The impact of financial parameters on agricultural cooperative and
investor-owned firm performance in Greece
Panagiota Sergaki1 and Anastasios Semos2
1
2
Panagiota Sergaki, Phd, Aristotle University of Thessaloniki
Anastasios Semos, Associated Professor, Aristotle University of Thessaloniki
Abstract: This paper attempts to examine how financial characteristics affect the
performance of Investor - Owned Firms (IOF) and Unions of Agricultural Cooperatives (UAC)
in Greece. Data has been collected over a period of five years for IOF (1995-1999) and six
years for UAC (1 995-2000). A theoretical model within a system of four simultaneous
equations presenting size, profitability, financial risk and business risk as dependent variables
has been developed. . Financial analysis results were used with the help of OLS, panel data
(fixed effect - random effect models), SUR and 3SLS techniques, to estimate those
parameters which would determine the profit performance of the IOF and the UAC as well as
to compare and contrast their financial structure and policies.
Keywords: agricultural cooperatives, profitability, size, financial risk, business risk,
simultaneous equations.
I. Introduction
Many empirical studies (Baker, S. 1973; Hurdle, 1974; Harris, F. 1986; Bernier, 1987;
Oustapassidis, K. and O. Notta, 1997; Kambhampati, 1999, Oustapassidis et al., 2000)
analyze and measure the relationships among profitability, size, financial risk (leverage) and
business risk. However, only few of them attempt to simultaneously analyze these factors.
This paper develops a four – equation theoretical model relating these variables and using
cross section and time series firm level data on 2533 greek manufacturing firms as well as on
93 Greek Unions of Agricultural Cooperatives. The results are extremely interesting as they
try to explain intertemporal changes in profit margin differences between different industries.
In addition, this work takes into consideration the existence of endogeneity bias between
profitability, concentration, financial and business risk in order to choose the appropriate
method of estimation. The paper uses firm level data, that is available in Greece to classify
manufacturing firms into the 20 4 -digit industries and estimates the value of variables and
their determinants.
II. Model specification
Most of studies of Structure-Conduct-Performance (SCP) relationships use OLS to estimate
single equation relationships assuming unidirectional causality running from structure to
conduct and then to performance. Some however, suggest not only that market structure
influence conduct and performance, but that market conduct and performance are likely to
feed back and influence market structure too. Thus, a single equation model would suffer
from simultaneous equation bias, and it would produce weak and inconsistent relationships.
For these econometric reasons a four-equation model was developed in which profits,
market share, financial risk (leverage) and business risk are jointly determined. The model
was tested using panel data for all the IOF in each greek industry that occupied more than 10
employees from 1995 till 1999 (2533 firms) and for the Unions of Agricultural Cooperatives
that have available data (93 out of 118) from 1995 to 2000 in Greece.
The model takes the general form provided below:
1
PR=
MS=
FR=
BR=
f(MS,FR,BR,X)
f(PR,FR,BR,Ψ)
f(PR,MS,BR,Z)
f(PR,MS,FR,Ω)
PR :profitability
MS:market share
FR:financial risk
BR:business risk
(indicator of performance)
(indicator of market structure)
(indicator of conduct)
(
”
) and
X,Ψ,Ζ,Ω: vectors of exogenous variables
Following the relevant literature, the price cost margin (PCM) index can be used when
available to test SCP relationships in empirical firm level studies. Also concentration and other
independent variables, which affect industrial structure and conduct, should be included to
give:
PR= a0+a1MS +a2FR +a3BR +a4CR4+a5DIV +a6CAPTURN
where:
CR4: concentration ratio of the industrial sector, DIV: diversification level of the firm
CAPTURN: capital intensity of the firm
Apart from the price cost margin, market share is a basic parameter for the examination of
SCP relationships in empirical industrial studies. The theoretical model of market share
equation (MS) includes profit rate ratio, financial risk ratio, business risk ratio as well as
growth rate ratio.
