Effects of banks on debt sensitive small businesses

Description
The purpose of this paper is to identify which small businesses are most “debt sensitive”,
or most likely to be affected by banking market conditions

Journal of Financial Economic Policy
Effects of banks on “debt-sensitive” small businesses
Allen N. Berger Philip Ostromogolsky
Article information:
To cite this document:
Allen N. Berger Philip Ostromogolsky, (2009),"Effects of banks on “debt-sensitive” small businesses",
J ournal of Financial Economic Policy, Vol. 1 Iss 1 pp. 44 - 79
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Effects of banks on
“debt-sensitive” small businesses
Allen N. Berger
Moore School of Business, University of South Carolina, Columbia,
South Carolina, USA and
Wharton Financial Institutions Center, Tilburg University,
Tilburg, The Netherlands, and
Philip Ostromogolsky
Yale University, New Haven, Connecticut, USA
Abstract
Purpose – The purpose of this paper is to identify which small businesses are most “debt sensitive”,
or most likely to be affected by banking market conditions.
Design/methodology/approach – For the primary debt sensitivity categories, the paper
hypothesizes that bank conditions are most likely to have signi?cant effects on ?rms in size classes
and industries that are “on the bubble” for credit availability (probability of credit close to 0.50), rather
than those with “relatively easy” or “relatively dif?cult” access to credit (probability much higher or
lower, respectively). The secondary classi?cations also require that loans fund a substantial
proportion of assets for the ?rms in the category that have loans. These hypotheses are tested using a
comprehensive data set of US small businesses by size class and industry matched with variables
measuring bank market power, market structure, and ef?ciency in the ?rm’s local markets.
Findings – Findings show that the data are consistent with the hypotheses, with the strongest
support for the hypotheses occurring using the secondary classi?cations. In terms of policy
implications, the ?ndings suggest that the credit availability of small, debt-sensitive ?rms may be
reduced by within-market mergers that increase concentration in rural markets, but that the more
common type of recent consolidation – creating larger banks that operate in more markets – may be
associated with an increase in credit availability for these sensitive ?rms. Such an increase in credit
availability would be magni?ed if consolidation resulted in increased bank operating ef?ciency.
Originality/value – The paper offers insights into the effect of banks on “debt-sensitive” small
businesses.
Keywords Banks, Small enterprises, Banking, Economic conditions, Debts, United States of America
Paper type Research paper
1. Introduction
Much of the banking research over the last decade has focused on the effects of banks
on small businesses, but several important research and policy questions remain.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G21, G28, L11, O33
This work was partially completed while Ostromogolsky was a Research Assistant at the
Board of Governors of the Federal Reserve System, June 2003-2006.
The authors thank Ron Borzekowski, Nate Miller, and Gang Xiao for valuable help in
preparing this research, Nicola Cetorelli, George Christodoulakis, and the anonymous referees for
comments on the paper, and Reid Dorsey-Palmateer for outstanding research assistance. The
authors also thank Bob Avery, Lamont Black, Brian Bucks, Andrew Cohen, Diana Hancock,
Arthur Kennickell, and audience members at the Chicago Federal Reserve Bank Structure and
Competition conference for very constructive comments on the presentation.
JFEP
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44
Journal of Financial Economic Policy
Vol. 1 No. 1, 2009
pp. 44-79
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576380910962385
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First, it is unclear whether small businesses are best served on net in more versus less
competitive banking markets. The exercise of bank market power may help small
businesses served using one lending technology – relationship lending – but hurt
small ?rms served using other technologies. Second, the net effect of changes in bank
market structure from consolidation remains in question. That is, it is unclear whether
the supply of small business credit is increased or decreased from the shifting of
market shares from small, single-market institutions – or “local community banks” –
to large, geographically dispersed banking organizations – or “mega banks”. Third,
the effects of bank ef?ciency on the supply of small business credit – the extent to
which small businesses are better served by institutions that are closer to the ef?cient
frontier – has not been addressed empirically to our knowledge.
In this paper, we try to both narrow and broaden the focus of this literature. We try to
narrow the focus by identifying and testing which small businesses are most likely to be
affected by bank market power, market structure, and ef?ciency. While some authors
have addressed this issue, our hypotheses appear to be quite different. For our primary
debt sensitivity categories, we hypothesize that bank conditions are most likely to have
signi?cant effects on small businesses in size classes and industries that are “on the
bubble” for credit availability (probability of credit close to 0.50), rather than those with
“relativelyeasy” or “relatively dif?cult” access to credit (probabilitymuch higher or lower,
respectively).
The logic here is quite simple – we argue that the effects of the key exogenous variables
are maximized near the center of the distribution. This is similar to the familiar S-curve
speci?ed in logit or probit analysis. The effect of exogenous variables should be maximized
inthe center of the distribution, andminimizednear boththe upper andlower tails, although
we have nospeci?c distributioninmind. We simplyargue that ?rms that withclose to a0.50
probability of obtaining a loan should be more sensitive to bank conditions than ?rms with
very high and low probabilities. Firms with very high probabilities are likely to obtain
credit for almost any banking conditions and those with very low probabilities that are
unlikely to acquire debt ?nancing under virtually any market conditions.
For our secondary classi?cations of which ?rm sizes and industries are likely to be
more sensitive to bank market conditions, we add the requirement that loans fund a
substantial proportion of assets for the small businesses in the category that have loans.
The goal is to ensure that this fundingis important whenit is available so that havingthe
loans is of economic signi?cance to the ?rm. The secondary classi?cation method has
the advantage of identifying ?rms more clearly as debt-sensitive. However, it has the
disadvantage of reducing the number of industries that can be analyzed by deleting size
class-industry pairs with relatively low loan-to-asset ratios when they have loans.
We also try to broaden the focus of the research literature by studying the effects of
bank market power, market structure, and ef?ciency in the same analysis. Many recent
empirical papers examine the effects of bank market power on small business credit
with mixed results. A smaller number of recent studies test the effects of bank market
structure using the market shares of banks of different sizes and geographic spreads.
No empirical studies of which we are aware directly examine the question of the effects
of bank ef?ciency on small businesses. By studying the effects of these banking factors
together on the small businesses that are most likely to be affected by them, we may
shed more light on the larger research and policy questions of both whether and how
banks “matter” to small businesses.
“Debt-sensitive”
small businesses
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We use data from the Survey of Small Business Finances (SSBF) on small business
size, industry, and credit to formulate hypotheses about the sizes and industries that are
likely to be more or less “debt-sensitive”. We then test these hypotheses using
information from the US census on the numbers of business establishments by size,
industry, and location, combined with data frombank regulatory reports for commercial
bank location and condition. We match virtually all the business establishments in the
USA with virtually all the commercial banks in virtually all the local metropolitan and
rural markets in the USAfor a 12-year period from1991 to 2002. We exclude ?rms in the
?nance, real estate, nonpro?t, and agriculture industries – industries that typically have
very different credit availability issues – leaving a total of over 30,000 market-year
observations.
In de?ning and measuring debt sensitivity, we focus on overall access to loans,
rather than just bank loans. We argue that bank loans and credit from other sources –
such as commercial ?nance companies, thrifts, and other ?nancial institutions – are
likely to be largely substitutable. In some cases, ?rms may borrow from these other
institutions because of unfavorable banking conditions, but commercial banks should
be considered as contenders for almost all loans, given that they use almost all of the
lending technologies for small businesses employed by different types of ?nancial
institutions, and banks are almost always conveniently located to make these loans.
For most small businesses, debt sensitivity essentially amounts to external ?nance
sensitivity, since access to equity markets is quite limited.
We recognize a potential source of endogeneity if industry conditions are responsive
to the ?rms we identify as more and less debt-sensitive. However, as shown below, we
focus most of our attention on the smallest size class of ?rms with 1-4 employees.
These ?rms account for less than 5 percent of total employment, so it is unlikely that
banks increase or decrease their market shares or ef?ciency signi?cantly in response to
these ?rms.
Our concept of debt sensitivity differs signi?cantly from the alternative concept of
“external dependence” for an industry employed by Rajan and Zingales (1998) and
others. External dependence is based on the external ?nance for investments by large,
publicly-traded US manufacturing ?rms – ?rms that generally have signi?cant access
to both external debt and equity markets. Industries that ?nance more of their
investments with external funds are considered to be more dependent. External
dependence is designed to describe the differences in technological needs for ?nancing
across industries, such the gestation period or production cycle between investment and
resulting future cash ?ows.
External dependence is quite useful for many purposes, but may not most be
appropriate for very small US businesses that typically do not have access to external
equity and which have loans only about half the time. We argue that for these ?rms,
access to any external funding is primary importance, and the amount of credit granted
if loans are issued is a secondary concern. Moreover, the technological needs for
investment funding for large, publicly traded manufacturing ?rms may not be
descriptive of the technological needs for funding of very small ?rms in the same
industries, and cannot be used for small businesses in the service sectors. Very small
?rms may also have de?ciencies in working capital ?nancing as well as investment
capital funding. For these ?rms, we argue that the probability of having loans is more
useful for re?ecting their access to credit.
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A second alternative that has been used is the median loan/asset ratio for small
businesses in different manufacturing industries – viewing industries with higher ratios
as more dependent on external debt (Cetorelli and Strahan, 2006). This is closer to our
concept because it uses small business debt, but differs in some important ways. This use
of loan/asset ratios focuses onindustries inwhichsmall businesses receive the most credit,
rather than on industries with ?rms on margin of receiving any credit. The median
loan/asset ratio for small businesses in industry also does not differentiate between the
smallest and largest of these ?rms, which we show below appear to have very different
access to loans. While we use the median loan/asset ratio for ?rms with loans in our
secondary classi?cation for debt sensitivity, we do not focus on the highest values.
A third alternative is to focus on the rate of new incorporations of businesses in a
state. Black and Strahan (2002) show that this is a good indicator of the number of
“starts” of very small ?rms, and so may well represent a size class and point in the
?nancial growth cycle when ?rms are highly sensitive to banking market conditions.
Their tests are somewhat analogous to our examination of debt sensitivity based on
different size classes of small businesses. However, they are not able to compare their
very small ?rms to a larger size class, and they are not able to differentiate among
industries with their data source.
To preview our main hypothesis tests, we regress the log of ?rms per capita in
different size classes and in different primary and secondary debt sensitivity categories
on credit supply variables measuring bank market power, bank market structure, bank
ef?ciency, and some controls. We then test for differences across these regressions. The
results are statistically and economically signi?cant and consistent with our hypotheses
that the numbers of very small ?rms per capita in categories identi?ed as particularly
debt-sensitive – or “on the bubble” for credit availability – are more responsive to
banking conditions than are ?rms in categories identi?ed ex ante as less sensitive.
However, not all of the bank conditions are found to be signi?cant. We also ?nd the most
support for the hypotheses using our secondary debt sensitivity classi?cations, despite
the fact that we have to drop a number of industries to construct the categories.
In terms of policy implications, our results suggest that allowing for mergers that
increase the market shares of large, multimarket banks may increase the credit
availability of debt-sensitive small businesses. This is particularly so if these mergers are
likely to increase ef?ciency, as may occur if more ef?cient banks take over less ef?cient
competitors. However, credit availability for these sensitive ?rms may be reduced when
within-market mergers that increase rural market concentration are permitted.
Section 2 describes our classi?cation of debt-sensitive small business sizes and
industries and our main hypotheses. Section 3 presents the regression model and
discusses the variables and the data and reviews some of the literature that has tested
the key bank market variables. Section 4 gives the results of our regressions and
hypothesis tests, and Section 5 concludes.
2. Classi?cation of debt-sensitive small businesses and our hypotheses
In this section, we identify the types of small businesses – by both size and industry –
that are likely to be more or less debt-sensitive than other small ?rms. We employ data
from the 1998 SSBF, a widely used research tool for small business ?nance with a
wealth of detailed information on a large number of US small businesses and their
access to ?nancial services. The survey provides information on ?rms up to the limit of
“Debt-sensitive”
small businesses
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500 full-time-equivalent employees – the Small Business Administration de?nition of a
small business. We exclude respondents without assets or a valid Standard Industrial
Classi?cation (SIC) code from which to determine the ?rm’s industry. We also exclude
?rms in the ?nance, real estate, nonpro?t, and agriculture industries, as the credit
availability issues for such ?rms are quite different.
2.1 Debt sensitivity by ?rm size class
We measure ?rm size by number of employees to be consistent with our data on
business establishments in the local market described below. Table I shows the
number of ?rms in each of three employee size classes, the proportion of these ?rms
with loans (P LOAN), and the median loans-to-assets ratio for ?rms that have loans
(LOAN RATIO). We base our identi?cation of debt sensitivity on overall access to
loans, not just bank loans. Bank loans and other loans are likely to be largely
substitutable because banks use almost all the lending technologies and are located in
virtually every local market. Thus, banks could make almost all of the loans if bank
market conditions were ideal[1].
The most striking statistics in Table I are the values of P LOAN(1-4) and
P LOANð20þÞ of 46.5 and 87.5 percent, respectively. The difference in the two values,
P LOANð20þÞ 2P LOAN(1-4), is 41.0 percent points, and is both statistically and
economically signi?cant. P LOAN(1-4) is close to the theoretical probability of
obtaining a loan of 0.50 where the effects on credit availability of banking conditions
and other factors is likely to be maximized. Thus, these smallest of small businesses
with 1-4 employees would seem to typify ?rms that are “on the bubble” for credit
availability, with marginal access to credit. In contrast, the value of P LOAN(20 þ ) of
87.5 percent is quite high, and appear to represent ?rms with high-credit availability,
or relatively easy access to debt.
In addition, loans appear to be much more important economically to the smallest
?rms when they have loans. As shown in the table, the one to four employee ?rms
?nance about half of their assets with loans when they have loans, as opposed to about
one-third for ?rms with 20þ employees. The difference, LOAN RATIOð20þÞ2LOAN
RATIO(1-4), is statistically signi?cant as well as economically signi?cant.
Based on these ?gures as well as conventional wisdom and prior research, we
hypothesize that small businesses with one to four employees are more debt-sensitive
than those with 20þ employees both overall and within their same industry. There are
a number of likely reasons for this, including greater informational transparency; lower
risk; and economies of scale in loan values, given that larger ?rms tend to have larger
credits.
For our intermediate ?rm size class of ?ve to 19 employees, P LOANð5-19Þ 2P
LOAN(1-4) is also positive and signi?cant and LOANRATIO(5-19) 2 LOANRATIO(1-4)
is again negative and signi?cant. However, the differences are much smaller, so we
make noexplicit hypothesis that these ?rms are less debt-sensitive thanthe one tofour size
class.
While the data in Table I are by ?rm size only and not by industry, the arguments
should generally hold within industry as well, and we test this hypothesis below. That
is, larger ?rms in the same industry are expected to be more transparent, lower risk,
and have larger loans that cost less per dollar than smaller ?rms. Importantly, there is
also signi?cant heterogeneity even within a size class across industries, as shown next.
JFEP
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Table I.
Proportions of small
businesses with loans
(P LOAN) and median
loan/asset ratio
for ?rms with loans
(LOAN RATIO)
“Debt-sensitive”
small businesses
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1
6

