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Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis

Author

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  • Dueñas, Marco
  • Ortiz, Víctor
  • Riccaboni, Massimo
  • Serti, Francesco

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.

Suggested Citation

  • Dueñas, Marco & Ortiz, Víctor & Riccaboni, Massimo & Serti, Francesco, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Working papers 79, Red Investigadores de Economía.
    • Handle: RePEc:rie:riecdt:79
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    File URL: https://arxiv.org/pdf/2104.04570.pdf
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    2. Campi, Mercedes & Dueñas, Marco, 2022. "Clusters and Resilience during the COVID–19 Crisis: Evidence from Colombian Exporting Firms," IDB Publications (Working Papers) 12527, Inter-American Development Bank.

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    More about this item

    Keywords

    Machine Learning; International Trade; COVID-19;
    All these keywords.

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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