Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments

Journal of Economic Surveys, Forthcoming

67 Pages Posted: 11 Jan 2019

See all articles by Hai-Anh Dang

Hai-Anh Dang

World Bank - Development Data Group (DECDG); IZA Institute of Labor Economics; Indiana University Bloomington - School of Public & Environmental Affairs (SPEA); Global Labor Organization (GLO); Vietnam National University Ha Noi

Dean Jolliffe

World Bank, DECDG; IZA Institute of Labor Economics; Global Labor Organization (GLO); Johns Hopkins University, Paul H. Nitze School of Advanced International Studies (SAIS), Students

Calogero Carletto

World Bank; World Bank - Development Research Group (DECRG)

Multiple version iconThere are 2 versions of this paper

Date Written: December 31, 2018

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.

Keywords: poverty, mobility, imputation, consumption, wealth index, synthetic panels, household survey

JEL Classification: C15, I32, O15

Suggested Citation

Dang, Hai-Anh H. and Jolliffe, Dean and Carletto, Calogero, Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments (December 31, 2018). Journal of Economic Surveys, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3308544

Hai-Anh H. Dang (Contact Author)

World Bank - Development Data Group (DECDG) ( email )

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HOME PAGE: http://sites.google.com/site/haianhhdang/

IZA Institute of Labor Economics ( email )

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Indiana University Bloomington - School of Public & Environmental Affairs (SPEA) ( email )

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Global Labor Organization (GLO) ( email )

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Vietnam National University Ha Noi ( email )

Dean Jolliffe

World Bank, DECDG ( email )

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HOME PAGE: http://www.deanjolliffe.net

IZA Institute of Labor Economics ( email )

P.O. Box 7240
Bonn, D-53072
Germany

HOME PAGE: http://www.iza.org/en/webcontent/index_html

Global Labor Organization (GLO) ( email )

Collogne
Germany

HOME PAGE: http://https://glabor.org/

Johns Hopkins University, Paul H. Nitze School of Advanced International Studies (SAIS), Students ( email )

1740 Massachusetts Avenue, NW
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Calogero Carletto

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

World Bank - Development Research Group (DECRG)

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MSN3-311
Washington, DC 20433
United States

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