X-Efficiency and Management Quality in Commercial Banks

	Robert DeYoung

	Office of the Comptroller of the Currency

	Economic & Policy Analysis

	Working Paper 94-1

	January 1994

* The opinions expressed in this paper are those of the author and do not necessarily reflect those of the Office of the Comptroller of the Currency or the Department of the Treasury. 

Any whole or partial reproduction of material in this paper should include the following citation:  DeYoung, Robert, “X-Efficiency and Management Quality in Commercial Banks,”

Office of the Comptroller of the Currency, E&PA Working Paper 94-1, January 1994.

______________________________________________________________________________________

The author thanks Gary Whalen for graciously providing a data set of examination ratings for national banks.  

The author also thanks Philip Bartholomew, Allen Berger, Jeffrey Brown, Mike Carhill, Edward Dumas, Alton

Gilbert, Dennis Glennon, Kevin Jacques, Lawrence Leong, Thomas Lutton, and Daniel Nolle for helpful

comments, David Becher for research support, and Claire Emory for editorial assistance.  All errors are the

responsibility of the author.

Please address questions to Robert DeYoung, Financial Economist, Bank Research Division, Office of the

Comptroller of the Currency, 250 E Street SW, Washington, DC  20219.  Phone (202) 874-5427, Fax (202) 874-

5394, E-mail  robert.deyoung@.occ.treas.gov.

* Any whole or partial reproduction of material in this paper should include the following citation:  DeYoung, Robert, “X-Efficiency and Management Quality in Commercial Banks,”

Office of the Comptroller of the Currency, E&PA Working Paper 94-1, January 1994.

Introduction

	Econometric studies of banking costs reveal surprisingly large cost differences

between otherwise similar banks.  After controlling for differences in size, product mix,

number of branches, and a variety of market factors, these studies typically find that the

average bank incurs costs that are 20 to 25 percent higher than costs at the most efficient

banks.  These excess costs are commonly referred to as X-inefficiencies to distinguish them

from scale or scope inefficiencies.  Estimates of X-inefficiency are considerably larger than

estimates of scale and scope economies and appear to be comprised mostly of technical

inefficiency, although some studies have also found nontrivial amounts of allocative

inefficiency.  

	A number of empirical studies have found relationships between X-inefficiency and

bank structure, organizational form, and market environment.1  Studies have found higher levels of banking X-inefficiency in states with greater regulatory restrictions, and have also

found that banks in multibank holding company organizations – allegedly formed to

circumvent such restrictions – are more X-inefficient than branch banking organizations. 

There is evidence that X-inefficiency is larger in banks where the CEO chairs the board of

directors, perhaps because this arrangement makes monitoring more difficult and reduces the

effectiveness of incentive mechanisms.  Some studies have found that X-inefficiency is less

severe in larger banks, perhaps because large banks experience greater pressure from owners concerning bottom-line profits, are better able to attract capable managers, and/or operate in

metropolitan markets that are both more competitive (creating pressure to control costs) and

more densely populated (allowing more efficient delivery systems). 

	These results suggest a dichotomy:  some X-inefficiency occurs because conditions

outside managers' control (e.g., restrictive branching laws, low population density) prevent

them from operating their banks efficiently, while the rest of X-inefficiency is due to, or is

exacerbated by, bad management practices (e.g., managerial laxity when rivalry is not

intense, utility-maximizing behavior by managers).  No previous empirical studies have

attempted to separate the proportion of X-inefficiency imposed on managers from the proportion caused by managers.  

	This study compares X-inefficiency in well-managed national banks to X-inefficiency

in poorly managed national banks.  Because management quality cannot be observed directly,

national banks are assigned to these two groups based on the management component of the

CAMEL rating assigned to them by examiners from the Office of the Comptroller of the

Currency (OCC).  The intergroup difference in X-inefficiency is estimated using a thick

frontier cost approach that filters out cost differences that are beyond the control of

managers.  The results are compared to estimates of total X-inefficiency in order to

approximate the relative importance of management quality in determining overall X-

inefficiency in banks.  On average, poorly managed banks exhibited about 12 percent more

X-inefficiency than did well-managed banks.  This result is around two-thirds as large as

total X-inefficiency.

	Section I summarizes the empirical literature on X-efficiency in banking.  Section II describes how bank examiners assign management quality ratings to banks and shows how

these ratings are related to various measures of bank performance.  Section III presents the

statistical cost model used to generate estimates of X-inefficiency.   Section IV describes the

data.  Section V contains preliminary results.  Section VI draws some conclusions from the

results and discusses the potential for future research.

I.  Empirical Literature on Cost Inefficiency in Banks

	The empirical literature on cost efficiency in banks has burgeoned during the past five

years.  Berger, Hunter, and Timme (1993) and Evanoff and Israilevich (1991a) contain good

reviews of this literature.  In general, these studies find that banks of similar size and

product mix incur widely divergent costs that vary by amounts far larger than the savings

available from scale and scope economies.  Many of the studies focus on what Leibenstein

(1966) called "X-inefficiencies," i.e, costs incurred over and above the minimum cost

necessary to sustain output at its current level.  Leibenstein coined the term to describe

management laxity that arises in firms with market power, but in the banking cost literature

it has come to be used in a more general sense to describe any excess cost of production not

caused by suboptimal scale or scope.  

I.A  Estimates of X-Inefficiency in Banks 

	X-inefficiencies are usually estimated by computing the distance between a bank's

actual costs and a "frontier-efficient" or "best practices" cost function representing the lower

bound of costs attainable only by the most cost-efficient banks.  Empirical studies using these methods generally control for cost differences due to scale, product mix, branching status,

and regional variation in input prices, and often decompose the resulting estimates of X-

inefficiency into technical inefficiency (employing unnecessary inputs or paying above market

prices for inputs) and allocative inefficiency (employing inputs in suboptimal combinations).

