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Review of Industrial Organization

Erschienen in:

14.01.2016

Community Bank Performance: How Important are Managers?

verfasst von: Dean F. Amel, Robin A. Prager

Erschienen in: Review of Industrial Organization | Ausgabe 2/2016

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Abstract

Community banks have long played an important role in the U.S. economy, providing loans and other financial services to households and small businesses within their local markets. In recent years, technological and legal developments, as well as changes in the business strategies of larger banks and non-bank financial service providers, have purportedly made it more difficult for community banks to attract and retain customers, and hence to survive. Indeed, the number of community banks and the shares of bank branches, deposits, banking assets, and small business loans that are held by community banks in the U.S. have all declined substantially over the past two decades. Nonetheless, many community banks have successfully adapted to their changing environment and have continued to thrive. This paper uses data from 1992 through 2011 to examine the relationships between community bank profitability and various characteristics of the banks and the local markets in which they operate. We divide these characteristics into two categories—those that are exogenous to the control of bank managers and those that reflect the decisions or actions of bank management. We find that variables from both categories significantly influence bank profitability. Statistical tests indicate that variables within managers’ control account for between 70 and 97 % of the total explanatory power of regression equations that explain variations in performance across community banks, which suggests that managers are extremely important to community bank performance.

Vorheriger Artikel Can Financing Constraints Explain the Evolution of the Firm Size Distribution?

Nächster Artikel Where Are All the New Banks? The Role of Regulatory Burden in New Bank Formation

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As of 30 September 2015, there were still nearly 5500 commercial banks and thrift institutions with less than $1 billion in assets (a standard criterion for defining the term “community bank”) that operated in the United States. Throughout this paper, the term “bank” will be used to refer to both commercial banks and thrift institutions.

The FDIC defines problem institutions as “those institutions with financial, operational, or managerial weaknesses that threaten their continued financial viability.” FDIC Quarterly Banking Profile, Third Quarter 2015, pp. 30–31.

Kupiec and Lee (2012) provide some evidence on this point, but they do not explicitly estimate the contributions of these two sets of factors in explaining bank performance.

Some research uses $500 million in assets as the cutoff between small (or community) and large banks, and a few papers use even smaller levels, such as $300 million or $400 million, but these lower cutoffs usually refer to assets from the 1990s or earlier. Two papers by Bassett and Brady (2001 and 2002) use definitions that are not based on a specific size, defining community banks as all commercial banks other than the 100 largest; this translated into banks under about $330 million in assets at the time of these studies.

In banking, unlike in many other industries, it is still common to use reported profitability as a measure of performance. This is likely due to the more highly regulated nature of banking, in which regulators regularly examine the safety of bank portfolios and dictate capital levels in order to control the risk of failure. While reported returns on assets and equity might be misleading when considering a sample of the largest banking organizations—where banks may specialize in a variety of different wholesale financial activities – for the small, retail banks considered in this paper and the papers cited here, reported returns likely give an accurate picture of the firms’ performance.

While individual year regressions are run only for the volatile 2007–2011 period, as a robustness check the regressions for the three earlier 5-year periods were run with dummy variables to capture yearly fixed effects. The addition of these yearly dummy variables had almost no effect on the significance of the explanatory variables and had no effect on the variables that are central to our analysis.

F-tests confirm that there are statistically significant differences in regression coefficients across time periods and between urban and rural markets within a time period.

We also estimate a model with bank fixed effects for each of the 5-year periods in our sample and for the entire 20-year sample. Results from these regressions are discussed in footnote 39.

The HHI is the sum of squared deposit market shares, divided by 10,000 to yield a value between zero and one.

For small banks, intrastate branching restrictions are more relevant than are interstate banking laws, because most interstate acquisitions are by large banking organizations that target other large banking organizations.

The latter expectation is supported by the work of Adams et al. (2007), Cohen and Mazzeo (2007), Hannan and Prager (2004), and Kiser (2004), among others.

See, for example, Adams and Amel (2007).

