Tag Archives: international comparisons

Obscene Gestures from Around the World

1The phrase “social construction” refers to the fact that things, symbols, places, sounds — basically everything — is devoid of meaning until we, collectively, agree as to what something means.  Once that happens, it has been “socially constructed” and we can refer to it as a “social construct.”

The fact that gestures have any meaning at all, and that they can have different meanings in different places, is a simple example of this basic sociological concept.  Enjoy this one minute compilation of examples!

Via Blame It On The Voices.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

Money Doesn’t Bring Happiness? A Reconsideration with New Data

Cross-posted at Montclair SocioBlog.

Forty years ago Richard Easterlin proposed the paradox that people in wealthier countries were no happier than those in less wealthy countries.  Subsequent research on money and happiness brought modifications and variations, notably that within a single country, while for the poor, more money meant fewer problems, for the wealthier people — those with enough or a bit more — enough is enough.  Increasing your income from $100,000 to $200,000 isn’t going to make you happier.

It was nice to hear researchers singing the same lyrics we’ll soon be hearing in commencement speeches and that you hear in Sunday sermons and pop songs (“the best things in life are free”; “mo’ money mo’ problems”).  But this moral has a sour-grapes taste; it’s a comforting fable we non-wealthy tell ourselves all the while suspecting that it probably isn’t true.

A recent Brookings paper by Betsey Stevenson and Justin Wolfers adds to that suspicion.  Looking at comparisons among countries and within countries, they find that when it comes to happiness, you can never be too rich.

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Stevenson and Wolfers also find no “satiation point,” some amount where happiness levels off despite increases in income.  They provide US data from a 2007 Gallup survey:

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The data are pretty convincing.  Even as you go from rich to very rich, the proportion of “very satisfied” keeps increasing.  (Sample size in the stratosphere might be a problem: only 8 individuals reported annual incomes over $500,000;100% of them, though, were “very happy.”)

Did Biggie and Alexis get it wrong?

Around the time that the Stevenson-Wolfers study was getting attention in the world beyond Brookings, I was having lunch with a friend who sometimes chats with higher ups at places like hedge funds and Goldman Sachs.  He hears wheeler dealers complaining about their bonuses. “I only got ten bucks.”  Stevenson and Wolfers would predict that this guy’s happiness would be off the charts given the extra $10 million.  But he does not sound like a happy master of the universe.

I think that the difference is more than just the clash of anecdotal and systematic evidence.  It’s about defining and measuring happiness.  The Stevenson-Wolfers paper uses measures of “life satisfaction.”  Some surveys ask people to place themselves on a ladder according to “how you feel about your life.”  Others ask

All things considered, how satisfied are you with your life as a whole these days?

The GSS uses happy instead of satisfied, but the effect is the same:

Taken all together, how would you say things are these days – would you say that you are very happy, pretty happy, or not too happy?

When people hear these questions, they may think about their lives in a broader context and compare themselves to a wider segment of humanity.  I imagine that Goldman trader griping about his “ten bucks” was probably thinking of the guy down the hall who got twelve.  But when the survey researcher asks him where he is on that ladder, he may take a more global view and recognize that he has little cause for complaint.  Yet moment to moment during the day, he may look anything but happy.  There’s a difference between “affect” (the preponderance of momentary emotions) and overall life satisfaction.

Measuring affect is much more difficult — one method requires that people log in several times a day to report how they’re feeling at that moment — but the correlation with income is weaker.

In any case, it’s nice to know that the rich are benefitting from getting richer.  We can stop worrying about their being sad even in their wealthy pleasure and turn our attention elsewhere.  We got 99 problems, but the rich ain’t one.

Jay Livingston is the chair of the Sociology Department at Montclair State University. You can follow him at Montclair SocioBlog or on Twitter.

Variation and Universality in Children’s Play

UBC Sociology student Pat Louie tweeted us a touching set of photographs by artist Gabriele Galimberti.  Each image is a child with his or her favorite toys.

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Chiwa – Mchinji, Malawi

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Stella – Montecchio, Italy

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Pavel – Kiev, Ukraine

The photographs reveal a universality — pride in favorite toys and the love of play — but, writes Ben Machell at Galimberti’s website, “how they play can reveal a lot.”  The children’s life experiences influenced their imaginative play:

…the girl from an affluent Mumbai family loves Monopoly, because she likes the idea of building houses and hotels, while the boy from rural Mexico loves trucks, because he sees them rumbling through his village to the nearby sugar plantation every day.

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Watcharapom – Bangkok, Thailand

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Arafa & Aisha – Bububu, Zanzibar

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Orly – Brownsville,Texas

Galimberti, interviewed by Machell, also observed class differences in entitlement to ownership:

The richest children were more possessive. At the beginning, they wouldn’t want me to touch their toys, and I would need more time before they would let me play with them. In poor countries, it was much easier. Even if they only had two or three toys, they didn’t really care. In Africa, the kids would mostly play with their friends outside.

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Julia – Tirana, Albania

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Botlhe – Maun, Botswana

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Cun Zi Yi – Chongqing, China

These photographs are reminiscent of another wonderful photography project featuring kids and their toys.  JeonMee Yoon photographed boys with all their blue stuff and girls with all their pink stuff.  The results are striking.  Likewise, there’s a wonderful set of photographs by James Mollison, counterposing portraits with children’s sleeping arrangements across cultures.  These are all wonderful projects that powerfully illustrate global and class difference and inequality.

Images borrowed from Feature Shoot.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

Health Care Costs, Greed, and “Socialism”

Cross-posted at Montclair SocioBlog.

The Washington Post has provided some data on medical costs across a selection of countries (Argentina, Canada, Chile, and India in grey; France, Germany, Switzerland, and Spain in blue; and the U.S. in red). The data reveal that American health care is very expensive compared to other countries.

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No wonder the US spends twice as much as France on health care.  In 2009, the U.S. average was $8000 per person; in France, $4000.  (Canada came in at $4800).  Why do we spend so much?  Ezra Klein quotes the title of a 2003 paper by four health-care economists:  “it’s the prices, stupid.”

And why are US prices higher?  Prices in the other OECD countries are lower partly because of what U.S. conservatives would call socialism – the active participation of the government.  In the U.K. and Canada, the government sets prices.  In other countries, the government uses its Wal-Mart-like power as a huge buyer to negotiate lower prices from providers.  (If it’s a good thing for Wal-Mart to bring lower prices for people who need to buy clothes, why is it a bad thing for the government to bring lower prices to people who need to buy, say, an appendectomy? I could never figure that out.)

There may also be cultural differences between the U.S. and other wealthy countries, differences about whether greed, for lack of a better word, is good.  How much greed is good, and in what realms is it good?  Klein quotes a man who served in the Thatcher government:

Health is a business in the United States in quite a different way than it is elsewhere.  It’s very much something people make money out of. There isn’t too much embarrassment about that compared to Europe and elsewhere.

So we Americans roll along, paying several times what others pay for medical procedures, doctor visits, and drugs.

Jay Livingston is the chair of the Sociology Department at Montclair State University. You can follow him at Montclair SocioBlog or on Twitter.

The Truth About Gender and Math

New data about the science aptitude of boys and girls around the world inspires me to re-post this discussion from 2010.
Math ability, in some societies, is gendered.  That is, many people believe that boys and men are better at math than girls and women and, further, that this difference is biological (hormonal, neurological, or somehow encoded on the Y chromosome).

But actual data about gender differences in math ability tell a very different story.  Natalie Angier and Kenneth Chang reviewed these differences in the New York Times.  They report the following (based on the US unless otherwise noted):

•  There is no difference in math aptitude before age 7.  Starting in adolescence, some differences appear (boys score approximately 30-35 points higher than girls on the math portion of the SAT).  But, scores on different subcategories of math vary tremendously (often with girls outperforming boys consistently).

•  When boys do better, they are usually also doing worse.   Boys are also more likely than girls to get nearly all the answers wrong.  So they overpopulate both tails of the bell curve; boys are both better, and worse, than girls at math.

•  That means that how we test for math ability is a political choice.  If you report who is best at math, the answer is boys.  If you report average math ability, it’s about the same.

•  How you decide to test math ability is also political.  Even though boys outperform girls on the SAT, it turns out those scores do not predict math performance in classes.  Girls frequently outperform boys in the classroom.