	Four different techniques, which differ based on maintained assumptions about error

term(s), have been used to generate estimates of X-inefficiency in banks.  The econometric

frontier approach (EFA) separates the error term in an econometric cost function into two

components:  a symmetric, random disturbance, and a one-sided, nonrandom component that

is assumed to be related to inefficiencies.  Using EFA methods, Ferrier and Lovell (1990)

found that costs at the average bank were about 26 percent greater than the frontier,

approximately two-thirds of which was allocative inefficiency, Pi and Timme (1993) found

that the average bank in their sample of large, publicly traded banks incurred controllable

costs that were approximately 11 percent higher than the frontier, and Bauer, Berger, and

Humphrey (1993) found that X-inefficiencies averaged around 16 percent over an 11-year

period.   

	The thick frontier approach (TFA) uses a priori information on unit costs to select subsamples of efficient and inefficient banks, then uses standard econometric techniques to

estimate "thick" (as opposed to discrete edges) upper and lower cost boundaries.  The error

terms within each of the two subsamples are assumed to reflect only random error and luck,

whereas the difference between the two subsamples is assumed to reflect only inefficiencies

and market factors.  This method was pioneered by Berger and Humphrey (1991), who found

that the average bank had unit costs around 25 percent greater than the efficient lower bound, over two-thirds of which was technical inefficiency.  Using the thick frontier approach,

Bauer, Berger, and Humphrey (1993) found that X-inefficiencies averaged around 21 percent

over an 11-year period.  

	The distribution-free approach (DFA) generates measures of cost inefficiency using

the residuals from a standard econometric cost model estimated separately over a series of

years.  This method assumes that, because the randomness in the error terms will cancel out

over time, the interyear average residual contains only information about X-efficiency. 

Berger and Humphrey (1992) used this method to generate an ordinal efficiency index to test

for cost efficiencies in large bank mergers. 

	Data envelopment analysis (DEA) uses a nonparametric (linear programming) method

to generate a lower envelope of the production function.  Since DEA is a nonstochastic

approach, random error is assumed away, and any cost differences due to random events are

included in inefficiency.  Using DEA methods, Ferrier and Lovell (1990) found that the

average bank used 21 percent more inputs than necessary, approximately three-quarters of

which was technical inefficiency.  Aly, Grabowski, Pasurka, and Rangan (1990) found that

banks could have employed 35 percent fewer inputs without reducing output.  Elyasiani and

Mehdian (1990), who used a technique that measured technical but not allocative efficiency,

found that banks overemployed inputs by about 12 percent.

I.B  Determinants of Cost Inefficiency in Banks

	There are many, non-mutually exclusive explanations for why some banks are more

X-inefficient than others, and the set of empirical studies linking X-inefficiency in commercial banks to these phenomena is growing.2  None of these studies, however, tests the relationship between management quality and X-efficiency.3 

	One line of inquiry focuses on agency theory.  Using an EFA approach, Pi and

Timme (1993) found that X-inefficiency in large, publicly traded commercial banks

decreased as CEO stock ownership increased as long as the CEO was not also the chairperson of the board of directors.  When the two positions were consolidated, X-

inefficiency increased with the percentage of the firm owned by the CEO.  These results suggest that stock ownership can be an effective way to reduce owners' monitoring costs and

discourage utility-maximizing behavior by managers, but that such arrangements become

ineffective and X-inefficiencies can result when too much power is concentrated in the hands

of the CEO.

	Another line of inquiry looks at differences in organizational form across banks. 

Using DEA methods, Grabowski, Rangan, and Rezvanian (1993) found that X-inefficiency is

larger in multibank holding companies than in branch banking organizations.  The authors

conclude that, to the extent that multibank holding companies are organizational

arrangements designed to circumvent product and geographic market restrictions, removal of

regulatory barriers will improve efficiency in banking markets by reducing X-inefficiency. 

Newman and Shrieves (1993) found the opposite – that multibank holding company

organizations have about a 2 percent cost advantage over branch banking organizations – although they did not use a cost frontier approach to estimate their cost model. 

	There is evidence that branching and other restrictions prevent banks from operating

as efficiently as possible.  Evanoff and Israilevich (1991b) found that X-inefficiency in large

banks is greater in regions characterized by more restrictive state level regulation, and also

found that X-inefficiency in these banks decreased after the financial deregulation of the early

1980s.  Evanoff, Israilevich, and Merris (1990) estimated that inefficiency due to regulation

amounts to about 2 percent of total costs for large commercial banks.

	Many of the studies mentioned above compare estimates of X-inefficiency across

institutions of different sizes.  Large banks may be more X-efficient than small banks if they

face greater pressure from owners concerning bottom-line profits or are better able to attract

capable managers.  In addition, large banks tend to be located in highly competitive, densely

populated metropolitan areas, which may create pressure to control costs and/or require

fewer branch locations to provide a given amount of financial services.  Evidence on the

relationship between cost-based X-inefficiency and commercial bank scale is mixed.  Aly, et

al (1990) found a positive and statistically significant relationship between scale and only one

out of four alternate measures of X-inefficiency.  Berger and Humphrey (1991) found that X-

inefficiency at first falls, then rises, with scale.  Bauer, Berger, and Humphrey (1993) found

that X-inefficiency increases with size when using EFA methods, but is not related to size

when using TFA methods.  Berger, Hancock, and Humphrey (1993) extended the concept of

X-inefficiency to include output inefficiency (e.g., suboptimal revenues) in addition to cost-

based input inefficiencies and found that this measure of X-inefficiency is negatively related

to scale.  	Ironically, only one study (to the author's knowledge) has attempted to find the negative relationship between X-inefficiency and market rivalry originally hypothesized by

Leibenstein.  Berger and Humphrey (1992) regressed an ordinal measure of X-inefficiency on

a set of variables that included market concentration and found no relationship between

concentration and X-efficiency.  It may not be possible to establish this relationship

empirically given the difficulty of constructing meaningful measures of market concentration

for banks that operate branches (or banking companies that operate banks) in multiple

geographic markets. 

