The Origins of Androgyny in Baby Names

Apologies in advance for the abundance of baby name post recently. I had another thought after posting yesterday, tracing some of the names Lieberson, Dumais, and Baumann (2000) identified as following the pattern of androgyny they identify in their paper using baby name data from births of white babies in Illinois.

In Philip Cohen’s post, he identified the 25 least sex-dominant names in 2018. He was interested in the relative prevalence of parents selecting extremely sex-dominant names for their children and how that prevalence might have changed. But it made me think that, using that method we might also be able to trace the various patterns through which androgynous names become androgynous. So, I charted shifts in the numbers of babies (by sex) given each of the names Philip identified in his post (below).

25 least sex-dominant names

If you look at these names, you might note that some follow similar trajectories. Look, for instance, at the trajectories of the names Finley, Oakley, Remy, Justice, Jael, Ocean, and Gentry, for instance (charted alone below). When Lieberson, Dumais, and Baumann wrote about androgynous names, they presented these names as “accidentally androgynous.” And certainly some androgynous names follow this pattern. The name “Jamie” might be an example of this. Jamie was a name given to both boys and girls in roughly equal number through about 1980 when it started to become a more popular name (still among both boys and girls) and then the name drops off dramatically for boys and becomes a “girl” name… until 2018, when it dropped in popularity enough among girls that it is again among the names that are less sex-dominant. That the name was selected for both boys and girls through 1980 could have been a product of “accident” in the way Lieberson, Dumais, and Baumann present it – parents selecting the name might not have intentionally selected a name because it was androgynous. Rather, the name might have simply become androgynous. But Finley, Oakley, Remy, Justice, Jael, Ocean, and Gentry do not follow that pattern.

likely intentionally androynous names.png

These names all seem to emerge relatively rapidly and are used in roughly equal numbers to name boys and girls. This pattern might be an illustration of what androgynous names appearing not by accident, but by design–parents intentionally selecting androgynous names. I know many parents who intentionally selected names they felt were androgynous. Alex Haden wrote about the phenomenon in the New York Times in 2016.

I’m not aware of any studies that trace different routes to androgeneity in baby names (though that may be because this is well outside my research area). But that strikes me as an interesting idea. If names have different pathways to androgeneity, it might be the case that these different paths are connected to distinct fortunes of androgynous names. If there is a way to identify what we might call “likely intentionally androgynous names” from “likely accidentally androgynous names” for instance, we could look into whether the names have more longevity and whether they show gender asymmetrical paths following becoming more popular.

Some of the names appear to follow really different trajectories for boys and girls. Some look like likely candidates for Lieberson, Dumais, and Baumann’s argument about the contaminating effect of femininity for boys names. “Jamie” seems to follow that pattern most clearly from the names Philip identified. And “Dakota” might also follow this pattern (though I’m wondering if there’s a high-profile woman named “Dakota” who became prominent in the early 1990s – it’s too early, I think, for Dakota Fanning).

Dakota and Jamie

What’s difficult about this is that the names Philip identified are currently androgynous. And Lieberson, Dumais, and Baumann’s analysis examines the fates of androgynous names. We can’t see the fates of the names that were most androgynous in 2018 yet. It will take time for those patterns to be visible. All I’m doing here is examining the various paths each of these names took to becoming androgynous in 2018. But lots of these names appear to follow radically different paths to androgyny.

I don’t have any big idea, and I’m not pursuing this. But this time I read Lieberson, Dumais, and Baumann’s article, I was struck by their characterization of androgynous names as “accidental,” examining the “chance factors that affect the gender makeup of a name.” It’s not only chance factors that produce androgynous names. Some are androgynous on purpose. And I wonder if and how that might matter.

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Trends in Androgynous U.S. Baby Names

Looking at gender and gender inequality through baby name data is something I’ve posted about before. Recently, Philip Cohen wrote a smart post thinking through how to measure whether androgynous baby names are on the rise. And it started a conversation on Twitter in which Charles Seguin weighed as well. The ideas and conversation are all revolving around a paper Stanley Lieberson, Susan Dumais and Shyon Baumann published in the American Journal of Sociology in 2000 – “The Instability of Androgynous Names: The Symbolic Maintenance of Gender Boundaries.” It’s a brilliant paper. I love it for the simplicity of the argument.

