I have a pretty major update for one of my articles.
It has to do with Justice Jackson's comment that when Black newborns are delivered by Black doctors, they're much more likely to survive, justifying racially discriminatory admissions.
We now know she was wrong🧵
So if you don't recall, here's how Justice Jackson described the original study's findings.
She was wrong to describe it this way, because she mixed up percentage points with percentages, and she's referring to the uncontrolled rather than the fully-controlled effect.
After I saw her mention this, I looked into the study and found that its results all seemed to have p-values between 0.10 and 0.01.
Or in other words, the study was p-hacked.
If you look across all of the paper's models, you see that all the results are borderline significant at best, and usually just-nonsignificant, which is a sign of methodological tomfoolery and results that are likely fragile.
With all that said, I recommended ignoring the paper.
Today, a reanalysis has come out, and it doesn't tell us why the coefficients are all at best marginally significant, but instead, why they're all in the same direction.
The reason has to do with baby birthweights.
So, first thing:
(A) At very low birthweights, babies have higher mortality rates, and they're similar across baby races;
(B) At very low birthweights, babies have higher mortality rates, and they're similar across physician races.
Second thing: Black infants tend to have lower birthweights.
MIxed infants tend to birthweights in-between Blacks and Whites, and there's a mother effect, such that Black mothers have smaller mixed babies than White mothers (selection is still possible)
(A) Black babies with high birthweights disproportionately go to Black doctors;
(B) The Black babies sent to White doctors disproportionately have very low birthweights.
If you control for birthweight when running the original authors' models, two things happen.
For one, they fit a lot better.
For two, the apparently beneficial effect of patient-doctor racial concordance for Black babies disappears:
At this point, we have to ask ourselves why the original study didn't control for birthweight. One sentence in the original paper suggests the authors knew it was a potential issue, but they still failed to control for it.
PNAS also played an important role in keeping the public misinformed because they didn't mandate that the paper include its specification, so no one could see if birthweight was controlled. If we had known the full model details, surely someone would have called this out earlier.
Ultimately, we have ourselves yet another case of PNAS publishing highly popular rubbish and it taking far too long to get it corrected.
Let me preregister something else:
The original paper will continue to be cited more than the correction with the birthweight control.
The public will continue to be misled by the original, bad result. PNAS should probably retract it for the good of the public, but if I had to bet, they won't.
So people like Justice Jackson will continue to cite it to support their case for racial discrimination.
They'll continue doing that even though they're wrong.
World War I devastated Britain and likely slowed down its technological progress🧵
The reason being, the youth are the engine of innovation.
Areas that saw more deaths saw larger declines in patenting in the years following the war.
To figure out the innovation effects of losing a large portion of a generation's young men who were just coming into the primes of their lives, the authors needed four pieces of data.
The first were the numbers and pre-war locations of soldiers who died.
The next components were the numbers and locations of patent filings.
If you look at both graphs, you see obvious total population effects. So, areas must be normalized.
You know how most books on Amazon are AI slop now? If you didn't, look at the publication numbers.
Compare those to the proportion Pangram flags as AI-generated. It's fully aligned with the implied numbers based on the rise over 2022 publication levels!
Similarly, the rise of pro se litigants has come with a rise in case filings detected as being AI-generated, and with virtually zero false-positives before AI was around.
Pierre Guillaume Frédéric le Play argued that France's early fertility decline was driven by its inheritance reforms, where estates had to be split up equally to all of the kids, including the girls.
There's likely something to this!🧵
For reference, the French Revolution ushered in a number of egalitarian laws.
A major example of these had to do with inheritance, and in particular with partibility.
In some areas of France, there was partible inheritance, and in others, it was impartible.
Partible inheritance refers to inheritance spread among all of a person's heirs, sometimes including girls, sometimes not.
Impartible inheritance on the other hands refers to the situation where the head of an estate can nominate a particular heir to get all or a select portion.
In terms of their employment, religion, and sex, people who joined the Nazi party started off incredibly distinct from the people in their communities.
It's only near the end of WWII when they started resembling everyday Germans.
Early on, a lot of this dissimilarity is due to hysteresis.
Even as the party was growing, people were selectively recruited because they were often recruited by their out-of-place friends, and they were themselves out-of-place.
It took huge growth to break that.
And you can see the decline of fervor based on the decline of Nazi imagery in people's portraits.
And while this is observed by-and-large, it's not observed among the SS, who had a consistently higher rate of symbolic fanaticism.
"Food deserts" are an example of social scientists getting causality backwards
They saw poor people eating unhealthy foods and blamed local supply
They should have blamed demand!
Using data from 13 years of supermarket entries, there's basically no effects on healthy eating🧵
The significant effects are probably not meaningful. They're more likely under the null with this gigantic dataset (p's of 0.003 and 0.005 with a total sample size of ~2.9m)
Entry did affect sales for new stores, but not existing ones. It also affected more local places more.
When new supermarkets open up, they do nab a share of local grocery sales, but the effect on healthy eating in total, among low-income households, and in food deserts, just isn't there.
I simulated 100,000 people to show how often people are "thrice-exceptional": Smart, stable, and exceptionally hard-working.
I've highlighted these people in red in this chart:
If you reorient the chart to a bird's eye view, it looks like this:
In short, there are not many people who are thrice-exceptional, in the sense of being at least +2 standard deviations in conscientiousness, emotional stability (i.e., inverse neuroticism), and intelligence.
To replicate this, use 42 as the seed and assume linearity and normality