Who gives, who gains?
Progressivity and Preferences
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, the 2012 CES Meetings at Boston, the University of Minnesota, and the
Hebrew University in Jerusalem. We are grateful for the feedback received in all these
occasions. We are also grateful to Ben Ansell, Lucy Barnes, Larry Bartels, German
Feierherd, Orit Kedar, Herbert Kitschelt, Isabela Mares, Yotam Margalit, Irfan
Nooruddin, David Rueda and Ines Valdez for their comments on earlier versions. The
usual disclaimer applies.
Who gives, who gains?
Progressivity and Preferences
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? If politics is about who gets what the
answer should follow naturally: income. However, a robust and consistent pattern documents
large levels of variation in the capacity of income to predict redistributive preferences (see
Figure 1 below). In other words, countries vary largely in the extent to which rich people support
redistribution and poor people oppose it. In this paper we return to the intuition that politics is
indeed about who gets what at which price to argue that the field has overlooked an important
institutional mechanism in the process of preference formation, namely the degree of
progressivity in the tax-transfer system. Progressivity shapes how concentrated (who) both
benefits (what) and taxes (the price) are.
We argue that, by shaping the distribution of citizens’ expected net benefits, the organization of
the tax and transfer system mediates the impact of income on preferences for redistribution. For
example, self-interested rich citizens may well support social policies if they are regressive. As
the degree of progressivity varies hugely across countries and social policy domains, the design
of the tax and transfer system is bound to play a key role in explaining why income is a better
predictor of attitudes toward redistribution in some countries. To give a preview of our core
result, we find that income is a better predictor of preferences towards redistribution in those
societies with more progressive fiscal systems. To the extent that inequality or the size of the
welfare state matter for the formation of social policy preferences, we argue and show that they
do so indirectly at best, by enhancing (or muting) the implications of progressivity.
Our argument contributes to the scholarship on the micro-foundations of the welfare state by
identifying an important institutional mechanism that affects preference formation. Preferences
respond to the actual distribution of who gets what (i.e., progressivity) which is determined
jointly by the tax system, the transfer structure, inequality, and risk. Existing contributions
typically focus exclusively on individual-level determinants of social policy preferences,
emphasizing mechanisms as diverse as self-interest,1 values and believes,2 and other-regarding
preferences.3 We build on the micro-level literature and offer a micro-level account of social
policy attitudes, but we advance an argument that links institutional characteristics to preference
formation. Linking micro- and macro-level arguments and analyzing “preferences in context”
(Gingrich and Ansell 2012) strikes us as a pressing research agenda.4
1
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; Margalit 2013), or class-based explanations (Svallfors 2004).
2
Examples include 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; Kangas 2003; Kangas et al. 1995; Scheve and Stasavage
2006; Shayo 2009).
3
Examples include references to group loyalty (Luttmer 2001), altruism (Rueda 2013), the importance of relative
status (Corneo and Gruner 2000; Wilensky 1975; Lupu and Pontusson 2011) and race or ethnicity (Alesina, Glaeser,
and Sacerdote 2001).
4
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; Gelissen 2002; Jaeger 2006; Jaeger 2009; Jakobsen 2010; Mehrtens 2004; Svallfors 1997), but this
literature tends not to focus on causal mechanisms. As far as we know, progressivity does not play a role in these
accounts.
3
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 are effectively representative, redistributive policies and outcomes will
reflect, at least in part, the degree of support for redistribution existing in a society. In turn,
according to layman’s intuition – and economists’ formalizations (Meltzer and Richard 1981;
Romer 1975; Robertson 1976) – , one should expect income to be closely correlated with
redistributive references. If politics is about who gets what, 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 around
2006, and are recovered from a multi-level model.5 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
Portugal? What explains the fact that income seems to have the same effect on preferences for
redistribution in Sweden and the United States, two radically different welfare 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?
The literature offers three clues to account for cross-national differences in the importance of
income as a predictor of preferences. Two of them point to redistributive motives and link
income slopes to either pre-tax inequality or the size of the welfare state. The connection
between income slopes and pre-tax income inequality follows rather directly from median voter
accounts of preferences, most notably Meltzer and Richards’ (Meltzer and Richard 1981): As the
5
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).
Our analysis refers to roughly 2005/2006 (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.
4
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. In addition to
inequality, a second aspect of contention over redistribution is the size of the effort: the larger the
amount of transfers at stake in fiscal policy, the higher the importance of income as a
determinant of preferences. Hence, one may expect that income slopes should be larger in more
generous welfare states.
A third logic builds on the premise that welfare states are primarily insurance systems tailored to
solve inter-temporal trade-offs in economies organized around different types of skills (EstevezAbé, Iversen, and Soskice 2001; Iversen and Soskice 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 might expect income to be a stronger predictor of
redistributive preferences.
Yet, however compelling in their simplicity, none of these three logics seems to work. Figure 2
plots income slopes as reported in Figure 1against four variables: the Gini coefficient of pre-tax
income inequality (top left panel) and post-tax income inequality (top right panel), the size of the
welfare state, measured as total public social expenditure as % of GDP (bottom left panel), and
the incidence of vocational training (Iversen and Soskice 2001, 888), i.e. the share of people of a
certain age-cohort that goes through vocational training programs (bottom right panel).
- Figure 2 about here Across the board, Figure 2 conveys a consistent message: income slopes do not correlate well
with pre-existing patterns of inequality, nor the size of the welfare state, nor the skill
composition of the labor force. As such, the patterns of variation depicted in Figure 1 do not sit
well with the dominant theoretical approaches in the field. The rest of the paper develops an
alternative argument that points to the degree of progressivity in the fiscal system as the key
mechanism, largely overlooked by the literature so far, accounting for the cross-national
variation in income slopes on preferences for redistribution.
