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JOURNAL OF INTERDISCIPLINARY RESEARCH
values of this indicator, as mention in Figure 2, we can see, that
the highest average values were in case of Slovakia and Poland,
both over 10 %. The lowest value of average unemployment was
in the Czech Republic, in the amount of 6,15 %.
Figure 2 Average unemployment rate in V4 countries, 2003 - 2015
Source: own processing
Typical indicator for insurance and, of course, other industries, is
concentration ratio. We calculated Cr
5
, that means concentration
of 5 biggest insurance companies on the market. Market share of
insurance companies was calculated according to amount of total
written premium of each insurance company. The development
of Cr
5
in V4 countries gained slightly declining trend.
Figure 3 Concentration ratio 5
Source: own processing
Very important finding was, in the average value of that
indicator in V4 countries. The average values of concentration in
the 2003-2015 shown by Figure 4, show that the Slovak
Republic and the Czech Republic has a significantly higher
concentration rate than Poland and Hungary.
Figure 4 Average values of Cr
5
in V4 countries
Source: own processing
In this section we offer the findings from the verification of the
relationship among the specified variables. The relationship
among the GCI index and the selected markers is monitored
primarily through the Kendall coefficient, which describes the
linear relationship among the observed variables, with the
following results.
Table 1 Linear Correlation of the GCI Index and selected
indicators
UNEM
GDP
WP
life
Pen
Cr
5
Czech
Republic
-
0,1600
0,3842
-0,3159
-
0,4384
0,2896
Hungary
-
0,3091
-
-
0,5778**
0,0811
0,6221**
0,0270
Poland
-
0,3626
-0,3358
0,0534
0,0552
-0,3203
Slovak
Republic
0,0779
0,5360*
-
0,8572**
0,3676
0,0520
* the level of significance <0,05
** the level of significance <0,01
Based on the results from Table 1 we note the linear relationship
of the GCI index with the prescribed premium in two countries
(Hungary and the Slovak Republic). In the case of Slovakia, a
statistically significant linear series correlation with a year-on-
year change in GDP was confirmed and, in the case of Hungary,
a statistically significant linear market-to-market correlation was
confirmed. With the rising value of the GCI index, life insurance
premiums are decreasing, and at the same time a year-on-year
change in GDP and market concentration in selected countries is
rising.
The above-identified statistically significant relationships are
subsequently described using a simple regression analysis
method, the results of which are captured by the following
graphs.
The relation between the GCI index and the written premium in
life insurance, respectively market concentration in Hungary can
be described by these simple models
GCI = 78,1762 - 0,324166*WP
life
,
GCI = 47,1591 + 0,22529*Cr
5
,
whose strengths, respectively the quality, expressed by the
coefficient of determination (R
2
WPlife
= 0,7686; R
2
Cr5
= 0,7166)
pointing to the high impact of other factors.
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