CEPSA 2011, Vienna - Multilevel Politics: Intra- and Inter-level Comparative Perspectives
West and East: Still Mind the Gap?
Aggregate Analysis of Electoral Behavior in Central Europe1
Petr Voda – Michal Pink
First draft only for presentation at conference venue
Abstract
A series of changes has taken place in Central Europe after the fall of communism. Some of these
changes have been driven by the increased influence and relevance of a multilevel government, the
highest level in the hierarchy being Brussels and the lowest, the closest municipality. A question
remains as to whether these changes have been of a uniform nature, or whether they have influenced
different states – and even different locations within the same state – in different ways. This paper
focuses on patterns of electoral behavior in terms of the spatial distribution of election results and the
impact of social structure. It asks whether these patterns have become more similar in the Central
European region. The basic hypothesis is that the influence of socioeconomic characteristics is
becoming ever more similar with the continuing integration of Central Europe. The first phase of the
analysis will be quantitative in nature. We employ ordinary least squares regression and geographical
weighted regression, using data from elections and censuses, to describe similarities and differences
in the influence of electoral behavior determinants among countries (Austria, the Czech Republic,
Slovakia and Hungary), as well as the development of these factors. The second phase studies the
alignment of results in terms of important dates in the integration process.
Introduction
The countries of the EU have experienced different histories over the last half-century. A key
distinction dividing members is between those countries in the East which were under
nondemocratic communist regimes and those of the West, where development was
democratic. Countries on both sides of this division lie side-by-side in Central Europe, with
the dividing line running through Germany, between Germany and the Czech Republic,
Austria and the Czech Republic and Austria and Slovakia, Hungary and Slovenia. At times in
the past, these countries have been very close – almost identical in many respects – as well as
very different, as was the case from the 1950s until the late 1980s. Since that time, they have
once again grown closer and likely more similar, as well.
Over the last 20 years, many new actors have appeared on the political stage in these
countries, particularly on the eastern side of the dividing line. Numerous integration projects
have taken place in the region. The most influential would be the coming of the EU. Austria
acceded in 1995. Poland, the Czech Republic, Slovakia, Slovenia and Hungary became part of
the EU in 2004, after a lengthy entrance procedure. These milestones have played an
1
This text has been prepared as part of postdoctoral grant supported by the GACR—Political Regionalism in the
Czech and Slovak Republics and Changes in Voting Maps 1993–2010, No. 407/09/P042.
1
important role in changing the politics, policies and polities of countries in the region.
European integration are obviously very important for changing politics, policies as well as
polities of all these countries (see Carbone 2010, Goetz, Hix 2001, KriegerBoden, Morgenroth, Petrakos 2008 etc.). But how deep are these changes? A lot of articles
suppose some change in manifestos of parties. However, we do not know, how affected voters
are.
Our focus will not be on parties, policies or actors, but rather on the relationship between
parties and their voters in elections. We make the following assumptions: just after the
revolutions of 1989, Western and Eastern countries differed greatly from each other. In the
West, political parties obtained votes from different social groups and made different policies.
Over time, though, with capitalism taking hold, increasing wages and unemployment and, of
course, with the EU integration process, both societies and politics in the East have changed.
The question is: how have connections between parties and society developed in Western and
Central Europe?
The answer is not easy to find. Data from several sources and multivariate statistical methods
will be used to provide one. Manifestoes, party activities, governments and parliamentary
bodies will not be the focus; we will instead concentrate exclusively on the election results for
relevant parties in general elections. There are several potential ways to work with these
results. We will use data for the micro-regional level and employ "traditional" OLS
regression. To address problems associated with OLS regression in studying nonstationary
relationships, geographically weighted regression techniques will be used to take special
notice of the manner in which the relationship between election results and certain causal
factors varies across space (see Kavanagh 2006).
Regression analysis will be used to make comparisons. The focus will be on determining
whether parties within the same family but in differing countries show similar support
patterns and whether the changes these patterns undergo are similar in nature. It is here that a
key issue arises. In progressing from counting GWR and interpreting the results to making
comparisons between countries, we suddenly have only a highly restricted space to control
for. Thus it is not certain whether the changes observed have truly arisen from the causes
theorized, or whether another factor may be impacting the development of electoral behavior
in the countries selected.
To determine an answer to the second question, Rokkan’s theory of cleavages will be applied.
