gender: politics

On the Data is Beautiful subreddit, a user going by the name fencelizard recently took a look at gender differences in full-time staff salaries in the last four U.S. Presidential administrations. This is only a quick descriptive picture (notes on the methodology below), but it highlights an important point about organizations: inequality doesn’t always neatly align with ideology.

Both the wide and the narrow median pay gaps are bipartisan. While the Clinton and the early Trump administrations have the widest gaps in median earnings, the George W. Bush and Obama administrations were the closest to gender parity (the gap was not statistically significant in the Obama years).

Of course, these gaps mean different things in different administrations. The parity among Bush staffers looks like it came from pay cuts on both sides, with more men remaining in a higher salary range, while the Obama administration had a much more even distribution across men and women. Part of the pattern for the Trump administration could be due to understaffing in general.

Nevertheless, it is interesting to see how the salary distribution for women staffers has remained relatively consistent and lower than fluctuating salaries for men. Sociologists know that inequality can be embedded in the day-to-day operations of institutions like schools, prisons, and government offices. Bias certainly can and does play a role in this process, but the ideological support that we often associate with such biases—like political preferences—doesn’t always have to be the deciding factor for whether inequality happens.

Some background on the analysis from fencelizard:

Salary data was sourced from white house press releases for Trump (PDF tables; FML) and Obama (UTF-8 csv’s; thanks Obama), and from the Washington Post for Bush (http://www.washingtonpost.com/wp-srv/politics/administration/whbriefing/2004stafflistb.html) and Clinton (https://www.washingtonpost.com/archive/politics/1993/11/01/salaries-at-clintons-white-house/9c96f5b6-02c5-4888-87ee-dc547d8d93f0/?utm_term=.aa70a4af1649). Supposedly full salary data for Bush I exists too, but I couldn’t find it anywhere online.
I cleaned up the data in R, used the ‘gender’ package to guess staffers’ gender from their first names, and made the plots with ggplot2 and gridExtra. I used a Wilcox test to compare the distribution of salaries across genders for each president. Asterisks in the figure above indicate significantly different distributions.

Evan Stewart is a Ph.D. candidate in sociology at the University of Minnesota. You can follow him on Twitter.

Photo by Ted Eytan; flickr creative commons.

President Trump recently declared that Obamacare is “essentially dead” after the House of Representatives passed legislation to replace existing health care policy. While members of the Senate are uncertain about the future of the proposed American Health Care Act (AHCA) — which could ultimately result in as many as 24 million people losing their health insurance and those with pre-existing conditions facing increasing health coverage costs — a growing number of Americans, especially women, are sure that the legislation will be bad for their health, if enacted.

On the same day that the House passed the Republican-backed plan, for example, a friend of mine revealed on social media that she had gotten her yearly mammogram and physical examination. She posted that the preventative care did not cost anything under her current employer benefit plan, but would have been prohibitively expensive without insurance coverage, a problem faced by many women across the United States. For instance, the American Cancer Society reports that in 2013 38% of uninsured women had a mammogram in the last two years, while 70% of those with insurance did the same. These disparities are certainly alarming, but the problem is likely to worsen under the proposed AHCA.

Breast care screenings are currently protected under the Affordable Care Act’s Essential Health Benefits, which also covers birth control, as well as pregnancy, maternity, and newborn care. The proposed legislation supported by House Republicans and Donald Trump would allow individual states to eliminate or significantly reduce essential benefits for individuals seeking to purchase health insurance on the open market.

Furthermore, the current version of the AHCA would enable individual states to seek waivers, permitting insurance companies to charge higher premiums to people with pre-existing conditions, when they purchase policies on the open market. Making health insurance exorbitantly expensive could have devastating results for women, like those with a past breast cancer diagnosis, who are at risk of facing recurrence. Over 40,000 women already die each year from breast cancer in our country, with African-American women being disproportionately represented among these deaths.

