Discussion Paper No. 616

AVAILABILITY OF FIRMS’ INFORMATION AND
THEIR CHOICE OF EXTERNAL CREDIT:
EVIDENCE FROM THE DATA OF
SMALL FIRMS

Wako Watanabe

September 2004

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Availability of Firms’ Information and their
Choice of External Credit: Evidence from theData of Small Firms∗
Wako Watanabe
Osaka University†
First draft: September 2003
This version: September 2004

Abstract
The main purpose of this paper is to present the empirical findings derived fromthe data of small firms that the availability of private and public information on the
borrowing firm leads to diverse borrowing patterns among firms. Exploring logit
models to characterize the firm’s choice of a financial source, we find that firms whose
information is poorly recorded, or who are publicly less recognized, are more likely tochoose institutional lending over trade credit but as the recorded information becomes
more organized and firms become more transparent, they tend to graduate to a greateruse of trade credit.
JEL Classification: C25, G21, G32
Keywords: availability of information, institutional lending, trade credit, nested
logit models∗

I would specially like to thank Ben Bernanke, Han Hong, and Guido Lorenzoni for their advice andencouragement. Thanks also to participants at the Princeton Macro/International Student Workshop, thegraduate seminar at Osaka University, and a public economics seminar at Keio University for their helpfulcomments. I thank Phillip Bond, Shinichi Nishiyama and Charles Yuji Horioka for their comments andadvice that improved the paper substantially. Any remaining errors are my own. I am also indebted to
Courtney Carter of the Board of Governors of the Federal Reserve System for assisting me with the data.
†
Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka 567-0047,
Japan. Email: watanabe@iser.osaka-u.ac.jp

1

Introduction

This paper intends to provide the empirical answer to the question: “Why do specialist financial intermediaries who engage solely in financial services exist?”. The relevant answers
have been addressed concerning banks that are the largest subset of specialist financial intermediaries.1 Freixas and Roche (1997) name four roles of a bank: oﬀering access to apayment system, transforming assets (for instance, transforming of terms of assets), managing risk (of the investment project), and processing information and monitoring borrowers.
They place emphasis on the fourth role, of managing asymmetric information, which, theyclaim, has been the major theoretical contribution of the last 20 years to our understandingof the banking function. They explain:(I)t is reasonable to assume that banks have a specific part to play in managing
some of the problems resulting from imperfect information on borrowers. Banks
may invest in an informational technology that allows them to screen the diﬀerent
demands for loans they are confronted with and to monitor the projects, thus
limiting the risk that the borrower may implement a project diﬀerent from theone agreed upon initially.
Thus, the contemporary banking theory of asymmetric information restates the fundamental but vague question into the more manageable question: “Are specialist financial
intermediaries more advanced (eﬃcient) in processing information (on borrowers) and monitoring borrowers than other competing lenders that provide funds to the same customers?”
We know that the recent development of financial instruments allow various unintermediatedfinancial instruments to fulfill transformation of assets and risk management. If specialist
intermediaries lack informational advantages, the size of institutional loans must be negligible and only as large as that of the pooled funds required for the settlement of payment
needs. The size of institutional loans in the real world, however, certainly exceeds thismagnitude.
Small firms have been characterized as being bank dependent for their external financing
as they are blocked from access to unintermediated financial markets. Trade credit, on theother hand, has been analyzed rather as non-financial business practice among firms. In
fact, however, trade credit can be viewed as a form of short term credit granted by firms
supplying goods and services to buyer firms, and interestingly small firms are almost equallytrade credit dependent as well as bank dependent. 43 percent and 37 percent of small firms’
liability are trade credit and institutional borrowing, respectively. An analysis of small
1

It is not our intention to restrict the interpretation of our empirical findings to only banks since suchspecialist intermediaries include non-bank financial companies too.

1

firms’ financial decision must consider their liability management of two largest items. Thisapproach also allows us comparison of financial institutions as specialist lenders with tradecredit providing firms as non-specialist lenders.
Using the 1998 Survey of Small Business Finances (SSBF) we investigated the relationship of the informational characteristics of small firms and the pattern of their borrowing
demand. In particular we explored how the managerial and accounting information of thefirms is recorded and made available to the lenders and how the openness of the managerial
decision making process, and the firms’ public recognition lead to diﬀerent borrowing patterns among them. We modeled the firm’s borrowing application behavior with a discrete
choice econometric model. Use of data on firms’ applications for loans and trade credit,
which include data on denied loans, allows us to identify borrowing demand and is superior
to the conventional use of balance sheet data that are themselves only a collection of agreed
contracts matching lending demand and supply.
Our main findings are: 1. Informationally opaque and less publicly known young firms
are more likely to choose institutional loans over trade credit; 2. They tend to switch to agreater use of trade credit besides institutional loans as they become more established andpublicly known, and are more forthcoming in recording detailed and accurate information on
their management and accounting. Such findings support the notion of the informational
advantage of institutional lenders.
The remainder of the paper is organized as follows. Section 2 discusses the backgroundof our empirical exercise. While reviewing the related theoretical and empirical literature,
it explains the contractual characteristics of trade credit and distinguishes it from the institutional lending based on its informational structure. Section 3 describes the data andintroduces the empirical method. Section 4 shows and interprets the results. Section 5
concludes the paper.

2

Background and the Related Literature

2.1

The Costly State Verification Model

The costly state verification model (Townsend [1979], Williamson [1987], Bernanke, Gertler
and Glichrist [1999]) shows that the lender’s oﬀer on the terms of contract reflects the costthat the lender expends to verify the state of the project the borrower firm exercises.2 Since
2

The model assumes that the return of the firm’s project is the private information to the firm without
the lender’s monitoring. Unless the lender exercises costly ex-post monitoring, upon the borrower’s default,
he cannot know whether the borrower firm cheats the lender when the invested project is indeed successfulor he truthfully reports that the project fails.

2the higher cost of monitoring compresses the lender’s expected profit from lending at any
lending rate, the oﬀering rate has to be forced up to make up for such loss of profit. The
more eﬃcient monitoring allows the lender to oﬀer the cheaper funds to the firm. The firmcompares contractual conditions that he would agree on with possible lenders and chooses
the most favorable lender.
The size of the monitoring cost varies across lender-borrower pairs. It depends on thelender’s monitoring technology, the borrower firm’s transparency and public recognition,and the lender’s special knowledge and expertise regarding the firm. Financial institutions
staﬀed by competent and experienced auditing oﬃcers and equipped with the appropriate
rating program can audit any firm more eﬃciently than those not so prepared. Any lender,
regardless of its monitoring eﬃciency, can audit more transparent and more publicly known
firms with greater ease than it can more opaque and less publicly recognized ones. Lenders
who specialize in specific industries or those who have special relationships with certain
borrowing firms and have access to hard to obtain information take advantage of overseeing
such industries and firms more eﬃciently than other lenders.
Since the higher lending rate has two opposing eﬀects on the lender’s expected profit—a higher return upon the firm’s success and an increased likelihood of the borrower firm’s
default—the lender’s expected profit does not necessarily increase monotonically in the lending rate. Thus, the profit maximizing lender does not intend to increase lending supply
monotonically in the lending rate, either. If the lending demand is suﬃciently high, thesupply and demand curves will not necessarily intersect, thus, leading to the Williamson’s(1987) equilibrium credit rationing.3

2.2

Small Business Finance and Trade Credit

Before further developing the discussion on the theoretical background, we shift our focus
briefly to the reality of small business finance. The major sources of external finance bysmall firms are institutional lenders, mostly so-called relationship lenders, and trade credit, amedium of short term credit.4 Trade credit is a major alternative to institutional borrowing
for small firms that are restricted access to unintermediated finance. The data in the 1998
SSBF show that 66 percent of small firms that employ less than 500 people use trade credit
3

Williamson (1987) shows the interesting case of a backward bending lending supply curve with a uniquemaximum supply of loans. In this case attainment of equilibrium credit rationing is ensured given a
suﬃciently high lending demand.
4
Neither equity finance nor bond finance is visible presumably because of the small amount of externalfunds needed. Bolton and Freixas (2000a, 2000b) argue that the only option available to the riskiest startups is equity finance. It appears that such firms in the 1998 Survey of Small Business Finances (SSBF) do
not issue equity publicly but either the owner, his family, or his acquaintances provide additional equity if
it is needed.

3and on average 43 percent of the total credit owed by the firms is trade credit.5 Indeed both
figures exceed the corresponding figures on relationship lending (44 percent and 37 percent).
Trade credit is a type of lending contract made between non-financial firms. A supplierfirm of goods and services allows a buyer to defer the payment. The lag between the dateof delivery of goods or services rendered and the date of payment means that the credit isgranted by the supplier firm to the buyer firm.
There are two types of a trade credit contract. A complex two-part terms contract has
four components: the discount rate, the discount due date, the net due date, and the penalty
rate. These four components are set up so that the buyer is given an incentive to repay atthe earlier time. The buyer firm is oﬀered a one to two percent discount on sold goods if it
pays within a shorter period of time. Thus missing the discount due date would require thebuyer an expensive opportunity cost.6 Further down the road, if the buyer fails to pay by
a certain date called the net due date, the buyer is in default and a penalty rate is imposedon him. However, if as the buyer firm makes its remittance by the discount due date, thecredit is cost free. A typical two-part terms contract structure is depicted in Figure 1. The
discount is not oﬀered under a net contract. The rest of its contractual structure is thesame as that of a two-part terms contract.7

2.3

Specialist and Non-specialist Intermediaries

Unlike the conventional empirical works that intend to examine advantages of relationshiplenders over non-relationship lenders, our empirical interest is in the relative advantage ofthe specialist financial intermediary, who is engaged solely in financial business, over the
non-specialist, who is engaged in both financial and non-financial activities.8 Institutional5

A detailed description of the SSBF survey and the descriptive statistics on the survey data will appear
in section 4.
6
Under the typical contract with 10 days as the discount due date, 2 percent as the discount rate, and30 days as the net due date, missing the discount due date would require the buyer to pay an annual 43.9
percent of opportunity cost, what we call the implicit interest rate.
The implicit interest rate ri is calculated according to the following formula.
ri =

µ

100
100 − rd

¶ N ET360−DIS−1

where rd is the discount rate, N ET is the number of days from the date of derivery to the net due date,and DIS is the number of days to the net due date.
7
For the detailed discussion on trade credit contract, see Ng, Smith, and Smith (1999). They discussthat a choice between two types of contracting form gives suppliers a greater flexibility in oﬀering terms ofcontract though suppliers do not vary prices (interest rates) much across buyers within an industry (Petersen
and Rajan ([1994], [1997]).
8
The supplier - buyer relationship can be as long lasting and strong as the lender - borrower relationship.
So many of supplier firms providing trade credits can be also relationship lenders. The literature review

4lenders and trade credit providing firms are good real world examples of specialists and nonspecialists respectively. Bond (2004) discusses that the specialist emerges when the total
aggregate monitoring costs in the economy are reduced with the intermediary rather than
without. According to his discussion trade credit is a more eﬃcient form of financial intermediation than institutional lending, all other things being equal. Since the non-specialist
intermediary does not have to monitor itself, intermediation by the non-specialist reduces
the aggregate costs of monitoring in comparison to the intermediation by the specialist.
Figure 2 depicts the specialist intermediation and the non-specialist intermediation ina simple environment with only two borrowing firms (entrepreneurs). The non-specialist
intermediary has to monitor only one entrepreneur whereas the specialist intermediary has
to monitor and verify both entrepreneurs, and hence the total (aggregate) monitoring costs
are higher with the non-specialist intermediary if the unit monitoring cost is the same forboth the specialist and the non-specialist. Therefore the specialist intermediary emerges
only when it is substantially more advanced in monitoring borrowers than the non-specialist.

