We estimate a model of credit risk for portfolios of Small and Medium-sized enterprises,

conditional on being a non-profit or for-profit firms. The estimation is based on a unique dataset on

Italian firms provided by a large commercial bank. We show that the main variables to identify

creditworthiness are different for non-profit and for-profit firms. Traditional balance sheet

information seems to be less crucial for non-profit firms

Luppi, Barbara

Marzo, Massimiliano

Scorcu, Antonello E.

AMS Acta

1       Credit risk and Basel II: Are non-profit firms financially different?     Barbara Luppi*, Massimiliano Marzo†, Antonello E. Scorcu‡            Abstract    We estimate a model of credit risk for portfolios of Small and Medium-sized enterprises, conditional on being a non-profit or for-profit firms. The estimation is based on a unique dataset on Italian firms provided by a large commercial bank. We show that the main variables to identify creditworthiness are different for non-profit and for-profit firms. Traditional balance sheet information seems to be less crucial for non-profit firms.   JEL Classification: G21, G28  Key words: SME finance; Basel II; Retail banking; Non-profit                                                            * Corresponding author: CEFIN and Dipartimento di Economia Politica, Viale Berengario, 22, 44100 Modena. Email address: barbara.luppi@unimore.it † Dipartimento di Scienze Economiche, Piazza Scaravilli, 2 40126 Bologna. Email address: massimiliano.marzo@unibo.it ‡ RCEA and Dipartimento di Scienze Economiche, Strada Maggiore, 45 40125 Bologna. Email address: antonello.scorcu@unibo.it   2   I. Introduction  The New Basel Capital Accord, known as Basel II, introduces capital requirement rules for banks depending on the borrower’s riskiness. Credit ratings will play a crucial role in the future, as shown in Czarnitzki et all. (2007). Each borrower receives a credit score on the basis of bank’s internal credit risk rating system, designed to assess creditworthiness on the basis of objective criteria.   Basel II  makes more urgent the need to establish a sound method to estimate the probability of default (PD), providing both good prediction and explicative capabilities. However, a great heterogeneity characterizes credit risk modelling and no standard and robust model emerges. It is therefore important to assess the role of firm’s specific characteristics in determining credit risk.   The present work investigates whether non-profit firms differ from for-profit firms in terms of firm’s characteristics that play the most significant role in the prediction of PD. Credit risk models for non-profit and for-profit firms might not coincide. It is often claimed that non-profit firms aim at maximising social value and have therefore a different objective function with respect to for-profit organizations. Some of the standard measures of economic performance might not be relevant in the evaluation of non-profit organizations. A standard credit risk model therefore might not capture these specific characteristics and might not assess adequately PD, probably inducing inefficient rationing on the credit market for non-profit firms. Moreover, since 2002 Italian non-profit firms are allowed to issue financial bonds on capital marketsi, the possibility to raise funds on capital markets in plain competition with for profit firms makes the evaluation of riskiness of non profit organizations even more important, given their peculiar characteristics and operating methods.  The structure of the rest of the note is as follows. In section II the methodology is discussed, in section III the data are described. In section IV we present and discuss our results. Conclusions are presented in Section V.    II: Model and Methodology  We estimate credit risk models specific for a retail portfolio of loans granted to non-profit Small and Medium enterprises (SME), and for- profit SMEs. From a methodological perspective, according to the nature of data available and methodological results as in Crouchy, Galai and Mark (2001), a logit regression is applied to the population of firm, conditioning on firm’s type. For each firm in the sample, indexed i=1,…n, the values of a binary response variable Yi and a vector of K covariates ( )iKiii xxxx ,...,, 21=  are assumed to be known. In the logit framework, the response variable is distributed according to a Bernoulli distribution: ( ) ( ) ( )( ) iiiiii xxxXYxp ββ′+′====exp1exp1Pr  where ( )1=iYp denotes the PD of firm i, and the logit regression coincide with the linear predictor: ( )( ) iKKiiii xxxxpxp ββββ +++=′=⎟⎟⎠⎞⎜⎜⎝⎛− ...1ln 110  where 0β  represents the overall intercept and ( )Kβββ ,...1=  a vector of K regression coefficients.  