Category Archives: Me @ work

New books on the block: Enduring Bonds, The Family (2e), Contexts (3e)

Suddenly I have news on three books to offer.

My brand new book is called Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. It is in press at the University of California Press, to be published in February (order from UCPress or Amazon).

EnduringBonds-cover

It’s a collection of essays that originated on this blog, all substantially revised and updated. For several chapters this meant combining posts in a series to make a longer essay, including those on sexual dimorphism in popular culture, marriage promotion, parenting and children’s names, and the Regnerus Affair.

The title comes from Anthony Kennedy’s Obergefell decision (at the suggestion of Judy Ruttenberg, my wife and the one with two history degrees, who knows about pulling titles out of primary sources). It’s about the good and the bad of bonds. From the introduction:

Kennedy wrote, “The nature of marriage is that, through its enduring bond, two persons together can find other freedoms, such as expression, intimacy, and spirituality.” It took the late justice Antonin Scalia, a conservative Catholic, to point out that marriage isn’t really about freedom. In his furious dissent, Scalia mocked the idea that people find “freedoms” in the “enduring bond” of marriage. “One would think Freedom of Intimacy is abridged rather than expanded by marriage,” he scoffed. “Ask the nearest hippie.” Scalia had a point.

I hope you like it, for you or your students.

The Family (2e)

Also on the block is the second edition of The Family: Diversity, Inequality, and Social Change. It’s also in press, available for adoption next fall (I don’t recommend ordering right now from Amazon, but you can check the book page at Norton for exam copies and instructional materials). In addition to integrating marriage equality throughout the book, and hundreds of updated references, the new edition benefits from reviews by many instructors compiled by Norton, leading to more material on gender identity, aging and old people, and role of technology. I also wrote a “trend to watch” feature for each chapter, with data-driven speculation about the future for classroom discussions. Norton will release it with their new InQuizitive instructional tool, which is state-of-the-art pedagogy. The new edition was a lot of work (for a lot of people) but I think it was worth it.

2eCover

The Contexts Reader (3e)

Finally, as we wind down our editorial tenure (sniff!) the editorial team of Syed Ali, Letta Page, and me have produced a new edition of the Contexts Reader, also with Norton (order info). It’s more than 60 of our favorite pieces from the magazine we’ve been editing for the last three years, most of them new for this edition (and with a beautiful cover photo from Scott Matthews, who has provided most of our cover images). Undergraduates are a huge part of the Contexts readership, and we’re super proud that this book has been a big part of thousands of students’ introductions to sociology. (Also, the royalties from this one go to the American Sociological Association, not us!)

reader3ecover

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Science finds tiny things nowadays (Malia edition)

We have to get used to living in a world where science — even social science — can detect really small things. Understanding how important really small things are, and how to interpret them, is harder nowadays than just finding them.

Remember when Hanna Rosin wrote this?

One of the great crime stories of the last twenty years is the dramatic decline of sexual assault. Rates are so low in parts of the country — for white women especially — that criminologists can’t plot the numbers on a chart.

Besides being wrong about rape (it has declined a lot, but it’s still high compared with most countries), this was a funny statement about science (I’ve heard we can even plot negative numbers now!). But the point is we have problems understanding, and communicating about, small things.

So, back to names.

In 2009, the peak year for the name Malia in the U.S., 1,681 girls were given that name, according to the Social Security Administration, or .041% of the 4.14 million children born that year (there are no male Malias in the SSA’s public database, meaning they have never recorded more than 4 in one year). That year, 7.5% of women ages 18-44 had a baby. If my arithmetic is right, say you know 100 women ages 18-44, and each of them knows 100 others (and there is no overlap in your network). That would mean there is a 30% chance one of your 10,000 friends of a friend had a baby girl and named her Malia in 2009. But probably there is a lot of overlap; if your friend-of-friend network is only 1,000 women 18-44 then that chance would fall to 3%.

