Focused on impact evaluation, economics, and (lately) animal welfare
geoffrey
Karma: 602
I’m loving this series so far. I got two questions if you’ve got some time to answer them.
What categories do you use for time-tracking? I find research tasks unusually hard to categorize.
Do you find that earlier stages in the Ideation → Exploration → Understanding → Distillation pipeline take more time to get good at? My experience is that I improve at later stages far earlier and far faster than earlier stages (passable at Distillation before Understanding, passable at Understanding before Exploration, passable at Exploration before Ideation). And anecdotally, I heard people can take a very long time to come up with a good research idea.
I found it very valuable but (barring any major changes like there being 5 new organizations of similar impact) I wouldn’t find an expanded or updated version that useful.
Interesting! I think my intuition going into this has always been stretching so that’s something I could rethink
Ah I missed the point about the relationship getting flatter before. Thanks for flagging that.
I think I’m more confused about our disagreement now. Let me give you a toy example to show you how I’m thinking about this. So there’s three variables here:
latent life satisfaction, which ranges from 0 to infinityreported life satisfaction, which ranges from 0 to 10 and increases withlatent life satisfactionprobability of divorce, which ranges from 0% to 100% and decreases withlatent life satisfaction
And we assume for the sake of contradiction that rescaling is true. One example could be:
In t=1,
latent life satisfaction = 1 * reported life satisfactionBoth are bounded from [0,10]
In t=2,
latent life satisfaction = 2 * reported life satisfication.Reported life satisfaction still ranges from [0,10]
But latent life satisfaction now ranges from [0,20]
Let’s say that’s true. Let’s also assume people divorce less as they get happier (and let’s ignore my earlier ‘divorce gets easier’ objection). One example could be:
In t=1 and t=2,
probability of divorce = 0.40 - latent life satisfaction/100. That implies:In t=1, probability of divorce ranges from [0.40, 0.30]
In t=2, probability of divorce ranges from [0.40, 0.20]
And so if I got the logic right, rescaling should accentuate (make steeper) the relationship between
probability of divorceandreported life satisfaction. But I think you’re claiming rescaling should attenuate (make flatter) the relationship. So it seems like we’re differing somewhere. Any idea where?
This is really neat analysis idea.
At the same time, my hunch is that all three of these exit actions have gotten easier to do and more common from 1990 to 2022. I believe divorce has gotten less stigmatized, the job market rewards more hopping around, and (I think) hospitalization has been recommended more.
If that “easier-to-do” effect is large enough, then it’d be compatible with a very wide range of happiness trends (rising/falling/stable + rescaling/no rescaling) Wondering if you have any thoughts on that.
Hoping to post some shallow takes on global health and development this year!
This is incredible. I skimmed all the sections and I’m impressed with the quality, scope, concreteness, and kindness throughout.
This is an area where Probably Good blows the 80,000 Hours version out of the water. I’ll be pointing people here for a one-stop shop for all their job searching needs and almost certainly coming back to this in the future.
Quick anecdote: I found this dynamic surprising in all the paths you mentioned: academia, research, EA research, and non-profit work. But I realized it very quickly for academia (one month) and painfully slowly for non-profits (several years).
In academia, the markers of success are transparent, the divide between “good” and “bad” jobs is sharp, and even very successful professors complain about the system.
On the other extreme, the non-profit sector is much fuzzier about credentials and career progression. So I might see an employee who graduated from a school like mine and think I could be that person, but not realize that they took on increasing amounts of responsibilities over various low-paid volunteer roles or not realize how much insider information they absorbed from their parents.
This caused me to underestimate how many skills I needed to build. Then I underestimated how long it would take to “make it”, which meant setting goals too high for myself and almost giving up too early.
Hey Ozzie, a few quick notes on why I react but try not to comment on community based stuff these days:
I try to limit how many meta-level comments I make. In general I’d like to see more object-level discussion of things and so I’m trying (to mixed success) to comment mostly about cause areas directly.
Partly it’s a vote for the person I’d like to be. If I talk about community stuff, part of my headspace will be thinking about it for the next few days. (I fully realize the irony of making this comment.)
It’s emotionally tricky since I feel responsibility for how others react. I know how loaded this topic was for a younger me, and I feel an obligation to make younger me feel welcome
These conversations often feel aspirational and repetitive. Like “there should be more X” is too simple. Whereas something like “there should be more X. Y org should be responsible for it. Tradeoffs may be Z. Failure modes are A, B, and C.” is concrete enough to get somewhere.
