Tag Archives: global

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:

tfr4017

tfrchanges

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

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

change in birthrates by age 1989-2016.xlsx

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

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

country fertilitiy trends.xlsx

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

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

birth model simple 2016.xlsx

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

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

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


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

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

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81 countries made more progress than the USA on women’s representation

The Inter-Parliamentary Union has a great archive of women’s representation in parliaments in most countries, from 1997 to 2016. I made this figure using the numbers for the lower houses (or single houses, if only one), which in the USA is the House of Representatives.

From 1997 to 2016, women rose from 12% to 19% of House members. During that time, for 163 countries, the average rose from 10% to 21%. When I cut the list down to 137, arbitrarily excluding a lot of very small countries, the USA slipped from 54th place to 84th place. Here’s the breakdown of changes in those countries (click to enlarge):

countries ranked by women's representation in parliament, 1997-2016

At this rate, in just 36 more years the House will get to the level of women’s representation that Hanna Rosin said Congress was at in 2012.


Previous posts:


Note: The code for making this figure in Stata looks like this:

gr twoway scatter rank16 rank97, mlabel(country) mlabposition(0) msymbol(i)

Before tinkering with the appearance and titles in the graph editor.

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Brave new racist nativist political world

[This was posted on July 27 and then revised on July 29 after Hillary Clinton’s speech]

It’s all harmless political shenanigans until a racist mob murders Vincent Chin.*

It’s amazing how the new figureheads of both major parties are now pretending to oppose globalization, outsourcing, and the corporate “free trade” agenda that they both have spent their professional lives furthering. It wasn’t long ago that I taught in my stratification class that this agenda was the one thing we could be sure both parties and the big money behind them wouldn’t give up. Never say never, but I’m still pretty sure that’s still true.

There are good reasons to oppose this agenda, but most of them aren’t in America. If you want to talk about slave labor, exploitation, and environmental degradation in the new manufacturing centers of the world, then I would be happy to listen to you talk about the harmful effects of those practices “here at home” too. But if you just want to bash China, then you’re a racist, and no thank you.

Case in point, Pennsylvania Senator Bob Casey at the Democratic National Convention the other day. Here’s his speech, followed by some of the text and my comments:

Casey quoted his father, the former governor:

“The sweat and blood of working men and women who built Pennsylvania forged the industrial revolution in our country, and outproduced the world.”

How touching, attributing the industrial revolution the efforts of the working class. It reminds me of when another brave Pennsylvania governor, Democrat Robert Pattison, reached across the aisle, helping out Republican industrialists by lending them the National Guard to put down the Homestead steelworkers.

I assume today’s Democratic politician will now go on to recognize the working class of today’s manufacturing centers, who, through their sweat and blood are outproducing the world and building the middle class in their countries. Oh right, Casey is American.

What about Donald Trump? Donald trump says he stands for workers, and that he’ll put American first, but that’s not how he’s conducted himself in business. Where are his, quote, tremendous products made? Dress shirts: Bangladesh. Furniture: Turkey. Picture frames: India. Wine glasses: Slovenia. Neckties: China. China! Why would Donald Trump make products in every corner of the world, but not in Altoona, Erie, or here in Philadelphia? Well, this is what he said, quote, outsourcing is not always a terrible thing. Wages in America quote, are too high. And then he complained about companies moving jobs overseas because, quote, we don’t make things anymore. Really? … [examples of stuff made in America]. Donald Trump hasn’t made a thing in his life, except a buck on the backs of working people. If he is a champion of working people, I’m the starting center for the 76ers! The man who wants to make America great, doesn’t make anything in America! If you believe that outsourcing has been good for working people, and has raised incomes for the middle class, then you should vote for Donald Trump. … We need to making good paying jobs for everyone here at home, so that everyone who works hard can get ahead and stay there.

Yes, the great conflict of our time is between “China” and “working people.” Maybe one we can all link arms and together put down striking Chinese workers to keep the price down on our iPhones and Wal-Mart junk.

The Democratic National Convention was very on-message. In Hillary Clinton’s acceptance speech the next day, she said:

If you believe that we should say “no” to unfair trade deals, that we should stand up to China, that we should support our steelworkers and autoworkers and homegrown manufacturers — join us.

