How Do Start-Up Firms Finance Their Assets?
Evidence from the Kauffman Firm Surveys
Abstract:
Using data from the Kauffman Firm Surveys to provide evidence on how U.S. start-up firms
finance their assets, we find that about 25% of firms report 100% equity financing of their initial
assets. For the remaining 75% of start-ups, we analyze their sources of credit, which we separate
into three groups—trade credit, personal credit, and business credit. At start-up, we find that the
majority of firms (55%) rely upon personal credit, but that a sizable fraction of firms also use
business credit (44%) and trade credit (24%). As firms develop, they decrease the use of personal
credit and increase the use of business credit. In addition, we examine which firm and owner
characteristics explain a start-up’s decisions to use credit and, conditional upon using credit,
what types to use. We find that firms are more likely to use credit at start-up when they are
larger, more profitable, more liquid, have more tangible assets; and when their primary owner
has more experience and more education. Black-owned firms are significantly less likely to use
credit at start-up. Among firms that use credit, we find that larger firms are more likely to use
trade and business credit but less likely to use personal credit; firms with more current and
tangible assets are more likely to use both trade and business credit but are less likely to use
personal credit; firms with better credit scores are more likely to use business credit; corporations
are more likely to use both trade and business credit but are less likely to use personal credit;
firms with several owners are more likely to use business credit but are less likely to use personal
credit; owners with more prior business start-ups are less likely to use personal credit; and
female owners are more likely to use personal credit.
Keywords: availability of credit, bank credit, capital structure, entrepreneurship, Kauffman,
KFS, start-up, trade credit
JEL Classifications: G21, G32, J71, L11, M13
0
1.
Introduction
How do start-up firms finance their assets? How does the use of credit change from the
firm’s start-up through the first critical years of business growth and development? These are
important decisions facing a nascent entrepreneur as she seeks to give birth and grow and
develop a new company. Yet, remarkably little is known about the use of credit by start-up and
young firms.
In this study, we shed new light on this topic by utilizing data from the Kauffman Firm
Surveys (KFS), which track a nationally representative sample of U.S. start-up firms from 2004
with annual surveys covering 2004 – 2009 (and beyond). We analyze the use of credit by
entrepreneurial firms at the time of their establishment, and examine the types of financing
growing companies receive during the first years of operations. With this unique dataset, we test
the fundamental theories of capital structure and examine the substitutability and connections
among the alternative sources of credit finance for closely held start-up firms.
Our study can be summarized as follows. First, we examine firms that use no credit to
finance their assets. As Cole (2010) documents, a significant portion of privately held U.S. firms
employ no debt of any kind in their capital structure, relying instead entirely upon owners’
equity. We find similar results for U.S. start-ups; more than one in five report 100% equity
financing of their initial assets. We then track zero-debt firms over the first five years of their life
and find that the portion of firms reporting zero debt remains relatively constant at about 25%.
Second, for the remaining 75% of start-ups, we analyze their sources of credit, which we
separate into three groups—trade credit, personal credit, and business credit. We find that, at
start-up, the majority (55%) of firms rely upon personal credit, and also a sizable fraction of
firms use business credit (45%) and trade credit (24%). As firms mature and develop, the
percentage of firms that use personal credit decreases, while the percentage of firms that use
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business credit increases. The percentage of firms that use trade credit remains relatively stable
during the first five years of business operations. Interestingly, there was little change in the
percentage of firms that used credit during the 2008 financial crisis. The most notable difference
during the crisis is between the fraction of firms that used business credit in 2008 (58%) versus
the fraction of firms that used business credit in 2009 (56%). A more detailed analysis of
business credit categories shows that this change came from a decreased use of business credit
cards issued on owner name.
The next part of our analysis examines the amount of credit used by young firms at the
firm’s start-up and during the first years of business operations. Our results show that firms more
than double the amount of trade credit used from the firm’s start-up to the first year of
operations. In contrast, the average amount of personal credit steadily decreases during the first
six years of operations. The average amount of business credit utilized in any given year shows
an increasing trend, but does not rise nearly as much as does the amount of trade credit. When
scaled by total liabilities, we find that firms, at start-up, finance almost fifty percent of their
liabilities with personal credit. However, as firms mature and develop, there is a trend to finance
a greater portion of liabilities with business credit, and, to a lesser extent, with trade credit, at the
expense of personal credit.
The final part of the paper analyzes the factors that explain a start-up’s decision to use
credit; the decision as to what types of credit to use; and, conditional upon using credit, how
credit is allocated across business, personal and trade credit. The richness of KFS data allows us
to examine the relation between firm and owner characteristics and credit use. We first perform
univariate tests for differences in the means of various firm and owner characteristics between
firms that do and firms that do not use credit and then conduct multivariate analysis. For those
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firms that do use credit, we compare firm and owner characteristics of each credit category to the
characteristics of “no credit” group.
Based upon our multivariate tests using logistic regression analysis, we find that firms are
more likely to use credit at start-up when they are larger, more profitable, more liquid, have more
current and tangible assets; and when their primary owner has less experience in the same
industry and has more education. Black-owned firms are significantly less likely to use credit at
start-up.
Among firms that use credit, we find that larger firms are more likely to use both trade
and business credit but less likely to use personal credit; firms with more current and tangible
assets are more likely to use both trade and business credit; firms with better credit scores are
more likely to use business credit; corporations are more likely to use both trade and business
credit but less likely to use personal credit; firms with multiple owners are more likely to use
business credit but less likely to use personal credit; older owners are less likely to use trade
credit; owners with prior start-up experience are less likely to use personal credit; and female
owners are more likely to use personal credit.
We complement logistic regression analysis with two-sided Tobit regressions examining
the factors that explain what percentage of the firm’s total liabilities is financed by trade credit,
business credit or personal credit. Consistent with the results outlined above, we find that larger
firms finance a greater portion of their liabilities with trade credit and a smaller portion with
personal credit; firms with more current assets finance a larger portion of liabilities with trade
credit and business credit but a smaller portion with personal credit; firms with higher credit
scores finance a greater part of liabilities with business credit and a smaller part with personal
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credit; corporations rely more on business credit and less on personal credit; female-owned firms
finance a greater part of their liabilities with personal credit and a smaller part with trade credit.
Why is our study important? According to the U.S. Department of Treasury and Internal
Revenue Service, there were more than 23 million nonfarm sole proprietorships, more than 2
million partnerships with less than $1 million in assets and more than 5 million corporations with
less than $1 million in assets that filed tax returns for 2006. These small firms are vital to the
U.S. economy. According to the U.S. Small Business Administration, small businesses account
for half of all U.S. private-sector employment and produced 64% of net job growth in the U.S.
between 1993 and 2008. Also, recent research by Haltiwanger, Jarmin and Miranda (2010)
indicates that the majority of all job creation is accounted for by start-up firms at their creation
and that majority of job destruction is accounted for by start-up firms during their early years,
when many of those firms fail. Therefore, a better understanding of what types of firms use
credit and the sources of credit finance can help policymakers to take actions that will lead to
more jobs and faster economic growth. Furthermore, a better understanding of credit use by
young entrepreneurial firms during economic downturns can help policymakers to create an
environment with fewer credit constraints for nascent entrepreneurs.
Our study contributes in several important ways to both the entrepreneurship and finance
literatures. First, we contribute to the growing literature that analyzes data on start-up firms from
the Kauffman Firm Survey. (See, e.g., Coleman and Robb 2009; Fairlie and Robb 2009; Cole
2011; Coleman and Robb, 2011; Robb and Watson, 2011; and Robb and Robinson, 2012). We
present new evidence on the use of credit by start-up firms during their first five years of
existence.
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Second, we contribute to the strand of the capital-structure literature that focuses on
privately held firms. (See, e.g., Ang, 1992; Berger and Udell, 1998; Cole, 2008; Ang, Cole and
Lawson, 2010; and Robb and Robinson, 2012). We provide new evidence on the mix of credit
upon which privately held firms rely at start-up and during their first five years of life.
Third, we contribute to the trade-credit literature on privately held firms. (See, e.g.,
Petersen and Rajan, 1997; Cunat, 2007; Cole, 2010; and Giannetti et al., 2011.) We document
the importance of trade credit to start-up firms during their first five years of life, including its
explosive growth during the first year.
Finally, we provide new evidence to the growing literature on zero-debt firms. (See, e.g.,
Strebulaev, 2006; and Cole, 2010). We document that about 25% of privately held firms are
financed exclusively with owners’ equity at start-up, and that this percentage changes by very
little during the firms’ first five years of life.
The remainder of our study is structured as follows. In section 2, we review relevant
studies from the literature on the liability structure of small firms and develop hypotheses. In
section 3, we describe our data and methodology. In section 4, we present our results, following
by a summary and conclusions in section 5.
2.
Literature Review and Hypotheses
2.1.
Capital Structure
Harris and Raviv (1991) review the theories of capital structure. The pecking order theory
of financing demonstrates that capital structure is driven by firms' desire to finance new
investments, first internally, then with low-risk debt, and finally with equity (Myers, 1984).
According to the trade-off theory, a company chooses how much debt and how much equity to
use by balancing the costs (i.e., the dead-weight costs of bankruptcy) and benefits (i.e., tax
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saving) of debt (Kraus and Litzenberger, 1973). A large body of empirical research has
developed around these theories. We limit our review of the literature to the studies examining
closely held firms.
Cole (2008) uses data from the Federal Reserve’s Survey of Small Business Finances
(SSBFs) to examine whether these theories explain the capital structure of small, privately held
firms in the U.S. Small firms are limited in their external borrowing to financial intermediaries
such as banks, finance companies, and other business lending institutions. His results are broadly
supportive of the pecking-order theory, in that leverage is negatively related to firm size, age,
profitability and credit quality; and positively related to tangibility and limited liability.
Robb and Robinson (2012) examine data from the 2004 – 2007 iterations of the
Kauffman Firm Survey and document that, in contrast to the widely-held view about
entrepreneurial finance, the outside capital is extremely important at the earliest stage of a firm's
life. They document a clear financing pattern of first outside debt, then owner equity, debt from
insiders, followed by outside equity, and by owner debt. Surprisingly, the least used source of
capital at the earliest stages of business development is inside equity. Robb and Robinson (2012)
conclude that newly founded firms rely heavily on formal debt financing rather than on informal
funding from friends and family.
Cole (2010) uses data from the Fed’s SSBFs to analyze differences between small U.S.
firms that do and do not use credit. He documents that one in five small firms uses no credit, one
in five uses trade credit only, one in five uses bank credit only, and two in five use both bank
credit and trade credit. He finds that firms using no credit are significantly smaller, more
profitable, more liquid and of better credit quality than non-borrowing firms. Cole concludes that
this evidence is generally consistent with the pecking-order theory of firm capital structure.
