Matching Returning Donors to Projects on Philanthropic Crowdfunding Platforms

Yicheng Song, Zhuoxin Li, Nachiketa Sahoo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. It is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating-a unique feature of philanthropic giving. We estimate our model using a comprehensive data set from DonorsChoose.org-the largest crowdfunding platform for K-12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign, both motivations play a symbiotic role: Egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last 14 weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating themto more viable projects, funded 17%more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors' total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors' utility.

Original languageEnglish (US)
Pages (from-to)355-375
Number of pages21
JournalManagement Science
Volume68
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Bibliographical note

Funding Information:
Since its foundation in 2000, DonorsChoose has helped public school teachers raise more than $878 million from over four million supporters, benefiting over 36 million students in the United States (DonorsChoose. org 2019b). More than 80% of all public schools in the United States have used this platform. Fundraising on this platform works as follows. First, a teacher posts a funding request for a class project on DonorsChoose. org outlining its purpose, providing a project plan, and listing necessary materials to complete the project. Within a typical campaign period of 120 days, donors may contribute any amount toward the funding target. If the total amount of donations reaches the funding target by the end of the campaign period, the project is considered successfully funded and no more funding is accepted. DonorsChoose purchases the materials and mails them to the teacher’s school. Teachers and students typically acknowledge donors by sending them Thank You notes, which summarize how the funding has helped them. If a project does not raise the full requested amount within the campaign period, the project is considered unsuccessful and is not funded. Although often donors get credit to support another project, we estimate that they do so for less than 15% of donations to unsuccessful campaigns. Typically, either the teacher behind the original project or the platform assigns the money to another project.

Publisher Copyright:
© 2022 INFORMS Inst.for Operations Res.and the Management Sciences. All rights reserved.

Keywords

  • Consideration set
  • Crowdfunding platforms
  • Online philanthropy
  • Recommender systems
  • Revenue optimization
  • Structural econometric model

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