In the conversation between Harris and Harden, something Harris said made me think that the genetic explanation for race differences in what they’re calling “outcomes,” such as educational attainment, intelligence, or other phenotypic “complex behavior traits” is like a truther conspiracy theory for people like Harris and Charles Murray. Any time they think they have poked a hole in the “racism explains everything” worldview, they believe it’s evidence for genetics.
Harden takes positions that are at odds with the viewpoint of many sociologists. She believes intelligence is measurable, and heritable (people get it from their parents), and has important causal effects on people’s lives. But she doesn’t believe that explains why Whites score higher than some other groups on IQ tests. In the interview, Harden had patiently explained that in the absence of evidence of group differences in genetics — which there is not — you can’t assume the direction of as-yet undiscovered genetic differences, even when you have group differences in phenotypes and evidence of heritability at the individual level. She said:
“It’s a really, really, really basic statistical point, which is that if you know the direction of the association within a group, you don’t know anything about whether that plays out between groups, not even in the sign of that direction…. It could be that Africans are at a genetic advantage for cognitive ability that’s been swamped by environmental risks and adversity. … That’s labeled the ecological fallacy, that’s like a basic statistical point. … We have no information, no default, about what is the relationship between differences in genetic ancestry and the causes of these cognition differences that we see on average, between groups. And in the absence of any data, and really good data, the only priors we have are informed by what? So that’s why I think the prior that there is a genetic difference, and what is more that it works in this particular direction, is not informed by the science.”
Murray and Harris can’t see this, or refuse to. They keep “defaulting” to the assumption that because traits like intelligence are somewhat heritable at the individual level, and there are group differences in the observed traits (“outcomes”) — therefore group differences are at least partly the result of genetics. It’s just a matter of time till we find out. Harris responds:
“My default assumption here … for the hundred things we care about in a person, given how much we are learning about the role that genes play in making us who we are, physically, and psychologically … we will find that genes are involved for virtually everything, to some degree. And in many cases it will be the difference that really makes most of the difference, and this is true for individuals, and we will find it true for groups.”
When he says it’s an assumption, and he won’t change it regardless of the lack of evidence, there’s no point in arguing. He’s not talking about science. I would make stronger arguments against the enterprise itself — the idea of trying to find genetic group differences to explain inequality between groups — than she did in the interview, but in any event Harden is clear that Murray and Harris are wrong on this point, as many others have explained as well. (Of course, the existence of meaningful genetic differences between ancestral populations in complex behavioral traits like intelligence is itself purely speculative. Variations evolve at random and might end up sticking if they provide a survival advantage, like lack of skin pigmentation, but it’s not likely humanity was divided up into different populations where some groups were selected for intelligence and others weren’t — unlike skin pigmentation, intelligence is handy for everyone.) Anyway, that’s all backstory to the quote below. Harris keeps saying his real concern is with intellectual honesty and the perils of cancel culture. And he says this:
“The real question is what is the cause of all these disparities. The real problem politically, at the moment, is when you’re talking about White-Black differences in American society, differences in outcome, differences in, you know, inner-city crime, differences in wealth inequality, all of it – anything that people could care about – the only acceptable answer in many quarters, to account for these differences, is White racism, or systemic racism, right, institutional racism. Some holdover effect from slavery and Jim Crow. And a failure to see it that way, just reflexively, is synonymous with being a racist, or being unaware of the depth of racism. White fragility – we’re having this conversation at the moment when the best selling book in the country is White Fragility, right. So to be doubtful that White racism accounts for all of these disparities – you know, White racism, again, in the year 2020, not to be in any doubt about the ugly history of racism in American, to be in doubt about whether racism explains the number of shootings we’re going to see in Chicago this weekend – and the fact that I can predict with something like 100 percent certainty that most of those shootings will be Black-on-Black crime, right, is it White racism that explains that? To have doubt about that will cast you as a malevolent imbecile in many, many quarters, now, and you risk reputational destruction. And the only safe space is to say, ‘Of course it’s White racism, that’s the problem we gotta solve.’ And that is such a stultifying and frankly dishonest place to be, intellectually, at the moment, and it’s closing down conversation on dozens of important topics, and it puts us in a position, insofar as we’re fighting from this trench, right, we’re all just hunkered down against all possible future insights, in this spot, it is deeply unstable, because we will find out things – differences among groups, again now speaking widely about all human difference, among all groups – and differences among individuals, that are simply not amenable to a politically correct analysis, and, again, there’s this inconvenient fact that we have these differences between Asians and Whites, right, so if White racism accounts for every possible difference between Whites and Blacks in society, is there a pro-Asian racism that’s account for the fact that Whites are performing so badly on IQ tests? That’s hard to argue for.”
You have to love how he goes from “dozens” of questions, and “all human difference, among all groups” straight to violence and IQ. But the whole rant is the tell that it’s a truther-style conspiracy theory. When a 9/11 truther finds any discrepancy or incomplete element in the official explanation for the 9/11 attacks — like something about the melting point of steel, or a missing document or garbled radio transmission — they assume it’s evidence the CIA did it. See! How can you believe them?! This is what I’m telling you! In fact, any complex scientific story will have potential discrepancies and inadequacies yet to be explained (“science is not designed for proving absolute negatives”). But those things are not evidence for a particular different theory — unless they really are. Once they plant the idea of their counternarrative, any weakness in the accepted story becomes evidence for their side. So, if “Asians” do better on IQ tests than Whites do, and if Black people kill each other in Chicago, facts that supposedly undermine the “White racism causes everything” story, it’s basically evidence that Blacks are genetically inferior — probably or maybe, blah, blah, blah racism — and you just can’t admit it.
Just in case you were wondering whether people who make this kind of assumption are thinking scientifically, they’re not. That’s a thought I had after some reading and listening. I think I’ll read her book.
Joe Pinsker at the Atlantic has written, “Amazon Ruined the Name Alexa,” that develops the story of the name, which I started tracking with a pick drop in 2017, writing: “You have to feel for people who named their daughters Alexa, and the Alexas themselves, before Amazon sullied their names. Did they not think of the consequences for these people? Another bad year for Alexa. After a 21.3% drop in 2016, another 19.5% last year.”
Amazon did not exactly ruin the life of every Alexa, but the consequences of its decision seven years ago are far-reaching—roughly 127,000 American baby girls were named Alexa in the past 50 years, and more than 75,000 of them are younger than 18. Amazon didn’t take their perfectly good name out of malice, but regardless, it’s not giving it back.
From the peak year of 2015, when there were 6,050 Alexas born in the US, the number fell 79% to 1272 in 2020, the biggest drop among names with at least 1000 girls born in 2015. Here’s that list:
Pinsker got Amazon on the record not commenting on the problem they created for actual humans named Alexa, who he reports are being bullied in school — they are not only named after a robot, but a subservient female one, so no surprise. Amazon said only, “Bullying of any kind is unacceptable, and we condemn it in the strongest possible terms.”
Cutting room floor
I am only quoted in the story saying, “We don’t usually think about the individuals who are already born when this happens, but the impact on their lives is real as well.” No complaint about that, of course. But since my interview with Pinsker was over email, I can share my other nuggets of insight here, with his questions:
I saw that you first blogged about this in 2018 (when you were remarking on the 2017 name data). Did you just happen to stumble upon Alexa’s declining popularity yourself, or did someone else point it out to you?
I wrote a program that identifies that names with biggest changes, and Alexa jumped out. One interesting thing about naming patterns is that dramatic changes are quite rare. Names rise and fall over time, but they rarely show giant leaps or collapse as dramatically as Alexa did after 2015.
When you look at what has happened to the name Alexa since Amazon’s Alexa was released in late 2014, how much of the name’s declining popularity do you attribute to Amazon? (Is it common for names to plummet in popularity as quickly as Alexa has since 2014?)
The Social Security national name data is a mile wide and an inch deep. We have a tremendous amount of name data, but it is all just counts of babies born — we have no direct information about who is using what names, or why. So any attribution of causal processes is speculative unless we do other research. That said, because dramatic changes are so rare, it’s usually pretty easy to explain them. For example, some classic 1970s hits apparently sparked name trends: Brandy (Looking Glass, 1972), Maggie (Rod Stewart, 1971), and of course Rhiannon (Fleetwood Mac, 1975). I defy you to find someone named Rhiannon, born in the US, who was born before 1975. We can also observe dramatic changes even among uncommon names, such as a doubling of girls named Malia in 2009 (the Obamas’ daughter’s name).
At one point, you mentioned on your blog that Hillary was another name that became less popular after becoming culturally ubiquitous. Are there any other examples you’re aware of, where a name’s cultural ubiquity tanks its popularity?
On the other hand, there are disaster stories, like Alexa. Hillary was rising in popularity before 1992, and then tanked. Monica declined dramatically after 1998 (after the Clinton sex scandal). Ellen became much less common suddenly the year after Ellen DeGeneres came out as gay in 1997. And Forrest, which had been on the rise before 1994, plummeted after Forrest Gump came out and virtually disappeared.
We don’t usually think about the individuals who are already born when this happens, but the impacts on their lives is real as well. The name trends tell us something about the social value of a name (and unlike other commodities, in the US at least there is no limit to the number of people who can have a name). People who were named Adolph before Hitler, Forrest before Forrest Gump, or Alexa before Amazon live with the experience of a devalued name. Many of them end up changing their names or using nicknames — or just getting used to people making jokes about their name every time they meet someone new, have attendance called, or go to the department of motor vehicles.
If I’m reading the SSA data correctly, there were 1,272 Alexas born last year in the U.S. I know this is speculative, but would you guess that most of these parents aren’t aware of the name of Amazon’s device? Or is it that they’re aware, and just don’t care?
Some don’t know, some don’t care, some probably think it’s cool. For some it may be a family name. I am fascinated to see that Alexis and Alexia have also seen five-year declines of more than 60% in name frequency. I wonder if that is because of concern over Alexa devices mishearing those names — certainly a reasonable concern — or maybe just association with the product making those names seem derivative or tacky. It’s hard to say.
Philip N. Cohen criticized the use of generation labels. Generations are one of many analytical lenses researchers use to understand societal change and differences across groups. While there are limitations to generational analysis, it can be a useful tool for understanding demographic trends and shifting public attitudes. For example, a generational look at public opinion on a wide range of social and political issues shows that cohort differences have widened over time on some issues, which could have important implications for the future of American politics.
In addition, looking at how a new generation of young adults experiences key milestones such as educational attainment, marriage or homeownership, compared with previous generations in their youth, can lend important insights into changes in American society.
To be sure, these labels can be misused and lead to stereotyping, and it’s important to stress and highlight diversity within generations. At Pew Research Center, we consistently endeavor to refine and improve our research methods. Therefore, we are having ongoing conversations around the best way to approach generational research. We look forward to engaging with Mr. Cohen and other scholars as we continue to explore this complex and important issue.
Kim Parker, Washington
I was happy to see this, and look forward to what they come up with. I am also glad to see that there has been no substantial defense of the current “generations” research regime. Some people on social media said they kind of like the categories, but no researcher has said they make sense, or pointed to any research justifying the current categories. With regard to her point that generations research is useful, that was in our open letter, and in my op-ed. Cohorts (and, if you want to call a bunch of a cohorts a generation, generations) matter a lot, and should be studied. They just shouldn’t be used with imposed fixed categories regardless of the data involved, and given names with stereotyped qualities that are presumed to extend across spheres of social life.
Several people have asked me for suggestions. My basic suggestion is to do like you learned in social science class, and use categories that make sense for a good reason. If you have no reason to use a set of categories, don’t use them. Instead, use an empty measure of time, like years or decades, as a first pass, and look at the data. As I argued here, there is not likely to be a set of birth years that cohere across time and social space into meaningful generational identities.
In the Op-Ed, I wrote this: “Generation labels, although widely adopted by the public, have no basis in social reality. In fact, in one of Pew’s own surveys, most people did not identify the correct generation for themselves — even when they were shown a list of options.” The link was to this 2015 report titled, “Most Millennials Resist the ‘Millennial’ Label” (which of course confirms a stereotype about this supposed generation). I was looking in particular at this graphic, which I have shown often:
It doesn’t exactly show what portion of people “correctly” identify their category, but I eyeballed it and decided that if only 18% of Silents and 40% of Millennials were right, there was no way Gen X and Boomers were bringing the average over 50%. Also, people could choose multiple labels, so those “correct” numbers was presumably inflated to some degree by double-clickers. Anyway, the figure doesn’t exactly answer the question.
The data for that figure come from Pew’s American Trends Panel Wave 10, from 2015. The cool thing is you can download the data here. So I figured I could do a little analysis of who “correctly” identifies their category. Unfortunately, the microdata file they share doesn’t include exact age, just age in four categories that don’t line up with the generations — so you can’t replicate their analysis.
However, they do provide a little more detail in the topline report, here, including reporting the percentage of people in each “generation” who identified with each category. Using those numbers, I figure that 57% selected the correct category, 26% selected an incorrect category, 9% selected “other” (unspecified in the report), and 8% are unaccounted for. So, keeping in mind that people can be in more than one of these groups, I can’t say how many were completely “correct,” but I can say that (according to the report, not the data, which I can’t analyze for this) 57% at least selected the category that matched their birth year, possibly in combination with other categories.
The survey also asked people “how well would you say they term [generation you chose] applies to you?” If you combine “very well” and “fairly well,” you learn, for example, that actual “Silents” are more likely to say “Greatest Generation” applies well to them (32%) than say “Silent” does (14%). Anyway, if I did this right, based on the total sample, 46% of people both “correctly” identified their generation title, and said the term describes them “well.” I honestly don’t know what to make of this, but thought I’d share it, since it could be read as me misstating the case in the Op-Ed.
The text and figures of this short paper are below, and it’s also available as a PDF on SocArXiv, in more citable form. The Stata code and other materials are up as well, here. It’s pretty drafty — very happy to hear any feedback.
Preamble: When Sabrina Tavernise, Claire Cain Miller, Quoctrung Bui and Robert Gebeloff wrote their excellent New York Times piece, Why American Women Everywhere Are Delaying Motherhood, they elevated one important aspect of the wider conversation about falling fertility rates — the good news that women with improving economic opportunities often delay or forego having children because that’s what they’d rather do.
But it’s tricky to analyze this. Consider one woman they quote, who said, “I can’t get pregnant, I can’t get pregnant… I have to have a career and a job. If I don’t, it’s like everything my parents did goes in vain.” Or another, who is waiting to have children till she finishes a dental hygienist degree, who said, “I’m trying to go higher. I grew up around dysfunctional things. I feel like if I succeed, my children won’t have to.” If people can’t afford decent childcare (yet), or won’t have a job that pays enough to afford the parenting they want to provide until they finish a degree — so they delay parenthood while investing in their careers — are they not having a baby because there are promising opportunities, or because of economic insecurity? These are edge cases, I guess, but it seems like they extend to a lot of people right now. That’s what motivated me to do this analysis.
Hard times and falling fertility in the United States
by Philip N. Cohen
Recent reports have suggested that falling fertility in the US since the 2008 recession is being driven by women with advantaged status in the labor market taking advantage of career opportunities. This paper takes issue with that conclusion. Although high incomes are associated with lower fertility in general, both in the cross section and over time (within and between countries), economic crises also lead to lower fertility. I offer a new descriptive analysis using data from the American Community Survey for 2000-2019. In the U.S. case, the fertility decline was widespread after the 2008 recession, but most concentrated among younger women. Although women with above average education have long had lower birth rates, the analysis shows that birth rates fell most for women in states with higher than average unemployment rates, especially among those with below average education. This is consistent with evidence that birth rates are falling, and births delayed, by economic insecurity and hardship.
A New York Times article by Sabrina Tavernise et al. was titled, “Why American Women Everywhere Are Delaying Motherhood” (Tavernise et al. 2021). Although it did not provide a simple answer to the question, it did offer this: “As more women of all social classes have prioritized education and career, delaying childbearing has become a broad pattern among American women almost everywhere.” And it included a figure showing birth rates falling faster in counties with faster job growth. Reading that article, the writer Jill Filipovic concluded, “the women who are driving this downturn [in fertility] are those who have the most advantage and the greatest range of choices, and whose prospects look brightest” (Filipovic 2021). This paper takes issue with that conclusion.
Clearly, one driver of delayed childbearing is the desire to maximize career opportunities, but there is also the weight of uncertainty and insecurity, especially regarding the costs of parenting. Filipovic (2021) also wrote, “Children? In this economy?” These two tendencies appear to generate opposing economic effects: A strong economy gives mothers more rewarding opportunities that childrearing threatens (reducing fertility), while also providing greater economic security to make parenting more affordable and desirable (increasing fertility). These two pathways for economic influence on fertility trends are not easily separable in research – or necessarily exclusive in personal experience. In what follows I will briefly situate falling US fertility in the wider historical and global context, and then offer a descriptive analysis of the US trend in births from 2000 to 2019, focusing on relative education and state unemployment rates.
Review and context
Historically, economic growth and development have been key determinants of fertility decline (Herzer, Strulik, and Vollmer 2012; Myrskylä, Kohler, and Billari 2009), although by no means the only ones, and with coupling that is sometimes loose and variable (Bongaarts 2017). In the broadest terms, both historically and in the present, higher average incomes at the societal level are strongly associated with lower fertility rates; and this relationship recurs within the United States as well, as shown in the cross section in Figure 1.
Figure 1. Total fertility rate by GDP per capita: Countries and U.S. states, 2019. Note: Markers are scaled by population. US states linear fit weighted by population. Source: World Bank, US Census Bureau, National Center for Health Statistics, Bureau of Economic Analysis.
A lower standard of living is associated with higher birth rates. However, economic crises cause declines in fertility (Currie and Schwandt 2014), and this was especially true around the 2008 recession in the U.S. (Comolli 2017; Schneider 2015) and other high-income countries (Gaddy 2021). The crisis interrupted what had been a mild recovery from falling total fertility rates in high-income countries, leading to a decline from 1.74 in 2008 to 1.57 by 2019 (Figure 2).
Figure 2. Total fertility rate in the 10 largest high-income countries: 1990-2019. Note: Countries with at least $30,000 GDP per capita at PPP. Source: World Bank.
Figure 2 shows that the pattern of a peak around 2008 followed by a lasting decline is widespread (with the notable exceptions of Germany and Japan, whose TFRs were already very low), although the post-crisis decline was much steeper in the U.S. than in most other high income countries. Figure 3 puts the post-crisis TFR decline in global context, showing the change in TFR between the highest point in 2007-2009 and the lowest point in 2017-2019 for each country, by GDP per capita. (For example, the U.S. had a TFR peak of 2.12 in 2007, and its lowest point in 2017-2019 was 1.71 in 2019, so its score is -.41.) Fertility decline is positively associated with per capita income, as low-income countries continued the TFR declines they were experiencing before the crisis. However, among the high-income countries the relationship reversed (the inflection point in Panel A is $36,600, not shown). Thus, the sharp drop in fertility in the U.S. after the 2008 economic crisis is indicative of a larger pattern of post-crisis fertility trends. Globally, fertility is higher but falling in lower-income countries; fertility is lower in high-income counties, but fell further during the recent period of economic hardship or uncertainty. As a result of falling at both low and high ends of the economic scale, therefore, global TFR declined from 2.57 in 2007 to 2.40 in 2019 (by these World Bank data).
Figure 3. Difference in total fertility rate between the highest point in 2007-2009 and the lowest point in 2017-2019, by GDP per capita. Note: Markers scaled by population; largest countries labeled. Source: World Bank.
The mechanisms for these relationships – higher standard of living and rising unemployment both lead to lower fertility – defy simple characterization. The social scale (individual to global) may condition the relationship; there may be different effects of relative versus absolute economic wellbeing (long term and short term); development effects may be nonlinear (Myrskylä, Kohler, and Billari 2009); and the individual or cultural perception of these social facts is important as well (Brauner-Otto and Geist 2018). Note also that, as fertility rates fall with development, the question of having no children versus fewer has emerged as a more important distinction, which further complicates the interpretation of TFR trends (Hartnett and Gemmill 2020).
In the case of recent U.S. recessions, the negative impact on fertility was largest for young women. After the 2001 recession, birth rates only fell for women under age 25. In the wake of the more severe 2008 economic crisis, birth rates fell for all ages of women up to age 40 (above which rates continued to increase every year until 2020) although the drop was still steepest below age 25 (Cohen 2018). For the youngest women, births have continued to fall every year since, while those over age 35 saw some rebound from 2012 to 2019 (Figure 4). Clearly, during this period many women postponed births from their teens or twenties into their thirties and forties. The extent to which they will end up with lower fertility on a cohort basis depends on how late they continue (or begin) bearing children (Beaujouan 2020).
Figure 4. Annual change in U.S. births per 1,000 women, by age: 2001-2020. Source: National Center for Health Statistics.
Contrary to the suggestion that fertility decline is chiefly the result of improving opportunities for women, the pattern of delaying births is consistent with evidence that structural changes in the economy, the decline in goods-producing industries and the rise of less secure and predictable service industry jobs, are largely responsible for the lack of a fertility rebound after the 2008 recession, especially for Black and Hispanic women (Seltzer 2019). Lower education is also associated with greater uncertainty about having children among young people (Brauner-Otto and Geist 2018). For women in more precarious circumstances, especially those who are not married, these influences may be observed in the effect of unemployment rates on birth rates at the state level (Schneider and Hastings 2015). The available evidence supports the conclusion that the 2008 recession produced a large drop in fertility that did not recover before 2020 at least in part because the economic uncertainty it amplified has not receded – making it both a short-term and long-term event.
