1
SME financing and the choice of lending technology in Italy: complementarity or
substitutability?
Francesca Bartoli, Giovanni Ferri
**, Pierluigi Murro
***, Zeno Rotondi
****
Abstract
This paper investigates SME financing in Italy. The literature distinguishes
between two main different lending technologies (LTs) for SMEs: transactional
and relationship LTs. We find that banks lend to SMEs by using both LTs
together, independently of the size and proximity of borrowers. Moreover, we
show that the use of soft information decreases the probability of firms being
credit rationed. Finally, we find that more soft information is produced when
the bank uses relationship LT as primary technology individually or coupled
with transactional LT. Our results support the view that LTs can be
complementary, but reject the hypothesis that substitutability among LTs is
somehow possible for outsiders by means of hardening of soft information.
Keywords: Lending technologies, bank-firm relationship, soft information,
hard information, small business finance
JEL Classifications: G21, G30, O16
1. Introduction
Among academics and policymakers there is a clear perception that small and medium-
sized enterprises (SMEs) lack adequate financing and need to receive special assistance
(see, e.g., Vos et al., 2007). Recent research shows that also large banks provide large
amounts of funding and other services to small firms (e.g., De la Torre et al., 2010).
Nevertheless, uunder the current paradigm in SMEs lending research, large banks are
believed to specialize towards relatively large, informationally transparent firms using
hard information, while it is held that small banks have advantages in lending to smaller,
less transparent firms using soft information. Hence large banks would tend to specialize
in the transaction-based lending technology, while small bank in the relationship lending
technology. Theories based on incomplete contracting suggest that small organizations
have a comparative advantage in activities that make extensive use of soft information.
The model of Stein (2002) predicts that large banks will tend to shift away from small-
business lending, because this is an activity that relies more heavily on the production of
soft information, which cannot be verifiably documented in a report that the loan officer
can pass on to his superiors. Hence, the loan officer’s incentives to produce high-quality
information are weak when she works in a large bank. On the contrary, in the case of
larger firm lending can be based more heavily on verifiable information, such as firm’s
financial statements, the balance sheet, etc.. In this case the model suggests that a large
bank will have no problem at providing incentives for hard information production.
UniCredit; e-mail: [email protected];
Lumsa University; e-mail: [email protected];
Luiss University; e-mail: [email protected];
UniCredit; e-mail: [email protected];
(corresponding author). The views put forward in the paper belong exclusively to the authors and do not
involve in any way the institutions of affiliation. Pierluigi Murro gratefully acknowledges financial support
from UniCredit and University of Bologna, Research Grant on Retail Banking and Finance.
2
The above paradigm has found some support in the empirical literature (see, e.g., Berger
et al., 2005). There have been some important refinements, but the conclusion on the
dichotomy between large versus small banks in the choice of lending technologies still
holds. In turn, Berger and Udell (2006) argue that a common oversimplification is the
treatment of transactions technologies as homogeneously unsuitable for lending to
informationally opaque SMEs. Moreover, Berger and Black (2011) suggest that large
banks do not have equal advantages in all of these transactions technologies and such
advantages are not all increasing monotonically in firm size. They also analyze lines of
credit without fixed-asset collateral in order to focus on relationship lending and confirm
that small banks have a comparative advantage in relationship lending. But their
comparative advantage appears to be strongest for lending to larger firms.
Hence, the current paradigm on SME lending emphasizes the dichotomy between large
and small banks by predicting that relationship lending will be used to the exclusion of
transaction-based lending technologies and vice versa, i.e. suggesting that these
alternative types of lending technologies tend to be mutually exclusive due to the
presence of incentives in specializing in one of them. In other words, the possibility of
complementarity is not neglected but is considered a less likely outcome in practice and
therefore has so far gathered very little attention in the literature.1
The aim of the present paper is to fill this gap in the literature and investigate the
possibility for banks of combining lending technologies for financing SMEs,
independently of their size. In particular, we argue that the paradigm that suggests that
large financial intermediaries are disadvantaged in relationship-based lending to opaque
SMEs is misleading. We show that complementarity among transactions and relationship
lending technologies is indeed a prevailing phenomenon, compared to specialization in
one primary lending technology, and that complementarity is higher for large banks
compared to small local banks.
To address these issue, we use a novel component of survey micro-data allowing us to
learn the lending technology used by the firm’s main bank. The data refer to the end of
2006 and come from the tenth wave of the Survey of Italian Manufacturing Firms (SIMF)
run by UniCredit banking group.2 This survey constitutes an ideal testing ground for two
main reasons. First, the data set provides unusually detailed information on the
relationship between the firm and its main bank, based directly on firms’ responses to
survey questions. Second, the small and medium size of the businesses in our sample and
the central role of banks in the external financing of investment renders Italy an ideal
environment to study the firm-main bank relationship. In fact, in Italy stock and bond
markets are relatively underdeveloped so that SMEs that are denied loans by banks are
usually forced to scale down their investment plans.
In the first part of our empirical analysis we study the specific features and the
deployment of lending technologies that appear to be more widespread toward SMEs. In
1 To our knowledge of the literature, an exception is represented by Uchida et al. (2008, 2006) who have
found the existence of complementarity among lending technologies for the case of Japan, by using an
approach for identifying lending technologies similar to that followed in the present paper, although the
analysis developed by them is rather different. They argue that further research based on other countries
should be developed in order to assess whether their findings should be interpreted as being inconsistent
with the prior literature or rather better interpreted as a reflection of an idiosyncratic situation prevailing in
Japan. 2 Formerly the survey was run by Mediocredito Centrale and Capitalia banking group.
3
particular, we provide empirical evidence on the existence and relevance of
complementarity between transaction-based and relationship-based lending technologies.
A possible explanation of the novelty of our result compared to the existing literature can
be found in the methodology. In fact, the identification strategies used in the literature for
the two alternative lending technologies imply by construction that they are mutually
exclusive (see, for instance, Berger and Black, 2011). On the contrary, our identification
strategy does avoid this problem by defining lending technologies in terms of their
specific features allowing by construction the possibility that the bank is using at the
same time more than a single lending technology when financing a given firm.
The second part of the analysis addresses the role of lending technologies in the
production of soft information under both hypotheses of a single primary lending
technology or complementarity among lending technologies. Previous literature has
associated soft information with relationship lending only. Various papers suggest that
more hierarchical banks, such as large and foreign banks, are relatively less capable of
processing and quantifying soft information and transmitting it through the channels of
large and complex organizations (Berger et al., 2001; Stein, 2002). However, Petersen
(2004) conjectures that transactional lenders might be able to “harden” soft information
to boost their local competitive stance and allow them to compete more aggressively
outside core markets.
