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The Impact of Sovereign Ratings on
Eurozone SMEs Credit Rationing
Michael Demoussis and Konstantinos Drakos and Nicholas
Giannakopoulos
University of Patras, Athens University of Economics and Business,
University of Patras
2016
Online at https://mpra.ub.uni-muenchen.de/76364/
MPRA Paper No. 76364, posted 22 January 2017 17:10 UTC
The Impact of Sovereign Ratings on Eurozone SMEs Credit Rationing
Michael Demoussis, Konstantinos Drakos, and Nicholas Giannakopoulos†
Abstract
In this study we investigate whether sovereign credit ratings have any discernible impact on credit
rationing in Euro zone countries. We utilize firm-level data from the Survey on the Access to
Finance of SMEs for the period 2009-2013 conducted by the ECB. A negative association between
the rating of sovereign creditworthiness and credit rationing is identified, while credit rationing
varies substantially even among countries with the highest quality of sovereign bonds. Credit
rationing is lower in sovereigns with high quality ratings and higher in sovereigns near default.
These results remain intact when fundamental firm characteristics (e.g. firm’s age and size, sector
of economic activity, financial situation etc.) are taken into consideration. This indicates that the
interconnection of sovereign debt risk with domestic credit market outcomes is robust.
Keywords: Credit rationing; Firms; Sovereign debt; Euro zone
JEL Classification: E51; H63; G2; C35

Professor, Department of Economics, University of Patras, Greece.
Corresponding author. Email: [email protected], Associate Professor, Department of Accounting and Finance,
Athens University of Economics and Business, Greece.
†
Assistant Professor, Department of Economics, University of Patras, Greece.

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1. Introduction
The presence of asymmetric information between lenders and borrowers, and more importantly
the inability of market participants to overcome them, leads to an equilibrium outcome known as
credit rationing, where lenders may find it optimal to cut off credit rather than increase loan rates,
since the latter may drive off the loan market all but the least creditworthy applicants or elicit
riskier behavior from borrowers (Jaffee and Russell, 1976; Stiglitz and Weiss, 1981). The extant
empirical literature has shown that smaller and informationally more opaque businesses are
typically more harshly hit by credit rationing, due to the amplified adverse selection and moral
hazard problems that characterize the lender (bank)-borrower (firm) relationship (Berger and
Udell, 1998; Ang, 1991). In practice, quantity (non-price) rationing by lenders can take one of two
alternative forms: (i) partially fulfilling loan requests, what is called Type-1 rationing, or (ii)
rejecting loan requests altogether, known as Type-2 rationing (Keeton, 1979). In the Stiglitz and
Weiss (1981) environment borrowers are credit rationed if they are unable to finance externally,
at the current market loan rate, investment projects with positive net present values. However, to
fully gauge the borrower-lender relationship one has to also take into account the potential
borrowers who choose not to apply, the so-called “discouraged” borrowers, who may arise due to
various application costs and/or a self-rationing mechanism (Jappelli, 1990; Mushinski 1999; Kon
and Storey, 2003; Chakravarty and Yilmazer, 2008; Han et al., 2009; Brown et al., 2011; Drakos
and Giannakopoulos, 2011; Freel et al. 2012; Popov, 2013; Ferrando and Mulier, 2015; Colea and
Sokolyk, 2016).
Credit rationing may exert detrimental effects on firm investment, employment and
survival prospects, especially for small and financially opaque firms (Ang, 1991; Hu and
Schiantarelli, 1994; Jensen and McGuckin, 1997; Berger and Udell, 1998; Audretsch and Elston,
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2002; Chodorow-Reich, 2014; Duygan-Bump, et al., 2015). Despite the extensive literature on the
within-country determinants of credit rationing in the euro area (Becchetti et al., 2011; Rottmann
and Wollmershaüser, 2013; Kremp and Sevestre, 2013; Farihna and Felix, 2015), there is an
apparently limited literature on the phenomenon regarding Euro zone SMEs as a whole (Casey
and O’Toole, 2014; Holton et al., 2015; Ferrando et al., 2015).i The latter studies although they
provide some empirical evidence regarding the effects of macroeconomic conditions on credit
rationing they do not explicitly take into account the potential impact of sovereign risk ratings.
The link between sovereign’s ability to repay its debts and the supply of credit in the business
sector has recently gained increasing attendance in academia, the financial sector and policy
makers. For example, Popov and Horen (2015) analyze the syndicated bank lending channel during
the Euro area sovereign debt crisis and found that banks with significant exposures to sovereign
debt issued by countries whose access to international bond markets became impaired (Greece,
Ireland, Italy, Portugal, and Spain) was on average lower by 21.3 per cent than lending by banks
without a significant exposure. In addition, Adelino and Ferreira (2016) show that banks with
ratings at the sovereign bound (around default) reduce their lending significantly more than
otherwise similar banks whose ratings are not at the sovereign bound following a sovereign
downgrade. In the present study, we utilize firm-level data from the Survey on the Access to
Finance of SMEs (SAFE, hereafter), covering the period between 1st half of 2009 and October
2012-March 2013, which is a database administered by the European Central Bank in order to
exploit the cross-country variation in sovereign bond ratings, which encapsulate the effects of
several country-specific macroeconomic fundamentals (e.g., GDP per capita, GDP growth,
external debt etc.), as well as other qualitative macroeconomic idiosyncrasies (Cantor and Parker,
1996; Beirne and Fratzscher, 2013).
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The rationale for conducting the present study relies on the fact that sovereign debt risk is
interconnected with domestic banking sector risk (Arteta and Hale, 2008; Gennaioli et al., 2014;
Correa et al., 2014). From this point of view to the extent that banks maintain a large exposure to
home country sovereign bonds, this interconnection could bring about losses on these portfolios
which are expected to weaken their balance sheets (Arezki et al., 2011). In addition, a downgrade
of home sovereign’s creditworthiness would erode the collateral value of sovereign bonds that
banks usually pledge for wholesale funding, but also for borrowing from the central bank.
Moreover, sovereign rating downgrades tend to precede domestic banks' downgrades, given banks'
high vulnerability to sovereign distress, compared to domestic firms in other sectors, as well as,
by reducing the benefits that banks derive from implicit and explicit government guarantees
(Committee on the Global Financial System, 2011). All the above are expected to manifest
themselves on a relatively impaired bank access to funding and a higher cost when obtaining it.
This has certainly been the case for banks from the peripheral euro area countries for which there
is ample evidence that they face higher wholesale and deposit funding costs. Moreover, while the
mix of bank funding in the major Euro zone countries has remained rather intact compared to the
pre-crisis period, for banks in peripheral countries the funding composition has changed
significantly, with customer deposits declining as a share of total assets and increased dependence
on central bank liquidity. Hence, it is obvious that these developments are bound to adversely
affect bank lending policies in the form of tighter lending terms and conditions and possibly higher
rates of credit rationing.
