Why People Are So Averse to Facts

Originally posted at Sociological Images.

Facts about all manner of things have made headlines recently as the Trump administration continues to make statements, reports, and policies at odds with things we know to be true. Whether it’s about the size of his inauguration crowd, patently false and fear-mongering inaccuracies about transgender persons in bathrooms, rates of violent crime in the U.S., or anything else, lately it feels like the facts don’t seem to matter. The inaccuracies and misinformation continue despite the earnest attempts of so many to correct each falsehood after it is made.  It’s exhausting. But why is it happening?

Many of the inaccuracies seem like they ought to be easy enough to challenge as data simply don’t support the statements made. Consider the following charts documenting the violent crime rate and property crime rate in the U.S. over the last quarter century (measured by the Bureau of Justice Statistics). The overall trends are unmistakable: crime in the U.S. has been declining for a quarter of a century.

Now compare the crime rate with public perceptions of the crime rate collected by Gallup (below). While the crime rate is going down, the majority of the American public seems to think that crime has been getting worse every year. If crime is going down, why do so many people seem to feel that there is more crime today than there was a year ago?  It’s simply not true.

There is more than one reason this is happening. But, one reason I think the alternative facts industry has been so effective has to do with a concept social scientists call the “backfire effect.” As a rule, misinformed people do not change their minds once they have been presented with facts that challenge their beliefs. But, beyond simply not changing their minds when they should, research shows that they are likely to become more attached to their mistaken beliefs. The factual information “backfires.” When people don’t agree with you, research suggests that bringing in facts to support your case might actually make them believe you less.  In other words, fighting the ill-informed with facts is like fighting a grease fire with water.  It seems like it should work, but it’s actually going to make things worse.

To study this, Brendan Nyhan and Jason Reifler (2010) conducted a series of experiments. They had groups of participants read newspaper articles that included statements from politicians that supported some widespread piece of misinformation. Some of the participants read articles that included corrective information that immediately followed the inaccurate statement from the political figure, while others did not read articles containing corrective information at all.

Afterward, they were asked a series of questions about the article and their personal opinions about the issue. Nyhan and Reifler found that how people responded to the factual corrections in the articles they read varied systematically by how ideologically committed they already were to the beliefs that such facts supported. Among those who believed the popular misinformation in the first place, more information and actual facts challenging those beliefs did not cause a change of opinion—in fact, it often had the effect of strengthening those ideologically grounded beliefs.

It’s a sociological issue we ought to care about a great deal right now. How are we to correct misinformation if the very act of informing some people causes them to redouble their dedication to believing things that are not true?

Where do LGBT people in the U.S. Live?

Originally posted at Sociological Images

I love gender and sexual demography.  It’s incredibly important work.  Understanding the size and movements of gender and sexual minority populations can help assess what kinds of resources different groups might require and where those resources would be best spent, among others things.  Gary J. Gates and Frank Newport initially published results from a then-new Gallup question on gender/sexual identity in 2012-2013 (here).  At the time, 3.4% of Americans identified as either lesbian, gay, bisexual, or transgender.  It’s a big deal – particularly as “identity” is likely a conservative measure when it comes to assessing the size of the population of LGBT persons.  After I read the report, I was critical of one element of the reporting: Gates and Newport reported proportions of LGBT persons by state.  As data visualizations go, I felt the decision concealed more than it revealed.

From 2015-2016, Gallup collected a second round of data. These new data allowed Gates to make some really amazing observations about shifts in the proportion of the U.S. population identifying themselves as LGBT.  It’s a population that is, quite literally on the move.  I posted on this latter report here.  The shifts are astonishing – particularly given the short period of time between waves of data collection.  But, again, data on where LGBT people are living was reported by state.  I suspect that much of this has to do with sample size or perhaps an inability to tie respondents to counties or anything beyond state and time zone.  But, I still think displaying the information in this way is misleading.  Here’s the map Gallup produced associated with the most recent report:

During the 2012-2013 data collection, Hawaii led U.S. states with the highest proportions of LGBT identifying persons (with 5.1% identifying as LGBT)–if we exclude Washington D.C. (with 10% identifying as LGBT).  By 2016, Vermont led U.S. states with 5.3%; Hawaii dropped to 3.8%.  Regardless of state rank, however, in both reports, the states are all neatly arranged with small incremental increases in the proportions of LGBT identifying persons, with one anomaly–Washington D.C.  Of course, D.C. is not an anomaly; it’s just not a state. And comparing Washington D.C. with other states is about as meaningful as examining crime rate by European nation and including Vatican City.  In both examples, one of these things is not like the others in a meaningful sense.

In my initial post, I suggested that the data would be much more meaningfully displayed in a different way.  The reason D.C. is an outlier is that a good deal of research suggests that gender and sexual minorities are more populous in cities; they’re more likely to live in urban areas.  Look at the 2015-2016 state-level data on proportion of LGBT people by the percentage of the state population living in urban areas (using 2010 Census data).  The color coding reflects Census regions (click to enlarge).

Vermont is still a state worth mentioning in the report as it bucks the trend in an impressive way (as do Maine and New Hampshire).  But I’d bet you a pint of Cherry Garcia and a Magic Hat #9 that this has more to do with Burlington than with thriving communities of LGBT folks in the towns like Middlesex, Maidstone, or Sutton.

I recognize that the survey might not have a sufficient sample to enable them to say anything more specific (the 2015-2016 sample is just shy of 500,000).  But, sometimes data visualizations obscure more than they reveal.  And this feels like a case of that to me.  In my initial post, I compared using state-level data here with maps of the U.S. after a presidential election.  While the maps clearly delineate which candidate walked away with the electoral votes, they tell us nothing of the how close it was in each state, nor do they provide information about whether all parts of the state voted for the same candidates or were regionally divided.  In most recent elections traditional electoral maps might leave you wondering how a Democrat ever gets elected with the sea of red blanketing much of the nation’s interior.  But, if you’ve ever seen a map showing you data by county, you realize there’s a lot of blue in that red as well–those are the cities, the urban areas of the nation.  Look at the results of the 2016 election by county (produced by physicist Mark Newman – here).  On the left, you see county level voting data, rather that simply seeing whether a state “went red” or “went blue.”  On the right, Newman uses a cartogram to alter the size of each county relative to its population density.  It paints a bit of a different picture, and to some, it probably makes that state-level data seem a whole lot less meaningful.

Maps from Mark Newman’s website: http://www-personal.umich.edu/~mejn/election/2016/

The more recent report also uses that state-level data to examine shifts in LGBT identification within Census regions as well.  Perhaps not surprisingly, there are more people identifying as LGBT everywhere in the U.S. today than there were 5 years ago (at least when we ask them on surveys).  But rates of identification are growing faster in some regions (like the Pacific, Middle Atlantic, and West Central) than others (like New England).  Gates suggests that while this might cause some to suggest that LGBT people are migrating to different regions, data don’t suggest that LGBT people are necessarily doing that at higher rates than other groups.

The recent shifts are largely produced by young people, Millennials in the Gallup sample.  And those shifts are more pronounced in those same states most likely to go blue in elections.  As Gates put it, “State-level rankings by the portion of adults identifying as LGBT clearly relate to the regional differences in LGBT social acceptance, which tend to be higher in the East and West and lower in the South and Midwest. Nevada is the only state in the top 10 that doesn’t have a coastal border. States ranked in the bottom 10 are dominated by those in the Midwest and South” (here).

When we compare waves of data collection, we can see lots of shifts in the LGBT-identifying population by state (see below; click to enlarge).  While the general trend was for states to have increasing proportions of people claiming LGBT identities in 2015-2016, a collection of states do not follow that trend.  And this struck me as an issue that ought to provoke some level of concern.  Look at Hawaii, Rhode Island, and South Dakota, for example.  These are among the biggest shifts among any of the states and they are all against the liberalizing trend Gates describes.

us-lgbt-state-shiftsPresentation of data is important.  And while the report might help you realize, if you’re LGBT, that you might enjoy living in Vermont or Hawaii more than Idaho or Alabama if living around others who share your gender or sexual identity is important to you, that’s a fact that probably wouldn’t surprise many.  I’d rather see maps illustrating proportions of LGBT persons by population density rather than by state.  I don’t think we’d be shocked by those results either.  But it seems like it would be provide a much better picture of the shifts documented by the report than state-level data allow.

Possibly the most exhaustive study of “manspreading” ever conducted

Originally published at Sociological Images.

“Manspreading” is a relatively new term.  According to Google Trends (below), the concept wasn’t really used before the end of 2014.  But the idea it’s describing is not new at all.  The notion that men occupy more space than women is one small piece of what Raewyn Connell refers to as the patriarchal dividend–the collection of accumulated advantages men collectively receive in androcentric patriarchal societies (e.g., wages, respect, authority, safety).  Our bodies are differently disciplined to the systems of inequality in our societies depending upon our status within social hierarchies.  And one seemingly small form of privilege from which many men benefit is the idea that men require (and are allowed) more space.

captureIt’s not uncommon to see advertisements on all manner of public transportation today condemning the practice of occupying “too much” space while other around you “keep to themselves.”  PSA’s like these are aimed at a very specific offender: some guy who’s sitting in a seat with his legs spread wide enough in a kind of V-shaped slump such that he is effectively occupying the seats around him as well.

I recently discovered what has got to be one of the most exhaustive treatments of the practice ever produced.  It’s not the work of a sociologist; it’s the work of a German feminist photographer, Marianne Wex.  In Wex’s treatment of the topic, Let’s Take Back Our Space: Female and Male Body Language as a Result of Patriarchal Structures (1984, translated from the German edition, published in 1979), she examines just shy of 5,000 photographs of men and women exhibiting body language that results from and plays a role in reproducing unequal gender relations.

The collection is organized by an laudable number of features of the various bodily positions.  Interestingly, it was published in precisely the same year that Erving Goffman undertook a similar sociological study of what he referred to as “gender display” in his book, Gender Advertisements–though Goffman’s analysis utilized advertisements as the data under consideration.

Like Goffman, Wex examined the various details that made up bodily postures that seem to exude gender, addressing the ways our bodies are disciplined by society.  Wex paired images according to the position of feet and legs, whether the body was situated to put weight on one or two legs, hand and arm positions, and much much more.  And through this project, Wex also developed an astonishing vocabulary for body positions that she situates as the embodied manifestations of patriarchal social structures.  The whole book organizes this incredible collection of (primarily) photographs she took between 1972 and 1977 by theme.  On every page, men are depicted  above women (as the above image illustrates)–a fact Wex saw as symbolizing the patriarchal structure of the society she sought to catalog so scrupulously.  She even went so far as to examine bodily depiction throughout history as depicted in art to address the ways the patterns she discovered can be understood over time.

If you’re interested, you can watch the Youtube video of the entire book.

Just How Big Was the 2017 Women’s March?

By: Tristan Bridges and Tara Leigh Tober

Originally posted at Sociological Images.

The 2017 Women’s March was a historic event. Social media alone gave many of us the notion that something happened on an incredibly grand scale. But measuring just how “grand” is an inexact science. Women’s Marches were held around the world in protest of Trump on the day following his inauguration. Subsequently, lots of folks have tried to find good ways of counting the crowds. Photos and videos of the crowds at some of the largest marches are truly awe-inspiring. And the media have gotten stirred up attempting to quantify just how big this march really was.

