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Bank debt and trade credit for SMEs in Europe: firm-, industry-, and country-level determinants

Authors:
  • Montpellier Business School, Montpellier, France
  • University of Messina (Italy)

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This paper examines differences in the ability to obtain capital—bank loans and trade credit—between firms, industries, and countries using survey data on European small and medium-sized enterprises (SMEs) from 2009 to 2014. The results show that firm age and firm size are positively linked to SMEs’ access to bank loans, but only firm size is positively related to the provision of trade credit. The results also provide empirical support for a complementary rather than a substitutive effect between bank loans and trade credit. Manufacturing SMEs have a significantly higher likelihood of receiving bank loans and trade credit than non-manufacturing SMEs. We find differences across countries in terms of the relevance of firm age and firm size for obtaining capital. In addition, we point at specific country-level variables that explain why obtaining credit is easier in some countries. We perform additional analyses to confirm our baseline results and provide directions for future research.
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Bank debt and trade credit for SMEs in Europe: firm-,
industry-, and country-level determinants
This manuscript is a previous version of an article published in Small Business Economics, DOI:
10.1007/s11187-017-9926-y, copyright Springer Science+Business Media, LLC, available online at:
http://dx.doi.org/10.1007/s11187-017-9926-y
Guillaume Andrieua, Raffaele Staglianòa, Peter van der Zwanb,c
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: g.andrieu@montpellier-bs.com, r.stagliano@montpellier-bs.com.
b Leiden Law School, Institute of Tax Law and Economics, Department of Business
Studies, Leiden University, 2311 ES Leiden, the Netherlands. E-mail:
p.w.van.der.zwan@law.leidenuniv.nl
c Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000
DR, Rotterdam, the Netherlands.
Abstract
This paper examines differences in the ability to obtain capitalbank loans and trade credit
between firms, industries and countries using survey data on European small and medium-
sized enterprises (SMEs) from 2009 to 2014. The results show that firm age and firm size are
positively linked to SMEs’ access to bank loans, but only firm size is positively related to the
provision of trade credit. The results also provide empirical support for a complementary
rather than a substitutive effect between bank loans and trade credit. Manufacturing SMEs
have a significantly higher likelihood of receiving bank loans and trade credit than non-
manufacturing SMEs. We find differences across countries in terms of the relevance of firm
age and firm size for obtaining capital. In addition, we point at specific country-level
variables that explain why obtaining credit is easier in some countries.
Key words: Bank loans; Trade credit; Information asymmetry; SMEs
JEL classification: E44; G32; G33
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1. Introduction
Small and medium-sized enterprises (SMEs), which are 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). Because banks are more likely to
provide loans to firms with more assets (Cosh et al. 2009), i.e., to larger firms, SMEs are
more dependent on alternative forms of financing, such as trade credit (Berger and Udell
1998; Petersen and Rajan 1997). 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). Suppliers have various (non-)financial motivations for granting trade
credit. Trade credit is a way of stimulating sales, for example by offering more favorable
terms with increasing quantities. Trade credit also makes it possible to construct long-term
relationships with customers and help them in difficult periods. Furthermore, it allows
customers to evaluate the quality of goods before paying and therefore is a signal of high
standards (García-Teruel and Martínez-Solano 2010; Klapper et al. 2012). 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 (Jensen and Meckling 1976). 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
negotiations (“applications”).
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Such direct measures of a firm’s access to bank loan and trade
credit have generally been unavailable. The central concept is debt capacity, which refers to
the ability of a firm to obtain all or part of its demand for debt financing (Cosh et al. 2009;
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We define “application” as a situation when a firm enters into negotiation with a bank to obtain a loan or with
a supplier to obtain trade credit. However, trade credit negotiations can be informal or formal, because trade
credit is characterized by more informal relationships compared to bank loans.
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Levenson and Willard 2000; Ang and Smedema 2011).
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Debt capacity may have several
financing sources, such as bank financing and trade credit. Although numerous studies have
investigated the determinants of debt capacity in terms of bank loans, evidence for the
determinants of obtaining trade credit is much scarcer. We have the following four research
aims.
First, we focus on firm size and firm age as relevant firm-level characteristics that
determine whether a requested bank loan or trade credit is granted. Debt financing restrictions
may be severe for small and young firms, thereby hindering the entrepreneurs’ efforts to
develop their businesses. It has been argued that trade credit is a good alternative source of
finance for SMEs (Diamond 1989; García-Teruel and Martínez-Solano 2010), highlighting
the relevance of taking the investigation of trade credit into account in the context of SMEs.
Second, we focus on whether bank financing and trade credit should be considered as
“complements” or “substitutes” (Giannetti et al. 2011; Agostino and Trivieri 2014). SMEs are
inclined to use multiple sources of finance (Moritz et al. 2016). 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 and vice versa (Berger and Udell
1998). Yet, trade credit can also be considered as a complement: backing by suppliers is a
positive signal for a bank during the screening process of a potential borrower. It may thus
reasonably be asked whether trade credit is a positive signal that makes banks less reluctant to
lend.
Third, we investigate whether application success (for bank loans or trade credit)
depends on the sector in which an SME is active. Previous studies (Hall et al. 2000; Taketa
and Udell 2007) suggest that sectors may have a relevant impact on financial choices, and,
hence, that industry differences may be present regarding the provision of credit. Indeed,
Taketa and Udell (2007) find that the availability of the financing form depends on the
industry in which Japanese SMEs are active.
Fourth, we investigate country differences regarding debt capacity. The presence of
country differences for the success of application outcomes can be expected for several
reasons. For example, Casey and O’Toole (2014) investigate whether financial restrictions for
SMEs are more severe in countries that have suffered more profoundly from the crisis, and
they find a high degree of heterogeneity across countries. In the present paper we investigate
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While in this paper we focus on a firm’s access to outside finance, other studies examine other questions
related to debt financing. For example, Canton et al. (2013) examine perceived financial flexibility by
investigating the expected capacity of a firm to access external financing.
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whether the importance of firm age and firm size for obtaining credit depends on the country,
and we determine which country-level variables explain application success.
Our paper contributes to the existing literature in two ways. First, we empirically
investigate firm, industry, and country differences in the provision of bank loans and trade
credit. We use a proxy for debt capacity based on SMEs’ direct experiences with bank loan
and trade credit applications. This approach differs from many studies on SMEs’ access to
finance that tend to concentrate on bank loans alone. Second, we unravel the linkage between
bank loan and trade credit applications to determine whether bank lending and trade credit are
complements or substitutes. To our knowledge, no empirical study conducted in Europe has
yet compared these two forms of financing based on application decision outcomes. For this
purpose, we make use of multiple observations for an SME across years.
Our analysis is based on twelve waves (2009-2014) of the SME Access to Finance
survey carried out on behalf of the European Commission. We underline the importance of
distinguishing between bank loans and trade credit. Our results reveal that firm size and firm
age are relevant variables to explain why European SMEs obtain bank loans, whereas only
firm size is relevant for trade credit, with the largest firms having a higher probability of
receiving trade credit. Regarding the dependency between the two sources of finance, we find
that bank financing and trade credit are complementary rather than substitutive. We also find
differences in loan application success depending on the sector in which an SME is active.
