Nathaniel Hendren
@nhendren82 · joined 30 Jul 2016
Professor of Economics at MIT. Co-Director of Policy Impacts (https://t.co/57eyCxxbj4) and Opportunity Insights. Co-Lead Editor of the JPUBEC (He/him/his)
11 410Followers
647Following
1 047Posts total
2.3MViews on collected posts
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@nhendren82 @jmwooldridge @DAcemogluMIT A beautiful tribute to your mentor. ❤️
8.8K views · 30 likes
· 2 reposts · 0 replies
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Maybe there are some people in this profession about whom you could write a legitimate personal hit piece. Daron is not one of them. He's selfless, endlessly curious, and a true treasure for our profession.
10.2K views · 310 likes
· 11 reposts · 2 replies
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The convos I had with Daron about it reached a level of depth far beyond what ever ended up in my JMP and it's eventual ecma publication. He just shared a curiosity. As a student, it was exhausting to keep up with. I now take it as a model for how we should approach our work.
9.5K views · 69 likes
· 3 reposts · 1 replies
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To this day, I'm one of the few economists whose papers don't start with a vector of Acemoglu citations. His efforts in my work were if anything a distraction relative to his line of work that ultimately won the Nobel.
9.4K views · 66 likes
· 2 reposts · 1 replies
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To this day, I can tell you why the mean residual life function is so cool and characterizes market non-existence.
10K views · 50 likes
· 1 reposts · 1 replies
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On the empirics, I faced an extremal quantile estimation problem. Daron pushed me to show how it relates to Victor Chernozhukov's work but differs from boundary biases arising in kernal estimators (neither of these things ended up in the paper, just shared thoughts in our minds).
9.7K views · 43 likes
· 2 reposts · 1 replies
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When I wanted to run a regression to measure a conditional expectation, he had me read about nonparametric estimators and chebyshev polynomials, because why would a linear regression be the best?
11.2K views · 73 likes
· 1 reposts · 1 replies
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When I'd worked through some theory and wanted to do some comparative statics, I did some work with parameterized models. That clearly didn't satisfy Daron. He pushed me to read Shaked and Shantikumar's work on stochastic orderings to characterize the assumptions I actually need.
10.7K views · 58 likes
· 1 reposts · 1 replies
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When I started to use subjective probability elicitations to measure beliefs, he pushed me into the depths of the savage axioms and blackwell's notion of the value of information to get clear about what I was measuring. To this day, Savage's foundation of statistics is a favorite
12.2K views · 68 likes
· 3 reposts · 1 replies
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Charting these new waters, there is NO ONE I would have ever wanted to have by my side other than Daron. Chatting with him weekly, I was challenged and encouraged in ways no one else could have done.
15.5K views · 85 likes
· 3 reposts · 2 replies
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Daron pushed me deep into the theory of asymmetric information (I think he can still state the difference between Riley 1979 ECMA and Wilson 1977 equilibria by memory). He knows not just the main papers in the field, but the details of all the papers in the related field journals
13.4K views · 76 likes
· 2 reposts · 1 replies
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I wrote a thesis on insurance markets that took a different route to thinking about how info asymmetries affect the market. Instead of looking for contracts that were adversely selected, I asked whether the threat of adverse selection was preventing contracts from existing.
15.7K views · 75 likes
· 3 reposts · 1 replies
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OK, a personal @DAcemogluMIT story. Little known fact: Daron was my advisor. Surprising perhaps because I do public economics (I may be his only empirical PF student?). He shaped my understanding of how to do economics.
146.7K views · 899 likes
· 72 reposts · 4 replies
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First it was Bowling Alone, now it's "Typing Alone". Important work from @NataliaHEmanuel and @emma_k_h
5K views · 37 likes
· 3 reposts · 1 replies
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Published an op ed in @nytimes: remote work hurts mental health.
Let me explain why
🧵⤵️ https://t.co/xE9XpyFLtb
1.9M views · 2.2K likes
· 241 reposts · 712 replies
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This is an excellent paper and a must read for any graduate student interested in corporate tax policy
12.1K views · 47 likes
· 4 reposts · 1 replies
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I cover all of this and more in a working paper: https://t.co/XNOYLK795F. First line of abstract: This article corrects a 60-year history of mis-application of the neoclassical theory of investment to interpret empirical work and guide policy analysis.
18.5K views · 62 likes
· 11 reposts · 2 replies
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We are glad to announce: 𝗥𝗶𝗰𝗵𝗮𝗿𝗱 𝗠𝘂𝘀𝗴𝗿𝗮𝘃𝗲 𝗩𝗶𝘀𝗶𝘁𝗶𝗻𝗴 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿𝘀𝗵𝗶𝗽 𝟮𝟬𝟮𝟲 awarded to @nhendren82 (MIT).
He will deliver the 18th CESifo and IIPF Richard Musgrave Lecture on 15 April 2026 in Munich @ifo_Institut
ℹ️ https://t.co/BRAgPhSsyL https://t.co/ROh96cPh9n
9.2K views · 13 likes
· 3 reposts · 0 replies
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🚨*New Paper Alert*🚨
What are the most effective ways to fight climate change? What are the returns to subsidies for wind and solar? What about EV subsidies or energy efficient appliance rebates? How about raising revenue through fuel taxes or cap and trade auctions?
1/: https:/
109.2K views · 401 likes
· 105 reposts · 18 replies
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Face aux comptes de taille comparable
13 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.
Vues médianes10 705ce compte924médiane pour 10K–100K
Portée, %93.82%ce compte3.62%médiane pour 10K–100K
Engagement, %0.59%ce compte1.52%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 10 705 | 924 | 11.6× |
| Portée (vues ÷ abonnés) | 93.82% | 3.62% | 25.9× |
| Taux d'engagement | 0.59% | 1.52% | 0.39× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
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
5K18 Jun
146.7K20 Aug
15.7K
13.4K
15.5K
12.2K
10.7K
11.2K
9.7K
10K
9.4K
9.5K
10.2K
8.8K
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
0.82%18 Jun
0.67%20 Aug
0.50%
0.59%
0.59%
0.59%
0.56%
0.67%
0.47%
0.52%
0.74%
0.77%
3.16%
0.36%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes50.8%4 694 in total
Reposts5.1%475 in total
Replies8.1%752 in total
Quotes5.9%542 in total
Bookmarks30.1%2 783 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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