Visualizing family modernization, 1900-2016

After this post about small multiple graphs, and partly inspired by two news reports I was interviewed for — this Salt Lake Tribune story about teen marriage, and this New York Times report mapping age at first birth — I made some historical data figures.

These visualizations use decennial census data from 1900 to 1990, and then American Community Survey data for 2001, 2010, and 2016; all data from IPUMS.org. (I didn’t use the 2000 Census because marital status is messed up in that data, with a lot of people who should be never married coded as married, spouse absent; 2001 ACS gets it done.)

An important, simple way of illustrating the myth-making around the 1950s is with marriage age. Contrary to the myth that the 1950s was “traditional,” a long data series show the period to be unique. The two trends here, teen marriage and divorce, both show the modernization of family life, with increasing individual self-determination and less restricted family choices for women.

First, I show the proportion of teenage women married in each state, for each decade from 1900 to 2016. The measure I used for this is the proportion of 19- and 20-year-olds who have ever been married (that is, including those married, divorced, and widowed). It’s impossible to tell exactly how many people were married before their 20th birthday, which would be a technical definition of teen marriage, but the average of 19 and 20 should do it, since it includes some people are on the first day of their 19th year, and some people are on the last day of their 20th, for an average close to exact age 20.

I start with a small multiple graph of the trend on this measure in every state (click all figures to enlarge). Here the states are ordered by the level of teen marriage in 2016, from Maine lowest (<1%) to Utah (14%):

This is useful for seeing that the basic pattern is universal: starting the century lower and rising to a peak in 1960, then declining steeply to the present. But that similarity, and smaller range in the latest data, make it hard to see the large relative differences across states now. Here are the 2016 levels, showing those disparities clearly:

Neither the small multiples nor the bars help you see the regional patterns and variations. So here’s an animated map that shows both the scale of change and the pattern of variation.

This makes clear the stark South/non-South divide, and how the Northeast led the decline in early marriage. Also, you can see that Utah, which is such a standout now, did not have historically high teen marriage levels, the state just hasn’t matched the decline seen nationally. Their premodernism emerged only in relief.

Divorce

Here I again used a prevalence measure. This is just the number of people whose marital status is divorced, divided by the number of married people (including separated and divorced). It’s a little better than just the percentage divorced in the population, because it’s at least scaled by marriage prevalence. But it doesn’t count divorces happening, and it doesn’t count people who divorced and then remarried (so it will under-represent divorce to the extent that people remarry). Also, if divorced people die younger than married people, it could be messed up at older ages. Anyway, it’s the best thing I could think of for divorce rates by state all the way back to 1900.

So, here’s the small multiple graph, showing the trend in divorce prevalence for all states from 1900 to 2016:

That looks like impressive uniformity: gradual increase until 1970, then a steep upward turn to the present. These are again ordered by the 2016 value, from Utah at less than 20% to New Mexico at more than 30% — smaller variation than we saw in teen marriage. That steep increase looks dramatic in the animated map, which also reveals the regional patterns:

Technique

The strategy for both trends is to download microdata samples from all years, then collapse the files down to state averages by decade. The linear figures are Stata scatter plots by state. The animated maps use maptile in Stata (by Michael Stepner) to make separate image files for each map, which I then imported into Photoshop to make the animations (following this tutorial).

The downloaded data, codebooks, Stata code, and images, are all available in an Open Science Framework project here. Feel free to adapt and use. Happy to hear suggestions and alternative techniques in the comments.

Families and Modern Social Theory, revised syllabus

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

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

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

Assignments

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

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

Evaluation

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

Universal learning

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

Device ban

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

Difficult subjects.

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

Academic integrity

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

Schedule and readings

August 30: Introduction

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

Part I: Modernity

September 6: What is modernity?

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

September 13: Modern relationships

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

September 20: Habitus and field

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

September 27: Discipline

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

Part II: Families

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

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

October 11: New families

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

October 18: Economics over all

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

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

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

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

October 25: No seminar meeting

November 1: Family economics

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

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

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

November 15: Black families, uncertainty, and exclusion.

