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Marital Happiness, Marital Status, Health, and Longevity



Married individuals are healthier and live longer than those who are never married, divorced, or widowed. But not all marriages are equal: unhappy marriages provide fewer benefits than happy ones. This study examined health and longevity across a nationally representative sample of U.S. adults, combining measures of marital status and marital happiness to compare those who were “very happy” in marriage to those who were “pretty happy” in marriage, “not too happy” in marriage, never married, divorced or separated, or widowed. We employed the General Social Survey–National Death Index to illuminate the associations among marital status, marital happiness, general happiness, and self-rated health and mortality risk. Compared to individuals who were “very happily” married, those who were “not too happy” in marriage were over twice as likely to report worse health and almost 40% more likely to die over the follow-up period, net of socioeconomic, geographic, and religiosity factors. Those not too happy in marriage also had equal or worse health and mortality risk compared to those who were never married, divorced or separated, or widowed. Results further indicate that general happiness underlies much of the relationship between marital happiness and better health and longevity. The literature on the health and longevity benefits of marriage is well established, but our results suggest that individuals in unhappy marriages may be a vulnerable population. We conclude that subjective well-being and relationship quality contribute to the health benefits of marriage.
Journal of Happiness Studies
1 3
Marital Happiness, Marital Status, Health, andLongevity
ElizabethM.Lawrence1 · RichardG.Rogers2 · AnnaZajacova3 ·
© Springer Nature B.V. 2018
Married individuals are healthier and live longer than those who are never married,
divorced, or widowed. But not all marriages are equal: unhappy marriages provide fewer
benefits than happy ones. This study examined health and longevity across a nationally
representative sample of U.S. adults, combining measures of marital status and marital
happiness to compare those who were “very happy” in marriage to those who were “pretty
happy” in marriage, “not too happy” in marriage, never married, divorced or separated,
or widowed. We employed the General Social Survey–National Death Index to illuminate
the associations among marital status, marital happiness, general happiness, and self-rated
health and mortality risk. Compared to individuals who were “very happily” married, those
who were “not too happy” in marriage were over twice as likely to report worse health and
almost 40% more likely to die over the follow-up period, net of socioeconomic, geographic,
and religiosity factors. Those not too happy in marriage also had equal or worse health and
mortality risk compared to those who were never married, divorced or separated, or wid-
owed. Results further indicate that general happiness underlies much of the relationship
between marital happiness and better health and longevity. The literature on the health and
longevity benefits of marriage is well established, but our results suggest that individuals
in unhappy marriages may be a vulnerable population. We conclude that subjective well-
being and relationship quality contribute to the health benefits of marriage.
Keywords Marital status· Marital happiness· Self-rated health· Mortality· General
happiness· General Social Survey· United States
1 Introduction
Married people are healthier and live longer than those who are single, separated, divorced,
or widowed (Dupre et al. 2009; Rogers 1995). The positive association includes better
mental health (Horwitz etal. 1996; Kessler and Essex 1982; Wadsworth 2015) and a range
* Elizabeth M. Lawrence
1 University ofNevada, Las Vegas, 4505 S. Maryland Pkwy, LasVegas, NV, USA
2 University ofColorado Boulder, Boulder, CO, USA
3 University ofWestern Ontario, London, ON, Canada
E.M.Lawrence et al.
1 3
of physical health factors, such as fewer health conditions and faster recovery from illness
(Umberson etal. 2006; Waite 1995). Yet there is heterogeneity in marital quality (Miller
etal. 2013). Unhappy, poorly-functioning marriages may be as harmful to health as happy
marriages are beneficial. What remains unclear is the impact on health and mortality risk
for marital status versus marital happiness: are unhappy marriages associated with better or
worse health than single, divorced, or widowed marital statuses?
Our study contributes to the literature on the health effects of marital status and marital
quality in several novel ways. First, we merge two bodies of research to shed light on health
and marriage: a population health perspective that highlights disparities across marital sta-
tus and a psychological approach that focuses on how marital quality shapes individual
health. We examine one indicator of marital quality—happiness in marriage—and its rela-
tionships to two important, widely used health outcomes: self-rated health (SRH) and mor-
tality risk. Although marriage has been linked to health and longevity, examining whether
and how much individuals benefit from very happy versus less happy marriages can clarify
why and under what conditions marriage protects individual health. We compare health
across different levels of marital happiness to being never married, divorced or separated,
or widowed. These comparisons provide insight into the role of marriage in the health of
In comparing the effects of marital status and happiness on health, moreover, we take
into account several important covariates that may influence their relationship, including
general happiness. General happiness could indicate the extent to which marital happiness
reflects general well-being. This is the first study, to our knowledge, to consider the com-
bined influence of marital status, marital happiness, and general happiness on health and
mortality among a representative sample of U.S. adults.
1.1 Marital Status andHealth
Numerous studies have documented better health among married than unmarried adults,
although the causality of the association has not been definitively proven. Healthier indi-
viduals are more likely to get and stay married (Fu and Goldman 1996; Goldman 1993;
Waldron etal. 1996). As the same time, being married has beneficial health effects, includ-
ing better SRH (Kane 2013; Lindström 2009; Liu and Umberson 2008; Rohrer etal. 2008)
and reduced mortality (Liu 2009). The protective effects work through the promotion of
healthy behaviors; the regulation of risky behaviors; increased material well-being, includ-
ing greater access to health insurance; and greater levels of social support and connections
(Carr and Springer 2010; Holt-Lunstad etal. 2010; Kane 2013; Rogers 1995; Rohrer etal.
2008; Umberson etal. 2010; Wood etal. 2007). Central to research on the health and lon-
gevity benefits of marriage is the “buffering hypothesis,” which asserts that individuals
with strong social support can better cope with stress, mitigating its health consequences
(Rook 1984). Social support is associated with improved cardiovascular, neuroendocrine,
and immune functioning (Robles and Kiecolt-Glaser 2003; Uchino 2006).
1.2 Marital Happiness andHealth
The effects of marriage on health differ depending on marital functioning (Kiecolt-Gla-
ser and Newton 2001; Gallo etal. 2003). Marital quality is associated with better physi-
cal health (Miller etal. 2013), better SRH (Bookwala 2005), and reduced physical illness
Marital Happiness, Marital Status, Health, andLongevity
1 3
(Wickrama etal. 1997). King and Reis (2012) found that marital quality was also associ-
ated with lower mortality among recipients of coronary artery bypass grafting.
Although happy marriages may buffer physiological responses to stressors, marriages
of poor quality may add to everyday and chronic stress. Problematic social interactions,
termed “social strain” by Rook (1990), evoke negative psychological and physiologi-
cal responses. Negative spousal behavior, such as being critical or hostile, is associated
with poorer health (Bookwala 2005), and marital stress is associated with poorer prog-
nosis among women with coronary heart disease (Orth-Gomer et al. 2000). In an experi-
mental setting, hostile marital interactions slowed wound healing (Kiecolt-Glaser et al.
2005). There is also evidence of effects of marital quality on mental health. For instance,
Ross (1995) reported that unhappy relationships were associated with the highest levels
of depression. Furthermore, researchers have found that individuals who exit poor quality
marriages fare better than those who remain in the difficult circumstances (Hawkins and
Booth 2005; Kalmijn and Monden 2006; Waite etal. 2009) or never enter them in the first
place: compared to those in unhappy marriages, never married adults have better psycho-
logical well-being (Williams 2003). These findings suggest that good marriages may buffer
stress whereas bad marriages may aggravate it.
1.3 Marital Status, Marital Happiness, andHealth
In sum, research has shown that those who are married, and among those who are mar-
ried, those with higher marital quality, have better SRH and longevity (Hawkins and Booth
2005; Robles et al. 2014). Because these prior studies examining marital quality either
used small, nonrepresentative samples, examined limited health outcomes, or included
only married couples, there is much to learn by comparing unhappy marriages to being
happily married or not being married. It is important to consider the role of marital happi-
ness in relation to the role of marital statuses, because the health advantages of marriage
compared to being single or separated may (or may not) be evident even among couples
with strained marriages.
1.4 The Role ofGeneral Happiness (and Other Covariates)
General happiness may be particularly important in the relationship between marital hap-
piness, marital status, and health and longevity. Happier individuals are healthier and
live longer (Diener and Chan 2011; Lawrence etal. 2015; Liu etal. 2016; Zajacova and
Dowd 2014). At the same time, general happiness and marital status are strongly corre-
lated (Proulx etal. 2007; Vanassche etal. 2013; Veenhoven 1994; Wadsworth 2015), as are
marital happiness and overall happiness and life satisfaction (Carr et al. 2014; Chapman
and Guven 2016; Dush etal. 2008). However, we do not know the extent to which correla-
tions between marital and general happiness shape health and longevity. General happiness
may underlie the effects of marital status and marital happiness on health and longevity. If
happiness in marriage leads to happiness in life, or vice versa, then general happiness may
account for the health benefits of happiness in marriage. In contrast, if marital and general
happiness have distinct mechanisms for health, we could find that marital and general hap-
piness have separate, independent effects.