(
P
T
)
2.2 Debt sensitivity by ?rm size class and industry using our primary and secondary
classi?cation rules
Table II, shows our primary debt sensitivity categories, which are based on both ?rm
size class and industry. Given our ?ndings on ?rm size above, we sort the industries
according to the proportions of ?rms in the one to four employee size class with loans
(P LOAN(1-4)). Based on SIC codes, we order the 32 industries with representation in
the SSBF from highest to lowest values for P LOAN(1-4). As shown, the proportion of
these very small ?rms with loans varies from as high as 75.0 percent for the lodging
industry to only 21.4 percent for educational services. Thus, even among very small
?rms, those in some industries are much more likely to have external credit than
others, suggesting that size class alone is not suf?cient for identifying debt
sensitivity.
Thus, for our primary classi?cation, we divide the industries into those with a high
proportion of one to four employee ?rms with loans, P LOAN(1-4) $ 0.60, a medium
proportion, 0.40 , P LOAN(1-4) , 0.60, and a low proportion, P LOAN(1-4) # 0.40.
We hypothesize that ?rms with one to four employees in industries with a medium
loan proportion are “on the bubble” for credit availability and are more debt-sensitive
by virtue of the close proximity of their P LOAN(1-4) to the 0.50 mark. We postulate
less sensitivity for very small ?rms in industries with high-loan proportions or
“relatively easy” credit availability and for those in low-proportion industries with
“relatively dif?cult” credit availability.
As shown in the table, ?rms with one to four employees in 16 of the 32 industries are
classi?ed as more debt-sensitive using our primary classi?cation rule. These ?rms
constitute well over half of the ?rms with one to four employees because of the
presence of some very large individual industries (e.g. business and technical services).
We also note that most of the highly sensitive ?rms and employees are not in
manufacturing industries. As discussed above, prior attempts to identify dependence
on external ?nance or debt in some cases focus only on manufacturing.
Table III, gives our secondary debt sensitivity categories, which are again based on
both ?rm size class and industry. The secondary categories include the same rules for
the loan proportion – a medium P LOAN(1-4) value – and add a requirement for the
median loan/asset ratio for ?rms with loans – a medium or high LOAN RATIO(1-4)
value. That is, to further differentiate the categories, we add the requirement that
LOAN RATIO(1-4) . 0.40 (loans fund more than 40 percent of assets) for the both the
high and medium P LOAN(1-4) groups, and that LOAN RATIO(1-4) # 0.40 (loans fund
40 percent or less of assets) for the low-P LOAN group. This yields more separation
between the high- and medium-P LOAN groups on the one hand and the low-P LOAN
group on the other hand. Industries that do not meet these joint requirements are not
assigned to a secondary category.
As shown, these requirements on LOAN RATIO reduce the total number of
industries classi?ed from 32 to 22. The secondary classi?cation method has the bene?t
of more clearly identifying ?rms as debt-sensitive, but it comes at the cost of fewer
observations to analyze in drawing conclusions.
Table IV and Table V provide tests of ?rm size differences in loan proportion
(P LOAN) using our primary and secondary debt sensitivity categories, respectively.
For each of the industries and for each of the primary and secondary categories, we test
whether the differences P LOAN(5-19) 2 P LOAN(1-4) and P LOAN(20 þ ) 2 P
JFEP
1,1
50
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(
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Table II.
Primary debt sensitivity
categories
“Debt-sensitive”
small businesses
51
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0
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4
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l
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s
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n
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S
o
u
r
c
e
:
1
9
9
8
S
S
B
F
Table II.
JFEP
1,1
52
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
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r
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h
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-
4
e
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l
o
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s
(
b
y
i
n
d
u
s
t
r
y
)
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e
b
t
s
e
n
s
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t
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v
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t
y
c
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n
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t
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2
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(
c
o
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d
)
Table III.
Secondary debt
sensitivity categories
“Debt-sensitive”
small businesses
53
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
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C
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R
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Y

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V
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A
t

2
1
:
3
4

2
4

J
a
n
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a
r
y

2
0
1
6

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u
r
c
e
:
1
9
9
8
S
S
B
F
Table III.
JFEP
1,1
54
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
D
e
b
t

S
e
n
s
i
t
i
v
i
t
y
C
a
t
e
g
o
r
y
C
a
t
e
g
o
r
y

M
e
m
b
e
r

I
n
d
u
s
t
r
i
e
s
N
u
m

f
i
r
m
s

w
i
t
h

1
–
4

e
m
p
l
o
y
e
e
s
T
o
t
a
l

e
m
p
l
.
P

L
O
A
N
(
1
–
4
)
N
u
m

f
i
r
m
s

w
i
t
h

5
–
1
9

e
m
p
l
o
y
e
e
s
T
o
t
a
l

e
m
p
l
.
P

L
O
A
N
(
5
–
1
9
)
P

L
O
A
N
(
5
–
1
9
)

–

P

L
O
A
N
(
1
–
4
)
N
u
m

f
i
r
m
s

w
i
t
h

2
0
+

e
m
p
l
o
y
e
e
s
T
o
t
a
l

E
m
p
l
.
P

L
O
A
N
(
2
0
+
)
P

L
O
A
N
(
2
0
+
)

–

P

L
O
A
N
(
1
–
4
)
L
o
d
g
i
n
g
1
2
2
8
0
.
7
5
0
9
9
7
0
.
8
8
9
0
.
1
3
9

1
1
8
2
9
1
.
0
0
0
0
.
2
5
0
*

I
n
d
u
s
t
r
i
a
l

e
q
u
i
p
m
e
n
t
1
5
4
1
0
.
7
3
3
1
0
1
0
2
0
.
8
0
0
0
.
0
6
7

3
1
3
,
0
3
4
0
.
8
7
1
0
.
1
3
8

A
p
p
a
r
e
l

s
t
o
r
e
s
1
3
2
8
0
.
6
9
2
8
5
8
0
.
8
7
5
0
.
1
8
3

6
4
8
2
0
.
6
6
7
–
0
.
0
2
6

W
o
o
d

&

p
a
p
e
r

p
r
o
d
u
c
t
s
1
6
4
4
0
.
6
8
8
4
2
9
1
.
0
0
0
0
.
3
1
3

3
2
3
,
7
8
0
0
.
9
0
6
0
.
2
1
9
*

T
r
u
c
k
i
n
g

&

w
a
r
e
h
o
u
s
i
n
g
2
1
4
1
0
.
6
1
9
1
7
1
6
7
0
.
7
0
6
0
.
0
8
7

2
0
1
,
3
8
7
0
.
8
5
0
0
.
2
3
1
*

P
r
i
n
t
i
n
g

&

p
u
b
l
i
s
h
i
n
g
2
5
6
1
0
.
6
0
0
1
4
1
1
1
0
.
9
2
9
0
.
3
2
9
*
*

1
7
1
,
9
1
4
0
.
9
4
1
0
.
3
4
1
*
*

U
t
i
l
i
t
i
e
s

&

s
a
n
i
t
a
r
y
5
1
2
0
.
6
0
0
4
4
0
1
.
0
0
0
0
.
4
0
0

2
1
0
4
1
.
0
0
0
0
.
4
0
0

7

H
I
G
H

P

L
O
A
N
(
1
–
4
)

i
n
d
u
s
t
r
i
e
s
1
0
7
2
5
5
0
.
6
6
4
6
6
6
0
4
0
.
8
4
8
0
.
1
8
5
*
*
*
1
1
9
1
1
,
5
3
0
0
.
8
9
1
0
.
2
2
7
*
*
*
A
u
t
o
m
o
t
i
v
e
2
2
5
3
0
.
5
9
1
2
2
1
8
8
0
.
8
6
4
0
.
2
7
3
*
*

3
9
3
0
1
1
0
.
9
7
4
0
.
3
8
3
*
*
*
B
u
i
l
d
i
n
g

&

g
a
r
d
e
n

m
a
t
r
l
s
.
1
7
3
8
0
.
5
8
8
1
5
1
4
7
0
.
6
0
0
0
.
0
1
2

1
3
6
8
1
0
.
7
6
9
0
.
1
8
1

C
o
m
m
u
n
i
c
a
t
i
o
n
7
1
7
0
.
5
7
1
7
5
1
0
.
7
1
4
0
.
1
4
3

8
6
5
5
1
.
0
0
0
0
.
4
2
9
*
*

G
e
n
.

m
e
r
c
h
a
n
d
i
s
e

&

f
o
o
d
3
6
9
4
0
.
5
5
6
2
2
1
7
8
0
.
6
8
2
0
.
1
2
6

2
5
2
,
3
9
5
0
.
9
2
0
0
.
3
6
4
*
*
*
W
h
o
l
e
s
a
l
e

t
r
a
d
e
9
7
2
3
0
0
.
5
4
6
6
9
6
7
8
0
.
7
5
4
0
.
2
0
7
*
*
*
7
8
6
,
6
9
4
0
.
8
9
7
0
.
3
5
1
*
*
*
A
m
u
s
e
m
e
n
t

&

r
e
c
r
e
a
t
i
o
n
1
5
2
8
0
.
5
3
3
8
8
1
0
.
8
7
5
0
.
3
4
2

2
0
1
,
4
2
8
0
.
8
5
0
0
.
3
1
7
*
*

T
r
a
n
s
p
o
r
t
a
t
i
o
n
1
7
4
1
0
.
5
2
9
1
4
1
4
4
0
.
5
7
1
0
.
0
4
2

1
9
2
,
1
1
4
0
.
7
8
9
0
.
2
6
0
*

H
o
m
e

f
u
r
n
i
s
h
i
n
g

s
t
o
r
e
s
4
0
9
3
0
.
5
2
5
3
1
2
2
8
0
.
7
7
4
0
.
2
4
9
*
*

1
0
4
8
3
1
.
0
0
0
0
.
4
7
5
*
*
*
C
o
n
s
t
r
u
c
t
n
.

&

c
o
n
t
r
a
c
t
i
n
g
1
6
5
3
5
5
0
.
5
2
1
9
3
8
1
3
0
.
7
5
3
0
.
2
3
1
*
*
*
9
8
7
,
9
5
0
0
.
8
7
8
0
.
3
5
6
*
*
*
H
e
a
l
t
h

s
e
r
v
i
c
e
s
7
1
1
8
3
0
.
5
0
7
4
6
3
8
6
0
.
7
6
1
0
.
2
5
4
*
*
*
3
8
3
,
4
7
6
0
.
8
4
2
0
.
3
3
5
*
*
*
E
l
e
c
t
r
o
n
i
c
s
1
2
2
4
0
.
5
0
0
6
6
8
0
.
6
6
7
0
.
1
6
7

2
2
1
,
8
5
0
0
.
9
5
5
0
.
4
5
5
*
*
*
E
a
t
i
n
g

&

d
r
i
n
k
i
n
g

p
l
a
c
e
s
2
5
6
6
0
.
4
8
0
5
5
5
0
9
0
.
5
6
4
0
.
0
8
4

9
0
7
,
0
5
2
0
.
8
1
1
0
.
3
3
1
*
*
*
M
i
s
c
.

m
a
n
u
f
a
c
t
u
r
i
n
g
1
5
3
6
0
.
4
6
7
6
5
4
0
.
8
3
3
0
.
3
6
7

1
0
7
7
8
0
.
9
0
0
0
.
4
3
3
*
*

A
u
t
o

&

r
e
p
a
i
r

s
e
r
v
i
c
e
s
1
4
5
3
1
0
0
.
4
6
2
4
2
3
5
7
0
.
7
6
2
0
.
3
0
0
*
*
*
9
5
3
3
0
.
8
8
9
0
.
4
2
7
*
*

M
i
s
c
.

r
e
t
a
i
l
1
3
3
2
8
4
0
.
4
5
9
5
3
4
5
1
0
.
7
7
4
0
.
3
1
5
*
*
*
2
1
1
,
7
2
4
0
.
7
1
4
0
.
2
5
6
*
*

B
u
s
i
n
e
s
s

&

t
e
c
h
.

s
e
r
v
i
c
e
s
4
1
3
8
5
5
0
.
4
0
9
1
5
7
1
,
3
2
6
0
.
7
7
7
0
.
3
6
8
*
*
*
1
4
5
1
3
,
0
0
5
0
.
8
7
6
0
.
4
6
7
*
*
*
1
6

M
E
D
I
U
M

P

L
O
A
N
(
1
–
4
)

i
n
d
u
s
t
r
i
e
s
1
,
2
3
0
2
,
7
0
7
0
.
4
7
3
6
4
6
5
,
6
5
9
0
.
7
4
1
0
.
2
6
8
*
*
*
6
4
5
5
3
,
8
2
9
0
.
8
7
1
0
.
3
9
8
*
*
*
S
t
o
n
e

&

m
e
t
a
l
1
0
2
1
0
.
4
0
0
1
5
1
3
6
0
.
8
0
0
0
.
4
0
0
*
*

3
6
3
,
5
3
9
0
.
8
8
9
0
.
4
8
9
*
*
*
C
h
e
m
.
,

p
e
t
r
o
l
.

&

p
l
a
s
t
i
c
s
5
1
0
0
.
4
0
0
7
7
5
0
.
8
5
7
0
.
4
5
7
*

2
0
2
,
1
1
4
0
.
9
0
0
0
.
5
0
0
*
*

S
o
c
i
a
l

s
e
r
v
i
c
e
s
3
7
6
5
0
.
3
7
8
2
1
1
8
3
0
.
5
2
4
0
.
1
4
5

1
5
9
6
6
0
.
8
6
7
0
.
4
8
8
*
*
*
M
o
v
i
e
s
8
1
9
0
.
3
7
5
6
4
7
0
.
6
6
7
0
.
2
9
2

3
1
2
4
0
.
6
6
7
0
.
2
9
2

P
e
r
s
o
n
a
l

s
e
r
v
i
c
e
s
1
4
2
2
9
7
0
.
3
3
8
2
9
2
4
3
0
.
4
8
3
0
.
1
4
5

1
4
9
6
0
0
.
7
8
6
0
.
4
4
8
*
*
*
T
e
x
t
i
l
e
s
,

a
p
p
a
r
e
l

&

l
e
a
t
h
e
r
1
5
3
3
0
.
2
6
7
7
6
2
0
.
8
5
7
0
.
5
9
0
*
*
*
1
8
1
,
7
8
4
0
.
8
8
9
0
.
6
2
2
*
*
*
F
o
o
d

&

t
o
b
a
c
c
o
4
5
0
.
2
5
0
4
4
8
1
.
0
0
0
0
.
7
5
0
*
*

1
0
1
,
2
4
3
1
.
0
0
0
0
.
7
5
0
*
*
*
M
i
n
i
n
g
4
1
1
0
.
2
5
0
3
2
8
0
.
0
0
0
–
0
.
2
5
0

6
7
3
6
0
.
8
3
3
0
.
5
8
3
*

E
d
u
c
a
t
i
o
n

s
e
r
v
i
c
e
s
1
4
2
7
0
.
2
1
4
3
2
8
1
.
0
0
0
0
.
7
8
6
*
*
*
3
4
4
9
1
.
0
0
0
0
.
7
8
6
*
*
*

9

L
O
W

P

L
O
A
N
(
1
–
4
)

i
n
d
u
s
t
r
i
e
s
2
3
9
4
8
8
0
.
3
3
5
9
5
8
5
0
0
.
6
3
2
0
.
2
9
7
*
*
*
1
2
5
1
1
,
9
1
5
0
.
8
8
0
0
.
5
4
5
*
*
*
N
o
t
e
s
:

S
i
g
n
i
f
i
c
a
n
t

a
t

t
h
e

*
1
0
,

*
*
5

a
n
d

*
*
*
1

p
e
r

c
e
n
t

l
e
v
e
l
s
,

r
e
s
p
e
c
t
i
v
e
l
y
.

