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

Feng, Guo-Hua; Zhang, Xiaohui

doi:10.1016/j.jbankfin.2012.02.008

Cited by 56 publications

(43 citation statements)

References 25 publications

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“…Numerous studies such as Mester (1997, 2003), Hughes and Mester (1998), Wheelock and Wilson (2001), Feng and Zhang (2012 among many others have used the above framework in their analysis of banking technologies. However, two points are worth mentioning here.…”

Section: Credit Uncertaintymentioning

confidence: 99%

Estimation of banking technology under credit uncertainty

2014

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Credit risk is crucial to understanding banks' production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk by using ex-post realizations of banks' uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank's production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks' technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks' production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks' supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature.

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Section: Credit Uncertaintymentioning

confidence: 99%

Estimation of banking technology under credit uncertainty

2014

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“…These studies include Wilson (2011, 2012), Mester (2013), andSerletis (2010). However, Feng and Zhang (2012) find no evidence consistent with economies of scale for large U.S. commercial banks; Restrepo-Tobón and Kumbhakar (2014) find no evidence of significant economies of scale for large U.S. banks and bank holding companies; and Davies and Tracey (2014), after controlling for Too-big-to-fail factors, find no evidence of scale economies for a sample of large U.S. banks. Wilson (2011, 2012) investigate scale economies of U.S. credit unions, commercial banks, and bank holding companies.…”

Section: Related Literaturementioning

confidence: 99%

“…However, in a recent paper, Feng and Zhang (2012) failed to reject constant or decreasing RTS for large and small banks. They use an output distance function to model banks' technology and Bayesian techniques for estimation.…”

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confidence: 96%

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Obelix vs. Asterix: Size of US commercial banks and its regulatory challenge

2015

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Big banks pose substantial costs to society in the form of increased systemic risk and government bailouts during crises. Should regulations limit the size of banks? To answer this question, regulators need to assess the potential costs of such regulations. If big banks enjoy substantial scale economies, that is, lower average costs as bank size increases, size limit regulations may be inefficient and may reduce social welfare. However, the literature offers conflicting results regarding the existence of economies of scale for the biggest U.S. banks. We contribute to this literature using a novel approach to estimate nonparametric measures of scale economies and total factor productivity (TFP) growth. For U.S. commercial banks, we find that around 73% of the top one hundred banks, 98% of medium and small banks, and seven of the top ten biggest banks by asset size exhibit substantial economies of scale. In addition, scale economies contribute positively and significantly to their TFP growth. The existence of substantial scale economies at commercial banks raises an important challenge for regulators to pursue size limit regulations.Keywords bank regulation · economies of scale · returns to scale · nonparametric methods JEL Classification L51 · G21 · G28 · D24 · L25 · C14

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“…Omitting heterogeneity variables has been identified to lead to biased estimations of inefficiency. In the banking literature, Bos et al (2009) identify important effects of observed heterogeneity on efficiency levels and rankings, while Feng and Zhang (2012) find that failure to consider unobserved heterogeneity results in misled efficiency rankings and mismeasured technical efficiency, productivity growth, and returns to scale. Observed and unobserved heterogeneity sources are important to be considered.…”

Section: A Stochastic Frontier Model With Random Inefficiency Coefficmentioning

confidence: 99%

Heterogeneous Effects of Risk-Taking on Bank Efficiency: A Stochastic Frontier Model with Random Coefficients

Sarmiento

Galán

2014

SSRN Journal

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We estimate a stochastic frontier model with random inefficiency parameters, which allows us not only to identify the role of bank risk-taking on driving cost and profit inefficiency, but also to recognize heterogeneous effects of risk exposure on banks with different characteristics. We account for an integral group of risk exposure covariates including credit, liquidity, capital and market risk, as well as bank-specific characteristics of size and affiliation. The model is estimated for the Colombian banking sector during the period 2002-2012. Results suggest that risk-taking drives inefficiency and its omission leads to over (under) estimate cost (profit) efficiency. Risk-taking is also found to have different effects on efficiency of banks with different size and affiliation, and those involved in mergers and acquisitions. In particular, greater exposures to credit and market risk are found to be key profit efficiency drivers. Likewise, lower liquidity risk and capital risk lead to higher efficiency in both costs and profits. Large, foreign and merged banks benefit more when assuming credit risk, while small, domestic and non-merged banks institutions take advantage of assuming higher market risk.

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

Cited by 56 publications

References 25 publications

Estimation of banking technology under credit uncertainty

Estimation of banking technology under credit uncertainty

Obelix vs. Asterix: Size of US commercial banks and its regulatory challenge

Heterogeneous Effects of Risk-Taking on Bank Efficiency: A Stochastic Frontier Model with Random Coefficients

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