A Comparison of Methods for Poverty Estimation in Developing Countries

Sumon Das, Stephen Haslett

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)368-392
Number of pages25
JournalInternational Statistical Review
Volume87
Issue number2
DOIs
Publication statusPublished - Aug 2019

Keywords

  • CENSUS
  • ELL
  • M-QUANTILE MODELS
  • M-quantile
  • MEAN-SQUARED ERROR
  • SMALL-AREA ESTIMATION
  • empirical best prediction
  • small area estimation
  • unit record census data

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