That is, SMEs in the manufacturing industry have a significantly higher probability of
receiving the requested bank loan or trade credit than SMEs in non-manufacturing sectors. In
terms of country differences, we find heterogeneity across countries in terms of the
importance of firm age and firm size, and we point at specific country-level variables that are
important for obtaining credit.
The paper is organized as follows. Section 2 provides a review of the literature. In
Section 3, the data, variables and methodology are presented. The main results and additional
analyses are presented in Section 4. Concluding remarks follow in Section 5.
2 Literature review and hypothesis development
As stated by Jensen and Meckling (1976), in any lending situation, information held by
the lender and borrower is asymmetric. Information asymmetry refers to the situation where
insiders (the SMEs) are better informed about themselves than outsiders such as banks,
suppliers, investors and shareholders. Adverse selection may result from information
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asymmetries, and it indicates that lenders find it difficult to distinguish good borrowers from
bad borrowers.
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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. Yet, insiders often have no incentive to provide
information to outsiders. Credit screening therefore provides an imperfect image of a firm’s
solvability, because certain aspects, such as its long-term strategy, future business
development, and the quality of managers or products, do not appear in a purely financial
analysis. SMEs may then be subject to financing restrictions, in which case SMEs with good
projects may be denied access to finance or are charged high interest rates (Sharpe 1990).
2.1 Firm size and age as determinants of bank financing and trade credit
The literature has shown that firm age and firm size are important determinants of debt
access for SMEs. Young firms experience more problems due to information asymmetries
than older firms, because they have a less successful track record than older firms due to their
limited accounting history (Diamond 1989; Canton et al. 2013). 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. 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.
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Outsiders may therefore be less likely to receive positive
signals on the quality of young SMEs than insiders. Furthermore, financing restrictions for
SMEs should be more severe during crisis periods. lmstrom and Tirole (1997) propose a
model in which firms with different levels of initial capital ask for funding. They take into
account different types of macroeconomic shocks, such as credit crunches, and show that
firms with lower levels of initial capital are hit more seriously by such global financial
restrictions. SMEs in particular, having less capital, are predicted to be more weakened by
such shocks.
Hyytinen and Pajarinen (2008) empirically investigate how a firm’s size and age relate
to information asymmetries. These authors find an inverse link between a firms information
opacity (measured from bank ratings) and its age. Interestingly, Hyytinen and Pajarinen
(2008) find no link between information opacity and firm size. In contrast, Canton et al.
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Moral hazard issues occur after the transaction when the agent wants to maximize its own benefits at the
expense of the principal (e.g., diverting the funds to bad projects).
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Arm’s length financing refers to a situation in which the investor has no other information than public
information and a poor capacity to renegotiate a debt contract (e.g., a bondholder).
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(2013) study perceived bank loan accessibility and show that small and young SMEs perceive
more difficulties to obtain bank loans than larger and older SMEs. Levenson and Willard
(2000) study credit line accessibility from financial institutions in the US in the late 1980s.
They observe that 6.36% of the SMEs in their sample are unable to obtain financing, and
4.22% choose not even to apply because they anticipate a denial decision. They also show
that restricted firms are the smallest ones, confirming the positive link between firm size and
loan accessibility. Robb (2002) confirms that in the US, younger firms have a higher
probability of being denied when they apply for a bank loan than older firms. Freel et al.
(2012) report results in the UK suggesting that discouraged firms are smaller or lack close
relationships with banks or service firms. Chakravarty and Xiang (2013) also show 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.
Similar to banks’ screening processes, the provision of trade credit is influenced by
information asymmetries and one may expect that firms’ age and size also affect this type of
financing. To our knowledge, few papers have zoomed in on firm size and firm age as
determinants of trade credit. García-Teruel and Martínez-Solano (2010) focus on the
determinants of trade credit using data from seven European countries, and find that granted
trade credit represents, on average, 22% of total assets. They find positive relationships
between firm size and firm age on the one hand and the trade credit received by SMEs on the
other.
Distinguishing between two firm-level determinants (firm age and firm size) and two
forms of capital (bank financing and trade credit) results in the following four hypotheses:
Hypothesis 1a Firm age is positively related to application success for bank financing.
Hypothesis 1b Firm size is positively related to application success for bank financing.
Hypothesis 1c Firm age is positively related to application success for trade credit.
Hypothesis 1d Firm size is positively related to application success for trade credit.
2.2 Trade credit versus bank financing
The literature has shown that SMEs may more easily signal their quality to suppliers of
trade credit (Biais and Gollier 1997) than to banks that use a screening process. Mian and
Smith (1994) mention the example of the regular visits of a manufacturer’s sales
representative to its customers. In addition, inputs (e.g., transacted goods) represent strong
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collateral in trade credit transactions: they represent more value for a supplier than for a bank
because the former “… can repossess the merchandise and resell it on more favorable terms”
(Mian and Smith, 1994, p. 76). Burkart and Ellingsen (2004, p. 570) highlight a main
difference between cash and inputs considering the risk of diversion, where diversion is
defined as “… any use of resources which does not maximize the lenders’ expected returns”.
They argue that customers represent less risk for suppliers than for banks, since it is less easy
for customers to divert inputs than cash: inputs are used for current activities and are a good
collateral of the transaction. This implies that firms that apply for trade credit should be less
subject to credit constraints. Also, information asymmetries and problems of adverse selection
and moral hazard are less severe for trade credit applications than for bank loan applications
(García-Teruel and Martínez-Solano 2010). Trade credit suppliers have been found to be less
rigid in their liquidation policies than banks (Huyghebaert and Van de Gucht 2007).
What is the relationship between bank loan and trade credit applications? Empirically,
Peterson and Rajan (1997) document that 70% of US SMEs provide trade credit to their
customers and 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 a
positive signal that makes banks less reluctant to lend. Biais and Gollier (1997, p. 905)
theoretically show that, as suppliers obtain better information on the borrowers’ quality, the
granting of trade credit proves that they accept bearing the default risk of the buyer and that
“… it has good information about the latter”. Banks may then simply observe the access to
trade credit to reduce their own information asymmetry. Casey and O’Toole (2014) show with
European data that firms that are credit rationed are 9% more likely to use trade credit.
However, contrary to our approach, they only consider the current usage of trade credit and
not the application outcome. Agostino and Trivieri (2014) show that banks in Italy take trade
credit information into account when they make lending decisions, also suggesting a
complementary rather than a substitutive mechanism between the two sources of finance. In
particular, they show that the positive effect of trade credit financing on obtaining bank
financing is all the more important, as the relationships with banks are younger.
Our second hypothesis is:
Hypothesis 2 Application success for trade credit is positively related to application
success for bank financing.
