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2017 LEAN Impact Assessment: Quantitative Findings

2018-01-05

The Local Effective Altruism Network (LEAN) is a Rethink Charity project initiated in 2015, which focuses on providing material and informational assistance to university and local EA groups around the world.

This document is the first in the LEAN Impact Assessment Series, summarising relevant data from the 2017 Local Group Survey, which is used to assess the EA local group network and the effectiveness of LEAN’s services.

The assessment utilises a mixed method social research strategy, including both quantitative and qualitative components. Our quantitative research relies upon relevant [1] data from the 2017 Local Group Survey [2], which was conducted in collaboration with the Effective Altruism Foundation (EAF) and the Centre for Effective Altruism (CEA) mid-2017. Our qualitative research is made up of over 30 semi-structured interviews that LEAN held by video call with group organisers around the world, ranging from 20 to 40 minutes in duration.

Findings are divided into two overarching categories: “EA Groups” and “Support and Resources”. The first includes data related to the scale, nature and impact of groups, (e.g. membership numbers or funds raised). The second includes data on particular group support strategies, and their popularity and impact.

In this report, and in the qualitative finding summary to follow, we offer descriptive commentary only, leaving full strategic analysis and interpretation for a later article.

Table of Contents

EA Groups

Support and Resources

Personal Feedback
Practical Support and New Ideas
Group Video Calls
Written Guides
Websites and Technical Support
Premium Meetup.com Subscription
Local Group Newsletter
Group Organisers’ Mentoring Programme
Group Organisers’ Facebook Community
EA Groups Slack Team

Conclusion
Acknowledgements

Survey Sample

The 2017 Local Group Survey was sent by LEAN staff to every EA group organiser on record [3] via email and Facebook message. The survey was also posted in EA Facebook groups, and advertised in the EA Newsletter and the Local Groups Newsletter.

Although 374 entries were submitted to the survey (138 of which identified as group leaders and 236 as group members), 292 entries remained once the data was cleaned [4]. Among these, 98 identified as organisers, and 194 as members.

The survey was split into sections containing questions reserved for organisers, and questions open to organisers and members. Where groups had more than one organiser, they were asked to nominate one organiser to complete the questions designated for organisers. Surplus organisers completed the survey as members. Organisers answered on behalf of their groups for the organiser questions, and on behalf of themselves for the member questions.

Some questions attracted many more responses than others, and several participants chose to skip certain questions. Throughout this report, response levels are indicated as fractions of the total respondent category, in order to signal this difference. For example, if there are 75 responses to a question restricted to organisers, this is displayed as 75/98 where 98 is the total number of organisers that completed the survey.

Our methodology is explained in more detail in a forthcoming post in this series.

EA Groups

Group Demographics

Number of Groups and Group Size


Organisers reported an average of 50 group members, and a median of 10. The data are partially determined by the different criteria respondents used for defining membership. For instance, some used the size of their Facebook or Meetup groups, while others counted only individuals who had attended group activities on a regular basis. Based on these numbers though, slightly more than 78% of members are within the top 10% of groups by size.

Group Type


56 participating organisers were from local groups, and 37 organisers were from University groups. Of course, many members of local city based groups may still be university students. Some groups may be better understood as hybrids between the two categories. It is also possible that as the EA community is aging, EAs who joined the movement during university are progressing into local non-university groups.

Group Leader Succession

As a rough indicator of the stability of groups, we asked organisers to estimate the likeliness that their groups would continue were the current organisers to step down.

The results suggest a degree of vulnerability, and dependence on particular organisers.

EA Activities of Organisers and Members

We asked members and organisers whether or not they had ever engaged in the following activities:


We supplied no participation count for this question because respondents were only given the option to add a mark if they engaged in the relevant activity. Therefore, a lack of marks from a respondent could mean either a lack of engaging in the question, or it could mean that the respondent doesn’t engage in any of the activities. Any participation count would therefore not be informative.

An additional limitation of these results is the fact that categories such as “volunteered at an EA organisation” were not sufficiently defined. For example, some respondents interpreted time spent organising their groups as voluntary work for an EA organisation, while others did not.


In an open ended addendum to the question, respondents reported additional ways of investing time in EA:

  • Thinking about EA
  • Direct EA work
  • Producing EA content
  • Researching EA
  • EA informed career transition
  • Applying for EA related grants
  • Pitching EA to individuals
  • EA aligned policy work
  • Earning to give

This suggests that members (and organisers) of EA groups are engaged in Effective Altruist activities and the movement more broadly. Almost by definition, group membership involves social interaction with other EAs. It is clear, however, that this is just one of many activities that members are involved in.

Commitment and Lifestyle Changes

Increasing Engagement with EA

Perhaps the most important success criteria for EA groups is their ability to attract people to Effective Altruism, and to retain their interest and commitment. We included the following questions in the Local Group Survey in order to gauge this:

The following table and graph summarise data on how many respondents considered themselves Effective Altruists prior to attending their first group meeting:


It may appear striking that some organisers did not consider themselves EAs until after their first group meeting. Note that it may be that organisers were converted to EA after attending their first group meeting as a member, but became an organiser after becoming an EA. It may also be the case that some respondents are reluctant to apply the label ‘EA’ to themselves e.g. until they’ve started doing something they see as concrete for EA (such as organising an EA group).

While it is difficult to discern causation from these figures, they do at least confirm that a sizeable number individuals become EAs only after beginning to attend a group. This confirms that groups are not merely reaching people who already identity as EAs and nor are they merely reaching non-EAs who never subsequently come to identify with the movement.

This graph displays the number of people who group organisers estimated attended each group’s events with little or no familiarity with Effective Altruism:

Responses are widely distributed, with a small number of groups reporting very large numbers, but most responses clustered around 30 new event attendees with little familiarity with EA, and with most responses (44/78) falling between 5 and 50. It is important to see this in the context of group size, with all but 10 of the groups who responded to this question had <50 group members. The ratio between group members and total event attendees who were unfamiliar with EA varied widely, between 1:0.375 (a group with 40 members and 15 new event attendees) and 1:43 (a group with 14 members and 600 event attendees who were unfamiliar with EA).

How much of a factor was group involvement for engagement with EA?

When asked how much of a factor group involvement was for their engagement with EA, respondents gave the following answers:

Most members (102/178) and organisers (45/72) report involvement with their EA group to be ‘large’ or ‘very large’ factor for their engagement with EA, with the remainder of the rest being ‘moderate’ responses.

Number of members becoming ‘actively committed’ to EA due to groups

We also asked for counterfactual estimates of the number of members who became ‘actively committed’ to EA (for example, lifestyle changes, direct action or donating money to effective causes) as a result of involvement in groups.

The median number of estimated counterfactual active commitments is 5 and the majority (43/63) fall between 1 and 10. A small number of groups report substantially higher numbers (e.g. 50-100 counterfactual commitments). The top 10/63 groups account for slightly more than half the reported commitments (352/617.5).

Looking at this in relation to ‘group size’ finds a positive correlation between the number of members a group has and its number of reported active commitments.

Thought and Behavior Change Since Being Involved with a Local Group

We asked all respondents whether their world views or behaviours had changed since becoming involved in an EA group.


We also asked them to indicate whether or not any changes reported were likely to be impactful.

A substantial majority of members and organisers alike report that the way they think about the world and behave has changed since being involved with a local group and that they expect to have more social impact as a result of this change. This does not necessarily suggest causation between the local group and their increased engagement and efficacy.

How many of your current members do you expect to choose careers based on EA recommendations or thinking?



Most groups report between 1 and 5 members choosing careers based on EA principles (median 4). The mean (7.29) is dragged upwards by a small number of groups with much higher (up to 50) numbers of career choices based on EA.

A natural question to ask is how this relates to group size. Are the largest groups simply accounting for many more of these outcomes (due to their much greater size)? The first graph shown here would seem to suggest this, with the group with the largest number of EA career choices by some way, also being the largest, and all but one of the groups with the highest number of EA career choices having >100 members.

