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).


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 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.


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

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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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.



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


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).

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.


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.


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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Philip Cohen at 50, having been 14 in 1981

This is a sociological reflection about life history. It’s about me because I’m the person I know best, and I have permission to reveal details of my life.

I was born in August 1967, making me 50 years old this month. But life experience is better thought of in cohort terms. Where was I and what was I doing, with whom, at different ages and stages of development? Today I’m thinking of these intersections of biography and history in terms of technology, music, and health.


We had a TV in my household growing up, it just didn’t have a remote control or cable service, or color. We had two phones, they just shared one line and were connected by wires. (After I moved out my parents got an answering machine.) When my mother, a neurobiologist, was working on her dissertation (completed when I was 10) in the study my parents shared, she used a programmable calculator and graph paper to plot the results of her experiments with pencil. My father, a topologist, drew his figures with colored pencils (I can’t describe the sound of his pencils drawing across the hollow wooden door he used for a desktop, but I can still hear it, along with the buzz of his fluorescent lamp). A couple of my friends had personal computers by the time I started high school, in 1981 (one TRS-80 and one Apple II), but I brought a portable electric typewriter to college in 1988. I first got a cell phone in graduate school, after I was married.

The first portable electronic device I had (besides a flashlight) was a Sony Walkman, in about 1983, when I was 16. At the time nothing mattered to me more than music. Music consumed a large part of my imagination and formed the scaffolding of most socializing. The logistics of finding out about, finding, buying, copying, and listening to music played an outsized role in my daily life. From about 1980 to 1984, most of the money I made at my bagel store job went to stereo equipment, concerts, records, blank tapes for making copies, and eventually drums (as well video games). I subscribed to magazines (Rolling Stone, Modern Drummer), hitchhiked across town to visit the record store, pooled money with friends to buy blank tapes, spent hours copying records and labeling tapes with my friends, and made road trips to concerts across upstate New York (clockwise from Ithaca: Geneva, Buffalo, Rochester, Syracuse, Saratoga, Binghamton, New York City, Elmira).

As I’m writing this, I thought, “I haven’t listened to Long Distance Voyager in ages,” tapped it into Apple Music on my phone, and started streaming it on my Sonos player in a matter of seconds, which doesn’t impress you at all – but the sensory memories it invokes are shockingly vivid (like an acid flashback, honestly) – and having the power to evoke that so easily is awesome, in the old sense of that word.

Some of us worked at the Cornell student radio station (I eventually spent a while in the news department), whose album-oriented rock playlist heavily influenced the categories and relative status of the music we listened to. The radio station also determined what music stayed in the rotation – what eventually became known by the then-nonexistent term classic rock – and what would be allowed to slip away; it was history written in real time.

It’s like 1967, in 1981

You could think of the birth cohort of 1967 as the people who entered the world at the time of “race riots,” the Vietnam and Six Day wars, the Summer of Love, the 25th Amendment (you’re welcome!), Monterey Pop, Sgt. Peppper’s, and Loving v. Virginia. Or you could flip through Wikipedia’s list of celebrities born in 1967 to see how impressive (and good looking) we became, people like Benicio del Toro, Kurt Cobain, Paul Giamatti, Nicole Kidman, Pamela Anderson, Will Ferrell, Vin Diesel, Phillip Seymour Hoffman, Matt LeBlanc, Michael Johnson, Liev Schreiber, Julia Roberts, Jimmy Kimmel, Mark Ruffalo, and Jamie Foxx.

But maybe it makes more sense to think of us as the people who were 14 when John Lennon made his great commercial comeback, with an album no one took seriously – only after being murdered. The experiences at age 14, in 1981, define me more than what was happening at the moment of my birth. Those 1981 hits from album-oriented rock mean more to me than the Doors’ debut in 1967. My sense of the world changing in that year was acute – because it was 1981, or because I was 14? In music, old artists like the Moody Blues and the Rolling Stones released albums that seemed like complete departures, and more solo albums – by people like Stevie Nicks and Phil Collins – felt like stakes through the heart of history itself (I liked them, actually, but they were also impostors).

One moment that felt at the time like a historical turning point was the weekend of September 19, 1981. My family went to Washington for the Solidarity Day rally, at which a quarter million people demonstrated against President Reagan and for organized labor, a protest fueled by the new president’s firing of the PATCO air traffic controllers the previous month (and inspired by the Solidarity union in Poland, too). Besides hating Reagan, we also feared a nuclear war that would end humanity – I mean really feared it, real nightmare fear.


