Community Banks and The Boundaries Of The Firm 26 …scottjon/documents/Commun… · · 2006-04-18Further Evidence on the Uniqueness of Community Banks and Boundaries of the Firm:

Further Evidence on the Uniqueness of Community Banks and Boundaries of the Firm: Service Quality and Small Business

Jonathan A. Scott Associate Professor of Finance

Fox School of Business and Management Temple University Philadelphia, PA

March, 2006

Abstract

Using survey data from 1987 through 2001, this paper analyzes whether a comparative advantage exists for community banks in service delivery to small businesses. This performance measure is based on ratings of a set of characteristics related to service delivery: accessibility of loan officer, number of services offered, capability of staff, staff turnover. After controlling for the self-selection of small banks based on characteristics that are important to the conduct of their banking business, owners of small firms are found to rate small bank service delivery better than large bank service delivery. This conclusion holds over the entire period that includes dramatic consolidation within the banking system as well as technological change in the delivery of financial services. Keywords: community banks, organizational architecture, small business Telephone: 215.204.7605 Fax: 215.204.1697 E-mail: [email protected] I want to thank Scott Hein for helpful comments on an earlier version.

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Further Evidence on the Uniqueness of Community Banks and Boundaries of the Firm: Service Quality and Small Business

1. Introduction

Deregulation and technology have accelerated the reduction in both the number of

small banking organizations and their share of total banking assets (DeYoung et al,

2004). The use of credit scoring in small firm underwriting has also expanded –

especially by large banks attempting to increase their share of small business lending,

striking at the core of relationship lending (Berger et al, 2005, Akhavein et al 2005),

while some recent evidence suggests that relationship lending is not profitable for

commercial bank (Ergungor, 2005). The confluence of these forces raises questions

about the future of community banks and whether they have any competitive advantage

in the delivery of banking services – especially to small firms that are a key customer

segment.

Despite these competitive threats, new community bank charters continue to be

approved by the FDIC and publicly traded community banks often trade at higher

multiples than their large bank counterparts. Thus, community banks appear to be

surviving even as they face external threats of competition, technology and deregulation.

Recent theoretical papers have developed a rationale for the uniqueness of community

banks based on their organizational architecture (e.g. Berger et al, 2004, Berger and

Udell, 2002, Brickley et al, 2003), with supporting empirical evidence in Berger et al,

2004 and Brickley et al, 2003.

This paper provides empirical evidence on another dimension where community

banks may be unique: service quality. Survey data from a sample of small firm owners in

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the United States at three points in time (1987, 1995, and 2001) is used to test the

hypothesis that small firms rate community bank service quality better than larger banks.

Each survey asks the owners if they have noted a change (better, no change, or worse) in

a set of characteristics of the financial institution they deal with most often (e.g.

accessibility of the account manager, number of services offered, capability of staff, staff

turnover). The empirical strategy is to relate bank size and other control variables to a

service quality index that summarizes small firm reports of changes in these

characteristics of service quality.

The paper is connected to the literature on the uniqueness of community banks in

several ways. First, the literature has generally assumed that endogeneity exists between

firm size and their choice of a bank when testing for an association between bank size

and banking outcomes for small firms (e.g. Berger et al, 2004, Scott, 2004). This paper

documents that small firms self-select bank size based on characteristics they rate

important in the conduct of their financial business – thus providing evidence of a

specific linkage that identifies a source of this endogeneity.

Second, the paper examines whether small banks, as a result of their

organizational architecture, are able to create a competitive advantage in service quality

for small firms as suggested by Brickley et al (2003). Evidence is provided to show that

owners conducting their financial business at small banks are more likely to report

improved service quality compared to those that have a larger bank as their primary

financial institution. This association, which is both statistically and economically

significant, is consistent with the idea that community banks are able to create a

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competitive advantage based on their organizational architecture for this customer

segment.

Finally, the paper offers some perspective on how changes in technology and

bank structure have affected the ability of small banks to serve small businesses over

time. While the survey data used in this paper do not permit a direct test of the effect of

technology or bank structure on the importance of local information, they do permit an

analysis from a pre-technology, pre-consolidation period (1987) to 2001 when the rate of

change and penetration of technology and bank consolidation may have begun to

stabilize. The paper documents that this service quality advantage has persisted over time

despite improvements in technology and consolidation that may have given larger banks

significant advantages in product and service delivery through scale economies.

The data used in the paper are taken from the periodic Credit, Banks and Small

Business survey of the membership of the National Federation of Independent Business

(NFIB). Like any survey, these responses have potential biases and measurement errors.

For example, the responses may not be representative of the NFIB membership from

which the survey respondents are solicited; or the NFIB membership may not be

representative of overall small firm experience in the United States. The respondents

may also not have understood the questions asked of them, creating an error in the

measurement of the key dependent variable relative to the desired theoretical construct –

a measure of service quality. Each of these issues is considered in the interpretation of

the results presented in the paper.

The remainder of the paper is as follows. Section 2 provides an overview of why

community banks may be different. In section 3 the survey data used in the analysis is

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presented. The characteristics central to a small firm banking relationship used in the

NFIB surveys are analyzed in section 4 while the empirical predictions and model

specification are presented in section 5. The empirical results are presented in section 6

and conclusions are offered in section 6.

2. What makes community banks unique?

Why should community banks be unique? The answer is not clear given that the

assets in the banking industry are not firm-specific. In addition, technology allows banks

of all size to access various services demanded by customers and provides substantial

latitude to choose their degree of horizontal and vertical integration (Brickley, et al,

2003). Thus, if community banks are unique, it must be related to how their

organizational architecture is adapted to meet the needs of various customer segments.

Two related strands of literature have identified how organizational architecture

provides community banks with a comparative advantage in serving the banking needs of

small firms.1 First, Berger and Udell (2002) and Berger, et al (2004) describe how

organizational architecture and the delegation of decision making affect the type of

information that will be used in credit granting decisions. Organizations with flatter

organizational structures, with ownership and decision rights largely invested in the same

individuals, are argued to be more amenable to processing soft information that can be

1 DeYoung et al (2003) provide a descriptive approach to the uniqueness of community banks by

developing a strategic map of the banking industry that takes into consideration how deregulation,

technological advances and increased competition have affected the banking industry structure. They argue

that small banks may have the ability to differentiate themselves by offering banking services to small

businesses that, in their grid, fall in the high cost, more personalized service quadrant.

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important to resolving asymmetric information problems often associated with small

firms.

A second approach, presented in Brickley, et al (2003), is based on incentive

contracting theory. Here Brickely, et al (2003) argue that small, locally owned banks can

have a competitive advantage within specific environments as a result of granting

significant decision rights to the local office managers. They provide evidence that

community banks have more concentrated ownership in Texas, which has a history of

unit banking, and California, which has a history of branch banking, to show that this

organization is not dependent on regulatory legacies. Thus, Brickely, et al (2003)

conclude that a more concentrated ownership structure and delegated decision rights can

allow small banks to gain a comparative advantage based on location, customer base, or

distribution of knowledge.

A number of empirical papers have provided indirect evidence in support of these

theories. Cole et al (2004) and Berger et al (2005) provide evidence on the uniqueness of

loan underwriting by smaller banks, Carter et al (2004) on small bank risk-adjusted small

business loan returns, and Scott (2004) on small bank production of soft information.2 In

several of these papers instruments are used for bank size arguing that small firms will

self-select small banks (e.g. Berger et al, 2005, Scott, 2004). Although reasons for why

endogeneity may exists are described in these papers, the only empirical evidence

provided is a discussion of the difference in magnitude of the coefficient on the

instrumented versus un-instrumented bank size variable. This paper provides evidence

why bank size and firm size may be endogenous by documenting that those small firms

2 However, Ergungor (2005) offers evidence that relationship loans do not add value to small community

banks.

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self-select small or large banks, depending upon the characteristics that are important to

them in conducting their banking business.

Small business customers are an ideal customer segment for testing whether

organizational architecture permits the creation of an organizational strategy based on

service quality to small firms. Small firms generally have limited time and financial

resources, as well as limited demand for more complex banking services. Local decision

rights may enable small banks to provide flexibility in their service offerings or policies

to meet ‘one-off’ requests of small firm owners (within the parameters of banking

regulations) that would not be possible within a larger banking organization. In addition,

local decision rights accompanied with concentrated ownership allow the small firm

owner to deal with the owners of the bank, providing more continuity and peer-to-peer

contact than would happen in a branch of a larger bank.

