Tag Archives: demography

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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African American marital status by age, Du Bois replication edition

At the 1900 Paris Exposition, sociologist W. E. B. Du Bois presented some the work of his students. In The Scholar Denied: W. E. B. Du Bois and the Birth of Modern Sociology, Aldon Morris writes:

Du Bois’s meticulousness as a teacher is apparent in the charts and graphs that he prepared with his students. For example, as part of his gold medal-winning exhibit for the 1900 Paris Exposition, Du Bois and his students produced detailed hand-drawn artistically colored graphs and charts that depicted the journey of black Georgians from slavery to freedom.

Some of collection is shown in this post at the Public Domain Review (shared by Tressie McMillan Cottom yesterday); the full collection is online at the Library of Congress (LOC).

The one that caught my eye was this, showing marital status (“conjugal condition”) by age and sex for the Black population. I can’t find the source details in the LOC record, so I don’t know if it’s Georgia or national, but I presume it’s from tabulations of 1890 decennial census or earlier:

33915v

It’s artistic and meticulous and clearly informative, beautiful. So I tried to make a 2015 update to complement it. I used data from the 2015 American Community Survey via IPUMS.org, and did it a little differently.* Most importantly, I added two more conjugal conditions, cohabiting and separated/divorced. Second, I used five-year age groupings all the way up, instead of ten. Third, I detailed the age groups up to age 85. Here’s what I got:

du bois marstat replication.xlsx

Some very big differences: Much smaller proportions of African Americans married now. Also, much later marriage. In the 1900 figure more than 30% of men and 60% of women have been married by age 25; those numbers are 5-6% now. I don’t know how they counted separated/divorced people in 1900, but those numbers are high now at 31% for women and 24% for men at age 60-64. Widowhood is later now, as 42% of women were widowed before age 65 in 1900, compared with only 13% now (of course, that’s off a lower marriage rate, and remarried people are just counted as married). And of course cohabitation, which the chart doesn’t show for 1900. Note I included people in same-sex as well as different-sex couples.

So, thanks for indulging me. I hope you don’t think it’s frivolous. I just love staring at the old charts, and going through the (very different) steps of replicating it was really satisfying. (I also just love that in another 100 years someone might look back on this and say, “Wait, which one was Earth again?”)

Note: If you want to compare them side-by-side, here’s a go at that. The age ranges don’t line up perfectly but you can get the idea (click to enlarge):


* SAS code, ACS data, images, and the spreadsheet used for this post are shared as an Open Science Framework project, here.

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‘Start a family’ started to mean ‘have children’ more recently than you think

Or more recently than I thought, anyway.

It looks like the phrase “start a family” started to mean “have children” (after marriage) sometime in the 1930s, and didn’t catch on till the 1940s or 1950s, which happens to be the most pro-natal period in U.S. history. Here’s the Google ngrams trend for the phrase as percentage of all three-word phrases in American English:

startfamngram

Searching the New York Times, I found the earliest uses applied to fish (1931) and plants (1936).

Twitter reader Daniel Parmer relayed a use from the Boston Globe on 8/9/1937, in which actress Merle Oberon said, “I hope to be married within the next two years and start a family. If not, I shall adopt a baby.”

Next appearance in the NYT was 11/22/1942, in a book review in which a man marries a woman and “brings her home to start a family.” After that it was 1948, in this 5/6/1948 description of those who would become baby boom families, describing a speech by Ewan Clague, the Commissioner of Labor Statistics, who is remembered for introducing statistics on women and families into Bureau of Labor Statistics reports. From NYT:

claguenyt

That NYT reference is interesting because it came shortly after the first use of “start a family” in the JSTOR database that unambiguously refers to having children, in a report published by Clague’s BLS:

Trends of Employment and Labor Turn-Over: Monthly Labor Review, Vol. 63, No. 2 (AUGUST 1946): …Of the 584,000 decline in the number of full-time Federal employees between June 1, 1945 and June 1, 1946, almost 75 percent has been in the women’s group. On June 1, 1946, there were only 60 percent as many women employed full time as on June 1, 1945. Men now constitute 70 percent of the total number of full-time workers, as compared with 61 percent a year previously. Although voluntary quits among women for personal reasons, such as to join a veteran husband or to start a family, have been numerous, information on the relative importance of these reasons as compared with involuntary lay-offs is not available…

It’s interesting that, although this appears to be a pro-natal shift, insisting on children before the definition of family is met, it also may have had a work-and-family implication of leaving the labor force. Maybe it reinforced the naturalness of women dropping out of paid work when they had children, something that was soon to emerge as a key battle ground in the gender revolution.

Note: Rose Malinowski Weingartner, a student in my graduate seminar last year, wrote a paper about this concept, which helped me think about this.

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Fertility trends and the myth of Millennials

The other day I showed trends in employment and marriage rates, and made the argument that the generational term “Millennial” and others are not useful: they are imposed before analyzing data and then trends are shoe-horned into the categories. When you look closely you see that the delineation of “generations” is arbitrary and usually wrong.

Here’s another example: fertility patterns. By the definition of “Millennial” used by Pew and others, the generation is supposed to have begun with those born after 1980. When you look at birth rates, however,  you see a dramatic disruption within that group, possibly triggered by the timing of the 2009 recession in their formative years.

I do this by using the American Community Survey, conducted annually from 2001 to 2015, which asks women if they have had a birth in the previous year. The samples are very large, with all the data points shown including at least 8,000 women and most including more than 60,000.

The figure below shows the birth rates by age for women across six five-year birth cohorts. The dots on each line mark the age at which the midpoint of each cohort reached 2009. The oldest three groups are supposed to be “Generation X.” The three youngest groups shown in yellow, blue, and green — those born 1980-84, 1985-89, and 1990-94 — are all Millennials according to the common myth. But look how their experience differs!

cohort birth rates ACS.xlsx

Most of the fertility effect on the recession was felt at young ages, as women postponed births. The oldest Millennial group was in their late twenties when the recession hit, and it appears their fertility was not dramatically affected. The 1985-89 group clearly took a big hit before rebounding. And the youngest group started their childbearing years under the burden of the economic crisis, and if that curve at 25 holds they will not recover. Within this arbitrarily-constructed “generation” is a great divergence of experience driven by the timing of the great recession within their early childbearing years.

You could collapse these these six arbitrary birth cohorts into two arbitrary “generations,” and you would see some of the difference I describe. I did that for you in the next figure, which is made from the same data. And you could make up some story about the character and personality of Millennials versus previous generations to fit that data, but you would be losing a lot of information to do that.

cohort birth rates ACS.xlsx

Of course, any categories reduce information — even single years of age — so that’s OK. The problem is when you treat the boundaries between categories as meaningful before you look at the data — in the absence of evidence that they are real with regard to the question at hand.

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