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This paper explores the determinants of access to finance for small and medium enterprises (SMEs) in the context of three Central European countries: Czech Republic, Slovak Republic, and Hungary. The data set of the research is obtained from the BEEPS survey, which is conducted by the World Bank and the European Bank for Reconstruction and Development. This paper empirically analyses firms not only from the SMEs point of view, but also shows results for micro, small and medium enterprises separately. Additionally, we have analysed the determinants of access to finance for SMEs at each country level for an in-depth understanding of country-level variations in SME financing. The results indicate that micro firms and firms owned and operated by women are experiencing a shortage of credits from banks. On the other hand, we found a positive relationship between the pledge of collateral and access to finance. With respect to the medium firms, we found evidence that innovative firms have a larger amount of credit from banks. The empirical results also suggest that the loan size increases as the interest rates increase in particular for SMEs on the whole and for micro-firms, although the interest rate is in a negative relationship with the loan size in Czech Republic.
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REVIEW OF ECONOMIC PERSPECTIVES NÁRODOHOSPODÁŘSKÝ OBZOR
VOL. 17, ISSUE 3, 2017, pp. 263285, DOI: 10.1515/revecp-2017-0014
© 2017 by the authors; licensee Review of Economic Perspectives / Národohospodářský obzor, Masaryk University,
Faculty of Economics and Administration, Brno, Czech Republic. This article is an open access article distributed under
the terms and conditions of the Creative Commons Attribution 3.0 license, Attribution Non Commercial No Derivatives.
Determinants of SME Finance: Evidence from Three
Central European Countries
Ashiqur Rahman,
1
M. Twyeafur Rahman,
2
Jaroslav Belas1
Abstract: This paper explores the determinants of access to finance for small and
medium enterprises (SMEs) in the context of three Central European countries: Czech
Republic, Slovak Republic, and Hungary. The data set of the research is obtained from
the BEEPS survey, which is conducted by the World Bank and the European Bank for
Reconstruction and Development. This paper empirically analyses firms not only from
the SMEs point of view, but also shows results for micro, small and medium enterprises
separately. Additionally, we have analysed the determinants of access to finance for
SMEs at each country level for an in-depth understanding of country-level variations in
SME financing. The results indicate that micro firms and firms owned and operated by
women are experiencing a shortage of credits from banks. On the other hand, we found
a positive relationship between the pledge of collateral and access to finance. With
respect to the medium firms, we found evidence that innovative firms have a larger
amount of credit from banks. The empirical results also suggest that the loan size
increases as the interest rates increase in particular for SMEs on the whole and for
micro-firms, although the interest rate is in a negative relationship with the loan size in
Czech Republic.
Key words: Access to finance, SMEs, Czech Republic, Slovak Republic, Hungary
JEL Classification: G21, O16
Received: 30 January 2017/ Accepted: 18 August 2017 / Sent for Publication: 12 September 2017
Introduction
A number of studies have focused on SMEs and bank financing due to the extreme
importance of SMEs to the world economies (Beck et al., 2006; Ayyagari et al., 2007;
Lee et al., 2015; Hanedar et al., 2014; Belas and Sopkova, 2016). Ayyagari et al. (2007)
showed that SMEs are solely responsible for the creation of about 60 percent of
employment in the manufacturing sector in their analysis of 76 developed and
developing countries. Beck et al. (2006) using the World Business Environment Survey
1
Department of Enterprise Economics, Tomas Bata University in Zlin, Mostni 5139, 76001 Zlin,
Czech Republic.
Corresponding author: Ashiqur Rahman. Email: rahman@fame.utb.cz
2
Department of Economics, Strathclyde Business School, 130 Rottenrow, G4 0QU, Glasgow,
UK.
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(WBES) found that lack of long-term bank finance is the second most important
financing difficulty faced by the SMEs, while high-interest rates and collateral
requirement are on the first and third place. Regardless of significant contribution to the
economy, the survival rate of SMEs is significantly lower than that of large corporate
firms due to various reasons, including restricted access to bank finance, high interest
rates, lack of skilled labour force, existence of technological and financial risks, severe
competition from large firms etc.
Particularly, SMEs face credit discrimination from banks because of their information
opacity. It is quite common that SMEs do not have audited financial statements and, in
fact, it is difficult for the SMEs to show their credit quality, hence, they are credit
rationed by banks (Berger and Udell, 2002; Petersen and Rajan, 2002). In the face of
information opacity, commercial banks make loan decisions based on their own credit
rating models that depend on their own methodological structure. Due to the ambiguous
nature of the credit rating models and information asymmetry between banks and the
SMEs, banks can impose not only higher prices of the loans, but also non-price related
restrictions in SME lending, for example, collateral, shorter maturity, and smaller loan
size (Hanedar et al., 2014; Godlewski and Weill, 2011; Ortiz-Molina and Penas, 2008;
Hernandez-Canovas and Koeter-Kant, 2011; Farinha and Felix, 2015; Kirschemann,
2016). In contrast, large firms can produce better financial statements, which can help
them to get easy access to bank finance (Cenni et al., 2015; Leon, 2015; Knyazeva and
Knyazeva, 2012; Berger and Udell, 2002).
The data of this study came from the survey of BEEPS V, which is a joint project of the
European Bank for Reconstruction and Development (EBRD) and the World Bank
(WB). BEEPS conducted surveys in 30 transition economies covering Europe, Eastern
Europe, Central Asia and Turkey. In this paper, we aim to explore the determinants of
access to finance for SMEs in three Central European countries - Czech Republic,
Slovak Republic, henceforth CR, SKR and Hungary. We have purposefully selected
these countries, as our persuasion is that these countries have similar economic
conditions and hence exploring the bank financing differences may highlight important
findings for SMEs. On the other hand, research shows that SMEs contribute about 65%
of total employment in the Czech Republic, 59% in the Slovak Republic, and 46% in
Hungary (Ayyagari et al., 2007). Considering the importance of SMEs in the economic
systems of these three countries, investigating the factors that may affect access to bank
finance can help the SMEs to overcome the shortage of bank finance and subsequently
can enable them to invest more in activities with added economic value.
