Tag Archives: divorce

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

kid on string phone in front of computer screen

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.

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.

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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):

YearsMarried

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):
Divorce by age at marriage

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):

Divorce By Age And Duration


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.

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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.

ap1

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job changing effect 2015 ACS-CPS

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.

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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.


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.

t1

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.

f1

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.)

t2

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).

job changing effect 2015 ACS-CPS

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

Cohen, Philip N. 2013. “The Persistence of Workplace Gender Segregation in the US.” Sociology Compass 7 (11): 889–99. http://doi.org/10.1111/soc4.12083.

Cohen, Philip N. 2014. “Recession and Divorce in the United States, 2008–2011.” Population Research and Policy Review 33 (5): 615–28. http://doi.org/10.1007/s11113-014-9323-z.

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.

Kalleberg, Arne L. 2013. Good Jobs, Bad Jobs: The Rise of Polarized and Precarious Employment Systems in the United States 1970s to 2000s. New York, NY: Russell Sage Foundation.

Pugh, Allison J. 2015. The Tumbleweed Society: Working and Caring in an Age of Insecurity. New York, NY: Oxford University Press.

Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0.

Sarah Flood, Miriam King, Steven Ruggles, and J. Robert Warren. Integrated Public Use Microdata Series, Current Population Survey: Version 4.0. [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D030.V4.0.

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Now-you-know data graphic series

As I go about my day, revising my textbook, arguing with Trump supporters online, and looking at data, I keep an eye out for easily-told data short stories. I’ve been putting them on Twitter under the label Now You Know, and people seem to appreciate it, so here are some of them. Happy to discuss implications or data issues in the comments.

1. The percentage of women with a child under age 1 rose rapidly to the late 1990s and then stalled out. The difference between these two lines is the percentage of such women who have a job but were not at work the week of the survey, which may mean they are on leave. That gap is also not growing much anymore, which might or might not be good.

2. In the long run both the dramatic rise and complete stall of women’s employment rates are striking. I’m not as agitated about the decline in employment rates for men as some are, but it’s there, too.

3. What looked in 2007 like a big shift among mothers away from paid work as an ideal — greater desire for part-time work among employed mothers, more desire for no work among at-home mothers — hasn’t held up. From a repeated Pew survey. Maybe people have looked this from other sources, too, so we can tell whether these are sample fluctuations or more durable swings.

4. Over age 50 or so divorce is dominated by people who’ve been married more than once, especially in the range 65-74 — Baby Boomers, mostly — where 60% of divorcers have been married more than once.

 

5. People with higher levels of education receive more of the child support they are supposed to get.

 

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How low is too low for divorce?

I have no idea, but I raised the possibility that there is a too low in this essay for Timeline.

I wrote:

We should ask whether falling divorce rates are always a good thing. Most people getting married would like to think they’ll stay together for the long haul, but what is the right amount of divorce for a society to have?

It seems like an odd question, but divorce really isn’t like crime. Less crime is inarguably good, but we do want some divorces. Otherwise it means people are stuck in bad marriages. If you have no divorce that means even abusive marriages can’t break up. If you have a moderate amount, it means pretty bad marriages can break up but people don’t treat it lightly.

When you put it that way, moderate sounds best. Even as we shouldn’t assume families are always falling apart more than they used to, we should consider the pros and cons of divorce, rather than insisting less is always better.

You can read the whole thing here. In addition to a picture of Donald and Ivana Trump, the piece features my figure:

divtrend

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