Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation

Abstract

Abstract The increasing growth of forced displacement worldwide has brought more attention to measuring poverty among refugee populations. However, refugee data remain scarce, particularly regarding income or consumption. We offer a first attempt to measure poverty among refugees using cross-survey imputation and administrative and survey data collected by the United Nations High Commissioner for Refugees (UNHCR). Employing a small number of predictors currently available in the UNHCR registration system, the proposed methodology offers out-of-sample predicted poverty rates that are not statistically different from actual poverty rates. These estimates are robust to different poverty lines, perform well according to targeting indicators, and are more accurate than those based on asset indexes or proxy means tests. They can also be obtained with relatively small samples. We additionally show that it is feasible to provide poverty estimates for one geographical region based on existing data from another similar region.