EA Survey 2020: Demographics
EA Survey 2020: Community Demographics
We collected 2,166 valid responses from EAs in the survey
The composition of the EA community remains similar to last year, in terms of age (82% 34 or younger), race (76% white) and gender (71% male)
The median age when EAs reported getting involved in the community was 24
More than two thirds (69%) of our sample were non-students and <15% were undergraduates.
Roughly equal proportions of non-student EAs report being in for-profit (earning to give), for-profit (not earning to give), non-profit (EA), non-profit (not EA), government, think tank/lobbying/advocacy careers.
More respondents seem to be prioritizing career capital than immediate impact
The demographic composition of the EA community has been much discussed. In this post we report on the composition of the EA Survey sample. In future posts we will investigate whether these factors influence different outcomes such as cause selection or experience of the EA community.
This year, we removed questions about religion, politics, and diet to make room for an additional set of requested questions. However, we intend to re-include these next year and at least every other year going forward.
The normal caveats about not knowing the degree to which the EA Survey sample is representative of the broader EA population apply. Ultimately, since no one knows what the true composition of the EA community is, it is impossible for us to know to what degree the EA Survey is representative. We discuss these issues further here in a hosted dynamic document (with data, code, and commentary). In that document we investigate how key characteristics of our sample are sensitive to which source (e.g. EA Newsletter, Facebook link) referred participants to the EA Survey and to measures that seem like they may be proxies for participation rates (such as willingness to be contacted about future surveys).
Note: full size versions of graphs can be viewed by opening them in a new tab.
The proportion of respondents who were male, female or other was very similar (to within a percentage point) to last year.
Our race/ethnicity question allowed respondents to pick multiple categories. We re-coded respondents based on whether they selected only one category or multiple. While around 83% of respondents selected ‘White’ (compared to 86.9% last year), some of these respondents also selected other categories. Taking this into account, around 75.9% of the sample selected only white.
The EA community remains disproportionately young, with a median age of 27 (mean 29). Moreover, there is an extremely sharp dropoff in the frequency of respondents from around age 35. Around 80% of our respondents are younger than this age, which means that there are fewer respondents in our sample older than 34 than there are women or non-white respondents.
Changes in Age over Time
That said, as we noted last year, the average age of EA Survey respondents appears to have increased fairly steadily over time. This makes sense because EAs (like non-EAs) get one year older annually. However, this effect is counterbalanced by new EAs joining the movement each year, and these new members being younger than average. In this year’s survey, the average age of respondents was slightly lower than last year (though still higher than 2014-2015).
Age of First Getting Involved in EA
The age at which people typically first get involved in EA has previously been discussed (e.g. here).
The median age when respondents reported having first gotten involved in EA was 24 (with a slightly higher mean of 26). Notably, while young, this is slightly older than the typical age of an undergraduate student (which is often thought of as a common time for getting into EA).
We also observe that the average age when people report getting involved in EA appears to increase across cohorts in our data, with people who got involved in more recent years reporting having heard about EA at an older age.
That said, we speculate that this might be explained by differential attrition. We have previously found that the respondents with the lowest self-reported engagement are, on average, considerably older (a finding which we replicated in the EAS 2020 sample). This could explain or reflect, a tendency for older people in the EA community to be more likely to drop out over time, which would also lead to the observed pattern that respondents from more recent cohorts are older. This tendency could be explained by the EA community being overwhelmingly composed of young people, which may lead to older individuals being more likely to disengage. Of course, it could also be that some factor leads to older people being less likely to join EA (leading to the community being overwhelmingly young people) and leads to older individuals being more likely to drop off.
It is also possible that EA has actually begun to recruit people who are, on average, older, in more recent years. This could be explained, for example, by the fact that the average age of people involved in EA is gradually increasing, making older people more likely to get involved. Note that this need not be explained by ‘affinity effects’ i.e. older people feeling more welcomed and interested in the community because people in the community are older, but by existing EAs recruiting progressively older people through their personal networks (given that older individuals’ personal networks may likewise be older).
This year, for the first time, we included an open comment question asking about sexual orientation that had been requested to examine the EA community’s level of diversity in this area.
Respondents were coded as straight/heterosexual, non-straight/non-heterosexual or unclear (this included responses which were uninterpretable, responses which stated they did not know etc.). We based our coding categories around the most commonly recurring response types. Overall, 73.4% of responses were coded as straight/heterosexual, while 19.9% were coded as non-straight (6.6% were unclear).
We observed extremely strong divergence across gender categories. 76.9% of responses from male participants identified as straight/heterosexual, while only 48.6% of female responses identified as such.
“First Generation” Students
This year, we were requested to ask whether respondents were in the first generation of their family to attend university. 17.2% of respondents reported being first generation students.