	It is possible that studies mistakenly identify banks that provide higher quality service

as X-inefficient if high quality services require banks to purchase more and/or more

expensive inputs than banks offering services of lesser quality.  However, measurement

problems make it difficult to separate cost-based X-inefficiency from cost-intensive service

quality in banking.  Standard measures of service or product quality (e.g., customer

turnover, defect rates) are either not applicable or not available for banks.  Available proxies

for service quality (e.g., FTE labor per deposit account, branches per deposit account) are

also not useful, because they are highly correlated with costs, and hence with X-inefficiency,

by construction.  Using a profit function rather than cost function to estimate X-inefficiency

might control for levels of service quality – presumably, the market is willing to pay more

for higher quality service – but separating service quality from other determinants of the

resulting estimates of X-inefficiency would still be difficult.

II.  Measuring the Quality of Bank Management

	Under the Federal Deposit Insurance Corporation Improvement Act of 1991 (FDICIA), every commercial bank is examined annually for safety and soundness by either

its primary federal regulator (the FDIC, the Federal Reserve System, or the OCC) or by its

state regulator.4  The most important product of the annual exam is the "CAMEL" rating.  The CAMEL rating is a composite of five performance dimensions – capital adequacy (C),

asset quality (A), management/administration (M), earnings (E), and liquidity (L) – each of

which ranges in whole numbers from 1 ("strong performance") to 5 ("unsatisfactory").5  The CAMEL rating also ranges from 1 ("...basically sound in every respect") to 5 ("...extremely

high immediate or near term probability of failure"), although it is not an arithmetic

combination of the five performance ratings.

	Examiners determine the C, A, E, and L ratings based on a combination of objective

information and subjective judgment, with the primary focus on the former.  Ratings in these

four areas are based mostly on quantifiable measures of financial performance (e.g., capital

ratios, profitability ratios, earnings retention, percentage of nonperforming loans, deposit

volatility, etc.), often using the performance of peer institutions as a benchmark.  These four

ratings are each influenced to a lesser degree by examiners' subjective evaluation of

nonquantifiable phenomena (e.g., the adequacy of procedures and policies, the demonstrated

ability of managers to respond to unforeseen developments, and any special circumstances

that may be influencing the bank's performance).  

	Examiners base the M rating on more subjective analysis.  Examiners attempt to gauge whether management demonstrates leadership and administrative ability, is technically

competent, shows the ability to respond to changing circumstances, and has adequate internal

controls in place – qualities that are also important for controlling costs.  Examiners consider

whether the board has made provisions for managerial succession, and how well management

complies with the internal controls that are in place – indicators of how well the bank's board

of directors monitors management, which in turn might be predictive of principal-agent

problems.  Compliance with regulatory statutes and tendencies toward self-dealing are also

considered – qualities arguably related to the integrity of management – as well as

management's willingness to serve the banking needs of the community.   

	The joint distribution of M ratings and CAMEL composite ratings for 3,523 national

banks as of year-end 1992 is shown in Table 1.6  The majority of banks (59 percent) were assigned CAMEL ratings of 2 ("fundamentally sound, but may reflect modest weaknesses

correctable in the normal course of business"), and nearly as many banks (58 percent) were

assigned M ratings of 2 ("satisfactory performance").  Banks with high CAMEL ratings tend 

to have high M ratings, and vice versa – the correlation between M and CAMEL was

+.819.  Highly positive ex post correlations among the performance ratings are not surprising, given that the C, A, M, E and L ratings measure phenomena that are interrelated

ex ante by definition.  In this case, however, a strong correlation raises the concern that M ratings are not independent measures of management quality, i.e., that examiners assign M

ratings based primarily on banks' financial performances.  To investigate further, the

following OLS regression was estimated using Table 1 data:

	M = .773 + .147*C + .280*A + .184*E + .131*L

	    (.025) (.018)   (.016)   (.013)   (.016)  

Standard errors appear in parentheses.  R2 = .58, which implies that 42 percent of the 

information in M is independent of C, A, E and L.  In recognition of the discrete dependent 

variable, an ordered logit model:7

	Probability(M=j) = exp(β'X)/(1 + exp(β'X))

was also estimated, where j = (1,5) .  The resulting maximum likelihood coefficients were:

	β'X = αj - .487*C - 1.120*A - .673*E - .382*L 

where estimated α = 2.203, 6.648, 9.843, or 12.544, respectively, for j = 1, 2, 3, or 4.  (The probability that M=5 is the complement of the other four probabilities.)  All of the

estimated parameters were significantly different from zero at the 1 percent level.  The

model classified 72 percent of the M ratings correctly, i.e., in 28 percent of banks, M could

not be inferred based solely on C, A, E and L.  The independence of the M rating from the

other performance ratings is discussed further in Section V.

	Mean values for selected measures of financial performance are shown in Table 2 for banks in different M categories.  All financial ratios are for year-end 1991, the midpoint of

the eight-quarter period over which the M ratings were collected.  Complete financial data 

____________________________________________________________________

Table 1:  Joint Distribution of M Ratings and CAMEL Ratings

Most recent exam ratings as of year-end 1992.  Data for 3,523 national banks.

