AMERICAN JOURNAL OF SOCIAL AND MANAGEMENT SCIENCES
ISSN Print: 2156-1540, ISSN Online: 2151-1559, doi:10.5251/ajsms.2010.1.2.102.123
© 2010, ScienceHuβ, http://www.scihub.org/AJSMS
Banking relationship and credit terms: empirical evidence from
Portuguese small firms
Miguel Neves Matias1, Zélia Serrasqueiro2 and Carlos Arriaga Costa3
1
Management and Economics Department of Polytechnic Institute of Leiria and Management
and Sustainability Research Center Leiria, Campus 2, Morro do Lena - Alto do Vieiro, 2411901 Leiria Apartado 4163, Portugal
e-mail: [email protected]
2
Management and Economics Department, Beira Interior University, Estrada do Sineiro,
6200-209, Covilhã Portugal, and CEFAGE Research Center, Évora University
e-mail: [email protected]
3
Economics Department of Minho University, Campus de Gualtar
4710 - 057 Braga, Portugal
e-mail:[email protected]
ABSTRACT
Based on a sample of Micro and Small-sized Enterprises (MSE) financed by a large Portuguese
bank, this study seeks to assess the importance of the banking relationship for MSE to obtain
better credit terms, i.e. higher credit limit, lower risk premium and less probability that collateral
must be provided. We conclude that long term banking relationship alone is not important for
firms to obtain better bank credit terms. However, greater borrowing concentration allows MSE to
obtain a lower risk premium and a higher limit of credit. The magnitude of these impacts are
affected by some specificities related to firm size, economic-financial performance and the
position of the lending bank in the local banking market.
Keywords: Banking relationship; Credit terms; Small firms.
INTRODUCTION
Small businesses, which play a key role in generating
employment and creating economic wealth, face
restrictions in accessing bank credit and in the terms
of loan conditions. The significant difficulties in
accessing external finance arise partly from small
firms´ financial fragility and the legal opacity of
information that they give to the lenders. The
relationship between accounting information and the
economic-financial performance, actually in evidence,
is frequently in question, given the freedom of
accounting "elaboration", rarely audited, not rated
and with multiple schemes for presenting their
financial statements, leading to the minimization of
the tax burden (Le Guirriec, 1996). It is in this context
that assessment of the banking relationship acquires
vital importance in determining the credit risk of Small
and Medium-sized Enterprises (SME).
SME operate in a particular context, characterised by
heavy interdependence; between the "personal
sphere" of the business-owner and the "business
sphere"; and the administrative and financial
organization are characterized by informality of
relationships; and, by the vital and dominant
importance of the business-owner (Levin and Travis,
1987; Belletante and Levratto; 1995).
Indeed, the profile, know-how, and reputation of the
SMEs´ business-owner emerge as the most
important determinants in credit decisions (Levratto,
2001). However, most of those attributes are included
in the so-called “soft information”, i.e. information of a
qualitative nature that is not generally recorded
uniformly in databases, and is not easily or faithfully
transmitted between the various organs deciding
bank credit (Stein, 2002).
It should be noted that the changes imposed by the
Basel II Capital Agreement have hastened the need
for banks to acquire tools of a quantitative nature
which allow, in the field of credit risk, establishment of
capital requirements more adjusted to the risks of the
assets present in bank (credit) portfolios. This
process has required sophistication of the systems of
managing individual credit risk, with the aim of
Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
granted to SME, this study aims to contribute for the
literature on the topic with a more rigorous definition
of the variables used in measuring the banking
relationship and a greater control of the influence of
factors outside the investigation, highlighting the
importance of the bank market structure and the
distance from the bank’s credit department, allowing
also in this way, a broader concept of the various
implications of this phenomenon in financing SME. It
intends to contribute to firms recognizing objectively
observable determinants of the terms of finance
obtained, highlighting those which are directly linked
to the banking relationship, so as to minimize
difficulties in the relationship and information
asymmetry, with a view to achieving a more efficient
performance at the lending bank.
making the rating and scoring models more sensitive
to knowledge about the client, and mainly, to his/her
relationship with the bank.
However, the capacity and intensity of the banking
relationship cannot always be inserted in a rating or
scoring model, because the relationship is a more
and more complex, dynamic and multi-faceted
concept, and so it is not always quantifiable or
immune to the subjectivity of assessment by the bank
manager who is the intermediary in the credit
relationship. It therefore becomes difficult to
reproduce, rigorously and uniformly, in the client
database and introduce and contemplate, at each
moment, in a standard model of credit risk
assessment.
The current study is structured as follows, after this
introduction; section 2 presents a bibliography
review; section 3 presents the empirical analysis
carried out; and finally in section 4 we draw the
conclusions from this study.
Several studies (Riding et al., 1994; Blackwell and
Winters, 1997; Johnson, 1998; Athavale and
Edmister, 1999) show that repeated transactions over
time between banks and SME contribute to reduce
information asymmetry between the parties,
diminishing the bank’s need to control SME activity
and, consequently, it becomes possible for them to
obtain an overall improvement in credit terms.
Portugal has a bank-based financial system with an
undeveloped stock market in comparison with stock
markets of USA and other European countries.
Portuguese SME represent about 99.8% of the
Portuguese business sector, being fundamental for
increased employment and the country’s economic
growth. Most Portuguese SME do not have the
necessary requirements to gain access to the stock
market. Portuguese SME are extremely volatile in
terms of operating income, have limited access to a
variety of sources of finance and are strongly
dependent on bank debt, making them vulnerable to
economic fluctuations.
The impact of the banking relationship on SME credit:
theory
and
empirical
evidence:
Theoretical
investigation has showed that the relationship
between the borrower and the bank ensures a
climate of confidentiality, improves contractual
flexibility, reduces agency problems through
increased control and allows the creation of
reputation. However, the abstract nature of the
theoretical benefits of the banking relationship has
drawn investigators to observe its concrete effects,
dealing more with the analysis of its impact in terms
of access to credit, credit limits, risk premium,
collateral requirements and the ease of renegotiating
the contract terms, thereby facilitating implicit long
term contracting.
The majority of empirical investigations have
concluded that the banking relationship increases
access to and improves loan conditions and,
consequently increases firm performance and value.
The main conclusions are generally reached through
evaluation and measuring of the two main indicators
of the banking relationship: the length of the
relationship and the extent of bank products
acquired.
Considering the importance of bank debt for
Portuguese SME, especially for the smaller firms, the
aim of this study is to identify and analyse the
influence of the banking relationship on the loan
terms of small firms in Portugal, using a sample of
Portuguese Micro and Small Enterprises (MSE)
taking out short-term credit. Besides assessment of
the existing banking relationship, descriptive
indicators of the quality of the firm, its management
and its economic-financial performance are also
analysed.
However, it should be noted that the conclusions of
the research carried out are far from consensual,
since not only benefits are found in the relationship.
Problems related to the banking relationship emerge,
Compared to the majority of studies investigating the
influence of the banking relationship on credit terms
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when the value created is not shared equally
between the bank and the financed firm. And so the
banking relationship includes some disadvantages
that affect both the bank and the borrowing firm, and
which Boot (2000) sums up as the “soft-budget
constraint problem” and the “hold-up problem”.
record greater access to credit than those with a
shorter relationship with the bank.
In a later investigation that used the same set of data,
Berger and Udell (1995) argue that Petersen and
Rajan (1994) failed to determine a relationship
between interest rates and length, because they did
not focus on the most important form of commercial
bank loan: the line of credit. Berger and Udell (1995)
concluded that the interest rates charged on lines of
credit diminish as the length of the relationship
increases. The authors argue that the results are
consistent with the fact that a banking relationship
involves disclosure of “soft information” which
improves contractual conditions for the firm, and so
banks do not appear to apply monopoly pricings.
The first problem concerns a weaker monetary
restriction to which the borrowing firm is subject and
which is explained by the bank’s greater ease in
granting credit (and a higher amount than the risk
actually presents) to the firm with which a certain
relationship of confidence is already consolidated,
based on the collection and regular analysis of soft
information (Dewatripont and Maskin, 1995; Bolton
and Scharfstein, 1996). While the second problem
arises from the information monopoly that the bank
develops over the length of the finance and could
mean it practising higher pricings than those it would
define in conditions of equal share of the benefits
(Greenbaum et al. 1989; Sharpe 1990; Rajan, 1992;
von Thadden, 1995), particularly in local markets
where the bank enjoys significant market power.
Considering the studies by Petersen and Rajan
(1994) and Berger and Udell (1995), Cole (1998)4
shows that the likelihood of access to credit
increases according to the length of the relationship
in the first year, but does not increase thereafter.
Bodenhorn (2003) and Brick et al. (2007) also find
evidence of benefits from the length of the credit
relationship in terms of interest rates and the
probability of attaching collateral to the credit.
