Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465231166334
Journal of Health and Social Behavior
2023, Vol. 64(3) 336–353
© The Author(s) 2023
DOI: 10.1177/00221465231166334
journals.sagepub.com/home/hsb
Original Article
The inverse relationship between socioeconomic sta-
tus (SES) and mental health is a cornerstone of the
general argument linking social inequality to well-
being (Adler et al. 1994; Aneshensel 2009; Pearlin
1989; Thoits 2010). The SES–mental health associa-
tion is also one of the most consistent in social sci-
ence (Thoits 2010), spanning over a century of
research (Dohrenwend and Dohrenwend 1969; Faris
and Dunham 1939; Hollingshead and Redlich 1958;
Jarvis 1855; Srole et al. 1962) and continuing to the
present day (Miech and Shanahan 2000; Warren
2009). This consistency within countries, predomi-
nantly estimated in the North American context, sup-
ports a social causation interpretation, net of social
selection (Warren 2009; Wheaton 1978), indicating
SES as a primary social determinant of mental health.
However, cross-national comparisons across coun-
tries demonstrate that the strength of the association
between SES indicators, such as education, occupa-
tion, and income, and mental health outcomes differs
(Rai et al. 2013), implying contextual social determi-
nants may be driving differences in the effect of SES
between countries while also demonstrating the gen-
erality of the existence of this association across very
different national contexts (Präg, Mills, and Wittek
2016).
At the same time, attention has also focused on
the role of the institutional arrangements of welfare
state policies as generalized social determinants of
1166334 HSBXXX10.1177/00221465231166334Journal of Health and Social BehaviorParbst and Wheaton
research-article2023
1University of Toronto, Toronto, ON, Canada
Corresponding Author:
Matthew Parbst, Department of Sociology, University of
Toronto, 725 Spadina Avenue, Toronto, ON M5S 2J4,
Canada.
Email: matt.parbst@mail.utoronto.ca
The Effect of Welfare State
Policy Spending on the
Equalization of Socioeconomic
Status Disparities in Mental
Health
Matthew Parbst1
 and Blair Wheaton1
Abstract
This article examines whether and how the relationship between socioeconomic status (SES) and
depression is modified by welfare state spending using the 2006, 2012, and 2014 survey rounds of the
European Social Survey (ESS) merged with macroeconomic data from the World Bank, Eurostat, and
SOCX database (N = 87,466). Welfare state spending effort divided between social investment and social
protection spending modifies the classic inverse relationship between SES and depression. Distinguishing
policy areas in both social investment and social protection spending demonstrates that policy programs
devoted to education, early childhood education and care, active labor market policies, old age care,
and incapacity account for differences in the effect of SES across countries. Our analysis finds that social
investment policies better explain cross-national differences in the effect of SES on depression, implying
policies focused earlier in the life course matter more for understanding social disparities in the mental
health of populations.
Keywords
depression, social investment, social protection, socioeconomic status, welfare state

Parbst and Wheaton
337
health, often due to their effects on SES (Beckfield
et al. 2015; Ólafsdóttir and Beckfield 2020;
Reynolds 2021). To assess the effects of policy, we
utilize spending effort as an operational indicator of
policy differences, recognizing that policies may
exist with varying levels of funding and that the
hypothetical presence of a policy without support-
ive spending is likely to have little to no effect
(Castles 2008). Recent reviews demonstrate that the
spending effort in general areas of social policy is
associated with SES differences in mental health,
where increases in spending generally reduce SES
differences both between (McAllister et al. 2018)
and within countries (Simpson et al. 2021).
Between-country differences highlight the impor-
tance of contextual differences in policy environ-
ments for SES inequalities in mental health,
averaged over time, whereas within-country analy-
sis demonstrate changes in either the direction or
magnitude of specific policy effects over a given
period. Between-country studies find greater levels
of active labor market (Niedzwiedz et al. 2016) and
sickness benefit spending (van der Wel et al. 2015)
reduce educational differences in mental health.
Within-country analyses demonstrate that SES
inequalities are impacted by the direction of spend-
ing effort so that increases reduce and reductions
increase
inequalities
across
family
policies
(Lebihan and Mao Takongmo 2018; Milligan and
Stabile 2011), social assistance (Herbst 2013), and
old age security (Carrino, Glaser, and Avendano
2020).
Our approach incorporates both between- and
within-country effects of policy by using cross-
level interactions to understand if cumulative his-
torical realities or short-term changes through time
in policy investment have distinct and/or differen-
tial impacts on the association between SES and
mental health at the individual level (Bell and Jones
2015). Each taps a distinct realm of the role of state
policy. Between-country differences, partitioned
from within-country differences over time, reflect
overall averaged differences across countries that
persist through time. Between-country differences
allow for the identification of cumulative impacts
more straightforwardly. For example, the cumula-
tive effects of relatively stable levels of investment
in childcare could affect both the consequences of
having children and the expectation of support if
you do not but foresee this possibility. This scenario
suggests that policy investments can have indirect
effects on decision-making and experience at the
individual level, possibly encouraging entry into
the labor force. Providing access to labor force
entry can in turn reduce the worst consequences of
low SES for mental health. Within-country differ-
ences reflect the effects of changes in policy within
countries over time. In this case, recent changes in
access options may be as important as the level of
support, following the hypothesis that changes in
resources can affect well-being as much as or even
more than the level of resources (Davis 1984). In
our approach, both level of and changes in spending
are separated and captured as distinct possibilities.
An important issue this allows for is the case where
policy patterns between and within countries trend
in opposite directions, resulting in conflated and
counteracting forces that could mask the effects of
each. This could happen, for example, when coun-
tries with historically high levels of spending effort
in a particular social policy—resulting in a weaker
association between SES and mental health—begin
spending less over time, thus reactivating the link
between SES and mental health.
We argue that distinguishing cumulative histori-
cal levels of spending from within-country trends
provides a more holistic and thus complete theoreti-
cal understanding of how policy processes may
impact the relationship between individual social
status (or position) and mental health. Our approach
also implies that the impact of policy on mental
health is not ahistorical. It is important to resolve
whether the current state of linkages between social
statuses and mental health depend on current fund-
ing or the historical carry-forward of the norms set
in place by average levels of funding through time.
As such, we seek to add to the institutional per-
spective on the role of social policy in the associa-
tion between SES and mental health by asking:
What policies are capable of weakening that
association—and thus of reducing the mental health
risk for lower SES groups—both historically
between countries and via trends in spending over
time within countries? In general, we specify these
effects as different cross-level interactions in a
three-level multilevel model using an approach sug-
gested by Bell and Jones (2015) and Giesselmann
and Schmidt-Catran (2019).
In answering this question, we focus on both the
social protection and social investment policy para-
digms and their impacts on SES inequalities. First,
social protection policies seek to mitigate the
unforeseen and adverse life circumstances accom-
panying disadvantaged statuses by promoting a
minimum standard of living (Titmuss 1974;
Wilensky 1975). The main mechanism of social
protection policies is the transfer of income to indi-
viduals or households through either the traditional

338
Journal of Health and Social Behavior 64(3)
focal policies of the paradigm, such as pensions,
sickness, and unemployment benefits (Esping-
Andersen 1990), or the expanded policies of trans-
fers to families and welfare services, such as health
care and housing (Castles 2008; Esping-Andersen
and Myles 2009). Second, the social investment
paradigm focuses on policies that promote human
capital formation and increased household labor
market participation (Esping-Andersen et al. 2002;
Hemerijck 2017). Focal policies include early edu-
cation and care programs (ECEC), general educa-
tion, postsecondary education and vocational
training, active labor market policies (ALMPs), and
old age care (Kvist 2015). Both perspectives ulti-
mately focus on methods by which welfare states
reduce inequality and poverty (Brady 2009; Plavgo
and Hemerijck 2021) but highlight different path-
ways to achieve these outcomes. We argue that the
differences in the mechanisms involved in these
pathways may have differential consequences for
the link between SES and mental health. Social
investment policies focus on individual-level pre-
vention of pathways leading to inequality and pov-
erty, such as poor education and labor market
attachment. Social protection policies reflect reac-
tive policies and are more responsive to immediate
needs, for example, by transferring income or the
provision of services when individuals cannot meet
their needs from the labor market.
We focus on depression as the mental health
outcome. Even though we necessarily focus on a
single measure, variations in depression in a popu-
lation remain a central marker of differences in the
mental health of populations overall given its prev-
alence, its seriousness and chronicity as a problem,
and its overlap with associated mental health indi-
cators, such as anxiety (Kessler et al. 1995, 2005;
Mirowsky and Ross 2003). By studying depression,
we also focus on the state’s role in the alleviation of
suffering rather than the maximization of well-
being, which may be more volatile in general popu-
lations (Diener and Diener 1996) and also depends
more heavily on interpersonal rather than macro-
social inputs (Diener et al. 1999; Veenhoven 2000).
We assess each component of SES on mental
health both across general policy processes of the
welfare state and by disaggregating each of the pro-
cesses to determine first, which components of SES
are affected most and second, which kinds of spe-
cific policies have the most significant impact. As
such, we argue our study makes three contributions
to the research tradition linking SES to mental
health. First, it assesses whether the relationship
between SES and depression varies significantly
across and within 21 modern welfare states.
Second, this study specifies and tests some of the
institutional policy mechanisms (indicated by
social investment vs. social protection spending
effort) utilizing cross-level interactions that allow
for the detection of differential policy effects on the
relationship between SES and depression and that
are beyond what can be considered in a main effects
analysis of SES differences (Bell and Jones 2015).
Third, we disaggregate specific policies in these
policy paradigms to specify better which areas of
the welfare state matter most in reducing SES-
based disparities in mental health. Overall, our
results suggest that greater social policy spending
effort provides “cumulative” mental health advan-
tages (Thoits 2010) at the individual level.
Background
SES and Mental Health at the
Individual Level
Education is argued to be the primary causal force
linking SES to mental health across the life course
(Mirowsky and Ross 2007). A central role of educa-
tion in the production of mental health is the social-
ization of beliefs and skills that together result in
higher levels of important personal coping
resources, such as cognitive flexibility or a sense of
control over life outcomes (Ross and Mirowsky
2010; Wheaton 1983). Those with high education
tend to have more complex jobs, resulting in more
intellectual flexibility (Kohn and Schooler 1982).
Education also has indirect effects through its effect
on economic resources, such as workplace authority
and income, that help maintain better mental health
(Kessler 1982; Turner, Wheaton, and Lloyd 1995).
However, the mental health benefits to education
are not limitless. There are potentially diminishing
returns to education on mental health among the
highly educated, who receive low economic returns
relative to their educational attainment (Bracke, Van
De Straat, and Missinne 2014).
Whereas increasing education predominantly
places individuals in advantageous life circum-
stances beneficial for mental health (Miech and
Shanahan 2000; Ross and Mirowsky 2010), occupa-
tion (Kohn et al. 1990) and income (Lynch, Kaplan,
and Shema 1997) also may have independent effects
on mental health. Occupational status, for example,
reflects structural job conditions that have both
direct and indirect effects on mental health (Kohn
and Schooler 1973; Link, Lennon, and Dohrenwend
1993), including the mental health advantages of

