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Measuring people’s preferences

A survey of 1,800 low-income individuals in Ghana and Kenya shows how potential beneficiaries trade-off between saving a life versus giving cash.

International development leaders frequently make complex resource allocation decisions that require weighing trade-offs between different types of good outcomes. For example, given limited resources, which should be prioritized: a program that increases household income or one that saves lives? When comparing diverse charities, GiveWell makes these decisions transparent by asking staff members to provide their ‘moral weights.’ These judgments are based on philosophical reasoning, intuition, and data on beneficiary lives, and extrapolation of preferences from studies of less relevant populations. Prior to this study, there was a clear lack of data on how potential beneficiaries of such interventions trade-off between different outcomes. This study represents a step to fill this gap for strategic international development decision-making.

An enumerator interviews a survey participant of the beneficiary preferences project supported by GiveWell.

An enumerator interviews a survey participant of the beneficiary preferences project supported by GiveWell.


We surveyed over 1,800 low-income individuals across four diverse regions in Ghana and Kenya. Three main methods1 were used to capture how respondents trade-off between averting deaths of individuals of different ages and increasing consumption:

  1. We asked individuals for their willingness-to-pay (WTP) to reduce the risk of death for themselves and their children.2
  2. We asked respondents to take the perspective of a decision-maker in their community and choose between programs that:
    1. Save lives of different ages;
    2. Save lives and provide cash transfers.

We also collected qualitative data on beneficiaries’ reasoning when making these trade-offs, and data on beneficiaries’ lives that can be used to inform GiveWell staff’s moral weights.3


  • Respondents place a higher value on averting a death4 than predicted by most extrapolations from studies in high-income countries (HICs).
    • Our central estimate of value placed on averting death for individuals 5 and older was $40,721, which is 1.7 times higher than the current GiveWell staff median.
  • Respondents consistently value the lives of individuals under 5 higher than individuals 5 and older, which is consistent with HIC studies but contrary to median GiveWell moral weights.
    • Our central estimate of value placed on averting death for individuals under 5 was $65,906, which is 4.9 times higher than the current GiveWell staff median.

Qualitative data suggests these high valuations are driven by a large proportion of individuals making two arguments. The first argument asserts the importance of accounting for the potential held by all individuals to achieve high economic and social value over their life course. A second common argument is that life holds inherent value and therefore no amount of money is sufficient to forego the chance to save a life.


For GiveWell, incorporating the preferences captured in this study and described above would result in:

  1. Placing a higher value on averting deaths relative to doubling consumption,
  2. Placing a higher value on averting the death of individuals under 5 than individuals 5 and older.

This would lead to higher relative cost-effectiveness of charities whose good is achieved primarily by averting the death of young children (e.g. Helen Keller International, Malaria Consortium, and Against Malaria Foundation, etc.).5

Beyond GiveWell, this study demonstrates that it is possible to inform the complex, moral trade-offs faced in development by capturing the preferences of the people affected by these decisions. It also represents a substantial addition to the existing literature on individual cross-outcome preferences, in which low-income populations and particularly those in Sub-Saharan Africa, have previously been severely underrepresented.

In a development sector that is generally reluctant to rigorously compare different types of
outcomes, we believe this study could make the use of portfolio-level cost-effectiveness analysis more appealing. We encourage additional research to further develop an understanding of beneficiary preferences across program areas and from different populations.

  1. 1. The three methods were selected after extensive piloting of over 15 different approaches. They were selected based on: 1) their relevance to GiveWell moral weights; and 2) the reliability of the methods to collect accurate data.
  2. 2. Value of Statistical Life (VSL) is an approach commonly used by governments to inform cost-effectiveness analysis.
  3. 3. This includes: 1) primary data on the subjective well-being of our respondents, and how this correlates with individual characteristics; and 2) primary qualitative data on the economic and emotional impact of household deaths; and 3) secondary data analysis of the relative economic contribution of different household members in a typical beneficiary population.
  4. 4. This includes death and risk of death for themselves, and individuals in their family and their community.
  5. 5. If the moral weights were changed to fully reflect the quantitative results of this study, the ranking of current GiveWell top charities would shift substantially and Helen Keller International, Malaria Consortium, and Against Malaria Foundation would be the most cost-effective charities (with charities such as Sightsavers and GiveDirectly becoming less cost-effective). It would also make it substantially easier for other charities whose main outcome is saving lives, particularly of children under 5, to reach and surpass GiveWell’s cost-effectiveness threshold (2-3x as cost-effective as cash).