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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.

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  • 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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    Citations

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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. Guohua Feng & Chuan Wang, 2021. "Determinants of profitability of community banks in the USA: a cost-frontier-based decomposition approach," Empirical Economics, Springer, vol. 60(6), pages 2969-2992, June.
    4. 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.
    5. 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.
    6. Robert McKeown, 2017. "Where Are The Economies Of Scale In Canadian Banking?," Working Paper 1380, Economics Department, Queen's University.
    7. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    8. Sarmiento, Miguel & Galán, Jorge E., 2017. "The influence of risk-taking on bank efficiency: Evidence from Colombia," Emerging Markets Review, Elsevier, vol. 32(C), pages 52-73.
    9. 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.
    10. Chaohua Dong & Jiti Gao & Bin Peng, 2018. "Varying-coefficient panel data models with partially observed factor structure," Monash Econometrics and Business Statistics Working Papers 1/18, Monash University, Department of Econometrics and Business Statistics.
    11. Gregory McKee & Albert Kagan, 2018. "Community bank structure an x-efficiency approach," Review of Quantitative Finance and Accounting, Springer, vol. 51(1), pages 19-41, July.
    12. Guohua Feng & Bin Peng & Xiaohui Zhang, 2017. "Productivity and efficiency at bank holding companies in the U.S.: a time-varying heterogeneity approach," Journal of Productivity Analysis, Springer, vol. 48(2), pages 179-192, December.
    13. 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.
    14. Guohua Feng & Chuan Wang & Xibin Zhang, 2019. "Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach," Journal of Productivity Analysis, Springer, vol. 51(1), pages 1-19, February.
    15. Chaohua Dong & Jiti Gao & Bin Peng, 2018. "Series estimation for single-index models under constraints," Monash Econometrics and Business Statistics Working Papers 5/18, Monash University, Department of Econometrics and Business Statistics.
    16. Mamatzakis, Emmanuel & matousek, roman & vu, anh, 2019. "The interplay between problem loans and Japanese bank productivity," MPRA Paper 92960, University Library of Munich, Germany.
    17. Galán, Jorge E. & Sarmiento, Miguel, 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.
    18. 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.
    19. Robert McKeown, 2017. "Costs, Size And Returns To Scale Among Canadian And U.s. Commercial Banks," Working Paper 1382, Economics Department, Queen's University.
    20. Al-Khasawneh, Jamal Ali & Essaddam, Naceur & Hussain, Tashfeen, 2020. "Total productivity and cost efficiency dynamics of US merging banks: A non-parametric bootstrapped analysis of the fifth merger wave," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 199-211.
    21. Diego A. Restrepo-Tobón & Subal C. Kumbhakar & Kai Sun, 2013. "Are U.S. Commercial Banks Too Big?," Documentos de Trabajo CIEF 010943, Universidad EAFIT.
    22. 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.
    23. Veiga, Helena & Ramos, Sofía B. & Galán, Jorge, 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.
    24. 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.
    25. 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.

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    More about this item

    Keywords

    Productivity; True random effects stochastic distance frontier; Bayesian estimation;
    All these keywords.

    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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