The argument Lieberson, Dumais and Baumann make revolves around tracing a rise in androgynous names given to babies (using data for white births in the state of Illinois between 1916 and 1989). They analyze what they characterize as the “accidental ways” androgynous names develop, and “asymmetric growth patterns.” Among the findings described in the paper is a powerful illustration of the cultural devaluation of femininity. As Lieberson, Dumais and Baumann put it:

“A central assumption is that androgyny is evaluated differently, depending on whether parents are naming a daughter or a son. We have seen that parents of daughters also respond to the number (or percentage) of boys with the name, but they are slower to retreat from using it. As a consequence, androgynous names end up as a predominantly female name more often than as a predominantly male name.” (HERE: 1282).

Philip charts a slow increase in the proportion of U.S. babies given names that are not sex dominant in the extreme. Charles Seguin is working on a paper analyzing this in much more sophisticated detail than I am here. But it made me go back through Lieberson, Dumais, and Baumann’s article to look at the names they identified among white babies born in Illinois to look at those names among all babies born in the U.S. and over a period of time that stretches farther in both directions.

They identify a collection of the 45 most androgynous names in their sample. And Philip developed a similar list, using national data for 2018, identifying a list of the 25 most common names that were given between 40% and 60% baby girls born in 2018. I’m really excited to see Charles Seguin’s paper when it is published. Because the data available today are just a lot more comprehensive. It made me really appreciate Lieberson’s A Matter of Taste in a whole new light, thinking about how he must have dug up all of those data on name trends, how much of it might have been transcribed, etc. It’s really impressive.

Lieberson, Dumais, and Baumann graph a collection of these names to illustrate the trend they identified in the article–the contaminating effect of femininity. Those figures are below. The dashed lines chart proportions of girls given the name, while the solid lines show those proportions for boys. So, whenever you see the dashed line increase and the solid line decline, the name was effectively feminized (i.e., it became a “girl” name) and whenever you see the solid line rise and the dashed line decrease, the name was effectively masculinized (i.e., it became a “boy” name). Their point is that once a name becomes androgynous and parents realize that, they will retreat faster from those names when giving them to sons. Androgynous names, according to Lieberson, Dumais, and Baumann are “unstable”–they argue that androgynous names that achieve a certain level of popularity don’t remain androgynous and that they’re much more likely to tip toward girls than boys because of the cultural devaluation of femininity.

Lieberson, et al 1Lieberson, et al 2Lieberson, et al 3

It got me thinking about their puzzle. I love teaching it. It’s such an awful example of gender inequality. And it’s so simple. But I’d never charted the names on their lists against national data. So, I did that. And in general, it produced similar results. Of those 12 names, Lieberson, Dumais, and Baumann showed three that were masculinized over time (Angel, Sean, and Corey). National data show similar results, but added another name that looks different in the Illinois data they collected: Cary. In national data, baby boys named “Cary” did decline after 1960, coinciding with a small increase in the number of baby girls named “Cary,” but the lines didn’t cross the way they do in Lieberson, Dumais, and Baumann’s data. Still, 8 of these 12 names were feminized.

Androgynous U.S. Baby Names.png

I don’t know how to identify threshold effects in data like these. But I’m struck that this might be useful. Philip’s post charts an increase in U.S. parents giving their children names that are less sex-dominant than they used to. But, to examine whether this trend will also shape the fortunes of these newly androgynous names is more difficult because we have to wait to see what happens to the names.

Because Charles Seguin goes by “Charlie” and Philip identified “Charlie” as the most popular androgynous name given to babies born in the U.S. in 2018, we thought through various iterations of names given to babies beginning with “Charl.” And this is the other point that makes studying the fates of androgynous names given to children today or recently more difficult. Lieberson, Dumais, and Baumann suggest that androgynous names often become androgynous in an accidental sort of way. Many parents today intentionally select androgynous names for their children. And Charlie is an interesting example, because their is more than one option for thinking through how to spell the name. Below are a few options along with frequencies of births to boys and girls given each name over time.

