No. 2/2012
19
EBITDA / EBIT AND CASH FLOW BASED ICRs:
A COMPARATIVE APPROACH IN THE AGRO-FOOD
SYSTEM IN ITALY
Mattia Iotti – Giuseppe Bonazzi
University of Parma, Via del Taglio 10, 43123 Parma (PR), Italy
mattia.iotti@unipr.it, giuseppe.bonazzi@unipr.it
In the article, even though it presents a joint reflection of the authors, Mattia Iotti wrote the
following paragraphs: “Introduction”, “Methodology” and “Data Analysis and Empirical Results”;
Giuseppe Bonazzi wrote the paragraphs “Literature Review” and “Conclusions”. The authors
would like to thank arch. Chiara Panini for help in data collecting and analisiaziendale.it for free
use of the database.
Abstract: The interest coverage ratios (ICRs) are used to quantify the ability of firms to pay
financial debts; ICRs are then considered by banks such as covenants in the financing term
sheet, and are used by researchers and the rating agencies to estimate the probability of default
of firms. Typically, ICRs calculation is based on profit margins, such as EBITDA and EBIT;
EBITDA and EBIT approximate, but do not directly express, cash flows available to pay
financial debts. The article aims to evaluate whether there are significant differences in results
using ICRs based on EBITDA or EBIT and ICRs based on different definitions of cash flow
(CF). The application is made to a sample of firms characterized by high absorption of capital
operating in the Italian agro-food sector. The article highlights that there are statistically
significant differences using ICRs EBITDA and EBIT based and ICRs based on different CF
definitions.
Keywords: EBITDA / EBIT based interest coverage ratios, Italian agro-food firms, debt
repayment, operating cash flow
JEL Classification: Q13, Q14, G31, G32
Introduction
Several studies were conducted about the general theme of assessing a firm’s capacity to
sustain the financial cycle in order to be able to pay the cost of debt and to refund the financial
debts borrowed in terms of net financial position (NFP). The firm's ability to support the
financial cycle does not reside only in the ability to generate profit, but it is the situation in
which the cash flows generated by operations are sufficient to pay the cost of debt and refund
the debt; for this purpose interest coverage ratios (ICRs) are applied. These ratios express the
firm's ability to pay the cost of debt traditionally using the profit margins generated by
operations, such as EBITDA (earnings before interest, taxes, depreciation, and amortization)
and EBIT (earnings before interest and taxes). However, EBITDA and EBIT do not express
directly cash flow (CF) available to pay the cost of debt, but approximates CF. If ICRs
calculated with EBITDA / EBIT based approach are different from ICRs calculated with CF
based approach it is possible to state that the use of ICRs EBITDA / EBIT based approach
Financial Assets and Investing
20
provides distorted information about the firm's ability to sustain the cycle of indebtedness; it
could then be useful to modify the traditional approach to ICRs ratios, expressing these with a
CF based approach, especially for firms with a high level of capital requirement such as agrofood
firms. This type of firms is a part of the agro-food system defined as the set of
interdependent elements with the aim to satisfy food needs of the population in a given space
and time (Malassis 1979; Malassis and Padilla 1986).
The article is divided into five parts. This part is the introduction of research that explains the
reasons and objectives of the work; the second part is the literature review that explains how
the work fits within the framework of studies already conducted and what new elements it
provides; the third part describes the methodology used to calculate the ratios and sample
selection; the fourth part discusses the data analysis and empirical results obtained; and the
fifth and final part outlines the conclusions of the work, also expressing the limits and possible
future research developments.
