Category Archives: Me @ work

ASA meeting Twitter network graph

The American Sociological Association meetings, which ended earlier this week, had a rollicking good Twitter stream. Now Marc Smith has analyzed the tweeters who used the hashtag #asa14 (and related), and their interactions, to produce network graphs of the meeting’s tweeted undercurrent. I looked through one of the graphs, which I’ll describe briefly.

Smith used NodeXL, and generated a whole gallery of graphs. Just looking in your browser is difficult because the resolution is too low to identify people, but you can download the giant Excel files he made, or use the interactive graphs which allow you to hover over points and see their handles. That’s how I figured out the following graph, which represents 18,000 tweets from Sunday and Monday, the middle of the conference (click to enlarge, but it won’t help that much). Details here, my description below.

Graph-25487The top left, G1, is the heaviest traffic. This was a lot of leftists in active discussions of Ferguson, Missouri, Mike Brown, and Alice Goffman (and her book On The Run). At the center of that mass seems to be Jessie Daniels from CUNY (who describes herself, fittingly, as an instigator), UT-Austin sociology, Conditionally Accepted, Dr. Compton, and C.J. Pascoe, among others. I can’t find the dot for Tressie McMillan Cottom (Tressiemcphd) — who has the highest betweenness centrality of any individual on the graph, and was the most frequently replied-to tweeter — but it’s probably in G1 somewhere.

Moving clockwise, the next cluster (G3) is centered around the official feed of the ASA, @ASAnews, with a lot of tweets about the conference theme, publishers and their booths, and journals.

Clockwise to G5, you get another cluster with a lot of Mike Brown and Ferguson, but this one more focused on education and academia, including Lean In. At the center of G5 is Sara Goldrick-Rab.

The top right, G6, is where I ended up. It has several lose center points, including me (familyunequal), Tina Fetner, and two people who tweeted ASA content that got picked up by a lot of non-sociologists: Mark Abraham (urbandata) and Str8Grandmother. Also up there is Karl Bakeman (my editor at Norton), the Norton sociology feed, and Contexts magazine.

Next on the far right is G10, which has a lot of critical race discussion (#troublewithwhitewomen), as well as information technology. I can’t tell the theme of G9, which includes Lisa Wade and Nathan Palmer (sociologysource).

The orange oval in G7 is centered around the Émile Durkheim feed (“Invented Sociology, and don’t let any Germans tell you otherwise”). This was probably his most popular tweet this time out, with going on 100 retweets:

durkheimcup

In fact, the graph data shows that the G7 sector basically comprises the community formed around this tweet.

The bottom center sector, G4, clusters around Think Progress. Note the strong ties to the top left, where the Ferguson traffic was heaviest. G4 is a key group for transmitting leftist politics into and out of ASA. The feminist Leta Hong Fincher is the node that connects this group to that fan of people off the bottom right of the cluster.

Finally, the bottom left group, G2, is centered on education and technology, with clusters around Liz Meyer, Marc Smith, Gina Neff, and others I’m not familiar with.

So

There are lots of social layers and clusters across the ASA, which could be grouped by specialty, department, age, race/ethnicity, nationality, sexuality, and so on. The Twitter network just happens to leave an easy data trail. I mention all these individuals not to play into a star system, but because it’s easier to name someone than to attempt to categorize them. I’m open to other interpretations of this graph.

I’m getting very sappy in my old age about my love for sociology and sociologists. But as I look over these figures, I think that if I had to pick 5,000 people to spend a weekend with, who all had only one thing in common, I think ASA members was a good choice.

 

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Doing math one-handed? Inequality and the marriage problem (#asa14)

I’m at the American Sociological Association meetings in San Francisco, on my way over to present the following slides at a session on “Closing the Economic Marriage Gap: The Policy Debate.” Looks like a great session, organized by Melanie Heath, Orit Avishai, and Jennifer Randles, and including Andrew Cherlin, Sarah Halpern-Meekin, Mignon Moore, and Ronald Mincy – with a discussion by Barbara Risman.

I’ve uploaded the slides for my talk, here.

The background is in this post, which I wrote in 2011, called, “Is it a ‘marriage problem’?” Here it is again:

Is it a “marriage problem”?

A self-described liberal (Andrew Cherlin) and conservative (W. Bradford Wilcox) pair of academics have produced a “policy brief”* for the Brookings Institution entitled, The Marginalization of Marriage in Middle America.