It is a further question, however, whether the largest groups are better at making conversions (e.g. getting members to make EA career choices). The graph below would not support this conclusion. We see here only a weak correlation, but it might appear that the largest groups (responsible for the most EA career choices) in absolute terms, have a relatively lower % of members making EA career choices. This does not seem sufficient to suggest that larger EA groups are worse at making conversions however. A plausible explanation might be that smaller groups contain a disproportionate number of dedicated EAs (for example, a small group with 5 members might contain two EAs sufficiently dedicated to found and run a group), compared to the largest groups which may have many hundreds of new members.

If applicable, how much of a factor are or were EA principles in planning your career?



A majority of members and organisers alike report that EA principles were a large or very large factor in planning their careers. Notably, though perhaps unsurprisingly, organisers disproportionately indicated that they were a “very large” factor in planning their careers, whereas among members there were relatively more moderate and large responses.

Examples of Notable Group Members Becoming Active EAs

Organisers responded to the question: “Please name any current or past group members who have gone on to become active in the wider EA community. This would include going on to work at an EA organization, starting an EA project, becoming a thought leader in the movement, earning to give, and/or representing EA in other public ways.” We refer to these individuals as ‘hits’ who initiated or increased their level of involvement in EA after group involvement.

In total 121 ‘hits’ were reported in this open comment question. Note that low response rates may obscure the number of group organisers who would report 0 ‘hits.’

How valuable do you find your group’s activities?


As this graph shows, majorities of both organisers and members rate their group’s activities as valuable or very valuable. Notably, members appear strikingly more positive than organisers, “very valuable” being their most frequent response by some way (80), followed by valuable, with 135 out of 144 selecting these two options, whereas organisers’ responses are centred around valuable (30) and moderately valuable (22).

Funds moved

Group organisers provided estimates of the money raised through group fundraising activities, the money raised through the private donations of members, and the counterfactual GWWC pledges raised by groups.


While substantial funds have been collectively raised by groups, the majority of the funds come from a small number of groups.

Nevertheless an appreciable number of groups have fundraised significant (i.e. $100 to $1000) amounts, as seen below (note the log scale on the y axis).

Note that we would expect that non-response would be higher for groups who have not run fundraisers or who raised very little, so there may be a longer ‘tail’ of groups raising $0.

This table summarises group organisers’ estimates of the private donations made by group members who counterfactually would not have donated (but for group involvement):

Total estimated counterfactual private donations are dominated by a small number of groups reporting very high figures (note the log scale on the x axis). However, a substantial number of groups are reporting significant sums being donated. An important caveat is that respondents may have over-estimated some of the figures for various reasons (for example, including future donations).

Note, as above, that non-response rates may conceal a higher number of groups who would estimate very low donations.

Finally, organisers provided estimates of the counterfactual Giving What We Can pledges secured through their groups.


Most organisers report few counterfactual pledges (pledges which would not have been taken without the influence of the group), with most reporting between 1 and 5. Indeed, the vast majority of responses fall within 1 and 11, while 2 groups report 40 and 75 counterfactual pledges respectively, and 16 report 0 counterfactual pledges.

Overall, these data on funds are speculative, and should be treated as such. However, it appears that groups have a non-trivial role in the movement of funds to effective causes.

LEAN Support and Resources

In this part of the report we summarise evidence regarding the usefulness of services which LEAN provides in assisting the operation of groups [5]. More data on groups’ experiences of outside support will be shared in the qualitative report.

General feedback on LEAN and other EA organisations

We asked organisers:

“What outside help has been the most useful to the operation of your group?” (Respondents could select multiple options.)

“Other” was made up of specific University student unions, larger EA groups in similar regions (EA NTNU, EA London and EA ANU), value aligned local organisations, and individual EAs.

CEA (39) was the organisation most often selected as ‘most useful’ to the operation of groups by organisers. Note that respondents could select multiple organisations if they wished. LEAN (22) and EAF (22) were joint second most commonly selected organisations. TLYCS was selected 11 times, and remaining options were each selected 2 or fewer times. CEA-affiliated 80,000 Hours (21) and Giving What We Can (13) were selected separately by some respondents. A favourable bias towards LEAN is possible given the fact that LEAN distributed the survey.

Feedback on Specific Services and Resources

Personal Feedback

LEAN offers organisers personal support, on demand, via video call, social media and email. We asked organisers: “In your opinion, how useful is personal feedback and support via social media, email and video call?”


A clear majority report that personal feedback and support of this kind is useful or very useful.

Practical support and new ideas

We asked organisers: “In your opinion, how useful is it to receive practical support and new ideas for group activities?”

Practical support and new ideas for group activities are generally rated as useful or very useful (75/80), with only (3/8) finding them either not useful or not at all useful.

Video Calls

LEAN hosts video calls to help share best practices between groups. Organisers responded to the question: “In your opinion, how useful is it to host video calls about group management topics?”

A majority of organisers reported video calls about group management to be either useful or very useful.

Written Guides

LEAN is among many EA individuals and organisations to have produced written content for EA organisers. We asked organisers: “In your opinion, how useful are written guides (with a focus on practical and strategic aspects of organising groups)?”


A clear majority of respondents (65/78) considered written guides to be useful or very useful with only 1 respondent out of 78 offering a negative rating.

Websites and Technical Support

LEAN provides hosting, domains and basic content management for over fifty EA group websites. Organisers were asked: “If your group uses a website, do you believe that it makes a non-trivial difference in the effectiveness of your group’s outreach efforts?”

While a majority of the groups who used (group) websites find them significantly useful, a notable minority find them no more than trivially useful.

In addition, we asked: “In your opinion, how useful is technical support (for instance, subscriptions for online services, free websites, group email addresses)?”

A majority (52/73) of respondents report that technical support of this nature is either useful or very useful, compared to 13 and 8 groups being neutral or not finding it useful, respectively.

Premium Meetup.com subscription

LEAN provides free Meetup.com accounts for interested organizers. Organisers were asked: “If your group uses Meetup.com, please give an estimation of the % more attendees you have attracted as a result of using the platform in addition to – or instead of – alternatives?”


*It should be noted that most EA Groups don’t use Meetup.com and would not have been able to answer this question.

While many groups gained modest increases in members from using meetup.com (median 15%, mean 21.42%), a small number gained very significant increases.

Local Group Newsletter

LEAN leads a regular newsletter for EA groups with support from EAF and CEA. We asked organisers: “In your opinion, how useful is the Local Group Newsletter?”

Many more respondents rated the local groups newsletter (N.B. not the EA Newsletter) as useful or very useful, (32) than not useful or not at all useful (34), though many were neutral (24).

These results should be contextualised, however, by responses at the end of the survey which asked whether respondents wished to be added to the local organiser newsletter:

This shows that 49 organisers or more have not received the newsletter, which limits the usefulness of the earlier responses.

Group Organisers’ Mentoring Programme

With support from CEA, LEAN launched a mentoring trial programme, connecting experienced organisers with new ones from August 2017. We asked organisers: “In your opinion, how useful is the EA Organiser’s Mentoring Programme?”

The majority (32) of respondents found this program neither useful nor useless, with 16 finding it useful or very useful and 4 finding it not useful. These results may indicate that the majority of organisers are simply unfamiliar with the program due to its recent release.

EA Organisers’ Facebook Community

LEAN supports a Facebook group for EA Organisers in collaboration with EAF and CEA.
We asked organisers: “In your opinion, how useful is the Facebook community of group organisers?”

A decisive majority of organisers found the Facebook group to be useful or very useful.

EA Groups Slack Team

LEAN supports a Slack channel for EA Groups in collaboration with EAF and CEA. Organisers were asked: “In your opinion, how useful is the EA Groups Slack Team?”

Slightly more organisers found the Slack team to be useless (13) rather than useful (10), with the majority (34) being neutral.

Conclusion

Evaluating the impact of LEAN and the strategic implications of these results will be deferred until the LEAN Assessment Strategy report, which will follow in this series of articles. We will also draw on the qualitative data we have gathered in a separate report to help interpret these findings.

