0
Religious Voting and Class Voting in
24 European Countries
–
A Comparative Study
Oddbjørn Knutsen
Department of Political Science,
University of Oslo
Paper prepared for presentation at the XVII International Sociological
Association (ISA) World Congress of Sociology in
Gothenburg, 11-17 July, 2010.
RC18 Political Sociology,
Panel RC18.13.
Comparative Class and Religious Voting
1
Introduction
The study of the relationship between social structure and party choice is a classic topic
within political science and political sociology. The relationship between social structure and
party choice is part of most reports from election studies in many countries, but there are not
many works that make systematic comparative studies of the relationship. Most of the books
that are supposed to be comparative are organised with country-specific chapters and
present some comparative patterns in the introductory and/or concluding chapters (Lipset &
Rokkan 1967a; Rose 1974a; Dalton, Flanagan & Beck 1984; Franklin et al. 1992). However,
several more recent books or book chapters have tried to analyse the relationship in a more
systematic comparative setting (Nieuwbeerta 1995; Knutsen 2004; 2006, Oskarson 2005).
In their seminal essay on the development of the conflict structure in Western democracies,
Lipset and Rokkan (1967b: 15-23) focused on the historical origins and the major conflicts
between the political parties. They identified four central cleavages which had their
anchorage in the social structure:
1) The centre–periphery cleavage was anchored in geographical regions and related to
different ethnic and linguistic groups as well as religious minorities.
2) The conflict between the Church and the State pitted the secular state against the
historical privileges of the churches and over control of the important educational institutions.
This cleavage has more specifically polarised the religious section against the secular
section of the population.
3) The conflict in the labour market involved owners and employers versus tenants,
labourers and workers.
4) Finally, the conflict in the commodity market was between buyers and sellers of
agricultural products, or more generally, between the urban and the rural population.
Previous research has shown that religion and social class have been the most important
structural variables for explaining party choice. Rose and Urwin (1969) conducted one of the
first comparative analyses of the topic, examining the social basis of party support in 16
western democracies. They found that religion and social class were more important than
the other cleavages in the Lipset-Rokkan model, and that, contrary to conventional wisdom,
“religious divisions, not class, are the main social basis of parties in the Western world
today” (Rose and Urwin 1969: 12). In a comparative study that included most West
European countries (Rose 1974b: 16-18) compared the impact of religion, social class and
region on left-right voting on the basis of data from mainly the 1960s, and found that religion
and social class was much more important than region, but that religion was much more
important than social class in all the Catholic and religiously mixed countries. Only in Britain
and the Scandinavian countries was social class the most important predictor for left-right
party choice.
These findings are confirmed in numerous single-country studies. My own comparative
longitudinal study of eight West European countries from the early 1970s to the late 1990s
showed that the strength of the religious and class cleavages confirmed previous analyses in
the sense that the religious cleavage was more significant in the Catholic countries and the
religiously-mixed countries while the class cleavage was of more significant than religion in
Britain and the Nordic countries. There were few changes in the relative importance of these
main cleavages over time although there was a decline in many countries. The impact of the
religious and class cleavages were, however, approaching in most countries due to the fact
that the cleavages that traditionally had the largest impact showed the clearest sign of
decline. Religious variables were, nevertheless most important for explaining party choice in
most countries also in the late 1990s (Knutsen 2004: chap. 7).
In a comparative longitudinal study of six Western democracies Brooks, Nieuwbeerta and
Manza (2006) found, however, that class voting was larger than religious voting in most
2
countries. This study included, however, only one of the two aspects of religious voting (see
below), namely religious denominations.
In this chapter I examine the impact of the two most important social cleavages according to
many researchers, namely religion and social class. It is a comparative study of 24 countries
based on a common comparative data set, namely the European Social Survey.
Three variables tap aspects of the religious cleavage, namely religious denomination, church
attendance and a direct measure of religiosity. I consider these variables to tap the two faces
of the religious cleavage which will be explained below.
The conflict in the labour market is traditionally associated with class cleavage, and in a
broad sense, social class, education and income can be considered as class variables or
variables that tap the class cleavage. However, it is important to emphasis that in advanced
industrial societies such variables can also tap other aspects of societal conflict. This is
explained in details in the section on the class variables below.
Research problems and data
This paper is a comparative study of the impact of religious and class variables on party
choice in 24 countries in the 2000s. The overall research problems are how the religious and
class variables vary in their capacity to explain voting behaviour in 24 European countries.
The more concrete research problems are as follows:
1. What is the comparative strength of religious and class voting in European countries in the
first part of the 2000s (2002-06)?
2. Which party families contribute to religious and class voting (polarisation)? Are there
significant cross-country differences in this respect?
3. Do the religious and class variables polarise the parties along the traditional left-right
division or do we see patterns of cross-cutting in the sense that the religious and class
variables group the parties in ways that do not follow the traditional left-right division?
The data source for this paper is a cumulative file based on the three first rounds of the
European Social Survey carried out in 2002, 2004 and 2006. All countries that have
participated in at least two waves are included apart from Ukraine.
The data for the various waves are weighted for the following reason. The sample size for
the various waves varies, for some countries considerably. The intention with the weighting
is to balance the sample size between the various ESS rounds. The principle for the
weighting is to take the total N in each country and divide it by two or three depending on the
number of rounds the countries have participated in. The number of cases in the various
waves in then the same in each country, and the total N for each country is the same as in
the data set before the weighting.
This study does not examine trends in the impact of the religious cleavage over time.
Table 1 shows countries that are included in the study and the number of respondents in the
cumulative file. The table also shows that a few of the countries only have participated in two
of the three relevant waves. Since the former East and West Germany have fairly different
religious structures and different recent historical experiences which are relevant for the
social cleavages, I have decided to keep them as different units/countries in this study.1
1
This decision is influenced by cooperation with German scholars on a book project on “Society and Democracy
in Europe” where different researchers should analyse the relationship between social structure and different
political dependent variables. Due to the fact that social structures still are different in the two parts of Germany
3
< Table 1 about here >
The countries are grouped into five regions which deserves a few comments.
The category - the Islands – comprise two countries which are located is the same region,
but have very different party systems and religious structures, although they sometimes are
grouped together under the umbrella Anglo-Saxon. I have kept Britain and Ireland in a
separate category partly due to their geographical location and partly because this category
is a “residual” category which cannot reasonably to be grouped among the other categories.
France is grouped among the Central European countries although it is a borderline case to
Southern Europe. I partly used empirical criteria to decide between the Central and Southern
European category. On the variables tapping religious identification and religiosity, France
has for example a very different location than the „pure‟ Southern European countries.
Finally I have grouped East Germany among the Eastern European countries2 due to the
historical experiences with the Communist legacy.
The notion “Western Europe” will be used below to describe the four regions that do not
comprise the Eastern Europe category.
I will use the “region” of countries below to sum up some main trends for the patterns for the
24 countries by presenting average figures for each of the regions and then compare the
regions.
Research strategy and methodology
For studying the relationship between the religious and class variables and party choice in a
comparative setting the political parties are grouped into 10 party families. These party
families are the Conservative, Christian, Liberal, Agrarian, Ethnic-regional, Radical Rightist,
Green, Social Democrats, Left Socialist and Communist party families. The distribution of the
party choice variable across the 24 countries is shown in Appendix Table 1.3 The largest
party family according to the data is the Social Democrats followed by the Conservative,
Christian and Liberal party families. The other party families are much smaller. The average
size has partly to do with the fact that several party families are present in only some of the
countries. Only the Social Democrats are present in all countries. The Liberal and Christian
parties are present in 20 and 18 countries, respectively, while the Conservative, Radical
Right and Green parties are present in 14 to 16 countries. The other party families are
present in 6 to 10 countries. For details in this respect, see the Appendix table.
Due to the large number of countries and the complexity of the research topic, I start many
of the empirical analyses by presenting some general findings which are based on the main
patterns in the data. The analyses which represent these main patterns are based on a
pooled data analysis. The data from the 24 countries are pooled into one analysis where the
party choice variable is party families. In this pooled analysis each country is assigned the
same weight since the focus is on the average pattern across countries. Each country
represents one unit and all countries are weighted equally.4
and influence political phenomena in different ways, it was decided that the “two Germanys” should be
considered as separate units.
2
I use the notion Eastern Europe instead of Central and Eastern Europe because the former is shorter and since
another region is labelled Central Europe.
3
4
The classification of the parties into party families can be obtained from the author.
The number of cases with a party choice for all 24 countries were 71538 after the weight which set the number
of cases equal for the (two or) three surveys. The number of cases in each country was then set to 71538/24
which, after rounding, is 2980.
4
The party choice variable is based on the respondents‟ reported party choice during the last
national election in their country.
The division between parties to the left and those to the right is central in much literature on
predictors of party choice. This study examines both the impact of religious variables and
class variables on all parties in the party systems, and the impact on the left–right division of
parties. It aims to compare the impact on these two ways of treating the party choice
variable.
The traditional way of determining which parties to include in the socialist or leftist group is
that they should belong to the Communist, Left Socialist and Social Democrat party families.
(Bartolini and Mair 1990: 42–43; Franklin et al. 1992; Nieuwbeerta 1995: 36).5 The Green
parties were not part of the industrial conflict structure, and therefore did not belong to the
established left. The Green parties have been characterised as the post-industrial left. Many
of the issues and values that the Greens inject into the political arena are new, challenging
the established conflict structure and the established left. Therefore, I present and compare
the results of two analyses in which the parties in the Green party family are placed within
the non-socialist and the socialist party group respectively.6 The first grouping of the leftist
parties is referred to as the traditional left–right division below.
In the surveys the Green party family is present in 14 of the 24 countries. The portion of the
respondents reporting that they voted for the Greens varies from 15-16 % in Austria and
West Germany to 2-4% in Britain, the Czech Republic, Italy and Ireland. The countries
where the Greens are not present in the data set, are five East European countries (all apart
from the Czech Republic and East Germany), Denmark, Norway, Greece and Portugal.
The division between leftist and rightist parties is often used in analyses of the relationship
between social structure and party choice, but it is seldom asked whether this division really
taps all the impact of social structure on the entire set of parties. Indeed, researchers seldom
develop measures that can be used for this purpose. A major research problem in the
present work is to examine the extent to which the impact of the religious variables tends to
overlap or cut across the traditional left–right division of parties.