It is clear that the Euro zone encompasses countries with sovereign debt problems of
varying intensity, and even a cursory look at the raw data shows that for the group of countries
with more acute sovereign debt difficulties credit rationing has increased considerably. In contrast,
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credit rationing seems to have actually eased in Euro zone countries with a perceived sustainable
sovereign debt. Perhaps this is a facet of the “flight-to-safety” phenomenon. Thus, as far as credit
rationing is concerned, the Euro zone is an amalgamation of countries with heterogeneous profiles.
A similar picture emerges in the Euro zone as highlighted by Drakos (2013) who argues that bank
lending terms and conditions have become tighter for Greece, Ireland, Portugal and Spain
compared to the rest of Euro zone member states. Thus, the presence of the sovereign debt crisis
allows -if not compels- the augmentation of credit rationing determinants with potential factors,
over and above those that one would employ under normal conditions. In particular, one may
advocate that the sovereign debt crisis, at least on a theoretical basis, exerts a detrimental and
unambiguous -in terms of direction- impact on credit rationing. In addition, this potential impact
may well be non-uniform across countries, given their heterogeneous degrees of involvement in
the sovereign debt crisis. The channel via which the sovereign debt crisis may affect firms' credit
rationing is indirect and necessarily passes through its impact on banks' loan decision-making
process (Jimenez et al., 2012; Bofondi et al., 2013), since after all this process shapes credit
rationing.
From a methodological point of view the use of matched loan applications and contracts is
an ideal research design for studying credit rationing (Banerjee and Duflo, 2014; Kirschenmann,
2016). However, matched data at the firm-level are not available for the majority of Euro zone
countries and thus the examination of the effect of the sovereign debt crisis is impossible in terms
of exploiting the relevant cross-country variation. In contrast, the use of survey-based dataset is
expected to shed more light on this issue. For instance, Casey and O’Toole (2014) use the SAFE
dataset (2009-2011) to investigate whether bank lending-constrained SMEs are more likely to use
or apply for alternative external finance including trade credit, informal lending, loans from other
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companies, market financing (issued debt or equity) and state grants. Holton et al. (2015) study
the determinants of SMEs’ credit access by combining firm micro data from the ECB’s SAFE
survey (2009-2011) with macroeconomic variables focusing on simultaneous crises in the real
economy (e.g., gross domestic product and domestic demand), the financial/sovereign market and
the effects of private sector indebtedness. Lastly, Ferrando et al. (2015) utilize selected waves of
the SAFE dataset (2009-2014) to investigate the effect of sovereign stress and of unconventional
monetary policy on small firms’ financing patterns during the euro area debt crisis.
Utilizing the SAFE dataset, we define, in the present study credit rationing at the firm-level
taking into account the fact that a specific firm may have not applied for a bank loan due to
sufficient internal funds. In order to measure country risk we utilize country-level data (eleven
Euro zone member states) regarding the sovereign bond ratings by the three most popular rating
agencies (Standard & Poor’s, Moody’s and Fitch). The alphanumerical rating corresponds to the
rating of the last month of each of the SAFE wave while for analytical purposes it has been
transformed to a numerical variable following Mondes et al. (2016, Table 1). For estimation
purposes we adopt probit models with sample selection in order to estimate the impact of firmspecific attributes, country-specific effects and time effects on rationing, conditional on firm’s
demand for bank loans. As a first step we provide evidence on the determinants of credit rationing
at the country level and then we proceed by estimating the effects of sovereign bond ratings on
credit rationing for the Euro zone as a whole. Our results indicate that credit rationing exhibits a
rather heterogeneous profile across Euro zone SMEs. For example, credit rationing is increasing
for young and small firms, for those of single ownership, with lower turnover and deteriorated
credit history, with limitations in the availability of credit and for those operating in the services
sector. While credit rationing remains stable across the examined period for the Euro zone as a
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whole, we found that there are significant differences across countries. With regard to risk ratings
we found that credit rationing varies significantly amongst firms in the Euro zone, while a negative
association between sovereign rating and credit rationing has been uncovered. These indirect
effects encompass time invariant differences regarding sovereign credit ratings and changes
pertaining to the downgrading of a sovereign’s creditworthiness. The contribution of the present
study to the relevant literature is twofold. First, it performs a detailed analysis of credit rationing
for Euro zone SMEs and second, it explores the link between sovereign credit rating and credit
rationing during the sovereign debt crisis period. In addition, it complements the work of Casey
and O’Toole (2014), Holton et al. (2015), and Ferrando et al. (2015) who utilize the SAFE dataset
to analyze several aspects of credit constraints in Euro zone SMEs and incorporate macroeconomic
variables at the country level to capture possible heterogeneous effects. Our study, in addition,
utilizes the widely publicized sovereign risk ratings in order to investigate whether country risk
can be associated with higher rationing prevalence.
The paper is organized as follows. Section 2 describes the data and provides the definitions
of the variables utilized in the study. We also provide descriptive statistics comparing cross
sectional aspects. In Section 3 we present the econometric specification and the empirical strategy
for modeling credit rationing. Estimation results and sensitivity analysis are discussed in Section
4. The paper concludes with Section 5 which contains a brief summary of the major findings.
2. Data sources and summary statistics
The evidence on the access to finance of enterprises in the European Union was rather scarce due
to lack of comparable, timely, and frequent data for SMEs. To fill this gap, the European Central
Bank (ECB) and the European Commission (EC) has decided in 2008 to collaborate on designing
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the Survey on Access to Finance of Enterprises (SAFE)ii which is a cross-sectional survey repeated
every six months in order to assess the latest developments in the financing conditions (i.e.,
financial situation, financing needs and access to financing) for firms in the euro area. The first
round of SAFE conducted between 17 June and 23 July 2009 and refer to the financial
developments that took place during the 1st half of 2009. Given the onset of the sovereign debt
crisis in Euro zone coincides with the 1st round of available micro data on credit market outcomes,
we use, in the present study, firm-level data from the 1st round (1st half 2009) of the SAFE database
up until the 8th round (October 2012 - March 2013). Since the survey is conducted twice a year
(every six months), our sample consists of eight waves. The survey basically covers micro, small
and medium-sized firms, based on the level of employment, but also includes a small percentage
of large firms (not used in the econometric analysis that follows). Industry-wise, firms belong to
the Mining and Manufacturing, Construction, Wholesale or Retail Trade, Transport and Other
Services sectors while country-wise Austria, Belgium, Finland, France, Germany, Greece, Ireland,
Italy, Netherlands, Portugal and Spain are continuously covered. The initial sample includes
information on 53,771 firms and after the exclusion of the sample of large firms we remain with
49,618 valid observations which are allocated in the examined period as follows: 5,129 (1st half
2009), 4,786 (2nd half 2009), 4,906 (March-September 2010), 6,941 (September 2010-February
2011), 6,968 (April-September 2011), 6,969 (October 2011-March 2012), 6,959 (April-September
2012) and 6,960 (October 2012-March 2013). Since the number of firms for each time-country
pair varies, the appropriate population weights (provided by the SAFE database) have been
applied.