U.S. Baby Charl, 1880-2018

Charles also suggested that “Noa” was a name he thought was going to become a much more popular androgynous name – interesting because the name “Noah” was the second most popular name given to baby boys in 2018. Interestingly, both names started to ascend in popularity right around the same time – around 1995.

U.S. Baby Noa, 1880-2018.pngI don’t have an argument to make here. I’m just interested how the trend toward the increasingly intentionally androgynous naming of children might affect the relative stability of androgynous names over time and whether we will continue to see asymmetric contamination effects by gender.

Gender Gap in Top Ten Baby Names – 2018 Update

I’ve been tracking shifts in the proportion of U.S. babies given top ten names among boys and girls since 2015. I think it’s a really fascinating trend and I use it some of my classrooms. The basic lesson is that popular baby names used to be a whole lot more popular than they are today. And the gender gap in just how popular the most popular baby names are has shrunk over time. As of 2017, for the first time since we can measure it using data from the Social Security Administration, the trend lines for girls and boys crossed. Since 2017, the top 10 most popular girls names are more popular than the top ten most popular boys names.

In 2019, I learned that I was not the first to notice this, or the first to graph the proportions of Americans giving babies top ten names to their boys and girls. Andrew Gelman published a piece in the New York Times in 2013 on the rise the proportion of American boys given a name ending with the letter “n.” He also wrote a blog post including two graphs he wished NYT had used for the story. One shows the rise in the proportion of baby boys given a name ending in “n.” And the other shows the proportions of baby boys and girls given top ten names by year (through, I’m assuming, 2012). I edited my original post to link to and credit Gelman’s figure.

And if we go back a bit further, Philip Cohen looked at this trend among girls in 2009 in a Huffington Post article. While Cohen was not looking at the gender gap in name popularity, he was interested in the shifts in names and naming trends that relate to what Stanley Lieberson referred to as the “modernization theory of name trends” in A Matter of Taste. Cohen was interested in both which name were most popular contemporarily vs. in the past as well as how the level of popularity of those popular names shifted over time.

Gelman’s more central discovery about the rise in the preponderance of boys given names ending in “n” was revisited again with a really cool animated visualization by Kieran Healy showing shifts in the distributions of last letters of boy and girl names among babies born over time. You can see the rise of “n” on the figure for boys and the steady dominance of names ending with “a” and “e” for girls.

Anyway, consider this my annual update on the trend Gelman identified in 2013 on shifts in the proportion of the prevalence of top ten baby names given to boys and girls as of 2018. The trend from 2017 continued. Top ten girl names remain (just slightly) more popular than top ten boy names, reversing a huge a very long-standing trend. Here is the updated figure.

baby top 10 - 2018

And here’s a figure that looks only at the figure since 2000.

baby top 10 - 2018.1

Smart stuff. I enjoy following this trend each year along with all of the other things we can consider just by looking at baby name data.

Urbanicity and LGBT Demographics

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.

LGBT by urban pop.png

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.

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Special thanks to Connor Gilroy for answering my RStats question on Twitter, enabling me to figure out the code.

Baby Names and #MeToo, Take 2

By: Tristan Bridges and Philip N. Cohen

Last year in January, we wrote about name contamination as a telling illustration of how gender inequality is perpetuated. While a great deal of research and popular graphics examine the social forces behind which baby names become popular, name contamination refers to the ways social and cultural forces operate in steering people away from certain names. Thus, while we’d both love to see data showing that Donald Trump has effectively contaminated the name “Donald” for children born in the U.S., the data don’t support that. Donald is a name that was once rapidly rising in popularity – in the first few decades of the 20th century. But in the middle of the 1930s, “Donald” stared a free fall in popularity. The name became dramatically less popular among Americans in fairly short order. This is what scholars are talking about when they examine names that seem to be “contaminated.”

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The source of the contamination? It’s Donald Duck, the character created by Walt Disney Productions in 1934. After Donald Duck, people just have never selected the name “Donald” for their babies in the same numbers. In his book, A Matter of Taste: How Names, Fashions, and Culture Change, Stanley Lieberson used Donald Duck as an example of “The Symbolic Contamination of Names,” tracking the name in California data and concluding the decline was probably triggered by the success of the Disney character.  Trump did not have this effect. In fact, Trump’s election and presidency has yet to register any effect on the number of baby boys in the U.S. named “Donald” – it just keeps trending steadily downward. Maybe Trump’s long-time celebrity means any further contamination associated with his name was already baked into the trends.

On the other hand, Melania and Ivanka – the two highest-profile women of the Trump family – might be provoking some identifiable name contamination. Both are quite rare names, with less than 400 girls given either name in 2018. And yet they have trended together quite closely since 1988, and both increased in 2016 and 2017 before taking a steep downward turn in 2018. We’ll have to keep an eye on those.

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But last year, we were particularly interested in the name “Harvey.” The year 2017 was a big one for Harveys. Between the sexual violence of Harvey Weinstein and Hurricane Harvey. In our initial post, we discussed it as a quasi-natural experiment in what it takes for a man’s name to be contaminated? And this all happened in the middle of a very recent trend of the name Harvey becoming much more popular among Americans in very recent history. Most names that were once popular but are no longer never return to their former popularity. Some bounce back, like “Emma,” “Eleanor,” or “Oliver” have in recent history. Harvey was bouncing back, part of a resurgence of popularity of names that were previously popular in the first half of the 20th century.

Harvey appears to have been contaminated in 2017. Like “Forrest” following Forrest Gump, “Monica” following the news of President Bill Clinton’s affair with Monica Lewinsky, “Ellen” following Ellen DeGeneres coming out publicly (see here), “Hillary” following Bill Clinton’s term in office as President, “Alexa” following Amazon’s release of their digital assistant, and many others, Harvey seems to have started to become contaminated. One way of looking at this is by examining changes in the rank of the name among boys born in the U.S. (below).

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While we see that Harvey was quickly rising up the ranks of name in recent years, the ranking of Harvey seemed to change its trajectory of popularity quickly in 2017. But, we also know that popular names used to be a lot more popular than they are currently. So, name rank is not a great measure to consult when considering whether a name has been contaminated.

Another measure to use is to consider the number of children born each year given the specific name. This might seem silly. But, as Philip has shown before, the best predictor of how many children are given any specific name this year is how many children received that name last year. When names buck this trend, the number of children given the specific name either increases substantially or decreases. Below, we charted the actual numbers of boys in the U.S. given the name Harvey since 1940 (the end of the period in which Harvey was popular in the first half of the 20th century).

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Here too, you see the sharp decrease in the number of Harveys born in 2018. But we can also look at the rate of Harveys born to consider name contamination – that is, the number of boys given the name Harvey per number of boys born in a given year – to see if that rate changed. That figure is below, charting the number of boys born and named Harvey each year, per 1,000 boys born that year.

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And finally, in our initial post, we examined name contamination by visualizing change in the rates of babies given potentially contaminated names by reporting name rates associated with the name in question relative to the name rate of the year in which the contamination may have occurred. Below, you can see that figure. Anything above the “100” line was a higher rate relative to the rate of babies named Harvey in 2017. And anything below is a lower rate relative to the rate of babies named Harvey in 2017.

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By all of these measures, Harvey appears to have been contaminated. Name contamination is an important indicator of society’s values. Shifts in the name “Forrest” tells us something about inequality associated with ability. Changing in naming babies “Ellen” tells us something important about sexual inequality. Shifts in the names “Monica” and “Hillary” offer new powerful illustrations of the double binds women face in societies shaped by institutionalized forms of gender inequality. “William” was not contaminated following Bill Clinton’s affair. The popularity of the name “Clarence” did not noticeably change course following Anita Hill’s public accusations of sexual harassment. Harvey did.

