Variables_of_Interest_2|climate change]], [[#_Variables_of_Interest_3|resource export dependence]], [[#_Variables_of_Interest_4|rate of per capita GDP growth]], [[#_Variables_of_Interest|infant mortality]], [[#_Variables_of_Interest_1|life expectancy]], [[#_Variables_of_Interest|malnutrition]], [[#_HIV.2FAIDS_Submodule_1|HIV prevalence rate]], [[#_Education|primary net enrolment]] and [[#_Variables_of_Interest_5|adult education years]]. Scenario interventions could target one of these elements or several, cutting across IFs modules.

== Parameters to Affect Capacity ==

{| border="1" cellspacing="1" cellpadding="1" width="618" style="width:618px;"
|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:110px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:240px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:138px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govrevm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Government Revenue Multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''firmtaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Firm tax multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''hhtaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Household tax rate multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''ssweltaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Social Security multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''indirecttaxrm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVREV

| nowrap="nowrap" style="width:240px;height:22px;" |
Indirect tax multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption Multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govcorruptseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVCORRUPT

| nowrap="nowrap" style="width:240px;height:22px;" |
Corruption target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''goveffectseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
GOVEFFECT

| nowrap="nowrap" style="width:240px;height:22px;" |
Effectiveness target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualm'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualsetar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''govregqualseyrtar'''

| nowrap="nowrap" style="width:110px;height:22px;" |
REGQUALITY

| nowrap="nowrap" style="width:240px;height:22px;" |
Regulatory quality target

| nowrap="nowrap" style="width:138px;height:22px;" |
Standard error target

|-
| nowrap="nowrap" style="width:130px;height:22px;" |
'''econfreem'''

| nowrap="nowrap" style="width:110px;height:22px;" |
ECONFREE

| nowrap="nowrap" style="width:240px;height:22px;" |
Economic freedom multiplier

| nowrap="nowrap" style="width:138px;height:22px;" |
Multiplier

|}
The governance module includes parameters connected to both sub-dimensions of capacity: government revenue and government effectiveness. '''Govrevm''' is a direct multiplier of government revenue, '''GOVREV'''; assigning values to this parameter would signify either growing or diminishing government capacity to gather revenue. However, various, more nuanced, government revenue collecting programs can be simulated via several taxation parameters. The module includes multipliers to the tax rate paid by firms ('''firmtaxrm''') and households ('''hhtaxrm'''), taxes paid on social security ('''ssweltaxrm''') and indirect taxes such as sales tax or value added tax ('''indirecttaxrm'''). For a full discussion of government revenue interventions see the [[#_The_Economic_Module|economic module]] discussion.

The governance module includes parameters connected to both sub-dimensions of capacity: government revenue and government effectiveness. '''Govrevm''' is a direct multiplier of government revenue, '''GOVREV'''; assigning values to this parameter would signify either growing or diminishing government capacity to gather revenue. However, various, more nuanced, government revenue collecting programs can be simulated via several taxation parameters. The module includes multipliers to the tax rate paid by firms ('''firmtaxrm''') and households ('''hhtaxrm'''), taxes paid on social security ('''ssweltaxrm''') and indirect taxes such as sales tax or value added tax ('''indirecttaxrm'''). For a full discussion of government revenue interventions see the [[#_The_Economic_Module|economic module]] discussion.

Each of the components of government effectiveness represents a potential target for scenario intervention. The variable '''GOVCORRUPT''', measuring corruption levels, is connected to a multiplicative parameter, '''govcorruptm'''. Although corruption is the only representation of effectiveness tied to the '''GOVINDCAPAC''', users may wish to explore the consequences of changes to other measures. Multipliers '''govregqualm '''and'''econfreem''' directly control the level of a country’s regulatory quality ('''REGQUALITY''') and economic freedom ('''ECONFREE'''). Increases to regulatory quality link forward to broadband connectivity and other information and communication technology (ICT) indicators; and changes in economic freedom affect multifactor productivity through social capital. Finally, users can manipulate the '''GOVEFFECT''' variable, a formulation of effectiveness taken from the World Bank’s World Governance Indicators project. '''Goveffectm '''is a straightforward multiplier, with assigned values acting to scale effectiveness up or down.

Users can alternatively choose to manipulate governance quality indicators via standard error targeting. Two parameters are linked to each corruption, regulatory quality and effectiveness. The first parameter sets the target increase or decrease in the index, expressed in standard errors away from the expected function, and the second parameter sets the number of years that will pass before the target is reached. These parameters are '''govcorruptsetar '''and '''govcorruptseyrtar '''for corruption, '''govregqualsetar '''and '''govreqqualseyrtar''' for regulatory quality, and '''goveffectsetar''' and '''goveffectseyrtar '''for government effectiveness.

== Parameters to Affect Inclusiveness ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''democm'''
|DEMOCPOLITY
|Democracy level multiplier
|Multiplier
|-
|'''swseffects'''
|DEMOCPOLITY
|Swing state effect
|Switch
|-
|'''democimpus'''
|DEMOCPOLITY
|Democracy impetus by leading power
|Coefficient
|-
|'''democimpoth'''
|DEMOCPOLITY
|Democracy impetus by other powers
|Coefficient
|-
|'''DEMOCWDIR'''
|N/A
|Democracy wave direction (-1=down; 0=off; 1=up)
|Switch
|-
|'''democwvlen'''
|DEMOCPOLITY
|Democracy wave length in years
|Limit
|-
|'''democwvmax'''
|DEMOCPOLITY
|Democracy wave upper limit
|Limit
|-
|'''democpolitysetar'''
|DEMOCPOLITY
|Democracy SE target
|Standard error target
|-
|'''democpolityseyrtar'''
|DEMOCPOLITY
|Democracy SE year
|Standard error target
|-
|'''freedomm'''
|FREEDOM
|Freedom multiplier
|Multiplier
|-
|'''gemm'''
|GEM
|Gender empowerment multiplier
|Multiplier
|-
|'''gemsetar'''
|GEM
|Gender empowerment target
|Standard error target
|-
|'''gemseyrtar'''
|GEM
|Gender empowerment year target
|Standard error target
|}

The two sub-dimensions of government inclusiveness are democracy and gender empowerment. Users wishing to explore alternative scenarios around levels of democracy can choose from an array of leverage points. First, the multiplier '''democm''' allows direct manipulation of the variable '''DEMOCPOLITY''' for a country or region. Users choosing this brute force method should direct their attention to linkages in multifactor productivity, state fragility, and the overall '''GOVINDINCLUS''' index. Scenario interventions could also target any of the deeper drivers of democracy. In the model, forecasts of democracy levels are a function of gender empowerment, the youth bulge, dependency on raw material exports and energy prices. Two other parameters—disabled in the base case—can affect '''DEMOCPOLITY'''. The first simulates a neighborhood affect: if '''swseffects''' is set to a value of 1, swing states in a geographic region will be pressured to conform to their neighbors’ democratic practices—or lack thereof. The second additional method is rather complicated and will not be of interest to most users. The variable '''DEMOCWAVE''' either adds to or subtracts from the democracy levels determined by the model, simulating a wider neighborhood, in fact global, effect known as a wave of democracy. The magnitude of upswings or downswings is specified exogenously through several parameters: '''democimpus, '''the impetus provided to democracy by the leading power; '''democimpoth''', the impetus provided to democracy by other powers; '''democwvlen''', the wave of democracy’s duration in years; and, '''democwvmax''', the maximum rise in democracy levels. When a democracy wave is active, two variables track the process: '''DEMOCWVCOUNT''' measures the duration of the wave’s run and recession, and '''DEMOCWVDIR '''contains the direction of the wave, whether it supports or hinders the consolidation of democracy. As an alternative to other types of intervention, users may override the polity function through the use of external targeting. '''Democpolitysetar '''specifies a standard error target, and '''democpolityseyrtar''' specifies the year in which this target will be achieved.

 The degree of civilian participation – on the part of all citizens – is another driver of inclusiveness. Whereas the polity measure of democracy ('''DEMOCPOLITY''') measures formal, electoral inclusion, the variable '''FREEDOM '''is a more comprehensive measure of civil participation. '''FREEDOM''' is computed from levels of GDP and general educational attainment—among both men and women; and like the other elements inclusion, it is linked to a brute force multiplier, '''freedomm'''. Unlike the other parameters, however, standard error targeting is not available for the civil society freedom variable.

The second dimension of government inclusiveness, gender empowerment, offers a final target for scenario analysis. The three primary drivers of the gender empowerment measure ('''GEM''') are youth bulge size, GDP per capita at purchasing power parity, and years of formal education obtained by females over age fifteen. Users can build alternate scenarios around a combination of driving factors, or, more directly, through a multiplicative parameter. The multiplier '''gemm '''allows users to directly control the progression/regression of gender empowerment in a country or region. Additional parameters control standard error targeting around '''GEM''': used together, '''gemsetar '''and'''gemseyrtar '''establish a target value, measured in distance from the expected, cross-sectional function, and the time required to reach the target.

== Prepackaged Scenarios ==

An installation of IFs includes scenarios used in each entry in the Pardee Institute’s Potential Patterns of Human Progress (PPHP) series. Scenarios created for the governance volume include: a high conflict scenario; a scenario representing challenges to the world’s governments; a set of scenarios that speed up economic growth via multifactor productivity; and, finally, a set of government strengthening scenarios. This section will briefly describe the parameter interventions used to construct several of the enhanced government scenarios, located in the folder Strengthened Governance.