2.1. Measuring credit rationing
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As discussed earlier, non-price credit rationing may be observed in equilibrium as a result of
informational asymmetries between lenders and borrowers. In our analysis we will use the broadest
definition of credit rationing (Type 1 and 2) and also take into account discouraged borrowers
(firms that did not apply for credit, even though they needed it). The SAFE dataset includes
appropriate questions for the identification of credit rationing (questions Q7A.A and Q7B.A of the
SAFE questionnaire). In particular, using only valid information (excluding cases with DK/NA
answers on Q7A.A and Q7B.A, reducing the working sample size to 48,386 firms) we define the
credit rationing variable in the form of a dummy indicator; taking the value 0 when a firm is not
rationed, and the value of 1 when rationed, identified as follows: (a) a firm applied for credit and
its application was rejected, (b) a firm applied for credit and its demand was partially satisfied,
(c) a firm did not apply for credit because of fear of rejection (discouraged borrowers). We should
point out that those firms stating that they refused to accept the bank’s loan offer because the
interest rate was thought to be too high are not considered to be rationed, since rationing is
conditional on the ongoing interest rate (Stiglitz and Weiss, 1981).
For analytical purposes we exclude from the analysis those firms that did not apply for
bank loans for other reasons and the remaining observations count to 37,394 firms. However, those
firms that did not apply for a bank loan because they have sufficient internal funds are considered
to be a specific subsample (22,387 observations) that may provide evidence on the self-selection
mechanism of firms regarding the demand for bank loans. In order to avoid problems of selectivity
biases arising from the fact credit rationing is most likely conditional on the absence of sufficient
internal funds (Drakos and Giannakopoulos, 2011) we appropriately include this subsample in our
empirical analysis. Given these selection criteria our effective sample contains 15,006 firms of
which 6,919 were subjected to rationing (Table 1). Looking at Figure 1 we note that credit rationing
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in the Euro zone is characterized by remarkable stability (around 46 per cent) and it varies
substantially both, between Euro zone countries and within specific countries.
[Insert Table 1 here]
[Insert Figure 1 here]
2.2. Sovereign bond credit ratings
The most popular sovereign bond ratings reviews are conducted and reported by Standard & Poor’s
(www.standardandpoors.com), Moody’s (www.moodys.com) and Fitch (www.fitchratings.com).
As a rule, sovereign bond ratings are presented in an alphanumerical mode and capture the whole
set of sovereign’s bond quality (from the highest quality to default). However, when ratings are
included in typical regression analysis most analysts transform the alphanumerical ratings into
numerical values (Cantor and Parker, 1996; Reinhart, 2002; Alfonso et al., 2012; Baum et al.,
2016; Mondes et al., 2016). In the present study we follow the scaling presented in Mondes et al.,
(2016) which ranges from 1 (default) to 23 (highest quality). It is worth noting that as far as credit
ratings are concerned, the triple-A group of countries (Austria, Finland, Germany, Netherlands)
exhibits remarkable stability while this is not the case for the non-triple-A countries. In the
examined period, the majority of the Euro zone countries witnessed downgrading of their
creditworthiness at least once. For example, according to Moody’s, France lost its Aaa rating in
November of 2012 while Belgium and Spain were downgraded once moving from Aa1 to Aa3 and
Aa2, respectively. Italy was also downgraded once albeit by three notches, moving from Aa2 to
A2. Portugal was downgraded twice, starting from Aa3 in 2009 and ending at A3 in the 1st
semester of 2011. Greece's credit rating was downgraded three times and while it was assessed as
A2 in 2009, it reached B1 in the 1st semester of 2011 and C in the 1st semester of 2012. Finally,
Ireland started as a triple-A country and then experienced a sequence of four downgrades and
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ended up rated as Baa3 in the 2nd semester of 2011. A visual display of the relative position of the
creditworthiness of Euro zone countries at the beginning (January 2009) and the end (January
2013) of the examined period is presented in Figure 2.
[Insert Figure 2 here]
Furthermore, the relationship between credit rationing and Moody’s ratings is negative (see
Figure 3) indicating that as rating deteriorates (improves) credit rationing increases (decreases).
According to the raw data, credit rationing in triple-A countries stood at 32.9 per cent, in Ba1
countries at 66.2 per cent and in Greece (the only C country) at 84.4 per cent. Also, credit rationing
is not stable across time. For instance, credit rationing for Ba1 countries ranges from 54.0 per cent
in wave 6 to 81 per cent in wave 7. Furthermore, credit rationing on average for triple-A countries
is continuously decreasing after the second half of 2009.
[Insert Figure 3 here]
2.3. Firm-specific heterogeneity
Given that -in the presence of informational asymmetries- price (loan rate) is not an effective credit
allocation mechanism, lenders must resort to some observed borrower characteristics in order to
reach a decision on the fate of the loan application. It should be clear that rationing is negatively
linked to the capacity of market forces to circumvent information asymmetries. The empirical
literature on the demand and supply of credit at the firm level identifies a wide range of explanatory
variables that might affect credit rationing, conditional on the demand for bank loans. These
variables typically include firm size, ownership, legal status, financing needs (short-term and longterm), availability of internal resources, interest expenses, financial and non-financial risk
indicators, such as leverage, profitability, firm’s age, firm’s rating and firm’s ability to provide
collateral (Freel et al., 2012; Kremp and Sevestre, 2013; Rottmann and Wollmershäuser, 2013;
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Farinha and Felix, 2015; Carbo-Valverde et al., 2016; Kirschenmann, 2016). The SAFE database
includes information on a variety of firm-specific (borrower) characteristics which could be useful
for the construction of variables that are usually employed in empirical studies as credit rationing
determinants. In particular, we utilize indicators for basic firm characteristics (ageiii, firm sizeiv,
ownership status and sector of economic activity) and information on the direction of change of
firm’s financial conditions and performance (turnover, profits, credit history, net interest expenses,
debt to assets ratio)v. Missing observations on any of these variables are deleted and the final
working sample counts to 30,555 firms. The total number of firms that are not subject to selfselection is 13,377 and the remaining 17,718 firms did not applied for bank loan because of
sufficient internal funds. Summary statistics for the utilized variables are present at Table 2,
separately for the rationed and non-rationed sub-samples. We observe that rationed firms
(compared to not rationed) are younger and smaller in size, single owned, have lower probabilities
of increased turnover and profitability, have lower chances of improved credit history, face
increased net interest expenses and higher debt to assets ratio and lower credit availability. Lastly,
firms in manufacturing seem to face less rationing while those in the construction sector are
considered to be more rationed.
[Insert Table 2 here]
3. Empirical strategy
In this section we present the adopted empirical strategy in order to identify the effects of sovereign
bond ratings on credit rationing. Initially, we estimate country specific credit rationing regressions
in an attempt to understand the factors that explain the within-country credit rationing variation.
Then we proceed with estimates at the Euro zone level and we focus on the impact of sovereign
bond ratings on credit rationing.
