Labor - Revision history

Wikiadmin at 22:59, 7 September 2018

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			Please cite as: Irfan, T. Mohammod. 2018. ''"IFs Labor Model Documentation."'' Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY <https://pardee.du.edu/wiki/Labor>

	Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.		Workers in an economy supply the expertise and the efforts needed to produce goods and services. In return the labor receives wages that they use to meet their current and future consumption needs. On one hand, shortage of labor with required skills prevents economies from realizing their growth potential. On the other hand, individuals falling short of the right qualifications might remain unemployed or underemployed failing to secure income needed for a decent living. The ongoing adjustments to find the best match between skills, jobs and wages can only be studied through a dynamic model of the labor market.

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	= References =		= References =
			<references /></div></div></div>
	<references />

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	The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients.  We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions. The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref>		The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients.  We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions. The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref>
	<div><div id="ftn1"></div></div></div></div></div></div></div></div>		<div><div id="ftn1"></div></div></div></div></div></div></div></div>

	= Labor Model Flowcharts =		= Labor Model Flowcharts =

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	<references />		<references />
	~~</div></div></div></div></div></div></div></div></div></div>~~

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	== Informal Labor ==		== Informal Labor ==

	IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors~~[[#_ftn1\|[1]]]~~. However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.		IFs forecast labor and GDP share of the informal sector. Informal labor forecast is not explicitly endogenized in the labor market though. They are rather driven by development, skill and regulatory factors.<ref>IFs economic model documentation has a detail description of the informal economy model.</ref> However, the productivity and revenue impacts of changes in informality affects output and thus labor demand implicitly as a very distal driver.

	= References =		= References =

Wikiadmin at 22:54, 7 September 2018

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	The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.		The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.

	[[File:LaborCoefficientFunctions.png\|frame\|center\|~~665x225px~~\|Labor coefficient functions by skill type for the agriculture and the manufacturing sector ]]		[[File:LaborCoefficientFunctions.png\|frame\|center\|665x445px\|Labor coefficient functions by skill type for the agriculture and the manufacturing sector]]

	These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.		These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.

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	The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.		The IFs model divides economic activities into six economic sectors – agriculture, energy, materials, manufacture, services and information, and communication technologies. Workers in the IFs labor model are disaggregated into two skill types. While the skill composition varies by the technology used in the sector and starts tilting towards the more skilled with the progress in technology, absolute number of labors needed to produce the same output goes down with technological development for both skilled and unskilled labor. This is illustrated in the next figure which plots the changes in labor requirement against GDP per capita at PPP, a proxy for level of development. Agriculture is a much less skill-intensive process than the manufacture, however, with technological progress skill requirement improves rapidly in both sectors. The IFs labor model computes these labor requirement functions in the model pre-processor. As we have already described in the pre-processor section, the computation of these functions use GTAP data on employment by occupation and economic activity. Appendices 3 and 4 lists sector and occupation mapping between GTAP and IFs.

			[[File:LaborCoefficientFunctions.png\|frame\|center\|665x225px\|Labor coefficient functions by skill type for the agriculture and the manufacturing sector ]]

	These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.		These functions are used to compute the labor coefficients (LABCOEFFS), i.e., number of skilled and unskilled labor needed to produce unit amount of output with the technology available, for which we use GDP per capita at PPP as a proxy.

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	== Labor by skill level ==		== Labor by skill level ==

	The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP~~[[#_ftn1\|[1]]]~~  and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.		The labor model forecasts labor supply (LABSUP) by two skill categories. The variable (''LABSUP'') is initialized in the pre-processor by reading the employment by skill/occupation (''LABEMPS'') data from GTAP<ref>We collapse GTAP’s 57 sectors into the six economic sectors of IFs. GTAP collapses the nine occupation categories of ISCO-88 into five. In IFs those five categories are collapsed into a binary – skilled and unskilled. The sectoral and skill mappings are described in two appendices of these document.</ref>  and adding the unemployment numbers. We assume same unemployment rate (''LABUMEMPR'') for skilled and unskilled labor.

