Category Archives: In the news

To tell the truth (right-wing front edition)

lyingcross

The mission of the Institute for Family Studies is “strengthening marriage and family life, and advancing the well-being of children, through research and public education.” As of this morning, this includes not a single use of the words “gay,” “lesbian,” or “same-sex” anywhere on their website, according to Google. They routinely post links to articles and research “of note,” that might interest readers who believe in their mission. So, why never mention the gay?

Or — dramatic pause — is that really their whole mission? The IFS website lists seven “senior fellows.” Don’t tell the others, but W. Bradford Wilcox is the only one getting paid $50,000 per year (in 2013). Their 2013 fundraising included $50,000 from the Bradley Foundation, which also supported Wilcox’s effort to fund the Regnerus study; and $20,000 from the Vine and Branches foundation, which lists the purpose of the donation as “religious” (the foundation’s eligibility criteria include, “Christian organizations that overtly express their faith through programming”).

So, do you really believe this?

As a nonpartisan, nonsectarian, and not-for-profit institute committed to the study of family life, IFS works with scholars, writers, and supporters without regard to academic discipline, party, or ideology.

The only thing that bothers me about this, besides the values, is the blatant, routine dishonesty. Why do respectable people just tolerate that?

Not to get into minutiae, but also, would it kill him to have any women among the nine officers of his shadowy, bogus non-profit foundation?

Note: I first wrote about IFS here, but only some of that info is still accurate.

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The scientific racism in Roof’s statement (can we get Wade and Murray on the record?)

Get it? Scientific racism.

Get it? Scientific racism.

The 2,400-word statement posted by Dylann Roof before he carried out the Charleston massacre — murdering nine Black people in an A.M.E. Church prayer meeting — is a clear statement of his terrorist political intentions (it’s described here, and archived here).

It also includes some very banal scientific racism which could, in slightly fancier language, have been written by Nicholas Wade or Charles Murray. Roof wrote:

Anyone who thinks that White and black people look as different as we do on the outside, but are somehow magically the same on the inside, is delusional. How could our faces, skin, hair, and body structure all be different, but our brains be exactly the same? This is the nonsense we are led to believe. Negroes have lower Iqs, lower impulse control, and higher testosterone levels in generals. These three things alone are a recipe for violent behavior. If a scientist publishes a paper on the differences between the races in Western Europe or Americans, he can expect to lose his job.

With regard to Roof’s first two sentences, you might compare them with Wade’s (all quotes found in my article):

It is reasonable to assume that if traits like skin color have evolved in a population, the same may be true of its social behavior, and hence the very different kinds of society seen in the various races and in the world’s great civilizations differ not just because of their received culture—in other words, in what is learned from birth—but also because of variations in the social behavior of their members, carried down in their genes.

On the second point — IQ, impulse control, and testosterone in Blacks — Roof is also in line with Wade. Wade inflates the weak case that the (rare) MAO-A “warrior gene” makes Blacks more violent than Whites, genetically. And then on the question of violence and impulse control, Wade explains that Africa remains poor because it is genetically stuck in tribalism (read: poor impulse control) despite the awesome helpfulness of the colonial powers on that continent (“Tribal behavior is more deeply ingrained than mere cultural prescriptions. Its longevity and stability point strongly to a genetic basis”). And also violent, as the higher-than-average homicide rate in Africa represents “a difference that does not prove but surely allows room for a genetic contribution to greater violence in the less developed world.”

On the claim of a forced silence about these (supposed) truths among the scientific community, Murray and Wade also are in agreement with Roof. This is a major theme for Wade. Instead of “expect to lose his job,” Murray says a scientist who focuses on the genetics of racial difference will face “professional isolation and stigma.” The point is the same. 

Given the closeness of his statements to their ideas, I think it would be helpful for Wade and Murray to explain how Roof is or is not accurate, and then explicitly denounce Roof’s associated actions. Of course Wade and Murray would never countenance racial murder of the kind perpetrated by Roof, and I would never put them in the same category — except insofar as their ideas are in fact similar. I would hate for Roof’s white supremacist friends to read the silence from Wade and Murray as endorsement of his view of racial genetics and its use as a rationale for violence.