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2.3 Role of industries and institutions
The industry of the firms in particular, in the face of similar prevailing circumstances,
may have a relevant influence on financing choices (Harris and Raviv 1991; Mian and Smith
1992). Hall et al. (2000) use UK data to show that capital structure determinants are driven by
the firm’s sector of activity. Previous studies, without distinguishing between different types
of debt, generally focus on the relationship between sectors and leverage ratios (Van der Wijst
and Thurik, 1993; Jordan et al., 1998). These studies find that firms in manufacturing sectors
that typically have a greater concentration of tangible assets (e.g., higher liquidation value),
have better access to debt financing. Yet, only a few papers investigate the relationship
between industry and bank financing. La Rocca et al. (2010) find that firms in manufacturing
sectors use more bank loan financing and obtain long-term debt more easily, due to lower
information asymmetries.
Some papers show that trade credit terms are determined by sectors (Ng et al. 1999;
Klapper et al. 2012). Giannetti et al. (2011) also find an influence of suppliers’ sectors on the
amount of accounts receivable. They further differentiate between the type of goods produced
and show that suppliers of differentiated products (unlike standardized ones) have larger
accounts receivable, suggesting that the nature of the inputs influences suppliers’ trade credit
policies. It is more difficult to break the relationship or to divert these inputs when the
suppliers offer unique products. Taketa and Udell (2007) analyze SMEs in manufacturing and
non-manufacturing sectors and show that sector determines an SME’s probability of obtaining
the required finance. Psillaki and Eleftheriou (2015) further confirm that firms in traditional
or manufacturing sectors obtain trade credit more easily than firms in non-manufacturing
industries. In manufacturing industries processing basic raw materials, it is easier to repossess
and resale the inputs; then, firms belonging to this sector may have easier access to external
financing. It should be observed that the importance of age, size and the application success
of the alternative form of financing is lower due to the reduced information asymmetries in
the manufacturing sector (Mian and Smith 1992; Psillaki and Eleftheriou 2015).
Because of the described benefits of being active in the manufacturing sector, we
formulate a hypothesis about differences in the probability of obtaining finance between
manufacturing and non-manufacturing industries (Hypothesis 3). We also hypothesize that
information symmetries are less of a concern for manufacturing SMEs such that the positive
relationship between age/size and application success is expected to be negatively moderated
by sector (manufacturing versus non-manufacturing; Hypothesis 4). Similarly, we expect the
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positive relationship between application success for trade credit and bank financing to be
negatively moderated by the manufacturing sector (Hypothesis 5).
Hypothesis 3 Being active in the manufacturing sector is positively related to
application success for bank financing and trade credit.
Hypothesis 4 Being active in the manufacturing sector negatively moderates the
positive relationships between firm age and firm size, and application success for bank
financing and trade credit.
Hypothesis 5 Being active in the manufacturing sector negatively moderates the
positive relationship between application success for trade credit and application success for
bank financing.
Macroeconomic factors have been demonstrated to determine SME financing choices
(e.g., Demirgüç-Kunt and Maksimovic 2001). The financial restrictions of SMEs are also
influenced by the quality of the institutions available in a country. Canton et al. (2013) show
that SMEs in countries with a higher concentration of the banking sector find it easier to
obtain bank financing. Demirgüç-Kunt and Maksimovic (2001) study trade credit in 39
countries. They show that a strong banking system is associated with a higher availability of
trade credit, whereas the quality of the legal system reinforces the use of bank debt relative to
trade credit.
The studies in the literature have also analyzed SME financing restrictions within the
context of the recent financial crisis that began in 2007. Psillaki and Eleftheriou (2015)
compare trade credit and bank financing in a sample of French SMEs before and during the
financial crisis. Their study only considers bank loans repayable within one year rather than
long-term loans, and they focus only on certain industries. They show 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. Casey and O’Toole (2014) discuss cross-country
differences in the importance of financial constraints and the propensity to use alternative
sources of finance when firms are constrained. According to these authors SMEs from
distressed countries did not suffer from more stringent financing restrictions than SMEs that
are active in countries not severely hit by the crisis. At the same time, SMEs in these
distressed countries are more likely to apply for alternative financing. Taketa and Udell
(2007) demonstrate that during the Japanese banking crisis, trade credit and bank financing
are more complementary than substitutive.
In the present paper, we investigate whether country-level variables influence the
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relationship between firm age and firm size on the one hand, and application success on the
other. Also, we determine which country-level variables are related to an SME’s application
success for bank loans and trade credit. Because there is a lack of earlier literature on these
topics, we do not formulate hypotheses here.
3 Data, methodology and variable definitions
3.1 Dataset
The dataset (Survey on the Access to Finance of Enterprises: SAFE) enables a study of
the determinants of SMEs’ debt capacity in a multi-country context. The data are collected
using fixed telephone lines, and respondents are the owner, financial manager/director, or
chief financial officer. The SAFE survey has been conducted in various waves since 2009 on
behalf of the Directorate General for Enterprise and Industry of the European Commission, in
cooperation with the European Central Bank. Our analysis considers 12 waves over the period
January 2009September 2014. The original dataset covers 72,849 firm-wave observations
for 11 countries: Austria, Belgium, Estonia, Finland, France, Germany, Greece, Ireland, Italy,
Netherlands and Portugal. In total, 26% of the observations in this dataset reflect applications
for debt financing, and 19% reflect applications for trade credit financing in the six months
prior to the interview. The sample we use in the baseline regression analyses consists of
applying firms only (16,687 firm-wave observations in case of bank loans and 11,562 firm-
wave observations in case of trade credit)
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. A subset of firms has been followed over time for
a consecutive number of time periods; this sample is used for our analysis of the
interrelationship between the two sources of finance.
3.2 Methodology
We proceed with the following baseline binary probit model to test Hypotheses 1a to
1d, which relate SMEs’ debt capacity to firm age, firm size and several control variables:
     
where we use a specification for bank loans and a specification for trade credit.
Furthermore, Φ is the cumulative normal distribution, and the subscript i denotes the firm, j
5
These samples are a bit smaller than one would expect on the basis of the numbers presented above. This is
related to the fact that the question on application success (see below) was not answered, or because of missing
values for the control variables.
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denotes the country, and t denotes the wave. β is Kx1 and Xijt is the ijt-th firm observation on
K explanatory variables (including firm age and firm size and control variables; see below for
an overview of the variables). We also include country dummy variables (), time dummy
variables (), and industry controls in all regressions. To ease interpretation and enhance
comparability across variables and specifications, we report marginal effects (at the means of
the variables). In some model specifications, we replace the country dummy variables with
specific country-level variables (see below).
We extend equation 1 to test Hypothesis 2 by adding a variable measuring an SME’s
success in applying for trade credit in the bank loan specification. Similarly, we add a variable
measuring an SME’s success in applying for bank loans in the trade credit specification.
These two application success variables (to test Hypothesis 2) are defined by the outcomes
observed in the previous wave. Information about application (success) for the previous wave
is available for a subset of SMEs which enables us to link application success in the previous
period to application success in the current period.
To test Hypotheses 3, we focus on the estimates of the marginal effects for a variable
reflecting the manufacturing sector (value 1 for manufacturing, and value 0 for all non-
manufacturing sectors). Hypothesis 4 is tested by adding interaction terms between the
manufacturing sector and firm age/size. Hypothesis 5 is tested by adding an interaction term
between the manufacturing sector and lagged application success.
