As more and more surveys are including questions attempting to capture the size of the LGBT identifying population in the U.S., I’ve been interested. I’m interested in the measures being used, what they’re able to capture, how those estimates vary when we compare surveys, and the specific wording of questions used.
In the past few years, Gallup’s estimates (after partnering with The Williams Institute and Gary Gates) have received an incredible amount of attention. Reporting on the most recent wave of data collected, Gallup reported that 4.5% of the U.S. population identifies as LGBT. It’s a bold claim. And, as with most estimates, it is most likely a conservative estimate.
Because of how Gallup asks this question, we cannot disaggregate lesbians, gay men, bisexuals, and transgender folks from one another. They’re just lumped together. And much of the reason for this is… wait for it… probably best blamed on straight folks. Why? Designing survey questions that can reliably and accurately assess sexual identities is challenging for lots of reasons I’ve discussed before. But one reason worth noting is that straight folks – heterosexual people – are among the biggest hurdles. Enough heterosexual people cannot make sense of questions inquiring about their sexual identities that we worry about “false positives” (straight people not knowing how to answer and responding that they are “bisexual” or “lesbian” or anything other than straight not because they identify that way, but because they don’t understand the question). Read (or listen to) how sexual demographer Gary Gates puts in when talking about the simple question Gallup has asked survey respondents in two waves now:
“It’s a simple yes or no answer. One of the challenges that we’ve observed in measuring sexual orientation, and this may sound humorous to people, but heterosexuals often don’t know what their sexual orientation is and don’t routinely call themselves either heterosexual or straight. And so when you have questions where you’re asking people what they are, that very big population sometimes makes mistakes and it creates what we call ‘measurement error’ or ‘false positives.’ And it basically puts people in the LGB category that really aren’t…. With the Gallup question, you’re not asking that group what they are, you’re asking what they aren’t. And they more or less know that. So they may not use terms like heterosexual or straight. But they know they’re not gay.”
-Gary Gates (HERE)
In the Gallup Poll, respondents are asked, “Do you personally identify as lesbian, gay, bisexual, or transgender?” The responses are a simple yes/no. And all because straight folks and cisgender folks are so likely to misunderstand the question and inaccurately report their sexuality (and possibly gender).
So, it’s a conservative estimate that doesn’t allow us to break the LGBT population apart as much as we might like if we’re interested in understanding where growth in the population is and isn’t happening. But, because Gallup collects such a large sample, they are able to report on state-level estimates of LGBT populations throughout the U.S. I’ve written about this before. I updated a figure I previously produced for a lecture I’m giving and thought I’d share in case it’s useful to others as well.
We know that states with larger shares of the population living in urban areas have higher proportions of LGBT identifying individuals. There is more than one hypothesis about why this is or might be the case. I’ve charted these data before, but I added a new element to the figure below. Now it charts proportion identifying as LGBT by state by proportion of the state population living in urban areas AND data points vary by size according to the size of state populations relative to one another.
Personally, I’m excited to see the 2017-2018 data (which I imagine might be released soon) because from the work I’ve been reading, a great deal of growth in LGBT-identifying population is happening in the South in the U.S. And on the figure above, few southern states are above one standard deviation above the trend line (with the exception of Georgia). I wonder what this figure will look like when we map the population with more recent data. I have a feeling things have changed and I’m interested to see how.
Special thanks to Connor Gilroy for answering my RStats question on Twitter, enabling me to figure out the code.