Carolyn Tsao
I am a Ph.D. Candidate in Economics at Princeton University.
I study how non-wage amenities affect job choice and worker productivity, with a focus on teacher labor markets.
I am on the 2024/25 academic job market.
My CV is available here.
Email: ctsao [at] princeton [dot] edu
Job Market Paper
It's Not (Just) About the Money: Pay and the Value of Working Conditions in Teaching [PDF]
Winner of the Best Paper Award at the 2024 CESifo/ifo Junior Workshop on Economics of Education
This paper quantifies the extent to which teachers earn rents, explicitly taking both pay and non-wage job attributes into account. Using quasi-experimental designs, novel administrative data, and a choice experiment, I estimate the gap in pay and the gap in the value of working conditions between teaching and teachers' next-best jobs, and sum the gaps to obtain an estimate of the teaching rent. Employing a fuzzy regression kink design that leverages variation in who becomes certified to teach in Kentucky, I show that teaching pays a premium of $18-21,000/yr (33-40% of the teaching salary) more than teachers' next-best options. A similar pay gap arises from event studies around teacher exits. Importantly however, I also find that working conditions in teaching are relatively poor. Fielding a stated preference experiment to Kentucky teachers, I estimate that teachers are willing to pay 29-35% of their salary to switch to their next-best job, solely for better working conditions. My results indicate that teaching offers negligible rents; instead, teaching pays a large premium that mostly functions as a compensating differential. Extending my point estimates to other states suggests that over $145 billion is being spent annually to compensate teachers for poor working conditions in the US, consistent with reports of teacher shortages today.
Working Papers
Managers in Public Schools [PDF]
To what extent do public school principals affect student outcomes, and how do managerial practices differ between more and less effectiveness principals? Using administrative data on teacher-principal-student links in two U.S. states, I estimate principal and teacher effectiveness using a two-way manager-worker fixed effect framework. Variance decompositions show that principal effectiveness explains less of the variance in student outcomes than teacher effectiveness does. That said, switching to a more effective principal improves school outcomes: event studies around principal moves and retirements show that receiving a more effective principal improves student outcomes (increased test scores, decreased absenteeism) and teacher outcomes (greater teacher retention, increased student test scores within teacher). Importantly, novel survey evidence suggests that principal effectiveness is attributable to particular managerial practices: linking the estimated principal effects to survey data on teachers' perceptions of their school leadership, I find that at schools that employ more effective principals, teachers are more likely to report the use of data-driven instructional practices but also a lack of trust and mutual respect between administration and staff.
The Effects of Prohibiting Marriage Bars: The Case of U.S. Teachers [PDF] (Under review)
with Amy Kim
Married women in the early 20th century U.S. faced “marriage bars,” a form of employer discrimination that barred them from paid employment. However, because the end of marriage bar use coincided with shifting social norms and labor market conditions, it is unclear how the end of marriage bars affected women’s employment. We study the effects of the legislative prohibition of marriage bars in teaching during the 1930s. A difference-in-differences design shows that the prohibitions increased the share of married women teachers, partly by pushing unmarried women out of the labor force, and modestly increased women's labor force participation.
Work in Progress
Manager Feedback Style and Worker Productivity [Extended abstract]
with Calvin Jahnke and Gabor Nyeki
This paper studies how a manager's tone when giving feedback to workers affects individual productivity and output quality. We construct a novel panel dataset that links software engineers and managers to their email communications and code contributions on the largest open source software project, the Linux kernel. We identify tones used in the emails (e.g., toxic, polite, encouraging) using natural language processing and machine learning techniques. We find a strong negative relationship between manager toxicity and engineer productivity. Using an instrumental variables design to address endogeneity in a manager's choice of tone, we find that receiving toxic feedback from a manager reduces the likelihood that an engineer completes a programming task, increases the amount of time to task completion, and decreases the likelihood that an engineer completes more tasks in the next 30 days.