Pardee Wiki - User contributions [en]

Importing data (general instructions)

2017-09-25T16:59:06Z

MeredithMoon:

= Introduction =

This section describes the import and update processes for data tables in IFsHistSeries.Mdb. Updating World Value Survey data (IFsWVSCohort.MDB) requires following a slightly different procedure.

Data import for IFs is a three step process:
<ol style="list-style-type:lower-roman;">
<li>Extraction, preparation and blending of source data (and meta-data) into MS Access tables formatted for IFs database. This step might involve a blending process when data is updated.</li>
<li>Vetting of MS Access tables containing extracted data (and meta-data)</li>
<li>Merging of the MS Access table (and meta-data) to IFs master database (IFsHistSeries.Mdb   and Datadict.mdb)</li>
</ol>

In this section we shall discuss how to extract data from external sources, prepare extracted data for IFs database and blend extracted data with existing data. When updating an existing data table we usually preserve data not reported in the more recent source to maintain consistency between old and new data, a process we call blending.

There are three ways to import data from external sources and prepare it for IFs as a new series or as an update of an existing series:
<ol style="list-style-type:lower-roman;">
<li>Manual import, which basically involves reorganizing external data into an IFs template spreadsheet and copying that data to an MS Access table   </li>
<li>Automated import that uses IFs software application to extract data from Excel spreadsheets and prepare Access tables using a Country Concordance table between external data source and IFs.</li>
<li>Batch update, another IFs application feature that automates updating all IFs tables from the same external source as long as external data is available in a required format.</li>
</ol>

= Manual Import Using IFs Template Spreadsheet =

The steps for the manual import are:
<ol style="list-style-type:lower-roman;">
<li>Enter or reorganize external data into IFs template spreadsheet. The IFs team uses an Excel spreadsheet template containing 186 IFs countries, country FIPS code and year columns going from 1960 to 2014. The name of the Excel file is "Template Data Form 6.XLS." This file can be found in the IFs\Data folder. (Template spreadsheets can also be generated from IFs application's import screen)</li>
<li>Delete year column in the template spreadsheet which do not have any data.</li>
<li>Create an MS Access table with the same structure (i.e., containing the same year columns) as the template spreadsheet. This new Access table needs to be created inside "IFsDataImport.Mdb" file in the IFs\Data folder. If you are updating an existing table, you should give the table the same name as the existing table. For a new table, prefix the table name with "Series" and follow the naming convention described in section 2.2.</li>
</ol>

Tip: Rather than creating an altogether new Access table, take an existing data table, i.e., any of the IFsHistSeries.mdb tables, copy it to a new name and change the structure by going to "Design View" in MS Access. In fact, it is best to start from an existing table so that the data field specifications are strictly followed. To add a year to the new table, insert an empty column (in the design view), copy an existing year column, paste it on the empty column and change the column (or field) name to the label of the year you need. (If you are using an existing Access table to create a new table, please delete the Earliest and MostRecent columns from the Access table as this will be populated automatically by IFs application.)
<ol style="list-style-type:lower-roman;">
<li>Copy and paste the data from Excel template spreadsheet to Access table. Make sure you sort both the spreadsheet and the Access table alphabetically by country name before you copy and paste.</li>
<li>Add a row to the Datadict table inside IFsDataImport.Mdb file. If you are importing new data, create an empty row and add meta-data (please be as complete as possible). If you are updating data, copy the row from master copy of Datadict (DataDict.Mdb) and edit as needed (you must edit last IFs update)</li>
</ol>

Tip: You can skip steps iii, iv and v and use IFs' automated import (see next section for description) feature instead

'''Passing Extracted Data for Vetting'''

Extracted data, saved in Data\IFsDataImport.Mdb, need to be sent to the person who will do the vetting. Please also pass all source information (Excel spreadsheet data or pdf file/s or the website from which data is taken). Some initial scrutiny of the data before it is sent for vetting is recommended.

= Automated Import Using ImportXLS: Single Import =

External data available in Excel format known to IFs can be extracted into Access tables using IFs application's automated ImportXLS (the process works only for Excel files and hence the name) feature that work by importing one time series at a time. If you have subnational models broken out in IFs on your computer, you need to switch back to the full 186 country model. Instructions on how to do this can be found in the [[SubRegionalization_Handbook|SubRegionalization Handbook]]. This process will take around five minutes, so can be done as frequently as needed. 

This feature can be accessed through IFs menu system:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS File.

[[File:ImportDataFromXLSFile.png|RTENOTITLE]]

This opens up a form shown below:

[[File:ImportDataWizard.png|RTENOTITLE]]

The form is self-explanatory for the most part. You have to start by opening the source Excel file (option on the top bar) and selecting the spreadsheet which contains source. You then need to choose one from the six options showing the format of the data organization in the source spreadsheet (see the right side of the import screen). The default of these six options (the one at the bottom) works in most cases, however you can compare each option to your data by selecting a data organization option and viewing the layout in the image directly below the final option.

The list of steps to follow when importing data can be found in the "Help" menu option, which opens up a dialog box with the basic list.

[[File:ImportDataInstructions.png|RTENOTITLE]]

This list is fleshed out here in more detail and should be followed carefully:

'''1. Open the Excel file containing formatted source data (option found on top bar)'''

'''2. Select one spreadsheet when the source file contains more than one'''

'''3. Select source data format using illustrated options'''

    Each option can be previewed by selecting it and seeing the visualization directly below

'''4. Identify the Excel cells containing country and year labels.'''

If you do not remember when it starts, you can click ''View Excel File ''to check - the file must be closed after viewing

The ''Series Name Start at'' field will only appear if you have selected a ''Multiple Series'' option in the ''Choose Excel Source Data Format'' field. In this case, you choose the column of the data series          you want to import. 

'''5. Data Conversion Operator and Factor'''

'''????'''

'''6. Choose aggregation rule'''

An aggregation rule tells the model how to aggregate country level data up to different country groupings that can be selected in the model. For instance, if you choose SubSaharan Africa as a region, the model needs to know whether to add, average, use a weighted average for all countries in the grouping. 

There are five different aggregation rules to choose from: POP, GDP, AVG, SUM, LND. If the dataset is being updated, the aggregation rule should populate. If you are importing a new series, you have to choose the aggregation rule. You can look in the Data Dict at similar series to get an idea of what should be chosen or below are brief explanations of each, along with a few examples of datasets that use each rule.

     1. POP (population): uses a weighted average in which population of a country is the weight; used in demographics, health, and education series; used in series with per capita designations or when the variable is based on a percent of households (HHs)

     2. GDP: uses a weighted average in which GDP at MER of a country is the weight; used in economic series and when price is a consideration in the dataset

     3. AVG (average): takes the average of each country's data; used in indices 

     4. SUM: sums each country's data; used in many energy and labor series and when the data is a count

     5. LND (land): uses a weighted average using land cover as weight

'''7. Choose Country Concordance table '''

Country Concordance lists are used in converting external data to IFs format. "Country Concordance" options are shown in a dropdown list at the bottom of the screen. The prepackaged lists that you see in the dropdown were built over the years from the major international databases like World Bank's WDI, UNESCO, IEA etc. If you do not see a list that you can use you need to either use a manual import described above in section 3.1.1 or leave ImportXLS for now, build and update a country list and then come back to import. Unless a data source has a large number of tables that we will be regularly updating we do not spend the time in building and updating a new country concordance table. As a general rule, decimals should be truncated after the 4th decimal place. 

'''8. Identify missing values, if any. Data source and names can be put in later. '''

'''9. Enter a name and a definition for the imported table. If the variable is new, the name must be different from any existing table name. '''

If you are importing a new variable into the model, ask the person who assigned you this task for a specific name. If not provided at first, this can be added in the Data Dict after the import is complete. If you would like to update an existing table rather than import a new one, you can select the name of the table from a dropdown box just above the variable name box. Selecting an existing table will automatically bring in dictionary information from the master copy of the data dictionary.

'''10. Click Import when you are ready. '''

'''11. Click on View Excluded after importing the data'''

You'll see the countries that were not imported, if any. This may have happened because the IFs model does not have those countries in the model or because the name was incorrect and did not concord. If the name was incorrect, go back into the source file and change the country name and re-import the data. 

'''12. After accepting imported data, you'll see a form to enter data dictionary information. '''

Here are some tips on what to enter in the different fields in the data dict that do not auto-populate from the import: 

''Series ''& ''Cohort'': the entry for Series is Yes and for Cohort is No

''Source Definition'': the exact definition from the data source

''Extended Source Definition'': any information in addition to the source definition, such as any calculations or modifications you or the creators of the dataset performed on the data 

''Name in Source'' & ''Code in Source'': only relevant for batch pulls since there are multiple series in the pull

''Notes'': enter your initials as data puller/importer and the data vetter will then enter their initials

''Forecast'': click Yes if the series imported is a forecast and not a historical data series

[[File:DataDictWizard.png|RTENOTITLE]]

'''13. Imported data will go to IFs\Data\IFsDataImport.mdb.'''

This is the file you will send to the person responsible for vetting and merging the data into the model.

'''14. Dictionary information will go to IFs\Data\IFsDataDict.mdb. '''

Any changes that need to be made to the data dict can be made directly here after the data is imported.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

= Batch Update Using ImportXLSBatch =

Another form has been created that allows import from an Access table. This needs to be described, and shown.

Batch update, another IFs application feature, allows updating all IFs tables pertaining to a certain external source at once. This works only for those sources where data is available in a recognized format and each source series is identified with a fixed code that IFs data dictionary can save and match during subsequent updates. At this moment, batch update works for AQU, WDI, UNESCO (UIS), IMF and FAO.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Update Data in Batch.

The form that opens up is shown below. The grid on the right of the form will be empty initially, but fills up as soon as the user selects a source name from the dropdown box sitting above the grid. You can select all series for batch update (a checkbox is available for this) or decide to update the ones that you select by clicking on the "Update" column in the grid. There is an additional option of selecting only those variables per data source which are used in the Pre-Processor, which can be selected by checking the ‘Pre-Proc’ box on the batch import form seen below. Of course, the import process will update only those series that are found in the source Excel that you'll have to open using the menu at the top.

Batch update feature works by extracting multiple data series from a single spreadsheet at a time. From the box listing all spreadsheets in the workbook that you just opened, you will have to select the one that contains all the series dat.

Before using the batch import process, new variables must be added to the datadict and the source column of the datadict must be formatted for batch import by including one of the qualified series listed above (FAO, IMF, AQU, etc). Additionally, each data series in the external spreadsheet is matched with an IFs data table through a "series code" that is previously collected in the IFs "datadict" table. For WDI, these codes appear as combination of two to four letter code sections separated by periods (e.g., AG.PRD.FOOD.XD). For UNESCO education data, we take series names used by UIS, exactly as they type it, as series codes. For FAO data, Element code and Item code used by FAO are used by IFS (accordingly, there will be two series code  boxes in the batch update form when you are updating FAO data; identify Element code as the first one and Item code as the second one).

The decimal places box is used for specifying the decimal places that will be kept in extracted tables. As a general rule, decimals should be truncated after the 4th decimal place. The setting works for those tables which do not have such setting established already (as shown in the grid above).

[[File:BatchImportWizard.png|RTENOTITLE]]

The steps are explained in a Help dialog shown below:

[[File:BatchImportInstructions.png|RTENOTITLE]]

Clicking the "Import Batch" button will import (and update) the series that are selected in the grid one at a time and show a progress bar as this is being done.

Imported tables can be subjected to automated blending and vetting using the new vetting/comparison interface described later in this document.

== Source Data for Batch Update ==

World Development Indicator (WDI) data is released annually and is usually available as a single Excel file at their website:

[http://data.worldbank.org/data-catalog/world-development-indicators/ http://data.worldbank.org/data-catalog/world-development-indicators/]

FAO and UIS data do not come in single file as WDI. What we need to do is download multiple series on related issues (e.g., all cereal production data or all primary education data) as single Excel files. (Kate McGrath has prepared instructions on getting FAO and UIS data from their websites).

IMF data is available only by accessing our IMF e-Library account, registered to Mohammod Irfan.

== Passing Extracted Batch Data for Vetting ==

Extracted data will be saved in Data\IFsDataImportBatch.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.
<div><div id="_com_1"></div></div>
= Automated Import Using ImportXLS: Province Import =

Importing data for a provincial model can be done with the Province Data Import Feature. IFs does not need to be sub-regionalized to use this feature.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS Provinces

The form that opens up is shown below. The format reflects the single series import process already described. A recognized excel format must be selected from the list on the right. The location of Province names, Years, and Series from your chosen excel data sheet must be designated. The provincial country concordance table must also be selected from the drop down list. The form contains the option to view the excel sheet you are importing, and to view the country concordance tables. Lastly, the variable you are importing needs to be selected from the drop down list. Existing variables will populate the definition field. New variables will need to be named using the Ifs naming convention reviewed above, and defined.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

Importing data (general instructions)

2017-09-25T16:50:19Z

MeredithMoon:

= Introduction =

This section describes the import and update processes for data tables in IFsHistSeries.Mdb. Updating World Value Survey data (IFsWVSCohort.MDB) requires following a slightly different procedure.

Data import for IFs is a three step process:
<ol style="list-style-type:lower-roman;">
<li>Extraction, preparation and blending of source data (and meta-data) into MS Access tables formatted for IFs database. This step might involve a blending process when data is updated.</li>
<li>Vetting of MS Access tables containing extracted data (and meta-data)</li>
<li>Merging of the MS Access table (and meta-data) to IFs master database (IFsHistSeries.Mdb   and Datadict.mdb)</li>
</ol>

In this section we shall discuss how to extract data from external sources, prepare extracted data for IFs database and blend extracted data with existing data. When updating an existing data table we usually preserve data not reported in the more recent source to maintain consistency between old and new data, a process we call blending.

There are three ways to import data from external sources and prepare it for IFs as a new series or as an update of an existing series:
<ol style="list-style-type:lower-roman;">
<li>Manual import, which basically involves reorganizing external data into an IFs template spreadsheet and copying that data to an MS Access table   </li>
<li>Automated import that uses IFs software application to extract data from Excel spreadsheets and prepare Access tables using a Country Concordance table between external data source and IFs.</li>
<li>Batch update, another IFs application feature that automates updating all IFs tables from the same external source as long as external data is available in a required format.</li>
</ol>

= Manual Import Using IFs Template Spreadsheet =

The steps for the manual import are:
<ol style="list-style-type:lower-roman;">
<li>Enter or reorganize external data into IFs template spreadsheet. The IFs team uses an Excel spreadsheet template containing 186 IFs countries, country FIPS code and year columns going from 1960 to 2014. The name of the Excel file is "Template Data Form 6.XLS." This file can be found in the IFs\Data folder. (Template spreadsheets can also be generated from IFs application's import screen)</li>
<li>Delete year column in the template spreadsheet which do not have any data.</li>
<li>Create an MS Access table with the same structure (i.e., containing the same year columns) as the template spreadsheet. This new Access table needs to be created inside "IFsDataImport.Mdb" file in the IFs\Data folder. If you are updating an existing table, you should give the table the same name as the existing table. For a new table, prefix the table name with "Series" and follow the naming convention described in section 2.2.</li>
</ol>

Tip: Rather than creating an altogether new Access table, take an existing data table, i.e., any of the IFsHistSeries.mdb tables, copy it to a new name and change the structure by going to "Design View" in MS Access. In fact, it is best to start from an existing table so that the data field specifications are strictly followed. To add a year to the new table, insert an empty column (in the design view), copy an existing year column, paste it on the empty column and change the column (or field) name to the label of the year you need. (If you are using an existing Access table to create a new table, please delete the Earliest and MostRecent columns from the Access table as this will be populated automatically by IFs application.)
<ol style="list-style-type:lower-roman;">
<li>Copy and paste the data from Excel template spreadsheet to Access table. Make sure you sort both the spreadsheet and the Access table alphabetically by country name before you copy and paste.</li>
<li>Add a row to the Datadict table inside IFsDataImport.Mdb file. If you are importing new data, create an empty row and add meta-data (please be as complete as possible). If you are updating data, copy the row from master copy of Datadict (DataDict.Mdb) and edit as needed (you must edit last IFs update)</li>
</ol>

Tip: You can skip steps iii, iv and v and use IFs' automated import (see next section for description) feature instead

'''Passing Extracted Data for Vetting'''

Extracted data, saved in Data\IFsDataImport.Mdb, need to be sent to the person who will do the vetting. Please also pass all source information (Excel spreadsheet data or pdf file/s or the website from which data is taken). Some initial scrutiny of the data before it is sent for vetting is recommended.

