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...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2022
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e http://hdl.handle.net/10986/37519 |
| Summary: | 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. |
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