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!

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.)

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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.

A little ‘generation’ debunking data exercise

For the whole series follow the generations tag.

Read and sign the experts’ open letter to the Pew Research Center on generation labels.

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One of the problems with the fake “generations” discourse is it confuses people on the importance of age (how old people are) versus cohort (when they were born). At any one point in time, these two concepts are measured the same way — asking people how old they are. But if you follow people over time, they answer two different questions. The first tells you how younger people are different from older people (for example, old people are more likely to die of heart attacks, because their hearts wear out as they age), while the second tells you how people’s lives may have changed as they live them. So, young people today live in a world where their grandparents are less likely to die of heart attacks because of improvements in medicine, which changes the nature of childhood and then adult life as well. That’s a cohort story.

One of the problems with the way the Pew Research Center reports on their polls is they usually survey people at one point in time (age), and then describe the results by “generation” (cohorts). Here’s a figure they just published:

This seems to tell a clear “generations” story: “OK, Boomer,” that story goes, “it’s time to get serious about climate change.” But why didn’t Pew just title the figure, “younger people more active than older people addressing climate change”? You’d have to ask them their reasons, but their “generations” framing sure is popular. (Pause for outrage over the fact that, not only do the figures not tell readers the ages of these “generations,” treating them as if they are obvious to everyone, but even in the text of the report, where they do mention in passing the birth years for Millennials and Gen Z, they never give the birth years for Gen X and Boomers.)

But what if it’s not a “generations” (cohort) story, but an age story? How would you know? You would ask the same question to people over time, and see how their attitudes may have changed over the course of their lives. That’s difficult and expensive, and besides the questions you might want to ask change over time. But you can come close by asking a random sample of people the same questions over time — something the clever sociologists behind the General Social Survey figured out 50 years ago, and they’ve been doing it ever since. In fact, the General Social Survey (GSS) since 1973 has been asking people, “are we spending too much, too little, or about the right amount on the environment?” (in another version of the question they added, “improving and protecting” before “the environment”; I use both questions). It’s not exactly what Pew asked, but it gives us an idea. And that idea is: this is much more of age story than a “generations” (cohort) story.

Since the 1970s, between 70% and 75% of Americans under 35 have said we’re spending “too little” on “improving and protecting the environment.” (Note this age breakdown is arbitrary, using a common definition of “young adult.”) It’s not all an age story, though. Over time, this age gap has closed some, which means some of those pro-environment spending young people have grown into pro-environment spending older people — which is a cohort story. But you can’t reasonably tell a dramatic “generations” story about “Generation Z” unless you compare them to people at the same age in the past.

That cohort story

Cohorts, and the life course perspective with which they are associated, are important, however. So telling the cohort story could be valuable. If you were going to tell that story, how would you do it?

First, you could look at survey respondents by individual year of birth, which is the most detailed breakdown you can get from the GSS. One problem with that is sample size. Breaking the sample up into 90 cohorts or so leaves you with as few as 21 respondents and no more than 1,200 for any one cohort who answered this question. The other problem is it’s harder to interpret because you have a lot of data points.

Second, you could go with some generation scheme you import from another source, from conventional wisdom, or something that might get you more clicks. That might mean using cohorts based on political trends in general, or economic or demographic trends. That is, you could pick a category scheme from somewhere else and hope it’s relevant to this analysis, too. This is the Pew approach. The crucial thing to do in this case, which is what Pew does not do, is acknowledge that you have done something either arbitrarily or based on theory, and that it might or might not be a reasonable fit to your data. Don’t just assume they’re the right categories and pretend no others exist.

Third, in the absence of a clear theory or other data to suggest a way to group individual years, you go with something “empty” and simple, such as binning them by decade of birth.

Fourth, you could try to use empirical methods to identify the best groupings from the data. If you’re better at statistics than me, like Bruce Western and Meredith Kleykamp, you could do something like their “Bayesian Change Point Model for Historical Time Series Analysis” (free copy). Or you could use other methods, or eyeball it. I have experimented with this using a few different GSS trends. Based on variance explained, my very messy results have failed to do better than single years or decades, and done either better or worse than Pew’s generations depending on how I do it. So for now I’ll just show you the results from the first three options here.

Here is Stata code for what follows (and to cut the data by cohort and look for patterns in the variance explained by cohort, which I’m not showing in the post).

Using the three methods — single years of birth, Pew generations, and decades of birth — I made six linear probability models (OLS regressions with a dichotomous dependent variable), with one for each scheme with and without a set of demographic control variables: age (and age-squared), sex, race/ethnicity, region, and education. The main way I compare these models in the table below is with the adjusted R², which is a measure of variance explained by the model, taking into account the number of variables used. Looking at the adjusted R² for models 2, 4, and 6 shows that individual years explain the most (.0447), followed by decades (.0440) and then Pew’s generations (.0437). These aren’t big differences, but then the models aren’t explaining that much of this trend altogether. (When I added presumably-endogenous measures for political ideology, political party identification, and church attendance, which are all correlated with views on the environment, I got this adjusted R² up to .0874.) Note I only used people born from 1910 to 1999 (age 19 in 2018; GSS only interviews adults age 18+).

The linear probability model results are easy to interpret because the coefficients tell you how far each variable moves the average case from 0 (doesn’t think we spend too little) toward 1 (thinks we spend too little). For example, in model 6, the average person born in the 1950s is 11.2% more likely to think this than someone born in the 1910s (controlling for age and the other factors).

