Kids these days really know how to throw off a narrative on gender and families

The most important thing is that Stephanie Coontz has written another very good, and very important, New York Times essay. It describes a “slippage” in support for gender equality among young people these days, and warns that without improved work-family policies, progress toward egalitarian family arrangements may be imperiled. The piece also announced a package of short papers in a Council on Contemporary Families symposium, which provided the supporting evidence. (This kind of work, incidentally, is why I’m a proud member, and board member, of CCF.) If you haven’t read Stephanie’s essay, I recommend reading it now, and if you forget to come back here that’s fine.

Anyway, an unfortunate confluence of events created some chaos after the piece came out. First, the NYTimes wrote a headline, “Do Millennial Men Want Stay-at-Home Wives?”, that emphasized only one piece of the evidence. It referred to a figure showing General Social Survey data on the trend in very young men and women (ages 18-25) disagreeing with the statement, “It is much better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family.” (That is the classic FEFAM question, to GSS fans, asked since 1977. I’ve used it myself, and it figures in the key analysis of stalled gender progress by Cotter, Hermsen, and Vanneman.)

This was the figure, showing a marked divergence between men and women:

The second event was the unfortunate timing: between the time Stephanie wrote the piece and the day it appeared, the General Social Survey released its 2016 round of data (it’s been running every two years). The survey is fickle. It’s very good quality and has many great demographic and attitude items running for 40 years, making it the best source for analyzing many social trends. But it’s not that big. In 2014 it had 2,867 respondents, of whom only 141 were ages 18-25. So it wasn’t surprising that the 2016 numbers were different from the 2014 numbers, but the scale of the blip was shocking, as reported independently by Emily Beam and Neal Caren. Here is what the updated trend looks like:

Yikes. As exciting as it is for survey analysts to see such a wild swing, it’s not what anyone wants to see the day after their NYTimes piece drops. We can’t know yet what happened, but on further inspection, at least we can say that it’s not limited to the youngest group and its small sample. Among men ages 26-54, the percentage disagreement with FEFAM also jumped, from 73.7 to 78.3 (women 26-54 were up one point).* In fact, 2014 may have been as big a blip as 2016, you just wouldn’t notice because it continued the trend.

Anyway, back for a minute to the main point. Joanna Pepin, who co-wrote one of the symposium pieces with David Cotter (and who is also an advisee of mine), has pointed out that the divergence between men and women is secondary to the main trend, which is the reversal of progress on FEFAM for both men and women since the mid-1990s. They used the Monitoring the Future survey, and find a big drop in FEFAM disagreement among high school seniors — regardless of gender. Here’s their key figure, with the FEFAM trend shown in green (their full paper is available on SocArXiv):

So that is the most important news: a big reversal among young adults on attitudes toward homemaker-breadwinner family arrangements.

Now, If you’ve now read Stephanie’s piece, and Joanna’s, and you’re back, here’s a little more on the minor kerfuffle that arose over the new data.

When to call a trend a trend

I don’t think Stephanie was wrong to use the GSS trend, although it might have been better to widen the age range, or pool the data over several years. The bigger problem was the headline selling that divergence as the main story, which it wasn’t in the grand scheme. (The fact that so many jumped on the story shows how good they are at headline writing.) But even that wasn’t really wrong, given the information they had. The Op-Ed staff checked the facts, and the facts were the facts. Until yesterday.

To confirm this, I ran some tests on the gender divergence in the data they used (I started with code that Neal shared; it’s at the bottom). I started at 1994, the last peak of the trend, to look for the divergence after that, which is what Stephanie referred to. First, here is what you get if you run a logistic model that controls for race/ethnicity and individual years of age (two things that changed over the last two decades), and enters the years individually in an interaction with gender (those are 95% confidence intervals).

If you stop at 2014, it looks like men are pulling away from women (in the direction of “traditional” attitudes), but it’s not definitive. And obviously 2016 is an issue. To help with the small samples, I ran a linear test of the year trend, that is, entering year as a continuous variable instead of individual years. I did it ended at 2014 and then through 2016. Here are the results:

In the 1994-2014 model, the Male*Year interaction is statistically significant at conventional levels, which in my opinion means it’s legit to say men were pulling away from women. Of course 2016 ruined that; if you had 2016 and didn’t use it, that would be really wrong. There are other ways to slice it, but at some point we have to call a trend a trend and deal with it. It was a reasonable decision. Of course, new data always comes along (until the last trend of all, whatever that is), no trend lasts forever; it’s just a shame when it comes along the next day. In addition, though I’m not showing it because it’s boring, if you didn’t disaggregate the trends by gender, you would also see a significant decline in FEFAM disagreement after 1994, which gets to Joanna’s point.

