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: www.thekohlhepps.com. We document our journey navigating faith, careers, and starting a family.
MA in Economics, 2020
BA in Economics, 2016
BA in Political Science, 2016
This paper studies the interaction between competition and the internal organization of firms using a new model and novel data. In the model, firms with different organizational efficiency choose their entire organization structure, including who to hire and the task-content of each worker’s job, in order to compete in a differentiated product market. The model illustrates that firms face a quality-wage-complexity trade-off when choosing their organizational structure. I show the model features a unique equilibrium under testable condition, can be solved using a globally convergent algorithm and can be identified using a measure of organizational complexity. I use the model to study the beauty industry. Millions of task assignments across competing hair salons illustrate that organizational complexity is positively correlated with firm revenue, employment, and labor, consistent with the model. I estimate the model, and use it to study the impact of a counterfactual convergence of management practices and a counterfactual sales tax on hair services in Los Angeles.
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.