In 1942, the U.S. government forcibly removed more than 110,000 ethnically Japanese people from their homes and sent them to internment camps in remote parts of the country.
People are resilient, but losing everything is hard.
How did victims' lives turn out?🧵
First, we need background.
Japanese citizens began arriving to the U.S. in the latter part of the 19th century.
The scale of migration was substantial. By 1942, 40% of Hawaii was Japanese (Hawaii wasn't a state until 1959).
This influx of immigrants quickly became a political problem.
1886-1911, more than 400,000 Japanese set out to American lands. Citizens called for an end, resulting in the Gentleman's Agreement of 1907:
The U.S. wouldn't harass its Japanese and Japan would restrict emigration.
Immigration from Japan was cut down to virtually nothing from 1924 to 1952, creating a "missing generation" of people and distinguishing the first-generation "Issei" from their American-born "Nisei" children.
By 1940, Hawaii had 160,000 or so Japanese residents and the U.S. proper (recall, Hawaii was not a state) had an additional roughly 120,000.
As you can see, the largest portion of them were in California, in both Census and interment camp-derived figures.
On December 7th, 1941, Japan attacked Pearl Harbor, resulting in the deaths of 2,008 sailors, 109 Marines, 208 soldiers, and a further 68 civilians and ten others, along with the destruction of almost 200 aircraft, four battleships, and more.
With Japan's declaration of war, Issei transformed into enemy aliens on U.S. soil.
The first governmental response was for the FBI to start rounding up community leaders, resulting in the detention of 222 Italian, 1,221 German, and 1,460 Issei men that month.
The Ni'ihau Incident that happened after the bombing of Pearl Harbor also started to cement into American's minds the problem of enemy aliens.
Shigenori Nishikaichi crash landed his Zero after the attack and two Japanese island residents agreed to help him.
The Haradas (an Issei couple) and Ishimatsu Shintani helped Shigenori get his equipment + papers + torch his plane while kidnapping three native Hawaiians
The Hawaiians fought back. They killed Nishikaichi, Yoshio Harada killed himself, and Shintani and Yoshio's wife were caught
Around that time, FDR and Attorney General Biddle made statements calling for Americans to respect the rights of minorities including enemy aliens.
But shortly after that on February 19, FDR signed Executive Order 9066, allowing the military to set up exclusion zones.
The EO didn't specify anything for Japanese Americans, but it didn't have to, because Japan was busy frightening American civilians and military personnel.
On February 23rd, Japan bombed an oil field near Santa Barbara.
From November 1944 to April 1945, the Japanese had been launching Fu-Go balloon bombs that ended up dropping incendiary munitions in California and fourteen other states.
The Japanese also attacked a baker's dozen U.S. ships off the California coast.
Americans were so afraid of a Japanese invasion that they inflicted damage on themselves in the "Battle of Los Angeles."
The fear was rightful: The Japanese had subs 20 miles from California on December 24, 1941 and California only had sixteen modern airplanes protecting it!
Leveraging the powers granted by the EO, the military split the West Coast into two military areas and began distributing signs encouraging Japanese people to go East.
The voluntary migration scheme failed and the War Relocation Authority was set up to administer ten camps scattered across the U.S., for 110,000 Japanese Americans living on the West Coast
These relocated people had to get out quickly, selling possessions at "fire sale" prices
It's from this background that the analysis begins:
Arellano-Bover used Census, Japanese-American Research Project, and War Relocation Authority data to identify interned Japanese Americans so data on their socioeconomic outcomes could be put to use.
If we look at home ownership after the war, we see that the interned Japanese were definitely negatively impacted:
In the period 1946-52, they had significantly lower homeownership rates than Japanese Americans who weren't interned.
But look at 1953 to the '60s. Recovery?
Homeownership is about an asset. If we look at income data, we actually see that the Japanese who would go on to be interned had lower incomes than the non-interned Japanese in 1940, and equal incomes by 1950-60.
So the internment... raised incomes?
The answer to this seems to be "Yes."
Not only did the Japanese who were interned recover, they caught up despite starting further behind the Japanese who weren't interned.
This result is actually very robust!
So we have to ask Why?
Let's check attitudes towards work.
Bupkes. The interned and non-interned Japanese don't differ in work attitudes, so they couldn't get ahead that way.
What about attachment to Japan and Japanese culture?
