Impacts of Effective Altruism on Donor Behaviour: A Randomised Discrete Choice Experiment
Abstract
In a pre-registered randomised online experiment, we test the effect of an effective altruism information treatment on donations to non-governmental organisations (NGOs). Donor behaviour is measured through an incentivised discrete choice experimental approach. Despite power to pick up reasonably small effects, we find no effect of the treatment on donations to advocacy organisations, suggesting that the critique against effective altruism as ignoring institutional, systemic, and political issues meets with little empirical support. Some results even suggest that effective altruism may increase donor support for advocacy. In addition, effective altruism leads to increased support for organisations with an international focus, a result driven by respondents with less universalistic distributional preferences and less trust in NGOs. This suggests that effective altruism may reduce parochialism in donor behaviour.
Study specs
Randomised online discrete choice experiment with pre-registration and incentivised donation measurement.
- Institution
- Norwegian School of Economics
- Study Type
- Experimental Study
- Year
- 2026
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Impact of effective altruism information treatment on donor behaviour and NGO support preferences.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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