Working Papers

Training Specificity and Occupational Mobility: Evidence from German Apprenticeships (Revise & Resubmit, Econometrica)

Previously circulated under the title "Are Chemists Good Bankers? Returns to the Match Between Training and Occupation";

RES Junior Symposium Best Paper Award, April 2019

Apprenticeships play a key role in enabling successful school-to-work transitions in many countries but, in the presence of imperfect information, the specificity of this type of training may entail important costs for those working outside their training fields. I study this issue in one of the most prominent training settings, the German apprenticeship system. Using administrative data and a broad occupational classification, I find that 40% of individuals work in occupations different from their training. To identify the returns to training-occupation combinations, I use vacancy instruments and extend methodological approaches in high-dimensional selection settings. The cost of mismatch is substantial. Lacking training in one's occupation entails an average wage penalty of 12%, the equivalent of an additional year of training. The penalty increases with the task distance between training and occupation. My findings suggest that retraining is crucial to mitigate the adverse consequences from imperfect information in specialized training settings.

Temporal-Difference Estimation of Dynamic Discrete Choice Models (with K. Adusumilli) (Revise & Resubmit, Review of Economic Studies)

We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-likelihood function. We suggest two approaches: The first -linear semi-gradient- provides approximations to the recursive terms using basis functions. The second -Approximate Value Iteration- builds a sequence of approximations to the recursive terms by solving non-parametric estimation problems. Our approaches are fast and naturally allow for continuous and/or high-dimensional state spaces. Furthermore, they do not require specification of transition probabilities. In dynamic games, they avoid integrating over other players' actions, further heightening the computational advantage. Our proposals can be paired with popular existing methods such as pseudo-maximum-likelihood, and we propose locally robust corrections for the latter to achieve parametric rates of convergence. Monte Carlo simulations confirm the properties of our algorithms in practice. 

Research in Progress

The Effect of Labor Market Entry Conditions on Job Match Quality: Evidence from Apprenticeship Graduates

Using administrative data on the largest group of labor market entrants in Germany - graduates from an apprenticeship in a specific occupation - this paper studies the effect of entry conditions on long-term job match quality. I find that higher entry unemployment leads to earnings losses that are driven by full-time wage effects. At the same time, job match quality, as measured by the likelihood of working in the training occcupation or the task similarity between training and occupation, falls. Lower match quality accounts for around 20% of the estimated wage effects. A conceptual framwork sheds light on underlying mechanisms. Lower levels of matching may arise from matching inefficiencies or in response to occupation-specific shocks. Matches persist due to skill depreciation. The framwork entails empirical predictions that I test using novel data on occupation-specific unemployment. The findings point to long-term productivity effects from initial conditions and indicate that at least two thirds of matching responses are inefficient.