Do rich people like bad data tweets about poor people? (Bins, slopes, and graphs edition)

Almost 2,000 people retweeted this from Brad Wilcox the other day.

Brad shared the graph from Charles Lehman (who noticed later that he had mislabeled the x-axis, but that’s not the point). First, as far as I can tell the values are wrong. I don’t know how they did it, but when I look at the 2016-2018 General Social Survey, I get 4.3 average hours of TV for people in the poorest families, and 1.9 hours for the richest. They report higher highs (looks like 5.3) and lower lows (looks like 1.5). More seriously, I have to object to drawing what purports to be a regression line as if those are evenly-spaced income categories, which makes it look much more linear than it is.

I fixed those errors — the correct values, and the correct spacing on the x-axis — then added some confidence intervals, and what I get is probably not worth thousands of self-congratulatory woots, although of course rich people do watch less TV. Here is my figure, with their line (drawn in by hand) for comparison:

Charles and Brad’s post got a lot of love from conservatives, I believe, because it confirmed their assumptions about self-destructive behavior among poor people. That is, here is more evidence that poor people have bad habits and it’s just dragging them down. But there are reasons this particular graph worked so well. First, the steep slope, which partly results from getting the data wrong. And second, the tight fit of the regression line. That’s why Brad said, “Whoa.” So, good tweet — bad science. (Surprise.) Here are some critiques.

First, this is the wrong survey to use. Since 1975, GSS has been asking people, “On the average day, about how many hours do you personally watch television?” It’s great to have a continuous series on this, but it’s not a good way to measure time use because people are bad at estimating these things. Also, GSS is not a great survey for measuring income. And it’s a pretty small sample. So if those are the two variables you’re interested in, you should use the American Time Use Survey (available from IPUMS), in which respondents are drawn from the much larger Current Population Survey samples, and asked to fill out a time diary. On the other hand, GSS would be good for analyzing, for example, whether people who believe the Bible is the “the actual word of God and is to be taken literally, word for word” watch TV more than those who believe it is “an ancient book of fables, legends, history, and moral precepts recorded by men” (Yes, they do, about an hour more.) Or looking at all the other social variables GSS is good for.

On the substantive issue, Gray Kimbrough pointed out that the connection between family income and TV time may be spurious, and is certainly confounded with hours spent at work. When I made a simple regression model of TV time with family income, hours worked, age, sex, race/ethnicity, education, and marital status (which again, should be done better with ATUS), I did find that both hours worked and family income had big effects. Here they are from that model, as predicted values using average marginal effects.

The banal observation that people who spend more time working spend less time watching TV probably wouldn’t carry the punch. Anyway, neither resolves the question of cause and effect.

Fits and slopes

On the issue of the presentation of slopes, there’s a good lesson here. Data presentation involves trading detail for clarity. And statistics have both have a descriptive and analytical purpose. Sometimes we use statistics to present information in simplified form, which allows better comprehension. We also use statistics to discover relationships we couldn’t otherwise — such as multivariate relationships that you can’t discern visually. The analyst and communicator has to choose wisely what to present. A good propagandist knows what to manipulate for political effect (a bad one just tweets out crap until they get lucky).

Here’s a much less click-worthy presentation of the relationship between family income and TV time. Here I truncate the y-axis at 12 hours (cutting off 1% of the sample), translate the binned income categories into dollar values at the middle of each category, and then jitter the scatterplot so you can see how many points are piled up in each spot. The fitted line is Stata’s median spline, with 9 bands specified (so it’s the median hours at the median income in 9 locations on the x-axis). I guess this means that, at the median, rich people in America watch about an hour of TV per day less than poor people, and the action is mostly under $50,000 per year. Woot.

Finally, a word about binning and the presentation of data (something I’ve written about before, here and here). We make continuous data into categories all the time, starting from measurement. We usually measure age in years, for example, although we could measure it in seconds or decades. Then we use statistics to simplify information further, for example by reporting averages. In the visual presentation of data, there is a particular problem with using averages or data bins to show relationships — you can show slopes that way nicely, but you run the risk of making relationships look more closely correlated than they are. This happens in the public presentation of data when analysts are showing something of their work product — such as a scatterplot with a fitted line — to demonstrate the veracity of their findings. When they bin the data first, this can be very misleading.

Here’s an example. I took about 1000 men from the GSS, and compared their age and income. Between the ages of 25 and 59, older men have higher average incomes, but the fit is curved with a peak around 45. Here is the relationship, again using jittering to show all the individuals, with a linear regression line. The correlation is .23

That might be nice to look at but it’s hard to see the underlying relationship. It’s hard to even see how the fitted line relates to the data. So you might reduce it by showing the average income at each age. By pulling the points together vertically into average bins, this shows the relationship much more clearly. However, it also makes the relationship look much stronger. The correlation in this figure is .65. Now the reader might think, “Whoa.”