The collection of governance scenarios includes one scenario for each of the three dimensions: Strengthened Security, Strengthened Capacity SE1 and Strengthened Inclusiveness SE1. Each scenario includes a single parameter shift that targets one sub-dimension of security, capacity, or inclusion. Users wishing to load the three interventions simultaneously can select the combined scenario, Strengthened Governance SE1. The boost to capacity operates through '''govrevm''', the multiplier tied to the revenue sub-dimension of capacity; the parameter is set to 1.1 for the majority of the forecast horizon, raising revenue collection each year. The intervention in inclusion targets the democracy sub-dimension. For the entire forecast horizon, from 2010 to 2100, '''democpolitysetar''' is set to 1, establishing an improvement target in democracy levels of one standard error. As the corresponding year parameter, '''democpolityseyrtar''', is not changed, it takes on its default value of 10.0. This means that the target of one standard error will be reached in 10 years. The final intervention simulates a decline in intra-state violence the world over. The additive factor '''sfintlwaradd''' interpolates from 0 to -1 over thirty years; after reaching negative 1, it remains at that value through 2100.

The final scenario in the governance set, Strengthened Policies, features interventions to deeper drivers of each governance dimension, including: educational improvements at primary, lower secondary, upper secondary and tertiary schooling levels; reductions in childhood malnutrition; increased government transfers and international development assistance; steady decline in total fertility rates across western, central and eastern Africa as well as Oceania; rising agricultural yields; less solid fuel use, and a corresponding reduction in atmospheric particulate matter.

The two comprehensive scenarios contained in the PPHP Governance folder represent distinct methods of alternative forecast analysis. Strengthened Governance SE1 explores the effects of directly manipulating governance indicators. Strengthened Policies, on the other hand, explores the forward linkages of governance drivers.

= International Politics Module =

== Variables of Interest ==

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| '''Variable Name'''
| '''Description'''
|-
| '''POWER''' 
| Total power index 
|-
| '''CPOW''' 
| Conventional power 
|-
| '''NPOW''' 
| Nuclear power 
|-
| '''WCPOW''' 
| World nuclear power 
|-
|'''WNPOW'''
|World nuclear power
|-
| '''PCONGREAT''' 
| Power concentration of great powers 
|-
| '''PCONGREATEU''' 
| Power concentration of great powers, single Europe 
|-
| '''PCONGREATEUF''' 
| Power concentration of great powers, flexibly defined, with single Europe 
|-
| '''THREAT''' 
| Probability of militarized conflict 
|-
| '''THREATREC''' 
| Threat received 
|-
| '''THREATSENT''' 
| Threat received 
|-
| '''THREATSENT''' 
| Threat sent 
|-
| '''WGDP''' 
| World GDP 
|-
| '''WTRADE''' 
| World trade 
|-
| '''GOVEMBED''' 
| Government embeddedness 
|-
| '''CWAR''' 
| Conventional war 
|-
| '''CWARPB''' 
| Conventional war probability 
|-
| '''NWAR''' 
| Nuclear war 
|-
| '''NWARPB''' 
| Nuclear war probability 
|-
| '''CIVDM''' 
| Civilian damage (multiplier) 
|-
| '''DEMOCPOLITY''' 
| Level of democracy 
|-
| '''DIPLOEMBASSY10''' 
| Number of embassies held in other countries 
|-
| '''DIPLOIGO''' 
| International organization membership 
|-
| '''DIPLOTREATY''' 
| Treaty participation
|}
<div><div id="ftn1">
10For '''DIPLOEMBASSY, DIPLOIGO '''and '''DIPLOTREATY''', there exist three corresponding variables with the suffix '''MAX''' that cap forecast values.
</div></div>
Unlike other systems, such as demographics or economics, there is no standard organizational structure used to represent international politics. In order to establish a theoretical structure that describes interactions between states, IFs draws on in-house expertise, as well as work conducted by other research organizations, academics and governments and, of course, international relations theory and empirical work. The two foundational elements in the model are power and threat levels, which trigger alliances, conflicts and military spending. IFs takes conceptual and theoretical inputs from projects that have been dedicated to quantifying power and threat dynamics, including J. David Singer’s Correlates of War project, initiated at the University of Michigan, which produced the Composite Index of National Capability (CINC) –a measure of hard power, the CINC formulation includes demographic, economic and military components. IFs reaches out for data to a variety of sources including the Stockholm International Peace Research Institute’s (SIPRI) datasets on military expenditures, arms trade, and international conflicts. The IFs international politics module contributes to existing work in the field by forecasting measures of hard power and of threat levels between states. It is increasingly also developing data and forecasting capability around diplomacy.

IFs includes several formulations of power. The variable '''POWER''' is an index, computed in the model, that measures each country’s share of world power. The index is comprised of weighted elements for: GDP, population, GDP per capita (as a proxy for technological sophistication), government size, public military spending, conventional power, and nuclear power. Levels of conventional ('''CPOW''') and nuclear ('''NPOW''') power have their own dynamics, but both are primarily dependent on military spending patterns, as captured in the variable '''GDS''' and an associated parameter that determines the amount of funding available for nuclear weapons research. (For a full discussion of government spending flowing to the military and to other destinations, see the economic section.) '''NPOW''' is not an index – like '''POWER''' or '''CPOW''' – but rather the number of nuclear warheads owned by each country. Two additional variables measure global power. The indicator of the world’s stock of nuclear power, '''WNPOW''', is measured in megatons. And '''WCPOW''' is an aggregate value of all countries’ conventional power, expressed as an index.

Depending on the configuration of power at a given point in time, the international system may be multipolar, bipolar, or unipolar. A set of variables in the model capture the degree to which power is concentrated in the hands of a few nations. The index '''PCONGREAT''' measures the percentage of global power held by dominant, or great powers— as values approach zero, smaller shares of power would be held by so-called great powers. '''PCONGREATEU''' measures the percentage of global power held by great powers, considering the European Union to be a single actor. '''PCONGREATEUF''' measures the percentage of global power held by great powers, with the European Union considered a single actor and the category “great power” flexibly defined; the flexible definition refers to the phasing in of great power status above a threshold share of global power up to full great power status upon attaining a higher share. And finally, the variable '''PCONSYS''' is an index indicating the power concentration of the global system, regardless of the number of great powers.

In IFs, the balance of power between actors in the international system links forward to threat ('''THREAT''') of militarized interstate disputes (MIDs). The model calculates the threat that any state poses to any other state as a product of historical conflict data and a predictive function. Three especially important factors drive the dyadic function: contiguity, whether or not either of the countries is a great power, and the existence of a territorial dispute between the countries. Additional drivers of international threat include world trade ('''WTRADE'''), energy production ('''WENP'''), and strength of democracy ('''DEMOCPOLITY'''). We also compute a level of government embeddedness ('''GOVEMBED''') that may interact with power structures in future model development. (Government embeddedness is a concept taken from international political economy; a government is considered embedded if its actions are constrained by domestic institutions, firms and households). The results of computations are stored in an index, '''THREAT''', which can be interpreted very roughly as the probability that a pair of countries will experience a militarized dispute in a given year (but expressed in percentage terms of 0-100 instead of probabilistic ones of 0-1). Countries can be theorized as both recipients and deliverers of threat: '''THREATREC''' is of the total of all threat received from all senders; '''THREATSENT''' is, as the name indicates, the total threat sent by a country. And the variable '''WTHREAT''' is an average of threat levels between all country pairs, thus offering an indication of global military tension.

The model includes a specialized display for threat dynamics and other dyadic (country-to-country) interaction. To access the display select the Data Analysis option from the IFs main menu, then select Dyadic Interaction Display. On the resulting form, users can explore the relationship between any two countries via the list boxes labeled CountryA and CountryB. In addition to '''THREAT''', a slew of international political variables are available for dyadic display in the Variable box: Midpresent, FatalityBinary, StandAlliance, StandDiplo, StandGO, Stand UN, StandTrade, StandCOI, StandLiberalisHigherMoreLib, StandRealismLowerMoreTension.

As an additional feature, the international politics module includes an optional war simulation. When the war option is activated, the model will stochastically (randomly) introduce a war, based on dyadic threat levels described above. The variables '''CWARPB''' and '''NWARPB''' measure the probability that a conventional or nuclear war will break out in a given year, whether or not the switch for the war option is activated. When the switch is on, a second pair of variables identifies the occurrence or absence of conventional war and nuclear war: '''CWAR''' and '''NWAR''' take on binary values, 1 indicating the presence of war, and 0 indicating the absence of war. Once a war event takes place, the countries involved will experience fatalities and material damage. '''CIVDM''' tracks the human costs to overall population and the capital lost in the fighting.

In addition to forecasting power, threat and conflict, the IFs model forecasts several diplomatic measures. The model currently includes variables containing the number of embassies each country has abroad ('''DIPLOEMBASSY'''), international organization membership ('''DIPLOIGO'''), and treaty membership ('''DIPLOTREATY'''). Like the other variables in IFs, these measures of soft power are forecast out to 2100 for each country.

Parameters in the international politics model allow users to create alternative scenarios that explore uncertainty around the international power structure, dyadic threat levels, the outbreak of war, and diplomacy.

== Parameters Affecting Power ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:104px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:281px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:120px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Factor controlling military spending conversion

| nowrap="nowrap" style="width:120px;height:22px;" |
Convergence factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowldcf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Higher impact of military spending in LDCs

| nowrap="nowrap" style="width:120px;height:22px;" |
Convergence factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''drcpow'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Depreciation rate of conventional power

| nowrap="nowrap" style="width:120px;height:22px;" |
Rate of change

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''cpowdf'''

| nowrap="nowrap" style="width:104px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:281px;height:22px;" |
Conventional power, damage done by war

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''govnucwpadd'''

| nowrap="nowrap" style="width:104px;height:22px;" |
NPOW

| nowrap="nowrap" style="width:281px;height:22px;" |
Nuclear weapons stockpile change

| nowrap="nowrap" style="width:120px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpwghtpow'''

| nowrap="nowrap" style="width:104px;height:22px;" |
POWER

| nowrap="nowrap" style="width:281px;height:22px;" |
Contribution of various components to power index

| nowrap="nowrap" style="width:120px;height:22px;" |
Index weight

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpgreatthresh'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONGREAT

| nowrap="nowrap" style="width:281px;height:22px;" |
Systemic power share required for threshold of great power status

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpgreatlev'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONGREAT

| nowrap="nowrap" style="width:281px;height:22px;" |
Systemic power share required for full great power status

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpconmax'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONSYS

| nowrap="nowrap" style="width:281px;height:22px;" |
Maximum power concentration

| nowrap="nowrap" style="width:120px;height:22px;" |
Limit

|-
| nowrap="nowrap" style="width:127px;height:22px;" |
'''wpconmin'''

| nowrap="nowrap" style="width:104px;height:22px;" |
PCONSYS

| nowrap="nowrap" style="width:281px;height:22px;" |
Minimum power concentration

| nowrap="nowrap" style="width:120px;height:22px;" |
Limit

|}

A suite of parameters is available to users who wish to explore power dynamics in the IFs model. Conventional power ('''CPOW''') can be manipulated using the parameters '''cpowf''', '''cpowldcf''', and '''drcpow'''. The first controls the elasticity between military spending and conventional power accrual; higher values of '''cpowf''' increase the impact that military spending has on conventional power measures. '''Cpowldcf''' controls the impact of military spending within LDCs, where military spending has historically had a greater impact on measures of power, largely because the costs of military personnel are lower. And '''drcpow''' controls the depreciation rate of conventional power. This parameter controls the rate at which past investments in conventional power decline as a contribution to power in successive years. The destructiveness of interstate conflict has a negative impact on conventional power measures. The impact that conventional war has on combatant nations, as measured by '''CIVDM''', can be controlled by '''cpowdf'''—the higher the parameter value, the greater the civilian deaths and capital destruction. Users may also increase or decrease a country’s stock of nuclear weapons via the parameter '''govnucwpadd'''. Nuclear weapons capacity influences total power through the nuclear power component, '''NPOW'''.

The aggregate measurement of power is similarly connected to parameter levers. The parameter '''wpwghtpow '''allows users to customize the set of weights used in the calculation of the power index ('''POWER'''). Individual weights are attached to each of the index’s components: population, GDPatPPP, GDP, GDPxGDPPCatPPP, GDPxGDPPC, Govt, MilSpend, ConvPow, NucPow, NucWarHeads, EnergyNetExport, TradeTotal, FDIInflows, AidNet, HumCapWkPopHDI, GovRev, InnovationR&D, and ConnectivityICTProd.

Users can also alter the role of great powers in the international political system. '''Wpgreatthresh''' controls the threshold level of the power index that, when achieved, signals the basic level of a nation’s transition into (or out of) being a great power. '''Wpgreatlev''' controls the power share held by full great powers, allowing users to model greater or lower levels of power diffusion. And, '''wpconeusw''' is a switch parameter that controls whether the EU is considered a single great power in the global power structure.

Two final parameters place limits on the levels of power that any country can experience. '''Wpconmax''' and '''wpconmin '''set the maximum and minimum level of power that a country can reach in forecast years.

== Parameters Affecting Threat Levels ==

{| border="1" cellspacing="1" cellpadding="1" width="637" style="width:637px;"
|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''Parameter'''

| style="width:86px;height:20px;" |
'''Variable of Interest'''

| style="width:252px;height:20px;" |
'''Description'''

| style="width:156px;height:20px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpgreat1'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Dyadic dispute probability, one great power

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpgreat2'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Dyadic dispute probability, two great powers

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wppowtran1'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Power transition threshold

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wppowtran2'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Increase in dispute probability with 10% greater power parity

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcon'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with power concentration

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpconsq'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with power concentration

| style="width:156px;height:20px;" |
Coefficient

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcontiguity'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Threat associated with geographic proximity

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpdemdist'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to political distance

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpdemmin'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to the minimum democracy level in the dyad

| style="width:156px;height:20px;" |
Limit

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''glocom'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Global community multiplier

| style="width:156px;height:20px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpcommun'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Response of threat to global community level

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wptrade'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Change in threat with systemic trade

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''ally'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Alliance membership switch

| style="width:156px;height:20px;" |
Switch

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpally'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Reduction in threat with allied dyad

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''terdispute'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Territorial dispute or absence

| style="width:156px;height:20px;" |
Exogenous specification

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpterdisp'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Conflict arising from territorial disputes

| style="width:156px;height:20px;" |
Elasticity

|-
| nowrap="nowrap" style="width:143px;height:20px;" |
'''wpthrconv'''

| style="width:86px;height:20px;" |
THREAT

| style="width:252px;height:20px;" |
Threat level convergence

| style="width:156px;height:20px;" |
Convergence Speed

|}

All parameters modifying threat affect the same variable, probability of dyadic conflict ('''THREAT'''). '''Wpgreat1 '''and '''wpgreat2''' specify the likelihood of disputes involving one and two great powers, respectively. The reason these parameters exist is that great powers have historically been much more likely to experience conflict than have other states. A subset of great powers, namely the global leader in power and any power challenging that leadership and transition itself into system leadership, is especially likely to be engaged in conflict. '''Wppowertran1''' sets the dyadic power ratio for those two countries that identifies the beginning of a power transition and thereby triggers an increase in conflict threat levels. For example, a '''wppowertran1''' value of 0.7 means that a threat increase would begin once the weaker dyad member achieve a power score ('''POWER''') equal to 70 percent of the more powerful member’s score. '''Wppowertran2 '''then establishes increase in the likelihood of conflict associated with increased power parity. More specifically, a '''wppowertran2''' value of 0.05 means that, once a power transition has begun, each further reduction in a dyad’s power differential of 10 percent leads to an increase in threat levels of 5 percent.

Regardless of the number of great powers or the occurrence of a power transition, the concentration of power in the global system of great powers ('''PCONGREAT'''), as described earlier, can affect the level of threat of conflict (THREAT). Users may wish to take note of, '''wpcon '''and '''wpconsq''', the two coefficients that control the relationship between power concentration and conflict likelihood. '''Wpcon''' directly links concentration to threat and '''wpconsq''' links the square of power concentration to threat.

Several additional parameters relate to the probability or threat of conflict in a dyad . '''Wpcontiguity''' controls the threat of dispute generated by geographic proximity, one of the strongest determinants of conflict. '''Wpdemdist '''alters threat generated in response to political incompatibility—i.e., divergent levels of democracy—between two members in a dyad (the greater the distance, the greater the threat). '''Wpdemmin '''relates conflict to the democracy level of the less democratic member of a dyad (if democracy of that member rises, the default value of that parameter would lower threat). A pair of parameters together establish the relationship between the global community and threat. '''Glocom '''represents an exogenous specification of the cohesiveness of the global community, and'''wpcommun '''is an elasticity between international cohesion and the probability of conflict. '''Wptrade''' modifies the response of threat to the level of systemic trade. The parameter '''ally '''is a switch parameter that turns on or off alliance memberships, while the parameter '''wpally '''reduces the probability of dispute if two dyadic members are allied. And finally, '''terdispute''' defines the presence or absence of a territorial dispute within a dyad, while '''wpterdisp''' changes the likelihood of conflict arising from such disputes.

One other parameter is worth noting. '''Wpthrconv''' defines the convergence speed of historical threat levels within a dyad (which in initialization for the first model year reflect the reality of past conflicts) to the level of dyadic threat as calculated using all of the drivers and relationships indicated above. If that convergence speed, expressed in terms of the number of years for full convergence, is very slow (a high value for years needed), then the path dependency of past conflict will continue much more strongly to affect future likelihood than if the convergence speed is very fast (a low value for years).

== Parameters Affecting War Simulation ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:100px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:295px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:120px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''wpthrwar'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Portion of disputes that escalate to war

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''waron'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWAR, NWAR

| nowrap="nowrap" style="width:295px;height:22px;" |
War switch

| nowrap="nowrap" style="width:120px;height:22px;" |
Switch

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarbase'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war probability addition to base computation

| nowrap="nowrap" style="width:120px;height:22px;" |
Additive factor

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war forcing

| nowrap="nowrap" style="width:120px;height:22px;" |
Switch

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cwarsv'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Conventional war severity

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''nwarf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
NWARPB

| nowrap="nowrap" style="width:295px;height:22px;" |
Probability that conventional war will escalate to nuclear

| nowrap="nowrap" style="width:120px;height:22px;" |
Coefficient

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''cdmf'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Civilian war damage factor

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''nwarsv'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Nuclear war severity

| nowrap="nowrap" style="width:120px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:118px;height:22px;" |
'''elndpow'''

| nowrap="nowrap" style="width:100px;height:22px;" |
CIVDM

| nowrap="nowrap" style="width:295px;height:22px;" |
Elasticity of nuclear damage with nuclear power

| nowrap="nowrap" style="width:120px;height:22px;" |
Elasticity

|}
Threat ('''THREAT''') within a dyad is, as indicated above, related to the probability of conflict but not identical to it, because scaling of the various contributions to the threat variable is complicated and uncertain. The parameter '''wpthrwar''' translates threat within a dyad into a probability of conventional conflict ('''CWARPB''') in that same dyad. There will never be an actual conventional war, however, unless the war switch is turned on ('''waron'''=1). When that switch is on and '''CWARPB''' for a dyad is non-zero, conventional war in any dyad-year occurs randomly in relationship to that war probability. For any given dyad-year a user can change the probability of conventional war with the additive parameter '''cwarbase'''. Or wars can be forced with the parameter switch, '''cwarf''', when it is set to 100 or above.

