Measuring long‐run gasoline price elasticities in urban travel demand

Abstract

I develop a structural model of urban travel to estimate long‐run gasoline price elasticities. I model the demand for transportation services using a dynamic discrete‐choice model with switching costs and estimate it using a panel dataset with public market‐level data on automobile and public transit use in Chicago. Long‐run own‐ (automobile) and cross‐ (transit) price elasticities are substantially more elastic than short‐run elasticities. Elasticity estimates from static and myopic models are downward biased. I use the estimated model to evaluate the response to several counterfactual policies. A gasoline tax is less regressive after accounting for the long‐run substitution behavior.

Estimating the CO2 emission and revenue effects of carbon pricing

“…Another important and potentially disruptive trend that the model is not able to capture, as this is not observed in the data used for the estimation, is the progressive electrification of sectors coupled with cleaner electricity generation. 17 Simulations of an increase of ECRs in road transport based on the empirical estimates reported above cannot show a fast take-up of electric vehicles as such variation is not yet observed in the data. Yet, the electrification of road transport and other sectors will gradually raise the responsiveness of fossil fuel demand to carbon pricing as clean electricity generation become increasingly widespread.…”

Striking Evidence: The Impact of Railway Strikes on Competition from Intercity Bus Services in Germany

“…The conceptual framework guiding our investigation is based on Donna (2021), who models the role of switching costs in the choice of transport mode. Preferences of passengers can be represented with the following equation: 4…”

“…First, we add to the classic literature relating to the way in which individuals decide between alternatives. There is a long-standing debate on rational decision-making (Simon, 1955;Weitzman, 1979;Morgan and Manning, 1985) and constraints such as search costs (Baumol and Quandt, 1964;Ben-Akiva and Morikawa, 1990), information imperfections or simply habits, specifically when it comes to the choice of transport mode (Moser et al, 2018;Donna, 2021). A permanent increase in demand for intercity bus services could also be explained by learning.…”

Futures Market for Demand Responsive Travel Pricing

“…Understandably, travelers do not want to pay more for travel beyond the sunk cost of fuel and automobile ownership, nor do they wish to be subjected to unpredictable congestion-related costs that they have limited control over. Moreover, dynamic pricing creates equity concerns if it simply extorts travelers if no other reasonable alternative travel options (e.g., transit) exist ( 2 , 9 , 17 ).…”

“…Given the unpredictable nature of traffic congestion, the public disdain for dynamic tolls is understandable since regular travelers are exposed to both price risk and travel time risk ( 1 ). For lower income travelers who tend to have stricter time constraints and commute longer distances, a dynamic toll presents a regressive tax ( 2 ), posing a major equity concern if no reasonable travel alternative exists. Lastly, automobile travel is notoriously inelastic, dampening the congestion mitigating benefits achievable through dynamic pricing.…”