Literature review
Financial ratios are used (Barnes, 1987) to evaluate the firm’s capacity to repay its debts, to
quantify business and managerial success and even to consider the statutory regulation of a
firm's performance. The ratios are applied for an easier comparison between different firms; in
fact, they are more significant than absolute values in comparison (Barnes, 1987). Whittington
(1980) identified two principal uses of financial ratios. The first is to compare a firm's ratio
with a general standard, and the second is in order to estimate empirical relationships also for
predictive objectives. The former dates back to the end of the nineteenth century when US
banks started to apply the ratio of current assets to current liabilities to evaluate firm’s credit
rating (Horrigan, 1968; Dev, 1974; Ohlson, 1980, Hillegeist et al., 2004). In 1919 du Pont
Company began to use a ratio system to evaluate its operating results, starting the application
of financial ratios for managerial purposes. The bankruptcy prediction literature starts with the
seminal work of Altman (1968) that tried to explain corporate bankruptcy status in the United
States applying accounting and financial ratios. The importance of these ratios is considered in
several research studies (Leland, 1994 and 1998) where it is shown that an interest coverage
ratio covenant could reduce asset volatility. The covenants often considered in bank loan
agreements (Gray et al., 2006) are leverage and current ratio, interest coverage ratios are also
frequently used, expressing as EBIT or EBITDA to interest expense ratio, having an earnings
based approach. These are the most important financial covenants in terms of frequency as
expressed in different studies (Dichev et al., 2002; Demerjian, 2011).
The ratios are also applied by rating agencies and banks to assess the rating of companies, in
particular (Dothan, 2006) banks use ratios that express the ability to pay debt service (interest
coverage ratios and cash flow measures), profitability (return on equity and return on assets)
and leverage (debt to assets and debt to equity ratio). The banks usually include the minimum
interest coverage ratio (ICR) that the firm must comply with in the term sheet for financing;
the text of the covenants by the definition of the minimum ICR could be in terms of earnings
before interest and taxes (EBIT) and/or in terms of earnings before interest, taxes,
depreciation, and amortization (EBITDA). EBITDA is an accounting measure frequently
No. 2/2012
21
recommended as a proxy for cash flow; the definitions of accruals versus cash flow were
conducted also by Healy (1985) and Sloan (1996); these researchers have been considered the
standard in the accounting literature until the introduction of a more comprehensive model by
Richardson (2005); this model is considered useful in explaining the financial performance
and the relationship between accounting and cash flow data. Some researchers (Esty, 2004)
use the cash flow based ICRs approach in analyzing project finance operations. An interesting
paper (Trejo-Pech et al., 2006), exploring the relationship between earnings (EBITDA) and the
cash flow measure for agro-food firms data in USA, shows that EBITDA differ from CF and
should not be used as a CF proxy. We believe that the work of Trejo-Pech has the advantage
of making the analysis of comparison between EBITDA and CF in a specific business sector,
namely the agro-food firms. In fact, different sectors may have different dynamics of
absorption of capital in fixed assets and working capital; in this way it could not be possible to
compare sectors that have different characteristics in terms of production, investments,
revenues and payments cycle. In Italy, some researchers (such as Dono et al., 2010) have
analyzed the role of sustainability indicators of the business cycle, including ICRs, in the case
of agricultural firms evaluated by public agencies in order to test the firms’ access to state
funds. In some other Italian studies (as in Cupo et al., 2008) ICRs were applied to analyze
agricultural firms credit standing considering Basel II Accord. We intend to expand the work
of Trejo-Pech, also considering the work of these Italian researchers, applying ratios that
express the ability to pay financial debts with a methodology considering earnings and
financial ratios net of financial charge.