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Stop me before I fake again

In light of the news on social science fraud, I thought it was a good time to report on an experiment I did. I realize my results are startling, and I welcome the bright light of scrutiny that such findings might now attract.

The following information is fake.

An employee training program in a major city promises basic job skills and as well as job search assistance for people with a high school degree and no further education, ages 23-52 in 2012. Due to an unusual staffing practice, new applications were for a period in 2012 allocated at random to one of two caseworkers. One provided the basic services promised but nothing extra. The other embellished his services with extensive coaching on such “soft skills” as “mainstream” speech patterns, appropriate dress for the workplace, and a hard work ethic, among other elements. The program surveyed the participants in 2014 to see what their earnings were in the previous 12 months. The data provided to me does not include any information on response rates, or any information about those who did not respond. And it only includes participants who were employed at least part-time in 2014. Fortunately, the program also recorded which staff member each participant was assigned to.

Since this provides such an excellent opportunity for studying the effects of soft skills training, I think it’s worth publishing despite these obvious weaknesses. To help with the data collection and analysis, I got a grant from Big Neoliberal, a non-partisan foundation.

The data includes 1040 participants, 500 of whom had the bare-bones service and 540 of whom had the soft-skills add-on, which I refer to as the “treatment.” These are the descriptive statistics:

fake-descriptives

As you can see, the treatment group had higher earnings in 2014. The difference in logged annual earnings between the two groups is significant at p

fake-ols-results

As you can see in Model 1, the Black workers in 2014 earned significantly less than the White workers. This gap of .15 logged earnings points, or about 15%, is consistent with previous research on the race wage gap among high school graduates. Model 2 shows that the treatment training apparently was effective, raising earnings about 11%. However, The interactions in Model 3 confirm that the benefits of the treatment were concentrated among the Black workers. The non-Black workers did not receive a significant benefit, and the treatment effect among Black workers basically wiped out the race gap.

The effects are illustrated, with predicted probabilities, in this figure:

fake-marginsplot

Soft skills are awesome.

I have put the data file, in Stata format, here.

Discussion

What would you do if you saw this in a paper or at a conference? Would you suspect it was fake? Why or why not?

I confess I never seriously thought of faking a research study before. In my day coming up in sociology, people didn’t share code and datasets much (it was never compulsory). I always figured if someone was faking they were just changing the numbers on their tables to look better. I assumed this happens to some unknown, and unknowable, extent.

So when I heard about the Lacour & Green scandal, I thought whoever did it was tremendously clever. But when I looked into it more, I thought it was not such rocket science. So I gave it a try.

Details

I downloaded a sample of adults 25-54 from the 2014 ACS via IPUMS, with annual earnings, education, age, sex, race and Hispanic origin. I set the sample parameters to meet the conditions above, and then I applied the treatment, like this:

First, I randomly selected the treatment group:

gen temp = runiform()
gen treatment=0
replace treatment = 1 if temp >= .5
drop temp

Then I generated the basic effect, and the Black interaction effect:

gen effect = rnormal(.08,.05)
gen beffect = rnormal(.15,.05)

Starting with the logged wage variable, lnwage, I added the basic effect to all the treated subjects:

replace newlnwage = lnwage+effect if treatment==1

Then added the Black interaction effect to the treated Black subjects, and subtracted it from the non-treated ones.

replace newlnwage = newlnwage+beffect if (treatment==1 & black==1)
replace newlnwage = newlnwage-beffect if (treatment==0 & black==1)

This isn’t ideal, but when I just added the effect I didn’t have a significant Black deficit in the baseline model, so that seemed fishy.

That’s it. I spent about 20 minutes trying different parameters for the fake effects, trying to get them to seem reasonable. The whole thing took about an hour (not counting the write-up).

