Altruists often would like to get good predictions on questions that don’t necessarily have great market significance. For example:

• Will a replication of a study of cash transfers show similar results?
• How much money will GiveWell move in the next five years?
• If cultured meat were price-competitive, what percent of consumers would prefer to buy it over conventional meat?

If a donor would like to give money to help make better predictions, how can they do that?

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Update 2020-01-04: This essay contains a number of important mistakes. See Correction on Giving Now vs. Later.

Disclaimer: I am not an investment advisor and nothing in this essay serves as investment advice.

Introduction

Robin Hanson: If More Now, Less Later

The rate of return on investment historically has been higher than the growth rate–or, as they say, r > g. If you save your money to donate later, you can earn enough interest on it that you eventually have the funds to donate a greater amount. Because r > g, you should invest your money for as long as you can before donating¹–or so the argument goes.

Traditionally, we’d apply a discount rate of g to future donations, because that’s the rate at which people get richer and therefore the rate at which money becomes less valuable for them. But this ignores some important factors that affect how much we should discount future donations, and we can create a much more detailed estimate. This essay will explore that in detail. Exactly what factors determine the investment rate of return and the discount rate on poverty alleviation? Can we gain any information about which is likely greater?

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Some people have observed that small and large donors follow different giving patterns. Small donors who give out of their salary—that is, most people—tend to donate money more or less as soon as they earn it (usually within a year). Large donors—e.g., extremely wealthy people and foundations—tend to slowly distribute their money and hold on to most of it¹. For example, large foundations typically donate little more than the legally required 5% of assets each year. Why do they behave differently?

I don’t believe this difference is surprising, and actually it’s not really even a difference.

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This is a collection of writings on where people are donating. It only includes writings that I am aware exist (obviously) and that are written by effectiveness-minded people.

My descriptions are paraphrased from the linked writings as much as possible. The writing in this post includes combinations of my own and the linked writers’ words. My summaries often do not do the original writers justice, so I recommend reading all of the linked articles if you are interested.

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Summary: The stock market can be modeled as Omega in Newcomb’s problem. On average, an asset will only outperform if the market predicts that you won’t buy it. So you cannot say “if I had bought that, I would have made a lot of money”, just as in Newcomb’s problem you can’t say “if I had taken both boxes, I would have gotten more money than if I only took one”.

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Summary: All expected value distributions must either (1) have a mean of infinity, or (2) have such thin tails that you cannot ever reasonably expect to see extreme values. When we’re estimating the utility distribution of an intervention, both of these options are bad.

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I have removed Disqus and replaced it with built-in static comments. Disqus comments are disabled, but still visible on any old posts¹. New posts going forward will only use the new static comment system.

I had been wanting to switch off Disqus for a while. It has a few disadvantages:

1. I have no control over comments except for the moderation tools Disqus provides.
2. I have no control over how comments are displayed.
3. I don’t know what Disqus is doing or might do with commenters’ personal information.

The new comment system does exactly what I want it to do and nothing more.

Edited to add: If anyone’s interested, I’m using the Jekyll Static Comments plugin by Matt Palmer, with a few personal modifications.

Notes

Summary: Large donors may create better incentives for both charities and small donors if, rather than providing fixed funding to a charity, they offer to match all donations to that charity over a relatively long time horizon.

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Summary: Investment advisors typically recommend that you put somewhere between 50% and 75% of your stock investments into US stocks and the rest into international markets. Most individual investors have 70% or more of their stock money in the US or their home country, a phenomenon that’s aptly called home country bias. But there are reasons to believe that even 50% is too much.¹²

Disclaimer: I am not an investment advisor and this is not investment advice.

Published 2017-03-26; last updated 2026-01-24.

A note from 2026: US equities handily beat international equities from 2017 to 2025. I still believe the arguments I originally wrote are mostly correct, although I was overconfident about their strength; and there was a good justification for home country bias that I wasn’t aware of, namely, expropriation risk.

I originally wrote that US investors might want to invest only 0–30% in US stocks. I now believe that’s taking it too far. I believed that the strongest argument for under-weighting US stocks was income diversification, but the correlation between domestic equities and personal income isn’t high enough to justify a 0% weighting.

Although I was overconfident in 2017 and wrong about some things, I believe I was correct to criticize most justifications for home country bias, and I don’t believe US equities’ subsequent outperformance was predictable ex ante; see Asness, Ilmanen & Villalon’s International Diversification—Still Not Crazy after All These Years (2023).

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Ever since I’ve been writing essays, I have always accumulated many more essay ideas than I end up actually writing. I frequently have an idea, write a paragraph or two, and then realize I have nothing left to say. Rather than leaving these unpublished, I am trying an experiment. This post contains a compilation of some of these ideas that were too short for essays.

In this issue, we discuss:

• putting your money where your mouth is
• the linguistics of swear words
• cause prioritization
• religion
• more cause prioritization, because that’s obviously been my favorite topic recently

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