There are for original cleavages: urban vs. rural, center vs. periphery, owners vs. employees
and church vs. state (Lipset, Rokkan 1967: 9-23). Each of these cleavages could give rise to a
2
party. The four cleavages are based upon the structure of society during the 19th century,
when parties arose around conflicts between the aims of the urban and rural population,
between owners and employees, between religious and secular individuals and between
inhabitants of the center and the periphery. Particular sides in the conflict have changed over
the course of the last century, but its bases likely remain the same. Some people are more
motivated to vote for certain parties as opposed to others, because their social status differs
(Evans 2004:42-68).
For finding an answer to the question Rokkan’s theory of cleavages will be applied. There are
four original cleavages: urban – rural, center – periphery, owners – employees and church –
state (Lipset, Rokkan 1967: 9-23). On each of these cleavages some party could exist. These
four cleavages are based on the structure of society in 19th century, when parties arose around
conflicts between aims of urban a rural population, between owners and employees, between
religious and secular and between inhabitants of the center and periphery. These sides of
conflict changed throughout the last century but the bases are probably still the same. Some
people are more motivated to vote certain parties than others because they have a different
social status (Evans 2004: 42-68).
Several articles dealing with electoral behavior in early 90’s in post-communist countries
assume the Rokkan’s theory inappropriate in context of new democracies because of
historical and geographical bases of this theory as well as specific context of states after
falling communist regimes. Dalton (2000: 925-926) notices that emerging party systems are
unlikely to be based on stable group-based cleavages, especially when the democratic
transition happened rapidly as in Eastern Europe. Also new electorates are unlikely to hold
long-term party attachments that might guide their behavior. That is the reason, why the
patterns of electoral choice in new democracies are more involved by short-term factors like
candidate images and issue positions.
Several studies also exist concerning the impact of European integration on electoral
behavior. Gabel (2001: 52-54) sees research on the topic focused above all on the question of
issue voting (see de Vries 2007). He notes the example of Great Britain, in which EU issues
have created new electoral cleavages, and examines the importance of European monetary
policy in economics-based voting behavior. Tillman (2008) also addresses the impact of
attitudes towards European economic issues on voting behavior and national politics.
Such an approach imposes a number of limitations, especially in the comparative dimension
of analysis. The data itself may be hard, but changing conditions and modified meanings over
time and through space make it function rather more like soft data. The fact that a vote was
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cast for certain party in certain elections in certain country does not entitle us to compare the
meaning of the vote with one cast for similar party in election in another country. The
meaning may be better described by applying more general concepts. Thus the Rokkan’s
theory and Bayme’s classification of parties are used. But this will only mitigate, not
eliminate the limitations. These concepts may help us to locate cases in which the level of
comparability is higher than in others.
Other way to provide comparability is to select countries on an areal basis. We chose Austria,
the Czech Republic, Slovakia and Hungary with logic of Mill’s method of most similar cases.
All countries have similar history and cultural bases. The countries share common frontiers,
allowing the degree to which societies are contained within the geographic area of the country
to be determined, as well as what development is like in the Western and Eastern blocs. These
countries are also similar in terms of their political regimes. Each country has a parliamentary
democracy. The only distinction lies in the unitary character of the Czech Republic, Slovakia
and Hungary; Austria, by contrast, may be described as a federal state. All states, however,
have autonomous regional governmental elections, meaning the parliamentary elections have
almost the same meaning and sense in all of these countries.
The party systems of all the countries under consideration are fairly close. All of them are
pluralistic as regards the number of parties and are moderate as regards type under Sartori's
classification. The only exception is the Czech system, which is assumed to be semi-polarized
(Strmiska 2005). However, individual systems differ in time and space in terms of the number
of relevant parties. This probably forms the fundamental limitation on our conclusions and the
research as a whole. This becomes even more critical when we focus on development. It is
obvious that the party system has changed markedly since 1990, but it may be less obvious
that the parties themselves have also changed. There is no party in which the same
personalities are saying the same things as was said in 1990. Questions related to party
systems are important because the nature of a party’s system creates possibilities for voters.
From our perspective, the different structure of party systems might result in different bases of
electoral support for the parties.
There is a real threat that the composition of a party's electoral base will not adequately
represent the party's ideological profile. This depends upon the voters' ability to make
decisions concerning their vote. Their votes may be influenced by several other factors
described by psychological models and by issue-based models, for example: sympathy for a
particular candidate, identification with a party, or agreement with the party's attitude on some
key issue. All of these factors taken together cannot be captured in a single article. In this
4
case, then, the research problem has been simplified to include only the influence of social
stratification on election results.
