Women in parliaments, by income

Say what you want about the United States of America, but we don’t have the world’s lowest percentage of women in the national legislature.

Here are the countries with at least 5 million people in 2013, arrayed by income and percentage of women in parliament (click to enlarge)*:

Source: My figure form http://wdi.worldbank.org

On the plus side, the USA leads the world in per capita income among countries with fewer than 19 percent women in its national legislature (except for the United Arab Emirates.)

* Note: Rwanda, with per-capita income of $1,430, has 64% women in parliament, but I didn’t include it because expanding the scale that far shrank the rest of the graph too much. Also note Canada is accidentally mislabeled as Cameroon.

Not your feminist grandmother’s patriarchy

Originally published at The Atlantic under the title, “America is still a patriarchy.”

Male dominance may be weakening, but it’s not gone.

In this election, women were the majority of voters, and the majority of them voted for Obama. The weaker sex clearly was men, contributing less than half the vote, the majority of whom preferred the loser. This is not new. As with Obama, men and whites also failed to unseat Bill Clinton in his reelection after voting for him the first time.

This story tests my ability to think systematically about power and inequality. How is it possible to understand an unprecedented transformation in women’s relative status while also acknowledging men’s continued dominance? Must we just list data points, always just including an “on the other hand” caveat to our real narrative?

I have been described as part of a “feminist academic establishment” that insists on taking the glass-half-empty view—as someone who likes to engage in “data wars” over the details of gender inequality. But what I actually try to do is keep the change in perspective.

In our academic research on gender inequality, my colleagues and I study variation and change. That means figuring out why women’s employment increased so rapidly, why some labor markets have smaller gender gaps, why some workplaces are less segregated, why couples in some countries share housework more, why women in some ethnic groups have higher employment rates, and so on.

The patterns of variation and change help us understand how gender inequality works. Systemic inequality doesn’t just happen. People (in the aggregate) get up in the morning and do it every day. To understand how it works, we need to see how it varies (for example, some people resist equality and some people dedicate their lives to it). Someone who studies inequality but doesn’t care about change and variation is not a social scientist.

Patriarchy

“It’s easy to find references to patriarchs, patriarchy or patriarchal attitudes in reporting on other countries,” writes Nancy Folbre:

Yet these terms seem largely absent from discussions of current economic and political debates in the United States. Perhaps they are no longer applicable. Or perhaps we mistakenly assume their irrelevance.

In fact—my interpretation of the facts—the United States, like every society in the world, remains a patriarchy: they are ruled by men. That is not just because every country (except Rwanda) has a majority-male national parliament, and it is despite the handful of countries with women heads of state. It is a systemic characteristic that combines dynamics at the level of the family, the economy, the culture and the political arena.

Top political and economic leaders are the low-hanging fruit of patriarchy statistics. But they probably are in the end the most important—the telling pattern is that the higher you look, the maler it gets. If a society really had a stable, female-dominated power structure for an extended period of time even I would eventually question whether it was really still a patriarchy.

In my own area of research things are messier, because families and workplaces differ so much and power is usually jointly held. But I’m confident in describing American families as mostly patriarchal.

Maybe the most basic indicator is the apparently quaint custom of wives assuming their husbands’ names. This hasn’t generated much feminist controversy lately. But to an anthropologist from another planet, this patrilineality would be a major signal that American families are male-dominated.

Among U.S.-born married women, only 6 percent had a surname that differed from their husband’s in 2004 (it was not until the 1970s that married women could even function legally using their “maiden” names). Among the youngest women the rate is higher, so there is a clear pattern of change—but no end to the tradition in sight.

Of course, the proportion of people getting married has fallen, and the number of children born to non-married parents has risen. Single parenthood—and the fact that this usually means single motherhood—reflects both women’s growing independence and the burdens of care that fall on them (another piece of the patriarchal puzzle). This is one of many very important changes. But they don’t add up to a non-patriarchal society.

