UC Santa Barbara

In this dissertation I study a new emerging labor market as well as a very old question about education. In all three chapters, I collect and build complex datasets and combine formal theoretical modeling with advanced econometric techniques. This allows me to answer interesting questions about peer effects in education, as well as employer learning and competitiveness in online labor markets.

In Chapter 1, Richard Startz and I propose an improved estimator of peer effects using network data. The ability to estimate peer effects in network models has been advanced considerably by the IV model of Bramoulle, Djebbari and Fortin (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 Bramoulle, Djebbari and Fortin (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. To highlight the benefits of our proposed estimator, we provide an empirical application where we estimate peer effects in individual schools.

In Chapter 2, I study whether employers learn from public, subjective, performance reviews. 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.

In Chapter 3, I analyze the competitiveness of online labor markets. A number of new labor market innovations have promised a more competitive marketplace. This paper tests this claim by comparing an online labor market with traditional labor markets. First, using data from an online labor market, I estimate a two-way fixed effects model and compare my estimates to the existing literature on traditional labor markets. Then, using a model of imperfect competition, I relate the estimated employer-specific wage premiums with the structural parameters governing market competitiveness. Combined with reasonable assumptions about differences in these labor markets, I show that the estimated differences in the variance of employer-specific wage premiums imply differences in competitiveness. My results suggest that online labor markets are more competitive than traditional labor markets.