In Table 2, I have illustrated my approach for analysing the impact of the various sociostructural variables on party choice. The table shows an imaginary cross-tabulation of the
relationship between religious denomination and party choice in a party system with five
parties where parties A and B are the leftist parties, and C, D and E are the non-leftist
parties. The illustration is based on a situation with only one dominant denomination where
the values on the denomination variables are “no denomination and “Roman Catholic
denomination”.
< Table 2 about here >
In this imaginary example 75% of the sample indicates that they belong to the Roman
Catholic denomination. This can be seen from the marginal of the table. The first part of the
table shows the support for each of the five parties, percentage difference (hereafter PDI)
between those who belong to the Roman Catholic denomination and those who do not
belong to any denomination who vote for each party, the log-odds ratios (hereafter lor) for
the differences between “no denomination and “Roman Catholic denomination” and the
absolute magnitude of the percentage differences |PDI|.
5
It is the socialist (or leftist) parties that are defined positively; the non-socialist parties are those not defined as
socialist, according to the conventional rules for the socialist/non-socialist division.
6
It should be emphasised that this applies only to the Green parties that are grouped into the Green party family,
not those that are grouped among the Left Socialist parties. Both of these party families could be characterised
as New Left parties, but they have different history and partly also political profile regarding location on the
economic left-right dimension.
5
The lor-scores are not affected by the size of the parties in contrast to percentage difference
measures and can be used for comparing the impact of religious denomination on the
various parties in the party system. In the table we see that the absolute magnitudes of the
lor-scores for party C and D are larger than for party B although the latter has a larger PDIscore (in absolute magnitude) than the two other parties. Thus, religious denomination has a
larger impact on parties C and D than on party B.
While lor is relevant for comparing individual parties, PDI is highly relevant for analysing the
impact of religious denomination on the whole party system; that is on “party choice”. For the
impact on party choice larger parties normally contribute most. The larger percentage
differences are the most important for the overall impact of the structural variable on the
party system, and a larger percentage difference is more easily obtained for a large party. A
relevant PDI measure for the impact of religious denomination on party choice is obtained by
summing the absolute values of the various PDIs and dividing by two. This is done in the
column to the right in the table.
In the last part of the Table 1 I have summed up the support from for the leftist and rightist
parties and calculated similar measures for left–right party choice. In this case the impact of
religious denomination on left–right party choice is considerably smaller than the total impact
from the first part of the table according to the PDI. The reason for this is easy to see from
the first part of the table. The impact of religious denomination seems to cut across the left–
right division to a certain degree. The leftist party A gets stronger support from the Roman
Catholics, while the opposite occurs for party B. We find a similar division among the rightist
parties. The left–right division taps only 11 of the total 26 percentage points (hereafter pp.)
which is the total impact of religious denomination on party choice. The portion 11/26 = 0.42
is the degree of overlap, that is the degree to which the impact of religious denomination on
left–right party choice taps the whole impact of religious denomination‟s impact on party
choice. The inverse concept in this respect is cross-cut, which in the example is 15/26 =
0.58.7
If the impact of religious denomination on left–right party choice is the same as the total
impact (26 pp.) in this case, the overlap is 1.00 and if religious denomination has no impact
on left–right party choice the overlap is 0.00.8
The focus on overlap and crosscut is also the main reason why I use PDI as a statistical
measure in the bivariate analyses of the relationship between the socio-structural variables
and party choice. As shown above, there is a close relationship between this measure and
the degree of overlap and crosscut since this measure is the basis for these calculations.
In the example above, the religious denomination variable has only two values and it is fairly
easy to calculate the degree of overlap. If there is more than one significant denomination,
the degree of overlap can be calculated by estimating the degree of overlap between no
denomination and each of the denominations and then by weighting the figures on the basis
of the sizes of the denominations in the sample (see under “religious denominations” and
explanations under Table 4 below). When the religious or class variable is a variable with
several values and has a higher level of measurement, there might be a problem where to
set the cutting points for calculating the overlap. This might be complicated because the
distributions of the religious and class variables can be very different cross-nationally (see
the discussion under the analysis of “Religiosity and church attendance” below).
I use the eta-coefficient and Cramer‟s V (for religious denomination and social class) as my
main statistical measures for tapping the relationship between the structural variables and
party choice. It should be underscored that for these measures all values for the religious
7
15 are calculated by subtracting the sum of the |PDI| from the second table (11 pp.) from the sum from the first
table (26 pp.).
8
For details and additional illustrative examples, see Knutsen (2004: 34-41).
6
and class variables are treated as separate values. They are not collapsed as for the PDI
measure. For measuring class voting several other measures are used.
The two ways of treating the party choice variable are sometimes referred to as ”total party
choice” (based on all parties as separate values) and “left-right party choice” (based on a
dichotomous variable).
The multivariate analyses are based on logistic regression for left-right party choice and
multinomial logistic regressions for total party choice. Logistic regression are preferred to be
used for analyses of dichotomized dependent variables, and multinomial logistic regression
(MNLR) can be used to perform multivariate analyses with nominal-level dependent
variables like total party choice. I use the pseudo R squared measures of model fit from
these analyses to compare the impact of religious variables and class variables,
respectively, in different countries and to compare the relative impact of religion and social
class. I use one of the measures, namely Nagelkerke‟s R2 which has the property that it
varies between 0 and 1.9
It should first be emphasized that even thought these measures are considered as
equivalent to explained variance, they are not tapping explained variance. For multinomial
logistic regression with a nominal level dependent variable, explained variance is not
meaningful. Instead, these measures tap the improvement in the log likelihood model from a
baseline model with parameters for the independent variables equalling zero (Pampel 2000:
48-52).
There is no standardised measure for each independent variable in MNLR or in logistic
regression. I therefore use the pseudo R squared measure also for comparing bivariate
correlations between total and left-right party choice, and different religious and class
variables. An additional reason for using pseudo R squared as a measure of bivariate
relationships is that these statistical methods can treat both nominal and interval variables as
independent variables. It is then possible to compare the impact of variables with different
levels of measurement in a simple way.
Pseudo R squared measures are then less easily interpretable than linear R squared, and
the various measures show different figures. These measures are then not the same as
“explained variance”, but they are useful for examining the relative importance of various
groups of variables and to compare the impact of the same variables across countries as in
the present case. Instead of the notion “explained variance” I use the notion “explanatory
power” frequently below.
It is unproblematic to compare the fit of different models with the same dependent variable. It
is somewhat controversial whether one exactly can compare the explanatory power for the
same set of variables from different data sets which in this paper means different countries.
In this paper I assume that the problems with comparing such models are small. I also try to
compare the results based on pseudo R squared with other statistical correlation measures
in order to examine if they show the same relative strength of different variables.
Organisation of the paper
I have organised the article in the following way: I first examine religious voting in Europe,
then class voting and finally I compare the impact of religious variables and class variables
on party choice. This part also contains multivariate analyses of comparative variations in
the impact of both religious and class variables.
9
For an overview of different measures tapping the goodness of fit in logistic regression analyses, see Menard
(2002: 17-27)
7
For each section I first examine the relationship between the structural variables and party
families based on the pooled data. The analysis based on the party families for all the 24
countries combined will be called the pooled analysis below. I then focus on comparative
patterns by examining cross-national variations in the strength of the relationship between
the religious variable and party choice, and the class variables and party choice, where party
choice is treated as a nominal level variable. Due to lack of space I do not report in detail
where the parties within the various countries are located on the religious and class
variables, but when the pattern deviates considerably from the pooled analysis of party
families, this is reported.
Then I examine the comparative patterns for the strength of the correlations for left–right
party choice based on the PDI measure, and the degree of overlap.
Finally, I examine the total explanatory power of the three religious variables on total party
choice and left-right party choice and compare the impact of these variables. The same is
done in the section on the class variables.
8
Religious voting10
Introduction
In their seminal article on the development of the party cleavages in western democracies,
Lipset and Rokkan (1967b) were impressively detailed about the development of the
religious cleavage. The religious cleavage was first shaped by the Protestant Reformation,
which created divisions between Catholics and Protestants. These divisions had political
consequences because the control of the nation-building process often became intermixed
with the religious cleavage. Protestants frequently found themselves allied with nationalist
forces in the struggle for national autonomy. In Anglican England and the Calvinist
Netherlands, the Protestant church supported national independence and became a central
element of the emerging national political identity. In other nations, religious conflicts also
ran deep, but these differences side-tracked the nation-building process (Dalton 1990: 66;
Martin 1993: 100-108).
Gradually the political systems of Europe accommodated themselves to the changes
wrought by the Reformation. The French Revolution renewed religious conflicts in the
nineteenth century. Religious forces – both Catholic and Protestant – mobilised to defend
church interests against the Liberal, secular movement spawned by the events in France.
Conflicts over church/state control, the legislation of mandatory state education and
disestablishment of state religion occurred across the face of Europe. These conflicts often
were intense, as in the Kulturkämpfe in Germany and Switzerland. In reaction to these liberal
attacks, new religious political parties formed in Germany, the Netherlands, Switzerland,
Austria, Italy and Belgium. These parties ranged from the Calvinist Anti-Revolutionary Party
in the Netherlands (named in reaction to the French Revolution) to the Vatican-allied
Catholic Partito Populare in Italy (Dalton 1990: 66-67). The party alignments developed at
the start of the twentieth century institutionalised the religious cleavage in politics, and many
basic features of these party systems have endured to the present.
The religious cleavage has two aspects: the various religious communities of which people
are members, including a category for those who are not a member of any religious
community (religious denomination); and how religious they are – independent of the
religious community they belong to (Bean 1999: 552; Dalton 2008: 152-158). This latter
aspect is normally measured by frequency of church attendance.
Many researchers have noted that there is a somewhat paradoxical situation related to the
importance of the religious cleavage. Only a small number of political issues clearly follow
the religious/secular conflict line. By the same token, there are very few issues that are
completely divorced from them. Despite the paucity of explicitly religious issues and the lack
of religious themes in most campaigns, religious beliefs have proven to be a strong predictor
of party choice in many West European democracies. Smith (1989: 20) has therefore
characterised the religious cleavage as a passive rather than an active force in shaping
political behaviour.
Perhaps the most important reason why religion continues to play an influential role for voter
choice is that religious conflicts helped determine the structure of the modern party system
and therefore still affect the electoral choices open to the voter. The religious cleavage is
also important because it reflects deeply held human values, which have a great potential for
influencing behaviour. Although religious issues are not very prominent on the political
agenda, religious values are related to a wide range of social and political beliefs: work
ethics, achievement aspirations, life-style norms, parent-child relations, morality, social
relations, attitudes toward authority and acceptance of the state. Religion signifies a
10
This section is based on Knutsen (2010).