= Automated Import Using ImportXLS: Single Import =

External data available in Excel format known to IFs can be extracted into Access tables using IFs application's automated ImportXLS (the process works only for Excel files and hence the name) feature that work by importing one time series at a time. If you have subnational models broken out in IFs on your computer, you need to switch back to the full 186 country model. Instructions on how to do this can be found in the [[SubRegionalization_Handbook|SubRegionalization Handbook]]. This process will take around five minutes, so can be done as frequently as needed. 

This feature can be accessed through IFs menu system:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS File.

[[File:ImportDataFromXLSFile.png|RTENOTITLE]]

This opens up a form shown below:

[[File:ImportDataWizard.png|RTENOTITLE]]

The form is self-explanatory for the most part. You have to start by opening the source Excel file (option on the top bar) and selecting the spreadsheet which contains source. You then need to choose one from the six options showing the format of the data organization in the source spreadsheet (see the right side of the import screen). The default of these six options (the one at the bottom) works in most cases, however you can compare each option to your data by selecting a data organization option and viewing the layout in the image directly below the final option.

The list of steps to follow when importing data can be found in the "Help" menu option, which opens up a dialog box with the basic list.

[[File:ImportDataInstructions.png|RTENOTITLE]]

This list is fleshed out here in more detail and should be followed carefully:

'''1. Open the Excel file containing formatted source data (option found on top bar)'''

'''2. Select one spreadsheet when the source file contains more than one'''

'''3. Select source data format using illustrated options'''

    Each option can be previewed by selecting it and seeing the visualization directly below

'''4. Identify the Excel cells containing country and year labels.'''

If you do not remember when it starts, you can click ''View Excel File ''to check - the file must be closed after viewing

The ''Series Name Start at'' field will only appear if you have selected a ''Multiple Series'' option in the ''Choose Excel Source Data Format'' field. In this case, you choose the column of the data series          you want to import. 

'''5. Data Conversion Operator and Factor'''

'''????'''

'''6. Choose aggregation rule'''

An aggregation rule tells the model how to aggregate country level data up to different country groupings that can be selected in the model. For instance, if you choose SubSaharan Africa as a region, the model needs to know whether to add, average, use a weighted average for all countries in the grouping. 

There are five different aggregation rules to choose from: POP, GDP, AVG, SUM, LND. If the dataset is being updated, the aggregation rule should populate. If you are importing a new series, you have to choose the aggregation rule. You can look in the Data Dict at similar series to get an idea of what should be chosen or below are brief explanations of each, along with a few examples of datasets that use each rule.

     1. POP (population): uses a weighted average in which population of a country is the weight; used in demographics, health, and education series; used in series with per capita designations or when the variable is based on a percent of households (HHs)

     2. GDP: uses a weighted average in which GDP at MER of a country is the weight; used in economic series and when price is a consideration in the dataset

     3. AVG (average): takes the average of each country's data; used in indices 

     4. SUM: sums each country's data; used in many energy and labor series and when the data is a count

     5. LND (land): uses a weighted average using land cover as weight

'''7. Choose Country Concordance table '''

Country Concordance lists are used in converting external data to IFs format. "Country Concordance" options are shown in a dropdown list at the bottom of the screen. The prepackaged lists that you see in the dropdown were built over the years from the major international databases like World Bank's WDI, UNESCO, IEA etc. If you do not see a list that you can use you need to either use a manual import described above in section 3.1.1 or leave ImportXLS for now, build and update a country list and then come back to import. Unless a data source has a large number of tables that we will be regularly updating we do not spend the time in building and updating a new country concordance table. As a general rule, decimals should be truncated after the 4th decimal place. 

'''8. Identify missing values, if any. Data source and names can be put in later. '''

'''9. Enter a name and a definition for the imported table. If the variable is new, the name must be different from any existing table name. '''

If you are importing a new variable into the model, ask the person who assigned you this task for a specific name. If not provided at first, this can be added in the Data Dict after the import is complete. If you would like to update an existing table rather than import a new one, you can select the name of the table from a dropdown box just above the variable name box. Selecting an existing table will automatically bring in dictionary information from the master copy of the data dictionary.

'''10. Click Import when you are ready. '''

'''11. Click on View Excluded after importing the data'''

You'll see the countries that were not imported, if any. This may have happened because the IFs model does not have those countries in the model or because the name was incorrect and did not concord. If the name was incorrect, go back into the source file and change the country name and re-import the data. 

'''12. After accepting imported data, you'll see a form to enter data dictionary information. '''

Here are some tips on what to enter in the different fields in the data dict: 

''Source Definition'': the exact definition from the data source

''Extended Source Definition'': any information in addition to the source definition, such as any calculations or modifications you or the creators of the dataset performed on the data 

''Name in Source'' & ''Code in Source'': only relevant for batch pulls since there are multiple series in the pull

[[File:DataDictWizard.png|RTENOTITLE]]

'''13. Imported data will go to IFs\Data\IFsDataImport.mdb.'''

This is the file you will send to the person responsible for vetting and merging the data into the model.

'''14. Dictionary information will go to IFs\Data\IFsDataDict.mdb. '''

Any changes that need to be made to the data dict can be made directly here after the data is imported.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

= Batch Update Using ImportXLSBatch =

Another form has been created that allows import from an Access table. This needs to be described, and shown.

Batch update, another IFs application feature, allows updating all IFs tables pertaining to a certain external source at once. This works only for those sources where data is available in a recognized format and each source series is identified with a fixed code that IFs data dictionary can save and match during subsequent updates. At this moment, batch update works for AQU, WDI, UNESCO (UIS), IMF and FAO.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Update Data in Batch.

The form that opens up is shown below. The grid on the right of the form will be empty initially, but fills up as soon as the user selects a source name from the dropdown box sitting above the grid. You can select all series for batch update (a checkbox is available for this) or decide to update the ones that you select by clicking on the "Update" column in the grid. There is an additional option of selecting only those variables per data source which are used in the Pre-Processor, which can be selected by checking the ‘Pre-Proc’ box on the batch import form seen below. Of course, the import process will update only those series that are found in the source Excel that you'll have to open using the menu at the top.

Batch update feature works by extracting multiple data series from a single spreadsheet at a time. From the box listing all spreadsheets in the workbook that you just opened, you will have to select the one that contains all the series dat.

Before using the batch import process, new variables must be added to the datadict and the source column of the datadict must be formatted for batch import by including one of the qualified series listed above (FAO, IMF, AQU, etc). Additionally, each data series in the external spreadsheet is matched with an IFs data table through a "series code" that is previously collected in the IFs "datadict" table. For WDI, these codes appear as combination of two to four letter code sections separated by periods (e.g., AG.PRD.FOOD.XD). For UNESCO education data, we take series names used by UIS, exactly as they type it, as series codes. For FAO data, Element code and Item code used by FAO are used by IFS (accordingly, there will be two series code  boxes in the batch update form when you are updating FAO data; identify Element code as the first one and Item code as the second one).

The decimal places box is used for specifying the decimal places that will be kept in extracted tables. As a general rule, decimals should be truncated after the 4th decimal place. The setting works for those tables which do not have such setting established already (as shown in the grid above).

[[File:BatchImportWizard.png|RTENOTITLE]]

The steps are explained in a Help dialog shown below:

[[File:BatchImportInstructions.png|RTENOTITLE]]

Clicking the "Import Batch" button will import (and update) the series that are selected in the grid one at a time and show a progress bar as this is being done.

Imported tables can be subjected to automated blending and vetting using the new vetting/comparison interface described later in this document.

== Source Data for Batch Update ==

World Development Indicator (WDI) data is released annually and is usually available as a single Excel file at their website:

[http://data.worldbank.org/data-catalog/world-development-indicators/ http://data.worldbank.org/data-catalog/world-development-indicators/]

FAO and UIS data do not come in single file as WDI. What we need to do is download multiple series on related issues (e.g., all cereal production data or all primary education data) as single Excel files. (Kate McGrath has prepared instructions on getting FAO and UIS data from their websites).

IMF data is available only by accessing our IMF e-Library account, registered to Mohammod Irfan.

== Passing Extracted Batch Data for Vetting ==

Extracted data will be saved in Data\IFsDataImportBatch.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.
<div><div id="_com_1"></div></div>
= Automated Import Using ImportXLS: Province Import =

Importing data for a provincial model can be done with the Province Data Import Feature. IFs does not need to be sub-regionalized to use this feature.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS Provinces

The form that opens up is shown below. The format reflects the single series import process already described. A recognized excel format must be selected from the list on the right. The location of Province names, Years, and Series from your chosen excel data sheet must be designated. The provincial country concordance table must also be selected from the drop down list. The form contains the option to view the excel sheet you are importing, and to view the country concordance tables. Lastly, the variable you are importing needs to be selected from the drop down list. Existing variables will populate the definition field. New variables will need to be named using the Ifs naming convention reviewed above, and defined.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

Importing data (general instructions)

2017-09-21T23:08:33Z

MeredithMoon:

= Introduction =

This section describes the import and update processes for data tables in IFsHistSeries.Mdb. Updating World Value Survey data (IFsWVSCohort.MDB) requires following a slightly different procedure.

Data import for IFs is a three step process:
<ol style="list-style-type:lower-roman;">
<li>Extraction, preparation and blending of source data (and meta-data) into MS Access tables formatted for IFs database. This step might involve a blending process when data is updated.</li>
<li>Vetting of MS Access tables containing extracted data (and meta-data)</li>
<li>Merging of the MS Access table (and meta-data) to IFs master database (IFsHistSeries.Mdb   and Datadict.mdb)</li>
</ol>

In this section we shall discuss how to extract data from external sources, prepare extracted data for IFs database and blend extracted data with existing data. When updating an existing data table we usually preserve data not reported in the more recent source to maintain consistency between old and new data, a process we call blending.

There are three ways to import data from external sources and prepare it for IFs as a new series or as an update of an existing series:
<ol style="list-style-type:lower-roman;">
<li>Manual import, which basically involves reorganizing external data into an IFs template spreadsheet and copying that data to an MS Access table   </li>
<li>Automated import that uses IFs software application to extract data from Excel spreadsheets and prepare Access tables using a Country Concordance table between external data source and IFs.</li>
<li>Batch update, another IFs application feature that automates updating all IFs tables from the same external source as long as external data is available in a required format.</li>
</ol>

= Manual Import Using IFs Template Spreadsheet =

The steps for the manual import are:
<ol style="list-style-type:lower-roman;">
<li>Enter or reorganize external data into IFs template spreadsheet. The IFs team uses an Excel spreadsheet template containing 186 IFs countries, country FIPS code and year columns going from 1960 to 2014. The name of the Excel file is "Template Data Form 6.XLS." This file can be found in the IFs\Data folder. (Template spreadsheets can also be generated from IFs application's import screen)</li>
<li>Delete year column in the template spreadsheet which do not have any data.</li>
<li>Create an MS Access table with the same structure (i.e., containing the same year columns) as the template spreadsheet. This new Access table needs to be created inside "IFsDataImport.Mdb" file in the IFs\Data folder. If you are updating an existing table, you should give the table the same name as the existing table. For a new table, prefix the table name with "Series" and follow the naming convention described in section 2.2.</li>
</ol>

Tip: Rather than creating an altogether new Access table, take an existing data table, i.e., any of the IFsHistSeries.mdb tables, copy it to a new name and change the structure by going to "Design View" in MS Access. In fact, it is best to start from an existing table so that the data field specifications are strictly followed. To add a year to the new table, insert an empty column (in the design view), copy an existing year column, paste it on the empty column and change the column (or field) name to the label of the year you need. (If you are using an existing Access table to create a new table, please delete the Earliest and MostRecent columns from the Access table as this will be populated automatically by IFs application.)
<ol style="list-style-type:lower-roman;">
<li>Copy and paste the data from Excel template spreadsheet to Access table. Make sure you sort both the spreadsheet and the Access table alphabetically by country name before you copy and paste.</li>
<li>Add a row to the Datadict table inside IFsDataImport.Mdb file. If you are importing new data, create an empty row and add meta-data (please be as complete as possible). If you are updating data, copy the row from master copy of Datadict (DataDict.Mdb) and edit as needed (you must edit last IFs update)</li>
</ol>

Tip: You can skip steps iii, iv and v and use IFs' automated import (see next section for description) feature instead

'''Passing Extracted Data for Vetting'''

Extracted data, saved in Data\IFsDataImport.Mdb, need to be sent to the person who will do the vetting. Please also pass all source information (Excel spreadsheet data or pdf file/s or the website from which data is taken). Some initial scrutiny of the data before it is sent for vetting is recommended.

= Automated Import Using ImportXLS: Single Import =

External data available in Excel format known to IFs can be extracted into Access tables using IFs application's automated ImportXLS (the process works only for Excel files and hence the name) feature that work by importing one time series at a time. If you have subnational models broken out in IFs on your computer, you need to switch back to the full 186 country model. Instructions on how to do this can be found in the [[SubRegionalization_Handbook|SubRegionalization Handbook]]. This process will take around five minutes, so can be done as frequently as needed. 

This feature can be accessed through IFs menu system:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS File.

[[File:ImportDataFromXLSFile.png|RTENOTITLE]]

This opens up a form shown below:

[[File:ImportDataWizard.png|RTENOTITLE]]

The form is self-explanatory for the most part. You have to start by opening the source Excel file (option on the top bar) and selecting the spreadsheet which contains source. You then need to choose one from the six options showing the format of the data organization in the source spreadsheet (see the right side of the import screen). The default of these six options (the one at the bottom) works in most cases, however you can compare each option to your data by selecting a data organization option and viewing the layout in the image directly below the final option.

The list of steps to follow when importing data can be found in the "Help" menu option, which opens up a dialog box with the basic list. This list is fleshed out here in more detail and should be followed carefully.

'''Steps to Import Data'''

'''1. Open the Excel file containing formatted source data (option found on top bar)'''

'''2. Select one spreadsheet when the source file contains more than one'''

'''3. Select source data format using illustrated options'''

     - Each option can be previewed by selecting it and seeing the visualization directly below

'''4. Identify the Excel cells containing country and year labels.'''