There’s no new information or analysis in the report, so I won’t dwell on it. But I’d like to use it to point out a logical problem with pro-marriage social science in general. Here’s an excerpt from the introduction, with my comment following:

This policy brief reviews the deepening marginalization of marriage and the growing instability of family life among moderately-educated Americans: those who hold high school degrees but not four-year college degrees and who constitute 51 percent of the young adult population (aged twenty-five to thirty-four). … [b]oth of us agree that children are more likely to thrive when they reside in stable, two-parent homes. … Thus, we conclude by offering six policy ideas, some economic, some cultural, and some legal, designed to strengthen marriage and family life among moderately-educated Americans. … To be sure, not every married family is a healthy one that benefits children. Yet, on average, the institution of marriage conveys important benefits to adults and children. … The fact is that children born and raised in intact, married homes typically enjoy higher quality relationships with their parents, are more likely to steer clear of trouble with the law, to graduate from high school and college, to be gainfully employed as adults, and to enjoy stable marriages of their own in adulthood. Women and men who get and stay married are more likely to accrue substantial financial assets and to enjoy good physical and mental health. In fact, married men enjoy a wage premium compared to their single peers that may exceed 10 percent. At the collective level, the retreat from marriage has played a noteworthy role in fueling the growth in family income inequality and child poverty that has beset the nation since the 1970s. For all these reasons, then, the institution of marriage has been an important pillar of the American Dream, and the erosion of marriage in Middle America is one reason the dream is increasingly out of reach for men, women, and children from moderately-educated homes.

It’s obvious empirically that adults and children in married-couple families, on average, are doing better on many measures than those not in such families. The logical problem is when people conclude from this pattern that the obvious response is to “strengthen marriage and family life.” But, why not try to reduce that disparity instead?

This is the logical equivalent of the Republican mantra that “We don’t have a revenue problem in Washington; we have a spending problem.” That’s only true if you’re doing one-handed math. And the same holds for marriage.

Yes, there is less marriage, and many people are less well off without it. Does that mean we have a “marriage” problem, or a family inequality problem? Is there any other way to help people develop high quality relationships with their parents, complete more education, get better jobs, accrue financial assets and maintain good physical and mental health?

In the categorical math of inequality, you can try (with little chance of success in this case) to reduce the number of people in the disadvantaged category (non-married families), or you can try to reduce the size of the disparity between the two categories.

*I’m not sure, but I think a “policy brief” is a blog post about policy matters, produced on the PDF letterhead of a foundation. Not that there’s anything wrong with that. As far as I can tell, this one is a non-peer-reviewed essay which handles sourcing like this: “the findings detailed in this policy brief come from a new report by Wilcox, When Marriage Disappears: The New Middle America.” As I’ve pointed out (here andhere), Wilcox’s reports at the National Marriage Project are also non-peer-reviewed essays with a lot of substantially misleading and erroneous content.

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Marriage, divorce, remarriage, age, education (Coontz tabs edition)

Stephanie Coontz has an excellent Op-Ed on the front of today’s New York Times Sunday Review, which draws out the implications for family instability of the connection between increasing gender equality on the one hand, and increasing economic inequality and insecurity on the other. The new instability is disproportionately concentrated among the population with less than a college degree. To help with her research, I gave Stephanie the figure below, but it didn’t make the final cut. This shows the marriage history of men and women by education and age. She wrote:

According to the sociologist Philip N. Cohen, among 40-somethings with at least a bachelor’s degree, as of 2012, 63 percent of men and 59 percent of women were in their first marriage, compared to just 43 percent of men and 42 percent of women without a bachelor’s degree.

I highlighted those numbers in the figure. Also striking is the higher percentage of divorced people among those with less than a BA degree (and higher widowhood rates). Click to enlarge: age marriage history Cross-posted on the Families As They Really Are blog.

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Nicholas Wade followup, deeper dive edition

I’m very happy with the editing and fact-checking they did at Boston Review for my review of Nicholas Wade’s book, A Troublesome Inheritance: Genes, Race and Human History, and I don’t want to undermine their work (thanks to managing editor Simon Waxman and associate web editor Nausicaa Renner). If you only have time to read 4,000 words on it, their version is what you should read. It’s up here for free.

But in the thousands of words that ended up on the cutting room floor, there were a few ideas I’d like to post here, for the very interested reader.

Photo from Flickr Creative Commons by epSos.de

Photo from Flickr Creative Commons by epSos.de

Human bones

A number of critics have said that Wade’s early chapters are good, and the book only gets crazy-racist in the second half when he starts attributing social behavior to races and tracing global economic disparities to evolution by natural selection. But I did want to stress that he’s got plenty wrong in the early part of the book as well. In particular, I highlighted the question, why did human bones get thinner in the millennia before they settled down? This isn’t something we worry over much, but I think it’s an important clue to his biases and assumptions. From the published review:

To establish that genes determine social behavior, Wade looks to ancient history, when humans first settled in agricultural communities. “Most likely a shift in social behavior was required,” he writes, “a genetic change that reduced the level of aggressivity common in hunter-gatherer groups.” Of course, many elements were involved—climate change and geography, population pressure, the presence of various plants and animals, advances in tools and weapons, and human biological evolution—but there is no evidence that a behavioral genetic change was required.

