*TLDR: if welfare compounds then risk-aversion is good.*

Within EA circles, the question of splitting donations pops up every once in a while. Should you donate all your money to the singular top-rated charity your singular top-rated cause area, or is there reason to split your donations between various different causes or interventions?

People other than me have written and talked about this under various headers, I’ll list a small subset. Reasons not to diversify (Giving What We Can). Reasons to diversify: the value of information, explore vs exploit (Amanda Askell @ 80k). Reasons both for and against: risk aversion, diminishing returns, EV maximization (Slate Star Codex). In-depth blog post with mahy arguments both for and against (EA forum). Not listed but probably talked about before: splitting your donations gives you extra practice at donating which might lead to you making better donation decisions in the future.

In this post I want to make an argument in favour of splitting donations based on compounding economic returns and measurement error. Specifically, compounding returns favour more consistent growth over a slightly higher but variable growth.

Let’s consider a 100-year time horizon. Suppose that there are 100 charities, C_1,\dots,C_{100}, whose effectiveness is heavily-tailed: donating $1000 to charity C_i allows them to produce i*\$1000 in welfare after a year. Charity evaluator BestowCapably measures the effectiveness of every charity C_i every year j and finds an effectiveness of i + s_{i,j}, where the s_{i,j} are independently normally N(0, \sigma^2) distribution. Let’s assume BestowCapably’s measurement error \sigma does not go down over time.

The way I think of these quantities is that effectiveness is a heavy-tailed distribution and that measurement error is multiplicative (instead of additive).

We assume all welfare gains are reinvested in charity the next year, so that the gains compound over years. The initial welfare is 1. We consider three different donation strategies: donate everything to the single best rated charity, split the donation between the top three rated charities, or split the donation between the top ten rated charities. We plot the compounded welfare after 100 years versus \sigma below.

In the above graph, we see that,for low measurement error, donation splitting is worse than donating everything to the best charity, but for high measurement error, the situation reverses and splitting donations wins out.

## Section of doubt

The code I’ve used (included below) to simulate the scenario has a couple *researcher degrees of freedom*. It is unclear whether measurement error should scale with charity effectiveness. I used Gaussian noise without any justification. My choice of range of \sigma to plot was chosen to have a nice result. The range of charity effecicies has close to no justification. The same stable result can be gotten by donating everything to AMF and nothing to speculative cause areas. The splitting incentive I illustrated only holds at the margin, not for the average donation. Because \sigma is fixed, the magnitude of the effect of donation splitting in this model depends heavily on the number of charities (less charities means greater effect).

Nonetheless, if you care about multi-year impacts, it might be wise to consider more than just short-term expected value. Risk-aversion translates to expected counterfactual impact when results compound.

## Appendix: Python code

```
import random
import matplotlib.pyplot as plt
import math
charitycount = 100
yearstocompound = 100
# The charities are {1,...,n}
# Charity i has effectiveness i
# Effectiveness measurement carries exp noise of size stddev
# Outputs list of (i, i + noise)
def measurecharities(n, stddev):
charities = []
for effectiveness in range(1,n+1):
charities.append((effectiveness,random.gauss(effectiveness,stddev)))
return charities
# Given list of tuples (x, y),
# calculates the average of x's for
# the k tuples with highest y value.
def avgtop(list, k):
sortedlist = sorted(list, key=lambda tup: tup[1], reverse=True)
sum = 0.0
for i in range(k):
sum += sortedlist[i][0]
return sum/k
# Split donations among k charities
for k in [1,3,10]:
x = []
y = []
# We plot the effect for different noise magnitudes
for stddev in range(1,251):
logwelfare = 0.0
for i in range(yearstocompound):
welfaregain = avgtop(measurecharities(charitycount, stddev), k)
logwelfare += math.log(welfaregain)
x.append(stddev)
y.append(max(1,logwelfare))
plt.plot(x, y,label=k)
plt.legend()
plt.xlabel('Error in measuring effectiveness')
plt.ylabel('Log(' + str(yearstocompound) + '-year compounded welfare gains)')
plt.title('Donating to top k out of ' + str(charitycount) + ' charities')
plt.show()
```