Who deserves help?
Religion and preferences for welfare state responsibility
for different social groups.
Paper prepared for the annual meeting of the Belgian–Dutch Political Science
Association, Leuven 2010
Daniel Stegmueller
Graduate School of Economic and Social Sciences,
University of Mannheim,
Department of Sociology,
University of Frankfurt
Contents
1 Introduction: religion, deservingness and the welfare state
2
2 Theoretical arguments
2
3 Hypotheses
5
4 Data and operationalization
7
5 Modeling ordinal correlated responses from different countries
11
6 Results and Discussion
13
7 Conclusion
19
1 Introduction: religion, deservingness and the welfare state
Recent research has shown that throughout the Western world religious individuals strongly and consistently oppose redistribution (Guiso et al. 2003, 2006). Why is
that the case? To answer this question we have to move away from the broad picture painted when examining individual’s preferences for income redistribution. We
can gain more insights why individuals oppose or support government activity, by
looking at more specific welfare policies targeted at different social groups.
The welfare state and its policies have a strong ‘material’ foundation, being the results of workers movements (Korpi 2006), politics catering to the interests of the
middle classes (Esping-Andersen 1985), or even employers self-interested support
for social programs (Mares 2001). Even its ‘non-material’ justification is build on the
notion of rights acquired via citizenship (Marshall 1950 [1997]). Moral judgements
are (at least in principle) not part of welfare deliverance. Religious individuals do
invoke strong judgments when evaluating recipients of welfare. They are more likely
than their secular counterparts to judge individuals who they perceive to be responsible of their own fate (due to character flaws or immoral behavior) as undeserving of
welfare. Therefore, religious individuals oppose redistribution through the state, because they loose control over “who gets what and why”. The state might redistribute
money and services to the wrong – read: undeserving – group of people.
We can put that argument to a test, by comparing individual preferences for welfare
state responsibility for two social groups. I choose two types of recipients of state
welfare, located at different ends of the deservingness spectrum: the sick and the
unemployed. Contrasting the effect of religious identity on each of these dependent
variables allows me to explicate, that religious individuals do indeed oppose government action that is geared towards group that they perceive to be non-deserving.
The paper proceeds as follows: Sections 2 and 3 present the theoretical argument
and derive observable implications. In Section 4 I discuss the data set and operationalization of central concepts. I develop a simultaneous bivariate ordinal probit
model, that allows me to test for differences in effects between two dependent variables. Results are presented in Section 6. Section 7 concludes the paper.
2 Theoretical arguments
A rank order of welfare deservingness Research has shown the existence of a general rank order in the public’s perception of deservingness of different social groups
(Oorschot 2000, 2006). Generally, the rank order follows the principle of attributed
responsibility. Individuals who are perceived to be responsible for their current predicament, are deemed to be less deserving of government welfare. Therefore, in the view
of most citizens, needy groups like the sick and the old deserve help trough welfare
transfers, whereas the poor and unemployed are mostly delineated as undeserving
recipients (Meier-Jaeger 2007). Those judgments are not only made by external ob-
2
servers of the state of an individual, but often also by the individual itself. Interviews
with unemployed recipients of welfare often uncover feelings of lack of self-respect
and “negative self-characterizations” (e.g. Horan and Austin 1974).
From the vantage point of citizen’s self-interest the delineation between deserving
and undeserving groups is a logical response to the existing risk structure of advanced industrial societies. The risk of unemployment is a result of individual decisions and investments which can, to a certain extent, be calculated by the individual. Contrary, becoming sick is a risk that potentially affects anyone in an almost
random fashion. It is therefore rational for self-interested citizens to opt for collectivized insurance of unforeseeable social risks (Kangas 1997; Bowles and Ginits 2000;
Oorschot 2002; Meier-Jaeger 2007).
The perspective of welfare state support as a result of rational considerations can
serve as an useful abstraction of citizen’s general preferences. However, for individuals possessing value systems different from the “average population”, cool, rational
calculations are replaced by moral evaluations and judgements. If individuals deem
certain groups to be less deserving because of certain moral qualities (or rather: lack
thereof), they will show even less support for welfare state intervention on their behalf of that group than the general population.
Religion and judgments of deservingness Even in modern societies religion still is
a major factor in determining political preferences (Manza and Brooks 1997; Brooks
et al. 2006; Elff 2007). In a secularizing environment, religion becomes more salient
as its position on social and moral issues is juxtaposed against secular proposals,
that are depicted as leading to the decline of a society’s moral order (Madeley 1991).
Individuals who self-categorize themselves as religious will then adapt their norms
and preferences to those prevalent among that group (see Tajfel 1981; Hogg et al.
1995; Huddy 2001), leading to an increasing relevance of religious identity for political preferences in modern societies (Brooks et al. 2006: 93, Shayo 2009).1
While recent research has found a negative effect of religion on general preferences
for redistribution (Guiso et al. 2003, 2006), I argue that contemporary religious individuals oppose redistributive welfare polices for specific groups. Whilst not necessarily opposed to the general idea of ameliorating the inequalities and social ills
generated by markets (e.g. Wiepking and Bekkers 2008), they object to the provision
of welfare by a secular state. Welfare state policies based on the notion of rights
(Marshall 1950), ignore religious or moral criteria for the provision of welfare. In
contrast, providing welfare voluntarily, through donations or participation in charitable church activities, leaves welfare deliverance in the hands of religious groups.
(cf. de Swaan 1988; Schneider and Ingram 1993; Oorschot 2000) Helping an individ1 In
fact, social psychological research has demonstrated that individuals perceive properties of the
group with which they identify as more extreme than they are (Tajfel 1981). In a dynamic perspective this would predict more extreme views among (strong) religious adherents as secularization
progresses.
3
Table 1: Blaming the needy. Differences between religious and non-religious
individuals. Predicted probabilities (in %) with 95% uncertainty bounds.
Dependent variable
non-religious
religious
(1)
Reason for being in need: lazyness
15.9
[14.8 - 17.1]
20.2
[19.4 - 21.0]
(2)
Reason for being poor:
drunkeness and lack of morals
36.1
[34.5 - 37.9]
40.0
[38.3 - 41.6]
(3)
Reason for being poor:
lack of effort
29.3
[27.7 - 30.9]
36.4
[34.8 - 38.0]
Note: Posterior predicted probabilities from probit models based on 20.000 MCMC
draws. All models control for age, gender, education, income, being self-employed
and unemployed. (1) Data from European Value Survey 1999/2000 for 15 Western
societies. (2) and (3): Data from International Social Justice Project 1991/1996 for
5 Western societies.
ual then means more than material welfare – it can be combined with attempts of
moralizing its behavior and beliefs (Salamon 1996; Deacon 2002).2
To see if this boundary condition is true, I present in Table 1 probit models estimated
for three variables, that represent negative character traits attributed to the poor as
explanation or justification of their current state.3 Each model is estimated controlling for central socio-economic characteristics and I report predicted probabilities
of agreeing to those statements for religious and non-religious individuals. Comparing those probabilities and their associated confidence intervals reveals quite clearly
that religious individuals differ from secular citizens. They are more likely to hold the
view that everyone has the possibility to move out of poverty and so people that are
in need must be so because of their own faults. More than secular individuals they
consider the poor to be lazy drunks that lack proper moral and willpower.
