The Financial Performance Of Privately Held Us Commercial Banks: A Profit Efficiency Approach

How Does Bank Holding Company Profit Efficiency Vary With Public versus Private Ownership?

Aigbe Akhigbe a a The University of Akron Akron, Ohio

James E. McNulty b b Florida Atlantic University Boca Raton, FL 33431

Bradley A. Stevenson c c The University of Akron Akron, Ohio

Abstract We examine the effect of the type of ownership on bank holding company performance, as measured by profit efficiency, for 1996 to 2006. We find that privately owned BHCs are more profit efficient than their publicly owned counterparts. Controlling for the effect of other variables, the difference in profit efficiency is less than one percentage point but it is statistically significant. Our analysis is consistent with the separation of ownership and control in banking being associated with somewhat weaker financial performance. The difference is primarily attributable to bank holding companies with assets under one billion dollars when profit efficiency is measured using ROA but is consistent across all BHC sizes when profit efficiency is measured using ROE.

JEL Codes: G21; D02 Keywords: Bank Holding Company, Profit Efficiency, Bank Efficiency, Bank Governance, Stochastic Frontier Analysis a Department of Finance, The College of Business, The University of Akron, 302 Buchtel Mall, Akron, Ohio 44325-4803; (330) 972-6883; e-mail: [email protected]. b Department of Finance, Barry Kaye College of Business, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431-0991; (561) 297-2708; email: [email protected]. c Corresponding Author: Department of Finance, The College of Business, The University of Akron, 302 Buchtel Mall, Akron, Ohio 44325-4803; (330) 972-6332; e-mail: [email protected]. 1. Introduction

Financial researchers have long been interested in the factors that explain differences in the financial performance of various types of commercial banks.

Contributing to that literature is a recent study by Kwan (2004) which points out that differences in the financial performance of privately owned and publicly owned banks may provide important information about the importance of capital market signals in disciplining financial managers. Since privately owned banks are institutions where separation of ownership and control is less, he asks if these banks have better financial performance than publicly traded banks. He finds better financial performance at privately held bank holding companies and interprets this finding as providing empirical support for the argument that separation of ownership and control, which occurs when ownership becomes dispersed in publicly-traded banks, may weaken the financial performance of these banks.

Kwan uses return on assets as the measure of financial performance and conducts a pair-wise comparison of private and publicly held banks over a sixteen-year period

(1986-2001). This approach does not control for other factors affecting performance.

We advance this discussion by putting the analysis in a profit efficiency framework. We include variables for public vs. private ownership (as well as the other factors affecting financial performance) in several equations examining the correlates of profit efficiency. Profit efficiency is a sophisticated econometric financial performance metric that measures how a bank’s financial performance compares to a best practice frontier. Since, it takes asset composition, liability composition and other factors into

2 consideration, profit efficiency methodology is considered superior to pairwise comparisons of profitability ratios such as return on assets.

Most private banks are smaller than their publicly-owned counterparts. Small banks have differences in asset composition, liability composition and capitalization.

Many of them (over 60% according to Akhigbe and McNulty (2003)) also operate in smaller, less competitive markets where they have greater opportunities to control loan yields and liability costs. We can control for these differences in a profit efficiency analysis. The differences in performance after these factors are controlled for should thus partly reflect the differences in corporate governance mechanisms emphasized by

The differences in corporate governance practices between the two sets of banks can be illustrated by the following quotation:

Relatively few Tenth District Community banks … are publicly traded or are subject to new laws that would require them to change their corporate governance practices. Indeed, many are small in asset size, family owned, closely held and owner-managed. Given these characteristics, the governance process at community banks tends to be less formal and structured than that required for publicly traded companies (Myers and Padget (2004, p. 39).

While differences in corporate governance between the two types of banks are pronounced, three additional factors may be important in explaining performance differences. First, as the quotation points out, a significant portion of the privately owned banks in some sections of the country are owned by one family. When family members are owners, they may have the objective of providing employment for future generations of family members. This may make them more risk averse than other banks. If these banks take less than the optimum amount of risk, their profitability may be less.

Furthermore, because the corporate structure may partially remove labor market

3 mechanisms and replace them with nepotism, family owned banks (a subset of the group of privately owned banks) may have weaker financial performance. On the other hand, a corporate structure which emphasizes long-run performance rather than quarterly earnings per share gains may allow managers to make better financial decisions. Banks with conservative strategies often outperform other banks over long periods of time.

Thus, the effect could go in either direction. A study of all of the firms in the S&P 500 by Anderson and Reeb (2003) finds that family-owned firms perform better than non family firms. Their result suggests that, all else equal, the presence of family owned banks in our sample of privately held banks will enhance the performance of privately held banks relative to publicly traded banks.

Second, anecdotal evidence, based on a family-owned Miami, Florida bank with

$3 billion in assets, suggests that some of these banks have a patriarchal approach toward their employees, with a policy of rewarding long-standing employees and assisting employees during periods of adversity. Bank owners may consider these employees as part of an extended family who have helped the bank survive and prosper. These policies can result in significantly lower employee turnover, and hence higher productivity1.

While this approach may not be effective at larger banks, it may influence the financial performance of smaller and even medium sized banks. In banks at which salary scales for many employees are determined by mid-level managers, which would be the case at most larger publicly traded banks, the emphasis on employee retention and loyalty may be less. This second factor would again suggest better financial performance at privately

1 Studies cited by Rose (2003) suggest that banks that pay somewhat higher salaries and have lower turnover as a result have better financial performance. Since more experienced employees can be more productive, they each manage more assets; thus the ratio of employees to assets is less, which offsets the higher compensation per employee.

4 held banks, when other factors are controlled for. The abovementioned bank is one of the top performing banks in the country in its size group.

Third, smaller and medium size banks have a different business model than large banks (DeYoung, Hunter and Udell (2004)). In particular they make greater use of loan relationships to determine the composition of their commercial loan portfolio. A firm which has a loan relationship with a loan officer at a family-owned bank also has an implicit relationship with the entire family. These loan relationships would be expected to be particularly durable and thus have a further positive influence on financial performance at these banks2. At privately owned banks which are not family owned, the same factors may be at work; the primary loan customers may have durable relationships with the principal owners of the bank.

