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:
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):
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.)
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):
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):
And here’s the list of metro areas, sorted by diversity-adjusted intermarriage propensity:
|Diversity-adjusted intermarriage propensity|
|Atlanta-Sandy Springs-Roswell, GA||.147|
|Louisville/Jefferson County, KY-IN||.170|
|St. Louis, MO-IL||.204|
|Dallas-Fort Worth-Arlington, TX||.219|
|New York-Newark-Jersey City, NY-NJ-PA||.220|
|Virginia Beach-Norfolk-Newport News, VA||.224|
|New Orleans-Metairie, LA||.229|
|Houston-The Woodlands-Sugar Land, TX||.235|
|Los Angeles-Long Beach-Anaheim, CA||.239|
|Milwaukee-Waukesha-West Allis, WI||.284|
|Tampa-St. Petersburg-Clearwater, FL||.286|
|San Francisco-Oakland-Hayward, CA||.287|
|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|
|Minneapolis-St. Paul-Bloomington, MN-WI||.345|
|San Antonio-New Braunfels, TX||.388|
|Las Vegas-Henderson-Paradise, NV||.406|
|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.