Length of the banking relationship: The length of
the banking relationship has been used as a proxy of
the solidness of the relationship, and therefore, a
determinant of the main loan terms obtained by SME.
In general, the results obtained in studies carried out
in American firms seem not to be easily transposed
to the European case, particularly those related to the
impact in terms of credit costs. Elsas and Krahnen
(1998), based on data about credit granted by large
German banks, do not identify significant
relationships between the length of the banking
relationship and interest rate or greater access to
credit. Similarly, Elsas (2005) and Harhoff and
Körting (1998) do not identify a significant
relationship between the length of the banking
relationship and the cost of, or access to credit by
German SME. However, Harhoff and Körting (1998)
obtain a significant and negative relationship between
the length of the banking relationship and the
requirement for collateral. Angelini et al. (1998) and
Auria et al. (1999), conclude, in the case of Italy, that
length of the banking relationship is positively
associated with the risk premium of credit. Also
Degryse and Van Cayseele (2000), for Belgium, and
Vigneron (2007), for France, arrived at similar results.
The benefits of the length on credit terms concerning
the interest rate obtained on the credit find theoretical
support in the studies by Diamond (1989; 1991).
Later, Boot and Thakor (1994) show on basis of a
theoretical model that, at first, firms may have to bear
high interest rates, but as the credit relationship
extends going through a series of successful credits,
the interest rate decreases and there is also more
probability of diminished collateral attached to the
credit.
In the first empirical studies that assess the
importance of the length of the banking relationship in
obtaining better loan conditions, particularly greater
availability of credit and lower interest rates, the
investigations by Berger and Udell (1995) and
Petersen and Rajan (1994) stand out.
Petersen and Rajan (1994)3 conclude that the length
of the banking relationship does not have statistically
significant influence on the interest rate on credit that
the bank offers to the firm. However, it appears to
influence positively the availability of bank credit to
clients. Firms with longer banking relationships
Regarding the impact of the length of the relationship
in terms of the amount of credit obtained, little
empirical investigation exists. We make special
mention of the work by Fernando et al. (2002) who
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find some advantages of a long banking relationship
in the amount of credit obtained by small NorthAmerican firms. As for the effect in terms of the
probability of attaching collateral, Boot and Thakor
(1994) demonstrate theoretically that firms with
longer banking relationships are less likely to provide
collateral to loans. Berger and Udell (1995), Cowling
(1999) and Brick et al. (2007) also find an inverse
relationship between the length of the banking
relationship and the probability of attach collaterals to
the loans, based on a sample of small NorthAmerican firms. Also Vigneron (2007), based on a
sample of borrowers at a large French bank, finds
evidence of an inverse relationship between the
quality of the collateral attached to the loan and the
length of the banking relationship.
HernándezCánovas and Martínez-Solano (2006) conclude for
Spain, dividing the sample collected in two subsamples according to the median length of the
relationship, that for relationships under 15 years, the
bank would exercise its monopoly power by imposing
higher risk premiums and by increasing the
probability of attaching collateral to the granting
credit.
Concerning particularly the impact on the interest
rate, Bédué and Lévy (1997) find that the more a firm
concentrates its loans in just one bank, the lower the
cost of credit. Worthington (1999) arrives at a similar
result, concluding that the interest rate associated
with a loan granted to a new client was between 40
and 50 points higher than that offered to a client with
earlier experience of credit. Auria et al. (1999),
Blackwell and Winters (1997), Elsas (2005) find
empirical evidence that shows that greater borrowing
concentration with the lending bank allows also a
reduction in interest rate.
The extent of banking products acquired from the
lending bank: The strength or intensity of a banking
relationship is also defined in terms of the extent of
financial products/services offered by the bank and
used by the borrowing SME. There is little evidence
to document the influence of the extent of products
acquired on the loan terms offered to borrowing firms.
The borrowing concentration with the lending bank or
the number of credit products has been used more
frequently. Together with this last indicator, there is
also extensive investigation that analyses the
theoretical and empirical impact of the number of
banking relationships on credit terms obtained.
Structure of the bank market: Analysis of the bank
market structure in the credit relationship with SME
has multiple features, presents great specificity and
interacts with other variables, that are not always
understood or duly pondered, and is therefore
complex and ambiguous. In spite of theoretical
models in the area of industrial economics generally
alerting to the inefficiencies of market power,
reflected in less availability of credit and higher
pricings, a more concentrated market can give value
to the credit relationship, by allowing a reduction of
information asymmetry and agency problems
(Guzman, 2000).
Indeed, in order to minimize the difficulty of obtaining
data that permit assessment of the extent of acquired
products and the related information that can be
extracted, sometimes only the financing aspect is
used, through determination of the borrowing
concentration with the bank. This method is used by
several authors (Blackwell and Winters, 1997;
Charlier, 1998; Harhoff and Körting, 1998; Athavale
and Edmister, 1999; Auria et al., 1999) that conclude
that a greater concentration of credit with the lender
is associated with lower cost of credit, greater access
to credit or even more granting credit.
The investigation carried out is not consensual in the
conclusions reached6. It has generated divergent
results, presenting a relationship which is sometimes
positive and sometimes negative between the degree
of market power in the banking system and the value
of the banking relationship, with diverse and rather
unclear effects on credit terms for SME.
As for the impact in terms of the credit limit obtained,
Charlier (1998) concludes most firms that
concentrated their commitments with the main bank
saw their credit limit almost doubled, compared to the
other firms making up the analysed sample.
Analysing the impact in terms of the probability of
attaching collateral, Forestieri and Tirri (2002) and
Ziane (2003) found empirical evidence of a greater
concentration of credit with the main bank increasing
the probability of attaching collateral.
The defenders of the virtues of banking concentration
in the banking relationship, reflected in beneficial
credit terms for SME, support their stance with the
fact that banking relationships facilitate a continuous
flow of information between the debtor-firm and the
creditor-bank, which can in turn guarantee
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most investigators who tackle this subject begin by
admitting that future research is still necessary to
examine and explain these conclusions in greater
detail (Boot, 2000; Boot and Schmeits, 2005;
Degryse and Ongena, 2007; Udell, 2008).
continuous (uninterrupted) access to bank finance,
and so it will be in a less competitive financial system
that banks may give more importance to client
relationships (Boot and Schmeits, 2005; Petersen
and Rajan, 1995). In competitive banking markets,
soft information tends to get scattered, its collection
being therefore more costly for the lending bank
integrated in that market, and consequently that cost
will have to be reflected in higher credit pricings,
particularly for small borrowers (Marquez, 2002).
Empirical analysis: Based on the review and
systematization of the literature referred to above, we
begin the empirical investigation, carried out from the
position of the granter of credit and where we assess
the effects of the banking relationship, the quality of
the financed firm and some specific conditions on the
loan terms obtained by SME.
However, despite the various virtues referred to the
influence of banking concentration on SME access to
credit, for other investigators, credit terms do not
seem to benefit from a market structure characterised
by high banking concentration or reduced competitive
intensity. Boot and Thakor (2000), based on a
theoretical model, demonstrate that as the intensity of
inter-bank
competitiveness
increases,
banks
concentrate more on the credit relationship but with
less value for the borrowing firm.
Research hypotheses: The banking relationship,
evaluated by its length and by the degree of
borrowing concentration with the lending bank, is
positively associated with the loan conditions for SME
assessed in respect of interest rate, credit limit and
collateral requirements.
Firms with a long relationship with a certain bank
(associated to which there are also previous
successful credit experiences) and with a greater
borrowing concentration with the lending bank have
better loan conditions7 than those obtained by firms
with a recent relationship, or with a reduced
borrowing concentration with that bank.
Processes of banking concentration reduce credit to
SME (Berger et al., 1998) and are associated with
significant spreads (Degryse and Ongena, 2005).
Concentration seems to destroy existing relationships
and marginalize small banks (more focused on credit
to this type of firm). The increased competitiveness in
the bank credit market forces the reduction of
spreads, but can also motivate banks to compensate
for the reduced financial income, through deepening
the relationship, “tying” clients through the sale of
other products that generate commission, via crossselling
As referred to, given that loan conditions are
assessed concerning risk premium, credit limit and
attachment of collateral, the following three
hypotheses arise. A greater banking relationship with
a SME allows for a reduction of information
asymmetry (better knowledge of the borrower, based
on the “soft information” acquired through his banking
behaviour over time), with beneficial effects in less
intense monitoring, allowing the lending bank to grant
lower pricings. Considering this argument, we
formulate the following hypothesis:
The empirical investigation by Blackwell and Winters
(2000) finds an inverse relationship, for the case of
lines of credit destined to small firms, between
competition in the local banking markets and average
interest rates. Beck et al. (2004), from a database
containing information about the obstacles in
accessing credit and the financing patterns in 74
countries, also conclude that movements of banking
concentration, whatever their intensity, increase
firms’ difficulty in accessing credit, that impact
tending to decrease as firm size increases.
H1: the banking relationship is negatively associated
with the risk premium (spread) of the loan, ceteris
paribus.
The banking relationship also encourages more
information clarity in the elements of accounting
made available, and so the proportionate reduction of
risk allows the lending bank to grant short-term
operations with a higher credit limit. On basis of this
argument, we formulate the second hypothesis:
To summarize, the relationship, between the degree
of concentration of banking markets and the value of
the credit relationship for SME, does not seem to be
monotonic. It is therefore no wonder that investigation
carried out is not consensual in terms of its impact on
the loan conditions obtained by SME. Therefore,
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Characterisation of firms making up the sample: In
the universe of SME, the investigation centred its
analysis specifically on MSE, as these firms are even
more exposed to serious problems of information
asymmetry, due to their reduced size and absence of
rating (Hernández-Cánovas and Martínez-Solano,
2006), the even greater importance of the banking
relationship being recognized in obtaining better loan
conditions. Besides, the preference for MSE was to
meet the aim of reaching a greater degree of
homogeneity, and banish completely the possibility of
observed firms turning to the capital market for
finance. The option for the “short-term limit” form of
credit is related to the fact of it not being used for any
specific previously defined investment, assessed and
later monitored by the bank, and requiring periodic
revision of the credit terms, where (re)evaluation of
the quality of the existing banking relationship
becomes crucially important (Fernando et al. 2002).
H2: the banking relationship is positively associated
with the credit limit (amount), ceteris paribus.
By allowing a reduction of information asymmetry, the
banking relationship provides a minimization of the
problems arising from adverse selection and moral
hazard, which can also reduce the possibility of
attaching collateral (e.g. real guarantees, rather than
personal guarantees) to the credit obtained by the
financed firm. On basis of this explanation, we
formulate the following hypothesis:
H3 the banking relationship is negatively associated
with the quality of the guarantees attached to the
loan, ceteris paribus.
Research Sample: We obtained a suitable sample of
436 observations from a large Portuguese CI
containing information related to the characteristics of
the banking relationship of SME holding a short-term
line of credit, in the form of a current account, with a
limit not above 500.000 €., in a situation considered
normal8. The data were collected in 2002 and refer to
the centre region of Portugal. Relating to statistical
treatment applied in this study, with special
prominence for the use of multiple and logistic linear
regression models, it was also necessary to subject
the sample to a process of detection, analysis and
later elimination of abnormal observations (outliers
and points of leverage), referring to the dependent
and explanatory variables, with the elimination of 10
observations, the number of observations in the
sample settling at 426.
In particular, the selection of borrowers observes the
following criteria:
- Portuguese MSE, according to the ruling European
definition at the time, i.e., firms with fewer than 50
employees, an annual business volume of no more
than 7 million euros or an annual total balance sheet
not exceeding 5 million euros and meeting the
independence criterion defined in the Commission’s
Recommendation 96/280/CE, of 3 April 1996;
- firms unrelated to a group, understood as not
owning or being owned9 by other firms in more than
25% of equity capital;
- bank credit is used for its main economic activity,
defined in terms of the NACE (Nomenclature of
Classification of Economic Activities);
- firms with organized accountability10;
- firms with loans and deposits with the bank;
- firms with headquarters situated in the same
administrative area11 as the branch of the bank where
the loan was granted.
The composition the research sample by industry
sectors is presented in appendix 1 table A1.
The past financial-economic information gathered
refers to the last period known on that date which
was 2001. For reasons of banking secrecy, and also
statistical secrecy, the information supplied omitted
any data which could lead to identification of the
borrowers. The information about the type of place
and structure of the local bank market was collected
from the Annual Statistical Account of the National
Statistics Institute (INE) and the Biannual Bulletin of
the Portuguese Association of Banks.
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Appendix
Table A1: Composition of research sample by industry sectors
N umb er o f F irms
Ind ustry sector
Sa mple
Vo lu me o f Sales
Universe
S ampl e
Un iverse
B an k D ebt
Sa mple
Un ive rse
A gric ulture
5 ,40 %
2, 49 %
5, 91 %
1,4 6%
5,6 2%
1 ,34 %
E xt ra ctive Ind ust ry
0 ,70 %
0, 31 %
0, 70 %
0,5 5%
0,1 6%
0 ,90 %
M an ufa ctu ring I ndu stry
19 ,48 %
14, 22 %
1 7, 38 %
2 3,6 2%
1 1,6 4%
21 ,46 %
Con struct io n and Rea l Est ate
30 ,75 %
28, 23 %
3 5, 61 %
1 8,7 6%
6 0,7 2%
46 ,40 %
Con struct io n
27 ,46 %
12, 28 %
3 4, 38 %
1 0,4 0%
6 0,0 6%
20 ,31 %
Com m erce
28 ,64 %
32, 12 %
3 0, 66 %
4 5,5 0%
1 5,9 0%
17 ,80 %
2 ,82 %
9, 41 %
1, 70 %
2,7 2%
1,2 3%
2 ,08 %
1 ,64 %
6, 17 %
1, 94 %
4,9 9%
1,1 0%
5 ,03 %
10 ,56 %
7, 04 %
6, 10 %
2,4 0%
3,6 3%
4 ,98 %
A ccom m od atio n
Tran spo rt
E duc ation , He alth an d Oth er Se rvice s
Table 1: Definition of variables
VARIABLE
DESIGNATION
DEFINITION
I. Terms of Credit Operation
Spread (in %) that is added to the market index (Euribor rate), and expresses the risk premium of the
loan attributed by the lending bank (in classes).
RPM
Risk premium
LIM
Credit limit
Natural logarithm of the limit (amount) of the credit operation (in thousands of euros) held with the
lending bank
COLL
Collateral
Dummy variable with the value of "1" when the guarantee associated with the credit operation is a
collateral, with a value equal to the loan and "0" in the remaining situations.
II. Banking Relationship
LBR
Length of banking
relationship
Number of years of relationship with the lending bank (in classes)
DBC
Borrowing
Concentration
Bank debt concentration (in %) with the lending bank (in classes)
III. Firm Quality
REP
Reputation
SIZE
Size
Natural logarithm of Liquid Assets (in thousands of euros)
DEBT
Debt
= (Liabilities / Liquid Assets)*100
SAL
Sales
GLIQ
General Liquidity
OPI
DEVP
Operating
Income
Deviation of EBITDA
to Sales compared to
the median for the
activity
Number of years of activity (in classes)
Natural logarithm of value of sales + services provided (in thousands of euros)
= (Current Assets / Current Liabilities )*100
= Operating Revenue - Operating Costs (in thousands of euros)
= ESi / ESNACE
ES being ratio EBITDA to Sales (in %); i, the firm and NACE, the Code of Economic Activity (to 5
digits) of the activity firm i belongs to
IV. Bank’s Local Market Power
MKTS
Bank’s local
market power
Market share (in %) in terms of the number of branches of the lending bank in the local market (unit
of local government) where the borrowing firm has its headquarters, in relation to the total number of
bank branches there
V. “Industry” Effect
IND
Industrial
firm
CONST
Construction
firm
Dummy variable with the value of “1” if credit is used in an INDUSTRIAL firm and “0” otherwise.
Dummy variable with the value of “1” if credit is used in a CONSTRUCTION or REAL ESTATE firm
and “0” otherwise
VI. Location
RGB
Regional branch
Dummy variable with the value of "1" if the place (unit of local government) where the credit is
granted is where the regional credit department is located, and “0” otherwise.
VII. Control Variables
DEC
Decision
Number of decision organs of the credit operation
PART
Partners
Number of firm partners/shareholders, with participation of at least 5%
the need to introduce other forms of risk assessment
in the study, and therefore simplified the analysis. By
selecting a non-specialized line of credit accessible
indiscriminately to all MSE, we avoided the credit
terms obtained being correlated with the special
framework of a special type of credit more accessible
to a certain sector or branch of activity or sub-group
Characterisation of the type of bank loan
analysed: From the outset, our preliminary concern
was that the information gathered on credit should
refer to only one determined line of non-specialized
finance available indistinctly to all the MSE chosen
for the study. By choosing not to introduce various
different forms of finance in the analysis, we avoided
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of firms (e.g. lines of credit with special rates due to
an agreement with an association in a certain sector),
or with other discriminatory motives outside the
investigation, besides the quality of the existing
relationship between the bank and the financed firm
and the economic-financial performance in evidence.
For the previous reasons, in the diversity of credit
products generally contracted by MSE, the one
whose definition of credit terms depends most on the
quality of the existing relationship and the risk profile
of the applicant rather than the project, business, or
activity, is the short-term (renewable) loan, in the
form of a credit limit, destined to minimize cyclical
financial needs arising from the firm’s operating
activity, which is also the predominant form of
financing in small firms.