All three factors may positively influence financial performance at privately held banks relative to their publicly-held counterparts. There are also reasons why financial performance of private banks can be less favorable. For example, the family or the other owners could dissipate resources on personal consumption or seeking political favoritism.

Such effects may be short-lived if regulatory intervention is effective. We look at financial performance over a longer period of time in part to deal with this issue.

The importance of the effect of corporate governance on performance is highlighted by the results of a profit efficiency study by DeYoung, Spong and Sullivan

(2001). Their focus is the effect of granting outside managers an ownership stake in the firm. (This, of course, is different from our research objective.) They look at a random sample of 266 closely held community banks in the Kansas City Federal Reserve District.

2 Durable relationships have a positive relationship with firm performance (Bae, Kang and Lim, 2002). In some cases, however, loyalty to the firm may make the bank less likely to monitor the firm during conditions of financial distress.

5 They find an inverted U-shaped relationship between managerial ownership and financial performance. Predicted profit efficiency improves from 68% to 77% as the managers’ shareholdings rise from zero to seventeen percent of total shareholdings. After that threshold, performance declines, presumably because managers become entrenched.

They conclude that this nine percentage point difference suggests that “underutilization of managerial shareholdings as a corporate control device is a chronic and expensive problem at small, closely held financial institutions” (DeYoung, Spong and Sullivan

(2001, p. 1237). In other words, few privately owned institutions have developed corporate governance mechanisms that will optimize their financial performance. When we consider that most bank holding companies are privately held, these findings take on added importance and the issue warrants further study. (As Table 1 indicates, 13,409 of our 17,748 observations are on privately-held BHCs.)

This research is also important because most corporate governance research on small community banks is, by necessity, usually based on a relatively small sample of banks (often from in one Federal Reserve district) because measuring corporate governance incentives requires data from the banks’ examination reports. (This data is generally only available to researchers within the regulatory community.) Some examples are the study cited immediately above as well as Spong and Sullivan (2007a,

2007b)3. They use examination report data from the Kansas City Federal Reserve

District for banks less than one billion in assets to evaluate the effect of board composition, the fraction of each board member’s wealth that is tied up in the bank’s

3 Another example of corporate governance research based on confidential data generated in the regulatory process is DeYoung (2007).

6 stock, and other variables, on the bank’s performance and risk. They find, as in the earlier study, that governance does affect performance. Understanding the effect that ownership has on performance can be a valuable complement to this research, even though other factors in addition to governance also affect performance.

In this paper we analyze the differences in financial performance between private and publicly held US commercial banks using profit efficiency as the measure of performance. We control for other factors affecting performance in addition to ownership, such as size, and asset and liability composition. We believe that this should provide a valuable extension of the research by Kwan (2004).

We find the same relation as Kwan, better financial performance at private banks.

While private banks are generally smaller, this effect does not appear to be driving the results, at least at it applies to the comparison of banks over and under $1 billion in assets. There are two reasons for this conclusion. First, as noted, we control for asset size and other factors affecting performance in the regression equations. Second, when we run separate regressions for various asset size groups, the variable denoting private ownership is only significant in the equation for small banks, i.e., those with under $1 billion in assets. This finding is important in understanding the incentive structure of the

United States commercial banking system and the way in which corporate governance influences financial performance at banks. Nonetheless, the effect of private ownership may be partially explained by factors other than differences in corporate governance mechanisms.

7 The rest of the paper proceeds as follows. Section two covers the methodology and variables used in the data analysis. Section three discusses the data and section four discusses the empirical results. Section five concludes.

2. Methodology, Variable Selection, and Expected Variable Relationships

2a. Frontier Analysis

The efficiency of a bank can be measured using stochastic frontier analysis (SFA) where the financial performance of individual banks is compared to the best achievable performance based on other institutions in the sample. To conduct this analysis we first need to identify a sample of banks with publicly traded equity and a sample of banks with privately held equity. We collect data for bank holding companies from the Federal

Reserve Bank of Chicago’s Bank Holding Company database for 1996-2006 for a total of eleven years of data. The data is the condition and income data that each bank holding company (BHC) must report on form FRY-9C which is similar to what is commonly called a “call report” for individual banks. Individual bank holding companies are identified as public or private by hand-matching the BHCs from the Federal Reserve data to data from Research Insight on publicly traded BHCs.

For this study, one frontier is created per year for all BHCs in the sample and each

BHC is included even if it has only one year of available data.4 The creation of a frontier for each year allows for the regression coefficients to vary over time and provide a flexible estimation procedure. Differences for publicly traded and privately held BHCs

4 As suggest in Bauer, Berger, Ferrier and Humphrey (1998), we used stochastic frontier analysis where all years were combined to create uniform coefficients for all years and we used a distribution free analysis method as well to compare with the results of the analysis here for consistency in the rankings of BHCs. Since the results of all methods were very comparable, we only present the SFA results by year in the paper.

8 are analyzed using two different measures for performance when calculating the frontier.

The two measures used are return on assets (ROA) and return on equity (ROE).

Calculating the efficiency measure allows us to analyze each BHCs’ performance relative to their peers. As will be discussed in the next section this will enable us to use this efficiency measure as a dependent variable and use regression analysis to explain the performance of BHCs. Efficiency scores can range from 0, least efficient, to 1, most efficient. For example, if a particular BHC in the sample has an efficiency score of .86, this means that the BHC has achieved 86% of its potential efficiency.

In our analysis, we also disaggregate the sample based on the size of the BHC.

As Akhigbe and McNulty (2003) and DeYoung and Hasan (1998) point out, small banks may differ from large banks in their production technologies and strategies. To take this into account, three separate size classes are examined -- one for BHCs with greater than

$10 billion in total assets, one for between $10 billion and $1 billion in total assets, and one for BHCs less than $1 billion in total assets.

