Are middle children going extinct?

In The Cut, Adam Sternbergh has a piece called, “The Extinction of the Middle Child They’re becoming an American rarity, just when America could use them the most.”

This is good for me to read, because I’ve been asked to include more material about sibling relationships in the next edition of my textbook, The Family, and it’s not my expertise. Thinking about sibling relationships is good, but the demography here is off. Sternberg writes:

According to a study by the Pew Research Center in 1976, “the average mother at the end of her childbearing years had given birth to more than three children.” Read that again: In the ’70s, four kids (or more) was the most common family unit. Back then, 40 percent of mothers between 40 and 44 had four or more children. Twenty-five percent had three kids; 24 percent had two; and 11 percent had one. Today, those numbers have essentially reversed. Nearly two-thirds of women with children now have two or one — i.e., an oldest, a youngest, but no middle.

It is true there are a lot fewer U.S. families with more than two children today than there were in 1976. However, by my reckoning (see below), in the most recent data (2016), 38 percent of mothers age 40-44 who have had any children have had three or more. So, there’s a middle child in more than a third of families. And, crucially, that number hasn’t dropped in the last 25 years. I’ll explain.

The best regular national survey for this is the Current Population Survey’s June Fertility Supplement, which is administered to a national sample by the Census Bureau more or less every two years. They ask women, “Altogether how many children have you ever given birth to?” The traditional way to measure total number of children born for a cohort of women is to take the average of that number for women who are ages 40-44. (I would rather do it at ages 45-49, but they didn’t always ask it for women over 44.)

This is what you get for the surveys from 1976 to 2016 (remember these are the years the women reached the end of their childbearing years).

You can see how it’s a little tricky. First, the biggest changes were over by the 1990s, when the last of the Baby Boom parents reached their forties (their first kids were born 25 years earlier). The biggest changes after that were in the number of women having no children, which rose until 2006 but then fell, possibly as access to fertility treatments improved. (Note in all this we’re calling all the children one woman has a “family,” but really it’s a sibling set; some will be living with other people and some will have died, so it’s not a measure of family life in the household sense of family. And it’s all based on children women have, so if there are different fathers in these families we wouldn’t know it, and if these children have half-siblings with a different mother we wouldn’t know it, but that’s the way it goes.)

It’s hard to see what this means for the prevalence of middle children in the country, because the no-children women aren’t relevant. So if your question is, “what proportion of families with children have any middle children?” you would want to do it like this, which excludes the childfree women, and combines all those with three or more:

This shows the big drop in middle-child families that Sternbergh started with, but it puts it in perspective: the change was over by the 1990s, and since then it’s been basically flat at 35+ percent. So, things have changed a lot from the days of the Baby Boom, but the same article could have been written, demographically speaking, in 1992. (Note that the drop in total fertility rate since 2008 [see this] hasn’t yet shown up in completed fertility since it’s among younger women.)

It’s not clear whether the unit of analysis should be the family or the child, however. This says 38 percent of women produce middle-child families. But how many children have the experience of being a middle child (defined as a child with at least one older and one younger sibling)? That might make more sense if you’re interested, as Sternbergh is, in the effect of middle children on the culture. So just multiplying out the number of children per woman, and counting the number of middle children as the total minus two for all sibships of three or more (I think I did it right), you get this:

(Again, this assumes no one died before their mother turned 44, which did change over this period, especially as violent crime rates among young men fell. You could do something fancy to estimate that.)

So, it looks to me that, for the last 25 years, about 20-25 percent of children have been middle children.

On the other hand, looking in the longer run — much further back than Sternbergh’s starting point of 1976 — it’s clear that the proportion of children growing up as middle children has declined drastically. One quick and dirty way to show that is in children’s living arrangements. The final figure uses Census data (decennial till 2000, then American Community Survey) to show how many children are living as the only child, one of two, a middle child, or as the oldest/youngest in a three-plus family. This is messy because it’s just whoever is living together at the moment. So this is answering a question more like, “what proportion of children at any given time are living with an older and a younger sibling?” Here’s the trend:

(Note that, thanks to IPUMS.org coding, this does count people as having older siblings if the sibling is older than 18, as long as they’re living in the household. But I’m only including kids living in the household of at least one parent.)

