Framing social class with sample selection

A lot of qualitative sociology makes comparisons across social class categories. Many researchers build class into their research designs by selecting subjects using broad criteria, most often education level, income level, or occupation. Depending on the set of questions at hand, the class selection categories will vary, focusing on, for example, upbringing and socialization, access to resources, or occupational outlook.

In the absence of a substantive review, here are a few arbitrarily selected examplar books from my areas of research:

• Mothering While Black: Boundaries and Burdens of Middle-Class Parenthood, by Dawn Marie Dow
• The Tumbleweed Society: Working and Caring in an Age of Insecurity, by Alison Pugh [my review]
• Negotiating Opportunities: How the Middle Class Secures Advantages in School, by Jessica McCrory Calarco.
• For the Family?: How Class and Gender Shape Women’s Work, by Sarah Damaske

This post was inspired by the question Caitlyn Collins asked the other day on Twitter:

#soctwitter, I'd love thoughts on how to operationalize social class categories for sampling in qualitative research. Yes, multidimensional evals with income, ed, occupation.. but I want best practices for actual schema to be used as filters for fieldwork, w people, face to face.

— Caitlyn Collins (@CaitlynMCollins) June 27, 2020

She followed up by saying, “Social class is nebulous, but precision here matters to make meaningful claims. What do we mean when we say we’re talking to poor, working class, middle class, wealthy folks? I’m looking for specific demographic questions, categories, scales sociologists use as screeners.” The thread generated a lot of good ideas.

Income, education, occupation

Screening people for research can be costly and time consuming, so you want to maximize simplicity as well as clarity. So here’s a way of looking at some common screening variables, and what you might get or lose by relying on them in different combinations. This uses the 2018 American Community Survey, provided by IPUMS.org (Stata data file and code here).

• I used income, education, and occupation to identify the status of individuals, and generated household class categories by the presence of absence of types of people in each. That means everyone in each household is in the same class category (a choice you might or might not want to make).
• Income: Total household income divided by an equivalency scale (for cost of living). The scale counts each adult as 1 person, each child under 18 as .70, and then scales that count by ^.70. I divided the resulting distribution into thirds, so households are in the top, middle, or bottom third. Top third is what I called “middle/upper” class, bottom third is “lower class.”
• Education: I use BA degree to identify households that have (middle/upper) or don’t (lower) a four-year college graduate present. This is 31% of adults.
• Occupation: I used the 2018 ACS occupation codes, and coded people as middle/upper class if their codes was 10 to 3550, which are management, business, and financial occupations; computer, engineering, and science occupations; education, legal, community service, arts, and media occupations; and healthcare practitioners and technical occupations. It’s pretty close to what we used to call “managerial and professional” occupations. Together, these account for 37% of workers.

So each of these three variables identifies an upper/middle class status of about a third of people.

For lower class status, you can just reverse them. The except is income, which is in three categories. For that, I counted households as lower class if their household income was in the bottom third of the adjusted distribution. In the figures below, that means they’re neither middle/upper class nor lower class if they’re in the middle of the income distribution. This is easily adjusted.

Venn diagrams

You can make Venn diagrams in Stata using the pvenn2 add-on, which I naturally discovered after making these. If  you must know, made these by generating tables in Stata, downloading this free plotter app, entering the values manually, copying the resulting figures into Powerpoint and applying the text there, then printing them to PDF, and extracting the images from PDF using Photoshop. Not recommended workflow.

Here they are. I hope the visuals might help people think about for example, who they might get if they screened on just one of these variables, or how unusual someone is who has a high income or occupation but no BA, and so on. But draw your own conclusions (and feel free to modify the code and follow your own approach). Click to enlarge.

First middle/upper class:

Then lower class:

I said draw your own conclusions, but please don’t draw the conclusion that I think this is the best way to define social class. That’s a whole different question. This is just about simply ways to select people to be research subjects. For other posts on social class, follow this tag, which includes this post about class self identification by income and race/ethnicity.

