We have just explained real convergence factors using machine learning

Piotr Wójcik and Bartłomiej Wieczorek
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Bartłomiej Wieczorek: Data Science Lab WNE UW

No 2020-38, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: There are several competing empirical approaches to identify factors of real economic convergence. However, all of the previous studies of cross-country convergence assume a linear model specification. This article uses a novel approach and shows the application of several machine learning tools to this topic discussing their advantages over the other methods, including possibility of identifying nonlinear relationships without any a priori assumptions about its shape. The results suggest that conditional convergence observed in earlier studies could have been a result of inappropriate model specification. We find that in a correct non-linear approach, initial GDP is not (strongly) correlated with growth. In addition, the tools of interpretable machine learning allow to discover the shape of relationship between the average growth and initial GDP. Based on these tools we prove the occurrence of convergence of clubs.

Keywords: cross-country convergence; conditional convergence; determinants; machine learning; non-linear (search for similar items in EconPapers)
JEL-codes: C14 C52 O47 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
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https://www.wne.uw.edu.pl/index.php/download_file/5905/ First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-38

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