I
n

d
e
s
c
e
n
d
i
n
g

o
r
d
e
r

b
y

P

L
O
A
N
(
1
-
4
)
S
o
u
r
c
e
:

S
S
B
F
F
i
r
m
s

w
i
t
h

1
–
4

E
m
p
l
o
y
e
e
s
F
i
r
m
s

w
i
t
h

5
–
1
9

e
m
p
l
o
y
e
e
s
F
i
r
m
s

w
i
t
h

2
0
+

E
m
p
l
o
y
e
e
s
M
o
r
e

s
e
n
s
i
t
i
v
e
?
?
?
?

s
e
n
s
i
t
i
v
i
t
y
L
e
s
s

s
e
n
s
i
t
i
v
e
L
O
W
P

L
O
A
N
(
1
–
4
)
H
I
G
H
P

L
O
A
N
(
1
–
4
)
M
E
D
I
U
M
P

L
O
A
N
(
1
–
4
)
Table IV.
Tests of ?rm size
differences in loan
proportion (P LOAN)
using primary debt
sensitivity categories
“Debt-sensitive”
small businesses
55
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
D
e
b
t

s
e
n
s
i
t
i
v
i
t
y
c
a
t
e
g
o
r
y
C
a
t
e
g
o
r
y

m
e
m
b
e
r

i
n
d
u
s
t
r
i
e
s
N
u
m

f
i
r
m
s

w
i
t
h

1
–
4

e
m
p
l
o
y
e
e
s
T
o
t
a
l

e
m
p
l
.
P

L
O
A
N

(
1
–
4
)
L
O
A
N

R
A
T
I
O
(
1
–
4
)
N
u
m

f
i
r
m
s

w
i
t
h

5
–
1
9

e
m
p
l
o
y
e
e
s
T
o
t
a
l

e
m
p
l
.
P

L
O
A
N

(
5
–
1
9
)
L
O
A
N

R
A
T
I
O
(
5
–
1
9
)
P

L
O
A
N
(
5
–
1
9
)

–

P

L
O
A
N
(
1
–
4
)
N
u
m

f
i
r
m
s

w
i
t
h

2
0
+

e
m
p
l
o
y
e
e
s
T
o
t
a
l

e
m
p
l
.
P

L
O
A
N

(
2
0
+
)
L
O
A
N

R
A
T
I
O
(
2
0
+
)
P

L
O
A
N
(
2
0
+
)

–

P

L
O
A
N
(
1
–
4
)
I
n
d
u
s
t
r
i
a
l

e
q
u
i
p
m
e
n
t
1
5
4
1
0
.
7
3
3
0
.
9
7
9
1
0
1
0
2
0
.
8
0
0
0
.
1
4
2
0
.
0
6
7

3
1
3
,
0
3
4
0
.
8
7
1
0
.
2
5
1
0
.
1
3
8

W
o
o
d

&

p
a
p
e
r

p
r
o
d
u
c
t
s
1
6
4
4
0
.
6
8
8
0
.
9
1
2
4
2
9
1
.
0
0
0
0
.
3
9
3
0
.
3
1
3

3
2
3
,
7
8
0
0
.
9
0
6
0
.
4
9
4
0
.
2
1
9
*

T
r
u
c
k
i
n
g

&

w
a
r
e
h
o
u
s
i
n
g
2
1
4
1
0
.
6
1
9
0
.
8
0
5
1
7
1
6
7
0
.
7
0
6
0
.
6
5
7
0
.
0
8
7

2
0
1
,
3
8
7
0
.
8
5
0
0
.
5
4
7
0
.
2
3
1
*

U
t
i
l
i
t
i
e
s

&

s
a
n
i
t
a
r
y
5
1
2
0
.
6
0
0
1
.
8
9
8
4
4
0
1
.
0
0
0
2
.
1
3
3
0
.
4
0
0

2
1
0
4
1
.
0
0
0
0
.
1
7
8
0
.
4
0
0

4

H
I
G
H

P

L
O
A
N
(
1
–
4
)

&

M
E
D
I
U
M

o
r

L
A
R
G
E

L
O
A
N

R
A
T
I
O
(
1
–
4
)

5
7
1
3
8
0
.
6
6
7
0
.
9
1
1
3
5
3
3
8
0
.
8
0
0
0
.
5
7
4
0
.
1
3
3

8
5
8
,
3
0
5
0
.
8
8
2
0
.
3
9
5
0
.
2
1
6
*
*
*
B
u
i
l
d
i
n
g

&

g
a
r
d
e
n

m
a
t
r
l
s
.
1
7
3
8
0
.
5
8
8
0
.
8
2
4
1
5
1
4
7
0
.
6
0
0
0
.
2
8
3
0
.
0
1
2

1
3
6
8
1
0
.
7
6
9
0
.
1
9
6
0
.
1
8
1

C
o
m
m
u
n
i
c
a
t
i
o
n
7
1
7
0
.
5
7
1
0
.
8
9
8
7
5
1
0
.
7
1
4
0
.
4
2
5
0
.
1
4
3

8
6
5
5
1
.
0
0
0
0
.
3
1
4
0
.
4
2
9
*
*

G
e
n
.

m
e
r
c
h
a
n
d
i
s
e

&

f
o
o
d
3
6
9
4
0
.
5
5
6
0
.
4
4
5
2
2
1
7
8
0
.
6
8
2
0
.
5
0
7
0
.
1
2
6

2
5
2
,
3
9
5
0
.
9
2
0
0
.
3
5
4
0
.
3
6
4
*
*
*
A
m
u
s
e
m
e
n
t

&

r
e
c
r
e
a
t
i
o
n
1
5
2
8
0
.
5
3
3
0
.
5
1
9
8
8
1
0
.
8
7
5
0
.
3
2
9
0
.
3
4
2

2
0
1
,
4
2
8
0
.
8
5
0
0
.
3
1
0
0
.
3
1
7
*
*

T
r
a
n
s
p
o
r
t
a
t
i
o
n
1
7
4
1
0
.
5
2
9
0
.
8
0
6
1
4
1
4
4
0
.
5
7
1
0
.
7
9
5
0
.
0
4
2

1
9
2
,
1
1
4
0
.
7
8
9
0
.
3
9
7
0
.
2
6
0
*

H
o
m
e

f
u
r
n
i
s
h
i
n
g

s
t
o
r
e
s
4
0
9
3
0
.
5
2
5
0
.
5
4
7
3
1
2
2
8
0
.
7
7
4
0
.
3
1
8
0
.
2
4
9
*
*

1
0
4
8
3
1
.
0
0
0
0
.
2
6
0
0
.
4
7
5
*
*
*
C
o
n
s
t
r
u
c
t
n
.

&

c
o
n
t
r
a
c
t
i
n
g
1
6
5
3
5
5
0
.
5
2
1
0
.
4
4
0
9
3
8
1
3
0
.
7
5
3
0
.
4
3
5
0
.
2
3
1
*
*
*
9
8
7
,
9
5
0
0
.
8
7
8
0
.
2
2
1
0
.
3
5
6
*
*
*
E
l
e
c
t
r
o
n
i
c
s
1
2
2
4
0
.
5
0
0
0
.
5
0
0
6
6
8
0
.
6
6
7
0
.
7
0
5
0
.
1
6
7

2
2
1
,
8
5
0
0
.
9
5
5
0
.
2
3
7
0
.
4
5
5
*
*
*
E
a
t
i
n
g

&

d
r
i
n
k
i
n
g

p
l
a
c
e
s
2
5
6
6
0
.
4
8
0
0
.
5
0
6
5
5
5
0
9
0
.
5
6
4
0
.
7
2
5
0
.
0
8
4

9
0
7
,
0
5
2
0
.
8
1
1
0
.
4
8
4
0
.
3
3
1
*
*
*
M
i
s
c
.

m
a
n
u
f
a
c
t
u
r
i
n
g
1
5
3
6
0
.
4
6
7
0
.
9
6
4
6
5
4
0
.
8
3
3
0
.
6
2
3
0
.
3
6
7

1
0
7
7
8
0
.
9
0
0
0
.
4
6
7
0
.
4
3
3
*
*

A
u
t
o

&

r
e
p
a
i
r

s
e
r
v
i
c
e
s
1
4
5
3
1
0
0
.
4
6
2
0
.
6
2
0
4
2
3
5
7
0
.
7
6
2
0
.
3
2
8
0
.
3
0
0
*
*
*
9
5
3
3
0
.
8
8
9
0
.
2
7
5
0
.
4
2
7
*
*

M
i
s
c
.

r
e
t
a
i
l
1
3
3
2
8
4
0
.
4
5
9
0
.
4
4
8
5
3
4
5
1
0
.
7
7
4
0
.
2
6
4
0
.
3
1
5
*
*
*
2
1
1
,
7
2
4
0
.
7
1
4
0
.
3
1
0
0
.
2
5
6
*
*

B
u
s
i
n
e
s
s

&

t
e
c
h
.

s
e
r
v
i
c
e
s
4
1
3
8
5
5
0
.
4
0
9
0
.
4
7
7
1
5
7
1
,
3
2
6
0
.
7
7
7
0
.
3
9
6
0
.
3
6
8
*
*
*
1
4
5
1
3
,
0
0
5
0
.
8
7
6
0
.
2
7
2
0
.
4
6
7
*
*
*
1
3

M
E
D
I
U
M

P

L
O
A
N
(
1
–
4
)

&

M
E
D
I
U
M

o
r

L
A
R
G
E

L
O
A
N

R
A
T
I
O
(
1
–
4
)

1
,
0
4
0
2
,
2
4
1
0
.
4
6
2
0
.
4
8
6
5
0
9
4
,
4
0
7
0
.
7
3
3
0
.
4
0
5
0
.
2
7
1
*
*
*
4
9
0
4
0
,
6
4
8
0
.
8
6
1
0
.
3
0
7
0
.
4
0
0
*
*
*
M
o
v
i
e
s
8
1
9
0
.
3
7
5
0
.
3
4
0
6
4
7
0
.
6
6
7
0
.
3
6
5
0
.
2
9
2

3
1
2
4
0
.
6
6
7
0
.
5
3
5
0
.
2
9
2

T
e
x
t
i
l
e
s
,

a
p
p
a
r
e
l

&

l
e
a
t
h
e
r
1
5
3
3
0
.
2
6
7
0
.
1
3
5
7
6
2
0
.
8
5
7
0
.
4
6
9
0
.
5
9
0
*
*
*
1
8
1
,
7
8
4
0
.
8
8
9
0
.
4
0
6
0
.
6
2
2
*
*
*
F
o
o
d

&

t
o
b
a
c
c
o
4
5
0
.
2
5
0
0
.
0
0
7
4
4
8
1
.
0
0
0
0
.
0
8
0
0
.
7
5
0
*
*

1
0
1
,
2
4
3
1
.
0
0
0
0
.
3
3
5
0
.
7
5
0
*
*
*
M
i
n
i
n
g
4
1
1
0
.
2
5
0
0
.
1
3
3
3
2
8
0
.
0
0
0
–
0
.
2
5
0

6
7
3
6
0
.
8
3
3
0
.
3
1
3
0
.
5
8
3
*

E
d
u
c
a
t
i
o
n

s
e
r
v
i
c
e
s
1
4
2
7
0
.
2
1
4
0
.
0
1
9
3
2
8
1
.
0
0
0
0
.
3
3
2
0
.
7
8
6
*
*
*
3
4
4
9
1
.
0
0
0
0
.
2
7
6
0
.
7
8
6
*
*
*
5

L
O
W

P

L
O
A
N
(
1
–
4
)

&

L
O
W

L
O
A
N

R
A
T
I
O
(
1
–
4
)

4
5
9
5
0
.
2
6
7
0
.
1
3
1
2
3
2
1
3
0
.
7
3
9
0
.
3
4
7
0
.
4
7
2
*
*
*
4
0
4
,
3
3
6
0
.
9
0
0
0
.
3
5
5
0
.
6
3
3
*
*
*
F
i
r
m
s

w
i
t
h

1
–
4

e
m
p
l
o
y
e
e
s
F
i
r
m
s

w
i
t
h

5
–
1
9

E
m
p
l
o
y
e
e
s
F
i
r
m
s

w
i
t
h

2
0
+

E
m
p
l
o
y
e
e
s
M
o
r
e

s
e
n
s
i
t
i
v
e
?
?
?
?

S
e
n
s
i
t
i
v
i
t
y
L
e
s
s

s
e
n
s
i
t
i
v
e
H
I
G
H

P

L
O
A
N
(
1
–
4
)

&

H
I
G
H

o
r

M
E
D
I
U
M

L
O
A
N

R
A
T
I
O
(
1
–
4
)
M
E
D
I
U
M

P

L
O
A
N
(
1
–
4
)

&

H
I
G
H

o
r

M
E
D
I
U
M

L
O
A
N

R
A
T
I
O
(
1
–
4
)
L
O
W

P

L
O
A
N
(
1
–
4
)

&

L
O
W

L
O
A
N

R
A
T
I
O
(
1
–
4
)
N
o
t
e
s
:

S
i
g
n
i
f
i
c
a
n
t

a
t

t
h
e

*
1
0
,

*
*
5

a
n
d

*
*
*
1

p
e
r

c
e
n
t

l
e
v
e
l
s
,

r
e
s
p
e
c
t
i
v
e
l
y
.

