Tag Archives: media

Changing Hispanic racial identity, or not

Hector Cordero-Guzman called my attention to a controversy over Hispanics changing their racial identities. Here is a quick rehash and a few comments. (Spoiler: the New York Times ran a bad story.)

At the Population Association of America, Carolyn Liebler, a sociologist at the University of Minnesota, and James Noon, who works on administrative records at the Census Bureau, presented preliminary results from a comparison of individual race/ethnic responses to the 2000 and 2010 Decennial Censuses. After analyzing millions of individual Census responses, they reported in their abstract that 6% of people changed their race or Hispanic origin classification between 2000 and 2010.

Details of the analysis apparently are not publicly available, but D’Vera Cohn, a writer at the Pew Research Center, reported on their findings, under the headline, “Millions of Americans changed their racial or ethnic identity from one census to the next.” Is this a lot of change? It’s hard to say without a comparison (and without the analysis details). “Millions” does not really mean “a lot,” but it sounds like it does. If the Census race/ethnic identity questions don’t fit people’s self-concept very well then a certain amount of bouncing around is to be expected.

The focus was on Hispanics, whose place in the racial classification scheme is squishy (including immigrants who came at different ages from countries with different racial schemes and ancestral origins, living in different parts of the country with different racial attitudes, some concentrated in dense communities and some dispersed, some economically marginalized and some much more upwardly mobile, etc.). According to D’vera Cohn, 2.5 million Hispanics were “some other race” in 2000 and “white” in 2010, while 1.3 million were “white” in 2000 and “some other race” in 2010.

I might conclude from that that it’s messy and the categories don’t work very well. But it’s also possible that this reflects fluid identities, and people actually change how they see themselves in a systematic way over time. The PAA abstract says “responses and corresponding identities can change over time,” which leaves open the possibility that the change is in measurement in addition to identity, but the hypothesis they suggest are about identity (hypothesizing that women, young people, and people in the West have more complex or less stable identities).

Identity shift is how New York Times Upshot writer Nate Cohn interpreted the D’Vera Cohn report. Under the headline, “More Hispanics Declaring Themselves White,” he converted that bidirectional flow into “net 1.2 million” changing from “some other race” to “white,” and proceeded to run away with the implications. It’s a good example of using any number greater than zero to confirm something you already believe. For example, he wrote:

The data also call into question whether America is destined to become a so-called minority-majority nation, where whites represent a minority of the nation’s population. Those projections assume that Hispanics aren’t white, but if Hispanics ultimately identify as white Americans, then whites will remain the majority for the foreseeable future.

Hm. The “net” flow from “some other race” to “white” is 1.2 million. That is 3% of the 2000 Hispanic population, or 2% of the 2010 population. So even if it’s truly an identity change, does that save the White majority in the long run?

Anyway, as Cordero-Guzman points out in a detailed discussion, referring to a post by Manuel Pastor, the Census questions changed between 2000 and 2010, with Census adding, in bold, “For this census, Hispanic origins are not races” to the form in 2010. Since many Hispanics write “Hispanic” under “some other race,” this probably discouraged them from choosing “some other race” in 2010.

Cordero-Guzman also points out that the context in which the question is asked (and in which the respondents live) is important. For example, 82% of Puerto Ricans on the island use “white” on the American Community Survey, while in New York City only 45% do. Does their identity — in the sense of how they really think of themselves — change when they are in New York, or do they interpret the question differently because they are answering a question in a different social setting? You can’t quantify that difference, probably, but I wouldn’t assume it’s just an identity change.

In a follow-up post, Nate Cohn acknowledges the wording changes — “an important detail” — but returns to the assimilation-upward mobility story. He should have just acknowledged that he jumped to conclusions in the first post and overreached in the race to produce an important, “data-driven” post. (Nate Cohn may have consulted actual experts, but if he did he didn’t include them in the post. It’s just data, I guess.)

The information economy did it

There is a lesson here in the new information economy. Academic conferences used to be less in the public eye. A preliminary analysis, shared with other researchers, then a Pew writer posts on the results, and the Times splashes them all over, all before a paper is even available. I think the Times story is basically wrong — the data as reported are not independent evidence of “assimilation.” (So, the person with the biggest megaphone was the person who was most wrong — surprise!) But the analysis could well be an important piece of research in a larger literature, and I think it’s good to present preliminary research at conferences. You can’t stop reporters from racing to be wrong, but I do think it would be better to distribute the paper publicly when it’s presented.