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

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

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

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

Part III: Development and change

November 22: Modernity, development, and demography

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

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

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

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

November 29: Decoupling, families, and modernity

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

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

 

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.

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.

Interview: Independence, uncertainty, defamilialization

My photo from Flickr: https://flic.kr/p/Fet9Dc

I had an hour-long discussion about the decline of marriage on WYPR, Baltimore public radio, a couple weeks ago. You can listen to it here. I transcribed a short section that summarizes some of the points I find myself making in different contexts. This is a light edit (including taking out a couple things I disagree with myself on).

Independence

Q: Tell us more about some of the factors that are at work here. Some people say, well, back in the day my grandmother of course got married because she wasn’t going to have much of a job anyway, but now women have great jobs, so, that’s why they’re not getting married. True or false?

A: That is probably the biggest factor. Not just employment, but really independence. For women especially but for young adults overall. And that is, increased educational opportunities, increased employment opportunities, and the extended young adulthood, or what some people call extended adolescence. Just going to college into your 20s, and delaying that entry into marriage.

I’m not sure that if people are marrying less or marrying later we should equate this with a decline in respect or the importance of marriage. In some ways marriage is more important now that it’s more often a choice. That is people elevate it in their minds or in the culture because – when everybody had to be married, and it was virtually universal around 1960, it wasn’t something that that people personally chose. And so, yes it was important in the sense that everyone was doing it, but now it’s reached the point where people are much more likely to say: This marriage is not good, it’s not working, it’s not what I imagine a marriage should be, therefore we’re going to divorce. Or: We have a vision for marriage which is exalted, and we want to have our marriage take place when we have arrived, and we’re ready to own a house or a decent place to live, we have good jobs, to provide something for our children – and therefore because of that high view of marriage, we’re going to delay marriage. And so that may end up reducing the numbers of married people also, but not because people don’t value marriage.

Uncertainty

One way to think about the high divorce rate – which people are aware of – is it’s a kind of uncertainty that hangs over people. But it’s only one kind. In many ways life is less predictable and more uncertain than it was a few decades ago. And that just makes it difficult for a person to make long-term plans and commitments. We see this in the economic sphere, definitely, where people change careers and jobs more often than they did in the past. In housing, where they may change where they live more often. In a variety of ways our lives are less predictable. And when you don’t know what the future holds in one arena it’s very hard to make a commitment in another. You wouldn’t want to pick your job and make a lifetime commitment to it before you know what your college major is going to be. And in the same way, it’s difficult to make a commitment in marriage before you know what career you’re going to have, or how long you’re going to spend in school. So the uncertainty in one realm translates into cautiousness in others.

[Here I recommended All the Single Ladies by Rebecca Traister and Going Solo by Eric Klinenberg.]

There are different kinds of freedom in play here, and they’re somewhat contradictory. If you have a long-term commitment, that gives you one kind of freedom, for example the freedom to experiment, to make changes in your lifestyle, to change jobs, to take time off from work. Or things that you can do with the security of knowing that the other person is there to back you up. On the other hand, of course, the freedom of being single is a different kind of freedom, is the freedom to not have the set of burdens and obligations that do come from marriage or any kind of long-term commitment. So I do think it’s possible to consider the pros and cons that go in both ways, and it does get back to that idea of uncertainty in life, and the idea of tying oneself down to a long-term commitment in the absence of predictability in all the other aspects of life just seems increasingly disjointed to people. It doesn’t resonate with a lot of people.

The economic argument for marriage has always been that – like contracts, in the economy in general – when you make a commitment, it increases predictability, and you can make long-term plans and investments. For example, you can take a year off to invest in some training, and not worry that you’re going to end up losing income in the long run. And then you also have the economies of scale, two people sharing one refrigerator and one car is more efficient. And then there also are effects of marriage on people’s behavior. The fact that people are relying on you may make people, especially men, behave more responsibly. That may not have to happen within marriage, but the idea of having people depend on you may make people, for example, focus on their career advancement more than other kinds of ambitions.