Parbst and Wheaton
339
more complex, more creative, and more meaningful
work also involving greater control over the terms
of work relative to lower status jobs that are routine
and manual in nature (Kohn et al. 1990). Higher
incomes decrease depression by reducing levels of
financial strain and economic hardship (Mirowsky
and Ross 2003) but more broadly, by creating choice
and thus fostering a sense of mastery. Finally, SES
in general structures the availability of social
resources individuals may draw on (Thoits 1995),
particularly through the effect of income on access
to social capital (Song 2011).
Cross-National Welfare State Impacts
on SES and Generalized Health
Studies on the impact of welfare states on SES have
examined self-reported health (Bambra et al. 2009;
Bambra, Netuveli, and Eikemo 2010; Dahl and van
der Wel 2013; Eikemo et al. 2008), the World Health
Organization mental well-being index (van der Wel
et al. 2015), and depression (Hansen, Slagsvold, and
Veenstra 2017; Niedzwiedz et al. 2016). A majority
of the research in this area utilizes Esping-
Andersen’s (1990) three regime typology to test dif-
ferences in the welfare state’s impact of SES on
health (Bergqvist, Yngwe, and Lundberg 2013),
although studies focusing on social protection
spending find greater spending reduces educational
differences in self-rated health (Dahl and van der
Wel 2013) and mental well-being (van der Wel et al.
2015).
Two cross-national studies focus on the relation-
ship between education and depression. Niedzwiedz
et al. (2016) find a moderating effect of active labor
market spending on the association between levels
of education and depression. However, evidence for
this conclusion can be questioned because they do
not consider the random (unmeasured) effects of
countries or time due to the pooling of waves in
their sample, thus potentially conflating between-
and within-country processes. Finally, Hansen et al.
(2017) utilized separate ordinary least sqaures
regressions to calculate the odds of the risk of
depression for seniors (age 60–80) for 10 countries.
Although not testing any policies directly, they
observe smaller differences in the effect of educa-
tion on depression in socially democratic welfare
states and the largest within post-Soviet states.
Research Questions
We ask two main research questions and consider
the relative importance of social investment and
social protection in each of them. Our first research
question addresses considerations of the average
differences between countries and their effects on
mental health: Are enduring differences in the
spending levels between countries associated with a
weakening of the association between SES and
mental health?
This question emphasizes the importance of
considering average policy climates across time.
Policy levels are relatively stable over short periods
due to “path dependency” logic where removal or
reduction of benefits is contested (Pierson 2000).
We expect to find enduring differences between
countries that continuously spend higher versus
lower amounts on social investment and social pro-
tection spending. We argue that this expectation
reflects a “cumulative advantage” for mental health,
especially among individuals in societies where
continuous investment at high levels promotes
overall relative structural advantages that individu-
als may benefit from, especially for those with low
status. Whereas changes over time in a country test
the impact of the direction and magnitude of social
policy effects, the average context between coun-
tries assesses differences due to generalized differ-
ences in the levels of social policy that persist
through time (Thoits 2010).
Our second question focuses on the impact of the
direction and magnitude of changes in social policy
over time: Do changes in social investment and
social protection policies help account for variation
in the inverse association between SES and mental
health in countries over time? In assessing both
questions, we assess which of our two types of poli-
cies are most relevant to the weakening of the link
between SES and mental health and the specific
policies that may be doing so. Our data cover a rela-
tively short historical period (2006–2014). As a
result, this may hamper our ability to detect the full
impact of changing policies, especially because path
dependency logic suggests that change is incremen-
tal and takes time. Nevertheless, we contend that
explicitly separating between- and within-country
trends is important both conceptually and empiri-
cally (Bell, Fairbrother, and Jones 2019). This ques-
tion parallels the classic status versus change issue
in the literature on general well-being: Is it level of
income or changes in income that are most relevant
to well-being in general (Davis 1984)?
Both questions we address imply that some
combination of social investment and social protec-
tion spending will modify the SES mental health
association. We assess this by examining the mod-
erating impact of social investment and social

340
Journal of Health and Social Behavior 64(3)
protection spending and their component policies
for each component of SES on depression.
Data and Methods
Individual data were taken from the 2006, 2012, and
2014 survey rounds of the European Social Survey
(ESS) of representative samples of adults (restricted
to 18 and above). Rounds had to be specifically cho-
sen based on the availability of depression mea-
sures. Rounds varied in sample size from 35,210 to
42,351, and typical within-country samples per year
varied from 949 to 2,925 and were combined for a
total pooled sample of 113,078. Included in this
study were 21 countries: Austria, Belgium, Czech
Republic, Denmark, Estonia, Finland, France,
Germany, Ireland, Israel, Lithuania, Netherlands,
Norway, Poland, Portugal, Slovakia, Slovenia,
Spain, Sweden, Switzerland, and the United
Kingdom. Countries excluded from this analysis
were Bulgaria, Cyprus, Hungary, Iceland, Ukraine,
and the Russian Federation, either due to the
unavailability or quality of welfare state data or
inclusion in the ongoing ESS at only one point in
time. Using listwise deletion, we obtained a total
analytic sample of 83,091, with attrition occurring
mainly from occupation (6,588) and income mea-
sures (16,496).
Dependent Variable
Depression was an averaged index of eight survey
items taken from the widely used Center for
Epidemiologic Studies Depression Scale (CES-D;
Van de Velde et al. 2010). The eight CES-D scale
items used here load consistently on a single latent
construct and behave similarly across countries and
demographic groups (Van de Velde et al. 2010). We
adjusted averaged item scores to range from 0, or no
weekly symptoms of depression, to 24, which repre-
sents reporting the most severe response on every
survey item.1 Each CES-D item question asked
respondents to report on the last week only. Question
items included were: felt depressed, felt everything
you did was an effort, your sleep was restless, you
were happy, you felt lonely, you enjoyed life, you
felt sad, and you could not get going. Depression
had a Cronbach’s alpha of .84.
SES
Education was measured in years ranging from 0 to
25.2 We included a squared education term to account
for diminishing returns to education (Bracke et al.
2014). Household occupational status translated
ISCO-88 occupational codes in the 2006 survey
wave and ISCO-08 occupational codes in 2012
and 2014 surveys into the International Socio-
Economic Index of Occupational Status (ISEI) scale
(Ganzeboom and Treiman 1996) utilizing the updated
ISEI-08 coding (Ganzeboom 2010). The ISEI index
was applied to the respondent’s reported occupation
and, if indicated, their partner’s occupation returning
the highest score ranging from 1.10 to 8.90. Finally,
household income was calculated in deciles for both
survey years and ranges from 1 to 10.3
State-Level Moderators
Following Kuitto (2016), social investment was a
measure combining four areas of welfare state
spending reported as a percentage of total gross
domestic product (GDP): education, ECEC, ALMP,
and old age care. Education spending was extracted
from the World Bank (2021).4 ECEC, ALMP, and
old age care were extracted from the SOCX
Organization for Economic Cooperation and
Development (OECD) spending database (OECD
2021).5 Also following Kuitto (2016), we measured
social protection spending as a percentage of GDP
of total cash transfers spending on incapacity, old
age, survivors, unemployment, and other social
exclusion spending, taken from the SOCX database
(OECD 2021).6 However, we also included service
spending on health care and housing in this cate-
gory, also taken from the SOCX database, due to its
effects on poverty and inequality and thus effects on
a minimum standard of living (Esping-Andersen
and Myles 2009). We present the disaggregation of
each policy paradigm in Table 1.
Controls
Controls in this analysis included confounders not
on the causal pathway between individual SES and
depression, such as age and gender (Mirowsky and
Ross 2003) and father’s educational status, to incor-
porate the intergenerational transmission of SES. At
the country level, we controlled for economic devel-
opment as measured by GDP per capita based on
purchasing power parity (divided by 1,000) in con-
stant 2011 international dollars taken from the
World Bank (2021) and income inequality, as mea-
sured by the GINI coefficient for household market
income, ranging from 0 to 1 where 1 equals com-
plete inequality (Solt 2019).7 Both country mea-
sures impact psychological well-being (Wilkinson
and Pickett 2010). Finally, we estimated the effects