Unfortunately, as the percentage of first generation students in the broader population varies across countries and has changed dramatically over time (reducing in more recent years), these numbers are not straightforward to interpret. 11.5% of our respondents currently living in the US (who, of course, may not have attended university in the US) reported being first generation students, compared to 20.5% of UK resident respondents. In the US, in 2011-2012, 33% of students enrolled in postsecondary education had parents who had not attended college. As the number of first generation students in the US has been decreasing with time, however, these numbers have plausibly declined further since 2011-2012.
We would also expect our sample to contain a disproportionately low number of first generation students, because our sample contains a disproportionately high number of students from elite higher education institutions, which tend to have lower numbers of first generation students (for example, Harvard appears to have only ~15% first generation students).
Financial or employment instability
We were also requested to ask respondents whether financial or employment instability was a major source of worry for them or their family when they were a child. As this is not an established question that is employed more broadly (in contrast to standard childhood SES or early life stress measures), we can’t make a rigorous comparison between the EA Survey sample and the wider population. We can observe, however, that only a small minority of respondents reported these factors often or always being a worry for them. Still, these results are hard to interpret, since they likely track varying elements of people’s objective SES, subjective SES and their propensity for anxiety or worry.
Careers and Education
We cut a number of questions about careers from this year’s EA Survey, in order to accommodate requests for other new questions. As such, we have added results about careers back into this general demographic post, rather than releasing them in a separate careers and skills post.
The majority of our respondents (68.7%) were non-students, while a little under one third (31.3%) were students.
A plurality were employed full-time (40.8%), many more than the next largest category, undergraduate students, who comprised less than 15% of our sample.
Last year we observed that our sample was extremely highly educated, with >45% having a postgraduate degree (and plausibly a higher percentage of people who would go on to have a postgraduate degree). Unfortunately, as we had to remove the question about respondents’ level of education, we cannot comment on that directly here. However, we can still observe that, among current students, a large number (45%) are currently studying for postgraduate degrees.
Last year we asked about respondents’ career path and skills and experience. And, in previous years, we asked more fine-grained questions about the kind of job or industry people work in. This year, both sets of questions were cut to make room for new questions. This year we asked about respondents’ current career. Respondents could select multiple options.
While the most common responses, by a long way, in the sample as a whole, were ‘building flexible career capital’ and ‘still deciding what to pursue’, when we look at the responses of non-students and students separately we see that, among non-students, responses are split very close to equally among different careers, with fewer selecting ‘building flexible career capital’ or ‘still deciding what to pursue.’
On its face, this would seem to suggest that no particular broad type of career particularly dominates among the EA community. Of course, this in itself doubtless means that certain broad career types (e.g. academia, non-profits) are much more common in EA than the general population. And, of course, more specific career types (e.g. software engineering) are likely still over-represented.
Career type across groups
We examined differences in the proportions of (non-student) respondents aged over 16 in each type of career, based on gender, race and level of self-reported engagement in EA.
Male vs Female
A significantly higher percentage of male respondents appear to be in for profit (not earning to give) careers, than female respondents.
We also compared the proportions in different career types for respondents who selected only the ‘white’ category, in contrast to those who selected any categories other than white. (Again, we include only non-students over 16).
We found no significant differences.
Level of Engagement
We also compared EAs lower in self-reported engagement to those who were highest in it.
Predictably, almost all those reporting specifically EA non-profit work were in the higher engagement groups (working in an EA org is specifically cited in the engagement scale as an example of being very highly engaged).
Career capital vs immediate impact
We also asked directly, to what extent respondents were currently prioritizing career capital or prioritizing having an impact in the next couple of years.
As before, while we saw a strong tendency towards prioritizing career capital in the sample as a whole, when we examined non-students specifically we saw a much more even split (albeit leaning towards prioritizing career capital).
Last year we were requested to ask respondents which universities they attended for their Associate’s and/or Bachelor’s degree, while this year we asked respondents to list any universities they had attended.
As was the case last year, we found that a disproportionately large number of respondents attended elite universities. 8.7% of respondents had attended the University of Oxford or Cambridge (slightly higher than the 7.7% who reported attending one of these institutions for their Associate’s or Bachelor’s degree last year).
We will explore the relationship between some of these variables and other measures on the EA Survey in future posts.
The annual EA Survey is a project of Rethink Priorities. This post was written by David Moss, with contributions from Jacob Schmiess and David Reinstein. Thanks to Peter Hurford, Neil Dullaghan, Jason Schukraft, David Bernard, Joan Gass and Ben West for comments.
We would also like to express our appreciation to the Centre for Effective Altruism for supporting our work. Thanks also to everyone who took and shared the survey.