This paper proposes to test whether small banks have a comparative advantage

over large banks in terms of owner assessment of service quality – a dimension of

banking services that has not been examined in the literature. The paper does not propose

to offer a test between the Berger et al (2005) thesis of allocation of decision rights versus

the Brickley et al (2003) thesis of incentive contracting as explanations for service quality

advantages. However, good service quality can be experience by information opaque

small firms that are borrowers or non-borrowers. For the latter, delegated decision rights

and the ability to use soft information for credit decisions are largely irrelevant. Thus, a

significant association between bank size and service quality that does not differ between

borrower and non-borrowers may be more suggestive that the incentive contracting

explanation explains the advantage.

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2. Where do small firms bank?

2.1 Credit, Banks and Small Business Surveys

The data in this study come from the Credit, Banks and Small Business (CBSB)

survey conducted by the National Federation of Independent Business in 1987, 1995, and

2001. There were 1921 respondents in the 1987 survey, 3642 in the 1995 survey and

2223 in the 2001 survey. For each survey, the questionnaire was mailed twice within a

two-week interval to the random sample of members and duplicate responses were

eliminated. The response rates have varied over time: 26% in 1987, 20 percent in 1995,

and 18 percent in 2001.3 Although self-reported, there is no reason to believe that there is

any systematic self-reporting bias in the responses to the survey questions. The data are

similar to that gathered by the Board of Governors of the Federal Reserve System in their

Survey of Small Business Finance through a commissioned telephone survey of small

firms drawn from the Dun and Bradstreet files. Neither the NFIB nor SSBF surveys

attempt to verify the accuracy of the self-reported data.4

Historically, there has been little survey response bias with respect to number of

employees, sales, industry, and region of the NFIB membership. The characteristics of

the NFIB membership do not show large differences from those of all small businesses,

although the discrepancies widened in the 2001 survey. Even if the survey respondents

were not completely representative of the small business population, the objective of the

research is only to identify significant association between bank size and service

3 A similar decline in the response rate has been experienced with the Board of Governor’s Survey of Small

Business Finance. 4 The Fed collects a significant amount of income statement and balance sheet data that are cross-checked

for consistency through internally developed algorithms.

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performance. Representativeness of the data would be more important, for example, if

the objective was to infer some population response, such as an average rating of bank

service performance. Nonetheless, as part of the sensitivity analysis of the baseline

results, the effect of weighting the data by United States Census Bureau proportions

(using a 1997 base) will be examined on the 2001 survey results.

2.3 Data description

Key demographic statistics are presented in Table 1 for each survey for all firms

that reported a commercial bank as their primary institutions. Respondents reported bank

size intervals and the name of their bank. All responses were cross-checked for accuracy

when a bank name was given. Although the data on bank size in the surveys are not

sufficiently detailed to investigate ownership concentration, we rely on the Brickley, et al

(2003) work and assume that the Texas experience is generally representative of other

regions in the United States.5 A small bank or CFI (community financial institution) is

defined as a bank with assets less than $1,000 million and large financial institutions

(LFIs) are banks with assets over $1,000 million. This definition is somewhat arbitrary

because community banks can also be defined in terms of deposit mix or loan mix

(DeYoung et al, 2004), while the legal definition of a community financial institution is

much lower.6 However, this asset threshold has frequently been used in the literature

(Brickley et al, 2003, Cole et al, 2004). An alternative definition using a $500 million

5 Brickely, et al (2003) provides evidence for California community banks as well. 6 See CFR, 12 USC 1422(13). Community banks were defined as banks with total assets that averaged

under $500 million for the previous three years as of the date of enactment, with adjustments made

annually based on CPI inflation.

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threshold to define a CFI was also used and had no material effect on the results

presented below.

The proportion of small firm owners using a community bank as their primary

financial institution fell from 65 percent in 1987 to 47 percent in 2001 – yet the

proportion of owners located in urban locations remained between 61 and 65 percent.

The regional distribution of firms has also been fairly stable. Table 2 shows that the

proportion of firms banking at CFIs in metropolitan areas (MSAs) decreased from 58

percent in both 1987 to 36 percent in 2001. This decrease could be attributable to a

higher incidence of merger activity involving CFIs in urban areas, resulting in a fewer

small bank charters. There are some significant regional changes with the proportion of

firms banking at CFIs falling more than average in the Midwest and Plains states. This

shift also may be attributable to merger activity, but a rigorous analysis of this

explanation is beyond the scope of the paper.

The firm characteristics within the surveys have shown some changes over time.

Both firm age and size (as measured by fulltime equivalent employees) have increased.

Comparisons of form of business are somewhat difficult because the ‘corporation’

category was changed to add ‘S-corporation’ in 1995. If the S-corporations are really

proprietorships, then form of business has been relatively stable over the 14 year period.

Some changes have occurred in industry distribution, reflecting changes in the overall

economy. Construction (which includes home remodeling) and agriculture (which

includes nursery/lawn care) have increased, while retail has decreased – perhaps

reflecting the effect of ‘big box’ stores on the number of smaller retailers in smaller urban

markets.

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2.3 Characteristics of large and small bank borrowers

Table 3 shows the means of the key firm descriptors based on whether their

primary financial institution is a CFI or LFI. Owners banking at CFIs tend to be

disproportionately located in the Plains states, which may reflect a unit-banking legacy

before deregulation of branching restrictions. Reports of merger and acquisition activity

show no apparent effect on the selection of a CFI as the primary financial institution.

About 22 percent of the respondents in 1995 and 2001 (this question was not asked in

1987) chose CFIs as their primary financial institution, although some may have changed

in response to a merger involving their former bank.7

Table 3 also suggests that owners at CFIs have a different demographic profile

than those at LFIs. Owners currently doing business at CFIs tend be younger (but only

significant in the 2001 survey) and consistently smaller (in terms of employment and

sales), which suggests a possibly more risky profile. Owners at CFIs also tend to be

organized more frequently as proprietorships and less likely as corporations, an outcome

that is likely correlated with size. There are a few significant industry differences, but

none are significant across the three surveys.

3. What is important for small firms to conduct their banking business?

3.1 Characteristics central to a banking relationship

A unique aspect of the CBSB surveys is the inclusion of a set of characteristics

central to the conduct of their financial business that owners rank for importance to their

banking business. These characteristics, which have been part of the surveys since they

began in 1980, include knows my business, provides helpful advice, wants cheapest

7 In the 2001 survey, owners were asked to give a reason why they changed banks; 18 percent reported

changing banks because of a merger. This question was not asked in the 1987 or 1995 survey.

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money, location, reliable as a source of credit, knows my industry, speedy decisions, easy

access to the loan officer, wide range of services, knows local market, and social contact

with loan officer. The importance rankings are based on a scale of 1 (not important) to 5

(important) in 1995 and 2001. In 1987 the rankings were based on a scale of 1 to 3; the

deal with one person only characteristic in 1997 was replaced by social contact with loan

officer characteristic in 1995.

Many of these characteristics address critical aspects of relationship lending where

locally-owned small banks with delegated decision authority may have a comparative

advantage. For example, the characteristics knows your business, knows your industry,

knows the local market/community, and social contact with loan officer capture aspects of

soft information that a small bank is more likely to use in credit decisions, as opposed to

large banks that might be driven more by credit scoring technologies (see Berger and

Udell, 2002). Locally owned and staffed banks are likely to be more knowledgeable

about local markets, provide better continuity in staffing and have the flexibility to adapt

products to specific customer needs.

The relative ranking of the importance ratings is fairly constant over time (Table 1).

Small firms place the highest importance on knows my business, followed by reliable

source of credit, speedy decisions, and easy access to loan officer. All of the importance

ratings are significantly correlated at the .01 level, but the correlations are not high (Table

4). Comparing the 2001 to 1995 survey, there are few changes in the ratings except for

location, where the average rating increased to 4.26 from 3.87. The increased importance

of location may reflect the effects of mergers, which could have led to branch or main

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office closures, because no noticeable change in the distribution of owners by location

(region or MSA) occurred between surveys.

