When should an effective altruist donate?

William MacAskill (Global Priorities Institute, Oxford University)

GPI Working Paper No. 8-2019, published as a chapter in Giving in Time

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Effective altruism is the use of evidence and careful reasoning to work out how to maximize positive impact on others with a given unit of resources, and the taking of action on that basis. It’s a philosophy and a social movement that is gaining considerable steam in the philanthropic world. For example, GiveWell, an organization that recommends charities working in global health and development and generally follows effective altruist principles, moves over $90 million per year to its top recommendations. Giving What We Can, which encourages individuals to pledge at least 10% of their income to the most cost-effective charities, now has over 3500 members, together pledging over $1.5 billion of lifetime donations. Good Ventures is a foundation, founded by Dustin Moskovitz and Cari Tuna, that is committed to effective altruist principles; it has potential assets of $11 billion, and is distributing over $200 million each year in grants, advised by the Open Philanthropy Project. [...]

Other working papers

Maximal cluelessness – Andreas Mogensen (Global Priorities Institute, Oxford University)

I argue that many of the priority rankings that have been proposed by effective altruists seem to be in tension with apparently reasonable assumptions about the rational pursuit of our aims in the face of uncertainty. The particular issue on which I focus arises from recognition of the overwhelming importance…

Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)

The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …