Tag Archives: demography

Fox News took my quotes out of context and added wrong information

Following up on Part 1, discussed here, Parts 2 and 3 of the Fox News series on demography and social change also featured quotes from me. Part 2 used a reasonable quote in a reasonable way, but Part 3 did not.

Part 2 is a good teaching lesson in sky-is-fallingism, a Fox News signature. As they’ve done before, they literally start with a 1950s TV show as if it were historical footage, and then proceed to the chaotic now.

“If Tommy suddenly woke up today, he’d be an aging Baby Boomer, receiving benefits from a Social Security trust fund that is more than 2 trillion dollars in debt. He might be tending to his aches and pains with medical marijuana, now legal in 33 states. He might see his childhood friends are legally married [showing gay male weddings] while almost half the mommies in the U.S. are not.”

Cut to racial minority students in UCLA gear. Etc. The most extreme cut is between the Heritage Foundation person saying, of Democrats, “We’re the party of government, and that way if we have voters attached to government programs they’re going to stick with us,” before, literally, cutting to archival Mao and Stalin footage, with the voice-over:

“That, while the hard lessons of socialism — 70 million dead in China, 20 million dead in the Soviet Union — that happened during Communism, are often neglected in colleges, now focused on social justice curricula.”

Great stuff, good for teaching. Anyway, my quote in the piece is just saying young people nowadays don’t like to be lectured about traditional values. They just frame it like that’s a bad thing. Here it is:

Part 3 is where they misused my quotes, in two places. The episode is about how low fertility leads to immigration, which creates chaos and causes populism. Plenty wrong in here, but I’m just focusing on my beefs. First, on immigration, they say:

“Europe’s accommodation of refugees fleeing ISIS and the civil war in Syria, has proved a bridge too far.”

Philip Cohen: “Immigration poses challenges to the dominant culture. It’s obviously politically fraught.”

Cut to rioting footage. Narrator: “From Greece to Italy, Germany, France, and the Nordic countries, clashes have erupted. Nationalist politicians are forcing a reckoning with multiculturalism.”

According to my own recording of the interview, however, what I said immediate after, “It’s obviously politically fraught,” was this:

“On the other hand, there’s a great pent-up demand for immigration. There are plenty of people who want to come here. The immigrants who come here tend to be the better off, more highly skilled and educated people from the countries that they’re coming from, contrary to some stereotypes, so they end up strengthening the U.S. economy even as they improve their own wellbeing. So if you can get over all the challenges and conflict that sometimes comes along with rapid immigration, what you end up with is an answer to the population [problem].”

Lesson learned. Not surprising they didn’t use my pro-immigration other hand. I should have anticipated that better and made the other hand the only hand in my comment. However, they had invited me to discuss Millennials and marriage, so I wasn’t prepared for immigration.

The piece has distracted tangents into robots in Japan and the one-child policy in China. I also wasn’t prepared for the one-child policy on that day, but I always have a take ready on that. Here’s what I said, according to my recording:

“One thing to know about China is the birthrate had fallen a lot before the one-child policy. So even if you like the idea … [they interrupted to say they had bumped the focus, so I should start my answer again] …One thing that’s important to realize about China is that population growth had already slowed a lot before the one-child policy started, so they really didn’t need the one-child policy to slow down population growth. And it was quite draconian. It went against what most people wanted for their families. The implementation of it was very repressive. It included forced sterilization, and abortion, and very harsh penalties for people who had extra children. So it was really a human rights disaster.”

In the piece, however, they used the part about forced sterilization and the human rights disaster, but didn’t use where I said, “they really didn’t need the one-child policy to slow down population growth” — and replaced it with voice-over that said, “overpopulation compelled the Communist government to force a one-child policy on the populous.” So they took out something true and added something false.

To see how wrong that it, here is the trend in total fertility rate (births per woman) from 1960 to 2016. This shows how much birth rates had come down in China under policies that promoted smaller families along with women’s healthcare, education, and employment, by the time China implemented the one-child policy in 1980:

china-1980-tfr

I put India and Nigeria on the chart to show how successful China already was relative to other large, poor countries with high fertility in the 1960s. There was no demographic justification for the one-child policy, and the fact that it became draconian and repressive is a clue to how out of step it was with the family lives of the Chinese people.

The reason this matters is not particularly important for the Fox News piece, but it’s very important to understand that progress on reducing fertility is better achieved through empowerment and development than through command and repression. Now that we’re seeing countries interested in increasing fertility, this is important historical context. (Here’s a good review article by Wang Feng, Baochang Gu, and Yong Cai [paywalled | bootlegged])

Anyway, regardless of the implications, it just goes against accuracy and honesty to remove true information for false information.