She gave no definition of what it means to “Stand up to China,” though her website says she will insist on trade deals that raise wages and create good-paying jobs (presumably meaning in the US). That’s not important — the important thing communicated to her audience is she’s against China and for American workers. Then she went through the same list of Trump production locations that Casey did, before concluding, “Donald Trump says he wants to make America great again – well, he could start by actually making things in America again.” The current U.S. trade deficit in goods (as opposed to services) is about $62 billion — per month. Virtually all Americans are dependent on imported goods (including, apparently, Clinton, whose Nina McLemore suits are made from European and Asian fabrics). No major politician is seriously against this. Trump hiring U.S. workers to make his ties would make about as much difference as Clinton buying clothes with U.S. fabrics, which is basically none. It’s just symbolism, and the symbolism here is China is bad. Unless you join this kind of talk with explicit concern for the (much greater, obviously) suffering and exploitation of Chinese workers, I think this just feeds American racism.

Decades later, Vincent Chin resonates with me. There is debate about whether racism was the real motivation behind Vincent Chin’s murder, and it wasn’t as simple as a random lynch mob. Despite the legend, it is not the case that the auto workers just killed him because they falsely believed he was Japanese. But a witness at the bar said they blamed him for them being out of work before they fought. She said:

I turned around and I heard Mr. Ebens say something about the ‘little motherfuckers.’ And Vincent said, ‘I’m not a little motherfucker,’ and he said, ‘Well, I don’t know if you’re a big one or a little one.’ Then he said something about, ‘Well, because of y’all motherfuckers we’re out of work.’*

After losing the first round, Ronald Ebins and his stepson, Michael Nitz, hunted Chin down and killed him with a baseball bat, a crime for which they ultimately served no jail time.

My 8-year-old Chinese immigrant daughter, who learns all about how racism and bullying are bad and MLK is great in her neoliberal public American elementary school, is routinely offended and hurt by the China-bashing she hears from Democrats as well as Trump (she supported Bernie but is willing to back Hillary to stop Trump).

Hillary says we should protect our children from having to listen to Trump’s nastiness — she even has ad on that, which I’ve personally witness liberals tearing up over:

So, what about the people making speeches at your convention, spitting out the word China! like it’s a disease? “What example will we set for them?”

If the new normal of politics is both parties bashing foreigners  while they pretend to oppose globalization — and then pursue the same policies anyway, which, face it, you know they will — then what have we gained? It seems to me there is a small chance Clinton will negotiate better trade deals, to the benefit workers (U.S. or Chinese), and a much greater chance her rhetoric will stoke nativism and racism. As Trump’s megaphone has drawn the White supremacists out from under their rocks, the new fake-anti-TPP Hillary has given a bigger platform to this kind of obnoxious chauvinism.

* The 1987 documentary Who Killed Vincent Chin, which includes that clip, is worth watching (it’s online here, for now).

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US teen birth rates remain high, and they’re not falling for the reasons you’ve heard

Everyone is excited by the decline in the teen birth rate in the US. But And here are a few things you should know about it.

This chart shows the birth rates for women ages 15 to 19 in 192 countries, plus the world and the UN-defined rich countries, for 1991 and 2011. Dots below the black line show countries where the teen birth rate fell. The red line shows the overall relationship between 1991 and 2011. Dots below the red line had greater than expected reduction in teen births.

teen births global

Source: My graph United Nations data.

The chart shows four things:

1. Teen birth rates are falling globally. From 1991 to 2011, the birth rate for women ages 15 to 19 fell from 65 to 46 births per 1,000 women worldwide.

2. US has higher teen birth rates than any other rich country. At 33 per 1,000, the US has more teen births than Pakistan (28), but fewer than India (36). For high income countries, by the UN definition, the rate is 19. The rate for the Euro area is 7.

3. The teen birth rate is falling faster in the US than in the world overall. The world rate fell 29% from 1991 to 2011, while the drop in the US was 44%.

In the US, there are a lot of factors related to falling teen births. But they’re mostly about how it’s happening, not why it’s happening. For example, Vox published a list of factors, as did Pew before them, that are reasonable: the recession, more birth control, more Medicaid money for family planning, cultural pressure, and less sex.

But to understand why this is happening, you have to stop thinking about teenagers as some sort of separate subspecies. They are just young women. Soon they will be in their 20s. The same women! So the short answer for why falling teen birth rates happening is this:

4. Teen birth rates in the US are falling because women are postponing their births generally.

You can see this if you line up teens next to women of other ages. Here are the changes in birth rates for women, by age, from 1989 to 2012.

birthratechangebyage

Source: My graph from National Center for Health Statistics data.

See how the trend for the last decade is parallel for 15-17, 18-19, and 20-24? As those rates fell, birth rates rose for the 30+ community. The younger women are, the fewer births they’re having; the older they are, the more births they’re having. Teenage women are women! They do it for all the reasons it’s happening around the world: some because they are delaying marriage, some to pursue education and careers, some to see the world, and so on.

Here is another way to look at this. Here are the 50 US states, from the 2000-2012 American Community Survey. This shows that states with lower teen birth rates (those are per 100, on the y-axis), have higher birth rates for 25-34 year-old women relative to 20-24 year-old women. I’ll explain:

teenbirthstates

Teen births rates and the ratio of birth rates ages 25-34 / 20-24. US states, 2010-2011

Where more women have children ages 25-34 relative to 20-24, there are fewer teen births. So, in Alabama, about 3% of women 15-19 had a baby per year, and in that state the birth rates are about the same for women 25-34 as 20-24. Alabama is an early-birth state. But in New Hampshire, only 1% of teens had a baby, and women 25-34 were almost 2.5-times more likely to have a baby than women 20-24. New Hampshire is a late-birth state. What’s happening with teens reflects what’s happening with older women.

To some significant degree, it’s not about teenagers, it’s about women delaying births.* I would love it if reporting on teen births would always compare them to older women.

*Notice I didn’t just exaggerate and say, “it’s not about teenagers.” I added “to some significant degree.” That’s the difference between a post that is selling you (your clicks) to someone versus a post that’s trying to explain things as clearly as possible.

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Global inequality, within and between countries

Most of the talk about income inequality is about inequality within countries – between rich and poor Americans, versus between rich and poor Swedes, for example. The new special issue of Science magazine about inequality focuses that way as well, for example with this nice figure showing inequality within countries around the world.

But what if there were no income inequality within countries? If everyone within each country had the same income, but we still had rich and poor countries, how unequal would our world be? It turns out that’s an easy question to answer.

Using data from the World Bank on income for 131 countries, comprising 91% of the world population, here is the Lorenz curve showing the distribution of gross national income (GNI) by population, with each person in each country assumed to have the same income (using the purchasing power parity currency conversion). I’ve marked the place of the three largest countries: China, India, and the USA:

lorenza1

The Gini index value for this distribution is .48, which means the area between the Lorenz curve and the blue line – representing equality, is 48% of the lower-right triangle. (Going all the way to 1.0 would mean one person had all the money.)

But there is inequality within countries. In that Science figure the within-country Ginis range from .24 in Belarus to .67 in South Africa. (And that’s using after-tax household income, which assumes each person within each household has the same income. So there’s that, too.)

The World Bank data I’m using includes within-country income distributions broken into 7 quantiles: 5 quintiles (20% of the population each), with the top and bottom further broken in half. If I assume that the income is shared equally within each of these quantiles, I can take those 131 countries and turn them into 917 quantiles (just assigning each group its share of the country’s GNI). These groups range in average income from $0 (due to rounding) in the bottom 10th of Bolivia and Guyana, or $43 per person in the bottom 10th of the Democratic Rep. of Congo, up to $305,800 per person in the top 10th of Macao.

To illustrate this, here are India, China, and the USA, showing average incomes for the quantiles and the countries as a whole:

lorenza2

This shows that the average income of China’s top 10th is between the second and third quntiles of the US income distribution, and the top 10th of India has an average income comparable to the US 10-19th percentile range. Obviously, this breakdown shows a lot more inequality.

So here I add the new Lorenz curve to the first figure, counting each of those 917 quantiles as a separate group with its own income:

lorenza3

Now the Gini index has risen a neat 25%, to an even .60. Is that a big difference? Clearly, between country inequality — the red line — is vast. If every country were a household, the world would be almost as unequal as Nigeria. In this comparison, you could say you get 80% of the income inequality to show up just looking at whole countries. But of course even that obscures much more, especially at the high end, where there is no limit.

Years ago I followed the academic debate over how to measure inequality within and between countries. If I were to catch up with it again, I would start with this article, by my friends Tim Moran and Patricio Korzeniewicz. That provoked a debate over methods and theory, and they eventually published this book, which argues: “within-country analyses alone have not adequately illuminated our understanding of global stratification.” There is a lot more to read, but their work, and the critiques they’re received, is a good place to start.

Note: I have put my Excel worksheet for this post here. It has the original data and my calculations, but not the figures.

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Marriage is declining globally: Can you say that?

Three issues on the decline of marriage: how universal is it, is delay the same thing as decline, and how can you predict it?

1. The worldwide decline in marriage

I basically argued that marriage decline in the U.S. is universal and inevitable. The headline for the post at the Atlantic was “How to Live in a World Where Marriage Is in Decline,” and at Sociological Images it was, “Marriage Is Over: Live with It.”

For context, I hinted at the globalness of this trend, but here I’ll give a little more detail. I consulted three sources: This Eurostat database on demographics from Europe, this United Nations compilation of marriage rates and marital status from 2008, the Demographic and Health Surveys (DHS) conducted by USAID, and this amazing IPUMS compilation of microdata from censuses around the world.

From Eurostat I got the crude marriage rate, which is marriages per year for every thousand people in the population. They have data for every year for most countries, so I took decade averages for each decade since the 1970s. Here it is:

eurostat-marriage-rates

The big countries on the left account for 78% of the population, and they all show a decline in marriage rates for every decade. Most of the little countries show the same pattern. Overall, 89% of the population lives in a country with that pattern.

Because it is affected by changes in the age distribution, the crude marriage rate is not ideal. So with the UN data, like I did with the US data in original post, I use the percentage of women married. Here it is for those ages 30-34 for almost the whole world, 174 countries, using two data points each, the first around 1985 and the second around 2002 (I’ve scaled the dots to represent population size). Countries below the diagonal line have falling proportions of women married:

un-currently-married

In this selection of countries 87% of the population is living in a country with falling marriage prevalence, but that includes the biggest – China and India – which still have virtually universal marriage (98+%). Still, almost all the other big ones show declines (I’ve labeled Pakistan, Russia, Mexico, Brazil, the US, Japan, Italy, Germany and France). The only countries with more than 50 million people that show increases are the Philippines, Vietnam, Egypt, and the Dem. Rep. of Congo.

That is an impressive array of countries with declining marriage prevalence. However, the big countries with big drops in marriage are mostly rich: France, Italy, Germany, Japan and the US. Is this a rich-country phenomenon? One thing that sets apart some of those countries — as Gøsta Esping-Andersen pointed out in a comment here — is their high rates of non-marital cohabitation, either replacing marriage or as part of a pattern of delayed marriage. Delayed or reduced marriage is often part of package of demographic changes, including high income and low fertility. (The exception is Japan, where cohabitation rates, although rising, are still low, with just 21% of Japanese women born in the 1970s having ever cohabited, compared with two-thirds of US women.)

One of the sources for that UN compilation is the DHS, which has been collecting fertility-related data in poor countries since the 1980s. To look more closely at some poor countries, I used the DHS data compiler to get the proportion of women ever married in the ages 25-49, for the 17 countries that had a data for the 1980s, 1990s (except missing a few), and 2000s (most from late in the decade):

dhs-ever-married

This shows the decline of marriage is common in poor countries as well, albeit smaller declines from high levels in recent decades (note I’ve scaled it from 70% to 100%).

Finally, after registering as an IPUMS International user, I downloaded data on 40 million women ages 25-49 from 26 countries, using the oldest census from the 1980s, one from the 1990s, and the most recent one from the 2000s for each (so two to four censuses per country).* This source is great because it’s got age and education variables, which I’ll use below. Here is the before-and-after graph using these data, again showing the percentage of 25-49 year-old women married:

ipums-international-marriage

Here, only Vietnam and Indonesia show increases in marriage prevalence. The big countries with declines are France, the US, and Brazil; medium-size countries include Argentina, Spain, Colombia, Venezuela, Chile and Turkey. And all the little countries show declines: Morocco, Ecuador, Greece, Malawi, Canada, Austria, Senegal, Thailand, Portugal, Hungary, Ireland, Switzerland, Costa Rica, Panama, Jamaica (I don’t know what’s up in Jamaica).

In almost all of these countries, women with more education are less likely to be married (the U.S. and Jamaica are exceptions to this; in Hungary, Argentina, Canada, and Ireland only those with university completed are less likely to be married). And in all countries older women are more likely to be married. So if world populations are aging (which should increase marriage prevalence) and women are gaining access to education (which should decrease it), we might get misleading results from simple rates. With the IPUMS data there is an indicator of whether people have finished secondary schooling and university education in all these countries. So I can adjust for those (and run a linear probability regression with 40 million cases across 26 countries in the time in takes to walk the dog). Here is the result:

ipums-international-marriage-ols

With the adjustments, Thailand, Indonesia and Vietnam have increased marriage prevalence. In most cases the adjustments reduce the time effect, reflecting the spread of education.

Conclusion: The decline of marriage is worldwide although not universal, and it’s probably not accounted for by age and education changes.**

2. Is delay the same as decline?

But what if some or even all of this worldwide trend – or the trend in some countries, like the US – is really from people delaying marriage rather than forgoing it? Some people argue, reasonably, that delaying marriage can be a sign of its increased social significance. This is the “capstone” concept popularized by Andrew Cherlin, and it’s been raised in blog comments many times.

There is good evidence for this. In the US at least, despite the delay in marriage, and the drop in the marriage rate, the great majority of Americans have been married at least once by the time they reach their fifties. Here are the percentages ever married (that is, including separated, divorced and widowed) by age for cohorts born from 1930 to 1990 (using IPUMS data).

US-cohorts-ever-born

The steps downward from back to front show that, at each age, successive cohorts are less likely to have been married. But the high levels on the far right show that much of this is just delay. By 2010, 87% of about-50-year-olds – those born around 1960 – were ever married. That said, the decline is still pronounced – that 87% is down from 95%. Thus, marriage is being both delayed and foregone, albeit to different extents.

The measure used is very important. Consider these scenarios:

  • If everyone suddenly put off their marriage till next year, the marriage rate this year would be zero.
  • If everyone got married at age 50, the percentage of people married in the ages 25-49 would be zero.
  • Even if no one got married ever again, we would still have a majority of adults married for a while. (Idea for demography exam question: how long?)
  • If everyone decided marriage was just so important as a capstone that they would only get married on their death beds, the marriage rate would be low, the percent of the population married would be low, but the percentage who ever got married would be 100%.

So what’s the right way to look at this? Questions of meaning in demography are amorphous. I can’t say from these numbers how people think about marriage, especially across all these countries. That doesn’t mean the question isn’t important, but in one sense these numbers mean it’s undeniable that marriage is less important – fewer people are doing it, and doing it for a smaller portion of their lives. (More on this here.) That goes especially for the question of family structure for children, who are increasingly likely to be born to parents who aren’t married.

3. Says who?

So marriage decline is both worldwide and real. Can we predict what will happen, that the decline will continue? The scenarios above all are possible. But I think they’re unlikely. In the post I started with, I included an illustration of the marriage rate from 1940 to the present, and argued we should plan for a future in 2040 where the rate would be somewhere between 0 (complete collapse) and the 2000 level (strong rebound but still low by historical standards). Some tweeter gave that the award for “today’s most hilariously amateur data analysis.”

I’ve made short-term predictions by extrapolation (such as the name Mary), which is fine unless the trend changes. And I’ve made some based on evidence from leading indicators, such as Google searches for divorce– or fertility-related terms. But what about the long-term trend on marriage? Here’s a cautionary tale.

In 1955, Talcott Parsons wrote the introduction to his co-authored book, Family, Socialization and Interaction Process. The book advanced a theory about how families worked that relied on a static concept of complementarity between male and female roles. The idea was that this structure was pretty ideal, the breadwinner-homemaker model was highly evolved, and change was unlikely. So Parsons’ introduction sought to tamp down speculation that a sea change was underway. Unfortunately, he did that using data through about 1950, focusing his attention on whether changes observed in the 1930s were continuing, especially increasing divorce and falling fertility. Not to worry, he wrote, mistakenly seeing the beginning of the baby boom as a new plateau. With regard to women’s employment, he went on:

…there can be no question of symmetry between the sexes in this respect, and we argue, there is no serious tendency in this direction. … The number [of women] in the labor force who have small children is still quite small and has not shown a marked tendency to increase.

And the rest is history:

parsons-prediction

Parsons and I are only two generations apart (I took theory from a guy who co-authored a book with him). And all this change was pretty fast. So, why should I be confident in predicting the future of marriage – continued decline, with chance of rebound not exceeding 2000 rates by 2040? Were Parsons and I both blinded by our assumptions?

Sure. But one difference between us is our access to demographic data. It’s surprising to me that the great thinkers of social science mostly wrote in the olden days, when they had so little information. In just a few hours I summoned these global statistics covering decades and assembled them into a worldwide trend. Doesn’t it increase our confidence in the power of the trend to know that it’s so universal? And of course that’s just a drop in the bucket. Shouldn’t all this information yield more, better knowledge and better predictions?

Even if I’m not as smart a theorist as Parsons, I can eyeball the US and global trends and conclude that a major reversal in marriage is not likely: there is no recent precedent for that, and, with the exception of some countries that still have universal marriage, no major exceptions in the direction of change. That’s how I get from “marriage has declined globally” to “marriage is declining globally.”

(This post was a lot of work so gimme a couple days off now, okay?)

*This IPUMS data includes people who are in “consensual unions” that aren’t legally married, which is less than 5% of the total. IPUMS does have some other censuses, but I excluded ones that weren’t from the right period or had something unusual about the coding, sample or measures. In the regression version I excluded individuals with missing education levels, a very small percentage. The IPUMS data are weighted to reflect population totals.

**In addition to IPUMS International, which compiled the data, these statistical offices made it available for use.

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Data visualizations: Is U.S. society becoming more diverse?

Trying to summarize a few historical trends for the last half century (because what else is there to do?), I thought of framing them in terms of diversity.

Diversity is often an unsatisfying concept, used to describe hierarchical inequality as mere difference. But inequality is a form of diversity — a kind of difference. And further, not all social diversity is inequality. When people belong to categories and the categories are not ranked hierarchically (or you’re not interested in the ranking for whatever reason), the concept of diversity is useful.

There are various ways of constructing a diversity index, but I use the one sometimes called the Blau index, which is easy to calculate and has a nice interpretation: the probability that two randomly selected individuals are from different groups.

Example: Religion

Take religion. According to the 2001 census of India, this was the religious breakdown of the population:

RELIGION Number Proportion
Hindus 827,578,868 .805
Muslims 138,188,240 .134
Christians 24,080,016 .023
Sikhs 19,215,730 .019
Buddhists 7,955,207 .008
Jains 4,225,053 .004
Others 6,639,626 .006
Religion not stated 727,588 .001
Sum of squared proportions .667
Diversity .333

Diversity is calculated by summing the squares of the proportions in each category, and subtracting the sum from 1. So in India in 2001, if you picked two people at random, you had a 1/3 chance of getting people with different religions (as measured by the census).

Is .33 a lot of religious diversity? Not really, it turns out. I was surprised to read on the cover of this book by a Harvard professor that the United States is “the world’s most religiously diverse nation.” When I flipped through the book, though, I was disappointed to see it doesn’t actually talk much about other countries, and does not seem to offer the systematic comparison necessary to make such a claim.

With our diversity index, it’s not hard to compare religious diversity across 52 countries using data from World Values Survey, with this result:

wvs-religious-diversityThe U.S. is quite diverse — .66 — but a number of countries rank higher.

Of course, the categories are important in this endeavor. For example, Turkey and Morocco are both 99% “Muslim.” So is Iraq, but in Iraq that population is divided between people who identify as Muslim, Shia and Sunni, so Iraq is much more diverse. You get the same effect by dividing up the Christians in the U.S., for example.

Increasing U.S. diversity

Anyway, back to describing the last half century in the U.S. On four important measures I’ve got easy-to-identify increasing diversity. What do you think of these (with apologies for the default Microsoft color schemes):

religious-diversityrace-ethnic-diversity

household-diversity

age-at-marriage-men-60-11a

The last one is a little tricky. It’s common to report that the median age at marriage has increased since the 1950s (having fallen before the 1950s). But I realized it’s not just the average increasing, but the dispersion: More people marrying at different ages. So the experience of marriage is not just shifting rightward on the age distribution, but spreading out. Here’s another view of the same data:

age-at-marriage-men-60-11b

These are corrected (5/11/2013) from the first version of this post. I have now calculated these using the this report from the National Center for Health Statistics for 1960, and comparing it with the 2011 American Community Survey for those married in the previous year.

I have complained before that using the 1950s or thereabouts as a benchmark is misleading because it was an unusual period, marked by high conformity, especially with regard to family matters. But it is still the case that since then diversity on a number of important measures has increased. Over the period of several generations, in important ways the people we randomly encounter are more likely to be different from ourselves (and each other).

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