Job Market Paper
Estimating Production Functions with Latent Team Structures: An Analysis of Nursing Homes
[draft]
Abstract
I consider robust specification and estimation of production functions when the researcher observes a disaggregated vector of endogenous labor inputs. Drawing on personnel and organizational economics, I develop a latent model of matching teams of worker types with bundles of tasks under time constraints and costly team formation. I adapt its implications into a penalized and shape-constrained GMM estimator and establish its consistency. Applying it to the US nursing home industry, I estimate revenue generation and predict counterfactual labor demand and health outcomes under a proposed targeted minimum staffing mandate. I find that the policy improves care quality for long-stay patients but has mixed effects for short-stay patients: it narrows disparities and raises bottom-decile quality, but reduces mean and top-decile quality.
Other Working Papers
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Minimax Regret Treatment Rules with Finite Samples when a Quantile is the Object of Interest
w. Patrik Guggenberger and Nikita Pavlov[draft]
Abstract
Consider the setup in which a policymaker is informed about the population by a finite sample and based on that sample has to decide whether or not to apply a certain treatment to the population. We work out finite sample minimax regret treatment rules under various sampling schemes when outcomes are restricted onto the unit interval. In contrast to Stoye (2009) where the focus is on maximization of expected utility the focus here is instead on a particular quantile of the outcome distribution. We find that in the case where the sample consists of a fixed number of untreated and a fixed number of treated units, any treatment rule is minimax regret optimal. The same is true in the case of random treatment assignment in the sample with any assignment probability and in the case of testing an innovation when the known quantile of the untreated population equals 1/2. However if that quantile exceeds 1/2 then never treating is the unique optimal rule and if it is smaller than 1/2 always treating is optimal. We also consider the case where a covariate is included.
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Robust Nonparametric Testing of Conditional Independence
Abstract
Testing for equality between two conditional probability functions can show up in a wide variety of economic settings. When covariates are high dimensional or continuous, we propose discretization of the covariate space as the tuning parameter in the contingency table approach to testing. Through Monte Carlo simulations, we observe that it has superior size control and power against alternatives while being robust to choice of the tuning parameter compared to testing based on series estimation. We show that testing for racial bias in judicial decisions reduces to a test of equality of conditional recidivism probabilities across races under certain assumptions. We apply this framework to parole decisions in the state of Georgia and find evidence of racial bias.
Instructor at Penn State
Introductory Microeconomics (undergraduate), Summer 2021
Teaching Assistant at Penn State
Introductory Econometrics, Honors (undergraduate), Spring 2023
Money and Banking (undergraduate), Fall 2022
Introductory Econometrics (undergraduate), Spring 2022
Introductory Macroeconomics (undergraduate), Fall 2020, Spring 2021, Fall 2021