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Petersen and Rajan (1994) use data from the 1987 SSBF to analyze the importance of
relationships to the availability of credit. They document the importance of firm-lender
relationships in the allocation of credit. Because of the relative opacity of small firms, those
firms with stronger relationships with their prospective lenders are more likely to receive credit.
Petersen and Rajan (1994) find that close ties with creditors lead to greater availability of credit
at lower interest rates.
2.2.
Trade Credit
Although trade credit is considered to be an expensive alternative to bank debt, several
studies emphasize that trade credit is extremely important to small business finance. Berger and
Udell (1998) note that a sizable 16% of total small-business assets are financed by trade credit.
Cuñat (2002) documents that trade credit represents 34% of total debt in small U.S. firms.
Fisman and Love (2003) and Burkart and Ellingsen (2004) show that trade credit constitutes an
important source of funding for firms with difficult access to financial markets. Similarly,
Petersen and Rajan (1994) find that small firms rely heavily on trade credit, especially
when bank financing is limited. Ferris (1981) argues that a small amount of trade credit may be
optimal from the viewpoint of transactions costs, liquidity, and cash management. Extending this
reasoning, Berger and Udell (1998) suggest that trade credit may often be the best or the only
available source of external funding for working capital. However, the authors suggest that as
small businesses become older and more informationally transparent, their relationships with
financial institutions mature and they become less dependent on trade credit.
Other studies emphasize the informational advantage of suppliers of trade credit relative
to bank lenders and argue that trade creditors may be able to solve incentive problems by
threatening to withhold future supplies and may have advantages in repossessing the supplied
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goods in the event of default (Mian and Smith, 1992). Berger and Udell (1998) also suggest that
suppliers of trade credit may be able to provide extra funds during crises in the banking industry.
Biais and Gollier (1997) demonstrate theoretically that trade credit alleviates information
asymmetries present between firms and bank lenders that otherwise would preclude financing of
positive NPV projects. Furthermore, they show theoretically that small firms react to monetary
contractions by using more of trade credit, consistent with the empirical results of Nilsen (1994).
Petersen and Rajan (1994, 1997) suggest that trade credit plays an important role because of its
strength in addressing information problems.
Meltzer (1960) develops a theory where a supplier uses trade credit to price discriminate
among its customers. Creditworthy customers will pay promptly so as to get any available
discounts while risky customers will find the price of trade credit to be attractive relative to other
options. The supplier also discriminates in favor of the risky firm because the supplier holds an
implicit equity stake in the customer, equal to the present value of future profits from sales to the
customer, and wants to protect that equity position by extending temporary short-term financing.
Meltzer (1960) concludes that trade creditors redistribute traditional bank credit during periods
of tight money, so that trade credit serves as a substitute for bank credit when money is tight.
Nilsen (2002) provides strong empirical support for Meltzer’s conclusions. Burkart and Ellingsen
(2004) extend Meltzer’s theory and develop a model showing that the cushion against tight
money is most valuable for entrepreneurs with intermediate amounts of wealth. Very wealthy
entrepreneurs do not need the cushion, and very poor entrepreneurs have their trade credit limits
move together with their bank credit limits.
Smith (1987) develops a theory that provides explanation for selecting trade credit as a
screening device. The author argues that trade credit facilitates the sorting of low from high
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default risk buyers. In this view, trade credit is provided because the value of default risk
information to a seller who has made an investment in the buyer can exceed its value to
prospective third-party financiers.
Huyghebaert, Van de Gucht and Van Hulle (2007) argue and show empirically that
entrepreneurs trade off the lower cost of bank debt against the more lenient liquidation policy of
suppliers when determining their debt mix. They find that entrepreneurs in industries with high
historical start-up failure rates and entrepreneurs who tend to highly value private benefits of
control use less bank debt to avoid a later default on their bank loans. For these firms, the loss of
control rents following default does not offset the lower financing expenses when borrowing
exclusively from the bank.
Cole (2010) analyzes data from the 1993, 1998, and 2003 iterations of the SSBFs; he
finds that private firms that use trade credit are larger, more liquid, of worse credit quality, and
less likely to be a firm that primarily provides services. Among firms that use trade credit, the
amount used as a percentage of assets is positively related to liquidity and negatively related to
credit quality and is lower at firms that primarily provide services. In general, these results are
consistent with the financing-advantage theory of trade credit. Furthermore, consistent with
Burkart and Ellingsen’s (2004) theory, Cole (2011) concludes that trade credit and bank credit
are complements.
Giannetti et al. (2011) analyze data from the 1998 iteration of the SSBFs; they find that
suppliers of differentiated goods offer more trade credit than do suppliers of standardized goods
and that firms using trade credit receive this credit at relatively low cost.
Robb and Robinson (2012) analyze data from the 2004 – 2007 iterations of the KFS and
document that average firm uses less than half as much trade credit as it does outside debt, and
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almost twice as many firms rely on outside debt than on trade credit. This finding for nonestablished start-up firms contradicts the notion that trade credit is especially important in
scenarios where trade creditors possess information that banks might not be able to obtain (e.g.,
Peterson and Rajan, 1997).
2.3.
Hypotheses Development
Our primary hypotheses relate to the differences between firms that use credit (“use-
credit firms”) and firms that use no credit (“no-credit firms”). This eliminates a large number of
potential explanatory variables, such as firm leverage and the outcome of the firm’s most recent
loan application, as these variables can only take on certain values when the firm uses credit.
However, there remain a large number of variables of interest that we can use to test our
hypotheses—many of which are tied to the pecking-order theory and trade-off theory of capital
structure.
The pecking-order theory of capital structure suggests that profitable firms, firms with
more “financial slack,” and firms in certain industries that require little in the way of tangible
assets use less debt than other firms. Therefore, we expect that no-credit firms have higher return
on assets, have more cash (our proxy for financial slack), have fewer tangible assets and are
more likely to be in the service industries (insurance/real estate, business services and
professional services) than use-credit firms. Corporations enjoy limited liability and therefore
should be more likely to use credit, so we expect to find a positive relation between corporate
form of organization and the use of credit.
Behavior finance suggests that owners of no-credit firms act irrationally, as these firms
are failing to take advantage of either the interest-free financing from typical trade credit terms
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or the debt-tax shield from bank financing, as well as the opportunity to leverage up their return
on equity. The managers of these firms may simply be financially unsophisticated, or may have
an irrational aversion to debt of any form and prefer to pay cash for all purchases. We
hypothesize that no-credit firms are more likely to be located in rural areas, and have owners
with less experience and less education than other types of firms. We also examine whether the
primary owner’s race or gender determine the firm’s use of credit.
Among use-credit firms, we also test hypotheses regarding the choices among using trade
credit, business credit, and personal credit. (See Table 1 for a comprehensive list of the different
sources of credit). One version of the price-discrimination theory suggests that trade creditors are
more lenient in the event of default than are bank creditors, who tend to hold more secure
positions in liquidation largely due to collateralization. Therefore, we expect to find that firms
with worse credit quality rely more on trade credit than do other firms. We use the D&B credit
score as a proxy for the firm’s credit quality.
Owners with greater private benefits of control should allocate more of their liabilities to
trade credit so as to ensure that they do not lose those benefits of private control. We follow the
corporate governance literature in measuring the benefits of private control using the “wedge”
between ownership and control (see Claessens et al., 2000). The larger is the primary owner’s
ownership percentage, the less she has to gain from self-dealing and perquisite consumption. The
logic of this measure follows from the seminal paper on agency costs and ownership structure by
Jensen and Meckling (1976), which is tested using small-business data by Ang et al. (2000).
When the primary owner owns 100% of the firm, each dollar of perquisite consumption costs
him one dollar, but as ownership falls to α, where 0% < α < 100%, the primary owner’s cost fall
to α times one dollar. In other words, the primary owner realizes the full benefit of the perquisite
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consumption, but the cost of the perquisite consumption is only her ownership percentage times
the value of the perquisites. We hypothesize that the ownership percentage should be negatively
related to the use of any type of credit.
According to the financing-advantage theory, bank creditors are more likely than trade
creditors to liquidate a firm when liquidation value is greater than the value of the firm as a going
concern because they have a much smaller implicit equity stake in the firm. Therefore, we
hypothesize that firms with greater liquidation value will use more trade credit. We proxy the
value of assets in liquidation using the ratio of tangible assets to total assets, where tangible
assets are defined as the sum of land and depreciable assets. Alternatively, firms with more
tangible assets may prefer to finance them with credit of matched maturity, so that they prefer to
use more bank credit. If this is the case, then we expect tangible assets to have a negative relation
with the use of trade credit and a positive relation to the use of bank credit.
Petersen and Rajan (1997, p. 678) point out that the interest-free financing from typical
trade credit terms (2/10 net 30) “dominates paying cash,” so that one should “expect all firms to
borrow during the initial period.” Yet our survey data show that many firms do not borrow. We
hypothesize that firms in certain industries have little or no need for trade credit, such as those in
insurance/finance, business services and professional services. In contrast, other firms have very
large financing needs, such as those in construction and manufacturing. Firms in industries
characterized by large investments in tangible and depreciable assets, such as construction,
manufacturing, and transportation, also should be more likely to use bank credit.
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3.
Data and Methodology
3.1
Data and Sample Description
We use data from the Kauffman Firm Survey (KFS) to examine the use of credit by start-
up firms. This annual survey follows 4,928 privately held firms that were established in 2004.
Currently, the survey results are available for the baseline year (2004) and five follow-up years
(2005 – 2009).1 The KFS identified start-up firms by using a random sample from Dun &
Bradstreet’s database list of new businesses established during 2004, excluding wholly owned
subsidiaries of existing businesses, businesses inherited from someone else, not-for-profit
organizations, and firms that had any kind of business activity prior to 2004. The KFS represents
the largest and the most comprehensive data on U.S. start-up firms. Along with detailed
information on the firm’s use of credit, the KFS provides data on various firm and owner
characteristics. The richness of the KFS data allows us to explore the determinants of the use of
different types of credit by start-up firms. (For more detailed information about the KFS data, see
Ballou et al., 2008; Robb et al., 2009; and Cole, 2011).
Similar to prior studies using the KFS data, we define a firm’s primary owner as the
firm’s owner who has the highest percentage ownership.2 In cases where two or more owners
have the same percent ownership, the owner who works the most hours in the firm is defined as
the primary owner. In cases where two or more owners report the same ownership and the same
number of work hours, a series of other variables (i.e., owner’s education, age, work experience,
amount of initial equity invested, other start-up experience, and race) is used to create a ranking
of owners in order to define the primary owner.
1
The KFS has plans for three additional follow-up surveys, the first of which was underway at
the time this manuscript was prepared.
2
See, e.g., Ballou et al. (2008) and Robb et al. (2009). The KFS provides information on the ten
largest percentage owners of each firm.
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Table 1 defines credit, firm, and owner characteristics variables used in the study. The
main variables of interest describe the firm’s use of credit. We separate different types of credit
into three groups: trade credit, business credit, and personal credit.
