Redistribution and Preference Formation
Pablo Beramendi (Duke University, [email protected])
&
Philipp Rehm (Ohio State University, [email protected])
Abstract
Why is income a good predictor of attitudes toward redistribution in some countries but
not in others? In this paper we argue that the tax-benefit structure of countries plays a role
in shaping social policy attitudes, an aspect largely overlooked by the literature so far.
Attitudes are proportional to expected net benefits, which is determined by what
individuals receive (probability of receiving a transfer; a transfer’s structure) and what
individuals contribute (taxes) to the system. The level of concentration (progressivity)
determines the distribution of both, thereby accounting for cross-national variations in the
distribution of preferences about the welfare state. The paper develops an argument on
the mechanisms driving the relationship between progressivity and preferences. We then
evaluate the argument on the basis of a cross-national design. Our findings indicate, quite
robustly, that the progressivity of the tax-benefit system is a major determinant of the
predictive power of income on preferences for redistribution.
Acknowledgments
Previous versions of this paper were presented at the 2011 MPSA meetings, the political
economy workshop at the University of Oxford, the comparative politics seminar at
Columbia University, the University of Zürich, the Juan March Institute, Madrid, Yale
University, and the 2012 CES Meetings at Boston. We are grateful for the feedback
received in all these occasions. We are also grateful to Larry Bartels, German Feierherd,
Herbert Kitschelt, Isabela Mares, Yotam Margalit, Irfan Nooruddin and David Rueda for
their comments on earlier versions. The usual disclaimer applies.
Redistribution and Preference Formation
Abstract
Why is income a good predictor of attitudes toward redistribution in some countries but
not in others? In this paper we argue that the tax-benefit structure of countries plays a role
in shaping social policy attitudes, an aspect largely overlooked by the literature so far.
Attitudes are proportional to expected net benefits, which is determined by what
individuals receive (probability of receiving a transfer; a transfer’s structure) and what
individuals contribute (taxes) to the system. The level of concentration (progressivity)
determines the distribution of both, thereby accounting for cross-national variations in the
distribution of preferences about the welfare state. The paper develops an argument on
the mechanisms driving the relationship between progressivity and preferences. We then
evaluate the argument on the basis of a cross-national design. Our findings indicate, quite
robustly, that the progressivity of the tax-benefit system is a major determinant of the
predictive power of income on preferences for redistribution.
Acknowledgments
[Redacted to preserve anonymity]
What drives people’s attitudes towards redistribution? As the economic crisis forces countries to
revisit fundamental aspects of their social contract, a better grasp of how citizens form their
views on the scope for the political reallocation of resources seems ever more pressing. In recent
years, comparative political economists have come a long way towards unraveling the
institutional foundations of different public insurance regimes and their implications on
distributive outcomes. From political regimes themselves, to electoral systems, economic
coordination, or federalism, the institutional foundations of redistribution and inequality have
dominated the agenda in recent years. This institutionalist focus, however, has come with a price
tag, namely the relative lack of attention towards the origins of citizens’ preferences for
redistribution and the processes through which these preferences are formed. This seems
particularly unfortunate in light of the suggestion and evidence that public opinion on social
policy matters for social policy outputs (Brooks and Manza 2006a, 2006b, 2007).
Fortunately, a recent wave of scholarship has begun to explore the determinants of social policy
attitudes, relying in a variety of theoretical frameworks and methodological bets. Existing
approaches can be classified into three types. First, some scholars rely on self-interest as the
primary source of social policy attitudes – income and its variability (“risk”) are the key
variables in these approaches. See, for example the literature on social upward mobility (Alesina
and La Ferrara 2005; Bénabou and Ok 2001; Piketty 1995), insurance (Cusack, Iversen, and
Rehm 2006; Iversen and Soskice 2001; Moene and Wallerstein 2001; Rehm 2009; Sinn 1995;
Varian 1980), or class-based explanations (Svallfors 2004). Second, other scholars rely on values
and beliefs as sources of social policy attitudes. Norms of “deservingness”, standards of fairness,
beliefs about the causes of inequality, partisan ideology, altruism, national identity, religion, and
many other factors have been explored (Alesina and Angeletos 2005; Bénabou and Tirole 2006;
Fong 2001; Kangas 1997, 2003; Kangas et al. 1995; Scheve and Stasavage 2006; Shayo 2009).
Third, some accounts rely on interpersonal preferences to explain social policy attitudes.
Examples include references to group loyalty (Luttmer 2001), the importance of relative status
(Corneo and Gruner 2000; Lupu and Pontusson 2011; Wilensky 1975) and race or ethnicity
(Alesina, Glaeser, and Sacerdote 2001).
In this paper, we take a closer look at the argument that self-interest shapes attitudes towards
public insurance and redistribution. A core proposition of that argument is that expected net
benefits determine attitudes. However, expected net benefits not only depend on individual-level
characteristics such as income and risk but also on the design and organization of public
insurance systems shapes citizens’ preferences. For example, self-interested rich citizens may
well support social policies if they are regressive – and the degree of progressivity varies hugely
across countries and social policy domains. Existing scholarship has largely neglected this
important point.1
In what follows, we explore theoretically and empirically why income varies in its ability to
predict preferences for redistribution. To give a preview of our core result, we find that income is
1
To be sure, there is a growing literature that explores the correlation between different welfare regimes (EspingAndersen 1990) and aggregate support for redistribution (Arts and Gelissen 2001; Bean and Papadakis 1998;
Gelissen 2000, 2002; Jaeger 2006, 2009; Jakobsen 2010; Mehrtens 2004; Svallfors 1997), but this literature tends
not to focus on causal mechanisms.
1
a better predictor of preferences towards redistribution in those societies with more progressive
fiscal systems. Throughout the paper we define progressivity as the concentration of net benefits
in the lower strata on the pre-tax and transfers income distribution. This finding has a number of
implications for several literatures. First, it highlights an important institutional component in the
process of preference formation, largely overlooked by the literature so far. Second, it provides a
framework to approach the political consequences of fiscal reforms, an important issue in the
context of the current financial crisis. Third, it identifies the process linking economic patterns
(inequality), macro-responses (progressivity), and political attitudes, thereby shedding light on
the forces behind the self-sustained nature of politico-economic configurations across countries.
The paper proceeds as follows. Section I motivates the question by presenting the key theoretical
puzzle. Section II develops the theoretical argument. Section III outlines the empirical strategy.
