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 andcrucial for non-profit firms. Classification-JEL: G21, G28SME finance; Basel II; Retail banking; Non-profit

Antonello E. Scorcu

Barbara Luppi

Massimiliano Marzo

English

Research Papers in Economics

The Rimini Centre for Economic Analysis
Legal address: Via Angherà, 22 – Head office: Via Patara, 3 - 47900 Rimini (RN) – Italy
www.rcfea.org -  secretary@rcfea.org
WP 30-07
BARBARA LUPPI
CEFIN
and
University of Modena and Reggio Emilia, Italy
MASSIMILIANO MANZO
University of Bologna, Italy
and
Rimini Centre for Economic Analysis, Italy
ANTONELLO E. SCORCU
University of Bologna, Italy
and
Rimini Centre for Economic Analysis, Italy
“CREDIT RISK AND BASEL II:
ARE NON-PROFIT FIRMS FINANCIALLY
DIFFERENT?”
Copyright belongs to the author. Small sections of the text, not exceeding three
paragraphs, can be used provided proper acknowledgement is given.
The Rimini Centre for Economic Analysis (RCEA) was established in March 2007.
RCEA is a private, non-profit organization dedicated to independent research in
Applied and Theoretical Economics and related fields. RCEA organizes seminars and
workshops, sponsors a general interest journal The Review of Economic Analysis, and
organizes a biennial conference: Small Open Economies in the Globalized World
(SOEGW). Scientific work contributed by the RCEA Scholars is published in the
RCEA Working Papers series.
The views expressed in this paper are those of the authors. No responsibility for them
should be attributed to the Rimini Centre for Economic Analysis.The Rimini Centre for Economic Analysis
Legal address: Via Angherà, 22 – Head office: Via Patara, 3 - 47900 Rimini (RN) – Italy
www.rcfea.org -  secretary@rcfea.org
WP 30-07
BARBARA LUPPI
CEFIN
and
University of Modena and Reggio Emilia, Italy
MASSIMILIANO MAZO
University of Bologna, Italy
and
Rimini Centre for Economic Analysis, Italy
ANTONELLO E. SCORCU
University of Bologna, Italy
and
Rimini Centre for Economic Analysis, Italy
Credit risk and Basel II:
Are non-profit firms financially different?
Copyright belongs to the author. Small sections of the text, not exceeding three
paragraphs, can be used provided proper acknowledgement is given.
The Rimini Centre for Economic Analysis (RCEA) was established in March 2007.
RCEA is a private, non-profit organization dedicated to independent research in
Applied and Theoretical Economics and related fields. RCEA organizes seminars and
workshops, sponsors a general interest journal The Review of Economic Analysis, and
organizes a biennial conference: Small Open Economies in the Globalized World
(SOEGW). Scientific work contributed by the RCEA Scholars is published in the
RCEA Working Papers series.
The views expressed in this paper are those of the authors. No responsibility for them
should be attributed to the Rimini Centre for Economic Analysis.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
Barbara  Luppi:  CEFIN  and  Dipartimento  di  Economia  Politica,  Viale  Berengario,  22,  I-44100  Modena,
barbara.luppi@unimore.it
Massimiliano  Marzo:  RCEA  and  Dipartimento  di  Scienze  Economiche,  Piazza  Scaravilli,  2  I-40126  Bologna,
massimiliano.marzo@unibo.it
Antonello  E.  Scorcu:  RCEA  and  Dipartimento  di  Scienze  Economiche,  Strada  Maggiore,  45  I-40125  Bologna,
antonello.scorcu@unibo.itI.  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 al. (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 models emerge. It is therefore important
to assess the role of firm’s specific characteristics in determining credit risk.
The present work investigates whether non-profit (NP) and for-profit (FP) firms differ in terms of
characteristics that play a significant role in the prediction of PD. Credit risk models for NP and FP
firms might not coincide. It is often claimed that NP firms aim at maximising social value and have
therefore a different objective function with respect to FP organizations. Some of the standard
measures of economic performance might not be relevant in the evaluation of NP organizations. The
standard credit risk models developed for FP firms might not capture these specific characteristics and
not assess adequately PD, probably inducing inefficient credit rationing for NP firms.
Moreover, since 2002 Italian NP firms are allowed to issue financial bonds on capital markets
1, the
possibility to raise funds on capital markets in plain competition with FP 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 NP Small and Medium
enterprises (SME), and FP SMEs.
According to the nature of data available and the methodological results in Crouchy, Galai and Mark
(2001), a logit regression is applied to the population of firm, conditioning on firm’s type (for-profit
and non-profit). For each firm, indexed i=1,…n, the values of a binary response variable Yi and a
                                                
