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Hedging Price Volatility Using Fast Transport

  • David L. Hummels
  • Georg Schaur

Purchasing goods from distant locations introduces a significant lag between when a product is shipped and when it arrives. This is problematic for firms facing volatile demand, who must place orders before knowing the resolution of demand uncertainty. We provide a model in which airplanes bring producers and consumers together in time. Fast transport allows firms to respond quickly to favorable demand realizations and to limit the risk of unprofitably large quantities during low demand periods. Fast transport thus provides firms with a real option to smooth demand volatility. The model predicts that the likelihood and extent to which firms employ air shipments is increasing in the volatility of demand they face, decreasing in the air premium they must pay, and increasing in the contemporaneous realization of demand. We confirm all three conjectures using detailed US import data. We provide simple calculations of the option value associated with fast transport and relate it to variation in goods characteristics, technological change, and policies that liberalize trade in air services.

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File URL: http://www.nber.org/papers/w15154.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15154.

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Date of creation: Jul 2009
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Publication status: published as Hummels, David L. & Schaur, Georg, 2010. "Hedging price volatility using fast transport," Journal of International Economics, Elsevier, vol. 82(1), pages 15-25, September.
Handle: RePEc:nbr:nberwo:15154
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  1. Hummels, David, 2001. "Time as a Trade Barrier," GTAP Working Papers 1152, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
  2. W. J. Baumol & H. D. Vinod, 1970. "An Inventory Theoretic Model of Freight Transport Demand," Management Science, INFORMS, vol. 16(7), pages 413-421, March.
  3. Micco, Alejandro & Serebrisky, Tomas, 2006. "Competition regimes and air transport costs: The effects of open skies agreements," Journal of International Economics, Elsevier, vol. 70(1), pages 25-51, September.
  4. Jonah B. Gelbach & Doug Miller, 2009. "Robust Inference with Multi-way Clustering," Working Papers 99, University of California, Davis, Department of Economics.
  5. David Hummels, 2007. "Transportation Costs and International Trade in the Second Era of Globalization," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 131-154, Summer.
  6. Aizenman, Joshua, 2004. "Endogenous pricing to market and financing costs," Journal of Monetary Economics, Elsevier, vol. 51(4), pages 691-712, May.
  7. Harrigan, James & Venables, Anthony J., 2006. "Timeliness and agglomeration," Journal of Urban Economics, Elsevier, vol. 59(2), pages 300-316, March.
  8. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
  9. Carolyn L. Evans & James Harrigan, 2005. "Distance, Time, and Specialization: Lean Retailing in General Equilibrium," American Economic Review, American Economic Association, vol. 95(1), pages 292-313, March.
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