Much of the new "gig economy" relies on reputation systems to reduce problems of asymmetric information. In most cases, these reputation systems function well by soliciting unbiased feedback from buyers and sellers. However, certain features of online labor markets create incentives for employers to misreport worker performance. This paper tests whether employers learn about worker productivity from public, subjective, performance reviews using data from a large online labor market. Starting with a simple model of employer learning in the presence of potentially biased reviews, I derive testable hypotheses about the relationship between public information and wages, worker attrition, and contract renewals. I find that these public reviews provide substantial information to the market and that other firms use them to learn about the productivity of workers. I also find evidence that these reviews affect how long workers stay in the labor market. Finally, using data on applications, I provide evidence of a mechanism for informative reviews. I show that workers punish firms that leave negative reviews by refusing to work for them again. Together, this body of evidence suggests that reputation systems in online labor markets provide significant information to both workers and firms and help reduce problems of asymmetric information.
The ability to estimate peer effects in network models has been advanced considerably by the IV model of Bramoullé et al. (2009). While such IV estimates work well for very sparse networks, they exhibit very weak power for networks of even modest densities. We review and extend the findings of Bramoullé et al. (2009) and then propose an alternative estimator. We show that our new estimator works approximately as well as IV in very sparse networks and performs much better in networks of moderate density.