Tag Archives: inequality

No, early marriage is not more common for college graduates

Update: IFS has taken down the report I critiqued here, and put up a revised report. They have added an editor’s note, which doesn’t mention me or link to this post:

Editor’s Note: This post is an update of a post published on March 14, 2018. The original post looked at marriage trends by education among all adults under age 25. It gave the misimpression that college graduates were more likely to be married young nowadays, compared to non-college graduates.


At the Institute for Family Studies, Director of Research Wendy Wang has a post up with the provocative title, “Early Marriage is Now More Common For College Graduates” (linking to the Internet Archive version).

She opens with this:

Getting married at a young age used to be more common among adults who didn’t go to college. But the pattern has reversed in the past decade or so. In 2016, 9.4% of college graduates ages 18 to 24 have ever been married, which is higher than the share among their peers without a college degree (7.9%), according to my analysis of the most recent Census data.

And then the dramatic conclusion:

“What this finding shows is that even at a young age, college-educated adults today are more likely than their peers without a college degree to be married. And this is new.”

That would be new, and surprising, if it were true, but it’s not.

Here’s the figure that supports the conclusion:

figure1wendyupdate-w640

It shows that 9.4% of college graduates in the age range 18-24 have been married, compared with 7.9% of those who did not graduate from college. (The drop has been faster for non-graduates, but I’m setting aside the time trend for now.) Honestly, I guess you could say, based on this, that young college graduates are more likely than non-graduates to “be married,” but not really.

The problem is there are very very few college graduates in the ages 18-19. The American Community Survey, which they used here, reports only about 12,000 in the whole country, compared with 8.7 million people without college degrees ages 18-19 (this is based on the public use files that IPUMS.org uses; which is what I use in the analysis below). Wow! There are lots and lots of non-college graduates below age 20 (including almost everyone who will one day be a college graduate!), and very few of them are married. So it looks like the marriage rate is low for the group 18-24 overall. Here is the breakdown by age and marital status for the two groups: less than BA education, and BA or higher education — on the same population scale, to help illustrate the point:

ifs1ifs2

If you pool all the years together, you get a higher marriage rate for the college graduates, mostly because there are so few college graduates in the younger ages when hardly anyone is married.

To show the whole thing in terms of marriage rates, here is the marital status for the two groups at every age from 15 (when ACS starts asking about marital status) to 54.

ifs3

Ignoring 19-21, where there are a tiny number of college graduates, you see a much more sensible pattern: college graduates delay marriage longer, but then have higher rates at older ages (starting at age 28), for all the reasons we know marriage is ultimately more common among college graduates. In fact, if you used ages 15-24 (why not?), you get an even bigger difference — with 9.4% of college graduates married and just 5.7% of non-college graduates. Why not? In fact, what about ages 0-24? It would make almost as much sense.

Another way to do this is just to look at 24-year-olds. Since we’re talking about the ever-married status, and mortality is low at these ages, this is a case where the history is implied in the cross-sectional data. At age 24, as the figure shows, 19.9% of non-college graduates have been married, compared with 12.9% of college graduates. Early marriage is not more common for college graduates.

In general, I don’t recommend comparing college graduates and non-graduates, at least in cross-sectional data, below age 25. Lots of people finishing college below age 25 (and increasingly after that age as well). There is also an important issue of endogeneity here, which always makes education and age analysis tricky. Some people (mostly women) don’t finish college because they get married and have children).

Anyway, it looks to me like someone working for a pro-marriage organization saw what seemed like a story implying marriage is good (that’s why college graduates do it, after all), and one that also fits with the do-what-I-say-not-what-I-do criticism of liberals, who are supposedly not promoting marriage among poor people while they themselves love to get married (a critique made by Charles Murray, Brad Wilcox, and others). And, before thinking it through, they published it.

Mistakes happen. Fortunately, I dislike the Institute for Family Studies (see the whole series under this tag), and so I read it and pointed out this problem within a couple hours (first on Twitter, less than two hours after Wang tweeted it). It’s a social media post-publication peer review success story! If they correct it.

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Explain to me again how marriage is the problem here

This is one of those things you share with all your friends on social media.

how-marriage-is-the-problem-here

Black married parents are 2.4-times more likely to be in poverty, are 2.1-times more likely to be unemployed, and have one-ninth the median net worth compared with White married parents. So explain to me again how marriage is the problem here.

Why?