It is a form of short-term credit destined to
temporary working capital needs, arising from the
firm’s operating activity. The bank monitors and
revalidates its use periodically, which contributes to
gaining more and better information about the firm,
also allowing the result of periodic assessment of the
risk of the operation to have possible repercussions
in altered terms for the operation in the following
period. In this attribution of a maximum value of
short-term finance appropriate to the perceived risk of
the client, besides the quality of the existing banking
relationship11, there is evaluation of descriptive
indicators of the quality of the firm, namely
assessment of management and economic-financial
performance.
In this way, we isolate the problem of the loans
terms (amount, interest rate or collateral
requirements) of the MSE included in the sample
being influenced by external circumstances coming
together at certain moments in time, caused by a
change in the bank’s internal credit policy, or by the
firm’s performance in the period before the first (or
other) credit contract, which could happen in a
medium or long-term credit, since loan terms are
generally defined on the date the credit starts and do
not change during the contract.
subsequently
variables.
other
complementary
determinant
Definition of the research variables: As already
mentioned, the principal aim of this research is to
assess the importance of the banking relationship for
small firms accessing bank finance, reflected in the
loan conditions obtained. To do so, the first concern
was to define the dependent variables that would
characterise the credit terms obtained and introduce
a first set of explanatory variables that would capture
the quality of the existing banking relationship, and
Another variable related to the contract terms of the
selected loan is the credit limit (LIM) defined as the
natural logarithm (ln) of the limit (amount) of the
short-term credit operation, borrowed by the
observed firm from the lending bank (variable
presented in this way in the study by Peltoniemi,
2007).
Based on the previously presented investigation
review, we now clarify objectively the main concepts
and variables to be used in this study. The indicators
included in the models tested were listed from the
main studies published about this subject and already
referred to in the theoretical and empirical review.
The information extracted aimed to correspond to
seven distinct components: considering the
dependent variables they refer to generic contract
terms of the type of loan selected; and, concerning
the independent variables they include characteristics
identifying the borrowing MSE banking relationship;
firm’s economic-financial characteristics; identification
of the local banking market structure; possible
existence of “industry effects”; relevance of the
distance from the regional credit department; control
variables.
Dependent variables: contract terms of the
selected loan : The risk premium (RPM) is defined
as the spread (in %) that is added to the market index
(Euribor rate), and expresses the risk premium of the
loan attributed by the lending bank (in classes). The
risk premium is calculated in the same way as in
several studies (Berger and Udell, 1995; Elsas and
Krahnen, 1998; Forestieri and Tirri, 2002;
Hernández-Cánovas and Martínez-Solano, 2006).
Although it is common in the type of credit observed
to set various types of commission (commission
associated with the study, management, renewal
and/or immobilization), which has the effect of
increasing the cost of credit, the fact that the values
for the research sample are almost invariably the
same (and therefore unrelated to the quality of the
operation or the proponent of credit), leads us to
believe they are not defined as a function of the
perceived risk of the operation or the client, and so
they were not included in this analysis.
Collateral (COLL) is a dummy variable with the value
of "1" when the guarantee associated with the credit
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
operation is a collateral, with a value equal to the
loan and "0" in the remaining situations (variable
presented in this way in the study by Peltoniemi,
2007). The literature in the field also suggests that
attachment of more and better guarantees reduces
the cost of the credit. Indeed, the guarantee attached
to the credit is associated with a certain consumption
of capital by the bank. We generally find that loans
with collateral attached will have a lower opportunity
cost for the bank than a credit operation attached to
personal
guarantees
(whatever
the
overall
assessment of the operation’s risk), and so this
variable can also be an explanatory factor of the final
cost of the operation. We chose to introduce the
variable “collateral” only as dependent variable, not
including the “risk premium” variable in the set of
independent variables. The collateral effect in
definition of the risk premium is not considered
relevant, in harmony with the conclusions obtained by
Fredriksson (2007) and Degryse and Van Cayseele
(2000).
and Winters, 1997; Cowling, 1999; Cardone et al.,
2005; Hernández-Cánovas and Martínez-Solano,
2006; Brick et al., 2007) as an indicator of the stability
of the banking relationship. This study measures the
length of the banking relationship (LBR) by the
number of years of relationship with the lending bank,
since becoming a customer, in classes. The degree
of borrowing concentration variable (DBC) with the
lending bank used in this study is a substitute of the
number of lending banks used by the borrowing firm,
which can also transmit an approximate picture of the
stability of the banking relationship. This variable was
used in several studies (Blackwell and Winters, 1997;
Auria et al., 1999; Ewert and Schenk, 1998; Ziane,
2003).
Firm quality: The first and main indicator for
assessing the credit risk of SME continues,
inevitably, to be their past financial-economic
performance, judging by the quantitative techniques
for measuring credit risk in SME, which include
various variables characterising their quality. Added
to which are indicators of reputation and firm size
which we designate as characterising their
informational opacity.
From observation of the bivariate correlations
between the selected dependent variables12 we find
low values (in absolute terms). The highest value
detected (-0,581) refers to the correlation between
the variables of “risk premium” and “credit limit”. This
value is not worrying, considering the fact that in the
process of analysing firm credit, the cost of credit is
set independently of the definition of the credit limit
granted/to be granted (Peltoniemi, 2007), and so the
hypothesis of possible endogeneity between selected
dependent variables is negligible.
Almost all empirical studies carried out that analyse
specifically the influence of the banking relationship
on SME finance incorporate in their models
independent variables identifying the reputation and
size of the financed firm and its economic-financial
performance which represent the borrower’s risk and
are associated positively with the credit terms
obtained (Blackwell and Winters, 1997; Steijvers and
Voordeckers, 2002).
Next, we present the independent variables used in
the models, in six groups: indicators of the banking
relationship, firm quality, local market strength of the
lending bank, “industry” effect, location and control
variables.
Concerning assessment of informational opacity, the
characteristics of reputation and firm size are
observed, selecting the variables REP and SIZE,
respectively. The reputation variable (REP) was
measured by the number of years the firm has been
active. This variable is associated with the firm’s past,
and it indicates its reputation and benefits credit
terms. This is the most commonly used variable in
empirical research into topics to do with SME capital
structure or finance and also inevitably in assessing
the banking relationship. Firm age, associated with its
past, indicates its reputation and benefits credit
terms, particularly in terms of cost and attachment of
collateral to the credit. The firm size variable (SIZE) is
defined by the natural logarithm of the firm’s Liquid
Assets. This variable is generally used to represent
Banking relationship: From what has been said, the
banking relationship is considered to be an important
criterion of risk analysis weighing decisively in the
credit terms attributed to the borrower. Owing to the
limitations in access to information about the sample
obtained, the banking relationship is assessed by the
length of the relationship under analysis and by the
degree of borrowing concentration with the lending
bank.
The length of the banking relationship is a frequently
used variable in various empirical studies (Petersen
and Rajan, 1994; Berger and Udell, 1995; Blackwell
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
firm size13 in investigatory studies carried out in the
field of firm finance.
accurate approximation to the operating capacity to
create a liquid margin, rather than net income.
A firm’s performance, and consequently also its risk,
can be assessed from the financial and economic
point of view. From the financial point of view,
concerning assessment of the need for short-term
finance, an examination of firm stability is important.
Short-term finance requires the debt service to be
essentially interlinked with the solidity of the firm
balance sheet, since the main concern is to ensure
adequate conversion of assets (e.g. stock, clients)
held, in the operating activities, and so it is important
to introduce indicators of the degree of firm debt, its
liquidity and the size of the activity undertaken. From
the economic point of view, analysis of profitability
indicators gets special attention, particularly
profitability arising from the MSE operating activity
(where the finance is used).
The analytical importance of the economic-financial
indicators lies in the possibility of making
comparisons with other firms in the same sector14.
However, not all the indicators allow extraction of
good information by comparing them with reference
values. The value of the indicator of sales profitability
should vary little among firms belonging to the same
sector, so it is therefore an indicator whose
comparability seems useful. A value found that is
under the sector average means insufficient sales
volume, excessive purchases or high production
costs, which shows the absence of competitive
advantage and therefore an added credit risk.
We used the variable relating to the deviation of
gross profitability of sales compared to the median for
the activity (DEVP), as it is an indicator that shows
the relative deviation of the margin of the firm’s
operating activity (which is independent of the
amortization or provision policies followed by the
firm), compared to the average for the activity sector.
Introduction of this indicator aimed to find the firm’s
competitive advantage, within its activity sector. To
compare the indicator of gross sales profitability of
each firm with the sector average, we turned to the
Bank of Portugal, Balance-Sheet Database, using
when possible only the sub-set of SME, rather than
the total sample15, for each NACE selected to 5
digits. Inclusion of this variable aimed also to reduce
some accounting irrelevance that SME give to Credit
Institution.
The selection criterion for the economic-financial
indicators was based initially on a review of the
various investigations carried out in the field of credit
scoring and rating models for SME. Aiming to
determine the degree of intensity of recourse to debt
to finance a firm, we used the level of debt (DEBT),
measured by the ratio of Liabilities to Liquid Assets.
