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 personalised job recommendations. We deploy it at scale on the largest online job board in Sweden, and design a clustered two-sided randomised experiment to evaluate its impact on job search and labour-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 0.7 percent 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 10 percent. We propose a decomposition exercise of the net employment effects into three channels. The most important channel corresponds to the increase in the number of applications due to recommendations (first channel), partly offset by the lower conversion into employment of marginal applications (second channel). Congestion effects (third channel) are not a significant contributor to the overall effect. We also find larger employment effects when recommended vacancies are less popular, and for recommendations that broaden search further away in geographical and occupational distance.
Date
17.7.2023
, 11:30 am to 12:15 pm
Speaker
Roland Rathelot (École d’ingenieurs pour l’économie (ENSAE), Institute Polytechnique de Paris (IP) and Center for Research in Econnomics and Statistics (CREST), Paris)
Venue
Institute for Employment Research
Regensburger Straße 104
90478 Nürnberg
Room Re100 E10
or online via Skype
Registration
Researchers who like to participate, please send a e-mail to IAB.Colloquium@iab.de