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Data and code: osf.io/w2yvf/

The COVID-19 epidemic in rural U.S. counties

I’ve been working on the COVID-19 epidemic in rural U.S. counties, and have now posted a paper on SocArXiv, here: https://osf.io/preprints/socarxiv/pnqrd/. Here’s the abstract, then some figures below:

Having first reached epidemic proportions in coastal metropolitan areas, COVID-19 has spread around the country. Reported case rates vary across counties from zero to 125 per thousand population (around a state prison in the rural county of Trousdale, Tennessee). Overall, rural counties are underrepresented relative to their share of the population, but a growing proportion of all daily cases and deaths have been reported in rural counties. This analysis uses daily reports for all counties to present the trends and distribution of COVID-19 cases and deaths in rural counties, from late March to May 16, 2020. I describe the relationship between population density and case rates in rural and non-rural counties. Then I focus on noteworthy outbreaks linked to prisons, meat and poultry plants, and nursing homes, many of which are linked to high concentrations of Hispanic, American Indian, and Black populations. The growing epidemic in rural counties is apparently driven by outbreaks concentrated in these institutional settings, which are conducive to transmission. The impact of the epidemic in rural areas may be heightening due to their weaker health infrastructure and more vulnerable populations, especially due to age, socioeconomic status, and health conditions. As a result, the epidemic may contribute to the ongoing decline of health, economic, and social conditions in rural areas.

Here are COVID-19 cases in rural counties across the country. Note that the South, Mid-Atlantic, Michigan, and New England have the most (fewer in West and upper Midwest). When you look at cases per capita, you see the concentration in the South and isolated others.

COVID is still underrepresented in rural counties, but their share of the national burden is increasing, as they keep adding more than 2,000 cases and just under 100 deaths per day.

Transmission dynamics are different in rural counties. They show a weaker relationship between pop density and cases. This suggests to me that there are more idiosyncratic factors at work (prisons, meat plants, nursing homes), which are high concentrations of vulnerable people.

These are the rural outbreak cases I identified, for which I could find obvious epidemic centers in institutions: Prisons, meatpacking and poultry plants, and nursing homes. These 28 select counties account for 15% of the rural burden.

In addition to the institutional concentration, these outbreak cases also show distinct overrepresentation of Hispanic, American Indian, and Black populations. Here are some of the outbreak cases plotted against minority concentrations.

And here’s a table of those selected cases:

Lots more to be done, obviously. It’s a strong limitation to be restricted to case and death counts at the county level. Someone could go get lists of prisons and meatpacking plants and nursing homes and run them through this, etc. But I wanted to raise this issue substantively. By posting the paper on SocArXiv, without peer review, I’m offering it up for comment and criticism. Also, I’m sharing the code (which links to the data, all public): https://osf.io/wd2n6/. Messy but usable.

A related thought on writing a paper about COVID19 right now: The lit review is daunting. There are thousands of papers, most on preprint servers. Is this bad? No. I use various tools to decide what’s reliable to learn from. If it’s outside my area, I’m more likely to rely on peer-reviewed journals, or those that are widely citied or reported. But the vast quantity available still helps me see what people are working on, what terms, and types of data they use. I learned a tremendous amount. Much respect to the thousands of researchers who are doing what they can to respond to this global crisis.

The arriving divorce decline

In “The Coming Divorce Decline” I showed the U.S. divorce rate falling from 2008 to 2017, and predicted that, because the married population was being stocked with increasingly non-divorce-prone marriages, the rate would continue to fall. After the first draft (based on 2016 data), divorce fell in 2017, providing the first support for my prediction before the paper was even “published” (accepted for Socius). Now the 2018 data is out, and divorce has become less common still.

Here’s a quick update.

Based on the number of divorces reported in the survey each year, by sex, and the number of married people, I calculate the refined divorce rate, or the number of divorces per 1,000 married people. That fell another 3% for both women and men in 2018, to 15.9 and 14.3 respectively (the rates differ because these are self reports and women report more).

