A-1

Appendix 1: An Exploratory Analysis of Individual Bank Performance

The body of this paper, and the accompanying tables, describe in detail differences in the

regulatory regimes in the EU and G-10 countries. In some cases the differences are relatively

minor, whereas in other cases they are quite significant. In particular, the U.S. appears to be

“out-of-step” in a number of respects with the majority of the other industrial countries examined.

As a first step in addressing whether the differences in regulatory regimes that have been

identified in these countries significantly affect the performance of individual banks, an

exploratory empirical analysis was conducted. Although our main aim in this appendix is to

suggest some potentially fruitful avenues for future research, it is interesting to note how our

results may add information to the debate on the range of powers permissible to banks.

The primary focus of inquiry is on the effects of restricted versus unrestricted banking

activities on bank performance. There are two basic points of view. The banking “industry”

position is that allowing banks a broader range of permissible activities will allow them to realize

economies of scope (and, possibly, scale), which in turn will lead to lower prices and higher

profitability. Diversifying into a broader range of activities, moreover, could result in less

overall risk for a bank, as downturns in the demand for one product line are offset by increases

in demand for another. A second point of view, which can be called the “overreaching” position,

is that broadening the range of activities in which banks are permitted to engage will not result

in either economies of scope or scale, and hence neither higher profits nor reduced risk. Instead,

banks will tend to add product lines without being able to integrate them adequately into the

overall operation of the organization, and/or inexpert management of new product lines will

detract from proper management of traditional lines of business.
(1) yi

' $0% E

m

j'1

$jXij % E

n

j'm%1

$jDij % ,i,

where E,i' 0, E,i, ́

i ́

' F

2

if i'i ́, E,i,i ́' 0 if i ... i ́,

and i indexes banks, and j indexes the explanatory variables.

The construction of proxies used in studies of bank performance is subject to well-known issues, 57

primarily related to unavailable data and thus the appropriateness of relying on available data. For a

recent discussion of such issues, and their attendant implications for analysis, see Berger and Mester

(forthcoming). The focus here is on examining the profitability aspect of the range of the desired

dimensions of bank performance, in part because there is an extensive literature upon which to draw for

guidance, and in part because of harsher limitations across countries in suitable data for constructing

standard proxies for other relevant measures of bank performance. 

A-2

In order to test which of these two viewpoints is the most accurate, ideally one would like

to assess the effect of various banking activities on measures of scope and scale economies and,

more generally, measures of profitability and risk. As a first step, however, and in keeping with

the exploratory nature of the analysis, the focus here is on investigating possible influences on

bank profitability only from a standard set of bank-specific explanatory variables along with new

variables taking account of cross-country differences in the regulatory environment in which

banks operate.57

The specific analysis conducted in an attempt to provide information useful in assessing

the different points of view is based upon an earlier study of individual bank performance across

selected countries by Barth, Gropper and Jahera (forthcoming), which in turn was largely

motivated by a study of U.S. individual bank performance by Berger (1995). In particular, four

alternative specifications of the following general model are estimated:

Rate-of-return on capital (ROE) is included in the model as the measure of performance

(i.e., profitability) of individual banks and therefore is the dependent variable (y). This is a
For examples of relatively early studies using this performance measure, see Bartholomew (1995) and Short 58

(1979). Gilbert (1984) provides a comprehensive survey of the earlier bank performance studies.

A-3

commonly employed, albeit criticized, measure of performance in the banking literature.58

Berger (1995, p. 440), for example, states that his “goal is to explain the effect of capital on

earnings (measured in equations with ROE as the dependent variable).” Barth, Gropper and

Jahera (forthcoming), like Berger (1995), also employ ROE as the dependent variable. Still other

studies employ this same variable as a measure of bank performance, but sometimes as one of

two or more alternative performance measures. This is the case in the studies by Swamy, Barth,

Chou and Jahera (1996), Goldberg and Rai (1996), Gorton and Rosen (1995), and Schranz

(1993), among others. In many cases, the empirical results are essentially unchanged with respect

to the particular measure of bank performance chosen, especially as between ROE and ROA

(rate-of-return on assets).

