Abstract

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 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 0.4% higher earnings over the 6 months following first exposure to recommendations. Younger, low-skilled, unemployed job seekers, and those who are initially broad in their job search experience even larger treatment effects. We also find larger effects when recommended vacancies are less popular, and for recommendations that broaden search further away in geographical and occupational distance. Our results illustrate the promise of AI technology for market clearing, and provide guidelines for future developments of job recommender systems.

Roland Rathelot, CREST, ENSAE, IPParis