When I run the model from the paper again on the new data (on women only), I can show the drop in the adjusted odds of divorce, updating Figure 1 of the paper (the 2018 change in an unadjusted model is significant at p=.06; adjusted is p=.14, the adjusted change from 2016 is significant at p=.002).

For other takes on the latest data, see this report on the marriage-divorce ratio from Valerie Schweizer, and this on geographic variation from Colette Allred, both at the National Center for Family and Marriage Research.

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• The data and code for the paper are available here. This update uses the same code with one new year of data.
• If you like my new Stata figure scheme (modified from Gray Kimbrough’s Uncluttered) you’re welcome to it: here.
• Slides from my presentation this fall at the European Divorce Conference are here.
• Divorce posts are gathered under this tag.

Family diversity, new normal

Family diversity is not just a buzzword (although it is that), and it’s not just the recognition of diversity that always existed (although it is that). There really is more actually-existing diversity than there used to be.

In The Family, I use a figure with five simple household types to show family conformity increasing from 1900 to a peak in 1960 — and then increasing diversity after that. I’ve updated that now for the upcoming third edition of the book.

In 2014, I wrote a report for the Council on Contemporary Families called “Family Diversity is the New Normal for America’s Children,” which generated some news coverage and a ridiculous appearance with Tucker Carlson on Fox & Friends. A key point was to demonstrate that the declining dominant family arrangement after 1960 — the male-breadwinner-homemaker family — was replaced by a diversity of arrangements rather than a new dominant form. Here I’ve updated one the main figures from that report, which shows that “fanning out from a dominant category to a veritable peacock’s tail of work-family arrangements.”

For this update, I take advantage of the great new IPUMS mother and father pointers to identify children’s (likely) parents, including same-sex couple parents who are cohabiting as well as those who are married. Census doesn’t collect multiple parent identifiers in the Decennial Census or American Community Survey, and IPUMS has tackled the issue of how to best presume or guess about these with a consistent and well-documented standard. In this figure, 0.42% of children ages 0-14 (about 250,000) are living in the households of their same-sex couple parents. I also rejiggered the other categories a little, but the basic story is the same.

I published a version of this figure for K-12 educators in Educational Leadership magazine in 2017. I wrote:

Today, teachers need to have a more inclusive mindset that recognizes the diversity of family structures. Although there are reasons for concern about some of the changes shown in the data, the driving factors have often been positive. For example, changes in family roles reflect increased educational and occupational opportunities for women and greater gender equality within families. Fathers are expected to play an active role in parenting—and usually do—to a much greater degree than they did half a century ago.

My advice to teachers is:

The key points of diversity in family experiences that teachers should watch for are family structure (such as who the student lives with), family trajectories (the transitions and changes in family structure), and family roles (who cares and provides for the student). Using principles from universal design, teachers can promote language and concepts that work for all students. Done right, this is an opportunity to broaden the learning experience for everyone—to teach that care, intimate relationships, and family structures can include people of different ages, genders, and familial connections.

So that’s my update.

The changing household age range, U.S. 1900-2017

One way to understand daily interaction, and intergenerational resource exchange, is just to look at the structure of households. This doesn’t tell you everything that goes on in households, but it gives some strong clues. And we can measure it going back more than a century, thanks to IPUMS.org’s collection of Census microdata.

In 1900, the most common situation for an American was to live in a household where the age difference between the oldest and youngest person was about 38 years. Now the most common situation is an age range of 0 — either living alone, or with someone of the exact same age. And there are a lot more people living in households with only similar-aged adults, with age ranges of less than 10.

In between 1900 and 2017, life expectancy increased, the age at first birth increased, and the tendency to live in multigenerational households fell and then rose again. So the household structure story is complicated, and this is just one perspective.

But it’s one indicator of how life has changed. Line up your household from youngest to oldest, look to your left and look to your right — how far can you see?

 

Data and Stata code (for all decades 1900-2000, then individual years to 2017) are available on the Open Science Framework, here.

White children are 2.7-times more likely than Black children to live with a parent who has a PhD

For a reflection Amy Harmon was working on, a followup to her article on the experience of Black mathematicians in American academia, I took a shot at the question: How many children have parents with PhDs?

The result was the highlighted passage (17 words and a link!) in her piece:

[all the racial biases that contribute to Black underrepresentation include] the well-documented racial disparities in public-school resources, the selection of students for gifted programs — and the fact that having a parent with a Ph.D. is helpful to getting one in math, while black children are less than half as likely as white children to live with such a parent.

To get there: I used data from the U.S. Census Bureau via IPUMS.org: The 1990 5% Public Use Microdata Sample (decennial census); and the 2000, 2010, and 2017 American Community Surveys.

I coded race/ethnicity into four mutually-exclusive categories: Single-race White, Black, and Asian/Pacific Islander (API); and Hispanic (including those of any race). I dropped from the analysis non-Hispanic children with multiple races reported, and American Indian / Alaska Natives (for whom about 0.5 percent lived with a PhD parent in 2017).

IPUMS made a tool that attaches values of parents’ variables to children with whom they share a household. I used that to calculate the highest level of education of each child’s coresident parents. In the Census data, children may have up to two parents present (which may be of the same sex in 2010 and 2017). Children living with no parent in the household were not included.

This let me calculate the percentage of children living (at the moment of the survey) with one or more parents who had a PhD. For each of the four groups the percentage of children living with a parent who has a PhD roughly doubled between 1990 and 2017. API children had the highest chance of living with a PhD parent, reaching 6.8 percent in 2017. The percentages for the other groups were: Whites, 2.7 percent; Blacks, 1.0 percent; and Hispanics, 0.7 percent:

The 2.7% for White children, versus, 1.0% for Black children, is the basis for her statement above.

Details (including the whole parents’ education distribution), data, codebook, and code, are available on the Open Science Framework at: https://osf.io/ry3zt/ under CC-BY 4.0 license.

Math bias

Both of Amy’s pieces are important reading for academics in many disciplines, including sociology, to reflect on the experience of Black colleagues in the environments we inherit and reproduce.

With regard to math, Amy points out that Black exclusion is not just about denying economic opportunity, it’s also about denying the public the benefits of all the lost Black math talents — and about denying Black potential mathematicians the joy and satisfaction of a passion for math realized.

As Daniel Zaharopol, the director of a program for mathematically talented low-income middle-school students, put it when I interviewed him for a 2017 article: “Math is beautiful, and being a part of that should not be limited to just some people.”

And Amy makes a good case that math bias and its outcomes contribute directly to racism much more broadly:

Some misguided people claim that there are not many black research mathematicians because African-Americans are not as intelligent as other races. These people, whom I have reported on for other stories in recent months, almost invariably use mathematical accomplishment as their yardstick for intelligence. They note that no individuals of African descent have won the Fields Medal, math’s equivalent of the Nobel Prize. They lack any genetic evidence to explain the gap in average I.Q. scores between white and black Americans that they cite as the basis of their belief, or reason to think that a genetic trait would be impervious to social or educational intervention, or that high I.Q. is key to math ability, which Timothy Gowers, a 1998 Fields medalist, has attributed largely to “the capacity to become obsessed with a math problem.”

But I have been reporting on these topics for several years, and I am acutely aware that math prowess factors heavily into the popular conception of intelligence. There’s a vicious cycle at work: The lack of African-American representation in math can end up feeding pernicious biases, which in turn add to the many obstacles mathematically talented minorities face. Which was one more reason it seemed especially important to hold up to the light all the racial biases that contribute to that underrepresentation.

The rise of Jewish boys’ names in the US

Names are cultural as the personal is political for marginalized groups.

I’ve had these numbers sitting around for a while, since I noticed Nazis on Twitter calling me “Shlomo” as an insult, and was just spurred to write them up by a fascinating Twitter thread from someone who goes by Benjamin (בנימן טבלוב). He writes in response to criticism of Jews who change their names from their “real” European names to Hebrew names, specifically Israeli Prime Minister Benjamin Netanyahu, whose father changed the family name from Mileikowsky after they moved from Europe to Palestine in 1920. (Netanyahu is terrible in every way, that’s not the point.)

Benjamin explained that the Jews of northern and eastern Europe historically practiced patronymic naming exclusively, naming children after their fathers, as in: Jacob, son of Isaac, son of Abraham. (The most famous contemporary patronymic society is Iceland, although they sometimes use matronyms now, too.) It was only with the bureaucratization of modern citizenship in eighteenth and nineteenth century Austria, Prussia, Russia, France, and Bavaria, that Jews were forced to take permanent surnames, and these were often not of their choosing, based things like on places, occupations, or even insults. Besides being generally dehumanizing, this system of Jewish surnames also eventually made it easy to round Jews up for the Holocaust (see the Kaplan and Bernays’ The Language of Names, and this paper, for some history). An exception, incidentally, is the use of the priestly honorific terms Cohen and Levy, which were already in place (e.g., Philip, son of Marshall the Cohen) and then became permanent surnames. I assume Israeli politicians aren’t ditching the name Cohen for something more Hebrew sounding.

So when Jews went to Palestine, they often took new Hebrew names; but when they came to America they took more English names, and then gave their kids mainstream American names. The history of coercive naming in Europe makes it easier to see why this might not have been so objectionable to the Jewish immigrants in the early twentieth century. Kaplan and Bernays quote an immigrant to New York who said, “Nothing good ever came to us while we bore them [old names]; possibly we’ll have more luck with the new names.” (My grandmother was born Tzivya (צִבְיָה), which became Cywja when she boarded a ship from Poland in 1921, and then eventually Sylvia.)

Jewish names today

Today it’s probably safe to say most Jewish children in the U.S. don’t have Jewish first names per se (although they sometimes have a Hebrew name they use just for religious occasions). Here I look at the trends for seven Jewish boys’ names I found on various naming websites: Shlomo, Chaim, Eliezer, Mordechai, Moshe, Yosef, and Zev. These were the most popular ones I could think of (feel free to suggest others).

First a little data on Yiddish and Hebrew in America. This is all from the Decennial Census and then, after 2000, the American Community Survey, which asked about “mother tongue” (language spoken at home as a child) from 1910 to 1970 (except 1950), and language spoken at home after that. The Census doesn’t ask about religion.

Yiddish was the language spoken by the big wave of Jewish immigrants in the early twentieth century. Hebrew is the primary official language of Israel, and the religious language of Judaism. This shows the percentage of people in the U.S. who spoke Yiddish or Hebrew from 1910 to 2017.* The peak in 1930 is 1.1 percent, during the immigration boom. The 1970 peak reflects the only year “mother tongue” was asked of non-immigrants as well as immigrants. By 1980 only one-in-500 Americans spoke Yiddish or Hebrew at home.

The second thing about Yiddish and Hebrew is children. There are a declining number of old immigrants speaking Yiddish, and no new immigrants speaking Yiddish. So most people speaking Yiddish as their language today are probably the descendants of those immigrants, orthodox Jews participating in ethnic revival or preservation. The same goes for people speaking Hebrew at home, except by now some of these could be immigrants from Israel and their children. (By 2000 Hebrew speakers outnumbered those speaking Yiddish.) Here’s the percentage of Yiddish and Hebrew speakers that were under 18 for the same years.

It was low in 1930, when they were mostly working-age immigrants, and then in 1960 when their kids were grown. The percentage under age 18 increased after 1960, and now 40 percent of Yiddish speakers are children (which is not the case for Hebrew). And, this is key: the proportion of all U.S. children speaking Yiddish at home has more than doubled since 1980, from 5 to 11 per 10,000. If these numbers are to be believed.

Names

The sample numbers here are small, but the ACS sample is also picking up about 150 Yiddish or Hebrew speaking women per year having babies, which implies that population is having about 10,000 babies per year, or about 26 out of every 10,000 babies born in the country.