The approach used in the present analysis for identifying lending technologies allows us
to shed new light providing a better understanding of the above issues. In order to tackle
these issues, the second part of our analysis is divided into two steps. Firstly, we measure
the production of soft information by the main bank and investigate its impact on the
probability that a firm is credit-rationed. The results show that soft information lowers the
probability that SMEs are credit rationed. Secondly, we try to understand the impact of
transaction-based and relationship lending technologies in the production of soft
information by the main bank. We find that the production of soft information increases
when either the relationship lending technology is used alone or together with the
transactions lending technology. By contrast, when the transactions lending technology is
used alone it seems to be ineffective in producing soft information. The implications of
these findings are twofold. First, the way soft information becomes embodied in the
lending decision might still differ between relational and transactional technologies.
Second, substitutability between lending technologies for outsiders by means of
hardening of soft information might be rather unfeasible.
The paper is structured as follows. Section 2 briefly discusses the literature on lending
technologies and the role of soft information for financial intermediaries. Section 3
presents the dataset, as well as the methodology we use to construct the variables
employed. Sections 4 presents the empirical evidence on lending technologies and the
role of soft information. Section 5 concludes.
2. Related literature
2.1 Lending technologies
Banks lend to SMEs by means of a variety of technologies. Berger and Udell (2006)
define a lending technology as a unique combination of primary information source,
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screening and underwriting policies/procedures, loan contract structure, and monitoring
strategies/mechanisms. Different banks use different lending technologies (Rajan, 1992).
Thus, the choice of the main bank is a key component for the strategy of any firm, in
particular for SMEs that usually depend on bank financing as a source of external
funding. Among the various lending technologies used to finance firms, the literature has
thus far focused mostly on two classes: transaction-based lending technologies and
relationship lending technologies. According to the prevailing paradigm large banks hold
a comparative advantage in transactional lending, while the smaller-sized or local banks
have an edge in relationship lending (Stein, 2002).
There is a growing literature on the lending technologies that banks use to finance SMEs.
The empirical research has tried to test the results derived from the theoretical models. In
particular, several papers have analyzed the impact of relationship lending on the
financing of SMEs. For the United States, Cole (1998) finds that a lender is less likely to
grant credit to a firm if the customer relationship has lasted for one year or less, or if the
firm deals with other financial counterparts. On data for Italy, Angelini et al. (1998) show
that the intensity of relationship banking reduces the probability that borrowing firms will
be rationed, even though the lending rates charged by the banks tend to increase as the
bank-firm relationship lengthens. For Belgian enterprises, Degryse and Van Cayseele
(2000) detect the role of relationship banking along two different dimensions: borrowing
rates increase as the bank-firm relationship lengthens, while borrowing rates decrease
when the scope of the bank-firm relationship – defined as the purchase of additional
information intensive services (other than the loan) – increases.
Recently, both the theoretical and the empirical strands of the literature analyze also the
transaction-based lending technologies. Often, the literature has used the transaction
lending label for any type of loan based on information that is easily verifiable by
outsiders. By contrast, some authors underline that transaction lending is not a single
homogeneous lending technology but should be separated into a number of distinct
transaction technologies used by financial institutions. Berger and Udell (2006) suggest
that transactions technologies include financial statement lending, small business credit
scoring, asset-based lending, factoring, fixed-asset lending, and leasing. They define and
describe each of these lending technologies, highlight its distinguishing features, and
show how the technology addresses the opacity problem. Also the empirical literature
tries to explain the transaction-based lending technologies. A number of studies focus on
each individual technology separately. For example, Berger and Frame (2007) study
credit scoring and Udell (2004) asset-based lending.
2.2 Hard and soft information
The two classes of lending technologies can be primarily distinguished by means of the
type of information a bank uses in granting and monitoring the loan. Transaction-based
lending technologies are typically based (primarily) on hard quantitative information,
such as information derived from the borrowers’ balance sheets and/or the collateral
guarantees they offer (Berger and Udell, 2006). Instead, relationship lending technologies
assign a key role to soft information, qualitative information obtained via personal
interaction/acquaintance and that is difficult to codify (Rajan, 1992). For these reasons,
the literature suggests that a transaction lending technology is more desirable for more
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transparent firms, while considering the relationship lending technology to be more
appropriate for opaque firms (suffering more intense asymmetries of information).
Although the literature generally distinguishes the lending technologies on the basis of
the type of information which is exchanged between the firm and the bank, only few
papers try to study in detail what is meant by hard and soft information. According to
Petersen (2004) hard information is quantitative, easy to store and transmit in impersonal
ways, and its content is independent of the collection process. Instead, soft information is
qualitative, often communicated in text, and so not easy to store. Also, soft information
contents depend on the collector of the information. In fact, soft information is gathered
personally and the decision maker is the same person as the information collector. Thus,
according to Stein (2002), less hierarchical banks are better able to use soft information in
their decisions.
However, Uchida et al. (2012) suggest that loan officers appear to be capable of
producing as much soft information at large banks as they do at small banks. Their
findings are related to a strand of literature dealing with the effects of organizational
variables on soft information production. Liberti and Mian (2009) show that delegating
decision-making at a lower level in a large multi-national bank’s hierarchy is likely to be
more soft information-intensive. Similarly, Agarwal and Hauswald (2010) find that
branches in a large bank that are more independent produce more soft information.
Related empirical evidence, going in the same direction, is provided by Mocetti et al.
(2010), who examine the interaction between information technology and banking
organization. In particular, they show that banks equipped with more information and
communication technology capital and resorting to credit scoring delegate credit
decisions relatively more to local branch managers in small business lending activities. In
turn, Benvenuti et al. (2010) show that the propensity to lend to SMEs tends to diminish
as bank size increases. This effect is partly offset by organizational measures
strengthening local loan officers’ independent decision-making power, by affecting
branch manager’s degree of delegation of credit power, incentives schemes and turnover.
The distinction among lending technologies derives from the idea that there are two types
of production functions using distinct inputs: hard and soft information. However, the
nature of information is not exogenously fixed. In fact, the lenders practices show us that
it may be possible to change the nature of information. For example, Frame et al. (2001)
find that credit scoring is associated with an increase in the portfolio share of U.S. small-
business loans, reducing information costs between borrowers and lenders. Moreover,
Berger et al. (2005) show results consistent with the hypothesis that the use of credit
scoring increases SME credit availability, in particular for relatively risky credits.