Empirical research explored many factors that affect the access to finance for SMEs,
such as information asymmetry, firm characteristics, availability of collateral, borrower
characteristics, lender characteristics, bank market structure and others (Hernandez-
Canovas and Martinez-Solano, 2010; Cenni et al., 2015; Berger and Udell, 2002;
Chakraborty and Hu, 2006; Menkhoff et al., 2012; Irwin and Scott, 2010; Stefani and
Vacca, 2015; Petersen and Rajan, 2002; Beck et al., 2011; Leon, 2015; Godlewski and
Weill, 2011). It could mean that firms with low information asymmetry, lower risk and
pledging collateral to banks might get a larger loan. On the other hand, firms with
higher information asymmetry, poor borrower quality or higher probability of defaults
can receive a smaller loan size or may face credit rationing. Therefore, we may argue
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that the firms which can show better credit quality to banks might receive larger loans
and firms with poor credit quality or higher information asymmetry may be credit
rationed or can only obtain a small loan. We used loan size, as a proxy to measure the
hypotheses of access to finance and examining the effect of firm size, firm age, female
owner, firm innovativeness, crime as a proxy of firm riskiness, collateral and interest
rates in relation to access to finance.
This study may have a significant impact on policy making for the Central European
countries. Moreover, our data set allows us to divide the analysis based on countries and
thus, we can find out the important factors that affect the access to credit for SMEs from
the country perspective. Overall, the paper makes a significant contribution to
understanding the SME finance in the context of bank-based European countries and
adds value to the SME bank financing literature.
The structure of the paper is organised as follows. Section 2 reviews the literature and
the hypotheses. Section 3 describes the data set and model as well as descriptive
statistics. Section 4 presents our empirical results and it is followed by the concluding
remarks.
Literature Review and Hypotheses
Studies used firm size as a proxy for better credit quality and showed that it can
positively affect the access to credit (Cenni et al., 2015; Hernandez-Canovas and
Martinez-Solano, 2010; Cole, 1998). As the firm gets larger, it can acquire more
tangible assets that can be useful for banks in assessing the credit risk of the firm
(Gompers, 1995). At the same time, large firms can gain more bargaining power and
they can negotiate with banks the credit terms which may facilitate loans with fewer
restrictions and larger loan sizes (Cenni et al., 2015). Brancati (2015) showed that micro
firms in the Italian market are more credit constrained than the small or medium firms
as information opacity is even more severe for the micro firms. It is obvious that micro
firms have a lower level of asset tangibility and it is difficult to assess their future
growth rate. Similarly, large firms can more easily show better information transparency
to banks by producing audited financial statements (Ortiz-Molina and Penas, 2008;
Berger and Udell, 2002; Petersen and Rajan, 2002). Overall, the above studies show that
lower information opacity of large firms and reduced information asymmetry can
positively affect the access to bank finance for SMEs. Therefore, we expect that the firm
size may be positively related to the access to finance.
On the other hand, research shows that younger firms are more vulnerable to having
restricted access to bank finance because information transparency is lower. It also
argues that younger firms have a lower level of asset intensity and because of it they are
credit rationed (Ferri and Murro, 2015). Similarly, banks are reluctant to lend money to
younger firms, as it is found that survival rates of younger firms are lower than of older
firms (Dierkes et al., 2013). Kirschemann (2016) in her study found out that younger
firms are more likely to be credit rationed since they previously did not receive any
loans from banks and as a result, it is difficult for banks to judge the loan repayment
history. Moreover, access to credit also depends on the survival analysis of firms and
Shumway (2001) showed that default rates of younger firms are higher than those of the
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older firms. From a bank-borrower relationship point of view, older firms can make a
long-term relationship with banks which is less likely for the younger firms. Thus,
based on the relationship banking, older firms can receive more credit from banks
(Comeig et al., 2015; Cenni et al., 2015; Uchida et al., 2012; Bolton et al., 2013).
Bearing in mind the above-mentioned literature, we hypothesised that there may be a
positive relationship between firm age and access to bank finance.
Hypothesis 1: Firm size is positively related to access to finance because of better
information transparency.
Hypothesis 1a: Firm age is positively related to access to finance.
Gender discrimination in loan markets is under severe scrutiny from both policy makers
and researchers. It is a serious concern that the firms owned and operated by women
face difficulties in getting access to bank finance due to stereotype gender
discrimination (Carter and Rosa, 1998). Financial institutions refuse to provide women
with credit, as it is difficult for banks to make a correct evaluation of their credit risk
due to lack of skills, technical knowledge and previous experience (Irwin and Scott,
2010). Moreover, women are reluctant to accept bank credit since they are afraid to lose
control over their business (Watson et al., 2009). Stefani and Vacca (2015) in the
context of Germany, Italy, France and Spain found that women are less motivated to get
loans from banks since they are afraid that their application will be rejected. Hence,
women are more interested to use credit from their family members, friends and
relatives. The research also showed that women-owned firms mainly operate in the
service and retail sectors and as a result, they do not have sufficient collateral to pledge
and due to this they are credit rationed. Alesina et al. (2013) found that women-owned
firms in Italy pay higher interest rates than the men-owned, but they did not find any
evidence that women-owned firms in Italy are riskier than male-owned firms. A study
by Muravyev et al. (2009) by examining the BEEPS data also found some financing
difficulties for women-based firms. Research found that women are credit rationed not
only due to their business characteristics, but also because of their individual
characteristics, such as lack of education, experience and less family support (Garwe
and Fatoki, 2012). A similar study by Belluchi et al. (2010) in the context of Italian
women-based SMEs shows that firms owned and operated by female entrepreneurs face
stricter credit conditions from banks, for example lower credit limits, higher collateral
and interest rates on their loan contract. Hence, the study suggests that women-owned
firms face more financial constraints than the male-owned firms. Taking the above-
mentioned arguments in consideration, we hypothesized that women-owned firms may
face more credit constraint from banks and it may also lower their credit limits on the
loan contracts and because of that access to finance may be negatively related to female
ownership.
H2: Female ownership of firms is negatively related to access to finance.
