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New Vehicle Consumption and Fuel Efficiency: A Nested Logit Approach

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  • McCarthy, Patrick S.
  • Tay, Richard S.

Abstract

Using data from a 1989 household survey of new vehicle buyers, this paper develops and estimates a nested logit model of new vehicle demands. In comparison with the more restrictive multinomial logit model, which assumes that the error terms are uncorrelated, the results support a nested structure of vehicle choice in which the error terms for purchases within the same fuel efficiency category are correlated. Among the findings, improvements in vehicle size, safety and quality increase a make/model's demand. Females, lower income households, younger consumers, non-white purchasers, and buyers in more densely populated areas exhibit higher demands for more fuel efficient vehicles. The results also indicate that vehicle demands have an approximate unitary elasticity with respect to capital cost and are elastic with respect to operating costs.

Suggested Citation

  • McCarthy, Patrick S. & Tay, Richard S., 1998. "New Vehicle Consumption and Fuel Efficiency: A Nested Logit Approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 34(1), pages 39-51, March.
    • Handle: RePEc:eee:transe:v:34:y:1998:i:1:p:39-51
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    Citations

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    Cited by:

    1. Sánchez Navarro, Dennis, 2013. "Análisis de elasticidades en el mercado automotor colombiano (2009 - 2011) mediante un modelo logit anidado [Analysis Of Elasticity In Colombian Automotive Market (2009 - 2011) Through A Nested Log," MPRA Paper 46043, University Library of Munich, Germany.
    2. Lee, Jongsu & Cho, Youngsang, 2009. "Demand forecasting of diesel passenger car considering consumer preference and government regulation in South Korea," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 420-429, May.
    3. Collantes, Gustavo, 2010. "Do green tech policies need to pass the consumer test?: The case of ethanol fuel," Energy Economics, Elsevier, vol. 32(6), pages 1235-1244, November.
    4. Horne, Matt & Jaccard, Mark & Tiedemann, Ken, 2005. "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions," Energy Economics, Elsevier, vol. 27(1), pages 59-77, January.
    5. Sabreena Anowar & Naveen Eluru & Luis F. Miranda-Moreno, 2014. "Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 441-473, July.
    6. Mannberg, Andrea & Jansson, Johan & Pettersson, Thomas & Brännlund, Runar & Lindgren, Urban, 2014. "Do tax incentives affect households׳ adoption of ‘green’ cars? A panel study of the Stockholm congestion tax," Energy Policy, Elsevier, vol. 74(C), pages 286-299.
    7. Martin Achtnicht, 2012. "German car buyers’ willingness to pay to reduce CO 2 emissions," Climatic Change, Springer, vol. 113(3), pages 679-697, August.
    8. Anna Fernández-Antolín & Matthieu Lapparent & Michel Bierlaire, 2018. "Modeling purchases of new cars: an analysis of the 2014 French market," Theory and Decision, Springer, vol. 84(2), pages 277-303, March.
    9. Galarraga, Ibon & Kallbekken, Steffen & Silvestri, Alessandro, 2020. "Consumer purchases of energy-efficient cars: How different labelling schemes could affect consumer response to price changes," Energy Policy, Elsevier, vol. 137(C).
    10. Hang, Wen & Li, Xuhong, 2010. "Application of system dynamics for evaluating truck weight regulations," Transport Policy, Elsevier, vol. 17(4), pages 240-250, August.

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