II. The Argument: Progressivity, Redistribution, and Preferences
Different welfare states rely on different mixes of taxes/contributions and benefits/entitlements,
and different societies are characterized by different income and risk distributions. In
conjunction, these factors influence who benefits and who loses from social policy, who actually
gets what and at what cost. The distribution of expected net benefits, in turn, should have stark
consequences for the politics of the welfare state and for individuals’ predispositions towards it.
Some systems pitch winners against losers, while others are much less zero sum.
5
The size of the welfare state – arguably the most prominent dependent variable in the social
policy literature – has no obvious connection to the distribution of expected net benefits, and
therefore no clear link to interests and politics. In fact, any given distribution of expected net
benefits can be the result of any number of combinations of transfers, taxes, inequality and risk.
For example, some social policy programs are largely paid for by the rich and largely used by the
poor. These (progressive) programs are characterized by a very uneven distribution of expected
net benefits, and we would expect them to cleave citizens along income lines. In other programs,
premiums are scaled to risk – these programs follow actuarial principles with a much more even
distribution of expected net benefits; income should be much less important in shaping attitudes.
And some public policies are financed by everybody but are disproportionally consumed by the
rich. These (regressive) policies should cleave citizens’ attitudes along income – but the rich
should actually favor these policies, while the poor should oppose them.
Of course, that the politics of welfare states isn’t just about the Benjamins has been recognized
before. The welfare regime literature uncovered different types of welfare states (EspingAndersen 1990), which finance and distribute benefits in very different ways. Social policy
preferences shape (Brooks and Manza 2007) or are being shaped by these regimes (Arts and
Gelissen 2001). The social insurance literature shows how incorporating risk into the analysis
changes our understanding of welfare state politics (Baldwin 1990; Mares 2003; Iversen and
Soskice 2001; Rehm, Hacker, and Schlesinger 2012; Moene and Wallerstein 2001; Moene and
Wallerstein 2003). Risk exposure offers a motive for affluent citizens to support social policy
programs. The small literature on taxation and social policy uncovered what became to be known
as “the paradox of redistribution” (Korpi and Palme 1998).6
However, our rich understanding of welfare state politics at the macro-level has not been
leveraged for explaining its micro-level underpinnings. We argue that the complex macro-level
interplay of financing (taxes/contributions), generosity (benefits/entitlements), inequality and
risk leads to different distributions of expected net benefits (across programs; across countries;
over time), which in turn shape individual-level social policy attitudes. We further argue that the
complex macro-level mix can be approximately summarized by the concentration of benefits and
taxes across the income distribution – something we call progressivity. In particular, in more
progressive systems,7 we expect income to be more closely connected to social policy attitudes
(income slopes are more negative).
To illustrate the logic, we next present a model that builds on the canonical Meltzer-Richard
formalization (Meltzer and Richard 1981) [MR]. Citizens have an exogenously given level of
income (wi), which is taxed by function ti. Taxes are collected and handed out as a flat-rate
benefit c. Taxation leads to disincentive effects (labor supply decreases as taxation increases),
which we capture (indirectly) by function L. Individuals’ utility is then:
= 1 − + − [1]
6
“The more we target benefits at the poor only and the more concerned we are with creating equality via equal
public transfers to all, the less likely we are to reduce poverty and inequality” (Korpi and Palme 1998, 681–682).
7
Benefits are concentrated at the bottom of the income scale, costs are concentrated at the top of the income scale;
inequality is high and income-risk correlations are negative and high.
6
This formulization departs from the MR model in two main ways. First, it simplifies the original
model by taking incomes as exogenous. Second and more interestingly, ti varies by individuals,
adding a progressive tax design to the original model. In particular, we follow de Donder and
Hindrik (De Donder and Hindriks 2003) and set:
= + [2]
With this quadratic income tax function, individual i’s tax payment is:
= + [2’]
Parameter alpha captures a proportional tax rate (everybody pays, say, 20% of their income as
taxes). Parameter beta is the progressivity tax parameter, with β > 0 indicating a (marginally)
progressive income tax and β < 0 representing a (marginally) regressive one.8 In the MR model,
β=0, i.e. taxes are proportional.
Balanced budgets require that the sum of benefits equals the tax take, which implies:
= + [3]
where w is the average wage.
Note that tax policies are bidimensional: benefit c depends on α and β. Without strong
assumptions it is not possible to find a median voter type solution to such a model (since it is
always possible to find a majority coalition to defend the status quo, leading to cycling).
However, this simple set up allows us to explore the impact of progressivity (β) on preferred
spending levels (c). Rewriting the utility function9 and taking the derivative with respect to c
leads to the following expression:
= − + 1 − − + [4]
Setting this expression to zero lets us solve for optimal c, which we call c*:
∗ = − + [5]
Ignoring w (since it is constant) leads to a simplified expression of c*:
∗ ∝ − + [6]
Comparative statics on this expression reveal the following:
8
9
De Donder and Hindrik set c≥0, 0≤α≤1, and –(α/2)≤β≤(1-α)/2.
Modeling disincentive effect as L=(witi)2/2, solving [3] for α and substituting the expression into [1].
7
∗
= − +
Or
∗
with
=
1 −
= − + 1 −
[7]
[8]
[9]
For positive values of β (i.e. in progressive systems), citizens with above average incomes
(wi>w) want zero benefits (no taxes, no transfers), while those below it want a positive benefit;
the poorer a person, the higher the preferred benefit. In somewhat regressive systems (for fairly
negative values of β, i.e. β+ε<0), the relationship between preferred benefits and income flips to
positive.10 The pogressivity parameter mediates the preference gap between different income
levels: the gap between the most and least preferred benefit level increases as β raises.11 In other
words, the income differences in preferred levels of redistribution increase in β. In more
progressive systems, income is a better (more negative) predictor of redistribution preferences.
