How Do Firms Choose Between Intermediary and
Supplier Finance?
Dimitar Antov: Cambridge Group, Chicago, United States of America
Christina Atanasova: Simon Fraser University, Burnaby, Canada
CONTACT: Christina Atanasova, Simon Fraser University, 8888 University Drive, BC V5A
1S6 Burnaby, Canada.
Email: [email protected]
Abstract
We examine the dynamics of firm’s choice of short-term financing between intermediated loans and trade credit. We argue that trade credit facilitates the access to
and improves the terms of conventional loans. We model the idea that trade credit
is a favorable signal of the creditworthiness of the borrower. Hence, some firms will
use trade credit in addition to conventional institutional loans despite its higher cost.
Our empirical results support the predictions of the theoretical model we develop.
We show that firms with high agency costs rely heavily on supplier financing. For
these firms trade credit has a significant positive effect on the level of intermediated
borrowing.
*
We would like to thank Vincent Hogan, Roberto Rigobon and the seminar participants at York
University, Simon Fraser and Universite de Paris-Dauphine for helpful suggestions.
1
1
Introduction
Suppliers regularly sell goods to their retail customers with a delayed payment the so called trade credit. Petersen and Rajan (1997) state that trade credit is the
single most important source of short-term external finance for firms in the United
States. Similar observations have been made for U.K. (see Pike and Cheng, 2001)
and Japanese firms (see Miwa and Ramseyer, 2004). A natural question to ask is
under what conditions suppliers and their customers prefer to use trade credit rather
than loans obtained from financial intermediaries. A common message emerging from
previous studies is that trade credit rates are often much higher than rates on conventional institutional loans. Pricing trade credit, however, is difficult. Suppliers do
not charge an explicit interest rate so previous studies have relied on extracting an
interest rate from the hypothesized cash-discount terms1 .
Many studies in the area have attempted to explain why firms use trade credit
given its higher cost. This paper argues that one important benefit of trade credit in
the cycle of firms’ financing is that it can serve as a reputation signal. When there are
no informational asymmetries between borrowers and lenders, all positive net present
value projects will be funded. On the other hand, if entrepreneurs cannot credibly
convey the creditworthiness of their projects trade credit can be used as a means
1
Peter and Rajan (1997), for example assume that suppliers offer the "2/10 Net 30" terms which
translates into an annual interest rate of over 40 percent.
2
of acquiring reputation. We examine how firms choose whether to borrow from a
supplier or a financial intermediary in the context of a simple theoretical model. In
our model, which is closely related to Diamond (1991), suppliers have a liquidation
advantage but financial intermediaries have a lower cost of capital. If the use of trade
credit is perceived by financial intermediaries as a favorable signal about the credit
worthiness of the borrower, then firms will use supplier finance despite its higher
cost. This role of trade credit implies that new benefits arise from borrower-lender
relationships. These benefits concern the availability of longer-term financing and the
willingness of financial intermediaries to extend loans to firms using trade credit.
We test the predictions of the model using a large panel of limited liability companies for the period 1994 to 2003. The sample comprises of both public and private
companies and has a comprehensive coverage from all industries for which the assumption that a supplier of an intermediate good provides trade credit to a customer
is valid. In contrast to studies which analyze cross sectional samples, our data allow
us to examine important time related issues. We are able to examine the dynamic adjustments firms make over time to their use of trade credit and intermediated finance.
More importantly we study whether trade credit and institutional loans are serially
correlated. Our model suggests that a synergy, which extends over time, arises when
firms diversify their borrowing. If a firm’s access to institutional loans in the future is
driven by the use of trade credit today, then the level of trade credit today is linked
3
to the level of future bank loans.
The empirical methodology used in the paper addresses the problem of simultaneity which is pervasive in this line of research. Both institutional loans and trade
credit are choice variables which implies that the standard OLS estimates are biased.
Using either lags of trade credit (the endogenous variable) or current period values of
exogenous variables in an instrumental variables estimation may not be a valid alternative. As discussed above the simultaneity bias extends over time so lags of trade
credit may be correlated with current levels of bank loans. In addition, exogenous
instruments are difficult to find as the information set of the supplier and the financial
intermediary are closely linked. We solve the problem of simultaneity by appealing
to the relatively new literature on identification through heteroskedasticity.
Our empirical analysis confirms the theoretical predictions of the model. We find
that the more trade credit is being used, the more institutional loans become available
to borrowers. The fact that firms are capable of servicing suppliers’ debt, which has a
higher cost, conveys important information to bank loan officers about the likelihood
of the borrower’s repayment. Everything else being equal, financial intermediaries
prefer to lend to borrowers who use high levels of trade credit than to those using low
levels of trade credit.
The remainder of this paper is organized as follows. Section 2 provides a brief re4
view of the related literature on trade credit as a source of short-term finance. Section
3 presents the theoretical model and formulates the testable hypothesis. Section 4
describes our data, the variables used in the empirical analysis and provides summary
statistics. Section 5 discusses the empirical strategy and the main results of the study.
Then, we provide a robustness check to the estimation results. The paper concludes
with a summary and discussion of future research opportunities.
2
Related Literature
There are many studies addressing the question of why firms choose to borrow from
a supplier rather than from a financial intermediary. A common feature of these
studies is that suppliers have an advantage over lenders in financing the borrower.
This advantage is related to either product market characteristics or market structure.
Our theory is similar in spirit to Frank and Maksimovic (2004). They argue that
suppliers have a comparative advantage in lending when they are able to recover a
larger liquidation value from the asset sold via trade credit than a bank is able to
recover from a collateralized asset in the case of default. A repossessed asset may be
worth more to the supplier than to the bank because the supplier is in the business of
selling this good. Moreover liquidation values may be important even when default
does not actually occur, due to the possibility of strategic renegotiations. Habib and
Johnsen (1999) also suggest that when borrowers are unable to repay their credit
5
obligations, suppliers are more efficient in redeploying the repossessed assets to an
alternative use than banks are. This advantage makes trade credit cheaper than
bank loans, thus offsetting the lower cost that banks face in raising funds on the
deposit market.
We should admit, however, that our framework stresses one perspective of trade
credit at the expense of some others. A widespread notion in the trade credit literature
is that interfirm finance substitutes for bank credit because suppliers have access to
privileged information about their customers (see Biais and Gollier, 1997). The use of
trade credit is justified because the suppliers are better informed or can monitor better
their customers than conventional lenders. Burkart and Ellingsen (2004) question
the plausibility of such advantage since banks typically have a greater expertise in
evaluating and monitoring borrowers. Also in practice supplier’s lending activity is
closely tied to the value of the asset. The papers that advocate monitoring advantage
fail to explain why trade credit is limited to the value of the inputs.
An alternative hypothesis is that trade credit use mitigates moral hazard problems.
Burkart and Ellingsen (2004) argue that the advantage of using trade credit is derived
from the fact that the commitment value of illiquid inputs prevents the possible
misuse by the borrower of liquid funds such as bank loans. Similarly, a supplier may
be willing to extend (more) credit because the borrower is more likely to repay him
6
than to repay the bank (see Cunat, 2007). For example, if the supplier is essential
for the entrepreneur’s future business due to the lack of alternative producers, the
