Bank debt and trade credit for SMEs: international
evidence
Guillaume Andrieua, Raffaele Staglianòa, Peter van der Zwanb
a
Montpellier Business School and Montpellier Research in Management, 2300 Avenue
des Moulins, 34185 Montpellier Cedex 4, France, Tel.: +33 (0)4 67 10 28 64 (Fax: +33 (0)4
67 45 13 56). E-mail: [email protected], [email protected]
b
Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000
DR, Rotterdam, the Netherlands, and Erasmus Happiness Economics Research Organisation
(EHERO), Erasmus University Rotterdam. Email: [email protected]
Abstract
This paper examines the links between firm age, firm size and the ability to obtain capital in a
sample of European SMEs. The results indicate that age and size are positively linked to debt
capacity. Furthermore, our analysis reveals that it is crucial to distinguish between bank debt
financing and trade credit. Young and small firms are more subject to denial due to the higher
moral hazard they represent for a bank. Only very young firms are more constrained for trade
credit. The results of simultaneous analysis show that trade credit is positively related to bank
credit financing, thus providing empirical support for the complementarity of these forms of
financing.
Key words: Debt capacity, Bank loans; Trade credit; Information asymmetry
JEL classification: G32; G33
We have benefited from the constructive comments of Roy Thurik, as well as the participants of the 2014
Finance, Risk and Accounting Perspectives FRAP (Oxford, UK), the 2014 Paris Financial Management
Conference (Paris, France), the 2015 Interdisciplinary European Conference on Entrepreneurship Research
IECER (Montpellier, France), and the 2015 Workshop: "Economics of Entrepreneurship and Innovation" (Trier,
Germany). All remaining errors are our own. Financial support from “Labex Entreprendre” is gratefully
acknowledged.
1
1. Introduction
Small and medium-sized enterprises (SMEs) — defined in the current paper as firms
with 250 employees at most — depend on regular cash inflows to ensure their survival and
growth. It is important to understand the determinants of their access to credit because SMEs
create the majority of jobs (De Wit and De Kok 2014) and contribute substantially to the
growth of modern economies (Carree and Thurik 2003). Bank financing and trade credit are
two major sources of SME finance (Berger and Udell 1998). A trade credit is offered by
suppliers when there is a delay between the provision of goods and/or services and their
actual payment by the SME (Biais and Gollier 1997). There are various (non-)financial
motivations for suppliers to grant trade credit (García-Teruel and Martínez-Solano 2010;
Klapper et al. 2011), and firms that supply trade credit have been found to be more profitable
than non-suppliers (Martinez-Sola et al. 2014).
In an ideal finance marketplace, SMEs with good projects experience no restrictions to
gaining access to external finance, whereas SMEs with poor projects are financially restricted.
However, when a lender screens a potential borrower, information asymmetries cannot be
avoided, because the lender is less informed about the viability of the borrower and its
projects than the borrower itself. Information asymmetries are thought to be particularly
strong for small and young firms because of their restricted credit history and track record,
and their lower ability to provide collateral.
The present study focuses on bank loans and trade credit as two often-used sources of
finance for SMEs by examining the SMEs’ direct experiences with bank loan and trade credit
applications.1 The central concept is debt capacity, which may have several financing sources,
such as bank financing and trade credit, and which refers to the ability of a firm to obtain all
or part of its demand for debt financing (Cosh et al. 2009; Levenson and Willard 2000; Ang
and Smedema 2011).2 Direct measures of firms’ access to bank financing and trade credit
have generally been unavailable. Although numerous studies have investigated the
determinants of debt capacity in terms of bank loans, evidence for trade credit supplied to
1
Note that the current study focuses only on bank credit and trade credit access and not other finance
mechanisms. For example, Cosh et al. (2009) examined the determinants of capital obtained from several forms
of financing (i.e., venture capital, hire purchase or leasing, factoring, invoice discounting). Recent studies have
examined the role of the crowdfunding industry (Ahlers et al. 2015; Colombo et al. 2015) and the impact of
venture capital finance on the firm’s financial structure (Haro-de-Rosario et al. 2015).
2
While in this paper we focus on a firm’s access to outside finance, other studies have examined other
questions related to debt financing. For example, Canton et al. (2013) examined perceived financial flexibility by
investigating the expected capacity of a firm to access external financing. There are also studies that have dealt
with the discouragement of small firms about bank debt applications (e.g., Levenson and Willard 2000; Freel et
al. 2012; Chakravarty, and Xiang 2013).
2
SMEs is much scarcer. We zoom in on firm size and firm age as relevant characteristics that
determine a firm’s debt capacity. Young and small firms may suffer more from information
asymmetries than older and larger firms, and this is likely to impede their access to bank
loans. Debt financing restrictions may be severe for these firms, thereby hindering the
entrepreneurs’ efforts to develop their businesses. As a result, young and small SMEs may
resort to trade credit as an alternative source of finance. It may therefore be expected that
young and small SMEs have fewer chances of receiving a bank loan but more chances of
accessing trade credit than old and large firms (Diamond 1989; Rajan and Zingales 1995).
In other words, trade credit can be regarded as a “substitute” for SMEs that cannot be
financed by banks: SMEs that already have access to bank loans are less likely to seek access
to trade credit (Berger and Udell 1998). Yet the relationship between firm age and firm size
on the one hand and receiving trade credit on the other hand are not as clear as hypothesized
(García-Teruel and Martínez-Solano 2010). It may reasonably be asked whether trade credit
can also be considered as a positive signal that makes banks less reluctant to lend. The present
paper therefore also focuses on whether bank financing and trade credit can be considered as
“complements” or “substitutes” (Giannetti et al. 2011; Agostino and Trivieri 2014).
Our paper makes a contribution to the existing literature in at least two ways. First, we
empirically investigate the determinants of debt capacity—specifically firm age and size—
using a proxy of debt capacity based on SMEs’ direct experiences with bank loan and trade
credit applications. Second, we unravel the linkage between bank loan and trade credit
applications to determine whether trade credit and bank lending are complements or
substitutes. To our knowledge, no empirical study has yet compared these two forms of
financing in the context of SMEs that apply for finance.
For this study, we use the 2009 Flash Eurobarometer survey on access to finance from
the European Commission (no. 271), conducted in the European Union Member States,
Iceland, and Norway. The data used in this study include information about access to external
sources of finance and financial activities for 7,831 SMEs with fewer than 250 employees.
We found that it is crucial to consider different types of debt financing. Our results
revealed that both size and age are relevant variables to explain why European SMEs obtain a
positive decision for bank financing, whereas only age seemed relevant for trade credit. This
evidence suggests that young and small firms are more subject to denial decisions due to the
3
higher adverse selection and moral hazard issues3 they represent for a bank. On the other
hand, only very young firms are more constrained for trade credit. It seems that it is easier for
SMEs, after a few years of existence, to convince suppliers to obtain trade credit compared to
bank financing. Our result also confirm that there is no substitution effect, but complementary
effect between the two types of financing.