Wow! In 1850 half of all U.S. children had an older and a younger sibling in the household with them. Now it’s below 20 percent. Still no drop since 1990, but the long-term change is impressive. So if all that personality stuff is true, then that’s a big difference between the olden days and nowadays. Definitely going to put this in the book.

That thing where you have a lot of little graphs (single-parent edition)

Yesterday I was on an author-meets-critics panel for The Triple Bind of Single-Parent Families: Resources, Employment, and Policies to Improve Well-Being, a new collection edited by Rense Nieuwenhuis and Laurie Moldonado. The book is excellent — and it’s available free under Creative Commons license.

Most of the chapters are comparative, with data from multiple countries. I like looking at the figures, especially the ones like this, which give a quick general sense and let you see anomalies and outliers. I made a couple, too, which I share below, with code.

Here’s an example, showing the proportion of new births to mothers who aren’t married, by education, for U.S. states.  For this I used the 2012-2016 combined American Community Survey file, which I got from IPUMS.org. I created an sample extract that included only women who reported having a child in the previous year, which gives me about 177,000 cases over the five years. The only other variables are state, education, and marital status. I put the raw data file on the Open Science Framework here. Code below.

My first attempt was bar graphs for each state. This is easiest because Stata lets you do graph means with the bar command (click to enlarge).

The code for this is very simple. I made a dummy variable for single, so the mean of that is the proportion single. Edcat is a four-category education variable.

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti(“Proportion not married”) by(state)

The bar graph is easy, and good for scanning the data for weird cases or interesting stories. But maybe it isn’t ideal for presentation, because the bars run from one state to the next. Maybe little lines would be better. This takes another step, because it requires making the graph with twoway, which doesn’t want to calculate means on the fly. So I do a collapse to shrink the dataset down to just means of single by state and edcat.

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

Then I use a scatter graph, with line connectors between the dots. I like this better:

You can see the overall levels (e.g., high in DC, low in Utah) as well as the different slopes (flatter in New York, steeper in South Dakota), and it’s still clear that the single-mother incidence is lowest in every state for women with BA degrees.

Here’s the code for that graph. Note the weights are now baked into the means so I don’t need them in the graph command. And to add the labels to the scatter plot you have to specify you want that. Still very simple:

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti(“Proportion not married”) lcolor(green) msymbol(O) connect(l) by(state)

Sadly, I can’t figure out how to put one title and footnote on the graph, rather than a tiny title and footnote on every state graph, so I left titles out of the code and I then added them by hand in the graph editor. Boo.

Here’s the full code:

set more off

clear
quietly infix ///
 byte statefip 1-2 ///
 double perwt 3-12 ///
 byte marst 13-13 ///
 byte fertyr 14-14 ///
 byte educ 15-16 ///
 int educd 17-19 ///
 using "[PATHNAME]\usa_00366.dat"

/* the sample is all women who reported having a child in the previous year, FERTYR==2 */
 
replace perwt = perwt / 100

format perwt %10.2f

label var statefip "State (FIPS code)"
label var perwt "Person weight"
label var marst "Marital status"
label var educd "Educational attainment [detailed version]"