I put the complete fake files here: code, data.

Would I get caught for this? What are we going to do about this?

BUSTED UPDATE:

In the comments, ssgrad notices that if you exponentiate (unlog) the incomes, you get a funny list — some are binned at whole numbers, as you would expect from a survey of incomes, and some are random-looking and go out to multiple decimal places. For example, one person reports an even $25,000, and another supposedly reports $25251.37. This wouldn’t show up in the descriptive statistics, but is kind of obvious in a list. Here is a list of people with incomes between $20000 and $26000, broken down by race and treatment status. I rounded to whole numbers because even without the decimal points you can see that the only people who report normal incomes are non-Blacks in the non-treatment group. Busted!

fake-busted-tableSo, that only took a day — with a crowd-sourced team of thousands of social scientists poring over the replication file. Faith in the system restored?

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Cohabitation in the marriage trend

The other day I complained about the low value added from a commercial marriage soothsayer. Making predictions about marriage in the short run isn’t very important (because short-run change is modest), and in the long run is much more complicated than the simple models I used. One very important complication that we in the United States are ill-prepared to deal with is cohabitation, raised in a comment yesterday by Gosta Esping-Andersen.

After a scare last fall over funding for the marital events and marital history questions in the Census Bureau’s American Community Survey (ACS), the government decided to keep the questions (I wrote about it here, here, and here). With these questions, we know a lot about the timing of marriage and divorce, in addition to births, from the biggest annual survey we have. However, we don’t know much about cohabitation. We know if people are cohabiting as “unmarried partners,” but only if they are doing so in a home owned or rented by one of the partners. And we don’t know how long they’ve been living together, or if someone used to cohabit but no longer does (cohab breakups aren’t recorded like divorces).

This isn’t so bad in the U.S., compared to some other countries where cohabitation tends to me more serious and long-lasting, but it still is a significant blind spot in our demographic data system. For example, according to an analysis of data from the National Survey of Family Growth (much smaller and less frequent than the ACS), by the Nation Center for Family and Marriage Research, the majority of unmarried women having births (57%) are in cohabiting relationships, which amounts to a quarter of all births. The proportion of single new-mothers living with someone is higher among Whites and Hispanics (two-thirds) than among Blacks (one-third).

Ultimately, the reason we care whether parents are married, or cohabiting, is because we want to know who’s going to take care of the children, and pay for them, and what their developmental environment will be. Marital status or living arrangements are a rough way to measure these things.

Marriage trends

Anyway, what role does cohabitation play in the decline in marriage? If people were just redefining their commitments, choosing cohabitation instead of marriage, that would mean something different than if they were just spending more of their lives truly single.

Frustratingly, the best annual data on cohabitation now comes from the Census Bureau’s Current Population Survey (CPS), rather than the ACS, which means it’s not paired with the marital events and history questions. In the CPS, since 2007 (a change I discussed here), we know if someone is cohabiting even if the couple is living in someone else’s household (such as a parent or roommate). So here’s a look at where cohabitation fits in to the marriage trends for young adults, from 2007 to 2014 (for these trends, I counted people as married only if they were not separated, and I counted people as cohabiting if they said they were living with a boyfriend or girlfriend even if they were married but separated):

Microsoft PowerPoint - marcohab-07-14.pptx

The figure shows that, even with the increase in cohabitation for 25-34-year-olds, singleness is still increasing. This is especially true for those in the peak marriage age of 25-29, for whom marriage has decreased 9% while cohabitation has increased only 4%. Strikingly, it also shows that cohabitation now is more common among 20-24-year-olds than marriage; I don’t remember noticing that before.*

So, at least in these broad strokes, cohabitation doesn’t account statistically for recent declines in marriage. But it is important: if you just focus on marriages, you miss the trend toward higher rates of cohabitation among unmarried people.

