"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. I estimate the cost of mismatch using vacancy instruments and extend methodological approaches in high-dimensional selection settings. Lacking training in one's occupation entails an average wage penalty of 14%, the equivalent of two years of work experience. 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 Karun 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 densities. 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.Â
"Labor Market Entry Conditions and Occupational Mismatch: 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 occupational mismatch. I find that higher entry unemployment leads to persistent earnings losses that are driven by full-time wage effects. At the same time, occupational matching, as measured by work in the training occupation or the task similarity between training and occupation, persistently falls. Occupational mismatch accounts for around 20% of the estimated wage effects. A conceptual framework sheds light on underlying mechanisms and shows that lower levels of matching can arise from adverse general or occupation-specific shocks. The framework entails empirical predictions that I test using novel data on occupation-specific unemployment. The findings highlight that the high-quality provision of specific skills can protect apprenticeship graduates from unemployment due to adverse entry conditions, but may expose them to occupational mismatch that can entail long-term productivity effects.
"Human Capital Investments over the Business Cycle" (with Jaime Arellano-Bover)
"Knowledge Flows within Firms" (with Louis-Pierre Lepage, Maddalena Ronchi, Giulia Vattuone)
"Access and Returns to Elite University Education" (with Ghazala Azmat, Jack Britton, Nick Ridpath, Emma Tominey)