Determinants of Credit Rationing for Manufacturing Firms Any Potential Effects from Basel 2?*

Cristiano Zazzara Capitalia Banking Group (cristiano.zazzara@capitalia.it)

University “Luiss-Guido Carli” (czazzara@luiss.it)

First version: September 30, 2005

This version: October 6, 2005

Preliminary and incomplete version: Please do not circulate without the permission of the author

Abstract This work discusses the determinants of credit availability for a sample of about 13,000 Italian manufacturing firms, based on a unique database gathered by the Capitalia banking group over the period 1995-2003. Among other findings, the evidence shows that firms with stronger lending relationships are less likely of being credit rationed compared to firms engaging in multiple bank lending. This study also finds that firms financial variables matter in credit rationing, where less profitable and especially riskier firms are more likely to be credit rationed. Finally, the main result emerging from an event study analysis is that, under the Basel 2 environment, riskier large firms are more likely to be rationed than riskier small firms, both because they are more costly in terms of bank capital and also because the loss in the case of default is higher for banks compared to smaller firms. Furthermore, as concerns bank-firm relations, evidence shows that under Basel 2 stronger lending relationship is beneficial to the firm, as it faces a lower probability of being credit rationed.

JEL classification: G21, G34.

Keywords: Credit Rationing, Lending Relationship, Risk Rating, Basel 2.

* The author thanks Tony Riti and Domenico Curcio for excellent research assistance, and Sergio Lugaresi for useful discussions. A special thank goes to Giovanni Butera of Moody’s KMV who provided access to the RiskCalc Italy™ dataset. The opinions expressed do not reflect the Institutions the author is affiliated with.

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1. Introduction

The current literature on credit rationing is based on the seminal papers by Jaffee and Russell

(1976) and Stiglitz and Weiss (1981), modeling credit rationing as an equilibrium phenomenon

where asymmetric information between lenders and borrowers creates potential for adverse

selection. Consequently, lenders may cut off credit rather than raise loan rates to curtail the supply

of credit, just as by raising rates lenders may drive off all but the least creditworthy applicants or

elicit riskier behaviour from borrowers. It follows that borrowers are credit rationed if they are

unable to fund, at any price, profitable investment opportunities.

During recessionary phases of the business cycle, fears of credit crunch or credit rationing [see

Owens and Schreft (1993) and Ding et al. (1998) for more details on the definitions and

identification of credit tightening] become widespread in the business community, though such

concerns are not always well-founded and supported by statistical evidence of credit rationing at

aggregate level. For example, some concerns about credit rationing in the Italian economy have

recently appeared in the financial press, but so far the official statistics from the Italian banking

industry seem to suggest that at aggregate level there has been no rationing, not even during the

recent downturn: “As in the previous five years, in 2003 the growth of bank lending in Italy

outstripped that in the euro area and credit conditions remained expansionary” [Bank of Italy

(2004)].

However, during downturns some marginal borrowers are more likely to be denied credit, if they

have seen their creditworthiness downgraded by banks and have no longer met minimal lending

standards. But this type of tightening is a rational response to the borrower’s changing conditions,

and does not necessarily imply credit rationing. Denial of credit to creditworthy borrowers is a

cause for concern, as it affects the general level of economic activity1, while tightening in response

to declining repayment capability among borrowers is a rational business decision and key to a

sound and stable banking system. If the bank receives new (and not favourable) information on the

borrowing firm’s creditworthiness, it may adjust its behaviour vis-à-vis the firm and tighten the

1 On the relationship between the economic and lending cycles see Bernanke and Blinder (1988); Bernanke and Gertler (1989); Bernanke and Gilchrist (1996); and Driscoll (2004).

3

firm’s financial constraints by reducing credit lines, terminating individual loans, requiring

additional collateral to discipline the firm’s management, and/or increasing the risk premium.

In practice, however, it is very difficult to ascertain when a situation of restrictive lending

behaviour should be classified as credit rationing, unless the analysis is performed on the results of

a specific survey providing data on loan application/rejection. Examples of survey data are the 1993

National Survey of Small Business Finance [described by Cole and Wolken (1995)] and the Survey

of Consumer Finances (SCF) for the U.S.A. [see Chakravarty and Yilmazer (2004) for a recent

analysis], and the Mediocredito-Capitalia’s Surveys of Manufacturing Firms for Italy (SIMFs) [see

Bianco et al. (1999), Angelini, Generale (2005), Becchetti et al. (2005), Rotondi (2005) for a

description].

In light of the above considerations, this paper undertakes an econometric analysis of the

determinants of credit rationing in the Italian manufacturing sector using the firm level data

collected from the latest three Mediocredito-Capitalia’s SIMFs. We explore the influence of various

characteristics on the likelihood of credit tightening in the Italian manufacturing sector. First, we

concentrate on firms characteristics, and include in the study age of firm, size of firm, exporting

firms and group membership. Second, we examine industry characteristics, such as district

participation and type of industry in terms of technology level. Third, we evaluate whether

establishing strong lending relationships translates into lower probability for a firm being credit

constrained by the banking system. An impressive body of empirical research has been built up

over the past decade, documenting the role of relationships in the availability and cost of bank

credit to small business [for a comprehensive review, see Boot (2000), Ongena and Smith (2000)].

We also test the impact of characteristics of the firm’s geographical area, including a dummy for the

firms located in southern regions. We finally extend our model including financial variables at the

firm level in order to evaluate the association between risk & profitability and credit rationing.

Furthermore, this paper attempts to evaluate the potential impact of the proposed Basel 2 risk-based

capital requirements on credit rationing. This is done by testing whether the above multiple

regression function differs between the two surveys run over the period 1995-2000 (considered as

the Pre-Basel 2 period) and the latest survey referred to 2001-2003. We chose the above mentioned

Pre-Basel 2 period because for most of those years there was any talk of a Basel reform.