Volume of sales (SAL) is the variable indicating the
size of operating activity carried out in the year, and
is used in these terms in various empirical studies on
this subject. The General Liquidity variable (GLIQ)
used in this study is determined by the relationship
between current assets and current liabilities. This
ratio shows to what extent current liabilities are
covered by assets that are expected to be converted
in liquid financial means, in a period supposedly
corresponding to the expiry of short-term debts.
Bank’s local market power: With the aim of
controlling the impact of the lending bank’s market
share on the credit terms offered to borrowing firms,
in terms of risk premium and credit limit, ceteris
paribus, it was necessary to introduce a variable
indicating the bank’s market strength in the local
banking market. With the aim of controlling the
impact of the lending bank’s market quota, on the
credit terms granted to borrowing firms, ceteris
paribus, it was necessary to introduce a variable
indicating the bank’s market power, in the local
banking market. This variable corresponds to the
market share (MKTS) in terms of the number of
branches of the lending bank, in relation to the total
number of bank branches in a given local market
(local government area) where the borrowing firm has
its headquarters. This variable assumes values
between “0” and “1”. High values of this indicator
This variable has negative effects on the probability
of getting a loan according to Bebczuk (2004). As the
more liquid assets are, the easier it is to decapitalize
the firm when bankruptcy approaches. Besides, high
levels of liquidity can lead the bank believe that the
firm intends to transfer the risk to the bank to transfer
its own risk to the lender in the event of default. This
variable does not appear very often in empirical
studies dealing with this topic.
Operating income (OPR) is determined by the
difference between the operating revenue and the
operating expenses. This variable expresses the
profitability of the firm’s activity and allows a more
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
mean greater market power held by the lending bank
and consequently less intensity of competition in that
market.
local authority area as the regional credit
management or regional branch where the credit
relationship exists, can reflect better loan terms,
compared to location near local branches. According
to the empirical study by Cowling and Westhead
(1996), the relationship with the local branch,
although providing better service, is penalized in
terms of interest rates. According to the empirical
study by Cowling (1999), when the credit decision is
made at the local branch, rather than at a regional
office, the incidence of collateralisation increases.
Industry effect: The theory defines the “industry”
effect as one of the explanatory factors of finance
options, and does not focus on its direct influence on
loan conditions. Even so, this hypothesis is generally
raised in the main empirical studies dealing with this
subject.
Volatility of income generation (associated with the
industry sector) due to the specific risk of the
business and in turn the credit operation, can also
influence the credit terms granted. In the empirical
studies analysed, this hypothesis has sometimes
been tested, obtaining statistical significance,
especially in the construction and manufacturing
sectors.
Control variables: Finally, we introduce two
regression variables which, although not contributing
to solve any theoretical or empirical problem pointed
out in the literature of the field, influence loan terms
for SME, and so we introduce them here in order to
control results.
Being normal, in the decision matrix of banking credit,
that credit operations registering unadjusted (more
favourable) conditions in relation to the previously
determined risk, have their approval based on a high
number of decision organs, we chose to insert a
variable controlling that fact. Irrespective of the
perceived credit risk or the typical banking decision
matrix, a credit operation being decided by a greater
number of hierarchical organs will have better credit
terms. In this way, the decision variable (DEC)
determined by the number of decision organs in the
credit operation aims to control situations where,
despite an overall credit risk that could indicate
granting of certain terms, these will be improved if a
greater number of decision organs becomes part of
the operation.
In addition, similarly to what is found in the majority of
empirical studies analysing determinants of the
banking relationship, we decided to introduce a
variable relating to the number of partners in the firm
(PART), so as to control the impact of the firm’s
partnership structure on credit terms.
Based on the studies by Harhoff and Körting (1998)
and Hernández-Cánovas and Martínez-Solano
(2006), we decided to include some dummy variables
indicating the sector industry of the financed firms
which could influence credit terms, ceteris paribus.
The variables considered were the following:
manufacturing firm (IND) which is a dummy variable
with the value of "1" if the credit is used in a
manufacturing firm and “0” otherwise; construction
firm (CONST) which is a dummy variable with the
value of "1" if the credit is used in a construction or
real estate firm and “0” otherwise.
Bank Localization: Since besides the borrowing
firm’s bank branch, the regional department of credit
always interferes in the credit decision, it is relevant
to introduce a variable to assess the importance of
the distance from regional credit department of the
lender, concerning loan terms, in this way allowing us
to draw conclusions as to the influence of distance on
the value of the banking relationship and the ease
with which “soft information” circulates
Model estimation: This study uses multiple linear
regression models (MRLM), with recourse to the
method of ordinary least squares (OLS), to evaluate
the impact of the selected independent variables on
the risk premium and the credit limit, and a logit
model, with recourse to the method of maximum
likelihood estimation, to evaluate the impact of the
dichotomic variable “Collateral”.
In this way, we introduce the regional branch (RGB)
variable, a dummy variable with the value of "1" if the
place (unit of local government) where the credit is
granted is where the regional credit department is
located, and “0” otherwise. A variable introduced by
Cowling (1999) and Vigneron (2007).
Although generally speaking, proximity to the lending
branch may mean added value to the relationship
(Degryse and Ongena 2005), location in the same
These options, besides appearing the most suitable
for this study, also make it easier to compare the
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
results obtained with those reached in other studies
that follow the same methodology.
designated SIZE) with credit terms (particularly the
credit limit) the sample was divided by the median of
the SIZE variable in two sub-samples, with the aim of
exercising some control over this variable and on
which the previously defined econometric models
were also tested.
Similarly to what was done in the majority of the
empirical studies analysed, we introduced the
described pre-selected independent variables in a
single econometric model, for each dependent
variable under analysis. Given the importance, the
extent and the foreseeable high correlation of the
size variable (through the value of net assets,
Analysis and discussion of the results: The
descriptive statistics of research variables appear in
table 2.
Table 2: Descriptive statistics
Variables*
Average
Median
Standard
Deviation
Minimum
Percentiles
Maximum
10
25
75
N
90
Dependent Variables
RPM
5,5
5,0
1,8
1,0
9,0
3,0
4,0
7,0
8,0
426
LIM
3,9
3,9
0,9
1,4
6,2
2,7
3,2
4,6
5,0
426
COLL
0,2
0,0
0,4
0,0
1,0
0,0
0,0
0,3
1,0
426
Independents - Banking Relationship
LBR
2,9
3,0
0,8
1,0
4,0
2,0
2,0
3,0
4,0
426
DBC
2,9
3,0
1,1
1,0
4,0
1,0
2,0
4,0
4,0
426
426
Independents - Firm quality
REP
4,5
4,0
1,4
1,0
7,0
3,0
4,0
5,0
6,0
SIZE
5,8
5,8
1,2
2,1
10,2
4,2
5,1
6,6
7,3
426
80,1
82,0
29,5
2,6
275,0
46,3
65,1
93,6
103,5
426
426
DEBT
SAL
5,4
5,7
1,6
0,0
8,9
3,9
4,8
6,4
7,0
GLIQ
1,1
0,9
1,9
0,0
31,7
0,3
0,6
1,1
1,6
426
27,2
12,0
75,3
-185,0
727,0
-14,3
1,0
30,0
79,6
426
1,8
1,2
11,2
-101,6
172,4
0,0
0,6
2,1
3,7
426
0,1
0,1
0,0
0,5
0,0
0,1
0,2
0,3
426
OPI
DEVP
Independent - Bank’s Local Market Power
MKTS
0,2
Independents - “Industry” effect
IND
0,2
0,0
0,4
0,0
1,0
0,0
0,0
0,0
1,0
426
CONST
0,3
0,0
0,4
0,0
1,0
0,0
0,0
1,0
1,0
426
0,0
0,5
0,0
1,0
0,0
0,0
1,0
1,0
426
5,0
1,0
2,0
3,0
Independent - Location
RGB
0,3
Independents - Control Variables
DEC
2,0
2,0
0,9
1,0
PART
2,3
2,0
0,9
1,0
1,0
426
(*)
over 50% of the observed operations have no
collateral attached. As for the characteristics of the
existing banking relationship, we find that the
average length of relationship is 7 years and the
degree of borrowing concentration with the lending
bank is 64%.
The complete designation of each variable can be
found in Table 1.
We find that the average (and the median) amount of
each loan, was around 50.000 €. (value as a
logarithm in thousands of euros being 3,9), with 25%
of operations being no more than 25.000 €. (value as
a logarithm in thousands of euros being 3,2). The
average risk premium of the observed operations
was in the spread interval (2% – 2,25%). Concerning
guarantees attached to the operations, we see that
6,0
1,0
2,0
3,0
3,3
426
In the bivariate correlation matrix, presented in table
3, we observe that the dependent variables present
statistically significant correlations with the majority of
explanatory variables, with only two coefficients
registering over 50%.
