Women account for half of a country’s potential talent base and numerous studies have shown that women’s participation in the workplace provides the nation with considerable competitiveness. Yet, despite an increasing breadth of educational and work opportunities, research has shown that women are more unhappy than they were in the 1970s. Some women, however, have made the successful and determined effort to seek fulfillment by voluntarily leaving an organization, pursuing their personal vision, and transitioning to their desired reality. This qualitative case study explored how 24 college-educated women learned to recreate their careers and find personal fulfillment. The purpose of this study was to understand how some women voluntarily left secured positions and how they applied the concept of a personal vision to transition to what was for them, a new desired reality. Personal vision is defined by Boyatzis et al. (2019) as an expression of an individual’s ideal self, expressing someone’s aspirations, dreams, and ambitions. The sample was a purposeful one. The primary method of data collection was participant semi-structured interviews. The second source of data was a group of six women with a personal vision who had not yet made the transition to their desired reality. The third source of data was document review, including CVs, LinkedIn, and social media accounts for cross-reference checking. The data was initially coded by research questions and the findings were coded by behavioral categories derived from the Conceptual Framework. Four conclusions emerged from the analysis. These include confirmation that women need to be motivated in order to achieve personal fulfillment, personal issues must be reconciled before women can begin their transition from an established situation to their desired reality, informal learning methods are key in helping women make their transition, and transitioning to a desired reality is unlikely without the support from others. Several recommendations for future research as well as recommendations for individuals, coaches, HR Practitioners and women’s networks and organizations are offered.

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.