The estimation procedure relies both on firm’s quantitative and qualitative information. For each firm type we implement a stepwise variable selection process, based on a likelihood-ratio test with significance level set at 5%.   We perform a series of robustness tests. Restricted versions of the model are estimated conditional on being a for-profit or a non-profit firm. Three alternative restricted models are estimated including only a subset of explanatory variables: economic performance ratios, liquidity 3  measures and debt structure proxies. We perform likelihood ratio tests on restricted models and we always reject the null hypothesis that the omitted variables in the general model have no impact on PDs. Moreover we reject the hypothesis that models for for-profit and non-profit firms are not significantly different.     The estimated model is robust to alternative specifications (explanatory variables in levels, instead of ratios and probit specification). We check for heteroskedasticity in errors in the probit estimation, with heteroskedastic variance in the form  Var(ɛ)=exp(γ′z)  where z indicates the vector of variables included in the variance specification. We perform the variance heteroskedasticity test under different specifications of z, including all explanatory variables and subsets of them, measuring liquidity or economic performance.   III. Data   The analysis is based on a data base provided by a large Italian commercial bank. The sample consists of about 4,000 firms having annual sales of less than 10 million Euros in the period 2001-02, classified as Small and Medium enterprises according to Basel II. One eighth of the sample is represented by non-profit firmsii.  The data set displays unique featuresiii with respect to all the other studies on Italian firms, as Fabi et al (2005), Bocchi-Lusignani (2004) and Quagliarello (2007). First, the data set is representative of a retail portfolio for a commercial bank; the sample is designed to replicate the distribution of exposures of the bank's overall retail portfolio geographically, by sector of activity and firm's size. The data base contains detailed information on firms, including financial variables coming from balance sheets and other firm specific variables. The dataset specifies variables regarding the quality of the customer-bank relationship, allowing to evaluate credit risk for retail portfolios, within the Basel II framework, using similar data available for the bank internal credit risk evaluation system. However, the default with respect to Basel II definition includes not only loans classified as non performing, substandard and loans past due 90 days, but also delayed payments by the firm, as we observe even firms which are not up to date with payments of bank debts, but are not necessarily going bankruptiv. In the following we use the label “stress” instead of default. Panel A of Table 1 shows the distribution of defaults across non-profit and for-profit firms and Panel B the summary statistics of variables used in the estimation, conditional on firm type.   IV. Results Table 2 presents the results of the estimation conditional on being either a non-profit or a for-profit firm.  Significant differences in the credit risk models emerge. Crucially standard measures of economic performances are not statistically significant in the prediction of PD of non-profit firms. EBIDTA/TA and ROI do not influence PDs for non-profit firms, while they are highly significant and negatively correlated with PDs in the case of for-profit SMEs. An analogous result is obtained for measures of liquidity as CF/TA and for proxy of financial structure as SD/TD.  