Albareto et al. (2008), reporting the results of an Italian survey conducted by the Bank of
Italy in 2007, illustrate that medium and large banks use soft information in their credit
scoring models. Their empirical evidence for Italy supports a trend of convergence in
recent years in the structures of lending organization between local and large banks, with
a greater degree of decentralization of credit decisions. Finally, another example of a
change in the nature of information is group lending, such as that involving Mutual
Guarantee Institutions (MGIs). Columba et al. (2010) and Bartoli et al. (2013) suggest
that banks, especially large ones, appreciate this kind of lending technology in order to
lend to SMEs. They find that MGIs, through peer monitoring and joint liability, help
banks to mitigate SMEs’ asymmetric information problems.
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3. Data and empirical model
3.1 Methodology and data description
We model the complementarity among lending technologies as:
.111 iiii uxzy (1)
where yi is the lending technology used to finance firm i, zi is the vector of control
variables, xi accounts for the presence of relationship lending and ui is the vector of
heteroskedastic-robust standard errors. We estimate these equations using an OLS
model.3
Our main data source is the Tenth wave of the Survey on Italian Manufacturing Firms
(SIMF), run by the UniCredit banking group in 2007. Every three years this survey
gathers data on a sample of Italian manufacturing firms having more than 10 employees.
The 2007 wave consists of 5,137 enterprises. All the firms with more than 500 employees
are included, while those having a number of employees in the range 11 to 500 are
sampled according to a stratified selection procedure based on their size, sector, and
geographic localization. The main strength of this database is the very detailed
information it collects on individual firms. In particular, the 2007 wave features
information regarding the firm’s: a) ownership structure; b) number and skill degree of
employees; c) attitude to invest in R&D and whether it has made innovations; d) extent of
internationalization and exports; e) quality of the financial management and relationships
with the banking system. This information refers to the three years previous to the survey
year, in our case 2004-2006.4 The firms in the sample cover approximately 9% of the
reference universe in terms of employees and about 10% in terms of value added. Thus,
the sample is highly representative of the economic structure of Italian manufacturing.
Italy provides an ideal testing ground for examining the characteristics of lending
technologies that banks use to finance SMEs. The industrial structure is, in fact,
characterized by the large presence of small and medium-sized firms for which banks are
the main source of external funding. In 2009, the total value of stocks traded (% of GDP)
was 64.2%, compared with 258.8% in the United States. In this framework, SMEs are
financially vulnerable due to their almost exclusive dependence on bank financing as a
source of external finance.
Table 2 presents the descriptive statistics. At the average, the firms have been in business
for 22 years; beyond 60% of them have fewer than 50 employees (below 4% of the firms
have more than 500 employees); 70% of them are localized in the North. Only 1% are
listed on the Stock Exchange, while 37% have their balance sheet certified by external
auditors. Using Pavitt’s taxonomy (Pavitt, 1984), the distribution among sectors shows
that almost half of the enterprises belong to traditional sectors, while only 5% have their
business in the high tech sectors. Moving on to their financial set up, the average length
of the relationship with the main bank is 17 years; 49% of the firms have a national bank
3 As a robustness check, we estimate model (1) using a Tobit model. The results, available upon request,
are qualitatively similar. 4 The survey is public and available upon request to UniCredit banking group.
7
as their main banking counterpart, 10% entrust a larger-sized cooperative bank, 7%
feature a savings bank as their main bank, 5% entrust a smaller-sized cooperative mutual
bank, while 28% of the firms have another type of bank as their main bank. Finally,
multiple banking is extensive: on average firms do business with five banks and the share
of loans obtained from the main bank is 32% of the total banking loans received.5
Particularly relevant for our analysis, the 2007 wave of the survey features a peculiarity
with respect to the previous waves. Specifically, an entirely new set of questions is
specifically tailored to investigate in depth the relationship between the firm and its main
bank. In this paper we focus particularly on two questions where the firm is asked to state
which of the characteristics – choosing from a given list – have been important in the
firm’s selection of its main bank, as well as stating which characteristics, in the firm’s
view, best describe the way its main bank grants credit. Unsurprisingly, given the fact
that answering this section of the survey was relatively more time-consuming, only one
third of the surveyed enterprises (exactly 1,541 firms) answered these questions. We
cannot rule out self-selection. In other words, it is possible that the choice by a firm to
answer this part of the questionnaire was not random. However, we think that this self-
selection in the respondents would bias the results only if it were systematically related to
one or more of the explanatory variables.
To complement the survey, we also use data from other sources (see Table 1 for details
on the variables). We employ data from the Bank of Italy on the presence of banks in
local markets. We use data provided by the Italian National Statistics Office (ISTAT) on
civil suits and population per judicial district, as well as on the value added and
population of provinces. Finally, we employ data on social capital by Guiso et al. (2004).
3.2 Lending technology indices
We consider four indicators of lending technology similar to those in Uchida et al.
(2006). We capture the characteristics of different lending technologies using the
question “In your view, which criteria does your bank follow in granting loans to you?”.
In answering this question the firm was required to give a weight (going, in descending
order, from 1, very much, to 4, nil) to fifteen factors (see the Appendix). Most of these
factors are related to one of the lending technologies. We then link the factors that we
believe to be most closely associated with each lending technology based on the Berger
and Udell (2006) classification scheme. For reason of comparability of our results with
those in Uchida et al. (2008, 2006), we focus only on four lending technologies from this
classification.6
First, financial statement lending, is a transactions technology based primarily on the
strength of a borrower’s financial statements. Berger and Udell (2006) argue that banks
underwrite commercial loans using the financial statement lending technology for firms
5 Employing data on self-reporting firms might raise concerns about firms overstating or understating their
characteristics. We do not believe this has occurred for two main reasons. On the one hand, the Italian law
(l. 675/1996) on the treatment of personal data forbids using them for objectives other than that mentioned
in the survey, namely the elaboration of statistical tables. Hence, firms should have no incentive to lie.
Second, the personnel employed in the survey are highly qualified, firms’ answers pass through several
filters and double checks by this personnel. 6 Berger and Udell (2006) consider six different transaction-based lending technologies: (i) financial
statement lending, (ii) small business credit scoring, (iii) asset-based lending, (iv) factoring, (v) fixed-asset
lending, and (vi) leasing, together with relationship lending.