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3.1. The determinants of credit rationing
In order to estimate rationing elasticities with respect to firm specific attributes we employ models
of the form:
𝑅𝑖𝑡 = 𝛽𝑋𝑖𝑡 + 𝑓𝑡 + 𝜀𝑖𝑡
(1)
where, 𝑅𝑖𝑡 is the credit rationing indicator for firm 𝑖 observed in period 𝑡, 𝑋𝑖𝑡 is a set of observed
characteristics of firm 𝑖 (e.g., firm’s age, firm’s size, sector of economic activity, etc.), 𝑓𝑡 are
unrestricted intercepts for different time periods, and 𝜀𝑖𝑐𝑡 is an error term. One potential source of
bias in the estimation of the credit rationing equation relates to the selective nature of the “credit
approval” procedure. Essentially, one needs to keep in mind that in our identification process of
the credit rationed firms, those firms which have sufficient internal funds do not participate in the
credit approval procedure. In other words, a firm with sufficient internal funds who did not apply
for a loan, could not be classified as rationed or not-rationed. Therefore, if there were common
factors influencing the demand for a bank loan and the credit rationing mechanism, then the
estimated effect of these factors on rationing -without taking into account firms that did not need
a loan would be biased. Thus, equation (1) is a probit model with sample selection (Cameron and
Trivedi, 2005) where the selection equation relates to whether a firm reports that the reason for not
applying for a bank loan is the sufficiency of its internal funds.
3.2. Credit rationing and sovereign bond ratings
In order to estimate rationing elasticities with respect to sovereign bond ratings we employ models
of the form:
𝑅𝑖𝑐𝑡 = 𝛼𝛢𝑐𝑡 + 𝛽𝑋𝑖𝑐𝑡 + 𝑑𝑐 + 𝑓𝑡 + 𝜀𝑖𝑐𝑡
(2)
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where, 𝑅𝑖𝑐𝑡 is the credit rationing indicator for firm 𝑖 observed in country 𝑐 in period 𝑡, 𝛢𝑐𝑡 is the
sovereign rating in country 𝑐 in period 𝑡, 𝑋𝑖𝑐𝑡 is a set of observed characteristics of firm 𝑖 (e.g.,
firm’s age, firm’s size, sector of economic activity, etc.), 𝑑𝑐 and 𝑓𝑡 are unrestricted intercepts for
different country and time periods, and 𝜀𝑖𝑐𝑡 is an error term. In equation (2) we assume that the
effect of rating on rationing in the Euro zone is linear. Alternative assumptions (up to fourth-order
polynomials) will also be utilized later.
The realization that the sovereign debt rating variable in equation (2) has no "𝑖" subscript
has two important implications. The first refers to its effect on credit rationing (fixed effect) and
the second to the estimated standard error. Specifically, firms located in the same country may
share some common components of variance not entirely attributable either to their measured
characteristics (𝑋𝑖𝑐𝑡 ) or to the sovereign debt rating (𝛢𝑐𝑡 ). In this case, the error components 𝜀𝑖𝑐𝑡
will be positively correlated across firms from the same country, and the conventional formula for
the estimated standard error of the sovereign debt rating effect will be significantly downward
biased. This means that the rationing elasticities are far less precisely estimated than their typical
t-ratios would suggest. To overcome this problem we correct for common variance components
within groups of firms by clustering the standard errors at country and time levels (Moulton, 1990).
The inclusion of year and country dummies in equation (2) is of great importance. For
instance, the implicit assumption underlying a possible exclusion of country dummies is that credit
rationing responds to the "transitory" and "permanent" components of sovereign credit rating with
the same intensity. In contrast, the inclusion of country-specific fixed effects allows the permanent
component of credit rationing to have an arbitrary correlation with the permanent component of
sovereign bond ratings and, in addition, uses only the deviations (transitory component) of credit
rationing and sovereign ratings from their respective average values, to estimate the rationing
14
elasticity. For the Euro zone this matters, because average sovereign bond ratings across memberstates are negatively correlated with the average credit rationing rates (see Figure 3 where credit
rationing is decreasing as ratings deteriorate). This correlation pertains to both, permanent and
transitory components of credit rationing and sovereign ratings. As a result the Euro zone rationing
elasticity tends to be larger when country dummies are excluded. If country dummies did not
exerted an effect on the estimated rationing elasticities, then one could assert that "permanence"
in the geographic patterns of sovereign rating dominates. This exercise serves the purpose of
uncovering econometric evidence for the presence (or absence) of transitory shocks regarding the
effect of sovereigns' creditworthiness on credit rationing.
4. Empirical results
4.1 Cross-country credit rationing estimates
Table 3 reports the estimation results (corrected for sample selection) regarding the impact of the
explanatory variables on credit rationing for each country separately. Three sources of
heterogeneity are controlled for: (a) firm-specific attributes and sectoral dummies (proxies for the
demand side), (b) firm’s perception regarding banks’ willingness to provide loans (proxy for for
supply side) and (c) time-period effects (exogenous shocks). With the exception of Austria,
Finland, Greece and Portugal, the age of the firm appears to be a significant correlate and in
particular as the age of the firm increases the probability of rationing decreases. The size of the
firm is also important (except Finland) and has a negative effect on credit rationing. In Germany,
Ireland and Italy, single ownership of the firm is associated with increased rationing. Changes in
firms' turnover does not seem to play a role in credit rationing with the exception of Greece, Ireland
and Italy. In the cases of Greece and Italy firms with increased turnover report lower credit
15
rationing while in Ireland there is a weak evidence (at the 10 per cent level of statistical
significance) of a positive correlation between increased turnover and credit rationing. Firm
profitability is correlated with rationing only for the case of Greece. The credit history of a firm
seems to be inversely related to credit rationing in the case of Austria and Spain. Firms with
increased net interest expenses are more rationed in Belgium, Finland, France, Greece, Italy and
Spain. When the debt to assets ratio increases the probability of rationing is higher in Belgium and
Spain and lower in the case of Finland. When the credit availability is improved the likelihood of
credit rationing decreases in all countries with the exception of Austria where the effect is not
different from zero in statistical terms. With regard to the effects of the sector of economic activity
we found that credit rationing is higher in manufacturing firms in Austria and France and in the
construction sector in Italy and Spain. In contrast lower rationing is observed in wholesale and
retail trade firms in Ireland and Netherlands. Regarding the time effects, we observe that -on
average- credit rationing has increased since the 1st half of 2009 in France, Germany, Greece,
Ireland, Italy, Netherlands and Spain but this time effect is of different magnitude and not linear
in all cases.