* UPDATE

Here are some figures showing the relative prevalence of cohabitation versus marriage, by sex, age, and year, using the same data and definitions as above. Restricting the data to those who are married or cohabiting, these figures show the percentage cohabiting, so over 50% means more people are cohabiting than are married (spouse present). Green is more cohabitation, red is less. Moving down the figures is time, and to the right is age, so older people are more likely to be married, and cohabitation increased from 2007 to 2014. By 2014, cohabiting was more common for men up to age 25, for women up to age 23. Because the samples are relatively small the estimates bounce around, so I smoothed the figures by averaging adjacent cells.

marcohab-07-14.xlsx

marcohab-07-14.xlsx

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Demographic Intelligence, low bar edition

U.S. marriage rates are falling generally, so that’s the real news. And it’s important. In fact, one classic projection has the rate hitting zero at 2042. But the news shenanigans are in the prediction business.

I first wrote here about Demographic Intelligence, a profit-making venture founded by Brad Wilcox (full file). They prey on companies’ ignorance about demography and the news media’s desire to stay ahead of the story, making ridiculous claims like “99% accuracy” in their forecasts. Here’s an update.

In a Washington Post Wonkblog entry meaninglessly titled, “Why parents should stop hoping their kids will get married,” we read:

“Millennials are such a big generation, we’re going to have more people of prime marriage age in the next five years than we’ve had at any time in U.S. history. For that alone, we’d expect an uptick in marriage rates,” said Sam Sturgeon, president of Demographic Intelligence.

Setting aside the knowledge-sucking obsession with generational marketing terms, let’s just hold the president of a company with “demographic” in the title to a slightly-higher-than-complete-nonsense standard of demographic intelligence. The median age at marriage is now 28 (combining men and women). At that age there were 4.3 million people in 2013. That is actually the highest number of people ever at the median age at marriage. For example, in 1900, when the median age at marriage was 24, there were only 1.5 million people that age. Wow!

However, intelligent demographer Sturgeon said “for that alone, we’d expect an uptick in marriage rates.” And marriage rates are based on population size. that 4.3 million people at age 28 in 2013 was only 1.35% of the population, while the 1.5 million people at age 24 in 1900 was 1.96% of the population. In fact, the pattern is the opposite of what Sturgeon said: we have never had fewer people — proportionately — in the prime marrying age. Double wow!

Here is the population distribution by age from 1900 to 2013, from the IPUMS.org online table maker (try it, it’s easy!). The color coding helpfully shows where the number is above average (red) versus below average (blue). I’ve highlighted the five-year age interval that contains the median marriage age for each decade:

Microsoft PowerPoint - uspop-age-dist-marriage-age.pptx

That the marriage rate is falling — Sturgeon’s expert prediction (see below) is that it will reach an all-time low in 2016 (as it has in 16 of the last 33 years) — is in large part driven by this age composition trend.

How accurate is that forecast?

Demographic Intelligence boasts “99% accuracy” in its wedding forecasts. And these forecasts, they say, are very useful:

This unique forecast is especially valuable as the federal marriage statistics are usually released 12 to 24 months after the date to which they apply, making official data of limited usefulness to the wedding industry. Our forecast is available 24 months before weddings happen, thereby offering a tremendous value to companies that focus on weddings and ancillary businesses.

Now, I’m all in favor of wasting the wedding industry’s money, but I don’t like deceiving the public. So I have to tell you: for every year from 2001 to 2012, if you had simply used last year’s marriage rate to predict this year’s, you would have averaged 98.3% accuracy. That is the deer-in-headlights method of forecasting. In fact, the deer-in-the-headlights forecast for 2012 — that is, assuming no change from 2011 — yields an astonishing accuracy of 99.87% (see below). Not bad! I’ll sell that to you for just 98% of what Demographic Intelligence is charging (except you’re already paying for my services, so you’re welcome).