8
with a strong financial condition based on an assessment of verified (i.e., audited)
financial statements. From the list of fifteen criteria shown in the Appendix, we use the
initial four. These factors (financial solidity, profitability, growth of sales and ability of
the firm to repay its debt) represent qualities that are best assessed by an analysis of the
firms’ audited financial statements. From these four factors we created the financial
statement lending index, LT_FS, by calculating the average of the four dummy variables
which take a value of one if the firm answered 1 (very much) to the four relevant lending
factors, respectively. The virtue of using an average index is that it can be directly
compared with the other (averaged) indices, as we explain below, since all the indices are
constructed from dummy variables and thus take a value in the [0,1] range.7 Next, we
focus on fixed-asset lending. Fixed-asset lending technologies involve lending against
assets that are long-lived and are not sold in the normal course of business (e.g.,
equipment, motor vehicles, or real estate). The factors that are related to fixed-asset
lending are items 5, 6 and 8. We make a clear difference between real estate lending and
other fixed-asset lending, and so we construct two indices. The first, LT_RE, is a dummy
variable that takes the value one if the firm answered 1 (very much) to lending factor no.
5. Second, LT_OF, is an average of the two dummy variables which take a value of one
if the firm answered 1 (very much) to lending factors no. 6 and 8, respectively.8 Indeed,
as a robustness check, we aggregate the three transactional lending technologies in a
single index (we label it LT_TRANS).9 We take the three transactional lending
technologies as well as the aggregated index as the endogenous variable.
Finally, as the key explanatory variable, we consider the relationship lending technology.
Under relationship lending, the financial institution relies primarily on soft information
gathered through contact over time with the SME, its owner and the local community to
address the opacity problem. We construct the relationship lending index, LT_RL, using
the factors that seem most related to soft information accumulation by banks through
close relationships. The index is an average of six dummy variables which take a value of
one if the firm answered 1 (very much) to lending factors, no. 9, 10, 11, 13, 14 and 15,
respectively.
These indices are not likely to be perfect proxies for the use of different lending
technologies, since they are based on the firms’ perception of the lending factors used by
the bank in underwriting its loans, and thus may not be precisely capturing the banks’
screening process. However, constructing these indices using the firms' perspectives has
some advantages. In fact, previous researches on SME finance suffer from the problem
that the lending technologies are usually not identified (Kano et al., 2011). Our data allow
us to perceive the actual features of the bank at the time the firm is asked. Thus, we can
distinguish between lending technologies. Such information was not available in the prior
literature.
3.3 Credit rationing and soft information
7 We also conducted preliminary analysis using the first principal component of the principal component
analysis over the dummy variables. The results, available upon request, are qualitatively similar. 8 Note that the basic technology used in real estate lending and other fixed-asset lending is the same, and
the distinction is solely based on the type of collateral. 9 This index is an average of seven dummy variables which take a value of one if the firm answered 1 (very
much) to lending factors, no. 1, 2, 3, 4, 5, 6 and 8, respectively. In Table 3 we report the partial correlation
between LT_TRANS index and LT_RL index.
9
In the second part of the empirical analysis we address the role of soft information on the
probability that a firm is credit-rationed. To define our indicator of credit rationing, we
use the firms’ answers to three questions of the survey that are not necessarily related to
the main bank. The questions are:
1. In 2006 would your firm have wished a larger amount of loans at the prevailing
interest rate agreed with the bank?
2. In 2006, did the firm demand more credit than it actually obtained?
3. To obtain more credit, were you willing to pay a higher interest rate?
The variable of credit rationing is a dummy variable taking value one if the firm answers
yes to the first question and to at least one of the other two, zero otherwise. In order to
construct a proxy variable for the production of soft information we consider a
methodology similar to that used in Scott (2004) and Uchida et al. (2012). We use the
question of the survey: “Which characteristics are key in selecting your main bank?”. In
answering this question the firm was required to give a value, with descending order of
importance, from 1 to 4 to fourteen factors (see the Appendix for the details on this
question). We focus on the following characteristics:
a. The bank knows you and your business.
b. Frequent contacts with the credit officer at the bank.
The variable Soft is a dummy variable that takes value one if the firm chose the highest
value for both the above characteristics a and b, zero otherwise.
3.4 Control variables
In this section, we discuss the other variables included in the regressions. We first
classify banks into two types: large banks and local banks. The variable for large banks,
is a dummy that takes value one if the main bank is a national bank or a foreign bank,
whereas the variable for local banks, is a dummy taking value one if the main bank is
smaller-sized mutual bank, larger-sized cooperative bank, a saving bank or “other type of
bank”. We use different variables to represent firm characteristics as controls: the age of
the firm; the logarithm of the number of employees, as a proxy for size; a dummy
variable that takes value one if the firm is a corporation, zero otherwise; and the degree of
financial leverage, given by the ratio of total loans to the sum of the total loans and the
firm’s assets. We control also for the firm’s geographic localization, defining two
dummies for whether a firm is located in the Center or in the South of Italy, and its sector
based on a two-digit ATECO sectors. Moreover, we insert some variables describing the
characteristics of the local economic environment: the GDP per capita in the province in
2004, the length of the first-degree trial by the courts located in the province in 2004 and
the provincial level of social capital as measured in Guiso et al. (2004). Finally, we insert
some variables to control for the structural characteristics of the banking sector, such as
the average value of the Herfindhal-Hirschman index of concentration on bank loans in
the province during 1991-2004 period and the average number of branches per thousands
inhabitants in the province during 1991-2004 period in the province.
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4. Findings
4.1 Complementarity between lending technologies
Comparing across the various lending technology indices, we first ascertain which one is
more widespread. We analyze the degree of complementarity between the various
technologies, by looking at the correlation among the indices as well as via multivariate
regressions of the indices. First, we consider the relative importance of each lending
technology individually, by directly comparing the magnitude of each index. Table 2
shows the summary statistics of these variables. The lending factors related to financial
statement technology are relatively more frequently emphasized, in fact the index of
financial statement technology is the largest among the four indices. This result shows
that financial statement is the most widespread lending technology. Moreover, this
finding is robust also whether we check for bank type. The level of LT_FS is 0.193 for
the firms whose main bank is a national bank, 0.092 for those that interact with a local
bank. The relationship lending index is the second largest index (0.053 for local banks
and 0.087 for national banks), followed by the real estate lending index and the other
fixed-asset lending. However, the presence of a ranking in the use of lending technologies
does not rule out the possibility of complementarity. In fact, it can be reasonably argued
that different technologies require screening and monitoring processes that are similar in
nature and intensity, so that these may be used simultaneously instead of being strictly
distinct from each other.
In order to examine in a descriptive way the issue of complementarity, we construct two
dummy variables equal to one if the main bank uses as primary lending technology either
the relationship or the transactional lending technology, zero otherwise. We construct a
third dummy variable equal to one if the bank exploits both lending technologies as
primary lending technology.10
The summary statistics confirm that complementarity
among lending technologies is more frequent than specialization in one specific
technology. This result is robust also when we distinguish between local and national
banks, although complementarity is relatively more frequent for national banks compared
to local banks.
