Tag Archives: demography

Demographic facts your students should know cold

Here’s an update of a series I started in 2013, and updated in 2016.

Is it true that “facts are useless in an emergency“? Depends how you define emergency I guess. I used to have a little justification for why we need to know demographic facts, as “the building blocks of first-line debunking.” It’s facts plus arithmetic that let us ballpark the claims we are exposed to all the time. The idea was to get our 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.

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 such as Trump’s “I’ve created over a million jobs since I’m president”? (The U.S. population grew by about 1.3 million between the 2016 election and the day he said that; CNN has a jobs tracker.)

What’s a number for? Lots of people disparage the nitpickers when they find something wrong with the numbers going around. But everyone likes a number that appears to support their argument. The trick is to know the facts before you know the 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.

facts-cartoon

Here’s the list of current 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 260 million and 390 million!).

This is only 25 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:

Fact Number Source
World Population 7.4 billion 1
US Population 326 million 1
Children under 18 as share of pop. 23% 2
Adults 65+ as share of pop. 15% 2
Official unemployment rate 4.3% 3
Unemployment rate range, 1970-2017 4% – 11% 4
Labor force participation rate, age 16+ 63% 9
Labor force participation rate range, 1970-2015 60% – 67% 9
Non-Hispanic Whites as share of pop. 61% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 18% 2
Asians as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 13% 2
Adults age 25+ with BA or higher 30% 2
Median household income $54,000 2
Total poverty rate 14% 8
Child poverty rate 20% 8
Poverty rate age 65+ 9% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 324 million 5
4th most populous country, Indonesia 258 million 5
5th most populous country, Brazil 206 million 5
Male life expectancy at birth 76 6
Female life expectancy at birth 81 6
National life expectancy range 50 – 85 7

Sources:
1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

4. Google public data: http://bit.ly/UVmeS3

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

Handy one-page PDF: Demographic Facts You Need to Know 2017

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Made in America, by immigrants: children

Immigrants make a lot of great things in the USA, like communities and ideas and political organizations. And they also make American children. So for Made in America Week, a quick look at children born in the U.S. whose parents were not. That is, children made in America by immigrants.

For this table I used the American Community Survey, made available by IPUMS, and selected children ages 0-17 who live with two parents. Then I narrowed that group down to those for whom both parents were born in one of the top 20 countries (or regions), from those listed in the birthplace variable (described here), including the USA. The table shows the birthplace of mother and father (same-sex parent couples are excluded). The blue outer band shows the children who have at least one US-born parent. The green diagonal shows the number of children with two parents who immigrated from the same country. For the rest, the colors highlight larger cells, growing darker as cells surpass 1000, 5000, and 10,000. I’ll mention a few below.

You’ll have to click to enlarge:

Children made in America by immigrants

The green cells are the largest in each row and column, except the blue US-born-parent cells. In most cases the green cell is larger than the blue ones — for example, there are 3.5 million U.S. born children who live with two Mexican-born parents, outnumbering the 950,000 who have a Mexican-born father and U.S.-born mother, and 650,000 in the reverse case. But in some cases the green cell is very small, for example England, as there are more than 100,000 children with one England-born and one U.S.-born parent, but only 4,000 who have two England-born parents.

In other cases there are big gender differences reflecting migration and marriage patterns. So there are 10,000 children with a Chinese-born mother and Vietnamese-born father, but only 6,000 of the reverse. Also, in the case of Asia parents, there are more U.S.-born kids with Asian-born mothers and U.S.-born fathers than the reverse, presumably reflecting the greater tendency of Asian women to marry White men (this doesn’t apply to Laos and India).

Anyway, happy Made in America week.

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A step toward civilization (and have more children), Shanghai edition

Over the course of two weeks in China, I saw several versions of signs like this:

IMG_1319

“A small step forward, a big step for civilization” (向前一小步, 文明一大步).

This one is posted in the old-town section of Nanxun (now a tourist attraction), naturally, above a urinal.* Invoking civilization may be overblown for the problem of men standing too far away (which didn’t seem to be especially extreme, compared to U.S. urinals), but China has a long tradition of using dramatic slogans to call citizens to higher common purpose. Here was one that struck me, in downtown Shanghai:

20170619-DSC_0587

Every family striving to become a civilized family; everyone involved in its creation (家家争做文明家庭; 人人叁与创建活动).

This is from the Shanghai public health authorities. (No, I don’t know Chinese, but I love trying to use a dictionary, and I ask people.) The fascinating thing about that is the composition of the civilized family pictured: father, mother, two grandparents, and two children. 