3.2 Bank size and importance ratings

Table 3 also includes the mean ratings of the importance characteristics broken

down by CFI and LFI. In 1987 there were few significant differences in owner ratings of

importance, but by 2001 owners at CFIs rated eight of the 11 characteristics significantly

higher (knows my business, helpful advice, reliable credit source, knows my industry,

speedy decision, access to loan officer, knows local market and social contact).

A more formal evaluation of the relationship between importance rankings and

bank size is presented in Table 5, where logistic regression is used to model the owner

choice of bank size as a function of the importance ratings of the 11 characteristics

related to owner conduct of their banking business and control variables for firm

characteristics (years in business, size, form of business, and industry classification) for

each survey.

These results reaffirm the conclusions drawn from Table 3 regarding the self-

selection of banks by owners depending on what they need in a banking relationship.

In 1987, before the merger wave began, small firms appear to have somewhat randomly

selected banks with respect to the importance of the service characteristics because only

three of the characteristics were significant. By 1995, five characteristics were positively

associated with the choice of a community bank: knows my business, reliable credit

source, easy access to loan officer, knows local market and social contact with loan

officer. This trend continued in 2001, although easy access to loan officer and knows

local market are no longer significant.

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This increase in the number of significant characteristics, especially those related to

relationship banking (e.g. knows my business, reliable credit source, easy access to loan

officer, knows local market and social contact with loan officer ), is a matter of

speculation. It could be due to well-publicized problems with mergers and acquisitions

during the 1990s. Or it could be the increased emphasis on technology at larger banks,

the antithesis of relationship banking desired by many small firms. While the data are

not sufficiently rich to answer these questions, it is clear that owners are more likely to

select a community bank after 1995 if relationship banking dimensions are rated

important to conducting their banking business.

Overall, the results in Tables 3 and 5 support the idea that small firms do not

randomly select their bank size. Neither large nor small banks have an inherent

advantage in meeting the banking service needs of small firms as revealed by what is

important to owners in the conduct of their banking business and the size of the bank they

choose to do business with. While large banks may be organizationally better suited to

meet the needs of small firms that rate location and wide range of services offered most

important, they do not appear to have an organizational advantage in meeting service

needs such as knows my business and reliable source of credit. Thus, these results

provide a direct linkage between characteristics of small firms and their choice of bank

size, reinforcing the need to control for endogeneity between characteristics of the firm

and the choice of bank size when analyzing small firm banking outcomes.

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4. Service quality change and bank size

4.1 Service quality change ratings

The survey respondents are asked if they have noticed a change in a set of

characteristics that describe various aspects of service quality (accessibility of account

manager, number of services offered, capability of staff, and staff turnover) and then rate

the change as ‘better (=1)’, ‘no change (=0)’, or ‘worse (=-1)’. Four of the characteristics

are common across the three surveys, while the 2001 survey added two more: quality of

service and ease of doing financial business. The mean results for each characteristic are

presented in Table 1, along with a Service Quality Change Index that sums the rating for

each characteristic. From Table 1 it can be seen that there has been a slight deterioration

in ratings of accessibility of account manager and capability of staff between 1987 and

2001. Ratings of staff turnover decreased dramatically between 1987 and 2001 (-.05 to -

.26), while the ratings for number of services offered has increased (.11 to .25).

The use of this variable as a measure of service quality poses several conceptual

problems. The first is that it is not an ‘arms-length’ measure of service and depends on

the owners interpretation of the attributes listed in the survey. For example, Dick (2005)

used advertising expenditures and number of employees as proxies for quality of service

to show that service quality increases in larger markets and is higher for dominant banks.

Unfortunately, the survey data have incomplete information on the banks used by the

owners and bank-related proxies of service quality cannot be generated. However, if the

lack of asset- or technology specificity of banking assets results in the decision rights and

organizational architecture mattering more in service quality, then the proxies used by

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Dick (2005) may be only loosely connected to service quality. In this case, using a

customer’s assessment of quality may be a better alternative.

The other conceptual problem is whether the owner assessments reflect actual

changes in service quality or some other change in the owner’s relation with the bank.

For example, owners that are frequent borrowers may have a better assessment of service

than owners using the bank only for transactions services. Or, owners that have not been

successful in their most recent loan attempt may have a negative opinion about the

change in the quality of service, independent of whatever efforts the bank has made to

improve service. Each of these questions is addressed in the empirical tests below.

There is a remarkable consistency across time in CFI service quality as shown in

Table 3. For every survey, owners with CFIs as their primary bank rate accessibility of

account manager and staff turnover significantly higher. Capability of staff is rated

higher by these owners in 1987 and 2001. Finally, for the two new attributes in the 2001

survey, quality of service and ease of doing business, owners at CFIs rate their bank’s

performance significantly higher. The Service Quality Change Index, which incorporates

all of the characteristics, is significantly higher at CFIs for every survey.

4.2 Model specification

The baseline model relates the Service Quality Change Index (the sum of the -1,

0, or +1 ratings for each question) to bank size and a set of control variables as shown in

equation (1). The control variables include years in business, full-time equivalent

employees, form of business, and 1-digit SIC industry classification.

(1) Service Quality Change Index = a0 + b1CFI + b2ln(years in business) + b3ln(FTE) + b4form of business +b51-digit SIC industry classification + e

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The null hypothesis is that b1=0, i.e. the choice of small firm owners to bank at a

CFI is independent of the Service Quality Change Index. Rejection of the null hypothesis

would provide support the proposition that CFIs have a comparative advantage over

larger banks in the quality of bank service as reported by to small firms.

Ordered probit is used to estimate the model. Linear regression would treat all of

the differences within the service quality index as equal, e.g. a change between -2 and -1

is equivalent to a change between 1 and 2. These differences, however, are only a

ranking and thus ordered probit would be more appropriate (Greene, 2000). The

downside is that the marginal effects of the independent variables on the dependent

variable are more difficult to interpret.

4.3 Endogeneity of bank size and errors in variable

Although the results presented in section 3.2 suggest that bank size is endogenous,

a two-stage model similar to a Hausman test (described in Wooldridge, 2002) is used to

formally test for endogeneity in equation (1). In the first stage, the bank size (CFI=1, 0

otherwise) variable is estimated as a function of the control variables in equation (1) and

a set of instruments described below and represented by y in equation (2). The residuals

from this equation, u, are used in the second stage where they are entered into equation

(1’). If the coefficient on the residual term u, is significant, the null hypothesis of no

endogeneity is rejected, and a 2SLS approach should be used with the instruments in y as

the identifying variables for the choice of bank size.

(2) CFIi = a1 + bx +cy + u, where x is the set of predictors in (1) and y includes the

instruments for bank size.

(1’) Service Quality Change Index= a0 + bx +cy + γu + e’

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The instruments used for the choice of a CFI include region, MSA employment, a

Herfindahl-Hirschman Index of deposit concentration, and whether a recent merger

occurred. The 1987 survey does not have sufficient information to compute a HHI or to

identify population density via a zip code. As such, the instrumental variable approach

will have less explanatory power because the explanatory variables are limited to city

size and region of the country.

Another possible problem in the multivariate estimation is that the owners did not

understand how to interpret the questions about the change in service quality. However,

these questions have been asked in all of the surveys since their inception in 1980 and the

respondents have either had some experience with preparing the questionnaire or reading

reports about the surveys distributed by the NFIB. Even if there is some error in the

measurement of the Service Quality Change Index, there is no reason to expect that the

error is correlated with any of the independent variables. Thus, the coefficient estimates

should be unbiased and consistent if there are no other specification problems.

5. Empirical results

5.1 Baseline estimates

The ordered probit results are presented in Table 6, where Panel A has the 1987

estimates, Panel B the 1995 estimates and Panel C the 2001 estimates. Only the years in

business and FTE control variables are included in the table for ease of presentation.

The results in Column (1) reveal that the residual term estimated from equation (2) is

significant in 1995 and 2001, which allows rejection of the null hypothesis of no

endogeneity for the CFI variable. To provide consistency in the presentation of the

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results in Table 6, the instrumented CFI variable is presented in Panel A for the 1987

survey even though the null hypothesis of no endogeneity cannot be rejected.

The coefficient on the instrumented bank size variable in column 2 is positive and

significant for all survey years in the baseline specification as expected, i.e. owners at

CFIs consistently report a higher Service Quality Change Index than those at LFIs. Years

in business has no effect on the owner service quality assessment, while firm size is

positively associated with service quality assessment in 1987 and 1995, it is not by 2001.