Acknowledgements

This report was written by Richenda Herzig. David Moss, Peter Hurford and Richenda Herzig analysed the 2017 Local Group Survey data. Editorial input was provided by Peter Hurford and Tee Barnett. Thanks to Ellen McGeoch for assisting in survey design and formatting for the 2017 Local Group Survey. Thanks to David Vatousious for distributing the survey across the network and for recruiting participants. Thanks also to Kaitlin Alcantara for data entry and filtering.

We are highly grateful to Greg Lewis for his input as an external advisor.

We would also like to express our thanks to Harri Besceli from the Centre for Effective Altruism (CEA) and Jonas Vollmer from the Effective Altruism Foundation (EAF), who collaborated in writing the 2017 Local Group Survey. We are grateful to CEA for generously supplying free EA t-shirts to respondents.

Last but not least, a big thank you to all organisers and members who took and shared the survey!

Endnotes

[1] The LEAN Impact Assessment is distinct from the 2017 Local Group Survey. While the survey results supply a substantive base for the assessment, the survey was a collaborative project between the Centre for Effective Altruism, the Effective Altruism Foundation, and The Local Effective Altruism Network (LEAN). Findings from the survey that were not relevant to this assessment may be shared at a later date.

[2] Due to the number of personal identifiers in the data set, it is not possible at this point in time to make the raw survey results publicly available. At a later date it may be possible to release partial anonymised findings.

[3]LEAN collaborates with CEA and EAF to maintain up to date, comprehensive records of EA groups and their organisers.

[4] Entries were deleted if they were blank, or sufficiently incomplete as to render the submitted data useless. Other deletions included garbled or illegible responses and duplicates.

[5] Of the support categories included in this section, some have historically been provided only by LEAN, whereas others have been provided by various individuals and organisations in EA.

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EA Survey 2017 Series Part 8: How do People Get Into EA?

2017-11-17

This is the eighth article in the EA Survey 2017 Series. You can find supporting documents at the bottom of this post, including previous EA surveys conducted by Rethink Charity, and an up-to-date list of articles in the series.

By Anna Mulcahy, Tee Barnett, and Peter Hurford

Summary

We asked self-identified EAs how they first heard about the movement and what resource or tool persuaded them to get more involved. It’s important to bear in mind, however, the limitations related to both the questions and also the respondents ability to recall from possibly long periods ago[1].

 

  • The number of people joining the EA movement each year continues to increase year-on-year.
  • The top five sources of introduction to EA in descending order are ‘Personal Contact’, ‘Lesswrong’, ‘Other book, article, or blog post’, ‘SlateStarCodex’, and ‘80,000 Hours’
  • As of 2016, LessWrong dropped out of the top five list of introductory sources after being one of the top three from 2009 to 2015.
  • The top five sources of engagement for new EAs in 2017 in descending order are ‘GiveWell’, ‘Book or Blog’, ‘80,000 Hours’, ‘Personal Contact’, and ‘Giving What we Can’.

What year did EAs first get involved with EA?

EA survey results from 2017 show an increase in the number of new members, confirming trends published in “Is EA Growing? Some EA Growth Metrics for 2017”. Results show growth of nearly 20% in the number of new recruits to the community for 2016, compared to 2015. This certainly reflects gains in recruitment year-on-year, though without efforts to track attrition rates it is possible that the total community growth could be less than what is suggested here.

Chart showing what years EAs got involved with the community.

How did people first hear about EA?

All-time figures for first introductions to EA were topped by ‘Personal Contact’ and “Lesswrong’ with ‘Other book, article, or blog post’ coming in a distant third. Scott Alexander’s SlateStarCodex (SSC) and 80,000 Hours round out the top five. Important to note is a considerable proportion of individuals who selected ‘Other’, which would place it as the third most popular answer if counted among specific referral sources.

Responses were then cross-referenced against the question, “In roughly which year did you first get involved in EA?”. This allowed for the 2017 results to be interpreted within a longer arc of EA surveys conducted in the last few years, and provided some indication about how the most successful sources for spreading the word about EA have changed over time.

 

We can also see how referrers have changed over time by cross-referencing people’s self-report of how they got involved with the year they report joining the movement. When interpreting the 2017 results within this context, we find that getting introduced to EA through personal networks has historically been most common (Table 3).

As for year-on-year trends according to particular referral sources, we can find several examples of noteworthy changes over time. For instance, Lesswrong was a wellspring of new EAs for several years before the community faded. 80,000 Hours is typically among the top referrers, and while SlateStarCodex has as also been important over the years according to this survey, potential for survey bias due to over-sampling SSC readers persists.

From 2009 to 2011, Giving What We Can ranked highly in response to the question “How did you first hear about EA?”. However, after 2011 it progressively fell in popularity and did not even rank in the top five ways of first hearing about EA from 2014 to 2016. In addition, the number of people who learned about EA through 80,000 hours almost doubled from 2014 to 2015 (see Table 4)[2]. Slate Star Codex has also shown increasing success as a referral source for EA since 2014 (see Table 5). Again, care must be used when interpreting these trends, as there have been fluctuations in how much each group promoted the EA Survey.

As mentioned previously, despite LessWrong dropping out of the top five in 2016, the historical strength of the rationalist website in drawing EA-adjacent individuals suggests that it may have been an obvious choice for EA Grants to support the newest iteration of LessWrong.

Comparison with a Survey of the EA Facebook Group

The EA Facebook group has become a popular place for the EA Community. Indeed, 54.6% of EAs in our survey sample report being in the group, and almost 18% of EA survey respondents were referred from Facebook. Notably, when people join the EA Facebook group, as a condition of joining, every member is asked to report how they heard about EA as freeform text. Julia Wise and other EA FB moderators collected a convenience sample of 100 responses collected in late 2017 and produced the following results:

To compare this to our data, we selected the 406 EAs who self-reported being a member of the EA Facebook group and who said they joined in 2016 or 2017 (though this would only go up to April-June 2017 when the survey was active). Among this subsample in our survey, the top five results were 19% saying personal contact, 17% saying “other”, 10% saying 80,000 Hours, 7% saying Doing Good Better, and 6% saying a TED Talk. This matches closely with the results gathered from Facebook despite a different data collection method (forced response for group membership vs. voluntary survey taking) and reporting methods (self-report from choices including others vs. self-reported freeform text with no prompts).

What got people more involved with EA?

Respondents were also asked what motivated them to get involved with EA. While the previous question can indicate the reach and accessibility of EA resources, this question can be used to indicated how effective these resources are at persuading people to join the EA community and actively participate.  

 

Introduction sources and sources of further engagement are not always one in the same. As seen in Table 7, personal networking did not come out as the top source for actually getting people involved in EA, though it remains within the top five. Once introduced to EA, it would appear GiveWell, books and/or blogs, and 80,000 Hours are the three most potent ways to keep engage new EAs. This may come as no surprise considering these answer options offer a wealth of in-depth information. LessWrong would presumably also fall into this camp, but the rationalist website may have fallen down the list due to reasons cited above. EA Global (EAG) performed quite well considering the relatively brief amount of time new EAs have to engage at a given conference.

Endnotes

[1] As mentioned in previous articles, care should be taken when interpreting EA survey results. Questions to identify where people first heard about EA are open to significant human error as respondents are required to rely on memory and recall something that may have happened up to 5 or more years ago. Furthermore, respondents could have heard about EA from multiple sources in a short period of time, but may not be able to pinpoint exactly which of those sources they heard about it from first. Having ‘cannot remember’ as an option can only reduce errors from memory recall up to a point.

 

The same potential for error applies when asking respondents to recall what caused them to actually get involved in EA. Although for this question they were given the opportunity to select multiple answers, as multiple factors often contribute to such a decision, so it relied less on accurate recall of a single, specific event.

 

[2] This may be the case for a few reasons. 80,000 Hours assisted this year in distributing the survey, which was not the case in 2016 because no EA survey was conducted. According to CEO and Co-founder, Ben Todd, 80,000 Hours web traffic nearly doubled each year for the past few years. And finally, the Effective Altruism Facebook group survey posted by Julia Wise illustrates the popularity of 80,000 Hours as a popular referral source among new members.

