Category Archives: Me @ work

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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What do doctors, lawyers, police, and librarians Google?

Now with college teachers!

What do doctors, lawyers, police, and librarians Google? I’ll tell you. But first — if you are going to take this too seriously, please stop now.

Data and Method

Using IPUMS to extract data from the 2010-2012 American Community Survey, I count the number of people ages 25-64, currently employed, in a given occupation. I divide that by each state’s population in that age range (excluding Washington DC from all analyses). I enter those numbers into the Google Correlate tool to see which searches are most highly correlated with the distribution of each occupation across states (the tool reports the top 100 most correlated searches). In other words, these are searches that maximize the difference between, for example, high-lawyer and low-lawyer states — searches that are relatively popular where there are a lot of lawyers, and relatively unpopular where there are not a lot of lawyers.

Is this what lawyers actually Google? We can’t know. But I think so. Or maybe what people who work in law firms do, or people who live with lawyers. It’s a very sensitive tool. I made this case first in the post, Stuff White People Google. Check that out if you’re skeptical.

For each occupation, I first offer a few highly correlated searches that support the idea that the data are capturing what these people search for. Then I list some of the interesting other hits from each list.

Results

Police

Police per adult

Police per adult

The map of police per adult looks pretty random, but the list of correlated search terms doesn’t. On the list are “security training,” “tsa jobs,” “waist belt,” “weight vest,” and “air marshals.”

After all the security stuff, the only major category left in the 100 searches most correlated with police in the population is women. Specifically, their search taste includes tough actress Rachel Ticotin, body builder Denise Masino, Brazilian actress Alice Braga, actress Rosario Dawson, and, “israeli women.” (Remember, Google suppresses known porn terms, so this is just what got through the filter.) It’s a leap from this data to the statement, “police search for images of these women,” but this is who they would find if that were the case (is this a “type”?):

policewomensearches

Librarians

Librarians per adult

Librarians per adult

On the other hand, librarians. They are the smallest occupation I tried: the average state population aged 25-64 is only one tenth of one percent librarians. Yet, their distribution leaves an unmistakable trace in the Google search patterns. It especially seems to pick up terms associated with public libraries. Correlated terms include, “cataloguing,” and “quiet hours.” And then there are terms one might ask a librarian about, classic reference-desk questions such as, “which vs that,” “turn off track changes,” “think tanks,” “9/11 commission,” and “irs form 6251″; and term paper topics like Shakespeare titles or “human development report.”

What about the librarians themselves, or those close to them? Could it be they who are searching for Ann Taylor dresses, Garnet Hill free shipping, Lands End home, and textile museums? We can’t know for sure. Of course, if anyone knows how to cover their search tracks, it might be this crowd.

Doctors

Doctors per adult

Doctors per adult

You know they’re doctors, because the search terms most correlated the map include “md, mph,” “md, phd,” “nejm,” “journal medicine,” “tedmed,” and “groopman.” What else do they like? Chic Corea, Tina Fey, Larry David, Mad Men (season 1) and The West Wing, Laura Linney, John Oliver, Scrabble 2-letter words, and a bunch of Jewish stuff.

Lawyers

Lawyers per adult

Lawyers per adult

That’s the map of lawyers per adult across states. Is it really lawyers? The top 100 searches correlated with the distribution shown above include “general counsel,” and then a lot of financial terms like, “world economic forum,” “international finance corporation,” and “economist intelligence.” Then there are international travel terms, like, “rate euro dollar,” “royal air,” and “swiss embassy.”

Looks like lawyers in lawyer-land are richer and more finance-oriented than lawyers in general. On the cultural side, they search for clothing terms Massimo Dutti, Hugo Boss, and Benetton. They apparently like to eat at Zafferano in London, and drink Caipirinhas. Also, they like “vissi,” which is an aria from Tosca but also a Cypriot celebrity; I lean toward the latter, because Queen Rania is also on the list. Finally, they combine their interests in law, finance, and wealthy attractive women by searching for Debrahlee Lorenzana, the “too-hot-for-work” banker.

By popular demand: Post-secondary teachers

postsecondaryperadult

Finally, here without comment are the results for “post-secondary teachers,” which includes any college teacher who didn’t instead specify a specialty, such as “psychologist” or “economist.” (It’s hard to see on the map, but Rhode Island is the highest.) I broke the results into four rough categories:

Academic

attribution
balderdash
bmi index
body image
citation style
cpdl
critical theory
debt to equity
debt to equity ratio
democracy in america
dihedral
economic inequality
economic statistics
economists
educause
edward elgar
effect size
email forward
equals sign
exogenous
feminists
google scholar
growth rates
homomorphism
inflation rate
inflation rates
intelligibility
international study
isomorphic
journal of
journal of nutrition
marginal propensity
marginal propensity to consume
mediating
meters per second
milieu
overlaying
piano sonata
prefrontal
prefrontal cortex
profile of
psychology studies
quick ratio
rejection letter
returns to scale
routledge
scholar
subgroup
superscript
transglutaminase
ways to end a letter

Personal

1% milk
2006 olympics
best pump up songs
crib safety
easy halloween costume
graco snug
handel
ipod history
jackson superbowl
janet jackson superbowl
mastermind game
maxim online
minesweeper
most popular names
napping
national sleep foundation
olympic figure skating
olympics 2006
pairs figure skating
positioning
refereeing
sandra boynton
senior hockey
snl clips
stuff magazine
stumbled upon
toilet training
verum

Musical

1812 overture
acapella group
acapella groups
africa toto
ave verum
for the longest time
it breaks my heart
pdq bach
taylor swift

Birth control

apri
apri birth control
aviane

Conclusions

Poor social scientists, generations of them spending their lives raising a few thousand dollars to ask a few thousand people a few hundred stilted, arbitrary survey questions. Meanwhile, coursing through the cable wires below their feet, and through the air around them, billions of data bits carry so much more potential information about so many more people, in so many intimate aspects of their lives, then we could even dream of getting our hands on. Just think of the power!

RingfrodoNote: I’ve done many posts like this. Some use time series instead of geographic variation, some use terms from Google Books ngrams. Browse the series under the Google tag, or check out this selection:

 

 

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Family Inequality wins Charm Quark

I’m pleased to report that the blog has been awarded the Charm Quark, which is third place in the Politics and Social Science category for 2014, from 3 Quarks Daily.

charmquark

The write-up for the award is here. The judge was Mark Blyth, and the post he read was my debunking of the State of Utah’s claim that banning same-sex marriage would make it more likely for kids to be raised by straight married parents. Blyth put my post in the category of “Bullshit Police,” writing:

If social science has a public function this is it. Theory generation and hypothesis testing and all that grad school stuff is all fine and well, but at the end of the day the job is to take the claims of those that want us to think X is Y and sniff it to see if its bullshit. … the winner in this pot is Philip Cohen for his Family Inequality piece on the state of Utah and same sex parenting. Take a causal argument. Test it. Test it again. Pronounce it bullshit. Move along. Move along. Fantastic stuff and first class ‘bullshit police’ work.

It’s very nice to have my work recognized this way. It’s especially gratifying that it was a piece that included original data analysis (and even fixed-effects regressions). I hope I did it right!

3 Quarks is a filter blog that presents posts on “science, design, literature, current affairs, art, and anything else we deem inherently fascinating” six days a week, and original pieces on Mondays. I hope you will visit the site and see what they have to offer.

Thanks to Mark Blyth and the 3 Quarks folks for the boost.

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Peak women, labor force participation edition

I had a great visit at the University of Pennsylvania the other day, and gave a talk titled, “What Happened to the Gender Revolution?” It was an elaboration of the op-ed I wrote last fall, in which I sketched out the stall in progress toward gender equality (a recurring theme, not my discovery) and offered some ideas about getting it moving again.

One objection I got during the talk (rather belligerently, from Herbert Smith) was that I was making a big deal out of women’s labor force share peaking at just under half the total, which is a natural place to peak and so we shouldn’t expect it to keep going up.

peak-woman

My first response was that the feminism-has-gone-too-far gang (Hanna Rosin, Kay Hymowitz, Christina Hoff Sommers, etc.) complains as if women’s progress has already shot past 50/50. Although it hasn’t on almost all measures, there’s also no reason why women couldn’t become dominant. Judging from history, one gender dominating the labor market is hardly an impossibility. So women’s labor force share tapering off as it approaches 50% shouldn’t be considered a natural phenomenon.

But second, and for this I blame my presentation, women’s share of the labor force isn’t the best measure because it depends also on men’s labor force participation, too, which has been falling since the 1960s. So maybe it’s best to focus on women’s participation rates instead (it is on this measure that the U.S. has slipped behind many other rich countries).

Here are the labor force participation rates for women by age, education, race/ethnicity, and marital status, from 1962 to 2013, from the Current Population Survey, with men for comparison. The dots show the peak year for each trend (click to enlarge).

wlfp

Women’s overall share of the labor force hit 46% in 1994, and has spent the last 20 years within a point of that (as both men’s and women’s rates fell). But if you look at all these groups it’s clear that doesn’t represent the simple slide of women into the home plate of equality. Every line here rose for decades before hitting a peak between 1996 and 2001. And they peaked at different levels: Women with BA degrees peaked at 85%, Black women peaked at 80%, Hispanic women peaked at 68%. Married women peaked at 75%, single women at 82%. And so on.

Maybe all these trends are not being driven by the same underlying forces. But I’m pretty sure it’s not a complete coincidence.

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What’s in a ratio? Teen birth and marriage edition

Even in our post-apocalypse world, births and marriages are still related, somehow.

Some teenage women get married, and some have babies. Are they the same women? First the relationship between the two across states, then a puzzle.

In the years 2008-2012 combined, 2.5 percent of women ages 15-19 per year had a baby, and 1 percent got married. That is, they were reported in the American Community Survey (IPUMS) to have given birth, or gotten married, in the 12 months before they were surveyed. Here’s the relationship between those two rates across states:

teenbirthmarriage1The teen birth rate  ranges from a low of 1.2 percent in New Hampshire to 4.4 percent in New Mexico. The teen marriage rate ranges from .13 percent in Vermont to 2.3 percent in Idaho.

But how much of these weddings are “shotgun weddings” — those where the marriage takes place after the pregnancy begins? And how many of these births are “gungo-ho marriages” — those where the pregnancy follows immediately after the marriage? (OK, I made that term up.) The ACS, which is wonderful for having these questions, is somewhat maddening in not nailing down the timing more precisely. “In the past 12 months” is all you get.

Here is the relationship between two ratios. The x-axis is percentage of teens who got married who also had a birth (birth/marriage). On the y-axis is the percent of teens who had a birth who also got married (marriage/birth).

teenbirthmarriageIf you can figure out how to interpret these numbers, and the difference between them within states, please post your answer in the comments.

 

 

 

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Open thread on the way some people, right, sort of really talk these days

Speaking extemporaneously in public is difficult. Since I’ve been on radio and TV a few times, and then reviewed the tapes afterward, I’ve developed my own internal criticism (drowning out that critic’s voice is sometimes difficult even while I’m talking). And I’ve also become even more aware of how people talk, to the point of speaking back lines I hear, trying out alternative expressions, and generally driving myself nuts.

Anyway, all that “really, sort of, right,” seems to be ascending toward some kind of peak. I heard this passage on the radio recently (no need to identify the speaker, is there?), and had to jot it down. The discussion was about Google and other tech workers and their buses to San Francisco. That’s enough context:

Look, I think, I mean, so all the data suggests, right, from the recent Census in the last two years, that obviously that center city areas are growing faster than suburban areas. But I think what’s actually interesting that’s happening, when you start to think about the city/suburbs divide, is really what we’re starting to see is are cities and suburbs become more and more alike. And that is to say that cities are having to deal with a lot of the issues that suburban areas have dealt with for a long time, right: crime, density, housing, all those issues. And now I think what we’re starting to see is suburbs, for instance, having to think about themselves becoming more attractive to folks who are looking for this urban lifestyle. So you’re starting to see suburban areas really focus on this idea of creative place-making: how do you really create a unique, authentic place, where people want to live. I think the other interesting thing is for suburbs is that they’re connected on transit, right – this idea of transit-oriented development is really important – how can they be connected to the city in terms of becoming a really sort of key node here. And so, you know, I think what we’re seeing, again, is this sort of shift, right, is what we call sort of this blending, of both cities and suburbs. You know, and just for a second to go back to the point about sort of young people and sort of being – not thinking about community as much – I think what’s interesting is you sort of see this shift of technology workers, back to city centers. What’s interesting is that a lot of technology workers are wanting to live in city centers because they want to have access to a unique, diverse community, they want to be engaged in their communities, so you see more of them taking public transit, you see more of them sharing resources. So it is about I think this sort of you know, it is perhaps a different perspective, but it is about sort of this engagement that we’re starting to see among young technology workers, Millennials, Creatives, etc., that are really going to sort of not be the problem for our cities, but really help us think about the solutions and what’s sort of to try to fix those issues.

Without picking on individuals (too late), any thoughts?

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