Of course, demographers like projections, and I’m no exception. It is frustrating that official marriage statistics lag “real time” so much more than other important statistics, such as the unemployment rate or the number of named storms per season. That’s why in 2013 I announced a marriage forecast contest to predict the 2012 marriage rate, and provided some trends in key variables for you to experiment with (in a spreadsheet here): Google searches for wedding invitations, bridal showers, and wedding gifts; the unemployment rate, the Index of Consumer Sentiment, and the number of women ages 20-39:

There was so little interest in my contest (go figure), that I never got around to updating the results. So here goes. We now know from official statistics that there were 2,131,000 marriages in 2012, which, for a population of 313,914,040, yields a marriage-per-1000 rate of 6.788, down from 6.797 in 2011. Using different combinations of these variables, I generated projections using linear regressions. As I noted, the no-change performed very well, at 99.87% accuracy. But the winning model was actually the one that used the Google search trends only, which predicted 2,133,647 weddings, an astonishing 99.88% accurate. If Google is not using their data to get filthy rich — oh wait.

Anyway, in this exercise I’m just predicting the next year in the series — it gets a little trickier if you want to go four years out. And demographic projections are a serious science. But this prediction business is just wasting money and confusing people.

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Updated age education birth figures

As fertility continues in the news (see last week’s post on rising birth rates for women with higher education), I am preparing for a planned appearance today on the Kojo Nnamdi Show, on the “grandparent deficit” associated with births at advanced parental age. So I updated some old figures I made in 2012.

These come from two posts:

  • Poverty Poses a Bigger Risk to Pregnancy Than Age, which argued that a focus on parental age was distracting us from economic inequality. I concluded: When it comes to parents’ age versus social class, the challenges are not either/or. We should be concerned about both. But addressing the health problems of parents—especially mothers—with less than a college degree and below-average incomes is the more pressing issue—both for potential lives saved or improved and for social equality.
  • Births to mothers in their forties are less common now than in the old days, which explained that, although first births at older ages are more common, the birth rate among older women is lower now than it was during the Baby Boom. That is, women aren’t more likely to have a kid at age 40 now — they’re just more likely to have their first at that age.

Here are three figures I’ve updated.

The first shows the distribution of births by education within each age group of mothers. It shows, for example, 85% of women under 20 who had a birth in 2013 had a high school education or less. The highest levels of education are found among women have babies in their late 30s (note these are not just first births):

work.xlsxThe next one shows the same information, but now arranged as percentages of all births. This shows, for example, that 27% of all births are to women in their late 20s, with the majority of those having some college education or less:

work.xlsxFinally, the odd phenomenon in which, although the percentage of all births to women age 40+ has increased to the point that it surpassed the Baby Boom years, the birth rate for women that age is still much lower than it was:

advanced age trends.xlsxSo the average 40-year-old was more likely to have a baby in 1960 than today (15.5 per 1000 versus 10.5 per 1000), but a baby born today is more likely to have a mother 40 or older (2.3% versus 2.8%). That’s because more people were having births at all ages in 1960. The U-shape here reflects two historical trends: first, the total number of children per woman declined, which meant fewer born at older ages because people stopped earlier. Then, as marriage age increased, along with women’s education, women started delaying their first births, which led to increasing birth rates — and proportions of births — at older ages.

Sources:

The source for the first two figures is my analysis of 2013 ACS data from IPUMS.org. The last one is from National Center for Health Statistics reports: here, here, here, and here; as well as a couple of old Statistical Abstracts, here and here.

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Why are women with advanced degrees having more children?

There are a few puzzles in the latest news on U.S. fertility trends.

Preamble

The issues behind fertility trends and patterns are complex, reflecting changing social and well as biological influences, and demanding careful attention to methods. Birth rates can be measured as annual events (such as the percentage of women having a birth in a given year) or as life-course outcomes (such as the percentage of women who reach age 45 without ever having a birth). Comparisons over time are confounded by changes in the composition of the population with regard to age, and some subgroups are subject to changing composition as a result of social or cultural, rather than biological, trends. For example, consider that a woman may spend the years from age 22 to age 43 as a college graduate, and then register an advanced degree at age 44. That means her births in the previous 20 years count among those with BAs, while her “completed fertility” would be counted among those with advanced degrees.

The news

It’s been a confusing few days for fertility-news watchers, so I’ll try to muddle it up a little more. I ran some numbers for a conversation I had with New York Times Upshot reporter Claire Cain Miller, which she reported under the title, “Births to Single Mothers Are Down, Except for Those 35 and Older.” I’ll show those here. They go along with the various headlines about Gretchen Livingston’s new Pew report, “Childlessness Falls, Family Size Grows Among Highly Educated Women,” reported by Brigid Schulte as, “Why educated women are having more babies.”

Here’s Miller’s chart, based on federal registered birth data. Note these are birth rates for women who aren’t married, which is not the same as the percentage of births occurring to women who aren’t married:

miller-unwed

For unmarried women of all ages except 15-17, birth rates increased from 2002 to 2007. As I’ve shown before for women overall, the trend shows the increasing delay of childbearing, with a steeper rise for women ages 30-34 than for those in their early 20s. After 2007, however, reflecting the recession, birth rates fell for all unmarried women except those ages 35 and up. The conventional explanation for this has been that individuals and couples delayed births when they were financially squeezed, but those running up against the end of their fertile years couldn’t delay without risking infertility.

To see how this is working for single women in particular (and single here includes those who are cohabiting), it’s helpful to break it down by age and education. Older women face the biological clock issue regardless of their education level, and women with less education had greater exposure to recession-related hardship. What I showed Miller was this chart, which I made from American Community Survey (ACS) data provided by IPUMS.org. The solid lines are all unmarried women ages 15-44 — red for less than BA, blue for BA plus — while the dotted lines are just the older subgroup, 35-44. This shows that the volatility is greatest for women without BAs. And there is no real recession decline for the 35+ groups:

unmarried births ACS 01-13.xlsx

Based on that, Miller wrote:

During the recession, the decline in single motherhood was entirely attributable to women without college degrees, according to census data analyzed by Philip Cohen, a sociologist at University of Maryland who writes a blog called Family Inequality.

These are “women for whom the hardships of single motherhood are most acute,” Mr. Cohen said. “This could be deliberate planning, or it could reflect relationship problems or economic stress undermining their family plans.”

Among older women who are unmarried, ages 35 to 39, however, the birthrate was 48 percent higher in 2012 than in 2002, according to the National Center for Health Statistics. The increase was driven by college-educated women, according to Mr. Cohen’s analysis. “The delay in general fits a long-term pattern: that family formation is increasingly delayed until women are more established, spend more time in education and more time developing their careers,” he said.

This is tricky because of course single women without BAs do have higher birth rates, so it’s not like poor women just can’t afford to have children — but as a group they were affected more by the crisis. What that means is that a greater proportion of them were affected in such a way as to reduce their fertility than among other groups.

Falling childfreeness

Although it seems contradictory on the surface, this is consistent with Livingston’s headline: Childlessness Falls, Family Size Grows Among Highly Educated Women. Although my figure only shows single women, look at the BA-holding 35+ women: their birth rates rose about 50% from the beginning of the decade till the recession, from about 10 per 1000 to about 15 per 1000, a rate they held through the recession.

But Livingston’s data are “completed” cohort fertility — estimated by the number of children women have had when they’re surveyed in the ages 40-44. Here’s her rather shocking chart:

ST_2015-05-07_childlessness-01

My chart was annual birth rates. But hers is more interesting because it captures the life course more. What is it that is making women with advanced degrees have bigger families — and making fewer of them have no children at all?