Think about it. The image below is taken of some of the crowds in Los Angeles. The caption Getty Images associates with the image includes the estimate “Hundreds of thousands of protesters…” But, was it 200,000? Or was it more like 900,000? Do you think you could eyeball it and make an educated guess? We’d bet you’d be off by more than you think. Previous research has found, for instance, that march participants and organizers are not always the best source of information for how large a protest was. If you’re there and you’re asked how many people were there, you’re much more likely to exaggerate the number of people who were actually there with you. And that fact has spawned wildly variable estimates for marches around the U.S. and beyond.


More than one set of estimates exist attempting to figure this out. The estimates that have garnered the most media attention (deservedly) are those produced by Jeremy Pressman and Erica Chenoweth. They collected as many estimates as they could for marches all around the world to try to figure out just how large the protest was on a global scale. Pressman & Chenoweth collected a range of estimates, and in their data set they classify them by source as well as providing the lowest and highest estimates for each of the marches for which they were able to collect data. You can see and interact with those estimates visually below in a map produced by Eric Compas (though some updates were made in the data set after Compas produced the map).

By Pressman & Chenoweth’s estimates, the total number of marchers in the U.S. was between 3,266,829 and 5,246,321 participants. When they include marches outside the U.S. as well they found that we can add between 266,532 and 357,071 marchers to that number to understand the scale of the protest on an international scale. That is truly extraordinary. But, the range is still gigantic. The difference between their lowest and highest estimate is around 2.1 million people! Might it be possible to figure out which of these estimates are better estimates of crowd size than others?

Nate Silver at FiveThirtyEight.com tried to figure this out in an interesting way. They only attempted to answer this question for U.S. marches alone. And Silver and a collection of his statistical team produced their own data set of U.S. marches. They collected as many crowd estimates as they could for all of the marches held in the U.S. And there are lots of holes in their data that Pressman and Chenoweth filled. March organizers collect information about crowd size and are eager to claim every individual who can be claimed to have been present. But, local officials estimate crowd sizes as well because it helps to give them a sense of what they will need to prepare for and respond to such crowds. As a part of this, some marches had estimates from march organizers, news sources, official estimates, as well as estimates from non-partisan experts (so-called crowd scientists)–this is especially true of the larger marches. Examining their data, they discovered that for every march in which they had both organizer and official estimates, the organizers’ estimate was 50-70% higher than the officials’ estimates. As Silver wrote: “Or put another way, the estimates produced by organizers probably exaggerated crowd sizes by 40 percent to 100 percent, depending on the city” (here). The estimates Silver produced at FiveThirtyEight are mapped below.

You can interact with the map to see Nate Silver’s team estimate, but also the various estimates on which that estimate is based. And you may note that the low and high estimates are often the same for Silver and for Pressman & Chenoweth (though not always). Additionally, there were a good number of marches in FiveThirtyEight’s data set that lacked any estimates at all. And those marches are not visible on the map above. Just to consider some of what is missing, you might note that there are no marches on the map immediately above in Puerto Rico, though Silver’s data set includes four marches there–all with no estimates.

Interestingly, Silver took a further step of offering a “best guess” based on patterned differences between types of estimates they found for marches for which they had more than a single source of data (more than one estimate). For instance, where there were only organizers’ estimates, they discounted that estimate by 40%, assuming that it was exaggerated. They discounted news estimates by 20% for similar reasons. Sometimes, non-partisan experts relying on photographs and videos provide estimates were available, which were not discounted (similar to official estimates).

It might be possible then, as Pressman & Chenoweth collected many more estimates, to fine-tune Silver’s formula and possibly come up with an even more accurate estimate of crowd sizes at marches around the world based on the source of the estimate. It’s a fascinating puzzle and a really interesting and simple way of considering how to resolve it with a (likely) conservative measure.

By these (likely conservative) estimates, marches in the U.S. alone drew more than 3,000,000 people across hundreds of separate locations across the nation. In the U.S. alone, FiveThirtyEight estimated that 3,234,343 people participated (though, as we said, some marches simply lacked any source of data in the data set they produced). And that number, you might note, is strikingly close to Pressman & Chenoweth’s low estimate for the U.S. (3,266,829). Even by this conservative estimate, this would qualify the 2017 Women’s March as certainly among the largest mass protests in U.S. history. It may very well have been the largest mass protest in American history. And in our book, that’s worth counting.

