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Cost recovery from congestion tolls with random capacity and demand

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  • Lindsey, Robin

Abstract

According to the Cost Recovery Theorem the revenues from congestion tolls pay for optimal capacity of a facility if user costs are homogeneous of degree zero in usage and capacity, and if capacity is perfectly divisible and supplied with a unit cost elasticity. This paper examines the robustness of the Theorem to demand and capacity uncertainty in the context of highway travel. Three main results are derived. (1) The Theorem holds in expected values when users are perfectly informed about the state and the toll is set optimally as a function of the state. (2) The Theorem also holds in expected values when users are imperfectly informed about the state, the toll is set optimally given the information that users have, and the price elasticity of demand is the same in all states. (3) The Theorem does not hold if the toll is set using less information than users have. Whether an expected surplus or deficit results depends on the sign of the correlation across states between the Pigouvian toll and the ratio of the price elasticity of demand to the price. Examples show that the correlation can be positive or negative depending on the functional form of the demand and cost curves and the way the curves shift between states.

Suggested Citation

  • Lindsey, Robin, 2009. "Cost recovery from congestion tolls with random capacity and demand," Journal of Urban Economics, Elsevier, vol. 66(1), pages 16-24, July.
  • Handle: RePEc:eee:juecon:v:66:y:2009:i:1:p:16-24
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    2. Lindsey, Robin & de Palma, André, 2014. "Cost recovery from congestion tolls with long-run uncertainty," Economics of Transportation, Elsevier, vol. 3(2), pages 119-132.
    3. Qiumin Liu & Vincent A.C. van den Berg & Erik T. Verhoef & Rui Jiang, 2024. "Pricing in the Stochastic Bottleneck Model with Price-Sensitive Demand," Tinbergen Institute Discussion Papers 24-011/VIII, Tinbergen Institute, revised 22 Oct 2024.
    4. André de Palma & Mogens Fosgerau, 2011. "Dynamic Traffic Modeling," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 9, Edward Elgar Publishing.
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    9. Guo, Qianwen & Sun, Yanshuo & Li, Zhi-Chun & Li, Zhongfei, 2017. "An integrated model for road capacity choice and cordon toll pricing," Research in Transportation Economics, Elsevier, vol. 62(C), pages 68-79.
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    11. Wang, Tao & Liao, Peng & Tang, Tie-Qiao & Huang, Hai-Jun, 2022. "Deterministic capacity drop and morning commute in traffic corridor with tandem bottlenecks: A new manifestation of capacity expansion paradox," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    12. Gardner, Lauren M. & Boyles, Stephen D. & Waller, S. Travis, 2011. "Quantifying the benefit of responsive pricing and travel information in the stochastic congestion pricing problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(3), pages 204-218, March.
    13. Hörcher, Daniel & Graham, Daniel J., 2018. "Demand imbalances and multi-period public transport supply," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 106-126.
    14. André de Palma & Mogens Fosgerau, 2010. "Dynamic and Static congestion models: A review," Working Papers hal-00539166, HAL.
    15. Itoh, Ryo, 2018. "Is transportation infrastructure cost recoverable under the risk of disasters?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 457-465.
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