Several methodological issues arise in terms of the comparability of electoral systems as well
as party systems. Chief among these are the Hungarian elections, which take place under the
rules of a mixed system (see Sedo 2009). Collections in the other countries (Austria, the
Czech Republic and Slovakia) use a proportional system. The problem arises because no
election results are available for areas smaller than electoral districts. Thus, results from the
majority party must be used. This represents a huge limitation in making comparisons, since
the conditions of the majority system are quite different to those in the proportional system. In
particular, there are strong incentives for strategic voting. Smaller parties are heavily
impacted, because there is almost no point in voting for them in a majority system (see
Abramson, et al. 2010). Hungary will nevertheless remain in the analysis but these potential
limitations must be borne in mind.
Data
The research question makes clear that data is needed concerning elections and societal
factors. The data used in this analysis derive from two sources. Electoral results for each
general election are taken from the central election commissions of the countries selected.
These election results will serve as the dependent variable for the first portion of the study.
The dates at which the elections were held are suitable for purposes of comparison. Every
country involved had elections in 1990, 2002 and 2006. There were elections in three
countries in 1994, 1998 and 2010 (see Table 1). Temporal context, then, presents virtually no
problem.
Table 1: General Elections in Central Europe in 1990 - 2010
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2001 2002 2005 2006 2007 2008 2009 2010
Austria
Czech
Republic
Hungary
Slovakia
Source: Parties and Elections
Societal data is derived from censuses that took place in 1991 and 2001 in all selected
countries. Independent variables have been selected using a multi-step process. First, cleavage
indicators under Rokkan’s theory are selected. All independent variables have some
relationship to a particular cleavage. The following variables have been selected: proportion
of self-employed persons, proportion of highly educated persons and the unemployment rate
5
as indicators for the economic cleavage; proportion of agricultural workers and urbanization
rate as indicators for the urban-rural cleavage; ethnic divisions in society for the centerperiphery cleavage; and for the church-state cleavage, the proportion of Christians.
In the second step, variables with no comparable data and variables lacking data for all
countries and censuses are eliminated. This leaves only the proportion of Christians,
agricultural workers, persons with a university education, unemployment and the rate of
urbanization. Finally, all variables demonstrating high global or local multicollinearity are
eliminated. The resulting list of variables is as follows: proportion of Christians, proportion of
agricultural workers and number of highly educated persons.
All data are tied to regional NUTS4 units, except for data relating to Hungary. The units
concerned are called ORP in the Czech Republic (Municipalities with Extended Powers),
Okres in Slovakia and Politische Bezirke in Austria. Results for Hungarian general elections
were available only for electoral districts, while census data was available for different census
regions. Data for the analysis was calculated using overlapping areas. Table 2 shows the basic
characteristics of regions. The number of units is also number of cases of further regression
analyses. Austrian, Hungarian and Slovakian units are very similar in average number of
inhabitants as well as average area. Czech micro-regions are smaller.
Table 2: Regional Units of Analysis
Number Averege number
of units of inhabitants
Average
area (km2)
Austria
121
68264,46
693,15
Czech Republic
206
51129,95
349,51
Slovakia
79
68305,92
620,70
Hungary
156
64012,82
596,35
Source: own calculation, based on Statistik Austria, ČSÚ, SŠÚ and valastazs.hu
Method
The focus is on relations between society and political parties. This means we require
indicators which tell something about the state of society, indicators concerning political
parties and a method which allows us to put the two together. Because we wish to inquire into
the relationship between election results and the social characteristics of particular localities,
we need a method capable of handling a large number of variables. For normally distributed
data, the best option is regression analysis. The most important results of the analysis are
6
contained in the value of (adjusted) R-squared and the beta coefficients. Adjusted R-squared2
shows how much variability in the dependent variable is explained by variability in the
independent variables (Field 2009: 206-207) and indicates how well the model fits. The Beta
coefficient shows the explanatory power of particular independent variables. Higher values
mean higher dependence of the dependent variable on that independent variable. There is no
need to employ inferential statistics, since the entire population of regional units is used for
the calculations (see Soukup, Rabušic 2007).
