Using Machine Learning to Understand the Heterogeneous Earnings Effects of Exports

Johanna Muffert () and Erwin Winkler
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Johanna Muffert: FAU Erlangen Nuremberg

No 17667, IZA Discussion Papers from IZA Network @ LISER

Abstract: We study how the effects of exports on earnings vary across individual workers, depending on a wide range of worker, firm, and job characteristics. To this end, we combine a generalized random forest with an instrumental variable strategy. Analyzing Germany's exports to China and Eastern Europe, we document sharp disparities: workers in the bottom quartile (ranked by the size of the effect) experience little to no earnings gains due to exports, while those in the top quartile see considerable earnings increases. As expected, the workers who benefit the most on average are employed in larger firms and have higher skill levels. Importantly, however, we also find that workers with the largest earnings gains tend to be male, younger, and more specialized in their industry. These factors have received little attention in the previous literature. Finally, we provide evidence that the contribution to overall earnings inequality is smaller than expected.

Keywords: machine learning; earnings; inequality; exports; skills; labor market (search for similar items in EconPapers)
JEL-codes: C52 F14 J23 J24 J32 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2025-02
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp, nep-cna, nep-eur, nep-int, nep-lma and nep-tra
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