Cody Cook

We study the impacts of New York City's Central Business District (CBD) Tolling Program, the first cordon-based congestion pricing scheme in the United States. Using a generalized synthetic controls approach that compares outcomes in NYC to contemporaneous outcomes in other cities, we find that the policy increased speeds on CBD roads by 11%, with little-to-no effect on air quality, transactions at shops and restaurants, or overall foot traffic in the CBD. Speeds also increased outside the CBD, especially on roads commonly traversed by drivers traveling to/from the CBD. These spillovers lead to faster trips throughout the metro area, including for many unpriced trips. We develop a simple model to bound the effects on driver welfare. If drivers have a Value of Travel Time (VOTT) of $40/hour, then we estimate that driver welfare increased by at least $14.3 million/week, before any revenue recycling or environmental benefits. Passenger vehicles headed to the CBD are only better off if their VOTT exceeds $153/hour, but the modest speed improvements for the many unpriced trips reduce the overall 'break-even' VOTT of drivers to at most $21/hour. Finally, we show how characteristics of local travel patterns and road networks can inform the potential impacts of introducing cordon-based congestion pricing in other cities.

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Media: New York Times, The Upshot, Financial Times, The Guardian, Wired

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 affordable housing tenants, we first show that, despite fixed eligibility requirements, developments in higher-opportunity neighborhoods disproportionally house tenants who are higher income, more educated, less likely to have children, and less likely to be Black or Hispanic. To quantify the welfare implications, we build a model in which households choose from both market-rate and affordable housing options, where the latter are rationed by private developers. Building in higher-opportunity neighborhoods costs more, but increases household welfare and reduces racial and economic segregation. However, the welfare gains accrue to more moderate-need and white (non-Hispanic) households at the expense of other households. Using the estimated model, we show that the shift in the distribution of assistance is primarily due to a `crowding out' effect: households that only apply for assistance in higher-opportunity neighborhoods crowd out those willing to apply regardless of location. Relative to the initial choice of location, policy levers available post-construction—such as lowering the income limits used for means-testing—have only limited effects.

This paper studies the welfare and distributional effects of dynamically priced highway toll lanes, the most common form of congestion pricing in the United States. 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 who choose their departure time under imperfect information about travel times and prices. Once the uncertainty is resolved, they then choose whether to take the priced (faster) or unpriced (slower) lanes. We estimate the model using data on toll transactions, historical traffic conditions, and driver characteristics for I-405 in Washington State. We find that tolling a subset of highway lanes increases welfare in aggregate, especially when the newly priced lanes were previously carpool-only. A key component of these gains is the "option value" of tolling: even drivers who infrequently take the priced lanes benefit from having the option to pay for speed when traffic is worse than expected. Moreover, the largest gains accrue to drivers in the bottom income quartile, primarily due to the spatial distribution of lower- and higher-income drivers rather than preference heterogeneity. 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.

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

Unlike many other goods allocated through centralized mechanisms, affordable housing is durable: who receives a unit today affects the supply of units available to reallocate in the future. We build a dynamic model of the allocation mechanism that endogenizes the arrival rate of vacant units. Households in the model make decisions on both whether to apply and, if allocated a unit, whether to move out each period. Policy changes that affect the move-out rate (e.g., giving households more choice when applying or allowing tenants to swap units) lead to a tradeoff between providing longer stays in subsidized housing to fewer households or shorter stays to more households. Optimal policy depends on dynamic considerations such as how match quality, need, and any treatment effects on households evolve over time.

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