Because all used data have spatial attributes, spatially weighted regression should be a
superior technique because it allows for spatial autocorrelation. Some events are more likely
to take place because they are happening globally and are not due to local conditions. This
tends to invalidate the assumed independence of error terms, creating a potential challenge for
classical statistical inference (Fotheringham 2008: 243). Fotheringham (2002: 27) identifies
several advantages of the method. It is based on the traditional regression framework with
which most readers will be familiar and it incorporates local spatial relationships into the
regression framework in an intuitive and explicit manner. Geographically Weighted
Regression (GWR) departs from the traditional assumption that all causes have the same
response at every location. Instead, it takes spatial dependency into count. The result is not
only a single number for each parameter estimated, but a table showing different values for
each parameter at distinct locations.
In addition to the overall regression, GWR takes into account the effects of independent
explanatory variables on the dependent variable. The results of a GWR analysis are similar to
those of OLS regression. Model fit is measured by the sum of the squared residuals. The
smaller the value, the better the GWR model fits the observed data. Goodness of fit is
measured by R-squared, the same as in OLS described above. There are several differences
between GWR and OLS. The effective number of degrees of freedom is a function of
bandwidth, so the adjustment may be quite marked compared to a global model like OLS. The
effective number reflects a tradeoff between the variance of the fitted values and the bias in
the coefficient estimates and is related to the choice of bandwidth. In addition to regression
residuals, the Output feature class includes fields for condition number indicating
multicollineatity, local R-squared, local explanatory variable coefficients and coefficient
Standard Errors (Fotheringham 2002).
2
Calculations for the adjusted R-squared value normalize the numerator and denominator by their degrees of
freedom. This has the effect of compensating for the number of variables in a model, and consequently, the
Adjusted R2 value smaller than the R2 value.
7
Outcomes of general elections in 1990 - 2010
Before turning to the results of the analysis, let us briefly describe the subject matter of the
analysis: election turnout and election results in all elections to the lower chamber of national
parliaments. The figures are limited to results for parties occupying a relevant place in at least
three elections. Outcomes for parliamentary elections in Austria are highly stable throughout
the period of the study.
Figure 1: Electoral Turnout and Results of General Elections in Austria in 1990 - 2010
100
90
80
70
SPÖ
60
ÖVP
50
GRÜNE
40
FPÖ
30
Turnout
20
10
0
1990
1994
1995
1999
2002
2006
2008
Source: Parties and Elections (http://parties-and-elections.de/austria.html)
Electoral turnout is very high, around 80% in all cases (see Figure 1). The Social Democratic
Party of Austria (SPÖ) is the highest vote getter in every election. The only exception to this
comes in 2002, when the vote share of the Austrian People's Party (ÖVP) increased. These
two parties form the chief poles of electoral competition in every election during the period of
study. During the same period, the Freedom Party of Austria (FPÖ) dropped 17%. The final
party of relevance is the Green Party. In the two latest elections, a new party, BZÖ, arises to
challenge the system.
In the Czech Republic, election outcomes are not as stable as they are in Austria. There has
been significant change since 1990. Voter turnout decreased from 96% in 1990 to 62% in
2010 (see Figure 2). Party changes have also taken place. The Czech Social Democratic Party
(CSSD) and the Civic Democratic Party (ODS) were the chief players after the initial
elections, when competition primarily focused on the Civic Forum3 and the Communist Party
3
The civic Forum was broad movement. After elections in 1990 started disintegration. One of successor, and
the most important one, is th Civic Democratic Party (see Strmiska 2005).
8
of Czechoslovakia. Only two other parties present at that time are still part of the system in
addition to CSSD and ODS. They are the Communist Party and the Christian-Democratic
Union. There is some space for liberal parties, which are very unstable in the Czech Republic.
The Civic Democratic Union (ODA), Union of Freedom (US) and TOP09 may all be fairly
described as liberal as, with some reservations, may be the Green Party.
Figure 2: Electoral Turnout and Results of General Elections in the Czech Republic in 1990 –
2010,
100.00
90.00
80.00
70.00
ODS
60.00
CSSD
50.00
KSCM
40.00
KDU-CSL
30.00
Turnout
20.00
10.00
0.00
1990
1992
1996
1998
2002
2006
2010
Source: Parties and Elections (http://parties-and-elections.de/czechrepublic.html)
Elections in Hungary share features with the Czech elections. The party system was
simplified in the 1990s and voter turnout has also been unstable. In the case of Hungary,
however, voter turnout began at a relatively low figure of "only" 65% in 1990, decreasing to
50% by 2002, then beginning to grow once again (see Figure 3). Competition between the
Hungarian Socialist Party (MSZP) and Hungarian Civic Union Fidesz was the focal point of
elections, but in the most recent elections in 2010, MSZP’s vote share decreased substantially.