There are several tricky things here, which I’ll show with data in a minute. They are:

  • The advanced-degree group has grown less select as it has grown — more women are entering this category. In particular, there are more Black and Hispanic women going beyond BAs, as well as presumably more women from poor backgrounds. So that might increase the birthrates of the group.
  • On the other hand, although marriage is more common among women with more education — and growing increasingly so — the proportion married among women going for advanced degrees has still fallen. Since married women have more children, this should lower fertility of higher-education women. (A quick check shows a slight decline in the proportion married among advanced degree holders under age 45 from 1990 to 2013, from 68% to 66%.)
  • Finally, as more women get BA degrees and go straight into additional schooling, the average age of women getting advanced degrees has fallen. That gives them more time to rack up births before hitting 44. (To make matters impossibly complicated, if they hold off on having children till they finish their advanced degrees, they will probably be younger when they graduate, as some graduate students with children might tell you.)

Remember that people make decisions about childbearing and education at the same time. If more women decide to get advanced degrees with the goal of having more children from a position of strength, then the statistics will show more women with advanced degrees having children — even if the decisions weren’t made in the order we assume.

It’s hard to get at this with the data we have. The population data we have on education and family characteristics doesn’t tell you when people got their degrees, which means those late 44-year-old medical school graduates are hard to pin down. Ideally, then, we’d have a measure of who is attending school, which would tell us who is on the way toward a degree. But the data from the Current Population Survey that Livingston used didn’t have measure of school attendance for people over age 25 until 2013. So I used the 1990 decennial Census and the 2013 ACS, which both have a measure of school attendance. Unfortunately, the 1990 Census doesn’t identify births, so I counted women as having had a birth if they were living in their own (or their husbands’) households with an “own child” age 0, which is not bad.

I took all the women ages 20-44 who already had a BA degree or higher, were attending school, and were living in their own (or their husbands’) households. In 1990 this was 3.4% of all women in that age group, and by 2013 it was 4.7% — a much bigger group. In 1990 3.5% of them had an infant, but that had increased to 4.6% by 2013. This is consistent with the Livingston finding that they are going to get advanced degrees and reach age 40-44 with more kids (if they experienced this birth rate difference every year, the completed fertility rates would be much higher for the later cohort).

Here are the breakdowns of the two cohorts according to the risk factors for childbearing I just described:

BAs attending school.xlsx

Notice: There are more in their prime childbearing ages (25-34), fewer married, and more Black and Hispanic. As it turns out, a regression analysis shows that the age change accounts for about a quarter of the increase in childbearing, while the change in marital status goes the other way about 8%, meaning they would have had even more kids if more were married. The race/ethnic effects are very small.

That also means the increase in fertility is not just compositional, the result of demographic changes. There is still an increasing tendency to have a child in this group, holding constant these factors. Adjusting for marital status and race/ethnicity, here are the predicted probabilities of having a birth in 1990 and 2013, by age:

BA-school-birth-pred

Although the younger average age is a big factor, then, there is also a higher chance of having a birth at every age for college graduates pursuing advanced degrees. Why?

Interpretation

The optimistic interpretation of rising fertility for women with advanced degrees is that the cultural and organizational context has changed the childbearing calculus. The husbands or partners of these women are more supportive now. And their workplaces — or the workplaces they anticipate entering — have grown more accepting of professional women with children. Some schools have childcare and lactation spaces for graduate students. So having children may seem more reasonable. It’s also possible — and this is not contradictory — that the growth of this group has been driven by those who are less narrowly focused on their careers. To be a woman pursuing an advanced degree in 1990 you had to be a little more of a pioneer than you do now, so that path may have attracted a different group of women.

On the other hand, this is consistent with an inequality story: that those with better jobs and economic security, and family stability, have a growing advantage when it comes to raising children. Looking forward, I worry that the logistics of successful parenting are becoming an insurmountable challenge for too many people who don’t have enough control over their work lives. If we don’t improve the situation with healthcare, childcare, and family leave, then we risk increasingly making children a luxury that fewer families believe they can afford.

We are trying to fit our rapidly evolving social lives within the relatively narrow biological limits of human reproduction. The inconvenient truth is that the biological prime years for reproduction are also essential years for developing our human capital and adult relationships. We need collective efforts in the form of social policy to manage this compression.

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