Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data‐scarce Environments

Abstract

Questions that often come up in contexts where household consumption data are unavailable or missing include: what are the best existing methods to obtain poverty estimates at a single snapshot in time? and over time? and what are the best available methods to study poverty dynamics? A variety of different techniques have been developed to tackle these questions, but unfortunately, they are presented in different forms and lack unified terminology. We offer a review of poverty imputation methods that address contexts ranging from completely missing and partially missing consumption data in cross‐sectional household surveys, to missing panel household data. We present the various existing methods under a common framework, with pedagogical discussion on their intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, we also offer a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.

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“…Hence, all sorts of gaps in the text are expressed by one of these properties. Such combinations have been used in other fields as well to refer to data gaps [33,34]. The results were drawn after analyzing the full contexts of the sentences and paragraphs detected by the software.…”

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“…Many surveys add other modules to the core consumption/expenditure section, to collect information on education, labor market, nutrition, and housing conditions. The most common practice is to use consumption, expenditure, and income to derive a money-metric measure to calculate poverty rates (Dang et al 2019). 4 National governments and international organizations use HBSs to inform poverty monitoring systems which track progress towards achieving regional/global poverty targets and which help formulate operational policies.…”