The observed and predicted values for each outcome of the Service Quality

Change Index are presented in Table 7. The predicted probabilities tend to be higher for

than the observed distribution for negative values of the Service Quality Change Index

and lower than the positive. The marginal effect of banking at a CFI on the Service

Quality Change Index is shown in Figure 1.8 An owner banking at a CFI has a lower

probability of reporting lower index values (-2, -3, etc.) and a higher probability of

reporting higher index values (1, 2, 3, etc.).

These marginal effects are economically meaningful, especially for the 1995 and

2001 surveys. An owner at a CFI has a .43 lower probability of reporting a Service

Quality Change Index of -3 or lower in 2001 and a .17 probability of reporting an index

value of 2 or higher. Similarly for 1995, an owner at a CFI has a .35 lower probability of

reporting an index value of -2 or lower and a .07 value of reporting an index value of 2 or

higher. The marginal effects on the Service Quality Change Index for 1987 are much

8 The change in the probabilities for the various Service Quality Change Index values is computed by

comparing the probability of the Service Quality Change Index at CFI=1 and CFI=0 while holding the

other variables at their sample means. See Greene (2000).

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lower, in part because the coefficient on the CFI variable is much lower than the other

two surveys.

Because these marginal effects are change in the probabilities, they can be

compared over time. The most striking conclusion from Figure 1 is that the effect of a

CFI choice on the probability of reporting higher service quality performance has

increased between 1987 and 2001. At a minimum, the marginal effects suggest that no

diminution in the effect of choice of bank size on the ratings of service quality over time,

despite the dramatic changes in the banking sector over this period.

The persistence of the small bank service quality advantage during a period of

dramatic consolidation in the banking industry and technological change is consistent

with the idea that organizational architecture can create a competitive advantage. In other

words, community banks with localized ownership and delegated decision authority are

unique - not just smaller versions of large banks - and have not been disadvantaged by the

potential economies of scale and branching abilities of large banks. This finding also

complements the finding of Cole et al (2004) who found higher loan application

acceptance rates at small banks and Scott (2004) who found that small banks do a better

job of producing soft information.

The results do not permit any precise conclusion about the role of delegated

decision rights or incentive contracting in explaining the significance of the instrumented

bank size variable. However, the lack of significance of years in business, a key proxy

for the degree of information opacity, as well as the lack of significance of firm size in

the 2001 survey does support the idea that incentive contracting is at least as important as

delegated decision rights for the service quality advantage of small banks. If delegated

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decision rights were primarily responsible for better service quality firm age or size, the

association between these variables and service quality should have been shown some

pattern of significance across the surveys.

5.2. Sensitivity tests

The effect of bank size on service quality ratings may be a function of location.

Two reasons have been offered why location should matter. Petersen and Rajan (1995)

argue that competition reduces the incentive of banks to invest in private information

because they must break even on a period-by-period basis and cannot share future

surpluses with information-opaque borrowers, possibly resulting in worse credit

outcomes for small firms. Brickley et al (2003) also argue that in smaller markets local

decision making offers a more efficient means to handle a broader array of products.

Taken together, the benefits of local decision making would be expected to be less in

larger markets resulting in poorer service quality.

In column 3 of Table 6, the instrumented bank size variable is interacted with a

1/0 MSA variable to test whether the Service Quality Change Index is negatively affected

by location. If it is, the coefficient on this variable should be negative, diminishing the

better reports of change in service quality. Although owners at small banks in urban areas

report lower service quality change ratings in 1995 and 2001, the coefficient on the

interactive variable is not significant (Table 6, column 3). Thus, location is not

responsible for the positive association between bank size and service quality reported by

small firms. Perhaps the explanation for this result lies in Dick’s (2005) finding that

markets concentration remains similar regardless of size.

21

By including all firms in the baseline estimate, an upward bias may be imparted to

the instrumented CFI variable because the sample may include owners who have recently

changed to community banks to obtain the service quality they desire. When the sample

is limited to firms that did not change banks (Table 6, column 4), the significant positive

association between bank size and the Service Quality Change Index remains (except for

the 1987 survey). Thus, it is very unlikely that the strong positive association is due to

owners switching to CFIs to obtain better service.9

The responses to the Service Quality Change Index could represent perceptions of

owners that are not related to service quality. For example, owners that borrow and rely

more on the community bank’s investment in knowledge about their business may be

more likely to give favorable service ratings than owners that have strictly a transaction

relation with their bank. This difference is important in trying to differentiate between a

delegated decision rights and incentive contracting explanation for the CFI service

quality advantage documented thus far. To test for this effect, the instrumented bank size

variable is allowed to vary by loan application status (applied for a loan; never applied).

As shown in column 5 of Table 6, application status has no effect on the significance; a

Wald linear restriction test confirms that the magnitude of the instrumented bank size

effect does not differ between borrowers and non-borrowers. At a minimum, these

results suggest that the incentive contracting story for the service quality is at least as

important as the delegated decision rights story.

9 A 1/0 variable that takes a value of 1 if the owner recently reported changing banks (within the past four

years) was interacted with the instrumented CFI variable and then used in an estimation that included all

observations. If there was an upward bias in reporting good service because this was the reason for

changing banks, the coefficient on this variable should be positive. While it was positive in all surveys, it

was not significant, thus complementing the estimation results from the limited sample.

22

The responses to the Service Quality Change Index could also differ based on the

outcome of the most recent loan, where owners turned down would rate service quality

lower regardless of the actual quality of service. Similar to application status, the

instrumented bank size variable is allowed to vary by the outcome of the most recent loan

application (approved; denied). Unlike the application status, service quality changes

were rated better for those owners whose most recent application was approved (column

6, Table 6). In the 1987 survey, the coefficient on bank size was actually negative for

those turned down on their most recent request, but became positive in the later surveys.

The coefficient on the instrumented bank size variable is allowed to vary by

region. Owners are disproportionately represented in the Plains states where there is a

unit banking legacy that may overweight the correlation between bank size and the

Service Quality Change Index. This disproportional influence may arise because of

specialized knowledge resulting from a long history of localized ownership. If so, the

instrumented bank size coefficient for Plains states should be higher than the other

regions. The instrumented bank coefficient does show some variation by region (column

7, Table 6), but not as expected. While a linear restriction test rejects the null hypothesis

of equal coefficients by region, the coefficient on the Plains interactive variable is not

greater than the coefficient on the other regional interactive variables. Although region

is an imperfect proxy for regulatory legacy, there appears to be no systematic advantage

that small banks in former unit banking states have over those without this legacy.

While the NFIB data are generally representative of the small business sector, the

sample of firms from the 2001 survey is somewhat larger in terms of employment per

firm when compared to the enterprise data compiled by the U.S. Census Bureau in 1997

23

(see Appendix A). Thirty-nine (39) percent of the NFIB sample is in the 0-4 employee

category compared to 59 percent for the Census data, while 19 percent of the NFIB

sample is in the 20-499 category compared to 12% for the Census data. NFIB firms tend

to be under-represented in the service industry, over-represented in agriculture and

manufacturing, under-represented along the Eastern seaboard, and over-represented in the

Midwest and Plains states. To adjust for these discrepancies, the 2001 survey data were

weighted using the Census employment, industry, and region distributions and the model

was re-estimated. As can be seen in the results in Table 8, Line 2, there was no effect on

the expected signs and significance of the instrumented bank size variable, although the

magnitudes changed compared to the baseline results repeated from Table 6, Column 1 in

Line 1.

The model is also estimated with the individual components of the service quality

index and the results are shown in Table 8, Line 3 (Service Quality Change Index

components). With the exception of the 1987 Survey where only three of the four

components are significant, all of the individual components of the Service Quality

Change Index are significant for the 1995 and 2001 surveys.

Overall, the results show that owners at CFIs consistently rate the change in

service quality better over time, and this change is economically meaningful. The

sensitivity analysis rules out many alternative explanations for the observed strong,

positive association that is observed. While it is certainly possible that some omitted

variable correlated with the decision to bank at a CFI is driving the results, it is not

attributable to location, time at their current bank, borrowing status, or the outcome of the

24

most recent loan. Additional tests (not shown) could not attribute the positive relation of

instrumented back size to the size of the firm, years in business, or form of business.