Section IV presents the results. Finally, section V summarizes the findings, discusses some of the
limitations in the paper, and points to future lines of inquiry.
I. The Puzzle: Income and Preferences for Redistribution
Insofar as democracies manage to achieve effectively a minimum degree of political
representation, redistributive policies and outcomes will reflect, at least in part, the degree of
support for redistribution existing in a society (Brooks and Manza 2006a, 2006b, 2007). The
latter link has often been treated as a matter of course. Following layman’s intuition, one should
expect income to be closely correlated with redistributive references. It would appear almost
self-evident that poor people like redistribution for the same reasons that rich people resist it.
However, available evidence suggests that the issue requires some additional thought. Figure 1
displays the size of the income-coefficient when predicting attitudes on the following ISSP
question:
“On the whole, do you think it should be or should not be the government’s responsibility
to: Reduce income differences between the rich and poor” [1. Definitely should not be; 2.
Probably should not be; 3. Probably should be; 4. Definitely should be]
- Figure 1 about here The bars indicated the size of the income coefficient from regressing redistribution attitudes on
income and a small set of controls (gender, education, age); these coefficients refer to 2006, and
are recovered from a multi-level model.2 The layman’s expectation appears correct in that
income almost always has a negative effect on support for redistribution across all nations, but
the diversity in the magnitude of the effect is striking (Dion and Birchfield 2010; Dion 2010).
Why is income such a strong predictor of redistributional attitudes in New Zealand, but not in
2
To produce these slopes, we estimate hierarchical linear models predicting social policy attitudes with income and
a set of controls (education, gender, and age), with random intercepts and random slopes. We then recover countryspecific income-slopes (income-gradients) and their standard errors from best linear unbiased predictions (BLUPs).
While there is some variation over time within countries, the main differences remain fairly stable over time. In the
remainder of the paper, our analysis refers to roughly 2006 (ISSP Research Group 2006). We limit our sample to
respondents aged 18-65 since our macro-level measure of progressivity refers to the same working age population.
2
Portugal? What explains the fact that income seems to have the same effect on preferences for
redistribution in Sweden and the United States? Why does income seem to be a worse predictor
of support for redistribution in France or Finland? Why are richer respondents in Portugal more
likely than poorer respondents to express support for redistribution?
One obvious candidate to account for cross-national differences in the importance of income as a
predictor of preferences is the level of pre-tax inequality. Such expectation follows quite directly
from median voter accounts of preferences, most notably Meltzer and Richards’ (Meltzer and
Richard 1981): As the gap between the rich and the poor increases, so does the gap between the
median voter income and the average income in society, thereby fostering support for higher
taxes and redistribution. Under those circumstances the degree of polarization around the welfare
state is likely to increase, rendering income a stronger predictor of preferences for redistribution.
Interestingly, a partisan logic would also yield similar predictions (Hibbs 1977). Given an
increase of inequality, left parties will find it in their interest to target their mobilization efforts to
low income voters, whereas right parties will work to protect the interests of high income voters.
The expected outcome becomes, again, a polarization in terms of redistributive preferences along
the income scale.
Yet, however compelling in their simplicity, these logics do not seem to work. Figure 2 plots the
Gini coefficient of pre-tax income inequality (top panel) and post-tax income inequality (bottom
panel) against the size of the income effect on the citizens’ preferences for the government’s role
in bridging the differences between the rich and the poor, as reported earlier in Figure 1. Clearly,
there appears to be no systematic relation between the incidence of pre-tax inequality and the
importance of income as a predictor of redistribution.
- Figure 2 about here Likewise, the observable patterns of association between the degree of contention over
redistribution and the size of the welfare state do not make much intuitive sense. Figure 3 plots
the income slopes reported in Figure 1 against the effort devoted to social spending expressed as
a percentage of GDP (top panel) and the importance of social policy, measured by the percentage
share of public cash transfers in household disposable income (bottom panel). One may reason
that the larger the amount of transfers at stake in fiscal policy, the higher the importance of
income as a determinant of preferences. But this does not seem to be the case: there is no
correlation between the income-gradient and the size of the welfare state (measured as total
public social expenditure as % of GDP) and there is actually a positive correlation between
income gradients and the importance of transfers for household disposable income, suggesting
that income is a worse predictor of redistributional attitudes in larger welfare states.
- Figure 3 about here Figures 2 and 3 reveal the limitations of theoretical models based exclusively on redistributive
motives. They simply do not go very far in accounting for the variation in income slopes on
attitudes toward redistribution. A possible alternative builds on the notion that welfare states are
primarily insurance systems tailored to solve inter-temporal trade-offs in economies organized
around different types of skills (Estevez-Abé, Iversen, and Soskice 2001; Iversen and Soskice
3
2001; Mares 2003). Those economies organized around the production of goods intensive in
specific skills by firms competitive in international markets develop welfare states to ameliorate
the risks incurred by workers with little transferability to other sectors during economic
downturns. In contrast, workers with general skills face less risk, and their skill’s transferability
reduces their demand for comprehensive insurance programs. According to this logic, in those
countries with labor markets where general skills are prevalent, we would expect income to be a
stronger predictor of redistributive preferences. Figure 4 sets out to explore this expectation by
displaying the relationship between the income slopes (as defined above) and the incidence of
vocational training (Iversen and Soskice 2001, 888), i.e. the share of people of a certain agecohort that goes through vocational training programs. Once again, Figure 4 contains a nonfinding: the variation in income slopes does not reflect differences in the composition of skills
within the labor force across countries.
- Figure 4 about here Taken together, Figures 1 to 4 present a systematic pattern of variation that does not sit well with
the dominant theoretical approaches in the field. By drawing from and combining these and other
theoretical approaches, we put forward a different logic that points to the degree of progressivity
in the fiscal system as a key mechanism largely overlooked by the literature so far.
II. The Argument: Progressivity, Redistribution, and Preferences
The central claim of this paper is that understanding preferences for redistribution requires
analyzing how different macro-configurations of benefits (transfers), costs (taxes), and risk shape
citizens’ calculations. In developing this argument, we integrate the social insurance and taxation
literatures to identify the progressivity of taxes and transfers as a major, and largely neglected,
factor driving the process of preferences formation.
