Do Large Banks have Lower Costs? New Estimates of Returns to Scale for U.S. Banks

Wheelock, David C.; Wilson, Paul W.

doi:10.2139/ssrn.1493261

Cited by 96 publications

(155 citation statements)

References 35 publications

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“…23 The empirical evidence suggests that there is little (if any at all) qualitative difference in scale economies across the models: virtually all banks are found to exhibit IRS regardless whether the ex-ante or ex-post cost function is being estimated. These findings of IRS are consistent with those recently reported in the literature despite the differences in methodology (e.g., Feng & Serletis, 2010;Hughes & Mester, 2013;Wheelock and Wilson, 2012). However, examining the Spearman's rank correlation coefficients of the scale economies estimates across the models (see Table 4) suggests striking differences in rankings of the banks.…”

Section: [Insert Figure 2 Here]supporting

confidence: 89%

“…The results, however, do not differ qualitatively across the ex-ante and ex-post models if one controls for unobserved bank-specific effects. In the latter case, we find that virtually all U.S. commercial banks (regardless of the size) operate under increasing returns to scale, which is consistent with findings recently reported in the literature despite the differences in methodology (e.g., Feng & Serletis, 2010;Hughes & Mester, 2013;Wheelock and Wilson, 2012). Interestingly, if we leave bank-specific effects uncontrolled (as, for instance, the three above-cited studies do), the results change dramatically: the ex-ante models then indicate that 23-35% and 33-34% of large banks exhibit decreasing and constant returns to scale, respectively.…”

Section: Introductionsupporting

confidence: 91%

“…The rank correlation coefficient between the ex-ante models and ex-post models is around 0.3 for small banks, 0.45 for medium size banks and 0.6 for large banks. 24 [insert Table 4 here] Notably, the results on economies of scale change dramatically if we do not account for unobserved bank-specific heterogeneity when estimating the models, as commonly done in the literature (e.g., Hughes et al 2001;Wheelock and Wilson, 2012). We find that biases in returns to scale estimates from the ex-post Models III through V are no longer uniformly negative across all bank-size categories.…”

Section: [Insert Figure 2 Here]mentioning

confidence: 73%

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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: [Insert Figure 2 Here]supporting

confidence: 89%

Section: Introductionsupporting

confidence: 91%

Section: [Insert Figure 2 Here]mentioning

confidence: 73%

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Estimation of banking technology under credit uncertainty

2014

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“…For example, Hughes and Mester (2011) and Wheelock and Wilson (2012) report evidence of scale economies at all sizes, which cannot be attributed solely to an implicit "too big to fail" (TBTF) subsidy. Across the empirical banking literature as a whole, however, the evidence as to whether large banks operate at lower average costs than their smaller counterparts is rather weak and contradictory (Davies and Tracey, 2012).…”

Section: Introductionmentioning

confidence: 99%

The Size Distribution of US Banks and Credit Unions

Goddard

Hong

McKillop

et al. 2014

International Journal of the Economics of Business

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This study examines the firm size distribution of US banks and credit unions. A truncated lognormal distribution describes the size distribution, measured using assets data, of a large population of small, community-based commercial banks. The size distribution of a smaller but increasingly dominant cohort of large banks, which operate a high-volume low-cost retail banking model, exhibits power-law behaviour. There is a progressive increase in skewness over time, and Zipf's Law is rejected as a descriptor of the size distribution in the upper tail. By contrast, the asset size distribution of the population of credit unions conforms closely to the lognormal distribution. JEL Classification: G21; L10; L16Keywords: Firm Size distribution, Zipf's Law, Gibrat's Law, Banks, Credit Unions __________________________________________________________________________ + The authors gratefully acknowledge the helpful comments of two anonymous referees on an earlier draft of this paper. The usual disclaimer applies. IntroductionThis study examines the empirical size distribution of US banks and credit unions. It is well known that most empirical firm-size distributions are highly skewed. If firm sizes are subject to proportional random growth, consistent with Gibrat's Law so that log sizes follow random walks, a lognormal cross-sectional firm-size distribution emerges over time (Gibrat, 1931;Sutton, 1997). There is, however, extensive evidence that the lognormal provides a poor approximation to empirical firm-size distributions in the upper tail, which typically exhibit greater skewness than is consistent with lognormality. Certain modifications toGibrat's Law, however, are capable of producing a cross-sectional size distribution that exhibits power-law behaviour. i A strand in the empirical literature examines the application of lognormal and power-law distributions to cross-sectional firm-size data (Simon and Bonnini, 1958;Quandt, 1966;Lucas, 1978;Cabral and Mata, 2003).Pareto (1897) describes the distribution of a collection of N subjects ranked by size, where the density function, denoted f( ), obeys a power law in the upper tail:where X is size,  is the size threshold above which the Pareto distribution applies, and  is constant. Zipf's Law describes the special case =1 (Zipf, 1949 Estimation MethodLet X i denote the assets of firm i in a particular year, and let x i =ln(X i ). Let X [i] denote the value of the i'th observation when the firms are ranked in descending order of asset size,, and let x [i] =ln(X [i] ). Let  k = X [k] denote some threshold value of k that is, initially, assumed to be pre-selected, and let  k = x [k] .We examine two candidate distributions for X i : , where  is the standard normal density function.For (ii), the likelihood function that would be formed over the two segments of the distribution is discontinuous at the truncation point  k . Accordingly, we estimate the parameters  k , 2 k  , k,  k and  k in two stages. Stage 1and sample variance of allwhere  is the sta...

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“…Foreign banks may deal better with risk exposure given cheaper access to funding sources or more diversification (see Chen and Liao, 2011). Similar effects could be faced by large institutions or those operating in different markets, mainly associated to scale economies (Bos and Kool, 2006;Wheelock and Wilson, 2012). Moreover, recent studies show that large banks face lower cost on both deposits and interbank funds mainly because their creditors infer that those banks are too-important-to-fail and will be saved by the government in case of failure to avoid contagion into the financial system (Bertay et al, 2013;Santos, 2014;IMF, 2014).…”

Section: Introductionmentioning

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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Do Large Banks have Lower Costs? New Estimates of Returns to Scale for U.S. Banks

Cited by 96 publications

References 35 publications

Estimation of banking technology under credit uncertainty

Estimation of banking technology under credit uncertainty

The Size Distribution of US Banks and Credit Unions

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

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