Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes
Better understanding the geography of women’s labor market outcomes within countries is important to inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which a method that combines simulated survey d...
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| Format: | Working Paper |
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World Bank, Washington, DC
2022
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| Online Access: | http://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e http://hdl.handle.net/10986/37519 |
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okr-10986-375192022-06-11T05:10:38Z Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes Merfeld, Joshua D. Newhouse, David Weber, Michael Lahiri, Partha SMALL AREA ESTIMATION DATA INTEGRATION GEOSPATIAL DATA LABOR FORCE PARTICIPATION UNEMPLOYMENT WOMEN'S LABOR MARKET OUTCOMES ECONOMIC EMPOWERMENT LOCAL LABOR PARTICIPATION LOCAL EMPLOYMENT ESTIMATES MUNICIPAL UNEMPLOYMENT RESULTS GENDERED EMPLOYMENT DATA HUMAN CAPITAL Better understanding the geography of women’s labor market outcomes within countries is important to inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation. 2022-06-10T15:09:30Z 2022-06-10T15:09:30Z 2022-06 Working Paper http://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e http://hdl.handle.net/10986/37519 English Policy Research Working Papers;10077 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Policy Research Working Paper Publications & Research Mexico |
| repository_type |
Digital Repository |
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Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
SMALL AREA ESTIMATION DATA INTEGRATION GEOSPATIAL DATA LABOR FORCE PARTICIPATION UNEMPLOYMENT WOMEN'S LABOR MARKET OUTCOMES ECONOMIC EMPOWERMENT LOCAL LABOR PARTICIPATION LOCAL EMPLOYMENT ESTIMATES MUNICIPAL UNEMPLOYMENT RESULTS GENDERED EMPLOYMENT DATA HUMAN CAPITAL |
| spellingShingle |
SMALL AREA ESTIMATION DATA INTEGRATION GEOSPATIAL DATA LABOR FORCE PARTICIPATION UNEMPLOYMENT WOMEN'S LABOR MARKET OUTCOMES ECONOMIC EMPOWERMENT LOCAL LABOR PARTICIPATION LOCAL EMPLOYMENT ESTIMATES MUNICIPAL UNEMPLOYMENT RESULTS GENDERED EMPLOYMENT DATA HUMAN CAPITAL Merfeld, Joshua D. Newhouse, David Weber, Michael Lahiri, Partha Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| geographic_facet |
Mexico |
| relation |
Policy Research Working Papers;10077 |
| description |
Better understanding the geography of
women’s labor market outcomes within countries is important
to inform targeted efforts to increase women’s economic
empowerment. This paper assesses the extent to which a
method that combines simulated survey data from urban areas
in Mexico with broadly available geospatial indicators from
Google Earth Engine and OpenStreetMap can significantly
improve estimates of labor force participation and
unemployment rates. Incorporating geospatial information
substantially increases the accuracy of male and female
labor force participation and unemployment rates at the
state level, reducing mean absolute deviation by 50 to 62
percent for labor force participation and 25 to 52 percent
for unemployment. Small area estimation using a nested error
conditional random effect model also greatly improves
municipal estimates of labor force participation, as the
mean absolute error falls by approximately half, while the
mean squared error falls by almost 75 percent when holding
coverage rates constant. In contrast, the results for
municipal unemployment rate estimates are not reliable
because values of unemployment rates are low and therefore
poorly suited for linear models. The municipal results hold
in repeated simulations of alternative samples. Models
utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary
information generate more accurate predictions than
area-level models specified using the same auxiliary data.
Overall, integrating survey data and publicly available
geospatial indicators is feasible and can greatly improve
state-level estimates of male and female labor force
participation and unemployment rates, as well as municipal
estimates of male and female labor force participation. |
| format |
Working Paper |
| author |
Merfeld, Joshua D. Newhouse, David Weber, Michael Lahiri, Partha |
| author_facet |
Merfeld, Joshua D. Newhouse, David Weber, Michael Lahiri, Partha |
| author_sort |
Merfeld, Joshua D. |
| title |
Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_short |
Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_full |
Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_fullStr |
Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_full_unstemmed |
Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_sort |
combining survey and geospatial data can significantly improve gender-disaggregated estimates of labor market outcomes |
| publisher |
World Bank, Washington, DC |
| publishDate |
2022 |
| url |
http://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e http://hdl.handle.net/10986/37519 |
| _version_ |
1764487383103832064 |