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

43 Pages Posted: 4 Dec 2019 Last revised: 5 Dec 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; Vietnam Academy of Social Sciences (VASS) - Centre for Analysis and Forecasting

Paolo Verme

World Bank Group; University of Turin - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: December 3, 2019

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.

Suggested Citation

Dang, Hai-Anh H. and Verme, Paolo, Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity? (December 3, 2019). World Bank Policy Research Working Paper No. 9076, Available at SSRN: https://ssrn.com/abstract=3498004

Hai-Anh H. Dang (Contact Author)

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

1818 H. Street, N.W.
MC2-846
Washington, DC 20433
United States

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

IZA Institute of Labor Economics ( email )

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

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

1315 East Tenth Street
Bloomington, IN 47405
United States

Global Labor Organization (GLO) ( email )

Collogne
Germany

Vietnam National University Ha Noi ( email )

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

1 Lieu Giai Street
Hanoi
Vietnam

Paolo Verme

World Bank Group ( email )

Washington, DC 20433
United States

University of Turin - Department of Economics ( email )

Via Po, 53
Torino, 10124
Italy

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
9
Abstract Views
118
PlumX Metrics