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Personalized fundraising: A field experiment on threshold matching of donations

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  • Adena, Maja
  • Huck, Steffen

Abstract

We study a form of threshold matching in fundraising where donations above a certain threshold are topped up with a fixed amount. We show theoretically that threshold matching can induce crowding in if appropriately personalized. In a field experiment, we explore how thresholds should be chosen depending on past donations. The optimal choice of thresholds is rather bold, approximately 75% above past donations. Additionally, we explore how thresholds should be set for new donors as a function of their personal characteristics and demonstrate the benefits of personalization as opposed to setting a general threshold that applies to all recipients of a fundraising call.

Suggested Citation

  • Adena, Maja & Huck, Steffen, 2022. "Personalized fundraising: A field experiment on threshold matching of donations," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1-20.
    • Handle: RePEc:eee:jeborg:v:200:y:2022:i:c:p:1-20
    DOI: 10.1016/j.jebo.2022.05.003
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    Cited by:

    1. Castillo, Marco & Petrie, Ragan & Wardell, Clarence, 2023. "Barriers to charitable giving," Journal of Public Economics, Elsevier, vol. 224(C).
    2. Castillo, Marco & Petrie, Ragan, 2020. "Optimal Incentives to Give," IZA Discussion Papers 13321, Institute of Labor Economics (IZA).
    3. Shusaku Sasaki & Hirofumi Kurokawa & Fumio Ohtake, 2022. "An experimental comparison of rebate and matching in charitable giving: The case of Japan," The Japanese Economic Review, Springer, vol. 73(1), pages 147-177, January.
    4. Drouvelis, Michalis & Marx, Benjamin M., 2022. "Can charitable appeals identify and exploit belief heterogeneity?," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 631-649.
    5. Maja Adena & Michalis Drouvelis & Steffen Huck, 2023. "Introduction to the special issue on ‘new directions in understanding philanthropic activities’," Oxford Economic Papers, Oxford University Press, vol. 75(4), pages 885-889.
    6. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    7. Adena, Maja & Hager, Anselm, 2020. "Does online fundraising increase charitable giving? A nation-wide field experiment on Facebook," Discussion Papers, Research Unit: Economics of Change SP II 2020-302, WZB Berlin Social Science Center.
    8. Nadine Chlaß & Lata Gangadharan & Kristy Jones, 2023. "Charitable giving and intermediation: a principal agent problem with hidden prices," Oxford Economic Papers, Oxford University Press, vol. 75(4), pages 941-961.
    9. Francesco Bripi & Daniela Grieco, 2023. "Participatory incentives," Experimental Economics, Springer;Economic Science Association, vol. 26(4), pages 813-849, September.
    10. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.

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    More about this item

    Keywords

    Charitable giving; Field experiments; Matching donations; Personalization;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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