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Urban Transportation Economics

Paper Session

Saturday, Jan. 7, 2023 2:30 PM - 4:30 PM (CST)

New Orleans Marriott, Balcony I
Hosted By: Transportation and Public Utilities Group
  • Chair: Ian Savage, Northwestern University

Uber Versus Trains? Worldwide Evidence from Transit Expansions

Jonathan D. Hall
,
University of Toronto
Marco Gonzalez-Navarro
,
University of California-Berkeley
Harrison Wheeler
,
University of California-Berkeley
Rik Williams
,
Uber Technologies, Inc.

Abstract

There is a contentious debate on whether ride-hailing complements or substitutes public transportation. We address this question using novel data and an innovative identification strategy. Our identification strategy relies on exogenous variation in local transit availability caused by rail expansions. Using proprietary, anonymized trip data from Uber for 35 countries, we use a dynamic difference-in-differences strategy to estimate how transit expansions affect local Uber ridership in 100 m distance bands centered on the new train station. Our estimates compare Uber ridership within a distance band before and after a train station opens relative to the next further out distance band. Total effects are obtained by aggregating relative effects at all further distance bands. We find that a new rail station opening increases Uber ridership within 100 m of the station by 60%, and that this effect decays to zero for distances beyond 300 m. This sharp test implies Uber and rail transit are complements.

Unintended Effects of Tax Hikes: from Ridership to Congestion

Bryan Weber
,
CUNY-College of Staten Island
Paolo Cappellari
,
CUNY-College of Staten Island
Ali Moghtaderi
,
George Washington University

Abstract

This paper examines the effects of a $2.75 congestion tax on ride-share and taxi usage in New York City. We use a difference-in-differences method to evaluate both the change in rides and the coinciding decline in pickups. We find a significant decline in rides originating from the taxed area and estimate the price elasticity of rides in this area, and the deadweight loss of the policy. We also measure a significant decline in collisions during this period and a reduction in injuries, suggesting that the policy has effects outside of the ride-share market that partially counteract this deadweight loss.

Inequitable Inefficiency: A Case Study of Rail Transit Fare Policies

Zakhary Mallett
,
Cornell University

Abstract

Research on transit fare equity often measures equity based on a disparity in the fare per mile paid by different groups of riders. This cost-benefit measurement overlooks the cost sharing nature of transit; as more riders consume a service, the average cost per rider declines. Using an average cost per rider metric to assign trip costs, and origin-destination fare data to estimate trip-level cost recovery through fares, I estimate the spatial and temporal variability of cost recovery across two rail systems, BART and MARTA. I find that cost recovery patterns are spatially monocentric, and that the weekday peak period recovers more of its costs through fares than other time periods. I offer ideas on why these findings appear divergent to past research.

How New Card Acquisition Fee Affects Transit Card Purchase and Use Patterns: Evidence from Washington D.C.

Meiping Sun
,
Fordham University
Jing Wang
,
Columbia University

Abstract

Transit authorities in many cities have introduced automated fare media by expanding fare payment to electronic, magnetic-stripe contact cards and more recently to smartcards. Most transit smart cards come with a refundable or non-refundable one-time acquisition fee to cover the card costs and ensure uninterrupted transit service in case the rider inadvertently has a negative balance. Most empirical studies on the demand elasticity of rides analyze fare increases. The effect of the ubiquitous new transit card fee is not clear. In October 2013, the Washington Metropolitan Area Transit Authority (WMATA) system reduced the one-time non-refundable acquisition fee from $5 to $2. Using a causal inference approach, a difference-in-difference model, I examine the demand elasticity of “SmarTrip” card purchases and the demand elasticity of rides..

Discussant(s)
Max Gillman
,
University of Missouri-St. Louis
Kenneth Button
,
George Mason University
James Nolan
,
University of Saskatchewan
Shih-Hsien Chuang
,
Northwest Missouri State University
JEL Classifications
  • L9 - Industry Studies: Transportation and Utilities
  • R4 - Transportation Economics