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A Negative Binomial model for the donations count in Fundraising Management

Author

Listed:
  • Luca Barzanti

    (University of Bologna)

  • Martina Nardon

    (Ca’ Foscari University of Venice)

Abstract

Forecasting expected gifts is a key task in Fundraising Management. In this study, we propose modeling a gift as an individual risk that can be analyzed from multiple perspectives: the occurrence, frequency, and timing of donations, as well as their monetary amounts. We focus specifically on modeling the number of donations as a Poisson random variable whose intensity parameter depends on individual donor characteristics. By introducing a Gamma-distributed heterogeneity factor, a Negative Binomial model arises as a natural extension of the starting framework. This approach enables the estimation of both the expected number of donations and the probability of a gift through Negative Binomial regression. We illustrate the methodology with an empirical application.

Suggested Citation

  • Luca Barzanti & Martina Nardon, 2025. "A Negative Binomial model for the donations count in Fundraising Management," Working Papers 2025: 28, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2025:28
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    References listed on IDEAS

    as
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    4. 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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    Keywords

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    JEL classification:

    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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