Do efficient institutions score well using ratio analysis? An examination of commercial banks in the 1990s.

Lacewell, Stephen K.

INTRODUCTION

Commercial banks operating in today's economic system are a far cry from the financial institutions of earlier decades. The traditional definition of a bank as defined by Rose (2002) is "a financial intermediary accepting deposits and granting loans", which at first glance seems fairly mundane. However, modern banks are becoming increasingly technical in both scale and scope. Coupled with the ever-changing landscape of banking is the undeniable fact that for our financial system to remain productive it must be characterized by the virtues of strength and stability. This requires a competent and progressive regulatory system that is accurately able to determine the performance of financial institutions.

Although there is arguably no one correct measure of bank performance, the area of performance measurement can be divided into two rather large streams of research: bank efficiency measures and accounting-based financial ratios. The various statistical methods for measuring bank efficiency are rather new compared to traditional ratio analysis. However, various efficiency techniques are increasingly mentioned in academic studies as a complement to, or substitute for, financial ratio analysis which constitutes such a large portion of the CAMELS rating system utilized by financial institution regulatory agencies in their determination of a firm's safety and soundness.

This paper seeks to determine if the financial ratios and efficiency scores of banks provide much of the same information. That is, do banks with strong ratios also exhibit strong efficiency scores? This is accomplished in a three-stage process. Stage one is the calculation of both alternative profit efficiency scores and cost efficiency scores, using the stochastic frontier approach (SFA), for all banks operating in the United States during the years 1996 and 1999. This model is termed the national model per Mester (1997) due to the fact that all banks, for which sufficient data are available, are used to estimate the desired efficient frontier. Stage two involves the formulation of financial ratios that are, according to previous research, highly correlated with each of the CAMELS rating components. The final stage is the comparison of the results of the first two stages when the population of banks is segmented into thirds, consisting of high, medium, and low performing banks.

As mentioned earlier many studies have proposed the addition of some form of efficiency measure to the current CAMELS rating. With this in mind it is hypothesized that banks which score high using financial ratios will also tend to perform well using more complicated efficiency techniques. The results of this study will be of interest to many parties due to the fact that determining a correct measure of bank performance must take into account the high degree of competitiveness, technical change, customer-base diversity, and other areas of the firm's operating environment.

LITERATURE REVIEW

While the area of production frontiers was introduced by Farrell (1957), the stochastic frontier, also called the composed error, is relatively new having been introduced by Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977). Many of the first papers on this topic were applied to manufacturing data, as were other efficiency methods. Much study has taken place regarding the early problems associated with this method (1). Stochastic frontier analysis (SFA) is today, however, one of the most popular efficiency estimation techniques due in part to its robustness and relative ease of use.

Among the first to examine the relationship between financial performance, measured by accounting-based ratios, and production performance proxied by efficiency indices, are Elyasiani, Mehdian, and Rezvanian (1994). They find a significant association between financial ratios and bank efficiency and suggest that efficiency analysis should be considered as a supplement to financial ratio analysis by regulatory agencies and bank managers. Their article focuses, however, on large banks and utilizes a rather small sample. Thus, the true nature of the relationship is not explored across a wide variety of banks operating in the U.S. One study which provides a very brief although interesting attempt to integrate the information provided by efficiency measures with that found in CAMELS ratings is by Simeone and Li (1997). This study, which focuses on a limited sample of 35 closed Rhode Island credit unions ranging in asset size from $131 thousand to $338 million, seeks to determine if stochastic frontier analysis (SFA) measures of efficiency would have been useful in identifying and preventing the failure of the aforementioned credit unions. The authors determine that SFA can be considered a good substitute for, or a valid supplement to, the CAMELS rating due to the fact that SFA avoids the subjective and difficult management rating utilized by CAMELS. Another study which examines how financial ratios can be used in conjunction with Data Envelopment Analysis (DEA), an alternative efficiency estimation technique, is performed by Yeh (1996). He seeks to demonstrates how the use of DEA in conjunction with financial ratio analysis can help to aggregate the confusing array of financial ratios into meaningful dimensions that somehow link with the operating strategies of a bank. His study utilizes a rather small sample of six Taiwanese banks over a nine-year period resulting in a total of 54 DEA efficiency scores. Factor analysis is used to classify 12 financial ratios based on their financial attributes so as to aid in the specification of their respective implications and determination as to whether the ratios examined adequately express a firm's financial profile. A comparison is then made regarding the factor scores relative to each group with different DEA efficiencies to allow for an overall comparison. The four factors identified as accounting for approximately 87% of the common variance in the measured ratios are related to capital adequacy, profitability, asset utilization and liquidity. When compared to the high, medium and low DEA groups it is shown that the highest scoring DEA group has the highest scores in the first three factor categories listed above. The conclusion of the article alludes to the fact that if the inputs and outputs are chosen properly, DEA can provide crucial information about a bank's financial condition and management performance and can assist examiners as an early-warning tool in the regulation process.

