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Economics of Energy Use in Transportation

The NBER's conference on Economics of Energy Use in Transportation took place May 2-3 in Washington DC. Kate S. Whitefoot of Carnegie Mellon University and Research Associates Meghan R. Busse of Northwestern University and Christopher R. Knittel of MIT organized the meeting, sponsored by the Alfred P. Sloan Foundation and the U.S. Department of Energy. These researchers' papers were presented and discussed:


Erich Muehlegger, University of California, Davis and NBER, and David S. Rapson, University of California, Davis

Estimating Demand for Electric Vehicles in Low- and Middle-income Households


Steven T. Berry, Kenneth Gillingham, and James A. Levinsohn, Yale University and NBER

Technological Innovation and Per-Mile Automobile Insurance: Effects on Patterns of Vehicle Usage

In the United States, the transportation sector accounts for 70 percent of oil consumption and 30 percent of greenhouse gas emissions. It has long been discussed that per-mile automobile insurance (also known as "pay-as-you-drive" insurance) may lead consumers to better internalize some of the externality costs from driving. With the innovation of very low-cost GPS technology that can monitor the number of miles driven, per-mile insurance is just recently becoming an offering in many states. Berry, Gillingham, and Levinsohn use data from MetroMile, a startup based in San Francisco, to examine how per mile insurance can lead to adverse selection, whereby low-mileage and thus low-risk drivers switch to per-mile insurance, and reduced driving by per-mile insurance customers. The results will be important for informing policymakers about how patterns of vehicle usage may change, with implications for emissions and other driving externalities.


Samuel Stolper, University of Michigan

Local Pass-Through and the Regressivity of Taxes: Evidence from Automotive Fuel Markets

The regressivity of taxation is conventionally determined by inspecting relative quantities -- i.e., whether poorer households devote more of their budget to the taxed good than richer ones. However, relative price impacts matter as well, and ignoring heterogeneous tax pass-through can lead to mistaken conclusions about distributional impacts. Stolper shows this empirically by estimating pass-through of retail diesel taxes in the Spanish market for automotive fuel. A novel informational mandate provides access to a national, station-daily panel of retail diesel prices and characteristics and allows Stolper to investigate market composition at a fine level. Event study and difference-in-differences regression reveal that, while retail prices rise nearly one-for-one (100%) with taxes on average, station-specific pass-through rates range from at least 70% to 120%. Greater market power -- measured by brand concentration and spatial isolation -- is strongly associated with higher pass-through, even after conditioning on detailed demand-side characteristics. Furthermore, passthrough rises monotonically in area-average house prices. While a conventional estimate of the Spanish diesel tax burden suggests roughly equivalent incidence across the wealth distribution, overlaying the effect of heterogeneous pass-through reveals the tax to be unambiguously progressive.


James B. Bushnell, University of California, Davis and NBER, and Jonathan E. Hughes, University of Colorado at Boulder

Energy Consumption, Emissions and Modal Substitution in U.S. Freight Transportation

Bushnell and Hughes exploit newly available micro data on goods movement in the U.S. to model shippers' freight mode choices. Because freight modes have vastly different fuel intensities, shippers' choices have large implications for fuel consumption and emissions. They find higher fuel prices yield substantial shifts from less to more fuel efficient modes, particularly rail. The researchers extend the model to analyze recently enacted heavy duty truck fuel economy standards. Surprisingly, fuel economy standards can increase emissions and fuel consumption by shifting shipments to less fuel efficient modes. As a result, savings can be substantially less than predictions that ignore modal substitution.


