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Psychology of Men & Masculinities
Precarious Manhood and Men’s Physical Health Around the World
Joseph A. Vandello, Mariah Wilkerson, Jennifer K. Bosson, Brenton M. Wiernik, and Natasza Kosakowska-Berezecka
Online First Publication, August 1, 2022. http://dx.doi.org/10.1037/men0000407
CITATION
Vandello, J. A., Wilkerson, M., Bosson, J. K., Wiernik, B. M., & Kosakowska-Berezecka, N. (2022, August 1). Precarious
Manhood and Men’s Physical Health Around the World. Psychology of Men & Masculinities. Advance online publication.
http://dx.doi.org/10.1037/men0000407
Precarious Manhood and Men’s Physical Health Around the World
Joseph A. Vandello
1
, Mariah Wilkerson
1
, Jennifer K. Bosson
1
, Brenton M. Wiernik
1
, and
Natasza Kosakowska-Berezecka
2
1
Department of Psychology, University of South Florida
2
Institute of Psychology, University of Gdańsk
Cultural beliefs about the requirements of manhood may have implications for men’s physical health. In a
cross-cultural examination of 62 countries, we explored whether country-level endorsement of precarious
manhood beliefs (PMBs) was associated with various country-level risk-related health behaviors and
outcomes. In countries that more strongly endorsed PMBs, men had higher rates of risky health behaviors
(e.g., smoking, venomous animal contact) and risk-related health outcomes (e.g., liver cirrhosis mortality,
drownings), and lower life expectancy. The average size of correlations of PMB with health behaviors and
outcomes was moderate (rs=.21, .26), while correlations of PMB with men’s life expectancy were
relatively strong (rs=−.56, −.57). Overall, men live over six fewer years in countries higher versus lower in
PMB. The relationships between PMB and health behaviors and outcomes were attenuated but did not
completely disappear when controlling for country-level indicators of development and gender equality.
These findings suggest that country-level beliefs about gender, and not just men’s own masculinity and
masculinity-related beliefs, may have important connections to men’s health.
Public Significance Statement
Country-level beliefs about the precariousness of manhood status are related to men’s risk-related health
behaviors and outcomes across cultures. In countries in which people most strongly endorse precarious
manhood beliefs, men live over six fewer years than in cultures inwhich people least endorse such beliefs.
Keywords: precarious manhood beliefs, country-level indicators, health outcomes, risk behaviors, life
expectancy
Supplemental materials: https://doi.org/10.1037/men0000407.supp
Men the world over have poorer health outcomes than women,
particularly for life-threatening conditions (Kruger & Nesse, 2006;
Vandello et al., 2019). Women outlive men globally by an average
of over 4 years, a gender gap that holds in virtually every nation
(Mateos et al., 2020). At least part of the reason for this gender
disparity is that men make riskier health choices than women do.
In the United States, behavioral risks account for about half of all
mortality (Mokdad et al., 2004). Men’s relatively poorer health is
well-documented, but we still lack an understanding of how cultur-
ally shared beliefs about manhood (i.e., the social status of being a
male adult) are associated with men’s risky health choices and poor
outcomes. Here, we test the hypothesis that country-level beliefs
about the requirements of manhood—specifically, the belief that
manhood is a precarious status that must be proven and defended
(Vandello et al., 2008)—predict men’s risky behaviors and poor
health outcomes. Specifically, in this preregistered study (see https://
osf.io/746a5) we examine precarious manhood beliefs (PMBs)
across 62 countries to see if they predict nation-level variation in
risky behaviors and health outcomes for men.
Masculinity and Health
Researchers have long implicated aspects of male gender roles
in men’s risky behaviors (i.e., behaviors like binge drinking or
reckless driving which may impact a person’shealth)and
relatively poor health outcomes (e.g., liver cirrhosis, heart disease;
Courtenay, 2000;Harrison, 1978;Levant & Wimer, 2014).
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Joseph A. Vandello https://orcid.org/0000-0001-9318-4002
This research was funded by a grant from the National Science Center in
Poland (Grant 2017/26/M/HS6/00360) awarded to Natasza Kosakowska-
Berezecka. No portion of ideas or data appearing in the article were
previously disseminated at conferences or other publications.
The data are available at https://osf.io/b5nh2/files/.
The preregistered design and analysis plan are accessible at https://
osf.io/746a5.
Correspondence concerning this article should be addressed to Joseph
A. Vandello, Department of Psychology, University of South Florida,
4202 E. Fowler Avenue, Tampa, FL 33620, United States. Email:
vandello@usf.edu
Psychology of Men & Masculinities © 2022 American Psychological Association
ISSN: 1524-9220 https://doi.org/10.1037/men0000407
1
However, masculinity has a complex relationship with health
(Gerdes & Levant, 2018); reviews of widely used scales measur-
ing male role norm endorsement (Levant et al., 1992;Mahalik et
al., 2003) yield mixed findings. Endorsement of some male role
norms (e.g., self-reliance, self-sufficiency) predicts increases in
substance use problems and lower rates of health-promoting
behaviors (Gerdes & Levant, 2018;Himmelstein & Sanchez,
2016), whereas other male role norms (e.g., emotional control)
appear to buffer men from some negative health outcomes (Levant
&Wimer,2014) or simultaneously promote both healthy and
unhealthy behaviors (Gerdes & Levant, 2018).
Research examining the association of self-ascribed masculine
traits with health also paints a complex picture. For instance, agentic
traits such as competitiveness and assertiveness, which are pre-
scribed for men more strongly than women (Bosson et al., 2022;
Prentice & Carranza, 2002), tend to be associated with better health,
more health-promoting behaviors, and better adjustment to illness
(Helgeson & Lepore, 1997,2004). However, unmitigated agency,
an extreme focus on the self to the neglect of others, is associated
with poorer health behaviors and outcomes (Helgeson, 2012;
Helgeson & Fritz, 1999). For example, college students higher in
unmitigated agency reported more risky health behaviors (e.g.,
reckless driving, substance use; Danoff-Burg et al., 2006), and
male prostate cancer survivors high in unmitigated agency experi-
enced adverse changes in urine and bowel function (Helgeson &
Lepore, 2004).
Some work also examines associations between masculinity and
health outcomes at the country level. Perhaps the most well-known
work comes from Hofstede’s (2011) dimensions of national
cultures. Hofstede described a dimension of masculinity (vs.
femininity) as “related to the division of emotional roles between
women and men”with masculine cultures being those in which
there is maximum emotional and social role differentiation between
the genders. In masculine cultures, men are expected (much more
than women) to be assertive and ambitious, whereas in feminine
cultures both men and women are expected to be modest and caring.
Because a cultural emphasis on masculinity is associated with
greater male gender role stress (Arrindell et al., 2013), men may
experience cultural pressure to respond to gender role violations in
ways that risk their health. However, correlations of cultural
masculinity with country-level health behaviors and outcomes are
modest, and other Hofstede dimensions (e.g., individualism, power
distance) predict county-level health better than the masculinity
dimension does (Mackenbach, 2014).
Collectively, these studies suggest that traditional masculine
norms and traits are often, but not always, associated with risky
health behaviors and poorer health outcomes. However, this
research is predominantly conducted in Western, educated, indus-
trialized, rich, democratic (Henrich et al., 2010) nations and may not
generalize to other cultures or countries. In addition, past studies
have focused on specific masculine norms and traits (such as self-
reliance, unmitigated agency). In contrast, research on precarious
manhood (Bosson et al., 2021;Vandello & Bosson, 2013) empha-
sizes general beliefs about the nature of manhood itself. The
precarious manhood hypothesis argues that people around the
world conceptualize manhood (more than womanhood) as a
social status that must be earned and defended rather than a
biological state or developmental stage. Here, we ask whether
this conceptualization of manhood predicts behaviors that can be
harmful to men’s physical health. Specifically, we test whether
country-level PMBs predict men’s risky behaviors and poor health
outcomes. Individuals and countries differ in their endorsement of
PMBs, and exploring variation in this endorsement may shed light
on the connection between masculinity and men’s health.