More formally, the cross-partial with respect to β is negative:
∗
= −1
[10]
The results in [8] and [10] summarize our argument. Our model shows how progressivity and
income slopes are connected. Of course, our model is very abstract and simplifies as much as
possible. Moreover, we model progressivity via the tax function. In reality, the progressivity of a
social policy program is the result of many factors, as mentioned above (financing, benefit,
inequality, income-risk correlations, etc.). In our empirical analysis below we will employ
measures of progressivity that take these various sources into consideration.
We also assume the status quo level of progressivity to be exogenous and given to the
individual’s process of preference formation. We believe this to be a reasonable theoretical
assumption. This paper analyzes the effect of progressivity on income slopes, not on the origins
of progressivity. And the latter is clearly a given for any individual entering politics: what
individuals experience as the status quo are the results of previous compromises on the politics
of risk management and fiscal burden allocation.12
Our argument suggests that institutional differences translate directly into the process of
preference formation. 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
10
The flipping point is not β=0, but somewhat to the left of that (loosely speaking, that’s because of the disincentive
effects of taxation, which show up in ε)
11
Note that for positive β the most preferred level of c is zero; the gap still increases because poor citizens prefer a
positive level of c. Progressivity parameter β myltiplies that gap.
12
On the politics behind these compromises: Stigler (1970); Dixit and Londregan (1995, 1998); Beramendi and
Cusack (2009).
8
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 progressivity – resulting from the interplay of 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 four 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?
(3) Over time: a dynamic analysis of the relationship between tax progressivity and income
slopes in Sweden during the period 1968-2010.
(4) A natural experiment, based on the German case before and after re-unification, where we
can exploit an exogenous change in the degree of progressivity and assess its implications for
income slopes.
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
9
across countries. It is therefore reassuring that the estimated income-gradients from alternative
data-sources with better and more comparable income variables are comparable.13
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). Our
core explanatory variable – discussed in detail below – is available for about the same year (and
not for other years). We therefore restrict our analysis to ISSP 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 is “progressivity” – parameter β in the model presented above.
How should it be measured? One prominent measure in the literature is the redistributive effect
taxes and transfers have, which is typically measured as the (proportional) reduction in the gini
coefficient comparing market and disposable income distributions (Bradley et al. 2003;
Kenworthy and Pontusson 2005).14
Standard measures capturing the overall reduction in inequality due to taxes and transfers, such
as the difference in Gini coefficients before and after taxes and transfers, provide a summary of
the scope of redistribution but do not speak to the directionality of the policy effects behind
observable redistribution nor about the subspace of the income distribution in which the
reallocation of resources actually takes place. For instance a 10 percent reduction in pre-tax
inequality may reflect transfers from the top to the middle, from the middle to the bottom, or
13
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
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.
14
In the literature on measurement of inequality, this is known as the Reynolds-Smolensky index.
10
from the top to the bottom. These are three very different scenarios in terms of progressivity and
the politics of social policy – and yet the overall reduction in the Gini coefficient for pre-tax
inequality offers no leverage to distinguish between them. This is not to say though that
redistribution and progressivity are unrelated.
Following Kakwani and Lambert the overall redistributive impact of the fiscal system –
measured by the difference between market and disposable income gini coefficients – can be
broken into two components (Kakwani 1977; Kim and Lambert 2009): the scope of the effort
and its progressivity. The relationship can be formally stated as follows:15
!"#$%
+
− &'(&!)*$ = ,+ =
% - .)/ 0 /
,%.)
[10]
where t denotes the tax level, b denotes the benefit level and βT and βB indicate the progressivity
of taxes and benefits. Assuming balanced budgets (t=b=α), we can rewrite this equality as:
!"#$%
− &'(&!)*$ = 1 + | 3 |
[11]
In words: the overall reduction in inequality due to taxes and transfers can be decomposed into
the product of the size of the welfare state (α) and the progressivity of its taxes (βT) and benefits
(βB). It is our contention that progressivity is a central ingredient of welfare state politics.
The literature has followed Kakwani in measuring βT and βB. Kakwani defines progressivity as a
tool to “measure deviations of the tax [or transfer] system from proportionality” (Kakwani 1977,
74), which are commonly captured by concentration curves of taxes and benefits, respectively.
The concentration of taxes is derived by plotting the share of taxes paid against rank-ordered
income groups. In a progressive system, those at the bottom of the income scale pay a lower
share of taxes and the concentration curve is below the 45-degree line, while it is above the 45degree line for richer income groups. The concentration coefficient of taxes (βT) sums the area
between the concentration curve and the 45-degree line in a way that more positive values
indicate more progressive systems.
Progressivity of benefits is measured analogously. The concentration of benefits is derived by
plotting the share of benefits received 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. The concentration coefficient of benefits (βB) sums 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. More negative values
indicate more progressive systems.
15
The first equality has been established by Kakwani (Kakwani 1977, equation 3.2), the second by Lambert (Kim
and Lambert 2009, equation 3).
11
In the empirical analysis below, we will capture progressivity by the concentration of benefits
(βB) as well as a measure of overall progressivity (βT-|βB|).16 In both cases, a value of zero
indicates proportionality. With respect to the concentration of benefits, negative values indicate
more progressive benefits systems, and we expect a positive correlation between income slopes
(welfare state contestation) and that measure. With respect to the combined measure of
progressivity – taking into account taxes and benefits – positive values indicate more progressive
systems, and we expect a negative relationship between income slopes and overall progressivity.