The other day I picked on someone’s fact meme, and wondered what makes these things work, without offering a constructive alternative. I can’t answer the question I asked in that post (how old are the fathers of teen mothers’ children?), but I can answer some other questions about families and Black-White inequality. So that’s what I did.

Feel free to take these facts (or any others) and make something better.

How?

Here are my sources:

Poverty: 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. The poverty rates were 5% for White married parents and 11.9% for Black married parents. The poverty variable goes from 0 to 501, with 0-99 being below the poverty line, so you specify the recode like this: poverty(r:0-99 “poor”; 100-501 “not poor”). Here’s how you fill out the boxes in the online analysis tool:

povacscode

Unemployment: Again, 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. For this one you limit it to people in the labor force (empstat(1-2)) to get the unemployment rate. I did it for men and women combined, getting unemployment rates of 3.1% for White married parents and 6.6% for Black married parents. The numbers are higher for women (3.7% versus 7.3%) but the Black/White ratio is a little worse for men (2.6% versus 5.8%). Here’s how:

unempacscode

Median net worth: I used the Survey of Consumer Finances from 2013, available here. These are also non-Hispanic Black and White parents living with children. The median net worths were $150,500 for Whites and $16,000 for Blacks (Hispanics, incidentally, have $18,750, and the rest are just coded “other”). This data set combines married people with those who are “living with partner,” so this comparison includes cohabitors. (I don’t know how that affects the results, but I’m sure there’s still lots of inequality.) I put my STATA code in an Open Science Framework project here, so feel free to play with it yourself.

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No Black women are not the “most educated” group in the US

I don’t know where this started, but it doesn’t seem to be stopping. The following headlines are all completely factually wrong, and the organizations that published them should correct them right away:

The Root: Black Women Now the Most Educated Group in US

Upworthy: Black women are now America’s most educated group

SalonBlack women are now the most educated group in the United States

GoodBlack Women Are Now The Most Educated Group In The U.S.

And then the video, by ATTN:, on Facebook, with 6 million views so far. I won’t embed the video here, but it includes these images, with completely wrong facts:

bweduc1

bweduc2

What’s true is that Black women, in the 2009-2010 academic year, received a higher percentage of degrees within their race/ethnic group than did women in any other major group. So, for example, of all the MA degrees awarded to Black students, Black women got 71% of them. In comparison, White women only got 62% of all White MA degrees. Here is the chart, from the data that everyone linked to (which is not new data, by the way, and has nothing to do with 2015):

bwdegchart

For Black women to be the “most educated group,” they would have to have more degrees per person than other groups. In fact, although a greater percentage of Black women have degrees than Black men do, they have less education on average than White women, White men, Asian/Pacific Islander women, and Asian/Pacific Islander men.

Here are the percentages of each group that holds a BA degree or higher (ages 25-54), according to the 2010-2014 American Community Survey, with Black women highlighted:

bwdegchartBA

23% of Black women ages 25-54 have BA degrees or more education, compared with 38% of White women. This does not mean Black women are worse (or that White women are better). It’s just the actual fact. Here are the percentages for PhD degrees:

bwdegchartPhDJPG

Just over half of 1% of Black women have PhDs, compared with just over 1% of White women – and almost 3% of Asian/PI women. White women are almost twice as likely to have a PhD and Black women, Asian/PI women are more than 5-times as likely.

Racism is racism, inequality is inequality, facts are facts. Saying this doesn’t make me racist or not racist, and it doesn’t change the situation of Black women, who are absolutely undervalued in America in all kinds of ways (and one of those ways is that they don’t have the same educational opportunities as other groups). There are some facts in these stories that are true, too. And of course, why Black women (and women in general) are getting more degrees than men are is an important question. But please don’t think it’s my responsibility to research and present all this information correctly before it’s appropriate for me to point out the obvious inaccuracy here. You don’t need this meme to do the good you’re trying to do by sharing these stories.

Our current information economy rewards speed and clickability. Journalists who know what they’re doing are more expensive and slower. Making good graphics and funny GIFs is a good skill, but it’s a different skill than interpreting and presenting information. We can each help a little by pausing before we share. And those of us with the skills and training to track these things down should all pitch in and do some debunking once in a while. For academics, there is little extra reward in this (as evidenced by my most recent, sup-par departmental “merit” review), beyond the rewards we already get for our cushy jobs, but it should be part of our mission.

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Why I snarked on a 538 blog post (and I’m sorry)

Gaza. What does inequality have to do with it? (Photo by gloucester2gaza)

Gaza. What does inequality have to do with it? (Photo by gloucester2gaza)

The first thing that bugged me about this blog post by Jay Ulfelder at Five Thirty Eight was not the most important thing. The first thing I reacted to was that Ulfelder opened by asking whether “economic inequality causes political turmoil,” and then chastising, “Just because a belief is widely held, however, does not make it true,” before offering only evidence from economics studies. So I tweeted this obnoxious thing:

It was obnoxious, and I apologize. That response was part of my routine, defensive, complaining about how complex sociological work is neglected in favor of glib economics (e.g., here, here, here). But I do substantively object to the piece. If I had taken the time to figure out what really bugged me about it I could have sent a more constructive Tweet. Oh well, you never get a second chance to make a first snarky response.

What really bugged me is that the piece reduced this question of world-historic importance to a matter of microdata quality and measurement:

In fact, it’s still hard to establish with confidence whether and how economic inequality shapes political turmoil around the world. That’s largely because of the difficulty in measuring inequality…

Despite the slipperiness of “whether and how,”* Ulfelder’s point is definitely that we are “not there yet” on the question of “the belief that inequality causes political crises.” Still, maybe this is a case of trying to sell a narrow empirical piece as something bigger than it is — in which case it’s also a lesson in how people overreact when you do that.

I have to examine my own motives here, because this is one of those times when someone’s empirical claims threaten something that I don’t routinely subject to empirical testing. If there is an actual article of faith in my sociological worldview — and I would not really use the word faith to describe it, it’s more like a foundational understanding — it’s that inequality causes conflict, which causes social change. Ulfelder notes this is attributed partly to Marx, which is one reason why I and so many other sociologists hold it dear, but it’s also because it’s actually true. But that depends on what you mean by true, and here I think I disagree with Ulfelder, who writes:

With such incomplete and blurry information about the crucial quantities, why are so many of us so sure that economic inequality is a principal cause of political turmoil? Careful observation is one answer. Aristotle and Marx drew inferences about the destabilizing effects of inequality from their deep knowledge of the societies around them.

He never explains why this isn’t good enough, instead wandering into a critique of contemporary activist claims, based in part on an argument that “the seminal economic study” on the question is methodologically flawed (I’m sure it is).

This reducing of the question is too reductionist. I would be very interested to know whether within-country economic inequality, measured at the national level, if accurately measured, could help predict which countries would experience political turmoil, if that could be measured with a single indicator. But that’s not answering the question of whether inequality causes political turmoil — it’s one very narrow slice of that giant historical question, for which we have many sources of data and many affirmative answers.

Use a little of Marx’s “deep knowledge of societies” to consider, for example, the anti-colonial revolutions in many countries after 1945. Do you need to test a within-country economic inequality measure to know that such “turmoil” was one consequence of inequality? Of course, the timing and nature of those revolts is an interesting question to be addressed through research, but is such research asking whether inequality causes conflict?

What about slave revolts? What if someone found that harsher slavery regimes were not more likely to explode in revolt than those in which the slaves had enough food and water — would that tell you that inequality does not “cause” conflict? (Inequality causes conflict; that’s why they’re called slave revolts.)

Even, what about the civil rights movement, women’s movement, gay rights movement, or Black Lives Matter?

Does inequality cause conflict? Yes. Of course the relationship is not necessarily linear or simplistically univariate, which is the subject of lots of great sociology (and probably some minor work in other disciplines). But this is the kind of complex issue that data journalism nowadays loves to turn into yes-or-no, show-me-the-scatterplot short blog posts. I’ve done some of that myself, of course — and if I do it with something that’s a vital part of your analytical worldview, feel free to send me a snarky tweet about it.

* Nothing against this expression in general, it’s just slippery in this case because it might or might not be moving the goalposts from the opening question. 

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Sex ratios as if not everyone is a college graduate

Quick: What percentage of 22-to-29-year-old, never-married Americans are college graduates? Not sure? Just look around at your friends and colleagues.

Actually, unlike among your friends and colleagues, the figure is only 27.5% (as of 2010). Yep, barely more than a quarter of singles in their 20s have finished college. Or, as the headlines for the last few days would have it: basically everyone.

The tweeted version of this Washington Post Wonkblog story was, “Why dating in America is completely unfair,” and the figure was titled “Best U.S. cities for dating” (subtitle: “based on college graduates ages 22-29”). This local news version listed “best U.S. cities for dating,” but never even said they were talking about college graduates only. The empirical point is simple: there are more women than men among young college graduates, so those women have a small pool to choose from, so we presume it’s hard for them to date.* (Also, in these stories everyone is straight.) In his Washington Post excerpt the author behind this, Jon Birger, talks all about college women. The headline is, “Hookup culture isn’t the real problem facing singles today. It’s math.” You have to get to the sixth paragraph before you find out that singles means college and post-college women.

In his Post interview the subject of less educated people did come up briefly — if they’re men:

Q: Some of these descriptions make it sound like the social progress and education that women have obtained has been a lose-lose situation: In the past women weren’t able to get college educations, today they can, but now they’re losing in this other realm [dating]. Is it implying that less educated men are still winning – they don’t go to college but they still get the pick of all these educated, more promiscuous women?

A: Actually, it’s the opposite. Less educated men are actually facing as challenging a dating and marriage market as the educated women. So for example, among non-college educated men in the U.S. age 22 to 29, there are 9.4 million single men versus 7.1 million single women. So the lesser-educated men face an extremely challenging data market. They do not have it easy at all.

It’s almost as if the non-college-educated woman is inconceivable. She’s certainly invisible. The people having trouble finding dates are college-educated women and non-college-educated men. By this simple sex-ratio logic, it should be raining men for the non-college women. Too bad no one thought to think of them.

Yes, the education-specific sex ratio is much better for women who haven’t been to college. That is, they are outnumbered by non-college men. But it’s not working out that well for them in mating-market terms.

I can’t show dating patterns with Census data (and neither can Birger), but I can show first-marriage rates — that is, the rate at which never-married people get married. Here are the education-specific sex ratios, and first-marriage rates, for 18-34-year-old never-married women in 279 metropolitan areas, from the 2009-2011 American Community Survey.** Blue circles for women with high school education or less, orange for BA-holders (click to enlarge):

educ-marpool

Note that for both groups marriage rates are lower for women when there are more of them relative to men — the downward sloping lines (which are weighted by population size). Fewer men for women to choose from, plus men eschew marriage when they’re surrounded by desperate women, so lower marriage rates for women. But wait: the sex ratios are so much better for non-college women — they are outnumbered by male peers in almost every market, and usually by a lot. Yet their marriage rates are still much lower than the college graduates’. Who cares?

I don’t have time to get into the reasons for this pattern; this post is media commentary more than social analysis. But let’s just agree to remember that non-college-educated women exist, and acknowledge that the marriage market is even more unfair for them. Imagine that.***


* I once argued that this could help explain why gender segregation has dropped so much faster for college graduates.

** It was 296 metro areas but I dropped the extreme ones: over 70% female and marriage rates over 0.3.

*** Remember, if we want to use marriage to solver poverty for poor single mothers, we have enough rich single men to go around, as I showed.

A little code:

I generated the figure using Stata. I got the data through a series of clunky Windows steps that aren’t easily shared, but here at least is the code for making a graph with two sets of weighted circles, each with its own weighted linear fit line, in case it helps you:

twoway (scatter Y1 X1 [w=count1], mc(none) mlc(blue) mlwidth(vthin)) ///

(scatter Y2 X2 [w=count2], mc(none) mlc(orange_red) mlwidth(vthin)) ///

(lfit Y1 X1 [w=count1], lc(blue)) ///

(lfit Y2 X2 [w=count2], lc(orange_red)) , ///

xlabel(30(10)70) ylabel(0(.1).3)

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How (and how much) academics talk about inequality, in one chart

Reader advisory: When I say “in one chart,” I never really mean it.

Updated with new chart at the end.

Because someone asked, here is the article count from Web of Science (an academic journal database with emphasis on science), showing the frequency of articles (of all types) according to the inequality-related phrases in their titles. This is obviously not an exhaustive list of work on these subjects, but I did want to show all combinations of race, class, and gender (click to enlarge).

strat terms.xlsx

  • “Social inequality” now completely dominates, but it once was second to “social stratification.”
  • The most common of the three-word combinations is “race, class, and gender.”
  • “Gender, race, and class” has almost always been second.
  • “Gender, class, and race” made a run in the late 1990s, but has since faded.

I’ve written a little more about language and intersectional concerns here.

Update:

Don Tomaskovic-Devey sent along this figure, which shows newspaper articles using inequality related terms. The dotted line shows articles with rich, wealthy, top 1%, top one %, while the solid line shows income inequality. He suggests the dotted line may reflect an Occupy Wall Street effect, while the solid line shows the Thomas Piketty framing process:

lexisineq

 

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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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