Anatoly Karlin offered a graph a few years ago of Kaiser Family Foundation data on life expectancy by state by race, along with some interesting speculations on possible reasons behind the differences.

The extreme high side outlier for whites is Washington D.C., which I like to imagine as elderly lawyers whose grandchildren want them to move to Montgomery County, but who insist on staying on in Georgetown in case Secretary Kerry needs them to drop by with urgent advice on the Latvian crisis.

In contrast, blacks did worse in Washington DC than in any of the 50 states.

The black-white gap in DC is 12.7 years, compared to 4.3 years for the country as a whole. (Washington DC, which is far more liberal than any state, always has these amazing examples of racial inequality.) The second biggest black-white gap is, of course, Wisconsin, at half of DC’s gap, 6.3 years.

The worst state for both of the longest lived groups, Asians and Hispanics, is Hawaii. Whites, in contrast, do quite well in Hawaii, with Hawaii being the 5th longest lived state for whites.

Minnesota is #2 longest-lived for whites, #1 for blacks, #2 for Hispanics, but not so hot for Asians (Hmongs).

The worst state for whites is, of course, West Virginia.

It should be noted that the recent paper by Raj Chetty on life expectancies by “commuting zone” adjusts away differences in life expectancy due to differences in racial makeup based on national average life expectancies per race. A lot of people looking for clues in the latest Chetty paper haven’t figured this out yet.

Awareness of this racial adjustment puts in perspective some of Chetty’s more puzzling findings. For example, the NYT article on Chetty’s study celebrates Greater Birmingham, Alabama as long-lived, which it’s not in the unadjusted real world, because its population is heavily made up of blacks and whites, with relatively few of the longer-lived Asians and Latinos. But relative to its racial makeup, Birmingham is doing better in 2014 than in 2001, probably because blacks are shooting each other and giving each other fatal cases of AIDS less often today than in the recent past.

I’ve pointed out the huge role that race played in Chetty’s 2013 and 2015 studies of income mobility: it turned out that much of the local differences he hoped to attribute to obscure policy variations were simply caused by different races regressing to different means.

Now, though, Chetty has adjusted away national average differences in race in his rankings of metropolitan areas.

But this still leaves subracial differences: e.g., Asians in Hawaii and Minnesota, the two worst states for Asian life expectancy, are different in nature and nurture from Asians on average in the whole country.

Similarly, the worst states for Hispanic life expectancy are Hawaii (where many Latinos are descendants of Puerto Rican sugar cane cutters) and New Mexico (where Hispanics have been indigenous for 400 years). In contrast, the best life expectancy for Hispanics is Virginia (where Latinos tend to be energetic immigrant sojourners taking advantage of the post-9/11 economic boom among Beltway Bandits by working downscale jobs). When their health wears down, they usually hope to take their savings and move to some place cheaper to relax.

Chetty is always looking for policy advice that will leap out at him from the local differences. If only we could figure out using Big Data what some local governments are doing right and others are doing wrong, then we could fix the whole country!

But Chetty usually winds up baffled because the noticeable patterns in his data seldom reflect the influence of local laws in any replicable way. For example, why does Virginia have long lived Hispanics? Virginia attracted an influx of healthy Hispanic immigrants to work service jobs because the CIA and Pentagon missed the call on 9/11, so the Bush Administration and Congress spent a gazillion dollars on the War on Terror in DC’s Virginia suburbs, which brought in lots of ambitious legal and illegal aliens to be servants for the Beltway Bandits.

But, you may ask:

What did we learn?

… I guess we learned not to do it again.

That’s probably over-optimistic, however.