Health, the welfare state and turnout* · 2014-08-18 · Health, the welfare state and turnout*...

Health, the welfare state and turnout*

Peter Söderlunda Lauri Rapelib

Hanna Wassc Mikko Mattilad

Abstract

Studies generally show that poor personal health decreases turnout in elections. Little is known,

however, about what explains why turnout gaps between those with good and poor health differ

between countries. In this study, we examine whether health-related inequalities in electoral

participation are more or less pronounced in countries with a more egalitarian welfare system.

Two competing hypotheses can be set. First, welfare state development may narrow the turnout

gap between the healthy and unhealthy by equalizing the opportunities and lowering the hurdles

to voting. Alternatively, unequal economic and social settings can disproportionately mobilize

citizens with poor health, implying a wider turnout gap in more extensive welfare systems.

These hypotheses are tested using six rounds of the European Social Survey across 32 countries

(2002–2013). The empirical results support the latter hypothesis: at the macro level, more

unequal countries actually had smaller turnout gaps between citizens with poor and good health.

Keywords

health, welfare states, inequality, voter turnout, mobilization

* Paper presented at the 8th

ECPR General Conference, University of Glasgow, Glasgow, 3–6

September, 2014 a Department of Political and Economic Studies, University of Helsinki. E-mail: [email protected]

b Aronia Research and Development Institute. E-mail: [email protected]

c Department of Political and Economic Studies, University of Helsinki. E-mail: [email protected]

d Department of Political and Economic Studies, University of Helsinki. E-mail:

[email protected]

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Introduction

Some citizens have greater resources and motivation to be politically active. Health is one of

many factors which contribute to disparities in political resources and action. Several studies

show that poor personal health decreases political participation as well as political efficacy and

interest (see Mattila et al, 2013). Citizens with poor health are less likely to vote given the

physical and mental effort required (Denny & Doyle, 2007a, 2007b). Scholars also often link

health status to other individual-level determinants of political behavior: e.g. social status,

psychological engagement and participation in social networks (Denny & Doyle, 2007b;

Peterson 1987). But it is largely unknown which contexts are more conducive for political

mobilization among individuals experiencing poor health. A potentially important factor is the

magnitude of the welfare state. Some studies show that turnout and political engagement is

higher in developed welfare states (Anderson and Beramendi, 2008; Lister, 2007) or states with

lower income inequality (Solt, 2008). But even though high welfare state development raises

overall turnout, it is less clear if and how it affects the participation gap between the healthy and

unhealthy.

This study analyzes if and how health and type of welfare state interact to affect voter turnout. In

that case, health inequalities in turnout are influenced by variations in redistributive social

policies pertaining to spending on health care and egalitarian income distribution. In some

contexts, the turnout gap between those experiencing poor and good health is narrower. Two

competing hypotheses will be tested. The ability hypothesis proposes that greater welfare state

development narrows the turnout gap between those with poor and good health by equalizing

the opportunities and lowering the hurdles for political participation among the latter. This

form of resource theory would imply that participation is more equal in an equal society

because personal resources relevant for political participation are more evenly distributed, while

greater inequality increases bias in the electorate due to greater differences between different

strata of society. The motivation hypothesis states that the turnout gap is smaller in unequal

social and economic settings if citizens with poor health are motivated to vote to shape electoral

outcomes. In that case it would support a form of conflict theory meaning that low levels of

social and economic equality provide a strong motivation for action by voting for politicians and

parties that are supportive of greater economic redistribution and social spending such as public

health care.

We seek to add to the literature on how the institutional, social and economic context narrows

or widens the turnout gap between social groups with different resources and incentives to

participate. Much research has shown that different political institutions influence the turnout

gap between, for example, generally disadvantaged and advantaged groupings (Perea 2002),

politically interested and uninterested (Söderlund et al, 2011), more and less knowledgeable

(Fischer et al, 2008) and highly and lesser educated (Gallego, 2010). The socioeconomic

context (economic inequality) may have a similar contingent effect on the turnout gap between

high- and low-income groups (Solt, 2008).

The study proceeds by presenting the theoretical framework and formulating the rival

hypotheses which predict how the relationship between health and voter turnout is modified by

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equity in the welfare state. The second section presents the data and the empirical design. The

data included six rounds (2002–2013) of the European Social Survey across 32 countries. The

third section presents the results of multilevel regression models that were employed to test the

hypotheses concerning how economic equality and health expenditures modify the relationship

between health and turnout. Finally, the fourth section discusses the main findings.

Theoretical framework and hypotheses

This section formulates two hypotheses about how the welfare context may affect the turnout

gap between healthy and unhealthy citizens. Welfare state is a vague concept and difficult to

validate empirically since there are so many aspects to it. In the widest sense a welfare state

system can be understood as state intervention and a set of policies which provide socio-

economic equality and security by means of economic redistribution through taxation,

unemployment benefits, education, public health care services, etc. (Schubert et al, 2009). The

key question is how and why the welfare state context has a contingent effect by influencing the

strength of the relationship between health and turnout. A contingent effect refers to a situation

when a contextual variable influences the relation between two variables at the individual level

(see Anderson, 2007, pp. 595–596). The mechanisms at both the individual and contextual

levels need to be elucidated because “any theory of contextual effects really is a theory of two

theories” (Anderson and Dalton, 2011, p. 243). We take influence from a conceptual

framework presented by Harder and Krosnick (2008) who state that the likelihood of voting is a

multiplicative function of three classes of causes: ability, motivation, and difficulty.1 We focus

on the first two factors, however, as both individual characteristics and the political context

affect turnout by shaping the ability and motivation to vote. Difficulty of voting, on the other

hand, depends on external factors such as availability of information, legal and institutional

barriers and access to polling stations (Harder and Krosnick, 2008). Although such contextual

factors may be highly relevant for the mobilization of citizens with poor health, it is beyond the

scope of this study. Furthermore, we also take influence of studies of economic inequality and

turnout which have included two competing theories: resource and conflict theories.