Threat ('''THREAT''') within a dyad is, as indicated above, related to the probability of conflict but not identical to it, because scaling of the various contributions to the threat variable is complicated and uncertain. The parameter '''wpthrwar''' translates threat within a dyad into a probability of conventional conflict ('''CWARPB''') in that same dyad. There will never be an actual conventional war, however, unless the war switch is turned on ('''waron'''=1). When that switch is on and '''CWARPB''' for a dyad is non-zero, conventional war in any dyad-year occurs randomly in relationship to that war probability. For any given dyad-year a user can change the probability of conventional war with the additive parameter '''cwarbase'''. Or wars can be forced with the parameter switch, '''cwarf''', when it is set to 100 or above.

The probability of a nuclear war ('''NWARPB''') is zero unless there is a conventional war (CWAR=1). If there is a conventional war, the probability of a nuclear war is determined by the severity of conventional war ('''cwarsv''') and the probability of escalation ('''nwarf'''). If '''nwarf''' is set to 100 or above, it will force a nuclear war when a conventional one occurs between nuclear powers.

Once a conventional or nuclear war takes place, several parameters determine how damaging the effects will be. '''Cdmf''' is a multiplier that serves as a civilian war damage factor–the higher the parameter’s value, the greater the casualties and physical destruction. Two related parameters, '''cwarsv '''and '''nwarsv''', control the severity of conventional and nuclear war. '''Elndpow''' is an elasticity of nuclear war severity and nuclear weapon numbers.

== Parameters Affecting Diplomacy ==

{| border="1" cellspacing="1" cellpadding="1" width="632" style="width:632px;"
|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''Parameter'''

| nowrap="nowrap" style="width:148px;height:22px;" |
'''Variable of Interest'''

| nowrap="nowrap" style="width:257px;height:22px;" |
'''Description'''

| nowrap="nowrap" style="width:92px;height:22px;" |
'''Type'''

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diploembassymaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOEMBASSYMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum embassies abroad

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diploigomaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOIGOMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum international organization membership

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|-
| nowrap="nowrap" style="width:135px;height:22px;" |
'''diplotreatymaxm'''

| nowrap="nowrap" style="width:148px;height:22px;" |
DIPLOTREATYMAX

| nowrap="nowrap" style="width:257px;height:22px;" |
Maximum bilateral treaty participation

| nowrap="nowrap" style="width:92px;height:22px;" |
Multiplier

|}

The diplomacy portion of the IFs international politics module is under development. Currently, three multiplicative parameters exist that allow users to manipulate forecast values of embassies abroad ('''DIPLOEMBASSY'''), international organization membership ('''DIPLOIGO)''' and bilateral treaty agreements ('''DIPLOTREATY'''). Forecast values of each measure of soft power are limited by cap variables— '''DIPLOEMBASSYMAX''', '''DIPLOIGOMAX''', and '''DIPLOTREATYMAX'''. Users can raise or lower these caps via the parameters '''diploembassymaxm''', '''diploigomaxm''' and '''diplotreatymaxm'''.

== Prepackaged Scenarios ==

There are few prepackaged scenarios which make use of the international politics parameters. Two sets featuring limited interventions are located under the Understandings of Causality heading in the Prepackaged Scenarios folder. Under the heading Power Capabilities are a number of different formulations of global power which modify the parameter '''wpwghtpow'''. These modifications are designed to reproduce the distributions of global power that have been discussed by academic researchers. For instance, the scenario COW-Like 3-Element at PPP increases the weighting for population, GDP at PPP, and military spending in the calculation of power. Three other scenarios in this folder (Brecke Formulation, Hillebrand-Herman Base and COW-Like 3-Element at Market) create alternative structuring of the POWER variable. Two other scenarios in this folder, Slow China Formulation, and Slow China Formulation with growth, model a slow growth in power for China by underweighting the value of military spending, GDP, GDPPPC, and population, while overweighting GDPPC.

In the folder labeled Threat International are two scenarios that model the threat of international war. These two scenarios modify the parameter '''wpthrconv''' in order to frame various assumptions about the dynamics of threats at the international level.

Finally, one scenario in the World Integrated Scenario Sets demonstrates the use of power parameters. This scenario is located in the MENA Scenarios and is called Iran Nukes + Sanctions. The scenario models the possible effect of Iran acquiring nuclear weapons and the potential US response. This scenario increases '''govnucwpadd '''to simulate the effect of Iran acquiring nukes, and then alters the calculation of the power index to underweight population, GDPPP, GDPxGDPPCatPPP, and military spending, while overweighting the effect of nuclear warheads, net exports of energy, trade flows, FDI inflows, net aid, government revenue, human capital, innovation and R&D, and connectivity of ICT. It also decreases exports ('''xshift'''), protectionism ('''protecm'''), oil and gas production ('''enpm'''), to demonstrate the possible impact of an international response that favored increasing sanctions against the country.

= Parameter Dictionary =

== Population ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''tfrm'''
|TFR, CBR
|Total fertility multiplier
|Multiplier
|-
|'''contrusm'''
|CONTRUSE
|Contraceptive use multiplier
|Multiplier
|-
|'''eltfrcon'''
|TFR
|Elasticity of total fertility rate to contraception use
|Elasticity
|-
|'''tfrmin'''
|TFR
|Long term TFR convergence value
|Limit
|-
|'''mortm'''
|DEATHS
|Mortality multiplier (not cause specific)
|Multiplier
|-
|'''hlmortm'''
|DEATHS
|Mortality multiplier by cause
|Multiplier
|-
|'''wmigrm'''
|MIGRATE, MIGRANTS
|World migration rate multiplier
|Multiplier
|-
|'''migrater'''
|MIGRATE, MIGRANTS
|Net migration rate (inward)
|Rate of change
|-
|'''migrateoutm'''
|MIGRANTSOUT, MIGRANTS
|Migration rate outward multiplier
|Multiplier
|-
|'''migrateinm'''
|MIGRANTSIN, MIGRANTS
|Migration rate inward multiplier
|Multiplier
|-
| '''workageentry'''
|POPPREWORK, POPWORKING
|Working age determinant
|Exogenous specification
|-
| '''workageretire'''
|POPWORKING, POPRETIRED
|Retirement age determinant
|Exogenous specification
|}

== Health ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''hlmortm'''
|DEATHCAT, HLYLL, HLDALY
|Multiplier on Mortality (by cause)
|Multiplier
|-
|'''hlmorbm'''
|YLD
|Multiplier on morbidity
|Multiplier
|-
|'''hlstddthsw'''
|DEATHCAT
|Switches DEATHCAT from absolute numbers to deaths/1000
|Switch
|-
|'''watsafem'''
|WATSAFE, INFMOR
|Percentage of population with access to safe water
|Multiplier
|-
|'''sanitationm'''
|SANITATION, INFMOR
|Percentage of population with access to improved sanitation
|Multiplier
|-
|'''malnelimstartyr'''
|MALNPOP
|Prevalence of undernutrition
|Standard error target start year
|-
|'''malnelimtargetyr'''
|MALNPOP
|Prevalence of undernutrition
|Standard error target year
|-
|'''malelimprecisesw'''
|MALNPOP
|Prevalence of undernutrition
|Switch
|-
|'''ylm'''
|YL
|Yield multiplier on agriculture
|Multiplier
|-
|'''hivm'''
|HIVCASES
|Rate of HIV infection
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|-
|'''envpm2pt5m'''
|ENVPM2PT5
|Increases levels of environmental pollution
|Multiplier
|-
|'''hlsmokingm'''
|HLSMOKING
|Increases rate of smoking
|Multiplier
|-
|'''hlobesitym'''
|HLOBESITY
|Prevalence of obesity
|Multiplier
|-
|'''hlbmim'''
|HLBMI
|Multiplier on body mass index
|Multiplier
|-
|'''deathtrpvm'''
|DEATHTRPV
|Multiplier on traffic deaths per vehicle
|Multiplier
|-
|'''deathtrpvsetar, deathtrpseyrtar'''
|DEATHTRPV
|Standard error target for traffic deaths per vehicle
|Relative target Value/Year
|-
|'''hlmortmodsw'''
|CDR
|Reduces crude death rate in Africa, Europe, Southeast Asia, West Pacific
|Switch
|-
|'''hltechshift'''
|CDR
|Rate of change in health technology
|Additive factor
|-
|'''hltechlinc'''
|CDR
|Rate of change in health technology in low income countries
|Additive factor
|-
|'''hltechssa'''
|CDR
|Rate of change in health technology in Sub-Saharan Africa
|Additive factor
|-
|'''hltechbase'''
|CDR
|Rate of change in health technology at base
|Exogenous specification
|}

