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  • 1
    Online Resource
    Online Resource
    Improving Estimates of Mean Welfare and Uncertainty in Developing Countries (2023)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (57 pages)
    Parallel Title: Erscheint auch als Merfeld, Joshua D Improving Estimates of Mean Welfare and Uncertainty in Developing Countries
    Keywords: Development Policy ; Geospacial Data ; Household Census Data ; Machine Learning ; Macroeconomics and Economic Growth ; Poverty Reduction ; Prediction of Poverty ; Prediction of Wealth ; Welfare
    Abstract: Reliable estimates of economic welfare for small areas are valuable inputs into the design and evaluation of development policies. This paper compares the accuracy of point estimates and confidence intervals for small area estimates of wealth and poverty derived from four different prediction methods: linear mixed models, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws samples from unit-level household census data from four developing countries, combines them with publicly and globally available geospatial indicators to generate small area estimates, and evaluates these estimates against aggregates calculated using the full census. Predictions of wealth are evaluated in four countries and poverty in one. All three machine learning methods outperform the traditional linear mixed model, with extreme gradient boosting and boosted regression forests generally outperforming the other alternatives. The proposed residual bootstrap procedure reliably estimates confidence intervals for the machine learning estimators, with estimated coverage rates across simulations falling between 94 and 97 percent. These results demonstrate that predictions obtained using tree-based gradient boosting with a random effect block bootstrap generate more accurate point and uncertainty estimates than prevailing methods for generating small area welfare estimates
    DOI: 10.1596/1813-9450-10348
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Small Area Estimation of Monetary Poverty in Mexico using Satellite Imagery and Machine Learning (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (72 pages)
    Parallel Title: Erscheint auch als Newhouse, David Small Area Estimation of Monetary Poverty in Mexico using Satellite Imagery and Machine Learning
    Keywords: Inequality ; Information and Communication Technologies ; Machine Learning ; Poverty ; Poverty Assessment ; Poverty Eradication ; Poverty Mapping ; Poverty Reduction ; Poverty, Environment and Development ; Satellite Data ; Small Area Estimation ; Sustainable Development Goals
    Abstract: Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups
    DOI: 10.1596/1813-9450-10175
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    The Welfare Implications of COVID-19 for Fragile and Conflict-Affected Areas (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (37 pages)
    Parallel Title: Erscheint auch als Tabakis, Chrysostomos The Welfare Implications of COVID-19 for Fragile and Conflict-Affected Areas
    Keywords: Access and Equity in Basic Education ; Access of Poor To Social Services ; Agriculture ; Conflict ; Covid In Conflict-Affected Households ; COVID-19 Restriction Social Impact ; Education ; Food and Nutrition Policy ; Food Insecurity ; Food Security ; Fragility ; Health, Nutrition and Population ; Household Welfare ; Inequality ; Pandemic Social Impact ; Violence
    Abstract: Understanding the impacts of the COVID-19 pandemic on households' welfare in areas at the admin-1 level subject to fragility, conflict, and violence is important to inform programs and policies in this context. Harmonized data from high-frequency phone surveys indicate that, at the onset of the pandemic, a higher fraction of households in areas affected by fragility, conflict, and violence reported income declines and a higher fraction of respondents reported that they had stopped working since the beginning of the crisis. Households in areas affected by fragility, conflict, and violence were far less likely to report receiving government assistance than those in other areas. These findings suggest that the initial effects of the pandemic exacerbated preexisting economic gaps between areas affected by fragility, conflict, and violence and other areas, indicating that an even larger effort will be necessary in areas affected by fragility, conflict, and violence to recover from COVID-19, with implications for funding needs and policy as well as program design
    DOI: 10.1596/1813-9450-10081
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes (2022)
    Washington, D.C : The World Bank
    Details
    Language: English
    Pages: 1 Online-Ressource (47 pages)
    Parallel Title: Erscheint auch als Merfeld, Joshua D Combining Survey and Geospatial Data can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes
    Keywords: 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
    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
    DOI: 10.1596/1813-9450-10077
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
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