De Young (1997) explores the challenges and misconceptions of measuring cost efficiency at financial institutions. Situations are illustrated in which accounting-based expense ratios are misleading and show that statistics-based efficient cost frontier approaches often measure cost efficiency more accurately. The author utilizes the stochastic cost frontier (SCF) approach to analyze 1994 data on 9,622 commercial banks. It is concluded that utilizing the SCF in unison with accounting-based ratios allows for a more accurate analysis of a bank's overall cost efficiency. A somewhat similar study by Siems and Barr (1998) uses a constrained-multiplier, input-oriented, DEA model to create a robust quantitative foundation to benchmark the productive efficiency of U.S. banks. It is found that the most efficient banks are relatively successful in controlling costs and also hold a greater amount of earning assets. The more efficient banks also earn a significantly higher return on average assets, hold more capital and manage less risky and smaller loan portfolios than less efficient institutions. Also, it is confirmed that banks which receive higher CAMEL ratings by bank regulators are significantly more efficient. Strong banks (rated as a 1 or 2) are shown to be significantly more efficient that weak banks (rated a 3, 4 or 5). Thus, it is concluded that more efficient banks tend to be higher performers and safer institution. Other studies of interest include Horvitz (1996), Taylor, Thompson, Thrall, and Dharmapala (1997), Thompson, Brinkman, Dharmapala, Gonzalaz-Lima, and Thrall (1997), Berger and Davies (1998) and Kantor and Maital (1999).

As evidenced by the above array of literature, the area of bank efficiency measurement is vast. Many studies have been performed regarding cost, revenue, and profit efficiency. Although studies have been performed which touch on the relationship between efficiency measures, financial ratio performance, and CAMELS ratings, none have been conducted as yet which combine all of these factors in the way of the examination undertaken here.

DATA AND METHODOLOGY

The data used in this study are obtained from the Sheshunoff BankSearch Commercial and Savings Banks database for the years 1996 and 1999, respectively. A sample of all banks for which there is available data is obtained for the two years with 7,514 banks for 1999 and 8,179 banks for 1996. The alternative profit and cost efficiency scores are then calculated. The sample is then decomposed, by efficiency scores, into thirds. The first group is the high-efficiency group and will represent the top one-third of banks, or banks with the highest X-efficiency scores (alternative profit or cost). The second group represents banks in the middle third of efficiency scores and group three includes banks with the lowest efficiency scores. The mean of each financial ratio for banks in each group is also provided. This will allow the determination of whether higher efficiency banks have consistently higher performance in the financial ratio category.

Efficiency Estimation

To provide for more robust findings both alternative profit efficiency and cost efficiency are estimated in this study. The alternative, or nonstandard, profit efficiency model, as given by Berger and Mester (1997) and Humphrey and Pulley (1997), differs from the standard profit efficiency model in that it measures how efficient a bank is at earning its maximum available profit given its output levels. Alternative profit efficiency is especially useful when there is a violation of at least one of the underlying assumptions of cost and standard profit efficiency. These assumptions include:

(i) the quality of banking services has no substantial unmeasured variations;

(ii) a bank can achieve its optimum volume and mix of output, meaning outputs are completely variable;

(iii) a bank cannot affect output price due to perfectly competitive output markets; and

(iv) output prices are accurately measured allowing for unbiased standard profit efficiency estimation.

