Testing for Racial Bias in Police Traffic Searches
(Job Market Paper)
I construct a flexible test for racial bias in police traffic searches that is valid amid sample selection and statistical discrimination.
The test uses instrumental variables that shift the distribution of drivers stopped without shifting the officer’s search preferences.
These instruments enable the test to be performed separately for each officer, thus permitting unrestricted heterogeneity in their preferences and beliefs.
By modeling search decisions stochastically, I allow the direction and intensity of bias to depend on the officer's beliefs, and I derive sharp bounds on various measures of intensity.
I apply the test to 50 officers in the Metropolitan Nashville Police Department, and find evidence suggesting 8 to 14 officers are biased, with 7 being biased against minorities.
I also find evidence suggesting the intensity of bias decreases for riskier drivers.
ivmte: An R Package for Implementing Marginal Treatment Effect Methods
(submitted; with Alexander Torgovitsky)
Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, psychology, and other fields.
When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994).
Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations.
We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments.
We show how to implement the framework using the ivmte package for R.
Inference for Support Vector Regression with l1 Regularization
(AEA Papers and Proceedings, 2021; with Yuehao Bai, Hung Ho, Guillaume A. Pouliot)
We provide large sample distribution theory for support vector regression (SVR) with l1-norm, along with error bars for the SVR regression coefficients.
Although a classical Wald confidence interval obtains from our theory, its implementation inherently depends on the choice of a tuning parameter which scales the variance estimate and thus the width of the error bars.
We address this shortcoming by further proposing an alternative large sample inference method based on the inversion of a novel test statistic which displays competitive power properties and does not depend on the choice of a tuning parameter.