3.3 Variable definitions
In line with the earlier literature (e.g., Biais and Gollier 1997; Burkart and Ellingsen
2004), we consider the determinants of application success for bank loans and for trade credit
separately. That is, the first dependent variable focuses on bank loans (Application success
bank financing), whereas the second dependent variable focuses on trade credit (Application
success trade credit). We focus on the following question about the successfulness of an
SME’s application for bank financing and trade credit: “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, 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? Both variables take a value of 1 if the answer is “Applied and got everything”
or “Applied but only got part of it”, and a value of 0 if the answer is “Applied but refused
because cost too high” or “Applied but was rejected.”
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Table 1 presents the acceptance rates for bank financing and trade credit financing,
defined as the percentage of successful requests (“Applied and got everything” and “Applied
but only got part of it” versus all requests). The mean acceptance rate was approximately
67.4% for SMEs that applied for bank financing and 65.4% for SMEs that applied for trade
credit financing.
-Insert Table 1 about here-
The vector Xijt of equation 1 contains firm age, firm size, and the firm-level control
variables. Dummy variables capture the impact of firm age: age<2 is a dummy variable equal
to 1 if the firm was founded fewer than 2 years ago (used as the reference category in our
analyses); age 2-5 is equal to 1 if the firm age is between 2 and 5 years; age 6-10 is equal to 1
if the firm age is between 6 and 10 years; age>10 equals 1 if the firm is older than 10 years.
For firm size, the following variables are included: employees 1-9 takes a value of 1 for
micro firms with 19 employees (used as the reference category in our analyses); employees
10-49 takes a value of 1 for small firms with 1049 employees; and employees 50-249 takes a
value of 1 for medium-sized firms with 50249 employees
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.
We include a set of control variables in the regressions. First, we include profit growth
to capture the impact of the firms performance on application success. The variable Profit
growth takes a value of 1 if a firm’s profit has increased over the past six months (Casey and
O’Toole 2014), and 0 otherwise. To capture ownership, we use dummy variables for each
type of ownership. We distinguish among: a) public shareholders, b) family or entrepreneurs,
c) other firms, d) venture capital firms/individual investors, e) single ownership, and f)
another type of ownership. Previous studies find that ownership structure has an impact on
access to bank loans (Canton et al., 2013) and trade credit (Psillaki and Eleftheriou 2015). To
control for the industry in our baseline model, we distinguish among four industries:
manufacturing, construction, trade, and services (note that for Hypotheses 3-5 the non-
manufacturing sectors will be merged into one category).
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We also control for wave effects by
including dummy variables.
6
We do not include a continuous specification for age and employees because such a continuous measure is not
available for each wave.
7
Several previous studies include tangibility in examining financial decisions, but this variable was not
available. Gompers (1995) shows 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.
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Data on country-specific variables are taken from the World Bank website for the years
2008 to 2013 (the country-specific variables are measured one year prior to the actual wave of
the survey). First, following Demirgüç-Kunt and Maksimovic (2001), we consider the growth
rate of gross domestic product (GDP growth) because firms in fast-growing economies may
be more in need of credit than firms in non-expanding economies. We also use variables to
measure financial development and the size of the real sector that previous studies have found
to predict the use of external finance (Demirgüç-Kunt and Maksimovic 2001). Furthermore,
we consider two proxies for the development of the financial system that may influence a
firm’s capacity to access external capital. We use domestic credit provided to the private
sector as a percentage of GDP [Ln(Domestic Credit)] and the number of commercial bank
branches per 100,000 adults [Ln(Bank branches)]. Finally, to measure the size of the real
sector, we use the ratio of trade to GDP (Trade) and the variable Inflation to control for price
distortions.
4. Results
A correlation matrix for the firm-level independent and control variables is provided in
Table 2. The low correlations between variables generate no serious concerns with regard to
multicollinearity. Table 2 also presents descriptive statistics for the entire sample of firms that
applied for bank and trade credit financing.
-Insert Table 2 about here-
4.1 Main results
Models 1 and 2 of Table 3 present the baseline results with Application success bank
financing and Application success trade credit as the dependent variables, respectively. There
are notable differences in the determinants of application success for each type of financing.
When we consider Model 1, we observe that firm age and firm size are significantly and
positively related to the probability of application success for bank financing. Hence, older
and larger SMEs are significantly more likely to receive the requested bank loan than younger
and smaller SMEs. Specifically, the impact of firm age is significant and positive for SMEs
that have been in existence for at least 6 years. Concerning firm size, firms with at least 10
employees have a significantly higher probability of retrieving their requested bank loan than
14
micro firms (1-9 employees). In particular, the differences in application success for bank
finance are significant and more marked for firms having more than 50 employees versus
micro firms (the probability of receiving the requested bank loan is 8 percentage-points higher
for firms with between 50 and 249 employees than for firms with between 1 and 9
employees).
These results are consistent with the first two Hypotheses (1a and 1b). Overall, the
results suggest that banks consider an “age threshold” when they screen firms. Both firm size
variables have significant coefficients in our bank loan specification.
In contrast, the trade credit model (Model 2; Application success trade credit) reveals a
significant and positive impact only for firm size (SMEs with at least 50 employees).
Hypothesis 1c is not supported whereas hypothesis 1d is partially supported. These results are
in line with García-Teruel and Martínez-Solano (2010), who find a significant impact of firm
size on received trade credit
8
.
-Insert Table 3 about here-
To investigate Hypothesis 2, we examine the link between success in obtaining one
source of finance and a previous success in obtaining the alternative source of finance.
Based on contingency tables and statistical tests for independence (Appendix: A.1 and
A.2), we find a general dependency between bank loan and trade credit application success
for a subsample of firms that applied for both types of financing. SMEs that applied for and
obtained one type of financing also applied for and obtained the alternative type of financing.
At the same time, firms that failed to obtain one type of financing were unsuccessful in
obtaining the alternative type of financing.
We use a binary probit model to empirically investigate the relationship between the
two types of financing, and we focus on the subsample of SMEs that applied for both types of
finance in consecutive waves. Models 3 and 4 of Table 3 present these results. It turns out that
trade credit application success in the previous period significantly and positively predicts
bank loan application success in the current period (Model 3). Also, bank loan application
success in the previous period significantly and positively predicts trade credit application
success in the current period (Model 4). Hence, creditors consider previous application
8
However, our empirical focus is different because García-Teruel and Martínez-Solano (2010) consider the
level of trade credit rather than direct experiences with bank loan and trade credit applications.
15
success in assessing a current application. It seems that by reducing the sample of firms to
only those that had been involved in both types of access to external finance, one form of
financing is affected by the other. These results confirm Hypothesis 2 on the complementary
effect between trade credit and bank financing application success.