The key intuition underpinning the insurance literature (Baldwin 1990; Iversen and Soskice
2001; Mares 2003; Moene and Wallerstein 2001, 2003; Rehm 2009) is that risk exposure and
risk aversion offer a set of motives that help understand why people in the upper end of the
income distribution support insurance and redistribution. A more recent stream of scholarship
has taken one step forward by modeling preferences as a function of the joint distribution of
income and risks, stressing the point that redistributive and insurance motives are not
independent from one another (Rehm, Hacker, and Schlesinger 2012). Crucially, though, this
literature has yet to fully incorporate the role of either taxes (t) or benefits (b) in the way
individuals derive their expected benefits. For instance, while Moene and Wallerstein (2001)
introduce the distinction between targeted and universal benefits in their model, they set aside
the issue of the allocation of costs across income groups. Likewise, Iversen and Soskice (2001)
not only ignore taxation, but also the possibility that benefits are allocated, as in most insurance
systems, in a non proportional way. Finally, Rehm et al. (2012) set aside the influence of welfare
states/nature of insurance systems in the process of preference formation.
In what follows, we show how the link between the progressivity of the fiscal system
(Beramendi and Cusack 2009; Cusack and Beramendi 2006) and individuals’ calculation of
4
expected benefits is essential to solve the puzzle of why countries vary in the predictive power of
income and the degree of contestation over redistribution. By determining citizens’ expected
benefits, progressivity mediates the connection between inequality, the size of transfers, and
preferences for redistribution.
Our analytical strategy incorporates simultaneously the role of risks, benefits, and taxes in
shaping expected incomes, and ultimately, preferences themselves. We approach this link with a
rational choice framework in which actors’ driving motivation is defined by the expected net
benefit of taxes and transfers (public insurance). It is helpful to think about expected net benefits
in terms of who gets what at which price. This implies that analyses of the determinants of
aggregate levels of support for redistribution must incorporate a conceptualization of three items:
i)
ii)
iii)
what, that is to say, the size of the benefit received
who, that is the distribution of risk
price, that is to say the distribution of taxes and contributions that need to be raised to
meet the cost of benefits
This intuition can be captured in a highly abstract model, in which an individual’s i expected
utility E(Ui) is defined as:
(1)
E(U i ) = (1− pi )(1− t i )y i + pibi
where all parameters are agent specific and:
• pi: probability of (or share of time) needing the benefit (“risk”), which implies that (p/(1€ the individual risk ratio
p)) defines
• bi: size of the benefit
• ti: taxes and contributions paid by individual i
• yi: individual’s pre-tax income
If we just focus on the parameters in (1) that directly relate to social policy, we can assume that
individuals support social policy if expected benefits exceed expected costs, or:
pibi > (1− pi )c i = (1− pi )t i y i
(2)
Note again that all parameters in equations (1) and (2) are agent specific, including taxes and
benefits. Such a model is quite complex, and analytically intractable, but it communicates the
€
basic point that expected
net benefits are influenced by a variety of factors.
We build on the assumption that institutional arrangements are sticky. The fundamental features
of the fiscal system involve long term arrangements that do not vary easily from year to year or
even election to election. While changes do take place, as shown by a recent literature on tax
structures (Beramendi and Rueda 2007; Ganghof 2006), they take decades to consolidate. As a
result, individuals define their preferences over redistribution given a fiscal system in place, from
which they derive their expected benefits. In other words, we assume that the key parameters in
expression (1) above are observable to individuals when they evaluate different proposals on
social policy.
5
While this model cannot be solved analytically, it helps clarifying the distinctiveness of our own
approach. The tax-benefit structure translates directly into individual calculations through the
three channels spelled out in (2): risk (p/(1-p)), benefit (transfers, b), and cost (taxes, t). To
understand the connection between the macro-processes and individual calculations, it is
essential to clarify the implications of progressivity for both the expenditure and the revenue
side. Progressivity implies that those with lower pre-tax income receive a higher share of
transfers, and those with a higher pre-tax income contribute a higher share of taxes to the
common pool. Thus, there is a direct link between the general notion of progressivity and the
concentration of benefits at the lower end of the distribution and the concentration of taxes at the
upper end.
The concentration of benefits is a function of three factors: the probability of having to receive
the benefit (i.e. the risk), the duration of time remaining in the bad state of the world, and the size
of the benefit across different income groups. In turn, the concentration of taxes is a function of
the probability of remaining in the good state of the world (the inverse of the risk) and the size of
the tax contribution across income groups. The former shape the calculation of net benefits
though p and b, that is through the concentration of benefits among the poor; the latter does it
through (1-p) and t, that is through the concentration of costs in the upper end of the income
distribution. Ultimately, overall progressivity is defined by the joint concentration of taxes and
transfers, in particular whether the concentration of taxes and transfers correlate with one another
and in what direction. Interestingly, this logic places the main focus of causality at the macro
level, i.e., in the degree of concentration of taxes and transfers that characterizes the fiscal
system. By implication, the political configurations underpinning different levels of progressivity
shape as well the predictive power of income gradients.
Figure 5 represents the distribution of expected benefits under three alternative institutional
scenarios. In a highly progressive scenario (A), both taxes and transfers show very high levels of
concentration and redistribution becomes strictly a zero-sum game: what the poor get comes
strictly out of high income earners’ pockets. The latter, in turn, receive little to no share of the
benefits. Accordingly, the expected net benefit of these two groups departs from zero in opposite
directions. It is under these institutional conditions that we expect the highest levels of
polarization and, as a result, a stronger impact of income on preferences for redistribution.
- Figure 5 about here In contrast, in a pure insurance system (B) neither taxes nor transfers are concentrated. A
majority of citizens contributes to the system and, when in need, draws out of it roughly in
proportion to what has been contributed earlier. As a result, there is very little effective
redistribution. Under the conditions of an actuarially fair system we expect much lower levels of
contestation over the fiscal system, and therefore income should be less effective a predictor of
preferences.3
3
If insurance is a normal good and is non-redistributive, demand for insurance should actually increase with income,
at least for sufficiently high levels of risk aversion (Moene and Wallerstein 2001).
6
Finally, taxes and transfers can be combined in a mixed-system (C). In this latter case,
substantial levels of redistribution, which should elicit some polarization between recipients and
contributors, co-exist with the fact that a good share of the middle classes are net recipients visà-vis the welfare state. The latter works, in our account, to ameliorate distributive tensions in
society. Accordingly, in those societies with a mixed fiscal system, we would expect income to
be a stronger predictor of preferences than in (B), but less so than in (A).
Crucially, these institutional factors shape the progressivity of social insurance. Different
distributions of risk, income, and their correlation, however, have a further impact on who ends
up as a net beneficiary or net loser. Ultimately, because of the interplay of institutional
arrangements and income-risk-distributions, the degree of progressivity is an empirical matter.