Precarious Manhood, Risk, and Physical
Health Across Countries
Precarious manhood refers to the belief that manhood is an
achieved social status that must be earned and constantly defended,
because it can be lost or taken away. While this belief is pervasive
in varying degrees across cultures (Bosson et al., 2021), comparable
beliefs about womanhood status are rare (Gilmore, 1990;Vandello
& Bosson, 2013;Vandello et al., 2008). According to precarious
manhood theory, when men’s gender status is threatened, they may
become motivated to restore manhood by broadcasting masculine
competence to others. One effective way of doing this is by
enacting aggressive or risky behaviors that demonstrate toughness
and courage (e.g., Bosson et al., 2009;Weaver et al., 2013). This
may help explain why men are stereotyped as more risk-seeking
than women (Siegrist et al., 2016).
Of course, risky behaviors can have consequences for health in
both the short- and long term. Men, compared with women,
disproportionately participate in risky hobbies such as hunting
and extreme sports, and they take more risks across a range of
domains, such as driving, drug and alcohol use, and smoking
(Byrnes et al., 1999;Frick, 2021). All of these activities heighten
risks to health. Some risk-taking behaviors can result in immediate
injury or death, such as with accidents or drug overdoses, whereas
others accumulate over time to affect health more gradually, as with
a lifetime of heavy drinking, smoking, or unhealthy dietary choices
(Hu & Willett, 2002;Klatsky et al., 1992;Woloshin et al., 2008).
Ultimately, a lifetime of risk-taking can shorten men’s life
expectancy.
Here, we ask if PMBs have links with country-level variation in
men’s risky behaviors and health outcomes. If men use risky
behaviors as a means of proving their manhood, then we should
find that men’s rates of risky behaviors and risk-related health
outcomes are higher in countries that more strongly endorse
PMBs. Countries that view manhood as more precarious likely
exert more pressure on men to uphold male role norms of toughness
and courage via regular risk-taking activities, pursuit of risky
occupations, and lower rates of preventive and health-promoting
behaviors (e.g., Gilmore, 1990). Cumulatively, these country-level
pressures should be evident in men’s poorer physical health out-
comes and shorter life spans.
Although PMBs are evident around the world (Gilmore, 1990;
Vandello & Bosson, 2013), there is cross-country variance in the
extent to which people endorse these beliefs. Recently, we vali-
dated a brief four-item measure of PMB scale in a sample of over
33,000 participants in 62 countries (Bosson et al., 2021). The items
conveyed beliefs that manhood is difficult to earn (“Other people
often question whether a man is a ‘real man’”,“Some boys do not
become men no matter how old they get”) and easy to lose (“It is
fairly easy for a man to lose his status as a man,”“Manhood is not
assured—it can be lost”). Importantly, the PMB displayed metric
isomorphism across the individual and country levels, meaning that
scores collected from individuals represent a meaningful property
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2VANDELLO ET AL.
attributable to the country as a whole (Tay et al., 2014). Moreover,
country-level PMB scores correlated negatively with country-
level gender equality and human development (United Nations
Development Program, 2019;World Economic Forum, 2021), and
they correlated positively with ambivalent gender ideologies about
women and men (Glick & Fiske, 1996,1999). Thus, people view
manhood as harder to earn, and easier to lose, in countries charac-
terized by more patriarchal gender hierarchies and traditional sex-
based labor divisions; lower wealth, education, and life expectancy;
and more traditional, binary gender stereotypes and ideologies. To
explain these correlations, we theorize that country-level PMBs
function to socialize boys and men to internalize qualities—such
as toughness and competition—that will facilitate their success in
countries where the male gender role requires more danger, struggle,
intragroup competition, and risk (Gilmore, 1990).
This study makes several novel contributions. First, although
past investigations ask how individual men’s masculinity-related
beliefs and traits are connected to health, we ask here whether
broader, culturally shared beliefs about the nature of manhood—
beliefs shared by both men and women—relate in a theoretically
meaningful way to men’s health behaviors and outcomes. This is
important because country-level PMBs may shape men’s outcomes
independently of their personal masculine gender role endorsement
or self-perceived masculine traits. After all, one need not see
oneself as masculine, nor endorse traditional male gender role
norms (of say, assertiveness, aggression, stoicism), to be affected
by a cultural milieu that views manhood as a tenuous and imper-
manent status that is hard to earn and easy to lose. In the present
study, we use countries as a convenient shorthand for cultures,
though we acknowledge that countries are imperfect approxima-
tions of cultures, which exist within and across national boundaries.
Second, we connect these cultural beliefs to country-level, objec-
tive measures of men’s health behaviors and outcomes, rather than
to men’s subjective, self-reported perceptions of or attitudes toward
health. Third, we explore the links between PMBs and men’s
physical health in a globally diverse sample of 62 countries,
many of which are underrepresented in investigations of psychol-
ogy, culture, and health.
The Role of Women
Although the focus of the present study is on men’s health, the
precarious manhood perspective assumes that both men’sand
women’s beliefs about manhood should play vital roles in men’s
health. Consider that women, as socializers of children, play an
important role in teaching children about gender-relevant beliefs and
values (e.g., Gelman et al., 2004;Witt, 1997). Women also valorize
men’s risk-taking in the context of romantic relationships (Bassett &
Moss, 2004;Sylwester & Pawlowski, 2011), and penalize men who
violate gender roles in lab experiments (cf. Rudman et al., 2012).
Thus, women who view manhood as precarious may question the
masculinity of men who wear masks to guard against COVID-19, or
who eat salads instead of steaks, or who back down from a fight. If
so, then women’s evaluations may contribute to the pressure that
men feel to take health-related risks to “be a real man”Women’s
perceptions of the precarity of manhood likely influence men’s risky
behaviors and health outcomes indirectly by shaping cultural mas-
culinity norms. More directly, women’s beliefs about manhood may
influence men’s health outcomes if women act as primary
health care navigators for the male members of their families
(Norcross et al., 1996).
For these reasons, and unlike past studies on masculinity and
health, the present research uses samples consisting of responses
from both men and women. Specifically, we examined the country-
level PMBs of both men and women. Men’s and women’s PMBs
within countries tend to be strongly correlated (Bosson et al., 2021),
and in countries lower in gender equality and human development,
women tend to endorse PMBs even more strongly than men. Thus,
it is important to include women’s beliefs in predicting men’s health
behaviors outcomes. In cultures in which women, as well as men,
endorse precarious manhood most strongly, we expected men to
take greater risks with their health.
Overview and Hypotheses
This study, which uses data from a large-scale, cross-cultural
investigation of gender beliefs (see https://osf.io/fqd4p/), examined
the links of country-level PMB (Bosson et al., 2021) with men’s
objective rates of health-related risk behaviors and risk-related
health outcomes across 62 countries. We distinguished between
“risky behaviors”and “health outcomes”as an organizational device
for analyses, while recognizing that some variables straddle the
line between behavior and outcome (e.g., do “transportation acci-
dents”reflect a behavior—risky driving—or an outcome—possible
injury or death?).