So the individual-year model performs best, and Pew’s generations perform worst. Here’s what they predict for each cohort, again using models 2, 4, and 6, at the mean of the controls:

The single-year panel (A) shows the messiness of single years, as at the end of the series we have fewer and fewer cases — all the people born in 1999 in the GSS were interviewed in one survey, 2018, and they were all ~19, so we don’t have much to go on; it’s better to pool some of those years for more stable estimates. (You can also see that wider confidence interval for the Pew generations panel (B), where there are only 108 respondents in Generation Z in the data [born before 2000].) Still, you can see that the single-year panel suggests a mild wave, peaking in the 1950s, sliding for people born in the 1970s and 1980s, and rising again. Pew, which cuts the data at 1965, 1981, and 1997, mostly misses this — but it shows up in the decades panel (C). People born in the 1950s and 1960s were most likely to think we spend too little on the environment (65%), those born in the 1970s and 1980s a little less so (63%), and then those born in the 1990s spiked back upward to 68% (this last estimate based on 996 people born in the 1990s).

So, there are some interesting cohort trends here, captured worse by the Pew categories than by decades or single years of birth. Still, none of these cohort differences is as big as the simple age difference I started with. (If you add control variables to the figures at the top, the age gap remains 12% to 19%.) Young people have been, and remain, more likely to say we spend too little money on the environment. That’s not a bad story.

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Given its popularity, it’s important to note the Pew model performs worst among these three. Of course, if Pew’s generations were real things, clearly defined with respect to other social trends, associated with social identities, and with empirical research to support their relevance, it might still be justified to analyze this question according to those categories. (For example, even though we don’t measure “race” perfectly, the variable as measured is associated with lots of important disparities and trends, and people feel affinities with their racial identities, so it’s not wrong to look at something like attitudes toward the environment by “race,” as commonly defined.) But the Pew categories are not justified in that way. Any cohort binning will show you something, especially for trends that are strongly patterned across time and age. But it doesn’t follow that any common binning is the best — that’s a question for research, research that hasn’t been done.

So, to return to where I started: Read and sign the experts’ open letter to the Pew Research Center on generation labels.

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Full disclosure: I am an unpaid member of the General Social Survey advisory board. (And, like most people who say “full disclosure,” I’m mentioning this mostly to impress you, and secondarily to disclose any potential conflict of interest.)

Open letter to the Pew Research Center on generation labels

I posted a draft of this, with a discursive preamble, yesterday. To see all the posts on generations, here’s the tag.

Sign the letter here.

We are demographers and other social scientists, writing to urge the Pew Research Center to stop using its generation labels (currently: Silent, Baby Boom, X, Millennial, Z). We appreciate Pew’s surveys and other research, and urge them to bring this work into better alignment with scientific principles of social research.

1. Pew’s “generations” cause confusion.

The groups Pew calls Silent, Baby Boom, X, Millennial, and Z are birth cohorts determined by year of birth, which are not related to reproductive generations. There is further confusion because their arbitrary lengths (18, 19, 16, 16, and 16 years, respectively) have grown shorter as the age difference between parents and their children has lengthened.

2. The division between “generations” is arbitrary and has no scientific basis.

With the exception of the Baby Boom, which was a discrete demographic event, the other “generations” have been declared and named on an ad hoc basis without empirical or theoretical justification. Pew’s own research conclusively shows that the majority of Americans cannot identify the “generations” to which Pew claims they belong. Cohorts should be delineated by “empty” periods (such as individual years, equal numbers of years, or decades) unless research on a particular topic suggests more meaningful breakdowns.

3. Naming “generations” and fixing their birth dates promotes pseudoscience, undermines public understanding, and impedes social science research.

The “generation” names encourage assigning them a distinct character, and then imposing qualities on diverse populations without basis, resulting in the current widespread problem of crude stereotyping. This fuels a stream of circular debates about whether the various “generations” fit their associated stereotypes, which does not advance public understanding.

4. The popular “generations” and their labels undermine important cohort and life course research

Cohort analysis and the life course perspective are important tools for studying and communicating social science. But the vast majority of popular survey research and reporting on the “generations” uses cross-sectional data, and is not cohort research at all. 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.

5. The “generations” are widely misunderstood to be “official” categories and identities

Pew’s reputation as a trustworthy social research institution has helped fuel the false belief that the “generations” definitions and labels are social facts and official statistics. Many other individuals and organizations use Pew’s definitions in order to fit within the paradigm, compounding the problem and digging us deeper into this hole with each passing day.

6. The “generations” scheme has become a parody and should end.

With the identification of “Generation Z,” Pew has apparently reached the end of the alphabet. Will this continue forever, with arbitrarily defined, stereotypically labeled, “generation” names sequentially added to the list? Demographic and social analysis is too important to be subjected to such a fate. No one likes to be wrong, and admitting it is difficult. We sympathize. But the sooner Pew stops digging this hole, the easier it will be to escape. A public course correction from Pew would send an important signal and help steer research and popular discourse around demographic and social issues toward greater understanding. It would also greatly enhance Pew’s reputation in the research community. We urge Pew to end this as gracefully as possible — now.

As consumers of Pew Research Center research, and experts who work in related fields ourselves, we urge the Pew Research Center to do the right thing and help put an end to the use of arbitrary and misleading “generation” labels and names.

Draft: Open letter to the Pew Research Center on generation labels

This post has been updated with the final signing statement and a link to the form. Thanks for sharing!

I have objected to the use of “generation” divisions and names for years (here’s the tag). Then, the other day, I saw this introduction to an episode of Meet the Press Reports, which epitomized a lot of the gibberishy nature of generationspeak (sorry about the quality).

OK, it’s ridiculous political punditry — “So as their trust in institutions wanes, will they eventually coalesce behind a single party, or will they be the ones to simply transform our political system forever?” — but it’s also generations gobbledygook. And part of what struck me was this: “millennials are now the largest generation, they have officially overtaken the Baby Boom.” Well-educated people think these things are real things, official things. We have to get off this train.

If you know the generations discourse, you know a lot of it emanates from the Pew Research Center. They do a lot of excellent research — and make a lot of that research substantially worse by cramming into the “generations” framework that they more than anyone else have popularized — have made “official.”

After seeing that clip, I put this on Twitter, and was delighted by the positive response:

What if we got a large number of social scientists, demographers, and journalists to sign a very collegial, lightly footnoted appeal to Pew to stop using generation labels, to never name another generation. Would it work? Would you sign it?

— Philip N Cohen (@familyunequal) May 23, 2021

So I wrote a draft of an open letter to Pew, incorporating some of the comments from Twitter. But then I decided the letter was too long. To be more effective maybe it should be more concise and less ranty. So here’s the long version, which has more background information and examples, followed by a signing version, with a link to the form to sign it. Please feel to sign if you are a demographer or other social scientist, and share the link to the form (or this post) in your networks.

Maybe if we got a lot of signatories to this, or something like it, they would take heed.

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Preamble by me

Pew’s generation labels — which are widely adopted by many other individuals and institutions — encourage unhelpful social science communication, driving people toward broad generalizations, stereotyping, click bait, sweeping character judgment, and echo chamber thinking. When people assign names to generations, they encourage anointing them a character, and then imposing qualities onto whole populations without basis, or on the basis of crude stereotyping. This fuels a constant stream of myth-making and myth-busting, with circular debates about whether one generation or another fits better or worse with various of its associated stereotypes. In the absence of research about whether the generation labels are useful either scientifically or in communicating science, we are left with a lot of headlines drawing a lot of clicks, to the detriment of public understanding.

Cohort analysis and the life course perspective are important tools for studying and communicating social science. We should study the shadow, or reflection, of life events across people’s lives at a cultural level, not just an individual level. In fact, the Pew Research Center’s surveys and publications make great contributions to that end. But the vast majority of popular survey research and reporting in the “generations” vein uses data analyzed by age, cross-sectionally, with generational labels applied after the fact — it’s not cohort research at all. We shouldn’t discourage cohort and life course thinking, rather we should improve it.

Pew’s own research provides a clear basis for scrapping the “generations.” “Most Millennials Resist the ‘Millennial’ Label” was the title of a report Pew published in 2015. This is when they should have stopped — based on their own science — but instead they plowed ahead as if the “generations” were social facts that the public merely failed to understand.

This figure shows that the majority of Americans cannot correctly identify the generational label Pew has applied to them.

The concept of “generations” as applied by Pew (and many others) defies the basic reality of generations as they relate to reproductive life cycles. Pew’s “generations” are so short (now 16 years) that they bear no resemblance to reproductive generations. In 2019 the median age of a woman giving birth in the U.S. was 29. As a result, many multigenerational families include no members of some generations on Pew’s chart. For example, it asks siblings (like the tennis-champion Williams sisters, born one year apart) to identify as members of separate generations.

Perhaps due to their ubiquitous use, and Pew’s reputation as a trustworthy arbiter of social knowledge, many people think these “generations” are official facts. Chuck Todd reported on NBC News just this month, “Millennials are now the largest generation, they have officially overtaken the Baby Boom.” (NPR had already declared Millennials the largest generation seven years earlier, using a more expansive definition.) Pew has perhaps inadvertently encouraged these ill-informed perspectives, as when, for example, Richard Fry wrote for Pew, “Millennials have surpassed Baby Boomers as the nation’s largest living adult generation, according to population estimates from the U.S. Census Bureau” — despite the fact that the Census Bureau report referenced by the article made no mention of generations. Note that Chuck Todd’s meaningless graphic, which doesn’t even include ages, is also falsely attributed to the U.S. Census Bureau.

Generations are a beguiling and appealing vehicle for explaining social change, but one that is more often misleading than informative. The U.S. Army Research Institute commissioned a consensus study report from the National Academies, titled, Are Generational Categories Meaningful Distinctions for Workforce Management? The group of prominent social scientists concluded: “while dividing the workforce into generations may have appeal, doing so is not strongly supported by science and is not useful for workforce management. …many of the stereotypes about generations result from imprecise use of the terminology in the popular literature and recent research, and thus cannot adequately inform workforce management decisions.”

As one of many potential examples of such appealing, but ultimately misleading, uses of the “Millennial” generation label, consider a 2016 article by Paul Taylor, a former executive vice president of the Pew Research Center. He promised he would go beyond “clichés” to offer “observations” about Millennials — before describing them as “liberal lions…who might not roar,” “downwardly mobile,” “unlaunched,” “unmarried,” “gender role benders,” “upbeat,” “pre-Copernican,” and as an “unaffiliated, anti-hierarchical, distrustful” generation who nevertheless “get along well with their parents, respect their elders, and work well with colleagues” while being “open to different lifestyles, tolerant of different races, and first adopters of new technologies.” And their “idealism… may save the planet.”

In 2018 Pew announced that it would henceforth draw a line between “Millennials” and “Generation Z” at the year 1996. And yet they offered no substantive reason, just that “it became clear to us that it was time to determine a cutoff point between Millennials and the next generation [in] order to keep the Millennial generation analytically meaningful, and to begin looking at what might be unique about the next cohort.” In asserting that “their boundaries are not arbitrary,” the Pew announcement noted that they were assigning the same length to the Millennial Generation as they did to Generation X — both 16 years, a length that bears no relationship to reproductive generations, nor to the Baby Boom cohort, which is generally considered to be 19 years (1946-1964).