Only “structural” variables as S/TA and TD/TA have a significant impact on PDs for both non-profit and for-profit firms. The evidence indicates that non-profit firms do have peculiarities that are not captured by standard measures of economic performance. This implies that standard credit risk models that rely only on firm’s profitability, liquidity and financial structure may imperfectly assess PD of a non-profit firm, incurring in the risk of credit rationing. We perform an out-of sample prediction of the model estimated for for-profit firms on the sample of non-profit firms. The model performance in terms of correct classification of sound and unsound firms is reduced by more than 10%, determining a higher average PD for non-profit firms. 4  The firm sector of activity and regional location affect differently PD estimates conditional on juridical structure. Non-profit firms in northern and central Italy are less risky than the ones located in the South. From an historical perspective, the cooperative movement is more deeply rooted in north-central Italy and seems to have a competitive advantage in terms of higher financial stabilityv. On the contrary, for-profit firms located in northern Italy are riskier. Only firms operating in transportation sector are riskier independently of juridical structure, while service sector is riskier for non-profit firms and manufacture in case of for-profit SMEs.  The credit risk models for non-profit and for-profit firms share some common features. PDs are inversely correlated with firm's size, measured in terms of number of workers (NW) and sale volume (S/TA)vi, confirming Basel II’s assumptions.   The dataset includes also a proxy of the quality of the firm-bank relationship, indicating whether the bank does not register any problem in the lending relationship in the year under scrutiny. The variable plays an important role in PD estimation: the better the quality of the relationship between the bank and a specific firm, the lower is firm's PD, especially in quantitative terms for non-profit firms.   An overall performance of the credit risk model is given by the accuracy ratio, equal to 83.89% for for-profit firms and 97.74% for non-profit firms - which is good compared to other studies on Italian firms where it ranges between 50 and 70%.    IV. Conclusions  We provide a comparative analysis of credit risk associated with loans granted either to non-profit or for-profit SMEs. Within the framework of Basel II, we provide striking evidence of the need to tailor the credit risk model in order to capture specific features of firms constituting bank’s retail portfolio. Contrary to evidence for for-profit firms, non-profit firms present special features such that standard measures of profitability, liquidity and debt structure do not play any role in predicting PDs. This suggests also the opportunity to develop models able to capture at best the specific features of this group of firms.  We find also common effects on PDs for non-profit and for-profit firms in terms of structural variables, sales, total debts. Additionally, size matters also within the retail segment. 5   References Bocchi, L., and G. Lusignani (2004) Le nuove Regole di Basilea 2: prime valutazioni di impatto sul Rapporto Banca-Impresa in Italia, Banca, Impresa, Società vol. XXIII (2), 209--237. Cannata, F., F. Fabi, and S. Laviola, (2002) Rating interni e gestione del rischio di credito: la misurazione delle probabilità di insolvenza, Bancaria, 4, 45--62 Crouchy, M., D. Galai and R. Mark (2001) A Comparative Analysis of Current Credit Risk Models, Journal of Banking and Finance, 24, 59--117 Czarnitzki, D. and K. Kraft (2007) Are Credit Ratings valuable information?, Applied Financial Economics, forthcoming. Fabi, F., S. Laviola and P. Marullo Reedtz, (2005) The New Basel Capital Accord and banks' lending decisions, Journal of Financial Stability, 1, 501--521 Laitinen, E. K. (1999) Predicting a corporate credit analyst's risk estimate by logistic and linear models, International Review of Financial Analysis, 8(2), 97--121 Luppi, B., M. Marzo, A. E. Scorcu (2006) A credit risk model for Italian SMEs, mimeo. Quagliarello, M. (2007), Bank’s riskiness over the business cycle: a panel analysis on Italian intermediaries, Applied Financial Economics, 17(2), 119--138 Saurina, J., and C. Trucharte, (2004) The Impact of Basel II on Lending to Small-and-Medium-Sized Firms: A Regulatory Policy Assessment Based on Spanish Credit Register Data, Journal of Finance Services Research, 26, 121—144  6   Table 