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Furthermore, since the first announcement of the banks’ capital reform made public in June 1999

was a general statement of purpose with neither technical details (risk-based capital requirement

formulas) nor the timetable of its implementation, we believe the new Basel 2 environment has

started to exert its effects during the period of the last survey (that is, 2001-2003).

The outline of the rest of the paper is as follows. Section 2 describes the data and the definition of

variables used in the study. Section 3 discusses the empirical methodology and the research

hypotheses. Section 4 examines the empirical results from the regression analysis on the

determinants of credit rationing. Section 5 evaluates the likely impact of the upcoming Basel 2 rules

on credit rationing, and Section 6 concludes.

2. Source of Data and definition of variables

The data for the present study on credit rationing in the Italian manufacturing sector come from

the Surveys of Italian Manufacturing Firms (SIMFs)2 run by Mediocredito Centrale, a credit

institution member of the Capitalia Banking Group (the fourth largest bank in the country)3. The

first survey was conducted in 1992 (covering the period 1989-1991), the second survey in 1995

(covering 1992-1994), the third survey in 1998 (covering 1995-1997), the fourth survey in 2001

(covering 1998-2000), and the latest one in 2004 (covering the period 2001-2003)4,5. We

concentrate in the latest three survey questionnaires, since firms are asked whether they encounter

financial constraints according to the same following questions: 1. “In the previous year the firm

wanted more loans at the prevailing market conditions.”, 2. “The firm asked for more credit but

was turned down by the bank.”, 3. “To obtain more credit the firm would have accepted to pay a

slightly higher loan rate.” 6

In accordance with the definitions currently used in the literature, we consider the following

definitions of credit rationing:

2 These surveys cover labor force, export strategies, finance, and innovation & technology. 3 Mediocredito Centrale became part of the Capitalia Banking Group in July 2002. 4 This latest Survey expanded the industry coverage including services, construction, and electricity as well. This Survey was run by the Research Department of Capitalia, which now has the responsibility of the project. 5 For further information on these surveys (currently available only in Italian), see www.capitalia.it. 6 For details on questions and definitions of financially constrained firms related to the Mediocredito-Capitalia surveys, see Angelini, Generale (2005).

5

Weak Rationing [see, for example, Angelini, Generale (2005)]: firms desiring more credit,

identified by firms answering “yes” to question 1,

Financial Constraint [see, for example, Angelini, Generale (2005)]: firms desiring more

credit and asked for it but were turned down by the bank, identified by firms answering

“yes” to questions 1 and 2.

Strong Rationing [see, for example, Mattesini, Messori (2004)]: firms desiring more credit

and asked for it but were turned down by the bank, and that to obtain more credit would

have accepted to pay a slightly higher loan rate. These firms answered “yes” to all three

questions7.

The above definitions of credit rationing are translated into dummy variables (see Table 1 for

descriptive statistics), which constitute the dependent variables of our binary response models (see

next section for details on the empirical methodology).

In Table 2 we report a description of the variables used to identify the determinants of credit

rationing, together with summary descriptive statistics, such as mean, standard deviation, minimum

and maximum. Below we provide detailed information on these variables.

As concerns Firm characteristics:

• AGE, the age of the firm is equal to the number of years since the date of establishment. Data as

of years 1998, 2001, 2003.

• SIZE, the number of employees. Data as of years 1998, 2001, 2003.

• EXPORT, dummy variable equal to 1 if the firm exports, 0 otherwise.

• GROUP, dummy variable equal to 1 if the firm belongs to a group, 0 otherwise.

As concerns Industry characteristics:

The following variables refer to an industry classification in terms of technology level (see Table

3), based on the OECD Product Classification provided by Hatzichronoglou (1997):

• IND1, dummy variable equal to 1 if the firm belongs to Medium-Low Technology Industry, 0

otherwise.

7 In the latest survey run in 2004, researchers at Capitalia eliminated the word slightly from question 3 in order to better define the concept of Strong Rationing.

6

• IND2, dummy variable equal to 1 if the firm belongs to Medium-High Technology Industry, 0

otherwise.

• IND3, dummy variable equal to 1 if the firm belongs to High Technology Industry, 0 otherwise.

• DISTRICT, dummy variable equal to 1 if the firm belongs to one of the sixty-two industrial

district areas identified by Mediobanca (2004). Each district is characterized by the combination

of location (province) and industry sectors, these latter classified according to the ATECO

system.

As concerns Region-level characteristics:

• SOUTH, dummy variable equal to 1 if the firm is located in the Southern regions of Italy.

According to the SIMF classification, we select firms located in the following regions: Abruzzo,

Basilicata, Campania, Molise, Apulia, Sardinia, and Sicily.

As concerns Lending relationships:

• NUMBANC, number of banks with which the firm has a commercial relation. Multiple-bank

lending is widely diffused in Italy, as documented, for example, by Ongena and Smith (2000),

and it is therefore reasonable to control for this variable. Data as of years 1998, 2001, 2003.

• SKEW, is an indicator firstly proposed by Krahnen et al. (2002), and also tested by Guelpa and

Tirri (2004), that combines the number of banks per each firm and the share of firm’s bank debt

held by the main lender i. In formula, NumbancDebt Bank Total

Debt sBanki 1'− , where the higher the ratio,

the stronger the lending relationship between the firm and the bank. Data as of years 1998, 2001,

2003.

As concerns Firm financial variables:

• PERF, the firm’s return on assets (ROA). Data come from the AIDA™ database8 and are

referred to years 1998, 2001, 2003.

• RISK, the 1-year probability of default coming out from the RiskCalc™ Italy model developed

by Moody’s KMV (2002, 2005). RiskCalc™ Italy, firstly released in October 2002 and regularly

revised to take into account all evolutions in the financial and regulatory environment, is the first

8 This is a product by Bureau Van Dijk which contains financial information (in the form of company accounts, ratios, activities, ownership, subsidiaries, and management) on 280,000 Italian companies.

