Lindsey Currier
About me
I'm a PhD candidate in Economics at Harvard University. My research focuses on urban economics, industrial organization, and public economics, with a particular interest in transportation and infrastructure. My work has been supported by the NSF Graduate Research Fellowship. I previously worked as a Data Scientist at Uber and earned dual degrees in Economics and Mathematics from the University of Chicago.
Working Papers
Competition in U.S. Infrastructure (Job-Market Paper)
Lindsey Currier
Presented at NBER Transportation Meeting (2025);
Volpe Center (2025);
North American UEA Meeting (2025)
[pdf]
Abstract
Can limited competition in procurement auctions explain the high, and rising, price of road infrastructure in the U.S.? I assemble a new dataset covering the near-universe of state highway auctions between 2002 and 2024. I first document thin competition: one- or two-bidder auctions account for a third of awards, and this share has risen over the past decade. Using spatial variation in inter-state bidder locations, I then estimate the average causal effect of competition on prices; an additional bidder reduces prices by ten percent. To decompose bids in the data into markups and production costs, I develop a semi-parametric structural auction model that incorporates bidders’ uncertainty over the number of competitors they face. I show that price increases over the past decade are primarily attributable to increasing markups, not increasing production costs. Limited competition, in turn, is consistent with patterns generated by fixed costs of entry, but not broad construction-sector fixed costs. Embedding the markup estimates in an entry model, I estimate large auction and market entry costs, consistent with an important role for procurement complexity and regulatory barriers.
Infrastructure Inequality: Who Pays the Cost of Road Roughness? (Nov 2024; revise & resubmit, Quarterly Journal of Economics)
Lindsey Currier, Edward L. Glaeser, & Gabriel Kreindler
[pdf]
Abstract
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.
Pricing and Quality in Public Transit
Lindsey Currier & Lia Petrose
Presented at European UEA Meeting (2024)
Abstract
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.
Suppliers and Demanders of Flexibility: The Demographics of Gig Work (March 2021)
Judy Chevalier, Keith Chen, Lindsey Currier & Peter Rossi
[pdf]
Abstract
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.Published Papers
Urban Mobility and the Experienced Isolation of Students (Nature Cities, Vol 1 Iss 1: 73–82, Jan 2024)
Cody Cook, Lindsey Currier & Edward Glaeser
Presented at AREUEA Meeting (2024)
[pdf]