     - If you do not remember when it starts, you can click ''View Excel File ''to check - the file must be closed after viewing

     - The ''Series Name Start at'' field will only appear if you have selected a ''Multiple Series'' option in the ''Choose Excel Source Data Format'' field. In this case, you choose the column of the data series        you want to import. 

'''5. Data Conversion Operator and Factor'''

'''????'''

'''6. Choose aggregation rule'''

An aggregation rule tells the model how to aggregate country level data up to different country groupings that can be selected in the model. For instance, if you choose SubSaharan Africa as a region, the model needs to know whether to add, average, use a weighted average for all countries in the grouping. 

There are five different aggregation rules to choose from: POP, GDP, AVG, SUM, LND. If the dataset is being updated, the aggregation rule should populate. If you are importing a new series, you have to choose the aggregation rule. You can look in the Data Dict at similar series to get an idea of what should be chosen or below are brief explanations of each, along with a few examples of datasets that use each rule.

     1. POP (population): uses a weighted average in which population of a country is the weight; used in demographics, health, and education series; used in series with per capita designations or when the variable is based on a percent of households (HHs)

     2. GDP: uses a weighted average in which GDP at MER of a country is the weight; used in economic series and when price is a consideration in the dataset

     3. AVG (average): takes the average of each country's data; used in indices 

     4. SUM: sums each country's data; used in many energy and labor series and when the data is a count

     5. LND (land): uses a weighted average using land cover as weight

'''7. Choose Country Concordance table '''

The country concordance tables have been created for sources we import on a regular basis, since each source uses a different country list often with different country names. Country names must be concorded with the country names in IFs when importing data. The country concordance tables automate this process with predetermined lists.

'''8. Identify missing values, if any. Data source and names can be put in later. '''

'''9. Enter a name and a definition for the imported table. If the variable is new, the name must be different from any existing table name. '''

If you are importing a new variable into the model, ask the person who assigned you this task for a specific name. If not provided at first, this can be added in the Data Dict after the import is complete. 

'''10. Click Import when you are ready. '''

'''11. Click on View Excluded after importing the data'''

You'll see the countries that were not imported. This may have happened because the IFs model does not have those countries in the model or because the name was wrong. 

[[File:ImportDataInstructions.png|RTENOTITLE]]

As mentioned above, Country Concordance lists are used in converting external data to IFs format. "Country Concordance" options are shown in a dropdown list at the bottom of the screen. The prepackaged lists that you see in the dropdown were built over the years from the major international databases like World Bank's WDI, UNESCO, IEA etc. If you do not see a list that you can use you need to either use a manual import described above in section 3.1.1 or leave ImportXLS for now, build and update a country list and then come back to import. Unless a data source has a large number of tables that we will be regularly updating we do not spend the time in building and updating a new country concordance table. As a general rule, decimals should be truncated after the 4th decimal place. 

If you would like to update an existing table rather than import a new one, you can select the name of the table from a dropdown box just above the variable name box. Selecting an existing table will automatically bring in dictionary information from the master copy of the data dictionary.

Once the external data is successfully imported you will be able to see the data in a grid. Once you accept the data, you will be allowed to input dictionary information (meta data) for the table you imported. A form will open up for this purpose (see figure below)

[[File:DataDictWizard.png|RTENOTITLE]]

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

Automated Import Using ImportXLS:  186 Countries

IFs data tables cover 183 countries now. But we plan to extend IFs to 186 countries by including Seychelles, Kosovo and South Sudan. Just below the “Import Data from XLS File" there is another menu option titled "Import Data from XLS File 186". The form that opens will follow the same procedure as 183 country import, but will create two tables one with 186 countries and one with 183, both in the same location.

= Batch Update Using ImportXLSBatch =

Another form has been created that allows import from an access table. This needs to be described, and shown.

Batch update, another IFs application feature, allows updating all IFs tables pertaining to a certain external source at once. This works only for those sources where data is available in a recognized format and each source series is identified with a fixed code that IFs data dictionary can save and match during subsequent updates. At this moment, batch update works for AQU, WDI, UNESCO (UIS), IMF and FAO.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Update Data in Batch.

The form that opens up is shown below. The grid on the right of the form will be empty initially, but fills up as soon as the user selects a source name from the dropdown box sitting above the grid. You can select all series for batch update (a checkbox is available for this) or decide to update the ones that you select by clicking on the "Update" column in the grid. There is an additional option of selecting only those variables per data source which are used in the Pre-Processor, which can be selected by checking the ‘Pre-Proc’ box on the batch import form seen below. Of course, the import process will update only those series that are found in the source Excel that you'll have to open using the menu at the top.

Batch update feature works by extracting multiple data series from a single spreadsheet at a time. From the box listing all spreadsheets in the workbook that you just opened, you will have to select the one that contains all the series dat.

Before using the batch import process, new variables must be added to the datadict and the source column of the datadict must be formatted for batch import by including one of the qualified series listed above (FAO, IMF, AQU, etc). Additionally, each data series in the external spreadsheet is matched with an IFs data table through a "series code" that is previously collected in the IFs "datadict" table. For WDI, these codes appear as combination of two to four letter code sections separated by periods (e.g., AG.PRD.FOOD.XD). For UNESCO education data, we take series names used by UIS, exactly as they type it, as series codes. For FAO data, Element code and Item code used by FAO are used by IFS (accordingly, there will be two series code  boxes in the batch update form when you are updating FAO data; identify Element code as the first one and Item code as the second one).

The decimal places box is used for specifying the decimal places that will be kept in extracted tables. As a general rule, decimals should be truncated after the 4th decimal place. The setting works for those tables which do not have such setting established already (as shown in the grid above).

[[File:BatchImportWizard.png|RTENOTITLE]]

The steps are explained in a Help dialog shown below:

[[File:BatchImportInstructions.png|RTENOTITLE]]

Clicking the "Import Batch" button will import (and update) the series that are selected in the grid one at a time and show a progress bar as this is being done.

Imported tables can be subjected to automated blending and vetting using the new vetting/comparison interface described later in this document.

== Source Data for Batch Update ==

World Development Indicator (WDI) data is released annually and is usually available as a single Excel file at their website:

[http://data.worldbank.org/data-catalog/world-development-indicators/ http://data.worldbank.org/data-catalog/world-development-indicators/]

FAO and UIS data do not come in single file as WDI. What we need to do is download multiple series on related issues (e.g., all cereal production data or all primary education data) as single Excel files. (Kate McGrath has prepared instructions on getting FAO and UIS data from their websites).

IMF data is available only by accessing our IMF e-Library account, registered to Mohammod Irfan.

== Passing Extracted Batch Data for Vetting ==

Extracted data will be saved in Data\IFsDataImportBatch.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.
<div><div id="_com_1"></div></div>
= Automated Import Using ImportXLS: Province Import =

Importing data for a provincial model can be done with the Province Data Import Feature. IFs does not need to be sub-regionalized to use this feature.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS Provinces

The form that opens up is shown below. The format reflects the single series import process already described. A recognized excel format must be selected from the list on the right. The location of Province names, Years, and Series from your chosen excel data sheet must be designated. The provincial country concordance table must also be selected from the drop down list. The form contains the option to view the excel sheet you are importing, and to view the country concordance tables. Lastly, the variable you are importing needs to be selected from the drop down list. Existing variables will populate the definition field. New variables will need to be named using the Ifs naming convention reviewed above, and defined.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

Importing data (general instructions)

2017-09-21T21:24:45Z

MeredithMoon:

= Introduction =

This section describes the import and update processes for data tables in IFsHistSeries.Mdb. Updating World Value Survey data (IFsWVSCohort.MDB) requires following a slightly different procedure.

Data import for IFs is a three step process:
<ol style="list-style-type:lower-roman;">
<li>Extraction, preparation and blending of source data (and meta-data) into MS Access tables formatted for IFs database. This step might involve a blending process when data is updated.</li>
<li>Vetting of MS Access tables containing extracted data (and meta-data)</li>
<li>Merging of the MS Access table (and meta-data) to IFs master database (IFsHistSeries.Mdb   and Datadict.mdb)</li>
</ol>

In this section we shall discuss how to extract data from external sources, prepare extracted data for IFs database and blend extracted data with existing data. When updating an existing data table we usually preserve data not reported in the more recent source to maintain consistency between old and new data, a process we call blending.

There are three ways to import data from external sources and prepare it for IFs as a new series or as an update of an existing series:
<ol style="list-style-type:lower-roman;">
<li>Manual import, which basically involves reorganizing external data into an IFs template spreadsheet and copying that data to an MS Access table   </li>
<li>Automated import that uses IFs software application to extract data from Excel spreadsheets and prepare Access tables using a Country Concordance table between external data source and IFs.</li>
<li>Batch update, another IFs application feature that automates updating all IFs tables from the same external source as long as external data is available in a required format.</li>
</ol>

= Manual Import Using IFs Template Spreadsheet =

The steps for the manual import are:
<ol style="list-style-type:lower-roman;">
<li>Enter or reorganize external data into IFs template spreadsheet. The IFs team uses an Excel spreadsheet template containing 186 IFs countries, country FIPS code and year columns going from 1960 to 2014. The name of the Excel file is "Template Data Form 6.XLS." This file can be found in the IFs\Data folder. (Template spreadsheets can also be generated from IFs application's import screen)</li>
<li>Delete year column in the template spreadsheet which do not have any data.</li>
<li>Create an MS Access table with the same structure (i.e., containing the same year columns) as the template spreadsheet. This new Access table needs to be created inside "IFsDataImport.Mdb" file in the IFs\Data folder. If you are updating an existing table, you should give the table the same name as the existing table. For a new table, prefix the table name with "Series" and follow the naming convention described in section 2.2.</li>
</ol>

Tip: Rather than creating an altogether new Access table, take an existing data table, i.e., any of the IFsHistSeries.mdb tables, copy it to a new name and change the structure by going to "Design View" in MS Access. In fact, it is best to start from an existing table so that the data field specifications are strictly followed. To add a year to the new table, insert an empty column (in the design view), copy an existing year column, paste it on the empty column and change the column (or field) name to the label of the year you need. (If you are using an existing Access table to create a new table, please delete the Earliest and MostRecent columns from the Access table as this will be populated automatically by IFs application.)
<ol style="list-style-type:lower-roman;">
<li>Copy and paste the data from Excel template spreadsheet to Access table. Make sure you sort both the spreadsheet and the Access table alphabetically by country name before you copy and paste.</li>
<li>Add a row to the Datadict table inside IFsDataImport.Mdb file. If you are importing new data, create an empty row and add meta-data (please be as complete as possible). If you are updating data, copy the row from master copy of Datadict (DataDict.Mdb) and edit as needed (you must edit last IFs update)</li>
</ol>

Tip: You can skip steps iii, iv and v and use IFs' automated import (see next section for description) feature instead

'''Passing Extracted Data for Vetting'''

Extracted data, saved in Data\IFsDataImport.Mdb, need to be sent to the person who will do the vetting. Please also pass all source information (Excel spreadsheet data or pdf file/s or the website from which data is taken). Some initial scrutiny of the data before it is sent for vetting is recommended.

= Automated Import Using ImportXLS: Single Import =

External data available in Excel format known to IFs can be extracted into Access tables using IFs application's automated ImportXLS (the process works only for Excel files and hence the name) feature that work by importing one time series at a time. If you have subnational models broken out in IFs on your computer, you need to switch back to the full 186 country model. Instructions on how to do this can be found in the [[SubRegionalization_Handbook|SubRegionalization Handbook]]. 

This feature can be accessed through IFs menu system:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS File.

[[File:ImportDataFromXLSFile.png|RTENOTITLE]]

This opens up a form shown below:

[[File:ImportDataWizard.png|RTENOTITLE]]

The form is self-explanatory for the most part. You have to start by opening the source Excel file and selecting the spreadsheet which contains source. You then need to choose one from the six options showing the format of the data organization in the source spreadsheet (see the right side of the import screen). The default of these six options (the one at the bottom) works in most cases. The "Help" menu option in the form opens up a dialog box with a step by step guide as shown in the figure below:

[[File:ImportDataInstructions.png|RTENOTITLE]]

As mentioned above, Country Concordance lists are used in converting external data to IFs format. "Country Concordance" options are shown in a dropdown list at the bottom of the screen. The prepackaged lists that you see in the dropdown were built over the years from the major international databases like World Bank's WDI, UNESCO, IEA etc. If you do not see a list that you can use you need to either use a manual import described above in section 3.1.1 or leave ImportXLS for now, build and update a country list and then come back to import. Unless a data source has a large number of tables that we will be regularly updating we do not spend the time in building and updating a new country concordance table. As a general rule, decimals should be truncated after the 4th decimal place. 

If you would like to update an existing table rather than import a new one, you can select the name of the table from a dropdown box just above the variable name box. Selecting an existing table will automatically bring in dictionary information from the master copy of the data dictionary.

Once the external data is successfully imported you will be able to see the data in a grid. Once you accept the data, you will be allowed to input dictionary information (meta data) for the table you imported. A form will open up for this purpose (see figure below)

[[File:DataDictWizard.png|RTENOTITLE]]

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

Automated Import Using ImportXLS:  186 Countries

IFs data tables cover 183 countries now. But we plan to extend IFs to 186 countries by including Seychelles, Kosovo and South Sudan. Just below the “Import Data from XLS File" there is another menu option titled "Import Data from XLS File 186". The form that opens will follow the same procedure as 183 country import, but will create two tables one with 186 countries and one with 183, both in the same location.

= Batch Update Using ImportXLSBatch =

Another form has been created that allows import from an access table. This needs to be described, and shown.

Batch update, another IFs application feature, allows updating all IFs tables pertaining to a certain external source at once. This works only for those sources where data is available in a recognized format and each source series is identified with a fixed code that IFs data dictionary can save and match during subsequent updates. At this moment, batch update works for AQU, WDI, UNESCO (UIS), IMF and FAO.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Update Data in Batch.

The form that opens up is shown below. The grid on the right of the form will be empty initially, but fills up as soon as the user selects a source name from the dropdown box sitting above the grid. You can select all series for batch update (a checkbox is available for this) or decide to update the ones that you select by clicking on the "Update" column in the grid. There is an additional option of selecting only those variables per data source which are used in the Pre-Processor, which can be selected by checking the ‘Pre-Proc’ box on the batch import form seen below. Of course, the import process will update only those series that are found in the source Excel that you'll have to open using the menu at the top.

Batch update feature works by extracting multiple data series from a single spreadsheet at a time. From the box listing all spreadsheets in the workbook that you just opened, you will have to select the one that contains all the series dat.

Before using the batch import process, new variables must be added to the datadict and the source column of the datadict must be formatted for batch import by including one of the qualified series listed above (FAO, IMF, AQU, etc). Additionally, each data series in the external spreadsheet is matched with an IFs data table through a "series code" that is previously collected in the IFs "datadict" table. For WDI, these codes appear as combination of two to four letter code sections separated by periods (e.g., AG.PRD.FOOD.XD). For UNESCO education data, we take series names used by UIS, exactly as they type it, as series codes. For FAO data, Element code and Item code used by FAO are used by IFS (accordingly, there will be two series code  boxes in the batch update form when you are updating FAO data; identify Element code as the first one and Item code as the second one).