2.4

Monitoring Technology and Transparency

It is the commonly held conventional wisdom that the advanced monitoring technology of thespecialist lender allows it to audit opaque firms with obscure accounting and management
records as easily as it can transparent firms with higher quality of written records.9 A
specialist lender equipped with better monitoring capability is able to oﬀer funds to a viablefirm whose written documents on accounting and management are poorly recorded morecheaply than a non-specialist. For instance, the advanced monitor has informal accessthrough its close contacts with the manager to information that is not recorded in either
the hard copy or electronic files. Unlike recorded hard information such soft information is
hard to obtain without expertise and special knowledge.
However, the opposite scenario is also easily justifiable. The eﬃcient lender could begood at processing and analyzing the recorded information rather than extracting the soft
information through informal activities.10 Therefore a specialist lender oﬀers cheaper funds
to firms with better management and accounting records. On the other hand, a nonspecialist lender is indiﬀerent to firms regardless of availability of information on them,since the recorded information, which would be useful for the advanced auditor to judge
will appear in section 2.5.
9This point is particularly made in the relationship lending literature. For the relationship lending theory,
see Diamond and Rajan (2000, 2001) and Kashyap, Rajan and Stein (2002).
10
For instance Petersen and Rajan (2002) discuss that the historically observed widening distance of the(specialist) lender and the borrower is likely to reflect the fact that detailed hard information is easily
obtainable electronically thanks to the rapid development of information technology.

5the firm’s solvency and viability, and to verify the firm’s default, is nothing but a blank
paper. Therefore the superior auditor charges a higher interest premium against opaquefirms, whereas the inferior auditor provides funds to them at the same cost as the funds tomore transparent borrowers.
In reality both cases must coexist. It is, therefore, an empirical question as to whichcase is more of relative importance. If the former dominates the latter, we observe thatthe more opaque small firm chooses institutional lending (a specialist) over trade credit (anon-specialist), and vice versa.

2.5

Empirical Literature

There are relatively few empirical works that attempt to relate the firms’ financial decisions
or the cost of lending directly to their informational characteristics. Based on the Duﬃe andLando (2002) model of the credit spreads of bonds issued by firms with imperfect accounting and using the percentile ranking of the firm’s transparency of accounting information
calculated from the data published by the Association for Investment and Management Research, Yu (2004) finds that the spread attributable to the extent of the bond issuing firms’
accounting imperfection is positive and statistically significant.
The closely related work attempting to examine the influence of the firm’s informational
characteristics on the cost of borrowing is Petersen and Rajan (2002). Interpreting that thegeographical closeness to the borrower firm implies better monitoring, with 1987 and 1993
SSBF surveys, they estimate the eﬀect of the distance predicted by the firm’s informational
characteristics on the probability that the institutional lender accepts the application and thecontractual lending rate, and find that the eﬀect has become less important. They discussthat the ease with which the lender can now acquire the borrower information through thedevelopment of information technology does not require the direct contact that the physicalcloseness allowed in the past. Using Argentine data on banks and small firms Berger,
Klapper, and Udell (2001) find that large foreign owned firms, whose decisions on lending
are likely to be made at their headquarters physically far from the small firms, are more
inclined to supply loans to small firms in a period of financial distress than would small
domestic banks. With 1987 SSBF data Petersen and Rajan (1994) find that an older firm
is charged the lower interest rate on the most recently accepted loan. As a related areaof study there are a large number of works examining the role of the relationship in small
business finance.11
11With 1987 data Petersen and Rajan (1994) find that the length of the relatiionship does not havesignificant negative eﬀect on the interest rate charged on the most recently accepted loans, whereas Berger
and Udell (1995) with the same data find the negative eﬀect of the length of relationship on the premium
over prime rate for loans issued under lines of credit. With 1993 data Cole (1998) examines the eﬀect of pre-6

Petersen and Rajan (1997) extrapolate from 1987 SSBF data that the demand for tradecredit measured by the accounts payable over assets increases with the firm’s age, and decreases in the length of a relationship with a financial institution, suggesting that an older
firm uses less institutional lending and replaces its financial needs with trade credit. Petersen and Rajan (1994) also find that the use of trade credit measured by the percentageof trade credits that were paid after the due date and the negative of discounts taken by theborrowing firm decreases as firms age and is higher for a corporation. However, to the bestof our knowledge there is not a single paper that directly examines the role of informational
characteristics on the small firm’s choice of external credit from trade credit and institutional
lending, two of equally important financial sources for small firms.

3

Data and Empirical Methodology

3.1

Data Description

The Survey of Small Business Finances (SSBF) is conducted by the Board of Governors of
the Federal Reserve System and collects information on for-profit, non-financial, non-farm,non-subsidiary business enterprises in the United States that have fewer than 500 employees.
The information collected includes the firm’s legal origin, various owner characteristics suchas age, education, management experience, ownership concentration, the firm’s geographicalexpansion, firm age, use of financial services, the firm’s relationships with financial institutions, and the firm’s income and balance sheets. The SSBF has provided the most reliabledata set on the management and financial information of small firms, and is widely used byeconomics and finance researchers.12
The 1998 survey is the third in a series of surveys along with the 1987 and 1993 surveys.
The number of firms surveyed was 3,561. Reflecting the development of information technology in the late 1990s, the 1998 survey collects data on the firm’s use of computers forbusiness purposes. This allows us to construct the more appropriate variable that captures
how well hard information on the firm’s accounting and management is recorded. Previous
surveys have asked the firm what types of written records the firm uses, if there are any inexisting relationships such as the firm’s possession of deposit accounts and the length of relationship on thelender institution’s probability to extend the existing contract and find the significant eﬀect of relationshipvariables. Degryse and Cayseele (1999) using the large Belgian contract level data on bank lending to small
firms find that the length of relationship and the main bank reduces the contractual lending rate. Hoshi,
Kashyap, and Scharfstein (1991) find that the Japanese firms in a keiretsu corporate group, who are expected
to have close ties to the main bank in the group, are more insulated from liquidity shocks in their investments
than firms not aﬃliated with a keiretsu.
12
The complete set of works that use the SSBF are available on the web page of the Federal Reserve Boardof Governors at http://www.federalreserve.gov/pubs/oss/oss3/abstract.html.

7

answering the survey. Such information may serve as a proxy for the state of maintenance of
information, but it is not a direct measure. It may be that the firm with very good written
records simply did not have the records within the respondent’s reach. In this case, use of
written records does not have economic meaning on the firm’s business operation.

3.2

Econometric Model

We model the small firm’s external financial demand as the firm’s discrete choice of financial
sources. The survey questionnaires of the SSBF include the firm’s recent applications foritems in liabilities. The advantage of using data on applications rather than post contract
financial statements data is that one can identify the firm’s demand from a certain financial
source no matter if the application from such a source is granted or not. Firms unsuccessful
in their credit applications have a credit demand but do not show it as a liability on theirown balance sheets.
We assume that the small firm decides whether to apply to institutional lending or not
and whether to apply to trade credit or not.13 Under this assumption there are four statesin one of which any given firm falls: no demand for trade credit or of institutional lending(no demand of external credit); only trade credit; only institutional lending; and both tradecredit and institutional lending. Each firm chooses one of these financing alternatives
depending on its informational and demographic characteristics. We employ probabilistic
choice models discussed in Amemiya (1985) to characterize the firm’s choice. More precisely,
we estimate the following conditional probability that the firm i chooses a financial source(mix) j.
Pij = Pr(di = j | xi ), j = 0, 1, 2, and 3
di is the dichotomous variable that takes on values 1 if the firm i has no external credit
demand, 2 if it applies to only trade credit, 3 if it applies to only institutional lending, and 4
if it applies to both trade credit and institutional lending. One widely used functional form
for the conditional expectation Pij is the logit family. Its mathematical simplicity allows
us to estimate the model parameters relatively easily. The models can be derived from theoptimal choice of the source of external credits by the firm. Denoting Uij as the firm i’sutility from choosing the financial source j and J = {0, 1, 2, 3} we get,
Pij = Pr (Uij > Uik , ∀k ∈ J\j | xi ) , j = 0, 1, 2, and 3
We further assume that Uij =xi β ∗j +εij so that the firm’s utility is composed of a determin13

Other alternatives for external finance by small firms are almost non-existent in the data.

8

istic part attributable to the firm j’s various characteristics and the stochastic disturbance.
We assume that there are k variables that capture the firm’s characteristics so that xi is a
1 × k column vector. The logit family specifies the (joint) distribution function of εij to be(the function of) the extreme value function given by exp [− exp (−εij )].
We examine three specifications: the multinomial logit model; the nested logit model;
and the nested multinomial logit model. Their diﬀerences are based on assumptions madeon the structure of the decision making. It is likely the case that only firms in need ofexternal credits proceed to the decision of choosing one of the three alternative external credit
sources. That is, the firm must decide at first whether or not to apply for external credits
at all regardless of any specific source, and then choose only trade credit, only institutional
lending, or both of them. The three specifications diﬀer in the way they deal with such astructure of the decision making process. 14
Multinomial logit model
The multinomial logit model does not take into account the firm’s two-stage sequential
decision. (the first figure of Figure 3) The firm chooses one of all four alternatives simultaneously. The model then assumes the independence of stochastic disturbance εij across any
alternative j.
One can derive the probability that firm i chooses an alternative j as the function ofindices xi β l ’s as,¢¡
exp xi β jPji =, j = 0, 1, 2, and 3, β 0 = 0
3
P
1 + exp (xi β l )l=1Note that one cannot identify all parameter vectors for four diﬀerent states. We canonly identify the diﬀerence of the parameter vector for the comparison group (β ∗0 ) and eachof the remaining three β j = β ∗j − β ∗0 .
Nested multinomial logit model
In the nested multinomial model the two-stage nature of the firm’s decision making
process is explicitly imposed. The firm decides whether to borrow or not before choosing
the specific source of credits. (the second figure of Figure 3) The decision tree for the nested
multinomial logit model is portrayed in the second figure of Figure 3. One can write the(unconditional) probability of not borrowing and the conditional probability of choosing thesource j respectively with parameters β and γ j ’s as,
P0i = Pr (di = 0) =14

1
1 + exp (xi β)

For a formal derivation of models, see Amemiya (1985).

9¡¢
exp xi γ j
Pr (di = j | di 6= 0) =2
P
1 + exp (xi γ l )l=115Nested logit model
The nested logit model is the most general model of the three.16 It allows correlation
among the firm’s utilities from diﬀerent sources of external credits but does not impose
the explicit two-stage decision structure. (the third figure of Figure 3) One obtains theunconditional probability that the firm i does not apply to any of external credits Pi0 andthe probability of choosing an external credit source j conditional on that the firm i does
apply to one of three external credit sources respectively as,
P0i =
1+a

½3
P

1

¾ρ

exp (ρ−1 xi β l )

l=1

¢
¡
exp ρ−1 xi β j
P2ji = Pr (di = j | di 6= 0) = 3, j = 1, 2, and 3
P0−1
exp (ρ xi β l )l=1where the subscript 2 represents the second stage, and ρ and a are model parameters. In
particular, ρ is the correlation parameter. 17
Marginal eﬀects
We estimate these models by the maximum likelihood estimation. In non-linear discrete
choice models coeﬃcient vectors themselves are of little interest to us unlike in linear models
in which coeﬃcients themselves are marginal eﬀects of changes in independent variableson the dependent variable. We are interested in either levels of the unconditional andsecond stage conditional probabilities per se for a certain value for a vector of characteristics
variables xi or the eﬀect of marginal change in a certain variable in the vector xi on theprobabilities (the marginal eﬀect).
i =j)
The marginal eﬀect on the unconditional probability is given by ∂ Pr(dfor a con∂xmi
tinuous variable xm . The marginal eﬀect for a binary (dummy) variable xm0 given by
15

exp(xi β)Note, Pr(di = j) = Pr (di = j | di 6= 0) Pr (di 6= 0) = 1+exp(xi β)exp(xi γ j )2
P1+
exp(xi γ l )l=1

16Nested multinomial logit and multinomial logit models are special cases of the nested logit model.
17
The unconditional probability of choosing an alternative j (6= 0) can be obtained by Pji = Pr (di 6= 0)
Pr (di = j | di 6= 0) = (1 − P0i ) P2ji . The formula degenerates to the formula for the multinomial logit
model if ρ = a = 1. β 1 = 0 and ρ = 1 in the second equation of the formula result in the formula for thesecond stage multinomial logit model in the nested multinomial logit model.

10Pr (di = j) |xm0 i =1 − Pr (di = j) |xm0 i =0 , represents the associated change in the probability
with a change of the m0 th variable from 0 to 1. The marginal eﬀects for the conditional
probability are analogous. Since these marginal eﬀects are the functions of xi , one needs
to compute the sample average to evaluate the eﬀects numerically. In calculating the eﬀectof each single variable for each observation, which is then used for averaging over all theobservations, other variables are kept at the original value of the observation.18 19

3.3

Variables

Dependent variable
Use of the data on the firm’s applications of loans and trade credit that include rejected
these attempts allows us to identify lending demand and is superior to the conventional useof balance sheet data that are themselves only a collection of agreed contracts matchinglending demand and supply.
Regarding institutional lending, the SSBF asks not only how many times the firm applied
for new loans but also if there were times when the firm needed credit but did not apply
because it thought the application would be turned down. Since the firm that did not apply
for loans for fear of being rejected by the institution can be interpreted to have had demand
for loans, we should include both actual applications and voluntary withdrawals.20 Table
1 shows the descriptive statistics on the variables used to construct the dependent variable
and the dependent variable itself.
It appears that quite a few firms withdrew voluntarily from applying for a loan (22percent) and a majority of them (63 percent) never applied for a loan afterward. Amongthe firms that applied for the loan afterward, 60 percent of them were never successful inany of their applications. Thus only 15 percent of firms that had ever withdrawn fromapplication fearing rejection were indeed accepted by some lenders in the end. Among firmsthat had applied for a loan at least once, most of them (83 percent) obtained loans eventuallyand half of the firms who were successful at least once stopped submitting applications with
their first successful application. Roughly speaking, there are two model groups within firms
demanding institutional lending. The first group of firms do not even attempt applying for aloan and withdraw voluntarily in the last three years. The second group searches around for18

As each probability is a non-linear function of xi β l ’s, the marginal eﬀect for each variable is not afunction of only a coeﬃcient for the variable of interest itself but a function of all parameters including
parameters for other alternatives and other model parameters if they are modeled so. Thus neither thestatistical significance nor the insignificance at any given significance level of a coeﬃcient in the linear index
of the model xi β l does not say much about the statistical significance of the marginal eﬀect.
19
In estimating standard errors of marginal eﬀects, the standard delta method is used.
20
Petersen and Rajan (2002) employ the same approach to define firms that were rejected loan applications.

11

lenders until they are successful, then stop shopping. The mode of the number of demands
for institutional lending within a three-year period is 1.21
Regarding trade credit, the SSBF asks both use of trade credit and experiences of rejectionof trade credit by a supplier firm in the last one year period. Two thirds of firms used tradecredit, whereas only 1 percent of firms had been denied and never had trade credit in the
one year period.22
We construct the dependent variable by combining the data on the application to institutional lending and on use of trade credit and arranges firms into four groups, firms without
demand for external credits (21 percent), firms demanding only trade credit (38 percent),
firms demanding only institutional lending (11 percent), and firms demanding both tradecredit and institutional lending (30 percent).
Though the period of time during which institutional lending is surveyed is much longer(three years) than that for trade credit (one year), combining both sets of data does notcause serious problems since firms are almost split between those using trade credit andthose not using it, and this division among firms seems to be a steady state phenomenon.
Firms that used trade credit in the survey year are very likely to have used trade credit in
the previous two years, too, whereas those that did not in the year are not very likely tohave stopped use of trade credit all of a sudden after two years of trade credit use. This isa reasonable assumption also because use of trade credit is a high frequency event and not
an event which takes place only once or twice in three years. Indeed firms that use tradecredit on average used trade credit for 72 percent of all purchases they made.
Independent variables
Table 2 shows the descriptive statistics of independent variables that are meant to measure the firm’s informational characteristics.
RECORD is a dummy variable which takes a value of 1 if the respondent used somekind of written records to answer the survey questionnaires and 0 if he/she relied on his/her
memory. If the firm keeps the written records in an organized manner, they must be readily
available to the firm’s manager (respondent). Well organized records must also be more
informative to outside lenders. Seventy nine percent of firms used some kind of written
records.23
21

The SSBF investigates the decomposition of institutions to determine which firms made their mostrecent approved and denied applications separately. 91.8 percent of approved and 88.4 percent of denied
applications were made to depository institutions. This means that in analyzing the sample of small firms
institutional lending and bank lending are almost synonymous. The decomposition of institutional lenders
to which firms withdraw without an actual attempt is not available in the survey, but it appears reasonable
to assume that such firms consider applying mostly to banks as well.
22
The low rejection rate of applied trade credit suggests that it is unlikely that large number of firms
withdraw applying to credit voluntarily.
23
It may be the case that the respondent simply did not use the written records to answer the survey

12

PCFMAN is a dummy variable which takes a value of 1 if the firm uses computers for
its accounting. Use of computers for accounting would leave more accurate records of thefirm’s accounting information. As we described earlier, this variable is new in the 1998 dataand our primary interest in estimating discrete choice models. Seventy percent of firms use
computers for accounting.
RECORD and PCFMAN are meant to be direct measures of availability of hard information to lenders. Besides, two additional variables are included as indirect proxies. They
are dummy variables indicating whether the firm is managed by an owner (OWNER) or
whether it is a corporation (CORP). A firm managed by a hired manager is more likely
to leave better detailed and informative hard information for communications between theowner and the manager. Corporations are required to have higher standard of accounting
records.
The dummy variable indicating whether its sales region stretches nation wide (NATIONAL) is also included. In addition such continuous variables as the number of branches(BRANCH), owner experience (C_EXP), owner age (C_OAGE) and firm’s age (C_FAGE)
are included as independent variables. They are all meant to measure the firm’s transparency and public recognition. The geographical expansion of the firm, an older ownerwith longer experience, and a longer firm history all tend to make the firm more recognizedby the general public.24
Demographic variables such as the industry, the geographic location, and the ethnic origin
are included to control the firm’s other fixed characteristics. A complete set of independent
variables and their explanations are listed in Table 3.

4

Results

4.1

Estimation results

Comparison of models and parameter estimates
In conducting the empirical analysis firms whose answers on questionnaires concerning
recent loan applications and trade credit applications were questionable are dropped. Firms
that report holding zero gross assets are not likely to be reflecting their true balance sheets,
and therefore, are dropped. 3477 firms remain in the sample.
Table 4 shows the parameter estimates and the maximized log likelihoods of three esalthough it has the informative written records. If this is true for large number of firms that did not use
written records, RECORD may have little economic meaning.
24
CORP captures the firm’s transparency besides availability of written records since corporations are
required to maintain a higher standard of disclosure.

13

timated models. The data support the sequential structure of the firm’s decision making.
The general log likelihood principle proposed by Vuong (1989) shows that the nested logit
model is superior to the multinomial logit model but is statistically equivalent to the nested
multinomial logit model.25 Table 5 shows the marginal eﬀects of variables on the conditional
probabilities of choosing a source of external credit for three models. Both point estimatesand standard errors appear to be independent of the choice of a model. Therefore our further
discussion will be based on the results of the most general nested logit model. Many of thecoeﬃcients for variables that are meant to capture the availability of the firm’s informationand its transparency are estimated relatively accurately, supporting the choice of variables.
Among 27 coeﬃcients that represent the first nine elements of coeﬃcient vectors for threechoices of external credit source, 13 are statistically significant. In particular, coeﬃcientson CORP and BRANCH are significant across all the alternatives.26 27 28
Table 5 presents the marginal eﬀects of individual variables on the firm’s unconditional
probabilities of financial choices and conditional probabilities of choices of external credits(Panel A). In order to show that results are robust across the models, marginal eﬀects
on conditional probabilities computed with multinomial logit and nested multinomial logit
models are attached (Panel B). Table 6 presents the average, taken from observations,of levels of unconditional probabilities for each financial choice (Panel A) and conditional
probabilities for each choice of external credit source (Panel B) when each dummy variableindicated as the title of column entries is held constant either at 0 or at 1 across observations,and other variables are kept at the original observed values. 29 The rest of this subsection
presents our empirical findings in Tables 5 and 6 and supplemental figures for continuous
25
The nested logit model nests the multinomial logit model, whereas the nested logit model and the nested
multinomial logit models are strictly non-nested. Under Vuong’s framework, the standard likelihood ratio
statistic (15.8196), which follows the chi-square distribution with 2 degrees of freedom, selects the nested logit
model over the multinomial logit model at the 1 percent significance level. In contrast, the standardized log
likelihood ratio test statistic (0.8432), which follows the standard normal distribution, does not distinguish
the nested logit model from the nested multinomial logit model at the 10 percent significance level.
26
Dummy variables PCFMAN and RECORD are only two variables that measure the secular informational
characteristics.
27
Some of such variables may capture not only the informational characteristics but other eﬀects. For
instance the firm’s legal status and branching may capture its endogenous management strategy rather than
fixed eﬀect characteristics.
28
The point estimate of the correlation parameter ρ is negative and statistically significant. It is hard tointerpret the negative estimate of ρ since it normally takes the value in the range from 0 (perfect correlation)
to 1 (no correlation, multinomial logit). The negative ρ, however, is not ruled out in the econometric theory.
The estimate of ρ may not be so reliable. Indeed the estimate of the model which assumes α = 1 but keeps ρas a free parameter results in the positive and statistically insignificant ρ. We find that results on estimatedprobabilities and marginal eﬀects are robust to such model assumptions.
29
For each dummy variable, a number in one of the cells in Table 5 is equal to a number in the corresponding
cell in the second panel (a dummy variable is held at 1) of Table 6 less a number in the corresponding cellin the first panel (a dummy variable is held at 0) of Table 6.