It is apparent from the above assumptions that the data used for this study would violate at least assumptions i and ii. Thus, alternative profit estimation is chosen as the profit efficiency measure of choice over standard profit efficiency.

The alternative profit frontier function is:

[pi] = [pi](y,w,[u.sub.[pi]],[v.sub.[pi]]), (1)

where [pi] represents the variable profits of the bank, y is a vector of variable output quantities, w is a vector of prices for variable inputs, [u.sub.[pi]] represents profit inefficiency and [v.sub.[pi]] is random error.

The alternative profit efficiency score for any bank can be calculated once the alternative profit frontier has been constructed. The alternative profit efficiency of bank i is calculated as the predicted actual observed profit of bank i divided by the predicted maximum profit of the best practice bank, i.e., the predicted maximum profit across all banks, adjusted for random error. This calculation is given by the following:

Alt[pi][Eff.sub.i] = [[??].sup.i] / [[??].sup.max], (2)

where [[??].sup.max] represents the predicted maximum profit, associated with the best practice bank, across N banks in the sample and [[??].sup.i] denotes the predicted actual profit for the ith bank, with i = 1, ..., N. The calculated raw profit efficiency scores are then truncated at the top 5 and 10 percent levels, per Berger (1993), so as to eliminate any distortion which may be caused by outliers when the maximum profit is used. The truncated profit efficiency scores can range from 0 to 1 with 1 representing the most efficient bank or the best practice bank. The profit efficiency score represents the percentage of profits or resources that are used efficiently. Thus, a bank that receives a profit efficiency score of 0.75 is 75% efficient or consequently loses 25% of its potential profits relative to the best practice bank facing similar operating conditions.

A modified intermediation approach is used for the analysis, which views a bank's primary goal as that of intermediating funds between savers and borrowers and uses the dollar volume of various deposit accounts and loan categories as output variables. Input variables include the cost of funds utilized in the process of transferring funds between savers and borrowers. The modification to this approach occurs due to the inclusion of nontraditional activities. Due to increased competition banks are placing increased emphasis on nontraditional activities. Rogers (1998b) finds that bank efficiency measures which do not account for these nontraditional activities as an output tend to understate the true bank efficiency measure.

Considering the aforementioned information, the variables included for analysis include the following:

 Input Variables (Cost) Output Variables (Quantity)

1) Labor 1) Demand Deposits
2) Physical Capital 2) Time and Savings Deposits
3) Time and Savings Deposits 3) Real Estate Loans
4) Purchased Funds 4) Other Loans
 5) Net Noninterest Income

Given the above inputs and outputs, and based on Berger's (1993) similar model specification, the empirical profit frontier model is given as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

where: j = 1, ..., 5 outputs, k = 1, ..., 4 inputs, [pi] = total profit [y.sub.j] = the amount of output j, [w.sub.k] = the input price of k, and [[epsilon].sub.[pi]] = the natural residual or total error

If the two components of the disturbance term, [u.sub.[pi]] and [v.sub.[pi]], meet the following assumptions:

[u.sub.[pi]] ~ [absolute value of N(0, [[sigma].sup.2.sub.u[pi]]), [v.sub.[pi]] ~ [absolute value of N(0, [[sigma].sup.2.sub.v[pi]]), (4)

then per Jondrow, et.al. (1982) the natural residual, [[epsilon].sub.[pi]], will be decomposed into an inefficiency measure, [u.sub.[pi]], and random noise, [v.sub.[pi]].

