Tag Archives: demography

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.

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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.

 

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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.

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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

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Update: Adjusted divorce risk, 2008-2014

Quick update to yesterday’s post, which showed this declining refined divorce rate for the years 2008-2014:

On Twitter Kelly Raley suggested this could have to do with increasing education levels among married people. As I’ve reported using these data before, there is a much lower divorce risk for people with BA degrees or higher education.

Yesterday I quickly (but I hope accurately) replicated my basic model from that previous paper, so now I can show the trend as a marginal effect of year holding constant marital duration (from year of marriage), age, education, race/ethnicity, and nativity.*

2014 update

This shows that there has been a decrease in the adjusted odds of divorce from 2008 to 2014. You could interpret this as a continuous decline with a major detour caused by the recession, but that case is weaker than it was yesterday, looking at just the unadjusted trend.

If it turns out that increase in 2010-2012 is related to the recession, it’s not so different from my original view — a recession drop followed by rebound, it’s just that the drop is less and the rebound is more, and took longer, than I thought.  In any event, this should undermine any effort to resuscitate the old idea that the recession caused a decline in divorce by causing families to pull together during troubled times.

This does not contradict the results from Kennedy and Ruggles that show age-adjusted divorce rising between 1980 and 2008, since I’m not trying to compare these ACS trends with the older data sources. For time beyond 2008, they wrote in that paper:

If current trends continue, overall age-standardized divorce rates could level off or even decline over the next few decades. We argue that the leveling of divorce among persons born since 1980 probably reflects the increasing selectivity of marriage.

That would fit the idea of a long-term decline with a stress-induced recession bounce (with real-estate delay).

Alternative interpretations welcome.

* This takes a really long time for Stata to compute on my sad little public-university computer because it’s a non-linear model with 4.8 million cases – so please don’t ask for a lot of different iterations of this figure. I don’t have my code and output cleaned up for sharing, but if you ask me I’ll happily send it to you.

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Divorce rate plunge continues

When I analyzed divorce and the recession in this paper, I only had data from 2008 to 2011. Using a model based on the predictors of marriage in 2008, I thought there had been a drop in divorces associated with the recession in 2009, followed by a rebound back to the “expected level” by 2011. So, the recession reduced divorces, perhaps temporarily.

That was looking iffy when the 2013 data showed a big drop in the divorce rate, as I reported last year. With new data now out from the 2014 American Community Survey, that story is seeming less and less adequate. With another deep drop in 2014, now it looks like divorce rates are on a downward slide, but in the years after the recession there was a bump up — so maybe recession-related divorces (e.g., those related to job loss or housing market stressors) took a couple years to materialize, producing a lull in the ongoing plunge. Who knows.

So, here is the latest update, showing the refined divorce rate — that is, the number of divorces in each year per 1,000 married people in that year.*

divorce rates.xlsx

Lots to figure out here. (As for why men and women have different divorce rates in the ACS, I still haven’t been able to figure that out; these are self-reported divorces, so there’s no rule that they have to match up [and same-sex divorces aren’t it, I think.])

For the whole series of posts, follow the divorce tag.

* I calculate this using the married population from table B12001, and divorces in the past year from table B12503, in the American Factfinder version of the ACS data.

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Age composition change accounts for about half of the Case and Deaton mortality finding

This paper by Anne Case and Angus Deaton, one of whom just won a Nobel prize in economics, reports that mortality rates are rising for middle-aged non-Hispanic Whites. It’s gotten tons of attention (see e.g., “Why poor whites are dying of despair” in The Week, and this in NY Times).

It’s an odd paper, though, in its focus on just one narrow age group over time. The coverage mostly describes the result as if conditions are changing for a group of people, but the group of people changes every year as new 45-year-olds enter and 54-year-olds leave. That means the population studied is subject to change in its composition. This is especially important because the Baby Boom wave was moving through this group part of that time. The 1999-2013 time frame included Baby Boomers (born 1945-1964) from age 35 to age 68.

My concern is that changes in the age and sex composition of the population studied could account for a non-trivial amount of the trends they report.

For example, they report that the increased mortality is entirely concentrated among those non-Hispanic White men and women who have high school education or less. But this population changed from 1999 to 2013. Using the Current Population Survey — which is not the authority on population trends, but is weighted to reflect Census Bureau estimates of population trends — I see that this group became more male, and older, over the period studied. That’s because the Baby Boomers moved in, causing aging, the population reflects women’s advances in education, relative to men, circa the 1970s. Here are those trends:

12208749_1027485723940137_783054584452711772_n

It’s odd for a paper on mortality trends not to account for account for sex and age composition changes in the population over time. Even if the effects aren’t huge, I think that’s just good demography hygiene. Now, I don’t know exactly how much difference these changes in population composition would make on mortality rates, because I don’t have the mortality data by education. That would only make a difference if the mortality rates differed a lot by sex and age.

However, setting aside the education issue, we can tell something just looking at the whole non-Hispanic White population, and it’s enough tor raise concerns. In the overall 45-54 non-Hispanic White population, there wasn’t any change in sex composition. But there was a distinct age shift. For this I used the 2000 Census and 2013 American Community Survey. I could get 1999 estimates to match Case and Deaton, but 2000 seems close enough and the Census numbers are easier to get. (That makes my little analysis conservative because I’m lopping off one year of change.)

Look at the change in the age distribution between 2000 and 2013 among non-Hispanic Whites ages 45-54. In this figure I’ve added the birth year range for those included in 2000 and 2013.

casedeatonage

That shocking drop at age 54 in 2000 reflects the beginning of the Baby Boom. In 2000 there were a lot more 53-year-olds than there were 54-year-olds, because the Baby Boom started in 1946. (Remember, unlike today’s marketing-term “generations,” the Baby Boom was a real demographic event.) So there was a general aging, but also a big increase in 54-year-olds, between 2000 and 2013, which will naturally increase the mortality rate for that year.

So, to see whether the age shift had a non-trivial impact on the number of deaths in this population, I used one set of mortality rates: 2010 rates for non-Hispanic Whites by single year of age, published here. And I used the age and sex compositions as described above (even though the sex composition barely changed I did it separately by sex and summed them).

The 2010 age-specific mortality rates applied to the 2000 population produce a death rate of 3.939 per 1,000. When applied to the 2013 population they produce a death rate of 4.057 per 1,000. That’s the increase associated with the change in age and sex composition. How big is that difference? The 2013 death rate implies 118,313 deaths in 2013. The 2000 death rate implies 114,869 deaths in 2013. The difference is 3,443 deaths. Remember, this assumes age-specific death rates didn’t change, which is what you want to assess effects of composition change.

So I can say this: if age and sex composition had stayed the same between 2000 and 2013, there would have been 3,443 fewer deaths among non-Hispanic Whites in the ages 45-54.

Here is what Case and Deacon say:

If the white mortality rate for ages 45−54 had held at their 1998 value, 96,000 deaths would have been avoided from 1999–2013, 7,000 in 2013 alone.

So, it looks to me like age composition change accounts for about half of the rise in mortality they report. They really should have adjusted for age.

Here is my spreadsheet table (you can download the file here):

casedeatontab

As always, happy to be credited if I’m right, and told if I’m wrong. But if you just have suggestions for more work I could do, that might not work.

Follow up: Andrew Gelman has three excellent posts about this. Here’s the last.

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