|Type||Thesis or Dissertation - Doctor of Philosophy|
|Title||Robust inference in poverty mapping|
Small area estimation (SAE) methods are widely used for estimating poverty indicators at
finer levels of a country’s geography. Three unit-level SAE techniques – the ELL method
(Elbers, Lanjouw, and Lanjouw, 2003), also known as the World Bank method, the
Empirical Best Prediction (EBP) method (Molina and Rao, 2010) and the M-Quantile (MQ)
method (Tzavidis et al., 2008) have all been used to estimate micro-level FGT poverty
indicators (Foster, Greer, and Thorbecke, 1984). These methods vary in terms of their
underlying model assumptions particularly differences in consideration of random effects.
This thesis provides results from a numerical comparison of the statistical performance of
these three methodologies in the context of a realistic simulation scenario based on a recent
Bangladesh poverty study. This comparison study shows that the ELL method is the better
performer in terms of relative bias but also significantly underestimates the MSEs of its
small area poverty estimates when its underlying area homogeneity assumption is violated.
A modified MSE estimation method for ELL-type poverty estimates is therefore developed
in this thesis. This method is robust to the presence of significant unexplained between-area
variability in the income distribution.
|»||Bangladesh - Household Income and Expenditure Survey 2000|