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Optimal Incentives to Give

Author

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  • Castillo, Marco

    (Texas A&M University)

  • Petrie, Ragan

    (Texas A&M University)

Abstract

We examine optimal incentives for charitable giving with a large-scale field experiment involving 26 charities and over 112,000 unique individuals. The price of giving is varied by offering a fixed match if the donation meets a threshold amount (e.g. "give at least $25 and the charity receives a $25 match"). Responses are used to structurally estimate a model of charitable giving. The model estimates are employed to evaluate the effectiveness of various counterfactual match incentive schemes, taking into account the goals of the charity and donor preferences. Two of these optimal incentives were subsequently implemented in a follow-up field study. They were found to be effective at implementing the desired goals, as predicted by theory and our simulations. Our findings highlight the pitfalls of relying on a particular parameterization of a policy to evaluate effectiveness. The best-guess incentives in our initial field experiment turned out to be ineffective at increasing donations because optimal incentives should have been set higher.

Suggested Citation

  • Castillo, Marco & Petrie, Ragan, 2020. "Optimal Incentives to Give," IZA Discussion Papers 13321, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp13321
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    File URL: https://docs.iza.org/dp13321.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    charitable giving; mechanism design; field experiment;
    All these keywords.

    JEL classification:

    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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