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

41 Pages Posted: 28 May 2020

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; Vietnam Academy of Social Sciences (VASS) - Centre for Analysis and Forecasting

Dean Jolliffe

World Bank

Calogero Carletto

affiliation not provided to SSRN

Multiple version iconThere are 3 versions of this paper

Date Written: July 2019

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: Consumption, Household survey, Imputation, Mobility, Poverty, Synthetic panels, Wealth index

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 (July 2019). Journal of Economic Surveys, Vol. 33, Issue 3, pp. 757-797, 2019, Available at SSRN: https://ssrn.com/abstract=3608912 or http://dx.doi.org/10.1111/joes.12307

Hai-Anh H. Dang (Contact Author)

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

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

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Vietnam National University Ha Noi ( email )

Vietnam Academy of Social Sciences (VASS) - Centre for Analysis and Forecasting ( email )

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Dean Jolliffe

World Bank

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Washington, DC 20433
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Calogero Carletto

affiliation not provided to SSRN

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