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
Table 3: Correlation matrix
RPM
LIM
COLL
LBR
DBC
REP
SIZE
DEBT
SAL
GLIQ
OPI
DEVP
MKTS
IND
CONST
RGB
DEC
RPM
LIM
-0,581 **
COLL
-0,369 **
LBR
0,191 ** -0,028
DBC
-0,116 *
-0,012
0,041
-0,095 *
REP
0,077
0,056
-0,012
0,622 ** -0,098 *
SIZE
-0,368 **
0,663 **
0,186 **
0,088
-0,241 **
0,114 *
DEBT
-0,003
0,004
0,105 *
-0,112 *
0,017
-0,234 ** -0,033
SAL
0,004
0,067
-0,196 **
0,212 ** -0,220 **
0,227 **
GLIQ
0,039
-0,006
0,040
0,025
0,000
0,148 ** -0,125 ** -0,401 ** -0,010
OPI
-0,234 **
0,370 **
0,083
0,042
-0,102 *
0,067
0,405 ** -0,048
0,231 **
0,030
DEVP
-0,077
0,078
0,007
-0,027
0,020
-0,047
0,048
-0,030
0,006
0,128 **
MKTS
0,209 ** -0,209 ** -0,045
0,135 **
0,070
0,056
-0,166 ** -0,168 **
0,024
0,042
-0,097 *
0,016
IND
0,040
-0,128 ** -0,032
-0,004
0,000
-0,020
0,014
-0,029
0,060
-0,102 *
-0,058
-0,040
CONST
-0,203 *
0,377 **
0,193 **
0,006
0,096 *
-0,023
0,247 **
0,109 *
-0,108 *
0,070
0,205 ** -0,007
-0,024
RGB
-0,190 **
0,147 **
0,068
0,009
0,002
0,041
0,067
0,057
-0,056
-0,018
0,095
-0,005
-0,416 ** -0,162 **
0,064
DEC
-0,549 **
0,446 **
0,288 ** -0,207 ** -0,018
-0,146 **
0,344 **
0,272 ** -0,148 ** -0,105 *
0,001
-0,100 *
-0,165 ** -0,039
0,222 **
0,155 **
PART
-0,178 **
0,200 **
0,010
-0,184 **
0,167 **
0,078
0,122 *
0,109 *
-0,133 ** -0,008
0,010
0,095 *
0,380 **
-0,086
0,017
0,030
0,384 ** -0,144 **
-0,098 *
-0,038
-0,129 **
0,136 **
-0,318 **
0,124 *
The complete designation of each variable can be found in Table 1.
(*) Correlations significant at 5%
(**) Correlations significant at 1%
Regarding the correlation between the dependent
variables of “Risk premium” (RPM) and “Collateral”
(COLL) we get a negative value but close to zero,
which indicates that attaching higher quality
guarantees tries to partly reduce problems arising
from adverse selection, since when more or better
guarantees are offered, lower interest rates are
defined.
The “Risk premium” variable (RPM) is negatively
correlated with the “Credit management” control
variable (DEC) (r=-0,549, p<0,01), which agrees with
the indications initially put forward, that to obtain
lower risk premiums, operations would have to pass
through a greater number of decision organs, ceteris
paribus. The “Credit Limit” variable (LIM) is positively
correlated with the “Size” variable (SIZE) (r=-0,663,
p<0,01), indicating that the bigger the firm, the higher
the credit limit, ceteris paribus. We also confirm a
positive, and significant though not very pronounced
correlation between the RPM variable and the SIZE
variable.
In addition, the explanatory variables do not show a
correlation completely in agreement with the
relationships (signs) initially proposed in the
hypotheses set out. Concerning assessment of the
banking relationship on credit terms, the “length of
the relationship” (LBR) presents contrary signs to
those expected in relation to the RPM and LIM
variables, indicating the disadvantage of a longer
lasting
relationship
for
credit
terms.
The
“Concentration” variable (DBC) presents the
expected signs in relation to the “Risk Premium”
(RPM) and “Collateral” (COLL) variables.
Besides, we find a significant and pronounced
correlation between the “Credit management”
Concerning the correlation between dependent
variables, as would be expected, the dependent
variables of “Risk premium” (RPM) and “Credit limit”
(LIM) also present high negative correlation (r=0.581, p<0,01), which indicates that higher amounts
of credit are associated with lower risk premiums,
ceteris paribus.
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
hypothesis 3 and which was also tested on the total
sample (LOGIT 1) and on the two sub-samples: firms
whose size is equal to or smaller than the total
sample (LOGIT 2) and firms that are larger than the
median of the total sample (LOGIT 3), respectively,
generated the results summarized in table 6.
variable (RGB) and the “Bank’s local market strength”
variable (MKTS), observing therefore that in local
authority areas with a regional credit department
(RGB=1), the market quota of the lending bank is
low, and vice versa. As for the other explanatory
variables, in general, the signs obtained by the
significant correlation coefficients are as expected.
Determinants of risk premium: Concerning
hypothesis 1, formulated about the relationship
between the banking relationship and the credit risk
premium , we conclude, according to MRLM 1 (with
dependent. variable RPM), that the length of the
relationship (LBR) is positively associated with the
risk premium of the loan, whatever the size of the
firm, going against the formulated hypothesis and the
main existing theory. This result was obtained by
Angelini et al. (1998) and Auria et al. (1999) for Italy,
by Degryse and Van Cayseele (2000) for Belgium, as
well as by Vigneron (2007) for France. So, the results
obtained in the current study are in line with the main
European studies carried out.
The two econometric models, that have as
dependent variables the “risk premium” (RPM) and
the “credit limit” (LIM), allowing evaluation of
hypotheses 1 and 2 respectively, and which were
tested on the total sample (MRLM 1 and MRLM 4)
and on two sub-samples: firms whose size is equal to
or smaller than the median of the total sample
(MRLM 2 and MRLM 5) and firms that are larger than
the median of the total sample (MRLM 3 and MRLM
6), respectively, generated the results summarized in
tables 4 and 5.
The logit model, with the dichotomic dependent
variable “Collateral” (COLL), allowing evaluation of
Table 4: Determinants of the risk premium, depending on the MSE size (SIZE)
Dependent Variable
Independent
Variables
(Constant)
RPM
(N=426)
MRLM 1
8,131 ***
( 13,193 )
RPM
RPM
(LNAC ≤ 5,8 / N=210)
(LNAC > 5,8 / N=216)
MRLM 2
7,687 ***
(
8,926 )
MRLM 3
7,075 ***
( 6,8113 )
Banking Relationship
LBR
DBC
(
(
0,255
2,625
-0,266
-4,838
***
)
***
)
(
(
0,240
1,691
-0,320
-4,78
*
)
***
)
(
(
0,141
1,273 )
-0,300 ***
-4,686 )
Firm Quality
REP
SIZE
DEBT
SAL
GLIQ
OPI
DEVP
(
(
(
(
(
(
(
-0,083
-1,442
-0,122
-1,82
0,011
4,4532
-0,043
-0,848
0,060
1,3554
-0,004
-3,354
-0,009
-2,384
)
*
)
**
)
(
)
(
0,005
0,0746
-0,171
-1,295
0,022
3,9679
0,038
0,8592
0,012
0,752
-0,003
-3,502
0,009
0,6389
(
-0,447
-0,368 )
(
-0,092
-1,06
0,107
1,22
0,009
3,115
-0,054
-0,82
0,115
1,771
-0,013
-2,201
-0,013
-1,348
(
2,674 ***
2,918 )
)
*
)
***
)
(
(
)
(
)
***
)
**
)
(
(
(
)
(
)
***
)
(
(
(
(
)
)
***
)
)
)
***
)
)
Bank’s Local Market Power
ICON
(
1,756 **
2,447 )
“Industry” Effect
IND
CONST
(
0,026
0,17
)
-0,072
-0,445 )
(
-0,185
1,200 )
(
(
0,380 *
1,833 )
-0,273
-1,41
)
(
-0,169
-0,799 )
0,018
0,090 )
(
-0,016
-0,08
)
(
-0,477 **
-2,468 )
(
(
Location
RGB
Control Variables
DEC
PART
Nº obs.
2
Adjusted R
F statistic
(
(
-1,043
-13,05
-0,167
-2,222
***
)
**
)
(
(
-1,206
-11,99
-0,185
-1,69
***
)
*
)
(
(
-0,895
-11,16
-0,117
-1,694
426
210
216
0,498
27,107
0,618
23,616
0,590
21,693
***
)
*
)
The complete designation of each variable can be found in Table 1. The “t” statistic appears in brackets.
(*) significant at 10%; (**) significant at 5% and (***) significant at 1%.
115
Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
rate, judging from the sign associated with the
coefficient of the independent variable referred to.
This result is in agreement with the conclusions
obtained by Cowling and Westhead (1996). However,
only in the case of larger MSE (MRLM 3) was there
statistical significance.