The correlation between the two equations (whether a firm has sufficient internal funds and
whether a firm is rationed) is statistically significant and according to the Wald test the null
hypothesis regarding the exclusion of all explanatory variables in the credit rationing equation is
rejected in the case of Belgium, France, Ireland and Netherlands.vi In addition, there are substantial
differences in the predicted probability of rationing across countries independently of the rating in
sovereign risk. For example, significant differences in the predicted probability of rationing appear
to be present in triple-A countries (.236 in Austria, .182 in Finland, .310 in Germany, and .682 in
the Netherlands). Thus, credit rationing differences have been observed even among countries with
16
the highest quality of sovereign bonds during the recent sovereign debt crisis. Overall, credit
rationing varies substantially in the Euro zone, and moreover it exhibits different time paths in the
examined period. This implies that country risk, captured through the sovereign debt risk ratings
is expected to exert a negative effect on credit rationing over and above the firm-specific attributes
of Euro zone SMEs.
[Insert Table 3 here]
4.2. The impact of sovereign bond ratings on credit rationing
Table 4 presents the estimation results (marginal effects) of four alternative model specifications
regarding the impact of sovereign credit rating on credit rationing. In Column 1 we present our
benchmark model which refers to the empirical model described by equation (2) for the pooled
sample of Euro zone SMEs without controlling for the effects of the sovereign bond rating.
Columns 2, 3 and 4 include estimates for the full specification of equation (2) which takes into
account the ratings of the Standard & Poor’s, Moody’s and Fitch, respectively. With regards to the
results presented in Column 1 we observe that firms older than 10 years have 9.5 percentage points
higher probability of credit rationing compared to firms aged 10 years or less. In addition, the
probability of credit rationing is higher for small firms (i.e., with 10-49 employees) and medium
firms (i.e., those with 50-249 employees) by 12.3 and 17.6 percentage points, respectively
(compared to micro firms, i.e., with 1-9 employees). When the owner of the firm is a single person
the probability of rationing is higher by 4.7 percentage points compared to firms with more than
one owners (firms, businesses, family enterprises etc.). For firms with improved credit history over
the past 6 months the probability of credit rationing is lower by 3.2 percentage points compared to
those with unchanged/deteriorated credit history. Firms with increased net interest expenses have
17
6.9 percentage points higher probability of credit rationing relative to firms with
unchanged/decreased net interest expenses. When the firm considers that the credit availability
regarding bank’s willingness to provide loans is improved over the past 6 months the probability
of credit rationing is 26.0 percentage points lower than firms with unchanged/deteriorated credit
availability. In addition, firms have 3.2 percentage points lower probability of credit rationing in
the wholesale/retail trade sector compared to firms operating in the services sector.
Credit rationing seems to incorporate significant time and country effects throughout the
examined period. Using the first quarter of 2009 as the reference period, we found that credit
rationing is increasing in subsequent semesters, reaching a peak in the period April 2012September 2012. In addition, credit rationing seems to be higher for firms operating in all countries
(using Austria as the reference group) with the exception of Finland. However, the magnitude of
the country-effects varies across countries with Ireland, Netherlands and Greece to witness the
highest extent of credit rationing. To the extent that the country risk factor (i.e., sovereign debt
ratings) is positively correlated with country and time indicators we expect that its inclusion in the
empirical models will result in lower estimates of these indicators.
The inclusion of the Standard & Poor’s rating (Column 2) indicates that firms operating in
countries with high quality ratings face lower probabilities of credit rationing. For instance, when
the rating increases by one point in the scale 1 to 23, then the probability of credit rationing for the
typical firm in the Euro zone decreases by 2.0 percentage points. It should be noted that in this
model specification (compared with the benchmark model presented at Column 1) the effects of
firm-specific variables remain practically intact, both in magnitude and statistical significance.
However, differences appear in the estimated effects of the time and country indicators. We
observe that the inclusion of the Standard and Poor’s rating resulted in smaller estimated time
18
effects for the period April 2011-March 2013 while for the period January 2009-Febraury 2011
these effects are the same as those presented in Column 1. This implies that the behavior of credit
rationing in the Euro zone exhibits a time variation independent of the effect of sovereign rating
only for a specific sub-period (January 2009-Febraury 2011) while afterwards (April 2011-March
2013) credit rationing seems to be correlated with specific time effects.
The inclusion of the sovereign debt rating is associated with significant reductions in the
effects of the country indicators. For example, the magnitude of the country effects is reduced in
the case of Spain, Greece, Ireland and Portugal but continue to exert a statistically significant
impact on credit rationing. In the case of Belgium and Italy the effects have also been reduced but
now they are not statistically different than zero while the effects of Germany, Finland, France and
Netherlands remain unchanged. Thus, credit rationing (a) in Austria, Belgium and France appears
to be exclusively due to firm-specific attributes and sovereign debt ratings, (b) in Greece, Ireland,
Portugal and Spain due to firm-specific attributes, sovereign debt ratings and country specific
idiosyncrasies and (c) in Germany, Finland, France and Netherlands due to firm-specific attributes
and country specific idiosyncrasies. Pretty much the same results were obtained when we utilized
the Moody’s and Fitch ratings (Columns 3 and 4, respectively). Overall, our results suggest that
credit rationing encompasses two distinct effects regarding the impact of sovereign ratings on
credit rationing: a permanent and a transitory one. Permanent differences are captured by country
dummies while transitory differences (i.e., short-run deviations of the rating variable from its longrun mean) by the autonomous effect of the rating variable on credit rationing.
[Insert Table 4 here]
4.3. Predicted probabilities of credit rationing for types of sovereign bonds
19
Table 4 presents estimated probabilities of credit rationing, conditional on the firm’s need for bank
loans for different types of sovereign bond ratings (all three rating agencies are considered). In this
case, we adopt equation (2) and instead of using numerical ratings we utilize 5 groups of sovereign
ratings (Alfonso et al., 2012, Table 1) i.e., “highest/high quality”, “strong payment capacity”,
“adequate payment capacity”, “likely to fulfil obligations” and “high/very high credit risk/near
default/ default”. The obtained results suggest again that credit rationing is higher for firms
operating in countries with increased credit risk for any of the three rating agencies. The estimated
conditional probabilities are very close regarding the Moody’s and Fitch ratings while the credit
rationing probabilities seem to be lower for the ratings of Standard & Poor’s and in particular for
the cases of high credit risk and near default. Overall, the obtained results indicate rather clearly
that firm-specific credit rationing is higher for firms operating in countries with bonds of
speculative grade.
[Insert Table 5 here]
5. Conclusions
The objective of the present study was to investigate credit rationing across firms in Euro zone
countries and its relation to sovereign credit ratings, and to explain the observed credit rationing
differentials between all possible pairs of Euro zone countries. Towards this end, we utilized firmlevel data, drawn from the European Central Bank Survey on the Access to Finance of SMEs
covering the period between the 1st half of 2009 and October 2012-March 2013. Given our
capacity to identify directly whether a firm is credit rationed or not, we were able to compare the
degree of credit rationing amongst firms in the Euro zone and to test whether the existing
differences are attributed to firm, country and time specific sources. According to our results, credit
20
rationing varies substantially between Euro zone countries. Incorporating in the econometric
analysis of credit rationing the potential impact of sovereign ratings, an unambiguous negative
association has been uncovered. This relationship is found to be robust under different model
assumptions regarding the relationship between the demand for bank loans and credit rationing.