The decimal places box is used for specifying the decimal places that will be kept in extracted tables. As a general rule, decimals should be truncated after the 4th decimal place. The setting works for those tables which do not have such setting established already (as shown in the grid above).

[[File:BatchImportWizard.png|RTENOTITLE]]

The steps are explained in a Help dialog shown below:

[[File:BatchImportInstructions.png|RTENOTITLE]]

Clicking the "Import Batch" button will import (and update) the series that are selected in the grid one at a time and show a progress bar as this is being done.

Imported tables can be subjected to automated blending and vetting using the new vetting/comparison interface described later in this document.

== Source Data for Batch Update ==

World Development Indicator (WDI) data is released annually and is usually available as a single Excel file at their website:

[http://data.worldbank.org/data-catalog/world-development-indicators/ http://data.worldbank.org/data-catalog/world-development-indicators/]

FAO and UIS data do not come in single file as WDI. What we need to do is download multiple series on related issues (e.g., all cereal production data or all primary education data) as single Excel files. (Kate McGrath has prepared instructions on getting FAO and UIS data from their websites).

IMF data is available only by accessing our IMF e-Library account, registered to Mohammod Irfan.

== Passing Extracted Batch Data for Vetting ==

Extracted data will be saved in Data\IFsDataImportBatch.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.
<div><div id="_com_1"></div></div>
= Automated Import Using ImportXLS: Province Import =

Importing data for a provincial model can be done with the Province Data Import Feature. IFs does not need to be sub-regionalized to use this feature.

The batch update feature can be reached through IFs main menu:

IFs -> MainMenu->Extended Features -> Manage Country Data->Historic Data File Processing-> Import Data from XLS Provinces

The form that opens up is shown below. The format reflects the single series import process already described. A recognized excel format must be selected from the list on the right. The location of Province names, Years, and Series from your chosen excel data sheet must be designated. The provincial country concordance table must also be selected from the drop down list. The form contains the option to view the excel sheet you are importing, and to view the country concordance tables. Lastly, the variable you are importing needs to be selected from the drop down list. Existing variables will populate the definition field. New variables will need to be named using the Ifs naming convention reviewed above, and defined.

== Passing Extracted Data for Vetting ==

Extracted data will be saved in Data\IFsDataImport.Mdb. Dictionary info is saved in a table named "datadict" inside this same .mdb. You need to send this .mdb and all external source Excel to the person who will do the vetting. Some initial scrutiny of the data before it is sent for vetting is recommended. The new automated vetting feature of IFs (described in the vetting section below) can be useful.

SubRegionalization Handbook

2017-09-07T21:32:57Z

MeredithMoon:

= Foundational Elements =

== A Note on Terminology ==

Sub-regional units go by many names internationally. When we refer to a sub-region in this document, we may use a variety of names to refer to the same entity. In IFs, there is a bias toward the use of “province” to refer to a sub-regional unit, but it is functionally equivalent to states, departments, territories, or any other name for a sub-regional unit.

Much of the following discussion will involve variables included in the model, such as GDP or POPULATION. These may be referred to as variable or series. The two terms are used interchangeably here. 

== Database Modification ==

=== IFsHistSeriesXXX.mdb ===

Before a country in IFs can be divided into sub-regions, a new data file called ''IFsHistSeriesXXX.mdb,'' for example ''IFsHistSeriesChina.mdb,'' must be created. That file is a variation of the file called ''IFsHistSeries.mdb'', which contains data tables for all countries across time, for all variables.

For each data table in ''IFsHistSeriesXXX.mdb'', rows correspond to all states/provinces/departments in the specified country. The file includes a table called ''DataDict  ''which is analogous to the file ''DataDict.mdb ''and provides information on names, sources, and specialized procedures followed to prepare the state/province/department data.  

To subregionalize countries that do not already have a ''IFsHistSeriesXXX.mdb'' file, users will need to create one. While this can be done manually, it is faster and simpler to just add the file with a feature in IFs. Before creating the table, however, make sure that a file called ''IFsHistSeriesxxx.mdb'' exists in ''C:\Users\Public\IFs\DATA. ''After that is done, follow these steps:

*Click the path: '''Extended Features''' > '''Change Country Subregionalization''' ''>'' '''Add New Historical Series File'''
*Select the country that is intended to be subregionalized and click ''OK''

This does a couple of things. First, it creates a new ''IFsHistSeries.mdb'' for the country you selected in ''C:\Users\Public\IFs\DATA''. The file will include tables for the 4 essential series and a datadict. It also adds the country to the ''Provinces'' tables in ''Provinces.mdb''. 

The entire process of sub-regionalization affects five files:''IFs.mdb, IFsHistSeries.mdb, IFsCoVatra.mdb, IFsCoVatraSeries.mdb,''and ''IFsWVSCohort.mdb''. For each of the files, the process looks in the ''IFs\Data'' folder for an equivalent file with the name of the country at the end — again, ''IFsHistSeriesChina.mdb'' is an example — then creates a copy in the''IFs\Runfiles'' directory. Modification is done in the copies on the ''\Runfiles ''directory as well to the original files in the ''\Data'' directory. Data for individual sub-regional units and the country as a whole are preserved, and can be copied back and forth between ''\Data'' and ''\Runfiles'' directories. This allows the user to switch between model runs with different sub-regionalizations, and reset everything to the starting point without any states/provinces/departments sub-regionalized.

=== DataDict.mdb ===

'''Path: C:/''' >''' Users''' > '''Public''' > '''IFs''' >''' DATA''' > '''DataDict.mdb'''

''DataDict.mdb'' documents the variable names, sources, units of measurement, years of data coverage, and aggregation rules for all historical data series in the model.

The''IFsHistSeriesXXX.mdb'' file has a copy of the ''DataDict'' table, and all series in ''IFsHistSeriesXXX.mdb'' must have a corresponding ''DataDict'' row in both places; otherwise, the model will not recognize them.  If the fields ''Years'' and''Aggregation'' are not correctly filled in the series may not read correctly.

=== Provinces.mdb ===

'''Path: C:/''' > '''Users''' > '''Public''' > '''IFs''' > '''DATA''' > '''Provinces.mdb'''

In addition to the creation of ''IFsHistSeriesXXX.mdb'', initiating the sub-regionalization process requires modifications to tables that exist in the file ''Provinces.mdb''. The alterations to specific tables in ''Provinces.mdb'' are outlined below:

'''All Provinces'''. The name and FIPS code of the target country should be added to a line in this table (those names should be identical to those used for the country in ''IFs.mdb'' and ''IFsHistSeries.mdb'' files; and province break-out should only be done when the Full version of IFs is being used with each country represented as a separate region).  The ''AllProvinces'' field should be checked. The only circumstances under which it is not checked is when the user is selecting one, or a few, provinces for use in the model in addition to the entire country — see the discussion of the ''Regionalization'' table below.

'''Provinces'''. In this table all of the provinces or states should be identified. The first column is the target country (the same for all provinces). The second is a unique FIPS Code created by the user (since country-specific codes are three letters and cannot be duplicated, two-letter codes normally work well for these). Finally, the name of the sub-regional unit should be specified.

The addition of province or state names to this table can be done manually, but it is also automated with the "Add New Historical Series File” option, which creates ''IFsHistSeriesXXX.mdb'' with only the basic tables needed. Users can access this automated option from the main menu of IFs by following the path: 

'''Extended Features''' > '''Change Country Sub-Regionalization''' > '''Add New Historical Series File'''

The province/state names need to coincide exactly with the ones that are used in the tables of the ''IFsHistSeriesXXX.mdb'' file for variables holding data. The province/state names also need to coincide with the province/state names that are used on the global map file to display the breakdown of countries into provinces/states. The exact names used in the map file can be found in ''IFs_Province.mdb'' using a query of the form: N.B: select Province from provinces where Country='Republic of India.'

'''Regionalization'''. This table repeats the target ''Country'' and ''Full Province Name'' as in the ''Provinces'' table. The new feature is the addition of a ''Region'' name. Each province could be a separate ''Region'', in which case the names should be less than 10-12 letters so as to allow easy display in IFs tables and graphs. Most often the provinces will be grouped into sub-regions of the target Country, in which case it is recommended that the ''Region'' name be the target country name or some abbreviated part of it followed by the sub-region, again a total of no more than 10-12 letters. Leading with the Country name facilitates the use of alphabetic listings of IFs.

If the user is only adding a single province (or a very small number) to the model and not splitting a country into regions based on all states/provinces, the lines added to the ''Regionalization'' table will be only for the added states/provinces (e.g. Hawaii could be added in addition to the US by adding a line to this table for it only, not other US states, and then not checking AllProvinces in the AllProvinces table). Even if all provinces/states are in the data set, as with India, this process allows a single state of India to be pulled out and added to IFs. 

'''Warning''': if a user only adds sub-regions rather than dividing a country into sub-regions based on all of them, there will be double counting of the country and the added unit(s) in the model. Only selected variables in IFs, such as ''WPOP'' and variables of the agricultural model have been set up for correction of such double counting in the run of the model.

The process of creation of regions can be done within IFs, under: 

'''Extended Features''' > '''Change Country Sub-Regionalization''' > '''Change Sub-Regionalization'''

=== IFsxxx.mdb ===

This file holds many tables, but only a few are of interest for sub-regionalization:

''PopAgeCohortCountryFemale ''and ''PopAgeCohortCountryMale. ''These files store data on the age-sex cohort breakdown of a population. If possible, use 2012 data because we populate the country-level tables with 2012 UNDP data. The first column, ''Cohort0'', is reserved for infants (age <1) but is no longer filled with data. ''Cohort1'' is for ages 1-4. ''Cohort2 ''and onward are 5-year age groups (e.g. Cohort2 is ages 5-9, etc.). Data accuracy is important because if there are large jumps between cohorts, errors can occur when running the model through 2100. If these data are not available, these tables read from the country-level data. 

''HealthDetailedDeathsCtry''. These files store data on mortality rates by sex of a population. Raw mortality data must be ascribed to the 15 mortality sub-types in IFs to match this file structure. These categories are as follows:

*Other communicable diseases
*Malignant neoplasms
*Cardiac
*Digestive
*Respiratory
*Other noncommunicable diseases
*Road injuries
*Other unintentional injuries
*Intentional injuries
*Diabetes
*HIV
*Diarrhea
*Malaria
*Respiratory infections
*Mental health

If these data are not available, this file will read from the country-level data that already exist in IFs.

''PopMortalityCohortCountryFemale ''and ''PopMortalityCohortCountryMale.''This table stores the survivor tables of a population by sex. These data series are calculated based on the mortality data from the table ''HealthDetailedDeathsCtry''. This is calculated by the likelihood that a person at any given age will survive to the next cohort, given the prevailing trends in mortality. If these data are not available, this file will read from the country-level data that already exist in IFs.

Users may need to modify table ''EconBaseSector''. Unlike other base files in''IFs.mdb'', rows are not automatically added to this table when additional countries (in this case sub-regions) are added. If too many sub-regions are added, this can cause problems when rebuilding the base. ''EconBaseSector'' has six rows for each country, so for example, if the user wants to break India into 36 sub-regions, a total of 1326 rows will be needed (221 countries*6). As of July 2014, the default number of rows for this table is 1304. Additional rows must be added to the table before adding sub-regions to ensure they are copied to ''\RUNFILES''.
<div><div><div id="_com_3"></div></div></div><div></div>
===  IFsXXX.DAT ===

'''C:''' >''' User''' > '''Public''' > '''IFs''' > '''Data''' > '''IFsXXX.DAT''' 
<div>Generating sub-national forecasts within IFs will inevitably require exogenous adjustments in some cases to produce reasonable output. Forecast tuning is only done on rare occasions, when there is not an apparent structural fix to the problem. However, there are some cases when it is the appropriate course of action.</div>
When it is determined that tuning is necessary, the user needs to build the suitable scenario and save the scenario file (.sce extension). Then, open up the .sce file (''C:\Users\Public\IFs\Scenario'') and copy and paste the scenario code into a ''IFsXXX.DAT'' file. If a .DAT file does not exist for your country, simply copy and paste one available for another country (South Africa, United States), change the country name, and delete the contents. Three very important notes:

*.DAT files most likely will not be able to be accessed without changing the extension. Simply add “.txt” at the end of the file name and click OK when Windows warns you about modifying the file extension. Once this is done, you will be able to open it with wordpad or notepad.
*When copying the code from the .sce to the .DAT file, be sure NOT to include, “CUSTOM,” “COMMENT,” and “START.” See the following example. The .DAT file should only include the highlighted script:

[[File:Sce.png|RTENOTITLE]]

*Once the ''IFsXXX.DAT'' file is done, users will have to delete the broken-out model and re-subregionalize. The process will incorporate the newly created .DAT file into the base case. 

= Provincial Data Processing =

The model executes 4 processes when provinces are added (see ''Adding Sub-Regions ''for procedural steps):

*Checks for essential series in ''IFsHistSeriesXXX.mdb''
*Normalizes and fills holes in essential series
*Normalizes base-year values in preprocessor variables present in ''IFsHistSeriesXXX.mdb'' when provinces are added
*Estimating values for preprocessor series NOT present in ''IFsHistSeriesXXX.mdb'' when provinces are added

 The following sections will cover these processes in detail. 

== Checking the Essential Series ==

As described in section 1, data for sub-regions in IFs are stored in ''IFsHistSeriesXXX.mdb''. In order for sub-regions to be added, data for the essential series is required. The 4 variables are described below.

{| border="1" cellspacing="1" cellpadding="1" style="width:500px;"
|-
| Variable
| Definition
|-
|
GDP2011

| Gross Domestic Product in 2011 Constant Dollars
|-
| GDP2011PCPPP
| Gross Domestic Product per Capita in 2011 Constant Purchasing Power Parity International Dollars
|-
| Population
| Population in Millions
|-
| LandArea
| Land Area
|}

When sub-regions are added, the model checks that each sub-region has at least one year of data for each of these required variables. If this is not true, then (1) the specific state/province/department cannot be used, (2) it is excluded from the process, and (3) the system will give a message indicating the problem. With these four series, the shell model is established. The user can add incrementally build upon this by adding more data. Also note, sub-regional data must be for years that are available in the country-level data. For example, if only 2011 population data is available for sub-regions, national-level population data must include 2011 as well. 

The model will not break-out if ''GDP2011'' does not have year columns back to 1960, whether or not there is sufficient data to fill all those year columns. The ''GDP2011'' table in the ''IFsHistSeriesXXX.mdb'' does not have to have to be structurally identical (have the exact same years) as the ''GDP2011'' table in ''IFsHistSeries.mdb.''

== Normalization and Hole-Filling in Essential Series ==

Due to the special importance of these series, the model normalizes and fills in data across all years for each sub-region. Normalization means it modifies sub-regional values with respect to country-level data. To do this, the model checks the base year (2010) for all sub-regions. If base-year data is not available, it uses the closest available year. The aggregation rules for ''GDP2011'', ''Population'', and''LandArea'' are all ''SUM'', so the model simply multiplies the country base-year value by each sub regional contribution:

<math>\frac{subregion\ value\ [base]}{\sum{subregion\ values\ [base]}}*country\ value\ [base]</math>

Since ''GDP2011PCPPP'' is defined as a rate, is filled out a little differently. Here, the sub-regional values are multiplied by their respective weights for ''Population'', then summed for a weighted average. Then a multiplier is computed by dividing the country-level ''GDP2011PCPPP'' value by this weighted average. The final normalized data are the product of the multiplier and the sub regional values.

