Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-04-30T19:38:05.795Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting Exporters with Machine Learning

Published online by Cambridge University Press:  21 June 2023

Francesca Micocci  and
Armando Rungi Open the ORCID record for Armando Rungi [Opens in a new window]
Francesca Micocci
Affiliation:
Laboratory for the Analysis of Complex Economic Systems, IMT School for Advanced Studies, piazza San Francesco 19, 55100 Lucca, Italy
Armando Rungi*
Affiliation:
Laboratory for the Analysis of Complex Economic Systems, IMT School for Advanced Studies, piazza San Francesco 19, 55100 Lucca, Italy
*
Corresponding author: Armando Rungi; Email: armando.rungi@imtlucca.it
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

exportingmachine learningtrade promotiontrade financecompetitivenessF17C53C55L21L25
Type
Original Article
Information
World Trade Review , Volume 22 , Issue 5 , December 2023 , pp. 584 - 607
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The World Trade Organization

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ackerberg, D.A., Caves, K., and Frazer, G. (2015) ‘Identification Properties of Recent Production Function Estimators’, Econometrica 83, 2411–2451.CrossRefGoogle Scholar
Ahrens, A., Hansen, C.B., and Schaffer, M.E.. (2020) Lassopack: Model Selection and Prediction with Regularized Regression in Stata. arXiv preprint arXiv:1901.05397.CrossRefGoogle Scholar
Alfaro, L., Charlton, A., and Kanczuk, F.. (2009) ‘Plant Size Distribution and Cross-Country Income Differences’, NBER International Seminar on Macroeconomics. Chicago, IL, The University of Chicago Press, 243–272.CrossRefGoogle Scholar
Almeida, H., Campello, M., and Weisbach, M.S. (2004) ‘The Cash Flow Sensitivity of Cash’, The Journal of Finance 59, 1777–1804.CrossRefGoogle Scholar
Altman, E.I. (1968) ‘Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy’, The Journal of Finance 23, 589–609.CrossRefGoogle Scholar
Altman, E.I. (2000) Predicting Financial Distress of Companies: Revisiting the z-Score and Zeta Models. Stern School of Business, New York: New York University, 9–12.Google Scholar
Athey, S. (2018) ‘The Impact of Machine Learning on Economics’, in A. Agrawal, J. Gans, and A. Glodfarb (eds.), The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, 507–547.Google Scholar
Athey, S., Imbens, G., Metzger, J., and Munro, E. (2021) ‘Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations’, Journal of Econometrics.CrossRefGoogle Scholar
Aw, B.Y., Lee, Y., and Vandenbussche, H.. (2023) ‘Quantifying Consumer Taste in Trade: Evidence from the Food Industry’, CESifo Working Paper No. 10234.Google Scholar
Baier, S.L., Bergstrand, J.H., and Mariutto, R. (2014) ‘Economic Determinants of Free Trade Agreements Revisited: Distinguishing Sources of Interdependence’, Review of International Economics 22, 31–58.CrossRefGoogle Scholar
Bargagli-Stoffi, F., Riccaboni, M., and Rungi, A.. (2020) ‘Machine Learning for Zombie Hunting. Firms’ Failures and Financial Constraints’, EIC Working Paper Series 1/2020, IMT School for Advanced Studies.CrossRefGoogle Scholar
Barro, R.J., and Sala-i Martin, X. (1997) ‘Technological Diffusion, Convergence, and Growth’, Journal of Economic Growth 2, 1–26.CrossRefGoogle Scholar
Békés, G., and Muraközy, B. (2012) ‘Temporary Trade and Heterogeneous Firms’, Journal of International Economics 87, 232–246.CrossRefGoogle Scholar