Cost efficiency consists of a comparison of an observed bank's cost to a best practice bank's cost in the production of a homogenous output bundle while facing the same operating conditions. The best practice bank is considered to be the minimum cost producer and any deviation from this provides a measure of the observed cost inefficiency. Determining the level of cost efficiency amounts to estimating a cost function which relates variable costs to the prices of variable inputs, the quantities of variable outputs, and allows for the presence of both random error and inefficiency. Such a cost frontier can be written as:

C = C(w, y, uc, vc) (5)

where C measures variable costs, w is a vector of prices of variable inputs, y is the vector of quantities of variable outputs, uc represents the cost inefficiency factor, and vc denotes random error. The random error component, vc, incorporates both a "luck" factor and measurement error which may give rise to high or low costs in the short-run. The cost inefficiency factor, uc, contains both allocative and technical inefficiencies. Allocative inefficiency results from choosing the wrong input combinations given the relative prices of inputs while technical inefficiency stems from using an excessive quantity of the inputs to produce y. The inefficiency score for any bank, given as bank i, can be calculated once the cost frontier has been constructed. The cost efficiency of bank i is calculated as the predicted cost of the best practice bank, i.e., the minimum predicted cost across all banks, needed to produce a given output quantity, divided by the predicted actual observed cost of bank i, adjusted for random error. This calculation is given, per Berger and Mester (1997), as the following:

Cost[EFF.sub.i] = [[??].sup.min] /[[??].sup.i], (6)

where [[??].sup.min] is the minimum predicted cost, associated with the best practice bank, across N banks in the sample and [[??].sup.i] is the predicted actual cost for the ith bank, with i = 1, ..., N. The calculated raw cost efficiency scores are then truncated at the top 5 and 10 percent levels, per Berger (1993), so as to eliminate any distortion which may be caused by outliers when the minimum cost (or profit) is used. The truncated cost efficiency scores can range from 0 to 1 with 1 representing the most efficient bank or the best practice bank. The cost efficiency score represents the percentage of costs or resources that are used efficiently. Thus, a bank that receives a cost efficiency score of 0.75 is 75% efficient or consequently wastes 25% of its costs relative to the best practice bank facing similar operating conditions. Descriptive statistics for the banks analyzed as well as cost and profit efficiency estimates are found in Exhibits 2 through 4.

Selection and Calculation of Financial Ratios

Once the efficiency estimates have been calculated the next step of the analysis involves the selection of variables which theoretically correlate to each of the CAMELS rating categories used by examiners. Due to the non-availability of data needed to calculate all of the financial ratios chosen for the analysis, the sample size of banks included in stage two of the study is reduced. (2) The final sample consists of 4,376 banks in 1999 and 5,158 banks in 1996. Exhibit 5 provides various financial ratio means for both years of examination.

The selection of accounting-based financial ratios which accurately represent a bank's CAMELS rating is the most difficult yet meaningful undertaking of the empirical portion of this study for a number reasons. First, CAMELS ratings are proprietary information, which means that only regulatory personnel and researchers with regulatory associations have access to this data. Second, CAMELS ratings are based on a combination of objective and subjective information. Although a large portion of a bank's rating is derived from the analysis of various financial ratios corresponding to a specific CAMELS component, an important aspect of the rating results from examiner subjectivity. Thus, items such as differences among regulatory agencies, examiner experience, and inconsistencies among examination districts arguably have an effect on the ratings received by banks. Finally, empirical literature on this topic is scarce due to the aforementioned proprietary nature of the data. Literature on the financial performance of banks is found in great supply but few researchers have tackled the more elusive CAMELS modeling issue unless they have access to private CAMELS data (see Cole et.al., 1995 and DeYoung, 1998). The problems of a study of this type not withstanding, it is very realistic to conclude that most of the CAMELS categories can be proxied by financial ratios corresponding to the component in question per previous studies by Cole, et al. (1995) and Cole and Gunther (1998).

The one area that meets with a greater degree of subjectivity is the management component (M). A study by DeYoung (1998) suggests that there is a high degree of correlation between the M rating and the overall financial performance of a bank. Other variables such as unit costs and insider loans are shown to be good predictors of the M rating as well. As various financial ratios are used in this study as proxies of the C, A, E L, and S components, the M component will be proxied by the amount of insider loans, overhead expense, and the number of full-time equivalent employees to average assets, which mirrors Gilbert, et. al. (1999). Although in no way a perfect measure of management quality, these variables should provide useful insight into an otherwise unmeasurable rating component.