Birth rates recovered some for older women, however – over 30 or so – which is consistent with fertility delay. But this delay does not necessarily favor the opportunity cost versus economic constraint explanations. On one hand are people with higher levels of education (anticipated or realized) who plan to wait until their education is complete. On the other hand are those with less education who are most economically insecure, whose delays reflect navigating the challenges of relationship instability, housing, health care, childcare and other costs with lesser earning potential. This latter group may end up delaying either until they attain more security or until they face the prospect of running out of childbearing years. Both groups are deliberately delaying births partly for economic reasons, but the higher-education group is much more likely to have planned births while the latter have higher rates of unintended or mistimed births (Hayford and Guzzo 2016).
The opportunity cost of women’s childbearing, in classical models, is simply the earnings lost from time spent childrearing – the product of the hours of employment lost and the expected hourly wage (Cramer 1979). Although rising income potential for women has surely contributed to the long-run decline of fertility rates, in the U.S. that mechanism has not been determinative. Women experienced large increases in earnings for decades during which fertility rates did not fall. As the total fertility rate rose from its low point in 1976 (1.74) to the post-Baby Boom peak in 2007 (2.12) – defying the trend in many other high-income countries – the average weekly earnings of full-time working women ages 18-44 rose by 16% in constant dollars (Figure 5).
Figure 5. Median weekly earnings of full-time employed women ages 18-44, and total fertility rate. Source: Current Population Survey Annual Social and Economic Survey, and Human Fertility Database.
Clearly, other factors beyond lost earnings calculations are at work. However, there is no simple way to distinguish those who make direct cost comparisons, where investments in time and money take away from other needs and opportunities, from those who delay out of concern over future economic security, which weighs on people at all income levels and generates reluctance to make lifelong commitments (Pugh 2015). But the implications of these two effects are opposing. For people who don’t want to lose opportunities, a strong economy with abundant jobs implies lower fertility. For people who are afraid to commit to childrearing because of insecurity about their economic fortunes, a weak economy should decrease fertility. The experience of the post-2008 period provides strong evidence for the greater weight of the latter mechanism.
US births, 2000-2019
If opportunity costs were the primary consideration for women, one might expect an inverse relationship between job market growth and fertility rates: more jobs, fewer babies; fewer jobs, more babies. This is the pattern reported by Tavernise et al. (2021), who found that birthrates after the 2008 crisis fell more in counties with “growing labor markets” – which they attribute to the combination of improving opportunities for women and the high costs of childcare. However, their analysis did not attend to chronological ordering. They identified counties as having strong job growth if they were in the top quintile of counties for labor market percent change for the period 2007 to 2019, and compared them with counties in the bottom quintile of counties on the same measure with regard to birth rates (author correspondence). Thus, their analysis used a 2007-2019 summary measure to predict birth rates for each year from 1990 to 2019, making the results difficult to interpret.
In addition to using contemporaneous economic data, whereas Tavernise et al. (2021) used county-level birth rates, in this analysis I use individual characteristics and state-level data. I construct indicators of individual- and state-level relative advantage during the period before and after the 2008 economic crisis, from 2000 to 2019. Individual data are from the 2000-2019 American Community Survey (ACS) via IPUMS (Ruggles et al. 2021). I include in the analysis women ages 15-44, and use the fertility question, which asks whether they had a baby in the previous 12 months. I analyze this as a dichotomous dependent variable, using ordinary least squares regression. Results are graphed as marginal effects at the means, using Stata’s margins command. The sample size is 9,415,960 million women, 605,150 (6.4%) of whom had a baby in the previous year (multiple births are counted only once).
In models with controls, I control for age in five-year bins, race/ethnicity (White, Black, American Indian, Asian/Pacific Islander, Other/multiple-race, and Hispanic), citizenship (U.S.-born, born abroad to American parents, naturalized, and not a citizen), marital status (married, spouse absent, separated, divorced, widowed, and never married), education (less than high school, high school graduate, some college, and BA or higher degree), as well as (in some models) the state unemployment rate (lagged two years), and state fixed effects. State unemployment rates are from Local Area Unemployment Statistics (Bureau of Labor Statistics 2021). ACS person weights are used in all analyses.
For states, I use the unemployment rate in each state for each year, and divide the states at the median, so those with the median or higher unemployment for each year are coded as high unemployment states, and low unemployment otherwise (this variable is lagged two years, because the ACS asks whether each woman has had a birth in the previous 12 months, but does not specify the month of the birth, or the date of the interview). For individuals, the identification of economic advantage is difficult with the cross-sectional data I use here, because incomes are likely to fall in the year of a birth, and education may be determined endogenously with fertility as women age (Hartnett and Gemmill 2020), so income and education cannot simply be used to identify economic status. Instead, I identify women as low education if they have less than the median level of education for women of their age in their state for each year (using single years of age, and 26 categories of educational attainment), and high education otherwise. Thus, individual women in my sample are coded as in a high or low unemployment state relative to the rest of the country each year, and as having high or low education relative other women of their age and state and year. Using the ACS migration variable, I code women into the state they lived in the previous year, which is more likely to identify where they lived when they determined whether to have a baby (which also means I exclude women who were not living in the U.S. in the year before the survey).