There are a lot of ways to study cultural patterns today, including analysis of big data sources, such as social media posts and archival text mining. Names have something in common with these methods, but we like to study names because, unlike Facebook posts or tweets, they represent decisions that people consider very consequential – very personal, symbolically representing weighty statements of identity – and they are measured on a very large scale over a very long period. On the other hand, it’s impossible to know from these data exactly why people use the names they do. Name contamination is a good case because sharp reversals of fortune, such as that shown by “Harvey,” have readily interpretable causes.

In this case, although people debated the merits of the #MeToo movement, and what its impact would or should be, it appears that fewer people wanted to be associated with its leading man. Or maybe it was just the hurricane.

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Note: Thanks to Charles Seguin for noting that Stanley Lieberson first discussed “Donald” as a good illustration of name contamination in A Matter of Taste.

2018 GSS Update on the U.S. LGB Population

By: D’Lane Compton and Tristan Bridges

The 2018 General Social Survey data was just recently publicly released. Along with collections of social scientists interested in shifts in the various opinions measured on the GSS, we were eager to see how things shifted. We were particularly interested in the GSS demographic questions on sexual identity. As of 2018, it has officially been one decade that GSS has been asking the question. Over the course of that period of time, GSS has conducted six waves of data collection. To characterize your sexual identity on the survey, you can self-classify as “heterosexual or straight,” “gay, lesbian, homosexual,” “bisexual,” or “don’t know.” (Note: measuring sexuality on surveys poses a series of notorious challenges. This is one way of thinking about asking people about sexuality, but one among many options – see here for a discussion.)  As we have both noted previously, different surveys ask this question in slightly different ways, which makes direct comparisons across instruments messy. While the proportions shift around a bit by survey, the trends discovered on each about growth in LGB identity and the populations among whom that growth appears to be happening have been generally comparable across instruments in the past.

Some data on shifts in the LGBT population don’t share the data publicly allowing people to assess where change is happening. Gallup reports on the data they have collected in two waves of surveys in this way. But, as Tristan has noted before, reporting on shifts in the LGBT population treats the group as homogenous and artificially presents growth in LGBT identity as though it might be equally distributed among the L’s, G’s, B’s, and T’s. But that’s not true. Bisexual women account for the lion’s share on the growth in LGBT identification. And as Tristan and Mignon Moore showed in 2016, young Black women account for a disproportionate amount of the growth in LGB identification.

Gallup reported that much of the change in LGBT identification between 2012 and 2016 could be accounted for by young people, women, college-educated people, people of color, and those who are not religious (see here for a summary of the highlights). Data from GSS too has shown an increase in LGB identification between 2008 and 2018. The GSS is a smaller survey than Gallup. But the trends in sexual identification appear to be on trajectories and reasonable if we consider increases in social tolerance rates and queer visibility in mainstream media. They are not extreme and lie within smaller or similar ranges of measures from prior “national surveys” of sexual behavior, including but not limited to Kinsey’s (1948, 1953) work and Laumann et al.’s (1994) findings from the early 90s.

Below, we charted shifts in those identifying as lesbian and gay alongside those identifying as bisexual. Consistent with what Tristan showed in 2016, bisexual identification continues to be increasing at a steeper rate.

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In fact, when you look at the proportions identifying as lesbian and gay or bisexual in 2008 and compare those with the proportions identifying as lesbian and gay or bisexual in 2018, lesbian and gay identifications have not really moved much. But bisexual identities continue to increase every year.

This is consistent with other national survey work. For example, the National Survey of Family Growth (NSFG) conducted by the U.S. National Center for Health Statistics was initially designed to be the national fertility survey of the United States and sought to explain trends and group differences in birth rates, such as contraception, infertility, sexual activity, and marriage. Compton, Farris and Chang 2015 found that almost nine percent of the women in the sample and about four percent of the men in the sample in 2008 and 2002 reported behavior as bisexual; that is, having sex with at least one male and at least one female partner in their lifetime. With regard to self-identification as bisexual for women, in 2008 0.43% and in 2002 0.26 percent. For men, in 2008 0.38% and in 2002 0.69% identified as being sexual. From this data we see how behavior and identity do not always align (which other work has noted as well). But these data do support the trends identified in GSS data, and might allow us to speculate that perhaps with increases in social tolerance rates people are more likely to claim bisexual identities than used to be the case regardless of their participation in the behavior.

Previous work has also suggested that much of the growth in LGB identities is happening among women. The GSS data show that the shift appears to be primarily happening among bisexual women. Indeed, bisexuality continues to be a more popular sexual identity than lesbian among women, but a less popular sexual identity than gay among men, as others have shown. As of 2018, almost 6% of women responding to the survey identified as bisexual compared with 1.5% in 2008. Comparatively, shifts in lesbian identities among women and both gay and bisexual identities among men really haven’t shifted much.

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And similar to previous analyses (here, here, and here), this shift is particularly pronounced among the young. The figure below shows changes in lesbian and gay identities alongside shifts in bisexual identities for four separate age cohorts. The real shift in among bisexual identification among 18-34 year-olds. Between 7 and 8% identified as  bisexual on the 2018 GSS survey. This is all the more interesting when you look at the 2008 data on these figures. Bisexuality did not stand out in these data in 2008. In fact, in 2008, more people identified as lesbian and gay than bisexual. This shift has emerged and grown in an incredibly short period of time.

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As other data has shown as well (see here and here, for instance), people of color account for a disproportionate amount of this shift. Black bisexuals accounted for almost 7% of Black respondents on the 2018 GSS. That’s a big shift as well. And while bisexual and lesbian/gay identities were moving along similar trajectories for Black Americans through 2016, as of 2018, bisexuality was much more common (as has been true for White respondents and those of other races… yes “other race” is actually the category GSS uses… and no, it’s not a good idea).

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Gallup’s survey found that, as of 2016, education had become less predictive of LGB identification than it was in 2012. On the chart below, we show that this is generally true, but those with less than a high school education stand out in interesting ways here. Lesbian and gay identifying people with less than a high school education account are the least likely of any educational group to identify this way. Conversely, respondents with less than a high school education are also the most likely to identify as bisexual.This is interesting and probably deserves more scholarship to explain what precisely this means. But the contrast is more stark looking at the 2018 data (below).

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Similarly, the subjective class identification data for sexual identity are all over the place. Part of this is a product of the vast majority of GSS respondents self classifying as working and middle class (see below). The slight trend that does seem generally visible is that lower classes have higher rates of LGB identification. Upper class (identifying) bisexuals are an outlier here, but they have also shifted a great deal in the last three waves of data collection and this may also be a product of “upper class” having the lowest numbers of respondents subjectively identifying with that class status. So, small n’s sometimes make for spiky graphs.

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Additionally, consistent with a growing body of scholarship on sexual minorities outside of major cities in the northeast and west, the steady increase in lesbian and gay identification in the South is notable (see below). Indeed, just looking at lesbian and gay identifications on the figure below, the south and west have the highest rates of LG identification in the GSS sample. Bisexuality adds another dimension. Indeed, bisexuality seems to have become more common in the midwest between 2016 and 2018, while lesbian and gay identifications seemed to fall out of favor. This same trend can be seen for LGB folks in the northeast here as well (see below). And, finally, while more respondents identify as bisexual than lesbian and gay (combined), that trend is not true in the south, where – as of 2018 – more respondents identified as lesbian and gay than as bisexual. These facts too suggest we need more scholarship on the emergence of bisexuality and on sexual minorities in different parts of the country than where they have been most frequently studied.

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Finally, rates of change among LGB identifying people by political party offers some interesting insights as well. As was true in the 2016 data as well, Democrats continue to have the highest rates of LGB identification and Republicans have the the lowest rates. The biggest change is the drop in rates of lesbian and gay identification among Independents. Whether these changes are indicative of people changing party identification or sexual identification would be interesting to know, but is not answerable with these data.