Ability hypothesis

First, the ability hypothesis predicts that economic equality and welfare services reduce the

turnout gap between those with poor and good health (see Figure 1a). Assumingly resources

such as time, money and skills are more evenly distributed between advantaged and

disadvantaged groups in more comprehensive welfare states. At the individual level, ability to

vote relates to an individual’s personal resources: e.g., time, money, information and cognitive

skills. Ability is traditionally linked to demographic factors such as age, education and income

(Harder and Krosnick, 2008) and in line with the civic voluntarism model which emphasizes

personal resources, psychological engagement and access to recruitment networks (Verba et al,

1995). Health tends to go hand in hand with these factors which affect levels of participation

(Denny and Doyle, 2007b; Mattila et al, 2013; Peterson, 1987).

[FIGURES 1A AND 1B ABOUT HERE]

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Strong welfare states are conducive for widespread personal resources and abilities. As

Rothstein points out, welfare states with universal social policies contribute to economic equality

and equality of opportunities. Such programs create material equality by means of redistribution

and equality of opportunities by being based on the principle of equal treatment (Rothstein,

2011, pp. 151-152). Welfare state provisions provide universal access to collective goods such

as higher education, health care and social security benefits which moderate social inequalities.

In contrast, inequality in the form of wide income disparities tends to be detrimental to social

cohesion, population health and political participation (Kawachi and Kennedy, 1997). Hence,

welfare benefits alleviate the situation for disadvantaged citizens in strong welfare states and

established democracies so that these citizens “do not have to struggle for their existence, and

this enables various kinds of social communication, participation and trust to flourish” (Wallace

and Pichler, 2007, p. 50). Spending on universal programs for education is in the long run also

conducive for equality of opportunities (Rothstein, 2011, p. 154). In fact, levels of civic literacy

(i.e. political knowledge) have been shown to be higher in welfare states with higher income

equality and social spending (Milner, 2002, pp. 173–174). Moreover, Grönlund and Milner

(2006) found that political knowledge is less dependent on education in egalitarian welfare states

than in inegalitarian countries.

Turnout should therefore be higher for people with poor health in universal welfare system as

the resource differentials are generally smaller between advantaged and disadvantaged groups.

As the opportunities for participation in elections are equalized in strong welfare states, the

turnout gap between those with poor health and good health is narrower. In contrast, greater

inequality in resources in less developed welfare states imply that those with poor health suffer

from deficits in personal resources and are thus discouraged from political participation. This

assumption is in line with the resource theory in the study of economic inequality and turnout.

Proponents of this theory also stress the importance of resources such as time, money,

education and skills. The main argument is that resources are more evenly distributed in

societies with low economic inequality, which, in turn, translates into higher overall turnout.

When economic inequality is high, however, resources that facilitate participation are more

concentrated to the rich, while the poor have fewer resources to devote to politics. Thus

economic equality reduces the turnout gap between the poor and the rich, while inequality

expands the turnout gap (Solt, 2008; see also Anderson and Beramendi, 2008). An alternative

interpretation is represented by the relative power theory which suggests that the context of

inequality impact the shape of politics whereby those with few resources participate less since

they feel their chances of influencing political outcomes are small (Goodin and Dryzek, 1980;

Solt, 2008).

The ability hypothesis thus predicts a positive contingent effect of welfare state development:

increasing social and economic equality reduces the turnout gap by fostering turnout of

unhealthy citizens, while not affecting turnout as much among healthy ones. The formal

hypothesis is as follows:

H1: The turnout gap between citizens with poor and good health decreases with greater

economic redistribution and public health expenditures (ability hypothesis).

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Motivation hypothesis

Second, the motivation hypothesis states that economic equality expands the turnout gap

between people with poor and good health (see Figure 1b). In that case, citizens with poor

health are, relative to those with good health, less motivated and mobilized in developed welfare

states than in less developed ones. Generally, motivation to vote can arise from a person having

strong preferences for political alternatives and the wish to influence the outcomes of elections.

There are of course a host of other motivational factors such as personal sense of duty, peer

pressure and psychological and social rewards (Harder and Krosnick, 2008). At the individual

level, even though poor health acts as a barrier to voting, a potential counteracting factor is that

unhealthy citizens are familiar with the health system and assign a higher priority to public

health services. Denny and Doyle (2007a) showed that respondents with poor health in Ireland

were more motivated to vote if they were dissatisfied rather than satisfied with the health service.

Thus, the turnout gap between those with poor health and good health narrowed with

increasing dissatisfaction (even though dissatisfied people had lower turnout rates overall).

