Ben Harrell 🌐

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Ben Harrell 🌐

Ben Harrell 🌐

@elben

Asst Prof of Economics @Trinity_U | health insurance, health policy, & health disparities. Abundance bro. Past: @vanderbiltecon @LGBTPolicyLabVU and @aysps

San Antonio, TX Katılım Mayıs 2008
3K Takip Edilen4.9K Takipçiler
Noora
Noora@gynemimesis·
@StatisticUrban Wrong. I ran my own numbers using the okcupid dataset, and gays earn 15% less after controlling for age and race
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Hunter📈🌈📊
Hunter📈🌈📊@StatisticUrban·
Married gay men have absurdly high incomes.
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Ben Harrell 🌐
Ben Harrell 🌐@elben·
@nomadj1s @besttrousers @jessespafford Basically the same insight as the hospital mergers literature. Local markets matter most for effective market power, and are defined by a complex combination of features commute times, ancillary services, and other factors that limit substitutability.
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Joseph Steinberg
Joseph Steinberg@jbsteinberg·
@NathanJRobinson FYI: the anonymous writer Unlearning Economics is not an actual economist. They're just an online weirdo with a grudge against the economics profession going back almost 20 years.
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Ben Harrell 🌐
Ben Harrell 🌐@elben·
A few more spots to fill! If you're interested in being a discussant or chair of a session in health equity at ASHEcon 2026 pls shoot me a DM!
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Ben Harrell 🌐 retweetledi
Ben Harrell 🌐
Ben Harrell 🌐@elben·
@wwwojtekk We may never step in the same river (or policy regime) twice, but sometimes they're close enough to motivate good inference. History rhymes!
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Ben Harrell 🌐
Ben Harrell 🌐@elben·
@AlexCaswen @tbsama03 I think there’s a strong case to be made for preferential admission of young men into college for this reason. The point of putting your thumb on the scale is to ensure outcomes map onto the population distribution.
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Noah Smith 🐇🇺🇸🇺🇦🇹🇼
I've always been against racial discrimination of any kind. But because I got into (and out of) academia before this started, and because I've never really worked a corporate job, I didn't know how widespread it was. No one really talked about it until the 2020s.
T. Greer@Scholars_Stage

This piece is justly being shared around. All I will add is this: if you want to understand the venom behind DOGE, the attacks on academia, and the grants being cut across the government... this is the thing to read, really. This is the main reason that all is happening.

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Ben Harrell 🌐
Ben Harrell 🌐@elben·
@RuxandraTeslo A genuine question: would course correction from several generations of white male dominance over all other groups not empirically look like a decline in the relative power of white men? Was that not the entire point of improving the prospects of women and minorities?
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Ruxandra Teslo 🧬
Ruxandra Teslo 🧬@RuxandraTeslo·
I think discrimination against white men in academia, especially humanities, is a big deal. But, arguably, an even greater source of discrimination is one's political opinions. Anything slightly right of centre is punished imo more than race, gender etc..
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Ben Harrell 🌐
Ben Harrell 🌐@elben·
@tbsama03 @Noahpinion Yeah. I think we should have priced this kind of stuff into our mental model. Not in a “nothing to see here way,” but in a “this was to be expected” way.
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John Cawley
John Cawley@cawley_john·
One last (sobering) update on the #EconJobMarket before the winter holidays; data as of 2025-12-14. In terms of # of job listings on JOE, this is the worst job mkt in recent yrs for PhD Economists. # jobs down 20% from last yr, even 18.9% lower than during COVID (2020).
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John A. List
John A. List@Econ_4_Everyone·
I saw the below picture over on the always astute @instrumenthull. When I used to teach Econometrics, I followed this very approach: econometrics involves three distinct objects and two distinct intellectual tasks. This framework is also beautiful in that it reveals exactly why experiments are so powerful. With observational data, the identification arrow is where all the heavy lifting happens. You want to know a causal effect (your parameter), but you can only observe correlations in the data (potential estimands). The gap between correlation and causation is the identification problem. Random assignment cuts through this problem by construction. When you randomly assign people to treatment or control, you guarantee that the two groups are comparable on average, not just on observables, but on everything, including things you cannot measure.
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alz
alz@alz_zyd_·
tl;dr: causal inference is a kind of prediction because causal inference is just trying to predict the effects of policy interventions
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Ole Peters
Ole Peters@ole_b_peters·
@LughSpear The circularity is this: Joan places a brick. A: moving bricks must have utility for her. B: Why is Joan moving bricks? A: It has utility for her. B: Ah. Evolutionary arguments are an alternative: a house prolongs Joan’s life. Prediction: we may find her moving bricks.
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