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

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  • Merfeld,Joshua David
  • Newhouse,David Locke
  • Weber,Michael
  • Lahiri,Partha

Abstract

Better understanding the geography of women’s labor market outcomes within countries is importantto inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which amethod that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators fromGoogle Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation andunemployment rates. Incorporating geospatial information substantially increases the accuracy of male and femalelabor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested errorconditional random effect model also greatly improves municipal estimates of labor force participation, as themean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holdingcoverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliablebecause values of unemployment rates are low and therefore poorly suited for linear models. The municipal results holdin repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliaryinformation 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 improvestate-level estimates of male and female labor force participation and unemployment rates, as well as municipalestimates of male and female labor force participation.

Suggested Citation

  • Merfeld,Joshua David & Newhouse,David Locke & Weber,Michael & Lahiri,Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-DisaggregatedEstimates of Labor Market Outcomes," Policy Research Working Paper Series 10077, The World Bank.
    • Handle: RePEc:wbk:wbrwps:10077
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    1. Stephan Klasen, 2019. "What Explains Uneven Female Labor Force Participation Levels and Trends in Developing Countries?," The World Bank Research Observer, World Bank, vol. 34(2), pages 161-197.
    2. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    3. Isabel Molina & Ayoub Saei & M. José Lombardía, 2007. "Small area estimates of labour force participation under a multinomial logit mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 975-1000, October.
    4. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    5. Yolanda Marhuenda & Isabel Molina & Domingo Morales & J. N. K. Rao, 2017. "Poverty mapping in small areas under a twofold nested error regression model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1111-1136, October.
    6. Ray Chambers & Nicola Salvati & Nikos Tzavidis, 2016. "Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 453-479, February.
    7. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
    8. Yan Li & Partha Lahiri, 2019. "A Simple Adaptation of Variable Selection Software for Regression Models to Select Variables in Nested Error Regression Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 302-317, December.
    9. Zhang, Yiyun & Li, Runze & Tsai, Chih-Ling, 2010. "Regularization Parameter Selections via Generalized Information Criterion," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 312-323.
    10. Andreea L. Erciulescu & Nathan B. Cruze & Balgobin Nandram, 2019. "Model‐based county level crop estimates incorporating auxiliary sources of information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 283-303, January.
    11. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    12. David B Lobell & George Azzari & Marshall Burke & Sydney Gourlay & Zhenong Jin & Talip Kilic & Siobhan Murray, 2020. "Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 202-219, January.
    13. Federico Belotti & Partha Deb & Willard G. Manning & Edward C. Norton, 2015. "twopm: Two-part models," Stata Journal, StataCorp LP, vol. 15(1), pages 3-20, March.
    14. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
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    1. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.

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    More about this item

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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