Financial theory regarding the operation of banking firms provides some insight into the use of certain financial ratios to proxy the six categories of a CAMELS rating. These ratios and their definitions are given in Exhibit 1. Risk-based capital is chosen to represent the capital component. Although there are many other capital measures, the level of risk-based capital is chosen because of the importance regulators have placed on this measure in recent years. The ratios of past due loans, nonaccrual loans, and the allowance for loan and lease loss are chosen to represent asset quality. The three management quality ratios--insider loans, overhead expense, and the number of full-time employees--are discussed previously. They are expected to exhibit negative relations with profit efficiency. This is fairly self-explanatory in terms of overhead expense and the number of employees. Banks with lower overhead and fewer employees per million dollars of assets should be more efficient. The amount of insider loans would also be expected to be negatively related to efficiency because a higher proportion of insider loans may indicate closely held or family owned institutions which tend to be smaller and more conservative than other banks. Operating income, return on equity, and noninterest income are chosen to represent the earnings component. All of these are expected to show a positive relation with efficiency since all are directly related to the profits of a bank. Liquidity is represented by liquid assets, jumbo CDs, and core deposits. Bank liquidity is a desirable characteristic in the eyes of regulators. Thus, it would seem pertinent that banks with more liquidity may also be considered more efficient. The final CAMELS category, interest rate sensitivity, is represented by the one year gap. There is no explicit assumption made regarding the relationship of this variable with the efficiency estimate.

EMPIRICAL RESULTS

As given in Exhibits 6 and 7, the comparison of mean financial ratios between high, medium, and low profit efficiency banks provides some unique findings. Exhibit 6 displays the means of the fourteen financial ratios and their differences between high, medium, and low alternative profit efficient groups. It is apparent that the level of risk-based capital (RBC) is much higher for highly profit efficient banks than for medium and low efficiency institutions. The percentage of nonaccrual loans (NONACCRL) is found to be lower, on average, for highly efficient banks with a minimal difference between mid-ranked institutions and a much more pronounced difference between low-ranked banks. Also, the ratios for insider loans (IL), overhead expense (OE), and full-time-equivalent employees (FTE ) in highly efficient banks are low relative to the other two groups, with the one exception being low efficiency banks in 1996. A rather interesting finding, which is consistent with Yeh's 1996 study, is that the ratios for every profitability category--operating income (OI), return on equity (ROE), and noninterest income (NII)--display an inverse relationship with profit efficiency scores. That is, the most profit efficient institutions exhibit the lowest profitability ratios and vice versa. This finding is puzzling to researchers trained in the art of analyzing accounting-based financial ratios to determine financial institution performance. However, it does illustrate the difference between ratio analysis and efficiency estimation, but fails to bridge the gap between these two methods. In the liquidity area, highly profit efficient banks display a higher percentage of liquid asset (LA) and core deposit (COREDEP) ratios than their lower rated counterparts. High efficiency institutions also have a more negative one-year gap (ONEGAP), on average, for both years of analysis.

It is extremely interesting to compare the average asset size of the banks in each category. While many institutions are taking the "bigger is better" attitude the findings of this study are in complete disagreement. The average asset size of highly profit efficient banks is slightly over $212 million, $444 million for banks in the medium efficiency category, and just over $2 billion in the bottom third of efficiency scores for 1999. The numbers from 1996 are similar. This inverse relationship between efficiency and asset size is consistent with previous studies as discussed below.

Exhibit 7, which provides mean financial ratios and their differences between cost efficiency groups, follows a similar pattern to that given in Exhibit 6. One noticeable difference between the output given in each table is found in the area of profitability ratios. OI shows a direct relationship with the level of cost efficiency for both years. Highly cost efficient banks have the highest ROE for both years. The medium and low efficiency categories are reversed in 1999 but display a positive relationship in 1996. NII is shown to be higher for low cost efficient banks, due possibly to the higher direct costs associated with many noninterest income products. An examination of asset size points to an inverse relationship between asset size and cost efficiency, duplicating the results when profit efficiency is used in Exhibit 6. This inverse relationship between efficiency and size is consistent with those of Bauer, Berger, and Humphrey (1993) and Rogers (1998) but contrasts with the findings of Elyasiani and Mehdian (1995).

