Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235407 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9037
Publisher: 
Center for Economic Studies and Ifo Institute (CESifo), Munich
Abstract: 
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.
Subjects: 
fundraising
charitable giving
gift exchange
targeting
optimal policy learning
individualized treatment rules
JEL: 
C93
D64
H41
L31
C21
Document Type: 
Working Paper
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.