It's my great pleasure to announce that, after seven months of hard work and planning fallacy, the EA Survey is finally out.
It's a long document, however, so we've put it together in an external PDF.
Introduction
In May 2014, a team from .impact and Charity Science released a survey of the effective altruist community. The survey offers data to supplement and clarify those anecdotes, with the aim of better understanding the community and how to promote EA.
In addition it enabled a number of other valuable projects -- initial seeding of EA Profiles, the new EA Donation Registry and the Map of EAs. It also let us put many people in touch with local groups they didn’t know about, and establish presences in over 40 new cities and countries so far.
Summary of Important Findings
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The survey was taken by 2,408 people, 1,146 (47.6%) of whom provided enough data to be considered, and 813 of whom considered themselves members of the EA movement (70.9%) and were included for the entire analysis.
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The top three sources people in our sample first heard about EA from were LessWrong, friends, or Giving What We Can. LessWrong, GiveWell, and personal contact were cited as the top three reasons people continued to get more involved in EA. (Keep in mind that EAs in our sample might not mean all EAs overall… more on this later.)
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66.9% of the EAs in our sample are from the United States, the United Kingdom, and Australia, but we have EAs in many countries. You can see the public location responses visualized on a map!
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The Bay Area had the most EAs in our sample, followed by London and then Oxford. New York and Washington DC have surprisingly many EAs and may have flown under the radar.
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The EAs in our sample in total donated over $5.23 million in 2013. The median donation size was $450 in 2013 donations.
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238 EAs in our sample donated 1% of their income or more, and 84 EAs in our sample give 10% of their income. You can see the past and planned donations that people have chosen to made public on the EA Donation Registry.
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The top three charities donated to by EAs in our sample were GiveWell's three picks for 2013 -- AMF, SCI, and GiveDirectly. MIRI was the fourth largest donation target, followed by unrestricted donations to GiveWell.
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Poverty was the most popular cause among EAs in our sample, followed by metacharity and then rationality.
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33.1% of EAs in our sample are either vegan or vegetarian.
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34.1% of EAs in our sample who indicated a career indicated that they were aiming to earn to give.
The Full Document
You can read the rest at the linked PDF! -->
A Note on Methodology
One concern worth putting in the forefront is that we used a convenience sample, trying to sample as many EAs as we can in places we knew where to find them. But we didn't get everyone.
It’s easy to survey, say, all Americans in a reliable way, because we know where Americans live and we know how to send surveys to a random sample of them. Sure, there may be difficulties with subpopulations who are too busy or subpopulations who don’t have landlines (though surveys now call cell phones).
Contrast this with trying to survey effective altruists. It’s hard to know who is an EA without asking them first, but we can’t exactly send surveys to random people all across the world and hope for the best. Instead, we have to do our best to figure out where EAs can be found, and try to get the survey to them.
We did our best, but some groups may have been oversampled (more survey respondents, by percentage, from that group than are actually in the true population of all EAs) or undersampled (not enough people in our sample from that subpopulation to be truly representative). This is a limitation that we can’t fully resolve, though we’ll strive to improve next year. At the bottom of this analysis, we include a methodological appendix that has a detailed discussion of this limitation and why we think our survey results are still useful.
You can find much more than you’d ever want in the methodological appendix at the bottom of the PDF.
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In sum, this is probably the most exhaustive study of the effective altruism movement in existence. It certainly exhausted us!
I'm really excited about the results and look forward to how they will be able to inform our movement.
I agree that could work, although doing it is not straightforward - for technical reasons, there aren't many instances where you get added precision by doing a convenience survey 'on top' of a random sample, although they do exist.
(Unfortunately, random FB sample was small, with something like 80% non-response, thus making it not very helpful to sample sampling deviation from the 'true' population. In some sense the subgroup comparisons do provide some of this information by pointing to different sub-populations - what they cannot provide is a measure as to whether these subgroups are being represented proportionally or not. A priori though, that would seem pretty unlikely.)
As David notes, the 'EA FB group' is highly unlikely to be a representative sample. But I think it is more plausibly representative along axes we'd be likely to be interested in the survey. I'd guess EAs who are into animal rights are not hugely more likely to be in facebook in contrast to those who are into global poverty, for example (could there be some effects? absolutely - I'd guess FB audience skews young and computer savvy, so maybe folks interested in AI etc. might be more likely to be found there, etc. etc.)
The problem with going to each 'cluster' of EAs is that you are effectively sampling parallel rather than orthogonal to your substructure: if you over-sample the young and computer literate, that may not throw off the relative proportions of who lives where or who cares more about poverty than the far future; you'd be much more fearful of this if you oversample a particular EA subculture like LW.
I'd be more inclined to 'trust' the proportion data (%age male, %xrisk, %etc) if the survey was 'just' of the EA facebook group, either probabilistically or convenience sampled. Naturally, still very far from perfect, and not for all areas (age, for example). (Unfortunately, you cannot just filter the survey and just look at those who clicked through via the FB link to construct this data - there's plausibly lots of people who clicked through via LW but would have clicked through via FB if there was no LW link, so ignoring all these responses likely inverts anticipated bias).