Can automatic recommendations improve the job finding rate and the labor-market outcomes of Swedish job seekers?
Time period: 2018-01-01 to 2021-12-31
Project leader: Lena Hensvik
Other personnel: Thomas Le Barbanchon, Roland Rathelot
Type of award: Unclassified
Total fundning: 5 030 000 SEK
Understanding the search process of job seekers and employers is at the heart of labor economic research and policy. Still our understanding of the nature and consequences of job search is inadequate, mainly due to lack of data. Our project proposes to provide new empirical evidence on the search strategies of both job seekers and of recruiters in the Swedish labor market. This evidence will enhance our understanding of the information asymmetries at the root of search frictions.We will leverage the extraordinary opportunities offered by online job boards, which record search activities in detail. We will match for the first time these data with administrative data from unemployment-employment registers. This will enable us to jointly observe search activity and core economic outcomes (wage, job duration) on very large samples.The data allow us to provide a rich description of the search patterns of Swedish job seekers and firms. In addition, we will evaluate the impact of randomized controlled trials, where we recommend new matches to both job seekers and recruiters. This will test for the extent of geographical and skill mismatch in the labor market and test if the private information that job seekers gather when they screen vacancies on job boards can be used to improve the matching efficiency.We expect our direct empirical evidence on search strategies to trigger new developments in search theories. Our results will also guide policy-makers who design job boards and the unemployment insurance system. While online job portals have flourished in the recent years, we still know very little about the impact on unemployment duration and the quality of employment. This project will shed light on the most important information frictions and test improvements. We hope that our research will help to reduce frictional unemployment and to increase productivity through a reduction of mismatch in the labor market.