CONCLUSION

There is no refuting the fact that banks today are more complicated entities than ever before. The added duties and services, permitted by the passage of laws such as the Gramm-Leach-Bliley Act, place a greater importance on the reliability of regulators to adequately assess a bank's efficiency and financial performance due to the allowance of increased risk-taking scenarios. In turn, the methods regulators utilize to assess the viability and productivity of banks must increase in sophistication to handle the added complexity of today's banking environment.

Furthermore, the areas of accounting-based financial ratios and efficiency are much debated in terms of the best measure of bank performance. While most studies tend to examine the two areas in isolation, this study chooses to merge the areas of bank efficiency and financial ratio performance. It examines the relationship between financial ratios deemed highly correlated with a bank's CAMELS rating and measures of alternative profit and cost efficiency to determine when and if the two should be used in combination, as suggested by previous studies.

The findings show that there is a high degree of consistency between banks with strong financial ratios and banks that are rated highly efficient. This is consistent with previous studies by Yeh (1996) and Siems and Barr (1998). The major exception to this claim is found in the profitability ratios category, which is also consistent with Yeh (1996). This result, however, highlights the fact that the more technical efficiency estimation techniques may interpret data in a different manner than researchers and practitioner using traditional financial ratios.

This study expands on the claim by previous researchers that an efficiency indicator should be added to the current bank rating system used by regulators. However, this study uses only the parametric stochastic frontier efficiency approach. A similar analysis using other parametric and nonparametric techniques would provide more insight into this area.

Furthermore, while a strong introduction to the problem, the research presented in this paper contains only two years of data. The use of a more comprehensive time frame would serve to better justify the results found here. Finally, the choice of the financial ratios used to simulate a CAMELS rating is arbitrary to say the least. As long as the CAMELS system remains proprietary information it is a researcher's best guess as to the accuracy of the ratios chosen to represent a bank's rating. Thus, making the CAMELS rating available to researchers not affiliated with a regulatory agency would greatly enhance the study of this area. This in turn would provide beneficial results to bankers, regulators, and academicians alike.

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Stephen K. Lacewell, Murray State University

Exhibit 1: Financial Ratios Representing Each CAMELS Category

 VARIABLE DESCRIPTION

Capital Adequacy (C)

Risk-Based Capital (RBC) Total capital divided by risk-
 weighted assets

Asset Quality (A)

Nonaccrual Loans (NONACCRL) Nonaccrual loans divided by average
 assets

Allowance for Loan and Lease Allowance for loan and lease loss
Loss (ALLL) divided by average loans and leases

Charge-Offs (COFF) Charged-off loans and leases
 divided by average loans and leases

Management Quality (M)

Insider Loans (IL) Loans to insiders divided by
 average assets

Overhead Expense (OE) Overhead expense divided by average
 assets

FTE Employees (FTE) Number of full-time equivalent
 employees divided by millions of
 dollars of average assets

Earnings (E)

Operating Income (OI) Total operating income divided by
 average assets

Return on Equity (ROE) Total income divided by total
 stockholder's equity

Noninterest Income (NII) Total noninterest income divided by
 average assets

Liquidity (L)

Liquid Assets (LA) Liquid assets divided total assets

Jumbo CDs (JMBOCD) $100,000+ time deposits divided by
 total assets

Core Deposits (COREDEP) Core deposits plus equity divided
 total assets

Sensitivity (S)

1 Year Gap (ONEGAP) Rate sensitive assets repricing
 within 1 year minus rate sensitive
 liabilities repricing within one
 year divided by total assets

Exhibit 2: Summary Statistics of Total Assets for All Banks
Analyzed for 1999 and 1996

All Banks 1999 1996 Difference

Mean 610,219 501,730 108,489
Std. Dev 8,762,162 5,346,773 3,415,389
Minimum 2,306 2,374 -68
Maximum 571,732,000 272,429,000 299,303,000
No. of Obs. 7,514 8,179 -665

Note: Mean, Std. Dev., Minimum and Maximum values are in
thousands of dollars.