Differences that matter

The social critic Barbara Ehrenreich—in a 1976 essay she might or might not like to be reminded of—urged feminists to acknowledge distinctions that matter rather than tar everything with the simplistic brush of “patriarchy.” Using China as an example, she wrote:

There is a difference between a society in which sexism is expressed in the form of female infanticide and a society in which sexism takes the form of unequal representation on the Central Committee. And the difference is worth dying for.

China presents an extreme case, with an extremely harsh patriarchy that was fundamentally transformed—into a different sort of patriarchy. By the late 1970s female infanticide (as well asfootbinding) had indeed been all but eradicated, which represented a tremendous improvement for women, saving millions of lives. Since the advent of the one-child policy in the 1980s, however, female infanticide has given way to sex-selective abortion (and female representation on the ruling committees has dropped), representing an important transformation. Calling China a “patriarchy” is true, but by itself doesn’t much help explain the pattern of and prospects for change.

Like Ehrenreich, I think we need to look at the variations to understand the systemic features of our society. Men losing out to women in national elections is an important one. Given the choice between two male-dominated parties with platforms that don’t differ fundamentally on the biggest economic issues despite wide differences in social policy, women voters (along with blacks, Latinos and the poor) bested men and got their way. I wouldn’t minimize that (more than I just did), or ignore the scale and direction of change. The American patriarchy has weakened.

I expect some readers will go right to their favorite statistics or personal experiences in order to challenge my description of our society as patriarchal. In that tit-for-tat, men leading the vast majority of the most powerful institutions, and that American families usually follow the male line, become just another couple of data points. But they shouldn’t be, because some facts are more important than others.

Obama Top Chef Romney Founding Fathers

If you don’t collect data on individual web users, and don’t have a big-data budget, you can still learn a lot about how people voted in this presidential election from some creative probing of the Google Correlate database. The power of the tool is in uploading your own data (such as vote tallies) to see what searches mirror your target pattern.

For example, the map on the left is what I uploaded: the ratio of Obama votes to Romney votes in each state, as of Thursday morning. The map on the right, from Google, is the relative frequency of searches for “top chef.” The two patterns have a correlation of .88 on a scale of 0 to 1.

Maybe it’s a complete coincidence that Michelle Obama appeared on a Top Chef program earlier this year. But out of the 100 Google searches that most closely match that vote pattern, eight are aboutTop Chef. Others on the list include “spliff” (never heard of it), “mos def” and various reggae artists, as well as “itchiness.”

On the other hand, searches for “founding fathers quotes” follow the Romney/Obama ratio just as closely:

Most of the searches on the top-100 Romney-state list (all correlated about the same .84) are about simple, non-obscene pleasures, such as “clean jokes,” “clean funny jokes,” “funny commercials”; and home-schooling materials, like “flag clipart,” “in god we still trust,” and “printable scrapbook.” After the kids are in bed, though, someone is Googling “hot cheerleader,” before quickly toggling back over to “sean hannity” when he hears mom coming up the stairs.

Demography and destiny for voters

Posted on TheAtlantic.com as Why Demographics Can’t Fully Predict How People Vote

Two-thirds of single women voted for Obama, according to the exit polls taken on the day of the election. On the other hand, the majority—53 percent—of married women went for Romney. With marriage on the decline, figured one pundit,

If [Republicans] are unable to attract the support of more unmarried female voters in future elections, they could face years in the political wilderness.

And Fox News reported:

Marital status was a more significant factor than gender this year. Women, a traditional Democratic voting group, backed Obama by 11 points—about the same as by 13 points in 2008. Even so, married women backed Romney by 7 points (an improvement from McCain’s +3 showing). Men backed Romney (52-45 percent), and married men backed him by an even wider margin (60-38 percent).

But these conclusions are overdrawn, because “unmarried women” are a data category more than a lived social group. Most people who aren’t married will get married. And people who are likely not to get married—such as poor people with fewer marriage prospects—tend to have traditional viewsabout marriage anyway. So what distinguishes unmarried women? Birth control is a common explanation. But almost everyone thinks birth control is okay these days, and although single women might be more worried about access, that’s because they’re less likely to have health insurance. In short, even if some women do have a strong identity as members of the single-women category, most unmarried women are just passing through.

What is grouping good for?

From the large sample interviewed by the major media’s exit polling consortium, we can see that simple demography can make some very strong predictions. At the extreme, with just two data points—race and gender—we can guess how a person voted 96 percent of the time, if she’s a black woman.

Source: From exit poll breakdowns based on polling by Edison Research.

But that doesn’t tell us why people voted the way they did. On average, for example, black women also have lower incomes, are younger, and have completed less education than whites—and they have worse healthcare.

Source: National Health Interview Survey.

So did black women vote for Obama because they are proud of him as a black man and identify with his personal experience, or because his policies are more beneficial for them as workers, concerned family members, or medical patients?

And the closer a group gets to the 50/50 middle of the odds breakdown, the harder it is to guess what’s going on from the demographics. When the plane I was on Tuesday night landed, I turned on my iPhone to see who had won Ohio. Waiting for the page to load, I looked around and saw a guy a few rows back, smiling and giving me a thumbs up. He must have noticed I’m a white man, almost two-thirds of whom went for Romney. But did he think I was Jewish, or gay? Maybe it was my rumpled casual-business wear, shaggy hair, and dead-giveaway academic backpack. Or my facial expression.

In real life, even with people we don’t know personally, the cues we get from interactions and behavior explain more than simple demographics, although they all go into the quick mix of judgments we are forced to form on short notice throughout the day. In statistical terms, the basic demographic variables leave a lot of variance unexplained.

These demographic categories are useful for prediction at the group level, as punditry has proved. Show me a room full of randomly-chosen Mormons and I can guess that 78 percent of them voted for Romney. But give me 30 seconds with one of them personally and I might figure out whether she is the one who didn’t.

This paradox is why the “big data” people were so central to the campaigns. If you can get someone to give you a few key bits of information—even just a zip code—and then track them as they wander around the web after leaving your site, your models can be much more powerful than the demographics alone. It’s the equivalent of sizing up someone by their shoes, haircut, and the flight they’re on, rather than just sex and race.

Have we passed the homogamy tipping point?

A tipping point is when a small step along a continuous path suddenly makes a big, irreversible difference in some outcome. Here’s one illustration (which I got here):

The little bits of action have been having a little impact, until suddenly one more little bit of action creates a big impact — the straw breaks the camels back. In real life these moments are very hard to predict, and even in retrospect it’s not so clear what did it.

Legal homogamous marriage is spreading in the U.S. (and the world). The restriction on marriage increasingly looks like a crude, irrational violation of equal rights. A growing share of the public sees no reason to object to gay and lesbian marriage.

This doesn’t follow a tipping-point pattern, at least not yet. But here is a case where 50% is potentially a tipping point rather than a mere milestone or watershed. That is because of 50%+1 winner-take all voting. And that’s why this week’s successful ballot measures in three states in favor of legal homogamous marriage– Maine, Maryland and Washington — are so important.

But the tipping point actually may have preceded them, when marriage rights broke out of New England, in Washington, D.C. in 2010, by a city council vote. (Iowa went earlier by court order.)

When New York followed DC by legislative action in 2011, the percentage of the population in marriage-equality states more than doubled, from 5% to 11%. Even without the judicial branch, which will do something in the next year or so, the trajectory here is steeply upward.

In a truly Rovian moment of prognostication, Brian Brown from the National Organization for Marriage gave this analysis earlier this year:

Proponents of same-sex marriage have created a myth of inevitability, and folks in the polling world have used language that has often helped them… The only poll that counts is the voters, and if you look at that, we’ve won every single one. If you look at trend lines, the trend lines are in our direction.

And that makes sense (that is, the opposite of what Brown said): if we’ve shifted to a majority, or close to it, nationally, then plenty of states and local areas are well beyond 50%, and that’s enough for ballot measures where they are permitted.

As a result of social movement action, legal challenges, changing attitudes, and cohort replacement, marriage equality appears to be spreading, like a forest fire or disease epidemic — only better.

Have Obama haters lost traction?

Maybe it’s because Donald Trump isn’t really a true hero to anti-socialist, anti-Muslim, racist Americans.

For whatever reason, there has been a real slump in the number of people typing “obama gun” (will he take our guns away?), “obama muslim” (the idea used to run at about 20%), “obama socialist” (the republic “hangs in the balance“), and “obama citizen” (thank you, Snopes) into the Google search box since the 2008 election.

Here’s the Google trend (and the search link):

We don’t know how much these fears, versus other concerns, will affect votes against him this year, although there have been some good efforts to track the effects of anti-Black racism on his vote tally.

Naturally, not everyone who Googles these things believes the underlying stories or myths. But it seems likely they either believe them, are considering them, heard someone repeat them, or are arguing with someone who believes them, etc. So I’m guessing – just guessing – that these trends track those beliefs.

But maybe four years of Obama as an actual president has softened up the hard-line hatred in some quarters. What do you think?

Guns dividing America (Google edition)

Whenever I get a good indicator broken down by state, I head over to Google Correlate to see how it connects to America’s search behavior. Often what I find is a gun connection. This is very big in searches related to the election, so I’ll start with that before giving a couple other examples.

Odds of Romney winning

Taking yesterday’s New York Times 538 forecast chance of Mitt Romney winning each state, I entered the numbers into the search correlation machine. As you can see from the map on the left, these are very polarized numbers, with 42 of the states being above 90% or below 10%. Of the 100 Google searches whose relative frequency is most correlated with this pattern across states, 31 are about guns. Here is the search most correlated (.82) with Romney’s odds of winning: “marlin 30-30,” which is a classic rifle (available at Walmart):

Unintentional deaths

News the other day was about the lives lost to unintentional injuries for people under age 20 — the most common causes of death in that age range. About half of this is from motor vehicle accidents, with most of the rest distributed between drowning, suffocating, fires, falls, and poisoning. The CDC put out a report that included a state breakdown, reported in terms of “years of potential life lost” per 100,000 population. That is just the number of deaths times the number of years between the age at which the death occurred and age 75 (so a death at age 1 is 74 years lost, a death at age 19 is 56).

The big inequalities here are in gender and geography. Males are about 1.8-times more likely to die from this stuff. And the most dangerous state (Mississippi) has more than 4-times the losses of the safest (Massachusetts). There are race differences as well — with American Indians having high rates — but the Black/White difference is not that large (Latino ethnicity wasn’t identified).

How are these rates of lost life correlated with search behavior? Guns. Among the 100 searches that most closely follow the pattern of deaths, 62 were about guns, starting with number 1: “shotgun for sale,” with a correlation of .93.

There were also 14 searches about cars and trucks on the list (mostly Ford F150s and Chevy trucks), four about wedding dresses and rings (“discount wedding dresses”) and three about Fox News.

Divorce

I did this twice with divorce rates. Using the 2008-2009 divorce rates per 1,000 married women, I found a good gun correlation with gun searches, with “colt .45 automatic” scoring a .86:

There were 27 more gun-related searches on that 08-09 divorce-correlation list. I updated that for the new 2011 rates, and again came up with a list of gun-related searches (and other military or survivalist stuff). Here is the Norinco SKS and 2011 divorce rates, correlation .84:

Someone who knows more than me could probably read more into the searches for different kinds of guns and gun-related stuff — for example, the difference between sniper accessories, shotguns and handguns. These different gun results show variations in their geographic patterns.

Anyway, I can’t think of what else besides search data tells us so much about so many people’s behavior — not their stated interests, their reported behavior, their tax forms, or their consumption patterns. And yet I can’t really put my finger on what it does tell us. It’s a million miles wide and not that deep, but it’s endlessly fascinating. If someone can figure out how to explain the value of what this all shows, I’m all ears.