MDF and the Nationalist party MIEP remain in the system, the latter renamed Jobbik prior to
the most recent elections.
Figure 3: Electoral Turnout and Results of General Elections in Hungary in 1990 – 2010
9
100
90
80
70
MSZP
60
FIDESZ
50
SZDSZ
40
MDF
30
MIEP
20
Turnout
10
0
1990
1994
1998
2002
2006
2010
Source: Parties and Elections (http://parties-and-elections.de/hungary.html)
Figure 4: Electoral Turnout and Results of General Elections in Slovakia in 1990 - 2010
100
90
80
SDL/SV/SMER-SD
70
DS/SDKU-DS
60
SNS
50
MKP/Híd
40
LS-HZDS
30
KDH
20
Turnout
10
0
1990
1992
1994 1998
2002
2006
2010
Source: Parties and Elections (http://parties-and-elections.de/slovakia.html)
The most complicated party system is that of Slovakia, which has registered more change than
any other of the selected countries (see Figure 4). Many voters have ceased voting. Voter
turnout was 95% in 1990, but only 55% in 2006. During the 1990s, the populist People's Party
– Movement for a Democratic Slovakia (HZDS) was the leader. Recent elections have seen
Direction – Social Democracy (SMER) come to the fore. The second strongest party at
present is the Slovak Democratic and Christian Union (SDKU). The system also includes
representatives of the Christian-Democratic, nationalist and liberal party traditions, as well as
parties representing ethnic minorities.
10
The figures and information shown above reveal several similarities. Competition for voters
mainly takes place between two parties. In all cases, one of these is Social Democratic and the
other (liberal) conservative. The only exception is Slovakia, to a certain extent. There are
many parties in every election. One of them normally obtains significantly more votes than
the competition. Voter turnout is much higher and more stable in Austria than in the other
countries, but was also quite high in the Czech Republic and Slovakia during the early 1990s.
These countries have since witnessed a marked drop.
For purposes of comparison, it is useful to group parties into similar cases. For this purpose,
Bayme’s concept of political party families (Bayme 1985) or Lewis's modification for
Central-Eastern Europe (Lewis 2000) may be applicable. Both classifications have been
criticized in terms of their appropriateness for the post-communist context (Fiala, Strmiska
2004:13-25). They have been judged as being static, generic and West-centric in nature. But
no alternative solution has been offered, thus we will employ Bayme’s theory, adding to it an
"Others" category (see Table 1).
Table 1: Parties According to Bayme’s Classification in Selected Countries in 1990-2010
Communist
s
Austria
Czech
Rep
Hungar
y
Slovaki
a
KPÖ
KSCM
KSS, ZRS
SocialDemocr
ats
SPÖ
CSSD
Green
Liber
al
Conservativ
e
G
SZ
ÖVP
ODS
MSZP
LMP
Smer,
SDĽ
SZ
FPÖ
ODA,
US,
TOP0
9
SDZS
Z
SaS,
ANO
Christia
n-Dem
Extrem
e Right
KDUČSL
BZÖ
SPRRSČ
Fidesz
MDF
SDKÚ
KDH
MIEP,
Jobbik
SNS
Other
Minorities
VV,
LSU
HSD-SMS
VPN,
LSHZDS
Híd, MDK
Source: Strmiska et al. (2005)
There are about 40 relevant parties according to Sartori's rules in at least in 9 categories. But
only two party families are present in every country at every time. These are the Social
Democrats and the Conservatives4. Electoral support for parties of these two families will be
analyzed.
Analysis
This section will discuss the results of ordinary least squares regression and geographically
weighted regression. The analyses seek to answer the question of whether the electoral base of
parties in Central-Eastern Europe have become more similar to those of their counterparts in
4
The precise classification is not neccesessary to achieve the goal of this text. It is only way to better specify the
conditions of comparison.
11
Central-Western Europe. Voter turnout and results for social democratic and conservative
parties are explained by several indicators of social structure: the proportion of Christians in
the society, the proportion of agricultural workers in the economically active population and
the proportion of people with a higher education in the population 15 years of age or older.
