Research
Peer Reviewed Publications
(Review of Economic Studies, 2023) with John List, Ian Muir, and Devin Pope
Left-digit bias (or 99-cent pricing) has been discussed extensively in economics, psychology, and marketing. Based on observational data from Lyft sessions followed by a field experiment conducted with 21 million Lyft passengers, we provide evidence of large discontinuities in demand at dollar values. Approximately half of the downward slope of the demand curve occurs discontinuously as the price of a ride drops below a dollar value (e.g. $14.00 to $13.99). Our results showcase the robustness of an important behavioral bias for large, modern companies and its persistence in a highly-competitive market.
with John List and Ian Muir
Accepted at Econometric Reviews
This study investigates the optimal use of covariates in reducing variance when analyzing experimental data. We show that finding the variance-minimizing strategy for making use of pre-treatment observables is equivalent to estimating the conditional expectation function of the outcome given all available pre-randomization observables. This is a pure prediction problem, which recent advances in machine learning (ML) are well-suited to tackling. Through a number of empirical examples, we show how ML-based regression adjustments can feasibly be implemented in practical settings. We compare our proposed estimator to other standard variance reduction techniques in the literature. Two important advantages of our ML-based regression adjustment estimator are that (i) they improve asymptotic efficiency relative to other alternatives, and (ii) they can be implemented automatically, with relatively little tuning from the researcher, which limits the scope for data-snooping.
Working Papers
Pretrained Regression Adjustment With An Applications to Clinical Trials
Most recent revision: 04/02/2024
Regression adjustment is a powerful technique allowing researchers to use available covariates to reduce the variance of treatment effect estimators derived from experimental data. While its asymptotic theory promises a high degree of variance reduction, I show that the sample sizes typical of many real-world clinical trials datasets often constrain regression adjustment methodologies from attaining their theoretical potential in practice. Based on this observation, I propose a novel methodology that allows researchers conducting small-scale experiments to robustly borrow observations from large-scale observational datasets, thereby mitigating these finite sample concerns. A back-of-the-envelope calculation suggests that my methodology at scale can potentially reduce spending on clinical trials for new-drug development by $1B annually while holding statistical power constant.
Stress Testing a Structural Model of Subscriptions: Robust Inference on Intensive Margin Demand
with Aaron Bodoh-Creed, Brent Hickman, John List, Ian Muir
Most recent revision: 08/18/2023
In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse-selection models. Despite significant data limitations, we are able to derive informative bounds on demand under counterfactual price changes. These bounds arise because empirically plausible DGPs must respect the Law of Demand and the observed shift(s) in aggregate demand resulting from a known exogenous price change(s). These bounds facilitate robust policy prescriptions using rich, internal data sources similar to those available in many real-world applications. Our partial identification approach enables viable nonlinear pricing design while achieving robustness against worst-case deviations from baseline model assumptions. As a side benefit, our identification results also provide useful, novel insights into optimal experimental design for pricing RCTs.
Minimum Cost Minimum Income Guarantees
Most recent revision: 07/27/2023
A recent public debate in the United States surrounds the proposal to use universal basic income (UBI) as a mechanism for alleviating the worst effects of poverty. A potential problem with UBIs is their high cost, when scaled up to all of society. In this paper, I contribute to this debate by estimating and solving a structural model of how governments can guarantee income to the poorest member of society at minimum cost. I adapt recent identification insights from the empirical contract theory literature to show identification of classical optimal income taxation models. When labor supply elasticities are more consistent with the findings of the micro literature (i.e., in the ballpark of 0.3-0.6), I find that the optimal marginal tax rate is in the range of 60-75\%. When elasticities are more consistent with the findings of the macro literature (i.e. 1-1.4), I find that the optimal marginal tax rate is roughly 40\%. The sensitivity of the optimal policy to structural primitives suggests that further experimentation with minimum-income guarantee programs would be valuable. I derive a number of identification results for extensions to my basic model and argue that these extensions provide a guide for what additional treatment variation and data collection efforts future experiments should engage in to facilitate learning about policy-relevant parameters.
Judging Nudging: Understanding the Welfare Effects of Nudges Versus Taxes
with John List, Matthias Rodemeier, and Sutanuka Roy
Most recent revision: 09/23/2023
While behavioral non-price interventions (“nudges”) have grown from academic curiosity to a bona fide policy tool, their relative economic efficiency remains under-researched. We develop a unified frame- work to estimate welfare effects of both nudges and taxes, while allowing for normative ambiguity about how nudges map into utility. We showcase our approach by creating a database of more than 300 carefully hand-coded point estimates of non-price and price interventions in the markets for cigarettes, influenza vaccinations, and household energy. While nudges are effective in changing behavior in all three markets, they are not necessarily the most efficient policy. When nudges are debiasing, they are more efficient in the market for cigarettes, while taxes are more efficient in the vaccine and energy market. Interestingly, these conclusions also often hold when nudges are deceptive rather than debiasing. We identify two key factors that govern the difference in results across markets: i) an elasticity-weighted standard deviation of the behavioral bias, and ii) the magnitude of the average externality. Nudges dominate taxes whenever i) exceeds ii). Finally, we consider cases in which nudges cause direct psychic costs or benefits to consumers.
Separability, Identification, and Extrapolation in a Family of Adverse Selection Models
Most recent revision: 9/11/2023
Empirical adverse selection studies (e.g., Luo Et Al (2018) and D'Hauotfoeuille and Fevrier (2020)) typically model agent heterogeneity as arising from a multiplicatively separable functional form and show nonparametric identification within this family. In this paper, I synthesize and generalize the nonparametric identification results in this literature. First, I show that while a certain form of separability is crucial for identification, this separability need not be multiplicative per se. Second, within the class of separable models, I analyze identification in a "first-order" framework by showing how infinitesimal policy responses can be used to nonparametrically identify this class of models. Third, having derived this family of separable models, I show that prior identification results in the literature continue to hold, with few modifications for a certain subset of separable models. My results allow me to clarify the role of separability as encoding a particular way to extrapolate from the local effects of small policy changes to the global effects of large policy changes. I revisit a number of applications and show which counterfactuals may be especially sensitive to the assumed form of separability.
The Generality and Generalizability of Aggregate Ratio Parameters
Most recent revision: 10/24/2023
Many parameters in empirical economics are identified by taking the ratio of two average causal effects. A famous example of this strategy, which I refer to as the "aggregate ratio parameter", is the Wald estimator from the instrumental variables (IV) model. I show that this paradigm is more general than the IV example and unifies quantities ranging from the equivalent price metric in behavioral economics to the diversion ratio in merger analysis. I generalize well-known results for IV and show that the aggregate ratio parameter identifies only the average of individual-level ratio parameters within a suitably defined "complier" population. Thus, the concerns raised in the context of IV that estimands may have limited policy relevance/external validity apply more broadly to empirical objects other than treatment effects. I show how to use covariates to empirically probe the importance of these external validity concerns and how to correct estimates to bring the identified complier population closer to the policy-relevant population along observable dimensions. I use my framework to re-analyze data from a diverse range of empirical examples.
with Pradhi Aggarwal, Alec Brandon, Ariel Goldszmidt, Justin Holz, John List, Ian Muir, and Thomas Yu
Most recent revision: 12/9/2022
Prior research finds that, conditional on an encounter, minority civilians are more likely to be punished by police than white civilians. An open question is whether the actual encounter is related to race. Using high-frequency location data of rideshare drivers operating on the Lyft platform in Florida, we estimate the effect of driver race on traffic stops and fines for speeding. Estimates obtained across traditional and machine learning approaches show that, relative to a white driver traveling the same speed, minorities are 24 to 33 percent more likely to be stopped for speeding and pay 23 to 34 percent more in fines. We find no evidence that these estimates can be explained by racial differences in accident and re-offense rates. Our approach provides key insights into the total effect of civilian race on outcomes of interest and highlights the methodological import of combining high-frequency data and machine learning to evaluate critical social issues.
Why Do Robust Standard Errors Hardly Change Inference?
Most recent revision: 09/21/2022
A folk wisdom in applied econometrics is that adjusting OLS standard errors for heteroskedasticity rarely leads to large changes whereas adjusting for other statistical considerations such as clustering is likely to be more consequential. In this note, I derive a tractable formula for the ratio between robust and conventional standard errors for a given covariate of interest in a multi-variate linear regression. This formula helps clarify when and in which direction we should expect robust standard errors to differ from conventional standard errors. I argue that a motivating example of heteroskedasticity in econometrics pedogogy is ambiguously signed because factors pushing in opposite directions will tend to cancel each other out.
Works in Progress
Structural Meta-Analysis of the Tax Bunching Literature
with Matthias Rodemeier
Identification of Random Coefficients Models without Linearity
Behavioral Nonlinear Pricing
with Matthias Rodemeier and Nicola Gennaioli