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 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. 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 incidence of vocational training (the large the
incidence of vocational training, the less contested social policy), and the magnitude of the
welfare state (the more is at stake, the more contested should welfare states be). Neither of these
variables performed well in bivariate analysis (see Figure 2), but it nevertheless may make sense
to include them as control variables. Welfare state size seems particularly relevant,17 and we
therefore include three different measures thereof (percentage share of public cash transfers in
household disposable income; total social expenditure as % of GDP; average tax wedge).18
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. Moreover, 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.19
16
To arrive at a measure of overall progressivity, we take the concentration coefficient of taxes (βT) and subtract the
absolute value of the concentration coefficient of benefits (βB). Higher values indicate more progressive systems.
17
Equation [11] above suggests that we should control for the overall size of the welfare state.
18
Defined as: “Sum of personal income tax and employee plus employer social security contributions together with
any payroll tax less cash transfers, expressed as a percentage of labour costs.”
19
In robustness checks, we have also controlled for a variety of other variables. These include factors that are
discussed in the growing literature exploring the link between income and voting (De La O and Rodden 2008; Huber
and Stanig 2007; Shayo 2009). We included control variables that measure religious fractionalization (Alesina et al.
2003); the share of confessional groups (such as Catholics or Protestants), with data from the Religion and State
Project (Fox 2004; Fox 2008); ethnic fractionalization (Alesina et al. 2003) to account for the possibility that ethnic
issues alter the link between income and redistributional preferences; and variables containing information on
immigration stocks and flows (such as inflow of asylum seekers, inflow of foreigners, stock of foreign-born
population, etc.). None of these change the results we report below.
12
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-price
question). 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 3 displays bivariate correlations between these
two core explanatory variables and the income-slopes. The figure shows that the more
redistributive benefits, the more contested they are. This is what we expected from our
theoretical framework.20 The close fit between income-slopes and the concentration of (taxes
and) benefits, as displayed in Figure 3 is remarkable.
- Figure 3 about here 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;
size of the welfare state measured in various ways; incidence of vocational training; 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 in the top panel of around
0.18 suggests that a one standard deviation (0.19) change in the concentration of benefits
changes the income slope by about 0.034. Since the income-slope ranges from about to -0.15 to
about -0.02, this is a 20% change.21
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
20
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): 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.
21
Income slopes: Mean=-.07; SD=.04; Min=-.15; Max=.02; N=21. Concentration of cash benefits: Mean=-.11;
SD=.19; Min=-.43; Max=.31; N=21. Overall progressivity: Mean=.57; SD=.2; Min=.21; Max=.54; N=18.
13
concentration measures of social policy domains. While there is often more than one possible
match, many mappings are straightforward. In particular, the ISSP RoG survey includes the
following social policy attitudinal items22 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 4 shows the results, pooling the four social policy
domains together.
- Figure 4 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 (not shown),
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. The
correlational evidence presented so far cannot identify the causal effect of progressivity on
preferences for redistribution. Two potential concerns stand out: first, a necessary yet insufficient
condition for a causal effect of progressivity on income slopes is that these two variables co-vary
over time; second, for causality to be identified, an exogenous increase (decrease) of
progressivity should lead to a subsequent increase (decrease) in the magnitude of income slopes.
We address each of these two points in turn.
22
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.”
14
Unfortunately, data limitations prevent us from performing a viable time-series cross-sectional
analysis.23 However, in some cases, there are enough data on both progressivity and income
slopes to evaluate the relationship over time. We make use of a time series of progressivity
indicators in Sweden from 1968-2009. Bengtsson et al. (2012) compute these indicators on the
basis of the Swedish Longitudinal Individual Data Base (LINDA), a dataset that captures a 3.35
percent random sample of the Swedish population and incorporates demographic information as
well as entries for all tax payments and deductions registered by tax authorities and population
censuses. This source allows us to trace the evolution of income tax progressivity in Sweden for
over forty years.24 The top-panel in Figure 5 traces the evolution of progressivity in Sweden.
- Figure 5 about here During this time, two major reforms took place. The first one, in 1971, increased significantly
the levels of income tax progressivity. Before 1971 the Swedish tax system had two distinctive
features: first, couples and singles were taxed under two different schedules; second,
proportional local income taxes were deductible from national income taxes. The 1971 reform
made all income from employment taxable on the basis of a single scale, regardless of marital
status, and eliminated the possibility to deduct local taxes from national taxes, thereby
significantly increasing the levels of progressivity. The second major reform, enacted by the first
conservative government to gain office in decades, took place in 1991 and worked in the
opposite direction. The 1991 tax reform enacted a massive reduction of the top marginal tax rate
from 80 to 50 percent and a simplification of the tax code such that about 85 percent of the
population were no longer required to file an income tax (Bengtsson, Holmlund, and
Waldenstrom 2012; Steinmo 2002, 850). Instead, all tax payers pay a proportional tax of 20%
(which eventually increased to 30% in most districts) and the top 20% of income earners pay an
additional national tax rate or either 20 or 25 % depending on their pre-tax income level. In
addition, capital and corporate marginal tax rates dropped significantly. Overall, the reform
reduced massively the pre-existing levels of progressivity in the system.
Accordingly, we expect the income slopes to increase after the 1971 reforms and to decrease
after 1991. Figure 5 plots the evolution of progressivity between 1968 and 2009 (top-left panel)
along with the income slopes on preferences for redistribution in twelve Swedish National
Election Studies between 1964 and 2006 (bottom-left panel). It also displays a scatterplot
between these two variables, along with a fit-line (top-right panel).25
23
The main constraint is the absence of estimates for the concentration curves over time, our core explanatory
variable.
