The GiveWell Blog

We aim to cost-effectively direct around $1 billion annually by 2025

A little over a decade ago in 2010, GiveWell directed around $1.5 million to the charities we recommended. In 2021, we expect we’ll raise at least $500 million, and may raise as much as $560 million or more.

We never anticipated that we’d grow this large this quickly. We’ve seen rapid growth from donors of all sizes, the most recent of which is a commitment of $300 million from Open Philanthropy.

While this growth comes with challenges—we’re working hard to hire enough researchers—it’s a testament to our donors’ trust in us and enthusiasm for our mission.

But these big numbers are relatively small in the long-term scope of what GiveWell hopes to achieve. We believe there are billions of dollars’ worth of annual cost-effective giving opportunities that we have yet to identify.

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New research on moral weights

Each year, GiveWell identifies more great giving opportunities than we are able to fully fund. As a result, in our charity recommendation decisions, we necessarily face very challenging questions, such as: How much funding should we recommend for programs that reduce poverty versus programs that reduce deaths from malaria? How should we prioritize programs that primarily benefit children versus adults? And, how do we compare funding those programs with others that have different good outcomes, such as reducing suffering from chronic health issues like anemia?

We recently received results from research we supported to help us answer these questions from the perspective of communities similar to those our top charities operate in. This blog post provides a brief summary of the project and results. Additional details are available on this page.

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Publishing more frequent updates to our cost-effectiveness model

We’ve recently made a number of adjustments to improve our research process. Not all of them are easily visible outside of the organization.

This post is to highlight one of them: Publishing more frequent updates to our cost-effectiveness model throughout the year.


This post will explain:

  • What changed in how we make updates to our cost-effectiveness model. (More)
  • Why we made this change. (More)
  • How to engage with updates to our model. (More)

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Why we don’t use subnational malaria mortality estimates in our cost-effectiveness models


We recently completed a small project to determine whether using subnational baseline malaria mortality estimates would make a difference to our estimates of the cost-effectiveness of two of our top charities, the Against Malaria Foundation and Malaria Consortium. We ultimately decided not to include these adjustments because they added complexity to our models and would require frequent updating, while only making a small difference (a 3-4% improvement) to our bottom line.

Though this post is on a fairly narrow topic, we believe this example illustrates the principles we use to make decisions about what to include in our cost-effectiveness model.

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Revisiting leverage

Many charities aim to influence how others (other donors, governments, or the private sector) allocate their funds. We call this influence on others “leverage.” Expenditure on a program can also crowd out funding that would otherwise have come from other sources. We call this “funging” (from “fungibility”).

In GiveWell’s early years, we didn’t account for leverage in our cost-effectiveness analysis; we counted all costs of an intervention equally, no matter who paid for them.((For example, see row 3 of our 2013 cost-effectiveness analysis for Against Malaria Foundation.)) For example, for the Schistosomiasis Control Initiative (SCI), a charity that treats intestinal parasites (deworming), we counted both drug and delivery costs, even when the drugs were donated. We did this because we felt it was the simplest approach, least prone to significant error or manipulation.

Over the last few years, our approach has evolved, and we made some adjustments for leverage and funging to our cost-effectiveness analyses where we felt they were clearly warranted.

In our top charities update at the end of 2017, we made a major change to how we dealt with the question of leverage by incorporating explicit, formal leverage estimates for every charity we recommend.

This change made our cost-effectiveness estimates of deworming charities (which typically leverage substantial government funding) look more cost-effective than our previous method. For example, our new method makes SCI look 1.2x more cost-effective than in the previous cost-effectiveness update. More details are in the table at the end of this post.

We also think the change makes our reasoning more transparent and more consistent across organizations.

In this post, we:

  • Describe how our treatment of leverage and funging has evolved.
  • Highlight two major limitations of our current approach.
  • Present how much difference leverage and funging make to our cost-effectiveness estimates.

Details follow.

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How uncertain is our cost-effectiveness analysis?

When our cost-effectiveness analysis finds robust and meaningful differences between charities, it plays a large role in our recommendations (more on the role it plays in this post).

But while our cost-effectiveness analysis represents our best guess, it’s also subject to substantial uncertainty; some of its results are a function of highly debatable, difficult-to-estimate inputs.

Sometimes these inputs are largely subjective, such as the moral weight we assign to charities achieving different good outcomes (e.g. improving health vs. increasing income). But even objective inputs are uncertain; a key input for anti-malaria interventions is malaria mortality, but the Institute for Health Metrics and Evaluation estimates 1.6 times more people died in Africa from malaria in 2016 (641,000) than the World Health Organization does (407,000; pg. 41).((Differences in their methodologies have been discussed, with older figures, in a 2012 blog post by the Center for Global Development.))

Before we finalized the charity recommendations we released in November, we determined how sensitive our results were to some of our most uncertain parameters.

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