Four alternative combinations of explanatory variables (X’s and D’s) are also included in

the empirical model. The general approach to the specifications of the model is to take

progressively into account bank-specific variables (X’s), country-specific macroeconomic

variables (X’s) (since the banks being analyzed are located in different countries), and regulatoryspecific variables (D’s) in explaining bank performance. The bank-specific variables (X’s) are

total assets, equity-capital-to-asset ratio, loan-to-asset ratio, non-interest-expense-to-net-revenue

ratio, and three-bank concentration ratio (although this latter variable is really a banking-wide,

country-specific variable). These are commonly employed explanatory or control variables in

many bank performance studies. In particular, in a study of performance for a sample of banks

in four European countries, Molyneux, Lloyd-Williams and Thornton (1992) include a capital-to-
Danton (1992) and Frankel and Montgomery (1991), use similar sample sizes. For each country, the banks 59

chosen for the sample account for a significant minority, or a substantial majority, of the banking assets in that

country.

A-4

asset ratio and a loan-to-asset ratio to account for bank-specific risk, on the grounds that their

dependent variable (total interest revenue 

per dollar of assets) is not risk-adjusted. In a similar vein, Samolyk (1994, p. 7) states that

“Differences in loan/asset ratios and bank capitalization are important factors in assessing the relative

profitability and risk of banks.” The other bank-specific variables employed here are found in other

studies of bank performance. There are numerous studies of performance and very few, if any,

employ the exact same set of bank-specific or, more generally, control variables. The

macroeconomic variables (X’s) are the percentage change in the GDP deflator, the percentage change

in real GDP, and the long-term interest rate minus the short-term rate. The regulatory variables are

dummy variables (D’s) indicating whether specific bank activities are unrestricted or not. In addition,

two dummy variables (D’s) are included to take into account whether the deposit-insurance scheme

in a particular country is funded ex post or ex ante and whether an individual bank is located in an

EU country or not. As in the case of the measure of performance employed here, future work should

consider alternative explanatory variables, as well as alternative estimation techniques to ordinary

least squares.

The individual bank data used in estimating the empirical model were obtained from Bank

Scope (IBCA) and cover the 19 separate EU and G-10 countries. The sample of 142 banks

consists of the largest 5 to 10 banks in each of these countries. The cross-sectional analysis is 59
Austria, Finland and Sweden joined the EU in 1995, and therefore are not counted as member countries of the 60

EU in the regression analysis. It should also be noted that future work should broaden both the analysis by

including other years and the coverage by including other countries.

A-5

for 1993. Table A.1.a presents information on the number of banks from each country and on 60

the mean values of the variables included in the model. The mean values for other bank-specific

variables are
In this regard, it is important to note that there are differences in accounting practices across countries. 61

Yet, no source appears to exist that would allow one to adjust for different accounting conventions across

countries.

For further discussion of some of these and other issues in the broader context of potential econometric 62

problems and a potential resolution strategy, see Swamy, Barth, Chou and Jahera (1996).

A-6

 included for informational purposes only. Clearly, there is substantial variation in the mean

values of these variables across countries.

All of the bank-specific variables are ex post, accounting measures, which is also the case

in many other studies of bank performance, including those studies that focus exclusively on

assessing differences in the efficiency, or inefficiency, among individual banks. In this regard, 61

Boyd and Gertler (1993, p. 19) state that “clean market value assessments of the overall portfolio

are unavailable.” Gorton and Rosen (1995), moreover, state that “we would like... to provide

evidence of the ex ante...return...[but] it is not possible to determine what bank managers think

the expected return...is.” Schranz (1993, p. 307) simply states that “an accounting measure of

profitability...is used rather than a market measure such as the rate of return on a firm’s common

stock because risk-adjusted rates of return should be equivalent across regulatory regimes if

markets are efficient.” More generally Rhoades (1995, p. 22) states that “At a conceptual level,

there is merit to criticism of accounting profits for measuring economic concepts associated with

resource allocation notions. However, such criticism may be of dubious importance in practice

and especially when analyzing cross sections of firms in a single industry.” In any event, the goal

here is not to resolve all these issues, but instead simply to specify an empirical model that

generally comports with many of the previous bank performance studies, except for the inclusion

of regulatory variables.62
A-7

The empirical results of estimating the model given by equation (1) are reported in Table