Gender Gaps and the Stalled Gender Revolution

Originally posted at Sociological Images.

Gender gaps are everywhere.  When we use the term, most people immediately think of gender wage gaps.  But, because we perceive gender as a kind of omni-salient feature of identity, gender gaps are measured everywhere.  Gender gaps refer to discrepancies between men and women in status, opportunities, attitudes, demonstrated abilities, and more. A great deal of research focuses on gender gaps because they are understood to be the products of social, not biological, engineering.  Gender gaps are so pervasive that, each year, the World Economic Forum produces a report on the topic: “The Global Gender Gap Report.”

I first thought about this idea after reading some work by Virginia Rutter on this issue (here and here) and discussing them with her.  When you look for them, gender gaps seem to be almost everywhere.  As gender equality became something understood as having to do with just about every element of the human experience, we’ve been chipping away at all sorts of forms of gender inequality.  And yet, as Virginia Rutter points out, we have yet to see gender convergence on all manner of measures.  Indeed, progress on many measures has slowed, halted, or taken steps in the opposite direction, prompting some to label the gender revolution “stalled.”   And in many cases, the “stall” starts right around 1980.  For instance, Paula England showed that though the percentage of women employed in the U.S. has grown significantly since the 1960s, that progress starts to slow in the 1980s.  Similarly, in the 1970s a great deal of progress was made in desegregating fields of study in college.  But, by the early 1980s, about all the change that has been made had been made already.  Changes in the men’s and women’s median wages have shown an incredibly persistent gender gap.

A set of gender gaps often used to discuss inherent differences between men and women are gaps in athletic performance – particularly in events in which we can achieve some kind of objective measure of athleticism.  In Lisa Wade and Myra Marx Ferree’s Gender: Ideas, Interactions, Institutions, they use the marathon as an example of how much society can engineer and exaggerate gender gaps.  They chart world record times for women and men in the marathon over a century.  I reproduced their chart below using IAAF data (below).

marathon-world-record-progression-by-gender

In 1963, an American woman, Merry Lepper, ran a world recording breaking marathon at 3 hours, 37 minutes, and 7 seconds.  That same year, the world record was broken among men at 2 hours, 14 minutes, and 28 seconds.  His time was more than 80 minutes faster than hers!  The gender gap in marathon records was enormous.  A gap still exists today, but the story told by the graph is one of convergence.  And yet, I keep thinking about Virginia Rutter’s focus on the gap itself. I ran the numbers on world record progressions for a whole collection of track and field races for women and men.  Wade and Ferree’s use of the marathon is probably the best example because the convergence is so stark.  But, the stall in progress for every race I charted was the same: incredible progress is made right through about 1980 and then progress stalls and a stubborn gap remains.

Just for fun, I thought about considering other sports to see if gender gaps converged in similar ways. Below is the world record progression for men and women in a distance swimming event – the 1500-meter swim.

1500-meter-swim-world-record-progression-by-gender

The story for the gender gap in the 1500-meter swim is a bit different.  The gender gap was smaller to begin with and was primarily closed in the 1950s and early 60s.  Both men and women continued to clock world record swims between the mid-1950s and 1980 and then progress toward faster times stalled out for both men and women at around that time.

One way to read these two charts is to suggest that technological innovations and improvements in the science of sports training meant that we came closer to achieving, possibly, the pinnacle of human abilities through the 1980s.  At some point, you might imagine, we simply bumped up against what is biologically possible for the human body to accomplish.  The remaining gap between women and men, you might suggest, is natural.  Here’s where I get stuck… What if all these gaps are related to one another?  There’s no biological reason that women’s entry into the labor force should have stalled at basically the same time as progress toward gender integration in college majors, all while women’s incredible gender convergence in all manner of athletic pursuits seemed to suddenly lose steam.  If all of these things are connected, it’s for social, not biological reasons.

