Arbeitspapier
Optimal Targeting in Fundraising: A Machine-Learning Approach
Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity’s fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed solicitation costs. Among past donors, optimal targeting substantially increases donations (net of fundraising costs) relative to bench-marks that target everybody or no one. Instead, individuals who were previously asked but never donated should not be targeted. Further, the charity requires only publicly available geospatial information to realize the gains from targeting. We conclude that charities not engaging in optimal targeting waste resources.
- Sprache
-
Englisch
- Erschienen in
-
Series: CESifo Working Paper ; No. 9037
- Klassifikation
-
Wirtschaft
Field Experiments
Altruism; Philanthropy; Intergenerational Transfers
Public Goods
Nonprofit Institutions; NGOs; Social Entrepreneurship
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Thema
-
fundraising
charitable giving
gift exchange
targeting
optimal policy learning
individualized treatment rules
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Cagala, Tobias
Glogowsky, Ulrich
Rincke, Johannes
Strittmatter, Anthony
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Economic Studies and Ifo Institute (CESifo)
- (wo)
-
Munich
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Cagala, Tobias
- Glogowsky, Ulrich
- Rincke, Johannes
- Strittmatter, Anthony
- Center for Economic Studies and Ifo Institute (CESifo)
Entstanden
- 2021
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