Estimating Experienced Racial Segregation in U.S. Cities Using Large-Scale GPS Data [paper]
with Susan Athey, Matt Gentzkow, and Tobias Schmidt. Proceedings of the National Academy of Sciences.
We introduce a novel measure of segregation, experienced isolation, that captures individuals’ exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we measure experienced isolation by race. We find that the isolation individuals experience is substantially lower than standard residential isolation measures would suggest, but that experienced andresidential isolation are highly correlated across cities. Experienced isolation is lower relative to residential isolation in denser, wealthier, more educated cities with high levels of public transit use, and is also negatively correlated with income mobility. Individuals are more isolated close to home and at locations like churches and schools, and less isolated at entertainment, retail, and eating establishments.
Many proposed surface water transfers undergo a series of regulatory reviews designed to mitigate hydrological and economic externalities. While these reviews help limit externalities, they impose substantial transaction costs that also limit trade. To promote a well-functioning market for surface water in California, we describe how a new kind of water right and related regulatory practices can balance the trade-off between externalities and transaction costs, and how a Water Incentive Auction can incentivize a sufficient number of current rights holders to swap their old rights for the new ones. The Water Incentive Auction adapts lessons learned from the US government’s successful Broadcast Incentive Auction.
Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error [paper]
with Brad Ross. Submitted.
We propose a sensitivity analysis for Synthetic Control (SC) treatment effect estimates to interrogate the assumption that the SC method is wellspecified, namely that choosing weights to minimize pre-treatment prediction error yields accurate predictions of counterfactual post-treatment outcomes. Our data-driven procedure recovers the set of treatment effects consistent with the assumption that the misspecification error incurred by the SC method is at most the observable misspecification error incurred when using the SC estimator to predict the outcomes of some control unit. We show that under one definition of misspecification error, our procedure provides a simple, geometric motivation for comparing the estimated treatment effect to the distribution of placebo residuals to assess estimate credibility. When we apply our procedure to several canonical studies that report SC estimates, we broadly confirm the conclusions drawn by the source papers.
What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic? [paper]
with Hunt Allcott, Levi Boxell, Jacob Conway, Matt Gentzkow, and Benny Goldman. Submitted.
We provide new evidence on the drivers of the early US coronavirus pandemic. We combine an epidemiological model of disease transmission with quasi-random variation arising from the timing of stay-at-home-orders to estimate the causal roles of policy interventions and vol- untary social distancing. We then relate the residual variation in disease transmission rates to observable features of cities. We estimate significant impacts of policy and social distancing responses, but we show that the magnitude of policy effects is modest, and most social distancing is driven by voluntary responses. Moreover, we show that neither policy nor rates of voluntary social distancing explain a meaningful share of geographic variation. The most important predictors of which cities were hardest hit by the pandemic are exogenous characteristics such as population and density.