In that chart, I used the GSS and found something many people replicate:
1. Cross-sectionally, there's a relationship between being married and life satisfaction. It's similar for men and women.
2. Within persons—causally!—marriage boosts life satisfaction, but more for women.
Leveraging the same within-person design, we can use the Add Health dataset to look at stress and depression.
For both sexes, the effects are indistinguishable.
But they're also mostly not real: it's just that people who get married tend to be less stressed and depressed!
Now, here's something to qualify the GSS stuff from before.
When the marriage is "happy" there's a huge life satisfaction delta: +0.83 SDs!
When the marriage is "unhappy", the people are still about as happy as the unmarried are.
That's how big the selection is.
Here's another curious addition:
In the AddHealth, the effect on depression is nonsignificant in both sexes.
But, exiting a marriage makes men much more depressed, while barely moving the needle for women.
Presumably this reflects why divorce happens.
We can also look at the time course of life satisfaction around marriage and we'll see something that directly makes the OP look a little foolish.
Using the AddHealth again, we see a bump right after marriage, with more persistent mood benefits for women!
What about longevity?
Well, being unmarried looks bad for both sexes, and to not so dissimilar degrees meta-analytically,
It's not quite the same, but we can get closer to understanding the longevity impacts causally by exploiting spontaneous causes of marriage dissolution.
Namely, death.
Accidents should affect the sexes very differently if the OP is right. But short and long-term? Similar.
And I'm happy to note, these findings are supported in the HRS, too, so they hold up across a few generations of American living. They also hold in some disparate European cohorts.
Hurrah!
Now, as a general rule: stop believing in interactions! They're rarer than you think.
And no disrespect to OP. A lot of people have good reasons for not having noticed marriage is good for both sexes.
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.
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.