Cross-National Support for the Welfare State Under Wealth Inequality

Published date01 November 2023
DOIhttp://doi.org/10.1177/00104140231168364
AuthorAmalie Sofie Jensen,Andreas Wiedemann
Date01 November 2023
Subject MatterArticles
Article
Comparative Political Studies
2023, Vol. 56(13) 19591995
© The Author(s) 2023
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DOI: 10.1177/00104140231168364
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Cross-National Support
for the Welfare State
Under Wealth Inequality
Amalie Sof‌ie Jensen
1
and Andreas Wiedemann
2
Abstract
Wealth is often more unequally distributed than income, and there are considerable
differences across countries. In this paper, we argue that wealth inequality helps
explain cross-national variation in support for (and the size of) the welfare state
because assets serve as private insurance. When wealth, particularly liquid assets, is
unequally distributed across the income spectrum and high-income groups hold
most assets, strong reinforcing preferences in favor of or against social policies result
in antagonistic welfare politics and less government spending. When assets are more
equitably distributed across the income spectrum, cross-cutting preferences
emerge as more people support either insurance or redistribution. Welfare politics
is consensual and facilitates a broader welfare coalition and more government
spending. We analyze original cross-national survey data from nine OECD
countries and provide evidence in support of our argument. Our f‌indings suggest
that wealth inequality reshapes the role of income in structuring welfare politics.
Keywords
social welfare programs, political economy, European politics, wealth
inequality
1
University of Copenhagen, Copenhagen, Denmark
2
Princeton University, Princeton, NJ, USA
Corresponding Author:
Andreas Wiedemann, Department of Politics and School of Public and International Affairs,
Princeton University, Princeton, NJ 08544, USA.
Email: awiedemann@princeton.edu
Income inequality has grown considerably across and within countries in
recent decades. Much less is known about how wealth is distributed across
countries or, more importantly, among individuals. Recent work based on
surveys and administrative tax data has shown that wealth is even more
unequally distributed than income (e.g., Chwieroth & Walter., 2019;Fuller
et al., 2020;Kuhn et al., 2020;Zucman, 2019). In China, Europe, and the
United States, the top 10% of the population own more than 70% of the total
wealth. The share of wealth held by the top 1% in the United States grew from
25% to 30% in the 1980s to around 40% in 2016 (Piketty, 2014;Saez &
Zucman, 2016). These numbers mask the fact that a growing share of
households live paycheck to paycheck and set aside little or no savings to
protect them from f‌inancial shocks (Kaplan et al., 2014). For example, Lusardi
et al. (2011) f‌ind that over 25% of American survey respondents, including
high-income respondents, could not come up with $2000 within 30 days for
unanticipated expenses such as a major car repair or a large medical co-
payment; 19% could only cover such expenses through payday loans or
selling items at pawnshops. Almost half of all US respondents indicated that
they could certainly notor probably notcome up with the f‌inancial
resources to address a f‌inancial shock of this magnitude. Hacker et al. (2014)
expand the notion of economic risk to include income losses, unexpected
medical expenses, and limited assets to weather f‌inancial shocks, and doc-
ument that the share of Americans that are insecure by that metric has in-
creased steadily since the mid-1980s.
These patterns are not limited to the United States. Kaplan et al. (2014)
show that households living paycheck to paycheck (with very little savings)
represent about 30% of the population in Canada, Germany, and the United
Kingdom and 20% or less in Australia, France, Italy, and Spain. Nor are these
patterns limited to low-income households. The same study estimates that
two-thirds of American households that live paycheck to paycheck hold a
large amount of wealth in assets such as housing or retirement accounts but
have very little or no liquid wealth. Thus, studies of householdsf‌inances
should measure multiple components of wealth and degrees of liquidity rather
than just total wealth to fully understand wealth inequality (Pfeffer & Waitkus,
2021).
Yet we know little about the policy implications of this uneven distri-
bution of different types of wealth acrossandwithincountries.Inthispaper,
we are particularly interested in how it affects support for the welfare state.
Most political economy models that consider material self-interest as the
foundation of social policy preferences are based on individualscurrent or
expected income (Meltzer & Richard, 1981;Romer, 1975;Rueda &
Stegmueller, 2019) and economic shocks that negatively affect peoples
incomes (Rehm, 2016;Rehm et al., 2012). More recently there has been an
increasing focus on how wealthboth illiquid housing equity and liquid
1960 Comparative Political Studies 56(13)
assets (Ansell, 2014;Hacker et al., 2014;Hariri et al., 2020),participation
in f‌inancial markets (Chwieroth & Walter., 2019;Margalit & Shayo, 2021),
and access to credit and indebtedness (Ahlquist & Ansell, 2017;Markgraf &
Rosas, 2023;Wiedemann, 2021,2022)inf‌luence peoples policy prefer-
ences, fairness perceptions (Scheve & Stasavage, 2016), and their incentives
to opt out of public goods provision in light of private alternatives
(Busemeyer & Iversen., 2020). These arguments have helped advance our
understanding of redistributive politics, but often fail to take the joint
distribution of income and wealth into account (see Fuller et al., 2020,foran
important exception). Assets are an important aspect of peoplessocial
policy preferences because they allow them to privately insure against the
negative consequences of economic shocks such as unemployment. People
with large amounts of assets may be less likely to supportthe welfare state, in
particular social insurance. What matters most, however, is how the ability to
self-insure is distributed across the income spectrum.
In this paper, we argue that wealth inequalityand in particular the degree
to which income and assets holdings are correlatedand the degree of asset
liquidity help explain variation in support for social policies across individuals
as well as variation in the size of the welfare state across countries. At the
micro level, we argue that the joint incomeasset distribution yields four
stylized groups with distinct social policy preferences. Most prior research has
focused on the f‌irst two groups: what we call the economically precarious
group with low incomes and low assets (which strongly supports the welfare
state) and the truly wealthygroup with high incomes and high assets (which
strongly opposes it). But the two cross-pressured groups have been largely
overlooked: those with high incomes and low assets (income buffered)or
low incomes and high assets (asset buffered). These groups are moderately
supportive of the welfare states insurance or redistribution function and play
an important role in welfare state politics. For example, high-income people
with few liquid assets have different social policy preferences than high-
income people with lots of private savings since the former are more vul-
nerable to income losses than the latter. Only focusing on income misses this
dimension.
These predictions have macro-level implications for the political coalitions
behind the welfare state. The sizes of the four groups vary considerably across
countries as a function of wealth inequality across income groups. The
correlation between income and assets determines the strength of the political
cleavages between economically precarious and truly wealthy voters. When
assets are unequally distributed across the income spectrum, such that low-
income people have few assets and high-income people have many, pref-
erences in favor of and against social policies are reinforcing and will result in
antagonistic redistributive politics and, ultimately, a smaller welfare state. By
contrast, a weaker correlation between income and assetsand a more
Jensen and Wiedemann 1961

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