However, this association is non-linear and indirect. In particular, credit rationing is higher for
firms operating in countries that are rated lower in the scale of sovereign bond ratings and
increasing for firms operating in sovereigns who have been subjected to downgrading, reflecting
country-related idiosyncratic reasons. The present study enriches the relevant literature, which
suggests that that the ongoing sovereign debt crisis in the Euro zone has affected credit markets
and primarily the supply of credit in periphery countries through the bank balance-sheet channel
(Jimenez et al., 2012; Bofondi et al., 2013). Nevertheless, credit rationing differences have been
observed even among countries with the highest quality of sovereign bonds during the recent
sovereign debt crisis indicating that credit rationing may be affected by bank balance-sheet risk
valuations unrelated to sovereign debt.
The findings of the present paper regarding the correlations between credit rationing and
country, time and firm specific attributes should not be interpreted as causal ones. For instance,
the casual effect of a major event in the credit market, cannot be ascertained with the data at hand
since there are certain time specific events (and/or country-specific) that occurred in the examined
period and may have led to changes in the path of credit rationing. Such events are associated with
a country's deterioration of credit ratings, and might include the launch of a bailout program and/or
austerity policies, bank recapitalizations and reforms, etc. These events or a subset of them could
be observed concurrently (running on a common trend) making the task of isolating and thus
21
identifying their impact on credit rationing difficult or impossible. Future research should focus
further on these identification issues.
According to our results, the incidence of credit rationing in the Euro zone is partly
associated with country specific idiosyncrasies which can be captured by credit rating assessments.
Thus, from a macroeconomic point of view observed credit rationing at a given point in time could
be an equilibrium outcome. However, this outcome encompasses significant informational
asymmetries which can play an important role in the re-allocation of credit within Euro zone firms.
For example, firms of different size face different access to the credit market. Thus, differences in
the distribution of firm size within the Euro zone are associated with differences in credit rationing.
From a policy perspective, a reduction of credit rationing differences between countries in the Euro
zone will require the convergence of firm characteristics which however is not feasible in the short
term -if at all- and thus differences in credit rationing are expected to be a permanent phenomenon.
In any case, fundamental firm characteristics (e.g. firm’s age and size, sector of economic activity,
financial situation etc.) are able to explain the observed credit rationing differentials amongst firms
in the Euro zone. In addition, domestic credit market outcomes are interconnected with sovereign
debt risk.
22
Acknowledgments
This paper uses data from the EC/ECB Survey on the access to finance of SME's for which a
confidentiality declaration has been signed.
23
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Source: SAFE (First half 2009-October 2012-March 2013).
Figure 1 Credit rationing in Euro zone and by country across survey periods
29
Source: Standard & Poor's.
Source: Moody's.
Source: Fitch.
Figure 2 Sovereign bond ratings in Euro zone (2009 and 2013)
30
Source: SAFE (First half 2009-October 2012-March 2013) and ratings from the websites of
Standard & Poor’s, Moody’s and Fitch.
Notes: The correlation coefficient between credit rationing and sovereign bond ratings for
Standard & Poor’s, Moody’s and Fitch is -.601, -.650 and -.647, respectively.
Figure 3 Credit rationing and Moody's ratings in Euro zone SMEs
31
Table 1. Summary statistics on credit rationing
Number of firms
Frequency (%)
Did not apply for a bank loan
Did not apply because of sufficient internal funds
22387
Rationed firms
Did not apply because of possible rejection (discouraged)
3103
44.8
Applied and partially satisfied
2327
33.6
Applied but was rejected
1489
21.5
A: Total
6919
100.0
Not Rationed firms
Applied and got everything
7772
96.1
Applied but refused because cost too high
315
3.9
B: Total
8087
100.0
Credit rationed firms: [A/(A+B)]
46.1
Source: SAFE (First half 2009-October 2012-March 2013).
Notes: Numbers are based on the responses in questions Q7A and Q7B of the SAFE questionnaire referring to bank loan (new or
renewal).
32
Table 2. Distribution of firm-specific attributes between not rationed and rationed firms in Euro zone
Variables
Not rationed
Rationed
Firm’s age
Age less than 10 years
24.11
32.75
Age 10 years or more
75.89
67.25
Firm-size
From 1 employee to 9 employees
33.42
53.15
From 10 employees to 49 employees
33.73
27.56
From 50 employees to 249 employees
32.84
19.29
Ownership
A natural person, one owner only
24.65
29.35
Other entities
75.35
70.65
Turnover
Turnover increased
37.99
26.43
Turnover unchanged/decreased
62.01
73.57
Profits
Profits increased
23.81
15.91
Profits unchanged/decreased
76.19
84.09
Firm’s credit history
Credit history improved
24.46
14.96
Credit history unchanged/deteriorated
75.54
85.04
Net interest expenses
Net interest expenses increased
43.39
55.53
Net interest expenses unchanged/decreased
56.61
44.47
Debt to assets ratio
Debt to assets ratio increased
33.69
40.29
Debt to assets ratio unchanged/decreased
66.31
59.71
Banks’ willingness to provide a loan (Credit availability)
Improved
16.34
4.11
Unchanged/deteriorated
83.66
95.89
Sector of economic activity
Manufacturing
19.79
15.96
Construction
10.69
13.90
Whole sale/retail trade
19.59
19.29
Services
49.92
50.85
Observations
7240
6137
Source: SAFE (First half 2009-October 2012-March 2013).
Notes: Other entities include Public shareholders, Family or entrepreneurs, Other firms or business associates, Venture capital firms
or business angels, Other. Data have been weighted using population weights.