entrepreneur has a stronger incentive to default on the bank loan than on the trade
credit obligation.
Chemla (2005), on the other hand, abstracts from issues of asymmetric information
and considers the interaction between takeover treats and the use of trade credit. He
argues that trade credit is granted to finance an investment allowing a takeover when
the supplier expects to benefit from the takeover. The supplier, however, will refuse
to extend trade credit when she expects the takeover to hurt her.
Empirical evidence in Bukart, Ellingsen and Giannetti (2004) provides little support for the information advantage hypothesis and the collateral liquidation hypothesis. Having a larger share of inputs provided by firms in related business lines, for
example, does not increase the firm’s ability to obtain trade credit. On the other hand,
their findings show that the scope for the customer’s moral hazard is an important
determinant of the availability of trade credit.
Limited empirical research exists that shares some similarities with this paper.
Alphones, Ducret and Severin (2006) provide evidence that small firms in the US use
trade credit to improve their reputation. Their results show that trade credit can serve
as a signal about firms’ quality and its use increases the availability of bank loans.
7
Garcia-Appendini (2006) empirically examines whether banks incorporate "soft" information about firms’ trade credit repayments into their lending decisions. The
evidence shows that transaction banks are unwilling to lend to firms that make trade
credit payments after the due date. Relationship banks on the other hand, disregard
such information. Finally, in an empirical model that allows for sorting and selectivity among firms that decide to use trade credit and those who choose not to, Antov
(2006) shows that synergy arises from combining supplier’s finance with institutional
borrowing.
The main difference between our study and these others is that we focus on the
dynamics of firms’ choice of short-term external funding. Fluck, Holtz-Eakin, and
Rosen (1998) investigate the choice of external and internal finance in the life cycle
of small businesses. One limitation of their dataset is that it does not include any
information on trade credit. In contrast, we analyze the choice between two forms of
external financing, one of which is trade credit. Our theoretical model allows us to
see the empirical results within one unified framework and to formulate alternative
hypothesis more clearly. In addition, our data comprise of both public and private,
and large and small firms. Hence, our empirical results are robust with respect to
sample biases related to size, age and access to public capital markets.
8
3
Theoretical Framework
This section develops a model of how an entrepreneur chooses to finance the purchase of inputs and describes the testable implications derived from this model. In
our model, an entrepreneur can borrow either from a supplier or from a financial
intermediary. Although financial institutions have a lower cost of capital, they are
inefficient at redeploying assets when the entrepreneur is unsuccessful. Suppliers have
a liquidation advantage in that they are able to redeploy efficiently the re-possessed
inputs to their next best use. Trade credit providers, however, have a higher cost of
capital. Proposition 3 shows that when firms cannot convey credibly their credit worthiness, they will choose to diversify and borrow from both type of lenders. Despite
the higher cost of funds associated with trade credit, it serves as a signal that firms
have good prospects. Everything else being equal, banks prefer to lend to a borrower
who uses trade credit rather than to a borrower who does not. In addition, the higher
the trade credit amount the more attractive is the borrower from the banks’ perspective. Proposition 4 shows that the fraction of trade credit financing will decrease
with reputation effects. This is because the likelihood that credit rationing occurs,
in the sense that the borrowers’ demand for conventional loans is not fully met by
the lenders’ supply, decreases as borrowers acquire reputation over time. Therefore
the longer a firm’s history of successful repayments, the smaller the fraction of trade
credit financing relative to bank loans.
9
3.1
Assumptions
Consider an economy with three types of risk neutral agents: entrepreneurs who have
access to a concave production technology g(.), suppliers of an intermediate good
(input) and financial intermediaries (banks) that provide loans. Lenders live only for
a single period and at the beginning of every period the entrepreneurs face a new set
of lenders. The lenders are competitive, earn zero expected profit and their number
exceeds the number of entrepreneurs. For simplicity we assume that the time value
of money is zero.
There are two different types of limited liability entrepreneurs. The entrepreneur’s
type is her private information. The two types are:
• Type G: Entrepreneurs invest k units of input and realize output g(k) > k with
probability pG = 1. Note that we normalize the price of one unit of input to
one.
• Type B: Entrepreneurs succeed in the same way as type G with probability
pB < 1 and fail with probability (1 − pB ). We assume that pB < k/g(k).
This implies that type B entrepreneurs are a negative NPV investment for the
financial intermediaries, whose cost of funds is the riskfree rate.
Every period, each entrepreneur can undertake only one project. Entrepreneurs
10
borrow funds to finance the purchase of k units of input sold by the supplier. Entrepreneurs borrow either from banks or from suppliers through a delayed payment of the
purchased inputs (trade credit). If the entrepreneur is successful all inputs are used
in the production process. If she fails some of the inputs can be retrieved. Output is
consumed at the end of the same period it is produced (no saving is allowed).
3.2
Trade Credit and Bank Loans
Trade credit can be advantageous since when projects are unsuccessful suppliers are
more efficient at redeploying the repossessed inputs than banks are. The suppliers
can produce the input but they also do not have cash. They can cover (part of) their
production cost either by receiving cash payment, or, if they obtain delayed payment,
by borrowing from the bank. It is assumed that the production costs are sufficiently
low relative to the value of the sale, that is production is always profitable. The
suppliers produce k units of the input at a cost c(k, λ) where c(k, λ) = c1 (k) + c2 (λ)
is a convex cost function2 . Efficient redeployment requires the supplier of inputs to
incur an ex ante specific (non-contractable) cost c2 (λ). This allows the supplier to
identify the extent to which the retrieved inputs can be reused in their next best use
and what their value will be. The idea is that the inputs are specific to the production
technology of the entrepreneur but this specificity can be dampened at some cost. In
2
There is no loss in generality in assuming additivity of the cost function.
11
the state of default the supplier is able to realize λk from the repossessed inputs,
where λ ∈ [0, 1]. A high level of λ corresponds to a highly redeployable asset, whereas
a specialized asset will have a low value of λ. For simplicity, we assume the following
parametric form for the cost function c2 (λ) =
λ2
.
2
The banks are less well positioned to obtain value from the repossessed input as
they do not specialize in the redeployment of assets. However, when the seller finances
her receivables using bank credit, she is locked in a lending relationship where the
bank has all the bargaining power. In the case of default, the bank can sell any unused
output to the supplier and extract all the rent from her. Houston and James (1996)
and Weinstein and Yafeh (1998) provide evidence in support of the rent extraction
from banking relationship monopolies.
Banks are financial specialists and can raise funds on the deposit market. Their
cost of funds is normalized to zero. On the other hand, the marginal cost of funds
for the supplier r(st ) is increasing and convex with r(0) = 0. This function is a
reduced form that captures the premium banks will charge suppliers due to moral
hazard or adverse selection problems. This is in the spirit of Cantillo (1996) where
the agency costs between a borrow and a lender(s) are like an added cost of capital.
This assumption is also used in the theoretical analysis of Biais and Gollier (1997),
while Mian and Smith (1992), provide empirical evidence of the suppliers’ higher costs
12
of lending.
3.3
Entry and Exit into the Credit Market
At time t = 1, the fraction of type B entrepreneurs is ϕ1 , where 0 < ϕ1 < 1. The
faction of type G entrepreneurs is 1−ϕ1 . Entrepreneurs’ types are set at the beginning
(t = 1) and do not change at different periods. At time t > 1, there is a history, h,
of each entrepreneur that will condition lenders’ beliefs about her type. The history
consists of the dates of successful repayments or default3 . Entrepreneurs die at an