== HIV/AIDS ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''hivm'''
|HIVRATE
|HIV infection rate, multiplier of percent of population infected
|Multiplier
|-
|'''hivtadvr'''
|HIV CASES, HIVRATE
|Technical advance rate in of control of infection
|Rate of change
|-
|'''hivpeakr'''
|HIVCASES, HIVRATE
|HIV infection rate at year of peak
|Target value
|-
|'''hivpeakyr'''
|HIVRATE
|Sets year of epidemic peak
|Target year
|-
|'''hivincr'''
|HIVCASES
|HIV increase rate, only used prior to 2000
|Rate of change
|-
|'''aidsdrtadvr'''
|AIDSDTHS, AIDSRATE
|AIDs death rate, technical annual advance rate in control
|Rate of change
|-
|'''aidsdratem'''
|AIDSRATE
|AIDs death rate as % of HIV infection rate, multiplier
|Multiplier
|}

=== Severe Acute Malnutrition (SAM) Submodule ===
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''malnchpadj'''
|MALNCHPSAMINC
|Used to adjust the incidence correction factor
|Adjustment factor
|-
|'''malnchpsamm'''
|MALNCHPSAM
|Used to change prevalence of SAM
|Multiplier
|-
|'''samadmissionadd'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Additive factor
|-
|'''samadmissionm'''
|SAMADMISSION
|Used to increase or decrease the number of admissions for treatment
|Multiplier
|-
|'''samadmissionsw'''
|SAMADMISSION
|Allows users to forecast SAM admissions in three ways (0) forecasts all countries according to the relationship with GDPPCP, (1) holds all initial zero values constant over time (default value), and (2) holds all initial values constant.
|Switch
|-
|'''samavertedconvyr'''
|SAMAVERTED
|Allows users to change the years to converge to 100
|Convergence factor
|-
|'''samavertedm'''
|SAMAVERTED
|Allows users to change the number of averted cases
|Multiplier
|-
|'''malnchpsamtostunt'''
|MALNCHPSAM and HLSTUNT
|Controls the degree to which MALNCHPSAM affects HLSTUNT
|Adjustment factor
|-
|'''samrrsw'''
|MALNCHPSAM and MALNCHP
|Used to substitute the severity of MALNCHP with that of MALNCHPSAM
|Switch
|-
|'''samtomalnchpsw'''
|MALNCHP
|allows users to specify scenarios in which successful treatment does not result in full weight
|Switch
|}

== Education ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''edpriintngr'''
|EDPRIINTN
|Primary net intake rate
|Annual growth rate
|-
|'''edterintgr'''
|EDTERINT
|Tertiary intake rate
|Annual growth rate
|-
|'''edseclowrtrangr'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Annual growth rate
|-
|'''edsecupprtrangr'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Annual growth rate
|-
|'''edprisurgr'''
|EDPRISUR
|Primary survival rate
|Annual growth rate
|-
|'''edtergradgr'''
|EDTERSUR
|Tertiary survival rates
|Annual growth rate
|-
|'''edpriintntrgtyr'''
|EDPRIINTN
|Primary net intake rate
|Target
|-
|'''edseclowrtrantrgtyr'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Target
|-
|'''edsecupprtrantrgtyr'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Target
|-
|'''edterinttrgtyr'''
|EDTERINT
|Tertiary Intake Rate
|Target
|-
|'''edprisurtrgtyr'''
|EDPRISUR
|Primary survival rate
|Target
|-
|'''edtergradtrgtyr'''
|EDTERGRAD
|Tertiary graduation rates
|Target
|-
|'''edpriintnm'''
|EDPRIINTN
|Primary net intake rate
|Multiplier
|-
|'''edterintm'''
|EDTERINT
|Tertiary intake rate
|Multiplier
|-
|'''edseclowrtranm'''
|EDSECLOWRTRAN
|Lower secondary transition rate
|Multiplier
|-
|'''edsecupprtranm'''
|EDSECUPPRTRAN
|Upper secondary transition rate
|Multiplier
|-
|'''edprisurm'''
|EDPRISUR
|Primary survival rate
|Multiplier
|-
|'''edtergradm'''
|EDTERGRAD
|Tertiary survival rates
|Multiplier
|-
|'''gdsm (education)'''
|GDS
|Government spending on education
|Multiplier
|-
|'''edbudgon'''
|GDS
|Education funding priority
|Exogenous specification
|-
|'''gdsedm'''
|GDS
|Education spending distribution
|Multiplier
|-
|'''edexppconv'''
|EDEXPERPRI
|Education spending per student, primary
|Convergence speed
|-
|'''edexpslconv'''
|EDEXPERSEC
|Education spending per student, secondary
|Convergence speed
|-
|'''edexpsuconv'''
|EDEXPERSEC
|Education spending per student, secondary
|Convergence speed
|-
|'''edexptconv'''
|EDEXPERTER
|Education spending per student, tertiary
|Convergence speed
|-
|'''edqtqltrm'''
|EDEXPERPRI, EDEXPERSEC , EDEXPERTER and GDS
|Education quantity-quality balance
|Multiplier
|-
|'''edprigndreqintn'''
|EDPRIINTN
|Gender parity for primary intake
|Convergence speed
|-
|'''edprigndreqsur'''
|EDPRISUR
|Gender parity for primary survival
|Convergence speed
|-
|'''edseclowrgndreqtran'''
|EDSECLWRTRAN
|Gender parity for lower secondary transition
|Convergence speed
|-
|'''edsecupprgndreqtran'''
|EDSECUPPRTRAN
|Gender parity for upper secondary transition
|Convergence speed
|-
|'''edtergndreqint'''
|EDTERINT
|Gender parity for tertiary intake
|Convergence speed
|-
|'''edtergndreqgrad'''
|EDTERSUR
|Gender parity for tertiary survival
|Convergence speed
|-
|'''edqualpriallm'''
|EDQUALAG15PRI
|Multiplier on primary education learning quality
|Multiplier
|-
|'''edqualsecallm'''
|EDQUALAG15SEC
|Multiplier on secondary education learning quality
|Multiplier
|-
|'''edqualag15m'''
|EDQUALAG15
|Multiplier on total learning quality for adults
|Multiplier
|-
|'''mfpedqual'''
|TFP
|Elasticity of education quality to multifactor productivity
|Elasticity
|-
|'''edmodelsw'''
|N/A
|Education switch between old and new model (0=old model; 1=new model)
|Switch
|-
|'''edpridrpgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edprienrgrdm'''
|EDPRICR / EDPRIDRPGRD / EDPRIENRGGRD
|Education, primary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|-
|'''edseclowrdrpgrdm'''
|EDSECLOWRDRPGRD/ EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific dropout rate, multiplier base 1
|Multiplier
|-
|'''edseclowrenrgrdm'''
|EDSECLOWRENRGGRD/ EDSECLOWRTOT
|Education, lower secondary, grade-specific enrollment rate, multiplier base 1
|Multiplier
|}