Established explanations Besides the role that religion plays in explaining individual preferences for welfare state activity, previous research has established a broad
2 One
should note, that this is still a value free paper in Weber’s sense. If deliverance of welfare by
private religious groups is beneficial to democracy or not, has clearly to be decided outside the
realm of this paper. (For a sophisticated discussion see Mueller 2009).
3 The reason for the (somewhat unusual) strategy of presenting a table instead of enumerating previous research is that only few sociological studies are available that deal with the topic of popular
explanations of poverty and need (e.g. Oorschot 2000, 2006; Lepianka et al. 2010). When those include religion, it is treated as a ‘control’ variable and entered into the model together with dozens
of other variables, which are mediators between religion and the dependent variable. This makes
it hard, if not impossible, to gauge the effect of religion from their results, as some authors themselves notice (Lepianka et al. 2010: 66).
4
range of factors, mainly based on individuals socio-economic position. 4 In general,
individuals who have secure, high paying service-class jobs and posses high level of
skills will not support government intervention in the economy. On the other hand,
individuals in insecure, low-skill low-wage occupations and those without income
are expected to support welfare state arrangements. On the country level, the role
of political institutions has been emphasized. Differences in welfare state arrangements and social policies shift individuals’s support for welfare state activity, with the
general expectation that the “social democratic” systems produce more support.5
3 Hypotheses
Religion From this general arguments, expectations about the effect of each explanatory concept on support for welfare state responsibility can be derived. Since
the sick are generally perceived to be ‘victims’ of forces beyond their control, religious individuals will not invoke strong value judgements. Identification with either
one of the two major christian denominations should have no clear effect on preferences for welfare state measures benefiting the sick. On the other hand, I expect to
find a substantive negative effect on welfare state responsibility for the unemployed,
who are at the bottom of the deservingness spectrum. Table 2 gives an overview of
the expected direction of effects for each social group.
The core of my argument about the role of religion in differentiating between deserving and undeserving recipients of welfare is built on the difference of effects between
the two groups of recipients. To formulate this hypothesis more precisely, let U and
S be the extent of state responsibility for unemployed and sick people. Individuals identify with one of the religious categories {C , P,S}, i.e. they regard themselves
as either Catholic, Protestant or Secular.6 Now, the difference hypothesis (H1) states
that the opposition of religious individuals to welfare measures for the unemployed
is expected to be larger than to measures for the sick, or, more precisely:
UC < S C ∧ UP < S P
Therefore the hypothesis can be regarded as confirmed iff both conditions are true.
Since previous research has shown that religious identification, not behavior, is the
major determinant of preferences related to the welfare state, I expect to find no
substantive effect of church attendance.
This difference between religious and secular individuals represents and important
4 Recent
works include Svallfors 2002, 2003; Gelissen 2000; Andreß and Heien 2001; Blekesaune and
Quadagno 2003; Linos and West 2003; Mehrtens III 2004; Rudolph and Jillian 2005; Edlund 2006;
Meier-Jaeger 2006, 2008, 2009; Blekesaune 2007.
5 For recent examples, see Arts and Gelissen 2001, 2002; Svallfors 2002, 2003; Meier-Jaeger 2009;
Mehrtens III 2004; Blekesaune and Quadagno 2003.
6 I exclude the ‘other’ category for the sake of simplicity and since no clear expectations can be derived for this heterogeneous group.
5
societal cleavage, in the sense of “borders between social categories” which generate the potential of social conflict (Svallfors 2007: 9). Since the debate centers
around abstract question of welfare policies, differences in teachings between denominations are relatively unimportant in determining support or opposition to
them. Therefore, the old antagonism between Catholicism and Protestantism emphasized by scholars of religion (e.g. Greeley 1989) should be less relevant here. Instead, the main dividing line lies between religious and secular individuals (Wuthnow 1988; Norris and Inglehart 2004). Consequently, the religious cleavage hypothesis (H2) posits that the difference in resistance against state responsibility for the unemployed should be larger between religious and secular individuals than between
denominations, or, symbolically:
∆(UC + UP ,UN ) > ∆(UC ,UP )
where ∆ yields the difference between its two arguments. This makes clear that we
have to test an inequality. The Bayesian framework used in the empirical part of this
paper provides a principled way to do this.
Religion also exerts an influence on the contextual level. Where religious groups
constitute a strong opponent to the secular part of society, they are more likely to
make their opinions heard. Therefore, each individual is more likely to encounter
negative views on the redistributive role of the state. The concept that best captures
this latent conflict on the macro level is polarization. It takes into account the size
of the opposing groups – religious vs. secular – as well as their distance from one
another (see Duclos et al. 2004 and Permanyer 2008 for the axiomatic derivation of
its properties). Maximum polarization exists when two equally large groups with
maximum distance oppose on another. Consequently, the religious polarization hypothesis (H3) predicts that the higher the level of religious polarization in a country,
the lower its citizen’s support for redistribution will be.
Established factors Regarding the established socio-economic factors, education
and income should reduce the support for welfare state intervention (Meltzer and
Richards 1981; Moene and Wallerstein 2001). There is no a priori reason to expect
effect differences for different target groups. Members of the so called service class
(jobs characterized by a high degree of autonomy and security, c.f. Goldthorpe and
McKnight 2006) oppose government intervention. A disadvantaged social class position and membership in one of the transfer classes is connected with increased endorsement for welfare state activity (Svallfors 2004). Since their interests are mainly
based on their position in the labor market, I expect to find stronger effects for unemployment than for sickness, especially for those that are currently unemployed or
direct recipients of government income (Alber 1984).
On the macro level, I take into account the systematic differences in countries’ welfare arrangements, in order to disentangle the effect of religion and political institutions and policies. Since the simple construct of ‘across the board’ social spending
6
Table 2: Expected effects of explanatory variables.
Sick
Unemployed
Church attendance
Religious identity
Catholic
Protestant
Other
0
0
0
0
.
−
−
.
Socio-economic factors
Gender
Education
Income
Social class
Transfer classes
+
−
−
+
+
+
−
−
+
+
Country characteristics
Religious polarization
Social-liberal stratification
Conservative stratification
0
−
+
−
−
+
Note: A dot indicates that no clear expectation can be formed.
is insufficient to capture the concept of welfare state decommodification (Scruggs
2006; Scruggs and Allan 2006), I control for the generosity for social welfare measures. Furthermore, welfare states differ not only in their generosity, but also in the
system of stratification its policies are trying to achieve (Esping-Andersen 1990). The
clear opposites in this respect are the social democratic and the liberal view on how
a society should be organized. I expect that the more a country is characterized by
a social democrati as opposed to a liberal pattern of stratification, the more its citizens will support state responsibility for both needy groups. Countries characterized
by more conservative stratification policies are expected to be populated by citizens
which show more support for state responsibility (Meier-Jaeger 2009).
4 Data and operationalization
To examine the influence of religion on individual’s differential support for social
groups, I use data from the International Social Survey Programme’s module “Role
of Government IV” fielded in 2006. I use all available 14 advanced western welfare states, namely Australia, Canada, Denmark, Finland, France, Germany, Ireland,
Netherlands, New Zealand, Norway, Sweden, Switzerland, Great Britain and the United
States, yielding interviews with over 18000 respondents.
