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Centre for Effective Altruism More to explore on 'Differences in impact' online This article examines the question of how to maximize positive impact when making charitable donations. It discusses two major organizations in the effective altruism movement: GiveWell and Open Philanthropy. GiveWell prioritizes evidence-backed organizations in global health and wellbeing, while Open Philanthropy supports both high-risk, high-reward work and work that could take a long time to pay off. The article then explores the methodologies used for assessing cost-effectiveness, highlighting the importance of considering various impact measures beyond just cost-effectiveness. Further, it examines newer strategies for improving human wellbeing, including the use of mobile money services like Wave and the incubation of charities through Charity Entrepreneurship. Finally, the article delves into criticisms of using cost-effectiveness estimates, emphasizing the need for careful consideration of long-term effects, local context, and potential biases. – AI-generated abstract.

More to explore on 'Differences in impact'

Centre for Effective Altruism

Effective Altruism Forum, July 2, 2022

Abstract

This article examines the question of how to maximize positive impact when making charitable donations. It discusses two major organizations in the effective altruism movement: GiveWell and Open Philanthropy. GiveWell prioritizes evidence-backed organizations in global health and wellbeing, while Open Philanthropy supports both high-risk, high-reward work and work that could take a long time to pay off. The article then explores the methodologies used for assessing cost-effectiveness, highlighting the importance of considering various impact measures beyond just cost-effectiveness. Further, it examines newer strategies for improving human wellbeing, including the use of mobile money services like Wave and the incubation of charities through Charity Entrepreneurship. Finally, the article delves into criticisms of using cost-effectiveness estimates, emphasizing the need for careful consideration of long-term effects, local context, and potential biases. – AI-generated abstract.

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