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US teen birth rates remain high, and they’re not falling for the reasons you’ve heard

Everyone is excited by the decline in the teen birth rate in the US. But And here are a few things you should know about it.

This chart shows the birth rates for women ages 15 to 19 in 192 countries, plus the world and the UN-defined rich countries, for 1991 and 2011. Dots below the black line show countries where the teen birth rate fell. The red line shows the overall relationship between 1991 and 2011. Dots below the red line had greater than expected reduction in teen births.

teen births global

Source: My graph United Nations data.

The chart shows four things:

1. Teen birth rates are falling globally. From 1991 to 2011, the birth rate for women ages 15 to 19 fell from 65 to 46 births per 1,000 women worldwide.

2. US has higher teen birth rates than any other rich country. At 33 per 1,000, the US has more teen births than Pakistan (28), but fewer than India (36). For high income countries, by the UN definition, the rate is 19. The rate for the Euro area is 7.

3. The teen birth rate is falling faster in the US than in the world overall. The world rate fell 29% from 1991 to 2011, while the drop in the US was 44%.

In the US, there are a lot of factors related to falling teen births. But they’re mostly about how it’s happening, not why it’s happening. For example, Vox published a list of factors, as did Pew before them, that are reasonable: the recession, more birth control, more Medicaid money for family planning, cultural pressure, and less sex.

But to understand why this is happening, you have to stop thinking about teenagers as some sort of separate subspecies. They are just young women. Soon they will be in their 20s. The same women! So the short answer for why falling teen birth rates happening is this:

4. Teen birth rates in the US are falling because women are postponing their births generally.

You can see this if you line up teens next to women of other ages. Here are the changes in birth rates for women, by age, from 1989 to 2012.

birthratechangebyage

Source: My graph from National Center for Health Statistics data.

See how the trend for the last decade is parallel for 15-17, 18-19, and 20-24? As those rates fell, birth rates rose for the 30+ community. The younger women are, the fewer births they’re having; the older they are, the more births they’re having. Teenage women are women! They do it for all the reasons it’s happening around the world: some because they are delaying marriage, some to pursue education and careers, some to see the world, and so on.

Here is another way to look at this. Here are the 50 US states, from the 2000-2012 American Community Survey. This shows that states with lower teen birth rates (those are per 100, on the y-axis), have higher birth rates for 25-34 year-old women relative to 20-24 year-old women. I’ll explain:

teenbirthstates

Teen births rates and the ratio of teen birth rates ages 25-34 / 20-24. US states, 2010-2011

Where more women have children ages 25-34 relative to 20-24, there are fewer teen births. So, in Alabama, about 3% of women 15-19 had a baby per year, and in that state the birth rates are about the same for women 25-34 as 20-24. Alabama is an early-birth state. But in New Hampshire, only 1% of teens had a baby, and women 25-34 were almost 2.5-times more likely to have a baby than women 20-24. New Hampshire is a late-birth state. What’s happening with teens reflects what’s happening with older women.

To some significant degree, it’s not about teenagers, it’s about women delaying births.* I would love it if reporting on teen births would always compare them to older women.

*Notice I didn’t just exaggerate and say, “it’s not about teenagers.” I added “to some significant degree.” That’s the difference between a post that is selling you (your clicks) to someone versus a post that’s trying to explain things as clearly as possible.

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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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Education, not income, drives Piketty searches

Proving once again that effort is not always correlated with income, I present this critique of a Justin Wolfers blog post…

A lot of people have written reviews of Piketty. The first few pages of a Google search revealed all these (I added Heather Boushey, who wrote a good one)*:

piketty-reviewers

I believe that is diversity, because every human being is different.

Anyway, where to begin? Justin Wolfers wrote a little post, not a review, but it caught my attention. The headline of was, “Piketty’s Book on Wealth and Inequality Is More Popular in Richer States.” Distractable, that’s where I began.

Wolfers’ culminating line, “Vive la révolution!”, suited Scott Winship, who looked over Wolfer’s figures before sniping, “the buzz around the book has come mostly from rich liberal states along the Boston-to-Washington corridor.” But I think they’re both misinterpreting.

According to the Google search data Wolfers used, these were the top 10 states for “piketty” searches (Washington, D.C. excluded): Massachusetts, New York, Connecticut, Maryland, New Jersey, Illinois, Pennsylvania, Wisconsin, Oregon, California.

It looks to me that it’s actually education driving the search data. And that is a big difference. Let me explain.

Do data?

Microsoft Word tells me that the reading grade level of the publisher’s excerpt is 16.3, so it takes a 16th-grade education to read it. (Note that the “Boston-to-Washington corridor,” which was supposed to sound like a small sliver of the country, has 26% of the country’s college graduates.) So consider income versus college completion, which we can now take as a proxy for being able to read Piketty.

Wolfers writes, “I can’t tell you where Piketty has been least popular, because below a certain level of search activity, Google doesn’t release the actual numbers.” So he proceeds to leave 24 states out of his analysis (this will become important). Using per-capita income (converted to z-scores), and dropping 24 states plus the ridiculous outlier of DC, this is Wolfers’ income result (my calculations; he just showed scatter plots):

pik1

OK, leaving out the bottom half of the Piketty distribution, there is a strong positive relationship between per capita income and Piketty Google searches. Congratulations, you can have three jobs as an economist!

I kid Wolfers. But, come on! I don’t know what kind of data operation they’re running over there at the Upshot, but I would expect Wolfers to take it up a notch. First, control for college completion (percent of folks ages 25+ with a BA or more, also z-scored). See how it shows… oops:

pik2

The income effect is reduced but the education effect isn’t significant. (See how I showed you that instead of just going right to the results that support my argument?)

But go back to Wolfers leaving out the bottom half of the Piketty distribution. What’s wrong with that? I’m sure there’s some statistical way of explaining that, but just eyeballing it you’d have to say dropping those cases could cause trouble. The censored cases all have values of -.64 on the search variable. The relationship with income is weaker when the censored cases are included (shown in the red line) versus when he limits it to the top half of Piketty states (blue line):

pik-scatter1

What to do about this? An easy thing is just to include the censored cases at their values of -.64, just pretending -.64 is a legitimate value. That gives:

pik3

Now the income effect is reduced about three-quarters, and the college completion effect is three-times as large (with a t-stats to match).

But that’s not the best way to handle this. If only economists had invented a way of modeling data with censored dependent variables! Just kidding: there’s Tobin’s Tobit. This kind of model says, I see your censored dependent variable, and I crash it through the bottom of the distribution as a function of its linear relationship to your independent variables. So instead of all being -.64, it lets the censored cases be as low as they want to be, with values predicted by income and college completion. Sort of. Anyway, here’s that result:

pik4

Now income is crushed, reduced to literal insignificance. What matters is the percentage of the population that has completed college. It’s not that rich people like Piketty, it’s that college graduates do. Maybe because that’s who can read it. (I don’t know, I haven’t tried.)

What do economists read?

Of course, mine and Wolfers’ are both pretty crude analyses. There are only two reasons his was published on a major news site and mine was buried over here on an obscure sociology blog: (a) he writes for a major news site, and (b) his weak analysis lends itself to an emerging snarky narrative in which rich leftists are seen to whine about inequality but real people can’t be bothered (the main point of Winship’s review) — just reinforcing the echo-chamber model of knowledge consumption that people who are into “data-driven” news like to appear to have risen above.

For a real explanation, Wolfers (and Winship) need look no further than the rest of the Google Correlate results page to see the obvious fact that searches for Piketty are simply correlated with interest in economics. Here’s the search that is most highly correlated with searches for “piketty” across U.S. states: “world bank gdp” (r=.98):

pik-scatter2

Here are some other searches correlated with “piketty” at .94 or higher:

economic consulting firms
eu data protection
exchange rate data
gdp by sector
inflation target
journal of labor economics
london school economics
nber working paper
oecd statistics
oxford economics
panel data stata
stock market capitalization
the economist intelligence unit
us current account deficit
world bank statistics

Well, there goes your rich, liberal, “American left” theory of who’s driving the Piketty phenomenon. It might be true, but it’s not confirmed by the Google search data. My hot new theory: college educated people who are also interested in economics are disproportionately interested in Piketty.

* The reviewer pool: Mervyn King (The Telegraph), Paul Krugman (New York Review of Books), Tyler Cowen (Foreign Affairs), James K. Galbraith (Dissent), Daniel Schuchman (Wall Street Journal), Justin Fox (Harvard Business Review), Michael Tanner (National Review), John Cassidy (New Yorker), Martin Wolf (Financial Times), Jordan Weissmann (Slate), Steven Pearlstein (Washington Post), Scott Winship (National Review), Heather Boushey (Challenge)

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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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Ridiculous NY Times Magazine data graphics

A series of ridiculous data graphics posts from the NY Time Magazine, collected in one post (with crummy photo-pic renderings).

These are examples of the abuse of data graphic techniques to spread ignorance, distract people from anything of actual importance, and contribute to the perception that statistics – especially graphic statistics – are just an arbitrary way of manipulating people rather than a set of tools for exploring data and attempting to answer real questions. (If you are already convinced of this and just want to see awesome real graphics, I would start with Healy and Moody’s Annual Review of Sociology paper.)

First, an innocent graphic that merely wastes space and contributes nothing — it really communicates less than the 8 simple data points it has because the bats all over are just confusing and the points are in no order (who even notices that the number of segments each bat is cut into is the data point?):

nyt-bats

 

Maybe a little better, I suppose, is this one, where the number of trees shown at least corresponds to the data points. But you would still learn more, faster, from a simple list:

nyt-trees

Here is an interesting mistake. I first thought these bars were out of order, but it turns out it’s just the top part of the bars that are out of order. If they were flat-topped bars it would be okay:

nyt-bars

Here’s one that combines useless graphics with data that is itself completely misleading. These are the fees associated with different parks in NY City. But the units of time are different. What is the point of comparing the annual tennis fee to the hourly roller hockey fee? At least they didn’t make the cards different sizes to show this meaningless comparison more clearly.

nyt-parkfees

The magazine also does text “analytics.” These are on the letters page, and they show the type of letters received. This is interesting to sociologists, who sometimes try to find ways to categorize text. They make two errors here that render these meaningless or worse.

First, they sometimes present them in order – as represented by graphic elements – when the sentiments expressed are not in that logical order. Like this one, in which the dial and shading implies these are in some logical order, but they aren’t:

nyt-four3They also did that here, with the shading implying some continuum that is not present. (In this one, also, is it the proportion of the state’s area the determines the size of the cuts, or the angle of the cuts at the center?). Come on!

nyt-four2A final point holds for all these letter “analytics.” You really shouldn’t determine the number of categories you are going to use before you read the texts, “Here, go break these letters into four categories.” For the love of God, they don’t even have an “other” category, and always ways add to 100%.

nyt-four1

 

 

 

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Michigan Black college completion falters (with consequences)

Yesterday the Supreme Court ruled that Michigan voters have the Constitutional right to ban the state’s government from using race-specific policies. The immediate implication for Michigan, and other states, is for university admissions polices. So now if the state wants to pass a law allowing children of alumni easier admission to the University of Michigan, it’s a simple act of the legislature; but if they want to consider race in their admissions, they will need to amend the state constitution.

The University of Michigan has been at the center of national affirmative action debates for several decades (at least since I arrived there in 1988). I previously reported that court decisions against the state’s affirmative action policy led to a precipitous decline in Black students entering the University in the 2000s, as shown in this graph:

That’s just the University of Michigan, an important school, but only one. (The New York Times has a graphic showing enrollment trends in a series of states with affirmative action bans.) For the whole state of Michigan, Black college graduation rates fell further behind the national average over the last decade. Here is the percent of Black 25-29 year-olds who have completed college, from 1970 to 2012, nationally versus in Michigan alone, for women (left) and men (right):

michigan-black-grad-rates

Source: 1970-2000 Decennial Censuses and 2010-2012 American Community Survey, via IPUMS.

During the 2000s, the national-Michigan gap widened from 2.3 points to 4.1 points for men, and from 3.4 to 4.8 points for women.

I am not expert in the legal arguments over this, so I can’t analyze the decision (here’s one good take). But regardless of whether it’s bad law, I think it’s bad policy.

Yesterday in a tweet I picked on the new, data-heavy news operations run by (from left to right) David Leonhardt (NY Times Upshot), Ezra Klein (Vox), and Nate Silver (Five Thirty Eight) for having very White-looking staff teams:

thenewteams

I don’t know any more about what goes into their hiring decisions than I do about what goes into University of Michigan admission decisions (and I know they have staff beyond these featured writers). I’m sure they all want talented people with a wide range of perspectives and skills. But the outcome in both the media and college situations is bad. It limits the perspectives presented, undermines progress toward racial-ethnic equality, and contributes to the inertia that stymies the potential of future leaders.

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