Electoral turnout
The dependence of voter turnout on social structure changes over time in each country,
always with a pattern unique to that country: turnout in Hungary increases, turnout in Austria
and Slovakia decreases, and turnout in the Czech Republic fluctuates. The strength of the
relationship differs, as well. The model can account for three-quarters of the variance in voter
turnout in Austria, around 30-50% of variance in Hungary and the Czech Republic and only
10-20% of variance in Slovakia. The spatial character of the dependency also differs. In
Austria OLS provides results which are almost identical to GWR, but this is not true for the
Czech Republic, Hungary or Slovakia. In these latter countries, the effect of the independent
variables differs in different locations.
Table 2: Adjusted R-squared of OLS regression and GWR for Electoral Turnout
Austria
Czech
Republic
Slovakia
Hungary
OLS
GWR
OLS
OLS
OLS
GWR
OLS
GWR
1990
0,76
0,77
0,31
0,37
0,21
0,30
0,36
0,66
2002
0,45
0,43
0,46
0,52
0,12
0,26
0,57
0,66
2006
0,51
0,51
0,43
0,50
0,11
0,46
0,56
0,61
Social Democrats
This section analyzes results for the Social Democratic Party of Austria, the Czech Social
Democratic Party, the Hungarian Socialist Party and the Slovak parties Democratic Left and
Direction – Social Democracy. The model clearly loses explanatory power for Austria. The
OLS regression model explained 55% of variance in 1990, but only 20% of variance in 2008.
The manner in which the independent variables affect election results has also changed. Rsquared is almost identical for GWR and OLS in 1990. But by 2010, R-squared under the
GWR model is almost twice as high as that of OLS, indicating that identical variables now
vary in their influence depending upon location.
Table 3: Results of OLS regression for SPÖ
12
OLS
GWR
1990
0,55
0,66
1994
0,42
0,70
1999
0,21
0,38
2002
0,25
0,41
2006
0,21
0,39
2008
0,19
0,38
The Czech Social Democratic Party saw a change in support between the 1992 and 1996
elections. Since that time, there has been almost no dependency relationship involving
CSSD’s election results as indicated by OLS regression. The GWR shows another picture.
CSSD’s results show a higher level of dependency, since 1992 (with exception 2002) constant
over time, on the independent variable set.
Table 4: Results of OLS regression for ČSSD
OLS
GWR
1990
0,57
0,63
1992
0,39
0,44
1996
0,11
0,30
1998
0,12
0,33
2002
0,02
0,18
2006
0,16
0,36
2010
0,18
0,36
Both OLS and GWR reveal almost no dependency on religiosity or rural and educational
characteristics for MSZP’s results. With the decline of support for MSZP which occurred in
2010, the party's support became at least partially dependent upon these variables.
Table 5: Results of OLS regression for MSZP
1990
0,08
0,11
OLS
GWR
1994
0,12
0,18
1998
0,05
0,14
2002
0,12
0,24
2006
0,11
0,21
2010
0,28
0,33
Results from Slovakia must take into account the existence of four different social democratic
parties. All of them (except the Common Choice coalition in 1994) showed a high level of
independence from the social structure indicators, most easily visible from 2002, when SMER
first participated in elections.
Table 6: Results of OLS regression for SD, SV, SDĽ and SMER
OLS
GWR
1990
0,08
0,46
1992
0,07
0,58
1994
0,34
0,51
1998
0,14
0,29
2002
-0,01
0,05
2006
0,09
0,24
2010
0,07
0,20
As regards social democratic parties, parties in the East follow common sense in having a low
dependency for social democratic results on our selected independent variables. The only
exception is the Czech Social Democratic Party in the early 1990s. Once again, there is an
obvious drop in the explanatory power of the model when it comes to Austria.
Conservative Parties
This final section deals with results concerning the electoral base of conservative parties.
13
Explanatory power for the model is almost the same for the election results of ÖVP as for
SPÖ. Once again, there is a significant drop in the model's explanatory power. The OLS
regression model accounts for around 70% of variance in the early 1990s, but by 2008 can
explain only 45%. In contrast to the results for SPÖ, GWR and OLS models come out almost
the same and there is no significant change, as was the case with SPÖ.
Table 7: Results of OLS regression for ÖVP
OLS
GWR
1990
0,68
0,71
1994
0,65
0,70
1999
0,48
0,51
2002
0,45
0,48
2006
0,47
0,50
2008
0,46
0,48
Election results for ODS depend less on the selected variables than do the results for ÖVP.
