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Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?

Hai-Anh Dang () and Paolo Verme ()

No 12822, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper offers a first attempt to measure poverty among refugees using cross-survey imputations and administrative and survey data collected by the United Nations High Commissioner for Refugees in Jordan. Employing a small number of predictors currently available in the United Nations High Commissioner for Refugees registration system, the proposed methodology offers out-of-sample predicted poverty rates. These estimates are not statistically different from the actual poverty rates. The estimates are robust to different poverty lines, they are more accurate than those based on asset indexes or proxy means tests, and they perform well according to targeting indicators. They can also be obtained with relatively small samples. Despite these preliminary encouraging results, it is essential to replicate this experiment across countries using different data sets and welfare aggregates before validating the proposed method.

Keywords: poverty imputation; Syrian refugees; household survey; Jordan; missing data (search for similar items in EconPapers)
JEL-codes: C15 I32 J15 J61 O15 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2019-12
New Economics Papers: this item is included in nep-ara, nep-dev and nep-mig
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Forthcoming - forthcoming in: Journal of Population Economics

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Related works:
Working Paper: Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity ? (2019) Downloads
Working Paper: Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity? (2019) Downloads
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