The paper is organized as follows. Section 2 provides a review of literature. In Section
3, the data, variables and methodology are presented. The main results and robustness checks
are conducted in Section 4. Concluding remarks follow in Section 5.
2 Literature review
As stated by Jensen and Meckling (1976), in any lending situation there is information
asymmetry between the lender and the borrower. Information asymmetry refers to the
situation where insiders (the SMEs) are better informed about themselves than outsiders like
banks, suppliers, investors, and shareholders. Adverse selection may result from information
asymmetries, and it indicates that lenders may have difficulties to distinguish good borrowers
from bad borrowers. Credit screening is the process by which a lender tries to obtain
information about the borrower’s quality, which is indicated, for example, by liquidity or
leverage ratios, in order to reduce information asymmetries between lender and borrower. In
addition, insiders often have no incentive to provide information to these outsiders. Credit
screening provides an imperfect image of a firm’s solvability, because certain aspects do not
appear in a purely financial analysis, such as long-term strategy, future business development,
and the quality of managers or products. As a consequence, SMEs may be subject to
financing restrictions, in which case SMEs with good projects may be denied access to
finance, or charged high interest rates. In addition, SMEs that find it difficult to signal their
quality to the bank may thus be discouraged from applying for a bank loan. Kon and Storey
(2003) developed a theory that predicts that both and good borrowers can suffer from
discouragement due to both information asymmetry and application costs.4
2.1 Firm size and age as determinants of bank financing and trade credit
It has been argued that young and small firms experience more problems from
3
Adverse selection issues happens when the principal (e.g., a bank) ignores the true quality and skills of the
agent (e.g., the firm) before the transaction. Moral hazard issues happens after the transaction when the agent
wants to maximize its own benefits at the expanse of the principal (e.g., diverting the funds in bad projects).
4
Han et al. (2009) investigated the theory on US data. They showed that the riskier borrowers were more likely
to be discouraged. The result confirms the adverse selection effect described above: these firms decide by
themselves not to apply.
4
information asymmetries than older and larger firms. For example, young firms have a less
successful track record than older firms because they have a limited accounting history
(Diamond 1989; Canton et al. 2013). In addition, large firms have more diversified project
portfolios and are therefore less risky (Rajan and Zingales 1995). Small or young firms may
also have less collateral (e.g., fewer tangible assets or capital) to guarantee that they will be
able to repay their debts. Hölmstrom and Tirole (1997) show theoretically that any global
restriction on credit such as a credit crunch hit poorly-capitalized firms more seriously.
Acquiring information about a debtor’s quality is a learning process, as well, as shown in
particular by Rajan (1992) in her comparison between informed and arm’s length debt.5
Outsiders may therefore be less likely to receive positive signals on the quality of young
SMEs than insiders.
The relationship between firm size and firm age on the one hand and information
asymmetries on the other is empirically investigated in Hyytinen and Pajarinen (2008). These
authors find an inverse link between a firm’s information opacity (measured from bank
ratings) and the firm’s age. Interestingly, Hyytinen and Pajarinen (2008) find no link between
information opacity and firm size. In contrast, Canton et al. (2013), in their study of perceived
bank loan accessibility, showed that small and young firms find it more difficult to be
financed by banks than older and larger ones. Levenson and Willard (2000) studied credit line
accessibility from financial institutions in the US in the late 1980s. They observed that 6.36%
of the SMEs in their sample were unable to obtain financing and 4.22% chose not even to
apply because they anticipated a denial decision. They also showed that restricted firms were
the smallest ones, confirming the link between size and loan accessibility. Freel et al. (2012)
reported similar results in the UK: the discouraged firms were smaller or lacked close
relationships with banks or service firms. Chakravarty and Xiang (2013) also showed that
older firms are more likely to apply for debt financing in developing countries, and that strong
relationships with the banking system reinforce this link.
The first aim of the present paper is to determine whether these results will also be
observed for SMEs that apply for financing in Europe. The first empirical question is thus:
Are firm age and firm size relevant factors in explaining access to bank financing and trade
credit (debt capacity)?
Arm’s lenghth financing refers to a situation in which the investor has no other information than public one
and poor capacity to renegotiate a debt contract (e.g., a bondholder).
5
5
2.2 Trade credit versus bank financing
Trade credit has several advantages over bank financing from the supplier’s viewpoint:
suppliers obtain information more easily on the quality of firms that are likely to be credit
constrained (Biais and Gollier 1997). They can also closely monitor customers. In addition,
inputs (e.g., transacted goods) are strong collateral for suppliers. Burkart and Ellingsen (2004)
argue that suppliers take less risk than banks since it is less easy to divert inputs than cash.
This implies that firms that apply for trade credit should be less subject to credit constraints.
Information asymmetries and problems of moral hazard and adverse selection are less severe
for trade credit applications than for bank loan applications (García-Teruel and MartínezSolano 2010).
What is the relationship between bank loan and trade credit applications, and the roles
of firm age and firm size in this relationship? Empirically, Petersen and Rajan (1997) show
that better-quality firms in the US obtain more trade credit. However, trade credit is expensive
and is therefore used more intensively by firms that have restricted access to bank financing.
In contrast, Giannetti et al. (2011) show that US firms receive trade credit at low cost.
Giannetti et al. (2011) also prove that trade credit and bank lending are more likely to be
complements than substitutes Receiving trade credit can be considered as a positive signal
that makes banks less reluctant to lend. Giannetti et al. (2011) also showed that firms
requiring standardized products (unlike differentiated ones) obtain less trade credit,
suggesting that the nature of inputs influences suppliers’ trade credit policies.
Psillaki and Eleftheriou (2014) compare trade credits and bank financing in French
SMEs before and during the financial crisis that began in 2007. Their study only considered
bank loans repayable within one year and not long-term loans, and they focused only on
certain industries. They showed that bank financing acts more as a complement than as a
substitute for trade credit for some sectors and that this effect is stronger during a financial
crisis, with differences between sectors. Agostino and Trivieri (2014) showed that banks in
Italy consider trade credit information when they make lending decisions, also suggesting a
complementary rather than a substitutive mechanism between the two sources of finance. This
effect is stronger when banking relationships are younger and is still observed after several
years of lending relationships.
To our knowledge, very few papers have tested the relationship between firm size and
firm age and trade credit. Using Spanish data, Sánchez-Vidal and Martín-Ugedo (2012) show
that young and small firms use more trade credit than old and large firms. However, they
make the comparisons only with short-term debt. García-Teruel and Martínez-Solano (2010)
6
focus on the determinants of trade credit using data from seven European countries. Although
these authors expected that small and young firms would have fewer possibilities to access
external financing and hence would be more likely to use trade credit than old and large firms,
they actually found positive relationships between firm size and firm age on the one hand and
the trade credit received by SMEs on the other.
Building on this research, we here investigate whether firm age and firm size are
relevant variables to explain financing restrictions for SMEs at the European level. We also
investigate the link between trade credit and bank financing application (e.g., are they
complement or substitutes?).