<math>\frac{country\ GDP\ per\ capita\ [base]}{\sum{subregion\ GDP\ per\ capita\ [base]}*\ POP\ weight\ [base]}*\ subregion\ GDP\ per\ capita\ [base]</math>

Where ''POP weight'' is:

<math>\frac{subregion\ POP\ [base]}{\sum{subregion\ POP\ [base]}}</math>

These normalization procedures are important for a couple reasons. First and foremost, they allow the model to produce reasonable forecasts. They also allow the user to use any type of unit for the essential series. This may be quite helpful because sub regional data is often harder to find than national figures, especially PPP data.

== Normalization of Preprocessor Variables Present in IFsHistSeriesXXX.mdb When Provinces are Added ==

For preprocessor (non-essential) variables included in ''IFsHistSeriesXXX.mdb'' before adding provinces, base year values are normalized. Variables used in the preprocessor are marked in the field ''UsedInPreProcessor'' (formerly ‘UseforProvince’) in the ''DataDict'' table in the ''IFsHistSeriesXXX.mdb''. Non-preprocessor variables will not be normalized. In general, normalization is important because the preprocessor draws upon base year (2010) data in order to initialize values of the model run. Simply put, provincial values must be consistent with the values for the total country. The process is similar to what we saw for the essential series. The main difference is that the model only normalizes the base year value. Values for other years will not be changed and no hole-filling occurs. First, the model computes a multiplier for provincial data by dividing the country value in the base year by the weighted average, simple average, or sum (designated by aggregation field) of the provinces. If base year data are not available for the country or provinces, the model looks to the Most Recent column for provinces and country data. Note that this could mean the multiplier is computed with data from different years. Multipliers are calculated as:

<math>\frac{country\ [base]}{\sum{subregion\ [base]\ *\ weight\ [base]}}</math>

Where weight is based on the aggregation rule of the variable (generally ''GDP'' or ''POP''): 

<math>\frac{subregion\ [base]}{\sum{subregion\ [base]}}</math>

The computed multiplier is stored in the multiplier field for the series row in the datadict table of ''IFsHistSeriesXXX.mdb'' in ''\RUNFILES''. 

If provincial data differs significantly from country data, likely due to different units, users can check the ''ApplyMultAll'' field of the ''DataDict'' table in ''IFsHistSeriesXXX''. This signals the model to apply the multiplier to all previous years of provincial data. This should be done before sub-regions are added.

== Estimating Values for Preprocessor Series Not Present in IFsHistSeriesXXX.mdb When Provinces Are Added ==

For variables used in preprocessor not included in ''IFsHistSeriesXXX.mdb'' before adding sub regions, the model jumps to ''IFsHistSeries.mdb'' and estimates values across all years data is available at the country level. This can happen 2 ways:

*If the aggregation rule (as seen in the ''DataDict.mdb'', not the ''DataDict'' table in ''IFsHistSeriesXXX.mdb'') is ''SUM'' then we use the disaggregation rule, usually either ''GDP'' or ''POP'', to formulate weighted estimates for sub-regions. Thus, the sum of the estimates will always equal the county value in a given year.
*If the aggregation rule is not ''SUM'', the value at the national level is simply applied to all sub-regions for a given year. These should always be rate variables like total fertility rate and life expectancy. 

Experienced users of IFs, or other models, may be familiar with other commonly used estimation techniques, like cross-sectional regression. These techniques are applied when rebuilding the base, not during the process of adding sub-regions. In any case, suppose that fertility data were not available for a particular sub-region, but we had GDP per capita figures for the region. The model would fill in the expected values for a country, based on the sub-region’s GDP per capita. Cross-sectional relationships such as TFR-GDP derive from IFs historical data for all countries. A list of all computed functions used within the model can be found under extended features. All variables can be estimated through a combination of such cross-sectional relationships and national level data.

== Data Gathering and Prioritization ==

With the four-essential series imported and the shell model in operation, the user can begin exploring available data sources to fill other preprocessor variables. While estimate-driven forecasts (from the shell model) are a starting point, they offer limited value to policy-makers. Each time a new series is added to ''IFsHistSeriesXXX.mdb'', the quality of sub-regional forecasts improves becoming anchored less on estimates and more on empirics.

=== Data Sources ===

Research on potential data sources for sub-national data is the first step in the data gathering process. Preference should be given to official government sources and census bureau data. Human Development reports frequently provide important sub-national data series and can direct researchers to key data sources. Researchers should search for ministry or department data resources as well because they tend to have the module specific data that is needed. For example, the Department of Energy will likely have energy data available sub-regionally. Provinces may also have their own databases which may be worthwhile data sources. It is better to use a single data source for each module's data. For instance, pulling all education data from a single data source is preferred over using multiple data sources. This is not always possible and having some data is better than no data at all. 

Each potential data source should be thoroughly researched before any data is pulled into the model. This will decrease the necessity to pull the same series more than once after a better data source is found. All data sources should be catalogued by the series that are available from the source, the format that the data source is in, the temporal coverage available from the data source, the provincial coverage available, how frequently the data is published, and the overall quality of the data source (eg. is this a trustworthy data source, are there potential errors or problems found in the data, is the data in a difficult to pull format?).

=== Data Pulling Prioritization ===

Some series are more important than others to the IFs preprocessor algorithms. Understanding the IFs initialization procedure can help inform the prioritization of data gathering efforts. For each module there are several series that contribute especially heavily to the robustness of the model. Overall, priority should be given to series that impact demography, the economy, and human development. This includes but is not limited to; Age-Sex cohorts, Infant Mortality, Life Expectancy (total, male, and female), Value Added, Land Use, Crude Birth Rates, Crude Death Rates, Urban Population, Imports and Exports, Government Consumption, Government Revenues, Mortality by Cause, Education Years, Education Enrollment Rates, Agricultural Production, and Energy Production/Consumption. 

= Procedural Elements =

The following sections present a guided walkthrough of some of the procedural elements of sub-regionalization in IFs. As such, “the user” will be referred to as “you” for the remainder of the document.

== Importing Data and Updating Data in IFs ==

Updating provincial data is a multi-step process. This walk-through will begin with the assumption that you have found the data and you are ready to introduce it into the model. Do not open your IFs model until you’ve made your data changes. With the model open, your changes will not properly register.

=== Importing Function for Provincial Data in IFs ===

IFs version 7.05 introduced a function that allows the user to import provincial data into the model. The process is nearly identical to importing data in the standard 186 country format. You do NOT need to have the model broken out into sub-regions to use this feature. Below are the steps for using this feature.

*Open IFs and go to: '''Extended Features''' > '''Manage Country Data''' > '''Historical Data Processing''' > '''Import data from XLS file provinces'''

'''[[File:ImportProvincialData.png|RTENOTITLE]]'''

*This brings you to the following window:

[[File:ImportProvincialDataBox.png|RTENOTITLE]]

*Select ''Open Excel File'' and double click the file that has the provincial data you want to import
**Note, while the menu path from above says ''XLS file'', this feature can use both XLS and XLSX files. To use XLSX files, you need to use the drop down box located in the bottom right of the ''Open Excel File'' window:

[[File:ImportTimeSeriesxlsxWindow.png|RTENOTITLE]]

*With your file selected, you will be prompted to select the correct Excel Sheet. To see your selected Excel file, click on View Excel. The file will be in read-only mode, so you cannot make changes to the file from here. Make sure to close the Excel file before importing the data, having it open can interfere with the importing process.
*On the right side under ''Choose Excel Source Data Format ''the user must select the appropriate data formatting. The bottom option is initially selected by default since most data sets follow this format – State/Province names in rows in rows (vertical) and years in columns. You will notice that the graph below the formatting options changes to visually represent the specific data orientation that you choose 
*Next, the user needs to provide the location of state/province names and years in the Excel file. The contents of the selected cell will appear in the blank box to the right of the dropdown menus. Choose the column and row of the state/province name that appears first in your data (e.g. Andaman and Nicobar Islands) and the column/row of the earliest listed year (e.g. 1960)
*Use the dropdown menus to select the appropriate country (e.g. India) and concordance table (concordance tables are covered in next section)
*Under ''Variable (or table) Name'', select the correct variable name for the data (e.g. ''GDP2011PCPPP''). For a new variable, which will be a series that is not a preprocessor series and does not exist in the list of tables, select ''new''. If the variable is new and unit conversion is required (e.g. thousands to millions), user will need to specify an operator and conversion factor. These will automatically be filled in if an existing variable is selected. 
*After completing previous steps and closing the Excel file if it is open, click Import. This will take you to the following window:

[[File:IndiaGDP2005ImportWindow.png|RTENOTITLE]]

*Click ''View Excluded'' to see any states/provinces in the source Excel file that were not imported. For example, if one of the listed states/provinces is India, this will be included in the list. It is a common error that state/province names are spelled incorrectly on the source file; this button will alert the user that they need to correct the Excel file and reimport the data.
*Clicking Accept will bring you to the data dictionary (datadict) window:

[[File:DataDictWindow.png|RTENOTITLE]]

*Again, if an existing variable (not ‘new’) was selected, a lot of this will be filled in. Users will be prompted to fill in required fields after pressing OK if they are blank.
*Imported data can be found here:
**''' C:''' > '''User''' > '''Public''' > '''IFs''' > '''Data''' > '''IFsDataImportXXX.mdb'''

=== Concordance Tables ===

Concordance tables are lists that translate the country (or state/province) names found in data source materials to IFs names. For example, if data was being imported from an Excel document from the [[Indian_Planning_Commission|Indian Planning Commission]], the model will import data under the sub-region ''A & N islands'' and categorize it as ''Andaman and Nicobar Islands'', the sub-region name in IFs. This saves the user from having to manually translating sub-region names on the source document every time they import new data.

Sub-regional translation tables are located in''IFs\Data\Provinces.mdb''. To add a list for a new source:

*Open the translation table for the appropriate country
*Click ''View'', located in upper left corner of window, to modify the table design
*In the first blank cell under ''Field Name'', type the name of your new source and select ''Short Text'' as the data type:

[[File:SubregionalConcordanceTable.png|RTENOTITLE]]

*Click ''View'' again to return to translation lists. Notice the new source now has a column.
*Add the exact sub-region names used in the new source to cells that correlate to the IFs countries. If the new source does not have data for a particular sub-region, leave the cell blank.
*When all sub-regions are entered, save and close Access. Restart IFs and your new concordance list should now be an option in the province data import window. 

=== Manual Import ===

Users can also manually add provincial data, instead of using the feature within the model. Users can manually import data with any version of IFs, 7.05 is not needed. The data structure of International Futures uses Microsoft Access. For the South African provincial data file, go to:

'''C:''' > '''User''' >'''Public''' > '''IFs''' > '''Data''' > '''IFsHistSeries South Africa.mdb'''

This file is the home to all of the historical provincial data that has been gathered for the provinces (in this case, of South Africa).  For the purposes of this tutorial, we will use the example of population.

From the South African 2011 Census, we can now add data for the population total in 2011.

Depending on the data source that you may have, you could want to either change existing data or add a new year of data.  Complete the data import process in Microsoft Access before opening IFs.

=== Adding or Deleting SubRegions ===

Go to '''Extended Features '''>''''' '''''Change Country Sub-Regionalization > '''Add or Delete SubRegions'''. This will open the menu below:

[[File:AddDeleteSubRegion.png|RTENOTITLE]]

With this menu open, select the country name that is desired to be sub-regionalized within the model in the upper-right box within the window. Below this box under ''Please select region name and click Add or Delete to Process ''select ''XXXALL'' and click on ''Add''. You will be prompted with the following:

[[File:Addingsubregions.png|RTENOTITLE]]

Choose ''Yes ''this process begins rebuilding the model with the sub-regional breakout. Depending on the processing power of your computer, the first portion of this Add Sub-regions process will take between 3 and 8 hours.

When this is complete, you will be prompted with a window stating ''Process Complete'', select ''Okay''.

Then the Rebuild Base form should open. Rebuilding the Base Case will place all initial conditions and parameters into the Base Case, but all computed values will be set at 0 until the model is re-run. All boxes on the Rebuild Base form should be checked and the last year should be 2100, then select ''Rebuild Base''.

[[File:Rebuildbase.png|RTENOTITLE]]

After the base is rebuilt, you should be prompted to run the model with the window below. Run the model to 2100 and select ''Start Run''.

[[File:Runmodel.png|RTENOTITLE]]

Once the model finishes running to 2100, you will get the message below.

[[File:Rewritebasecase.png|RTENOTITLE]]

Select ''OK'' to this message and then click ''Run Successful – Click to Continue''.

=== Switching Models ===

It is also possible to switch between different model runs (i.e. between the national level and a sub-regional model). For instance, you have just completed a sub-regionalization that divides China into single provinces. Now, you would like to use data for the whole country as a Base Case again. To remove all sub-regions, open the window for ''Add or Delete Subregions''. Click on ''Switch Provinces'' and choose ''No State/Province'' from the right side box of Available Runs. Click ''Switch'' to restore the model to pre-sub-regionalization status.

At this point you will be asked if you want to save your current configuration. It is important to select ''Yes'' in order switch back to the preferred sub-regionalization the next time you want to revert back to the sub-regional version. This process only takes 5 - 10 minutes, so it is possible to switch back and forth on a regular basis. 

==== Switching Models to Process New Data ====

To process newly imported data for the preferred sub-regionalization, the existing sub-region setup (used for switching) needs to be removed first and the model needs to be broken out again. To do this users must switch their model from the broken out version to the ''No State/Province'' version as described above. The option to delete a sub-region setup is in the same window as to add sub-regions. After the model has been switched to No State/Province select the country from the box on the right, ''Available Runs:'' and click ''Remove ''to delete the sub-region setup. Deleting a sub-region setup will remove all files created in the process of adding the sub-region setup, and the corresponding model run will no longer be available from the Switch Provinces option.

= Appendix =

In this example, we will look at how normalized estimates are calculated for Female and Male Life Expectancy (LifExpectMale and LifExpectFemale). Below are abridged datadict entries for the two variables. Since there are values for the Multiplier field, we know this was taken from RUNFILES after sub regions were added. Also, the aggregation rule for the male series was changed from null to GDP for the sake of demonstration. The next steps will detail the steps for calculation the multipliers and the estimated base-year values. 

=== ===

{| border="1" cellspacing="1" cellpadding="1" style="width:900px;"
|-
| style="width: 142px;" | Variable
| style="width: 140px;" | Units
| style="width: 148px;" | Multiplier
| style="width: 131px;" | ApplyMultAll
| style="width: 153px;" | Aggregation
| style="width: 143px;" | UsedinPreprocessor
| style="width: 155px;" | UseProvData
|-
| style="width: 142px;" | LifeExpectFemale
| style="width: 140px;" | Years
| style="width: 148px;" | 0.98
| style="width: 131px;" | No
| style="width: 153px;" | 
| style="width: 143px;" | Yes
| style="width: 155px;" | No
|-
| style="width: 142px;" | LifeExpectMale
| style="width: 140px;" | Years
| style="width: 148px;" | 0.97
| style="width: 131px;" | No
| style="width: 153px;" | GDP
| style="width: 143px;" | Yes
| style="width: 155px;" | No
|}

We will start with the female series. Below is a sample table for what was included in IFsHistSeriesIndia.mdb, along with the 4 essential series (earlier years are left out to save space).