9
Weltanshauung that extends into the political area (Dalton 1990: 86). Religious faith is
strongly connected not only to party choice. The connection encompasses political ideology,
issue outlook, and attitudes towards a wide range of political objects (Wald 1987: chap. 3).
Several studies have examined the impact of the religious cleavage (the two faces of it or
only one) over time and in a comparative setting (Dalton 1990: 82-88; Dalton 2008: 152-160,
Elff 2007: 279-284; Inglehart 1977: 216-225, 245-249), and numerous studies have focused
on trends within a single country. The main findings from these studies are that although
there has been a considerable change in the distribution on the religious cleavage variables
in the direction of a more secular mass public, the correlation with party choice has shown a
surprising persistence at a high level. For example, Dalton (2008: 159) compares the impact
of religion on voting with the impact of social class in a comparative longitudinal study and
concludes that “the trends for religious voting do not show the sharp drop-off found for class
voting”.
My own longitudinal study of eight West European countries from the early 1970s to the late
1990s based on Eurobarometer data showed, however, considerable decline in the impact
of religion on party choice in the countries where the religious cleavage has been most
pronounced in the 1970s, Belgium, France, Italy and the Netherlands. Due to these declines
there was a trend towards convergence in the impact of the religious variables on party
choice at a somewhat lower average level than in the 1970s. There were, however, also
signs of a considerable persistence in the impact of religion in the other countries (Knutsen
2004: chap. 2, 3, 234-236).
Similar findings are reported in van der Brug, Hobolt and de Vreese (2009: 1274-79) on the
basis of the European Election Studies. They found that there was a significant decline in the
impact of religious variables on party choice from 1989 to 1999, but then a small increase
from 1999 to 2004 based on data from the countries that were EU-members for the whole
period they examined.
Operationalisation of the religious variables. Comparative patterns regarding
religious structures and beliefs.
I start the empirical analysis with presenting the three variables that tap the religious
cleavage and a comparative analysis of the distributions of these variables.
The three variables tapping religious affiliation and religiosity in the European Social Survey
are based on the following questions:
Religious denomination
“Do you consider yourself as belonging to any particular religion or denomination?”
Those who answer “yes” are then asked “Which one” and the interviewer should code the
reply into one of several response alternatives.
It is evident from the annotations provided to aid translation that the first of these questions
is supposed to tap identification with a religious community, not official membership.11
To the extent that religious denomination can be considered as a structural variable (see the
discussion below), the question is then not ideal for tapping religious structure since the
notion “consider yourself” is used.
Self-declared religiosity
11
In the source questionnaire the annotation in a footnote is as follows: ”Identification is meant, not official
membership”.
10
The second question tapping self-declared religiosity is asked in the following way:
“Regardless of whether you belong to a particular religion, how religious would you say that
you are?”
The respondents are then shown a card with a scale from 0 (Not at all religious) to 10 (Very
religious). This question is strictly self-declared religiosity since the respondents are asked to
place themselves on a scale. It is not a question about concrete religious beliefs.
Church attendance
The question on church attendance was asked to all respondents in the following way:
“Apart from special occasions such as weddings and funerals, about how often do you
attend religious services nowadays?”
The respondents were then shows a card with the following alternatives:
Every day
More than once a week
Once a week
At least once a month
Only on special holy days
Less often
Never
(10.00)
(8.33)
(6.77)
(5.00)
(3.33)
(1.67)
(0.00)
The variable is in this paper transformed into a ratio-scale with values from 0 to 10 in
accordance with the figures in parentheses to the right-hand side. These figures were not on
the card which the respondents got.
Table 3 shows the percentage of the samples in the various countries which replied that they
considered themselves as belonging to any religious denomination and the means on the
two other variables, namely the self-declared religious and the church attendance variable. A
high degree of religiosity and religious identification have the highest values on all these
three variables.
< Table 3 about here >
There are large comparative differences for all the religious variables and the ranking of the
countries are very similar. This is also indicated when the scores on the three variables for
the 24 countries are correlated. They are all strongly correlated (0.84-0.86) indicating that all
three variables tap a religious/secular cross-national dimension.
The highest level of religiosity and religious identification are found in the Southern
European countries and in Ireland, Poland and Slovakia, while the lowest level is found in
The Nordic countries and in several East European countries. France also has a low level of
religiosity and religious identification. These differences are also reflected in the average
scores in the latter party of the table (see Table 3D).
On the religious denomination variable we can examine two different aspects, namely the
portion who indicates that they do (not) consider themselves to belong to any religious
denomination, and the dominant denomination among those who declare that they do
adhere to a denomination. Since the portion who considers they not to belong to any
religious denomination varies so largely between countries, I delimit the analysis of the
comparative variations in religious denominations to group the countries according to the
dominant denominations in the various countries. This corresponds to the historical
differences between the countries in this respect.
11
Protestant
The Nordic countries,
(Britain (even when Northern Ireland is excluded) is a borderline case to the religiouslymixed countries and can also be grouped there).
Roman Catholic
Austria, Belgium, Ireland, Luxembourg, France, Italy, Spain, Portugal
Poland, Slovenia, Slovakia.
Orthodox
Greece
Religiously mixed
The Netherlands, Switzerland, West Germany, East Germany, Hungary (Roman Catholics
and Protestants)
Britain (Established Protestant churches, non-conformists and Roman Catholics)
Estonia (Protestant and Orthodox)
Several other Christian denominations and also non-Christian religions were also coded in
the surveys, but they comprised each only a few percentages of the samples. For example,
the sum of the various non-Christian religions (including “Jewish”, “Islam”, “Eastern religions”
and “Other non-Christian religions”) comprise less than 4% in all samples in all countries.
Below I only examine the voting pattern or these categories in the pooled data.
12
The impact of the religious variables on party choice
Religious denominations
The religious denominations that people belong to can be considered as the structural
aspect of the religious cleavage (Knutsen and Scarbrough 1995: 499-500). The
denomination that families belong to, is frequently transferred from generation to generation.
The religious structures are very different cross-nationally as we have seen above.
Based on the pooled data I will mainly comment on the voting behaviour of the Roman
Catholics and the Protestants in relation to those who do not consider themselves to belong
to any denomination.
The first comment, however, is related to the voting behaviour of those who belong to the
Islamic and other non-Christian religions. They are inclined to support the leftist parties, in
particular the Social Democrats and the Greens.
The comparison of those who belong to the Roman Catholic denomination and those who do
not belong to any denomination, shows that in addition to the Christian parties, the
Conservative and Ethnic-regional parties have a larger portion of the vote from the Roman
Catholics while all other party families (apart from the Radical Right where there is no
difference) get stronger support from those who do not belong to any denomination. Among
the parties that get strongest support among the non-affiliated the differences are largest for
the Greens and the Left Socialists according to the lor scores and among the Social
Democrats, Left-Socialists and Liberals according to PDI.
Some of the same patterns are found based on a comparison between the non-affiliated and
the Protestants. The Radical Right, however, is inclined to get stronger support from those
who belong to a Protestant denomination, and there is no difference for the Conservative
parties.
For the comparison of both the Catholics and the Protestants with the unaffiliated we find
that all leftist party families get stronger support from those who are not affiliated with any
religious denomination, while in particular the Liberal parties among the non-leftist parties do
get stronger support from those who are not affiliated. Since the Green parties get stronger
support from the unaffiliated, the overlap is largest when these parties are grouped among
the leftist parties. The degree of overlap is somewhat smaller for the Protestants (0.49 and
0.60 when the Greens are placed in the leftist group) than for the Roman Catholics (0.57 and
0.72, respectively) based on the pooled data.
Tables 4 A and B shows that the strength of the denominational cleavage in a comparative
perspective by means of the Cramer‟s V coefficient and the PDI measure.
< Table 4 about here >
The impact of religious denomination on party choice is largest in the Netherlands according
to both Table A and B, but there are several differences regarding the ranking of countries.
One particularly large deviation is Poland which has a comparatively low correlation
according to Cramer‟s V, but is ranked second according to PDI in Table B. This difference
is probably caused by the skewed distribution on the religious denomination variable (see
Table 3A). Such a distribution does not course a large correlation for correlation coefficients
like CV, but the PDI measure indicate that the Catholics and the affiliated nevertheless vote
very differently.
The impact of religions denomination on party choice is - according to the average etacorrelations in Table E - largest in Central Europe while there are only small differences
between the other regions (Cramer‟s V 0.149-0.172) apart from the Island category where
the impact is small. The ranking of the regions is somewhat different based on the PDImeasure.
13
Table C shows the PDI measure which is based on the left–right division of parties, and
where those who do not belong to any religious denomination are compared with those who
belong to the major denomination(s).12 In all countries those who do not belong to any
religious denomination are more likely to support the leftist parties than those who belong to
one of the major religious denominations although the differences are small in Estonia and
Slovakia. We note that the correlation with left–right party choice is not considerably larger in
the Netherlands than in the many of the other countries with a strong denominational voting
differences according to Table A and B.
The impact of religious denomination on left-right party choice is – according to the average
scores in Table E – largest in the Central European countries, followed by Southern, the
Islands, Northern Europe and finally Eastern Europe.
Table D shows that the degree of overlap is very high in most countries. Only in five
countries is the degree of overlap less than 0.50. In average level of overlap in the various
regions is fairly similar for all regions apart from Eastern Europe. While the average varies
between 0.70 and 0.83 for the former regions, the corresponding figure for the Eastern
European countries is 0.41. We note in particular the low degree of overlap in Estonia,
Slovakia, Slovenia and the Czech Republic.
The low degree of overlap in several East European countries is mainly caused by Liberal
parties who do get stronger support from those who do not belong to any religious
denomination. The same applies to the Netherlands where the degree of overlap also is
smaller than in most West European countries. The Netherlands has two liberal parties (VVD
and D66) which both get strongest support from the non-affiliated. The lower degree of
overlap in the Netherlands explains why the left–right correlation is comparatively smaller
than the correlation with party choice in Tables A and B.
In Denmark and Norway where the overlap is fairly low, the most important component
explaining this is that the radical rightist parties get stronger support from the non-affiliated.
The same applies to Belgium.
In Greece the voting pattern of those who belong to the Orthodox Church and those who do
not consider themselves to belong to any denomination is small. The degree of overlap is
also relatively small. The major reason for the fact that religious voting in Greece cut across
the left-right division of parties is that the social democratic PASOK get strongest support
from those who belong to the Orthodox Church.
