%0 Report
%9 EIC working paper series
%A Micocci, Francesca
%A Rungi, Armando
%C Lucca
%D 2021
%F eprints:4082
%I IMT Institute for Advanced Studies Lucca
%K Keywords: exporting; machine learning; trade promotion; trade finance; competitiveness.  JEL Codes: F17; C53; C55; L21; L25
%N 3
%T Predicting Exporters with Machine Learning
%U http://eprints.imtlucca.it/4082/
%X 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.