Such disparities draw attention to the connection between inequality and health, patterns long documented by sociologists. Recent work by David R. Williams and his colleagues, for instance, examines how racism and class inequality help to explain why the breast cancer mortality rate in 2012 was 42% higher for Black women than for white women. Limiting affordable access to health care — which the AHCA would most surely do — would exacerbate these inequalities, and further jeopardize the health and lives of the most socially and economically vulnerable among us.

Certainly, everyone who must purchase insurance in the private market, particularly those with pre-existing conditions stand to lose under the AHCA. But women are especially at risk. Their voices have been largely excluded from discussion regarding health care reform, as demonstrated by the photograph of Donald Trump, surrounded by eight male staff members in January, signing the “global gag order,” which restricted women’s reproductive rights worldwide. Or as illustrated by the photo tweeted  by Vice-President Pence in March, showing him and the President, with over twenty male politicians, discussing possible changes to Essential Health Benefits, changes which could restrict birth control coverage, in addition to pregnancy, maternity, and newborn care. And now, as all 13 Senators slated to work on revisions to the AHCA are men.

Women cannot afford to be silent about this legislation. None of us can. The AHCA is bad for our health and lives.

Jacqueline Clark, PhD is an Associate Professor of Sociology and Chair of the Sociology and Anthropology Department at Ripon College. Her research interests include inequalities, the sociology of health and illness, and the sociology of jobs, work, and organizations.

Sometimes you have to take the long view.

This week Bill O’Reilly — arguably the most powerful political commentator in America — was let go from his position at Fox News. The dismissal came grudgingly. News broke that he and Fox had paid out $13 million dollars to women claiming O’Reilly sexually harassed them; Fox didn’t budge. They renewed his contract. There was outcry and protests. The company yawned. But when advertisers started dropping The O’Reilly Factor, they caved. O’Reilly is gone.

Fox clearly didn’t care about women — not “women” in the abstract, nor the women who worked at their company — but they did care about their bottom line. And so did the companies buying advertising space, who decided that it was bad PR to prop up a known sexual harasser. Perhaps the decision-makers at those companies also thought it was the right thing to do. Who knows.

Is this progress?

Donald Trump is on record gleefully explaining that being a celebrity gives him the ability to get away with sexual battery. That’s a crime, defined as unwanted contact with an “intimate part of the body” that is done to sexually arouse, gratify, or abuse. He’s president anyway.

And O’Reilly? He walked away with $25 million in severance, twice what all of his victims together have received in hush money. Fox gaves Roger Ailes much more to go away: $40 million. Also ousted after multiple allegations of sexual harassment, his going away present was also twice what the women he had harassed received.

Man, sexism really does pay.

But they’re gone. Ailes and O’Reilly are gone. Trump is President but Billy Bush, the Today host who cackled when Trump said “grab ’em by the pussy,” was fired, too.  Bill Cosby finally had some comeuppance after decades of sexual abuse and rape. At the very least, his reputation is destroyed. Maybe these “victories” — for women, for feminists, for equality, for human decency — were driven purely by greed. And arguably, for all intents and purposes, the men are getting away with it. Trump, Ailes, O’Reilly, Bush, and Cosby are all doing fine. Nobody’s in jail; everybody’s rich beyond belief.

But we know what they did.

Until at least the 1960s, sexual harassment — along with domestic violence, stalking, sexual assault, and rape — went largely unregulated, unnoticed, and unnamed. There was no language to even talk about what women experienced in the workplace. Certainly no outrage, no ruined reputations, no dismissals, and no severance packages. The phrase “sexual harassment” didn’t exist.

In 1964, with the passage of the Civil Rights Act, it became illegal to discriminate against women at work, but only because the politicians who opposed the bill thought adding sex to race, ethnicity, national origin, and religion would certainly tank it. That’s how ridiculous the idea of women’s rights was at the time. But that was then. Today almost no one thinks women shouldn’t have equal rights at work.