This evidence is more robust for smaller firms (MRLM
2) and in places where the lending bank has greater
market strength. We can therefore conclude lending
banks make use of their information monopoly (hold
up problem), due to the possibility of accumulated
information, making it disadvantageous for the firm to
prolong the banking relationship, as a way to obtain
better credit terms. In the case of the larger firms in
the sample (MRLM 3), the length of the credit
relationship, despite presenting a positive relationship
with the risk premium, is not significant, in harmony
with the conclusions also obtained by Ziane (2003)
for France or Cardone et al. (2005) for Spain.
Regarding the “control variables”, the quite significant
impact and with the expected sign of the coefficients
associated with the “Decision” variable (DEC) stands
out. The coefficients of the “Partners” variable
(PART) record the expected signs, but without
statistical significance.
Determinants of credit limit: Concerning hypothesis
2, concerning the relationship between the banking
relationship and the credit limit attributed, we
conclude, according to MRLM 4 (dep. var. LNMONT),
that the coefficient associated with the length of the
banking relationship presents a negative sign,
contrary to what was expected in the hypothesis
formulated. However, the estimated coefficient is not
statistically significant.
However, we conclude, according to MRLM 1, MRLM
2 and MRLM 3 that the more a firm concentrates its
credit with the lending bank (DBC), the lower the risk
premium of the loan, in harmony with the conclusions
obtained by Bédué and Levy (1997) for France,
Harhoff and Körting (1998) for Germany, and Auria et
al. (1999) for Italy. Besides the banking relationship,
we see that firm size (through the SIZE variable)
presents a positive impact on the risk premium
attributed (coefficient with negative sign), but only in
the total sample (MRLM 1). As for firm age (REP),
although presenting the expected sign, it is not a
statistically significant variable in any of the models
analysed.
Once again, and also in accordance with MRLM 4,
and with greater emphasis in smaller firms (MRLM 5),
there is the marked importance of another indicator of
the banking relationship, the borrowing concentration
with the lending bank, as a way to obtain higher
credit limits, in harmony with existing theory and with
the empirical conclusions obtained by Charlier
(1998), confirming therefore the hypothesis
formulated.
Regarding the remaining variables concerning firm’s
financial-economic performance, the importance of
“debt” (DEBT) and “profitability of operating activity”
(OPI) indicators stand out, their statistically significant
coefficients presenting the expected signs in the 3
models, in harmony with the conclusions obtained in
the main empirical studies carried out. Debt records a
negative impact on the risk premium obtained and
profitability records an inverse effect. General liquidity
(GLIQ) is not statistically significant, in terms of its
impact on the risk premium of operations.
The variables assessing firm quality, both the
variables characterising information opacity and
those characterising economic-financial performance,
generally present the expected signs. Beginning with
the variables characterising information opacity
(“reputation” - REP and “size” - SIZE), we conclude
that firm reputation (age), despite only being a
significant variable when considering the total sample
(MRLM4), it presents the expected signs in the 3
models, i.e. the greater the reputation, the higher the
credit limit obtained. Firm size is seen to be
significant and also with a positive impact on the
credit limit in the 3 models.
Concerning “Bank’s local market strength” (MKTS)
we obtain a significant negative impact on credit
terms in MRLM1 and MRLM2, an increase in the
bank’s market strength being associated with worse
loan terms for MSE and vice versa. In the subsample made up of larger MSE (MRLM3) that effect
is not noticeable. As for the “industry effect”, its
impact is not significant. Branch location near
regional branch (RGB) was shown to be important in
improving loan terms, reflected in reduced interest
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
Table 5: Determinants of the credit limit, depending on the MSE size (SIZE)
Dependent Variable
Independent
Variables
(Constant)
LIM
(N=426)
LIM
LIM
(SIZE ≤ 5,8 / N=210)
(SIZE > 5,8 / N=216)
MLRM 4
0,797 ***
( 3,574 )
MLRM 5
1,694 ***
( 6,458 )
MLRM 6
1,274 **
( 2,583 )
-0,015
( -0,397 )
0,075 ***
( 3,593 )
-0,013
( -0,287 )
0,086 ***
( 4,068 )
-0,037
( -0,838 )
0,026
( 0,894 )
0,064
( 2,498
0,354
( 11,569
-0,001
( -0,519
-0,025
( -1,074
0,020
( 3,1627
0,002
( 6,3899
0,003
( 0,8938
0,034
( 1,162
0,088
( 3,100
0,000
( 0,112
0,066
( 1,76
-0,002
( -0,244
0,003
( 1,28
0,009
( 2,422
0,023
( 0,697
0,393
( 6,306
0,001
( 0,254
-0,083
( -3,933
0,023
( 2,325
0,001
( 4,939
-0,003
( -0,735
Banking Relationship
LBR
DBC
Firm quality
REP
SIZE
DEBT
SAL
GLIQ
OPR
DEVP
**
)
***
)
***
)
)
***
)
***
)
)
)
***
)
***
)
)
***
)
***
)
)
)
***
)
)
***
)
**
)
***
)
)
Bank’s Local Market Power
MKTS
-0,491 **
( -2,491 )
-0,308 **
( -1,477 )
-0,043
( -0,124 )
-0,180
( -2,996
0,228
( 3,1152
***
)
***
)
-0,134
( -2,195
0,242
( 2,917
***
)
***
)
0,032
( 0,382 )
0,279 ***
( 2,789 )
0,269
( 7,3328
0,053
( 1,7776
***
)
*
)
0,173
( 3,573
0,071
( 2,267
***
)
*
)
0,224 ***
( 5,467 )
0,051
( 1,572 )
“Industry” effect
IND
CONST
Control Variables
DEC
PART
Nº obs.
2
Adjusted R
F statistic
426
210
216
0,63
52,830
0,524
17,490
0,604
24,458
The complete designation of each variable can be found in Table 1. The “t” statistic appears in brackets.
(*) significant at 10%; (**) significant at 5% and (***) significant at 1%.
117
Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
in the manufacturing sector are exposed to greater
restrictions in terms of credit limit than firms in the
construction sector, in agreement with Fernando et
al. (2002). The greater size of credit destined to
construction firms can be related to the fact of this
type of credit being directed to the integral
development of investments in fixed assets, whereas
for manufacturing firms it may be destined exclusively
to finance working capital needs.
Regarding the variables characterising economicfinancial performance, we find that the indicators of
firms’ financial stability, reflected in the level of debt
(DEBT) and profitability of operating activity (OPI) are
the ones presenting statistical significance and the
expected signs. In the models tried on the total
sample (MRLM4), debt registers a negative impact
on the credit limit obtained and profitability registers a
positive impact. General liquidity (GLIQ), in turn,
presents coefficients that are significant from a
statistical point of view, although its impact is reduced
and presents opposite signs in the sub-samples
analysed (MRLM5 and MRLM6). In the case of
smaller firms (MRLM5), the impact of liquidity is
negative. This result corroborates the arguments of
Bebczuk (2004). However, in the case of larger firms
(MRLM6) the impact of general liquidity on the credit
limit is positive. This result suggests that banks
attribute less risk to larger firms with higher levels of
general liquidity, to which they will associate a higher
level of cash-flow.
Finally, concerning the “control variables”, we
highlight the positive and quite significant impact in all
models and with the expected sign of the “Decision”
variable (DEC). This result suggests that when a
greater number of decision organs are involved in the
credit operation, the credit limit was raised. The
“Partners” variable (PART) registered a positive
impact on the credit limit and presents statistical
significance in MRLM4 and MRLM5. However, the
number of partners seems to lose relevance when
firms are larger (MRLM6). This result is not
surprising, given that larger firms are managed by
professional managers, and it is they who negotiate
credit with banks, firm ownership structure (including
the figure of the owner-manager) tending to lose
importance.
For the “bank’s local market strength” variable
(MKTS), we obtain a significant negative impact on
the amount of credit in models MRLM4 and MRLM5,
an increase in the bank’s market strength being
associated with worse credit terms for firms, in terms
of credit limit. These results are close to the ones
obtained by Repetto et al. (2002) and are in harmony
with the conclusions obtained by the theoretical
models that warn of the inefficiencies of market
strength for the consumer, reflected here in less
availability of credit.
Determinants of collateral requirements:
Concerning hypothesis 3 formulated to test the
relationship between the banking relationship and the
guarantees attached to credit, although the variables
indicative of the banking relationship introduced in
the logit models do not present statistical
significance, finding in general a greater impact of
some variables indicating firm quality, similarly to
what was found in the study by Elsas and Krahnen
(1999), it is nevertheless possible to draw some
conclusions, bearing in mind the signs presented by
the coefficients.
As for larger firms (MRLM6), although the
relationship between the bank’s local market strength
and the amount of credit is negative, it is not
significant. This result agrees with the main
conclusions of the study by Beck et al. (2004).
Regarding the “industry effect”, we find the impacts of
the activity sectors analysed are not indifferent. Firms
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
Table 6: Determinants of collateral, depending on the MSE size (SIZE)
Dependent Variable
Independent
Variables
(Constant)
(
COLL
(N=426)
COLL
COLL
(LNAC ≤ 5,8 / N=210)
(LNAC > 5,8 / N=216)
LOGIT 1
LOGIT 2
LOGIT 3
-3,699 ***
9,762
)
(
-1,575
0,549
)
(
-3,661 *
2,681
)
Banking Relationship
LBR
DBC
(
(
-0,206
1,073
)
0,115
0,995
)
(
(
-0,525 *
2,336
)
-0,021
0,012
)
(
(
-0,068
0,064
)
0,142
0,794
)
Firm Quality
REP
SIZE
DEBT
SAL
GLIQ
OPI
DEVP
(
(
(
(
(
(
(
0,153
1,504
0,442
9,205
0,008
2,26
-0,361
13,214
0,122
1,725
0,001
0,406
0,004
0,142
)
***
)
(
)
***
)
(
)
(
)
(
)
(
(
(
0,227
1,281
0,427
2,251
0,014
3,946
-0,576
7,773
0,019
0,004
0,002
0,034
-0,001
0,013
)
(
)
**
)
***
)
(
)
(
)
(
)
(
(
(
0,042
0,059
0,638
5,338
-0,018
2,646
-0,330
7,186
0,097
0,039
0,001
0,064
-0,012
0,206
)
**
)
*
)
***
)
)
)
)
“Industry” Effect
IND
CONST
(
0,192
0,348
)
0,304
0,961
)
(
0,0350
0,016
)
(
(
0,154
0,106
)
-0,475
0,685
)
(
-0,0760
0,028
)
(
(
0,381
0,598
)
0,880 **
3,988
)
(
0,2070
0,276
)
(
Location
RGB
Control Variables
DEC
PART
Nº obs.
2
Nagelkerke R
(
(
0,435 ***
6,932
)
-0,152
0,995
)
(
(
-0,335 **
1,125
)
0,337
1,838
)
(
(
0,774
12,497
-0,379
3,349
426
210
216
0,217
0,170
0,345
***
)
*
)
-2LogL Statistic
410,765
178,326
208,836
The complete designation of each variable can be found in Table 1. The “Wald” statistic appears in brackets.
(*) significant at 10%; (**) significant at 5% and (***) significant at 1%.
We conclude that the banking relationship, assessed
by its length, is important for reduced probability of
attaching collateral to the credit, this effect being
more significant in the case of smaller firms (LOGIT
2), in harmony with what was demonstrated
theoretically by Boot and Thakor (1994). It is also in
agreement with the conclusions extracted from the
empirical work of Berger and Udell (1995) for the
case in North America and Vigneron (2007) for
France, who found evidence of an inverse
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
In this study, based on a sample of MSE financed by
a large national bank, the value of the banking
relationship for loan terms is assessed by the length
of the banking relationship and by the degree of
borrowing concentration with the lending bank.
relationship between the quality of the collateral
attached to the credit operation and the length of the
banking relationship.
However, borrowing concentration with the lending
bank (DBC) seems to increase the probability of the
borrower attaching collateral to the credit, judging
only by the sign obtained in LOGIT 1 and to a certain
extent in harmony with the conclusions obtained by
Forestieri and Tirri (2002) for Italy and by Ziane
(2003) for France. But that variable is not significant
and presented different signs, but also with
significance, in the attempt to control the “size” effect
(LOGIT 2 and 3).
Generally speaking, a longer-lasting banking
relationship seems not to bring benefits to MSE, in
terms of improved conditions of credit, which ties in
with the main European studies carried out. However,
a greater concentration of credit with the lending
bank generally presents a positive impact on loan
conditions, in the form of lower interest rates and
higher credit limits.
Indeed, the length of the banking relationship only
presents benefits concerning the reduction of
collateral requirements and only in the case of larger
firms, and is not reflected in lower risk premiums or
higher credit limits. This indicator is seen to be quite
weak in the impact it registers on loan terms. This
conclusion is in harmony with the principal studies
carried out in European countries, namely Spain,
France, Belgium and Germany, and unlike the
conclusions obtained in more representative studies
carried out mainly in USA, based on samples drawn
from the NSSBF.
Regarding the impact of firm quality on the
guarantees attached, firm size (SIZE), the activity
and level of debt (DEBT) affect the guarantees of the
loan in the expected direction, allowing the solution of
problems arising from moral hazard. This agrees with
Brau (2002) and Elsas and Krahnen (1999) and
confirms hypothesis 3 of this study.
The impacts of the different industry sectors are not
indifferent (particularly industry and construction). In
the case of larger MSE (LOGIT 3), firms in the
construction sector would be more likely to see
collateral attached to their credit than firms in the
manufacturing sector, similarly to the conclusions of
Elsas and Krahnen (1998), Harhof and Körting
(1998), and Hernández-Cánovas and MartínezSolano (2006).
On its own, the length of the banking relationship
does not reveal the degree of solidity of the
relationship, as the length of the relationship in
isolation does not transmit any relevant information to
the bank, and therefore some empirical studies
carried out (Blackwell and Winters, 1997; Ewert and
Schenk, 1998 and Uzzi, 1999 among others) have
added a complementary variable indicating the
number of bank products acquired, especially
“capturing” products that allow at least two types of
benefits for the lending bank: improved customer
profitability (which, in an overall analysis, can allow to
compensate for possible reductions in the risk
premium on the credit) and accumulation of soft
information from the way the various products are
used, with direct benefits for the credit terms obtained
by SME.
As for the impact of location on collateral
requirements, besides not obtaining statistically
significant values, the signs were also contradictory,
depending on whether we considered the sub-sample
composed of smaller firms (LOGIT 2) or larger ones
(LOGIT 3), in the case of smaller firms obtaining a
positive sign of the coefficient, revealing proximity of
a regional department to be beneficial in terms of
attaching guarantees of poorer quality, in harmony
with Cowling (1999).
CONCLUSIONS
The risk of bank credit for SME generally measured
through models of credit rating and scoring, gives
special importance to past economic-financial
performance, which due to the high informational
opacity that these firms present, ends up forming the
main determinant of the high credit risk perceived by
banks. Refocusing on the value of the banking
relationship should not be considered a backward
step in the techniques of evaluating credit risk, but
rather the need to go back to basics.
Therefore, from analysis of the process of evaluating
credit, we see that the length of the banking
relationship, if devoid of any record allowing
assessment of past behaviour of movements and the
quality of existing accounts, particularly credit ones,
does not in the end make a positive contribution to
determining perceived risk, and consequently credit
terms agreed.
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Am. J. Soc. Mgmt. Sci., 2010, 1(2): 102-123
affected by significant market strength in the local
banking market) lets the bank establish less
competitive rates above the average market pricing
(up to a given limit) especially for small MSE, the
differential of which would form the cost of changing
to another Credit Institution.
The degree of concentration of bank credit with the
bank presents greater analytical value in evaluating
credit, and consequently in defining the credit terms
obtained by SME. Indeed, a greater concentration of
credit with the bank shows a firm’s loyalty to its bank
and/or, alternatively, also the costs of changing,
especially for smaller MSE, with greater economicfinancial risk and/or in places where the bank enjoys
significant market strength.
To sum up, SME that try to benefit from the
advantages of a loyal (concerning concentration of
their credit) and lasting relationship with a bank
should bear in mind that the information monopoly
acquired by the lending bank over the course of the
relationship, together with some opacity by the
borrowing firm and the bank’s greater strength in the
local market, can mean disadvantages for the firm.
Therefore, it should try mobility in terms of changing
banks, something which is only viable in places
where the local banking market is not very
concentrated.
The degree of concentration of credit, together with
some length of the banking relationship, has crucial
importance in reducing the effects arising from
adverse selection, as it lets the credit analyst obtain
very important information that reveals experience of
credit, which if positive, will favour the terms of new
credit granted to the firm, ceteris paribus.
A firm’s informational opacity and economic-financial
performance, considered characteristics of its
intrinsic quality, are also determinants of loan terms
obtained. Besides the banking relationship, reduced
informational opacity together with good economicfinancial performance observed in the period
preceding the granting of credit, influence positively
the conditions of bank finance obtained. In general,
we obtained the expected signs in the coefficients
associated with the main variables revealing firm
quality (indicators of informational opacity and
economic-financial performance), in harmony with the
conclusions obtained in the main empirical studies
carried out.
Briefly, the banking relationship will be beneficial for
all parties in the bank credit market (for banks as
supply side and for SME as demand side), and
consequently for the economy in general, as long as
there is no abuse of informational advantage by any
party in the relationship (particularly the party that
generally has more negotiating power – the lending
bank) and concerning a possible reduction of
incentives to monitor the development of granting.
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