____________________________________________________________________

			CAMEL Rating

		  1 	  2 	  3  	4 and 5	Total

 M Rating 

	1	 259	  48	   0	   0	  	 307

	2	 217	1718	 110	   6	  	2051

	3	   1	 319	 451	  66	  	 837

   4 and 5	   0	   0	  69	 259	  	 328

    Total	 477	2085	 630	 331

____________________________________________________________________

were available for 3,345 banks.  

	M ratings vary with financial performance in the expected direction, and all of the

reported correlation coefficients in Table 2 are significantly different from zero at the 1

percent level.  M ratings improve as profitability (ROA, ROE) and as capital levels

(equity/assets) increase, and get worse as asset quality (OREO/real estate loans) deteriorates. 

The percentage of loans made to executive officers in M 4- or 5-rated banks is triple the

percentage of insider loans in M 1-rated banks, evidence that the M rating may contain

information about principal-agent problems.  Fee income as a percentage of assets is

negatively related to M, suggesting that highly rated managers moved their banks quickly

into nontraditional products and services.  

	M ratings are positively related to the efficiency ratio (noninterest costs/revenue). 

Furthermore, the evidence indicates sizeable technical inefficiencies in poorly run banks – M

4- or 5-rated banks employed one-third more workers per asset dollar, and operated two-

thirds more branches per deposit dollar, than did M 1-rated banks.  

	M ratings tended to get worse as banks got smaller.8  The average M 4- or 5-rated bank ($320 million in assets) was about three-quarters the size of the average M 1-rated bank

($441 million).  Given that national banks range in assets from less than $10 million to over

$1 billion, however, it is unlikely that bank size is the major determinant of the other results

shown in Table 2. 

 ____________________________________________________________________

Table 2:  Performance Variable Means by M Rating

Year-end 1991 data for 3,345 national banks.  

All reported correlations are significantly different from zero at the 1 percent level.

____________________________________________________________________

					    M Rating           Correlation

				  1 	  2 	  3   4 and 5      with M  

Return on Assets		.017  .012  .006   -.010	 -.445

Return on Equity		.198  .147  .053  -1.216	 -.106

Equity/Assets		.089	.085	.078	  .060	 -.254

OREO/RE Loans		.005	.011	.021	  .040	  .284

Inside Loans/Assets	.0003 .0004 .0005   .0008	  .074

Fees/Assets			.0032 .0019 .0016   .0010	 -.073

Nonint. Costs/Revenues	.622	.684	.770	  .918	  .402

Branches/$M of Deposits	.037	.042	.047	  .054	  .133

FTEs/$M of Assets 	.507	.527  .585	  .680	  .244

Assets ($M)*		$441	$411	$364	  $320	

N				 289	1970	 808	   278

* OCC multinational banks excluded from asset means.

____________________________________________________________________

III.  Measuring X-Inefficiency

	Assuming that M ratings are valid measures of management quality in commercial

banks, the results in Table 2 suggest that poorly managed banks are more X-inefficient than

well-managed banks.  The cost differences revealed in Table 2, however, are not controlled

for interbank differences in product mix, input prices, organizational form, or branching laws

that can affect costs.  This section develops a statistical model to estimate the difference in

X-inefficiency between banks with M ratings of 1 ("fully effective" management) and banks

with M ratings of 4 or 5 ("generally inferior" or "incompetent" management) while

controlling for these conditions.9

III.A  Cost Model 

	Following Berger and Humphrey (1991), a pair of thick cost frontiers were generated

by estimating the following multiproduct translog cost model separately for well-managed

and poorly managed banks:10

where:

C		= interest expense plus noninterest expense.

Yi 		= output vector, i = 1,5 (commercial and industrial loans, real estate loans,

		   consumer loans, securities, and transactions deposits)

Wm 		= input price vector, m = 1,4 (wage rate, price of physical capital, interest

		   rate paid for deposited funds, interest rate paid on purchased funds)

LIMIT	= dummy equal to 1 in limited branching states.

UNIT	 	= dummy equal to 1 in unit banking states.

BHC		= dummy equal to 1 if bank is a member of a bank holding company.

Sm 		= the share of C generated by expenditures on input m.

ε,η		= error terms assumed to capture random cost fluctuations.

	The wage rate equals salaries and benefits divided by FTE labor; the price of physical

capital equals depreciation expense divided by the original price of physical assets; the price

of deposited funds equals interest expense on transactions, savings, and time deposits

(excluding CDs > $100,000) divided by these balances; and the price of purchased funds

equals interest expense on large CDs, Fed funds, foreign deposits, and other borrowed funds

divided by these balances.  Transactions deposits equal demand deposits, NOW accounts, and

other nonsavings and nontime deposits, and are included as a proxy for the amount of

payment and liquidity services produced by the bank.  BHC controls for cost differences due

to organizational form.  LIMIT and UNIT are included to control for the impact of

branching restrictions on costs.   

	Two maintained assumptions are necessary to yield the thick frontiers:

A1.	The error terms within the well-managed (M1) and the poorly managed (M45) samples reflect only random chance and luck.  

A2.	The cost differences between the M1 and M45 samples are not due to random error but can be divided into two classes of determinants:  

	a.	conditions special to an individual bank or local banking market that are beyond the short-run control of management (e.g., prices of inputs, mix of

demand for products, organizational form, or branching laws), or 

	b.	conditions that management has the power to reduce or eliminate (e.g., agency effects or lax cost control when competitive rivalry is not intense) or that are

the result of intrinsic differences in the abilities of managers.  

Stated differently, the assumptions imply that M1 banks use a production technology that is

distinctly different from the production technology used by M45 banks, that these

technologies reflect the "quality" (experience, education, native ability, integrity, etc.) of

bank management, and that managers can influence costs only through the amount and

combination of inputs they hire.  These assumptions obviously do not hold exactly, but they

are reasonable approximations for the purpose at hand.11 

	Although the number of branches operated by a bank has been shown elsewhere to be

a significant determinant of costs, no branch variable is included in the cost function. 