Bank supervisors employ a rating system known as “CAMELS” to rate the safety and soundness of their banks: Ratings (1 is the best; 5 is the worst) are assigned for each of six components (Capital, Assets, Management, Earnings, Liquidity, and Sensitivity to market risk), and the six components are then combined to generate a composite rating for the bank.

Banks were first permitted to become S-corporations in 1997. An S corporation generally does not pay corporate income taxes on its profits; instead, the shareholders pay income taxes on their proportionate shares of the corporation’s profits. Previous research (e.g., Hein et al. (2005), Gilbert and Wheelock (2007), and Cyree et al. (2010)) has found a relationship between S corporation status and bank profitability.

Per capita income is adjusted to constant 2005 dollars using the same deflator used for bank assets.

This is the standard approach that is taken by the Federal Reserve System when screening bank merger applications for competitive effects.

Discussions with bank examiners raised concerns that bank management ratings might be raised or lowered to reflect the overall performance of the bank rather than being independent evaluations of the quality of bank management. If this were true, changes in bank returns might affect the management rating rather than vice versa. As a robustness check, we estimated the model using the most recent management rating as of 1 year prior to the start of the observation year. Results do not vary from those reported here.

Analysis of variance shows that cross-sectional differences are about four times as important as time-series effects in explaining the variation of return on equity in our sample, which suggests that there are substantial differences in profitability to be explained by cross-sectional variables.

It is also interesting that, while the mean management rating increased (got worse) in the 2007–2011 period, especially in urban markets, it never reached the levels of the 1992–1996 period. Apparently, the financial difficulties of the early 1990s, which resulted in a large number of failures in 1992 and earlier years, were blamed on management to a greater extent than the difficulties experienced in the most recent recession.

As noted above, using return on assets yields results very similar to those for return on equity. Key results are also the same using a measure of risk-adjusted return on equity (RAROC), defined as the return on equity divided by the standard deviation of the return on equity over the 5-year period from which an observation is taken. Differences in results using RAROC are described further in footnote 34.

When ROA is used as the dependent variable, the coefficient on the unemployment rate for banks that operate in rural markets is positive and insignificant in the first two periods and negative and significant in the last two periods.

It is also positive and significant at the .05 level in rural markets in 2007 in the individual year regressions.

In rural markets, bank age has negative but insignificant coefficients in 2009 and 2010. When the dependent variable is ROA, the coefficient on bank age is still negative in each period for rural markets, but significant only in the first period. In urban markets, the coefficient is always positive, with statistical significance in three of the four periods.

For each variable that may be within the control of bank management, we considered the possibility of reverse causality from profitability to that variable. The only variable for which we believe this is a potentially important concern is our measure of management quality, and we address the reverse causality issue in our discussion of that variable. In the case of bank size, such reverse causality is unlikely because large changes in bank size are most often the result of mergers and acquisitions rather than growth through the reinvestment of retained earnings. Also, there is no reason to expect reverse causality from profitability to S-corporation status or portfolio composition. The use of brokered deposits tends to reflect opportunities for lending in excess of banks’ ability to attract retail deposits. A reverse causality from profitability to the use of brokered deposits might be observed in banks that are near the point of failure, but such banks make up a very small portion of our sample.

Recall that higher values of this variable indicate poorer management quality.

As a robustness check, we also estimated the model using the most recent management rating as of 1 year prior to the start of the observation year. The magnitudes of the estimated coefficients on this lagged management rating are a bit smaller than those reported here, but the coefficients remain negative and highly significant. Estimated coefficients on other variables are generally unaffected by this change in specification. Results from this estimation are available from the authors upon request.

Gilbert and Wheelock (2007) find that, even after adjusting for corporate income taxes, S corporation banks have higher earnings than C corporation banks. Restrictions on the number of shareholders cause S corporations to be closely held. Cyree et al. (2010) hypothesize that this may lead to a better alignment between shareholder and management interests and a consequent reduction in agency costs. Correcting for sample selection bias in the decision to elect S corporation status, Cyree, Hein and Koch find evidence consistent with this hypothesis. SCORP is excluded from the equations that are estimated for the first period because banks were not allowed to become S-corporations until 1997.

