Improving Estimates of Mean Welfare and Uncertainty in Developing Countries:
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 method...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Washington, D.C
The World Bank
2023
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| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (57 Seiten) |
| DOI: | 10.1596/1813-9450-10348 |
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| 520 | 3 | |a 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 | |
| 650 | 4 | |a Development Policy | |
| 650 | 4 | |a Geospacial Data | |
| 650 | 4 | |a Household Census Data | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Macroeconomics and Economic Growth | |
| 650 | 4 | |a Poverty Reduction | |
| 650 | 4 | |a Prediction of Poverty | |
| 650 | 4 | |a Prediction of Wealth | |
| 650 | 4 | |a Welfare | |
| 700 | 1 | |a Newhouse, David |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Merfeld, Joshua D |t Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |d Washington, D.C. : The World Bank, 2023 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:27:55Z |
| indexdate | 2025-02-20T07:20:12Z |
| institution | BVB |
| language | English |
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| publisher | The World Bank |
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| spellingShingle | Merfeld, Joshua D. Improving Estimates of Mean Welfare and Uncertainty in Developing Countries Development Policy Geospacial Data Household Census Data Machine Learning Macroeconomics and Economic Growth Poverty Reduction Prediction of Poverty Prediction of Wealth Welfare |
| title | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |
| title_auth | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |
| title_exact_search | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |
| title_exact_search_txtP | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |
| title_full | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries Joshua D. Merfeld |
| title_fullStr | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries Joshua D. Merfeld |
| title_full_unstemmed | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries Joshua D. Merfeld |
| title_short | Improving Estimates of Mean Welfare and Uncertainty in Developing Countries |
| title_sort | improving estimates of mean welfare and uncertainty in developing countries |
| topic | Development Policy Geospacial Data Household Census Data Machine Learning Macroeconomics and Economic Growth Poverty Reduction Prediction of Poverty Prediction of Wealth Welfare |
| topic_facet | Development Policy Geospacial Data Household Census Data Machine Learning Macroeconomics and Economic Growth Poverty Reduction Prediction of Poverty Prediction of Wealth Welfare |
| url | https://doi.org/10.1596/1813-9450-10348 |
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