have/have not made investments in the year and to what
extent. This index presumes to highlight the real development of a farm and the connected potential for potential
development.
• Assigned score 0 for a value of 0% (no investments have
been made);
• assigned score 1 for values between 0.01% and 50%;
• assigned score 2 for values between 50.01% and 100%;
• assigned score 3 for values greater than 100%.
It assigns a growing merit in proportion to the ability and
willingness of investment demonstrated over time by the
entrepreneurs, an index of determination, by the latter, in
taking care of the interests and business development.
D: Cap_ESE_PROP/Sup_TOT. Ratio between working
capital and total area (Euro/ha). This index highlights the
exploitation of the available area of the company; therefore,
with this indicator, it is presumed to be able to highlight the
productivity of the company.
• Assigned score 1 for values between 0 and 1,500;
• assigned score 2 for values between 1,501 and 3,000;
• assigned score 3 for values greater than 3,000.
This indicator aims to highlight the intensity of use of the
Utilized Agricultural Area (UAA), under the assumption that
the UA matches with the Total Agricultural Area (SAT).
The construction of a weighted quotient of performance
indicators for the creditworthiness of individual farms, is as
follows:
Q ¼ ð15% Ã AÞ þ ð30% BÞ þ ð25% Ã CÞ þ ð30% Ã DÞ
As illustrated above, the quotient can vary between a
minimum of 0.75 and a maximum of 3. The scoring scale
adopted corresponds to the following ranges of values:
A (5): for companies that have reported a weighted
quotient equal to 3.00 (maximum value);
B (4): for companies that have reported a weighted
quotient between 2.26 and 2.99;
C (3): for companies that have reported a weighted
quotient between 1.51 and 2.25;
D (2): for companies that have reported a weighted
quotient between 1.01 and 1.50;
E (1): for companies that have reported a weighted
quotient between 0.75 and 1.00;
The PDs associated with the different rating classes are
shown in Table 3.
In accordance with this scale, the results reported in the
various years considered for three classifications performed
are those shown below. The percentages indicated represent
the ratio between the number of companies falling within
each rating class compared to the total of companies included in the specific classification in each year of reference. In
detail, the authors continued to calculate the rating scale for
the years between 2004 and 2007, for nonspecialized companies, for the specialized in breeding and for those specialized in crop/horticultural/fruit production, as identified
by the RICA. The results, as described above, are identified
in Table 4.
This research paper highlighted how the distribution of
companies by rating classes is unbalanced toward classes 4
and 3 (ratings B and C), central to the classification system
used, by drawing a normal Gaussian distribution with a
marked positive asymmetry. It is interesting to underline
how in the period considered, for the companies specialized
in breeding there has been an increase in the concentration of
the same, in the highest rating classes (5 and 4). Reverse
trend instead for the specialized in various crops. This result,
even if stopped at 2007 due to the availability of data;
therefore, out of the period of severe crisis in livestock due
to the crisis in commodity prices on international markets,
seems to reward the greater propensity to invest in this type
of company and its greater size in terms of working capital
and land.
In this regard, it seems opportune to recall how the normal distribution (Latini 2004) is the distribution of continuous probability mostly used in statistical analysis because it
has so far managed to provide an apparently correct
description of most natural and economic-industrial
phenomena.
The rating agencies are required to make available to the
public the ex-post analyses of the ratings issued in previous
years by relating each category with the cumulative default
rates to verify ex post the predictive power of the rating. The
accuracy of the assignment of each rating class to the issuers
is carried out through the ex-post evaluation of the issuer’s
performance with the calculation of the cumulative default
rates (Lucarelli 2006; Szego 1999). Since the present study
Table 3 Default Probability
using the new methodology
Numbers
Letters
PD ranges (%)
5
A
0,00–0,05
4
B
0,05–0,50
3
C
0,50–2,00
2
D
2,00–5,00
1
E
>5,00
114
F. Capitanio et al.
extent. This index presumes to highlight the real development of a farm and the connected potential for potential
development.