Religiosity and church attendance
One may argue that frequency of church attendance and the eleven-point scale tapping
(self-declared) religiosity, taps much of the same aspect of the religious cleavage. Research
carried out by Jagodzinski and Dobbelaere (1995: 87–90) has shown very strong correlation
between church attendance and more direct measures of religiosity at the individual level. On
the basis of European Value Surveys (I and II), they found that the correlations vary between
0.41 and 0.73 in different countries. Jagodzinski and Dobbelaere concluded that these two
variables tap the same aspect of the religious cleavage. Church attendance can then be
considered as an indirect measure of religiosity, and is not a structural aspect of the religious
cleavage in the same way as religious denomination.
12
For the religiously mixed countries the PDI is calculated by weighting the figures for the various denominations,
see note to Table 4.
14
This is confirmed in analyses based on the cumulative ESS file. The correlation between
church attendance and religiosity is on average 0.58 for the 24 countries. The correlations
vary between 0.51 and 0.68 apart from Greece where it is lower (0.45).13
Based on the pooled data for the 24 countries the correlation with party choice is somewhat
larger for church attendance (0.273) than for (self-declared) religiosity (0.218). The average
location of the party families on the two scales shows that they are ranked completely
identically. In addition to the Christian party family, religious voters support Conservative,
Agrarian and Ethnic-regional parties, while secular voters support all the leftist party families
(including the Greens) and also the Liberal parties. The average for the Radical Rightist
parties is close to the average for all party voters for both variables.
The degree of overlap is very high, 0.83 when the Greens are grouped among the leftist
parties for church attendance, and 0.82 for religiosity based on the pooled data.14
For the comparative analysis the PDI measure is calculated by collapsing the three
categories for those who report that they attend church 1) every day, 2) more than once a
week and 3) once a week. This collapsed category is then compared with those who never
attend church. The distribution on the church attendance variable varies considerably
between countries as we have seen above and in most countries a small percentage report
that they attend church more than once a week (categories 1 and 2 above). However, for a
few countries the portion is (much) larger than in the other countries. This applies to Poland,
Ireland, Slovakia, Italy, Portugal and Greece (see Table 3B). I have – as an alternative –
collapsed only two categories (1 and 2 above) for these countries. This reduces the religious
portion of the samples considerably and increases the PDI measure somewhat. I have
nevertheless chosen to rely on a consistent coding for all countries for calculating the PDI
and the degree of overlap. The PDI measures for the 24 countries (based on the consistent
coding and the alternative coding) are both strongly correlated with the strength of the etacoefficients reported in Table A (0.90 and 0.91, respectively).
The strength of the correlations between church attendance and party choice and religiosity
and party choice, respectively, are similar although the correlations for church attendance
are somewhat larger.15 The difference is, however, somewhat smaller than for the pooled
data set: The averages correlations with party choice for the 24 countries are 0.300 and
0.262 for church attendance and religiosity, respectively. The correlation between church
attendance and party choice is larger than the correlation for religiosity for 20 of the 24
countries. Here, I therefore focus on church attendance.
< Table 5 about here >
There are strong variations in the correlation between party choice and church attendance.
The correlations are strongest in the Netherlands, Norway, Slovenia and Finland and
weakest in Britain and Estonia.
Somewhat surprisingly the average strength of the correlation is strongest in the Nordic
countries and then Central Europe, followed by Eastern and Southern Europe, while the
average correlation is the Islands is smallest due to the extremely low correlation in Britain
(see Table E).
13
These correlations at the individual level should not be mixed with the correlations between the comparative
means based on the 24 countries reported earlier, which are considerably stronger, 0.85
14
The degree of overlap is 0.69 and 0.70 respectively when the Greens are not grouped among the leftist
parties. The degree of overlap for church attendance was calculated by comparing those who attend church at
least once a week with those who never attend church (see text), while for religiosity overlap this was calculated
by collapsing the two extreme categories on the 11-point scale and then using these collapsed categories as a
basis for comparison.
15
The similarity is expressed when the eta-correlations for church attendance and religiosity are correlated for
the 24 countries. This correlation is 0.90.
15
The Green parties get consistently across the countries where they are significant in the
data, stronger support from the secular segment of the population. They are therefore
grouped among the leftist parties in the analyses of the left-right division below.
The ranking of the countries based on the left–right division of the parties shows a different
ranking on the top of the list (see Table C). Three countries are now ranked ahead of
Norway and an additional fourth country ahead of the Netherlands and Slovenia is among
the countries where the correlation with left–right party choice is lowest.
The strength of the correlation between church attendance and left-right party choice is
largest in Central Europe according to the average correlation, followed by the Nordic
countries and Southern Europe, while the average correlation is considerably smaller in
Eastern Europe and the Islands as can be seen from Table E.
The degree of overlap is remarkably high for most countries. In the seven countries where
the overlap is lowest (less than 0.60) we find the same four East European countries that
had the lowest degree of overlap on the religious denomination variable (see Table 4D) and
Denmark, the Netherlands and Norway.
In Southern and Central Europe and the Islands the average level of overlap is remarkable
high according to the average score while the figures for the Nordic countries and Eastern
Europe are smaller (see Table E).
For the East European countries it is again the Liberal parties that contribute to almost all
crosscut by gaining considerably more support among the secular segment of the voters.
The same applies to The Netherlands and voters for the radical rightist List Pim Fortuyn also
recruited more voters from the secular segment. In Belgium where overlap also is
comparatively small (see Table 5D) the Liberal and Radical Rightist parties contribute
equally is this respect. Both party families16 get strongest support from the secular segment.
In Norway, both the Conservative and Radical Rightist parties gain strongest support from
the secular segment. The same applies to the Agrarian Liberals and the radical rightist
Danish People‟s Party in Denmark. In Norway the components attached to the two
mentioned parties are fairly similar, while the pattern for the Agrarian Liberals is dominant in
Denmark.
The Conservative parties in the Nordic countries are interesting cases regarding the religious
cleavage. Apart from the Danish Conservative Party, these parties gain considerably
stronger support from the secular segment: The support from those who attend church once
a week and more frequently and those who never attend church, respectively are 14% and
20% in Finland, 6% and 20% in Norway, and 9% and 23% in Sweden. In Denmark we do not
find the same pattern for the Conservative party (11% and 6%) but for the considerably more
successful Agrarian Liberal Party (19% and 32%) which has taken many voters from the
Conservatives during the last 15-20 years.
We do not find equivalent tendencies among the leftist parties: All leftist parties get strongest
support from the secular segment of the population. There is, however, one important
exception, the social democratic PASOK in Greece get stronger support from those who
attend church frequently. This is the main explanation for why the overlap in Greece is fairly
low.17
16
I use the notion “party families” since there are two parties within each family, one Flemish and one
Francophone.
17
All the mentioned causes of low degree of overlap for frequency of church attendance are also found for the
self-declared religiosity variable.
16
Multivariate analyses
I first analyse the total explanatory power of the three religious variables from multinomial
logistic regression analysis indicated by the pseudo explained variance measure
Nagelkerke‟s R2 where party choice is treated as a nominal level variable and all parties are
included as separate values.18
Based on the pooled data the explanatory power of the religious variables indicated by
Nagelkerke‟s R2 is 0.164, but this varies largely cross-nationally as can be seen from Table
6A and the corresponding part of Table E.
< Table 6 about here >
The average impact is strongest in Central Europe followed by Northern and Eastern
Europe, and surprisingly small in Southern Europe and smallest on the Islands.
Nevertheless, the differences according to the average regional explanatory power are small
between the three first mentioned regions.
The impact of the religious variables in some of the Central European countries is
remarkable small in some of the countries compared to other studies. For example, in
France, Luxembourg and West Germany the explanatory power is less than the average for
all countries.
The explanatory power of the religious variables in Southern Europe is also small: We note
from the table that the impact of religious variables is smaller than the average in three of
these countries namely Greece, Portugal and Italy. Only in Spain is the explanatory power
slightly above the average for all countries.
The small impact of the religious variables in Italy is remarkable compared with previous
findings. This indicates that there has been a large decline in the impact of these variables
from the First to the Second Republic, probably associated with the dramatic changes in the
party system.
I have then examined the explanatory power of the socio-structural variables on the left–right
party choice by means of logistic regressions. The party choice variables are in these
analyses dichotomised into leftist and rightist parties and the Green parties are grouped
among the rightist and leftist party groups respectively.
I have systematically compared the impact of the religious variables on left-right party choice
by grouped the Green parties among the leftist and the non-leftist parties. For the religious
variables the explanatory power is largest when the Greens are grouped among the leftist
parties since these parties consistently tend to get stronger support from the secular
segment of the populations. For example, the explanatory power of the religious variables on
left-right party choice is 0.059 when the Green parties are placed among the leftist parties
and 0.049 when they are grouped among the non-left parties according to the pooled data.
Table B shows also considerable variation in the explanatory power (again measured by
Nagelkerke‟s R2) on left-right party choice when the Green parties are located among the
leftist parties.
The comparatively explanatory power of the religious variables is not so large in the
Netherlands and Switzerland as for total party choice, and we find Slovakia at the bottom of
the list even thought the impact of the religious variables on total party choice is clearly
18
The three regions variables have different levels of measurement. Religious denomination is a nominal level
variable, while church attendance and self-declared religiosity in this paper are considered as interval level
variables. In the MNLR and the logistic regression (in Table 6B) these different levels of measurement are taken
into account by treating the religious denomination variable as a” factor” variable, while the two other variables
are treated as so-called “covariates”.
17
above average in Table A. The correlation between the explanatory power from Tables A
and B is nevertheless considerable, 0.60 for the 24 units.
Explanatory power is largest in the Central European countries according to the average
scores (0.106) while differences are small between the other regions (0.048-0.064) as
shown in the equivalent part of Table E.
The table contains two measures tapping the differences in the impact of the religious
variables on total party choice and left-right party choice, namely (a) the absolute difference
which simply is the differences in explanatory power for total party choice and left-right party
choice based on Nagelkerke‟s R2 ,19 and (b) log-odds ratios which takes into account “floor”
and “ceiling” effects, that is: lor controls for the fact that both the impact of religious and class
variables are small in some countries and larger in other. Lor then measure the relative
difference between the impact of religion on total party choice and left-right party choice.
We see from Table C that the absolute difference between the explanatory powers are
largest in the countries with the highest explanatory power in Table A, but this is not so
pronounced when the relative differences indicated by the lor-scores in Table D is examined.
We find several countries where the religious variables have a small impact on total party
choice in the top of the list (Estonia and Denmark) while Austria - which belongs to the
countries where the religious variables have large explanatory power in Table A - is one of
the countries where the relative difference is smallest.
Can the differences in explanatory power indicated by the figures in Table C and D be
explained by the differences in degree of overlap? We should expect that a high degree of
overlap should be associated with a small difference between the impact of the religious
variables on total party choice compared with left-right party choice and visa versa, a low
degree of overlap should be associated with a large difference. This follows straightforward
from the notion of overlap. A high degree of overlap implies that the left-right division of
parties taps a large portion of the impact of the religious variables on total party choice.
When the two measures of difference are correlated with the average overlap for religious
denomination and church attendance (see Table 13 below), it turns out that the correlations
are fairly strong -0.54 for the absolute difference and -0.82 for the relative difference based
on lor.20
19
The explanatory power from Table B is subtracted from the corresponding figure in Table A.
20
The coefficients are negative since a large difference implies a low level of overlap.
18
Class voting
Introduction
Social class represents the classic structural cleavage in industrial society. In Seymour
Martin Lipset and Stein Rokkan‟s seminal work on the formation of social cleavages in
Western democracies the class cleavage was first and foremost a cleavage in the labor
market between owners and employers on the one side and tenants, laborers and workers
on the other. It sprang out of the Industrial Revolution and proved much more uniformly
divisive than the other major cleavages they focused upon (Lipset & Rokkan 1967b: 14, 21,
35). The rising masses of workers resented their working conditions and the insecurity of
their contracts. The result was the formation of a variety of labor unions and the
development of nation-wide socialist parties. The fact that the labor market cleavage was so
uniformly divisive in a comparative setting implied that it tended to bring the party systems
closer to each other in their basic structure. While conflicts and compromises along the other
cleavages, especially the centre-periphery and the state-church cleavage lines, tended to
generate national developments of the party systems in divergent directions, the ownerworker cleavages moved the party system in the opposite direction. "... the owner-worker
cleavage tended to bring the party system closer to each other in their basic structure"
(Lipset & Rokkan 1967b: 35). In this respect Rokkan and Lipset focused most on the parties
of the left, neglecting to some degree to focus in detail on the parties that represented the
interests of the owners and employers in a comparative context (Steed & Humphreys 1988:
400-402). The Russian Revolution, however, also brought about a more divisive party
structure among parties that articulated the interests of the workers. In some countries there
emerged significant communist parties which created a split among the socialist parties,
while the communists became an insignificant force in other countries (Bartolini 2000: 86120, chap. 9; Lipset & Rokkan 1967b: 46-50).
The cleavage in the labor market is the central class cleavage, but not the only one
according to Lipset and Rokkan. The other cleavage is the conflict in the commodity market
between peasants and others employed in the primary sector and those who wanted to buy
the products from the primary sector, particularly the urban population. This cleavage also
sprang out of the Industrial Revolution. The peasants wanted to sell their wares at the best
possible prices and to buy what they needed from the industrial and urban producers at low
costs, while the urban population often had opposite economic interests (Lipset & Rokkan
1967b: 20-21). This is then essentially an urban-rural conflict. Such conflicts did not
invariably prove party-forming. They could be dealt with within broad party fronts or could be
channeled through interest organisations into more narrow arenas of functional
representation and bargaining. In this paper the farmers and other producers in the primary
industries are grouped as one category on the class variable and considered as a
component of the social class variable.
The impact of the class variables on party choice in advanced industrial democracies are
complex due to the fact that both Old and New Politics influence the way different status
groups vote.
According to the Old Politics perspective education, for example, was an indicator of social
status and class interests. Those with lower education voted for the traditional parties of the
left, primarily Social Democratic parties and Communist parties, while those with higher
education voted for the parties that articulated the class interests of the upper middle class
and the bourgeoisie, in particular Liberal and partly also Conservative parties. This version of
how education has an impact on voting behaviour has become less important over time, but
we still expect this pattern to be significant.
The New Politics version is inspired by changes in the political landscape in connection with
socio-structural change and changes in conflict structure of the advanced societies. The new
version discusses the new electoral orientation in connection with changes in political values
19
and the conflict structure in advanced societies. The higher educated strata and the new
middle class will be more likely to have post-materialist or libertarian values, while the lower
educated strata and the working class will have more traditional materialist and also
authoritarian values (Dalton 2008: 90-91; Inglehart 1977: 72–84; 1990: 162–168; Knutsen
2004: chap. 5). With regard to education, it is strong evidence that the causal structural base
for the New Politics cleavage is education level, not social class (Stubager 2009, 2010).
In terms of electoral behaviour, the new middle class and the better-educated strata are most
likely to support “the post-material left”, that is, mainly the Green and Left Socialist parties. And
as post-materialist issues become more important, this may stimulate a materialist and
authoritarian counter-reaction whereby part of the working class and those with least education,
side with Conservative and Radical Rightist parties to reaffirm the traditional materialist
emphasis on economic growth, military security, and law and order (Inglehart 1984: 28; 1997:
244–251; Kitschelt 1994; 1995).
In this paper a broad notion of the class cleavage is used. I include education and household
income in addition to social class as variables to tap the overall class conflict.
Education
There are two variables in the ESS data set that tap education level. One is a seven value
variable based on various concrete levels of education from not completed primary
education to second stage of tertiary level of education. The other variable is a variable
where the respondents are asked how many years of full-time equivalent education they
have completed. These variables show very similar cross-national differences in education
level. The education level is largest in Central and Northern Europe and on the Islands,
lower in Eastern Europe and decisively lowest in Southern Europe. According to the data the
education level is lowest in the four Southern European countries, Poland Slovenia and
Hungary. The eta-coefficients between the two education level variables and the country
variable are 0.32-0.33.
In the analysis below I use the seven value variable and treat it as a ratio variable where the
various education levels are assigned values from 0 to 6.21
According to the pooled data the correlation between education and party choice is 0.162
(eta). It is evident that the old and new version works simultaneously. The Greens and the
Left Socialist party families have the support of the electorate with the highest education
level, followed by parties belonging to the Liberal party family. Below the average for all
voters we find the Old Left, the Communists with the decisively lowest education level, and
then Social Democrats and the New Right (with fairly similar education level) and finally the
Conservative party voters. The remaining party families (Christian, Agrarian and Ethnicregional) have average scores close the average for all party voters.
With regard to the left–right division, the leftist parties gain stronger support from the lowereducated strata, in particular when the Greens are not included in the leftist group in the
pooled data. When we compare the highest and the lowest categories on the sevencategory variable, PDI is –11 and declines to –10 when the comparison is made on the basis
of collapsing the two highest and lowest categories. This decreases to –4 and –3,
respectively when the Greens are included in the leftist parties.
21
The various values are 0) Not completed primary education, 1) Primary or first stage of basic education, 2)
Lower level secondary or secondary education, 3) Upper secondary education, 4) Post-secondary, not tertiary
education, 5) First stage of tertiary education, and 6) Secondary stage of tertiary education.
20
The degree of overlap is 0.50, but decreases to 0.15 when the Greens are included in the
leftist parties.22 When the Greens are included in the leftist group, education cuts very much
across the left–right division of parties.
It is first and foremost the tendency for Conservative and then Left Socialist party voters that
contribute to the fact that the degree of overlap is fairly low even when the Greens are
excluded from the leftist parties.
Table 7 shows the comparative strength of the correlations between education and party
choice.
< Table 7 about here >
There are large variations in the correlations between education and party choice according
to the measures that tap the total correlation. The eta-correlation which is based on keeping
all values on the education variable and the PDI-correlations in Table A and B show
*basically the same patterns and ranking of the countries.23
The correlations are on average considerably stronger in the Nordic countries, while there
are smaller differences between the other regions, but the correlations are smaller in
Southern Europe and the Islands than in Central and Eastern Europe according to Table E.
There is also large variation regarding the correlations between left–right party choice and
education. In Table 7, the Green parties are not included in the leftist party group since
Green party voters tend to belong to the higher educated strata. By excluding the Greens
from the leftist parties we focus on the pure leftist party families, and we focus on the best fit
models, the treatment of the Greens which provides the highest explanatory power. In my
comments below I also mention some important results when the Greens are included in the
leftist party groups below. The signs for the PDI for the left–right division of parties (and for
the overlap) must be interpreted in the light of the fact that the leftist parties and higher
education are assigned higher values on the two variables. A negative sign implies that the
lower educated strata are most likely to support the parties of the left.
With regard to the correlations with left-right party choice (cf. Table C), we note first that the
absolute magnitude of the mean correlation is much smaller for left-right party choice than
for total party choice (cf. Table B). The negative correlation is largest in the Nordic countries
according to the mean correlations reported in Table E, and then on the Islands, while there
are very small average correlations for the other regions.
Considering the correlation for all parties and for the left–right division of parties in
comparison based on the PDI-measure (Tables B and C), we find that for some of the
countries where the total correlations are large (see Table B), this is caused mainly by leftist
parties that gain stronger support from the lower educated strata. This is the pattern for
Finland, Sweden and the Czech Republic. For some of these countries, like Norway and
Slovakia, the total impact of education cuts across the left–right division of parties since the
latter correlation is very small. For some countries like Switzerland and Poland, the tendency
for leftist parties to gain strongest support from the higher educated strata is a significant
component of the total correlation. In six countries there is a significant tendency for the
leftist parties to gain stronger support from the higher educated strata, and for an additional
five countries the correlation is close to zero (plus/minus 4 pp.). The former group of
countries comprise three Eastern European countries (Slovenia, Poland and East Germany),
two central European countries (Switzerland and France) and one Southern European
country (Italy). We note that the average degree of overlap is fairly low indicating that even
22
Overlap is calculated by comparing the distribution of the vote for the two highest and the two lowest
categories on the education variable.
23
The correlation between the two coefficients based on the 24 countries (units) is 0.81.
21
when the Greens are not grouped among the leftist parties, the impact of education tends,
on average, to cut across the left–right division of parties.
When the Greens are grouped among the leftist parties (not shown in the table), the mean
correlation is zero, and the mean overlap is exactly .00 indicating that the impact of
education on average cuts across the left–right division of parties completely, and there is
about the same number of countries where there is a significant tendency for the higher
educated strata to the more likely to support the leftist parties as the opposite.
The analysis below is based on the traditional left–right division where the Greens are not
grouped among the leftist parties.