33
Table 3. Determinants of credit rationing
Austria
Belgium
Finland
France
Germany
Greece
-.046
-.127**
-.111
-.137***
-.138***
-.043
(.050)
(.051)
(.083)
(.031)
(.036)
(.057)
Firm size: 10-49 employees
-.088
-.145***
-.037
-.150***
-.169***
-.069
(.057)
(.045)
(.051)
(.035)
(.037)
(.057)
Firm size: 50-249 employees
-.186***
-.158
.004
-.221***
-.256***
-.160**
(.057)
(.057)
(.063)
(.037)
(.037)
(.064)
Single owner
.055
.032
.015
.020
.054*
-.075
(.045)
(.049)
(.058)
(.028)
(.029)
(.060)
Turnover increased
-.027
-.057
-.007
-.043
.001
-.138**
(.045)
(.055)
(.048)
(.029)
(.031)
(.067)
Profits increased
-.055
-.015
-.029
-.028
.009
.168**
(.047)
(.060)
(.054)
(.033)
(.034)
(.083)
Credit history improved
-.091**
-.002
-.035
-.007
-.048
.076
(.046)
(.052)
(.074)
(.034)
(.030)
(.071)
Net interest expenses increased
.075
.082*
.146**
.137***
.015
.161**
(.047)
(.048)
(.063)
(.026)
(.029)
(.047)
Debt to assets ratio increased
.037
.085*
-.078*
.017
.006
.056
(.050)
(.046)
(.042)
(.025)
(.033)
(.053)
Credit availability improved
-.084
-.266***
-.168***
-.242***
-.179***
-.536***
(.063)
(.042)
(.051)
(.030)
(.034)
(.129)
Manufacturing
.127**
-.017
.024
.095***
-.005
-.014
(.060)
(.058)
(.065)
(.032)
(.031)
(.065)
Construction
.052
-.003
.002
-.041
.028
-.060
(.075)
(.062)
(.069)
(.039)
(.042)
(.095)
Whole sale/retail trade
-.062
.023
-.045
-.044
-.037
-.042
(.045)
(.050)
(.064)
(.028)
(.035)
(.058)
2nd half 2009
.115
.247**
-.041
.060
.103**
.208
(.127)
(.120)
(.187)
(.043)
(.045)
(.146)
March 2010- September 2010
-.043
.043
.033
.154***
.112**
.320**
(.107)
(.124)
(.212)
(.051)
(.047)
(.127)
September 2010- February 2011
.023
-.071
.055
.151***
.194***
.273***
(.090)
(.093)
(.184)
(.049)
(.047)
(.101)
April 2011- September 2011
-.073
.032
-.121
.221***
.111**
.245**
(.090)
(.097)
(.172)
(.047)
(.053)
(.112)
October 2011- March 2012
-.063
-.008
.007
.164***
.007
.315***
(.090)
(.095)
(.174)
(.047)
(.051)
(.103)
April 2012- September 2012
-.060
-.047
-.077
.184***
.016
.413***
(.094)
(.093)
(.174)
(.045)
(.053)
(.110)
October 2012- March 2013
.029
.022
.001
.213***
-.085*
.461***
(.097)
(.094)
(.176)
(.048)
(.044)
(.102)
Wald-test (Rho=0)
1.96
2.92*
.38
5.41**
.35
.01
[p-value]
[.162]
[.087]
[.533]
[.020]
[.554]
[.941]
Predicted probability (conditional)
.236
.330
.182
.334
.310
.684
Number of firms (total)
1817
1990
1615
4835
4322
1588
Number of firms (uncensored)
1254
1195
1229
2572
2758
594
Number of firms (censored)
563
795
386
2263
1564
994
Source: SAFE (First half 2009-October 2012-March 2013).
Notes: Reported estimates are conditional marginal effects drawn from probit models with sample selection. The dependent variable
is the dummy indicator as presented in Table 1. Robust standard errors in parentheses. Reference groups: Firm size 1-9 employees,
Services sector and 1st half 2009. Sampling weights have used.
Statistical significance: *<.10, **<.05, ***<.01.
Age more than 10
34
Table 3 (continued). Determinants of credit rationing
Ireland
Italy
Netherlands
Portugal
Spain
Age more than 10
-.091*
-.066*
-.096**
-.070
-.087***
(.054)
(.035)
(.048)
(.059)
(.026)
Firm size: 10-49 employees
-.094*
-.118***
-.142***
-.168***
-.079***
(.053)
(.032)
(.050)
(.056)
(.026)
Firm size: 50-249 employees
-.173***
-.113***
-.196***
-.088
-.068**
(.066)
(.033)
(.061)
(.064)
(.027)
Single owner
.080*
.090**
.059
.008
-.013
(.045)
(.039)
(.047)
(.073)
(.030)
Turnover increased
.106*
-.058
-.079*
-.026
-.032
(.058)
(.036)
(.047)
(.059)
(.034)
Profits increased
-.049
.054
-.026
-.064
.020
(.061)
(.047)
(.049)
(.074)
(.043)
Credit history improved
-.075
.016
-.034
-.024
-.074**
(.060)
(.042)
(.045)
(.076)
(.035)
Net interest expenses increased
.060
.081**
-.007
-.013
.043*
(.046)
(.031)
(.040)
(.048)
(.025)
Debt to assets ratio increased
-.014
.038
-.031
-.057
.064***
(.056)
(.029)
(.039)
(.050)
(.024)
Credit availability improved
-.402***
-.276***
-.421***
-.155*
-.292***
(.080)
(.040)
(.064)
(.088)
(.044)
Manufacturing
.031
.004
-.054
-.009
.015
(.053)
(.031)
(.063)
(.053)
(.029)
Construction
.055
.096**
.014
-.036
.087***
(.077)
(.049)
(.055)
(.073)
(.031)
Whole sale/retail trade
-.097**
-.020
-.095*
-.028
.011
(.044)
(.038)
(.055)
(.059)
(.028)
2nd half 2009
.015
.108**
.201**
-.127
.054
(.216)
(.052)
(.089)
(.109)
(.039)
March 2010- September 2010
-.045
.078
.123
-.026
-.015
(.199)
(.056)
(.096)
(.100)
(.042)
September 2010- February 2011
.290*
.114**
.196**
-.007
.014
(.160)
(.051)
(.083)
(.085)
(.044)
April 2011- September 2011
.347**
.064
.244***
.160*
-.046
(.162)
(.055)
(.082)
(.084)
(.045)
October 2011- March 2012
.219
.227***
.132
.103
.029
(.160)
(.056)
(.093)
(.095)
(.043)
April 2012- September 2012
.266*
.244***
.275***
.148
.059
(.159)
(.052)
(.077)
(.105)
(.045)
October 2012- March 2013
.326**
.153***
.105
.012
.124***
(.158)
(.055)
(.086)
(.094)
(.043)
Wald-test (Rho=0)
18.3***
1.40
13.09***
2.60
.14
[p-value]
[.001]
[.237]
[.001]
[.107]
[.703]
Predicted probability (conditional)
.645
.462
.682
.522
.557
Number of firms (total)
1723
4455
1907
1430
4873
Number of firms (uncensored)
1051
2157
1372
745
2251
Number of firms (censored)
672
2298
535
685
2622
Source: SAFE (First half 2009-October 2012-March 2013).
Notes: Reported estimates are conditional marginal effects drawn from probit models with sample selection. The dependent variable
is the dummy indicator as presented in Table 1. Robust standard errors in parentheses. Reference groups: Firm size 1-9 employees,
Services sector and 1st half 2009. Sampling weights have used.
Statistical significance: *<.10, **<.05, ***<.01.
