Tag Archives: divorce

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.

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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.

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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.

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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.)

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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:

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Vasectomy reversal, divorce, and American optimism

That would be a good title for a longer essay (feel free to use it).

“Studies suggest that up to 6% of such men will request vasectomy reversal,” wrote the authors of a chapter in Clinical Care Pathways in Andrology. “Divorce with remarriage is by far the most common reason for vasectomy reversal.”

So, when in the divorce process do people start Googling “vasectomy reversal”? Is it men with younger girlfriends, considering leaving their wives? Women considering marrying a divorced man? Divorced couples considering another round of kids?

I don’t know, but Google does, or they could if they looked into it. I’ve only gotten as far as the strong relationship between searches for “vasectomy reversal” and state divorce rates:

vasectomy-divorce

I like to think of it as the optimism rooted in the American spirit. We always look forward to the next renewal, the next reboot, rebranding, or escape. Not because I really think it’s true, I just like to think of it that way.

The Google data is from their Trends tool, the divorce data is from the ACS via IPUMS.org.)


(This is a return to an old post, in which I first noticed this relationship, with new data. Now I think divorce per population makes more sense for Google correlations, rather than divorce per married population, which I used before.)

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The liberalization of divorce attitudes proceeds apace

The 2016 Gallup poll results on what is morally acceptable versus morally wrong came out over the summer, and they show that U.S. attitudes toward divorce continue to grow more positive. The acceptable attitude has gained 5 points in the last 5 years:

divorcegallup

This parallels results from the General Social Survey, which asks, “Should divorce in this country be easier or more difficult to obtain than it is now?” The latest GSS is still 2014, but it also shows a marked increase in the liberal easier view over the same time period:

divorcegss

See more under the divorce tag.

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