In practice, two common specifications are used for the functional form. The first is the translog, and the other is the Fourier-flexible form. The Fourier-flexible form adds onto the translog form by including Fourier trigonometric terms to the model. Some studies (McAllister and McManus (1993) and Mitchell and Onvural (1996)) show that the

Fourier-flexible form is a better approximation due to its flexibility and its quality of being a global approximation. As in Akhigbe and McNulty (2003) and DeYoung and

Hassan (1998), a hybrid function of the Fourier form is used here where the trigonometric variants of the output variables are used. The function is as follows:

9 4 4 4 3 min 1 lnROA  ROA  .01     i lnwi     ij lnwi lnw j   k lnyk   i1 2 i1 j1 k 1 1 3 3 5 1 5 5 4 3  km lnyk lnym    r lnz r    rs lnz r lnz r   ik lnwi lnyk   2 k 1 m1 r 1 2 r1 s1 i1 k 1 4 5 3 5 3   ir lnwi lnz r    kr lny k lnz r   [ l cos X i   l sin X l ]  (1) i1 r1 k 1 r1 l 1 3 3 [ mn (cos X m  X n )   mn (sin X m  X n )]  m1 n1 3 3 3 [ opq (cos X o  X p  X q )   opq (sin X o  X p  X q )]  v  u o1 p1 q1

where ROA = net income divided by total assets, w1 = Price of non-deposit related borrowing, w2 = Price of deposits, w3 = Price of labor, w4 = Price of property, plant and equipment, y1 = Loans, y2 = Non-interest income, y3 = Off-balance sheet items (letters of credit, loan commitments, etc.), z1 = Risk (assets 90 days past due and non-accrual status divided by total assets), z2 = Shareholder’s equity , z3 = Average for Risk in the state

5 where the BHC is headquartered, z4 = HHI and z5 = Total Assets. The X variables in the equation are transformations of the Y variables so that they fall in the interval (0,2π).

The model is the same when ROE is used to measure performance except that the dependent variable changes to use ROE instead of ROA. For ROA and ROE, the absolute value of the minimum ROA or ROE in the sample year plus .01 is added to the

BHC’s ROA or ROE to avoid taking the log of a negative number.6

In order to capture the operating environment, the following variables are included to reflect the inputs and outputs of the BHC as well as other BHC specific characteristics that may affect performance. Off-balance sheet items, total loans, and fee revenue are included to capture all outputs by the BHC. For off-balance sheet activity,

5 The HHI measure was calculated using the FDIC’s summary of deposits (branch office) data. The HHI measure was matched with each BHC based on the BHC’s home office state and county. 6 The scaling and use of certain terms in addition to costs and revenues of the bank follow recommendations of Berger and Mester (1997).

10 several studies [Clark and Siems (2002), Stiroh (2000), Lieu, Yeh, and Chiu (2005), etc.] indicate that excluding off-balance sheet activities creates a downward bias in efficiency.

In other words, excluding off-balance-sheet items makes banks appear less efficient than they really are compared to their peers. Physical capital and human resource costs proxy for the inputs the bank uses to generate its product. Also, the cost of deposits and non- deposit borrowing are inputs for traditional banking activities. The level of non-accrual and past-due assets at the state level are included to provide a risk measure of the BHC’s operating environment. HHI is a proxy for the level of competition faced by the bank based on its location. Total assets are a proxy for economies of scale afforded banks by larger size. Lastly, shareholder’s equity is included in the model to measure a bank’s insolvency risk, determine the size of equity funding of lending and other activities in addition to deposits, and adjust for a bank’s risk aversion [Hughes et al. (1996a, 1996b, and 1997)].

The efficiency measure is computed using the associated residual for each observation. This error term is then converted into a measure of profit efficiency measure as:

ˆ PEi  expˆi  max i ˆi  (2)

In the frontier analysis, the maxi ˆi  for each year is used since separate frontiers are created for each year. Therefore, the BHCs error for 1997 will be compared to the highest error term for 1997, etc. As noted in Bauer, Berger, Ferrier and Humphrey

(1998), from the error term in the efficiency regressions, SFA assumes a normal distribution for the noise in the error term and the normal assumption is a half-normal distribution for the inefficiency. In our results, the distribution of error terms most

11 closely resembles a normal distribution. However, Bauer, et al (1998) note that regardless of the assumption made for the distribution of efficiency scores, while individual firm efficiencies may vary, the benefit of using SFA is that the rankings are consistent no matter what distributional assumptions are used. In this paper, since we are concerned with comparing the firms within a group that are publicly traded versus those that are not publicly traded, having a precise individual efficiencies is not as important as having relative efficiencies that are accurate. Regardless, as a double check, distribution free analysis (DFA) was also used on the data where no assumptions need to be made in the model. The empirical results and rankings are the same as with SFA. Since SFA allows us to analyze each year individually while DFA requires averaging over years, we present the results using SFA in our paper.

3b. Regression Analysis and Expected Outcomes

After the efficiency measures are generated, we use the efficiency measures as dependent variables in OLS regressions.7 The model is as follows:

PE = f (PUB, Section 20, RelNPA, AssetGr, FeeRev, DD, LGDD, 1-TL,

LnAssets, Salary, Y97, Y98, Y99, Y00, Y01, Y02, Y03, Y04, Y05, Y06)

PE = our estimate of efficiency;

PUB = 1 if the BHC is publicly traded and 0 otherwise;

Section 20 = 1 if the BHC had previously had a Section 20 subsidiary and 0

otherwise;

7 DeYoung and Hasan (1998) provide a justification for this procedure. See page 580 of their article for an explanation.

12 RelNPA = the difference between the non-performing assets of the bank and the

average non-performing assets for the state;

AssetGr = the asset growth over the previous year;

FeeRev = total noninterest income divided by total revenue;

DD = demand deposits divided by total deposits;

LGDD = deposits over $100,000 divided by total deposits;

1-TL = one minus total loans divided by total assets;

LnAssets = log of total assets;

Salary = total salary and benefits as a percentage of total assets;

Y97, Y98, etc. = 1 if the year of the observation is in 1997, 1998, etc. and 0

otherwise.