I actually spent a fascinating few hours reading the scientific literature on evolution and bone structure, and saw no mention of the reduction in human aggressive behavior as a cause of human bones becoming weaker. To elaborate, Wade thinks natural selection gave people genes for thinner bones because strong bones became less necessary for survival as people fought each other less. He thinks genetic change in behavior led to genetic change in bones. Please correct me if I’m wrong, but I don’t see any literature at all to back this up (Wade doesn’t cite any).

In fact, if I read it right, we might have thinner bones today than people did 50,000 years ago even though our bone genetics haven’t changed much, as a result of diet and lifestyle changes alone. How is that possible? When the bones of young people bear less weight they don’t grow as thick when they’re adults. This is the issue of tool use and the declining “habitual loads” on human limbs. It might also extend to our skulls because we’re not grinding pre-agricultural superfoods with our teeth all day long. Biological anthropologist Christopher Ruff writes: “In a few years, the strength of a person’s bone structure can change as much as the total average change over the past 2 million years of human evolution.” He cites classic research showing the bones of tennis players’ arms are thicker on the side they hold the racket. There is an alternative view that genetic adaptation did drive changes in bone size, having to do with climate change (here is some of that debate). But nothing about aggression I could find.

This point about the bones not-so-subtly underlies his later argument about Africa’s poverty, which he attributes in part to the genetic propensity toward violence among its people. Rather than aggression being an asset as society evolved, Wade speculates that, in the centuries leading up to the first settlements, “the most bellicose members of the society were perhaps killed or ostracized” (again, no evidence). Cue footage of UN peacekeepers landing in Africa.

Anyway, it’s potentially an important lesson in the malleability of human bodies through life experience rather than (only) through genetic change. The implication is that each generation may still be genetically ready to have thick bones again, but we just keep lucking out and being born into societies with tools and soft foods, so we don’t need to grow them. I find that amazing. I don’t want to push it too far, but I imagine that a lot of behavioral things are like that, too. Evolution has brought us to the point where we have vast potential to grow in different ways, and huge differences between people can emerge as a result our life experiences.

More on the “warrior gene”

In the review I included some discussion of the MAO-A studies:

Wade devotes considerable attention to MAO-A, the gene that encodes the enzyme monoamine oxidase A, which is related to aggression. He singles out studies showing that a rare version of the gene is associated with violence in U.S. male adolescents. Out of 1,200 young men surveyed in the National Longitudinal Study of Adolescent Health, eleven particularly violent young men carried the 2R version of MAO-A, subsequently known as the “warrior gene.” Nine of those eleven were African American, comprising 5 percent of the black male adolescents in the study.

Sometimes in genetics there is some gene or coding that produces some measureable effect, and that’s how most people seem to think about genetics most of the time – there is “a gene for” something. In the days before today’s genome-wide association (GWA) studies, before scientists had the means to investigate hundreds of thousands of genetic markers at a time, they often looked for effects of such “candidate” genes. This approach was valuable, especially when the role of specific genes was known (as in the case of the BRCA1 gene, associated with higher risk of breast cancer). However, with most diseases, and even more so with behavior, which is presumed to be more complicated than single-gene mechanisms, candidate gene studies were (are) often fishing expeditions, with a high risk of false-positive results, amplified by selective publication of positive findings. It is quite possible that’s at least part of what happened with MAO-A and aggression.

Most studies about MAOA have been gene-environment interaction studies, where some version of MAOA has a statistical association with a behavior only in the presence of a particular social factor, such as a history of child abuse (e.g., this one). This kind of study is tricky and offers a lot of opportunity to fish around for significant effects (which I’m specifically not accusing any particular person of doing). The MAO-A 2R studies he cites weren’t interaction studies. But a couple of cautions are important. First, that 2R version of MAO-A is very rare, and the two studies Wade cites about it (here and here) both used the same sample from Add Health – 11 boys with the variant. Two studies doesn’t mean two independent results. You could never get a drug approved based on that (I hope). Second, as far as I can tell there was no strong reason a priori to suspect that this 2R variant would be especially associated with violence. So that’s a caution. I have to say, as I did in the review, that it may be correct. But the evidence is not there (and you shouldn’t say “not there yet,” either). Those two studies are the entire evidentiary basis for Wade saying that genes that shape social behavior vary by race (“one behavioral gene … known to vary between races”.) I didn’t find any other studies that show MAO-A 2R varies by race (though maybe there are some).