Belloni, A., and Chernozhukov, V. (2013) ‘Least Squares After Model Selection in High-Dimensional Sparse Models’, Bernoulli 19, 521–547.CrossRefGoogle Scholar
Belloni, A., Chernozhukov, V., and Hansen, C. (2014) ‘High-Dimensional Methods and Inference on Structural and Treatment Effects’, Journal of Economic Perspectives 28, 29–50.CrossRefGoogle Scholar
Belloni, A., Chernozhukov, V., Hansen, C., and Kozbur, D. (2016) ‘Inference in High-Dimensional Panel Models with an Application to Gun Control’, Journal of Business & Economic Statistics 34, 590–605.CrossRefGoogle Scholar
Belloni, A., Chernozhukov, V., Fernández-Val, I., and Hansen, C. (2017) ‘Program Evaluation and Causal Inference with High-Dimensional Data’, Econometrica 85, 233–298.CrossRefGoogle Scholar
Bernard, A.B., and Jensen, J.B. (1999) ‘Exceptional Exporter Performance: Cause, Effect, or Both?’, Journal of international economics 47, 1–25.CrossRefGoogle Scholar
Bernard, A.B., and Jensen, J.B. (2004) ‘Why Some Firms Export’, Review of economics and Statistics 86, 561–569.CrossRefGoogle Scholar
Bernard, A.B., Jensen, J.B., and Lawrence, R.Z. (1995) ‘Exporters, Jobs, and Wages in US Manufacturing: 1976–1987’, Brookings papers on economic activity. Microeconomics 1995, 67–119.CrossRefGoogle Scholar
Bleich, J., Kapelner, A., George, E.I., and Jensen, S.T. (2014) ‘Variable Selection for Bart: An Application to Gene Regulation’, The Annals of Applied Statistics 8, 1750–1781.CrossRefGoogle Scholar
Breiman, L. (2001) ‘Random Forests’, Machine Learning 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984) Classification and Regression Trees. Belmont, CA: Wadsworth & Brooks.Google Scholar
Breinlich, H., Corradi, V., Rocha, N., Ruta, M., Santos Silva, J., and Zylkin, T.. (2021) ‘Machine Learning in Gravity Models: An Application to Agricultural Trade’, Research Working Paper 9629, World Bank.Google Scholar
Chen, J., and Chen, Z. (2008) ‘Extended Bayesian Information Criteria for Model Selection with Large Model Spaces’, Biometrika 95, 759–771.CrossRefGoogle Scholar
Chen, H.J., and Chen, S.J. (2012) ‘Investment-Cash Flow Sensitivity Cannot be a Good Measure of Financial Constraints: Evidence from the Time Series’, Journal of Financial Economics 103, 393–410.CrossRefGoogle Scholar
Chipman, H.A., George, E.I., and McCulloch, R.E. (2010) ‘Bart: Bayesian Additive Regression Trees’, The Annals of Applied Statistics 4, 266–298.CrossRefGoogle Scholar
Chor, D., and Manova, K. (2012) ‘Off the Cliff and Back? Credit Conditions and International Trade During the Global Financial Crisis’, Journal of International Economics 87, 117–133. Symposium on the Global Dimensions of the Financial Crisis.CrossRefGoogle Scholar
Cravino, J., and Levchenko, A.A. (2016) ‘Multinational Firms and International Business Cycle Transmission*’, The Quarterly Journal of Economics 132, 921–962.CrossRefGoogle Scholar
Crozet, M., Head, K., and Mayer, T. (2011) ‘Quality Sorting and Trade: Firm-Level Evidence for French Wine’, The Review of Economic Studies 79, 609–644.CrossRefGoogle Scholar
Del Prete, D., and Rungi, A. (2017) ‘Organizing the Global Value Chain: A Firm-Level Test’, Journal of International Economics 109, 16–30.CrossRefGoogle Scholar
Dhyne, E., Ludwig, P., and Vandenbussche, H.. (2023) ‘Export Entry and Network Interactions: Evidence from the Belgian Production Network’, Technical Report, National Bank of Belgium.Google Scholar
Ellison, G., and Glaeser, E. (1997) ‘Geographic Concentration in US Manufacturing Industries: A Dartboard Approach’, Journal of Political Economy 105, 889–927.CrossRefGoogle Scholar
Fazzari, S.M., Hubbard, R.G., and Petersen, B.C. (1988) ‘Financing Constraints and Corporate Investment’, Brookings Papers on Economic Activity 19, 141–206.CrossRefGoogle Scholar
Fontagné, L., Secchi, A., and Tomasi, C. (2018) ‘Exporters’ Product Vectors Across Markets’, European Economic Review 110, 150–180.CrossRefGoogle Scholar
Gaulier, G., Santoni, G., Taglioni, D., and Zignago, S.. (2013) ‘In the Wake of the Global Crisis. Evidence from a New Quarterly Database of Export Competitiveness’, Policy Research Working Paper 9629, World Bank.CrossRefGoogle Scholar
Geishecker, I., Schröder, P.J., and Sörensen, A. (2019) ‘One-Off Export Events’, Canadian Journal of Economics/Revue canadienne d’éonomique 52, 93–131.CrossRefGoogle Scholar
Geman, S., and Geman, D. (1984) ‘Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images’, IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.CrossRefGoogle Scholar
Gopinath, G., Kalemli-Özcan, Ş, Karabarbounis, L., and Villegas-Sanchez, C. (2017) ‘Capital Allocation and Productivity in South Europe’, The Quarterly Journal of Economics 132, 1915–1967.CrossRefGoogle Scholar
Gopinath, M., Batarseh, F.A., and Beckman, J.. (2020) ‘Machine Learning in Gravity Models: An Application to Agricultural Trade’, Working Paper 27151, National Bureau of Economic Research.CrossRefGoogle Scholar
Grossman, G.M., and Helpman, E. (1990) ‘Comparative Advantage and Long-Run Growth’, The American Economic Review 80, 796–815.Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2017) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.Google Scholar
Hastings, W.K. (1970) ‘Monte Carlo Sampling Methods Using Markov Chains and Their Applications’, Biometrika 57, 97–109.CrossRefGoogle Scholar
Hill, J., Linero, A., and Murray, J. (2020) ‘Bayesian Additive Regression Trees: A Review and Look Forward’, Annual Review of Statistics and Its Application 7, 251–278.CrossRefGoogle Scholar
Hottman, C.J., Redding, S.J., and Weinstein, D.E. (2016) ‘Quantifying the Sources of Firm Heterogeneity *’, The Quarterly Journal of Economics 131, 1291–1364.CrossRefGoogle Scholar
Joseph, A. (2020) ‘Parametric Inference with Universal Function Approximators’, Bank of England Staff Working Paper no. 784.Google Scholar
Kapelner, A., and Bleich, J. (2013) ‘Bartmachine: Machine Learning with Bayesian Additive Regression Trees’, arXiv preprint arXiv:1312.2171.Google Scholar
Kapelner, A., and Bleich, J. (2015) ‘Prediction with Missing Data via Bayesian Additive Regression Trees’, Canadian Journal of Statistics 43, 224–239.CrossRefGoogle Scholar
Leamer, E., and Stern, R. (1970) Quantitative International Economics. Boston: Allin and Bacon, Chapter 7, 171–182.Google Scholar
Lin, F. (2015) ‘Learning by Exporting Effect in China Revisited: An Instrumental Approach’, China Economic Review 36, 1–13. www.sciencedirect.com/science/article/pii/S1043951X15000887, doi:10.1016/j.chieco.2015.07.004.CrossRefGoogle Scholar
Lin, F. (2017) ‘Credit Constraints, Export Mode and Firm Performance: An Investigation of China's Private Enterprises’, Pacific Economic Review 22, 123–143.CrossRefGoogle Scholar
Liu, X. (2012) ‘Classification Accuracy and Cut Point Selection’, Statistics in Medicine 31, 2676–2686.CrossRefGoogle Scholar