1
 Bond issues by non-profit firms are allowed under conditions stated in Deliberazione of 3/5/1999 of CICR published in
8/7/1999.vector of K covariates  ( ) iK i i i x x x x ,..., , 2 1 =  are known. In the logit framework, the response variable is
distributed according to a Bernoulli distribution:
( ) ( ) ( )
( )i i
i
i i i x
x
x X Y x p


 +

= = = =
exp 1
exp
1 Pr
where ( ) 1 = i Y p denotes the PD of firm i, and the logit regression coincide with the linear predictor:
( )
( )
iK K i i
i
i x x x
x p
x p
    + + + =  =  


 



...
1
ln 1 1 0
where 0   represents the overall intercept and  ( ) K    ,... 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 FP or a NP firm. Three alternative restricted models are estimated including only a subset of
explanatory variables: economic performance ratios, liquidity 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 FP and NP 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(_)=exp(__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, classified as Small and Medium
enterprises according to Basel II. One eighth of the sample is represented by NP firms
2.
                                                
2 We limit our attention to limited liability non-profit firms called “società cooperativa a responsabilità limitata” (SCRL).The data set displays unique features
3
 with respect to all the other studies on Italian firms, as Fabi et al.
(2005), Bocchi and 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 comprises 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, with respect to Basel II our default measure 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
bankrupt
4. In the following we use the label “stress” instead of default.
Panel A of Table 1 shows the distribution of defaults across NP and FP 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 NP or a FP 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 NP firms.
EBIDTA/TA and ROI do not influence PDs for NP firms, while they are highly significant and
negatively correlated with PDs in the case of FP 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 NP and FP
firms. The evidence indicates that NP 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 NP firm, incurring
in the risk of credit rationing. We perform an out-of sample prediction of the model estimated for FP
firms on the sample of NP 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 NP firms.
The firm sector of activity and regional location affect differently PD estimates conditional on juridical
structure. NP 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
                                                
3 Most of existing studies draw samples from national registers without direct reference to banks' portfolios.
4 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 nonprofit and
for-profit firm with respect to this feature. See Luppi et al. (2006).seems to have a competitive advantage in terms of higher financial stability
5. On the contrary, FP 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 NP firms and manufacture in case
of FP SMEs.
However, the credit risk models for NP and FP 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)
6,
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 NP firms.
An overall performance of these credit risk models is given by the accuracy ratio, equal to 83.89% for
FP firms and 97.74% for NP firms – a value higher than the one found in other studies on Italian firms,
as in Cannata et al. (2002) where it ranges between 50 and 70%.
V.  Conclusions
We provide a comparative analysis of credit risk associated with loans granted either to NP or FP
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 FP firms, NP 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 NP and FP firms in terms of structural variables, sales, total
debts. Additionally, size matters also within the retail segment.
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
                                                
5 A share of non distributed earnings of the non-profit firms is compulsory allocated as reserves.
6 Saurina and Trucharte (2004) provided similar evidence for Spanish economy.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 and 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, 121-144Table 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 Std Dev. Min. Max. Mean Std Dev. 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.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 R
2 61.25% 24.84%
Number of observations 389 3599
Area under ROC 97.74% 83.89%
Notes: *** indicates statistical significance at 1% level; **  indicates statistical significance at 5%;
* indicates statistical significance at 10%

Bank’s riskiness over the business cycle: a panel analysis on Italian intermediaries,

Le nuove Regole di Basilea 2: prime valutazioni di impatto sul Rapporto Banca-Impresa in Italia,

provided similar evidence for Spanish

Rating interni e gestione del rischio di credito: la misurazione delle probabilità di insolvenza,

The New Basel Capital Accord and banks' lending decisions,

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

http://www.rcfea.org/RePEc/pdf/wp30_07.pdf

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

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