I am a PhD candidate in economics at UCLA. I will be on the 2022-2023 job market. My CV is available here.
My research focuses on how human resource decisions within firms shape markets. I enjoy projects that involve capturing interesting theoretical forces with data.
I am a proud husband and father. My wife and I write a blog where we summarize each week of our marriage here.
MA in Economics, 2020
BA in Economics, 2016
BA in Political Science, 2016
This paper studies how task assignment decisions within the firm interact with products and labor markets outside the firm. Using data documenting the minute-by-minute assignment of millions of tasks to workers across competing salons, I show firms account for a large share of task variation, and the complexity of a firm’s internal organization increases with revenue, employment, and prices. Based on these facts, I develop a model where firms with different internal organization costs choose how to assign workers with multidimensional skills to tasks in order to compete in a differentiated product market. An equivalence result allows the model to remain tractable from theoretical analysis through estimation. I estimate the model for Manhattan hair salons and study two counterfactual policies. Raising the minimum wage from $15 to $20 reallocates employment across salons and changes the task specialization of different worker types. These forces combine in equilibrium to generate negative wage spillovers for some workers and positive wage spillovers for others. Eliminating the sales tax on services improves service quality through increased task-specialization. This benefits workers through higher wages but reduces consumer welfare as price increases outpace quality improvements.
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.
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.
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.