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Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?

Author

Listed:
  • Inanoglu, Hulusi

    (Federal Reserve Board)

  • Jacobs, Michael, Jr.

    (Pricewaterhouse Coopers LLC)

  • Liu, Junrong

    (IFE Group)

  • Sickles, Robin

    (Rice University)

Abstract
This paper analyzes the provision of banking services--the multioutput/ multi-input technology that is utilized by banks in their role in the provision of banking services, including both balance-sheet financial intermediation businesses and off-balance-sheet activities. We focus on the largest financial institutions in the U. S. banking industry. We examine the extent to which scale efficiencies exist in this subset of banks in part to address the issue of whether or not there are economic justifications for the notion that these banks may be "too-big-to-fail." Our empirical study is based on a newly developed set data based on Call Reports from the FDIC for the period 1994-2013. We contribute to the post-financial crisis "too-big-to-fail" debate concerning whether or not governments should bail-out large institutions under any circumstances, risking moral hazard, competitive imbalances and systemic risk. Restrictions on the size and scope of banks may mitigate these problems, but may do so at the cost of reducing banks' scale efficiencies and international competitiveness. Our study also utilizes a suite of econometric models and assesses the empirical results by looking at consensus among the findings from our various econometric treatments and models in order to provide a robust set of inferences on large scale banking performance and the extent to which scale economies have been exhausted by these large financial institutions. The analyses point to a number of conclusions. First, despite rapid growth over the last 20 years, the largest surviving banks in the U.S. have decreased their level of efficiency. Second, we find no measurable returns to scale across our host of models and econometric treatments and in fact find negative correlation between bank size and the efficiency with which the banks take advantage of their scale of operations. In addition to the broad policy implications of our analysis our paper also provides an array of econometric techniques, findings from which can be combined to provide a set of robust consensus-based conclusions that can be a valuable analytical tool for supervisors and others involved in the regulatory oversight of financial institutions.

Suggested Citation

  • Inanoglu, Hulusi & Jacobs, Michael, Jr. & Liu, Junrong & Sickles, Robin, 2015. "Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?," Working Papers 15-016, Rice University, Department of Economics.
    • Handle: RePEc:ecl:riceco:15-016
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    References listed on IDEAS

    as
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    Cited by:

    1. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, vol. 5(3), pages 1-21, July.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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