MS= b0+b1PR+b2FR+b3BR +b4GROT
where GROT refers to the growth rate of the firm
Since financial risk (FR) is correlated with some of the elements of market structure and
performance, it is desirable to include FR in the system of equations in order to explain the
profitability level of the industry. The theoretical model of the financial risk equation includes
profit rate ratio, market share, business risk ratio, efficiency ratio as well as an indicator of
the firm age.
FR=c0+c1PR+c2MS+c3BR+c4NWTU+c5YOFES
where
NWTU: net worth over turnover of the firm
YOFES: firm age
Finally, the business risk equation includes profit rate ratio, market share ratio, financial
risk ratio, turnover over the number of employees ratio as well as export intensity ratio.
BR= d0+d1PR+d2MS+d3FR+d4TE+d5EXAG
where
TE: turnover / # permanent employees of the firm
EXAG: export intensity ratio (exports / total sales)
The regression analysis results reveal the effects of the independent variables on the
dependent variables for both IOF and UAC.
2
III. Data and measurement of variables
A total of 2533 manufacturing firms are classified into 20 Greek manufacturing industries
according to their principle product for the years 1995 through 1999. The definition of these
industries corresponds to the 2-digit Greek Standard Industrial Classification (SIC). Also, 93
UAC are used for comparative reasons for the years 1995 through 2000. Data for IOF is
drawn from ICAP’s annual reports. These reports provide individual balance sheet and income
statement data for all manufacturing IOF. Data for UAC is gathered with the help of personal
interviews as well as from their annual reports. The results of the financial analysis are
exported with the help of OLS, panel data (fixed and random effect models), SUR and 3SLS
techniques and describe the impact of several parameters on the performance of IOF and
UAC in Greece. More specifically we include in the model the following parameters:
Profitability:
Market share:
CR4 :
net income over turnover of the firm
sales of each firm over the total industrial sales
concentration ratio of the industry according to the sales of the four biggest
firms of each industrial sector
Financial risk: total liabilities over net worth of the firm
Business risk: deviation of firm profit from the industrial average profits
CAPTURN:
firm capital over its sales
Diversification: the number of different firm activities
Growth:
annual firm sales over firm sales in the previous year
Export intensity: exports over total firm sales
Efficiency:
a) net worth over turnover and b) turnover over the number of permanent
staff
YOFES:
firm age
No advertising sales ratio is included in this equation as a separate market structure variable.
Although advertising has often been treated as a market structure variable that has a
separate positive effect on profitability, data is not available for the majority of firms and
cooperatives and as a result it is not included.
IV. Analysis
The analysis of data is done with the help of panel data technique (fixed effect - random
effect) which can control for individual heterogeneity, can give more informative data, more
variability, more degrees of freedom and it is able to better identify and measure effects that
are simply not detectable in pure cross section or pure time series data (Nerlove, 1971, Judge
et al, 1985). In the fixed effect method, we assume that all industries have identical
coefficients except for the intercept. In case of random effect model there is an unobservable
individual effect that is stable through time but different for each IOF. According to F-test,
the appropriate method of estimating profitability, financial and business risk equation is the
fixed effect method. On the contrary, in size equation we use the random effect method
(table 1).
According to Hausman – Wu test (Martin, 1993; Greene,1997) there is endogeneity
problem in all equations and as a result an instrumental variable technique (3SLS-2SLS)
should be used. (table 2). According to Langrange multiplier statistic (λ=4638.6), there exist
contemporaneous correlation bias across the four-equation system (the theoretical value of
X2 for 4 degrees of freedom is 9.49 at 5% level of significance). Finally, we test the existence
of identification problems (order and rank conditions) in each equation separately in order to
be able to apply a system of simultaneous equations. The results show that all four equations
3
are over-identified. We can therefore apply 3SLS to estimate jointly the four equations.
However, we also apply SUR for comparison reasons (tables 3-6).
In the profitability equation (table 3) all the independent variables are statistically
significant (3SLS method). The market share ratio, leverage, concentration, diversification as
well as capital over turnover ratio have a strong positive effect on profitability. On the
contrary, business risk has negative effect on profitability. This occurs because both business
risk and leverage, which are negatively correlated, are included in the same equation.
According to 3SLS method, R2 is 17.4% indicating that the independent variables explain by
this amount the variability of the dependent variable.
In the size equation (table 4) the dependent variable is the market share. Growth has
positive effect on size while business risk and leverage are negatively correlated. According to
R2, the independent variable explains by 8.5% the variability of the dependent variable (SUR
method). In the leverage equation (table 5) profitability as well as business risk have a
positive effect on leverage while efficiency and market share have a negative impact on
leverage (3SLS method). A 1% increase on efficiency level decreases leverage level by
0.79%. R2 is 69.2%. Finally, in the business risk equation (table 6) market share and
leverage have positive effect on business risk whereby R2 is 70.7% (3SLS method).
Apart from the analysis of IOF, the impact of the same financial factors on Greek Unions
of Agricultural Cooperatives has also been examined. Cooperatives have been portrayed as a
form of business enterprise in a market economy, which is particularly structured to serve the
special needs and interests of its owner – members who have mutual benefits. Agricultural
Cooperatives in the EU are presently in a state of transformation. The economic, social and
legal environment of cooperatives is changing, resulting in the fact that the latter are
accordingly in need of adopting new measures to adapt themselves to this new environment.
To mention but a few of these changes: withdrawal of government from the market within
the last decade, increase in international trade, new technological developments, changing
consumer demands, concentration and integration process in other segments of the product
and marketing chain and so on. All these factors have a major impact on the development of
agricultural cooperatives, placing them under great pressure to adapt themselves to new
realities.
In the year 2000 there were 118 Unions of Agricultural Cooperatives in Greece. The level
of sales has increased in recent years. However, the high leverage level as well as the high
operating cost level results in difficulties in adopting expensive strategies that raise the
competitiveness of the firm. The lack of capital leads to the increase of borrowed capital
indicating higher financial risk. The net profit margin of cooperatives is negative from 1995 to
2000. On the contrary, the average profit margin on greek IOF is positive for the same
period. The evaluation and comparison of the cooperatives performance with that of IOF is
becoming imperative under these circumstances.
The same system of equations is examined for the UAC in Greece. In the profitability
equation (table 3) market share has a positive effect on profitability. A 1% increase of market
share raises profitability by 0.91% (SUR method). Leverage and business risk are negatively
correlated. In the size equation (table 4) profitability, business risk and growth have positive
effect on size (SUR method). 1% increase on profit raises size level by 0.02% (SUR). In the
leverage equation (table 5) profitability, market share as well as business risk have positive
effect on leverage (SUR- 3SLS method). Finally, the business risk equation (table 6) indicates
that the profitability has a negative impact on business risk whereby a 1% increase of
profitability leads to decrease of business risk by 1.89%. On the other hand, market share
and leverage have a positive effect on business risk (SUR method).
V. Conclusions
It has been widely recognized that the economic health of Greek cooperatives has been
failing in recent years and much needs to be done to bring the latter into line with current
transformations that their European counterparts are undergoing.
Comparing the results from the analysis of IOF and Cooperatives we conclude that:
4

In the profit equation of IOF, size, leverage, concentration, capital/sales as
well as diversification have a positive effect on a firm’s profitability whereas business
risk has a negative effect (3SLS method)

In the profit equation of Unions of Agricultural Cooperatives, size, leverage,
capital/sales as well as diversification have a positive effect on firm’s profitability
whereas business risk has a negative effect (3SLS method). The results derived from
the analysis of cooperatives absolutely agree with those of the analysis conducted
for private firms. The only exception concerns the impact of concentration on
profitability which is expected as all cooperatives belong to the same industry and
concentration affects all cooperatives similarly.

In the leverage equation of private firms, profitability, business risk as well as
the age of the firm positively affect leverage level. On the contrary, market share as
well as efficiency negatively affect the leverage level of private firms. The results of
the leverage equation of agricultural cooperatives agree with those of private firms.
The only exception refers to the positive impact of size on the cooperative leverage
level

In the size equation of agricultural cooperatives, profitability, business risk as
well as growth affect positively the cooperative size. In the IOF equation, financial
risk as well as growth affect positively the firm size (SUR method)

Small size UAC and IOF have no economies of scale. In addition, they face
obstacles in application of competitive strategies and have high production cost, low
market share and low profit margins
Some proposals to enhance the econy of UAC and IOF are the following:
 Increase of net worth for the application of competitive strategies aiming at the
increase of firm’s market share
 Increase of size through mergers
 Evaluation of alternative scenarios of external growth
 Adoption of competitive strategies (e.g. product differentiation, advertising, reliable
distribution channels, R+D, innovations)
 Better exploitation of economies of scale
 Restriction of fixed costs and expansion to trade activities with greater value added

Focus on specialized parts of the market which do not interest big firms
It is obvious that a number of exogenous variables affect the level of profitability, size,
leverage and business risk of a firm or a cooperative. Consequently, testing the hypotheses
against models with even more exogenous variables would be desirable for further research.
5
REFERENCES
Baker, S. (1973). Risk, Leverage and Profitability. Review of Economics and Statistics, 55
(Nov), pp503-7.
Bernier G. (1987). Market Power and Systematic Risk: An empirical analysis using Tobin’s q
ratio. Journal of Economics and Business, pp. 91-99.
Greene, H.W., (1997). Econometric Analysis, 3rd edition, International edition.
Harris, F. (1986). Market structure and price cost performance under endogenous profit
risk. The Journal of Industrial Economics. Vol 35.
Hurdle, G.J (1974). Leverage, Risk, Market Structure and Profitability. Review of Economics
and Statistics, 56(4), pp.478-85.
ICAP Hellas, 1995-2001. Annual Data of the Greek companies, Athens, 1995-1999
(series).
Judge, G., et al (1985). The theory and practice of Econometrics, New York: John Wiley
and Sons.
Kambhampati U. (1999). A study of the structure, conduct and performance of Indian
Industry. Review of Industrial Organization, 14, pp. 91-94
Maddala, G.,(1971). The use of variance components models in Pooling cross section and
time series data. Econometrica (39) pp 341-358.
Martin S., (1993). Advanced Industrial Economics. Blackwell Publishers, Oxford, U.K.
Nerlove, M.1971. A note on Error Components Models. Econometrica (39), pp 383-396.
Oustapassidis, K. and O. Notta. (1997). Profitability of Cooperatives and InvestorOwned firms in the Greek Dairy Industry. Journal of Rural Cooperation 25 (1) pp.33-43.
Oustapassidis, K. et al (2000). Efficiency and Market Power in Greek Food Industries.
American Journal of Agricultural Economics, Aug., 1982, pp.623-629.
6
INDEX
Table 1: The Hausman test
Equations
Degrees of