7
Table 3: Preferences for welfare state responsibility for
unemployed and sick individuals in 14 advanced welfare
states
Unemployed
Country
Norway
Finland
Ireland
Denmark
Sweden
Germany
Netherlands
France
Canada
Switzerland
Great Britain
Australia
United States
New Zealand
Sick
mean
sd
mean
sd
3.22
3.16
3.09
3.07
3.07
2.83
2.82
2.81
2.72
2.68
2.58
2.57
2.55
2.41
0.69
0.72
0.83
0.79
0.74
0.80
0.75
0.82
0.85
0.62
0.85
0.84
0.91
0.87
3.89
3.80
3.87
3.85
3.55
3.50
3.65
3.50
3.63
3.12
3.71
3.70
3.43
3.66
0.36
0.44
0.35
0.40
0.66
0.59
0.50
0.69
0.59
0.63
0.49
0.50
0.75
0.53
Note: Calculated from multiply imputed data ISSP 2006
Dependent variable Two items measure respondents’ support for welfare measures targeted towards the sick and unemployed on a 4 point scale.7 Table 3 shows
the distribution of the responses for each country. The ‘rank order of deservingness’
is clearly visible here. In each and every country, support for welfare state activity
to the benefit of the sick is much greater than for the unemployed. We see particularly clear opposition in the so called ‘liberal’ welfare states, especially in the United
States, Australia and New Zealand.
Explanatory variables: Religious factors Religious denomination is a nominal variable assigning individuals to one of the following categories: No denomination, Catholics, Protestants or other.8 The wide variety of denominations in the United States
is of course not well represented by couching them into the “protestant” label. But
since our population of interest is that of advanced welfare states, this coarse categorization is an appropriate strategy to achieve equivalence (van Deth 1998). Church
attendance is captured using a quasi-metric variable for the frequency with which
7 The
exact wording is: “On the whole, do you think it should or should not be the government’s
responsibility to ... (1) provide health care for the sick ... (2) provide a decent standard of living for
the unemployed.”
8 The “other” group is rather heterogeneous consisting of muslims and Eastern faiths which are too
small to include on their own.
8
our respondents visit church (if at all), ranging from never to several times a week.
Descriptive statistics of the variables used can be found in Table 8 in the appendix.
A proper measure of religious polarization needs to account not only for the size of
religious and secular groups, but also for the distance between them. In a classic
paper, Esteban and Ray (1994) provide a sophisticated account of how to construct
a measure of polarization using group size and group distance.9 Finding a distance
measure is not easy, which is why it is often left out of widely used polarization and
fractionalization indices (e.g Montalvo and Reynal-Querol 2002, 2005a, b; Alesina
et al. 2003). However, using distances is essential to the definition of polarization:
holding the size of opposing groups constant, an increase in distance between those
groups is hypothesized to to lead to higher conflict potential (Duclos et al. 2004; Permanyer 2008). Using data from the ISSP 1998 religion II survey, which includes an assessment of an individual’s strength of religiosity, I create average distances between
those belonging to a denomination and those who do not (for a similar approach
using World Value Survey data, see Permanyer 2008). Following Esteban and Ray
(1994) I now model the polarization, P, between religious (r ) and secular (s ) groups
as a function of group size and distance:
P(π, y ) = π1+α
πs abs y r − y s .
r
The distance, or alienation (Esteban and Ray 1994: 831), between groups is captured
in the second half of the equation by including the absolute distance in religiosity, y ,
between them. Group size enters in the first half of the equation, where the population share, π, of each group is captured and scaled by a factor α, which represents
the degree of polarization sensitivity (Esteban and Ray 1994: 833).10 It is this feature
that makes this measure one of polarization between groups instead of just one of
inequality (indeed setting α = 0 yields the Gini measure).
Explanatory variables: Established factors A respondent’s education is measured
as the number of years he or she spent in full-time education. Income is measured in
country specific units and is standardized to have a within-country mean of zero and
a standard deviation of one. Membership in one of the transfer classes is included as
a dummy variable for being unemployed, retired, or not in labor force for a variety of reasons (permanent disability, doing housework etc.). Social class is included
by coding detailed occupation-by-employment-status units into a categorical class
scheme. The ISSP does not include information on the number of people someone
supervises at work, which is needed to construct an accurate version of the EriksonGoldthorpe-Portocarero scheme (cf. Ganzeboom and Treiman 1996, 2003). I there9 Most
of the theoretical derivations of the polarization measure are done with respect to income
polarization. However, there is nothing in the assumptions of these measures that would preclude
their application to other forms of “social polarization” (Duclos et al. 2004).
10 Admissible values of α lie between 0 and 1.6 (Esteban and Ray 1994: 833-4). I use a conservative α
value of 0.25, however, choosing higher values leads to similar rank ordering of countries.
9
fore use an adapted version, the European Socio-economic Classification (Bihagen
et al. 2005; Harrison and Rose 2006; Rose and Harrison 2010). It uses supervisory status (binary) to construct a class scheme that is largely similar to EGP and is superior
to a strategy that only uses ISCO88 occupational codes to construct class positions.11
To reduce the complexity of the model (i.e. the number of dummy variables) I use
a reduced 5 class version of the ESeC scheme, contrasting managers, lower supervisors, self-employed/employers and white and blue collar workers.12
Welfare state generosity is captured using detailed information on the benefit structure of three major social security programmes (Scruggs 2006; Scruggs and Allan
2006). The extent of each program’s decommodification is a linear combination
of replacement rate (RR), minimum contribution before being qualified (QP), waiting period to first payment (WP) and maximum length of benefit payments (PL),
weighted by the coverage rate (Cov) of the program:13
Decommodification = (2RR + P L − W P −QP) ∗ Cov
My measure of overall welfare generosity is formed by combining z-standardized
decommodification measures for all three programs. Scruggs (2006) provides a detailed description on data collection and operationalizations.
Stratification policy is operationalized following Esping-Andersen (1990) using data
from Scruggs and Allan (2008). Social democratic stratification is captured by benefit
universalism and equality. My indicator for universalism is the proportion of a country’s workforce that is eligible for pensions, sickness and unemployment benefits.
Equality of benefits is calculated as the average after-tax ratio of standard to maximum benefits. The two main ingredients of the Conservative dimension are corporatism and etatism. The extent of corporatism is indicated by the number of distinct
public pension funds for major occupational groups, which provide advantageous
conditions for higher status positions. Etatism is operationalized as the amount of
government’s expenditure on pensions of its employees. Lastly, the Liberal stratification pattern is assessed by the extent of market based arrangements, as indicated
by the ratio of private health expenditures to total health spending. The prevalence
of means-testing is given by the ratio of means-tested poor relief expenditure to total
social spending. As above, the overall index for each stratification pattern is calculated using standardized scores. Following previous research (Hicks and Kenworthy
2003), I performed an eigendecomposition of the correlation matrix of those three
indices, to construct two orthogonal dimensions: one contrasting social democratic
11 Code
for the construction of the scheme is available on my website (user.unifrankfurt.de/~dstegmue)
12 Details on construction of the correspondence between nine and five class scheme are displayed
in Table 9 in the appendix.
13 The replacement rate is the ratio of (after-tax) benefit of a typical (single) worker – i.e. someone
earning an average production worker’s wage – to his or her after-tax income. It is calculated
using detailed information on benefit structure, APW wage and income and security tax structure
of each country. Following Esping Andersen (1990), it is weighted by 2.
10
and liberal stratification14 and one capturing the extent of conservative stratification
policies.
