Marriage equality is official now that it’s in The Family textbook

Well, actually, it’s in a special addendum to the textbook that W. W. Norton is just releasing.

The book I wrote, The Family: Diversity, Inequality, and Social Change, hit the streets a year ago today. Marriage equality plays a significant part in the story, much larger than the proportion of the population that is directly affected by the changing law. That’s because of the high-stakes nature of the debate for so many people, and because of its symbolic acceptance of rising family diversity — the main theme of the book.

So when the law suddenly, and fundamentally, changed this summer, we decided we needed an update for instructors teaching this fall. The three-page supplement reviews the political and legal events leading up to the June 26 Obergefell decision, and the logic of the legal questions addressed — along with a little context on the place of marriage equality in the story of family change. I hope it’s helpful for you.

The update is now available on the Norton website, here, and on my teaching page. While you’re at it, you should visit the book’s homepage, and see what we have in store for you if you teach family sociology (and request an exam copy), here.


  • A symposium with 12 writers and researchers addressing the concept, “After marriage equality,” which Syed Ali and I edited for Contexts.
  • My whole series of blog posts on marriage equality is archived under the homogamy tag.


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Does doing difference deny dominance? (vocal fry, sports sex testing, and resting bitch face edition)

Does women’s behavior make them less equal?

“Guess what,” Camille Paglia said the other day in Salon. “Women are different than men!”

Usually when people point out gender differences, they don’t just mean men and women are different, they mean “women are different from men.” As an archetypal example, in “Do women really want equality?” Kay Hymowitz argued that women don’t want to model their professional lives on male standards, and therefore they don’t really want equality:

This hints at the problem with the equality-by-the-numbers approach: it presumes women want absolute parity in all things measurable, and that the average woman wants to work as many hours as the average man, that they want to be CEOs, heads of state, surgeons and Cabinet heads just as much as men do.

So the male professional standard is just there, and the question is what women will do if they want equality. Of course, what women (and men) want is a product of social interaction, so it’s not an abstract quality separate from social context. But also, I’m no statistician but I know that when there is a gap between two variable quantities (such as men’s and women’s average hours in paid work), moving one of them isn’t the only way to bring them closer together. In other words — men could change, too.

What about vocal fry and uptalk?

Naomi Wolf would add these speech patterns to the list of women’s self-inflicted impediments:

“Vocal fry” has joined more traditional young-women voice mannerisms such as run-ons, breathiness and the dreaded question marks in sentences (known by linguists as uptalk) to undermine these women’s authority in newly distinctive ways.

So the male speech pattern is just there, and the question is what women will do if they want equality. In opposition is the argument made here:

Teaching young women to accommodate to the linguistic preferences, a.k.a. prejudices, of the men who run law firms and engineering companies is doing the patriarchy’s work for it. It’s accepting that there’s a problem with women’s speech, rather than a problem with sexist attitudes to women’s speech.

So some feminists want more respect for vocal fry, saying: “when your dads bitch about the way you talk it’s because they’re just trying to not listen to you talk, period, so fuck your dads.” This stance is not just feminist, it’s young feminist:

[Vocal fry] is the speaking equivalent of “you ain’t shit,” an affectation of the perpetually unbothered. It’s a protective force between the pejorative You — dads, Sales types, bosses, basically anyone who represents the establishment — and the collective Us, which is to say, a misunderstood generation that inherited a whole landscape of bullshit because y’all didn’t fix it when you had the goddamn chance.

Elevating vocal fry to a virtue would be more persuasive if the common examples weren’t mostly rich women talking about basically nothing. As an old dad who has done nothing to fix society, I personally bitched about the way the two women interviewed for this NPR story fried and uptalked their way through an excruciating seven-minute conversation about the awesomeness of selfie culture.

Of course, this being a patriarchal society, double standards abound. Men fry their vocals, too, and no one cares. (I myself transcribed this awesome piece of run-on from a young man on the radio once, but I didn’t blame him for holding all men back.) And then there’s resting bitch face, “a face that, when at ease, is perceived as angry, irritated or simply … expressionless,” according to Jessica Bennett (whose RBF is not to be trifled with). But only for women:

“When a man looks stern, or serious, or grumpy, it’s simply the default,” said Rachel Simmons, an author and leadership consultant at Smith College. “We don’t inherently judge the moodiness of a male face. But as women, we are almost expected to put on a smile. So if we don’t, it’s deemed ‘bitchy.’ ”

Many men feel that RBF is a blight on their scenery — one they have the right to demand improvement upon — which is why they tell random women on the street to smile. Plus, they just like exercising informal personal power over random women who aren’t conforming with various social rules, including the rule that you show your love for patriarchy at all times.

