Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246325 
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 2108
Publisher: 
Johannes Kepler University of Linz, Department of Economics, Linz
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 benchmarks 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

Files in This Item:
File
Size
805.48 kB





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