Here is the trend in girls named Malia, relative to the total number of girls born, from 1960 to 2016:

names.xlsx

To make it easier to see the Malias, here is the same chart with the y-axis on a log scale.

names.xlsx

This shows that Malia has been on a long upward trend, from less than 50 per year in the 1960s to more than 1,000 per year now. And it also shows a pronounced spike in 2009, the year Malia peaked .041%. In that year, the number of people naming daughters Malia jumped 75% before declining over the next three years to resume it’s previous trend. Here is the detail on the figure, just showing the Malia in 2005-2016:

names.xlsx

What happened there? We can’t know for sure. Even if you asked everyone why they named their kid what they did, I don’t know what answers you would get. But from what we know about naming patterns, and their responsiveness to names in the news (positive or negative), it’s very likely that the bump in 2009 resulted from the high profile of Barack Obama and his daughter Malia, who was 11 when Obama was elected.

What does a causal statement like that that really mean? In 2009, it looks to me like about 828 more people named their daughters Malia than would have otherwise, taking into account the upward trend before 2008. Here’s the actual trend, with a simulated trend showing no Obama effect:

names.xlsx

Of course, Obama’s election changed the world forever, which may explain why the upward trend for Malia accelerated again after 2013. But in this simple simulation, which brings the “no Obama” trend back into line with the actual trend in 2014, there were 1,275 more Malias born than there would have been without the Obama election. This implies that over the years 2008-2013, the Obama election increased the probability of someone naming their daughter Malia by .00011, or .011%.

That is a very small effect. I think it’s real, and very interesting. But what does it mean for anything else in the world? This is not a question of statistical significance, although those tools can help. (These names aren’t a probability sample, it’s a list of all names given.) So this is a question for interpreting research findings now that we have these incredibly powerful tools, and very big data to analyze with them. The number alone doesn’t tell the story.

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Teach it! Family syllabus supplements for Fall 2017

This year we were working on the second edition of my book The Family: Diversity, Inequality, and Social Change, which will be out in 2018. And my new book, a collection of essays, will also be out for Spring: Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible, from University of California Press. But I’ve still produced a few blog posts this year, so I can provide an updated list of potential syllabus supplements for this fall.

In addition to the excellent teaching materials to support The Family from Norton, there is also an active Facebook group for sharing ideas and materials (instructors visit here). And then I provide a list of blog posts for family sociology courses (for previous lists, visit the teaching page). So here are some new, and some old, organized by topic. As always, I appreciate your feedback.

1. Introduction

2. History

3. Race, ethnicity, and immigration

4. Social class

5. Gender

6. Sexuality

7. Love and romantic relationships

  • Is dating still dead? The death of dating is now 50 years old, and its been eulogized so many times that its feelings are starting to get hurt.
  • Online dating: efficiency, inequality, and anxiety: I’m skeptical about efficiency, and concerned about inequality, as more dating moves online. Some of the numbers I use in this post are already dated, but this could be good for a debate about dating rules and preferences.
  • Is the price of sex too damn low? To hear some researchers tell it in a recent YouTube video, women in general — and feminism in particular — have ruined not only sex, but society itself. The theory is wrong. Also, they’re insanely sexist.

8. Marriage and cohabitation

9. Families and children

10. Divorce, remarriage, and blended families

11. Work and families

12. Family violence and abuse

13. The future of the family

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Donald is not the biggest loser (among winning and losing names)

From 2015 to 2016 there was a 10% drop in U.S. boys given the name Donald at birth, from 690 to 621, plunging the name from 900th to 986th in the overall rankings. Here is the trend in Donalds born from 1880 to 2016, shown on a log scale, from the Social Security names database.

donald-name-trend

That 2016 drop is relatively big in percentage terms, but it’s been dropping an average of 6% per year since 1957 (it dropped 26% in the 8 years after the introduction of Donald Duck in 1934). I really wish it was a popular name so we could more easily see if the rise of Donald Trump is a factor in this. With so few new Donalds, and the name already trending downward, there’s no way to tell if Trump fanatics may be counterbalancing regular people turned off to the name.

Stability over change

How big is a fall of 69 births, which seems so trivial in relation to the 3.9 million children born last year? Among names with more than 5 births in each year, only 499 fell more, compared with 26,052 that fell less or rose. So Donald is definitely a loser.

But I am always amazed at how little change there is in most names from year to year. It sounds obvious to describe a trend as rising or falling, but names are scarily regular in their annual changes given that the statistics from one year to the next reflect independent decisions by separate people who overwhelmingly don’t know each other.

Here is away of visualizing the change in the number of babies given each name, from 2015 to 2016. There is one dot for each name. Those below the diagonal had a decrease in births, those above had an increase; the closer to the line the less change there was. (To adjust for the 1% drop in total births, these are shown as births per 1,000 total born.)

2015-2016 count change

No name had a change of more than 1700 births this year (Logan dropped 1697, a drop of 13%; Adeline increased 1700, or 71%). There just isn’t much movement. I find that remarkable. (Among top names, James stands out this year: 14,773 born in 2015, rising by 3 to 14,776 in 2016.)

Here’s a look at the top right corner of that figure, just showing names with 3 per 1,000 or more births in either 2015 or 2016:

2015-2016 count change 3per1000

Note that most of these top names became less popular in 2016 (below the diagonal). That fits the long-term trend, well known by now, for names to become less popular over time, which means name diversity is increasing. I described that in the history chapter of my textbook, The Family; and going back to this old blog post from 2011. (This great piece by Tristan Bridges explores why there is more diversity among female names, as you can see by the fact that they are outnumbered among the top names shown here.)

Anyway, since I did it, here are the top 20 winners and losers, in numerical terms, in 2016. Wow, look at that catastrophic 21% drop in girls given the name Alexa (thanks, Amazon). I don’t know what’s up with Brandon and Blake. Your explanations will be as good as mine for these.

namewinners

namelosers

For the whole series of name posts on this blog, follow the names tag, including a bunch on the name Mary


Here’s the Stata code I used (not including the long-term Donald trend), including the figure and tables. The dataset is in a zip file at Social Security, here. There is a separate file for each year. The code below runs on the two latest files: yob2015.txt and yob2016.txt.

clear
import delimited [path]\yob2016.txt
sort v2 v1
rename v3 count16
save "[path]\n16.dta", replace
clear
import delimited [path]\yob2015.txt
sort v2 v1
rename v3 count15
merge 1:1 v2 v1 using [path]\n16.dta
drop _merge

gen pctchg = 100*(count16-count15)/count15
drop if pctchg==. /* drops cases that don't appear in both years (5+ names) */

gen countchg = count16-count15
rename v2 sex
rename v1 name

gsort -count16
gen rank16 = _n

gsort -count15
gen rank15 = _n

gsort -countchg
gen riserank=_n

gsort countchg
gen fallrank=_n

gen rankchg = rank15-rank16

format pctchg %9.1f 
format count15 count16 countchg %15.0fc

gen prop15 = (count15/3978497)*1000 /* these are births per 1000, based on NCHS birth report for 15 & 16 */
gen prop16 = (count16/3941109)*1000

*winners table
sort riserank
list sex name count15 count16 countchg pctchg rank15 rank16 rankchg in 1/20, sep(0)

*losers table
sort fallrank
list sex name count15 count16 countchg pctchg rank15 rank16 rankchg in 1/20, sep(0)

*figure for all names
twoway (scatter prop16 prop15 if sex=="M", mc(blue) m(Oh) mlw(vvthin)) (scatter prop16 prop15 if sex=="F" , m(Oh) mc(pink) mlw(vvthin))

*figure for top names
twoway (scatter prop16 prop15 if sex=="M" & (prop15>=3 | prop16>=3), ml(name) ms(i) mlabp(0)) (scatter prop16 prop15 if sex=="F" & (prop15>=3 | prop16>=3), ml(name) ms(i) mlabp(0))

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Families and Modern Social Theory, revised syllabus

I’m teaching Families and Modern Social Theory again. This is a graduate seminar that meets a theory requirement for our PhD program, mostly taken by students in their first year or two. This revised version adds the new edition of Stephanie Coont’s book The Way We Never Were and Allison Pugh’s The Tumbleweed Society. Feel free to follow along. Comments welcome.

Families and Modern Social Theory: Fall 2017 Syllabus (PDF version)

This course is designed to build knowledge about theories of modernity, with emphasis on modern families. Thus, it combines some core theories of modernity (Giddens, Bourdieu, Foucault), with key theoretical debates about families and intimate relationships (economics and economic sociology, gender, race), and social change (development and new family forms).

Assignments

Students are expected to complete the assigned readings and upload a weekly comment to ELMS by 5pm the day before the seminar meeting each week. The comment should be less than 500 words, and include a specific issue from the readings that you would like to discuss, with your question or comment. Please do not summarize the readings – at all.

Students will write three more elaborate thought papers engaging the readings from the previous weeks. These exploratory essays will be approximately 2000 words, and make a critical argument, offering a hypothesis to explore, or making empirical connections between the course material and other research, bringing in some sources from outside the course. This is a chance for you to explore your own work in relation to the concepts and research in the course.

Evaluation

Evaluation will be based on participation, weekly writings, and exploratory essays.

Universal learning

The principle of universal learning means that our classroom and our interactions should be as inclusive as possible. Your success in this class is important to me. If there are circumstances that may affect your performance in this class, please let me know as soon as possible so that we can work together to meet both your needs and the requirements of the course. Students with particular needs should contact the UMD Disability Support Service (http://www.counseling.umd.edu/DSS/), which will forward the necessary information to me. Please do it now instead of waiting till late in the semester.

Device ban

Students may not use laptops, tablet computers, or mobile phones in class. Exceptions may be granted on an individual basis.

Difficult subjects.

The content of this course may include topics that are difficult for some people to confront or discuss. I cannot anticipate what those topics are, or who will be affected, but I can be sensitive and work with students who let me know of their needs. If there is a topic you are unable to discuss or need to be warned about, please notify me so we can make appropriate arrangements for your work. However, we cannot prevent all students from being exposed to topics or ideas that they find objectionable or offensive.

Academic integrity

Students must be familiar with the UMD Code of Academic Integrity (http://president.umd.edu/sites/president.umd.edu/files/documents/policies/III-100A.pdf). In this course there is zero tolerance for academic dishonesty.

Schedule and readings

August 30: Introduction

Cohen, Philip N. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 1, “A Sociology of the Family.”

Part I: Modernity

September 6: What is modernity?

Giddens, Anthony. 1990. The Consequences of Modernity. John Wiley & Sons

September 13: Modern relationships

Giddens, Anthony. 1993. The Transformation of Intimacy: Sexuality, Love, and Eroticism in Modern Societies. 1st edition. Stanford University Press.

September 20: Habitus and field

Bourdieu, Pierre. 1998. Practical Reason: On the Theory of Action. Stanford University Press.

September 27: Discipline

Foucault, Michel. 2012. Discipline & Punish: The Birth of the Prison. Knopf Doubleday Publishing Group.

Part II: Families

October 4: U.S. family history [FIRST PAPER DUE]

Coontz, Stephanie. 2016. The Way We Never Were: American Families and the Nostalgia Trap. Revised edition. New York: Basic Books.

October 11: New families

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

October 18: Economics over all

Blau, Francine D., Marianne A. Ferber, and Anne E. Winkler. 2013. The Economics of Women, Men and Work. 7 edition. Boston: Pearson. Chapters 3 & 4.

The Austin Institute. 2014. The Economics of Sex. https://www.youtube.com/watch?v=cO1ifNaNABY.

Cohen, Philip N. 2014. “Is the Price of Sex Too Damn Low?” Family Inequality. February 24. https://familyinequality.wordpress.com/2014/02/24/price-of-sex/.

England, Paula. 1989. “A Feminist Critique of Rational-Choice Theories: Implications for Sociology.” The American Sociologist 20 (1): 14–28.

October 25: No seminar meeting

November 1: Family economics

Boushey, Heather. 2016. Finding Time: The Economics of Work-Life Conflict. Cambridge, Massachusetts: Harvard University Press.

November 8: Economic sociology of intimacy [SECOND PAPER DUE]

Zelizer, Viviana A. 2009. The Purchase of Intimacy. Princeton University Press.

November 15: Black families, uncertainty, and exclusion.

Burton, Linda M., and M. Belinda Tucker. 2009. “Romantic Unions in an Era of Uncertainty: A Post-Moynihan Perspective on African American Women and Marriage.” Annals of the American Academy of Political and Social Science 621 (January): 132–48.

Geronimus, Arline T. 2003. “Damned If You Do: Culture, Identity, Privilege, and Teenage Childbearing in the United States.” Social Science & Medicine 57 (5): 881–93. doi:10.1016/S0277-9536(02)00456-2.

Collins, Patricia Hill. 2001. “Like One of the Family: Race, Ethnicity, and the Paradox of US National Identity.” Ethnic and Racial Studies 24 (1): 3–28. doi:10.1080/014198701750052479.

Dow, Dawn Marie. 2016. “The Deadly Challenges of Raising African American Boys: Navigating the Controlling Image of the ‘Thug.’” Gender & Society 30 (2): 161–88. doi:10.1177/0891243216629928.

Part III: Development and change

November 22: Modernity, development, and demography

Thornton, Arland. 2001. “The Developmental Paradigm, Reading History Sideways, and Family Change.” Demography 38 (4): 449–65. doi:10.2307/3088311

Greenhalgh, Susan. 2003. “Science, Modernity, and the Making of China’s One-Child Policy.” Population and Development Review 29 (2): 163–96.

Kirk, Dudley. 1996. “Demographic Transition Theory.” Population Studies 50 (3): 361–87. doi:10.1080/0032472031000149536.

Lesthaeghe, R. “The Second Demographic Transition in Western Countries: An Interpretation.” In Mason, Karen Oppenheim, and An-Magritt Jensen (eds.). 1995. Gender and Family Change in Industrialized Countries. Clarendon Press.

November 29: Decoupling, families, and modernity

Stacey, Judith. 2011. Unhitched: Love, Marriage, and Family Values from West Hollywood to Western China. New York University Press.

15. December 6: Topic TBA [THIRD PAPER DUE]

 

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Demographic facts your students should know cold

Here’s an update of a series I started in 2013, and updated in 2016.

Is it true that “facts are useless in an emergency“? Depends how you define emergency I guess. I used to have a little justification for why we need to know demographic facts, as “the building blocks of first-line debunking.” It’s facts plus arithmetic that let us ballpark the claims we are exposed to all the time. The idea was to get our radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it.

It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements such as Trump’s “I’ve created over a million jobs since I’m president”? (The U.S. population grew by about 1.3 million between the 2016 election and the day he said that; CNN has a jobs tracker.)

What’s a number for? Lots of people disparage the nitpickers when they find something wrong with the numbers going around. But everyone likes a number that appears to support their argument. The trick is to know the facts before you know the argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.

facts-cartoon

Here’s the list of current demographic facts you need just to get through the day without being grossly misled or misinformed — or, in the case of journalists or teachers or social scientists, not to allow your audience to be grossly misled or misinformed. Not trivia that makes a point or statistics that are shocking, but the non-sensational information you need to make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 260 million and 390 million!).

This is only 25 facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). They are rounded to reasonable units for easy memorization. All refer to the US unless otherwise noted. Most of the links will take you to the latest data:

Fact Number Source
World Population 7.4 billion 1
US Population 326 million 1
Children under 18 as share of pop. 23% 2
Adults 65+ as share of pop. 15% 2
Official unemployment rate 4.3% 3
Unemployment rate range, 1970-2017 4% – 11% 4
Labor force participation rate, age 16+ 63% 9
Labor force participation rate range, 1970-2015 60% – 67% 9
Non-Hispanic Whites as share of pop. 61% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 18% 2
Asians as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 13% 2
Adults age 25+ with BA or higher 30% 2
Median household income $54,000 2
Total poverty rate 14% 8
Child poverty rate 20% 8
Poverty rate age 65+ 9% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 324 million 5
4th most populous country, Indonesia 258 million 5
5th most populous country, Brazil 206 million 5
Male life expectancy at birth 76 6
Female life expectancy at birth 81 6
National life expectancy range 50 – 85 7

Sources:
1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

4. Google public data: http://bit.ly/UVmeS3

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

Handy one-page PDF: Demographic Facts You Need to Know 2017

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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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