A.1.b. In the first specification, only country dummy variables are included, with the U.S. the

excluded country. In half of the countries, the estimated coefficients are negative and statistically

significant, indicating that individual banks in those countries performed, on average, worse than

banks in the U.S. Although these results do indicate there are differences in individual bank

performance across countries, they provide no clear information as to which macroeconomic,

bank and regulatory factors account for this situation.

The second specification replaces the country dummy variables with only the five bankspecific variables. In this case, two of the variables are negative and statistically significant: the

loan-to-asset ratio and the non-interest-expense-to-net-revenue ratio. The latter variable is a

rough measure of efficiency and therefore would be expected to be negatively related to ROE.

Given all the banking problems in recent years it is not unexpected to find that the greater the

percentage of a bank’s assets in loans, the worse that bank’s performance. This latter finding,

moreover, is consistent with some of the results for the sample of European banks reported by

Molyneux, Lloyd-Williams and Thornton (1992).

The third specification adds the macroeconomic variables to the bank-specific variables.

In this case, three of the bank-specific variables are now statistically significant and all have

negative signs. Two of these variables perform the same as in the previous specification. The

additional significant variable is the equity capital-to-asset ratio. This particular variable has a

negative effect on ROE, which is essentially the same result reported in Barth, Gropper and

Jahera (forthcoming). With respect to the macroeconomic variables, only the percentage change

in real GDP enters significantly, and with an expected positive sign.
Note that the regulatory dummy variables capture whether or not respective banking powers are available to 63

banks in a given country; they are not continuous variables measuring the extent to which each bank in the sample

exploited the powers available to it.

A-8

The fourth and last specification adds the regulatory variables both to the bank-specific

and to the macroeconomic variables. In addition, a dummy variable for EU membership is

63

included to account for the substantial interrelatedness of activities in the member countries. In

this case, the results for the bank-specific variables are essentially unchanged. With respect to

the macroeconomic variables, the percentage change in the GDP deflator is now significant and

positive as expected, in addition to the percentage change in real GDP retaining its significantly

positive sign. The EU membership variable is negative and statistically significant, indicating

that, on average, the EU-banks under performed relative to non-EU banks in 1993. All of the

regulatory variables are statistically insignificant based upon a two-tailed test. However, if one

argues that the regulatory authorities should be most concerned about negative effects on

performance, then a one-tailed test might be appropriate. In this case, one of the regulatory

variables is both positive and statistically significant. In particular, the results indicate that

unrestricted insurance activities have a significantly positive effect on ROE.

Despite the fact that the securities, insurance, real estate, and ownership variables when

considered individually are not significant (perhaps due to multicolinearity), it nevertheless is

possible that these “activities” variables taken together could have a significant effect on bank

performance. To determine whether or not this is indeed the case, an F-test of the joint

significance of the five “activities” variables was conducted. Based upon this test, one cannot

reject the null hypothesis that the variables as a group have no impact. However, when one

considers just the
In an examination of securities activities, Benston (1990, p. 162) concludes that “...there is no reason to believe 64

that banks would be more likely to become insolvent or in any way put additional strains on the federal ‘safety

net’ were the Glass-Steagall Act repealed. Whether the banking industry would become much more profitable,

and hence safer, also is doubtful. Banks and their customers probably would make some gains from economies of

scope as banking and securities operations were conducted together.” Also, Litan (1992, p. 542) concludes that

“Broader interstate authority, coupled with broader product-line freedom with suitable safeguards, clearly would

reduce the risks associated with banking. It is less likely, however, that it would enhance the industry’s average

profitability, since most of the cost savings would be passed on in the form of lower prices of financial services

for consumers.” Furthermore, in a study more closely in the spirit of this paper, Steinherr and Huveneers (1994,

p. 305) test “...with a set of 88 banks in 18 countries the effect of regulatory and other structural variables on the

level and variability of bank performance.” They “...conclude that universal banks indeed achieve a better riskreturn tradeoff.” More specifically, they find that “Economies of scope are not visible...”, but the variability of

income is reduced. For other studies of these issues, see Boyd, Graham and Hewitt (1993), Brewer (1989),

Kroszner and Rajan (1994), Kwast (1989), Mote (1992), Puri (1994), Rosen, Lloyd-Davies, Kwast and Humphrey

(1989), and Wall (1987).

