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John A List, Ian Muir, Gregory Sun

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This study investigates how to use regression adjustment to reduce variance in experimental data. We show that the estimators recommended in the literature satisfy an orthogonality property with respect to the parameters of the adjustment. This observation greatly simplifies the derivation of the asymptotic variance of these estimators and allows us to solve for the efficient regression adjustment in a large class of adjustments. Our efficiency results generalize a number of previous results known in the literature. We then discuss how this efficient regression adjustment can be feasibly implemented. We show the practical relevance of our theory in two ways. First, we use our efficiency results to improve common practices currently employed in field experiments. Second, we show how our theory allows researchers to robustly incorporate machine learning techniques into their experimental estimators to minimize variance.

Pradhi Aggarwal, Alec Brandon, Ariel Goldszmidt, Justin Holz, John A List, Ian Muir, Gregory Sun, Thomas Yu

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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 study provides key insights into the total effect of civilian race on outcomes of interest and highlights the potential value of private sector data to help inform major social challenges.

John A List, Matthias Rodemeier, Sutanuka Roy, Gregory Sun

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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 framework to estimate welfare effects of both nudges and taxes. 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. We find that nudges are more efficient in the market for cigarettes, while taxes are more efficient in the energy market. For influenza vaccinations, optimal subsidies likely outperform nudges. Importantly, two key factors 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). Combining nudges and taxes does not always provide quantitatively significant improvements to implementing one policy tool alone.

John A List, Ian Muir, Devin Pope, Gregory Sun

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Left-digit bias (or 99-cent pricing) has been discussed extensively in economics, psychology, and marketing. Despite this, we show that the rideshare company, Lyft, was not using a 99-cent pricing strategy prior to our study. Based on observational data from over 600 million 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). If our short run estimates persist in the longer run, we calculate that Lyft could increase its profits by roughly $160M per year by employing a left-digit bias pricing strategy. Our results showcase the robustness of an important behavioral bias for a large, modern company and its persistence in a highly-competitive market.

Aaron Bodoh-Creed, Brent R Hickman, John A List, Ian Muir, Gregory Sun

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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.