Super Mario and Cultural Globalization

Originally posted at Sociological Images.

The 2020 Summer Olympics will be held in Japan. And when the prime minister of Japan, Shinzo Abe, made this public at the 2016 Olympics in Rio de Janeiro, Brazil, he did so in an interesting way. He was standing atop a giant “warp pipe” dressed as Super Mario. I’m trying to imagine the U.S. equivalent. Can you imagine the president of the United States standing atop the golden arches, dressed as Ronald McDonald, telling the world that we’d be hosting some international event?

1f58463f2d95bf88502fb36a2e3a26e2.jpg

Prime minister Abe was able to do this because Mario is a cultural icon recognized around the world. That Italian-American plumber from Brooklyn created in Japan is truly a global citizen. The Economist recently published an essay on how Mario became known around the world.

Mario is a great example of a process sociologists call cultural globalization. This is a more general social process whereby ideas, meanings, and values are shared on a global level in a way that intensifies social relations. And Japan’s prime minister knew this. Shinzo Abe didn’t dress as Mario to simply sell more Nintendo games. I’m sure it didn’t hurt sales. In fact, in the past decade alone, Super Mario may account for up to one third of the software sales by Nintendo. More than 500 million copies of games in which Mario is featured circulate worldwide. But, Japan selected Mario because he’s an illustration of technological and artistic innovations for which the Japanese economy is internationally known. And beyond this, Mario is also an identity known around the world because of his simple association with the same human sentiment—joy. He intensifies our connections to one another. You can imagine people at the ceremony in Rio de Janeiro laughing along with audience members from different countries who might not speak the same language, but were able to point, smile, and share a moment together during the prime minister’s performance. A short, pudgy, mustached, working-class, Italian-American character is a small representation of that shared sentiment and pursuit. This intensification of human connection, however, comes at a cost.

We may be more connected through Mario, but that connection takes place within a global capitalist economy. In fact, Wisecrack produced a great short animation using Mario to explain Marxism and the inequalities Marx saw as inherent within capitalist economies. Cultural globalization has more sinister sides as well, as it also has to do with global cultural hegemony. Local culture is increasingly swallowed up. We may very well be more internationally connected. But the objects and ideas that get disseminated are not disseminated on an equal playing field. And while the smiles we all share when we connect with Mario and his antics are similar, the political and economic benefits associated with those shared smirks are not equally distributed around the world. Indeed, the character of Mario is partially so well-known because he happened to be created in a nation with a dominant capitalist economy. Add to that that the character himself hails from another globally dominant nation–the U.S. The culture in which he emerged made his a story we’d all be much more likely to hear.

Shifts in the U.S. LGBT Population

Originally posted at Sociological Images.

Counting the number of lesbian, gay, bisexual, and transgender people is harder than you might think.  I’ve written before on just how important it is to consider, for instance, precisely how we ask questions about sexuality.  One way scholars have gotten around this is to analytically separate the distinct dimensions of sexuality to consider which dimension they are asking about.  For research on sexuality, this is typically done by considering sexual identities as analytically distinct from sexual desires and sexual behaviors.  We like to imagine that sexual identities, acts, and desires all neatly match up, but the truth of the matter is… they don’t.  At least not for everyone.  And while you might think that gender might lend itself to be more easily assessed on surveys, recent research shows that traditional measures of sex and gender erase our ability to see key ways that gender varies in our society.