The essay that followed this announcement attempted to draw distinctions between Millennials and Generation Z, but it could not delineate a clear division, because none can be drawn. For example, it mentioned that “most Millennials came of age and entered the workforce facing the height of an economic recession,” but in 2009, the trough year for that recession, Millennials by Pew’s definition ranged from age 13 to 29. The other events mentioned — the 9/11 terrorist attacks, the election of Barack Obama, the launch of the iPhone, and the advent of social media — similarly find Millennials at a range of ages too wide to be automatically unifying in terms of experience. Why is being between 12 and 28 at the time of Obama’s election more meaningful a cohort experience than being, say, 18 to 34? No answer to this is provided, because Pew has determined the cohort categories before the logical scientific questions can be asked.

Consider a few other hypothetical examples. In the future, we might hypothesize that those who were in K-12 school during the pandemic-inflicted 2020-2021 academic year constitute a meaningful cohort. That 13-year cohort was born between 2003 and 2015, which does not correspond to one of Pew’s predetermined “generations.” For some purposes, an even narrower range might be more appropriate, such as those who graduated high school in 2020-2021 alone. Under the Pew generational regime, too many researchers, marketers, journalists, and members of the general public will look at major events like these through a pre-formed prism that distorts their ability to pursue or understand the way cohort life course experiences affect social experience.

Unlike the other “generations” in Pew’s map, the Baby Boom corresponds to a unique demographic event, painstakingly, empirically demonstrated to have begun in July 1946 and ended in mid-1964. And being part of that group has turned out to be a meaningful experience for many people — one that in fact helped give rise to the popular understanding of birth cohorts as a concept. But it does not follow that any arbitrarily grouped set of birth dates would produce a sense of identity, especially one that can be named and described on the basis of its birth years alone. It is an accident of history that the Baby Boom lasted 18 years — as far as we know having nothing to do with the length of a reproductive generation, but perhaps leading subsequent analysts to use the term “generation” to describe both Baby Boomers and subsequent cohorts.

The good researchers at Pew are in a tough spot (as are others who rely on their categories). The generations concept is tremendously appealing and hugely popular. But where does it end? Are we going to keep arbitrarily dividing the population into generations and giving them names — after “Z”? On what scientific basis would the practice continue? One might be tempted to address these problems by formalizing the process, with a conference and a dramatic launch, to make it even more “official.” But there is no scientific rationale for dividing the population arbitrarily into cohorts of any particular length for purposes of analyzing social trends, and to fix their membership a priori. Pew would do a lot more to enhance its reputation, and contribute to the public good, by publicly pulling the plug on this project.

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Open letter to the Pew Research Center on generation labels

Sign the letter here.

We are demographers and other social scientists, writing to urge the Pew Research Center to stop using its generation labels (currently: Silent, Baby Boom, X, Millennial, Z). We appreciate Pew’s surveys and other research, and urge them to bring this work into better alignment with scientific principles of social research.

1. Pew’s “generations” cause confusion.

The groups Pew calls Silent, Baby Boom, X, Millennial, and Z are birth cohorts determined by year of birth, which are not related to reproductive generations. There is further confusion because their arbitrary lengths (18, 19, 16, 16, and 16 years, respectively) have grown shorter as the age difference between parents and their children has lengthened.

2. The division between “generations” is arbitrary and has no scientific basis.

With the exception of the Baby Boom, which was a discrete demographic event, the other “generations” have been declared and named on an ad hoc basis without empirical or theoretical justification. Pew’s own research conclusively shows that the majority of Americans cannot identify the “generations” to which Pew claims they belong. Cohorts should be delineated by “empty” periods (such as individual years, equal numbers of years, or decades) unless research on a particular topic suggests more meaningful breakdowns.

3. Naming “generations” and fixing their birth dates promotes pseudoscience, undermines public understanding, and impedes social science research.

The “generation” names encourage assigning them a distinct character, and then imposing qualities on diverse populations without basis, resulting in the current widespread problem of crude stereotyping. This fuels a stream of circular debates about whether the various “generations” fit their associated stereotypes, which does not advance public understanding.

4. The popular “generations” and their labels undermine important cohort and life course research

Cohort analysis and the life course perspective are important tools for studying and communicating social science. But the vast majority of popular survey research and reporting on the “generations” uses cross-sectional data, and is not cohort research at all. 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.

5. The “generations” are widely misunderstood to be “official” categories and identities

Pew’s reputation as a trustworthy social research institution has helped fuel the false belief that the “generations” definitions and labels are social facts and official statistics. Many other individuals and organizations use Pew’s definitions in order to fit within the paradigm, compounding the problem and digging us deeper into this hole with each passing day.

6. The “generations” scheme has become a parody and should end.

With the identification of “Generation Z,” Pew has apparently reached the end of the alphabet. Will this continue forever, with arbitrarily defined, stereotypically labeled, “generation” names sequentially added to the list? Demographic and social analysis is too important to be subjected to such a fate. No one likes to be wrong, and admitting it is difficult. We sympathize. But the sooner Pew stops digging this hole, the easier it will be to escape. A public course correction from Pew would send an important signal and help steer research and popular discourse around demographic and social issues toward greater understanding. It would also greatly enhance Pew’s reputation in the research community. We urge Pew to end this as gracefully as possible — now.

As consumers of Pew Research Center research, and experts who work in related fields ourselves, we urge the Pew Research Center to do the right thing and help put an end to the use of arbitrary and misleading “generation” labels and names.

In the war between armed police and the unarmed public, the police should unilaterally disarm.