exogenous rate θ and this exogenous separation occurs after projects’ outcomes are
realized. At the end of each period a set of new entrepreneurs with new histories enter
the borrowing pool4 . Type B entrepreneurs who are unsuccessful and fail are excluded
from the credit market forever. At time t the proportion of type B entrepreneurs with
history h is ϕB (h) = ϕt (h) and the proportion of type G entrepreneurs with history
h is ϕG (h) = 1 − ϕt (h) and ϕt (h) ≤ ϕt (h − 1). The proportions of the two types of
entrepreneurs are public information.
3
To rule out money burning equilibia we assume that interest rates paid are not part of the
history.
4
We allow for borrowers with different histories to enter, not only those without an established
financial record. The rationale for this is that for many new firms, the credit history of the owner
can provide information to the lender. Alternatively, new entrants, who have operated some other
technologies in the past, are able to offer collateral, which makes them more attractive to lenders.
13
Conditional on extending loans to everybody in the pool, we set exit and entry
rates (ξ t ) equal, i. e. for a group of G and B type entrepreneurs with a particular
history h, the exit rate of borrowers equals the entry rate of borrowers with the same
history h. Exit rate for borrowers with history h at time t is (1−pB )ϕt (h)+θpB ϕt (h)+
θ(1 − ϕt (h)). These are all type B entrepreneurs who were unsuccessful, a fraction
θ of type B entrepreneurs who were successful but died and a fraction θ of type G
entrepreneurs who die.
As the entry and exit rates are the same, the proportion of the two types of new
entrepreneurs entering the pool of borrowers, regardless of their history record, is
the same as the initial one, i.e. ϕ1 . This means that at time t > 1, ϕ1 ξ t (h) type B
entrepreneurs with history h will enter the pool and (1−ϕ1 )ξ t (h) type G entrepreneurs
with the same history will enter the pool. Figure 1 shows the dynamics of the entry
and exit to the borrowing pool. Next we show how the pool composition evolves over
time. The fraction of type B entrepreneurs, with history h, reaches a constant level
in the limit. Borrowers’ histories continue to evolve as time goes by, but the fraction
of type B entrepreneurs for a particular history becomes constant5 . This result is
stated in the following lemma.
5
There are two dimensions to keep track of. The first one is the time dimension, while the second
is the history dimension. Both the time and history dimensions become irrelevant in the limit in
terms of revealing information about borrowers.
14
Lemma 1 The composition of type B entrepreneurs with history h, reaches a fixed
point at some time t.
The proof is provided in the Appendix. An important implication of the lemma is
that the proportion of negative NPV entrepreneurs declines but never becomes zero.
Let πt (h) denote the probability that a project of a borrower with history h is
successful. Then π t (h) = 1 − ϕt (h) + pB ϕt (h). Using Lemma 1 and the fact that π t (h)
is decreasing in the fraction of type B borrowers with given histories, the following
lemma is established.
Lemma 2 The probability π that a lender will be repaid by a borrower of particular
history h increases over time. Once the fraction of borrowers of type B reaches the
fixed point ϕ∗ this probability approaches its maximum value of π∗ =
1−(1−θ)pB −ϕt (1−pB )
.
1−(1−θ)pB −(1−θ)ϕ1 (1−p)
The proof is provided in the Appendix.
3.4
Definition and Characterization of Equilibria
At t, the timing of the moves in our model is as follows.
• Entrepreneurs choose the level of investment kt and what fraction st of kt will
be financed by trade credit and therefore a fraction (1−st ) that will be financed
by bank loans.
15
• Entrepreneurs with history h offer a debt contract with face value L to the bank
and a debt contract with a face value D to the supplier.
• Nature draws randomly the proportion of type B entrepreneurs that are unsuccessful.
• For entrepreneurs with history h, output g(k) is realized with probability [(1 −
ϕt (h))pG + ϕt (h)pB ] and repayments are made or output of 0 is realized with
probability [ϕt (h)(1 − pB )] and default occurs.
• ξ t (h) entrepreneurs with history h exit the borrowing pool and ξ t (h) entrepreneurs with history h enter the borrowing pool.
Entrepreneurs cannot influence the outcome of the project, so there is no ex-ante
moral hazard on their part. Nature decides randomly what fraction of the type B
entrepreneurs will be successful and what fraction will fail. Limited liability implies
that the repayments [sD + (1 − s)L] from type G and successful type B entrepreneurs
cannot exceed g(k). When the failed type B entrepreneurs declare default, inputs
are repossessed and redeployment value of λk is realized. When the state of default
occurs, the priority rule is determined by s6 . Lenders cannot distinguish between the
6
Prioritized debt is not feasible in our set up. Absolute priority of the bank debt creates a moral
hazard problem for the supplier. If the supplier is paid cash (with money borrowed from the bank)
to make the investment, she does not have an incentive to invest in redeployment so the inputs will
16
two types when faced with a borrower and the only observable characteristic is the
history h. Output realizations are also observable and debt contracts are enforceable,
i.e. the borrower cannot run away with the output7 .
In equilibrium, type B entrepreneurs choose the same level of investment and the
fraction of it financed by trade credit as type G entrepreneurs. Any deviation from
this strategy will reveal their type. In our model, a separating equilibrium cannot
exist. Suppose a separating equilibrium existed, where type G borrowers receive risk
free bank loans since they are a positive NPV for the financial intermediaries. Type B
borrowers cannot be offered loans at the risk free rate since they are a negative NPV
for the banks. In this equilibrium, type B borrowers using trade credit are required
to pay higher rates than type G borrowers. It is sufficient for one type B borrower
to mimic a type G borrower and use bank loans for such a separating equilibrium to
collapse. Therefore, we focus on pure strategy pooling equilibria.
have no value when repossessed by the bank. Seniority of supplier’s debt, on the other hand, might
cause banks to cease lending to entrepreneurs. Note that this problem disappears if the bank and
the supplier were merged.
7
Although this is a strong assumption, allowing ex-post moral hazard on the side of the borrowers
does not change the empirical implications derived from the model. A model that allows borrowers
to run away with the output requires an incentive compatible contract that makes it worthwhile for
entrepreneurs to make payments when they have the funds to do so. Multiple equilibria arise in
such a model but the result reported in Proposition 3 continues to hold.
17
The return on a bank loan is determined by the zero profit condition:
πt (h)L(kt , st |h)(1 − st )kt + (1 − π t (h))λ(st |h)(1 − st )kt
(1)
= (1 − st )kt
L(st |h) =
1 − (1 − π t (h))λ(st |h)
π t (h)
(2)
where λ(st |h) is the redeployment level chosen by supplier.
Suppliers determine a level of λ that maximizes their profits. Efficient level of λ
is achieved when in the case of default the supplier is the sole residual claimant.
λ∗t = arg max[(1 − π t (h))λt kt − c(kt , λt )]
(3)
The fact that suppliers pay for making inputs redeployable but share the benefits
with the bank creates a distortion. Side payments from banks to suppliers will not
correct this distortion since the redeployment investment is non-contractible. The
level chosen by the supplier is:
λ(st |h) = arg max[(1 − π t (h))λt st kt − c(kt , λt )]
λ(st |h) = (1 − π t (h))st kt
18
(4)
and only when st = 1 does the redeployment investment equals the efficient level.
When the supplier does not provide credit st = 0, she does not make any redeployment
investment. Since
∂λ(st |h)
∂st
> 0 and
∂λ(st |h)
∂π t (h)
< 0, we note that the level of redeployment
investment is increasing in the fraction of trade credit financing of the input and
decreasing in the probability of repayment.
The face value of the trade credit contract, D, at time t for borrows with history
ht is determined by the zero-profit condition for suppliers:
πt (h)D(kt , st |h)st kt + (1 − π t (h))λt st kt − c(kt , λt )
(5)
= (1 + r(st ))st kt
This leads to:
c(kt , λt )
(1 + r(st )) − (1 − π t (h))λt
+
π t (h)
π t (h)st kt
r(st )
c(kt , λt )
+
D(st |h) = L(st |h) +
π t (h) π t (h)st kt
D(st |h) =
where
r(st )
πt (h)
reflects the higher cost of funds of trade credit providers and
accounts for the production and redeployment costs.
19
(6)
c(kt ,λt )
π t (h)st kt
Assume that each entrepreneur borrows from a single bank or a single supplier8 .
Entrepreneurs maximize profits by simultaneously deciding on the level of inputs k
and the composition of sources of funds, i.e. entirely financed by bank loans (s = 0),
entirely finance by trade credit (s = 1) or some combination of the two (0 < s < 1).
After entrepreneurs decide on k and s, the project is undertaken and nature draws