Note this didn’t change the slope much (it still runs from about $30k to $60k), it just put all the dots closer to the line. Finally, here it is pulling the averages together in horizontal bins, grouping the ages in fives (25-29, 30-34 … 55-59). The correlation shown here is .97.

If you’re like me, this is when you figured out that reducing this to two dots would produce a correlation of 1.0 (as long as the dots aren’t exactly level).

To make good data presentation tradeoffs requires experimentation and careful exposition. And, of course, transparency. My code for this post is available on the Open Science Framework here (you gotta get the GSS data first).

Gender segregated sociology today

This updates a series of posts that have addressed gender in academic sociology, starting in 2011 and updated in 2015, along with various tweets (to see random fact tweets from me on Twitter, Google familyunequal “now you know”).

Gender in academic sociology is complicated because the profession is running pretty female these days, with more than half the U.S. PhDs going to women since 1994, and more than 60% overall since 1999. So although there are various kinds of exclusion, it’s not as simple as excluding women from the discipline, and the representation of women’s representation depends on the choice of denominators. For example, a recent report found that, in the top 100 U.S. sociology departments in 2012, women were 60% of the assistant professors, 54% of the associate professors, and 34% of the full professors. This probably reflects a combination of age and tenure, with this year’s full professors representing yesteryear’s hiring, as well as women having lower rates of progression up the hierarchy.

Also, feminists (myself included) cheer the entry of women into formerly male-only professions but bemoan their concentration into female ghettos, but there is no bright line beyond which one process transforms into the other (don’t get me started on “tipping points“).

But however we want to interpret the trends, we have to know the trends. So here are some, starting with updates on previous reports (degrees, sections, elections), and then some new ones (journal articles, peer reviewers).*

Degrees

The National Science Foundation reports the number and gender of PhD recipients by discipline (since 2006, earlier). This is what we get (smoothed with three-year averages): mostly more than 60% female since the late-1990s, with women accounting for most of the growth.

Sections

Sociology is a very broad discipline, including people who specialize in many distinct substantive and methodological areas. Within the American Sociological Association (ASA), we divide into 49 sections, which serve as a mechanism to organize conferences and journals, and to give awards (people can belong to as many as they want, for a small charge). The sections are pretty segregated by gender. Here are the gender compositions of each, from Sex and Gender (86% female) to Mathematical Sociology (22%):

Elections

The ASA leadership is elected in annual balloting by the membership, which is open to anyone who wants to join as long as they claim some affiliation with the discipline (the price ranges from $51 for students to $377 for people with incomes over $150,000). The association elected its first president in 1906, its first woman in 1952 and its second woman in 1973. In the last 10 years 7 of the presidents have been women.

Is the shift toward women presidents because there are more women in the association, or in the hierarchy of the association, or because of the preference of the membership? The president, along with all the other elected officers, are selected for the ballot by a nominations committee. In recent years it has become conventional wisdom that men usually lose to women in these elections because ASA members vote for a woman if they don’t have a strong preference between candidates, but I don’t know how well-founded that perception is.

Here is the gender of candidates for top positions (president, vice president, secretary, and council members), and the gender of the winners, from 2007 to 2018. Note that in the last three years they have nominated fewer women, but except for 2016 the membership has voted for more women (with 2018 being having the widest gap yet):

Papers

I’ve only done a little of this, but here is a quick look at the gender of authors in two of the highest-status sociology journals for the last several years. American Sociological Review (an ASA journal published by Sage), and American Journal of Sociology (an independent journal published by University of Chicago), 35% of authors in the last 11 issues have been women (by my assessment, regular articles only)

Someone could easily do a much more serious assessment of gender in sociology journal authorship.

Reviewers

For the last few months I have been working on peer review in sociology and the social sciences — how it works, how it doesn’t, and how it might be improved. (Here are slides from a talk I gave, with Micah Altman at MIT). One of my concerns about peer review is its general lack of accountability; no one supervises the process, generally, as the only person who knows everything at a journal is the editor, and the only thing the public sees is the published outcome. And yet publication peer review determines all manner of statuses in academia.

Looking for externally accessible data that might shine a light on the process, I checked for reviewer acknowledgement lists, which some journals publish at the end of a volume (lots of journals don’t apparently, including Social Forces, Sociological Methods and Research, Social Science Research, Sociological Forum, Sociological Theory, Work & Occupations, Social Currents, and Mobilization.) I used the genderize.io API to count the genders of the reviewers, using 80% confidence for the first pass, and then personally checking or Googling other names (I didn’t do all the names, but almost).

The reviewer gender shares are a little higher for ASR and AJS than they were for authors, with the former having somewhat more women. Publication in one of these two journals is the probably most important gatekeeping mechanism to the upper echelons of the discipline. The methods journal has the lowest representation of women, the gender journal as the highest. Unknown here is the proportion of women among the pool of reviewers solicited by the editors.

So, that’s my report.

* These data all treat gender as sex as binary, either because the data were reported that way, or because I coded them from names. I don’t address race, ethnicity, or other traits for the same reason.

On my 10 year cancerversary, medical assistance for migrants

From the Doctors Without Borders report (see below)

Update: I’m delighted and gratified that we met the donation goal described below. Thank you.

It snuck up on me again, the anniversary of my cancer experience, which came and went, more or less, in 2008, ten years ago. Last year I wrote about the experience a little:

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.

Unlike the anxiety I have now, the fear and sadness I felt that summer were almost overwhelming. Today, 10 years later, with no detectable disease (not that I’m looking), I am thinking of the millions of people who have no access to the kind of medical care I had, who face similar or worse medical conditions in infinitely worse social conditions.

With so much energy in the US being diverted to our political crisis, for good reason, I want to pause for some humanitarian assistance abroad. Doctors Without Borders is a great organization doing vital work around the world. This year I am honoring their efforts to provide medical assistance to migrants fleeing violence and instability in Central America (here’s a report on the conditions there, and their work.).

I will match contributions to Doctors Without Borders up to $1000 for this campaign (plus the $80 or so GoFundMe will charge to collect it). It’s a small token of appreciation for my good fortune.

Here’s the GoFundMe link: Emergency Global Healthcare. I’ll make my contribution directly to Doctors Without Borders after it reaches $1000 or stops growing. Thank you for considering it.

And below is what I wrote on the five-year anniversary.

That summer, when she was four, my daughter made this picture of me.

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My 5-year cancerversary

I didn’t even register it right away. Five years ago this Memorial Day I got my diagnosis of follicular lymphoma, a form of non-Hodgkin’s lymphoma. It was late on the Friday afternoon when the surgeon called with the biopsy results. He never said the word “cancer,” but recommended I see an oncologist. He was a very nice guy, and told me I was going to live to be an old man. Within 15 minutes I had read that follicular lymphoma is usually incurable. (The UpToDate database I used now puts it this way: “most cases of follicular lymphoma are not curable with currently available therapies.”) It was a long long weekend.

Usually follicular lymphoma – a blood cancer – is advanced before it’s first discovered. In the next few weeks, one oncologist told me the median survival was between 10 and 20 years. I was 40 with a wife and 4-year-old daughter. I asked her why she was an oncologist. She said she was interested in end-of-life issues. Also, the nicest people get cancer.

Eventually we determined that I had what apparently was a rare case of Stage I, which may be curable. I had 18 days of painless radiation and didn’t (physically) miss a day of work. Lucky is a funny word for this.

Five years later I don’t have an oncologist anymore. It’s the first line on my medical chart but not a to-do list item. When we moved away, my Bayesian-minded oncologist wrote in his farewell note, using his best handwriting: “Your chance for cure is reasonable: pre-test probability is low. Early detection is not helpful. If you get an enlarged lymph node, get biopsied.” Maybe that’s oncology speak for: “Relax, good luck!”

Anyway, there were lots of people I never told, including the chair of my department and some good friends and colleagues. Maybe that’s because it went from incurable (yikes, too much information) to possibly-cured (so stop complaining already) so quickly – before the start of the new semester – so I didn’t know how to bring it up or what to say.

For most people with this disease, the story is different. Thankfully, we’ve had a revolution in lymphoma treatment, and it’s usually a very long story. Most people live many years, and I’m told the new treatments usually aren’t that bad. (Easy for me to say.) Chance of surviving (that is, dying from something else) is pretty good. Experts debate whether the word “cure” should be used more.

Meanwhile, now there are two kinds of people in the world: people with a better prognosis, and people with a worse prognosis. Of course that’s always been true. But this experience sometimes makes me dwell on that, which increases my tendency to draw a sharp resentment/sympathy line according to this criterion. That isn’t healthy because it obscures the more important bases upon which to relentlessly judge people and compare myself to them.

I’m writing this because I remembered how lonely and scared I felt back then – when I didn’t even know where on the scale to put myself. Nothing aggravates the modern identity like incalculable risk. Fortunately, I had the greatest family and friend support – and medical care – anyone could ask for. Life got back to normal. We adopted another daughter. There are other risks to worry about.

But I’m thinking that somewhere someone with no idea what to do next is getting news like I did and Googling “follicular lymphoma.” If that’s someone you know, or it is you, maybe it will help to know about one more person who’s still living about as normal a life as I was before. Feel free to drop me a note.

Craptastic

Your bright future written in the sign over a urinal (Photo: pnc)

I don’t know where it came from, but sometime after the 2016 election the word craptastic started rolling around in my head. Eventually it congealed into the title of something I want to write.

Some people use craptastic to mean “so bad it’s good,” like bad food you love. But to me it’s that thing you say when you thought something was going well — maybe turning around from a bad situation — and it suddenly turns out to be even worse than you thought. An early use appears in a 2007 young adult novel called Two Foot Punch:

“Come on. Now that we know where Derek is, we can get help!”

“Not yet,” I say. My voice becomes weak, even for a whisper. “He told the guys that if anyone comes, or if something goes wrong, they’re going to kill Derek.” …

Rain leans against the duct, shaking her head. “Craptastic.”

The situation with Derek was bad, but then they found out where he was (lucky break!), but it turns out if they act on that he will be killed (craptastic!).

Joy-Ann Reid has a descriptive piece up at Daily Beast called “The Enormous Emotional Toll of Trumpism,” in which she writes:

Dr. Jeffrey R. Gardere, Ph.D., a clinical psychologist, said some of his patients over the past nine months “have expressed much frustration, unhappiness and stress with the present political climate,” and that he is seeing increased instances of “dysphoria, and sometimes the related eating and sleeping interruptions.”

We all know this is happening. My theory for Craptastic is that the catastrophic thinking and uncontrollable feelings of impending doom go beyond the very reasonable reaction to the Trump shitshow that any concerned person would have, and reflect a sense that things are turning around in a suddenly serious way, rupturing what Anthony Giddens describes as the progress narratives of modernity people use to organize their identities. People thought things were sort of going to keep getting better, arc of the moral universe and all that, but suddenly they realize what a naive fantasy that was. It’s not just terrible, it’s craptastic.

If that’s true, I suppose, it would be felt more strongly by relatively privileged people, who had the luxury of believing their good lives were just a little ahead of the lives of those obviously much worse off, so being happy wasn’t a betrayal of humanity, it was just a little premature. Now, they feel not just bad, but worse. (My insider perspective on this is a plus, right?)

I suspect that if America lives to see this chapter of its decline written, Trump will not be as big a part of the story as it seems he is right now. And that impending realization is one reason for the Trump-inspired dysphoria that so many people are feeling.

(Cohen forthcoming)*

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* If you love this idea and want to help make it happen, please contact my agent. Or I guess be my agent.

Family Demography seminar syllabus

Taipei shopping district / pnc

Here’s my syllabus for Family Demography this semester. Play along at home!

I went for contemporary readings for most subjects, rather than classic readings. I’ll talk about the background myself, and I added an origin/impact analysis assignment, where students dig into the front end of the papers and figure out where they’re coming from – and then follow the citations to see where they went (if they’re not brand new). If I had my stuff together I’d have a better list of background readings as a supplement, but we have comprehensive exam readings lists for that, too. Anyway, we’ll see how that works.

I hope this is useful. Feel free to add your own supplemental readings and suggestions in the comments.

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Introduction

This course is designed to build knowledge on the key theories, empirical patterns, and contemporary debates in the study of family demography, with lesser attention to methodology. (Some students previously took my seminar Families and Modern Social Theory; those who haven’t may find interesting background material in that syllabus: http://www.terpconnect.umd.edu/~pnc/FMST-syllabus.pdf.)

Students are expected to read assigned material and write a response paper each week, and a summary essay or research report at the end of the semester. In addition, each student will do an origin/impact analysis of one of the assigned readings and make a brief presentation to the class. Evaluation will be based on participation, weekly writings, the presentation, and the final paper.

Universal learning

The principle of universal learning means that our classroom and our interactions 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.

Rules

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.

Classroom conduct. Students should not come to class late, as this creates a distraction for those who are participating. If your schedule regularly does not permit you to be in class from beginning to end, do not take the course. Students who need to leave early should sit at the back and leave quietly. Students may not use laptops, tablet computers, or mobile phones in class. If you have a need for keeping your phone handy in class notify the professor in advance for an exception.

Discussion. We will discuss course readings and related material, as well as current events, social issues, and politics. Everyone is free to express personal opinions and disagree with others, including the professor – just raise your hand. All discussion must be polite and respectful, and differences of opinion are tolerated. The professor will work to ensure the classroom is a safe space for all of use to participate freely. Please let me know if you have any concerns or suggestions for accomplishing this.

SCHEDULE

January 31

Theoretical perspectives in demography

Samek, Diana, Bibiana D. Koh, and Martha A. Rueter. 2013. “Overview of Behavioral Genetics Research for Family Researchers.” Journal of Family Theory & Review 5 (3): 214–33. doi:10.1111/jftr.12013.

Ferree, Myra Marx. 2010. “Filling the Glass: Gender Perspectives on Families.” Journal of Marriage and Family 72(3):420-439.

Elder, Glen H., Jr. 1998. “The Life Course as Developmental Theory.” Child Development 69(1):1-12.

February 7

Demographic transition

Kirk, D. 1996. “Demographic Transition Theory.” Population Studies 50 (3): 361-.

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

Balbo, Nicoletta, Francesco C. Billari, and Melinda Mills. 2013. “Fertility in Advanced Societies: A Review of Research.” European Journal of Population 29 (1): 1–38. doi:10.1007/s10680-012-9277-y.

Feng, Wang. 2011. “The Future of a Demographic Overachiever: Long-Term Implications of the Demographic Transition in China.” Population and Development Review 37: 173–90.

February 14

Fertility in poor countries

Yount, Kathryn M., Sarah Zureick-Brown, Nafisa Halim, and Kayla LaVilla. 2014. “Fertility Decline, Girls’ Well-Being, and Gender Gaps in Children’s Well-Being in Poor Countries.” Demography 51 (2): 535–61. doi:10.1007/s13524-014-0282-0.

Feng, Wang, Baochang Gu, and Yong Cai. 2016. “The End of China’s One-Child Policy.” Studies in Family Planning 47 (1): 83–86. doi:10.1111/j.1728-4465.2016.00052.x.

Kravdal, Oystein. 2012. “Further Evidence of Community Education Effects on Fertility in Sub-Saharan Africa.” Demographic Research 27 (November): 646–. doi:10.4054/DemRes.2012.27.22.

Bongaarts, John, and Christophe Z. Guilmoto. 2015. “How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050.” Population and Development Review 41 (2): 241–69. doi:10.1111/j.1728-4457.2015.00046.x.

February 21

Second demographic transition

Geist, Claudia. 2017. “Marriage Formation in Context: Four Decades in Comparative Perspective.” Social Sciences 6 (1): 9. doi:10.3390/socsci6010009.

Lesthaeghe, Ron. 2010. “The Unfolding Story of the Second Demographic Transition.” Population and Development Review 36 (2): 211-.

Goldscheider, Frances, Eva Bernhardt, and Trude Lappegard. 2015. “The Gender Revolution: A Framework for Understanding Changing Family and Demographic Behavior.” Population and Development Review 41 (2): 207–+. doi:10.1111/j.1728-4457.2015.00045.x.

Cohen, Philip N. 2011. “Homogamy Unmodified.” Journal of Family Theory & Review 3 (1): 47–51.

February 28

U.S. History

Ruggles. Steven. 2015. “Patriarchy, Power, and Pay: The Transformation of American Families, 1800-2015.” Demography 52: 1797-1823. (His lecture version at PAA.)

Cherlin, Andrew J. 2004. “The Deinstitutionalization of American Marriage.” Journal of Marriage and Family 66 (4): 848–61.

Ruggles, Steven. 2007. “The Decline of Intergenerational Coresidence in the United States, 1850 to 2000.” American Sociological Review 72 (6): 964–89. doi:10.1177/000312240707200606.

Cohen, Philip N. 2014. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 2, “History.”

March 7

Marriage and social class

Cherlin, Andrew J. 2014. Labor’s Love Lost: The Rise and Fall of the Working-Class Family in America. New York: Russell Sage Foundation.

Cohen, Philip N. 2014. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 8, “Marriage and cohabitation.”

March 14

Fatherhood: race, class, and multiple-partner fertility

Edin, Kathryn and Timothy Nelson. 2013. Doing the Best I Can: Fatherhood in the Inner City. University of California Press.

March 21

Spring break

March 28

Transition to adulthood

Crosnoe, Robert, and Monica Kirkpatrick Johnson. 2011. “Research on Adolescence in the Twenty-First Century.” Annual Review of Sociology 37:439–60.

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.

Billari, Francesco C., and Aart C. Liefbroer. 2010. “Towards a New Pattern of Transition to Adulthood?” Advances in Life Course Research 15 (2–3, SI): 59–75. doi:10.1016/j.alcr.2010.10.003.

Ghimire, D. J., W. G. Axinn, S. T. Yabiku, and A. Thornton. 2006. “Social Change, Premarital Nonfamily Experience, and Spouse Choice in an Arranged Marriage Society.” American Journal of Sociology 111 (4): 1181–1218.

April 11

Economic conditions and family outcomes

Sweeney, Megan M., and R. Kelly Raley. 2014. “Race, Ethnicity, and the Changing Context of Childbearing in the United States.” Annual Review of Sociology 40:539–58.

Currie, Janet, and Hannes Schwandt. 2014. “Short- and Long-Term Effects of Unemployment on Fertility.” Proceedings of the National Academy of Sciences 111 (41): 14734–39. doi:10.1073/pnas.1408975111.

Schneider, Daniel, Kristen Harknett, and Sara McLanahan. 2016. “Intimate Partner Violence in the Great Recession.” Demography 53 (2): 471–505. doi:10.1007/s13524-016-0462-1.

April 18

Policy, race, and nonmarital births

England, Paula. 2016. “Sometimes the Social Becomes Personal: Gender, Class, and Sexualities.” American Sociological Review 81 (1): 4–28.

Cohen, Philip N. 2015. “Maternal Age and Infant Mortality for White, Black, and Mexican Mothers in the United States.” Sociological Science 3 (January): 32–38.

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.

Cohen, Philip N. Forthcoming. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage promotion [Excerpts]” 24pp. [to be provided]

April 25

More U.S. inequality issues

Musick, Kelly, and Robert D. Mare. 2006. “Recent Trends in the Inheritance of Poverty and Family Structure.” Social Science Research 35 (2): 471–99. doi:10.1016/j.ssresearch.2004.11.006.

Western, Bruce, and Christopher Wildeman. 2009. “The Black Family and Mass Incarceration.” Annals of the American Academy of Political and Social Science 621 (1): 221–242.

Two selections from Families in an Era of Increasing Inequality (2015) edited by Paul R. Amato, Alan Booth, Susan M. McHale, and Jennifer Van Hook, 3–23. National Symposium on Family Issues 5. Springer International Publishing.

McLanahan, Sara, and Wade Jacobsen. “Diverging Destinies Revisited.”

Cohen, Philip N. 2015. “Divergent Responses to Family Inequality.”

May 2

Family structure and child wellbeing

Regnerus, Mark. 2012. “How Different Are the Adult Children of Parents Who Have Same-Sex Relationships? Findings from the New Family Structures Study.” Social Science Research 41 (4): 752–70. doi:10.1016/j.ssresearch.2012.03.009.

Rosenfeld, Michael J. 2015. “Revisiting the Data from the New Family Structure Study: Taking Family Instability into Account.” Sociological Science 2 (September): 478–501. doi:10.15195/v2.a23.

Cohen, Philip N. Forthcoming. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage equality in social science and the courts.” 19pp. [to be provided]

Gates, Gary J. 2015. “Marriage and Family: LGBT Individuals and Same-Sex Couples.” Future of Children 25(2):67-87.

May 9

Divorce, Remarriage and Stepfamilies

Amato, Paul R. 2010. “Research on Divorce: Continuing Trends and New Developments.” Journal of Marriage and Family 72(3):650-666.

Kennedy, Sheela, and Steven Ruggles. 2014. “Breaking Up Is Hard to Count: The Rise of Divorce in the United States, 1980–2010.” Demography 51 (2): 587–98. doi:10.1007/s13524-013-0270-9.

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

Anderson, Lydia R. 2016. “Divorce Rate in the U.S.: Geographic Variation, 2015.” National Center for Marriage and Family Research. http://www.bgsu.edu/ncfmr/resources/data/family-profiles/anderson-divorce-rate-us-geo-2015-fp-16-21.html.

Cohen, Philip N. 2016. “Life Table Says Divorce Rate Is 52.7%.” Family Inequality. June 8. https://familyinequality.wordpress.com/2016/06/08/life-table-says-divorce-rate-is-52-7/.

Bennett, Neil G. 2017. “A Reflection on the Changing Dynamics of Union Formation and Dissolution.” Demographic Research 36 (12): 371–90. doi:10.4054/DemRes.2017.36.12.

 

Race/ethnicity and slacking at work

From John Henry: An American Legend, by Ezra Jack Keats

I gave some comments to an Economist writer for a story they just published, “New research suggests that effort at work is correlated with race.” They used a snippet of what I said, so I figured I’d dump the rest here (because the piece is not bylined, I’m not using the reporter’s name).

The article is about an NBER working paper (not yet peer reviewed) by, Daniel Hamermesh, Katie Genadek, and Michael Burda. It’s officially here, but I put a copy up in case you don’t have am NBER subscription.) The analysis uses the American Time Use Survey to see whether time at work spent not working varies by race/ethnicity, and they find that it does. The abstract:

Evidence from the American Time Use Survey 2003-12 suggests the existence of small but statistically significant racial/ethnic differences in time spent not working at the workplace. Minorities, especially men, spend a greater fraction of their workdays not working than do white non-Hispanics. These differences are robust to the inclusion of large numbers of demographic, industry, occupation, time and geographic controls. They do not vary by union status, public-private sector attachment, pay method or age; nor do they arise from the effects of equal-employment enforcement or geographic differences in racial/ethnic representation. The findings imply that measures of the adjusted wage disadvantages of minority employees are overstated by about 10 percent.

When the Economist contacted me, I consulted several colleagues for their response. Reeve Vanneman pointed out that minority workers might slack off at work because they are discriminated against, and Liana Sayer pointed out that the activity measures in the ATUS may not be not precise enough to say what if any “non-work” activity is actually contributing to the bottom line – the paper doesn’t detail what these “non-work” activities are. My own critique was that, before we start attributing work behavior to “culture,” we might consider whether work reporting behavior varies by “culture” as well (the ATUS uses self-reported time diaries). The authors did a little monkeying around with the General Social Survey to address that, but I found it unpersuasive.

Anyway, you can read the Economist article yourself. I would have preferred they killed the article, because I don’t think the paper sustains its conclusions, but they did a reasonable job of reporting it. And here are the full comments I sent them:

The analysis in the paper does not support the conclusion that wage disparities between blacks and whites are overstated. There just isn’t enough there to make that claim. As the authors note, the problem of differential reporting is an obvious concern. Their analysis of the “importance of work” questions in the GSS seems immaterial – it’s just not the same question.

This is exacerbated by the problem that they don’t describe the difference between work-related non-work activities and non-work-related non-work activities. We just don’t know enough about what they’re doing to draw the conclusion that the work-related activities are really productivity enhancing while the non-related activities are really not. (Consider trying to parse the effect of eating alone at your desk versus eating with a team-member in the cafeteria. Which is productivity enhancing?) It is always the case that jobs differ between blacks and whites in ways surveys do not capture – that’s the whole question of the wage gap. Controlling for things like industry and occupation helps but it’s the tip of the iceberg. For example, the difference between small and large employers, and between those with formal management procedures and those without, is not captured here.

Finally, consider the possibility of reverse-causality. What if blacks are discriminated against and paid less than whites for the same level of productivity – or treated poorly in other ways – a very reasonable hypothesis? Might that not lead those black workers to be less devoted to their employers, and spend more time on other things when no one is looking? I wouldn’t blame them.

In short, the paper uses a lot of ambiguous information, which is interesting and suggestive, to draw a conclusion that is not warranted. It’s part of a tradition in economics of assuming there must be some rational basis for pay disparities, and looking really hard to find it, rather than treating employer motivations more skeptically and trusting the voluminous evidence of racist bias in the labor market.

In the email exchange, they asked for followup on the evidence of racial bias, so I added this:

The best evidence of discrimination is from audit studies. This is one of the best. That author, Michael  Gaddis at Penn State, can talk much more about it, but the point is that even when you can’t identify an individual act of racism, in the aggregate employer behavior shows a preference for whites — as we can tell by imposing experimental conditions in which the only thing different between resumes is the names. Other approaches include studying disparities in performance evaluation (e.g., this [by Marta Elvira and Robert Town]), or analyzing discrimination case files directly (e.g., this [by Ryan Light, Vincent Roscigno, and Alexandra Kalev]).

That all got reduced to this, in the article: “Worse treatment by managers of minority workers may itself encourage slacking, says Philip Cohen.” (Though they went on to cite evidence that workers work less when their managers are biased against them.)

On the other hand

As I think about it more, there is another important angle on this, which goes back to Reeve’s comment, and also something in the conclusion to the Economist article:

Within hours of publication, Mr Hamermesh received vitriolic messages and was labelled a racist in an online forum popular among economists. Mr Hamermesh, an avowed progressive, who refers to Donald Trump only by amusing nicknames and resigned from a post at the University of Texas over a state law permitting the open carrying of firearms, finds this unfair. He notes that Americans work too much. His preferred solution would not be for some groups to work more, but for others to work less.

There is an understandable anti-racist tendency to want to avoid a story of minority workers as lazy and shiftless – which is a character flaw. But there is a resistance story to tell as well, and the liberal anti-racist approach papers it over. For this, we need historian Robin D. G. Kelley, who wrote a brilliant paper called, “‘We Are Not What We Seem’: Rethinking Black Working-Class Opposition in the Jim Crow South” (free copy here). Here’s a relevant excerpt, in which he cites W. E. B. Du Bois:

Part of the reason [labor historians have not written more about workplace theft and sabotage by Southern Blacks], I think, lies in southern labor historians’ noble quest to redeem the black working class from racist stereotypes. In addition, company personnel records, police reports, mainstream white newspaper accounts, and correspondence have left us with a somewhat serene portrait of black folks who only occasionally deviate from what I like to call the “cult of true Sambohood.” The safety and ideological security of the South required that pilfering, slowdowns, absenteeism, tool breaking, and other acts of black working-class resistance be turned into ineptitude, laziness, shiftlessness, and immorality. But rather than reinterpret these descriptions of black working-class behavior, sympathetic labor historians are often too quick to invert the images, remaking the black proletariat into the hardest working, thriftiest, most efficient labor force around. Historians too readily naturalize the Protestant work ethic and project onto black working people as a whole the ideologies of middle-class and prominent working-class blacks. But if we regard most work as alienating, especially work done amid racist and sexist oppression, then a crucial aspect of black working-class struggle is to minimize labor with as little economic loss as possible. Let us recall one of Du Bois’s many beautiful passages from Black Reconstruction: “All observers spoke of the fact that the slaves were slow and churlish; that they wasted material and malingered at their work. Of course they did. This was not racial but economic. It was the answer of any group of laborers forced down to the last ditch. They might be made to work continuously but no power could make them work well.”

Working hard for the man’s benefit is not the only way to build character.

Family syllabus supplements for Spring 2017

People using my book for in their classes get excellent teaching materials from Norton to use. They also have a Facebook group for sharing ideas and materials (instructors visit here). For extra support, and to maximize timeliness, I also regularly update this list of blog posts that might help you with your course, whether or not you’re using my book.

As in previous lists, there are recent posts and some older favorites. Plenty of good material is still available on the supplements 2013, 2014, and 2015. As always, I appreciate feedback on what works and what doesn’t.

1. Introduction

• Interview: Independence, uncertainty, defamilialization: I did a radio interview on family trends, and drew on some themes related to modernity and individualization that show up in Chapter 1.
• Why I called it The Family, and what that has to do with Cosby: Explaining the origins of the “family as an institutional arena” frame I used in the book — and how the secrets in real and imaginary families like the Huxtables illustrate the concept.
• Deciphering a well-told data story, cars are good for kids edition: A parable of misleading statistical stories inspired by recent claims about marriage, with tips for critical reading.

