EA Survey 2017 Series: Cause Area Preferences

By Eve McCormick

The an­nual EA Sur­vey is a vol­un­teer-led pro­ject of Re­think Char­ity that has be­come a bench­mark for bet­ter un­der­stand­ing the EA com­mu­nity. This post is the third in a multi-part se­ries in­tended to provide the sur­vey re­sults in a more di­gestible and en­gag­ing for­mat. You can find key sup­port­ing doc­u­ments, in­clud­ing prior EA sur­veys and an up-to-date list of ar­ti­cles in the EA Sur­vey 2017 Series, at the bot­tom of this post. Get no­tified of the lat­est posts in this se­ries by sign­ing up here.

Sig­nifi­cant plu­ral­ity within the com­mu­nity means EAs have differ­ent ideas as to which causes will have the most im­pact. As in pre­vi­ous years, we asked which causes peo­ple think are im­por­tant, first pre­sent­ing a se­ries of causes, and then let­ting peo­ple an­swer whether they feel the cause is “The top pri­or­ity”, “Near the top pri­or­ity”, through to “I do not think any EA re­sources should be de­voted to this cause”.

As in pre­vi­ous years (2014 and 2015), poverty was over­whelm­ingly iden­ti­fied as the top pri­or­ity by re­spon­dents. As can be seen in the chart above, 601 EAs (or nearly 41%) iden­ti­fied poverty as the top pri­or­ity, fol­lowed by cause pri­ori­ti­za­tion (~19%) and AI (~16%). Poverty was also the most com­mon choice of near-top pri­or­ity (~14%), fol­lowed closely by cause pri­ori­ti­za­tion (~13%) and non-AI far fu­ture ex­is­ten­tial risk (~12%).

Causes that many EAs thought no re­sources should go to­ward in­cluded poli­tics, an­i­mal welfare, en­vi­ron­men­tal­ism, and AI. There were very few peo­ple who did not want to put any EA re­sources into cause pri­ori­ti­za­tion, poverty, and meta causes.

Over­all, cause pri­ori­ti­sa­tion among EAs re­flects very similar trends to the re­sults from 2014 and 2015. How­ever, the pro­por­tion of EAs who thought that no re­sources should go to­wards AI has dropped sig­nifi­cantly since the 2014 and 2015 sur­vey, down from ~16% to ~6%. We find this sup­ports the com­mon as­sump­tion that EA has be­come in­creas­ingly ac­cept­ing of AI as an im­por­tant cause area to sup­port. Global poverty con­tinues to be over­whelm­ingly iden­ti­fied as top-pri­or­ity de­spite this no­tice­able soft­en­ing to­ward AI.

How are Cause Area Pri­ori­ties Cor­re­lated with De­mo­graph­ics?

The de­gree to which in­di­vi­d­u­als pri­ori­tised the far fu­ture varied con­sid­er­ably ac­cord­ing to gen­der iden­tity. Only 1.6% of donat­ing women said that they donated to far fu­ture, com­pared to 10.9% of men (p = 0.00015). Dona­tions to or­gani­sa­tions fo­cus­ing on poverty were less varied ac­cord­ing to gen­der, with 46% of women donat­ing to poverty, com­pared to 50.6% of men (not statis­ti­cally sig­nifi­cant).

The iden­ti­fi­ca­tion of an­i­mal welfare as the top pri­or­ity was highly cor­re­lated with the amount of meat that EAs were eat­ing. The chart be­low shows the pro­por­tion of EAs who iden­ti­fied an­i­mal welfare as a top pri­or­ity ac­cord­ing to gen­der. Con­sid­er­ably more EAs who iden­ti­fied as fe­male ranked an­i­mal welfare as a top or near top pri­or­ity (~47%), as op­posed to ~35% males. The sec­ond chart shows the dietary choices of those who iden­ti­fied an­i­mal welfare as the top pri­or­ity. Those who iden­ti­fied an­i­mal welfare as top or near top pri­or­ity were over­whelm­ingly veg­e­tar­ian or ve­gan (~57%), much more than the EA rate of ~20%, which looks promis­ing when com­pared to the es­ti­mated pro­por­tion of US cit­i­zens aged 17+ who are veg­e­tar­ian or ve­gan (2%).