We study more deeply the phenomenon of complementarity by estimating regressions
among the indices. In particular, we examine four different specifications, considering as
dependent variable the three transaction-based lending technologies and an aggregate
index. Table 4 reports the results. The main explanatory variable is the bank’s use of
relationship lending. In all the specifications, the coefficient for relationship lending is
positive and significant at less than the 1% level. Specifically, taking into account the
magnitude of the coefficients, relationship lending results more closely tied with financial
statement lending (0.72) than with the two fixed-asset lending technologies (real estate
and other fixed-asset, both with a coefficient of about 0.59). In column 4, we consider the
10
In particular, the dummy variable for the use of relationship lending as primary lending technology is
equal to one if the index LT_RL is larger than the 75% percentile of the distribution, while the index
LT_TRANS is lower than 75%. The dummy variable for transactional lending as primary lending
technology is defined with similar thresholds. Finally, the dummy variable for multiple primary lending
technologies is equal to one if both LT_RL and LT_TRANS are larger than 75% percentile. As a
robustness check, we use also other thresholds for the construction of these indexes. The results are similar.
11
transaction-based lending technologies in aggregate. The value of the coefficient of this
index is 0.66. The estimates show complementarity between relationship lending and
transaction-based lending considered in aggregate and in each single component. In other
words, the findings suggest that different technologies could complement each other and
loans could contain characteristics of different technologies at the same time. These
results also provide information about what combination of lending technologies is the
most important. According to the magnitude of the estimated coefficients, financial
statement lending is relatively more correlated with relationship lending. Furthermore,
taking into account the mutual interrelationship among the four lending technologies, the
two fixed-asset lending technology and the relationship lending technology are less used
at the same time.
Finally, in the analysis of the other determinants of the lending technology choice, we
find that local banks use less the transactional lending technologies. In fact, if the main
bank is local the probability of using the transactional technologies decreases and the
effect is strongest for the financial statement technology.
Regarding the other control variables considered, we find that the judicial inefficiency
decreases the probability of using real estate technology and the other-fixed asset
technology. The rationale for this result is that the judicial inefficiency, reducing the
expected return from the fixed assets pledged as collateral, decreases the incentive of
using these technologies. By contrast, a higher level of social capital is associated with a
larger use of financial statement technology.
4.2 Robustness checks
To check that our findings are robust, we report the results from running the regressions
on sub-samples of observations. First, we split the sample based on the type of the main
bank of the firm. Our aim is to investigate whether the contemporaneous use of
transaction-based lending and relationship lending is a phenomenon that characterizes
smaller-sized/territorial banks more than large banks, as suggested by the literature (see,
e.g., Berger and Black, 2011). For the sake of simplicity we consider only the
complementarity between transaction-based lending technologies taken in aggregate and
relationship lending. Results are reported in Table 5 (column 1-2). The estimated coefficients are positive and significant at less than 1% level, either when the firm’s main
bank is a large bank (coefficient equal to 0.64), or when it is a local one (coefficient equal
to 0.70).
In columns 3-6 we report the estimates for the regression on the determinants of
transactional lending technology for several sub-samples based on firms’ characteristics.
First, we distinguish between small and large firms, based on the number of employees
(columns 3 and 4). The impact of relationship lending on transactional lending is
significant for both firms with less and more than 30 employees (the median number of
employees in our sample). The coefficients are qualitatively similar. These results remain
virtually unchanged if we split the sample at the median value of sales. Finally, when we
split the sample according to the age of the firms (columns 5 and 6), we find that
relationship lending is significant in explaining transactional lending technologies for all
the firms independently of their age. These findings suggest that the importance of the
complementarity of lending technologies is not affected by firm’s characteristics (size
and age) and type of main bank (local versus large).
12
4.3 The role of soft information
In the previous sections, we find that there is pervasive complementarity among lending
technologies. An interesting issue raised by our finding lies in studying how soft
information enters in this picture. For this reason, in this section we investigate the
impact of soft information on the probability that a firm is credit rationed and how the
lending technology used by the firm’s main bank impacts on the soft information
production.11
Table 6 reports the results. In particular, columns 1 and 2 show regression
results and marginal effects for the probability of being credit rationed.12
As we expected,
the findings show that soft information has a negative and significant impact on credit
rationing in all regressions. The marginal effect of soft information is -0.19, it is
significant at the 5% level and economically sizable. This finding shows that the
production of soft information can reduce credit rationing. This is consistent with results
in the previous literature that show the positive effects of soft information for borrowers
(e.g. Uchida et al., 2012).
In columns 3 and 4, we consider the cases of individual and multiple primary lending
technologies - i.e. specialization in one lending technology versus complementarity with
specialization on both lending technologies - and examine their impacts on the production
of soft information. In order to perform this analysis we consider again the dummy
variables used in the descriptive analysis reported in section 4.1.
Following the literature (see, e.g., Petersen, 2004), if hardening of soft information is
feasible we would expect that transactional lending technology has a statistically
significant impact on the production of soft information. Our results show that
relationship lending increases the production of soft information. Moreover, the
complementarity among lending technologies seems to be also useful for the bank in the
production of soft information. These findings imply that more soft information is
produced when the main bank uses a relationship lending technology as primary lending
technology individually or in combination with transactional lending technology also
taken as primary lending technology. By contrast, transactional lending as primary
technology individually is not significant. This finding implies that substitutability
between relationship and transactional lending technologies may not be feasible by means
of hardening of soft information.
5. Conclusions
In this paper we have investigated the firm-main bank relationship by using a large
sample of Italian manufacturing firms, featuring a pervasive presence of small and
11
As explained in Section 3.3, the measures of credit rationing are not necessarily related to the main bank
(i.e., it could be that firms perceived that they are credit rationed by other banks). Therefore, the results
need to be interpreted with some caveats. Unfortunately, the low number of observation used in this
regression does not give us the possibility to better investigate this relation. 12
In order to tackle for potential problems of small sample bias we performed Montecarlo bootstrapping.
Results showed to be stable with 100 replications. In addition to the control variables used in the first part
of the analysis, we consider the length of the firm-main bank relationship and the firm’s profitability,
measured by the average value of the firm’s return on assets in the 2004-2006 period.
13
medium-sized enterprises. We start considering a recent strand of literature stressing that
banks want to serve SMEs and find this segment profitable, especially as margins in other
banking markets narrow due to intensified competition (De la Torre et al., 2010). This
literature finds that, partly thanks to the enormous progress in information and
communication technologies, even large and foreign banks (normally arm’s-length
lenders) may now be capable of lending to SMEs referring also to firm’s soft information.
