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Measuring people’s preferences and values to inform funding decisions

IDinsight and GiveWell completed a two-year effort to identify preferences of potential aid recipients to inform its giving. We surveyed ~2000 people in rural Ghana and Kenya with similar characteristics to those served by GiveWell-endorsed charities using rigorous methods to capture their preferences and values. Compared to GiveWell’s prior assumptions, communities placed greater emphasis on life-saving interventions versus cash, and a higher value on averting deaths of young children versus older children or adults. These results helped update GiveWell’s 2019 model and demonstrated how preferences can directly influence large-scale funding allocations.

Measuring preferences in Jirapa, Ghana. ©IDinsight/Will Slotznick

IDInsight Beneficiary Preferences Final Report - 2 MB

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Beneficiary Preferences Results Summary - 458 KB

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Decision-maker’s challenge

GiveWell is a non-profit which aims to find ‘outstanding giving opportunities’ by determining ‘how much good a given program accomplishes (in terms of lives saved, and/or lives improved) per dollar spent.’ When recommending charities to donors, GiveWell compares interventions targeting different outcomes, such as deaths averted or increases gains in household consumption.

GiveWell wanted to understand how potential participants in the programs of its top charities value different good outcomes. In particular, GiveWell needed evidence to inform its approach to two key questions:

Valuing health vs. income: For example, how much should we value averting the death of a one-year-old relative to doubling the income of an extremely poor household?
Age-weighting: For example, how much should we value averting the death of a one-year-old relative to averting the death of a 30-year-old?

Impact opportunity

Funders and other decision-makers frequently weigh trade-offs between different types of good outcomes. The preferences and values of individuals participating in programs are often missing in funding decision-making. In part, this is due to a lack of rigorous data on how individuals in low-income countries trade-off between different outcomes. The absence of people’s preferences can hamper the relevance and overall social impact of development efforts. IDinsight and GiveWell worked together to address this gap.

The study informed GiveWell’s top charity recommendations in 2019 and is expected to continue to inform GiveWell’s decisions annually (GiveWell directs $140 million in funding for international programs.1.

Our approach

IDinsight developed, adapted, and tested a variety of methods to generate evidence on people’s preferences in low-income countries. We then scaled up the most promising methods and undertook a 2,000 household survey across four locations in Kenya and Ghana to capture their preferences and values.

For instance, we explored how much households value risk reductions (e.g. willingness to pay for a vaccine that would reduce mortality of a child from 10/1000 to 1/1000) and lives of people of different ages (e.g. preference for a program that saves 0-5 year olds or 20-40-year-olds).


Among other notable results:

  • IDinsight found that survey respondents placed higher value on saving lives relative to increasing consumption2
  • Survey respondents valued the lives of children under five higher than individuals over five.
  • The study produced detailed qualitative findings on how respondents make these trade-offs and their moral reasoning.

How this research informed GiveWell’s grantmaking:

Based on IDinsight’s evidence, GiveWell updated its approach “to place more emphasis on programs that avert deaths (relative to those that reduce poverty) and to value programs averting deaths at all ages more equally (relative to our previous assumption of valuing programs that avert deaths of individuals over 5 years old more highly)”. GiveWell plans to revisit its approach as it completes its analysis of the IDinsight study.

For other funders and decision-makers, the study demonstrates that it is possible to capture participants’ preferences to inform the complex moral trade-offs development practitioners make. While more data is required from a broader range of contexts, these results can immediately inform a host of benefit-cost analyses and strategic policy decisions, which currently rely on extrapolation from high-income countries.

  1. 1. This is based on data from 2018.
  2. 2. This is compared to the value predicted by extrapolations from similar data captured in high-income countries.