{| width="541" border="1" style="width:900px;"
|-
| colspan="7" width="541" height="19" | SeriesLifExpectFemale
|-
| width="151" height="19" | Country
| width="65" | FIPS_CODE
| width="65" align="right" | 2006
| width="65" align="right" | 2010
| width="65" align="right" | 2011
| width="65" | Earliest
| width="65" | MostRecent
|-
| width="151" height="19" | Andaman and Nicobar Islands
| width="65" | AN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Andhra Pradesh
| width="65" | AP
| width="65" align="right" | 65.5
| width="65" |  
| width="65" align="right" | 70.9
| width="65" align="right" | 59.8
| width="65" align="right" | 70.9
|-
| width="151" height="19" | Arunachal Pradesh
| width="65" | AR
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Assam
| width="65" | AS
| width="65" align="right" | 59.3
| width="65" |  
| width="65" align="right" | 64.8
| width="65" align="right" | 51.9
| width="65" align="right" | 64.8
|-
| width="151" height="19" | Bihar
| width="65" | BR
| width="65" align="right" | 60.4
| width="65" |  
| width="65" align="right" | 68.7
| width="65" align="right" | 51.5
| width="65" align="right" | 68.7
|-
| width="151" height="19" | Chandigarh
| width="65" | CH
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Chhatisgarh
| width="65" | CT
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Dadra and Nagar Haveli
| width="65" | DN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Daman and Diu
| width="65" | DD
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Delhi
| width="65" | DL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Goa
| width="65" | GA
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Gujarat
| width="65" | GJ
| width="65" align="right" | 65.2
| width="65" |  
| width="65" align="right" | 72.5
| width="65" align="right" | 59.3
| width="65" align="right" | 72.5
|-
| width="151" height="19" | Haryana
| width="65" | HR
| width="65" align="right" | 67.3
| width="65" |  
| width="65" |  
| width="65" align="right" | 59
| width="65" |  
|-
| width="151" height="19" | Himachal Pradesh
| width="65" | HP
| width="65" align="right" | 66.3
| width="65" |  
| width="65" align="right" | 71.3
| width="65" |  
| width="65" align="right" | 71.3
|-
| width="151" height="19" | Jammu & Kashmir
| width="65" | JK
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Jharkhand
| width="65" | JH
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Karnataka
| width="65" | KA
| width="65" align="right" | 67.1
| width="65" |  
| width="65" align="right" | 72.3
| width="65" align="right" | 62
| width="65" align="right" | 72.3
|-
| width="151" height="19" | Kerala
| width="65" | KL
| width="65" align="right" | 76.3
| width="65" |  
| width="65" align="right" | 77.6
| width="65" align="right" | 71.5
| width="65" align="right" | 77.6
|-
| width="151" height="19" | Lakshadweep
| width="65" | LD
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Madhya Pradesh
| width="65" | MP
| width="65" style="text-align: right;" |  56.9
| width="65" |  
| width="65" |  
| width="65" align="right" | 51.9
| width="65" |  
|-
| width="151" height="19" | Maharashtra
| width="65" | MH
| width="65" align="right" | 57.9
| width="65" |  
| width="65" align="right" | 65.3
| width="65" align="right" | 62.1
| width="65" align="right" | 65.3
|-
| width="151" height="19" | Manipur
| width="65" | MN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Meghalaya
| width="65" | ML
| width="65" align="right" | 68.4
| width="65" |  
| width="65" align="right" | 72.5
| width="65" |  
| width="65" align="right" | 72.5
|-
| width="151" height="19" | Mizoram
| width="65" | MZ
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Nagaland
| width="65" | NL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Orissa
| width="65" | OR
| width="65" align="right" | 59.6
| width="65" |  
| width="65" align="right" | 67.3
| width="65" align="right" | 53
| width="65" align="right" | 67.3
|-
| width="151" height="19" | Pondicherry
| width="65" | PY
| width="65" align="right" | 70.4
| width="65" |  
| width="65" align="right" | 72.8
| width="65" |  
| width="65" align="right" | 72.8
|-
| width="151" height="19" | Punjab
| width="65" | PB
| width="65" style="text-align: right;" |  69.6
| width="65" |  
| width="65" |  
| width="65" align="right" | 63.6
| width="65" |  
|-
| width="151" height="19" | Rajasthan
| width="65" | RJ
| width="65" align="right" | 62.3
| width="65" |  
| width="65" align="right" | 70.7
| width="65" align="right" | 53.8
| width="65" align="right" | 70.7
|-
| width="151" height="19" | Sikkim
| width="65" | SK
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Tamil Nadu
| width="65" | TN
| width="65" align="right" | 67.4
| width="65" |  
| width="65" align="right" | 71.8
| width="65" align="right" | 57.4
| width="65" align="right" | 71.8
|-
| width="151" height="19" | Tripura
| width="65" | TR
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Uttar Pradesh
| width="65" | UP
| width="65" align="right" | 59.5
| width="65" |  
| width="65" align="right" | 66.9
| width="65" align="right" | 48.5
| width="65" align="right" | 66.9
|-
| width="151" height="19" | Uttaranchal
| width="65" | UL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | West Bengal
| width="65" | WB
| width="65" align="right" | 65
| width="65" |  
| width="65" |  
| width="65" align="right" | 58
| width="65" |  
|}

Below is the same table but from RUNFILES after the process of adding sub regions is complete (C:\Users\Public\IFs\RUNFILES). Note two things: 2010 estimates have been added and MostRecent values have been added where appropriate, as highlighted in orange. 

{| width="541" border="1" style="width:900px;"
|-
| colspan="7" width="541" height="19" | SeriesLifExpectFemale
|-
| width="151" height="19" | Country
| width="65" | FIPS_CODE
| width="65" align="right" | 2006
| width="65" align="right" | 2010
| width="65" align="right" | 2011
| width="65" | Earliest
| width="65" | MostRecent
|-
| width="151" height="19" | Andaman and Nicobar Islands
| width="65" | AN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Andhra Pradesh
| width="65" | AP
| width="65" align="right" | 65.5
| width="65" align="right" | 69.65
| width="65" align="right" | 70.9
| width="65" align="right" | 59.8
| width="65" align="right" | 70.9
|-
| width="151" height="19" | Arunachal Pradesh
| width="65" | AR
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Assam
| width="65" | AS
| width="65" align="right" | 59.3
| width="65" align="right" | 63.65
| width="65" align="right" | 64.8
| width="65" align="right" | 51.9
| width="65" align="right" | 64.8
|-
| width="151" height="19" | Bihar
| width="65" | BR
| width="65" align="right" | 60.4
| width="65" align="right" | 67.49
| width="65" align="right" | 68.7
| width="65" align="right" | 51.5
| width="65" align="right" | 68.7
|-
| width="151" height="19" | Chandigarh
| width="65" | CH
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Chhatisgarh
| width="65" | CT
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Dadra and Nagar Haveli
| width="65" | DN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Daman and Diu
| width="65" | DD
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Delhi
| width="65" | DL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Goa
| width="65" | GA
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Gujarat
| width="65" | GJ
| width="65" align="right" | 65.2
| width="65" align="right" | 71.22
| width="65" align="right" | 72.5
| width="65" align="right" | 59.3
| width="65" align="right" | 72.5
|-
| width="151" height="19" | Haryana
| width="65" | HR
| width="65" align="right" | 67.3
| width="65" align="right" | 66.11
| width="65" |  
| width="65" align="right" | 59
| width="65" align="right" | 67.3
|-
| width="151" height="19" | Himachal Pradesh
| width="65" | HP
| width="65" align="right" | 66.3
| width="65" align="right" | 70.04
| width="65" align="right" | 71.3
| width="65" |  
| width="65" align="right" | 71.3
|-
| width="151" height="19" | Jammu & Kashmir
| width="65" | JK
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Jharkhand
| width="65" | JH
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Karnataka
| width="65" | KA
| width="65" align="right" | 67.1
| width="65" align="right" | 71.02
| width="65" align="right" | 72.3
| width="65" align="right" | 62
| width="65" align="right" | 72.3
|-
| width="151" height="19" | Kerala
| width="65" | KL
| width="65" align="right" | 76.3
| width="65" align="right" | 76.23
| width="65" align="right" | 77.6
| width="65" align="right" | 71.5
| width="65" align="right" | 77.6
|-
| width="151" height="19" | Lakshadweep
| width="65" | LD
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Madhya Pradesh
| width="65" | MP
| width="65" style="text-align: right;" |  56.9
| width="65" align="right" | 55.89
| width="65" |  
| width="65" align="right" | 51.9
| width="65" align="right" | 56.9
|-
| width="151" height="19" | Maharashtra
| width="65" | MH
| width="65" align="right" | 57.9
| width="65" align="right" | 64.15
| width="65" align="right" | 65.3
| width="65" align="right" | 62.1
| width="65" align="right" | 65.3
|-
| width="151" height="19" | Manipur
| width="65" | MN
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Meghalaya
| width="65" | ML
| width="65" align="right" | 68.4
| width="65" align="right" | 71.22
| width="65" align="right" | 72.5
| width="65" |  
| width="65" align="right" | 72.5
|-
| width="151" height="19" | Mizoram
| width="65" | MZ
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Nagaland
| width="65" | NL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Orissa
| width="65" | OR
| width="65" align="right" | 59.6
| width="65" align="right" | 66.11
| width="65" align="right" | 67.3
| width="65" align="right" | 53
| width="65" align="right" | 67.3
|-
| width="151" height="19" | Pondicherry
| width="65" | PY
| width="65" align="right" | 70.4
| width="65" align="right" | 71.51
| width="65" align="right" | 72.8
| width="65" |  
| width="65" align="right" | 72.8
|-
| width="151" height="19" | Punjab
| width="65" | PB
| width="65" style="text-align: right;" |  69.6
| width="65" align="right" | 68.37
| width="65" |  
| width="65" align="right" | 63.6
| width="65" align="right" | 69.6
|-
| width="151" height="19" | Rajasthan
| width="65" | RJ
| width="65" align="right" | 62.3
| width="65" align="right" | 69.45
| width="65" align="right" | 70.7
| width="65" align="right" | 53.8
| width="65" align="right" | 70.7
|-
| width="151" height="19" | Sikkim
| width="65" | SK
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Tamil Nadu
| width="65" | TN
| width="65" align="right" | 67.4
| width="65" align="right" | 70.53
| width="65" align="right" | 71.8
| width="65" align="right" | 57.4
| width="65" align="right" | 71.8
|-
| width="151" height="19" | Tripura
| width="65" | TR
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | Uttar Pradesh
| width="65" | UP
| width="65" align="right" | 59.5
| width="65" align="right" | 65.72
| width="65" align="right" | 66.9
| width="65" align="right" | 48.5
| width="65" align="right" | 66.9
|-
| width="151" height="19" | Uttaranchal
| width="65" | UL
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
| width="65" |  
|-
| width="151" height="19" | West Bengal
| width="65" | WB
| width="65" style="text-align: right;" |  65
| width="65" align="right" | 63.85
| width="65" |  
| width="65" align="right" | 58
| width="65" align="right" | 65
|}

Since no 2010 values were available when the process to add sub regions was initiated, the model looked to the MostRecent column. Next, the model needs to fill in null values, which it does by simply using the national-level base-year value from IFsHistSeries.mdb. In this case, the value for India is 66.675 years, as highlight in orange. Once each state has a usable value, the model reads the aggregation rule from the datadict table in IFsHistSeriesIndia.mdb, as seen above. Since the rule is null, it defaults to a simple average (arithmetic).

{| border="1" cellspacing="0" cellpadding="0" width="401" style="width:401px;"
|-
| nowrap="nowrap" style="width:206px;height:16px;" |
SeriesLifExpectFemale

| nowrap="nowrap" style="width:99px;height:16px;" |
 

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Country

| nowrap="nowrap" style="width:99px;height:16px;" |
MostRecent

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Andaman and Nicobar Islands

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Andhra Pradesh

| nowrap="nowrap" style="width:99px;height:16px;" |
70.9

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Arunachal Pradesh

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Assam

| nowrap="nowrap" style="width:99px;height:16px;" |
64.8

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Bihar

| nowrap="nowrap" style="width:99px;height:16px;" |
68.7

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Chandigarh

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Chhatisgarh

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Dadra and Nagar Haveli

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Daman and Diu

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Delhi

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Goa

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Gujarat

| nowrap="nowrap" style="width:99px;height:16px;" |
72.5

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Haryana

| nowrap="nowrap" style="width:99px;height:16px;" |
67.3

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Himachal Pradesh

| nowrap="nowrap" style="width:99px;height:16px;" |
71.3

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Jammu & Kashmir

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Jharkhand

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Karnataka

| nowrap="nowrap" style="width:99px;height:16px;" |
72.3

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Kerala

| nowrap="nowrap" style="width:99px;height:16px;" |
77.6

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Lakshadweep

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Madhya Pradesh

| nowrap="nowrap" style="width:99px;height:16px;" |
56.9

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Maharashtra

| nowrap="nowrap" style="width:99px;height:16px;" |
65.3

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Manipur

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Meghalaya

| nowrap="nowrap" style="width:99px;height:16px;" |
72.5

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Mizoram

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Nagaland

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Orissa

| nowrap="nowrap" style="width:99px;height:16px;" |
67.3

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Pondicherry

| nowrap="nowrap" style="width:99px;height:16px;" |
72.8

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Punjab

| nowrap="nowrap" style="width:99px;height:16px;" |
69.6

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Rajasthan

| nowrap="nowrap" style="width:99px;height:16px;" |
70.7

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Sikkim

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Tamil Nadu

| nowrap="nowrap" style="width:99px;height:16px;" |
71.8

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Tripura

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Uttar Pradesh

| nowrap="nowrap" style="width:99px;height:16px;" |
66.9

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
Uttaranchal

| nowrap="nowrap" style="width:99px;height:16px;" |
66.675

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
West Bengal

| nowrap="nowrap" style="width:99px;height:16px;" |
65

| nowrap="nowrap" style="width:95px;height:16px;" |
 

|-
| nowrap="nowrap" style="width:206px;height:16px;" |
 

| nowrap="nowrap" style="width:99px;height:16px;" |
67.934

| nowrap="nowrap" style="width:95px;height:16px;" |
= mean value

|}

Lastly, the model needs to calculate the multiplier to be used for normalization. In other words, what value will change the mean to the national value? This is obtained by simply dividing the national value by the mean:

<math>\frac{country}{subcountry\ mean}=\frac{66.675}{67.934}=0.98</math>

Note this is the multiplier seen in the datadict above. This is multiplied against each ''MostRecent'' data point to obtain the normalized values seen in figure 3.

