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Efficiency and productivity of the US banking industry, 1998-2005: evidence from the Fourier cost function satisfying global regularity conditions

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  • Guohua Feng

    (Department of Econometrics and Business Statistics, Monash University, Victoria, Australia)

  • Apostolos Serletis

    (Department of Economics, University of Calgary, Alberta, Canada)

Abstract

This paper provides estimates of bank efficiency and productivity in the United States, over the period from 1998 to 2005, using (for the first time) the globally flexible Fourier cost functional form, as originally proposed by Gallant (1982), and estimated subject to global theoretical regularity conditions, using procedures suggested by Gallant and Golub (1984). We find that failure to incorporate monotonicity and curvature into the estimation results in mismeasured magnitudes of cost efficiency and misleading rankings of individual banks in terms of cost efficiency. We also find that the largest two subgroups (with assets greater than 1 billion in 1998 dollars) are less efficient than the other subgroups and that the largest four bank subgroups (with assets greater than $ 400 million) experienced significant productivity gains and the smallest eight subgroups experienced insignificant productivity gains or even productivity losses. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Guohua Feng & Apostolos Serletis, 2009. "Efficiency and productivity of the US banking industry, 1998-2005: evidence from the Fourier cost function satisfying global regularity conditions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 105-138.
  • Handle: RePEc:jae:japmet:v:24:y:2009:i:1:p:105-138
    DOI: 10.1002/jae.1021
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    References listed on IDEAS

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    Cited by:

    1. Diego Restrepo-Tobón & Subal Kumbhakar & Kai Sun, 2015. "Obelix vs. Asterix: Size of US commercial banks and its regulatory challenge," Journal of Regulatory Economics, Springer, vol. 48(2), pages 125-168, October.
    2. Feng, Guohua & Serletis, Apostolos, 2010. "Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 127-138, January.
    3. repec:eee:ejores:v:261:y:2017:i:3:p:1125-1140 is not listed on IDEAS
    4. Feng, Guohua & Zhang, Xiaohui, 2012. "Productivity and efficiency at large and community banks in the US: A Bayesian true random effects stochastic distance frontier analysis," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1883-1895.
    5. Stefano Caiazza & Alberto Franco Pozzolo & Giovanni Trovato, 2016. "Bank efficiency measures, M&A decision and heterogeneity," Journal of Productivity Analysis, Springer, vol. 46(1), pages 25-41, August.
    6. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    7. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
    8. Tsionas, Mike G., 2017. "The profit function system with output- and input-specific technical efficiency," Economics Letters, Elsevier, vol. 151(C), pages 111-114.
    9. Emir Malikov & Diego Restrepo-Tobón & Subal Kumbhakar, 2015. "Estimation of banking technology under credit uncertainty," Empirical Economics, Springer, vol. 49(1), pages 185-211, August.
    10. Makieła, Kamil, 2016. "Bayesian inference in generalized true random-effects model and Gibbs sampling," MPRA Paper 69389, University Library of Munich, Germany.
    11. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    12. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    13. repec:eee:econom:v:204:y:2018:i:2:p:131-146 is not listed on IDEAS
    14. Kamil Makieła, 2017. "Bayesian Inference and Gibbs Sampling in Generalized True Random-Effects Models," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 9(1), pages 69-95, March.

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