For use as outcome measures, we identified as many variables as
possible, reported at the aggregated country-level for most of the
countries in our data set that could serve as health-related risk
behaviors and risk-related health outcomes. See Table 1,for
operationalizations, sources, numbers of countries for which
data are available, and health-relevance of these variables. Note
that our selection of outcome measures was theoretically driven,
and we initially considered a larger set of variables than those
described in Table 1. However, some relevant variables reflecting
our constructs of interest (e.g., condom or seatbelt use as indices of
health-related risk behaviors) were ultimately abandoned due to
insufficient availability of country-level data or lack of clear
association with risk-taking (e.g., heart disease). The final prere-
gistered risk-related variable set included four health-related risk
behaviors (smoking, high volume drinking, substance use disor-
ders, and venomous animal contact) and eight risk-related health
outcomes (lung cancer, liver cirrhosis mortality,
1
drowning, death
from venomous animal contact, death from injuries,
2
transportation
accidents, COVID-19 infections, and COVID-19 deaths). We also
included overall and healthy life expectancy as exploratory health
outcomes.
We included several controls. First, to establish the discriminant
validity of the PMB, we compared the PMB’s association with risk-
related behaviors and health outcomes to its association with nonrisk
behaviors and outcomes. We reasoned that, if country-level PMB is
a cultural ideology that socializes men to prove manhood via risk-
taking, then PMB should correlate more strongly with men’s risky
behaviors and health outcomes than with men’s nonrisk behaviors
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1
In our preregistration, we incorrectly listed this variable as “prevalence
of liver cirrhosis”rather than mortality from liver cirrhosis.
2
In our preregistration, we incorrectly listed this variable as “death from
accidents”rather than death from injuries.
PRECARIOUS MANHOOD AND MEN’S PHYSICAL HEALTH 3
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Table 1
Health-Related Risk Behaviors and Risk-Related Health Outcomes
Variable Index Data source Associated health outcomes Behavioral risk factor
Behavior (Number of countries)
Smoking (n=58) Percentages of men and women
(aged 15+) who smoke any form
of tobacco
World Health Organization (2018a) Cancer, heart disease, strokes, diabetes
(Centers for Disease Control [CDC],
2021a)
High-volume drinking
(n=26)
Percentages of men and women
drinking more than 1 ounce of
ethanol (or equivalent) per day for
the preceding year
Wilsnack et al. (2009) Accidents, injuries, poisoning, heart
disease, cancer, and cirrhosis (Centers
for Disease Control [CDC], 2021b)
Substance use disorders
(n=59)
Percentages of men and women
diagnosed with drug use disorders
Institute for Health Metrics and
Evaluation (2018b)
Accidents, injuries, heart disease, strokes,
and cancer (National Institute of Drug
Abuse [NIDA], 2020)
Venomous animal contact
(n=58)
Age standardized rates of venomous
animal contact resulting in injuries
Institute for Health Metrics and
Evaluation (2018a)
Injury, neurological, muscular, and
gastrointestinal damage, paralysis, and
death (Harris & Goonetilleke, 2004)
Health outcome
(Number of countries)
Lung cancer (n=59) Rates of lung cancer per
100,000 people
International Agency for Research on
Cancer (2021)
Smoking (Mayo Clinic, 2021)
Liver cirrhosis
(n=59)
Death rates (aged 15+) from liver
cirrhosis per 100,000 people
World Health Organization (2021c) Long-term alcohol use (National Institute
of Diabetes and Digestive and Kidney
Diseases [NIDDK], 2021)
Drowning (n=47) Death by drowning per 100,000 people World Health Organization (2021b) Risky water activities (WHO, 2021b)
Death from venomous
animal contact
(n=58)
Mortality rate from contact with
venomous animals
Institute for Health Metrics and
Evaluation (2018a)
Agriculture, hunting, and outdoor work ()
Death from injuries
(n=59)
Death by injury as a percent
of population
World Bank (2021a) Risk-taking in work or leisure
activities (Turner et al., 2004)
Transportation accidents
(n=47)
Transportation accidents per 100,000
people
World Health Organization (2021c) Risky and aggressive driving
(National Highway Traffic Safety
Administration, 2021)
COVID-19 infections
(n=53)
Percentage of confirmed COVID cases
that are male
GlobalHealth5050 (2021) Mask, social distancing, and vaccination
refusal (Centers for Disease Control
[CDC], 2021c)
COVID-19 deaths
(n=52)
Percentage of confirmed COVID deaths
that are male
GlobalHealth5050 (2021) Mask, social distancing, and vaccination
refusal; avoidance of hospitals (CDC,
2021c)
Life expectancy
(n=58)
Average number of years a newborn
could be expected to live
World Health Organization (2021a)
Healthy life expectancy
(n=58)
Average number of years that a person
can expect to live in “full health”by
taking into account years lived in
less than full health due to disease
and/or injury.
World Health Organization (2021a)
4VANDELLO ET AL.
and health outcomes. For nonrisk behaviors, we used five behaviors:
borrowing money, saving money, shopping online, literacy, and
living in urban areas. Note that some of these behaviors may reflect a
degree of risk-taking (e.g., borrowing money can be risky), and
some show cross-cultural gender differences (e.g., literacy gaps
favor boys and men globally; Schwab et al., 2017). Nonetheless, we
selected these variables because they have less direct relevance for
physical health compared to the set of risky behaviors. For non-risk-
related health outcomes, we used leukemia, multiple sclerosis,
psoriasis, Alzheimer’s disease, appendicitis, and mortality rates
from air pollution. The causes of these health outcomes vary, but
importantly, they are generally unassociated with risk-taking and
gender (see https://www.mayoclinic.org).
As additional controls, we also examined whether the PMB’s
links with men’s risk-related behaviors and health outcomes would
remain once accounting for cross-country differences in (a) wo-
men’s rates of risk-related behaviors and health outcomes,
(b) human development (the inequality-adjusted Human Develop-
ment Index [IHDI]; United Nations, 2021), (c) access to physicians,
and (d) gender inequality (the Global Gender Gap index [GGGI];
World Economic Forum, 2021). We reasoned that, if country-level
PMB imposes unique pressures on men’s gendered behaviors, then
links of PMB with men’s risk behaviors and health outcomes should
persist when controlling for women’s rates of the same behaviors
and outcomes. Moreover, country-level human development, access
to physicians, and gender inequality reflect (to varying degrees) a
country’s wealth, access to desirable resources such as education
and health care, and traditional gender arrangements and beliefs.
These are all theoretically relevant third variables that could at least
partially explain the links between PMB and men’s health outcomes.
Preregistered hypotheses (https://osf.io/746a5/?view_only=f879
165889ee4aa4ac8206f5493a54a9) were as follows:
Hypothesis 1: Country-level PMB will positively predict men’s
rates of health-related risk behaviors.
Hypothesis 2: Country-level PMB will positively predict men’s
rates of risk-related health outcomes.
Hypothesis 3: On average, country-level PMB will positively
predict men’s rates of health-related risk behaviors more
strongly than they will predict men’s rates of nonrisk behaviors.
Hypothesis 4: On average, country-level PMB will positively
predict men’s rates of risk-related health outcomes more
strongly than they will predict men’s rates of nonrisk-related
health outcomes.
Hypothesis 5: The above associations (H1–H4) will hold when
controlling for women’s rates of the same behaviors and health
outcomes.
We posed several exploratory questions: Does country-level
PMB predict general overall and healthy life expectancy? Will tests
of Hypotheses 1–4 remain significant when controlling for country-
level indices of human development (IHDI), physician availability,
and gender equality (GGGI)?