	:<math>LABSUP_{r,t=1,sk}=sum_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math>		:<math>LABSUP_{r,t=1,sk}=sum_{s=1 to 6}(LABEMPS_{r,s,t=1}/(1-(LABUNEMPR_{r,t=1}/100))</math>

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	== Labor Model Pre-processor ==		== Labor Model Pre-processor ==

	IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed~~[[#_ftn1\|[1]]]~~. Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.		IFs system has a data preprocessor that prepares the initial conditions for the model using historical databases and various assumptions and estimated relationships to fill in the missing data and make data adjustments as needed.<ref>For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf</ref> Pre-processing of labor data takes place in two IFs pre-processing modules. Labor participation rate data, which is closely related to demography, is processed in the population pre-processor. Unemployment rate and labor demand data are processed in the economic pre-processor.
	<div>~~<br/>~~		<div><div id="ftn1">
	~~----~~
	<div id="ftn1">
	[[#_ftnref1\|[1]]] For more details, please see ‘The Data Pre-Processor of International Futures (IFs)” by Barry B. Hughes (with Mohammod Irfan) at [http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf http://www.ifs.du.edu/assets/documents/preprocessorv1_0.pdf]

	=== Pre-processing Labor participation rate and unemployment ===		=== Pre-processing Labor participation rate and unemployment ===

	For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data~~[[#_ftn1\|[1]]]~~. For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan~~[[#_ftn2\|[2]]]~~.		For initializing labor participation rates by sex (LABPARR) the model uses the historical values from the base year or the most recent year with data.<ref>The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.</ref> For countries with no data we use regression relationships of the participation rates, for men and for women, with income per capita. The relationships, shown in the next figure, are not great. However, the functions affect only five countries for which we do not have any data at all: Grenada, Kosovo, Micronesia, Seychelles and South Sudan.<ref>We should try to collect participation rate for these countries from country sources.</ref>
			<div><div id="ftn2">
	~~ ~~		IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan<ref>These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)</ref>) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.
	<~~div~~><br/>		<div><div id="ftn1">
	~~----~~
	~~<div id="ftn1">~~
	~~[[#_ftnref1\|[1]]] The data tables that the IFs model pre-processor use for initializing labor participation rates are: SeriesLaborParRate15PlusFemale%, SeriesLaborParRate15PlusMale%.~~
	</div><div id="ftn2">
	~~[[#_ftnref2\|[2]]] We should try to collect participation rate for these countries from country sources.~~

	IFs data series SeriesLaborUnemploy% is used for the initialization of unemployment rates. That series has annual unemployment rates for one or more years between 1980 and 2016, for 181 of the 186 IFs countries. For five countries (Grenada, Kosovo, Micronesia, Taiwan and South Sudan~~[[#_ftn1\|[1]]]~~) there is no data at all. To fill in the missing data we use a regression function of unemployment rate against GDP per capita. Like the participation rate functions, this function does also not have much of an explanatory power.
	<div>~~<br/>~~
	~~----~~
	<div id="ftn1">
	[[#_ftnref1\|[1]]] These are pretty much the same countries for which we do not have any participation rate data. This indicates ILO might have some administrative limitation in reporting data for these countries (notice Kosovo, Seychelles etc in the list)

	=== Pre-processing labor demand and unemployment from GTAP ===		=== Pre-processing labor demand and unemployment from GTAP ===

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	Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.		Labor coefficients are defined as the amount of labor needed to produce one unit of value added in a certain sector of the economy. The coefficients depend on the level of technology. The model uses GDP per capita as an indicator of the level of technological development. IFs pre-processor estimates labor coefficient functions for labor of different skill levels for the different sectors of the economy.

	The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients.  We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions.  The functions fit quite well with a power law formulation~~[[#_ftn1\|[1]]]~~.		The functions are derived from GTAP data we described earlier. The model pre-processor reads data on factor payments and aggregates data from 57 GTAP sectors to six IFs sectors. Shares of payment going to skilled and less-skilled workers in each of the sectors are then computed. Countries are grouped according to their level of technological development as represented by per capita income. For each group labor coefficients are obtained by taking an average of the country coefficients.  We also convert labor payments data to labor headcount data using per capita income as a proxy for average wage. Labor coefficients and income are then plotted into a power function relationship. The figure below plots some of those labor functions. The functions fit quite well with a power law formulation.<ref>This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</ref>
	~~<div><br/>~~		<div><div id="ftn1"></div></div></div></div></div></div></div></div>
	~~----~~
	<~~div id="ftn1"~~>~~[[#_ftnref1\|[1]]]~~ This is interesting given the prevalence of power law in all sorts of scale-up activities (West 2017).</div></div></div></div></div></div></div></div></div></div>

	= Labor Model Flowcharts =		= Labor Model Flowcharts =

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	The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref> for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.		The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref> for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.
	~~<div><br/>~~
	~~----~~
	~~<div id="ftn1">~~
	~~[[#_ftnref1\|[1]]] The name of the IFs table is SeriesLaborUnemploy%~~
	~~</div><div id="ftn2">[[#_ftnref2\|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</div></div>~~

	== Scope of IFs Labor Model ==		== Scope of IFs Labor Model ==

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	IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.		IFs model uses ILO data for labor participation rates and for the unemployment rate. The data in IFs are collected from World Bank’s World Development Indicators (WDI) database. According to their documentation, WDI obtained the data from the ILO.