According to our maintained assumptions, differences in the regressors between the upper and lower frontiers are due to exogenous factors beyond the control of the bank and hence do not reflect efficiency, while differences in the parameters between the upper and lower frontiers do reflect differences in efficiency.  The regressors in equation (1) – output mix,

input prices, branching laws, and holding company organization – are all arguably beyond

the short-run control of bank management (see section III.B), but management clearly has the

short-run ability to close existing branches or (within limits in some states) open new

branches.  Hence, branches were excluded from the cost function, and their effect on costs is

contained in the regression residual.

III.B.  Estimating Cost Inefficiency Associated with Management Quality

	The predicted percent difference in unit costs between banks in the M45 and M1 cost

frontiers is given by:

where estimated unit costs ACp,m  =  Cp(Xm)/Am and:

	Cp(.) 	= predicted total costs using the estimated parameters from equation (1) when

		   the model is estimated for banks in sample p, p = (M1,M45).

	Xm	= the vector of variable means for banks in sample m, m = (M1,M45).

	Am	= the average assets held by banks in sample m, m = (M1,M45).

Thus, DIFFM is the percentage by which the predicted unit cost of a poorly managed bank

exceeds, on average, the predicted unit costs of a well-managed bank, where well-managed

and poorly managed are defined by examiners' M ratings.

	By construction, the cost discrepancies captured in DIFFM can be traced to two

differences between the estimated M45 and M1 cost frontiers:  differences in the estimated

equation (1) parameters, or differences in the mean values of the regressors used to evaluate equation (1).  Differences in the parameters between the M45 and M1 frontiers are attributed

to the different production technologies being used by the banks – and hence, attributed to

differences in decisions made by managers – in the two samples.  In contrast, differences in

the mean values of the regressors between the M45 and M1 cost frontiers are attributed to

exogenous factors beyond the control of banks, such as local market conditions (input prices,

state branching laws), customer demand (output level, output mix), or conditions that

managers cannot alter in the short-run (organizational form).  Thus, the portion of DIFFM

owing to these exogenous factors is given by:

where ACM1,M45 is the estimated unit cost for a bank operating with the estimated parameters of the average M 1-rated bank, but which receives exogenously the market conditions faced

by the average M 4- or M 5-rated bank.  The predicted percentage difference in unit costs

between well-managed and poorly managed banks that cannot be attributed to exogenous

market conditions is given by:

INEFFM is the percentage by which the predicted unit cost of the average poorly managed

bank exceeds the predicted unit costs of the average well-managed bank, after controlling for

the impact of exogenous factors on cost.  It approximates the X-inefficiency in commercial

banks due to differences in management quality.

	MARKETM is expected to be small, but positive.  Assuming that bank examiners control adequately for market conditions, the M ratings they assign will be unrelated to

conditions beyond the control of bank managers, and MARKETM will be zero.12  However, the maintained assumption that the exogenous variables are beyond the short-run control of

management does not hold perfectly.  For example, input prices could be related to

management quality if poorly managed banks reprice their deposits less often than well-

managed banks reprice theirs.  This X-inefficiency will be captured in MARKETM.

III.C  Management-Related X-Inefficiency as a Percentage of Total X-Inefficiency 

	INEFFM can be compared to estimates of total (management-related plus

nonmanagement-related) X-inefficiency to approximate the portion of X-inefficiency in banks

associated with differences in management quality.  Again following Berger and Humphrey

(1991), the following sampling technique was used to select high-cost and low-cost banks

from the population of national banks in 1991.  The population of banks was placed in order

by asset size, and then partitioned into deciles.  Within each asset decile, banks were ordered

by average cost (the sum of interest and noninterest costs as a percentage of assets) and then

partitioned into quartiles.  The lowest cost quartiles from each of the asset deciles were

assumed to contain the most cost-efficient or "best practices" banks; this subset of banks is

denoted Q1.   Similarly, the highest-average-cost quartiles were assumed to contain the most

cost-inefficient banks; this subset of banks is denoted Q4.

	Thick frontier cost functions were estimated using cost model (1) and (2) for both the

cost-efficient and the cost-inefficient samples.  The maintained assumptions presented above about the error terms continue to hold.  The estimation results were used to generate a

corollary to INEFFM:

INEFFQ is the predicted percentage difference in unit costs between the most cost-efficient

banks and most cost-inefficient banks, after controlling for market factors.  A priori, one would expect the cost residual INEFFQ to be larger than the cost residual INEFFM, because

samples Q1 and Q4 are determined directly by costs, whereas samples M1 and M45 are

determined based on a phenomenon (M ratings) that is merely related to costs. 

IV.  Data

	There were 3,345 national banks for which complete end-of-year 1991 data were

available.  Thus, to determine the Q1 and Q4 subsamples, there were 335 banks in each asset

decile (3,345÷10), 84 banks in each quartile within these asset deciles (335÷4), and 840

banks in the upper and lower unit cost quartiles (84*10).  Table 3a contains descriptive

statistics for Q1 and Q4 banks. 

	Descriptive statistics for M1 and M45 banks are shown in Table 3b.  Of the 3,345

national banks in the overall sample, there were 289 banks in the M1 subsample and 278

banks in the M45 subsamples.  A rating of M=1 is "indicative of management that is fully 

____________________________________________________________________

Table 3a:  Descriptive Statistics for Upper and Lower Cost Quartile Banks

Year-end 1991 data for 1,681 national banks.  OCC multinational banks excluded.

Dollar amounts in thousands.