The sur­vey also in­di­cated a clus­ter­ing of cause pri­ori­ti­sa­tion ac­cord­ing to ge­og­ra­phy. Most no­tably, 62.7% of re­spon­dents in the San Fran­cisco Bay area thought that AI was a top or near top pri­or­ity, com­pared to 44.6% of re­spon­dents out­side the Bay (p = 0.01). In all other lo­ca­tions in which more than 10 EAs re­ported liv­ing, cause pri­ori­ti­sa­tion or poverty (and more of­ten the lat­ter) were the two most pop­u­lar cause ar­eas. For years, the San Fran­cisco Bay area has been known anec­do­tally as a hotbed of in­ter­est in ar­tifi­cial in­tel­li­gence. In­ter­est­ing to note would be the con­cen­tra­tion of EA-al­igned or­ga­ni­za­tions lo­cated in an area that heav­ily fa­vors AI as a cause area [1].

Fur­ther­more, en­vi­ron­men­tal­ism was one of the low­est rank­ing cause ar­eas in the Bay Area, New York, Seat­tle and Ber­lin. How­ever, it was more fa­vored el­se­where, in­clud­ing in Oxford and Cam­bridge (UK), where it was ranked sec­ond high­est. Also, with the ex­cep­tion of Cam­bridge (UK) and New York, poli­tics was con­sis­tently ranked ei­ther low­est or sec­ond low­est.

[1] This para­graph was re­vised on Septem­ber 9, 2017 to re­flect the Bay Area as an out­lier in terms of the amount of sup­port for AI, rather than declar­ing AI an out­lier as a cause area.

Dona­tions by Cause Area

Dona­tion re­port­ing pro­vides valuable data on be­hav­ioral trends within EA. In this in­stance, we were in­ter­ested to see what tan­gible efforts EAs were mak­ing to­ward sup­port­ing spe­cific cause ar­eas. We pre­sented a list and asked to which or­ga­ni­za­tion EAs donated. We will write a post about gen­eral dona­tion habits of EAs in the next sur­vey.

As in 2014, the most pop­u­lar or­gani­sa­tions in­cluded some of GiveWell’s top-rated char­i­ties, all of which were fo­cused on global poverty. Once again, AMF re­ceived by far the most in to­tal dona­tions in both 2015 and 2016. GiveWell, de­spite only at­tract­ing the fourth high­est num­ber of in­di­vi­d­ual donors in both 2015 and 2016, was sec­ond in terms of amount per dona­tion re­ceived each year.

Meta or­gani­sa­tions were the third most pop­u­lar cause area, in which CEA was by far the most favoured in terms of num­ber of donors and com­bined size of dona­tions in both years. Mercy for An­i­mals was the most pop­u­lar out of the an­i­mal welfare or­gani­sa­tions in both years in num­ber of donors, though the Good Food In­sti­tute re­ceived more in dona­tions than MFA in 2016. MIRI was the most pop­u­lar or­gani­sa­tion fo­cus­ing on the far fu­ture, which was the least pop­u­lar cause area over­all by dona­tion amount (though the fact that only two far fu­ture or­gani­sa­tions were listed may ex­plain this, at least in part). How­ever, the least pop­u­lar or­gani­sa­tions among EAs were spread across cause ar­eas: Sight­savers and The END Fund were the two least pop­u­lar, fol­lowed by Fau­n­a­lyt­ics, the Foun­da­tional Re­search In­sti­tute and the Malaria Con­sor­tium. The rel­a­tive un­pop­u­lar­ity of Sight­savers, The END Fund and the Malaria Con­sor­tium, de­spite their fo­cus on global poverty, may re­late to the fact that they were only con­firmed on GiveWell’s list of top-recom­mended char­i­ties quite re­cently and are not in GiveWell’s de­fault recom­men­da­tion for in­di­vi­d­ual donors.

