I am a freelance writer based in Brooklyn. Previously I worked at Glo Foundation, Riskified, and Code Ocean. I thru-hiked the Appalachian Trail in 2021.
👋 which meta-analyses did you look at? I have looked into this a subject a bit and would be curious to read more. Thanks!
I think you are going to have a very hard time convincing EAs that this should be a core, or even peripheral, EA cause area.
In your previous piece, you cite Matt Desmond's estimate that 5.4 million Americans live in extreme poverty. I think this is probably an overestimate, but taking it at face value, that's less than 1% of all the people who live in extreme poverty globally. Accordingly, if global poverty is your top priority, the impartial altruism principle implies that the extreme poor in America should receive less than 1% of the attention you devote to it. Exceeding that 1% attention budget is going to be a very high burden of proof to meet, given our strong shared commitments to on-the-margin thinking (there are many, many social programs and organizations devoted to the welfare of the poor in America) and impartiality.
On a different note; if you want to work on reducing poverty in America, what's to be gained by applying an EA label to it or convincing others to do so? My 2c: it's perfectly well and good to work on causes that motivate you and where your work is plausibly +EV, labels be damned.
Very nice report, and thank you for sharing it here.
I am currently working on a meta-analysis of interventions intended to reduce MAP consumption -- first draft published on the forum here. My main question about this paper is: did you all collect (or consider collecting) MAP consumption outcomes? I like the behavioral outcomes you collect, and I think that giving money is not at all a cheap signal. I'd also be interested in whether a week later people are still thinking about it in a way that affects their purchases. (I am guessing that the mTurk-based design made collecting these kinds of follow-up data challenging.)
Very nice! Apropos of:
Which settings are most conducive to running rigorous experiments on dietary change interventions, and how can these settings be accessed/used? (For example, college cafeterias allow data on purchases to be used, so that researchers don’t have to rely on self-reports.)
I'd suggest retirement communities. We have a fair bit of data on changing the eating habits of college students (I review the most rigorous studies in that literature in this meta-analysis), but much less on adults, and essentially nothing on older people. Like college cafeterias, retirement communities offer a lot of opportunities for oblique monitoring, as well as a bevy of potential qualitative outcomes (e.g. staff members who know that 'Daisy always orders the chicken' might observe that she finishes less of her meat after watching the movie 'Babe'). My advisor suggested this empirical strategy a few weeks ago and I think it's on the money.
As it happens, my coauthor and I suggest some studies we'd like to see in section 6.4 of that meta-analysis, and I'm working now and getting some of these off the ground. I'd be glad to hear any feedback you might have.
Pro-immigration orgs probably meet the bill, e.g. https://malengo.org/ or https://freemigrationproject.org/ (see here for discussion: https://vipulnaik.com/blog/my-q1-2022-donation-to-free-migration-project/)
I don't know much about these org's efficacy, but we generally have good reason to think that more immigration will lead to more growth: https://www.aeaweb.org/articles?id=10.1257%2Fjep.25.3.83
Thank you, I had not seen this Vox piece, Lewis Bollard's thread, or pieces you link to.
My attention is now mostly on expanding a different piece but if time permits, I'll return to this one and incorporate the above evidence. A few quick thoughts
anyway, much to read!
Wow that’s a very large number! I shall take a look at the paper, thank you. My first thought is that I don’t think 1/5 premature deaths is attributable to MAP — overeating, maybe, but that could be true of plant-based diets too.
Chek out Maya Mathur’s slides on the state of nudging research: https://osf.io/encd5
I also wrote a recent meta-analysis of MAP reduction research that identifies some high-quality RCTs as well as collates some prior systematic reviews: https://forum.effectivealtruism.org/posts/k9qqGZtmWz3x4yaaA/environmental-and-health-appeals-are-the-most-effective
From a moral philosophy/psychology perspective, check out Lucius Caviola at Harvard or Eric Schwitzgebel : http://www.faculty.ucr.edu/~eschwitz/
These aren’t exactly syllabi, but I think that between these researchers you’ll be able to put something nice together.
I would be obliged to see the final product when you have one!
Thank you for engaging Alex!
(The following is a lightly edited version of an email I sent to Alex earlier this weekend)
I just fixed that typo, TY [I actually fixed it in draft on Saturday and forgot to implement]
1. Comparing online to IRL studies -- I will think about how to integrate, e.g. a study that finds similar results wr.t. the effects of intergroup contact on prejudice, but I'm not sure how much this generalizes across the behavioral sciences.
2. You're right about motivations; for the EA forum and a preprint I think we can take for granted that people agree that we should collectively eat fewer animal products, and truth be told I'm not sure what kind of journal we're going to aim for yet, so we left that kind of underspecified.
3. There are some studies that compare multiple strategies within one sample! See Feltz et al. (2022), Norris (2014) and Piester et al. (2022), though admittedly these are generally trying to test multiple implementations of one theoretical perspective, as opposed to your idea which puts the theoretical approaches head to head. I also think that's promising. I am soon to put a research agenda on this subject together and I will think about how to incorporate that.
Thanks for engaging as always!
P.S. I went to a lovely vegan donut shop in Beacon this weekend and the person working there mentioned that they don't always emphasize the vegan labels for certain customers because of the mixed connotations. Then again, a lot of the vegan places near me have vegan in their name --seasoned vegan, slutty vegan, and next stop vegan come to mind. This is probably a regional/NYC thing but still something I've been more on the look out for since reading your paper.
You are 100% right about this, my mistake. First, I read your first comment too fast (I placed 'binary' on the wrong side of the equation, as you noticed), and second, I think that the original paragraph confuses percentage change with percentile change. I removed the section.
I still want the final draft to present some intuitive, drawing-on-stats-that-we-learned-in-HS way to put standardized mean effect sizes into impact estimate terms, but I think we need to think more about this.
Thanks for engaging! FWIW I ran through your code and everything makes sense to me