This could imply that substitutability between relationship and transactional lending
technologies may to some extent be possible for outsiders by means of hardening of soft
information. Another possibility, not explored in the literature, could be that the
alternative lending technologies can indeed be complementary, i.e. used in combination
for the same firm. In this paper we tried to address these issues.
Our results highlight that a given firm may receive credit via different lending
technologies, hence supporting the hypothesis of complementarity among lending
technologies. We show that this form of complementarity is found not only at large banks
but also at smaller-sized ones. Moreover, after showing that soft information lowers the
probability of credit rationing, we find that the production of soft information depends
crucially on whether the firm’s main bank uses mainly relationship lending technology
alone or combined with transaction-based lending technology, taken both as a primary
lending technologies. On the contrary, when the main bank uses mainly transaction-based
lending technology there is no production of soft information.
Thus, it appears that the way soft information becomes embodied in the lending decision
might still differ between relational and transactional banks/technologies. These findings
suggest that complementarity among lending technologies, pursued by organizational
measures aimed at increasing the degree of delegation and lowering the turnover of
branch manager, might be more effective for the loan decision process rather than new
soft information communication techniques.
14
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16
Appendix
Table 1. Variables definition and sources
Variable Definition and source (in parentheses)
LT_FS
Index for financial statement lending technology. We use a question available in the
Survey: “In your view, which criteria does your bank follow in granting loans to
you?”. In answering this question the firm was required to give a weight (going, in
descending order, from 1, very much, to 4, nil) to fifteen factors. From the list of
fifteen criteria shown in the Appendix, we use the initial four. For each of the four
characteristics we constructed a dummy variable, which takes a value of one if the
firm chose 1. LT_FS is the average of these four dummy variables. (Survey on
Italian Manufacturing Firms)
LT_RE
Index for real-estate lending technology. We use a question available in the Survey:
“In your view, which criteria does your bank follow in granting loans to you?”. In
answering this question the firm was required to give a weight (going, in descending
order, from 1, very much, to 4, nil) to fifteen factors. LT_RE, is a dummy variable
that take the value one if the firm answered 1 (very much) to lending factor no. 5.
(Survey on Italian Manufacturing Firms)
LT_OF
Index for other fixed-asset lending technology. We use a question available in the
Survey: “In your view, which criteria does your bank follow in granting loans to
you?”. In answering this question the firm was required to give a weight (going, in
descending order, from 1, very much, to 4, nil) to fifteen factors. LT_OF, is an
average of the two dummy variables which take a value of one if the firm answered 1
(very much) to lending factors no. 6 and 8, respectively. (Survey on Italian
Manufacturing Firms)
LT_TRANS
Index for the transactional lending technology. To construct this index, we aggregate
the three transactional lending technologies (LT_FS, LT_RE and LT_OF) in a single
index. (Survey on Italian Manufacturing Firms)
Credit Rationed
Dummy taking a value of one if the firm answers yes to the question “In 2006 would
your firm have wished a larger amount of loans at the prevailing interest rate agreed
with the bank?”, and yes to at least one of the following two questions: “In 2006, did
the firm demand more credit than it actually obtained?” or “To obtain more credit,
were you willing to pay a higher interest rate?”. (Survey on Italian Manufacturing
Firms)
Soft
We use the following question of the Survey: “Which characteristics are key in
selecting your main bank?”. In answering this question the firm was required to give
a value, with descending order of importance, from 1 to 4 to the two following
characteristics (among others): “The bank knows you and your business” and
“Frequent contacts with the credit officer at the bank”. The variable Soft is a dummy
that takes value one if the firm chose the highest value for both the above two
characteristics. (Survey on Italian Manufacturing Firms)
LT_RL
Index for relationship lending technology. We use a question available in the Survey:
“In your view, which criteria does your bank follow in granting loans to you?”. In
answering this question the firm was required to give a weight (going, in descending
order, from 1, very much, to 4, nil) to fifteen factors. LT_RL, is an average of six
dummy variables which take a value of one if the firm answered 1 (very much) to
lending factors, no. 9, 10, 11, 13, 14 and 15, respectively. (Survey on Italian
Manufacturing Firms)
National bank Dummy variable taking value one if the main bank is either a national bank or a
foreign bank; 0 otherwise. (Survey on Italian Manufacturing Firms)
Local bank
Dummy variable taking value one if the main bank is a smaller-sized cooperative
mutual banks, a larger-sized Volksbank type cooperative banks, a saving bank or
“other type of bank”; 0 otherwise. (Survey on Italian Manufacturing Firms)
17
Number of Banking relations Log of the total number of firm’s reference banks. (Survey on Italian Manufacturing
Firms)
Corporation Dummy variable taking value one if firm is a public limited company; 0 otherwise.
(Survey on Italian Manufacturing Firms)
Leverage Ratio of firm’s total loans to the sum of firm’s total loans and firm's equity as of the
end of December 2006. (Survey on Italian Manufacturing Firms)
Size Log of the firm’s number of employees as of the end of December 2006. (Survey on
Italian Manufacturing Firms)
Age Log of the age of firm since foundation, in years. (Survey on Italian Manufacturing
Firms)
HHI Average value of the Herfindhal Hirschman index of concentration on bank loans in
the province during 1991-2004 period. (Statistical Bulletin of the Bank of Italy)
Provincial GDP Log of the value of the GDP in the province as of the end of December 2004.
