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.
More than thirty countries globally have automatic non-filing options for taxpayers.
Many people claim these help to make the tax system more fair by taking out tax hassle and guesswork.
But German data suggests they might make the tax system less progressive🧵
The first thing to note is that the lower the income, the greater the odds of not filing, with almost 90% of those earning just €10,000 choosing not to file.
At an income of about €50,000, the relationship asymptotes at roughly 30% non-filers.
Another thing to note is that, consistent with the tax system being progressive in general, lower-income individuals are entitled to refunds more often.
The simple way to do this is to remove Sub-Saharan Africa from a regression of log(GDP PPP Per Capita), for which I'm using 2019 to avoid the pandemic and get closer to the sampling years.
Like this, we get:
Measured IQ: 71.96
Predicted IQ: 74.86
Predicted, sans SSA: 76.78
In other words, no big difference.
But, you might say, aren't logs doing the work? Well, they're appropriate here, so no, but without them, we get:
How bad are Richard Lynn's 2002 national IQ estimates?
They correlate at r = 0.93 with our current best estimates.
It turns out that they're really not bad, and they don't provide evidence of systematic bias on his part🧵
In this data, Lynn overestimated national IQs relative to the current best estimates by an average of 0.97 points.
The biggest overestimation took place in Latin America, where IQs were overestimated by an average of 4.2 points. Sub-Saharan Africa was underestimated by 1.89 pts.
Bias?
If you look at the plot again, you'll see that I used Lynn's infamously geographically imputed estimates.
That's true! I wanted completeness. What do the non-imputed estimates look like? Similar, but Africa does worse. Lynn's imputation helped Sub-Saharan Africa!
If you use a relative standard to measure poverty, establishing the bottom x% are poor regardless of their absolute income, you will never win the war on poverty.
But President Johnson wanted an absolute standard. With such a standard, the poverty rate has fallen more than 90%🧵
The first thing you have to do to get to this is to adjust the equivalence scale of the official poverty rate, by recreating the poverty thresholds based on the square root of the number of family members rather than the formula used for the Official Poverty Measure (OPM):
The next things you have to do are to change the unit poverty is calculated at to the more reasonable unit, household. Then, you use post-tax income, and finally, you have to get to "full-income" by accounting for transfers, including health insurance (incl. Medicare/caid).