Tag Archives: demography

Update: Adjusted divorce risk, 2008-2014

Quick update to yesterday’s post, which showed this declining refined divorce rate for the years 2008-2014:

On Twitter Kelly Raley suggested this could have to do with increasing education levels among married people. As I’ve reported using these data before, there is a much lower divorce risk for people with BA degrees or higher education.

Yesterday I quickly (but I hope accurately) replicated my basic model from that previous paper, so now I can show the trend as a marginal effect of year holding constant marital duration (from year of marriage), age, education, race/ethnicity, and nativity.*

2014 update

This shows that there has been a decrease in the adjusted odds of divorce from 2008 to 2014. You could interpret this as a continuous decline with a major detour caused by the recession, but that case is weaker than it was yesterday, looking at just the unadjusted trend.

If it turns out that increase in 2010-2012 is related to the recession, it’s not so different from my original view — a recession drop followed by rebound, it’s just that the drop is less and the rebound is more, and took longer, than I thought.  In any event, this should undermine any effort to resuscitate the old idea that the recession caused a decline in divorce by causing families to pull together during troubled times.

This does not contradict the results from Kennedy and Ruggles that show age-adjusted divorce rising between 1980 and 2008, since I’m not trying to compare these ACS trends with the older data sources. For time beyond 2008, they wrote in that paper:

If current trends continue, overall age-standardized divorce rates could level off or even decline over the next few decades. We argue that the leveling of divorce among persons born since 1980 probably reflects the increasing selectivity of marriage.

That would fit the idea of a long-term decline with a stress-induced recession bounce (with real-estate delay).

Alternative interpretations welcome.

* This takes a really long time for Stata to compute on my sad little public-university computer because it’s a non-linear model with 4.8 million cases – so please don’t ask for a lot of different iterations of this figure. I don’t have my code and output cleaned up for sharing, but if you ask me I’ll happily send it to you.


Filed under In the news

Divorce rate plunge continues

When I analyzed divorce and the recession in this paper, I only had data from 2008 to 2011. Using a model based on the predictors of marriage in 2008, I thought there had been a drop in divorces associated with the recession in 2009, followed by a rebound back to the “expected level” by 2011. So, the recession reduced divorces, perhaps temporarily.

That was looking iffy when the 2013 data showed a big drop in the divorce rate, as I reported last year. With new data now out from the 2014 American Community Survey, that story is seeming less and less adequate. With another deep drop in 2014, now it looks like divorce rates are on a downward slide, but in the years after the recession there was a bump up — so maybe recession-related divorces (e.g., those related to job loss or housing market stressors) took a couple years to materialize, producing a lull in the ongoing plunge. Who knows.

So, here is the latest update, showing the refined divorce rate — that is, the number of divorces in each year per 1,000 married people in that year.*

divorce rates.xlsx

Lots to figure out here. (As for why men and women have different divorce rates in the ACS, I still haven’t been able to figure that out; these are self-reported divorces, so there’s no rule that they have to match up [and same-sex divorces aren’t it, I think.])

For the whole series of posts, follow the divorce tag.

* I calculate this using the married population from table B12001, and divorces in the past year from table B12503, in the American Factfinder version of the ACS data.

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Filed under In the news

Age composition change accounts for about half of the Case and Deaton mortality finding

This paper by Anne Case and Angus Deaton, one of whom just won a Nobel prize in economics, reports that mortality rates are rising for middle-aged non-Hispanic Whites. It’s gotten tons of attention (see e.g., “Why poor whites are dying of despair” in The Week, and this in NY Times).

It’s an odd paper, though, in its focus on just one narrow age group over time. The coverage mostly describes the result as if conditions are changing for a group of people, but the group of people changes every year as new 45-year-olds enter and 54-year-olds leave. That means the population studied is subject to change in its composition. This is especially important because the Baby Boom wave was moving through this group part of that time. The 1999-2013 time frame included Baby Boomers (born 1945-1964) from age 35 to age 68.

My concern is that changes in the age and sex composition of the population studied could account for a non-trivial amount of the trends they report.

For example, they report that the increased mortality is entirely concentrated among those non-Hispanic White men and women who have high school education or less. But this population changed from 1999 to 2013. Using the Current Population Survey — which is not the authority on population trends, but is weighted to reflect Census Bureau estimates of population trends — I see that this group became more male, and older, over the period studied. That’s because the Baby Boomers moved in, causing aging, the population reflects women’s advances in education, relative to men, circa the 1970s. Here are those trends:


It’s odd for a paper on mortality trends not to account for account for sex and age composition changes in the population over time. Even if the effects aren’t huge, I think that’s just good demography hygiene. Now, I don’t know exactly how much difference these changes in population composition would make on mortality rates, because I don’t have the mortality data by education. That would only make a difference if the mortality rates differed a lot by sex and age.

However, setting aside the education issue, we can tell something just looking at the whole non-Hispanic White population, and it’s enough tor raise concerns. In the overall 45-54 non-Hispanic White population, there wasn’t any change in sex composition. But there was a distinct age shift. For this I used the 2000 Census and 2013 American Community Survey. I could get 1999 estimates to match Case and Deaton, but 2000 seems close enough and the Census numbers are easier to get. (That makes my little analysis conservative because I’m lopping off one year of change.)

Look at the change in the age distribution between 2000 and 2013 among non-Hispanic Whites ages 45-54. In this figure I’ve added the birth year range for those included in 2000 and 2013.


That shocking drop at age 54 in 2000 reflects the beginning of the Baby Boom. In 2000 there were a lot more 53-year-olds than there were 54-year-olds, because the Baby Boom started in 1946. (Remember, unlike today’s marketing-term “generations,” the Baby Boom was a real demographic event.) So there was a general aging, but also a big increase in 54-year-olds, between 2000 and 2013, which will naturally increase the mortality rate for that year.

So, to see whether the age shift had a non-trivial impact on the number of deaths in this population, I used one set of mortality rates: 2010 rates for non-Hispanic Whites by single year of age, published here. And I used the age and sex compositions as described above (even though the sex composition barely changed I did it separately by sex and summed them).

The 2010 age-specific mortality rates applied to the 2000 population produce a death rate of 3.939 per 1,000. When applied to the 2013 population they produce a death rate of 4.057 per 1,000. That’s the increase associated with the change in age and sex composition. How big is that difference? The 2013 death rate implies 118,313 deaths in 2013. The 2000 death rate implies 114,869 deaths in 2013. The difference is 3,443 deaths. Remember, this assumes age-specific death rates didn’t change, which is what you want to assess effects of composition change.

So I can say this: if age and sex composition had stayed the same between 2000 and 2013, there would have been 3,443 fewer deaths among non-Hispanic Whites in the ages 45-54.

Here is what Case and Deacon say:

If the white mortality rate for ages 45−54 had held at their 1998 value, 96,000 deaths would have been avoided from 1999–2013, 7,000 in 2013 alone.

So, it looks to me like age composition change accounts for about half of the rise in mortality they report. They really should have adjusted for age.

Here is my spreadsheet table (you can download the file here):


As always, happy to be credited if I’m right, and told if I’m wrong. But if you just have suggestions for more work I could do, that might not work.

Follow up: Andrew Gelman has three excellent posts about this. Here’s the last.


Filed under Research reports

Lifetime chance of marrying for Black and White women

I’m going to Princeton next week to give a talk at the Office of Population Research. It’s a world-class population center, with some of the best trainers and trainees in the business, so I figured I’d polish up a little formal demography for them. (I figure if I run through this really fast they won’t have time to figure any mistakes I made.)

The talk is about Black and White marriage markets, which I’ve written about quite a bit, including when I posted the figure below, showing the extremely low number of local same-race, employed, single men per women Black women experience relative to White women — especially when they have less than a BA degree.

This figure was the basis for a video we made for my book, titled “Why are there so many single Black women?” For years I’ve been supporting the strong (“Wilsonian“) case that low marriage rates for Black women are driven by the shortage of “marriageable” men — living, employed, single, free men. I promised last year that Joanna Pepin and I were working on a paper about this, and we still are. So I’ll present some of this at Princeton.

Predictions off

Five years ago I wrote about the famous 2001 paper by Joshua Goldstein and Catherine Kenney, which made lifetime marriage predictions for cohorts through the Baby Boom, the youngest of whom were only 30 in the 1995 data the paper used. That’s gutsy, predicting lifetime marriage at age 30, so there’s no shame that they missed. They were closer for White women. They predicted that 88.6% of White women born 1960-1964 would eventually marry, and by the age 49-53 (in the 2013 American Community Survey) they were at 90.2%, with another 2.3% likely to marry by my estimates (see below). For Black women they missed by more. For the 1960-1964 cohort, they predicted only 63.8% would ever marry, but 71.3% were already married by 2013, and I’m projecting another 7.5% will marry. (I also wrote about a similar prediction, here.) If they actually get to 79%, that will be very different from the prediction.

Their amazing paper has been cited another 100 times since I wrote about it in 2010, but it doesn’t look like anyone has tried to test or extend their predictions.

Mass incarceration

Interestingly, Goldstein and Kenney undershot Black women’s marriage rates even though incarceration rates continued to rise after they wrote — a trend strongly implicated in the Black-White marriage disparity. This issue has increased salience today, with the release of a powerful new piece by Ta-Nehisi Coates in the Atlantic (my old job), which exposes the long reach of mass incarceration into Black families in ways that go way beyond the simple statistics about “available” men. The large ripple effects implied by his analysis — drawing from his own reporting and research by Deva Pager, Bruce Western, and Robert Sampson — suggest that any statistical model attempting to identify the impact of incarceration on family structure is likely to miss a lot of the action. That’s because people who’ve been out of prison for years are still affected by it, as are their relationships, their communities — and their children in the next generation.

Some new projections

I should note that some readers unfamiliar with demographic analysis may find parts of what follows morbidly depressing.

To set up the marriage market analysis I’m doing with Joanna — which isn’t ready to show here yet — I’m going to introduce some marriage projections at the talk. These use a different method than Goldstein and Kenney, because I have a different kind of data. This is a lifetable approach, in which I use first-marriage rates at every age to calculate how many women would get married at least once before they die if they lived 2010 over and over again from birth to death. I can do this because, unlike Goldstein and Kenney in 2001, I now have the American Community Survey (ACS), which asks a giant sample of people if they have married in the previous year, and how many times they’ve been married before, so I can calculate a first-marriage rate at every age. To this I add in death rates — making what we call a multiple-decrement life table — so that there are two ways out of the birth cohort: marriage or death. (Give me marriage or give me death.)

The way this works is you start with 100,00 people, and each year some of them die and some of them get married — according to the rates you have measured at one point in time. For example, in my tables, of 100,000 Black women at the start of year 0, only 98.7% make it to age 15, the first year they can be counted as married in the data. By the time you get down to age 30, there are only 67,922 left, as 2,236 have died and 29,843 have married for the first time. And so on down to the bottom. In the last row of the table, when they are all dead, you calculate how many got married before dying.*

The bottom line: 85.3% of White women, and 78.4% of Black women born and stuck in 2010 forever are projected to marry before they die — a surprisingly small gap. The first figure shows you that basic result:

NHBW life tables 2010.xlsx

Note that my projections of 85.3% of White women and 78.4% of Black women ever marrying are lower than, for example, the roughly 96% of White women and 91% of Black that were actually ever-married at age 85+ in 2010 (reported here), for several reasons. First, I count dead people against the ever-married number (additionally, married people live longer, not necessarily because they’re married). Second, today’s 90+ year-olds mostly got married 70 years ago, when times were different; my estimates are a projection of nowadays.

A very interesting age pattern emerges here, which is relevant to the incarceration and “available men” question. If you look back at the figure, notice that the big difference in marriage opens up early — peaking at 28 points by age 33, before narrowing to 7 points at the end.The big difference in marriage is that White women marry earlier. In fact, as the next figure shows, after age 33 Black women are more likely to marry than are White women. I don’t think I knew that. Here are the number marrying at each age:

NHBW life tables 2010.xlsx

Specifically, although White women are twice as likely to marry in their mid-twenties, of our fictional 100,000 women stuck in 2010, just 15.6% of White women, compared with 36.8% of Black women end up marrying after age 33.

The other way of looking at this — and an answer to a common question about marriage rates — is to see the chances of marrying after a given age if you haven’t married yet. This figure shows, for example, that a White women who lives to age 45 without marrying has a 26% chance of someday marrying, compared with a whopping 49% for Black women.

NHBW life tables 2010.xlsx

It is surprising that Black women, with lower cumulative odds of marrying at every age in the cohort, are so much more likely to marry conditional on getting to their 40s without marrying. Maybe you’ve got a better interpretation of this, but this is mine. Black women are not against marriage, and they are not ineligible for marriage in some way (even though most of these single women are already mothers**). Rather, they have not married earlier because they couldn’t find someone to marry. That’s because of all the Black men who are themselves dead, incarcerated or unemployed (or scarred by those experiences in their past) — or married to someone else. So within their respective marriage markets (which remain very segregated), the 45-year-old single White woman is much more likely to be someone that either doesn’t want to marry or can’t marry for some reason, while the 45-year-old single Black woman is more active and eligible in the marriage market. This fits with the errors in the earlier predictions, which failed to pick up on the upward shift in marriage age for Black women — marriage delayed rather than foregone.

What do you think of that interpretation? If you have a better idea I’ll mention you at Princeton next week.

Note: I found so many mistakes as I was doing this that it seems impossible there are any more. Nevertheless, caveat emptor: This analysis hasn’t been peer reviewed yet, so consider it only as reliable the latest economist’s NBER paper you read about on the front page of the every newspaper and website on earth. (And if you’re a journalist feel free to refer to this as a new working paper.)

* Technical notes: I used death rates from 2010 (found here), and marriage rates from the five-year ACS file for 2008-2012 (which has 2010 as its midpoint), from IPUMS.org. I adjusted the death rates because never-married people are more likely to die than average (I told you this was depressing). I had to use a 2007 estimate of mortality by age and marital status for that (found here), which is not that precise because it was in 10-year increments, which I didn’t bother to smooth because they didn’t have much effect anyway. The details of how to do a multiple-decrement lifetable are nicely described (with a lot of math) by Sam Preston here (though if you really want to replicate this, note one of his formulas is missing a negative sign, so plan to spend an extra few days on it). To help, I’m sharing my spreadsheet here, which has the formulas. (Note that survival in the life table doesn’t refer to being alive, it refers to being both alive and never-married.) The mortality and marriage rates are for non-Hispanic women; the never-married adjustment is for all women. For the marriage rates I used all Black and White women regardless of what other races they also specified (very few are multiple-race when you exclude Hispanics).

** In 2010, 63% of never-married Black women who lived in their households had at least own of their own children living with them.


Filed under Me @ work

Grandparents day: Still no need to send a card

I felt guilty this afternoon when I noticed a lot of people clicking on this old post about Grandparents Day. I should have updated it sooner. Better late than never, here is the updated trend of children (ages 0-14) living in the households of their grandparents, by poverty status:

children living with grandparents.xlsx

It looks like that near-poor group may have been given a boost by the recession. But the trend is basically upward for everyone.

My comments from a few years ago are still OK:

Interestingly, as the figure shows, the jump in multigenerational living was greatest for the non-poor (those over 200% of the poverty line). In addition to fallout from job losses, one can imagine this includes families displaced by foreclosure and job loss, grandparents who can’t afford to move into retirement communities because they can’t sell their homes, and other complications of the real estate crash.

The children most likely to live with grandparents, however, are the near-poor — those between 100% and 200% of the poverty line. This might include a lot of would-be poor families in which the grandparents are employed, bringing the total family income over the poverty line.

My older research into multigenerational living produced compelling evidence that these arrangements are usually not a first choice in the U.S. these days — because the more money people have, the less likely they are to share housing. Still, the effect of all this could be more intergenerational solidarity and close relationships. But I wouldn’t assume that.


Filed under In the news

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):


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)


Filed under In the news

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.degree i.race i.hispan i.citizen
margins i.agemarc#i.mardurc


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