 

Yao Ming and Ye Li

Yao Ming and Ye Li

Modern evolution

Does natural selection still apply to humans? Of course. But I can’t see how it works very efficiently in modern societies, because our demography seems like a poor launching pad for genetic revolutions. Most threats to our survival now occur after we’ve had the opportunity to have children. And it’s getting worse (which means better). The decline in child mortality and the extension of life expectancy beyond the childbearing years means that relatively few people are left of out of the breeding community. That’s how I was raised to understand natural selection: individuals with stronger, better traits breed more than those with weaker, worse traits. In the U.S. today, 97.8% of females born live to age 40, and 85% of those have a birth, so 83% of females born become biological mothers. And a good part of modern childlessness is voluntary, rather than the consequence of a genetic weakness. Even as recently as 1900, in contrast, Census data and mortality statistics show that only 53% of females born lived to be age 40 and had a surviving child. So I don’t know how evolution is working today, but except for really bad health conditions I’m skeptical.

Of course, we have selective breeding producing subpopulations that have concentrations of genetic traits. Yao Ming’s parents were both basketball players, and his wife is 6′ 3″. So they’re on their way to producing a subpopulation of really tall Chinese people. But most social divides we have are not like that — they aren’t based on genetic traits. So I don’t see that being very effective either. To take Wade’s example of Jews and math ability (a chapter I didn’t write about because I was already 3,000 words long), you would need to have Jews not only have good math genes, and only reproduce with each other, but they’d also have to cast out those kids who were relatively bad and math and put the boys and girls who were relatively good at math together. That could happen, but it would be inefficient and very slow, and next thing you know some historical event or trend would come along and mess it all up.

Even the much-discussed increasing tendency of college graduates to marry each other — which gives us about three-quarters of couples today being on the same side of the college/non-college divide — is just sloppy and slow by selective-breeding standards. Maybe it could produce a race of people who like baby joggers and The Economist, but given the low levels of isolation between groups and the length of human generations I just think any progress in that direction would be so slow as to be swamped by other processes pushing in all different directions.

Australia

Wade used Australia to argue against Jared Diamond, whose account of world history, Guns, Germs and Steel, dismisses genetic evolution as an explanation, making him the villain in Wade’s story. How is it, Wade wonders, that Paleolithic Age native Australians were unable to build a modern economy, but Europeans could waltz onto the continent and be successful so easily? He writes:

If in the same environment … one population can operate a highly productive economy and another cannot, surely it cannot be the environment that is decisive … but rather some critical difference in the nature of the two people and their societies.

That’s one of the worst head-scratchers in the book. Does Wade really think that Europeans just dropped in to Australia on an equal footing with the local population, and had to figure out how to thrive there on their raw genetic merits, proving their superiority by their relative success? It can’t be that “the nature of the two people and their societies” means the boats, weapons, technology and modern state social organization the Europeans possessed, because then he has made Diamond’s point. So the “nature” he’s referring to must be genetics. To the reader who has a passing familiarity with modern social science, this is just jarring.

Does cancer genetics help?

To help show the dead-end of Wade’s very mechanical view of genetic influence, I drew out an example from cancer genetics (with a little help from my brother-in-law, Peter Kraft, who is not responsible for this interpretation).

What if we found that genetic factors contributed to social behavior in any of the ways Wade imagines? Speculative as that is at present, it is of course a possibility. Most people are concerned about the implications for genocide and eugenics, for good reason. But even if our scientific motives were pure, the functional utility of such information would be questionable.

Consider a comparison to the much better understood genetics of disease. Take prostate cancer, which is known to have a family history component. Genome wide association studies have identified some genetic markers that are significantly associated with the risk of developing prostate cancer, such that a genetic test can identify which men are at highest risk. However, a review of the statistical evidence in the journal Nature Reviews Genetics pointed out that, even among the high-risk group only about 1.1% of men would come down with prostate cancer in a five-year period. That’s much higher than the 0.7% expected in the general population, but what do you do with that information? Invasive procedures, medications, or preventative surgery on millions of men would not be worth it in order to prevent a small number of cases of prostate cancer – the side effects alone would swamp the benefits. On the other hand, we don’t need any genetic tests to tell smokers to quit, or urge people to eat better and exercise.

This is just one example. Risk factors for this and other diseases are the subject of intense research, and there are actionable results out there, too. But I suspect that genetic influences on social behavior, if discovered, would present an extreme version of this problem: slight genetic tendencies implying tiny increases in absolute risks – and interventions with huge costs and side effects – all while more effective solutions stare us in the collective face.