Inequality in less generous welfare states would, assumingly, provide an incentive for

participation for disadvantaged groups (i.e. demands for social protection). Unhealthy citizens

are in that case mobilized to vote by dissatisfaction with the current situation even though they

have less personal resources. This implies that motivation matters more, while discrepancies in

personal resources are of minor importance.2 Low levels of social and economic equality may

provide a strong motivation for action by voting for politicians and parties that are supportive of

greater economic redistribution and social spending such as public health care. There is, for

example, the possibility that leftist parties in particular target socially disadvantaged citizens and

mobilize them more effectively in unequal settings (see Anderson and Beramendi, 2012). As a

consequence, the turnout gap is reduced between those with poor and good health. In contrast,

the likelihood of mobilization of the less healthy is lower in countries where the situation is

relatively good given ingrained social welfare and widely redistributed resources. People with

poor health are inactive as voters because they are satisfied with how the welfare state performs

and no particular struggle is needed to receive vital outputs from the political system.

This line of reasoning aligns with conflict theory in economic inequality research. Some

scholars assume that economic inequality fosters participation by increasing conflicts among the

rich and the poor. Both groups will turn out to vote to influence the course of politics. The less

economically privileged clamor for shared wealth and vote for parties that are likely to pursue

redistributive policies (Brady, 2004; Meltzer and Richard, 1981). The relatively rich may also be

mobilized to consolidate their gains and vote for anti-tax parties, but all in all, the turnout gap

should be reduced. Personal resources are downplayed in this tradition of research, while

motivation is emphasized: “conflict theories assume that all individuals have the same political

skills” and “what differentiates one individual from one another is their own interest, and

individual interest depends on one’s position on the income ladder (Jaime-Castillo, 2009).

To sum up the second hypothesis, if the motivation to vote among those with poor health in

welfare states is low, other personal resources which favor the healthy will widen the turnout

gap. Thus, there will be a negative contingent effect of welfare state development whereby

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increasing equality widens the turnout gap between health and unhealthy citizens. The

hypothesis is formulated as:

H2: The turnout gap between citizens with poor and good health increases with greater

economic redistribution and public health expenditures (motivation hypothesis).

Data, measures and method

Data

The analysis is based on the cross-national survey samples of individuals undertaken in the

European Social Survey (ESS). The European Social Survey is in many respects an ideal data

set that covers many countries over time and includes variables for self-reported turnout, health

status and perceptions of welfare state performance. The centrally coordinated cross-national

survey of social and political attitudes in Europe involves face-to-face interviews with

representative samples of persons selected by random probability sampling methods. Survey

data taken in six rounds between 2002 and 2013 across 32 countries are analyzed. The data are

unbalanced since all countries did not participate in every survey.

The better option would be to have post-election survey responses to attain as reliable data as

possible for turnout. Since election dates and interview dates vary between countries, only

responses that were obtained within 2 years after a parliamentary election are included in the

analysis (and only country samples with 500 respondents or more). The reason for setting a

time limit is to minimize recall errors with regard to self-reported turnout as well as to address

that a person’s health status is likely to change as the time gap between an election and an

interview increases. A possibility would have been to include respondents who answered the

survey a half a year or one year after the latest parliamentary election. The problem with ESS

data is that the number of countries decreases and the number of respondents in some country

samples becomes too low. We decided to set the cutoff point at 2 years after the last

parliamentary election to increase the number of countries and observations. The choice is of

course arbitrary, but it allows us to analyze a reasonably large number of observations across

many countries: 32 countries, 82 country samples and over 137,000 respondents (after

respondents with missing values were excluded).3 Finally, these individual-level data are merged

with macro-level data measuring national welfare state performance and other country

characteristics.

Dependent and independent variables

The key variables are individual turnout, health and subjective and objective measures of

welfare state conditions (see Appendix 1 for specific coding). The measures for turnout and

health status are straightforward in the sense that they are based on responses provided by the

individuals themselves. The dependent variable is self-reported turnout in the most recent

national parliamentary election (1=voted, 0=did not vote). Personal health is based on self-

reported general health status on a five-point ordinal scale. Responses were recoded into three

categories and converted into dummy variables: good, fair and poor health (with poor health as

the reference category).

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Both subjective and objective indicators are used to capture welfare state performance and

development. By employing measures at different levels, we seek to distinguish between

compositional effects (characteristics of individuals living in different countries) and contextual

effects (properties of countries). The subjective measures of welfare state system performance

are the respondents’ satisfaction with income redistribution and state of health services in their

country on a 0–10 scale. The objective measures are a country’s levels of income equality and

public health expenditures. Welfare states differ widely in Europe in terms of, for example,

economic redistribution and government spending on health services (Schubert et al, 2009).

Choosing empirical indicators is more complex given all the various institutions and activities

which may characterize the welfare state (Lister 2007, pp. 28–29). In this study, income

distribution will provide a more general proxy for the level of welfare institutions which tend to

be more redistributive. The other measure is deliberatively chosen to concern health

services/expenditures since people with poor health are expected to have personal experiences

with and more interest in the state of health services in their country. Income equality

(0=perfect inequality, 100=perfect equality) is a reversed Gini Index of household income

inequality, while public health expenditures represent the percentage of a country’s gross

domestic product. Both indicators represent the means for the period 2002–2012.

Measures of subjective and objective welfare state performance are interacted with personal

health to see if the turnout gap narrows or widens between citizens with poor and good health

with increasing equality. The interaction variables are computed by multiplying the two health

dummies by the welfare state indicators: between health and subjective evaluations of welfare

state performance (good health×economic redistribution and fair health×state of health

services), on the one hand, and between health and objective indicators of welfare state

arrangements (good health×income equality and fair health×public health expenditures), on the

other hand.

Individual and contextual effects

Even though the subjective and objective measures of welfare state conditions are similar in

terms of content, they carry different information and may potentially have differing effects on

turnout. Objective measures will account for between-country differences as they reflect national

conditions. This also means that instead of summaries of individual-level surveys responses,

measures of actual macro-level structural factors should be included. Subjective perceptions of

welfare state performance will account for within-country differences (or between-individual

heterogeneity) since, in each country, some individuals have more negative views than others.

How individuals feel about welfare institutions and policies is a mix of personal and collective

experiences (Kumlin, 2002). In the empirical analyses, we need to avoid that the objective

measures are confounded by individual-level evaluations. Thus between-cluster variation is

removed from the predictors by group-mean centering (or centering within context). Subjective

evaluations of the economy contain a mixture of both within- and between-country variation.

Group-mean centering produces scores that are uncorrelated with economic performance

variables at the aggregate level. Instead, the computed individual-level subjective evaluations will

be relative to those of other respondents within a particular country and survey round. In the

first step, the mean of the individual responses was calculated for each country (j) and survey

7

round (r). In the second step, group-centered values were attained by subtracting the respective

mean score from the individual response ( ̅ ).

Data analysis

Multilevel mixed-effects logistic regression models for binary responses (voted or abstained) are

fitted using the maximum likelihood method. Multilevel models allow for predictors at both the

micro and macro levels and can be used for examining how contextual variables modify the

relationships between individual-level predictors and outcomes. These models also allow for the

dependency presented in nested data by adjusting for the clustering at each of the levels. The

data have a two-level hierarchical structure with individuals at level 1 nested within countries at

level 2. After assessing that random intercept models are appropriate for our data, we test

whether the slopes between health and turnout differ across the 32 countries. Random intercept

models assume that the intercepts are different (or random), but the slopes of the independent

variables are the same for all countries. Random slope models allow that the effects of

individual predictors are allowed to vary across countries. Separate sets of models for income

redistribution/equality and health care/expenditures are tested since the variables are

interrelated.

Control variables

A variety of individual and institutional factors may facilitate or discourage political

participation. At the individual level, a group of sociodemographic variables in the resource

model are included as control variables: age, age squared, gender, education and living with

partner (see Smets and van Ham, 2013). At the macro level, compulsory voting, margin of

victory and age of democracy are controlled for. Compulsory voting and margin of victory

(between the first- and second-placed parties) are among the most important predictors in cross-

national analyses of turnout (see Blais, 2006; Geys, 2006). Old democracy is a dummy variable

distinguishing between old established democracies in Western Europe (pre-1990) and more

recent democracies in Eastern Europe (and Turkey). Turnout in newer, such as post-

communist, democracies tends to be lower, likely due to a mix of contextual variables

(Bernhagen and Marsh, 2007). Finally, to account for possible temporal effects, a set of dummy

variables indicating round of the European Social Survey (rounds 2 to 6) with round 1 as the

reference category.

Results

Descriptive analysis

The empirical part starts by shortly describing levels of self-reported turnout and the number of

people with different health status in different countries. Self-reported turnout was on average

79 percent with a variation between 50 percent (Lithuania) and 95 percent (Denmark). It is well

known that turnout rates in surveys are much higher than actual turnout rates due to

overrepresentation of actual voters and social desirability bias (see e.g. Selb and Munzert, 2013).

But the correlation between actual (aggregate) turnout at the country level and self-reported

turnout (mean by country and ESS round) is high: r = 0.86, p < 0.01, N = 82. Overall, 63

percent reported their health was good or very good, 28 percent it was fair and 9 percent is was

bad or very bad. Western European respondents were more prone to report their health was

8

good (70 percent on average) than Eastern European ones (50 percent on average).

Correspondingly, more people in Eastern Europe reported bad health (14 percent) than in

Western Europe (7 percent).

To obtain a preliminary idea of how the relationship between health and turnout take different

forms in different types of welfare states, welfare state development (x-axis) and self-reported

turnout (y-axis) are plotted in Figures 2 and 3. Country-level means for all waves are calculated.

Each figure is paneled according to health status (good, fair and poor health) to show how the

slopes vary. The bivariate plots lend support to hypothesis 2 which states that the turnout gap

between citizens with poor and good health increases with higher welfare. Overall, there is a

positive relationship between welfare state conditions and turnout. The slopes are steepest for

people with good health (left-hand panels), while they are less steep for those with fair health

(middle panels). But neither levels of income equality nor health expenditures appear to

increase or decrease turnout among citizens with poor health (right-hand panels).

[FIGURES 2 AND 3 ABOUT HERE]

According to the raw scores, the greatest turnout gaps in favor of healthy people (10 percentage

points or higher) are found in a mix of countries: Switzerland, Italy, Netherlands, Czech

Republic, Austria, Hungary, Belgium and Spain. Conversely, people with poor health are about

equally likely to vote as those with fair and/or good health in two systems with compulsory

voting (Cyprus and Turkey) and countries with high a proportion of people with bad health

(Ukraine, Russia, Portugal, Latvia and Bulgaria). Thus there is a need to conduct multivariate

analyses to control for confounding factors in order to get a better picture of the relationship

between welfare state development and turnout gaps between the unhealthy and healthy.