label define statefip_lbl 01 "Alabama"
label define statefip_lbl 02 "Alaska", add
label define statefip_lbl 04 "Arizona", add
label define statefip_lbl 05 "Arkansas", add
label define statefip_lbl 06 "California", add
label define statefip_lbl 08 "Colorado", add
label define statefip_lbl 09 "Connecticut", add
label define statefip_lbl 10 "Delaware", add
label define statefip_lbl 11 "District of Columbia", add
label define statefip_lbl 12 "Florida", add
label define statefip_lbl 13 "Georgia", add
label define statefip_lbl 15 "Hawaii", add
label define statefip_lbl 16 "Idaho", add
label define statefip_lbl 17 "Illinois", add
label define statefip_lbl 18 "Indiana", add
label define statefip_lbl 19 "Iowa", add
label define statefip_lbl 20 "Kansas", add
label define statefip_lbl 21 "Kentucky", add
label define statefip_lbl 22 "Louisiana", add
label define statefip_lbl 23 "Maine", add
label define statefip_lbl 24 "Maryland", add
label define statefip_lbl 25 "Massachusetts", add
label define statefip_lbl 26 "Michigan", add
label define statefip_lbl 27 "Minnesota", add
label define statefip_lbl 28 "Mississippi", add
label define statefip_lbl 29 "Missouri", add
label define statefip_lbl 30 "Montana", add
label define statefip_lbl 31 "Nebraska", add
label define statefip_lbl 32 "Nevada", add
label define statefip_lbl 33 "New Hampshire", add
label define statefip_lbl 34 "New Jersey", add
label define statefip_lbl 35 "New Mexico", add
label define statefip_lbl 36 "New York", add
label define statefip_lbl 37 "North Carolina", add
label define statefip_lbl 38 "North Dakota", add
label define statefip_lbl 39 "Ohio", add
label define statefip_lbl 40 "Oklahoma", add
label define statefip_lbl 41 "Oregon", add
label define statefip_lbl 42 "Pennsylvania", add
label define statefip_lbl 44 "Rhode Island", add
label define statefip_lbl 45 "South Carolina", add
label define statefip_lbl 46 "South Dakota", add
label define statefip_lbl 47 "Tennessee", add
label define statefip_lbl 48 "Texas", add
label define statefip_lbl 49 "Utah", add
label define statefip_lbl 50 "Vermont", add
label define statefip_lbl 51 "Virginia", add
label define statefip_lbl 53 "Washington", add
label define statefip_lbl 54 "West Virginia", add
label define statefip_lbl 55 "Wisconsin", add
label define statefip_lbl 56 "Wyoming", add
label define statefip_lbl 61 "Maine-New Hampshire-Vermont", add
label define statefip_lbl 62 "Massachusetts-Rhode Island", add
label define statefip_lbl 63 "Minnesota-Iowa-Missouri-Kansas-Nebraska-S.Dakota-N.Dakota", add
label define statefip_lbl 64 "Maryland-Delaware", add
label define statefip_lbl 65 "Montana-Idaho-Wyoming", add
label define statefip_lbl 66 "Utah-Nevada", add
label define statefip_lbl 67 "Arizona-New Mexico", add
label define statefip_lbl 68 "Alaska-Hawaii", add
label define statefip_lbl 72 "Puerto Rico", add
label define statefip_lbl 97 "Military/Mil. Reservation", add
label define statefip_lbl 99 "State not identified", add
label values statefip statefip_lbl

label define educd_lbl 000 "N/A or no schooling"
label define educd_lbl 001 "N/A", add
label define educd_lbl 002 "No schooling completed", add
label define educd_lbl 010 "Nursery school to grade 4", add
label define educd_lbl 011 "Nursery school, preschool", add
label define educd_lbl 012 "Kindergarten", add
label define educd_lbl 013 "Grade 1, 2, 3, or 4", add
label define educd_lbl 014 "Grade 1", add
label define educd_lbl 015 "Grade 2", add
label define educd_lbl 016 "Grade 3", add
label define educd_lbl 017 "Grade 4", add
label define educd_lbl 020 "Grade 5, 6, 7, or 8", add
label define educd_lbl 021 "Grade 5 or 6", add
label define educd_lbl 022 "Grade 5", add
label define educd_lbl 023 "Grade 6", add
label define educd_lbl 024 "Grade 7 or 8", add
label define educd_lbl 025 "Grade 7", add
label define educd_lbl 026 "Grade 8", add
label define educd_lbl 030 "Grade 9", add
label define educd_lbl 040 "Grade 10", add
label define educd_lbl 050 "Grade 11", add
label define educd_lbl 060 "Grade 12", add
label define educd_lbl 061 "12th grade, no diploma", add
label define educd_lbl 062 "High school graduate or GED", add
label define educd_lbl 063 "Regular high school diploma", add
label define educd_lbl 064 "GED or alternative credential", add
label define educd_lbl 065 "Some college, but less than 1 year", add
label define educd_lbl 070 "1 year of college", add
label define educd_lbl 071 "1 or more years of college credit, no degree", add
label define educd_lbl 080 "2 years of college", add
label define educd_lbl 081 "Associates degree, type not specified", add
label define educd_lbl 082 "Associates degree, occupational program", add
label define educd_lbl 083 "Associates degree, academic program", add
label define educd_lbl 090 "3 years of college", add
label define educd_lbl 100 "4 years of college", add
label define educd_lbl 101 "Bachelors degree", add
label define educd_lbl 110 "5+ years of college", add
label define educd_lbl 111 "6 years of college (6+ in 1960-1970)", add
label define educd_lbl 112 "7 years of college", add
label define educd_lbl 113 "8+ years of college", add
label define educd_lbl 114 "Masters degree", add
label define educd_lbl 115 "Professional degree beyond a bachelors degree", add
label define educd_lbl 116 "Doctoral degree", add
label define educd_lbl 999 "Missing", add
label values educd educd_lbl