Our measure of progressivity is ex-post, and therefore takes this complex set of factors into
account.4
There is no gainsaying the process by which societies end up in each of these scenarios is a
matter of politics. A recent stream of literature shows how the forging of encompassing proredistributive coalitions underpinning large, egalitarian welfare states requires net benefits to be
spread across a broad spectrum of groups in the income distribution.5 By contrast, fiscal systems
with high levels of concentration are bound to yield relatively smaller redistributive systems
(Korpi and Palme 1998; OECD 2008; Prasad and Deng 2009; Wilensky 2002). In either case,
there are bound to be feedback processes that make progressivity endogenous in the medium run.
The point of our model is not to deny such endogeneity but rather to identify the impact of the
organization of fiscal systems on citizen’s views about their scope, allocative properties, and
potential reform. Hence the assumption that citizens evaluate their expected benefits given a
rather stable set of institutional conditions.
4
At this point we want to make clear that the degree of progressivity of the fiscal system is the only dimension of
theoretical interest or leverage in our model. Figure 5 does not allow us to make claims about the implications of
cross-national differences in the levels of absolute welfare of the poor, nor about the implications of differences in
terms of sheer welfare effort (size of the welfare budget). The latter has already been shown to have little predictive
power over the saliency of social policy as a political issue (figure 3 above).
5
The political logic behind this claim goes as follows. As the fiscal burden increases, it becomes politically
unsustainable for the cost to be borne out only by the upper end of the pre-tax income distribution. Progressivity of
fiscal transfers becomes especially problematic because taxes on labor remains the most important source of revenue
generation for advanced industrial societies while alternative sources of revenue, in particular capital taxation
(Mares 2006; Pontusson 1992; Przeworski and Wallerstein 1988) become less of an option over time (Cusack and
Beramendi 2006). Indeed, as the relative importance of labor taxes increases, the internal stability of redistributive
coalitions comes into question: workers in the upper half of the earnings distribution, a central element of Social
Democratic constituencies, grow increasingly resistant to bear the bulk of the cost of the welfare state alone and
threaten with exit towards more accommodating options. As groups dominated by swing voters with high political
clout (Dixit and Londregan 1995, 1998), redistributive coalitions cannot afford to ignore their concerns on the
revenue side. A straightforward response to ensure electoral survival and prevent/revert this potential exit of core
members of the coalition is to tax labor income less progressively, yielding a new equilibrium in which large
redistributive transfers require, for their own sustainability, lower levels of concentration. A related way of thinking
about the implications of too high, too progressive taxes on the upper end of the earnings distribution is in terms of a
violation of Director’s Law (Stigler 1970). To the extent that an increasing share of the middle classes bears a large
share of the fiscal burden of the welfare state, the purported advantage of middle income groups, regardless of the
institutional environment, dwarfs relative to the rich (de facto sheltered from taxation) and the poor (net
beneficiaries of progressive tax schemes).
7
With this proviso in mind, the implications emerging from these different scenarios bring out the
following insight on the determinants of preferences for redistribution: what matters for
understanding the role of income in preference formation is not the level of inequality per se, nor
the level of generosity, but the set of rules that govern the allocation of costs and benefits across
income groups. Pre-existing levels of progressivity shape individual’s preferences formation.
Speaking directly to the questions above, we derive the following testable empirical implication:
The impact of income on preferences over redistribution is higher in systems with higher
levels of progressivity
The remainder of the paper offers empirical assessments of this expectation.
III. Empirical Strategy
Our argument is that the tax-benefit structure in combination with the distribution of income and
risk influences the predictive power of income when it comes to attitudes towards social
insurance programs. We test this claim in two different ways:
(1) Across countries: do income-slopes (from predicting redistributional / social insurance
attitudes) vary systematically with the degree of progressivity?
(2) Across social policy domains, across countries: do income-slopes vary with the degree of
progressivity in different social policy domains in different countries?
Our dependent variable is the predictive power of income for social insurance attitudes. To
compute a reliable measure, we estimate hierarchical linear models predicting social insurance
attitudes with income and a set of controls (education, gender, and age), with random intercepts
and random slopes. We then recover country-specific income-slopes (and their standard errors)
from best linear unbiased predictions (BLUPs). These income-slopes are our dependent variable
(and we use the inverse of the standard errors as weights in all further country-level estimates).
Figure 1 above displayed these slopes.
Larger (more negative) coefficients indicate that social policy issues are a more salient cleavage
in a society. One advantage of this approach is that it allows us to take into account other
relevant factors: the partial correlation coefficient of social policy attitudes and income is net of
control variables. Perhaps the largest challenge for the income-slope approach is that the income
data in our public opinion survey are of problematic quality and not necessarily comparable
across countries. It is therefore reassuring that the estimated income-gradients from alternative
data-sources with better and more comparable income variables are comparable6
6
We convert the country-specific family income variables in the ISSP surveys into income noviles. But not all
countries report detailed income data. For Portugal, for example, we only have six income categories, and we
therefore drop that country from our analysis involving survey data. To get a sense of the robustness of our estimates
of the dependent variables, we also relied on the European Social Survey (ESS) (ESS 2008). In particular, the ESS
2002, 2004, 2006, and 2008 contain the following survey item: “Using this card, please say to what extent you agree
8
Our main source for measuring our dependent variable is the International Social Survey
Program’s (ISSP) “Role of Government” (RoG) module IV (ISSP Research Group 2006). While
income-slopes do show some variation over time (a point we will come back to below), they tend
to be fairly stable. We therefore restrict our analysis to 2006. To construct our dependent
variable, we recover income slopes on the following attitudinal items:
•
On the whole, do you think it should or should not be the government's responsibility to ...
o Reduce income differences between rich and poor
o Provide decent standard of living for the old
o Provide decent standard of living for the unemployed
o Provide health care for the sick
o Provide decent housing for those who can’t afford it
The answer categories are 1 “Definitely should not be”, 2 “Probably should not be”, 3
“Probably should be”, and 4 “Definitely should be.”
Our main focus is on the redistribution item (government responsibility to reduce income
difference between rich and poor). In turn, for the comparative analysis of contestation across
social policy domains, we rely on the items asking about government responsibility for the old,
unemployed, sick, and those in need of housing.
