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

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

Here’s the abstract:

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

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

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

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

Earlier updates and media reports are here.

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.

Host, parasite, and failure at the colony level: COVID-19 and the US information ecosystem

Trump campaign attempts to remove satirical cartoon from online retailer | Comics and graphic novels | The Guardian

This cartoon is offensive. And yet.


A few months ago I did some reading about viruses and other parasites, inspired by the obvious, but also those ants that get commandeered by cordyceps fungi, as seen in this awesome Richard Attenborough video:

Besides the incredible feat of programming ants to disseminate fungus spores, the video reveals two other astounding facts about this system. First, worker ants from afflicted colonies selflessly identify and remove infected ants and dump their bodies far away, reflecting intergenerational genetic training as well as the ability to gather and process the information necessary to make the diagnosis and act on it. And second, there are many, many cordyceps species, each evolved to prey upon only one species, reflecting a pattern of co-evolution between host and parasite.

This led me to reading about colony defenses in general, including not just ants but things like wasps and termites that leave chemical protection for future generations, and bees getting together to make hive fevers to ward off parasitic infections. I don’t find a video of exactly a hive fever, but this one is similar: It’s bees using their collective body temperature to cook a predatory hornet to death:

Incredible. That got me thinking about how information management and dissemination is vital to colony-level defenses against parasites. They need to process and transmit information to work together in the arms race against parasites (especially viruses) that usually evolve much more rapidly than they do.

And you may know where this is going: How the US failed against SARS-CoV-2. In an information arms-race, life and death struggle against a parasitic virus that mutates exponentially faster than we can react — who knows how many experimental trials it took to design SARS-CoV-2? — this kind of efficient information system is what we need. And it worked in some ways, as humanity identified the virus and shared the data and code necessary to take action against it. But clearly we failed in other ways — communicating with our fellow citizens, dislodging the disinformation and misinformation that clouded their understanding and led so many to sacrifice themselves at the behest of a corrupt political organization and its demented leader.

Is this social evolution, I asked (despairingly), in which the Chinese system of government proves its superiority for survival at the colony level, while the US democratic system chokes on its own infected lungs. Worse, is the virus programming us to exacerbate our own weaknesses — yanking our social media chains and our slavery-era political institutions, like the rabies virus, which infects the brain and then explodes out through the salivary glands of a zombified attack animal. Colonies of ants rise or fall based on how they respond to parasites, which themselves are evolving to control ant behavior, as they evolve together. How exceptional are humans? Maybe we just do it faster, in social evolutionary time, rather than across many generations of breeding. Fascinating, but kind of dark. lol.

Anyway, naturally my concern is with information systems and scholarly communication. How human success against the virus has come from the rapid generation and dissemination of science and public health information (including preprints and data sharing). And failure came from disinformation and information corruption. Dr. Birx in the role the rabid raccoon, watching herself lose her grip on scientific reality as the authoritarian leader douses the public health information system with bleach and sets it on fire with an ultraviolet ray gun “inside the body.”

So I wrote a short paper titled, “Host, parasite, and failure at the colony level: COVID-19 and the US information ecosystem,” and posted it on SocArXiv: socarxiv.org/4hgam.* It includes this table:

hpit2


* I barely took high school biology. In college I took “Climate and Man,” and “Biology of Human Affairs.” That’s pretty much it for my life sciences training, so don’t take my word for it. Comments welcome.

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.

New COVID-19 and Health Disparities lecture

I recorded a new version of the lecture I created last spring: COVID-19 and Health Disparities. It defines health disparities, introduces the theory of fundamental causes, and then describes COVID-19 disparities by race/ethnicity and age with reference to education and occupational inequality. For intro sociology students.

Using data from Bureau of Labor Statistics (inspired by this piece from Justin Fox), I showed the percentage of workers working at home according to the median wage in their occupations, illustrating how people in lower-paid occupations aren’t working at home, while professionals and managers are:

And, using age- and race/ethnic-specific mortality rates from CDC, with population denominators from the 2018 ACS (I don’t know why I can’t find the denominators CDC uses), I made this:

The greatest race/ethnic disparities are in the working ages, which suggests they are driven at least partly by occupational inequality.

The lecture 23 minutes, slides with references and links are here.

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