Cody Cook

How does the location of affordable housing affect tenant welfare, the distribution of assistance, and broader social objectives such as racial and economic integration? Using administrative data on households living in units funded by the Low-Income Housing Tax Credit (LIHTC), we first show that tenant characteristics such as income, race, education, and family structure vary widely across neighborhoods, despite common eligibility thresholds. To quantify the welfare implications, we develop and estimate a residential choice model in which households choose from both market-rate and affordable housing options, where the latter are priced below-market and must be rationed. Moving a new development to a neighborhood with less poverty and better access to good schools and jobs increases aggregate tenant welfare and reduces both racial and economic segregation. However, it is also more costly to provide and disproportionately benefits more moderate-need, non-Black/Hispanic households. This change in the distribution of assistance arises in part because of the rationing process: households that only apply for assistance in opportunity-rich neighborhoods crowd out other households willing to apply anywhere. Relative to the choice of where to build, policy levers available post-construction—such as lowering the eligibility thresholds—have only limited effects on outcomes.

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. The model is estimated using data on toll transactions, historical traffic conditions, and driver characteristics from 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. Low-income drivers have the longest I-405 commutes and they face low prices relative to their time savings from the priced lanes. They also have high option values of tolling because they are more price-sensitive, so they are more likely to be marginal when deciding between the priced and unpriced lanes. Finally, we show how simple revisions to the pricing algorithm can increase aggregate welfare and achieve redistributive goals.

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Media: Marginal Revolution

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 to fewer households or shorter stays to more households. Optimal policy will depend on dynamic considerations, such as how match quality, need, and any treatment effects on households evolve over time. To take the theory to the data, we are working on a data-sharing agreement with New York City, where units are allocated through centrally-run lotteries.