Taggert Brooks

Husband, Father, Dean, Professor, Economist, Cyclist.

[Taggert Brooks @brookstagg]

Ripped from the headlines. More college credit for high schoolers on the horizon. Our shared mental model of learning seems off.

[Taggert Brooks @brookstagg]

@johnarnold This experiment has been done. https://t.co/PyJk5r36R0

[Jay Van Bavel, PhD @jayvanbavel]

Social media discourse is driven by false #polarization

Analyzing 1.7 million social media posts, a series of controlled experiments, and a large global survey covering 40 countries (total N = 49,222), we find that

(1) strong ideologues post their opinions significantly more often than moderate individuals
(2) extreme attitudes are posted more often than moderate attitudes.
(3) This general pattern replicates robustly across 40 countries
(4) the opinions that users post are more hostile and toxic than the beliefs they keep to themselves.

Across studies we find extreme opinions are overrepresented on social media by at least 6% and up to 22% compared to their true prevalence in our samples.
https://t.co/NKovC9Uisj

This project was led by @small_schulz and @ClaireRobertson and includes @H_Sjastad @AshuAshok @LisaFOswaldo @junghwanyang @simonsaysnothin and @andyguess

[Paul Novosad @paulnovosad]

Over half of people who end up homeless had contact with child welfare services, fewer than a quarter graduated from high school.

Homelessness is predictable from data on government service use. (Data is from the full population of British Columbia.)

[Crémieux @cremieuxrecueil]

The increase in gambling disorder rates is much more marked among exactly who you expect:

Young men.

The effects on women and the old have been much milder.

[Crémieux @cremieuxrecueil]

The medical community has cured a mountain of diseases in the past several decades.

Diseases cured thread🧵

In 2013, hepatitis C was cured by direct-acting antivirals.

[Taggert Brooks @brookstagg]

What are the reasons Warsh might be intentionally less transparent about his and the fomc's view on the state of economy and future direction of monetary policy?

[Noam Dworman @noam_dworman]

Second for second, @tylercowen packs more substance into a talk than anyone I'm aware of. This is a clear, non-hysterical, and somewhat soothing discussion of our AI future.

[Claudia Persico (@claudiapersico.bsky.social) @ClaudiaLPersico]

We asked four LLMs how exposed your job is to AI. They could NOT agree. Management: 15% vs 90% Legal: 10% vs 75% Healthcare: 5% vs 60% Same rubric. Same jobs. Same data. Different AI, completely different answer. New @nberpubs working paper with @MichelleYinPhD & @hoa_vuxuan! 1/

[Taggert Brooks @brookstagg]

@ernietedeschi My guess is that AI exposed industries are particularly uncertain about the future outlook for task and jobs in their industry and thus have just slowed hiring, impacting new grads the most. But it is less about AI itself causing permanent change to the hiring of the new grads.

[Ethan Mollick @emollick]

Every system that was regulated, either explicitly or implicitly, by the fact that they were effortful for humans (letters of recommendation, lawsuits, government filings, essays) will break.

[Daron Acemoglu @DAcemogluMIT]

Dear followers, please see this conversation with Jon Stewart and David Autor on AI, work, inequality and learning. I personally had a great time.

[Timothy B. Lee @binarybits]

This essay by @alexolegimas is the best thing I've ever read on why AGI won't lead to mass unemployment. A compelling argument backed up by substantial empirical data.

[Ernie Tedeschi @ernietedeschi]

New from @Stripe Economics today:
One of the biggest labor market uncertainties is whether AI will displace human work at scale. Evidence so far is still early & mixed. The story of travel agents—a clear case of tech displacement—is instructive. And not entirely bleak. 🧵 /1

[Alex Imas @alexolegimas]

AI cannot reduce inequality if this is what the adoption margin looks like.

https://t.co/IUw1MZ8y8G

[Taggert Brooks @brookstagg]

@robertwiblin @Jess_Riedel @jburnmurdoch Is it really because they are "smarter"? Or are they just reflecting regression to the mean?