In the last four models of Table 3, we add country variables to the regression
specifications to control for specific macroeconomic conditions. Specifically, we find that
GDP growth is significantly and positively associated with the probability of obtaining bank
financing and trade credit. The first proxy for financial development, Ln(Domestic credit), is
significantly and positively related to the probability of obtaining bank financing. The second
proxy, Ln(Bank branches), is significantly and positively associated with the probability of
obtaining trade credit. Globally, this finding implies that countries with an efficient financial
system are, on average, characterized by a favorable financial environment for SMEs. Finally,
Trade and Inflation play a significant role for trade credit application success rather than bank
loan application success.
Hypotheses 3, 4, and 5 are tested in Table 4. Columns 1 and 2 of Table 4 replace the
industry dummies from Table 3 with a manufacturing versus non-manufacturing dummy
variable. Overall, we find that firms in the manufacturing industry have a significantly higher
probability of obtaining external financing, confirming Hypothesis 3. Following Taketa and
Udell (2007), Table 4 adds interaction terms between the manufacturing sector on the one
hand, and firm age (Columns 3 and 4), firm size (Columns 3 and 4) and lagged application
success (Columns 5 and 6) on the other hand. The results show the importance of the
manufacturing sector in shaping the magnitude of the baseline relationships. The relationship
between firm age and an SME’s application success for bank financing and trade credit is
negatively moderated by the manufacturing industry.
9
For firm size we do not find a
moderation effect for either type of financing; hence, Hypothesis 4 is partially supported. We
do not find significant coefficients of the interaction terms between the manufacturing dummy
and application success in obtaining the alternative form of financing. Thus, Hypothesis 5 is
not supported. In sum, our sector-specific analysis highlights the role of industries in the
9
Additional Wald tests in Column 3 of Table 4 reveal that the sum of the coefficients of the age dummy
variables and the interaction terms lead to non-significance for age 6-10 and age>10, and a significant negative
coefficient for age 2-5 (p-value<0.10). For trade credit (Column 4) we find significant negative coefficients for
age 2-5, age 6-10, and non-significance for age>10. In sum, there is some evidence that younger SMEs in the
manufacturing sector have a higher likelihood of obtaining credit than older SMEs. To further check the
robustness of our findings, we provide an analysis by partitioning the sample of firms into non-manufacturing
and manufacturing firms. We find that the relationships between firm age/size and application outcomes are
weaker in the manufacturing sector than in the non-manufacturing sectors. For reasons of brevity, these results
are not tabulated but are available upon request.
16
context of application success: in the manufacturing sector, suppliers consider the value of the
inputs delivered to be strong collateral (Biais and Gollier 1997; Mian and Smith 1992 and
1994).
-Insert Table 4 about here-
Finally, we investigate differences at the country level. Therefore, Table 5 shows the
marginal effects of firm age and firm size for the 11 countries. Control variables as in Table 3
are also included. Clearly, there is large heterogeneity in terms of the importance of firm age
and firm size for application success. These country-by-country results appear to be in line
with earlier analyses in the European economic environment (Casey and OToole 2014) that
find strong heterogeneity across countries. In particular, for bank financing (Table 5, Panel
A), the marginal effect of firm age is statistically significant mainly in German legal-origin
countries such as Austria, Estonia, and Germany. Firm size is important for bank loan
application success in seven out of 11 countries. Concerning trade credit (Table 5, Panel B),
an impact of firm age is generally absent while for firm size we find a significant impact for
Belgium, Greece, Italy, and the Netherlands.
10
-Insert Table 5 about here-
4.2 Additional analyses
Sample selection and discouraged borrowers. Models 1 and 2 in Table 6 show the
results for binary probit models with sample selection that consist of an outcome equation
(Application success) and a selection equation (applying or not).
11
For model identification, it
is necessary to include a variable that is correlated with the decision to apply, but not for
Application success. We use the variable D_subsidiaries, which 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
10
In a further analysis, we also consider the relationship between the two types of financing for each country.
The results (which are not reported) confirm a positive relation between bank loans and trade credit financing.
Too few observations in the panel data were available for each country. This implies that, to obtain an estimate
of the relationship, we run a simple regression model with the response variable y as one type of financing and
the predictor variable x as the other type of financing in the previous period.
11
The related survey question on the decision to apply was 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?
17
autonomous financial decisions”, and 0 otherwise. For subsidiary firms, we expect a low
probability of applying for external financing, which is verified in a single equation probit
model with the decision to apply as the dependent variable. We also find that this variable is
not significantly associated with an SME’s access to external finance. Importantly, the
coefficients for the outcome equation (application success)
12
are in line with our previous
results. In addition, we find that the error terms of both equations in the sample selection
model are not significantly correlated (p-values>0.10 for both dependent variables). Hence,
selection does not seem to be a concern in our case.
To further check the robustness of our findings, we take into account discouraged
borrowers. Kon and Storey (2003) develop a theory that predicts that good and bad borrowers
can become discouraged due to information asymmetry and application costs. Empirical
studies have examined discouragement in the case of bank loan applications (Levenson and
Willard 2000; Han et al. 2009; Freel et al. 2012; Chakravarty, and Xiang 2013). To assess the
role of discouragement, we use the definition of Freel et al. (2012, p. 400): “… firms that
chose not to apply for fear of rejection”. According to this definition, we re-estimate Models 1
and 2 of Table 6 and consider the fact that SMEs that did not apply because of a fear of
rejection. This implies that in the selection equation, the dependent variable has a value of 1 if
the firm applied for external financing and 0 if the firm did not apply because of a fear of
rejection (these are the discouraged borrowers). Models 3 and 4 in Table 6 show the results.
Overall, qualitatively similar results are found.
-Insert Table 6 about here-
Ordered probit model. Table A.3 in the appendix (Models 1 and 2) shows the results of
ordered probit regressions to account for the ordered nature of our dependent variables. The
dependent variable in this case takes a value of 3 if the firm received all financing (100%), a
value of 2 if it received most financing (75%99%), a value of 1 if it received some financing
(1%74%) and a value of 0 if it received no financing (0%). The coefficients we obtain for
firm age and firm size confirm our previous results. We find that, where significant, the
coefficients are positive, underlining that the very small (19 employees) and very young (age
<2) firms are less able to obtain debt financing.
12
For brevity, the results for the selection equation (applied yes/no) are not tabulated but are available upon
request.
18
Firm size. We also consider a different measure of firm size. We re-perform our
analysis in Appendix Table A.3 (Models 3 and 4) using annual turnover instead of the number
of employees (distinguishing between Turnover<2 million euros, Turnover 2-10 million
euros, and Turnover 11-50 million euros). The findings are qualitatively similar to the results
of Models 1 and 2 in Table 3.
Application decision and current usage of bank and trade credit. We applied an
additional analysis to check the relationship between the decision to apply for bank financing
and its current usage and between the decision to apply for trade credit and its current usage.
Each of these variables of current usage is binary and takes the value of 1 if the firm used it in
the past 6 months and 0 otherwise. Significant results are found for variables that measure the
current usage of debt financing. Firms that are experienced with the use of bank and trade
credit are more likely to ask for financing. Specifically, we find that firms that are
experienced with bank loans are 21.8% more likely to ask for bank financing and that firms
that are experienced with trade credit are 31.2% more likely to ask for trade credit. The results
are statistically significant at the 1% level (complete tables are available upon request). This
implies that recent lending experiences can boost future access to external financing.
5. Discussion and conclusion
We use the SAFE database (2009-2014) from the European Commission, which
includes financial data for SMEs (fewer than 250 employees). We analyze outcomes of SME
applications for bank loans and trade credit. Bank loans are the largest source of finance
among SMEs (Petersen and Rajan 1994). At the same time, SMEs are dependent on
alternative sources of finance, such as trade credit, given their restricted track record and
reputation (Berger and Udell 1998; Petersen and Rajan 1997).
There are two major outcomes of the present study. First, we find that the probabilities
of receiving trade credit or a bank loan have different determinants. Second, we demonstrate
the dependency between the two sources of finance.
Regarding the determinants of receiving a bank loan and trade credit, we find that older
and larger SMEs are more likely to receive bank financing, while the results are less
convincing for trade credit. That is, firm age is unrelated to the probability of receiving trade
credit, and for firm size, there is no significant difference in the probability of receiving trade
credit for the smallest firms (less than 10 employees) and those between 10 and 49
employees. A relatively small marginal effect is found for the largest SMEs. Our results
suggest a different screening process between the two financing options. The relationships
19
with suppliers of credit and information asymmetries play a more substantial role for bank
loans than for trade credit. Future research should reveal whether this is indeed the case by
using continuous measures of firm age and firm size and measures of information
asymmetries (Hyytinen and Pajarinen, 2008).
We also add to the literature in terms of the interrelationship between sources of
finance. Our results confirm that trade credit and bank financing are complementary rather
than substitutive. That is, in our specification, we take account of an SME’s successful
application for bank finance in the past when using application success for trade credit as the
dependent variable. At the same time, a successful application for trade credit in the past was
added to the model with application success for bank finance as the dependent variable. We
find a significant positive coefficient for the lagged variable in either case, which points to a
complementarity effect between the two sources of finance. In other words, a successful
application for either source of finance is beneficial for an SME’s chance of receiving the
other source of finance. The provision of finance through either source can be seen as a signal
lenders use when deciding whether or not to grant financing (Giannetti et al. 2011; Psillaki
and Eleftheriou 2015; Agostino and Trivieri 2014).
This study also focuses on the determinants of receiving bank loans and trade credit at
the industry level and country level. We find that application success is greater for SMEs in
the manufacturing sector compared to SMEs in the other sectors, and that firm age and firm
size have less profound impacts on application outcomes in the manufacturing sector than in
non-manufacturing sectors. Regarding differences across countries, we find the following.
While a significant relationship between firm age and access to bank loans is found in our
main sample, a country-specific analysis reveals statistical significance in a few countries
only. Despite the relatively small and varying sample sizes across countries, we believe this is
an indication of heterogeneity across countries in terms of the importance of firm age and
firm size for application success for bank loans and trade credit. These findings are consistent
with recent cross-country empirical studies (Casey and OToole 2014). Finally, our analysis
reveals different country-level determinants of bank loan and trade credit application success.
In addition to the possible avenues for future research described above, further
empirical analysis, with the inclusion of credit scores or ratings, should reveal whether it is
indeed the case that only good creditors are granted access to additional amounts of credit.
Such research could also include objective financial indicators of SMEs that were unavailable
for the present study. For example, we included a subjective measure of profit growth in our
analyses, whereas previous research pointed to profit or assets as being important in lending
20
decisions. Another limitation is the restricted size of the panel dataset. With more waves
becoming available in the coming years, it will be possible to analyze the dynamics of SME
financing more narrowly and to apply more-advanced panel data techniques.
A clear avenue for further research is the integration of other forms of finance. For
example, Cosh et al. (2009) examine the determinants of capital obtained from several forms
of financing, such as venture capital, hire purchase or leasing, factoring, and invoice
discounting. Moritz et al. (2016) and Masiak et al. (2017) use a cluster analysis approach and
identify several SMEs financing profiles. There are also recent studies that examine the role
of crowdfunding (Ahlers et al. 2015; Colombo et al. 2015) and the impact of venture capital
finance on a firm’s financial structure (Haro-de-Rosario et al. 2016). Further research is also
needed to develop better proxies for asymmetric information to improve the analyses of
access to debt financing and the role of dynamics in the lending relationship (especially for
debt financing). For example, Roberts (2015) shows that the terms of renegotiation (i.e.,
modifications in the contractual constraints) are an integral part of bank lending that impact
the behavior of the contracting parties. This suggests that the dynamics of renegotiation
throughout the relationship influence the role of the initial contract terms and, consequently,
the initial accessibility to debt financing. Finally, the analysis of potentially differential
determinants across several forms of finance can be extended from firm age and firm size to
other variables, such as financial indicators or ownership structure.
In sum, our results of the analyses of the application decision outcomes of European
SMEs add new insights into their financial restrictions and the way in which multiple sources
of finance are used over time. These findings may be useful for policymakers who are
committed to encouraging the growth and viability of SMEs. Our results demonstrate the
usefulness of financial data collection initiatives over time, and future research can profit
from more extensive longitudinal datasets with the inclusion of a wide array of financial
sources.
21
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24
Table 1. Application (success) information for bank financing and trade credit for each
country
Country
SMEs that
applied for
bank
financing
Application
success for
bank
financing (as
% of SMEs
that applied)
SMEs that
applied for
trade credit
Application
success for
trade credit
(as % of
SMEs that
applied)
Austria
902
0.833
366
0.842
Belgium
1,159
0.792
427
0.696
Estonia
2,130
0.540
663
0.597
Finland
3,207
0.835
3,025
0.888
France
679
0.806
554
0.661
Germany
3,181
0.817
858
0.846
Greece
1,078
0.387
1,212
0.394
Ireland
564
0.489
1,185
0.647
Italy
3,225
0.633
2,642
0.722
Netherlands
503
0.467
378
0.513
Portugal
859
0.638
708
0.675
Total
17,487
0.674
12,018
0.654
Note: These data refer to the entire sample of firms that have applied for bank loans and/or for trade credit
financing.
29
Table 4. Application success, firm age and firm size. Manufacturing versus non-
manufacturing SMEs (marginal effects are shown)
(1)
(2)
(3)
(4)
(5)
(6)
Application
success
bank
financing
Application
success trade
credit
Application
success bank
financing
Application
success
trade credit
Application
success bank
financing
Application
success
trade credit
Previous application
success trade credit
0.2890***
(0.036)
Previous application
success bank financing
0.2420***
(0.033)
Manuf. Dummy
0.1108**
(0.037)
0.1013*
(0.060)
0.0908*
0.1412**
0.1090**
0.5180**
(0.048)
(0.060)
(0.040)
(0.177)
Manuf. Dummy ×
Previous application
success trade credit
-0.0399
(0.064)
Manuf. Dummy ×
Previous application
success bank financing
-0.0357
(0.045)
Firm age: 2-5
-0.0151
-0.0361
0.0073
-0.0108
0.0366
0.2410
(0.018)
(0.024)
(0.015)
(0.039)
(0.285)
(0.229)
Firm age: 6-10
0.0313
-0.0220
0.0497**
0.0140
0.0056
0.180
(0.024)
(0.032)
(0.020)
(0.039)
(0.266)
(0.208)
Firm age: >10
0.0826***
0.0160
0.0994***
0.0408
0.0151
0.192
(0.026)
(0.024)
(0.019)
(0.034)
(0.251)
(0.228)
Employees 10-49
0.0537***
-0.00163
0.0542***
0.0089
0.0295
0.0695***
(0.015)
(0.013)
(0.017)
(0.013)
(0.045)
(0.023)
Employees 50-249
0.0736***
0.0172
0.0672**
0.0224
0.0597
0.0541*
(0.024)
(0.019)
(0.032)
(0.022)
(0.054)
(0.029)
Manuf. Dummy ×
Firm age: 2-5
-0.1160**
-0.118*
-0.173
-0.547**
(0.058)
(0.067)
(0.427)
(0.269)
Manuf. Dummy ×
Firm age: 6-10
-0.1070*
-0.1600**
-0.1030
-0.5380*
(0.059)
(0.059)
(0.424)
(0.298)
Manuf. Dummy ×
Firm age: >10
-0.1030**
-0.1250**
-0.0754
-0.4780*
(0.049)
(0.061)
(0.399)
(0.268)
Manuf. Dummy ×
Employees 10-49
0.0050
-0.0305
0.0799
-0.0815
(0.019)
(0.020)
(0.064)
(0.088)
Manuf. Dummy ×
Employees 50-249
0.0238
-0.0128
0.114
0.0076
(0.035)
(0.017)
(0.091)
(0.084)
Profit growth
0.0673***
0.0642***
0.0656***
0.0625***
0.0463**
0.0414
(0.008)
(0.010)
(0.008)
(0.009)
(0.023)
(0.029)
Ownership
Public shareholders
0.0318
0.0358
0.0302
0.0338
-0.0459
0.0523
(0.029)
(0.033)
(0.029)
(0.034)
(0.062)
(0.048)
Family/entrepreneurs
0.0368***
0.0234**
0.0365***
0.0229**
-0.00551
0.0633*
(0.010)
(0.009)
(0.010)
(0.009)
(0.026)
(0.037)
Other firms
0.0514***
0.0391**
0.0501***
0.0377**
-0.0147
0.125***
(0.017)
(0.016)
(0.017)
(0.016)
(0.039)
(0.027)
Venture capital firms
-0.0165
-0.0620*
-0.0198
-0.0642*
-0.0873
0.102
(0.027)
(0.034)
(0.026)
(0.034)
(0.139)
(0.070)
Other
0.0541**
-0.0193
0.0529**
-0.0214
0.0553
-0.2550***
30
(0.021)
(0.030)
(0.021)
(0.031)
(0.073)
(0.088)
Country dummies
YES
YES
YES
YES
YES
YES
Wave dummies
YES
YES
YES
YES
YES
YES
Observations
16,687
11,562
16,687
11,562
1,775
1,745
Log likelihood
-10565
-7474
-9,636
-7,014
-1,052
-1,037
McFadden’s R2
0.087
0.060
0.088
0.062
0.135
0.103
McKelvey & Zavoina
R2
0.168
0.125
0.170
0.128
0.255
0.206
Notes: For the description of the variables, see Section 3. Marginal effects calculated at the means are presented.
Robust clustered (by country) standard errors are between parentheses. Reference categories: Age: <2;
Employees: 19; Ownership: Single person.
*, **, *** denote significance at the 10%, 5% and 1% levels, respectively.
31
Table 5. Application success, firm age and firm size across countries (marginal effects
are shown)
Panel A:
Bank
Financing
Country
Firm
age:2-5
Firm age:6-
10
Firm
age:>10
Employees
10-49
Employees
50-249
Obs
McFadden's
R2
Austria
0.028
0.204**
0.146*
-0.003
0.042
824
0.054
Belgium
0.022
0.007
0.044
0.092***
0.081**
1,113
0.084
Estonia
0.015
0.119*
0.142**
0.106***
0.141***
1,940
0.073
Finland
0.012
-0.015
0.028
0.020
0.028
3,095
0.051
France
0.134*
-0.190
-0.155
0.109
-0.065
656
0.062
Germany
0.022
0.014
0.102**
0.074***
0.133***
3,040
0.058
Greece
-0.302
-0.272
-0.169
-0.003
0.105**
1,056
0.045
Ireland
-0.100
0.007
0.031
0.057
0.050
546
0.061
Italy
-0.042
0.016
0.042
0.051**
0.056**
3,093
0.066
Netherlands
-0.105
0.025
0.091
0.187***
0.267***
486
0.110
Portugal
-0.267
-0.136
-0.142
0.074*
-0.005
833
0.069
Panel B:
Trade Credit
Austria
0.041
0.036
0.055
-0.029
0.066
318
0.105
Belgium
0.268
0.088
0.163
0.112**
0.194***
417
0.069
Estonia
-0.022
0.013
0.068
0.028
0.021
587
0.100
Finland
0.002
-0.012
0.019
0.006
0.023
2922
0.045
France
0.064
0.191
0.225
-0.001
0.019
522
0.131
Germany
0.012
0.026
-0.009
0.014
0.016
814
0.043
Greece
-0.234
0.263*
-0.163
0.027
0.129***
1198
0.046
Ireland
-0.131
0.013
0.032
-0.044
0.061
1150
0.055
Italy
-0.037
0.021
0.028
0.042*
0.039**
2,521
0.065
Netherlands
0.159
0.017
0.170
0.117*
0.201***
368
0.067
Portugal
0.057
0.044
0.030
0.070
-0.033
687
0.049
Notes: For the description of the variables, see Section 3. Marginal effects calculated at the means are presented.
All of the other variables and controls (not tabulated), included in Table 3, are enclosed. Reference categories:
Age: <2; Employees: 19; Ownership: Single person. Sector: Services.*, **, *** denote significance at the 10%,
5% and 1% levels, respectively.
32
Table 6. Additional analyses: Selection model and discouraged firms
(1)
Selection model
(2)
Selection
model
(3)
Selection model
and discouraged
firms
(4)
Selection model
and
discouraged firms
Application success bank
financing
Application
success trade
credit
Application
success bank
financing
Application
success trade
credit
Firm age: 2-5
-0.0009
-0.0330
0.0016
-0.0310
(0.019)
(0.023)
(0.015)
(0.027)
Firm age: 6-10
0.0376*
-0.0214
0.0419*
0.0167
(0.022)
(0.033)
(0.022)
(0.027)
Firm age: >10
0.081 ***
0.0155
0.0847***
0.0021
(0.022)
(0.027)
(0.024)
(0.024)
Employees 10-49
0.0405 **
0.0190
| 0.0390**
0.0432***
(0.016)
(0.059)
(0.019)
(0.011)
Employees 50-249
0.0396*
0.0788**
0.0534*
0.0411***
(0.022)
(0.036)
(0.029)
(0.013)
Profit growth
0.0551***
0.0689***
0.0563***
0.0323*
(0.012)
(0.008)
(0.012)
(0.017)
Ownership
Public shareholders
0.0367
0.0179
0.0226
0.0327
(0.028)
(0.082)
(0.032)
(0.028)
Family/entrepreneurs
0.0299***
0.0299 *
0.0302**
0.0004
(0.010)
(0.021)
(0.032)
(0.006)
Other firms
0.0515***
0.0408**
0.0470***
0.0040
(0.014)
(0.018)
(0.016)
(0.017)
Venture capital firms
-0.0103
-0.0379*
-0.0193
-0.0861***
(0.023)
(0.043)
(0.027)
(0.028)
Other
0.0513**
-0.0379
0.0459**
-0.0136
(0.017)
(0.067)
(0.022)
(0.034)
Sector: Manufacturing
-0.0055
0.0189
0.0004
-0.0123
(0.008)
(0.079)
(0.009)
(0.017)
Sector: Construction
-0.0409**
-0.0290
-0.0418**
-0.0439***
(0.017)
(0.073)
(0.017)
(0.012)
Sector: Trade
-0.0014
0.0432
-0.0012
0.0004
(0.007)
(0.065)
(0.009)
(0.014)
Country dummies
YES
YES
YES
YES
Wave dummies
YES
YES
YES
YES
Observations
67,366
62,705
21,539
14,195
Censored observations
50,687
51,148
4,860
2,638
Uncensored observations
16,679
11,557
16,679
11,557
Wald χ2 test (ρ=0)
0.235
0.773
0.202
0.312
Notes: For the description of the variables, see Section 3. Marginal effects calculated at the means are presented.
Robust clustered (by country) standard errors are between parentheses. Reference categories: Age: <2;
Employees: 19; Ownership: Single person; Sector: Services.
*, **, *** denote significance at the 10%, 5% and 1% levels, respectively.
33
Appendix Supplementary Tables
A.1: Contingency table for firms that obtain both trade credit at time t-1 and bank
financing at time t
Notes: The relationship between the two types of financing is analyzed via a contingency table. The tests of
statistically significant independence (Pearson’s chi2 (1), likelihood-ratio, Cramer's V, Fisher’s exact) confirm
the existence of a strong coherent link between the two types of financing.
Previous application
success trade credit =0
Previous
application success trade
credit =1
Total
Application success
bank financing =0
437
54.97%
65.03%
358
45.03%
31.29%
795
100%
43.78%
Application success
bank financing =1
235
23.02%
34.97%
786
76.98%
68.71%
1,021
100%
56.22%
Total
672
37%
100%
1,144
63%
100%
1,816
100%
100%
Pearson chi2(1) = 195.75 (Pr = 0.000)
Likelihood-ratio chi2(1) = 197.55 (Pr = 0.000)
Cramer's V = 0.3283
Fisher's exact = 0.000
1-sided Fisher's exact = 0.000
34
A.2: Contingency table for firms that obtain both trade credit at time t and bank
financing at time t-1
Notes: The relationship between the two types of financing is analyzed via a contingency table. The tests of
statistically significant independence (Pearson’s chi2 (1), likelihood-ratio, Cramer's V, Fisher’s exact) confirm
the existence of a strong coherent link between the two types of financing.
Application success
trade credit =0
Application
success trade credit =1
Total
Previous application
success bank financing =0
409
53.12%
60.15%
361
46.88%
32.46%
770
100%
42.97%
Previous application
success bank financing =1
271
26.52%
39.85%
751
73.48%
67.58%
1,022
100%
57.03%
Total
680
37.95%
100%
1,112
62.05%
100%
1,792
100%
100%
Pearson chi2(1) = 131.95 (Pr = 0.000)
Likelihood-ratio chi2(1) = 132.37 (Pr = 0.000)
Cramer's V = 0.2714
Fisher's exact = 0.000
1-sided Fisher's exact = 0.000
35
A.3 Additional analyses (with application success as the dependent variable)
(1)
Ordered Probit
(2)
Ordered Probit
(3)
Binary probit
(alternative firm size
variable)
(4)
Binary probit
(alternative firm size
variable)
Application success
bank financing
Application
success trade credit
Application success
bank financing
Application success
trade credit
Firm age: 2-5
-0.0801
-0.1406
-0.0033
-0.0175
(0.348)
(0.213)
(0.019)
(0.024)
Firm age: 6-10
0.0869
-0.0906
0.0449*
0.0008
(0.280)
(0.403)
(0.025)
(0.033)
Firm age: >10
0.2506***
0.0071
0.0943***
0.0288
(0.001)
(0.946)
(0.026)
(0.024)
Employees 10-49
0.2228***
0.0777***
(0.000)
(0.010)
Employees 50-249
0.3113***
0.1558***
(0.000)
(0.000)
Turnover 2-10 M
0.0440*
-0.0092
(0.025)
(0.013)
Turnover 11-50 M
0.0750***
0.0455***
(0.02)
(0.017)
Profit growth
0.1885***
0.1955***
0.0657***
0.0620***
(0.000)
(0.000)
(0.008)
(0.009)
Ownership
Public shareholders
0.1387**
0.0988
0.0231
0.0311
(0.026)
(0.180)
(0.0314)
(0.034)
Family/entrepreneurs
0.1306***
0.0854***
0.0370***
0.0214**
(0.000)
(0.007)
(0.010)
(0.009)
Other firms
0.1534***
0.1291***
0.0480***
0.0333**
(0.000)
(0.006)
(0.018)
(0.016)
Venture capital firms
-0.0450
-0.1399
-0.0253
-0.0741**
(0.630)
(0.197)
(0.026)
(0.034)
Other
0.2030**
0.0114
0.0514**
-0.0214
(0.011)
(0.912)
(0.023)
(0.032)
Sector: Manufacturing
-0.0016
-0.0102
-0.0021
-0.00766
(0.951)
(0.750)
(0.011)
(0.017)
Sector: Construction
-0.1263***
-0.1410***
-0.0457***
-0.0471***
(0.000)
(0.001)
(0.017)
(0.013)
Sector: Trade
0.0149
0.0766**
-0.00610
0.0162
(0.575)
(0.018)
(0.011)
(0.014)
Country dummies
YES
YES
YES
YES
Wave dummies
YES
YES
YES
YES
Observations
16,687
11,562
16,397
11,415
Log likelihood
-13,289
-8,988
-9,456
-6,916
Pseudo R2
0.067
0.053
0.088
0.062
McFadden's R2
McKelvey & Zavoina
R2
0.170
0.130
Notes: For the description of the variables, see Section 3. For the binary probit model (Models 3 and 4), marginal
effects calculated at the means are presented. Robust clustered (by country) standard errors are between
parentheses. Reference categories: Age: <2; Employees: 19; Turnover: < 2 million euros; Ownership: Single
person; Sector: Services.
*, **, *** denote significance at the 10%, 5% and 1% levels, respectively.
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... As a result, small businesses typically do not have access to public capital markets and are dependent on financial institutions for external finance (Berger and Udell 2002). Conversely, large firms usually benefit from internal capital markets and face less financing constraints (Andrieu et al. 2018). ...
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