Predicting Exporters with Machine Learning

Francesca Micocci () and Armando Rungi
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Francesca Micocci: IMT School for Advanced Studies Lucca

No 03/2021, Working Papers from IMT School for Advanced Studies Lucca

Abstract: In this contribution, we exploit machine learning techniques to predict out-of-sample firms' ability to export based on the financial accounts of both exporters and non-exporters. Therefore, we show how forecasts can be used as exporting scores, i.e., to measure the distance of non-exporters from export status. For our purpose, we train and test various algorithms on the financial reports of 57,021 manufacturing firms in France in 2010-2018. We find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with a prediction accuracy of up to 0:90. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporters. Eventually, we argue that exporting scores can be helpful for trade promotion, trade credit, and to assess firms' competitiveness. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to 44% more cash resources and up to 2:5 times more capital expenses to reach full export status.

Keywords: exporting; machine learning; trade promotion; trade finance; competitiveness (search for similar items in EconPapers)
JEL-codes: C53 C55 F17 L21 L25 (search for similar items in EconPapers)
Pages: 41
Date: 2021-07, Revised 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-int and nep-ore
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Published in EIC working paper series

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http://eprints.imtlucca.it/4082/ First version, 2021

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Working Paper: Predicting Exporters with Machine Learning (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ial:wpaper:3/2021

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