7

Basel 2 compliant rating model based on publicly available data9 to assess private firm’s

creditworthiness, and constitutes a benchmark in the Italian financial industry10. For details on

the variables included in the RiskCalc Italy™ model and their weights, see Table XXX [THIS

TABLE WILL BE INCLUDED AFTER THE REVIEW BY MOODY’S KMV]. Balance-

sheet data used to calculate the 1-year probability of default are obtained from the AIDA™

database and are referred to years 1997 and 1998 (first survey), 2000 and 2001 (second survey),

2002 and 2003 (third survey).

In the next section we describe the empirical methodology employed in this study and our research

hypotheses.

3. Empirical Methodology and Research Hypotheses

We follow the conventional practice of using a discrete and limited dependent variable model11

to analyze the determinants of credit rationing.

The likelihood of credit tightening is modelled as:

iiy µβ += 'iX [1]

where:

1 if iy > 0, i.e. firm i is credit rationed =iy [2]

0 otherwise

and iX is the set of exogenous (independent) explanatory variables and iµ the error term.

The probability of credit rationing is modelled as a logit model:

'

'

exp1exp)1(

ββ

i

i

X X

+==iyprob [3]

9 That is, publicly available balance-sheet data. 10 RiskCalc™ Italy was built in collaboration with two leading Italian banks -- Capitalia and San Paolo-IMI -- that regularly uses it as a benchmark for their internal credit risk estimates. 11 For an excellent review of regression models for categorical and limited dependent variables, see Long J. S. (1997).

8

Since we use data from three surveys, we run an independently pooled cross section to take into

account cross-sectional and time series aspects, where year dummy variables are included to

account for aggregate time effects12. By pooling random samples drawn from the same population,

but at different points in time, we can get more precise estimators and test statistics with more

power. Furthermore, pooling cross sections over time is also useful to evaluate the impact of a

certain event or policy changes, such as for example the introduction of Basel 2 in the financial

system (see section 5).

We develop our analysis building a model for each definition of credit rationing explained above,

where the independent variables cover the relevant profiles of firm characteristics, industry

characteristics, region-level characteristics, lending relationships, and firm financial variables. The

choice of the independent variables has been driven by existing studies using similar firm-level data

(for a brief review of this literature, see section 1). We also control for time effects, including year

dummies for each survey (we choose the year before the survey was run, that is 1997, 2000, 2003).

Running regressions under various definition of credit rationing using the same set of parameters

allows us to better evaluate the robustness of the estimates, and to highlight the possible different

explanatory power in each specification.

We now describe the research hypotheses and specify the regression model.

• Firm Characteristics

We postulate that the probability of being rationed is influenced by the following factors: age of

firm (AGE), firm size measured by total employees (SIZE), a dummy variable equal to 1 if the firm

exports and equal to 0 otherwise (EXPORT), a dummy variable equal to 1 if the firm belongs to a

group and equal to 0 otherwise (GROUP).

• Industry Characteristics

Credit rationing may be more probable in some industries compared with others. We include two

types of variables to capture industry characteristics. First, an interesting hypothesis would be that

firms in high-technology industries are less likely of being rationed compared to those in low

12 For details on pooled logit regressions, see Wooldridge (2002a).

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technology industry. We investigate the possibility of differences in the likelihood of credit

tightening in the different types of industries classified by technological levels. Using the

classification scheme of the various industries by their technological characteristics proposed by

Hatzichronoglou (1997) -- reported in Table 3 -- we label an industry as one of the following: (a)

low technology (IND0), (b) medium-low technology (IND1), (c) medium-high technology (IND2),

(d) high technology (IND3).

Second, we test the effect of being part of an industrial district on rationing, since theoretical and

empirical studies acknowledge that firms located inside industrial districts may have an advantage

in terms of lower cost of credit and lower probability of encountering financial constraints [see, for

example, Stiglitz, (1994), Finaldi et al., (2001)]. We control for this characteristic using a dummy

variable based on the industrial district classification proposed by Mediobanca (2004).

• Region-level Characteristics

Credit rationing may also be determined by region-level factors [see, for example, Guiso et al.

(2004)], since many differences in the economic and legal environment are present across

geographical areas in Italy. To test if local conditions matter, we include a dummy variable

(SOUTH) equal to 1 for regions located in southern regions, and equal to 0 otherwise.

• Lending Relationships

After controlling for all the previous factors (firm, industry and region characteristics), we want to

test if firms with stronger bank-lending relationships are less likely of being credit constrained than

firms with weaker lending relationships. This proposition is modelled with the inclusion of two

variables: the firm’s number of lending banks (NUMBANC) and the skewness of bank debt

(SKEW) firstly proposed by Krahnen et al. (2002), this latter being a comprehensive indicator of

lending relationship (see previous section for details). Since the variable SKEW includes also the

variable NUMBANC, these variables alternate in different specification models in order to test the

robustness of the lending relationship hypothesis. [RESULTS OF THE ESTIMATES

INCLUDING THE VARIABLE SKEW ARE NEITHER REPORTED NOR COMMENTED]

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• Firm’s Performance and Risk

We want to test if firms being rationed are less profitable and riskier than those that are not

financially constrained. The proposition that these variables determine credit rationing is modelled

with the inclusion of two variables: the firm’s ROA (PERF) and the 1-year probability of default

(RISK) coming out from the RiskCalc™ Italy model developed by Moody’s KMV (2002, 2005).

The inclusion of this latter variable enables us to test whether banks are able to screen and monitor

borrowers [see Allen (1990) for a review of this literature]. As a consequence, we expect the

probability of being rationed increases with the riskiness of the firm. Providing evidence on

whether risk rating (which is the cornerstone of the Basel 2 regime) is a determinant of credit

rationing is also important since there is a scarcity of research in the Italian environment on this

aspect13.

From equation [3], the logit model including all the above-mentioned explanatory variables may be

written in the following log-linear form14:

µββββ

βββββ

βββββ

+++++

++++++

++++=

−

YEARSRISKPERF(SKEW) NUMBANC

SOUTHDISTINDINDIND

GROUPEXPORTSIZEAGE1

log

12111010

98372615

43210pp

[7]

where p is the probability of a firm being credit rationed, and YEARS are dummy variables for

years 2000 and 200315.