Research shows that innovation is significantly important for the long-term growth of
firms in order to attract new customers. By innovating, a firm can create a competitive
advantage over its competitors which helps to earn extra profit margin for the
innovative firm (Baregheh et al., 2009). Previous studies found that European SMEs are
more likely to depend on bank loans to support their innovative ideas because they
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cannot raise funds from the external financial market (Lee et al., 2015; Freel, 2007).
However, the lack of support from commercial banks is negatively affecting the ability
of the firms to innovate (Mohnen and Roller, 2005). Investments in innovative activities
are usually risky since returns from the investments are uncertain (Hall, 2002). Lee et al.
(2015) in the context of UK showed that innovative firms look for more external
sources of finance than the non-innovative firms. They also show that innovative firms
are more likely to be credit rationed than the non-innovative ones. Pederzoli et al.
(2013) showed that default rates of the innovative firms are higher than those of the
firms that do not innovate. They argue that in most of the cases R&D investments for
SMEs do not pay off as it was estimated before and hence innovative SMEs experience
more defaults. Brancati (2015) studied the financing possibilities for innovative firms in
the Italian market and found that hi-tech firms are credit rationed by banks more than
the non-technological or non-innovative firms. The author argues that commercial
banks cannot evaluate the growth prospects of innovative firms and that may lead to the
lack of finance. Because of the uncertainties related to the innovative SMEs, they are
considered as risky investment by banks and, hence, it is more likely that innovative
SMEs may receive lower amount of loans from banks. Therefore, we suppose that there
may exist a negative relationship between firm innovativeness and access to finance.
H3: Firm innovativeness is negatively related to access to finance.
Empirical research examines the borrower risk profile and financial constraints for
SMEs from various perspectives. Because of higher borrower risk, lenders may reduce
the loan size and hence, SMEs may face more credit rationing (Kirschemann, 2016).
Ortiz-Molina and Penas (2008) showed that risky borrowers receive loans with shorter
maturity. Godlewski and Weill (2011) found that high-risk firms provide more collateral
than the less risky firms do. Therefore, the literature suggests that riskier borrowers are
more financially constrained and they experience more stringent credit terms than the
less risky firms. We examine the firm riskiness in terms of theft, robbery, arson and
vandalism. It is likely that the losses which SMEs incurred due to theft or robbery can
have a significant negative effect on their profit margin. This may raise question about
their survival. Hander et al. (2014) using the data provided by BEEPS showed that the
firms which face crime and lose products due to theft and robbery are required to
provide more collateral as it signals higher credit risk to the lender. Therefore, we argue
that the riskier firms are more likely to be financially constrained than firms with low-
risk profile. Because of that access to finance may be negatively related to firm
riskiness.
H4: Firm riskiness is negatively related to access to finance.
The collateral requirement in a loan contract is a conventional way of reducing credit
risk to the borrower. Due to information asymmetry in SME lending, commercial banks
face difficulties in pricing the loans and lending decision leading to credit rationing may
be difficult for them (Stiglitz and Weiss, 1981). Hence, to show better credit quality to
banks, firms usually pledge collateral and by doing so, they can reduce credit rationing.
Research also shows that collateral is a positive signal for banks to reduce adverse
selection and moral hazard as it is less likely that poor quality borrowers may pledge
collateral. Because loan defaults may cause the poor-quality borrowers to lose control
over the asset and hence poor quality borrowers have less incentives to provide
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collateral (Bester, 1987; Chan and kanatas, 1985; Besanko and Thakor, 1987;
Godlewski and Weill, 2011; Hainz et al., 2013). Therefore, the above-mentioned
literature concluded that collateral acts as a signalling device for the lenders to sort out
quality borrowers from the risky borrowers. Thus, if collateral is in fact a signal for
better borrower quality, pledging collateral may positively affect the access to finance
for SMEs because of lower credit risk.
H5: Availability of collateral increases access to finance.
Higher interest rates are significant obstacles for small business lending and SMEs are
discouraged to take loans from banks, as they cannot agree with the price of the loans.
Beck et al. (2006) used the World Business Environment Survey (WBES) and showed
that a high interest rate is the most important financing obstacle for SMEs among 12
examined financing obstacles. Farinha and Felix (2015) found that banks with lower
interest rates received more loan applications as compared to banks with higher interest
rates in Portugal. A study also showed that higher interest rate is one of the most
significant factors for SMEs causing loan default as the higher price of loans increases
the debt burden for SMEs (Chaibi and Ftiti, 2015). Nevertheless, many factors affect
interest rates on loan contract, such as relationship lending, availability of collateral,
credit market concentration and competition, bank size and bank ownership type,
borrower characteristics, firm characteristics, loan maturity, loan size and others (Berger
and Udell, 2002; Cole, 1998; Carter et al., 2004; Rahman et al., 2016a; Menkhoff et al.,
2012; Steijvers et al., 2010; Godlewski and Weill, 2011; Berger et al., 2011; Brick and
Palia, 2007; Chakraborty and Hu, 2006; Hernandez-Canovas and Martinez-Solano,
2010; Petersen and Rajan, 2002; Bonini et al., 2015; Beck et al., 2011; Mian, 2003;
Rahman et al., 2016b; Neuberger and Rathke-Doppner, 2015; Stefani and Vacca, 2015).
An empirical research shows that borrowers are discouraged to get loans from banks
when the cost of loans are too high because it increases their debt burden and that can
negatively affect the value of the firm (Hernandez-Canovas and Martinez-Solano,
2010). As such, we expect to find a negative relationship between access to finance and
interest rates, as higher borrowing costs may discourage the borrowers to take larger
loans from the bank.
H6: Interest rate is negatively related to access to finance.
Statistical Model and the Variables
We run the following ordinary least square regression in order to achieve the objectives
of the paper.
Ln (LoanSize) =
𝜷
0 +
𝜷
1 FirmSize +
𝜷
2
FirmAge+𝜷
3
FirmAgeSquare +𝜷
4
Female +𝜷
5
Innovation +𝜷
6 Crime +
𝜷
7
Collateral+ 𝜷
8
InterestRate +𝝁
The dependent variable loan size, which is a proxy for access to finance, is converted
from the local currencies to US dollars to give the analysis more uniformity. We
obtained the loan size information from the BEEPS survey question where the firm
manager was asked about the particular question “Referring only to this most recent
loan or line of credit, what was its value at the time of approval”.