	~~ ~~		Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.<ref>The name of the IFs table is SeriesLaborUnemploy%</ref>

	Unemployment rate data in IFs is also collected from WDI. Like the participation rates WDI also obtains their unemployment data from ILO.~~[[#_ftn1\|[1]]]~~

	For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document.		For employment and labor demand data IFs uses Purdue University’s Global Trade Analysis Project (GTAP) database. GTAP collects and compiles factor payments, imports, and intersectoral flow data to calibrate CGE models of national economies for trade and other analyses. In their ninth release in 2016, GTAP published data for 140 countries and regions for the year 2011. The earlier GTAP releases, which the IFs model used for its previous versions, compiled data for the years 2004 and 2007. GTAP data release aggregates economic activities into 57 commodities and activities following International Standard Industrial Classification (ISIC). The IFs model maps the 57 GTAP sectors into six economic sectors of IFs – agriculture, energy, material and mining, manufacture, services and ICT. Appendix 2 presents two tables listing the sectors mapping between IFs and GTAP, and GTAP and ISIC. GTAP further disaggregates labor in each of the commodities/activities into five occupation and skill categories following the nine category International Standard Classification of Occupations (ISCO-88). The IFs model collapses five GTAP occupation categories into the simple IFs dichotomy of skilled and unskilled. The mapping of occupations and skills are presented in the third appendix of this document.

	The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database~~[[#_ftn2\|[2]]]~~ for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.		The data in the main GTAP database, prepared for CGE modeling, are all in dollar unit and thus do not include labor headcounts. We have used a ‘satellite’ GTAP database<ref>See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</ref> for labor headcounts by skill and sector. The labor counts were also used to plot labor requirement functions for each of the IFs economic sectors and skill categories. The wage share of skilled and unskilled labor in each sector was computed using the labor headcounts and labor payments.
	<div><br/>		<div><br/>
	----		----
	<div id="ftn1">		<div id="ftn1">
	[[#_ftnref1\|[1]]] The name of the IFs table is SeriesLaborUnemploy%		[[#_ftnref1\|[1]]] The name of the IFs table is SeriesLaborUnemploy%
	</div><div id="ftn2">		</div><div id="ftn2">[[#_ftnref2\|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.</div></div>
	[[#_ftnref2\|[2]]] See Weingarden and Tsigas, 2010 for the details on the preparation of this database.

	== Scope of IFs Labor Model ==		== Scope of IFs Labor Model ==

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	[[File:LaborSubsets.png\|frame\|right\|Relationship among various labor measurement]]		[[File:LaborSubsets.png\|frame\|right\|Relationship among various labor measurement]]

	Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database		Incompatibility can arise in the treatment of various population groups for the computation of the denominator for participation and unemployment rates.<ref>For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf for a discussion </ref> ILO makes their best efforts to make adjustments in the data for the sake of international comparison. For example, ILO asks countries that deviate from ILO guidelines to collect data needed to convert national figures to ILO figures. It is likely that some differences might have slipped past the adjustment process. We use ILO data and continue to update our database
	~~<div><div id="ftn1">~~		<div><div id="ftn4"><div id="ftn1">
			The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather<ref>Please see the webpage for documentation on GTAP labor data statistic: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248</ref>, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.
	~~</div></div>~~<div~~><br/~~><div id="ftn4">~~<div>~~
	~~----~~
	<div id="ftn1">
	[[#_ftnref1\|[1]]] For example, the USA excludes people in the defense services and those in the prisons or mental asylums in their computation of the civilian non-institutional working-age population. There are also variations in the treatments of students, those recently laid-off, and family workers. Please see [https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf https://stats.bls.gov/opub/mlr/2000/06/art1full.pdf] for a discussion

	The GTAP data that we use for the demand side of the labor model is taken as labor headcounts and is thus immune from ambiguities around rate computation. As far as we could gather~~[[#_ftn1\|[1]]]~~, the data includes both the formal and informal employment. We also need mention here that the GTAP database reconciles the labor data to calibrate the general equilibrium modeling that they do for the trade analyses. The data could thus be somewhat different from data collected through direct surveys. As a CGE model IFs is benefited by using calibrated data.
	~~<div><br/>~~
	~~----~~
	<div id="ftn1">[[#_ftnref1\|[1]]] Please see the webpage for documentation on GTAP labor data statistic: [https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248 https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=3248]</div></div></div></div></div></div>

	== Sources of Labor Data ==		== Sources of Labor Data ==