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.”


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.


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).


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).


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.


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.


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.


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.


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.


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.


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).


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).


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.


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.


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.


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.]


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

San Fran.png

Here’s Chicago:


And Chicago’s Strava runners:


Los Angeles:


And L.A.’s Strava enthusiasts:


New York City:


NYC Strava users:


Detroit, Michigan:


Detroit Strava users:


and, finally, Houston, Texas:


And the Strava scene in Houston:


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.

Much Ado About Nothing?: The Story of an Erratum that Barely Was

By Tristan Bridges and Kristen Barber

Originally posted at Feminist Reflections.

Scholarly publications are not necessarily free from error. Researchers like Mark Regnerus have operational problems that skew their claims. Others publish with typos. And still others make mistakes in translating data to graphs, tables, or other infographics. Peer review can only catch so much, because reviewers don’t often have access to the full data set, at least not when dealing with qualitative data. Or, in the case of Regnerus’ Social Science Research publication, blinded reviewers overlook egregious errors in conceptualization and conflicts of interest in project funding (see here for a nuanced critique).

About a year ago, we both discovered an error in a 1976 research note published in the American Journal of Sociology that resulted in an Erratum in the journal’s May 2018 issue. The error appears in a really interesting article by Sociologist Dwight E. Robinson on shifts in men’s facial hair fashions over the course of 130 years in London. Robinson tracked representations of facial hair as a case study of fashion trends as measurable bits of culture. Comparing shifts in men’s facial hair to shifts in women’s skirt lengths, for example, he made claims that “men are just as subject to fashion’s influence as women” (here: 1133).

In the research note, Robinson calculated the relative frequencies of five different styles of men’s facial hair (clean shaven, moustaches, sideburns, moustache & sideburns, and full beards), and different combinations of these styles, from images published in the Illustrated London News between 1842 and 1972. This project shows dramatic shifts in configurations of men’s facial hair over the period studied, with a spike in different styles at different times but an overall decline in facial hair since the late 1800s. Robinson also reported on this shift in Harvard Business Review a year prior, in an article comparing this trend to still more cultural shifts in fashions.

Plotting his findings allowed Robinson to visualize this shift over time, and visualizations help to more readily appreciate the cyclical nature of cultural shifts in fashion (like changes in the popularity of baby names, for instance). They help make discernible something that might be otherwise difficult to appreciate. Below, we’ve stacked all the relative frequencies in a chart to display this shift (also in Sociology NOW, 3e, Chapter 4). It’s really an incredible change, and such a neat way to talk about shifts in fashion. Some fashions have short cycles (like styles of clothing, for instance), while fashions associated with other things (like popular baby names) have longer cycles. Facial hair fashions, according to Robinson’s research, appear to follow a fashion cycle more similar to baby names than to styles of clothing.

But… in the American Journal of Sociology article, there are a collection of errors in the Appendix table from which we collected these relative frequencies. These errors are reproduced in both the  AJS and Harvard Business Review. Robinson may not have realized these mistakes because he plotted shifts in facial hair styles on separate graphs both publications (see images below).

The graphs are produced from relative frequencies of a raw count of men’s facial hair styles in each year of published issues of the Illustrated London News. When we requested Robinson’s submission files from the American Journal of Sociology to consult when assessing the error, they no longer had them. This would have been in hard copy and that filing system, we were told, did not include his submission materials. We also tried to collect submission files from Harvard Business Review, which no longer has the files. Because of this, the Editorial Board at AJS decided they were unable to correct the errors in an erratum; they did agree to at least publish something stating that errors were indeed made. After all that investigation, we ended up with this Erratum:

This erratum is a bit non-committal. But it was what the journal was willing to print. Don’t get us wrong, these errors don’t have the same policy implications as the egregious Regnerus study that suggests children of gay parents don’t meet markers of success similar to kids’ of straight parents. We do feel, however, that the errors can and should be corrected with the available information.

Robinson’s errors appear to most likely be the result of mistakes make in calculating something simple: relative frequency. Because Robinson included all of the figures in the appendix, he allowed us to calculate these frequencies ourselves for verification. Journals should do this when they can, to make scholarly claims more transparent and to offer other scholars data that could be used in different ways, to perhaps answer different questions. Indeed, more journals are including data files as a part of the available materials for download, now that things are online. Below is the Appendix from the article published in the American Journal of Sociology.

