After reviewing a paper for JAMA Network Open I was invited to write a comment about it. The paper is here, reporting a large drop in the percentage of mothers who are planning or thinking about having another child in a sample from New York City in mid-2020. After summarizing the results, I wrote this:
Before the COVID-19 pandemic, the US was in a period of declining fertility following the 2008 financial crisis and subsequent recession—a decline that was linked to economic precarity and hardship . Then, in 2020, the total number of US births decreased 3.8%, which was the largest annual decline on a percentage basis since the early 1970s. The decreases were steeper at the end of the year, −6% in November and −8% in December, compared with 2019 . In some large states with public monthly reports (California, Florida, and Ohio), it appears that January and February 2021 had fewer births still, with some recovery in the months that followed . This timing suggests a direct association with the onset of the pandemic and closures that began in the spring of 2020. The evidence presented by Kahn and colleagues  supports this interpretation and suggests that when people faced the uncertainty and hardships associated with the pandemic, one common response was to pull back from plans to add children to their families. Future research will examine whether family decision-making in more advantaged families was similarly affected.
The current evidence concerns shifts in pregnancy planning. However, in the US, a substantial portion of births results from unintended or mistimed pregnancies, and these are concentrated among disadvantaged women . The inability to predict, much less control, the trajectory of their lives leads many women to postpone the lifelong commitments implied by intentional births, but also makes unintentional pregnancy more likely. How the pandemic may have affected such births is not yet known. If mobility restrictions, unemployment, illness, care work burdens, and social distancing all reduced social interaction, coupled with increased motivation to prevent pregnancy, we may suspect unintended births will have declined as well.
The impacts of the pandemic within and between families points to the complex interrelationships among family structure, health disparities, and social inequality in the US . The COVID-19 pandemic has been an inequality-exacerbating event on a large scale, widening existing health disparities, especially along the lines of socioeconomic status, race, and ethnicity. Excess mortality among Black and Hispanic populations in 2020, directly and indirectly related to the pandemic, far outstripped that seen among non-Hispanic White populations and contributed to the decrease in overall US life expectancy that exceeded that seen in peer countries . In light of disparate impacts of COVID-19 itself and the social and economic fallout of the pandemic, research should concentrate on widening inequalities in fertility and family well-being, and their relationship to health disparities.
Corresponding Author: Philip N. Cohen, PhD, Maryland Population Research Center, Department of Sociology, University of Maryland, Parren J. Mitchell Art Sociology Building, College Park, MD 20742 (email@example.com).
Conflict of Interest Disclosures: None reported.
Kahn LG, Trasande L, Liu M, Mehta-Lee SS, Brubaker SG, Jacobson MH. Factors associated with changes in pregnancy intention among women who were mothers of young children in New York City following the COVID-19 outbreak. JAMA Netw Open. 2021;4(9):e2124273. doi:10.1001/jamanetworkopen.2021.24273
Seltzer N. Beyond the great recession: labor market polarization and ongoing fertility decline in the United States. Demography. 2019;56(4):1463-1493. doi:10.1007/s13524-019-00790-6
Cohen PN. Baby bust: falling fertility in US counties is associated with COVID-19 prevalence and mobility reductions. SocArXiv, March 17, 2021. doi:10.31235/osf.io/qwxz3
Hartnett CS, Gemmill A. Recent trends in US childbearing intentions. Demography. 2020;57(6):2035-2045. doi:10.1007/s13524-020-00929-w
Thomeer MB, Yahirun J, Colón-López A. How families matter for health inequality during the COVID-19 pandemic. J Fam Theory Rev. 2020;12(4):448-463. doi:10.1111/jftr.12398
Woolf SH, Masters RK, Aron LY. Effect of the covid-19 pandemic in 2020 on life expectancy across populations in the USA and other high income countries: simulations of provisional mortality data. BMJ. 2021;373(n1343):n1343. doi:10.1136/bmj.n1343
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.
Here’s the 2021 update of a series I started in 2013. A few pandemic-specific facts below.
If anyone tells you that “facts are useless in an emergency,” give them a bad grade. Knowing basic demographic facts lets us run a quick temperature check on the pot we’re slowly boiling in — which we need to survive. The idea is to get your radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it (and tell the same stories over and over).
This year, in pursuit of this mission, I created the Demographic Fact A Day Twitter account, which started tweeting one fact per day at the start of 2021. Some of these are more advanced, some very simple. Here’s a figure from that account, for a taste:
Everyone likes a number that appears to support their perspective. But that’s no way to run (or change) a society. The trick is to know the facts before you create or evaluate an argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.
The list below are demographic facts you need just to get through the day without being grossly misled or misinformed — or, in the case of journalists or teachers or social scientists, not to allow your audience to be grossly misled or misinformed. Not trivia that makes a point or statistics that are shocking, but the non-sensational information you need to make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 266 million and 400 million!).
This is only a few dozen facts, not exhaustive but they belong on any top-100 list. This year, many of the most important facts are about the pandemic, but they’re not included here — these are some of what you need to understand the upheavals of the day. Feel free to add additional facts in the comments (as per policy, first-time commenters are moderated).
The numbers are rounded to reasonable units for easy memorization. All refer to the US unless otherwise noted. Most of the links will take you to the latest data:
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.
CNN’s Dr. Sanjay Gupta has a podcast called Chasing Life about coming out of the pandemic. Associate producer Grace Walker interviewed me for an episode titled, “Let’s Talk About Making Babies (Or Deciding Not To).” In it reporter Chloe Melas starts with the story of a Black couple (two women, one of them trans) seeking to have children. At about minute 21, she turns to the fertility decline in the US. The transcript of that part is below. This episode would be good for teaching.
Chloe Melas: But we can’t forget – not everyone wants to have children. And that’s OK. According to the CDC, the number of births in the United States fell by 4% last year – the largest annual decline since 1973. Given the global pandemic, for demographers like Philip Cohen of the University of Maryland, this isn’t too surprising.
Philip Cohen: What we’ve learned in the last century or so is that when there are crises birth rates go down. It’s partly deliberate, that is, people decide to hold off on having children, or decide against having children, because they’re unsure about the future, they’re unsure they’ll be able to care for them, they think they might lose their job, they think their mother might lose her job – all the things that go into the calculations of when and whether to have children.
CM: 2020 is not an outlier. Cohen says birthrates have been on a downward trend for quite a while.
PNC: We were sort of focusing on issues like work-family balance, childcare, healthcare, housing, the expenses of raising children, and the difficulty of raising children, which had been putting pressure on people to reduce their number of children. That’s the main reason. At the same time, when people have more opportunities to do other things in their lives, they’re also inclined to have fewer children, or delay having children. So especially for women, when opportunities improve, the number of children they have tends to go down, because on average they’re more likely to choose something else.
CM: Hispanic women in particular are seeing some of the largest declines. From 2007 to 2017 birth rates fell by 31%. Experts attribute this drop to more Hispanic women joining the workforce, and waiting longer to start families than previous generations. Overall, the data doesn’t lie. Fewer people are having kids. That could lead to smaller kindergarten classrooms, as well as larger demands on Social Security, given the aging population. But Cohen and others think there could be positives, too. For example, fewer people means less of an environmental impact on the planet. So it’s really a glass half empty, glass half full kind of situation. The point is, I think this pandemic has really made many of us reflect on what we want our future to look like, including our future families. Some have been inspired to freeze their eggs, some to seek out help for infertility, and some have decided against having kids while others have been inspired to do so.
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.
Micah Altman and I have written a paper using the new Open Editors dataset from Andreas Pacher, Tamara Heck, and Kerstin Schoch. They scraped up data on almost half a million editors (editors in chief, editors, editorial board member) at more than 6000 journals from 17 publishers (most of the big ones; they’ve since added some more). Micah and I genderized them (fuzzily), geolocated them in countries, and then coded the journals as either open access or not (using the Directory of Open Access Journals), and according to whether they practice transparency in research (using the Transparency and Openness Promotion signatories). After just basic curiosity about diversity, we wondered whether those that practice open access and research transparency have better gender and international diversity.
The results show overwhelming US and European dominance, not surprisingly. And male dominance, which is more extreme among editors in chief, across all disciplines. Open access journals are a little less gender diverse, and transparency-practicing journals a little more internationally diverse, but those relationships aren’t strong. There are other differences by discipline. A network analysis shows not much overlap between journals, outside of a few giant clusters (which might indicate questionable practices) although it’s hard to say for sure — journals should really use ORCIDs for their editors. Kudos to Micah for doing the heavy lifting on the coding, which involved multiple levels of cleaning and recoding (and for making the R markdown file for the whole thing available).
Lots of details in the draft, here. Feedback welcome!
Data from the Social Security Administration show that the names Kobe and Gianna had the greatest increase in popularity of any names in the country in 2020; as Kobe boys increased from 499 to 1500 and Gianna girls from 3408 to 7826. Kobe Bryant and his daughter Gianna died in a helicopter crash on January 26 last year, one of the dramatic national news events eclipsed by the pandemic (George Floyd’s daughter, now 7 years old, is also named Gianna).
The Kobe count of 1500 was surpassed only in 2001, during his first run of NBA championships, but the number per 1000 births was higher in 2020. Here is the trend:
And the Gianna trend, with a similar increase off a much higher base. Gianna became the 12th most common name given to girls in 2020.
Other news from the pandemic year in naming
Besides Gianna, not much change in the top 20 names, by gender, as Olivia, Emma, Liam, and Noah continued their dominance. Most of the top 20 names declined in popularity last year.
Outside the top names, the biggest drop in percentage terms (among those with at least 1000 births) was Alexa, who fell another 36%, from 1995 to 1272. Alexa has had a historically catastrophic decline since Amazon gave the name to its robot shopping companion (discussed last year).
Finally, Mary remains dormant, with 2188 girls getting the name in 2020, a drop of 21 from 2209. I told the story of Mary going back to the Revolutionary War on this blog and in Enduring Bonds. Still ripe for a comeback (jinx). Here’s an updated Figure 1:
The Social Security Data and Stata code for this analysis is here under CC0 license: osf.io/m48qc/. Note SSA updates their denominators every year; I have a file of those in here too.