Exhibit 3: Descriptive Statistics of Variables Used in the 1996
and 1999 SFA Profit and Cost Frontier National Models

 Year: 1999 and 1996

 1999
Variables: Mean Std. Dev.

Total Profit (a) 19,758 341,355

Total Cost (a) 24,570 303,358

Input Price:
Price of Labor (a) 39.50 9.09
Price of Capital (b) .3472 .3535
Cost of Deposits 3.90 .65
Cost of Purch. Funds 4.63 1.12

Output Quantity:
Transaction Deposits (c) 80,382 916,932
Time & Savings Dep (c) 233,290 2,853,011
Real Estate Loans (c) 179,734 2,315,876
Other Loans (c) 215,566 3,491,460
Net Nonint. Income (c) 13,591 214,203

No. of Observations 7,514

 Year: 1999 and 1996

 1996
Variables: Mean Std. Dev.

Total Profit (a) 14,741 221,818

Total Cost (a) 21,115 176,773

Input Price: 35.14 8.58
Price of Labor (a) .3747 .3802
Price of Capital (b) 4.15 .63
Cost of Deposits 4.92 1.30
Cost of Purch. Funds

Output Quantity: 92,243 759,512
Transaction Deposits (c) 185,332 1,295,194
Time & Savings Dep (c) 133,856 1,065,328
Real Estate Loans (c) 182,113 2,237,613
Other Loans (c) 9,687 128,553
Net Nonint. Income (c)

No. of Observations 8,179

 Difference

Variables: Mean Std. Dev.

Total Profit (a) 5,017 119,537

Total Cost (a) 3,455 126,585

Input Price: 4.36 0.51
Price of Labor (a) -.0275 -.0267
Price of Capital (b) -.25 .02
Cost of Deposits -.29 -.18
Cost of Purch. Funds

Output Quantity: -11,861 157,420
Transaction Deposits (c) 47,958 1,557,817
Time & Savings Dep (c) 45,878 1,250,548
Real Estate Loans (c) 33,453 1,253,847
Other Loans (c) 5,904 85,650
Net Nonint. Income (c)

No. of Observations -659

Note: (a) Values are in thousands of dollars per full-time
equivalent employee

(b) Values are in dollars per dollar of fixed assets

(c) Values are in thousands of dollars

Exhibit 4: Summary Statistics of Profit and Cost Efficiency
Estimates Obtained from the National Model

 1999 1996
 5% 10% 5% 10%
 Trun- Trun- Trun- Trun-
 cation cation cation cation

Profit Mean .39722 .48845 .37692 .46221
 Efficiency Std. Dev .24424 .26787 .24928 .27488
 Estimates Minimum .00911 .01169 .02568 .03286
 Maximum 1.00000 1.00000 1.00000 1.00000
 No. of 7,514 7,514 8,179 8,179
 Observations

Cost Mean .3882 .4752 .4124 .5025
 Efficiency Std. Dev .2457 .2700 .2372 .2584
 Estimates Minimum .0114 .0146 .0145 .0184
 Maximum 1.00000 1.00000 1.00000 1.00000
 No. of 7,520 7,520 8,179 8,179
 Observations

Exhibit 5: Financial Ratio Summary Statistics for All
Banks (1999 and 1996)

 Variable Mean Std. Dev. No. of Obs.
 1999 1996 1999 1996 1999 1996

 RBC 14.31 15.19 5.66 5.62
 NONACCRL .45 .49 .62 .63
 ALLL 1.51 1.62 .68 .77
 COFF .43 .50 .70 1.40
 IL 1.46 1.47 1.55 1.51
All OE 3.28 3.38 1.30 1.47
Banks FTE .45 .51 .15 .18 4,376 5,158
 OI 1.59 1.80 1.10 .87
 ROE 12.72 13.39 8.12 7.12
 NII .95 .99 1.40 1.33
 LA 12.05 16.35 7.24 8.03
 JMBOCD 11.98 10.32 6.87 6.28
 COREDEP 81.86 85.71 9.48 8.44
 ONEGAP -19.89 -9.51 14.82 13.96

Note: Definitions given in Exhibit 1.

