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How does fuel economy of vehicles affect urban motor vehicle travel in the USA?

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Abstract

This paper applies the ordinary quantile regression approach to examine the impact of fuel economy of vehicles and gasoline prices on motor vehicle travel. The dataset used includes observations with a survey date before September 2008 from the 2009 National Household Travel Survey to avoid potential problems from the wild volatility of gasoline prices in late 2008 and early 2009. The regression results indicate that for every 10 % increase in fuel economy of vehicles, annual vehicle miles traveled increase by 0.9 to 1.7 % along its distribution. For every 10 % increase in average gasoline prices, annual vehicle miles decline by 0.86 to 2.65 % along the vehicle miles traveled (VMT) distribution.

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Notes

  1. Similar definitions are given by Sorrell (2007) (definition E3–E5 on page 28).

  2. The instrumental variable quantile regression approach is based on the assumptions that there exist instrumental variables Z that are statistically related to the fuel economy of vehicle (denoted Edg) and both X (explanatory exogenous variables) and Z are independent of the error terms. The process of obtaining the instrumental variable quantile regression estimator involves the following steps: step 1, for a given value of λ i(k), run the ordinary quantile regression of y i  − γ(k)Edg on X i and \( {\widehat{Z}}_i \) to obtain the estimates \( \widehat{\beta}\left({\lambda}^i(k),k\right),\widehat{\lambda}\left({\lambda}^i(k),k\right) \); step 2, test \( \widehat{\lambda}\left({\lambda}^i(k),k\right) \) = 0 and save the Wald statistic, W i ; step 3, for all the values in a pre-specified support for λ i(k), repeat steps 1 and 2. The value that minimizes W is the instrumental variable quantile regression estimate (for detailed information, see Chernozhukov and Hansen (2008)). The programming to run this regression is available on Hansen’s research website.

  3. As discussed by Brand, “The average price at the pump for unleaded regular gasoline was $4.09 a gallon in July, and was below $1.70 in December 2008.” To make the measure of gasoline price consistent and comparable, the sample is restricted in the way described.

  4. Professional/managerial is not included in the final model specification since it is not statistically significant for all the quantiles used.

  5. http://www.motortrend.com/new_cars/specifications/

  6. Curb weight is the weight of the vehicle with all fluids and components but without the drivers, passengers, and cargo.

  7. The correlation coefficients of the weight dummies and fuel economy are between −0.086 and 0.53.

  8. The average VMT is calculated for each quantiles used in the regression. Then the weights are calculated as the percentage of the observations in terms of VMT in each range of the average VMT obtained.

  9. For detailed information, see https://www.fhwa.dot.gov/policy/otps/innovation/issue1/impacts.htm.

  10. Possible factors include vehicle prices, the time estimated to cover the premium paid for fuel-efficient vehicles (e.g., the difference between hybrid and regular vehicles), expectations of gasoline price change, the increase in vehicle prices due to technology improvement necessary to meet tougher fuel economy standards, and the average life expectancy of vehicles.

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  1. Department of Marketing, Economics and Sports Business, Northern Kentucky University, Nunn Drive, BC 338, Highland Heights, KY, 41099, USA

    Qing Su

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  1. Qing Su
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Su, Q. How does fuel economy of vehicles affect urban motor vehicle travel in the USA?. Energy Efficiency 8, 339–351 (2015). https://doi.org/10.1007/s12053-014-9302-6

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