Does it make sense for EA’s to be more risk-seeking in earning to give?
Repost from Charity Entrepreneurship’s blog
Historically it has been argued that EAs who are looking to make large donations may have more reasons to be risk-neutral than people who are aiming to make money for personal, happiness-focused reasons. This is in part due to the impact of donations not suffering the same diminishing returns as money on happiness. However, we believe there is an additional, significant reason for people who are doing E2G (earning to give) to be even more sympathetic to risk. In short; as income goes up, a larger percentage of the income is donated. This leads to dichotomized earning outcomes and a higher net donation per dollar.
Theoretical example:
Say we have ten EAs, each of whom can do E2G in a low-risk way (e.g. working in software) or a high-risk way (e.g. founding a software startup). In both cases, they all have signed the GWWC pledge and plan to donate 10%+ to an effective charity.
Low-risk outcome:
In the low-risk scenario, each does E2G and earns $100k, donating 10%. Across the group this results in $10k x 10 people = $100k donations. This is a really great outcome, but let’s look at the higher risk situation with the same expected income value ($1,000,000).
Higher variance scenario:
In this situation, nine out of ten people have startups that do not work that well, resulting in a yearly income of $50k. However, one person earns considerably more; $550k. Most of the lower earners stick with their donation percentage, with eight continuing to give 10% and one no longer giving. This results in $40k of donations. However; the earner of $550k feels 10% seems a little underwhelming relative to their newfound wealth, and instead donates 50%. This results in $315k being donated to charities out of the same $1,000,000 earned by the high-risk group.
Although the financial earnings in these two groups were identical, the donation outcomes differed by more than 3 times ($100k vs $315k). The higher variance scenario did considerably more good.
Real-world data:
This is a theoretical example with made-up numbers. How do we know that people who earn more will, in fact, donate more? Thankfully we have some data on this topic from both within the EA movement and outside of it. Inside the EA movement, we have compelling data showing this trend happens both in GWWC and non-GWWC members; this is likely the most relevant data when thinking about giving EA career advice. However, outside of the EA world there is also some confused data that tentatively points in the same direction.
Countervailing factors:
There are some other factors that can affect this calculation pretty significantly. If half of the ten low-risk EAs have donation matching at their workplace, that increases their net donations pretty significantly (x1.5), although still not enough to change the endline outcome. Also, if you assume that every EA fundraises a bit from people in their same income group it gets even less clear.
On the other hand, a big, beneficial factor of dichotomous income is that it opens up more optimal career change paths. In the low-risk situation, any one of those people changing careers affects the net donations by 10%, however, in the higher variance scenario, nine of the ten people could change careers while only affecting 1.5% of the total donations made. Of course, the cost for the highest income person becomes a lot higher, but assuming the people are of roughly equal talent at the start, having differentiated outcomes allows easier career changes with less percentage of donations lost. Those who manage to launch a successful start-up business are also less likely to change careers in general. Another positive is that in our experience, for-profit entrepreneurship typically builds better skill sets than many other E2G paths.
On the basis of this reasoning and evidence, we feel more attention should be aimed at encouraging E2Gs to aim towards higher risk career paths, such as for-profit entrepreneurship.
This seems to be a different angle on the diminishing personal utility of income, combined with artifacts of fixed percentage pledges? Doing, say, a startup, gives some probability distribution of financial outcomes. The big return ones are heavily discounted personally. Insofar as altruism tips you over into pursuing a startup path it’s because of your valuation of donations you expect yourself to make in those worlds.
But it seems like double counting to say this is on top of “the impact of donations not suffering the same diminishing returns as money on happiness”.
It definitely seems right for people to consider progressive rather than flat proportion donation schedules for themselves in high variance careers though, basically self-insuring some of the risk of failure/lower earnings to consumption utility.
Thanks for laying out the math here. Given the high variance we’ve seen in the community, it does suggest E2G’rs should go for high-risk high-reward choices.
There is one further consideration that I think needs to be added to the higher variance scenario which comes from concentrating donations from fewer people.
If all the E2G people donate to the same charity, then it makes sense to have higher variance in giving as you laid out. However if the E2G people give to different charities, then donations are now skewed towards the preferred charity of the lucky entrepreneur.
One way to think of this is in terms of dollars donated per thought-hour. Assume each donor spends 10 hours thinking about where to donate, and that the lucky entrepreneur spends 20 hours deciding where to donate. In the lower variance scenario, there are ($10,000 * 10 / (10 * 10) hours) = $1,000 dollars donated per thought hour. In the higher variance scenario, there are ( (($40,000 / (10 hrs * 8 people)) * $40,000 + ($275,000 / 20 hours)* $275,000) / $315,000 = $12,067 dollars donated per thought-hour. We’ve traded a 3.15x increase in donations for ~4x (12067 / (3.15*1000)) less thoughtfulness.
So while it’s great to have more money to donate, it’d be nice for the E2G givers to pre-commit in advance to a target charity, fund, donor lottery, or collective decision making process to not dilute the thoughtfulness behind donations. Another option is that the lucky entrepreneur could simply allocate donation decisions for a small portion of their giving to the other E2G people (say $5k each) and it would gain back all the lost thoughtfulness.
If we are taking the assumed donor behavior as given, and if the sole objective is maximizing donations to charity, this makes sense. But there is an available option that would be better for both the EA that is earning to give and the charity. The E2Ger could take the $100k job and donate 32%. With even slightly diminishing marginal utility of consumption, the E2Ger would be better off consuming $68k with certainty than having a 80% chance of consuming $45k, a 10% chance of consuming $50k, and a 10% chance of consuming $275k. And the charity would get slightly more in expectation ($32k rather than $31.5k).
In practice, I think there is usually a tradeoff between risk and expected value when choosing among E2G jobs/careers, so choosing riskier options and donating a higher percentage when outcomes are favorable will tend to be the right policy. I’m just not sure that the main argument presented here strengthens the case for doing so.
This is something I’m thinking about for my personal situation, and I strongly agree with this comment (but don’t have a lot of actual data to back this view).
Considering the subset of people that are donating everything after rent and food, this model might predict lower total donations for the higher variance distribution (I expect rent and food costs to increase once you have a more intense and higher risk job, because of opportunity costs).
But I think that in that case it’s still very likely that choosing riskier options will have a much higher expected impact. I think I would model income with a ~lognormal distribution with higher variance and higher EV than the one in the post (I would expect many to get way less than 50k, <1% to get in debt, and a few to get much more than 500k, but would love to see data on this). [Edit: thinking about this more, it’s not at all obvious to me that the EV would be higher in the more risky approach, instead of lower, for the average person that’s undecided]
Since most EAs are younger than ~45, the biggest advantage could be in terms of career capital as mentioned in the post. I expect riskier work tends to be higher effort, lead to more upskilling, and move comparative advantage away from e.g. “optimize the company obscure proprietary database” to things that are more generally useful for direct work.
So I think the post actually undersells the main advantages of being risk-seeking in earning to give.
I think it also undersells the personal costs. Asking people giving 10k of 100k to work harder with a 90% chance of losing income (possibly in a significant way), all for a chance of becoming a millionaire, seems a big ask.
So I wonder:
Would most people prefer to give more than 10% given the choice, like you mentioned?
Is the expected value of riskier options actually higher in practice?
How could we quantify the difference in career capital?