Method
As part of another project (see https://osf.io/fqd4p/), we surveyed
college student samples from 62 nations (N=33,417) on their
gender beliefs and attitudes. Data were collected between January
2018 and February 2020. From that data set, Bosson et al. (2021)
created country-level PMB scores. To locate worldwide behavior
and health data, we searched publicly available databases for
country-level data split by binary gender. For most variables,
data were available for most, but not every, country for which
we had PMB scores, and thus sample sizes vary across individual
analyses.
Precarious Manhood Beliefs
Table 2 presents scores on the four-item PMBs Scale for the 62
countries in our sample, along with three country-level control
variables: IHDI, physicians, and GGGI. PMB scores were derived
from a confirmatory factor analysis and are presented as factor
scores (theoretical range is −2.1 to 2.1; M=0, SD =1.00). For each
country, a PMB score was calculated by taking the mean factor score
from the student samples. See Bosson et al. (2021), for details
regarding sample composition and psychometric validation of the
PMB.
Health-Related Risk Behaviors and
Risk-Related Health Outcomes
Table 1 details how we operationalized and retrieved all the risk
behaviors and health outcomes.
Nonrisk Behaviors and Health Outcomes
For nonrisk behaviors, we retrieved men’s and women’s data
from the World Bank’s (2021a) Gender Data Portal as follows.
Borrowed money: percentage (Age 15+) who borrowed any money
in the past year. Saved money: percentage (Age 15+) who saved any
money in the past year. Shopped online: percentage (Age 15+) who
bought something online in the past year. Adult literacy rates:
percentage (Age 15+) who are literate. Living in urban areas:
percentage who live in urban areas. For nonrisk health outcomes,
we retrieved prevalence rates for men and women per 100,000
people from the Institute for Health Metric and Evaluation (2021)
for appendicitis,psoriasis,leukemia, Alzheimer’s disease and other
dementias, and multiple sclerosis. We retrieved mortality attributed
to household and ambient air pollution for men and women per
100,000 people from the World Bank’s (2021a,2021b) Gender Data
Portal.
Control Variables
The IHDI indexes a nation’s development, comprising measures
of health, education, and economics, adjusted for inequalities
on each dimension (United Nations, 2021). Scores can range
from 0 to 1, with higher scores indicating more developed countries.
Physicians per 1,000 people (World Bank, 2021b) indexes the
health resources available to a country’s citizens. The GGGI indexes
women’s disadvantages, relative to men’s, in economic, educational,
health, and political domains (World Economic Forum, 2021).
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PRECARIOUS MANHOOD AND MEN’S PHYSICAL HEALTH 5
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Table 2
Precarious Manhood Beliefs and Other Country-Level Variables for 62 Countries
Country N
PMB
IHDI GGGI Physicians per 1,000MSD
Albania 239 0.72 1.09 .71 .77 1.2
Argentina 424 −0.32 1.04 .73 .75 4.0
Armenia 282 0.05 1.07 .70 .68 4.4
Australia 664 0.04 1.01 .87 .73 3.7
Belgium 1,951 −0.30 0.93 .86 .75 3.1
Bosnia and Herzegovina 219 −0.12 1.28 .67 .71 2.2
Brazil 1,150 −0.03 1.01 .57 .69 2.2
Canada 913 0.03 0.89 .85 .77 2.6
Chile 237 −0.06 1.09 .71 .72 2.6
China 600 0.17 0.78 .64 .68 2.0
Colombia 615 −0.16 1.02 .60 .76 2.2
Croatia 363 0.47 0.89 .78 .72 3.0
Czechia 423 −0.04 1.00 .86 .71 4.1
Denmark 255 −0.30 0.87 .88 .78 4.0
England 744 −0.10 0.98 .86 .77 2.8
Finland 314 −0.78 0.86 .89 .83 3.8
France 422 −0.41 0.97 .82 .78 3.3
Georgia 197 0.39 1.17 .72 .71 7.1
Germany 1,864 −0.49 0.94 .87 .78 4.2
Ghana 329 0.53 1.12 .44 .67 0.1
Greece 282 −0.20 0.92 .79 .70 5.5
Hungary 768 0.41 0.95 .79 .68 3.4
India 388 −0.01 0.97 .59 .69 0.9
Indonesia 255 0.18 0.81 .70 .70 0.4
Iran 174 0.66 0.90 .69 .58 1.6
Ireland 571 0.10 0.94 .89 .80 3.3
Italy 2,419 0.07 0.95 .78 .71 4.0
Japan 397 0.49 0.72 .84 .65 2.4
Kazakhstan 344 0.52 0.98 .77 .71 4.0
Kosovo 433 0.80 1.05 —.77 —
Lebanon 134 0.42 0.98 —.60 2.1
Lithuania 355 0.19 1.12 .79 .75 6.4
Luxembourg 181 −0.06 1.11 .83 .73 3.0
Malta 254 0.23 1.01 .82 .69 2.9
Mexico 343 −0.18 0.99 .61 .75 2.4
Morocco 294 0.05 1.04 —.61 0.7
Nepal 219 0.21 0.96 .45 .68 0.7
Netherlands 893 −0.36 0.89 .88 .74 3.6
New Zealand 216 0.05 0.85 .86 .80 3.6
Nigeria 461 0.65 1.06 .35 .64 0.4
Northern Ireland 303 −0.06 1.01 .86 .77 —
Norway 210 −0.42 0.95 .90 .84 2.9
Pakistan 573 0.18 0.88 .38 .56 1.0
Philippines 468 0.26 0.94 .59 .78 0.6
Poland 843 0.34 1.00 .81 .74 2.4
Portugal 173 −0.39 0.86 .76 .74 5.1
Romania 253 0.36 1.03 .73 .72 3.0
Russia 698 0.41 1.03 .74 .71 4.0
Serbia 720 0.27 1.12 .71 .74 3.1
Slovakia 622 0.29 0.98 .81 .78 3.4
South Africa 415 0.40 0.97 .47 .72 0.9
Spain 1,235 −0.52 0.95 .78 .80 3.9
Suriname 182 0.32 1.02 .56 .71 1.2
Sweden 671 −0.46 0.98 .88 .82 4.0
Switzerland 581 −0.44 0.94 .89 .78 4.3
Turkey 1,495 −0.34 1.11 .68 .64 1.8
UAE 510 0.38 1.00 —.72 2.5
Ukraine 285 0.55 0.94 .73 .66 3.0
Uruguay 187 −0.32 0.84 .71 .72 5.1
United States 786 0.15 1.01 .81 .74 2.6
Vietnam 408 0.17 0.85 .59 .70 0.8
Wales 213 0.07 1.05 .86 .77 —
Total sample 33,417 0.00 1.00
Note. PMB =Precarious Manhood Beliefs Scale (Bosson et al., 2021); IHDI =Inequality adjusted Human Development Index (United Nations, 2021);
GGGI =Global Gender Gap Index (Mateos et al., 2020).
6VANDELLO ET AL.
Scores can range from 0 to .99, with higher scores indicating more
gender parity.
