Thank you for visiting my website. I am a PhD student in economics at UCLA. My research focuses on how individual decisions shape labor market outcomes. I have a general interest in capturing interesting theoretical forces with data.
MA in Economics, 2020
BA in Economics, 2016
BA in Political Science, 2016
In this paper we demonstrate tips are sensitive to service quality even when future interaction is unlikely. Using a novel data set covering 150,000 hair salon appointments where customers can be observed over time, we are able to exploit variation in service quality and exogenous separation rates. This allows us to separate the dynamic and direct effect of service quality on tips. We show that an important part of tipping behavior is a social norm for quality: clients tip based on perceived quality even when they do not expect to see the stylist again. At the same time, dynamic concerns make tips more sensitive to quality. We show in a stylized dynamic model how such a social norm for quality can support greater effort provision in equilibrium. Our results support the view of tipping as a social norm which encourages cooperation.
A firm hires a recruiter to find a worker. Search over candidates is noisy: the recruiter does not know productivity exactly but instead forms an estimate of a worker’s productivity expectation and variance. We analyze how delegation under the commonly used refund contract between the firm and the recruiter affects search. We find the contract induces hiring mismatch, where the recruiter focuses on finding low variance instead of high expected productivity workers. More heterogeneity in worker productivity variance causes greater hiring mismatch and welfare loss. Our model predicts variance-based statistical discrimination in hiring like that suggested by the Heckman-Siegalman critique. We also analyze the relationship between direct and delegated search, establishing delegation is equivalent to adding noise to the search technology.
This paper investigates the relationship between individual workplace injury risk and labor supply. I utilize a novel panel data set of traffic officers. Unique aspects of overtime assignment, including randomization, leave of coworkers, and informal trading enable identification. I find daily labor supply is downward sloping in injury risk: officers are less likely to work when they are more likely to be injured. This self-selection leads to an observed injury rate which is 8.5 times smaller than the underlying average injury rate. I show this has wide-ranging implications for labor supply elasticities, the value of statistical injuries, and overtime assignment.