In the next section we proceed discussing the econometric results of the above model specification.

13 To our knowledge, the only contributions dealing explicitly with credit risk at the firm level as a determinant of credit rationing are those of Guelpa, Tirri (2004), and Becchetti et al. (2005). 14 For ease of exposition, we present the model in this form, overlooking firm and time subscripts. 15 The dummies for low technology industries (IND0) and for year 1998 are excluded from the specification in order to avoid the “dummy variable trap”. For further details, see Wooldridge (2002b).

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4. What Determines Credit Rationing?

In this section we discuss the maximum likelihood estimation results, where regression in the

form of equation [7] is run for each credit rationing definition (for a total of three pooled logit

regressions). These results are summarized in Table 4.

• Firm Characteristics

(a) Age of Firms: The positive sign for the coefficient of the variable representing firm’s age

indicates that older firms are more likely of being credit rationed compared to younger firms.

However, the coefficient of the age of firm is very small and is not statistically significant at the

10% level in the regression based on the Strong Rationing definition. We notice this outcome is

against expectation and its modest relevance may indicate that age matters more for new or very

young firms, and less for well established firms [evidence provided by Guelpa and Tirri (2004) on a

different sample of Italian firms confirms this result].

(b) Firm Size: The coefficient of the variable representing firm size is negative in all regressions but

its magnitude is very small. Furthermore, the coefficient is not statistically significant in the

Financial Constraint and Strong Rationing regressions, while it is significant at the 1% level in the

regression related to Weak Rationing. This outcome indicates that -- in the 1995-2003 period -- the

probability of credit constraints, in the strongest versions, is not decreasing in firm size.

(c) Export and Group Membership: The coefficient of the dummy variable representing firms that

produce for international markets is small -- albeit positive in some specifications (Financial

Constraint and Strong Rationing models) and negative in others (Weak Rationing model) -- and

never statistical significant at the conventional level (the standard errors are very high in all cases).

Similar results are found for the dummy variable GROUP, where coefficients are small in

magnitude and with very high standard errors in all regressions, even if they report the expected

negative signs in all regressions.

Our evidence does not support any significant relationship between credit rationing versus firms

that export and firms that are part of a group.

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• Industry Characteristics

(d) Type of Industry by Technological Characteristics: From the regression results, the empirical

relationship between high technology industries and firms’ probability of being credit rationed is

negative in all specifications, compared to the positive relationship16 emerging for medium-low and

medium-high technology industries. Even if the coefficient of high tech industries is relative small

compared to those of other explanatory variables and never statistical significant at the conventional

level, this result may be considered as a mild indication of how the probability of being credit

rationed varies systematically by the level of technology of the industry sector.

(e) Industrial District: This dummy variable is never significant and the sign of the coefficient is

unexpected, that is positive, is some regressions (Financial Constraint, Strong Rationing) and

negative in others (Weak Rationing). This result confirms the findings of Guelpa, Tirri (2004) who

cannot reject the hypothesis that the likelihood of credit tightening for a firm is not influenced by its

participation in an industrial district, this latter being defined according to Mediobanca (2004).

• Region-level Characteristics

(f) Location in Southern regions: The coefficient of this indicator shows a positive sign and a

relatively high magnitude in all regressions. Furthermore, the estimates are rather robust, being

statistically significant at the 1% level in the Weak Rationing and Financial Constraint regressions,

and at the 5% level in the Strong Rationing one. This evidence is consistent with other recent

studies [Becchetti (2005), Guelpa and Tirri (2005), Guiso et al. (2004), Rotondi (2005)] showing

that firms located in southern regions are more likely to be tightened. One the factors driving this

result may be related to the fact that, as shown by Guiso et al. (2004), southern regions are less

financially developed than others, reporting lower internal growth rates, firm creation and the

number of existing firms is lower, and per capita GDP growth is lower than in more developed

regions.

16 Only in the case of weak rationing, medium-low technology industries report a negative sign of the coefficient, but its magnitude is very small.

13

• Lending Relationships

(g) Number of banks: The parameter of NUMBANC is of positive sign, albeit small in magnitude,

and statistically significant at the 1% level in all regressions. Therefore, firms with stronger

relationships show a lower probability of being credit rationed, and this result is robust to all the

definitions of rationing employed.

• Firm Financial Characteristics

(h) Performance: The parameter of firms’ ROA is of negative sign, very high in magnitude, and

statistically significant at the 1% level in all Financial Constraint, Weak Rationing, and Strong

Rationing regressions. This result indicates that firms with higher profitability are less likely to be

credit rationed, and also confirms the evidence of recent literature in the field [see, for example,

Becchetti et al. (2005)17].

(i) Risk: The parameter of firms’ probability of default (1-year) according to the RiskCalc™ Italy

model is positive and statistically significant at the 1% level in all regressions. It is also, by far, the

highest in magnitude among all the regressors, indicating that riskier firms tend to be credit rationed

more than firms with better creditworthiness. Coupled with the previous evidence on profitability,

this result confirms that more financially sound firms have a lower probability of being credit

tightened. This result confirms the ability of banks in screening borrowers over the period under

examination, showing banks’ rational behaviour to the borrowers’ changing creditworthiness18.

• Year Dummies

After controlling for all the above factors, we now evaluate the pattern of credit rationing over the

three surveys, spanning over the years 1995-2003. In our estimates the base year is 1997,

17 Becchetti et al. (2005) use ROI, ROS, and ROE as measures of firm’s performance. 18 We notice that, compared to other existing studies referred to the Italian market [Becchetti et al. (2005), Guelpa, Tirri (2004)], our evidence shows a stronger role of the risk rating variable as a determinant of credit rationing. In our view, apart from the different model specifications, this may be due to the use in our analysis of the RiskCalc™ Italy model which seems to perform better than other existing models not calibrated to the Italian market [such as, for example, the Z-score developed by Altman (1968)] in assessing the firm’s probability of default.