3 Data, variable definitions and methodology
3.1 Data
Our analysis is based on the Flash Eurobarometer survey on access to finance (no. 271)
conducted in the European Union Member States and Iceland. Data were collected using fixed
telephone lines. Respondents were chief executive officers or chief financial officers.6 The
dataset enables a study of the determinants of SMEs’ debt capacity in a multi-country sample.
The focus is on two related questions in the survey. The first question reads as follows:
“For each of the following ways of financing, could you please indicate whether you applied
for them over the past 6 months, or if you did not apply because you thought you would be
rejected, because you had sufficient internal funds, or you did not apply for other reasons?”
Table 1 shows the percentage of SMEs applying for bank financing and trade credit for each
country.7 There are 7,831 SMEs in our initial sample with 25% (1,981 SMEs) of SMEs that
have applied for bank financing in the 6 months prior to the interview, and 11% (899) of
SMEs that have applied for trade credit financing.
The second question focuses on the success of these applications for bank financing and
trade credit. The relevant question is as follows: “If you applied for and tried to negotiate for
this type of financing over the past 6 months, did you receive all the financing you requested,
6
The surveys were conducted on behalf of the Directorate General for Enterprise and Industry of the European
Commission, in cooperation with the European Central Bank. The survey excluded companies in the following
sectors: agriculture, fishing, public administration, financial services, and activities of households, extraterritorial organizations and holding companies.
7
The questionnaire also included an additional category of external financing called: “Other external
financing” that included “loans from other lenders, overdrafts, credit lines, equity or debt issuance, leasing,
factoring, etc., but excludes bank loans and trade credit.” We excluded this category because its description was
too broad and addressed different research questions. See, for example, Cosh et al. (2009). The authors explored
rejection rates considering differences between several outside finance sources.
7
did you receive only part of the financing you requested, or did you receive it only at
unacceptable costs or terms and conditions so you did not take it, or did you receive nothing
at all?” Last two columns of Table 1 presents the acceptance rates for bank financing and
trade credit financing, defined as the percentage of requests not being denied. In general, we
observe high levels of acceptance but with a difference between bank debt and trade credit.
That is, the mean acceptance rate was around 82% among SMEs that applied for debt
financing and 85% among SMEs that applied for trade credit financing.
The final sample used in the regression analyses is reduced, although firms may have
said that they had applied for funding, a number of respondents did not answer the question
on the effective access to credit (148 firms for debt loan and 71 firms for trade credit). We
also have missing values on the independent variables used in the regression models that
impact on the number of observation in each model specification.
-
Here Table 1-
3.2 Dependent variables
For our multivariate analysis, we focus on the second question regarding the success of
the applications, and construct three dependent variables. The first variable is a “global” debt
capacity variable that takes a value of 1 if the answer was “Applied and got everything” or
“Applied but only got part of it” for both sources of finance, and 0 otherwise (Application
success). In line with earlier literature (e.g., Biais and Gollier, 1997; Burkart and Ellingsen
2004), we also consider the determinants of bank loans and trade credit separately. That is, the
second dependent variable focuses on bank loans (Application success for bank financing),
whereas the third dependent variable zooms in on trade credit(Application success for trade
credit). Both variables take a value of 1 if the answer was “Applied and got everything” or
“Applied but only got part of it”, and took a value of 0 if the answer was “Applied but refused
because cost too high” or “Applied but was rejected” for the specific source of finance.
8
3.3 Firm age and firm size
We use natural logarithm of the number of years since the date of an SME’s founding
(Age) and the natural logarithm of the number of employees (Size) to investigate whether
there is a relationship between the dependent variables and firm size/age8.
3.4 Control variables
We use several control variables to control for firm-specific characteristics and include
industry dummies. Furthermore, D_innovation is a dummy variable that takes a value of 1 if a
firm has introduced new products or new processes in the past year. This variable tests
whether innovative activities are able to open up financial opportunities (e.g., Francis et al.
2012). To control for firm productivity, we include the variable Change in Markup that took a
value of 1 if a firm’s markup has decreased over the past year; a value of 2 if it has remained
unchanged, and a value of 3 if it has increased. Markup was defined as the selling price minus
production cost per unit.
Other control variables refer to a firm’s turnover growth expectations and financing
expectations. The variable Expected Growth takes a value of 1 if a firm’s expected turnover
growth is negative, a value of 2 if it is expected to remained unchanged, a value of 3 if it is
expected to be moderate (below 20%), and a value of 4 if it is expected to be high (over 20%).
To control for industry effects, we distinguish between seven sectors: mining,
construction, manufacturing (including electricity, gas and water supply), wholesale or retail
trade, transport, real estate, other services.9
Much of the financial economics research has found that a country’s financial
development is a key determinant of economic growth (e.g., Pagano 1993; Hassan et al. 2011;
Law and Singh 2014). Moreover, the development of financial systems may influence a
firm’s capacity to access external capital beyond what the firm’s characteristics would seem
to justify. We expect that between-country variations in financial development would
influence a firm’s access to external finance, and we use three measures of the development
of financial systems. The data are from the World Bank dataset for the year 2008. We use
proxies for financial development that previous studies have shown to be related to economic
growth (e.g., Pagano 1993; Hassan et al. 2011), such as domestic credit provided by the
banking sector as a percentage of GDP (Domestic Credit). To reflect the amount and quality
8
Firm age and firm size are transformed into logarithms to control for skewness. Using the continuous
variables for firm size and firm age does not alter our results.
9
Several previous studies have included tangibility in examining financial decisions but this variable was not
available. Gompers (1995) showed that tangibility and industry sector are correlated. This implies that industry
variables can also be a proxy for the agency problems arising in a firm.
9
of information about borrowers available to lenders, we also used the variable Depth of Credit
Information, an index ranging from 0 to 8, with higher values indicating the availability of
more credit information through either public or private credit registries10. Houston et al.
(2010) found that information sharing helped reduce adverse selection problems in loan
screening.
3.5 Methodology
Empirically, to relate external financing capacity to firm age and firm size, as well as to
the control variables listed above, we propose two models. In our baseline specification, we
run the following binary probit model:
Pr(𝐴𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑠𝑢𝑐𝑐𝑒𝑠𝑠 𝑓𝑜𝑟 𝑒𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝑓𝑖𝑛𝑎𝑛𝑐𝑒 )𝑖𝑗 = Φ(𝑋𝑖𝑗 𝛽 + 𝑋𝑗 𝛾)
where Φ is the cumulative normal distribution. The subscript i denotes a firm and j denotes a
country. β is Kx1 and Xij is the ijth firm observation on K firm-level explanatory variables. γ
is Gx1 and Xj is the jth country observation on G financial development variables. To ease
interpretation and enhance comparability across variables and specifications, we report
marginal effects instead of coefficients.
We also check whether our inferences are vulnerable to selection issues. The probability of
denial could be lower for firms that choose to apply and, in this sense, better firms might selfselect for application and this self-selection might explain the application outcome. To
address this selection issue, we use a probit model with sample selection consisting of an
outcome equation and a selection equation. Application success (the outcome equation) is
observed only when a firm decides to apply (the selection equation).