Methodology
The economic approach quantifies the result of the management cycle in terms of profit
considering the moment in which the value is generated by the firm’s activity; this is in fact an
accounting approach; the financial approach considers the management result in terms of cash
inflow and outflow, as streams of money (Kwon, 1989; Copeland et al., 2000). The economic
approach uses an analysis of positive and negative voices of income to calculate profit. We can
express the economic approach, defining the value of production, for a generic time t, in the
income statement, as follows:
where St are sales at time t, It and It-1 are inventories at time t and t-1, respectively; ∆It,t-1 is the
variation of inventories between time t-1 and time t. The value of production (VP) is a stream
value (Dechow et al., 2002). The operative monetary costs are raw material costs (Mt), costs
for services (St), rent and leasing costs (Rt), labor costs (Lt), others operative costs (Ot). In the
income statement we summarize non-monetary operating costs as amortization of tangible and
intangible fixed assets (Dt) and depreciation and risk provisions (At). The balance of financial
operations, in terms of interest income and interest expenses is expressed as follows:
p
t
a
tt IISF −=)2(
where
a
tI are interest incomes at time t,
p
tI are interest expenses at time t. The balance of
extraordinary voices of income (SXt), at time t, is given by:
ttttttt VPISIIS =∆+=−+ −− 1,1)()1(
Financial Assets and Investing
22
p
t
a
tt XXSX −=)3(
where
a
tX is an extraordinary income while
p
tX is an extraordinary expense. We can then
express the income statement as follows:
aT
tttt
ttttttttttt
ΠSZSFEBIT
;EBIT)A(DEBITDA;EBITDA)OLRS(MVP
=++
=+−=++++−)4(
where EBITDA approximates the creation of liquidity about the costs, such as net of nonmonetary
costs ( tt AD + ); EBIT is the operating income margin expressing an intermediate
operative current income;
aT
tΠ is profit before taxes while profit after taxes (
pT
tΠ ) is given as
follows:
pT
t
Y
t
aT
t ΠTΠ =−)5(
where
pT
tΠ expresses the remuneration, at a given time t, of the equity capital given by
equityholders while
Y
tT are income taxes.
The financial approach differs from the economic approach; the economic approach quantifies
intermediate income margins while the financial approach expresses different types of cash
flow available. The cash flow statement is the table used to quantify the cash flow generation
(Wallace et al., 1997; Krolick, 1998; Penman, 2004):
t
p
t
a
tttttttt
ttttt
p
t
a
ttt
pT
t
FCFE)I(IUFCF;UFCF)A(D)FA(FAOCF
;OCFNWCNWCCF;CF)I(I)A(DΠ
=−+=+−−−
=−−=−−++
−
−
1
1)()6(
where CFt is cash flow at time t, OCFt is the operating cash flow at time t, UFCFt is the
unlevered free cash flow at time t, FCFEt is free cash flow to equity at time t, NWC is net
working capital. We have ∆+
NWCt,t-1=>∆OCFt,t-1
expressing that an increase in net working
capital implies an increase in absorption of liquidity that reduces operating cash flow and vice
versa in the case of negative variation (∆-
NWCt,t-1=>∆+
OCFt,t-1). An increase in inventories
(∆+
It,t-1), that is a positive income voice, implies a reduction in the operating cash flow:
1,1, −−
±
∆⇒∆ tttt OCFI m
with |||| 1,1, −−
±
∆=∆ tttt OCFI m
. Given OCFt, the liquidity absorption
due the fixed asset investment has an effect on UFCFt, having that [(FAt - FAt-1) – (Dt + At)] >
0 => ∆UFCFt,t-1,
and vice versa. UFCFt is therefore the cash flow available, given the
investments in fixed assets, at time t, to remunerate the financial debt and equity capital.
Given the income statement and cash flow statement structure, we would propose in the article
a panel of 12 ratios that are applied to the firm’s sample date, also considering comparisons
between different ratio’s results. The ratios are divided into 2 categories; the ratios from S1 to
S6 are marked with “ea” letters expressing an earning approach and assume the denomination
from S1ea to S6ea; the ratios from S7 to S12 are marked with “cfa” letters expressing a cash
flow approach and assume the denomination from S7cfa to S12cfa. The traditionally
formulated ratios are expressed with an asterisk (S1ea*, S2ea*, S3ea*, S4ea*), where we
consider S1ea* and S2ea* as ICR with economic approach (respectively EBITDA and EBIT
based ICR); the ratios suggested in the article, if characterized by innovation considering a
No. 2/2012
23
financial value in the evaluation of sustainability, are marked with two asterisks (S7cfa**,
S8cfa**, S9cfa**, S10cfa**), where S7cfa** and S8cfa** are ICRs with the financial
approach (respectively OCF and UFCF based ICR). We use OCF and UFCF because we do
not consider CF adequately expressive of cash flow available to pay the cost of debt as it does
not consider the absorption of financial resources due to the cycle of working capital. The
ratios that we consider innovative because of the expression of ability to pay NFP with a value
net of the interest charge are indicated with three asterisks (S5ea***, S6ea***, S11cfa***,
S12cfa***), where S5ea*** and S8cfa*** have the earning approach while S11cfa*** and
S12cfa*** concern the financial approach.
