Socioeconomic Network Heterogeneity and Pandemic Policy Response
with Mohammad Akbarpour, Aude Marzuoli, Simon Mongey, Abhishek Nagaraj, Matteo Saccarola, Pietro Tebaldi, and Shoshana Vasserman [pdf][website]
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,
Work in Progress
Local Amenities and Neighborhood Change
I study the impact of neighborhood change on higher and lower income residents through the channel of neighborhood amenities. Using data on over 200 million visits to amenities, I first document four facts describing amenity consumption: 1) higher-income areas have more local amenities, 2) higher-income residents visit more amenities, 3) the distribution of clientele income varies by amenity type, and 4) residents travel further for some types of amenities than others. Motivated by these facts, I model preferences for amenities by income, conditioning on access by using the distance from a resident's home and work locations. For certain amenities (e.g., pizza places), higher and lower income residents have aligned preferences, while for others (e.g., restaurant chains) preferences diverge. In both the cross-section and time-series, I find evidence for the endogenous response of amenities to neighborhood demographics: cross-sectionally, amenities in higher income neighborhoods are preferred by higher income residents and over time, restaurants preferred by lower income residents are more likely to exit from neighborhoods that are gentrifying. The results suggest a role for policies that affect the composition of local amenities to reduce the level of income segregation in cities, which has been steadily increasing over the past 40 years.
The Gender Pay Gap in the Gig Economy: Evidence from over a Million Uber Drivers
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.
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.