14

variables.
Availability of the firm’s hard information
As we will detail below, our empirical findings on the relationship between the availabilityof the recorded information on small firms and their credit demand are summarized in threemain points. First, these firms whose accounting and management information are thought
to be better kept are more likely to seek some form of external credit than those firms whose
information is poorly recorded. Second, firms with poor written records start with institutional borrowing, and add trade credit as their second choice of financing as their records
become better organized. Third, the empirical results from the dummy variable PCFMAN
shows the clearest evidences, whereas the results from other variables are supportive of thefindings from PCFMAN.
Direct measures (PCFMAN, RECORD)
The probability that firms using computers for accounting (firms with PCFMAN=1)
apply for some external credit is 82.3 percent, whereas the probability that firms without
computer assistance (PCFMAN=0) apply to it is only 72.1 percent. That is, use of computers for accounting increases the firm’s demand of external credit by 10.2 percentage points.30
Such firms are 4.3 percent more likely to choose trade credit only (34.8 percent for the firms
not using computers for accounting as opposed to 39.1 percent for firms without them. For
any dummy variable to be mentioned, the first percentage is the probability of each choicefor the less transparent firm and the second one is that for the more transparent firm impliedby the variable, unless noted otherwise), 9.0 percentage points more likely (23.9 percent, 32.9
percent) to apply to both lending and trade credit, and 3.1 percentage points less likely (13.3
percent, 10.3 percent) to choose lending only. All of these eﬀects are statistically significant.
Thus, introduction of computers for managing accounting raises the unconditional likelihoodof applying to trade credit, exclusive use of not, by 13.3 percentage points. On the other
hand, it raises the likelihood of applying for institutional lending only by 5.9 percentage
points.
Indeed the shift from institutional borrowing to trade credit is the shift from demand of
institutional lending to demand of trade credit besides lending. Conditional on the firm’s
need of external credit, the probability that the firm applies only to lending falls by 7.0
percentage points (20.0 percent,13.0 percent) and the probability that it applies both tolending and trade credit rises by 7.2 percentage points (32.1 percent, 39.3 percent) as thefirm introduces computers for accounting. Both eﬀects are statistically significant at the 130

Note that reported figures for marginal eﬀects are point estimates. They accompany standard errors.
Thus these figures are merely expectations (averages) over randomization based on the empirical distributionof model parameters and not deterministic.

15

percent level. 31 The total likelihood of choosing trade credit, exclusive use or not, increases
by 7.0 percentage points (80.0 percent, 87.0 percent). In contrast, the total probability tochoose lending barely increases.
The findings on the influence of RECORD on the firm’s financing requests are consistent
with the findings for PCFMAN. On average, firms using written records to answer thesurvey are more likely to apply to external credit than firms answering the survey withoutthem, and are more likely to apply for both institutional lending and trade credit. The
marginal eﬀect on the unconditional probability that the firm applies to lending only andthe eﬀect on the probability that it applies to trade credit only is negligible. Regarding
the chances that firms in need of credit choose one of the three alternatives, firms answering
the survey with some kind of written records are less likely to choose lending only and more
likely to choose the mix of lending and trade credit. None of the reported marginal eﬀects,
however, are significant.
Indirect measures
Firms managed by an employee (OWNER=0) are 4.7 percentage points more likely toapply for external credit than owner managed firms (78.5 percent, 83.1 percent) and theeﬀect is significant. Employee managed firms are more likely to apply for trade credit only,
more likely to apply for both trade credit and lending, and less likely to apply for lending
only, though none of these eﬀects is significant. The unconditional probability of applying
to trade credit rises by 8.0 percent (74.9 percent, 67.0 percent) as the owner managed firm
hands its management over to a hired manager. As for the conditional probabilities of the
choice of a financial source, the owner managed firm is 5.4 percentage points more likely
to choose lending only than the employee managed firm by 5.4 percent (15.7 percent, 10.4
percent, significant at the 10 percent significance level) and is less likely to choose the othertwo alternatives.
Corporations (CORP=1) are 7.1 percentage points more likely to apply for some external
credit than non-corporations (15.5 percent, 22.6 percent), and 5.1 percent points more likely
to apply to both trade credit and institutional lending (29.0 percent, 34.1 percent). Theseeﬀects are statistically significant. The unconditional likelihood that the corporation chooses
trade credit only is higher than the likelihood that a non-corporation chooses one, but thediﬀerence in likelihoods between two types of firms, 2.9 percent, is small and not significant.
The (unconditional) marginal eﬀect on the choice of lending only is insignificant but negative.
As for the marginal eﬀects on the conditional probabilities when the firm needs credit, acorporation is more likely to choose the mix of lending and trade credit and less likely to
31
Two eﬀects almost cancel each other out and leave little room for the likelihood change of applying for
trade credit only. Marginal eﬀects for alternatives are summed up to 0 since corresponding probabilities are
summed up to 1.

16choose lending only than a non-corporation. (Neither of the marginal eﬀects is significant.
The conditional likelihood of choosing trade credit only remains unchanged across diﬀerent
types of legal entities.
The firm’s public recognition
More branches encourage the firm to attempt to give up self-reliance and to seek tradecredit, supporting the evidence from examining variables on availability of the firm’s hard
information. In contrast, older firms, firms owned by more experienced or older owners, are
not necessarily more likely to seek external credits than otherwise. Such time factors tend
to induce firms to shift from lending only to trade credit only rather than to mix both typesof borrowing.
Marginal eﬀects of the increased number of branches on unconditional probabilities arenegative and significant for a choice of no application to any external credit, and positive andsignificant for choices of trade credit only and the mix of trade credit and lending. However,
the only conditional choice on which the marginal eﬀect of marginal increase in the numberof branches is statistically significant is a positive eﬀect on the choice of the mix of lending
and trade credit. The marginal eﬀect on the conditional probability of choosing lending onlyis negative but not significant. The eﬀect for the choice of trade credit only is negligible.
As the number of branches increases, dependence on internal funds vanishes and is replaced
by demand of the mix of trade credit and institutional lending (Figure 4). Very few firms(1.9 percent) at the top 99 percentile of the number of branches (at least 11 branches) are
estimated to refrain from the external credit voluntarily, whereas nearly half of them (44.0
percent) are estimated to apply both for trade credit and lending institutions. In contrast,
the corresponding figures are 22.5 percent and 29.2 percent for firms with only one site ofbusiness.32 33
Marginal eﬀects of operating nation wide as opposed to operating locally on both conditional and unconditional probabilities are positive for lending only and negative for tradecredit only. These eﬀects, however, are not significant. A possible explanation for thisis that the variable NATIONAL captures the geographical network of lenders rather than
the firm’s characteristics. While financial lending institutions represented by banks haverelatively large geographical networks and are able to satisfy the financial needs of firms expanding their sales channels nationwide, supplier firms providing trade credit to small firms,
whose trade partners, many of whom are also likely to be small firms with small numbers ofbusiness sites, are less likely to match their financial needs.
32

Firms operating only a single branch are the dominant majority and constitute 79 percent in the sample.
Indeed the unconditional probability of applying only for trade credit keeps increasing in the number ofbranches until it reaches11, and then starts to decrease. Ninety nine percent of firms, however, have lessthan 12 branches. The negative relationship does not alter the course of the discussion.
33

17

Figure 5 shows the estimated relationship between the owner experience and the firm’s
financial choice. The more experienced owner demands external credit (the relationshipis not statistically significant). Figures on the unconditional and conditional probabilities
consistently show that the more experienced owner is more likely to choose trade credit only
and less likely to choose lending only. The likelihood that the least experienced owner (inthe first year of his business career) is dependent solely on institutional lending is substantial(21.2 percent). In contrast, the owner’s thirty-year experience in the small business reduces
the likelihood by almost half. The decline in bank dependence is almost replaced by therise in dependence on trade credit (42.8 percent to 50.6 percent).34
Figure 6 shows the estimated relationship between the firm’s age and the firm’s financial
choice. The marginal eﬀect of the firm age on the unconditional probability of being self
reliant for its financing is negligible. On the other hand, younger firms are more likely tochoose institutional lending and older firms are more likely to be dependent on trade credit.
For instance thirty-years of staying in business increases the likelihood that the firm chooses
trade credit only from 40.9 percent to 55.1 percent, reduces the likelihood that it chooses
lending only from 17.6 percent to 12.0 percent and the likelihood that it chooses the mix oflending and trade credit from 41.4 percent to 32.9 percent
Figure 7 shows the relationship between the owner age and the firm’s financial choice.
Unconditionally the firm is more likely to withdraw from external credit and less likely toapply to lending institutions as the owner ages. All eﬀects but that for trade credit only
are significant. 35 The likelihoods that a forty year old owner chooses to be self-reliant,chooses lending only, lending and trade credit, and trade credit only are 17.6 percent, 12.7
percent, 32.5 percent, and 37.2 percent, respectively. Corresponding figures for 60 year old
are 24.1 percent, 9.2 percent, 28.3 percent, and 38.4 percent. The eﬀect on the conditional
likelihood of choosing trade credit is positive and significant, but eﬀects of the other twoalternatives are negative but not statistically significant.
The firm’s demography and its financial choiceFirms whose owner has advanced degrees (degree granting college or higher) are more
likely to choose trade credit and less likely to choose lending only than firms owned by theless educated. Minority owners are more likely to choose institutional lending and lesslikely to be either exclusively dependent on trade credit or to be self-financed (no demandfor external credit). Female owners are less likely to seek external credit. They are likely
34

Owners with 30 year experience of doing business are at the top 86.3 percentile of the sample. The
median of owner experience is 18 years. Thirty year old firms are at the top 91.4 percentile and the medianfirm age is 11 years.
35
This may indicate that the terms of borrowing contracts in general are unfavorable to older individuals.
Interest rates charged may also be higher. Faced with only expensive credits are available, they would
choose to be self-financed.

