Abstract
SummarySmall 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.
Highlights
Traditional national surveys are designed to obtain national and regional statistics
The distributions of area-specific relative bias (RB) and relative root MSE (RRMSE) are shown as percentages in Figure 1 and Figure 2, respectively
The mean and median RBs of the estimated root mean square error (RMSE) shown in Table S5 clearly shows that ELL.2L and MQ severely underestimated the true RMSE for head count ratio and poverty gap (PG), while highly overestimated for poverty severity (PS) with higher RRMSE
Summary
Follow this and additional works at: https://ro.uow.edu.au/eispapers Part of the Engineering Commons, and the Science and Technology Studies Commons. Recommended Citation Das, Sumonkanti and Haslett, Stephen, "A Comparison of Methods for Poverty Estimation in Developing Countries" (2019). Faculty of Engineering and Information Sciences - Papers: Part B. Faculty of Engineering and Information Sciences - Papers: Part B. 2906. https://ro.uow.edu.au/eispapers1/2906
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