24
Since we do not have access to LINDA, we have to rely on published data to approximate progressivity.
Bengtsson, Holmlund, and Waldenstrom (2012, Table A7) publish time-series on the evolution of the gini
coefficient of market and disposable income, respectively, as well as the so-called re-ranking effect. We
approximate progressivity by “vertical redistribution”, which can be calculated as: Gini(market)Gini(disposable)+reranking effect (Kim and Lambert 2009, equation 4). This measure is inferior to the concentration
based measures we employ in the analysis above, but it addresses some of the shortcomings of the gini-reduction
measure.
25
The dependent variable comes from a question that asks respondents to react to the following statement: “Social
reforms in this country have gone so far that the state ought to reduce rather than increase social benefits and support
for people”. Respondents have four options: 1 "agree completely"; 2 "agree on the whole"; 3 "disagree on the
whole"; 4 "disagree completely" A negative slope for income indicates that wealthier respondents are more likely to
agree with the statement. In turn, income is measured as a five scale variable: 1 "low income"; 2 "fairly low
15
- Figure 5 about here The longue durée analysis of the Swedish case reveals a pattern consistent with our theoretical
expectations. As the progressivity of the fiscal system increased between 1972 and 1990, so did
the income slopes. By contrast, the 1991 reform caused a change in trends that reduced and
ultimately stabilized the importance of income as a predictor of preferences for redistribution.
The change in trends is less sharp than one would ideally like to see – we think for two reasons.
First, the change in progressivity is contemporaneous to a major economic crisis that triggers
automatic stabilizers and reduces the tax contributions of low income citizens (via
unemployment effects). Hence, the effects of the reform did take some time to become apparent.
Second, the return of the Social Democrats to power in 1994 implied a marginal adjustment of
the 1991 Tax Reform. While the fundamental spirit of the 1991 reform, that is the need to
preserve high wage earners for paying an exceedingly high share of the tax burden, remained in
place, the new government introduced a series of changes that scaled back the scope of the
reform. These adjustments included a 5% increase in the top marginal tax rate and a drop in the
VAT for food of 50% (Steinmo 2002, 852). These changes slowed down the reduction of
progressivity and its impact on income slopes. Overall, however, there is clear, strong and
negative relationship between the degree of progressivity and the impact of income slopes in
Sweden over time, as shown in the top-right panel of Figure 5. Higher levels of progressivity
imply a stronger negative impact of income, as predicted by our argument.
This result, however, does not address the joint endogeneity between progressivity and
preferences for redistribution over time as neither the 1971 nor the 1991 reforms can be said to
be exogenous with respect to the pre-existing distribution of preferences. To address this
problem we exploit German Reunification (1989-1990) as a natural experiment where by virtue
of sharp increases in the pool of recipients from the East and ad hoc modifications of the tax and
transfers system the levels of progressivity increased sharply (Beramendi 2012). The West
incorporated five new poorer Länder and twenty million new and largely poorer citizens,
triggering an increase of progressivity through four mechanisms: (1) the concentration of
financing effort among tax payers in the West;26 (2) the concentration of risk among the new
citizens in the East; (3) the increase in unemployment induced by the recession following Reunification; and (4) the rapid incorporation into the benefit system of the new pool of welfare
dependents into the system.27 The fundamentals of the fiscal system did not change, despite a
massive alteration in the geography of income and labor markets. Rather, it assimilated the new
members in a short period of time, triggering an unprecedented redistributive effort from the
West to the East and a substantial increase in the levels of progressivity in the system.
Together, these processes combined two types of redistribution: interpersonal redistribution from
rich to poor individuals in both the East and West, and inter-territorial redistribution from
income"; 3 "neither low nor high income"; 4 "fairly high income"; 5 "high income". The analysis also controls for
age, gender, marital status, education, and whether the respondent is affiliated with a union.
26
For example, a 7.5% surcharge on personal and corporate income, introduced in 1996, was met primarily by
Western tax payers.
27
For instance, Eastern citizens were incorporated into the unemployment insurance system as early as 1990, and to
social assistance and public insurance pensions by 1992. For a more detailed account of the rapid process of
incorporation of Eastern lander into the existing welfare structures, see Beramendi (2012:149-159).
16
Western taxpayers to Eastern recipients. The latter, however, decreased marginally over time as
the cost and the size of transfers to the East became a major focus of political contention in the
late 1990s. From a geographic perspective, progressivity will be at its highest in the newly
unified Germany, reflecting the joint effect of interpersonal and inter-territorial redistribution. Of
the two Germanys, the East will be one with the lowest degree of progressivity as its population
has an overconcentration of welfare beneficiaries, whereas in Western Germany the majority of
tax payers coexists with a sizable group of welfare recipients. Hence, income slopes should be
larger in the new Germany than in Western Germany, and larger in the West than in the East. In
turn, in terms of dynamics, according to the model, increasing progressivity should translate into
larger (i.e. more negative) income slopes.
To assess the validity of these predictions, we rely on the German General Social Survey,
ALLBUS (GESIS - Leibniz-Institut für Sozialwissenschaften 2012). In particular, we explore the
predictive power of income on the response to the following statement: “the state should secure
income in times of hardship”28 with responses ranging from strong disagreement (1) to strong
agreement (4).29 Figure 6 reports the income slopes for Germany, West Germany, and East
Germany.