14

corresponding to the first survey. As emerges from Table 4, the coefficient on YEAR_00 and

YEAR_03 shows a different behaviour according to various model specifications. Particularly:

• For the Weak Rationing regression, we see a statistically significant increase in credit rationing

in 2000 (YEAR_00 is positive), while this increase is very modest and not significant at the

conventional statistical level in 2003 (YEAR_03 has a very high standard error).

• For the Financial Constraint regression, the coefficients on both dummies YEAR_00 and

YEAR_03 are positive and statistically significant at the 1% level. Furthermore, the coefficient

of YEAR_03 is larger than that of YEAR_00. Given the two year dummies are positive and

significant, this means that -- holding all the controlling factors fixed -- firms show an increase

in the probability of being rationed in the years 2000 and 2003 compared to 1997. However, this

finding should not come as a surprise. In fact, this variation in rationing may be partly due to the

worsening of macroeconomic conditions during that time, a cycle factor we did not control to

avoid the overlapping with the year dummies. Particularly, during years 2002 and 2003 the

business cycle hit a slowdown phase, with a yearly growth rate of GDP equal to only 0.1%

compared to a yearly growth rate of 2.6% in 2000 and 3.9% in 199719.

• For the Strong Rationing regression, the coefficients on YEAR_00 is positive and significant at

the 1% level, while the coefficient of YEAR_03 is positive and smaller than that of YEAR_00,

and its significance is at the 5% level.

We conclude by noticing that the effect of the year dummies (even though they are almost always

positive and statistically significant) on the probability of being rationed is relatively lower than

that of other explanatory variables (such as, for example, RISK and ROA)20.

We now turn to discuss the result of an event study analysis to test the potential effects of Basel 2

on credit rationing.

5. Will Basel 2 induce credit rationing?

The Basel 2 process began in June 1999 when the Basel Committee on Banking Supervision

(1999) issued a consultative document proposing the new risk-based capital requirements according

19 Source: ISTAT, Italy Real GDP (year on year, seasonally adjusted).

15

to risk ratings (external or internal, depending on the banks’ know how on this issue). The first

technical document describing in details the new regulatory formulas for bank capital was released

in January 2001 [Basel Committee on Banking Supervision (2001)], and after that there was a large

discussion between the regulatory bodies and the banking industry, where the Basel Committee

released a revised version of the 2001 document [Basel Committee on Banking Supervision (2003)]

and conducted also three quantitative impact studies (so-called QIS) related to these proposals.

Finally, the Basel Committee issued the final proposals of the new Basel 2 regulatory framework in

June 2004 [Basel Committee on Banking Supervision (2004)]. The Accord will come into effect in

its entirety at the end of 200721, a date which has been postponed various times since the original

proposal by the Basel Committee just to take into account all comments from the industry.

However, even if not yet implemented, we believe that, according to the evolution of the above

reform process, Basel 2 started to exert its effects on the financial system since the year 2002. Our

assumption is also driven by the fact that, during that time, there were clear signs of compliance

towards Basel 2 rules in banks’ business plans of major Italian banks22. Therefore, it is worth trying

to assess the potential effect of Basel 2 on credit rationing exploiting our unique database on Italian

firms23.

Our main hypotheses are the following:

• Will the introduction of Basel 2 risk ratings increase credit rationing for Italian firms?

• To what extent small and medium enterprises will be affected by this regulatory change?

To test these effects we create two sub-group of firms, one including firms from the first two

surveys (1995-2000) -- which we call Pre-Basel 2 group -- and the other including firms from the

third survey (2001-2003), where this latter group should incorporate the effects of Basel 2. We then

run separate estimates for each sub-group of firms. Finally, in order to determine whether observed

20 In the logit model, the ratio between coefficients is a measure of the relative magnitude of the marginal effect for two variables [see Long (1997) for details]. 21 The Basel Committee considers this implementation date for the most advanced approaches, while year-end 2006 will be the starting date of Basel 2 for the basic approaches. 22 See, for example, the Business Plan 2003-2005 of Capitalia (downloadable at www.capitalia.it) -- the fourth largest bank in Italy --, presented to the financial community in October 2002. 23 Sironi and Zazzara (2003) indicated a potential risk of credit rationing in Italy according to the 2001 proposals of the Basel Committee. In that version, capital requirement for small, medium and large companies did not vary by turnover thresholds.

16

differences in the two sets of coefficients are significant, the following regression is estimated with

observations where the separate samples are pooled:

µββ

ββββ

ββββββ

ββββββ

ββββββ

ββββββ

ββββ

ββββββ

βββββ

++

+++

+++

+++

+++

++

++++

++++++

++++=

−

DUMMY

DUMMYDUMMY

DUMMYDUMMYDUMMY

DUMMYDUMMYDUMMY

DUMMYDUMMYDUMMY

DUMMYDUMMYDUMMY

pp

*LARGE*RISK

*MEDIUM*RISK*SMALL*RISK

*PERF*NUMBANC*SOUTH

*DIST*IND*IND

*IND*GROUP*EXPORT

*SIZE*AGE*

LARGE*RISKMEDIUM*RISK SMALL*RISKPERF

NUMBANCSOUTHDISTINDINDIND

GROUPEXPORTSIZEAGE1

log

14

1312

11109

83726

1543

210

14131211

1098372615

43210

[8]

where:

DUMMY = binary variable equal to 1 if firm is in the Pre-Basel 2 group and 0 otherwise.

SMALL = firms with turnover less than € 5 millions.

MEDIUM = firms with turnover between € 5 and € 50 millions.

LARGE = firms with turnover greater than € 50 millions.

The above turnover thresholds are those proposed by Basel 2 to differentiate capital requirement

according to size. In general, the new formulas are constructed so that capital requirement is

decreasing in size, based on the assumption that default correlation is lower for smaller firms [for

details, see Basel Committee (2003)].

The coefficients iββ represent the difference between the coefficients from the two samples, and

the statistical significance of these coefficients is a test of whether statistical differences exist.