____________________________________________________________________

			 lower quartile       upper quartile 

				     % of 		    % of

				    assets		   assets

Assets		$418,610		 $440,076

Cost			  22,815   5.45%	   33,215   7.55%    

C&I Loans		  64,295  15.35%	   73,976  16.81%

Consumer Loans	  45,874  10.96%	   59,322  13.48%

RE Loans		  85,808  20.50%	  125,484  28.51%

Securities		  98,001  23.41%	   75,188  17.09%

Transactions Dep.	  97,852  23.38%	   99,274  22.56%

			 lower quartile       upper quartile 

Wage ($1,000/FTE)		$28.415		  $30.817

Rate paid on:

 Deposited Funds		  4.29%		    5.08%

 Purchased Funds		  6.51%		    7.66%

 Physical Capital		 43.50%		   56.72%

BHC Dummy		 	  0.763		    0.723

Unit Dummy			  0.112		    0.022

Limit Dummy			  0.351		    0.289

M Rating			  2.126		    2.753

____________________________________________________________________

____________________________________________________________________

Table 3b:  Descriptive Statistics for M = 1 and M = 4 or 5 Banks

Year-end 1991 data for 567 national banks.  OCC multinational banks excluded.

Dollar amounts in thousands.

____________________________________________________________________

			   M = 1 banks         M = 4,5 banks  

				     % of 		     	   % of	

				    assets		         assets	

Assets		$441,116	            $320,413

Cost			  27,798   6.30%		 24,051   7.51%  

C&I Loans		  69,596  15.78%		 54,636  17.05%

Consumer Loans	  80,912  18.34%		 25,404   7.93%

RE Loans		  91,130  20.66%		111,275  34.73%

Securities		  93,385  21.17%		 52,715  16.45%

Transactions Dep.	  94,804  21.49%		 76,442  23.86%

			 lower quartile       upper quartile 

Wage ($1,000/FTE)		$28.613		  $30.439

Rate paid on:

  Deposited Funds	 	  4.84%		    4.85%

  Purchased Funds	 	  6.65%		    7.56%

  Physical Capital 	 38.97%		   53.84%

BHC Dummy		  	  0.889		    0.612

Unit Dummy			  0.045		    0.033

Limit Dummy			  0.467		    0.207

M Rating		 	  1.000		    4.225

N			  	    289		      276

____________________________________________________________________

effective," a rating of M=4 is "indicative of a management that is generally inferior in ability," and a rating of M=5 is assigned to "institutions where incompetence has been

demonstrated."  M 4-rated banks were added to M 5-rated banks to form the subsample of

"poorly managed" banks because only 62 banks had M ratings of 5.  

	All variables used in the cost model were constructed from end-of-year 1991 data

from the Reports of Condition and Income (call report), except for LIMIT and UNIT, which

were constructed using information from Amel (1991).  All CAMEL ratings were read from

the National Bank Surveillance Video Display System (NBSVDS) and are used with the

permission of the Office of the Comptroller of the Currency.   	

V.  Results

	Estimates of overall X-inefficiency (DIFFQ, MARKETQ, INEFFQ) are reported in

Table 4, and estimates of management-related X-inefficiency (DIFFM, MARKETM,

INEFFM) are reported in Table 5.  In both tables, the results are segregated into asset

classes to reveal possible relationships between size and X-inefficiency.   (Parameter

estimates for the Q1, Q4, M1, and M45 cost frontiers are reported in the Appendix.) 

	The estimates of overall X-inefficiency are consistent with those found in other studies of commercial banks.  INEFFQ (overall X-inefficiency after adjusting for exogenous

influences) averaged about 19 percent and ranged between 15 and 38 percent.  Contrary to the

hypothesis that larger banks are able to operate closer to the cost frontier, INEFFQ increases

as banks get larger.  In contrast, MARKETQ (X-inefficiency attributable to exogenous

conditions) 

____________________________________________________________________

Table 4:  Total Cost Inefficiency

Year-end 1991 data for upper quartile and lower quartile national banks.  

AC = predicted total costs as a percentage of total assets.

Assets in millions of dollars.  

____________________________________________________________________

	 	     lower quart.	upper quart.

  Assets  		 N    AC 	 N    AC 	  DIFFQ  MARKETQ  INEFFQ

$   0-25		123  .057	114  .077	  34.2%   17.7%	16.5%

   25-50		187  .057	198  .078	  37.7%   21.6%	16.0%

   50-100		215  .056	208  .076	  35.7%   17.4%	18.3%

  100-300		202  .054	206  .072	  33.3%   13.5%	19.8%

  300-500		 35  .055	 33  .071	  28.2%   12.9%	15.4%

  500-1,000		 27  .050	 26  .069	  37.2%    1.6%	35.7%

1,000+		 51  .044	 56  .058	  29.1%   -4.5%	33.6%

Total			840		841		

Mean 							  34.8%   15.5%	19.3%

____________________________________________________________________

________________________________________________________________________

Table 5:  Management-Related Cost Inefficiency

Year-end 1991 data for selected national banks.  

Assets in millions of dollars.  AC = predicted total costs as a percentage of total assets.