- Figure 6 about here The evolution of income slopes in Germany after Re-unification validates our theoretical
expectations. Income slopes are larger in the newly unified Germany than in either of its
constituent parts throughout the period. In terms of dynamics, it is important to distinguish
between the West and the East. In the West, due to the concentration of tax payers, income
slopes are large from the beginning and strengthen marginally over time. Indirectly, this reflects
a conflict of motives. Western citizens want to sustain a comprehensive insurance system in the
West but grow increasingly opposed towards the scale and the efficiency implications of the
unification effort. In the East, slopes increase sharply during the period 1991-2010. Early on in
the process there is virtual unanimity in the East about the need and desirability of massive
income transfers. However, as time goes by, preferences sort along income lines, especially from
the late 1990s onwards when the scope and nature of the post-unification effort becomes the
subject of significant political contention. Overall, the evolution of income slopes in Germany
after Re-unification lends support to our theoretical expectations in a context in which the
increase in the level of progressivity was clearly exogenous.
V. Conclusion
We began this paper by showing that the importance of income as a predictor of redistribution
varies greatly across rich democracies (Figure 1), but not in a way we would expect from the
dominant theoretical approaches in the field (Figure 2). What, then, does explain welfare state
28
German question wording: “Der Staat muss dafuer sorgen, dass man auch bei Krankheit, Not, Arbeitslosigkeit und
im Alter ein gutes Auskommen hat.”
29
Reported estimates are, as before, BLUP from multilevel models, with individuals nested in time. Control
variables include gender, age, marital status, and education. As before, the sample is restricted to respondents aged
18-60.
17
contestation? We have argued that progressivity is a key ingredient for understanding the politics
of the welfare state, since it decidedly shapes the distribution of expected net benefits, i.e. who
gets what at which price. And we have shown that, indeed, a higher concentration of taxes and
transfers (higher progressivity) produces higher levels of polarization over social policy (more
negative income slopes). This finding is robust across a wide range of different tests (crossnational; cross-domain; time-series; exogenous shock).
We believe it is futile to understand the politics of the welfare state without considering
progressivity. Politics is, after all, the question of who gets what (at which price), and
progressivity crucially shapes expected net benefits. This implies that there are large payoffs
from analyzing systematically and jointly the role of taxes (not just transfers) and the distribution
of risk (not just income). For example, consider Figure 7, which shows a clear negative
correlation between welfare state size and progressivity.30 Our approach helps us understand the
politics behind this finding: in more progressive systems, welfare state contestation will be
higher, slowing welfare state expansion.
- Figure 7 about here By unpacking the linkage between who actually gets what and preference formation, this paper
has a number of implications. First, it unveils an important mechanism in the formation of
citizens’ attitudes towards the welfare state. The mechanism highlights the role of an institutional
component largely neglected in the literature, namely fiscal institutions.31 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 provides a corrective to the dominant social policy
paradigms, which tend to focus on the role of income inequality and welfare state size.
In closing, we highlight three avenues for future research. First, progressivity may itself reflect
pre-existing levels of support for redistribution. We have presented evidence on 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 longrun (Cusack and Beramendi 2006; Beramendi and Rueda 2007). A natural next step in this
agenda is to explain the political origins of different equilibria in terms of concentration and
generosity.
Second, 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
30
This is somewhat different from the “paradox of redistribution”, which is concerned with targeting and poverty
reduction.
31
For another analysis on the role on institutions as context for preference formation, see Gingrich and Ansell
(2012).
18
logic assumes in terms of preference formation. For example, we do not test whether expected
net benefits are good predictors of social policy benefits. To be sure, previous research has
established aspects of this mechanism, showing that income and risk are strong predictors of
social policy attitudes. We doubt that there are cross-national surveys that provide respondentspecific estimates of expected net benefits, but they may be derived 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.
Third, we argue that the expected benefits of social policy are determined by the interplay of
income, risk, benefits, and financing. We measure progressivity by the concentration of benefits
(and the concentration of taxes), which does not unravel the impact of these different sources on
progressivity. Future research should disentangle and measure the different contributors to
progressivity directly. At a minimum, this would give a richer picture of who gets what at which
price. And that is what arguably politics is all about.
19
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23
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 (ISSP):
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.