Anyway, score one for sociology Twitter. People came up with the data, shared code and results, and discussed interpretations. It got back to Stephanie and the NYTimes editors, and within a day they added an addendum to the original piece:

Update: After this article was posted, 2016 data from the General Social Survey became available, adding some nuance to this analysis. The latest numbers show a rebound in young men’s disagreement with the claim that male-breadwinner families are superior. The trend still confirms a rise in traditionalism among high school seniors and 18-to-25-year-olds, but the new data shows that this rise is no longer driven mainly by young men, as it was in the General Social Survey results from 1994 through 2014.

This is pretty much how it’s supposed to work. As the Car Guys used to say, if you never stall you’re wearing out your clutch (sorry, Millennials). If you never overshoot an analysis of trends you’re probably waiting too long to get the information out.

* Note: I originally accidentally described this as “over 25.” 

---

You can get the data here. Here’s the STATA code:

/* recodes */

recode fefam (1/2=0) (3/4=1), gen(fefam_d)
gen young=age>=18&age<=25
recode sex (2=0), gen(male)

/* the model for the figure */

logit fefam_d i.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]
margins year##male
marginsplot

/* the models for the table */

logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 & year<=2014 [pweight = wtssall]
logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]

Two examples of why “Millennials” is wrong

When you make up “generation” labels for arbitrary groups based on year of birth, and start attributing personality traits, behaviors, and experiences to them as if they are an actual group, you add more noise than light to our understanding of social trends.

According to generation-guru Pew Research, “millennials” are born during the years 1981-1997. A Pew essay explaining the generations carefully explains that the divisions are arbitrary, and then proceeds to analyze data according to these divisions as if are already real. (In fact, in the one place the essay talks about differences within generations, with regard to political attitudes, it’s clear that there is no political consistency within them, as they have to differentiate between “early” and “late” members of each “generation.”)

Amazingly, despite countless media reports on these “generations,” especially millennials, in a 2015 Pew survey only 40% of people who are supposed to be millennials could pick the name out of a lineup — that is, asked, “These are some commonly used names for generations. Which of these, if any, do you consider yourself to be?”, and then given the generation names (silent, baby boom, X, millennial), 40% of people born after 1980 picked “millennial.”

“What do they know?” You’re saying. “Millennials.”

Two examples

The generational labels we’re currently saddled with create false divisions between groups that aren’t really groups, and then obscure important variation within the groups that are arbitrarily lumped together. Here is just one example: the employment experience of young men around the 2009 recession.

In this figure, I’ve taken three birth cohorts: men born four years apart in 1983, 1987, and 1991 — all “millennials” by the Pew definition. Using data from the 2001-2015 American Community Surveys via IPUMS.org, the figure shows their employment rates by age, with 2009 marked for each, coming at age 26, 22, and 18 respectively.

Each group took a big hit, but their recoveries look pretty different, with the earlier cohort not recovered as of 2015, while the youngest 1991 group bounced up to surpass the employment rates of the 1987s by age 24. Timing matters. I reckon the year they hit that great recession matters more in their lives than the arbitrary lumping of them all together compared with some other older “generations.”

Next, marriage rates. Here I use the Current Population Survey and analyze the percentage of young adults married by year of birth for people ages 18-29. This is from a regression that controls for year of age and sex, so it can be interpreted as marriage rates for young adults (click to enlarge).

From the beginning of the Baby Boom generation to those born through 1987 (who turned 29 in 2016, the last year of CPS data), the marriage rate fell from 57% to 21%, or 36 percentage points. Most of that change, 22 points, occurred within the Baby Boom. The marriage experience of the “early” and “late” Baby Boomers is not comparable at all. The subsequent “generations” are also marked by continuously falling marriage rates, with no clear demarcation between the groups. (There is probably some fancy math someone could do to confirm that, with regard to marriage experience, group membership by these arbitrary criteria doesn’t tell you more than any other arbitrary grouping would.)

Anyway, there are lots of fascinating and important ways that birth cohort — or other cohort identifiers — matter in people’s lives. And we could learn more about them if we looked at the data before imposing the categories.

Couple fact patterns about sexuality and attitudes

Working on the second edition of my book, The Family, involves updating facts as well as rethinking their presentation, and the choice of what to include. The only way I can do that is by making figures to look at myself. Here are some things I’ve worked up recently; they might not end up in the book, but I think they’re useful anyway.

1. Attitudes on sexuality and related family matters continue to grow more accepting or tolerant, but acceptance of homosexuality is growing faster than the others – at least those measured in the repeated Gallup surveys:

2. Not surprisingly, there is wide divergence in the acceptance of homosexuality across religious groups. This uses the Pew Religious Landscape Study, which includes breakouts for atheists, agnostics, and two kinds of “nones,” or unaffiliated people — those for whom religion is important and those for whom it’s not:

3. Updated same-sex behavior and attraction figures from the National Survey of Family Growth. For some reason the NSFG reports don’t include the rates of same-sex partner behavior in the previous 12 months for women anymore, so I analyzed the data myself, and found a much lower rate of last-year behavior among women than they reported before (which, when I think about it, was unreasonably high – almost as high as the ever-had-same-sex-partner rates for women). Anyway, here it is:

FYI, people who follow me on Twitter get some of this stuff quicker; people who follow on Instagram get it later or not at all.

’16 and Pregnant’ and less so

From Flickr/CC: https://flic.kr/p/6dcJgA

Regular readers know I have objections to the framing of teen pregnancy, as a thing generally and as a problem specifically, separate from the rising age at childbearing generally (see also, or follow the teen births tag).

In this debate, one economic analysis of the effect of the popular MTV show 16 and Pregnant has played an outsized role. Melissa Kearney and Phillip Levine showed that was more decline in teen births in places where the show was popular, and attempted to establish that the relationship was causal — that the show makes people under age 20 want to have babies less. As Kearney put it in a video promoting the study: “the portrayal of teen pregnancy, and teen childbearing, is something they took as a cautionary tale.” (The paper also showed spikes in Twitter and Google activity related to birth control after the show aired.)

This was very big news for the marriage promotion people, because it was taken as evidence that cultural intervention “works” to affect family behavior — which really matters because so far they’ve spent $1 billion+ in welfare money on promoting marriage, with no effect (none), and they want more money.

The 16 and Pregnant paper has been cited to support statements such as:

• Brad Wilcox: “Campaigns against smoking and teenage and unintended pregnancy have demonstrated that sustained efforts to change behavior can work.”
• Washington Post: “By working with Hollywood to develop smart story lines on popular shows such as MTV’s ’16 and Pregnant’ and using innovative videos and social media to change norms, the [National Campaign to Prevent Teen and Unplanned Pregnancy] has helped teen pregnancy rates drop by nearly 60 percent since 1991.”
• Boston Globe: “As evidence of his optimism, [Brad] Wilcox points to teen pregnancy, which has dropped by more than 50 percent since the early 1990s. ‘Most people assumed you couldn’t do much around something related to sex and pregnancy and parenthood,’ he said. ‘Then a consensus emerged across right and left, and that consensus was supported by public policy and social norms. . . . We were able to move the dial.’ A 2014 paper found that the popular MTV reality show ’16 and Pregnant’ alone was responsible for a 5.7 percent decline in teen pregnancy in the 18 months after its debut.”

I think a higher age at first birth is better for women overall, health permitting, but I don’t support that as a policy goal in the U.S. now, although I expect it would be an outcome of things I do support, like better health, education, and job opportunities for people of color and people who are poor.

Anyway, this is all just preamble to a new debate from a reanalysis and critique of the 16 and Pregnant paper. I haven’t worked through it enough to reach my own conclusions, and I’d like to hear from others who have. So I’m just sharing the links in sequence.

The initial paper, posted as a (non-peer reviewed) NBER Working Paper in 2014:

Media Influences on Social Outcomes: The Impact of MTV’s 16 and Pregnant on Teen Childbearing, by Melissa S. Kearney, Phillip B. Levine

This paper explores how specific media images affect adolescent attitudes and outcomes. The specific context examined is the widely viewed MTV franchise, 16 and Pregnant, a series of reality TV shows including the Teen Mom sequels, which follow the lives of pregnant teenagers during the end of their pregnancy and early days of motherhood. We investigate whether the show influenced teens’ interest in contraceptive use or abortion, and whether it ultimately altered teen childbearing outcomes. We use data from Google Trends and Twitter to document changes in searches and tweets resulting from the show, Nielsen ratings data to capture geographic variation in viewership, and Vital Statistics birth data to measure changes in teen birth rates. We find that 16 and Pregnant led to more searches and tweets regarding birth control and abortion, and ultimately led to a 5.7 percent reduction in teen births in the 18 months following its introduction. This accounts for around one-third of the overall decline in teen births in the United States during that period.

A revised version, with the same title but slightly different results, was then published in the top-ranked American Economic Review, which is peer-reviewed:

This paper explores the impact of the introduction of the widely viewed MTV reality show 16 and Pregnant on teen childbearing. Our main analysis relates geographic variation in changes in teen childbearing rates to viewership of the show. We implement an instrumental variables (IV) strategy using local area MTV ratings data from a pre-period to predict local area 16 and Pregnant ratings. The results imply that this show led to a 4.3 percent reduction in teen births. An examination of Google Trends and Twitter data suggest that the show led to increased interest in contraceptive use and abortion.

Then last month David A. Jaeger, Theodore J. Joyce, and Robert Kaestner posted a critique on the Institute for the Study of Labor working paper series, which is not peer-reviewed:

Does Reality TV Induce Real Effects? On the Questionable Association Between 16 and Pregnant and Teenage Childbearing

We reassess recent and widely reported evidence that the MTV program 16 and Pregnant played a major role in reducing teen birth rates in the U.S. since it began broadcasting in 2009 (Kearney and Levine, American Economic Review 2015). We find Kearney and Levine’s identification strategy to be problematic. Through a series of placebo and other tests, we show that the exclusion restriction of their instrumental variables approach is not valid and find that the assumption of common trends in birth rates between low and high MTV-watching areas is not met. We also reassess Kearney and Levine’s evidence from social media and show that it is fragile and highly sensitive to the choice of included periods and to the use of weights. We conclude that Kearney and Levine’s results are uninformative about the effect of 16 and Pregnant on teen birth rates.

And now Kearney and Levine have posted their response on the same site:

Does Reality TV Induce Real Effects? A Response to Jaeger, Joyce, and Kaestner (2016)

This paper presents a response to Jaeger, Joyce, and Kaestner’s (JJK) recent critique (IZA Discussion Paper No. 10317) of our 2015 paper “Media Influences on Social Outcomes: The Impact of MTV’s 16 and Pregnant on Teen Childbearing.” In terms of replication, those authors are able to confirm every result in our paper. In terms of reassessment, the substance of their critique rests on the claim that the parallel trends assumption, necessary to attribute causation to our findings, is not satisfied. We present three main responses: (1) there is no evidence of a parallel trends assumption violation during our sample window of 2005 through 2010; (2) the finding of a false placebo test result during one particular earlier window of time does not invalidate the finding of a discrete break in trend at the time of the show’s introduction; (3) the results of our analysis are robust to virtually all alternative econometric specifications and sample windows that JJK consider. We conclude that this critique does not pose a serious threat to the interpretation of our 2015 findings. We maintain the position that our earlier paper is informative about the causal effect of 16 and Pregnant on teen birth rates.

So?

There are interesting methodological questions here. It’s hard to identify the effects of interventions that are swimming with the tide of change. In fact, the creation of the show, the show’s popularity, the campaign to end teen pregnancy, and the rising age at first birth may all be outcomes of the same general historical trend. So I’m not that invested in the answer to this question, though I am very interested.

There are also questions about the publication process, which I am very invested in. That’s why I work to promote a working paper culture among sociologists (through the SocArXiv project). The original paper was posted on a working paper site without peer review, but NBER is for economists who already are somebody, so that’s a kind of indirect screening. Then it was accepted in a top peer-reviewed journal (somewhat revised), but that was after it had received major attention and accolades, including a New York Times feature before the working paper was even released and a column devoted to it by Nicholas Kristof.

So is this a success story of working paper culture gone right — driving attention to good work faster, and then also drawing the benefits of peer review through the traditional publication process? (And now continuing with open debate on non-gated sites). Or is it a case of political hype driving attention inside and outside of the academy — the kind of thing that scares researchers and makes them want to retreat behind the slower, more process-laden research flow which they hope will protect them from exposure to embarrassment and protect the public from manipulation by the credulous news media. I think the process was okay even if we do conclude the paper wasn’t all it was made out to be. There were other reputational systems at work — faculty status, NBER membership, New York Times editors and sources — that may be as reliable as traditional peer review, which itself produces plenty of errors.

So, it’s an interesting situation — research methods, research implications, and research process.

How broken is our system (hit me with that figure again edition)

Why do sociologists publish in academic journals? Sometimes it seems improbable that the main goal is sharing information and advancing scientific knowledge. Today’s example of our broken system, brought to my attention by Neal Caren, concerns three papers by Eran Shor, Arnout van de Rijt, Charles Ward, Aharon Blank-Gomel, and Steven Skiena (Shor et al).

May 13, 2016 update: Eran Shor has sent me a response, which I posted here.

In a paywalled 2013 paper in Journalism Studies, the team used an analysis of names appearing in newspapers to report the gender composition of people mentioned. They analyzed the New York Times back to 1880, and then a larger sample of 13 newspapers from 1982 through 2005. Here’s one of their figures:

The 2013 paper was a descriptive analysis, establishing that men are mentioned more than women over time.

In a paywalled 2014 article in Social Science Quarterly (SSQ) the team followed up. Except for a string-cite mention in the methods section, the second paper makes no reference to the first, giving no indication that the two are part of a developing project. They use this figure to motivate the analysis in the second paper, with no acknowledgment that it also appeared in the first:

Shor et al. 2014 asked,

How can we account for the consistency of these disparities? One possible factor that may explain at least some of these consistent gaps may be the political agendas and choices of specific newspapers.

Their hypothesis was:

H1: Newspapers that are typically classified as more liberal will exhibit a higher rate of female-subjects’ coverage than newspapers typically classified as conservative.

After analyzing the data, they concluded:

The proposition that liberal newspapers will be more likely to cover female subjects was not supported by our findings. In fact, we found a weak to moderate relationship between the two variables, but this relationship is in the opposite direction: Newspapers recognized (or ranked) as more “conservative” were more likely to cover female subjects than their more “liberal” counterparts, especially in articles reporting on sports.

They offered several caveats about this finding, including that the measure of political slant used is “somewhat crude.”

Clearly, much more work to be done. The next piece of the project was a 2015 article in American Sociological Review (which, as the featured article of the issue, was not paywalled by Sage). Again, without mentioning that the figure has been previously published, and with one passing reference to each of the previous papers, they motivated the analysis with the figure:

Besides not getting the figure in color, ASR readers for some reason also don’t get 1982 in the data. (The paper makes no mention of the difference in period covered, which makes sense because it never mentions any connection to the analysis in the previous paper). The ASR paper asks of this figure, “How can we account for the persistence of this disparity?”

By now I bet you’re thinking, “One way to account for this disparity is to consider the effects of political slant.” Good idea. In fact, in the depiction of the ASR paper, the rationale for this question has hardly changed at all since the SSQ paper. Here are the two passages justifying the question.

From SSQ:

Former anecdotal evidence on the relationship between newspapers’ political slant and their rate of female-subjects coverage has been inconclusive. … [describing studies by Potter (1985) and Adkins Covert and Wasburn (2007)]…

Notwithstanding these anecdotal findings, there are a number of reasons to believe that more conservative outlets would be less likely to cover female subjects and women’s issues compared with their more liberal counterparts. First, conservative media often view feminism and women’s rights issues in a relatively negative light (Baker Beck, 1998; Brescoll and LaFrance, 2004). Therefore, they may be less likely to devote coverage to these issues. Second, and related to the first point, conservative media may also be less likely to employ female reporters and female editors […]. Finally, conservative papers may be more likely to cover “hard” topics that are traditionally (that is, conservatively) considered to be more important or interesting, such as politics, business, and sports, and less likely to report on issues such as social welfare, education, or fashion, where according to research women have a stronger presence (Holland, 1998; Ross, 2007, 2009; Ross and Carter, 2011).

From ASR:

Some work suggests that conservative newspapers may cover women less (Potter 1985), but other studies report the opposite tendency (Adkins Covert and Wasburn 2007; Shor et al. 2014a).

Notwithstanding these inconclusive findings, there are several reasons to believe that more conservative outlets will be less likely to cover women and women’s issues compared with their more liberal counterparts. First, conservative media often view feminism and women’s issues in a relatively negative light (Baker Beck 1998; Brescoll and LaFrance 2004), making them potentially less likely to cover these issues. Second, and related to the first point, conservative media may also be less likely to employ female reporters and female editors. Finally, conservative papers may be more likely to cover “hard” topics that are traditionally considered more important or interesting, such as politics, business, and sports, rather than reporting on issues such as social welfare, education, or fashion, where women have a stronger presence.

Except for a passing mention among the “other studies,” there is no connection to the previous analysis. The ASR hypothesis is:

Conservative newspapers will dedicate a smaller portion of their coverage to females.

On this question in the ASR paper, they conclude:

our analysis shows no significant relationship between newspaper coverage patterns and … a newspaper’s political tendencies.

It looks to me like the SSQ and ASR they used the same data to test the same hypothesis (in addition to whatever else is new in the third paper). Given that they are using the same data, how they got from a “weak to moderate relationship” to “no significant relationship” seems important. Should we no longer rely on the previous analysis? Or do these two papers just go into the giant heap of studies in which “some say this, some say that”? What kind of way is this to figure out what’s going on?

Still love your system?

It’s fine to report the same findings in different venues and formats. It’s fine, that is, as long as it’s clear they’re not original in the subsequent tellings. (I personally have been known to regale my students, and family members, with the same stories over and over, but I try to remember to say, “Stop me if I already told you this one” first.)

I’m not judging Shor et al. for any particular violation of specific rules or norms. And I’m not judging the quality of the work overall. But I will just make the obvious observation that this way of presenting ongoing research is wasteful of resources, misleading to readers, and hinders the development of research.

• Wasteful because reviewers, editors, and publishers, are essentially duplicating their efforts to try to figure out what is actually to be learned from these overlapping papers — and then to repackage and sell the duplicative information as new.
• Misleading to readers because we now have “many studies” that show the same thing (or different things), without the clear acknowledgment that they use the same data.
• And hindering research because of the wasteful delays and duplicative expenses involved in publishing research that should be clearly presented in cumulative, transparent fashion, in a timely way — which is what we need to move science forward.

Open science

When making (or hearing) arguments against open science as impractical or unreasonable, just weigh the wastefulness, misleadingness, and obstacles to science so prevalent in the current system against whatever advantages you think it holds. We can’t have a reasonable conversation about our publishing system based on the presumption that it’s working well now.

In an open science system researchers publish their work openly (and free) with open links between different parts of the project. For example, researchers might publish one good justification for a hypothesis, with several separate analyses testing it, making clear what’s different in each test. Reviewers and readers could see the whole series. Other researchers would have access to the materials necessary for replication and extension of the work. People are judged for hiring and promotion according to the actual quality and quantity of their work and the contribution it makes to advancing knowledge, rather than through arbitrary counts of “publications” in private, paywalled journals. (The non-profit Center for Open Science is building a system like this now, and offers a free Open Science Framework, “A scholarly commons to connect the entire research cycle.”)

There are challenges to building this new system, of course, but any assessment of those challenges needs to be clear-eyed about the ridiculousness of the system we’re working under now.

Previous related posts have covered very similar publications, the opposition to open access, journal self-citation practices, and one publication’s saga.

Delayed parenting and anti-poverty policy

Here’s a preview of talk today at Brown University’s population center.

My basic argument is that policies intended to prevent poverty by delaying parenthood are mostly misplaced, especially with regard to Black women. Not that delaying parenthood is bad per se, but delaying parenthood in the absence of other improvements in people’s conditions is ineffectual in the aggregate, and actually harmful for some populations.

The delayed childbearing argument features prominently in the recent “consensus” on anti-poverty strategy reached by the American Enterprise Institute / Brookings working group I wrote about here. They say:

It would be better for couples, for children, and for society if prospective parents plan their births and have children only when they are financially stable, are in a committed relationship (preferably marriage), and can provide a stable environment for their child.

Isabel Sawhill, a leading proponent of delayed childbearing as anti-poverty strategy, says in her book Generation Unbound, that she is not telling poor people not to have children, but she sort of is. She writes:

It is only fair to expect parents to limit the number of children they have to something they can afford.

The evidence I offer to help argue that this approach is unhelpful includes this paper (the actual new research for the talk), which shows the risk of infant mortality rising with parent age for Black mothers, a pattern strikingly different from White and Hispanic mothers’ (see a discussion here). Here’s that result:

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. Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.

Of course, infant mortality is thankfully very rare, but it’s the extreme measure for the underlying pattern of women’s health. When infant mortality in a group is higher, their average health is usually worse.

I’m adding to that the following descriptive figures on children’s poverty rates according to how old their mothers were when they were born. This is by necessity limited to children who are still living with their mothers, because I used the Current Population Survey. I show this for all children (black lines), and then for those whose mothers have never married (red lines). The solid lines are official poverty-line rates, and the dotted lines use the Supplemental Poverty Measure. The latter shows lower poverty rates for children whose mothers were younger, because it reflects transfer income and welfare support as well as income from unmarried cohabiting partners.

For children overall (black lines), being born to an older mother appears beneficial in terms of poverty rates. This fits the standard story, in which delaying births allows women to go further in school and their careers, and get married, as well as being more mature and so on. However, for those whose mothers remain unmarried the relationship is much weaker, and there is no relationship to the SPM. To me this undermines the policy of delay with regard to women who have low probability of marriage during their child-bearing years. Which brings me back to Black women.

I estimated the same pattern by race/ethnicity, this time just using the SPM, in a model that controls for child age, sex, nativity, geography, and mother’s marital status (ever- versus never-married). I didn’t control for education, because schooling is also an outcome of birth timing (so if young mothers don’t go to college for that reason, this would show them more likely to be poor as a result). Here’s the result:

For White women there is a strong relationship, with lowest poverty rates for children whose mothers were in their 30s when they were born. For Black and Hispanic women the relationship is much weaker (it actually looks very similar when you control for education as well, and if you use the continuous income-to-needs ration instead of the poverty-line cutoff).

My conclusion is that I’m all for policies that make family planning available, and U.S. women should have better access to IUDs in particular (which are much more common in other rich countries) — these need to be part of better medical care for poor people in general. But I don’t favor this as a poverty-reduction strategy, and I reject the “responsibility” frame for anti-poverty policy evident in the quotes above. I prefer education, jobs, and income support (which Sawhill also supports, to her credit). See Matt Bruenig on the Brookings “Success Sequence” and my op-ed on income support.

Ideals and intentions

Consider this from Sawhill. In her book Generation Unbound, she writes:

‘poor and minority women … themselves do not want to have as many children as they are currently having. Unintended pregnancy rates are much higher among the poor, minority groups, and the less-educated … [free, better contraception] can help poorer and less-educated women align their behavior with their intentions.’ (p. 138)

I think we need to take a little more complicated view of intentions here. She is referring to what demographers call “unintended” births, which means the woman recalls that she was not intending to get pregnant at the time — she either wanted to get pregnant some time in the future, or never. As you can see, such unintended pregnancies are very common:

However, most poor women think the ideal family size is large. Among young women, 65% of women who didn’t finish high school, and 48% of those with high school degrees but no BA, believe 3 or more children is the ideal for a family:

For lots of their births, poor women were not ready, or not planning to get pregnant. But it’s also common for poor people to never achieve their ideal conditions for having children — good job, marriage, housing, education, and so on. In that case, with the clock running on their (and their mothers’) health, unintended childbearing is more complicated than just a behavior problem to be solved. It may reflect a compromise between unachievable goals.

In addition to making sure everyone has the reproductive healthcare they need (including more effective contraception), I think we should also help people achieve their long-term ideals — including having the children they want to have — rather than (just) help them realize their short-term intentions.

Maternal age and infant mortality paper forthcoming

Update: The paper is now published here. 

The working paper I wrote about here has now been accepted for publication in Sociological Science. Although the results haven’t changed substantially, I revised it since the last post, so you should use this copy instead. Here’s the abstract:

Maternal Age and Infant Mortality for White, Black, and Mexican Mothers in the United States

This paper assesses the pattern of infant mortality by maternal age for White, Black, and Mexican mothers, using 2013 Period Linked Birth/Infant Death Public Use File from the Centers for Disease Control. The results are consistent with the “weathering” hypothesis, which suggests that White women benefit from delayed childbearing while for Black women early childbearing is adaptive because of deteriorating health status through the childbearing years. For White women, the risk (adjusted for covariates) of infant death is U-shaped – lowest in the early thirties – while for Black women the risk increases linearly with age. Mexican-origin women show a J-shape, with highest risk at the oldest ages. The results underscore the need for understanding the relationship between maternal age and infant mortality in the context of unequal health unequal health experiences across race/ethnic groups in the U.S.