Why you’ll never establish the existence of distinct “generations” in American society

An update from Pew, today’s thoughts, and then another data exercise.

Pew response

After sending it the folks in charge at the Pew Research Center, I received a very friendly email response to our open letter on generation labels. They thanked me and reported that they already had plans to begin an internal discussion about “generational research” and will be consulting with experts as they do, although the timeline was not given. I take this to mean we have a bona fide opportunity to change course on this issue, both with Pew (which has outsized influence) and more widely in the coming months. But the outcome is not assured. If you agree that the “generations” labels and surrounding discourse are causing more harm than good, for researchers and the public, I hope you will join with me and 140+ social scientists who have signed the letter so far, by signing and sharing the letter (especially to people who aren’t on Twitter). Thanks!

avocado toast

Why “generations” won’t work

Never say never, but I don’t see how it will be possible to identify coherent, identifiable, stable, collectively recognized and popularly understood “generation” categories, based on year of birth, that reliably map onto a diverse set of measurable social indicators. If I’m right about that, which is an empirical question, then whether Pew’s “generations” are correctly defined will never be resolved, because the goal is unattainable. Some other set of birth-year cutoffs might work better for one question or another, but we’re not going to find a set of fixed divisions that works across arenas — such as social attitudes, family behavior, and economic status. So we should instead work on weaning the clicking public from its dependence on the concept and get down to the business of researching social trends (including cohort patterns), and communicating about that research in ways that are intelligible and useful.

Here are some reasons why we don’t find a good set of “generation” boundaries.

1. Mass media and social media mean there are no unique collective experiences

When something “happens” to a particular cohort, lots of other people are affected, too. Adjacent people react, discuss, buy stuff, and define themselves in ways that are affected by these historical events. Gradations emerge. The lines between who is and is not affected can’t be sharply drawn by age.

2. Experiences may be unique, but they don’t map neatly onto attitudes or adjacent behaviors

Even if you can identify something that happened to a specific age group at a specific point in time, the effects of such an experience will be diffuse. To name a few prominent examples: some people grew up in the era of mass incarceration and faced higher risks of being imprisoned, some people entered the job market in 2009 and suffered long-term consequences for their career trajectories, and some people came of age with the Pill. But these experiences don’t mark those people for distinct attitudes or behaviors. Having been incarcerated, unemployed, or in control of your pregnancy may influence attitudes and behaviors, but it won’t set people categorically apart. People whose friends or parents were incarcerated are affected, too; grandparents with unemployed people sleeping on their couches are affected by recessions; people who work in daycare centers are affected by birth trends. And, of course, African Americans have a unique experience with mass incarceration, rich people can ride out recessions, and the Pill is for women. When it comes to indicators of the kind we can measure, effects of these experiences will usually be marginal, not discrete, and not universal. (Plus, as cool new research shows, most people don’t change their minds much after they reach adulthood, so any effects of life experience on attitudes are swimming upstream to be observable at scale.)

3. It’s global now, too

Local experiences don’t translate directly to local attitudes and behavior because we share culture instantly around the world. So, 9/11 happened in the US but everyone knew about it (and there was also March 11 in Spain, and 7/7 in London). There are unique things about them that some people experienced — like having schools closed if you were a kid living in New York — but also general things that affected large swaths of the world, like heightened airline security. The idea of a uniquely affected age group is implausible.

4. Reflexivity

Once word gets out (through research or other means) about a particular trait or practice associated with a “generation,” like avocado toast or student debt, it gets processed and reprocessed reflexively by people who don’t, or do, want to embody a stereotype or trend for their supposed group. This includes identifying with the group itself — some people avoid it and some people embrace it, and some people react to who does the other things in other ways — until the category falls irretrievably into a vortex of cultural pastiche. The discussion of the categories, in other words, probably undermines the categories as much as it reinforces them.

If all this is true, then insisting on using stable, labeled, “generations” just boxes people into useless fixed categories. As the open letter puts it:

Predetermined cohort categories also impede scientific discovery by artificially imposing categories used in research rather than encouraging researchers to make well justified decisions for data analysis and description. We don’t want to discourage cohort and life course thinking, we want to improve it.

Mapping social change

OK, here’s today’s data exercise. There is some technical statistical content here not described in the most friendly way, I’m sorry to say. The Stata code for what follows is here, and the GSS 1972-2018 Cross-Sectional Cumulative Data file is free, here (Stata version); help yourself.

This is just me pushing at my assumptions and supplementing my reading with some tactile data machinations to help it sink in. Following on the previous exercise, here I’ll try out an empirical method for identifying meaningful birth year groupings using attitude questions from the General Social Survey, and then see if they tell us anything, relative to “empty” categories (single years or decades) and the Pew “generations” scheme (Silent, Baby Boom, Generation X, Millennials, Generation Z).

I start with five things that are different about the cohorts of nowadays versus those of the olden days in the United States. These are things that often figure in conversations about generational change. For each of these items I use one or more questions to create a single variable with a mean of 0 and a standard deviation of 1; in each case a higher score is the more liberal or newfangled view. As we’ll see, all of these moved from lower to higher scores as you look at more recent cohorts.

  • Liberal spending: Believing “we’re spending too little money on…” seven things: welfare, the environment, health, big cities, drug addiction, education, and improving the conditions of black people. (For this scale, the measure of reliability [alpha] is .66, which is pretty good.)
  • Gender attitudes: Four questions on whether women are “suited for politics,” working mothers are bad for children, and breadwinner-homemaker roles are good. High scores mean more feminist (alpha = .70).
  • Confidence in institutions: Seven questions on organized religion, the Supreme Court, the military, major companies, Congress, the scientific community, and medicine. High scores mean less confidence (alpha = .68).
  • General political views from extremely conservative to extremely liberal (one question)
  • Never-none: People who never attend religious services and have no religious affiliation (together now up to about 16% of people).

These variables span the survey years 1977 to 2018, with respondents born from 1910 to 1999 (I dropped a few born in 2000, who were just 18 years old in 2018, and those born before 1910). Because not all questions were asked of all the respondents in every year I lost a lot of people, and I had to make some hard choices about what to include. The sample that answered all these questions is about 5,500 people (down from almost 62,000 altogether — ouch!). Still, what I do next seems to work anyway.

Clustering generations

Once I have these five items, I combine them into a megascale (alpha = .45) which I use to represent social change. You can see in the figure that successive cohorts of respondents are moving up this scale, on average. Note that these cohorts are interviewed at different points in time; for example, a 40-year-old in 1992 is in the same cohort as a 50-year-old in 2002, while the 1977 interviews cover people born all the way back to 1910. That’s how I get so many cohorts out of interviews from just 1977 to 2018 (and why the confidence intervals get bigger for recent cohorts).

The question from this figure is whether the cohort attitude trend would be well served by some strategic cutpoints to denote cohorts (“generations” not in the reproductive sense but in the sense of people born around the same time). Treating each birth year as separate is unwieldy, and the samples are small. We could just use decades of birth, or Pew’s arbitrary “generations.” Or make up new ones, which is what I’m testing out.

So I hit on a simple way to identify cutpoints using an exploratory technique known as k means clustering. This is a simple (with computers) way to identify the most logical groups of people in a dataset. In this case I used two variables: the megascale and birth year. Stata’s k means clustering algorithm then tries to find a set of groups of cases such that the differences within them (how far each case is from the means of the two variables within the group) are as small as possible. (You tell it k, the number of groups you want.) Because cohort is a continuous variable, and megascale rises over time, the algorithm happily puts people in clusters that don’t have overlapping birth years, so I get nicely ordered cohorts. I guess for a U-shaped time pattern it would put young and old people in the same groups, which would mess this up, but that’s not the case with this pattern.

I tested 5, 6, and 7 groups, thinking more or fewer than that would not be worth it. It turns out 6 groups had the best explanatory power, so I used those. Then I did five linear regressions with the megascale as the dependent variable, a handful of control variables (age, sex, race, region, and education), and different cohort indicators. My basic check of fit is the adjusted R2, or the amount of variance explained adjusted for the number of variables. Here’s how the models did, in order from worst to best:

Cohort variable(s)Adjusted R2
Pew generations.1393
One linear cohort variable.1400
My cluster categories.1423
Decades of birth.1424
Each year individually.1486

Each year is good for explaining variance, but too cumbersome, and the Pew “generations” were the worst (not surprising, since they weren’t concocted to answer this question — or any other question). My cluster categories were better than just entering birth cohort as a single continuous variable, and almost as good as plain decades of birth. My scheme is only six categories, which is more convenient than nine decades, so I prefer it in this case. Note I am not naming them, just reporting the birth-year clusters: 1910-1924, 1925-1937, 1938-1949, 1950-1960, 1961-1974, and 1975-1999. These are temporary and exploratory — if you used different variables you’d get different cohorts.

Here’s what they look like with my social change indicators:

Shown this way, you can see the different pace and timing of change for the different indicators — for example, gender attitudes changed most dramatically for cohorts born before 1950, the falling confidence in institutions was over by the end of the 1950s cohort, and the most recent cohort shows the greatest spike in religious never-nones. Social change is fascinating, complex, and uneven!

You can also see that the cuts I’m using here look nothing like Pew’s, which, for example, pool the Baby Boomers from birth years 1946-1964, and Millennials from 1980 to 1996. And they don’t fit some stereotypes you hear. For example, the group with the least confidence in major institutions is those born in the 1950s (a slice of Baby Boomers), not Millennials. Try to square these results with the ridiculousness that Chuck Todd recently offered up:

So the promise of American progress is something Millennials have heard a lot about, but they haven’t always experienced it personally. … And in turn they have lost confidence in institutions. There have been plenty of scandals that have cost trust in religious institutions, the military law enforcement, political parties, the banking system, all of it, trust eroded.

You could delve into the causes of trust erosion (I wrote a paper on confidence in science alone), but attributing a global decline in trust to a group called “Millennials,” one whose boundaries were declared arbitrarily, without empirical foundation, for a completely unrelated purpose, is uninformative at best. Worse, it promotes uncritical, determinist thinking, and — if it gets popular enough — encourages researchers to use the same meaningless categories to try to get in line with the pop culture pronouncements. You get lots of people using unscrutinized categories, compounding their errors. Social scientists have to do better, by showing how cohorts and life course events really are an important way to view and comprehend social change, rather than a shallow exercise in stereotyping.

Conclusion

The categories I came up with here, for which there is some (albeit slim) empirical justification, may or may not be useful. But it’s also clear from looking at the figures here, and the regression results, that there is no singularly apparent way to break down birth cohorts to understand these trends. In fact, a simple linear variable for year of birth does pretty well. These are sweeping social changes moving through a vast, interconnected population over a long time. Each birth cohort is riven with major disparities, along the stratifying lines of race/ethnicity, gender, and social class, as well as many others. There may be times when breaking people down into birth cohorts helps understand and explain these patterns, but I’m pretty sure we’re never going to find a single scheme that works best for different situations and trends. The best practice is probably to look at the trend in as much detail as possible, to check for obvious discontinuities, and then, if no breaks are apparent, use an “empty” category set, such as decades of birth, at least to start.

It will take a collective act of will be researchers. teachers, journalists, and others, to break our social change trend industry of its “generations” habit. If you’re a social scientist, I hope you’ll help by signing the letter. (I’m also happy to support other efforts besides this experts letter.)


Note on causes

Although I am talking about cohorts, and using regression models where cohort indicators are independent variables, I’m not assessing cohort effects in the sense of causality, but rather common experiences that might appear as patterns in the data. We often experience events through a cohort lens even if they are caused by our aging, or historical factors that affect everyone. How to distinguish such age, period, or cohort effects in social change is an ongoing subject of tricky research (see this from Morgan and Lee for a recent take using the GSS) , but it’s not required to address the Pew “generations” question: are there meaningful cohorts that experience events in a discernibly collective way, making them useful groups for social analysis.

Kids these days really know how to throw off a narrative on gender and families

The most important thing is that Stephanie Coontz has written another very good, and very important, New York Times essay. It describes a “slippage” in support for gender equality among young people these days, and warns that without improved work-family policies, progress toward egalitarian family arrangements may be imperiled. The piece also announced a package of short papers in a Council on Contemporary Families symposium, which provided the supporting evidence. (This kind of work, incidentally, is why I’m a proud member, and board member, of CCF.) If you haven’t read Stephanie’s essay, I recommend reading it now, and if you forget to come back here that’s fine.

Anyway, an unfortunate confluence of events created some chaos after the piece came out. First, the NYTimes wrote a headline, “Do Millennial Men Want Stay-at-Home Wives?”, that emphasized only one piece of the evidence. It referred to a figure showing General Social Survey data on the trend in very young men and women (ages 18-25) disagreeing with the statement, “It is much better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family.” (That is the classic FEFAM question, to GSS fans, asked since 1977. I’ve used it myself, and it figures in the key analysis of stalled gender progress by Cotter, Hermsen, and Vanneman.)

This was the figure, showing a marked divergence between men and women:

scfefam

The second event was the unfortunate timing: between the time Stephanie wrote the piece and the day it appeared, the General Social Survey released its 2016 round of data (it’s been running every two years). The survey is fickle. It’s very good quality and has many great demographic and attitude items running for 40 years, making it the best source for analyzing many social trends. But it’s not that big. In 2014 it had 2,867 respondents, of whom only 141 were ages 18-25. So it wasn’t surprising that the 2016 numbers were different from the 2014 numbers, but the scale of the blip was shocking, as reported independently by Emily Beam and Neal Caren. Here is what the updated trend looks like:

scfefam-16

Yikes. As exciting as it is for survey analysts to see such a wild swing, it’s not what anyone wants to see the day after their NYTimes piece drops. We can’t know yet what happened, but on further inspection, at least we can say that it’s not limited to the youngest group and its small sample. Among men ages 26-54, the percentage disagreement with FEFAM also jumped, from 73.7 to 78.3 (women 26-54 were up one point).* In fact, 2014 may have been as big a blip as 2016, you just wouldn’t notice because it continued the trend.

Anyway, back for a minute to the main point. Joanna Pepin, who co-wrote one of the symposium pieces with David Cotter (and who is also an advisee of mine), has pointed out that the divergence between men and women is secondary to the main trend, which is the reversal of progress on FEFAM for both men and women since the mid-1990s. They used the Monitoring the Future survey, and find a big drop in FEFAM disagreement among high school seniors — regardless of gender. Here’s their key figure, with the FEFAM trend shown in green (their full paper is available on SocArXiv):

figure-3

So that is the most important news: a big reversal among young adults on attitudes toward homemaker-breadwinner family arrangements.

Now, If you’ve now read Stephanie’s piece, and Joanna’s, and you’re back, here’s a little more on the minor kerfuffle that arose over the new data.

When to call a trend a trend

I don’t think Stephanie was wrong to use the GSS trend, although it might have been better to widen the age range, or pool the data over several years. The bigger problem was the headline selling that divergence as the main story, which it wasn’t in the grand scheme. (The fact that so many jumped on the story shows how good they are at headline writing.) But even that wasn’t really wrong, given the information they had. The Op-Ed staff checked the facts, and the facts were the facts. Until yesterday.

To confirm this, I ran some tests on the gender divergence in the data they used (I started with code that Neal shared; it’s at the bottom). I started at 1994, the last peak of the trend, to look for the divergence after that, which is what Stephanie referred to. First, here is what you get if you run a logistic model that controls for race/ethnicity and individual years of age (two things that changed over the last two decades), and enters the years individually in an interaction with gender (those are 95% confidence intervals).

fefam-yr.JPG

If you stop at 2014, it looks like men are pulling away from women (in the direction of “traditional” attitudes), but it’s not definitive. And obviously 2016 is an issue. To help with the small samples, I ran a linear test of the year trend, that is, entering year as a continuous variable instead of individual years. I did it ended at 2014 and then through 2016. Here are the results:

fefam-log

In the 1994-2014 model, the Male*Year interaction is statistically significant at conventional levels, which in my opinion means it’s legit to say men were pulling away from women. Of course 2016 ruined that; if you had 2016 and didn’t use it, that would be really wrong. There are other ways to slice it, but at some point we have to call a trend a trend and deal with it. It was a reasonable decision. Of course, new data always comes along (until the last trend of all, whatever that is), no trend lasts forever; it’s just a shame when it comes along the next day. In addition, though I’m not showing it because it’s boring, if you didn’t disaggregate the trends by gender, you would also see a significant decline in FEFAM disagreement after 1994, which gets to Joanna’s point.

Anyway, score one for sociology Twitter. People came up with the data, shared code and results, and discussed interpretations. It got back to Stephanie and the NYTimes editors, and within a day they added an addendum to the original piece:

Update: After this article was posted, 2016 data from the General Social Survey became available, adding some nuance to this analysis. The latest numbers show a rebound in young men’s disagreement with the claim that male-breadwinner families are superior. The trend still confirms a rise in traditionalism among high school seniors and 18-to-25-year-olds, but the new data shows that this rise is no longer driven mainly by young men, as it was in the General Social Survey results from 1994 through 2014.

This is pretty much how it’s supposed to work. As the Car Guys used to say, if you never stall you’re wearing out your clutch (sorry, Millennials). If you never overshoot an analysis of trends you’re probably waiting too long to get the information out.

* Note: I originally accidentally described this as “over 25.” 


You can get the data here. Here’s the STATA code:

/* recodes */

recode fefam (1/2=0) (3/4=1), gen(fefam_d)
gen young=age>=18&age<=25
recode sex (2=0), gen(male)

/* the model for the figure */

logit fefam_d i.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]
margins year##male
marginsplot

/* the models for the table */

logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 & year<=2014 [pweight = wtssall]
logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]

Couple fact patterns about sexuality and attitudes

Working on the second edition of my book, The Family, involves updating facts as well as rethinking their presentation, and the choice of what to include. The only way I can do that is by making figures to look at myself. Here are some things I’ve worked up recently; they might not end up in the book, but I think they’re useful anyway.

1. Attitudes on sexuality and related family matters continue to grow more accepting or tolerant, but acceptance of homosexuality is growing faster than the others – at least those measured in the repeated Gallup surveys:

gallupmoral

2. Not surprisingly, there is wide divergence in the acceptance of homosexuality across religious groups. This uses the Pew Religious Landscape Study, which includes breakouts for atheists, agnostics, and two kinds of “nones,” or unaffiliated people — those for whom religion is important and those for whom it’s not:

relhomoaccept

3. Updated same-sex behavior and attraction figures from the National Survey of Family Growth. For some reason the NSFG reports don’t include the rates of same-sex partner behavior in the previous 12 months for women anymore, so I analyzed the data myself, and found a much lower rate of last-year behavior among women than they reported before (which, when I think about it, was unreasonably high – almost as high as the ever-had-same-sex-partner rates for women). Anyway, here it is:

nsfgsamesexupdate

FYI, people who follow me on Twitter get some of this stuff quicker; people who follow on Instagram get it later or not at all.

Visualizing attitude differences

This didn’t turn into something more substantial, so I’m just leaving it here as is. I like the idea of visualizing attitude (or other) differences by race/ethnicity, sex, and generation (or other characteristics) with distances. Plus my daughter is learning about x-y coordinates in math.

I got these just using the General Social Survey online analysis tool (here). These are the question texts.

HELPBLK (1-5):

Some people think that African-Americans have been discriminated against for so long that the government has a special obligation to help improve their living standards; they are at point 1. Others believe that the government should not be giving special treatment to African-Americans; they are at point 5. a. Where would you place yourself on this scale, or haven’t you made up your mind on this?

FEFAM: Strongly agree to strongly disagree (1-4):

It is much better for  everyone involved if the man is the achiever outside the home and the woman takes care of the home and family.

I used two categories each of race (Black/White), Sex (man/woman), and Generation (18-44/45+). Scores are shown as differences between each group’s mean score and the population average.

Short story: Whites show little difference by age, gender, or generation on the race question, but big differences by gender and age on the gender question. Blacks are similar to Whites on the gender question except that younger Blacks men are less opposed to breadwinner-homemaker family arrangements than are younger Whites (especially men).

I also added some other groupings for comparison:

No real point to make about this except that I like the idea of representing these patterns like this. Someone should make a GSS tool that does this for you on any question. With confidence intervals. (If there are already are such tools, please advise.)