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Transportation Externalities

Paper Session

Sunday, Jan. 7, 2024 10:15 AM - 12:15 PM (CST)

Marriott Rivercenter, Conference Room 9
Hosted By: Transportation and Public Utilities Group
  • Chair: Ian Savage, Northwestern University

Heterogeneous Speed, Reliability, and Traffic Externalities

Ian Herzog
,
Huron University College

Abstract

This paper estimates traffic's effect on travel times and congestion externalities on a broad sample of London roads which enables detailed heterogeneity analysis. A fixed effects approach that compares across times of day finds that 10% more traffic increases travel time by 4%, erodes reliability, and marginal effects are smallest on high-capacity roads. Quantifying a traffic externality model gives substantial congestion costs and I explore how magnitudes depend on modelling assumptions. Computing trip-level externalities suggests that public transit creates substantial congestion relief benefits and that Central London's Congestion Charge is too high to reflect time savings alone

Privatized Provision of Public Transit

Lucas Conwell
,
University College London

Abstract

Workers in developing countries waste significant time commuting, and gaps in public transit constrain access to productive jobs. In many cities, privately-operated minibuses provide 50–100% of urban transit, at the cost of long wait times and poor personal safety for riders. Can policymakers improve upon the privatized provision of public transit via subsidies, which leverage increasing returns in wait times, or technological upgrades? I build a micro-founded model of privatized shared transit where minibuses load passengers from a queue. I then estimate the model with newly-collected data on minibus and passenger queues in Cape Town and stated user preferences for exogenously-varied commute attributes. I find that governments should subsidize minibuses and their passengers because neither internalize their beneficial spillovers to wait times. This optimization shortens queues, fills buses faster, and particularly benefits low-skill workers. Government actions to expand minibus stations or improve security bring even more substantial welfare gains.

Hear Ye, Bear Ye: Housing Prices, Noise Levels, and Noise Inequality

Jeffrey Cohen
,
University of Connecticut
Cletus Coughlin
,
Federal Reserve Bank of St. Louis
Felix Friedt
,
Macalester College

Abstract

The relationship between house prices and transportation noise has been studied for many locations, but the underlying factors and issues of heterogeneity have not been as extensively explored. Transportation noise – both air and road – can be pervasive in major metropolitan areas, and there is much heterogeneity in the noise exposure faced by many residents across geographic space. High housing prices in urban centers can impede some residents in moving from louder to less noisy areas. This paper relies on a Census tract-level dataset on road and aviation noise covering the contiguous U.S. for 2016 and 2018, along with American Community Survey data, to address the question of how house prices can be a barrier to avoiding noise for some residents. In the first known comprehensive analysis of this type combining these datasets for multiple years, we first explore which tracts, states and demographic groups experience disproportionate amounts of noise. Then, we use quantile regressions to demonstrate the inter-relationships between house prices and demographics, and how these interactions are correlated with noise. We find evidence for some tracts that when house prices are relatively high, higher Black population tracts are associated with additional noise exposure.

Does Uber Reduce Public Transit Ridership? Evidence & Impacts in the San Francisco Bay Area

Laura Grant
,
Claremont McKenna College
Lianne Sturgeon
,
Scripps College

Abstract

Many public transit authorities believe ride-hailing reduces ridership, thus hurts tran- sit solvency. If so, municipalities may also face increased congestion and pollution. We develop methods measuring metro-specific, hourly impacts. Our estimates use a decade of route-by-hour data from the San Francisco Bay Area transit. We employ difference- in-differences with weather and traffic as route-specific exogenous, hourly shocks, which we interact with ride-hailing availability and find large short-run decreases in transit ridership. Then, comparing ridership trends, before and after ride-hailing begins, we find long-run year-over-year losses of over 10 percent. Fare revenue loss and social cost are each around $100 million in 2013, growing to $200 million in 2018.

Discussant(s)
Andrew Waxman
,
University of Texas
Ian Savage
,
Northwestern University
Xi Yang
,
University of North Texas
Dustin Frye
,
University of Wisconsin-Madison
JEL Classifications
  • R4 - Transportation Economics