A firm is classified as using trade credit if that firm reported using trade credit at any time
during the survey year. A firm is classified as using business credit if that firm reported using
credit in any of the following categories: business bank loan, business credit line, business loan
from nonbank institutions, business credit card, business credit card issued on owner’s name, or
business loan from the government, other businesses, or other sources. A firm is classified as
using personal credit if it reported using credit in any of the following categories: personal bank
loan by the primary owner, or by other owners; and the primary owner’s, or the other owners’
use of personal credit cards for business purposes.
Firm characteristics examined in the study include financial measures, industry, form of
business organization, and ownership structure. Owner characteristics include age, gender, race,
education, and prior business and start-up experience of the primary owner.
3.2.
Methodology
We employ both univariate and multivariate tests in our analysis.
3.2.1. Univariate Tests
First, we conduct univariate tests for the differences in means of various firm and owner
characteristics between firms that do and firms that do not use credit. For those firms that do use
credit, we analyze differences in means for those that use trade credit, business credit, and
personal credit, respectively. We compare firm and owner characteristics of each of these
categories to the characteristics of “no credit” firms for that type of credit. In addition to the
differences in means tests, we examine differences in proportions of various categories of start-
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up firms that use credit, trade credit, business credit, or personal credit. The categories of start-up
firms are defined based on various firm characteristics. Table 1 presents a summary of the
variable definitions.
3.2.2 Multivariate Tests
Second, we examine the firm’s use of credit in a multivariate framework using the
following weighted binomial logistic-regression model.
Use Credit = f (Firm Characteristics, Owner Characteristics)
(1)
where:
Use Credit is the dependent variable which takes on a value of one if the firm indicated
that it used credit and a zero otherwise;
Firm Characteristics is a vector of variables related to the firm that are expected to
influence availability of credit, such as credit score, size, profitability; and
Owner Characteristics is a vector of variables related to the primary owner that are
expected to influence availability of credit, such as prior work and start-up experience,
age, education, race, ethnicity, and gender.
For those firms that do use credit, we examine the determinants of the decision to use
trade credit, business credit or personal credit using the weighted logistic-regressions model as
described in (1). The model is estimated for three different dependent variables: (1) Trade Credit
takes on a value of one if the firm indicated that it used trade credit and a zero otherwise; (2)
Business Credit takes on a value of one if the firm indicated that it used business credit and a
zero otherwise; and (3) Personal Credit takes on a value of one if the firm indicated that it used
personal credit and a zero otherwise.
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Complementing the logistic analysis outlined above, for those firms that do use credit, we
estimate two-sided Tobit regressions examining which firm and owner characteristics explain
whether the firm’s total liabilities are financed by trade credit, business credit or personal credit,
respectively:3
T/B/P Credit Prt. = f (Firm Characteristics, Owner Characteristics)
(2)
where:
T/B/P Credit Prt. is equal to the proportion of total liabilities financed by trade credit,
business credit, or personal credit, respectively; and
Firm Characteristics and Owner Characteristics are as defined above for eq. (1).
The lower limit on the dependent variable is set equal to zero and the upper limit is set
equal to one.
4.
Data Analysis and Empirical Results
4.1.
Credit Use from the Firm’s Start-Up through the First Years of Operations
In this section, we examine the use of credit by start-up firms, and any patterns in credit
use from the firm’s start-up through the first five years of operation. Panels A, B and C of Table
2 present firm distributions in each survey year, based upon whether or not the firm used a given
type of credit or a given combination of credit types.
Panel A examines the full sample of firms; it shows that between 75 and 78% of young
closely held firms use some type of credit during the first five years of business operations. The
analysis of different credit categories shows some interesting trends in credit use. First, the
percentage of firms that use trade credit remains relatively stable, in the range of 24 - 27%.
3
We employ two-sided tobit regressions because the dependent variable is censored on the left
side at zero and on the right side at one. We estimate our models using the STATA statistical
package.
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Second, the use of business credit increases steadily from 44% at the firm’s start-up to a high of
59% by the third and fourth years of operation.4 Third, the use of personal credit steadily
declines from 55% at start-up to 39% in 2009.
The break-up of business credit into finer categories shows that its increased use during
the first years of operation is attributable to the increased use of credit cards and credit lines.
Credit obtained using a business credit card issued on owner’s name is the most widely used
category of business credit. Almost 29% of firms use this type of financing at the firm’s start-up,
but this percentage increases each year to a peak of 45% in 2008. Credit obtained using a
business credit in the firm’s name is the next most widely used category, rising from 24% at
start-up to 32% in 2006 before leveling off. Also noteworthy is the trend in the use of business
credit lines. Even though credit line is not the largest source of financing, its usage almost
doubles from 9% at start-up to 16% in 2008. Surprisingly, less than 7% of the firms use a
business bank loan at start-up or at any time during their first five years of operation, except in
2007.
Breaking up the use of personal credit into finer categories reveals that the declining use
of personal credit from the firm’s start-up through the following five years is typical for all
personal credit categories. For example, while 17% of firms use personal bank loans by primary
owner at the firm’s start-up, this percentage declines in each year to 9% in 2009. The primary
owner’s personal credit card is the largest personal credit category. However, its usage steadily
decreases from 46% at start-up to 34% in 2009. The use of personal bank loans by other owners
also declines from the firm’s start-up through the following five years. Almost three percent of
4
The use of business credit decreases slightly to 56% in 2009. This result could be due to the
financial crisis rather than due to the firm’s life cycle.
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start-up firms use this type of personal credit at the firm’s start-up, but this percentage declines to
less than one percent in 2009.
The results in Panel A also show that there was little change in use of credit by firms
surviving through the 2008 financial crisis. A comparison of the results for 2008 and 2009 shows
that the most notable difference is percentage of firms using business credit, which declined from
58.1 to 55.5%. A more detailed analysis of business credit categories shows that this change
comes from a decrease in the use of business credit cards issued on owner name, which fell to
from 45.3% to 41.8%. In addition, the use of business credit line decreased from 16.3 to 14.6%.
The use of personal credit also declined from 4.8% in 2008 to 38.7% in 2009. While these
changes are quite small in economic terms, other changes in credit use are even less notable.
Panel B in Table 2 examines firms that use only one type of credit. This panel includes
firms that rely on some type of credit (category “Credit (any type)” in Panel A) and excludes
firms that use no credit. The results in Panel B closely resemble the results presented in Panel A.
Out of firms that rely on just one type of credit, trade credit is the least “popular” of the three
credit categories. About six percent of firms use only trade credit at start-up and this percentage
remains relatively constant during the first five years of operation.
In contrast, about 16% of the firms use only business credit and 29% use only personal
credit at start-up. As firms mature, they consistently rely more on business credit and less on
personal credit as the only source of borrowed funds. Specifically, the use of business credit as
the sole credit category almost doubles from 16% in 2004 survey to 29-30% in the 2007 – 2009
surveys. The use of personal credit as the sole credit category decreases from almost 29% in
2004 to about 15-16% in 2006 – 2009. This evidence complements Robb and Robinson (2012),
who document that the fraction of new capital coming into the firm that is made up of outside
- 18 -
debt increases as the firm matures, and supports life-cycle theories of small-firm financing such
as Berger and Udell (1998).
Panel C reports the distribution of firms in each survey year that use two different types
of credit and distribution of firms that use all three types of credit. The sample examined in Panel
C is the same as the sample in Panel B and includes only firms that use some type of credit.
Panel C shows that between 10and 14% of firms rely on all three types of credit in any survey
year. The percentage of firms that use both business credit and personal credit remains quite
stable, at about one-quarter of firms, from the firm’s start-up through the first five years of
business operations. The percentage of firms that use both trade credit and business credit
steadily increases from six percent at start-up to 13% in subsequent years. Combined with
evidence in Panel A of Table 2, we can infer that this trend is due to the increase in business
credit use during the first years of business operations. In contrast, the percentage of firms that
use both trade credit and personal credit decreases from 7.4% at start-up to less than four percent
in 2007 - 2009, which is attributed to the decrease in the use of personal credit during the first
years of operations documented in Panel A of Table 2.
Overall, Table 2 shows some interesting patterns in the use of different types of credit
from the firm’s start-up and through the first five years of business operations. In the next
section, we examine the amounts of different types of credit utilized by young entrepreneurial
firms.
4.2.
Analysis of the Amounts of Credit Used by Young Entrepreneurial Firms
In this section, we examine the amounts of credit used by entrepreneurial firms at start-up
and during the five subsequent years of business operations. Table 3 presents the mean values of
the amounts of different types of credit by year. The sample includes firms that have survived
- 19 -
through 2009. The credit-amount variables are winsorized at the 99th percentile to limit the
impact of outliers.
The results show that firms more than double the average amount of trade credit used
from the start-up year through the first year of operation, from $25,907 to $51,300. In contrast,
the average amount of personal credit steadily decreases through the first five years of
operations, from $18,487 to $8,642. The average amount of business credit utilized in any given
year shows an increasing trend, rising from $21,272 at start-up to $23,147 in 2009.
The analysis of credit amounts divided by total liabilities demonstrates that, at the startup, firms finance almost 50% of their liabilities with personal credit, 30% with trade credit and
20% with business credit. However, as firms mature and develop, there is a trend to finance a
greater portion of liabilities, with business credit and, to a lesser extent with trade credit, at the
expense of personal credit. In 2009, the financing mix has changed to 32% personal credit, 32%
trade credit and 34% personal credit.
The analysis of changes in the amount of credit utilized by young entrepreneurial firms
during 2004 – 2009 presents an opportunity to examine changes in credit use during economic
downturns. This analysis may reveal the extent of credit constraints faced by growing ventures
during difficult economic times and provides valuable information to policymakers and
entrepreneurs. Our preliminary analysis shows that there was a drop in the amount of credit used
by young entrepreneurial firms in 2008 and 2009. The decrease in the amount of credit used
during these years seems to be inconsistent with the life-cycle increasing pattern in the amount of
credit used during 2004 – 2007. The analysis of credit sub-categories shows that there was a ten
percent drop in the amount of bank credit and 14% drop in the amount of credit card financing
from 2008 to 2009. The next version of the paper will present a more detailed analysis of
- 20 -
changes in the amount of credit used by young entrepreneurial firms during the recent financial
crisis. Specifically, we attempt to differentiate whether these changes can be attributed to
changes in the firm’s life cycle or were provoked by the financial crisis.
In the next section we focus on 2004 KFS survey, examining entrepreneurial firms at
their time of start-up. Specifically, we examine firm and owner characteristics of start-up firms
and analyze the differences in firm and owner characteristics between firms that use credit and
firms that do not use credit.
4.3.
Analysis of Credit Use at the Firm’s Start-Up
4.3.1. Descriptive Statistics of Start-Up Firms
Table 4 presents sample descriptive statistics of start-up firms using data from the 2004
KFS. On average, start-up firms generated about $230,000 in revenues, $486 in net income, and
had $347,000 in total assets by the end of 2004. On average, start-up firms had $38,000 in cash
and over $180,000 in tangible assets by the end of the start-up year.
