Angelina Li

Data Analyst @ Centre for Effective Altruism
Working (0-5 years experience)


Hiya! I work on data stuff at CEA. I used to be the content lead on the EA Global team at CEA, and before that I did economic consulting. Here's an old website I might update at some point.

Think I'm making a mistake? Want to give me feedback? Here's my admonymous.


Thanks! I conducted most of the analytics underlying the post. I sympathize with the issue you point out here! The explanation is kind of boring: the data has limitations that make more granular analyses tricky.

In 2022, the EA Global team collected race/ethnicity data exclusively using free-response fields in the application and feedback forms. For this post, we asked assistants working for the events team to hand code each unique response to two fields: (i) whether or not someone is POC, and (ii) which US Census race / ethnicity category this corresponded with. On (ii), I chose this mostly to be consistent with how e.g. the EA Survey in 2020 coded race/ethnicity data, and to allow for easier further analysis.

This second hand categorization is necessarily less accurate than what people would have marked themselves. In particular, our disaggregated race/ethnicity counts are probably less accurate than the “is POC” / “not POC” labeling. As an example, if someone reports they are “Thai / Indian”, I don’t have great guesses for whether they would have marked themselves down as “Asian” or “Multiracial”, but it seems fairly likely to me that they would fit under the “people of color” umbrella. Incidentally, I suspect this kind of issue might be why the EA Survey reports a much larger percentage of multiracial EAs than we do in our attendance numbers.

For speakers, as mentioned in the footnotes most speakers did not give us race / ethnicity data, and so I hand coded a binary “is POC” flag myself. For a variety of reasons coding a more granular flag would have taken much more effort, so we skipped that exercise.

As a second general problem, all of the data we are working with is pretty small, splitting the race/ethnicity data up more granularly makes each cohort smaller, and doing meaningful statistics on small samples is hard.

For the two above reasons, we presented mostly findings on the less granular level here. We might eventually take a look at this question, but I expect this would be a non-trivial lift, so we are currently not prioritizing it over other projects.

As an aside, the events team as a whole is conscious of the dynamic where the term “people of color” hides some important nuance, and doesn’t try to optimize for only this binary categorization when thinking about diversity considerations. (I no longer work on the EA Global team and am passing this on from speaking with the team.)

Wow, thank you so much for this! I was looking for exactly this type of product a couple of months ago, and was feeling frustrated at the lack of good options in this niche.

Really excited to try this out!

It'll be hard to see you go, Max!

I’ve loved to be a part of a culture where staff are valued and empowered to do things.

I think of you as playing a major part in creating that culture (thank you :) ). I remember being really impressed when joining CEA how you take time to individually message & appreciate staff, meet regularly with everyone 1:1, and take staff feedback really seriously.

I admire you a ton, and I'm sorry this whole thing has taken such a toll. That really sucks. I'm very glad you're getting more rest these days, and am excited to hear about what you do next!

Some low effort thoughts:

  • If this is meant as a living resource, maybe move the first 2-3 paragraphs to the bottom of the post, and leave just a one line explainer at the top, to make it easier to skim ("There are now more free or discounted services available to EAs and EA orgs. Here is an updated list, which is mostly a repost of [this].")
  • Maybe worth linking to your anti karma farming comment in the post so ppl can find it easier?

Other things that might belong here:

My impression is AISS does a bunch of things outside from the health consulting thing fwiw, like maintaining this and this.

Thanks, this is an interesting post! (I was going back reading some posts on aquatic animal welfare and came across this).

I think the crux for me is:

I assume that the welfare gains of the fish stunner (the elimination of asphyxiation suffering of a small wild fish), is equivalent to the welfare gains of a cage-free system or higher welfare broiler breed for a chicken during a half-day.

I think this paragraph is hiding a lot of the work for me. I'd be interested in reading  a follow up (even in a quick BOTEC-y form) for:

  • How much better (per individual) do we expect stunning to be relative to asphyxiation
  • How should we weight this quality reduction relative to suffering from other animals

I guess I don't know enough to assess whether the heuristic of comparing "stunning v. asphyxiation" to "half-day of cage-free / higher welfare breed v. baseline" is sensible.

There are multiple regions where this can be done: Peruvian coasts, Black Sea, Sea of Japan, Yellow Sea, and East China Sea, South African coasts etc. 

Also curious where this list of examples is generated from?

Have you considered brushing this up and sending this as an idea for Charity Entrepreneurship to consider incubating as a charity (or consider applying yourself if you think you would be a good fit)?

I found this an interesting framing, thank you! I hadn't heard of the multidimensional poverty index before. 

(1) Do you know how widely this measure is currently being used in e.g. development research, charity evaluation? I was kind of surprised at how specific some of the components of the index are (e.g. I imagine the below is kind of hard to straight forwardly calculate based on past surveys -- not sure if all of these questions are standard to ask?).

Deprived if the household does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle, motorbike or refrigerator and does not own a car or truck.

(2) Minor point: I wonder if you will reach more of your intended audience by changing the title of this post to "The Capability Approach (to Improving Human Welfare)" or something. I initially pattern matched the word "capability" in this title onto something about AI, since I think on the EA forum folks talk more about capability in terms of AI systems than anything else.

Wow, 9 placements in 3 months is incredible!! I'd be curious to see that in terms of FTE equivalents (is it mostly part time staff?).

What are you counting as a "placement" here -- someone first noticing a persons' name on Pineapple, or did you also ask about counterfactuality (would the employee have applied otherwise, or would the employer have reached out from another source?).

I'd also kind of be interested (if you have capacity to share) if the folks placed are "new" to EA (operationalized maybe as 'no former full time job at an EA affiliated org'), or were already in the labor pool.

I like "Is this going to mislead people?" or "Am I communicating as honestly as I can?" for communications, in the spirit of Chana's onion test post for personal integrity (which I think about often).

(Edit: really like this list! :) )

FWIW, some resources I find useful in mapping just bullet (1):

I agree it's still a messy space. Although I worry about this failure mode for anyone thinking about adding new standards.

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