Household consumption surveys do not typically cover refugee populations, and consequently poverty estimates for refugees are rare. This paper examines the
performance of cross-survey imputation methods to estimate poverty for a sample of refugees in Chad, by combining non-income refugee data from UNHCR’s Profile Global
Registration System (ProGres) database (or other household datasets) with existing sets of non-nationally representative refugee consumption data. The authors also estimate the
accuracy of the current humanitarian targeting strategy and compared it with the targeting strategy based on imputed consumption in the light of international evidence.
The analysis is based on data collected by humanitarian organizations, including: (a) ProGres registration data containing socioeconomic variables (such as household size, marital status, gender, age, country of origin, and region of residence) but not consumption and expenditure data; (b) census-like ‘targeting’ data collected for the purposes of
categorizing refugees into wealth groups for cash, food, and livelihood assistance, containing demographic data (household size, gender, age, country of origin, and region of residence), data on asset and animal ownership, and information on coping strategies, but not consumption and expenditure data; and (c) ‘Post-Distribution Monitoring’ data collected by World Food Programme (WFP) from a sample of refugees to provide insights into how refugees use food assistance and containing data on consumption and expenditure.
Key findings:
- The limited set of variables available in ProGres registration data predict household consumption (welfare) reasonably well. This result is robust to different poverty lines, sets of regressors, and other econometric modeling assumptions.
- Adding variables related to asset and animal ownership provides predictions that are very close to the ones with only the variables available in the ProGres dataset.
- The current targeting strategy in Chad, which is used jointly by the National Commission on the Welcoming and Resettlement of Refugees (CNARR), UNHCR, and WFP, is accurate in predicting household welfare. However, this targeting strategy could be further improved by reducing the inclusion and exclusion errors.
The authors conclude that if these results are replicated in other contexts, poverty predictions for refugees could be expanded at scale, with good prospects for the improvement of targeted programs.