A piece of radio news copy I wrote and read at WVBR, probably 1983. The slashes are where I’m going to take a breath. “Local AQX” is the name of the tape cartridge with the sound bite (“actuality”) from Alfred Kahn, and “OQ:worse” means that’s the last word coming out of the clip.

On the same day as Solidarity, while we were in D.C., was Simon and Garfunkel’s Concert in Central Park. They were all of 40 (literally my mother’s age), tired old people with a glorious past (I’m sure I ignored the rave reviews). As I look back on these events – Reagan, the Cold War, sell-out music – in the context of what I thought of as my emerging adulthood, they seemed to herald a dark future, in which loss of freedom and individuality, the rise of the machines, and runaway capitalism was reflected in the decline of rock music. (I am now embarrassed to admit that I even hated disco for a while, maybe even while I listened 20 times, dumbstruck, to an Earth, Wind, and Fire album I checked out of the library.)

I don’t want to overdramatize the drama of 1981; I was basically fine. I came out with a penchant for Camus, a taste for art rock, and leftism, which were hardly catastrophic traits. Still, those events, and their timing, probably left a mark of cynicism, sometimes nihilism, which I carry today.


About 1984, with Daniel Besman (who later died) in Ithaca. Photo by Linda Galgani.

Data aside

Maybe one reason 1981 felt like a musical watershed to me is because it really was, because pop music just got worse in the 1980s compared to the 1970s. To test (I mean prove, really) that hypothesis, I fielded a little survey (more like a game) that asked people to rate the same artists in both decades. I chose 58 artists by flipping through album charts from 1975-1984 and finding those that charted in both decades; then I added some suggestions from early respondents. To keep the task from being too onerous, as it required scoring bands twice from 1 (terrible) to 5 (great), once for each period, and some people found it difficult, I set the survey to serve each person just 10 artists at random (a couple of people did it more than once). The participants were 3/4 men, 3/4 over 40, and 3/4 White and US-born; found on Facebook, Twitter, and Reddit. The average artist was rated 11 times in each period (range 5 to 19). (Feel free to play along or share this link; I’ll update it if more come in.)

The results look very bad for the 1980s. The average change was a drop of .59, and only three acts showed noticeable improvement: Pat Benatar, Michael Jackson, and Prince (and maybe Talking Heads and the lowly Bryan Adams). Here is the full set (click to enlarge):

Technology and survival

I don’t think I would have, at age 14, given much weight to the idea that my life would repeatedly be saved by medical technology, but now that seems like business as usual, to me anyway. I guess as long as there’s been technology there have been people who owe their lives to it (and of course we’re more likely to hear from them than from those who didn’t make it). But the details are cohort-specific. These days we’re a diverse club of privileged people, our conditions, or their remnants, often hidden like pebbles wedged under the balls of our aging feet, gnawing reminders of our bodily precarity.

Family lore says I was born with a bad case of jaundice, probably something like Rh incompatibility, and needed a blood transfusion. I don’t know what would have happened without it, but I’m probably better off now for that intervention.

Sometime in my late teens I reported to a doctor that I had periodic episodes of racing heartbeat. After a brief exam I was sent home with no tests, but advised to keep an eye on it; maybe mitral valve prolapse, he said. I usually controlled it by holding my breath and exhaling slowly. We found out later, in 2001 – after several hours in the emergency room at about 200 very irregular beats per minute – that it was actually a potentially much more serious condition called Wolff-Parkinson-White syndrome. The condition is easily diagnosed nowadays, as software can identify the tell-tale “delta wave” on the ECG, and the condition is listed right there in the test report.


Two lucky things combined: (a) I wasn’t diagnosed properly in the 1980s (which might have led to open-heart surgery or a lifetime of unpleasant medication), and; (b) I didn’t drop dead before it was finally diagnosed in 2001. They fixed it with a low-risk radiofrequency ablation, just running a few wires up through my arteries to my heart, where they lit up to burn off the errant nerve ending, all done while I was almost awake, watching the action on an x-ray image and – I believed, anyway – feeling the warmth spread through my chest as the doctor typed commands into his keyboard.

Diverticulitis is also pretty easily diagnosed nowadays, once they fire up the CT scanner, and usually successfully treated by antibiotics, though sometimes you have to remove some of your colon. Just one of those things people don’t die from as much anymore (though it’s also more common than it used to be, maybe just because we don’t die from other things as much). I didn’t feel like much like surviving when it was happening, but I suppose I might have made it even without the antibiotics. Who knows?

More interesting was the case of follicular lymphoma I discovered at age 40 (I wrote about it here). There is a reasonable chance I’d still be alive today if we had never biopsied the swollen lymph node in my thigh, but that’s hard to say, too. Median survival from diagnosis is supposed to be 10 years, but I had a good case (a rare stage I), and with all the great new treatments coming online the confidence in that estimate is fuzzy. Anyway, since the cancer was never identified anywhere else in my body, the treatment was just removing the lymph node and a little radiation (18 visits to the radiation place, a couple of tattoos for aiming the beams, all in the summer with no work days off). We have no way (with current technology) to tell if I still “have” it or whether it will come “back,” so I can’t yet say technology saved my life from this one (though if I’m lucky enough to die from something else — and only then — feel free to call me a cancer “survivor”).

It turns out that all this life saving also bequeaths a profound uncertainty, which leaves one with an uneasy feeling and a craving for antianxiety medication. I guess you have to learn to love the uncertainty, or die trying. That’s why I cherish this piece of a note from my oncologist, written as he sent me out of the office with instructions never to return: “Your chance for cure is reasonable. ‘Pretest probability’ is low.”


From my oncologist’s farewell note.

Time travel

It’s hard to imagine what I would have thought if someone told my 14-year-old self this story: One day you will, during a Skype call from a hotel room in Hangzhou, where you are vacationing with your wife and two daughters from China, decide to sue President Donald Trump for blocking you on Twitter. On the other hand, I don’t know if it’s possible to know today what it was really like to be me at age 14.

In the classic time travel knot, a visitor from the future changes the future by going back and changing the past. The cool thing about mucking around with your narrative like I’m doing in this essay (as Walidah Imarisha has said) is that it by altering our perception of the past, we do change the future. So time travel is real. Just like it’s funny to think of my 14-year-old self having thoughts about the past, I’m sure my 14-year-old self would have laughed at the idea that my 50-year-old self would think about the future. But I do!


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Let’s use award incentives to promote open scholarship (at ASA this year!)

At the American Sociological Association of America meetings in Montreal next month, I will begin a one-year term as chair of the Family Section. I’m honored to have been elected to this position, and will do my best to make a positive contribution in that role. Besides doing the job in the normal ways — organizing our sessions at the conference next year, coordinating committees, and so on — I will bring a proposal to the section’s council to open our graduate student paper award. Here’s what I mean.

Steps toward solutions

Sociology has an inertia problem with regard to open scholarship. Lots of us understand that it would be better if our work was shared faster and more freely. That would be better for the generation and dissemination of new knowledge, it would promote collaboration, reduce costs to the public, and increase our capacity for engagement with each other and the public. Unfortunately, the individual steps toward that goal are unclear or daunting. Many of us need promotion and tenure, which requires prestige, which is still driven by publication in the paywalled journals that work against our open goals: they slow down dissemination, restrict access to our work, and bilk our institutions with exorbitant subscription fees.

To help overcome this inertia, a group of us have created SocArXiv, a non-profit, open access, open source archive of social science research that allows free, immediate publication of papers at any stage of the publication process. When and if the papers are published in a peer-reviewed journal, the preprint version can link to the journal version, providing a free copy of the paywalled paper. (Here’s an example of a new paper published in American Sociological Review, with a free copy on SocArXiv, which includes a link to the ASR version). In the meantime, the paper is available to our peers and the public. It provides a time-stamped record of the development of our original ideas, and is discoverable through Google Scholar and other search tools. People can still get their jobs and promotions, but the quality, efficiency, and reach of our research is improved. And part of what we are rewarding is open scholarship itself.


Using awards

SocArXiv, of which I’m director, is trying to get the word out and encourage the use of our system, and open scholarship in general. One of our new ideas is opening paper awards. This may help people get in the habit of openness — and start to see its benefits — and also work against the negative impression that many people have of open access as a cesspool of low quality work. We hope this intervention will be especially effective coming early in the career of up-and-coming scholars.

Using its grant money and support from academic libraries, SocArXiv is offering sections of the ASA — like the Family Section — $400 to transport their paper award winner to the conference next year, if they using the archive as the submission platform for their awards. I’m bringing this proposal to the Family Section (and one just like it to the Population Section, of which I’m Secretary Treasurer).

We hope the open paper award will become a common best practice in our association — still providing the prestige and reward functions of the award, but also promoting best practices with regard to open scholarship, increasing our visibility, building the scholarly communication infrastructure of the future, and generating buzz for our conference and our research.

There are possible objections to this idea. Here are a few, with my responses:

  • Sharing unpublished work will lead to someone stealing their ideas. You protect yourself by posting it publicly.
  • We shouldn’t promote the dissemination of research that hasn’t been peer reviewed yet. We do this all the time at conferences, and SocArXiv allows posting updated versions that replace the original when it is revised.
  • This would impose a burden on people submitting papers. Being considered for an award is a privilege, not a right; it’s OK to require a short, free submission process.
  • Sharing a paper publicly will compromise its publishability later. All ASA journals, and all journals worthy of our support, allow posting preprints prior to publication. Here’s a list of 25 top journals and their policies.


In the case of the Family Section, it looks like no change in the bylaws is needed, because they don’t specify the submission process for the graduate student paper award. They state:

Best Graduate Student Paper Award. The committee will be chaired by the Section Chair. Two additional members of the Section will be appointed by the Section Chair. The committee will select a best paper from among nominations submitted. Papers, dealing with a family-related topic, may be either published or unpublished and must have been writted by a graduate student (or group of graudate students) while still enrolled in a graduate program. The award, in the form of a Plaque and citation, shall be presented at a Section Reception (or, in the event no reception is held, at a Business Meeting of the Section).

Instead, I think we can just revise the call for award nominations, like this:

The Family Section Outstanding Graduate Student Paper Award

​Deadline: 3/13/2018

Graduate students are invited to submit an article-length paper on the family. The paper should represent a finished product rather than a proposal for future work. The submission can be based on a course paper, a recently published journal article, a manuscript under review at a journal, or a conference presentation. Co-authored papers are acceptable if all authors are students, although the prize will be shared. The paper must have been written when the author was enrolled in a graduate program. The paper may not exceed 30 pages or 11,000 words. Submissions are made by posting the paper on SocArXiv and sending a link to the paper to the committee chair, Philip N. Cohen, at pnc@umd.edu. To submit your paper, go to SocArXiv.org, and click “Add a preprint.” If you don’t yet have an account, you will fill out a short form — it’s free, non-profit, and won’t spam you! For assistance, contact socarxiv@gmail.com or consult the FAQ page. Please indicate whether you would like your paper to be included in a public list of submissions (this will not affect your chances of winning). The winner will receive a plaque and travel reimbursement up to $400 to attend the 2018 Family Section reception at the ASA meetings.

The Family Section Council will consider this proposal next month in Montreal. Please let us know what you think!

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Made in America, by immigrants: children

Immigrants make a lot of great things in the USA, like communities and ideas and political organizations. And they also make American children. So for Made in America Week, a quick look at children born in the U.S. whose parents were not. That is, children made in America by immigrants.

For this table I used the American Community Survey, made available by IPUMS, and selected children ages 0-17 who live with two parents. Then I narrowed that group down to those for whom both parents were born in one of the top 20 countries (or regions), from those listed in the birthplace variable (described here), including the USA. The table shows the birthplace of mother and father (same-sex parent couples are excluded). The blue outer band shows the children who have at least one US-born parent. The green diagonal shows the number of children with two parents who immigrated from the same country. For the rest, the colors highlight larger cells, growing darker as cells surpass 1000, 5000, and 10,000. I’ll mention a few below.

You’ll have to click to enlarge:

Children made in America by immigrants

The green cells are the largest in each row and column, except the blue US-born-parent cells. In most cases the green cell is larger than the blue ones — for example, there are 3.5 million U.S. born children who live with two Mexican-born parents, outnumbering the 950,000 who have a Mexican-born father and U.S.-born mother, and 650,000 in the reverse case. But in some cases the green cell is very small, for example England, as there are more than 100,000 children with one England-born and one U.S.-born parent, but only 4,000 who have two England-born parents.

In other cases there are big gender differences reflecting migration and marriage patterns. So there are 10,000 children with a Chinese-born mother and Vietnamese-born father, but only 6,000 of the reverse. Also, in the case of Asia parents, there are more U.S.-born kids with Asian-born mothers and U.S.-born fathers than the reverse, presumably reflecting the greater tendency of Asian women to marry White men (this doesn’t apply to Laos and India).

Anyway, happy Made in America week.

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