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To test the hypotheses, we arranged the variables according to firm and loan
characteristics. Regarding the firm-specific characteristics, we observed five (FIRM
AGE, FIRM SIZE, FEMALE, INNOVATION, CRIME/THEFT) variables that can affect
commercial bank decisions for granting credit to the firms. FIRM SIZE is examined
through the number of full-time employees the firm was employing during the survey
period. It is more likely that larger firms can gain more bargaining power and acquire
more assets that can show better credit quality of the firm. Hence, we expect to find a
positive relationship between FIRM SIZE and LOAN SIZE. FIRM AGE is the number of
years the firm is in existence with continuous operation. We added also firm age
squared in the model in order to capture the non-linearity. We believe that as a firm gets
older, it can more easily prove its credit worthiness to the bank by presenting its past
business track records and it can make a long-term relationship with the bank.
Therefore, we expect to find a positive relationship between access to finance and FIRM
AGE. FEMALE (1) dummy represents if the firm is owned by female and zero
otherwise. FEMALE dummy is employed to find whether women-owned firms are
facing any financial constraints in the loan market. As literature shows, women-owned
firms are facing more credit rationing than the male-owned firms do. In that context, we
expect to find a negative relationship between LOAN SIZE and FEMALE.
INNOVATION (1) dummy represents if the firm has introduced any new products and
services within the last three years and otherwise zero. It is widely accepted that the
returns from the innovation and R&D activities are uncertain and as a result, firms with
innovation activities are experiencing lack of finance from banks. Hence, we expect to
find a negative relationship between INNOVATION and LOAN SIZE. CRIME/THEFT
(1) dummy represents if the firm experienced any losses caused by theft, robbery,
vandalism or arson and zero otherwise. CRIME/THEFT shows the firm riskiness of
defaults and we expect that firms that experienced losses due to theft and robbery are
more likely to receive smaller loans from banks and thus, we expect a negative
relationship between CRIME/THEFT and LOAN SIZE. One could question how the
validity of the claim that innovation (INNOVATION) activity of the firm and
information regarding firm’s past losses due to crime, vandalism or arson
(CRIME/THEFT) could be established in the context of our current research? It is
worthwhile to mention that, we completely rely on the voluntary disclosure of all
information from the SMEs during the period of BEEPS survey. Moreover, depreciation
on R&D activities or how much firms spent on R&D in terms of total sales could be
more appropriate proxy to find out the innovation tendency of the SMEs, however the
survey did not have any information regarding this topic, hence, we used innovation
activity of the firms to investigate the relationship between innovation and access to
finance.
The loan characteristic variables of the paper include two items, presence of collateral
and interest rates. COLLATERAL (1) is a dummy variable that represents if the firm has
pledged any sort of collateral while getting credit from the bank and zero otherwise. As
research shows that collateral signals a better credit quality of the borrower by
eliminating moral hazard and adverse selection problem, we expect to find a positive
connection between COLLATERAL and LOAN SIZE. INTEREST RATE is the rate of
interest that is charged on the loan contract. The research assumes to find a negative
relationship between INTEREST RATE and LOAN SIZE since a higher interest rate will
discourage the borrowers to access larger loans from the bank.
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Table 1 Variable Definitions and Sources
Variable
Definition
Source
LOAN SIZE
Value of the loan in terms of ($)
Own calculation
FIRM SIZE
Number of full-time employees
BEEPS
INNOVATION
Dummy = 1 if the firm has launched any new products or services within the last three years and zero otherwise
BEEPS
Loan Characteristics
Note: The table presents variable definitions of our study. BEEPS = Business Environment and Enterprise Performance Survey.
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Data and Descriptive Statistics
The data set we have used for the analysis is obtained from the BEEPS V survey, which
is a joint project of the European Bank for Reconstruction and Development (EBRD)
and the World Bank (WB). BEEPS survey V was conducted in between 2012-2014 in
30 developed, developing and emerging markets to examine the business environment
conditions of SMEs in the examined countries. The data set covers 15,883 enterprises,
which range from micro, small, medium to large firms. The paper defined SMEs
according to the conventions of both OECD and BEEPS - the number of employees is
less than 250. We did not consider any subsidiaries or business partner that are linked
with the SMEs because these external entities may also influence the bank decision in
lending to the SMEs and that may distort the aim of the research.
The loan amount that we used for our empirical analysis is drawn from the BEEPS
survey V (2012-2014), and we found that most of the recent loans of the SMEs were
approved during the period of 2010-2011 and afterwards. It may be stressed that after
the recent financial crisis banks are providing more loans to the SMEs. However, the
survey did not cover how many loans are taken by the firm in the same fiscal year
which would have helped us in better understanding the characteristics of the firms that
are taking more loans per year and also their investment strategy.
The BEEPS data set covers 254 firms in the CR, where 236 firms are covered by the
BEEPS V and 18 firms were from earlier surveys. Out of these 254 firms, 16 firms had
more than 250 employees so we had to exclude them from empirical analysis and finally
obtained 238 SMEs for analysis.
In terms of SKR, the BEEPS survey examined 276 firms but due to poor data quality it
dropped 8 firms and reported 268 firms in the main database. To comply with the aim of
this paper, we excluded the large firms and obtained 260 SMEs.
Finally, we found information about 310 firms in Hungary. Out of them, data on 247
firms were covered by the BEEPS latest survey and data on 63 others were obtained
from the pooled survey. After deleting the large firms and other missing data, we were
able to use 295 firms which are within the scope of this paper.
Altogether we obtained data on 793 SMEs from the three mentioned countries. Among
these 793 firms, 268 firms are classified as micro firms, 385 firms as small firms and
140 firms as medium firms. The paper used BEEPS definition for firm level
classification, therefore, a firm is considered as micro firm when the number of
employees is less than 10, small firms are identified when the number of employees is
more than 9 but fewer than 50 (10-49) and medium firms are defined as the firms
having between 50-249 employees.
