Tag Archives: demography

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.)

year married in 2016.xlsx

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.

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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:

2008-2017 divorce marriage.xlsx

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.


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Breaking Millennial divorce drop news explained

[With updates as new stories come in.]

Millennials are fun to disparage.

Phones and selfies are all that they cherish.

And what’s par for the course, they have ruined divorce.

‘Cuz Millennials hang on to their ______.

Wait Wait Don’t Tell Me, 9/29/18

The divorce paper I posted two weeks ago, “The Coming Divorce Decline,” suddenly took off in the media the other day (blog post | paper | data and code). I’ve now written an op-ed about the findings for The Hill, including this:

I am ambivalent about these trends. Divorce is often painful and difficult, and most people want to avoid it. The vast majority of Americans aspire to a lifelong marriage (or equivalent relationship). So even if it’s a falling slice of the population, I’m not complaining that they’re happy. Still, in an increasingly unequal society and a winner-take-all economy, two-degree couples with lasting marriages may be a buffer for the select few, but they aren’t a solution to our wider problems.

Here’s my media scrapbook, with some comment about open science process at the end.

The story was first reported by Ben Steverman at Bloomberg, who took the time to read the paper, interview me at some length, send the paper to Susan Brown (a key expert on divorce trends) for comment, and produce figures from the data I provided. I was glad that his conclusion focused on the inequality angle from my interpretation:

“One of the reasons for the decline is that the married population is getting older and more highly educated,” Cohen said. Fewer people are getting married, and those who do are the sort of people who are least likely to get divorced, he said. “Marriage is more and more an achievement of status, rather than something that people do regardless of how they’re doing.”

Many poorer and less educated Americans are opting not to get married at all. They’re living together, and often raising kids together, but deciding not to tie the knot. And studies have shown these cohabiting relationships are less stable than they used to be.

Fewer divorces, therefore, aren’t only bad news for matrimonial lawyers but a sign of America’s widening chasm of inequality. Marriage is becoming a more durable, but far more exclusive, institution.

The Bloomberg headline was, “Millennials Are Causing the U.S. Divorce Rate to Plummet.” Which proved irresistible on social media. I didn’t use the terms “millennials” (which I oppose), or “plummet,” but they don’t fundamentally misrepresent the findings.

Naturally, though, the Bloomberg headline led to other people misrepresenting the paper, like Buzzfeed, which wrote, “Well, according to a new study, millennials are now also ‘killing’ divorce.” Neither I nor Bloomberg said anyone was “killing” divorce; that was just a Twitter joke someone made, but Buzzfeed was too metameta to pick up on that. On the other hand, never complain about a Buzzfeed link, and they did link to the paper itself (generating about 800 clicks in a few days).

Then Fox 5 in New York did a Skype interview with me, and hit the bar scene to talk over the results (additional footage courtesy of my daughter, because nowadays you provide your own b-roll):

The next day Today did the story, with additional information and reporting from Bowling Green’s National Center for Family and Marriage Research, and Pew.

The Maryland news office saw the buzz and did their own story, which helped push it out.

An article in Atlantic featured an interview with Andrew Cherlin putting the trends in historical context. Rachelle Hampton in Slate tied the divorce trend to a Brookings report showing marriage is increasingly tied to higher education. On KPCC, AirTalk hosted a discussion with Megan Sweeney and Steven Martin. On Wisconsin Public Radio, Stephanie Coontz widened the discussion to put changes in marriage and divorce in historical perspective.

Rush Limbaugh read from the Bloomberg article, and was just outraged: “Now, who but deranged people would look at it this way?”

How anybody thinks like this… You have to work to be this illogical. I don’t know where this kind of thing comes from, that a plummeting divorce rate is a bad sign for America in the left’s crazy world of inequality and social justice and their quest to make everybody the same. So that’s just an example of the… Folks, that is not… That kind of analysis — and this is a sociology professor at the University of Maryland. This is not stable. That kind of thinking is not… It’s just not normal. Yet there it is, and it’s out there, and it’s be widely reported by the Drive-By Media, probably applauded and supported by others. So where is this coming from? Where is all of this indecency coming from? Why? Why is it so taking over the American left?

The Limbaugh statement might have been behind this voicemail I received from someone who thinks I’m trying to “promote chaos” to “upend the social order”:

I had a much more reasonable discussion about marriage, divorce, and inequality in this interview with Lauren Gilger in KJZZ (Phoenix public radio).

The Chicago Tribune editorial board used the news to urge parents not to rush their children toward marriage:

This waiting trend may disturb older folks who followed a more traditional (rockier?) path and may be secretly, or not so secretly, wondering if there’s something wrong with their progeny. There isn’t. Remember: Unlike previous generations, many younger people have a ready supply of candidates at their fingertips in the era of Tinder and other dating apps. They can just keep swiping right. Our advice for parents impatient to marry off a son or daughter? Relax. The older they get, the less likely you’ll be stuck paying for the wedding.

The Catholic News Agency got an expert to chime in, “If only we could convince maybe more of them to enter into marriage, we’d be doing really well.”

I don’t know how TV or local news work, but somehow this is on a lot of TV stations. Here’s a selection.

Fox Business Network did a pretty thorough job.

Some local stations added their own reporting, like this one in Las Vegas:

And this one in Buffalo:

And this one in Boise, which brought in a therapist who says young people aren’t waiting as long to start couples therapy.

Jeff Waldorf on TYT Nation did an extended commentary, blaming capitalism:

Open science process

Two things about my process here might concern some people.

The first is promoting research that hasn’t been peer reviewed. USA Today was the only report I saw that specifically mentioned the study is not peer reviewed:

The study, which has not been published in a peer-reviewed journal, has been submitted for presentation at the 2019 Population Association of America meeting, an annual conference for demographers and sociologists to present research.

But, when Steverman interviewed me I emphasized to him that it was not peer-reviewed and urged him to consult other researchers before doing the story — he told me he had already sent it to Susan Brown. Having a good reporter consult a top expert who’s read the paper is as good a quality peer review as you often get. I don’t know everything Brown told him, but the quote he used apparently showed her endorsement of the main findings:

“The change among young people is particularly striking,” Susan Brown, a sociology professor at Bowling Green State University, said of Cohen’s results. “The characteristics of young married couples today signal a sustained decline [in divorce rates] in the coming years.”

For the story to be clear enough to become a news event, the research often has to be pretty simple. That’s the case here: what I’m doing is looking at an easily-identified trend and providing my interpretation of it. If this has to be peer-reviewed, then almost anything an academic says should be. Of course, I provided the publicly verifiable data and code, and there are a lot of people with the skills to check this if it concerned them.

On the other hand, there is a lot of research that is impossible to verify that gets reported. Prominent examples include the Alice Goffman ethnographic book and the Raj Chetty et al. analysis of confidential IRS data. These were big news events, but whether they were peer reviewed or not was irrelevant because the peer reviewers had no way to know if the studies were right. My conclusion is that sharing research is the right thing to do, and sharing it with as much supporting material as you can is the responsible way to do it.

The second concern is over the fact that I posted it while it was being considered for inclusion in the Population Association of America meetings. This is similar to posting a paper that is under review at a journal. Conference papers are not reviewed blind, however, so it’s not a problem of disclosing my identity, but maybe generating public pressure on the conference organizers to accept the paper. This happens in many forms with all kinds of open science. I think we need to see hiding research as a very costly choice, one that needs to be carefully justified — rather than the reverse. Putting this in the open is the best way to approach accountability. Now the work of the conference organizers, whose names are listed in the call for papers, can be judged fairly. And my behavior toward the organizers if they reject it can also be scrutinized and criticized.

Although I would love to have the paper in the conference, in this case I don’t need this paper to be accepted by PAA, as it has already gotten way more attention than I ever expected. PAA organizers have a tough job and often have to reject a lot of papers for reasons of thematic fit as well as quality. I won’t complain or hold any grudges if it gets rejected. There’s a lot of really good demography out there, and this paper is pretty rudimentary.


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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!


Filed under Research reports

Demographic facts your students should know cold in 2018

birth gumHere’s an update of a series I started in 2013.

Is it true that “facts are useless in an emergency“? Depends how you define emergency I guess. Facts plus arithmetic let us ballpark the claims we are exposed to all the time. The idea is to get our radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it.

It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements such as Trump’s, “I’ve created over a million jobs since I’m president,” referring to a period when the U.S. population grew by 1.3 million?

What’s a number for? Lots of people disparage the nitpickers when they find something wrong with the numbers going around. But everyone likes a number that appears to support their argument. The trick is to know the facts before you know the argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.

Here’s the list of current demographic facts you need just to get through the day without being grossly misled or misinformed — or, in the case of journalists or teachers or social scientists, not to allow your audience to be grossly misled or misinformed. Not trivia that makes a point or statistics that are shocking, but the non-sensational information you need to make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 262 million and 394 million!).

This is only 30 facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). They are rounded to reasonable units for easy memorization. All refer to the US unless otherwise noted. Most of the links will take you to the latest data:


Fact Number Source
World Population 7.5 billion 1
U.S. Population 328 million 1
Children under 18 as share of pop. 23% 2
Adults 65+ as share of pop. 16% 2
Official unemployment rate 3.9% 3
Unemployment rate range, 1970-2018 3.9% – 11% 3
Labor force participation rate, age 16+ 63% 9
Labor force participation rate range, 1970-2017 60% – 67% 9
Non-Hispanic Whites as share of pop. 61% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 18% 2
Asians as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 13% 2
Adults age 25+ with BA or higher 30% 2
Median household income $55,300 2
Total poverty rate 13% 8
Child poverty rate 18% 8
Poverty rate age 65+ 9% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 327 million 5
4th most populous country, Indonesia 261 million 5
5th most populous country, Brazil 207 million 5
U.S. male life expectancy at birth 76 6
U.S. female life expectancy at birth 81 6
Life expectancy range across countries 51 – 85 7
World total fertility rate 2.4 10
U.S. total fertility rate 1.8 10
Total fertility rate range across countries 1.2 – 7.2 10

1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

10. World Bank

Handy one-page PDF: Demographic Facts You Need to Know in 2018


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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%):

teen marriage 1900-2016

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:

teen marriage states 2016.xlsx

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.


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:



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.


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Fertility trends explained, 2017 edition

Not really, but some thoughts and a bunch of figures on the 2017 fertility situation.

There was a big drop in the U.S. fertility rate in 2017. As measured by the total fertility rate (TFR), which is a projection of lifetime births for the average woman based on one year’s data, the drop was 3.1%, from 1.82 projected births per woman to 1.76. (See this measure explained, and learn how to calculate it yourself, in my blockbuster video, “Total Fertility Rate.”) To put that change in perspective, here is the trend in TFR back to 1940, followed by a plot of the annual changes since 1971:



That drop in 2017 is the biggest since the last recession started. In fact, we have seen no drop that big that’s not associated with a time of national economic distress, at least since the Baby Boom. In 2010, I noted that the drop in fertility at that time preceded the official start of the recession and the big unemployment spike. There is now some more systematic evidence (pointed out by Karen Benjamin Guzzo) that fertility falls before economic indicators turn down. Which makes this New York Times headline a little funny, “US Births Hit a 30-Year Low, Despite Good Economy.” This is a pretty solid warning sign, although not definitive, of an economic downturn coming in the next year or so. (On the other hand, maybe it’s a Trump effect, as people are just freaking out and not thinking positively about the future; something to think about.)

Whatever the role of immediate economic conditions, the long-term trend is toward later births, which is generally going to mean fewer births — both because people who want later births tend to want fewer births, and because some people run out of time if they start late. And that is not wholly separable from economic factors, of course. People (especially women) delay childbearing to improve their economic situation, as they improve their economic situation when they delay births (if they have the right suite of economic opportunities). To show this trend, I’ve been updating this figure for a few years (you’ll find it, and a description, in my book Enduring Bonds).

change in birthrates by age 1989-2016.xlsx

The real reason I made this figure was to highlight the interconnected nature of teen births. Birth rates for teens have fallen dramatically, but it’s been along with drops among younger women generally, and increases among older women — it’s about delaying births overall. Note, however, that 2017 is the first time since the depths of the last recession that birth rates fell for all age groups except women over age 40.

So, sell stock now. But it is hard to know for sure what’s a local temporal reaction and what’s just the way things are going nowadays. For that it’s useful to compare the U.S. to other countries. The next figure shows the U.S. and 15 other hand-picked countries, from World Bank data. Rising fertility in the decade before the last recession wasn’t so unusual. We are a little like Spain and France in this figure, who had rising fertility then and falling now. But Germany and Japan are still rising, at least through 2016. All this is at below-replacement levels (about 2.0), meaning eventually these rates lead to population decline, in the absence of immigration. The figure really shows the amazing fertility transformation of the last half century, especially in giant countries like China, India, and Brazil. Who would have thought we’d live to see Brazil have lower fertility rates than the U.S.? It’s been that way for more than a decade (click to enlarge).

country fertilitiy trends.xlsx

Anyway, it’s my position that our below-replacement fertility levels are themselves nothing to worry about at present. There are still lots of people who want to move here (or, there were before Trump). And we can live with low fertility for a long time before the population starts to decline in a meaningful way. Eventually it will be a good idea to stop perpetual population growth anyway, so we may as well start working on it. This is better than trying to shape domestic policy to increase birth rates.

That said, there is an argument that Americans are having fewer children than they want to because of our stone age work-family policies, especially poor family leave support and the high costs of good childcare. I’m sure that’s happening to some degree, but it’s still the case that more privileged people, who should be able to overcome those things more readily — people with college degrees and Whites — have lower fertility rates than people who are getting squeezed more. People who assume their kids are going to college are naturally concerned with rising higher education costs, both their own loan payments and their kids’ future payments. So it’s a mixed bag story. Here are the predictors of childbearing for women ages 15-44 in the 2016 American Community Survey. These are the probabilities of having had a birth in the previous 12 months, estimated (with logistic regression) at the mean of all the variables shown.*

birth model simple 2016.xlsx

Interesting that there’s only a small foreign-born fertility edge in this multivariate model. In the unadjusted data, 7.4% of foreign-born versus 6.0% of U.S.-born women had a baby, but that’s mostly accounted for by their age, education, and race/ethnicity.

To summarize: 2017 was a big year for fertility decline (at all but the highest ages), the economy is probably about to tank, and the U.S. fertility rate is still relatively high for our income level, especially for racial-ethnic minorities.

Happy to have your thoughts in the comments. For more, check the fertility tag.

* Here’s the Stata code for the regression analysis. It’s just some simple recodes of the ACS data from IPUMS.org. Start with a file of women ages 15-44, with the variables you see here, and then do this to it:

recode educd (0/61=1) (62/64=2) (65/90=3) (101/116=4), gen(edcat)
label define edlbl 1 "Less than high school"
label define edlbl 2 "High school graduate", add
label define edlbl 3 "Some college", add
label define edlbl 4 "BA or higher", add
label values edcat edlbl
gen raceth=race
replace raceth=4 if race==5 | race==6 /* now 4 is all API */
replace raceth=5 if hispan>0
drop if race>5
label define raceth_lbl 1 "White"
label define raceth_lbl 2 "Black", add
label define raceth_lbl 3 "AIAN", add
label define raceth_lbl 4 "API", add
label define raceth_lbl 5 "Hispanic", add
label values raceth raceth_lbl
egen agecat=cut(age), at(15(5)50)
gen forborn=citizen!=0
gen birth=fertyr==2
logit birth i.agecat i.raceth i.forborn i.edcat i.marst [weight=perwt]
margins i.agecat i.raceth i.forborn i.edcat i.marst


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