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Productivity and efficiency at large and community banks in the US: A Bayesian true random effects stochastic distance frontier analysis

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  • Feng, Guohua
  • Zhang, Xiaohui

Abstract

This paper compares the productivity and efficiency of large banks and community banks in the United States over the period 1997–2006. This comparison is performed by estimating a true random effects stochastic distance frontier model—a model that is capable of disentangling unobserved heterogeneity from inefficiency—within a Bayesian framework. We find that failure to consider unobserved heterogeneity results in a misleading ranking of banks and mismeasured technical efficiency, productivity growth, and returns to scale. Our results show that, compared with community banks, large banks have experienced much higher productivity growth and higher levels of returns to scale. Our estimates of total factor productivity growth show a clear downward trend for both large and community banks, and our decomposition of the output-distance-function-based Divisia productivity index indicates that technical change is the driving force behind this trend.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbfina:v:36:y:2012:i:7:p:1883-1895 DOI: 10.1016/j.jbankfin.2012.02.008
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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. Kao, Chiang & Liu, Shiang-Tai, 2014. "Measuring performance improvement of Taiwanese commercial banks under uncertainty," European Journal of Operational Research, Elsevier, vol. 235(3), pages 755-764.
    3. Feng, Guohua & Gao, Jiti & Peng, Bin & Zhang, Xiaohui, 2017. "A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks," Journal of Econometrics, Elsevier, vol. 196(1), pages 68-82.
    4. Chaohua Dong & Jiti Gao & Bin Peng, 2016. "Another Look at Single-Index Models Based on Series Estimation," Monash Econometrics and Business Statistics Working Papers 19/16, Monash University, Department of Econometrics and Business Statistics.
    5. Robert McKeown, 2017. "Where are the economies of scale in Canadian banking?," Working Papers 1380, Queen's University, Department of Economics.
    6. 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.
    7. Sarmiento Paipilla, N.M. & Galán, Jorge E., 2015. "The Influence of Risk-taking on Bank Efficiency : Evidence from Colombia," Discussion Paper 2015-036, Tilburg University, Center for Economic Research.
    8. repec:kap:jproda:v:48:y:2017:i:2:d:10.1007_s11123-017-0515-5 is not listed on IDEAS
    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. Sarmiento, Miguel & Galán, Jorge E., 2014. "Heterogeneous effects of risk-taking on bank efficiency : a stochastic frontier model with random coefficients," DES - Working Papers. Statistics and Econometrics. WS ws142013, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
    12. Robert McKeown, 2017. "Costs, size and returns to scale among Canadian and U.S. commercial banks," Working Papers 1382, Queen's University, Department of Economics.
    13. Diego A. Restrepo-Tobón & Subal C. Kumbhakar & Kai Sun, 2013. "Are U.S. Commercial Banks Too Big?," DOCUMENTOS DE TRABAJO CIEF 010943, UNIVERSIDAD EAFIT.
    14. Diego Restrepo-Tobón & Subal Kumbhakar, 2015. "Nonparametric estimation of returns to scale using input distance functions: an application to large U.S. banks," Empirical Economics, Springer, vol. 48(1), pages 143-168, February.
    15. Galán, Jorge & Ramos, Sofía B. & Veiga, Helena, 2015. "An analysis of the dynamics of efficiency of mutual funds," DES - Working Papers. Statistics and Econometrics. WS ws1517, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. repec:eee:ememar:v:32:y:2017:i:c:p:52-73 is not listed on IDEAS
    17. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    18. Cristina Bernini & Paola Brighi, 2012. "Modeling the effects of Geographical Expansion Strategies on the Italian Minor Banks' Efficiency," Working Paper series 72_12, Rimini Centre for Economic Analysis.

    More about this item

    Keywords

    Productivity; True random effects stochastic distance frontier; Bayesian estimation;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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