Among these 793 firms we found 227 firms that obtained loans from banks and about
75 per cent of the loans were secured with collateral. The survey shows that about 40
per cent of firms have at least one owner who is female. The data set also highlights that
about 30 per cent of the firms have launched new products and services within the
period of last three years. Considering the crime factors in the examined countries, it is
quite surprising that about 20 per cent of the firms reported that they have incurred
losses due to the theft, robbery or arson. Seeing these results, it may signal that SMEs
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are still facing hostile business conditions in the European countries. On average, the
firms in the sample received loans with 5 per cent interest rate. The detailed results can
be seen in table 2.
With respect to the firm level analysis, we found that 55 micro firms received bank
loans with an average interest rate of about 5.30 per cent and nearly 71 per cent of the
loans were secured. Interestingly, women-owned firms are more present in the micro
segment than any other segments. About 47 per cent firms in this segment have a female
owner. It may signal that women prefer to establish firms that are easier to manage.
Within the segments of small and medium firms, 119 small firms and 53 medium firms
received loans from banks. The descriptive study shows that the average interest rates
for the small firms was approximately 4.96 per cent while the average interest rate for
the medium firms were around 4.35 per cent, which is the lowest rate among the
segments. However, the average value of collateral suggests that about 74 per cent of
loans are secured for small firms and about 82 per cent loans are pledged with collateral
for the medium firms. Hence, it suggests that medium firms pledge more collateral than
micro or small firms. According to the results, it may signal that banks in these three
countries require higher collateral from firms which have more assets to pledge as
collateral. Therefore, firms with more assets can be a suitable choice for banks to
impose collateral requirements on the loan contract.
Table 3 presents the country level descriptive statistics and the results show that average
firm age is about 17 years, which is similar in all three countries. We can also see that
women own both in CR and SKR similar share of firms; about 33 in CR and 30 per cent
in SKR. In contrary, female ownership is significantly higher in Hungary where women
own 53 per cent of firms. Results from the CR show that about 50 per cent of firms have
developed new products and services within the last three years. On the other hand, only
18 per cent of firms in SKR and 21 per cent of firms in Hungary have innovative
activities. It reflects that firms in CR have a stronger innovation orientation in
comparison to the firms in SKR or Hungary. The data also shows that about 35 per cent
of firms in the CR reported that they incurred losses as a result of theft, robbery and
arson, which is much higher than in SKR and Hungary. Interestingly, the descriptive
results suggest that the collateral requirement is similar for small business lending in
these countries (about 75 per cent of firms provide collateral for bank loans). It may
mean that these three countries share similar creditor protection rights, which may
harmonise the collateral requirements for SMEs. Finally, the survey finds that SMEs in
CR pay higher interest rates (average interest rate is 5.6 %) than in SKR (average
interest rate 4.5 %) and Hungary (average interest rate 4.6%). As firms in CR face much
higher obstacles due to crime and theft, this may reflect that firms in CR are riskier than
those in two other countries.
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Table 2 Descriptive Statistics Firms’ Level
Variable
SMEs
Micro
Small
Medium
Obs.
Mean
Std. dev.
Min
Max
Obs.
Mean
Std. dev.
Min
Max
Obs.
Mean
Std.
dev.
Min
Max
Obs.
Mean
Std. dev.
Min
Max
FIRM SIZE
793
32.6
45.69
1
245
268
6.2
1.897
1
9
385
20.36
9.489
10
49
140
117
52.06
50
245
FIRM AGE
785
16.98
7.024
1
69
267
15.75
6.44
2
53
380
16.71
6.506
1
69
138
19.91
8.543
3
63
FEMALE (%)
793
39.5
0
1
268
46.6
0
1
385
34.5
0
1
140
39.3
0
1
INNOVATION (%)
793
29.1
0
1
268
26.5
0
1
385
31.7
0
1
140
27.1
0
1
CRIME/THEFT (%)
793
19.3
0
1
268
13.8
0
1
385
21
0
1
140
25
0
1
COLLATERAL (%)
342
74.9
0
1
92
70.7
0
1
174
74.1
0
1
76
81.6
0
1
INTEREST RATE
263
4.91
3.865
1
27
72
5.264
4.988
1
27
137
4.956
3.408
2
20
54
4.352
30.2
2
19
LOAN SIZE
($1000)
227
1360
1070
0.88
1540
55
3041
2070
2507
1540
119
342.2
812
0.880
5003
53
1902
6429
0.45
4550
This table reports descriptive statistics for dependent variable and independent variables at firm level. FEMALE, INNOVATION, CRIME/THEFT and
COLLATERAL are dummy variables. Source: BEEPS (2015).
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Table 3 Descriptive Statistics by Country
Variable
Czech Republic
Slovak Republic
Hungary
Obs.
Mean
Std. dev.
Min
Max
Obs.
Mean
Std. dev.
Min
Max
Obs.
Mean
Std. dev.
Min
Max
FIRM SIZE
238
31.5
42.5
2
235
260
35.2
48
1
245
295
31.3
46.1
2
242
FIRM AGE
238
17.4
5.34
1
25
257
17
6.56
2
60
290
16.6
8.49
2
69
FEMALE (%)
238
33
0
1
260
30
0
1
295
53
0
1
INNOVATION (%)
238
50
0
1
260
18
0
1
295
21
0
1
CRIME/THEFT (%)
238
35
0
1
260
13
0
1
295
12
0
1
COLLATERAL (%)
124
75
0
1
107
75
0
1
115
75
0
1
INTEREST RATE
83
5.64
2.83
2
19
78
4.51
3.94
2
27
102
4.64
4.45
1
20
LOAN SIZE (1000 $)
89
484
893
75
5003
69
860
5466
3
45531
69
2990
1868
0.880
15400
This table reports descriptive statistics for dependent variable and independent variables at country level. FEMALE, INNOVATION, CRIME/THEFT
and COLLATERAL are dummy variables. Source: BEEPS (2015).
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Correlation Analysis
The data set we used for our analysis consists of cross sectional data, hence, we cannot
test the possibility of autocorrelation on our examined variables. However, the authors
run correlation matrix and presented the results in order to find out whether there is a
collinearity in the model. In table 4 we show the level of correlation between the
independent variables and it suggests that this study might not have the collinearity
problem.
