DATA GAPS, DATA INCOMPARABILITY, AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA-SCARCE ENVIRONMENTS

摘要

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.