What has happened at Fox News, in Bill Cosby’s hotel rooms, in the Access Hollywood bus, and on election day is proof that sexism is alive and well. But it’s not as healthy as it once was. Thanks to hard work by activists, politicians, and citizens, things are getting better. Progress is usually incremental. It requires endurance. Change is slow. Excruciatingly so. And this is what it looks like.

Lisa Wade, PhD is a professor at Occidental College. She is the author of American Hookup, a book about college sexual culture, and a textbook about gender. You can follow her on Twitter, Facebook, and Instagram.

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.

Tara Leigh Tober, PhD is a professor at The College at Brockport, SUNY.  She studies the sociology of memory, is writing a book on how the Irish have remembered being neutral during WWII, and is presently engaged in a study on mass shootings in the U.S.  You can follow her on Twitter here.

Tristan Bridges, PhD is a professor at the University of California, Santa Barbara. He is the co-editor of Exploring Masculinities: Identity, Inequality, Inequality, and Change with C.J. Pascoe and studies gender and sexual identity and inequality. You can follow him on Twitter here. Tristan also blogs regularly at Inequality by (Interior) Design.

1Recently Nadya Tolokonnikova was interviewed by NPR about Pussy Riot’s latest video. In it, Tolokonnikova explores themes of racism, xenophobia, and misogyny and its influence on governance through a graphic and violent imagined America under a Trump presidency. Trigger warning for… most things:

Tolokonnikova is making a statement about American politics, but she is clearly informed by Putin’s performance of masculinity and how that has translated into policy measures and electoral success. When he took office in early 2000, Putin needed to legitimize his power and counteract the global impression of Russian weakness after the collapse of the Soviet Union.

The projection of masculinity was a PR strategy: fishing and riding a horse shirtless, shooting a Siberian tiger, and emerging from the Black Sea in full scuba gear. These actions combined with bellicose foreign policy initiatives to portray Putin as assertive and unrelenting.

In the book, Sex, Politics, & Putin, Valerie Sperling makes a case that his strategy was successful. She investigates the political culture under Putin and argues there is popular support for Putin’s version of masculinity and its implications for femininity, even among young women. As a consequence, the gender and sexual politics of Russia have deviated from those of wider Europe, as indicated by the rise of the Russian slur “gayropa.”

The machismo and misogyny embodied by Putin have also translated into policy: the “gay propaganda” law, for example, and the ban on international adoption to gay couples. In his 2013 address to the Federal Assembly, Putin framed these policies as necessary to combat the “destruction of traditional values.”

While there is no systematic research on the role of masculinity in Trump’s rise to the national political stage in the US just yet, and while the nature of the link between Putin and Trump remains unclear (if one truly even exists), we should consider Putin’s Russia a cautionary tale. His performances of masculinity – his so-called “locker room talk,” discussion of genitalia size, and conduct towards pageant contestants — could go from publicity stunt to public support to actual policy measures. His bombastic language about defeating ISIS and the need for more American “strength” at home and abroad, for example, could easily translate into foreign policy.

Coverage of Trump during this election cycle is credited for hundreds of millions in profits for news agencies and Trump himself has enjoyed an unprecedented level of coverage. While Trump has benefited from far more airtime than Putin did in 2000, he has not been able to find the same level of popular support. At least not yet. When Putin rose to status as a national figure in Russia his approval rating was approximately 60%, and it grew from there to levels most American politicians only dream of. If Trump is willing and able to adopt other components of Putin’s leadership style, there is precedent for the possibility that his presidency could truly turn American back.

Alisha Kirchoff is a sociology PhD student at Indiana University-Bloomington. She has previously lived and worked in Russia and is currently working on research in political sociology, law and society, organizations, and gender. Her latest project is on fertility intentions and family policies in Putin’s Russia. You can follow her on twitter.

1Just after the United Kingdom voted to leave the European Union, a commentator at the lauded US News and World Report claimed that the “general consensus” was that the vote was a “veritable dumpster fire.” Since then, most citizens of the EU, many Americans, and lots of UK citizens, including many who voted to leave, seem to think that this was a terrible decision, sending the UK into treacherous political and economic territory.