 

[3]: The full text of the question was “Which factors were important in ‘getting you into’ Effective Altruism, or altering your actions in its direction? Check all that apply.”

Credits

Post written by Anna Mulcahy, Tee Barnett, and Peter Hurford, with edits from Ben Todd.

 

The annual EA Survey is a volunteer-led project of Rethink Charity that has become a benchmark for better understanding the EA community. 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.

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

 

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

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SHIC Workshop Experiment and Revised Impact Strategy 2018

2017-10-31

By Tee Barnett and Baxter Bullock

This post details our shift in priorities for the Students for High-Impact Charity (SHIC) program over time, and briefly outlines the revised methods of delivering this approach. We conclude the article by announcing a new SHIC workshop experiment slated to launch in early 2018.

Summary & Sections

I – Background

II – Initial Strategy

III – SHIC Workshop Experiment

IV – Our Revised Approach

I – Background

Shortly after the release of our interim report in early 2017, SHIC became a project under the Rethink Charity umbrella. The organizational reshuffling and insight from 2016 prompted us to reconsider our program objectives and outreach methods for making the largest impact with students.  

 

The program’s overall objectives are to inform students about the greatest problems facing humanity, equip them with the cognitive tools for thinking about how to address these problems, and then suggest possible solutions and avenues for taking effective action.

II – Initial Strategy

Our initial outreach strategy prioritized making SHIC accessible, hoping to reach the largest number of students possible in order to produce broad-scale shifts in perspective and behavior (e.g. survey results across all participants), and perhaps even a significant inflection point for a small fraction. Since mid-2016, we’ve relied solely on volunteer student leaders and teachers to run the program with student participants. The interim report expands on how we found this implementation model rather unstable. We found difficulties in data collection most problematic.

 

Gathering feedback on the curriculum, testing program efficacy, and achieving scale, were largely rolled into a single process. By running the introductory program with as many students as possible, we expected to collect actionable data while simultaneously scaling our presence around the world.

 

SHIC confronted several practical questions that affected how we interpreted our success metrics. As an example, we’d initially intended to inspire dozens of SHIC groups around the world to fundraise for effective charity as a broad indicator of program effectiveness. We reasoned that fundraising participation and performance could serve as a proxy for student engagement. Fundraising also served to hedge uncertainty regarding the overall value of SHIC by prompting donations to high-impact charities, possibly enough to offset the cost of the project. After observing very inconsistent and underwhelming student fundraising numbers, it became clear that using dollars donated as a central goal of program success warranted revisiting.

 

More importantly, we questioned the underlying assumptions of our success metrics. As a program dedicated to helping students achieve the largest prosocial impact, it wasn’t clear that high fundraising numbers or broad shifts in survey results would necessarily be indicative of lasting impact. We increasingly felt that inspiring meaningful value shifts and shaping long-term plans for a smaller proportion of participants would be more impactful.

 

For instance, we had serious reservations that program objectives appeared to privilege present-day action over longer-term skill building. Our previous model did not consider the opportunity-cost of fundraising comparatively nominal amounts for charity, versus skill building in order to have a larger impact in the future. In fact, optimizing SHIC too much in either direction (towards long-term career building or towards short-term action) could be detrimental. Put too much present-day focus in the program, and students likely achieve relatively little good and neglect skill building for the future. Put too much emphasis in skill building for the future, and risk squelching a natural passion for helping others.

 

III – SHIC Workshop Experiment

After reevaluating the program as a whole, we’ve decided on an implementation model that better reflects our revised objectives. By early 2018, SHIC will conduct the most ambitious experiment on the program to date, training in-house instructors to bring SHIC workshops to schools across Vancouver.

 

We gauge interest in the program by opening with mass Giving Game events conducted by SHIC. Provided that a large enough group of students are interested in moving on to the introductory program in a given school, SHIC instructors will return to conduct the full workshop in two additional 1.5-hour installments.

 

An additional component to this experiment will involve collecting longitudinal data on the medium- to long-term effects of our program on student giving behavior. Thanks to our collaboration with Charitable Impact Foundation (CHIMP), a Vancouver-based donation platform that uses a donor-advised fund to facilitate gifts to other registered charities.

 

We will be able to track the giving behavior of workshop participants by individually assigning online Chimp accounts. On a monthly basis, each participant account will be credited with a set amount of money that can be disbursed to any charity in Canada, and also effective charities across the border thanks to our partnership with the Priority Foundation. We hypothesize the SHIC program will at least moderately influence the giving preferences of students.

 

In addition to the longitudinal giving data, surveys administered throughout the subsequent year, qualitative interviews, and classroom-level scouting will help SHIC identify a select cohort of high-potential students eligible for additional programming and mentorship opportunities.

IV – Our Revised Approach

Compared to the inconsistency we experienced with a volunteer implementation model, an instructor approach allows for more rapid feedback and adjustment, reliable data collection, tighter quality control over messaging, and may motivate students to become generally more engaged. By late 2018, we expect to have several vantage points from which to assess the impact of SHIC.

 

More importantly, the instructor model is our best attempt at taking a more targeted approach at influencing students. SHIC will remain committed to keeping our message accessible – students around the world can still download our entire curriculum for free and run their own student clubs, for example – but the majority of our resources and effort will pursue meaningful impact on an individual level. We’ve updated our program to achieve this in the following ways:

 

  • Using data to identify high-potential students –  SHIC will collect a variety of metrics to find students most inclined to engage with effective charity in the long-term. Primarily through periodic surveying, qualitative interviews and classroom-level scouting, we will identify a select cohort of students eligible for additional programming, mentorship, and career opportunities.
  • Action oriented toward future impact – Our program incorporates more informed insight on balancing long-term skill-building and present-day action. As an example, rather than exclusively recommending raising money for effective charities, we may instead attempt to connect students with high-impact internship opportunities. This new approach empowers students in the present day, facilitates skill-building, and helps build experience and career capital that could reap long-term benefits. The career path carved out by Owen Shen provides a real-world example of this approach. Owen became involved with the rationalism and effective altruism community at the age of 16, eventually volunteering with the SHIC curriculum and outreach teams for a number of months. Owen created his own rationality-focused blog, and subsequently earned a contractor position with the Center for Applied Rationality (CFAR). Our overarching objective is to orient students toward a future with the highest potential for prosocial impact in a way similar to Owen.
  • Optimizing curriculum for critical thinking – SHIC goes far beyond philanthropic education. Our application of logic, ethics, epistemology, and metacognition to complex social problems provide transferable skills students can utilize in other educational domains. Students and teachers will explore the scientific method, thematic learning, lateral thinking, and the formulation of an alternative outlook on conventional problem solving. See ‘Level 6 – Cognitive Quirks’ for the sort of thematic principles we will incorporate more of moving forward.

 

More information on the SHIC workshop experiment will be made public in the coming months. You can get notified of the latest developments with this specific project by signing up here.

Credits

Post written by Tee Barnett and Baxter Bullock, with invaluable edits and input from Peter Hurford, David Moss and Catherine Low. A special thanks to 80,000 Hours for publishing their process over the years and communicating the importance of long-term plan changes, and to CHIMP for their inspiration and technical support. We’re happy to discuss this post further in the comments section. You can email Tee at [email protected], and Baxter at [email protected].

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EA Survey 2017 Series Part 7: Have EA Priorities Changed Over Time?

2017-10-07

By Peter Hurford and Tee Barnett

This is the seventh article in the EA Survey 2017 Series. You can find supporting documents at the bottom of this post, including our prior EA surveys, and an up-to-date list of articles in the series. 

Summary

  • We use past survey data to shed light on community shifts in cause area preferences over time.
  • Our evidence suggests that EAs are becoming more favorable toward AI and less favorable toward politics.
  • EAs in both the 2015 and 2017 surveys shifted away from viewing poverty as a “top” or “near top” cause.
  • Newcomers in the 2015 survey were less accepting of global poverty than veterans. However, the reverse was true in the 2017 survey, with newcomers being more accepting of global poverty than veterans.
  • There is no indication that EAs are getting less interested in animal welfare with time.