== Economics ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''labparm'''
|LAB
|Labor participation rate multiplier
|Multiplier
|-
|'''labfemshrm'''
|LAB
|Female labor force participation rate multiplier
|Multiplier
|-
|'''labretagem'''
|LAB
|Retirement age, labor force participation multiplier
|Multiplier
|-
|'''labunemprm'''
|LABUNEMPR
|Labor unemployment rate multiplier
|Multiplier
|-
|'''labmarketsw'''
|LABS
|Labor demand to economic production switch (1=on)
|Switch
|-
|'''labdemsm'''
|LABDEMS
|Labor force, multiplier on demand
|Multiplier
|-
|'''labcoeffsm'''
|LABCOEFFS
|Labor demand, multiplier on technology coefficients
|Multiplier
|-
|'''workageentry'''
|POPRETIRED
|Age at which individuals enter the labor force
|Exogenous specification
|-
|'''workageretire'''
|POPRETIRED
|Age at which individuals leave the labor force
|Exogenous specification
|-
|'''invm'''
|IDS
|Investment by destination sector
|Multiplier
|-
|'''mfpadd'''
|MFP components
|Multifactor productivity, additive
|Additive factor
|-
|'''mfpleadr'''
|MFP
|MFP growth rate of the technological lead country
|Growth Rate
|-
|'''mfpedspn'''
|GDS-educ
|Increase portion of spending going to education
|Elasticity
|-
|'''malnelimstartyr'''
|MALNPOP
|Undernutrition elimination standard error target
|Standard error target
|-
|'''malnelimtargetyr'''
|MALNPOP
|Undernutrition elimination standard error target
|Standard error target
|-
| '''malelimprecisesw '''
|MALNPOP
|Undernourishment elimination
|Switch
|-
|'''ylm'''
|YL
|Land yield
|Multiplier
|-
|'''aginvm'''
|AGINV
|Investment in agriculture
|Multiplier
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|-
|'''econfreem'''
|ECONFREE
|Economic freedom
|Multiplier
|-
|'''goveffectm'''
|GOVEFFECT
|Government effectiveness
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Government corruption
|Multiplier
|-
|'''freedomm'''
|FREEDOM
|Freedom/democracy
|Multiplier
|-
|'''mfpinfrindtrad'''
|INFRAINDTRAD
|Traditional infrastructure
|Elasticity
|-
|'''mfpinfrindict'''
|INFRAINDICT
|ICT infrastructure
|Elasticity
|-
|'''mfpenpri'''
|WEP
|World energy price
|Elasticity
|-
|'''gdsm, R&D'''
|GDS, R&D
|Connectivity
|Multiplier
|-
|'''govrevm'''
|GOVREV
|Government revenue multiplier
|Multiplier
|-
|'''gdsm'''
|GDS
|Government spending by destination sector
|Multiplier
|-
|'''gdsedm'''
|GDS
|Education spending distribution multiplier
|Multiplier
|-
|'''govhhtrnpenm'''
|GOVHHTRN
|Government pension spending multiplier
|Multiplier
|-
|'''firmtaxrm,'''
|SAVINGS
|National tax rates on firms, households, and indirect taxation
|Multiplier
|-
|'''hhtaxrm (skilled, unskilled)'''
|SAVINGS
|National tax rates on households, broken down by skilled and unskilled
|Multiplier
|-
|'''indirecttaxrm'''
|SAVINGS
|National tax rates on firms, households, and indirect taxation
|Multiplier
|-
|'''ginidomr'''
|GINIDOM
|Domestic Gini Index
|Growth Rate
|-
|'''ginidomm'''
|GINIDOM
|Domestic Gini Index multiplier
|Multiplier
|-
|'''gdprext'''
|GDP
|GDP growth rate
|Growth Rate
|-
|'''xshift'''
|X, XS
|Export shift as a result of trade promotion
|Additive factor
|-
|'''protecm'''
|M, MS
|Protectionism in trade, multiplier on import prices
|Multiplier
|-
|'''termtrm'''
|TERMTR
|Terms of trade balance, higher favors global south
|Multiplier
|-
|'''xfdistockm'''
|XFDISTOCK
|Inward stocks of FDI multiplier
|Multiplier
|-
|'''xfdistoutm'''
|XFDISTOUT
|Outward stocks FDI multiplier
|Multiplier
|-
|'''xfdiwgrm'''
|XFDI STOCK, XFDIOUT
|World FDI growth rate multiplier
|Multiplier
|-
|'''xportfoliom'''
|XPORTFOLIO
|Inward portfolio investment multiplier
|Multiplier
|-
|'''xportstoutm'''
|XPORTFOLIO
|Outward portfolio investment stock multiplier
|Multiplier
|-
|'''aiddon'''
|AID
|Aid foreign loan donations as % GDP
|Additive factor
|-
|'''aidrecm'''
|AID
|Aid receipts as % GDP
|Multiplier
|-
|'''aidlpm'''
|AID
|Aid foreign loan percentage multiplier
|Multiplier
|-
|'''labinformshrm'''
|LABINFORMSHR
|Labor informal share multiplier
|Multiplier
|-
|'''gdpnformshrm'''
|GDPINFORMSHR
|GDP informal share multiplier
|Multiplier
|-
|'''govbusindxm'''
|GOVBUSINDEX
|Government business index multiplier
|Multiplier
|-
|'''tefinfadjm'''
|TEFF
|Multiplier of impact of informal GDP share on productivity
|Multiplier
|-
|'''taxinfadjm'''
|HHTAXR, FIRMTAXR, SSWELTAXR
|Multiplier of impact of informal GDP share on tax rates
|Multiplier
|-
|'''randdexpm'''
|RANDEXP
|Total (public and private) expenditure on research and development multiplier
|Multiplier
|-
|'''firmtaxrm'''
|FIRMTAX
|Tax rate on firm’s multiplier
|Multiplier
|-
|'''labinformcoeffbus'''
|LABINFORMSHR
|Informal labor share, coefficient of business index
|Coefficient
|-
|'''labinformcoeffed'''
|LABINFORMSHR
|Informal labor share, coefficient of education years
|Coefficient
|-
|'''labinformcoeffhhtrn'''
|LABINFORMSHR
|Informal labor share, coefficient of household transfers
|Coefficient
|-
|'''labinformcoefftax'''
|LABINFORMSHR
|Informal labor share, coefficient of tax rate
|Coefficient
|-
|'''gdpinformcoeffcorrupt'''
|GDPINFORMSHR
|Informal GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpinformcoeffintercept'''
|GDPINFORMSHR
|Informal GDP share, intercept of model
|Coefficient
|-
|'''gdpinformcoefflabinf'''
|GDPINFORMSHR
|Informal GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpinformcoeffrandd'''
|GDPINFORMSHR
|Informal GDP share, coefficient of R&D spending
|Coefficient
|-
|'''gdpshadowcoeffintercept'''
|GDPSHADOWSHR
|Shadow economy GDP share, intercept of model
|Coefficient
|-
|'''gdpshadowcoefflabinf'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of informal labor share
|Coefficient
|-
|'''gdpshadowcoeffcorrupt'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of government corruption
|Coefficient
|-
|'''gdpshadowcoeffrandd'''
|GDPSHADOWSHR
|Shadow economy GDP share, coefficient of R&D spending
|Coefficient
|}

== Infrastructure ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''infraroadm'''
|INFRAROAD
|Multiplier on road density expected/demanded
|Multiplier
|-
|'''infraroadpavedpcntm'''
|INFRAROADPAVEDPCNT
|Multiplier on percentage of roads paved
|Multiplier
|-
|'''Infraroadraitrgtval, Infraroadraitrgtyr'''
|INFRAROADRAI
|Target value/years after 2010 for achievement for population percentage living w/in 2km of an all-weather road
|Absolute target
|-
|'''infraroadraisetar, infraroadraiseyrtar'''
|INFRAROADRAI
|Standard error target and years after 2010 for achievement for rural road access index
|Relative target
|-
|'''infraelecgencapm'''
|INFRAELECGENCAP
|Multiplier on electricity generation capacity
|Multiplier
|-
|'''infraelecaccm'''
|INFRAELECACC
|Multiplier on electricity access
|Multiplier
|-
|'''enelecshrendemm'''
|INFRAELECDEM
|Multiplier on ratio of electricity use to total primary energy use
|Multiplier
|-
|'''infraelectranlossm'''
|INFRAELECTRANLOSS
|Multiplier on the loss of electricity in transmission and distribution
|Multiplier
|-
|'''ensolfuelm'''
|ENSOLFUEL
|Multiplier on the reliance on solid fuel as a source of energy
|Multiplier
|-
|'''infraelecacctrgtval/ infraelecacctrgtyr'''
|INFRAELECACC
|Target value and years after 2010 for target achievement for population percentage w/ access to electricity
|Absolute target
|-
|'''infraelecaccsetar/ infraelecaccseyrtar'''
|INFRAELECACC
|Standard error target and years after 2010 for population percentage w/ access to electricity
|Relative target
|-
|'''ensolfueltrgtval/ ensolfueltrgtyr'''
|ENSOLFUEL
|Target value and years after 2010 for target achievement for percentage of households reliant on solid fuels
|Absolute target
|-
|'''ensolfuelsetar/ ensolfuelseyrtar'''
|ENSOLFUEL
|Standard error target and years after 2010 for target achievement for percentage of households reliant on solid fuels
|Relative target
|-
|'''watsafenewsw'''
|N/A
|Switch to activate 5-tier or 3-tier water model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''sanithhmodel2sw'''
|N/A
|Switch to activate 5-tier or 3-tier sanitation model (0=3-tier model; 1=5-tier model)
|Switch
|-
|'''landirareaequipm'''
|LANDIRAREAEQUIP
|Multiplier on land area equipped for irrigation
|Multiplier
|-
|'''sanitationm'''
|SANITATION
|Multiplier on percentage of people with access to sanitation
|Multiplier
|-
|'''watsafem'''
|WATSAFE
|Multiplier on percentage of people with access to safe water
|Multiplier
|-
|'''watwastem'''
|WATWASTE
|Multiplier on percentage of people connected to wastewater collection system,
|Multiplier
|-
|'''watwastetreatm'''
|WATWASTETREAT
|Multiplier on percentage of people connected to wastewater treatment
|Multiplier
|-
|'''sanitationtrgtval/ sanitationtrgtyr'''
|SANITATION
|Target value and years after 2010 for target achievement for population percentage with improved access to improved sanitation, by category of
|Relative target
|-
|'''watsafetrgtval/ watsafetrgtyr'''
|WATSAFE
|Target value and years after 2010 for target achievement for population percentage without access to improved sources of water
|Relative target
|-
|'''sanitnoconsetar/ sanitimpconsetar/ sanithhconsetar/ sanitnoconseyrtar'''
|SANITATION
|Standard error targets and years after 2010 for target achievement for population percentage with varying access to sanitation
|Relative target
|-
|'''sanithhm'''
|SANITHH
|Multiplier on percentage of people with access to sanitation by type
|Multiplier
|-
|'''sanithhbasictrgtval'''

'''sanithhbasictrgtyr'''
|SANITHH
|Target value and years from the base to reach at least basic sanitation services
|Relative target
|-
|'''sanithhsafetrgtval'''

'''sanithhsafetrgtyr'''
|SANITHH
|Target value and years from the base to reach safely managed sanitation services
|Relative target
|-
|'''waterhhm'''
|WATERHH
|Multiplier on percentage of people with access to water by type
|Multiplier
|-
|'''waterhhbasictrgtval'''

'''waterhhbasictrgtyr'''
|WATERHH
|Target value and years from the base to reach at least basic water services
|Relative target
|-
|'''waterhhsafetrgtval'''