This may be due to the civic nature of the party; ÖVP is a partially Christian-Democratic
party. But the dependence on social structure characteristics is reversed. The explanatory
power of the OLS regression increases from 25% in 1992 to 47% in 2006, before falling again
in 2010. The GWR model, however, shows a higher dependency, indicating high spatial
autocorrelation in the OLS regression estimates. Obviously, it is less important in 2010 than
in 1992.
Table 8: Results of OLS regression for ODS
OLS
GWR
1990
0,24
0,41
1992
0,25
0,43
1996
0,18
0,30
1998
0,41
0,55
2002
0,45
0,57
2006
0,47
0,59
2010
0,26
0,36
Election results for Fidesz are almost independent of the explanatory variables for all
elections. In 1994 and 1998, there is perfect independence, with the GWR model showing
very low values, as well. There is thus no dependency on the explanatory variables and no
dependency on results in neighboring regions in the 1990s. Since 2002, however, there has
been very little evidence of dependency for the selected indicators of social structure in OLS
results, but somewhat greater values for the GWR results.
Table 9: Results of OLS regression for Fidesz
OLS
GWR
1990
0,14
0,13
1994
0,00
0,03
1998
0,00
0,03
2002
0,23
0,39
2006
0,17
0,28
2010
0,15
0,33
Just as was the case for Social Democratic parties, there has been more than one conservative
party in Slovakia active in the period since 1990. The role was played by the Democratic
Party until 1994, by the Slovak Democratic Coalition in 1998 and, since 2002, has been
played by the Slovak Democratic Christian Union. The 1990 results show great strength,
14
perhaps because of the uniqueness of the situation after the fall of the communist regime and
the role of the civic movement Public Against Violence (VPN). These results, then, are
similar to those in Austria and the Czech Republic, with the exception of the existence of
different parties. The results for DS showed the highest level of dependence on our
explanatory variables, those for SDK and SDKU less so, but the results of the latter parties are
more dependent on those in neighboring regions than is the case for DS.
Table 10: Results of OLS regression for DS, SDK and SDKÚ
OLS
GWR
1990
0,09
0,11
1992
0,64
0,68
1994
0,64
0,69
1998
0,35
0,53
2002
0,48
0,75
2006
0,44
0,70
2010
0,42
0,61
The results for ÖVP and the Slovak conservative parties are obviously highly dependent upon
the independent variable because of the significant influence of religiosity present. The Czech
Civic Democratic Party shows less dependence and Fidesz, none at all.
Conclusions
This text deals with trends of electoral behavior in West and East Central Europe. The texts
from early 90’s mention the distinction between old and new European democracies. Analysis
made in this contribution shows some differences at the starting point of this analysis. The
electoral turnout and electoral results of Social Democrats and Conservatives were explained
by several indicators of social structure suggested by Rokkan’s theory of cleavages
(religiosity, proportion of agricultural workers and highly educated people) through OLS
regression and geographically weighted regression.
The results show that the explanatory power of model was high in early 90’s in Austria in the
case of electoral turnout, social democrats even conservatives. On the other hand, it was
significantly lower in case of electoral turnout and all Social democratic parties and
Conservative parties in the Czech Republic, Slovakia and Hungary.
While in Austria the explanatory power of both models is lower, their power increases in all
countries in the East. This means that Austrian parties are becoming less and less dependent
upon social groups, while their counterparts in the Czech Republic, Slovakia and Hungary are
experiencing greater dependence. Some caution must be taken in interpreting the results for
Hungary, since election outcomes in that country are based upon a majoritarian electoral
15
system. Thus, the answer on proposed question talks patterns visible at aggregate level
became more and more similar.
We found out something like unification of influence of social structure on electoral behavior,
but we examined nothing about possible causes of this situation. Changes in party support and
the party base behind these changes are probably not directly motivated by external factors
like European integration. There is no meaningful change in explanatory power between 2002
and 2006, although the Czech Republic, Hungary and Slovakia entered into the EU between
these two elections. Thus, the described trends seem to be rather independent on European
integration. Also if the R-squared tends ever higher, electoral results are better explained by
the selected variables than they are other variables (such as European integration). But
European integration may function as an intervening variable, making the picture less clear
than it would otherwise be.
16
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