Tag Archives: ethnicity

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):


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):


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.


Filed under In the news, Me @ work

Marriage rates among people with disabilities (save the data edition)

Cross posted on the Families as They Really Are blog.

Disability is a very broad concept, representing a wide array of conditions that are not easily captured in a simple demographic survey. However, disabilities are very prevalent, especially in an aging society, and the people who experience disabilities differ in important ways from those who do not. Previously I reported — in a preliminary way — that people with disabilities are much more likely to divorce than those without. Here I present some numbers on marriage rates.

This isn’t the kind of thorough, probing analysis this subject requires. But I have two reasons to do it now. First is that I hope to motivate other people to pursue this issue in greater depth. And second, I want to highlight the importance of the data I’m using — the American Community Survey (ACS) — because it might be not available for much longer. These questions have been slated for demolition by the U.S. Census Bureau on cost-saving grounds. I put details about this issue — and how to register your opinion with the federal government — at the end of the post.


The ACS asks five disability questions (I put the shorthand label after each):

  1. Is this person deaf or does he/she have serious difficulty hearing? (Hearing)
  2. Is this person blind or does he/she have serious difficulty seeing even when wearing glasses? (Vision)
  3. Because of a physical, mental, or emotional condition, does this person have serious difficulty concentrating, remembering, or making decisions? (Cognitive)
  4. Does this person have serious difficulty walking or climbing stairs? (Ambulatory)
  5. Does this person have difficulty dressing or bathing? (Independent living)

These aren’t perfect questions, but they cover a lot of ground, and the ACS — which involves about 3 million households — can’t get into too much detail.

One great thing about having these questions on the giant ACS is you can use the data to get all the way down to the local level, or into small race/ethnic groups. And with the marital events questions, you can combine disability information and marriage information.

Marriage rates*

Using marital events (did you get married in the last year), marital history (how many times have you been married), detailed race and ethnicity breakdowns, and the disability questions above, I produced the following figure. This uses the combined 2008-2012 ACS data because these are small groups, but even with five years of data these groups get quite small. There are about 90,000 non-Hispanic Whites with a cognitive disability in my sample, but only 356 people who are both White and American Indian with a hearing disability (the smallest group I included). This sample is people ages 18-49 who have never been married (or just got married).


The overall marriage rate for never-married people ages 18-49 is 71.8 per 1,000. For people with disabilities it’s 41.1 (shown by the blue line). So that’s much lower than for the general population. But there is a very wide variation across these groups, from 15.5 per thousand for Blacks with disabilities in independent living all the way up to above the national average for Whites and White/American Indians with hearing disabilities. (For every condition, Blacks with disabilities have the lowest marriage rates.)

I don’t draw any conclusions here, except that this is an important subject and I hope more people will study it. Also, we need data like this.

In previous posts demonstrating the value of this data source, I wrote about:

Whether you are a researcher or some other member of the concerned public, I hope you will consider dropping the government a line about this before the end of the year.

The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).

Comments will be accepted until December 30.

* Erratum: In the original post I described this as a “first-marriage rate.” However, in checking over the code I used, I see that I used all marriages in the nominator, and never-married people in the denominator. Therefore, this is more accurately described as “marriages per 1000 never-married people.” It would have made more sense to just put first marriages in the numerator. For reference, the first-marriage rates were 54.0 for the total never-married 18-49 population, and 24.5 for those with a disability. I regret the error. 

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Ethnic vehicles, White edition

The New York Times reports that Chrysler is bringing back the Jeep Cherokee.

Jim Morrison, the director of marketing for Jeep, said:

[the car] is a new, very capable vehicle that has the Cherokee name and Cherokee heritage. Our challenge was, as a brand, to link the past image to the present. … We want to be politically correct, and we don’t want to offend anybody.

The Times article includes a slideshow of ethnic vehicles of the past, include Dakota, Comanche, Seneca and Pontiac, but also Viking and Scottsman.

The contemporary ethnic vehicle, however, makes subtle reference to heritage, yet draws on well known associations (not to say “stereotypes”) with the objectified group – in a way that doesn’t offend anybody. In what I’m sure is an unoriginal exercise, and inspired by too much time watching Mad Men, I experimented with a few other possibilities:


Jew: When a car is smart enough already.

frenchcarFrench: You don’t have to be rude.

irishcarIrish. Whimsy plus.


Eat boiled food. Play bagpipes. Grow sideburns. Drive. Scott.

swisscarSwiss. Just because you can’t see the wealth doesn’t mean it isn’t there.

I could have done more, but didn’t want to dilute the brand. (Blogger: you didn’t get into this career cuz you wanted to work all day long.)


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