Table 4 Correlation Analysis
Firm
Size
Firm
Age
Female
Ownership
Innovati
on
Crime/Th
eft
Collater
al
Interest
rate
Firm Size
1.0000
Firm Age
0.0365
1.0000
Female
Ownership
-0.0336
0.0584
1.0000
Innovation
-0.0505
-0.0962
-0.0333
1.0000
Crime/Theft
0.1081
0.0419
-0.0392
0.1444
1.0000
Collateral
0.0423
0.0904
-0.0281
0.0131
0.0208
1.0000
Interest rate
-0.0658
-0.0409
-0.0164
-0.0082
0.0798
0.0337
1.0000
This table reports correlation analysis between the independent variables. Source: authors own
estimation
Empirical Results
We present estimation results across firm size and across countries. We separate
regression results to understand bank lending behaviour for micro, small and medium
firms. Moreover, the paper presents cross-country regression results for SME financing
to understand the differences in country level. Therefore, the analyses of the paper have
valuable attributes to foster knowledge about SME financing behaviour not only from
firm-level differences perspective but also on country level.
Table 5 presents the regression results for full sample and we show results from firm
level segmentation perspective. With respect to the SMEs, we found that the coefficient
of FIRM SIZE is statistically significant at 1 per cent and positively associated with our
dependent variable which is LOAN SIZE. This indicates that as the firm size increases
the loan size also increases. However, this result is not true for the micro firms when we
look at it from the firm size perspective. The negative coefficient of the relationship
between loan size and micro firms suggests that micro firms get lower amount of credits
from banks in our examined countries. Brancati (2015) found similar results in the
context of Italian market. The result may suggest that larger firms can show better credit
quality by reducing information opacity and that helps them to get more loans from
banks. Thus, we may say that reduction of information asymmetry can improve the
financing possibilities of firms in the loan markets.
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In the segment of SMEs, unexpectedly, we found that FIRM AGE is negatively related
to LOAN SIZE but it is not statistically significant. Petersen and Rajan (1995) also found
a negative relationship between the loan size and firm age in the context of USA. They
found that mature and older firms need a relatively lower amount of debt from financial
institutions since they have reserve cash for investment. Moreover, this result can be
interpreted from the capital structure theory of firms. It could mean that the firms which
are mature and already in the markets for a long time have accumulated more internal
assets and can invest their retained earnings (Myers and Majluf, 1984). As a result,
firms which are older require smaller amounts of loans from banks. The hypothesis is
supported when we look at the micro firms. Therefore, we can say that as a micro firm
matures, it can provide more information to banks in the form of past track record or it
is also able to get loans by forming a good relationship with banks (Brancati, 2015;
Neuberger et al., 2006).
We found a negative relationship between FEMALE ownership of firms and access to
bank finance. However, the result is not statistically significant on the SMEs level. A
statistically significant result is found for the micro firms. Hence, this study provides
empirical evidence that women-owned firms get a lower amount of credit from the
formal financial institutions than the male-owned firms do. Our results can be
interpreted from different perspectives. Firstly, it might be caused by female owners
receiving lower amount of finance due to the bank stereotype gender discrimination
(Carter and Rosa, 1998). Similarly, women-owned firms may lack access to finance
because they do not have enough assets to pledge as collateral to banks (Lee et al.,
2015). In our case, it is more relevant that women-owned micro firms may have less
physical assets to pledge as collateral to the bank and thus they face higher credit
restrictions from banks.
Unexpectedly we did not find statistically significant results between INNOVATION and
LOAN SIZE in the segment of SMEs or full sample. However, we found statistically
significant positive result at 10 per cent level between INNOVATION and access to
finance only in the case of medium sized firms. Thus, we can infer that innovative firms
are not penalized by commercial banks in our examined countries. The positive sign of
innovation and access to finance signals that commercial banks do value the innovation
activities of the firms by providing financial support. It could mean that commercial
banks provide funds to innovative firms by assuming that innovative firms have more
growth prospects in the market.
We show that CRIME/THEFT is only statistically significant for micro enterprises. The
result suggests that commercial banks perceive micro firms as riskier if they incur any
losses due to robbery, theft or arson and based on that micro firms can be denied a
larger loan. It is legitimate to argue that micro firms have limited resources and if they
face additional losses because of criminal activities, it can seriously hamper their
possibility of survival. Hence, it could mean that banks are stricter when rating the
micro firms which reported that CRIME/THEFT had affected their business because it
increases their probability of loan default.
The paper found that COLLATERAL has a positive sign and the results are statistically
significant across all firm sizes. According to the results, the current study provides
additional support that availability of collateral can ease the financing possibility for
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SMEs. It is possible that collateral signals better credit quality and confidence of the
borrower in loan repayment capacity in the examined countries (Bester, 1987; Chan and
kanatas, 1985; Besanko and Thakor, 1987). On the other hand, it could mean that
collateral has a disciplinary role and because of that banks are willing to lend to SMEs
(Chakraborty and Hu, 2006; Menkhoff et al., 2012; Brick and Palia, 2007). Hence, the
result suggests collateral is a significant determinant of SME finance in our examined
countries.