Our key explanatory variable, the level of progressivity in the fiscal system, captures how
concentrated benefits and costs (taxes and social contributions) of social policy programs are. In
particular, we use and combine two measures:
• The concentration of cash benefits
• The concentration of household taxes minus the concentration of cash benefits
The latter is our indicator of the overall progressivity in the fiscal system. Concentration curves
plot the share of benefits received (or taxes paid) against rank-ordered income groups. In a
progressive system, those at the bottom of the income scale receive a higher share of benefits and
the concentration curve is above the 45-degree line, while it is below the 45-degree line for
richer income groups. Concentration coefficients sum the area between the concentration curve
and the 45-degree line, where the area above the 45-degree line has a negative sign, while the
area below the 45-degree line has a positive sign. Therefore, a value of zero means that all
income groups receive an equal share of transfers (or pay an equal share of taxes), while higher
absolute values indicate more progressive systems. In particular, more negative values on the
concentration of cash benefits mean that benefits are targeted at the bottom of the income scale,
while more positive values on the concentration of taxes measure indicate that the rich pay more
taxes than the poor. We take these measures from the OECD (Förster and Whiteford 2009;
OECD 2008), and they refer to the mid-2000s. The OECD also provides concentration measures
or disagree with each of the following statements: The government should take measures to reduce differences in
income levels. [The answer categories are 1 “Disagree strongly”, 2 “Disagree”, 3 “Neither agree nor disagree”, 4
“Agree”, 5 “Agree strongly”]. While the income data in the ESS 2008 survey are better suited for our purposes
(since they are reported in national income deciles), the ESS sample is restricted to European countries. We
therefore prefer to use the ISSP surveys. However, a comparison of the estimates of contestation from the two
different data sources shows a fair degree of overlap. Results are available upon request.
9
of cash benefits for various social policy domains. Together with an indicator of the importance
of benefits (percentage share of public cash transfers in household disposable income), these
measures are displayed in Table 1.
- Table 1 about here Our theoretical framework predicts that the importance of social policy as a political cleavage
will be larger, the higher the levels of progressivity, that is the higher the concentration of net
benefits in the lower end of the pre-tax and transfer distribution. But, clearly, the tax-benefit
structure is not the only plausible factor influencing how contested welfare states are. We already
noted that three plausible explanatory variables are income inequality (higher inequality should
lead to more contestation, per Meltzer-Richard), the magnitude of the welfare state (the more is
at stake, the more contested should welfare states be), and the incidence of vocational training
(the large the incidence of vocational training, the less contested social policy). Neither of these
variables performed well in bivariate analysis (see Figures 2-4), but it nevertheless may make
sense to include them as control variables.
While American Politics scholars focus a lot on contestation of preferences (Evans 2003, 2003),
parties (the literature is extremely voluminous; for a review, see Hetherington 2009) or both,
there are only few comparative contributions that explore polarization of social policy attitudes
(Rehm, Hacker, and Schlesinger 2012). However, there is a growing literature exploring the link
between income and voting (Huber and Stanig 2007; De La O and Rodden 2008), which offers
some clues regarding potential determinants of income. The main argument in this subfield focus
around the claim that a second dimension “distracts” the poor from their “objective” interests
and/or limits the formation of comprehensive pro-redistributive coalitions by introducing group
differentials what otherwise would be homogenous class/income groups (Shayo 2009).
The usual suspect explanatory variables for second dimension are religion and ethnic
fractionalization. We therefore include a control variable that measures religious
fractionalization (Alesina et al. 2003). We also explored measures of religiosity that contain the
share of confessional groups (such as Catholics or Protestants), with data from the Religion and
State Project (Fox 2004, 2008), with very similar (non-)results. We also control for ethnic
fractionalization (Alesina et al. 2003) to account for the possibility that ethnic issues alter the
link between income and redistributional preferences. Additional robustness checks with control
variables containing information on immigration stocks and flows (such as inflow of asylum
seekers, inflow of foreigners, stock of foreign-born population, etc.) do not change the results we
report below.
Since it is plausible that attitudes towards government intervention are shaped by dominant (left)
parties and unions, we include control variables for left party dominance (cumulative share of
cabinet posts for left parties) and trade union density.
Finally, the overall level of risk in a society may shape attitudes towards social policy and have
an impact on the link between income and redistribution preferences. To take this possibility into
account, we control for the unemployment rate. Table 2 lists all variables and their sources.
10
- Table 2 about here IV. Findings
We now turn to the results. We are primarily concerned with the correlation of welfare state
contestation and the structure of tax-benefit systems (the who-gets-what-at-which-prize
question). Because the probability of receiving benefits and/or the size of benefits vary more
across countries and social-policy domains than the financing of these benefits, and in order to
keep the number of figures at a manageable number, we will concentrate on two explanatory
variables only: the concentration of cash benefits and the concentration of net benefits, namely
joint impact of benefit and financing concentration (“overall progressivity”).
Figure 6 displays bivariate correlations between these two core explanatory variables and the
income-slopes. The figures show that the more redistributive benefits, the more contested they
are. This is what we expected from our theoretical framework. There are also good reasons why
the results are generally stronger when we solely look at the benefit structure (top panel) as
opposed to both the benefit and financing structure (bottom panel). As mentioned above, benefit
structures vary more across countries than tax structures. This certainly does not imply that
financing structures can or should be neglected. But it implies that in our empirical
investigations, their effects are smaller than those generated by the structure of benefits.
- Figure 6 about here The close fit between income-slopes and the concentration of (taxes and) benefits, as displayed
in Figure 6 is remarkable. We now explore whether these correlations withstand the inclusion of
(more or less plausible) control variables. As elaborated above, we include the following
controls: income inequality; magnitude of transfers; incidence of vocational training; religious
fractionalization; ethnic fractionalization; left party dominance; trade union density; and the
unemployment rate. We include these control variables one at a time. Table 3 displays the results
when we predict income-slopes with the concentration of cash benefits, while Table 4 has the
results when we predict income-slopes with overall progressivity (concentration of taxes –
concentration of benefits).
- Table 3 and Table 4 around here The results are easy to report: the key explanatory variables in Table 3 and Table 4 – the
concentration of benefits and overall progressivity, respectively – turn out to be statistically
significant in all models, while none of the control variables is found so. The substantive impact
of cash benefit concentration is also significant. The estimated slope of around 0.2 suggests that
a one standard deviation (0.19) change in the concentration of benefits changes the income slope
by about 0.038. Since the income-slope ranges from about to -0.149 to about -0.016, this is a
23% change.7
7
Slopes: Mean=-.072; SD=.042; Min=-.149; Max=.016; N=21. Concentration of cash benefits: Mean=-.11; SD=.19;
Min=-.431; Max=.315; N=21
11
Based on our theoretical framework, we expected cash benefit concentration and overall
progressivity to be statistically and substantively significant predictors of the saliency of social
policy as a political issue. In contrast, the finding that none of the more or less standard variables
in the comparative political economy literature took us by surprise.