2. History

• This word ‘generation,’ I do not think it means what you think it means: How people talk about “Millennials,” and what a historical cohort means.
• Diversity is the new normal (pdf): A short report I wrote explaining family demographic trends, comparing the 1950s and today.

3. Race, ethnicity, and immigration

• Why Heritage is wrong on the new Census race/ethnicity question: The Census Bureau has good ideas about improving data collection on race and ethnicity. Will the new regime let them do it?
• Black men raping White women: BJS’s Table 42 problem: American racists have always loved the Black rapist myth. Unfortunately, the government helped them with a badly constructed table.
• Six grueling demographic indicators of Detroit’s decline (and some pictures): The population dropped 64% since 1950, and shifted from 16% Black to more than 80% Black. The city that remains is a demographic disaster.

4. Social class

• US policy fails at reducing child poverty because it aims to fix the poor: An essay I wrote for the Washington Post argues for getting poor people more money instead of getting them to change their family behavior.
• Policy, politics, and promoting education versus marriage: Instead of blaming poor women for not being married, how about decreasing the number of poor women, by increasing access to higher education?
• Turns out marriage and income inequality go pretty well together: Does the decline of marriage increase economic inequality? Yes and no.

5. Gender

• My, what dimorphic parents you have! Updating Disney dimorphism to include Moana, with a look at extremely different parents.
• Transgender discrimination is sex discrimination: A brief argument for using the law on sex discrimination to protect transgender rights, in response to the former Obama administration’s education reforms.
• Pregnancy discrimination and the gender gap, involuntary job choice edition: One example helps illustrate the problem of measuring the gender gap by comparing men and women in the same jobs.

6. Sexuality

• Couple fact patterns about sexuality and attitudes: Updating trends on attitudes toward homosexuality; and same sex behavior, identity, and attraction.
• What was I supposed to do, not report the results? What if it’s true that women are more likely to wear red on dates — does that mean our sexual behavior is “hard wired”? I don’t think so.

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

• We can’t build our social system around marriage anymore: Marriage has declined in every state every decade since 1980. So what are you going to do about it?
• This “Supporting Health Marriage,” I do not think it means what you think it means: More than a billion dollars taken from the welfare program to promote marriage, and not a single healthy marriage to show it.
• Does gay marriage make straight men hate children? In the steadily losing battle against marriage equality, one of the more pernicious claims is that gay marriage threatens to turn all men against children.

9. Families and children

• The fathers behind teen births (or, statistical memes and motivated blind trust): A combination demography and media literacy post, in which I pick on a popular meme. (I followed up with a meme attempt of my own.)
• International adoption to the US has fallen 75%: Updating the trend, with detail on the countries of origin, and some explanation for what’s going on with China.
• Delayed parenting and anti-poverty policy: Policies intended to prevent poverty by delaying parenthood are mostly misplaced, especially with regard to Black women.

10. Divorce, remarriage, and blended families

I never put this on the blog, but here’s my update for divorce rates through 2015.

• The liberalization of divorce attitudes proceeds apace: More and more Americans think divorce is morally acceptable, and should be easier to get.
• Life table says divorce rate is 52.7%: Spelling out my method for estimating the risk of divorce from marriage through death (or divorce, whichever comes first).
• Check that: Most marrying people are remarrying above age 31: As the age at first marriage rises, second and third weddings are taking a larger share of the limelight.

11. Work and families

• Why male and female ‘breadwinners’ aren’t equivalent (in one chart): Pushing back on the tendency to call women who earn $.01 more than their husbands “breadwinners.”
• How Can We Jump-Start the Struggle for Gender Equality? In this New York Times essay, I argue for paid family leave, reduced work hours, and public child care.
• What drives the rise of stay at home fathers? How you interpret the trend depends on how you define stay at home fathers. With the “hardcore” definition, stay at home mothers still outnumber stay at home fathers 24-to-1.

12. Family violence and abuse

• Survivor bias and the 92% of Southern Black men who support spanking: With NFL family violence in the news, I asked why so many Americans rationalize spanking by referring to their own childhoods.
• Getting beyond how the ‘Factual Feminist’ is wrong about the prevalence of rape: It’s not perfect, but if you had to pick a number it’s reasonable to estimate that one in five women will be a victim of rape in their lifetime.
• Final proof there is no human tragedy Brad Wilcox will not exploit in order to promote marriage: Did he really just say that women should get married if they don’t want to be raped? (In fact, marriage and rape have both fallen a lot in the last few decades.)

13. The future of the family

• Thought leader for a day: Families in uncertain times: I gave an interview on diversity and uncertainty, and — yes — plasticity in family life.
• Get your dependency ratio off my lawn: Old people are doing more work than they used to. A new way of showing how much weight they pull now.
• Tripping on tipping points: Minority births are now the majority. Is this a tipping point, a milestone, or a watershed? On the importance of accurately representing trends.