Gallup just released a new publication authored by Gary J. Gates.  Gates has written extensively on gender and sexual demography and is responsible for many of the population estimates we have for gender and sexual minorities in the U.S.  This recent publication just examines shifts in the past 5 years (between 2012 and 2016).  And many of them may appear to be small.  But changes like this at the level of a population in a population larger than 300,000,000 people are big shifts, involving huge numbers of actual people.  In this post, I’ve graphed a couple of the findings from the report–mostly because I like to chart changes to visually illustrate findings like this to students.  [*Small note: be aware of the truncated y axes on the graphs.  They’re sometimes used to exaggerate findings.  I’m here truncating the y axes to help illustrate each of the shifts discussed below.]

lgbt-demo-1

The report focuses only on one specific measure of membership as LGBT–identity.  And this is significant as past work has shown that this is, considered alongside other measures, perhaps the most conservative measure we have.  Yet, even by that measure, the LGBT population is on the move, increasing in numbers at a rapid pace in a relatively short period of time.  As you can see above, between 2012 and 2016, LGBT identifying persons went from 3.5%-4.1% of the U.S. population, which amounts to an estimated shift from 8.3 million people in 2012 to more than 10 million in 2016.

lgbt-demo-2-generations

The report also shows that a great deal of that increase can be accounted for by one particular birth cohort–Millennials.  Perhaps not surprisingly, generations have become progressively more likely to identify as LGBT.  But the gap between Millenials and the rest is big and appears to be growing.  But the shifts are not only about cohort effects.  The report also shows that this demographic shift is gendered, racialized, and has more than a little to do with religion as well.

The gender gap between proportion of the population identifying as LGBT in the U.S. is growing.  The proportion of women identifying as LGBT has jumped almost a full percentage point over this period of time.  And while more men (and a larger share of men) are identifying as LGBT than were in 2012, the rate of increase appears to be much slower.  As Gates notes, “These changes mean that the portion of women among LGBT-identified adults rose slightly from 52% to 55%” (here).

lgbt-demo-3-gender-and-race

The gap between different racial groups identifying as LGBT has also shifted with non-Hispanic Whites still among the smallest proportion of those identifying.  As you can see, the shift has been most pronounced among Asian and Hispanic adults in the U.S.  Because White is the largest racial demographic group here, in actual numbers, they still comprise the largest portion of the LGBT community when broken down by race.  But, the transitions over these 5 years are a big deal.  In 2012, 2 of every 3 LGBT adults in the U.S. identified as non-Hispanic White.  By 2016, that proportion dropped to 6 out of every 10. This is big news.  LGBT people (as measured by self-identification) are becoming a more racially diverse group.

They are also diverse in terms of class.  Considering shifts in the proportion of LGBT identifying individuals by income and education tells an interesting story.  As income increases, the proportion of LGBT people decreases.  And you can see that finding by education in 2012 as well–those with less education are more likely to be among those identifying as LGBT (roughly).  But, by 2016, the distinctions between education groups in terms of identifying as LGBT have largely disappeared.  The biggest rise has been among those with a college degree.  That’s big news and could mean that, in future years, the income gap here may decrease as well.

There were also findings in the report to do with religion and religiosity among LGBT identifying people in the U.S.  But I didn’t find those as interesting.  Almost all of the increases in people identifying as LGBT in recent years have been among those who identify as “not religious.”  While those with moderate and high levels of religious commitment haven’t seen any changes in the last five years.  But, among the non-religious, the proportion identifying as LGBT has jumped almost 2 percentage points (from 5.3% in 2012 to 7.0% in 2016).

All of this is big news because it’s a powerful collection of data that illustrate that the gender and sexual demographics of the U.S. are, quite literally, on the move.  We should stand up and pay attention.  And, as Gates notes in the report, “These demographic traits are of interest to a wide range of constituencies.”  Incredible change in an incredibly short period of time.  Let the gender and sexual revolution continue!

Edit (1/17/17): The graph charting shifts by age cohort may exaggerate (or undersell) shifts among Millennials because the data does not exclude Millennials born after 1994.  So, some of those included in the later years here wouldn’t have been included in the earlier years because they weren’t yet 18.  So, it’s more difficult to tell how much of that shift is actually people changing identity for the age cohort as a whole as opposed to change among the youngest Millennials surveyed.