I
n

d
e
s
c
e
n
d
i
n
g

o
r
d
e
r

b
y

P

L
O
A
N
(
1
-
4
)
S
o
u
r
c
e
:

S
S
B
F Table V.
Tests of ?rm size
differences in loan
proportion (P LOAN)
using secondary debt
sensitivity categories
JFEP
1,1
56
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
LOAN(1-4) are statistically and economically signi?cant. The results and how they
vary across the debt sensitivity categories are quite interesting. It seems quite clear
that the data are consistent with our hypothesis that small businesses with one to four
employees are more debt-sensitive than those with 20 þ employees within their same
industry. The values of P LOAN(20 þ ) 2 P LOAN(1-4) are generally positive, large,
and statistically signi?cant in the overwhelming majority of cases for both the primary
and secondary categories.
It is also clear that this effect is much higher, the lower is the P LOAN(1-4) category.
This occurs because the values of P LOAN(20 þ ) do not vary much across categories
– staying between about 85 and 90 percent with loans – whereas P LOAN(1-4) falls
precipitously from about two-thirds with loans to about one-third or one quarter with
loans. Thus, it appears that industry does not matter much for the largest of small
businesses – almost all of which are associated with high-credit availability – but
industry is quite important for the smallest of small businesses.
The ?ndings for P LOAN(5-19) 2 P LOAN(1-4) are qualitatively similar, but
quantitatively smaller. The differences are generally positive and higher when the P
LOAN(1-4) category is lower, but the results are less often statistically and
economically signi?cant. Recall that based on the ?ndings in Table I, we make no
explicit hypothesis that the ?rms with ?ve to 19 employees are less debt-sensitive than
those with one to four employees.
Finally, we recognize that in some cases, ?rms have no loans because they do not
want to borrow, rather than any credit constraints. The SSBF provides some additional
evidence that this is not a decisive factor. Speci?cally, we ?nd that ?rms in our most
debt-sensitive group are much less often granted loans when they indicate on the SSBF
that they “want” credit (applied for a loan in the prior three years).
3. Regression model, variables, and data sets
3.1 Regression model and endogenous variables
We regress the log of ?rms per capita in a given size class or in a given primary or
secondary debt sensitivity category on credit supply variables measuring bank market
power; bank market structure (presence and shares by size and geographic structure);
and bank cost or pro?t ef?ciency. We also include control variables for market and
time period:
lnðMKT FIRMSÞ ¼ f ðBank market power; bank market presence and shares
by size and geography; bank cost or profit efficiency;
market control variables; time fixed effectsÞ;
ð1Þ
where MKT FIRMS is the log of the number of establishments of a given size or
category per 1,000 population in the local market. Establishments are physical
locations at which business is conducted or services or industrial operations are
performed. They are not necessarily identical with a company, which may own and
operate one or more establishments. The establishment data are taken from county
business patterns (US Census Bureau), which has annual information on the location,
employment size, and industry of all establishments in the nation[2].
For convenience, we use the term “?rm” to describe either an establishment or small
business and draw conclusions about small businesses. We acknowledge that the
“Debt-sensitive”
small businesses
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correspondence between the number of employees in an establishment and in a small
business sometimes deviate from one another because a business may have multiple
establishments. As examples, a company may own several fast-food franchises,
convenience stores, or service stations. Nonetheless, given our focus on the one to four
employee size class, it seems likely that vast majority of these very small
establishments are either coincident with or owned by very small businesses.
We argue that the number of very small ?rms per capita in certain industries is a
good candidate for the effects of bank credit supply to small businesses, as measured
by our bank market power, structure, and ef?ciency variables. The literature suggests
that these bank market characteristics affect small business credit availability
generally, and the SSBF data analyzed above suggests that very small ?rms –
particularly those in industries identi?ed as “on the bubble” or on the margin for
credit – are the most sensitive to this credit availability. Thus, if differences in banks’
small business credit supplies have any signi?cant effects on small businesses, they
should affect the numbers of these marginal ?rms. Viewed in this fashion, our tests
may also be interpreted as more precise investigations of the larger research and policy
question of whether banks “matter” to small businesses.
The number of these ?rms per capita may also be a particularly good summary
statistic for the effects of bank credit supply. Measures of the performance of very
small businesses using ?nancial statements – if they were also available by ?rm
location, size, and industry for all ?rms in the nation – would re?ect only the marginal
bene?ts for those that entered and survived. In contrast, the number of ?rms per capita
also incorporates the number of ?rms that exited the market or did not enter in the ?rst
place.
To test our main hypotheses, we run the model in equation (1) for the three ?rm size
classes and separately for our primary and secondary categories that are based on both
size and industry. Using our identi?cation above, when the ?rm size classes are
speci?ed, the 1-4 employee category is hypothesized to be more debt-sensitive than the
20 þ category. When the primary or secondary categories are speci?ed, the medium
loan proportion ?rms are postulated to be more sensitive than the high- and
low-proportion ?rms.
The hypothesis tests consist of determining whether the one category of ?rms (1-4
size class or medium loan proportion) is statistically and economically signi?cantly
more sensitive than other categories to our three sets of bank market conditions
(market power, structure, and ef?ciency). Thus, under the maintained assumption that
our identi?cation of debt sensitivity is valid, we test our hypotheses that these credit
supply variables have signi?cantly greater effects on ?rms per capita in a category
identi?ed as more debt-sensitive than on ?rms per capita in a category identi?ed as
less sensitive.
We conduct separate estimations for metropolitan (METRO) and rural (RURAL)
markets. The former are agglomerations of counties designated as metropolitan
statistical areas (MSAs) or New England county metropolitan areas for the year 2002,
and the latter include all other counties. These local markets are standard in antitrust
and research on banking and small business lending because most retail services,
including small business loans, are provided within these markets[3].
We expect much greater test power in rejecting the null hypothesis of no different
effect of the bank market variables across ?rm categories in RURAL markets.
JFEP
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There is more variation in market conditions in RURAL counties – METRO markets
are generally more highly competitive with signi?cant presence and shares of all bank
types. There are also many more observations on RURAL markets, further increasing
test power.
3.2 Exogenous variables
The data are annual observations for METRO and RURAL markets for the years
1991-2002. We show sample means, minimums, and maximums by market for both
market types for the exogenous variables in Table VI. All ?nancial values are
expressed in real 1994 dollars, de?ated using the consumer price index. The main data
sources for the key exogenous variables are bank call reports for bank balance sheet
and income items and FDIC summary of deposits for the locations of bank branches.
The exogenous variables (other than the time ?xed effects) are measured as
averages over the prior three years to reduce measurement error and endogeneity. For
market power, we use concentration as measured by the Her?ndahl index for bank
branches (including head of?ces) in the market (HERF). The use of branches – rather
than the quantities of deposits or loans – reduces endogeneity problems. Banks choose
their branch locations and typically leave these ?xed for the short-term, whereas
customers may respond to the exercise of market power by changing deposits and
loans more quickly. We include only bank branches, and exclude savings and loans
that take deposits, but typically do not supply much small business credit. Not
surprisingly, RURAL markets are generally highly concentrated, whereas METRO
markets are typically moderately concentrated.
The research literature is ambiguous on the net effect of market power on small
business credit availability. Market power may have a negative effect on the amount of
credit supplied using any lending technology through the traditional
structure-conduct-performance model. However, there may be an increase in credit
supplied using one lending technology – relationship lending. This is because market
power helps the bank enforce a long-term implicit contract in which the borrower
receives a subsidized interest rate in the short-term, and then pays a higher rate in a
later period (Petersen and Rajan, 1995). The empirical results for lending to small
businesses are mixed, with some studies ?nding generally unfavorable effects from
market power (Karceski et al., 2005; Cetorelli and Strahan, 2006), and others ?nding
favorable effects (Petersen and Rajan, 1995; Cetorelli, 2004).
For bank market structure, we include variables for the presence and market shares
of three types of banks – “local community banks”, “multicommunity banks”, and
“mega banks”. We de?ne local community banks (LOCAL COMM) as those with
branches in a single local market and gross total assets (GTA) of $5 billion or less;
multicommunity banks (MULTI COMM) as institutions those with branch of?ces in
multiple markets and GTA # $1 billion; and mega banks (MEGA) as the remainder
with GTA . $5 billion or in multiple markets with GTA . $1 billion.
These three de?nitions conform reasonably well with the research literature and
conventional wisdom about community banking and relationship lending (DeYoung
et al., 2004). LOCAL COMM ?t with the notion that an institution must be in only
one community and small enough to “know” that locality – its leaders, its business
climate, and its customer base – yielding a potential comparative advantage
in relationship lending based on “soft” information to very small businesses.
“Debt-sensitive”
small businesses
59
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Table VI.
Exogenous variables
in regressions
JFEP
1,1
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Table VI.
“Debt-sensitive”
small businesses
61
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U
N
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A
t

2
1
:
3
4

2
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a
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(
P
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)
MULTI COMM ?t an even narrower de?nition of size, potentially improving any
advantage in relationship lending, but their presence in multiple markets may inhibit
their abilities to use this lending technology. Finally, MEGA essentially correspond to
the idea of institutions that are either too large to know the local community well or
have a combination of geographic dispersion and size that prevents specialization in
relationship lending to small ?rms. Nonetheless, these institutions may have
comparative advantages in transactions lending technologies – such as ?nancial
statement lending, small business credit scoring, and asset-based lending – that are
based on “hard” information (Berger and Udell, 2006).
Unfortunately, the theory still does not suggest which bank size-geography group is
likely to provide the most credit availability on net for debt-sensitive ?rms. While the
smaller small businesses are more likely to be served by relationship lending, they may
alternatively be served using some of the transactions lending technologies. The
empirical literature on the effects of the market shares of large and small banks is
mixed. For example, one study ?nds that new business incorporations respond
positively to large bank market share (Black and Strahan, 2002), while another study
?nds virtually no difference in credit availability or price of credit of large bank market
share (Berger et al., 2007a, b). Finally, some recent literature suggests that MEGA may
have increased how aggressively they compete due to deregulation and technological
changes over time that favor larger, more geographically dispersed organizations
(Berger and Mester, 2003; Berger et al., 2007a, b).
The presence and shares of LOCAL COMM, MULTI COMM, and MEGA banks
suggest that all three size-geography groups are almost always present in METRO
markets, and MEGA banks have the greatest share. In RURAL markets, by contrast,
LOCAL COMM banks have the largest share and MEGA institutions are present
slightly less than half of the time.
We include either the cost or pro?t ef?ciency of banks in the ?rm’s market. Pro?t
ef?ciencyis the more inclusive concept, but we also include cost ef?ciencybecause it is more
commonly speci?ed in the literature and the predictions may differ as discussed below.
We specify cost and pro?t ef?ciency ranks, which are uniform over time, rather than
ef?ciency levels, which vary from year to year because our model includes multiple years.
The ef?ciency variables are derived fromthe residuals of OLS variable cost and pro?t
functions that are estimated for virtually all banks in the nation in the same
size-geography group in the same year. For example, we regress the variable costs of
MULTI COMMbanks for 1993 on measures of market prices of variable inputs, quantities
of variable outputs and ?xedoutputs/inputs, and controls for market environment for that
year[4]. We assume that the bankwiththe lowest cost residual is the most ef?cient MULTI
COMM bank in 1993, the one with the highest residual is least ef?cient, and so forth in
between these extremes. We convert the ranks of these residuals into a uniformscale over
[0, 1], such that most ef?cient bank has a rank of 1.00, the least ef?cient has a rank of 0.00,
and a bank that is more ef?cient than 70 percent of the banks in the category and year has
a rank of 0.70. Pro?t ef?ciency rank is derived analogously using the variable pro?t
function, ranking higher residuals as more ef?cient. COST EFF of MULTI COMM
BANKS is the weighted mean of the cost ef?ciency ranks of MULTI COMM banks in
market m averaged over years t 2 1, t 2 2 and t 2 3. That is, we measure how well
the banks in this market performed relative to banks in the same size-geography group in
the same three years[5].
JFEP
1,1
62
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o
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o
a
d
e
d

b
y

P
O
N
D
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C
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E
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S
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A
t

2
1
:
3
4

2
4

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a
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(
P
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)
There is no empirical research of which we are aware directly on the topic of the
credit availability effects of bank frontier ef?ciency, although other studies have used
bank ?nancial ratios or labor productivity ratios (Black and Strahan, 2002). In terms of
expected effects, the most cost-ef?cient banks tend to have the lowest costs of lending,
all else equal, which may be passed on in part to loan customers in terms of lower
prices and greater availability. The effects of pro?t ef?ciency ranks are more
ambiguous. A pro?t-ef?cient bank may pass along to business loan customers some of
the bene?ts of cost ef?ciency and some of any revenue ef?ciencies earned from other
activities. However, pro?t ef?ciency may also incorporate the effects of market power
in loan pricing, which may reduce small business credit, leaving the overall sign of the
pro?t ef?ciency ranks unknown.
The statistics shown in Table VI suggest the banks in METRO markets tend to be
more ef?cient than those in RURAL markets. As well, within METRO markets, the
average market ef?ciency ranks are all slightly below0.50, despite the fact that the mean
rank across banks is always 0.50 by construction (uniformdistribution over [0, 1]). This
suggests that even within METRO markets, the most ef?cient banks tend to be in the
markets with most banks and the lowest average market shares (since the means in
Table VI weight all markets equally).
The control variables include ?rst- and second-order terms of population to account
for market size. We also include state geographic regulation variables. These
regulations had important effects on local market competition in the early part of the
sample, but have become less relevant as banks can now have (almost) nationwide
operations (subject to a 10 percent deposit cap achieved through mergers). The time
?xed effects control for other differences in competition and macroeconomic conditions
over the sample period.
4. Results of regressions and hypothesis tests
Tables VII-XV present our regressions and test results by employee size class
(Tables VII-IX) and by our primary and secondary classi?cations for debt sensitivity
classi?cations (Tables X-XV). In Tables VII, X and XIII, we display regressions of
the log of ?rms per capita in the market (ln (MKT FIRMS)) on the bank market
variables (BK MKT) and controls (CTRLS). We show results for ?rms with different
predicted debt sensitivity to test our main hypotheses. Speci?cally, the BK MKT
coef?cients should be statistically and economically more signi?cant for ?rms that
are hypothesized to be more debt-sensitive than for ?rms predicted to be less
sensitive. In Tables VIII, XI and XIV, we show the statistical hypothesis tests for
differences in coef?cients of the banking variables between ?rm size classes or
primary or secondary classi?cations. Finally, in Tables IX, XII and XV, we tabulate
the quantitative effects of these differences in coef?cients to test for economic
signi?cance.
In all cases, separate regressions are shown for METRO and RURAL markets. Each
observation in the regressions is a market-year combination. There are 3,816
observations for the METRO regressions, re?ecting over 300 METRO markets per
year over the 12 years of the sample. There are 26,904 RURAL observations, re?ecting
the much larger number of these markets.
We present ?ndings for four speci?cations, each with different combinations of the
BK MKT exogenous variables. Speci?cation I includes only bank market power – as
“Debt-sensitive”
small businesses
63
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(
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)
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e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table VII.
Regressions of log of
?rms per capita (ln (MKT
FIRMS)) in different size
classes on bank market
variables (BK MKT) and
market controls (CTRLS)
JFEP
1,1
64
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
I
I
I
I
I
I
I
I
I
I
I
V
I
V
I
I
I
I
I
I
I
I
I
I
I
I
I
V
I
V
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
B
a
n
k

m
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p
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w
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H
E
R
F
–
0
.
1
5
5
0
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0
5
0
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0
7
3
0
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0
7
1
0
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0
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8
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3
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9
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3
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–
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3
4
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3
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3
5
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*
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[
–
0
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7
1
]
[
1
.
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6
]
[
0
.
3
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1
.
3
5
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[
0
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3
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1
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4
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0
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4
3
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9
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5
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6
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6
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7
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4
0
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7
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–
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3
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]
[
7
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2
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]
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a
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k

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0
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7
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2
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3
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M
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I