Sansei (third-generation Japanese) were just as likely to have Japanese-speaking grandparents and citizenship/Americanness-wise, if anything, the interned were a bit less American.
This probably isn't it either.
Here's the meat:
In 1940, Japanese on the West Coast were disproportionately likely to be farmers and unskilled laborers, whereas the Japanese who migrated East and were thus less likely to be interned worked more often in skilled occupations.
This migration-related occupational stratification must not have been very selective by ability, because the interned/non-interned converged.
They also converged, in part, because the interned used the experience to move and change jobs.
The camps had more socioeconomic diversity than the places internees came from, so they were exposed to a diversity of opportunities and their family ties binding them to certain occupations were broken.
There were frictions the camps help them to overcome!
It was common to hear stories about internees entering poor and vowing to make it big when they got out, like this pictured one.
And that's what they did: interned Japanese Americans overcame the experience and wound up, miraculously, better off for it.
If you're interested in learning more about this amazing example of human resilience in the face of discriminatory adversity, go read the paper, here: cambridge.org/core/journals/…
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0. The sample for the population result is *everyone* with ≥1/16 and ≤15/16 African admixture, the within-family result is for the siblings among them. I didn't plot between-families results, but they're pulled from the same distribution as the population result, so when I have those computed I'll replot, but they shouldn't be any different (will take a few hours, maybe be tomorrow, w/e).
1. Yes, datasets will be merged and sharper results will be obtained for the article on this that's coming out ~shortly. Can't be done for all phenotypes in the UKBB, but can be done for IQ at least. For example, we don't have lipoprotein(a) (lp(a)) measurements in some of the samples of young American kids, some of them don't have objective skin color measurements, etc. As a side note, this result holds up with brain size (not shown) in the UKBB, but I'm unsure if it holds up in the young samples that'll be merged with it, as they're still developing in most cases.
Anyway, the IQ p-value is p = 0.008 (two-tailed) within families and extremely low between them. The within-family result is robust, will get sharper with more data, and is also sharpened by using a latent variable instead of a score, and by correcting for measurement error (not needed with the LVM). Score shown for simplicity and ease of others with UKBB access replicating this. Also, accounting for error in the admixture computation, the p-value would drop a bit further, but not by very much since error is very small.
2. The "IQ per unit of admixture" is statistically indistinguishable between the population and within-family results, and yes, it explains most of the Black-White difference in IQ. I just wanted comparably-scaled results for all the traits here, so you're seeing r's. It's pleasant that the within-family variance reductions aren't enormous for siblings, which is what we expect even with quite high heritabilities given their genetic relatedness. It's the same result we've seen with American data, and it's also nice to see that in the case of this trait, the global admixture result *can* be interpreted like the within-family one. Presumably this only holds with measurement invariance, as we see in the U.K. when comparing Whites and Blacks there. Since we see this in the U.S. too, it's likely that the previous, already-published within-family null—which had a sizable effect in the correct direction which also could not be distinguished from the global r—was just a false-negative.
3. This result replicates with other degrees of relatedness, but we might lose the causal interpretation with those ones because the estimand for, say, a cousin test is different given the identity of the "C" variance component shifts for that comparison.
4. What is architectural sparsity and why is it relevant? Consider this table from nature.com/articles/s4159… (cc: @hsu_steve):
Basically, sparsity refers to the number of variants involved in a trait. It also refers to their effect size distribution. So, for Alzheimer's, for example, the trait is highly polygenic, but APOE explains more variance than the entire rest of the PGS, so while being under highly polygenic control, it remains moderately sparse.
If you're still not grokking what I mean here, consider some distributions of cumulative effects across the chromosomes. Here's the result for lp(a), which is already known to be influenced by essentially one gene. Guess which chromosome it's located on:
Consider, as a comparison, creatinine, which is considerably less sparse, and thus has effects distributed across all chromosomes:
Now, consider what this does to within-family admixture assessments. Skin color, for example, is controlled by only a handful of genes. This means that global admixture shouldn't tag it very much between siblings. The same is even more true for lp(a). And in fact, now we have confirmation of this!
