Publications

Frick, K. M. (2026). Convergence to Collusion in Algorithmic Pricing. International Journal of Industrial Organization, Article 103339. [Stable link] [arXiv]

Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research shows that they can learn to collude, but how quickly and reliably they can do so has remained an open question. I study average-reward soft actor-critic algorithms that set continuous prices in the workhorse environment of this literature: a repeated Bertrand duopoly with symmetric firms, deterministic logit demand, and constant marginal costs, where agents observe only market prices and their own profits. The algorithms can converge to a collusive outcome within 50,000 periods, and in 36% of simulations learned punishments make one-period deviations unprofitable, so that high prices are mutual best responses. Successful simulations show cooperative behaviour supported by reward-punishment schemes that discourage deviations. When high prices are not supported by punishments, profitable deviations are typically on the order of hundredths of a percent.

Frick, K. M., Hazard, Y., Mayaux, D. & Zuber, T. (2025). Skill Distance Between Occupations and Post‑Training Professional Transitions of Jobseekers. Economie et Statistique / Economics and Statistics, 547, 49–67. [Stable link]

Does vocational training help correct structural imbalances in the labour market? We propose a new measure of the skills distance between occupations, obtained by fine-tuning a large language model on a sample of job offers. Using this method, we demonstrate that the “return to employment” differential between jobseekers with and without training is driven by a reallocation of workers towards occupations that are very different from their previous posts in terms of the skills required. From a purely reallocative perspective, however, the return to employment differential associated with vocational training does not appear to be driven by more jobseekers moving to occupations where employers are struggling to recruit.

Working papers

Estimating government worker skills (with Jonas Gathen)

[Submitted] [arXiv]

We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from 1988-2014, showing two main applications. First, government skills have continuously declined relative to the private sector, driven by the most skilled workers ending up in the private sector. Second, the Indonesian government pays a wage premium of 43% conditional on skills.

Work in progress

Heterogeneous Labor Market Power

Selection and Productivity in the Public Sector: Evidence from Italian Teachers

R packages

  • MST-based k-means clustering (code, CRAN)
  • Sparse LU Decomposition via SuiteSparse (code, CRAN)
  • Fuzzy difference-in-differences estimators, as introduced in de Chaisemartin and D’Haultfoeuille (2018) (code, CRAN, documentation)
  • Principal curves of oriented points, as introduced in Delicado and Huerta (2003) (code, CRAN, documentation)