recode educd (0/61=1) (62/64=2) (65/90=3) (101/116=4), gen(edcat)

label define edlbl 1 "<HS"
label define edlbl 2 "HS", add
label define edlbl 3 "SC", add
label define edlbl 4 "BA+", add
label values edcat edlbl

label define marst_lbl 1 "Married, spouse present"
label define marst_lbl 2 "Married, spouse absent", add
label define marst_lbl 3 "Separated", add
label define marst_lbl 4 "Divorced", add
label define marst_lbl 5 "Widowed", add
label define marst_lbl 6 "Never married/single", add
label values marst marst_lbl

gen married = marst==1 /* this is married spouse present */
gen single=marst>3 /* this is divorced, widowed, and never married */

gr bar (mean) single [weight=perwt], over(edcat) bar(1,color(green)) yti("Proportion not married") by(state)

collapse (mean) single psingle=single [fw=perwt], by(state edcat)

gr twoway scatter single edcat , xlab(1 2 3 4, valuelabel) yti("Proportion not married") lcolor(green) msymbol(O) connect(l) by(state)

 

 

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).

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).

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.*

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.

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* 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

A step toward civilization (and have more children), Shanghai edition

Over the course of two weeks in China, I saw several versions of signs like this:

“A small step forward, a big step for civilization” (向前一小步, 文明一大步).

This one is posted in the old-town section of Nanxun (now a tourist attraction), naturally, above a urinal.* Invoking civilization may be overblown for the problem of men standing too far away (which didn’t seem to be especially extreme, compared to U.S. urinals), but China has a long tradition of using dramatic slogans to call citizens to higher common purpose. Here was one that struck me, in downtown Shanghai:

Every family striving to become a civilized family; everyone involved in its creation (家家争做文明家庭; 人人叁与创建活动).

This is from the Shanghai public health authorities. (No, I don’t know Chinese, but I love trying to use a dictionary, and I ask people.) The fascinating thing about that is the composition of the civilized family pictured: father, mother, two grandparents, and two children. 

Fertility rates in China are well below replacement level, as they are in other East Asian countries, meaning the average woman will have fewer than two children in her lifetime and the population will eventually shrink (barring immigration). China’s total fertility rate nationally is probably at about 1.5. In Shanghai, a metro area with some 20 million people, the norm was already one child per family before the one-child policy was implemented in 1980, and fertility has continued to fall; it most recently clocked in at a shockingly low .88 per woman as of 2008.

Reasons for ultra-low fertility are varied and contested, but likely culprits include expensive housing and education costs for children. It was reported to me informally that about half of children can go to college-track high schools instead of vocational schools, and that is determined by a standardized test administered at the end of middle school. That puts tremendous pressure on parents with middle-class aspirations. Which helps explain the extensive system of expensive supplemental private education, as promoted by this ad I saw in an upscale mall:

School advertisement, Shanghai

The website for this company promises, “Super IQ, Wealth of Creativity, Instant Memory Capacity.” How many kids are you going to send to this private program?

One of the five perfect, super-involved parents at the parent-child class is a man, which may or may not seem like a lot. Of the many people taking their kids to school on scooters, I didn’t see a lot with more than one child, and the only picture I got was of one piloted by the apparent dad (note also something you don’t see here much: schoolboy in pink shirt):

Man taking children to school, Shanghai

This recalls another probable cause of low-low fertility, the gender-stuck family and employment practices that keep women responsible for children and other care work (scooter dads notwithstanding). In conjunction with women outperforming men in college graduation rates these days (as in the U.S.), this indirectly reduces fertility by leading to delayed marriage, and directly reduces fertility by causing parents to decide against a second child.

Grandparent, parent, child, in Hangzhou

The weak system of care hurts on both ends, with people having fewer children because raising them is expensive, and people needing children to take care of old people because public support is lacking. This may be one reason why grandparents can have a positive effect on parents’ motivation to have children, as reported by Yingchun Ji and colleagues (including Feinian Chen, who hosted my visit). The fact that it is common for grandparents to provide extensive care for their grandchildren, as Feinian Chen has described (paywall), presumably helps strengthen their pronatal case.

Lots of pictures of grandparents taking care of a single grandchild to choose from. Here’s one, from the (awesome) Shanghai Museum:

Grandparent and child, Shanghai

The one-child policy ended in 2016, and couples no longer have to get permission to have a first or second child (but they do for a third or more). This change alone, although a better-late-than-never thing, may not do much to increase birth rates. That is the conclusion from studies of families for whom the policy was relaxed earlier. Sadly, although birth rates were already falling dramatically in the 1970s and the one-child policy was not responsible for the trend, the policy still (in addition to large scale human rights abuses) created many millions of one-child families that will struggle to meet intergenerational care obligations in the absence of adequate public support. (Here’s a good brief summary from Wang Feng, Baochang Gu, and Yong Cai.)

This is a challenge for civilization.

The pictures here, and a few hundred more, are on my Flickr site under creative commons license.

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* Americans who love the funny translations of signs in China may be in for some disappointment, as the Standardization Administration has announced plans to implement thousands of stock translations in the service sector nationwide.

Births to 40-year-olds are less common but a greater share than in 1960

Never before have such a high proportion of all births been to women over 40 — they are now 2.8% of all births in the US. And yet a 40-year-old woman today is one-third less likely to have a baby than she was in 1947.

From 1960 to 1980, birth rates to women over 40* fell, as the Baby Boom ended and people were having fewer children by stopping earlier. Since 1980 birth rates to women over 40 have almost tripled as people started “starting” their families at later ages, but they’re still lower than they were back when total fertility was much higher.

Sources: Birth rates 1940-1969, 1970-2010, 2011, 2012-2013, 2014-2015; 2016; Percent of births 1960-1980, 1980-2008.

Put another way, a child born to a mother over 40 before 1965 was very likely the youngest of several (or many) siblings. Today they are probably the youngest of 2 or an only child. A crude way to show this is to use the Current Population Survey to look at how many children are present in the households of women ages 40-49 who have a child age 0 (the survey doesn’t record births as events, but the presence of a child age 0 is pretty close). Here is that trend:

In the 1970s about 60 percent of children age 0 had three or more siblings present, and only 1 in 20 was an only child. Now more than a quarter are the only child present and another 30 percent only have one sibling present. (Note this doesn’t show however many siblings no longer live in the household, and I don’t know how that might have changed over the years).

This updates an old post that focused on the health consequences of births to older parents. The point from that post remains: there are fewer children (per woman) being born to 40-plus mothers today than there were in the past, it just looks like there are more because they’re a larger share of all children.

* Note in demography terms, “over 40” means older than “exact age” 40, so it includes people from the moment they turn 40.

Fertility trends and the myth of Millennials

The other day I showed trends in employment and marriage rates, and made the argument that the generational term “Millennial” and others are not useful: they are imposed before analyzing data and then trends are shoe-horned into the categories. When you look closely you see that the delineation of “generations” is arbitrary and usually wrong.

Here’s another example: fertility patterns. By the definition of “Millennial” used by Pew and others, the generation is supposed to have begun with those born after 1980. When you look at birth rates, however,  you see a dramatic disruption within that group, possibly triggered by the timing of the 2009 recession in their formative years.

I do this by using the American Community Survey, conducted annually from 2001 to 2015, which asks women if they have had a birth in the previous year. The samples are very large, with all the data points shown including at least 8,000 women and most including more than 60,000.

The figure below shows the birth rates by age for women across six five-year birth cohorts. The dots on each line mark the age at which the midpoint of each cohort reached 2009. The oldest three groups are supposed to be “Generation X.” The three youngest groups shown in yellow, blue, and green — those born 1980-84, 1985-89, and 1990-94 — are all Millennials according to the common myth. But look how their experience differs!

Most of the fertility effect on the recession was felt at young ages, as women postponed births. The oldest Millennial group was in their late twenties when the recession hit, and it appears their fertility was not dramatically affected. The 1985-89 group clearly took a big hit before rebounding. And the youngest group started their childbearing years under the burden of the economic crisis, and if that curve at 25 holds they will not recover. Within this arbitrarily-constructed “generation” is a great divergence of experience driven by the timing of the great recession within their early childbearing years.

You could collapse these these six arbitrary birth cohorts into two arbitrary “generations,” and you would see some of the difference I describe. I did that for you in the next figure, which is made from the same data. And you could make up some story about the character and personality of Millennials versus previous generations to fit that data, but you would be losing a lot of information to do that.

Of course, any categories reduce information — even single years of age — so that’s OK. The problem is when you treat the boundaries between categories as meaningful before you look at the data — in the absence of evidence that they are real with regard to the question at hand.

Weathering and delayed births, get your norms off my body edition

You can skip down to the new data and analysis — or go straight to my new working paper — if you don’t need the preamble diatribe.

I have complained recently about the edict from above that poor (implying Black) women should delay their births until they are “financially ready” — especially in light of the evidence on their odds of marriage during the childbearing years. And then we saw what seemed like a friendly suggestion that poor women use more birth control lead to some nut on Fox News telling Rebecca Vallas, who spoke up for raising the minimum wage:

A family of three is not supposed to be living on the minimum wage. If you’re making minimum wage you shouldn’t be having children and trying to raise a family on it.

As if minimum wage is just a phase poor people can expect to pass through only briefly, on their way to middle class stability — provided they don’t piss it away by having children they can’t “afford.” This was a wonderful illustration of the point Arline Geronimus makes in this excellent (paywalled) paper from 2003, aptly titled, “Damned if you do: culture, identity, privilege, and teenage childbearing in the United States.” Geronimus has been pointing out for several decades that Black women face increased health risks and other problems when they delay their childbearing, even as White women have the best health outcomes when they delay theirs. This has been termed “the weathering hypothesis.” In that 2003 paper, she explores the cultural dynamic of dominance and subordination that this debate over birth timing entails. Here’s a free passage (where dominant is White and marginal is Black):

In sum, a danger of social inequality is that dominant groups will be motivated to promote their own cultural goals, at least in part, by holding aspects of the behavior of specific marginal groups in public contempt. This is especially true when this behavior is viewed as antithetical or threatening to social control messages aimed at the youth in the dominant group. An acknowledgment that teen childbearing might have benefits for some groups undermines social control messages intended to convince dominant group youth to postpone childbearing by extolling the absolute hazards of early fertility. Moreover, to acknowledge cultural variability in the costs and consequences of early childbearing requires public admission of structural inequality and the benefits members of dominant groups derive from socially excluding others. One cannot explain why the benefits of early childbearing may outweigh the costs for many African Americans without noting that African American youth do not enjoy the same access to advanced education or career security enjoyed by most Americans; that their parents are compelled to be more focused on imperatives of survival and subsistence than on encouraging their children to engage in extended and expensive preparation for the competitive labor market; indeed, that African Americans cannot even take their health or longevity for granted through middle age (Geronimus, 1994; Geronimus et al., 2001). And one cannot explain why these social and health inequalities exist without recognizing that structural barriers to full participation in American society impede the success of marginalized groups (Dressler, 1995; Geronimus, 2000; James, 1994). To acknowledge these circumstances would be to contradict the broader societal ethic that denies the existence of social inequality and is conflicted about cultural diversity. And it would undermine the ability the dominant group currently enjoys to interpret their privilege as earned, the just reward for their exercise of personal responsibility.

But the failure to acknowledge these circumstances results in a disastrous misunderstanding. As a society, we have become caught in an endless loop that rationalizes, perhaps guarantees, the continued marginalization of urban African Americans. In the case at hand, by misunderstanding the motivation, context, and outcomes of early childbearing among African Americans, and by implementing social welfare and public health policies that follow from this misunderstanding, the dominant European American culture reinforces material hardship for and stigmatization of African Americans. Faced with these hardships, early fertility timing will continue to be adaptive practice for African Americans. And, reliably, these fertility and related family “behaviors” will again be unfairly derided as antisocial. And so on.

Whoever said demography isn’t theoretical and political?

A simple illustration

In Geronimus’s classic weathering work, she documented disparities in healthy life expectancy, which is the expectation of healthy, or disability-free, years of life ahead. When a poor 18-year-old Black woman considers whether or not to have a child, she might take into account her expectation of healthy life expectancy — how long can she count on remaining healthy and active? — as well as, and this is crucial, that of her 40-year-old mother, who is expected to help out with the child-rearing (they’re poor, remember). Here’s a simple illustration: the percentage of Black and White mothers (women living in their own households, with their own children) who have a work-limiting disability, by age and education:

Not too many disabilities at age 20, but race and class kick in hard over these parenting years, till by their 50s one-in-five Black mothers with high school education or less has a disability, compared with one-in-twenty White mothers who’ve gone on to more education. That looming health trajectory is enough — Geronimus reasonably argues — to affect women’s decisions on whether or not to have a child (or go through with an accidental pregnancy). But for the group (say, Whites who aren’t that poor) who have a reasonable chance of getting higher education, and making it through their intensive parenting years disability-free, the economic consequence of an early birth weighs much more heavily.

Some new analysis

As I was thinking about all this the other day, I went to check on the latest infant mortality statistics, since that’s where Geronimus started this thread — with the observation that White women’s chance of a baby dying decline with age, while Black women’s don’t. And I noticed there is a new Period Linked Birth-Infant Death Data File for 2013. This is a giant database of all the births — with information from their birth certificates — linked to all the infant deaths from the same year. These records have been used for analyzing infant mortality dozens of times, including in pursuit of the weathering hypothesis, but I didn’t see any new analyses of the 2013 files, except the basic report the National Center for Health Statistics put out. The outcome is now a working paper at the Maryland Population Research Center.

The gist of the result is, to me, kind of shocking. Once you control for some basic health, birth, and socioeconomic conditions (plurality, parity, prenatal care, education, health insurance type, and smoking during pregnancy), the risk of infant mortality for Black mothers increases linearly with age: the longer they wait, the greater the risk. For White women the risk follows the familiar (and culturally lionized) U-shape, with the lowest risk in the early 30s. Mexican women (the largest Hispanic group I could include) are somewhere in between, with a sharp rise in risk at older ages, but no real advantage to waiting from 18 to 30.

I’ll show you (and these rates will differ a little from official rates for various technical reasons). First, the unadjusted infant mortality rates by maternal age:

Infant Death Rates, by Maternal Age: White, Black, and Mexican Mothers, U.S., 2013. Infant death rates per 1,000 live births for non-Hispanic white (N = 1,925,847), non-Hispanic black (N = 533,341), and Mexican origin (N = 501,390) mothers. Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.

These raw rates show the big health benefit to delay for White women, a smaller benefit for Mexican mothers, and no benefit for Black mothers. But when you control for those factors I mentioned, the infant mortality rates for young Black and Mexican mothers are lower — those are the mothers with low education and bad health care. Controlling for those things sort of simulates the decisions women face: given these things about me, what is the health effect of delay? (Of course, delaying could contribute to improving things, which is also part of the calculus.) Here are the adjusted age patterns:

Adjusted Probability of Infant Death, by Maternal Age: White, Black, and Mexican Mothers, U.S., 2013. Predicted probabilities of infant death generated by Stata margins command, adjusted for plurality, birth order, maternal education, prenatal care, payment source, and cigarette smoking during pregnancy; models estimated separately for white (A), black (B), and Mexican (C) mothers (see Tab. 1). Error bars are 95% confidence intervals. (A separate test showed the linear trend for Black women is statistically significant.) Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.

My jaw kind of dropped. Infant mortality is mostly a measure of mothers’ health. Early childbearing looks a lot crazier for White women than for Black and Mexican women, and you can see why the messaging around delaying till your “ready” seems so out of tune to the less privileged (and that really means race more than class, in this case). Why wait? If women knew they had higher education, a good job, and decent health care awaiting them throughout their childbearing years, I think the decision tree would look a lot different.

Of course, I have often said that delayed marriage is good for women. And delayed childbearing would be — should be — too, as long as it doesn’t put the health of the mother and her children at risk (and squander the healthy rearing years of their grandparents).

Please check out the working paper for more background and references, and details about my analysis.