•  And, since girls often outperform boys in a practical setting, math aptitude (even measured at the levels of outstanding instead of average performance) doesn’t explain sex disparities in science careers (most of which, incidentally, only require you to be pretty good at math, as opposed to wildly genius at it).   In any case, scoring high in math is only loosely related to who opts for a scientific career, especially for girls. Many high scoring girls don’t go into science, and many poor scoring boys do.

Now, let’s look at some international comparisons:

•  Boys do better in only about ½ of the OECD nations. For nearly all the other countries, there were no significant sex differences. In Iceland, girls outshine boys significantly.

•  In Japan, though girls perform less well than the boys, they generally outperform U.S. boys considerably.  So finding that boys outperform girls within a country does not mean that boys outperform girls across all countries.

•  Still, even in Iceland, girls overwhelmingly express more negative attitudes towards math.

So what’s the real story here?  Well, one study found that the gender gap in math ability and the level of gender inequality in a society were highly correlated. That is, “…the gender gap in math, although it historically favors boys, disappears in more gender-equal societies.”

Part of the problem, then, is simply that  girls and boys internalize the idea that they will be bad and good at math respectively because of crap like the “Math class is tough!” Barbie (sold and then retracted in 1992):

However, girls’ insecurity regarding their own math ability isn’t just because they internalize cultural norm, their elementary school teachers, who are over 90% female, sometimes do to and they teach math anxiety by example.  A recent study has shown that, when they do, girl students do worse at math.  From the abstract (this is pretty amazing):

There was no relation between a teacher’s [level of] math anxiety and her students’ math achievement at the beginning of the school year.  By the school year’s end, however, the more anxious teachers were about math, the more likely girls (but not boys) were to endorse the commonly held stereotype that “boys are good at math, and girls are good at reading” and the lower these girls’ math achievement.  Indeed, by the end of the school year, girls who endorsed this stereotype had significantly worse math achievement than girls who did not and than boys overall.

So, with only the possible exception of genius-level math talent, men and women likely have equal potential to be good (or bad) at math.  But, in societies in which women are told that they shouldn’t or can’t do math, they don’t.  And, as Fatistician said, “math is a skill.”  People who think practicing it is pointless won’t practice it.  And those who don’t practice, won’t be any good at it… Y chromosome or no.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

Family, Race, Religion: The U.S. is Becoming More Diverse

Cross-posted at Racialicious and Family Inequality.

Trying to summarize a few historical trends for the last half century, I thought of framing them in terms of diversity.

Diversity is often an unsatisfying concept, used to describe hierarchical inequality as mere difference. But inequality is a form of diversity — a kind of difference. And further, not all social diversity is inequality. When people belong to categories and the categories are not ranked hierarchically (or you’re not interested in the ranking for whatever reason), the concept of diversity is useful.

There are various ways of constructing a diversity index, but I use the one sometimes called the Blau index, which is easy to calculate and has a nice interpretation: the probability that two randomly selected individuals are from different groups.

Example: Religion

Take religion. According to the 2001 census of India, this was the religious breakdown of the population:

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Diversity is calculated by summing the squares of the proportions in each category, and subtracting the sum from 1. So in India in 2001, if you picked two people at random, you had a 1/3 chance of getting people with different religions (as measured by the census).

Is .33 a lot of religious diversity? Not really, it turns out. I was surprised to read on the cover of this book by a Harvard professor that the United States is “the world’s most religiously diverse nation.” When I flipped through the book, though, I was disappointed to see it doesn’t actually talk much about other countries, and does not seem to offer the systematic comparison necessary to make such a claim.

With our diversity index, it’s not hard to compare religious diversity across 52 countries using data from World Values Survey, with this result:

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The U.S. is quite diverse — .66 — but a number of countries rank higher.

 

Increasing U.S. Diversity

Anyway, back to describing the last half century in the U.S. On four important measures I’ve got easy-to-identify increasing diversity:

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The last one is a little tricky. It’s common to report that the median age at marriage has increased since the 1950s (having fallen before the 1950s). But I realized it’s not just the average increasing, but the dispersion: More people marrying at different ages. So the experience of marriage is not just shifting rightward on the age distribution, but spreading out. Here’s another view of the same data:

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I calculated these using the Panel Study of Income Dynamics from 1968 (for those married in the years 1950-1968) and comparing it with the 2011 American Community Survey for those married in the previous year. There might be a better way, of course.

I have complained before that using the 1950s or thereabouts as a benchmark is misleading because it was an unusual period, marked by high conformity, especially with regard to family matters. But it is still the case that since then diversity on a number of important measures has increased. Over the period of several generations, in important ways the people we randomly encounter are more likely to be different from ourselves (and each other).

Philip N. Cohen is a professor of sociology at the University of Maryland, College Park, and writes the blog Family Inequality. You can follow him on Twitter or Facebook.

Assault Deaths Within the United States

Cross-posted at Kieran Healy’s blog.

The chart in “America is a Violent Country” has been getting a lot of circulation. Time to follow up with some more data. As several commentators at CT noted, the death rate from assault in the U.S. is not uniform within the country. Unfortunately, state-level and county-level mortality data are not easily available for the time period covered by the previous post — though they do exist, going back to the 1940s. What I have to hand is a decade’s worth of US mortality data courtesy of CDC WONDERcovering 1999 to 2009. I extracted the assault deaths according to the same criteria the OECD uses (for the time period in question, ICD-10 codes X85-Y09 and Y87.1). The estimates are adjusted to the 2000 U.S. population, which isn’t identical to the standard OECD adjustment. But the basic comparability should be OK, for our purposes.

First, it’s well-known that there are strong regional differences in the assault death rate in the U.S. by state and region. Here’s what the patterns look like by state from 1999 to 2009 (click for a larger PNG or PDF):

This figure excludes the District of Columbia, which has a much higher death rate but is also a city. Also missing are a few states with small populations and low absolute numbers of assault deaths — Wyoming, North Dakota, Vermont — such that the CDC can’t generate reliable age-adjusted estimates for them. If you want a “small-multiple” view with each state shown separately from high to low, here you go.

The legend for the figure above arranges the states from high to low, reading top to bottom and left to right. Although it’s clear that geographical region isn’t everything, those tendencies are immediately apparent. Let’s look at them using the official census regions (click for a larger PNG or PDF):

As is well known, the South is more violent than the rest of the country, by some distance. Given the earlier post, the natural thing to do is to put these regional trends into the cross-national comparison and see — for the decade we have, anyway — how these large U.S. regions would fare if they were OECD countries. Again, bear in mind that the age-adjustment is not quite comparable (click for a larger PNG or PDF):

Despite their large differences, all of the U.S. regions have higher average rates of death from assault than any of the 24 OECD countries we looked at previously. The placid Northeast comes relatively close to the upper end of the most violent countries in our OECD group.

Finally, there’s the question of racial and ethic incidence of these deaths within the United States. Here are the decade’s trends broken out by the race of the victim, rather than by state or region (click for a larger PNG or PDF):

The story here is depressing. Blacks die from assault at more than three times the U.S. average, and between ten and twenty times OECD rates. In the 2000s the average rate of death from assault in the U.S. was about 5.7 per 100,000 but for whites it was 3.6 and for blacks it was over 20. Even 3.6 per 100,000 is still well above the OECD-24 average, which – if we exclude the U.S. – was about 1.1 deaths per 100,000 during the 2000s, with a maximum value of 2.9. An average value of 20 is just astronomical. And this is after a long period of decline in the death rate from assault.

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Kieran Healy is a professor of sociology in the Kenan Institute for Ethics at Duke University.  His research is primary concerned with the moral order of a market society. You can follow him on twitter and at his blog.

America Is a Violent Country

Cross-posted at Kieran Healy’s blog.

The terrible events in Connecticut prompted me to update an old post about comparative death rates from assault across different societies. The following figures are from the OECD for deaths due to assault per 100,000 population from 1960 to the present.

As before, the most striking features of the data are (1) how much more violent the U.S. is than other OECD countries (except possibly Estonia and Mexico, not shown here), and (2) the degree of change — and recently, decline — there has been in the U.S. time series considered by itself. Note that “assault” as a cause of death does not distinguish the mechanism of death (gunshot, stabbing, etc). If anyone knows of a similar time series for gun-related deaths only, let me know.

(Click for a larger PNG or PDF.)

Here are the individual time series:

(Click for a larger PNG or PDF.)

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Kieran Healy is a professor of sociology in the Kenan Institute for Ethics at Duke University.  His research is primary concerned with the moral order of a market society. You can follow him on twitter and at his blog.