Data analysis was conducted on a sample of 250 firms of the agro-food sector operating in 4
northern regions of Italy (Piemonte, Lombardia, Emilia-Romagna and Veneto). The data was
made available free of charge by the analisiaziendale.it company and it was randomly drawn
from the financial statements in the AMADEUS database considering 2005 as the extraction
year; the data extraction covers the 5-year period from 2006 to 2010 and uses the financial
statements filed by corporations and cooperatives each year at the Registrar of Companies. In
the sample, 87 firms operate in the meat sector (ATECO-ISTAT code 15.1), 4 operate in the
fish transformation industry (ATECO-ISTAT code 15.2), 51 operate in the vegetables
preserving industry (ATECO-ISTAT code 15.3), 8 operate in the oils and fats processing
industry (ATECO-ISTAT code 15.4), 67 operate in the milk and dairy industry (ATECOISTAT
code 15.5), 33 operate in the pasta and bakery products industry (ATECO-ISTAT code
15.8). These types of firms are interesting because several studies (Iotti et al., 2011) have
shown that in agro-food there is a high capital absorption in the cycle of fixed assets and in the
working capital cycle; this is due to processing of agricultural raw materials that often requires
high investments in plant and machinery and also determines the absorption of capital due to
the aging cycle of production, especially for cured meats and hard matured cheeses.
The 2005 data were used to provide availability of the 2006 cash flow statements. The random
extraction from the AIDA database of the 250 firms in the sample took into account the
following extraction parameters:
1. The extracted firms are limited liability companies (società a responsabilità limitata,
società per azioni, società in accomandita per azioni) as defined in the Italian Civil
Code; in the sample cooperative firms are not included.
2. The extracted firms are classified as agro-food firms in accordance with the
classification of the Italian National Institute of Statistics (ISTAT).
3. The extracted firms have all data available for the years from 2006 to 2010 (except for
firms that failed or ceased the activity during the period 2006 / 2010); during the 5year
period 2 companies were declared bankrupt (1 in 2006 and 1 in 2009 ) and 13
firms closed for voluntary liquidation (2 in 2006, 3 in 2007, 1 in 2008, 4 in 2009, 3 in
2010).
4. The turnover of the extracted firms is between 2 and 50 million Euros, in compliance
with the EU definition for small and medium-sized enterprises (SMEs).
Financial Assets and Investing
24
A total of 1,207 year-firms have been considered. The analysis tests the following 8 null
hypotheses:
H1: the S1ea* and S7cfa** ratios have equal means (medians) in the firm’s sample;
H2: the S2ea* and S8cfa** ratios have equal means (medians) in the firm’s sample;
H3: the S3ea* and S9cfa** ratios have equal means (medians) in the firm’s sample;
H4: the S4ea* and S10cfa** ratios have equal means (medians) in the firm’s sample;
H5: the S3ea* and S5ea*** ratios have equal means (medians) in the firm’s sample;
H6: the S4ea* and S6ea*** ratios have equal means (medians) in the firm’s sample;
H7: the S9cfa*** and S11cfa*** ratios have equal means (medians) in the firm’s sample;
H8: the S10ea** and S12cfa*** ratios have equal means (medians) in the firm’s sample.
In order to verify the different hypotheses we first applied an approach with parametric
statistics (paired sample Student's t-test); if the D statistic of Kolmogorov-Smirnov shows that
the distribution of 1 or 2 ratios considered in testing the different hypothesis is not normally
distributed, we also apply a non-parametric statistic (Wilcoxon paired sample t-test) to verify
the hypothesis; in fact, t-Student statistics could be correctly applied only in the case of a
sample with normal distribution. The data analysis were performed using the statistical
package SPSS, issue 19.
