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Making friends with your neighbors? Agglomeration and tacit collusion in the lodging industry

  • Li Gan
  • Manuel A. Hernandez

Agglomeration is a location pattern frequently observed in service industries such as hotels. This paper empirically examines if agglomeration facilitates tacit collusion in the lodging industry using a quarterly dataset of hotels that operated in rural areas across Texas between 2003 and 2005. We jointly model a price and occupancy rate equation under a switching regression model to endogenously identify a collusive and non-collusive regime. The estimation results indicate that clustered hotels have a higher probability of being in the potential collusive regime than isolated properties in the same town. The identification of a collusive regime is also consistent with other factors considered to affect the sustainability of collusion like cluster size, seasonality and firm size, and the results are robust to alternative cluster definitions.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16739.

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Date of creation: Jan 2011
Date of revision:
Publication status: published as Li Gan & Manuel A. Hernandez, 2013. "Making Friends with Your Neighbors? Agglomeration and Tacit Collusion in The Lodging Industry," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 1002-1017, July.
Handle: RePEc:nbr:nberwo:16739
Note: IO LE
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  1. John Connor, 2005. "Collusion and price dispersion," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 335-338.
  2. Irmen, A. & Thisse, J.-F., . "Competition in multi-characteristics spaces: Hotelling was almost right," CORE Discussion Papers RP 1305, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Kyle Bagwell, 2004. "Collusion and Price Rigidity," Theory workshop papers 658612000000000081, UCLA Department of Economics.
  4. John Haltiwanger & Joseph E. Harrington Jr., 1991. "The Impact of Cyclical Demand Movements on Collusive Behavior," RAND Journal of Economics, The RAND Corporation, vol. 22(1), pages 89-106, Spring.
  5. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
  6. Joseph E. Harrington, Jr, 2005. "Detecting Cartels," Economics Working Paper Archive 526, The Johns Hopkins University,Department of Economics.
  7. Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
  8. Friedman, J. & Thisse, J-F., 1991. "Infinite horizon spatial duopoly with collusive pricing and noncollusive location choice," CORE Discussion Papers 1991004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Christopher R. Knittel & Victor Stango, 2001. "Price ceilings as focal points for tacit collusion: evidence from credit cards," Working Paper Series WP-01-12, Federal Reserve Bank of Chicago.
  10. Janet S. Netz & Beck A. Taylor, 2002. "Maximum Or Minimum Differentiation? Location Patterns Of Retail Outlets," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 162-175, February.
  11. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
  12. Helsley, Robert W. & Strange, William C., 1990. "Matching and agglomeration economies in a system of cities," Regional Science and Urban Economics, Elsevier, vol. 20(2), pages 189-212, September.
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