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“Empirical forecasting of slow-onset disasters for improved emergency response:

Existing program evaluation methods such as difference-in-difference estimators or propensity score matching are designed to examine the average impact of a program. By design they can only examine changes in a particular summary statistic of an outcome indicator, most commonly the mean or the median or a particular quantile. However, we are often interested not only in the mean impact of an intervention, or the average treatment effect, but also the differential impact on different subpopulations such as the rich and the poor, the well-nourished and the malnourished, or some finer disaggregation of the welfare domain. In principle, one could examine the program impact on various subpopulations by applying existing program evaluation techniques on smaller and smaller subsamples of the data. In practice, this approach faces three main problems. First, it is cumbersome both for carrying out the analysis and for interpreting the results. Second, one faces arbitrary choices of how to split the sample. And third, increasing the number of subgroups leads smaller sample sizes and wider confidence intervals in the regression estimates. To circumvent these problems this article suggests a novel approach to program evaluation which combines stochastic dominance with difference-indifference methods.