Data analysis and empirical results
Data analysis was carried out by verifying the assumptions made in 8 equal means for couples
of financial ratios. The analysis was conducted with the parametric approach (paired sample
Student's t-test). The results of the analysis show that the null hypotheses (equality of means)
must be rejected at 95% level of significance for all couples of ratios analyzed using the
parametric approach.
The Kolmogorov-Smirnov D statistic shows that all distributions of the ratios do not follow
the normal distribution; in past years, several researchers have shown the non normality of the
financial ratios distribution in their analysis (McLeav, 1982; Barnes, 1982; Ezzamel et al.,
1987).
Therefore, a non-parametric approach was applied for paired data (Wilcoxon paired sample ttest)
to test the null hypotheses (in this case equality of medians). We can then reject the null
hypotheses for all pairs of ratios having 95% level of significance with the parametric
approach.
The research can then show some remarkable results in terms of application:
1. The ICRs EBITDA and EBIT based (S1ea* and S2ea*), with the earnings based
approach, are significantly different compared to ICRs OCF and UFCF based (S7cfa**
and S8cfa**), with the cash flow based approach; we then suggest to apply the cash
flow based ratios; therefore, the application of ICRs earnings based, both in the
quantification of bank covenants for credit operations and the analysis of the ratings,
leads to a distorted result; this result is significantly different than the real result that
could be calculated with the ICRs cash flow based.
No. 2/2012
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2. The ratios expressing the firm’s capacity to repay financial debts, EBITDA and EBIT
based (S3ea* and S4ea*), with the earnings based approach, are statistically different if
compared with OCF and UFCF based ratios (S8cfa** and S9cfa**), with the cash flow
based approach. We then suggest to apply the cash flow based ratios; the S3ea* and
S4ea* application, in order to quantify covenants in financing operations and to
quantify the firm’s rating causes a distorted result; this result is significantly different
than the real result that is calculated with the S8cfa** and S9cfa** cash flow based ratios.
3. The ratios applied to express the firm’s capacity to repay financial debts EBITDA and
EBIT based (S3ea* and S4ea*) and OCF and UFCF based (S8cfa** and S9cfa**), do not express this
attitude in a correct way; in fact, the analyzed couple of ratios (S3ea* and S5ea***, S4ea* and
S6ea***, S9cfa*** and S11cfa***, S10ea** and S12cfa***) show that the ratios not considering the interest
charge, as traditionally applied (S3ea*, S4ea*, S9cfa***, S10ea**), are significantly different than the
suggested ratios that are calculated considering the cost of debt (S5ea***, S6ea***, S11cfa***,
S12cfa***); if we apply traditional ratios we could overestimate the firm's ability to repay its
debts and, therefore, provide distorted information on the sustainability of debt service.
Conclusion
The evaluation of the cost of debt payment, carried out applying ICRs, and the analysis of the
ability to repay financial debts, are essential to quantify: a) the sustainability of the business
cycle, b) the calculation of the firm’s rating, c) the covenants in bank financing operations,
d) the managerial assessment of sustainability of the business cycle. This importance is higher
in firms where the economic and the financial cycle have a time lag, due to the absorption of
capital in the cycle of investments in fixed assets and in working capital, as in the case of agrofood
firms. Firms with a greatest mismatch between the economic and the financial cycles
often operate in the aging of meat and dairy products, such as cured meats and hard cheeses; in
this sector of the agro-food system it would be useful to perform future studies.
The article proposes the use of ICRs expressing directly the financial ability to pay the cost of
debt (ICRs cash flow based), and ratios that express the repayment of financial debt with cash
flows net of the cost of interest charge. All the suggested ratios are statistically different
compared to the traditional ratios used in banking covenants and, often, for the calculation of
the firm’s rating. The analysis thus shows that it is preferable to use the suggested ratios rather
than the traditional ones; the former are able to express the sustainability of the business cycle
more correctly.
The analysis is based, however, on a sample of only 1.207 observations of Italian data, and for
some agro-food segments. It may therefore be important, even for the work of other
researchers, that research proceeds to test the suggested ratios in other sectors and in other
states to get a more reliable evaluation of the correctness of the suggested ratios.
Financial Assets and Investing
26
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DOI: 10.5817/FAI2012-2-2
Financial Assets and Investing
28
Appendix
Fig. 1 Ratios applied in the research on the firm’s sample data
Ratio Formula Meaning
S1ea* EBITDA / I Expresses the firm’s capacity to pay interest on financial debts using an earning measure such as
EBITDA as a proxy of CF.
S2ea* EBIT / I Expresses the firm’s capacity to pay interest on financial debts using an earning measure such as EBIT
as a proxy of CF.
S3ea * EBITDA / NFP Expresses the firm’s capacity to repay NFP using an earning measure such as EBITDA as a proxy of CF.
S4ea* EBIT / NFP Expresses the firm’s capacity to repay NFP using an earning measure such as EBIT as a proxy of CF.
S5ea*** (EBITDA – I) /
NFP
Expresses the firm’s capacity to repay NFP using an earning measure such as EBITDA as a proxy of CF
net of the interest charge I.
S6ea*** (EBIT – I) / NFP Expresses the firm’s capacity to repay NFP using an earning measure such as EBIT as a proxy of CF net
of the interest charge I.
S7cfa** OCF / I Expresses the firm’s capacity to pay interest on financial debts using a financial measure such as OCF.
S8cfa** UCFC / I Expresses the firm’s capacity to pay interest on financial debts using a financial measure such as UFCF.
S9cfa** OCF / NFP Expresses the firm’s capacity to repay NFP using a financial measure such as OCF.
S10cfa** UFCF / NFP Expresses the firm’s capacity to repay NFP using a financial measure such as UFCF.
S11cfa*** (OCF – I) / NFP Expresses the firm’s capacity to repay NFP using a financial measure such as OCF net of the interest
charge I.
S12cfa*** (UFCF – I) / NFP Expresses the firm’s capacity to repay NFP using a financial measure such as UFCF net of the interest
charge I.
Source: Authors’ calculation.
No. 2/2012
29
Fig. 2 Descriptive statistics
N Min Max Mean Dev std. Asymmetry Kurtosis
Stat Stat Stat Stat Standard dev Stat Stat Stat Stat Standard dev
S1ea* 1207 -19.4783 4644.6753 102.0290 17.3214 601.7792 7.184 .07000 51.169 .141
S2ea* 1207 -40.2643 677.8350 17.6106 2.5600 88.9417 6.841 .07000 47.687 .141
S3ea* 1207 -271.2103 .6081 -9.3428 1.2665 44.0032 -5.167 .07000 25.643 .141
S4ea* 1207 -33.6988 112.4473 .6571 .4428 15.3838 6.146 .07000 43.932 .141
S5ea*** 1207 -268.4176 .5433 -8.6917 1.1829 41.0993 -5.356 .07000 28.358 .141
S6ea*** 1207 -33.1619 115.2400 1.3082 .4493 15.6111 6.285 .07000 44.692 .141
S7cfa** 1207 -66.6192 14188.4805 252.0930 52.2130 1813.9810 7.531 .07000 55.058 .141
S8cfa** 1207 -11081.8052 922.7290 -171.3491 40.9255 1421.8320 -7.496 .07000 54.784 .141
S9cfa** 1207 -128.2280 92.6552 -1.3146 .5993 20.8225 -2.269 .07000 26.955 .141
S10cfa** 1207 -36.0901 141.1224 1.7976 .5459 18.9671 6.447 .07000 45.799 .141
S11cfa*** 1207 -92.3714 95.4479 -.6635 .5081 17.6545 .294 .07000 23.863 .141
S12cfa*** 1207 -14.5495 143.9152 2.4481 .5374 18.6726 7.217 .07000 51.919 .141
Source: Authors’ calculation.
Fig. 3 Paired-sample statistics – parametric approach – t-Student statistic
Confidence interval 95%
Cfr. between couple of ratios
(H0: Ratio1 mean = Ratio2 mean in the
couple) Min. Max.
t-Sudent
stat. D.F. Sig. (2-tailed)
§: Sign. 0.05 (2-sided test)
Ratios Couple 1 S1ea* - S7cfa** -218.9614 -81.1665 -4.273 1206 .000 §
Ratios Couple 2 S2ea* - S8cfa** 107.9492 269.9702 4.576 1206 .000 §
Ratios Couple 3 S3ea* - S9cfa** -10.7174 -5.3390 -5.857 1206 .000 §
Ratios Couple 4 S4ea* - S10cfa** -1.5846 -.6951 -5.029 1206 .000 §
Ratios Couple 5 S3ea* - S5ea*** -.9102 -.3920 -4.931 1206 .000 §
Ratios Couple 6 S4ea* - S6ea*** -.9101 -.3920 -4.931 1206 .000 §
Ratios Couple 7 S9cfa** - S11cfa*** -.9101 -.3920 -4.931 1206 .000 §
Ratios Couple 8 S10cfa**- S12cfa*** -.9101 -.3920 -4.931 1206 .000 §
§: Sign. 0.05 (2-sided test) Source: Authors’ calculation.
Financial Assets and Investing
30
Fig. 4 Kolmogorov-Smirnov D statistic on normality of distribution
Null hypothesis (H0) Test Sign, Decision
S1ea* distribution is normal with mean 102.03 and standard deviation 601.78 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S2ea* distribution is normal with mean 17.61 and standard deviation 88.94 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S3ea* distribution is normal with mean -9.34 and standard deviation 44.00 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S4ea* distribution is normal with mean 0.66 and standard deviation 15.38 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S5ea*** distribution is normal with mean -8.69 and standard deviation 41.10 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S6ea*** distribution is normal with mean 1.31 and standard deviation 15.61 Kolmogorov-Smirnov D statistic .002 §Reject null hypothesis
S7cfa** distribution is normal with mean 252.09 and standard deviation 1,813.98 Kolmogorov-Smirnov D statistic .003 §Reject null hypothesis
S8cfa** distribution is normal with mean -171.35 and standard deviation 1,421.83 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S9cfa** distribution is normal with mean -1.31 and standard deviation 20.82 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S10cfa** distribution is normal with mean 1.80 and standard deviation 18.97 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S11cfa*** distribution is normal with mean 0.66 and standard deviation 17.65 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
S12cfa*** distribution is normal with mean 2.45 and standard deviation 18.67 Kolmogorov-Smirnov D statistic .000 §Reject null hypothesis
§: Sign. 0.05 (2-sided test)
Source: Authors’ calculation.
Fig. 5 Paired-sample statistics - non parametric approach – T-Wilcoxon paired sample test
Cfr. between couple of ratios
(H0: Ratio1 median = Ratio2 median in the
couple)
T-Wilcoxon
paired sample
test
T-Wilcoxon
paired sample test
(standardized
value) Sig. (2-tailed)
§: Sign. 0.05 (2-sided test)
Ratios Couple 1 S1ea* - S7cfa** 193,689.000 -14.103 .000 §
Ratios Couple 2 S2ea* - S8cfa** 233,197.000 -10.842 .000 §
Ratios Couple 3 S3ea* - S9cfa** 308,067.000 -4.660 .000 §
Ratios Couple 4 S4ea* - S10cfa** 335,953.000 -2.358 .018 §
Ratios Couple 5 S3ea* - S5ea*** 216,672.000 -12.206 .000 §
Ratios Couple 6 S4ea* - S6ea*** 216,660.000 -12.184 .000 §
Ratios Couple 7 S9cfa** - S11cfa*** 216,672.000 -12.206 .000 §
Ratios Couple 8 S10cfa**- S12cfa*** 216,082.000 -11.890 .000 §
§: Sign. 0.05 (2-sided test)
Source: Authors’ calculation.