So, who’s naming their sons Shlomo, Chaim, Eliezer, Mordechai, Moshe, Yosef, and Zev? Now switching to the Social Security names database, I find that these names together accounted for 1,943 boys born in 2017 (that’s 9.9 out of every 10,000 US boys born). What’s interesting is that none of these boys’ names reached the threshold for reporting in the database — five children — until 1942. This is remarkable given that Yiddish was in decline by then. And they’ve all been growing more common since that time. So all those Yiddish immigrants in 1920 weren’t naming their sons Moshe, or at least not legally, but now a growing (though small) proportion of their descendants are.

I can’t tell if Yiddish or Hebrew speakers are giving their sons these names. But there must be some connection between the rise of these names and the increase in the proportion of children speaking Yiddish at home. It might not be same people teaching their kids Yiddish, but they may be part of the same (highly localized) revival.

I’ve put the Social Security names data, and my SAS code for extracting name trends, on the Open Science Framework here.

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* An earlier version had much higher prevalence of Yiddish and Hebrew before 1980 because I was accidentally just showing the percentages among immigrants.

Decadally-biased marriage recall in the American Community Survey

Do people forget when they got married?

In demography, there is a well-known phenomenon known as age-heaping, in which people round off their ages, or misremember them, and report them as numbers ending in 0 or 5. We have a measure, known as Whipple’s index, that estimates the extent to which this is occurring in a given dataset. To calculate this you take the number of people between ages 23 and 62 (inclusive), and compare it to five-times the number of those whose ages end in 0 or 5 (25, 30 … 60), so there are five-times as many total years as 0 and 5 years.

If the ratio of 0/5s to the total is less than 105, that’s “highly accurate” by the United Nations standard, a ratio 105 to 110 is “fairly accurate,” and in the range 110 to 125 age data should be considered “approximate.”

I previously showed that the American Community Survey’s (ACS) public use file has a Whipple index of 104, which is not so good for a major government survey in a rich country. The heaping in ACS apparently came from people who didn’t respond to email or mail questionnaires and had to be interviewed by Census Bureau staff by phone or in person. I’m not sure what you can do about that.

What about marriage?

The ACS has a great data on marriage and marital events, which I have used to analyze divorce trends, among other things. Key to the analysis of divorce patterns is the question, “When was this person last married?” (YRMARR) Recorded as a year date, this allows the analyst to take into account the duration of marriage preceding divorce or widowhood, the birth of children, and so on. It’s very important and useful information.

Unfortunately, it may also have an accuracy problem.

I used the ACS public use files made available by IPUMS.org, combining all years 2008-2017, the years they have included the variable YRMARR. The figure shows the number of people reported to have last married in each year from 1936 to 2015. The decadal years are highlighted in black. (The dropoff at the end is because I included surveys earlier than those years.)

Yikes! That looks like some decadal marriage year heaping. Note I didn’t highlight the years ending in 5, because those didn’t seem to be heaped upon.

To describe this phenomenon, I hereby invent the Decadally-Biased Marriage Recall index, or DBMR. This is 10-times the number of people married in years ending in 0, divided by the number of people married in all years (starting with a 6-year and ending with a 5-year). The ratio is multiplied by 100 to make it comparable to the Whipple index.

The DBMR for this figure (years 1936-2015) is 110.8. So there are 1.108-times as many people in those decadal years as you would expect from a continuous year function.

Maybe people really do get married more in decadal years. I was surprised to see a large heap at 2000, which is very recent so you might think there was good recall for those weddings. Maybe people got married that year because of the millennium hoopla. When you end the series at 1995, however, the DBMR is still 110.6. So maybe some people who would have gotten married at the end of 1999 waited till New Years day or something, or rushed to marry on New Year’s Eve 2000, but that’s not the issue.