Parbst and Wheaton
341
of each category of spending, controlling for the
other category in the same model. Thus, for exam-
ple, if we estimated the effect of social investment in
general or a component of social investment (educa-
tion spending), this was in the presence of the asso-
ciated effects of social protection.
Finally, we considered each ESS round as a con-
trol allowing for the observation of secular changes
in our dependent variable across time.
Analysis
We employed three-level hierarchical linear models
(HLMs) to answer our research questions, incorpo-
rating both between-country averaged differences
over time and within-country trends. In these mod-
els, the individual was Level 1, the country-year
was Level 2, and the country was Level 3
(Giesselmann and Schmidt-Catran 2019). Our ana-
lytic strategy addressed core issues in using multiple
cross-sectional, cross-national studies to estimate
differences between countries and over time within
countries using pooled time-series cross-sectional
data structures and small number of countries
(Schmidt-Catran, Fairbrother, and Andreß 2019).
We followed the approach of Giesselmann and
Schmidt-Catran (2019) to estimate our cross-level
interactions between spending and SES but
extended their model to include a random coeffi-
cient for SES at both the country (Level 3) and
country-year levels (Level 2) utilizing a within–
between coding structure (Bell and Jones 2015). We
did so because first, it is consequential for estimates
of Level 3 variables in the model (Heisig, Schaeffer,
and Giesecke 2017) and second, to account for
unobserved differences in the effect of SES on
depression both between and within countries (Bell
et al. 2019). The use of the three-level model along
with the between and within coding in HLM allowed
for the simultaneous estimation of both effects.
Our HLM strategy also accounted for small
cluster sizes (Level 3 N) by utilizing a Satterthwaite
approximation following Elff et al. (2021) given
our number of countries was 21. All models used
ESS poststratification weight. We describe a full
accounting for the model structure in Appendix A in
the online version of the article.
In our models, we distinguished “between”
from “within” effects. This distinction had a some-
what different meaning applied to state-level policy
differences versus individual measures of SES. At
the state level, the between measure reflected the
average level of spending over time at the country
level, while the within measure reflected changes in
levels of spending in each country over time. At the
individual level, the between measure of SES in
effect controlled for the average level of SES over
time in each country, while the within measure
reflected the relative position of individuals on SES
relative to that mean. Including the between effect
of SES controlled for the compositional mean of
SES across countries. Individual differences in SES
were captured by the within measure—analogous
to a group-mean centered approach at the individ-
ual level, but with the group mean included in the
model (Hox 2010).
Robustness Checks
Following Schmidt-Catran et al. (2019) and Van der
Meer, Te Grotenhuis, and Pelzer (2010), we con-
ducted an outlier analysis and report the Cook’s D in
Appendix B in the online version of the article. All
countries with a Cook’s D value higher than 4/N
were removed from analysis to examine their influ-
ence on model results, and any model modifications
are noted in tables. Outlier model results are avail-
able by request.
Results
Descriptive Overview
Descriptive information in Table 2 highlights the
general trends of the combined sample. Beginning
with individual-level variables, depressive symp-
toms remain constant through Round 3 and Round 6
at 5.61 symptoms per week and decrease to 5.25 in
Round 7 with relatively consistent variation across
waves. Turning to controls, over half of the sample
is female in all three waves. From the 2006 to 2014
Table 1.  Social Investment and Social Protection
Policy Paradigm Overview.
Policy Paradigm
Disaggregated Policies
Social
investment
Early childhood education and
care (ECEC)
Education
Active labor market policies
Old age care
Social
protection
Family transfers
Unemployment transfers
Old age transfers
Survivors transfers
Incapacity transfers
Health
Housing

342
Table 2.  Descriptive Overview of European Social Survey Rounds 3, 6, and 7.
Round 3 (N = 24,702)
Round 6 (N = 29,950)
Round 7 (N = 28,439)

Mean or % (SD)
Range
Mean or % (SD)
Range
Mean or % (SD)
Range
Dependent variable
Depression
5.61 (4.01)
0–24
5.60 (4.12)
0–24
5.24 (3.96)
0–24
Individual-level variables
Controls
Female
52%
52%
52%

Father’s level of education
Above secondary
15%
19%
19%

Age
48.82 (16.90)
18–97
5.44 (17.09)
18–102
5.83 (17.23)
18–114
Focal independent variables
Education (years)
12.67 (4.12)
0–25
12.99 (3.98)
0–25
13.18 (3.93)
0–25
Household occupation
4.68 (2.18)
1.10–8.90
4.63 (2.20)
1.10–8.90
4.74 (2.22)
1.10–8.90
Household income (deciles)
5.61 (2.45)
1–10
5.31 (2.77)
1–10
5.35 (2.75)
1–10
Country-level variables
Controls
Gross domestic product (thousands)
39.85 (10.77)
18.27–63.85
37.57 (10.23)
23.22–63.00
40 (9.43)
25.31–63.42
Gini
.47 (.04)
.39–.52
.48 (.04)
.37–.52
.47 (.04)
.37–.52
Focal moderators
Social protection
18.47 (3.92)
11.91–25.45
19.73 (4.43)
13.30–28.92
2.06 (4.41)
13.23–28.97
Family cash
1.03 (.48)
.32–2.08
.98 (.61)
.20–2.43
.98 (.56)
.18–2.10
Unemployment
1.01 (.76)
0–3.16
1.09 (.98)
0–3.24
1.05 (.88)
0–3.24
Old age cash
6.67 (2.13)
2.40–10.33
7.34 (2.09)
4.20–12.20
7.78 (2.37)
4.12–12.25
Health
5.48 (1.44)
2.45–7.92
5.96 (1.54)
2.95–8.72
6.13 (1.60)
2.91–8.81
Survivors cash
1.04 (.76)
0–2.16
.91 (.76)
.01–2.34
.96 (.75)
.01–2.34
Incapacity cash
2.16 (1.04)
1.22–3.42
2.01 (.49)
1.53–3.31
2.00 (.54)
1.28–3.29
Housing
.43 (.44)
0–1.78
.54 (.73)
0–3.32
.41 (.39)
0–1.54
Social investment
7.29 (1.95)
5.03–12.31
7.57 (2.31)
4.89–12.90
7.67 (2.31)
5.01–12.93
Early education and care programs
.64 (.35)
.25–1.33
.75 (.37)
.32–1.58
.78 (.36)
.38–1.64
Education
5.37 (.90)
3.72–7.73
5.62 (1.11)
3.91–7.66
5.63 (1.12)
3.99–7.67
Active labor market policies
.69 (.33)
.07–1.46
.65 (.42)
.14–1.93
.69 (.43)
.13–2.03
Old age care
.56 (.65)
0–2.13
.54 (.65)
0–2.16
.57 (.66)
0–2.18

Parbst and Wheaton
343
waves, there is an increasing percentage of individ-
uals whose father’s education is above secondary,
ranging from 15% in Round 3 to 19% in Round 7.
The average age increased slightly, from nearly 49
years to around 51 years. Finally, SES trends show
both education and occupation slightly increasing
over time while income levels marginally decreased
on average.
Country-level controls are mostly stable across
waves except for GDP per capita. GDP per capita
declined in Round 6 and then increased beyond
Round 3 levels in Round 7. Gini remained rela-
tively constant across waves.
Social protection and investment spending
increased across this time, and a nontrivial amount
of spending differences exists across areas.
Comparatively, states spend more on social protec-
tion spending on average. Social protection spend-
ing also generally has the larger average increase
across waves, with a proportionate increase of
about 2% of GDP. In social protection spending, the
two largest areas of spending are old age cash ben-
efits and health. These two areas have driven the
overall increase in social protection because all
other programs remain relatively constant. Social
investment only marginally increased, with less
than a 0.5% increase over this period due to mar-
ginal increases in education and ECEC. All within-
country trends are in Appendix C in the online
version of the article.
Estimating the Role of State-Level Policies in
the SES–Depression Relationship
Table 3 examines the relationship between the three
indicators of SES and depression. Random coeffi-
cient models are presented to reflect the possibility
that the effect of SES varies across countries, based
on unspecified country-level differences. The test of
the random variation of coefficients for SES is a
logical first step that signals the possibility, or not,
of specific cross-level interactions. We also include
a country-year random slope that acknowledges
within-country trends.
Models for the fixed effects of SES, including
the random effects noted previously, are tested in
Models 1 through 3—one each for each component
of SES. In Models 1, 2, and 3, we build a model for
the fixed effects of SES by considering the standard
causal sequence for education, occupation, and
income (Sewell, Hauser, and Featherman 1976).
Thus, in the model testing the fixed and random
effects of occupation, we control for education and
include a random effect for occupation (Model 2),
and in the model from income, we control for both
education and occupation and include a random
effect for income (Model 3). The fixed effects in
these models include controls at the individual level
for the survey round,8 gender, age, and father’s
education.
Our focus in Model 1 is on the within and
between effects of education and the squared term
for the possibility of diminishing returns of educa-
tion. The focus of our research questions is on the
within measures at the individual level, while the
between measures capture aggregate differences in
average SES only but must be controlled. Table 3
shows that the within differences in education are
negatively related to depression, while the between
differences are not. This is consistent with expecta-
tion. State-level differences in SES are likely to be
smaller than individual differences within coun-
tries. The squared education term at the individual
level is also significant in the expected positive
direction, indicating a diminishing return to high
levels of education. This model also shows a sig-
nificant random slope component of the effect of
education between countries and within countries,
signaling that this association does vary across
countries, and this may signal important variation
in the effect of SES due to state-level differences.
Finally, the random effect for the squared education
term is only significant at the between-country
level.
Models 2 and 3 add occupational status and
income progressively to the education model. The
effect of occupational status before adding income is
notably larger than in the final model—reflecting
partial mediation by the effect of income—and
again, the random coefficients in this model are sig-
nificant, in this case, both between and within coun-
tries. Model 3 also indicates that there is a between
effect of occupation. Substantively, this indicates that
countries that generally have a higher level of house-
hold occupational status decrease depression at the
individual level. Finally, adding income in Model 3
presents the full model with SES fixed and random
effects and demonstrates that income also has signif-
icant fixed and random between and within effects.
Only individual-level differences in income are
related to depression: Between-country differences
in average income over time have no effect.
All SES components in this final model have
distinct negative associations with depression—no
one component absorbs the effects of other compo-
nents. This finding is important because analyses
only using one or two components will underesti-
mate the total effect of SES. Overall, Table 3

344
Journal of Health and Social Behavior 64(3)
Table 3.  Random Coefficient Models’ Fixed and Random Effects for Socioeconomic Status Predicting
Depression (N = 83,091).
(1) Education
(2) Household
Occupation
(3) Household Income
Fixed components
Intercept
8.248
(5.614)
9.116
(5.593)
8.149
(5.806)
Controls
  Round 6
−.255**
(.079)
−.297***
(.070)
−.242*
(.102)
  Round 7
−.424***
(.081)
−.488***
(.071)
−.414**
(.104)
  FemaleW
.837***
(.026)
.837***
(.026)
.713***
(.026)
  FemaleB
4.562
(5.985)
8.241
(6.005)
10.653
(6.825)
Father’s education
  Above secondaryW
.027
(.037)
.143***
(.037)
.186**
(.037)
  Above secondaryB
1.729
(2.514)
5.264
(2.739)
5.396
(3.234)
  AgeW
.008***
(.001)
.013***
(.001)
.008***
(.001)
  AgeB
.013
(.098)
−.048
(.096)
−.081
(.117)
Focal associations
  EducationW
−.436***
(.062)
−.289***
(.016)
−.247***
(.015)
  EducationB
−.716
(.510)
.485
(.498)
.368
(.526)
  EducationW
2
.011***
(.002)
.009***
(.001)
.008***
(.001)
  EducationB
2
.019
(.025)
−.024
(.024)
−.018
(.025)
  OccupationW
−.243***
(.019)
−.123**
(.008)
  OccupationB
−1.765**
(.538)
−1.481*
(.565)
  IncomeW
−.288***
(.020)
  IncomeB
.067
(.349)
Random components
Between country
  Intercept
.877*
(.430)
.311*
(.134)
.386*
(.202)
  Education
.071**
(.028)

  Education2
.000*
(.000)

  Occupation
.006**
(.002)

(continued)

Parbst and Wheaton
345
(1) Education
(2) Household
Occupation
(3) Household Income
  Income
.006**
(.003)
Within country
  Intercept
.051***
(.015)
.054***
(.016)
.087***
(.025)
  Education
.001*
(.000)

  Occupation
.002*
(.001)

  Income
.004**
(.001)
Note: All within-country effects are denoted by W, and all between-country effects are denoted by B. Data are from
Rounds 3, 6, and 7 of the European Social Survey.
*p < .05. **p < .01. ***p < .001 (two-tailed).
Table 3.  (continued)
demonstrates a consistency with SES findings in
the literature and the generality of its association
with mental health within countries. At the same
time, the significance of the accompanying random
effects for all components of SES also suggests the
possibility of cross-level interactions with state-
level policy differences.
Table 4 presents models that include both indi-
vidual-level and contextual effects and thus assess
both of our research questions. Results are pre-
sented separately for within versus between compo-
nents of both SES and spending, although again,
our focus is on the within effects. Each component
of SES includes two models: the first testing the
effect of social investment (SI) and the second test-
ing the effect of social protection (SP) while also
controlling for the other form of spending.
Results include controls for the contextual
effects of GDP and Gini. Except for the household
income models for SI and SP spending and the edu-
cation model for SI, the within effect of GDP per
capita is negative and significant, indicating that
increasing economic development levels across
time reduce depression. There are also negative
between effects for GDP per capita across all mod-
els. This indicates that countries with historically
higher GDP per capita also tend to have lower lev-
els of depression. However, counter to expecta-
tions, the effect of income inequality—as measured
by Gini—is not significant at either the between or
within level of analysis.
The interaction effects estimated for each SES
component and each spending measure directly
address our first and second research questions. We
consider first the interaction between differences in
SES and spending within countries, primarily to
address
potential
unobserved
confounding
(Giesselmann and Schmidt-Catran 2019). None of
these interactions are significant, as expected. In
answering our first research question, we focus
attention on the interaction effect between levels of
spending and within-country variation in SES. All
significant interactions in these results involve SI
spending. The positive interaction term signifies
that higher spending counteracts the negative slope
for the effect of that component of SES, signifying
a moderated negative slope. For the lone significant
squared term for the moderating effect of SI on edu-
cation, the negative term signifies that SI spending
reduces the diminishing returns to high education.
The most consistent result here is the interaction
between individual differences in SES and overall
social investment differences between countries
over time. This finding suggests that a general cli-
mate of social investment does reduce depression
differences between high and low SES at the indi-
vidual level. In results not displayed, we note that
all SI models explain away the negative depression
trend across waves, suggesting it to be an important
mechanism for societal well-being.
Regarding our second question, we fail to detect
any within-country trends in policy that mitigate
the effects of SES across the observed period in
these data. We noted earlier the possible problems
of detecting these sources of influence over delim-
ited time frames.

346
Journal of Health and Social Behavior 64(3)
Table 4.  Between and Within Moderating Fixed Effects of Social Investment and Social Protection
Spending on Socioeconomic Status and Depression (N = 83,091).
SES Component
Education
Household Occupation
Household Income
Welfare State Policy
SI
SP
SI
SP
SI
SP
Intercept
9.133
(5.365)
8.990
(5.335)
12.242*
(4.971)
12.291*
(4.905)
12.244
(5.469)
12.067
(5.46)
Individual level
Focal associations
  SES Componentw
−1.035***
(.159)
−.641
(.300)
−.411***
(.053)
−.307**
(.089)
−.497
(.052)
−.311**
(.099)
  SES componentb
−.303
(.480)
−.298
(.477)
−1.206
(.576)
−1.201
(.568)
.428
(.350)
.407
(.349)
  Education2
w
.026***
(.005)
.015
(.009)
.009***
(.001)
.009***
(.001)
.008***
(.001)
.008***
(.001)
  Education2
b
.007
(.024)
.007
(.024)
−.000
(.022)
.001
(.021)
.007
(.025)
.006
(.025)
Contextual level
Controls
  GDP per capitaw
−.087
(.043)
−.097*
(.045)
−.091*
(.042)
−.090*
(.041)
.020
(.065)
.006
(.065)
  GDP per capitab
−.067**
(.016)
−.068**
(.016)
−.053**
(.015)
−.053**
(.015)
−.051*
(.017)
−.051*
(.017)
  GINIw
5.092
(3.121)
4.868
(3.140)
3.716
(3.025)
3.332
(2.996)
3.934
(4.634)
3.623
(4.733)
  GINIb
1.561
(3.330)
1.551
(3.311)
−1.155
(3.334)
−1.135
(3.290)
.259
(3.789)
.118
(3.783)
Focal moderators
  Social investmentw
−1.676
(2.182)
−.038
(.111)
−.304
(1.559)
.004
(.098)
−1.388
(1.027)
−.120
(.153)
  Social investmentb
−.107
(.072)
−.118
(.070)
−.084
(.067)
−.104
(.065)
−.138
(.076)
−.119
(.076)
  Social protectionw
−.029
(.040)
−.248
(.294)
−.040
(.044)
.129
(.274)
.015
(.068)
−.041
(.257)
  Social protectionb
.022
(.037)
.028
(.037)
.004
(.033)
.007
(.033)
.012
(.037)
.011
(.037)
Interactions
  SESb × Policyw
.128
(.166)
.017
(.022)
.068
(.323)
−.036
(.061)
.220
(.180)
.002
(.050)
  SESw × Policyb
.082**
(.021)
.011
(.015)
.023**
(.007)
.003
(.005)
.029***
(.007)
.001
(.005)
  Ed2
w × Policyb
−.002*
(.001)
.000
(.000)

  SESw × Policyw
−.007
(.012)
−.002
(.004)
−.023
(.019)
−.007
(.006)
.025
(.023)
.001
(.007)
Note: All within-country effects are denoted by w, and all between-country effects are denoted by b. Data are from
Rounds 3, 6, and 7 of the European Social Survey. SI = social investment; SP = social protection; SES = socioeconomic
status; GDP = gross domestic product; Ed = education.
*p < .05. **p < .01. ***p < .001 (two-tailed).

Parbst and Wheaton
347
We display the moderating between-country
effect of social investment spending on the impact
of SES in Figure 1. This figure displays predicted
values of depression by levels of education, occu-
pation, and household income, respectively, at three
levels of SI spending—low, average, and high,
where “low” and “high” represent 1 SD below and
above the mean of all countries and average is the
mean for all countries. Each component of SES is
country-mean centered with standard deviation
units above and below the mean on the x-axis.
Examining differences between the three levels of
spending demonstrates a clear pattern that general-
izes across all components of SES: The slope for
the effect of SES is weaker as the level of social
investment increases. It is important to note that
this figure especially makes clear the fact that indi-
viduals face different levels of depression at the
same level of low status depending on the country
in which they live. Differences at high levels of
SES are not affected, and specifically, depression
levels are not worse due to state investments at high
levels of status. Put another way, the impact of
social policy here is where it is intended to be and
does not degrade mental health levels of the advan-
taged. Generally, this finding suggests that the con-
sequences of fundamental individual differences in
social status depend in part on social context.
Finally, we turn to Table 5 to assess our two
research questions across disaggregated social
investment and protection policies. We present a
table indicating whether between- and within-
policy differences interactions are significant for
the effect of SES.9 Each significant interaction is
noted by direction (±) and level of significance. In
SI policies, we find a consistent pattern of overall
between-country SI spending moderating the
effects of education, occupation, and income. There
are two exceptions: ALMPs do not reduce the posi-
tive slope for education squared and between ECEC
does not moderate the effect of household occupa-
tion. Social protection has much more limited influ-
ence on the SES–depression association: Only
incapacity spending moderates the effect of house-
hold income on depression.
Out of 24 possible models involving social pro-
tection spending, only one has significant interac-
tion effects in comparison to nearly all models of
social investment. These findings conform to our
expectations that living in countries with higher
levels of certain types of spending bestows a mental
health advantage for those who live there, specifi-
cally through the reduction of status differences in
mental health, and that SI spending specifically is
more consequential for this association across the
SES spectrum.
Discussion and
Conclusion
Our study contributes to the foundational history of
research on the association between SES and mental
health by incorporating an institutional perspective
of social investment and social protection spending
and applying it between and within countries cross-
nationally across time. This builds on earlier cross-
national studies (Niedzwiedz et al. 2016; Präg et al.
2016; Rai et al. 2013) using an analytic strategy that
explicitly accounts for between- and within-country
trends across time. In doing so, we find a significant
Figure 1.  Between-Country Moderating Effect of Social Investment Spending on Education,
Occupation, and Income on Depression.
Note: Data are from Rounds 3, 6, and 7 of the European Social Survey.

348
Journal of Health and Social Behavior 64(3)
within effect of each component of SES on depres-
sion at the individual level, adding to evidence con-
sistent with a social causation interpretation of this
association (Gangl 2010) outside of the North
American context, while also finding significant
policy effects that alter this association.
In answering our first research question, we find
evidence to support the notion that SI spending is
more consequential for the SES–mental health
association. Both generally and in terms of specific
disaggregated policies, SI reduces mental health
inequities between low and high status across all
components of SES. Of particular importance is the
effect of SI on the education and depression link.
We find—in addition to a general mitigation of the
implications of status differences—an impact of SI
that also linearizes the effect of education and thus
potentially extends its impact to both low and high
status. This finding contributes to other findings on
the nonlinear effects of education in Europe
(Bracke et al. 2014). Our findings also are consis-
tent with Niedzwiedz et al.’s (2016) finding of
ALMPs reducing educational inequalities in
depression, but we generalize this finding to other
forms of SI and across components of SES. Social
protection has a much more limited impact. The
lone significant SP policy that we find is the moder-
ating effect of incapacity cash transfers on the asso-
ciation between household income and depression.
Nevertheless, we demonstrate that more generous
welfare states matter for reducing mental health
inequalities and argue that this article is one of the
first to find direct evidence for Thoits’s (2010) con-
ception of cumulative advantage at the macro level
where individuals who live in high spending coun-
tries enjoy mental health advantages. Our evidence
also points to the importance of the timing of policy
interventions in the life course because SI policies
are typically more focused on formative stages of
the life course and less on later life.
Our second research question focusing on
within-country change in policies yielded little evi-
dence for the cross-level effects of policy. These
findings are in fact consistent with other within-
country studies on mental health that observe rela-
tively small changes, both positive and negative, in
the level of spending on mental health outcomes in
the population (Lee and Wolf 2014; Pak 2020). For
example, over the years we observe in this study,
spending remains relatively stable across most
spending areas in both SI and SP. However, one
benefit of our model specification is that we were
Table 5.  Disaggregation of Focal Between (B) and Within (W) Interaction Fixed Effects on Within
Effects of Socioeconomic Status (N = 83,091).
SES Component
Educationw
Educationw
2
Household
Occupationw
Household Incomew
Between and within
B
W
B
W
B
W
B
W
Social investment
  ECEC
X+**
X–**
X+***

  Education
X+**
X–**
X+**
X+**

  ALMPs
X+**
X+**
X+*

  Old age care
X+**
X–*
X+*
X+**

Social protection
  Family cash

  Unemployment

  Old age cash

  Health care

  Survivors cash

  Incapacity cash
X+*

  Housing

Note: X marks significant interactions and direction. All within-country effects are denoted by W, and all between-
country effects are denoted by B. Models adjusted for influence include: household income and ECEC (Denmark,
Finland, Poland), all components of SES and ALMPs (Denmark for education and household income, Poland and
Norway for household occupation), education and old age care (Poland), and household income and incapacity
(Denmark and Norway). Data are from Rounds 3, 6, and 7 of the European Social Survey. SES = socioeconomic
status; ECEC = early education and care programs; ALMPs = active labor market policies.
*p < .05. **p < .01. ***p < .001 (two-tailed).