Now, how does this change when the aggregation rule is GDP, as is the case with the male series? The process is the same as the female series through filling null values in the ''MostRecent'' column. The national value for males is 63.62 years:

{| border="1" cellspacing="0" cellpadding="0" width="338" style="width:338px;"
|-
| nowrap="nowrap" style="width:233px;height:13px;" |
SeriesLifExpectMale

| nowrap="nowrap" style="width:105px;height:13px;" |
 

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Country

| nowrap="nowrap" style="width:105px;height:13px;" |
MostRecent

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Andaman and Nicobar Islands

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Andhra Pradesh

| nowrap="nowrap" style="width:105px;height:13px;" |
66.90

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Arunachal Pradesh

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Assam

| nowrap="nowrap" style="width:105px;height:13px;" |
63.60

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Bihar

| nowrap="nowrap" style="width:105px;height:13px;" |
68.60

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Chandigarh

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Chhatisgarh

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Dadra and Nagar Haveli

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Daman and Diu

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Delhi

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Goa

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Gujarat

| nowrap="nowrap" style="width:105px;height:13px;" |
69.20

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Haryana

| nowrap="nowrap" style="width:105px;height:13px;" |
66.50

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Himachal Pradesh

| nowrap="nowrap" style="width:105px;height:13px;" |
68.90

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Jammu & Kashmir

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Jharkhand

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Karnataka

| nowrap="nowrap" style="width:105px;height:13px;" |
68.00

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Kerala

| nowrap="nowrap" style="width:105px;height:13px;" |
73.20

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Lakshadweep

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Madhya Pradesh

| nowrap="nowrap" style="width:105px;height:13px;" |
57.00

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Maharashtra

| nowrap="nowrap" style="width:105px;height:13px;" |
64.50

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Manipur

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Meghalaya

| nowrap="nowrap" style="width:105px;height:13px;" |
68.90

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Mizoram

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Nagaland

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Orissa

| nowrap="nowrap" style="width:105px;height:13px;" |
64.30

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Pondicherry

| nowrap="nowrap" style="width:105px;height:13px;" |
69.70

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Punjab

| nowrap="nowrap" style="width:105px;height:13px;" |
67.40

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Rajasthan

| nowrap="nowrap" style="width:105px;height:13px;" |
67.60

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Sikkim

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Tamil Nadu

| nowrap="nowrap" style="width:105px;height:13px;" |
64.20

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Telangana

| nowrap="nowrap" style="width:105px;height:13px;" |
68.60

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Tripura

| nowrap="nowrap" style="width:105px;height:13px;" |
63.62

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Uttar Pradesh

| nowrap="nowrap" style="width:105px;height:13px;" |
59.40

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
Uttaranchal

| nowrap="nowrap" style="width:105px;height:13px;" |
66.00

|-
| nowrap="nowrap" style="width:233px;height:13px;" |
West Bengal

| nowrap="nowrap" style="width:105px;height:13px;" |
69.20

|}

Instead of taking the average as was done for females, the model uses a weighted average based on GDP data. In other words, each life expectancy value is multiplied by the state’s proportional GDP contribution to the total GDP for all states, then the resulting values are summed.

{| border="1" cellspacing="0" cellpadding="0" width="574" style="width:573px;"
|-
| nowrap="nowrap" style="width:161px;height:17px;" |
SeriesLifExpectMale

| nowrap="nowrap" style="width:65px;height:17px;" |
 

| nowrap="nowrap" style="width:217px;height:17px;" |
 

| nowrap="nowrap" style="width:130px;height:17px;" |
 

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Country

| nowrap="nowrap" style="width:65px;height:17px;" |
MostRecent

| nowrap="nowrap" style="width:217px;height:17px;" |
Weights (State GDP/Total GDP for States)

| nowrap="nowrap" style="width:130px;height:17px;" |
(MostRecent)*(Weights)

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Andaman and Nicobar Islands

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.001

| nowrap="nowrap" style="width:130px;height:17px;" |
0.04

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Andhra Pradesh

| nowrap="nowrap" style="width:65px;height:17px;" |
66.90

| nowrap="nowrap" style="width:217px;height:17px;" |
0.081

| nowrap="nowrap" style="width:130px;height:17px;" |
5.42

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Arunachal Pradesh

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.001

| nowrap="nowrap" style="width:130px;height:17px;" |
0.08

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Assam

| nowrap="nowrap" style="width:65px;height:17px;" |
63.60

| nowrap="nowrap" style="width:217px;height:17px;" |
0.016

| nowrap="nowrap" style="width:130px;height:17px;" |
0.99

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Bihar

| nowrap="nowrap" style="width:65px;height:17px;" |
68.60

| nowrap="nowrap" style="width:217px;height:17px;" |
0.028

| nowrap="nowrap" style="width:130px;height:17px;" |
1.94

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Chandigarh

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.003

| nowrap="nowrap" style="width:130px;height:17px;" |
0.18

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Chhatisgarh

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.017

| nowrap="nowrap" style="width:130px;height:17px;" |
1.05

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Dadra and Nagar Haveli

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.000

| nowrap="nowrap" style="width:130px;height:17px;" |
0.02

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Daman and Diu

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.000

| nowrap="nowrap" style="width:130px;height:17px;" |
0.01

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Delhi

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.035

| nowrap="nowrap" style="width:130px;height:17px;" |
2.23

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Goa

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.005

| nowrap="nowrap" style="width:130px;height:17px;" |
0.30

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Gujarat

| nowrap="nowrap" style="width:65px;height:17px;" |
69.20

| nowrap="nowrap" style="width:217px;height:17px;" |
0.072

| nowrap="nowrap" style="width:130px;height:17px;" |
5.00

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Haryana

| nowrap="nowrap" style="width:65px;height:17px;" |
66.50

| nowrap="nowrap" style="width:217px;height:17px;" |
0.036

| nowrap="nowrap" style="width:130px;height:17px;" |
2.40

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Himachal Pradesh

| nowrap="nowrap" style="width:65px;height:17px;" |
68.90

| nowrap="nowrap" style="width:217px;height:17px;" |
0.008

| nowrap="nowrap" style="width:130px;height:17px;" |
0.55

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Jammu & Kashmir

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.008

| nowrap="nowrap" style="width:130px;height:17px;" |
0.51

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Jharkhand

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.018

| nowrap="nowrap" style="width:130px;height:17px;" |
1.12

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Karnataka

| nowrap="nowrap" style="width:65px;height:17px;" |
68.00

| nowrap="nowrap" style="width:217px;height:17px;" |
0.057

| nowrap="nowrap" style="width:130px;height:17px;" |
3.87

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Kerala

| nowrap="nowrap" style="width:65px;height:17px;" |
73.20

| nowrap="nowrap" style="width:217px;height:17px;" |
0.037

| nowrap="nowrap" style="width:130px;height:17px;" |
2.68

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Lakshadweep

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.000

| nowrap="nowrap" style="width:130px;height:17px;" |
0.01

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Madhya Pradesh

| nowrap="nowrap" style="width:65px;height:17px;" |
57.00

| nowrap="nowrap" style="width:217px;height:17px;" |
0.037

| nowrap="nowrap" style="width:130px;height:17px;" |
2.08

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Maharashtra

| nowrap="nowrap" style="width:65px;height:17px;" |
64.50

| nowrap="nowrap" style="width:217px;height:17px;" |
0.144

| nowrap="nowrap" style="width:130px;height:17px;" |
9.26

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Manipur

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.001

| nowrap="nowrap" style="width:130px;height:17px;" |
0.08

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Meghalaya

| nowrap="nowrap" style="width:65px;height:17px;" |
68.90

| nowrap="nowrap" style="width:217px;height:17px;" |
0.002

| nowrap="nowrap" style="width:130px;height:17px;" |
0.14

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Mizoram

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.001

| nowrap="nowrap" style="width:130px;height:17px;" |
0.06

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Nagaland

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.002

| nowrap="nowrap" style="width:130px;height:17px;" |
0.10

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Orissa

| nowrap="nowrap" style="width:65px;height:17px;" |
64.30

| nowrap="nowrap" style="width:217px;height:17px;" |
0.027

| nowrap="nowrap" style="width:130px;height:17px;" |
1.76

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Pondicherry

| nowrap="nowrap" style="width:65px;height:17px;" |
69.70

| nowrap="nowrap" style="width:217px;height:17px;" |
0.002

| nowrap="nowrap" style="width:130px;height:17px;" |
0.13

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Punjab

| nowrap="nowrap" style="width:65px;height:17px;" |
67.40

| nowrap="nowrap" style="width:217px;height:17px;" |
0.031

| nowrap="nowrap" style="width:130px;height:17px;" |
2.11

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Rajasthan

| nowrap="nowrap" style="width:65px;height:17px;" |
67.60

| nowrap="nowrap" style="width:217px;height:17px;" |
0.047

| nowrap="nowrap" style="width:130px;height:17px;" |
3.17

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Sikkim

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.001

| nowrap="nowrap" style="width:130px;height:17px;" |
0.07

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Tamil Nadu

| nowrap="nowrap" style="width:65px;height:17px;" |
64.20

| nowrap="nowrap" style="width:217px;height:17px;" |
0.081

| nowrap="nowrap" style="width:130px;height:17px;" |
5.21

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Telangana

| nowrap="nowrap" style="width:65px;height:17px;" |
68.60

| nowrap="nowrap" style="width:217px;height:17px;" |
0.041

| nowrap="nowrap" style="width:130px;height:17px;" |
2.81

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Tripura

| nowrap="nowrap" style="width:65px;height:17px;" |
63.62

| nowrap="nowrap" style="width:217px;height:17px;" |
0.002

| nowrap="nowrap" style="width:130px;height:17px;" |
0.16

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Uttar Pradesh

| nowrap="nowrap" style="width:65px;height:17px;" |
59.40

| nowrap="nowrap" style="width:217px;height:17px;" |
0.083

| nowrap="nowrap" style="width:130px;height:17px;" |
4.94

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
Uttaranchal

| nowrap="nowrap" style="width:65px;height:17px;" |
66.00

| nowrap="nowrap" style="width:217px;height:17px;" |
0.012

| nowrap="nowrap" style="width:130px;height:17px;" |
0.77

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
West Bengal

| nowrap="nowrap" style="width:65px;height:17px;" |
69.20

| nowrap="nowrap" style="width:217px;height:17px;" |
0.064

| nowrap="nowrap" style="width:130px;height:17px;" |
4.42

|-
| nowrap="nowrap" style="width:161px;height:17px;" |
 

| nowrap="nowrap" style="width:65px;height:17px;" |
 

| nowrap="nowrap" style="width:217px;height:17px;" |
 Total =

| nowrap="nowrap" style="width:130px;height:17px;" |
65.69

|}

The last step is the same as before. What value will change this weighted average to the national average?

<math>\frac{country}{subregion\ weighted\ mean}=\frac{63.62}{65.69}=0.97</math>

Once again, this is the multiplier seen above. This is used to transform each ''MostRecent'' data point into a normalized 2010 value. 
<div><div><div id="_com_1"></div></div></div>

Introduction to IFs

2017-09-05T20:12:52Z

MeredithMoon:

= Purposes =

'''International Futures (IFs) is a tool for thinking about long-term global trends. It assists with:'''

*Understanding the state of the world
*Exploring trends and considering where they might be taking us
*Learning about the dynamics of global systems

'''Thinking about the future we want to:'''

*Clarify goals/priorities
*Develop alternative scenarios (if-then statements) about the future
*Investigate the leverage various agent-classes have in shaping the future

'''Assumptions that underlie IFs development and use:'''

*Global issues are becoming more significant as the scope of human interaction and human impact on the broader environment grow
*Goals and priorities for human systems are becoming clearer and are more frequently and consistently enunciated
*Understanding of the dynamics of human systems is growing rapidly
*The domain of human choice and action is broadening

'''What can you investigate with IFs? Examples include:'''

*Environmental Sustainability: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage
*Social/Political Change: Life expectancy, literacy rate, democracy level, status of women, value change
*Demographic Futures: Population levels and growth, fertility, mortality, migration
*Food and Agriculture: Land use and production levels, calorie availability, malnutrition rates
*Energy: Resource and production levels, demand patterns, renewable energy share
*Economics: Sectoral production, consumption, and trade patterns and structural change
*Global System: Country and regional power levels

= IFs Issues and Modules: Visual Representation =

[[File:IFsOverviewChart.jpg|frame|center|Visual representation of IFs structure]]

Among the philosophical premises of the International Futures (IFs) project is that the model cannot be a "black box" to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.

There is available (see topics under Understanding the Mode in the contents of this help system):

*[[Understand_IFs#Dominant_Relations|Dominant Relations]] of the model structure
*[[Understand_IFs#2.2|Structure-Based and Agent-Class Driven Modeling]]
*[[Understand_IFs#Equation_Notation|Equation Notation]]
*[[Introduction_to_IFs#IFs_Bibliography|IFs Bibliography]] of data and data sources

= IFs Issues and Modules: Quick Survey =

The '''population''' module:

*represents 22 age-sex cohorts to age 100+
*calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers
*computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life
*represents migration and HIV/AIDS
*includes a newly developing submodel of formal education across primary, secondary, and tertiary levels

The '''economic''' module:

*represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)
*computes and uses input-output matrices that change dynamically with development level
*is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time
*contains an endogenous production function that represents contributions to growth in multifactor productivity from R&D, education, worker health, economic policies ("freedom"), and energy prices (the "quality" of capital)
*uses a Linear Expenditure System to represent changing consumption patterns
*utilizes a "pooled" rather than the bilateral trade approach for international trade
*is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows

The '''agricultural''' module:

*represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail
*maintains land use in crop, grazing, forest, urban, and "other" categories
*represents demand for food, for livestock feed, and for industrial use of agricultural products
*is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes
*overrides the agricultural sector in the economic module unless the user chooses otherwise

The '''energy''' module:

*portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable
*represents consumption and trade of energy in the aggregate
*represents known reserves and ultimate resources of the fossil fuels
*portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources
*is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes
*overrides the energy sector in the economic module unless the user chooses otherwise

The two '''socio-political''' sub-modules:

Within countries or geographic groupings

*represents fiscal policy through taxing and spending decisions
*shows six categories of government spending: military, health, education, R&D, foreign aid, and a residual category
*represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)
*represents the evolution of democracy
*represents the prospects for state instability or failure

Between countries or groupings of countries

*traces changes in power balances across states and regions
*allows exploration of changes in the level of interstate threat
*represents possible action-reaction processes and arms races with associated potential for conflict among countries

The implicit '''environmental''' module:

*is distributed throughout the overall model
*allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions

The implicit '''technology''' module:

*is distributed throughout the overall model
*allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy
*explicitly represents the extent of electronic networking of individuals in societies
*is tied to the governmental spending model with respect to R&D spending 

= IFs Background =

International Futures (IFs) has evolved since 1980 through three "generations," with a fourth generation now taking form.

The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.

The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.

The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.

The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.

The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).

Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.

The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council ([http://www.ifs.du.edu http://www.ifs.du.edu]). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.

= Geographic Representation of the World =

186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.

''Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:''

[[File:Geo 186.png|frame|right|Visual representation of IF's definition of regions/countries/groups/glists]]

''*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image located at the bottom of this Help topic.''

When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.

Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.

Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the [[Extended_Features#Manage_Groups/Regions|Managing Regionalization]] function.

Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.

Users may also want to [[Extended_Features#Change_Grouping/Regionalization|create]] their own groups or [[Extended_Features#Identify_Groups_or_Country/Region_Members|explore]] what countries are members of what groups.

= IFs Time Horizon =

'''Future Forecasts.''' IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.

'''Historical Analysis and "Forecasts."''' IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time. 