In the next subsections we discuss the econometric results of the various models of credit rationing

before and after Basel 2, according to our chosen time threshold.

17

5.1. Weak Rationing

The results of estimating the combined model (see Table 5) show a different magnitude and

statistical significance of the coefficients on the explanatory variables. Particularly, for the Basel 2

group, many coefficients are statistically significant (such as, those related to the variables SIZE,

EXPO, SOUTH, NUMBANC, PERF, and RISK*SMALL, RISK*MEDIUM, RISK*LARGE). On

the contrary, the dummy PRE_BASEL 2 is not statistical significant, indicating that, on average, the

Basel 2 period did not affect credit rationing in the weakest form. The important result that emerges

from this estimation is related to the variables PRE_BASEL2*RISK*MEDIUM and

PRE_BASEL2*RISK*LARGE, where their coefficient are very large in magnitude and statistically

significant at the 10% and 5% level respectively. This output shows that before Basel 2, the risk

rating was not a relevant determinant of weak credit rationing for medium and large firms, while

after Basel 2 risk rating is a strongest determinant for this group of firms (the coefficient of the

variable RISK_LARGE is the highest among the explanatory variables).

To sum up, we believe that the definition of weak rationing is too broad in content to qualify the

credit rationing phenomenon, therefore we now turn the attention to the most relevant definitions of

credit rationing.

5.2. Financial Constraint

Under this specification, the dummy PRE_BASEL 2 is of negative sign and its magnitude is

relatively high. However, looking at the standard error of the estimates, the statistical coefficient of

this dummy is not statistically significant at the conventional level. Therefore, this result supports

the hypothesis that in the Basel 2 period there are no clear signs of more credit rationing.

The estimates show that the only statistical difference between the coefficients on the explanatory

variables is that related to lending relationships (NUMBANC), where the coefficient -- albeit small

in magnitude -- is statistical significant at the 5% level. This output indicates that in the Basel 2

period stronger lending relationship is beneficial to the firm, as it faces a lower probability of

tightening. This result is of utmost importance because the stronger the lending relationship, the

higher the firm’s informational transparency for the bank, a factor that prior to Basel 2 did not exert

any influence on credit rationing.

18

Other important variables to observe are those related to the interaction between risk and size.

PRE_BASEL2*RISK*SMALL, PRE_BASEL2*RISK*MEDIUIM, PRE_BASEL2*RISK*LARGE

variables -- albeit not statistically significant -- show relatively high coefficients, indicating that

important shifts are taking place in the Basel 2 period. Particularly, small firms in the pre-Basel 2

period were more rationed according to risk, while medium and large firms showed an opposite

trend. This means that before Basel 2 risk rating was less relevant in defining credit rationing in

terms of size. This result becomes clearer when looking at the estimates for the Basel 2 period

(coefficients of the variables RISK*SMALL, RISK*MEDIUM, RISK*LARGE): risk rating matters

in credit rationing (all coefficients have the expected sign and are statistically significant at the 1%

level), but as a decreasing function of size. That is, for the same increase in risk (probability of

default), the likelihood of being rationed increase with size, being greater for medium and large

firms compared to small firms. This finding indicates the banks are changing attitudes in risk

discipline, just in line with the new Basel 2 rules for calculating capital requirements (the new

formulas, in fact, imply a notable reduction in the risk weights for small-medium enterprises).

Therefore, the main result emerging from this analysis is that riskier large firms are more likely to

be rationed under the Basel 2 environment, both because they are more costly in terms of bank

capital and also because the loss in the case of default is higher for banks compared to smaller

firms.

Finally, we concentrate the attention on the SOUTH variable, where the difference in coefficients

between the two periods is economically large -- even if with a high variability around the expected

values. This result signals that the probability of being rationed for southern firms has undertaken a

downward trend under Basel 2.

5.3. Strong Rationing

Under this definition of credit rationing, the dummy PRE_BASEL 2 is positive, indicating that on

average the Basel 2 period was characterized by lower strong rationing. However, given the

imprecision of the estimate, this result is not statistically significant.

Also in this case, the estimates show that the difference between the coefficients on the explanatory

variable related to lending relationships (NUMBANC) has the expected sign and its coefficient is

19

significant at the 5% level. This output confirms that in the Basel 2 period stronger lending

relationship is beneficial to the firm, as it faces a lower probability of being strongly rationed.

As concern the risk variables, we notice that the coefficients of PRE_BASEL2*RISK*MEDIUM

and PRE_BASEL2*RISK*LARGE are economically large but not statistically significant at the

conventional level. This result indicates that risk rating matters in credit rationing, but as a

decreasing function of size. Particularly, this evidence is in support of the hypothesis that riskier

large firms are more likely to be strongly rationed under the Basel 2 environment, for the same

reasons mentioned in the previous sub-section.

Finally, as concerns the SOUTH variable, from this estimation we cannot reject the hypothesis that

southern firms have the same probability of strong credit rationing before and after Basel 2. In fact,

the coefficient of the interaction variable PRE_BASEL2*SOUTH is small in magnitude and not

statistically significant, albeit its sign is positive (denoting an increase in credit rationing before

Basel 2).

6. Conclusions

The econometric analysis carried out on the Mediocredito/Capitalia Surveys on Italian

Manufacturing firms (SIMFs) over the period 1995-2003 indicates that firms with stronger lending

relationships are less likely of being credit rationed compared to firms engaging in multiple bank

lending. This result confirms the evidence provided by relevant literature [see, for example,

Petersen and Rajan, (1994)] that documents the benefits of lending relationships in terms of better

credit condition and greater availability of credit.

This study also finds that firms financial variables matter in credit rationing, where less profitable

(those with lower ROA) and especially riskier firms (those with higher probability of default,

measured through the RiskCalc™ Italy model developed by Moody’s-KMV) are more likely to be

credit rationed. This result is robust under the various definitions of rationing employed.

Finally, our analysis on the determinants of credit rationing shows that location factors matter in

this context, even if empirical evidence indicates that the probability of being rationed for southern

firms is decreasing under Basel 2.

