How to measure poverty among refugees when data is scarce – the case of Chad

To better understand whether we are on track to end extreme poverty, and how individual countries are faring, it is important to regularly measure poverty. Measuring poverty is especially important in developing countries because it allows governments to determine who are the most vulnerable and to effectively target development programs.

But tracking progress aimed at reducing poverty requires the availability of high-quality poverty data, such as household consumption surveys. Unfortunately, many countries face challenges in collecting this type of data, especially poorer countries and those afflicted by fragility, conflict and violence (FCV). An estimated 500 million people --including the majority of displaced persons-- live in FCV settings for which there is no data on poverty or what exists is outdated . Comparable poverty data for refugees and hosts is almost non-existent, although the World Bank and the United Nations High Commissioner for Refugees (UNHCR) have prioritized this effort under the World Bank UNHCR Joint Data Center on Forced Displacement.

Refugees in Chad are just one example.  In the past decade Chad has experienced political, social and economic instability, resulting in a decline in people’s well-being. But despite Chad’s current economic situation, it continues to host a high number of refugees. It is among the top 10 refugee hosting countries in the world and the Sub-Saharan African country with the largest portion of refugees as a percent of the national population—about 3%.

At the time of writing our recently published paper, Estimating Poverty among Refugee Populations, A Cross-Survey Imputation Exercise for Chad, comparable welfare data for refugees and nationals did not exist in Chad. Our paper combines UNHCR survey and national administrative data to estimate welfare for refugees in Chad.

First, we used cross-survey estimation methods to investigate how several existing datasets –as well as different variables within them—fare in the identification of poor households. Three datasets were used:

1. the UNHCR Global Registration System (proGres) for refugee case management;
2. a census-type dataset for assistance targeting; and
3. a combination of the two.
   
   We considered the different sets of variables below:

• demographic and geographic (region of residence and country of origin) or Model 1
• demographic and geographic and animal and asset ownership or Model 2
• demographic and geographic, animal and asset ownership and measurable coping strategies or Model 3

We found that the limited set of variables available in the UNHCR registration data (Model 1) predict household welfare reasonably well (consumption, in this case). Adding variables related to animal and asset ownership produces similar results. Moreover, comparisons of these welfare predictions with estimates from the other sets of survey data available for the analysis produce similar welfare figures.

 

Second, we looked at the current targeting strategy in Chad, used jointly by the National Commission on the Welcoming and Resettlement of Refugees (CNARR), UNHCR, and World Food Programme (WFP). We found that this is accurate in predicting household welfare but can be improved by imputing consumption using the methodology proposed in this paper.

This study is important because it gives us new approaches for measuring poverty of refugees. Although similar early studies have been done in Jordan, the Chad study is a good opportunity to apply and further validate this method in a different geographic and income level setting.  In this study, we looked at a richer and more diverse set of data, including registration data, census-type targeting data, and a household consumption survey. We also looked at the imputation method against different poverty lines, including the food poverty line, the national poverty line, the international poverty line, and various other simulated values. We compared the targeting performance of our method with the targeting method currently used in Chad to administer cash assistance to refugees as well as with the global experience.

The results of our research are encouraging, and if replicated in other contexts, poverty predictions can be scaled up and be used to inform targeted development programs for other refugee populations. 

This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership" funded by UK aid from the United Kingdom's Department for International Development (DFID).

Top photo © Reclaiming the Future, used under a CC BY-NC 2.0 license