________________________________________________________________________

		        M = 1      M = 4,5 	

										   INEFFM÷

  Assets  	       N    AC     N    AC 	DIFFM MARKETM INEFFM  INEFFQ     

$   0-25		23  .058	83  .072	23.5%	  5.8%  17.7%   107.5%

   25-50		42  .063	81  .076	20.8%	 11.6%   9.1%    57.1%

   50-100		80  .063	45  .073	15.9%	  5.1%  10.8%    59.0%

  100-300		90  .062	40  .071	14.5%	  3.6%  10.9%    55.1%

  300-500		24  .062	10  .068	 9.9%	  3.5%   6.4%    41.6%

  500-1,000		16  .062	 2  .067	 8.5% -11.4%  19.9%    55.8%

1,000+		14  .049	17  .054	12.2%	 -3.1%  15.3%    45.5%

Total		     289	     278		

Mean							16.7%	  4.6%   12.1%   65.4%

________________________________________________________________________

comprised about 45 percent of DIFFQ (unadjusted X-inefficiency), a larger portion than found in other thick frontier cost studies.13  This may be due to differences in the way the cost model is constructed – among other differences, earlier studies have specified separate

equations for operating costs and interest costs, included variables to measure the number of

branch offices, and estimated the model separately for banks in branching and unit banking

states.14    

	Banks judged by examiners to be poorly managed displayed substantial X-inefficiency

relative to banks judged by examiners to be well-managed.  INEFFM (management-related

X-inefficiency after adjusting for conditions beyond the control of management) averaged

about 12 percent and ranged between 6 percent and 20 percent.  However, the cost residual

between M1 and M45 banks was uniformly smaller than the cost residual between Q1 and

Q4 banks.  Overall, INEFFM averaged about 65 percent the size of INEFFQ.  As expected,

MARKETM was relatively small, averaging 4.6 percent, or about 28 percent of the total cost

residual DIFFM.  There appears to be no relationship between management-related X-

inefficiency and bank size.  

	In Section II, M ratings were shown to be positively related to financial performance,

and it was suggested that bank examiners might not assess management quality independently

from the other performance ratings.  If so, then the portion of X-inefficiency associated with management quality may be less than 28 percent of banking costs, and differences in

management quality might account for less than 65 percent of total X-inefficiency in banks. 

To address this concern, the results of the ordered logit model presented in Section II were

used to separate the M rating into two components:  information coincident with that portion

of a bank's financial performance represented by the C, A, E, and L ratings, and information

that is orthogonal to these four performance ratings.  Each bank's actual M rating was

compared to the M rating predicted for it (i.e., the M category assigned the highest

probability estimate) by the logit model.  A bank was assumed to be well-managed if its

actual M rating was better than its predicted M rating, and poorly managed if its actual M

rating was worse than its predicted M rating.  This procedure resulted in 515 well-managed

banks and 558 poorly managed banks.15  Thick cost frontiers were estimated for these two sets of banks, and versions of DIFF, MARKET, and INEFF were generated.  

	The results are reported in Table 6.16  On average, the X-inefficiency (INEFFL) associated with this "pure" measure of management quality increased costs by only about 11⁄2

percent, and equaled only about 7 percent of total X-inefficiency (INEFFQ).  There are two

basic ways to interpret these results.  At one extreme, one could conclude that management

quality plays only a small role in determining cost differences between banks.  At the other

extreme, one could conclude that management quality plays a large role in determining inter-

bank cost differences, but that roughly 90 percent of its impact (7 percent ÷ 65 percent) is 

________________________________________________________________________

Table 6:  Management-Related Cost Inefficiency from Ordered Logit

Year-end 1991 data for selected national banks.  Assets in millions of dollars.  

AC = predicted total costs as a percentage of total assets.  Predicted M = management

rating with highest estimated probability in the ordered logit model.

________________________________________________________________________

		    actual M <   actual M > 

	          predicted M  predicted M	

										   INEFFL÷

  Assets  	       N    AC     N    AC 	DIFFL MARKETL INEFFL  INEFFQ     

$   0-25		 57 .066	117 .066	-0.1%	  0.4%  -0.5%   - 0.3%

   25-50		 84 .066	151 .067	 1.9%	  2.3%  -0.4%   - 0.3%

   50-100		126 .066	142 .064	-2.5%	 -4.7%   2.2%    12.2%

  100-300		138 .062	107 .062	-0.3%	 -2.3%   2.0%    10.1%

  300-500		 42 .063	 17 .063	-0.0%	 -3.5%   3.5%    22.7%

  500-1,000		 25 .061	 10 .059	-4.4%  -4.4%   0.0%     0.0%

1,000+		 43 .041	 14 .053	30.9%	 24.4%   6.5%    19.4%

Total		      515	      558		

Mean							 1.2%	 -0.2%   1.4%     7.5%

________________________________________________________________________

reflected in measurable financial performance captured by examiners in the C, A, E, and L

ratings (e.g., good underwriting standards that result in fewer costly nonperforming loans, or

cost controls that result in higher operating margins), while the other 10 percent of its impact

is associated with less tangible financial outcomes (e.g., good internal controls that result in

lower agency costs).  	

VI.  Conclusions

	It seems a straightforward proposition that good managers will run their banks

efficiently and bad managers will run their banks inefficiently.  The results found here for

national banks in 1991 confirm this common sense proposition.  After adjusting for exogenous

factors beyond the control of management, unit costs at national banks with  "inferior" or

"incompetent" management (according to bank examiners) averaged about 12 percent higher

than unit costs at banks with "fully effective" management.  This difference amounts for

roughly two-thirds of total banking X-inefficiency found both here and in other studies.  This

suggests that a large portion of the cost inefficiencies present in commercial banks is

associated with – though perhaps not caused by – the quality of the managers that run those

banks.  When the 12 percent figure was disaggregated, about 90 percent was attributed to

differences in management quality associated with subpar financial performance (e.g., poor

asset quality or low earnings) and the remaining 10 percent was attributed to differences in

management quality not reflected by financial performance (e.g., poor internal controls that

might foster principal-agent problems).  

	Some caution should be exercised in interpreting these results, which rest on several assumptions about the characteristics of error terms in the statistical model, as well as on the

assumption that M ratings sufficiently capture interbank differences in management quality. 

Although no good substitute measure of management quality exists, techniques that make

different assumptions about the error terms are available (e.g., stochastic frontier or data

envelopment), and might be used to test the robustness of the results found here. 