Missing data I address missing data with multiple imputation using the chained
equations approach (Raghunathan et al. 2001; Buuren et al. 2006; Buuren 2007). The
commonly used methods of listwise deletion and mean imputation lead to biased
and misleading results, which is especially problematic when a variable like income
is part of the theoretical model (see for a review of missing data issues Allison 2001;
King et al. 2001). Essentially, multiple imputation creates a posterior distribution for
the missing data conditional on the observed data, and draws randomly from this
distribution to create multiple replications of the original dataset. The model analysis is performed on each of these replicates and then averaged with standard error
adjusted appropriately to reflect the uncertainty of the imputed values (cf. Rubin
1987; Little and Rubin 2002). I computed five imputations on which the following
results are based.
5 Modeling ordinal correlated responses from different countries
Simultaneous random effects ordered probit model Since the dependent variables of this study are categorical, I use an ordered probit formulation (see, among
many, Winship and Mare 1984; Amemiya 1985; Greene and Hensher 2008). It introduces a latent variable y ∗ which is related to the k category observed variable y via
k − 1 thresholds τ.

1 if y ∗ ≤ 0,


2 if 0 < y ∗ ≤ τ1 ,
y=
∗


3 if τ1 < y ≤ τ2 ,
4 if τ2 ≤ y ∗ .
The first threshold is normalized to zero, since an intercept is included in the linear
predictor. Now we can model the response of individual i from country j to each
dependent variable k (k = 1, 2) as a function of p covariates:
y i j k ∗ = β0k +
P
X
βp k x i p + ξ j k
p =1
€
Š
Cor y 1∗ , y 2∗ = ρ
In order to model the dependence of the two dependent variables I estimate them
as a system of equations via a bivariate probit specification allowing for a correlation
ρ between the y ∗ ’s. A schematic depiction of the model is shown in Figure 1 which
14 The
weights for the linear combination are -0.91 and 0.88, respectively.
11
y1
y 1∗
ξ1
y2
ρ
X
y 2∗
ξ2
ψ
Figure 1: Latent variable formulation of a simultaneous ordered probit model with random
effects
follows the usual convention (e.g Skrondal and Rabe-Hesketh 2004) of using circles
for latent variables and boxes for observed quantities.
To capture unobserved differences in response behavior of individuals from different countries, the cut-points are free to vary across countries (cf. Molenberghs and
Verbeke 2005: ch. 18) by giving them a normal distribution, centered above zero and
freely estimated variance ψξk (see Sun et al. 2000; Congdon 2005; Gill 2008). Since
the distribution of responses is quite different for both variables (see Table 3) I allow
for different random effects for each of them which may be correlated. Thus their
distribution is assumed to follow a multivariate normal distribution:
ξ j k ∼ M V N (0, Ψ),
with variance covariance matrix
–
™
ψ2ξ1
ψξ1 ψξ2
Ψ=
.
ψξ1 ψξ2
ψ2ξ2
Specification tests of this somewhat complicated model setup can be performed
straightforwardly by restricting some of the parameters, e.g. by setting ξ j 1 = ξ j 2 ,
and comparing the deviance of the model.
I estimate the model in a Bayesian framework (see Lynch 2007 or Koop 2003 for introductions), using non-informative independent normal priors for the regression
coefficients p with mean zero and a large variance (1000). The prior for the variance
covariance matrix of the random effects ξ follows an inverse Wishart distribution
with 3 degrees of freedom and a scale matrix I ∗ 0.02 (c.f. Gelman et al. 2004). The
correlation ρ of the latent residuals of y ∗ is modeled using the prior suggested by
Barnard et al. (2000). Models are estimated using a blocked Metropolis-Hastings
sampler (Chib and Greenberg 1995) run for 310,000 iterations, with the first 10,000
iterations discarded as burn-in. Chains were thinned by a factor of ten to yield 30,000
12
samples on which the following inferences are based.15
Model selection I estimated a series of models to test for different specifications.
Table 4 shows model deviance as well as the Deviance Information criterion (DIC),
which ‘penalizes’ for growing model complexity and is the preferred measure for
complex Bayesian models involving latent variables (Spiegelhalter et al. 2002). Using it to compare complex models, a rule of thumb is that a DIC difference of 5 to 10
provides some evidence to prefer one model over another, while a difference greater
than 10 indicates a clear rejection of one model in favor of the other.16 Starting from
an empty model, only containing threshold parameters, an intercept and random
country effects, it is clear that the addition of 17 socio-economic covariates (M1) as
well as four indicators of religion (M2) substantially improve the fit of the model.
Model 3 tests for equality of the regression parameters, implying that no effect differences between both dependent variables exist – which would flat out refute the
arguments made in Section 2. But results clearly show that this model does not fit the
data well, as does a model that assumes that random effects are identical for both dependent variables. Adding welfare state characteristics and my measure of religious
polarization (M5) improves model fit to a somewhat smaller extent. Nonetheless,
the DIC difference shows that a model including only micro predictors (M2) has to
be rejected in favor of this one. An alternative model (M6) which includes widely
used economic controls – social spending as percentage of GDP, GDP per capita and
unemployment rate (e.g. Blekesaune 2007) – shows no improvement over the micro
model. Since both specifications tests reject common covariate and random effects,
all inferences of the following sections will be based on model specification M5.17
6 Results and Discussion
Model results The parameter estimates of model 5 are displayed in Table 5, which
shows them with their associated 95% credible intervals.18 Comparing effect size
and direction of religious variables between both model equations, we see our expectations largely borne out. Effects for Catholics and Protestants are small and not
statistically reliable (i.e. indistinguishable from zero) for equation 1 which taps support for welfare state activity directed towards the sick. In other words, religious
15 Several
tests are used to diagnose the absence of convergence (c.f. Brooks and Roberts 1998). Furthermore, using priors with even larger variances did not change results.
16 Just as the Akaike information criterion, DIC compares predictive ability of competing models. It is
not an absolute criterion in search of a ‘true’ model (Burnham and Anderson 2003).
17 I do not present results for M2, since effects of individual level covariates are virtually the same as
in Model 5.
18 Credible intervals are the Bayesian analogue to the frequentist confidence interval. However, owing
to the straightforward meaning of posterior probability in Bayesian analysis, it actually can be
interpreted as region of confidence, i.e. the 95% probability that the estimated parameter lies in
this region (Bernardo and Smith 2000).
13
Table 4: Deviance and DIC of fitted models
Deviance
DIC
Individual characteristics
M0: Empty model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
M1: M0 + Socio-economic variables . . . . . . . . . . . . . . .
M2: M1 + Religious variables . . . . . . . . . . . . . . . . . . . . . .
52612
51738
51694
64230
63400
63290
Specification tests of M2
M3: Covariates equal over equations . . . . . . . . . . . . . . .
M4: Random effects equal over equations. . . . . . . . . .
51796
52449
63480
64740
Country characteristics
M5: M2 + Polarization, welfare state characteristics
M6: M2 + Polarization, economic controls . . . . . . . . .
51634
51693
63274
63300
Specification tests of M5
M7: Covariates equal over equations . . . . . . . . . . . . . . .
M8: Random effects equal over equations. . . . . . . . . .
51799
52345
63527
64530
individuals seem not to differ from their secular counterparts when it comes to the
state helping the sick. Things look quite different when turning to equation 2 for
the unemployed. Both denominations show a large and reliable negative effect, with
Protestants showing somewhat more opposition than Catholics (I examine this difference in more detail below.). Somewhat contrary to my formulated expectations,
church attendance exerts a small negative effect on preferences for state sickness
help. The argument that church attendance and involvement might provide an insurance alternative to the state (Scheve and Stasavage 2006) might help to explain
this finding.