The errors in the table (reproduced in the figures in both publications) are associated with the years: 1844, 1860, 1904, 1916, and 1959. In each case, the relative frequencies are miscalculated in the table.

  • 1844: The relative frequency of clean shaven should be 30%, not 47%.
  • 1860: The relative frequency of beards should be 40%, not 39%.
  • 1904: The relative frequencies of moustaches and beards should both be 34%, not 37% and 32% (respectively).
  • 1916: The relative frequencies of clean shaven and moustaches should be 34% and 65%, not 33% and 64% (respectively).
  • 1959: The relative frequencies of clean shaven and moustaches should be 78% and 22%, not 74% and 21% (respectively).

These errors do affect what the graphs look like. If they were corrected, we would see a slight rise in the popularity of representations of men with mustaches in the late 1950s. Now, is that a significant difference? Not really. Clearly, we went to more trouble here than necessary. But identifying (and correcting) research errors is as important to maintaining scholarly integrity as is conducting meticulous reviews of research before it’s published. Accountability is key to making sure we, as scholars, continue to understand research as a communal process that takes seriously the integrity of research, from the smallest details to the biggest biases.

2018 Update: Shifts in the U.S. LGBT Population

Gallup has been collecting data on LGBT identities since 2012. Each year a new wave comes out, I like to visualize it, because I think the figures tell a story more challenging to tell with words alone. Actually “measuring” someone’s sexuality is more challenging than you might think. And one of reasons different surveys produce different estimates of the gender and sexual minority population in any society is that they ask about sexuality differently. I’ve written before on just how challenging sexuality is to measure (and why). A great deal of survey research on the topic has sought to engage these challenges by analytically separating three separate dimensions of sexuality (sexual behaviors, sexual desires, and sexual identities). It’s popularly assumed that the various dimensions all line up in some neat and tidy way. But the fact of the matter is, for many people, they don’t. Indeed, recent work by Laurel Westbrook and Aliya Saperstein show that measuring sex and gender on surveys is not necessarily any easier. All of this has combined to make it challenging to make estimates about the size of any gender or sexual minority population. I was happy to see that Gallup’s report actually addressed this in 2018.

“Self-identification as LGBT is only one of a number of ways of measuring sexual and gender orientation. The general grouping of these four orientations (lesbian, gay, bisexual and transgender) into one question involves significant simplification, and other measurement techniques which ask about each of these categories individually yield different estimates. Additionally, self-identification of sexual orientation can be distinct from other measures which tap into sexual behavior or attraction.” (here)

Gallup’s new report, by Frank Newport was just recently released, and update their estimates of the size of the LGBT population in the U.S. through 2017. This recent publication charts change in LGBT identification in the U.S. over 6 years (2012-2017). And, they rely on what previous research has shown to be a variable that produces the most conservative numbers of LGBT–gender and sexual identity.

The shifts themselves may appear to be small. But, within a population of over 300,000,000 people, these shifts involve huge numbers of actual people. As I have in previous years, in this post, I’ve graphed a collection of findings from Gallup’s report. I use these to talk with students, but I also think graphs offer a powerful illustration of the shifts.

NOTE: It’s worth noting that I truncate the y axes on the figures. Sometimes this is done to exaggerate discoveries. In this case, I truncate the axes because I think it helps more clearly illustrate the shifts I’ll address below.

Over the short period of 6 years Gallup has collected data, the LGBT population has grown substantially. The size of the population has increased from 8.3 to over 11 million people who identify as LGBT in the U.S. The proportion of LGBT Americans jumped a full percentage point between 2012 and 2017–from 3.5% to 4.5% of the U.S. population.

LGBT 1.png

Mignon Moore and I recently published on some of the shifts in the LGB population using data from the General Social Survey. We found a great deal of growth among younger Americans, women, and Black women in particular. Gallup’s new data support these shifts as well with a much larger representative sample of Americans (340,000 interviews in the 2017 sample).

In fact, when we look at shifts in the U.S. LGBT population by age, almost all of the growth in the population has been among the young. (Generations are slippery sorts of  categories as suggesting someone born in 1979 vs. 1980  has a completely different experience and identity, unique from one another is sort of arbitrary. Yet, these data support research like Barbara Risman‘s new book, Where The Millennials Will Take Us: A New Generation Wrestles with the Gender Structure, showing that young people are more open with respect to gender and sexuality.)