Multivariate analysis

Next we perform more rigorous multivariate statistical modelling of the data to test the two

theoretical perspectives framed as competing hypotheses. Tables 1 and 2 display the results

from multilevel logistic regression analyses predicting turnout in the last national parliamentary

election. Results are presented as odds ratios (ORs) and 95% confidence intervals. Values above

1 indicate greater turnout and values below 1 indicate lower turnout. Good health is modelled

as a random coefficient that varies at the country level, but not fair health since it did not

improve predictions. Overall, the multivariate models seem to support the findings above,

which is in accordance with hypothesis 2 that the turnout gap between people with poor and

good health is larger in more comprehensive welfare states.

[TABLE 1 AND 2 ABOUT HERE]

In the empirical models we first take notice of the independent effects of the individual-level

predictors good and fair health. As expected, there is a significant positive relationship between

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health and turnout. Citizens with fair and good health are generally more likely to vote than

citizens with poor health. For people with fair health, the odds of voting are over 40 percent

higher than they are for people with bad health, all else equal (models 1 and 3). Having good

health increases the odds of voting by over 70 percent compared to people with bad health. If

translated into predicted probabilities, the likelihood of voting was about 0.80 among

respondents with good health, 0.78 among those with fair health and 0.71 among those with

poor health (when all other independent variables are set to their mean). This means the

turnout gap is on average nine percentage points between those with good and poor health.

Second, we assess the individual-level interactions between health and subjective evaluations

(health×subjective welfare state) to see if, and to what extent, bad health in combination with

negative evaluations of policies related to economic redistribution and health services stimulates

political participation. The subjective measures relating to satisfaction with income distribution

and health services are weakly positively correlated with turnout. Note, the variables were

group-mean centered (by country and ESS round) to obtain within-group regression slopes and

eliminate the clustering effects of a nested structure of data. In that case, the variables capture

differences between individuals within countries, not contextual differences between countries.

Both indicators are measured on a 0–10 scale. A one-unit increase in satisfaction with the

income distribution corresponds to a 3 percent increase in the odds of voting versus non-voting

(Table 1, Model 1). In similar vein, a one-unit increase in satisfaction with the health services

yields a 2 percent increase (Table 2, Model 3). These estimated effects remain stable when the

satisfaction variables are interacted with health status. But there are no interaction effects

between health and subjective evaluations of income redistribution and state of health services.

This means that people with bad health and negative evaluations are no more likely to turn out

to vote than those with similar health status but positive evaluations.

Third, and finally, we model the cross-level interactions based on health and welfare state

redistribution and expenditures. Most importantly, the question is if, and to what extent, citizens

with bad health are mobilized in egalitarian and high health spending countries in comparison

with inegalitarian and low health spending countries (health×objective welfare state). Note, both

objective indicators may theoretically vary between 0 and 100. But the actual spread for income

equality is over 19 points and for public health expenditures about 6 points. There are no

significant main (or direct) effects of the objective measures of income equality and health

expenditures. Both variables are statistically insignificant which indicates no differences in

turnout between individuals in different contexts (Models 1 and 3). To answer which of the two

hypotheses is correct we have to interpret the coefficients of the interaction terms. Even though

there are no main effects, there are significant cross-level interaction effects between individual

health and the objective welfare indicators. The values of the interaction terms are ORGood×Objective =

1.05 (p < 0.01) and ORFair×Objective = 1.03 (p < 0.01) in Model 2 and ORGood×Objective = 1.13 (p < 0.01) and

ORFair×Objective = 1.03 (p < 0.05) in Model 4.

To aid the interpretation of the interaction effects we present predicted probabilities based on

the estimates in Models 2 and 4. Health and the objective welfare state indicators are allowed to

vary, while the remaining independent variables are set to their mean. Figures 4 and 5 present

the linear slopes between the continuous objective welfare state measures (income equality and

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health expenditures) and the binary outcome (voter turnout) according to a person’s health

status. The slopes are quite similar to those presented above (based on simple bivariate

correlations), except for a negative slope between income equality and turnout among those

with poor health. The plots with predicted probabilities from the fixed part of the model

indicate that the turnout gap between those with good health and poor health increases the

more equal a country in terms of income and the higher the health expenditures. Turnout

levels appear to be similar across individuals with different health status when income equality

and health expenditures are low. The confidence intervals are, naturally, narrower around the

means for the welfare state indicators (71 for income equality and 6 for public health

expenditures). Overall, the multivariate analyses and the predicted probabilities bring further

support for hypothesis 2 that the turnout gap between citizens with worse and better health

increases with greater welfare state development.

[FIGURES 4 AND 4 ABOUT HERE]

Conclusions

The aim of this study was to explore if health-related inequalities in voter turnout are less or

more pronounced in egalitarian welfare systems which promote social equality by means of

redistributive policies and greater social expenditures such as health care. Seeing health as a

resource for political participation is quite plausible because health status is linked to the ability

to vote, both pertaining to material and psychological attributes (Harder and Krosnick, 2008;

Verba et al, 1995). From this point of view the positive relationship at the individual-level found

in this study was expected: with better personal health the likelihood to participate in elections

also grows. But we also need to consider the larger context and examine if and how the effects

of health on political participation vary across countries. We assumed that such variations could

be found between more and less developed welfare states. The empirical results showed that, at

the macro level, more unequal countries actually had smaller turnout gaps between citizens with

poor and good health.

