Predicting household resilience with machine learning: preliminary cross-country tests

Abstract

Using a unique cross-country sample from 10 impact evaluations of development projects, we test the out-of-sample performance of machine learning algorithms in predicting non-resilient households, where resilience is a subjective metrics defined as the perceived ability to recover from shocks. We report preliminary evidence of the potential of these data-driven techniques to identify the main predictors of household resilience and inform the targeting of resilience-oriented policy interventions.