Improving household resilience, therefore, becomes a central issue in any SSA policy agenda, in particular in areas such as the Sahel region, where household exposure to economic and climatic shock is harsh. While conceptualization of resilience has been sharpened over the last years (see for example World Bank, 2014), to our knowledge, efforts to measure resilience have not yet proved fully convincing. Given the urgent need of measuring resilience without waiting for the ‘ideal data set’, the present paper develops a methodology that uses existing data sets and can already provide some guidance on resilience based on the experience of the last decade. The quantitative measure we provide builds on the idea that a household is resilient if there is very little difference between the pre-shock welfare and the post-shock welfare. This paper contributes to the literature on measuring resilience by providing a method for estimating potential deviation of household income from expected permanent income by using repeated cross sections.

Inspired by the vast literature on counterfactual distributions and survey to survey estimation, we simulated the welfare outcome the household would have experienced if faced with a different state of the world compared to its current state. We first split our survey sample into households or individuals that experienced a shock, those named treated – and those which did not, the control. Next, we estimated separate welfare outcomes for those who received a shock and those who did not. We used these parameters to obtain the counterfactual distributions. The comparison of the permanent welfare level and the counterfactual distributions for each individual indicates who is likely to be resilient and who is not.

The results look promising. Using consumption as a welfare measure in Burkina Faso, Northern Nigeria, Niger and Senegal, we are able to define three distinct groups of households classified on the basis of their resilience. According to these estimates, around 33 percent of households are resilient in three of the four countries. Niger fares much worse, and the resilient group accounts for only 18 percent of the households. The number of chronically poor, those permanently below the poverty line, is around 49 percent in Niger, 33 percent in Burkina Faso, and 26 percent in Northern Nigeria and only 4 percent in Senegal. By contrast, when we look at welfare measures used to analyze the nutritional deficiency of children, we find that resilience levels range from 20 percent in Burkina Faso to 40 percent in Northern Ghana.

In summary, our model offers some potential insights for understanding resilience using only cross sections. In the absence of a panel, two or more cross-sectional household survey rounds linked to rainfall or any available measure of shocks could be enough to obtain a reasonable measure of resilience. This is quite important when deciding on targets for social protection and on scaling up programs to buffer shocks. However, more research is obviously needed to test the robustness of the methodology. In particular, it would be important to validate the model with a panel survey that follows households over several years and at the same time collects information on shocks faced by households, something we plan to do in future research.

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