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Charitable Giving Behavior and Emerging Patterns

Paper Session

Saturday, Jan. 4, 2025 8:00 AM - 10:00 AM (PST)

San Francisco Marriott Marquis, Foothill B
Hosted By: Association for the Study of Generosity in Economics
  • Chair: Daniel Hungerman, University of Notre Dame

The Giving Type? Heterogeneous Donation Styles and Lessons for Tax Policy

Nicolas Duquette
,
University of Southern California

Abstract

This paper explores a novel dimension of charitable giving behavior in observational data: the structure and timing of donations themselves.  Some people give to charity regularly, through automatic monthly contributions of fixed amounts. Others give to their favorite charities annually, at year end or on Giving Tuesday, which gives those donors better-timed tax deductibility and more control over amounts given, but with greater behavioral and transaction costs from the act of making the payment. Still others give, but do not do so regularly or consistently. The way people choose to give has real implications for charities themselves, which are better able to budget, plan, and build capacity when gifts are more consistent and predictable.

Our study exploits a fundraising database of over one billion donation events from tens of millions of individual donors to hundreds of large, donor-driven charities, over many years. In 2022 alone, the donations recorded in the database account for $7.3 billion in charitable contributions from over 24 million individual donors.  We describe the share of giving in the data from recurring monthly donations, from annual giving at specific times such as year-end or Giving Tuesday, and from irregular donors.  We report changes in the relative share of these giving types within charities over time, and we estimate predictive models for the extensive and intensive margins of giving as a function of giving history and giving style.

We then estimate heterogeneous effects of the 2017 Tax Cut and Jobs Act on donation amounts, timing, and giving style using a causal random forest (Athey et al 2019) and the social-value network random forest (Williams et al 2023).  The paper concludes with a discussion of policy lessons for fundraisers and policymakers on the relationship between tax policy, level and manner of giving, and revenue volatility for charitable organizations.

Do Firms Use Charitable Giving When Making Investment Decisions? Establishment-Level Evidence from M&As

Cara Vansteenkiste
,
University of Sydney

Abstract

I investigate how firms’ charitable giving affects investment decisions. Based on an establishment-level analysis of public and private M&As, I show that building social capital by providing charitable donations to an establishment’s local stakeholders increases the establishment’s takeover likelihood. I exploit geographical variation in ESOPs to capture firms’ incentives to donate and find that acquirers direct donations to both target management and employees. Local donations increase acquirer returns and reduce integration costs, and donation likelihood decreases after withdrawing from a deal. These results indicate that firms seek local stakeholder support when doing so can improve investment outcomes.

The Heterogeneous Impact of Changes in Default Gift Amounts on Fundraising

Kristine Koutout
,
Stanford University
Susan Athey
,
Stanford University
Undral Byambadalai
,
CyberAgent, Inc
Matias Cersosimo
,
Instacart
Shanjukta Nath
,
University of Georgia

Abstract

When choosing whether and how much to donate, potential donors often observe a set of default donation amounts known as an ``ask string.'' In an experiment with more than 400,000 PayPal users, we replace a relatively unused donation amount ($75) on PayPal's Giving Fund Website ask string with either a lower ($10) or a higher ($200) reference point to evaluate the impact on charitable giving. Relative to the status quo, we find that a higher reference point increases the total amount of money raised, while the lower reference point increases the number of donors, two objectives important to non-profits. Both interventions drive more people to choose a default amount compared to the status quo, where the alternatives are not donating or writing in an amount. Examining treatment effect heterogeneity and changes in the distribution of donations, we provide suggestive evidence about the mechanisms. We use data-driven machine learning methods to learn personalized policies that identify who should be shown the lower versus higher reference point. Personalization can increase the probability of choosing a default amount, and it can also alleviate the trade-off to non-profits between the total amount of money raised and the number of donors.

Discussant(s)
Christoph Schiller
,
Ohio State University
Jennifer Mayo
,
University of Missouri
Derrick Xu
,
University of Bristol
JEL Classifications
  • H4 - Publicly Provided Goods
  • C9 - Design of Experiments