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 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.

Cities provide access to stores, public amenities and other people, but that access may provide less benefit for lower-income and younger urbanites who lack money and means of easy mobility. Using detailed GPS location data, we measure the urban mobility and experienced racial and economic isolation of the young and the disadvantaged. We find that students in major metropolitan areas experience more racial and income isolation, spend more time at home, stay closer to home when they do leave, and visit fewer restaurants and retail establishments than adults. Looking across levels of income, students from higher-income families visit more amenities, spend more time outside of the home, and explore more unique locations than low-income students. Combining a number of measures into an index of urban mobility, we find that, conditional on income, urban mobility is positively correlated with home neighborhood characteristics such as distance from the urban core, car ownership, and social capital.

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.

---

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.

---

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.

---

Media: LAist, GSB Insights