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Economics of Road Network Ownership

Author

Listed:
  • Lei Zhang
  • David Levinson

    () (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

This paper seeks to understand the economic impact of centralized and decentralized ownership structures and their corresponding pricing and investment strategies on transportation network performance and social welfare for travelers. In a decentralized network economic system, roads are owned by many agencies or companies that are responsible for pricing and investment strategies. The motivation of this study is two-fold. First, the question of which ownership structure, or industrial organization, is optimal for transportation networks has yet to be resolved. Despite several books devoted to this research issue, quantitative methods that translate ownership-related policy variables into short- and long-run network performance are lacking. Second, the U.S. and many other countries have recently seen a slowly but steadily increasing popularity of road pricing as an alternative to traditional fuel taxes. Not only is the private sector encouraged to finance new roads, this transition in revenue mechanism also makes it possible for lower-level government agencies and smaller jurisdictions to participate in network pricing and investment practice. The issue of optimal ownership is no longer a purely theoretical debate, but bears practical importance. This research adopts an agent-based simulator of network dynamics to explore the implications of centralized and decentralized ownership on mobility and social welfare, as well as potential financial issues and regulatory needs. Components of the simulator: the travel demand model, cost functions, and key variables of pricing and investment strategies, are empirically estimated and validated. Results suggest that road network is a market with imperfect competition. While there is a significant performance lag between the optimal strategy and the current network financing practice in the U.S. (characterized by centralized control, fuel taxes, and budget-balancing investment), a completely decentralized network suffers from issues such as higher-than-optimal tolls and over-investment. For the decentralized ownership structure, appropriate regulation on pricing and investment practices is necessary. Further analysis based on simulation comparisons suggests that with appropriate price regulation, a decentralized road economy consisting of profit-seeking road owners could outperform the existing centralized control, achieve net social benefits close to the theoretical optimum, and distribute a high percentage of welfare gains to travelers. Decentralized control is especially valuable in rapidly changing environments because it promptly responds to travel demand. These results seem to favor the idea of privatizing or decentralizing road ownership on congested networks. Further tests on real-world transportation networks are necessary and should make an interesting future study.

Suggested Citation

  • Lei Zhang & David Levinson, 2006. "Economics of Road Network Ownership," Working Papers 200908, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:erno
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    File URL: http://hdl.handle.net/11299/179980
    File Function: First version, 2007
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    References listed on IDEAS

    as
    1. David Levinson & Ramachandra Karamalaputi, 2003. "Induced Supply: A Model of Highway Network Expansion at the Microscopic Level," Journal of Transport Economics and Policy, University of Bath, vol. 37(3), pages 297-318, September.
    2. Small, Kenneth A. & Yan, Jia, 2001. "The Value of "Value Pricing" of Roads: Second-Best Pricing and Product Differentiation," Journal of Urban Economics, Elsevier, vol. 49(2), pages 310-336, March.
    3. David Levinson, 2000. "Revenue Choice on a Serial Network," Working Papers 200001, University of Minnesota: Nexus Research Group.
    4. AndrÊ de Palma & Robin Lindsey, 2000. "Private toll roads: Competition under various ownership regimes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(1), pages 13-35.
    5. Gene M. Grossman (ed.), 1996. "Economic Growth," Books, Edward Elgar Publishing, volume 0, number 553.
    6. Lei Zhang & David Levinson, 2006. "The Economics of Transportation Network Growth," Working Papers 200710, University of Minnesota: Nexus Research Group.
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    9. Safirova, Elena & Gillingham, Kenneth & Houde, Sébastien, 2007. "Measuring marginal congestion costs of urban transportation: Do networks matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 734-749, October.
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    Cited by:

    1. Safirova, Elena & Gillingham, Kenneth & Houde, Sébastien, 2007. "Measuring marginal congestion costs of urban transportation: Do networks matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 734-749, October.

    More about this item

    Keywords

    Network economics; Modeling network dynamics; Road pricing; Transportation financing; Privatization.;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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