Table 5 Results of the Regressions Across Firms’ Size: Dependent Variable: Loan Size
Variables
SMEs
Micro Firms
Small Firms
Medium Firms
FIRM SIZE
0.0127***
-0.4001***
0.052
0.002
(0.0042)
(0.1362)
(0.0336)
(0.0079)
FIRM AGE
-0.0535
0.5954**
-0.0222
0.0349
(0.0693)
(0.02698)
(0.2183)
(0.044)
FIRM AGE SQUARE
0.0009
-0.0236
-0.0009
0.0084
(0.0014)
(0.0089)
(0.0061)
(0.0045)
FEMALE (Yes=1)
-0.176
-1.1648**
0.5372
-1.042
(0.3921)
(0.5203))
(0.6178)
(0.7621)
INNOVATION (Yes =1)
0.5737
0.1907
0.387
1.4462*
(0.405)
(0.5607)
(0.6525)
(0.8079)
CRIME/THEFT (Yes =1)
0.4334
-1.3635*
1.0898
0.0698
(0.4509)
(0.7406)
(0.7042)
(0.8138)
COLLATERAL (Yes =1)
1.6145***
2.5885***
1.4740**
0.1084*
(0.4654)
(0.6085)
(0.7057)
(1.165)
INTEREST RATE
0.0943*
0.1052*
0.0956
0.025
(0.0527)
(0.0582)
(0.088)
(0.1245)
Constant
9.3543***
8.8984***
8.1000***
11.3294***
(0.9243)
(2.0198)
(1.9721)
(1.913)
Number of Firms
195
48
104
43
R-squared (%)
14.7
54.6
12.2
13.9
Note: This table reports results from OLS regression models for the entire sample of firms (SMEs)
and firm level segmentation. The dependent variable is natural logarithm of loan amount (Loan
Size). Firm size is the number of full-time employees (FIRM SIZE) and firm age is the number of
years the firm has been in operation (FIRM AGE). Interest rate is the appropriate interest rates
charged on the loan (INTEREST RATE). Other explanatory variables are dummy variables
(FEMALE, INNOVATION, CRIME/THEFT, and COLLATERAL). Significance level: *** p<0.01,
** p<0.05, * p<0.1. Standard errors are in parenthesises
Source: Authors own estimation
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We find that the INTEREST RATE is statistically significant at 10 per cent level for the
SMEs and micro-firms. This indicates that as the rate of interest increases, the loan size
increases. It could mean that the higher the amount of loan the higher the risk.
Therefore, banks may impose a higher interest rate as the loan size increases. Moreover,
a large loan size can increase the moral hazard issue and for that reason, it might be
possible that banks charge higher interest rates to receive their compensation as quickly
as possible. One can raise question why a bank would provide credit to a borrower
knowing it was substantially risky? We argue that inter-bank competition may affect the
bank decision to provide credit to the risky borrowers and a high interest rate is an
incentive for the lenders to increase their profit margin.
Table 6 Results of the Regressions at Country-Level: Dependent Variable: Loan Size
Variables
Czech Republic
Slovak Republic
Hungary
FIRM SIZE
0.0166***
0.0157
0.0106
(0.0039)
(0.0135)
(0.0048)
FIRM AGE
-0.1608
-0.2201
-0.0881
(0.189)
(0.1582)
(0.1243)
FIRM AGE SQUARE
0.0052
0.0032
0.0021
(0.0059)
(0.00277)
(0.0029)
FEMALE (Yes= 1)
-0.2508
-0.0952*
-0.6525**
(0.3362)
(1.0541)
(0.577)
INNOVATION (Yes =1)
0.3734
1.1481
-0.2704
(0.3224)
(1.1798)
(0.5831)
CRIME/THEFT (Yes =1)
0.32
-0.0092*
-0.8404
(0.3133)
(1.3931)
(0.797)
COLLATERAL (Yes =1)
0.6456
3.3339***
0.4740*
(0.4196)
(1.2751)
(0.6112)
INTEREST RATE
-0.1765***
0.3821
0.0297
(0.0571)
(0.1853)
(0.0594)
Constant
12.7082***
7.3551***
11.8276
(1.5715)
(2.2071)
(1.4375)
Number of Firms
71
58
66
R-squared (%)
46.1
27.7
14.0
Note: This table reports results from OLS regression models at country level segmentation. The
dependent variable is natural logarithm of loan amount (Loan Size). Firm size is the number of
full-time employees (FIRM SIZE) and firm age is the number of years the firm has been in
operation (FIRM AGE). Interest rate is the appropriate interest rates charged on the loan
(INTEREST RATE). Other explanatory variables are dummy variables (FEMALE, INNOVATION,
CRIME/THEFT, and COLLATERAL). Significance level: *** p<0.01, ** p<0.05, * p<0.1
Standard errors are in parenthesises
Source: Authors own estimation
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Table 6 presents regression results at country level determinants of SME financing. We
control for the same variables as we did for the firm size level.
At first, the result suggests that FIRM SIZE has a positive effect on access to finance in
all countries. However, the result is statistically significant only in the case of CR. The
result stresses that banks in Czech Republic consider firm’s size to be a positive signal
while considering a loan application. This positive effect of firm size and loan size
shows further evidence that as firms get bigger it can signal positive information to
banks about their credibility. Moreover, the results also support that higher information
transparency can ease the possibility of getting a bank loan (Ferri and Murro, 2015;
Bolton et al., 2013).
FIRM AGE has a negative coefficient in each country, but it is not statistically
significant. This result signals that regardless of the country, mature firms ask for a
lower amount of bank loans. It is more likely that they invest their cash reserve or
financial slack.
The results for FEMALE dummy have significant negative coefficient with LOAN SIZE
in SKR and Hungary, but the result is not significant in the context of CR. This result
may indicate that banks in CR do not discriminate against loan size based on gender
differences. However, results from the SKR and Hungary are suggesting that female
borrowers do receive a smaller amount of credit from banks than male borrowers.
Although we did not examine at what basis female borrowers receive smaller amount of
loans, it may come from supply side gender discrimination effect from banks or it is
possible that female borrowers restrict themselves from asking for larger loans.
The paper did not find any significant effect of INNOVATION and access to finance in
our examined countries. Thus, we cannot deduce that the innovative firms are more
financially constrained than the non-innovative firms. This result may encourage
innovative firms to ask for bank loans as our result suggests that innovative firms get
similar preferences from banks as the non-innovators.
We found that CRIME/THEFT has a negative coefficient in the context of SKR, but not
in two other countries. Hence, we may infer that SMEs in SKR located in the area
where the frequency of crime is higher are more likely to be financially constrained by
banks. Hence, firm riskiness is an important determinant of access to finance in one out
of our three examined countries.
COLLATERAL has a positive effect on getting bank loans in each examined country.
However, only results from SKR and Hungary are statistically significant. This means
that collateral is significantly valued by the banks in these countries while lending to
SMEs. It is also possible that banks in SKR and Hungary take a conservative approach
in lending to SMEs and hence ask for collateral to protect their loan portfolio from bad
loans because in the event of defaulted loans a bank can liquidate the securitized
collateral and get back the extended loans, which is also proposed by Blazy and Weill
(2013). On the other hand, it may signal that SMEs in SKR and Hungary are more credit
worthy and they would like to show their credit quality by providing more collateral.