35
Table 4. Sovereign bond ratings and credit rationing
[1]
[2]
[3]
[4]
Standard & Poor’s
-.020*** (.005)
Moody’s
-.021*** (.005)
Fitch
-.022*** (.006)
Age more than 10
-.095*** (.012)
-.095*** (.012)
-.095*** (.012)
-.096*** (.012)
Firm size: 10-49 employees
-.123*** (.013)
-.123*** (.013)
-.124*** (.013)
-.124*** (.013)
Firm size: 50-249 employees
-.176*** (.019)
-.175*** (.018)
-.176*** (.018)
-.175*** (.018)
Single owner
.047*** (.012)
.047*** (.012)
.046*** (.012)
.047*** (.012)
Turnover increased
-.041*** (.014)
-.041*** (.014)
-.041*** (.014)
-.041*** (.014)
Profits increased
.008 (.016)
.008 (.016)
.008 (.016)
.007 (.016)
Credit history improved
-.032** (.012)
-.030** (.012)
-.029** (.013)
-.030** (.012)
Net interest expenses increased
.069*** (.015)
.067*** (.015)
.068*** (.015)
.067*** (.015)
Debt to assets ratio increased
.022 (.014)
.022 (.014)
.022 (.014)
.022 (.014)
Credit availability improved
-.260*** (.016)
-.259*** (.016)
-.258*** (.016)
-.259*** (.016)
Manufacturing
.014 (.011)
.012 (.011)
.012 (.011)
.013 (.011)
Construction
.032 (.021)
.033 (.021)
.034 (.022)
.034 (.021)
Whole sale/retail trade
-.032** (.012)
-.033** (.012)
-.032** (.013)
-.032*** (.012)
2nd half 2009
.086** (.033)
.088** (.037)
.088** (.037)
.086** (.040)
March 2010- September 2010
.076** (.031)
.074** (.032)
.070** (.033)
.066* (.034)
September 2010- February 2011
.115*** (.039)
.113*** (.039)
.101** (.039)
.103** (.041)
April 2011- September 2011
.097*** (.037)
.085** (.036)
.072* (.039)
.084** (.038)
October 2011- March 2012
.112*** (.035)
.082** (.039)
.053 (.039)
.072* (.042)
April 2012- September 2012
.140*** (.035)
.079** (.039)
.063 (.041)
.079** (.039)
October 2012- March 2013
.102*** (.048)
.039*** (.051)
.008 (.054)
.033 (.054)
Belgium (BE)
.077** (.039)
.059 (.038)
.044 (.038)
.055 (.038)
Germany (DE)
.107** (.044)
.115*** (.038)
.109*** (.036)
.110*** (.039)
Spain (ES)
.257*** (.033)
.207*** (.034)
.202*** (.035)
.209*** (.034)
Finland (FI)
-.063* (.034)
-.059* (.036)
-.062* (.036)
-.064* (.036)
France (FR)
.054* (.029)
.058* (.030)
.053* (.032)
.058* (.032)
Greece (GR)
.398*** (.042)
.161** (.071)
.122* (.067)
.154** (.072)
Ireland (IE)
.437*** (.048)
.375*** (.050)
.306*** (.053)
.323*** (.053)
Italy (IT)
.133*** (.048)
.046 (.039)
.061* (.032)
.053 (.038)
Netherlands (NL)
.405*** (.033)
.412*** (.033)
.408*** (.034)
.408*** (.033)
Portugal (PT)
.240*** (.040)
.102*** (.051)
.102** (.050)
.114** (.053)
Wald-test (Rho=0)
3.79*
6.51**
8.20***
7.43**
[p-value]
[.051]
[.010]
[.001]
[.006]
Predicted probability (conditional)
.420
.420
.420
.420
Number of firms (total)
30555
Number of firms (uncensored)
17178
Number of firms (censored)
13377
Source: SAFE (First half 2009-October 2012-March 2013) and ratings from the websites of Standard & Poor’s, Moody’s and Fitch.
Notes: Reported estimates are conditional marginal effects drawn from probit models with sample selection. The dependent variable
is the dummy indicator as presented in Table 1. Standard errors in parentheses corrected for clustering at country and survey period
levels. Firm size 1-9 employees, Services sector, 1st half 2009 and Austria (AT). Sampling weights have used.
Statistical significance: *<.10, **<.05, ***<.01.
36
Table 5. Predicted probability of credit rationing across types of sovereign bond ratings
Predicted probability
Standard error
Standard & Poor’s
Highest/High quality
.406
.018
Strong payment capacity
.386
.036
Adequate payment capacity
.517
.040
Likely to fulfil obligations
.513
.080
High/Very High/Near Default/Default
.625
.064
Moody's
Highest/High quality
.383
.012
Strong payment capacity
.515
.031
Adequate payment capacity
.550
.054
Likely to fulfil obligations
.610
.048
High/Very High/Near Default/Default
.756
.065
Fitch
Highest/High quality
.386
.013
Strong payment capacity
.521
.027
Adequate payment capacity
.596
.038
Likely to fulfil obligations
.590
.053
High/Very High/Near Default/Default
.792
.056
Source: SAFE (First half 2009-October 2012-March 2013) and ratings from the websites of Standard & Poor’s, Moody’s and Fitch.
Notes: Predicted probabilities are drawn from probit models with sample selection. The dependent variable is the dummy indicator
as presented in Table 1. The grouping of sovereign bond ratings (5 categories) is based on Alfonso et al. (2012, Table 1). The set of
independent variables include those presented on Table 4. Standard errors in parentheses corrected for clustering at country and
survey period levels. Sampling weights have used.
Statistical significance: *<.10, **<.05, ***<.01.
37
Endnotes
i
Of course there are many cross-county studies from non-euro Central and Eastern European countries (e.g., Popov
and Udell, 2012; Beck et al., 2014; Popov, 2015).
ii
http://www.ecb.europa.eu/stats/money/surveys/sme/html/index.en.html
iii
Firm age is usually viewed as an indicator of its quality, since longevity sends a signal for survival ability and quality
of management, as well as, the accumulation of reputational capital (Diamond, 1992). Moreover, the information gap
is relatively smaller for older firms given their longer track record (Petersen and Rajan, 1994; Cressy, 1996).
iv
A number of explanations have been proposed for small firm disadvantages in loan markets. For instance, their
higher relative probability of failure (Jensen and McGuckin, 1997), fixed costs in assessing application for finance
and proportionately higher monitoring costs (Boocock and Woods, 1997). In addition, smaller firms may have lower
collateral relative to their liabilities than larger ones, and unit bankruptcy costs are likely to decrease with size (Gertler
and Gilchrist, 1994; Hu and Schiantarelli, 1994; Gilchrist and Himmelberg, 1998; Audretsch and Elston, 2002;
Vijverberg, 2004).
v
The firm’s investment opportunity set may also affect the likelihood of rationing (Hubbard, 1990). We control for
the investment opportunity set by using sales growth, fixed assets growth, and profitability. We expect firms with
higher investment opportunity set to face lower probability of rationing. In the context of the Merton (1974) optionpricing model, leverage is used as an inverse proxy of firm credit quality because more levered firms, ceteris paribus,
face a greater likelihood of insolvency. In addition, leverage could exacerbate moral hazard problems since highly
levered borrowers may have a greater incentive to substitute high risk assets for low risk ones after a loan. In addition,
more profitable firms or firms with higher cash flow are expected to be able to borrow more from banks since they
are more likely to have the means to service their debt.
vi
We have also estimated models in which we exclude the firm’s perception regarding the willingness of the banks
to provide loans (during the last 6 months) from the credit rationing equation in order to facilitate the requirement of
the exclusion restriction in the probit models with selection. However, the results are identical as those presented in
the text.
38