Results
Analysis Plan
To test Hypotheses 1 and 2, we estimated simple correlations
between PMB and each behavior or outcome. Hypotheses 3 and 4
concern differences in mean correlations between risk-taking and
non-risk-taking behaviors or outcomes. After consulting with a
statistician (Author 4) about the most appropriate method, we
estimated the average correlation for each category of behaviors/
outcomes by fitting multilevel models with outcomes nested
within countries, with random intercepts for countries and random
slopes for behaviors/outcomes. This approach accounts for multiple
testing by partially pooling correlations across outcomes (see
Gelman et al., 2012). In the multilevel models, the key parameter
of interest is the coefficient for the PMB ×Risk category (non-risk-
taking vs. risk-taking) interaction. This coefficient indicates the
difference in mean PMB correlations between the two risk catego-
ries. For these analyses, we standardized the PMB and behavior/
outcome variables across countries so that model parameters can be
interpreted in the correlation metric. Because standardized variables
have M=0bydefinition, we fixed the global intercept in the models
to 0 and did not include random intercepts for behaviors/outcomes.
We fit these models using the lme4 packages in R (Bates et al., 2015;
R Core Team, 2021).
To test Hypothesis 5 and the exploratory hypotheses related to
robustness when controlling for other country-level factors, we
estimated partial correlations of PMB with each outcome, control-
ling for one control variable at time, and re-estimated the above
multilevel models adding one control variable at a time as a fixed
covariate.
Given our limited sample size (ns=26–61 countries per correla-
tion), we did not necessarily expect pvalues for all individual
correlations to reach statistical significance at the critical αlevel of
p<.05. In presenting results, we therefore attend primarily to the
average correlations for each risk category estimated from the
multilevel model and focus interpretation on the estimated effect
sizes and confidence intervals, paying less attention to pvalues (e.g.,
McShane et al., 2019). In keeping with the latest empirical bench-
marks for effect sizes in social psychological research (where
correlations of .12, .25, and .42 can be taken as thresholds for
“small,”“medium,”and “large”effects; see Lovakov & Agadullina,
2021), and in cross-cultural research (where average group-level
main effects are around ρ=.21; Taras et al., 2010), we consider an
r≥.20 to be a medium effect and an r≥.30 to be a large effect (cf.
Funder & Ozer, 2019).
Correlations of PMB With Men’s Risk
Behaviors and Health Outcomes
Hypotheses 1 and 2 state that country-level mean PMB positively
predicts men’s rates of the four risk behaviors and the eight physical
health outcomes. Table 3 reports the bivariate correlations of PMB
with the behaviors and health outcomes, as well as results from
multilevel models. Examining Hypothesis 1, PMB showed moder-
ate-to-strong positive correlations with men’s health risk-taking
behaviors (mean r=.21, 95% confidence interval, CI [.06, .36],
p=.006, based on the multilevel model), particularly smoking (r=
.30), high volume drinking (r=.31), and injuries from venomous
animal contact (r=.34). Examining Hypothesis 2, PMB also
showed moderate-to-strong positive correlations with men’s risk-
related health outcomes (mean r=.26, 95% CI [.04, .48], p=.021,
based on the multilevel model), particularly liver cirrhosis deaths
(r=.48), drownings (r=.47), and transportation accidents (r=.40).
Exploratory analyses revealed that PMB correlated strongly
negatively with men’s general life expectancy and healthy life
expectancy (see Table 3). Stated differently, in countries high
(1 SD above the mean) versus countries low (1 SD below the
mean) in PMB, men live on average 6.69 fewer years (70.68 vs.
77.37 years, 95% CI [4.03, 9.35]), and 6.17 fewer healthy years
(62.69 vs. 68.86 years, 95% CI [3.80, 8.55]). Figure 1 shows a
scatterplot of the association of country-level PMB with men’s
general life expectancy (the figure for men’s healthy life expectancy
is nearly identical).
Overall, there was support for Hypotheses 1 and 2, in that that
country-level PMB positively predicted men’s rates of health-
related risk behaviors and risk-related health outcomes, with average
correlations in the moderate range. Moreover, exploratory analyses
on life expectancy yielded large effects such that men live shorter
lives in countries higher in PMB. Given the limited number of
countries available for some behaviors and outcomes, we urge
caution against overinterpreting differences in the strength of
PMB correlations with measures within a risk category (e.g.,
PMB-smoking vs. PMB-venomous animal contact) and recommend
focusing on the estimated mean correlations for each risk category
instead.
Comparing Risk-Related to Low-Risk
Behaviors and Outcomes
Hypotheses 3 and 4 state that country-level mean PMB will
positively predict men’s rates of health-related risk behaviors and
risk-related health outcomes more strongly than they will predict
men’s rates of non-risk-related behaviors and health conditions.
Based on the multilevel models (see Table 3) and supporting
Hypothesis 3, risk-related behaviors were much more strongly
positively related to PMB than non-risk-related behaviors (differ-
ence in mean correlations β=.56, 95% CI [.39, .73], p<.001), with
risk-related behaviors showing a moderate positive mean correlation
with PMB (mean r=.21, 95% CI [.06, .36], p=.006) and non-risk-
related behaviors showing a strong negative mean correlation with
PMB (mean r=−.34, 95% CI [−.48, −.21], p<.001). Similarly,
supporting Hypothesis 4, risk-related health outcomes (mean r=
.26, 95% CI [.04, .48], p=.021) were more strongly positively
related to PMB than non-risk-related health outcomes (mean r=
−.08, 95% CI [−.36, .20], p=.588; difference in mean correlations
β=.34, 95% CI [.00, .68], p=.063).
Note, however, that the non-risk-related behaviors correlated
strongly negatively with PMB. With some nonrisk variables—for
instance, saving money and shopping online—these negative cor-
relations may reflect the higher economic development of countries
lower in PMB (PMB correlated with IHDI r=−.42, p<.001). Thus,
although Hypotheses 3 and 4 were technically supported, the large
differences between the PMB’s association with risk-related and
non-risk related variables were driven as much by negative
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PRECARIOUS MANHOOD AND MEN’S PHYSICAL HEALTH 7
associations of PMB with nonrisk variables as they were by positive
associations of PMB with risky behaviors and health outcomes.
Controlling for Women’s Behaviors and Outcomes
Hypothesis 5 states that the associations observed in tests of
Hypotheses 1–4 should remain when controlling for women’s rates
of the same behaviors and outcomes. We tested this hypothesis using
partial correlations and by adding the women’s rate as a covariate to
the multilevel models (except for analyses on COVID-19 infections
and deaths, as these rates are operationalized as “percentage that are
male,”and thus already control for women’srates).
As shown in Table 4, correlations of PMB with men’s risk-related
behaviors were weakened when controlling for women’s rates (mean
r=.17, 95% CI [−.00, .35], p=.060), with only the individual
correlation between PMB and men’s smoking remaining substantial
(r=.33). PMB correlations with non-risk-related behaviors were
much smaller when controlling for women’s rates of these behaviors
(mean r=−.07, 95% CI [−.23, .09], p=.376), so the difference in
mean correlations between risk-related and non-risk-related beha-
viors was reduced in magnitude by nearly half (difference in mean
correlations β=.25, 95% CI [.00, .48], p=.047).
Similarly, correlations of PMB with men’s risk-related health
outcomes were weakened when controlling for women’s rates
(mean r=.13, 95% CI [−.00, .26], p=.068). The difference in
mean correlations between risk-related and non-risk-related health
outcomes was also reduced in magnitude by nearly half when
controlling for women’s rates (difference in mean correlations
β=.16, 95% CI [−.03, .36], p=.100).
Correlations of PMB with men’s life expectancy were also
reduced when controlling for women’s life expectancy, but never-
theless remained moderately negative (general life expectancy r=
−.22, 95% CI [−.45, .04]; healthy life expectancy r=−.30, 95% CI
[−.52, −.04]).
