eprintid: 4082
rev_number: 12
eprint_status: archive
userid: 69
dir: disk0/00/00/40/82
datestamp: 2021-07-19 09:36:23
lastmod: 2021-07-19 09:39:43
status_changed: 2021-07-19 09:36:23
type: monograph
metadata_visibility: show
creators_name: Micocci, Francesca
creators_name: Rungi, Armando
creators_id: francesca.micocci@imtlucca.it
creators_id: armando.rungi@imtlucca.it
title: Predicting Exporters with Machine Learning
ispublished: pub
subjects: HB
subjects: HF
subjects: HG
divisions: EIC
full_text_status: public
monograph_type: imt_eic_working_paper
keywords: Keywords: exporting; machine learning; trade promotion; trade finance; competitiveness.
JEL Codes: F17; C53; C55; L21; L25
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.
date: 2021-07
date_type: published
number: 3
place_of_pub: Lucca
pages: 41
institution: IMT Institute for Advanced Studies Lucca
issn: 2279-6894
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citation:   Micocci, Francesca and Rungi, Armando  Predicting Exporters with Machine Learning.  EIC working paper series  #3/2021     ISSN 2279-6894.      
document_url: http://eprints.imtlucca.it/4082/1/WP_EIC_3-2021.pdf