1. Data Panel A: Structure of the sample Dependent variable: stress  Non-profit For-profit Total 0 271 2,287 3,558 1 18 318 336 Total 289 3,605 3894  Panel B: Summary statistics  Non-profit For-profit  Mean Standard deviation Minimum Maximum Mean Standard  Deviation Min Max BFR 1.7855 0.4112 0 2 1.7587 0.4279 0 2 AGE 2.4049 1.2299 1 5 2.6175 1.2462 1 5 NW 1.1419 0.5809 1 4 1.085 0.4306 1 4 SD/TD 0.79 0.2261 0.0125 1 0.8151 0.2031 0 1 TD/TA 0.9571 0.8358 0.0702 9.8524 0.903 0.6047 0 7.3333 NSF/ TA -0.0018       0.0933 -4.3634 0.875 0.0033 0.370 -13.9565 3.5806 NSF_P 0.0316      0.1749 0 1 0.4528    0.4978 0 1 CF/TA -.0209 0.6393 -9.2 0.6154 0.031 0.3563 -13.9565 3.8548 S/TA 0.2221         1.4640 0 46.153 1.9168    2.2406 0 62.5 XS 0.0239     0.1527 0 1 0.2313   0.4217 0 1 S 0.0149     0.1211 0 1 0.1740    0.3792 0 1 EBIDTA/ TA -0.024 0.3422 -4.3634 0.875 0.0887 0.4038 -13.587 6.1396 ROI -0.154 0.2150 -91.54 50.75 -0.123 0.2567 -45.16 48.15   BFR is 0 if no quality lending relationship, 1 if high quality for one year and 2 for at least two years. AGE: firm’s months of activity (5 classes: <23 months; 24-71; 72-143;144-288; >288). NW: firm’s workers (4 classes: 0-3 workers; 4-10;11-20; >20) SD/TD : short-term debt on total debt. TD/TA: total debt on total asset value. NSF/TA: net self financing flow on TA. CF/TA: cash flows on TA. S/TA: annual sale volume on TA. XS and S are dummies with value 1 in case of firm’s annual sale respectively less than 250.000 and 500,000 euro. EBIDTA/TA is EBIDTA on TA. North and South are regional dummies. Transport, Service and Manufacture are sectorial dummies. 7  Table 2. Logit estimation results  Dependent variable: stress                 Non-profit For-profit  Coefficient (standard error) Elasticity (standard error) Coefficient (standard error) Elasticity (standard error) BFR -4.474*** (1.349) -7.988 (2.410) -2.379*** (0.141) -4.001 (0.248) AGE         0.607* (0.391)         1.460 (0.940)         0.458*** (0.057)         1.138 (0.143) NW       -1.427 * (0.930)       1.630 (1.062)       -0.479 *** (0.198)       -0.502 (0.206) SD/TD     -3.344* (2.057)     -2.641 (1.625)     1.165*** (0.363)     0.891 (0.283) TD/TA      5.463*** (1.553)      5.228 (1.487)      0.357*** (0.104)     0.298 (0.088) NSF/ TA    8.929 *** (3.355)    -0.218 (0.082)    1.304 *** (0.359)    0.004 (0.001) NSF_P -4.072*** (1.674) -1.733 (0.712) -0.344*** (0.144) -0.168 (0.067) CF/TA -0.134 (0.634) 0.003 (-0.013) -0.883** (0.405) -0.027 (0.012) S/TA  -1.597*** (0.491) -4.782 (1.471) -0.178*** (0.047) -0.354 (0.092) XS 4.022 *** (1.494) 1.049 (0.362) 0.011  (0.171) -0.008 (0.041) S 5.226*** (1.804) -0.03 (0.674) 0.097 (0.179) -0.015 (0.032) EBITDA/ TA -0.279 (1.731) -0.004 (0.028) -0.439** (0.218) -0.035 (0.018) ROI -0.00005  (0.001)  -0.716  (0.309)  -0.001*** (0.0002)  -0.305  (0.119)  North -2.687 ** (1.162) 0.716 (0.309) -0.191 (0.152) -0.054 (0.043) South 4.950*** (1.614)  0.704 (0.229)  -0.024 (0.203)  -0.017 (0.024)  Transport 7.810** (2.331) 0.919 (0.274) 0.224** (0.287) 0.013 (0.013) Services 2.697 *** (1.355) 1.185 (0.596)-.0136    (0.198) -0.003 (0.041)Manufacture 2.084 *** (1.588) 0.274 (0.209) 0.488 *** (0.156) 0.135 (0.047) Constant -6.497** (3.298)  -0.022** (0.507)  Log-likelihood -26.1191 -808.0937 Pseudo R2 61.25% 24.84% Number of observations 389 3599 Area under ROC 97.74% 83.89% Notes:  *** indicates significance at 1% level,  **  at 5%  * at 10%. 8                                                   i Bond issues by non-profit firms are allowed under conditions stated in Deliberazione of 3/5/1999 of CICR published in 8/7/1999. ii We limit our attention to limited liability non-profit firms called “società cooperativa a responsabilità limitata” (SCRL). iii Most of existing studies draw samples from national registers without direct reference to banks' portfolios. iv Because of the default definition, estimated PD can be interpreted as an early warning to the bank in order to detect future situations of stress in loan repayments. There are no reasons to expect the existence of differences between non-profit and for-profit firm w.r.t. this feature. See Luppi et all. (2006). v A share of non distributed earnings of the non-profit firms is compulsory allocated as reserves. vi Saurina and Trucharte (2004) provide similar evidence for Spanish economy. 