The country-level effect of religious polarization is large and in the expected direction. Citizens in countries with a high antagonism between religious and secular
groups show less support for welfare state involvement. The effect follows the pattern on the individual level: it is decidedly negative for the unemployed and indistinguishable from zero. However its large variance suggests that some caution regarding its generality are in order.19
Regarding our socio-economic control factors, most effects are as expected. Especially the self-employed and individuals with high income show clear opposition of
welfare state involvement, irrespective of the group it concerns, while women and
individuals in lower class positions are in favor of it. We see a curvilinear relationship
with age and the well known pattern of higher female support for welfare measures.
Preferences for welfare state activity in support for the unemployed are especially
high among those who are currently unemployed and (counter intuitively) among
19 However,
in another application using only Western European countries I found similar effects
(Stegmueller 2010).
14
Table 5: Preferences for welfare state responsibility for two social groups. Simultaneous
ordered probit model with random effects. Estimates (posterior means) with credible intervals in brackets
Eq.1: Sick
Eq.2: Unemployed
Individual level variables
[ 0.108
[ 0.187
[ −0.033
[ −0.022
[ −0.169
: 0.220 ]
: 0.370 ]
: −0.014 ]
: −0.007 ]
: −0.109 ]
0.082
0.233
−0.015
0.006
−0.120
[ 0.035
[ 0.155
[ −0.023
[ 0.000
[ −0.146
: 0.130 ]
: 0.311 ]
: −0.007 ]
: 0.013 ]
: −0.094 ]
Denomination (ref.: none)
Catholic . . . . . . . . . . . . . . . . .
0.028 [ −0.056
Protestant. . . . . . . . . . . . . . .
−0.061 [ −0.138
Other . . . . . . . . . . . . . . . . . . .
0.014 [ −0.104
Church attendance . . . . .
−0.032 [ −0.047
: 0.113 ]
: 0.016 ]
: 0.133 ]
: −0.017 ]
−0.132
−0.182
−0.019
0.002
[ −0.205
[ −0.247
[ −0.121
[ −0.010
: −0.059 ]
: −0.116 ]
: 0.084 ]
: 0.015 ]
Female . . . . . . . . . . . . . . . . . .
Age . . . . . . . . . . . . . . . . . . . . .
Age2 . . . . . . . . . . . . . . . . . . . .
Education . . . . . . . . . . . . . . .
Income . . . . . . . . . . . . . . . . .
0.164
0.278
−0.023
−0.015
−0.139
Transfer class (ref.: working)
Unemployed . . . . . . . . . . . .
0.091 [ −0.065 :
Retired . . . . . . . . . . . . . . . . . .
0.050 [ −0.045 :
Not in LF . . . . . . . . . . . . . . . .
−0.010 [ −0.099 :
Socio-economic class (ref.: lower supervisors)
Managers . . . . . . . . . . . . . . .
−0.049 [ −0.124
Self-employed . . . . . . . . . .
−0.230 [ −0.339
White collar . . . . . . . . . . . . .
0.108 [ −0.013
Blue collar . . . . . . . . . . . . . .
0.079 [ −0.019
No class . . . . . . . . . . . . . . . . .
−0.011 [ −0.096
0.247 ]
0.145 ]
0.079 ]
0.862 [ 0.726 :
0.198 [ 0.121 :
0.202 [ 0.128 :
0.996 ]
0.277 ]
0.277 ]
: 0.026 ]
: −0.119 ]
: 0.230 ]
: 0.177 ]
: 0.073 ]
−0.005
−0.151
0.137
0.230
0.153
[ −0.069
[ −0.246
[ 0.039
[ 0.149
[ 0.081
: 0.059 ]
: −0.056 ]
: 0.236 ]
: 0.312 ]
: 0.226 ]
: 0.224 ]
: −0.037 ]
: 0.374 ]
: 0.064 ]
−1.632
−0.204
0.247
0.017
[ −2.385
[ −0.346
[ 0.100
[ −0.013
: −0.877 ]
: −0.062 ]
: 0.395 ]
: 0.047 ]
Country level variables
Religious Polarization . . .
Socialist-liberal Strat. . . .
Conservative Strat. . . . . . .
Welfare generosity . . . . . .
ψ2ξi . . . . . . . . . . . . . . . . . . . . .
ψξ1 ξ2 . . . . . . . . . . . . . . . . . . . .
ρ .......................
Deviance. . . . . . . . . . . . . . . .
DIC . . . . . . . . . . . . . . . . . . . . .
N (countries) . . . . . . . . . . . .
−1.123
−0.290
0.109
0.010
[ −2.477
[ −0.543
[ −0.153
[ −0.043
0.502 [ 0.205 : 1.189 ]
0.156 [ 0.063 :
0.102 [−0.061 : 0.364]
0.765 [0.733 : 0.797]
51634
63274
18910 (14)
0.372 ]
Note: Based on 30.000 MCMC draws obtained via Metropolis sampling. Multiply imputed data,
ISSP 2006. Credible intervals are 95% bounds for individual level variables, 90% bounds for
country level variables.
15
Table 6: Posterior probability that coefficient for Unemployment is smaller than
for Sickness
P(βp 1 < βp 2 )
Coefficient
Catholic
Protestant
Other
Church attendance
0.999
0.997
0.750
0.000
Note: Entries show Bayesian p-values based
on 30.000 MCMC draws from Model 5.
retired individuals. Similarly, the effect of education is not consistent for both groups
with marginally small support for unemployed and opposition to welfare responsibility for the sick. In countries with more liberal stratification policies, support for
welfare state involvement is lower, whereas conservative policies are connected with
higher preferences for welfare state measures directed at the unemployed.20 Interestingly, welfare generosity has no substantial effect – a result that is also obtained
when using the ‘inferior’ social spending per GDP measure.
Effect differences Going beyond the (difficult) interpretation of ordered probit coefficients, I proceed to examine the support for my micro level hypothesis by calculating quantities of interest from Model 5. The difference hypothesis (H1) stated
that identification with one of the major christian denominations leads to stronger
opposition to welfare state activity for the unemployed than for the sick. In Table 6
I calculate the probability that the parameter for Unemployment is larger than that
for Sickness. The probabilities close to one for both denominations indicate that we
can be certain that religious’ individuals resistance to government helping the unemployed is larger than for the more “deserving” group of the sick. This does provide
strong evidence for H1.
Figure 2 provides a complementary piece of evidence in support for the difference
hypothesis. It shows the model implied difference in effect size between religious
and secular individuals for our two social groups. In other words, it displays to which
extent religious individuals differ from secular ones in their opposition to welfare activity targeted towards unemployed and sick people. A measure of uncertainty about
this difference can be computed from MCMC samples from the model and is displayed as horizontal lines. Two points become clear from these calculations. Firstly,
the opposition of religious individuals to welfare measures for the unemployed is
large and statistically reliable (as indicated by the confidence bounds far away from
20 But
note that using 95% bounds pushes the confidence bound for the socialist-liberal coefficients
of equation 2 over zero.
16
Unemployment
●
Sickness
●
−0.20
−0.15
−0.10
−0.05
0.00
0.05
0.10
Figure 2: Effect differences between religious and secular individual’s support for welfare
measures for two social groups. Mean difference and 95% confidence bounds.
zero). The opposite picture shows in regard to sick individuals: the difference between religious and secular individuals is so small that we cannot be sure that it is
not zero. Secondly, Figure 2 clearly shows that the difference between those two differences is itself large. Calculating the exact numbers yields a difference of −0.14
with 95% confidence bound ranging from −0.22 to −0.06, thus showing that the opposition of religious individuals against welfare measures for the unemployed differs
from measures for the sick in a significant way.
This brings us to the examination of the religious cleavage hypothesis (H2) which
stated that differences in resistance against the state helping the unemployed should
be larger between religious and secular individuals than between denominations.
Figure 3 (constructed the same way as before) shows the difference between religious and secular individuals as well as between Catholic and Protestants in denying welfare state responsibility for the unemployed. We are already familiar with
the effect difference between secular and religious individuals. It is large and significant compared to the difference between the two denominations, whose uncertainty
range makes it indistinguishable from zero. The difference between this differences
is −0.21 with a confidence bound ranging from −0.30 to −0.13’ lending strong support to the cleavage hypothesis.
Religion and other socio-economic characteristics The previous paragraphs have
demonstrated a clear effect of religion on individuals preferences. In the final section, I examine the interaction between religion and socio-economic position since
Religious−Secular
●
Catholic−Protestant
●
−0.2
−0.1
0.0
0.1
0.2
Figure 3: Preferences for welfare state support for the unemployed. Differences between
religious and secular individual’s and between Catholics and Protestants. Mean difference
and 95% confidence bounds.
17
Table 7: Effect of religion and socio-structural characteristics on support for the unemployed. Difference between religious and secular individuals in
predicted probabilities for responding in the highest category (standard errors in parentheses).
Effect difference
religious – secular
High educated
with high income in
managerial occupation
−0.081
(0.024)
Low educated,
with median income
in blue collar occupation
−0.102
(0.025)
Low educated
with low income
currently unemployed
−0.123
(0.025)
Note: First differences calculated from 30.000 MCMC
draws from Model 5. Variables which are not mentioned above are held at their mean or sample proportion.
it possible that the effect of religion is strongly conditional on other socio-economic
factors (e.g. religion might only be relevant for low educated individuals). For the
results displayed in Table 7, I calculated the predicted probability of showing a clear
preference for welfare state responsibility for the unemployed setting parameters
for individual covariates to represent “typical” individuals. The displayed estimates
represent the difference in predicted probabilities between religious and secular individuals, ceteris paribus.21 Glancing over the three types of individuals in different
socio-economic conditions, it becomes clear that identifying with a major christian
religion always has a clear negative effect on support for the unemployed. Interestingly, the size of the difference gets slightly stronger down the socio-economic
ladder. The relevance of religion as determinant of welfare state preferences is especially emphasized when looking at the last type of individual. Being unemployed
and having low education and income, he or she is clearly a beneficiary of welfare
state activity. Nonetheless, the difference of more than 12 percentage points demonstrates that religious individuals show significantly less support for the welfare state
than their secular counterparts.
21 Since
the difference between men and women are minuscule, I decided to present a simpler table,
not divided by gender. The predicted probabilities can be interpreted as “‘population averaged”’,
holding gender at its sample proportion.
18
7 Conclusion
In this paper I argued that religious individuals differentiate who should get help by
the welfare state and who should not. Based on moral judgments, certain groups are
seen as more deserving than others and this “deservingness ranking” is translated
into their political preferences over welfare state responsibility.
Using a simultaneous bivariate ordered probit specification, I am able to perform a
strict test of the hypothesis that the effect of religious identification is larger for the
deserving than the undeserving group. And indeed, findings show a clear and negative effect of religious identity on preferences for welfare state responsibility for the
unemployed. Moreover, this pattern can also be found at the macro level. With a
refined measure from the theoretical literature (Esteban and Ray 1994), I show how
a country’s degree of religious polarization affects the preferences of its citizen’s: increased antagonism between secular and religious groups leads to lower support for
welfare state responsibility for the unemployed. Moreover, I argued that in modernized secular societies, when it comes to social polices, the main dividing line is
not between Catholics and Protestants. My findings confirm that religious individuals are united in their opposition to welfare state measures directed at undeserving
individuals, making the religious–secular divide the more important cleavage.
The results of this paper suggest, that religion is an important factor which should
not be ignored in research on the causes and consequences of welfare state policy. Religious identities are part of a “moral economy” (Mau 2003), where norms and
ideas influence individual preferences and beliefs. The strong effect of identification
with a certain denomination shows that motivations beyond self-interest determine
the extent to which citizens support or oppose redistributive policies.
19
Appendix
Table 8: Descriptive statistics of covariates. Means
and standard deviation.
Age
Female
Income (std.)
Education (years)
Religion
Church attendance
No denomination
Catholic
Protestant
Other
Transfer class
Working
Unemployed
Retired
Not in Labor Force
Socio-economic class
Manager
Lower supervisor
Self-Employed
White collar
Blue collar
No class position
Country characteristics
Religious Polarization
Socialist-liberal stratification
Conservative stratification
Welfare generosity
mean
sd
49.32
0.52
-0.06
12.99
16.75
0.50
1.03
3.95
3.16
0.24
0.26
0.43
0.07
2.11
0.43
0.44
0.49
0.25
0.59
0.03
0.24
0.13
0.49
0.18
0.43
0.34
0.28
0.23
0.07
0.07
0.12
0.21
0.45
0.42
0.26
0.26
0.33
0.41
0.40
0.19
0.05
1.16
0.20
1.05
1.01
3.96
Note: Calculated from multiply imputed ISSP 2006
data.
20
Table 9: Recoding from 9 to 5 class ESeC scheme
Esec
Description
recode
9 class version
1
Large employers, higher mgrs/professionals
2
Lower mgrs/professionals, higher supervisory/technicians
3
Intermediate occupations
4
Small employers and self-employed (non-agriculture)
5
Small employers and self-employed (agriculture)
6
Lower supervisors and technicians
7
Lower sales and service
8
Lower technical
9
Routine
5 class version
1
Managers & professionals
2
Intermediate and lower supervisory
3
Small employers and own account workers
4
White collar workin class
5
Blue collar working class
1+2
3+6
4+5
7
8+9
Note: See www.iser.essex.ac.uk/research/esec/user-guide for further details.
21
References
Alber, J. (1984). Versorgungsklasse im Wohlfahrtsstaat [Transfer class in the welfare
state]. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 36, 225–251.
Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S. and Wacziarg, R. (2003). Fractionalization. Journal of Economic Growth, 8, 155–194.
Allison, P. D. (2001). Missing Data. Thousand Oaks: Sage.
Amemiya, T. (1985). Advanced Econometrics. Harvard: Cambridge University Press.
Andreß, H.-J. and Heien, T. (2001). Four Worlds of Welfare Attitudes. A Comparison
of Germany, Norway, and the United States. European Sociological Review, 17,
337–356.
Arts, W. and Gelissen, J. (2001). Welfare States, Solidarity and Justice Principles: Does
the Type Really Matter? Acta Sociologica, 44, 283–299.
Arts, W. A. and Gelissen, J. (2002). Three worlds of welfare capitalism or more? A
state-of-the-art report. Journal of European Social Policy, 12, 137–158.
Barnard, J., McCulloch, R. and Meng, X.-L. (2000). Modeling covariance matrices
in terms of standard deviations and correlations, with application to shrinkage.
Statistica Sinica, 10, 1281–1311.
Bernardo, J. M. and Smith, A. F. M. (2000). Bayesian Theory. Chichester: Wiley.
Bihagen, E., Nermo, M. and Erikson, R. (2005). Class Schemas and Employment
Relations. Comparisons between the ESeC and the EGP class schemas using European data. Validation report prepared for the ESeC conference in Bled, Slovenia
29-30 June 2006.
Blekesaune, M. (2007). Economic Conditions and Public Attitudes to Welfare Policies. European Sociological Review, 23, 393–403.
Blekesaune, M. and Quadagno, J. (2003). Public Attitudes toward Welfare State Policies: a Comparative Analysis of 24 Nations. European Sociological Review, 19,
415–427.
Bowles, S. and Ginits, H. (2000). Reciprocity, Self-interest, and the Welfare State.
Nordic Journal of Political Economy, 26, 35–53.
Brooks, C., Nieuwbeerta, P. and Manza, J. (2006). Cleavage-based voting behavior
in cross-national perspective: Evidence from six postwar democracies. Social Science Research, 35, 88–128.
Brooks, S. P. and Roberts, G. O. (1998). Convergence assessment techniques for
Markov chain Monte Carlo. Statistics and Computing, 8, 319–335.
Burnham, K. P. and Anderson, D. (2003). Model Selection and Multi-Model Inference.
A Practical Information-Theoretic Approach. New York: Springer.
Buuren, S. v. (2007). Multiple imputation of discrete and continuous data by full
conditional specification. Statistical Methods in Medical Research, 16, 219–242.
Buuren, S. v., Brand, J. P. L., Groothuis-Oudshoorn, C. G. M. and Rubin, D. B. (2006).
Fully conditional specification in multivariate imputation. Journal of Statistical
Computation and Simulation, 76, 1049–1064.
22
Chib, S. and Greenberg, E. (1995). Understanding the Metropolis-Hastings Algorithm. The American Statistician, 49, 327–335.
Congdon, P. M. (2005). Bayesian Models for Categorical Data. Chichester: Wiley.
Deacon, A. (2002). Perpsectives on Welfare. Buckingham: Open University Press.
van Deth, J. W. (1998). Equivalence in comparative political research. In van Deth,
J. W. (Ed.), Comparative Politics. The problem of equivalence, London: Routledge.
pp. 1–19.
Duclos, J.-Y., Esteban, J. and Ray, D. (2004). Polarization: Concepts, Measurement,
Estimation. Econometrica, 72, 1737–1772.
Edlund, J. (2006). Trust in the Capability of the Welfare State and General Welfare
State Support: Sweden 1997–2002. Acta Sociologica, 49, 395–417.
Elff, M. (2007). Social Structure and Electoral Behavior in Comparative Perspective:
The Decline of Social Cleavages in Western Europe Revisited. Perspectives on Politics, 5, 277–294.
Esping-Andersen, G. (1985). Politics against Markets. The Social Democratic Road to
Power. Princeton: Princeton University Press.
Esping-Andersen, G. (1990). The Three World of Welfare Capitalism. Cambridge:
Polity Press.
Esteban, J.-M. and Ray, D. (1994). On the Measurement of Polarization. Econometrica, 62, 819–851.
Ganzeboom, H. B. and Treiman, D. J. (2003). Three Internationally Standardised Measures for Comparative Research on Occupational Status. In HoffmeyerZlotnik, J. H. and Wolf, C. (Eds.), Advances in Cross-National Comparison. A European Working Book for Demographic and Socio-Economic Variables, New York:
Kluwer Academic / Plenum Publishers. pp. 159–194.
Ganzeboom, H. B. G. and Treiman, D. J. (1996). Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification of
Occupations. Social Science Research, 25, 201–239.
Gelissen, J. (2000). Popular support for institutionalised solidarity: a comparison
between European welfare states. International Journal of Social Welfare, 9, 285–
300.
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004). Bayesian Data Analysis.
Boca Raton: Chapman & Hall.
Gill, J. (2008). Bayesian Methods. A Social and Behavioral Sciences Approach. Boca
Raton: Chapman & Hall.
Goldthorpe, J. H. and McKnight, A. (2006). The Economic Basis of Social Class. In
Morgan, S. L., Grusky, D. B. and Fields, G. S. (Eds.), Mobility and Inequality: Frontiers of Research in Sociology and Economics, Stanford: Stanford University Press.
pp. 109–136.
Greeley, A. (1989). Protestant and Catholic: Is the Analogical Imagination Extinct?
American Sociological Review, 54, 485–502.
23
Greene, W. and Hensher, D. (2008). Modeling Ordered Choices: A Primer and Recent
Developments. Working paper, Version 4, June 15, 2008.
Guiso, L., Sapienza, P. and Zingales, L. (2003). People’s opium? Religion and economic attitudes. Journal of Monetary Economics, 50, 225–282.
Guiso, L., Sapienza, P. and Zingales, L. (2006). Does Culture Affect Economic Outcomes? Journal of Economic Perspectives, 20, 23–48.
Harrison, E. and Rose, D. (2006). The European Socio-Economic Classification
(ESeC) User Guide. Institute for Social and Economic Research, University of Essex.
Hicks, A. and Kenworthy, L. (2003). Varieties of welfare capitalism. Socio-economic
review, 1, 27–61.
Hogg, M. A., Hardie, A., Elizabeth and Reynolds, K. J. (1995). Prototypical similarity,
self-categorization, and depersonalized attraction: a perspective on group cohesivness. European Journal of Social Psychology, 25, 159–177.
Huddy, L. (2001). From Social to Political Identity: A Critical Examination of Social
Identity Theory. Political Psychology, 22, 127–156.
Kangas, O. E. (1997). Self-interest and the Common Good: The Impact of Norms,
Selfishness and Context in Social Policy Opinions. Journal of Socio, 26, 475–494.
King, G., Honacker, J., Anne, J. and Kenneth, S. (2001). Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation. American
Political Science Review, 95, 49–69.
Koop, G. (2003). Bayesian Econometrics. Hoboken: Wiley.
Korpi, W. (2006). Power Resources and Employer-centered Approaches in Explanations of Welfare States and Varieties of Capitalism. Protagonists, Consenters, and
Antagonists. World Politics, 58, 167–206.
Lepianka, D., Gelissen, J. and Oorschot, W. v. (2010). Popular Explanations of Poverty
in Europe. Effects of Contextual and Individual Characteristics across 28 European
Countries. Acta Sociologica, 53, 53–72.
Linos, K. and West, M. (2003). Self-interest, Social Beliefs and Attitudes toward Redistributioin. Re-addressing the Issue of Cross-national Variation. European Sociological Review, 19, 393–409.
Little, R. J. and Rubin, D. B. (2002). Statistical Analysis with Missing Data. Hoboken:
Wiley.
Lynch, S. M. (2007). Introduction to Applied Bayesian Statistics and Estimation for
Social Scientists. New York: Springer.
Madeley, J. (1991). Politics and religion in Western Europe. In Moyser, G. (Ed.), Politics and religion in the modern world, London: Routledge. pp. 28–66.
Manza, J. and Brooks, C. (1997). The Religious Factor in U.S. Presidential Elections,
1960–1992. American Journal of Sociology, 103, 38–81.