'''waterhhsafetrgtyr'''
|WATERHH
|Target value and years from the base to reach safely managed water services
|Relative target
|-
|'''infratelem'''
|INFRATELE
|Multiplier on fixed-line telephone lines per 100 persons
|Multiplier
|-
|'''ictbroadm'''
|ICTBROAD
|Multiplier on fixed-line broadband subscriptions per 100 persons
|Multiplier
|-
|'''ictbroadmobilm'''
|ICTBROADMOBIL
|Multiplier on mobile broadband subscriptions per 100 persons
|Multiplier
|-
|'''ictbroadtrgtval, ictbroadtrgtyr'''
|ICTBROAD
|Target value and years after 2010 for target achievement for number of subscriptions per 100 persons
|Absolute target
|-
|'''ictbroadmobiltrgtval, ictbroadmobiltrgtyr'''
|ICTBROADMOBIL
|Target value and years after 2010 for target achievement for number of subscriptions per 100 persons; varies between 0 and 150
|Absolute target
|-
|'''ictbroadmobilsetar, ictbroadmobilseyrtar'''
|ICTBROADMOBIL
|Standard error target and years after 2010 for target achievement for ICT mobile broadband access
|Relative target
|-
|'''ictbroadsetar, ictbroadseyrtar'''
|ICTBROAD
|Standard error target and years after 2010 for target achievement for fixed broadband access
|Relative target
|-
|'''ictmobilsetar, ictmobilseyrtar'''
|ICTMOBIL
|Standard error target and years after 2010 for target achievement for mobile phone access
|Relative target
|-
|'''gdsm (infrastructure)'''
|GDS
|Gross domestic spending multiplier (infrastructure)
|Multiplier
|-
|'''infrainvnewpubshrm'''
|INFRAINVESTNEWPUB
|Portion of maintenance funding coming from public sources
|Multiplier
|-
|'''Infrainvmaintpubshrm'''
|INFRAINVESTMAINTPUB
|Portion of new infrastructure funding coming from public sources
|Multiplier
|-
|'''hhsizem'''
|GDS
|Household size multiplier
|Multiplier
|-
|'''infraroadpavedcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of paved roads
|Multiplier
|-
|'''infraroadunpavedcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of unpaved roads
|Multiplier
|-
|'''infraelecgencostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of electricity generation
|Multiplier
|-
|'''infraelecaccruralcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of rural electricity access
|Multiplier
|-
|'''infraeleaccurbancostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of urban electricity access
|Multiplier
|-
|'''watsafecostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of safe water
|Multiplier
|-
|'''watsafeimpcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of improved safe water sources
|Multiplier
|-
|'''sanitationcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of basic sanitation
|Multiplier
|-
|'''sanitationimpcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of improved sanitation
|Multiplier
|-
|'''watwastetreatcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of waste water treatment
|Multiplier
|-
|'''landircostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of equipping land for irrigation
|Multiplier
|-
|'''infratelecostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of telephone
|Multiplier
|-
|'''ictmobilcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of mobile
|Multiplier
|-
|'''ictbroadcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on unit cost of broadband
|Multiplier
|-
|'''ictbroadmobilcostm'''
|INFRAINVESTNEWPUB, INFRAINVESTMAINTPUB
|Multiplier on mobile broadband
|Multiplier
|}

== Agriculture ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''clpcm'''
|AGDEM
|Caloric demand for agricultural production
|Multiplier
|-
|'''clpccvm'''
|CLPCCV, MALNPOPP
|Multiplier on coefficient of variation for calories per capita
|Multiplier
|-
|'''elascd'''
|AGDEM
|Elasticity of crop demand to changes in food price
|Elasticity
|-
|'''elasmd'''
|AGDEM
|Elasticity of meat demand to changes in food price
|Elasticity
|-
|'''elasfd'''
|AGDEM
|Elasticity of fish demand to changes in food price
|Elasticity
|-
|'''indemm'''
|INDEM
|Industrial demand for crops
|Multiplier
|-
|'''tgrld'''
|AGP
|Target growth rate in cultivated land
|Initial
|-
|'''ylm'''
|YL
|Crop yield (agricultural production)
|Multiplier
|-
|'''ylmax'''
|YL
|Maximum crop yield
|Exogenous specification
|-
|'''envylchgadd'''
|ENVYLDCHNG
|Agricultural yield change
|Additive factor
|-
|'''slr'''
|AGP
|Livestock slaughter rate
|Exogenous specification
|-
|'''livhdpro'''
|AGP
|Livestock herd productivity gain with grain feeding
|Exogenous specification
|-
|'''aquaculm'''
|FISH
|Fish production through aquaculture, multiplier
|Multiplier
|-
| '''fishcatchm'''
|FISH
|Fish production through catch
|Multiplier
|-
|'''aquaculgr'''
|AQUACUL
|Growth in the use of aquaculture
|Growth Rate
|-
|'''aquaculconv'''
|AQUACUL
|The number years in which aquaculture growth rate converges to 0
|Convergence rate
|-
|'''aglosstransm'''
|LOSS
|Agricultural wastage
|Multiplier
|-
|'''aglossprodm'''
|LOSS
|Agricultural Production loss
|Exogenous specification
|-
|'''aglossconsm'''
|LOSS
|Agricultural Consumption loss
|Exogenous specification
|-
|'''aginvm'''
|YL, IDS (agriculture)
|Investment in agriculture
|Multiplier
|-
| '''ldcropm '''
|LD(crop land)
|Share of crop land in total land
|Multiplier
|-
|'''Elagind'''
|AGP
|Elasticity of industrial use of crops with price
|Elasticity
|-
|'''elinag1'''
|IDS (agriculture)
|Elasticity of investment in agriculture to profit levels
|Elasticity
|-
|'''elinag2'''
|IDS (agriculture)
|Elasticity of investment in agriculture to changes in profit level
|Elasticity
|-
| '''malnelimstartyr'''
|MALNPOP
|Undernourishment elimination target start year
|Standard error target start year
|-
| '''malnelimtargetyr'''
|MALNPOP
|Undernourishment elimination target year
|Standard error target year
|-
| '''malelimprecisesw '''
|MALNPOP
|Undernourishment elimination
|Switch
|}

== Energy ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''endemm'''
|ENDEM
|Energy demand
|Multiplier
|-
|'''elasde'''
|ENP, WEP
|Price elasticity of energy demand
|Elasticity
|-
|'''carbtax'''
|ENP
|Carbon tax
|Exogenous specification
|-
|'''enrgdpgr'''
|ENP, QE
|Growth in energy efficiency
|Rate of change
|-
|'''enpm'''
|ENP
|Production multiplier
|Multiplier
|-
|'''eprodr'''
|ENP
|Production growth rate
|Growth rate
|-
|'''resorm'''
|RESOR
|Conventional resource multiplier
|Multiplier
|-
|'''resorunconm'''
|RESORUNCON
|Unconventional resource multiplier
|Multiplier
|-
|'''rdm'''
|RESER
|Discovery multiplier
|Multiplier
|-
|'''etechadv'''
|QE
|Rate of technical advance in energy
|Exogenous specification
|-
|'''etechadvuncon'''
|QE
|Rate of technical advance in unconventional energy
|Exogenous specification
|-
|'''qem'''
|QE
|Capital output multiplier
|Multiplier
|-
|'''eninvm'''
|ENIV
|Energy investment
|Multiplier
|-
|'''enprix'''
|ENPRI
|Regional energy price
|Exogenous specification
|-
|'''enml, enxl'''
|ENM, ENX
|Limits on energy imports and exports
|Exogenous specification
|}

== Environment ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''enpm'''
|ENP
|Energy production by type
|Multiplicative
|-
|'''endemm'''
|ENDEM, WEP
|Energy demand multiplier
|Multiplicative
|-
|'''carbtax'''
|ENP, CARANN
|Carbon tax
|Exogenous Specification
|-
|'''qem'''
|ENP
|Capital-output ratio multiplier
|Multiplicative
|-
|'''resorm'''
|RESOR
|Resource multiplier
|Multiplicative
|-
|'''enrgdpgr'''
|ENP
|Energy efficiency
|Growth rate
|-
|'''carfuel1'''
|CARANN
|Carbon generated by gas
|Coefficient
|-
|'''carfuel2'''
|CARANN
|Carbon generated by natural gas
|Coefficient
|-
|'''carfuel3'''
|CARANN
|Carbon generated by coal
|Coefficient
|-
|'''envclimsens'''
|CO2PPM, WTEMP
|Time sensitivity of global temperature to carbon dioxide
|Limit
|-
|'''carabr'''
|CO2PPM, WTEMP
|Carbon absorbed by oceans
|Coefficient
|-
|'''carabrchng'''
|CO2PPM, WTEMP
|annual change in this absorption rate of carbon by oceans
|Coefficient
|-
|'''envco2fert'''
|AGP
|Crop sensitivity to environmental change
|Elasticity
|-
|'''envylchgadd'''
|ENVYLDCHNG
|Agricultural yield change
|Additive factor
|-
|'''envylchgm'''
|ENVYLDCHNG
|Agricultural yield change
|Multiplicative
|-
|'''forestm'''
|WFOREST
|World forest multiplier
|Multiplicative
|-
|'''tgrld'''
|WFOREST, LD
|Land growth by type
|Initial condition
|-
|'''carforst'''
|CO2PPM
|Level of carbon sequestered by forests
|Coefficient
|-
|'''watres'''
|Unknown
|Renewable water resources
|Exogenous specification
|-
|'''tgrld'''
|AGP
|Land growth by type
|Initial condition
|-
|'''ylm'''
|YL
|Agricultural yield multiplier
|Multiplier
|-
|'''envpm2pt5m'''
|ENVPM2PT5
|PM2.5 levels multiplier
|Multiplier
|-
|'''ensolfuelm'''
|ENSOLFUEL
|Solid fuel use multiplier
|Multiplier
|-
|'''ensolfuelsetar'''
|ENSOLFUEL
|Solid fuel use SE target
|Standard error target
|-
|'''ensolfuelseyrtar'''
|ENSOLFUEL
|Solid fuel use SE target
|Standard error target
|-
|'''ensolfueltrgtval'''
|ENSOL FUEL
|Solid fuel elimination target
|Target
|-
|'''ensolfueltrgtyr'''
|ENSOLFUEL
|Solid fuel elimination target
|Target
|-
|'''hlsolfuelsw'''
|DEATHCAT
|Relationship between solid fuel and respiratory diseases
|Switch
|-
|'''climeconimpsw '''
|CLIMECONIMP
|Climate change to economy impact switch (1=on)
|Switch
|-
|'''climeconimplin '''
|CLIMECONIMP
|Climate change to economy impact, linear term coefficient
|Co-efficient
|-
|'''climeconimpsq '''
|CLIMECONIMP
|Climate change to economy impact, squared term coefficient
|Co-efficient
|-
|'''climeconimpstk'''
|VADD
|Climate chnge to econ impact, portion affecting productivity or capital stocks
|Multiplier