Credit risk and Basel II: Are non-profit firms financially different?

AMS Acta - Alm@DL - Università di Bologna

1 
 
 
 
 
 
 
Credit risk and Basel II: 
Are non-profit firms financially different?  
 
 
 
Barbara Luppi*, Massimiliano Marzo†, Antonello E. Scorcu‡ 
  
 
 
 
 
 
 
 
 
 
Abstract 
 
 
 We estimate a model of credit risk for portfolios of Small and Medium-sized enterprises, 
conditional on being a non-profit or for-profit firms. The estimation is based on a unique dataset on 
Italian firms provided by a large commercial bank. We show that the main variables to identify 
creditworthiness are different for non-profit and for-profit firms. Traditional balance sheet 
information seems to be less crucial for non-profit firms.  
 
JEL Classification: G21, G28 
 
Key words: SME finance; Basel II; Retail banking; Non-profit 
 
 
 
 
 
 
 
 
 
 
                                                 
* Corresponding author: CEFIN and Dipartimento di Economia Politica, Viale Berengario, 22, 44100 Modena. Email 
address: barbara.luppi@unimore.it 
† Dipartimento di Scienze Economiche, Piazza Scaravilli, 2 40126 Bologna. Email address: 
massimiliano.marzo@unibo.it 
‡ RCEA and Dipartimento di Scienze Economiche, Strada Maggiore, 45 40125 Bologna. Email address: 
antonello.scorcu@unibo.it 
 
 
2 
 
 
I. Introduction 
 The New Basel Capital Accord, known as Basel II, introduces capital requirement rules for 
banks depending on the borrower’s riskiness. Credit ratings will play a crucial role in the future, as 
shown in Czarnitzki et all. (2007). Each borrower receives a credit score on the basis of bank’s 
internal credit risk rating system, designed to assess creditworthiness on the basis of objective 
criteria.  
 Basel II  makes more urgent the need to establish a sound method to estimate the probability 
of default (PD), providing both good prediction and explicative capabilities. However, a great 
heterogeneity characterizes credit risk modelling and no standard and robust model emerges. It is 
therefore important to assess the role of firm’s specific characteristics in determining credit risk.  
 The present work investigates whether non-profit firms differ from for-profit firms in terms 
of firm’s characteristics that play the most significant role in the prediction of PD. Credit risk 
models for non-profit and for-profit firms might not coincide. It is often claimed that non-profit 
firms aim at maximising social value and have therefore a different objective function with respect 
to for-profit organizations. Some of the standard measures of economic performance might not be 
relevant in the evaluation of non-profit organizations. A standard credit risk model therefore might 
not capture these specific characteristics and might not assess adequately PD, probably inducing 
inefficient rationing on the credit market for non-profit firms. 
Moreover, since 2002 Italian non-profit firms are allowed to issue financial bonds on capital 
marketsi, the possibility to raise funds on capital markets in plain competition with for profit firms 
makes the evaluation of riskiness of non profit organizations even more important, given their 
peculiar characteristics and operating methods. 
 The structure of the rest of the note is as follows. In section II the methodology is discussed, 
in section III the data are described. In section IV we present and discuss our results. Conclusions 
are presented in Section V.  
 
 
II: Model and Methodology  
We estimate credit risk models specific for a retail portfolio of loans granted to non-profit 
Small and Medium enterprises (SME), and for- profit SMEs. 
From a methodological perspective, according to the nature of data available and 
methodological results as in Crouchy, Galai and Mark (2001), a logit regression is applied to the 
population of firm, conditioning on firm’s type. For each firm in the sample, indexed i=1,…n, the 
values of a binary response variable Yi and a vector of K covariates ( )iKiii xxxx ,...,, 21=  are 
assumed to be known. In the logit framework, the response variable is distributed according to a 
Bernoulli distribution: 
( ) ( ) ( )( ) iiiiii x
xxXYxp β
β
′+
′====
exp1
exp
1Pr  
where ( )1=iYp denotes the PD of firm i, and the logit regression coincide with the linear predictor: 
( )
( ) iKKiii
i xxx
xp
xp ββββ +++=′=⎟⎟⎠
⎞
⎜⎜⎝
⎛
− ...1ln 110  
where 0β  represents the overall intercept and ( )Kβββ ,...1=  a vector of K regression coefficients. 
 The estimation procedure relies both on firm’s quantitative and qualitative information. For 
each firm type we implement a stepwise variable selection process, based on a likelihood-ratio test 
with significance level set at 5%.  
 We perform a series of robustness tests. Restricted versions of the model are estimated 
conditional on being a for-profit or a non-profit firm. Three alternative restricted models are 
estimated including only a subset of explanatory variables: economic performance ratios, liquidity 
3 
 
measures and debt structure proxies. We perform likelihood ratio tests on restricted models and we 
always reject the null hypothesis that the omitted variables in the general model have no impact on 
PDs. Moreover we reject the hypothesis that models for for-profit and non-profit firms are not 
significantly different. 
    The estimated model is robust to alternative specifications (explanatory variables in levels, 
instead of ratios and probit specification). We check for heteroskedasticity in errors in the probit 
estimation, with heteroskedastic variance in the form  
Var(ɛ)=exp(γ′z) 
 where z indicates the vector of variables included in the variance specification. We perform the 
variance heteroskedasticity test under different specifications of z, including all explanatory 
variables and subsets of them, measuring liquidity or economic performance. 
 