I also share these frustrations with career advice from 80,000 Hours and the EA Forum. There was time about 2 years back where my forum activity was a lot of snarky complaints (of questionable insight) about career advice and diversity.
Like you mentioned, the career advice usually leaves a lot to be desired in the concrete details of navigating a lack of mentors, lack of credentials, lack of financial runway, family obligations, etc. I’ve sometimes wondered about writing an article to fill in the gap, but it’s not exactly a “one article” sized hole. Maybe that’s a yearlong project you or I or someone else can work on someday.
As for my comment on “above-the-curve”, I think we’re in agreement but I could have worded this better. I don’t think the community is diverse but the initiatives are much higher quality than I see elsewhere. Usually, these initiatives range from bad to useless. Whereas this list of EA diversity initiatives feels mostly harmless or slightly positive nudges. A few feel like they’ll pay dividends in a few years.
This is great stuff. I often find it hard to remember a lot of initiatives have happened (despite having read 80% of this list already) so this timeline is a good reference
As an aside, I think others may benefit from reading about diversity initiatives outside EA to remember this is hard problem. It’s totally consistent for EA to be above-the-curve on this and still not move the needle much (directionally I think those two things are true but not confident on magnitudes), so linking some stuff I’ve been reading lately:
DEI Deconstructed: Your No-Nonsense Guide to Doing the Work and Doing It Right has two neat chapters at the beginning: one on corporate initiatives over the past 10 years and another on US initiatives since World War II. The tidbits on US military diversity initiatives (and their mixed results) were new to me and sadly not something I see talked about much elsewhere. The rest of the book seemed to be a corporate strategy workbook which I didn’t find useful but others might.
Affirmative Action and the Quality-Fit Trade-Off explores the stronger forms of affirmative action practiced at law school + elite undergraduate universities. It summarizes the economic theory and evidence on whether these stronger forms can “backfire” for minority students. Ultimately, it’s hard to say but mild forms seem good. Plato Stanford Encylopedia of Philosophy takes a legal and philosophical look at how the justification and practice for affirmative action has shifted over time.
Echoing what Eva said, I think you should consider waiting a year then apply for IDE / applied econ masters. An IDE program is probably the right fit given your goals, but I don’t know any beyond Yale’s IDE which expects you to already have worked in development first.
For Applied Econ, I like University of Maryland’s Applied Economics Master’s program. The program only requires Calc I and is very transparent about what it can do. Dev / global health placements, content, and networking will take a huge hit compared to IDE programs though.
You can use the year in the workforce to save money and take online classes on the side. Believe me, you’ll want the savings. Development and global health can be financially tough in early career.
In general, the econ and math background required isn’t too high for these type of real-world Master’s degrees. Working + getting good grades in first-semester calculus, first-semester probability and stats, intermediate micro, and intermediate macro may be enough for admission.
This is my favorite in the series so far. I really enjoy the tacit knowledge flavor of it and that some of these lessons generalize beyond the CBA domain.
Would also love this. I think a useful contrast will be A/B testing in big tech firms. My amateur understanding is big tech firms can and should run hundreds of “RCTs” because:
No need to acquire subjects.
Minimal disruption to business since you only need to siphon off a minuscule portion of your user base.
Tech experiments can finish in days while field experiments need at least a few weeks and sometimes years.
If we assume treatments are heavy tailed, then a big tech firm running hundreds of A/B tests is more likely to learn of a weird trick that grows the business when compared to a NGO who may only get one shot.
Something I’ve noticed more in the EA Forum is the increase in drive-by professional posts. Organizations will promote a idea, a job posting, or something else. Then they’ll engage as long as they’re on the front page before bouncing.
That’s fine in small amounts or if the author is a regular contributor. But if the author is just stopping by to do their public engagement, then it breaks the illusion of a community.
And for me, that is the aesthetic draw of the forum. It’s a place where expects and amateurs alike coexist in the same space, say things that are too rough for professional publication, and then respond to each other in real time.
It’s magical and unreal that I can develop these (admittedly shallow and sometimes parasocial) relationships with people. It’s cool that I have some chance of getting a leading expert to respond to my quarter-baked comment. It’s cool that people sometimes recognize me in real life from what I wrote online.