We consider several other important covariates in disentangling the relationships between
marital status and marital happiness, and health and longevity in the general U.S. popula-
tion. Sociodemographic factors that may play an important role include age, gender, race, and
E.M.Lawrence et al.
1 3
parenthood status. We also account for socioeconomic status (SES; education, income, and
employment), geographic location, and religiosity. These potential confounders are related to
both marital status/happiness, as well as health and longevity. Prior research has shown sub-
stantial variation in health, mortality, and marital rates across geographic locations (Fenelon
2013; Kreider and Simmons 2003; Montez etal. 2017). SES is a known determinant of health
and mortality (Elo 2009) and also strongly linked to marriage. Finally, religiosity is associ-
ated with higher likelihood of marriage and higher marital quality (Mahoney 2010), as well as
health and longevity (Hummer etal. 1999; Koenig 2012).
1.5 The Present Study
This study examines the relationship between marital status and marital happiness with SRH
and mortality risk in a large, nationally representative sample of U.S. adults. It is important to
keep in mind that ours is an observational study that describes associations and may not indicate
causality. Given previous research on the relationship between marital quality and health, we
anticipate that the results will support an “aggravating” effect of unhappy marriages. We expect
that compared to individuals who were happy in their marriage, those who were unhappy in
their marriage will suffer worse health and shorter lives. We also anticipate that those who were
unhappy in their marriage will have poorer outcomes compared to those who were never mar-
ried, divorced or separated, or widowed. We base this hypothesis on studies reporting a par-
ticularly toxic effect of unhappy marriages, including those who report that men and women
who leave unhappy marriages improve their health (Hawkins and Booth 2005; Kalmijn and
Monden 2006; Waite etal. 2009) and observe better psychological well-being among never
married compared to unhappily married individuals (Williams 2003). Finally, because research
has indicated that marital status, marital happiness, and general happiness are associated, and
each of these has been shown to have important health effects, we expect that general happiness
will attenuate the effects of marital status and marital happiness, but that both general and mari-
tal happiness will have independent associations with health and mortality.
Our measure of SRH captures a broad range of mental and physical health conditions, and is
strongly related to subsequent morbidity and mortality (Jylhä 2011). But because SRH was col-
lected concurrently with the independent variables, it is impossible to draw definitive conclu-
sions about the direction of effects; furthermore, SRH may be subject to reporting differences
across gender and SES (Dowd and Zajacova 2010; Zajacova and Dowd 2011). We therefore
also examined mortality as an objective indicator of health. As the last event in a person’s life,
it takes on added significance and avoids issues of reverse causality. We expect SRH to be more
sensitive to marital happiness because it is measured at the same time as marital status and hap-
piness, whereas death occurs at a later time, with many potential intervening events that intro-
duce statistical noise and reduce the effects of the variables reported during the survey.
2 Method
2.1 Data
We used the General Social Survey (GSS), a nationally representative cross-sectional
sample of noninstitutionalized English-speaking adults (aged 18 and over) in the United
States. This survey began collecting information on individuals’ behaviors and attitudes in
1972, and continues to do so every other year. The GSS sampled households and randomly
Marital Happiness, Marital Status, Health, andLongevity
1 3
selected one household member to be interviewed. Surveys from years 1978 to 2002 were
linked to mortality information through 2008 from the National Death Index (NDI) in the
General Social Survey-National Death Index (GSS-NDI) dataset (NORC 2011; Muennig
etal. 2011a, b). We used surveys from years 1988 to 2002, a 15-year time span with the 10
most recent waves of data.1
Analytic samples As explained below, the samples for SRH and mortality analyses
were not identical. For mortality analyses, the GSS-NDI for years 1988–2002 included
21,045 individuals. In this group, 21 individuals were missing information on age and were
excluded from analyses because age is necessary to define duration in survival models. In
2002, only a random subsample were asked the question on marital happiness, excluding
an additional 614 individuals who did not receive the question, leaving a sample of 20,410
for the analyses of mortality. To reduce respondent burden, SRH was collected from a ran-
dom subsample of respondents in all years except 1998 (when all respondents received the
question) and an additional 57 people had missing data for this outcome, which resulted in
a sample of 15,385 individuals for the SRH analyses.
2.2 Measures
To measure SRH, the GSS asked respondents “Would you say your own health, in general,
is excellent, good, fair, or poor?”2 We dichotomized SRH as fair or poor versus excellent or
good health, following precedent (e.g., Kondo etal. 2009; Siahpush etal. 2008). For mor-
tality, a matching algorithm linked GSS respondents to the National Death Index (NDI).
Over the follow-up period, 4266 respondents from our sample died (see Muennig et al.
Our key independent variable combined marital status and marital happiness. Marital
status included four categories: married, never married, divorced/separated, and widowed.
Those who were married were then asked: “Taking things all together, how would you
describe your marriage? Would you say that your marriage is very happy, pretty happy,
or not too happy?” From these two questions, we created one variable with six mutually
exclusive categories: very happy marriage (referent), pretty happy marriage, not too happy
marriage, never married, divorced or separated, and widowed.
All multivariate models controlled for year of survey, gender, race, and parenthood sta-
tus (if the respondent had any children). Mortality models incorporated age into the dura-
tion variable, and SRH models included age as a covariate. Gender was a binary variable
(1 = male; 0 = female). Race consisted of three mutually exclusive categories: White (refer-
ent), black, and other.
Subsequent models also controlled for SES, geographic location, and religiosity. Educa-
tional attainment, income-to-needs ratio, and employment status captured SES. Education
was recoded into four categories: less than high school, high school, some college includ-
ing associate’s degree, and college degree or higher (referent). Income-to-needs ratio was
the ratio of the household’s income to the poverty threshold given by the U.S. Census for
that year and household size. Income-to-needs categories then represented whether the ratio
was below 100, 100–199, 200–299, or 300%+ (referent). Employment status comprised
1 We also estimated models with all available years (1978–2002); findings were comparable to those shown
2 We used this four-point scale because it is consistently available for all years analyzed here; the more con-
ventional five-point scale was not administered until 2002.
E.M.Lawrence et al.
1 3
four categories: working full-time (referent), working part-time, retired, and other (includ-
ing temporarily not working, unemployed, students, keeping house, and “other,” which were
merged due to small cell sizes).3 The nine U.S. Census divisions were: New England, Mid-
dle Atlantic, East North Central, West North Central, South Atlantic, East South Central,
West South Central, Mountain, and Pacific. The division with the smallest percentage of
deaths, the Mountain division, was used as the referent. We used religious attendance as the
most relevant measure of religiosity (Hummer etal. 1999; Musick etal. 2004), categorizing
attendance as: never attending religious services, attending services less than once a week,
attending services once a week, and attending services more than once a week (referent).
Finally, an additional important covariate was general happiness, which was assessed
with the question, “Taking all things together, how would you say things are these days—
would you say that you’re very happy (referent), pretty happy, or not too happy these days?”
2.3 Analytic Approach
We analyzed the relationship between marital happiness and fair/poor SRH using logis-
tic regression models. We started with a base model that included the mutually exclusive
marital status/happiness categories, survey year, age, gender, race, and parenthood status.
Then we added SES, geographic location, and religiosity sequentially to determine their
associations with health status and the influence of marital status/happiness net of these
other factors. We used F tests to test for differences between all pairs of coefficients associ-
ated with the marital status/happiness dummies.
We also examined whether results were sensitive to the operationalization of our out-
come and the modeling approach. Appendix Tables5 and 6 present results from models
that retained the four categories and were estimated using OLS and ordinal logistic regres-
sion models. The substantive conclusions from the models were similar. These two alterna-
tive approaches violate important distributional assumptions, and thus we present results
from logistic regression. Further, we discuss results in terms of odds ratios because it is
common practice in research examining SRH and can be used to compare results across
studies (e.g., Kondo etal. 2009).
To analyze mortality, we used Cox proportional hazards models, which are com-
monly used to identify the association between variables and the duration of time to a
certain event, such as death. Cox models do not estimate a baseline hazard (in this case,
risk of death over age), but rather distinguish increases or decreases in risk associated
with independent variables. In this study, Cox models examined the risk of death across
age through specifying time and duration. We used age at interview as the time vari-
able; duration was calculated as the time from the interview to death or 2008, the end
of the follow-up period. We handled failure ties (observations with the same survival
times) using the Efron method (Hertz-Picciotto and Rockhill 1997). Tests of propor-
tional hazards indicated that our main independent variable of interest (marital status
and marital happiness) did not violate proportionality, a key assumption of Cox models.
For mortality, we estimated a series of nested models with the same predictors as
for SRH. For both outcomes, we also estimated models that included general happi-
ness, both with and without the marital status/happiness variable.
3 Models where each of these categories were disaggregated rather than merged yielded similar findings to
those shown.
Marital Happiness, Marital Status, Health, andLongevity
1 3
We used multiple imputation to retain the full sets of available respondents. As
described above, the SRH and mortality analyses had different samples, and separate
imputation models and analyses were applied for each of the two samples. The imputa-
tions used all independent and dependent variables. No values were imputed for age,
gender, race, work status, geographic division, survey year, SRH, or mortality status.
For the mortality analyses, 0.3% of values were imputed for the marital status and hap-
piness categorical variable, 0.3% for parenthood status, 0.3% for education, 10.5% for
income-to-needs ratio, 1.8% for religious attendance, and 4.1% for general happiness.
For the SRH analyses, 0.3% of values were imputed for marital status and happiness,
0.2% for parenthood status, 0.2% for education, 10.3% for income-to-needs ratio, 1.7%
for religious attendance, and 3.6% for general happiness. We used a fully conditional
specification (FCS) approach with chained equations using the mi impute chained com-
mand (StataCorp 2015), creating ten datasets for both imputation models.
3 Results
3.1 Descriptive Results
Table1 presents the descriptive statistics for the sample and for each of the marital
status/happiness categories. Nearly half of the respondents were married, less than a
quarter were never married, and just over a quarter were divorced/separated or wid-
owed. The largest group was those who are married and very happy in their marriage
(30.1% of the sample). In contrast, a very small number of individuals were married
and not happy in their marriage (1.3% of the sample).
Just over 22% of respondents reported fair or poor rather than excellent or good
health. Greater proportions of fair or poor health were observed among those who were
widowed or in not too happy marriages; conversely, those who were never married or
in very happy marriages demonstrated smaller proportions of fair or poor health. Dur-
ing the follow-up, 21% of the respondents died. Among widowed respondents, over
53% died, and among those unhappy in marriage, nearly 25% did. However, we caution
that these bivariate patterns across the marital status/happiness categories could be due
to demographic and socioeconomic compositional differences, and thus the patterns
require multivariate analysis.
Additionally, the descriptive statistics illustrate bivariate relationships between
marital status/happiness and general happiness. The higher percentage of very happy
individuals in a very happy marriage compared to the other groups is striking. Only a
small fraction of individuals (3.0%) who were in a very happy marriage were generally
unhappy. Those who never married, divorced or separated, or were widowed reported
somewhat similar happiness levels.
3.2 Multivariate Findings
Table2 shows odds ratios from logistic regression models of fair or poor compared to excel-
lent or good health. The base model reveals that compared to those in very happy marriages,
people in all other marital status/happiness categories had higher odds of reporting worse
health, net of basic covariates. Those who were unhappy in their marriage had the highest
E.M.Lawrence et al.
1 3
Table 1 Percentage Distribution by Marital Status and Happiness Categories, U.S. Adults Aged 18 and Over (1988–2002)
All Very happy Pretty happy Not too happy Never married Divorced/separated Widowed
Population 30.1% 16.8% 1.3% 22.9% 18.5% 10.4%
Decedents 20.9% 20.1 19.0 24.9 10.3 18.6 53.1
Self-rated healtha
Excellent or good 77.9 84.5 76.8 70.6 81.9 74.2 59.3
Fair or poor 22.1 15.5 23.2 29.4 18.1 25.8 40.7
Sociodemographic factors
18–44 54.4 51.3 51.6 53.1 85.6 51.1 5.2
45–64 27.7 32.6 34.3 35.6 10.2 38.6 20.9
65+ 18.0 16.2 14.1 11.4 4.2 10.3 73.9
Male 43.7 49.9 45.8 37.6 50.5 37.8 18.4
White 81.6 89.0 84.5 75.6 72.3 78.0 82.7
Black 13.4 6.3 10.6 17.3 20.4 17.5 15.0
Other race 5.0 4.6 4.9 7.1 7.2 4.5 2.3
Is a parent 71.5 84.5 89.0 92.2 22.7 83.8 88.2
Socioeconomic status
Less than HS 19.4 15.3 18.4 22.1 16.4 19.2 39.8
High school 30.4 30.9 34.0 25.5 25.6 31.2 32.5
Some college 26.3 25.0 24.9 29.2 30.8 29.2 17.0
College degree 23.9 28.7 22.7 23.3 27.2 20.5 10.7
Marital Happiness, Marital Status, Health, andLongevity
1 3
Table 1 (continued)
All Very happy Pretty happy Not too happy Never married Divorced/separated Widowed
Full time 52.6 56.2 55.3 53.5 55.7 62.1 14.1
Part time 10.7 9.8 11.2 10.7 14.9 8.7 6.9
Retired 14.1 13.4 11.4 9.7 4.1 10.0 49.8
Other 22.6 20.6 22.0 26.1 25.3 19.1 29.1
Income-to-needs ratio
< 100% 15.5 5.7 7.7 14.2 23.5 21.5 27.8
100–199% 29.8 30.4 37.4 34.8 26.2 24.4 32.2
200–299% 32.8 43.2 39.6 37.3 25.0 26.1 20.4
300%+ 22.0 20.7 15.2 13.8 25.3 28.0 19.7
New England 5.1 5.1 4.9 5.2 5.4 4.3 6.3
Middle Atlantic 14.4 13.0 15.3 16.3 16.8 12.7 14.1
E. Nor. Central 17.2 17.3 17.7 18.0 16.1 17.1 19.0
W. Nor Central 8.0 7.9 8.2 6.3 8.8 7.1 8.0
South Atlantic 18.5 19.7 18.3 14.0 17.0 19.0 18.6
E. Sou. Central 7.3 7.6 6.7 7.0 5.5 8.3 9.5
W. Sou. Central 9.6 9.8 9.2 9.2 8.6 10.5 9.7
Mountain 6.4 6.1 6.9 6.3 7.1 6.1 5.0
Pacific 13.6 13.5 12.9 17.7 14.6 14.9 9.8
E.M.Lawrence et al.
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Table 1 (continued)
All Very happy Pretty happy Not too happy Never married Divorced/separated Widowed
Religious attendance
Never 17.2 12.4 15.2 22.7 22.3 21.2 15.7
< once a week 56.3 53.6 57.9 52.6 60.9 59.7 45.4
Once a week 19.1 24.1 20.2 19.2 12.7 13.4 27.3
> once a week 7.4 9.8 6.7 5.5 4.2 5.7 11.6
General happiness
Very happy 31.0 59.3 12.2 7.0 22.4 18.6 23.7
Pretty happy 57.6 37.7 79.5 47.6 63.8 63.3 57.9
Not too happy 11.3 3.0 8.3 45.4 13.8 18.1 18.4
Source: GSS-NDI. N = 20,410
a N = 15,385 for self-rated health rows
Marital Happiness, Marital Status, Health, andLongevity
1 3
odds of reporting fair or poor health: 2.26 times ashigh (p < .001) than those in the referent
group. Controlling for SES (Model 2) attenuated the odds of reporting worse health for each
of the groups, particularly for the widowed group. Geographic location did little to change
the relationship between marital happiness and SRH (Model 3), but religious attendance fur-
ther attenuated the differences (Model 4). Still, the differences among marital status/happi-
ness categories remained significant and substantial even with these statistical controls. In
particular, the associations for those in pretty happy or not too happy marriages changed little
across the models. F tests (available upon request) demonstrated that those not-too-happily
married had higher odds (p < .10) of worse health compared to all other categories except the
divorced/separated. The pretty happily married had statistically significantly higher odds of
worse health compared to the widowed, but not compared to never married individuals.
Table3 displays results from Cox proportional hazard models of the association of mar-
ital happiness with mortality risk. Model 1 shows the hazard ratios for the marital status
and happiness categories, net of gender, race, and age (incorporated as duration). Com-
pared to respondents reporting very happy marriages, those in pretty happy marriages had
similar mortality risk, but each of the other groups, especially those in unhappy marriages,
demonstrated higher risk. Models 2–4 added controls for SES, location, and religious
attendance, respectively. These variables did little to change the results for the marital sta-
tus/happiness groups. Compared to those in very happy marriages, those in not-too-happy
marriages were 37% more likely to die over the study period, net of all covariates in Model
4. Additional comparisons (not shown but available on request) indicate that this increased
risk for adults in not-too-happy marriages did not differ significantly from those who were
never married, divorced/separated, or widowed.
Table4 shows how marital status/happiness and overall happiness, both separately and
combined, are associated with health and mortality. Each of these models included the full
set of controls (age, gender, race, parenthood, SES, geographic location, religiosity, and sur-
vey year). Model 1 for SRH and mortality duplicated the findings reported in the final mod-
els in Tables2 and 3. Model 2 omitted marital status/happiness, but instead included general
happiness, and Model 3 included both general and marital happiness jointly.4 Compared to
those who were very happy, respondents who were generally pretty happy or unhappy were
significantly more likely to have fair or poor health, net of covariates. The effect sizes were
fairly large, with those who were generally unhappy over four times the odds of reporting
worse health. Further, general happiness appeared to be fairly robust to the consideration of
marital status/happiness, as the odds ratios were attenuated only slightly between Models
2 and 3. Marital status and happiness categories were attenuated, with only two categories
remaining significantly different from the referent of very happy marriage.
A similar pattern emerged for mortality though magnitudes were smaller; compared
to those who were very happy, those who were pretty happy or not-too-happy displayed
increased mortality risk. The increased risk for these respondents remained robust to mari-
tal status and happiness categories, as evidenced by the similar hazard ratios in Models 2
and 3. The effects of marital status/happiness were attenuated with the inclusion of general
happiness. The effect of a not-too-happy marriage was reduced but retained a marginally
significant association with mortality risk.
Surprisingly, compared to very happy marriages, pretty happy marriages were associ-
ated with significantly lower mortality risks (Model 3). In the previous models (Table3),
pretty happy marriages are not significantly associated with mortality compared to very
happy marriages, which might signify that general happiness suppresses the relationship
4 Multicollinearity diagnostics indicated no problems, with variance inflation factors all below 3.5.
E.M.Lawrence et al.
1 3
Table 2 Odds ratios for fair/poor
self-rated health, U.S. adults aged
18 and over (1988–2002)
Source: GSS-NDI. N = 15,442
Referent is listed in parentheses
***p < .001; **p < .01; *p < .05; +p < .10
Model 1 Model 2 Model 3 Model 4
Marital status/happiness (Very happy marriage)
Never married 2.11*** 1.57*** 1.61*** 1.54***
Divorced/separated 1.96*** 1.90*** 1.91*** 1.80***
Widowed 1.77*** 1.26** 1.26** 1.22*
Pretty happy marriage 1.66*** 1.59*** 1.60*** 1.56***
Not too happy marriage 2.26*** 2.25*** 2.29*** 2.19***
Sociodemographic factors
Age 1.03*** 1.03*** 1.03*** 1.03***
Male (female) 1.01 1.17** 1.17** 1.13**
Race (white)
Black 1.69*** 1.31*** 1.27*** 1.31***
Other race 1.70*** 1.40*** 1.48*** 1.53***
Is a parent 1.21** 0.97 0.98 0.98
Socioeconomic status
Income-to-needs ratio (300% +)
 < 100% 2.43*** 2.39*** 2.40***
100–199% 1.65*** 1.64*** 1.67***
200–299% 1.25** 1.25** 1.26***
Education (college degree +)
Less than high school 3.04*** 2.91*** 2.77***
High school 1.87*** 1.83*** 1.78***
Some college 1.38*** 1.37*** 1.35***
Employment (full time)
Part time 1.13 1.15+1.16+
Retired 1.76*** 1.79*** 1.80***
Other 1.95*** 1.96*** 1.98***
Location (Mountain)
New England 0.96 0.98
Middle Atlantic 1.19 1.20+
E. Nor. Central 1.20+1.23*
W. Nor. Central 1.16 1.19
South Atlantic 1.15 1.19
E. Sou. Central 1.71*** 1.78***
W. Sou. Central 1.23+1.27*
Pacific 1.05 1.04
Religious attendance (> once a week)
Never 1.43***
Less than once a week 1.29**
Once a week 0.85+
Survey year 0.99* 1.00 1.00 0.99
Constant 0.03 0.02*** 0.01*** 0.01***
Marital Happiness, Marital Status, Health, andLongevity
1 3
Table 3 Hazard ratios for
mortality risk, U.S. adults aged
18 and over (1988–2009)
Source: GSS-NDI. N = 20,410
Referent is listed in parentheses
***p < .001; **p < .01; *p < .05; +p < .10
Model 1 Model 2 Model 3 Model 4
Marital status/happiness (very happy marriage)
Never married 1.16* 1.12+1.13+1.11
Divorced/separated 1.20*** 1.16** 1.16** 1.14*
Widowed 1.21*** 1.15** 1.14** 1.13*
Pretty happy marriage 0.97 0.95 0.96 0.94
Not too happy marriage 1.45** 1.41** 1.41** 1.37*
Sociodemographic factors
Male (female) 1.43*** 1.48*** 1.49*** 1.46***
Race (white)
Black 1.49** 1.38*** 1.37*** 1.39***
Other race 1.14 1.10 1.08 1.10
Is a parent 1.06 1.03 1.03 1.03
Socioeconomic status
Income-to-needs ratio (300% +)
< 100% 1.12+1.12+1.13+
100–199% 1.02 1.03 1.04
200–299% 0.94 0.95 0.95
Education (college degree+)
Less than high school 1.31*** 1.30*** 1.28***
High school 1.22*** 1.23*** 1.22***
Some college 1.19** 1.19** 1.18***
Employment (full time)
Part time 1.02 1.03 1.03
Retired 1.19** 1.19** 1.18**
Other 1.21*** 1.21*** 1.21***
Location (Mountain) 1.12+1.12+1.13+
New England 1.00 1.01
Middle Atlantic 1.06 1.06
E. Nor. Central 1.09 1.09
W. Nor. Central 0.85+0.86+
South Atlantic 1.10 1.11
E. Sou. Central 1.00 1.02
W. Sou. Central 1.25** 1.25**
Pacific 1.09 1.08
Religious attendance (> once a week)
Never 1.25**
Less than once a week 1.20**
Once a week 1.06
Survey year 0.99** 0.99* 0.99* 0.99**
E.M.Lawrence et al.
1 3
between pretty happy marriages and mortality. This singular finding may be due to the
numerous covariates, and particularly general happiness: about 80% of those who are in
pretty happy marriages reported being generally pretty happy.
Overall, these findings suggest that the relationship between marital happiness and
health was either mediated or confounded by general happiness—our models cannot dis-
tinguish between these causal models—but clearly marital happiness was related to general
happiness, which was associated with better health.
3.3 Sensitivity Analyses
We performed two robustness checks (available upon request) to determine whether
the results were sensitive to our modeling approach and specifications. First, we exam-
ined whether the results changed when we included the full range of interview years
(1978–2002); we found similar patterns to those shown. Second, we tested for mod-
eration by gender and race through including an interaction of these two demographic
characteristics with marital status/happiness. With one exception, we did not find mod-
eration, suggesting that marital status and happiness were related to health and mortal-
ity similarly across all population groups. The exception was a significant interaction
between black and widowed for both mortality and SRH, such that the disadvantages for
being widowed, compared to happily married, were exacerbated for black compared to
white individuals.
Table 4 Differences in fair/poor health and mortality risk by marital status/happiness and general happi-
ness, U.S. adults aged 18 and over (1988–2009)
Source: GSS-NDI
a N = 15,442
b N = 20,410
Models also control for gender, race, age (covariate in SRH models and entry in mortality models), parent-
hood status, education, income, employment, location, religion, and survey year. Referent is listed in paren-
theses. Odds ratios greater than 1 indicate greater likelihood of reporting worse health, and hazard ratios
greater than 1 indicate greater mortality risk
***p < .001; **p < .01; *p < .05; +p < .10
Fair/poor self-rated healthaMortality riskb
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Marital status/happiness (very happy marriage)
Never married 1.54*** 1.12 1.11 1.06
Divorced/separated 1.80*** 1.24** 1.14* 1.08
Widowed 1.22* 0.85+ 1.13* 1.08
Pretty happy marriage 1.56*** 1.14+ 0.94 0.90*
Not too happy marriage 2.19*** 1.10 1.37* 1.26+
General happiness (very happy)
Pretty happy 1.97*** 1.90*** 1.11** 1.11**
Not happy 4.51*** 4.33*** 1.22*** 1.19**
Marital Happiness, Marital Status, Health, andLongevity
1 3
4 Discussion
Research has consistently documented better health and longer lives for married compared
to unmarried adults. Another body of research has shown that marital quality shapes the
benefits of marriage. We merged these research strands to analyze the comparative effects
of marital status and marital happiness on health and longevity. To our knowledge, ours is
the first study to examine marital status and marital quality jointly in a large representative
sample of U.S. adults. We employed two of the most widely used health outcomes: SRH
and mortality, and also controlled for important potential confounders including general
The results indicate that those who are not too happy in their marriage have the worst
health and the shortest lives. Studies on the benefits of marital status have relied on the
buffering hypothesis to focus on qualities that exist in happy marriages, such as social sup-
port. Our findings support the aggravating hypothesis as well. Individuals who report that
they were not happy in their marriage exhibited equal or worse risk of fair/poor health and
mortality than those who never married, divorced or separated, and widowed. It could be
that poor health worsens marital quality and we further discuss this possibility below. Or, it
could be that individuals in unhappy marriages experience stress-inducing interactions or
forgo the social support of their spouse. The mechanisms that provide protective effects in
good marriages may instead aggravate stress and unhealthy behaviors for those in unhappy
An extensive research literature has shown that being married is associated with better
outcomes than not being married, as well as the importance of marital quality on health
outcomes. We contribute by showing the uniquely poor health among adults in unhappy
marriages. Consistent with previous studies, our results demonstrate that individuals who
are in low quality marriages have outcomes equal to or worse than those who dissolved
their relationship (Bulanda etal. 2016; Hawkins and Booth 2005; Waite etal. 2009) and
that unmarried individuals have better psychological well-being than those in unhappy
marriages (Williams 2003). While only a small proportion of married individuals reported
being unhappy in marriage, studies that combine all marriages together may fail to iden-
tify the particularly poor health of this group. This pulls the average health of the married
category downward. Thus, paradoxically, overlooking heterogeneity in marital quality may
understate the benefits of a good marriage.
We also explored the role of general happiness as a factor in the complex relation-
ship between marital happiness and health and longevity. General happiness appears
to underlie the relationships between marital happiness and health and mortality, sug-
gesting that marital happiness taps into and/or contributes to a broad dimension of
well-being. This result builds on the findings of prior studies that marriage most likely
selects happy people and also provides support and resources that contribute to further
happiness (Wadsworth 2015; Waite 1995). When we accounted for general happiness,
the effects of marital happiness were attenuated, although our analysis did not allow
us to determine the nature of the relationship between marital and general happiness.
Marital happiness could influence health and mortality through general happiness, or
general happiness could be a confounder. These results linking general and marital hap-
piness suggest that marital happiness may be a broad indicator of well-being, and not
just a signifier of the quality of interactions with one’s spouse. Overall, being in a happy
marriage is clearly linked to health and longevity, in part because of its association with
general happiness.
E.M.Lawrence et al.
1 3
4.1 Implications forResearch andPolicy
The conclusions of this study have important research and policy implications. Notably,
our study highlights the importance for future studies to consider variability within mar-
riage. Most health and mortality studies include a simple categorical measure of marital
status to control for this important characteristic. Accounting for marital quality or using a
simple measure of general happiness could better control for these important factors.
To understand how marriage impacts health and longevity, future research must
identify the characteristics of marriages that matter most for subjective well-being and
health. The results presented in this study point to the importance of psychological pro-
cesses as key. Future research combining psychological and demographic approaches
could yield greater insight, as it could exploit the variation both within and across mar-
ital-status groups. However, identifying the causal and selection processes that drive
observed associations will be important for better understanding when and why marital
happiness and marital status shape health. Longitudinal studies such as those conducted
by Miller etal. (2013), which examine both initial levels and change over time in happi-
ness, will help adjudicate between causal and selection processes. It will be important to
consider both aggravating and buffering mechanisms of marriage and how these mecha-
nisms become more or less pronounced over time for different groups of adults.
Understanding the outcomes of adults who are not married compared to those in low
quality marriages is particularly salient in today’s U.S. society: Recent estimates indicate
that the share of unmarried adults is at an historic high and is predicted to rise further
among younger cohorts (Ruggles 2015; Wang and Parker 2014). Moreover, the benefits
of marriage vary across sociocultural contexts (Vanassche et al. 2013) and thus future
research must take into account the changing meaning of marriage—for instance, the
increased prevalence and acceptance of cohabitation. Research will need to continue to
examine the health consequences of both marital status and marital quality amid the shift-
ing expectations about marriage and changing norms around selection into (and out of)
marriage. Natural experiments or interventions could shed light on the effects of marriage
as norms and meanings change. For example, the cost of marriage licenses, changes in
tax policies, or other changes over space or time serving as instruments for marital rates
could offer an approach to estimating causal effects of marriage on population-level mor-
tality rates. Future research should consider the role of marital status/happiness for health
among sexual minorities. Not only will such inclusion better represent the U.S. population
as marriages become more diverse (Fincham and Beach 2010), but differences in types of
marriage and selection into marriage may also illuminate the mechanisms and processes
through which social relationships accentuate or attenuate health and longevity.
4.2 Limitations
Our findings should be considered in light of several limitations. First, we use a single
item measure of marital happiness, which may not capture complexity or dimensionality
of marriage quality (see Rauer and Volling 2013). Yet many studies have found a single
question on marital happiness to be a meaningful indicator of marital quality (Corra etal.
2009; Rauer and Volling 2013; Waite et al. 2009). We are mindful of the cross-sectional
nature of the GSS survey. We have information on marital status, marital happiness, and
general happiness for one point in time, and cannot observe changes in marital happiness,
marital status, or health that may have occurred prior to or after the survey. An additional
Marital Happiness, Marital Status, Health, andLongevity
1 3
limitation is that although we controlled for a variety of factors, there may be additional
confounders that shape marital status, marital happiness, and health. For example, person-
ality traits may influence propensity to marry, happiness in marriage, as well as health.
Importantly, the structure of the data limits making causal inferences. Reverse causality
is possible because poor health can shape both marital status and marital happiness. We
mitigated concern of reverse causality in two ways. First, we used linked mortality data,
which allowed us to identify a temporal relationship and avoid reverse causation in inter-
preting associations with respect to mortality. Second, we replicated the mortality analyses
but omitted the 186 individuals who died within 1year of the interview to further reduce
the potential of our findings being driven by reverse causality. The results of these analyses,
shown in Appendix Table7, display very similar estimates from those reported in Table3,
with no substantive differences. Yet, like other studies examining subjective well-being and
health, we could not eliminate reverse causality as a potential explanation for our findings.
Poor health may worsen marital quality. The stress of illness could worsen interactions,
and spouses may experience financial or emotional strain associated with caring for an ill
family member. This explanation does not contradict our conclusions regarding increased
attention to heterogeneity in marriage-health relationship, since this process would also
indicate that some marriages are disadvantageous. Nonetheless, further research is war-
ranted to parse out reciprocal effects between marital happiness and health.
5 Conclusion
Marriage, while important, is not a panacea. A vast literature has shown that it is associated
with better health and longer lives, but our study indicates that this may only be accurate
for those who are pretty happy or very happy in marriage. At a time when marriage rates
continue to decline, some policymakers and commentators assume that increasing the mar-
riage rate will lead to greater wellbeing (see, for example, Aber etal. 2015: 32). But higher
rates of marriage may increase heterogeneity in marital quality. For individuals who suffer
in an unhappy marriage, divorce or separation may be a reasonable option, and avoiding an
unhappy marriage through remaining single is a viable alternative. Alternatives to focus-
ing on increasing marriage rates could be to improve relationship quality or boost general
happiness, which would have widespread benefits, including better health and longer lives.
Acknowledgements We thank the editor and the anonymous reviewers for their helpful comments, and
Columbia University, Mailman School of Public Health, and NORC at the University of Chicago for collect-
ing the data and making the linked files available to the research public. This research was supported by the
National Institutes of Health under Ruth L. Kirschstein National Research Service Award (F32 HD 085599)
from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).
We are grateful to the Carolina Population Center and the University of Colorado Population Center and
their NICHD center grants (P2CHD050924 and P2CHD066613, respectively) for general research support.
A previous version of this paper was presented at the Population Association of America annual meeting in
Chicago, Illinois, April 27, 2017.
See Tables5, 6 and 7.
E.M.Lawrence et al.
1 3
Table 5 Coefficients for
self-rated health OLS models,
U.S. adults aged 18 and over
Source: GSS-NDI. N = 15,442
Referent is listed in parentheses
***p < .001; **p < .01; *p < .05; +p < .10
Model 1 Model 2 Model 3 Model 4
Marital status/happiness (Very happy marriage)
Never married 0.23*** 0.12*** 0.13*** 0.11***
Divorced/separated 0.24*** 0.21*** 0.21*** 0.19***
Widowed 0.28*** 0.13*** 0.12*** 0.12***
Pretty happy marriage 0.22*** 0.19*** 0.19*** 0.18***
Not too happy marriage 0.34*** 0.32*** 0.32*** 0.30***
Sociodemographic factors
Age 0.01*** 0.01*** 0.01*** 0.01***
Male (female) − 0.01 0.04** 0.03** 0.02+
Race (white)
Black 0.19*** 0.09*** 0.08*** 0.09***
Other race 0.17*** 0.10*** 0.11*** 0.13***
Is a parent 0.05* − 0.05** − 0.05** − 0.05**
Socioeconomic status
Income-to-needs ratio (300%+)
< 100% 0.36*** 0.35*** 0.35***
100–199% 0.19*** 0.19*** 0.20***
200–299% 0.09*** 0.09*** 0.10***
Education (college degree+)
Less than high school 0.43*** 0.41*** 0.39***
High school 0.23*** 0.22*** 0.21***
Some college 0.13*** 0.13*** 0.12***
Employment (full time)
Part time 0.00 0.00 0.01
Retired 0.16*** 0.16*** 0.16***
Other 0.21*** 0.21*** 0.21***
Location (Mountain)
New England − 0.03 − 0.03
Middle Atlantic 0.08** 0.08**
E. Nor. Central 0.05+0.06*
W. Nor. Central 0.03 0.04
South Atlantic 0.03 0.04
E. Sou. Central 0.15*** 0.17***
W. Sou. Central 0.06+0.07*
Pacific 0.00 − 0.01
Religious attendance (> once a week)
Never 0.16***
Less than once a week 0.11***
Once a week − 0.02
Survey year 0.00 0.00 0.00 0.00
Constant 1.20*** 1.00*** 0.96*** 0.87***
Marital Happiness, Marital Status, Health, andLongevity
1 3
Table 6 Proportional odds ratios
for self-rated health ordinal
logistic regression models,
U.S. adults aged 18 and over
Source: GSS-NDI. N = 15,442
Referent is listed in parentheses. Cutpoints are the estimated thresh-
olds of the latent variable that lead to observed values of the observed
***p < .001; **p < .01; *p < .05; +p < .10
Model 1 Model 2 Model 3 Model 4
Marital status/happiness (very happy marriage)
Never married 1.76*** 1.39*** 1.41*** 1.36***
Divorced/separated 1.78*** 1.72*** 1.72*** 1.64***
Widowed 1.94*** 1.37*** 1.37*** 1.34***
Pretty happy marriage 1.78*** 1.70*** 1.70*** 1.66***
Not too happy marriage 2.33*** 2.29*** 2.29*** 2.20***
Sociodemographic factors
Age 1.03*** 1.02*** 1.02*** 1.03***
Male (female) 0.99 1.10** 1.10** 1.07+
Race (white)
Black 1.55*** 1.26*** 1.23*** 1.26***
Other race 1.53*** 1.33*** 1.37*** 1.40***
Is a parent 1.12** 0.89** 0.89** 0.90*
Socioeconomic status
Income-to-needs ratio (300%+)
< 100% 2.42*** 2.40*** 2.40***
100–199% 1.62*** 1.61*** 1.65***
200–299% 1.28*** 1.28*** 1.29***
Education (college degree+)
Less than high school 3.04*** 2.93*** 2.79***
High school 1.88*** 1.84*** 1.79***
Some college 1.46*** 1.45*** 1.43***
Employment (full time)
Part time 0.98 0.99 1.00
Retired 1.40*** 1.41*** 1.42***
Other 1.56*** 1.57*** 1.59***
Location (Mountain)
New England 0.95 0.95
Middle Atlantic 1.24** 1.25**
E. Nor. Central 1.14+1.16*
W. Nor. Central 1.09 1.11
South Atlantic 1.08 1.10
E. Sou. Central 1.49*** 1.55***
W. Sou. Central 1.15+1.19*
Pacific 1.00 0.99
Religious attendance (> once a week)
Never 1.47***
Less than once a week 1.33***
Once a week 0.96
Survey year 1.00 1.00 1.00 1.00
Cut points
Cut1 0.89 1.47 1.57 1.82
Cut2 3.09 3.82 3.93 4.19
Cut3 4.86 5.70 5.81 6.07
E.M.Lawrence et al.
1 3
Table 7 Hazard ratios for
mortality risk, U.S. adults aged
18 and over (1988–2009)
Source: GSS-NDI. N = 20,224
Referent is listed in parentheses. Individuals dying within 1year of the
interview have been omitted
***p < .001; **p < .01; *p < .05; +p < .10
Model 1 Model 2 Model 3 Model 4
Marital status/happiness (very happy marriage)
Never married 1.17* 1.13+1.14+1.12+
Divorced/separated 1.21*** 1.17** 1.17** 1.14*
Widowed 1.17** 1.11* 1.10* 1.09+
Pretty happy marriage 0.95 0.93 0.94 0.92
Not too happy marriage 1.41** 1.38* 1.37* 1.34*
Sociodemographic factors
Male (female) 1.41*** 1.47*** 1.47*** 1.44***
Race (white)
Black 1.49*** 1.38*** 1.37*** 1.39***
Other race 1.13 1.08 1.07 1.09
Is a parent 1.05 1.02 1.02 1.03
Socioeconomic status 1.41*** 1.47*** 1.47*** 1.44***
Income-to-needs ratio (300% +)
< 100% 1.14* 1.14* 1.14*
100–199% 1.03 1.04 1.05
200–299% 0.95 0.95 0.96
Education (college degree +)
Less than high school 1.29*** 1.28*** 1.26***
High school 1.21*** 1.22*** 1.21***
Some college 1.18** 1.18** 1.17**
Employment (full time)
Part time 1.03 1.04 1.05
Retired 1.14* 1.13* 1.13*
Other 1.18** 1.18** 1.19**
Location (Mountain) 1.14* 1.14* 1.14*
New England 1.01 1.01
Middle Atlantic 1.05 1.05
E. Nor. Central 1.08 1.09
W. Nor. Central 0.84+0.84+
South Atlantic 1.11 1.11
E. Sou. Central 1.00 1.02
W. Sou. Central 1.25** 1.25**
Pacific 1.09 1.07
Religious attendance (> once a week)
Never 1.25**
Less than once a week 1.22**
Once a week 1.05
Survey year 0.99*** 0.99** 0.99** 0.99**
Marital Happiness, Marital Status, Health, andLongevity
1 3
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... Similarly, recommend that inequality should be reduced in order to preserve well-being. More so, a variety of socio-cultural factors, such as marital status (Kislev, 2018;Lawrence et al., 2019;Stutzer & Frey, 2006), hope and support (Mason, 2023), education (Nikolaev & Rusakov, 2016), or religion (Frey, 2018;Minkov et al., 2020), have been identified as determinants of subjective well-being in the empirical literature. Political systems and governance factors have also been acknowledged as predictors of well-being (Helliwell, 2003;Ott, 2011;Njangang, 2019;Frijters et al., 2019). ...
... On the one hand, there is a well-documented literature on the determinants and indicators of well-being. Some authors attributed it to a range of socioeconomic indicators such as health (Danna & Griffin, 1999;Ellison & Smith, 1991;Law et al., 1998;Waddell & Burton, 2006) and education (Castriota, 2006;Nikolaev & Rusakov, 2016), inequality , technology transfer (Kouladoum et al., 2023) and social support (Ginja et al., 2018;Stronge et al., 2019), and others with demographic indicators such as age and gender (Kislev, 2018;Lawrence et al., 2019;Stutzer & Frey, 2006) and religion (Frey, 2018;Minkov et al., 2020). Political systems and governance factors have also been acknowledged as predictors of well-being (Helliwell, 2003;Ott, 2011;Njangang, 2019;Frijters et al., 2019). ...
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Unlike previous studies which focused on the economic effects of infrastructures, this paper contributes to the literature by analysing the contribution of infrastructure development to well-being, considered the aim of all efforts. The paper uses composite infrastructure indexes from the African Development Bank, to capture infrastructure quality and the life ladder index as proxy for subjective well-being on a sample of 29 African countries during the 2007–2018 period. Estimates are done using panel corrected standard errors, Tobit regression, and the generalised method of moments. Results show that infrastructure development boosts the well-being of Africans. Further analysis at the disaggregated level shows that information and communication technology (ICT) and electricity are the main drivers of happiness in the region. After testing for possible mediators, human capital is found to be the main channel through which infrastructure development enhance subjective well-being in Africa. Therefore, policies aiming to promote the well-being of Africans should consider investments in infrastructure development, especially ICT, electricity, transport, water supply, and sanitation services. This would in turn improve the performance of institutions and human capital, contributing to the well-being of Africans.
... The effect of sleeping hours or sleep duration on happiness has been another focus in the literature [27][28][29]. Some studies found that short sleep duration is associated with lower happiness in healthy adults [30], while some longitudinal studies have revealed that happiness negatively correlates with sleep duration [31]. ...
... The path model results in a highly optimistic estimate on the effects of sleep quality and duration on older adults and their happiness. The results are consistent with other international research focusing on the associations between sleeping hours or sleep duration and happiness [27,29,30]. Apart from a direct association between sleeping quality and happiness, the path model also reveals a strong indirect association between sleeping quality and happiness through subjective health. ...
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Background The study aimed to identify the associations of happiness and factors related to physical and mental health, leisure, and sports activities amongst older adults in Abu Dhabi. The sample comprised 1,004 participants in the third Abu Dhabi Quality of Life survey administered in 2019–2020. Methods The analysis used path analysis to develop a model incorporating the specified variables. The path model highlighted all direct and indirect associations between the variables. We also used variance analysis to test the differences in gender, marital status, and education attainment with happiness. Results Results show that sleep quality is most associated with happiness and subjective health. In addition, sleeping hours did not show any association with subjective health; but were associated with happiness. The result also confirms that mental health is negatively associated with happiness and subjective health. How often an elderly gets involved in sport and activities for at least 30 min significantly affects subjective health and happiness. Conclusions Happiness of older adults is best understood when we look at both direct and indirect effects using a path model. Their happiness is significantly associated with their subjective health, mental health, participation in sport and activities and sleep quality, Implications of the study were highlighted, along with future research directions.
... , longevity (Diener & Chan, 2011;Lawrence et al., 2015Lawrence et al., , 2019, quality of social interactions Lyubomirsky et al., 2005aLyubomirsky et al., , 2005b, and business performance (De Neve et al., 2013;Edmans, 2011Edmans, , 2012Oswald et al., 2015). Literature suggests that about 50% of individuals' SWB variance reflects a stable factor, with the remainder representing change or variation due to habits and mindsets (e.g., Amen, 2022;Nes & Røysamb, 2017). ...
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The link between effort and individual well-being has been the subject of contentious debate. Economic and some psychological models analyze effort as a cost or a disutility, while other philosophical and psychological theories argue that personal effort is a pivotal element for a flourishing life. These theories also distinguish between higher and lower pleasures. To assess the contested contribution of effort to personal well-being, we analyze survey data gathered from 1954 working adults aged 25 to 65 in Israel. We analyze their subjective assessments of the effort they exert in different life domains and support the validity of our analysis by comparing them to choice scenarios in each domain. The results contribute three key findings: 1. Effort in five life domains—work, leisure activities, friendship, community and health—as well as effort of managing work life balance, was found to be positively associated with at least one component of subjective well-being, while effort to make work more intrinsically rewarding was found to be associated with all three components—affect, cognition and meaning—of an individual’s subjective well-being. 2. These efforts are not strongly correlated among themselves, implying that people can choose how to allocate their efforts among the various life domains. 3. People’s assessments of their future subjective well-being are positively correlated with their expectations regarding future effort. These results suggest that effort and well-being are correlated through hedonic capital accumulation.
... In addition, while there is limited research on the reasons why female immigrants are happier than male immigrants, general happiness studies have shown that women tend to report higher levels of happiness [57,58]. The greater happiness among married immigrants compared to unmarried ones can be attributed to various factors, including a sense of belonging, increased social interaction, lower levels of depression, and emotional support [59,60]. This study also observes that in terms of gender and marital status female immigrants tend to be happier than male immigrants, and happiness tends to increase among married immigrants compared to those who are unmarried. ...
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In this study, the impact of the reasons for leaving their country (such as social, economic, and political) of Afghan asylum seekers who illegally entered Turkey from the eastern border on their life satisfaction in the country of destination was analyzed. The sample comprised 500 individuals (54.8% women; 42.4% < 30 age) who came as refugees from Turkey’s eastern border. Three-stages of analysis were carried out in the study: the Mantel–Haenszel test, ordered logit, and CART (Classification and Regression Trees) decision tree. The main findings obtained from these analyses show that individuals leaving their country for economic reasons and because of war/terrorism are happier, while those leaving their country because of religious and cultural pressures are unhappier. According to the results of the CART analysis, the most frequently repeated variables are economic and life satisfaction of individuals who are satisfied with their household income and save money is at its highest level. In the analysis it is also seen that the life satisfaction level of individuals who are not satisfied with their household income, leave their country for reasons other than economic reasons, and make a living on debt is very low. This study also focuses on the relationship between happiness and sustainable development (SD). It has associated the reasons for migrants leaving their countries with the Sustainable Development Goals (SDGs), highlighting the significance of happiness studies in achieving the SDGs.
... Furthermore, patients with carers or partners reported increased participation in cardiac rehabilitation programs, medication, diet, and exercise adherence, and reduced hospital readmissions compared to patients without a partner or carer [73]. These authors argued that the quality of marriage would be an important consideration for future studies as unhappy marriages have worse outcomes with regard to general QOL and distress than happy marriages in both the general population and among CVD patients [73,74]. ...
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Following surgery, over 50% of cardiac surgery patients report anxiety, stress and/or depression, with at least 10% meeting clinical diagnoses, which can persist for more than a year. Psychological distress predicts post-surgery health outcomes for cardiac patients. Therefore, post-operative distress represents a critical recovery challenge affecting both physical and psychological health. Despite some research identifying key personal, social, and health service correlates of patient distress, a review or synthesis of this evidence remains unavailable. Understanding these factors can facilitate the identification of high-risk patients, develop tailored support resources and interventions to support optimum recovery. This narrative review synthesises evidence from 39 studies that investigate personal, social, and health service predictors of post-surgery psychological distress among cardiac patients. The following factors predicted lower post-operative distress: participation in pre-operative education, cardiac rehabilitation, having a partner, happier marriages, increased physical activity, and greater social interaction. Conversely, increased pain and functional impairment predicted greater distress. The role of age, and sex in predicting distress is inconclusive. Understanding several factors is limited by the inability to carry out experimental manipulations for ethical reasons (e.g., pain). Future research would profit from addressing key methodological limitations and exploring the role of self-efficacy, pre-operative distress, and pre-operative physical activity. It is recommended that cardiac patients be educated pre-surgery and attend cardiac rehabilitation to decrease distress.
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This study employed a descriptive correlational design to investigate the collaborative role of teachers' profiles, assistive technology utilization, and their impact on teaching exceptional learners in public schools. A convenience sample of 63 teachers who had experience teaching exceptional learners in self-contained and inclusive classrooms in Mandaue City, Philippines, was surveyed to understand the relationships between teachers' profiles, assistive technology utilization, and the impact on learners with exceptionalities. The findings revealed that teacher profiles, particularly educational attainment and income, influenced the perceived effectiveness of middle-to-high technology. Teachers reported that assistive technology positively impacted learners' participation, independence, and skills. Based on these insights, a profile-aligned matrix action plan is recommended to equip special education and inclusion teachers to choose and implement appropriate technologies aligned with exceptional learners' needs. With appropriate government support, the integration of teachers' competencies, technology utilization, and learners' outcomes can be optimized to improve exceptional education through a systemic, profile-aligned approach.
Marriage is an important milestone for many adults, and notably in Indonesia, where marriage is also considered a personal accomplishment and social obligation. Research has found being married is associated with greater well‐being, but marriages also face challenges. Resilience, defined as successfully adapting to challenges, is a potential concept to help married individuals maintain or regain adaptation despite challenges in marriages. This is the first relationship study in Indonesia to examine resilience trajectories as represented by marital satisfaction. A weekly repeated measure design was conducted among 135 Indonesian married individuals. Participants reported their experiences of intradyadic and extradyadic stress, and marital satisfaction over 6 weeks. Growth mixture modeling and multinomial logistic regression were used to examine unobserved marital satisfaction trajectories and to estimate the impact of intradyadic and extradyadic stress on trajectory membership. Results suggested three unobserved trajectories: high, moderate, and low levels of marital satisfaction. Higher levels of intradyadic and extradyadic stress increased the probability of belonging to lower satisfaction trajectories. This evidence could be invaluable in helping Indonesian married individuals to better adapt to challenges they face. Future studies can explore protective factors associated with a high satisfaction trajectory to assist married Indonesians in successfully adapting to stress.
Why do people fall in love? Does passion fade with time? What makes for a happy, healthy relationship? This introduction to relationship science follows the lifecycle of a relationship – from attraction and initiation, to the hard work of relationship maintenance, to dissolution and ways to strengthen a relationship. Designed for advanced undergraduates studying psychology, communication or family studies, this textbook presents a fresh, diversity-infused approach to relationship science. It includes real-world examples and critical-thinking questions, callout boxes that challenge students to make connections, and researcher interviews that showcase the many career paths of relationship scientists. Article Spotlights reveal cutting-edge methods, while Diversity and Inclusion boxes celebrate the variety found in human love and connection. Throughout the book, students see the application of theory and come to recognize universal themes in relationships as well as the nuances of many findings. Instructors can access lecture slides, an instructor manual, and test banks.
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Objectives: To examine how disparities in adult disability by educational attainment vary across US states. Methods: We used the nationally representative data of more than 6 million adults aged 45 to 89 years in the 2010-2014 American Community Survey. We defined disability as difficulty with activities of daily living. We categorized education as low (less than high school), mid (high school or some college), or high (bachelor's or higher). We estimated age-standardized disability prevalence by educational attainment and state. We assessed whether the variation in disability across states occurs primarily among low-educated adults and whether it reflects the socioeconomic resources of low-educated adults and their surrounding contexts. Results: Disparities in disability by education vary markedly across states-from a 20 percentage point disparity in Massachusetts to a 12-point disparity in Wyoming. Disparities vary across states mainly because the prevalence of disability among low-educated adults varies across states. Personal and contextual socioeconomic resources of low-educated adults account for 29% of the variation. Conclusions: Efforts to reduce disparities in disability by education should consider state and local strategies that reduce poverty among low-educated adults and their surrounding contexts. (Am J Public Health. Published online ahead of print May 18, 2017: e1-e8. doi:10.2105/AJPH.2017.303768).
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Background: Poor health can cause unhappiness and poor health increases mortality. Previous reports of reduced mortality associated with happiness could be due to the increased mortality of people who are unhappy because of their poor health. Also, unhappiness might be associated with lifestyle factors that can affect mortality. We aimed to establish whether, after allowing for the poor health and lifestyle of people who are unhappy, any robust evidence remains that happiness or related subjective measures of wellbeing directly reduce mortality. Methods: The Million Women Study is a prospective study of UK women recruited between 1996 and 2001 and followed electronically for cause-specific mortality. 3 years after recruitment, the baseline questionnaire for the present report asked women to self-rate their health, happiness, stress, feelings of control, and whether they felt relaxed. The main analyses were of mortality before Jan 1, 2012, from all causes, from ischaemic heart disease, and from cancer in women who did not have heart disease, stroke, chronic obstructive lung disease, or cancer at the time they answered this baseline questionnaire. We used Cox regression, adjusted for baseline self-rated health and lifestyle factors, to calculate mortality rate ratios (RRs) comparing mortality in women who reported being unhappy (ie, happy sometimes, rarely, or never) with those who reported being happy most of the time. Findings: Of 719,671 women in the main analyses (median age 59 years [IQR 55-63]), 39% (282,619) reported being happy most of the time, 44% (315,874) usually happy, and 17% (121,178) unhappy. During 10 years (SD 2) follow-up, 4% (31,531) of participants died. Self-rated poor health at baseline was strongly associated with unhappiness. But after adjustment for self-rated health, treatment for hypertension, diabetes, asthma, arthritis, depression, or anxiety, and several sociodemographic and lifestyle factors (including smoking, deprivation, and body-mass index), unhappiness was not associated with mortality from all causes (adjusted RR for unhappy vs happy most of the time 0·98, 95% CI 0·94-1·01), from ischaemic heart disease (0·97, 0·87-1·10), or from cancer (0·98, 0·93-1·02). Findings were similarly null for related measures such as stress or lack of control. Interpretation: In middle-aged women, poor health can cause unhappiness. After allowing for this association and adjusting for potential confounders, happiness and related measures of wellbeing do not appear to have any direct effect on mortality. Funding: UK Medical Research Council, Cancer Research UK.
This study examines the relationship between later-life marital quality, marital dissolution, and mortality using discrete-time event history models with data from nine waves (1992–2008) of the Health and Retirement Study (n = 7388). Results show marital status is more important for men's mortality risk than women's, whereas marital quality is more important for women's survival than men's. Being widowed or divorced more than two years raises mortality risk for men, but later-life marital dissolution is not significantly associated with women's mortality risk, regardless of the type of dissolution or length of time since it occurred. Low-quality marital interaction is negatively related to women's odds of death, but none of the marital quality measures are significantly associated with mortality for men. Marital satisfaction moderates the relationship between widowhood and mortality for women, but the relationship between marital dissolution and mortality is similar for men regardless of marital quality prior to divorce/widowhood. Results suggest the importance of accounting for both marital status and marital quality when examining older individuals' mortality risk.
This article proposes explanations for the transformation of American families over the past two centuries. I describe the impact on families of the rise of male wage labor beginning in the nineteenth century and the rise of female wage labor in the twentieth century. I then examine the effects of decline in wage labor opportunities for young men and women during the past four decades. I present new estimates of a precipitous decline in the relative income of young men and assess its implications for the decline for marriage. Finally, I discuss explanations for the deterioration of economic opportunity and speculate on the impact of technological change on the future of work and families.
This is the first study to our knowledge to examine the relationship between happiness and longevity among a nationally representative sample of adults. We use the recently-released General Social Survey-National Death Index dataset and Cox proportional hazards models to reveal that overall happiness is related to longer lives among U.S. adults. Indeed, compared to very happy people, the risk of death over the follow-up period is 6% (95% CI 1.01-1.11) higher among individuals who are pretty happy and 14% (95% CI 1.06-1.22) higher among those who are not happy, net of marital status, socioeconomic status, census division, and religious attendance. This study provides support for happiness as a stand-alone indicator of well-being that should be used more widely in social science and health research.
For most people, social relationships undoubtedly function more often as assets than as liabilities. Yet social relationships clearly can be a source of stress as well as support and companionship, and evidence suggests that negative social exchanges have potent effects on psychological well-being. To achieve a more comprehensive understanding of how social bonds affect emotional and physical health, greater attention is needed to the problematic exchanges that occur within informal social networks. Researchers interested in the negative aspects of social relationships face many of the same conceptual and methodological issues that have challenged social support researchers. This paper examines several of these parallel issues, highlighting important gaps in our existing knowledge base regarding the manner in which negative interpersonal transactions affect well being.
Over the last 20 years the academic community has experienced a burgeoning interest in the causes and correlates of subjective well-being. One of the most consistent findings has been that married respondents report higher levels of happiness and life satisfaction than unmarried respondents. Despite its prevalence, scant empirical research has focused on the potential mechanisms driving this relationship. The current work draws on the Behavior Risk Factor Surveillance System along with 2000 US Census data to investigate the role of context and reference groups in shaping the relationship between marriage and well-being. The primary research question is whether marriage has a greater influence on life satisfaction when it is more common and thus more normative? The findings offer new insight into the marriage/well-being relationship and have broad implication for how we think about the study of the causes and correlates of subjective well-being.
This study reconceptualizes marital status as social attachment in order to examine the effect of marital status on well-being. Using data from a national probability sample of 2,031 adults aged 18 to 90, four levels on a continuum of social attachment are compared: no partner, partner outside the household, living with partner in the household, living with married partner in the household. Adjusting for age, sex, and race, results indicate the higher the level of social attachment, the lower the level of psychological distress, although living with a partner and being married are not significantly different. Social attachment, emotional support, and economic support significantly reduce distress and explain the positive effect of being married and the negative effect of being single or divorced on psychological well-being, although recent widows exhibit high levels of distress that are not explained. Although relationships generally improve well-being, unhappy relationships are worse than none at all.
This paper revisits the marriage and wellbeing relationship using variables reflecting marriage quality and data from the US, the UK and Germany. People in self-assessed poor marriages are fairly miserable and much less happy than unmarried people, even in the first year of marriages. However, people in self-assessed good marriages are even happier than the literature suggests. Women show greater range of responses to marriage quality than men. The effect of employment status and subjective health on happiness and the marriage effects on interpersonal trust and mental health change dramatically when marriage quality is controlled for. A strong link from happiness to marriage does not exist. However, happier people are more likely to stay single instead of being unhappily married, but less likely to stay single compared to being very happily married and happiness cannot predict staying single versus being pretty happily married.
Using latent growth curves, this study investigates the association between intraindividual changes in marital quality and physical illness for 364 wives and husbands in the rural Midwest. The results reveal that both the initial level of and the change in the marital quality of husbands and wives correlate with the initial level of and the change in physical health, after controlling for the influence of work stress, education, and income. Additional analyses imply that psychological well-being and behaviors that are health risks mediate or explain this association. The results provide stronger evidence for the association between marital quality and physical illness for both husbands and wives than has been obtained from cross-sectional studies or from longitudinal studies that have been limited to the investigation of interindividual differences.