Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes

Joshua D. Merfeld (), David Newhouse, Michael Weber () and Partha Lahiri ()
Additional contact information
Joshua D. Merfeld: KDI School of Public Policy and Management
Michael Weber: University of Chicago
Partha Lahiri: University of Maryland

No 15390, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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.

Keywords: small area estimation; data integration; geospatial data; labor force participation; unemployment; Mexico (search for similar items in EconPapers)
JEL-codes: C13 J21 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2022-06
New Economics Papers: this item is included in nep-big, nep-dev, nep-geo, nep-lma and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes (2022)
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