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.)
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.)