[Taggert Brooks @brookstagg]

@EvanLuthra You should be a little skeptical of non-economists making strong economic predictions. Come to think of it, you should be skeptical of anyone making strong predictions.

[John A. List @Econ_4_Everyone]

The most likely interaction any of us will have with the police is a traffic stop. That simple fact accentuates the import of the following question: when an officer decides to pull someone over, does race play a role? It sounds like a straightforward question. It is not. And the reason comes down to a measurement problem that has haunted this literature, and many others, for decades.

What do you need to properly explore this question? Three numbers: i) how many minority drivers were stopped, ii) how many minority drivers were on the road, and iii) how many minority drivers were actually speeding. Knowing all of these is a tall task, especially knowing who was speeding among those who were never pulled over.

I call this the denominator problem and measuring discrimination critically relies on solving it. Prior research has a bunch of creative: veil-of-darkness designs, daytime versus nighttime comparisons, benchmark approaches using census data. All clever. Yet, they do require certain assumptions that everyone is not comfortable making.

A paper that I just talked about at our student visitation day this past Friday (and I will talk about this week in my Economics for Everyone course) solves it differently. When I was back at Lyft, we leveraged records on the GPS location of every driver every few seconds. That gives us actual driving behavior — speed, location, time — for over 200,000 drivers before any police interaction occurs. We matched those records to official Florida speeding citations secured through a Freedom of Information Act request (kudos to Florida, still the only state to adhere to our FOIA).

As far as I know, this is one of those rare occurrences where we have solved the denominator problem: who was on the road and exactly how fast are they driving?

Guess what we found? You can find out here (it is depressing so I am not going to recount it): https://t.co/micQtqBSWp

But the broader lesson I want to make with my students is that the denominator problem is not unique to discrimination research. It shows up everywhere in social science. Hiring discrimination studies rarely observe the full applicant pool and almost never observe underlying qualifications. Health disparities research cannot see who never sought care. Criminal justice research fights this at every turn.

The general insight is this: selection into measurement is itself the phenomenon you are trying to study. In certain cases, partnerships with organizations can help to solve that key issue.

[Arin Dube @arindube]

I've spent a lot of time with Claude Code in the past 4 months. There have been 3 types of interactions. I want to share a bit here, since most of my fellow economists have a lot to say about AI, apparently.

1) Specific coding asks - too many to count, very much productivity enhancing in specific tasks. Saves time in routine programming. Great.

2) Working with CC with a lot of interaction to model and calibrate: a ton of hand holding but getting very quick feedback on ideas and iteration. I have only *one* successful example of that and it'll likely go into a publication. Very productive, but largely as a highly effective RA who happens to be able to give very fast feedback on ideas (like simulating a model, or estimating using data, to test out a hypothesis). And it took a lot of my time to make it work, with tons of time where CC was going to go off the deep end without my hand holding. So, my managerial capacity was absolutely a critical constraining factor. I can't do this for too many projects.

3) Letting CC "write a paper" from scratch on an interesting topic and prompts, and minimal detailed instructions. Here I found CC roaming free to be highly unpredictable in quality, and largely a disappointment. On matters I have expertise it would often go on the wrong track and reach a dead end unless I veered it away. This includes theory and empirics. When I let it just write a paper based on its findings the quality was questionable, and at the minimum would require major time commitment to make it viable. I have 3 such full-paper drafts. I doubt any of them will see the light of the day. Maybe someone smarter than me can seed CC with the right starting point to produce an enormous number of successful papers. Or, maybe not.

Bottom line: based on my very personal experience (not gonna say you all are like me), this makes me feel (1) there are very clear but limited productivity gains in the pipeline (2) people who appear to think we're at the cusp of the singularity will probably be deeply disappointed.

[Alex Imas @alexolegimas]

Every tech CEO for the last 30 years: College is useless. drop out of college and build something. Find a trade.

Also every tech CEO: *only hires from Stanford/MIT/Harvard*

Also every tech CEO: *spends millions getting their mid kids into Stanford/MIT/Harvard*