Authors:Sumonkanti Das (Shahjalal University of Science & Technology, Bangladesh & University of Wollongong, Australia)Stephen Haslett (Shahjalal University of Science and Technology, Bangladesh and University of Wollongong, Australia) (Speaker)
Small area estimation (SAE) has been widely used as an indirect estimation technique for geographic profiling of poverty indicators. Three unit-level SAE techniques: the method of Elbers, Lanjouw, and Lanjouw (2003) also known as ELL or World Bank method, the Empirical Best Prediction (EBP) method of Molina and Rao (2010), and the M-Quantile (MQ) method of Tzavidis et al. (2008), have been widely used to estimate the micro-level FGT poverty indicators: poverty incidence, gap and severity (Foster et al., 1984). The three methods have in common that they use both unit level survey data and (possibly model-based) unit level census data. However, they differ in their applicability because real data sets do not always follow their underlying assumptions. The performance of these three methods is compared in terms of small area poverty estimates and their standard errors. The effects of using a model-based unit record census data reconstructed from available cross-tabulations are discussed, as are the effects of small area-heterogeneity and cluster-heterogeneity in the over-arching superpopulation model. The three methods also have variants. A three-level nested-error regression model-based ELL method is applied for comparison with the standard two-level model-based ELL method which does not contain a random component at small area level, and with EBP and MQ. A comparison study uses a simulation based on 2003 data from Bangladesh. An important finding is that the number of small areas for which a method is able to produce sufficiently accurate estimates is more often been driven by the type of data available than by the model per se.
Keywords: Empirical Best Prediction; ELL; M-quantile; small area estimation; unit record census data.