Defamilialization

So it’s a challenge for our economy and our welfare state to think about: how can we ensure the wellbeing of people who do not have the two-person marriage – if we can’t assume people have that to back them up, economically speaking, and especially their children. But we’ve been going in that direction for a long time. The introduction of Social Security, retirement for older people, the public education system, we’ve been making investments in people to make them less reliant for their survival on their families for a long time, and in the long run that’s an important part of modern society. There’s a downside and an upside to that. The upside is people can act according to their own ambitions and desires individually, with more freedom than they could in the past. The downside is the expense for state institutions of caring for them and their children. It’s a complicated set of tradeoffs, and I think the important thing to realize is we can’t build our policies around the assumption that everybody and their parents are going to be married forever. And if we do that we’re going to leave a lot of people out, and put a lot of people at risk for real hardship.

Certain death? Black-White death dispersions

New research report, after rumination.

Knowing the exact moment of death is a common fantasy. How would it change your life? Here’s a concrete example: when I got a usually-incurable form of cancer, and the oncologist told me the median survival for my condition was 10 to 20 years, I treated myself to the notion that at least I wasn’t going to the dentist anymore (6 years later, with no detectable cancer, I’m almost ready to give up another precious hour to dentistry).

I assume most people don’t want to die at a young age, but is that because it makes life shorter or because it makes them think about death sooner? When a child discovers a fear of death, isn’t it tempting to say, “don’t worry: you’re not going to die for a long, long time”? The reasonable certainty of long life changes a lot about how we think and interact (one of the many reasons you can’t understand modernity without knowing some basic demography). I wrote in that cancer post, “Nothing aggravates the modern identity like incalculable risk.” I don’t know that’s literally true, but I’m sure there’s some connection between incalculability and aggravation.

Consider people who have to decide whether to get tested for the genetic mutation that causes Huntington’s disease. It’s incurable and strikes in what should be “mid”-life. Among people with a family history of Huntington’s disease, Amy Harmon reported in the New York Times, the younger generation increasingly wants to know:

More informed about the genetics of the disease than any previous generation, they are convinced that they would rather know how many healthy years they have left than wake up one day to find the illness upon them.

The subject of Harmon’s story set to calculating (among other things) whether she’d finish paying off her student loans before her first symptoms appeared.

The personal is demographic

So what is the difference between two populations, one of which has a greater variance in age at death than the other? (In practice, greater variance usually means more early deaths, and the risk of a super long life probably isn’t as disturbing as fear of early death.) Researchers call the prevalence of early death — as distinct from a lower average age at death — “life disparity,” and it probably has a corrosive effect on social life:

Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. (source)

That’s why reducing life disparity may be as important socially as increasing life expectancy (the two are highly, but not perfectly, correlated).

New research

Consider a new paper in Demography by Glenn Firebaugh and colleagues, “Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States.”

I previously reported on the greater life disparity and lower life expectancy among Blacks than among Whites. Here is Firebaugh et al’s representation of the pattern (the distribution of 100,000 deaths for each group):

Black deaths are earlier, on average, but also more dispersed. The innovation of the paper is that they decompose the difference in dispersion according to the causes of death and the timing of death for each cause. The difference in death timing results from some combination of three patterns. Here’s their figure explaining that (to which I added colors and descriptions, as practice for teaching myself to use an illustration program — click to enlarge):

The overall difference in death timing can result from the same causes of death, with different variance in timing for each around the same mean (spread); different causes of death, but with the same age pattern of death for each cause (allocation); and the same causes of death, but different average age at death for each (timing). Above I said greater variability in life expectancy usually means more early deaths, but with specific causes that’s not necessarily the case. For example, one group might have most of its accidental deaths at young ages, while another has them more spread over the life course.

Overall, the spread effect matters most. They conclude that even if Blacks and Whites died from the same causes, 87% of the difference in death timing would persist because of the greater variance in age at death for every major cause. There are differences in causes, but those mostly offset. Especially dramatic are greater variance in the timing of heart disease (especially for women), cancer, and asthma (presumably more early deaths), The offsetting causes are higher Black rates of homicide (for men) and HIV/AIDS deaths, versus high rates of suicide and accidental deaths among White men (especially drug overdoses).

