Breaking Millennial divorce drop news explained

[With updates as new stories come in.]

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Millennials are fun to disparage.

Phones and selfies are all that they cherish.

And what’s par for the course, they have ruined divorce.

‘Cuz Millennials hang on to their ______.

—Wait Wait Don’t Tell Me, 9/29/18

The divorce paper I posted two weeks ago, “The Coming Divorce Decline,” suddenly took off in the media the other day (blog post | paper | data and code). I’ve now written an op-ed about the findings for The Hill, including this:

I am ambivalent about these trends. Divorce is often painful and difficult, and most people want to avoid it. The vast majority of Americans aspire to a lifelong marriage (or equivalent relationship). So even if it’s a falling slice of the population, I’m not complaining that they’re happy. Still, in an increasingly unequal society and a winner-take-all economy, two-degree couples with lasting marriages may be a buffer for the select few, but they aren’t a solution to our wider problems.

Here’s my media scrapbook, with some comment about open science process at the end.

The story was first reported by Ben Steverman at Bloomberg, who took the time to read the paper, interview me at some length, send the paper to Susan Brown (a key expert on divorce trends) for comment, and produce figures from the data I provided. I was glad that his conclusion focused on the inequality angle from my interpretation:

“One of the reasons for the decline is that the married population is getting older and more highly educated,” Cohen said. Fewer people are getting married, and those who do are the sort of people who are least likely to get divorced, he said. “Marriage is more and more an achievement of status, rather than something that people do regardless of how they’re doing.”

Many poorer and less educated Americans are opting not to get married at all. They’re living together, and often raising kids together, but deciding not to tie the knot. And studies have shown these cohabiting relationships are less stable than they used to be.

Fewer divorces, therefore, aren’t only bad news for matrimonial lawyers but a sign of America’s widening chasm of inequality. Marriage is becoming a more durable, but far more exclusive, institution.

The Bloomberg headline was, “Millennials Are Causing the U.S. Divorce Rate to Plummet.” Which proved irresistible on social media. I didn’t use the terms “millennials” (which I oppose), or “plummet,” but they don’t fundamentally misrepresent the findings.

Naturally, though, the Bloomberg headline led to other people misrepresenting the paper, like Buzzfeed, which wrote, “Well, according to a new study, millennials are now also ‘killing’ divorce.” Neither I nor Bloomberg said anyone was “killing” divorce; that was just a Twitter joke someone made, but Buzzfeed was too metameta to pick up on that. On the other hand, never complain about a Buzzfeed link, and they did link to the paper itself (generating about 800 clicks in a few days).

Then Fox 5 in New York did a Skype interview with me, and hit the bar scene to talk over the results (additional footage courtesy of my daughter, because nowadays you provide your own b-roll):

The next day Today did the story, with additional information and reporting from Bowling Green’s National Center for Family and Marriage Research, and Pew.

The Maryland news office saw the buzz and did their own story, which helped push it out.

An article in Atlantic featured an interview with Andrew Cherlin putting the trends in historical context. Rachelle Hampton in Slate tied the divorce trend to a Brookings report showing marriage is increasingly tied to higher education. On KPCC, AirTalk hosted a discussion with Megan Sweeney and Steven Martin. On Wisconsin Public Radio, Stephanie Coontz widened the discussion to put changes in marriage and divorce in historical perspective.

Rush Limbaugh read from the Bloomberg article, and was just outraged: “Now, who but deranged people would look at it this way?”

How anybody thinks like this… You have to work to be this illogical. I don’t know where this kind of thing comes from, that a plummeting divorce rate is a bad sign for America in the left’s crazy world of inequality and social justice and their quest to make everybody the same. So that’s just an example of the… Folks, that is not… That kind of analysis — and this is a sociology professor at the University of Maryland. This is not stable. That kind of thinking is not… It’s just not normal. Yet there it is, and it’s out there, and it’s be widely reported by the Drive-By Media, probably applauded and supported by others. So where is this coming from? Where is all of this indecency coming from? Why? Why is it so taking over the American left?

The Limbaugh statement might have been behind this voicemail I received from someone who thinks I’m trying to “promote chaos” to “upend the social order”:

I had a much more reasonable discussion about marriage, divorce, and inequality in this interview with Lauren Gilger in KJZZ (Phoenix public radio).

The Chicago Tribune editorial board used the news to urge parents not to rush their children toward marriage:

This waiting trend may disturb older folks who followed a more traditional (rockier?) path and may be secretly, or not so secretly, wondering if there’s something wrong with their progeny. There isn’t. Remember: Unlike previous generations, many younger people have a ready supply of candidates at their fingertips in the era of Tinder and other dating apps. They can just keep swiping right. Our advice for parents impatient to marry off a son or daughter? Relax. The older they get, the less likely you’ll be stuck paying for the wedding.

The Catholic News Agency got an expert to chime in, “If only we could convince maybe more of them to enter into marriage, we’d be doing really well.”

I don’t know how TV or local news work, but somehow this is on a lot of TV stations. Here’s a selection.

Fox Business Network did a pretty thorough job.

Some local stations added their own reporting, like this one in Las Vegas:

And this one in Buffalo:

And this one in Boise, which brought in a therapist who says young people aren’t waiting as long to start couples therapy.

Jeff Waldorf on TYT Nation did an extended commentary, blaming capitalism:

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Open science process

Two things about my process here might concern some people.

The first is promoting research that hasn’t been peer reviewed. USA Today was the only report I saw that specifically mentioned the study is not peer reviewed:

The study, which has not been published in a peer-reviewed journal, has been submitted for presentation at the 2019 Population Association of America meeting, an annual conference for demographers and sociologists to present research.

But, when Steverman interviewed me I emphasized to him that it was not peer-reviewed and urged him to consult other researchers before doing the story — he told me he had already sent it to Susan Brown. Having a good reporter consult a top expert who’s read the paper is as good a quality peer review as you often get. I don’t know everything Brown told him, but the quote he used apparently showed her endorsement of the main findings:

“The change among young people is particularly striking,” Susan Brown, a sociology professor at Bowling Green State University, said of Cohen’s results. “The characteristics of young married couples today signal a sustained decline [in divorce rates] in the coming years.”

For the story to be clear enough to become a news event, the research often has to be pretty simple. That’s the case here: what I’m doing is looking at an easily-identified trend and providing my interpretation of it. If this has to be peer-reviewed, then almost anything an academic says should be. Of course, I provided the publicly verifiable data and code, and there are a lot of people with the skills to check this if it concerned them.

On the other hand, there is a lot of research that is impossible to verify that gets reported. Prominent examples include the Alice Goffman ethnographic book and the Raj Chetty et al. analysis of confidential IRS data. These were big news events, but whether they were peer reviewed or not was irrelevant because the peer reviewers had no way to know if the studies were right. My conclusion is that sharing research is the right thing to do, and sharing it with as much supporting material as you can is the responsible way to do it.

The second concern is over the fact that I posted it while it was being considered for inclusion in the Population Association of America meetings. This is similar to posting a paper that is under review at a journal. Conference papers are not reviewed blind, however, so it’s not a problem of disclosing my identity, but maybe generating public pressure on the conference organizers to accept the paper. This happens in many forms with all kinds of open science. I think we need to see hiding research as a very costly choice, one that needs to be carefully justified — rather than the reverse. Putting this in the open is the best way to approach accountability. Now the work of the conference organizers, whose names are listed in the call for papers, can be judged fairly. And my behavior toward the organizers if they reject it can also be scrutinized and criticized.

Although I would love to have the paper in the conference, in this case I don’t need this paper to be accepted by PAA, as it has already gotten way more attention than I ever expected. PAA organizers have a tough job and often have to reject a lot of papers for reasons of thematic fit as well as quality. I won’t complain or hold any grudges if it gets rejected. There’s a lot of really good demography out there, and this paper is pretty rudimentary.

