Storm, E. (2022), Task Specialization and the Native-Foreign Wage Gap, LABOUR Vol. 36(2), P.167-95.

Running RIF regressions to decompose wage differences along the distribution, this is the first study documenting that worker-level variation in tasks has played a key role in the widening of the German Native-Foreign Wage Gap. Comparing variation in Individual- vs Occupation-level task measures suggests idiosyncratic differences account for up to 34% of the explained wage gap. Importantly, natives specialize in high-paying interactive activities not only between but also within occupations. In contrast, foreign workers specialize in low-paying manual activities. This enhanced degree of task specialization accounts for 11% of the gap near the top of the distribution and 25% near the bottom, thus offering new insight into sources for imperfect substitution of native and foreign workers in the production function and consequently small migration-induced wage effects.

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Media Coverage: SZ, WDR (Neugier genuegt)

Working Papers

Task-Based Learning and Skill (Mis)Matching

This paper studies wage effects and job mobility as a result of skill mismatch in worker-occupation pairs. I develop a Roy model in which learning on the job induces workers to shift more time towards job-specific activities. Using a short task panel containing data on worker’s time allocation of job tasks, I test the model’s implications and present three main findings. First, workers who are overqualified in their initial occupation in regards to abstract tasks are more likely to switch to another job by up to 19 pp. Second, task-based learning only pays off with respect to acquisition of abstract skills and is associated with a return of up to 2-3% with each year of experience. Third, gains from task-based learning are heterogenous and benefit primarily workers in abstract-intensive occupations. Combined, my findings highlight the persistent effects of investments in job-specific skills on wage growth and job mobility.

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On the Measurement of Tasks: Does Expert Data Get it Right?

Using German survey and expert data on job tasks, this paper explores the presence of omitted-variable bias suspected in conventional task data derived from expert assessment. I show expert task data introduces omitted-variable bias in task returns on the order of 24-38%. Motivated by a theoretical framework, I argue this bias results from expert data ignoring workplace heterogeneity. My findings have important implications for the interpretation of conventional task models, which are motivated by a Roy model, as the resulting occupation-level task returns are substantially inflated. Moreover, a rigorous comparison of the statistical performance of various task models offers guidance for future research regarding choice of data and construction of task measures.

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Research in Progress

Task Returns and the Gender Pay Gap

Tasks and the Gains from Specialization

Did COVID-19 change task requirements on the job? Evidence from online job vacancies (w/ Ronald Bachmann, Niklas Benner & Rebecca Kamb)