Fertility rates in China are well below replacement level, as they are in other East Asian countries, meaning the average woman will have fewer than two children in her lifetime and the population will eventually shrink (barring immigration). China’s total fertility rate nationally is probably at about 1.5. In Shanghai, a metro area with some 20 million people, the norm was already one child per family before the one-child policy was implemented in 1980, and fertility has continued to fall; it most recently clocked in at a shockingly low .88 per woman as of 2008.

Reasons for ultra-low fertility are varied and contested, but likely culprits include expensive housing and education costs for children. It was reported to me informally that about half of children can go to college-track high schools instead of vocational schools, and that is determined by a standardized test administered at the end of middle school. That puts tremendous pressure on parents with middle-class aspirations. Which helps explain the extensive system of expensive supplemental private education, as promoted by this ad I saw in an upscale mall:

IMG_1100

School advertisement, Shanghai

The website for this company promises, “Super IQ, Wealth of Creativity, Instant Memory Capacity.” How many kids are you going to send to this private program?

One of the five perfect, super-involved parents at the parent-child class is a man, which may or may not seem like a lot. Of the many people taking their kids to school on scooters, I didn’t see a lot with more than one child, and the only picture I got was of one piloted by the apparent dad (note also something you don’t see here much: schoolboy in pink shirt):

IMG_1092

Man taking children to school, Shanghai

This recalls another probable cause of low-low fertility, the gender-stuck family and employment practices that keep women responsible for children and other care work (scooter dads notwithstanding). In conjunction with women outperforming men in college graduation rates these days (as in the U.S.), this indirectly reduces fertility by leading to delayed marriage, and directly reduces fertility by causing parents to decide against a second child.

20170625-DSC_1094

Grandparent, parent, child, in Hangzhou

The weak system of care hurts on both ends, with people having fewer children because raising them is expensive, and people needing children to take care of old people because public support is lacking. This may be one reason why grandparents can have a positive effect on parents’ motivation to have children, as reported by Yingchun Ji and colleagues (including Feinian Chen, who hosted my visit). The fact that it is common for grandparents to provide extensive care for their grandchildren, as Feinian Chen has described (paywall), presumably helps strengthen their pronatal case.

Lots of pictures of grandparents taking care of a single grandchild to choose from. Here’s one, from the (awesome) Shanghai Museum:

20170619-DSC_0623

Grandparent and child, Shanghai

The one-child policy ended in 2016, and couples no longer have to get permission to have a first or second child (but they do for a third or more). This change alone, although a better-late-than-never thing, may not do much to increase birth rates. That is the conclusion from studies of families for whom the policy was relaxed earlier. Sadly, although birth rates were already falling dramatically in the 1970s and the one-child policy was not responsible for the trend, the policy still (in addition to large scale human rights abuses) created many millions of one-child families that will struggle to meet intergenerational care obligations in the absence of adequate public support. (Here’s a good brief summary from Wang Feng, Baochang Gu, and Yong Cai.)

This is a challenge for civilization.

The pictures here, and a few hundred more, are on my Flickr site under creative commons license.


Americans who love the funny translations of signs in China may be in for some disappointment, as the Standardization Administration has announced plans to implement thousands of stock translations in the service sector nationwide.

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2016 U.S. population pyramid, with Baby Boom

I’m finishing up revisions for the second edition of The Family, and that means it’s time to update the population pyramids.

Because it’s not so easy (for me) to find population by age and sex for single years of age for the current year, and because there is a little trick to making population pyramids in Excel, and because I’m happy to be nearing the end of the revision, I took a few minutes to make one to share.

The data for single year population estimates for July 1, 2016 are here, and more specifically in the file called NC-EST2016-AGESEX-RES.csv, here. To make the pyramid in Excel, you multiply one of the columns of data by -1 and then display the results as absolute values by setting the number to a custom format, like this: #,###;#,###. Then in the bar graph you set the two series to overlap 100%.*

In this figure I highlighted the Baby Boom so you can see the tsunami rolling into the 70s now. Unlike when I discuss cohorts previously, when I let it slide, here I actually adjusted this from what you would get applying the official Baby Boom years (1946-1964) with subtraction from 2016. That would give you ages 52 to 70, but the boom obviously starts ate age 69 and ends at age 51 here, so that’s what I highlighted. Maybe this has to do with the timing within years (nine months after the formal end of WWII would be May 2, 1946). Anyway, this is not the official Baby Boom, just the boom you see.