For the model parameters to be identified, the selection equation should have at least
one variable that is not in the outcome equation. The practical problem with this model is the
identification of a valid identification variable that is correlated with credit application, but
not with application success. In the selection equation we use a variable called
D_subsidiaries. This variable is a dummy that takes a value of 1 if a firm claims to be “part of
a profit-oriented enterprise (e.g., a subsidiary or branch) not making autonomous financial
decisions.” For subsidiary firms, we consequently expected a low probability of applying for
10
Previous studies often include another proxy which domestic credit provided to the private sector as a
percentage of GDP. We decided not to consider this variable as it has a close correlation (0.9) with the variable
Domestic credit.
10
external financing. We also include the variable Financial_need. This variable is a dummy
that takes a value of 1 if a firm claims to have an “Increased needs for external financing” . To
verify the validity of the identification variables, we included these two variable in several
specifications of a single equation probit model that estimates the probability of accessing to
debt financing. The results indicated that it was not in any way associated with a firm’s access
to outside finance.
4. Results
4.1 Main results
A correlation matrix for the firm and country-level variables is provided in Table 2. The
low correlations between variables and the value of variance inflation factors (VIFs) generate
no serious concerns with regard to multicollinearity.
-Here Table 2-
Models 1 to 3 of Table 3 show the estimation results for binary probit models with
sample selection. Specifically, models 1 to 3 present coefficients from both the outcome
equation (application success) and selection equation (applied yes/no). The purpose of the
selection equation – and the correlation between the disturbance terms in the selection and
outcome equation – is to verify the presence of selection bias. Models 4 to 6 show the results
for “simple” binary probit models that do not control for selection bias. Models 1 and 4 focus
on both types of finance; Models 2 and 5 focus on bank loans, and Models 3 and 6 zoom in on
trade credit.
We find that the correlation coefficient between the errors terms of the outcome and
selection equations are not significantly different from zero. This implies that a sample
selection issue is not relevant in our data. In brief, the selection equation (the firm’s decision
to apply) presented in Models 1, 2, and 3 suggests that, where significant, firms that decided
to apply are larger, more innovative, more likely to have a negative change in markup and
lower expected growth, than firms that did not apply for credit. There are notable differences
with respect to firm age: Older firms are less likely to apply for trade credit whereas age does
not seem to be relevant for a firm’s application for bank loans. As the firm age rose, the firm
was less likely to apply for trade credit. This variable is insignificant in Model (1) and (2).
11
The results for the identification variable show that the D_subsidiaries was negatively
associated with the decision to apply for external debt financing and that the variable
Financial_need is positively linked with the probability to apply for external financing. These
findings confirm the expected sign for this variable.
Turning to our equation of interest, the results of binary probit models without sample
selection (Models 4, 5, and 6) suggest a number of interesting observations. Overall, to the
extent that increasing age and size indicate diminishing information asymmetries, it is not
surprising that older and larger firms are more likely to obtain capital, especially bank loans.
Specifically, our findings in Model 4 reveal that firm age has a positive impact on the
probability of obtaining both types of financing and that large SMEs are more likely to
receive the financing they applied for, in terms of both bank loans and trade credit. Models 5
and 6 further explore the issues addressed above by considering the difference between bank
financing and trade credit. There are notable differences in terms of which applications were
successful for the two types of finance. When we consider the probability of application
success for bank financing (Model 5), the age and size are positive and significant, suggesting
that old and large SMEs are more likely to receive debt capital. Firms that obtain trade credit
(Model 6) are more likely older. The coefficient variable of size is insignificant in the trade
credit model. Interestingly, older firms are more likely to obtain trade credit than bank
financing. In that respect and consistent with the findings of García-Teruel and MartínezSolano (2010) for the mature phase of the life cycle, access to trade credit may increase
because suppliers have a greater ability to obtain information due to the long-term contact
with their customers.
While we find several significant associations between the firm-level variables and the
probability of obtained the requested bank loan, we observe that many firm-level variables
enter the regressions insignificantly for trade credit. This tends to confirm previous studies
(e.g., Burkart and Ellingsen 2004; Giannetti et al. 2011; García-Teruel and Martínez-Solano
2010) that trade credit financing involves regular relationships with inputs representing strong
collateral in case of liquidation. Firms receiving debt financing (Model 5) are less innovative
and more likely to have positive change in markup.
With respect to the country financial development variables, we find that the variable
Domestic Credit is insignificant in all binary probit models and that the variable Depth of
Credit Info is negatively associated with a firm’s access to debt financing only (Model 5)
highlighting the differences between debt financing and trade credit financing.
12
4.2 Linkage between bank financing and trade credit
In the previous section, both decisions of application success regarding bank financing
and trade credit are treated separately, but the endogeneity between the two decisions is not
controlled for. In this section, we investigate the relationship between the two types of
financing focusing on the subsample of 459 SMEs that have applied for both types of credit
during the analyzed period. Specifically, since empirical evidence suggests that application
success regarding bank financing and trade credit are closely linked and there are common
determinants that affect both decisions simultaneously, to avoid a possible endogeneity
problem, we estimated a bivariate probit (also called biprobit), which allows errors to be
correlated and therefore estimates both equations simultaneously (Greene 2003) 11.
The results of the biprobit estimation of application success regarding bank financing
and trade credit can be seen in Table 4, Model 1. We report only coefficients, making the
marginal effects available upon request12. From the significance of the likelihood ratio test for
the correlation estimator Rho between the two binary variables, we may conclude that a
simultaneous model will generate consistent estimates. The Rho is positive in value which
means that these decisions are positively correlated. The results of keys determinants are
consistent with those obtained in the previous sections confirming a positive impact of firm
age and firm size on both type of sources of capital.
-Table 4 here -
The theory, but also our previous results, suggests that firm-level characteristics determine
how likely an SME is to gain access to debt financing, especially bank loans. Empirically, the
recent literature (e.g., Giannetti et al. 2011; Agostino and Trivieri 2014; Psillaki and
Eleftheriou 2014) includes trade credit in the bank loan equation because trade credit has
information content for banks. These studies tend to suggest than bank lending and trade
credit are complements more than substitutes and that banks consider trade credit as a positive
signal when screening potential borrowers. In this paper, we examine this point using two
dichotomous outcome variables—bank loans and trade credit— using an instrumental
variable approach by using an exclusion restrictions in trade credit equation. Specifically, we
11
We modeled this relationship using the routine biprobit in STATA 12.1.
To be precise, we omitted marginal effects analysis from this table because there are marginal effects on
different joint probabilities to be considered (Greene 1996).
12
13
introduced a new variable called Trade Credit Situation in the trade credit equation that is a
variable set equal to 1 if the respondents claimed that the availability of trade credit was
deteriorated, 2 if it remained unchanged, 3 if this availability was improved. In this way, we
captured the variation in unused trade credit that was not also correlated with the variation in
bank loan access.