The Prime Minister agreed to step down and, rather quickly, two women rose to the top of the replacement pool. Yesterday Theresa May was the lone contender left standing and today she was sworn in.

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Is it a coincidence that a woman is about to step into the top leadership position after the Brexit?

Research suggests that it’s not. In contexts as wide-ranging as the funeral business, music festivals, political elections, the military, and law firms, studies have found a tendency for women to be promoted in times of crisis. As a result, women are given jobs that have a higher risk of failure — like, for example, cleaning up a dumpster fire.  It’s called the “glass cliff,” an invisible hazard that harms women’s likelihood of success. One study found that, because of this phenomenon, the average tenure of a female CEO is only about 60% as long as that of the average male CEO.

As one Democratic National Committee chair once said: “The only time to run a woman is when things look so bad that your only chance is to do something dramatic.” Maybe doing “something dramatic” is why so many women are promoted during times of crisis, but the evidence suggests that another reason is because men protect other men from having to take precarious positions. This was the experience of one female Marine Corps officer:

It’s the good old boys network. The guys helping each other out and we don’t have the women helping each other out because there are not enough of us around. The good old boys network put the guys they want to get promoted in certain jobs to make them stand out, look good.

If women are disproportionately promoted during times of crisis, then they will fail more often than their male counterparts. And they do. It will be interesting to watch whether May can clean up this dumpster fire and, if she can’t, what her legacy will be.

Lisa Wade, PhD is a professor at Occidental College. She is the author of American Hookup, a book about college sexual culture, and a textbook about gender. You can follow her on Twitter, Facebook, and Instagram.

Cross-posted at Cyborgology.

Fake news among the alt-right has been central in post-election public discourse, like with Donald Trump’s dubiously sourced tweet about the “millions of illegal voters” supposedly driving Clinton’s substantial lead in the popular vote. Less attention, however, has been paid to the way “real” news is, to use the sociologist Nathan Jurgenson’s term, based in “factiness,” described as “the feel and aesthetic of ‘facts,’ often at the expense of missing the truth.”  Mainstream news gets cast as objective in part because journalists, stack of papers and obligatory pen studiously in hand, point to statistics that back up their reports. Such reliance on “data” can mask the way that humans are involved in turning things into numbers and numbers into stories. So here I present a cautionary tale.

It is a common truism that white male voters without college degrees disproportionately supported Trump in the 2016 election. Indeed, the notion that men with high school as their highest level of education were more likely to vote for Trump is an empirically supported fact. This data point spread widely throughout the campaign season, and bore out in the post-election analyses. But also in the post-election analyses — over which researchers poured in response to the statistically surprising result — another data point emerged that could have, but didn’t, change the narrative around this demographic voting bloc.

The data point that emerged was that white American men without college degrees have remained economically depressed since the 2008 recession and subsequent recovery. Although the U.S. economy has been steadily improving, the economic reality for this particular segment of the population has not. This is what Michael Moore talked about experientially (but not statistically), claiming that he knows the people who live in the rust belt, and they are struggling. He was right, the data show that they are struggling. Highlighting the economic reality for people without college degrees in the U.S. tells a very different story than highlighting the fact that they don’t have college degrees. The former renders an image of a voting contingent who, in the face of personal economic hardship that contrasts with national economic gain, are frustrated and eager to try something — anything — new. The latter renders an image of ignorance.

Data about education levels of voters is transformed by its coupling with economic trajectories. What’s been strange, is that although this coupling was discovered, it never really penetrated the larger “what happened” narrative. This is particularly strange given the meticulous and sometimes frantic search for explanation and the media’s public introspective quests to understand how so many got it all so wrong.

The transformative effect of the economic data point and its failure to effectively transform the story underlines two related things: data are not self-evident and narrative currents are hard to change.

