EA Survey 2018 Series: Donation Datapost by David_Moss · 2018-12-09T03:58:43.529Z · EA · GW · 18 comments
Summary Totals Donated Percentages of Income Donated What Explains Low Donations? Students Employment Income Self-reported reasons for donating less than desired Other Influences on Giving Career Giving What We Can Pledge Year First Heard of EA Predictors of Donation Which Charities are EAs Donating to? Conclusions Updates and corrections Credits Supporting Documents None 18 comments
- Median donations were slightly higher than in 2016 and total donations much higher
- A small number of very large donors account for the majority of the totals donated
- A majority of EAs report donating less than they would like due to financial constraints
This post explores donation data in the 2018 EA Survey, investigating how much people are donating, where they are donating and what influences their donations.
1891 out of 2607 (73%) self-identified EAs in our sample offered data about their donations. This is a significant increase from the 2017 Survey where we had donation data from 1019 EAs out of 1853 (54.9%).
As in previous years, there was a very wide range in amounts donated (note the 2018 Survey collects reports on amounts donated in 2017). All amounts are in USD ($).
This marks a significant increase in total amounts donated and median donations compared to 2015 and 2016 data.
As the graph below shows, a very small number of very large donations dwarf the size of most others.
The following histogram shows the number of donations of different sizes being made by EAs in our sample (note the log x axis).
A commenter [EA(p) · GW(p)] on a previous EA Survey post asked about the proportion of total EA donations that came from donations of certain sizes and requested a cumulative donations graph (below). Perhaps, even if the largest donations are many times larger than the rest, much of the total might still be coming from a very large number of smaller donors?
As the graph above shows, however, a relatively small portion of total donations recorded in our sample comes from those donating smaller amounts. Individuals donating $1000 or less in 2017 (47.8% of donors in our sample) accounted for $267,942.5 of donations or about 1.5% of the total. Those more in the ‘middle’, donating between $1000 and $10,000 include 40% of donors, and the substantial sum of $2,569,359.59, which is large relative to most EA projects, while still only being 14% of total donations. Conversely donors giving more than $100,000 (0.7% of donors) accounted for 57% of donations.
As the figures in the table above show, a donation of $1000 per year (or 5% of a $20,000 salary) would place one in the top half of EA donors (specifically, the 55th percentile), whereas being in the top 10% of donors would require donating $10,000 and the top 1% >$75,000.
Percentages of Income Donated
We also looked at the percentages of income that EAs were donating, based on the 1798 EAs who disclosed both income and donation data. As in previous years, most EAs were donating significantly less than the 10% Giving What We Can Pledge. However, as the graph below shows, there is a marked ‘bump’ in the donors giving at around the 10% figure, perhaps due to the Giving What We Can Pledge target around this amount, or due to the figure’s wider popularity as a target (e.g. in tithing).
The above graph shows a small number of respondents report donating more than 100% of their income in 2017. In all but three of these cases the sums reported for donations and income were very low (less than a couple of thousands dollars), so we do not view them as particularly implausible, though they may well represent unusual situations.
What Explains Low Donations?
In previous years, readers have commented on the large numbers of EAs who appear to be donating $0 or close to $0 and at their surprise at low donation levels overall. In earlier years, where donations and the Giving What We Can Pledge were emphasised more, compared to now where (directly) impactful career choice and upskilling EAs are emphasised more, this was perhaps a more a cause for surprise and concern. However, it is still worth investigating what explains low levels of donation.
It goes without saying that many EAs are students (45.2% in our sample this year), who might therefore be expected to be donating lower amounts.
True to expectations, we found that non-students donated significantly more than students:
Similarly, many EAs in our sample, though not students, may not be employed or fully employed. When we exclude both students and those who are not employed full-time, median donations are, again, substantially higher:
Despite this, the percent donated even among full-time employed non-students was very low, with a median of 3.7% income donated. Indeed, only the 80th percentile of full-time employed non-students was donating more than 10% (10.591%) with the 90th percentile donating 14.52%.
The fact that donations are higher among full-time employed non-students may well be expected to be largely due to higher income. Indeed, within the sample as a whole, we found that donations in 2017 were strongly correlated with the average of household and individual income in 2017 ( 0.9232, p<0.001) in the raw data. However, we should note that there are some large outliers driving this very strong relationship.
To account for full-time employed non-students who were nevertheless on a low income, we also looked at full-time employed non-students who reported earning more than $20,000 USD in 2017 (762 out of 1050 full-time employed non-students, of which 747 reported donation data).
While the median donation among this group was substantially higher than for the sample as a whole, the percent donated remains quite far short of the level of the Giving What We Can Pledge.
Self-reported reasons for donating less than desired
This year, for the first time, we asked respondents whether they were donating as much or less than they wanted to.
Across the sample as a whole, a majority (56.99%) reported donating less than they would like to due to financial constraints or some other reason (17.36%), whereas less than a quarter (24.42%) reported donating as much as they would like to.
