Originally Posted at Discoveries

After the 2016 Presidential election in the United States, Brexit in the UK, and a wave of far-right election bids across Europe, white supremacist organizations are re-emerging in the public sphere and taking advantage of new opportunities to advocate for their vision of society. While these groups have always been quietly organizing in private enclaves and online forums, their renewed public presence has many wondering how they keep drawing members. Recent research in American Sociological Review by Pete SimiKathleen BleeMatthew DeMichele, and Steven Windisch sheds light on this question with a new theory—people who try to leave these groups can get “addicted” to hate, and leaving requires a long period of recovery.

Photo by Dennis Skley, Flickr CC

The authors draw on 89 life history interviews with former members of white supremacist groups. These interviews were long, in-depth discussions of their pasts, lasting between four and eight hours each. After analyzing over 10,000 pages of interview transcripts, the authors found a common theme emerging from the narratives. Membership in a supremacist group took on a “master status”—an identity that was all-encompassing and touched on every part of a member’s life. Because of this deep involvement, many respondents described leaving these groups like a process of addiction recovery. They would experience momentary flashbacks of hateful thoughts, and even relapses into hateful behaviors that required therapeutic “self talk” to manage.

We often hear about new members (or infiltrators) of extremist groups getting “in too deep” to where they cannot leave without substantial personal risk. This research helps us understand how getting out might not be enough, because deep group commitments don’t just disappear when people leave.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

From Pizzagate to more plausible stories of palace intrigue, U.S. politics has more than a whiff of conspiracy in the air these days. In sorting fact from fiction, why do some people end up believing conspiracy theories? Social science research shows that we shouldn’t think about these beliefs like delusions, because the choice to buy in stems from real structural and psychological conditions that can affect us all.

For example, research in political science shows that people who know a lot about politics, but also show low levels of generalized trust, are more likely to believe conspiracy theories. It isn’t just partisan, either, both liberals and conservatives are equally likely to believe conspiracy theories—just different ones.

In sociology, research also shows how bigger structural factors elevate conspiracy concern. In an article published in Socius earlier this year, Joseph DiGrazia examined Google search trends for two major conspiracy theories between 2007 and 2014: inquiries about the Illuminati and concern about President Obama’s birth and citizenship.

DiGrazia looked at the state-level factors that had the strongest and most consistent relationships with search frequency: partisanship and employment. States with higher unemployment rates had higher search rates about the Illuminati, and more Republican states had higher searches for both conspiracies throughout the Obama administration.

These studies show it isn’t correct to treat conspiracy beliefs as simply absurd or irrational—they flare up among reasonably informed people who have lower trust in institutions, often when they feel powerless in the face of structural changes across politics and the economy.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

The New York Times has been catching a lot of criticism this week for publishing a profile on the co-founder of the Traditionalist Worker Party. Critics argue that stories taking a human interest angle on how alt-right activists live, and how they dress, are not just puff pieces that aren’t doing due diligence in reporting—they also risk “normalizing” neo-nazi and white supremacist views in American society.

It is tempting to scoff at the buzzword “normalization,” but there is good reason for the clunky term. For sociologists, what is normal changes across time and social context, and normalization means more than whether people choose to accept deviant beliefs or behaviors. Normalization means that the everyday structure of organizations can encourage and reward deviance, even unintentionally.

Media organizations play a key role here. Research on the spread of anti-Muslim attitudes by Chris Bail shows how a small number of fringe groups with extremist views were able to craft emotionally jarring messages that caught media attention, giving them disproportionate influence in policy circles and popular culture.

Organizations are also quite good at making mistakes, and even committing atrocities, through “normal” behavior. Research on what happened at NASA leading up to the Challenger disaster by Diane Vaughan describes normalization using a theory of crime from Edwin H. Sutherland where people learn that deviant behavior can earn them benefits, rather than sanctions. When bending the rules becomes routine in organizations, we get everything from corporate corruption up to mass atrocities. According to Vaughan:

When discovered, a horrified world defined these actions deviant, yet they were normative within the culture of the work and occupations of the participants who acted in conformity with organizational mandates

The key point is that normalization doesn’t just stop by punishing or shaming individuals for bad behavior. Businesses can be fined, scapegoats can be fired, and readers can cancel subscriptions, but if normalization is happening the culture of an institution will continue to shape how individual people make decisions.This raises big questions about the decisions made by journalists and editors in pursuit of readership.