The financial characteristics of a median firm, however, are quite different from the
characteristics of an average firm. The median firm generated only $7,500 in revenues, had only
$76,000 in total assets, $10,000 in cash, and $29,000 in tangible assets. Furthermore, the median
firm had a net loss of $300 during the start-up year.
Table 4 also shows that the standard deviations for firm variables are very large.
Moreover, the bottom 25th percentile of start-up firms has values of zero for revenue, cash,
current assets, and tangible assets, and $10,000 in net loss. The 75th percentile of start-up firms
has values for revenues and assets that are quite a bit smaller than the sample average values of
revenues and assets. These observations suggest that the distribution of start-up firms is highly
- 21 -
skewed with the presence of significant outliers. To address the skewness of the distribution, we
take the natural logarithms of firm financial characteristics’ variables in our subsequent analysis.
Table 4 also shows that about one-third of start-up firms are organized as corporations
and have more than one owner. At the firm’s start-up the primary owner, on average, is 45 years
old, has almost 13 years of experience in the same industry and has one prior start-up experience.
Twenty six percent of start-up firms have a female owner. In 79% of the firms, the primary
owner is white; in 8% of the firms, the primary owner is black; and, in 5% of the firms, the
primary owner is Asian or Hispanic. With respect to educational attainment, 66% of the owners
have some college education or hold a college (Bachelor’s) degree, and 21% hold a graduate
degree.
4.3.2. Use-Credit Firms vs. No-Credit Firms: Univariate Analysis
Table 5 presents univariate analysis of the differences in firm and owner characteristics
between start-up firms that use credit (“use-credit firms”) and start-up firms that do not use credit
(“no-credit firms”). We perform the analysis using data from the 2004 KFS and defining four
credit categories: any type of credit (Panel A), trade credit (Panel B), business credit (Panel C),
and personal credit (Panel D). Panel A includes the full sample of firms, while Panels B, C, and
D exclude firms that use no credit. Columns two and three in each panel present mean values of
a given variable for firms that use some type of credit (column 2) and for firms that use no credit
(column 3). Column 4 presents p-values for the test of the difference in mean values (t-test)
between firms that use credit and firms that do not use credit.
Overall, the results suggest that there are significant differences in firm and principal
owner’s characteristics between firms that borrow funds at the firm’s start-up and firms that do
not borrow at the start-up. Across all credit categories, borrowers are larger in terms of revenues,
- 22 -
total assets, the amount of cash, current assets, and tangible assets (except personal credit
category). These findings are consistent with those reported by Cole (2010) for all privately held
U.S. firms based upon data from the SSBFs.
Furthermore, corporations are more likely to borrow in all credit types, except personal
credit. This is consistent with the notion that entrepreneurs may organize the business as a
corporation to avoid the unlimited liability of sole proprietorships and partnerships. Thus, if the
liability issue is an important factor in choosing the form of business organization, it is not
surprising that corporations try to avoid the form of debt that carries the largest personal liability,
i.e., personal debt. Furthermore, consistent with the idea that corporations have better access to
capital, the result shows that corporations are able to raise funds from other sources rather than
borrow on personal account, which is very costly from the diversification viewpoint. Also,
compared to credit users, non-credit users have a higher credit risk in all credit categories, except
personal credit. This suggests that it is more difficult for firms with higher credit risk to raise
funds from trade credit or business credit. However, the credit risk of the firm is not an important
factor in getting personal credit. We suspect that the personal credit risk rather than the business
credit risk may play a role in borrowing on personal account.5 Finally, firms with several owners
are more likely to use trade credit and business credit and less likely to use personal credit. It is
possible that the owner of a single-owner firm decides to borrow on personal account to cover
business-related expenses so that she can retain a higher percentage of equity ownership by
limiting the number of owners.
Table 5 also shows that characteristics of the principal owners are different between usecredit firms and no-credit firms. Firms that use trade credit are more likely to have a principle
5
We do not have data on the personal credit scores of the firm’s owners.
- 23 -
owner who is white; firms that use trade credit and business credit are less likely to have a
principal owner who is black. In the trade-credit and business-credit categories, firms owned by
females are less likely to borrow than firms owned by males, whereas, in the personal-credit
category, firms owned by females are more likely to borrow. These results complement prior
findings that small firms owned by minorities experience more difficulties in obtaining financing
than firms owned by non-minorities. (See, e.g., Coleman, 2002; Blanchflower et al., 2003; Cole,
2009; Coleman and Robb, 2009; Fairlie and Robb, 2009; Robb and Watson, 2011).
Finally, Table 5 illustrates that the primary owner’s education and work experience may
affect the use of credit at the start-up. Firms that have owners with the graduate degrees are more
likely to use business credit but are less likely to use trade credit. Generally, trade credit is more
expensive than other types of financing, and it is possible that more educated owners are more
likely to realize that. Moreover, firms with the owners who have more experience in the same
industry are more likely to use trade credit. This may indicate that the owners with more
experience in the industry are likely to have established business relations with suppliers and
other providers of trade credit. Also, firms with owners who have less experience in the same
industry are more likely to rely on personal credit.
Table 6 complements the analysis presented in Table 5. It presents the differences in the
proportion of start-up firms that use credit and the proportion of firms that do not use credit
divided into several firm categories. For example, out of all firms that are organized as
corporations, 80% use some type of credit, 30% use trade credit, and 54% use business credit. In
contrast, out of all firms that have non-corporate form of business organization, 75% use some
type of credit, 22% use trade credit, and 41% use business credit. These differences are
statistically significant at the one percent level. Also, a higher proportion of firms with several
- 24 -
owners use trade credit or business credit than the proportion of firms with a single owner. Out
of firms that have a female owner, almost 19% use trade credit, 40% use business credit, and
57% use personal credit. This is in contrast to the 26% of firms that use trade credit, 46% of
firms that use business credit, and 54% of firms that use personal credit for the firms with a male
owner. Complementing the analysis in Table 5, Table 6 shows that a lower proportion of firms
with a black owner use credit in any credit category, and a higher proportion of firms with a
white owner use credit in any credit category.
4.4
Multivariate Analysis
The richness of KFS data allows us to examine the relation between firm and owner
characteristics and credit use at the firm’s start-up. In Table 7, we examine the firm’s decision to
use credit in a multivariate framework using a weighted binary logistic regression model, with
the dependent variable taking on a value of one if the firm uses any type of credit and zero
otherwise (column 1). In addition, for those firms that use credit, we separately estimate the
probability of using trade credit, business credit, and personal credit; the results of this analysis
appear in columns 2, 3 and 4, respectively. In each of the four columns, we report odds ratios
over t-statistics (in parentheses).
The results from Model 1, which estimates the probability of using any type of credit
appear in column 1. Compared to non-borrowers, borrowing start-up firms have a higher level of
revenue, cash, current assets, and tangible assets. The economic significance of these variables
ranges from almost four percent higher odds of using credit for one percentage increase in
ln(Cash+1) to almost eight percent increase in the odds of using credit for one percentage
- 25 -
increase in ln(Revenue+1).6 The table also shows that firms with higher ROA are more likely to
use credit. Each one unit increase in return-on-assets (which is 10 basis points) is associated with
four percent greater odds of obtaining credit. In general, corporations are 32% more likely to use
credit than other forms of business organizations combined. Furthermore, firms with greater
credit risk are less likely to borrow at the start-up. A one-step decrease in the firm’s credit
category is associated with 20% lower odds of obtaining credit.7 Overall, these results suggest
that more profitable, higher quality firms, with more solid asset base are more likely to borrow
capital at the firm’s start-up.
Table 7 also shows that several owner characteristics significantly relate to the
probability of obtaining credit at the firm’s start-up. Firms with owners who have more
experience in the same industry are less likely to borrow funds. Firms with owners who are
college educated or have a graduate degree are more likely to borrow. This effect is especially
strong for highly educated owners (measured by having a graduate degree). Firms with the
principal owner who holds a graduate degree are 2.6 times more likely to borrow than firms with
the owner who does not hold a graduate degree. Consistent with prior studies documenting lower
availability of credit to minorities, our results show that firms with Black owners are 60% less
likely to use any type of credit at start-up.
Columns 2 – 4 of Table 7 shows results for Models 2 – 4, which estimate the probability
of obtaining trade, business or personal credit, respectively; conditional upon using at least one
6
We add one to cash and revenue in order to deal with taking logs for firms reporting zero for
those variables.
7
Firm credit risk is a categorical variable based on the credit score of the firm. Credit scores
were derived from Dunn and Bradstreet (D&B) U.S. Ratings and Scores. Then, five credit risk
categories were created based on credit score percentiles. A firm with a credit risk of 1 has the
highest credit quality; a firm with a credit risk of 5 has the lowest credit quality.
- 26 -
type of credit.. The results show that there are notable differences between firms that use these
different sources of credit.
Firms that are larger in terms of the level of revenue generated by the end of the start-up
year are more likely to use trade credit and business credit, but are less likely to use personal
credit. Compared to firms that use trade credit or personal credit, firms that use business credit
have higher levels of cash. Firms that use trade credit or business credit also have higher levels
of current and tangible assets than firms that use personal credit. Corporations are more likely to
use trade credit and business credit, but are less likely to use personal credit. Firms that have
several owners are more likely to use business credit, but are less likely to use personal credit.
Greater credit risk has a significantly negative effect on the probability of using business credit,
but is not statistically significant in explaining the use of trade or personal credit.
Our results also show that several owner characteristics have significant effects on the
probability of using a given type of credit. Compared to firms that use either business credit or
personal credit, firms with older owners are less likely to use trade credit. Firms with owners
who had prior start-up experience are less likely to use personal credit than other types of credit.
Firms that are owned by females are 35% more likely to use personal credit. Firms with highly
educated owners are more likely to use credit across all three credit categories, but the results are
not statistically significant for trade credit users.
Finally, we examine whether there are differences in credit use among different
industries. Finance and insurance and real estate, rental and leasing are less likely to use trade
credit than firms in the agriculture services (omitted industry group). The results for other
industries are not statistically significant. The industry results are omitted from the table for the
sake of brevity, but are available upon request from the authors.
- 27 -
Table 8 reports the results of two-sided Tobit regressions of the determinants of credit
allocation among trade, business and personal credit. Here, our analysis sample only includes
start-up firms that use credit. The results shown in Panels A, B, and C are from a model where
the dependent variable is the amount of trade/business/personal credit divided by the amount of
total liabilities, respectively. Total liabilities is calculated as the sum of the amounts of trade
credit, business credit, and personal credit. In each panel, the regressions are estimated for three
specifications using firm characteristics only (specification 1), owner characteristics only
(specification 2), and then both firm and owner characteristics (specification 3). Industry controls
are included only in specification 3.