The main independent variable of interest in the regression analysis is the indicator, labeled PUB. This is determined by hand matching the BHC data from the

Federal Reserve Bank of Chicago with data from CRSP to identify the publicly traded

As mentioned in the introduction, BHCs that are privately held may benefit from the connection of ownership and control, possible greater risk aversion leading to more profitable lending over time, the potential of a patriarchal approach toward their employees which may generate higher productivity through higher retention, and a relationship lending focus at smaller banks that allows for better bank monitoring of the borrower. These factors would lead to the conclusion that privately held banks would perform better, i.e., be more profit efficient, than publicly traded banks. Thus we expect a negative and significant coefficient on PUB.

13 There are also reasons why financial performance of private banks can be less favorable. For example, if it’s a family owned bank the family could dissipate resources on personal consumption or seeking political favoritism. Such effects may be short-lived if regulatory intervention is effective so we look at financial performance over a longer period of time. However, if these effects were strong enough, we may see a negative coefficient on PUB.

In addition to PUB, the following other independent variables are included to explain the efficiency measures. The log of total assets (LnAssets) are included in the regression to control for the effect of size on efficiency due to economies of scale, and non-performing assets to total assets relative to the state level of non-performing assets

(labeled RelNPA) is included as a measure of the relative quality of firm assets and risk exposure.8 If there is a size effect in relation to bank efficiency, LnAssets should have a positive and significant relationship. RelNPA should have a negative and significant relationship, since this variable indicates poor performance (i.e. impending default) on the bank’s portfolio of assets.

A dummy variable indicating if a BHC has previously had a Section 20 subsidiary is included as well. Section 20 subsidiaries allowed commercial banks to engage in underwriting activities limited to certain classes of securities and with caps on revenue generated by this activity prior to the Gramm-Leach-Bliley Act in 1999. The expectation is that if a bank previously had a Section 20 subsidiary that they should have “learned the

8 Empirical evidence, for example in Giradone, Molyneux, and Gardener (2004), exists that indicates that size does not have a relationship with efficiency. However, other research such as Berger, Hancock, and

Humphrey (1993) indicates that banks of different size do have measurable differences in efficiency. Since evidence points in both directions, size is included in this paper.

14 ropes” of the business before GLBA and therefore should be more efficient relative to other commercial banks without prior experience. It is included here since the data cover the years immediately before and after the act. The amount of demand deposits relative to total deposits (DD) is expected conjectured to increase profit efficiency since DD are a low cost source of funds. In contrast, the amount of large deposits (over $100,000) relative to total deposits (LGDD) is expected to have a negative coefficient due to the higher cost of these funds. However, if more uninsured funds at the bank create increased monitoring on the part of depositors, the result may be a positive and significant coefficient for LGDD.

Salary and benefits as a percentage of total assets (SALARY) is expected to have a negative coefficient because higher levels per dollar of asset make the BHC have higher costs relative to their peers. 1-TL, or one minus total loans, is meant to capture the quiet life hypothesis as discussed in Akhigbe and McNulty (2003). Briefly, this hypothesis suggests that some banks pick only the best highest reward-to-risk-ratio loans and do not lend to others. This results in a less than optimal portfolio of loans. If this hypothesis is true, we should expect the coefficient on 1-TL to be negative and significant.

Asset growth from one year to the next, AssGr, should have a negative sign. As

BHCs grow faster, the anticipated effect may be less efficiency as rapid growth is a more difficult process to manage than slow, consistent growth.

3. Data

Table 1 presents descriptive statistics for all BHCs gathered from form FRY-9C for 1996 through 2006. This data is graciously provided by the Federal Reserve Bank of

15 Chicago on their website. In total, there are 17,748 data points. For some of the variables used in later regressions some BHCs have missing data. If those BHCs with missing data are removed from the sample, the final sample size is 17,528. Each data point represents a single BHC in a single year. Thus BHCs appear more than once in the sample if they exist in more than one year from 1996 to 2006.

[Insert Table 1: Descriptive Statistics for BHC Characteristics here]

As Table 1 indicates, privately held BHCs are significantly different from publicly traded BHCs along many dimensions. First, while there is not a significant difference in ROE between BHC types, the ROA of private BHCs is significantly higher than that of public BHCs. However, while statistically significant, the difference is very small, 1.10% for private BHCs and 1.07% for public BHCs. Second, RelNPA is statistically significantly higher for private BHCs relative to public BHCs. This measures the risk of the BHCs assets relative to the risk of other BHCs in the state. Therefore, it appears that, as suggested in the introduction, private BHCs take less risk than their public counterparts. On average the differences appear small, but these averages could mask considerable differences at individual banks. Public BHCs earn a significantly higher portion of fee revenue relative to their total revenue. Private BHCs use a significantly higher proportion of demand deposits and large deposits over $100,000. 1-

TL is also significantly higher for private BHCs as well. This indicates that private

BHCs invest more of their assets in items other than loans relative to public BHCs.

However, the percentage difference is small with a mean for private BHCs of 36.91% and a mean for public BHCs of 36.32%. As expected, asset growth (AssGr) and total assets

16 for public BHCs are significantly higher. Lastly, salary and benefit expense/assets for private and public BHCs is about the same and is not significantly different.

Tables 2 and 3 provide an examination of profit efficiency by year and for the sample overall. The tests here show that, without controlling for other BHC characteristics, using ROA, privately held BHCs are more profit efficient than their publicly traded counterparts in nine of the eleven years. In Table 3, which uses ROE, the results are similar.

[Insert Table 2: Mean Prediction Error (PE) Comparison for Private and Public Traded

BHCs using ROE here]

[Insert Table 3: Mean Prediction Error (PE) Comparison for Private and Public Traded

BHCs using ROA here]

4. Empirical Results for OLS Regressions

4a. Results for the Whole Sample using ROE and ROA to Determine PE

Table 4 contains the regression results for the entire sample of 17,528 data points that have all data for variables used in the regression. (Since some firms are missing data required for some of the independent variables, the sample size used in the regressions is reduced from 17,748 to 17,562.) Of the variables included in the regression, several are highly significant including the main variable of interest PUB.