Manova, K. (2012) ‘Credit Constraints, Heterogeneous Firms, and International Trade’, The Review of Economic Studies 80, 711–744.CrossRefGoogle Scholar
Melitz, M.J. (2003) ‘The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity’, Econometrica 71, 1695–1725.CrossRefGoogle Scholar
Melitz, M.J., and Ottaviano, G.I.P. (2008) ‘Market Size, Trade, and Productivity’, The Review of Economic Studies 75, 295–316.CrossRefGoogle Scholar
Melitz, M.J., and Redding, S.J. (2014) ‘Chapter 1 - heterogeneous firms and trade’, in Gopinath, G., Helpman, E., and Rogoff, K. (eds.), Handbook of International Economics, Volume 4 of Handbook of International Economics. Amsterdam: Elsevier, 1–54.Google Scholar
Merton, R.C. (1974) ‘On the Pricing of Corporate Debt: The Risk Structure of Interest Rates’, The Journal of Finance 29, 449–470.Google Scholar
Mullainathan, S., and Spiess, J. (2017) ‘Machine Learning: An Applied Econometric Approach’, Journal of Economic Perspectives 31, 87–106.CrossRefGoogle Scholar
Reis, J.G., Wagle, S., and Farole, T. (2010) Analyzing Trade Competitiveness: A Diagnostics Approach. Washington, DC: The World Bank.CrossRefGoogle Scholar
Richardson, J. (1971a) ‘Constant-Market-Shares Analysis of Export Growth’, Journal of International Economics 1, 227–239.CrossRefGoogle Scholar
Richardson, J. (1971b) ‘Some Sensitivity Tests for a “Constant-Market-Shares” Analysis of Export Growth’, The Review of Economics and Statistics 53, 300–304.CrossRefGoogle Scholar
Rivera-Batiz, L.A., and Romer, P.M. (1991) ‘International Trade with Endogenous Technological Change’, European Economic Review 35, 971–1001.CrossRefGoogle Scholar
Romer, P. (1994) ‘New Goods, Old Theory, and the Welfare Costs of Trade Restrictions’, Journal of Development Economics 43, 5–38.CrossRefGoogle Scholar
Rungi, A., and Del Prete, D. (2018) ‘The Smile Curve at the Firm Level: Where Value is Added Along Supply Chains’, Economics Letters 164, 38–42.CrossRefGoogle Scholar
Tybout, J.R. (2000) ‘Manufacturing Firms in Developing Countries: How Well do They do, and Why?’, Journal of Economic Literature 38, 11–44.CrossRefGoogle Scholar
Uddin, M.S. (2021) ‘Machine Learning in Credit Risk Modeling: Empirical Application of Neural Network Approaches’. in A. Hamdan, A.E. Hassanien, A. Razzaque, and B. Alareeni (eds.), The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. Studies in Computational Intelligence, vol 935. Springer Cham.Google Scholar
Van Biesebroeck, J., Konings, J., and Volpe Martincus, C. (2016) ‘Did Export Promotion Help Firms Weather the Crisis?’, Economic Policy 31, 653–702.CrossRefGoogle Scholar
Volpe Martincus, C., and Carballo, J. (2008) ‘Is Export Promotion Effective in Developing Countries? Firm-Level Evidence on the Intensive and the Extensive Margins of Exports’, Journal of International Economics 76, 89–106.CrossRefGoogle Scholar
Volpe Martincus, C., and Carballo, J. (2010a) ‘Entering New Country and Product Markets: Does Export Promotion Help?’, Review of World Economics 146, 437–467.CrossRefGoogle Scholar
Volpe Martincus, C., and Carballo, J. (2010b) ‘Export Promotion: Bundled Services Work Better’, The World Economy 33, 1718–1756.CrossRefGoogle Scholar
Volpe Martincus, C., Estevadeordal, A., Gallo, A., and Luna, J. (2010) ‘Information Barriers, Export Promotion Institutions, and the Extensive Margin of Trade’, Review of World Economics 146, 91–111.CrossRefGoogle Scholar
Supplementary material: File

Micocci and Rungi supplementary material
Download undefined(File)
File 2 MB