Small Area Estimation of Poverty under Structural Change

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Guidelines to Small Area Estimation for Poverty Mapping

“…In off-census years, the typical small area approach applied has been an area-level model, such as a Fay-Herriot or a sub-area-level model, such as the one proposed by Torabi and Rao (2014). Nevertheless, since the perceived gains in precision from area-level models are less than stellar, methods that combine unit-and area-level models have been proposed (see Nguyen 2012;Lange, Pape, and Pütz 2018;).…”

“…A similar approach to the one from Nguyen (2012) was presented by Lange, Pape, and Pütz (2018) as an alternative in cases when census and survey data are not from similar periods. However, the same inefficiency issues noted in Chapter 4 regarding ELL estimates would likely persist when considering a model using only area-level covariates.…”

A Map of the Poor or a Poor Map?

“…The results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, given the inherent high level of bias of methods relying solely on aggregate information (see Nguyen [9]; Lange et al [11], Masaki et al [12]) these should be used with caution and only under certain circumstances.…”

“…Lange et al [11] present an approach similar to that of Nguyen [9], which the authors suggest as an alternative in cases when census and survey data are not from similar periods, though the same issues noted above for the ELL method would likely persist in a model using only area-level covariates. Masaki et al [12] use a similar modeling approach to Nguyen [9], but take measures to address some of the shortcomings of a standard ELL approach and obtain EB estimators of Molina and Rao [5], which appear to be more precise.…”

“…UC − ELL c : ELL estimates under model in UC − CEB a , but random effects are specified at the PSU level, as was originally proposed by Nguyen [9] and then by Lange et al [11]. In cases where we use a transformation different from the natural logarithm, random location effects and household residuals are drawn from their empirical distribution.…”