Considering this result, it might be possible to say that collateral acts as a signalling
device for banks in sorting the high-quality borrowers from the bad borrowers, which is
highlighted by Bester (1987), Chan and Kanatas (1985), Besanko and Thakor (1987).
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Finally, we see that INTEREST RATE has a negative effect on loan size in CR and the
result is statistically significant. This result shows that when interest rates are high,
SMEs in this market demand a lower amount of bank loans as it increases their debt
burden. However, we did not find any significant results for the other two countries.
This result further supports our descriptive studies where we showed that banks in CR
charge higher interest rates than banks in SKR and Hungary.
Conclusion
In this paper, we examined the determinants of access to finance for SMEs in the
context of three Central European countries CR, SKR, and Hungary. The access to
finance was a proxy variable captured by the loan size. BEEPS V, , which is a joint
project of European Bank for Reconstruction and Development (EBRD) and the World
Bank (WB), provides the data set we used for our empirical analysis. We analysed five
borrower characteristics and two loan specific characteristics for assessing the
determinants of access to finance.
The results are mixed and we found that firm-level characteristics are more depended on
the firm classification (for example: micro, small and medium firms) rather than
comprehensive results for the whole SME segment. For example, while the result
suggests firm size has a positive relationship with access to finance for SMEs, it has a
negative coefficient for micro firms. That means micro firms are facing even more
financing obstacles from commercial banks. With respect to the firm age, we found
significant positive results for micro firms with access to finance. That means micro
firms can show better information quality to banks when they get older and mature. The
results for female ownership showed that women-owned firms experience more
financial constraints than the men-owned firms do. This suggests that the potential
gender discrimination in the loan market is also a concern for developed European
countries.
With respect to innovation, our result indicates that innovative SMEs are not more
financially constrained than the non-innovative firms. Rather a positive coefficient
suggests that innovative firms are encouraged by banks in the form of access to finance.
It is also possible to see that micro firms are facing financing barriers if they
experienced crime/theft. Hence, crime/theft adds additional financial barriers for micro
firms when they want to ask for loans from banks. The paper finds evidence that
collateral has a positive effect on loan size for all firms; it also reflects the fact that
banks in these three countries are more comfortable in collateral-based lending. Finally,
we found evidence that the interest rate positively affects access to finance in the
segment of SMEs on the whole, and for micro firms. It may reflect that as the loan size
increases, banks are also charging higher loan price due to increased risk with loan size.
On the other hand, micro firms are more vulnerable to defaults and for that reason,
banks may ask for higher rates from the micro firms.
With respect to the country level perspective, we find that only firm size and interest
rate are statistically significant in the CR. However, firm size has a positive effect while
interest rate has a negative effect on access to finance. Therefore, we can say that
commercial banks in the CR consider firm size to be a positive signal for extending
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loans to SMEs. Conversely, a higher interest rate in CR in comparison to SKR and
Hungary creates barriers for SMEs to asking for larger loans. In the context of SKR and
Hungary, we found that female ownership and the pledge of collateral are statistically
significant. According to our expectation, we found that female ownership reduces the
likelihood of accessing to finance for the SMEs but the pledge of collateral can enhance
it. Therefore, referring to our results we may say that gender discrimination is a
prevailing fact in the loan markets and it is not only the case for developing countries,
but also for the developed European markets. Finally, positive effect of collateral on
access to finance suggests that the pledge of collateral may increase the confidence level
of banks to extend credits to SMEs. Although we did not empirically examine whether
the positive effect of collateral on access to finance comes from the reduction of adverse
selection or moral hazard issue, it can be an interesting future research scope.
The results of this paper have a few policy implications. Firstly, an appropriate policy
could be helpful for the firms which are credit constrained and owned by women.
Implementing such a policy could encourage female entrepreneurs, which can foster
economic growth of the country. Secondly, as we confirmed that SMEs are credit
constrained due to the collateral requirement, it could be useful to rethink the collateral
requirements in particular for the SMEs. Finally, regulators may take initiatives to
reduce the interest rate for SMEs, which can foster the growth of the SMEs and,
therefore, contribute to the economy.
Acknowledgement: The authors are thankful to the Internal Grant Agency of FaME
TBU No. IGA/FaME/2017/010: Financial Constraints on Economic Activities, for
financial support to carry out this research.
Disclosure statement: No potential conflict of interests was reported by the author.
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... Through the research of Rahman et al. (2017), the authors discovered that availability of collateral was a positive signal for a bank lending to firms, with the collateral providing evidence of a borrower's loan repayment capacity, increasing the level of confidence of banks approving loans to the firms. Because of issues of opaque information and weak enforcement of debt collection, especially in emerging markets, financial institutions may apply an asset-based lending policy in which credit judgments are much based on the quality of collateral's assets (Berger & Udell, 2002). ...
... Previous studies utilized the size (value) of the loan as a proxy to measure the accessibility to finance (Cressy & Toivanen, 2001;Rahman et al., 2017). These researchers argued that successful borrowers might receive larger loans because they have a better credit quality, lower information asymmetry, lower risk, and a higher level of asset intensity for the collateral requirements of banks. ...
... That means the safer the collateral method, the higher probability of loan approval. This finding is in line with Rahman et al. (2017) that the more secure the collateral, the higher the confidence of financial institutions in approving loans, and thus less financing constraints on the part of the borrower. This finding also fits fairly well with the reports of Uchida (2011) and the concept of small financial institutions linking the bank guarantee with their credit decision. ...
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... This applies to studies of individual economies (see : Table 1) as well as cross-sectional studies. The latter include the research of Mateev et al. (2013) and Rahman et al. (2017). The first study covered the financial data of 3,257 SMEs for the period 2001-2005. ...
... For both factors, a positive relationship was found. Rahman et al. (2017) examined 793 SMEs from Czechia, Slovakia and Hungary operating in 2012-2014. For the typical internal factors, debt dependency was found only on the size of the company (the bigger the company, the higher the debt). ...
... The results of the CEE SMEs' country-specific factors study do not contradict the previously obtained results (Mateev et al., 2013;Rahman et al., 2017) and support H6. The institutional country-specific factors analysis provides results similar to those in Jõeveer (2013). ...
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