Cause Preference Shifts

Our previous posts in this series were largely descriptive, often reporting on 2015 and 2016 to provide an approximate snapshot of the current EA community. As the series progresses into late 2017, we’ll look to extract further insight from the data, which will include various longitudinal analyses, commentary on the Pledge, and potentially other angles upon request.

 

We turn first to a commonly held narrative within the community – that new EAs are typically attracted to poverty relief as a top cause initially, but subsequently branch out after exploring other EA cause areas. An extension of this line of thinking credits increased familiarity with EA for making AI more palatable as a cause area. In other words, the top of the EA outreach funnel is most relatable to newcomers (poverty), while cause areas toward the bottom of the funnel (AI) seem more appealing with time and further exposure. (For example, see Michael Plant’s post “The marketing gap and a plea for moral inclusivity”.) While we previously reported higher support for global poverty as a top cause, we find reason to support some version of a narrative suggesting that EAs are shifting away from global poverty.

 

There are two ways we’ve looked at changes in preference toward causes over time. First, we took the information on what year EAs joined the community, and compared the cause preferences of earlier EAs to newcomers. Our second method involved taking the population of EAs who took the EA Survey in both 2015 and 2017 and seeing how the same people changed their opinions of their  top cause over this two year gap. The first method has a larger sample size, while the second version captures intrapersonal attitude shifts over time. Both tell a similar tale.

 

Using the longitudinal method, there were 184 people who took both the 2015 and 2017 EA Surveys that we could match (using a hashed email address to preserve anonymity). To get a quick overview of cause preference change over time, we looked at the number of people who shifted toward a cause (they previously had not considered the cause to be a “top priority” or “near the top priority” in 2015, but now do as of 2017) and subtracted the number of people who shifted away from a cause (they previously had considered the cause to be “top” or “near top” and now don’t). This gave us a number we called a “net shift” from a cause.

 

Cause area preferences fluctuated slightly between the 2015 and 2017 EA surveys (Table 1). Poverty remains the clear community favorite, although the net shift in preference broken down by cause area reveals that interest has been waning in poverty since the 2015 EA survey, with a net shift of -8. Interestingly, politics has hemorrhaged the most interest (-13) in the wake of Brexit, Trump’s victory, and other significant political developments in traditional EA hubs. The biggest winner in net gains is AI (+29) and non-AI far future (+14), which suggests at least some directional movement toward long-term concerns over time.

Chart depicting net shift in cause area preference

We were compelled to take a closer look at the dropping interest in poverty, particularly due to its continued popularity in the aggregate and traditional status as an EA mainstay. Between the 2015 and the 2017 surveys, 14.13% of EAs in the longitudinal sample changed their mind about how much importance should be placed on the cause (Table 2), with 9.24% of these EAs no longer considering poverty as a “top” or “near top” cause, and 4.89% of EAs upgrading their estimation of poverty’s importance.

However, there has been more movement within the distinction between “top” and “near top”, with 19.02% of EAs in the longitudinal sample relegating poverty from being the top cause two years later and only 5.98% of EAs upgrading their estimation of poverty as the most important cause area (Table 3).

To look at this from another perspective, we took the 2017 EA Survey population and distinguished between whether an EA was more of a “veteran” who learned about EA in 2013 or earlier or was more of a relative newcomer who learned about EA in 2014 or later[1]. The hypothesis is that veteran EAs would have had more time to shift their beliefs in causes and may be predictive of how newcomers will eventually shift.

 

Taking initial preferences into consideration, EAs who joined in 2013 or earlier were far less likely to rank poverty as the “top” or “near top” priority than EAs who joined in 2014 or later (Table 4), though a majority of these veteran EAs still ranked poverty as the “top” or “near top” cause.

One potential explanation for this shift might not be a genuine change in opinion over time, but instead that veteran EAs were always less likely to be into poverty, whereas newer EAs are a lot more likely to be into poverty. To check our base assumption about whether there has been a significant influx of poverty-focused EAs in recent years, we looked back at the 2015 EA Survey and compared it to the 2017 EA Survey (Table 5).

As of the 2015 Survey, newcomers were actually relatively less accepting of global poverty than the veterans, but this effect reverses as of the 2017 EA Survey. This could point to a difference in attitudes for newcomers in 2015 and 2017 skewing the data, rather than newcomers from 2015 changing their minds over time.

 

The data is not entirely clear on whether initially interested EAs change their views away from poverty with time. The perceived separation between veteran EAs being less poverty-focused may be down to initial dispositions, rather than later conversions. The 2017 EA survey data does suggest that most newcomers enter the movement interested in poverty, which may have implications for movement building organizations to bear in mind.

Attitudes Toward AI

Turning to AI, not only has resistance to devoting resources to AI safety reduced substantially since the 2015 EA Survey, but we showed that this set of concerns is now actively competing with other cause areas for top priority billing.

There were more people changing their minds on AI than global poverty (Table 6), with 19.57% of EAs in our longitudinal sample choosing to upgrade the importance of AI in their view to a “top” or “near top” cause and only 3.8% of EAs choosing to downgrade it out of “top” and “near top”. When looking at just top cause area preference, the trends were roughly similar, with 13.04% of EAs in the longitudinal sample promoting AI to the top cause and 7.07% demoting AI from top cause to something else.

Among those veteran EAs who joined in 2013 or earlier, the support for AI as a “top” or “near top” priority was closer to 50-50, whereas for EAs who joined in 2014 or later, there is less support for AI as a “top” or “near top” cause (Table 7). The net shift of aggregate interest toward AI (Table 1), a broad trend favoring AI (Table 6), combined with our knowledge that newer EAs favor AI relatively less (Table 7), would seem to suggest that more exposure to EA increases the likelihood of becoming more inclined to support AI safety over time.

Attitudes Toward Animal Welfare

We were also curious to check the same for animal rights, to see how EA interest in helping animals as a cause has changed over the years.

Here we see that among the 2017 EA Survey respondents, unlike with AI, there is no statistically significant difference between the rate at which newcomers and veterans support animal rights (Table 9). Furthermore, there has been a net shift toward animal welfare among those who took both the 2015 and 2017 EA Surveys (Table 8). Thus, suggestions that EAs are getting less interested in animal welfare over time does not seem to be confirmed by EA Survey data.

Among the 2017 EA Survey respondents, newcomers to EA are relatively more likely to support politics than veterans, though the majority of both newcomers and veterans do not support politics as a “top” or “near top” cause (Table 11). Similarly, among those who took both the 2015 and 2017 EA Surveys, people are shifting away from thinking of politics as a “top” or “near top” cause (Table 10). This may mean that while politics is less popular as an EA cause overall, EAs tend to shift away from it over time. Likewise, it is interesting that it seems like contentious developments of late may have not had any sort of energizing effect on getting EAs interested in politics, as far as we can tell in this survey data.

Endnotes

[1]: This effect is statistically significant at p < 0.00001 for both. We chose 2013 because we felt it properly conveyed “veteran” status before a lot of popular growth in EA in 2014, but this effect remains the same in direction and statistical significance, with similar strength, regardless of your choice for cut-off year (tested with 2011, 2012, 2013, 2014, and 2015 as cut-off years).

Credits

Post written by Peter Hurford and Tee Barnett

 

The annual EA Survey is a volunteer-led project of Rethink Charity that has become a benchmark for better understanding the EA community. 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?

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.

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

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Monthly Donor Briefing: 09/2017

2017-10-06

Thanks for taking the time. These monthly donor briefings are designed to be quick reads that keep you in the know about Rethink Charity. More detailed updates will come in bimonthly/quarterly intervals.

 

Listen to three-minute audio version of this briefing here.

 

Two-sentence RC breakdown – RC is now maturing into a period of evaluation and proactivity since the consolidation of projects announced in May. Key highlights here include plans for a large-scale SHIC workshop experiment, the ongoing LEAN assessment, our release of the 2017 EA Survey, and major developments on regranting avenues in the UK and Canada.