One way of examining the comparative patterns is to divide the countries into three groups:
1) those where the Old Politics version of the impact of education is dominant, 2) those
where there is a high degree of crosscut, and 3) those where the old order is turned upside
down in the sense that the left has stronger support from the higher educated strata while
the right has stronger support from the lower educated strata.
We can define the first group as those countries where the degree of negative overlap is
higher than 0.50 in absolute magnitude. This group comprises eight countries. In all of these
countries the left parties gains stronger support from the lower educated stratum. In five of
these countries, it is particularly the Liberal or Conservative parties among the non-socialist
countries which gain the strongest support from the higher educated strata. In Austria,
Luxemburg and West Germany another pattern is dominant. While the (old) left parties gain
strongest support from the lower educated strata, it is the Greens that represent the opposite
pattern with much stronger support from the higher educated strata. The traditional
polarisation between the Old Left and Old Right with regard to the educational variable is
replaced by a polarisation between the Old Left and the New Left.
Eight additional countries have a high degree of crosscut but the overlap remains negative (0.01 to -0.49), indicating that the left still gain stronger support from the lower educated
strata.
The parties that contribute most significantly to the crosscut can be grouped into party
families, and we find the following main patterns:
1. Radical Rightist parties which gain stronger support from the lower educated strata are
found in Denmark, Norway, Belgium, the Netherlands and Slovakia. In Denmark, Norway
and Slovakia the different support by the higher and lower educated for these parties is the
most significant cross-cutting factor.
2. The Left Socialist parties gain considerably stronger support from the higher educated
strata in Denmark, Norway, the Netherlands, Spain and Portugal.
3. Christian parties gain strongest support from the lower educated strata in Belgium, the
Netherlands and Norway, and Conservative parties in Ireland, Spain and Portugal.
Finally, in the third group of countries the leftist parties gain stronger support from the higher
educated strata. This group comprises the remaining eight countries. This inverse overlap is
interesting but we note that it does not reach a very high level in any country. The parties
that contribute to this positive overlap can be grouped into three party families:
1. Again, Radical Rightist parties gain stronger support from the lower educated strata in
Switzerland, France and East Germany. In Switzerland this is the most significant
contribution and in France too, although the tendency for the Social Democrats to gain
stronger support from the higher educated strata, is of equal size.
2. The Social Democrats gain stronger support from the higher educated strata in all of these
countries apart from East Germany and Greece.
22
3. Christian and Conservative parties gain stronger support from the lower educated strata.
This factor is present in all countries, for Christian parties in Switzerland, Slovenia, East
Germany and Poland, and for Conservative parties in France, Italy, Greece and Slovenia.
The largest component for explaining the pattern in Italy is, for example, the tendency for
conservative Forza Italia to gain much stronger support from the lower educated strata.
4. The most significant factor in Poland is the trend for the two Agrarian parties to recruit
much stronger support from the lower educated strata.
5. In Greece the Left Socialist party gains considerably stronger support from the higher
educated strata.
Household income
The household income level variable is a 12 value variable where the respondents are asked
to place the household‟s total income in one of 12 categories shown in a card. This variable
is used as a ratio-level variable below.
The Household Income variable is somewhat lower correlated with party choice than
education in the pooled data (eta = 0.126) and considerably lower correlated based on the
average for the various countries (eta = 0.126 versus 0.217 for education). The strength of
the correlations between income and party choice, and education and party choice is very
strong (r = 0.74) for the 24 units and as high as 0.83 when the eta-coefficient for income is
correlated with the total PDI correlation for education. In brief, the comparative impact of
income is smaller than the impact of education and the comparative strength ranks the
countries very similarly. I will not report details regarding the impact of income on party
choice although income is included among the class variables in the multivariate analyses in
the next section.24
Social class
Introduction
Social class represents the classic structural cleavage in industrial society. In Lipset and
Rokkan‟s work class cleavage was first and foremost a cleavage in the labour market
between owners and employers on the one hand and tenants, labourers, and workers on the
other. It sprang out of the Industrial Revolution and proved much more uniformly divisive
across the various European countries than the other major cleavages.
The discussion of de-alignment in electoral research has often focussed first and foremost
on the decline in class voting, and the evidence for a decline in class voting in comparative
research is massive (Knutsen 2006; Nieuwbeerta 1995).
Social class can be measured in several ways. The original studies of class voting relied on
a simple distinction between the working class and other social classes, and the famous
Alford index of class voting (see details below) is based on this dichotomous class variable.
I have used the Erikson/Goldthorpe (hereafter EG) class schema, which was originally
developed in connection with social mobility studies (Goldthorpe 1980; Erikson, Goldthorpe
24
One important reason for this (in addition to smaller but comparative very similar impact as to education), is
that the distributions on the income variable are very different due to the high portion of missing values in several
countries. When these units are recoded to the mean category (as in this book project) the variable in several
countries get distributions that make it impossible to calculate degrees of overlap.
23
& Portocarero 1979; Erikson & Goldthorpe 1992) 25 but it has also been used in British
election studies (Heath, Jowell & Curtice 1985: chap. 2; Heath et al. 1991: chap. 5) and
comparative studies of class voting in Western democracies (Knutsen 2006; Nieuwbeerta
1995). It is considered the most influential conceptualization and operationalisation of social
class in European sociology (Evans 1992: 211–212).
The aim of the class schema is to differentiate positions based on the work situation
(authority and autonomy) as well as market situations (including income, degree of income
security, career prospects and source of income). The basic distinction in the schema is
within the category of employees. The distinction between employees involved in a service
relationship with their employers and those whose employment relationships are essentially
regulated by a labor contract is what underlies the way different employee classes have
been delineated. A “service relationship”, rather than one formulated in terms of a labour
contract, is found where the employees are required to exercise delegated authority or
specialized knowledge and expertise in the interest of their employing organization. Such
employees must be accorded a legitimate area of autonomy and discretion, and their
performance will depend on the degree of moral commitment that they feel towards the
organization rather than on the efficacy of external sanctions. To a significant extent the
organization must trust these employees to make decisions and to carry them through in
ways consistent with the values and goals of that organization (Goldthorpe 1982; Erikson &
Goldthorpe 1992: 42). It is on the basis of this fundamental distinction that the class schema
is drawn up. The classes used here are shown in Table 8.
< Table 8 about here >
The higher-level service class has positions which typically involve the exercise of authority
within a wide range of discretion, and with considerable freedom of control by others. The
lower-level service class comprises lower-grade professionals (typically called semiprofessionals) and lower-grade administrators and officials.
Routine non-manual do non-manual work, but they do not belong to the service class. They
are functionally associated with (but marginal to) the service class (Goldthorpe 1980: 40).
This is a class that may be regarded as “intermediate” in the sense that it comprises
positions with employment relationships that appear to take on mixed forms.
The working-class differentiates between skilled and unskilled workers. Supervisors of
manual workers (foremen) and lower-grade technicians are grouped among the skilled
workers.
The scheme does not comprise a single category for employers or large employers.
Because there are so few large employers EG classifies these in the higher-level service
class while small employers (with less than 10 employees) are classified as the petty
bourgeoisie together with self-employed without employees.
Self-employed in the primary sector (primarily farmers and fishermen) are classified in a
separate category apart from the other petty bourgeoisie.
In the ESS surveys the respondents‟ occupations are classified according to ISCO 1988.
Leiulfsrud, Bison & Jensberg (2005) have classified the various occupations according to the
ISCO coded into the EG-classes. Their scheme contained original 11 classes, but I have
collapsed these into 8 classes according to the schema discussed above. These represent
the major classes in central versions of the EG class schema.
Studies of class voting have undergone significant changes and one can differentiate
between three stages or „generations‟ (Knutsen 2007: 459–461; Nieuwbeerta 1995: chap.
25
It is also called the EGP class schema owing to the contribution of Portocarero in one of the articles referred to
above.
24
1). Below I briefly review these generations and how class voting has been measured
according to the various generations:
1) “Traditional (left–right) class voting” examines the left–right division of parties and
incorporates only two social classes (the manual/non-manual division). Traditional class
voting has been measured by the Alford index which is based on a percentage difference
measure or more recently the Thomsen index which is based on log-odds ratios.
2) “Overall or total left–right class voting” examines the left–right voting of all social classes.
This type of class voting has been tapped by the kappa index.26 The kappa index has
several desirable statistical properties, the most desirable being that the index is based on
log-odds ratios and is therefore not dependent on the marginal distributions of the
independent or dependent variables.
3) “Total class voting” considers class differences (based on a detailed class schema) in
voting between all the parties in the party system. Total class voting can be measured by
Cramer‟s V coefficient which is a standardised correlation coefficient for analysing the
relationship between two nominal-level variables. The kappa index is also relevant. Kappa
values can be calculated for each political party. For example, it is relevant for analyzing
total class voting where the research question is to compare the class profile of parties and
party families (see Knutsen 2006, chap. 4). One can also use a weighted kappa measure to
examine total class voting. The kappa values are then weighted according to the size of the
support for the various political parties. These values are summed up to capture the whole
party system.
Some scholars have argued that in advanced industrial democracies, it is important to study
class voting by employing more than two classes and also by analyzing all parties as
separate categories. There is some evidence that social cleavages, and the class cleavage
in particular, cuts across the left–right division of parties. The New Left parties gain stronger
support from the higher educated strata and the new middle class, while the New Right
parties gain strongest support from the less educated and the workers. Therefore, newer
research on class voting should consider all parties as separate categories (see Knutsen
2006).
Empirical analysis
Table 9 shows that relationship between party choice and social classes based on the
pooled data. Below I will comment the table according to the three generations of class
voting.
< Table 9 about here >
The support for the leftist parties among workers and other classes is shown in Table A.
Workers are the skilled and unskilled workers from the EG class schema, while other
classes are the combined support from all the other classes. I have calculated the log-odds
ratios in addition to the PDI measure. These two measures correspond to the Thomsen and
the Alford indexes, respectively. We see that traditional class voting is not large according to
the pooled data, about 9 pp. according to the Alford index.27 We note from the table that
support for the leftist parties is less than 50 per cent even in the working class.
26
The kappa index calculates several log-odds ratios between a reference category on the class variable and
each of the other classes, and uses the standard deviation of these log-odds ratios as a measure of class voting.
27
In all left–right class voting analyses the greens are not included among the leftist parties. I follow this tradition
here, and due to the complexity of the issue, I do not report the figures with the greens included among the leftist
parties.