Results presented in Table 8 suggest that different factors affect the firm’s decision on
whether to finance its liabilities with trade credit, business credit, or personal credit. Firms with a
higher level of revenue at the start-up year finance a greater part of their liabilities with trade
credit and a smaller part with personal credit. Firms with a more solid base of current assets
(inventory and accounts receivable) are more likely to use trade credit or business credit to
finance their liabilities, while firms with a lower level of current assets are more likely to use
personal credit. Firms with a lower level of cash finance a greater portion of liabilities with
personal credit. More profitable firms (measured by ROA) finance more of their liabilities with
business credit and less with personal credit. Furthermore, firms with a higher level of tangible
assets rely more heavily on business credit to finance their liabilities. Greater credit risk is
negatively associated with the amount of business credit financing indicating that firms with
higher credit risk (i.e., lower credit quality) finance a lower portion of liabilities with business
credit. The opposite result is found for personal credit: firms with higher credit risk finance a
greater portion of liabilities with personal credit. Results also show that, compared to other forms
- 28 -
of business organization, corporations finance a greater percentage of liabilities with business
credit and a lower percentage with personal credit. Overall, these results on firm characteristics
show that firms with more liquid assets finance a greater percentage of liabilities with trade
credit, firms with solid financial ratios and good credit scores more heavily rely on business
credit, and less profitable, lower quality firms rely more on personal credit. This suggests that
lower quality firms may not be able to get business credit and are forced to finance a greater
portion of liabilities with personal credit, which is less desirable from the diversification
perspective.
Table 8 also shows that several owner characteristics are significant determinants of the
amounts of total liabilities financed by either trade credit, business credit or personal credit. Prior
experience in the same industry has a positive effect on the portion of liabilities financed by trade
credit but a negative effect on the portion of liabilities finance by personal credit. However, the
coefficient on prior experience loses its significance once we control for firm and industry
characteristics. Results also show that firms whose primary owner has prior start-up experience
finance a lower percentage of liabilities with personal credit. These results suggests that prior
experience in the industry and with start-up firms help firms to develop business relations with
suppliers and credit providers so that firms can rely less on personal credit. This could also
indicate that prior experience helps to certify the quality of the firm; this, in turn, opens other
sources of capital for the firms, besides personal credit.
Finally, Table 8 shows that there are some differences between the race and gender of the
primary owner and the type of credit used to finance liabilities. Firms with female owners
finance a lower portion of liabilities with trade credit and a higher portion of liabilities with
personal credit. Furthermore, compared to firms with white owners, firms with black owners
- 29 -
finance a higher portion of liabilities with personal credit and a lower portion of liabilities with
business credit. However, this result loses its significance once we control for firm and industry
characteristics (model 3). It seems unlikely that this could be attributable to discrimination, as we
see no differences in the allocation of credit by Black- or Hispanic-owned firms.
5.
Conclusion
In this study, we use data from the Kauffman Firm Survey to analyze how U.S. start-up
firms finance their assets. We find that more than one in five firms report 100% equity financing
of their initial assets. The portion of firms reporting zero debt remains relatively constant at
about 25% over the first five years of their life.
For the remaining 75% of start-ups, we analyze their sources of credit, which we classify
into three categories—trade credit, personal credit, and business credit. We find that, at start-up,
the majority of firms (55%) rely upon personal credit, but that a large percentage of firms also
use business credit (44%) and trade credit (24%). As firms mature and develop, the credit mix
changes, with portion of firms using personal credit decreasing and the portion of firms using
business credit increasing. About a quarter of young firms use trade credit during each of the first
five years of operations.
We also examine which firm and owner characteristics explain a start-up’s decision to
use credit and, conditional upon using credit, what types to use. We find that firms are more
likely to use credit at start-up when they are larger, more profitable, more liquid, have more
tangible assets; and when their primary owner has more experience and more education. Blackowned firms are significantly less likely to use credit at start-up.
- 30 -
Among firms that use credit, we find that larger firms are more likely to use both trade
and business credit but less likely to use personal credit; firms with more current and tangible
assets are more likely to use both trade and business credit; firms with better credit scores are
more likely to use business credit; corporations are more likely to use both trade and business
credit but less likely to use personal credit; firms with multiple owners are more likely to use
business credit but less likely to use personal credit; older owners are less likely to use trade
credit; owners with prior start-up experience are less likely to use personal credit; and female
owners are more likely to use personal credit.
Our study contributes, in several important ways, to both the entrepreneurship and
finance literatures. First, we contribute to the growing literature that analyzes data on start-up
firms from the Kauffman Firm Survey. (See, e.g., Coleman and Robb 2009; Fairlie and Robb
2009; Cole 2011; Coleman and Robb, 2011; Robb and Watson, 2011; and Robb and Robinson,
2012). We present new evidence on the use of credit by start-up firms during their first five years
of existence.
Second, we contribute to the strand of the capital-structure literature that focuses on
privately held firms. (See, e.g., Ang, 1992; Berger and Udell, 1998; Cole, 2008; Ang, Cole and
Lawson, 2010; and Robb and Robinson, 2012). We provide new evidence on the mix of credit
upon which privately held firms rely at start-up and during their first five years of life.
Third, we contribute to the trade-credit literature on privately held firms. (See, e.g.,
Petersen and Rajan, 1997; Cunat, 2007; Cole, 2010; and Giannetti et al., 2011.) We document
the importance of trade credit to start-up firms during their first five years of life, including its
explosive growth during the first year.
- 31 -
Finally, we provide new evidence to the growing literature on zero-debt firms. (See, e.g.,
Strebulaev, 2006; and Cole, 2010). We document that about 25% of privately held firms are
financed exclusively with owners’ equity at start-up, and that this percentage changes by very
little during the firms’ first five years of life.
- 32 -
References
Ang, J., 1992, On the theory of finance for privately held firms, Journal of Small Business
Finance 1, 185–203.
Ang, J., Cole, R., and Lin, J., 2000, Agency costs and ownership structure, The Journal of
Finance 55, 81-106.
Ang, J., Cole, R., and Lawson, J., 2010, The role of owner in capital structure decisions: An
analysis of single-owner corporations, Journal of Entrepreneurial Finance 14, 1-36.
Ballou, J., T. Barton, D. Des Rouches, F. Potter, E.J. Reedy, A. Robb, S. Shane, and Z. Zhao,
2008, Kauffman Firm Survey: Results from the Baseline and First Follow-Up Surveys.
Berger, A., and Udell, G., 1998, The economics of small business finance: The roles of private
equity and debt markets in the financial growth cycle, Journal of Banking & Finance 22, 613–
673.
Biais, B., and Gollier, C., 1997, Trade credit and credit rationing, Review of Financial Studies
10, 903-937.
Blanchflower, D., Levine, P., and Zimmerman, D., 2003, Discrimination in the small business
credit market, Review of Economics and Statistics 84, 930-943.
Burkart, M., and Ellingsen, T., 2004, In-kind finance: A theory of trade credit, American
Economic Review 9, 569-590.
Claessens, S., Djankov, S., and Lang, L., 2000, The separation of ownership and control in East
Asian corporations, The Journal of Finance 58, 81-112.
Cole, R, 2008, What do we know about the capital structure of privately held firms? Evidence
from the Surveys of Small Business Finances, U.S. Small Business Administration Research
Study No. 324.
Cole, R., 2009, Who needs credit and who gets credit? Evidence from the Surveys of Small
Business Finances, In Small Business in Focus: Finance. A Compendium of Research by the
Small Business Administration Office of Advocacy, July, 95-133.
Cole, R., 2010, Bank credit, trade credit or no credit? Evidence from the Surveys of Small
Business Finances, U.S. Small Business Administration Research Study No. 365.
Cole, R., 2011, How do firms choose legal form of organization? U.S. Small Business
Administration Research Study No. 383.
- 33 -
Coleman, S., 2002, The borrowing experience of black and Hispanic-owned small firms:
Evidence from the 1998 Survey of Small Business Finances, The Academy of Entrepreneurship
Journal 8, 1-20.
Coleman, S. and Robb, A., 2009, A comparison of new firm financing by gender: Evidence from
the Kauffman Firm Survey data, Small Business Economics 33, 397-411.
Coleman, S. and Robb, A., 2011, Capital structure theory and new technology firms: Is there a
match? Management Research Review, forthcoming.
Fairlie, R. and Robb, A., 2009, Gender differences in business performance: Evidence from the
characteristics of business owners survey, Small Business Economics 33, 375-395.
Ferris, J., 1981, A transaction theory of trade credit use, Quarterly Journal of Economics 94,
243-270.
Fisman, R. and Love, I., 2003, Trade credit, financial intermediation and industry growth, The
Journal of Finance 58, 353-374.
Giannetti, M., Burkhart, M., and Ellingsen, T., 2011, What you sell is what you lend? Explaining
trade credit contracts, Review of Financial Studies 24, 1261-1298.
Haltiwanger, J., Jarmin, R. and Miranda, J., 2010, Who creates jobs? Small vs. large vs. young,
NBER Working Paper No. 16300.
Harris, M. and Raviv, A., 1991, The theory of capital structure, The Journal of Finance 46, 297355.
Heckman, J., 1979, Sample selection bias as a specification error, Econometrica 47, 153-161.
Huyghebaert, N., Van de Gucht, L. and Van Hulle, C., 2007, The choice between bank debt and
trade credit in business start-ups, Small Business Economics 29, 435-452.
Jensen, M., and Meckling, W., 1976, Theory of the firm: Managerial behavior agency costs and
capital structure, Journal of Financial Economics 3, 305-360.
Kraus A., and R.H. Litzenberger, 1973, A state-preference model of optimal financial leverage,
The Journal of Finance, 911-922.
Meltzer, A., 1960, Mercantile credit, monetary policy and the size of firms, Review of Economics
and Statistics 42, 429-436.
Mian, S., and Smith, C., 1992, Accounts receivable management policy: Theory and evidence,
The Journal of Finance 47, 169-200.
Myers, S., 1984, The capital structure puzzle, The Journal of Finance 39, 575-592.
- 34 -
Nilsen, J, 2002, Trade credit and the bank lending channel, Journal of Money, Credit & Banking
34, 226-253.
Petersen, M., and Rajan, R., 1994, The benefits of lending relationships: Evidence from small
business data, The Journal of Finance 46, 3-37.
Petersen, M., and Rajan, R., 1997, Trade credit: Theories and evidence, Review of Financial
Studies 10, 661–691.
Robb, A.M., R. Fairlie, and D.T. Robinson, 2009, Financial capital injections among new black
and white business ventures: Evidence from the Kauffman Firm Survey, working paper.
Robb, A.M. and D.T. Robinson, 2012, The capital structure of new firms, Review of Financial
Studies, forthcoming.
Robb, A.M., and J. Watson, 2011, Gender differences in firm performance: Evidence from new
ventures in the United States, Journal of Business Venturing, forthcoming.