The data weren’t wrong — people without college degrees were more likely to vote for Trump — but they were incomplete and in their partialness, quite misleading. That’s not a data problem, it’s a people problem. Data are not silent, but they are inarticulate. Data make noise, but people have to weave that noise into a story. The weaving process begins with survey construction, and culminates in analyses and reports. Far from an objective process, turning data into narrative entails nuanced decisions about the relevance of, and relationship between, quantifiable items captured through human-created measures. The data story is thus always value-laden and teeming with explicit and implicit assumptions.

Framing a contingent of Trump supporters through the exclusive metric of education without examining the interaction, mediating, and moderating effects of economic gains, was an intellectual decision bore out through statistical analyses. That is, pollsters, strategists, and commentators treated “lack of education” as the variable with key explanatory power. Other characteristics or experiences of those with low levels of education could/should/would be irrelevant.

Such dismissal created a major problem with regard to Democratic strategy. To situate a voting bloc as “uneducated” is to dismiss that voting bloc. How does one campaign to those voting in ignorance? In contrast, to situate a voting bloc as connected through an economic plight not only validates their position, but also gives a clear policy platform on which to speak.

But okay, after the election, analysts briefly shed light on the way that economics and education operated together to predict candidate preference. Why has this gotten so little attention? Why is education — rather than economics or the economic-education combination — still the predominant story?

The predominance of education remains because narrative currents are strong. Even when tied to newly emergent data, established stories are resistant to change. Narratives are embedded with social frameworks, and changing the story entails changing the view of reality. A key tenet of sociology is that people tend towards stability. Once they understand and engage the world in a particular way, they do social and psychological gymnastics to continue understanding and engaging the world in that way. To reframe (some) Trump voters as part of an economic interest group that has been recently underserved, is an upheaval of previous logics. Moreover, disrupting existing logics in this way forces those who practice those logics to, perhaps, reframe themselves, and do so in a way that is not entirely flattering or identity affirming. To switch from a frame of ignorance to a frame of economics is to acknowledge not only that the first frame was distorted, but also, to acknowledge that getting it wrong necessarily entailed ignoring the economic inequality that progressives take pride in caring so much about. Switching from ignorance to economics entails both a change in logic and also, a threat to sense of self.

Data are rich material from which stories are formed, and they are not objective. Tracing data is a process of deconstructing the stories that make up our truths — how those stories take shape, evolve, and solidify into fact. The “truth” about Trump voters is of course complex and highly variable. The perpetually missed nuances tell as much of a story as those on which predominant narratives hang.

Jenny L. Davis, PhD, is in the department of sociology at James Madison University. She studies social psychology, experimental research methods, and new and social media. She is also a contributing author and editor at Cyborgology.  You can follow her at @Jenny_L_Davis.

According to the Southern Poverty Law Center, the US saw a spike of hate incidents after the election of Donald Trump on November 8th. 867 real-world (i.e., not internet-based) incidents were reported to the Center or covered in the media in just 10 days. USA Today reports that the the Council on American-Islamic relations also saw an uptick in reports and that the sudden rise is greater than even what the country saw after the 9/11 attacks. This is, then, likely just a slice of what is happening.

The Center doesn’t present data for the days coming up to the election, but offers the following visual as an illustration of what happened the ten days after the 8th.

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If the numbers of reports prior to the 8th were, in fact, significantly lower than these, than there was either a rise in incidents after Trump’s victory and Clinton’s loss, or an increase in the tendency to report incidents. Most perpetrators of these attacks targeted African Americans and perceived immigrants.

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The most common place for these incidents to occur, after sidewalks and streets, was K-12 schools. Rosalind Wiseman, anti-bullying editor and author of Queen Bees and Wannabes, and sociologist CJ Pascoe, author of Dude, You’re a Fag, both argue that incidents at schools often reflect adult choices. Poor role models — adults themselves who bully or who fail to stand up for the bullied — make it hard for young people to have the moral insight and strength to do the right thing themselves.

Lisa Wade, PhD is a professor at Occidental College. She is the author of American Hookup, a book about college sexual culture, and a textbook about gender. You can follow her on Twitter, Facebook, and Instagram.