Mares, I. (2001). Firms and the Welfare State: When, Why, and How Does Social Policy Matter to Employers. In Hall, P. and Soskice, D. (Eds.), Varieties of Capitalism.
24
The Institutional Foundations of Comparative Advantage, Oxford: Oxford University Press. pp. 184–212.
Marshall, T. H. (1950 [1997]). Citizenship and Social Class. In Marshall, T. H. and
Bottomore, T. (Eds.), Citizenship and Social Class, London: Pluto Press.
Mau, S. (2003). The Moral Economy Of Welfare States: Britain And Germany Compared. New York: Routle.
Mehrtens III, J. F. (2004). Three worlds of public opinion? Values, variation and the
effect on social policy. International Journal of Public Opinion Research, 16, 115–
143.
Meier-Jaeger, M. (2006). What Makes People Support Public Responsibility for Welfare Provision: Self-interest or Political Ideology? Acta Sociologica, 49, 321–338.
Meier-Jaeger, M. (2007). Are the deserving needy really deserving everywhere. Crosscultural Heterogeneity and Popular Support for the Old and the Sick in Eight Western Countries. In Mau, S. and Veghte, B. (Eds.), Social Justice, Legitimacy and the
Welfare State, Ashgate. pp. 73–96.
Meier-Jaeger, M. (2008). Does left-right orientation have a causal effect on support
for redistribution? Causal analysis with cross-sectional data using Instrumental
Variables. International Journal of Public Opinion Research, 20, 363–373.
Meier-Jaeger, M. (2009). United But Divided: Welfare Regimes and the Level and
Variance in Public Support for Redistribution. European Sociological Review, 25,
723–737.
Meltzer, A. and Richards, S. (1981). A rational theory of the size of government. Journal of Political Economy, 89, 914–927.
Moene, K. O. and Wallerstein, M. (2001). Inequality, Social Insurance and Redistribution. American Political Science Review, 95, 859–874.
Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New
York: Springer.
Montalvo, J. G. and Reynal-Querol, M. (2002). Why ethnic fractionalization? Polarization, ethnic conflict and growth. Manuscript.
Montalvo, J. G. and Reynal-Querol, M. (2005a). Ethnic diversity and economic development. Journal of Development Economics, 76, 293–323.
Montalvo, J. G. and Reynal-Querol, M. (2005b). Ethnic polarization, potential conflict and civil wars. American Economic Review, 95, 796–815.
Mueller, D. C. (2009). Reason, Religion, and Democracy. Cambridge University Press.
Norris, P. and Inglehart, R. (2004). Sacred and Secular. Religion and Politics Worldwide. New York: Cambridge.
Oorschot, W. v. (2000). Who should get what, and why? On deservingness criteria
and the conditionality of solidarity among the public. Policy & Politics, 28, 33–48.
Oorschot, W. v. (2002). Individual Motives for contributing to welfare benefits in the
Netherlands. Pol, 30, 31–46.
Oorschot, W. v. (2006). Making the difference in social Europe: deservingness per-
25
ceptions among citizens of European welfare states. Journal of European Social
Policy, 16, 23–42.
Permanyer, I. (2008). Social Polarization: Introducing distances between and within
groups. Manuscript.
Raghunathan, T. E., Lepkowski, J. M., Van Hoewyk, J. and Solenberger, P. (2001). A
Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of
Regression Models. Survey Methodology, 27, 85–95.
Rose, D. and Harrison, E. (Eds.) (2010). Social Class in Europe: An Introduction to The
European Socio-economic Classification. London: Routledge.
Rubin, D. (1987). Multiple Imputation for Nonresponse in Surveys. Hoboken: Wiley.
Rudolph, T. J. and Jillian, E. (2005). Political Trust, Ideology, and Public Support for
Government Spending. American Journal of Political Science, 49, 660–671.
Salamon, L. M. (1996). Third Party Government. Ein Beitrag zu einer Theorie der
Beziehung zwischen Staat und Nonprofit–Sektor im modernen Wohlfahrtsstaat [A
Contribution to a Theory of the Relation between State and Non–profit Sector in
Modern Welfare States]. In Evers, A. and Olk, T. (Eds.), Wohlfahrtspluralismus:
vom Wohlfahrtsstaat zur Wohlfahrtsgesellschaft, Opladen: VS Verlag. pp. 79–102.
Scheve, K. and Stasavage, D. (2006). Religion and Preferences for Social Insurance.
Quarterly Journal of Political Science, 1, 255–286.
Schneider, A. and Ingram, H. (1993). Social Construction of Target Populations: Implications for Politics and Policy. American Political Science Review, 87, 334–347.
Scruggs, L. (2006). The generosity of social insurance, 1971-2002. Oxford Review of
Economic Policy, 22, 349–364.
Scruggs, L. and Allan, J. (2006). Welfare-state decommodification in 18 OECD countries: a replication and revision. Journal of European Social Policy, 16, 55–72.
Scruggs, L. L. and Allan, J. P. (2008). Social Stratification and Welfare Regimes for the
Twenty-First Century. Revisiting the Three Worlds of Welfare Capitalism. World
Politics, 60, 642–664.
Shayo, M. (2009). A Model of Social Identity with an Application to Political Economy: Nation, Class, and Redistribution. American Political Science Review, 103,
147–174.
Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Boca Raton: Chapman & Hall.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian
Measures of Complexity and Fit. Journal of the Royal Statistical Society Series B,
64, 583–639.
Stegmueller, D. (2010). Support for Redistribution in Western Europe. Assessing the
role of religion. Paper Presented at the Annual Meeting of the American Political
Science Association, Toronto 2010.
Sun, D., Speckman, P. L. and Tsutakawa, R. K. (2000). Random Effects in Generalized
Linear Mixed Models (GLMMs). In Dey, D. K., Ghosh, S. K. and Mallick, B. (Eds.),
Generalized Linear Models: A Bayesian Perspective, New York: Marcel Dekker. pp.
26
23–39.
Svallfors, S. (2002). Political Trust and Support for the Welfare State: Unpacking a
Supposed Relationship. In Rothstein, B. and Steinmo, S. (Eds.), Restructuring the
Welfare State: political institutions and policy change, New York: Palgrave Macmillan. pp. 184–205.
Svallfors, S. (2003). Welfare Regimes and Welfare Opinions. Social Indicators Research, 64, 495–520.
Svallfors, S. (2004). Class, Attitudes and the Welfare State: Sweden in Comparative
Perspective. Social Policy & Administration, 38, 119–138.
Svallfors, S. (2007). Introduction. In Svallfors, S. (Ed.), The Political Sociology of
the Welfare State, Institutions, Social Change, and Orientations, Stanford: Stanford
University Press. pp. 1–29.
de Swaan, A. (1988). In Care Of The State. Health Care, Education And Welfare In
Europe And The Usa In The Modern Era. Oxford: Oxford University Press.
Tajfel, H. (1981). Human groups and social categories. Studies in Social Psychology.
Cambridge: Cambridge University Press.
Wiepking, P. and Bekkers, R. (2008). Explaining differences in charitable giving in
Europe. Http://www.wiepking.com/papers/WiepkingBekkers2008.pdf.
Winship, C. and Mare, R. D. (1984). Regression models with ordinal variables. American Sociological Review, 49, 521–525.
Wuthnow, R. (1988). The Restructuring of American Religion: Society and Faith since
World War II. Princeton: Princeton University Press.
27