18

to choose lending only over other financing alternatives if they consider applying for someexternal credit. Family owned firms are less likely to seek external credit than non-family
owned firms.
None of the region dummies except a few entries are statistically significant. The estimated marginal eﬀects of these dummy variables are not small, but accompanying standard
errors are large.36 None of industry dummies are statistically significant.37

4.2

Discussion

The firm’s information availability and its financial choice
Records of the firm’s accounting and management may not be publicly available but
lenders may have access to them upon their request to the borrowing firm. Firms whose
information is poorly recorded are more likely to choose institutional lending than thosewith better records and less likely to choose trade credit. In contrast firms that record hard
information are unlikely to be bank dependent, and more likely to attempt to borrow fromboth banks and supplier firms.
Such findings are consistent with the view that specialist lenders can obtain information
about borrowers from undocumented sources. Such lenders are equipped with the expertisefor handling information, and are able to obtain the information of borrowing firms not only
through written documents or electronic documents recorded and kept by firms but also
through less formal measures such as meetings, phone conversations, and on site inspectionsof the borrowers’ production facilities, operating sales sites, and so on. Specialist lenders,then, process such soft information to assess the viability of borrowers. In other words,
specialist lenders are able to read between the lines of written documents with their special
monitoring technology and can oﬀer the cheap credits to informationally opaque firms.
Unlike specialist lenders, non-specialist lenders, represented by good supplying firmsthat allow buyers late payments, are less likely to possess such expertise. However, daily
36

The estimated marginal eﬀects on the unconditional likelihood as well as on the likelihood conditionalon the firm’s demand of credit imply that firms in East North Central are more likely to choose lending only
than firms elsewhere. The estimated marginal eﬀect on the unconditional likelihood for firms in West South
Central is negative and statistically significant . These eﬀects, however, are only statistically significant at
the 10 percent significance level.
37
There are several large point estimates of marginal eﬀects that draw our attention. The results on both
unconditional and conditional probabilities suggest that firms in construction and manufacturing industries
are more likely to use trade credit (a large positive sum of entries for trade credit and the mix of lending
and trade credit), and that those in communication and transportation, and finance industries are less likely
to use trade credit (a large negative sum of entries for trade credit and the mix of lending and trade credit).
Results on conditional probabilities of choosing a financial alternative suggest that firms in construction andmanufacturing are less likely to choose lending only, and that those in communication and transportation,and finance industries are more likely to choose lending only.

19transactions and delivery of goods and services may allow them for as close relationships
with borrowing firms as specialist lenders. They can exert leverage on a borrowing buyer
by threatening to cut oﬀ the future supply of goods and services if they ever detect a badsignal from a borrower. 38 Yet, our findings imply that such supplier firms perhaps lack theability to collect and process unrecorded soft information from borrowers. Thus they do not
oﬀer cheap trade credit to firms opaque to them. As a result the optimal choice for firms
with poor management and accounting records is to seek cheaper institutional borrowing.
There are three possible reasons why firms do not switch entirely to trade credit but toseek to add trade credit as an adjunct to institutional borrowing. First, the theory discussed
in Section 2 does not necessarily prove that the funds provided to firms with a higher qualityof recorded information by a superior monitor is cheaper than those provided to such firms
by a poor monitor. Firms may have an incentive to reduce their interest rate risks byborrowing from two sources. Second, institutional borrowing could involve the substantial
fixed transaction costs for a borrower firm. Giving up bank loans entirely would waste thefirm’s investment of such costs. Third, many firms’ motives for using trade credit are not
restricted to the financial. For instance they may use trade credit to smooth transactions
by reducing the number of bills paid.39 In this case firms choosing trade credit only could
constitute a group of such firms with non-financial motives.
Less publicly recognized firms, young firms with few branches, are also less likely tochoose bank lending over trade credit, supporting our findings concerning the availability of
the firm’s private information to lenders. These findings are consistent with the findingsof Petersen and Rajan (1994, 1997) that costs of institutional lenders fall as a firm ages.
Our findings show that the direct and indirect costs of trade credit, which is provided by anon-specialist, is more sensitive to a firm’s age than is the institutional lending.
Interpreting measures of general public recognition needs caution. First and most importantly, it is availability and accuracy of information to the lenders per se that mattersin determining the terms of the lending contract. Second, the owner’s business experienceand the firm’s age may involve the classical sample selection bias, and capture not only thefirm’s public recognition but also the talents and performance of the firm and its owner.
A long business history could mean that the firm has outperformed its market competitors
year after year. Alternatively, it could imply that the firm has lagged behind other firmsthat have grown much faster and become large entities. Third, younger firms are likely todemand longer-term credits (institutional borrowing) for financing setup costs.40
38

Such bargaining power could be the reason that supplier firms can act as non-specialist intermediaries.
See the discussion in Petersen and Rajan (1997).
39
Elliehausen and Wolken (1993), Petersen and Rajan (1997), and Ng, Smith and Smith (1999) provide
non-financial motives of use of trade credit.
40
Public recognition seems to encourage a firm to switch from bank dependence to exclusive trade credit

20

Inventory management and trade credit
Large but statistically insignificant industry eﬀects on use of trade credit are observedin the positive eﬀects of construction and manufacturing industries and the negative eﬀects
of communication and transportation, and the finance industries. Trade credit serves as adevice for supplier firm to manage its inventories eﬃciently. A supplier may oﬀer trade credit
on sales of goods beyond its warehousing capacity to customers that have a better ability tocarry them.41 Manufacturing firms usually have large warehouses for intermediate and final
goods. Likewise construction companies have a large quantity of equipment, machinery, andbuilding materials to store. Thus these industries are more likely to buy intermediate andfinal goods through trade credit arrangements. That there is no recognizable eﬀect on theservices industry is not inconsistent with its limited ability for storing materials.
Some caveats
There is the potential that independent variables are not exogenous and cause a simultaneity bias. Suppose there are two lenders, an institutional lender and a goods (services)supplier firm to the borrowing firm. If one of them required the borrowing firm to submit
its accounting records or the business plans, the borrowing firm, which previously had notused computers for accounting would now have an incentive to utilize its computer resources
or buy new ones for keeping up and organizing its accounting. Then the marginal eﬀect of
PCFMAN on the likelihood of choosing the lender requesting such documents conditional on
the firm’s need for external credit would be biased upwardly. Similarly if the borrowing firm
needed the well organized bookkeeping for repaying to specifically one of two, the marginal
eﬀect on the conditional likelihood of choosing the lender would be biased upwardly again.
However, such a bias would be likely only to underestimate our findings. Trade a credit
providing supplier firm, a lender of short-term credit, is less likely to require the submission of
specific documents on the borrower firm’s accounting or management.42 On the other hand,an institutional lender as a provider of longer term credit would request such documents froma borrower more frequently than a trade credit providing firm. As for the firm’s voluntary
use of computers for planned repayments, the longer term loans would require more detailed
planning and involve more complicated calculations than trade credit. This implies that
correcting biases, if there were any, would reinforce our conclusions even more.
Timings of institutional borrowing vary across the three year period prior to the dateof polling of the firms, whereas timings of trade credit contracts span the one year period
financing rather than mixing its demand for both. This may reflect firms’ preference change on length offinance.
41
See discussions in Emery (1987), Petersen and Rajan (1997) and Choi and Kim (2003).
42
The firm would simply use the generic rating services such as Dun & Bradstreet credit scoring if necessary.

21

prior to the date of inquiries.43 On the other hand, the survey questions the firm’s current
informational characteristics at the date of inquiries. Thus, unfortunately, independent
variables are not predetermined. It would be idealistic if the data on the availability of thefirm’s information before the three-year period prior to the date of inquires for each firm
were available to us so that independent variables could be predetermined.
This, however, does not cause a serious problem. The constructed dependent variable
collects applications to the same type of credit source into a single group, and captures thestatic (structural) preference of the firm. The construction of the variable does not take
into account the frequency of applications for each type of credit source. Therefore, in theempirical exercise, we are examining the structural relationship between static properties ofthe firm, implying that the timing of events is of little relevance.
PCFMAN is one of the few independent variables that could capture the timing of anevent as well as static characteristics. As for this variable, it is the timing of the firm’s
introduction of computers for accounting purposes that is relevant to the claim. If the firm
had introduced them later than the firm’s most recent application from each source, thevariable would be indeed predetermined. However, even if it is a late user of computers forbookkeeping, such a firm is likely to have kept better accounting records with low technology
methods including hard copies of well organized and detailed books and timely accounting
statements. The firm that uses computers just for online shopping, emailing, and web
surfing at the time of the survey (1999-2000) is less likely to have kept better records than
the firm with some level of computerized accounting system. In other words, PCFMAN
is not only the direct measure of the availability of the firm’s records but also the indirect
measure of the more general stance of the firm on recording information.

5

Conclusion

The main purpose of this paper is to present the empirical findings from the 1998 Surveyof Small Business Finances that the availability of private and public information on the
borrower firm and other informational characteristics of the firm leads to diverse borrowing
patterns among borrower firms.
Small firms often portrayed as being exclusively bank dependent in fact also use tradecredit as a source of external credit. The share of external credit in small business financeis as dominant as institutional loans. Though term structures of the two forms of financediﬀer, use of trade credit leaves the average balance in the firm’s balance sheet just as using
43

A date of survey for each firm varies in the two year period from 1999 to 2000. Therefore, a timing ofan attempted institutional borrowing covered ranges from 1996 to 2000. Likewise a timing of an attempted
trade credit contract ranges from 1998 to 2000.

22

an institutional lender would. As a total financial management, the firm makes a choice
between institutional borrowing and trade credit.
Institutional lenders are specialized in financial services whereas trade credit providing
firms play a dual role of non-financial service provider and lending service provider. Therefore, theoretically, institutional lenders, who are specialist lenders, emerge only when they aresubstantially more eﬃcient in monitoring and assessing the viability of borrower firms than
non-financial firms granting trade credit. Whether the firm that do not allow that recorded
accounting and management information be made available to lenders chooses institutionalborrowing or trade credit is an empirical question.
We explored a family of probabilistic logit models to characterize the firms’ choice ofa financial source. Constructing the four state dependent variable that indicates whether
firms applied to external credit or not, and if it did whether the source is trade credit,
institutional lending, or both of them, we then estimated how the availability of private
and public information about the firm to lenders aﬀects the likelihood, particularly the
likelihood conditional on the firm’s need for credit, that the firm would choose each credit
source. Use of the data on the firms’ applications, which include denied loans, allowed us
to identify borrowing demand. We paid special attention to the variable PCFMAN, thedummy variable indicating whether the firm employs computers for accounting or not, which
is based on the new questionnaires that appear for the first time in the 1998 version of theSSBF. The variable overcomes the shortcomings of other preexisting variables.
We find that firms whose information is poorly recorded, or that are publicly less recognized, are more likely to choose institutional lending over trade credit. As recorded information is better organized and firms become more transparent, they tend to switch to agreater use of trade credit besides institutional loans. This finding is robust across various
measures of availability of information to lenders. Our empirical findings are consistent withthe classical hypothesis which claims that institutional lenders are capable of auditing borrowers regardless of the availability of the recorded information. Firms with better private
recorded information tend to keep using bank lending as well as further use of trade credit
rather than switching entirely to trade credit.