C
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M
0
.
0
5
7
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0
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1
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3
[
0
.
9
5
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[
0
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0
5
]
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1
.
6
3
]
[
0
.
0
8
]
[
1
.
0
1
]
[
–
0
.
1
9
]
[
1
.
1
9
]
[
0
.
4
6
]
[
2
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1
3
]
[
0
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6
5
]
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1
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0
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8
3
]

M
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6
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0
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8
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8
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1
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6
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]
[
1
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6
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]
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0
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9
3
]
[
1
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2
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]
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a
n
k

m
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h
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I

C
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0
.
0
2
1
–
0
.
0
1
7
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3
1
–
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5
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0
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–
0
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0
]
[
0
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3
9
]
[
–
0
.
3
5
]
[
0
.
2
8
]
[
–
0
.
4
6
]
[
0
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1
4
]
[
–
1
.
0
2
]
[
0
.
3
3
]
[
–
1
.
1
0
]
[
0
.
1
9
]
[
–
1
.
0
6
]

M
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A
–
0
.
1
2
5
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–
0
.
0
5
8
–
0
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1
2
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–
0
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5
8
–
0
.
1
3
1
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–
0
.
0
6
0
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0
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1
5
7
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–
0
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1
3
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–
0
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1
4
9
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–
0
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1
3
9
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–
0
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1
7
2
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–
0
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1
3
5
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*
[
–
1
.
8
7
]
[
–
1
.
0
2
]
[
–
1
.
8
2
]
[
–
1
.
0
3
]
[
–
1
.
9
5
]
[
–
1
.
0
6
]
[
–
2
.
2
7
]
[
–
2
.
4
5
]
[
–
2
.
1
7
]
[
–
2
.
6
1
]
[
–
2
.
4
9
]
[
–
2
.
5
4
]
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
–
0
.
0
5
4
0
.
0
3
4
–
0
.
0
5
2
–
0
.
0
2
8
[
–
0
.
8
7
]
[
0
.
7
9
]
[
–
0
.
8
3
]
[
–
0
.
7
1
]

M
U
L
T
I

C
O
M
M
–
0
.
0
9
8
*
*
–
0
.
0
0
3
–
0
.
1
2
9
*
*
*
–
0
.
0
1
2
[
–
2
.
1
5
]
[
–
0
.
0
7
]
[
–
2
.
7
2
]
[
–
0
.
3
4
]

M
E
G
A
–
0
.
1
7
7
*
*
*
–
0
.
0
4
1
–
0
.
2
4
8
*
*
*
–
0
.
0
6
7
*
[
–
3
.
2
1
]
[
–
0
.
9
6
]
[
–
4
.
4
8
]
[
–
1
.
6
7
]
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
–
0
.
0
3
1
–
0
.
0
5
5
–
0
.
0
8
5
–
0
.
1
2
4
*
*
*
[
–
0
.
5
6
]
[
–
1
.
4
5
]
[
–
1
.
5
5
]
[
–
3
.
5
1
]

M
U
L
T
I

C
O
M
M
–
0
.
0
0
9
0
.
0
1
6
–
0
.
0
5
1
–
0
.
0
2
0
[
–
0
.
2
0
]
[
0
.
4
4
]
[
–
1
.
1
2
]
[
–
0
.
6
3
]

M
E
G
A
–
0
.
0
9
3
–
0
.
0
2
8
–
0
.
1
5
2
*
*
–
0
.
0
4
8
[
–
1
.
5
4
]
[
–
0
.
6
1
]
[
–
2
.
5
5
]
[
–
1
.
1
0
]
C
O
E
F
F
(
5
–
1
9
)

–

C
O
E
F
F
(
1
–
4
)
C
O
E
F
F
(
2
0
+
)

–

C
O
E
F
F
(
1
–
4
)
N
o
t
e
s
:

S
i
g
n
i
f
i
c
a
n
t

a
t

t
h
e

*
1
0
,

*
*
5

a
n
d

*
*
*
1

p
e
r

c
e
n
t

l
e
v
e
l
s
,

r
e
s
p
e
c
t
i
v
e
l
y
;

t

S
t
a
t
i
s
t
i
c
s

b
a
s
e
d

o
n

e
r
r
o
r
s

c
l
u
s
t
e
r
e
d

b
y

m
a
r
k
e
t
;

m
o
d
e
l
s

I
-
I
V

f
o
r

M
E
T
R
O

a
n
d
R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l

v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C
S
u
m
m
a
r
y

o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table VIII.
Tests of statistical
signi?cance of differences
in effects of bank market
variables (BK MKT) on
log of ?rms per capita (ln
(MKT FIRMS)) by size
class
“Debt-sensitive”
small businesses
65
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
I
I
I
I
I
V
I
V
I
I
I
I
I
I
I
V
I
V
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
B
a
n
k

m
a
r
k
e
t

p
o
w
e
r
?

H
E
R
F

=

0
.
0
8
0
.
0
0
6
2
0
.
0
0
5
8
0
.
0
0
7
9
0
.
0
0
5
9
–
0
.
0
0
7
1
0
.
0
2
7
6
–
0
.
0
0
5
9
0
.
0
2
8
6
B
a
n
k

m
a
r
k
e
t

p
r
e
s
e
n
c
e

?

L
O
C
A
L

C
O
M
M

=

1
–
0
.
0
0
5
0
–
0
.
0
0
2
0
–
0
.
0
1
6
0
0
.
0
4
0
0
–
0
.
0
4
3
0
0
.
0
2
3
0
–
0
.
0
2
3
0
0
.
0
7
5
0

?

M
U
L
T
I

C
O
M
M

=

1
0
.
1
0
5
0
0
.
0
0
3
0
0
.
0
6
5
0
–
0
.
0
0
6
0
0
.
1
3
2
0
0
.
0
1
9
0
0
.
1
0
0
0
0
.
0
2
3
0

?

M
E
G
A

=

1
0
.
0
6
8
0
0
.
0
3
8
0
0
.
0
3
4
0
0
.
0
3
0
0
0
.
0
8
0
0
0
.
0
5
1
0
0
.
0
4
9
0
0
.
0
4
0
0
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s
?

M
U
L
T
I

C
O
M
M

=

0
.
2
5
0
.
0
0
7
8
–
0
.
0
0
3
8
0
.
0
0
5
8
–
0
.
0
0
5
0
0
.
0
0
7
0
–
0
.
0
1
1
3
0
.
0
0
4
0
–
0
.
0
1
1
0

?

M
E
G
A

=

0
.
2
5
–
0
.
0
3
0
0
–
0
.
0
1
4
5
–
0
.
0
3
2
8
*
–
0
.
0
1
5
0
–
0
.
0
3
7
3
–
0
.
0
3
4
8
–
0
.
0
4
3
0
–
0
.
0
3
3
8
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
–
0
.
0
0
5
4
0
.
0
0
3
4
–
0
.
0
0
5
2
–
0
.
0
0
2
8
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
–
0
.
0
0
9
8
–
0
.
0
0
0
3
–
0
.
0
1
2
9
–
0
.
0
0
1
2
?

M
E
G
A

=

0
.
1
0
–
0
.
0
1
7
7
*
–
0
.
0
0
4
1
–
0
.
0
2
4
8
–
0
.
0
0
6
7
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
–
0
.
0
0
3
1
–
0
.
0
0
5
5
–
0
.
0
0
8
5
–
0
.
0
1
2
4
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
–
0
.
0
0
0
9
0
.
0
0
1
6
–
0
.
0
0
5
1
–
0
.
0
0
2
0
?

M
E
G
A

=

0
.
1
0
–
0
.
0
0
9
3
–
0
.
0
0
2
8
–
0
.
0
1
5
2
–
0
.
0
0
4
8
C
O
E
F
F
(
5
–
1
9
)

–

C
O
E
F
F
(
1
–
4
)
C
O
E
F
F
(
2
0
+
)

–

C
O
E
F
F
(
1
–
4
)
N
o
t
e
s
:

*
E
x
c
e
e
d
s

0
.
0
1

a
n
d

i
n
d
i
c
a
t
e
s

s
t
a
t
i
s
t
i
c
a
l

a
n
d

e
c
o
n
o
m
i
c

s
i
g
n
i
f
i
c
a
n
c
e
;

e
f
f
e
c
t
s

i
n

b
o
l
d

a
r
e

s
t
a
t
i
s
t
i
c
a
l
l
y

s
i
g
n
i
f
i
c
a
n
t

a
n
d

a
r
e

e
v
a
l
u
a
t
e
d

f
o
r

e
c
o
n
o
m
i
c
s
i
g
n
f
i
c
a
n
c
e
;

c
o
m
p
l
e
t
e

s
p
e
c
i
f
i
c
a
t
i
o
n
s

o
f

M
o
d
e
l
s

I
I
I

a
n
d

I
V

o
n
l
y

f
o
r

M
E
T
R
O

a
n
d

R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l
v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C
S
u
m
m
a
r
y

o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
*
***
*
* *
***
*
Table IX.
Tests of economic
signi?cance of differences
in effects of selected
changes in bank market
variables (BK MKT) on
changes in log of ?rms
per capita (ln (MKT
FIRMS)) by size class
JFEP
1,1
66
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
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a
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m
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p
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F
–
0
.
0
3
0
–
0
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2
4
7
*
*
*
–
0
.
0
7
3
–
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2
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1
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*
–
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2
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7
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2
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3
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*
*
0
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1
1
9
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6
5
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–
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6
6
1
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–
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6
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4
4
0
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*
0
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0
6
4
–
0
.
4
3
9
*
*
*
0
.
0
3
6
–
0
.
4
4
4
*
*
*
[
–
0
.
2
2
]
[
–
7
.
4
5
]
[
–
0
.
4
9
]
[
–
6
.
7
4
]
[
–
0
.
5
6
]
[
–
6
.
6
5
]
[
–
0
.
7
1
]
[
–
6
.
7
2
]
[
0
.
7
0
]
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–
1
8
.
4
7
]
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–
0
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0
1
]
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–
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7
.
1
1
]
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–
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8
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–
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6
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8
7
]
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–
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7
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4
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]
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0
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–
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4
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]
B
a
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k

m
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0
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4
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0
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0
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6
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0
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5
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7
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*
0
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2
0
[
1
.
0
3
]
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0
.
3
7
]
[
0
.
6
2
]
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–
1
.
2
8
]
[
1
.
0
4
]
[
–
0
.
3
6
]
[
2
.
9
0
]
[
1
.
8
9
]
[
1
.
7
2
]
[
–
1
.
3
6
]
[
2
.
8
6
]
[
–
0
.
1
4
]
[
2
.
7
5
]
[
2
.
7
3
]
[
1
.
8
2
]
[
0
.
8
3
]
[
2
.
6
3
]
[
1
.
0
7
]

M
U
L
T
I

C
O
M
M
–
0
.
0
4
4
0
.
0
1
8
–
0
.
0
5
5
–
0
.
0
0
7
–
0
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0
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7
0
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3
–
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1
0
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0
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0
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–
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1
–
0
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*
–
0
.
0
1
6
0
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0
2
3
–
0
.
0
3
2
–
0
.
0
1
2
–
0
.
0
6
3
*
–
0
.
0
2
0
–
0
.
0
2
1
–
0
.
0
1
0
[
–
1
.
0
7
]
[
1
.
1
0
]
[
–
1
.
2
8
]
[
–
0
.
3
5
]
[
–
1
.
0
6
]
[
1
.
1
5
]
[
–
0
.
1
9
]
[
–
0
.
0
8
]
[
–
1
.
3
2
]
[
–
1
.
6
8
]
[
–
0
.
2
9
]
[
1
.
0
5
]
[
–
1
.
0
7
]
[
–
0
.
8
3
]
[
–
1
.
9
2
]
[
–
1
.
1
6
]
[
–
0
.
5
9
]
[
–
0
.
5
5
]

M
E
G
A
0
.
0
4
5
*
0
.
0
2
7
*
–
0
.
0
0
9
0
.
0
0
3
–
0
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3
0
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0
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0
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9
*
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–
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7
–
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–
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9
–
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4
0
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–
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0
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3
–
0
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0
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8
–
0
.
0
5
3
*
*
*
0
.
0
0
5
–
0
.
0
5
9
*
*
*
[
1
.
7
1
]
[
1
.
7
6
]
[
–
0
.
2
9
]
[
0
.
1
5
]
[
–
0
.
4
1
]
[
0
.
0
2
]
[
1
.
9
6
]
[
2
.
7
2
]
[
–
1
.
0
1
]
[
–
0
.
3
0
]
[
–
0
.
7
2
]
[
–
0
.
5
3
]
[
1
.
2
6
]
[
–
0
.
2
4
]
[
–
0
.
3
3
]
[
–
2
.
9
5
]
[
0
.
1
9
]
[
–
3
.
2
2
]
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s

M
U
L
T
I

C
O
M
M
0
.
0
2
0
0
.
0
2
8
0
.
0
1
6
0
.
0
3
5
0
.
0
2
1
0
.
0
3
0
–
0
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0
3
3
0
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0
6
7
*
*
–
0
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0
4
7
0
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0
7
8
*
*
–
0
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7
0
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9
*
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0
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0
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3
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0
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0
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2
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4
5
*
0
.
0
0
6
0
.
0
4
1
[
0
.
4
1
]
[
1
.
0
1
]
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0
.
3
3
]
[
1
.
2
5
]
[
0
.
4
1
]
[
1
.
0
7
]
[
–
0
.
5
1
]
[
2
.
1
7
]
[
–
0
.
7
6
]
[
2
.
5
2
]
[
–
0
.
5
5
]
[
2
.
2
1
]
[
0
.
1
3
]
[
1
.
6
8
]
[
0
.
0
4
]
[
1
.
7
4
]
[
0
.
1
3
]
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1
.
5
6
]

M
E
G
A
0
.
0
2
9
0
.
0
9
5
*
*
0
.
0
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2
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4
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*
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0
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0
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6
9
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]
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2
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3
]
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2
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8
]
[
3
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8
7
]
[
2
.
2
6
]
[
4
.
3
6
]
[
2
.
3
8
]
[
4
.
0
6
]
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–
0
.
2
9
]
[
1
.
1
2
]
[
–
0
.
3
3
]
[
1
.
5
4
]
[
–
0
.
3
1
]
[
1
.
2
8
]
B
a
n
k

c
o
s
t

e
f
f
i
c
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e
n
c
y

L
O
C
A
L

C
O
M
M
0
.
0
1
9
0
.
0
7
2
*
*
*
0
.
0
5
4
0
.
1
5
0
*
*
*
0
.
0
2
9
0
.
0
5
3
*
*
[
0
.
5
6
]
[
2
.
6
1
]
[
1
.
0
4
]
[
4
.
7
7
]
[
1
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0
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]
[
2
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8
]

