Cody Cook

How does the location of affordable housing affect household welfare, the distribution of assistance, and broader societal objectives such as racial integration? Using administrative data on tenants of units funded by the Low-Income Housing Tax Credit (LIHTC), we first show that tenant characteristics such as race and proxies for need vary widely across neighborhoods. Despite fixed eligibility requirements, tenants of LIHTC developments in higher-opportunity neighborhoods are higher income, more educated, and far less likely to be Black. To quantify the welfare implications, we build and estimate a model in which households choose from both market-rate and affordable housing options, where the latter are rationed by developers. While affordable housing in higher-opportunity neighborhoods costs more, it also increases household welfare and reduces segregation. However, the welfare gains accrue to more moderate-need and white (non-Hispanic) households at the expense of other households. This distributional shift is primarily due to `crowding out': households that only apply for assistance in higher-opportunity neighborhoods crowd out those willing to apply regardless of location. Other policy levers—such as lowering the income limits used for means-testing—have only limited effects relative to the initial choice of location.

This paper studies the welfare and distributional effects of dynamically priced highway toll lanes. To quantify the equilibrium effects of tolling, we develop and estimate a model of driver demand, the road technology, and the pricing algorithm. The demand model features heterogeneous drivers choosing both where and when to drive, as well as uncertainty about prices and travel times. A key welfare channel is the option value of tolling: even drivers who infrequently take the priced lanes can benefit from having the option but not the obligation to pay for speed. We estimate the model using data on toll transactions, historical traffic conditions, and driver characteristics for the I-405 Express Toll Lanes in Washington State. Relative to a world in which the same number of highway lanes are all free, status-quo tolling increases aggregate welfare and benefits drivers in all income quartiles, driven in large part by the option value. Moreover, we find that drivers in the bottom income quartile gain the most under status-quo tolling. These heterogeneous welfare effects by income stem primarily from the spatial distribution of lower- and higher-income drivers, rather than from preference heterogeneity. Low-income drivers have longer I-405 commutes and therefore—due to the design of the pricing algorithm—often face an advantageous tradeoff between time savings and price. Finally, we show how simple revisions to the pricing algorithm can increase aggregate welfare and help achieve redistributive goals.

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Media: Marginal Revolution

This paper examines how preferences for neighborhood amenities vary by income. Using panel data on over 100 million visits to 1.4 million establishments, I build and estimate a discrete choice model of demand for restaurants, shops, personal services, and entertainment places. While preferences for specific establishments often vary by income, preferences for each neighborhood's overall access to amenities are highly aligned. Dense urban areas have a sufficient variety of amenities to offer broad appeal, while less dense areas have more limited access to amenities. For incumbent residents of gentrifying neighborhoods, counterfactual simulations suggest that the tailoring of amenities to higher-income entrants has only modest welfare effects relative to the effects of displacement to cheaper neighborhoods with worse access to amenities.

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

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.

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 where 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 tradeoff between flexible work arrangements and earnings.

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Media: LAist, GSB Insights