Three Essays in Economics

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

Abstracts from the NIHR INVOLVE Conference 2017

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It’s Not Just the Economy, Stupid: An Analysis of the Important Issues in Presidential Elections from 1972 to 2008

Mentor: Norman Schofield From the Washington University Undergraduate Research Digest: WUURD, Volume 7, Issue 1, Fall 2011. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Do download reports reliably measure journal usage? Trusting the fox to count your Hens?

Download rates of academic journals have joined citation rates as commonly used measures of research influence. But in what ways and to what extent do the two measures differ? This paper examines six years of download data for more than five thousand journals subscribed to by the University of California system. While down- load rates of journals are highly correlated with citation rates, the average ratio of downloads to citations varies substantially among academic disciplines. We find that, typically, the ratio of a journal’s downloads to citations depends positively on its im- pact factor. Surprisingly, we find that, controlling for citation rates, number of articles, academic discipline and year of download, there remains a “publisher effect,” with some publishers recording significantly more downloads than would be predicted from char- acteristics of their journals. Download statistics are recorded and supplied to libraries by journal publishers, often subject to confidentiality clauses. If libraries use download statistics to evaluate journals, they may want to account for publisher bias in these statistics

Do download reports reliably measure journal usage? Trusting the fox to count your hens?

Download rates of academic journals have joined citation rates as commonly-used indicators of the value of journal subscriptions.  While citation rates reflect worldwide influence, the value that a single library places on access to a journal is probably more accurately measured by the rate at which it is downloaded by local users.  If local download rates accurately measure local usage, there is a strong case for employing download rates to compare the cost-effectiveness of journals. We examine download data for more than five thousand journals subscribed to by the ten universities in the University of California system. We find that controlling for measured journal characteristics - citation rates, number of articles, and year of download - download rates, as captured by the ratio of downloads to citations, differs substantially between academic disciplines. This suggests that discipline specific adjustments to download rates are needed to construct a reliable tool for estimating local usage. Even after adding academic disciplines to the variables we control for, we find that there remain substantial ``publisher effects'', with some publishers recording significantly more downloads than would be predicted by the characteristics of their journals. While the usage tool can be modified to incorporate the publisher effect, this raises the question of what causes such substantial differences across publishers once journal and discipline characteristics are accounted for

Do download reports reliably measure journal usage? Trusting the fox to count your Hens?

Download rates of academic journals have joined citation rates as commonly used measures of research influence. But in what ways and to what extent do the two measures differ? This paper examines six years of download data for more than five thousand journals subscribed to by the University of California system. While down- load rates of journals are highly correlated with citation rates, the average ratio of downloads to citations varies substantially among academic disciplines. We find that, typically, the ratio of a journal’s downloads to citations depends positively on its im- pact factor. Surprisingly, we find that, controlling for citation rates, number of articles, academic discipline and year of download, there remains a “publisher effect,” with some publishers recording significantly more downloads than would be predicted from char- acteristics of their journals. Download statistics are recorded and supplied to libraries by journal publishers, often subject to confidentiality clauses. If libraries use download statistics to evaluate journals, they may want to account for publisher bias in these statistics