Skip to main navigation Skip to main content
info(at)avldigital.remove-this.de
Deutsch English
Narrow Search
Search narrowed by
Last searches

Results for *

Displaying results 1 to 2 of 2.

  1. Optimal targeting in fundraising
    a machine-learning approach
    Author: Cagala, Tobias; Glogowsky, Ulrich; Rincke, Johannes; Strittmatter, Anthony
    Published: [2021]
    Publisher:  Department of Economics, Johannes Kepler University of Linz, Linz-Auhof, Austria

    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... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 398
    Inter-library loan:
    No inter-library loan

     

    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.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/246325
    Series: Working paper / Department of Economics, Johannes Kepler University of Linz ; no. 2108 (April 2021)
    Subjects: Fundraising; charitable giving; gift exchange; targeting; optimal policy learning; individualized treatment rules
    Scope: 1 Online-Ressource (circa 52 Seiten), Illustrationen
  2. Optimal targeting in fundraising
    a machine-learning approach
    Author: Cagala, Tobias; Glogowsky, Ulrich; Rincke, Johannes; Strittmatter, Anthony
    Published: April 2021
    Publisher:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    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... more

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 63
    Inter-library loan:
    No inter-library loan

     

    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.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/235407
    Series: CESifo working paper ; no. 9037 (2021)
    Subjects: fundraising; charitable giving; gift exchange; targeting; optimal policy learning; individualized treatment rules
    Scope: 1 Online-Ressource (circa 40 Seiten), Illustrationen