Research

Public transit agencies face a budget-constrained trade-off between lower fares and higher service quality. Agencies value rider surplus, potentially weighted by income level, but also their own profit due to the budget constraint. The optimal choice of price depends on rider sensitivity to price, willingness to pay for quality, and heterogeneity across income groups. We study the MBTA using quasi-experimental variation — a subway fare increase and speed restrictions along certain routes — to estimate price and quality elasticities. We then embed these moments in a discrete-choice model with rich substitution across routes and heterogeneity by neighborhood income. We find that riders are highly price-sensitive and moderately responsive to slower service. Higher-income areas place greater weight on quality, while lower-income riders are more price elastic. Finally, we find that infra-marginal quality insensitive riders should incentivize the agency to over-invest in quality: the solution to the budget-constrained welfare maximization problem cannot explain underinvestment in quality.

Which Americans experience the worst infrastructure? What are the costs of living with that infrastructure? We measure road roughness throughout America using vertical acceleration data from Uber rides across millions of American roads. Our measure correlates strongly and positively with other measures of road roughness where they are available, negatively with driver speed. We find that road repair events decrease roughness and increase speeds. We measure drivers’ willingness-to-pay to avoid roughness by measuring how speeds change with salient changes in road roughness, such as those associated with town borders and road repaving events in Chicago. These estimates suggest that one standard deviation of road roughness in the US generates losses to drivers of $0.33 per driver-mile. Roads are worse near coasts, and in poorer towns and in poorer neighborhoods, even within towns. We find that a household that drives 3,000 miles annually on predominantly local roads will suffer $450 per year more in driving pain if they live in a predominantly Black neighborhood than in a predominantly White neighborhood. The relationship between road roughness and both race and income is substantially stronger in less populous and rich places. Road roughness has little ability to explain subsequent road resurfacing in eleven cities, which suggests American rides could be much smoother if the bumpiest roads were fixed first.

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.

Platform gig work such as rideshare driving involves workers supplying flexibility to the platform, for example, providing service when demand is high. It also can be attractive to workers who demand flexibility, for example, workers with irregular commitments in other jobs. Who benefits the most (and least) from flexible work arrangements? Workers who supply labor elastically provide flexibility to the platform and receive above the platform-average compensation. In contrast, workers who demand substantial flexibility from the platform may not achieve above-average remuneration but may nonetheless benefit from the availability of flexible work options. We characterize the demographics of Uber drivers; we demonstrate that the share of drivers that are women is highest for women from low-income Census tracts and among Black drivers. While within-demographic heterogeneity is large, we show that older workers demand less flexibility from the system and women demand disproportionately more. We show that workers from low-income census tracts are important suppliers of flexibility to the system.