Thank you for visiting my website. I am a PhD candidate in economics at UCLA. My research focuses on how human resource decisions within firms shape market outcomes. I generally enjoy any project that involves capturing interesting theoretical forces with data.
MA in Economics, 2020
BA in Economics, 2016
BA in Political Science, 2016
We develop a model in which a principal delegates sequential search over uncertain objects to an agent. We use the model to analyze how recruiters influence the search for talent. During search, the recruiter does not learn worker productivity but only forms a belief characterized by an expectation and a variance. We demonstrate that delegation is equivalent to making the search technology less accurate. Delegation results in moral hazard with a multitasking flavor, where the recruiter wastes effort finding low-variance workers at the expense of high-expectation workers. As workers become more homogeneous with respect to productivity variance delegation becomes more efficient. Our results provide a theoretical connection between delegation and variance-based statistical discrimination.
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.
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.