No. 2/2012
31
Fig. 6 Ratios correlation (Pearson’s approach)
S1ea* S2ea* S3ea* S4ea* S5ea*** S6ea*** S7cfa** S8cfa** S9cfa** S10cfa** S11cfa*** S12cfa***
Pearson correl. 1 ,381**
,014 ,004 ,013 -,002 ,991** -,961** ,014 ,007 ,011 ,002
Sig. (2-sided test) ,000 ,621 ,885 ,653 ,945 ,000 ,000 ,623 ,819 ,695 ,956
S1ea*
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,381**
1 ,069*
-,101**
,071*
-,108** ,260** -,112** -,044 -,071* -,059* -,079**
Sig. (2-sided test) ,000 ,016 ,000 ,014 ,000 ,000 ,000 ,131 ,014 ,040 ,006
S2ea*
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,014 ,069*
1 -,591**
,997**
-,778** ,030 -,021 ,056 -,598** -,107** -,770**
Sig. (2-sided test) ,621 ,016 ,000 ,000 ,000 ,297 ,456 ,053 ,000 ,000 ,000
S3ea*
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,004 -,101**
-,591**
1 -,644**
,956** -,010 ,001 ,721** ,916** ,825** ,906**
Sig. (2-sided test) ,885 ,000 ,000 ,000 ,000 ,737 ,969 ,000 ,000 ,000 ,000
S4ea*
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,013 ,071*
,997**
-,644**
1 -,811** ,030 -,021 -,024 -,661** -,184** -,818**
Sig. (2-sided test) ,653 ,014 ,000 ,000 ,000 ,298 ,464 ,409 ,000 ,000 ,000
S5ea***
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. -,002 -,108**
-,778**
,956**
-,811**
1 -,015 ,006 ,491** ,848** ,630** ,910**
Sig. (2-sided test) ,945 ,000 ,000 ,000 ,000
,596 ,833 ,000 ,000 ,000 ,000
S6ea***
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,991**
,260**
,030 -,010 ,030 -,015 1 -,988** ,003 -,013 -,001 -,018
Sig. (2-sided test) ,000 ,000 ,297 ,737 ,298 ,596 ,000 ,908 ,642 ,968 ,523
S7cfa**
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. -,961**
-,112**
-,021 ,001 -,021 ,006 -,988** 1 -,008 ,004 -,005 ,008
Sig. (2-sided test) ,000 ,000 ,456 ,969 ,464 ,833 ,000 ,785 ,896 ,865 ,782
S8cfa**
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,014 -,044 ,056 ,721**
-,024 ,491** ,003 -,008 1 ,754** ,985** ,583**
Sig. (2-sided test) ,623 ,131 ,053 ,000 ,409 ,000 ,908 ,785 ,000 ,000 ,000
S9cfa**
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,007 -,071*
-,598**
,916**
-,661**
,848** -,013 ,004 ,754** 1 ,842** ,970**
Sig. (2-sided test) ,819 ,014 ,000 ,000 ,000 ,000 ,642 ,896 ,000 ,000 ,000
S10cfa**
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,011 -,059*
-,107**
,825**
-,184**
,630** -,001 -,005 ,985** ,842** 1 ,702**
Sig. (2-sided test) ,695 ,040 ,000 ,000 ,000 ,000 ,968 ,865 ,000 ,000 ,000
S11cfa***
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
Pearson correl. ,002 -,079**
-,770**
,906**
-,818**
,910** -,018 ,008 ,583** ,970** ,702** 1
Sig. (2-sided test) ,956 ,006 ,000 ,000 ,000 ,000 ,523 ,782 ,000 ,000 ,000
S12cfa***
N 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207 1207
**. Corr. with sign. 0.01 (2-sided test), *. Corr. with sign. 0.05 (2-sided test). Source: Authors’ calculation.