24
Figure 2: Inequality, welfare state size, vocational training and income slopes
.05
PRT
0
KOR
-.05
CHE
-.1
IRL
JPN
FIN FRA
DEU
SWE
GBR
NOR
CZE
USA
NLD
CAN
AUS
DNK
POL
Income slope (w/ controls)
Income slope (w/ controls)
.05
NZL
-.15
-.05
-.1
PRT
KOR
ESP
HUN
IRL
POL
JPN
CHE
FRA
FIN
DEU
SWE
GBR USA
CZE
NOR
NLD
CAN
AUS
DNK
NZL
-.15
.3
.35
.4
.45
.5
.55
Gini coefficient (before taxes and transfers) (age 18-65)
Coef=.07, SE=.19, t=.4, adj. R2=-.05, N=19
.2
.25
.3
.35
.4
Gini coefficient (after taxes and transfers) (age 18-65)
Coef=.31, SE=.22, t=1.4, adj. R2=.05, N=21
.05
PRT
0
KOR
ESP
HUN
POL
JPN
CHE
FIN FRA
DEU
SWE
GBR
CZE
NOR
USA
NLD
CAN
AUS
DNK
IRL
-.05
-.1
NZL
-.15
5
10
15
20
25
Total social expenditure (% GDP)
Coef=0, SE=0, t=-.7, adj. R2=-.03, N=21
Income slope (w/ controls)
.05
Income slope (w/ controls)
0
PRT
0
ESP
-.05
-.1
-.15
30
IRL
USA
AUS
JPN
CAN
FRACHE
FIN
DEU
SWE
GBRNOR
NLD
DNK
NZL
0
10
20
30
40
Incidence of vocational training indicator (Iversen & Soskice)
Coef=0, SE=0, t=-.1, adj. R2=-.07, N=17
25
Figure 3: Concentration of benefits / benefits and taxes and income slopes
.05
PRT
Income slope (w/ controls)
0
KOR
ESP
HUN
IRL
POL
-.05
JPN
CHE
FIN
NOR
GBR
-.1
NLD
DNK
AUS
CAN
FRA
DEU
SWE
CZE
USA
NZL
-.15
-.4
-.2
0
.2
Progressivity public cash benefits, working age
Coef=.18, SE=.03, t=6.2, adj. R2=.65, N=21
.4
.05
Income slope (w/ controls)
0
KOR
POL
-.05
IRL
JPN
FRA
CHE
DEU
FIN
SWE
CZE
NLD USA
NOR
CAN
DNK
-.1
GBR
AUS
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
26
Figure 4: 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
P
D
0
H
-.02
-.04
H
H
H H
H
D
U
H
-.6
H
-.4
U
DH
D
D
P
P
P
P
P
D
P
H
U
-.06
D
P
DU D
P
P
D
D
U
U
UD
P
P U P
P
P P
U
U
U
H
U
U
U
U
H
U
P
U
H
D
P
U
U
-.2
0
Concentration of benefits
D=Disability, H=Housing, P=Pensions, U=Unemployment
Coef=.029, SE=.009, t=3.09, adj. R2=.116
.2
.4
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 attitudes32 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
32
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.”
27
Income slopes (w/ controls)
Income slopes (w/ controls)
Progressivity (vertical redistribution)
Figure 5: Sweden – income slopes and progressivity
.3
.25
.2
.15
.05
1968
0
1970
-.05
2006
1976
1979
1988
1985
1991
1994
1998
-.1
.1
.1
2002
1973
.15
.2
.25
.3
Progressivity (vertical redistribution)
1960
1970
1980
1990
2000
2010
1970
1980
1990
2000
2010
Coef=-.34, SE=.13, t=-2.6, adj. R2=.34, N=12
.05
0
-.05
-.1
1960
Note:
Top-left-panel: Progressivity estimates are calculated based on data derived from the Swedish Longitudinal
Individual Data Base (LINDA), as provided by Bengtsson, Holmlund, and Waldenstrom (2012, Table A7).
Progressivity is approximate by vertical redistribution, which can be calculated as (Kim and Lambert 2009, equation
4): Gini(market)-Gini(disposable)+reranking effect.
Bottom-left-panel: Incomes slopes are BLUPs (Best Linear Unbiased Predictors) from multi-level models
(individuals nested in time). Dependent variable: “Social reforms in this country have gone so far that the state ought
to reduce rather than increase social benefits and support for people” (1 agree completely, 2 agree on the whole, 3
disagree on the whole, 4 disagree completely). Control variables are gender, age, marital status, union membership
and education. Sample restricted to respondents aged 18-60. Data-source: Swedish National Election Studies.
Top-right-panel: Scatterplot of progressivity vs. income slopes.
28
Figure 6: Germany – evolution of income slopes
Income slope
0
-.02
-.04
-.06
1980
1985
1990
East
1995
West
2000
2005
2010
Total
Note:
Shown are BLUPs (Best Linear Unbiased Predictors) from multi-level models (individuals nested in
time). Dependent variable: preferences for “state should secure income in times of hardship” (1 strongly
disagree; 2 disagree; 3 agree; 4 strongly agree). Controls variables are gender, age, marital status and
education. Sample restricted to respondents aged 18-60 and (full- or part-time) employed.
Source: Based on the German General Social Survey (ALLBUS), 1980-2010 (GESIS - Leibniz-Institut
für Sozialwissenschaften 2012).
29
% of HH disposable income from public cash transfers
Figure 7: Progressivity and size of welfare states
AUT
POL
35
FRA
SWE
LUX
ITA
30
BEL
DEU
SVK
25
DNK
CZE
NOR
20
ISL
JPN
NLD
IRL
CHE
15
FIN
CAN
10
GBR
NZL
AUS
USA
.2
.4
.6
.8
Overall progressivity (concentration of taxes - concentration of benefits)
Coef=-27., SE=6.07, t=-4.6, adj. R2=.48, N=23
1
30
Table 1: Concentration measures
Transfers
AUS (Australia)
CAN (Canada)
CHE (Switzerland)
CZE (Czech Republic)
DEU (Germany)
DNK (Denmark)
ESP (Spain)
FIN (Finland)
FRA (France)
GBR (United Kingdom)
HUN (Hungary)
IRL (Ireland)
JPN (Japan)
KOR (South Korea)
NLD (Netherlands)
NOR (Norway)
NZL (New Zealand)
POL (Poland)
PRT (Portugal)
SWE (Sweden)
USA (United States)
Concentration of …
% DI
cash
benefits
HH
taxes
14.3
13.6
16
24.3
28.2
25.6
21.3
14.4
32.9
14.5
35.1
17.7
19.7
.
17.1
21.7
13
35.8
25.5
32.7
9.4
-0.43
-0.17
-0.18
-0.15
-0.07
-0.30
0.10
-0.26
0.10
-0.35
-0.03
-0.20
0.02
0.04
-0.22
-0.18
-0.33
0.17
0.31
-0.15
-0.12
0.49
0.47
0.21
0.42
0.44
0.33
.
0.42
0.35
0.49
.
0.53
0.36
0.36
0.44
0.35
0.49
0.38
.