 
III. Data  
 The analysis is based on a data base provided by a large Italian commercial bank. The 
sample consists of about 4,000 firms having annual sales of less than 10 million Euros in the period 
2001-02, classified as Small and Medium enterprises according to Basel II. One eighth of the 
sample is represented by non-profit firmsii.  
The data set displays unique featuresiii with respect to all the other studies on Italian firms, 
as Fabi et al (2005), Bocchi-Lusignani (2004) and Quagliarello (2007). First, the data set is 
representative of a retail portfolio for a commercial bank; the sample is designed to replicate the 
distribution of exposures of the bank's overall retail portfolio geographically, by sector of activity 
and firm's size. The data base contains detailed information on firms, including financial variables 
coming from balance sheets and other firm specific variables. The dataset specifies variables 
regarding the quality of the customer-bank relationship, allowing to evaluate credit risk for retail 
portfolios, within the Basel II framework, using similar data available for the bank internal credit 
risk evaluation system. 
However, the default with respect to Basel II definition includes not only loans classified as 
non performing, substandard and loans past due 90 days, but also delayed payments by the firm, as 
we observe even firms which are not up to date with payments of bank debts, but are not necessarily 
going bankruptiv. In the following we use the label “stress” instead of default. 
Panel A of Table 1 shows the distribution of defaults across non-profit and for-profit firms 
and Panel B the summary statistics of variables used in the estimation, conditional on firm type. 
 
 
IV. Results 
Table 2 presents the results of the estimation conditional on being either a non-profit or a 
for-profit firm.  
Significant differences in the credit risk models emerge. Crucially standard measures of 
economic performances are not statistically significant in the prediction of PD of non-profit firms. 
EBIDTA/TA and ROI do not influence PDs for non-profit firms, while they are highly significant 
and negatively correlated with PDs in the case of for-profit SMEs. An analogous result is obtained 
for measures of liquidity as CF/TA and for proxy of financial structure as SD/TD.  
Only “structural” variables as S/TA and TD/TA have a significant impact on PDs for both 
non-profit and for-profit firms. The evidence indicates that non-profit firms do have peculiarities 
that are not captured by standard measures of economic performance. This implies that standard 
credit risk models that rely only on firm’s profitability, liquidity and financial structure may 
imperfectly assess PD of a non-profit firm, incurring in the risk of credit rationing. We perform an 
out-of sample prediction of the model estimated for for-profit firms on the sample of non-profit 
firms. The model performance in terms of correct classification of sound and unsound firms is 
reduced by more than 10%, determining a higher average PD for non-profit firms. 
4 
 
The firm sector of activity and regional location affect differently PD estimates conditional 
on juridical structure. Non-profit firms in northern and central Italy are less risky than the ones 
located in the South. From an historical perspective, the cooperative movement is more deeply 
rooted in north-central Italy and seems to have a competitive advantage in terms of higher financial 
stabilityv. On the contrary, for-profit firms located in northern Italy are riskier. Only firms operating 
in transportation sector are riskier independently of juridical structure, while service sector is riskier 
for non-profit firms and manufacture in case of for-profit SMEs. 
 The credit risk models for non-profit and for-profit firms share some common features. PDs 
are inversely correlated with firm's size, measured in terms of number of workers (NW) and sale 
volume (S/TA)vi, confirming Basel II’s assumptions.  
 The dataset includes also a proxy of the quality of the firm-bank relationship, indicating 
whether the bank does not register any problem in the lending relationship in the year under 
scrutiny. The variable plays an important role in PD estimation: the better the quality of the 
relationship between the bank and a specific firm, the lower is firm's PD, especially in quantitative 
terms for non-profit firms.  
 An overall performance of the credit risk model is given by the accuracy ratio, equal to 
83.89% for for-profit firms and 97.74% for non-profit firms - which is good compared to other 
studies on Italian firms where it ranges between 50 and 70%.  
 