The coming divorce decline

Unless something changes outside the demogosphere, the divorce rate is going to go down in the coming years.

Divorce represents a number of problems from a social science perspective.

• • Most people seem to assume “the divorce rate” is always going up, compared with the good old days, which are supposed to be the whole past but are actually represented by the anomalous 1950s.
  • On other hand, social scientists have known for a few decades that “the divorce rate” has actually been declining since the 1980s. That shows up in the official statistics, with the simple calculation — known as the refined divorce rate — of the number of divorces per 1,000 married women.
  • On the third hand, the official statistics are very flawed. The federal system, which relies on states voluntarily coughing up their divorce records, broke down in the 1990s and no one fixed it (hello, California doesn’t participate). In the debate over different ways of getting good answers, a key 2014 paper from Sheela Kennedy and Stephen Ruggles showed that the decline in divorce after 1980 was mostly because the whole married population was getting older, and older people get divorced less. That refined divorce rate doesn’t account for age patterns. When you remove the age patterns from the data, you see a continuously increasing divorce rate. Yikes!
  • On the fourth hand, Kennedy and Ruggles stopped in about 2010. Since then, the very divorce-prone, multi-marrying, multi-divorcing Baby Boomers have moved further out of their peak action years, and it’s increasingly clear that divorce rates really are falling for younger people.

In my new analysis, which I wrote up as a short paper for submission to the Population Association of America 2019 meetings, I argue that all signs point to a divorce decline in the coming years. Here is the paper on SocArXiv, where you will also find the data and code. And here is the story, in figures (click to enlarge).

1. The proportion of married women who divorce each year has fallen 18% in the decade after 2008. (There are reasons to do this for women — some neutral, some good, some bad — but one good thing nowadays is at least this includes women divorcing women.) And when you control for age, number of times married, years married, education, race/ethnicity, and nativity, it has still fallen 8%.

2. The pattern of increasing divorce at older ages, described by Susan Brown and I-Fen Lin as gray divorce, is no longer apparent. In the decade after 2008, the only apparent change in age effects is the decline at younger ages, holding other variables constant.

3. The longer term trends, identified by Kennedy and Ruggles, which I extend to 2016, show that the upward trajectory is all about older people. These are prevalences (divorced people in the population), not divorce rates, but they are good for illustrating this trend.

4. In fact, when you look just at the last decade, all of the decline in age-specific divorce rates is among people under age 45. This implies there will be more older people who have been married a long time, which means low divorce rates. Also, their kids won’t be as likely to have divorced parents, although more kids will have parents who aren’t married, which might work in the other direction. (You can ignore then under-20s, who are 0.2% of the total.)

5. Finally, to get a glimpse of the future, I looked at women who report getting married in the year before the survey, and how they have changed between 2008 and 2016 on traits associated with the risk of divorce. They clearly show a lower divorce-risk profile. They are more likely to be in their first marriage, to have college degrees, to be older, and to have no children in their households (race/ethnicity appears to be a wash, with fewer Whites but more Latinas).

6. Finally finally, I also looked at the spouses of the newly-married women, and made an arbitrary divorce-protection scale, with one point to each couple for each spouse who was: age 30 or more, White or Hispanic, BA or higher education, first marriage, and no own children. Since 2008 the high scale scores have become more common and the low scores have become rarer.

7. It’s interesting that the decline in divorce goes against the (non-expert) conventional wisdom. And it is happening at a time when public acceptance of divorce has reached record levels (which might be part of why people think it’s growing more common — less stigma). Here are the trends in attitudes from Pew and Gallup:

That’s my story — thanks for listening!

Visualizing family modernization, 1900-2016

After this post about small multiple graphs, and partly inspired by two news reports I was interviewed for — this Salt Lake Tribune story about teen marriage, and this New York Times report mapping age at first birth — I made some historical data figures.

