The relatively stable rates of heterosexuality, bisexuality, and homosexuality observed across nations for both women and men suggest that non-social factors likely may underlie much variation in human sexual orientation

Prevalence of Sexual Orientation Across 28 Nations and Its Association with Gender Equality, Economic Development, and Individualism. Qazi Rahman, Yin Xu, Richard A. Lippa, Paul L. Vasey. Archives of Sexual Behavior, December 3 2019. https://link.springer.com/article/10.1007/s10508-019-01590-0

Abstract: The prevalence of women’s and men’s heterosexuality, bisexuality, and homosexuality was assessed in 28 nations using data from 191,088 participants from a 2005 BBC Internet survey. Sexual orientation was measured in terms of both self-reported sexual identity and self-reported degree of same-sex attraction. Multilevel modeling analyses revealed that nations’ degrees of gender equality, economic development, and individualism were not significantly associated with men’s or women’s sexual orientation rates across nations. These models controlled for individual-level covariates including age and education level, and nation-level covariates including religion and national sex ratios. Robustness checks included inspecting the confidence intervals for meaningful associations, and further analyses using complete-cases and summary scores of the national indices. These analyses produced the same non-significant results. The relatively stable rates of heterosexuality, bisexuality, and homosexuality observed across nations for both women and men suggest that non-social factors likely may underlie much variation in human sexual orientation. These results do not support frequently offered hypotheses that sexual orientation differences are related to gendered social norms across societies.

Keywords: Sexual orientation Homosexuality Culture Gender roles Gender equality Social construction