0.33
0.55
HH taxes
–cash
benefits
0.92
0.64
0.39
0.57
0.50
0.63
.
0.68
0.26
0.83
.
0.74
0.34
0.32
0.66
0.53
0.82
0.21
.
0.48
0.66
Concentration of cash benefits for …
Old age
pension
-0.47
-0.11
-0.19
-0.11
0.10
-0.49
0.04
-0.44
0.25
-0.21
0.01
-0.32
0.02
.
-0.16
-0.27
-0.32
0.26
0.33
-0.19
-0.04
Unempl
oyment
Benefits
-0.44
-0.06
-0.15
-0.28
-0.28
-0.22
0.02
-0.24
0.08
.
-0.25
-0.07
-0.11
.
0.03
-0.12
-0.38
0.13
0.20
-0.10
0.07
Housing
Benefits
Disability
Benefits
.
.
.
-0.66
0.00
-0.58
0.48
-0.61
-0.55
.
.
-0.46
.
.
-0.65
-0.65
-0.37
-0.26
0.13
-0.66
.
-0.35
.
.
-0.06
.
-0.18
0.11
0.07
0.14
-0.20
.
-0.27
.
.
-0.11
-0.06
-0.35
0.04
0.03
0.25
.
Source: OECD 2008, Table 4.3, 4.4; Figure 4.2.
31
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 (“% of HH disposable income
from public cash transfers”)
Average tax wedge (%)
Incidence of Vocational Training
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
http://stats.oecd.org/Index.aspx?DataSetCode=SOCX_AGG#
OECD 2008, Figure 4.2
OECD Taxing Wages
http://stats.oecd.org/Index.aspx?DataSetCode=AWCOMP
Defined as: “Sum of personal income tax and employee plus
employer social security contributions together with any payroll
tax less cash transfers, expressed as a percentage of labour
costs” (http://stats.oecd.org/glossary/detail.asp?ID=7273)
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)
Ethnic fractionalization
Calculated from the Comparative Political Dataset (Armingeon
Cumulative left parties in percentage of et al. 2009)
total cabinet posts, weighted by days
Based on variable gov_left1. Variable is the cumulative cabinet
share of left parties since 1990, for 2005.
Trade union density (OECD)
OECD http://stats.oecd.org/Index.aspx?DataSetCode=UN_DEN
Rate of Unemployment as % of
OECD (http://stats.oecd.org)
Civilian Labour Force
Religious fractionalization
32
Table 3: Predicting income-slopes with the concentration of cash benefits
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.18**
(0.04)
0.21**
(0.04)
Income-slope of regression:
preferences for redistribution=income + controls (ISSP)
Concentration of cash benefits
0.18**
(0.03)
Percentage share of public cash transfers
in household disposable income
Total social expenditure
(% of GDP)
Average tax wedge
0.18**
(0.04)
-0.00
(0.00)
0.19**
(0.03)
0.20**
(0.03)
0.20**
(0.03)
0.18**
(0.03)
0.17**
(0.03)
-0.00
(0.00)
-0.00
(0.00)
HH market income inequality (gini)
-0.24#
(0.12)
Incidence of vocational training
-0.00
(0.00)
cumulative Left parties in percentage of
total cabinet posts, weighted by days
Trade union density
0.00
(0.00)
0.00
(0.00)
Unemployment rate
Constant
-0.05**
(0.01)
20
0.649
-0.05*
(0.02)
19
0.637
-0.02
(0.02)
20
0.671
-0.03
(0.02)
20
0.663
0.05
(0.05)
18
0.676
-0.05**
(0.01)
16
0.636
-0.06**
(0.01)
19
0.634
-0.05**
(0.01)
19
0.516
-0.00
(0.00)
-0.03
(0.02)
20
0.668
No of cases
R2
Note: Standard errors are in parentheses. # p<0.1, * p<0.05, ** p<0.01.
a
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 aged18-65.
Concentration of cash benefits: higher values indicate less progressive systems.
33
Table 4: Predicting income-slopes with the overall progressivity
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
-0.11**
(0.03)
-0.13**
(0.04)
Income-slope of regression:
preferences for redistribution=income + controls (ISSP)
Overall progressivity (concentration of
taxes - concentration of cash benefits)
Percentage share of public cash transfers
in household disposable income
Total social expenditure
(% of GDP)
Average tax wedge
-0.12**
(0.03)
-0.10*
(0.05)
-0.00
(0.00)
-0.13**
(0.03)
-0.14**
(0.03)
-0.12**
(0.03)
-0.08#
(0.05)
-0.10**
(0.03)
-0.00*
(0.00)
-0.00
(0.00)
HH market income inequality (gini)
-0.13
(0.13)
Incidence of vocational training
-0.00
(0.00)
cumulative Left parties in percentage of
total cabinet posts, weighted by days
Trade union density
-0.00
(0.00)
-0.00
(0.00)
Unemployment rate
Constant
-0.02
(0.02)
-0.02
(0.05)
0.04
(0.03)
0.03
(0.03)
0.04
(0.06)
-0.04
(0.04)
-0.02
(0.02)
-0.01
(0.02)
-0.00
(0.00)
-0.00
(0.03)
No of cases
R2
17
0.421
16
0.312
17
0.543
17
0.488
17
0.420
14
0.148
16
0.346
17
0.392
17
0.393
Note: Standard errors are in parentheses. # p<0.1, * p<0.05, ** p<0.01.
a
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 aged18-65.
Overall progressivity: higher values indicate more progressive systems.
34
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Who gives, who gains? Progressivity and Preferences Pablo Beramendi (Duke University,