If you go based off of surveys like the ACS, CPS ASEC, or SIPP, you might end up with the wrong answer.
If you use administrative records, you'll find that people usually underreport the welfare they receive🧵
If you look at receipt of Temporary Assistance for Needy Families (TANF) and unemployment insurance, a similar pattern shows up:
What we're seeing here is something that affects not only the estimated prevalence of benefits receipt, but also the magnitude of it. In other words, people underreport the size of their welfare checks.
If we take these facts together, then we'll notice something interesting: America's poverty rates are overestimated.
Poverty is, in fact, somewhat less common, less severe, and considerably less racialized than is generally understood:
An open question is Why do people misestimate? Well, go look back at the first two charts again.
Did you notice that misestimation of benefits receipt was greater for Blacks and Hispanics than for Asians and Whites? That's an important detail here.
When people respond to surveys, errors in their responses are predicted by tons of variables, from sex to race to education to height and so on.
One of my favorite examples is that people with low levels of educations misreport their heights more often.
What surveys show us about welfare usage is different from the reality, and that has unfortunate consequences, like misleading us about the extent to which Americans are poverty-stricken and underserved by their governments.
What this also tells us is that, somewhere, some bureaucrat could have probably assembled this data and contradicted a years-old literature, because all the researchers were using survey data that turned out to be, in some ways, wrong!
If we want to ensure this doesn't keep happening, we'll need the government to be more open with administrative data.
- His license is suspended
- He was once a soldier for a Mafia family
- He's telling me about his time in Rikers
- He's showing me YouTube videos
- He's telling me his theories about Jews
He's telling me about gang wars he was in ad a kid.
He's wondering why all the Chinese girls are lined up - for an audition?
He says to go to Mother's Ruin for latin prostitutes.
All of this entirely unprompted.
"Yeah, these African guys, yeesh"
"I couldn't fuck that whore because I got the erectile dysfunction."
As a recap on my appearance, Eli Lilly is pursuing:
- A one-dose drug for preventing most heart disease
- A vaccine for chlamydia
- A vaccine for gonorrhea
- A vaccine for Epstein-Barr
- A drug that lets you stay awake longer and feel more rested
And remember, Eli Lilly's big break historically was the University of Toronto licensing them to produce insulin.
They started off by giving it out for free, saving the world's diabetics at a time when there was no treatment available.
They've always been a force for good.
I think
- The heart disease drug will succeed
-- Will it commercialize? It can, easily. But I'm 50/50 due to the competition
- Chlamydia and gonorrhea vax will succeed, but I don't see much commercial potential with Lilly
- EBV vaccine will fail with Lilly, succeed eventually
Are White women the primary beneficiaries of affirmative action?
That's a real claim that's commonly advanced by journalists, and the claim has gone so far that it's even made its way into academic publications and policy.
But the claim is completely false🧵
This claim doesn't make a lot of sense. After all, shouldn't the primary beneficiaries of affirmative action be the people who the policies primarily target?
In America, that's African Americans and, among them, women get an added benefit. How could it be Whites?
To figure out where the claim comes from, I started reading supposed sources.
Often enough, journalists will just take the claim for granted without providing *any* source.
It's just tacit knowledge now, and that's not good!
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.