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 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.

New working paper: The rising marriage mortality gap among Whites

I wrote a short working paper on U.S. mortality trends for the last decade. You can go straight to the paper on SocArXiv, or the code and output, if you want the full version.

The issue is that premature mortality has been rising for Whites, partly because of the opioid epidemic and also from suicide and alcohol, and also from other causes related to stress and hardship. (See, e.g., Case and Deaton, and Geronimus.) And a recent NCHS report showed that mortality nationally declined much more for married people since 2010.

So I got the Mortality Multiple Cause Files from the National Center for Health Statistics, for two years: 2007 and 2017. These are a complete set of death certificates, which include race/ethnicity, marital status, and education. I linked these to the American Community Survey, to create age-specific mortality rates by age, sex, marital status, and education, for non-Hispanic Whites, Hispanics, and Blacks, in the ages 25-74 (old enough to finished with college, but too young to die).

The basic result is that virtually all of the growth in premature death is among Whites, and further among non-married Whites. (Whites still dies less than Blacks, and more than Hispanics, at each age and marital status.)

Here is the figure of age-specific mortality rates, by race/ethnicity, sex, and marital status for 2007 and 2017. At the bottom of each column I calculated “marriage mortality ratios,” which are how much more likely single people are to die than married people. Note these death rates are deaths per 10,000, but they’re on a log scale so you can see changes where rates are very low.

In the figure you can see how much the marriage mortality ratio jumped up, for Whites only. Now, at the most extreme, single White men age 35-39 are more than 4-times more likely to die than married White men (that’s in the bottom left).

Then I zoom into Whites specifically, and do the same thing for four levels of education:

In the lowest education group of Whites (the far left), mortality rates for married and single people increased similarly, so the marriage mortality ratio didn’t increase. However, for the other education levels, death rates increased for single people more than married people, so the ratio increased (across the bottom). Even among White college graduates, there were increases in mortality for single people. I did not expect that.

My bottom line is that marriage is taking an ever-more prominent place in the social status hierarchy, and now we can add growing mortality inequality, at least among Whites, to that pattern.

Early version, comments welcome!

The Coming Divorce Decline, Socius edition

“The Coming Divorce Decline, ” which I first posted a year ago, has now been published by the journal Socius.  Three thousand people have downloaded it from SocArXiv, I presented it at the Population Association, and it’s been widely reported (media reports), but now it’s also “peer reviewed.” Since Socius is open access, I posted their PDF on SocArXiv, and now that version appears first at the same DOI or web address (paper), while the former editions are also available.

Improvement: Last time I posted about it here I had a crude measure of divorce risk with one point each for various risk factors. For the new version I fixed it up, using a divorce prediction model for people married less than 10 years in 2017 to generate a set of divorce probabilities that I apply to the newly-wed women from 2008 to 2017:

…the coefficients from this model are applied to newly married women from 2008 to 2017 to generate a predicted divorce probability based on 2017 effects. The analysis asks what proportion of the newly married women would divorce in each of their first 10 years of marriage if 2017 divorce propensities prevailed and their characteristics did not change.

The result looks like this, showing the annual probability falling from almost 2.7% to less than 2.4%:

The fact that this predicted probability is falling is the (now improved) basis for my prediction that divorce rates will continue to decline in the coming years: the people marrying now have fewer risk factors. (The data and code for all this is up, too).

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Prediction aside: The short description of study preregistration is “specifying your plan in advance, before you gather data.” You do this with a time-stamped report so readers know you’re not rejiggering the results after you collect data to make it look like you were right all along. This doesn’t always make sense with secondary data because the data is already collected before we get there. However, in this case I was making predictions about future data not yet released. So the first version of this paper, posted last September and preserved with a time stamp on SocArXiv, is like a preregistration of the later versions, effectively predicting I would find a decline in subsequent years if I used the same models — which I did. People who use data that is released on a regular schedule, like ACS, CPS, or GSS, might consider doing this in the future.

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Rejection addendum: Sociological Science rejected this — as they do, in about 30 days, with very brief reviews — and based on their misunderstandings I made some clarifications and explained the limitations before sending it to Socius. Since the paper was publicly available the whole time this didn’t slow down the progress of science, and then I improved it, so I’m happy about it.

Just in case you’re worried that this rejections means the paper might be wrong, I’m sharing their reviews here, as summarized by the editor. If you read the current version you’ll see how I clarified these points.

* While the analyses are generally sensible, both Consulting Editors point out the paper’s modest contribution to the literature relative to Kennedy and Ruggles (2014) and Hemez (2017). The paper cites both of these papers but does not make clear how the paper adds to our understanding derived from those papers. If the relatively modest extension in the time frame in this paper is sociologically consequential, the paper does not make the case clearly.

* There is more novelty in the paper’s estimates of the annual divorce probability for newly-married women (shown in Table 3 and Figure 3), based on estimating a divorce model for the most recent survey year, and then applying the coefficients from that model to prior years. This procedure was somewhat difficult for the readers to follow, but issues were raised, most notably the question of the sensitivity of the results to the adjustments made. As one CE noted, “Excluding those in the first year of marriage is problematic as newlyweds have a high rate of divorce. Also, why just married in the last 10 years? Consider whether married for the first time vs remarried matters. Also, investigate the merits of an age restriction given the aging of the population Kennedy and Ruggles point to.”

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.

