Equal-education and wife-more-education married couples don’t have sex less often

In my review of Mark Regnerus’s book, Cheap Sex, I wrote: “The book is an extended rant on the theme, ‘Why buy the cow when you can get the milk for free?’ wrapped in a misogynist theory about sexual exchange masquerading as economics, and motivated by the author’s misogynist religious and political views.”

Someone just reposted an old book-rehash essay of Regnerus’s called, “The Death of Eros.” In it he links to my post documenting the decline in sexual frequency among married couples in the General Social Survey. In marriage, Regnerus writes, “equality is the enemy of eros,” before selectively characterizing some research about the relationship between housework and sex. (Here’s a recent analysis finding egalitarian couples don’t have sex less.)

But I realized I never looked at sexual frequency in married couples by the relative education of the spouses, which is available in the GSS. So here’s a quick take: Married man-woman couples in which the wife has equal or more education don’t have sex less frequently.

I modeled sexual frequency (an interval scale from “not at all” = 0 to “4+ times per week” = 6 as a function of age, age-squared, respondent education, respondent sex, decade, and relative education (wife has lower degree, wife has same degree, wife has higher degree). The result is in this figure. Note the means are between 3 (“2-3 times per month”) and 4 (“weekly”). Stata code for GSS below.

OK, that’s it. Here’s the code (I prettied the figure a little by hand afterwards):

*keep married people
keep if marital==1

* with non-missing own and spouse education
keep if spdeg<4 & degree<4
recode age (18/29=18) (30/39=30) (40/49=40) (50/59=50) (60/109=60), gen(agecat)
recode year (1970/1979=1970) (1980/1989=1980) (1990/1999=1990) (2000/2008=2000) (2010/2016=2010), gen(decade)
gen erosdead = spdeg>degree
gen equal=spdeg==degree

gen eros=0
replace eros=1 if spdeg<degree & sex==1
replace eros=2 if spdeg==degree
replace eros=3 if spdeg>degree & sex==1

replace eros=1 if spdeg>degree & sex==2
replace eros=3 if spdeg<degree & sex==2

label define de 1 "wife less"
label define de 2 "equal", add
label define de 3 "wife more", add
label values eros de

reg sexfreq i.sex i.agecat i.decade i.degree i.eros [weight=wtssall]
reg sexfreq i.sex c.age##c.age i.degree i.eros##i.decade [weight=wtssall]
margins i.eros##i.decade
marginsplot, recast(bar) by(decade)

Note: On 25 Dec 2018 I fixed a coding error and replaced the figure; the results are the same.

New findings: The widening political divide over science

Gordon Gauchat published a paper in American Sociological Review in 2012 (paywalled), reporting that political conservatives in the United States lost confidence in the scientific community from the 1970s to 2010, while political liberals and moderates did not. As a result, a political divide opened up so that by 2010 conservatives had the lowest level of confidence in science.

I’ve written a short working paper that extends the trends through the 2016 General Social Survey. I find that political conservatives, Republicans, and Americans who attend religious services regularly, all demonstrate falling levels of confidence in the scientific community. Further, for the most recent period (2012-2016) educational attainment, for conservatives, is not associated with increased levels of confidence in science, except at the graduate degree level (and even there confidence is lower among conservatives). It’s as if conservative views on science are impervious to education.

I have only a few sentences about politics in the paper, but the gist of it is that this divide is bad, it undermines democracy, and plays into Trump’s authoritarian hands.

Here’s the figure showing the Republicans falling off the trust cliff: the trend in expressing “a great deal of confidence” in the scientific community, with controls, by party identification:

 And here’s the figure from the recent period, showing that conservatives have lower levels of confidence regardless of education level — and education has no relationship to confidence in science for them, up to the level of graduate degree at least.

I welcome your feedback.

The paper is here on SocArXiv, and the Stata code is here.

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.

Survey says 23% of Whites think Whites are more intelligent than Blacks

In response to a request from New York Times reporter Amy Harmon, I used the General Social Survey (GSS) to address the question: “How Many Whites think Whites are more intelligent than Blacks?”

She made the request as part of her research for this story about how White supremacists are selectively manipulating genetics research, under the banner of “race realism,” to spread their ideas. This analysis didn’t end up in her story, but I put the tables and a brief write-up, with the code, here: https://osf.io/xt4j8/.

GSS asks about both Whites and Blacks: “Do people in these groups tend to be unintelligent or tend to be intelligent?” The responses were coded on a seven-point scale from “unintelligent” to “intelligent.” Without asking people to make a comparison, then, the survey allows us to identify people who rate White intelligence higher than Black intelligence.

For a contemporary estimate, I pooled three surveys (2012, 2014, and 2016). You can see how Whites rated the intelligence of Blacks and Whites in this table. Cells on the diagonal show Whites who rated Blacks and Whites equally. Cells below the diagonal show the percentages of Whites who rated White intelligence higher.

The table shows that 23 percent of Whites assess the intelligence of Whites as greater than the intelligence of Blacks, according to the General Social Survey, compared with 8 percent who said the reverse. This 23 percent is down from more than 50 percent in 1990, but only a few points lower than it was a decade ago. The assessment that Whites are more intelligent than Blacks is more common among male, older, less formally educated, and conservative Whites, and (in multivariate models only) among Democrats compared to Republicans. Here are the marginal results from a linear regression model predicting whether Whites think Whites are more intelligent than Blacks.

This is just one slice of racist beliefs as told to survey takers. In a previous analysis, Sean McElwee and I showed that the tendency of Whites to describe Blacks as violent and lazy was more common among Trump supporters, and Republicans generally, but a substantial minority of Democrats expressed those views as well. Racism, in its structural as well as interpersonal forms, is a lot bigger and more complicated than expressed beliefs on a survey, but I think it’s useful to analyze patterns like this as well.

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!

How conservatism makes peace with Trump

 

Jonah Goldberg telling his Howard Zinn story to John Podhoretz on CSPAN.

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I  wrote a long essay on Jonah Goldberg’s book, Suicide of the West. Because it has graphs and tables and a lot of references, I made it a paper instead of a blog post, and posted it on SocArXiv, here. If you like it, and you happen to edit some progressive or academic publication that would like to publish it, please let me know! I’m happy (not really, but I will) to shorten it. There, I pitched it. Feedback welcome.

First paragraph:

This essay is a review of Suicide of the West: How the Rebirth of Tribalism, Populism, Nationalism, and Identity Politics is Destroying American Democracy, by Jonah Goldberg (Crown Forum, 2018), with a few data explorations along the way. I read the book to see what I could learn about contemporary conservative thinking, especially anti-Trump conservatism. Opposing Trump and the movement he leads is suddenly the most pressing progressive issue of our time, and it’s important not to be too narrow in mobilizing that opposition. Unfortunately, I found the book to be an extended screed against leftism with but a few pages of anti-Trump material grafted in here and there, which ultimately amounts to blaming leftism and immigration for Trump. And that might sum up the state of the anemic conservative movement. Goldberg’s own weak-kneed position on Trump is not resolved until page 316, when he finally concludes, “As much as I hold Trump in contempt, I am still compelled to admit that, if my vote would have decided the election, I probably would have voted for him” (316). In the end, Goldberg has charted a path toward a détente between his movement and Trump’s.