23

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Kashyap, Anil K, Raghuram G. Rajan and Jeremy C. Stein (2002), “Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit-Taking”,
Journal of Finance 57 (1): 33-73.
Ng, Chee K., Janet Kiholm Smith, and Richard L. Smith (1999), “Evidence on the Determinants of Credit Terms Used in Interfirm Trade”, Journal of Finance 54 (3): 1109-29.
Petersen, Mitchell A. and Raghuram G. Rajan (1994), “The Benefits of Lending Relationships: Evidence from Small Business Data”, Journal of Finance 49 (1): 3-37.
Petersen, Mitchell A. and Raghuram G. Rajan (1997), “Trade Credit: Theories and Evidence”, Review of Financial Studies 10 (3): 661-91.
Petersen, Mitchell A. and Raghuram G. Rajan (2002), “Does Distance Still Matter? The
Information Revolution in Small Business Lending”, Journal of Finance 57 (6): 253370.
Townsend, Robert E. (1979), “Optimal Contracts and Competitive Markets with CostlyState Verification”, Journal of Economic Theory 21: 265-93.
Vuon, Quan H. (1989), “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses”, Econometrica 57(2): 307-333.
25

Williamson, Stephen D. (1987), “Costly Monitoring, Loan Contracts, and Equilibrium
Credit Rationing”, Quarterly Journal of Economics 102 (1): 135-45.
Yu, Fan (2004), “Accounting Transparency and the Term Structure of Credit Spreads”,
forthcoming in the Journal of Financial Economics.

26

NET
DISC
DeliveryDiscount due date

Net due date

Discount rate to the purchased goods applies
No discount, no penalty

Penalty rate applies

Figure 1: Contractual details of trade credit

In te rm e d ia tio n b y an en tre p ren e u r 1 (n o n -sp e c ia lis t)
E n tre p re n e u r 2
E n tre p re n e u r 1

c re d ito rs
In te rm e d ia tio n b y o n e o f c re d ito rs (sp e c ia list)
E n tre p re n e u r 1

E n tre p re n e u r 2
cre d ito r 1

c re d ito rs

Figure 2: Specialist and non-specialist lenders (from Bond (2004))27

M u ltin o m ial lo git

N ested lo git
B an k o n ly

B an k o n ly

B an k an d trad e cred it

B an k an d trad e cred it

T rad e cred it o n ly

T rad e cred it o n ly

N o ex tern al cred it

N o ex tern al cred it

N ested m u ltin o m ial lo git
S tage 2
B an k o n ly

S tage 1

B an k an d trad e cred it

Y es

A p p ly to ex tern alcred its

T rad e cred it o n ly

No

Figure 3: Decision trees of logit models

28Unconditional probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

2

3

4

5

6

7

8

9

10

11

12

11

12

Number of branches
No external credit

Lending only

Lending/Trade credit

Trade credit only

Conditional probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

2

3

4

5

6

7

8

9

10

Number of branches
Lending only

Lending/Trade credit

Trade credit only

Figure 4: Number of branches and financial choice29

Unconditional Probabiities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

65

70

Owner experience
No external credit

Lending only

Lending/Trade credit

Trade credit only

Conditional Probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5

10

15

20

25

30

35

40

45

50

55

60

Owner experience
Lending only

Lending/Trade credit

Trade credit only

Figure 5: Owner experience and financial choice30

Unconditional probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5

10

15

20

25

30

35

40

45

50

45

50

Firm ageNo external credit

Lending only

Lending/Trade credit

Trade credit only

Conditional probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5

10

15

20

25

30

35

Firm age
Lending only

Lending/Trade credit

Trade credit only

Figure 6: Firm age and financial choice3140

Unconditional Probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
19

24

29

34

39

44

49

54

59

64

69

74

79

84

89

94

Owner ageNo external credit

Lending only

Lending/Trade credit

Trade credit only

Conditional Probabilities
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
19

24

29

34

39

44

49

54

59

64

69

74

Owner age
Lending only

Lending/Trade credit

Trade credit only

Figure 7: Owner age and financial choice

32

79

84

89

94

Table 1 Descriptive statistics on the variables used to construct the dependent variable

Voluntary withdrawal
Yes
No
487
2046
zero
63.0
75.5
Frequency
114318
of applying once
14.8
12.8
new loansmore than
172
347
once
22.3
11.7
773
2711
total
22.2
77.8
Top entries are frequencies. Bottom entries forindividual items are proportions to the column
total, and those for “Total” are proportions to theentire sample size.

Approval of applications
always not always none
363
0
67
38.3
0.0
7.1
127
32
35
13.4
3.4
3.7
76
15
25
8.0
1.6
2.6
142
34
58
15.0
3.6
6.1
708
81
160
74.6
8.5
16.9

1
Number ofapplications
for new
loans

2
3
>3
total

Top entries are frequencies. Bottom entries areproportions to the entire sample size.

Applications
approved denied
commercial
395
108
bank
50.1
44.8
savings
159
49
bank
20.2
20.3
126
32
Institutions S&L
16.0
13.3
44
25
credit union
5.6
10.4
65
27
non-bank
8.2
11.2
total
789
241

yes
Use of tradecreditno
total

Trade credit denied
yes
no
172
2147
5.0
61.8
34
1124
1.0
32.3
206
3271
5.9
94.1

Top entries are frequencies. Bottom entries areproportions to the entire sample size.

Top entries are frequencies. Bottom entries areproportions to the column total.

33

Table 2. Descriptive statistics of information related independent variables

RECORDPCFMAN
CORP
NATIONAL

Value
1
0
2732
745
78.6
21.4
2421
1056
69.6
30.4
849
2628
24.4
75.6
549
2928
15.8
84.2

mean
BRANCH
C_EXP
C_OAGE
C_FAGE

median

1.65
19.29
50.76
14.52

1
18
50
11

top 90 standard
percentile deviation
1
2
2.62
20
35
11.75
55
66
11.25
3
29
12.14

Mode

Top entries are frequencies.
Bottom entries are proportions to
the entire sample.

Table 3 List of independent variables
Variable types Variables
Description
RECORD Use of written records to answer the survey
Availability of
PCFMAN Use of computers for accounting
privateOWNERManaged by owner
information
CORP
Corporation
Geographical NATIONAL Operating nationwide
expansion
BRANCH The number of branches
C_EXP
Owner's managerial experiences
Age
C_FAGE
Firm age
C_OAGE Owner age
EDUC
Owner's education
MINORITY Minority owned
Demography
FEMALE Female owned
FAMILY Family owned
Mid Atlantic
East N Central
West N Central
RegionSouth Atlantic
dummies
East S Central
West S Central
MountainPacific
Construction
Manufacturing
Communication/transportation
Industry
Wholesaledummies
RetailFinance
Services
34

Table 4 Panel A Parameter estimates (Nested logit and multinomial logit models)Nested logit
RECORD (use of written record)
PCFMAN (computer use for accounting)
OWNER (managed by owner)
CORP (corporation)
NATIONAL (nationally operating)
BRANCH (number of branches)
C_EXP (owner experience)
C_FAGE (firm age)
C_OAGE (owner age)
EDUC (owner higher education)

β’s

MINORITY (minority owner)
FEMALE (female owner)
FAMILY (family owned)

Mid Atlantic
East N Central
West N Central
South Atlantic
East S Central
West S Central
MountainPacific

j=1
0.1607(0.1288)
0.6116***(0.1212)-0.2494(0.1870)0.5009***(0.1392)
0.1304(0.1719)0.2581***(0.0585)
0.0032(0.0081)-0.0182**(0.0071)-0.0312***(0.0079)-0.2975**(0.1346)
0.6347***(0.2148)-0.1711(0.1294)-0.4638**(0.1782)-0.2889(0.3096)-0.5300**(0.3176)-0.4624(0.3295)-0.2685(0.3014)-0.5540(0.3451)-0.1417(0.3365)-0.0929(0.3483)-0.3923(0.2986)j=20.0474(0.1519)0.3074(0.1951)-0.1624(0.2123)
0.3599**(0.1606)
0.0714(0.1775)0.2232***(0.0603)
0.0089(0.0083)0.0083(0.0096)-0.0177**(0.0076)-0.0934(0.1254)
0.1173(0.1836)-0.1401(0.1455)-0.2211(0.1871)-0.5244(0.3525)-0.3347(0.3431)-0.4362(0.3685)-0.3957(0.3357)-0.5900(0.3877)-0.4499(0.3543)-0.3890(0.3820)-0.4542(0.3302)

35

Multinomial logit
j=30.3278*(0.19621.3143***(0.3220)-0.9877**(0.4336)0.8146(0.2604)-0.3081(0.2694)
0.3148***(0.0795)
0.0466**(0.0204)
0.0177(0.0152)-0.0046(0.0128)0.2716(0.2321)-0.0533(0.2380)-0.6054***(0.2240)-0.4595(0.2643)-0.1094(0.4826)
0.4536(0.5880)-0.3256(0.5005)-0.2615(0.4498)-0.0613(0.5832)-0.9218*(0.4995)-0.3866(0.5075)-0.7260(0.4533)

j=1
0.1717(0.1156)
0.6530***(0.1083)-0.3759**(0.1755)
0.4993***(0.1303)-0.0662(0.1518)0.2494***(0.0589)0.0157**(0.0065)0.0009(0.0053)-0.0164***(0.0059)
0.0039(0.1036)
0.0223(0.1468)-0.3058***(0.1107)-0.2971**(0.1602)-0.3616(0.2866)-0.2111(0.2845)-0.3856(0.3043)-0.3147(0.2780)-0.4498(0.3195)-0.5922**(0.2941)-0.3605(0.3195)-0.4984*(0.2743)j=20.2416*(0.1242)0.8625***(0.1174)-0.3816**(0.1834)
0.5981***(0.1355)-0.0045(0.1558)
0.2721***(0.0591)
0.0138**(0.0070)-0.0163***(0.0059)-0.0252***(0.0063)-0.1356(0.1095)
0.4071***(0.1478)-0.3385***(0.1176)-0.4650***(0.1634)-0.2037(0.3036)-0.2855(0.3031)-0.4454(0.3245)-0.2734(0.2951)-0.3998(0.3401)-0.3922(0.3107)-0.1233(0.3352)-0.4806(0.2914)j=30.0084(0.1499)
0.1972(0.1415)
0.2833(0.2772)
0.2665(0.1814)0.2495(0.1903)0.2113***(0.0658)-0.0058(0.0093)-0.0241***(0.0090)-0.0299***(0.0077)-0.3847***(0.1382)
0.5826***(0.1685)-0.0475(0.1410)-0.2465(0.2118)-0.4895(0.3775)-0.8156**(0.3862)-0.4957(0.3988)-0.4255(0.3614)-0.8585*(0.4448)-0.0597(0.3704)-0.0950(0.4069)-0.3562(0.3534)