A-9

securities and insurance variables, the test results indicate that they are jointly significant at the

5 percent level. This latter finding provides very limited support for the position that loosening

the restrictions on banking activities with respect to two important financial services securities

and insurance underwriting, brokering, and dealing might enhance bank performance.

The exploratory empirical analysis conducted here fails to find any empirical evidence

that unrestricted securities, insurance, and real estate activities adversely, or positively, affect

bank performance. Nor do the empirical results indicate that broad powers with respect to bank

investment in nonfinancial firms, and nonfinancial firm investment in banks adversely, or

positively, affect performance. However, the joint significance of the securities and insurance

variables provides weak support for the “industry” claim that allowing banks a broader range of

permissible activities might lead to improved performance.64

Overall, while neither the “industry” nor the “overreaching” positions receives much

support from the regression analysis, the results are consistent with what can be characterized as

a third
According to Federal Reserve Bank of Cleveland President Jerry L. Jordan, “Banking companies should not be 65

required to get permission from regulators before doing something new. Rather, they should notify authorities of

their intentions. If regulators want to prevent the action, the burden should be on them to intervene in a timely

way to demonstrate that the costs exceed the benefits” (see Jordan (1996)).

A-10

 viewpoint on permissible banking powers. That “marketplace” view holds that broader activities

for banks assure neither improved, nor diminished, performance. This position, in turn, is

consistent with a public policy stance advocating reduced restrictions on banks, whose

performance would then be determined more by market forces and less by regulatory measures.65

Of course, extreme caution must be observed in drawing any policy implications from this

exploratory and tentative empirical analysis. Nonetheless, the analysis could serve as a useful

point of departure for further studies of bank performance seeking to take account of possible

effects of regulatory variables.
Table A.1.a

Sample of Large Banks in the EU and G-10 Countries: 1993

(percent, except as noted)

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg

Number of Banks 6 8 8 6 7 7 5 8 6 10

ROA 0.49 0.35 0.47 (0.75) 0.06 0.29 1.14 0.79 0.13 0.61

ROE 11.57 11.76 9.53 (16.21) 1.93 8.99 29.68 13.76 2.81 19.48

Net Interest Margin 2.40 1.76 3.06 1.62 1.45 1.80 2.14 4.70 2.40 0.93

Noninterest Expense-to-Asset Ratio 83.21 67.38 64.06 73.89 75.45 60.64 74.21 65.28 70.21 33.04

Equity Capital-to-Asset Ratio 4.22 2.99 4.78 4.34 3.18 3.19 3.28 5.94 4.62 3.18

Tier 1 Capital-to-Total Asset Ratio NA 0.08 2.75 3.91 2.36 NA NA 3.11 2.89 NA

Profits Before Tax-to-Total Asset Ratio 0.37 0.51 0.67 (0.75) 0.16 0.55 0.80 0.85 0.50 0.87

Total Assets (mil USD) 73,089 289,072 119,875 99,600 1,220,144 1,073,028 47,825 63,654 553,663 149,729

Off-Balance Sheet Items-to-Asset Ratio 5.23 103.97 9.96 18.37 32.90 20.49 10.04 5.27 12.08 13.27

Loans-to-Asset Ratio 53.00 34.60 47.83 52.93 41.49 65.78 28.14 58.19 53.54 24.86

Other Earning Assets-to-Asset Ratio 42.89 60.24 47.68 35.78 49.41 29.99 63.70 32.91 36.71 72.32

