Tag Archives: methods

For social relationships outside marriage

Stephanie Coontz has a great piece in tomorrow’s New York Times titled, “For a Better Marriage, Act Like a Single Person.” From her intro:

Especially around Valentine’s Day, it’s easy to find advice about sustaining a successful marriage, with suggestions for “date nights” and romantic dinners for two. But as we spend more and more of our lives outside marriage, it’s equally important to cultivate the skills of successful singlehood. And doing that doesn’t benefit just people who never marry. It can also make for more satisfying marriages.

From there she develops the case with, as usual, a lot of the right research. Well worth a read.

Stephanie used two empirical bits from my work:

No matter how much Americans may value marriage, we now spend more time living single than ever before. In 1960, Americans were married for an average of 29 of the 37 years between the ages of 18 and 55. That’s almost 80 percent of what was then regarded as the prime of life. By 2015, the average had dropped to only 18 years.

In many ways, that’s good news for marriages and married people. Contrary to some claims, marrying at an older age generally lowers the risk of divorce. It also gives people time to acquire educational and financial assets, as well as develop a broad range of skills — from cooking to household repairs to financial management — that will stand them in good stead for the rest of their lives, including when a partner is unavailable.

The first figure, the average years spent in marriage between the ages of 18 and 55 is very easy to calculate. You just sum the proportion of people married at each age. Here’s what it looks like, comparing 1960 (from the decennial Census) and 2015 (from the American Community Survey), both from IPUMS.org (click to enlarge):

YearsMarried

I think it’s a nice, simple way to show the declining footprint of marriage in American life. (I first did this, and described in the rationale, in 2010.)

The bit about older age at marriage being associated with lower odds of divorce is from this post. Here’s the result, showing odds of divorce in one year by age at marriage, with controls for duration, education, race/ethnicity, and nativity, for women in their first marriages (click to enlarge):
Divorce by age at marriage

There’s more discussion in the post, as well as in this followup post, which has this cool figure, where red is the highest odds of divorce and green is the lowest, and the axes are years married and age at marriage (click to enlarge):

Divorce By Age And Duration


My new book is out! Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. Available all the usual places, plus here at the University of California Press, where Chapter 1 is available as a sample, and where instructors can request a review copy.

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Data analysis: Are older newlyweds saving marriage?

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Is the “institution” still in decline if the incidence of marriage rebounds, but only at older ages?

In my new book I’ve revisited old posts and produced this figure, which shows the refined marriage rate* from 1940 to 2015, with a discussion of possible futures:

f15

The crash scenario – showing marriage ending around 2050, is there to show where the 1950-2014 trajectory is headed (it’s also a warning against using linear extrapolation to predict the future). The rebound scenario is intended to show how unrealistic the “revive marriage culture” people are. The taper scenario emerges as the most likely alternative; in fact, it’s grown more likely since I first made the figure a few years ago, as you can see by the 2010-2014 jag.

So let’s consider the tapering scenario more substantively — what would it look like? One way to get a declining marriage rate is if marriage is increasingly delayed, even if it doesn’t become less common; people still marry, but later. (If everyone got married at age 99, we would have universal marriage and a very low refined marriage rate.) I give some evidence for this scenario here.

These trends are presented with minimal discussion; I’m not looking at race/ethnicity or social class, childbearing or the recession; I’m not discussing divorce and remarriage and cohabitation, and I’m not testing hypotheses. (This is a list of research suggestions!) To make the subject more enticing as a research topic (and for accountability), I’ve shared the Census data, Stata code, and spreadsheet file used to make this post in this OSF project. You can use anything there you want. You can also easily fork the project — that is, make a duplicate of its contents, which you then own, and take off on your own trajectory, by adding to or modifying them.

Trends

For some context, here is the trend in percentage of men and women ever married, by age, from 1960. (“Ever married” means currently married, separated, divorced, or widowed.) This clearly shows both life-course delay and lifetime decline, but delay is much more prominent, at least so far. Even now, almost 90% of people have been married by age 60 or so, while the marriage rates for people under 35 have plummeted.

evmar6016

People become ever-married when they get first-married. We measure ever-married prevalence from a survey question on current marital status, but first-marriage incidence requires a question like the American Community Survey asks, “In the past 12 months, did this person get married?” Because they also ask how many times each person has been married, you can calculate a first marriage rate with this ratio:

(once married & married in the past 12 months) / (never married + (once married & married in the past 12 months))

Until recently it hasn’t been easy to measure first-marriage across all ages; now that we have the ACS marital events data (since 2008) we can. This allows us to look at the timing of first marriage, which means we can use current age-specific first-marriage rates to project lifetime ever-married rates under current conditions.