25
Table B shows class voting according to the second generation: The left–right division of
parties is retained, but the relationship is examined by treating the various “other” classes as
separate categories. As to the left–right divisions of parties, the leftist parties gain strongest
support from the two working class categories, followed by the routine non-manuals and the
service class. Support is smallest from the petty bourgeoisie and the farmers.
According to the third generation of class voting, all parties should be examined in analyses
of class voting in addition to all social classes. This is shown in Table C. The Old Left (Social
Democrats and Communist parties) gains strongest support from the working class, while
the New Left (Left Socialist and Greens) gains strongest support from the service class and
routine non-manuals. The Liberal parties gain strongest support from the higher-level service
class and the petty bourgeoisie, while the Conservative parties gain strongest support from
the farmers and the petty bourgeoisie. The Christian Democrats have a very even class
base, but nevertheless gain strongest support from the farmers. The same pattern
characterises the Agrarian party family, but the difference in support is nevertheless much
larger since it gains very limited support from classes other than the farmers. The class
differences in support for the Radical Right are not large, but these parties gain strongest
support from the workers and the petty bourgeoisie.
The kappa index expresses the degree to which the various party families are class party
families and takes into consideration that the various party families gain very different overall
support in the surveys. Somewhat surprisingly, it is the New Left parties, in particular the
Greens that have the largest values in addition to the Agrarian parties. The Old Left party
families have somewhat smaller values. The less distinct class profile is – as expected from
the reading of the table – the Christian Democrats. The Radical Rightist parties do not have
a very distinct class base either, according to the kappa index.
Table 10 shows the comparative strength of the of class voting according to the three
generations of class voting, the Alford index, the kappa index, Cramer‟s V, and the weighted
kappa index which taps the total correlation between party choice and the EG classes. In
Table 10 F the mean level of class voting is calculated. In this section these will be
commented after the comments on the broader comparative pattern from the various class
voting measures shown in Tables A-D.
< Table 10 about here >
Traditional class voting has been stronger in all the Nordic countries than in other Western
countries (Nieuwbeerta 1995: chap. 3). This obviously does not apply consistently any more
according to Table A.28 Traditional class voting is still highest in two of the Nordic countries,
Finland and Sweden, moderate in Denmark and about zero in Norway. Traditional class
voting is also significant in the Czech Republic, Britain and Austria. By contrast, we find
negative traditional class voting in Slovenia, Estonia, East Germany and Poland even if the
absolute magnitudes are small. The difference between the Czech Republic and the other
East European countries regarding traditional class voting is very pronounced. 29
Total left–right class voting (Table B) shows a somewhat different pattern according to the
kappa index. We find high kappa index in many of the same countries that has a high score
on the Alford-index, but in addition Denmark, the Netherlands and Switzerland have fairly
high kappa values. A major explanation for the high level of left–right class voting in the latter
countries is the very low level of support for the leftist parties among farmers (only 4% in all
28
I have also examined the Thomsen index (based on lor) for traditional class voting. It shows the same ranking
of the countries and the correlations between the Alford and the Thomsen indices is 0.95 for the 24 units.
29
For similar findings regarding the difference between the Czech Republic and the other East European
countries, and between the Eastern and Western European countries, see Gijsberts & Nieuwbeerta (2000).
26
three countries).30 There is still a fairly strong correlation between traditional class voting
(based on the Alford index) and total left–right class voting measured by the kappa index (r =
0.52) based on the indices for the 24 countries.
The first coefficient tapping total class voting is Cramer‟s V and shows a somewhat different
pattern according to Table C. Now all the Nordic countries are among those with the highest
correlations and also Poland and Switzerland have high coefficients even though they had
low negative traditional class voting.
Although there are several exceptions, the weighted kappa measure (see Table D) for total
class voting shows a fairly similar pattern compared with total left–right class voting and the
Cramer‟s V measure for total class voting. The correlation between the two measures for
total class voting is 0.66 based on the 24 units (countries).
As to region (see Table F) all four measures of class voting is on average largest in the
Nordic countries although we have seen that left-right class voting in Norway is low. For the
other regions there are different patterns for the various measures. Traditional class voting
(the Alford index) is smallest in Eastern European countries where we have seen some
evidence of negative class voting (see Table 10 A) compared with16 percentage points in
the Nordic countries and 9-12 in the other regions. For the other measures class voting
appears to be higher in Central European countries than in the South, East and on the
Islands.
In Table E, the degree of overlap is calculated. This measure is not based on any of the
three types of class voting discussed above, but – according to the logic of the overlap
concept – it is based on a dichotomous class variable (workers versus all other classes),
cross-tabulated against all parties. This PDI measure is then compared with the PDI based
on the left–right division of parties (the Alford index). In contrast to Table 7D, a positive
overlap means that workers tend to support the parties on the left and a negative overlap
that workers are ore likely to support the non-leftist parties.
We note that the large variation between the countries from 1.00 for Spain to less than 0.10
(in absolute magnitude) for Switzerland, France and Norway. For the same four countries
where the Alford index was negative, the overlap is negative.
The average overlap is fairly low, 0.46, indicating that the traditional division between leftist
and rightist parties to a large degree cuts across the left–right division.
The average degree of overlap is decisively smallest in Eastern Europe (se Table F). For the
other regions differences are smaller, ranking from 0.51 in Central Europe, 0.61 in the Nordic
countries, and around 0.70 in Southern Europe and on the islands.
I have examined what causes the low degree of overlap for the countries where overlap is
positive but less than 0.70, and have included Switzerland although the overlap is slightly
negative. I then examined the causes of negative class voting in Slovenia, Estonia, East
Germany and Poland.
The main cause for the low degree of overlap in Denmark, Norway, Belgium, the
Netherlands, France, Switzerland and Slovakia is the Radical Rightist parties‟ stronger
support from workers than from other classes. The second most important component in
Norway, Denmark and the Netherlands is the stronger support for the Left Socialist parties
among the other classes. This factor is the most important component in Greece and
Portugal. In Ireland, the conservative Fiánna Fail gets strongest support from the workers
and the same applies to the Christian Democrats in Switzerland and the Calvinist
30
Two countries score considerably lower (comparatively speaking) on total left-right class voting, i.e. Britain and
the Czech Republic. This can partly be explained by the high level of support for the leftist parties among farmers
(nearly 30%).
27
fundamentalist parties in the Netherlands. The ethnic-regional SMK in Slovakia also
contributes significantly to the low degree of overlap.
With regard to the four countries with significant negative overlap, it is the rightist parties
belonging to different party families that contribute most significantly. These parties are the
Agrarian and Christian parties in Poland, the liberal Centre Party in Estonia and all rightist
parties apart from the liberal LDS in Slovenia. In East Germany the CDU also gains
strongest support from the working class. Support for the Social Democrats is largest from
other classes in Slovenia and Estonia.
Multivariate analysis
As for the religious variables I have performed multivariate analyses which show the total
explanatory power of the three class variables in a comparative perspective.
< Table 11 about here >
In Table 11A I have shown the explanatory power of the three class variables on the
nominal-level party choice variables and in Table B on the left-right party choice variable. I
have examined the explanatory power of the class variables with different location of the
Greens (among the rightist and leftist parties, respectively), and, in contrast to the pattern for
the multivariate analyses of the religious variables, the largest explanatory power were
sometimes found where the greens where placed among the leftist and sometimes among
the rightist parties. The figures reported in the table are based on the best fit model where
the explanatory power is largest.
Not surprisingly, the ranking of the countries is fairly similar to those found in Tables 7 and
10. While previous findings on class voting have shown that class voting is larger in all the
Nordic countries, this is not the case according to Table A and B.
According to the corresponding part of Table E both total class voting and left-right class
voting is on average nevertheless highest in the Nordic countries while there are small
differences between the average explanatory powers for the other regions.
The ranking of the countries are fairly similar in the two tables, although we note that the
impact of the class variables on left-right voting is comparatively much smaller in Norway,
Poland and Slovakia in particular. The correlation between the explanatory powers for the 24
countries is fairly strong, 0.68.
Table C and D shows the absolute and relative differences in explanatory power between
total and left-right party choice calculated in the same way as outlined for the religious
variables in Table 6. There are considerably differences between the countries in how well
the left-right division of parties tap the total impact of class variables on party choice. Do
these differences reflect the differences in degree of overlap which we have found above for
education and social class? I have as for the religious variables calculated the average
degree of overlap which - for the class variables - is based on education and social class
(see table 13 below). The correlation between the lor-scores in Table 11 and the degree of
overlap for the religious variables are -0.58 and -0.74. In the countries where the impact of
the class variables cut across the left-right division of parties, we find comparatively larger
differences in the explanatory power of total party choice compared with left-right party
choice.
28
Comparing the impact of the religious and the class variables
Bivariate analysis and overlap
I first sum up the results from the analyses above by comparing the bivariate correlations
between the various socio-structural variables on party choice. In Table 12 A I have shown
the bivariate explanatory power of each of the six religious and class variables from
multinomial logistic regression analysis indicated by Nagelkerke‟s R2 31 and I have compared
these coefficients with the eta and CV coefficients.32 The variables are ranked according to
the strength of Nagelkerke‟s R2. Both of these analyses are based on the pooled data with
party families as the dependent variable. All coefficients are based on total party choice, not
left-right party choice.
< Table 12 about here >
According to Table A the three religious variables have the largest bivariate explanatory
power with religious denomination as the variable with the largest correlation. There is a
perfect fit between the strength of the Nagelkerke‟s R2 and the eta coefficients, at least with
regard to the ranking of the variables. For the two nominal-level structural level variables,
Religious Denomination and Social class, I have used Cramer‟s V coefficient. The strength
of Cramer‟s V and the eta coefficients cannot be compared, but Cramer‟s V for religious
denomination is considerably larger than for social class in accordance with the results from
multinomial logistic regressions.
In Table B the average coefficients from the analyses of the 24 countries are calculated.
These are reported above for most of the variables. These coefficients are somewhat higher
than for the corresponding coefficients for the pooled data, but they show very similar
relative strength. The ranking of the strength of the variables is the same as for the pooled
data, and all three religious variables have on average larger correlations as the class
variables.
According to the pooled data, the explanatory power of the three religious variables
altogether based on Nagelkerke‟s R2 is 0.164 and for the three class variables 0.073 based
on the pooled data (not shown in Table 12). A comparison of the explanatory power for the
various countries will be done below (see Table 14).