Fixed Assets-to-Asset Ratio 5.09 9.70 1.71 2.31 1.23 0.99 1.60 2.16 2.09 4.31

Non-Earning Assets-to-Asset Ratio 3.60 41.78 2.78 8.98 7.87 3.24 6.55 6.74 7.66 2.39

Customer and Short Term 

Funding-to-Asset Ratio

78.24 90.20 86.63 66.81 80.52 47.16 87.57 84.97 77.55 89.09

Other Funding-to-Asset Ratio 14.88 2.45 5.03 19.77 6.77 46.04 2.74 2.85 8.92 4.22

Other (Non-Interest Bearing)-to-Asset

Ratio

2.57 4.34 3.57 8.62 9.01 3.04 6.41 6.25 8.59 3.51

Loan Loss Reserves-to-Asset Ratio NA NA 2.09 NA 0.53 NA 0.94 1.69 1.27 NA

Loan Loss Provisions-to-Asset Ratio NA 3.78 1.11 1.72 0.63 0.51 0.27 0.68 0.54 2.80

Net-Charge Offs-to-Asset Ratio NA NA 1.11 NA 0.13 NA NA 0.84 NA NA

Hybrid Capital-to-Asset Ratio 8.06 NA 2.16 NA 0.51 0.49 NA 0.86 NA 0.05

Subordinated Debts-to-Asset Ratio NA 2.18 0.83 3.55 1.69 1.25 NA 1.79 1.00 1.18

Non-Performing Loans-to-Asset Ratio NA NA 0.19 3.41 0.26 NA NA 1.33 1.91 NA

Intangibles-to-Asset Ratio NA 0.09 0.03 0.07 0.28 0.00 0.08 NA 0.30 NA
Table A.1.a (continued)

Netherlands Portugal Spain Sweden United Canada Japan Switzerland U.S.

Kingdom

Number of Banks 6 7 9 8 9 8 10 7 10

ROA 0.41 0.79 (0.42) 0.11 0.51 0.48 0.12 0.73 1.13

ROE 10.33 13.01 (7.66) 2.68 12.47 8.77 3.29 10.96 16.57

Net Interest Margin 2.12 4.36 2.94 2.18 2.81 3.05 1.30 1.47 3.73

Noninterest Expense-to-Asset Ratio 70.11 61.34 65.13 48.32 63.17 62.86 61.99 61.74 67.66

Equity Capital-to-Asset Ratio 4.11 5.97 4.68 4.38 4.08 5.46 3.82 6.55 7.14

Tier 1 Capital-to-Total Asset Ratio NA 1.21 4.30 3.43 2.68 4.98 3.80 NA 6.85

Profits Before Tax-to-Total Asset Ratio 0.60 0.94 0.14 0.08 0.77 0.26 0.26 0.91 1.59

Total Assets (mil USD) 396,654 72,002 385,681 209,066 986,840 495,759 3,547,841 524,332 840,754

Off-Balance Sheet Items-to-Asset Ratio 16.55 16.89 5.82 8.09 33.92 55.80 10.58 0.29 NA

Loans-to-Asset Ratio 57.65 39.10 40.56 69.86 56.25 71.17 68.67 51.75 54.17

Other Earning Assets-to-Asset Ratio 36.20 43.24 50.73 22.93 32.94 23.25 27.56 39.97 30.90

Fixed Assets-to-Asset Ratio 1.45 3.67 2.81 2.03 1.60 1.22 0.70 2.37 2.37

Non-Earning Assets-to-Asset Ratio 4.70 14.02 5.89 5.18 9.21 4.37 3.04 5.90 12.56

Customer and Short Term 

Funding-to-Asset Ratio

83.82 85.75 86.97 54.62 80.76 83.14 87.71 75.06 66.27

Other Funding-to-Asset Ratio 7.10 2.17 3.36 33.49 4.73 2.41 4.22 10.03 18.40

Other (Non-Interest Bearing)-to-Asset

Ratio

4.54 3.80 3.64 7.40 10.43 8.99 3.37 6.64 8.19

Loan Loss Reserves-to-Asset Ratio NA 1.47 2.50 2.01 2.44 2.01 0.74 1.45 1.80

Loan Loss Provisions-to-Asset Ratio 0.35 0.70 1.31 1.66 0.86 (0.06) 0.28 0.67 0.62