When the dependent variable is ROA, the coefficient on real estate loans is negative in all four multi-year periods and significant in 1997–2001 and 2007–2011. The coefficient is negative and significant in all five of the single-year regressions.

If real estate loans are split into commercial real estate loans and other real estate loans and these two portfolio measures are included in the rural market regression, the estimated coefficient on the commercial real estate variable is significant and positive before the crisis; it is insignificant and negative during and after the crisis. The coefficient on the other real estate variable has varying signs and is generally insignificant. This change to the model has little effect on coefficients for other variables, including the coefficients on other portfolio components.

In urban markets, the model change that was described in the previous footnote yields positive significant coefficients on commercial real estate lending in the pre-crisis periods and a negative significant coefficient in the 2007–2011 period. The latter finding is consistent with Cole and White’s (2012) finding of a significant positive relationship between a bank’s portfolio share of commercial real estate loans and its probability of failure during 2009. The coefficients on other real estate lending are positive in the first 5-year period and negative and generally significant in the other periods. As in rural markets, this change to the model has little effect on the coefficients of other variables.

When the dependent variable is ROA, the coefficient on real estate loans is negative in all four time periods and significant after 1992–1996.

When ROA is the dependent variable, the coefficient on C&I lending becomes negative in 1997–2001 and becomes more significant over time.

If the equity-to-asset ratio is added as an explanatory variable, it has a significant negative coefficient for all regressions except the 2007–2011 regression on urban markets and the 2009–2011 annual urban regressions, where the coefficient is positive and significant. Adding the equity-to-asset ratio to the analysis has little effect on the coefficients on other variables and no effect on the variables of central interest in our analysis.

When the dependent variable is a risk-adjusted measure of return on equity (RAROC, see footnote 20), there is no effect on the significance level of the measure of management quality. In one of the two regressions (among the 18 reported) in Tables 2, 3, 4 and 5 where the portfolio shift variable does not have a significant negative coefficient when ROE is the dependent variable, the coefficient is significant when RAROC is used. However, there is also one incidence in which this variable loses its significance. There are a number of instances in which demographic or portfolio composition variables gain or lose significance when RAROC is substituted for ROE, but gains and losses of significance are closely balanced. The only variable with a notable difference between the two sets of regressions is AGE, which tends to have a more positive correlation with RAROC than it does with ROE.

Results of these estimations are not reported here, but are available from the authors upon request.

The phi coefficient is a measure of association between two binary variables.

Results are not reported here, but are available from the authors upon request.

The addition of this interaction term has little effect on the coefficients of other variables.

We also estimate a model with bank fixed effects for each of the 5-year periods in our sample and for the entire 20-year sample. Results of the fixed-effects regressions are broadly consistent with those reported here. In general, coefficients are less significant because they are reduced to explaining only within-firm variation over 5-or 20-year periods. The measure of management quality consistently has significant negative coefficients; in these regressions this variable is capturing the performance of the small subset of banks whose management rating was either upgraded or downgraded within a sample period. Measures of large portfolio shifts tend to have insignificant coefficients over 5-year periods, probably because portfolio shifts are measured over the most recent 3 years, so that there is little within-firm variation in portfolio shift indicators during any 5-year period. Over the entire 20-year sample, measures of large portfolio shifts have negative and significant coefficients, consistent with the results reported in Tables 2 through 5. Results of the fixed-effects estimations are available from the authors upon request.

Results of these estimations are not reported here, but are available from the authors upon request.

See Shorrocks (2013) for an explanation of this method.

Adams, R. M., & Amel, D. F. (2007). The effects of past entry, market consolidation, and expansion by incumbents on the probability of entry. Finance and Economics Discussion Paper 2007-51, Board of Governors of the Federal Reserve System.

Adams, R., Brevoort, K., & Kiser, E. (2007). Who competes with whom? The case of depository institutions. Journal of Industrial Economics, 55, 141–167.CrossRef

Amel, D. F. (1995). State laws affecting the geographic expansion of commercial banks (unpublished paper).