We also hand-coded the qualitative responses in the “please specify why” box. Responses could be classified as multiple different categories at once. Many people who selected this option also specified financial constraints as a reason, meaning that the total for this category was even higher. Most of the coded categories are fairly self explanatory. However, “Social” refers to social influence or social norms and most responses in this category explicitly referred to a spouse or partner. “Efficacy” refers to concerns about locating an effective charity to donate to or concern than any charities were effective donation targets. The graph below shows the frequency of these other reasons.
Other Influences on Giving
EAs work in a variety of careers and perhaps people aren't donating money, but rather donating their time or taking a lower salary to work at a directly impactful org. Indeed, those working in research roles donate substantially less than their peers and have a lower median income.
Giving What We Can Pledge
The observation that so many EAs in our survey were donating much less than 10% of their income to charity suggests the question: how much are self-reported GWWC Pledge takers donating?
According to this analysis, the median percent of income donated by someone who had taken the GWWC Pledge, in our sample, was 7.22%, short of the 10% target. Nevertheless, this could be influenced by GWWC Pledge takers being students, not employed or only recently having taken the Pledge. We will be exploring these issues in more detail in a dedicated post.
Year First Heard of EA
Median donations, income and percentage of income donated all increase fairly steadily the longer respondents have been in EA. 2010 bucks the trend slightly owing to one very high income-high donation individual and small number of EAs within that year. One intuitive reason why this might be is that those who have more recently heard of EA tend to be younger, more likely to be students (or may be earlier in their careers) and so have a lower income. In addition, new EAs might also be expected to be less likely to be extremely dedicated or willing to donate large sums right away. There may also be some survivorship bias, where those EAs who report first hearing of EA in earlier years, and are still taking the survey in 2018, may be more likely to dedicated, highly involved (and perhaps higher donating) EAs.
This veteran versus newcomer difference is apparent within non-students, pledge takers, those earning to give, and especially among high-income individuals. Without longitudinal data it is difficult to know if this is due to a time effect, or group-switching over time. There is very little difference between newcomer and veteran non-pledge takers, students, other career types, and other income-level individuals. This makes intuitive sense as this group are likely to now either have low incomes or not be expressly committed to donating a significant portion of income, irrespective of the length of time they have been in EA.
(Right click and open in new tab to expand image)
Predictors of Donation
We examined the effect of income, student status, number of years involved with EA, and membership in GWWC as potential predictors of donations. Individual income was the strongest predictor of donation amount, followed by a positive impact of membership in GWWC interacting with income (link to regression table). Before analysis, two outliers with large influence were removed (individual incomes >$5 million), and the data were log transformed, centered and scaled to improve normality. Household income was strongly correlated with individual income (~80%) and was therefore excluded from the model. This model (AIC=3114), was slightly preferred over a model with no interaction between GWWC and income (AIC=3121), and explained about 45% of the variation in the data. The model was also preferred over a simple regression of donation vs income (AIC=3498) which accounted for 37% of the variation in the normalized data.
As previously noted, there was a small negative impact of being a student on donation amount. As seen in the figure below, this effect was slightly mitigated by being a member of GWWC.
There was also a very small negative effect of being aware of EA for a shorter time. There was little correlation between between years of EA involvement and individual income (16%), so the explanation for this effect may have more to do with years of commitment to charitable giving than income-related factors such as student status.
Which Charities are EAs Donating to?
We received information about which specific charities respondents donated to from 494 out of the 1363 EAs who reported donating anything in 2017. Given this, information about totals of donations to specific charities should be treated with caution.
As in previous years, GiveWell charities, led by GiveDirectly, received among the most reported donations. However, unlike previous years, CEA tops the list for total reported donations. The Humane League attracted substantially more funding (mostly explained by one large donor) and donors than in 2016, among those who reported donations. In addition, AMF, GiveDirectly, GiveWell, and MIRI received the largest number of individuals donating to them.
Total EA donations within our sample are dominated by a fairly small number of very large donors. Nevertheless, median donations do seem to be slowly increasing, compared to earlier years. Furthermore, median donations and percentages of income donated are substantially higher when excluding students, those not fully employed or those on a low income.
 Unless otherwise stated “income” refers to the average of individual and household income, which were reported separately. These measures were extremely highly correlated and using either of the individual measures made no difference to our analyses where we tested this.
Updates and corrections
Corrected percentage donated values
Added column labels to percentiles tables at request
This post was written by David Moss, with contributions from Neil Dullaghan and Kim Cuddington.
Analysis conducted by Rethink Priorities staff, David Moss, Neil Dullaghan, Kim Cuddington and Peter Hurford.
Thanks to Peter Hurford and Tee Barnett for editing.
Other articles in the 2018 EA Survey Series:
Prior EA Surveys conducted by Rethink Charity:
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