Thanks!

Incidentally your comment just now prompted me to look at the cross-year cross-cohort data for this. Here we can see that in EAS 2019, there was a peak in podcast recruitment closer to 2016 (based on when people in EAS 2019 reported getting involved in EA). Comparing EAS 2019 to EAS 2020 data, we can see signs of dropoff among podcast recruits among  those who joined ~2014-2017 (and we can also see the big spike in 2020).
 

These are most instructive when compared to the figures for other recruiters (since the percentage of a cohort recruited by a given source is inherently a share relative to other recruiters, i.e. if one percentage drops between EAS 2019 and EAS 2020 another's has to go up). 

Comparing personal contact recruits we can see steadier figures across EAS 2019 and EAS 2010, suggesting less dropoff. (Note that the figures for the earliest cohorts are very noisy since there are small numbers of respondents from those cohorts in these surveys).

This is also reflected very clearly in EA Survey data. 

Here's the breakdown of which specific podcasts people cited in EAS 2020, for where they first heard about EA.

You can also get a sense of the magnitude of Sam Harris' podcast compared to other things like Doing Good Better from looking at the total number of mentions across response categories. (Respondents were asked to first indicate where they first heard about EA from a list of broad categories like 'Book', 'Podcast', and then asked to provide further details (e.g. what book or podcast) in an open comment. Only 60% of respondents to the first question gave further details so the numbers are commensurately lower.)

Taking these numbers at face value, Sam Harris seems to represent more than twice the recruitment effect of Doing Good Better, and slightly higher than half as much as Peter Singer.

One good reason  not to take these numbers at face value is that they will be influenced by how recently these factors were recruiting people. We see consistent signs of attrition across cohorts, so a factor which recruits people in 2020 will have a lot more of those people left in the sample than a factor which recruited a lot of people in 2015 (of whom probably >60% have dropped out by 2020).

People who first got involved at 18 (or 19) are about the same as people who got involved at 21 (i.e. a little bit lower than the peak at 20).

People who first got involved at 17 are about the same as people who first got involved 22-23.

For people who first got involved 15 or 16, the confidence intervals are getting pretty wide, because fewer respondents joined at these ages, but they're each a little less engaged, being most similar to those who first got involved in their mid-late 20s or 30s respectively.

In short, the trend is pretty smooth both before and after 20, but mid to late 30s it seems to level out a bit, temporarily.

You might want to open these images in new windows to see them full size.

And finally, this is  visually messy, but split by cohort, which could confound things otherwise.

We'll be presenting analyses of this using EAS2020 data in the Engagement post shortly.

Yes, these are all based on analyses which I did on EAS 2019 data.

Hedone seems appropriate given that Blackpool is famous for its 'pleasure beach.'

We show changes in the proportion of respondents coming from each source across cohorts using this year's data here. 

You can see the increase in absolute numbers coming from Podcasts and the % of each cohorts coming from Podcasts below. Because some portion of each cohort drop out every year, this should give an inflated impression of the raw total coming from the most recent cohort (2020) compared to earlier cohorts though. Comparing raw totals across years is not straightforward, because sample size varies each year (and we sampled fewer people in 2020 than earlier years as discussed here and here and although we think we can estimate our sampling rate for engaged EAs quite well, we're less certain about the true size of the more diffuse less engaged EA population (see here))- so the totals for ~2017 at the time were likely relatively higher.

Hi Oliver. Thanks for your question!

We actually just performed the same analyses as we did last year, so any references to significance are after applying the Bonferroni adjustment. We just decided to show the confidence intervals rather than just the binary significant/not significant markers this year, but of course different people have different views about which is better. 

Hi Linda. Thanks for the question!

Could I ask to clarify which question you are looking at? I assume maybe the importance for retention question? There we observe that non-white respondents are more likely to select EAGx specifically (about twice as large a percentage of non-white respondents selected EAGx) and indeed I expect this is driven by geography for the reasons you say. There is no significant difference for EAG, though a slightly higher percentage of non-white respondents selected that as well.) 

To answer your question about the analyses: the chi-square tests just look at whether there are differences in the proportions of white/non-white, male/non-male respondents selecting different categories don't attempt to control for other characteristics. So you should just read these as identifying differences between these groups, rather than as necessarily showing that these differences are explained by the groupings themselves. Note that just knowing the proportions, even if they're not causal may still be action-relevant, i.e. we might want to know what programs are actually helping a larger number of non-white EAs, even if this is ultimately explained by some third factor). In contrast, in the models at the end looking at predictors of NPS and change in level of interest in EA we do try to control for different influences simultaneously.

There was some discussion of the original acquisition here.

Historically, Charity Navigator has been extremely hostile to effective altruism, as you probably know, so perhaps this isn't surprising. 

Thanks for the reply!

Interestingly, EAG attendees don't seem straightforwardly newer to EA than EAS respondents. I would agree that it's likely explained by things like age/student status and more generally which groups are more likely to be interested in this kind of event.