 


Your full RC Briefing

Read time: 3 minutes

Project Updates

 

Rethink Charity

We’re taking a more focused approach to our projects since the rebranding in May. This includes throttling down on .impact (independent EA projects) and online peer-to-peer fundraisers, and reassessing the value our major programs (e.g. LEAN assessment). We’re currently prioritizing SHIC and international regranting efforts as the highest-impact use of our time.

 

–  We initiated the EA Survey 2017 Series reporting on 2015 and 2016 survey data with what we perceive as strong interest from the community.

– RC jointly established EAUK with EA London, an official effective altruism charity with regranting capability.

 

SHIC

– SHIC is planning a longitudinal workshop experiment set to launch in early 2018. This will put to the test our data-driven approach to identifying high-potential students most inclined to engage with effective charity in the long-term. More on this next month.

 

LEAN

– LEAN is currently undergoing its most comprehensive impact assessment to date. The future of LEAN within RC is contingent upon the outcome of the assessment.

 

Donor-Advised Funds (DAF)

– Plans are in motion to inherit Charity Science’s DAF that moved ~$600,000 to GiveWell top charities in 2017. The DAF will then be cause-neutral, expanding our services beyond GiveWell-recommended charities.

– Through a partnership with Chimp, we will be able to offer tax benefits to Canadians for both Canadian and non-Canadian charities. This was previously quite restrictive.

 

What’s Next

 

The shift in our priorities reflects what we think will achieve the highest multiplier effects over time, not simply high initial rates of return. This is why we’re favoring regranting efforts over online fundraisers, and allocating more bandwidth to SHIC while LEAN is under assessment.

 

We’re thrilled with your support – all of RC is pushing hard to keep it.

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EA Survey 2017 Series Part 6: Qualitative Comments Summary

2017-09-21

by June Lee

The annual EA Survey is a volunteer-led project of Rethink Charity that has become a benchmark for better understanding the EA community. This is the sixth article in our multi-part EA Survey 2017 Series. You can find supporting documents at the bottom of this post, including prior EA surveys, and an up-to-date list of articles in the EA Survey 2017 Series.

Could you, however loosely, be described as an “Effective Altruist”?

Several respondents support the underlying principles of the EA movement, but many suggested that they did not consider themselves part of the community because of their disagreement with some of the ideas, or their lack of donations to effective charities (often due to financial difficulties or perceived lack of commitment). Various respondents also seemed to view EA as a lofty, principle-based lifestyle that they had not yet attained and were therefore hesitant to label themselves “effective altruists.” A few comments suggested that the term “effective altruist” implied an underlying pretentiousness that respondents were unwilling to associate with.

If there was a local group near your home, would you attend?

For this question, people tended to respond in one of two ways: respondents in the first group tended to be active participants and/or leaders in their local EA group. Those that did not live in an area with a local EA group expressed interest in starting such a community. Respondents in the second group showed interest in attending occasional meetings. At the same time, these respondents also expressed some ambivalence about attending meetings. Distance and scheduling were common concerns; people also wanted to know how effective and structured the group meetings would be in reaching practical outcomes.

How welcoming do you find the EA community?

Responses varied widely based on the region and the particular forum being referenced. People generally commented that the online community feels off-putting to new members as the topics discussed are very specialized and members tend to be very well-informed. As a typical response went: “Sometimes the jargon and in depth conversations can be a bit alienating to someone without a philosophy or economics background.” Relating to this concern, a few respondents commented that it would be best to create a separate, more open space dedicated to bringing new members up to speed on EA ideas.

Another common theme was that the EA community tends to attract members with similar ethnic, socioeconomic, and educational backgrounds. Respondents noted that the lack of diversity often made it difficult for those outside the demographic to feel comfortable in the EA community.

Do insecurities about not being ‘EA enough’ sometimes prevent you from taking action or participating more in the EA community?

Many respondents expressed a certain degree of guilt for not having “done enough” as an effective altruist, especially when compared to more dedicated members of the EA community. This insecurity seems to largely be the result of internal sentiments (e.g. feeling that they do not have anything worthwhile to contribute), and at least partly attributable to a dynamic inside EA groups that does not fully accommodate new members.

Others expressed satisfaction with their current level of giving and the extent to which they had embraced EA ideas in their daily life.

How can we improve the EA survey?

In this question, respondents highlighted four critical areas of improvement for the survey content. First, they were concerned that so many of the questions asked about donations and participants’ income. According to responses, these questions were tedious and reflected poorly on the nature of EA. Second, several respondents raised serious concerns that the multiple choice questions did not account for all possible answers; for instance, one person noted that the careers list did not include a “retail” option but did have a “business” and “manual labor” option, appearing to exclude individuals of lower income classes. These respondents suggested that more multiple choice questions include an option for “other.” Furthermore, responses noted that many of the questions did not distinguish between EA as a set of principles for doing good and the EA community. Finally, respondents consistently noted that the survey was much longer than advertised and actually took 30-45 min.

Respondents also had specific complaints about the formatting of the survey. First, several voiced frustration that the positioning and color coding of the “Exit & Clear survey” caused them to mistake it for the “next” button and accidentally delete their responses. Others noted that it would be very convenient, both for the respondents and the writers of the survey, to sync individuals’ data from the GWWC My Giving website, eliminating the need for all the questions about donations and income. The survey also caused some problems for active participants of the EA movement. For questions that gauged respondents’ interest in setting up an EAHub profile or subscribing to a newsletter, there was no option for those who had already completed these items.

How did you hear about this survey?

The vast majority of respondents heard about the survey via the Slate Star Codex blog and open threads. Respondents frequently recalled accessing the survey via Facebook group pages such as the GWWC Community page, the Effective Animal Advocacy Discussion page, local EA group pages, and the Dank EA Memes page. A significant number heard about the survey directly from EA-affiliated organizations, including 80000 Hours, Rethink Charity (formerly known as Dot Impact), Students for High-Impact Charity, and Giving What We Can; leaders of these organizations either sent out email newsletters with the survey link or directly contacted individuals with information about the survey.

Credits

Post written by June Lee, 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?

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.

 

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

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EA Survey 2017 Series Part 5: Demographics II

2017-09-18

By: Katie Gertsch and Tee Barnett 

The annual EA Survey is a volunteer-led project of Rethink Charity that has become a benchmark for better understanding the EA community. This is the fifth article in our multi-part EA Survey 2017 Series. You can find supporting documents at the bottom of this post, including our previous piece on community demographics, prior EA surveys, and an up-to-date list of articles in the EA Survey 2017 Series. 

 

This article brings EA demographics back by popular demand. As in, demand for the metrics not covered in the previous post. We hope you enjoy this second look.

 

Race

The survey respondents identified as white by a wide majority. Among the 1,069 who self-identified regarding race, 88.9% identified as white, 0.7% identified as black, 3.3% identified as hispanic, 7.0% identified as asian, and 621 respondents preferred not to answer the question. It was possible to identify with as many races as one wanted, but only 3.59% answered ‘Yes’ to self-identify as more than one race, and only one person (0.09%) identified with three races.

 

 Responses to the question

While diversity comes in many forms, especially in a definitional sense, EA is unlikely to be characterized as racially diverse according to this survey. There may be considerable margin for error in these findings, not the least because such a large proportion of respondents did not answer. But the trope of EA being a predominantly white (89%) and male (70.1%) community, however, is not likely to fade anytime soon without directed effort.

 

A longitudinal analysis of the community’s racial composition cannot be conducted because no data on race was gathered in the 2015 survey.

 

Want to contribute more to this discussion? We recommend joining the Diversity & Inclusion in EA group on Facebook.

Race and Geographic Location

A crosstab of declared racial identity according to location revealed a vast white majority across the top five EA hubs around the world. New York City emerged as the most racially diverse EA hub in the community. This was statistically significant with p = 0.02, but it’s not clear how much we can read into this.

Percentage of respondents who identified as

Politics

Left-leaning EAs composed 64.8% of respondents, while ‘Centre’ (8.1%), ‘Centre Right’ and ‘Right’ (3.3%) accounted for a considerably smaller portion of the sample. Libertarian EAs constitute a sizeable proportion of the sample (8.7%)  a small group (6%) explicitly chose not to answer, and 9% refused to identify with any of the political spectrum. These percentages do not include the 785 people who took the survey but did not answer this question.