We turn now to the analysis disaggregated by policy domain (unemployment, pensions, health
care, and housing). To this end, we need to match survey items on social policy domains to
concentration measures of social policy domains. While there is often more than one possible
match, many mappings are straightforward. In particular, the ISSP RoG surveys include the
following social policy attitudinal items8 that can be easily matched with concentration measures
of benefits:
• Provide decent standard of living for the unemployed → concentration of unemployment
benefits
• Provide decent standard of living for the old → concentration of old age pensions
• Provide health care for the sick → concentration of disability benefits
• Provide decent housing for those who can’t afford it → concentration of housing benefits
When we explore the correlation between income-slopes (from multi-level models predicting a
social policy attitude) and concentration measures within each of these domains, we find the
expected positive correlations (the more concentrated benefits towards the poor, the more
contested the social policy area). Figure 7 shows the results, pooling the four social policy
domains together.
- Figure 7 about here Each symbol in the scatter-plot represents one of four social policy domains, for a given country:
unemployment (U), pensions (P), disability benefits (D), or housing benefits (H). As can be seen
from the Figure, there is a positive correlation between the concentration of benefits and the
income-slopes, within each domain (dotted lines) and overall (solid line). The overall correlation
is quite strong, and statistically significant (whether we include fixed effects for social policy
domains or not), as was the case with the overall progressivity measures.
To recapitulate, we have tested our hypothesis that social policy polarization (measured in terms
of the strength of income as a predictor of attitudes) can be explained by the progressivity of the
tax-benefit system. Our findings suggest, in line with our theoretical expectations, a strong
impact of the level of progressivity of the tax-transfer system. This finding is robust across
measures of concentration (cash benefits vs. benefits and taxes), income measures, and policy
domains, as well as to the inclusion of relevant control variables.
These are solid empirical grounds. Yet, however robust, the joint endogeneity between
progressivity and support for redistribution continues to loom large over these findings. None of
the results identify in full the causal effect of progressivity on preferences for redistribution.
Sadly, the field lacks enough data points to undertake the type of analysis that would bring us
8
These are based on ISSP survey items, with the following wording stem: “On the whole, do you think it should or
should not be the government's responsibility to ...”. The answer categories are 1 “Definitely should not be”, 2
“Probably should not be”, 3 “Probably should be”, and 4 “Definitely should be.”
12
closer to that point in a systematic and generalizable way. We can however exploit specific
experiences where we can be certain that the changes in the progressivity in the system is
actually exogenous to pre-existing citizens’ demands and see if the slope of income on
preferences for redistribution responds according to our theoretical expectations. These
experiences concern preferences for redistribution before and after Reagan’s tax reform in 1986,
which implied a sharp reduction in the pre-existing levels of progressivity, and preferences
before and after Germany’s reunification (1989-1990), which, by virtue of sharp increases in the
pool of recipients from the East, implied a sharp increase in the levels of progressivity in
Germany. Our theory suggests that we should see a decline in the predictive power of income on
redistributive preferences in the former case, and an increase in the latter.
The specialized literature on US elections suggests that Reagan’s re-election of 1984 was
anchored on two tenets: a period of sustained economic growth and the re-activation of
America’s unilateralism in foreign and military issues (as illustrated by the invasion of Granada).
Though the Democratic challenger, Walter Mondale, criticized Reagan’s tax policy during the
campaign, contentions among citizens over redistributive policy did not play a fundamental role
in the re-election of the government that would ultimately enact the 1986 Tax reform
(Abramson, Aldrich, and Rohde 1987; Cranville 2008). There is little room for concerns over
endogeneity in this particular case. Reagan’s reform implied a sharp decrease in the levels of
progressivity in income taxation. By way of illustration, the tax rate for the lowest income
bracket grew from 11 to 15% whereas the top rate dropped from 50 to 28%. Accordingly, we
should see a significant decline in the income slope on preferences for redistribution.
By contrast, Germany’s reunification offers an example of an exogenous increase in the levels of
progressivity. The West incorporated five new poorer Länder and twenty million new and
largely poorer citizens. The fundamentals of the fiscal system did not change, despite a massive
alteration in the geography of income and labor markets (Beramendi 2012). Rather, it assimilated
the new members in a short period of time, triggering an unprecedented redistributive effort from
the West to the East. The levels of progressivity increased through multiple mechanisms,
including changes in the tax code (e.g. the introduction of a Solidarity Surcharge of 7.5% on
personal and corporate income in 1996, met by Western tax payers) and the allocation of massive
additional benefits (b in the model above) to a massive amount of individuals incapable of
contributing to the revenue pool. Accordingly, we should see a significant increase in the income
slope on preferences for redistribution.
Table 5 evaluates these expectations. It shows for both countries the size of the income slope
over time on the basis of two questions on redistributive attitudes in the ISSP data. We present
data for the years 1985, 1990, 1996, and 2006.
- Table 5 about here In both cases, there is a clear change in the size of the income slope in the direction suggested by
our theory. In the US case, the size of the slope drops by around 35 to 40% between 1985 and
1990. In the case of Germany, the slope of income in Western Germany rises by between 5% and
72%, depending on the dependent variable. While the results are surely not definitive, they do
13
show effects over time that are both consistent with the theory and unaffected by reversed
causality.
V. Conclusion
We began this paper by noting the absence of relation between the level of pre-tax inequality and
the importance of income as a predictor of redistribution and a slight negative relationship
between the size of redistributive transfers and the predictive capacity of income. Both patterns
are puzzling from the perspective of standard models of redistribution: if anything, one would
expect income to be a better predictor of redistribution the higher the level of inequality and the
higher the stakes in terms of redistribution. What explains these puzzling findings?
Addressing this issue requires establishing the conditions under which income is a good
predictor of redistributive preferences, that is to say, the conditions under which preferences
about income redistribution are more polarized. Our approach in this paper is straightforward:
the key factor that explains contentions over redistribution is the design of the tax and transfers
system. Progressivity is inversely related to the size of redistributive transfers and positively
related to the level of conflict about the welfare state. A higher concentration of taxes and
transfers produces higher levels of polarization over redistribution, boosting up the importance of
income as a predictor of redistributive preferences. At the same time, higher levels of
concentration come at the price of a reduction in the pool of resources available for
redistribution.
