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Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter-city freight demand


  • Li, Zheng
  • Hensher, David A.
  • Rose, John M.


Freight demand elasticity studies vary significantly in terms of the demand measure, data type, estimation method, commodity type, etc. This wide variation makes it difficult to compare empirical estimates when the differences may arise in part from the methods and data used. In this paper we conduct a comparative analysis to identify systematic sources of influence on direct price elasticity estimates in the context of freight transport, distinguishing between road, rail, and sea transport, using published direct price elasticities from 12 elasticity-derivative studies from five countries. The study focuses on revealed preference elasticities defined by the freight rate for tonnes and tonne kilometres of inter-city freight movements. Systematic sources that explain differences in direct price elasticities include the demand elasticity measure, mode, commodity class, model estimation form, country, and temporal nature of data (e.g., cross-section). Analysts can utilise the model outputs to adjust the empirical evidence from specific studies to control for differences that impact on the behavioural implications of comparative evidence.

Suggested Citation

  • Li, Zheng & Hensher, David A. & Rose, John M., 2011. "Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter-city freight demand," Transport Policy, Elsevier, vol. 18(5), pages 727-734, September.
  • Handle: RePEc:eee:trapol:v:18:y:2011:i:5:p:727-734

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    References listed on IDEAS

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    2. Hensher, David A., 2008. "Assessing systematic sources of variation in public transport elasticities: Some comparative warnings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(7), pages 1031-1042, August.
    3. Kremers, Hans & Nijkamp, Peter & Rietveld, Piet, 2002. "A meta-analysis of price elasticities of transport demand in a general equilibrium framework," Economic Modelling, Elsevier, vol. 19(3), pages 463-485, May.
    4. Holmgren, Johan, 2007. "Meta-analysis of public transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1021-1035, December.
    5. Brons, Martijn & Nijkamp, Peter & Pels, Eric & Rietveld, Piet, 2008. "A meta-analysis of the price elasticity of gasoline demand. A SUR approach," Energy Economics, Elsevier, vol. 30(5), pages 2105-2122, September.
    6. Beuthe, Michel & Jourquin, Bart & Geerts, Jean-François & Koul à Ndjang' Ha, Christian, 2001. "Freight transportation demand elasticities: a geographic multimodal transportation network analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(4), pages 253-266, August.
    7. Hensher, David A. & Li, Zheng, 2010. "Accounting for differences in modelled estimates of RP, SP and RP/SP direct petrol price elasticities for car mode choice: A warning," Transport Policy, Elsevier, vol. 17(3), pages 191-195, May.
    8. E. Raphael Branch, 1993. "Short Run Income Elasticity of Demand for Residential Electricity Using Consumer Expenditure Survey Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 111-122.
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    Cited by:

    1. Winebrake, James J. & Green, Erin H. & Comer, Bryan & Corbett, James J. & Froman, Sarah, 2012. "Estimating the direct rebound effect for on-road freight transportation," Energy Policy, Elsevier, vol. 48(C), pages 252-259.
    2. Hensher, David A. & Collins, Andrew T. & Rose, John M. & Smith, Nariida C., 2013. "Direct and cross elasticities for freight distribution access charges: Empirical evidence by vehicle class, vehicle kilometres and tonne vehicle kilometres," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 1-21.


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