Responses to the question

Data on political preference was collected but not published in the 2015 EA Survey report, allowing us in 2017 to present longitudinal data on community-wide shifts in political orientation.

Responses to the question

From 2015 to 2017, the survey indicates a slight shift away from the political left in the EA community. The tables above show 27.27% of the 2015 ‘Left’ moved to the ‘Centre Left’, and 5.88% of the ‘Centre Left’ went “Centre”. There was also some polarization, as 46.15% of the “Centre” moved “Centre Left”.

 

Want to contribute more to this discussion? We recommend joining the Effective Altruists Discuss Politics group on Facebook.

Politics and cause area preference

When looking at the relationship between politics and other areas, we broke down political orientation into whether someone identified with the “Left” (i.e. they said they were “Left” or “Centre Left”) or did not identify with the left (i.e., they picked a different option like, “Centre”, “Centre Right”, “Right”, “Libertarian”). “Other” and “Prefer not to answer” were dropped from this variable. We found 682 respondents who were associated with a left-leaning position (left), 212 respondents who were not associated with a left-leaning position (non-left), and 943 people with no position.

 

A crosstab of political orientation and cause area preference revealed that individuals on the left are more likely to be interested in politics (28% of people on the left rate politics as a top or “near top” cause, compared to 22% of people not on the left), poverty (78% of people on the left rate poverty as a top or “near top” cause, compared to 72% of people not on the left), animal welfare (41% of people on the left say animal welfare is top or near top compared to only 28% of the non-left), and environmentalism (42% of people on the left say environmentalism is top or “near top”. compared to 21% of non-left).

 

Conversely, people on the left are less likely to care about AI (42% of people on the left rate AI as top or “near top” compared to 47% of people not on the left).

Politics and geographic location

Despite the San Francisco Bay Area being anecdotally associated with libertarians, it had the highest amount of people identifying with the left, with 82.9% of Bay Area respondents. Of the other five largest EA cities, London was 80.85% left, Oxford  was 76.92% left and Boston was 73.53% left, and New York City was 63.64% left. However, despite these percentages of left appearing quite different, there was no statistically significant trend in left vs. non-left that we could pick up in our data.

 

Politics and dietary habits

Results show a significant difference according to political affiliation, where 48.9% on the left identified as vegetarian or vegan, while only 29% on the non-left did.

 

This makes sense in the light of the above, looking at politics and cause area preference, where we see a significantly greater proportion (41%) of people on the left putting a high priority on animal welfare, compared to a smaller proportion sharing that level of priority from those on the non-left (28%).

Age and cause area preference

Using the median age of 27 as a dividing point, those below the median  grouped as ‘younger’ and those above the median as ‘older’, we compared cause area preference in these two groups. The group younger than the median age showed a preference for AI (53.1% compared to 37.9% of older people) and less of a preference for poverty (72% vs. 78% of older people).

Employment status

Employment status responses were lead by for-profit work (43.7%) and non-profit organizations (17.0%). There were similar numbers for self-employed (9.5%) and academics work (9.6%). Unemployed respondents made up 7.7%, while 6.8% reported working for a government entity, and 1.2% were homemakers. Those who are financially independent, through savings, passive income or a providing partner accounted for 4.6%.

Respondents employment data (in academia, self-employed, etc)

Field of study

Respondents were allowed to select more than one field of study. Most popular fields among EA’s, by a significant margin, proved to be computer science (18.9%) and maths (16.1%). Following that, philosophy (9.9%), other sciences (9.2%), social sciences (8.6%) and economics (8.4%). Less often chosen were the fields of humanities (7.1%), engineering (6.9%), physics (6.7%) and finally medicine (2.8%).

Responses about field of study

Year joined EA

Pardoning 2017 for being the current year, the last few years appear to have been strong for EA recruitment, though there may also be a survivorship bias with EAs who joined in previous years no longer identifying with EA or take the EA survey. Post-2013, we see double-digit percentage growth in the number self-identified EAs joining the community.

Responses to question asking which year they joined EA

Some additional metrics on  EA movement growth from Peter Hurford and Joey Savoie is available in “Is EA Growing? Some EA Growth Metrics for 2017”.

Credits

Post written by Katie Gertsch and Tee Barnett, with edits 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?

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.

 

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

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EA Survey 2017 Series Part 4: Donation Data

2017-09-12

By Huw Thomas 

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 fourth 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.

Our earlier post presented declared preferences among respondents, and donation reporting allows us to further contextualize behavioral trends within the EA community. The most recent survey of 1019 individuals collected donation data on both 2015 and 2016 donations. The survey was not distributed in 2016.

This post aims to compare donation data of the EA community, and within a couple specific subpopulations. You can find donation data according to cause area and organization preference in our “Cause Area Preferences” post.

Points of Interest

  • Self­-described EAs in our survey reported more than $6.6M in total donations to effective charities for 2015, and more than $9.8M in 2016.
  • Average donation amounts between 2015 and 2016 were heavily skewed upward by major donors, but the median donation amount rose $118.68.
  • Longitudinal survey data revealed consistent year-on-year donation growth.
  • Donors parting with $655.17 or more fall within the top 50% of EA donors. Gifts totalling $12,500 or more are among the top 10%.
  • 405 people who identify their career plan as “Earning to give” (ETG). In 2015, these people accounted for 63.0% of total reported donations. In 2016, ETG donations constituted 57.3% of total reported donations.  


How Much are EAs Donating?

Relatively high average donation rates seem to be commonly associated with effective altruists. So how much are EAs donating?
 

Self­-described EAs in our survey reported more than $6.6m in total donations to effective charities for 2015, and more than $9.8m in 2016. We standardized all the donations into US dollars and found that the average 2015 donation was $6,498 among respondents, while the average donation in 2016 was $9,510. These seemingly impressive are seriously skewed upward by a few major donors.
 

The more informative metric, the median donation, was $250 in 2015, and $655 in 2016. This increase was probably due, in part, to the fact that the survey was released in 2017, and so respondents were probably more involved with the movement in 2016 than in 2015 on average. We see evidence of this when comparing donation activity between years. The survey reveals that 150 respondents donated in 2016, but not in 2015. Only 29 donated in 2015, but not 2016. A total of 999 people provided data for both 2015 and 2016 donations.

 

Although personal donation amounts fluctuated between 2015 and 2016, the mean donation amount per person increased by $3,663.68. This obviously includes a huge variance, however, the median donation amount also increased by $118.68[1].

To help visualize the distribution of donation amounts, let’s look at it in terms of deciles. In other words, how much you would have to donate to be in the top X% of donors based on the reports that we have from the 2016 data.

Percentile values of total annual personal donations

In order to top the highest donation in our registry, you would have to donate over $1,934,550.

 

According to the survey, EA donations are highly skewed toward a handful of major donors. Many individuals could make it into the top 50% of EA donors by donating a small percentage of their income, but only a distinct minority are capable of making it into the top 1%.
 

Donations are clearly affected by student status. In 2016, the median donation of non-­students was $1,538, compared to the median donation of students at $154. The 258 students who donated gave $252,339.60 in total, while the 482 non-students who donated gave $7,242,580.64.
 

These donations may be over­reported, given that who donate less might be less inclined to share that information. We found, however, a relatively more forthcoming sample than expected. Among those who reported on donations, 29% in 2015 and 16.4% in 2016 reported donating $0.
 

If you made donations not reported in the survey, please report them via the EA Donation Registry, which allows you to anonymously contribute to the public total for the EA community – you can ­also share your own donations to inspire others.


Percentage of Income Donated

The mean percentage of income donated was 7.98% of in 2016[2], but again this is skewed. The median is 4.28%. While this may seem low when benchmarked against the 10% commitment of the Giving What We Can pledge, it is higher than the United States national average of around 2% of GDP[3]. To better illustrate the point, let’s look at how many people donate at or above a certain amount of income. Since many neglected to reveal their income, or made less than $10,000, this is based on a sample of 597 EAs.

