In this
contribution, we exploit machine learning techniques to evaluate whether and
how close firms are to become successful exporters. First, we train various
algorithms using financial information on both exporters and non-exporters in
France in 2010–2018. Thus, we show that it is possible to predict the distance
non-exporters are from export status. In particular, we find that a Bayesian
Additive Regression Tree with Missingness In Attributes (BART-MIA) performs
better than other techniques with an accuracy of up to 0.90. Predictions are
robust to changes in definitions of exporters and in the presence of
discontinuous exporting activity. Eventually, we discuss how our exporting
scores can be helpful for trade promotion, trade credit, and assessing
aggregate trade potential. 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 to get to foreign
markets.
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