These visualizations use decennial census data from 1900 to 1990, and then American Community Survey data for 2001, 2010, and 2016; all data from IPUMS.org. (I didn’t use the 2000 Census because marital status is messed up in that data, with a lot of people who should be never married coded as married, spouse absent; 2001 ACS gets it done.)

An important, simple way of illustrating the myth-making around the 1950s is with marriage age. Contrary to the myth that the 1950s was “traditional,” a long data series show the period to be unique. The two trends here, teen marriage and divorce, both show the modernization of family life, with increasing individual self-determination and less restricted family choices for women.

First, I show the proportion of teenage women married in each state, for each decade from 1900 to 2016. The measure I used for this is the proportion of 19- and 20-year-olds who have ever been married (that is, including those married, divorced, and widowed). It’s impossible to tell exactly how many people were married before their 20th birthday, which would be a technical definition of teen marriage, but the average of 19 and 20 should do it, since it includes some people are on the first day of their 19th year, and some people are on the last day of their 20th, for an average close to exact age 20.

I start with a small multiple graph of the trend on this measure in every state (click all figures to enlarge). Here the states are ordered by the level of teen marriage in 2016, from Maine lowest (<1%) to Utah (14%):

This is useful for seeing that the basic pattern is universal: starting the century lower and rising to a peak in 1960, then declining steeply to the present. But that similarity, and smaller range in the latest data, make it hard to see the large relative differences across states now. Here are the 2016 levels, showing those disparities clearly:

Neither the small multiples nor the bars help you see the regional patterns and variations. So here’s an animated map that shows both the scale of change and the pattern of variation.

This makes clear the stark South/non-South divide, and how the Northeast led the decline in early marriage. Also, you can see that Utah, which is such a standout now, did not have historically high teen marriage levels, the state just hasn’t matched the decline seen nationally. Their premodernism emerged only in relief.

Divorce

Here I again used a prevalence measure. This is just the number of people whose marital status is divorced, divided by the number of married people (including separated and divorced). It’s a little better than just the percentage divorced in the population, because it’s at least scaled by marriage prevalence. But it doesn’t count divorces happening, and it doesn’t count people who divorced and then remarried (so it will under-represent divorce to the extent that people remarry). Also, if divorced people die younger than married people, it could be messed up at older ages. Anyway, it’s the best thing I could think of for divorce rates by state all the way back to 1900.

So, here’s the small multiple graph, showing the trend in divorce prevalence for all states from 1900 to 2016:

That looks like impressive uniformity: gradual increase until 1970, then a steep upward turn to the present. These are again ordered by the 2016 value, from Utah at less than 20% to New Mexico at more than 30% — smaller variation than we saw in teen marriage. That steep increase looks dramatic in the animated map, which also reveals the regional patterns:

Technique

The strategy for both trends is to download microdata samples from all years, then collapse the files down to state averages by decade. The linear figures are Stata scatter plots by state. The animated maps use maptile in Stata (by Michael Stepner) to make separate image files for each map, which I then imported into Photoshop to make the animations (following this tutorial).

The downloaded data, codebooks, Stata code, and images, are all available in an Open Science Framework project here. Feel free to adapt and use. Happy to hear suggestions and alternative techniques in the comments.

How I engaged my way to excellent research success and you can too

Kid photo CC from MB Photography; collage by pnc.

Too often sociologists think of social media, or online communications generally, primarily as a way of broadcasting their ideas and building their audience, instead of as a way of deepening their engagement with different people and perspectives. You see this when academics start a twitter account right when their book is coming out. Nothing wrong with that, but it’s very limited. A crucial part of being a public scholar, public intellectual, or a public sociologist, etc., is reading, listening, and learning through engagement, and digital communication can enhance the metabolism of that process. Especially important is the chance to learn from people you don’t normally interact with. For all the complaints about social media bubbles, some true, social media also offers huge efficiencies for meeting and learning from new people.

As I’m writing an essay about this, I thought of my work on divorce as an example. So here’s that thread, condensed.

A divorce story

In 2008 I was teaching an undergraduate Family Sociology course at the University of North Carolina, and included a section on divorce based on other people’s research. I was also developing a proposal for my own textbook, which at the time framed family structures and events, including divorce, as consequences and causes of inequality. I was reading research about divorce along with many other family issues that were outside of my formal training and experience (the closest I had come to a family demography or family sociology course was a seminar on Gender, Work & Family in grad school).

Then in 2009, I wrote a post on my pretty new blog criticizing something bad the Brad Wilcox had written about divorce. I was trying to be newsy and current, and he was claiming that the recession was lowering divorce rates because hard times pulled people together. We didn’t yet know what would happen in the recession. (In the comments, Louise Roth suggested it would take time for divorces “caused” by the recession to show up, which turned out to be true.)

I kept on that path for a while, criticizing Wilcox again for similar work in 2011. By then — prompted by the combination of my reading, the blog debates, and the news coverage around families and the recession — I was working on a paper on divorce using the American Community Survey. I presented it at a demography meeting in the summer of 2011, then revised and presented it at the Population Association of America the following spring. I blogged about this a couple more times as I worked on it, using data on state variation, and Google searches, each time getting feedback from readers.

A version of the paper was rejected by Demography in the summer of 2011 (which generated useful reviews). Although now discredited as not peer-review-publishable (which no one knew), my commentary on divorce and the recession was nevertheless featured in an NPR story by Shankar Vedantam. Further inspired, I sent a new version of the paper (with new data) to Demographic Research, which also rejected it. I presented on the work a couple of times in 2012, getting feedback each time. By August 2012, with the paper still not “published,” I was quoted describing my “divorce/recession lull-rebound hypothesis” in New York magazine.

The news media pieces were not simply my work appearing in the news, in a one-directional manner, or me commenting on other people’s research, but rather me bringing data and informed commentary to stories the reporters were already working on. Their work influenced my work. And all along that news coverage was generating on- and offline conversations, as I found and shared work by other people working on these topics (like the National Center for Marriage and Family Research, and the Pew Research Center). Looking back over my tweets about divorce, I see that I covered divorce and religion, disabilities, economics, and race/ethnic inequality, and also critiqued media coverage. (Everything also got discussed on Facebook, in a smaller semi-private circle.)

By 2014 I finally got the paper — now with even newer data — published in a paywalled peer-reviewed journal, in Population Research and Policy Review. This involved writing the dreaded phrase, “Thank you very much for the opportunity to revise this paper again.” (Submitted October 2012, revision submitted August 2013, second revision submitted January 2014, final revision April 2014.) The paper, eventually titled, “Recession and Divorce in the United States, 2008-2011,” did improve over this time as new data provided better leverage on the question, and the reviewers actually made some good suggestions.

Also in 2014 the descriptive analysis was published in my textbook. The results were reported here and there, and expanded into the general area of family-recession studies, including this piece in the Conversation. I also developed a method of projecting lifetime divorce odds (basically 50%), for which I shared the data and code, which was reported on here. Along the way I also did some work on job characteristics and divorce (data and code, working paper). When I posted technical notes, I got interesting responses from people like economist Marina Adshade, whom I’ve never met.

From my presentation, “The open scholarship media strategy for maximum success.”

So that’s an engagement story that includes teaching, the blogosphere and social media, news media, peer-reviewed publishing, conference presentations and colloquium talks. I did research, but also argued about politics and inequality, and taught and learned demography. It’s not a story of how I used social media, or the news media, to get the word out about my research, although that happened, too. The work product, not just the “publications,” were all public to varying degrees, and the discussions included all manner of students, sociologists, reporters, and interested blog or Twitter readers, most of whom I didn’t know or wouldn’t have met any other way.

So I can’t draw a line dividing the “engagement” and the “research,” because they weren’t separate processes.

For social relationships outside marriage

Stephanie Coontz has a great piece in tomorrow’s New York Times titled, “For a Better Marriage, Act Like a Single Person.” From her intro:

Especially around Valentine’s Day, it’s easy to find advice about sustaining a successful marriage, with suggestions for “date nights” and romantic dinners for two. But as we spend more and more of our lives outside marriage, it’s equally important to cultivate the skills of successful singlehood. And doing that doesn’t benefit just people who never marry. It can also make for more satisfying marriages.

From there she develops the case with, as usual, a lot of the right research. Well worth a read.

Stephanie used two empirical bits from my work:

No matter how much Americans may value marriage, we now spend more time living single than ever before. In 1960, Americans were married for an average of 29 of the 37 years between the ages of 18 and 55. That’s almost 80 percent of what was then regarded as the prime of life. By 2015, the average had dropped to only 18 years.

In many ways, that’s good news for marriages and married people. Contrary to some claims, marrying at an older age generally lowers the risk of divorce. It also gives people time to acquire educational and financial assets, as well as develop a broad range of skills — from cooking to household repairs to financial management — that will stand them in good stead for the rest of their lives, including when a partner is unavailable.

The first figure, the average years spent in marriage between the ages of 18 and 55 is very easy to calculate. You just sum the proportion of people married at each age. Here’s what it looks like, comparing 1960 (from the decennial Census) and 2015 (from the American Community Survey), both from IPUMS.org (click to enlarge):

I think it’s a nice, simple way to show the declining footprint of marriage in American life. (I first did this, and described in the rationale, in 2010.)

The bit about older age at marriage being associated with lower odds of divorce is from this post. Here’s the result, showing odds of divorce in one year by age at marriage, with controls for duration, education, race/ethnicity, and nativity, for women in their first marriages (click to enlarge):

There’s more discussion in the post, as well as in this followup post, which has this cool figure, where red is the highest odds of divorce and green is the lowest, and the axes are years married and age at marriage (click to enlarge):

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My new book is out! Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. Available all the usual places, plus here at the University of California Press, where Chapter 1 is available as a sample, and where instructors can request a review copy.

Sixteen minutes on The Tumbleweed Society

At the American Sociological Association conference, just concluded, I was on an author-meets-critics panel for Alison Pugh’s book, The Tumbleweed Society: Working and Caring in an Age of Insecurity. The other day I put up a short paper inspired by my reading, on SocArXiv (data and code here).

Here is my talk itself, in an audio file, complete with 6 seconds of music at the beginning and the end, and a lot of the ums and tangents taken out, running 16 minutes. Download it here, or listen below. And below that are the figures I reference in the talk, but you won’t really need them.

Figure 2. Average predicted probability of divorce within jobs (from logistic model in Table 2), by turnover rate. Markers are scaled according to sample size, and the linear regression line shown is weighted by sample size.

Job turnover and divorce (preconference preprint)

As I was prepared to discuss Alison Pugh’s interesting and insightful 2015 book, The Tumbleweed Society: Working and Caring in an Age of Insecurity, on an author-meets-critics panel at the American Sociological Association meetings in Montreal next week (Monday at 4:30), I talked myself into doing a quick analysis inspired by the book. (And no, I won’t hijack the panel to talk about this; I will talk about her book.)

From the publisher’s description:

In The Tumbleweed Society, Allison Pugh offers a moving exploration of sacrifice, betrayal, defiance, and resignation, as people adapt to insecurity with their own negotiations of commitment on the job and in intimate life. When people no longer expect commitment from their employers, how do they think about their own obligations? How do we raise children, put down roots in our communities, and live up to our promises at a time when flexibility and job insecurity reign?

Since to a little kid with a hammer everything looks like a nail, I asked myself yesterday, what could I do with my divorce models that might shed light on this connection between job insecurity and family commitments? The result is a very short paper, which I have posted on SocArXiv here (with supporting data and code in the associated OSF project shared here). But here it is in blog form; someday maybe I’ll elaborate it into a full paper.

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Job Turnover and Divorce

Introduction

In The Tumbleweed Society, Pugh (2015) explores the relationship between commitments at work – between employers and employees – and those at home, between partners. She finds no simple relationship such that, for example, people who feel their employers owe them nothing also have low commitment to their spouses. Rather, there is a complex web of commitments, and views of what constitutes an honorable level of commitment in different arenas. This paper is inspired by that discussion, and explores one possible connection between work and couple stability, using a new combination of data from the Current Population Survey (CPS) and the American Community Survey (ACS).

In a previous paper I analyzed predictors of divorce using data from the ACS, to see whether economic indicators associated with the Great Recession predicted the odds of divorce (Cohen 2014). Because of data limitations, I used state-level indicators of unemployment and foreclosure rates to test for economic associations. Because the ACS is cross-sectional, and divorce is often associated with job instability, I could not use individual-level unemployment to predict individual-divorce, as others have done (see review in Cohen 2014). Further, the ACS does not include any information about former spouses who are no longer living with divorced individuals, so spousal unemployment was not available either.

Rather than examine the association between individual job change and divorce, this paper tests the association between turnover at the job level and divorce at the individual level. It asks, do people who work in jobs that people are likely to leave themselves more likely to divorce? The answer – which is yes – suggests possible avenues for further study of the relationship between commitments and stressors in the arenas of paid work and family stability. Job here turnover is a contextual variable. Working in a job people are likely to leave may simply mean people are exposed to involuntary job changes, which is a source of stress. However, it may also mean people work in an environment with low levels of commitment between employers and employees. This analysis can’t differentiate potential stressors versus commitment effects, or identify the nature (and direction) of commitments expressed or deployed at work or within the family. But it may provide motivation for future research.

Do job turnover and divorce run together?

Because individual (or spousal) job turnover and employment history are not available in the ACS, I use the March CPS, obtained from IPUMS (Flood et al. 2015), to calculate job turnover rates for simulated jobs, identified as detailed occupation-by-industry cells (Cohen and Huffman 2003). Although these are not jobs in the sense of specific workplaces, they provide much greater detail in work context than either occupation or industry alone, allowing differentiation, for example, between janitors in manufacturing establishments versus those in government offices, which are often substantially different contexts.

Turnover is identified by individuals whose current occupation and industry combination (as of March) does not match their primary occupation and industry for the previous calendar year, which is identified by a separate question (but using the same occupation and industry coding schemes). To reduce short-term transience, this calculation is limited to people who worked at least 20 weeks in the previous year, and more than 20 hours per week. Using the combined samples from the 2014-2016 CPS files, and restricting the sample to previous-year job cells with at least 25 respondents, I end up with 927 job cells. Note that, because the cells are national rather than workplace-specific, the size cutoff does not restrict the analysis to people working in large workplaces, but rather to common occupation-industry combinations. The job cells in the analysis include 68 percent of the eligible workers in the three years of CPS data.

For descriptive purposes, Table 1 shows the occupation and industry cells with the lowest and highest rates of job turnover from among those with sample sizes of 100 or more. Jobs with low turnover are disproportionately in the public sector and construction, and male-dominated (except schoolteachers); they are middle class and working class jobs. The high-turnover jobs, on the other hand, are in service industries (except light truck drivers) and are more female-dominated (Cohen 2013). By this simple definition, high-turnover jobs appear similar to precarious jobs as described by Kalleberg (2013) and others.

Although the analysis that follows is limited to the CPS years 2014-2016 and the 2015 ACS, for context Figure 1 shows the percentage of workers who changed jobs each year, as defined above, from 1990 through 2016. Note that job changing, which is only identified for employed people, fell during the previous two recessions – especially the Great Recession that began in 2008 – perhaps because people who lost jobs would in better times have cycled into a different job instead of being unemployed. In the last two years job changing has been at relatively high levels (although note that CPS instituted a new industry coding scheme in 2014, with unknown effects on this measure). In any event, this phenomenon has not shown dramatic changes in prevalence for the past several decades.

Figure 1. Percentage of workers (20+ weeks, >20 hours per week) whose jobs (occupation-by-industry cells) in March differed from their primary job in the previous calendar year.

Using the occupation industry codes from the CPS and ACS, which match for the years under study, I attach the job turnover rates from the 2014-2016 CPS data to individuals in the 2015 ACS (Ruggles et al. 2015). The analysis then uses the same modeling strategy as that used in Cohen (2014). Using the marital events variables in the ACS (Cohen 2015), I combine people, age 18-64, who are currently married (excluding those who got married in the previous year) and those who have been divorced in the previous year, and model the odds that individuals are in the divorced group. In this paper I essentially add the job turnover measure to the basic analysis in Cohen (2014, Table 3) (the covariates used here are the same except that I added one category to the education variable).

One advantage of the ACS data structure is that the occupation and industry questions refer to the “current or most recent job,” so that people who are not employed at the time of the survey still have job characteristics recorded. Although that has the downside of introducing information from jobs in the distant past for some respondents, it has the benefit of including relevant job information for people who may have just quit (or lost) jobs as part of the constellation of events involved in their divorce (for example, someone who divorces, moves to a new area, and commences a job search). If job characteristics have an effect on the odds of divorce, this information clearly is important. The ACS sample size is 581,891, 1.7 percent of whom reported having divorced in the previous year.

Results from two multivariate regression analyses are presented in Table 2. The first model predicts the turnover rate in the ACS respondents’ job, using OLS regression. It shows that, ceteris paribus, turnover rates are higher in the jobs held by women, younger people (the inflection point is at age 42), people married more recently, those married few times, those with less than a BA degree, Blacks, Asians, Hispanics, and immigrants. Thus, job turnover shows patterns largely similar to labor market advantage generally.

Most importantly for this paper, divorce is more likely for those who most recent job had a higher turnover rate, as defined here. In a reduced model (not shown), with just age and sex, the logistic coefficient on job turnover was 1.39; the addition of the covariates in Table 2 reduced that effect by 39 percent, to .84, as shown in the second model. Beyond that, job turnover is predicted by some of the same characteristics as those associated with increased odds of divorce. Divorce odds are lower after age 25, with additional years of marriage, with a BA degree, and for Whites. However, divorce is less common for Hispanics and immigrants. (The higher divorce rates for women in the ACS are not well understood; this is a self-reported measure, not a count of administrative events.)

To illustrate the relationship between job turnover and the probability of divorce, Figure 2 shows the average predicted probability of divorce (from the second model in Table 2) for each of the jobs represented, with markers scaled according to sample size and a regression line similarly weighted. Below 20 percent job turnover, people are generally predicted to have divorce rates less than 2 percent per year, with predicted rates rising to 2.5 percent at high turnover rates (40 percent).

Figure 2. Average predicted probability of divorce within jobs (from logistic model in Table 2), by turnover rate. Markers are scaled according to sample size, and the linear regression line shown is weighted by sample size.

Conclusion

People who work in jobs with high turnover rates – that is, jobs which many people are no longer working in one year later – are also more likely to divorce. A reading of this inspired by Pugh’s (2015) analysis might be that people exposed to lower levels of commitment from employers, and employees, exhibit lower levels of commitment to their own marriages. Another, noncompeting explanation would be that the stress or hardship associated with high rates of job turnover contributes to difficulties within marriage. Alternatively, the turnover variable may simply be statistically capturing other aspects of job quality that affect the risk of divorce, or there are individual qualities by which people select into both jobs with high turnover and marriages likely to end in divorce. This is a preliminary analysis, intended to raise questions and offer some avenues for analyzing these questions in the future.

References

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Cohen, Philip N. 2015. “How We Really Can Study Divorce Using Just Five Questions and a Giant Sample.” Family Inequality. July 22. https://familyinequality.wordpress.com/2015/07/22/how-we-really-can-study-divorce/.

Cohen, P. N., and M. R. L. Huffman. 2003. “Individuals, Jobs, and Labor Markets: The Devaluation of Women’s Work.” American Sociological Review 68 (3): 443–63. http://doi.org/10.2307/1519732.

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