|-
|'''watdemandelas'''
|WATDEM
|Elasticity from water price to water demand
|Elasticity
|-
|'''landirareaequipelas'''
|LANDIRAREAACTUAL
|Elasticity from water price to land equipped for irrigation
|Elasticity
|-
|'''desali'''
|WATERDESALINATED
|desalination responsiveness to change in stock levels of price
|PID factor
|-
|'''desald'''
|WATERDESALINATED
|desalination responsiveness to change in price
|PID factor
|}

== Governance ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''sfusehist'''
|N/A
|State failure switch to use history for base values(0=off; 1=on; 2=converge)
|Switch
|-
|'''sfintlwaradd'''
|SFINTLWAR
|State failure/internal war addition
|Additive
|-
|'''sfconv'''
|SFINTLWAR
|State failure convergence
|Convergence speed (years)
|-
|'''sfmassrep'''
|SFINTLWAR
|Mass repression factor
|Additive
|-
|'''wpextinterv'''
|SFINTLWAR
|External intervention factor
|Additive
|-
|'''govriskm'''
|GOVRISK
|Government risk multiplier
|Multiplicative
|-
|'''govriskweight'''
|GOVRISK
|Government risk weight
|Weight
|-
|'''govrevm'''
|GOVREV
|Government Revenue Multiplier
|Multiplier
|-
|'''firmtaxrm'''
|GOVREV
|Firm tax multiplier
|Multiplier
|-
|'''hhtaxrm'''
|GOVREV
|Household tax rate multiplier
|Multiplier
|-
|'''ssweltaxrm'''
|GOVREV
|Social Security multiplier
|Multiplier
|-
|'''indirecttaxrm'''
|GOVREV
|Indirect tax multiplier
|Multiplier
|-
|'''govcorruptm'''
|GOVCORRUPT
|Corruption Multiplier
|Multiplier
|-
|'''govcorruptsetar'''
|GOVCORRUPT
|Corruption target
|Standard error target
|-
|'''govcorruptseyrtar'''
|GOVCORRUPT
|Corruption target
|Standard error target
|-
|'''goveffectm'''
|GOVEFFECT
|Effectiveness multiplier
|Multiplier
|-
|'''goveffectsetar'''
|GOVEFFECT
|Effectiveness target
|Standard error target
|-
|'''goveffectseyrtar'''
|GOVEFFECT
|Effectiveness target
|Standard error target
|-
|'''govregqualm'''
|REGQUALITY
|Regulatory quality multiplier
|Multiplier
|-
|'''govregqualsetar'''
|REGQUALITY
|Regulatory quality target
|Standard error target
|-
|'''govregqualseyrtar'''
|REGQUALITY
|Regulatory quality target
|Standard error target
|-
|'''econfreem'''
|ECONFREE
|Economic freedom multiplier
|Multiplier
|-
|'''democm'''
|DEMOCPOLITY
|Democracy multiplier
|Coefficient
|-
|'''swseffects'''
|DEMOCPOLITY
|Swing state effect
|Additive factor
|-
|'''democimpus'''
|DEMOCPOLITY
|Democracy impetus by leading power
|Coefficient
|-
|'''democimpoth'''
|DEMOCPOLITY
|Democracy impetus by other powers
|Coefficient
|-
|'''DEMOCWDIR'''
|N/A
|Democracy wave direction (-1=down; 0=off; 1=up)
|Switch
|-
|'''democwvlen'''
|DEMOCPOLITY
|Democracy wave length
|Limit
|-
|'''democwvmax'''
|DEMOCPOLITY
|Democracy wave upper limit
|Limit
|-
|'''democpolitysetar'''
|DEMOCPOLITY
|Democracy SE target
|Standard error target
|-
|'''democpolityseyrtar'''
|DEMOCPOLITY
|Democracy SE year
|Standard error target
|-
|'''freedomm'''
|FREEDOM
|Freedom multiplier
|Multiplier
|-
|'''gemm'''
|GEM
|Gender empowerment multiplier
|Multiplier
|-
|'''gemsetar'''
|GEM
|Gender empowerment target
|Standard error target
|-
|'''gemseyrtar'''
|GEM
|Gender empowerment year target
|Standard error target
|}

== International Relations ==
{| class="wikitable"
|'''Parameter'''
|'''Variable of Interest'''
|'''Description'''
|'''Type'''
|-
|'''cpowf'''
|CPOW
|Factor controlling military spending conversion
|Convergence factor
|-
|'''cpowldcf'''
|CPOW
|Higher impact of military spending in LDCs
|Convergence factor
|-
|'''drcpow'''
|CPOW
|Depreciation rate of conventional power
|Rate of change
|-
|'''cpowdf'''
|CIVDM
|Conventional power, damage done by war
|Coefficient
|-
|'''govnucwpadd'''
|NPOW
|Nuclear weapons stockpile change
|Additive factor
|-
|'''wpwghtpow'''
|POWER
|Contribution of various components to power index
|Index weight
|-
|'''wpgreatthresh'''
|PCONGREAT
|Systemic power share required for threshold of great power status
|Coefficient
|-
|'''wpgreatlev'''
|PCONGREAT
|Systemic power share required for full great power status
|Coefficient
|-
|'''wpconeusw'''
|PCONGREAT
|EU great power switch
|Switch
|-
|'''wpconmax'''
|PCONSYS
|Maximum power concentration
|Limit
|-
|'''wpconmin'''
|PCONSYS
|Minimum power concentration
|Limit
|-
|'''wpgreat1'''
|THREAT
|Dyadic dispute probability, one great power
|Coefficient
|-
|'''wpgreat2'''
|THREAT
|Dyadic dispute probability, two great powers
|Coefficient
|-
|'''wppowtran1'''
|THREAT
|Power transition threshold
|Coefficient
|-
|'''wppowtran2'''
|THREAT
|Increase in dispute probability with 10% greater power parity
|Elasticity
|-
|'''wpcon'''
|THREAT
|Change in threat with power concentration
|Coefficient
|-
|'''wpconsq'''
|THREAT
|Change in threat with power concentration
|Coefficient
|-
|'''wpcontiguity'''
|THREAT
|Threat associated with geographic proximity
|Elasticity
|-
|'''wpdemdist'''
|THREAT
|Response of threat to political distance
|Elasticity
|-
|'''wpdemmin'''
|THREAT
|Response of threat to the minimum democracy level in the dyad
|Limit
|-
|'''glocom'''
|THREAT
|Global community multiplier
|Exogenous specification
|-
|'''wpcommun'''
|THREAT
|Response of threat to global community level
|Elasticity
|-
|'''wptrade'''
|THREAT
|Change in threat with systemic trade
|Elasticity
|-
|'''ally'''
|THREAT
|Alliance membership switch
|Switch
|-
|'''wpally'''
|THREAT
|Reduction in threat with allied dyad
|Elasticity
|-
|'''terdispute'''
|THREAT
|Territorial dispute or absence
|Exogenous specification
|-
|'''wpterdisp'''
|THREAT
|Conflict arising from territorial disputes
|Elasticity
|-
|'''wpthrconv'''
|THREAT
|Threat level convergence
|Convergence Speed
|-
|'''wpthrwar'''
|CWARPB
|Portion of disputes that escalate to war
|Coefficient
|-
|'''waron'''
|CWAR, NWAR
|War switch
|Switch
|-
|'''cwarbase'''
|CWARPB
|Conventional war probability addition to base computation
|Additive factor
|-
|'''cwarf'''
|CWARPB
|Conventional war forcing
|Switch
|-
|'''cwarsv'''
|CIVDM
|Conventional war severity
|Multiplier
|-
|'''nwarf'''
|NWARPB
|Probability that conventional war will escalate to nuclear
|Coefficient
|-
|'''cdmf'''
|CIVDM
|Civilian war damage factor
|Multiplier
|-
|'''nwarsv'''
|CIVDM
|Nuclear war severity
|Multiplier
|-
|'''elndpow'''
|CIVDM
|Elasticity of nuclear damage with nuclear power
|Elasticity
|-
|'''diploembassymaxm'''
|DIPLOEMBASSYMAX
|Maximum embassies abroad
|Multiplier
|-
|'''diploigomaxm'''
|DIPLOIGOMAX
|Maximum international organization membership
|Multiplier
|-
|'''diplotreatymaxm'''
|DIPLOTREATYMAX
|Maximum bilateral treaty participation
|Multiplier
|}