Anyway anyway, here’s Part 3:

 

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Decadally-biased marriage recall in the American Community Survey

Do people forget when they got married?

In demography, there is a well-known phenomenon known as age-heaping, in which people round off their ages, or misremember them, and report them as numbers ending in 0 or 5. We have a measure, known as Whipple’s index, that estimates the extent to which this is occurring in a given dataset. To calculate this you take the number of people between ages 23 and 62 (inclusive), and compare it to five-times the number of those whose ages end in 0 or 5 (25, 30 … 60), so there are five-times as many total years as 0 and 5 years.

If the ratio of 0/5s to the total is less than 105, that’s “highly accurate” by the United Nations standard, a ratio 105 to 110 is “fairly accurate,” and in the range 110 to 125 age data should be considered “approximate.”

I previously showed that the American Community Survey’s (ACS) public use file has a Whipple index of 104, which is not so good for a major government survey in a rich country. The heaping in ACS apparently came from people who didn’t respond to email or mail questionnaires and had to be interviewed by Census Bureau staff by phone or in person. I’m not sure what you can do about that.

What about marriage?

The ACS has a great data on marriage and marital events, which I have used to analyze divorce trends, among other things. Key to the analysis of divorce patterns is the question, “When was this person last married?” (YRMARR) Recorded as a year date, this allows the analyst to take into account the duration of marriage preceding divorce or widowhood, the birth of children, and so on. It’s very important and useful information.

Unfortunately, it may also have an accuracy problem.

I used the ACS public use files made available by IPUMS.org, combining all years 2008-2017, the years they have included the variable YRMARR. The figure shows the number of people reported to have last married in each year from 1936 to 2015. The decadal years are highlighted in black. (The dropoff at the end is because I included surveys earlier than those years.)

year married in 2016.xlsx

Yikes! That looks like some decadal marriage year heaping. Note I didn’t highlight the years ending in 5, because those didn’t seem to be heaped upon.

To describe this phenomenon, I hereby invent the Decadally-Biased Marriage Recall index, or DBMR. This is 10-times the number of people married in years ending in 0, divided by the number of people married in all years (starting with a 6-year and ending with a 5-year). The ratio is multiplied by 100 to make it comparable to the Whipple index.

The DBMR for this figure (years 1936-2015) is 110.8. So there are 1.108-times as many people in those decadal years as you would expect from a continuous year function.

Maybe people really do get married more in decadal years. I was surprised to see a large heap at 2000, which is very recent so you might think there was good recall for those weddings. Maybe people got married that year because of the millennium hoopla. When you end the series at 1995, however, the DBMR is still 110.6. So maybe some people who would have gotten married at the end of 1999 waited till New Years day or something, or rushed to marry on New Year’s Eve 2000, but that’s not the issue.

Maybe this has to do with who is answering the survey. Do you know what year your parents got married? If you answered the survey for your household, and someone else lives with you, you might round off. This is worth pursuing. I restricted the sample to just those who were householders (the person in whose name the home is owned or rented), and still got a DBMR of 110.7. But that might not be the best test.

Another possibility is that people who started living together before they were married — which is most Americans these days — don’t answer YRMARR with their legal marriage date, but some rounded-off cohabitation date. I don’t know how to test that.

Anyway, something to think about.

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Predicted divorce decline rolls on

With the arrival of the 2017 American Community Survey data on IPUMS.org, I have updated my analysis of divorce trends (paper | media reports | data and code).

In the first version of the paper, based on data from 2008 to 2016, I wrote:

Because divorce rates have continued to fall for younger women, and because the risk profile for newly married couples has shifted toward more protective characteristics (such as higher education, older ages, and lower rates of higher-order marriages), it appears certain that – barring unforeseen changes – divorce rates will further decline in the coming years.

I don’t usually make predictions, but this one seemed safe. And now the 2017 data are consistent with what I anticipated: a sharp decline in divorce rates among those under age 45, and continued movement toward a more selective pattern in new marriages.

Here is the overall trend in divorces per 100 married women, 2008-2017, with and without the other variables in my model:

divtrend

With the 2017 data, the divorce rate has now fallen 21% since 2008. To show the annual changes by age, I made this heatmap style table, with shading for divorce rates, rows for years, columns for age, and the column widths proportional to the age distribution (so 15-19 is a sliver, and 50-54 is the widest). The last row shows the sharp drop in divorce rates for women under age 45 in 2017:

2008-2017 divorce marriage.xlsx

To peek into the future a little more, I also made a divorce protective-factor scale, which looks just at newlywed couples in each year, and gives them one point for each spouse that is age 30 or more, White or Hispanic, has BA or higher education, is in a first marriage, and a point if the woman has no own children in the home at the time of the survey. So it ranges from 0 to 9. (I’m not saying these factors have equal importance, but they are all associated with lower odds of divorce.) The gist of it is new marriages increasingly have characteristics conducive to low divorce rates. In 2008 41% of couples had a score of 5 or more, and in 2017 it’s 50%.

mdpf

So divorce rates will probably continue to fall for a while.

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Breaking Millennial divorce drop news explained

[With updates as new stories come in.]


Millennials are fun to disparage.

Phones and selfies are all that they cherish.

And what’s par for the course, they have ruined divorce.

‘Cuz Millennials hang on to their ______.

Wait Wait Don’t Tell Me, 9/29/18

The divorce paper I posted two weeks ago, “The Coming Divorce Decline,” suddenly took off in the media the other day (blog post | paper | data and code). I’ve now written an op-ed about the findings for The Hill, including this:

I am ambivalent about these trends. Divorce is often painful and difficult, and most people want to avoid it. The vast majority of Americans aspire to a lifelong marriage (or equivalent relationship). So even if it’s a falling slice of the population, I’m not complaining that they’re happy. Still, in an increasingly unequal society and a winner-take-all economy, two-degree couples with lasting marriages may be a buffer for the select few, but they aren’t a solution to our wider problems.

Here’s my media scrapbook, with some comment about open science process at the end.

The story was first reported by Ben Steverman at Bloomberg, who took the time to read the paper, interview me at some length, send the paper to Susan Brown (a key expert on divorce trends) for comment, and produce figures from the data I provided. I was glad that his conclusion focused on the inequality angle from my interpretation:

“One of the reasons for the decline is that the married population is getting older and more highly educated,” Cohen said. Fewer people are getting married, and those who do are the sort of people who are least likely to get divorced, he said. “Marriage is more and more an achievement of status, rather than something that people do regardless of how they’re doing.”

Many poorer and less educated Americans are opting not to get married at all. They’re living together, and often raising kids together, but deciding not to tie the knot. And studies have shown these cohabiting relationships are less stable than they used to be.

Fewer divorces, therefore, aren’t only bad news for matrimonial lawyers but a sign of America’s widening chasm of inequality. Marriage is becoming a more durable, but far more exclusive, institution.

The Bloomberg headline was, “Millennials Are Causing the U.S. Divorce Rate to Plummet.” Which proved irresistible on social media. I didn’t use the terms “millennials” (which I oppose), or “plummet,” but they don’t fundamentally misrepresent the findings.

Naturally, though, the Bloomberg headline led to other people misrepresenting the paper, like Buzzfeed, which wrote, “Well, according to a new study, millennials are now also ‘killing’ divorce.” Neither I nor Bloomberg said anyone was “killing” divorce; that was just a Twitter joke someone made, but Buzzfeed was too metameta to pick up on that. On the other hand, never complain about a Buzzfeed link, and they did link to the paper itself (generating about 800 clicks in a few days).

Then Fox 5 in New York did a Skype interview with me, and hit the bar scene to talk over the results (additional footage courtesy of my daughter, because nowadays you provide your own b-roll):

The next day Today did the story, with additional information and reporting from Bowling Green’s National Center for Family and Marriage Research, and Pew.

The Maryland news office saw the buzz and did their own story, which helped push it out.

An article in Atlantic featured an interview with Andrew Cherlin putting the trends in historical context. Rachelle Hampton in Slate tied the divorce trend to a Brookings report showing marriage is increasingly tied to higher education. On KPCC, AirTalk hosted a discussion with Megan Sweeney and Steven Martin. On Wisconsin Public Radio, Stephanie Coontz widened the discussion to put changes in marriage and divorce in historical perspective.

Rush Limbaugh read from the Bloomberg article, and was just outraged: “Now, who but deranged people would look at it this way?”

How anybody thinks like this… You have to work to be this illogical. I don’t know where this kind of thing comes from, that a plummeting divorce rate is a bad sign for America in the left’s crazy world of inequality and social justice and their quest to make everybody the same. So that’s just an example of the… Folks, that is not… That kind of analysis — and this is a sociology professor at the University of Maryland. This is not stable. That kind of thinking is not… It’s just not normal. Yet there it is, and it’s out there, and it’s be widely reported by the Drive-By Media, probably applauded and supported by others. So where is this coming from? Where is all of this indecency coming from? Why? Why is it so taking over the American left?

The Limbaugh statement might have been behind this voicemail I received from someone who thinks I’m trying to “promote chaos” to “upend the social order”:

I had a much more reasonable discussion about marriage, divorce, and inequality in this interview with Lauren Gilger in KJZZ (Phoenix public radio).

The Chicago Tribune editorial board used the news to urge parents not to rush their children toward marriage:

This waiting trend may disturb older folks who followed a more traditional (rockier?) path and may be secretly, or not so secretly, wondering if there’s something wrong with their progeny. There isn’t. Remember: Unlike previous generations, many younger people have a ready supply of candidates at their fingertips in the era of Tinder and other dating apps. They can just keep swiping right. Our advice for parents impatient to marry off a son or daughter? Relax. The older they get, the less likely you’ll be stuck paying for the wedding.

The Catholic News Agency got an expert to chime in, “If only we could convince maybe more of them to enter into marriage, we’d be doing really well.”

I don’t know how TV or local news work, but somehow this is on a lot of TV stations. Here’s a selection.

Fox Business Network did a pretty thorough job.

Some local stations added their own reporting, like this one in Las Vegas:

And this one in Buffalo:

And this one in Boise, which brought in a therapist who says young people aren’t waiting as long to start couples therapy.

Jeff Waldorf on TYT Nation did an extended commentary, blaming capitalism:


Open science process

Two things about my process here might concern some people.

The first is promoting research that hasn’t been peer reviewed. USA Today was the only report I saw that specifically mentioned the study is not peer reviewed:

The study, which has not been published in a peer-reviewed journal, has been submitted for presentation at the 2019 Population Association of America meeting, an annual conference for demographers and sociologists to present research.

But, when Steverman interviewed me I emphasized to him that it was not peer-reviewed and urged him to consult other researchers before doing the story — he told me he had already sent it to Susan Brown. Having a good reporter consult a top expert who’s read the paper is as good a quality peer review as you often get. I don’t know everything Brown told him, but the quote he used apparently showed her endorsement of the main findings:

“The change among young people is particularly striking,” Susan Brown, a sociology professor at Bowling Green State University, said of Cohen’s results. “The characteristics of young married couples today signal a sustained decline [in divorce rates] in the coming years.”

For the story to be clear enough to become a news event, the research often has to be pretty simple. That’s the case here: what I’m doing is looking at an easily-identified trend and providing my interpretation of it. If this has to be peer-reviewed, then almost anything an academic says should be. Of course, I provided the publicly verifiable data and code, and there are a lot of people with the skills to check this if it concerned them.

On the other hand, there is a lot of research that is impossible to verify that gets reported. Prominent examples include the Alice Goffman ethnographic book and the Raj Chetty et al. analysis of confidential IRS data. These were big news events, but whether they were peer reviewed or not was irrelevant because the peer reviewers had no way to know if the studies were right. My conclusion is that sharing research is the right thing to do, and sharing it with as much supporting material as you can is the responsible way to do it.

The second concern is over the fact that I posted it while it was being considered for inclusion in the Population Association of America meetings. This is similar to posting a paper that is under review at a journal. Conference papers are not reviewed blind, however, so it’s not a problem of disclosing my identity, but maybe generating public pressure on the conference organizers to accept the paper. This happens in many forms with all kinds of open science. I think we need to see hiding research as a very costly choice, one that needs to be carefully justified — rather than the reverse. Putting this in the open is the best way to approach accountability. Now the work of the conference organizers, whose names are listed in the call for papers, can be judged fairly. And my behavior toward the organizers if they reject it can also be scrutinized and criticized.

Although I would love to have the paper in the conference, in this case I don’t need this paper to be accepted by PAA, as it has already gotten way more attention than I ever expected. PAA organizers have a tough job and often have to reject a lot of papers for reasons of thematic fit as well as quality. I won’t complain or hold any grudges if it gets rejected. There’s a lot of really good demography out there, and this paper is pretty rudimentary.

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The coming divorce decline

Unless something changes outside the demogosphere, the divorce rate is going to go down in the coming years.

Divorce represents a number of problems from a social science perspective.