Smith, J., 1987, Trade credit and informational asymmetry, The Journal of Finance 42, 863-872.
Strebulaev, I., and Yang, B., 2006, The mystery of zero-leverage firms, SSRN working paper,
Available at: http://ssrn.com/abstract=890719.
- 35 -
Table 1
Variable Definitions
Credit variables:
Credit (any type)
Firm reported that it used either trade credit, business credit, or personal
credit during the reference year.
Trade Credit
Firm reported that it used trade credit during the reference year.
Business Credit
Firm reported that it used business credit during the reference year.
Business credit includes either of the following categories: business bank
loan, business credit line, business loan from nonbank institutions,
business credit card, business credit card issued on owner’s name,
business loan from the government, business loan from other businesses,
business loan from other sources.
Personal Credit
Firm reported that it used personal credit during reference year. Personal
credit includes either of the following categories: personal bank loan by
the primary owner, personal bank loan by other owners, the primary
owner’s personal credit card used for business purposes, and the other
owners’ personal credit cards used for business purposes.
Firm Characteristics:
Sales
Assets
Net Income
ROA
Cash
Current Assets
Tangible Assets
Credit Risk
Annual revenue from sales of product or service
Total assets (sum of cash, current assets, and tangible assets)
Annual profit or loss (profit positive, loss negative)
Net income / total assets
Cash
Sum of accounts receivable and inventory
Sum of equipment, land/building, vehicles, other business property, and
other assets such as intangibles
Categorical variable (1 to 5) based on the credit score of the firm
derived from Dunn and Bradstreet U.S. Ratings and Scores. A firm
with a credit risk of 1 has the highest credit quality; a firm with a
credit risk of 5 has the lowest credit quality.
Corp
Multiown
Firm is organized as an S-corporation or C-corporation
Firm has more than one owner
Owner Characteristics:
Primary Owner
Ownership
Owner Age
Female
Asian
Black
Hispanic
White
Other Race
Owner with the highest percentage of firm ownership
Firm ownership (in %) by primary owner
Age of primary owner (in years)
Primary owner is female
Primary owner is Asian
Primary owner is Black
Primary owner is Hispanic
Primary owner is White
Primary owner is other than White, Asian, Hispanic, or Black
- 36 -
High School
Some College
College Degree
College Education
Graduate Degree
Prior Experience
Prior Start-ups
Primary owner is either a high school graduate, has some high school
education but no diploma, or has less than 9th grade education
Primary owner attended some college but does not have a Bachelor’s
degree
Primary owner has a Bachelor’s degree and may have attended a
graduate school but has no graduate degree
Primary owner has either attended some college, has a Bachelor’s degree
or may have attended a graduate school but has no graduate degree
Primary owner has a graduate degree (Master’s, Professional school, or
Doctorate)
Prior work experience (in years) of the primary owner in the same
industry
Number of prior business start-ups by the primary owner
Industry Classifications:
Agriculture, Forestry, Fishing and Hunting
Mining and Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate and Rental and Leasing
Professional, Management, and Educational Services
Administrative and Support and Waste Management and Remediation Services
Health Care and Social Assistance
Arts, Entertainment, and Recreation
Accommodation and Food Services
Other Services, including Public Administration
- 37 -
Two-Digit NAICS Code
11
21, 22
23
31-33
42
44-45
48-49
51
52
53
54, 55, 61
56
62
71
72
81, 92
Table 2
Distribution of Firms by Use of Different Types of Credit
The table reports the percentage of firms that use certain type of credit. The sample includes firms from 2004-2009 Kauffman Firm Surveys. Panel
A presents the distribution for the full sample of firms. Panels B and C include firms that use some type of credit and exclude firms that use no
credit. Survey weights applied. N/A denotes cases with less than ten observations.
Panel A: Percentage of firms that use a certain type of credit
Credit Category
Credit (any type)
Trade Credit
Business Credit
Business Credit Card Owner Name
Business Credit Card
Business Credit Line
Business Bank Loan
Business Loan from Nonbank Institution
Business Loan from Government
Business Loan from Other Businesses
Business Loan from Other Sources
Personal Credit
Personal Bank Loan by Primary Owner
Personal Bank Loan by Other Owners
Primary Owner's Credit Card
Other Owner's Credit Card
Number of Observations
2004
76.1%
23.8%
44.4%
28.7%
24.3%
8.6%
6.8%
1.7%
0.9%
0.3%
0.5%
54.9%
17.3%
2.6%
46.2%
5.7%
4,928
38
2005
76.4%
27.1%
54.8%
39.6%
31.4%
11.5%
6.2%
1.5%
0.7%
0.4%
0.3%
46.4%
13.8%
1.4%
39.4%
4.2%
3,998
2006
77.5%
25.8%
58.7%
44.3%
31.5%
14.1%
6.2%
1.6%
0.5%
0.4%
N/A
43.3%
13.4%
1.0%
35.8%
3.6%
3,390
2007
76.8%
25.6%
59.2%
44.2%
29.1%
14.8%
7.2%
1.8%
N/A
N/A
N/A
40.0%
10.7%
1.1%
34.1%
3.8%
2,915
2008
76.3%
24.6%
58.1%
45.3%
28.5%
16.3%
5.7%
1.1%
N/A
N/A
N/A
40.8%
10.8%
0.7%
35.2%
3.0%
2,606
2009
74.5%
24.6%
55.5%
41.8%
28.6%
14.6%
5.8%
1.4%
N/A
N/A
N/A
38.7%
9.3%
0.6%
34.4%
3.6%
2,408
(continues)
Table 2 (continued)
Panel B: Percentage of firms that use only one type of credit
Credit Category
Trade Credit Only
Business Credit Only
Personal Credit Only
Number of Observations
2004
5.5%
16.3%
28.7%
3,752
Panel C: Percentage of firms that use a given combination of credit types
Credit Category
2004
Trade Credit and Business Credit
6.0%
Trade Credit and Personal Credit
7.4%
Business Credit and Personal Credit
23.7%
Trade, Business, and Personal Credit
12.4%
Number of Observations
3,752
39
2005
5.3%
23.5%
17.3%
3,054
2006
5.3%
26.9%
14.8%
2,630
2007
4.8%
29.7%
15.0%
2,247
2008
4.5%
28.7%
15.6%
2,013
2009
6.0%
29.4%
15.6%
1,808
2005
10.5%
5.7%
23.8%
14.0%
3,054
2006
11.9%
4.3%
25.0%
11.8%
2,630
2007
13.4%
3.1%
21.9%
12.1%
2,247
2008
13.3%
3.8%
23.5%
10.6%
2,013
2009
12.6%
3.9%
22.1%
10.4%
1,808
Table 3
Distribution of Different Types of Credit Amounts by Year
The table reports yearly mean values of credit amounts. The sample includes survived firms from 2004-2009 Kauffman Firm Surveys that use
credit. The credit amount distributions are winsorized at 99th percentile to decrease the effect of outliers. Total Liabilities is the sum of amount of
trade credit, business credit, and personal credit. N is the number of non-missing observations. N/A denotes cases with less than ten observations.
2004
2005
2006
2007
2008
2009
2004-2009
mean
N
mean
N
mean
N
mean
N
mean
N
mean
N
mean
N
Amount of Trade Credit
$24,907
2,391
$51,330
2,279
$62,705
2,229
$62,563
2,190
$61,478
2,233
$60,531
2,396
$54,456
13,718
Amount of Business Credit
$21,272
2,401
$18,186
2,291
$19,693
2,242
$22,864
2,196
$23,090
2,238
$23,147
2,400
$21,418
13,768
Amount of Personal Credit
$20,103
2,392
$14,722
2,290
$13,671
2,242
$10,696
2,196
$11,284
2,238
$8,844
2,400
$12,987
13,758
Total Amount of Credit
$66,282
2,404
$84,238
2,293
$96,069
2,243
$96,123
2,198
$95,852
2,239
$92,522
2,403
$88,861
13,780
Amount of Business Bank Loan
$9,813
2,380
$5,959
2,276
$5,571
2,230
$7,129
2,183
$6,141
2,226
$7,314
2,381
$6,953
13,676
Amount of Business Credit Line
$2,604
2,385
$3,745
2,279
$5,064
2,233
$5,518
2,187
$7,191
2,221
$5,563
2,386
$4,999
13,691
Amount of Business Credit Card
$22
1,815
$19
1,591
$31
1,555
$20
1,562
$25
1,620
$22
1,740
$23
9,883
Amount of Business Credit Card Owner Name
$872
2,376
$1,914
2,273
$2,285
2,233
$3,089
2,184
$3,100
2,230
$2,810
2,391
$2,378
13,687
Amount of Business Loans from Nonbank Institutions
$295
2,388
$289
2,280
$217
2,234
$259
2,188
$200
2,223
$207
2,388
$243
13,701
$2,620
2,382
$3,327
2,265
$18,759
2,228
$13,296
2,181
$653
2,222
$5,661
2,385
$7,421
13,663
Amount of Business Loan from Other Businesses
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Amount of Business Loan from Other Sources
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Personal Bank Loan by Primary Owner
$13,459
2,370
$9,469
2,281
$9,906
2,229
$7,207
2,174
$6,775
2,214
$5,288
2378
$8,558
13,646
Personal Bank Loan by Other Owners
$3,898
802
$2,398
811
$1,208
910
$653
872
$1,288
859
$899
925
$1,621
5,179
Primary Owner's Credit Card
$2,366
2,360
$2,494
2,274
$2,390
2,221
$2,639
2,182
$2,919
2,226
$2,349
2392
$2,528
13,655
$380
801
$361
807
$167
908
$197
868
$302
861
$308
924
$280
5,169
Trade Credit / Total Liabilities
37.58%
1,346
60.93%
1,297
65.27%
1,284
65.09%
1,240
64.14%
1,293
65.42%
1,317
61.28%
7,777
Business Credit / Total Liabilities
32.09%
1,347
21.59%
1,299
20.50%
1,288
23.79%
1,242
24.09%
1,295
25.02%
1,319
24.10%
7,790
Personal Credit / Total Liabilities
30.33%
1,342
17.48%
1,300
14.23%
1,288
11.13%
1,242
11.77%
1,295
9.56%
1,320
14.61%
7,787
Business Credit Sub-categories
Amount of Business Loans from Government*
Personal Credit Sub-categories
Other Owner's Credit Card
Credit Amount as % of Total Liabilities
40
Table 4
Descriptive Statistics of Start-up Firms
The sample includes Kauffman Firm Survey 2004 start-up firms. Variable definitions are provided in Table 1. N is the number of nonmissing observations for a given variable.