Maybe this has to do with who is answering the survey. Do you know what year your parents got married? If you answered the survey for your household, and someone else lives with you, you might round off. This is worth pursuing. I restricted the sample to just those who were householders (the person in whose name the home is owned or rented), and still got a DBMR of 110.7. But that might not be the best test.

Another possibility is that people who started living together before they were married — which is most Americans these days — don’t answer YRMARR with their legal marriage date, but some rounded-off cohabitation date. I don’t know how to test that.

Anyway, something to think about.

The coming divorce decline

Unless something changes outside the demogosphere, the divorce rate is going to go down in the coming years.

Divorce represents a number of problems from a social science perspective.

• • Most people seem to assume “the divorce rate” is always going up, compared with the good old days, which are supposed to be the whole past but are actually represented by the anomalous 1950s.
  • On other hand, social scientists have known for a few decades that “the divorce rate” has actually been declining since the 1980s. That shows up in the official statistics, with the simple calculation — known as the refined divorce rate — of the number of divorces per 1,000 married women.
  • On the third hand, the official statistics are very flawed. The federal system, which relies on states voluntarily coughing up their divorce records, broke down in the 1990s and no one fixed it (hello, California doesn’t participate). In the debate over different ways of getting good answers, a key 2014 paper from Sheela Kennedy and Stephen Ruggles showed that the decline in divorce after 1980 was mostly because the whole married population was getting older, and older people get divorced less. That refined divorce rate doesn’t account for age patterns. When you remove the age patterns from the data, you see a continuously increasing divorce rate. Yikes!
  • On the fourth hand, Kennedy and Ruggles stopped in about 2010. Since then, the very divorce-prone, multi-marrying, multi-divorcing Baby Boomers have moved further out of their peak action years, and it’s increasingly clear that divorce rates really are falling for younger people.

In my new analysis, which I wrote up as a short paper for submission to the Population Association of America 2019 meetings, I argue that all signs point to a divorce decline in the coming years. Here is the paper on SocArXiv, where you will also find the data and code. And here is the story, in figures (click to enlarge).

1. The proportion of married women who divorce each year has fallen 18% in the decade after 2008. (There are reasons to do this for women — some neutral, some good, some bad — but one good thing nowadays is at least this includes women divorcing women.) And when you control for age, number of times married, years married, education, race/ethnicity, and nativity, it has still fallen 8%.

2. The pattern of increasing divorce at older ages, described by Susan Brown and I-Fen Lin as gray divorce, is no longer apparent. In the decade after 2008, the only apparent change in age effects is the decline at younger ages, holding other variables constant.

3. The longer term trends, identified by Kennedy and Ruggles, which I extend to 2016, show that the upward trajectory is all about older people. These are prevalences (divorced people in the population), not divorce rates, but they are good for illustrating this trend.

4. In fact, when you look just at the last decade, all of the decline in age-specific divorce rates is among people under age 45. This implies there will be more older people who have been married a long time, which means low divorce rates. Also, their kids won’t be as likely to have divorced parents, although more kids will have parents who aren’t married, which might work in the other direction. (You can ignore then under-20s, who are 0.2% of the total.)

5. Finally, to get a glimpse of the future, I looked at women who report getting married in the year before the survey, and how they have changed between 2008 and 2016 on traits associated with the risk of divorce. They clearly show a lower divorce-risk profile. They are more likely to be in their first marriage, to have college degrees, to be older, and to have no children in their households (race/ethnicity appears to be a wash, with fewer Whites but more Latinas).

6. Finally finally, I also looked at the spouses of the newly-married women, and made an arbitrary divorce-protection scale, with one point to each couple for each spouse who was: age 30 or more, White or Hispanic, BA or higher education, first marriage, and no own children. Since 2008 the high scale scores have become more common and the low scores have become rarer.

7. It’s interesting that the decline in divorce goes against the (non-expert) conventional wisdom. And it is happening at a time when public acceptance of divorce has reached record levels (which might be part of why people think it’s growing more common — less stigma). Here are the trends in attitudes from Pew and Gallup:

That’s my story — thanks for listening!

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.