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Time Series Analysis of Deregulatory Dynamics and Technical Efficiency: The Case of the U.S. Airline Industry

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  • Alam, Ila M Semenick
  • Sickles, Robin C

Abstract

As markets worldwide become less regulated, it becomes increasingly possible and timely to establish the presence of an empirical relationship between technical efficiency and market forces compelling agents to economize. This article, taking an innovative approach to test the hypothesis that competitive pressure enhances efficiency, constructs a methodology to examine time series of technical efficiency indices for cointegration and convergence. A panel of U.S. airlines, observed quarterly between 1970 and 1990, is used as a case study. Cointegration results are suggestive of long-run relationships between carriers; furthermore, convergence tests document less dispersion in firm performance over time. Copyright 2000 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

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Bibliographic Info

Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 41 (2000)
Issue (Month): 1 (February)
Pages: 203-18

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Handle: RePEc:ier:iecrev:v:41:y:2000:i:1:p:203-18

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Cited by:
  1. Byeong U. Park & Robin C Sickles & LĂ©opold Simar, 2002. "Semi parametric efficient estimation of dynamic panel data models," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C6-1, International Conferences on Panel Data.
  2. Lee, Chia-Yen & Johnson, Andrew L., 2012. "Two-dimensional efficiency decomposition to measure the demand effect in productivity analysis," European Journal of Operational Research, Elsevier, vol. 216(3), pages 584-593.
  3. Walter Briec & Kristiaan Kerstens, 2006. "Input, output and graph technical efficiency measures on non-convex FDH models with various scaling laws: An integrated approach based upon implicit enumeration algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 14(1), pages 135-166, June.
  4. Lee, Boon L. & Worthington, Andrew C., 2014. "Technical efficiency of mainstream airlines and low-cost carriers: New evidence using bootstrap data envelopment analysis truncated regression," Journal of Air Transport Management, Elsevier, vol. 38(C), pages 15-20.
  5. Jamasb, Tooraj & Pollitt, Michael & Triebs, Thomas, 2008. "Productivity and efficiency of US gas transmission companies: A European regulatory perspective," Energy Policy, Elsevier, vol. 36(9), pages 3398-3412, September.
  6. Aguirregabiria, Victor & Ho, Chun-Yu, 2012. "A dynamic oligopoly game of the US airline industry: Estimation and policy experiments," Journal of Econometrics, Elsevier, vol. 168(1), pages 156-173.
  7. Alam, Ila M Semenick & Morrison, Andrew R, 2000. "Trade Reform Dynamics and Technical Efficiency: The Peruvian Experience," World Bank Economic Review, World Bank Group, vol. 14(2), pages 309-30, May.

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