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The agency problem revisited: a structural analysis of managerial productivity and CEO compensation in large US commercial banks

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Abstract

The paper analyzes performance, incentives, and the inefficiencies that may arise due to agency problems and market power using a newly developed panel of large US commercial banks that have too-big-to-fail nature. We use a structural model to characterize managerial efficiency, which complements technical efficiency in standard stochastic frontier models. We incorporate managerial decisions, bank-specific characteristics, and market competition in deriving managerial efficiency. Data on the 50 largest commercial banks in the USA during 2000 and 2017 are collected from the Call Reports and are matched with CEO compensation from S&P’s ExecuComp database. The paper connects empirical evidence with economic theory and contributes to the literature on efficiency and management. The ultimate goal is to better understand the linkages among managerial performance, CEO compensation, and the size and scope of bank operations. Current results point to robust empirical findings. Economies of scale have steadily declined throughout the period and are not positively related to managerial performance and CEO compensation. The size of a bank does not seem to be justified by the evidence in that larger banks offer larger bonuses and tend to have lower managerial efficiency and diminishing scale economies.

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Notes

  1. The asset share is defined as the share in the consolidated assets, meaning all assets owned directly or indirectly by the company through any subsidiary and reflected on the company’s consolidated balance sheet.

  2. See, for example, Olley and Pakes (1996), Schmidt and Sickles (1984), and Kutlu (2018).

  3. For a more detailed comparison of DEA and SFA applied in the banking industry, see Ferrier and Knox Lovell (1990) and Bauer et al. (1998).

  4. For details, see Inanoglu and Jacobs (2009).

  5. The asset approach assumes that banks are intermediaries whose main function is to collect deposits from savers and transform them into loans and financial investments. The user cost approach considers a financial instrument as an output only when the net revenue exceeds the opportunity cost of funds or the costs of liability are smaller than the opportunity cost. Otherwise, it is an input. The value-added approach, however, does not exclusively differentiate inputs from outputs. It determines whether financial products are outputs, inputs, or intermediates depending on how much value the categories of the products generate. For a more detailed discussion of the approaches, see Berger Allen and Humphrey David (1992).

  6. The form of inverse demand functions is chosen in order to facilitate estimation and interpretation. We have examined other functional representations of the inverse demand equations, and our results are not qualitatively different from using semi-log specifications as well as models with second-order interactions.

  7. We test the validity of these instruments using Hansen’s J-test of overidentifying restrictions. We fail to reject J-test’s null hypothesis that instruments are uncorrelated with the error term, and the excluded instruments are correctly excluded from the equation.

  8. Baumol et al. (1982) use zero value for \(q_m^s\), but in the use of translog function \(\ln q_m^s\) is undefined. Thus, we choose an arbitrarily small number.

  9. All the inputs, i.e., labor, fixed capital, and deposits, are considered as normal goods.

  10. For details on the implementation, please refer to a paper on SFA using Stata by Belotti et al. (2013).

  11. Please refer to the Stata documentation on GMM for detailed derivation and implementation that uses consistent point estimates to obtain correct standard errors for the two-step or iterative GMM estimation.

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Authors and Affiliations

  1. Freddie Mac, 1551 Park Run Dr, McLean, VA, 22102, USA

    Shasha Liu

  2. Rice University, 6100 Main Street, Houston, TX, 77005, USA

    Robin Sickles

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  1. Shasha Liu
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  2. Robin Sickles
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Correspondence to Robin Sickles.

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Shasha Liu declares that she has no conflict of interest. Robin Sickles declares that he has no conflict of interest.

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Liu, S., Sickles, R. The agency problem revisited: a structural analysis of managerial productivity and CEO compensation in large US commercial banks. Empir Econ 60, 391–418 (2021). https://doi.org/10.1007/s00181-020-01982-5

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