Distance to Export: A Machine Learning Approach with Portuguese Firms

João Amador (), Paulo Barbosa and João Cortes

Working Papers from Banco de Portugal, Economics and Research Department

Abstract: This paper studies firms’ distances to becoming successful exporters. The empirical exercise uses rich data on Portuguese firms and assumes that there are significant features distinguishing exporters from 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 to shed light on the critical factors driving the transition to successful export ventures. Neural Networks outperform the other models and remain highly accurate when export definitions and training and testing strategies are changed. We show that the most influential variables for prediction are labor productivity and the share of imports from the EU in total purchases. Additionally, firms at the median distance to sell in international markets operate with about twice the assets of the group in the decile more distance from exporting. Firms in the decile closest to the export market operate with around 12 times more assets than those in the decile more distant from exporting.

JEL-codes: C53 C55 L2 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-cmp, nep-eur, nep-int and nep-sbm
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https://www.bportugal.pt/sites/default/files/documents/2024-12/WP202420.pdf

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Working Paper: Distance to Export: A Machine Learning Approach with Portuguese Firms (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:ptu:wpaper:w202420

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