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

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dc.contributor.authorMerfeld, Joshua D.
dc.contributor.authorNewhouse, David
dc.contributor.authorWeber, Michael
dc.contributor.authorLahiri, Partha
dc.date.accessioned2022-06-10T15:09:30Z
dc.date.available2022-06-10T15:09:30Z
dc.date.issued2022-06
dc.description.abstractBetter 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.en
dc.identifierhttp://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e
dc.identifier.doi10.1596/1813-9450-10077
dc.identifier.urihttps://hdl.handle.net/10986/37519
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Papers;10077
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectSMALL AREA ESTIMATION
dc.subjectDATA INTEGRATION
dc.subjectGEOSPATIAL DATA
dc.subjectLABOR FORCE PARTICIPATION
dc.subjectUNEMPLOYMENT
dc.subjectWOMEN'S LABOR MARKET OUTCOMES
dc.subjectECONOMIC EMPOWERMENT
dc.subjectLOCAL LABOR PARTICIPATION
dc.subjectLOCAL EMPLOYMENT ESTIMATES
dc.subjectMUNICIPAL UNEMPLOYMENT RESULTS
dc.subjectGENDERED EMPLOYMENT DATA
dc.subjectHUMAN CAPITAL
dc.titleCombining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomesen
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.date.disclosure2022-06-09
okr.date.lastmodified2022-06-09T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099321406092229138/IDU016f95e0806fc6044ea0b843007d5dc0ef17e
okr.guid099321406092229138
okr.identifier.doi10.1596/1813-9450-10077
okr.identifier.externaldocumentum33839458
okr.identifier.internaldocumentum33839458
okr.identifier.reportWPS10077
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099321406092229138/pdf/IDU016f95e0806fc6044ea0b843007d5dc0ef17e.pdfen
okr.region.countryMexico
okr.topicGender::Gender Monitoring and Evaluation
okr.topicGender::Gender and Urban Development
okr.topicSocial Protections and Labor::Labor Markets
okr.topicSocial Protections and Labor::Employment and Unemployment
okr.topicSocial Development::Social Capital
okr.unitHuman Capital Director (HHCDR)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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