[Insert Table 4: Analysis of Profit Efficiency]

The first regression column in Table 4 contains results using the PE measures calculated using ROE. Importantly, the coefficient on PUB is negative (-0.00732) and is also highly significant with a t-statistic of -7.85. The coefficient suggests that profit efficiency would be about 0.73 percentage points higher at privately held BHCs (e.g.,

17 75.00% vs. 75.73%). This supports the principal hypotheses advanced in this paper.

Since privately held firms lack a separation of ownership and control, the incentives of the owner/manager are aligned and maximum effort can be given in the operation of the bank (i.e. there is less incentive for shirking). Also, stronger ties to clients/borrowers and greater risk aversion may contribute to this result as well.

RelNPA, or the amount of non-performing assets, is positive (coefficient =

0.36025) and significant (t-stat = 5.96). This indicates that BHCs who have taken on riskier loan portfolios have benefited in terms of higher profit efficiency. This result also seems to suggest that the better performance of privately held banks during this period may not be the result of a lower risk profile, such as a conservative loan portfolio. This result is certainly sensitive to the period chosen (1996-2006) when overall loan losses were at very low levels and bank profitability was at record high levels. A similar regression for another time period would no doubt show different results. In the 1980’s for example, many banks with high risk loan portfolios failed.

FeeRev, the amount of non-interest income relative to total revenue, is significant

(t-stat = 6.70) and positive (coefficient = 0.03855) indicating that fee revenue bolsters financial performance. On the other hand, DD (demand deposits to total deposits) and

LGDD (time deposits > $100,000 to total deposits) are both positive and significant with coefficients of 0.00810 and 0.02414 and t-stats of 1.71 and 5.84, respectively. The positive coefficient on DD indicates that having access to a low cost source of funds is important to BHC efficiency. The positive coefficient on LGD suggests that banks with substantial funds over the insurance limit may be monitored more closely by institutional

18 investors such as money market mutual funds, giving management increased motivation to perform well.

Salary is also significant with a coefficient of -0.79304 and a t-stat of -12.87 indicating that a higher burden of salary and benefits is a drag on profit efficiency. Asset growth (AssetGr) is significant with a coefficient of -0.01991 and a t-stat of -5.95 indicating that high growth rates hinder profit efficiency. Lastly, all but one of the year dummies are significant at the .01 level and negative, relative to the omitted year, 1996.

The second regression column in Table 4 contains the regression results for the entire sample of 17,528 data points using ROA to calculate PE. The frontier analysis using ROA is virtually identical to the results using ROE. This important robustness test adds to the importance of these results. The estimated difference between public and private BHC profit efficiency (about 0.67%) is also virtually identical in the two sets of tables.

4b. OLS Regressions Results for the Sample Disaggregated by Size

Tables 5 and 6 present separate regressions for BHCs above $10 billion in total assets in the first column, between $10 billion and $1 billion in column two, and less than

$1 billion in column three.

[Insert Table 5: Analysis of Efficiency as Measured by ROE: Firms Disaggregated by

[Insert Table 6: Analysis of Efficiency as Measured by ROA: Firms Disaggregated by

The most important result here is that the negative relation between PUB and profit efficiency. In Table 5 where PE is measured by ROE, the relation is negative and

19 significant across all sizes of BHC. For Table 6, where PE is measured by ROA, the only significant relationship is for small BHCs. In both tables, the difference between small public and private BHCs 5 is over 1%, substantially above the 0.72% result for the entire sample. In Table 5, the difference between large private and public BHCs is about 0.65% and the difference for medium BHCs is about 0.47%. These differences are less than the difference for the overall sample, but they are still statistically significant.

5. Conclusion

Depending on the whether a bank holding company (BHC) is privately or publicly held, the management of the BHC may have different incentives. This is the classic case of the separation of ownership and control. Firms that are privately held are often owned by managers and families who, due to their high equity stake, exert high effort.

Managers of publicly held firms often have no or atomistic equity holdings in the firm for which they work giving them less incentive to exert effort and raise the value of the firm’s equity.

The data in the empirical analysis of this paper shows that this lack of an equity incentive on the part of management is related to poorer performance for publicly held

BHCs compared to privately held BHCs. These findings are robust to different measures of profitability and seem to be particularly pronounced for small BHCs under $1 billion in assets.

20 Table 1 Descriptive Statistics for BHC Characteristics Privately Held BHCs Publicly Traded BHCs Mean Median Mean Median ROA 1.10%*** 1.05% 1.07%*** 1.09% [0.80%] [0.53%] ROE 12.23% 11.82% 12.03% 12.66% [7.06%] [7.00%] RelNPA 0.01%*** -0.13% -0.02%*** -0.11% [0.65%] [0.46%] AssetGr 8.60%*** 7.40% 11.30%*** 9.30% [10.71%] [12.30%] FeeRev 13.85%*** 11.70% 16.40%*** 14.05% [9.90%] [10.31%] DD 14.01%*** 13.03% 13.60%*** 12.82% [8.43%] [7.14%] LGDD 15.65%*** 13.74% 14.84%*** 12.87% [9.36%] [8.74%] 1-TL 36.84%** 35.33% 36.33%** 34.80% [13.29%] [11.91%] Total Assets $2,788,523*** $314,768 $16,113,678*** $1,089,283 [$28,898,872] [$90,814,191] Salary 1.67% 1.56% 1.67% 1.58% [0.89%] [0.60%] N 13,216 4,312 T-tests for each variable are between the mean for privately held and publicly traded BHCs and the associated standard deviations are in brackets below the mean for each variable. RelNPA is the amount of assets 90 days or more past-due or in non-accrual status divided by the amount of total assets. AssetGr is the change in assets found by subtracting the amount of assets in year t-1 from the amount of assets in year t with that amount being divided by the amount of assets in year t-1. FeeRev is the amount of non- interest income earned by the BHC as a portion of all revenue earned by the BHC. DD is the portion of all deposits that are demand deposits. LGDD is the dollar amount of time deposits over $100,000 relative to the entire pool of deposits. 1-TL is the total amount of assets less total loans divided by total assets. Salary is the dollar amount of salary and benefit expense per year divided by total assets. *** Indicates significance at the .01 level. ** Indicates significance at the .05 level. * Indicates significance at the .10 level.

