Tag Archives: divorce

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.

Questions

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.

Data

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!

Sigh

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.

Conclusion

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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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:

top10divoccs

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.

agemar-divorce

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:

newheatmap

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:

educ-div

(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.

Aside

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:

age-ed-mar-count

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:

wolfinger2

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.)

Related:

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Fewer births and divorces, more violence: how the recession affected the American family

I wrote this for The Conversation. Read the original here.

Observers may be quick to declare social trends “good” or “bad” for families, but such conclusions are rarely justified. What’s good for one family – or group of families – may be bad for another. And within families, interests do not always align. Divorce is “bad” for a family in the sense of breaking it apart, but it may be beneficial, or even essential, for one or both partners or their children.

This kind of ambiguity makes it difficult to assess what kind of impact the recent recession and its aftermath had on families. But for researchers, at least, it offers a lot of job security – so many questions, so much going on. In any case, here’s where we stand so far.

The effect of the Great Recession on family trends in the United States has been dramatic with regard to birth rates and divorce, and has been strongly suggestive of family violence, but less clear for marriage and cohabitation.

Marriage rates declined, and cohabitation rates increased, but these trends were already underway, and the recession didn’t alter them much. When trends don’t change direction it’s difficult to identify an effect of a shock this broad. However, with both birth rates and divorce, clear patterns emerged.

Birth rates: a sharp drop
The most dramatic impact was on birth rates, which dropped precipitously, especially for young women, as a result of the economic crisis. How do we know? First, the timing of the fertility decline is very suggestive. After increasing steadily from the beginning of 2002 until late 2007, birth rates dropped sharply. (The decline has since slowed for some groups after 2010, but the US still saw record-low birth rates for teenagers and women ages 20-24 as late as 2012.)

Second, the decline in fertility was steeper in states with greater increases in unemployment. Although we don’t have the data to determine which couple did or did not have a child in response to economic changes, this pattern supports the idea that financial concerns convinced some people to not have a child.

That interpretation is supported by the third trend: the fertility drop was more pronounced among younger women – and there was no drop at all among women over 40. That may mean the fertility decline represents births postponed by families that intend to have children later – an option older women may not have – which fits previous research on economic shocks.

It seems likely that people who are on the fence about having a baby can be swayed by perceived financial hardship or uncertainty. From research on 27 European countries, we know that people with troubled family financial situations are more likely to say they are unsure whether they will meet their stated childbearing goals – that is, economic uncertainty doesn’t change their familial aims but may increase uncertainty in whether they will be met.

However, some births delayed inevitably become births foregone. Based on the effect of unemployment on birth rates in earlier periods, it appears a substantial number of young women who postponed births will end up never having children. By one estimate, women who were in their early 20s during the Great Recession are projected to have some 400,000 fewer lifetime births and an additional 1.5% of them will never have a birth.

Divorce rates: a counter-intuitive reaction
In the case of divorce, the pattern is counter-intuitive. Although economic hardship and insecurity adds stress to relationships and increases the risk of divorce, the overall divorce rate usually drops when unemployment rates rise.

Researchers believe that, like births, people postpone divorces during economic crises because of the costs of divorcing – not just legal fees, but also housing transitions (which were especially difficult in the Great Recession) and employment disruptions.

My own research found that there was a sharp drop in the divorce rate in 2009 that can reasonably be attributed to the recession. But, as we suspect will be the case with births, there appears to have been a divorce-rate rebound in the years that followed.
Domestic violence: a spike along with joblessness
Family violence has become much less common since the 1990s. The reasons are not entirely clear, but they certainly include the overall drop in violent crime, improved response from social service and non-governmental organizations, and improvements in women’s relative economic status. However, when the recession hit there was a spike in intimate-partner violence, coinciding with the sharp rise in men’s unemployment rates (I show the trends here).

As with the other trends, it’s hard to make a case based on timing alone, but the evidence is fairly strong that the economic shock increased family stress and violence. For example, one study showed that mothers were more likely to report spanking their children in the months when consumer confidence fell. Another study found a jump in abusive head trauma cases during the recession in several regions. And there have been many anecdotal and journalist accounts of increases in family violence, emerging as early as 2009. Are these direct results of the economic stress or mere correlation? It’s hard to say for sure.

The ultimate impact of these trends on American families will likely take years to emerge. The recession may have affected the pattern of marriage in ways we don’t yet understand – how couples selected each other, who got married and who didn’t – and may create measurable group of marriages that are marked for future effects as yet unforeseen. Like the young adults who entered the labor market during the period of high unemployment and whose career trajectories will be forever altered unfavorably, how these families bear the scars cannot be predicted. Time will tell.

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First look: 2013 divorce rates show big drop

The 2013 American Community Survey (ACS) is out, and with it the numbers we need to update the divorce rate trend.*

These are the estimates based on the giant sample: In 2013 there were 63,951,934 married men, and  63,619,135 married women. In 2013, 1,071,278 men and 1,197,095 women reported getting divorced in the 12 months prior to their interview. That means the refined divorce rates — divorces per 1,000 married people — were 16.8 for men and 18.8 for women. Wow! Look at the trend now:

divorce rates.xlsxThat’s a very big drop, almost as big as the 2009 drop. What does this mean? It’s too early to say without more investigation, but consider it in light of my analysis of the recession period. Starting with the 2008 data, it looked to me like there was a big drop at the start of the recession — which we figure is related to the costs of divorce (legal fees, real estate, other transition costs) — and then a rebound as the postponed divorces start to materialize.

I didn’t just guess that based on the trends. I used the variables associated with divorce in 2008 (age at marriage, marital duration, education, race/ethnicity, etc.) to predict divorces in the later years, and found that the prediction was higher than the observed number of divorces, suggesting a deficit of realized divorces.

That interpretation might still be true (which is good, because the paper was published less than a year ago). But the ACS marital events (e.g. ,divorce) data only go back to 2008, so it’s difficult to evaluate the 2009 drop in historical perspective. Now we have to wonder: What if 2009 was really closer to the “normal” rate of divorce decline, and really the recession just gave us the 2010-2012 increase, and no drop? I’m not ready to conclude that, of course. My analysis still makes sense. And there is previous research (cited in the paper) that shows declines in divorce during recessions. But 2013 is going to have to be explained somehow.

I have an idea. I’ll just hop over to the government’s official divorce statistics page to compare these ACS numbers with the actual number of divorces recorded. Wait, what?

nodivorcestats

Hm. Well, OK, there’s no “detailed data” from the vital statistics system anymore. But surely there is at least a simple count available? I’ll just click on “National marriage and divorce rate trends,” to get those. Um…

cdcdivrate

So these numbers only go through 2011, and they exclude 6 states, which together account for 20% of all US divorces. Here’s a good data exercise: find another rich country that doesn’t have a count of its own marriages and divorces.

As I have pointed out in increasingly alarmed tones (in this post and earlier), the ACS marital events and marital history questions have been slated for removal by the budget powers that be.** Because if there’s one thing we don’t want to spend money on, it’s information. Why bother? We can just do a Google search or use Big Data to count up #imfinallydivorced hashtags. Yes, I just made that up. But the way things are going we may soon be begging Facebook and Google to tell us what’s going on. As Vonnegut might say, “Good luck, America!”

Notes

* The public use files are up on the IPUMS.org site, and the online analysis tool is available for quick analysis, but for this I used the numbers from the full survey, available on the Census Bureau’s American FactFinder, tables 1YR_B12503 and 1YR_B12001. I realize it’s odd that the rates for men and women here differ (by more than would be possible even if lots of women are divorcing other women). This is a survey question, not a count of legal divorces.

**The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).

Comments will be accepted until December 30.

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Divorce, animated

Next up the in The Story Behind the Numbers series of animations for my book: Divorce (Chapter 10, “Divorce, Remarriage, and Blended Families). Using the demographic characteristics associated with divorce, from my paper here, the artists at Kiss Me I’m Polish set the story in charming abstract creatureville:

Here’s the relevant table from the paper. Positive coefficients mean the variable is associated with increased odds of divorce, negative is the reverse:

divtables

Meanwhile, I have written several posts about the planned cuts to the American Community Survey, which include the questions necessary to conduct this analysis: marital events (did you get divorced in the last year) and marital history (how many times have you been married, and when was the last time).

Here’s the information on how to register your opinion on these cuts:

The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).

Comments will be accepted until December 30, 2014.

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