Is this positive association due to differences in concentration of ownership

between large and small banks or the allocation of decision rights that affect the

importance and use of hard versus soft information? Given that bank assets are not firm-

specific, technology ‘levels’ the service delivery playing field, and soft information is not

critical in screening for delivery, the Brickley et al (2003) argument hat the allocation of

decision rights within community banks can create a comparative advantage for service

delivery for small firms is consistent with the outcomes documented in the paper.

Although the delegation of decision rights as described in Berger et al (2005) could be

equally important in explaining superior service quality delivery, owners report better

service quality regardless of whether they are borrowers or non-borrowers. Thus, there

really is no advantage in the acquisition of soft information for non-borrowers or for

those small banks that rely on credit scoring for their small firm underwriting.

6. Summary and conclusions

Localized decision-making can allow smaller banks to create an organizational

strategy based on the service quality, especially when bank assets are not firm-specific.

This strategy could create a comparative advantage for certain customer segments, such

as small businesses, that often rely on community banks for their funding. In this paper,

survey data obtained from the National Federation of Independent Business’ Credit,

Banks and Small Business surveys in 1987, 1995, and 2001 are used to test whether this

comparative advantage exists for service quality.

25

The paper makes three contributions to the literature on the role of community

banks in providing banking services to small firms. The first is to document that small

firms select their bank size based on the importance of characteristics central to the

conduct of their banking business. Owners that place a higher value on soft information

characteristics such as the banks knowledge of their business and market are more likely

to choose small banks, while those owners that value transactions needs of their banking

business higher (wide range of service, location) are more likely to choose a larger bank.

These findings provide direct evidence of the importance of controlling for endogeneity

when relating bank size to small firm banking outcomes.

Second, the paper documents that small banks have a comparative advantage in

service quality as reported by small firms. This advantage persists over the 14 year

period covered by the survey with little change in the magnitudes of the advantage

despite a dramatic change in bank structure attributable to mergers and significant

advances in technology that dramatically affected many aspects of service delivery. An

extensive number of sensitivity tests show that the significant association between

improvements in service quality and bank size is not due to location, duration of the

relation with their primary bank, borrowing status, or outcome of the most recent loan.

Thus, the service quality advantage is attributed to the organizational architecture of

community banks and is most likely attributable to the effect of the concentration of

ownership on the incentives of those on the front line to delivery quality service to small

firm customers.

Overall, the empirical results provide additional evidence about the uniqueness of

community banks and the boundaries of the firm. To the extent that bank size is

26

correlated with ownership concentration and allocation of decision rights, community

banks appear to have the flexibility to differentiate their ‘product’ based on the quality of

service. While technology may ultimately diminish this advantage, the results of this

paper suggest otherwise at this time.

Yet there are still a number of unanswered questions. The data do not allow for a

sharp test between the delegated decision rights versus incentive contracting

explanations. For example, do differences in underwriting technology (credit scoring

versus relationship lending) affect service quality assessment by small firms? What are

the specific features of the allocation of decision rights that result in superior service

quality delivery? Is it related to whether the bank is publicly traded or privately owned?

Is it related to the form of ownership, i.e. an S-corporation with more highly concentrated

shareholdings? And finally, are banks able to ‘monetize’ this comparative advantage into

superior operating performance? These issues remain for future research.

27

References

Akhavein, J., Frame, W.S., and White, L.J., 2005.“The Diffusion of Financial Innovations: An Examination of the Adoption of Small Business Credit Scoring by Large Banking Organizations.” Journal of Business, 78, 577–96.

Berger, A.N., Miller, N.H., Petersen, M.A., Rajan, R.G., Stein, J.C., 2005. “Does

Function Follow Organizational Form? Evidence from the Lending Practices of Large And Small Banks.” Journal of Financial Economics 76, 237-269.

Berger, A.N., Frame, S., Miller, N., 2005.“Credit Scoring and the Availability, Price and

Risk of Small Business Credit”. Journal of Money, Credit, and Banking 37, 191–222.

Berger, A. N., and Udell, G.F., 1995. “Relationship Lending and Lines Of Credit in Small Firm Finance.” Journal of Business 68, 351-382.

Berger, A. N., and Udell, G.F., 2002. “Small Business Credit Availability and Relationship Lending: The Importance of Bank Organisational Structure.” Economic Journal 112, F32-F53.

Brickley, J.A., Linck, J.S., Smith, C.W., 2003. “Boundaries Of The Firm: Evidence From The Banking Industry.” Journal of Financial Economics 70, 351-383.

Cole, R.A., Goldberg, L.G., White, L.J., 2004. “Cookie-Cutter Versus Character: The Micro Structure of Small Business Lending by Large and Small Banks.” Journal of Business.

DeYoung, R., Hunter, W.C., Udell, G.F., 2004. The Past, Present, and Possible Future for Community Banks? Journal of Financial Services Research 25, 85-133.

Dick, A. A., 2005. Market Size, Service Quality And Competition In Banking. Working paper (Journal of Money, Credit and Banking, forthcoming).

Ergungor, O.E., 2005. The Profitability Of Bank-Borrower Relationships, Journal of Financial Intermediation 14, 485-512.

Greene, W., 2000. Econometric Analysis (4th ed., Prentice-Hall, New York, New York). Petersen, M.A. and R. G. Rajan, 2002. Does Distance Still Matter? The Information

Revolution In Small Business Lending. Journal of Finance 57, 2533-2570.

28

Petersen, M.A. and Rajan, R.G., 1995. The Effect of Credit Market Competition on Lending Relationships, Quarterly Journal of Economics 110, 405-443.

Scott, J.A., 2004. Small Business and the Value of Community Financial Institutions.

Journal of Financial Services Research, 25, 207-230. Stein, J.C., 2003. “Information Production and Capital Allocation: Decentralized Vs.

Hierarchical Firms.” Journal of Finance 57, 1891-1921.

Wooldridge, J.W., 2002. Econometric Analysis of Cross Section and Panel Data. MIT

Press, Cambridge MA

Employment NFIB Census0-4 39% 59%5-9 25% 18%10-19 17% 11%20 and up 19% 12%

Total 100% 100%

RegionNew England 16% 21%South Atlantic 11% 18%Midwest 24% 16%Plains 14% 7%Mid-south/Southwest 13% 15%Mountain 10% 7%Pacific 12% 16%

Total 100% 100%

IndustryAgriculture 7%Construction 16% 12%Manufacturing/Mining 12% 6%Transportation/Comm. 4% 4%Wholesale 11% 8%Retail 21% 19%FIRE 7% 8%Services 23% 40%

Total 100% 98%

Appendix A. NFIB 2001 CBSB Survey versus 1997 U.S. Census Bureau Enterprise Distributions

This table compares the distribution of responses by employment, region and 1-digit SIC classification for the National Federation of Independent Business (NFIB) 2001 Credit, Banks and Small Business Survey to the 1997 U.S. Census Bureau Enterprise Survey (Census). The computation of the NFIB frequency distribution excludes no answer responses.

Marginal Effect of CFI on Service Quality Change Index

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Service Quality Change Index

Cha

nge

in p

roba

bilit

y

CFI (87) CFI (95) CFI (01)

Variable name and definition MeanStd. Dev. Mean

Std. Dev. Mean

Std. Dev.