And that feeling has been decreasing over time, which has made me lean more towards Slack, Discord, or even Twitter for real-time engagement. Meanwhile, I treat the Forum more as a searchable repository for EA-style research
This is a class act in reasoning transparency. I love how easy it is to skim and drill down into things for more detail. Same goes for the pre-print and replication code.
Nits:
I was confused what calibration meant since I think of this exercise as simulation. To me, calibration is taking some observed data and reverse engineering the elasticity. But this is starting with the elasticity values—taken from the Bouyssou et al. (2024) meta-analysis—and seeing how that goes forward in a pretend-tax situation.
I would have loved to hear about how trustworthy you found the Bouyssou et al. (2024) study, since it’s providing the elasticity values for your main analysis. That meta-analysis is really cool and probably the best we have. But it also feels like a bucket of numbers that doesn’t think too critically about its inputs. And cross-price elasticity feels like a tricky thing to estimate, something Rethink Priorities wrote about here.
But it’s still really cool. I like how simple this is conceptually and that (given some assumptions) carbon tax can be net-positive for all animals.
I love this tiny hearts on pink banner aesthetic. Especially in the middle of winter where I’m at.
I’m on a pause with donations for some personal and financial reasons, but if I was on the fence or was procrastinating, this absolutely would have pushed me to do so.
Personal reasons why I wished I delayed donations: I started donating 10% of my income about 6 years back when I was making Software Engineer money. Then I delayed my donations when I moved into a direct work path, intending to make up the difference later in life. I don’t have any regrets about ‘donating right away’ back then. But if I could do it all over again with the benefit of hindsight, I would have delayed most of my earlier donations too.
First, I’ve been surprised by ‘necessary expenses’. Most of my health care needs have been in therapy and dental care, neither of which is covered much by insurance. On top of that, friend visits cost more over time as people scatter to different cities, meaning I’m paying a lot more for travel costs. And family obligations always manage to catch me off-caught.
Second, career transitions are expensive. I was counting on my programming skills and volunteer organizing to mean a lot more in public policy and research. But there are few substitutes for working inside your target field. And while everyone complains about Master’s degrees, it’s still heavily rewarded on the job market so I ultimately caved in and paid for one.Finally, I’m getting a lot more from ‘money right away’ these days. Thanks to some mental health improvements, fancy things are less stressful and more enjoyable than before. The extra vacation, concert, or restaurant is now worth it, and so my optimal spending level has increased. That’s not just for enjoyment. My productivity also improves after that extra splurging, whereas before there wasn’t much difference in the relaxation benefit I got from a concert and a series of YouTube comedy skits.
If I had to find a lesson here, it’s that I thought too much about my altruistic desires changing and not enough on everything else changing. I opted to ‘donate right away’ to protect against future me rebelling against effective charity, worrying about value drift and stories of lost motivation. In practice, my preference for giving 10% has been incredibly robust. My other preferences have been a lot more dynamic.
Hey John, this is very cool to read. I like the focus on what surprised you as a founder (and maybe newcomer?) in the mental health field.
I’m curious to hear more about the implementation details. Could you tell me more about the length, intensity, and duration of a typical treatment program? I saw 6 sessions in a graph which makes me think this is once-a-week program for 1-2 hour sessions over 1-2 months
Less sessions is a reliable way to reduce cost, but my understanding is there’s a U-shaped curve to cost-effectiveness here. 1 session doesn’t have enough benefits but 100 sessions costs too much and doesn’t add more benefit.
Also, are you targeting specific conditions? I see improvement in insomnia but that can arise from a sleep intervention or a general CBT course too
I find searching for in-depth content on the EA Forum vastly better than Reddit. This isn’t just relating to EA topics. There are a few academic-ish subreddits that I like and will search when I’m interested in what the amateur experts think on a given topic. Finding relevant posts is about the same on Reddit but finding in-depth comments + related posts is very hard. I usually have to do some Google magic to make that happen.
Also on rare occasion, I end up liking a person’s writing style or thinking methods and want to deep dive into what else they’ve written about. On the EA Forum, about 100% of what I find will be tangential to things I care about. On Reddit, it’s more likely I’ll have to sift through lots of hobbyist content like about sports since it’s more of a “bring your whole self” platform.