randomly the proportion of successful and unsuccessful entrepreneurs.
The total payment (T P ) for a successful borrower with history ht is:
(7)
T P (kt , st |h) = (D(st |h)st + L(st |h)(1 − st )) kt
¶
µ
r(st )st
c(kt , λt )
kt +
=
L(st |h) +
π t (h)
π t (h)
¶
µ
2
((1 − π t (h))2 s2t kt2 c1 (kt )
1 − (1 − π t (h)) st kt r(st )st
+
+
kt +
=
π t (h)
π t (h)
2π t (h)
πt (h)
Unsuccessful type B entrepreneurs declare default and are excluded from the borrowing market forever. The supplier redeploys the repossessed inputs and a value of
λk is realized.
A borrower with history h solves the following problem:
8
For a borrower with history h, the number of banks providing loans is irrelevant. On the other
hand, if the level of the redeployment investment λ is additive across suppliers of the same inputs,
given the convexity of the redeployment costs, the entrepreneur will try to obtain trade credit from
as many suppliers as possible. For simplicity, we assume that λ is not additive across suppliers and
therefore it is optimal to use a single supplier of the inputs.
20
∞
X
max
(1 − θ)t pti [g(kt ) − T P (kt , st |h)] , i ∈ {G, B}
kt ,st
t=0
s.t.
(8)
g(kt ) ≥ T P (kt , st |h)
kt ≥ 0, 0 ≤ st ≤ 1
3.5
Testable Predictions
Proposition 3 Entrepreneurs choose to diversify their borrowing using bank loans
and trade credit at the same time i.e., s∗t ∈ (0, 1).
The proof is provided in the Appendix. Since T P is convex in s, the derivative on
the left bound is negative and on the right bound positive, T P is minimized at some
interior point of the interval s ∈ [0, 1]. The intuition behind Proposition 3 is that at
the margin firms will trade off the benefits of trade credit against its higher costs and
choose to diversify their borrowing.
Proposition 4 The fraction of trade credit financing extended by suppliers decreases
monotonically approaching a steady level as the borrower acquires more reputation
over time.
21
The proof is provided in the Appendix.
An important empirical implication of our model is that firms operating in industries characterized with high probability of failure of the type B technology (low value
of pB ) use on average greater amounts of trade credit compared to those operating
in industries with low failure probabilities. This implication is derived from the fact
that the derivative
4
∂π
∂pB
∂ϕ
= (pB − 1) ∂p
+ϕ =
B
θ2 ϕ1
(1−(1−θ)(pB +ϕ1 (1−pB )))2
is strictly positive.
Data Description
This section describes the data set and the variables that we use in our empirical
analysis. We work with an unbalanced panel of 8897 limited liability UK companies
drawn from the FAME database for the period 1994-2003. FAME provides data on
company accounts, ratios, activities, ownership and management for the largest 2.4
million UK firms. To select the companies in our sample we exclude (i) firms that are
not required to file full accounts; (ii) firms that do not have at least three consecutive
years of filed accounts; (iii) firms, which do not comply with the assumption that a
supplier of an intermediate good provides trade credit to a customer9 ; (iv) firms with
significant merger and acquisition activities.
9
We exclude firms in the following industries: financial intermediation, insurance and pension
funds, real estate activities, public administration and defence, education, health and social work,
recreation and sporting activities and extra-territorial organizations.
22
The FAME database covers both publicly traded firms and privately held entities.
Our sample allows us to test the predictions of the theoretical model developed in this
paper. Using data from Compustat and other databases comprised of publicly traded
firms poses limitations and difficulties in the interpretation of results pertaining to
trade credit use. The use of trade credit by large publicly traded firms could be
driven by tax and accounting considerations, rather than financial motives. Having a
sample of both public and private firms allows us to test the effect of trade credit on
the availability of institutional loans for public and private firms separately.
Table 1 describes the variables used in this study, broken down into categories. The
dependent variables are Conventional Institution Loans scaled by Total Assets and
Accounts Payable scaled by Total Assets. First, we consider variables that measure
the informational opacity/transparency, firms’ risk and profitability. We include size,
age, profitability ratio, quick ratio and tangible assets as we expect profitable and
less risky firms with ample collateral will rely less on trade credit. Informational
opaqueness translates into the adverse selection specified in the model. We expect
that the greater the opacity of smaller, younger firms, the stronger the reliance on
trade credit and the lower the availability of conventional bank loans.
Next, we include variables specific to bank loans and trade credit provision. Inventories and current assets proxy for the availability of collateral in a similar way that
23
tangible assets do. The one difference is that inventories, as well as current assets,
are of shorter maturity and might best serve as collateral for suppliers in particular.
Given that suppliers’s obligations are of shorter maturity and they are more flexible in
repossession the above two variables are likely to be more important for trade credit
than for bank loans. In addition, the level of inventories indicates the intensity of
sales transactions the firm undertakes. The higher is this intensity, the higher is the
expected use of trade credit financing as the higher is the amount of conventional
bank loans required to sustain this activity. Also, firms that grow at a faster pace are
regarded as the ones that have more investment opportunities. Petersen and Rajan
(1997) find evidence that firms experiencing both positive and negative sales growth
have higher levels of accounts payable. Given that suppliers have an advantage in
repossessing the inputs they supply it is not surprising that they are willing to lend
to firms whose sales growth has declined. This does not have to be the case for bank
financing. It is reasonable to expect that, from the bankers’ point of view, firms experiencing an increase in their sales will be more desirable borrowers than those with
declining sales.
Finally, previous research has suggested that cash holdings also has an effect on
both the availability of bank credit and the demand for trade credit. Opler et al
(1999) find that cash-rich firms have lower cash flows, are substantially smaller and
are in industries with highly volatile cash flows. We expect that such characteristics
24
may make cash-rich firms use more trade credit than cash poor firms.
Table 2 contains the summary statistics for the public and private companies in
our sample. In general, the public companies in our sample are larger and older than
the private companies. The median public firm has an age of 20 years and logarithm
of total assets of 9.2507. For the median private firm these numbers are 17 years and
7.9094. The table shows that the median public firm has a smaller fraction of their
assets financed by short-term conventional institutional loans, 7.13%, than for the
median private firm, 11.48%. Since private firms do not have access to public financial
market, they will naturally rely more on intermediated credit. However, both types of
companies have a substantial proportion of accounts payable to their assets, 13.65%
for the median public firm and 13.60% for the median private firm. In addition, the
median private firm has lower profitability and lower levels of fixed tangible assets
but higher levels of current asset and in particular cash holdings10 . These univariate
statistics suggest that there are important differences in the way public and private
firms choose to finance their operating activity. In the next section, we test explicitly
whether there is a difference in the sensitivity of bank loans to trade credit for public
and private companies.
10
The null that variables are drown from populations with the same median is rejected
for all variables except for age and quick ratio.
25
5
Empirical Strategy and Estimation Results
Our empirical methodology addresses the problem of simultaneity of the decision to
borrow from a financial intermediary and/or borrow from a supplier. We estimate a
system of simultaneous equations for bank loans and trade credit. An identification
problem arises in this context. As argued in the introduction, instruments which are
uncorrelated with bank loans but are correlated with trade credit are difficult to find.
Using lags of trade credit as instruments is also not a valid alternative as we argued
that the simultaneity bias in our case extends over time. Instead, we solve the problem
of identification by using the natural heteroskedasticity present in the data. The next
subsection describes the identification through heteroskedasticity procedure.
5.1
Simultaneity and Identification through Heteroskedasticity
We are interested in estimating the following simultaneous equation system for bank
loans (BL) and trade credit (T C).