Sometimes women should act more like men, because some of the behavior that men would otherwise own is about power and access and self-determination and other things that women want and deserve. And some gender differences are just little pieces of the symbolic architecture that helps establish that men and women are different, which means women are different, which means men are dominant. Difference for its own sake is bad for gender equality.

It’s tricky because we don’t have different audiences for different messages anymore, but we need two true messages at once: It’s wrong to discriminate against and shame women for their speech patterns, and it’s a good idea not to undermine yourself with speech patterns that annoy or distract men and old people.

What about sports?

One process people use to essentialize sex categories — to enhance rather than downplay gender differences — is sex segregated sports (which I last wrote about with regard to Caster Semenya). As is the case with many gender differences, our sports establishment and culture is built around male standards, which is why women are granted a protected sphere of difference . Writes Vanessa Heggie in a fascinating historical review of sex testing in international sports:

Sex testing, after all, is a tautological (or at least circular) process: the activities which we recognise as sports are overwhelmingly those which favour a physiology which we consider ‘masculine’. As a general rule, the competitor who is taller, has a higher muscle-to-fat ratio, and the larger heart and lungs (plus some other cardio-respiratory factors) will have the sporting advantage. It is therefore inevitable that any woman who is good at sport will tend to demonstrate a more ‘masculine’ physique than women who are not good at sport. What the sex test effectively does, therefore, is provide an upper limit for women’s sporting performance; there is a point at which your masculine-style body is declared ‘too masculine’, and you are disqualified, regardless of your personal gender identity. For men there is no equivalent upper physiological limit – no kind of genetic, or hormonal, or physiological advantage is tested for, even if these would give a ‘super masculine’ athlete a distinct advantage over the merely very athletic ‘normal’ male.

Heggie adds that, for every claim of gender fraud that turns out to be “true” — that is, a male or intersex person with an unfair advantage competing as a woman, which is vanishingly rare — there are countless cases of “suspicions, rumour, and inuendo” regarding women who are simply unusually big and muscular. As in wide swaths of the professional world, men are the standard, and successful women often look or act more like men — and then they are shamed or penalized for not performing their gender correctly.

There is a sex versus gender issue here, however. When men’s behavior or activity is the standard by which all are judged, there are gendered (social) reasons women have trouble competing — such as exclusion from training, hiring, promotion, and social networks, or socially-defined burdens (such as childcare) impeding their progress toward the top ranks. And then sometimes there are sex (biological) reasons women can’t win, such as in most organized sports.

Here are the world record times in the 800-meter foot race for men and women, from 1922 to the present:

For all the fuss over Caster Semenya’s natural hormone levels, she never got to within two seconds of Jarmila Kratochvílová‘s 1983 record of 1:53.3. It’s presumed that Kratochvílová was taking steroids, but not proven — though the longer the time that lapses since her record was achieved, the more that seems likely.

It’s very telling that no woman has beaten Kratochvílová’s record. In fact, after women made steady progress toward equality for four decades, men’s lead has increased by almost a second in the last four decades. In this contest of physiology, the fastest women apparently cannot compete with the fastest men. This makes a strong case for sex not gender as the difference-maker. But, as I’ve argued before, that does not mean we’re outside the realm of social construction, because the line has to be drawn somewhere to create the protective arena in which women can compete with each other, and that line is defined socially.

We solve the problem if we “stop pawning this fundamentally social question off onto scientists,” say Rebecca Jordan-Young and Katrina Karkazis. They want to “let all legally recognized women compete. Period.” But if it is fundamentally social, instead of biological, why are men’s times so much faster?

Aside: How deep a difference

Thinking about all this, I was half interested in what Camille Paglia had to say in Salon about the similarity between Bill Clinton and Bill Cosby — in some ways obvious, in some ways an obvious overreach — and I might even have looked up her book, Sexual Personae, if she hadn’t said the book “of course is far too complex for the ordinary feminist or academic mind!” So that rules me out.