CFI 1 if the owner's principal bank has assets less than $1000 million 0.65 0.48 0.56 0.50 0.47 0.50Urban location 1 if the firm is located in a MSA area (0 = non-MSA area) 0.61 0.49 0.65 0.48 0.63 0.48

Northeast 1 if the firm is located in New England or Mid-Atlantic Census 0.13 0.33 0.14 0.35 0.15 0.36South 1 if the firm is located in South Atlantic or East South Central 0.19 0.39 0.17 0.38 0.16 0.37Midwest 1 if the fimr is located in East North Central 0.14 0.34 0.21 0.41 0.24 0.43Plains 1 if the firm is located in West South Central or West North Central 0.22 0.42 0.22 0.41 0.22 0.41Pacific 1 if the firm is located in Pacific or Mountain 0.32 0.47 0.26 0.44 0.22 0.42

Recent merger 1 if the firm reported a merger/acquisition of primary bank in last 3 years NA NA 0.27 0.44 0.36 0.48B. Firm characteristicsAge Years in business 14.4 12.8 16.1 13.4 19.0 14.1FTE Full-time equivalent employees 15.3 30.8 15.9 44.8 16.9 50.6Form of business

Proprietorship 1 if the firm is organized as a proprietorship 0.34 0.47 0.29 0.46 0.25 0.44Partnership 1 if the firm is organized as a partnership or LLC 0.08 0.27 0.06 0.23 0.08 0.20Corporation 1 if the firm is organized as a corporation 0.57 0.50 0.43 0.49 0.41 0.49S-Corporation 1 if the firm is organized as an S-corporation NA NA 0.21 0.41 0.25 0.49

IndustryAgriculture 1 if a firm's primary activity is agriculture 0.04 0.19 0.08 0.27 0.07 0.25Manufacturing 1 if a firm's primary activity is manufacturing 0.13 0.34 0.13 0.34 0.12 0.33Construction 1 if a firm's primary activity is construction 0.11 0.31 0.13 0.33 0.15 0.36Transportation 1 if a firm's primary activity is transportation 0.03 0.17 0.03 0.17 0.04 0.19Wholesale 1 if a firm's primary activity is wholesale 0.10 0.29 0.06 0.24 0.10 0.30Retail 1 if a firm's primary activity is retail 0.24 0.43 0.22 0.41 0.20 0.40Finance 1 if a firm's primary activity is financial services 0.08 0.27 0.07 0.25 0.06 0.24Services 1 if a firm's primary activity is non-professional services 0.20 0.40 0.20 0.40 0.15 0.36Professional 1 if a firm's primary activity is professional services 0.08 0.27 0.05 0.21 0.07 0.25

2001

Means and standard deviations are presented for all firms that reported a commercial bank as their primary financial institution in the 1987, 1995, and 2001 National Federation of Independent Business Credit, Banks and Small Business Surveys. The summary statistics are also presented for all firms that report a commercial bank with assets below $1,000 million (CFI) and those with assets above $1,000 million (LFI) as of June 2001, 1995, and 1987. The sum of the CFI and LFI observations do not equal the total because ofcases where the bank size could not be determined from the information provided on the survey. No answer responses are excluded from the computation of the summary statistics.

1995

Region (combined Census regions)

A. Bank location, relationship and market structure

Table 1 . Small business owners and incidence of community banking

1987

Variable name and definition MeanStd. Dev. Mean

Std. Dev. Mean

Std. Dev.

Knows my business Rated from 5= very important to 1=not important 2.66 0.56 4.47 0.96 4.46 0.87Provides helpful advice Rated from 5= very important to 1=not important 1.81 0.76 3.25 1.35 3.34 1.20Wants cheapest money Rated from 5= very important to 1=not important 2.38 0.67 4.08 1.09 4.13 1.05Location important Rated from 5= very important to 1=not important 2.25 0.71 3.87 1.14 4.26 0.88Reliable credit source Rated from 5= very important to 1=not important 2.62 0.58 4.37 0.95 4.36 0.95Knows my industry Rated from 5= very important to 1=not important 2.08 0.74 3.55 1.31 3.65 1.15Speedy decision Rated from 5= very important to 1=not important 2.55 0.56 4.34 0.85 4.31 0.85Easy access to loan officer Rated from 5= very important to 1=not important 2.52 0.62 4.29 0.95 4.28 0.94Wide range of services Rated from 5= very important to 1=not important 2.22 0.67 3.62 1.13 3.78 0.99Knows local market Rated from 5= very important to 1=not important 2.30 0.69 3.75 1.20 3.84 1.08Social contact with loan officerb Rated from 5= very important to 1=not important 2.43 0.66 2.49 1.43 2.92 1.36

Accessibility of account mgr Worse = -1; No change = 0; Better = 1 0.05 0.54 0.04 0.54 -0.01 0.54Quality of service Worse = -1; No change = 0; Better = 2 -0.04 0.60Number of services Worse = -1; No change = 0; Better = 3 0.11 0.58 0.08 0.58 0.25 0.59Capability of staff Worse = -1; No change = 0; Better = 4 0.02 0.56 0.01 0.54 -0.01 0.55Staff turnover Worse = -1; No change = 0; Better = 5 -0.05 0.54 -0.06 0.54 -0.26 0.56Ease of doing business Worse = -1; No change = 0; Better = 6 0.02 0.63

C. Importance characteristics central to a banking relationshipa

1995 2001

b In the 1987 survey this characteristic was 'Do business with one person.'

D. Service quality characteristics

a The ratings in the 1987 survey are based on a 3 point scale ( 1 to 3)

1987

Table 2 : Banks size and location

Survey: 1987 1995 2001

Change in % at

CFI

Total 65 56 47 -18

MSA location 58 48 36 -22

RegionNortheast 57 45 43 -14South 58 57 42 -16Midwest 76 68 44 -32Plains 90 77 65 -25West 50 46 37 -13

% Banking at CFI

This table shows a breakdown of those reporting banking at a CFI (under $1 billion in assets) and an LFI ($1 billion in assets and higher) by two location variables: MSA (Metropolitan Statistical Area) and Region. Northeast includes the New England and Mid-Atlantic Census regions; South includes the South Atlantic and East South Central regions; Midwest includes the East North Central region; Plains includes the West North Central and West South Central regions; and West includes the Pacific and Mountain regions. The percentages reported for both variables are the percent of firms by bank size. 'Change in CFI' is the difference between the 2001 and 1987 percent reporting banking at a CFI.

A. Location and bank structure CFI LFI CFI LFI CFI LFIMSA location 0.55 0.74 * 0.55 0.78 * 0.49 0.76 *Region

Northeast 0.11 0.15 * 0.11 0.17 * 0.14 0.16South 0.16 0.23 * 0.16 0.19 * 0.15 0.18 *Midwest 0.16 0.10 * 0.22 2.00 0.23 0.25Plains 0.30 0.06 * 0.30 0.11 * 0.30 0.14 *Pacific 0.24 0.45 * 0.21 0.32 * 0.18 0.26 *

Recent merger NA NA 0.27 0.44 * 0.22 0.49 *B. Firm CharacteristicsYears in business 14.1 15.0 15.7 16.6 18.1 19.9 *FTE 13.7 18.2 * 11.8 21.1 13.7 19.6 *Form of business

Proprietorship 0.37 0.29 * 0.33 0.25 * 0.29 0.22 *Partnership 0.09 0.07 0.06 0.05 0.08 0.08Corporation 0.33 0.64 * 0.40 0.47 * 0.37 0.44 *

IndustryAgriculture 0.04 0.03 0.09 0.05 * 0.08 0.06Manufacture 0.12 0.14 0.13 0.13 0.12 0.12Construction 0.12 0.10 0.13 0.13 0.15 0.15Transportation 0.03 0.03 0.04 0.02 0.04 0.04Wholesale 0.09 0.11 0.05 0.07 * 0.09 0.12 *Retail 0.26 0.19 * 0.24 0.19 * 0.20 0.20Finance 0.07 0.08 0.07 0.07 0.07 0.06Services 0.18 0.24 * 0.18 0.22 * 0.15 0.15Professional 0.09 0.07 0.04 0.05 0.06 0.07

C. Characteristics important to a banking relationship

Knows my business 2.66 2.64 4.54 4.39 * 4.55 4.38 *Provides helpful advice 1.83 1.78 3.29 3.20 * 3.40 3.28 *Wants cheapest money 2.38 2.39 4.13 4.02 * 4.14 4.12Location important 2.23 2.28 3.85 3.89 4.23 4.28Reliable credit source 2.61 2.62 4.43 4.28 * 4.43 4.30 *Knows my industry 2.10 2.03 3.56 3.53 3.75 3.56 *Speedy decision 2.55 2.55 4.37 4.30 4.35 4.27 *Easy access to loan officer 2.53 2.49 4.37 4.18 * 4.35 4.22 *Wide range of services 2.21 2.23 3.66 3.58 * 3.78 3.77Knows local market 2.36 2.19 * 3.84 3.63 * 3.93 3.76 *Social contact with loan officer 2.42 2.46 2.62 2.32 3.06 2.80 *

D. Service quality change

Accessiblity of account manager 0.07 0.01 * 0.07 0.01 * 0.07 -0.06 *Quality of service 0.06 -0.10 *Number of services 0.10 0.09 0.11 0.07 0.27 0.23Capability of staff 0.06 -0.06 * 0.03 -0.02 0.07 -0.08 *Staff turnover -0.02 -0.10 * -0.01 -0.12 * -0.17 -0.33 *Ease of doing business * 0.08 -0.31 *

Service Quality Change Index 0.22 -0.05 * 0.20 -0.06 * 0.41 -0.39 *

Table 3 : Location, firm characteristics, importance characteristics, and service performance: CFI and LFI differences

200119951987

This table presents the mean values for the variables listed by CFI (assets under $1 billion ) and LFI (assets $1 billion and higher). See Table 1 for variable definitions. * indicate a significant difference between the CFI and LFI mean values at the .01 level

Table 4. Importance Rating Correlations

A. 1987 SurveyKnows my business

Provides helpful

suggest.

