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

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465221109202
Journal of Health and Social Behavior
2023, Vol. 64(1) 2–20
© American Sociological Association 2022
DOI: 10.1177/00221465221109202
journals.sagepub.com/home/hsb
Original Article
The United States continues to have unenviable
population health. U.S. life expectancy stagnated
for five years (2010 to 2014) and subsequently
declined for another three (2015 to 2017)—an
unheralded decline among high-income nations
(Crimmins and Zhang 2019; Woolf and Schoomaker
2019). Underneath these troubling trends are sub-
stantial and increasing disparities across U.S. states
(Montez et  al. 2020; Wilmoth, Boe, and Barbieri
2011; Woolf and Schoomaker 2019). For instance,
in 2018, the difference in life expectancy between
West Virginia (74.7) and Hawaii (81.9) was a
marked 7.2 years—up from 4.3 in 2000. To under-
stand why the United States is fairing so poorly,
scholars are increasingly pointing toward macro-
level structural explanations (e.g., Beckfield and
Bambra 2016; Gutin and Hummer 2021; Torche and
Rauf 2021). Income inequality and public policy
represent two such structural factors because both
mirror national trends in life expectancy, are
strongly tied to population health, and exhibit wide
differences across states (e.g., Kaufman et al. 2020;
Montez et  al. 2020; Pickett and Wilkinson 2015;
Venkataramani, O’Brien, and Tsai 2021).
Income inequality and public policy are two
major axes of the United States’s sociopolitical
landscape that have experienced fundamental shifts
in recent years. Income inequality in the United
States has been rising for over half a century
1109202 HSBXXX10.1177/00221465221109202Journal of Health and Social BehaviorMcFarland et al.
research-article2022
1Florida State University, Tallahassee, FL, USA
2University of Texas at San Antonio, San Antonio, TX,
USA
3Syracuse University, Syracuse, NY, USA
Corresponding Author:
Michael J. McFarland, PhD, Department of Sociology
and Center for Demography and Population Health,
Florida State University, 113 Collegiate Loop, P.O. Box
3062270, Tallahassee, FL 32306-2270, USA.
Email: mmcfarland@fsu.edu
Income Inequality and
Population Health: Examining
the Role of Social Policy
Michael J. McFarland1
, Terrence D. Hill2
,
and Jennifer Karas Montez3
Abstract
Studies of the relationship between income inequality and life expectancy often speculate about the role
of policy, but direct empirical research is limited. Drawing on the neo-materialist perspective, we examine
whether the longitudinal association between income inequality and life expectancy is mediated and
moderated by policy liberalism in U.S. states (2000–2014). More liberal policy contexts are characterized
by greater efforts to regulate the economy, redistribute income, and protect vulnerable groups and lesser
efforts to penalize deviant social behavior. We find that state-level income inequality is inversely associated
with policy liberalism and life expectancy. The association between income inequality and life expectancy
was not mediated by policy liberalism but was moderated by it. The association is attenuated in states
with more liberal policy contexts, supporting the neo-materialist perspective. This finding illustrates how
states like New York and California (with liberal policy contexts) can exhibit high income inequality and
high life expectancy.
Keywords
income inequality, life expectancy, public policy

McFarland et al.
3
(McCall and Percheski 2010; Schaeffer 2020) and
now exceeds that in peer countries (Organisation for
Economic Cooperation and Development 2021). It
has grown to such an extent that the top 20% of U.S.
households acquired more income in 2018 than did
the bottom 80% (Schaeffer 2020). At the same time,
the United States has undergone a rapid period of
hyper-political divergence in the types of policies
enacted across U.S. states. Policy environments
among U.S. states are increasingly moving further
right or left on the political spectrum (Montez et al.
2020). Although the U.S. government distributes
policy-making authority across levels of govern-
ment, states increasingly define the policy context in
which Americans live (Grumbach 2018).
Researchers, however, know little about the
implications of their intersection vis-à-vis popula-
tion health. This article draws from the extensive
literature on income inequality and population
health. Two issues from this literature are particu-
larly relevant. First, why is income inequality asso-
ciated with decreased life expectancy? Second,
under which conditions is the association more or
less pronounced? Some work has attempted to
answer the first question by pointing to potential
pathways related to human capital investments,
social welfare programs, public services, health
care infrastructure, social capital, and health behav-
ior (e.g., Clarkwest 2008; Dunn, Burgess, and Ross
2005; Elgar and Aitken 2011; Elgar, Stefaniak, and
Wohl 2020). Other studies have considered the sec-
ond question by demonstrating that the association
between income inequality and life expectancy or
mortality appears stronger in places that have less
economic development, greater financial insecurity,
and fewer social welfare programs (Curran and
Mahutga 2018; Rambotti 2015; Ross et  al. 2000,
2005; Wilkinson and Pickett 2008).
The present study builds on this robust literature
by simultaneously considering mortality conse-
quences of state policy environments (i.e., principles,
decisions, and methods of action proposed and
selected by governmental bodies). Although “pol-
icy” is regularly mentioned as an implication of
research linking income inequality and population
health (e.g., implicating policies on taxation and
redistribution schemes), studies rarely theorize about
the functions of policies or use direct measures of
them. When policy is measured, assessments are
generally restricted to a single policy or a narrow
range of policies. Consequently, a more comprehen-
sive measure of the policy environment might help
elucidate the association between income inequality
and mortality. We build on previous research by
examining whether the longitudinal association
between income inequality and life expectancy is
mediated and moderated by a comprehensive assess-
ment of policy environments in U.S. states from
2000 through 2014.
Income inequality and
population health
Places with more inequitable income distributions
have lower life expectancies (e.g., Curran and
Mahutga 2018; Elgar et al. 2020; Hill et al. 2019;
Hill and Jorgenson 2018; Jorgenson et  al. 2020,
2021; Pickett and Wilkinson 2015; Thombs et al.
2020; Wilkinson and Pickett 2009; Zheng 2012).
These associations persist across indicators of
income inequality (e.g., Gini coefficient, income
share measures, and the Theil entropy index) and
longevity (e.g., life expectancy, infant mortality).
In response to early and credible concerns over
cross-sectional designs, modeling decisions, and
statistical rigor (Beckfield 2004; Gravelle 1998;
Mellor and Milyo 2001, 2003), more recent national
and international studies have employed longitudi-
nal designs and a range of advanced statistical tech-
niques (Curran and Mahutga 2018; Hill et al. 2019;
Hill and Jorgenson 2018; Jorgenson et  al. 2020,
2021; Liao and De Maio 2021; Thombs et al. 2020;
Zheng 2012). Evaluating the consistency in the lit-
erature, Kondo and colleagues (2009:7) concluded
that their “meta-analysis of cohort studies including
around 60 million participants found that people
living in regions with high income inequality have
an excess risk for premature mortality.” Less is
known, however, about why income inequality is
linked to worse population health.
There are three general theoretical perspectives
to explain why income inequality might undermine
population health: the psychosocial perspective, the
social capital perspective, and the neo-materialist
perspective. Although these theoretical perspectives
have been contrasted in the literature (Clarkwest
2008; Elgar and Aitken 2011; Lynch et  al. 2000;
McLeod et al. 2003), they “are not mutually exclu-
sive” and are “likely to reinforce each other”
(Lochner et al. 2001:390). The distinction between
these perspectives lies primarily in the types of causal
mechanisms purported to link income inequality to
population health.
The psychosocial perspective points to deleterious
effects of relative deprivation. Such deprivation may
contribute to widespread negative self-appraisals
(e.g., a sense of worthlessness from social compari-
sons), alienation (e.g., a sense of powerlessness

4
Journal of Health and Social Behavior 64(1)
from the subjective experience of stratification),
emotional distress (e.g., anxiety from a sense of
insecurity and anger from a sense of injustice), risky
coping behaviors (e.g., heavy alcohol consumption
and smoking through self-medication), and, over
time, the breakdown of the physiological systems of
the human body (Hill et  al. 2019; Wilkinson and
Pickett 2008). Lynch and colleagues (2000:1201)
explain “that income inequality affects health
through perceptions of place in the social hierarchy
based on relative position according to income” and
that “such perceptions produce negative emotions
such as shame and distrust that are translated ‘inside’
the body into poorer health via psycho-neuro-endo-
crine mechanisms and stress induced behaviors such
as smoking.”
The social capital perspective asserts that income
inequality may undermine interpersonal trust, social
cohesion, cooperation, and collective political
efforts to support vulnerable populations of society
through processes related to widespread status com-
petition and class resentment (Elgar et al. 2020; Hill
et al. 2019). In support of this perspective, income
inequality has been associated with lower levels of
group membership, social trust, and civic engage-
ment in the United States and other countries (Elgar
et al. 2020; Wilkinson and Pickett 2009).
The neo-materialist perspective posits that income
inequality weakens broader commitments to the
general interests of society by concentrating wealth
and power among elites through political capture
and the subsequent demoralization of voters from
lower social classes. This, in turn, undermines resis-
tance to neoliberal policies like tax cuts, deregula-
tion, and divestment in community resources and
social services that promote public health and safety,
such as education and environmental regulations
(Clarkwest 2008; Coburn 2004; Curran and
Mahutga 2018; Elgar and Aitken 2011; Hill et al.
2019; Jorgenson et al. 2020; Jorgenson, Schor, and
Huang 2017; Lynch et al. 2000; McLeod et al. 2003;
Neumayer and Plümper 2016). According to this
perspective, political units that have a high amount
of income inequality are less likely to invest in their
populations in terms of human, physical, cultural,
civic, and health resources.
Intersection of income
inequality and policy
contexts
We view the links between income inequality and
population health as working through or being
modulated by the public policy environment or
both. In discussing these issues, we conceptualize
the policy environment as occurring on a liberal to
conservative political continuum, consistent with
recent formulations in political science (Grumbach
2018). Liberal-leaning policies tend to expand eco-
nomic regulations and protect marginalized groups,
while conservative-leaning policies tend to endorse
traditional values and disapprove of state interven-
tion in markets (Grumbach 2018).
Policy Contexts as a Mediator of
Income Inequality and Health
The idea that income inequality might undermine
population health through policy mechanisms com-
ports with the neo-materialist and social capital per-
spectives (Clarkwest 2008; Curran and Mahutga
2008; Elgar and Aitken 2011; Lynch et  al. 2000;
McLeod et  al. 2003). Lynch and colleagues
(2000:1202) explain that income inequality and
related political processes “influence the private
resources available to individuals and shape the
nature of public infrastructure—education, health ser-
vices, transportation, environmental controls, avail-
ability of food, quality of housing, occupational
health regulations—that form the ‘neo-material’
matrix of contemporary life.” Although income
inequality has been shown to undermine public
spending on education, library books, income redis-
tribution, income assistance, food assistance (food
stamps), health insurance, and medical care
(Clarkwest 2008; Coburn 2004; Curran and
Mahutga 2018; Elgar and Aitken 2011; Neumayer
and Plümper 2016; Ross et al. 2000, 2005), there is
little empirical evidence to suggest that policies
related to medical care and public social expendi-
tures might mediate the association between income
inequality and life expectancy (Clarkwest 2008;
Elgar and Aitken 2011; Le Grand 1987; Wilkinson
and Pickett 2009).
Although there is little evidence of an indirect
effect of income inequality on life expectancy
through policy, this mediation model is theoretically
viable because policy is known to shape the social,
economic, and behavioral determinants of popula-
tion health (Bambra, Smith, and Pearce 2019;
Dawes 2020; Montez et al. 2017; Woolhandler et al.
2021). For example, U.S. state policy regulates
access to human capital by funding schools, income by
setting a minimum wage, prenatal care via Medicaid,
childhood nutrition through the Supplemental
Nutrition Assistance Program, tobacco by way of
excise taxes, and much more. For these reasons,

McFarland et al.
5
Bambra and colleagues (2019) aptly frame policies
and the political choices behind them as the “causes
of the causes of the causes” of geographic inequali-
ties in population health.
While many studies have isolated the health
benefits of specific policies, such as minimum
wage (e.g., Kaufman et al. 2020), earned income
tax credits (e.g., Strully, Rehkopf, and Xuan 2010),
and Medicaid (e.g., Bhatt and Beck-Sagué 2018),
others have focused on the health consequences of
overarching policy contexts (Beckfield and Bambra
2016; Bradley et al. 2016; Montez et al. 2020). For
instance, lower life expectancies in the United
States relative to other high-income countries are
largely explained by the miserly social policies of
the United States (Beckfield and Bambra 2016).
Furthermore, U.S. states with a more liberal policy
context have exhibited longer life expectancies
over a 45-year period than states with more conser-
vative policy contexts (Montez et al. 2020).
Liberal policy contexts are often linked to good
population health because they shape the material
and social determinants of health in salubrious ways
(Brennenstuhl, Quesnel-Vallée, and McDonough
2012; McCartney et  al. 2019). They are generally
aimed at solving enduring social problems that com-
promise the health and well-being of populations,
especially vulnerable populations. For instance,
environmental policies protect populations by limit-
ing exposure to toxins. Health and welfare policies
support the sick and poor through financial assis-
tance and the provision of needed services. Civil
rights policies empower socially marginalized
groups by legislating against widespread institutional
discrimination. Educational spending invests in the
human capital of workers and the future of state
economies. In addition, a body of research using
cross-national data shows that social democratic wel-
fare state regimes offer more social protection,
including health care access, and tend to have better
population health outcomes than their less liberal
counterparts (e.g., Beckfield and Bambra 2016;
Brennenstuhl et al. 2012). The next section discusses
potential synergies between state policy contexts and
income inequality in relation to population health.
Policy Contexts as a Moderator of
Income Inequality and Health
Although previous studies of income inequality and
population health have emphasized the potential
mediating influence of policy, the neo-materialist
perspective may be extended by also framing policy
as an effect modifier of inequality (Curran and
Mahutga 2018; McLeod et al. 2003). In addition to
asking how policy might help to mediate or explain
the association between income inequality and life
expectancy, we might also ask whether the associa-
tion between income inequality and life expectancy
varies across policy environments. This idea is gen-
erally supported by the fact that countries with more
generous policies (e.g., pensions, unemployment
insurance, health care, and social services) tend to
limit the degree to which populations are dominated
by the market forces of neoliberalism and broader
systems of social stratification (Bambra 2007).
Following these lines of research, we expect that
the association between state-level income inequal-
ity and life expectancy will be moderated or attenu-
ated in U.S. states with more liberal policy
environments. The idea is that state policy contexts
that invest in the general welfare may help offset the
processes through which income inequality erodes
population health. While income inequality tends to
undermine social equality, social capital, and mate-
rial resources, liberal policy environments tend to
support the equitable distribution of goods and ser-
vices (Arts and Gelissen 2002; Bambra 2007).
Our expectation derives from studies finding that
the magnitude of the association between income
inequality and population health differs across pop-
ulation subgroups and country contexts. In general,
the effect of income inequality on health appears to
be strongest for disadvantaged individuals and
countries with weak economic safety nets. For
instance, the association between U.S. state-level
income inequality and individual-level mortality is
more pronounced among near-poor whites than their
nondisadvantaged counterparts (Lochner et  al.
2001). Studies also demonstrate that the association
between country-level income inequality and coun-
try-level life expectancy is stronger at higher levels
of relative poverty (e.g., Rambotti 2015).
The second indicator is that income inequality is
often unrelated to life expectancy and other indica-
tors of population health in countries with more
comprehensive social programs (Coburn 2004;
Laporte and Ferguson 2003; McLeod et al. 2003;
Ross et  al. 2000, 2005). Ross and colleagues
(2000:898) speculated that “the lack of a significant
association between income inequality and mortal-
ity in Canada may indicate that the effects of
income inequality on health are not automatic and
may be blunted by the different ways in which
social and economic resources are distributed in
Canada and in the United States.” Coburn (2004:49)
echoed this sentiment, arguing that “social support
programs, from Medicare to education and social

6
Journal of Health and Social Behavior 64(1)
infrastructure” are likely to “reduce the effect of
income inequality on health in Canada more so than
in the U.S.” Ross and colleagues (2005:108) later
broadened the comparisons by noting that “the
identification of a relationship between inequality
and mortality within the United States and Great
Britain, and the absence for Australia, Canada, and
Sweden, provides some evidence for the idea that
there is no necessary association between income
inequality and population health.”
The third indicator is that the association
between income inequality and life expectancy can
be weaker in countries with greater economic devel-
opment. A study by Curran and Mahutga (2018)
found that the effect of income inequality was more
pronounced in the poorest countries. They explained
that “poorer countries with high inequality may
invest less in public goods; however, unlike high-
income countries with high inequality, they lack the
economic and administrative resources to maintain
infrastructure for all” (p. 539).
2000 to 2014.  The 2000 to 2014 time period is of
particular interest because it followed major pieces of
national legislation that made states more important
in shaping income inequality and increased levels of
inequality nationally. The passage of welfare reform
in 1996, for instance, resulted in state policies having
more power to influence income inequality. Indeed,
liberal-leaning states saw slower rates of growth in
income inequality after this time than their conserva-
tive-leaning counterparts (Kelly and Witko 2012).
Our time period also comes on the heels of major
pieces of tax legislation (e.g., the 1997 Taxpayer
Relief Act, the 2001 and 2003 Bush Tax Cuts) that
disproportionately benefited high-income taxpayers
and expanded income inequality (Center on Budget
and Policy Priorities 2017). Furthermore, state-level
policy contexts polarized considerably during this
time, especially after 2000 (Grumbach 2018:Figure
1; Montez et al. 2020:Figure 3). By 2014, the left–
right divergence in state policy contexts was at its
highest point since 1970.
Hypotheses
Based on previous theory and research, we formu-
lated three hypotheses to guide subsequent analyses.
Hypothesis 1: Income inequality will be
inversely associated with life expectancy.
Hypothesis 2: The inverse association between
income inequality and life expectancy will be
mediated by policy liberalism.
Hypothesis 3: The inverse association between
income inequality and life expectancy will be
attenuated in states with higher levels of policy
liberalism.
Data and methods
The data for this study included state-year observa-
tions for 2000 to 2014 for all 50 states. Our analytic
sample included 750 observations (15 years × 50
states). This study combined state-level data from a
variety of sources described below.
Measures
Annual data on life expectancy by state were
obtained from the United States Mortality Database
(https://usa.mortality.org/). Life expectancy is an
ideal indicator of overall population health because
it reflects age-specific mortality rates spanning all
ages for a particular year.
Our key indicator of income inequality was the
income share of the top 10% of earners based off
pretax gross income reported to the Internal
Revenue Service. These data were obtained from
the U.S. State-Level Income Inequality Database,
developed by Mark Frank (2015) and constructed
from individual tax filing data available from the
Internal Revenue Service. From the same source,
we also obtained alternative measure of income
inequality to use in supplemental analyses. These
measures included the share of the top .1%, 1%,
and 5% of income earners and the Gini index.
The main source of data on state policies came
from Grumbach (2018). These measures, shown in
Table 1, contained 135 policies spanning 16 domains:
abortion, campaign finance, civil rights and liberties,
criminal justice, education, environment, gun control,
health and welfare, housing and transportation, immi-
gration, private sector labor, public sector labor,
LGBT rights, marijuana, taxes, and voting. Evidence
on how these policy domains are plausibly linked to
life expectancy are described elsewhere (Kemp,
Grumbach, and Montez 2022). For each state, the
data contained a score for each domain annually from
2000 through 2014.
The scoring mechanisms for each policy domain
were established by Grumbach (2018). First, 135
individual policies were classified as liberal or con-
servative. Liberal was defined as the use of state
power to regulate the economy, redistribute income
and wealth, protect vulnerable groups, or limit the
government’s ability to penalize irregular social
behavior. Conservative was defined as the reverse.

McFarland et al.
7
For example, gay marriage bans were classified as a
conservative policy. Second, individual policies
were scored on a 0 to 1 scale during the 2000 to
2014 period, with values ranging from conservative
(low) to liberal (high). For instance, a state’s score
for medical marijuana policy is 0 in years when it
was illegal and 1 in years when it was legal.
Nondichotomous policies (e.g., K–12 spending)
were normalized across all state-year observa-
tions—giving a 0 to 1 range. Next, annual scores
Table 1.  U.S. State Policy Domains and Their Constituent Policies.
Domain
Policies
Abortion
Abortion insurance restriction, abortion legal, consent post-Casey, consent pre-Casey,
emergency contraception, gestation limit, Medicaid covers abortion, parental notice,
partial birth abortion ban, physician required, waiting required
Campaign
finance
Corporate contribution ban, limit on individual contributions (yes, no), dollar limit on
individual contributions ($), limit on political action committee (PAC) contributions
(yes, no), dollar limit on PAC contributions ($), public funding elections
Civil rights and
liberties
Bible allowed in public schools, corporal punishment ban, discrimination ban public
accommodations, Equal Rights Amendment (ERA) ratification, fair employment
commission, gender discrimination ban, gender equal pay law, moment of silence
in public school, no-fault divorce, physician-assisted suicide, public breastfeeding,
Religious Freedom Rights Amendment, reporters right to source confidentiality,
state Americans with Disabilities Act (ADA), state ERA
Criminal justice
Death penalty repeal, determinate sentencing, DNA motions, three strikes,
truth-in-sentencing
Education
Charter school law; Higher education spending; K-12 spending; School choice
Environment
Bottle bill, California car emissions standards, endangered species, e-waste,
Greenhouse Gas (GHG) cap, renewables fund, solar tax credit, state National
Environmental Policy Act (NEPA)
Gun control
Assault weapon ban, background checks (dealers), background checks (private), Brady
law, dealer licenses required, gun registration, open carry, Saturday night special ban,
stand your ground
Health and
welfare
Affordable Care Act (ACA) exchange, Aid to Families with Dependent Children
(AFDC) payment level, AFDC Up, Children’s Health Insurance Program (CHIP)
eligibility (children), CHIP eligibility (infants), CHIP eligibility (pregnant women),
expanded dependent coverage, Medicaid adoption, Medicaid expansion, Pre-Balanced
Budget Act CHIP eligibility, senior prescription drugs, Temporary Assistance for
Needy Families (TANF) eligibility, TANF payment level, welfare drug test, welfare
time limit
Housing and
transportation
Growth management, Lemon law, rent control ban, tort limit
Immigration
Drivers’ licenses for undocumented, English official language, e-verify, e-verify ban, in-
state tuition for undocumented, state cash benefits for recent immigrants, state food
benefits for recent immigrants, state health benefits for recent immigrants
Labor (private
sector)
Disability insurance; local minimum wage ban, local sick leave law ban, minimum wage,
paid family leave, paid sick leave, prevailing wage, right to work, unemployment
compensation
Labor (public
sector)
Ban on agency fees (state), collective bargaining (firefighters), collective bargaining
(local), collective bargaining (police), collective bargaining (state), collective
bargaining (teachers)
LGBT rights
Civil unions and marriage, gay marriage ban, hate crime law, LGB discrimination ban
public accommodations, LGB employment discrimination ban, sodomy ban
Marijuana
Marijuana decriminalization, medical marijuana
Taxes
Corporate tax rate, earned income tax credit, estate tax, income tax, sales tax, tax
burden, top capital gains rate, top income rate
Voting
Absentee voting, early voting, motor voter, voter identification

8
Journal of Health and Social Behavior 64(1)
for each policy domain were calculated as the sum
of the liberal policy scores minus the sum of the
conservative policy ones. The summed scores for
each domain were then standardized across states
and years to give a 0 to 1 scale.
This study included various state-level time-
varying control variables, including percentage
Black, percentage Hispanic, median household
income (in thousands of dollars), percentage of the
population between 18 and 64 years old, population
size, percentage of population with a college
degree, and percentage of the population that was
foreign born. Controls were chosen based on prior
inequality and health research (e.g., Hill et al. 2019;
Hill and Jorgenson 2018; Rambotti 2015). Median
household income, in particular, was chosen to iso-
late the association in the income inequality and life
expectancy from overall levels of income in the
population. Similarly, we needed to assure this
association did not exist because more educated
people live in places with lower levels of inequality.
Control variables were obtained from the following
sources: the U.S. Bureau of Labor Statistics Local
Area Statistics Project, U.S. Census Bureau Small
Area Income and Poverty Estimates, U.S. Census
Bureau Population and Housing Estimates, the
2000 Decennial Census, and the 1% American
Community Survey PUMS files.
Analytical Strategy
Our analytical strategy utilized regression models
with fixed effects for states and years. Fixed effects
control for omitted variables that are time-invariant
by examining variability within states rather than
between states. We used the xtreg command in Stata
along with the fe option to estimate regression mod-
els with fixed effects for years and states, with robust
standard errors clustered by state. The time fixed
effects were accounted for by the inclusion of year-
specific intercepts. Linear models with fixed effects
assume linear additive effects. All models accounted
for serial correlation using the vce (robust) command
in Stata. In the tables and text, we provide coeffi-
cients, their uncertainty (standard errors or confi-
dence intervals), and p value thresholds up to .10 to
improve transparency and reproducibility of our
results (Wasserstein and Lazar 2016).
The influence of income inequality on policy
and life expectancy likely has a meaningful tempo-
ral lag (Clarkwest 2008; Mellor and Milyo 2003;
Zheng 2012). While the fixed effects approach has
several advantages, particularly the ability to rule
out time-invariant sources of potential spuriousness,
its interpretation vis-à-vis temporal ordering is
unclear. Two-way fixed effects coefficients can be
interpreted as the average difference in intraunit
changes in the dependent variable at time point t for
each one-unit intraunit increase in the explanatory
variable at time point t, averaged across time points.
Because the model addresses change averaged
across time points, they tell us little about the tem-
poral lag needed for an independent variable to
influence an outcome. To partially address this
issue, we show both conventional fixed effects
results and results after incorporating a five-year lag
variable for income inequality and policy liberalism.
We chose a five-year lag because the only study
(that we are aware of) to systematically examine
how the association between inequality and mortal-
ity changes by the lag time found that the associa-
tion becomes pronounced at five years (Zheng
2012). To model life expectancy from 2000 to 2014
with five-year lags on income inequality and policy
liberalism, we also incorporated inequality and pol-
icy indicators from 1995 to 1999.
Our specific modeling strategy was as follows.
First, we examined how changes in income inequal-
ity predict changes in policy liberalism. Next, we
examined the relationships among income inequal-
ity, policy liberalism, and life expectancy with six
models. Using these models, we were able to assess
the extent to which policy liberalism mediates or
moderates the association between income inequal-
ity and life expectancy.
Using several models, we tested whether the
association between income inequality and life
expectancy is mediated by policy liberalism.
Previous studies using fixed effects (Anthony,
DiPerna, and Amato 2014; Huang, Oshima, and
Kim 2010) used the Sobel (1982) test of indirect
effects. Studies show that the Monte Carlo method
for assessing mediation (MCMAM) has greater sta-
tistical power than the standard Sobel test (Bauer,
Preacher, and Gil 2006; MacKinnon, Lockwood,
and Williams 2004). Unlike the Sobel test, which
assumes that the sampling distribution of the indi-
rect effect is normally distributed, the MCMAM
uses model estimates and asymptotic variances and
covariances to simulate the sampling distribution
for the indirect effect. For this reason, we employed
the MCMAM to formally test the indirect effect of
income inequality on life expectancy through pol-
icy liberalism.
The formula for the indirect effect is
E a b
ab
j
j
a b
j
j
(
) =
+σ
 (Bauer et al. 2006). The aver-
age indirect effect is a function of the average effect
of X on M (a), the average effect of M on Y (b), and

McFarland et al.
9
the covariance between the two effects (σa b
j
j). To
simulate the sampling distribution of the average
indirect effect, a multinormal distribution is defined
with means equal to a b and
a b
j
j

, ,
σ
 and covariance
matrix equal to the estimated covariance matrix of
these estimates. Random values from this multinor-
mal distribution are entered into the aforementioned
formula to estimate the average indirect effect.
Resampling provides a simulated sampling distri-
bution for the average indirect effect.
The MCMAM uses parameter estimates and
their asymptotic variances to simulate random
draws from the joint distribution of a (the regression
of policy liberalism on income inequality) and b (the
regression of life expectancy on policy liberalism)
and to compute the product of these values. This
procedure was repeated 20,000 times, and the result-
ing distribution of a × b was used to estimate a 95%
confidence interval (CI) around the observed value
of a × b (Selig and Preacher 2008). The procedure
required (1) the coefficient for the regression of pol-
icy liberalism on income inequality, or a (Table 3,
Model 2); (2) the coefficient for the regression of
life expectancy on policy liberalism, or b (Table 4,
Model 5); (3) the asymptotic sampling variance of
a; (4) the asymptotic sampling variance of b; and (5)
the covariance of a and b. The MCMAM output
provided a 95% CI for the indirect effect.
Lastly, we estimated several models to test for
moderation by including an interaction term between
income inequality and policy liberalism. This model
is of the following form:
y
x
z
x z
it
it
it
it
i
t
it
*
*
*
* *
,
=
+
+
+
+
+
β
β
β
1
2
3
w
ε

(1)
where subscript i represents each unit of analysis
(i.e., state-year), subscript t represents year, y is life
expectancy for each state at each year, x represents
the income share of the top 10% income earners, z
represents policy liberalism, ∝i is the state-specific
disturbance term, wt is the year-specific disturbance
term that is constant across all states, and ε is the
disturbance term unique to each state at each year.
The “*” represent deviations from state-specific
means where
y
y
y
it
it
i
* =
−
, x
x
x
it
it
i
* =
−
, and
z
z
z
it
it
i
* =
−
. β3 is of particular interest because it
tells whether the association between changes in
income inequality and life expectancy varies by
policy liberalism.
To better understand how the association
between income inequality and life expectancy var-
ies by policy liberalism, we plotted the results from
Equation 1 and calculated marginal effects of
income inequality at various levels of policy liber-
alism. Marginal effects are calculated by taking the
partial derivative of Equation 1. Equation 2 shows
that the income inequality–life expectancy associa-
tion will vary by policy liberalism when β3  ≠ 0.

∂
∂
=
+
y
x
zit
*
*
*
β
β
1
3

(2)
Marginal effects allowed us to quantify the asso-
ciation between income inequality and life expec-
tancy at various levels of policy liberalism. Marginal
effects were calculated using the margins dydx com-
mand in Stata.
Results
Table 2 presents the descriptive statistics for our
study variables, including key variables at various
points in time. It shows notable variation in our key
study variables. For instance, in the most equitable
state (Alaska), the top 10% of wage earners con-
trolled 33% of income, but in the least equitable
state (New York), they controlled 60% in 2014. The
reported means also increased over time for all three
of our key variables.
Table 3, Model 2 shows that increases in income
inequality from five years prior predict decreases in
policy liberalism, net of state fixed effects and the
time-varying covariates. While the coefficient for
income inequality is negative in both models, it is
not statistically different from zero in Model 1,
which underscores the importance of considering
past income inequality when predicting subsequent
state policy liberalism.
Table 4 presents the results examining life expec-
tancy and reveals three important points. First,
Model 1 shows that increases in income inequality
are associated with decreases in life expectancy (b =
−.031; p < .001) when nonlagged, while Model 4
shows this association for lagged income inequality
is not statistically significant (b = –.007; p = .347).
The former association corresponded with a standard
deviation increase in income inequality being associ-
ated with a .15-year decrease in life expectancy. This
estimate was found by standardizing our income
inequality measure and rerunning Model 1 (not
shown). Second, both the nonlagged policy liberal-
ism score (Model 2, b = 2.164, p = .07) and the
lagged score (Model 5, b = 2.143, p < .05) are associ-
ated with life expectancy, net of income inequality.
Third, the association between income inequality
and life expectancy did not show any meaningful
µ

10
Journal of Health and Social Behavior 64(1)
change with the inclusion of policy liberalism in
either the lagged or nonlagged models. This latter
point hints that the association between income
inequality and life expectancy is not mediated by
policy liberalism. We next performed a formal medi-
ation analysis to assess the veracity of this claim.
The mediation analysis used information pro-
vided in Tables 3 and 4. The direct effect of income
inequality on life expectancy using the lagged
regression models is –.007 (Table 4, Model 4), while
the indirect effect is the product of –.002 (Table 3,
Model 2) and 2.143 (Table 4, Model 5), or –.004.
The total effect is the sum of the direct effect (–.007)
and indirect effects (–.004), or –.011. Figure 1 pres-
ents the uncertainty of these mediation estimates
using the MCMAM procedure. Given that the 95%
CI (–.007, .002) contains zero, our analysis supports
the null hypothesis of no mediation and suggests
that the indirect effect of income inequality on life
expectancy through policy liberalism is not likely
different from zero, 90% CI (–.006, .001). We also
tested our proposed mediation model using the tra-
ditional Sobel test, and the results of this test (z =
–1.50, SE = .003, p = .13) are also consistent with
the null hypothesis of no indirect effect. Evidence
for the null hypothesis is also found when estimates
from the nonlagged models are used (not shown).
Next, we examine whether policy liberalism
moderates the association between income inequal-
ity and life expectancy. Model 6 in Table 4 shows
that the association between lagged income inequal-
ity and life expectancy varies by policy liberalism
such that the association becomes weaker with
increases in policy liberalism (b = .142, p < .01).
Table 2.  Descriptive Statistics (N = 750).
Minimum
Maximum
Mean
Standard Deviation
Life expectancy–overall
73.39
81.70
77.92
1.70
Life expectancy–2000
73.39
79.51
76.87
1.45
Life expectancy–2007
74.38
80.60
78.01
1.59
Life expectancy–2014
74.93
81.70
78.73
1.70
Income share of top 10%–overall
32.97
62.17
44.13
5.12
Income share of top 10%–2000
33.27
58.93
43.58
5.82
Income share of top 10%–2007
36.08
62.17
46.02
5.32
Income share of top 10%–2014
32.97
60.42
46.10
5.31
Policy liberalism–overall
.18
.77
.45
.15
Policy liberalism–2000
.19
.63
.38
.13
Policy liberalism–2007
.20
.71
.42
.15
Policy liberalism–2014
.15
.74
.43
.18
Percentage Black
.31
37.51
10.51
9.53
Percentage Hispanic
.68
47.67
9.77
9.64
Percentage foreign born
27.00
1.00
8.21
5.98
Median household income (in thousands
of dollars)
30.19
73.85
47.87
8.63
Percentage population age 18–64 years
58.83
66.04
62.50
1.30
Total population (in thousands)
493.79
38,600.00
6,000.17
6,612.77
Percentage population college degree
10.71
30.56
18.81
3.99
Note: All variables are from state-level data from 2000 to 2014.
Figure 1.  Monte Carlo Distribution of Indirect
Effects of Income Inequality on Life Expectancy.

McFarland et al.
11
This pattern looks similar for the nonlagged model
(b = .096, p = .056). Furthermore, when alternative
measures of inequality are utilized, including the
income share of the top .1%, 1%, and 5%, the inter-
action term between income inequality and policy
liberalism remains significant at p < .05. To better
understand the finding from Model 6 in Table 4, we
examine these relationships graphically, in Figure 2,
and provide marginal effects.
Figure 2 shows how the association between
income inequality and life expectancy varies by pol-
icy liberalism based on the lagged model (Model 6 of
Table 4). Income inequality is more strongly
inversely associated with life expectancy among
states with relatively low levels of policy liberalism.
Among states with average to high levels of policy
liberalism, the association between income inequal-
ity and life expectancy is reduced to a statistical zero
throughout most of this half of the policy liberalism
distribution. Also intriguing, the combination of
high-income inequality and low policy liberalism
results in particularly low levels of life expectancy.
Life expectancies are all similar, and relatively high,
when inequality is low, as seen in the left side of
Figure 2. At higher levels of income inequality, this
pattern diverges, however. States with either low-
income inequality or high policy liberalism have rel-
atively high life expectancy, but states with both did
not have higher life expectancy than those with only
one. States with neither low inequality nor high pol-
icy liberalism had particularly low life expectancy.
Table 5 shows more precisely how the associa-
tion between income inequality and life expectancy
varies by policy liberalism. Income inequality is
most strongly related to life expectancy in states
with the lowest levels of policy liberalism. For
instance, in the lagged model, the coefficient for
income inequality when policy liberalism is at
the bottom 10th percentile is –.033 but is .005 at
the 75th percentile—more than a 100% increase.
Indeed, income inequality is not statistically associ-
ated with life expectancy by the 50th percentile
of policy liberalism. Overall, Table 5 presents evi-
dence that any harmful influence of income
Table 3.  Two-Way Fixed Effects Coefficients for the Regression of Policy Liberalism on Income
Inequality (N = 750).
Nonlagged
Lagged 5 Yearsa

Model 1
Model 2
Income inequality
  Income share top 10%
−.001
(.001)
−.002**
(.001)
Time-varying controls
  Percentage Black
.005
(.007)
.005
(.007)
  Percentage Hispanic (natural log)
−.143**
(.045)
−.150**
(.044)
  Median household income
.001
(.001)
.002
(.001)
  Percentage working age
.006
(.007)
.006
(.007)
  Total population (square root)
.000***
(.000)
.000***
(.000)
  Percentage college degree
.004**
(.001)
.004**
(.001)
  Percentage foreign born
.009*
(.003)
.008*
(.003)
Constant
.438
(.519)
.536
(.510)
R2
.452
.462
Note: Robust standard errors in parentheses.
aThese models also utilize measures of income inequality and policy liberalism from 1995 to 1999.
*p < .05, **p < .01, ***p < .001.

12
Journal of Health and Social Behavior 64(1)
inequality is smaller or nonexistent in states with
more liberal policy environments. In the following,
we probe this finding by disaggregating our mea-
sure of policy liberalism into specific policy
domains because it is plausible that a limited num-
ber of policy domains are driving these patterns
rather than the broader policy environment.
Table 6 shows the marginal associations between
income inequality and life expectancy for different
levels of each policy domain used to create the over-
all policy liberalism score using lagged models. Each
policy domain was included in the equivalent of
Model 6 in Table 4. At least two important points can
be gleaned from Table 6. First, it provides evidence
that our decision to measure policy liberalism as one
variable was a judicious one. Among the 16 policy
domains, 13 are consistent with the prior finding that
76.5
77
77.5
78
78.5
79
Life Expectancy
35
40
45
50
55
60
Income Inequality (Income Share of Top 10% Lagged 5 Years)
1st percentile
10th percentile
25th percentile
50th percentile
75th percentile
95th percentile
Policy Liberalism (Lagged 5 Years)
Figure 2.  Life Expectancy by Income Inequality
and Policy Liberalism Based on Model 6 from
Table 4.
Table 4.  Fixed Effects Coefficients Showing Life Exepctancy Regressed on Income Inequality and Policy
Liberalism (N = 750).
Nonlagged
Lagged by 5 Years

Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Independent variables
  Income share top 10%
−.031***
(.008)
−.030***
(.008)
−.069**
(.024)
−.007
(.008)
−.005
(.008)
−.064**
(.024)
  Policy liberalism
2.164†
(1.171)
−2.341
(3.138)
2.143*
(.982)
−4.354
(2.873)
  Income share × Policy liberalism
.096†
(.049)
.142**
(.052)
Time-varying control variables
  Percentage Black
.020
(.096)
.009
(.090)
.001
(.092)
.039
(.099)
.027
(.098)
.020
(.098)
  Percentage Hispanic (natural log)
−.827†
(.453)
−.518
(.443)
−.338
(.435)
−.921†
(.481)
−.663
(.465)
−.402
(.446)
  Median household income
.003
(.013)
−.001
(.012)
.001
(.011)
.011
(.013)
.008
(.013)
.008
(.012)
  Percentage working age
.139*
(.064)
.125*
(.059)
.102
(.057)
.121†
(.069)
.112†
(.063)
.091
(.060)
  Total population (square root)
.002**
(.001)
.003***
(.001)
.003***
(.001)
.002*
(.001)
.002**
(.001)
.002***
(.001)
  Percentage college degree
.037**
(.013)
.029*
(.012)
.027*
(.012)
.040**
(.013)
.033*
(.012)
.034**
(.012)
  Percentage foreign born
.087*
(.038)
.069*
(.034)
.060†
(.034)
.091*
(.039)
.078*
(.038)
.069†
(.038)
Constant
65.039***
(5.067)
64.09***
(4.467)
66.945***
(4.536)
65.195***
(5.414)
64.425***
(4.860)
67.661***
(4.760)
R2
.903
.907
.909
.899
.902
.905
Note: Robust standard errors in parentheses. Year fixed effects not shown.
aThese models also utilize measures of income inequality and policy liberalism from 1995 to 1999.
†p < .10, *p < .05, **p < .01, ***p < .001.

McFarland et al.
13
the association between income inequality and life
expectancy is weaker with increases in policy liberal-
ism, although only 5 interactions are statistically sig-
nificant from zero at conventional levels. The
negative association of income inequality is largest in
magnitude within states having the lowest levels of
policy liberalism, and it decreases as the policy liber-
alism percentile increases. Civil rights, taxation, and
education were the only policy domains to not meet
this general pattern. Second, certain policies were
more important than others in understanding the
intersection between income inequality and life
expectancy. In particular, the interactions between
income inequality and several policy domains,
including marijuana legalization, environmental pro-
tection, gun control, LGBT rights, and health and
welfare, were particularly pronounced. For instance,
the association between income inequality and life
expectancy was –.025 for states at the 10th percentile
for environmental protection but .001 for those at the
75th percentile—more than a 100% change. In short,
the evidence in Table 6 supports our argument that
overall state-level policy liberalism is important for
understanding the association between income
inequality and life expectancy while also pointing to
the differential importance of particular policy
domains in these relationships. The marginal effects
when income inequality and policy domains were
nonlagged five years were consistent with the overall
patterns reported here and shown in Table A1 in the
Appendix.
Robustness and Sensitivity Analyses
We performed several ancillary analyses that
strengthened evidence for this finding. First, to
ensure that states with extremely low or high
income inequality were not driving our key results,
we ran several additional models that excluded
each state from the analysis. Table A2 in the
Appendix shows tests for moderation based on
Model 6 from Table 4 after excluding the eight
states with the highest and lowest levels of income
inequality in 2014. These states include New York,
Florida, Connecticut, Massachusetts, Alaska,
Hawaii, Iowa, and Nebraska. It shows that the same
pattern of findings reported previously. We also ran
these models after excluding the other 42 states (not
shown). These results were also consistent with our
reported findings. Second, we tested whether our
results were driven by our measure of income
inequality by reestimating our analyses using the
share of the top .1%, 1%, and 5% of income earn-
ers. We found that choice of these income inequal-
ity measures did not alter our substantive findings.
These results are available on request.
We also included the Gini coefficient as an
additional measure of income inequality. However,
our findings for this measure differed substantively
from our more reliable findings for the other mea-
sures of income inequality. Income inequality, as
measured by the Gini coefficient, shared a stronger
positive association with life expectancy in states
with higher levels of policy liberalism. This pattern
is not unprecedented in the literature. For example,
Neumayer and Plümper (2016) found that the Gini
coefficient was positively associated with life
expectancy inequality among developed countries.
Hill and Jorgenson (2018) showed that the Gini
index shares a different relationship with life
expectancy than other inequality indicators. These
authors explained that most income inequality
indicators capture the extent to which there is a
concentration of income among the wealthy (i.e.,
Table 5.  Partial Slope Coefficients for the Association between Income Inequality and Average Life
Expectancy as a Function of Policy Liberalism (N = 750).
Percentiles for Policy
Liberalism
Income Share and Policy Liberalism
Nonlaggeda
Income Share and Policy Liberalism
Lagged 5 Yearsb
1
−.049 (.015)**
−.037 (.015)*
10
−.047 (.014)**
−.033 (.014)*
25
−.042 (.012)***
−.024 (.011)*
50
−.032 (.008)***
−.008 (.008)
75
−.022 (.007)**
.005 (.008)
95
−.009 (.011)
.024 (.012)†
Note: Huber-White robust standard errors in parentheses.
aPartial slope coefficients were calcuated based on Model 3 in Table 4.
bPartial slope coefficients were calcuated based on Model 6 in Table 4.
†p < .10, *p < .05, **p < .01, ***p < .001.

14
Journal of Health and Social Behavior 64(1)
the upper end of the income distribution) relative
to the rest of the population, but the Gini coeffi-
cient, by contrast, deals with how much the actual
income distribution deviates from the Lorenz curve
and does not directly capture the specific location
in the distribution where inequality is occurring.
For this reason, the Gini coefficient is more sensi-
tive to changes in the middle of the distribution
than to changes at the highs and lows of the distri-
bution (Burns 2015). Because our framework
implicates the higher end of the income distribu-
tion as having a disproportionate influence on pop-
ulation health, the use of the Gini index is not
appropriate. For these reasons, we highlight the
theoretical and empirical consistency of our results
across several income share measures.
Third, we replicated our analysis using sex-
specific life expectancy measures. This ancillary
analysis was conducted because (1) others have
found gender differences in the relationship
between income inequality and state-level mortal-
ity (e.g., Hill and Jorgenson 2018) and (2) gender-
stratified structural exposures are known to shape
population health (Homan 2019). Our finding that
the association between income inequality and
life expectancy varies by level of policy liberalism
did not differ by sex (these results are available
on request).
Finally, it is plausible that the key associations
reported previously were due to changes in other
time-varying characteristics, particularly economic
growth, poverty rates, and mass liberal ideology.
We included a measure of gross domestic product
per capita in all of our models. The inclusion of this
variable did not have any meaningful impact on
regression estimates and did not change our sub-
stantive results. Similarly, we included an indicator
measuring the poverty rate after excluding our mea-
sure of median income, and the inclusion of this
variable did not change any of our substantive
results. Furthermore, we controlled for the interac-
tion between income inequality and the poverty
rate. This did not affect our substantive results
either. Finally, we utilized a measure of mass liberal
ideology developed by Tausanovitch and Warshaw
(2013) derived on the policy preferences of 275,000
Americans. The inclusion of this measure did not
change our substantive results, which is consistent
with the idea that our key results are driven by
social policy rather than the underlying political
and cultural beliefs of the broader population. All of
these results are available on request.
Table 6.  Marginal Effects Showing the Association between Income Inequality and Life Expectancy as a
Function of Specific Policy Domains (N = 750).
Policy Domains
Interaction
Term
Coefficient
Policy
Liberalism
at 1st
Percentile
Policy
Liberalism
at 10th
Percentile
Policy
Liberalism
at 25th
Percentile
Policy
Liberalism
at 50th
Percentile
Policy
Liberalism
at 75th
Percentile
Policy
Liberalism
at 95th
Percentile
1
Abortion
.047†
−.030†
−.023
−.018
−.006
.006
.011
2
Campaign finance
.027
−.019
−.013
−.012
−.006
−.004
.005
3
Civil rights
−.005
−.004
−.006
−.007
−.007
−.008
−.009
4
Criminal justice
.034
−.022†
−.015
−.015
−.008
−.002
.005
5
Marijuana
.025*
−.013
−.013
−.013
−.013
.000
.022
6
Environment
.069**
−.029**
−.025**
−.020*
−.012
.001
.026*
7
Guns
.054*
−.025**
−.020*
−.020*
−.014†
−.004
.023
8
Housing and
transportation
.003
−.007
−.007
−.007
−.007
−.006
.001
9
Immigration
.025
−.015
−.013
−.013
−.010
−.006
.001
10
LGBT
.086***
−.039***
−.039***
−.025**
−.011
.004
.025*
11
Voting
−.016
.000
−.002
−.004
−.005
−.009
−.013
12
Taxes
.029
−.019*
−.017**
−.008
−.004
.001
.007
13
Education
−.045
.010
.003
−.002
−.006
−.011
−.017
14
Labor
.050
−.018
−.017
−.015
−.002
.001
.008
15
Public labor
.014
−.017
−.017
−.014
−.014
−.004
−.002
16
Health and welfare
.117**
−.053***
−.039**
−.025**
−.015†
−.003
.013
Note: Estimates from 16 fixed effects models with individual policy domains included in the interaction between lagged
policy liberalism and lagged income inequality. Models included all study covariates.
†p < .10, *p < .05, **p < .01, ***p < .001.

McFarland et al.
15
Discussion
Although numerous studies have shown that income
inequality is associated with lower life expectancy,
little is known about the role of policy environ-
ments. In an effort to build on previous work, we
tested whether the longitudinal association between
income inequality and life expectancy was mediated
and moderated by a comprehensive assessment of
state-level policy environments in the United States.
Our first hypothesis proposed that income
inequality would be inversely associated with life
expectancy. This hypothesis received support in our
nonlagged models. These findings corroborate the
conclusions of other recent studies of income
inequality and life expectancy (Curran and Mahutga
2018; Hill et  al. 2019; Hill and Jorgenson 2018;
Jorgenson et al. 2020, 2021).
Our second hypothesis posited that the inverse
association between income inequality and life
expectancy would be partially mediated by policy
liberalism. We found that income inequality was
inversely associated with policy liberalism. We also
observed that policy liberalism was positively asso-
ciated with life expectancy. Because the product of
two these paths (i.e., the indirect effect) was small
and not statistically significant from zero, our Monte
Carlo analysis supported a null interpretation of no
mediation. There are several reasons why this find-
ing is important. It represents the first longitudinal
mediation test of the neo-materialist perspective and
does not support the widely cited idea that income
inequality undermines population health through
policy mechanisms (Clarkwest 2008; Curran and
Mahutga 2008; Elgar and Aitken 2011; Lynch et al.
2000; McLeod et al. 2003). With this in mind, future
mediation studies should focus more on mecha-
nisms that flow from the psychosocial and social
capital perspectives on income inequality (Kawachi
et al. 1997; Wilkinson and Pickett 2009).
Our final hypothesis stated that the inverse asso-
ciation between income inequality and life expec-
tancy would be moderated or attenuated in states
with higher levels of policy liberalism. Our modera-
tion analyses support this hypothesis, showing that
income inequality exhibited a weaker or nonexistent
relationship with life expectancy among states with
average to high policy liberalism. This finding helps
us to understand how states like Massachusetts,
New York, and California can rank high on both
income inequality and life expectancy. When con-
sidered along with our null mediation model, these
results suggest that the neo-materialist perspective
might benefit from focusing more on framing policy
environments as a modifier of income inequality
than as a mechanism of income inequality (Curran
and Mahutga 2018; McLeod et al. 2003).
Our finding that policy liberalism moderates the
association between income inequality and life
expectancy may help reconcile cross-national
income inequality research. While the bulk of work
examining income inequality and life expectancy
(or mortality) across countries finds a negative asso-
ciation (see Pickett and Wilkinson 2015), some do
not. In particular, the association sometimes disap-
pears after accounting for time-invariant sources of
spuriousness (e.g., Blázquez-Fernández, Cantarero-
Prieto, and Pascual-Saez 2018; Hu, van Lenthe, and
Mackenbach 2015; Leigh and Jencks 2007).
Because cross-national research frequently differs in
study design characteristics that are strongly linked
to policy environments, such as countries and years
of investigation, the inclusion of country-level pol-
icy environment indicators as moderators may rec-
oncile these disparate findings. Our results, if they
are applicable across countries, suggest that associa-
tion between income inequality and life expectancy
(1) will be weaker in more policy liberal countries
and (2) will weaken over time to the extent that
countries embrace a more liberal policy orientation.
These two phenomena may, therefore, be obfuscat-
ing the expected association. Future work should
address this issue.
The results of our final hypothesis also implore
future work to continue probing who is most and
least likely to be affected by inequality. One impor-
tant aspect of policy liberalism is concern for socially
disadvantaged or marginalized populations. To the
extent that vulnerable members of society dispropor-
tionately benefited from policy liberalism, we would
expect subpopulation differences in the link between
income inequality and life expectancy—particularly
those along socioeconomic and racial lines.
Our analyses should be considered in the con-
text of three limitations. Although models with
fixed effects provide rigorous statistical tests, these
models have several potential limitations (Hill et al.
2020; Kropko and Kubinec 2020). First and fore-
most, fixed effects approaches do not reflect causal
associations. While our modeling strategy allowed
us to account for time-invariant and several time-
varying sources of heterogeneity, including mass
political ideology in ancillary models, we cannot
exclude the possibility of unobserved time-varying
factors. All said, our key results were robust to a
variety of alternate modeling choices, and the most
likely sources of spuriousness were included in our
models.

16
Journal of Health and Social Behavior 64(1)
We also note that our null mediation test could
reflect the conservative nature of models with fixed
effects. Given that our mediation analysis tested
whether the product of two paths (the association
between changes in income inequality and changes in
policy liberalism and the association between changes
in policy liberalism and changes in life expectancy)
was different from zero, we may have lacked the sta-
tistical power to find evidence for mediation. Although
we are encouraged by several statistically significant
tests in our focal regression models, the power of our
mediation analysis could still be limited by modest
within-state changes in income inequality, policy lib-
eralism, and life expectancy.
Finally, we note that changes in states’ popula-
tion composition during the study period could
impact our results. For example, education levels (a
strong predictor of mortality) may have risen more
in some states than others, some states may have
experienced in-migration of healthier individuals,
and other states may have experienced out-migra-
tion of healthy individuals. Although these pro-
cesses should not be ignored when interpreting our
findings, prior studies indicate that they do not
materially affect our findings (Couillard et al. 2021;
Fenelon 2013; Montez et al. 2019). A recent study
examined state-level trends in adult mortality dur-
ing the 1992 to 2016 period and concluded that the
diverging trends are primarily due to changes in
states’ policy contexts, not to changes in the educa-
tion or income levels of their populations (Couillard
et  al. 2021). Another study examined state-level
trends in the association between education level
and mortality during the 1985 to 2011 period and
found that changes in the education–mortality
association within states were not due to trends in
interstate migration or immigration (Montez et al.
2019).
Conclusion
The United States experienced momentous shifts
in income inequality and state-level policy envi-
ronments in recent years. This study examined
how the confluence of these major dimensions of
the political economy shaped population health
and thereby filled a lacuna in income inequality
and health research. For nearly four decades, stud-
ies of income inequality and population health
have been speculating about the role of policy.
Although the original neo-materialist perspective
framed policy as a mediator, more recent scholar-
ship has framed policy as a moderator. Our analy-
ses clearly favor the more recent framing, showing
that more liberal state policies have the capacity to
completely offset the effects of income inequality
on life expectancy.
One policy recommendation has been to reduce
income inequality. Our findings suggest that invest-
ments in liberal social policies that support the gen-
eral welfare of the population may also limit the
degree to which populations are dominated by the
market forces of neoliberalism and broader systems
of social stratification. For instance, states with
both high life expectancy and income inequality
(e.g., Connecticut, New Jersey, New York) tend to
invest in health and welfare programs that pay pop-
ulation health dividends. Our work suggests other
high-inequality states might benefit from similar
investments.

McFarland et al.
17
Appendix
Table A1.  Marginal Effects Showing the Association between Nonlagged Income Inequality and Life
Expectancy as a Function of Specific Policy Domains (N = 750).
Policy Domains
Interaction
Term
Coefficient
Policy
Liberalism
at 1st
Percentile
Policy
Liberalism
at 10th
Percentile
Policy
Liberalism
at 25th
Percentile
Policy
Liberalism
at 50th
Percentile
Policy
Liberalism
at 75th
Percentile
Policy
Liberalism
at 95th
Percentile
1
Abortion
.007
−.033*
−.032**
−.031**
−.030***
−.028**
−.026*
2
Campaign finance
.014
−.037**
−.033***
−.033***
−.030***
−.029**
−.024*
3
Civil rights
.006
−.034
−.033*
−.032**
−.031***
−.030***
−.029**
4
Criminal justice
.017
−.038**
−.035***
−.035***
−.032***
−.028**
−.025*
5
Marijuana
.025*
−.037***
−.037***
−.037***
−.037***
−.019†
−.013
6
Environment
.087***
−.061***
−.055***
−.045***
−.029***
−.018*
.015
7
Guns
.057**
−.044***
−.044***
−.038***
−.035***
−.024**
.013
8
Housing and
transportation
.007
−.032**
−.032**
−.032**
−.030***
−.028**
−.026*
9
Immigration
.001
−.031**
−.031**
−.031**
−.031***
−.031**
−.031*
10
LGBT
.070***
−.059***
−.047***
−.047***
−.036**
−.012
−.001
11
Voting
−.042
−.012
−.017
−.021*
−.026**
−.035***
−.045**
12
Taxes
.012
−.033***
−.033***
−.029**
−.028**
−.025**
−.023*
13
Education
−.050
−.010
−.012
−.025**
−.027**
−.040**
−.043**
14
Labor
.035
−.044**
−.038**
−.037**
−.030***
−.025**
−.020†
15
Public labor
.018
−.039**
−.039**
−.036**
−.024**
−.021**
−.021**
16
Health and welfare
.092*
−.068***
−.045***
−.040***
−.031***
−.022**
−.007
Note: Estimates from 16 fixed effects models with individual policy domains included in the interaction between policy
liberalism and income inequality. Models included all study covariates.
†p < .10, *p < .05, **p < .01, ***p < .001.
Table A2.  Tests for Moderation After Excluding States with the Four Highest and Lowest Levels of
Income Inequality in 2014.
Income Inequality and Policy Liberalism are Lagged 5 Years
New York
Florida
Connecticut Massachusetts
Alaska
Hawaii
Iowa
Nebraska
State Excluded
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Income share top
10%
−.055*
(.023)
−.069*
(.025)
−.063*
(.025)
−.063*
(.024)
−.065*
(.025)
−.065*
(.023)
−.061*
(.024)
−.064*
(.024)
Policy liberalism
−2.699
(2.265)
−4.626
(2.944)
−4.163
(3.004)
−4.174
(2.969)
−4.484
(2.996)
−4.753†
(2.763)
−3.888
(2.934)
−4.196
(2.925)
Income share ×
Policy liberalism
.111*
(.047)
.150**
(.054)
.138*
(.055)
.140*
(.054)
.142*
(.054)
.146**
(.051)
.134*
(.052)
0.140*
(.053)
Note: Robust standard errors in parentheses.
Estimates based on fixed effects analyses with all study covariates (not shown).
†p < .10, *p < .05, **p < .01.
ORCID iDs
Michael J. McFarland
 https://orcid.org/0000-0001-8793-
3169
Terrence Hill
 https://orcid.org/0000-0003-3798-7753
Jennifer Karas Montez
 https://orcid.org/0000-0001-
9472-615X
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Author Biographies
Michael McFarland is an associate professor in sociol-
ogy and a faculty associate of the Center for Demography
and Population Health at Florida State University. His
recent research focuses on the adverse effects of policing
for health, early life lead exposure and mental health, and
recently, social policy and health. Recent publications can
be found in the Proceedings of the National Academy of
Sciences (PNAS), Social Science & Medicine, and Energy
Economics.
Terrence Hill is a professor of sociology at the University
of Texas at San Antonio. His research examines social
inequalities in health and human suffering. To date, he
has published 138 peer-reviewed manuscripts. His publi-
cations appear in a range of journals, such as the Journal
of Health and Social Behavior, Social Science &
Medicine, Society & Mental Health, The Journals of
Gerontology, The Gerontologist, American Journal of
Public Health, Labour Economics, and Social Work.
According to GoogleScholar, his work has been cited
6,891 times.
Jennifer Karas Montez is a professor of sociology,
Gerald B. Cramer faculty scholar in aging studies, director
of the Center for Aging and Policy Studies, and codirector
of the Policy, Place, and Population Health Lab at Syracuse
University. Her research investigates trends and disparities
in population health since the 1980s and the growing influ-
ence of U.S. state policies and politics on those outcomes.
A major focus of her work is understanding why health
trends are particularly worrisome for women, for people
without a college degree, and for those living in states in
the South and Midwest.

    Scientific Research Study Methods:

Answer: We assembled a balanced 2000–2014 panel of all 50 U.S. states (750 state-year observations) by linking state life-expectancy series from the U.S. Mortality Database to (i) Internal-Revenue-Service data on the income share captured by the top 10 percent of earners and (ii) a 0–1 composite “policy-liberalism” index that standardizes 135 statutes across 16 domains (tax, health-and-welfare, labor, environment, civil rights, etc.). We estimated two-way fixed-effects regressions—both contemporaneous and with five-year lags—that regressed life expectancy on income inequality, policy liberalism, and their interaction, adjusting for time-varying state sociodemographics (race/ethnicity mix, age structure, education, median household income, foreign-born share, and population size) and clustering errors by state. We then applied Monte-Carlo simulations of indirect effects to test whether policy liberalism mediates, and interaction terms to test whether it moderates, the inequality–longevity association. This design isolates within-state temporal shifts and enables us to determine whether more liberal, redistributive policy packages buffer the population-health harms of rising income inequality.