Here's the core idea: If talent is uniformly distributed and opportunity is equal, then Nobelists will come out of the woodwork, from random families & places.
If every laureate is born rich, or in the West, or has a teacher mom, it means a lot of our geniuses are being missed.
We researched the childhood background of every laureate in the sciences. We excluded Peace and Literature, since those committees sometimes intentionally select people who were born poor — doesn’t happen in the sciences.
(We included econ, cue the not-a-real-Nobel truthers) 6/N
In economic history, the best measure of a kid’s childhood is often the father’s occupation. It predicts SES, and is often the only thing you can find.
Moms occupations are more sparse in the historical record, and many are housewives, which doesn’t tell you much about SES. 7/N
For every laureate, we identified the predicted education and income rank of their fathers. (We found data on 715/739 laureates in the sciences).
Looks like we can reject that uniform distribution idea — about half come from the top 5%. 8/N
They are not universally from elite families — take Daniel Tsui, the child of illiterate farmers from Henan China.
He somehow made it to Augustana College in Illinois, the University of Chicago, and Bell Labs, where he made Nobel-worthy discoveries in quantum physics. 9/N
Or Har Gobind Khorana, the child of a village taxation clerk, the only literate family in a little village in Punjab.
He made it to Liverpool, Cambridge, and finally Wisconsin, where he did foundational work on how DNA is translated into proteins. 10/N
The father occupation that is the most common for a Nobel Laureate: business owner! Some large businesses, but also a lot of small ones.
Doctors, professors, engineers are also common, and more disproportionate relative their population share. 11/N
Only 3% of laureates grew up on farms — like this year’s Medicine winner, Victor Ambros (also from Hanover & Dartmouth, woot woot!).
Other notable laureates from farming families: David Card, Frederick Banting, Alexander Fleming. 12/N
Since we have 125 years of prize data, we can ask whether we have gotten any better at creating access for brilliant people from less elite backgrounds.
These graphs show the father income and education ranks over time. 13/N
The average ed rank of a Nobel laureate father was 95 in 1901, and is 88 today.
For the optimists: we’re creating opportunity for twice as many people as we used to!
For the pessimists: it will be another 688 years before we get to the benchmark equal opportunity rank of 50!
Women face a lot of barriers in the sciences, especially in our sample cohorts (~1835–1975). Only 28/735 laureates are women.
Female laureates come from more elite backgrounds — suggesting family advantages made up for some of the barriers faced by women in the sciences. 15/N
Which world region has been the best at nurturing top scientists from ordinary families? We thought it might be Eastern Europe, with its Soviet mass education.
But in fact it is the land of opportunity 🇺🇸🇺🇸🇺🇸 16/N
By every measure, Nobel laureates born in the United States come from less elite backgrounds than laureates born elsewhere. 17/N
We dug deeper into those U.S. born laureates, by linking their birth places to the Opportunity Atlas.
Not surprisingly, we get more laureates from non-elite families in places with more upward mobility. (We also get more laureates overall from these places) 18/N
More surprisingly, we get more laureates in places with more *downward mobility*.
When there is lots of churn, and children from rich families are not guaranteed to be rich, we produce more top scientists. 19/N
This is interesting! Why do we produce more successful scientists when rich kids seem to do worse — especially when scientists mostly come from rich families?
20/N
A couple of ideas: 1. People work harder when their outcomes aren’t guaranteed 2. We get better allocation of talent when there is a lot of economic churn
Causation isn’t correlation, so put this one into “food for thought”.
21/N
But it's consistent with other theory and evidence that increasing access to opportunity makes a better society for everyone, not just the poor people getting more opportunities. 22/N
All our work so far is looking only at fathers’ occupations, NOT at birth countries.
But the child of a tailor in India has far fewer life opportunities than the child of a tailor in the U.S., especially in earlier birth cohorts. 23/N
We incorporate income differences across countries, using historical GDP data to rank laureates’ families in a synthetic global distribution.
The results are a lot less optimistic. 24/N
In the global income distribution, the average Nobel laureate comes from a family at the 94th percentile — implying that 90% of global scientific talent is not achieving its potential.
And this measure has barely improved at all in 125 years. 25/N
Stephen Jay Gould’s concern is as important today as it was in 1980.
Brilliant people, with the potential to make world-changing scientific discoveries, are living and dying in poverty, without ever getting the chance to nurture their talents.
26/N
We are getting better at creating pathways for high potential people to succeed in the sciences. But we have a long to way to go.
We address genetics, bias in prize committees, contributions to society outside of the sciences, among others. I’ll post another thread on some of these in a bit.
28/27
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Intelligence matters way more for age 0–25 than for age 25–50. Since this is a formative period, people often overrate intelligence.
But the really successful people at 40 aren't the ones who were brilliant, they're the ones who kept at it, who kept working, who kept learning.
For example, at age 20, there isn't so much difference between people who are into and aren't into reading books.
If you're smart, you can ace everything, be at the top, even if you're not investing that much.
But compare the person who reads 25 books a year at age 40 to the person who reads 2 books a year.
Quick wit doesn't matter anymore — the person who reads is just vastly more knowledgeable in a way that high IQ or quick-wittedness can't possibly compensate.
3/
This was surprising — respondents to the AEA social media survey are still 10x more likely to be reading Twitter than reading BlueSky.
Rumors of Twitter's demise are greatly exaggerated?
Some more notes from the report in the thread 1/
I'm doing fine, but everyone else is having a bad experience!
>70% are positive or neutral on their own social media experience, but consistently <50% think social media is beneficial. 2/
I feel like there was some negativity bias among the report authors.
For instance, they highlight (Table 6) that women and LGBTQ have less positive views of social media, but they do not highlight (from Appendix) that Black and Asian respondents had *more* positive views. 3/
A spicy datacolada post on the challenge of evaluating papers where the data are proprietary and experiments are difficult to replicate.
My main thought: re-analysis like this is vastly under-supplied.
Link and some thoughts in thread. 1/
Link:
The general absence of public dialogue like this around most published papers is a major flaw of how econ works.
This kind of discussion does happen in (closed) seminars and in the (secret) referee process, but it is of public interest. 2/datacolada.org/122
e.g., when reading a published paper, it seems useful to know — did any of the referees read the pre-analysis plan?
Regarding the major potential biases, were they discussed and the author convinced the referees? Or did the author luckily draw 3 refs who didn't ask about it? 3/