Wants cheapest money

Location important

Reliable credit

sourceKnows my

industrySpeedy decision

Easy access to

loan officer

Wide range of services

Knows local

market

Social contact with loan officer

Knows my business 1.000 0.249 0.108 0.051 0.322 0.368 0.270 0.335 0.125 0.234 0.307Provides helpful suggest. 1.000 0.144 0.061 0.158 0.326 0.194 0.204 0.219 0.246 0.176Wants cheapest money 1.000 0.119 0.342 0.199 0.189 0.257 0.151 0.165 0.272Location important 1.000 0.151 0.085 0.136 0.119 0.280 0.187 0.117Reliable credit source 1.000 0.324 0.342 0.428 0.210 0.246 0.375Knows my industry 1.000 0.306 0.302 0.233 0.387 0.264Speedy decision 1.000 0.570 0.360 0.306 0.300Easy access to loan officer 1.000 0.388 0.335 0.406Wide range of services 1.000 0.471 0.203Knows local market 1.000 0.210Deal with one person 1.000

B. 1995 SurveyKnows my business 1.000 0.347 0.140 0.045 0.311 0.449 0.282 0.328 0.216 0.352 0.196Provides helpful suggest. 1.000 0.179 0.117 0.219 0.396 0.212 0.230 0.342 0.355 0.271Wants cheapest money 1.000 0.210 0.369 0.215 0.269 0.322 0.228 0.182 0.144Location important 1.000 0.172 0.126 0.202 0.145 0.314 0.226 0.181Reliable credit source 1.000 0.376 0.419 0.529 0.275 0.277 0.149Knows my industry 1.000 0.334 0.345 0.312 0.483 0.259Speedy decision 1.000 0.598 0.373 0.340 0.199Easy access to loan officer 1.000 0.418 0.381 0.270Wide range of services 1.000 0.530 0.311Knows local market 1.000 0.350Social contact with loan officer 1.000

C. 2001 SurveyKnows my business 1.000 0.390 0.190 0.050 0.350 0.451 0.395 0.402 0.254 0.366 0.250Provides helpful suggest. 1.000 0.212 0.049 0.280 0.442 0.277 0.300 0.338 0.360 0.265Wants cheapest money 1.000 0.181 0.451 0.260 0.336 0.420 0.289 0.214 0.168Location important 1.000 0.167 0.075 0.173 0.120 0.281 0.178 0.167Reliable credit source 1.000 0.409 0.432 0.590 0.310 0.303 0.211Knows my industry 1.000 0.422 0.405 0.328 0.475 0.315Speedy decision 1.000 0.622 0.379 0.374 0.239Easy access to loan officer 1.000 0.419 0.387 0.280Wide range of services 1.000 0.502 0.331Knows local market 1.000 0.390Social contact with loan officer 1.000

This table presents the correlation coefficients for the owner ratings of importance of each of the 11 characteristics to the conduct of their banking business. The 1987 survey results are presented in Panel A; 1995 in Panel B; and 2001 in Panel C. The characteristic "Social contact with loan officer was new to the 1995 survey and replaced 'Contact with 1 person' in the 1987 survey.Owners ranked each characteristics on a scale of 1 (not important) to 5 (very important) in the 1995 and 2001 survey; for the 1987 survey the ranking was based on a scale of 1 to 3. All of these coefficients are significant at the .01 level. The sample used to prepare this table was limited to all firms that reported using a bank as their primary financial institution. No answer responses were excluded from the analysis.

Correlation Coefficients

Coef. Std Err Coef. Std Err Coef. Std ErrImportance characteristics

Knows my business 0.012 0.103 0.152 0.049 *** 0.165 0.073 **

Provides helpful advice 0.004 0.092 -0.009 0.038 0.000 0.044Wants cheapest money -0.031 0.101 0.034 0.039 -0.061 0.047Location important -0.124 0.089 -0.115 0.044 *** -0.167 0.068 **

Reliable credit source -0.112 0.123 0.156 0.053 *** 0.131 0.053 **

Knows my industry 0.052 0.086 -0.166 0.038 *** 0.020 0.041Speedy decision -0.088 0.136 -0.079 0.049 0.015 0.074Easy access to loan officer 0.196 0.130 0.178 0.063 *** 0.045 0.074Wide range of services -0.177 0.094 * -0.071 0.052 -0.117 0.062 *

Knows local market 0.445 0.125 *** 0.089 0.047 * 0.076 0.050Social contact with loan officera -0.182 0.091 ** 0.130 0.028 *** 0.103 0.037 ***

Firm characteristicsLog of years in bus. 0.005 0.057 -0.273 0.049 *** -0.228 0.050 ***

Log of FTE -0.089 0.061 -0.029 0.055 0.018 0.061Partnership 0.268 0.168 0.173 0.174 0.281 0.119 **

Proprietorship 0.358 0.247 0.185 0.098 0.136 0.159S-corp 0.006 0.100 0.127 0.101Agriculture 0.078 0.294 0.020 0.155 0.019 0.179Construction 0.063 0.200 -0.267 0.097 *** -0.009 0.157FIRE -0.131 0.221 -0.101 0.132 0.141 0.168Manufacturing -0.054 0.335 0.204 0.243 0.126 0.257Professional -0.300 0.256 -0.507 0.161 *** -0.140 0.155Business services -0.375 0.271 -0.288 0.156 * 0.228 0.162Transportation -0.493 0.183 *** -0.488 0.093 *** -0.036 0.149Wholesale -0.179 0.272 -0.464 0.206 ** -0.230 0.183

Constant 0.956 0.458 ** -0.181 0.319 -0.639 0.379 *

No. of obs 1,392 2,914 1,847LR chi-square 153.6 353.8 151.2p-value 0.00 0.00 0.00

a In the 1987 survey this characteristic was 'Do business with one person.'

*** indicates significance at the .01 level, ** significance at the .05 level and * significance at the .10 level. Robust standard errors are reported that allow for clustering on firm size (FTE).

Table 5 : Determinants of bank size selection

The dependent variable is the bank size of the owner's primary financial institution (1=CFI, 0=other). The independent variables are defined in Table 1. Logistic regression is used to estimate the equations.

1987 Survey 1995 Survey 2001 Survey

Panel A: 1987 Coef.Robust Std Err Coef.

Robust Std Err Coef.





Robust Std Err

CFI 0.341 0.170 **

CFI-Residual -0.201 0.181CFI (IV) 0.339 0.153 ** 0.340 0.154 ** 0.098 0.260CFI (IV) x MSA 0.009 0.091

CFI (IV) x Borrower 0.342 0.155 **

CFI (IV) x Non-borrower 0.303 0.156 *

CFI (IV) x Denied -0.420 0.143 ***

CFI (IV) x Approved 0.254 0.090 ***

CFI(IV) x Plains 0.184 0.193CFI(IV) x West 0.138 0.325CFI(IV) x Midwest 0.448 0.219 **

CFI(IV) x South 0.502 0.283 *

CFI(IV) x Northeast 0.284 0.252Log of years in bus. -0.006 0.034 0.006 0.027 0.006 0.027 -0.048 0.042 0.006 0.027 -0.035 0.033 0.006 0.027Log of FTE 0.082 0.036 ** 0.100 0.031 *** 0.100 0.031 *** 0.129 0.058 *** 0.100 0.031 *** 0.127 0.023 *** 0.097 0.031 ***

No. of obs 1,176 1,467 1,467 496 1,467 1,467 1,467Wald chi-square 27.4 40.6 43.5 21.1 41.3 86.2 45.9pseudo R-squared 0.0056 0.0059 0.0059 0.0107 0.0060 0.0120 0.0076Wald linear restriction test for CFI(IV) χ2 stat P-value

