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Short-run fuel price responses: At the pump and on the road

Author

Listed:
  • Ritter, Nolan
  • Schmidt, Christoph M.
  • Vance, Colin

Abstract

We provide evidence that motorists respond to short-run fluctuations in fuel prices at the gas pump and not on the road. Employing variants of censored panel regression to control for censoring of the dependent variable, we find that the fuel price has a negative impact on the quantity of fuel purchased, but no consistently significant impact on the subsequent distance driven until the next refill. Over the short run, drivers thus appear to cope with high fuel prices by adjusting fuel purchases with each visit to the filling station, but without altering their daily mileage.

Suggested Citation

  • Ritter, Nolan & Schmidt, Christoph M. & Vance, Colin, 2016. "Short-run fuel price responses: At the pump and on the road," Energy Economics, Elsevier, vol. 58(C), pages 67-76.
    • Handle: RePEc:eee:eneeco:v:58:y:2016:i:c:p:67-76
    DOI: 10.1016/j.eneco.2016.06.013
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    References listed on IDEAS

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    1. Ritter, Nolan & Vance, Colin, 2013. "Do fewer people mean fewer cars? Population decline and car ownership in Germany," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 74-85.
    2. Espey, Molly, 1998. "Gasoline demand revisited: an international meta-analysis of elasticities," Energy Economics, Elsevier, vol. 20(3), pages 273-295, June.
    3. Jonathan E. Hughes & Christopher R. Knittel & Daniel Sperling, 2008. "Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand," The Energy Journal, International Association for Energy Economics, vol. 29(1), pages 113-134.
    4. Robert W. Crandall, 1992. "Policy Watch: Corporate Average Fuel Economy Standards," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 171-180, Spring.
    5. Lin, C.-Y. Cynthia & Prince, Lea, 2013. "Gasoline price volatility and the elasticity of demand for gasoline," Energy Economics, Elsevier, vol. 38(C), pages 111-117.
    6. Alan Sule & Honoré Bo E. & Hu Luojia & Leth-Petersen Søren, 2014. "Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation," Journal of Econometric Methods, De Gruyter, vol. 3(1), pages 1-20, January.
    7. Manuel Frondel & Jorg Peters & Colin Vance, 2008. "Identifying the Rebound: Evidence from a German Household Panel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 145-164.
    8. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
    9. Adonis Yatchew & Joungyeo Angela No, 2001. "Household Gasoline Demand in Canada," Econometrica, Econometric Society, vol. 69(6), pages 1697-1709, November.
    10. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," Energy Economics, Elsevier, vol. 34(2), pages 461-467.
    11. Manuel Frondel & Colin Vance, 2009. "Do High Oil Prices Matter? Evidence on the Mobility Behavior of German Households," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(1), pages 81-94, May.
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    Citations

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    Cited by:

    1. Frondel, Manuel, 2019. "CO2-Bepreisung in den nicht in den Emissionshandel integrierten Sektoren: Optionen für eine sozial ausgewogene Ausgestaltung," RWI Materialien 130, RWI - Leibniz-Institut für Wirtschaftsforschung.
    2. Simora, Michael & Vance, Colin, 2017. "Travel mode and tour complexity: The roles of fuel price and built environment," Ruhr Economic Papers 711, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    More about this item

    Keywords

    Panel data; Driving behavior; Tanking behavior; Fuel price; Two-sided censoring;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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