To complete the analogy: In other words, if – big if – we could identify them, should we incarcerate, surveil, or segregate a subpopulation with a small increased odds of committing crime – thereby preventing a tiny number of crimes while harming a large group of innocent people? And should we isolate and elevate the children of some other subpopulation because of their slightly higher odds of success in some endeavor? Or should we instead devote our resources to improving education, nutrition, employment and health care for the much larger population, based on the well-established benefits of those interventions? We know lots of effective ways to affect social behavior, including against “natural” inclinations.

I’m really not against scientific exploration of behavioral genetics. But the risk of exaggerated results and inflated importance seems so high that I doubt the research will be useful any time soon.

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Misogyny and masculinity, less edited

One point of all this work that I do speaking about sociology to people who aren’t academic sociologists — teaching, blogging, writing a textbook, speaking to the news media — is to help our research have a greater social impact. When a public tragedy occurs, such the Santa Barbara mass murder, there is a chance to widen the conversation and include a sociological perspective.

Photo by Robert Vitulano from Flickr Creative Commons

Photo by Robert Vitulano from Flickr Creative Commons

Sometimes I have the chance to do this even when my own research is not what’s most applicable. That’s great, but I try to be careful (and recommend that journalists speak to others as well). I hope I was right in this case. When Jessica Bennett – a journalist who writes incisively about gender and popular culture – asked me (among others) for a reaction, for what became this column, my first thought was about misogyny. I offered here these comments in an email:

There are two ways that misogyny could play into this case. The first possibility is that he simply hated women, a perspective that is highly accessible in US society. This is illustrated in a lot of pornography — rape or humiliation — and advertising, and articulated by a lot of men who objectify women and seek their conquest or abuse in order to express power or impress other men.

The other possibility is he was schizophrenic or otherwise disassociated from social reality. In that case, misogyny is just the vehicle his disordered brain latched onto. Paranoid people choose from the available entities when building up the fantasy of their persecution. The source of their persecution may not be real, but it is also not random. (The CIA may not be after you, but if it didn’t spy on and assassinated some people, schizophrenics wouldn’t be afraid of them.)

If a paranoid delusional young man believes women are persecuting him, he may be crazy but he is also picking up on the hatred and fear directed toward women that he sees around him.

No matter how you slice it, it is a tragedy that reflects the societal influence of hatred toward women. That is not the whole story of gender relations in our society, but it is definitely present and dangerous.

Then, when Bennett let me know she was interested in focusing the piece on masculinity, I added this (the excerpt she chose is underlined):

One issue is the narrow range of acceptable expressions of masculinity. This is one place where women have more flexibility than men (pants or dress). Especially in adolescence, the question is: If you can’t be good at sports or have sex, what makes you [a] man? Maybe it’s violence.

The alternative many men/boys learn to deal with, of course, is just not being an ideal man. [as mentioned,] most men don’t kill people. Partly that means learning to be ok with not achieving the ideal. So that’s a coping thing many men need to develop, and failure to develop that could be evidence of a problem.

I’m not an expert on masculinity studies. In the quote on masculinity that Bennett used, I was thinking specifically of the chapter by Barbara Risman and Elizabeth Seale, in which they interviewed middle schoolers about gender, concluding:

We find that both boys and girls are still punished for going beyond gender expectations, but boys much more so than girls. For girls, participation in traditionally masculine activities, such as sports and academic competition, is now quite acceptable and even encouraged by both parents and peers. We fi nd, indeed, that girls are more likely to tease each other for being too girly than for being a sports star. Girls still feel pressure, however, to be thin and to dress in feminine ways, to “do gender” in their self-presentation. Boys are quickly teased for doing any behavior that is traditionally considered feminine. Boys who deviate in any way from traditional masculinity are stigmatized as “gay.” Whereas girls can and do participate in a wide range of activities without being teased, boys consistently avoid activities defined as female to avoid peer harassment.

 

The chapter appears in the reader that Risman edited, titled Families as They Really Are (keep an eye out for a new edition!). Someone posted a bootleg copy of the chapter here.

As I read my comments now, I realize there are a lot of other ways to be “a man,” but what I was trying to get at is the concept of hegemonic masculinity, the dominant (in the sense of power) way of being “a man” in a particular cultural context. Of course there other ways to be happy and a man without hanging it on sports, sex, or violence. In reaction to the #YesAllWomen Twitter movement, some people have responded with “real men don’t rape” (which is ironically similar to the old feminist perspective that “rape is violence, not sex”). It attempts to preserve the basic status (men, sex) as good while making the oppressive or violent part deviant, not of the essence. Here is one tweet to that effect, from Michelle Ray:

Feminists seem to have no idea what a man is. Men don’t rape. Sick people who never learned to be men commit violence to solve their issues.