Here are the first-marriage rates for men and women, by age. Each set of bars shows the trend from 2008 to 2016. The left side shows men, by age; the right side shows women, by age; the totals for men and women are in the middle. This shows that first-marriage rates have fallen for men and women under age 35, but increased for those over age 35. The total first-marriage rate has rebounded from the 2013 crater, but is still lower than 2008.

1stmarage

This is a short-range trend, 9 years. It could be recession-specific, with people delaying marriage because of hardships, or relationships falling apart under economic stress, and then hurrying to marry a few years later. But it also fits the long-term trend of delay over decline.

The overall rates for men and women show that the 2014-2016 rebound has not brought first-marriage rates back to their 2008 level. However, what about lifetime odds of marriage? The next figure uses women’s age-specific first-marriage rates to project lifetime odds of marriage for three years: 2008, the 2013 crater, and 2016. This shows, for example, that at 2008 rates 59% of women would have married by age 30, compared with 53% in both 2013 and 2016.

1stmarproj

The 2013 and 2016 lines diverge after age 30, and by age 65 the projected lifetime ever-married rates have fully recovered. This implies that marriage has been delayed, but not forgone (or denied).

Till now I’ve shown age and sex-specific rates, but haven’t addressed other things that might changed in the never-married population. Finally, I estimated logistic regressions predicting first-marriage among never married men and women. The models include race, Hispanic origin, nativity, education, and age. In addition to the year and age patterns above, the models show that all races have lower rates than Whites, Hispanics have lower rates than non-Hispanics, foreign-born people have higher rates (which explains the Hispanic result), and people with more education first-marry more (code and results in the OSF project).

To see whether changes in these other variables change the story, I used the regressions to estimate first-marriage rates at the overall mean of all variables. These show a significant rebound from the bottom, but not returning to 2008 levels, quite similar to the unadjusted trends above:

1stmaradj

This is all consistent with the taper scenario described at the top. Marriage delayed, which reduces the annual marriage rate, but with later marriage picking up much of the slack, so that the decline in lifetime marriage prevalence is modest.


* The refined marriage rate is the number of marriages as a fraction of unmarried people. This is more informative than the crude marriage rate (which the National Center for Health Statistics tracks), which is marriages as a fraction of the total population. In this post I use what I guess you would call an age-specific refined first-marriage rate, defined above.

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Science finds tiny things nowadays (Malia edition)

We have to get used to living in a world where science — even social science — can detect really small things. Understanding how important really small things are, and how to interpret them, is harder nowadays than just finding them.

Remember when Hanna Rosin wrote this?

One of the great crime stories of the last twenty years is the dramatic decline of sexual assault. Rates are so low in parts of the country — for white women especially — that criminologists can’t plot the numbers on a chart.

Besides being wrong about rape (it has declined a lot, but it’s still high compared with most countries), this was a funny statement about science (I’ve heard we can even plot negative numbers now!). But the point is we have problems understanding, and communicating about, small things.

So, back to names.

In 2009, the peak year for the name Malia in the U.S., 1,681 girls were given that name, according to the Social Security Administration, or .041% of the 4.14 million children born that year (there are no male Malias in the SSA’s public database, meaning they have never recorded more than 4 in one year). That year, 7.5% of women ages 18-44 had a baby. If my arithmetic is right, say you know 100 women ages 18-44, and each of them knows 100 others (and there is no overlap in your network). That would mean there is a 30% chance one of your 10,000 friends of a friend had a baby girl and named her Malia in 2009. But probably there is a lot of overlap; if your friend-of-friend network is only 1,000 women 18-44 then that chance would fall to 3%.

Here is the trend in girls named Malia, relative to the total number of girls born, from 1960 to 2016:

names.xlsx

To make it easier to see the Malias, here is the same chart with the y-axis on a log scale.

names.xlsx

This shows that Malia has been on a long upward trend, from less than 50 per year in the 1960s to more than 1,000 per year now. And it also shows a pronounced spike in 2009, the year Malia peaked .041%. In that year, the number of people naming daughters Malia jumped 75% before declining over the next three years to resume it’s previous trend. Here is the detail on the figure, just showing the Malia in 2005-2016:

names.xlsx

What happened there? We can’t know for sure. Even if you asked everyone why they named their kid what they did, I don’t know what answers you would get. But from what we know about naming patterns, and their responsiveness to names in the news (positive or negative), it’s very likely that the bump in 2009 resulted from the high profile of Barack Obama and his daughter Malia, who was 11 when Obama was elected.

What does a causal statement like that that really mean? In 2009, it looks to me like about 828 more people named their daughters Malia than would have otherwise, taking into account the upward trend before 2008. Here’s the actual trend, with a simulated trend showing no Obama effect:

names.xlsx

Of course, Obama’s election changed the world forever, which may explain why the upward trend for Malia accelerated again after 2013. But in this simple simulation, which brings the “no Obama” trend back into line with the actual trend in 2014, there were 1,275 more Malias born than there would have been without the Obama election. This implies that over the years 2008-2013, the Obama election increased the probability of someone naming their daughter Malia by .00011, or .011%.

That is a very small effect. I think it’s real, and very interesting. But what does it mean for anything else in the world? This is not a question of statistical significance, although those tools can help. (These names aren’t a probability sample, it’s a list of all names given.) So this is a question for interpreting research findings now that we have these incredibly powerful tools, and very big data to analyze with them. The number alone doesn’t tell the story.

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’16 and Pregnant’ and less so

3419870216_fded1624d2_z

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

So?

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

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

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

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

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Cause and effect on myopia

It’s funny for a non-eye specialist to read articles about myopia, which in my line of work rarely means myopia, literally, which is nearsightedness. Takes some getting used to.

Anyway, in my book I use as an example of misleading correlations the link between night lights and myopia in children. Checking it to make sure it is still a good example to keep for the second edition, I was glad to see that it holds up well.

Here’s the story. In a 1999 paper (paywalled | sci-hub), Quinn and colleagues reported a “strong association between myopia and night-time ambient light exposure during sleep in children before they reach two years of age.” That is, kids who slept with night lights were more likely to be nearsighted. This was potentially big news, because we actually don’t fully understand why people become nearsighted, except we know it has to do with reading a lot and spending a lot of time indoors as a kid. They had some idea that light penetrating the eyelids at night might do something, but no real mechanism, just an association over a few hundred kids.

The paper didn’t have some important variables controlled, notably parents’ nearsightedness. Since the condition is also genetic, this was acknowledged as a problem. Still, they wrote:

Although it does not establish a causal link, the statistical strength of the association of night-time light exposure and childhood myopia does suggest that the absence of a daily period of darkness during early childhood is a potential precipitating factor in the development of myopia.

As I stress ad nausem in this post, the “strength” of an association is not an argument for its causal power. And neither is the number of studies in which the association is found. Real spurious findings can produce very strong, easily-reproducible results. And when researchers have a story to fit the rationale can seem strong. Also, the prospect of publishing in a top journal like Nature has to figure in there somewhere. (This problem is endemic in studies of, for example, family structure and child outcomes, among many other subjects.)

In this case there is a very nice explanation, which was reported less than a year later by Zadnik and colleagues (paywalled | sci-hub), who found no association between night lights and myopia – but they did report a very strong relationship between night lights and parents’ myopia. The same pattern was reported in another response to the Quinn paper, in the same issue, by Gwiazda and colleagues. It appears that nearsighted parents like to leave night lights on. Alternately, some other factor causes parental nearsightedness, child nearsightedness, and night light preference, such as education level (e.g., more-educated people read more and use night lights more).

Several other studies have also failed failed to confirm the night-light theory, and now the thing seems to have blown over. It’s not a perfect example, because the bivariate correlation isn’t always found, but I like it as a family-related case. So I think I’ll keep it in.

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On Asian-American earnings

In a previous post I showed that generalizations about Asian-American incomes often are misleading, as some groups have above-average incomes and some have below-average incomes (also, divorce rates) and that inequality within Asian-American groups was large as well. In this post I briefly expand that to show breakdowns in individual earnings by gender and national-origin group.

The point is basically the same: This category is usually not useful for economic statistics, and should usually be dropped for data on specific groups when possible.

Today’s news

What’s new is a Pew report by Eileen Patten showing trends in race and gender wage gaps. The report isn’t focused on Asian-American earnings, but they stand out in their charts. This led Charles Murray, who is fixated on what he believes is the genetic origin of Asian cognitive superiority, to tweet sarcastically, “Oppose Asian male privilege!” Here is one of Pew’s charts:

pewraceearn

The figure, using the Current Population Survey (CPS), shows Asian men earning about 14.5% more per hour than White men, and Asian women earning 11% more than White women. This is not wrong, exactly, but it’s not good information either, as I’ll argue below.

First a note on data

The CPS data is better for some labor force questions (including wages) than the American Community Survey, which is much larger. However, it’s too small a sample to get into detail on Asian subgroups (notice the Pew report doesn’t mention American Indians, an even smaller group). To do that I will need to activate the ACS, which is better for race/ethnic detail.

As a reminder, this is the “race” question on the 2014 American Community Survey, which I use for this post:

acsrace2014

There is no “Asian” or “Pacific Islander” box to check. So what do you do if you are thinking, “I’m Asian, what do I check?” The question is premised on that assumption that is not what you’re thinking. Instead, you choose from a list of national origins, which the Census Bureau then combines to make “Asian” (the first 7 boxes) and “Pacific Islander” (the last 3) categories. And you can check as many as you like, which is good because there’s a lot of intermarriage among Asians, and between Asians and other groups (mostly Whites). This is a lot like the Hispanic origin question, which also lists national origins — except that question is prefaced by the unifying phrase, “Is Person 1 of Hispanic, Latino, or Spanish origin?” before listing the options, each beginning with “Yes”, as in “Yes, Cuban.”

Although changes have not been announced, it is likely that future questions will combine the race and Hispanic-origin questions, and also preface the Asian categories with the umbrella term. This may mark the progress of getting Asian immigrants to internalize the American racial classification system, so that descendants from groups that in some cases have centuries-old cultural differentiation start to identify and label themselves as from the same racial group (who would have put Pakistanis and Japanese in the same “race” group 100 years ago?). It’s hard to make this progress, naturally, when so many people from these groups are immigrants — in my sample below, for example, 75% of the full-time, year-round workers are foreign-born.

Earnings

The problem with the earnings chart Pew posted, and which Charles Murray loved, is that it lumps all the different Asian-origin groups together. That is not crazy but it’s not really good. Of course every group has diversity within it, so any category masks differences, but in my opinion this Asian grouping is worse in that regard than most. If someone argued that all these groups see themselves as united under a common identity that would push me in the direction of dropping this complaint. In any event, the diversity is interesting even if you don’t object to the Pew/Census grouping.

Here are two breakouts. The first is immigration. As I noted, 75% of the full-time, year-round workers (excluding self-employed people, like Pew does) with an Asian/Pacific Islander (Asian for short) racial identification are foreign born. That ranges from less than 4% for Hawaiians, to around 20% for the White+Asian multiple-race people, to more than 90% for Asian Indian men. It turns out that the wage advantage is mostly concentrated among these immigrants. Here is a replication of the Pew chart using the ACS data (a little different because I had to use FTFY workers), using the same colors. On the left is their chart, on the right is the same data limited to US-born workers.

api1

Among the US-born workers the Asian male advantage is reduced from 14.5% to 4.2% (the women’s advantage is not much changed; as in Pew’s chart, Hispanics are a mutually exclusive category.) There are some very high-earning Asian immigrants, especially Indians. Here are the breakdowns, by gender, comparing each of the larger Asian-American groups to Whites:

api2

Seven groups of men and nine groups of women have hourly earnings higher than Whites’, while nine groups of men and seven groups have women have lower earnings. In fact, among Laotians, Hawaiians, and Hmong, even the men earn less than White women. (Note, in my old post, I showed that Asian household incomes are not as high as they look when they are compared instead with those of their local peers, because they are concentrated in expensive metropolitan markets.)

Sometimes when I have a situation like this I just drop the relatively small, complex group, which leads some people to accuse me of trying to skew results. (For example, I might show a chart that has Blacks in the worst position, even though American Indians have it even worse.)

But generalization has consequences, so we should use it judiciously. In most cases “Asian” doesn’t work well. It may make more sense to group people by regions, such as East-, South-, and Southeast Asia, and/or according to immigrant status.

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Life table says divorce rate is 52.7%

After the eternal bliss, there are two ways out of marriage: divorce or death.

I have posted my code and calculations for divorce rates using the 2010-2012 American Community Survey as an Open Science Framework project. The files there should be enough to get you started if you want to make multiple-decrement life tables for divorce or other things.

Because the American Community survey records year of marriage, and divorce and widowhood, it’s perfectly set up for a multiple-decrement life table approach. A multiple-decrement life table uses the rate of each of two exits for each year of the original state (in this case marriage), to project the probability of either exit happening at or after a given year of marriage. It’s a projection of current rates, not a prediction of what will happen. So, if you write a headline that says, “your chance of divorce if you marry today is 52.7%,” that would be too strong, because it doesn’t take into account that the world might change. Also, people are different.

The divorce rate of 52.7% can accurately be described like this: “If current divorce and widowhood rates remain unchanged, 52.7% of today’s marriages would end in divorce before widowhood.” Here is a figure showing the probability of divorce at or after each year of the model:

div-mdlt

So there’s 52.7% up at year 0. Marriages that make it to year 15 have a 30% chance of eventually divorcing, and so on.

Because the ACS doesn’t record anything about the spouses of divorce or widowed people, I don’t know who was married to whom, such as age, education, race-ethnicity, or even the sex of the spouse. So the estimates differ by sex as well as other characteristics. I estimated a bunch of them in the spreadsheet file on the OSF site, but here are the bottom lines, showing, for example, that second or higher-order marriages have a 58.5% projected divorce rate and Blacks have a 64.2% divorce rate, compared with 52.9% for Whites.

div-mdlt-tab

(The education ones should be taken with a grain of salt because education levels can change but this assumes they’re static.)

Check the divorce tag for other posts and papers on divorce.

The ASA-style citation to the OSF project would be like this:  Cohen, Philip N. 2016. “Multiple-Decrement Life Table Estimates of Divorce Rates.” Retrieved (osf.io/zber3).

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