Tag Archives: demography

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:

Fig2

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.

cpsbrown

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:

bw-kid-predict-no-educ

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:

unintended

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:

idealed

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.

6 Comments

Filed under Me @ work, Research reports

Old people are getting older and younger

The Pew Research Center recently put out a report on the share of U.S. older women living alone. The main finding they reported was a reversal in the long trend toward old women living alone after 1990. After rising to a peak of 38% in 1990, the share of women age 65+ living alone fell to 32% by 2014. It’s a big turnaround. The report attributes it in part to the rising life expectancy of men, so fewer old women are widowed.

Cg0IMkjWYAEz-aO

The tricky thing about this is the changing age distribution of the old population (the Pew report breaks the group down into 65-84 versus 85+, but doesn’t dwell on the changing relative size of those two groups). Here’s an additional breakdown, from the same Census data Pew used (from IPUMS.org), showing percent living alone by age for women:

pewage1

Two things in this figure: the percent living alone is much lower for the 65-69s, and the decline in living alone is much sharper in the older women.

The age distribution in the 65+ population has changed in two ways: in the long run it’s getting older as life expectancy at old age increases. However, the Baby Boom (born 1946-1964) started hitting age 65 in 2010, resulting in a big wave of 65-69s pouring into the 65+ population. You can see both trends in the following figure, which shows the age distribution of the 65+ women (the lines sum to 100%). The representation of 80+ women has doubled since 1960, showing longer life expectancy, but look at that spike in the 65-69s!

pewage2

Given this change in the trends, you can see that the decrease in living alone in the 65+ population partly reflects greater representation of young-old women in the population. These women are less likely to live alone because they’re more likely to still be married.

On the other hand, why is there such a steep drop in living alone among 80+ women? Some of this is the decline in widowhood as men live longer. But it’s an uphill climb, because among this group there is no Baby Boom spike of young-olds (yet) — the 80+ population is still just getting older and older. Here’s the age distribution among 80+ women (these sum to 100 again):

pewage3

You can see the falling share of 80-84s as the population ages. If this is the group that is less likely to live alone the most because their husbands are living longer, that’s pretty impressive, because the group is aging fast. One boost the not-alones get is that they are increasingly likely to live in extended households — since 1990 there’s been a 5% increase in them living in households of at least 3 people, from 13% to 18%. Finally, at this age you also have to look at the share living in nursing homes (some of whom seem to be counted as living alone and some not).

In addition to the interesting gerentological questions this all raises, it’s a good reminder that the Baby Boom can have sudden effects on within-group age distributions (as I discussed previously in this post on changing White mortality patterns). Everyone should check their within-group distributions when assessing trends over time.

Leave a comment

Filed under In the news

Has your marriage lasted 50 years? Congratulations, you’re old

Just kidding: Congratulations, you’re old and have had a long marriage.

The Washington Post magazine has a feature out today called “The secret to a long-lasting marriage.” I don’t have a general comment on it, because I only made it to the third paragraph, and it’s probably worth reading.

But the third paragraph is funny:

They have beaten the odds of death and divorce: Of all current U.S. marriages, only 7 percent have reached the 50-year mark, according to the National Center for Family and Marriage Research at Bowling Green State University.

It is certainly true that making it to the 50-year mark of marriage means you have beaten the odds of death and divorce. But that 7% figure has nothing to do with it, because it includes people who got married yesterday!

Here is the breakdown of when people got married, among people married right now (in the 2014 American Community Survey, which has to be the source for that statistic):

yrmar14

So the statistic is correct: only 7% of currently married people have been married for 50 years or more. Good for them! To bad for all those other people they were born so recently.

It’s all in the denominator. Sure, 50-year marrieds are rare, but compared to what?

With the ACS we can answer a more relevant question, which is this: among living people whose most recent marriage was 50 years ago or more, what is their current marital status? This is a little more encouraging: half are still married.

mars50p

So let’s restate the original congratulatory message like this:

They have beaten the odds of death and divorce: Of all people who tied the knot 50 or more years ago, and who haven’t yet died, only 50% percent have made it this far without divorcing or becoming widowed, according to the American Community Survey.

Many happy returns.

5 Comments

Filed under In the news

Marriage and gender inequality in 124 countries

Countries with higher levels of marriage have higher levels of gender inequality. This isn’t a major discovery, but I don’t remember seeing this illustrated before, so I decided to do it. Plus I’m trying to improve my Stata graphing.

I used data from this U.N. report on marriage rates from 2008, restricted to those countries that had data from 2000 or later. To show marriage rates I used the percentage of women ages 30-34 that are currently married. This is thus a combination of marriage prevalence and marriage timing, which is something like the amount of marriage in the country. I got gender inequality from the U.N. Development Programme’s Human Development Report for 2015. The gender inequality index combines the maternal mortality ratio, the adolescent birth rate, the representation of women in the national parliament, the gender gap in secondary education, and the gender gap in labor market participation.

Here is the result. I labeled countries with 49 million population or more in red; a few interesting outliers are also labeled. The line is quadratic, unweighted for population (click to enlarge).

You can see the USA sliding right down that curve toward gender nirvana (not that I’m making a simplistic causal argument).

Note that India and China together are about 36% of the world’s population. They both have nearly universal marriage by age 30-34, but women in China get married about four years later on average. That’s an important part of why China has lower gender inequality (it goes along with more educational access, higher employment levels, politics, history, etc.). China is a major outlier among universal-marriage countries, while India is right on the curve.

Any cross-national comparison has to handle this issue. China is 139-times bigger than Sweden. One way to address it is to weight the points by their relative population sizes. If you do that it actually doesn’t change the result much, except for China, which in this cases changes everything because in addition to being huge they broke the relationship between marriage and gender inequality. Here is the comparison. Now the dots are scaled for population, and the gray line is fit to all the countries except China, while the red line includes China (click to enlarge).

My conclusion is that the gray line is the basic story — more marriage, more gender inequality — with China as an important exception, but that’s up for interpretation.

I put the data and the code for making the charts in this directory. Feel free to copy and crib, etc.

10 Comments

Filed under Me @ work

When the map says race but all you can talk about is fatherhood

Raj Chetty and colleagues have a new paper showing that “childhood environments play an important role in shaping gender gaps in adulthood.” Essentially, boys from poor or or single parents are doing worse. Also, this gender difference is greater in Black and poor places.

The tricky thing with this data, and I don’t blame Chetty et al. for this, although I would like them to say more about it, is that they don’t know the race of the children. The data are from tax records, which allow you to know the income and marital status of the parents, but not the race. But they know where they grew up. So if they have a strong effect of the racial composition of the county kids grow up in, but they don’t know the race of the kids, you have to figure a big part of that is race of the kids — and by “you” I mean someone who knows anything about America.

So here’s their map of the gender difference in employment rates associated with having poor parents:

chetmap

To help make the point, here is their list of local areas at the top and bottom of the map:

chettab

I hope that is enough to make the point for the demographically literate reader.

I credit them in this paper for at least using percent Black as a variable, which they oddly omitted from a previous analysis. This allows the careful reader to see that this is the most important local-area variable — which makes perfect sense because it is doing the work of the individual data, which doesn’t include race.

racechettyeffects

Wow!

It’s important that these examples are all about employment rates. We know that the penalty for being a Black man is especially large for employment, partly because of the direct effects of mass incarceration, but also because of discrimination, some of which is directly related to incarceration and the rest of which may be affected by its aura. This is not something we measure well. Our employment reporting system does not include prison records. Prisoners are excluded from the Current Population Survey, but then included when they are released. So they show up as jobless (mostly) men.

Whenever you see something about how race affects poor men, you have to think hard about what incarceration is doing there — we can’t just rely on the data in front of us and assume it’s telling the whole story, when we know there is a massive influence not captured in the data.

This is exactly what marriage promoters delight in doing. I give just one example, a blog post by the Brookings Institution’s Richard Reeves, which — amazingly, astoundingly, remarkably, disappointingly, not surprisingly — discusses the effect of growing up poor and “less-educated” in Baltimore (Baltimore!) without once mentioning race or incarceration. Instead, he goes right to this:

Wanted: Fathers

Of course, there is much more to being a man than money: in fact, to define masculinity in breadwinning terms alone is a fatal move. As Barack Obama said on Fathers’ Day seven years ago, fathers are “teachers and coaches. They are mentors and role models.” But as he also said, “too many fathers are missing—missing from too many lives and too many homes.” In its poorest neighborhoods, America faces a fathering deficit, one that will make it even harder for the boys of today to make it as men in the new world.

Fatherhood is important. You could investigate a fathering deficit, but if you really cared about it you want to look at in the context of well-known, massive causes of harm to Black boys in America, chief among them racism and mass incarceration.

 

9 Comments

Filed under In the news

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.

7 Comments

Filed under Me @ work, Research reports

Must-know current demographic facts

Here’s an update of a post I wrote two years ago, with some additions.

One reason you, and your students, need to know these things is because they are the building blocks of first-line debunking. We use these facts, plus arithmetic, to ballpark the empirical claims we are exposed to all the time.

This followed my aggressive campaign to teach the undergraduate students in my class the size of the US population (I told you sociology isn’t an easy A). If you don’t know that — and some large portion of them didn’t — how can you interpret statements such as, “On average, 24 people per minute are victims of rape, physical violence, or stalking by an intimate partner in the United States.” In this case the source followed up with, “Over the course of a year, that equals more than 12 million women and men.” But, is that a lot? It’s a lot more in the United States than it would be in China. (Unless you go with, “any rape is too many,” in which case why use a number at all?)

statscartoon

I even updated the cartoon!

Anyway, just the US population isn’t enough. I decided to start a list of current demographic facts you need to know 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 know to make sense of those things when other people use them. And it’s really a ballpark requirement (when I tested the undergraduates, I gave them credit if they were within 20% of the US population — that’s anywhere between 258 million and 387 million!).

I only got as far as 25 facts, but they should probably be somewhere in any top-100. And the silent reporters the other day made me realize I can’t let the perfect be the enemy of the good here. I’m open to suggestions for others (or other lists if they’re out there).

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 7.3 billion 1
US Population 323 million 1
Children under 18 as share of pop. 23% 2
Adults 65+ as share of pop. 15% 2
Unemployment rate 5.0% 3
Unemployment rate range, 1970-2015 4% – 11% 4
Labor force participation rate, age 16+ 63% 4
Labor force participation rate range, 1970-2015 60% – 67% 4
Non-Hispanic Whites as share of pop. 62% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 17% 2
Asians as share of pop. 5% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 13% 2
Adults with BA or higher 29% 2
Median household income $53,000 2
Total poverty rate 15% 8
Child poverty rate 21% 8
Poverty rate age 65+ 10% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 323 million 5
4th most populous country, Indonesia 256 million 5
5th most populous country, Brazil 204 million 5
Male life expectancy at birth 76 6
Female life expectancy at birth 81 6
National life expectancy range 50 – 85 7

Sources:
1. http://www.census.gov/popclock/

2. http://quickfacts.census.gov/qfd/states/00000.html

3. http://www.bls.gov/

4. Google public data: http://bit.ly/UVmeS3

5. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2119rank.html

6. http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf

7. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2102rank.html

8. https://www.census.gov/hhes/www/poverty/about/overview/

Now with handy PDF: Family Inequality Must-Know Demographic Facts

13 Comments

Filed under Uncategorized