Jeremy J. Michalek, Ines Azevedo, Constantine Samaras, and Pedro Ferreira, Carnegie Mellon University, and Nicholas Muller, Carnegie Mellon University and NBER

Effects of On-Demand Ridesourcing on U.S. Vehicle Ownership, Travel Patterns, and Energy Use Externalities

Michalek, Azevedo, Samaras, Ferreira, and Muller estimate the effect of on-demand ride-hailing service availability by Transportation Network Companies (TNCs) Uber and Lyft on per-capita vehicle ownership, energy use, travel distances, and emissions in U.S. states from 2005 to 2015 using a difference-in-difference propensity score-weighted regression model. The results suggest that, on average during the examined period, TNC entry causes a state's per-capita vehicle registrations and volatile organic compound emissions to drop by 3% and 5%, respectively. The researchers also find that these effects are greater in relatively urban states than in relatively rural states. Additionally, travel distances are found to decline in states with average and low urbanization by 1% and 3% respectively, no effect is detected for highly urbanized states or on average across all states. Estimated effects on gasoline consumption do not pass robustness checks. Taken together, the externality cost implications of these TNC-induced trends represent an estimated $700 million to $7 billion in vehicular accident, congestion delay, noise, and air pollution social cost savings to the U.S. economy from 2005 to 2015.


Jackson Dorsey, Indiana University; Ashley Langer, University of Arizona; and Shaun McRae, ITAM

Fueling Alternatives: Evidence from Real-World Driving Data (slides)

Development of a transportation system based on an alternative fuel requires both drivers to invest in vehicles and fueling stations to invest in infrastructure. Dorsey, Langer, and McRae study the interaction between these decisions using real world driving data that identifies when and where drivers stop to purchase gasoline. They estimate a discrete choice model for the driver's choice of refueling location and show that drivers make a trade-off between the price of fuel and the time taken to deviate from their route. With these results, the researchers simulate the willingness of drivers to adopt alternative fuel vehicles under different assumptions about the density of the alternative fueling network. The results suggest that the marginal cost of each alternative fuel and the fixed cost of alternative fuel vehicles and alternative fueling stations can dramatically change the market equilibria and alter the role of government in helping to create a self-sustaining alternative fuel market, but that subsidizing a new alternative fuel network would not be prohibitively expensive.


Stephen P. Holland, University of North Carolina at Greensboro and NBER; Erin T. Mansur, Dartmouth College and NBER; Nicholas Muller, Carnegie Mellon University and NBER; and Andrew J. Yates, University of North Carolina at Chapel Hill

Environmental Benefits from Transportation Electrification

Holland, Mansur, Muller, and Yates determine the environmental benefit of using electric buses rather than diesel buses. The environmental benefit is about 70 million dollars per year in Los Angeles and above 10 million dollars per year in six other MSA's. They also explore three methods for determining spatially disaggregated estimates of marginal damages from electricity consumption. Two of the methods use OLS and one uses a machine learning technique (Lasso). Using data from Texas, which allows for a rich set of controls, the researchers show these methods lead to good agreement for the three largest load sub-regions. Moreover, the results suggest that two of the methods may work reasonably well in other parts of the country in which detailed controls are not available.


Ziyan Chu, Resources for the Future, and Yichen Christy Zhou, Clemson University

The Effect of Adopting NextGen Air Transportation System on Air Travel Performance: Evidence from High-frequency Air Traffic Data

The U.S. Federal Aviation Administration (FAA) has undergone a large scale multi-year modernization effort, called the Next Generation Air Transportation System (NextGen), and continues to invest in NextGen to improve airspace efficiency. To assess the efficacy of NextGen investments, Chu and Zhou estimate how NextGen projects affect air travel time and delays by exploiting the high-frequency air flight on-time performance data from 2010 to 2017. Using a difference-in-differences design, they find that adopting one additional category of NextGen projects in both departure and arrival airports would improve air travel time by 2.4 minutes, with most time savings resulting from reductions in departure delays and 14 percent from reductions in taxi time. The effect of NextGen is much stronger for flights on the right-tail of the distribution of air travel delays due to unexpected shocks such as poor weather and prior delays. Preliminary calculations suggest that the NextGen projects have lead to passenger time saving of 221 dollars per flight and the airline fuel saving of 45 dollars per flight, amounting to 1.3 billion dollars of private benefits in 2017.


 
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