Modified photo from Chris Yarzab: https://flic.kr/p/8bjkUo

One thing Duante Wright, Philando Castile, Walter Scott, Samuel DeBose, and Rayshard Brooks, have in common is that the police who killed them could have accomplished whatever they were legitimately supposed to be doing without a gun on their hip. The police in these incidents had no reason to anticipate violence in the interactions. There was no report of a violent crime, no weapons visible, no sign of anyone in imminent danger. Whether you think the police acted with racist malice, incompetence, or even reasonably, the fact is that if the police who killed them weren’t carrying guns no one would have died.

The structural approaches to police violence introduced in the last year, including reducing police funding to replace them with other agencies and services, involve big, complex proposals. For example, a recent law review article by Jordan Blair Woods reasonably suggests replacing police with unarmed civilian enforcers of traffic codes. These would require changing laws and restructuring government budgets.

A much simpler and immediately effective remedy to at least some of our problem is a simple matter of police department policy: don’t wear your guns.

Whether it was poor training, racism, malice, or just fatally bad luck that led Kimberly Potter to shoot Duante Wright with her gun instead of her Taser in Booklyn Center, Minnesota earlier this month, the body camera recording clearly shows she had nothing in her hands just seconds earlier. She didn’t enter the scene with her gun out because there was no reason to suspect violence, and in fact the only violence that occurred was her shooting Wright. If she hadn’t had a gun on her hip, he wouldn’t have died.

For all the talk of “de-escalation” in police interactions with the public, this simple solution is routinely overlooked. In any potentially violent conflict, the stakes are automatically raised to the level of the deadliest weapon present. Guns escalate conflict.

The policy details are important. In a society awash in guns (unlike many of those where police are usually unarmed), police here will sometimes need them for good reasons. You could start with some units dedicated to traffic enforcement, for example. Some police could have guns in a safe in the trunk of their car. Special units could be routinely armed. But the officers who come to your (my) house to discuss online death threats don’t need to be wearing firearms.

There are risks to police from such an approach, but the present default unreasonably assumes that carrying guns only reduces those risks. How often are unarmed police killed at traffic stops? If we don’t know the answer to that, maybe it hasn’t been sufficiently tried. If your response is, “one traffic cop killed is too many,” try applying that logic to the unarmed victims of police.

Even if you believe Darren Wilson, who said Michael Brown tried to take his gun in Ferguson, Missouri in 2014, possession of the gun was the basis of their violent conflict. Even if Darren Wilson had been just as racist in harassing Brown for walking in the street, no one would have died if Wilson hadn’t had a gun.

A Justice Department report on Michael Brown’s death noted, “Under well-established Fourth Amendment precedent, it is not objectively unreasonable for a law enforcement officer to use deadly force in response to being physically assaulted by a subject who attempts to take his firearm.” Well-established, perhaps, but that’s tragically circular – cop has a right to kill someone with his gun who tries to take his gun – because he has a gun.

If Duante Wright or Michael Brown or George Floyd had resisted arrest, punched an officer, or driven off to escape law enforcement, no one would have died. But that’s not all that would be different. If police in those situations, and millions of others, weren’t carrying guns, we could develop a new mutual understanding between the police and public: Police won’t “accidentally” kill you during a traffic stop or when reacting to nonviolent infractions, but if you do attack unarmed police, more police will show up later and they will have reason to be armed.

What might seem riskier to police upfront – leaving the gun in the trunk, or at the station – would certainly lead to fewer deaths of innocent, unarmed, nonviolent, people. Given the scale of innocent life taken in such incidents, and its effects on relations between the public and the police, that is a paramount concern for equity, civil rights, and law enforcement. But by reducing the stakes of individual interactions with police – automatically de-escalating them – it would probably also end up making the job safer for police as well.

Policing is dangerous work, work the police make more dangerous by introducing firearms into many interactions that should remain nonviolent. Would removing the holster from the standard uniform discourage people from becoming police? To some extent it might. But if not wearing a gun discouraged the kind of person for whom wearing a gun is the best part of the job, so much the better.

In the war between armed police and the unarmed public, the police should unilaterally disarm.

Citizen Scholar: new book under contract

PN Cohen photo

My new book, Citizen Scholar, is under contract with Columbia University Press (thanks to the support of editor, and Editorial Director, Eric Schwartz).

Some of the writing I’ve been doing here is part of the book’s development, including the piece on “policy implications,” essays on transparency and accountability in research, as well as talks and materials about preprints, open science and the pandemic, politics and science, and others. It’s time for a book (and also more talks, if you’d like to invite me!). I will post essays and excerpts as I go, here, and I welcome your critiques, suggestions, and ideas. The first post describes my ambitions, and plan, for the book.

I love Family Inequality and everyone here but it seemed awkward to repeatedly post stuff for the new book under this heading. So I set up a blog style page, and I’ll post links here, too (and I’ll figure out you can subscribe, for those who want their blog posts via email).

Wish me luck!

The American Sociological Association is collapsing and its organization is a perpetual stagnation machine

I am at the end of a three-year term as an elected member of the American Sociological Association (ASA) Committee on Publications, during which time, despite some efforts, I achieved none of the goals in my platform. I would like to say I learned a lot or whatever, but I didn’t. I enjoyed the time spent with colleagues at the meetings (and we did some rubber stamping and selecting editors, which someone had to do), but beyond that it was a waste of time. Here are some reflections.

First, some observations about sociology as a discipline, then about the ASA generally, and then about the situation with the Committee on Publications.

Sociology

Sociology has occupied a rapidly declining presence in US higher education for two decades. The percentage of bachelor’s, masters, and PhD degrees that were awarded in sociology peaked at the end of the last century:

Looking just at the number of PhDs awarded, you can see that among the social sciences, since the 2009 recession (scaled to 0 on this figure), sociology is one of the social sciences disciplines that has slipped, while psychology, economics, business, and political science have grown (along with STEM disciplines).

The American Sociological Association

So, sociology as an academic discipline is in decline. But how is ASA doing — is it fair for me to say “collapsing” in the title of this post? The shrinking of the discipline puts a structural squeeze on the association. In order to maintain its organizational dimensions it would need a growing share of the sociology milieu. The prospects for that seem dim.

On the plus side, the association publishes several prominent journals, which were the ostensible subject of our work on the publications committee. Metrics differ, but two ASA journals are in the top ten sociology journals by five-year citation impact factor in Web of Science (American Sociological Review and Sociology of Education [the list is here]). In the Google Scholar ranking of journals by h5-index, which uses different subject criteria, only one (ASR) is in the top 20 of sociology journals, ranking 20th out of the combined top 100 from economics, social science general, sociology, anthropology, and political science  (the list is here). In terms of high-impact research, among the top 100 most cited Web of Science sociology papers published in 2017 (an arbitrarily chosen recent year), seven were published in ASA journals (five in American Sociological Review [the list is here]). The 2020 Almetric Top 100 papers, those gaining the most attention in the year (from sources including news media and social media), includes 35 from humanities and social sciences, none of which were published by ASA (although several are by sociologists). So ASA is prominent but not close to dominant within sociology, which is similarly situated within the social sciences. In terms of publications, you can’t say ASA is “collapsing.” (Plus, in 2019 ASA reported $3 million in revenue from publications, 43 percent of its total non-investment income.)

But in terms of membership, the association is leading the way in the discipline’s decline. The number of members in the association fell 24 percent from 2007 to 2019, before nosediving a further 16 percent last year. Relative to the number of PhDs completing their degrees, as one scale indicator, membership has fallen 42 percent since 2007 — from 26 paying members per PhD awarded to 15. Here are the trends:

Clearly, the organization is in a serious long-term decline with regard to membership. How will an organization of sociologists, including organizational sociologists, react to such an organizational problem? Task force! A task force on membership was indeed seated in 2017, and two years later issued their report and recommendations. To begin with, the task force reported that ASA’s membership decline is steeper than that seen by 11 other unnamed disciplinary societies:

They further reported that only 36 percent of members surveyed consider the value of belonging to ASA equal to or greater than the cost, while 48 percent said it was overpriced. Further, 69 percent of members who didn’t renew listed cost of membership as an important reason, by far — very far — the most important factor, according to their analysis. Remarkably, given this finding, the report literally doesn’t say what the member dues or meeting registration fees are. Annual dues, incidentally, increased dramatically in 2013, and now range from $51 for unemployed members to $368 for those with incomes over $150,000 per year, apparently averaging $176 per member (based on the number of members and membership revenue declared in the audit reports).

Not surprisingly, then, although they did recommend “a comprehensive review of our membership dues and meeting registration fee structures,” they had no specific recommendations about member costs. Instead they recommended: Create new ways for sociologists to create subgroups within the association, “rethink the Annual Meeting and develop a variety of initiatives, both large and small,” remove institutional affiliations from name badges and make the first names bigger, give a free section membership to new members (~$10 value), anniversary- instead of calendar-based annual pricing, hold the meeting in a wider “variety of cities,” more professional development (mechanism unspecified), more public engagement, change the paper deadline a couple of weeks and consider other changes to paper submission, and provide more opportunities for member feedback. Every recommendation was unanimously approved by the association’s elected council. The following year membership fell another 16 percent, with some unknown portion of the drop attributable to the pandemic and the canceled annual meeting.

With regard to the membership crisis, my assessment is that ASA is a model of organizational stagnation and failure to respond in a manner adequate to the situation. The sociologist members, through their elected council, seem to have no substantial response, which will leave it to the professional staff to implement emergency measures as revenue drops in the coming years. One virtually inevitable outcome is the association further committing to its reliance on paywalled journal publishing and the profit-maximizing contract with Sage, and opposing efforts to open access to research for the public.

Committee on Publications

But it is on the publications committee, and its interactions with the ASA Council, that I have gotten the best view of the association as a perpetual stagnation machine.

I can’t say that the things I tried to do on the publications committee would have had a positive effect on ASA membership, journal rankings, majors, or any other metric of impact for the association. However, I do believe what I proposed would have helped the association take a few small steps in the direction of keeping up with the social science community on issues of research transparency and openness. In November I reported how, more than two years ago now, I proposed that the association adopt the Transparency and Openness Promotion Guidelines from the Center for Open Science, and to start using their Open Science Badges, which recognize authors who provide open data, open materials, or use preregistration for their studies. (In the November post I discussed the challenge of cultural and institutional change on this issue, and why it’s important, so I won’t repeat that here.)

The majority of the committee was not impressed at the beginning. At the January 2019 meeting the committee decided that an “ad hoc committee could be established to evaluate the broader issues related to open data for ASA journals.” Eight months later, after an ad hoc committee report, the publications committee voted to “form an ad hoc committee [a different one this time] to create a statement regarding conditions for sharing data and research materials in a context of ethical and inclusive production of knowledge,” and to, “review the question about sharing data currently asked of all authors submitting manuscripts to incorporate some of the key points of the Committee on Publications discussion.” The following January (2020), the main committee was informed that the ad hoc committee had been formed, but hadn’t had time to do its work. Eight months later, the new ad hoc committee proposed a policy: ask authors who publish in ASA journals to declare whether their data and research materials are publicly available, and if not why not, with the answers to be appended in a footnote to each article. And then the committee approved the proposal.

Foolishly, last fall I wrote, “So, after two years, all articles are going to report whether or not materials are available. Someday. Not bad, for ASA!” Yesterday the committee was notified by ASA staff that, “Council is pleased that Publications Committee has started this important discussion and has asked that the conversation be continued in light of feedback from the Council conversation.” In other words, they rejected the proposal and they’ll tell us why in another four months. There is no way the proposal can take effect for at least another year — or about four years after the less watered-down version was initially proposed, and after my term ends. It’s a perpetual stagnation machine.

Meanwhile, I reviewed 24 consecutive papers in ASR, and found that only four provided access to the code used and at least instructions on how to find the data. Many sociologists think this is normal, but in the world of academic social science, this is not normal, it’s far behind normal.

I don’t know if the Council is paying attention to the Task Force on Membership, but if they were it might have occurred to them that recruiting people to run for office, having the members elect them based on a platform and some expertise, having them spend years on extremely modest, imminently sensible proposals, and then shooting those down with a dismissive “pleased [you have] started this important discussion” — is not how you improve morale among the membership.

Remember that petition?

While I’m at it, I should update you on the petition many of you signed in December 2019, in opposition to the ASA leadership sending a letter to President Trump against a potential federal policy that would make the results of taxpayer-funded research immediately available to the public for free — presumably at some cost to ASA’s paywall revenues. At the January 2020 meeting the publications committee passed two motions:

1. For the Committee on Publications to express opposition to the decision by the ASA to sign the December 18, 2019 letter.
2. To encourage Council to discuss implications of the existing embargo and possible changes to the policy and to urge decisionmakers to consult with the scientific community before making executive orders.

We never heard back from the ASA Council, and the staff who opposed the petition were obviously in no rush to follow up on our entreaty to them, so it disappeared. But I just went back to March 2020 Council minutes, and found this profoundly uninformative tidbit:

Council discussed a recent decision of ASA’s authorized leadership to sign a letter expressing concern about an executive order related to scientific publishing rumored to be coming out with almost no notice or consultation with the scientific community. A motion was made by Kelly to affirm ASA’s policy for making time sensitive decisions about public statements and to confirm that the process was properly followed in this instance. Seconded by Misra. Motion carried with 16 for and 2 abstentions.

This doesn’t mention that the substance of the dispute, that the publications committee objected to the leadership’s statement, or the fact that more than 200 people signed a letter that read, in part: “We oppose the decision by ASA to sign this letter, which goes against our values as members of the research community, and urge the association to rescind its endorsement, to join the growing consensus in favor of open access to scholarship, including our own.” To my knowledge no member of the ASA leadership, whether elected sociologists or administrative staff, has responded publicly to this letter. Presumably, the terrible statement sent by the ASA leadership still represents the position of the association — the association that speaks for a rapidly dwindling number of us.

Side note: An amazing and revealing thing happened in the publications committee meeting where we discussed this statement in January 2020. The chair of the committee read a prepared statement, presumably written by the ASA staff, to introduce the voting on my proposal:

The committee has a precedent that many of you are already aware of, of asking people to leave for votes on the proposals they submitted…. This practice is designed to ensure that the committee members can have a full and open discussion. So, Philip, I’d like to ask you to recuse yourself now for the final two items, which you can simply do by hanging up the phone…

Needless to say, I refused to leave the meeting for the discussion on my proposal, as there is no such policy for the committee. (If you know of committee meetings where the person making a proposal — an elected representative — has to leave for the discussion and vote, please let me know.) It was just an attempt to railroad the decision, and other members stepped in to object, so they dropped it. The motion passed, and council ignored it, so seriously who cares, but still. (The minutes for this meeting don’t reflect this whole incident, but I have verbatim notes.) 

You will forgive me if, after this multi-year exercise in futility, I am not inclined to be optimistic regarding the Taskforce on Membership’s Recommendation #10: “Enhance and increase communications from ASA to members and provide opportunities for ASA members to provide ongoing feedback to ASA.” I have one more meeting in my term on the publications committee, but it doesn’t seem likely I’ll be there.

New paper: Baby Bust analysis of 124 counties in 2 states through February 2021

Having spent a few months collecting data on birth rates over the last year, and a few months pouring over pandemic data, I took the time to bring the two together and assess the relationship between some basic pandemic indicators and the latest fertility outcomes. The result is a short paper I titled, “Baby Bust: Falling Fertility in US Counties Is Associated with COVID-19 Prevalence and Mobility Reductions,” now available on SocArXiv, with links to the data and Stata code for replication. 

Here’s the abstract:

The United States experienced a 3.8 percent decline in births for 2020 compared with 2019, but the rate of decline was much faster at the end of the year (8 percent in December), suggesting dramatic early effects of the COVID-19 pandemic, which began affecting social life in late March 2020. Using birth data from Florida and Ohio counties through February 2021, this analysis examines whether and how much falling birth rates were associated with local pandemic conditions, specifically infection rates and reductions in geographic mobility. Results show that the vast majority of counties experienced declining births, suggestive of a general influence of the pandemic, but also that declines were steeper in places with greater prevalence of COVID-19 infections and more extensive reductions in mobility. The latter result is consistent with more direct influences of the pandemic on family planning or sexual behavior. The idea that social isolation would cause an increase in subsequent births receives no support.

Here’s the main result in graphic form, showing that births fell more in January/February in those counties with more COVID-19 cases, and those with more mobility limitation (as measured by Google), through the end of last May:

However, note also that births fell almost everywhere (87% of the population lives in a fertility-falling county), so it didn’t take a high case count or shutdown to produce the effect.

There will be a lot more research on all this to come, I just wanted to get this out to help establish a few basic findings and motivate more research. I’d love your feedback or suggestions.

Earlier updates and media reports are here.

What is life expectancy? (video)

My YouTube career may have peaked in 2015, with the now-classic video, Total Fertility Rate, which has been viewed almost 30,000 times (124 likes!). Since then the technical quality has improved, but not the viewership.

Recently I heard someone say (sorry, I can’t remember who) that they were looking for a short video explaining what life expectancy is. This was after the CDC reported that US life expectancy in the first half of 2020 decreased by 1 year, which generated some confusion. Outside of secondary effects, the pandemic did reduce life “expectancy” for people it didn’t kill, and here we are (still alive, so far) reading about it, so how could our life expectancy have been affected? did last year’s deaths mean people would live less long in the future? I said somewhere on twitter than “life expectancy” is a bad name for this common statistic, and I think it is. I don’t have a better name for it, though, and it’s probably to late to change anyway.

So, to help meet the current need, and to try to reach my past video glory, yesterday I produced, “What is life expectancy?“, a 6-minute explainer, using 3 graphs, to help people understand. I didn’t discuss the recent COVID results so as not to date the video, and I hope it will be useful in the future (that it has a long life expectancy).

Here it is:

The one-child policy was bad and so is “One Child Nation”

I was considering assigning the students in my Family Demography seminar to watch the documentary, One Child Nation: The Truth Behind the Propaganda, so I watched it. The movies uses the tragic family history of one of the directors, Nanfu Wang, to tell the story of the Chinese birth planning policy that began in 1979 and extended through many modifications until 2015. Nothing against watching it, but it’s not good. The one-child policy wasn’t good either, of course, leading to many violations of human rights and a lot of suffering and death.

Before watching the movie, I’m glad I read the review by Susan Greenhalgh, an anthropologist who spent about 25 years studying the one-child policy and related questions (summarized in three books and many articles, here). It’s short and you should read it, but just to summarize a couple of key historical points:

• The policy was “the cornerstone of a massively complex and consequential state project to modernize China’s population,” and can’t be understood in the context of birth control alone.
• Many people opposed and resisted the policy, but reducing birth rates was a commonly-understood goal, for both gender equality and economic development, and many women were glad the government supported them in that effort. The “vast majority” felt “deep ambivalence” about the policy, weighing individual desires against the perceived need to sacrifice for the common good.
• The policy was unevenly applied and enforced (it was especially harsh in the provinces featured in the film), and after 1993 enforcement became less egregious. 
• Exceptions were added starting in the early 1980s, until by the late 1990s the majority of the population was not subject to a one-child rule. 

There are some other specific errors and distortions, including the dramatic, incorrect claim that “the one-child policy [was] written into China’s constitution” in 1982 (as Greenhalgh writes, “the 1982 Constitution says only: “both husbands and wives are duty-bound to practice birth planning”). And the decision to translate all uses of the term “birth planning” as “one-child policy.” That said, the stories of forced abortions, sterilizations, and infanticide are wrenching and ring true.

I have two things to add to Greenhalgh’s review. First, a simple data illustration to show that China, really, is not a “one-child nation.” Using Chinese census data, here is the total number of children (by age 35-39) born to three groups of Chinese women, arranged according to their ages in 1980, about when the one-child policy began.

The shift left shows the decline in number of children born: the mean fell from 3.8 to 2.5 to 1.8 in these data. (Measuring Chinese fertility is complicated, but the census provides a reasonable ballpark.) But the main thing I want to show is that among the last group — those who were beginning their childbearing years when the policy took effect — 61% had two or more children. The idea that China became a “one-child nation” under the policy is false.

Second, the movie takes a hard turn in the middle and focuses on international adoption, and the illegal trafficking of mostly second-born girls to orphanages that sought to place them abroad. This was a very serious problem. But the movie tells the story of the most notorious scandal (for which many people served jail terms) as if it were the common practice, and centers on the savior-like behavior of American activists helping adopted children trace their familial roots. Granting that of course that corruption was terrible, and that the motivations of many (some?) adoptive parents (including me) were good, from China’s point of view it’s not a central story in the history of the one-child policy. As the movie notes, 130,000 Chinese children were adopted abroad during the period, during which time hundreds of millions were born.

Greenhalgh summarizes on this point, calling the film a:

“familiar coercion narrative, complete with villain (the state), victims (rural enforcers and targets), and savior (an American couple offering DNA services to match adopted girls in the U.S. with birth parents in China). The characters (at least the victims and saviors) have some emotional complexity, but they still play the stock roles in an oft-told tale. For American viewers, this narrative is comforting, because it provides a simple, morally clear way to react to troubling developments unfolding in a faraway, little understood land. And by using China (communist, state-controlled childbearing) as a foil for the U.S. (liberal, relative reproductive freedom), the film leaves us feeling smug about the assumed superiority of our own system.”

The many centuries of Chinese patriarchy are a dark part of the human story, and in some ways is unique. For example — relevant to this recent histyory — female infanticide and selling girls has a long history (a history that includes foot binding and other atrocities). The Chinese Communist Party, for all its misdeeds, did not create this problem. Gender inequality in China, including the decline in fertility — which was mostly accomplished before 1979 — has markedly improved since 1949. Greenhalgh concludes: “In China, before the state began managing childbearing, reproductive decisions were made by the patriarchal family. Since the shift to a two-child policy, they have been subject to the strong if indirect control of market forces. One form of control may be preferable to another, but freedom over our bodies is an illusion.”