For left-right party choice all three religious variables also have larger explanatory power
than the class variables, independent of the grouping of the Green parties. According to the
best fit model, the explanatory power of church attendance is 0.049, religiosity 0.032 and
religious denomination 0,022, while the explanatory power for social class is 0.016 and less
than 0.010 for education and household income (not shown in Table 12).
In Table 13 I have shown the average degree of overlap for the religious and the class
variables. I have based these averages on those variables reported in details above,
religious denomination and church attendance, and education33 and social class, for the
religious and the class variables, respectively.
< Table 13 about here >
31
Since multinomial logistic regression does not show a standardised coefficient indicating the impact of each of
the independent variables, I use this measure as an equivalent to a correlation coefficient.
32
In the multinomial logistic regressions all independent variables are treated as covariates, apart from religious
denomination and social class which are treated as factors due to their different levels of measurement.
33
The signs of the overlaps for education have been reversed before calculating the averages so that the pattern
for lower educated strata to vote for leftist parties indicates a positive overlap.
29
According to the mean degree of overlap for the 24 countries overlap is considerably larger
for the religious variables than for the class variables. In fact the degree of overlap is about
twice as high for the religious variables based on the averages for the 24 countries (0.69
versus 0.34).34 This implies that religious voting tends to follow the left-right division of
parties to a much higher degree than class variables which tend much more to cut across
the left-right division of parties. Furthermore, for the religious variables all overlaps are
positive which means that in all countries leftist parties tend to get strongest support from the
secular groups while the overlap is negative for 6 countries which imply that in these
countries the lower social strata tend to support the rightist parties to a larger degree then
the higher social strata.
Overlap (conventionally defined) is largest for the religious variables in all countries apart
from four countries where the overlap for the class variables is largest, namely Hungary,
Sweden, Britain and the Czech Republic. The differences in overlap for these countries are
small (0.07-0.11) apart from the Czech Republic where it is large (0.46). For Luxembourg the
overlap for the class and religious variables are exactly similar. Slovenia and Estonia are
special cases because the absolute magnitudes of the negative overlap for the class
variables are larger than the positive overlap for the religious variables.
Differences in overlap following the main trend where the overlap is largest for the religious
variables are especially strong in France, Ireland, Switzerland, Norway and Poland (0.510.72). In the three former countries the overlap for the religions variables are very high. For
Norway overlap for the religious variables are below the average (0.64) while the class
variables tend to cut across left-right party choice nearly completely (0.07).
The regional differences in overlap are fairly small as can be seen from Table D, apart from
the small average overlap for Eastern Europe. In Eastern Europe, the overlap for the
religious variables is smaller than for the other regions and the impact of the class variables
tends to cut across the left-division of parties completely. Religious and class voting in
Eastern Europe then tend to cut across the left-right division of parties to a much larger
degree than in Western Europe.
Multivariate analyses
Table 14 compares the explanatory power (Nagelkerke‟s R2) of the religious and the class
variables. The data in Table 14 A and B is the same as those reported in Tables 6 and 11,
but in contrast to those tables we here compare the strength of religious and class voting.
Table 14A is based on the results from multinomial logistic regression with total party choice
as the dependent variable while B is based on left-right party choice.
< Table 14 about here >
The table contains two measures tapping the differences in the impact of the religious and
the class variables, the absolute difference and the log-odds ratios which are explained
above.
From the means for all countries we note both from Table A and B that the impact of the
religious variables are larger than the impact of the class variables although the differences
are not so large as based on the pooled data outlined above.
34
The negative sign for 6 of the countries for the class variables reduces the average degree of overlap
considerably. It can be argued that this is a correct way of calculating the mean overlap since class variables in
these countries structure left-right party choice in an opposite or negative way compared to the other countries.
On the other hand, there is another type of overlap in these six countries, and the class variables really structure
left-right party choice, although in an unconventional way. Following this logic, we might argue that we should use
the absolute magnitudes of the overlap for calculating the mean overlap. Mean overlap increases to 0.50 when
this logic is used and is then still considerably smaller than the overlap for the religious variables.
30
As to Table A we note that for both measures that tap the differences between the religious
and social class variables, the religious variables are comparatively more significant than
social class in 15 countries, while the class variables have higher explanatory power in 8
countries. In Italy the explanatory power of religious and social class variables are exactly
the same. The religious variables are relatively most important in the Netherlands, Slovenia
and Switzerland, while class variables are more important in particular in Finland and
Denmark.
As to region previous findings about the relative difference between religion and class
variables are confirmed. Since these figures are not reported in Table 14, they are presented
in details here: Religious variables are on average more important in Central European
countries (average absolute difference in explanatory power is 0.070 and average lor is
0.37), and then in Eastern Europe (0.037/0.27) and Southern Europe (0.018/0.17), while
there are only small differences in the Islands. Only in the Nordic countries are the class
variables more important (-0.045/-0.28).
When we examine the impact on left-right party choice (see table 14B), we find both
similarities and differences compared to Table A. The religious variables have a higher
explanatory power in 17 countries and the class variables in 6 countries. In Slovakia
religious and class variables have about the same explanatory power on left-right party
choice.
The absolute difference is still largest in the Netherlands of the countries where the religious
variables have larger explanatory power than the class variables, but the differences in
Slovenia and Switzerland are not so pronounced. The relative difference (indicated by the
lor-scores) is now largest in Poland. This indicates that religious left-right voting is
comparatively largest compared with class voting. However, we have seen above (Table
14A) that total class voting is larger than total religious voting. This must be seen to reflect
the fact that total class voting is high in Poland but cuts very much across the left-right
division of parties. This does not apply to religious voting where the overlap is fairly high (see
Table 13 A and B) as we have seen above. Differences in degree of overlap are also the
case for the other countries where religious left-right voting is relatively most important,
Ireland, Greece and Italy. In these countries religious voting follows the left-right division
closely (overlaps 0.89-0.92), but class voting cut across the left-right division of parties to a
large degree (overlaps 0.11-0.29, see Table 13).
Left-right class voting is comparatively most important than religious left-right voting in the
Nordic country (apart from Norway), in the Czech Republic, Estonia and Britain. The Czech
Republic is an opposite case to Poland. The religious variables are most important for total
party choice while it is the opposite for left-right party choice. As can be seen from Table 13
above, the impact of the class variables follows closely the left-right division (0.92) while
there is a higher degree of cross-cut for the religious variables (0.46).
Also for left-right voting religious variables are relatively more important than social class
variables in Central (average absolute difference in explanatory power is 0.056 and average
lor is 0.76 and Southern Europe (0.035/0.84) followed by the Islands (0.023/0.38) and
smallest in Eastern Europe (0.014/0.20), while class variables are most important only in the
Nordic countries (-0.046/-0.61).
31
The impact of the whole model
Finally I have examined the explanatory power of the whole model comprising the three
religious and the three class variables based on the two ways of treating the party choice
variable. The results are shown in Table 15. The table shows that there are enormous
differences in how well the religious and class variables explain party choice in Europe. For
total party choice the five countries where the religious variables have largest explanatory
power, the Netherlands, Switzerland, Slovenia, Norway and Finland, are on the top of the
list. For left-right party choice (Table B) we find some of same countries but in another rank
order, and we do not find Norway which now is found at the lower side.
< Table 15 about here >
I have again calculated the differences between the explanatory powers between the two
analyses. The results can be seen from Table C and D. In absolute magnitudes the
differences are largest for Norway, the Netherlands and Slovakia, but for the relative
differences we find also Estonia and Greece among the countries where the impact of the
total model explains relatively more for total party choice than for left-right party choice.
The differences in explanatory power between the different ways of treating the party choice
variable are strongly influenced by the degree of overlap. When the average overlap for the
religious and class variables from Table 13 C is correlated with the difference between the in
Table 15 C and D, respectively, the correlations are 0.60 and 0.74 in absolute magnitudes.
32
Conclusions
In this paper I have systematically examined the total impact of the religious and class
variables on party choice by treating the party choice variable as a nominal-level variable
where all significant parties are included as separate categories in the analyses. The impact
on the whole party system has then been systematically compared with the impact on the
left–right division of parties with alternative treatments of the Green parties.
Religious and social class variables influences voters‟ party choice in complex ways and the
traditional way of analysing the relationship by dichotomising the party choice into leftist and
rightist parties is strongly in need of revision. This tradition might perhaps have been
problematic even in traditional industrial society. According to Lipset and Rokkan, only the
class variables were supposed to group the parties along the left–right division while the
other cleavages largely cut across that division. These expectations are seldom tested in
comparative electoral research.
This perspective can be a point of departure for summing up the main findings regarding the
impact of the religious and the class variables on party choice.
The results of this analysis are somewhat surprising. It is not the class variables, but the
religion variables that overlap strongest in their impact on party choice. In accordance with
previous findings, the religious variables have the strongest impact both on total party choice
and on left–right party choice in the pooled data and in most countries in the comparative
analyses. The impact on left–right party choice is also more consistent than the impact of the
class variables. Those who are affiliated with one a major religious denomination are more
likely to support the non-left parties in all countries compared with those who are not
affiliated. The findings for church religiosity are equivalent: In all countries a high degree of
church religiosity is associated with voting for the rightist parties.35
For the two class variables, Education and Social Class, we find a considerably less
consistent pattern. The higher educated strata are more likely to vote for the left parties in
several countries, even in some of the West European countries like Switzerland, France
and Italy, and the Alford index for class voting is about zero in France, Norway and
Switzerland. The most systematic statistics which show the differences between the religious
and the class variables in relation to how they influence left–right party choice is the degree
of overlap. The influence of the religious variables on party choice follows the left–right
division of parties more closely than the class variables. This is expressed by the average
degree of overlap which in absolute magnitudes is 0.71 for Church Attendance, 0.68 for
Religious Denomination, 0.46 for Social Class and 0.23 for Education. It is thus the religious
variables, not class variables that dominate in structuring left–right party choice in advanced
industrial democracies.
The detailed analyses of the impact of the class variables on party choice have shown that
the Old Politics patterns are still significant in many countries. However, although there are
various patterns that do not conform to the New Politics perspective, many of the main
reasons for the low overlap are caused by Radical Rightist, and by Green and Left Socialist
parties which gain support from the lower educated strata and the working class, and the
higher educated strata and the service class, respectively. New Politics appears then to be a
major cause of the breakdown of the old industrial order in many advanced industrial
countries.
35
For both of these findings, the correlations are minor for two East European countries, namely Estonia and
Slovakia, see Table 4C and Table 5C.
33
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