M
U
L
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I

C
O
M
M
0
.
0
1
7
0
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0
4
4
*
0
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1
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5
*
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0
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0
6
6
*
*
0
.
0
5
8
*
*
0
.
0
1
6
[
0
.
7
0
]
[
1
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B
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A

f
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p
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p
u
l
a
t
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o
n
Table X.
Regressions of log of
?rms per capita (ln (MKT
FIRMS)) in different
primary debt sensitivity
categories on bank
market variables (BK
MKT) and market
controls (CTRLS)
“Debt-sensitive”
small businesses
67
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.
0
3
]
[
–
0
.
4
5
]
[
0
.
0
0
]
[
–
0
.
3
7
]
[
–
0
.
4
5
]
[
0
.
1
2
]
[
0
.
6
2
]
[
–
0
.
0
7
]
[
–
1
.
1
7
]

M
E
G
A
–
0
.
0
1
3
–
0
.
0
2
2
0
.
0
2
8
0
.
0
1
1
0
.
0
1
5
0
.
0
1
4
–
0
.
0
3
1
–
0
.
0
5
2
*
*
0
.
0
2
9
–
0
.
0
4
6
0
.
0
3
4
–
0
.
0
4
6
[
–
0
.
3
2
]
[
–
0
.
9
1
]
[
0
.
6
0
]
[
0
.
3
2
]
[
0
.
3
0
]
[
0
.
4
2
]
[
–
0
.
8
5
]
[
–
2
.
3
4
]
[
0
.
6
5
]
[
–
1
.
4
8
]
[
0
.
7
0
]
[
–
1
.
4
3
]
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s

M
U
L
T
I

C
O
M
M
0
.
0
5
4
–
0
.
0
3
9
0
.
0
6
3
–
0
.
0
4
3
0
.
0
5
7
–
0
.
0
3
9
0
.
0
3
9
–
0
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4
0
.
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4
9
–
0
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0
3
3
0
.
0
4
3
–
0
.
0
2
8
[
0
.
6
6
]
[
–
0
.
9
4
]
[
0
.
8
0
]
[
–
1
.
0
2
]
[
0
.
6
9
]
[
–
0
.
9
2
]
[
0
.
5
0
]
[
–
0
.
5
9
]
[
0
.
6
5
]
[
–
0
.
8
2
]
[
0
.
5
3
]
[
–
0
.
6
9
]

M
E
G
A
–
0
.
0
9
1
–
0
.
0
6
9
–
0
.
0
8
3
–
0
.
0
8
0
–
0
.
0
8
5
–
0
.
0
7
4
–
0
.
1
2
9
*
*
–
0
.
1
3
1
*
*
–
0
.
1
2
7
*
*
–
0
.
1
3
7
*
*
*
–
0
.
1
3
6
*
*
–
0
.
1
3
3
*
*
[
–
1
.
3
8
]
[
–
1
.
2
1
]
[
–
1
.
2
9
]
[
–
1
.
4
0
]
[
–
1
.
2
8
]
[
–
1
.
2
9
]
[
–
2
.
0
6
]
[
–
2
.
5
2
]
[
–
2
.
0
6
]
[
–
2
.
6
6
]
[
–
2
.
1
5
]
[
–
2
.
5
6
]
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
–
0
.
0
3
4
–
0
.
0
7
8
*
–
0
.
0
2
5
–
0
.
0
9
7
*
*
[
–
0
.
5
6
]
[
–
1
.
8
6
]
[
–
0
.
4
2
]
[
–
2
.
4
8
]

M
U
L
T
I

C
O
M
M
–
0
.
0
9
8
*
*
–
0
.
0
2
2
–
0
.
0
5
6
–
0
.
0
5
[
–
2
.
2
8
]
[
–
0
.
5
8
]
[
–
1
.
2
6
]
[
–
1
.
4
1
]

M
E
G
A
–
0
.
0
9
3
*
–
0
.
0
5
1
–
0
.
1
2
2
*
*
–
0
.
0
1
2
[
–
1
.
7
4
]
[
–
1
.
2
0
]
[
–
2
.
3
5
]
[
–
0
.
3
1
]
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
0
.
0
3
1
–
0
.
0
4
7
0
.
0
3
2
–
0
.
0
3
7
[
0
.
5
5
]
[
–
1
.
2
8
]
[
0
.
6
2
]
[
–
1
.
0
7
]

M
U
L
T
I

C
O
M
M
0
.
0
0
4
0
.
0
3
6
–
0
.
0
2
3
0
.
0
4
6
[
0
.
1
0
]
[
1
.
0
4
]
[
–
0
.
5
1
]
[
1
.
4
4
]

M
E
G
A
–
0
.
0
6
4
–
0
.
0
5
8
–
0
.
1
1
7
*
*
–
0
.
0
0
9
[
–
1
.
0
8
]
[
–
1
.
2
5
]
[
–
1
.
9
7
]
[
–
0
.
2
0
]
C
O
E
F
F
(
H
I
G
H
,
P
R
I
)

–

C
O
E
F
F
(
M
E
D
,
P
R
I
)
C
O
E
F
F
(
L
O
W
,
P
R
I
)

–

C
O
E
F
F
(
M
E
D
,
P
R
I
)
N
o
t
e
s
:

S
i
g
n
i
f
i
c
a
n
t

a
t

t
h
e

*
1
0
,

*
*
5

a
n
d

*
*
*
1

p
e
r

c
e
n
t

l
e
v
e
l
s
,

r
e
s
p
e
c
t
i
v
e
l
y
;

t

S
t
a
t
i
s
t
i
c
s

b
a
s
e
d

o
n

e
r
r
o
r
s

c
l
u
s
t
e
r
e
d

b
y

m
a
r
k
e
t
;

m
o
d
e
l
s

I
-
I
V

f
o
r

M
E
T
R
O

a
n
d
R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l

v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C

S
u
m
m
a
r
y
o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table XI.
Tests of statistical
signi?cance of differences
in effects of bank market
variables (BK MKT) on
log of ?rms per capita (ln
(MKT FIRMS)) by
primary debt sensitivity
category
JFEP
1,1
68
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
I
I
I
I
I
V
I
V
I
I
I
I
I
I
I
V
I
V
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
B
a
n
k

m
a
r
k
e
t

p
o
w
e
r
?

H
E
R
F

=

0
.
0
8
–
0
.
0
0
5
2
0
.
0
3
2
6
*
–
0
.
0
0
3
0
0
.
0
3
3
0
*
0
.
0
0
6
3
0
.
0
1
7
2
*
0
.
0
0
8
2
0
.
0
1
7
7
*
B
a
n
k

m
a
r
k
e
t

p
r
e
s
e
n
c
e
?

L
O
C
A
L

C
O
M
M

=

1
–
0
.
0
5
0
0
0
.
0
0
5
0
–
0
.
0
8
3
0
–
0
.
0
0
4
0
–
0
.
0
2
9
0
0
.
0
4
7
0
–
0
.
0
5
6
0
0
.
0
2
3
0
?

M
U
L
T
I

C
O
M
M

=

1
0
.
0
1
6
0
0
.
0
3
1
0
–
0
.
0
3
1
0
0
.
0
0
0
0
0
.
0
0
8
0
0
.
0
1
8
0
–
0
.
0
0
5
0
–
0
.
0
3
3
0
?

M
E
G
A

=

1
0
.
0
2
8
0
0
.
0
1
1
0
0
.
0
1
5
0
0
.
0
1
4
0
0
.
0
2
9
0
–
0
.
0
4
6
0
0
.
0
3
4
0
–
0
.
0
4
6
0
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s
?

M
U
L
T
I

C
O
M
M

=

0
.
2
5
0
.
0
1
5
8
–
0
.
0
1
0
8
0
.
0
1
4
3
–
0
.
0
0
9
8
0
.
0
1
2
3
–
0
.
0
0
8
3
0
.
0
1
0
8
–
0
.
0
0
7
0
?

M
E
G
A

=

0
.
2
5
–
0
.
0
2
0
8
–
0
.
0
2
0
0
–
0
.
0
2
1
3
–
0
.
0
1
8
5
–
0
.
0
3
1
8
*
–
0
.
0
3
4
3
*
–
0
.
0
3
4
0
*
–
0
.
0
3
3
3
*
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
–
0
.
0
0
3
4
–
0
.
0
0
7
8
–
0
.
0
0
2
5
–
0
.
0
0
9
7
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
–
0
.
0
0
9
8
–
0
.
0
0
2
2
–
0
.
0
0
5
6
–
0
.
0
0
5
0
?

M
E
G
A

=

0
.
1
0
–
0
.
0
0
9
3
–
0
.
0
0
5
1
–
0
.
0
1
2
2
*
–
0
.
0
0
1
2
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
0
.
0
0
3
1
–
0
.
0
0
4
7
0
.
0
0
3
2
–
0
.
0
0
3
7
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
0
.
0
0
0
4
0
.
0
0
3
6
–
0
.
0
0
2
3
0
.
0
0
4
6
?

M
E
G
A

=

0
.
1
0
–
0
.
0
0
6
4
–
0
.
0
0
5
8
–
0
.
0
1
1
7
*
–
0
.
0
0
0
9
C
O
E
F
F
(
H
I
G
H
,
P
R
I
)

–

C
O
E
F
F
(
M
E
D
,
P
R
I
)
C
O
E
F
F
(
L
O
W
,
P
R
I
)

–

C
O
E
F
F
(
M
E
D
,
P
R
I
)
N
o
t
e
s
:

*
E
x
c
e
e
d
s

0
.
0
1

a
n
d

i
n
d
i
c
a
t
e
s

s
t
a
t
i
s
t
i
c
a
l

a
n
d

e
c
o
n
o
m
i
c

s
i
g
n
i
f
i
c
a
n
c
e
;

e
f
f
e
c
t
s

i
n

b
o
l
d

a
r
e

s
t
a
t
i
s
t
i
c
a
l
l
y

s
i
g
n
i
f
i
c
a
n
t

a
n
d

a
r
e

e
v
a
l
u
a
t
e
d

f
o
r

e
c
o
n
o
m
i
c
s
i
g
n
f
i
c
a
n
c
e
;

c
o
m
p
l
e
t
e

s
p
e
c
i
f
i
c
a
t
i
o
n
s

o
f

M
o
d
e
l
s

I
I
I

a
n
d

I
V

o
n
l
y

f
o
r

M
E
T
R
O

a
n
d

R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l
v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

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e

i
t
e
m
s
;

F
D
I
C

S
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;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table XII.
Tests of economic
signi?cance of differences
in effects of selected
changes in bank market
variables (BK MKT) on
changes in log of ?rms
per capita (ln (MKT
FIRMS)) by primary debt
sensitivity category
“Debt-sensitive”
small businesses
69
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O
b
s
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r
v
a
t
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s
3
,
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9
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H
I
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P
R
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P
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A
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(
P

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A
N
(
1
–
4
)

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0
.
6
0
)
M
E
D
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M

P
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L
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&

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(
1
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4
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4
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M
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e

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t
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1
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5

a
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d

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1

p
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r

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t

l
e
v
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s
,

r
e
s
p
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c
t
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y
;

y
e
a
r

d
u
m
m
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s
,

s
t
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b
a
n
k

b
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a
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c
h
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g

p
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v
a
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,

a
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p
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f
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c
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d
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m
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n
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s
h
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;

t

s
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a
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b
a
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d

o
n

e
r
r
o
r
s

c
l
u
s
t
e
r
e
d

b
y

m
a
r
k
e
t
;

m
o
d
e
l
s

I
-
I
V

f
o
r

M
E
T
R
O

a
n
d

R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
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a
r

o
b
s
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r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;
a
l
l

f
i
n
a
n
c
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v
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a
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i
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1
9
9
4

d
o
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l
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s
,

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d

u
s
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g

t
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C
P
I
S
o
u
r
c
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s
:

C
o
u
n
t
y

B
u
s
i
n
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s

P
a
t
t
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r
n
s

(
C
e
n
s
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s
)

f
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n
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m
b
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r

o
f

e
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t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C

S
u
m
m
a
r
y
o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table XIII.
Regressions of log of
?rms per capita (ln (MKT
FIRMS)) in different
secondary debt
sensitivity categories on
bank market variables
(BK MKT) and market
controls (CTRLS)
JFEP
1,1
70
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
I
I
I
I
I
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I
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V
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M
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A
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M
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B
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p
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3
3
3
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4
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8
7
0
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4
3
4
*
*
*
–
0
.
0
3
5
0
.
4
3
6
*
*
*
[
–
2
.
1
8
]
[
8
.
6
1
]
[
–
1
.
1
4
]
[
8
.
5
3
]
[
–
1
.
1
8
]
[
8
.
4
7
]
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–
0
.
9
5
]
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8
.
6
9
]
[
–
1
.
6
2
]
[
9
.
9
8
]
[
–
0
.
3
4
]
[
9
.
1
1
]
[
–
0
.
4
0
]
[
8
.
8
2
]
[
–
0
.
1
6
]
[
8
.
8
2
]
B
a
n
k

m
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r
k
e
t

p
r
e
s
e
n
c
e

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C
A
L

C
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M
M
–
0
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0
7
5
*
–
0
.
0
1
0
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0
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2
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9
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0
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*
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–
0
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1
3
0
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–
0
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0
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4
[
–
1
.
6
6
]
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–
0
.
3
8
]
[
–
0
.
5
3
]
[
1
.
5
1
]
[
–
1
.
9
8
]
[
1
.
9
1
]
[
–
1
.
8
0
]
[
–
0
.
0
1
]
[
–
0
.
3
7
]
[
2
.
7
8
]
[
–
2
.
5
4
]
[
–
0
.
1
2
]

M
U
L
T
I

C
O
M
M
–
0
.
0
0
8
0
.
0
1
8
0
.
0
6
4
0
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0
3
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0
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0
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4
1
*
0
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5
7
0
.
0
1
3
0
.
0
0
1
–
0
.
1
0
5
*
*
*
[
–
0
.
1
4
]
[
0
.
7
6
]
[
1
.
0
2
]
[
1
.
0
7
]
[
0
.
0
8
]
[
0
.
1
7
]
[
0
.
0
8
]
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–
1
.
7
6
]
[
0
.
8
9
]
[
0
.
4
7
]
[
0
.
0
1
]
[
–
3
.
7
5
]

M
E
G
A
–
0
.
0
3
2
–
0
.
0
5
9
*
*
0
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0
5
7
–
0
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0
0
3
0
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0
2
7
–
0
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0
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2
0
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6
–
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0
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0
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1
1
2
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0
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5
0
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0
9
0
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–
0
.
0
3
6
[
–
0
.
8
6
]
[
–
2
.
5
4
]
[
1
.
2
5
]
[
–
0
.
1
0
]
[
0
.
5
5
]
[
–
0
.
0
6
]
[
0
.
4
5
]
[
–
3
.
1
2
]
[
2
.
5
2
]
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–
0
.
8
1
]
[
1
.
8
4
]
[
–
1
.
1
4
]
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s

M
U
L
T
I

C
O
M
M
0
.
0
9
3
–
0
.
0
6
1
0
.
1
0
8
–
0
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0
6
9
*
0
.
0
9
8
–
0
.
0
6
2
0
.
0
0
5
–
0
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0
4
8
0
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0
1
2
–
0
.
0
6
4
0
.
0
0
7
–
0
.
0
5
5
[
1
.
1
5
]
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–
1
.
4
9
]
[
1
.
3
9
]
[
–
1
.
6
8
]
[
1
.
2
0
]
[
–
1
.
5
2
]
[
0
.
0
6
]
[
–
1
.
1
9
]
[
0
.
1
6
]
[
–
1
.
5
7
]
[
0
.
0
8
]
[
–
1
.
3
5
]

