Premium
DATA GAPS, DATA INCOMPARABILITY, AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA‐SCARCE ENVIRONMENTS
Author(s) -
Dang HaiAnh,
Jolliffe Dean,
Carletto Calogero
Publication year - 2019
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/joes.12307
Subject(s) - missing data , imputation (statistics) , poverty , terminology , computer science , econometrics , data science , data mining , economics , economic growth , machine learning , linguistics , philosophy
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.