Exhibit 6: Mean Financial Ratios and Their Differences Between
Profit Efficiency Groups

 1999
Financial Profit Efficiency Group
Ratio High Medium Low

RBC 16.0684 13.8943 12.9874
NONACCRL .4257 .4263 .4939
ALLL 1.5375 1.4578 1.5238
COFF .4161 .3732 .5108
IL 1.2717 1.5091 1.6106
OE 3.0026 3.1859 3.6458
FTE .4327 .4470 .4726
OI 1.3480 1.5524 1.8796
ROE 10.7019 12.6193 14.8520
NII .6839 .8348 1.3416
LA 13.2786 11.5840 11.2977
JMBOCD 11.1514 11.8752 12.9200
COREDEP 84.4736 82.4907 78.6104
ONEGAP -23.1534 -19.9068 -9.6138
Avg. Asset 212,097 444,926 2,192,416
Size (a)
Number of 1,458 1,459 1,459
Obs.

 1996
Financial Profit Efficiency Group
Ratio High Medium Low

RBC 17.0557 14.6675 13.8520
NONACCRL .4474 .4922 .5225
ALLL 1.6603 1.5837 1.6151
COFF .4715 .4310 .5850
IL 1.3092 1.5349 1.5725
OE 3.2681 3.4734 3.3959
FTE .5134 .5283 .4863
OI 1.5145 1.7719 2.1165
ROE 11.2436 13.4655 15.4630
NII .8562 1.0490 1.0610
LA 18.4179 16.1461 14.4770
JMBOCD 10.3052 10.1177 10.5386
COREDEP 86.7131 86.3127 84.1025
ONEGAP -13.5411 -9.3109 -5.6924
Avg. Asset 247,769 518,835 1,405,849
Size (a)
Number of 1,719 1,720 1,719
Obs.

(a) Asset values are in thousands of dollars

Exhibit 7: Mean Financial Ratios and Their Differences
Between Cost Efficiency Groups

 1999
Financial Cost Efficiency Group
Ratio High Medium Low

RBC 15.3240 14.0605 13.5638
NONACCRL .4145 .4299 .5015
ALLL 1.5164 1.5007 1.5020
COFF .3484 .3985 .5559
IL 1.3378 1.4816 1.5723
OE 2.7683 3.1968 3.8698
FTE .3790 .4548 .5186
OI 1.6797 1.5487 1.5517
ROE 12.9354 12.4578 12.7814
NII .6940 .8212 1.3455
LA 11.9697 11.5247 12.6655
JMBOCD 8.7936 11.7745 15.3814
COREDEP 86.2892 82.2179 77.0624
ONEGAP -19.4278 -20.4290 -19.8191
Avg. Asset 292,991 365,338 2,192,467
Size (a)
Number of 1,459 1,459 1,458
Obs.

 1996
Financial Cost Efficiency Group
Ratio High Medium Low

RBC 15.4356 15.2540 14.8852
NONACCRL .4287 .4526 .5808
ALLL 1.6316 1.5756 1.6518
COFF .4601 .4156 .6118
IL 1.3928 1.5017 1.5221
OE 3.1799 3.2833 3.6744
FTE .4541 .5140 .5599
OI 2.0655 1.8059 1.5313
ROE 15.3146 13.3087 11.5477
NII 1.0361 .8706 1.0595
LA 16.9137 15.6133 16.5136
JMBOCD 7.2771 9.7341 13.9519
COREDEP 89.5759 86.8843 80.6663
ONEGAP -8.5785 -9.7551 -10.2112
Avg. Asset 355,213 371,728 1,445,607
Size (a)
Number of 1,719 1,720 1,719
Obs.