These findings are quite robust and suggest significant payoffs to modeling preferences, in
contrast to earlier contributions, as a joint function of risk, benefits (transfers) and cost (taxes).
Nonetheless, neither the logic nor the findings in this paper are devoid of limitations. In closing
the paper we highlight three important issues as pointers for future research efforts.
First, the shadow of endogeneity continues to loom large over this paper. The idea that
progressivity may itself reflect pre-existing levels of support for redistribution motivates this
concern. We have presented evidence on the US and Germany that reveals an impact of
progressivity on preferences in circumstances where the case for exogeneity is plausible. This
evidence would suggest then that the concerns about endogeneity apply only in the long run. A
sizeable stock of research indicates that fiscal institutions have remained pretty sticky since their
consolidation between the inter-war and the post-war period. They do change, but very slowly
and within their own logic, in ways heavily constrained by their own feedback effects on
political coalitions. Thus, we believe that our assumption that citizens evaluate the expected net
benefits given a rather stable pre-existing design remains plausible. Having that said, however,
progressivity is itself the outcome of political contentions in the medium- to long-run. A natural
next step in this agenda is to explain the political origins of different equilibria in terms of
concentration and generosity.
Second, we have argued that the expected benefits of social policy are captured by the product of
p (risk, or share of time on benefits) and b (the size of the benefit), while its costs can be
understood as the product of t (taxes and social contributions) and the probability (or share of
14
time) of not experiencing a bad event (1-p). We measure the former with the concentration of
benefits, while the latter is measured by the concentration of taxes and contributions. These
measures strike us as good proxies, but future research should disentangle and measure the three
parameters (p, b, t) directly. At a minimum, this would give a richer picture of who gets what at
which prize.
Third, while our paper makes use of individual-level data to measure the dependent variables and
our core explanatory variables, it does not directly test the micro-level mechanisms that our logic
assumes in terms of preference formation. In particular, we did not shed light on the question
whether our three parameters (p, b, t) have the postulated effects on citizens’ social policy
attitudes. To be sure, previous research has established some of these individual-level links, but –
as far as we know – not all of them simultaneously. We doubt that there are cross-national
surveys that provide agent-specific estimates for p, b, and t. But we think these parameters can
be estimated at cohort-levels (such as income groups, or occupations), and then merged into
existing public opinion surveys. This seems one fruitful way to pursue the mechanisms
underpinning the link between material interests and preference formation.
15
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18
Figures and Tables
Figure 1: Income as predictor of redistributional attitudes
NZL
DNK
AUS
CAN
NLD
USA
NOR
CZE
GBR
SWE
DEU
FIN
FRA
CHE
JPN
POL
IRL
HUN
ESP
KOR
PRT
-.15
-.1
-.05
0
Slope of income from regression:
Redistribution = Income + Controls
.05
Note:
Shown are BLUPs (Best Linear Unbiased Predictors) from multi-level models.
Based on ISSP 2006.
Preferences for redistribution: “On the whole, do you think it should be or should not be the government’s
responsibility to reduce income differences between the rich and poor” [1. Definitely should not be; 2. Probably
should not be; 3. Probably should be; 4. Definitely should be].
Controls are education, gender, and age. Sample is restricted to employed respondents aged 18-65.
Standard errors are as follows: AUS (0.014), CAN (0.016), CHE (0.016), CZE (0.016), DEU (0.014), DNK (0.014),
ESP (0.011), FIN (0.015), FRA (0.014), GBR (0.018), HUN (0.017), IRL (0.017), JPN (0.015), KOR (0.013), NLD
(0.015), NOR (0.013), NZL (0.016), POL (0.014), PRT (0.014), SWE (0.014), USA (0.012).
19
Figure 2: Income inequality and income slopes
.05
PRT
Income slopes
0
KOR
IRL
-.05
POL
JPN
CHE
FIN
FRA
DEU
SWE
-.1
CZEGBR USA
NOR
NLD CAN
AUS
DNK
NZL
-.15
.3
.35
.4
.45
.5
Gini coefficient (before taxes and transfers) age 1865
.55
Coef=.07, SE=.19, t=.4, adj. R2=-.05, N=19
Income: Family income in noviles
.05
PRT
Income slopes
0
KOR
ESP
HUN
IRL
-.05
POL
JPN
CHEFRA
FIN
DEU
SWE
CZENOR
NLD
-.1
DNK
GBR
CAN
USA
AUS
NZL
-.15
.2
.25
.3
.35
Gini coefficient (after taxes and transfers) age 1865
.4
Coef=.31, SE=.22, t=1.4, adj. R2=.05, N=21
Income: Family income in noviles
20
Figure 3: Welfare state size and income slopes
.05
Income slopes
0
KOR
ESP
HUN
IRL
-.05
POL
JPN
CHE
-.1
USA
CAN
FIN
DEU
SWE
CZE GBR
NOR
NLD
AUS
FRA
DNK
NZL
-.15
5
10
15
20
25
Total Public Social Expenditure (%GDP)
30
Coef=0, SE=0, t=-1, adj. R2=0, N=20
Income: Family income in noviles
.05
PRT
Income slopes
0
ESP
HUN
IRL
-.05
POL
JPN
FIN
CHE
FRA
DEU
-.1
USA
GBR
CAN
SWE
NLD
AUS
-.15
NOR
CZE
DNK
NZL
10
15
20
25
30
35
Percentage share of public cash transfers in household disposable income
Coef=0, SE=0, t=2, adj. R2=.13, N=20
Income: Family income in noviles
21
