Small Bank Comparative Advantages in Alleviating Financial Constraints and Providing Liquidity ...

47 Pages·2015·0.27 MB·English

Small Bank Comparative Advantages in Alleviating Financial
Constraints and Providing Liquidity Insurance over Time†

Allen N. Berger
University of South Carolina, Columbia, SC 29208 U.S.A.
Wharton Financial Institutions Center, Philadelphia, PA 19104 U.S.A.
European Banking Center, The Netherlands
[email protected]

Christa H.S. Bouwman
Texas A&M University, College Station, TX 77843 U.S.A.
Wharton Financial Institutions Center, Philadelphia, PA 19104 U.S.A.
[email protected]

Dasol Kim
Case Western Reserve University, Cleveland, OH 44106 U.S.A.
[email protected]

November 2015

Abstract

Using novel monthly U.S. survey data from 1993-2012 on small business managerial perceptions
of their financial constraints, we address four questions related to the comparative advantages of
local small banks in alleviating such constraints. 1) Do small banks (still) have these comparative
advantages? YES. 2) Are these advantages greater during adverse economic conditions,
consistent with provision of liquidity insurance to their customers during such times? YES. 3)
Have these comparative advantages declined over time? NO. 4) Do small banks also have
comparative advantages in providing liquidity insurance to the customers of large banks
experiencing liquidity shocks during financial crises? YES.

JEL Classification Numbers: G21, G28, G34
Keywords: Small Businesses, Small Banks, Community Banks, Financial Constraints, Liquidity
Insurance, Relationship Lending

† The authors thank Lakshmi Balasubramanyan, Megan Clubb, Kristle Cortes, Ben Craig, Yuliya Demyanyk, Hans
Degryse, Michael Faulkender, Ron Feldman, Annalisa Ferrando, Joe Haubrich, Neeltje van Horen, Peter Illiev, Diana
Knyazeva, Tanakorn Makaew, Loretta Mester, Iulian Obreja, Steven Ong, Zacharias Sautner, Carlos Serrano, Jason
Sturgess, and seminar participants at the Securities and Exchange Commission, the Federal Reserve Bank of
Cleveland, the Office of Financial Research, and Texas A&M University, and conference participants at the China
International Conference in Finance, the European Finance Association Conference, the FDIC/JFSR Bank Research
Conference, the Federal Reserve Bank of St. Louis Community Banking Conference, and the 7th European Banking
Center Conference for useful comments. We owe a special thanks to the National Federation of Independent Business,
for making their data available.
1. Introduction
One of the most important issues in finance is the extent to which financial markets and institutions
are able to relieve financial constraints – providing firms with the funds to undertake positive net
present value projects (e.g., Fazzari, Hubbard, and Petersen, 1988). Small businesses, which
represent a significant fraction of total employment and economic growth in the United States,1
are often considered more financially constrained than large businesses due to a lack of hard,
quantitative information on which to base credit decisions, since they do not have audited financial
statements or publicly-traded securities (e.g., Petersen and Rajan, 1994; Hubbard, 1998; Carpenter
and Petersen, 2002). Banks can alleviate small business financial constraints via relationship
lending, lending based on soft, qualitative information2 gathered over the course of a relationship
in place of hard, quantitative information (e.g., Boot and Thakor, 2000). Small banks are typically
viewed as having comparative advantages in using soft information because such information is
easier to communicate within a small organization (e.g., Berger and Udell, 2002; Stein, 2002;
Berger, Miller, Petersen, Rajan, and Stein, 2005; Liberti and Mian, 2009; Canales and Nanda,
2012).3 Over the past few decades, the number of small banks has declined by more than 50% for
various reasons. This raises a concern that something important is being lost – the ability to
alleviate financial constraints of small businesses. To address this, the paper revisits one important
“old” question about the importance of small banks, and also raises three novel questions.
Question (1) is: Do small banks (still) have comparative advantages in alleviating financial
constraints of small businesses? This question is addressed empirically in the literature, but we