== Instructional Use ==

The standard modes for using IFs in a classroom are:

1. Assigning class members to an issue area or topic. Consider identifying specific questions for them to address.

2. Assigning class members to a country/geographic region. Again, specificity helps.

Most often, students will work independently or in groups on projects and share information after completing them. It is possible, however, to have students work interactively, by assigning them topics or regions, letting them begin work, and then have the interacting groups (or individuals) create a collective model run with the changes that each group proposes by topic or region. That process, although more difficult to organize, allows the class as whole to investigate the interaction of their topics or regions (and to share learning about model use).

There is a [http://portfolio.du.edu/bhughes web site] available in support of the educational use of IFs. You will find syllabi at that site. There are several [[Introduction_to_IFs#Publications_on_IFs|publications]] on IFs, including a book structured specifically for educational use.

Donald Borock has described his classroom use of IFs in print. Borock, Donald. 1996. "Using Computer Assisted Instruction to Enhance the Understanding of Policymaking," Advances in Social Science and Computers 4, 103-127.

= Acknowledgements =

The author gratefully recognizes critical contributions in the forms of:

:1. Testing and suggestions for development of IFs in one or more of multiple generations. By Donald Borock, Richard Chadwick, William Dixon, Dale Rothman, Phil Schrodt, Douglas Stuart, Donald Sylvan, Jonathan Wilkenfeld, and Ronald Inglehart.

:2. Computer assistance across many releases. By Michael Niemann, Terrance Peet-Lukes, Douglas McClure, Mohammod Irfan, and Jose Solorzano.

:3. Data gathering and general assistance. By James Chung, Padma Padula, Shannon Brady, David Horan, Michael Ferrier, Kay Drucker, Warren Christopher, and Anwar Hossain.

:4. Long-term encouragement and support. By Harold Guetzkow, Karl Deutsch, Richard Chadwick, Gerald Barney, and Ronald Inglehart.

:5. Association in related world modeling projects and projects building upon IFs. By Mihajlo Mesarovic, Aldo Barsotti, Juan Huerta, John Richardson, Thomas Shook, Patricia Strauch, and other members of the World Integrated Model (WIM) team. By Stuart Bremer, Peter Brecke, Thomas Cusack, Wolf Dieter-Eberwein, Brian Pollins, and Dale Smith of the GLOBUS modeling project. By Evan Hillebrand, Paul Herman, and others of the IFs for SAG project. By Rob Lempert and Steve Bankes at RAND, Santa Monica. By Robert Pestel, Jonathan Cave, Ronald Inglehart, Sergei Parinov, Pentti Malaska, and many others in the IFs for TERRA project.

:6. Financial assistance (without responsibility for the form of the evolving product). By the National Science Foundation, the Cleveland Foundation, the Exxon Education Foundation, the Kettering Family Foundation, the Pacific Cultural Foundation, the United States Institute of Peace, General Motors, the Strategic Assessments Group of the Central Intelligence Agency, the European Commission (Information Society Technology) Programme, the European Union Center of the University of Michigan, the National Intelligence Council (for web conversion), and Frederick S. Pardee.             

= Feedback =

Feedback on how to improve IFs is always appreciated, especially if you find something that is not working. Compliments are also accepted. Please contact. To send the IFs team an e-mail, click on [mailto:pardee.center@du.edu Pardee Center] in stand-alone versions or on the web.

= Support for IFs Use =

== Publications on IFs ==

To obtain additional information about IFs and its use, consult:

Barry B. Hughes and Evan E. Hillebrand, '''Exploring and Shaping International Futures.''' Boulder, CO: Paradigm Publishers, 2006. Specifically, see chapter 4.

Barry B. Hughes, '''International Futures: Choices in the Face of Uncertainty,''' 3rd ed. Boulder, CO: Westview Press, 1999. This volume is built around IFs and contains detailed suggestions for its use. Version 3.17 of IFs, which runs under Windows 95, is distributed with the third edition of the book. The second edition contained a version for Windows 3.1, and the first edition ran under DOS. Chapter 4 of the 2nd edition of IFs included Flow Charts of Worldviews , reproduced now in this Help system.

Barry B. Hughes, '''Continuity and Change in World Politics,''' 4th ed. Englewood Cliffs, N.J.: Prentice Hall, 2000. IFs can also usefully supplement this textbook on global politics.

Barry B. Hughes, "The International Futures (IFs) Modeling Project. 1999. '''Simulation and Gaming''' 30, No. 3 (September): 304-326.

== IFs Bibliography ==

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Bailey, Ronald, ed. 1995. ''The True State of the Planet''. New York: The Free Press.

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Barney, Gerald O., W. Brian Kreutzer, and Martha J. Garrett, eds. 1991. ''Managing a Nation'', 2nd ed. Boulder: Westview Press.

Barro, Robert J. 1997. ''Determinants of Economic Growth: A Cross-Country Empirical Study''. Cambridge, Mass: MIT Press.

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Borock, Donald M. 1996. "Using Computer Assisted Instruction to Enhance the Understanding of Policymaking," ''Advances in Social Science and Computers'' 4, 103-127.

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Brown, Lester R., et al. 1996 ''State of the World 1996''. New York: W.W. Norton.

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Cook, Earl. 1976. ''Man, Energy, Society''. San Francisco: W.H. Freeman.

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Henderson, Hazel. 1996. "Changing Paradigms and Indicators: Implementing Equitable, Sustainable and Participatory Development," in Jo Marie Griesgraber and Bernhard G. Gunter, ''Development: New Paradigms and Principles for the 21st Century''. East Haven, CT: Pluto Press, pp. 103-136.

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Clemens, Michael A. 2004. The Long Walk to School: International education goals in historical perspective. Econ WPA, March. [http://ideas.repec.org/p/wpa/wuwpdc/0403007.html http://ideas.repec.org/p/wpa/wuwpdc/0403007.html].

Cohen, Daniel, and Marcelo Soto. 2001. “Growth and Human Capital: Good Data, Good Results.” Technical Paper 179.  Paris: OECD.

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Hughes, Barry B. 2001. “Global Social Transformation: The Sweet Spot, the Steady Slog, and the Systemic Shift.” ''Economic Development and Cultural Change'' 49(2): 423-458. doi: 10.1086/452510.

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Hughes, Barry B., and Anwar Hossain. 2003. “Long-Term Socio-Economic Modeling: With Universal, Globally-Integrated Social Accounting Matrices (SAMs) in a General Equilibrium Model Structure.” Working Paper, University of Denver, Denver, CO. [http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf http://www.ifs.du.edu/assets/documents/economyandsamdocument46.pdf]

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== Health Bibliography ==

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Agénor, Pierre-Richard, Mustapha Kamel Nabli, and Tarik M. Yousef. 2007. “Public Infrastructure and Private Investment in the Middle East and North Africa.” In Mustapha Kamel Nabli, ed.,. Breaking the Barriers to Higher Economic Growth: Better Governance and Deeper Reforms in the Middle East and North Africa. Washington, DC: World Bank Publications, 399–422.

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Bhattacharyay, Biswa Nath. 2010. “Estimating Demand for Infrastructure in Energy, Transport, Telecommunications, Water and Sanitation in Asia and the Pacific: 2010-2020”. Working Paper no. 248. Asian Development Bank Institute, Tokyo. [http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/ http://www.adbi.org/working-paper/2010/09/09/4062.infrastructure.demand.asia.pacific/].

Bruinsma, Jelle. 2011. “The Resources Outlook: By How Much Do Land, Water and Crop Yields Need to Increase by 2050?” In Piero Conforti, ed.,. ''Looking Ahead in World Food and Agriculture: Perspectives to 2050''. Rome: Food and Agriculture Organization of the United Nations (FAO), 233–275. [http://www.fao.org/docrep/014/i2280e/i2280e.pdf http://www.fao.org/docrep/014/i2280e/i2280e.pdf].

Calderón, César, and Luis Servén. 2010a. “Infrastructure and Economic Development in Sub-Saharan Africa.” ''Journal of African Economies'' 19(Supplement 1): i13–i87. doi:10.1093/jae/ejp022. [http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13 http://jae.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i13].

Calderón, César, and Luis Servén. 2010b. “Infrastructure in Latin America”. World Bank Policy Research Working Paper. Report Number 5317. World Bank, Washington, DC.

Canning, David. 1998. “A Database of World Stocks of Infrastructure, 1950-1995.” ''The World Bank Economic Review'' 12(3): 529–548.

Canning, David, and Mansour Farahani. 2007. “A Database of World Stocks of Infrastructure: Update 1950-2005”. Harvard School of Public Health, Boston, MA. [http://www.hsph.harvard.edu/faculty/david-canning/data-sets/ http://www.hsph.harvard.edu/faculty/david-canning/data-sets/].

Cavallo, Eduardo Alfredo, and Christian Daude. 2008. “Public Investment in Developing Countries: A Blessing or a Curse?” RES Working Paper #4597. Inter-American Development Bank (IADB) - Research Department, OECD, Washington, DC.

Chatterton, Isabe, and Olga S. Puerto. 2006. ''Estimation of Infrastructure Investment Needs in the South Asia Region: Executive Summary''. Washington, DC: World Bank. [http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf http://siteresources.worldbank.org/INTSARREGTOPTRANSPORT/Resources/Inf_Investment_Needs_IC_version4.pdf].

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Estache, Antonio, and Ana Goicoechea. 2005. “A Research Database on Infrastructure Economic Performance”. Policy Research Working Paper no. 3643. World Bank, Washington, DC. [http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2005/06/16/000016406_20050616100502/Rendered/PDF/wps3643.pdf].

Ezzati, Majid, Alan D. Lopez, Anthony Rodgers, and Christopher J. L. Murray, eds. 2004. ''Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors''. Geneva, Switzerland: World Health Organization (WHO).

Fay, Marianne. 2001. “Financing the Future: Infrastructure Needs in Latin America, 2000-05”. Policy Research Working Paper no. 2545. World Bank, Washington, DC. [http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&id=id&accname=guest&checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB http://elibrary.worldbank.org/docserver/download/2545.pdf?expires=1375200693&id=id&accname=guest&checksum=DB7E5FB146EE28C93511B5ADAB2FD3CB].

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Hughes, Gordon, Paul Chinowsky, and Ken Strzepek. 2009. “The Costs of Adapting to Climate Change for Infrastructure”. Economics of Adaptation to Climate Change Discussion Paper no. 2. World Bank, Washington, DC.

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Kim, M. Julie, and Rita Nangia. 2010. “Infrastructure Development in India and China—A Comparative Analysis.” In William Ascher and Corinne Krupp, eds.,. ''Physical Infrastructure Development: Balancing The Growth, Equity, and Environmental Imperatives''. New York, NY: Palgrave Macmillan, 97–140.

Lora, Eduardo A. 2007. ''Public Investment in Infrastructure in Latin America: Is Debt the Culprit?'' Inter-American Development Bank Working Paper. Washington, DC: Inter-American Development Bank (IADB) - Research Department.

Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi, and Liangzhi You. 2010. ''Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options''. Washington, DC: International Food Policy Research Institute. [http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050 http://www.ifpri.org/publication/food-security-farming-and-climate-change-2050].

Organisation for Economic Co-operation and Development. 2006. ''Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity''. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development.

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Qiang, Christine Zhen-Wei, Carlo M. Rossotto, and Kaoru Kimura. 2009. “Economic Impacts of Broadband.” In World Bank, ed.,. ''2009 Information and Communications for Development: Extending Reach and Increasing Impact''. Washington, DC: World Bank, 35–50.

Rothman, Dale S. Mohammod T. Irfan, Eli Margolese-Malin, Barry B. Hughes, Jonathan Moyer, and Janet Dickson. 2013. ''Building Global Infrastructure. ''vol. 4, Patterns of Potential Human Progress series. Boulder, CO, and New Delhi, India: Paradigm Publishers and Oxford University Press. Stambrook, David. 2006. “Key Factors Driving the Future Demand for Surface Transport Infrastructure and Services.” In Organisation for Economic Cooperation and Development (OECD), ed.,. ''Infrastructure to 2030 Volume 1: Telecom, Land Transport, Water and Electricity''. Infrastructure to 2030. Paris: Organisation for Economic Cooperation and Development (OECD), 185–239.

World Health Organization, and UNICEF. 2013. ''Progress on Sanitation and Drinking-Water - 2013 Update''. Geneva: World Health Organization.

Yepes, Tito. 2008. “Investment Needs for Infrastructure in Developing Countries 2008-15”. Draft. World Bank, Washington, DC.

Yepes, Tito. 2005. ''Expenditure on Infrastructure in East Asia Region, 2006–2010''. East Asia Pacific Infrastructure Flagship Study. Manila: Asian Development Bank (ADB), Japan Bank for International Cooperation (JBIC), World Bank.

You, Liangzhi, Claudia Ringler, Ulrike Wood-Sichra, Richard Robertson, Stanley Wood, Tingju Zhu, Gerald Nelson, Zhe Guo, and Yan Sun. 2011. “What Is the Irrigation Potential for Africa? A Combined Biophysical and Socioeconomic Approach.” ''Food Policy'' 36(6): 770–782. doi:10.1016/j.foodpol.2011.09.001. [http://www.sciencedirect.com/science/article/pii/S030691921100114X http://www.sciencedirect.com/science/article/pii/S030691921100114X].

== [[Development_Mode_Features|Development Mode Features]] ==

Sandbox

2017-09-05T16:24:18Z

MeredithMoon:

[[Development_Mode_Features|Development_Mode_Features]]

[[Jake|jake ]]

[[SubRegionalization_Handbook|Brasil - Dados das unidades federativas]]

Additional resources

2017-09-05T16:23:11Z

MeredithMoon:

[http://pardee.du.edu/wiki/index.php?title=Data Data]

[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model validation and verification]

[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]

[[Preprocessor|Preprocessor]]

[[Sub-modules|Sub-modules]]

[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]

[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]

=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&action=edit&section=1 edit]] ===

[[Sandbox|Sandbox]]

Additional resources

2017-09-05T16:22:36Z

MeredithMoon:

[http://pardee.du.edu/wiki/index.php?title=Data Data]

[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model validation and verification]

[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]

[http://pardee.du.edu/wiki/index.php?title=Preprocessor Preprocessor]

[[Sub-modules|Sub-modules]]

[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]

[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]

=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&action=edit&section=1 edit]] ===

[[Sandbox|Sandbox]]

Additional resources

2017-09-05T16:21:35Z

MeredithMoon:

[http://pardee.du.edu/wiki/index.php?title=Data Data]

[http://pardee.du.edu/wiki/index.php?title=Model_validation_and_verification Model validation and verification]

[http://pardee.du.edu/wiki/index.php?title=Consolidation Consolidation]

[http://pardee.du.edu/wiki/index.php?title=Preprocessor Preprocessor]

[http://pardee.du.edu/wiki/index.php?title=Sub-modules Sub-modules]

[[SubRegionalization_Handbook|Sub-Regionalization Handbook]]

[http://pardee.du.edu/wiki/index.php?title=Version_notes Version notes]

=== In progress[[http://pardee.du.edu/wiki/index.php?title=International_Futures_(IFs)&action=edit&section=1 edit]] ===

[[Sandbox|Sandbox]]

Sandbox

2017-09-05T16:18:57Z

MeredithMoon:

[[Development_Mode_Features|Development_Mode_Features]]

[[Jake|jake ]]

Brasil - Dados das unidades federativas