Figure 4: Incidence of vocational training and income slopes
.05
PRT
Income slopes
0
ESP
IRL
-.05
JPN
FRA
FIN
DEU
SWE
-.1
USA
CAN
AUS
CHE
GBR
NLD
NOR
DNK
NZL
-.15
0
10
20
30
40
Incidence of vocational training indicator (Iversen & Soskice)
Coef=0, SE=0, t=-.1, adj. R2=-.07, N=17
Income: Family income in noviles
22
Figure 5: The distribution of expected benefits under different fiscal systems
23
Figure 6: Concentration of benefits / benefits and taxes and income slopes
.05
PRT
Income slopes
0
KOR
ESP
HUN
IRL
POL
-.05
JPN
FIN
CHE
FRA
DEU
SWE
CZEUSA
NOR
NLD CAN
GBR
-.1
AUS
DNK
NZL
-.15
-.4
-.2
0
.2
Progressivity public cash benefits, working age
.4
Coef=.18, SE=.03, t=6.2, adj. R2=.65, N=21
Income: Family income in noviles
.05
Income slopes
0
-.05
KOR
IRL
POL
JPN
FRA
-.1
CHE
FIN
DEU
SWE
NORCZE
USA
NLD
CAN
GBR
AUS
DNK
NZL
-.15
.2
.4
.6
.8
1
Overall progressivity (concentration of taxes – concentration of benefits)
Coef=-.12, SE=.03, t=-3.8, adj. R2=.44, N=18
Income: Family income in noviles
24
Figure 7: Correlation between concentration of benefits in various social policy domains
and income-slopes in matched social policy domains
P
Income-slope predicting attitude (ISSP)
.02
D
P
H
-.02
-.06
P
H
H
H
H H
H
H
-.6
P
D
U
U
H
-.4
DH
D
P
U
U
D
D
P
P
P
P
P
D
0
-.04
D
DU D
UD
P
P U P
P P
U U
U
U
UH
U
U
P
P
P
D
D
U
U
H
P
D
P
H
U
U
U
-.2
0
Concentration of benefits
.2
.4
D=Disability, H=Housing, P=Pensions, U=Unemployment
Coef=.029, SE=.009, t=3.09, adj. R2=.116
Note:
The unit of analysis is a social-policy-domain in different countries. The solid line is the pooled
regression line (for equation, see note of figure). The four dotted lines are separate regression lines for
each of the four social policy domains. The four social policy domains are disability (D), housing (H),
pensions (P), and unemployment (U). The mapping of social policy attitudes9 and concentration of benefit
domains is as follows:
• Provide decent standard of living for the unemployed → concentration of unemployment benefits
• Provide decent standard of living for the old → concentration of old age pensions
• Provide health care for the sick → concentration of disability benefits
• Provide decent housing for those who can’t afford it → concentration of housing benefits
9
These are based on ISSP survey items, with the following wording stem: “On the whole, do you think it should or
should not be the government's responsibility to ...”. The answer categories are 1 “Definitely should not be”, 2
“Probably should not be”, 3 “Probably should be”, and 4 “Definitely should be.”
25
Table 1: Concentration measures
Transfers
Concentration of …
HH taxes
cash
% DI
HH taxes
–cash
benefits
benefits
AUS (Australia)
14.3
-0.43
0.49
0.92
CAN (Canada)
13.6
-0.17
0.47
0.64
CHE (Switzerland)
16
-0.18
0.21
0.39
CZE (Czech Republic)
24.3
-0.15
0.42
0.57
DEU (Germany)
28.2
-0.07
0.44
0.50
DNK (Denmark)
25.6
-0.30
0.33
0.63
ESP (Spain)
21.3
0.10
.
.
FIN (Finland)
14.4
-0.26
0.42
0.68
FRA (France)
32.9
0.10
0.35
0.26
GBR (United Kingdom)
14.5
-0.35
0.49
0.83
HUN (Hungary)
35.1
-0.03
.
.
IRL (Ireland)
17.7
-0.20
0.53
0.74
JPN (Japan)
19.7
0.02
0.36
0.34
KOR (South Korea)
.
0.04
0.36
0.32
NLD (Netherlands)
17.1
-0.22
0.44
0.66
NOR (Norway)
21.7
-0.18
0.35
0.53
NZL (New Zealand)
13
-0.33
0.49
0.82
POL (Poland)
35.8
0.17
0.38
0.21
PRT (Portugal)
25.5
0.31
.
.
SWE (Sweden)
32.7
-0.15
0.33
0.48
USA (United States)
9.4
-0.12
0.55
0.66
Source: OECD 2008, Table 4.3, 4.4; Figure 4.2.
Concentration of cash benefits for …
Unemplo
Old age
Housing
Disability
yment
pensions
Benefits
Benefits
Benefits
-0.47
-0.44
.
-0.35
-0.11
-0.06
.
.
-0.19
-0.15
.
.
-0.11
-0.28
-0.66
-0.06
0.10
-0.28
0.00
.
-0.49
-0.22
-0.58
-0.18
0.04
0.02
0.48
0.11
-0.44
-0.24
-0.61
0.07
0.25
0.08
-0.55
0.14
-0.21
.
.
-0.20
0.01
-0.25
.
.
-0.32
-0.07
-0.46
-0.27
0.02
-0.11
.
.
.
.
.
.
-0.16
0.03
-0.65
-0.11
-0.27
-0.12
-0.65
-0.06
-0.32
-0.38
-0.37
-0.35
0.26
0.13
-0.26
0.04
0.33
0.20
0.13
0.03
-0.19
-0.10
-0.66
0.25
-0.04
0.07
.
.
26
Table 2: Sources of variables
Variable
Concentration of public cash benefits
(working age)
Concentration of HH tax (working age)
Overall progressivity
HH market income inequality (gini)
Total Public Social Expenditure as %
of GDP
Percentage share of public cash
transfers in household disposable
income
Incidence of Vocational Training
Religious fractionalization
Ethnic fractionalization
Cumulative left parties in percentage of
total cabinet posts, weighted by days
Trade union density (OECD)
Rate of Unemployment as % of
Civilian Labour Force
Source / Comment
OECD 2008, Tables 4.3, 4.4
Calculated as [concentration of HH tax – concentration of
public cash benefits]
OECD‘s ―Gini coefficient based on equivalised household
market income, before taxes and transfers (18–65 years only)
(http://stats.oecd.org/wbos/Index.aspx?DataSetCode=INEQUA
LITY)
Social Expenditure Database (SOCX) - OECD
www.oecd.org/els/social/expenditure
OECD 2008, Figure 4.2
Data from Torben Iversen (Iversen and Soskice 2001, 888)
Data available at:
http://www.anderson.ucla.edu/faculty_pages/romain.wacziarg/p
apersum.html (Alesina et al. 2003)
Calculated from the Comparative Political Dataset (Armingeon
et al. 2009)
Based on variable gov_left1. Variable is the cumulative cabinet
share of left parties since 1990, for 2005.
OECD
(http://stats.oecd.org/Index.aspx?DataSetCode=UN_DEN),
Mildly interpolated.
OECD (http://stats.oecd.org)
27