We explore how Artificial Intelligence can be leveraged to help frictional markets to clear. We design a collaborative-filtering machine-learning job recommender system that uses job seekers' click history to generate relevant personalized job recommendations.
- Speaker
- Date
- Monday 17 Feb 2025, 11:30 - 12:30
- Type
- Seminar
- Room
- 2-16
- Building
- Polak Building
(with Lena Hensvik and Roland Rathelot)
We deploy it at scale on the largest online job board in Sweden, and design a clustered two-sided randomized experiment to evaluate its impact on job search and labor-market outcomes. Combining platform data with unemployment and employment registers, we find that treated job seekers are more likely to click and apply to recommended jobs, and have 0.6% higher employment within the 6 months following first exposure to recommendations.
At the job-worker pair level, we document that recommending a vacancy to a job seeker increases the probability to work at this workplace by 5%. Leveraging the two-sided vacancy-worker randomization or the market-level randomization, we find limited congestion effects. We find that employment effects are larger for workers that are less-educated, unemployed, and have initially a large geographic scope of search, for jobs that are attached to several jobs, and are relatively older. Results also suggest that recommendations expanding the occupational scope yield higher effects.
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