The higher variance in causes of death seems consistent with problems of disease prevention and disparities in treatment access and quality. (I’m not expert on this stuff, so please don’t take it exclusively from me — read the paywalled paper or check with the authors if you want to pursue this.)

Are these differences in death timing enough to create differences in social life and outlook, or health-related behavior, between these two groups? I don’t know, but it’s worth considering.

Why Don’t Parents Name Their Daughters Mary Anymore?

Originally published on TheAtlantic.com.

Understanding the rapid decline of what was once America’s most popular name

Each year I mark the continued calamitous decline of Mary as a girls’ name in the United States. Not to be over-dramatic, but in the recorded history of names, nothing this catastrophic has ever happened before. Mary was the most common name given to girls every year from the beginning of record-keeping (at least back to 1800) through 1961 (except for a six-year dip to #2, behind Linda).

And then it happened. In 2011, according to the latest report from the Social Security Administration (SSA), Mary fell three more places, to 112th. In absolute numbers, the number of girls given the name Mary at birth has fallen 94 percent since 1961. Here is the trend:

The modernization theory of name trends, advanced most famously by the sociologist Stanley Lieberson, sees the rise of individualism in modern naming practices. “As the role of the extended family, religious rules, and other institutional pressures declines,” he wrote, “choices are increasingly free to be matters of taste.” Mary—both a traditional American name and a symbol religious Christianity—embodies this trend.

The other day I did a search of newspaper birth announcements for the name “Mary,” and turned up a lot of grandmothers named Mary. Here, from a recent day’s birth announcement page from Rock Hill, South Carolina, are three Marys in the grandparent generation, in three different families announcing the births of Mazie, Ja’Nae, and Asani. I diagrammed the family names:

Other generational sequences in recent announcements also mirror the history of common names: Mary–Jennifer–Madelyn; Mary–Ashley–Emily; Mary–Cora–Elizabeth.

In the tradition of treating statistical trends as horse races, I imagine that there is one person named Mary, who is constantly falling behind: first behind Linda, then Lisa, Jennifer, Ashley, Jessica, and so on, all the way to Isabella and now Sophia.

But that’s not how it happens—it just looks that way because of the amazing regularity in human behavior, which produces an orderly succession of names. Incredibly, out of 1.7 million girls’ names recorded by the SSA in 2011, I was able to predict to within 87 how many would be named Mary. By simply taking the number born in 2010 and subtracting the 5-year average decline, I predicted 2,584 would be born; the actual number was 2,671 (an error of 3.3 percent).

Somehow, out of the millions of individual decisions parents make, they produce steady trends like this. (If you’re as amazed as I am, consider a career in sociology! If not, please bear with me.)

So what does the Mary trend mean? First, it’s the growing cultural value of individuality, which leads to increasing diversity. People value names that are uncommon. When Mary last held the number-one spot, in 1961, there were 47,655 girls given that name. Now, out of about the same number of total births, the number-one name (Sophia) was given only 21,695 times. Conformity to tradition has been replaced by conformity to individuality. Being number one for so long ruined Mary for this era.

Second, America’s Christian family standard-bearers are not standing up for Mary anymore. It’s not just that there may be fewer devout Christians, it’s that even they don’t want to sacrifice individuality for a (sorry, it’s not my opinion) boring name like Mary. In 2011 there were more than twice as many Nevaehs (“Heaven” spelled backwards) born as there were Marys. (If there is anything more specific going on within Christianity, please fill me in.)

I’m not here to give advice to people who want to bring back the “traditional family.” But if I were I would recommend putting your names where your tradition is—and producing some more Marys.

There are precedents for bringing names back. My simple linear prediction method fails once in a while, when a name’s trend turns around. The greatest example is probably Emma. Emma was at number three when the SSA records begin, in 1880. She fell almost down to #500 by the 1970s. But after a decade of uncertainty she began a fantastic run, finally reaching number one in 2008.

I don’t know (yet) what makes a name turn around like that. Why Emma and not Mildred or Bertha, both former top-10s who fell into oblivion? But if any name has a chance for a similar resurgence, it might be Mary, at least as long as Christianity keeps hanging around.