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Table 3
Correlations of Precarious Manhood Beliefs With All Outcomes
Category Measure nr/Coefficient 95% CI tp
Behaviors
Risk-related Smoking 58 .30 [.04, .52] 2.32 .024
High-volume drinking 26 .31 [−.09, .62] 1.60 .123
Substance use disorders 59 −.05 [−.30, .21] −0.36 .723
Venomous animal contact 58 .34 [.09, .55] 2.71 .009
Mean r .21 [.06, .36] 2.75 .006
Non-risk-related Borrowing money 59 −.27 [−.49, −.02] −2.14 .037
Saving money 59 −.36 [−.57, −.12] −2.94 .005
Shopping online 59 −.48 [−.66, −.26] −4.16 <.001
Literacy 36 −.26 [−.54, .08] −1.55 .129
Living in urban areas 58 −.32 [−.53, −.06] −2.51 .015
Mean r −.34 [−.48, −.21] −5.06 <.001
MLM parameters Mean correlation (all behaviors) −.07 [−.17, .03] −1.21 .227
Difference: Mean r(risk) −Mean r(nonrisk) .56 [.39, .73] 5.88 <.001
Random SD of correlations .06 [.00, .17]
Outcomes
Risk-related Lung cancer 59 −.03 [−.28, .23] −0.20 .843
Liver cirrhosis mortality 59 .48 [.26, .66] 4.14 <.001
Drowning 47 .47 [.21, .67] 3.57 <.001
Death from venomous animal 58 .20 [−.06, .44] 1.53 .132
Death from injuries 59 .17 [−.09, .41] 1.34 .187
Transportation accidents 47 .40 [.12, .61] 2.90 .006
COVID-19 infections 53 .18 [−.09, .43] 1.31 .195
COVID-19 deaths 52 .26 [−.02, .50] 1.88 .065
Mean r .26 [.04, .48] 2.31 .021
Non-risk-related Leukemia 61 −.55 [−.70, −.34] −5.05 <.001
Multiple sclerosis 60 −.34 [−.55, −.09] −2.74 .008
Alzheimer’s disease 61 .34 [.10, .55] 2.79 .007
Appendicitis 61 −.34 [−.54, −.09] −2.74 .008
Death from air pollution 59 .51 [.29, .68] 4.48 <.001
Mean r −.08 [−.36, .20] −0.54 .588
MLM parameters Mean correlation (all behaviors) .09 [−.08, .26] 1.01 .312
Difference: Mean r(risk) −Mean r(nonrisk) .34 [.00, .68] 1.86 .063
Random SD of correlations .29 [.17, .44]
Cumulative life outcomes
General life expectancy 58 −.56 [−.71, −.35] −5.04 <.001
Healthy life expectancy 58 −.57 [−.72, −.37] −5.21 <.001
Note. MLM =multilevel model; n=number of countries providing data for the behavior/outcome; r/Coefficient =Pearson correlation or multilevel model
parameter (mean rfor risk-related and non-risk-related categories estimated from the multilevel models); 95% CI =95% confidence interval (Fisher z-based for
bivariate correlations, profile likelihood-based for multilevel model results), pvalues for multilevel model parameters computed using normal approximation;
focal parameters of interest highlighted in bold.
8VANDELLO ET AL.
Together, these results provide little support for Hypothesis 5, in
that most associations of PMB with men’s behaviors and health
outcomes were attenuated when controlling for women’s rates of
these behaviors and outcomes. However, the PMB’s links with
men’s life expectancy remained moderate when controlling for
women’s life expectancy.
Controlling for Human Development,
Physicians, and Gender Equality
As a further test of the robustness of the relationships of PMB
to men’s health behaviors and outcomes, we conducted a series
of partial correlations and multilevel model analyses to examine if
and how these relationships change when controlling for human
development (the IHDI), physicians per 1,000 people, and gender
equality (the GGGI). Table 5 summarizes these analyses. In all
cases, results did not meaningfully change when controlling for
any of these potential confounding factors. Accordingly, these
analyses suggest that resource and gender equality-related contex-
tual variables cannot account for the observed relationships of
country-level mean PMB and men’s risk-related behaviors and
health outcomes.
Exploratory Analyses With Cultural Masculinity
Although Hofstede’s (2011) cultural masculinity dimension
was not of primary interest, we conducted exploratory analyses
examining its association with PMB and with men’shealth
outcomes. The bivariate correlation between country-level mas-
culinity and PMB was r=.46, p=.003, indicating that these two
variables share conceptual overlap. However, as detailed in the
Supplemental Material analyses document, PMB was a stronger
and more consistent predictor of men’s health outcomes than
cultural masculinity.
Discussion
The precarity of manhood relative to womanhood is a widely
held belief around the world (Bosson et al., 2021;Gilmore, 1990;
Vandello et al., 2008). If members of a country endorse this belief
in general, they may socialize and pressure boys and men to prove
and defend their manhood status to others. Men in such cultures
may thus learn to perform risky behaviors that have short- and
long-term consequences for their health. In this preregistered
study, we tested this logic by examining whether country-level
PMBs are associated with men’s risky health behaviors and
negative health outcomes.
As predicted, we found that country-level PMBs predicted
men’s country-level risky health behaviors and risky health out-
comes, with moderate mean correlations (mean rs=.21 and .26).
While these average effect sizes may seem modest in conventional
terms, they are near the median of the distribution of effects
observed in social and cross-cultural psychology research, and
there is an increasing recognition that what have traditionally
been considered small statistical effects can be important when
considered at a larger scale and over time, particularly for health
behaviors (Gotz et al., 2022).
Moreover, the accumulation of various physical health risk
factors can add up to substantial effects. When considering overall
life expectancy as an outcome, there were strong associations
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Figure 1
Male Life Expectancy by Country-Level Mean Precarious Manhood Beliefs
PRECARIOUS MANHOOD AND MEN’S PHYSICAL HEALTH 9
between country-level PMBs and men’s total and healthy life
expectancy. Men in countries high in PMBs can be expected to
live over six fewer years than men in countries low in PMBs.
Notably, this association held and remained medium-to-large
in size even when controlling for country-level variation in
women’s life expectancy, human development, availability of
physicians, and gender equality. Although not preregistered and
thus exploratory, this finding is perhaps the most exciting and
powerful to emerge from this study: country-level endorsement
of the belief that manhood is “hard won and easily lost”uniquely
and strongly predict how long men in that country will live a
healthy life.
To test the discriminant validity of these associations, we
examined relationships between PMBs and behaviors and
outcomes that have no obvious direct relationship to risk.
These nonrisk behaviors and health outcomes were sometimes
positively correlated, sometimes uncorrelated, and sometimes
unexpectedly negatively correlated, with country-level PMBs.
Unfortunately, the unexpected negative associations muddied
the conclusions we can draw from analyses that compared the
PMB’s link with risk-related versus nonrisk-related behaviors
and outcomes (though the negative association with low-risk
outcomes was minimal). Although the sets of correlations dif-
fered significantly from each other, this was likely driven as
much by their opposite signs as by their differing effect sizes.
Nonetheless, the PMB displayed discriminant validity insofar as
it associated positively with risk-related behaviors and health
outcomes, and negatively with behaviors and health outcomes
unassociated with risk. As noted earlier, some of the variables we
selected to reflect nonrisk behaviors and outcomes are factors
that likely covary meaningfully with the economic development
of a country, such as literacy rates and death from air pollution.
Thus, it makes sense that these variables would correlate nega-
tively with PMBs, which presumably reveal something about the
competitiveness of daily life in environments that are physically
difficult or have scarce resources.