• Assigned score 0 for a value of 0% (no investments have
been made);
• assigned score 1 for values between 0.01% and 50%;
• assigned score 2 for values between 50.01% and 100%;
• assigned score 3 for values greater than 100%.
It assigns a growing merit in proportion to the ability and
willingness of investment demonstrated over time by the
entrepreneurs, an index of determination, by the latter, in
taking care of the interests and business development.
D: Cap_ESE_PROP/Sup_TOT. Ratio between working
capital and total area (Euro/ha). This index highlights the
exploitation of the available area of the company; therefore,
with this indicator, it is presumed to be able to highlight the
productivity of the company.
• Assigned score 1 for values between 0 and 1,500;
• assigned score 2 for values between 1,501 and 3,000;
• assigned score 3 for values greater than 3,000.
This indicator aims to highlight the intensity of use of the
Utilized Agricultural Area (UAA), under the assumption that
the UA matches with the Total Agricultural Area (SAT).
The construction of a weighted quotient of performance
indicators for the creditworthiness of individual farms, is as
follows:
Q ¼ ð15% Ã AÞ þ ð30% BÞ þ ð25% Ã CÞ þ ð30% Ã DÞ
As illustrated above, the quotient can vary between a
minimum of 0.75 and a maximum of 3. The scoring scale
adopted corresponds to the following ranges of values:
A (5): for companies that have reported a weighted
quotient equal to 3.00 (maximum value);
B (4): for companies that have reported a weighted
quotient between 2.26 and 2.99;
C (3): for companies that have reported a weighted
quotient between 1.51 and 2.25;
D (2): for companies that have reported a weighted
quotient between 1.01 and 1.50;
E (1): for companies that have reported a weighted
quotient between 0.75 and 1.00;
The PDs associated with the different rating classes are
shown in Table 3.
In accordance with this scale, the results reported in the
various years considered for three classifications performed
are those shown below. The percentages indicated represent
the ratio between the number of companies falling within
each rating class compared to the total of companies included in the specific classification in each year of reference. In
detail, the authors continued to calculate the rating scale for
the years between 2004 and 2007, for nonspecialized companies, for the specialized in breeding and for those specialized in crop/horticultural/fruit production, as identified
by the RICA. The results, as described above, are identified
in Table 4.
This research paper highlighted how the distribution of
companies by rating classes is unbalanced toward classes 4
and 3 (ratings B and C), central to the classification system
used, by drawing a normal Gaussian distribution with a
marked positive asymmetry. It is interesting to underline
how in the period considered, for the companies specialized
in breeding there has been an increase in the concentration of
the same, in the highest rating classes (5 and 4). Reverse
trend instead for the specialized in various crops. This result,
even if stopped at 2007 due to the availability of data;
therefore, out of the period of severe crisis in livestock due
to the crisis in commodity prices on international markets,
seems to reward the greater propensity to invest in this type
of company and its greater size in terms of working capital
and land.
In this regard, it seems opportune to recall how the normal distribution (Latini 2004) is the distribution of continuous probability mostly used in statistical analysis because it
has so far managed to provide an apparently correct
description of most natural and economic-industrial
phenomena.
The rating agencies are required to make available to the
public the ex-post analyses of the ratings issued in previous
years by relating each category with the cumulative default
rates to verify ex post the predictive power of the rating. The
accuracy of the assignment of each rating class to the issuers
is carried out through the ex-post evaluation of the issuer’s
performance with the calculation of the cumulative default
rates (Lucarelli 2006; Szego 1999). Since the present study
Table 3 Default Probability
using the new methodology
Numbers
Letters
PD ranges (%)
5
A
0,00–0,05
4
B
0,05–0,50
3
C
0,50–2,00
2
D
2,00–5,00
1
E
>5,00
114
F. Capitanio et al.