(ISTAT)
Judicial inefficency Log of the length of the first-degree trial by the courts located in the province in
2004. (ISTAT)
Social Capital
Voter turnout at the province level for all the referenda before 1989. These include
data referenda on the period between 1946 and 1987. For each province turnout data
were averaged across time. (Guiso, Sapienza and Zingales, 2004)
Branches in the province Average number of branches per thousands inhabitants in the province during 1991-
2004 period. (Statistical Bulletin of the Bank of Italy)
Center
Dummy variable taking value 1 if the bank branch where the credit relationship with
the firm takes place is located in Central Italy; 0 otherwise. (Survey on Italian
Manufacturing Firms)
South
Dummy variable taking value 1 if the bank branch where the credit relationship with
the firm takes place is located in Southern Italy; 0 otherwise. (Survey on Italian
Manufacturing Firms)
ROA Average value of the ratio of firm’s EBIT to firm’s total assets during 2004-2006
period. (Survey on Italian Manufacturing Firms)
Relationship Length Log of the length of the firm-main bank relationship. (Survey on Italian
Manufacturing Firms)
Mainly relationship lending
Dummy variable taking value 1 if the index LT_RL is larger than the 75% percentile
of the distribution, while the index LT_TRANS is lower than 75%. (Survey on
Italian Manufacturing Firms)
Mainly transactional lending
Dummy variable taking value 1 if the index LT_TRANS is larger than the 75%
percentile of the distribution, while the index LT_RL is lower than 75%. (Survey on
Italian Manufacturing Firms)
Complementarity
Dummy variable taking value 1 if both the indexes LT_RL and LT_TRANS are
larger than 75% percentile of their distribution. (Survey on Italian Manufacturing
Firms)
18
Table 2. Summary statistics
Full Sample Local banks National banks
Mean St. Dev Mean St. Dev Mean St. Dev
LT_FS 0.216 0.332 0.092 0.239 0.193 0.31
LT_RE 0.119 0.324 0.034 0.181 0.093 0.291
LT_OF 0.113 0.259 0.035 0.157 0.090 0.237
LT_TRANS 0.173 0.274 0.068 0.18 0.149 0.237
LT_RL 0.145 0.284 0.053 0.180 0.087 0.189
Soft Information 0.097 0.297 0.025 0.157 0.072 0.259
Credit Rationed 0.426 0.496 0.435 0.498 0.420 0.495
Local bank 0.503 0.500 1.000 0.000 0.000 0.000
National bank 0.497 0.500 0.000 0.000 1.000 0.000
Number of banks 4.973 3.959 5.482 4.602 5.985 4.416
Number of banks (ln) 1.383 0.668 1.487 0.617 1.592 0.611
Corporation 0.332 0.471 0.390 0.488 0.381 0.486
Leverage 0.899 0.113 0.891 0.114 0.892 0.109
Size 3.553 1.118 3.757 1.279 3.797 1.370
Age 22.663 14.388 22.746 15.020 23.625 15.898
HHI 0.111 0.048 0.112 0.047 0.116 0.050
Audit 0.376 0.485 0.214 0.410 0.461 0.499
Provincial GDP 10.192 0.219 10.197 0.203 10.179 0.218
Judicial inefficency 5.893 0.276 5.883 0.254 5.916 0.272
Social Capital 0.845 0.055 0.851 0.050 0.846 0.058
Branches 0.530 0.125 0.544 0.119 0.522 0.121
North 0.719 0.449 0.736 0.441 0.674 0.469
Center 0.162 0.369 0.170 0.376 0.197 0.398
South 0.118 0.323 0.094 0.293 0.129 0.336
ROA 0.025 2.097 0.052 0.079 0.052 0.058
Relationship length 2.595 0.782 2.691 0.738 2.559 0.758
Mainly relationship lending 0.051 0.221 0.044 0.206 0.064 0.244
Mainly transactional lending 0.074 0.262 0.055 0.228 0.119 0.324
Complementarity 0.232 0.422 0.072 0.259 0.172 0.378
19
Table 3. Correlation matrix
LT
TRANS LT_RL
Credit
Rationed Soft Local bank
Number of
banks Length ROA Age Size Center South Lever
LT_TRANS 1.000
Credit Rationed -0.009 1.000
Soft 0.175* -0.118 1.000
LT_RL 0.774* 0.003 0.197* 1.000
Local -0.191* 0.015 -0.108* -0.092* 1.000
Number of Banks 0.014 0.037 0.051* 0.030 -0.056 1.000
Leverage 0.013 0.095 -0.033 -0.024 -0.004 -0.077* 1.000
Size 0.038 -0.019 0.065* 0.090* -0.015 0.394* -0.258* 1.000
Age -0.006 -0.081 0.084* 0.017 -0.028 0.174* -0.183* 0.225* 1.000
Relationship length -0.025 -0.119 0.010 0.009 0.088* 0.077* -0.080* 0.108* 0.333* 1.000
Mainly rel. lend. -0.089* -0.027 0.033 0.154* -0.043 0.005 0.053* -0.064* -0.012 -0.017 1.000
Mainly trans. lend. 0.264* 0.042 -0.001 -0.145* -0.113* -0.034 0.057* -0.0754* 0.005 -0.027 -0.066* 1.000
Complementarity 0.795* -0.007 0.172* 0.790* -0.153* 0.022 -0.027 0.056* -0.031 -0.022 -0.128* -0.155* 1.000
Note: The table shows the pairwise correlation coefficients. (*) correlation coefficients significant at the 5% level or better.
20
Table 4. The determinants of lending technologies
(1) (2) (3) (4)
Variables LT_FS LT_RE LT_OF LT_TRANS
LT_RL 0.717*** 0.587*** 0.598*** 0.664***
0.056 0.084 0.068 0.049
Local bank -0.083*** -0.043*** -0.036*** -0.064***
0.018 0.016 0.012 0.013
Number of banks -0.013 0.010 0.005 -0.005
0.018 0.017 0.012 0.013
Corporation 0.043* 0.022 0.008 0.030*
0.023 0.021 0.015 0.016
Leverage 0.101 0.028 0.011 0.064
0.078 0.080 0.060 0.054
Size -0.016* -0.010 0.000 -0.011*
0.009 0.009 0.006 0.006
Age -0.001 -0.001 -0.000 -0.001
0.001 0.001 0.001 0.001
HHI 0.002 -0.011 0.004 0.001
0.233 0.166 0.146 0.151
GDP 0.022 -0.071 -0.088* -0.023
0.078 0.061 0.047 0.050
Judicial inefficiency -0.005 -0.163*** -0.113*** -0.059
0.066 0.057 0.043 0.046
Social Capital 0.636** 0.053 -0.098 0.343
0.322 0.257 0.212 0.221
Branches in the province -0.034 -0.168 -0.061 -0.061
0.107 0.102 0.078 0.080
Center -0.004 0.003 -0.004 -0.003
0.028 0.024 0.016 0.018
South 0.080 0.031 -0.031 0.041
0.069 0.060 0.035 0.044
Constant -0.601 1.776** 1.700*** 0.396
0.881 0.770 0.621 0.607
Observations 816 816 816 816
R-squared 0.272 0.229 0.337 0.381
Note: The table reports regressions coefficients. The dependent variables are the three transactional
lending technologies taken also in aggregate. For the definition of the explanatory variables see Table 1.
The regressions are estimated by OLS. The regressions include sector dummies. Robust standard errors are
reported below coefficients. (*): coefficient significant at 10% confidence level; (**): coefficient significant
at 5% confidence level; (***): coefficient significant at less than 1% confidence level. The table also
reports, as goodness-of-fit tests, the R-squared.
