IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T18:32:32ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2021-07-19T09:36:23Z2021-07-19T09:39:43Zhttp://eprints.imtlucca.it/id/eprint/4082This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40822021-07-19T09:36:23ZPredicting Exporters with Machine LearningIn 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.Francesca Micoccifrancesca.micocci@imtlucca.itArmando Rungiarmando.rungi@imtlucca.it