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Small Area Estimation of Poverty Under Structural Change

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  • Simon Lange
  • Utz Johann Pape
  • Peter Pütz

Abstract

Small area estimation is an important tool to produce estimates of poverty for regions with low or zero sample sizes. Estimates are typically obtained by combining a consumption survey reporting on poverty and a census providing the spatial disaggregation. This paper discusses an updating method that produces up‐to‐date small area estimates when only a dated census and a more recent survey are available and predictors are subject to drift over time, a situation commonly encountered in practice. Instead of using survey variables to explain consumption in the survey, the updating approach uses only variables constructed from the census. The proposed estimator has fewer data requirements and weaker assumptions than common small area estimators. Applications to simulated data and to poverty estimation in Brazil show an overall good performance, but also imply the importance of examining practical challenges in real‐world applications.

Suggested Citation

  • Simon Lange & Utz Johann Pape & Peter Pütz, 2022. "Small Area Estimation of Poverty Under Structural Change," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 264-281, December.
    • Handle: RePEc:bla:revinw:v:68:y:2022:i:s2:p:s264-s281
    DOI: 10.1111/roiw.12558
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    References listed on IDEAS

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    1. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
    2. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
    3. Simon Lange & Utz Johann Pape & Peter Pütz, 2022. "Small Area Estimation of Poverty Under Structural Change," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 264-281, December.
    4. Nguyen Viet Cuong, 2012. "A Method to Update Poverty Maps," Journal of Development Studies, Taylor & Francis Journals, vol. 48(12), pages 1844-1863, December.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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    Cited by:

    1. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    2. Newhouse David, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 45-50, August.
    3. Simon Lange & Utz Johann Pape & Peter Pütz, 2022. "Small Area Estimation of Poverty Under Structural Change," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 264-281, December.
    4. 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.
    5. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.

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