LGBT 2.png

But these shifts also are gendered, racialized, and classed. One of the most consistent shifts has been the growing gap between the numbers of women and men who identify as LGBT in the U.S. Since Gallup started collecting data in 2012, this gap has simply continued to grow. More women identify as LGBT than men, and just how much more continues to change each year.

LGBT 3.png

Those identifying as LGBT in the U.S. today are also becoming more racially diverse. While less than 4% of non-Hispanic white Americans identified as LGBT in 2017, more than 4.5% of Black Americans and Asians did, and more than 6% of Hispanic Americans as well as the racial categories Gallup collapses as “Others” (the “other” category was not included in the 2018 update).

LGBT 4.png

The other changes reported note shifts relative to income and education among LGBT-identifying Americans. With respect to education, Gallup’s data do not show meaningful differences among those with more or less education. Those differences that existed in 2012 seem to have largely eroded with growth in the LGBT population occurring among people with very different educational backgrounds.

LGBT 5.png

Despite this, LGBT population growth does continue to be stratified by class, according to Gallup’s report. Rates of LGBT identification among the class-advantaged have been stagnant over the 6 years of data collection, while rates among middle-income and lower-income LGBT identifying folks in the U.S. are growing.

LGBT 6.png

This is sad and likely to do with a combination of factors that perpetuate gender and sexual inequality. Part of it may be to do with the higher rates of homelessness among gender and sexual minorities as Brandon Andrew Robinson‘s research on LGBTQ homeless youth carefully documents. Some of it must also have to do with sexual discrimination on the job market as work like Emma Mishel‘s audit study showing the resumes with a small signification of possible lesbian identity were significantly less likely to be called for an interview. And likely it is all of this and more.

This is really an incredible amount of change in a very short period of time. The LGBT population is, quite literally, on the move. Tracking the needs of this population is and must be a goal that is continually revisited as the very composition of the population continues to shift.

Gender Gap in Top Ten Baby Names: 2017 Update

I’ve been tracking the proportion of baby girls and boys given top ten names in the U.S. for the past few years. It’s a remarkable shift. You can see the figure below through 2016 in the first chapter of Sociology NOW, 3e. We have an entire section of the introductory chapter that uses baby name trends to teach students how to think sociologically – and this is among my favorite examples from that section. Simply put, popular names used to be a whole lot more popular than they are today. It’s not just which names were popular that change, but how popular they were that has changed as well.

More than 1 in 20 boys born in the U.S. in 1880 were given the name John. The same was true of Mary for girls. And while the most popular names in 2017 are different (Liam and Emma), it’s their frequency that has interested me so much. While Liam was the most popular boy’s name in 2017, it was only given to 0.9539% of all boys born. So, fewer than 1 in 100 baby boys born in 2017 were given the most popular name. Similarly, the top name for girls last year (Emma) accounted for 1.0528% of all baby girls born. It’s just nowhere near the level of popularity.

Rather than tracking the frequency of the top boy and girl name, I’ve been tracking the proportion of boys and girls given top ten names each year. And the change is really amazing (see below). In blue, you can see the proportion of boys given a top ten boys’ name each year (since 1880), and in pink, you can see similar frequencies for top ten girls’ names.

Figure 1.png

I’ve always been struck by the erosion of the gender gap. The most popular boys’ names used to be almost twice as popular (among boys) as the most popular girls’ names were among girls. Boys given top ten names in 1880 accounted for 41.26% of all boys born that year. Girls given top ten names in 1880 accounted for only 22.98% of all girls born that year. I’ve written before explaining why the gap used to be so large and how sociologists explain why the gap shrunk.

Ever since I started tracking this, I’ve been interested in collect the data each year they’re released to see just how close the remaining gap is. I updated the figure last year and noted that top ten boys’ names were still more popular than top ten girls’ names – but the gap had shrunk to 0.01% (top ten boy names accounted for 7.63% of all boys born; top ten girl names accounted for 7.62% of all girls born). So, I was really interested to see whether the lines finally crossed in 2017. They did. Below, I’ve zoomed in on the figure above between 2000 and 2017 and truncated the y axis a bit so it is easier to visualize.

Figure 2.png

It’s a big deal. Since 1880, top ten names have never before accounted for a larger share of births among girls than among boys in any single year. Never. It’s just never happened. But in 2017, it happened. Top ten names were given to 7.48% of boys born in 2017 and 7.66% of girls born.

