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Our paper presents a methodology to study the heterogeneous effects ofeconomy-wide shocks and applies it to the case of the impact of the COVID-19crisis on exports. This methodology is applicable in scenarios where thepervasive nature of the shock hinders the identification of a control groupunaffected by the shock, as well as the ex-ante definition of the intensity ofthe shock's exposure of each unit. In particular, our study investigates theeffectiveness of various Machine Learning (ML) techniques in predicting firms'trade and, by building on recent developments in causal ML, uses thesepredictions to reconstruct the counterfactual distribution of firms' tradeunder different COVID-19 scenarios and to study treatment effect heterogeneity.Specifically, we focus on the probability of Colombian firms surviving in theexport market under two different scenarios: a COVID-19 setting and anon-COVID-19 counterfactual situation. On average, we find that the COVID-19shock decreased a firm's probability of surviving in the export market by about20 percentage points in April 2020. We study the treatment effect heterogeneityby employing a classification analysis that compares the characteristics of thefirms on the tails of the estimated distribution of the individual treatmenteffects.

Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms