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 data from urban areas in Mexico with broadl...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Washington, D.C
The World Bank
2022
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| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (47 Seiten) |
| DOI: | 10.1596/1813-9450-10077 |
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| 100 | 1 | |a Merfeld, Joshua D. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |c Joshua D. Merfeld |
| 264 | 1 | |a Washington, D.C |b The World Bank |c 2022 | |
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| 520 | 3 | |a 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 | |
| 650 | 4 | |a Data Integration | |
| 650 | 4 | |a Economic Empowerment | |
| 650 | 4 | |a Employment and Unemployment | |
| 650 | 4 | |a Gender | |
| 650 | 4 | |a Gender Monitoring and Evaluation | |
| 650 | 4 | |a Gendered Employment Data | |
| 650 | 4 | |a Geospatial Data | |
| 650 | 4 | |a Human Capital | |
| 650 | 4 | |a Labor Force Participation | |
| 650 | 4 | |a Labor Markets | |
| 650 | 4 | |a Local Employment Estimates | |
| 650 | 4 | |a Local Labor Participation | |
| 650 | 4 | |a Municipal Unemployment Results | |
| 650 | 4 | |a Small Area Estimation | |
| 650 | 4 | |a Social Capital | |
| 650 | 4 | |a Social Development | |
| 650 | 4 | |a Social Protections and Labor | |
| 650 | 4 | |a Unemployment | |
| 650 | 4 | |a Women's Labor Market Outcomes | |
| 700 | 1 | |a Lahiri, Partha |e Sonstige |4 oth | |
| 700 | 1 | |a Newhouse, David |e Sonstige |4 oth | |
| 700 | 1 | |a Weber, Michael |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Merfeld, Joshua D |t Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |d Washington, D.C. : The World Bank, 2022 |
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| doi_str_mv | 10.1596/1813-9450-10077 |
| format | Electronic eBook |
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| spellingShingle | Merfeld, Joshua D. Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes Data Integration Economic Empowerment Employment and Unemployment Gender Gender Monitoring and Evaluation Gendered Employment Data Geospatial Data Human Capital Labor Force Participation Labor Markets Local Employment Estimates Local Labor Participation Municipal Unemployment Results Small Area Estimation Social Capital Social Development Social Protections and Labor Unemployment Women's Labor Market Outcomes |
| title | Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_auth | Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_exact_search | Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes |
| title_exact_search_txtP | 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 Joshua D. Merfeld |
| title_fullStr | Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes Joshua D. Merfeld |
| title_full_unstemmed | Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes Joshua D. Merfeld |
| title_short | 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 |
| topic | Data Integration Economic Empowerment Employment and Unemployment Gender Gender Monitoring and Evaluation Gendered Employment Data Geospatial Data Human Capital Labor Force Participation Labor Markets Local Employment Estimates Local Labor Participation Municipal Unemployment Results Small Area Estimation Social Capital Social Development Social Protections and Labor Unemployment Women's Labor Market Outcomes |
| topic_facet | Data Integration Economic Empowerment Employment and Unemployment Gender Gender Monitoring and Evaluation Gendered Employment Data Geospatial Data Human Capital Labor Force Participation Labor Markets Local Employment Estimates Local Labor Participation Municipal Unemployment Results Small Area Estimation Social Capital Social Development Social Protections and Labor Unemployment Women's Labor Market Outcomes |
| url | https://doi.org/10.1596/1813-9450-10077 |
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