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Transportation and the Environment

Paper Session

Friday, Jan. 7, 2022 10:00 AM - 12:00 PM (EST)

Hosted By: Association of Environmental and Resource Economists
  • Chair: Madeline Werthschulte, University of Munster

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

Laura Grant
,
Claremont McKenna College
Lianne Sturgeon
,
Scripps College

Abstract

Uber and Lyft changed modern transit. Public transit authorities worry these Transportation Network Companies (TNCs) capture market share. In addition, if TNCs are substitutes, municipalities face concerns of increased congestion and air pollution. We analyze a decade of hour-by-route data from Bay Area Rapid Transit (BART). TNCs correlate with year-over-year losses of 9.8 to 11.1 percent | around 5.5 million fewer trips on BART, each year. Then, using weather, daylight, and traffic speed as exogenous short-run shocks, we find each induces signi ficant declines in ridership after TNCs are available. We then calculate social costs: year over year, BART loses $26 million in revenue and externality costs increase $4 to $16 million.

The Hidden Costs of Traffic Congestion

Madeline Werthschulte
,
Leibniz Centre for European Economic Research Mannheim and University of Munster.
Andreas Löschel
,
Ruhr-University Bochum
Laura Razzolini
,
University of Alabama
Michael Price
,
University of Alabama, Australian National University and NBER

Abstract

We assess the external costs of road traffic by studying the consequences of congestion on individual decision-making. To shed light on such "hidden" costs of congestion, we use a unique event, the closure of Interstate 59/20 through Birmingham, Alabama. Specifically, by conducting a field experiment with commuters affected and non-affected by congestion, we are able to uncover the relation between congestion, risk and time preferences, charitable giving and honesty. We show that road traffic gives rise to substantial, unanticipated externalities in terms of decreased risk aversion, increased impatience, and a reduced willingness to help others by providing public goods. These results provide evidence of important spill-over effects of congestion on daily decisionmaking, such as decisions involving health, education or financial choices, and contribute to the ongoing discussion on the stability of preferences. Further, our results show that existing cost-benefit assessments evaluating the introduction of an environmental
policy aimed at reducing road traffic underestimate the true external costs of congestion by neglecting its impact on decision-making.

Market Competition and the Adoption of Clean Technology: Evidence from the Taxi Industry

Raúl Bajo-Buenestado
,
University of Navarra

Abstract

This paper studies the impact of the intensity of market competition on firms’ willingness to adopt
green technologies. We exploit the staggered rollout of different ride-hailing platforms (most notably,
Uber) across different metropolitan areas in Spain as a natural experiment that provides time and city specific exogenous variation in the intensity of competition to study the impact on taxi drivers’ decisions to purchase “green” or “dirty” vehicles. We show that the entry of these platforms significantly
increased the takeout of green vehicles among professional drivers in incumbent (dominant) conventional
taxi companies, and decreased that of dirty vehicles. The exact opposite effect is observed in
the cities where these platforms were extremely unlikely to enter. These results speak directly to the
recent debate on whether competition policies should be relaxed to achieve certain environmental
targets.

Do Credit Constraints Explain the Energy Efficiency Gap? Evidence from the U.S. New Vehicle Market

Kevin Ankney
,
Georgetown University

Abstract

The “energy efficiency gap” refers to a puzzle characterized partly by consumer underinvestment in energy-efficient products (e.g., hybrid vehicles). These products cost more upfront than less efficient alternatives but would likely pay for themselves in the form of future energy savings. The energy efficiency gap serves as justification for regulations such as U.S. new vehicle fuel economy standards, the thinking being that mandating increases in energy efficiency is a “win-win” – consumers use less energy and save money.
But if pricier fuel-efficient cars would eventually pay for themselves, why don’t more consumers purchase them in the absence of regulation? One common explanation is that credit constraints – prohibitively high borrowing costs or a lack of access to credit – hinder consumers’ ability to make energy efficiency investments. However, limited evidence exists corroborating credit constraints as an explanation for the energy efficiency gap.

This paper provides evidence of the relationship between credit constraints and fuel economy demand in the U.S. new vehicle market. I use actual auto loan terms and self-reported credit histories from a survey of new vehicle buyers to estimate the relationship between consumer credit constraints and fuel economy choices. I find a statistically significant but economically minor relationship between auto loan interest rates and purchased vehicle fuel economy, indicating that credit constraints play little role in explaining why consumers fail to purchase energy-efficient cars. On average, increasing a consumer’s auto loan interest rate from 2% to 5% APR is associated with a 0.09 mile per gallon decrease in fuel economy purchased. For a typical auto loan, this 2-to-5 percentage point increase adds $2,313 in interest payments (a 156% increase), but 0.09 miles per gallon only saves $86 in lifetime fuel costs. This disparity calls into question the suggestion that credit constraints are a meaningful contributor to the energy efficiency gap.
Discussant(s)
Joseph Price
,
Brigham Young University and NBER
Antonio Bento
,
University of Southern California
Erich Muehlegger
,
University of California-Davis and NBER
Arthur van Benthem
,
University of Pennsylvania
JEL Classifications
  • K3 - Other Substantive Areas of Law
  • R1 - General Regional Economics