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:
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
Abstract
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.
Introduction
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).
U.S. recessions
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.
Discussion
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.
References
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.
———. 2021. “Baby Bust: Falling Fertility in US Counties Is Associated with COVID-19 Prevalence and Mobility Reductions.” SocArXiv. https://doi.org/10.31235/osf.io/qwxz3.
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.
Hartnett, Caroline Sten, and Alison Gemmill. 2020. “Recent Trends in U.S. Childbearing Intentions.” Demography 57 (6): 2035–45. https://doi.org/10.1007/s13524-020-00929-w.
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.
Seltzer, Nathan. 2019. “Beyond the Great Recession: Labor Market Polarization and Ongoing Fertility Decline in the United States.” Demography 56 (4): 1463–93. https://doi.org/10.1007/s13524-019-00790-6.
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.
Having spent a few months collecting data on birth rates over the last year, and a few months pouring over pandemic data, I took the time to bring the two together and assess the relationship between some basic pandemic indicators and the latest fertility outcomes. The result is a short paper I titled, “Baby Bust: Falling Fertility in US Counties Is Associated with COVID-19 Prevalence and Mobility Reductions,” now available on SocArXiv, with links to the data and Stata code for replication.
Here’s the abstract:
The United States experienced a 3.8 percent decline in births for 2020 compared with 2019, but the rate of decline was much faster at the end of the year (8 percent in December), suggesting dramatic early effects of the COVID-19 pandemic, which began affecting social life in late March 2020. Using birth data from Florida and Ohio counties through February 2021, this analysis examines whether and how much falling birth rates were associated with local pandemic conditions, specifically infection rates and reductions in geographic mobility. Results show that the vast majority of counties experienced declining births, suggestive of a general influence of the pandemic, but also that declines were steeper in places with greater prevalence of COVID-19 infections and more extensive reductions in mobility. The latter result is consistent with more direct influences of the pandemic on family planning or sexual behavior. The idea that social isolation would cause an increase in subsequent births receives no support.
Here’s the main result in graphic form, showing that births fell more in January/February in those counties with more COVID-19 cases, and those with more mobility limitation (as measured by Google), through the end of last May:
However, note also that births fell almost everywhere (87% of the population lives in a fertility-falling county), so it didn’t take a high case count or shutdown to produce the effect.
There will be a lot more research on all this to come, I just wanted to get this out to help establish a few basic findings and motivate more research. I’d love your feedback or suggestions.
My YouTube career may have peaked in 2015, with the now-classic video, Total Fertility Rate, which has been viewed almost 30,000 times (124 likes!). Since then the technical quality has improved, but not the viewership.
Recently I heard someone say (sorry, I can’t remember who) that they were looking for a short video explaining what life expectancy is. This was after the CDC reported that US life expectancy in the first half of 2020 decreased by 1 year, which generated some confusion. Outside of secondary effects, the pandemic did reduce life “expectancy” for people it didn’t kill, and here we are (still alive, so far) reading about it, so how could our life expectancy have been affected? did last year’s deaths mean people would live less long in the future? I said somewhere on twitter than “life expectancy” is a bad name for this common statistic, and I think it is. I don’t have a better name for it, though, and it’s probably to late to change anyway.
So, to help meet the current need, and to try to reach my past video glory, yesterday I produced, “What is life expectancy?“, a 6-minute explainer, using 3 graphs, to help people understand. I didn’t discuss the recent COVID results so as not to date the video, and I hope it will be useful in the future (that it has a long life expectancy).
I was considering assigning the students in my Family Demography seminar to watch the documentary, One Child Nation: The Truth Behind the Propaganda, so I watched it. The movies uses the tragic family history of one of the directors, Nanfu Wang, to tell the story of the Chinese birth planning policy that began in 1979 and extended through many modifications until 2015. Nothing against watching it, but it’s not good. The one-child policy wasn’t good either, of course, leading to many violations of human rights and a lot of suffering and death.
Before watching the movie, I’m glad I read the review by Susan Greenhalgh, an anthropologist who spent about 25 years studying the one-child policy and related questions (summarized in three books and many articles, here). It’s short and you should read it, but just to summarize a couple of key historical points:
The policy was “the cornerstone of a massively complex and consequential state project to modernize China’s population,” and can’t be understood in the context of birth control alone.