Net-Charge Offs-to-Asset Ratio NA 0.40 0.45 1.69 0.86 0.59 0.05 1.48 0.67

Hybrid Capital-to-Asset Ratio NA NA NA 4.30 1.32 0.23 1.99 NA NA

Subordinated Debts-to-Asset Ratio 1.98 1.33 1.12 2.91 1.52 2.18 14.61 1.07 NA

Non-Performing Loans-to-Asset Ratio NA 2.38 1.78 5.34 4.62 1.92 NA NA 1.67

Intangibles-to-Asset Ratio NA 0.32 0.22 0.04 NA 0.18 NA 0.02 0.56

SOURCE: BankScope, IBCA.
Table A.1.b

Alternative Regression Specifications for ROE

Explanatory Variables 1 2 3 4

Intercept 18.1645*** 32.3261*** 27.3881*** 23.7655***

(4.918) (6.899) (5.116) (3.018)

Bank Size, Loan Share, Efficiency and Concentration

Assets -0.0000 0.0000 0.0000

(-0.895) (0.066) (0.628)

Equity Capital-to-Asset Ratio -0.1194 -0.2174* -0.2226*

(-0.943) (-1.708) (-1.695)

Loan-to-Asset Ratio -0.1194*** -0.1711*** -0.1694***

(-2.964) (-2.930) (-2.778)

Non-Interest Expense-to-Net -0.1543*** -0.1674*** -0.2046***

Revenue Ratio (-2.725) (-3.107) (-3.170)

3 Bank Concentration Ratio -0.0708 -0.0271 -0.0060

(-1.654) (-0.619) (-0.092)

Macro economy

Percentage Change in GDP 0.9544 2.0279**

Deflator (1.587) (2.439)

Percentage Change in Real GDP 2.0901*** 1.7629*

(2.986) (1.682)

Long-Term Interest Rate Minus 0.4814 0.0704

Short-Term Rate (0.872) (0.075)

Powers, Ownership, Deposit Insurance and EU Membership

Securities Activities Unrestricted 1.9730

(=1) (0.408)

Insurance Activities Unrestricted 6.3882

(=1) (1.628)

Real Estate Activities Unrestricted -1.3385

(=1) (-0.342)

Bank Investment in Nonfinancial 2.8772

Firms (=1) (0.729)

Nonfinancial Firm Ownership of 1.3441

Bank (=1) (0.344)

Ex Post Insurance System (=1) -2.5291

(-0.723)

Geographical Branching Restricted 8.1494

(=1) (1.168)

EU Membership (=1) -8.6177**

(-2.248)
Table A.1.b (continued)

Explanatory Variables 1 2 3 4

Country Dummies

Austria -10.5667*

(-1.752)

Belgium -7.9875

(-1.442)

Canada -8.5475

(-1.543)

Denmark -16.5514***

(-2.988)

Finland -23.5345***

(-3.902)

France -12.9745**

(-2.254)

Germany -12.2958*

(-1

Greece 5.0377

(0.787)

Ireland -7.8275

(-1.224)

Italy -14.8902**

(-2.469)

Japan -15.0082***

(-2.873)

Luxembourg -0.0750

(-0.014)

Netherlands -11.2958*

(-1.873)

Portugal -6.9228

(-1.203)

Spain -4.8200

(-0.898)

Sweden -14.4725**

(-2.612)

Switzerland -9.2924

(-1.614)

United Kingdom -4.8783

(-0.909)

F-Statistic 2.303 5.378 6.304 3.829

Adjusted R-Squared 0.1426 0.1344 0.2313 0.2430

Sample Size 142 142 142 142

NOTE: t-Statistics are in parenthesis. By a two-tailed test *** indicates statistically significant at the 1% level; ** indicates statistically significant

at the 5% level; and * indicates statistically significant at the 10% level.