Australia & UK: Gay & bisexual men, men who ‘prefer not to say,’ and gay women are less satisfied with their lives; in the UK is the same but for gay women, who do not have a lower level of satisfaction

Sexual orientation and life satisfaction. David Bartram. Journal of Sociology, May 19, 2021. https://doi.org/10.1177/14407833211017672

Abstract: Existing quantitative research on sexual orientation and life satisfaction uses models with control variables that do not have a clear rationale. With a correct understanding of what control variables do, no controls are necessary to estimate the consequences of sexual orientation on life satisfaction. An analysis constructed from this perspective reveals gay and bisexual men in the UK and Australia are less satisfied with their lives (relative to heterosexual men). Bisexual women in both countries are less satisfied as well. Lesbians in Australia are less satisfied (relative to straight women) – but lesbians in the UK do not have lower satisfaction. These conclusions hold also in an analysis that considers the possibility that some non-heterosexual people might be unwilling to disclose their sexual orientation on surveys.

Keywords: Australia, control variables, life satisfaction, sexual orientation, United Kingdom

Conclusion

Having a non-normative sexual orientation in the UK and Australia comes with consequences – including a lower level of life satisfaction. The only exception to that general pattern is lesbian women in the UK. The gap is especially large for bisexuals, of both sexes, and in both countries. There is also a striking negative coefficient for Australian men who ‘prefer not to say’. It is not hard to imagine that there must be a reason some Australian men prefer not to disclose their sexual orientation (especially if what they prefer not to disclose is: not being straight).

Researchers should be confident in perceiving that the right way to model the impact of sexual orientation on SWB is to exclude ‘other determinants’ as controls in this context – because the other determinants of SWB cannot also be determinants of sexual orientation. That assertion forms the key recommendation for future research on this topic. Building larger models with many control variables might appear desirable simply because a more parsimonious model (especially one containing no controls) might seem unpersuasive (e.g. to potential anonymous reviewers). But size on its own is hardly a coherent criterion for constructing an analytical model whose purpose is to gauge a causal impact (see e.g. Gangl, 2010).

The fact that lesbians in the UK report life satisfaction on a par with that of heterosexual women (in contrast with the life satisfaction deficit among gay men) is a striking finding, perhaps at odds with what one might expect, given the context of stigma and discrimination that commonly confronts people belonging to a sexual minority. Being gay in a heteronormative society sometimes goes with a perceived loss of masculinity and thus a reduction in status more generally (Connell, 1995); the lower life satisfaction among gay men is arguably understandable in these terms. A similar dynamic might not apply to the experience of lesbians to the same extent, perhaps in part because women in general already have lower social status in a patriarchal society such as the UK.

There is a genuine limitation of the analysis presented here: the findings describe average impacts pertaining to the categories available in the data and thus do not capture the diversity of experiences that go under the heading of being ‘gay’, ‘lesbian’, ‘bisexual’, etc. That point applies as well to the way people might choose from the available options on a survey question, at a single point in time. There are many ways to be non-heterosexual and indeed to be heterosexual, with imprecise and fluid boundaries for many (Cover, 2019). The fact that the available response categories inhibit a more fine-grained analysis does not mean this point is anything other than a limitation. The analysis in this article gives us average effects of belonging to an indicated category; for some individuals a negative impact will exceed that average, while for others the impact might be less negative or perhaps even null (cf. Feinstein et al., 2015). Even so, any attempt to gauge effects in a finer grain must ensure a correct use of control variables, along the lines developed in this article.