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Small Area Estimation of Poverty under Structural Change / Lange, Simon.
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
View online- Format:
- Book
- Government document
- Author/Creator:
- Lange, Simon.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Censuses.
- Education.
- Educational Sciences.
- Employment and Unemployment.
- Inequality.
- Population Estimates.
- Poverty.
- Poverty Assessment.
- Poverty Diagnostics.
- Poverty Impact Evaluation.
- Poverty Lines.
- Poverty Monitoring and Analysis.
- Poverty Reduction.
- Small Area Estimation.
- Small Area Estimation Poverty Mapps.
- Social Protections and Labor.
- Local Subjects:
- Censuses.
- Education.
- Educational Sciences.
- Employment and Unemployment.
- Inequality.
- Population Estimates.
- Poverty.
- Poverty Assessment.
- Poverty Diagnostics.
- Poverty Impact Evaluation.
- Poverty Lines.
- Poverty Monitoring and Analysis.
- Poverty Reduction.
- Small Area Estimation.
- Small Area Estimation Poverty Mapps.
- Social Protections and Labor.
- Physical Description:
- 1 online resource (22 pages)
- Place of Publication:
- Washington, D.C. : The World Bank, 2018.
- System Details:
- data file
- Summary:
- Small area poverty maps allow for the design of policies based on spatial differences in welfare. They are typically estimated based on a consumption survey reporting on poverty and a census providing the spatial disaggregation. This paper presents a new method which allows for the estimation of up-to-date small area poverty maps when only a dated census and a more recent survey are available and predictors and structural parameters are subject to drift over time, a situation commonly encountered in practice. Instead of using survey variables to explain consumption in the survey, the new approach uses variables constructed from the census. The proposed estimator has fewer data requirements and weaker assumptions than common small area poverty map estimators. Applications to simulated data and to poverty estimation in Brazil show an overall good performance.
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