1 Small businesses account for 46% of private, non-farm gross domestic product in the United States as of 2008 (Kobe,
2012). Additionally, small businesses are responsible for 63% of net new jobs created between 1993 and 2013 (Headd,
2014).
2 An example of such information is knowledge of the character of the small business owner.
3 For example, in a small bank, the loan officer might also be the bank’s president and owner, whereas in a large bank,
the loan officer may have to justify decisions to senior managers and a credit committee.
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revisit it using a superior measure of small business financial constraints, including better controls
for investment opportunities, and employing data over a much longer sweep of time.
Question (2) is: Are these advantages greater during adverse economic conditions,
resulting in superior ability to provide liquidity insurance to their customers? This may be the case
for at least two reasons. First, as relationship lenders, small banks may be able to lend short-term
at a loss and recoup these losses in the long term through earnings on future loans or elsewhere in
the relationship (e.g., Petersen and Rajan, 1995).4 Second, soft information gathered through
relationship lending may be relatively more reliable than hard information when economic
conditions are adverse. For example, knowing the character of a small business owner may not
lose its effectiveness during downturns as much as credit scores.
Question (3) is: Have these advantages declined over time? This question is brought up,
but not addressed empirically in the literature. Advantages may have declined because
improvements in transactional lending technologies that rely on hard information, such as credit
scoring, have reduced the relative importance of soft information, aiding large banks (e.g., Berger
and Udell, 2006). In addition, the deregulation of the banking industry may have helping large
banks compete more effectively over large areas.
Question (4) is: Do small banks also have comparative advantages in providing liquidity
insurance to displaced customers of large banks experiencing liquidity shocks during financial
crises? This is a novel but important question, in particular in light of the recent financial crisis.
Ivashina and Scharfstein (2010) document that some large banks rationed credit during the crisis
when the relatively volatile, short-term purchased funds they relied on dried up. It is useful to

4 The literature has examined the provision of interest rate and liquidity insurance in banking relationships (Elsas and
Krahnen 1998; Berlin and Mester 1999; Bolton, Freixas, Gambacorta, Mistrulli 2013), but not in the context of bank
size.
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know whether small banks, which tend to rely on steady, core deposits (which may actually
increase during crises due to a flight to safety) provided liquidity insurance to small businesses
that are credit rationed by these large banks.5
We address these questions using novel survey data on a representative sample of U.S.
small businesses from the Small Business Economic Trends (SBET) survey, conducted by the
National Federation of Independent Businesses (NFIB), the largest U.S. small business
organization with over 350,000 small business members.6 The survey randomly samples firms on
a monthly basis from 1993:M6 to 2012:M12, and allows us to overcome data limitations faced in
the small business finance literature. In particular, we are able to directly observe managerial
assessments of financial constraints and investment opportunities, which is critical since accurately
measuring financial constraints and controlling properly for investment opportunities are major
empirical challenges in this literature. The survey asks borrowing firms whether their borrowing
needs are satisfied, capturing the extent to which firms are able to obtain credit when they really
want it, as opposed to using indirect constraint measures, such as loan spreads, loan balances, or
the use of trade credit, as in the existing literature. The survey also provides details on the firm’s
expectations about future firm performance and business conditions, enabling us to control for
direct measures of credit demand.
We regress a dummy that equals one if a firm perceives its borrowing needs as not satisfied
(i.e., it is financially constrained) on the local market share of small banks, defined as the
proportion of local branches (i.e., branches within a 50-kilometer radius of the firm) belonging to
small banks. The coefficient on small bank share (inversely) captures the comparative advantages

5 DeYoung, Gron, Torna, and Winton (forthcoming) find evidence that some small banks that focused on small
business lending exhibited relatively greater lending during the recent financial crisis, consistent with liquidity
insurance, but do not link this lending to liquidity shocks experienced by large banks.
6 Members include independent businesses and exclude franchises.
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of small banks in satisfying the financial needs of small business customers. We control for other
local bank, market, and firm characteristics, and include industry and year-month fixed effects.
We find that firms with better access to small banks are better able to satisfy their financing
needs, providing evidence of small bank comparative advantages, and yielding an answer to
Question (1) of yes. We offer a number of robustness checks that suggest the influences of omitted
variables and sample selection biases are unlikely to be large. We document that small bank
comparative advantages are greater when local economic conditions are worse, consistent with
provision of liquidity insurance to their customers, suggesting that the answer to Question (2) is
yes. We examine changes in comparative advantages and find that small bank comparative
advantages have not declined over time, implying that the answer to Question (3) is no.
Finally, we focus on the recent financial crisis to address Question (4), whether small banks
are better at providing liquidity insurance to displaced customers of banks experiencing liquidity
shocks during financial crises. Small businesses in the U.S. experienced more credit rationing than
larger firms during the financial crisis (e.g., Montorial-Garriga and Wang, 2012), and similar
findings are documented in international samples.7 We identify small businesses that were more
likely to be credit rationed by large banks experiencing liquidity shocks based on the local presence
of banks with significant exposure to the asset-backed commercial paper (ABCP) markets, which
were disrupted during the crisis. We confirm that small businesses with greater local presence of
ABCP banks were more likely to experience financial constraints following these shocks.
Importantly, greater access to small banks significantly mitigated these effects, providing evidence

7 These studies include Popov and Udell (2012), Cotugno, Monferra and Sampagnaro (2012), Jimenéz, Ongena,
Peydró, and Saurina (2012), and Iyer, Da-Rocha-Lopes, Peydró, and Schoar (2013).
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that small banks provide liquidity insurance to the displaced customers of these banks, yielding an
answer to Question (4) of yes.8
The remainder of this paper is organized as follows. Section 2 describes the data sources.
Section 3 explains the methodology used to address Questions (1) – (3). Section 4 contains the
results for these questions. Section 5 addresses Question (4). Section 6 draws conclusions and
policy implications.

