Extended Structure Preserving Estimation (ESPREE) for updating small area estimates of poverty

Abstract

Small area estimation techniques are now routinely used to generate local-level poverty estimates for aid allocation and poverty monitoring in developing countries. However, the widely implemented World Bank (WB) or Elbers, Lanjouw and Lanjouw [Econometrica 71 (2003) 355–364] (ELL) method can only be used when a survey and census are conducted at approximately the same time. The empirical best prediction (EBP) method of Molina and Rao [Canad. J. Statist. 38 (2010) 369–385] also requires a new census for updating. Hence, if small area estimation methods that use both survey and census unit record data are required, and the survey is rerun some years after the census, how to update small area estimates becomes an important issue. In this paper, we propose an intercensal updating method for local-level poverty estimates with estimated standard errors which we call Extended Structure PREserving Estimation (ESPREE). This method is a new extension of classical Structure PREserving Estimation (SPREE). We test our approach by applying it to inter-censal municipal-level poverty estimation and carrying out a validation exercise in the Philippines, comparing the estimates generated with an alternative ELL or EBP updating method due to Lanjouw and van der Wiede [Determining changes in welfare distributions at the micro-level: Updating poverty maps. (2006) Powerpoint presentation at the NSCB Workshop for the NSCB/World Bank Intercensal Updating Project] which uses time-invariant variables. The results show that the ESPREE estimates are preferable, generally being unbiased and concurring well with local experts’ opinion on poverty levels at the time of the updated survey.