Evaluating the Efficacy of Platform-Imposed Application Costs for Managing Congestion in Online Matching Markets

One key objective of matching platforms is to facilitate market clearing. When the supply and demand for services are unbalanced, congestion issues arise. Specifically, when workers can apply for unlimited employers/jobs with negligible application costs, employers may face excessive applications and difficulty processing the proposals of lower quality. Prior work has argued (theoretically) that a platform operator can potentiall address this imbalance by imposing application costs on workers, to screen out some low-skilled workers. This study empirically evaluates the efficacy of this intervention, an artificially introduced job application fee for workers, by leveraging data from a prominent online labor market in a quasi-experiment setting. We provide field evidence that application fees successfully improve matching outcomes. First, the application fee disproportionately reduces the number of bids submitted by low-skilled workers. Second, the application fee increases workers’ selectivity effort and differentiating effort, to signal their job fit and ability to employers. However, our results also document several unintended consequences induced by application fees, which are usually ignored in previous literature. First, the application fee reduces workers’ skill expansion, as workers become less likely to bid on jobs involving new or less familiar skills. Second, the application fee appears to worsen the cold-start problem– new workers without salient reputations have a harder time attracting employers. Third, the application fee increases the chosen workers’ unavailability, indicated by their increased chance of rejecting an offer, which is different from the previous assumptions. We discuss the implications of our findings for the design of matching platforms