21 Table 2 Mean Profit Efficiency (PE) Comparison for Private and Public Traded BHCs using ROE Year Privately Held BHCs Publicly Traded BHCs PE Std. Dev. N PE Std. Dev. N 1996 0.6531 0.0516 837 0.6507 0.0475 435 [0.6478] [0.6467] 1997 0.7522** 0.0612 871 0.7440** 0.0519 424 [0.7501] [0.7469] 1998 0.6586*** 0.0539 940 0.6498*** 0.0341 413 [0.6541] [0.6513] 1999 0.5990*** 0.0461 1,016 0.5905*** 0.0427 414 [0.5928] [0.5923] 2000 0.4963 0.0379 1,146 0.4947 0.0464 421 [0.4923] [0.4911] 2001 0.7403*** 0.0489 1,228 0.7332*** 0.0396 402 [0.7389] [0.7339] 2002 0.7037** 0.0348 1,370 0.6991** 0.0402 402 [0.6997] [0.7016] 2003 0.5732** 0.0346 1,566 0.5701** 0.0240 395 [0.5693] [0.5687] 2004 0.5481*** 0.0579 1,715 0.5364*** 0.0494 364 [0.5422] [0.5369] 2005 0.6799*** 0.0431 1,859 0.6693*** 0.0408 351 [0.6741] [0.6729] 2006 0.6697*** 0.0775 668 0.6576*** 0.0519 295 [0.6622] [0.6576] All Years 0.6431*** 0.0805 0.6360*** 0.0796 [0.6586] [0.6507] The mean PE measures in this table represent the performance of each BHC relative to a best practice frontier identified from the sample where the financial performance of the individual BHC is compared to the best practice frontier. Median PE values are given in brackets below the mean values for each year. The PE measures for the BHCs are created using a separate best practice frontier for each year. Values of the PE for each BHC may range from zero (least efficient) to one (most efficient). The “All Years” comparison is the mean of the individual years and the t-test is a pair-wise comparison between years.

*** Indicates a p-value difference between Private and Public BHCs of < .01 ** Indicates a p-value difference between Private and Public BHCs of < .05 * Indicates a p-value difference between Private and Public BHCs of < .10

22 Table 3 Mean Profit Efficiency (PE) Comparison for Private and Public Traded BHCs using ROA Year Privately Held BHCs Publicly Traded BHCs PE Std. Dev. N PE Std. Dev. N 1996 0.7467 0.0408 837 0.7460 0.0370 435 [0.7467] [0.7474] 1997 0.7194 0.0498 871 0.7151 0.0467 424 [0.7196] [0.7176] 1998 0.7158*** 0.0508 940 0.7081*** 0.0357 413 [0.7127] [0.7095] 1999 0.6790*** 0.0555 1,016 0.6708*** 0.0468 414 [0.6761] [0.6746] 2000 0.6585* 0.0374 1,146 0.6543* 0.0414 421 [0.6567] [0.6553] 2001 0.6453*** 0.0509 1,228 0.6384*** 0.0435 398 [0.6433] [0.6401] 2002 0.5760* 0.0429 1,370 0.5718* 0.0334 402 [0.5728] [0.5744] 2003 0.5294** 0.0436 1,566 0.5248** 0.0334 395 [0.5262] [0.5265] 2004 0.5889*** 0.0669 1,715 0.5766*** 0.0583 364 [0.5836] [0.5809] 2005 0.5215*** 0.0431 1,859 0.5120*** 0.0382 351 [0.5159] [0.5153] 2006 0.6795** 0.0697 668 0.6711** 0.0474 295 [0.6721] [0.6686] All Years 0.6418*** 0.0774 0.6353*** 0.0786 [0.6585] [0.6543] The mean PE measures in this table represent the performance of each BHC relative to a best practice frontier identified from the sample where the financial performance of the individual BHC is compared to the best practice frontier. Median PE values are given in brackets below the mean values for each year. The PE measures for the BHCs are created using a separate best practice frontier for each year. Values of the PE for each BHC may range from zero (least efficient) to one (most efficient). The “All Years” comparison is the mean of the individual years and the t-test is a pair-wise comparison between years.

*** Indicates a p-value difference between Private and Public BHCs of < .01 ** Indicates a p-value difference between Private and Public BHCs of < .05 * Indicates a p-value difference between Private and Public BHCs of < .10

23 Table 4 Analysis of Profit Efficiency (PE) PE Measured by ROE PE Measured by ROA Intercept 0.66437*** 0.76241*** [126.69] [142.22] PUB -0.00732*** -0.00668*** [-7.85] [-7.01] Section 20 0.000351 0.00170 [0.11] [0.51] RelNPA 0.36025*** 0.15964*** [5.96] [2.58] AssetGr -0.01991*** -0.03085*** [-5.95] [-9.03] FeeRev 0.03855*** 0.04302*** [6.70] [7.32] DD 0.00810* 0.01021** [1.71] [2.12] LGDD 0.02414*** 0.02189*** [5.84] [5.18] 1-TL -0.00550* -0.00700** [-1.86] [-2.32] LnAssets -0.000102 -0.000175 [-0.28] [-0.48] Salary -0.79304*** -0.91764*** [-12.87] [-14.56] Y97 0.09718*** -0.02847*** [52.06] [-14.92] Y98 0.00365** -0.03272*** [1.97] [-17.30] Y99 -0.05669*** -0.07086*** [-30.96] [-37.86] Y00 -0.15767*** -0.09025*** [-87.56] [-49.03] Y01 0.08444*** -0.10468*** [47.26] [-57.31] Y02 0.04770*** -0.17421*** [26.99] [-96.43] Y03 -0.08352*** -0.22209*** [-47.63] [-123.90] Y04 -0.11040*** -0.16400*** [-63.61] [-92.43] Y05 0.02175*** -0.23067*** [-12.63] [-131.05] Y06 0.01058*** -0.07258*** [5.03] [-33.73]

24 N 17,528 17,528 R2 0.7301 0.7102 F-Stat 2,368.02 2,145.40 F-Stat Probability <.0001 <.0001 Coefficients from the OLS regression are presented with their corresponding t-statistics in brackets. PUB equals one if the BHC is publicly traded and zero if the BHC is privately held. Section 20 indicates if the BHC had a Section 20 subsidiary prior to the passage of the Gramm-Leach Bliley Act in 1999. RelNPA is the amount of assets 90 days or more past-due or in non-accrual status divided by the amount of total assets. AssetGr is the change in assets found by subtracting the amount of assets in year t-1 from the amount of assets in year t then dividing that result by the amount of assets in year t-1. FeeRev is the amount of non-interest income earned by the BHC as a portion of all revenue earned by the BHC. DD is the portion of all deposits that are demand deposits. LGDD is the dollar amount of time deposits over $100,000 relative to the entire pool of deposits. 1-TL is the total amount of assets less total loans divided by total assets. LnAssets is the log of total assets. Salary is the dollar amount of salary and benefit expense per year divided by total assets. *** Indicates significance at the .01 level. ** Indicates significance at the .05 level. * Indicates significance at the .10 level.

25 Table 5 Analysis of Efficiency as Measured by ROE: Firms Disaggregated by Size TA > $10B $10B > TA > $1B TA < $1B Intercept 0.68131*** 0.69652*** 0.66567*** [27.91] [32.60] [54.97] PUB -0.00648** -0.00466*** -0.01009*** [-2.18] [-2.62] [-8.28] Section 20 0.00656* -0.000491 -0.02498*** [1.86] [-0.05] [-2.62] RelNPA 0.37106 0.34363 0.37356*** [1.11] [1.64] [5.74] AssetGr -0.00972 -0.03210*** -0.01723*** [-1.09] [-4.83] [-4.02] FeeRev -0.02017 0.06217*** 0.04374*** [-1.38] [4.83] [6.03] DD 0.01537 0.00603 0.00728 [0.81] [0.54] [1.31] LGDD -0.01856 0.05395*** 0.02065*** [-1.61] [6.28] [4.02] 1-TL 0.00501 0.01899*** -0.01162*** [0.49] [2.71] [-3.34] LnAssets -0.00209 -0.00280* 0.0000296 [-1.52] [-1.95] [0.03] Salary 0.53368** -1.07354*** -0.83321*** [2.05] [-7.93] [-11.37] Y97 0.10116*** 0.09586*** 0.09705*** [16.52] [21.69] [44.86] Y98 0.00763 0.00080 0.00386* [1.23] [0.18] [1.81] Y99 -0.05213*** -0.06187*** -0.05618*** [-8.28] [-14.08] [-26.59] Y00 -0.14673*** -0.15851*** -0.15870*** [-23.46] [-36.66] [-76.36] Y01 0.08965*** 0.07857*** 0.08466*** [14.43] [18.10] [41.12] Y02 0.05481*** 0.04230*** 0.04766*** [8.89] [9.68] [23.43] Y03 -0.07389*** -0.08813*** -0.08387*** [-11.77] [-20.50] [-41.46] Y04 -0.09158*** -0.11702*** -0.11080*** [-14.47] [-27.62] [-55.30] Y05 0.03442*** 0.01634*** 0.02154*** [5.49] [3.90] [10.82] Y06 0.02323*** 0.00701* 0.00885*** [3.45] [1.66] [3.25]

26 N 924 3,061 13,543 R2 0.7928 0.7172 0.7314 F-Stat 172.77 385.55 1,840.62 F-Stat Probability <.0001 <.0001 <.0001 Coefficients from the OLS regression are presented with their corresponding t-statistics in brackets. The first column includes BHCs with total assets in excess of $10 billion. The second column includes BHCs with total assets between $10 billion and $1 billion. The third column includes BHCs with total assets less than $1 billion. PUB equals one if the BHC is publicly traded and zero if the BHC is privately held. Section 20 indicates if the BHC had a Section 20 subsidiary prior to the passage of the Gramm-Leach Bliley Act in 1999. RelNPA is the amount of assets 90 days or more past-due or in non-accrual status divided by the amount of total assets. AssetGr is the change in assets found by subtracting the amount of assets in year t-1 from the amount of assets in year t and dividing that result by total assets in year t-1. FeeRev is the amount of non-interest income earned by the BHC as a portion of all revenue earned by the BHC. DD is the portion of all deposits that are demand deposits. LGDD is the dollar amount of time deposits over $100,000 relative to the entire pool of deposits. 1-TL is the total amount of assets less total loans divided by total assets. LnAssets is the log of total assets. Salary is the dollar amount of salary and benefit expense per year divided by total assets. *** Indicates significance at the .01 level. ** Indicates significance at the .05 level. * Indicates significance at the .10 level.

27 Table 6 Analysis of Efficiency as Measured by ROA: Firms Disaggregated by Size TA > $10B $10B > TA > $1B TA < $1B Intercept 0.78505*** 0.79619*** 0.75091*** [29.92] [37.11] [60.57] PUB -0.00467 -0.00174 -0.01061*** [-1.46] [-0.98] [-8.50] Section 20 0.00835** -0.00260 -0.01388 [2.20] [-0.26] [-1.42] RelNPA 0.34072 0.28054 0.14706** [0.95] [1.34] [2.21] AssetGr -0.00396 -0.04197*** -0.03272*** [-0.04] [-6.30] [-7.45] FeeRev -0.01127 0.05804*** 0.05441*** [-0.72] [4.49] [7.32] DD 0.02416 0.00834 0.00922 [1.18] [0.75] [1.62] LGDD -0.02428* 0.05646*** 0.01745*** [-1.95] [6.55] [3.32] 1-TL 0.00920 0.01751** -0.01379*** [0.83] [2.49] [-3.87] LnAssets -0.00251* -0.00311** 0.00101 [-1.71] [-2.16] [1.06] Salary 0.04257 -1.19878*** -0.94706*** [0.15] [-8.82] [-12.62] Y97 -0.02556*** -0.02801*** -0.02906*** [-3.88] [-6.31] [-13.12] Y98 -0.03061*** -0.03341*** -.03303*** [-4.57] [-7.54] [-15.09] Y99 -0.06564*** -0.07421*** -0.07099*** [-9.70] [-16.82] [-32.82] Y00 -0.08301*** -0.09275*** -0.09080*** [-12.35] [-21.37] [-42.68] Y01 -0.10056*** -0.10947*** -0.10484*** [-15.06] [-25.12] [-49.74] Y02 -0.16661*** -0.17801*** -0.17497*** [-25.16] [-40.58] [-84.01] Y03 -0.21276*** -0.22444*** -0.22344*** [-31.54] [-52.00] [-107.90] Y04 -0.14640*** -0.17078*** -0.16494*** [-21.52] [-40.14] [-80.41] Y05 -0.21831*** -0.23437*** -0.23170*** [-32.41] [-55.74] [-113.70] Y06 -0.05964*** -0.07497*** -0.07565*** [-8.24] [-17.67] [-27.17]