Model 2 of Table 4 presents results for IV probit model. The estimated coefficient of
trade credit application success is positive and significant in the bank debt equation.
Therefore, trade credit may have information content for banks debt. We also find that there is
an insignificant effect of the age and size variables in the model. The insignificant effects of
these variables may reflect a substitutive effect of these firm characteristics with debt
financing, signaling the quality of the SMEs.
This result complements our analysis: in addition to age and/or size, having one type of
financing is also considered as a signal from the investors to grant the alternative. We could
sum this up as “only a good creditor can get more credit.” This result confirms that the two
types of financing are complements more than substitutes (Giannetti et al. 2011; Psillaki and
Eleftheriou 2014, in the US; Agostino and Trivieri 2014, in Italy).
4.3 Robustness checks
In this section, we use dummy variables to investigate the impact of firm age and firm
size. Specifically, following Canton et al. (2013), D_young is a dummy variable equal to 1 if
age <10 if the firm was founded fewer than 10 years ago; D_middleage is equal to 1 if firm
age is between 10 and 20 years; and D_middleage2 is equal to 1 for an age between 20 and 30
years. Finally, D_old equals 1 if the firm is older than 30 years; D_old is used as the reference
category in our regression analyses.
Again, three dummies are used to measure the effect of firm size. The first variable,
called D_micro, takes a value of 1 if firms had 1–9 employees. The second, D_small, takes a
value of 1 if small firms had 10–49 employees. Finally, D_medium equals 1 for medium-sized
firms that had 50–249 employees. The group of 50–249 employees is used as the reference
category in the analyses.
-Table 5 here -
14
Table 5 (Model 1 to Model 3) presents the findings about the impact of these categorical
variables. The results of Model 1 are in line with the previous results on continuous variables
but interestingly our results do not imply a monotonic relationship. Specifically, the impact
of the categorical variables for firm age is negative and significant only for young firms (0-9
years) compared to older firms (more than 30 year old) and for micro firms (1-9 employees)
compared with bigger firms (50-249 employees). Model 2 reveals that young (0-9 years) and
middle-aged (10-20 years) SMEs are more likely to be denied access to bank loans compared
with firms over 30 years old and, moreover, there are no significant differences between 2030-year-old firms and firms more than 30 year old. Regarding firm size, we observed greater
differences between micro firms (1-9 employees) and medium-sized firms (50-249
employees) than between small firms (10-49 employees) and medium-sized firms. In contrast,
the trade credit model (Model 3) reveals a significant difference only with respect to the
youngest firms. The firm-size dummy variables have no significant coefficients. The results
for other variables are in line with those of bivariate probit estimation.
This evidence suggests that 20 years of existence is a threshold that banks consider
when screening firms. In addition, very small firms (size effect) have more difficulty
obtaining bank financing. In contrast, trade credit is more restricted only for the youngest
firms. Consistent with previous results (Table 3), this may be because very young firms have
not yet established regular relationships with suppliers and the suppliers thus have little
information on their quality, such that they are ultimately reluctant to do business with them.
These results based on European data agree with the theory that size and age are relevant
variables for credit decisions since young or small firms represent more moral hazard.
Interestingly, we prove that this effect is only observed for the youngest firms when they
apply for trade credit and that size does not matter. This tends to confirm our above results.
To control for between-country differences, we also ran multilevel mixed-effects
logistic regressions. Mixed-effects logistic regression is logistic regression containing both
fixed effects and random effects. As a standard logistic model, the fixed portion of the model
is identified by the constant term (intercept) and the coefficients of all independent variables.
The coefficients of the firm-level variables can be interpreted as the output from the previous
logit model. We specified the random effects at the country level, and this model had the
advantage of controlling for random variance at this level. Specifically, we incorporated a
random intercept to test whether a between-country variation exists in the debt capacity:
Pr(𝐷𝑒𝑏𝑡 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦)𝑖𝑗 = 𝐻(𝑋𝑖𝑗 𝛽 + 𝑢𝑖 )
15
for j= 1,…… 28 countries, with i = 1,…. nj firms in country j. Debt capacity relates to our
binary dependent variables; H(.) is the logistic cumulative distribution function, Xiy are our
independent variables and ui is the random intercept.
Table 5, Model 4 to Model 6, shows the results using the continuous variables for firm
age and firm size. The results of this analysis are quite consistent with those of bivariate
probit estimation even with this correction, despite some slight changes in the significance for
some of the variables. The intraclass correlation coefficient (ICC) for application success was
respectively 0.14, 0.25 and 0.16, indicating that 14%, 25% and 16% of the variance in the
dependent variable can be attributed to country-level effects.
We have also considered a different measure of firm size. We reestimate our analysis of
Table 3 (Model 4 to Model 6) using annual turnover instead of the number of employees.
Turnover for year 2008 is an ordinal variable that takes a value of 1 if turnover up to 2 million
euros ; a value of 2 if turnover is more than 2 million and up to 10 million euros and a value
of 3 if turnover is more than 10 million and up to 50 million euros. Table 5 (Model 7 to
Model 9) shows the results. The findings are qualitatively similar for Model 4 to Model 6 of
Table 3.
5. Concluding remarks
The central proposition in this study is that SME age and size are key determinants of
how a firm will directly experience application for bank financing or trade credit. For this
purpose, we used the 2009 Flash Eurobarometer Survey from the European Commission that
includes financing data for 7,831 SMEs with fewer than 250 employees.
We show systematic differences at the firm level in the ability of firms to obtain their
desired capital, depending of the type of debt chosen (i.e., bank financing or trade credit).
Older and larger SMEs are more likely to receive debt capital and firms that obtain trade
credit are likely to be older. Interestingly, we also find that older firms have a greater
probability of obtaining trade credit than bank financing. These results suggest that in the
mature phase of a firm’s life cycle, access to trade credit may increase because suppliers are
better able to obtain information due to long-term contact with their customers (García-Teruel
and Martínez-Solano 2010). Furthermore, firm size has a limited effect on trade credit
application success. Other results also indicate that other firm characteristics have a reduced
impact on the success of trade credit applications. We also confirm that trade credit and bank
financing are more complementary than substitutive and are therefore both good signals that
16
lenders can use when deciding whether or not to grant financing (Giannetti et al. 2011;
Psillaki and Eleftheriou 2014; Agostino and Trivieri 2014). Empirically, our results confirm
the main predictions of theories relative to bank financing and trade credit. They further
develop the empirical research on trade credit compared with bank financing from a broad
European perspective.
Using dummy variables to investigate the impact of the firm age and firm size
categories on the acceptance of requests for external financing, we find that small and young
firms have more difficulty obtaining bank financing. In contrast, trade credit is more restricted
only for the youngest firms. This result confirms the theory that young or small SMEs
represent greater moral hazard for a credit. The theory also assumes that this issue is less
severe in trade credit operations based on long-term relationships between sellers of products
and debtors, as well as on the good collateral represented by inputs (e.g., Burkart and
Ellingsen 2004; García-Teruel and Martínez-Solano 2010). The robustness checks also
confirm that taking into account the between-country variation provided results that are
consistent with the relation previously found between the dependent variables, age and size.