Parbst and Wheaton
349
able to observe cross-national differences in the
effect of continuously high versus low levels of
spending between countries, which demonstrated
that SI policies are important for cross-national dif-
ferences. Generally, our null findings suggest
focusing on longer periods of time with greater
spending variation or on specific policies in coun-
tries that may be more historically volatile.
Our article indirectly speaks to a core theme in
the long literature linking SES to mental health.
Going back to at least Faris and Dunham (1939) and
including the classic community studies of the mid-
twentieth century (Hollingshead and Redlich 1958)
and a history of investigations on the causal interpre-
tation of this relationship (Warren 2009; Wheaton
1978), the emphasis throughout has been on consis-
tency of this relationship across time, measures, and
samples. This consistency of findings aligns with the
central premises of fundamental cause theory (Link
and Phelan 1995), with the exception of interest in
SES variation by gender and race (Jackson and
Williams 2006). However, most of this research is
based on North American findings.
Our study suggests that there is variability in the
strength of the link between SES and mental health
not only at the individual level (Kessler and
MacRae 1981) but also at the macro level of state
policy while not questioning the consistency of this
association overall. Our article adds another layer
to the discussion of SES differences in mental
health by considering the social context within
which SES differences occur. The link between
SES and mental health is not exactly immutable or
unchanging across social contexts: Clearly, it
depends on features of social context not usually
specified in the traditional literatures in sociology
addressing this issue.
Our results also have broader implications for
social policy research in general. By distinguishing
the two theoretical pathways of social protection
and social investment, we demonstrated that social
investment spending more dramatically reduces the
inverse link between SES and depression. At the
same time, we find no systematic evidence suggest-
ing that changes in the direction or magnitude of
spending (i.e., within-country trends) impact the
SES–depression association, either due to the gen-
eral or specific domains of either social investment
or social protection policy. In effect, it is the consis-
tent historical presence of spending that matters.
While our study answers important questions
about cross-national differences in the link between
SES and mental health, it also raises other questions
that need to be answered in further research. A
central question concerns the mechanisms through
which social policies reduce the linkage between
SES and depression. Given evidence that more gen-
erous welfare states reduce levels of economic
strain (Levecque et al. 2011), a plausible next step
is to investigate how the role of configurations of
stress histories and coping resources at the individ-
ual level that play out over the life course—follow-
ing the tradition of the stress process (Pearlin 1989;
Pearlin et al. 1981)—are impacted by social invest-
ment and social protection. There are, in fact, some
clues in the results in Table 4. Policies that target
ECEC and education have broad effects on reduc-
ing the mental health disadvantages of low educa-
tion, occupation, and income, suggesting that the
impacts of these policies may operate fundamen-
tally through increased access to educational oppor-
tunities, which is consequential for both labor force
placement and mobility and indirectly for income.
In addition, low status individuals may dispropor-
tionately benefit from ECEC and old age care in
part because of increased labor force attachment
among women (Boeckmann, Misra, and Budig
2015; Costa-Font and Vilaplana-Prieto 2022), in
turn indirectly increasing household income while
also providing women with mental health benefits
(Kessler and McRae 1981). ALMPs may further
support this earlier advantage. These policy pro-
cesses may in turn alter the distribution of chronic
stress exposures over lives (Turner et al. 1995) and
the development of a sense of control or mastery
(Mirowsky and Ross 2007) within countries.
Our study is not without limitations. Due to
restrictions caused by the specific waves of the ESS
that included the measurement of depression, we
have limited waves of data. Ideally, we would want
more than the three waves available for analysis
and/or more widely spaced waves. For example, it
is recommended to have at least four or more waves
to detect the complex within-country effects under
investigation in this article (Giesselmann and
Schmidt-Catran 2019). In addition, our measures
do not deal with variation in policy delivery,
whether targeted or universal (Brady and Burroway
2012), that may more directly affect depression.
Our measures of policy also do not include mea-
sures of duration of benefits or their generosity for
each type of policy (Scruggs and Allan 2008),
which is demonstrated to affect mental well-being
(Glass, Simon, and Andersson 2016). Finally, varia-
tion in the effect of spending effort during this
period itself may have been due to other factors
such as the 2008 world financial crisis, an issue that
could partially confound observed associations.

350
Journal of Health and Social Behavior 64(3)
Despite these limitations, our analysis contrib-
utes theoretically and empirically to the growing
body of evidence that suggests social inequality in
mental health depends on the policy context one
lives within. Our findings also strongly suggest that
social policy may be another “fundamental cause”
of health inequalities (Beckfield et al. 2015) in
addition to previous focuses on individual-level
determinants (Link and Phelan 1995). Our findings
suggest that the reduction of human suffering is
indeed within the control of policymakers, and as
such, considerations of policy outcomes should
incorporate the understanding that changing policy
may also mean changing the balance of suffering
across an entire society.
ORCID iDs
Matthew Parbst
 https://orcid.org/0000-0002-5949-6122
Blair Wheaton
 https://orcid.org/0000-0002-6705-4594
SUPPLEMENTAL MATERIAL
Appendices A through D are available in the online version of
the article.
Notes
  1.	 Individuals who had more than four items of the
index missing were treated as missing at random
and deleted.
  2.	 Top coded at the 99.5% of the sample distribution
due to extreme values.
  3.	 Because of different codings of household income
between the 2006 and 2012 and 2014 waves, 2006
was coded into deciles through collapsing income cat-
egories into the appropriate distributional properties.
The same procedure is used in Glass et al. (2016).
  4.	 Belgium 2012 was extracted from the Eurostat data-
base. Britain 2012 is derived by averaging across
2011 and 2013 data points.
  5.	 Data extracted in 2017.
  6.	 All data available by request.
  7.	 Original scale was divided by 100.
  8.	 Survey wave is appropriate in this context com-
pared to each survey year because there is no sig-
nificant differences between years within each wave
respectively.
  9.	 Full models are in Appendix D in the online version
of the article.
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Author Biographies
Matthew Parbst is a PhD candidate in the Department of
Sociology at the University of Toronto. Mr. Parbst’s
research interests include social policy, social determi-
nants of health, and comparative research. His current
research focuses on examining how social policy modifies
individual-level social determinants of health relationships
with mental health in the cross-national perspective.
Blair Wheaton is currently the S.D. Clark Professor of
Sociology at the University of Toronto. He received his
PhD from the University of Wisconsin in 1976 and taught
at Yale University and McGill University before moving
to the University of Toronto in 1989. He was the first
recipient of the Leonard I. Pearlin Award for Distinguished
Contributions to the Sociology of' Mental Health in 2000
and was elected to the Sociological Research Association
in 2010. His research focuses on both the life course and
social contextual approaches to understanding mental
health over multiple life stages. Currently, he is following
up the children from the Toronto Family Study 30 years
later to investigate the long-term consequences of growing
up in gender-egalitarian households on work, family, and
health outcomes.

    Policy Problem:

Answer: Severe and enduring inequalities in depression and other common mental-health conditions follow the socioeconomic gradient: people with less education, lower-status jobs and modest household incomes experience substantially higher symptom loads than their more advantaged peers. New cross-national evidence from 87,000 adults in 21 European welfare states shows that this gap is not immutable—it narrows dramatically in countries that consistently devote larger shares of GDP to social-investment programmes such as early-childhood education and care, schools and training, active labour-market measures, and publicly funded elder care, while it remains wide where spending is low or restricted to after-the-fact cash transfers. Yet average social-investment effort hovers around 7–8 % of GDP and has risen only marginally since 2006, leaving millions in low-SES groups exposed to preventable psychological distress that drives health-care costs, undermines labour-force participation and widens productivity and life-expectancy gaps. In short, current welfare mixes prioritise income maintenance over human-capital formation, and this policy imbalance perpetuates avoidable mental-health inequalities.