	Finally, the results found here suggest that an active market for corporate control of

banks, which supposedly identifies bad managers and replaces them with better managers,

would be a potent weapon for reducing X-inefficiency.  The empirical literature on bank

mergers, however, raises doubts about the efficiency-enhancing potential of bank mergers. 

Although some studies of bank mergers have found post-merger improvements in stock prices,

earnings, loan-to-asset ratios, and labor expenses, the existing literature contains little evidence

of post-merger improvement in X-inefficiency.17  Future research might attempt to reconcile the results of these studies.    

References

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	A Review and Preview of Research Past, Present, and Future."  Journal of Banking and

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	Bank of Chicago, Economic Perspectives, July: 11-32 (1991a).

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	Appendix	

	Equation 1 parameter estimates for Q1, Q4, M1, and M45 samples.

	   α0		    α1	    	    α2	 	    α3		    α4	 	   α5    

Q1	4.40828*	-0.22839 	 0.54268*	 0.51123*	-0.00522	-0.10510 

Q4	3.59959*	-0.53617*	 0.38666*	-0.06678	 0.31591*	 0.49178*

M1	4.28801	 0.42704	-0.20161	 0.20593	-0.64860*	 1.07170*

M45	1.83230	-0.09688	 0.18903	-0.03932	-0.05907	 0.52832  	

	   β11		   β22	 	    β33	    β44	   β55	   	   β12	

Q1	0.065516*	0.02812*	 0.09727*	 0.11592*	0.14083*	-0.02441*

Q4	0.031516*	0.05541*	 0.20275*	 0.09241*	0.08866*	-0.02708*

M1	0.047351*	0.08056*	 0.13685*	 0.09322*	0.12620*	-0.02555 

M45	0.097173*	0.04369*	 0.16195*	 0.09774*	0.16431*	-0.02912*

	    β13	     β14		    β15	    β23	    β24	     β25

Q1	-0.02671*	 0.00482	-0.01789	 0.00542	-0.02870*	 0.02003

Q4	-0.02091*	-0.00412	 0.02577*	-0.01058*	-0.03359*	 0.02080*

M1	-0.02382*	-0.02844	 0.02595	-0.00773	-0.01259	-0.01032

M45	-0.02281	-0.00400	 0.03597	 0.01627	-0.01536	-0.02011

	    β34	    β35	    β45	    γ1		    γ2		    γ3	

Q1	-0.01513*	-0.05183*	-0.07208*	-0.59212*	 0.51480*	 0.75634*

Q4	-0.02966*	-0.13151*	-0.01327	 0.03623	 2.15105*	-0.74878*

M1	 0.01691	-0.11361*	-0.05043*	-0.61779	 2.31158*	-0.92748

M45	-0.04195*	-0.09108*	-0.02433	 0.72170	 1.31255*	-0.80376

	    γ5	  	      δ11	    δ22	    δ33	    δ44	    δ12	

Q1	 0.32097*	 0.14883*	 0.06987 	 0.07141*	-0.00385	 0.02142

Q4	-0.43850*	-0.00179	 0.30252*	 0.02773	 0.03397*	-0.10461*

M1	 0.23369	 0.16329	 0.42065*	 0.04218	 0.01857	-0.22455*

M45	-0.23049	-0.13777	 0.16800	-0.20137*	-0.00705	-0.03845

	    δ13	    δ14	    δ23	    δ24	    δ34 	    θ11

Q1	-0.13713*	-0.03312 	-0.09526  	 0.02702	 0.00995	 0.06126*

Q4	 0.05729	 0.04910	 0.05576*	-0.09331*	 0.01023	 0.10958*

Q1	 0.14302	-0.08177	-0.21995*	 0.02844	 0.03475	-0.03616

M45	 0.13326	 0.04295	 0.05520	-0.09111*	 0.05520	 0.03706 	

	    θ12	    θ13	    θ14	    θ21	    θ22	    θ23

Q1	-0.06769*	 0.02700	-0.02068*	-0.06556*	 0.11439*	-0.03880*

Q4	-0.05128*	-0.02080	-0.03748*	-0.05847*	 0.05658*	-0.04354*

M1	 0.03073	 0.00875	-0.00333	 0.00604	-0.05927	 0.03963 

M45	-0.04591	 0.00600	 0.00274	-0.01413	-0.05815	 0.06248 	 

	    θ34	    θ31	    θ32 	    θ33 	    θ34	    θ41

Q1	-0.01003	-0.07186*	 0.04111	 0.013074	 0.01766*	 0.02951

Q4	 0.04542*	 0.01723	-0.05603*	 0.022597	 0.01619	-0.05344*

M1	 0.01359	-0.01442	 0.02370	 0.001571	-0.01086	 0.10388*

M45	 0.00980	 0.00564	-0.02287	-0.016810	 0.03403*	 0.01978

( * indicates statistical significance at the 10 percent level.)

	Appendix	

	Continued.

	    θ42	    θ43	    θ44	    θ51	    θ52	    θ54

Q1	-0.01151	 0.01072	-0.02873*	 0.03268	-0.06448	 0.00261

Q4	-0.01695	 0.05048*	 0.01990*	 0.00118	 0.00325	 0.02957

M1	-0.04401	-0.03227	-0.02760	-0.08696	 0.04018	-0.00998

M45	-0.01830	 0.01310	-0.01459	-0.02755	 0.11351	-0.04927

	    θ54	    λBHC	    λUNIT	    λLIMIT	  R2   

Q1	 0.02918*	-0.00628	-0.03526*	-0.00900	0.9891

Q4	-0.03401*	 0.02548*	-0.03790	 0.00300	0.9909

M1	 0.05676	 0.05133*	-0.02776	-0.01655	0.9930

M45	-0.03669	 0.02316	-0.08143	 0.01137*	0.9917

( * indicates statistical significance at the 10 percent level.)