There was no clear positive and direct effect between objective indicators of welfare state

development and turnout (cf. Jaime-Castillo, 2009; Stockemer and Scruggs, 2012). Only among

those with good health high welfare state development seems to have clearly boosted turnout. A

likely scenario is that healthy persons have both necessary personal resources and incentives to

participate in developed welfare states (cf. Anderson and Beramendi, 2008). But this did not

apply for people with poorer health. Our ability hypothesis was rejected since equality did not

reduce the turnout gap between citizens with poor and good health. This hypothesis predicted

that equality would reduce the gap in personal resources and equalize the opportunities for

participation in elections.

Instead, the results corroborated our motivation hypothesis stating that unequal distribution of

economic resources in society provide disadvantaged groups, such as those with poor health,

with a greater incentive to vote. This can be understood from the perspective of conflict theory

11

in research on the relationship between economic inequality and turnout. Political mobilization

is, arguably, relatively high among people with poor health as a function of greater desire to

influence political outcomes. As a consequence, in less well developed welfare states, the

turnout gap between people with poor and good health is narrowed. In more comprehensive

welfare states, citizens with poor health have fewer incentives to influence politics if they are

satisfied with available social security networks. Hence, contentment seems to lead to passivity

while dissatisfaction leads to action. A finding that may argue against the motivation hypothesis

is that the effect of policy satisfaction (income distribution and health services) on turnout

increased monotonically. Thus the interaction between poor health and dissatisfaction did not

predict significant higher turnout.

Finally, a cautionary note is warranted concerning cultural differences as regards reporting

health status. Overly optimistic or pessimistic assessments can be more common in some

countries (Dorling and Bradford, 2009; Jürges, 2007). As a worst case scenario, this could imply

that the interaction effect between health and welfare state development is spurious due to

cultural differences. On the other hand, self-assessments of health reflect a very real subjective

sense of well-being. It undoubtedly has a real-life effect on behavior, regardless of objective

health status. Consequently, even if the scales by which individuals assess health may differ

between countries, the effects of health may still be comparable.

End notes

1 The function is expressed as: Likelihood of voting = (Motivation to vote × Ability to vote) / Difficulty of

voting (Harder and Krosnick, 2008, p. 527). 2 A counter-argument would be that the motivation to vote among people with poor health is greater in

more comprehensive welfare states than in less developed ones. Borrowing from the logic of Brady et al (1995, p. 274), societal equality would provide relatively disadvantaged segments of the population with

basic needs, leaving more capacity and motivation to participate in politics. Also, Lister (2007) argues

that universalist welfare state institutions and policies engender social norms of solidarity which, in turn,

are conducive to participation and effectively reduce turnout gaps between different strata in society.

Solidaristic social norms lead individuals to think that collective action such as voting is socially desirable

as well as help them understand how others are expected to behave. 3 Data are from ESS Round 1 (2002), ESS Round 2 (2004), ESS Round 3 (2006), ESS Round 4 (2008),

ESS Round 5 (2010), ESS Round 6 (2012). The countries (and number of rounds) analyzed are: Austria

(2), Belgium (3), Bulgaria (2), Switzerland (3), Cyprus (2), Czech Republic (2), Germany (3), Denmark

(4), Estonia (3), Spain (3), Finland (3), France (2),United Kingdom (3), Greece (3), Croatia (1), Hungary

(3), Ireland (3), Italy (2), Latvia (1), Lithuania (1), Luxembourg (1), Netherlands (6), Norway (3), Poland

(4), Portugal (4), Romania (1), Russia (2), Sweden (3), Slovenia (3), Slovakia (3), Turkey (1) and Ukraine

(2).

12

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15

Table 1. Predicting turnout in 32 countries by subjective income distribution

and objective income equality.

Model 1 Model 2

OR (95% CI) OR (95% CI)

Constant 3.07 (2.52–3.73)** 2.96 (2.44–3.60)**

ESS round

Round 2 (ref. ess 1) 0.96 (0.90–1.04) 0.96 (0.90–1.04)

Round 3 (ref. ess 1) 0.85 (0.80–0.89)** 0.85 (0.80–0.89)**

Round 4 (ref. ess 1) 0.98 (0.92–1.04) 0.98 (0.92–1.04)

Round 5 (ref. ess 1) 0.82 (0.78–0.86)** 0.82 (0.78–0.86)**

Round 6 (ref. ess 1) 0.78 (0.73–0.83)** 0.78 (0.73–0.82)**

Individual level

Female (ref. male) 1.02 (0.99–1.05) 1.02 (0.99–1.05)

Age/10 1.37 (1.36–1.39)** 1.37 (1.36–1.39)**

Age/10 squared 0.96 (0.96–0.96)** 0.96 (0.96–0.96)**

Years of education/10 2.39 (2.30–2.50)** 2.39 (2.30–2.50)**

Partner (ref. no partner) 1.45 (1.41–1.49)** 1.45 (1.41–1.49)**

Good health (ref. poor) 1.71 (1.54–1.89)** 1.79 (1.63–1.97)**

Fair health (ref. poor) 1.44 (1.37–1.51)** 1.50 (1.41–1.57)**

Income distribution: subjective 1.03 (1.02–1.03)** 1.02 (1.00–1.04)

Country level

Income equality: objective 1.00 (0.96–1.05) 0.98 (0.94–1.02)

Compulsory voting 2.00 (1.13–3.54)* 2.00 (1.13–3.54)*

Margin of victory/10 0.97 (0.94–0.99)* 0.97 (0.94–0.99)**

Old democracy 1.64 (1.11–2.42)* 1.63 (1.10–2.42)*

Individual-level interactions

Good × Income distribution — 1.02 (1.00–1.04)

Fair × Income distribution — 1.00 (0.98–1.02)

Cross-level interactions

Good × Income equality — 1.05 (1.03–1.07)**

Fair × Income equality — 1.03 (1.01–1.04)**

Random-effects parameters

Var(u0j): constant 0.27 0.28

Var(u1j): good health 0.07 0.05

Cov: (u0j, u1j) –0.01 –0.01

ICC 0.093 0.089

Log likelihood –64,350 –64,331

Countries (level 2) 32 32

Individuals (level 1) 137,403 137,403

Notes. The models are multilevel mixed-effects logistic regression models for binary responses.

The dependent variable is turnout coded as 1 (voted) or 0 (did not vote). Estimates reported

are odds ratios with 95% confidence intervals.

** p < 0.01; * p < 0.05.

16

Table 2. Predicting turnout in 32 countries by subjective state of health services

and objective public health expenditures.

Model 3 Model 4

OR (95% CI) OR (95% CI)

Constant 3.00 (2.45–3.67)** 3.00 (2.46–3.67)**

ESS round

Round 2 (ref. ess 1) 0.97 (0.90–1.04) 0.97 (0.90–1.04)

Round 3 (ref. ess 1) 0.85 (0.81–0.90)** 0.85 (0.81–0.90)**

Round 4 (ref. ess 1) 0.99 (0.93–1.05) 0.99 (0.93–1.05)

Round 5 (ref. ess 1) 0.83 (0.79–0.87)** 0.83 (0.79–0.87)**

Round 6 (ref. ess 1) 0.78 (0.74–0.83)** 0.78 (0.74–0.83)**

Individual level

Female (ref. male) 1.01 (0.98–1.04) 1.01 (0.98–1.04)

Age/10 1.37 (1.36–1.38)** 1.37 (1.35–1.38)**

Age/10 squared 0.96 (0.95–0.96)** 0.96 (0.95–0.96)**

Years of education/10 2.45 (2.35–2.56)** 2.45 (2.35–2.56)**

Partner (ref. no partner) 1.45 (1.41–1.50)** 1.46 (1.41–1.50)**

Good health (ref. poor) 1.73 (1.55–1.92)** 1.72 (1.56–1.88)**

Fair health (ref. poor) 1.46 (1.39–1.53)** 1.46 (1.39–1.54)**

Health services: subjective 1.02 (1.02–1.03)** 1.02 (1.01–1.04)**

Country level

Health expenditures: objective 1.03 (0.84–1.27) 0.99 (0.83–1.18)

Compulsory voting 2.08 (1.14–3.80)* 2.10 (1.15–3.82)*

Margin of victory/10 0.97 (0.94–0.99)* 0.97 (0.94–0.99)*

Old democracy 1.51 (0.86–2.64) 1.52 (0.87–2.67)

Individual-level interactions

Good × Health services — 1.00 (0.98–1.02)

Fair × Health services — 1.00 (0.98–1.02)

Cross-level interactions

Good × Health expenditures — 1.13 (1.07–1.20)**

Fair × Health expenditures — 1.03 (1.00–1.07)*

Random-effects parameters

Var(u0j): constant 0.26 0.26

Var(u1j): good health 0.07 0.05

Cov: (u0j, u1j) –0.01 –0.01

ICC 0.093 0.086

Log likelihood –65,071 –65,063

Countries (level 2) 32 32

Individuals (level 1) 138,631 138,631

Notes. The models are multilevel mixed-effects logistic regression models for binary responses.

The dependent variable is turnout coded as 1 (voted) or 0 (did not vote). Estimates reported

are odds ratios with 95% confidence intervals.

** p < 0.01; * p < 0.05.

17

Figures 1a and 1b. Turnout gaps between citizens with poor and good health decreases with

higher welfare (Ability hypothesis) and increases with higher welfare (Motivation hypothesis).

Poor health

Good health

Poor health

Good health

Low

Hig

h

Low

Hig

hLow

welfareHigh

welfareLow

welfareHigh

welfare

Ability hypothesis Motivation hypothesis

Tu

rno

ut

18

Figure 2. Self-reported turnout (mean) according to a country’s income equality and personal

health, 2002–2013 (including regression lines).

Figure 3. Self-reported turnout (mean) according to a country’s public health expenditure and

personal health, 2002–2013 (including regression lines).

