Research

I study how preferences for neighborhood amenities vary by income. Using data on over 150 million visits to restaurants, shops, personal services, and entertainment places, I estimate a model of demand for amenities. I find that higher and lower income urban residents have heterogenous preferences for individual establishments, which which often vary systematically along observable dimensions such as category, brand, and price level. Using the location and estimated quality of each establishment, I construct an aggregate Neighborhood Amenity Quality Index (NAQI) that measures the value of each neighborhood's overall access to amenities. Despite the heterogeneity in establishment-level preferences, neighborhood-level preferences exhibit strong positive correlation; higher and lower income residents generally agree on which neighborhoods have the best overall access to amenities. Densely populated neighborhoods close to the urban core benefit from residential agglomeration forces and have especially high quality access to amenities. Conditional on population density, neighborhoods with better amenity access tend to be richer, more educated, and have more expensive rents.

Do urban children live more isolated lives than urban adults? Using cellphone location data, we measure and compare the experienced racial isolation of students and adults. We find that students in cities experience significantly less integration in their day-to-day lives. The average student experiences 28% more racial isolation outside of the home than the average adult. Even when comparing students and adults living in the same neighborhood, exposure to devices associated with a different race is 21% lower for students. Next, we look at more broad measures of urban mobility and find that students spend more time at home, more time closer to home when they do leave the house, and less time at school than adults spend at work. Finally, we find correlational evidence that neighborhoods with more geographic mobility today also had more intergenerational income mobility in the past. We hope future work will more rigorously test the hypothesis that different geographic mobility patterns can explain why urban density appears to boost adult wages but reduce intergenerational income mobility.

We develop and implement a heterogeneous-agents network-based empirical model to analyze alternative policies during a pandemic outbreak. We combine several data sources, including information on individuals' mobility and encounters across metropolitan areas, information on health records for millions of individuals, and information on the possibility to be productive while working from home. This rich combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions. We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.

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Media: Stanford News, Plugging the Gap, Berkeley Haas

The growth of the gig economy generates worker flexibility that, some have speculated, will favor women. We explore this by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. We document a roughly 7% gender earnings gap amongst drivers. We show that this gap can be entirely attributed to three factors: experience on the platform (learning-by-doing), preferences over where to work (driven largely by where drivers live and, to a lesser extent, safety), and preferences for driving speed. We do not find that men and women are differentially affected by a taste for specific hours, a return to within-week work intensity, or customer discrimination. Our results suggest that there is no reason to expect the gig economy to close gender differences. Even in the absence of discrimination and in flexible labor markets, women's relatively high opportunity cost of non-paid-work time and gender-based differences in preferences and constraints can sustain a gender pay gap.

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Media: Freakonomics, Marginal Revolution, Washington Post, Financial Times, The Verge, Quartz, Brookings, Telegraph, Fortune, Bloomberg, The Economist

One way for older workers to ease into retirement is to move to the gig economy where they can freely choose hours and intensity of work. We look at age/wage profiles of workers in the traditional labor market and of Uber drivers. While the move to the gig economy generates flexibility, it also moves pay closer to a spot market individuals earn (presumably) their marginal product. Earnings for workers in traditional jobs increase steeply with age, while Uber earnings are steadily declining after age forty. This highlights the tradoff between flexible work arrangments and earnings.

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Media: LAist, GSB Insights

Who wins and who loses when we price a subset of highway lanes? Toll lanes allow drivers to sort into paying with money or with travel time, which might increase overall efficiency at the expense of lower-income drivers. On the other hand, the option value of paying for speed may outweigh a small increase in everyday travel time. In this project, we present a model of commuters’ route and departure time choice on a highway road network and estimate it using data from Seattle-area Express Toll Lanes. We use the model to evaluate the welfare and distributional consequences of the Express Lanes, and to compare the current pricing regime to counterfactual pricing policies.