Variable
Revenue ($)
Net Income ($)
Assets ($)
ROA
Cash ($)
Current Assets ($)
Tangible Assets ($)
Corp
Credit Risk
Rural
Multiown
Ownership
Owner Age
Prior Experience
Prior Start-ups
Female
Asian
Black
Hispanic
White
Other Race
High School
College Education
Graduate Degree
25th percentile
0
-10,000
3,400
-0.44
0
0
0
0.00
3.00
0.00
0.00
50.00
37.00
3.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
Mean
229,789
486
347,177
-20.69
37,682
125,983
186,548
0.30
3.40
0.17
0.36
80.49
44.99
12.84
1.02
0.26
0.05
0.08
0.05
0.79
0.03
0.12
0.66
0.21
Median
7,500
-300
20,000
-0.03
2,000
1,000
5,000
0.00
3.00
0.00
0.00
100.00
44.00
10.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
1.00
0.00
41
75th percentile
62,500
5,000
76,000
0.24
10,000
15,000
29,000
1.00
4.00
0.00
1.00
100.00
53.00
20.00
1.00
1.00
0.00
0.00
0.00
1.00
0.00
0.00
1.00
0.00
Standard Deviation
5,526,475
437,137
5,642,656
1,308.23
413,690
4,500,293
2,717,975
0.46
0.72
0.37
0.48
27.37
10.88
10.71
3.17
0.44
0.21
0.28
0.23
0.41
0.16
0.33
0.47
0.41
N
4,741
4,586
4,818
4,123
4,680
4,755
4,810
4,928
3,606
4,928
4,923
4,880
4,860
4,907
4,893
4,920
4,888
4,888
4,888
4,888
4,888
4,895
4,895
4,895
Table 5
Differences between Start-Up Firms that Use Credit and Start-Up Firms that do not Use Credit
The table reports differences in firm and owner characteristics between firms that use credit and firms that
use no credit. The sample includes Kauffman Firm Survey 2004 start-up firms. Panel A includes the full
sample of firms and shows differences in means between firms that use any type of credit and firms that
use no credit. Panels B, C, and D exclude firms that use no credit and present differences in means
between firms that use trade credit and firms that use no trade credit (Panel B), between firms that use
business credit and use no business credit (Panel C), and between firms that use personal credit and use
no personal credit (Panel C). Columns 2 and 3 report mean values of a given variable for firms that use
credit (column 2) and firms that use no credit (column 3). Column 4 reports p-values for the difference in
means t-test. Variable definitions are provided in Table 1. N is the number of non-missing observations
for a given variable. Survey weights applied. ***, **, and * indicate that the difference in means is
statistically significant at the 1%, 5%, and 10% level, respectively.
Panel A: Any Type of Credit
Firm Characteristics
ln(Revenue+1)***
ln(Total Assets+1)***
ROA
ln(Cash+1)***
ln(Current Assets+1)***
ln(Tangible Assets+1)***
Corp***
Credit Risk***
Rural
Multiown***
Owner Characteristics
Owner Age
Prior Experience*
Prior Start-ups
Female
Asian
Black***
Hispanic
White***
Other Race
High School**
Some College
College Degree
College Education
Graduate Degree***
Use Credit
Use No Credit
p-value
N
7.27
9.68
-0.51
6.13
6.04
7.70
0.29
3.40
0.16
0.36
4.13
7.24
-13.23
4.16
3.38
5.21
0.23
3.56
0.15
0.30
0.000
0.000
0.339
0.000
0.000
0.000
0.000
0.000
0.371
0.001
4,740
4,816
4,123
4,680
4,753
4,808
4,918
3,597
4,918
4,915
44.53
11.69
0.98
0.30
0.05
0.07
0.06
0.80
0.02
0.14
0.37
0.30
0.67
0.19
44.43
12.36
0.92
0.33
0.04
0.18
0.06
0.69
0.03
0.17
0.40
0.29
0.68
0.15
0.817
0.098
0.581
0.100
0.387
0.000
0.722
0.000
0.915
0.016
0.111
0.307
0.492
0.000
4,855
4,902
4,888
4,912
4,880
4,880
4,880
4,880
4,880
4,889
4,889
4,889
4,889
4,889
(continues)
42
Table 5 (continued)
Panel B: Trade Credit
Firm Characteristics
ln(Revenue+1)***
ln(Total Assets+1)***
ROA
ln(Cash+1)***
ln(Current Assets+1)***
ln(Tangible Assets+1)***
Corp***
Credit Risk***
Rural***
Multiown***
Owner Characteristics
Owner Age*
Prior Experience***
Prior Start-ups
Female***
Asian*
Black***
Hispanic
White***
Other Race
High School*
College Education
Graduate Degree***
Use Credit
Use No Credit
p-value
N
9.25
10.67
-0.54
6.95
8.03
8.80
0.35
3.31
0.19
0.42
6.37
9.21
-0.50
5.77
5.11
7.18
0.27
3.44
0.15
0.34
0.000
0.000
0.924
0.000
0.000
0.000
0.000
0.000
0.004
0.000
3,612
3,671
3,289
3,570
3,624
3,665
3,743
2,770
3,743
3,742
43.97
12.71
1.08
0.24
0.04
0.05
0.06
0.83
0.03
0.15
0.69
0.16
44.78
11.22
0.93
0.33
0.05
0.07
0.06
0.78
0.02
0.13
0.66
0.21
0.058
0.000
0.113
0.000
0.073
0.006
0.669
0.007
0.745
0.073
0.205
0.001
43
3,702
3,734
3,725
3,740
3,717
3,717
3,717
3,717
3,717
3,727
3,727
3,727
(continues)
Table 5 (continued)
Panel C: Business Credit
Firm Characteristics
ln(Revenue+1)***
ln(Total Assets+1)***
ROA
ln(Cash+1)***
ln(Current Assets+1)***
ln(Tangible Assets+1)***
Corp***
Credit Risk***
Rural
Multiown***
Owner Characteristics
Owner Age
Prior Experience
Prior Start-ups
Female***
Asian*
Black***
Hispanic
White
Other Race
High School
Some College***
College Degree***
College Education
Graduate Degree**
Use Credit
Use No Credit
p-value
N
7.69
10.19
-0.40
6.77
6.70
8.19
0.34
3.33
0.16
0.40
6.70
8.96
-0.67
5.26
5.13
7.00
0.23
3.49
0.16
0.31
0.000
0.000
0.362
0.000
0.000
0.000
0.000
0.000
0.866
0.000
3,618
3,678
3,294
3,575
3,630
3,672
3,751
2,775
3,751
3,750
44.64
11.88
1.00
0.28
0.05
0.05
0.06
0.81
0.03
0.13
0.34
0.32
0.66
0.21
44.35
11.39
0.94
0.33
0.04
0.08
0.07
0.78
0.02
0.14
0.41
0.27
0.68
0.17
0.475
0.213
0.517
0.004
0.096
0.003
0.248
0.113
0.696
0.382
0.000
0.005
0.239
0.024
44
3,709
3,742
3,733
3,748
3,724
3,724
3,724
3,724
3,724
3,734
3,734
3,734
3,734
3,734
(continues)
Table 5 (continued)
Panel D: Personal Credit
Firm Characteristics
ln(Revenue+1)***
ln(Total Assets+1)***
ROA
ln(Cash+1)***
ln(Current Assets+1)***
ln(Tangible Assets+1)
Corp***
Credit Risk***
Rural
Multiown***
Owner Characteristics
Owner Age
Prior Experience***
Prior Start-ups
Female***
Asian
Black
Hispanic
White
Other Race
High school
Some College***
College Degree**
College Education
Graduate Degree
Use Credit
Use No Credit
p-value
N
7.05
9.52
-0.54
5.89
5.89
7.64
0.28
3.43
0.16
0.34
7.86
10.08
-0.44
6.79
6.45
7.84
0.33
3.30
0.16
0.41
0.000
0.000
0.800
0.000
0.008
0.288
0.006
0.000
0.948
0.001
3,612
3,671
3,287
3,569
3,625
3,665
3,744
2,770
3,744
3,743
44.38
11.08
0.92
0.32
0.05
0.07
0.07
0.79
0.03
0.13
0.38
0.29
0.67
0.19
44.88
13.24
1.13
0.25
0.04
0.07
0.06
0.81
0.02
0.14
0.32
0.33
0.66
0.20
0.263
0.000
0.128
0.000
0.423
0.989
0.376
0.228
0.537
0.491
0.004
0.033
0.435
0.750
3,702
3,735
3,726
3,741
3,717
3,717
3,717
3,717
3,717
3,727
3,727
3,727
3,727
3,727
45
Table 6
Differences in the Proportions of Start-Up Firms (by Category) that Use Different Types of Credit
The sample includes Kauffman Firm Survey 2004 start-up firms. Variable definitions are provided in
Table 1. p-value if for the differences in proportions adjusted Wald test. N is the number of non-missing
observations for a given category. Survey weights applied.
Use Any Type of
Credit
Use Trade
Credit
Use Business
Credit
Use Personal
Credit
Corp
Yes (N= 1,481 )
No (N= 3,447)
p-value
N
80.1%
74.7%
0.000
4,915
29.7%
21.7%
0.000
4,904
53.7%
40.9%
0.000
4,912
55.0%
55.3%
0.900
4,898
Multiown
Yes (N= 1,762)
No (N=3,161)
p-value
N
79.5%
74.5%
0.001
4,915
28.9%
21.3%
0.000
4,904
51.4%
40.9%
0.000
4,912
54.7%
55.5%
0.614
4,898
Female
Yes (N= 1,267)
No (N= 3,653)
p-value
N
74.4%
77.1%
0.101
4,912
18.7%
26.3%
0.000
4,901
40.3%
46.4%
0.001
4,909
57.4%
54.3%
0.092
4,895
Asian
Yes (N= 223)
No (N=4,665)
p-value
N
79.0%
76.1%
0.386
4,880
19.4%
24.2%
0.139
4,870
51.5%
44.2%
0.066
4,878
59.5%
55.1%
0.257
4,864
Black
Yes (N= 415)
No (N= 4,473)
p-value
N
54.3%
78.5%
0.000
4,880
12.4%
25.1%
0.000
4,870
25.7%
46.4%
0.000
4,878
39.4%
56.9%
0.000
4,864
(continues)
46
Table 6 (continued)
Hispanic
Yes (N=266)
No (N=4,622)
p-value
N
77.3%
76.2%
0.722
4,880
23.1%
24.0%
0.755
4,870
41.7%
44.7%
0.393
4,878
58.1%
55.1%
0.388
4,864
White
Yes (N= 3,861)
No (N= 1,027)
p-value
N
78.7%
68.1%
0.000
4,880
25.7%
18.2%
0.000
4,870
46.5%
37.7%
0.000
4,878
56.6%
50.7%
0.003
4,864
High School
Yes (N=607)
No (N=4,288)
p-value
N
71.9%
77.1%
0.016
4,889
25.5%
23.7%
0.373
4,880
40.5%
45.3%
0.040
4,886
51.1%
56.0%
0.039
4,873
Some College
Yes (N= 1,739)
No (N= 3,156)
p-value
N
74.8%
77.2%
0.112
4,889
25.2%
23.2%
0.166
4,880
40.2%
47.2%
0.000
4,886
56.5%
54.6%
0.248
4,873
College Degree
Yes (N= 1,510)
No (N=3,385)
p-value
N
77.4%
75.9%
0.308
4,889
23.4%
24.2%
0.632
4,880
48.2%
43.0%
0.004
4,886
53.9%
55.9%
0.273
4,873
Graduate Degree
Yes (N= 1,039 )
No (N=3,856)
p-value
N
81.1%
75.3%
0.000
4,889
20.9%
24.6%
0.032
4,880
50.8%
43.2%
0.000
4,886
58.4%
54.6%
0.065
4,873
47
Table 7
Determinants of Credit Use at the Firm’s Start-Up
The table reports odds ratios from a weighted binomial logistic regression model examining the
determinants of the use of different types of credit at the firm’s start-up. The sample includes Kauffman
Firm Survey 2004 start-up firms. In Model 1, the dependent variable equals 1 if the firm uses any type of
credit (trade, business, or personal) and equals 0 if the firm uses no credit. Models 2-4 are estimated for
the sample of firms that use credit. In Model 2, the dependent variable equals 1 if the firm uses trade
credit and equals 0 if the firm uses business or personal credit. In Model 3, the dependent variable equals
1 if the firm uses business credit and equals 0 if the firm uses trade or personal credit. In Model 4, the
dependent variable equals 1 if the firm uses personal credit and equals 0 if the firm uses trade or business
credit. t-statistics are in parentheses. Variable definitions appear in Table 1. ROA is scaled by 1,000 to
improve the exposition of odds ratios for ROA. Industry dummies (based on two-digit NAICS code) are
included but omitted from the table for the sake of brevity. N is the number of observations. Survey
weights applied. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level,
respectively.
48
Table 7 (continued)
Firm Characteristics
ln (Revenue+1)
ROA
ln (Cash+1)
ln (Current Assets+1)
ln (Tangible Assets+1)
Corp
Multiown
Credit Risk
Rural
Owner Characteristics
ln (Owner Age)
1
2
Any Type of Credit Trade Credit
3
Business Credit
4
Personal Credit
1.079***
(6.62)
1.043**
(2.46)
1.037**
(2.51)
1.054***
(3.87)
1.061***
(4.36)
1.310**
(2.00)
1.002
(0.02)
0.801***
(-2.73)
0.833
(-1.22)
1.096***
(6.40)
0.001
(-0.81)
1.013
(0.83)
1.084***
(5.35)
1.048***
(2.88)
1.282*
(1.92)
1.074
(0.58)
0.911
(-1.23)
1.322*
(1.92)
1.024**
(2.15)
624.486
(0.84)
1.055***
(4.06)
1.041***
(3.34)
1.031**
(2.20)
1.560***
(3.82)
1.336***
(2.62)
0.844**
(-2.34)
0.964
(-0.27)
0.975**
(-2.09)
0.001
(-1.06)
0.98
(-1.39)
0.994
(-0.43)
1.024
(1.62)
0.774**
(-2.13)
0.744***
(-2.58)
1.124
(1.51)
0.903
(-0.72)
0.603**
(-2.05)
1.085
(1.35)
1.02
(1.07)
0.800
(-1.60)
0.775
(-0.87)
0.987
(-0.05)
0.712
(-1.30)
1.618
(1.37)
1.141
(0.76)
1.085
(0.38)
Yes
2,353
7.99
0.000
1.012
(0.06)
0.958
(-0.82)
0.973
(-1.08)
0.844
(-1.41)
1.177
(0.59)
1.017
(0.07)
0.845
(-0.77)
1.370
(0.92)
1.033
(0.20)
1.152
(0.74)
Yes
2,355
3.35
0.000
1.108
(0.44)
0.925
(-1.38)
0.968**
(-2.02)
1.348**
(2.20)
1.525
(1.35)
0.652*
(-1.81)
0.946
(-0.24)
1.560
(1.19)
1.349*
(1.86)
1.197
(0.92)
Yes
2,352
2.64
0.000
0.991
(-0.04)
ln(Prior Experience+1) 0.823***
(-3.29)
Prior Start-ups
1.004
(0.23)
Female
0.860
(-1.15)
Asian
0.859
(-0.49)
Black
0.385***
(-5.09)
Hispanic
0.868
(-0.57)
Other Race
1.111
(0.29)
College Education
1.335*
(1.74)
Graduate Degree
2.609***
(4.32)
Yes
Industry Controls
2,943
N
F-statistic
6.62
Model p-value
0.000
49
Table 8
Determinants of the Percentage of Total Liabilities Financed by Trade Credit, Business Credit, or
Personal Credit at the Firm’s Start-Up
The table reports the results from two-sided Tobit regressions examining the determinants of the
percentage of the firm’s total liabilities financed by trade credit (specification 1), business credit
(specification 2), or personal credit (specification 3) at the firm’s start-up. The sample includes Kauffman
Firm Survey 2004 start-up firms that use credit. The dependent variable in specification 1 is the amount of
trade credit divided by the amount of total liabilities; in specification 2 is the amount of business credit
divided by the amount of total liabilities; in specification 3 is the amount of personal credit divided by the
amount of total liabilities. Total liabilities equal the sum of the amounts of trade credit, business credit,
and personal credit. The lower limit for the dependent variable is zero and the upper limit is one.
Variable definitions appear in Table 1. ROA is scaled by 1,000 to improve the exposition of odds ratios
for ROA. Industry dummies (based on two-digit NAICS code) are included but omitted from the table for
the sake of brevity. N is the number of observations. Survey weights applied. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% level, respectively.
50
Table 8 (continued)
Panel A: Trade Credit/Total Liabilities
1
Firm Characteristics
ln(Revenue+1)
ROA
ln(Cash+1)
ln(Current Assets+1)
ln(Tangible Assets+1)
Corp
Multiown
Credit Risk
Rural
2
0.059***
(6.66)
4.274
(0.52)
0.011
(1.10)
0.050***
(5.35)
0.004
(0.35)
0.105
(1.31)
0.026
(0.34)
-0.051
(-1.08)
0.177*
(1.95)
Owner Characteristics
ln(Owner Age)
ln(Prior Experience+1)
Prior Start-ups
Female
Asian
Black
Hispanic
Other Race
College Education
Graduate Degree
Industry Controls
Constant
N
F-statistic
Model p-value
No
-1.097***
(4.80)
1,825
14.97
0.000
51
3
0.049***
(5.83)
4.054
(0.61)
0.013
(1.41)
0.033***
(3.49)
0.003
(0.27)
0.099
(1.30)
0.049
(0.67)
-0.045
(-1.01)
0.132
(1.53)
-0.197
(-1.41)
0.143***
(4.43)
0.000
(0.04)
-0.290***
(-3.66)
-0.231
(-1.35)
-0.376**
(-2.55)
-0.138
(-1.00)
-0.072
(-0.37)
-0.022
(-0.23)
-0.178
(-1.52)
No
0.321
(0.62)
2,732
5.55
0.000
-0.270*
(-1.81)
0.082**
(2.43)
0.009
(1.03)
-0.165*
(-1.94)
-0.167
(-1.00)
-0.003
(-0.02)
-0.124
(-0.80)
0.050
(0.31)
0.028
(0.26)
-0.033
(-0.25)
Yes
-0.034
(-0.05)
1,802
6.67
0.000
Table 8 (continued)
Panel B: Business Credit/Total Liabilities
1
Firm Characteristics
ln(Revenue+1)
ROA
ln(Cash+1)
ln(Current Assets+1)
ln(Tangible Assets+1)
Corp
Multiown
Credit Risk
Rural
2
-0.006
(-1.03)
12.505*
(1.89)
0.013
(1.62)
0.015**
(2.16)
0.019**
(2.29)
0.182***
(2.98)
0.078
(1.32)
-0.098**
(-2.52)
0.022
(0.30)
Owner Characteristics
ln(Owner Age)
ln(Prior Experience+1)
Prior Start-ups
Female
Asian
Black
Hispanic
Other Race
College Education
Graduate Degree
Industry Controls
Constant
N
F-statistic
Model p-value
No
-0.14
(-0.81)
1,639
5.19
0.000
52
3
-0.005
(-0.71)
12.028*
(1.66)
0.012
(1.58)
0.016**
(2.17)
0.018**
(2.24)
0.198***
(3.23)
0.076
(1.28)
-0.101**
(-2.51)
0.003
(0.04)
0.146
(1.39)
-0.010
(-0.41)
0.012*
(1.69)
-0.062
(-1.09)
0.125
(1.19)
-0.124
(-1.21)
-0.058
(-0.54)
-0.103
(-0.63)
-0.014
(-0.20)
0.132
(1.57)
No
-0.646
(-1.64)
2,458
1.88
0.043
-0.075
(-0.63)
-0.026
(-0.89)
0.003
(0.44)
-0.053
(-0.77)
0.153
(1.14)
0.012
(0.10)
-0.081
(-0.57)
0.011
(0.06)
-0.029
(-0.32)
0.016
(0.15)
Yes
0.561
(0.99)
1,619
2.76
0.000
Panel C: Personal Credit/Total Liabilities
1
Firm Characteristics
ln(Revenue+1)
ROA
ln(Cash+1)
ln(Current Assets+1)
ln(Tangible Assets+1)
Corp
Multiown
Credit Risk
Rural
2
-0.028***
(-4.52)
-12.344**
(-2.00)
-0.015**
(-1.98)
-0.039***
(-5.60)
-0.010
(-1.30)
-0.219***
(-3.58)
-0.102*
(-1.73)
0.099***
(2.63)
-0.171**
(-2.36)
Owner Characteristics
ln(Owner Age)
Prior Start-ups
Female
Asian
Black
Hispanic
Other Race
College Education
Graduate Degree
Industry Controls
Constant
N
F-statistic
Model p-value
-0.024***
(-3.77)
-11.268**
(-2.04)
-0.015**
(-2.06)
-0.029***
(-4.10)
-0.007
(-0.96)
-0.226***
(-3.70)
-0.112*
(-1.94)
0.096***
(2.59)
-0.120
(-1.63)
-0.006
(-0.05)
-0.089***
(-3.57)
-0.016**
(-1.99)
0.217***
(3.65)
0.080
(0.68)
0.286***
(2.66)
0.154
(1.44)
0.177
(1.07)
0.089
(1.18)
0.084
(0.92)
No
0.591
(1.43)
2,719
4.96
0.000
ln(Prior Experience+1)
No
0.986***
(5.70)
1,818
15.57
0.000
53
3
0.189
(1.57)
-0.042
(-1.51)
-0.018**
(-2.21)
0.169**
(2.53)
0.088
(0.64)
0.007
(0.05)
0.112
(0.94)
0.055
(0.32)
0.122
(1.41)
0.124
(1.17)
Yes
-0.202
(-0.36)
1,795
5.93
0.000