 1 −a   BLt 
 BLt 
 εt 


 = Φ(L) 
 + Ψ(L)Xt + 








−b 1
T Ct
T Ct
ηt
(9)
where a and b are the contemporaneous coefficients, Φ(L) and Ψ(L) are lag polynomials, Xt is a vector of control variables and εt and η t are the structural innovations.
The first equation, the bank loan equation, measures the effect of trade credit on the
26
level of bank loans and the structural residuals can be interpreted as the innovations
to bank loans which are independent of all control variables and other shocks. The
second equation, the trade credit equation, describes how bank loans affect trade
credit. The innovations to this equation can be interpreted as the changes in the level
of trade credit that are not explained by the covariates.
Standard econometric literature imposes restrictions either on Ψ(L) (exclusion
restrictions) or on Φ(L) (cointegration restrictions) in order to solve the problem of
identification. The problem arises since the reduced form of this model, given by:






 BLt 
 BLt 
 νt 

 = A−1 Φ(L) 
 + A−1 Ψ(L)Xt + 







T Ct
T Ct
ωt




(10)


 νt 
 1 −a 
 εt 
 and 
 = A−1 
.
cannot be identified from the data. In (10) A = 






−b 1
ωt
ηt
Next, we discuss the assumptions behind the identification through heteroskedas-
ticity (IH) procedure. The main identification assumption is that data are heteroskedastic. We model this heteroskedasticity as a multi-regime process11 . Also,
we assume that the innovations in (9) are identically distributed, zero mean and are
11
See Rigobon (2002) and Rigobon and Sack (2003) for applications where heteroskedasticity is
modeled as a GARCH process. Applications where heteroskedasticity is described by regimes shifts
are Rigobon (2003) and Hogan and Rigobon (2003).
27
uncorrelated. The assumption of no correlation can be relaxed by introducing an
unobservable common shock to bank loans and trade credit. The effect of a common
shock is analyzed in Section 5.3 as a robustness check to our main results. Additionally, we assume that the coefficients are the same across all realizations. This stability
of the parameters of the system is a crucial assumption. Even though this assumptions may seem strong, in the context of panel data regressions, standard instrumental
variables (IV) methods implicitly make the same assumption.
For the reduced form model (10), the only statistic we can compute is the covariance matrix of the data, i.e. the variance of bank loans, the variance of trade
credit and their covariance. For notational simplicity we abstract from the effect of
the control variables and consider only the simultaneity problem. Algebraically, the
covariance matrix of the reduced form is given by:


 σ 2ε + b2 σ 2η aσ 2ε + bσ 2η 
1


Ω=

(1 − ab)2 
a2 σ 2ε + σ 2η
(11)
where σ 2ε and σ 2η are the variances of the structural residuals. The covariance matrix
provides us with three moments, however, it is explained by four unknowns. This is
the standard problem in simultaneous equations.
Assume that we can split the data into two subsamples, for which the variances of
the residuals are different. In these two subsamples, we can estimate two covariance
28
matrices. Let Ωr denote the covariance matrix of the reduced form residuals in regime
r, given by:


 σ 2r,ε + b2 σ 2r,η aσ 2r,ε + bσ 2r,η 
1

 , r = 1, 2
Ω =

(1 − ab)2 
a2 σ 2r,ε + σ 2r,η
r
(12)
where where σ 2r,ε and σ 2r,η are the variances of the structural residuals in regime
r. There are six moment conditions in (12) that can be estimated from the data.
These are explained by six unknowns: the two contemporaneous parameters of the
structural model (9) and four variances. This implies that the system satisfies the
order condition. Finally, in order to recover the unknown coefficients in (12), we have
to verify that the six equations are linearly independent, i.e. the system also satisfies
the rank condition. Rigobon (2003) shows that the rank condition is satisfied when
σ 21,ε
σ22,η
6=
σ22,ε
.
σ 22,η
The IH methodology has a simple instrumental variable interpretation. In an IV
context, a valid instrument is one that moves one of the equations without affecting
the others. In the context of IH, the rise in the variance of the structural shock of one
equation serves as a probabilistic instrument. When the variance of the structural
shock increases in part of the subsample an expansion of the residuals follows. Such a
shift increases the likelihood that the equation schedule will be shifted which in turn
identifies the slope coefficient of interest. For more details see Rigobon (2003).
29
In the reduced form model (10), A−1 Φ(L) and A−1 Ψ(L) can be estimated consistently by standard OLS regressions. If we know the coefficients of the matrix A, then
recovering the rest of the coefficients of the structural model is trivial. Equation (9)
shows that the reduced form residuals have exactly the same properties as the endogenous variables. Therefore, the easiest way to estimate the matrix of contemporaneous
parameters A is a two step procedure. First, we estimate the reduced form model for
each regime and recover their residuals. Second, given an estimated covariance matrix
for each regime, we compute the contemporaneous coefficients by GMM, where the
moments for each regime are given by (12). We compute the standard errors using
the optimal weighting matrix for the GMM estimation.
5.2
Estimation Results
This section presents and interprets the empirical results. First, we analyze the effect
of trade credit on institutional loans, which is the focus of this paper. Then we turn
to the effect of the control variables on the levels of conventional institutional loans
and trade credit. Finally, we account for serial correlation and investigate the effect
of lags of trade credit in the bank loan equation.
For the IH procedure, we split our sample observations by one-digit SIC industry
code. The economic rationale for our split is as follows. Consider the variation of
trade credit practices among industries, for example. There is abundant evidence that
30
trade credit terms and availability differ significantly for suppliers across industries12 .
These differences are reflected in the duration of trade credit obligations, the amount
of discounts offered, the penalties imposed by suppliers for late payments, the speed
of transformation of inputs into outputs and the ease of liquidation by suppliers,
among other factors. Also, we expect that firms operating in high risk industries
will rely more heavily on trade credit and that firms in industries characterized by a
high degree of product market concentration are less likely to be denied trade credit
by their supplier (see Petersen and Rajan (1995) or Sharpe (1990) for specifications
of the market power’s effect on credit market financing). Therefore, the variance of
the factors determining the use of trade credit is expected to be heteroskedastic. In
addition, we perform formal statistical tests for heteroskedasticity in our sample. We
carry out a test of homoskedasticity of the reduced form residuals across the sample
split as in Hogan and Rigobon (2003). We regress the squared reduced form residuals
for the aggregate data and their products on the dummy variables that define the
regimes. The null hypothesis of zero slopes is rejected at all conventional levels of
significance13 .
12
See Ng et al., (1999) and Petersen and Rajan (1994) for variation of trade credit terms across
industries. See Mian and Smith (1992) for evidence of variation of trade credit terms within industries.
13
The F statistics are F( 8, 26291) = 37.78 and F( 8, 26291) = 39.62 for the bank loans and
trade credit equations and the critical values at 5% and 1% sigificance level are 1.9384 and 2.5110
31
We start our empirical analysis by deriving some benchmark results in order to
assess the importance of controlling for simultaneity. We perform preliminary OLS
with fixed effects and difference GMM regressions of bank loans on the control variables and trade credit. The difference GMM method implicitly accounts for fixed firm
specific effects and for endogeneity by instrumenting current levels of the endogenous
variable with its lags.
Table 3 shows the results for the estimated coefficient of trade credit in a regression
of bank loans on control variables and trade credit, estimated by OLS, difference
GMM and IH with fixed effects14 . First we analyze the results for private firms.
When we compare the IH results to the OLS results, we find that the elimination
of the endogeneity bias changes the sign and size of the coefficient of trade credit in
the bank loans regression. The serial cross-correlation between bank loans and trade
credit is reflected in the fact that the difference GMM estimates are close to the OLS
estimates. The IH results confirm the prediction of our model. There is a positive
effect of trade credit on the levels of bank loans. The estimation results for public firms
are very different. The effect of trade credit on the level of conventional institutional
loans is generally not significant. This finding is in line with the predictions of our
model that the effect is important only for firms with high agency costs.
respectively.
14
Time invariant firm or industry specific variables cannot be estimated in panel regressions with
fixed effects.
32
Next we analyze the effect of the control variables on both trade credit and bank
loans. Table 4 shows the results from the reduced form regressions with fixed effects
for public and private firms. For private firms the proportion of both trade credit
and bank loans to total assets increases with size whereas for public firms size has
an effect only on the levels of trade credit. Age, high levels of tangible assets, high
profitability and quick ratios increase the amount of external short-term financing
available to both public and private firms. As expected, current assets including
inventories have a significant effect on trade credit but not on bank loans. Also, sales
growth has a significant positive effect only for private firms. Cash holdings, on the
other hand, have a significant negative effect on trade credit and institutional loans
for both public and private firms. These findings are consistent with the reputation
based arguments which underpin our theoretical model.
Finally, we explore some time related issues regarding firms financing choice. Figure 2 illustrates the main point of the paper. The figure shows that for a private firm
with median sample characteristics, the proportion of trade credit over bank loans
has gradually decreased in the last ten years. In addition, when we regress the ratio
of trade credit to bank loans on age, the estimated parameter is -2.9254(1.3682)* for
private firms and -1.2079(1.2042) for public firms.
Next, we present formal evidence for the existence of serial correlation between
33
bank loans and trade credit by including the first and second order lags of trade credit
in the reduced form equation for institutional loans15 . We argued before that in a
typical bank loans regression, there are important serial correlation effects that are
not accounted for. Table 5 shows the estimation results for the lags of trade credit in
the specification for both public and private firms. The results are similar to those
in Table 3 and show that the effect of the lags is significant only for private firms.
In addition, the effect of the first order lag is much stronger. We interpret this as
an indication that current levels of trade credit facilitate the access and improve the
terms of future conventional institutional loans.
5.3
Robustness Analysis
The next step in our analysis is to study how robust our results are to the introduction
of a common shock. We estimate the following system of simultaneous equations for
bank loans and trade credit:

15






 1 −a   BLt 
 BLt 
 εt 


 = Φ(L) 
 + Ψ(L)Xt + ΓZt + 








−b 1
T Ct
T Ct
ηt
We restrict the lag length to two because of the time dimension of our panel.
34
(13)


 γ1 
 are the parameters of the common shock where the usual
where Γ(2×1) = 


1
normalization γ 2 = 1 is imposed16 . The common shock can be interpreted as the
sources of correlation between the structural residuals. Rigobon (2003) shows that
the number of common shocks is K < N(N − 1)/2, where N is the number of
exogenous variables. This constraint indicates that some of the covariances of the
structural shocks have to be restricted to be constant, or zero. Note that the right
hand side of the inequality is exactly the number of all possible contemporaneous
correlations among the structural shocks.
The likelihood ratio statistics for the null hypothesis of equivalence between the
contemporaneous coefficients in the specification with common shock Z and the one
without common shock is 12.47. Thus the null can be rejected at conventional levels
of significance. This result should not come as a surprise. Albeit different credit
instruments, trade credit and conventional loans are subject to common shocks, such
as credit quality, history of interactions with lenders, financial outlooks, competitive
environment and others. Table 6 shows the contemporaneous parameters of trade
credit in the bank loans equation. The main difference is in the reported standard
errors. Note that the inclusion of a common shock has strengthened our results.
16
The estimation of the model is carried out by GMM with the following moment conditions
AΩr A0 = ΓVz Γ0 + Ωrε .
35
6
Conclusions
In this paper, we have developed a theoretical model that seems to explain well how
firms choose to borrow in order to finance their short-term operating expenditures.
The model is based on a simple insight that trade credit may serve as a means of
acquiring reputation. We expect that those firm characteristics linked to the degree of
information opacity (size, age and access to public capital markets) will be associated
with companies that use more trade credit. Those characteristics that suggest a
firm is less likely to default (high and stable cash flows, high profitability, tangible
collateralizable assets) will facilitate access to intermediated funds. The evidence
presented in this study supports this line of analysis.
We utilized a non-standard procedure to solve the identification in a system of
simultaneous equations. We dealt with the endogeneity by exploring the natural
heteroskedasticity observed in the data. We present evidence that the relationship
between trade credit and conventional loans is far more complicated than suggested
by the existing research in the area. It is likely that the overall costs of borrowing and
the extent of credit rationing are key factors determining whether trade credit and
intermediated loans should be treated as complements or substitutes. Our finding
that higher levels of trade credit correspond to higher levels of institutional loans is
inconsistent with different hypotheses that trade credit is a substitute for bank loans.
36
In addition, our findings have important policy implications. Policies which emphasize the development of technologies that improve information provision, such as the
valuation and monitoring of informationally opaque firms, might be more effective
than the current policies of government credit guarantees. It would be interesting to
explore more fully how information provision in the credit market will affect firms’
borrowing prospects.
37
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41
Appendix
Proof of Lemma 1: The composition of borrowers of type B for particular history
h, that continues to evolve, reaches a fixed point at some time t. The fraction of type
B at time t is: ϕt+1 (h + 1) = (1 − θ)pB ϕt (h) + ξ t (h)ϕ0 , the first term of which is
the type B borrowers from the pool of history h at time t that were successful and
survived and the second term is the entry of type B borrowers with same history at
the end of period t, before the beginning of period t + 1. As history evolves the same
way as time does, the h subscript is suppressed for the simplicity of exposition. We
have:
ϕt+1 = (1 − θ)pB t + (θ + (1 − pB )ϕt (1 − θ))ϕ0
ϕt+1 = ϕt (1 − θ)(pB + ϕ0 (1 − pB )) + θϕ0
For ϕt = 0 we have ϕt+1 = θϕ0 and for ϕt = 1 we have ϕt+1 = (1 − θ)(pB + ϕ0 (1 −
pB )) + θϕ0 . Note, θϕ0 < ϕt+1 < 1. Then, since ϕt+1 is increasing and linear in ϕt ,
there exists a fixed point ϕ∗ =
θϕ0
.
1−(1−θ)(pB +ϕ0 (1−pB ))
any ϕ0 > 0, and ϕ∗ = ϕ0 = 0.
42
Note, the fixed point ϕ∗ < ϕ0 , for
Proof of Proposition 3 :
Consider the first derivative of T P (kt , st |h) with respect to st .
T P 0 (kt , st |h) =
µ
=
r(st ) + r0 (st )st − (1 − π t (h))2 kt + (1 − π t (h)2 st kt
π t (h)
¶
kt
Since α(0) = 0 we have:
0
T P (kt , st
T P 0 (kt , st
¶
−(1 − πt (h))2
kt2 < 0
= 0|h) =
π t (h)
µ
¶
r(1) + r0 (1)
= 1|h) =
kt > 0
π t (h)
µ
Also
00
T P (kt , st |h) =
µ
2r0 (st ) + r00 (st )st + (1 − π t (h)2 kt
πt (h)
¶
kt > 0
since r(0) = 0, r0 () ≥ 0 and r00 () ≥ 0.
T P is convex in s. Its derivative on the left bound is negative while the derivative
on the right bound is positive. Therefore, T P is minimized at some interior point of
the interval s ∈ [0, 1].
43
Proof of Proposition 4 :
Using the results from Proposition 3 and Lemma 2 it is straightforward to verify
that s∗ is strictly decreasing until the fixed point of ϕ is reached and then remains
constant for any t afterwards. From Lemma 2, it follows that for surviving borrowers reputation effects reduce the cost of credit. As π increases over time the
loan return, assuring zero profits for lenders, goes down. It is straightforward to
verify that T P (h) > T P (h + 1) for any t before the fixed point ϕ∗ is reached and
T P (h) = T P (h + 1) once the fraction of type B becomes stationary.
44
Figure 1: Dynamics of the entry and exit to the borrowing pool
45
0.5
0.4
0.3
0.2
0.1
0
year 1994 1995 1996 1997 1998 1999 2000 2001 2002
Figure 2: Evolution of the ratio of accounts payable to institutional loans for a private
firm with median sample characteristic: 1994-2003
46
Table 1: Variables Description
This table presents a description of the variables analyzed in Tables 2 to
6. All assets and liabilities are annual data measured in million GBP. Age is
measured in years.
Variable Name
Institutional Loans
Account Payable
Size
Age
Profitability ratio
Quick ratio
Tangible assets
Inventories
Current assets
Cash holdings
Sales growth
Description
Short-term bank loans, overdrafts and hire purchases
Total amount of accounts payable
Ln(Total assets)
Ln(1+Age)
Operating profit over total assets
Current assets minus inventories over current liabilities
Property, plant and equipment
Total value of firm’s inventories
Value of firm’s current assets
Total value of firm’s cash and bank deposits
Dummy equals to one for positive sales growth
47
Table 2: Summary Statistics
This table presents descriptive statistics for 1994 to 2003. Panel A reports
statistics for the public, quoted and public AIM firms in our sample. Panel
B reports statistics for the private limited firms. We perform t tests for
differences in mean and nonparametric median tests. The null of no difference
in the mean is rejected at all conventional significance levels for all variables
except profitability ratio and quick ratio. The null that variables are drown
from populations with the same median is rejected for all variables except for
age and quick ratio.
Variable Name
Panel A: Public Firms
Loans/Total Assets
Acc. Payable/Total Assets
Size
Age
Profitability ratio
Quick ratio
Tangible assets
Inventory/Total Assets
Current Assets/Total Assets
Cash/Total Assets
Panel B: Private Firms
Loans/Total Assets
Acc. Payable/Total Assets
Size
Age
Profitability ratio
Quick ratio
Tangible Assets/Total Assets
Inventory/Total Assets
Current Assets/Total Assets
Cash/Total Assets
Mean
Std deviation Median
0.0665
0.1599
9.5107
38.033
0.0167
1.7982
0.2702
0.1605
0.6015
0.1623
0.0984
0.1072
2.4841
33.806
0.8505
2.5942
0.2082
0.1289
0.2186
0.1787
0.3034
0.1965
7.9985
28.172
0.8806
1.6882
0.2670
0.1813
0.7069
0.1697
48
7.6708
1.7384
1.5308
29.019
165.933
2.7154
0.2078
0.1571
0.2284
0.1762
0.0713
0.1365
9.2507
20
0.0663
1.2762
0.2305
0.1468
0.6343
0.1039
0.1148
0.1360
7.9094
17
0.0565
1.2645
0.2191
0.1433
0.7555
0.1181
Table 3: Estimation Results for the Contemporaneous Parameter
This table contains regression results for the contemporaneous parameter
of trade credit in the bank loans equation. Panel A reports the results for
public firms and Panel B reports the results for private firms. The dependent
variable, in the OLS and difference GMM regressions, is conventional institutional loans over total assets. For the IH procedure, we estimate a system
of simultaneous equations with dependent variables conventional institutional
loans over total assets and account payables over total assets. Heteroskedasticity is modelled as a multi-regime process, where the regimes are defined by
the one digit SIC codes. Standard errors robust to cluster effects are reported
in parenthesis. The standard errors for the IH procedure are obtained using
the optimal weighting matrix for GMM.
Parameter Estimate for Acc. Payables/Assets
Panel A: Public Firms
OLS
Difference GMM
IH with fixed effects
-0.0988(0.1109)
0.01846(0.0800)**
0.0429(0.0397)
Panel B: Private Firms
OLS
Difference GMM
IH with fixed effects
-0.09544(0.02671)*
-0.01515(0.00354)*
0.02280(0.00470)*
*** significant at 10%; ** significant at 5%; * significant at 1%
49
Table 4: Estimation Results for the Reduced Form Parameters
This table contains regression results for the reduced form parameters in
the trade credit and in the bank loans equations. These reduced form equations are estimated with fixed firm specific effects and time dummies. Panel
A reports the results for public firms and Panel B reports the results for private firms. The dependent variable in the bank loan equation is conventional
institutional loans over total assets. The dependent variable in the trade
credit equation is account payables over total assets. Standard errors robust
to cluster effects are reported in parenthesis.
Dependent Variable
Parameter Estimates Institutional Loans Trade Credit
Panel A: Public Firms
Size
0.0219(0.0151)
0.0064(0.0017)*
Age
0.0043(0.0022)**
0.0030(0.0002)*
Profitability ratio
0.1269(0.0453)*
0.0365(0.0344)
Quick ratio
0.0252(0.0035)*
0.0025(0.0002)*
Tangible assets
0.0912(0.0494)*** 0.0082(0.0106)
Inventories
0.0612(0.0655)
0.0267(0.0078)*
Current assets
0.0721(0.0486)
0.1292(0.0100)*
Cash holdings
-0.1225(0.0449)*
-0.0859(0.0053)*
Sales growth
-0.0146(0.0081)
0.0128(0.0971)
Panel B: Private Firms
Size
0.0102(0.0055)***
Age
0.0025(0.0008)*
Profitability ratio
0.6923(0.0117)*
Quick ratio
0.0103(0.0008)*
Tangible assets
-0.0036(0.0357)
Inventories
0.0281(0.0259)
Current assets
0.0884(0.0339)*
Cash holdings
-0.1375(0.0185)*
Sales growth
0.0268(0.0033)*
0.0111(0.0025)*
0.0011(00005)**
0.0258(0.0117)*
0.0082(0.0011)*
0.0203(0.0159)
0.0603(0.0221)*
0.1863(0.0339)*
-0.1367(0.0144)*
0.0063(0.0024)*
*** significant at 10%; ** significant at 5%; * significant at 1%
50
Table 5: Estimation Results for the Lag Parameters
This table contains the estimated parameters of the first and second lag of
trade credit in the reduced form bank loans equation. The reduced form equation is estimated with fixed firm specific effects and time dummies. Panel A
reports the results for public firms and Panel B reports the results for private
firms. The dependent variable is conventional institutional loans over total
assets. Standard errors robust to cluster effects are reported in parenthesis.
Parameter Estimates
Panel A: Public Firms
Acc. Payables/Assetst−1
Acc. Payables/Assetst−2
0.0100(0.0746)
0.0082(0.0230)
Panel B: Private Firms
Acc. Payables/Assetst−1
Acc. Payables/Assetst−2
0.1695(0.0259)*
0.0926(0.0270)*
*** significant at 10%; ** significant at 5%; * significant at 1%
51
Table 6. IH estimates with a common shock
This table contains IH regression results for the contemporaneous parameter of trade credit in the bank loans equation. We allow for the presence
of a common shock. Panel A reports the results for public firms and Panel
B reports the results for private firms. We estimate a system of simultaneous equations with dependent variables conventional institutional loans over
total assets and account payables over total assets. The standard errors are
obtained using the optimal weighting matrix for GMM.
Parameter Estimate for Acc. Payables/Assets
Panel A: Public Firms
IH with common shock
0.0185(0.0251)
Panel B: Private Firms
IH with common shock
0.0555(0.0015)*
*** significant at 10%; ** significant at 5%; * significant at 1%
52