Figure 6 shows the unadjusted probability of birth for women in high- and low-unemployment states for the period 2000-2019. This shows the drop in birth rates after 2008, which is steeper for women who live in high-unemployment states, especially before 2017. This is what we would expect from previous research on the 2008 financial crisis: a greater falloff in birth rates where the economy suffered more.
Figure 6. Probability of birth in the previous year: 2000-2019, by state unemployment relative to the national media (marginal effects at the means). Women ages 15-44. Based on state of residence in the previous year; unemployment lagged two years.
Next, I split the sample again by women’s own education relative to the median for those of the same age, year, and state. Those less than that median are coded as low education, those at or higher than the median are coded as high education. Figure 7 shows these results (again, unadjusted for control variables), showing that those with lower education (the top two lines) have higher birth rates throughout the period. After 2008, within both the high- and low-education groups, those in high-unemployment states had longer and steeper declines in birth rates (at least until 2019). The steepest decline is among low-education, high-unemployment women: those facing the greatest economic hardship at both the individual and state level. Finally, Figure 8 repeats the model shown in Figure 7, but with the control variables described above, and with state fixed effects. The pattern is very similar, but the differences associated with state unemployment are attenuated, especially for those with low education.
Figure 7. Probability of birth in the previous year: 2000-2019, by education relative to the age-state median, and state unemployment relative to the national media (marginal effects at the means). Women ages 15-44. Based on state of residence in the previous year; unemployment lagged two years.
Figure 8. Probability of birth in the previous year: 2000-2019, by education relative to the age-state median, and state unemployment relative to the national media, with controls for age, race/ethnicity, citizenship, marital status, and state fixed effects (marginal effects at the means). Women ages 15-44. Based on state of residence in the previous year; unemployment lagged two years.
Although birth rates fell for all four groups of women in this analysis after the 2008 recession, these results reflect that paradoxical nature of economic trends and birth rates. Women with higher education (and greater potential earnings) have lower birthrates, consistent with the opportunity cost reasoning described in Tavernise et al. (2021) and elsewhere. However, women in states with higher unemployment rates – especially when they have high relative education – also have lower birthrates, and in these states saw greater declines after the 2008 crisis. This is consistent with the evidence of negative effects of economic uncertainty and stress. And it goes against the suggestion that stronger job markets drive down fertility rates for women with higher earning potential, at least in the post-2008 period. In the long run, perhaps, economic opportunities reduce childbearing by increasing job market opportunities for potential mothers, but in recent years this effect has been swamped by the downward pressure of economic troubles. US birth rates fell further in 2020, apparently driven down by the COVID-19 pandemic, which raised uncertainty – and fear for the future – to new heights (Cohen 2021; Sobotka et al. 2021). We don’t yet know the breakdown of the shifts in fertility for that year, but if the effects were similar to those of the 2008 economic crisis, we would expect to see greater declines among those who were most vulnerable.
Beaujouan, Eva. 2020. “Latest-Late Fertility? Decline and Resurgence of Late Parenthood Across the Low-Fertility Countries.” Population and Development Review 46 (2): 219–47. https://doi.org/10.1111/padr.12334.
Brauner-Otto, Sarah R., and Claudia Geist. 2018. “Uncertainty, Doubts, and Delays: Economic Circumstances and Childbearing Expectations Among Emerging Adults.” Journal of Family and Economic Issues 39 (1): 88–102. https://doi.org/10.1007/s10834-017-9548-1.
Bureau of Labor Statistics. 2021. “States and Selected Areas: Employment Status of the Civilian Noninstitutional Population, January 1976 to Date, Seasonally Adjusted.” 2021. https://www.bls.gov/web/laus/ststdsadata.txt.
Cohen, Philip N. 2018. Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. Oakland, California: University of California Press.
Comolli, Chiara Ludovica. 2017. “The Fertility Response to the Great Recession in Europe and the United States: Structural Economic Conditions and Perceived Economic Uncertainty.” Demographic Research 36 (51): 1549–1600. https://doi.org/10.4054/DemRes.2017.36.51.
Cramer, James C. 1979. “Employment Trends Ofyoung Mothers and the Opportunity Cost of Babies in the United States.” Demography 16 (2): 177–97. https://doi.org/10.2307/2061137.
Currie, Janet, and Hannes Schwandt. 2014. “Short- and Long-Term Effects of Unemployment on Fertility.” Proceedings of the National Academy of Sciences 111 (41): 14734–39. https://doi.org/10.1073/pnas.1408975111.
Gaddy, Hampton Gray. 2021. “A Decade of TFR Declines Suggests No Relationship between Development and Sub-Replacement Fertility Rebounds.” Demographic Research 44 (5): 125–42. https://doi.org/10.4054/DemRes.2021.44.5.
Hayford, Sarah R., and Karen Benjamin Guzzo. 2016. “Fifty Years of Unintended Births: Education Gradients in Unintended Fertility in the US, 1960-2013.” Population and Development Review 42 (2): 313–41.
Herzer, Dierk, Holger Strulik, and Sebastian Vollmer. 2012. “The Long-Run Determinants of Fertility: One Century of Demographic Change 1900–1999.” Journal of Economic Growth 17 (4): 357–85. https://doi.org/10.1007/s10887-012-9085-6.