Percentages of income donated by EAs

It is also possible that people compensate for 2016 donation deficits by donating more at different times. Note also that this finding also doesn’t capture the EAs that are saving now while waiting for better causes to donate to later.


Donations Among Earning to Give

Perhaps one of the more prescient questions in the community is how much ETG individuals are donating. This question includes all individuals who plan to pursue, or are already involved in ETG careers. In 2015, donations among the 405 ETG individuals in our survey totaled  $4,210,633.29. In 2016, donations totaled $5,672,334.74.

The median donation amount in 2015 for 255 ETG non-students is $237.65. For 2016, the median amount is $798.57, which is actually less than the median donation for non-students generally. This suggests that many ETG individuals  are aiming to give later, and perhaps building career capital in the meantime.

We can break this down further by analyzing how EAs responded to  “Do you believe that – for you at the moment – it is better to act now or invest to act better later?”. Among the 148 ETG non-students who answered “Act now”, the median donation was $4,510. Among the 51 non-students who answered “Act later”, the median donation was $712.08. This suggests that the low median donation for earning to give is due to people investing to give later.


Longitudinal Analysis

To look at how donation behavior changes between a subset of individuals, rather than among EA as a whole, we were able to follow a specific group of EAs who took both the 2015 and 2017 EA Surveys[4].

Donation information about EAs who responded to the survey in 2014, 2015, and 2017.

The table above reflects consistent year-on-year growth in donations among 184 individuals we tracked across the last three EA surveys. It’s worth noting, however, there is survivorship bias in this group, as EAs who cease donating might also be less likely to take the 2017 EA Survey.

 

Endnotes

[1]: The median increase is smaller than the difference between the medians for each year, because it only includes people who donated in both years.


[2]: Percent income percentages were performed only for people with income greater than $10K, as donations as a percentage of income became quite absurd with low incomes, including many people donating without any income at all. This was chosen prior to any analysis. Income here refers to self-reported individual income, as opposed to household income.


[3]: https://www.philanthropy.com/article/The-Stubborn-2-Giving-Rate/154691


[4]: The 2014 and 2015 EA surveys covered donation data of the prior year, while the 2017 EA survey covered 2015 and 2016 donation data. For everyone in the 2015 EA Survey and 2017 EA Survey who provided an email address, we hashed their email address using the MD5 hashing function and matched up email addresses between survey data while still ensuring anonymity. This variable is available as `ea_id` in all the public datasets. 180 people could be matched up between 2015 and 2017 surveys and 18 people could be matched up between all three surveys (2014, 2015, and 2017).

Credits

Post written by Huw Thomas, 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?

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.

 

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

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EA Survey 2017 Series Part 3: Cause Area Preferences

2017-09-01

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. 

 

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”.

Responses to a question asking which cause is the top priority.

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%).

Chart showing responses indicating which causes are the top or near the top priority.

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.

Chart showing responses indicating which causes respondents felt no EA resources should go towards.

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%).Chart showing respondents prioritization of animal welfare, broken down by gender, and a second chart showing proportion of vegetarians and vegans broken down by views on animal welfare.

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.

Chart listing number and size of donations to various EA supported organizations.

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.

Chart showing number of and size of aggregated donations divided by cause area in 2015 and 2016.

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.

 

Credits

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?

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.

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

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EA Survey 2017 Series Part 2: Community Demographics and Beliefs

2017-08-29

By: Katie Gertsch

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 second in a multi-part series intended to provide the survey results in a more digestible and engaging format. Important to bear in mind is the potential for sampling bias and other considerations outlined in the methodology post published here. 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. 

 

Summary

  • EAs remain predominantly young and male, though there has been a small increase in female representation since the 2015 survey.
  • The top five cities with the highest concentration of EAs include the San Francisco Bay Area, London, New York, Boston/Cambridge, and Oxford.
  • The proportion of EA’s that identify as atheist, agnostic, or non-religious came down from 87% in the 2014 and 2015 surveys to 80% in the 2017 survey.
  • The number who saw EA as a moral duty or opportunity increased, and the number who saw it as an only an obligation decreased.

Age


The EA community is still predominantly represented by a young adult demographic, with 81% of those giving their age in the EA survey falling between 20 and 35 years of age[1]. This year, ages ranged between 15 to 77, with a mean age of 29 and a median age of 27 (and a standard deviation of 10 years). The histogram below shows a visual representation of the distribution of ages.

Graph depicting ages of EAs.

[1] Ages were calculated by subtracting the self-reported birth year from 2017.

Gender

The survey respondents were male by a wide majority. Of the 1,080 who answered the question asking how they self-identified regarding gender, 757 (70.1%) identified as male, 281 (26.01%) identified as female, 21 (1.9%) respondents identified as “other”, and another 21 respondents preferred not to answer. This is similar to the 2015 survey, which had a 73% proportion of males.

Chart depicting responses to the question "In which country do you live?"

Consistent with the results of the previous survey, the US and UK are main hubs for EA, home to the majority (63.4%) of this year’s surveyed EAs. Additionally, the top five countries by population (US, UK, Germany, Canada, and Australia) from the 2015 survey remain the top five countries again in 2017. Australia and New Zealand both dropped ranking slightly, and we saw a small increase of EAs living in Northern European countries, such as Germany, Denmark, Sweden, the Netherlands, and the Czech Republic. Representation from Continental Europe overall rose from 14% to 18%.

Chart depicting responses to the question

The San Francisco Bay Area (which includes Berkeley, San Francisco, Oakland, Mountain View, Menlo Park, and other areas) remains the most populous area for EAs in our survey for this question, but only outnumbers respondents from London by a very small margin. This gap between London and the Bay Area has shrunk substantially from 2015.

 

Oxford, Boston/Cambridge (US) and Cambridge (UK) all show consistently high populations of EAs. Washington D.C. dropped from the fifth most densely populated EA city to eleventh. Newly reported additions include Berlin, Sydney, Madison, Oslo, Toronto, Zürich, Munich, Philadelphia, and Bristol.

Chart depicting responses to the question

The proportion of atheist, agnostic or non-religious people is less than the 2015 survey. Last year that number was 87% compared to 80.6% this year. That metric hadn’t changed over the last two surveys, so this could be an indicator that inclusion of people of faith in the EA community is increasing.

As noted in 2015, it has been suggested that greater efforts should be made on the part of EA to be more inclusive of religious groups. The numbers definitely still show room for growth in religious communities.

Chart depicting responses to question

The distribution of responses regarding a stance on moral philosophy is extremely similar to the last survey. In 2015, 56% selected Consequentialism (Utilitarian), 22% No opinion or not familiar with these terms, 13% Non-utilitarian consequentialism, 5% Virtue Ethics and 3% Deontology. Among respondents, the distribution of philosophical stances has not noticeably changed.

 

Do they see EA as an opportunity or an obligation?

This question was inspired by Peter Singer’s classic essay on whether doing a tremendous amount of good is an obligation or an opportunity, which inspired commentary by Luke Muehlhauser (see this post) and Holden Karnofsky (see this post), among others. Perhaps even more than a preferred moral philosophical stance, this helps us get a view to the participants’ motivation to be effective altruists.

 

The 2015 survey posed this question a little differently, presenting the choices as ‘Opportunity,’ ‘Obligation,’ or ‘Both’ instead of ‘Moral Duty’. Both surveys included ‘Other’ as a choice as well. About the same proportion chose ‘Both’ in 2015, as those who selected ‘Moral Duty’ this year. We could guess that there was a richer connotation understood by ‘Moral Duty’, over the more narrow, and somewhat negatively biased ‘Obligation’ option.

 

From 2015 to this year, those who saw EA as only an opportunity stayed the same, while those seeing it only as an obligation decreased significantly.

 

By offering ‘Moral Duty’ as a response, we may have given those who see participating in EA as primarily a dutiful action, a more neutral (less negative) and/or more principled (less self-focused) match to their personal interpretation.

 

Credits

Post written by Katie Gertsch, 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 Slate Star Codex, 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?

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.

 

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

 

Raw Data

Anonymized raw data for the entire EA Survey can be found here.

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