17
References
Ahlers, G. K., Cumming, D., Günther, C., & Schweizer, D. (2015). Signaling in equity crowdfunding.
Entrepreneurship Theory and Practice, doi: 10.1111/etap.12157.
Agostino, M., & Trivieri, F. (2014). Does trade credit play a signalling role? Some evidence from SMEs
microdata. Small Business Economics, 42(1), 131–151.
Ang, J., & Smedema, A. (2011). Financial flexibility: Do firms prepare for recession? Journal of Corporate
Finance, 17(3), 774–787.
Berger, A. N., & Udell, G. F. (1998). The economics of small business finance: The roles of private equity and
debt markets in the financial growth cycle. Journal of Banking & Finance, 22(6), 613-673.
Biais, B., & Gollier, C. (1997). Trade credit and credit rationing. Review of Financial Studies, 10(4), 903-937.
Burkart, M., & Ellingsen, T. (2004). In-kind finance: A theory of trade credit. American Economic Review, 569590.
Canton, E., Grilo, I., Monteagudo, J., & Van der Zwan, P. (2013). Perceived credit constraints in the European
Union. Small Business Economics, 41(3), 701-715.
Carree, M. A., & Thurik, A. R. (2003). The impact of entrepreneurship on economic growth. In Handbook of
entrepreneurship research (pp. 437-471). Springer US.
Chakravarty, S., & Xiang, M. (2013). The international evidence on discouraged small businesses. Journal of
Empirical Finance, 20, 63-82.
Colombo, M. G., Franzoni, C., & Rossi‐Lamastra, C. (2015). Internal social capital and the attraction of early
contributions in crowdfunding. Entrepreneurship Theory and Practice, 39(1), 75-100.
Cosh, A., Cumming, D., & Hughes, A. (2009). Outside Enterpreneurial Capital. The Economic Journal,
119(540), 1494-1533.
de Wit, G., & de Kok, J. (2014). Do small businesses create more jobs? New evidence for Europe. Small
Business Economics, 42(2), 283-295.
Diamond, D. W. (1989). Reputation Acquisition in Debt Markets. Journal of Political Economy, 828-862.
Freel, M., Carter, S., Tagg, S., & Mason, C. (2012). The latent demand for bank debt: characterizing
“discouraged borrowers”. Small Business Economics, 38(4), 399-418.
Francis, B., Hasan, I., Huang, Y., & Sharma, Z. (2012). Do banks value innovation? Evidence from US firms.
Financial Management, 41(1), 159-185.
García-Teruel, P. J., & Martínez-Solano, P. (2010). Determinants of trade credit: A comparative study of
European SMEs. International Small Business Journal, 28(3), 215-233.
Giannetti, M., Burkart, M., & Ellingsen, T. (2011). What you sell is what you lend? Explaining trade credit
contracts. Review of Financial Studies, 24(4), 1261-1298.
18
Gompers, P. A. (1995). Optimal Investment, Monitoring, and the Staging of Venture Capital. The Journal of
Finance, 50, 1461–1489.
Greene, W. (1996). Marginal effects in the bivariate probit model (Working Paper No. 96-11). New York, NY:
Stern School of Business.
Greene, W. (2003). Econometric analysis (5th ed.). Englewood Cliffs, NJ: Prentice Hall.
Han, L., Fraser, S., & Storey, D. J. (2009). Are good or bad borrowers discouraged from applying for loans?
Evidence from US small business credit markets. Journal of Banking & Finance, 33(2), 415-424.
Haro-de-Rosario, A., del Carmen Caba-Pérez, M., & Cazorla-Papis, L. (2015). The impact of venture capital on
investee companies: evidence from Spain. Review of Managerial Science, doi: 10.1007/s11846-015-01695.
Hassan, M. K., Sanchez, B., & Yu, J. S. (2011). Financial development and economic growth: New evidence
from panel data. The Quarterly Review of Economics and Finance, 51(1), 88-104.
Holmstrom, B., & Tirole, J. (1997). Financial intermediation, loanable funds, and the real sector. the Quarterly
Journal of Economics, 663-691.
Houston, J. F., Lin, C., Lin, P., & Ma, Y. (2010). Creditor rights, information sharing, and bank risk taking.
Journal of Financial Economics, 96(3), 485-512.
Hyytinen, A., & Pajarinen, M. (2008). Opacity of young businesses: Evidence from rating disagreements.
Journal of Banking & Finance, 32(7), 1234–1241.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership
structure. Journal of Financial Economics, 3(4), 305-360.
Klapper, L., Laeven, L., & Rajan, R. (2011). Trade credit contracts. Review of Financial Studies, hhr122.
Kon, Y., & Storey, D. J. (2003). A theory of discouraged borrowers. Small Business Economics, 21(1), 37-49.
Law, S. H., & Singh, N. (2014). Does too much finance harm economic growth?. Journal of Banking & Finance,
41, 36-44.
Levenson, A. R., & Willard, K. L. (2000). Do Firms Get the Financing They Want ? Measuring Credit Rationing
Experienced by Small Businesses in the U .S ., Small Business Economics, 14(2), 83–94.
Martínez-Sola, C., García-Teruel, P. J., & Martínez-Solano, P. (2014). Trade credit and SME profitability. Small
Business Economics, 42(3), 561-577.
Pagano, M. (1993). Financial markets and growth: an overview. European Economic Review, 37(2), 613-622.
Petersen, M. A., & Rajan, R. G. (1997). Trade credit: theories and evidence. Review of financial studies, 10(3),
661-691.
Psillaki, M., & Eleftheriou, K. (2014). Trade Credit, Bank Credit, and Flight to Quality: Evidence from French
SMEs. Journal of Small Business Management, doi:10.1111/jsbm.12106.
Rajan, R. G. (1992). Insiders and outsiders: The choice between informed and arm's‐length debt. The Journal of
Finance, 47(4), 1367-1400.
Rajan, R. G., & Zingales, L. (1995). What Do We Know about Capital Structure? Some Evidence from
International Data. The Journal of Finance, 50, 1421–1460.
19
Sánchez-Vidal, J., & Martín-Ugedo, J. F. (2012). Are the implications of the financial growth cycle confirmed
for Spanish SMEs? Journal of Business Economics and Management, 13(4), 637–665.
