Unemployment is a major economic and social issue of our society. Our project provides new empirical evidence on the job search strategies of workers. Our overall objective is to enhance our understanding of the source of frictions on the labor market, and to provide guidance for policy-makers.
We reached three main conclusions:
1. Providing personalized job recommendations to unemployed worker creates more matches on the labor market. We design, implement and test new machine-learning job recommender systems developed on the main online job board in Sweden (plastbanken.se). We find that job seekers follow their personalized job recommendations, and they benefit from it. Their employment increases during the 6 months following their first exposure to the recommender system (by 0.6 percent). The job recommender system helps to solve coordination failures on the labor market, occupational mismatch, and inattention bias during job search. The job recommender system generates only limited congestion on popular jobs. Overall, our project created around 500 extra matches in the Swedish labor market during one year.
2. Online job search leaves very useful data on online job boards, that can be used to analyze recent labor market evolution in real-time. During the COVID crisis, we provided and analyzed new real-time indicators of the state of the labor market based on online job search and vacancy posting activity. These indicators are particularly useful in uncertain times.
3. Gender gaps in commuting time between work and home contribute significantly to gender inequality in pay. We contrast how men and women search on the labor market, especially the type of jobs they are looking for. We find that women are looking for jobs closer to their home than men, and are willing to accept lower wages through that process.
Our results guide policy-makers, who design public job boards and transportation policies, and more broadly policies addressing sources of inefficiencies in the labor market.