If you say “men don’t rape,” that’s a nice way to try to make it cool to be a man against rape, to resist that image of masculinity. So I like it as an imperative. But as a description of society it’s not true, so there’s that. (A similar move happens in family discourse, sometimes, as when someone says about abuse within families, “real fathers don’t treat their children that way.” Of course, real fathers do good as well as evil — the questions are how and why, and what to do about it.)

Anyway, I would also recommend C. J. Pascoe’s ethnography, Dude, You’re a Fag, in which she discussed sex and masculinity with high school students. Here’s one excerpt:

If a guy wasn’t having sex, “he’s no one. He’s nobody.” Chad explained that some guys tried to look cool by lying about sex, but they “look like a clown, [they get] made fun of.” He assured me, however, that he was not one of those “clowns” force to lie about sex, bragging, “When I was growin’ up I started having sex in the eighth grade.”

And Pascoe concluding:

These practices of compulsive heterosexuality indicate that control over women’s bodies and their sexuality is, sadly, still central to definitions of masculinity, or at least adolescent masculinity. By dominating girls’ bodies boys defended against the fag position, increased their social status, and forged bonds of solidarity with other boys. However, none of this is to say that these boys were unrepentant sexists. Rather, for the most post, these behaviors were social behaviors. Individually boys were much more likely to talk empathetically and respectfully of girls. … Maintaining masculinity, though, demands the interactional repudiation of this sort of empathy in order to stave off the abject fag position.

That insight about interaction is crucial. To go above my pay grade a little (more), I might add that this division between the way one acts in “public” versus “private” is notoriously tricky and frustrating for people with some kinds of mental illness.

That’s just the tip of the masculinity-studies iceberg. Feel free to post other recommended readings in the comments.

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The most comprehensive analysis ever of the gender of New York Times writers

In this post I present the most comprehensive analysis ever reported of the gender of New York Times writers (I think), with a sample of almost 30,000 articles.

This subject has been in the news, with a good piece the other day by Liza Mundy — in the New York Times — who wrote on the media’s Woman Problem, prompted by the latest report from the Women’s Media Center. The WMC checked newspapers’ female byline representation from the last quarter of 2013, and found levels ranging from a low of 31% female at the NYT to a high of 46% at the Chicago Sun-Times. That’s a broad study that covers a lot of other media, and worth reading. But we can go deeper on the NYTimes. The WMC report, it appears (in full here), only focused on the A-section of each newspaper, with articles coded by topic according to unspecified criteria. Thanks to the awesome data collecting powers of my colleague Neal Caren, a sociology professor at UNC, we can do better.*

I started this project with a snap survey of the gender of writers on the front page of each section of NYTimes.com: result, 36% female from a sample of 164 writers. Then I followed the front page of the website for a month: result, 29% female from a sample of 421. For this, Neal gave me everything the NYTimes published online from October 23, 2013 to February 25, 2014 — a total of 29,880 items, including online-only and print items. After eliminating the 7,669 pieces that had no author listed (mostly wire stories), we tried to determine the gender of the the first author of each piece. To start, Neal gave me the gender for all first names that were more than 90% male or female in the Social Security name database in the years 1945-1970. That covered 97% of the total. For the remainder, I investigated the gender of all writers who had published 10 pieces or more during the period (attempting to find both images and gendered pronouns). That resolved all but 255 pieces, and left me with a sample of 21,440.** These are the results.

Women’s authorship

1. Women were the first author on 34% of the articles. This is a little higher than the WMC got with their A-section analysis, which is not surprising given the distribution of writers across sections.

2. Women wrote the majority of stories in five out of 21 major sections, from Fashion (52% women ), to Dining, Home, Travel, and Health (76% women). Those five sections account for 11% of the total.

3. Men wrote the majority of stories in the seven largest sections. Two sections were more than three-fourths male (Sports, 89%; and Opinion, 76%). U.S., World, and Business were between 66% and 73% male.

Here is the breakdown by section (click to enlarge):

nytpctfem

Gender words

Since we have all this text, we can go a little beyond the section headers served up by the NYTimes‘ API. What are men and women writing about? Using the words in the headlines, I compiled a list of those headline words with the biggest gender difference in rates of appearance. That is, I calculated the frequency of occurrence of each headline word, as a fraction of all headline words in female-authored versus male-authored stories.

For example, “Children” occurred 36 times in women’s headlines, and 24 times in men’s headlines. Since men used more than twice as many headline words as women, this produced a very big gender spread in favor of women for the word “Children.”  On the other hand, women’s headlines had 10 instances of “Iran,” versus 85 for men. Repeating this comparison zillions of times, I generated these lists:

NYTimes headline words used disproportionately in stories by

WOMEN MEN
Scene US
Israel Deal
London Business
Hotel Iran
Her Game
Beauty Knicks
Children Court
Home NFL
Women Billion
Holiday Nets
Food Music
Sales Case
Wedding Test
Museum His
Cover Games
Quiz Bitcoin
Work Jets
Christie Chief
German Firm
Menu Nuclear
Commercial Talks
Fall Egypt
Shoe Bowl
Israeli Broadway
Family Oil
Restaurant Shows
Variety Super
Cancer Football
Artists Hits
Shopping UN
Breakfast Face
Loans Russia
Google Ukraine
Living Yankees
Party Milan
Vows Mets
Clothes Kerry
Life Gas
Child Investors
Credit Plans
Health Calls
Chinese Fans
India Model
France Fed
Park Protesters
Doctors Team
Hunting Texas
Christmas Play

Here is the same table arranged as a word cloud, with pink for women and blue for men (sue me), and the more disproportionate words larger (click to enlarge):

nytmenwomenwords

What does it mean?

It’s just one newspaper but it matters a lot. According to Alexa, NYTimes.com is the 34th most popular website in the U.S., and the 119th most popular in the world — and the most popular website of a printed newspaper in the U.S. In the JSTOR database of academic scholarship, “New York Times” appeared almost four-times more frequently than the next most-commonly mentioned newspaper, the Washington Post.

Research (including this paper I wrote with Matt Huffman and Jessica Pearlman) shows that women in charge, on average, produce better outcomes for women below them in the organizational hierarchy. Jill Abramson, the NYTimes‘ executive editor, is the 19th most powerful woman in the world, behind only Sheryl Sandberg and Oprah Winfrey among media executives on that list. She is aware of this issue, and proudly told the Women’s Media Center that she had reached the “significant milestone” of having a half-female news masthead (which is significant). So why are women underrepresented in such prominent sections? That’s not a rhetorical question; I’m really wondering how this happens. The NYTimes doesn’t even do as well as the national average: 41% of the 55,000 “News Analysts, Reporters and Correspondents” working full-time, year-round in 2012 were women.

Organizational research finds that large companies are less likely to discriminate against women, and we suspect three main reasons: greater visibility to the public, which may complain about bias; greater visibility to the government, which may enforce anti-discrimination laws; and greater use of formal personnel procedures, which limits managerial discretion and is supposed to weaken old-boy networks. Among writers, however, an informal, back-channel norm still apparently prevails — at least according to a recent essay by Ann Friedman. Maybe NYTimes‘ big-company, formalized practices apply more to departments other than those that select and hire writers.

Finally, I am sorry I’m not doing this for race/ethnicity. It’s just a much different project to do that, because the names don’t tell you the identities as well. If someone wants to figure out the race/ethnicity of NYTimes authors (such as someone, say, inside their HR department) and send it to me, I would love to analyze it.

* Neal has a series of tutorials on analyzing text as data, and he has posted some slides on how to do this with the NYT’s application programming interface (API).

** A couple other notes. This is a count of stories by the gender of their authors, not a count of authors. If men or women write more stories per person then this will differ from the gender composition of authors. So it’s not a workplace study but a content study. It asks: When you see something in the NYTimes, what is the chance it was written by a woman versus a man? I combined Sunday Review (which was small) with Opinion, since they have the same editor and are the same on Sundays. I combined Style (which was small) into Fashion, since they’re “Fashion and Style” in the paper. I  combined T Mag (which was small) into T:Style, since they seem to be the same thing. Also, I coded Reed Abelson‘s articles as female because I know she’s a woman even though “Reed” is male more than 90% of the time.

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How well do teen test scores predict adult income?

Now with new figures and notes added at the end — and a new, real life headline and graph illustrating the problem in the middle!

The short answer is, pretty well. But that’s not really the point.

In a previous post I complained about various ways of collapsing data before plotting it. Although this is useful at times, and inevitable to varying degrees, the main danger is the risk of inflating how strong an effect seems. So that’s the point about teen test scores and adult income.

If someone told you that the test scores people get in their late teens were highly correlated with their incomes later in life, you probably wouldn’t be surprised. If I said the correlation was .35, on a scale of 0 to 1, that would seem like a strong relationship. And it is. That’s what I got using the National Longitudinal Survey of Youth. I compared the Armed Forces Qualifying Test scores, taken in 1999, when the respondents were ages 15-19 with their household income in 2011, when they were 27-31.*

Here is the linear fit between between these two measures, with the 95% confidence interval shaded, showing just how confident we can be in this incredibly strong relationship:

afqt-linear

That’s definitely enough for a screaming headline, “How your kids’ test scores tell you whether they will be rich or poor.”

In fact, since I originally wrote this, the Washington Post Wonkblog published a post with the headline, “Here’s how much your high school grades predict your future salary,” with this incredibly tidy graph:

earnings-gpa

No doubt these are strong relationships. My correlation of .35 means AFQT explains 12% of the variation in household income. But take heart, ye parents in the age of uncertainty: 12% of the variation leaves a lot left over. This variable can’t account for how creative your children are, how sociable, how attractive, how driven, how entitled, how connected, or how White they may be. To get a sense of all the other things that matter, here is the same data, with the same regression line, but now with all 5,248 individual points plotted as well (which means we have to rescale the y-axis):

afqt-scatter

Each dot is a person’s life — or two aspects of it, anyway — with the virtually infinite sources of variability that make up the wonder of social existence. All of a sudden that strong relationship doesn’t feel like something you can bank on with any given individual. Yes, there are very few people from the bottom of the test-score distribution who are now in the richest households (those clipped by the survey’s topcode and pegged at 3 on my scale), and hardly anyone from the top of the test-score distribution who is now completely broke.

But I would guess that for most kids a better predictor of future income would be spending an hour interviewing their parents and high school teachers, or spending a day getting to know them as a teenager. But that’s just a guess (and that’s an inefficient way to capture large-scale patterns).

I’m not here to argue about how much various measures matter for future income, or whether there is such a thing as general intelligence, or how heritable it is (my opinion is that a test such as this, at this age, measures what people have learned much more than a disposition toward learning inherent at birth). I just want to give a visual example of how even a very strong relationship in social science usually represents a very messy reality.

Post-publication addendums

1. Prediction intervals

I probably first wrote about this difference between the slope and the variation around the slope two years ago, in a futile argument against the use of second-person headlines such as “Homophobic? Maybe You’re Gay.” Those headlines always try to turn research into personal advice, and are almost always wrong.

Carter Butts, in personal correspondence, offered an explanation that helps make this clear. The “you” type headline presents a situation in which you — the reader — are offered the chance to add yourself to the study. In that case, your outcome (the “new response” in his note) is determined by the both the line and the variation around the line. Carter writes:

the prediction interval for a new response has to take into account not only the (predicted) expectation, but also the (predicted) variation around that expectation. A typical example is attached; I generated simulated data (N=1000) via the indicated formula, and then just regressed y on x. As you’d expect, the confidence bands (red) are quite narrow, but the prediction bands (green) are large – in the true model, they would have a total width of approximately 1, and the estimated model is quite close to that. Your post nicely illustrated that the precision with which we can estimate a mean effect is not equivalent to the variation accounted for by that mean effect; a complementary observation is that the precision with which we can estimate a mean effect is not equivalent to the accuracy with which we can predict a new observation. Nothing deep about that … just the practical points that (1) when people are looking at an interval, they need to be wary of whether it is a confidence interval or a prediction interval; and (2) prediction interval can (and often should be) wide, even if the model is “good” in the sense of being well-estimated.

And here is his figure. “You” are very likely to be between the green lines, but not so likely to be between the red ones.

CarterButtsPredictionInterval

2. Random other variables

I didn’t get into the substantive issues, which are outside my expertise. However, one suggestion I got was interesting: What about happiness? Without endorsing the concept of “life satisfaction” as measured by a single question, I still think this is a nice addition because it underscores the point of wide variation in how this relationship between test scores and income might be experienced.

So here is the same figure, but with the individuals coded according to how they answered the following question in 2008, when they were age 24-28, “All things considered, how satisfied are you with your life as a whole these days? Please give me an answer from 1 to 10, where 1 means extremely dissatisfied and 10 means extremely satisfied.” In the figure, Blue is least satisfied (1-6; 21%), Orange is moderately satisfied (7-8; 46%), and Green is most satisfied (9-10; 32%)

afqt-scatter-satisfied

Even if you squint you probably can’t discern the pattern. Life satisfaction is positively correlated with income at .16, and less so with test scores (.07). Again, significant correlation — not helpful for planning your life.

* I actually used something similar to AFQT: the variable ASVAB, which combines tests of mathematical knowledge, arithmetic reasoning, word knowledge, and paragraph comprehension, and scales them from 0 to 100. For household income, I used a measure of household income relative to the poverty line (adjusted for household size), plus one, and transformed by natural log. I used household income because some good test-takers might marry someone with a high income, or have fewer people in their households — good decisions if your goal is maximizing household income per person.

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