Tag Archives: socimages

New York City police killings: 1964 (life) – 1989 (art) – 2014 (life)

In July 1964, just after the passage of the Civil Rights Act, White New York City police officer Thomas Gilligan killed Black 15-year-old James Powell. After two days of peaceful protest, police and protesters clashed and six nights of violence followed. This is not James Powell being killed, just another guy being beaten:

3c36894r

In the summer of 1989, Spike Lee’s movie Do the Right Thing featured the killing of Radio Raheem by White police — using the already-infamous chokehold — after they swept into the sweltering neighborhood, where a fight had broken out. The climactic incident sparked an explosive riot (watch the scene on Hulu with membership):

deathofradioraheem

Now, another quarter century later, police on Staten Island have apparently choked 43-year-old Eric Garner to death after he refused to cooperate with whatever random demand they had, as captured on video (and posted by the Daily News):

choke18n-12-web

Now the chokehold is against police department rules, but the number of chokehold complaints — a statistic the department keeps — has been rising and last year reached 233, only a “tiny fraction” of which are substantiated. In the Daily News video, Garner is heard saying, “I can’t breathe” many times.

UPDATE: Spike Lee has now produced a video splicing together the chokehold scenes of Eric Garner and Radio Raheem. It’s embedded on Indiwire here.

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Global inequality, within and between countries

Most of the talk about income inequality is about inequality within countries – between rich and poor Americans, versus between rich and poor Swedes, for example. The new special issue of Science magazine about inequality focuses that way as well, for example with this nice figure showing inequality within countries around the world.

But what if there were no income inequality within countries? If everyone within each country had the same income, but we still had rich and poor countries, how unequal would our world be? It turns out that’s an easy question to answer.

Using data from the World Bank on income for 131 countries, comprising 91% of the world population, here is the Lorenz curve showing the distribution of gross national income (GNI) by population, with each person in each country assumed to have the same income (using the purchasing power parity currency conversion). I’ve marked the place of the three largest countries: China, India, and the USA:

lorenza1

The Gini index value for this distribution is .48, which means the area between the Lorenz curve and the blue line – representing equality, is 48% of the lower-right triangle. (Going all the way to 1.0 would mean one person had all the money.)

But there is inequality within countries. In that Science figure the within-country Ginis range from .24 in Belarus to .67 in South Africa. (And that’s using after-tax household income, which assumes each person within each household has the same income. So there’s that, too.)

The World Bank data I’m using includes within-country income distributions broken into 7 quantiles: 5 quintiles (20% of the population each), with the top and bottom further broken in half. If I assume that the income is shared equally within each of these quantiles, I can take those 131 countries and turn them into 917 quantiles (just assigning each group its share of the country’s GNI). These groups range in average income from $0 (due to rounding) in the bottom 10th of Bolivia and Guyana, or $43 per person in the bottom 10th of the Democratic Rep. of Congo, up to $305,800 per person in the top 10th of Macao.

To illustrate this, here are India, China, and the USA, showing average incomes for the quantiles and the countries as a whole:

lorenza2

This shows that the average income of China’s top 10th is between the second and third quntiles of the US income distribution, and the top 10th of India has an average income comparable to the US 10-19th percentile range. Obviously, this breakdown shows a lot more inequality.

So here I add the new Lorenz curve to the first figure, counting each of those 917 quantiles as a separate group with its own income:

lorenza3

Now the Gini index has risen a neat 25%, to an even .60. Is that a big difference? Clearly, between country inequality — the red line — is vast. If every country were a household, the world would be almost as unequal as Nigeria. In this comparison, you could say you get 80% of the income inequality to show up just looking at whole countries. But of course even that obscures much more, especially at the high end, where there is no limit.

Years ago I followed the academic debate over how to measure inequality within and between countries. If I were to catch up with it again, I would start with this article, by my friends Tim Moran and Patricio Korzeniewicz. That provoked a debate over methods and theory, and they eventually published this book, which argues: “within-country analyses alone have not adequately illuminated our understanding of global stratification.” There is a lot more to read, but their work, and the critiques they’re received, is a good place to start.

Note: I have put my Excel worksheet for this post here. It has the original data and my calculations, but not the figures.

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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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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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Does sleeping with a guy on the first date make him less likely to call back?

I have no idea. But there is a simple reason that it might seem like it does, even if it doesn’t.

Let’s imagine that a woman — we’ll call her “you,” like they do in relationship advice land — is trying to calculate the odds that a man will call back after sex. Everyone tells you that if you sleep with a guy on the first date he is less likely to call back. The theory is that giving sex away at a such a low “price” lowers the man’s opinion of you, because everyone thinks sluts are disgusting.* Also, shame on you.

Photo by Emily Hildebrand, from Flickr Creative Commons

So, you ask, does the chance he will call back improve if you wait till more dates before having sex with him? You ask around and find that this is actually true: The times you or your friends waited till the seventh date, two-thirds of the guys called back, but when you slept with him on the first date, only one-in-five called back. From the data, it sure looks like sleeping with a guy on the first date reduces the odds he’ll call back.

callback1

So, does this mean that women make men disrespect them by having sex right away? If that’s true, then the historical trend toward sex earlier in relationships could be really bad for women, and maybe feminism really is ruining society.

Like all theories, this one assumes a lot. It assumes you (women) decide when couples will have sex, because it assumes men always want to, and it assumes men’s opinion of you is based on your sexual behavior. With these assumptions in place, the data appear to confirm the theory.

But what if that those assumptions aren’t true? What if couples just have more dates when they enjoy each other’s company, and men actually just call back when they like you? If this is the case, then what really determines whether the guy calls back is how well-matched the couple is, and how the relationship is going, which also determines how many dates you have.

What was missing in the study design was relationship survival odds. Here is a closer look at the same data (not real data), with couple survival added:

callback2

By this interpretation, the decision about when to have sex is arbitrary and doesn’t affect anything. All that matters is how much the couple like and are attracted to each other, which determines how many dates they have, and whether the guy calls back. Every couple has a first date, but only a few make it to the seventh date. It appears that the first-date-sex couples usually don’t last because people don’t know each other very well on first dates and they have a high rate of failure regardless of sex. The seventh-date-sex couples, on the other hand, usually like each other more and they’re very likely to have more dates. And: there are many more first-date couples than seventh-date couples.

So the original study design was wrong. It should have compared call-back rates after first dates, not after first sex. But when you assume sex runs everything, you don’t design the study that way. And by “design the study” I mean “decide how to judge people.”

I have no idea why men call women back after dates. It is possible that when you have sex affects the curves in the figure, of course. (And I know even talking about relationships this way isn’t helping.) But even if sex doesn’t affect the curves, I would expect higher callback rates after more dates.

Anyway, if you want to go on blaming everything bad on women’s sexual behavior, you have a lot of company. I just thought I’d mention the possibility of a more benign explanation for the observed pattern that men are less likely to call back after sex if the sex takes place on the first date.

* This is not my theory.

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Can animated boys and girls be (almost) the same size?

A lot of the criticism I got for this post on Disney dimorphism was about how good animation inevitably exaggerates sex differences. (There are a lot of these comments on the Sociological Images version of the post and on the Slate re-write.) Here’s one example:

Cartoons aren’t meant to accurately portray people, EVER. They are meant to exaggerate features, so that they are more prominent and eye catching. So feminine features are made more feminine, and masculine features are made more masculine. … The less realistic the proportions, the more endearing and charming we find the character. The closer to realistic they are, the creepier/blander they can become.

Flipping through IMDB’s list of the top 500 animated movies reveals that Disney is certainly not alone in emphasizing the larger size of males. But there are a few successful counterexamples as well.

Here are some good ones where the male and female characters are similarly proportioned. Note these are not just random male and female characters but couples (more or less).

From Kiki’s Delivery Service by Hayao Miyazaki:

kiki-bike

From Dreams of Jinsha:

DreamsofJinsha

Even some old Disney movies have romantic moments between physically-similar males and females. The original Snow White (from the 1937 movie) was paired with a Prince Charming whose wrists were barely bigger than hers (plus, look at her giant/normal waist!):

snowwhite-prince

Disney non-human animal pairs were sometimes quite physically matched. Consider Bambi and Faline (Bambi, 1942):

Bambi-and-Feline

Or Dutchess and O’Malley from Aristocats (1970) in which their exaggerated femininity and masculinity are not conveyed through extreme body-size difference:

dutchess-omally

In other realms of animation, Marge and Homer Simpson, the most durable couple in animation history, have very similar features: heads, eyes, noses, ears. His arms are fatter and neither of them really have wrists, but I’d put this in the category of normal sex difference:

marge-homer

Of course, Lucy and Charlie Brown were virtually identical if you think about it:

lucy-charlie

I’m open to other suggestions.

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Disney’s dimorphism, ‘Help! My eyeball is bigger than my wrist!’ edition

(Addendum added at the end)

I can’t offer much in the crowded field of Disney gender criticism. But I do want to update my running series on the company’s animated gender dimorphism. The latest installment is Frozen.

Just when I was wondering what the body dimensions of the supposedly-human characters were, the script conveniently supplied the dimorphism money-shot: hand-in-hand romantic leads, with perfect composition for both eye-size and hand-size comparisons:

frozen-hands

With the gloves you can’t compare the hands exactly, but you get the idea. And the eyes? Yes, her eyeball actually has a wider diameter than her wrist:

frozen-eyeball

Giant eyes and tiny hands symbolize femininity in Disneyland.

While I’m at at, I may as well include Brave in the series. Unless I have repressed it, there is no romance story for the female lead in that movie, but there are some nice comparison shots of her parents:

brave-hands

Go ahead, give me some explanation about the different gene pools of the rival clans from which Merida’s parents came.

Since I first complained about this regarding Tangled (here), I have updated the story to include Gnomeo and Juliet (here). You can check those posts for more links to research (and see also this essay on human versus animal dimorphism by Lisa Wade). To just refresh the image file, though, here are the key images. From Tangled:

From Gnomeo:

At this point I think the evidence is compelling enough to conclude that Disney favors compositions in which women’s hands are tiny compared to men’s, especially when they are in romantic relationships.*

REAL WRIST-SIZE ADDENDUM

How do real men’s and women’s wrist sizes differ? I looked at 7 studies on topics ranging from carpal tunnel syndrome to judo mastery, and found a range of averages for women of 15.4 cm to 16.3 cm, and for men of 17.5 to 18.1 cm (in both cases the judo team had the thickest wrists).

‘Then I found this awesome anthropometric survey of U.S. Army personnel from 1988. In that sample (almost 4,000, chosen to match the age, gender, and race/ethnic composition of the Army), the averages were 15.1 for women and 17.4 for men. Based on the detailed percentiles listed, I made this chart of the distributions:

army-wrists

The average difference between men’s and women’s wrists in this Army sample is 2.3 cm, or a ratio of 1.15-to-1. However, if you took the smallest-wristed woman (12.9 cm) and the largest-wristed man (20.4), you could get a difference of 7.5 cm, or a ratio of 1.6-to-1. Without being able to hack into the Disney animation servers with a tape measure I can’t compare them directly, but from the pictures it looks like these couples have differences greater than the most extreme differences found in the U.S. Army.

*This conclusion has not yet been subject to peer review.

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