Distance to Export: A Machine Learning Approach with Portuguese Firms

Paulo Barbosa (), João Cortes () and João Amador ()
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Paulo Barbosa: ISEG - University of Lisbon and Portugal Trade & Investment
João Cortes: Portugal Trade & Investment

No 182, GEE Papers from Gabinete de Estratégia e Estudos, Ministério da Economia

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.

Keywords: Machine learning; Forecasting exporters; Trade promotion; Micro level data; Portugal (search for similar items in EconPapers)
JEL-codes: C53 C55 F17 L21 (search for similar items in EconPapers)
Date: 2024-07, Revised 2024-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-int and nep-sbm
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https://www.gee.gov.pt//RePEc/WorkingPapers/GEE_PAPERS_182.pdf First version, 2024 (application/pdf)

Related works:
Working Paper: Distance to Export: A Machine Learning Approach with Portuguese Firms (2024)
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