Pandemic Baby Bust situation update

[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):

births 18-20 state small multiple by month

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):

births countries 18-20 small multiple by month

Here are some recent items from elsewhere on this topic:

  • 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 Independent quoted 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.

Family Demography Seminar syllabus, 2021 edition

PN Cohen photo: https://flic.kr/p/2jw1ZhA.

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. 

February 3

Theoretical perspectives in demography

Bianchi, Suzanne M. 2014. “A Demographic Perspective on Family Change.” Journal of Family Theory & Review 6 (1): 35–44. https://doi.org/10.1111/jftr.12029. (preprint: http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4465124&blobtype=pdf).

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.

February 24

U.S. History

Ruggles. Steven. 2015. “Patriarchy, Power, and Pay: The Transformation of American Families, 1800-2015.” Demography 52: 1797-1823. (His lecture version at PAA.)

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.

Goldscheider, Frances, Eva Bernhardt, and Trude Lappegard. 2015. “The Gender Revolution: A Framework for Understanding Changing Family and Demographic Behavior.” Population and Development Review 41 (2): 207–+. doi:10.1111/j.1728-4457.2015.00045.x.

Smock, Pamela J., and Christine R. Schwartz. 2020. “The Demography of Families: A Review of Patterns and Change.” Journal of Marriage and Family 82(1):9–34. doi: 10.1111/jomf.12612. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jomf.12612.

March 10

Pandemic fertility

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. doi:10.1073/pnas.1408975111.

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/

March 17

Spring Break

March 24

COVID-19 and race/ethnic inequality

Sweeney, Megan M., and R. Kelly Raley. 2014. “Race, Ethnicity, and the Changing Context of Childbearing in the United States.” Annual Review of Sociology 40:539–58.

Hardy, Bradley L., and Trevon D. Logan. 2020. “Racial Economic Inequality amid the COVID-19 Crisis.” The Hamilton Project, Essay 17:2020. https://www.brookings.edu/wp-content/uploads/2020/08/EA_HardyLogan_LO_8.12.pdf

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/

April 7 [SECOND PAPER DUE]

Divorce

Kennedy, Sheela, and Steven Ruggles. 2014. “Breaking Up Is Hard to Count: The Rise of Divorce in the United States, 1980–2010.” Demography 51 (2): 587–98.

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.

April 14

Policy, race, and nonmarital births

England, Paula. 2016. “Sometimes the Social Becomes Personal: Gender, Class, and Sexualities.” American Sociological Review 81 (1): 4–28.

Cohen, Philip N. 2015. “Maternal Age and Infant Mortality for White, Black, and Mexican Mothers in the United States.” Sociological Science 3 (January): 32–38.

Geronimus, Arline T. 2003. “Damned If You Do: Culture, Identity, Privilege, and Teenage Childbearing in the United States.” Social Science & Medicine 57 (5): 881–93.

Cohen, Philip N. 2018. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage promotion [Excerpts]” 24pp. [to be provided]

Smith, Imari Z., Keisha L. Bentley-Edwards, Salimah El-Amin and William Darity, Jr. “Fighting at Birth: Eradicating the Black-White Infant Mortality Gap.” Samuel DuBois Cook Center on Social Equity and Insight Center for Community Economic Development. https://socialequity.duke.edu/wp-content/uploads/2019/12/Eradicating-Black-Infant-Mortality-March-2018.pdf

April 21

More U.S. inequality issues

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.

April 28

Family structure and child wellbeing

Regnerus, Mark. 2012. “How Different Are the Adult Children of Parents Who Have Same-Sex Relationships? Findings from the New Family Structures Study.” Social Science Research 41 (4): 752–70. doi:10.1016/j.ssresearch.2012.03.009.

Rosenfeld, Michael J. 2015. “Revisiting the Data from the New Family Structure Study: Taking Family Instability into Account.” Sociological Science 2 (September): 478–501. doi:10.15195/v2.a23.

Cohen, Philip N. 2018. Enduring Bonds: Families and Modern Inequality, Chapter: “Marriage equality in social science and the courts.” 19pp. [to be provided]

Perkins, Kristin L. 2019. “Changes in Household Composition and Children’s Educational Attainment.” Demography, January. https://doi.org/10.1007/s13524-018-0757-5.

May 5 [THIRD PAPER DUE]

Maternal mortality

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.

McMillan Cottom, Tressie. 2019. “Why Are Pregnant Black Women Viewed as Incompetent?” Time. January 8, 2019. http://time.com/5494404/tressie-mcmillan-cottom-thick-pregnancy-competent/.

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.

For reference: World Health Organization. 2014. “Trends in maternal mortality: 1990 to 2013. Estimates by WHO, UNICEF, UNFPA, The World Bank and the United Nations Population Division.” http://documents.worldbank.org/curated/en/937281468338969369/pdf/879050PUB0Tren00Box385214B00PUBLIC0.pdf.

COVID-19 mortality rates by race/ethnicity and age

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/.

COVID-19 Baby Bust update and data

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.

Are pandemic effects on birth rates already detectable?

As birth data approaches, maybe we can get beyond analyses like Google searches for pregnancy-related terms to see what’s happening with birth rates.

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.

Early pandemic demographic indicators

A couple new ones and a couple updates.

Pregnancy

The pandemic could be affecting the number of abortions, miscarriages, or infant deaths, but unless those effects are large it should be too early to see effects on the total birth rate, given that we’re only about 7 months into it here. So for possible birth indicators I did a little Google search analysis using the public Google Trends data.

I found three searches that were pretty well correlated in the weekly series: “am I pregnant”, “pregnancy test,” and “morning sickness”, which all should have something to do with the frequency of new pregnancies. I ran Google Trends back five years, created an index from these searches (alpha = .68) , smoothed it a little, and this is what I got:

There was already a big drop in 2019 from the previous three years (reasonable, based on recent trends), and then 2020 started out with a further drop. But then it spiked downward in March before rebounding back to its lower level. So, maybe that implies birth rates will keep falling but not off the charts compared with recent trends.

I also checked “missed period,” which was not well correlated with the others, and got this:

Again, 2019 was already showing some decline, and 2020 started out lower than that, and now searches for “missed period” are running lower than last year, but not more in the middle of the year than they were in the beginning. So, inconclusive for pandemic effect.

Weddings

Here’s a new take on the Google trends for weddings. I took the averages of searches for “wedding invitations”, “wedding shower”, “bridal shower”, “wedding shower”, and “wedding dresses” (alpha=.94). With a little smoothing, here is 2020 compared with the average of the four previous years (unlike pregnancy searches, this one didn’t show a marked decline in 2019 compared with previous years).

March and April showed catastrophic declines in searches for wedding topics, and the rebound so far has been weak. However, weddings aren’t the same as marriages. Maybe people who had to cancel their weddings still got married down at whatever the pandemic equivalent of the courthouse is. So here’s the same analysis just for the search term “marriage license.” This shows a steep but not as catastrophic drop-off in March and April, and a stronger rebound. So maybe the decline in drop in marriages won’t be as big as the drop in weddings.

Actual marriages

I previously showed the steep decline in recorded marriages in Florida. Here’s an update.

Florida lists recorded marriages by county and month, one month behind (see Table 17). They update as they go, so as of today August marriages are probably still not all recorded. The comparison with previous years shows a collapse in March and April, and then some rebounding. August is preliminary and will come up some.

Marriages in Florida normally peak between March and May. Of course it’s too early to say how many of these were just being postponed. The cumulative trend shows that through July Florida is down 24,000 marriages, or 27%, compared with last year.

Divorce ideation

When the going gets tough, the afflicted want to get divorced, but maybe they can’t. It’s expensive and time consuming and maybe people think it will upset the children even more. (I’ve written about divorce and recessions here and here). So my initial assumption going into the pandemic was that there would be a stall in divorces even though the intent to divorce would rise, followed by a rebound when people get a chance to act on their wishes.

Here I use Google search trends for four searches: “divorce lawyer”, “divorce attorney”, “get a divorce”, and “how to divorce”. The alpha for this index is .69 (when I just use the attorney and lawyer, the alpha is .86, but the result looks the same, so I’m showing the wider index). The results show a drop in divorce ideation in March into April, followed by a rebound to a level a little above the previous year average. Note this pandemic-spring drop is a lot less pronounced than the wedding and marriage collapses above.

Actual divorces

Divorces take time, of course. Like births, I wouldn’t expect to see definitive results right away. In fact, it’s hard to know how long divorces are in process before they show up as recorded. However, in my favorite real-time demography state, Florida, they have been recording divorces every month, and have a look at this:

It’s a giant plunge in recorded divorces, almost 60% in April, followed by a weaker rebound. Again, the records are not yet complete, especially for August, so we’ll see. But comparing these patterns, it might be that there was a short suspension in divorce ideation as people were distracted by the crisis, followed by a rebound which hasn’t yet translated into divorce filings. Googling about divorce seems cheap and easy (and faster) compared with pulling it off, but this might mean there is growing pent up demand for divorce, which is bad (and may imply greater risks of conflict and violence).

Young adults living “at home”

I previous wrote about young adults living with their parents and grandparents using the June and then July Current Population Survey data made available by IPUMS.org. Subsequently, the Pew Research Center did something very similar using the data through July (with additional breakdowns and historical context). Pew used living with parents, apparently including those in households where the parents are not the householders. I prefer my definition — young adults living in the home of parents (also, or grandparents) — which fits better with the popular concept of living “at home.” So if your parents come to live with you, that’s different.

Anyway, here’s the update through August, which shows the percentage of young adults living at home falling back some from the June peak. I will be very interested to follow this through the fall.

Update: By October this trend had returned almost back to pre-pandemic levels:

Stata code for the living at home analysis is available here: https://osf.io/2xrhc/.

The pandemic and its attendant economic crisis is having massive effects on many aspects of family life. These early indicators are just possible targets of future analysis. There is a lot of other related work going on, which I’ve not taken the time to link to here. Please feel free to recommend other work in the comments.

Demographic facts your students need to know right now (with COVID-19 addendum)

20200808-DSC_4900
PN Cohen photo / Flickr CC: https://flic.kr/p/2jw6stF

Here’s the 2020 update of a series I started in 2013. This year, after the basic facts, I’ll add some pandemic facts below.

Is it true that “facts are useless in an emergency“? I guess we’ll find out this year. Knowing basic demographic facts, and how to do arithmetic, lets us ballpark the claims we are exposed to all the time. 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).

It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements like, “The U.S. economy lost a record 20.5 million jobs in April“?

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.

These 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 264 million and 396 million!).

This is only a few dozen facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). They 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:

Number Source
World Population 7.7 billion 1
U.S. Population 330 million 1
Children under 18 as share of pop. 22% 2
Adults 65+ as share of pop. 17% 2
Official unemployment rate (July 2020) 10% 3
Unemployment rate range, 1970-2018 3.9% – 15% 3
Labor force participation rate, age 16+ 61% 9
Labor force participation rate range, 1970-2017 60% – 67% 9
Non-Hispanic Whites as share of pop. 60% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 19% 2
Asians / Pacific Islanders as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 14% 2
Adults age 25+ with BA or higher 32% 2
Median household income $60,300 2
Total poverty rate 12% 8
Child poverty rate 16% 8
Poverty rate age 65+ 10% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 327 million 5
4th most populous country, Indonesia 261 million 5
5th most populous country, Brazil 207 million 5
U.S. male life expectancy at birth 76 6
U.S. female life expectancy at birth 81 6
Life expectancy range across countries 51 – 85 7
World total fertility rate 2.4 10
U.S. total fertility rate 1.7 10
Total fertility rate range across countries 1.0 – 6.9 10

Sources

1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

10. World Bank


COVID-19 Addendum: 21 more facts

The pandemic is changing everything. A lot of the numbers above may look different next year. Here are 21 basic pandemic facts to keep in mind — again, the point is to get a sense of scale, to inform your consumption of the daily flow of information (and disinformation). These are changing, too, but they are current as of August 31, 2020.

Global confirmed COVID-19 cases: 25 million

Confirmed US COVID-19 cases: 6 million

Second most COVID-19 cases: Brazil, 3.9 million

Third most COVID-19 cases: India, 3.6 million

Global confirmed COVID-19 deaths: 850,000

Confirmed US COVID-19 deaths: 183,000

Second most COVID-19 deaths: Brazil, 121, 000

Third most COVID-19 deaths: India: 65,000

Percent of U.S. COVID patients who have died: 3%

COVID-19 deaths per 100,000 Americans: 50

COVID-19 deaths per 100,000 non-Hispanic Whites: 43

COVID-19 deaths per 100,000 Blacks: 81

COVID-19 deaths per 100,000 Hispanics: 55

COVID-19 deaths per 100,000 Americans over age 65: 400

Annual deaths in the U.S. (these are for 2017): Total, 2.8 million

Leading cause of death: Heart disease, 650,000

Second leading cause: Cancer: 600,000

Third leading cause: Accidents: 160,000

Deaths from flu and pneumonia, 56,000

Deaths from suicide: 47,000

Deaths from homicide: 20,000


Sources

COVID-19 country data: Johns Hopkins University Coronavirus Resource Center

U.S. cause of death data: Centers for Disease Control

U.S. age and race/ethnicity COVID-19 death data: Centers for Disease Control

 

 

Race/ethnic intermarriage trends, 2008-2018

Rising, with gender differences.

Since 2008 the American Community Survey has been asking respondents whether they got married in the previous 12 months. Using the race/ethnicity of spouses (when they are living together), you can estimate the proportion of new marriages that cross racial/ethnic lines.

Defining such “intermarriage” is not as simple as it sounds. Some people have multiple racial or ethnic identities. Some people marry across national-origin lines within panethnic groups (e.g., Mexicans marrying Puerto Ricans). Is a Black+White Dominican marrying a White Mexican, or a Black+White person marrying a Black person, “intermarriage”? In these estimates I drop people who are not Hispanic and specified more than one race, then combine Hispanic origin and race into one, mutually exclusive 5-category variable: White, Black, American Indian, Asian/Pacific Islander, Hispanic (of any race). In other words, intrapanethic marriage (Mexicans marrying Puerto Ricans, or Filipinos marrying Koreans) is not intermarriage. I’m not saying this is the best way; it combines conventional categories with convenience. I combine same-sex and different-sex marriages.

To present the results, I separate men and women (you’ll see why), and estimate predicted probabilities of intermarriage at the mean of controls for age and age-squared, using logistic regression with normalized weights. My Stata code is on the Open Science Framework; help yourself. (I previously did something very similar for states and metro areas.)

The results are in figures, with each race/ethnic group presented on its own scale (check the y-axes). I don’t present American Indians because the samples are small (about half the API sample) and the multirace group is large.

Results

Click the images to enlarge

white intermarriageblack intermarriage

api intermarriage

hispanic intermarriage

Why there are 3.1 million extra young adults living at home

Answer: The COVID-19 pandemic.

UPDATE: A new post updates this analysis for July 2020

Catherine Rampell tweeted a link to a Zillow analysis showing 2.2 million adults ages 18-25 moving in with their parents or grandparents in March and April. Zillow’s Treh Manhertz estimates these move-homers would cost the rental market the better part of a billion dollars, or 1.4% of total rent if they stay home for a year.

We now have the June Current Population Survey data to work with, so I extended this forward, and did it differently. CPS is the large, monthly survey that the Census Bureau conducts for the Bureau of Labor Statistics each month, principally to track labor market trends. It also includes basic demographics and living arrangement information. Here is what I came up with.*

Among people ages 18-29, there is a large spike of living in the home of a parent or grandparent (of themselves or their spouse), which I’ll call “living at home” for short. This is apparent in a figure that compares 2020 with the previous 5 years (click figures to enlarge):

six year trends

From February to April, the percentage of young adults living at home jumped from 43% to 48%, and then up to 49% in June. Clearly, this is anomalous. (I ran it back to 2008 just to make sure there were no similar jumps around the time of the last recession; in earlier years the rates were lower and there were no similar spikes.) This is a very large disturbance in the Force of Family Demography.

To get a better sense of the magnitude of this event, I modeled it by age, sex, and race/ethnicity. Here are the estimated share of adults living at home by age and sex. For this I use just June of each year, and compare 2020 with the pooled set of 2017-2019. This controls for race/ethnicity.

men and women

The biggest increase is among 21-year-old men and 20-year-old women, and women under 22 generally. These may be people coming home from college, losing their jobs or apartments, canceling their weddings, or coming home to help.

I ran the same models but broke out race/ethnicity instead (for just White, Black, and Latino, as the samples get small).

white black latino

This shows that the 2020 bounce is greatest for Black young adults (below age 24) and the levels are lowest for Latinos (remember that many Latinos are immigrants whose parents and grandparents don’t live in the US).

To show the total race/ethnic and gender pattern, here are the predicted levels of living at home, controlling for age:

raceth-gender

The biggest 2020 bounce is among Black men and women, with Black men having the highest overall levels, 58%, and White women having the lowest at 44%.

In conclusion, millions of young adults are living with their parents and grandparents who would not be if 2020 were like previous years. The effect is most pronounced among Black young adults. Future research will have to determine which of the many possible disruptions to their lives is driving this event.

For scale, there are 51 million (non-institutionalized) adults ages 18-29 in the country. If 2020 was like the previous three years, I would expect there to be 22.2 million of them living with their parents. Instead there are 25.4 million living at home, an increase of 3.1 million from the expected number (numbers updated for June 2020). That is a lot of rent not being spent, but even with that cost savings I don’t think this is good news.


* The IPUMS codebook, Stata code, spreadsheet, and figures are in an Open Science Framework project under CC0 license here: osf.io/2xrhc.

How big will the drop in weddings be? Big

With data snapshot addendum at the end.

In the short run, people are canceling their weddings that were already booked, or not planning the ones they were going to have this summer or fall. In the long run, we don’t know.

To look at the short run effect, I used Google Trends to extract the level of traffic for five searches over the last five years: wedding dressesbridal shower, wedding licensewedding shower, and wedding invitations (here is the link to one, just change the terms to get the others). These are things you Google when you’re getting married. Google reports search volume for each term weekly, scaled from 0 to 100.

Search traffic for these terms is highly correlated with each other across weeks, between .45 and .76. I used Stata to combine them into an index (alpha = .92), which ranges from 22 to 87 for 261 weeks, from May 2015 to last week.

For the graph, I smoothed the trend with a 5-week average. Here is the trend, with dates for the peaks and troughs (click to enlarge):

wedding plans searches.xlsx

The annual pattern is very strong. Each year people people do a lot of wedding searches for about two months, from mid-January to early-March, before traffic falls for the rest of year, until after Thanksgiving. There is a decline over these five years, but I don’t put too much stock in that because maybe the terms people use are changing over time.

But this year there is a break. After starting out with a normal spike in mid-January, searches lurched downward into February, and then collapsed to their lowest level in five years — at what should have been the height of the wedding Google search season.

Clearly, there will be a decline in weddings this spring and summer, or until we “reopen,” whatever that means. A lot of people just can’t get married. When you think about the timing of marriage, most people getting married in a given year are probably already planning to at least half a year in advance. So even if no one’s relationships are affected, and their long term plans don’t change, we’ll still see a decline in marriages this year just from canceled plans.

Beyond that, however, people probably aren’t meeting and falling in love as much. People who are dating probably aren’t as likely to advance their relationships through what would have been a normal development – dating, love, kids, marriage, and so on. So a lot of existing relationships – even for people who weren’t engaged – probably aren’t moving toward marriage. Even if they get back on track later, that’s a delay of a year or two or however long. This says nothing about people being stressed, miserable, sick (or worse), and otherwise not in any kind of mood.

In the longer term, what does the pandemic mean for confidence in the future? The crisis will undermine people’s ability to make long term decisions and commitments. Unless the cultural or cognitive model of marriage changes, insecurity or instability will mean less marriage in the future. This could be a long term effect even after the acute period passes.

What about a rebound? Eventually – again, whenever that is – there probably will be some rebound. At least, just practically, some people who put off marriage will go ahead and do it later. Although, as with delayed births, some postponed marriages probably will end up being foregone. On a larger scale, when people can get out and get together and get married again, there might well be a marriage bounce (and also even a baby boom). Presumably that would depend on a very positive scenario: a vaccine, an economic resurgence, maybe a big government boost, like after WWII. A surge in optimism about the future, happiness. That’s all possible. This also depends on the cultural model of marriage we have now, so that good times equals more marriage (and childbearing). In real life, any such effect might be small, dwarfed by big declines from chaos, fear, and uncertainty. I can’t predict how these different impulses might play against each other. However, on balance, my out-on-a-limb forecast is a decline in marriage.

kissing sailor

Data snapshot addendum

I didn’t realize there was monthly data available already. For example, in Florida they release monthly marriage counts by county, and they have released the April numbers. These show a 1% increase in marriages year-over-year in January, a 31% increase in February, then a 31% drop in March and a 72% drop in April [Since I first posted this, Florida added 477 more marriages in April, and a few in the earlier months, changing these percentages by a couple points as on June 5. -pnc.] Here is a scatter plot [updated] showing the count of marriages by county in 2019 and 2020. Counties below the diagonal have fewer marriages in 2020 than they did in 2019. Not surprising, but still dramatic to see it happening in “real time” (not really, just in quickly available data).

florida marriages.xlsx