It’s sort of amazing. What’s also interesting is that the two lines are starting to appear as though they might be on different trajectories moving forward. And it’s interesting to consider what this might mean and what it tells us about gender and gender inequality in the U.S. I’ll continue to follow this. And I may attempt to use a different number of names (like top 20 names, for instance) to see if there’s something funky about 10 that produces the appearance of a change that doesn’t show up when I change the size of the popular names tracked.

Review Symposium on Mark Regnerus’s “Cheap Sex: The Transformation of Men, Marriage, and Monogamy”

As book review editor with Men and Masculinities, I’m often having books reviewed outside my area of expertise. My goal has always been to make sure I’m reviewing books that represent the field, incorporating work by a diverse group of scholars, making sure to review the work done by women in the field, and including reviews from graduate students and faculty both in the U.S. and abroad. This year, the sociologist Mark Regnerus published a new book on masculinity and sexualityCheap Sex: The Transformation of Men, Marriage, and Monogamy. It received a great deal of publicity, and quickly.

Just to consider the scale of publicity of the book, it was covered in New York Magazine, The Wall Street Journal, The Washington Post (twice, once an op-ed by Regnerus himself), The Atlantic, Harper’s Bazaar, The Globe and Mail, the Chicago Sun-Times, in addition to more conservative venues like Fox News and the National Review (again, twice, and once by Regnerus himself). This is, quite literally, just a very few of the public venues that reported on this research. As a public sociologist interested in more sociological research reaching public audiences, I was completely blown away. It’s rare to receive a single story in some of these outlets reporting on important sociological work, let alone this kind of massive national attention and sustained dialogue. Even more interesting because, while the book includes a massive collection of new data and analysis, the argument he’s pursuing in the book has been pursued before (more on this in a bit).

The reviews of the book are mixed in the public outlets. Some simply summarize his argument and suggest that he proved it while others are critical of the argument and study to varying degrees. Either way, very quickly, the book became a piece of a national conversation about men, masculinity, and sex. Those blurbing the work were all celebratory in their comments (as book blurbs often are). Perhaps most impressive were social theorist Anthony Giddens‘ comments, who referred to the book as “a magisterial study of the changing sexual landscape today,” and predicted that it would “become a standard work of reference in the field.” High praise!

I decided the book merited a conversation in the field. So, with the editor’s blessings, I invited a collection of scholars to review different elements of the book as a part of a review symposium at Men and Masculinities. I’ve read just about every issue of the journal and I think we’ve done something novel here. Distinct from some symposiums like this at other journals, this one ended up being less congratulatory. In some ways, it’s an odd thing to publish. But, in other ways, I felt the book was part of a larger issue in the field. It pursues an argument we’ve encountered before–leaning on a biologically deterministic position regarding men’s alleged insatiable desire for sex, albeit with new data and a new take.

Regnerus’s argument is that women have started to demand less from men in exchange for sex and this has produced a world historical shift and crisis for gender, sexuality, monogamy, and marriage more generally. He borrows an economic theory (“exchange theory”) to propose this, and leans on a variety of claims from biologically deterministic positions and evolutionary psychology to support his position as well. And he also marshals an incredible amount of evidence from nationally representative surveys and a sample of interviews he collected. There’s a lot to this book. So, I wanted a collection of people capable of reading it from these different perspectives to help readers of Men and Masculinities make sense of the argument.

I’m sharing it here because i hope people read and share the reviews. Sociologist Paula England (a supporter of exchange theory within sociology) assesses his use of this framework and reviews the applicability of exchange theory to his discussion of sex. I invited the anthropologist and NPR blogger Barbara J. King to evaluate his use of biological and evolutionary theories and frameworks that he relies on to support some of the larger claims in the book. And I asked the sociologist Philip N. Cohen to review the data and analysis critically. All three are public scholars par excellence. And I hope they produced a symposium that can be a touchstone as we encounter work subject to some of the critiques of this book.

We’ve published it ahead of print and online at SocArXiv here: (for those of you outside of academia, this means it’s not yet published, but will be in a forthcoming issue). I hope you will read it and share it with friends and colleagues. When arguments like this reach outside of academia, critiques from their peers should follow that reach and be a part of that conversation as well. That’s how we use science to make the world a better place. It’s part of the process and project.