Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis
Authors
Dueñas, Marco
Ortiz, Víctor
Riccaboni, Massimo
Serti, Francesco
Editor
Publication date
2021-04-09
Document language
eng
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Abstract
By interpreting exporters’ dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms’ trade status. 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. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms’ outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.
Description
By interpreting exporters’ dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms’ trade status. 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. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms’ outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.
Códigos JEL
F14 - Empirical Studies of Trade, F17 - Trade Forecasting and Simulation, D22 - Firm Behavior: Empirical Analysis, L25 - Firm Performance: Size, Diversification, and Scope
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Keywords
Machine Learning, International Trade, COVID-19