M
E
G
A
–
0
.
1
3
1
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*
–
0
.
1
3
9
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0
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1
2
2
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1
5
3
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1
2
6
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–
0
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4
4
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–
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1
8
8
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–
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5
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9
5
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–
0
.
1
7
4
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1
9
4
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–
0
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1
6
0
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*
[
–
2
.
1
4
]
[
–
2
.
5
0
]
[
–
2
.
0
6
]
[
–
2
.
7
7
]
[
–
2
.
0
5
]
[
–
2
.
6
0
]
[
–
2
.
9
6
]
[
–
2
.
8
7
]
[
–
3
.
1
7
]
[
–
3
.
3
2
]
[
–
3
.
0
6
]
[
–
3
.
0
5
]
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
–
0
.
0
7
8
–
0
.
1
1
8
*
*
*
–
0
.
1
1
9
*
*
–
0
.
1
9
1
*
*
*
[
–
1
.
3
2
]
[
–
2
.
9
0
]
[
–
1
.
9
9
]
[
–
4
.
7
4
]

M
U
L
T
I

C
O
M
M
–
0
.
1
3
9
*
*
*
–
0
.
0
1
9
–
0
.
1
0
1
*
*
–
0
.
1
0
1
*
*
*
[
–
3
.
3
0
]
[
–
0
.
5
2
]
[
–
2
.
2
3
]
[
–
2
.
7
7
]

M
E
G
A
–
0
.
1
9
3
*
*
*
–
0
.
0
8
7
*
*
–
0
.
1
8
5
*
*
*
–
0
.
0
7
3
*
[
–
3
.
7
6
]
[
–
2
.
0
9
]
[
–
3
.
6
3
]
[
–
1
.
8
5
]
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y

L
O
C
A
L

C
O
M
M
0
.
0
7
4
–
0
.
1
2
7
*
*
*
0
.
1
0
4
*
*
0
.
0
0
5
[
1
.
3
8
]
[
–
3
.
5
2
]
[
2
.
0
5
]
[
0
.
1
3
]

M
U
L
T
I

C
O
M
M
–
0
.
0
1
0
0
.
0
3
0
0
.
0
2
4
0
.
1
2
4
*
*
*
[
–
0
.
2
3
]
[
0
.
9
0
]
[
0
.
4
9
]
[
3
.
7
7
]

M
E
G
A
–
0
.
1
2
4
*
*
–
0
.
0
9
1
*
*
–
0
.
1
3
6
*
*
–
0
.
0
4
7
[
–
2
.
1
7
]
[
–
2
.
0
2
]
[
–
2
.
2
4
]
[
–
1
.
0
9
]
C
O
E
F
F
(
H
I
G
H
,
S
E
C
)

–

C
O
E
F
F
(
M
E
D
,
S
E
C
)
C
O
E
F
F
(
L
O
W
,
S
E
C
)

–

C
O
E
F
F
(
M
E
D
,

S
E
C
)
N
o
t
e
s
:

S
i
g
n
i
f
i
c
a
n
t

a
t

t
h
e

*
1
0
,

*
*
5

a
n
d

*
*
*
1

p
e
r

c
e
n
t

l
e
v
e
l
s
,

r
e
s
p
e
c
t
i
v
e
l
y
;

t

S
t
a
t
i
s
t
i
c
s

b
a
s
e
d

o
n

e
r
r
o
r
s

c
l
u
s
t
e
r
e
d

b
y

m
a
r
k
e
t
;

m
o
d
e
l
s

I
-
I
V

f
o
r

M
E
T
R
O

a
n
d
R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l

v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C

S
u
m
m
a
r
y
o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table XIV.
Tests of statistical
signi?cance of differences
in effects of bank market
variables (BK MKT) on
log of ?rms per capita
(ln (MKT FIRMS)) by
secondary debt
sensitivity category
“Debt-sensitive”
small businesses
71
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
I
I
I
I
I
I
I
V
I
V
I
I
I
I
I
I
I
V
I
V
M
E
T
R
O
R
U
R
A
L
M
E
T
R
O
R
U
R
A
L
M
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R
O
R
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R
A
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M
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T
R
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R
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R
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L
B
a
n
k

m
a
r
k
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t

p
o
w
e
r
?

H
E
R
F

=

0
.
0
8
–
0
.
0
1
9
8
0
.
0
3
4
9
*
–
0
.
0
1
6
6
0
.
0
3
6
0
*
–
0
.
0
0
7
0
0
.
0
3
4
7
*
–
0
.
0
0
2
8
0
.
0
3
4
9
*
B
a
n
k

m
a
r
k
e
t

p
r
e
s
e
n
c
e
?

L
O
C
A
L

C
O
M
M

=

1
–
0
.
0
2
8
0
0
.
0
4
7
0
–
0
.
1
0
5
0
*
0
.
0
6
2
0
–
0
.
0
1
8
0
0
.
0
8
5
0
–
0
.
1
3
0
0
*
–
0
.
0
0
4
0
?

M
U
L
T
I

C
O
M
M

=

1
0
.
0
6
4
0
0
.
0
3
1
0
0
.
0
0
5
0
0
.
0
0
5
0
0
.
0
5
7
0
0
.
0
1
3
0
0
.
0
0
1
0
–
0
.
1
0
5
0
?

M
E
G
A

=

1
0
.
0
5
7
0
–
0
.
0
0
3
0
0
.
0
2
7
0
–
0
.
0
0
2
0
0
.
1
1
2
0
–
0
.
0
2
5
0
0
.
0
9
0
0
–
0
.
0
3
6
0
B
a
n
k

m
a
r
k
e
t

s
h
a
r
e
s
?

M
U
L
T
I

C
O
M
M

=

0
.
2
5
0
.
0
2
7
0
–
0
.
0
1
7
3
*
0
.
0
2
4
5
–
0
.
0
1
5
5
0
.
0
0
3
0
–
0
.
0
1
6
0
0
.
0
0
1
8
–
0
.
0
1
3
8
?

M
E
G
A

=

0
.
2
5
–
0
.
0
3
0
5
*
–
0
.
0
3
8
3
*
–
0
.
0
3
1
5
*
–
0
.
0
3
6
0
*
–
0
.
0
4
8
8
*
–
0
.
0
4
3
5
*
–
0
.
0
4
8
5
*
–
0
.
0
4
0
0
*
B
a
n
k

c
o
s
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
–
0
.
0
0
7
8
–
0
.
0
1
1
8
–
0
.
0
1
1
9
–
0
.
0
1
9
1
*
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
–
0
.
0
1
3
9
*
–
0
.
0
0
1
9
–
0
.
0
1
0
1
*
–
0
.
0
1
0
1
*
?

M
E
G
A

=

0
.
1
0
–
0
.
0
1
9
3
*
–
0
.
0
0
8
7
–
0
.
0
1
8
5
*
–
0
.
0
0
7
3
B
a
n
k

p
r
o
f
i
t

e
f
f
i
c
i
e
n
c
y
?

L
O
C
A
L

C
O
M
M

=

0
.
1
0
0
.
0
0
7
4
–
0
.
0
1
2
7
*
0
.
0
1
0
4
0
.
0
0
0
5
?

M
U
L
T
I

C
O
M
M

=

0
.
1
0
–
0
.
0
0
1
0
0
.
0
0
3
0
0
.
0
0
2
4
0
.
0
1
2
4
?

M
E
G
A

=

0
.
1
0
–
0
.
0
1
2
4
*
–
0
.
0
0
9
1
–
0
.
0
1
3
6
*
–
0
.
0
0
4
7
C
O
E
F
F
(
H
I
G
H
,
S
E
C
)

–

C
O
E
F
F
(
M
E
D
,
S
E
C
)
C
O
E
F
F
(
L
O
W
,
S
E
C
)

–

C
O
E
F
F
(
M
E
D
,

S
E
C
)
N
o
t
e
s
:

*
E
x
c
e
e
d
s

0
.
0
1

a
n
d

i
n
d
i
c
a
t
e
s

s
t
a
t
i
s
t
i
c
a
l

a
n
d

e
c
o
n
o
m
i
c

s
i
g
n
i
f
i
c
a
n
c
e
;

e
f
f
e
c
t
s

i
n

b
o
l
d

a
r
e

s
t
a
t
i
s
t
i
c
a
l
l
y

s
i
g
n
i
f
i
c
a
n
t

a
n
d

a
r
e

e
v
a
l
u
a
t
e
d

f
o
r

e
c
o
n
o
m
i
c
s
i
g
n
f
i
c
a
n
c
e
;

c
o
m
p
l
e
t
e

s
p
e
c
i
f
i
c
a
t
i
o
n
s

o
f

M
o
d
e
l
s

I
I
I

a
n
d

I
V

o
n
l
y

f
o
r

M
E
T
R
O

a
n
d

R
U
R
A
L

m
a
r
k
e
t
s
;

m
a
r
k
e
t
-
y
e
a
r

o
b
s
e
r
v
a
t
i
o
n
s
,

1
9
9
1
-
2
0
0
2
;

a
l
l

f
i
n
a
n
c
i
a
l
v
a
r
i
a
b
l
e
s

a
r
e

i
n

1
9
9
4

d
o
l
l
a
r
s
,

d
e
f
l
a
t
e
d

u
s
i
n
g

t
h
e

C
P
I
S
o
u
r
c
e
s
:

C
o
u
n
t
y

B
u
s
i
n
e
s
s

P
a
t
t
e
r
n
s

(
C
e
n
s
u
s
)

f
o
r

n
u
m
b
e
r

o
f

e
s
t
a
b
l
i
s
h
m
e
n
t
s

d
a
t
a
;

C
a
l
l

R
e
p
o
r
t
s

f
o
r

b
a
n
k

b
a
l
a
n
c
e

s
h
e
e
t

a
n
d

i
n
c
o
m
e

i
t
e
m
s
;

F
D
I
C

S
u
m
m
a
r
y
o
f

D
e
p
o
s
i
t
s

f
o
r

t
h
e

l
o
c
a
t
i
o
n
s

o
f

b
a
n
k

o
f
f
i
c
e
s
;

B
E
A

f
o
r

p
o
p
u
l
a
t
i
o
n
Table XV.
Tests of economic
signi?cance of differences
in effects of selected
changes in bank market
variables (BK MKT) on
changes in log of ?rms
per capita (ln (MKT
FIRMS)) by secondary
debt sensitivity category
JFEP
1,1
72
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
1
:
3
4

