Logo eprints

Predicting Exporters with Machine Learning

Micocci, Francesca and Rungi, Armando Predicting Exporters with Machine Learning. EIC working paper series #3/2021 ISSN 2279-6894.

[img] PDF - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (3MB)


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.

Item Type: Working Paper (EIC working paper series)
Uncontrolled Keywords: Keywords: exporting; machine learning; trade promotion; trade finance; competitiveness. JEL Codes: F17; C53; C55; L21; L25
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HF Commerce
H Social Sciences > HG Finance
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 19 Jul 2021 09:36
Last Modified: 19 Jul 2021 09:39
URI: http://eprints.imtlucca.it/id/eprint/4082

Actions (login required)

Edit Item Edit Item