AT

BE

BG

CH

CY

CZ

DE

DK

EE

ES FI

FRGB

GR

HR HU

IE

IT

LT

LU

LV

NL NO

PLPT

RORU

SE

SISK

TR UA

ATBE

BG

CH

CY

CZ

DE

DK

EE

ESFI

FR

GB

GR

HR HU

IE

IT

LT

LU

LV

NL

NO

PL

PT

RO

RU

SE

SISK

TRUA

AT

BE

BG

CH

CY

CZ

DE

DK

EE

ES

FI

FRGB

GR

HR

HUIE

IT

LT

LU

LV

NL

NO

PL

PT

RO

RU

SE

SISK

TR UA

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

55 60 65 70 75 80 55 60 65 70 75 80 55 60 65 70 75 80

Good health Fair health Poor health

Tu

rno

ut

Income equality

AT

BE

BG

CH

CY

CZ

DE

DK

EE

ESFI

FRGB

GR

HRHU

IE

IT

LT

LU

LV

NLNO

PLPT

RORU

SE

SISK

TRUA

ATBE

BG

CH

CY

CZ

DE

DK

EE

ESFI

FR

GB

GR

HRHU

IE

IT

LT

LU

LV

NL

NO

PL

PT

RO

RU

SE

SISK

TRUA

AT

BE

BG

CH

CY

CZ

DE

DK

EE

ES

FI

FRGB

GR

HR

HU IE

IT

LT

LU

LV

NL

NO

PL

PT

RO

RU

SE

SISK

TRUA

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10


Tu

rno

ut

Public health expenditures

19

Figure 4. Predicted turnout according to a country’s income equality and personal health (fixed

part of the model, dashed lines present 95% confidence intervals).

Figure 5. Predicted turnout according to a country’s public health expenditures and personal

health (fixed part of the model, dashed lines present 95% confidence intervals).

0.6

0.7

0.8

0.9

0.6

0.7

0.8

0.9

0.6

0.7

0.8

0.9

58 61 64 67 70 73 76 58 61 64 67 70 73 76 58 61 64 67 70 73 76


Pre

dic

ted

turn

ou

t

Income equality

0.6

0.7

0.8

0.9

0.6

0.7

0.8

0.9

0.6

0.7

0.8

0.9

3 4 5 6 7 8 9 3 4 5 6 7 8 9 3 4 5 6 7 8 9


Pre

dic

ted

turn

ou

t

Public health expenditures

20

Appendix A. Coding of variables and sources.

Variable Coding Source

Turnout Voted in the last national (parliamentary) election

1 = yes, 0 = no

1

Female 1 = female, 0 = male 1

Age/10 Years, divided by 10 1

Age/10 squared Years, divided by 10, squared

Education Years of full-time education completed

Scale 0–25, top-coded to 25 years, divided by 10

1

Partner Living with husband/wife/partner in the same household

1 = yes, 0 = no

1

Good/fair/poor health How is your health in general?

1 = good/very good, 2 = fair, 3 = bad/very bad, dummy

coded to good, fair and poor health

1

Income distribution: subjective Government should reduce differences in income levels

0–10 scale, 0 = agree strongly, 2.5 = agree, 5 = neither agree

nor disagree, 7.5 = disagree, 10 = disagree strongly

1

Health services: subjective State of health services in country nowadays

0–10 scale, extremely bad to extremely good

1

Income equality: objective Scale 0–100, reversed Gini coefficient that theoretically

ranges from 0 (one household receives all the income) to

100 (household incomes are distributed completely

equally).

2

Health expenditures: objective Sum of spending on health from taxes, health insurance

funds and external sources, percentage of each country’s

gross domestic product (GDP), mean 2002–2012

3

Compulsory voting 1 = sanctioned compulsory voting (Belgium, Cyprus,

Luxembourg and Turkey), 0 = constitutionally mandated

compulsory voting and non-mandatory voting

4

Margin of victory Difference in percentage points between first- and second-

place parties in parliamentary elections

5

Old democracy 0 = new democracy, 1 = old democracy, scored continuosly

between 6 and 10 on Polity IV’s democracy scale since the

end of the 1970s,

6

Sources: (1) European Social Survey, rounds 1–6 (http://www.europeansocialsurvey.org/), (2) Solt (2009), (3)

Database of Political Institutions 2012, Development Research Group of the World Bank

(http://econ.worldbank.org/), (4) Jensen and Spoon (2011), (5) ParlGov Database

(http://parlgov.org/stable/data.html) and (6) Polity IV Project: Political Regime Characteristics and Transitions,

1800–2012 (http://www.systemicpeace.org/polity/polity4.htm).

21

Appendix B. Descriptive statistics.

Variables M SD Min Max

Turnout 0.79 0.41 0 1

ESS Round

ESS 2 0.11 0.11 0 1

ESS 3 0.18 0.18 0 1

ESS 4 0.21 0.21 0 1

ESS 5 0.19 0.19 0 1

ESS 6 0.15 0.15 0 1

Socioeconomic background

Female 0.54 0.50 0 1

Age/10 4.91 1.74 1.8 9.9

Years of education/10 1.20 0.42 0 2.5

Health

Good 0.63 0.48 0 1

Fair 0.28 0.45 0 1

Subjective welfare

Income redistribution 2.78 2.60 0 10

Health services 4.91 2.61 0 10

Objective welfare

Economic equality 70.62 4.39 57.25 76.70

Health expenditures 6.42 1.61 2.66 10.27

Compulsory voting 0.07 0.26 0 1

Margin of victory 0.94 1.04 0.02 4.33

Old Democracy 0.66 0.47 0 1

Notes. N = 259,632. The variables are uncentered in the table, while

they are either grand-mean or group-mean centered in the empirical

analyses.

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