Bassett, W. F., & Brady T. F. (2002). What drives the persistent competitiveness of small banks? Finance and Economics Discussion Paper 2002-28, Board of Governors of the Federal Reserve System.

Bassett, W. F., & Brady, T. F. (2001). The economic performance of small banks, 1985–2000. Federal Reserve Bulletin, 87, 719–728.

Cohen, A., & Mazzeo, M. (2007). Market structure and competition among retail depository institutions. Review of Economics and Statistics, 89, 60–74.CrossRef

Cole, R. A., & White, L. (2012). Déjà vu all over again: The causes of U.S. commercial bank failures this time around. Journal of Financial Services Research, 42, 5–29.CrossRef

Cyree, K. B., Hein, S. E., & Koch, T. W. (2010). The impact of taxes and ownership on the performance and capital structure of S Corporation Banks (unpublished manuscript).

DeYoung, R. (1999). Birth, growth and life or death of newly chartered banks. Federal Reserve Bank of Chicago Economic Perspectives, 14–35.

DeYoung, Robert, Hunter, William C., & Udell, Gregory F. (2004). The past, present and probable future for community banks. Journal of Financial Services Research, 25, 85–133.CrossRef

Emmons, W. R., Gilbert, R. A., & Yeager, T. J. (2004). Reducing the risk at small community banks: Is it size or geographic diversification that matters? Journal of Financial Services Research, 25, 259–281.CrossRef

Federal Deposit Insurance Corporation (2012) FDIC Community Banking Study, December.

Gilbert, R. A., Meyer, A. P., & Fuchs, J. W. (2013). The future of community banks: Lessons from banks that thrived during the recent financial crisis. Federal Reserve Bank of St. Louis Review, March/April 115–43.

Gilbert, R. A., & Wheelock, D. C. (2007). Measuring commercial bank profitability: Proceed with caution (pp. 515–532). November/December: Federal Reserve Bank of St. Louis Review.

Hannan, T., & Prager, R. (2004). The competitive implications of multimarket bank branching. Journal of Banking & Finance, 28, 1889–1911.CrossRef

Hannan, T. H., & Prager, R. A. (2009). The profitability of small single-market banks in an era of multi-market banking. Journal of Banking & Finance, 33, 263–271.CrossRef

Hein, S. E., Koch, T. W., & MacDonald, S. S. (2005). On the uniqueness of community banks. Federal Reserve Bank of Atlanta Economic Review, 90 (First Quarter), 15–36.

Kiser, E. (2004). Modeling the whole firm: The effect of multiple inputs and financial intermediation on bank deposit rates. Finance and Economics Discussion Series 2004–07. Washington: Board of Governors of the Federal Reserve System.

Kupiec, P., & Lee, Y. (2012). What factors explain differences in return on assets among community banks? FDIC working paper.

Shorrocks, A. F. (2013). Decomposition procedures for distributional analysis: A unified framework based on the Shapley value. Journal of Economic Inequality, 11, 99–126.CrossRef

Stiroh, K. J. (2004). Do community banks benefit from diversification? Journal of Financial Services Research, 25, 135–160.CrossRef

Whalen, G. (2007). Community bank strategic lending choices and performance. Economics Working Paper 2007-3, Office of the Comptroller of the Currency.

Yeager, T. J. (2004). The demise of community banks? Local economic shocks Are not to blame. Journal of Banking & Finance, 28, 2135–2153.CrossRef

Zimmerman, G. C. (1996). Factors influencing community bank performance in California. Federal Reserve Bank of San Francisco Economic Review, 1, 26–42.

Titel: Community Bank Performance: How Important are Managers?
verfasst von: Dean F. Amel
Robin A. Prager
Publikationsdatum: 14.01.2016
Verlag: Springer US
Erschienen in: Review of Industrial Organization / Ausgabe 2/2016
Print ISSN: 0889-938X
Elektronische ISSN: 1573-7160
DOI: https://doi.org/10.1007/s11151-015-9497-5

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