Myrskylä, Mikko, Hans-Peter Kohler, and Francesco C. Billari. 2009. “Advances in Development Reverse Fertility Declines.” Nature 460 (7256): 741–43. https://doi.org/10.1038/nature08230.
Pugh, Allison J. 2015. The Tumbleweed Society: Working and Caring in an Age of Insecurity. 1 edition. New York, NY: Oxford University Press.
Ruggles, Steven, Sarah Flood, Sophia Foster, Ronald Goeken, Jose Pacas, Megan Schouweiler, and Matthew Sobek. 2021. “IPUMS USA: Version 11.0 [Dataset].” 2021. doi.org/10.18128/D010.V11.0.
Schneider, Daniel. 2015. “The Great Recession, Fertility, and Uncertainty: Evidence From the United States.” Journal of Marriage and Family 77 (5): 1144–56. https://doi.org/10.1111/jomf.12212.
Schneider, Daniel, and Orestes P. Hastings. 2015. “Socioeconomic Variation in the Effect of Economic Conditions on Marriage and Nonmarital Fertility in the United States: Evidence From the Great Recession.” Demography 52 (6): 1893–1915. https://doi.org/10.1007/s13524-015-0437-7.
Sobotka, Tomas, Aiva Jasilioniene, Ainhoa Alustiza Galarza, Kryštof Zeman, Laszlo Nemeth, and Dmitri Jdanov. 2021. “Baby Bust in the Wake of the COVID-19 Pandemic? First Results from the New STFF Data Series.” SocArXiv. https://doi.org/10.31235/osf.io/mvy62.
An update from Pew, today’s thoughts, and then another data exercise.
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.
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.
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:
One linear cohort variable
My cluster categories
Decades of birth
Each year individually
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.
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.
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:
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.
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.
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.
Open letter to the Pew Research Center on generation labels
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.
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.
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.
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.
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.
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.
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.
Five years ago today I wrote a post called “Basic self promotion” on here. There has been a lot of work and advice on this subject in the intervening years (including books, some of which I reviewed here). So this is not as necessary as it was then. But it holds up pretty well, with some refreshing. So here is a lightly revised version. As always, happy to have your feedback and suggestions in the comments — including other things to read.
If you won’t make the effort to promote your research, how can you expect others to?
These are some basic thoughts for academics promoting their research. You don’t have to be a full-time self-promoter to improve your reach and impact, but the options are daunting and I often hear people say they don’t have time to do things like run a Twitter account or write for blogs and other publications. Even a relatively small effort, if well directed, can help a lot. Don’t let the perfect be the enemy of the good. It’s fine to do some things pretty well even if you can’t do everything to your ideal standard.
It’s all about making your research better — better quality, better impact. You want more people to read and appreciate your work, not just because you want fame and fortune, but because that’s what the work is for. I welcome your comments and suggestions below.
Make a decent personal website and keep it up to date with information about your research, including links to freely available copies of your publications (see below). It doesn’t have to be fancy. I’m often surprised at how many people are sitting behind years-old websites. (I recently engaged Brigid Barrett, who specializes in academics’ websites, to redesign mine.)
Very often people who come across your research somewhere else will want to know more about you before they share, report on, or even cite it. Your website gives your work more credibility. Has this person published other work in this area? Taught related courses? Gotten grants? These are things people look for. It’s not vain or obnoxious to present this information, it’s your job. I recommend a good quality photo, updated at least every five years.
Make your work available
Let people read the actual research. For work not yet “published” in journals, post drafts when they are ready for readers (a good time is when you are ready to send it to a conference or journal – or earlier if you are comfortable with sharing it). This helps you establish precedence (planting your flag), and allows it to generate feedback and attract readers. It’s best to use a disciplinary archive such as SocArXiv (which, as the director, I highly recommend) or your university repository, or both. This will improve how they show up in web searches (including Google Scholar) indexed for things like citation or grant analysis, and archived. You can also get a digital object identifier (DOI), which allows them to enter the great stream of research metadata. (See the SocArXiv FAQ for more answers.)
When you do publish in journals, prefer open-access journals because it’s the right thing to do and more people can read your work there. If a paper is paywalled, share a preprint or postprint version. On your website or social media feeds, please don’t just link to the pay-walled versions of your papers, that’s the click of death for someone just browsing around, plus it’s elitist and antisocial. You can almost always put up a preprint without violating your agreements (ideally you wouldn’t publish anywhere that won’t let you do this). To see the policies of different journals regarding self-archiving, check out the simple database at SHERPA/RoMEO, or, of course, the agreement you signed with the journal.
I oppose private sites like Academia.edu, ResearchGate, or SSRN. These are just private companies making a profit from doing what your university and its library, and nonprofits like SocArXiv are already doing for the public good. Your paper will not be discovered more if it is on one of these sites.
I’m not an open access purist, believe it or not. (If you got public money to develop a cure for cancer, that’s different, then I am a purist.) Not everything we write has to be open access (books, for example), but the more it is the better, especially original research. This is partly an equity issue for readers, and partly to establish trust and accountability in all of our work. Readers should be able to see our work product – our instruments, our code, our data – to evaluate its veracity (and to benefit their own work). And for the vast majority of readers who don’t want to get into those materials, the fact they are there increases our collective accountability and trustworthiness. I recommend using the Open Science Framework, a free, nonprofit platform for research sharing and collaboration.
Actively share your work
In the old days we used to order paper reprints of papers we published and literally mail them to the famous and important people we hoped would read and cite them. Nowadays you can email them a PDF. Sending a short note that says, “I thought you might be interested in this paper I wrote” is normal, reasonable, and may be considered flattering. (As long as you don’t follow up with repeated emails asking if they’ve read it yet.)
If you’re reading this, you probably use at least basic social media. If not, I recommend it. This does not require a massive time commitment and doesn’t mean you have to spend all day doomscrolling — you can always ignore them. Setting up a public profile on Twitter or a page on Facebook gives people who do use them all the time a way to link to you and share your profile. If someone wants to show their friends one of my papers on Twitter, this doesn’t require any effort on my part. They tweet, “Look at this awesome new paper @familyunequal wrote!” (I have some vague memory of this happening with my papers.) When people click on the link they go to my profile, which tells them who I am and links to my website.
Of course, a more active social media presence does help draw people into your work, which leads to exchanging information and perspectives, getting and giving feedback, supporting and learning from others, and so on. Ideally. But even low-level attention will help: posting or tweeting links to new papers, conference presentations, other writing, etc. No need to get into snarky chitchat and following hundreds of people if you don’t want to. To see how sociologists are using Twitter, you can visit the list I maintain, which has more than 1600 sociologists. This is useful for comparing profile and feed styles.
People who write popular books go on book tours to promote them. People who write minor articles in sociology journals might send out some tweets, or share them with their friends on Facebook. In between are lots of other places you can write something to help people find and learn about your work. I still recommend a blog format, easily associated with your website, but this can be done different ways. As with publications themselves, there are public and private options, open and paywalled. Open is better, but some opportunities are too good to pass up – and it’s OK to support publications that charge subscription or access fees, if they deserve it.
There are also good organizations now that help people get their work out. In my area, for example, the Council on Contemporary Families is great (I’m a former board member), producing research briefs related to new publications, and helping to bring them to the attention of journalists and editors. Others work with the Scholars Strategy Network, which helps people place Op-Eds, or the university-affiliated site The Society Pages, or others. In addition, there are blogs run by sections of the academic associations, and various group blogs. And there is Contexts (which I used to co-edit), the general interest magazine of ASA, where they would love to hear proposals for how you can bring your research out into the open (for the magazine or their blog).
For more on the system we use to get our work evaluated, published, transmitted, and archived, I’ve written this report: Scholarly Communication in Sociology: An introduction to scholarly communication for sociology, intended to help sociologists in their careers, while advancing an inclusive, open, equitable, and sustainable scholarly knowledge ecosystem.
[Update: California released revised birth numbers, which added a trivial number to previous months, except December, where they added a few thousand, so now the state has a 10% decline for the month, relative to 2019. I hadn’t seen a revision that large before.]
Lots of people are talking about falling birth rates — even more than they were before. First a data snapshot, then a link roundup.
For US states, we have numbers through December for Arizona, California, Florida, Hawaii, and Ohio. They are all showing substantial declines in birth rates from previous years. Most dramatically, California just posted December numbers, and revised the numbers from earlier months, now showing a 19% 10% drop in December. After adding about 500 births to November and a few to October, the drop in those two months is now 9%. The state’s overall drop for the year is now 6.2%. These are, to put it mildly, very larges declines in historical terms. Even if California adds 500 to December later, it will still be down 18%. Yikes. One thing we don’t yet know is how much of this is driven by people moving around, rather than just changes in birth rates. California in 2019 had more people leaving the state (before the pandemic) than before, and presumably there have been essentially no international immigrants in 2020. Hawaii also has some “birth tourism”, which probably didn’t happen in 2020, and has had a bad year for tourism generally. So much remains to be learned.
Here are the state trends (figure updated Feb 18):
From the few non-US places that I’m getting monthly data so far, the trend is not so dramatic. Although British Columbia posted a steep drop in December. I don’t know why I keep hoping Scotland will settle down their numbers… (updated Feb 18):
Here are some recent items from elsewhere on this topic:
Laura Lindberg in Ms.: The Coming COVID Baby Bust. “Past patterns and emerging evidence suggest we are going to see a COVID Baby Bust. But how long will it last and how big will it be?”
That led to some local TV, including this from KARE11 in Minneapolis:
Good news / bad news clarification
There’s an unfortunate piece of editing in the NBCLX piece, where I’m quoted like this: “Well, this is a bad situation. [cut] The declines we’re seeing now are pretty substantial.” To clarify — and I said this in the interview, but accidents happen — I am not saying the decline in births is a bad situation, I’m saying the pandemic is a bad situation, which is causing a decline in births. Unfortunately, this has slipped. As when the Independentquoted the piece (without talking to me) and said, “Speaking to the outlet, Philip Cohen, a sociologist and demographer at the University of Maryland, called the decline a ‘bad situation’.”
The data for this project is available here: osf.io/pvz3g/. You’re free to use it.
For more on fertility decline, including whether it’s good or bad, and where it might be going, follow the fertility tag.
Acknowledgement: We have lots of good conversation about this on Twitter, where there is great demography going on. Also, Lisa Carlson, a graduate student at Bowling Green State University, who works in the National Center for Family and Marriage Research, pointed me toward some of this state data, which I appreciate.
In the Atlantic article, “The Rise of the Three-Parent Family” I was quoted saying, “the increasing visibility and legalization of three-parent arrangements ‘is one of the signs that our definition of family is opening up.'”
That led to an interview with a different journalist. I recorded my end of the interview, and re-enact it here as a five-minute commentary. Could be suitable for a class discussion.