Construction
Manufacturing
Communication/transportation

β’s

Wholesale
RetailFinance
Services
ConstantaρMaximized likelihood

Nested logit
j=1
j=2
0.34590.2196(0.9707)(1.0206)
0.42000.3075(0.9706)(1.0168)-0.1758-0.7457(1.0181)(1.0283)-0.1718
0.1332(0.9743)(1.0169)-0.2662
0.1067(0.9613)(1.0006)-1.0095-0.3165(0.9767)(1.0699)-0.6417-0.2980(0.9556)(0.9967)
4.16623.2854(83.5458) (83.5403)
1.7236(143.9665)-1.4681**(0.6273)-4177.3456

j=3
1.7424(1.4127)
1.5851(1.3928)-1.3744(1.3548)0.2567(1.3322)0.5083(1.3099)-2.3693(1.4404)-0.5832(1.2971)
3.0760(83.5437)

Multinomial logit
j=1
j=2
0.7256
0.5686(0.9113)(0.9086)0.7146
0.5711(0.9119)(0.9090)-0.9555-0.7189(0.9297)(0.9206)
0.2670-0.1456(0.9141)(0.9129)
0.3386-0.1440(0.9005)(0.8983)-0.9573-1.6460*(0.9110)(0.9153)-0.2569-0.7256(0.8964)(0.8936)
1.1752
1.8882*(1.0065)(1.0144)

j=3-0.6068(1.0737)-0.4039(1.0729)-0.2369(1.0713)-0.4184(1.0719)-0.6010(1.0504)-0.5211(1.0586)-0.6258(1.0427)
1.7691(1.2050)-4185.2554***, **, and * show that a coefficient is statistically significant at the 1 percent, the 5 percent, and the 10 percent level,
respectively. Standard errors are in parentheses. The same hold true for coming tables.

36

Table 4 Panel B Parameter estimates (Nested multinomial logit model)

β’sFirst stage logit
j≠10.1602
RECORD (use of written record)(0.1034)
0.6386***
PCFMAN (computer use for accounting)(0.0962)-0.3012OWNER (managed by owner)(0.1644)0.4994***
CORP (corporation)(0.1215)0.0191NATIONAL (nationally operating)(0.1376)0.2524***
BRANCH (number of branches)(0.0580)
0.0115**
C_EXP (owner experience)(0.0059)-0.0070
C_FAGE (firm age)
(0.0049)-0.0217***
C_OAGE (owner age)
(0.0053)-0.1171
EDUC (owner higher education)(0.0936)0.2858**MINORITY (minority owner)(0.1281)-0.2596**
FEMALE (female owner)(0.0983)-0.3596**
FAMILY (family owned)(0.1480)-0.3325
Mid Atlantic(0.2654)-0.3253
East N Central(0.2650)-0.4268
West N Central(0.2817)-0.3157
South Atlantic(0.2582)-0.4936*
East S Central(0.2944)-0.4097
West S Central(0.2702)-0.2353
Mountain(0.2941)-0.4693*
Pacific(0.2544)

37

Second stage multinomial logit
j=2
j=3
0.0725-0.1501(0.1083)(0.1466)
0.2050*-0.4903***(0.1057)(0.1392)-0.0258
0.6085**(0.1315)(0.2567)
0.0973-0.2337(0.0987)(0.1628)
0.05340.3182*(0.1227)(0.1759)
0.0233-0.0379(0.0146)(0.0356)-0.0028-0.0256***(0.0060)(0.0092)-0.0178***-0.0259***(0.0048)(0.0088)-0.0091-0.0157**(0.0056)(0.0077)-0.1418-0.3984***(0.0909)(0.1312)0.3747***0.5309***(0.1283)(0.1617)-0.0255
0.2889**(0.1039)(0.1370)-0.1692
0.0305(0.1175)(0.1894)0.1600-0.1261(0.2334)(0.3410)-0.1002-0.6491*(0.2293)(0.3484)-0.0442-0.1082(0.2514)(0.3618)
0.0612-0.0689(0.2239)(0.3235)
0.0390-0.3956(0.2675)(0.4138)0.20940.5555*(0.2429)(0.3351)0.22700.2468(0.2572)(0.3652)
0.03020.1884(0.2224)(0.3162)

Construction
Manufacturing
Communication/transportation

β’s

Wholesale
RetailFinance
Services
Constant

Maximized likelihood

First stage logit

Second stage multinomial logit

j≠10.4778(0.8184)0.5091(0.8195)-0.6702(0.8269)
0.0021(0.8212)-0.0006(0.8076)-1.0868(0.8147)-0.4992(0.8034)2.6243***(0.9063)j=2
j=3-0.0639-1.1938(0.7085)(0.9661)-0.0582-0.9964(0.7062)(0.9633)0.30730.8041(0.7346)(0.9751)-0.3330-0.5250(0.7127)(0.9640)-0.3960-0.7907(0.7040)(0.9466)-0.5960
0.6855(0.7305)(0.9621)-0.3765-0.2353(0.7015)(0.9399)
0.68400.7020(0.7964)(1.0898)-2549.5978-1630.7325

38

Table 5 Panel A Marginal effects for the nested logit model
Unconditional probabilities
No external Trade credit Lending/creditonly
Trade credit-2.3578
0.57432.5992
RECORD (use of written record)(1.8602)(2.0093)(1.9822))-10.2432*** 4.3446**
8.9555***
PCFMAN (computer use foraccounting)(2.3365)(2.1494)(2.7434)
4.6758*-3.3592-4.5946
OWNER (managed by owner)(2.6262)(2.8927)(3.1143)-7.0972***
2.92835.1183**
CORP (corporation)(2.0446)(2.1165)(2.4794)-0.3720-1.7151-0.3953
NATIONAL (nationallyoperating)(2.3808)(2.3837)(2.5023)-3.8643**1.6827**1.8962***
BRANCH (number of branches)(1.8448)(0.7592)(0.6145)-0.19480.2737**0.1132
C_EXP (owner experience)(0.1282)(0.1237)(0.1212)0.04050.3355***-0.2391**
C_FAGE (firm age)
(0.1017)(0.1051)(0.0996)
0.3284**0.0488-0.2058*
C_OAGE (owner age)
(0.1647)(0.1215)(0.1142)1.89012.7033-1.5981EDUC (owner higher education)(1.7132)(1.7780)(1.7445)-4.4522**-5.0594**
6.0276**MINORITY (minority owner)(2.1674)(2.5009)(2.7700)
3.7229**-2.7929-2.8284
FEMALE (female owner)(1.8746)(1.9387)(1.9229)5.3103**-0.0889-4.9326*
FAMILY (family owned)(2.3146)(2.4215)(2.7955)
5.5070-4.3479
1.2956
Mid Atlantic(4.9956)(4.3614)(4.9023)
5.0432
1.8485-1.8703
East N Central(5.0130)(4.4615)(4.6423)
7.0277-2.7795-2.3948
West N Central(5.4719)(4.6185)(4.8558)
4.9230-3.4999-0.1868
South Atlantic(4.7504)(4.2097)(4.5726)
8.1567-3.0820-1.3356
East S Central(5.8913)(4.9259)(5.2132)
6.5457-7.9743*-0.9176
West S Central(5.2807)(4.3697)(4.8129)
4.0120-5.3908
1.3112
Mountain(5.4431)(4.7449)(5.4050)
7.6032-5.1093-2.7773
Pacific(4.8550)(4.1144)(4.3784)-6.61745.30937.2607
Construction(11.4344) (14.3917) (15.8619))-7.28865.3621
6.8786
Manufacturing(11.0936) (14.3298)(15.7444)
10.8532-12.7962-1.0416
Communication/transportation(16.9037) (12.0126)(14.5462)-0.0034
4.1482-2.6057
Wholesale(13.7994) (13.5999)(14.1199)
0.2431
5.6335-2.6852
Retail(13.6683) (13.3761)(14.0479)
20.4747-9.3584-16.2643
Finance(18.1010) (11.7498)(10.5895)
7.8781-0.4160-6.6736
Services(13.7631) (13.1588)(13.8357)
N
735
1307
1046

39

Lending
only-0.8157(1.3564)-3.0569*(1.5706)
3.2781(2.1424)-0.9494(1.5268)
2.4825(1.7885)0.2854(0.4823)-0.1920**(0.0976)-0.1368(0.0855)-0.1714**(0.0799)-2.9952**(1.3176)
3.4840*(1.7837)
1.8984(1.3497)-0.2887(1.7679)-2.4546(2.7385)-5.0214*(2.6289)-1.8534(2.9185)-1.2363(2.7728)-3.7391(2.9706)
2.3463(3.2817)0.0676(3.2487)
0.2833(2.8321)-5.9526(6.3157)-4.9521(6.6870)2.9847(9.0796)-1.5391(7.8649)-3.1913(7.5743)
5.1480(9.2745)-0.7885(8.2144)389

Conditional probabilities
Trade credit Lending/Lending
only
Trade credit
only-0.5852
2.3314-1.7462(2.3981)(2.3415)(1.8965)-0.28417.2413*** -6.9571***(2.4844)(2.3394)(2.2377)-1.6368-3.71945.3562*(3.2239)(3.1644)(2.8317)-0.4392
3.2807-2.8415(2.3066)(2.2783)(1.9777)-2.6228-0.7628
3.3856(2.8018)(2.7131)(2.4456)-0.1705
0.7386**-0.5680(0.4066)(0.3755)(0.4909)0.2505*0.0680-0.3186**(0.1433)(0.1415)(0.1329)0.4619*** -0.2766**-0.1853(0.1154)(0.1190)(0.1186)
0.2717**-0.1124-0.1592(0.1270)(0.1264)(0.1011)4.8137**-1.0832-3.7305**(2.0542)(2.0343)(1.7510)-8.9385***5.3178*3.6207(2.8453)(2.8855)(2.2378)-1.5530-2.08433.6373*(2.3173)(2.2642)(1.8734)
2.9069-3.7588
0.8519(2.6983)(2.6886)(2.2970)-2.12484.3045-2.1798(5.2151)(5.3000)(3.9936)
6.12690.0094-6.1363*(5.2310)(5.1298)(3.6731)0.87880.0767-0.9554(5.6194)(5.5534)(4.3765)-1.4736
1.9865-0.5129(4.9968)(5.0085)(4.0006)
1.45672.1853-3.6420(6.0617)(6.0591)(4.5135)-6.9816
1.56565.4161(5.2904)(5.4534)(4.8866)-4.6916
3.47661.2151(5.6724)(5.8143)(4.7434)-2.1680-0.2933
2.4613(4.9363)(4.9128)(4.1782)
2.9331
6.0430-8.9761(16.1572)(15.7630)(7.8334)
2.53245.2676-7.8000(16.0262)(15.5851)(8.2162)-11.7131
3.41588.2973(15.4317)(15.7967) (14.5452)
5.3986-3.2070-2.1916(16.0128)(14.5938) (10.7203)
7.6008-3.1581-4.4427(15.7666)(14.5771) (10.2654)-1.2649-15.0697
16.3346(16.5796)(12.2786) (16.4507)
4.2684-5.04190.7735(15.6162)(14.6418) (11.2454)
1307
1046
389

