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

Hai-Anh Dang (), Dean Jolliffe () and Calogero Carletto ()

No 179, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: We offer a review of methods that have been employed to provide poverty estimates of poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross sectional household surveys, to missing panel household data. We focus on methods that aim to compare trends and dynamic patterns of poverty outcomes over time. We present the various existing methods under a common framework, with pedagogical discussion on the 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 (search for similar items in EconPapers)
JEL-codes: C15 I32 O15 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-dev and nep-sea
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https://www.econstor.eu/bitstream/10419/173752/1/GLO-DP-0179.pdf (application/pdf)

Related works:
Journal Article: DATA GAPS, DATA INCOMPARABILITY, AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA‐SCARCE ENVIRONMENTS (2019) Downloads
Working Paper: Data gaps, data incomparability, and data imputation: A review of poverty measurement methods for data-scarce environments (2018) Downloads
Working Paper: Data gaps, data incomparability, and data imputation: a review of poverty measurement methods for data-scarce environments (2017) Downloads
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