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
measured by HERF – and the control variables. This represents the most common
speci?cation for recent studies of credit availability, focusing on the net effects of bank
market power. Speci?cation II adds the bank market structure variables. These include
the presence and shares by bank size-geography groups – local community (LOCAL
COMM), multicommunity (MULTI COMM), and mega (MEGA) banks. We specify
dummies for the presence of all three groups, and include the shares of the latter two
groups, leaving LOCAL COMM share excluded as the base case. The shares measure
the marginal effects of banks in a group on competitive conditions in the market. The
inclusion of the presence dummies as well as the shares allows for the possibility that a
very small share for a group may have an important effect in terms of a “toehold” or
sunk costs that allow for the threat of more aggressive competition through future
expansion without the costs or delay of entry. Speci?cations III and IV add the cost and
pro?t ef?ciency ranks, respectively, of the banks in the three bank size-geography
groups. We include only one of the two ef?ciency concepts at a time because they have
potentially different predictions.
4.1 Results by size class
Table VII shows the regressions of ln (MKT FIRMS) on the bank market variables (BK
MKT) and controls (CTRLS) for the one to four, ?ve to 19, and 20 þ employee size classes.
Recall that we hypothesize that the number of market ?rms with1-4 employees per capita is
more sensitive to the BK MKT variables than the number with 20 þ employees, with no
clear prediction for the difference between the ?ve to 19 and one to four employee size
classes.
The HERF coef?cients are negative and statistically signi?cant in RURAL markets
for all three size classes and generally insigni?cant for the METRO markets, as
expected. To simplify matters, we focus attention on the complete Speci?cation IV
using pro?t ef?ciency for RURAL markets, but the coef?cients are almost the same in
all four speci?cations. The coef?cient of 20.0653 for the one to four employee size
class is not much larger in magnitude than the 20.579 coef?cient for the ?ve to 19 size
class, but is more than double the coef?cient of 20.295 for the 20 þ size class.
We test for statistical signi?cance of the differences incoef?cients across size classes in
Table VIII. As shown, the coef?cient differences between the ?ve to 19 and one to four size
classes – shown under the COEFF(5-19) 2 COEFF(1-4) heading – are not statistically
signi?cant. In contrast, all of the RURAL values of COEFF(20 þ ) 2 COEFF(1-4) show
statistical signi?cance for the difference between the RURAL coef?cients, consistent with
our hypothesis about differences between these two groups.
We examine the economic signi?cance of these differences in Table IX. As there are
no common metrics or standards for economic signi?cance, we make a few “rules” that
seem appropriate, although they are obviously somewhat subjective. First, we examine
economic signi?cance only when both the coef?cient is statistically signi?cant for the
category hypothesized to be most debt-sensitive and the difference in coef?cient
estimates across debt sensitivity categories is statistically signi?cant. Second,
we con?ne attention for economic signi?cance to the complete speci?cations of
Models III and IV to avoid issues of excluded variables. Third, we examine the effects
of an economically substantial change in the exogenous variable in question, which
differs across our exogenous variable groups. For example, we examine a change in
HERF of 0.08, which corresponds to a difference in antitrust treatment, and we
“Debt-sensitive”
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evaluate a change in the market share of MEGA banks of 0.25, approximately the
change that would occur if banks in RURAL markets consolidated to be similar to the
market structure of METRO markets.
Finally – and most subjective – we will call the difference economically signi?cant
if the effect of the coef?cient difference and change in exogenous variable moves the
predicted difference in the effects on the endogenous variables of market ?rms per
capita at least 1 percent. We argue that an additional 1 percent difference in the number
of ?rms per capita in a more sensitive category than in a less-sensitive category that is
also statistically signi?cant is an important difference because the number of ?rms is
such an important indicator of the ?nancial health of very small businesses. In
Tables IX, XII and XV, we indicate in italics the numbers that are evaluated for
economic signi?cance – coef?cient is statistically signi?cant for the most sensitive
hypothesized category and statistically signi?cantly different from other categories –
and we indicate with an asterisk (
*
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0.01, which we will call economically signi?cant.
As noted, for our substantial change in the exogenous variable for market power, we
evaluate an increase in HERF of 0.08 (DHERF ¼ 0.08). This corresponds to a substantial
difference in antitrust treatment. US antitrust authorities classify a HERF in the range of
0.10-0.18 as a moderately concentrated market, and a HERF over 0.18 as a highly
concentrated market, requiring more scrutiny for merger approval. The coef?cient
of 20.0653 for the MKT FIRMS(1-4) in the RURAL Speci?cation IV regression in
Tables VII, X and XIII implies a change in the dependent variable for DHERF ¼ 0.08 as
(20.653) £ 0.08 ¼ 20.05224 or about a 25.224 percent change in the 1-4 employee ?rms
per capita in RURAL markets, given the natural log form of the dependent variable. In
contrast, the corresponding coef?cient of 20.295 for the MKT FIRMS(20 þ ) regression
implies a (20.295) £ 0.08 ¼ 20.0236 or about a 22.36 percent change in the 20 þ ?rms
per capita in RURAL markets. The difference between these numbers, which may be
expressedmore simplyas [COEFF(20 þ ) 2 COEFF(1-4)] £ DHERF, is 0.02864 or about a
2.864 percent greater decline in one to four employee ?rms than in 20 þ employee ?rms,
whichis showninTable IX, whichis markedbyanasteriskfor economic signi?cance. The
difference is similarly economically signi?cant for the RURAL Speci?cation III test, as
shown in the table.
Turning to bank market presence, the only variable in Panel A that is consistently
statistically signi?cant for MKT FIRMS(1-4) is LOCAL COMM in the METRO
markets, so we con?ne attention to this variable and these markets. The data show a
very strong effect, with one to four employee ?rms per capita between about 7.5 and
12.5 percent higher when a LOCAL COMM is present. However, as shown in Tables
VIII, XI and XIV, the differences between the size classes are not statistical signi?cant,
so it may be the case that small businesses of all sizes bene?t from the competition
provided by having at least one LOCAL COMM in METRO markets. We do not
evaluate the economic signi?cance of the market presence variables because of this
lack of statistical signi?cance of the coef?cient differences. However, we note here for
completeness that for Tables IX, XII, and XV, the change in the presence variables to
be evaluated is 1 (e.g. DLOCAL COMM ¼ 1), since these are 0, 1 dummies.
For the bank market shares, the MEGA bank coef?cients are statistically signi?cant
in all the MKT FIRMS(1-4) regressions, and the LOCAL COMM coef?cients are
statistically signi?cant for the RURAL markets only, and are smaller in magnitude.
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Thus, very small businesses may be better-off if large and/or multimarket banks have
greater market shares at the expense of shares of LOCAL COMM banks. In Table VIII,
none of the MULTI COMM market share coef?cient differences are statistically
signi?cant, so we do not investigate the economic signi?cance of these differences. The
MEGA coef?cient differences are statistically signi?cant in all cases for
COEFF(20 þ ) 2 COEFF(1-4), and also statistically signi?cant for the METRO
markets for COEFF(5-19) 2 COEFF(1-4), so we investigate their economic signi?cance
in Panel C. We use DMEGA ¼ 0.25 as our metric for a substantial change in market
share for these banks. This seems reasonable, given the ?nding in Table VI that the
MEGA share in RURAL markets is about 30 percent below that in METRO markets in
our data set. All of these differences are in the range of about 23 to 24 percent,
suggesting a larger percentage effect on the very small businesses. Thus, for both
METRO and RURAL markets, for MEGA coef?cient differences are may be considered
to be statistically and economically signi?cantly different for the 20 þ size class, and
consistent with our hypothesis about the effects of this component of market structure.
The ?ndings also suggest economic and statistical signi?cant differences for the 5-19
employee size class in METRO markets.
Turning to the ef?ciency ?ndings, both cost and pro?t ef?ciency have some
positive, statistically signi?cant effects on MKT FIRMS(1-4). In Table VIII, some of the
METRO market cost ef?ciency COEFF(5-19) 2 COEFF(1-4) differences are
statistically signi?cant, as are some of the METRO and RURAL market cost and
pro?t ef?ciency COEFF(20 þ ) 2 COEFF(1-4) differences. To evaluate economic
signi?cance of these changes, we consider a change in ef?ciency of 0.10 or 10 percent
points, which would be a substantial increase – such as an improvement from the
median to the 60th percentile of the ef?ciency distribution. As shown in Table IX, all of
the statistically signi?cant changes are economically signi?cant, ranging from about
1.2 to 2.5 per cent.
To brie?y summarize our results by size class, we ?nd the regression results to be
consistent with our hypothesis that small businesses with one to four employees are
more debt-sensitive overall thanthose with 20 þ employees based ontheir sensitivityto
our banking variables. However, the statistically and economically signi?cant ?ndings
are limited to some variables in some types of markets. The difference in market power
applies to RURAL markets only; none of the market presence variables are statistically
signi?cantly different; the effects of market shares apply to MEGA banks in both
METRO and RURAL markets, but not to MULTI COMM banks; and the ef?ciency
effects are somewhat spotty, and smaller than some of the effects of the other variables.
4.2 Results by primary debt sensitivity categories
For our primary debt sensitivity categories in Tables X-XII, we again start by
examining the statistical signi?cance of the coef?cients for the category hypothesized
to be most debt-sensitive – the one to four employee size class in industries with a
MEDIUM P LOAN(1-4) ?rms (0.40 , P LOAN(1-4) , 0.60). In Table X, the effects of
our market power variable, HERF, are again statistically signi?cant only for RURAL
markets for the MEDIUM P LOAN(1-4) category. The HERF coef?cients are also
statistically signi?cant in HIGH P LOAN(1-4) and LOW P LOAN(1-4) categories for
RURAL markets, but smaller in magnitude. The ?ndings in Table XI are consistent
with statistical signi?cance for these RURAL market differences from both the HIGH
“Debt-sensitive”
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and LOW P LOAN(1-4) ?rms –, i.e. COEFF(HIGH, PRI) 2 COEFF(MED, PRI) and
COEFF(LOW, PRI) 2 COEFF(MED, PRI) are all statistically signi?cant for RURAL
markets, where “PRI” indicates that the primary debt sensitivity categories are used. In
Table XII, the economic effects are stronger for the differences from the HIGH P
LOAN(1-4) category – over 3 percent differences for both Speci?cations III and IV –
than the differences of just under 2 percent for the differences from the LOW P
LOAN(1-4) category.
Turning to the effects of bank market structure, the coef?cients for the presence of a
LOCAL COMM bank in a METRO market are economically large and statistically
signi?cant for the MEDIUM P LOAN(1-4) category, but the differences from the HIGH
and LOW P LOAN(1-4) categories are not statistically signi?cant. Again, we do not
pursue the market presence variables further.
The market share ?ndings in Tables X-XII for the MEDIUM MKT FIRMS(1-4)
regressions are similar to the ?ndings above in Tables VII-IX for all the MKT
FIRMS(1-4) ?rms. The MEGA bank coef?cients are statistically signi?cant for both
METROand RURAL markets in all the regressions, and the LOCAL COMMcoef?cients
are statistically signi?cant for the RURAL markets only. These ?ndings again suggest
bene?ts to having larger market shares for large and/or multimarket banks at the
expense of shares of LOCAL COMM. In Table XI, none of the MULTI COMM market
share coef?cient differences are statistically signi?cant, so we do not investigate their
economic signi?cance. The MEGAcoef?cient differences are not statistically signi?cant
for the COEFF(HIGH, PRI) 2 COEFF(MED, PRI) differences, but they are all
statistically signi?cant for all the COEFF(LOW, PRI) 2 COEFF(MED, PRI). The
?ndings in Table XII also suggest economic signi?cance of these differences in both
METRO and RURAL markets – about a 3 percent stronger effect for MEDIUM P
LOAN(1-4) ?rms than LOW P LOAN(1-4) ?rms.
Turning to the ef?ciency ?ndings, all of the cost and pro?t ef?ciencies are
statistically signi?cant for the MEDIUM P LOAN(1-4) category for RURAL markets,
although one of the pro?t ef?ciencies is negative and only signi?cant at the 10 percent
level. Half of the ef?ciencies are also signi?cant for METRO markets. Other than the
one negative effect, all of these are positive and exceed the corresponding ef?ciency
effects for both the HIGH and LOW P LOAN(1-4) category. Table XI suggests that
several of the differences are statistically signi?cant, but Table XII suggests very
limited economic signi?cance – just for COEFF(LOW, PRI) 2 COEFF(MED, PRI)
for METRO markets for the cost and pro?t ef?ciency of MEGA banks of just over
1 per cent.
To brie?y summarize our results by primary debt-sensitive category, we ?nd that
the data provide some support for our hypothesis that even within the smallest size
class of businesses with one to four employees, those in industries with a MEDIUM P
LOAN(1-4) are more debt-sensitive than those in industries with HIGH P LOAN(1-4)
and LOW P LOAN(1-4). We focus just on the differences in Table XII, Panel C that are
both statistically and economically signi?cant. The MEDIUM P LOAN(1-4) category is
only more sensitive than the HIGH P LOAN(1-4) category to the market power variable
HERF in RURAL markets, while differences between MEDIUM and LOW P
LOAN(1-4) industries are also signi?cant for both METRO and RURAL market shares
for MEGA banks and for the ef?ciencies of MEGA banks in METRO markets.
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As we will see next, the differences among the industries will be stronger for the
secondary categories that add the requirement that the LOAN RATIO is substantial.
4.3 Results by secondary debt sensitivity categories
Table XIII-XV shows the regressions for the secondary categories, which add the
requirement for a substantial median loan/asset ratio for industries in the high-and
medium-P LOAN groups (LOAN RATIO(1-4) . 0.40), and a low ratio (LOAN
RATIO(1-4) # 0.40) for the low-P LOAN group. The ?ndings are generally more
strongly consistent with our hypotheses than those for the primary categories shown
in Table X-XII. In the interest of brevity, we will discuss only the ?ndings that are
statistically and economically signi?cant in Table XV and how they differ from
Table XII.
The most important difference between our secondary and primary results is that
the secondary results are much more often statistically and economically signi?cant,
particularly for the differences between the HIGH and MEDIUM P LOAN(1-4)
industries. For the secondary classi?cation of industries, the HIGH and MEDIUM P
LOAN(1-4) differences are signi?cant not just for market power for RURAL markets,
but also for the presence of LOCAL COMM banks in METRO markets when pro?t
ef?ciency is speci?ed; for the market shares of MEGA banks in both METRO and
RURAL markets; and for some of the cost and pro?t ef?ciencies in both METRO and
RURAL markets. The main differences between LOW and MEDIUM industries in
Table XV are that the magnitudes of all the signi?cant cases in Table XII are all larger
in Table XV; that presence of LOCAL COMM banks in METRO markets is signi?cant
when pro?t ef?ciency is speci?ed; and that more of the cost ef?ciencies are statistically
and economically signi?cant.
5. Conclusion
We employ the concept of “debt sensitivity”, which differs in some important ways
from “external dependence” and other measures of the importance of external funding
used in the literature. We argue that debt sensitivity may be a useful tool for
identifying which sizes and industries of small businesses may be most affected by
banking market conditions, including bank market power, structure, and ef?ciency.
We formulate and test our hypotheses using two data sets on small business size,
industry, access to credit, and location, as well as data on the banks in their markets.
To be speci?c, we use responses from 3,272 ?rms to the 1998 SSBF on their size,
industry, and access to credit to form hypotheses about the sizes and industries of
small businesses that likely to be more or less “debt-sensitive”. We then test these
hypotheses with regression analysis that employs a comprehensive data set on
business establishments and their size, industry, and location from the US Census
Bureau and information on the banks in their markets from regulatory reports. The
regressions include 3,807 observations of metropolitan market-years and 26,904
observations of rural market-years from 1991 to 2002.
Our primary debt sensitivity classi?cations are based on whether ?rms in a size
class and/or industry have a loan probability between 0.40 and 0.60. We argue that
?rms with loan probability close to 50 percent are “on the bubble” for credit or have
marginal access to debt, and are more sensitive to local banking conditions than both
those with “relatively easy” credit availability (probability $ 60 percent) and those
“Debt-sensitive”
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with “relatively dif?cult” credit availability (probability # 40 percent). Our secondary
classi?cation adds the requirement that the median loan/asset ratio for ?rms in the
category with loans exceed 0.40 to ensure that the credit is a substantial source of
?nancing when it is available.
The empirical ?ndings are consistent with our hypotheses – the size classes and
industries hypothesized to be more debt-sensitive are statistically and economically
signi?cantly more sensitive to at least some of the bank market power, market
structure, and ef?ciency variables than those hypothesized to be less debt-sensitive.
The banking variables and market conditions that provide the most consistent support
for the hypotheses are the local market Her?ndahl index in rural markets and the share
of “mega” banks (assets . $5 billion or in multiple markets with assets . $1 billion) in
both metropolitan and rural markets. The cost and pro?t ef?ciency of “mega” banks
operating in metropolitan markets is also almost always statistically and economically
signi?cant.
In terms of policy implications, the ?ndings suggest that the credit availability
of small, debt-sensitive ?rms may be reduced by within-market mergers that
increase concentration in rural markets, but that the more common type of recent
consolidation – creating larger banks that operate in more markets – may be associated
with an increase in credit availability for these sensitive ?rms. The consolidation may
bring additional credit availability bene?ts if it results in increased ef?ciency.
Notes
1. We do not count trade credit as a loan here, given that banks do not compete as directly with
trade credit suppliers.
2. When two or more activities are carried on at a single location under a single ownership, they
are generally are grouped as a single establishment, and the establishment’s industry is
based on the major activity. The census uses SIC codes through 1997 and NAICS codes
thereafter. Therefore, for our industry identi?cation, we use the SIC codes for the years
1991-1997, and match these to approximately equivalent NAICS codes for 1998-2002. The
smallest size class of one to four employees includes establishments that did not report any
paid employees in the mid-March pay period at the time of the sample, but paid wages to at
least one employee at some time during the year. Those with no paid staff at any time during
the year are excluded from these establishment data by the census.
3. Some studies found that US banks have increased the distances at which they make small
business loans, more often lending outside these traditional geographic de?nitions of local
markets, but most small businesses still use local banks (Petersen and Rajan, 2002; Hannan,
2003; Brevoort and Hannan, 2006).
4. Variable inputs are purchased funds, core deposits, and labor; variable outputs are consumer
loans, commercial and industrial loans, real estate loans, other loans, and securities; ?xed
outputs/inputs are off-balance sheet items, physical capital, and ?nancial equity capital; and
market controls are population and total deposits. The cost and pro?t functions use the
Fourier-?exible functional form, which combines a conventional translog form with Fourier
trigonometric terms. See Berger and Mester (1997) for the exact speci?cation.
5. We exclude a small number of observations from the ef?ciency calculations because of
violations of data standards, and we include presence-of-ef?ciency dummies in the
regressions (not shown) to account for these. These dummies affect fewer than 1 percent of
observations. For the MEGA banks, we also exclude the handful of single-market banks
with GTA . $5 billion because they are so unusual and unrepresentative.
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Corresponding author
Allen N. Berger can be contacted at: [email protected]
“Debt-sensitive”
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79
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This article has been cited by:
1. Allen N. Berger, William Goulding, Tara Rice. 2014. Do small businesses still prefer community banks?.
Journal of Banking & Finance 44, 264-278. [CrossRef]
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