The re­sults solely for the 476 GWWC mem­bers in the sam­ple were similar to the above. Global poverty was the most pop­u­lar cause area, with ~41% re­spon­dents re­port­ing to hav­ing donated to or­gani­sa­tions within this cat­e­gory. This was fol­lowed by cause-pri­ori­ti­za­tion or­gani­sa­tions, to which ~13% donated.

Top Dona­tion Destinations

For both 2015 and 2016, the sur­vey re­sults sug­gest that GiveWell had the largest mean dona­tion size ($5,179.72 in 2015 and $6,093.822 in 2016). There­fore, de­spite re­ceiv­ing far fewer in­di­vi­d­ual dona­tions than AMF, the to­tal of GiveWell’s com­bined dona­tions in both years was al­most as large. Nev­er­the­less, AMF had the sec­ond largest mean dona­tion size ($2,675.39 in 2015 and $3,007.63 in 2016) fol­lowed by CEA ($2,796.66 in 2015 and $1,607.32 in 2016). Although GiveWell and CEA were not among the top three most pop­u­lar or­gani­sa­tions for in­di­vi­d­ual donors, they were, like AMF, the most pop­u­lar within their re­spec­tive cause ar­eas.

The top twenty donors by dona­tion size in 2016 donated similarly to the pop­u­la­tion as a whole. The top twenty donors donated the most to poverty char­i­ties, and speci­fi­cally AMF within that cause area. How­ever, the third most pop­u­lar or­gani­sa­tion among these twenty in­di­vi­d­u­als was CEA, which was not one of the top five high­est-ranked or­gani­sa­tions in ag­gre­gate dona­tions for ei­ther 2015 or 2016.


Post writ­ten by Eve McCormick, with ed­its from Tee Bar­nett and anal­y­sis from Peter Hur­ford.

A spe­cial thanks to Ellen McGeoch, Peter Hur­ford, and Tom Ash for lead­ing and co­or­di­nat­ing the 2017 EA Sur­vey. Ad­di­tional ac­knowl­edge­ments in­clude: Michael Sad­owsky and Gina Stuessy for their con­tri­bu­tion to the con­struc­tion and dis­tri­bu­tion of the sur­vey, Peter Hur­ford and Michael Sad­owsky for con­duct­ing the data anal­y­sis, and our vol­un­teers who as­sisted with beta test­ing and re­port­ing: Heather Adams, Mario Ber­aha, Jackie Burhans, and Nick Yeretsian.

Thanks once again to Ellen McGeoch for her pre­sen­ta­tion of the 2017 EA Sur­vey re­sults at EA Global San Fran­cisco.

We would also like to ex­press our ap­pre­ci­a­tion to the Cen­tre for Effec­tive Altru­ism, Scott Alexan­der via SlateS­tarCodex, 80,000 Hours, EA Lon­don, and An­i­mal Char­ity Eval­u­a­tors for their as­sis­tance in dis­tribut­ing the sur­vey. Thanks also to ev­ery­one who took and shared the sur­vey.

Sup­port­ing Documents

EA Sur­vey 2017 Series Articles

I—Distri­bu­tion and Anal­y­sis Methodology

II—Com­mu­nity De­mo­graph­ics & Beliefs

III—Cause Area Preferences

IV—Dona­tion Data

V—De­mo­graph­ics II

VI—Qual­i­ta­tive Com­ments Summary

VII—Have EA Pri­ori­ties Changed Over Time?

VIII—How do Peo­ple Get Into EA?

Please note: this sec­tion will be con­tinu­ally up­dated as new posts are pub­lished. All 2017 EA Sur­vey posts will be com­piled into a sin­gle re­port at the end of this pub­lish­ing cy­cle. Get no­tified of the lat­est posts in this se­ries by sign­ing up here.

Prior EA Sur­veys con­ducted by Re­think Char­ity (formerly .im­pact)

The 2015 Sur­vey of Effec­tive Altru­ists: Re­sults and Analysis

The 2014 Sur­vey of Effec­tive Altru­ists: Re­sults and Analysis