28 N 924 3,061 13,543 R2 0.7665 0.7157 0.7071 F-Stat 148.18 382.64 1,632.36 F-Stat Probability <.0001 <.0001 <.0001 Coefficients from the OLS regression are presented with their corresponding t-statistics in brackets. The first column includes BHCs with total assets in excess of $10 billion. The second column includes BHCs with total assets between $10 billion and $1 billion. The third column includes BHCs with total assets less than $1 billion. PUB equals one if the BHC is publicly traded and zero if the BHC is privately held. Section 20 indicates if the BHC had a Section 20 subsidiary prior to the passage of the Gramm-Leach Bliley Act in 1999. RelNPA is the amount of assets 90 days or more past-due or in non-accrual status divided by the amount of total assets. AssetGr is the change in assets found by subtracting the amount of assets in year t-1 from the amount of assets in year t and dividing that result by total assets in year t-1. FeeRev is the amount of non-interest income earned by the BHC as a portion of all revenue earned by the BHC. DD is the portion of all deposits that are demand deposits. LGDD is the dollar amount of time deposits over $100,000 relative to the entire pool of deposits. 1-TL is the total amount of assets less total loans divided by total assets. LnAssets is the log of total assets. Salary is the dollar amount of salary and benefit expense per year divided by total assets. *** Indicates significance at the .01 level. ** Indicates significance at the .05 level. * Indicates significance at the .10 level.

29 REFERENCES

Akhigbe A, McNulty JE (2003) The Profit Efficiency of Small U.S. Commercial Banks. Journal of Banking and Finance 27: 307-325

Anderson RC, Reeb DM (2003) Founding-Family Ownership and Firm Performance: Evidence from the S&P 500. Journal of Finance 63: 1301-1328

Bae K, Kang J, Lim CW (2002) The Value of Durable Bank Relationships: Evidence from Korean Banking Shocks. Journal of Financial Economics 64: 181-214

Bauer PW, Berger AN, Ferrier GD, Humphrey DB (1998) Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods. Journal of Economics and Business 50: 85-114

Berger A, Hancock D, Humphrey D (1993) Bank efficiency derived from the profit function. Journal of Banking and Finance 17: 317-347

Berger AN, Mester LJ (1997) Inside the black box: What determines differences in the efficiency of financial institutions. Journal of Banking and Finance 21: 895- 947

Clark JA, Siems TF (2002) X-Efficiency in Banking: Looking beyond the Balance Sheet. Journal of Money, Credit and Banking 30: 987 – 1,013

DeYoung R, Hunter WC, Udell GF (2004) The Past, Present and Probable Future for Community Banks. Journal of Financial Services Research 25: 85-134

DeYoung R, Hasan I, (1998) The performance of de novo commercial banks: A profit efficiency approach. Journal of Banking and Finance 22: 565-587

DeYoung R, Spong W, Sullivan RG (2000) Who’s Minding the Store? Motivating and Monitoring Hired Managers at Small, Closely Held Commercial Banks. Journal of Banking and Finance 26: 1209-1243

DeYoung R (2007) Corporate Governance at Community Banks: One Size Does Not Fit All. In Gup (2007) Corporate Governance of Banks. Edward Elgar.

Giradone C, Molyneux P, Gardener EPM (2004) Analyzing the determinants of bank efficiency: the case of Italian banks. Applied Economics 36: 215 – 227

Hughes JP, Lang W, Mester LJ, Moon C (1996a) Efficient banking under interstate banking. Journal of Money, Credit, and Banking 28: 1045 – 1071

30 Hughes JP, Lang W, Mester LJ, Moon C (1996b) Safety in numbers? Geographic diversification and insolvency risk. Proceedings, Federal Reserve Bank of Chicago: 202 – 218

Hughes JP, Lang W, Mester LJ, Moon C (1997) Recovering risky technologies using the almost ideal demand system: An application to U.S. banking. Journal of Financial Services Research 18: 5 – 27

Kwan S (2004) Risk and Return of Publicly Held vs. Privately Owned Banks. Economic Policy Review, Federal Reserve Bank of New York September: 97-107

Lieu P, Yeh T, Chiu Y (2005) Off-Balance Sheet Activities and Cost Inefficiency in Taiwan’s Banks. The Services Industry Journal 25: 925 – 944

Myers F, Padget J (2004) Corporate Governance: Where Do Tenth District Community Banks Stand? Financial Industry Perspectives, Federal Reserve Bank of Kansas City Fourth Quarter: 39-56.

McAllister PH, McManus D (1993) Resolving the scale efficiency puzzle in banking. Journal of Banking and Finance 17: 389-405

Mitchell K, Onvurall NM (1996) Economies of scale and scope at large commercial banks: Evidence from the Fourier flexible functional form. Journal of Money, Credit and Banking 28: 178-199

Rose PS (2003) Commercial Bank Management. McGraw Hill-Irwin

Spong KR, Sullivan RJ (2007a) Corporate Governance and Bank Performance. In Gup (2007) Corporate Governance of Banks. Edward Elgar

Spong KR, Sullivan RJ (2007b) Manager Wealth Concentration, Ownership Structure and Risk in Commercial Banks. Journal of Financial Intermediation 16: 229-248

Stiroh KJ (2000) How did bank holding companies prosper in the 1990s? Journal of Banking and Finance 24: 1703 – 1745