Let’s raise the legal age of marriage in Maryland

Today I sent the following letter to the Maryland House Judiciary Committee, which is scheduled to hold a hearing on these bills tomorrow. Under current law in Maryland, marriage is permitted as young as age 15 with parental consent and evidence of pregnancy or childbirth, and age 16-17 with one or the other, and these exceptions are granted by county clerks rather than judges. By my calculations, from 2008 to 2017, based on the American Community Survey, the annual marriage rate for girls ages 15-16 was 5 per 1000 in Maryland, behind only Hawaii, Nevada, and West Virginia. HB 855 would raise the age at marriage to 18, while HB 1147 would establish an emancipated minor status, requiring review by a judge, under which 17-year-olds could marry. For more on the effort to end child marriage in the U.S., visit the Tahirih Justice Center site.

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March 6, 2019

To the House Judiciary Committee:

I write in support of Maryland House Bill 855, concerning age requirements for marriage; and House Bill 1147, concerning the emancipation of minors.

My relevant background

• I am a Professor of Sociology, and family demographer, at the University of Maryland, College Park, where I have been on the faculty since 2012. I also earned my PhD at the University of Maryland, College Park, in 1999, and I live in Silver Spring.
• I have written two books and many peer-reviewed articles on family sociology, including on topics related to marriage and divorce, family structure, gender inequality, health and disability, infant mortality, adoption, race and ethnicity, and the division of labor.
• I have served as a consultant to the U.S. Census Bureau on the measurement of family structure, and testified before Congress on gender discrimination.

My support of the bills

In general, the rise of the age at marriage and childbearing in U.S. have been positive developments for women and children, allowing mothers to devote more years of early adulthood to education and career development, which is beneficial to both adults and their children.

Very early marriage in particular is detrimental to women’s opportunity to finish high school. More urgently, research and service work shows that very early marriage is usually unwanted, coerced, or forced. Very young women should not be expected to protect themselves legally or socially from such impositions, which are usually from older men and dominant family members. Very early marriage often follows statutory rape or other sexual assault, compounding rather than mitigating the harms of these crimes against children. Rather than protect a young woman, very early marriage instead provides protection from scrutiny for her abuser(s), and makes state intervention on her behalf all the more difficult to accomplish in the following years. The privacy and discretion we bestow upon families has benefits, of course, but it also makes the family a dangerous place for the victims of abuse.

Research, including my own, unequivocally shows that very early marriage leads to the highest rates of divorce. I have written several papers on divorce rates in the United States (see references). For illustration, here I used the same method of analysis, and present only the relationship between age at marriage and incidence of divorce. As you can see from the figure, divorce rates are highest by far – estimated at 2.5% per year – for women who married before age 18. This is about twice as high as divorce rates for those who marry in their 30s, for example. (These estimates hold constant other factors; data and code are available here.) The evidence is very strong.

I only reluctantly support increasing state restrictions on women’s freedom with regard to family choices, but in the case of marriage before adulthood I see the restriction as a protection from the exploitative behavior of others, rather than an imposition on young women’s rights.

At present in Maryland, exceptions allowing marriage before age 18 – based on pregnancy and/or parental consent – are granted without adequate legal review. Together, HB 855 and HB 1147 would set the minimum age at marriage in Maryland to 18, with an exception only for court emancipated minors of age 17. This would improve the state’s protection of young women from unwanted, coerced, forced, or ill-advised marriages without unduly restricting the freedom to marry for younger women (age 17), who may be emancipated by a court after a direct application and careful review of circumstances.

I urge your support for these bills. I would be happy to provide further information or testimony at your request.

Sincerely,

Philip N. Cohen

References

Cohen, Philip N. 2015. “Recession and Divorce in the United States, 2008-2011. Population Research and Policy Review 33(5):615-628.

Cohen, Philip N. 2018. “The Coming Divorce Decline.” SocArXiv. November 14. https://osf.io/preprints/socarxiv/h2sk6. To be presented at the Population Association of America meetings, 2019.

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.

Predicted divorce decline rolls on

With the arrival of the 2017 American Community Survey data on IPUMS.org, I have updated my analysis of divorce trends (paper | media reports | data and code).

In the first version of the paper, based on data from 2008 to 2016, I wrote:

Because divorce rates have continued to fall for younger women, and because the risk profile for newly married couples has shifted toward more protective characteristics (such as higher education, older ages, and lower rates of higher-order marriages), it appears certain that – barring unforeseen changes – divorce rates will further decline in the coming years.

I don’t usually make predictions, but this one seemed safe. And now the 2017 data are consistent with what I anticipated: a sharp decline in divorce rates among those under age 45, and continued movement toward a more selective pattern in new marriages.

Here is the overall trend in divorces per 100 married women, 2008-2017, with and without the other variables in my model:

With the 2017 data, the divorce rate has now fallen 21% since 2008. To show the annual changes by age, I made this heatmap style table, with shading for divorce rates, rows for years, columns for age, and the column widths proportional to the age distribution (so 15-19 is a sliver, and 50-54 is the widest). The last row shows the sharp drop in divorce rates for women under age 45 in 2017:

To peek into the future a little more, I also made a divorce protective-factor scale, which looks just at newlywed couples in each year, and gives them one point for each spouse that is age 30 or more, White or Hispanic, has BA or higher education, is in a first marriage, and a point if the woman has no own children in the home at the time of the survey. So it ranges from 0 to 9. (I’m not saying these factors have equal importance, but they are all associated with lower odds of divorce.) The gist of it is new marriages increasingly have characteristics conducive to low divorce rates. In 2008 41% of couples had a score of 5 or more, and in 2017 it’s 50%.

So divorce rates will probably continue to fall for a while.

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!