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A Comparison of Methods for Poverty Estimation in Developing Countries

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  • Sumonkanti Das
  • Stephen Haslett

Abstract

Small area estimation is a widely used indirect estimation technique for micro‐level geographic profiling. Three unit level small area estimation techniques—the ELL or World Bank method, empirical best prediction (EBP) and M‐quantile (MQ) — can estimate micro‐level Foster, Greer, & Thorbecke (FGT) indicators: poverty incidence, gap and severity using both unit level survey and census data. However, they use different assumptions. The effects of using model‐based unit level census data reconstructed from cross‐tabulations and having no cluster level contextual variables for models are discussed, as are effects of small area and cluster level heterogeneity. A simulation‐based comparison of ELL, EBP and MQ uses a model‐based reconstruction of 2000/2001 data from Bangladesh and compares bias and mean square error. A three‐level ELL method is applied for comparison with the standard two‐level ELL that lacks a small area level component. An important finding is that the larger number of small areas for which ELL has been able to produce sufficiently accurate estimates in comparison with EBP and MQ has been driven more by the type of census data available or utilised than by the model per se.

Suggested Citation

  • Sumonkanti Das & Stephen Haslett, 2019. "A Comparison of Methods for Poverty Estimation in Developing Countries," International Statistical Review, International Statistical Institute, vol. 87(2), pages 368-392, August.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:2:p:368-392
    DOI: 10.1111/insr.12314
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    Cited by:

    1. Adam Chwila & Tomasz Żądło, 2020. "On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 35-60, June.
    2. Arias-Salazar Alejandra, 2023. "Small Area Estimates of Poverty Incidence in Costa Rica under a Structure Preserving Estimation (SPREE) Approach," Journal of Official Statistics, Sciendo, vol. 39(4), pages 435-458, December.
    3. Md Jamal Hossain & Sumonkanti Das & Hukum Chandra & Mohammad Amirul Islam, 2020. "Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-16, April.
    4. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    5. Penelope Bilton & Geoff Jones & Siva Ganesh & Stephen Haslett, 2020. "Regression trees for poverty mapping," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 426-443, December.
    6. Chwila Adam & Żądło Tomasz, 2020. "On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 35-60, June.
    7. Batana,Yele Maweki & Masaki,Takaaki & Nakamura,Shohei & Viboudoulou Vilpoux,Mervy Ever, 2021. "Estimating Poverty in Kinshasa by Dealing with Sampling and Comparability Issues," Policy Research Working Paper Series 9858, The World Bank.

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