Anyway, in the interview she goes beyond the idea that men and women have different preferences and habits. Here is “why women are having so much trouble dealing with men in the feminist era”:

equality in the workplace is not going to solve the problems between men and women which are occurring in the private, emotional realm, where every man is subordinate to women, because he emerged as a tiny helpless thing from a woman’s body. Professional women today don’t want to think about this or deal with it.

Not recognizing such inherent conditions is a problem for modern feminism, she believes:

Guess what – women are different than men! When will feminism wake up to this basic reality? Women relate differently to each other than they do to men. And straight men do not have the same communication skills or values as women – their brains are different!

In this view, which you could (she does) loosely call Freudian, the sex difference and the gender difference are nearly unified, because the psychological basis for difference is universally present at birth. The short-sighted feminist attempt to erase gender difference thus makes both women and men miserable:

Now we’re working side-by-side in offices at the same job. Women want to leave at the end of the day and have a happy marriage at home, but then they put all this pressure on men because they expect them to be exactly like their female friends. If they feel restlessness or misery or malaise, they automatically blame it on men. Men are not doing enough; men aren’t sharing enough. But it’s not the fault of men that we have this crazy and rather neurotic system where women are now functioning like men in the workplace, with all its material rewards.

What is out of whack is women entering men’s sphere, apparently.

The political stakes attached to the nature and extent of difference between male and female people makes it an ever-important question. It underlies, for example, the opposition to marriage equality, as demonstrated in the terrible Catholic video series called Humanum, where you might hear such nuggets of wisdom as this:

In every human being there is a masculine part, and a feminine part, and as a man I get this feminine part from my mother or from the maternal image in my family, and I get this masculine image from the paternal part, from the paternal image in my family. And I get to make some equilibrium inside. And without this equilibrium my humanity is not really sane.

There is a difference between saying there is a difference between men and women and saying there is such a difference between men and women that your humanity is not complete unless you have both a mother and father.

Difference and dominance

Times like this, like it or not, are good times to revisit Catharine MacKinnon’s essay, “Difference and dominance: On sex discrimination.”*

There is a politics to this. Concealed is the substantive way in which man has become the measure of all things. Under the sameness standard, women are measured according to our correspondence with man, our equality judged by our proximity to his measure. Under the difference standard, we are measured according to our lack of correspondence with him, our womanhood judged by our distance from his measure. Gender neutrality is thus simply the male standard, and the special protection rule is simply the female standard, but do not be deceived: masculinity, or maleness, is the referent for both.

Between the rock of neutrality and the hard place of special protection. Difference and dominance.

In reality … virtually every quality that distinguishes men from women is already affirmatively compensated in this society. Men’s physiology defines most sports … their socially designed biographies define workplace expectations and successful career patterns, their perspectives and concerns define quality in scholarship, their experiences and obsessions define merit, their objectification of life defines art, their military service defines citizenship, their presence defines family, their inability to get along with each other — their wars and rulerships — defines history, their image defines god, and their genitals define sex.

So, check that referent. Of course, those women who work more hours, adopt male speech patterns and facial expressions, and run faster, may do better than those who do not (under the risk of overstepping). But why can’t women embrace gender difference in things like speech patterns, and wield them in the service of equality? They might. But under these conditions, enhancing gender differences works against inequality.

* There are several versions of this essay available by Googling. I’m quoting the one published in her 1988 book Feminism Unmodified.


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How we really can study divorce using just five questions and a giant sample

It would be great to know more about everything, but if you ask just these five questions of enough people, you can learn an awful lot about marriage and divorce.


First the questions, then some data. These are the question wordings from the 2013 American Community Survey (ACS).

1. What is Person X’s age?

We’ll just take the people who are ages 15 to 59, but that’s optional.

2. What is this person’s marital status?

Surprisingly, we don’t want to know if they’re divorced, just if they’re currently married (I include people are are separated and those who live apart from their spouses for other reasons). This is the denominator in your basic “refined divorce rate,” or divorces per 1000 married people.

3. In the past 12 months, did this person get divorced?

The number of people who got divorced in the last year is the numerator in your refined divorce rate. According to the ACS in 2013 (using population weights to scale the estimates up to the whole population), there were 127,571,069 married people, and 2,268,373 of them got divorced, so the refined divorce rate was 17.8 per 1,000 married people. When I analyze who got divorced, I’m going to mix all the currently-married and just-divorced people together, and then treat the divorces as an event, asking, who just got divorced?

4. In what year did this person last get married?

This is crucial for estimating divorce rates according to marriage duration. When you subtract this from the current year, that’s how long they are (or were) married. When you subtract the marriage duration from age, you get the age at marriage. (For example, a person who is 40 years old in 2013, who last got married in 2003, has a marriage duration of 10 years, and an age at marriage of 30.)

5. How many times has this person been married?

I use this to narrow our analysis down to women in their first marriages, which is a conventional way of simplifying the analysis, but that’s optional.


I restrict the analysis below to women, which is just a sexist convention for simplifying things (since men and women do things at different ages).*

So here are the 375,249 women in the 2013 ACS public use file, ages 16-59, who were in their first marriages, or just divorced from their first marriages, by their age at marriage and marriage duration. Add the two numbers together and you get their current age. The colors let you see the basic distribution (click to enlarge):

2011-2013 agemar figures.xlsx

The most populous cell on the table is 28-year-olds who got married three years ago, at age 25, with 1068 people. The least populous is 19-year-olds who got married at 15 (just 14 of them). The diagonal edge reflects my arbitrary cutoff at age 59.

Divorce results

Now, in each of these cells there are married people, and (in most of them) people who just got divorced. The ratio between those two frequencies is a divorce rate — one specific to the age at marriage and marriage duration. To make the next figure I used three years of ACS data (2011-2013) so the results would be smoother. (And then I smoothed it more by replacing each cell with an average of itself and the adjoining cells.) These are the divorce rates by age at marriage and years married (click to enlarge):

2011-2013 agemar figures.xlsx

The overall pattern here is more green, or lower divorce rates, to the right (longer duration of marriage) and down (older age at marriage). So the big red patch is the first 12 years for marriages begun before the woman was age 25. And after about 25 years of marriage it’s pretty much green, for low divorce rates. The high contrast at the bottom left implies an interesting high risk but steep decline in the first few years after marriage for these late marriages. This matrix adds nuance to the pattern I reported the other day, which featured a little bump up in divorce odds for people who married in their late thirties. From this figure it looks like marriages that start after the woman is about 35 might have less of a honeymoon period than those beginning about age 24-33.

To learn more, I go beyond those five great questions, and use a regression model (same as the other day), with a (collapsed) marriage-age–by–marriage-duration matrix. So these are predicted divorce rates per 1000, holding education, race/ethnicity, and nativity constant (click to enlarge)**:

2011-2013 agemar figures.xlsx

The controls cut down the late-thirties bump and isolate it mostly to the first year. This also shows that the punishing first year is an issue for all ages over 35. The late thirties just showed the bump because that group doesn’t have the big drop in divorce after the first year that the later years do. Interesting!


Here’s where the awesome data let us down. This data is very powerful. It’s the best contemporary big data set we have for analyzing divorce. It has taken us this far, but it can’t explain a pattern like this.

We can control for education, but that’s just the education level at the time of the most recent survey. We can’t know when she got her education relative to the dates of her marriage. Further, from the ACS we can’t tell how many children a person has had, with whom, and when — we only know about children who happen to be living in the household in 2013, so a 50-year-old could be childfree or have raised and released four kids already. And about couples, although we can say things about the other spouse from looking around in the household (such as his age, race, and income), if someone has divorced the spouse is gone and there is no information about that person (even their sex). So we can’t use that information to build a model of divorce predictors.

Here’s an example of what we can only hint at. Remarriages are more likely to end in divorce, for a variety of reasons, which is why we simplify these things by only looking at first marriages. But what about the spouse? Some of these women are married to men who’ve been married before. I can’t how much that contributes to their likelihood of divorce, but it almost certainly does. Think about the bump up in the divorce rate for women who got married in their late thirties. On the way from high divorce rates for women who marry early to low rates for women who marry late, the overall downward slope reflects increasing maturity and independence for women, but it’s running against the pressure of their increasingly complicated relationship situations. That late-thirties bump may have to do with the likelihood that their husbands have been married before. Here’s the circumstantial evidence:

2011-2013 agemar figures.xlsx

See that big jump from early-thirties to late-thirties? All of a sudden 37.5% of women marrying in their late-thirties are marrying men who are remarrying. That’s a substantial risk factor for divorce, and one I can’t account for in my analysis (because we don’t have spouse information for divorced women).

On method

Divorce is complicated and inherently longitudinal. Marriages arise out of specific contexts and thrive or decay in many different ways. Yesterday’s crucial influence may disappear today. So how can we say anything about divorce using a single, cross-sectional survey sample? The unsatisfying answer is that all analysis is partial. But these five questions give us a lot to go on, because knowing when a person got married allows us to develop a multidimensional image of the events, as I’ve demonstrated here.

But, you ask, what can we learn from, say, the divorce propensity of today’s 40-year-olds when we know that just last year a whole bunch of 39-year-olds divorced, skewing today’s sample? This is a real issue. And demography provides an answer that is at once partial and powerful: Simple, we use today’s 39-year-olds, too. In the purest form, this approach gives us the life table, in which one year’s mortality rates — at every age — lead to a projection of life expectancy. Another common application is the total fertility rate (watch the video!), which sums birth rates by age to project total births for a generation. In this case I have not produced a complete divorce life table (which I promised a while ago — it’s coming). But the approach is similar.

These are all synthetic cohort approaches (described nicely in the Week 6 lecture slides from this excellent Steven Ruggles course). In this case, the cohorts are age-at-marriage groups. Look at the table above and follow the row for, say, marriages that started at age 28, to see that synthetic cohort’s divorce experience from marriage until age 59. It’s neither a perfect depiction of the past, nor a foolproof prediction of the future. Rather, it tells us what’s happening now in cohort terms that are readily interpretable.


The ACS is the best thing we have for understanding the basic contours of divorce trends and patterns. Those five questions are invaluable.

* For this I also tossed the people who were reported to have married in the current year, because I wasn’t sure about the timing of their marriages and divorces, but I put them back in for the regressions.

** The codebook for my IPUMS data extraction is here, my Stata code is here. The heat-map model here isn’t in that code file, but this these are the commands (and the margins command took a very long time, so please don’t tell me there’s something wrong with it):

logistic divorce i.agemarc#i.mardurc i.race i.hispan i.citizen
margins i.agemarc#i.mardurc


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That Sunday New York Times Style section trend piece

Just folks trying to survive their divorces.

Just folks trying to survive their divorces.

Does it matter which one?

You know it from the opening paragraphs:

The women are architects, film industry executives, skin care consultants, product managers at tech companies, psychologists. They have worked in finance, publishing and television, though some had scaled back or left the work force when their children were born.

Divorce is what they have in common. Their stories are varied: the breadwinner wife whose husband’s career hadn’t quite taken off and who found comfort in an affair; the husband who never really adapted to parenthood; the wife with Ivy League degrees who stayed home with her child but lost her way in the marriage while the husband thrived in his international career.

Really. Divorce is what they have in common? How hard would it be to include a single mention of how rich and privileged these women are compared to the typical woman getting divorced? Penelope Green’s story never mentions the possibility.

Here is what a five-minute effort would have looked like:


These 10 occupations account for 25% of all women age 40+ who reported getting divorced in the previous year.

In addition, 34% of those just-divorced, 40+ women are not non-Hispanic Whites (14% Black, 13% Hispanic, 4% Asian/Pacific Islander).

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No, you should get married in your late 40s (just kidding)

Please don’t give (or take) stupid advice from analyses like this.

Since yesterday, Nick Wolfinger and Brad Wilcox have gotten their marriage age analysis into the Washington Post Wonkblog (“The best age to get married if you don’t want to get divorced”) and Slate (“The Goldilocks Theory of Marriage”). The marriage-promotion point of this is: don’t delay marriage. The credulous blogosphere can’t resist the clickbait, but the basis for this is very weak.

Yesterday I complained about Wolfinger pumping up the figure he first posted (left) into the one on the right:

wolfbothToday I spent a few minutes analyzing the American Community Survey (ACS) to check this out. Wolfinger has not shared his code, data, models, or tables, so it’s hard to know what he really did. However, he lists a number of variables he says he controlled for using the National Survey of Family Growth: “sex, race, family structure of origin, age at the time of the survey, education, religious tradition, religious attendance, and sexual history, as well as the size of the metropolitan area.”

The ACS seems better for this. It’s very big, so I can analyze just the one-year incidence of divorce (did you get divorced in the last year?), according to the age at which people married. I don’t have family structure of origin, religion, or sexual history, but he says those don’t influence the age-at-marriage effect much. He did not control for duration of marriage, which is messed up in his data anyway because of the age limits in the NSFG.

So, in my model I used women in their first marriages only, and controlled for marriage duration, education, race, Hispanic ethnicity, and nativity/citizenship. This is similar to models I used in this (shock) peer-reviewed paper. Here are the predicted probabilities of divorce, in one year, holding those control variables constant.


Yes, there is a little bump up for the late 30s compared with the early 30s, but it’s very small.

Closer analysis (added to the post 7/19), generated from a model with age-at-marriage–x–marital duration interactions, shows that the late-30s bump is concentrated in the first five years of marriage:


This doesn’t much undermine the “conventional wisdom” that early marriage increases the risk of divorce. Of course, this should not be the basis for advice to people who are, say, dating a person they’re thinking of marrying and hoping to minimize chance of divorce.

If you want to give advice to, say, a 15-year-old woman, however, the bottom line is still: Get a bachelor’s degree. You’ll likely earn more, marry later, and have fewer kids. If you or your spouse decide to get divorced after all that, it won’t hurt that you’re more independent. For what it’s worth, here are the education effects from this same model:


(The codebook for my IPUMS data extraction is here, my Stata code is here.)

Anyway, it’s disappointing to see this in the Wonkblog piece:

But the important thing, for Wolfinger, is that “we do know beyond a shadow of a doubt that people who marry in their thirties are now at greater risk of divorce than are people who wed in their late twenties. This is a new development.”

That’s just not true. I wouldn’t swear by this quick model I did today. But I would swear that it’s too early to change the “conventional wisdom” based only on a blog post on a Brad-Wilcox-branded site.


One interesting issue is the problem of age at marriage and education. They are clearly endogenous — that is, they influence each other. Women delay marriage to get more education, they stop their education when they have kids, they go back to school when they get divorced — or think they might get divorced. And so on. And, for the regression models, there are no highly-educated people getting married at really young ages, because they haven’t finished school yet. On the other hand, though, there are lots of less-educated people getting married for the first time at older ages. Using the same ACS data, here are two looks at the women who just married for the first time, by age and education.

First, the total number per year:


Then, the percent distribution of that same data:

age-ed-mar-distInteresting thing here is that college graduates are only the majority of women getting married for the first time in the age range 27-33. Before and after that most women have less than a BA when they marry for the first time. This is also complicated because the things that select people into early marriage are sometimes but not always different from those that select people into higher education. Whew.

It really may not be reasonable to try to isolate the age-at-marriage effect after all.


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The latest get-married-young thing tells you all you need to know

Just a quick note for people wondering about this new thing by Nicholas Wolfinger on Brad Wilcox’s blog. He says it used to be (before 1995) that getting married young increased the odds of divorce. Since then, however, he says getting married either before or after age 32 raises the odds of divorce.

Why is that? His explanation — in his very own words, from his very own post: “my money is on a selection effect.” In other words, do not follow the advice in the headline, which is: “Want to Avoid Divorce? Wait to Get Married, But Not Too Long.” Because if the mechanism is selection, then changing your behavior to ride that curve will not work.

I’m not getting into the methods, which are not revealed, despite a link for “more information” — there is no paper, no tables, no code or data. However, something is off, and the post is off-gassing a discernible essence of Wilcox’s influence. In the new blog post, they show this graph:

wolfinger1Wow, that’s a pretty big boomerang effect. If it weren’t a selection effect, it might really be relevant for personal decision-making. But when you follow the link for “more information” you see this graph:


The upward swing here is hardly enough to get your marriage promotion lather up. Clearly, something had to be improved from Wolfinger’s post from April and his post for Wilcox’s site in July. That’s the kind of data leadership we expect from this site. (Also, get rid of those dots, which show you the all those people with really low divorce odds at higher ages.)



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