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Distance to Export: A Machine Learning Approach with Portuguese Firms

Author

Listed:
  • Paulo Barbosa

    (ISEG - University of Lisbon and Portugal Trade & Investment)

  • João Cortes

    (Portugal Trade & Investment)

  • João Amador

    (Banco de Portugal and Nova SBE)

Abstract
This paper estimates how distant a firm is from becoming a successful exporter. The empirical exercise uses very rich data for Portuguese firms and assumes that there are non-trivial determinants to distinguish between exporters and non-exporters. An array of machine learning models - Bayesian Additive Regression Tree (BART), Missingness not at Random (BART-MIA), Random Forest, Logit Regression and Neural Networks – are trained to predict firms’ export probability and shed light on the critical factors driving the transition to successful export ventures. Neural Networks outperform the other techniques and remain highly accurate when we change the export definitions and the training and testing strategies. We show that the most influential variables for prediction are labour productivity, firms’ goods and services imports, capital intensity and wages.

Suggested Citation

  • Paulo Barbosa & João Cortes & João Amador, 2024. "Distance to Export: A Machine Learning Approach with Portuguese Firms," GEE Papers 182, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Jul 2024.
    • Handle: RePEc:mde:wpaper:182
    as

    Download full text from publisher

    File URL: https://www.gee.gov.pt//RePEc/WorkingPapers/GEE_PAPERS_182.pdf
    File Function: First version, 2024
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Machine learning; Forecasting exporters; Trade promotion; Micro level data; Portugal;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

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