Theses Doctoral

Essays in Behavioral/Experimental and Labor Economics: Information, Networks, and Institutions

Jiang, Michelle

The following dissertation is a series of three essays in behavioral/experimental and labor economics: (1) Information Asymmetry in Job Search, (2) Minority Turnout and Representation under Cumulative Voting. An Experiment, and (3) Networks and Labor Mobility: A Study of LinkedIn Profiles in the Biotechnology Sector.

Standard models of rational job search assume agents know the distribution of offered wages when deciding which jobs to accept. In Chapter 1, coauthored with Kai Zen, I test if incorrect beliefs about wages affect real-world job search behavior in a field experiment with 1100 senior-year undergraduate students in the graduating Class of 2023 at the University of California, Berkeley. Partnering with the Career Center, we present personalized information graphics on school-and-major-specific salary distributions to students in the treatment group. We first document novel evidence that even prior to labor market entry, errors exist in wage beliefs – some students overestimate the available distribution, while others underestimate the available distribution. Post-treatment, we find that students treated with correct information update their beliefs towards the truth, and this is reflected in changes in reservation wages. At the end of the school year, we find that in comparison to the control group, students who increased their reservation wage after treatment had higher total and base salaries conditional on employment, a result significant at the 5% level. However, these same students had a lower, but imprecisely estimated likelihood of being employed by June post-graduation. An opposite but symmetric effect occurred for students who decreased their reservation wage. Our results are consistent with job search models where workers with more optimistic expectations wait longer to accept a job, but accept higher wages. We compare our experimental estimates to simulated moments from the model and find that the mean experimental effect is close to the model in magnitude under reasonable parameters. Our paper suggests an economically important role for errors in beliefs about labor market conditions and shows the effectiveness of a light-touch information intervention on employment and earnings for first-time job seekers.

Chapter 2, coauthored with Alessandra Casella and Jeffrey Guo, asks how an alternative voting system can increase the voter turnout and representation of minorities. Under majoritarian election systems, securing the participation and representation of minorities remains an open problem, made salient in the US by its history of voter suppression. One remedy recommended by the courts is the adoption of Cumulative Voting (CV) in multi-member districts: each voter has as many votes as open positions but can cumulate votes on as few candidates as desired. Historical experiences are promising but also reflect episodes of minority activism. We present the results of a controlled lab experiment that isolates the role of the voting rule from other confounds. Although each voter is treated equally, theory predicts that CV should increase the minority's turnout relative to the majority and the minority's share of seats won. Our experimental results strongly support both theoretical predictions.

In Chapter 3, using LinkedIn profiles data on the biotechnology sector, it is possible to construct a measure of individuals' networks based on coworkers within the same firm and location. Exploiting such a measure, I intend to test the impact of network size on future employment in the biotechnology sector, which has frequent employee turnover due to unanticipated clinical trial failures. In doing so, I seek to answer the following question: Do larger or higher-quality networks cushion against negative employment shocks? According to preliminary evidence, connections help workers find jobs more quickly. Currently, the network measure is imperfect, the data set on which the preliminary results are based is small, and the assumptions underlying the statistical analysis can be questioned. However, all three limitations can be overcome. I highlight in this chapter the steps to be taken to do so.


This item is currently under embargo. It will be available starting 2026-04-18.

More About This Work

Academic Units
Thesis Advisors
Black Youngblood, Sandra
Casella, Alessandra
Ph.D., Columbia University
Published Here
April 22, 2024