Research on normalization also begs us to remember that some of the most horrifying crimes and accidents in human history are linked by a common process: the way organizations can reward deviant work. Just look at the “happy young folks” photographed by Karl Höcker in 1944…while they worked at Auschwitz.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

The CDC recently issued a press release announcing that rates of reported cases for sexually transmitted diseases are setting record highs. The new report offers reports of rates going back to 1941 in a table, so I made a quick chart to see the pattern in context and compare the more common conditions over time (HIV wasn’t included in this particular report).

It is important to note that a big part of changes in disease rates is usually detection. Once you start looking for a condition, you’ll probably find more of it until enough diagnoses happen for treatment to bring the rates down. Up until 2000, the U.S. did pretty well in terms of declining rates for cases of gonorrhea and syphilis. Zoom in on the shaded area from 2000 to 2016, however, and you can see a pretty different story. These rates are up over the last 16 years, and chlamydia rates have been steadily increasing since the start of reporting in 1984.

STDs are fundamentally a social phenomenon, especially because they can spread through social networks. However, we have to be very careful not to jump to conclusions about the causes of these trends. It’s tempting to blame dating apps or hookup culture, for example, but early work at the state level only finds a mixed relationship between dating app use and STD rates and young people also have higher rates of sexual inactivity. Rate increases could even be due in part to detection now that more people have access to health coverage and care through the Affordable Care Act. Just don’t wait for peer review to finish before going to get tested!

Inspired by demographic facts you should know cold, “What’s Trending?” is a post series at Sociological Images featuring quick looks at what’s up, what’s down, and what sociologists have to say about it.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

Where is your nearest garbage dump? Where does the local factory go when it needs to get rid of some particularly toxic chemicals? If there was a disaster, would you have to move? Could you?

Sociologists use shorthand terms like “environmental racism” to draw attention to the fact that poor communities and communities of color are often more likely to be exposed to hazardous materials, and cases like the Flint water crisis drive this point home.

Of course, housing inequality also means that nobody has to dump anything to put poor communities in hazardous positions. One recent example of this is the flooding in Houston after Hurricane Harvey. Over at Socius, Yuqi Lu gathered data on the median household income in neighborhoods across the Houston area from the American Community Survey and matched it with land elevation data from Google Maps.

In general, poorer neighborhoods in Houston sit at lower elevations, and thus are more susceptible to flood risks. This relationship is strongest in less-densely-populated areas, such as rural and suburban neighborhoods, but additional analysis in Lu’s article shows the relationship is robust.

The latest reports are in on human caused climate change. Regardless of whether we can act to turn it around in time, we’ll also have to recognize the fact that not everyone faces the same fallout from environmental hazards and natural disasters.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

The staff at How Much recently visualized summaries from a Federal Reserve analysis showing how much a college degree can matter for your net worth. It turns out education can really pay…if you’re white.

This illustrates an important sociological point. When we talk about structural inequality, critics often note that we shouldn’t disregard individuals’ efforts to work and earn a better life. Getting a college degree is one of the centerpieces of this argument. These gaps show it’s not that effort doesn’t matter at all, but that inequality in social conditions means those efforts yield wildly different outcomes.

Want to read more on higher education and America’s wealth gap? Check out Tressie McMillan Cottom’s Lower Ed, Thomas Shapiro’s Toxic Inequality, and Dalton Conley’s Being Black, Living in the Red.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

This weekend I was at the annual conference for the Society for the Scientific Study of Religion, where they held a memorial for sociologist Peter Berger. I thought of Berger and Luckmann’s classic The Social Construction of Reality in the airport on the way home. Whenever people say ritual is dying out, or socially constructed things “aren’t real,” I think of airport lines.

There are always two lines, but rarely any separation other than a sign like this. If you’re lucky, you can catch the gate agent making a big show of opening the “general boarding” lane, but everyone ends up at the same scanner right past the sign (usually only a minute or two after the “elite” passengers). From Berger and Luckmann (the Anchor Books paperback edition):

The developing human being not only interrelates with a particular natural environment, but with a specific cultural and social order which is mediated to him by the significant others who have charge of him (p. 48).

The symbolic universe orders and thereby legitimates everyday roles, priorities, and operating procedures…even the most trivial transactions of everyday life may come to be imbued with profound significance (p. 99).

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.

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 an assistant professor of sociology at University of Massachusetts Boston. You can follow him on Twitter.