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So what do we do with all of this? What can we take from the shifts? One thing that we ought to take from this is to take scholarship on bisexuality more seriously. Both of us have commented on this before. As a sexual identity, bisexuality is less studied than it ought to be. But bisexuality has continued to grow and continues to represent a larger number of people’s sexual identities than lesbian and gay combined. This is interesting for a number of reasons, but one is that much of the growth in the LGBT community might actually be the result of changes in the population of bisexual identifying people (and this is a group that is disproportionately composed of women). Whether bisexual identifying people understand themselves as a part of a distinct sexual minority, though, is a question that deserves more scholarship.

We should care about bisexuality as a sexual identity, because as a sexual identity, it is one that continues to be on the rise. And, as both of us has written about previously, if we are going to continue to group bisexuals with lesbian women and gay men when we report on shifts in LGB populations, this might be something that deserves better understanding and more attention. Context matters in how we understand identities and how they change or evolve over time.

Race, Education, Running and Spatial Inequalities

A while ago, I wrote a post at Sociological Images looking at the ways Dustin A. Cable’s map visualizing racial segregation in the U.S. compared with Kyle Walker’s map examining educational segregation in the U.S. My interest was in was in examining spatial inequality. In a nutshell, where you live matters. It plays an important role in what kinds of resources you have access to (or don’t). It shapes your future earnings, how much education you’re likely to receive (in addition to the quality of that education), how long you live, and much more.

I was interested in putting the two maps into conversation because sociologists who study inequality are interested in a specific social process wherein privilege and inequality tend to accumulate. That is, some kinds and qualities of resources (economic, social, cultural) are found in abundance in some contexts, to a lesser extent in others, and are virtually absent in many places. And you can see these accumulations. Non-white populations (specifically Black and Hispanic) are in high concentrations on the maps in the same areas lower levels of education are present.

Last week, Runner’s World ran a story that looks at where people run according to Strava’s maps to talk about racial segregation and it reminded me of this post. I went through and looked up some of the big cities to see how where most popular routes are for runners using Strava. [Note: I run and do not use Strava, and many runners I know do not either. So, this is not a measure of exercise in a given area; it’s a measure of how many people exercise using Strava.]

strava logo.pngIf you’re unfamiliar, Strava is a mobile app and website on which runners and cyclists are able to post their exercise and connect with others doing the same. So, it’s basically a facebook page for people who exercise and want to log their miles to go online and hold themselves and each other accountable (and probably to brag a little too).

But look at how racial segregation, educational segregation and Strava use map onto one another! Below, is the map I created so that you can see San Francisco, Berkeley, and San Jose, California in the same frame using Walker’s map of educational attainment (top) over Cable’s racial dot map (bottom). [Note the legends on each map when deciphering the meanings.]

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Here’s where people run using Strava in the same areas:

San Fran.png

Here’s Chicago:

Chicago-2.png

And Chicago’s Strava runners:

Chicago.png

Los Angeles:

L.A.-2.png

And L.A.’s Strava enthusiasts:

L.A..png

New York City:

New-York-2.png

NYC Strava users:

NYC.png

Detroit, Michigan:

Detroit-2.png

Detroit Strava users:

Detroit.png

and, finally, Houston, Texas:

Houston-2.png

And the Strava scene in Houston:

Houston.png

This could be an indication that white people are exercising more than are other groups (or, more accurately, that white people who exercise are simply a lot more likely to turn to Strava to tell everyone how much they exercised and where than are other racial groups). But it is also a kind of social network that people living in predominantly white areas seem to be tapped into that other groups are less likely to use (and part of this is both knowing about the Strava community and believing it would be worthwhile to join). It’s a social network white people seem to be using more than other groups. And that makes me wonder what else they might be getting from logging their extracurricular exercise on social media.

But it also made me think of Rashawn Ray‘s research on how varying racial compositions of neighborhoods influences Black men and women to engage in more of less physical activity in their leisure time. And it could provide a different kind of evidence for spatially-based health inequalities.

Segregation is one powerful way that inequalities persist–its also a way that many are kept blithely unaware of the existence of stark social inequalities. It can be hard to notice inequality when we’re segregated from each other (by race, class, education). But mapping where we run offers a powerful illustration of some of these inequalities – and the use of Strava is one small piece of how inequalities are sustained.