But, we know from other methods that lp(a) is effectively entirely genetically-caused between populations. This is just accepted within the medical community because it is an obvious fact that follows from its strong control by a single gene. You can also figure this out using local ancestry estimates. Basically, the correlation between genome-wide ancestry and ancestry at a causal loci is what we want to get at, and if you know the causal loci, you gain power by restricting your analysis to that area. This is what we find with lp(a) (not shown, but use your brain. The obviousness of this fact is why the author used lp(a) as an example).
Also, in some sense, the frequency of that locus between ancestries gives you what you need without doing all this within-family stuff, if you're confident it's causal. The effect estimate might still be biased by population structure though, so that's worth keeping in mind.
There will be a post soon with more details and the expanded set of results with the additional datasets, robustness tests, and plenty of other fun things to look at.
TL;DR: this is a spoiler, and it shows that, yes, you can explain the Black-White IQ difference in Britain mostly genetically, and the global admixture result that I've posted here before is equivalent to the within-family one. Woohoo for things that should hold up, in fact, holding up!
As a sort of replication of the Young paper, you cannot explain the difference in educational attainment (as years of education) in this way. Why? Well, hard to say. Compensatory factors like I found with the GCSEs? (Haven't read that post yet? Go check it out here: cremieux.xyz/p/explaining-a…). Poor phenotype quality? Very plausible, because education really is a huge garbage heap, but why would that be in the general population and not within families? Maybe it has to do with what other traits admixture tags? Maybe it does replicate, but we just can't see it, because the precision is too poor (possibly the lp(a) story too). Who knows!
Q: Will the combination with more datasets allow us to fix this educational attainment result?
A: No, because most of the other datasets involve young people, not people who have almost all completed their educations, as in the UKBB. That plus the generational change and international incomparability in the definition of educational attainment makes it too poor as a phenotype. Sorry!
Any questions?
Link to a fun previous post from the same dataset, showing a result that *does* hold up within families: x.com/cremieuxrecuei…
Link to another post mentioning the forthcoming article earlier: x.com/cremieuxrecuei…
I want to ping an old post that I've also posted some replications for.
Basically, parents are inequality averse, and they try to compensate for when one sibling is less gifted than another, reducing the ancestry/PGS effect on education within families.
Ever wondered why advertisements heavily feature Black actors when they're just 12-14% of the population?
I might have an explanation:
Black viewers have a strong preference for seeing other Blacks in media, whereas Whites have no racial preferences.
These results are derived from a meta-analysis of 57 pre-2000 and 112 post-2000 effect sizes for Blacks alongside 76 and 87 such effect sizes for Whites.
If you look at them, you'll notice that Whites' initial, slight preference declined and maybe reversed.
It's worth asking if this is explained by publication bias.
It's not!
Neither aggregately (as pictured), nor with results separated by race.
You're on trial, and the jury can't make up their minds. The decision is a coin flip: 50/50, you either get it or you don't.
Your odds of a given verdict depend on the "peers" making up your jury.
If you're Black and they're Black, your odds are good; if you're White, pray.
Though White jurors have, on average, no racial bias, the same can't be said for Black jurors.
Where the White jury gives you approximately the coin flip you deserve, the Black jury's odds for a verdict are like a coin rigged to come up heads 62% of the time.
Once you get to sentencing, things get even worse.
The White jury is still giving you a coin flip on a lighter or a harsher sentence, but the Black jury is giving lenient sentences to Blacks about 70% of the time.
Posts saying Charlie said things he didn't, oftentimes even including videos where he doesn't say what the post says he does, have convinced me.
There will be no organic temperature-lowering coming from the left, because they don't want it.
They believe the things they claim.
They actually believe the people they dislike *are* racists, fascists, homophobes, transphobes, all of it.
They are not reasonable, and they cannot form anything like a reasoned argument for these perceptions.
Even still, it's really what they think.
Accordingly, they will not lower the temperature.
You can't expect people to stop using slurs like "fascist" or "racist" or "sexist"—even if they are completely untrue and it's impossible to provide valid evidence for them!—if they believe they're true.
It shows that the gender wage gap is mostly about married men and their exceptional earnings.
In this thread, I'm going to explain why married men earn so much more than everyone else🧵
The question is:
Does marriage maketh man?
Or
Are all the good men married?
That is, does marriage lead men to earn more, or do men who earn more get married more often?
To answer this question, we have to work through the predictions of different theories.
For example, one of my favorite papers on the subject looked into three different hypotheses to explain the "marriage premium" to wages, and they laid out a few testable predictions: