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

TL;DR

This paper introduces a machine learning-based methodology to analyze the heterogeneous effects of economy-wide shocks, specifically COVID-19, on Colombian firms' export survival, overcoming challenges of identifying unaffected control groups.

Contribution

It develops a novel causal ML approach to estimate individual treatment effects in scenarios lacking clear control groups, applied to COVID-19's impact on exports.

Findings

COVID-19 reduced export survival probability by 20 percentage points

ML techniques effectively predict firms' trade outcomes

Heterogeneity analysis reveals varied firm responses to the shock

Abstract

Our paper presents a methodology to study the heterogeneous effects of economy-wide shocks and applies it to the case of the impact of the COVID-19 crisis on exports. This methodology is applicable in scenarios where the pervasive nature of the shock hinders the identification of a control group unaffected by the shock, as well as the ex-ante definition of the intensity of the shock's exposure of each unit. In particular, our study investigates the effectiveness of various Machine Learning (ML) techniques in predicting firms' trade and, by building on recent developments in causal ML, uses these predictions to reconstruct the counterfactual distribution of firms' trade under different COVID-19 scenarios and to study treatment effect heterogeneity. Specifically, we focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. On average, we find that the COVID-19 shock decreased a firm's probability of surviving in the export market by about 20 percentage points in April 2020. We study the treatment effect heterogeneity by employing a classification analysis that compares the characteristics of the firms on the tails of the estimated distribution of the individual treatment effects.