 
IV. Conclusions 
 We provide a comparative analysis of credit risk associated with loans granted either to non-
profit or for-profit SMEs. Within the framework of Basel II, we provide striking evidence of the 
need to tailor the credit risk model in order to capture specific features of firms constituting bank’s 
retail portfolio. Contrary to evidence for for-profit firms, non-profit firms present special features 
such that standard measures of profitability, liquidity and debt structure do not play any role in 
predicting PDs. This suggests also the opportunity to develop models able to capture at best the 
specific features of this group of firms. 
 We find also common effects on PDs for non-profit and for-profit firms in terms of 
structural variables, sales, total debts. Additionally, size matters also within the retail segment. 
5 
 
 
References 
Bocchi, L., and G. Lusignani (2004) Le nuove Regole di Basilea 2: prime valutazioni di impatto sul 
Rapporto Banca-Impresa in Italia, Banca, Impresa, Società vol. XXIII (2), 209--237. 
Cannata, F., F. Fabi, and S. Laviola, (2002) Rating interni e gestione del rischio di credito: la 
misurazione delle probabilità di insolvenza, Bancaria, 4, 45--62 
Crouchy, M., D. Galai and R. Mark (2001) A Comparative Analysis of Current Credit Risk Models, 
Journal of Banking and Finance, 24, 59--117 
Czarnitzki, D. and K. Kraft (2007) Are Credit Ratings valuable information?, Applied Financial 
Economics, forthcoming. 
Fabi, F., S. Laviola and P. Marullo Reedtz, (2005) The New Basel Capital Accord and banks' 
lending decisions, Journal of Financial Stability, 1, 501--521 
Laitinen, E. K. (1999) Predicting a corporate credit analyst's risk estimate by logistic and linear 
models, International Review of Financial Analysis, 8(2), 97--121 
Luppi, B., M. Marzo, A. E. Scorcu (2006) A credit risk model for Italian SMEs, mimeo. 
Quagliarello, M. (2007), Bank’s riskiness over the business cycle: a panel analysis on Italian 
intermediaries, Applied Financial Economics, 17(2), 119--138 
Saurina, J., and C. Trucharte, (2004) The Impact of Basel II on Lending to Small-and-Medium-
Sized Firms: A Regulatory Policy Assessment Based on Spanish Credit Register Data, Journal of 
Finance Services Research, 26, 121—144 
 
6 
 
 
Table 1. Data 
Panel A: Structure of the sample 
Dependent 
variable: stress  
Non-profit For-profit Total 
0 271 2,287 3,558 
1 18 318 336 
Total 289 3,605 3894 
 
Panel B: Summary statistics 
 Non-profit For-profit 
 Mean Standard 
deviation 
Minimu
m 
Maxim
um 
Mean Standard  
Deviation 
Min Max 
BFR 1.7855 0.4112 0 2 1.7587 0.4279 0 2 
AGE 2.4049 1.2299 1 5 2.6175 1.2462 1 5 
NW 1.1419 0.5809 1 4 1.085 0.4306 1 4 
SD/TD 0.79 0.2261 0.0125 1 0.8151 0.2031 0 1 
TD/TA 0.9571 0.8358 0.0702 9.8524 0.903 0.6047 0 7.3333 
NSF/ TA -0.0018       0.0933 -4.3634 0.875 0.0033 0.370 -13.9565 3.5806 
NSF_P 0.0316      0.1749 0 1 0.4528    0.4978 0 1 
CF/TA -.0209 0.6393 -9.2 0.6154 0.031 0.3563 -13.9565 3.8548 
S/TA 0.2221         1.4640 0 46.153 1.9168    2.2406 0 62.5 
XS 0.0239     0.1527 0 1 0.2313   0.4217 0 1 
S 0.0149     0.1211 0 1 0.1740    0.3792 0 1 
EBIDTA/ 
TA 
-0.024 0.3422 -4.3634 0.875 0.0887 0.4038 -13.587 6.1396 
ROI -0.154 0.2150 -91.54 50.75 -0.123 0.2567 -45.16 48.15 
 
 BFR is 0 if no quality lending relationship, 1 if high quality for one year and 2 for at least two 
years. 
AGE: firm’s months of activity (5 classes: <23 months; 24-71; 72-143;144-288; >288). 
NW: firm’s workers (4 classes: 0-3 workers; 4-10;11-20; >20) 
SD/TD : short-term debt on total debt. TD/TA: total debt on total asset value. 
NSF/TA: net self financing flow on TA. CF/TA: cash flows on TA. S/TA: annual sale volume on 
TA. XS and S are dummies with value 1 in case of firm’s annual sale respectively less than 250.000 
and 500,000 euro. 
EBIDTA/TA is EBIDTA on TA. 
North and South are regional dummies. Transport, Service and Manufacture are sectorial dummies. 
7 
 
Table 2. Logit estimation results  
Dependent variable: stress                 Non-profit For-profit 
 Coefficient 
(standard 
error) 
Elasticity 
(standard error) 
Coefficient 
(standard 
error) 
Elasticity 
(standard 
error) 
BFR -4.474*** 
(1.349) 
-7.988 
(2.410) 
-2.379*** 
(0.141) 
-4.001 
(0.248) 
AGE         0.607* 
(0.391) 
        1.460 
(0.940) 
        0.458*** 
(0.057) 
        1.138 
(0.143) 
NW       -1.427 * 
(0.930) 
      1.630 
(1.062) 
      -0.479 *** 
(0.198) 
      -0.502 
(0.206) 
SD/TD     -3.344* 
(2.057) 
    -2.641 
(1.625) 
    1.165*** 
(0.363) 
    0.891 
(0.283) 
TD/TA      5.463*** 
(1.553) 
     5.228 
(1.487) 
     0.357*** 
(0.104) 
    0.298 
(0.088) 
NSF/ TA    8.929 *** 
(3.355) 
   -0.218 
(0.082) 
   1.304 *** 
(0.359) 
   0.004 
(0.001) 
NSF_P -4.072*** 
(1.674) 
-1.733 
(0.712) 
-0.344*** 
(0.144) 
-0.168 
(0.067) 
CF/TA -0.134 
(0.634) 
0.003 
(-0.013) 
-0.883** 
(0.405) 
-0.027 
(0.012) 
S/TA  -1.597*** 
(0.491) 
-4.782 
(1.471) 
-0.178*** 
(0.047) 
-0.354 
(0.092) 
XS 4.022 *** 
(1.494) 
1.049 
(0.362) 
0.011  
(0.171) 
-0.008 
(0.041) 
S 5.226*** 
(1.804) 
-0.03 
(0.674) 
0.097 
(0.179) 
-0.015 
(0.032) 
EBITDA/ TA -0.279 
(1.731) 
-0.004 
(0.028) 
-0.439** 
(0.218) 
-0.035 
(0.018) 
ROI -0.00005 
 (0.001) 
 
-0.716 
 (0.309) 
 
-0.001*** 
(0.0002) 
 
-0.305 
 (0.119) 
 
North -2.687 ** 
(1.162) 
0.716 
(0.309) 
-0.191 
(0.152) 
-0.054 
(0.043) 
South 4.950*** 
(1.614) 
 
0.704 
(0.229) 
 
-0.024 
(0.203) 
 
-0.017 
(0.024) 
 
Transport 7.810** 
(2.331) 
0.919 
(0.274) 
0.224** 
(0.287) 
0.013 
(0.013) 
Services 2.697 *** 
(1.355) 
1.185 
(0.596)
-.0136 
   (0.198) 
-0.003 
(0.041)
Manufacture 2.084 *** 
(1.588) 
0.274 
(0.209) 
0.488 *** 
(0.156) 
0.135 
(0.047) 
Constant -6.497** 
(3.298) 
 -0.022** 
(0.507) 
 
Log-likelihood -26.1191 -808.0937 
Pseudo R2 61.25% 24.84% 
Number of observations 389 3599 
Area under ROC 97.74% 83.89% 
Notes:  
*** indicates significance at 1% level,  
**  at 5%  
* at 10%. 
8 
 
                                                 
i Bond issues by non-profit firms are allowed under conditions stated in Deliberazione of 3/5/1999 of CICR published in 
8/7/1999. 
ii We limit our attention to limited liability non-profit firms called “società cooperativa a responsabilità limitata” 
(SCRL). 
iii Most of existing studies draw samples from national registers without direct reference to banks' portfolios. 
iv Because of the default definition, estimated PD can be interpreted as an early warning to the bank in order to detect 
future situations of stress in loan repayments. There are no reasons to expect the existence of differences between non-
profit and for-profit firm w.r.t. this feature. See Luppi et all. (2006). 
v A share of non distributed earnings of the non-profit firms is compulsory allocated as reserves. 
vi Saurina and Trucharte (2004) provide similar evidence for Spanish economy. 


We estimate a model of credit risk for portfolios of small and
medium-sized enterprises, conditional on being a nonprofit (NP) or
for-profit (FP) firms. The estimation is based on a unique data set on
Italian firms provided by a large commercial bank. We show that the main
variables to identify creditworthiness are different for NP and FP firms.
Traditional balance sheet information seems to be less crucial for NP firms

Credit risk and Basel II: Are non-profit firms financially different?

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