20

The event study conducted to test the potential effects of Basel 2 reveals that risk rating matters in

credit rationing, but as a decreasing function of size. Therefore, the main result emerging from this

analysis is that riskier large firms are more likely to be rationed under the Basel 2 environment,

both because they are more costly in terms of bank capital and also because the loss in the case of

default is higher for banks compared to smaller firms. Furthermore, as concerns bank-firm relations,

evidence shows that under Basel 2 stronger lending relationship is beneficial to the firm, as it faces

a lower probability of being strongly rationed.

Besides its eventual use to design public policies directed to this sector, this work might help to

grasp a better understanding of the determinants of credit rationing in the Italian manufacturing

sector and of bank behaviour toward this segment of firms.

21

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24

Table 1 -- Descriptive statistics of the Credit Rationing Variable (1995-2003)

WEAK RATIONING

FINANCIAL CONSTRAINT

STRONG RATIONING Survey Data

Yes No Yes No Yes No

Total No. of Firms

Real GDP Growth

Rate (Yearly)

1995-1997 607 (13.67%)

3,834 (86.33%)

148 (3.33%)

4,293 (96.67%)

75 (1.69%)

4,366 (98.31%)

4,441 (100%) 3.90%

1998-2000 907 (19.84%)

3,665 (80.16%)

213 (4.66%)

4,359 (95.34%)

104 (2.27%)

4,468 (97.73%)

4,572 (100%) 2.60%

2001-2003 596 (14.57%)

3,496 (85.43%)

233 (5.69%)

3,859 (94.31%)

93 (2.27%)

3,999 (97.73%)

4,092 (100%) 0.10%

Overall Period (1995-2003)

2,110 (16.10%)

10,995 (83.90%)

594 (4.53%)

12,511 (95.47%)

272 (2.08%)

12,833 (97.92%)

13,105 (100%) 1.49%

Source: Our elaborations on data from Capitalia’s Surveys of Italian Manufacturing Firms (SIMFs) and ISTAT. Note: For each survey, the yearly growth rate of GDP (seasonally adjusted) is referred to the last year of each period. For the overall period, the GDP rate is a simple average of all the yearly growth rates from 1996 to 2003.

25

Table 2 – Definitions and Descriptive statistics: Variables used to identify the Determinants of Credit Rationing (1995-2003)

Name Notation Definition of the variable Mean Standard

Deviation Min Max

Firm Characteristics

Age AGE Age of the Firm since establishment 25.57 18.5 191 1

Size SIZE Number of employees 116.67 383.54 11 12,630

Export EXP Dummy variable = 1 if firm export, else = 0 0.71 0.45 0 1

Group GROUP Dummy variable = 1 if firm belongs to a group, else = 0

0.26 0.44 0 1

Industry Characteristics

Medium-Low Technology IND1

Dummy Variable = 1 if firm belongs to Medium-Low Tech Industry, else = 0

0.26 0.44 0 1

Medium-High Technology IND2

Dummy Variable = 1 if firm belongs to Medium-High Tech Industry, else = 0

0.24 0.42 0 1

High Technology IND3 Dummy Variable = 1 if firm belongs to High Tech Industry, else = 0

0.08 0.27 0 1

Industrial District DIST Dummy variable = 1 if firms belongs to a District, else = 0

0.19 0.39 0 1

Region Charachteristics

Southern Regions SOUTH Dummy Variable = 1 if firm is located in the south, else = 0

0.14 0.35 0 1

Lending Relationships

Number of banks NUMBANC Number of banks with which the firm has a commercial relation

5.69 3.81 1 30

Skewness of Bank Debt SKEW (Banki Debt/Total Bank

Debt) – (1/NUMBANC) 0.19 0.35 0 0.97

Firm Financial Variables Performance PERF Firm’s ROA 0.04 0.06 -0.29 0.30

Risk RISK Firm’s 1-year probability of default (RiskCalc Italy™)

0.01 0.02 0.00 0.24

Source: Our elaborations on Capitalia’s Surveys of Italian Manufacturing Firms (SIMF), Moody’s-KMV RiskCalc™ Italy and AIDA™ databases.

26

Table 3 -- Classification of Industries by Technological level Division Industry OECD Product Classification

15 Food Products and Beverages Low-Technology 16 Tobacco and Products Low-Technology 17 Textiles Low-Technology 18 Wearing Apparel; Dressing and Dyeing of Fur Low-Technology 19 Tanning and Dressing of Leagther; Luggage, Handbags,

Saddelery, Harness and Footwear Low-Technology

20 Wood; Products of Wood and Cork Except Furniture; Articles of Straw and Plaiting Materials Low-Technology

21 Paper and Paper Products Low-Technology 22 Publishing, Printing and Reproduction of Recorded Media Low-Technology 23 Coke, Refined Petroleum Products and Nuclear Fuel Low-Medium Technology 24 Chemicals and Chemical Products Low-Medium Technology 25 Rubber and Plastic Products Low-Medium Technology 26 Other Non-Metallic Mineral Products Medium-High Technology 27 Basic Metals Low-Medium Technology 28 Fabricated Metal Products, Except Machinery and Equipment Low-Medium Technology 29 Machinery and Equipment N.E.C. Medium-High Technology 30 Office, Accounting and Computing Machinery High Technology 31 Electrical Machinery and Apparatus N.E.C. High Technology 32 Radio, Television and Communication Equipment and

Apparatus High Technology

33 Medical, Precision and Optical Instruments, Watches & Clocks High Technology

34 Motor Vehicles, Trailers and Semi Trailers Medium-High Technology 35 Other Transport Equipment Medium-High Technology 36 Furniture; Manufacturing N.E.C. Low-Technology 37 Recycling Not Available

Source: Our elaborations based on the industry classification proposed by Hatzichronoglou (1997). Note: Industry sectors reported above are those considered in the present study.