Many people opposed and resisted the policy, but reducing birth rates was a commonly-understood goal, for both gender equality and economic development, and many women were glad the government supported them in that effort. The “vast majority” felt “deep ambivalence” about the policy, weighing individual desires against the perceived need to sacrifice for the common good.
The policy was unevenly applied and enforced (it was especially harsh in the provinces featured in the film), and after 1993 enforcement became less egregious.
Exceptions were added starting in the early 1980s, until by the late 1990s the majority of the population was not subject to a one-child rule.
There are some other specific errors and distortions, including the dramatic, incorrect claim that “the one-child policy [was] written into China’s constitution” in 1982 (as Greenhalgh writes, “the 1982 Constitution says only: “both husbands and wives are duty-bound to practice birth planning”). And the decision to translate all uses of the term “birth planning” as “one-child policy.” That said, the stories of forced abortions, sterilizations, and infanticide are wrenching and ring true.
I have two things to add to Greenhalgh’s review. First, a simple data illustration to show that China, really, is not a “one-child nation.” Using Chinese census data, here is the total number of children (by age 35-39) born to three groups of Chinese women, arranged according to their ages in 1980, about when the one-child policy began.
The shift left shows the decline in number of children born: the mean fell from 3.8 to 2.5 to 1.8 in these data. (Measuring Chinese fertility is complicated, but the census provides a reasonable ballpark.) But the main thing I want to show is that among the last group — those who were beginning their childbearing years when the policy took effect — 61% had two or more children. The idea that China became a “one-child nation” under the policy is false.
Second, the movie takes a hard turn in the middle and focuses on international adoption, and the illegal trafficking of mostly second-born girls to orphanages that sought to place them abroad. This was a very serious problem. But the movie tells the story of the most notorious scandal (for which many people served jail terms) as if it were the common practice, and centers on the savior-like behavior of American activists helping adopted children trace their familial roots. Granting that of course that corruption was terrible, and that the motivations of many (some?) adoptive parents (including me) were good, from China’s point of view it’s not a central story in the history of the one-child policy. As the movie notes, 130,000 Chinese children were adopted abroad during the period, during which time hundreds of millions were born.
Greenhalgh summarizes on this point, calling the film a:
“familiar coercion narrative, complete with villain (the state), victims (rural enforcers and targets), and savior (an American couple offering DNA services to match adopted girls in the U.S. with birth parents in China). The characters (at least the victims and saviors) have some emotional complexity, but they still play the stock roles in an oft-told tale. For American viewers, this narrative is comforting, because it provides a simple, morally clear way to react to troubling developments unfolding in a faraway, little understood land. And by using China (communist, state-controlled childbearing) as a foil for the U.S. (liberal, relative reproductive freedom), the film leaves us feeling smug about the assumed superiority of our own system.”
The many centuries of Chinese patriarchy are a dark part of the human story, and in some ways is unique. For example — relevant to this recent histyory — female infanticide and selling girls has a long history (a history that includes foot binding and other atrocities). The Chinese Communist Party, for all its misdeeds, did not create this problem. Gender inequality in China, including the decline in fertility — which was mostly accomplished before 1979 — has markedly improved since 1949. Greenhalgh concludes: “In China, before the state began managing childbearing, reproductive decisions were made by the patriarchal family. Since the shift to a two-child policy, they have been subject to the strong if indirect control of market forces. One form of control may be preferable to another, but freedom over our bodies is an illusion.”
[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.
This week it’s back to teaching Family Demography, a graduate seminar in the sociology department. This year a majority of the students are from other departments around campus, and of course the whole thing will be online. So we’ll see! I added a few weeks of pandemic related readings. And some things I never read before. Feel free to follow along. Feedback welcome.
This is the schedule, with readings. A lot of them are paywalled, I’m sorry to say, but you might have access to them. (You can always try sci-hub, which has stolen most academic articles for you, so you don’t have to steal them yourself.)
Family Demography
January 27
Introduction
Cohen, Philip N. 2021. “The Pandemic and The Family.” Supplement to The Family: Diversity, Inequality, and Social Change (3e). New York: W. W. Norton & Company.
Sigle, Wendy. 2016. “Why Demography Needs (New) Theories.” In Changing Family Dynamics and Demographic Evolution: The Family Kaleidoscope, edited by Dimitri Mortelmans, Koenraad Matthijs, Elisabeth Alofs, and Barbara Segaert. Cheltenham, UK: Edward Elgar Publishing. http://eprints.lse.ac.uk/86429/1/Sigle_Demography%20needs%20theories_2018.pdf.
Cohen, Philip N. 2021. The Family: Diversity, Inequality, and Social Change (3e). New York: W. W. Norton & Company. Chapter 1, “A Sociology of the Family.”
February 10
Demographic transition
Thornton, Arland. 2001. “The Developmental Paradigm, Reading History Sideways, and Family Change.” Demography 38 (4): 449–65. https://doi.org/10.2307/3088311.
Bongaarts, John. 2009. “Human Population Growth and the Demographic Transition.” Philosophical Transactions of the Royal Society B-Biological Sciences 364(1532):2985–90. 10.1098/rstb.2009.0137.
Pande, Rohini Prabha, Sophie Namy, and Anju Malhotra. 2020. “The Demographic Transition and Women’s Economic Participation in Tamil Nadu, India: A Historical Case Study.” Feminist Economics 26(1):179–207. https://umd.instructure.com/files/60782517/
February 17
Second demographic transition
Sassler, Sharon, and Daniel T. Lichter. 2020. “Cohabitation and Marriage: Complexity and Diversity in Union-Formation Patterns.” Journal of Marriage and Family 82(1):35–61. https://doi.org/10.1111/jomf.12617.
Cohen, Philip N. 2011. “Homogamy Unmodified.” Journal of Family Theory & Review 3 (1): 47–51.
Schneider, Daniel, Kristen Harknett, and Matthew Stimpson. 2018. “What Explains the Decline in First Marriage in the United States? Evidence from the Panel Study of Income Dynamics, 1969 to 2013.” Journal of Marriage and Family 80(4):791–811. https://doi.org/10.1111/jomf.12481.
Zaidi, Batool, and S. Philip Morgan. 2017. “The Second Demographic Transition Theory: A Review and Appraisal.” Annual Review of Sociology 43(1):473–92. https://10.1146/annurev-soc-060116-053442.
Bloome, Deirdre, and Christopher Muller. 2015. “Tenancy and African American Marriage in the Postbellum South.” Demography 52 (5): 1409–30. https://doi.org/10.1007/s13524-015-0414-1.
Cherlin, Andrew J. 2020. “Degrees of Change: An Assessment of the Deinstitutionalization of Marriage Thesis.” Journal of Marriage and Family 82(1):62–80. https://doi.org/10.1111/jomf.12605.
Cohen, Philip N. 2021. The Family: Diversity, Inequality, and Social Change (3e). New York: W. W. Norton & Company. Chapter 2, “History.”
March 3 [FIRST PAPER DUE]
U.S. Today
Guzzo, Karen Benjamin, and Sarah R. Hayford. 2020. “Pathways to Parenthood in Social and Family Contexts: Decade in Review, 2020.” Journal of Marriage and Family 82(1):117–44. https://doi.org/10.1111/jomf.12618.
Luppi, Francesca, Bruno Arpino, and Alessandro Rosina. 2020. “The Impact of COVID-19 on Fertility Plans in Italy, Germany, France, Spain, and the United Kingdom.” Demographic Research 43(47):1399–1412. doi: 10.4054/DemRes.2020.43.47.
Wilde, Joshua, Wei Chen, and Sophie Lohmann. 2020. COVID-19 and the Future of US Fertility: What Can We Learn from Google?Working Paper. 13776. IZA Discussion Papers. https://www.econstor.eu/handle/10419/227303
Wagner, Sander, Felix C. Tropf, Nicolo Cavalli, and Melinda C. Mills. 2020. “Pandemics, Public Health Interventions and Fertility: Evidence from the 1918 Influenza.” https://osf.io/preprints/socarxiv/f3hv8/
Vargas, Edward D., and Gabriel R. Sanchez. 2020. “COVID-19 Is Having a Devastating Impact on the Economic Well-Being of Latino Families.” Journal of Economics, Race, and Policy 3(4):262–69. 10.1007/s41996-020-00071-0.
Snowden, Lonnie R., and Genevieve Graaf. 2021. “COVID-19, Social Determinants Past, Present, and Future, and African Americans’ Health.” Journal of Racial and Ethnic Health Disparities 8(1):12–20. 10.1007/s40615-020-00923-3.
Reinhart, Eric, and Daniel L. Chen. 2020. “Incarceration and Its Disseminations: COVID-19 Pandemic Lessons From Chicago’s Cook County Jail.” Health Affairs 39(8):1412–18. 10.1377/hlthaff.2020.00652.
March 31
China and fertility policy
Bongaarts, John, and Christophe Z. Guilmoto. 2015. “How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050.” Population and Development Review 41 (2): 241–69. doi:10.1111/j.1728-4457.2015.00046.x.
Wang Feng, Baochang Gu, and Yong Cai. 2016. “The End of China’s One-Child Policy.” Studies in Family Planning 47 (1): 83–86. doi:10.1111/j.1728-4465.2016.00052.x.
Shen, Ke, Feng Wang, and Yong Cai. 2020. “Government Policy and Global Fertility Change: A Reappraisal.” Asian Population Studies 16(2):145–66. https://umd.instructure.com/files/60812839/
Wang, Feng. 2017. “Is Rapid Fertility Decline Possible? Lessons from Asia and Emerging Countries.” Pp. 435–51 in Africa’s population: In search of a demographic dividend. Springer. https://umd.instructure.com/files/60848754/
Cohen, Philip N. 2019. “The Coming Divorce Decline.” Socius 5:2378023119873497. 10.1177/2378023119873497.
Raley, R. Kelly, and Megan M. Sweeney. 2020. “Divorce, Repartnering, and Stepfamilies: A Decade in Review.” Journal of Marriage and Family 82(1):81–99. https://doi.org/10.1111/jomf.12651.
Brady, David, Ryan M. Finnigan, and Sabine Hübgen. 2017. “Rethinking the Risks of Poverty: A Framework for Analyzing Prevalences and Penalties.” American Journal of Sociology 123 (3): 740–86. https://doi.org/10.1086/693678.
Enns, Peter K., Youngmin Yi, Megan Comfort, Alyssa W. Goldman, Hedwig Lee, Christopher Muller, Sara Wakefield, Emily A. Wang, and Christopher Wildeman. 2019. “What Percentage of Americans Have Ever Had a Family Member Incarcerated?: Evidence from the Family History of Incarceration Survey (FamHIS).” Socius 5:2378023119829332. doi: 10.1177/2378023119829332.
Cooper, Marianne, and Allison J. Pugh. 2020. “Families Across the Income Spectrum: A Decade in Review.” Journal of Marriage and Family 82(1):272–99. https://doi.org/10.1111/jomf.12623.
Cohen, Philip N. 2018. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage equality in social science and the courts.” 19pp. [to be provided]
MacDorman, Marian F., Eugene Declercq, and Marie E. Thoma. 2017. “Trends in Maternal Mortality by Socio-Demographic Characteristics and Cause of Death in 27 States and the District of Columbia.” Obstetrics and Gynecology 129 (5): 811–18. https://doi.org/10.1097/AOG.0000000000001968.
MacDorman, Marian F., Eugene Declercq, and Marie E. Thoma. 2018. “Trends in Texas Maternal Mortality by Maternal Age, Race/Ethnicity, and Cause of Death, 2006-2015.” Birth 45 (2): 169–77. https://doi.org/10.1111/birt.12330.
Molina, Rose L., and Lydia E. Pace. 2017. “A Renewed Focus on Maternal Health in the United States.” New England Journal of Medicine 377 (18): 1705–7. https://doi.org/10.1056/NEJMp1709473.
Why are there such great disparities in COVID-19 deaths across race/ethnic groups in the U.S.? Here’s a recent review from New York City:
The racial/ethnic disparities in COVID-related mortality may be explained by increased risk of disease because of difficulty engaging in social distancing because of crowding and occupation, and increased disease severity because of reduced access to health care, delay in seeking care, or receipt of care in low-resourced settings. Another explanation may be the higher rates of hypertension, diabetes, obesity, and chronic kidney disease among Black and Hispanic populations, all of which worsen outcomes. The role of comorbidity in explaining racial/ethnic disparities in hospitalization and mortality has been investigated in only 1 study, which did not include Hispanic patients. Although poverty, low educational attainment, and residence in areas with high densities of Black and Hispanic populations are associated with higher hospitalizations and COVID-19–related deaths in NYC, the effect of neighborhood socioeconomic status on likelihood of hospitalization, severity of illness, and death is unknown. COVID-19–related outcomes in Asian patients have also been incompletely explored.
The analysis, interestingly, found that Black and Hispanic patients in New York City, once hospitalized, were less likely to die than White patients were. Lots of complicated issues here, but some combination of exposure through conditions of work, transportation, and residence; existing health conditions; and access to and quality of care. My question is more basic, though: What are the age-specific mortality rates by race/ethnicity?
Start tangent on why age-specific comparisons are important. In demography, breaking things down by age is a basic first-pass statistical control. Age isn’t inherently the most important variable, but (1) so many things are so strongly affected by age, (2) so many groups differ greatly in their age compositions, and (3) age is so straightforward to measure, that it’s often the most reasonable first cut when comparison groups. Very frequently we find that a simple comparison is reversed when age is controlled. Consider a classic example: mortality in a richer country (USA) versus a poorer country (Jordan). People in the USA live four years longer, on average, but Americans are more than twice as likely to die each year (9 per 1,000 versus 4 per 1000). The difference is age: 23% of Americans are over age 60, compared with 6% of Jordanians. More old people means more total deaths, but compare within age groups and Americans are less likely to die. A simple separation by age facilitates more meaningful comparison for most purposes. So that’s how I want to compare COVID-19 mortality across race/ethnic groups in the USA. End tangent.
Age-specific mortality rates
It seems like this should be easier, but I can’t find anyone who is publishing them on an ongoing basis. The Centers for Disease Control posts a weekly data file of COVID-19 deaths by age and race/ethnicity, but they do not include the population denominators that you need to calculate mortality rates. So, for example, it tells you that as of December 5 there have been 2,937 COVID-19 deaths among non-Hispanic Blacks in the age range 30-49, compared with 2,186 deaths among non-Hispanic Whites of the same age. So, a higher count of Black deaths. But it doesn’t tell you there are 4.3-times as many Whites as Blacks in that category. So a much higher mortality rate.
On a different page, they report the percentage of all deaths in each age range that have occurred in each race/ethnic group, don’t include their percentage in the population. So, for example, 36% of the people ages 30-39 who have died from COVID-19 were Hispanic, and 24% were non-Hispanic White, but that’s not enough information to calculate mortality rates either. I have no reason to think this is nefarious, but it’s clearly not adequate.
So I went to the 2019 American Community Survey (ACS) data distributed by IPUMS.org to get some denominators. These are a little messy for two main reasons. First, ACS is a survey that asks people what their race and ethnicity are, while death counts are based on death certificates, for which the person who has died is not available to ask. So some people will be identified with a different group when they die than they would if they were surveyed. Second, the ACS and other surveys allow people to specify multiple races (in addition to being Hispanic or not), whereas death certificate data generally does not. So if someone who identifies as Black-and-White on a survey dies, how will the death certificate read? (If you’re very interested, here’s a report on the accuracy of death certificates, and here are the “bridges” they use to try to mash up multiple-race and single-race categories.)
My solution to this is make denominators more or less the way race/ethnicity was defined before multiple race identification was allowed. I put all Hispanic people, regardless of race, into the Hispanic group. Then I put people who are White, non-Hispanic, and no other race into the White category. And then for the Black, Asian, and American Indian categories, I include people who were multiple race (and not Hispanic). So, for example, a Black-White non-Hispanic person is counted as Black. A Black-Asian non-Hispanic person is counted as both Black and Asian. Note I did also do the calculations for Native Hawaiian and Other Pacific Islanders, but those numbers are very small so I’m not showing them on the graph; they’re on the spreadsheet. Note also I say “American Indian” to include all those who are “non-Hispanic American Indian or Alaska Native.”
This is admittedly crude, but I suggest that you trust me that it’s probably OK. (Probably OK, that is, especially for Whites, Blacks, and Hispanics. American Indians and Asians have higher rates of multiple-race identification among the living, so I expect there would be more slippage there.)
Anyway, here’s the absolutely egregious result:
This figure allows race/ethnicity comparisons within the five age groups (under 30 isn’t shown). It reveals that the greatest age-specific disparities are actually at the younger ages. In the range 30-49, Blacks are 5.6-times more likely to die, and Hispanics are 6.6-times more likely to die, than non-Hispanic Whites are. In the oldest age group, over 85, where death rates for everyone are highest, the disparities are only 1.5- and 1.4-to-1 respectively.
Whatever the cause of these disparities, this is just the bottom line, which matters. Please note how very high these rates are at old ages. These are deaths per 100,000, which means that over age 85, 1.8% of all African Americans have died of COVID-19 this year (and 1.7% for Hispanics and 1.2% for Whites). That is — I keep trying to find words to convey the power of these numbers — one out of every 56 African Americans over age 85.
Please stay home if you can.
A spreadsheet file with the data, calculations, and figure, is here: https://osf.io/ewrms/.
Joe Pinsker at the Atlantic has a piece out on the coming (probable) baby bust. In it he reviews existing evidence for a coming decline in births as a result of the pandemic, especially including historical comparisons and Google search data. Could we see this already?
Pinsker writes:
The baby bust isn’t expected to begin in earnest until December. And it could take a bit longer than that, Sarah Hayford, a sociologist at Ohio State University, told me, if parents-to-be didn’t adjust their plans in response to the pandemic immediately back in March, when its duration wasn’t widely apparent.
If people immediately changed their plans in February, we might see a decline in births in October, but Hayford is right that’s early. And what about September, for which I’ve already observed declining births in Florida and California? If people who were pregnant already in January had miscarriages or abortions because of the pandemic, that would result in fewer births in September, but how big could that effect be? So maybe the Florida and California data are flukes, or data errors, or lots of pregnant people left those states and gave birth elsewhere (or pregnant people who normally come didn’t arrive). Perhaps more likely is that 2020 was already going to be a down year. As I told Pinsker:
“It might actually be that we were already heading for a record drop in births this year … If that’s the case, then birth rates in 2021 are probably going to be even more shockingly low.”
Anyway, we’ll find out soon enough. And to that end I’ve started assembling a dataset of monthly births where I can find them, which so far includes Florida, California, Oregon, Arizona, North Carolina, Ohio, Hawaii, Sweden, Finland, Scotland, and the Netherlands, to varying degrees of timeliness. As of today we have October data for some of them:
As of now Florida and California remain the strongest cases for a pandemic effect. But they are also both likely to add some more births to October (in November’s report, California increased the September number by 3%).
Anyway, lots of speculation while we’re killing time. You can get the little dataset here on the Open Science Framework: https://osf.io/pvz3g/. Check the date on the .csv or .xlsx file to see what I last updated it. I’ll add more countries or states if I find out about them.
At this writing we are a few days shy of 35 weeks from February 1st. If I read this right, 10% of US births occur at 36 weeks of gestation or less. But the most recent complete data I see is from August, so it’s early. However, most fertilized human eggs do not come to term, being lost either before (30%) or after (30-40%) implantation. That’s from a paper by Jenna Nobles and Amar Hamoudi, who write:
Evidence suggests that multiple mechanisms may be involved in pregnancy survival, including those that affect placental development and function, fetal oxidative stress, fetal neurological development, and likely many others. These, in turn, are shaped by more distal processes that affect maternal nutrition, maternal exposure to biological and psychosocial stress, maternal exposure to infection, and management of chronic conditions. Pregnancy survival varies with women’s body mass index, consumption of folic acid, and in some studies, reports of stressful life events (citations removed).
The pandemic might reasonably have contributed to a higher rate of pregnancy loss from these factors. And then there are abortions, which people have probably needed more even though they had less access to them (see this report from Guttmacher). So the net effect is unclear.
Setting aside how the pandemic might have affected fertility intentions and planning (I assume this is negative, as reported by Guttmacher), there might already be fewer births, from loss and abortion.
I haven’t looked at every state, but Florida and California report births by month. In Florida, there were 9.5% fewer babies born in August 2020 than in the previous year (they revise these as they go, but the August number has been stable for a little while, so probably won’t increase much). In California there were 9.6% fewer births in August of this year compared with last year. Here are the monthly trends, including the last three years (I included Florida’s September number as of today, but that will certainly rise):
This is going to be tricky because birth rates were already falling in many places. But the average decline in the last three years was 2.9% in California and 0.7% in Florida, so these numbers clearly outpace that naïve expectation. Also, what about spring? Maybe the pandemic was already causing a decline in live births in California in March (from immigrants not coming or staying in Mexico or other countries?), but if the decline in March was unrelated, then it’s not clear how to interpret the drop in August. So it will be complicated. But this is a bona fide blip in the expected direction, so I’m posting it with a question mark.
I assume other people will be way ahead of me on this, though I haven’t seen anything. Feel free to post other analyses in the comments.