Discussion

The central question addressed by the current research was:
Are national factors such as gender equality, economic
development, and individualism-collectivism related to the
national prevalence of various sexual orientations, across
28 nations? Our analyses also tested the frequently offered
hypothesis that sexual orientation rates may be associated
with gender norms and social roles (Bearman & Bruckner,
2002; Greenberg, 1988; Terry, 1999). The use of a large international
dataset allowed us to test whether countries that differed
in gender egalitarianism and rigidity of gender roles (as
indexed by national indicators of gender equality and gender
empowerment) also differed in the prevalence of various
sexual orientations. We found no compelling evidence that
this was the case. While the present results were not significant,
they demonstrate that several theoretically important
predictor variables (national levels of gender equality, economic
development, and individualism) were not much associated
with important outcome variables (sexual identity and
same-sex attractions) in a very large sample with sufficient
statistical power. The non-significant results were also inconsistent
with the notion that women’s sexual identities and
same-sex and other-sex attractions are more linked to cultural
and social factors than men’s were (Bailey et al., 2016;
Baumeister, 2000). Furthermore, there was no evidence that
national indices were more strongly related to identity than
to attraction-based measures of sexual orientation. Finally,
the pattern of associations did not seem to result from the
fact that prevalence rates were more variable, in general, for
women than men across nations. Indeed, when assessed in
terms of sexual identity, prevalence rates for male homosexual
identity were more variable than prevalence rates for
lesbian identity were.
Some factors that may be related to the prevalence of men’s
sexual orientation were not assessed in the current study.
One candidate supported by previous research is participants’
average number of older brothers in a given national sample
(and the correlated factor of the average size of participants’
family of rearing in a given national sample). Many studies
have shown that the more older biological brothers a man has,
the more likely he is to be gay (Blanchard, 2018). This “fraternal
birth order effect” is thought to result from biological
processes—each additional male fetus carried by a woman
increases the likelihood of maternal immunological reactions
against male factors in fetal tissue, and these immunological
reactions then influence the development of subsequent male
fetuses (Bogaert et al., 2018). A prediction that follows from
the fraternal birth order effect is that nations with larger mean
family sizes at the time of participants’ births should, on average,
have higher rates of male but not female homosexuality
among adult probands (Bogaert, 2004). Although not tested
in the current study, this hypothesis suggests the possibility
that biological as well social factors could be associated with
the prevalence of heterosexuality, bisexuality, and homosexuality,
across nations, and furthermore that associations with
biological as well as social factors may sometimes differ for
men and women.
The current study had several limitations. One pertains to
the sexual identity categories used. In some cultures, one’s
degree of sexual attraction to men and women is simply not
a basis upon which individuals construct identities. Cultural
variations in the construal of same-sex and other-sex
attractions have also been affected by our use of an English
language survey. While other cultures may sometime use
sexual identity terms that are comparable to those employed
in Western countries, such terms may have different meanings
across cultures, as for example when a man identifies as
“straight,” but nonetheless engages in sexual activity with
same-sex partners (e.g., Petterson et al., 2016). In some
cultures (e.g., those with “third gender” categories), sexual
orientation might be seen as a basis for identity, but at the
same time, some or all of the Western terms that are commonly
used to denote sexual orientation may not be employed
(e.g., Asthana & Oostvogels, 2001; Petterson et al., 2016).
Similar issues can even characterize some subcultures within
Western nations, in which asking members whether they are
“heterosexual,” “homosexual,” or “bisexual” is discouraged
(e.g., Denizet-Lewis, 2010). In the context of the current
study, it is worth noting that all participants, in fact, identified
themselves using one of the provided sexual identity
terms, and thus they seemed willing to use the categories of
“heterosexual,” “bisexual,” and “homosexual” as a basis for
self-classification.
A second limitation is that the national samples in the BBC
survey were not random or representative. Thus, each national
subsample is not necessarily representative of national patterns
overall. As the participants in all countries come from a sample of
BBC consumers, there may be cross-national homogeneity built
into the sampling frame. As noted earlier, participants tended to
be young, affluent, and educated (as well as able to understand the
English language). Compared to other cross-cultural studies on
college student samples, the BBC data included data from noncollege
populations who came from various locations within the
various countries and who varied in age and various demographic
characteristics.
One obvious direction for future research is to replicate
the current findings with data from representative samples of
men and women from diverse nations. Many of the nations
studied in the current study were European with a number of
notable exceptions (e.g., India, Japan, Malaysia, Philippines,
Singapore, Turkey). The unequal sample sizes across nations
(some nations contained more people than others) is unlikely
to bias the estimation of the parameters of interest. One of
the advantages of using multilevel models is their tolerance
of unequal samples and other unbalanced data structures.
Simulation studies suggest that group-level sample size is
somewhat more important than total sample size, and large
individual-level sample sizes can compensate for small numbers
of groups (for review, see Maas & Hox, 2005). Naturally,
any estimates of grand means (e.g., across all nations) will
be more weighted toward countries with larger sample sizes
which is why researchers should use multilevel models when
nesting is inherent in the study design.
It is also important to note that the concept of national
culture (insofar as that is captured by UN indices) has been
questioned by scholars in personality and social psychology.
While the concept of national cultures is disputed, other
research suggests there may be between-nation differences
in average personality traits and that some of these may
correlate with sociopolitical structures (e.g., having democratic
institutions; Barceló, 2017; Schmitt, Allik, McCrae, &
Benet-Martínez 2007). In this context, Hofstede’s measures
of individualism and collectivism have also been criticized.
As cultures (especially those in closer geographic proximity)
tend to become more similar (perhaps due to economic
factors such as globalization), it is possible that consistency
in psychological traits across cultures may also be driven by
globalized sexual norms. While the analysis presented here
accounts for the statistical dependencies introduced by these
issues, the findings are specific to the BBC sample examined.
Social attitudes toward sexual orientation may also have
changed since the BBC survey was taken. Thus, further tests
of these questions will be needed in other, more representative
and recent cross-cultural datasets.
The use of multilevel models allowed us to use nationlevel
data to draw inferences at the individual level. In other
words, it allowed us to test the potential influence of national
gender equality on individuals’ sexual identity and desire.
However, the relationship between variables could theoretically
be different at other levels of analysis. For example,
societal or structural-level gender egalitarianism could influence
intermediate proximate mechanisms, such as parental
gender socialization or internalization of gender stereotypes
(or other gender norms), which then influence the development
of sexual orientation differences. However, the effects
of factors such as parental socialization on sexual orientation
appear to be weak based on existing research evidence
(Bailey et al., 2016). Furthermore, many country-level variables
may be clustered in world regions (e.g., Europe, North
America). While multilevel model can accommodate such
effects (e.g., by simply adding another data level in a hierarchical
model), it is unlikely that levels of gender egalitarianism
differ sufficiently between countries within a world
region (e.g., between all European countries) for us to detect
such associations with sufficient statistical power.
Finally, it is worth noting that although the national samples
of men and women studied in the BBC survey were not
representative of their larger national populations in some
ways, the male and female samples were nonetheless well
matched on demographic factors such as age and education
levels. Thus, the apparent absence of sex differences in
the current study—e.g., there appeared to be no difference
between men and women in the relation between sociocultural
factors and sexual orientation—was present despite
the fact that male and female samples were matched on key
factors.
In conclusion, our analyses did not yield a significant
association between national indicators of gender equality,
economic development, and individualism-collectivism traits
and identity-based or desire-based measures of sexual orientation
across 28 countries in men and women. This provides
new evidence that questions the power of factors such as
gendered norms, gender roles, and gender socialization to
account for variations in the prevalence of sexual orientations
across nations. Future empirical studies are needed to better
test the extent to which national gender norms and economic
factors are related to variations in the expression of sexual
orientation across nations.