    • Most people seem to assume “the divorce rate” is always going up, compared with the good old days, which are supposed to be the whole past but are actually represented by the anomalous 1950s.
    • On other hand, social scientists have known for a few decades that “the divorce rate” has actually been declining since the 1980s. That shows up in the official statistics, with the simple calculation — known as the refined divorce rate — of the number of divorces per 1,000 married women.
    • On the third hand, the official statistics are very flawed. The federal system, which relies on states voluntarily coughing up their divorce records, broke down in the 1990s and no one fixed it (hello, California doesn’t participate). In the debate over different ways of getting good answers, a key 2014 paper from Sheela Kennedy and Stephen Ruggles showed that the decline in divorce after 1980 was mostly because the whole married population was getting older, and older people get divorced less. That refined divorce rate doesn’t account for age patterns. When you remove the age patterns from the data, you see a continuously increasing divorce rate. Yikes!
    • On the fourth hand, Kennedy and Ruggles stopped in about 2010. Since then, the very divorce-prone, multi-marrying, multi-divorcing Baby Boomers have moved further out of their peak action years, and it’s increasingly clear that divorce rates really are falling for younger people.

In my new analysis, which I wrote up as a short paper for submission to the Population Association of America 2019 meetings, I argue that all signs point to a divorce decline in the coming years. Here is the paper on SocArXiv, where you will also find the data and code. And here is the story, in figures (click to enlarge).

1. The proportion of married women who divorce each year has fallen 18% in the decade after 2008. (There are reasons to do this for women — some neutral, some good, some bad — but one good thing nowadays is at least this includes women divorcing women.) And when you control for age, number of times married, years married, education, race/ethnicity, and nativity, it has still fallen 8%.

ddf1

2. The pattern of increasing divorce at older ages, described by Susan Brown and I-Fen Lin as gray divorce, is no longer apparent. In the decade after 2008, the only apparent change in age effects is the decline at younger ages, holding other variables constant.

ddf2

3. The longer term trends, identified by Kennedy and Ruggles, which I extend to 2016, show that the upward trajectory is all about older people. These are prevalences (divorced people in the population), not divorce rates, but they are good for illustrating this trend.

ddf3

4. In fact, when you look just at the last decade, all of the decline in age-specific divorce rates is among people under age 45. This implies there will be more older people who have been married a long time, which means low divorce rates. Also, their kids won’t be as likely to have divorced parents, although more kids will have parents who aren’t married, which might work in the other direction. (You can ignore then under-20s, who are 0.2% of the total.)

ddf4

5. Finally, to get a glimpse of the future, I looked at women who report getting married in the year before the survey, and how they have changed between 2008 and 2016 on traits associated with the risk of divorce. They clearly show a lower divorce-risk profile. They are more likely to be in their first marriage, to have college degrees, to be older, and to have no children in their households (race/ethnicity appears to be a wash, with fewer Whites but more Latinas).

ddf5

6. Finally finally, I also looked at the spouses of the newly-married women, and made an arbitrary divorce-protection scale, with one point to each couple for each spouse who was: age 30 or more, White or Hispanic, BA or higher education, first marriage, and no own children. Since 2008 the high scale scores have become more common and the low scores have become rarer.

ddf6

7. It’s interesting that the decline in divorce goes against the (non-expert) conventional wisdom. And it is happening at a time when public acceptance of divorce has reached record levels (which might be part of why people think it’s growing more common — less stigma). Here are the trends in attitudes from Pew and Gallup:

ddf7

That’s my story — thanks for listening!

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Demographic facts your students should know cold in 2018

birth gumHere’s an update of a series I started in 2013.

Is it true that “facts are useless in an emergency“? Depends how you define emergency I guess. Facts plus arithmetic let us ballpark the claims we are exposed to all the time. The idea is to get our radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it.

It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements such as Trump’s, “I’ve created over a million jobs since I’m president,” referring to a period when the U.S. population grew by 1.3 million?

What’s a number for? Lots of people disparage the nitpickers when they find something wrong with the numbers going around. But everyone likes a number that appears to support their argument. The trick is to know the facts before you know the argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.

Here’s the list of current demographic facts you need 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 make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 262 million and 394 million!).

This is only 30 facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). 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 7.5 billion 1
U.S. Population 328 million 1
Children under 18 as share of pop. 23% 2
Adults 65+ as share of pop. 16% 2
Official unemployment rate 3.9% 3
Unemployment rate range, 1970-2018 3.9% – 11% 3
Labor force participation rate, age 16+ 63% 9
Labor force participation rate range, 1970-2017 60% – 67% 9
Non-Hispanic Whites as share of pop. 61% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 18% 2
Asians as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 13% 2
Adults age 25+ with BA or higher 30% 2
Median household income $55,300 2
Total poverty rate 13% 8
Child poverty rate 18% 8
Poverty rate age 65+ 9% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 327 million 5
4th most populous country, Indonesia 261 million 5
5th most populous country, Brazil 207 million 5
U.S. male life expectancy at birth 76 6
U.S. female life expectancy at birth 81 6
Life expectancy range across countries 51 – 85 7
World total fertility rate 2.4 10
U.S. total fertility rate 1.8 10
Total fertility rate range across countries 1.2 – 7.2 10

Sources:
1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

10. World Bank

Handy one-page PDF: Demographic Facts You Need to Know in 2018

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Visualizing family modernization, 1900-2016

After this post about small multiple graphs, and partly inspired by two news reports I was interviewed for — this Salt Lake Tribune story about teen marriage, and this New York Times report mapping age at first birth — I made some historical data figures.

These visualizations use decennial census data from 1900 to 1990, and then American Community Survey data for 2001, 2010, and 2016; all data from IPUMS.org. (I didn’t use the 2000 Census because marital status is messed up in that data, with a lot of people who should be never married coded as married, spouse absent; 2001 ACS gets it done.)

An important, simple way of illustrating the myth-making around the 1950s is with marriage age. Contrary to the myth that the 1950s was “traditional,” a long data series show the period to be unique. The two trends here, teen marriage and divorce, both show the modernization of family life, with increasing individual self-determination and less restricted family choices for women.

First, I show the proportion of teenage women married in each state, for each decade from 1900 to 2016. The measure I used for this is the proportion of 19- and 20-year-olds who have ever been married (that is, including those married, divorced, and widowed). It’s impossible to tell exactly how many people were married before their 20th birthday, which would be a technical definition of teen marriage, but the average of 19 and 20 should do it, since it includes some people are on the first day of their 19th year, and some people are on the last day of their 20th, for an average close to exact age 20.

I start with a small multiple graph of the trend on this measure in every state (click all figures to enlarge). Here the states are ordered by the level of teen marriage in 2016, from Maine lowest (<1%) to Utah (14%):

teen marriage 1900-2016

This is useful for seeing that the basic pattern is universal: starting the century lower and rising to a peak in 1960, then declining steeply to the present. But that similarity, and smaller range in the latest data, make it hard to see the large relative differences across states now. Here are the 2016 levels, showing those disparities clearly:

teen marriage states 2016.xlsx

Neither the small multiples nor the bars help you see the regional patterns and variations. So here’s an animated map that shows both the scale of change and the pattern of variation.

teen-marriage-1900-2016

This makes clear the stark South/non-South divide, and how the Northeast led the decline in early marriage. Also, you can see that Utah, which is such a standout now, did not have historically high teen marriage levels, the state just hasn’t matched the decline seen nationally. Their premodernism emerged only in relief.

Divorce

Here I again used a prevalence measure. This is just the number of people whose marital status is divorced, divided by the number of married people (including separated and divorced). It’s a little better than just the percentage divorced in the population, because it’s at least scaled by marriage prevalence. But it doesn’t count divorces happening, and it doesn’t count people who divorced and then remarried (so it will under-represent divorce to the extent that people remarry). Also, if divorced people die younger than married people, it could be messed up at older ages. Anyway, it’s the best thing I could think of for divorce rates by state all the way back to 1900.

So, here’s the small multiple graph, showing the trend in divorce prevalence for all states from 1900 to 2016:

div-mar-1900-2016

That looks like impressive uniformity: gradual increase until 1970, then a steep upward turn to the present. These are again ordered by the 2016 value, from Utah at less than 20% to New Mexico at more than 30% — smaller variation than we saw in teen marriage. That steep increase looks dramatic in the animated map, which also reveals the regional patterns:

divorce-1900-2016

Technique

The strategy for both trends is to download microdata samples from all years, then collapse the files down to state averages by decade. The linear figures are Stata scatter plots by state. The animated maps use maptile in Stata (by Michael Stepner) to make separate image files for each map, which I then imported into Photoshop to make the animations (following this tutorial).

The downloaded data, codebooks, Stata code, and images, are all available in an Open Science Framework project here. Feel free to adapt and use. Happy to hear suggestions and alternative techniques in the comments.

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