Empirical
Theoretical
Recommende
freedom
value
value*
d method
Profitability
6
10.638,00
12,59
Fixed effect
Size
4
0,61
9,49
Random effect
Financial Risk
5
212,36
11,07
Fixed effect
Business Risk
5
93,07
11,07
Fixed effect
 5% level of significance
Table 2: The Hausman – Wu test
Equations
Empirical value
Theoretical value *
Endogeneity bias
7,34
F(3359,8413)=1
Yes
66,82
F(4371,5296)=1
Yes
Financial Risk
2,25
F(3840,9423)=1
Yes
Business Risk
1,05
F(2308,4606)=1
Yes
Profitability
Size
 5% level of significance
Table 3: Profitability equation of IOF and UAC
Variables
SUR(IOF)
3SLS (IOF)
SUR (UAC)
3SLS (UAC)
(fixed effect)
coefficient
MS
0,57
TLNW
3,49
RISK1
-0,04
CR4
CAPTURN
DIV
R2
t-value
*1,17
E-03
coefficient
42,38
(fixed effect)
t-value
4,48
coefficient
t-value
0.91
2,43
0,58
11,78
0.10
- 2,16
-11,87
-11,11
-0.23
2.80
E-02
E-03
coefficient
0.88
1.77
0.40
-4.59
-0.37
0.64
t-value
*1.61
E-02
*0.94
-7.52
E-02
-0.33
E-02
-0,33
-2,76
0,09
2,39
0.55
0,43
9,77
0,43
56,77
0.09
0.39
0.17
E-03
-0,01
*0,21
0,03
*1,87
-0.01
-1.01
0.13
E-02
*-0.07
0.11
*0.11
76,0 %
17,4 %
12.4%
9.1%
DW
LM
2,0
10,7
1,9
2,0
10,7
1,9
F
4,9
F (3359,8413 ) = 7,3
Hausma
n Test
E-02
X2/df 6= 10638
 10% level of significance
7
Table 4: Size Equation of IOF and UAC
Variables
SUR(IOF)
3SLS (IOF)
SUR (UAC)
3SLS (UAC)
(fixed effect)
coefficient
t-value
coefficient
PR
2,91
E-05
FR
2,86
E-04
2,19
-6,17
-1,52
E-03
*-1,00
0,16
1,97
E-06
1,91
1,35
BR
GROT
R2
*1,29
(fixed effect)
t-value
coefficient
-2,98
E-05
*-0,08
0.02
E-03
*-1,60
-0.76
2,96
0.03
*0,24
0.49
E-06
8,5%
3,1%
DW
LM
t-value
E-04
coefficient
2.31
0.14
-1.53
0.12
6.48
0.09
2.95
-0.44
E-02
t-value
*1.59
E-02
*0.99
3.24
*-0.07
E-03
10.0%
7.6%
0,03
1,50
F
F(4371,5296)= 65,97
X2(4) = 0,61
Hausma
n Test
* 10% level of significance
Table 5: Financial Risk (leverage) equation of IOF and UAC
Variables
SUR(IOF)
3SLS (IOF)
SUR(UAC)
3SLS (UAC)
(fixed effect)
coefficient
t-value
coefficient
t-value
(fixed effect)
coefficient
t-value
coefficient
t-value
PR
0,03
4,35
3,65
13,07
54.45
2.86
59.18
*1.42
MS
16,73
2,54
-85,29
- 4,19
-52.79
-1.19
287.40
3.87
BR
12,41
43,65
21,21
12,68
32.02
2.14
7.28
*0.46
NWTU
-6,79
-6,43
-0,79
-12,82
0.26
1.17
*0.17
E-03
E-02
0.40
E02
YOFES
1,32
R2
DW
LM
F
E-04
0,95
1,88
E-04
1,26
73,8%
1,8
39,2
69,2%
1,9
2696,9
22,7
F(3840,9423)=2,3
Hausman
Test
-0.05
-0.84
3.9%
0.85
E-03
*0.02
6.0%
X2(5) = 212,4
* 10% level of significance
8
Table 6: Business Risk Equation of IOF and UAC
Variables
SUR(IOF)
3SLS (IOF)
SUR(UAC)
3SLS (UAC)
(fixed effect)
coefficient
PR
-2,88
MS
coefficient
*-0,11
1,95
-0,58
*-1,10
TLNW
0,08
ΤΕ
EXAG
R2
DW
LM
F
Hausman
Test
E-05
t-value
coefficient
t-value
coefficient
t-value
* 0,09
-1.89
-10.77
-2.74
-13.26
4,87
4,45
5.06
5.36
2.29
1.57
*62,66
0,05
6,86
0.46
E-02
2.16
0.01
2,56
E-11
*0,35
-3,41
E-11
*-0,02
0.34
E-10
1.01
0.15
-1,75
E-04
-0,10
-1,52
E-03
*-0,39
0.09
0.88
0.02
73,8%
1,8
4,3
E-04
t-value
(fixed effect)
70,7%
1,9
11.9%
1.03
*0.04
E-11
*0.22
13.1%
F(2308,4606)=1,05
X2(5) = 93,075
* 10% level of significance
9