2. Data Sources
We use monthly small business data, collected by the National Federation of Independent
Businesses (NFIB) in its Small Business Economic Trends (SBET) survey, from June 1993 to
December 2012.9 The NFIB randomly selects survey participants from its members each month.
The number of respondents is approximately 865 per month over the sample period and the key
dependent variable, NotSatisfied, is available for firms that classify themselves as borrowers, about
400 respondents per month.10,11 The identities of the firms are confidential, but we have access to
the 3-digit ZIP code location of the firm.
The SBET survey has key advantages over the more commonly used Survey of Small
Business Finance (SSBF), which surveys firms up to 500 full-time equivalent employees every

8 Beck, Degryse, de Haas, and van Horen (2015) provide evidence that access to relationship lenders reduces the
propensity of small and large European firms to become discouraged from seeking bank finance during the recent
financial crisis, but not before the crisis. Berger, Cerquiero, and Penas (2015) shows lending to U.S. startup firms is
higher in regions with a greater share of small banks just before the crisis, but this relationship disappears during the
crisis. Our paper uses direct measures of financial constraints on small businesses, and examines effects on borrowers
that may have been displaced by large banks experiencing liquidity shocks during the crisis.
9 Data are available for a longer time period: on a quarterly basis from 1973:Q1 until 1985:Q4 and on a monthly basis
from 1986:M1 onward. June 1993 is chosen as the start of the sample period given that firm location information (3-
digit ZIP code) is unavailable prior to that date.
10 The average number of respondents per month increases slightly over the sample period from 855 (1993-2002) to
872 (2003-2012). The number of observations that are used in the analysis increases in a similar fashion.
11 We discuss possible sample selection bias issues in Section 4.1.2.
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five years from 1988 – 2003, and the Kauffman Firm Survey (KFS), which follows firms that
started up in 2004 annually from 2004 – 2011. First, the SBET survey allows us to study firms’
survey responses over a much broader sweep of history using a long, continuous monthly time
series from 1993:M6 to 2012:M12 instead of using data collected every 5 years (SSBF) or only
annually from 2004 to 2011 (KFS). Second, it ontains firms that are more representative of small
businesses as a whole than the KFS, which contains only firms started in 2004. Most important,
unlike the SSBF and the KFS, the SBET survey includes the firm manager’s perceived financial
constraints, as well as perceptions on different aspects of the firm’s operations, including economic
outlooks, and general business conditions. This allows us to directly measure financial constraints
and other conditions of the firm from the perspective of the small business rather than resorting to
indirect measures, as discussed below.
For each firm, we identify the number and deposit size of nearby branches of banks using
the FDIC’s annual Summary of Deposits (SoD) dataset from June 1993 to June 2012. Additionally,
we obtain quarterly commercial bank information from the Call Reports and, if the bank belongs
to a bank holding company (BHC), the Y-9C BHC data from 1993:Q1 to 2012:Q3. The SoD, Call
Report, and Y-9C datasets are linked using the RSSD9001 identifier supplied in these datasets.
Finally, county-level population, wage, and unemployment rate data are obtained from the Bureau
of Labor Statistics.

3. Methodology Used to Address Questions 1, 2, and 3
This section begins by discussing the empirical design used to address the first three questions. It
then explains the control variables. Table 1 provides variable descriptions, sources, and summary
statistics.
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3.1 Empirical Design for Questions (1) – (3)
To address Question (1), do small banks (still) have comparative advantages over large banks in
alleviating financial constraints for small businesses, we use the following OLS regression model:

FinancialConstraints =  +  SmallBankShare
i,t 0 1 i,t-1
+  Other Local Bank & Market Characteristics
2 i,t-1
+  Firm Characteristics
3 i,t
+  +  +  (1)
ind t i,t

The key dependent variable is FinancialConstraints , financial constraints perceived by
i,t
management at small business i in month t. Our main proxy, NotSatisfied , is a dummy that equals
i,t
one for each firm responding “no” to the question “During the last three months, was your firm
able to satisfy its borrowing needs?,” and zero if the response is “yes.” Only firms that borrowed
or tried to borrow over the last three months answer this question. In the rest of the paper, we refer
to these firms collectively as “borrowers” for ease of exposition. Firms that did not try to borrow
do not answer this question and are thus excluded from the analyses. This facilitates interpretation
because it is ambiguous whether the non-borrowing firms are constrained or alternatively did not
need bank financing. An important advantage of NotSatisfied is that it directly corresponds with
managerial assessments of financial constraints, which should be more accurate than indirect
constraints measures used in the literature, such as loan rates (e.g., Petersen and Rajan, 1994), loan
balances (e.g., Berger, Cerquiero, and Penas, 2015), or use of trade credit (e.g., Berger, Miller,
Petersen, Rajan, and Stein, 2005).
The key explanatory variable of interest is SmallBankShare , the proportion of bank
i,t-1
branches belonging to small banks within a 50 kilometer radius of firm i in quarter t-1.12 This
variable captures a firm’s access to small banks, as opposed to its actual banking relationships,

12 We also examine distance thresholds of 40 and 100 kilometers, and find similar results.
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which are likely to be endogenous. Banks with gross total assets (GTA) up to $1 billion in 2005
real dollars are coded as small banks because this is the common research definition of community
banks, and others are coded as large banks.13,14 To calculate the distance between the firm and a
bank branch, we use the centroid of the firm’s 3-digit ZIP code (the only firm location data
available in the survey) and the centroid of the bank branch’s 5-digit ZIP code in the most recent
SoD data) as inputs in the haversine formula.15
We focus on the coefficient on SmallBankShare, , which measures the marginal impact
1
of access to small banks relative to large banks in the area on firms’ financial constraints, and
inversely captures small bank comparative advantage. A negative value for  would imply a
1
comparative advantage of small banks in relieving financial constraints for small businesses in
their markets.
We also distinguish between regular and non-regular borrowers, those that answer “yes”
and “no,” respectively, to the question “Do you borrow at least once every three months?” We
split the sample because repeated interactions between regular borrowers and their lenders likely
result in stronger bank-borrower relationships than for non-regular borrowers. Therefore, we
expect the magnitude of the coefficient  to be larger in magnitude (i.e., more negative) for non-
1
regular borrowers because local banking conditions should matter more for businesses that do not
already have strong relationships.

13 GTA equals total assets plus allowances for loan and lease losses and the allocated risk transfer. GTA may be
considered a superior measure of the size of the balance sheet than total assets, which excludes the latter items that
are part of the balance sheet that must be financed.
14 The results are similar when using a $10 billion threshold or using the community bank designations from the FDIC
Community Banking Study (2012) available at https://www.fdic.gov/regulations/resources/cbi/data.html.
15 The haversine formula estimates the kilometer distance between locations A and B as:
d = 2 R arcsin([sin2 (0.5(Y -Y )) + cos(Y ) cos (Y )sin2(0.5*(X -X ))]1/2)
A,B A B A B A B
where (X ,Y ) and (X ,Y ) are the coordinates for locations A and B, respectively, and R is the Earth’s radius at the
A A B B
poles, or 6,356.752 kilometers.
8
To properly account for other factors that may affect small business financial constraints,
we include two sets of control variables (discussed in detail in Section 2.3): (1) Other Local Bank
& Market Characteristics ; and (2) Firm Characteristics .16 The latter include survey answers
i,t-1 i,t
that allow us to not just capture firm size, type, and growth, but also managerial expectations
regarding future changes in general business conditions and individual firm performance. We also
include industry fixed effects ( based on the ten industry groupings available in the survey17
ind
and time (year-month) fixed effects (to purge the financial constraints measures of other
aggregate factors, such as business and interest rate cycles. Because the model residuals () are
unlikely to be independent across location and time, we use two-way clustered standard errors by
3-digit ZIP code and year-month.
To address Question (2), whether small banks are better at providing liquidity insurance
during adverse economic conditions, we distinguish between local and national economic
conditions. Local economic conditions should be most relevant for the provision of liquidity
insurance to the small bank’s own customers. National economic conditions are more relevant for
liquidity insurance to the customers of large banks, which are more often affected by conditions
outside the local market. Our focus here is on liquidity insurance to the small bank’s own
customers – we deal with insurance to the customers of large banks in Question (4). We run the
following OLS regression model:18

FinancialConstraints =  +  SmallBankShare
i,t 0 1 i,t-1
+  Other Local Bank & Market Characteristics
2 i,t-1
+  Firm Characteristics
3 i,t
+  SmallBankShare × Local Economic Conditions
4 i,t-1 i,t-1

16 The firm characteristics are not lagged since we only have information about each firm in the quarter it is surveyed.
17 The industry classifications are self-reported, and include agriculture, retail, wholesale, transportation,
manufacturing, construction, professional, services, financial, and other.
18 The model includes (local and national) economic conditions and bank funding conditions in levels, but these
variables drop out due to collinearity arising from the inclusion of year-month fixed effects.
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