TY  - RPRT
        AU  - Muffert, Johanna
AU  - Winkler, Erwin
        TI  - Using Machine Learning to Understand the Heterogeneous Earnings Effects of Exports
        PY  - 2025/Feb/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 17667
        UR  - https://www.iza.org/index.php/publications/dp17667
        AB  - 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.
        KW  - machine learning
KW  - earnings
KW  - inequality
KW  - exports
KW  - skills
KW  - labor market
        ER  -