Paul Novosad Profile picture
Oct 9 28 tweets 9 min read Read on X
What kind of childhood makes a top scientist? Is it enough to have all the right traits (brilliance, grit, etc) or do you need the right family too?

And why should we care? A 🧵 on our paper on the Nobel Laureates.

A teaser: the income distribution of the laureates' fathers.1/N Image
Why we should care: science is arguably the most important force for human progress, maybe by a lot. More discoveries, better lives for all of us.

If there’s a kid who could make a foundational discovery, we want to make sure they don’t spend their lives in the mines. 2/N Image
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
This is work with @thesamasher, @eni_iljazi (PhD student at Wharton) and Catriona Farquharson (predoc at Princeton).
You can read the full paper here:

4/Npaulnovosad.com/pdf/nobel-priz…
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 Image
Image
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 Image
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 Image
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 Image
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 Image
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 Image
Image
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! Image
Image
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 Image
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 Image
By every measure, Nobel laureates born in the United States come from less elite backgrounds than laureates born elsewhere.
17/N Image
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 Image
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 Image
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

jstor.org/stable/2937945Image
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
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 Image
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 Image
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:

N/N paulnovosad.com/pdf/nobel-priz…Image
Image
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

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Paul Novosad

Paul Novosad Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @paulnovosad

Jul 23
My preliminary takeaway from the 3-year cash transfer study:

Poverty is a lot more than just not having enough cash. Many factors interact to keep people in a bad equilibrium.

Once you're in that equilibrium, cash transfers alone don't change your steady state. 1/N
It's a bit obvious in retrospect.

But the recent zeitgeist has very much been one of "poverty is just low income, cash transfers are the answer."

Not to mention the Silicon Valley dreams of AI + UBI utopia.

2/N
I've been skeptical of the cash-as-solution idea either for America's labor market malaise or for our AI future.

There's a world of difference between earning enough cash to feed your family, vs. getting the same as a handout.

I'm reminded of this paper:
Read 4 tweets
Jun 15, 2023
📣 New working paper on residential segregation in India. We’ve been working for 5 years on this.

8 facts about residential segregation in India, from new administrative data. The situation is not great 🧵 1/N Image
We built a national neighborhood-level dataset covering all India, 2011–13.

It’s super local. A neighborhood = ~700 people, 1.5m in the country.

Data are from ~2012, this is about historical patterns, not the current govt.

3/N
Read 29 tweets
Jun 8, 2023
🚨Please stop calling this brain drain.

When there are high overseas returns to education, *more people get educated*.

Think about all the folk working their asses off for this exam, and then *staying in India*.

High returns for migrants are good, they build human capital. 1/N
"Brain drain" is mostly a myth. It assumes the stock of educated people is fixed.

It isn't— when engineers are getting amazing international opportunities, *more people train to be engineers.*

2/N
Check out Abarcar & Theoharides, who studied what happened when the U.S. recruited thousands of nurses from the Philippines.

Nursing enrollment shot up in the Philippines. *For every migrant nurse, 9 additional nurses were licensed.* 3/N
direct.mit.edu/rest/article/d… Image
Read 10 tweets
Jun 1, 2023
🤷🤷‍♀️New data: SHRUG 2 is out!! @devdatalab has been working on this for two years, a HUGE update to India’s coolest data platform:
1. Maps of *every* 2011 town and village, with ids
2. All data at every geography (villages, districts, ACs, etc)...
devdatalab.org/shrug

🧵1/N Image
3. Some of the only local data since 2018: Project Antyodaya village info + Facebook's satellite-detected wealth
4. Constituency-village keys
5. Detailed village- and town-level asset lists from SECC
6. Night lights, forest cover, air pollution

2/N
7. Keys matching virtually every combination of Indian geography
8. Consistent industry codes spanning 1990–present — industrial structure of *every* town and village from 1990–2013
3/N
Read 11 tweets
Dec 16, 2022
An updated graph of U.S. middle-aged mortality, this time with the crucial time series legend.

Other race/gender groups in thread 1/8
Mortality change among white men, age 50–54

2/N
Mortality change among Black women, age 50–54
Read 4 tweets
Dec 16, 2022
A 🧵 on our work on US mortality change, just out in AEJ:App, with @thesamasher and @charlierafkin.

We ask: how concentrated is the U.S. pre-Covid mortality crisis? Is everyone doing a little worse, or is a small subset doing catastrophically worse?

The graph is a spoiler 1/N
White Americans without a high school degree have rising middle-age mortality rates.

But is it because: (1) it has gotten worse to be at the bottom of the education distribution; or (2) these ppl are more negatively selected, since high school completion rates are going up?

2/N
Our paper highlights a statistical paradox — with education rising over time, it’s hard to interpret time series outcomes by education. E.g. the graph in this thread — you can’t interpret those time series lines — all three are biased downward! 3/N
Read 21 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(