Table 1. Application and success rate for bank financing and trade credit for each country
Country
Austria
Belgium
Bulgaria
Cyprus
Czech
Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Lithuania
Luxembou
rg
Malta
Netherlan
ds
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United
Kingdom
Total
All SMEs
observations
% SMEs
applying for bank
financing
% SMEs
applying for for
trade credit
% SMEs
application
success for bank
financing
0.857
0.954
0.571
0.931
0.774
% SMEs
application
success for trade
credit
1.000
1.000
0.600
0.800
0.750
194
187
201
101
198
0.242
0.262
0.189
0.297
0.166
0.015
0.133
0.129
0.138
0.065
192
98
95
874
909
195
205
100
85
886
103
98
95
0.125
0.163
0.073
0.273
0.234
0.435
0.141
0.150
0.164
0.352
0.087
0.224
0.200
0.073
0.061
0.042
0.051
0.029
0.112
0.029
0.290
0.247
0.126
0.048
0.132
0.052
0.809
0.600
1.000
0.859
0.841
0.741
0.814
0.800
0.769
0.891
0.750
0.789
0.941
1.000
0.600
1.000
0.857
0.807
0.736
0.800
0.888
0.950
0.971
0.800
0.750
0.750
448
282
0.247
0.166
0.256
0.102
0.740
0.707
0.963
0.760
102
292
100
100
195
897
189
410
0.205
0.229
0.170
0.380
0.128
0.382
0.206
0.175
0.039
0.089
0.010
0.060
0.087
0.254
0.026
0.190
0.857
0.851
1.000
0.842
0.652
0.777
0.843
0.774
0.666
0.875
1.000
1.000
0.666
0.773
1.000
0.893
7,831
0.253
0.114
0.820
0.853
20
21
Table 2. Descriptive Statistics, correlation matrix and variance inflation factors (VIF)
Obs
Mean
20.890
Standard
deviation
21.981
1. Age
2350
2. Size
1.000
2350
34.369
45.761
0.223
1.000
3. D_innovation
2346
0.414
0.493
-0.084
-0.123
1.000
4. Change in markup
2191
1.610
0.693
-0.018
-0.032
0.030
1.000
5. Expected Growth
2250
0.845
0.895
-0.117
-0.154
0.340
0.122
1.000
6. Sector:Mining
2350
0.008
0.087
0.028
0.029
-0.024
0.000
-0.009
1.000
7. Sector:Constr.
2350
0.141
0.348
-0.010
-0.015
-0.081
-0.007
-0.079
-0.036
1.000
8. Sector:Manuf.
2350
0.212
0.408
0.153
0.225
-0.031
-0.015
-0.077
-0.046
-0.210
1.000
9 Sector:Wholes.
2350
0.300
0.458
-0.009
-0.158
0.072
0.010
0.110
-0.057
-0.265
-0.339
1.000
10. Sector:Transp.
2350
0.033
0.179
0.011
0.035
-0.001
-0.012
0.004
-0.016
-0.075
-0.096
-0.121
1.000
11. Sector:Real es.
2350
0.021
0.144
-0.017
-0.057
0.026
0.004
0.022
-0.013
-0.060
-0.076
-0.096
-0.027
1.000
12. Sector: Other serv.
2350
0.286
0.452
-0.126
-0.033
0.015
0.012
0.013
-0.056
-0.256
-0.328
-0.413
-0.117
-0.093
1.000
13. Domestic Credit
2350
138.723
53.993
0.009
0.016
-0.106
-0.076
-0.149
-0.012
0.063
-0.048
-0.102
-0.024
-0.016
0.116
1.00
14. Depth of Credit Info
2350
4.683
1.236
0.029
0.060
-0.122
-0.028
-0.140
-0.021
0.001
0.023
-0.018
-0.029
-0.000
0.013
0.01
1.08
1.12
1.30
1.03
1.36
1.03
1.53
1.23
1.93
1.18
1.09
2.02
1.03
VIF
1
2
3
4
5
6
7
8
9
10
11
12
22
13
For the description of the variables, see Section 3 with the exception of variable Age and Size that are not in natural logarithms. We estimate these correlations using the entire
sample of firms that have applied for bank loans and/or for trade credit financing.
23
Table 3. Regression analyses of those firms that were successful in obtaining finance
Model 1
Selection model:
application success
Age
Size
D_innovation
Change in Markup
Expected Growth
D_subsidiaries
Financial_need
Country-level
variables
Domestic Credit
Depth of Credit Info
Industry dummies
Observations
Censored
observations
Uncensored
Applied
Yes/No
-0.006
(0.004)
0.020***
(0.003)
0.023***
(0.0065)
-0.016**
(0.009)
-0.008
(0.0073)
-0.048***
(0.010)
0.054***
(0.011)
Application
success
0.065**
(0.0327)
0.017
(0.033)
-0.039
(0.057)
0.065 *
(0.037)
0.012
(0.032)
0.001*
(0.000)
0.006
(0.006)
-0.000
(0.000)
-0.009
(0.015)
YES
YES
Model 2
Selection model: application
success for bank financing
Applied
Yes/No
-0.006
(0.005)
0.046***
(0.004)
0.052***
(0.017)
-0.027**
(0.013)
-0.021**
(0.008 )
-0.112***
(0.018)
0.140***
(0.017)
Model 3
Selection model: application
success for trade credit
Application
success
0.047***
(0.013)
0.032**
(0.014)
-0.066**
(0.032)
0.058***
(0.014)
0.015
(0.017 )
Applied
Yes/No
-0.013***
(0.005)
0.028***
(0.004)
0.050***
(0.010)
-0.022**
(0.011)
0.002***
(0.011)
-0.043***
(0.013)
0.074***
(0.013)
0.004
(0.000)
-0.004
(0.010)
0.000
(0.000)
-0.020**
(0.008)
0.000***
(0.000)
0.016
(0.011)
YES
YES
YES
6,806
6,347
6,696
5,064
6,761
5,989
459
1,632
772
Application
success
0.092***
(0.016)
0.031
(0.024)
-0.014
(0.052)
0.021
(0.029)
0.034
(0.034)
-0.001***
(0.000)
0.027
(0.018)
YES
Model 4
Probit model:
Application
success
Model 5
Probit Model:
Application
success for bank
financing
Model 6
Probit Model:
Application
success for
trade credit
0.087***
(0.032)
0.037**
(0.018)
-0.041
(0.069)
0.081**
(0.040)
0.011
(0.042)
0.042***
(0.013)
0.025***
(0.008)
-0.061**
(0.028)
0.054***
(0.012)
0.015
(0.015)
0.066***
(0.012)
0.005
(0.009)
-0.031
(0.032)
0.024
(0.017)
0.023
(0.022)
0.000
(0.000)
-0.008
(0.020)
0.000
(0.000)
-0.018***
(0.006)
-0.000
(0.000)
0.009
(0.011)
YES
YES
YES
459
1,632
772
24
observations
Number of countries
28
Log likelihood
-1,791.023
Model test
LR
test
of
independent
equations (rho = 0)
0.5486
chi2
McFadden's R2
MaximumLikelihood
R2 (Cox-snell R2)
McKelvey
&
Zavoina
R2
28
-4,235.703
28
-2,508.596
0.26
0.89
28
-260.46
259.16***
28
-727.92
542.26***
28
-293.53
273.35***
0.071
0.084
0.247
0.243
0.175
0.160
0.144
0.194
0.137
Model 1, Model 2 and Model 3 are a probit model with sample selection consisting of an outcome equation and a selection equation. Application success (the outcome
equation) is observed only when a firm decides to apply (the selection equation). Model 4, Model 5 and Model 6 are the binary probit model on the probability of application
success. For the description of the variables, see Section 3. Marginal effects (only) are presented. Robust clustered (by country) standard errors are in brackets. A constant is
included but not reported. McFadden’s R2, Maximum Likelihood R2 and McKelvey & Zavoina R2 are goodness of fit measure for probit model.*, **, *** denote
significance at the 10%, 5% and 1% levels, respectively.