27

Table 4 -- Maximum Likelihood Estimates of the Determinants of Credit Rationing (Pooled Logit Regressions over the period 1995-2003)

Independ. Variable Weak Rationing Financial Constraint Strong Rationing

Parameter Standard Error Parameter Parameter Parameter Standard

Error AGE 0.0008* 0.0002 0.0008** 0.0003 0.0006 0.0005

SIZE -0.0012* 0.0002 -0.0005 0.0004 -0.0001 0.0003

EXPORT -0.0523 0.0639 0.1021 0.1193 0.2430 0.1824

GROUP -0.1154 0.0776 -0.0832 0.1335 -0.0588 0.1881

IND1 -0.0004 0.0751 0.1759 0.1336 0.2114 0.2001

IND2 0.0602 0.0776 0.0521 0.1442 0.1989 0.2070

IND3 -0.0736 0.1194 -0.0188 0.2197 -0.1145 0.3394

DIST -0.0444 0.0789 0.0074 0.1432 0.1357 0.2042

SOUTH 0.6833* 0.0746 0.6502* 0.1299 0.5071** 0.1987

NUMBANC 0.0199* 0.0069 0.0333* 0.0098* 0.0392* 0.0114

PERF -4.4529* 0.5735 -4.0132* 0.9820 -3.8631* 1.4279

RISK 15.9929* 1.4444 15.5467* 1.7968 14.5383* 2.3692

YEAR_00 0.5070* 0.0708 0.4659* 0.1418 0.6302* 0.2021

YEAR_03 0.0362 0.0778 0.7061* 0.1403 0.4759** 0.2118

Intercept -2.0992* 0.0992 -4.1609* 0.1865 -5.1998* 0.2761

No. of Obs. 10,002 10,002 10,002

Pseudo R2 0.0711 0.0735 0.0654 Likelihood Ratio (LR) 612.078 249.178 117.919

Probability LR 0.000 0.000 0.000

Source: Our elaborations on Capitalia’s Surveys of Italian Manufacturing Firms (SIMF), Moody’s-KMV RiskCalc™ Italy and AIDA™ databases. Note: *, **, *** indicate statistical significance at the 1%, 5%, and 10% respectively.

28

Table 5 -- Potential Effects of Basel 2 on Credit Rationing (Pooled Logit Regressions on combined samples of firms before and after Basel 2)

Regression 1 Regression 2 Regression 3

Weak Rationing Financial Constraint Strong Rationing Independent Variable

Parameter Standard Error Parameter Standard

Error Parameter Standard Error

Intercept -1.9888* 0.1864 -3.4169* 0.2908 -4.9879* 0.5026 AGE 0.0040 0.0029 -0.0039 0.0048 -0.0059 0.0079 SIZE -0.0020* 0.0007 -0.0012 0.0008 -0.0014 0.0013

EXPORT -0.2256*** 0.1254 0.1129 0.2014 0.6472*** 0.3822 GROUP -0.1271 0.1372 -0.3021 0.2105 -0.3542 0.3454

IND1 -0.1885 0.1418 0.0519 0.2104 -0.1566 0.3516 IND2 -0.0341 0.1496 -0.0883 0.2351 -0.1513 0.3751 IND3 -0.2305 0.2477 -0.1180 0.3727 -0.6886 0.7243 DIST -0.0436 0.1486 -0.0209 0.2254 -0.0217 0.3564

SOUTH 0.6120* 0.1435 0.5057** 0.2184 0.4835 0.3601 NUMBANC 0.0411** 0.0166 0.0817* 0.0228 0.1059* 0.0345

PERF -5.4509* 1.0695 -5.0983* 1.5743 -4.6499*** 2.5428 RISK*SMALL 16.3770* 3.3257 13.7582* 3.6478 15.4833* 4.9430

RISK*MEDIUM 21.5983* 4.0264 19.43081* 4.2083 20.3470* 5.4752 RISK*LARGE 22.5747* 7.9267 23.0938* 8.3728 35.4770* 10.2130

DUMMY (Pre-Basel 2 period) 0.1250 0.2118 -0.4974 0.3524 0.2213 0.5751 mmmmmAGE*DUMMYmmmmm -0.0032 0.0030 0.0047 0.0048 0.0066 0.0080

SIZE*DUMMY 0.0012 0.0008 0.0009 0.0009 0.0013 0.0014 EXPORT*DUMMY 0.2363 0.1462 0.0004 0.2526 -0.5203 0.4393 GROUP*DUMMY 0.0450 0.1669 0.3809 0.2741 0.4034 0.4187

IND1*DUMMY 0.2555 0.1675 0.1881 0.2739 0.5107 0.4304 IND2*DUMMY 0.0923 0.1750 0.2018 0.2983 0.4560 0.4519 IND3*DUMMY 0.2254 0.2837 0.1310 0.4677 0.7396 0.8292 DIST*DUMMY -0.0199 0.1757 -0.0100 0.2946 0.1481 0.4388

SOUTH*DUMMY 0.1770 0.1686 0.2825 0.2747 0.0822 0.4357 NUMBANC*DUMMY -0.0288 0.0184 -0.0618** 0.0261 -0.0785** 0.0373

PERF*DUMMY 1.5399 1.2691 1.8069 2.0370 1.8195 3.1030 RISK*SMALL*DUMMY 0.3941 3.8453 2.0300 4.4206 -2.1285 5.9583

RISK*MEDIUM*DUMMY -8.8035*** 4.7735 -5.1703 5.2930 -5.6502 6.7202 RISK*LARGE*DUMMY -32.7185** 14.6620 -21.7485 17.8812 -27.0332 18.0629 Number of Observations 9,882 9,882 9,882

Pseudo R2 0.0692 0.0749 0.0718 Likelihood Ratio (LR) 591.087 251.173 128.052

Probability LR 0.000 0.000 0.000 Source: Our elaborations on Capitalia’s Surveys of Italian Manufacturing Firms (SIMF), Moody’s-KMV RiskCalc™ Italy and AIDA™ databases. Note: *, **, *** indicate statistical significance at the 1%, 5%, and 10% respectively.