21
Table 5. Robustness checks
(1) (2) (3) (4) (5) (6)
Variables LT_TRANS LT_TRANS LT_TRANS LT_TRANS LT_TRANS LT_TRANS
Local Banks National Banks Small Firms Large Firms Age < 21 years Age ≥ 21 years
LT_RL 0.699*** 0.642*** 0.648*** 0.676*** 0.707*** 0.616***
0.063 0.068 0.074 0.072 0.071 0.081
Local bank -0.074*** -0.060*** -0.051** -0.080***
0.023 0.018 0.021 0.020
Number of banks -0.002 -0.016 0.009 -0.010 0.011 -0.023
0.010 0.024 0.023 0.013 0.016 0.017
Corporation 0.021 0.040 0.006 0.034* 0.025 0.030
0.014 0.028 0.037 0.018 0.021 0.022
Leverage 0.018 0.114 0.035 0.055 -0.042 0.126**
0.052 0.105 0.083 0.066 0.092 0.061
Size -0.010* -0.009 -0.016 -0.003 -0.014 -0.007
0.006 0.012 0.018 0.009 0.008 0.009
Age -0.001** -0.000 -0.000 -0.001 -0.001 -0.000
0.000 0.001 0.001 0.001 0.001 0.001
HHI 0.190 -0.137 0.036 -0.112 0.331* -0.336**
0.196 0.213 0.259 0.151 0.194 0.157
GDP -0.014 -0.027 -0.013 -0.061 -0.005 -0.047
0.055 0.069 0.065 0.058 0.073 0.059
Judicial inefficiency -0.019 -0.089 -0.056 -0.063 0.004 -0.107*
0.039 0.077 0.054 0.057 0.063 0.064
Social Capital 0.259 0.250 0.229 0.480* 0.336 0.384
0.231 0.303 0.338 0.242 0.279 0.264
Branches in the
province 0.018 -0.119 0.021 -0.096 -0.062 -0.100
0.066 0.114 0.104 0.086 0.120 0.090
Center 0.014 -0.017 0.001 -0.001 0.014 -0.021
0.018 0.037 0.029 0.028 0.027 0.026
South 0.044 0.006 0.039 0.043 0.002 0.055
0.054 0.082 0.065 0.052 0.054 0.057
Constant 0.049 0.711 0.358 0.711 -0.102 0.930
0.587 0.850 0.801 0.707 0.924 0.736
Observations 411 405 344 472 398 418
R-squared 0.519 0.287 0.347 0.419 0.422 0.366
Note: The table reports regressions coefficients. The dependent variable is the transactional lending technologies taken in
aggregate. For the definition of the explanatory variables see Table 1. The regressions are estimated by OLS. The
regressions include sector dummies. Robust standard errors are reported below coefficients. (*): coefficient significant at
10% confidence level; (**): coefficient significant at 5% confidence level; (***): coefficient significant at less than 1%
confidence level. The table also reports, as goodness-of-fit tests, the R-squared.
22
Table 6. The role and production of soft information
(1) (2) (3) (4)
VARIABLES Credit Rationed Credit Rationed Soft information Soft information Coefficients Marginal Effects Coefficients Marginal Effects
Soft -0.530* -0.193**
0.277 0.093
Mainly relationship lending 0.750*** 0.153**
0.239 0.067
Mainly transactional lending 0.288 0.044
0.190 0.034
Complementarity 0.803*** 0.144***
0.108 0.026
Relationship length -0.190 -0.075 -0.010 -0.001
0.145 0.057 0.091 0.012
Number of banks 0.174 0.068 0.206 0.026
0.185 0.072 0.153 0.019
Leverage 1.398 0.549 -0.669 -0.086
1.227 0.482 0.464 0.062
ROA -3.686** -1.447** -0.793 -0.102
1.691 0.663 0.505 0.065
Size 0.085 0.033 0.035 0.005
0.101 0.039 0.054 0.007
Age -0.002 -0.001 0.007 0.001
0.007 0.003 0.006 0.001
HHI -1.355 -0.532 -1.116 -0.143
1.893 0.744 1.432 0.185
Provincial GDP 1.407** 0.552** 0.333 0.043
0.647 0.252 0.485 0.061
Branches in the province -1.164 -0.457 -1.277 -0.164*
1.103 0.432 0.776 0.096
Center 0.416* 0.165* 0.037 0.005
0.221 0.087 0.155 0.021
South 0.599 0.235 -0.386 -0.039
0.430 0.162 0.372 0.030
Constant -15.040** -4.191
6.596 5.152
Observations 186 186 1,112 1,112
Pseudo R-squared 0.123 0.123 0.111 0.111
Average predicted probability 0.428 0.066
Note: The table reports regressions coefficient and marginal effects. The dependent variables are the dummy
of credit rationing and the proxy for the production of soft information. For the definition of the explanatory
variables see Table 1. The regressions are estimated with Probit. The regressions include sector dummies.
Robust standard errors are reported below coefficients. (*): coefficient significant at 10% confidence level;
(**): coefficient significant at 5% confidence level; (***): coefficient significant at less than 1% confidence
level. The table also reports the Pseudo R-squared as goodness-of-fit tests and the average predicted
probability.
23
Survey questions
F1.15: Which of these characteristics are key in selecting your main bank?
1. The bank knows you and your business.
2. The bank knows a member of your Board of directors or the owners of the firm.
3. The bank knows your sector.
4. The bank knows your local economy.
5. The bank knows your relevant market.
6. Frequent contacts with the credit officer at the bank.
7. The bank takes quick decisions.
8. The bank offers a large variety of services.
9. The bank offers an extensive international network.
10. The bank offers efficient internet-based services.
11. The bank offers stable funding.
12. The bank offers funding and services at low cost.
13. The bank’s criteria to grant credit are clear.
14. The bank is conveniently located.
F1.17: In your view, which criteria does your bank follow in granting loans to you?
1. Ability of the firm to repay its debt (e.g. years needed to repay its debt).
2. Financial solidity of the firm (capital/asset ratio).
3. Firm’s profitability (current profits/sales ratio).
4. Firm’s growth (growth of sales).
5. Ability of the firm to post (not personal) real estate collateral.
6. Ability of the firm to post tangible non-real estate collateral.
7. Support by a guarantee association (e.g. loan, export, R&D, etc.).
8. Personal guarantees by the firm’s manager or owner.
9. Managerial ability on the part of those running the firm’s business.
10. Strength of the firm in its market (number of customers, commercial network).
11. Intrinsic strength of the firm (e.g. ability to innovate).
12. Firm’s external evaluation or its evaluation by third parties.
13. Length of the lending relationship with the firm.
14. Loans are granted when the bank is the firm’s main bank.
15. Fiduciary bond between the firm and the credit officer at your bank.
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