Borrower = non-borrower 0.19 0.66Denied = approved 29.38 0.00All regions equal 9.25 0.06

Panel B: 1995CFI 0.908 0.120 ***

CFI-Residual -0.809 0.132 ***

CFI (IV) 0.926 0.115 *** 0.927 0.115 *** 1.220 0.204 ***

CFI (IV) x MSA -0.090 0.071

CFI (IV) x Borrower 0.926 0.115 ***

CFI (IV) x Non-borrower 0.992 0.167 ***

CFI (IV) x Denied 0.281 0.155 *


Table 6 : Multivariate results of the effect of reported bank size on service quality (continued)(4) (5) (6) (7)

(5)Borrower/non-borrower effect

(6)Last loan outcome

effectEndogeneity test

The dependent variable is the performance ranking defined in Table 1; the independent variables are also defined in Table 1. The CFI is constructed from instruments based employment, deposit concentration and region of country. Panel A presents the estimates from the 1987 Credit, Banks and Small Business Survey, Panel B from the 1995 Survey, and Panel C from the 2001 Survey. Ordered probit is used to estimate the equations. Column 1 presents the endogeneity test for CFI; column 2 presents the baseline model with the instrumented CFI variable; column 3 adds the interactive instrumented CFI x MSA variable; column 4 restricts the sample to those owners that have not changed banks in 5 or more years; column 5 allows the CFI coefficient to vary by borrower vs. non-borrower, column 6 allows the CFI coefficient to vary by the outcome of the last loan, and column 7 allows the instrumented CFI coefficient to vary by region. The industry and form of business control variables are omitted from the presentation.

Table 6 : Multivariate results of the effect of reported bank size on service quality

(4)

No bank change

(7)

Baseline - IV MSA effect Regional effect

(1) (2) (3)

(1) (2) (3)

Panel B: 1995 Coef.Robust Std Err Coef.






Robust Std Err

CFI(IV) x Plains 1.180 0.116 ***

CFI(IV) x West 1.584 0.225 ***

CFI(IV) x Midwest 1.422 0.164 ***

CFI(IV) x South 1.304 0.175 ***

CFI(IV) x Northeast 1.571 0.227 ***

Log of years in bus. -0.019 0.031 -0.016 0.032 -0.017 0.032 -0.017 0.052 -0.016 0.032 -0.035 0.033 -0.022 0.033Log of FTE 0.137 0.023 *** 0.135 0.024 *** 0.133 0.024 *** 0.176 0.042 *** 0.136 0.024 *** 0.127 0.023 *** 0.166 0.025 ***

No. of obs 2,682 2,976 2,976 1,040 2,976 2,976 2,976Wald chi-square 132.7 155.8 179.3 97.5 156.6 163.8 234.7pseudo R-squared 0.0081 0.0075 0.0077 0.0141 0.0080 0.0120 0.0088Wald linear restriction test for CFI(IV) χ2 stat P-value


Panel C: 2001CFI 1.101 0.129 ***

CFI-Residual -0.893 0.137 ***

CFI (IV) 1.127 0.124 *** 1.133 0.123 *** 1.095 0.196 ***

CFI (IV) x MSA -0.014 0.089

CFI (IV) x Borrower 1.145 0.126 ***

CFI (IV) x Non-borrower 1.019 0.158 ***

CFI (IV) x Denied 0.412 0.233 *


CFI(IV) x Plains 1.134 0.127 ***

CFI(IV) x West 1.465 0.203 ***

CFI(IV) x Midwest 1.461 0.168 ***

CFI(IV) x South 1.639 0.195 ***

CFI(IV) x Northeast 1.436 0.164 ***

Log of years in bus. -0.006 0.024 -0.002 0.026 -0.003 0.026 0.031 0.040 -0.004 0.026 -0.012 0.027 0.000 0.024Log of FTE 0.024 0.027 0.042 0.025 * 0.043 0.024 * 0.022 0.037 0.039 0.025 0.040 0.024 0.055 0.025 **

No. of obs 1,779 1,996 1,996 901 1,996 1,996 1,996Wald chi-square 162.8 149.7 157.8 55.5 153.3 179.4 189.1pseudo R-squared 0.0123 0.0101 0.0102 0.0130 0.0103 0.0125 0.0117Wald linear restriction test for CFI(IV) χ2 stat P-value


Borrower/non-borrower effect

Last loan outcome effect Regional effectEndogeneity test Baseline - IV MSA effect No bank change

*** indicates significance at the .01 level, ** significance at the .05 level and * significance at the .10 level. Robust standard errors are estimated that allow for clustering on firm size (FTE)

Change in probability



Value Observed Predicted CFI (87) Observed Predicted CFI (95) Observed Predicted CFI (01)-6 0.019 0.070 -0.154-5 0.035 0.092 -0.114-4 0.027 0.085 -0.057 0.041 0.174 -0.249 0.049 0.102 -0.082-3 0.045 0.097 -0.037 0.050 0.126 -0.074 0.051 0.088 -0.045-2 0.067 0.116 -0.027 0.059 0.114 -0.030 0.083 0.123 -0.030-1 0.106 0.149 -0.014 0.081 0.123 0.000 0.097 0.119 0.0070 0.438 0.405 0.060 0.501 0.391 0.231 0.328 0.270 0.1631 0.146 0.084 0.035 0.109 0.040 0.056 0.122 0.066 0.0892 0.083 0.037 0.020 0.061 0.017 0.029 0.071 0.030 0.0543 0.047 0.017 0.011 0.033 0.007 0.014 0.049 0.017 0.0384 0.042 0.010 0.008 0.065 0.009 0.023 0.035 0.010 0.0275 0.028 0.007 0.0226 0.033 0.006 0.025

Total 1.000 1.000 0.000 1.000 1.000 0.000 1.000 1.000 0.000

Table 7 : Predicted versus actual probabilities and marginal effect of instrument bank sizeThis table presents the observed and estimated probabilities from the ordered probit analysis results shown in column 2 of Tables 7) for the 1987, 1995, and 2001 Credit, Banks and Small Business Surveys. The change in probability of the instrumented bank size variable (CFI) for the values of the Service Quality Change index is also shown. These marginal effects are the based on the instrumented bank size variable coefficient and the standard normal densities evaluated at the cut points (or threshold values) provided in the ordered probit analysis.

1987 Survey

Probability Probability Probability

1995 Survey 2001 Survey

Dependent variable Coef. Std Err Coef. Std Err Coef. Std Err1. Service Quality Index (column 2, Table 7) 0.339 0.153 ** 0.926 0.115 *** 1.127 0.124 ***

2. Service Quality Index (Census Weighted) 1.415 0.259 ***

3. Service Quality Change Index ComponentsAccessiblity of account manager 0.395 0.167 ** 0.721 0.128 *** 0.910 0.139 ***

Quality of service 0.949 0.141 ***

Number of services -0.200 0.180 0.850 0.122 *** 0.665 0.121 ***

Capability of staff 0.490 0.167 *** 0.784 0.148 *** 1.020 0.166 ***

Staff turnover 0.533 0.185 *** 0.881 0.132 *** 1.164 0.165 ***

Ease of doing business 0.978 0.155 ***

Table 8 : Sensitivity results of reported bank size on service quality

Panel A: 1987 Survey


CFI (IV) CFI (IV)CFI (IV)


*** indicates significance at the .01 level, ** significance at the .05 level and * significance at the .10 level. Robust standard errors are reporwith clustering for firm size (FTE).

The dependent variables for the sensitivity tests are presented in the rows and only the instrumented bank size coefficients are reported in the columns. The CFI is constructed from instruments based employment, deposit concentration and region of country. Panel A presents the estimates from the 1987 Credit, Banks and Small Business Survey, Panel B from the 1995 survey, and Panel C from the 2001 Survey. Ordered probit is used to estimate the equations. Line 1 presents the full model results from column 3, Table 7. Line 2 presents the results for the 2001 survey where the data are weighted by U.S. Census Enterprise data for region, employment and industry. Line 3 presents the results for the service attributes included in the Service Quality Index, where each is given a value of ‘-1’ for worse, ‘0’ for no change, and ‘1’ for better. The other control variables, e.g. firm age, size, industry and form of business, are omitted from the presentation.

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