25
Table 4. Endogeneity regression analysis
Model 1
Bi-probit model
VARIABLES
Application success for
bank financing
Model 2
IVprobit
2nd stage
Application success for
trade credit
Trade credit application success
Age
Size
D_innovation
Change in Markup
Expected Growth
Country-level variables
Domestic Credit
Depth of Credit Info
Industry dummies
Observations
Log likelihood
Rho
Likelihood-ratio test of rho=0:
Application
success for bank
financing
3.1567***
(0.398)
-0.0732
(0.104)
0.1217
(0.086)
-0.1733
(0.167)
0.1747
(0.124)
0.1538**
0.244**
(0.103)
0.145**
(0.063)
-0.238
(0.154)
0.272**
(0.137)
0.142
0.3465***
(0.078)
0.0811*
(0.042)
-0.1117
(0.175)
0.1717
(0.128)
0.0415
(0.131)
(0.127)
(0.077)
0.001
-0.000
0.002**
(0.001)
-0.066
(0.078)
YES
459
-313.7
0.907
48.447***
(0.002)
-0.082
(0.104)
YES
459
-313.7
(0.001)
-0.014
(0.069)
YES
426
-302.3
.
.
First stage results for IV probit:
Trade Credit situation
0.076***
Wald test of exogeneity
Wald test of all the slope
oefficients are jointly zero
(0.022)
13.05***
416.04***
Model 1 is a bivariate probit (also called biprobit) and Model 2 is an instrumental probit model. For the
description of the variables, see Section 3. Coefficients (only) are presented. For reasons of brevity, marginal
effects are not tabulated. These results are available under request. Robust clustered (by country) standard errors
are in brackets. Likelihood-ratio test of rho=0 for Model 1 support the validity to estimate simultaneously both
equations. The first stage results for IV probit, for Model 2, support the validity of instrumental variable
approach. A constant is included but not reported.
*, **, *** denote significance at the 10%, 5% and 1%
levels, respectively.
26
Table 5. Additional analysis.
Model 1
VARIABL
ES
Applica
tion
success
using firm size
and firm age
categories
Model 2
Applicati
on success for
bank financing
using firm size
and firm age
categories
Model 3
Applicat
ion success for
trade
credit
using firm size
and firm age
categories
Age
Size=employees
Model
4
Mixed
-effects
model:
application
success
Model
5
Mixedeffects model:
application
success
for
bank
financing
0.091*
**
(0.025
)
0.036*
*
(0.019
)
0.047**
*
(0.012)
0.027**
*
(0.008)
Model
Model
6
7
MixedApplic
effects model: ation success
application
using
firm
success
for turnover
trade credit
0.063
***
(0.017)
D_middleage
D_middleage2
D_micro
D_small
-0.224***
(0.071)
-0.047
(0.082)
-0.099
(0.088)
0.193***
(0.044)
-0.065
0.125***
(0.031)
0.094***
(0.022)
-0.066
(0.047)
0.098***
(0.027)
-0.048**
Applica
tion
success
for
bank
financing
using
firm
turnover
Model
9
Applic
ation success
for
trade
credit
firm
turnover
0.079*
*
(0.035)
0.042**
*
(0.012)
0.065*
**
(0.012)
0.080*
**
(0.030)
0.057**
*
(0.020)
0.017
0.009
(0.010)
Size=turnover
D_young
Model 8
(0.017)
0.167***
(0.034)
-0.078
(0.048)
-0.046
(0.045)
-0.0250
(0.031)
0.023
0.0241
-0.037*
-0.023
27
D_innovation
Change
markup
in
Expected Growth
(0.059)
(0.023)
(0.037)
-0.045
-0.052*
0.0663
(0.054
)
0.0687
*
(0.035
)
0.0085
(0.023)
(0.029)
0.0103
-0.017
-0.048
-0.029
(0.019)
(0.065)
(0.031)
(0.033)
0.019
0.0166
0.065
0.020
(0.014)
(0.018)
(0.040)
0.051**
*
(0.013)
(0.015)
0.049***
(0.015)
(0.060)
(0.028)
(0.051)
0.090**
0.051***
-0.037
(0.039)
(0.012)
(0.030)
0.014
(0.039)
0.015
(0.013)
0.024*
(0.014)
0.003
(0.043)
0.014
(0.016)
0.023
(0.023)
0.000
0.000
-0.000
0.000
0.000
-0.000
(0.000)
0.010
(0.000)
-0.011
(0.010)
(0.019)
(0.031
)
Financial
development
(country-level)
Domestic
Credit
Depth
Credit Info
of
(0.000)
-0.011
(0.020)
Industry
dummies
Observatio
ns
Number of
countries
Wald test
of
joint
significance
of
Age dummies
Wald test
of
joint
significance
of
Size dummies
Log
likelihood
(0.000)
0.018***
(0.005)
(0.000)
0.015**
(0.005)
(0.000)
0.011
(0.010)
YES
YES
YES
YES
YES
YES
YES
YES
YES
459
1,634
769
459
1,634
772
431
1527
734
28
28
28
28
28
28
28
28
28
12.48**
32.09**
35.80***
21.89**
*
13.39**
2.46
-723.653
-290.579
259.507
724.152
283.780
249.921
685.491
284.419
254.471
28
Model test
ICC
Compariso
n model
McFadden's R2
Maximum
Likelihood R2
(Cox-snell R2)
McKelvey &
Zavoina
R2
472.75*
**
742.16**
*
331.29**
*
29.12*
**
54.81**
*
33.27*
**
0.142
1.89*
0.249
23.74**
*
0.162
21.86*
**
189.68
***
293.02*
**
245.02
***
0.092
0.107
0.153
0.148
0.085
0.067
0.167
0.180
0.146
0.142
0.072
0.059
0.181
0.208
0.153
0.237
0.193
0.131
Model 1, Model 2 and Model 3 are a probit model with firm age and firm size categories. Reference categories when we use firm age categories are: Age>30 and Employees
50–249 (medium). Model 4, Model 5 and Model 6 are a Mixed-effects logistic regression is logistic regression containing both fixed effects and random effects. Model 7,
Model 8 and Model 9 are probit model with Turnover variable as a proxy of firm size. For the description of the variables, see Section 3. Marginal effects (only) are
presented. Robust clustered (by country) standard errors are in brackets with the exception of Mixed-effects model. A constant is included but not reported. ICC is the
intraclass correlation coefficient Comparison model test is LR test vs. logistic regression model. McFadden’s R2, Maximum Likelihood R2 and McKelvey & Zavoina R2 are
goodness of fit measure for probit model.*, **, *** denote significance at the 10%, 5% and 1% levels, respectively.
29
l.
30