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Table 4
Partial Correlations of Precarious Manhood Beliefs With All Outcomes, Controlling for Women’s Rates
Category Measure nr/Coefficient 95% CI tp
Behaviors
Risk-related Smoking 58 .33 [.08, .54] 2.61 .012
High-volume drinking 26 −.02 [−.41, .37] −0.12 .908
Substance use disorders 59 .11 [−.15, .36] 0.87 .394
Venomous animal contact 58 .05 [−.21, .31] 0.41 .686
Mean r .17 [−.00, .35] 1.88 .060
Non-risk-related Borrowing money 59 −.36 [−.56, −.11] −2.91 .006
Saving money 59 −.07 [−.32, .19] −0.55 .586
Shopping online 59 −.06 [−.31, .20] −0.48 .637
Literacy 35 −.18 [−.49, .16] −1.08 .297
Living in urban areas 58 .11 [−.15, .36] 0.84 .410
Mean r −.07 [−.23, .09] −0.89 .376
MLM parameters Mean correlation (all behaviors) .05 [−.07, .17] 0.81 .418
Difference: Mean r(risk) –Mean r(nonrisk) .25 [.00, .48] 1.99 .047
Random SD of correlations .16 [.07, .27]
Outcomes
Risk-related Lung cancer 59 .13 [−.13, .38] 1.02 .315
Liver cirrhosis mortality 59 .13 [−.13, .38] 1.01 .322
Drowning 47 .21 [−.08, .47] 1.47 .152
Death from venomous animal 58 .09 [−.17, .34] 0.68 .504
Death from injuries 59 .22 [−.04, .45] 1.67 .103
Transportation accidents 47 .06 [−.23, .34] 0.38 .708
Mean r .13 [−.00, .26] 1.83 .068
Non-risk-related Lukemia 61 −.30 [−.51, −.05] −2.40 .020
Multiple sclerosis 60 .17 [−.09, .41] 1.31 .199
Alzheimer’s disease 61 .35 [.11, .56] 2.89 .006
Appendicitis 61 −.37 [−.57, −.13] −3.04 .004
Death from air pollution 59 .31 [.06, .53] 2.50 .016
Mean r −.04 [−.18, .11] −0.49 .624
MLM parameters Mean correlation (all behaviors) .04 [−.06, .15] 0.86 .392
Difference: Mean r(risk) –Mean r(nonrisk) .16 [−.03, .36] 1.65 .100
Random SD of correlations .14 [.07, .23]
Cumulative life outcomes
General life expectancy 58 −.22 [−.45, .04] −1.67 .103
Healthy life expectancy 57 −.30 [−.52, −.04] −2.35 .024
Note. MLM =multilevel model; n=number of countries providing data for the behavior/outcome; r/Coefficient =Pearson correlation or multilevel model
parameter (mean rfor risk-related and non-risk-related categories estimated from the multilevel models); 95% CI =95% confidence interval (Fisher z-based for
bivariate correlations, profile likelihood-based for multilevel model results), pvalues for multilevel model parameters computed using normal approximation;
focal parameters of interest highlighted in bold.
10 VANDELLO ET AL.
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Table 5
Partial Correlations of Precarious Manhood Beliefs With All Outcomes, Controlling for IHDI, Physicians, or GGGI
Category Measure
IHDI Physicians per 1,000 GGGI
r/Coef. 95% CI pr/Coef. 95% CI pr/Coef. 95% CI p
Behaviors
Risk-related Smoking .28 [.01, .51] .041 .33 [.07, .54] .013 .19 [−.07, .43] .159
High-volume drinking .16 [−.24, .51] .450 .14 [−.26, .50] .514 .05 [−.35, .43] .828
Substance use disorders −.01 [−.27, .25] .928 −.07 [−.32, .20] .628 −.09 [−.34, .17] .479
Venomous animal contact .14 [−.13, .39] .313 .23 [−.03, .46] .085 .19 [−.08, .42] .168
Mean r .19 [.02, .37] .027 .22 [.07, .37] .005 .21 [.06, .37] .006
Non-risk-related Borrowing money −.15 [−.40, .12] .273 −.28 [−.50, −.02] .035 −.10 [−.35, .16] .449
Saving money −.20 [−.45, .06] .138 −.35 [−.56, −.11] .007 −.18 [−.41, .08] .188
Shopping online −.28 [−.50, −.01] .044 −.38 [−.58, −.13] .004 −.31 [−.52, −.05] .020
Literacy −.13 [−.45, .22] .472 −.10 [−.41, .24] .579 −.13 [−.44, .21] .460
Living in urban areas −.14 [−.39, .13] .320 −.23 [−.46, .03] .092 −.25 [−.48, .00] .057
Mean r −.36 [−.51, −.20] <.001 −.34 [−.48, −.20] <.001 −.34 [−.48, −.21] <.001
MLM Mean correlation (all behaviors) −.08 [−.20, .04] .191 −.06 [−.17, .05] .281 −.07 [−.17, .04] .242
Difference: Mean r(risk) –Mean r(nonrisk) .55 [.33, .77] <.001 .56 [.37, .75] <.001 .56 [.39, .73] <.001
Random SD of correlations .10 [.00, .21] .05 [.00, .17] .06 [.00, .17]
Outcomes
Risk-related Lung cancer .24 [−.02, .47] .076 .15 [−.11, .39] .259 −.00 [−.26, .26] .994
Liver cirrhosis mortality .35 [.10, .56] .008 .44 [.21, .63] <.001 .38 [.14, .58] .003
Drowning .36 [.09, .59] .013 .50 [.24, .69] <.001 .45 [.18, .65] .002
Death from venomous animal −.18 [−.43, .09] .190 −.02 [−.27, .24] .904 −.08 [−.33, .19] .578
Death from injuries −.12 [−.37, .15] .385 .04 [−.21, .30] .741 .12 [−.14, .37] .367
Transportation accidents .20 [−.09, .46] .172 .39 [.12, .61] .007 .20 [−.09, .46] .179
COVID-19 infections −.02 [−.30, .26] .868 .03 [−.25, .30] .864 −.10 [−.36, .17] .465
COVID-19 deaths −.01 [−.29, .27] .932 .02 [−.26, .30] .895 .05 [−.23, .31] .752
Mean r .27 [.04, .49] .019 .26 [.04, .49] .020 .26 [.04, .49] .021
Non-risk-related Lukemia −.39 [−.59, −.14] .003 −.44 [−.63, −.21] <.001 −.36 [−.56, −.12] .005
Multiple sclerosis −.04 [−.30, .22] .756 −.24 [−.47, .01] .067 −.10 [−.34, .16] .457
Alzheimer’s disease .41 [.17, .60] .002 .41 [.17, .60] .001 .23 [−.03, .45] .083
Appendicitis −.20 [−.44, .06] .132 −.26 [−.48, −.00] .049 −.24 [−.46, .01] .066
Death from air pollution .34 [.08, .55] .012 .39 [.15, .59] .003 .35 [.11, .56] .007
Mean r −.08 [−.36, .20] .553 −.07 [−.35, .20] .606 −.08 [−.36, .20] .590
MLM Mean correlation (all behaviors) .09 [−.09, .27] .318 .10 [−.08, .27] .295 .09 [−.08, .27] .310
Difference: Mean r(risk) –Mean r(nonrisk) .35 [−.00, .71] .054 .34 [−.02, .69] .063 .34 [−.02, .69] .063
Random SD of correlations .29 [.17, .44] .29 [.17, .44] .29 [.17, .44]
Cumulative life outcomes
General life expectancy −.45 [−.64, −.21] <.001 −.48 [−.66, −.26] <.001 −.43 [−.62, −.20] <.001
Healthy life expectancy −.45 [−.64, −.21] <.001 −.49 [−.67, −.27] <.001 −.43 [−.62, −.20] <.001
Note. MLM =multilevel model parameters; IHDI =Inequality-Adjusted Human Development Index; GGGI =Global Gender Gap Index. n=number of countries providing data for the behavior/
outcome; r/Coef. =Pearson correlation or multilevel model parameter (mean rfor risk-related and non-risk-related categories estimated from the multilevel models); 95% CI =95% confidence interval
(Fisher z-based for bivariate correlations, profile likelihood-based for multilevel model results), pvalues for multilevel model parameters computed using normal approximation; focal parameters of
interest highlighted in bold.
PRECARIOUS MANHOOD AND MEN’S PHYSICAL HEALTH 11
As additional tests of the robustness of the associations of PMBs
with men’s health behaviors and outcomes, we examined these
associations when controlling for women’s health behaviors and
outcomes, as well as for indicators of human development (IHDI),
access to doctors (physicians per 1,000 people), and gender equality
(GGGI). In contrast to the robustness of the PMB life-expectancy
associations—which remained medium-to-large in size against
all control variables—associations of the PMB with men’s beha-
viors and health outcomes were attenuated when controlling for
women’s rates. While the PMB’s association with men’s smoking
rates and death by injuries, drowning, and COVID-19 remained
medium-to-large when controlling for women’s rates, most other
individual-level associations were negligible in size when women’s
rates were controlled. This may be because men’s and women’s
health behaviors and outcomes are, on the whole, strongly correlated
within cultures. When accounting for the shared variance of
women’s behaviors and outcomes, the relationship of PMB to
men’s risk behaviors and health decreases substantially, suggesting
that nongendered cultural influences on behaviors and health may be
accounting for at least some of these relationships. Notably, how-
ever, the PMB’s associations with men’s risk behaviors and
health remained robust when controlling for country-level variables
including human development (IHDI), physicians per 1,000 people,
and gender equality (GGGI).
This study demonstrates the usefulness of the PMB as a country-
level indicator of beliefs about manhood (Bosson et al., 2021).
Previous research has established links between individual men’s
endorsement of male role norms (Levant et al., 1992;Mahalik et al.,
2003) or self-ascribed masculinity (Helgeson, 2012;Helgeson &
Lepore, 1997,2004) and health outcomes, yielding complex relation-
ships depending on the specific male norms or masculinity constructs
under investigation. The current findings are thus impressive because
country-level PMB scores reflect a widespread belief, shared by
individuals of different genders, about the elusive and tenuous nature
of manhood. Moreover, our outcome measures—health-related risk
behaviors and risk-related health outcomes—are objective indicators
that bypass people’s self-reports. However, we must caution against
drawing conclusions at the individual level from the present country-
level findings (the well-known ecological fallacy; Robinson, 1950).
Although the present study supports the hypothesis that country-level
variation in PMBs predicts country-level aggregate measures of
men’s risky behaviors and health, it remains to be seen whether
these relationships hold at the individual level, though at least one
study has shown that men higher in PMBs show larger stress
(cortisol) reactivity following feedback that they lack masculinity
(Himmelstein et al., 2019).
Limitations and Future Directions
A major limitation of this research is the fact that observations
are limited to 62 nations. On the one hand, the 62 nations from which
we drew manhood beliefs and health behaviors represent an
impressive sample of the globe, and data sets of this size and
variability are rare in the psychological literature. On the other
hand, 62 observations (or fewer, when accounting for missing data
for some variables) severely limited the types of analyses we could
conduct. For example, we were unable to test for interaction patterns.
And yet, country-level factors may moderate the associations between
PMB and men’s health. To speculate, differences in the collectivism
or tightness of countries might influence whether PMBs more or less
strongly predict men’s behaviors and outcomes (e.g., Stamkou et al.,
2019). While our sample size did not allow for such moderator
analyses, future studies should test more complex models using
multilevel modeling techniques that allow for cross-level interactions
between country- and individual-level variables.
In addition, the country-level precarious manhood scores were
drawn from college student samples, which are unrepresentative of
the larger populations of these nations (usually younger, wealthier,
and of higher social status than the average citizen). Thus, one might
question the generalizability of the present findings. Despite this
limitation, the associations of these country-level PMB scores with
health outcomes (which were based on the general population) were
often quite striking. This strengthens our confidence that the
country-level PMB scores indeed capture widely shared beliefs
about manhood.
Another limitation of this study is that the PMB was unexpectedly
negatively associated with some of the nonrisk variables we
selected for use in control analyses. Our selection of variables
was necessarily limited to data that are publicly available, globally
tracked, standardized, and aggregated by gender. This did not leave
many options for non-risk-related variables against which to com-
pare the risk-related variables. Nonetheless, in retrospect, the set of
variables we chose was not ideal. Future exploration with other
behaviors and outcomes not expected to be related to PMB would
strengthen the case for discriminant validity.
A common concern of large cross-national data sets is that
country-level comparisons are crude, as national boundaries are
imperfect reflections of cultures (Taras et al., 2016) and they are
not strictly independent observations. Ideally, we would have liked
to conduct hierarchical linear models that nested nations in
cultural regions, such as the World Values Survey’s nine cultural
regions (Haerpfer et al., 2020) or the United Nations’classification
of countries into 17 subregions (United Nations Statistics Division,
2021). However, multilevel models require around 30 observations
per cluster to make meaningful comparisons (Peterson et al., 2012),
which precluded such an analysis in the present data set. Neverthe-
less, even without more sophisticated analysis options, comparing
relationships across such a large representation of the globe can
uncover relationships that might go unnoticed if studies are limited
to samples within a single culture.
Relatedly, by focusing on between-country variation, we ignore
within-country variation. Countries typically have rich and nuanced
cultures within cultures (Cohen, 2014;Kitayama et al., 2006). The
current broad cross-country study provides a blueprint for how
future research can sort out how relationships might differ among
subcultures within countries. For instance, one could conduct finer,
regional-level analyses to compare subcultures within nations.
Alternatively, as mentioned previously, moderator variables can
add nuance to aggregate-level analyses of nations.
Finally, as noted earlier, we view the robust associations of
PMB with men’s life expectancy to be an especially exciting
direction for future research. We treated life expectancy as an
exploratory variable in analyses, given that it is influenced by
many factors that may be unrelated to gender and risk-taking.
And yet, our preliminary findings suggest that country-level varia-
tions in PMBs may hold promise for unpacking the global gender
gap in longevity. For instance, one study found that alcohol
consumption and life satisfaction significantly predicted gender
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12 VANDELLO ET AL.
differences in life expectancy across 54 countries (Rochelle et al.,
2015), with medium-to-large effects comparable to those reported
here. From the perspective of precarious manhood theory, country-
level PMB might plausibly predict both alcohol use and
life satisfaction among men, thereby offering a theoretical frame-
work to help interpret these findings.
Conclusions
The present cross-cultural study found evidence that a single
gender belief—the belief that manhood is a precarious social
status—relates to the health habits and outcomes of people in
countries representing over 80% of the global population. While
past research has linked masculinity to health, this is the first study,
and the largest in scale, to show that a basic belief about the nature of
manhood may have far-reaching implications for men around the
world. While we must be cautious in drawing causal conclusions
from correlational data, and health outcomes are invariably complex
and multiply determined, we hope that the present results act as a
catalyst for research that will further tease apart these associations
using diverse methods.
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