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IZA Discussion Paper No. 17667
February 2025
Using Machine Learning to Understand the Heterogeneous Earnings Effects of Exports
Johanna Muffert, Erwin Winkler

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.

Kommunikation
Mark Fallak
mark.fallak@liser.lu
+352 585-855-526
World of Labour
Olga Nottmeyer
olga.nottmeyer@liser.lu
+352 585-855-501
Netzwerkkoordination
Christina Gathmann
christina.gathmann@liser.lu

Das IZA@LISER-Netzwerk ist eine weltweite Gemeinschaft für exzellente Forschung in der Arbeitsmarktökonomie und angrenzenden Fachgebieten. Nach dem Wechsel von Bonn wird das Netzwerk nun am Luxembourg Institute of Socio-Economic Research (LISER) koordiniert.

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