Click to enlarge:

2016 pop pyramid


* I put the data file, the Census Bureau description, and the Excel file on the Open Science Framework here: https://osf.io/qanre/.

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Intermarriage rates relative to diversity

Addendum: Metro-area analysis added at the end.

The Pew Research Center has a new report out on race/ethnic intermarriage, which I recommend, by Gretchen Livingston and Anna Brown. This is mostly a methodological note, which also nods at some other issues.

How do you judge the amount of intermarriage? For example, in the U.S., smaller groups — Asians and American Indians — marry exogamously at higher rates. Is that because they have fewer same-race people to choose from? Or is it because Whites shun them less than they do Blacks, which are also a larger group. To answer this, you can look at the intermarriage rates relative to group size in various ways.

The Pew report gives some detail about different groups marrying each other, but the topline number is the total intermarriage rate:

In 2015, 17% of all U.S. newlyweds had a spouse of a different race or ethnicity, marking more than a fivefold increase since 1967, when 3% of newlyweds were intermarried, according to a new Pew Research Center analysis of U.S. Census Bureau data.

Here’s one way to assess that topline number, which I’ll do by state just to illustrate the variation in the U.S. (and then I repeat this by metro area below, by popular request).*

The American Community Survey (which I download from IPUMS.org) identified people who married within the previous 12 months, whom I’ll call newlyweds. I use the 2011-2015 combined data file to increase the sample size in small states. I define intermarriage a little differently than Pew does (for convenience, not because it’s better). I call a couple intermarried if they don’t match each other in a five-category scheme: White, Black, Asian/Pacific Islander, American Indian, Hispanic. I discard those newlyweds (about 2%) who are are multiracial or specified other race and not Hispanic. I only include different-sex couples.

The Herfindahl index is used by economists to measure market concentration. It looks like this:

H =\sum_{i=1}^N s_i^2

where si is the market share of firm i in the market, and N is the number of firms. It’s the sum of the squared proportions held by each firm (or race/ethnicity). The higher the score, the greater the concentration. In race/ethnic terms, if you subtract the Herfindahl index from 1, you get the probability that two randomly selected people are in a different race/ethnic group, which I call diversity.

Consider Maine. In my analysis of newlyweds in 2011-2015, 4.55% were intermarried as defined above. The diversity calculation for Maine looks like this (ignore the scale):

me

So in Maine two newlyweds have a 5.2% chance of being intermarried if you scramble up the marriage applications, compared with 4.6% who are actually intermarried. (A very important decision here is to use the newlywed population to calculate diversity, instead of the single population or the total population; it’s easy to change that.) Taking the ratio of these, I calculate that Maine is operating at 87% of its intermarriage potential (4.55 / 5.23). Maybe call it a diversity-adjusted intermarriage propensity. So here are all the states (and D.C.), showing diversity and intermarriage. (The diagonal line shows what you’d get if people married at random; the two illegible clusters are DC+NY and WA+KS; click to enlarge.)

State intermarriage

How far each state is off the line is the diversity-adjusted intermarriage propensity (intermarriage divided by diversity). Here is is in map form (using maptile):

DAMP

And here are the same calculations for the top 50 metro areas (in terms of number of newlyweds in the sample). I chose the top 50 by sample size of newlyweds, by which the smallest is Tucson, with a sample of 478. First, the figure (click to enlarge):

State intermarriage

And here’s the list of metro areas, sorted by diversity-adjusted intermarriage propensity:

Diversity-adjusted intermarriage propensity
Birmingham-Hoover, AL .083
Memphis, TN-MS-AR .127
Richmond, VA .133
Atlanta-Sandy Springs-Roswell, GA .147
Detroit-Warren-Dearborn, MI .155
Philadelphia-Camden-Wilmington, PA-NJ-D .157
Louisville/Jefferson County, KY-IN .170
Columbus, OH .188
Baltimore-Columbia-Towson, MD .197
St. Louis, MO-IL .204
Nashville-Davidson–Murfreesboro–Frank .206
Cleveland-Elyria, OH .213
Pittsburgh, PA .215
Dallas-Fort Worth-Arlington, TX .219
New York-Newark-Jersey City, NY-NJ-PA .220
Virginia Beach-Norfolk-Newport News, VA .224
Washington-Arlington-Alexandria, DC-VA- .224
New Orleans-Metairie, LA .229
Jacksonville, FL .234
Houston-The Woodlands-Sugar Land, TX .235
Los Angeles-Long Beach-Anaheim, CA .239
Indianapolis-Carmel-Anderson, IN .246
Chicago-Naperville-Elgin, IL-IN-WI .249
Charlotte-Concord-Gastonia, NC-SC .253
Raleigh, NC .264
Cincinnati, OH-KY-IN .266
Providence-Warwick, RI-MA .278
Milwaukee-Waukesha-West Allis, WI .284
Tampa-St. Petersburg-Clearwater, FL .286
San Francisco-Oakland-Hayward, CA .287
Orlando-Kissimmee-Sanford, FL .295
Boston-Cambridge-Newton, MA-NH .305
Buffalo-Cheektowaga-Niagara Falls, NY .305
Riverside-San Bernardino-Ontario, CA .311
Miami-Fort Lauderdale-West Palm Beach, .312
San Jose-Sunnyvale-Santa Clara, CA .316
Austin-Round Rock, TX .318
Kansas City, MO-KS .342
San Diego-Carlsbad, CA .343
Sacramento–Roseville–Arden-Arcade, CA .345
Minneapolis-St. Paul-Bloomington, MN-WI .345
Seattle-Tacoma-Bellevue, WA .346
Phoenix-Mesa-Scottsdale, AZ .362
Tucson, AZ .363
Portland-Vancouver-Hillsboro, OR-WA .378
San Antonio-New Braunfels, TX .388
Denver-Aurora-Lakewood, CO .396
Las Vegas-Henderson-Paradise, NV .406
Provo-Orem, UT .421
Salt Lake City, UT .473

At a glance no big surprises compared to the state list. Feel free to draw your own conclusions in the comments.

* I put the data, codebook, code, and spreadsheet files on the Open Science Framework here, for both states and metro areas.

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Births to 40-year-olds are less common but a greater share than in 1960

Never before have such a high proportion of all births been to women over 40 — they are now 2.8% of all births in the US. And yet a 40-year-old woman today is one-third less likely to have a baby than she was in 1947.

From 1960 to 1980, birth rates to women over 40* fell, as the Baby Boom ended and people were having fewer children by stopping earlier. Since 1980 birth rates to women over 40 have almost tripled as people started “starting” their families at later ages, but they’re still lower than they were back when total fertility was much higher.

40yrbirths

Sources: Birth rates 1940-1969, 1970-2010, 2011, 2012-2013, 2014-20152016; Percent of births 1960-1980, 1980-2008.

Put another way, a child born to a mother over 40 before 1965 was very likely the youngest of several (or many) siblings. Today they are probably the youngest of 2 or an only child. A crude way to show this is to use the Current Population Survey to look at how many children are present in the households of women ages 40-49 who have a child age 0 (the survey doesn’t record births as events, but the presence of a child age 0 is pretty close). Here is that trend:

sibs40p

In the 1970s about 60 percent of children age 0 had three or more siblings present, and only 1 in 20 was an only child. Now more than a quarter are the only child present and another 30 percent only have one sibling present. (Note this doesn’t show however many siblings no longer live in the household, and I don’t know how that might have changed over the years).

This updates an old post that focused on the health consequences of births to older parents. The point from that post remains: there are fewer children (per woman) being born to 40-plus mothers today than there were in the past, it just looks like there are more because they’re a larger share of all children.

* Note in demography terms, “over 40” means older than “exact age” 40, so it includes people from the moment they turn 40.

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Now-you-know data graphic series

As I go about my day, revising my textbook, arguing with Trump supporters online, and looking at data, I keep an eye out for easily-told data short stories. I’ve been putting them on Twitter under the label Now You Know, and people seem to appreciate it, so here are some of them. Happy to discuss implications or data issues in the comments.

1. The percentage of women with a child under age 1 rose rapidly to the late 1990s and then stalled out. The difference between these two lines is the percentage of such women who have a job but were not at work the week of the survey, which may mean they are on leave. That gap is also not growing much anymore, which might or might not be good.

2. In the long run both the dramatic rise and complete stall of women’s employment rates are striking. I’m not as agitated about the decline in employment rates for men as some are, but it’s there, too.

3. What looked in 2007 like a big shift among mothers away from paid work as an ideal — greater desire for part-time work among employed mothers, more desire for no work among at-home mothers — hasn’t held up. From a repeated Pew survey. Maybe people have looked this from other sources, too, so we can tell whether these are sample fluctuations or more durable swings.

4. Over age 50 or so divorce is dominated by people who’ve been married more than once, especially in the range 65-74 — Baby Boomers, mostly — where 60% of divorcers have been married more than once.

 

5. People with higher levels of education receive more of the child support they are supposed to get.

 

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