Table 5 Panel B Marginal effects for multinomial logit and nested multinomial models(conditional probabilities)
Multinomial logit
Trade credit Lending/Lending
only
Trade credit
only-0.1883
2.4281-2.2399
RECORD (use of written record)(2.4192)(2.3437)(1.9426)-0.7732
7.1013*** -6.3282***
PCFMAN (computer use foraccounting)(2.4811)(2.3256)(2.2024)-4.0322-3.5694
7.6016***OWNER (managed by owner)(3.2120)(3.1376)(2.7347)-0.25123.4414-3.1902
CORP (corporation)(2.3211)(2.2733)(1.9705)-3.0486-0.2555
3.3041NATIONAL (nationally operating)(2.7884)(2.6897)(2.4421)-0.15430.7109*-0.5565
BRANCH (number of branches)(0.3990)(0.3653)(0.4670)
0.1681
0.0688-0.2369*
C_EXP (owner experience)(0.1368)(0.1338)(0.1218)0.4548*** -0.2573**-0.1975*
C_FAGE (firm age)
(0.1125)(0.1143)(0.1180)0.2371*-0.1279-0.1092
C_OAGE (owner age)
(0.1239)(0.1222)(0.0958)
4.8506**-1.1148-3.7357*
EDUC (owner higher education)(2.0924)(2.0582)(1.7763)-10.1780***5.7499**4.4281*MINORITY (minority owner)(2.8249)(2.8961)(2.3074)-1.0522-2.0849
3.1372*
FEMALE (female owner)(2.3276)(2.2529)(1.8684)2.5918-4.0490
1.4572
FAMILY (family owned)(2.7340)(2.6965)(2.2796)-1.93394.2268-2.2929
Mid Atlantic(5.2570)(5.3120)(4.0050)
5.07691.4786-6.5554*
East N Central(5.2566)(5.1652)(3.6945)1.7266-0.7735-0.9531
West N Central(5.6537)(5.5187)(4.3970)-0.0206
1.5088-1.4882
South Atlantic(5.0393)(5.0003)(3.9193)
1.69443.2616-4.9560
East S Central(6.1227)(6.1066)(4.3724)-6.8030
1.73065.0724
West S Central(5.3409)(5.4476)(4.9226)-5.77373.9612
1.8125
Mountain(5.6988)(5.8633)(4.8589)-1.1999-0.3406
1.5405
Pacific(4.9805)(4.9131)(4.1156)
11.0691
3.4158-14.4849
Construction(16.0683)(15.5633)(7.1498)
9.49612.6028-12.0989
Manufacturing(16.0360)(15.4254)(7.5057)-8.6019
1.58247.0195
Communication/transportation(16.1202)(15.5261)(14.4338)
11.4419-5.7227-5.7192
Wholesale(15.8107)(14.2808)(9.2463)
14.2482-5.9738-8.2744
Retail(15.3972)(14.3061)(9.4292)
9.1966-17.8019
8.6053Finance(16.6356)(11.9824)(14.7992)
10.4325-8.6412-1.7913Services(15.3232)(14.5377)(11.1428)
N
1307
1046
389

40

Nested multinomial logit
Trade credit Lending/Lending
only
Trade credit
only-0.3182
2.4173-2.0991(2.4321)(2.3526)(1.9723)-0.49107.1833*** -6.6922***(2.5087)(2.3442)(2.2806)-3.3374-3.7700
7.1074**(3.2223)(3.1599)(2.8053)-0.2273
3.4149-3.1876(2.3286)(2.2830)(2.0023)-2.8996-0.4663
3.3659(2.8089)(2.7067)(2.4923)-0.1667
0.7237* -0.5570(0.4023)(0.3701)(0.4777)0.20640.0721 -0.2786**(0.1406)(0.1377)(0.1297)
0.4672***-0.2639** -0.2034*(0.1158)(0.1176)(0.1216)0.2547**-0.1230 -0.1317(0.1264)(0.1246)(0.1005)
4.9234**-1.1024 -3.8209**(2.0930)(2.0595)(1.8022)-9.7676***
5.6497*4.1180*(2.8385)(2.9015)(2.3105)-1.3558-2.0883
3.4441*(2.3415)(2.2659)(1.9184)2.7298-3.9664
1.2366(2.7388)(2.7046)(2.3298)-1.9778
4.2616 -2.2839(5.2719)(5.3297)(4.0744)5.7649
1.1467 -6.9115(5.2772)(5.1691)(3.7427)
1.4358-0.4280 -1.0078(5.6760)(5.5536)(4.4666)-0.6277
1.7385 -1.1108(5.0503)(5.0228)(4.0188)
1.78672.9506 -4.7373(6.1418)(6.1153)(4.5020)-7.06761.8001
5.2674(5.3518)(5.4768)(5.0123)-5.45133.81381.6375(5.7116)(5.8685)(4.8900)-1.6916-0.3090
2.0006(4.9931)(4.9325)(4.2210)8.52384.8184 -13.3422(16.2689) (15.7287)(7.5465)
7.19903.9120 -11.1111(16.2048) (15.5717)(7.9494)-10.3015
2.69967.6019(15.9738) (15.6850) (14.8743)
9.0102-4.7402 -4.2700(16.0944) (14.4613) (10.0712)
11.7496-4.7656 -6.9840(15.7256) (14.4808)(9.9182)
6.0250-17.0025 10.9775(16.9450) (12.1073) (15.9690)
7.9599-7.2374 -0.7225(15.6352) (14.6400) (11.3912)
1307
1046
389

Table 6 Panel A Unconditional probabilities of financial choice when one dummy variable is held fixed
Dummy variable=0
No external Trade credit Lending/Tradecredits
only
credit
22.9445
37.159628.0724
RECORD (use of written record)
PCFMAN (computer use for accounting)27.9457
34.7930
23.9425
Owner (managed by owner)
16.8665
40.651934.2903
CORP (corporation)
22.5901
37.0192
28.9679
NATIONAL (nationally operating)21.203437.827330.1586
BRANCH (number of branches)
C_EXP (owner experience)
C_FAGE (firm age)
C_OAGE (owner age)
EDUC (owner higher education)
20.178236.1070
30.9830MINORITY (minority owner)
21.799038.278629.3536
FEMALE (female owner)
20.042938.3766
30.9296
FAMILY (family owned)
16.4700
37.7347
34.3394
Mid Atlantic
20.5358
38.085929.9005
East N Central
20.5366
37.2495
30.3478
West N Central
20.668737.7441
30.2642South Atlantic
20.3866
38.148030.0734
East S Central
20.731937.717130.1589
West S Central
20.505438.4447
30.1847
Mountain
20.922537.933929.9900
Pacific
19.750338.5684
30.6192
Construction
21.694237.097129.3166
Manufacturing
21.7058
37.1545
29.4010
Communication/transportation
20.7455
38.0683
30.1686
Wholesale
21.138437.2584
30.3152
Retail21.133136.364230.5929
Finance19.9887
38.100731.1580
Services
17.7434
37.7870
32.9433
N
735
1307
1046

41

Lending
only
11.8235
13.3188
8.1913
11.4229
10.8106
12.7318
10.5688
10.650911.4559
11.4778
11.8662
11.3230
11.3920
11.392110.8652
11.1536
11.062111.8920
11.7387
11.0176
11.2880
11.909910.7527
11.5264389

Dummy variable=1
No external Trade credit Lending/Tradecredits
only
credit20.5867
37.733930.671717.7025
39.1376
32.8980
21.542337.2926
29.6957
15.492939.9475
34.0862
20.831436.112229.7633
22.0683
38.810329.3849
17.3468
33.2192
35.381223.7659
35.5836
28.101221.780237.6458
29.4068
26.042833.7379
31.1961
25.5798
39.0980
28.4774
27.6964
34.9646
27.869425.3096
34.6481
29.8866
28.8886
34.6351
28.8232
27.051030.4704
29.2671
24.9345
32.543131.301227.3536
33.4591
27.8420
15.0768
42.4064
36.5774
14.4172
42.5165
36.2796
31.5987
25.272129.1270
21.1350
41.4065
27.7095
21.3761
41.997727.9076
40.4633
28.7422
14.8937
25.621537.370926.2697
735
1307
1046

Lending
only
11.0078
10.2619
11.4694
10.4735
13.29319.736514.0529
12.549311.1672
9.0232
6.84489.469510.15567.653013.2115
11.221211.34545.9394
6.786714.00229.74908.718515.900710.7379389

Table 6 Panel B Conditional probabilities of financial choice when one dummy variable is held
fixed
Dummy variable=0
Trade credit Lending/Trade Lending
only
creditonly
48.141535.2245
16.6340
RECORD (use of written record
PCFMAN (computer use for accounting)
47.9638
32.051219.9851
Owner (managed by owner)
49.1842
40.4383
10.3776
CORP (corporation)
47.862536.3097
15.8278
NATIONAL (nationally operating)
48.0205
37.1492
14.8303
BRANCH (number of branches)
C_EXP (owner experience)
C_FAGE (firm age)
C_OAGE (owner age)
EDUC (owner higher education)
45.159437.661417.1792
MINORITY (minority owner)
48.9472
36.3988
14.6540
FEMALE (female owner)
48.1361
37.6667
14.1972
FAMILY (family owned)
45.190940.2611
14.5480
Mid Atlantic
47.9108
36.5315
15.5576
East N Central
46.8350
37.0870
16.0781West N Central
47.581937.0462
15.3718
South Atlantic
47.9159
36.6978
15.3863
East S Central
47.5663
36.9351
15.4986
West S Central
48.4265
36.9079
14.6656
Mountain
47.9734
36.8183
15.2083
Pacific
48.1263
37.1256
14.7480
Construction
47.3873
36.385416.2273
Manufacturing
47.4783
36.4976
16.0241
Communication/transportation
48.106236.9338
14.9599
Wholesale47.281637.2887
15.4297
Retail46.1258
37.6228
16.2515
Finance47.7276
38.001214.2712Services
45.9520
39.152114.8959
N
1307
1046
389

42

Dummy variable=1
Trade credit Lending/Trade Lending
only
creditonly
47.5563
37.5559
14.887847.679739.2924
13.0279
47.5474
36.7188
15.7338
47.4234
39.5904
12.9863
45.397736.3864
18.215949.9732
36.5782
13.4487
40.0086
41.7166
18.2747
46.5830
35.582417.834548.097736.5024
15.399945.7861
40.8361
13.377952.961937.09639.941848.460737.1229
14.416446.442338.6844
14.8733
49.0230
39.1204
11.8566
41.4449
38.4734
20.081743.2818
40.2948
16.423445.9584
36.8323
17.209350.320442.4284
7.251250.010741.76538.224136.393140.349723.2573
52.680234.0817
13.238153.7265
34.4647
11.8088
46.462722.931530.6058
50.2204
34.110215.66941307
1046
389