Paul Novosad ✓
@paulnovosad · NH · joined 22 Jan 2012
econ prof @dartmouth, founder @devdatalab r2: "a morass of disjointed streams of consciousness" 20% satire 🤷
175 934Followers
772Following
17 688Posts total
7.3MViews on collected posts
Ultimi post
Cerchiamo nuovi post…
I’ve noticed reading comprehension is steadily declining on this platform also
576 views · 13 likes
· 1 reposts · 0 replies
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The Indian Statistical Institute's annual growth conference in Delhi is just one of the best conferences around.
Submissions are due in one week, link in thread. Submit your papers! https://t.co/qRPj3oTklS
1.1K views · 16 likes
· 3 reposts · 1 replies
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@paulnovosad You dropped this. https://t.co/IzOAvrdkdw
15.5K views · 271 likes
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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
47.2K views · 614 likes
· 15 reposts · 54 replies
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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.
Read the paper for more details: https://t.co/ssbLSQcGyQ
N/N https://t.co/QsdT8EBguO
51.6K views · 525 likes
· 57 reposts · 9 replies
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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 https://t.co/qILDxClshF
64.6K views · 876 likes
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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 https://t.co/xtYh04hTlB
68.1K views · 631 likes
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One last thing.
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
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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
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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
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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
https://t.co/NOUmqHyoQa https://t.co/sZSqs0IlT6
68.2K views · 735 likes
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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
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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 https://t.co/KsggW5os8N
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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 https://t.co/A8U
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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 https://t.co/CyBGqchYXK
99.9K views · 882 likes
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By every measure, Nobel laureates born in the United States come from less elite backgrounds than laureates born elsewhere.
17/N https://t.co/YO7gPpC4aT
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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 ht
318K views · 1.1K likes
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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! ht
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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 https://t.co/6DBlZqzpRR
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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 https://t.co/DqDWpHcQWy
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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 https://t.co/zibsSL5Lwp
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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 https://t.co/29nVUpkef8
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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 https:
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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
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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 https://t.co/3vlkNF1dtK
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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
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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.
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· 124 reposts · 151 replies
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This is work with @thesamasher, @eni_iljazi (PhD student at Wharton) and Catriona Farquharson (predoc at Princeton).
You can read the full paper here: https://t.co/ssbLSQcGyQ
4/N
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So how is our society doing at finding and supporting the potential scientists who can improve this precarious existence?
Our idea was to look at the childhoods of the Nobel Laureates. Virtually all of them reached the pinnacle of discovery — where did they come from?
3/N
196.2K views · 1K likes
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Rispetto ad account della stessa dimensione
3 post degli ultimi 90 giorni, accanto alla fascia di 100K–1M follower. raggiunge meno persone dei pari, ma le coinvolge molto di più.
Visualizzazioni mediane987questo account4 200mediana per 100K–1M
Copertura, %0.56%questo account1.62%mediana per 100K–1M
Interazione, %1.85%questo account1.31%mediana per 100K–1M
| Metrica | Questo account | Mediana per 100K–1M | Rapporto |
|---|---|---|---|
| Visualizzazioni mediane per post | 987 | 4 200 | 0.23× |
| Copertura (visualizzazioni ÷ follower) | 0.56% | 1.62% | 0.35× |
| Tasso di interazione | 1.85% | 1.31% | 1.41× |
Altri account di questa fascia → Confronta con un altro account → Come sono costruiti questi parametri →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
76.9K9 Oct
67.9K
68.2K
67.4K
55.2K
58.2K
68.1K
64.6K
51.6K
47.2K
15.5K
1.1K8 Sep
987
576
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.99%9 Oct
0.70%
1.21%
1.24%
0.68%
1.03%
1.11%
1.65%
1.15%
1.45%
1.78%
1.85%8 Sep
0.81%
2.43%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.31%.
What the audience does
Likes82.8%22 902 in total
Reposts7.4%2 036 in total
Replies1.5%418 in total
Quotes1.0%264 in total
Bookmarks7.4%2 033 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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