High-Capacity Donors’ Preferences for Charitable Giving

Abstract

How can charities solicit high-capacity donors to provide the funds for matching grants and leadership gifts? In conjunction with Texas A&M University’s fundraising organizations, we conducted a field experiment to study whether high-income donors respond to nonpersonal solicitations. We also designed the experiment to test the impact of allowing for directed giving on the giving behavior of high-income donors and their willingness to direct their donations toward overhead costs. High-income donors are not responsive to letters or emails, regardless of whether they have the option to direct giving; we cannot conclude, therefore, that giving behavior is different for those who could direct giving compared with those who could not. Our results highlight the difficulties of motivating some high-income donors, especially when only impersonal communication is used.

Keywords

charitable giving alumni giving high-income donors field experiments

Introduction

Evidence suggests that matching grants, leadership gifts, and large donations that cover overhead costs are effective in inducing small donors to give.¹ Yet, the question of how nonprofit organizations might secure funds for such efforts is unanswered. There has also been little causal examination of the giving habits of high-income donors, despite their importance in the world of philanthropy.² Building on previous research on directed giving (Eckel et al., 2017), we conduct a field experiment on high-capacity prospective donors’ giving behavior in conjunction with the Texas A&M Foundation (TAMF). Texas A&M University (TAMU), the university’s capital-fundraising arm, did not previously solicit using direct mail, but its senior staff agreed to collaborate with us because of their interest in learning whether less labor-intensive approaches might be successful.

Our experimental design varies the set of choices to which these donors can direct their gifts, including the option to donate to the operations of TAMF, that is, overhead or administrative costs. But as a broader question, we begin by asking whether high-capacity donors respond to being solicited with an impersonal direct mailing, even from an organization with which they are familiar—namely, their alma mater.³

They did not. Response rates were extremely low and not meaningfully different from individuals who were not contacted in any manner by TAMF. We conclude that opportunities to direct one’s giving are not sufficient to induce high-capacity donors to make a gift if they are not already inclined to do so, and that impersonal solicitations are likely to be an ineffective method of solicitation for this particular group. Our contribution is twofold. First, we provide insight for nonprofits considering soliciting high-income donors in this manner. Second, our experiment is a framework for future researchers to examine questions of directed giving and overhead aversion in other contexts. We also note that the literature on charitable giving focuses almost entirely on donors’ reasons and motivation for starting to give. Over 15% of our sample had ever given to TAMF, but most were lapsed donors. Understanding why donors stop giving is important, because fundraising from previous donors is much more cost-effective than inducing new donors to give (Lindahl & Winship, 1992; Sargeant & Jay, 2004).⁴

The “Previous Research” section briefly discusses the literature on directed giving, overhead aversion, and high-income donors. The “Experiment Design” section lays out the design of the experiment, and the “Results” section presents the results. The “Conclusion” section concludes and offers suggestions for future research.

Previous Research

Studies on directed giving suggest that allowing donors to target their gifts has a positive impact on donations. In a field experiment at a public university, Eckel et al. (2017) find that the probability of giving is not affected by allowing for directed giving, but average donations conditional on giving increase substantially. Very few donors take up the option to direct. Similarly, Li et al. (2015) examine the impact of targeted giving in a lab experiment in which subjects are given the opportunity to donate to specific organizations from their earnings. They also find that targeting has a positive impact on donations. Kessler et al. (2019) examine perceived agency in a field experiment similar to this one, in which donors can indicate their priorities for an organization’s activities—though those preferences were not binding on the organization. They find that “rich and powerful” donors (as defined by living in very-high-income census tracts and having a job title indicating significant influence) give significantly more when given the option to express their charitable giving priorities. Whillans and Dunn (2018) find that fundraising messages that appeal to higher income donors’ sense of agency are more effective than those that emphasize shared goals.

Yet, it may be difficult to induce donors to give to an organization’s administrative expenses. The existing experimental literature on overhead costs finds strong evidence of overhead aversion (Caviola et al., 2014; Exley, 2020; Gneezy et al., 2014; Portillo & Stinn, 2018). Empirical research using charitable giving data also reveals a negative correlation between the amount donated and the amount charitable organizations spend on administrative and fundraising costs (Meer, 2014; Tinkelman & Mankaney, 2007). Overhead aversion may cause charities to focus on reducing overhead instead of on serving their causes most effectively. Gneezy et al. (2014) propose a solution to solve this problem: use donations from major donors to cover overhead costs so that potential donors will have an overhead-free donation opportunity. In a set of experiments, they find that informing potential donors that overhead costs are covered by an initial donation increases both the donation rate and total donations significantly relative even to matching grants, let alone a standard solicitation. Of course, the success of this approach relies on having the major donors willing to cover overhead costs.

Despite the importance of the donations of high-income households, there is little research studying this group’s giving behavior.⁵ There is some descriptive evidence, though results are mixed (James & Sharpe, 2007; Korndörfer et al., 2015; Meer & Priday, 2020). Here, we focus on experimental studies that compare rich and poor subjects, focusing on differences in their response to fundraising practices. In a field experiment targeting a sample of high-capacity potential donors (those who were estimated to have a median annual giving capacity greater than US$25,000), Levin et al. (2016) find that the behavior of wealthier alumni is consistent with many prior studies of ordinary donors. Andreoni et al. (2017) and Smeets et al. (2015) find different results that wealthy donors are more prosocial in specific settings. They are more likely to return a misdelivered envelope (Andreoni et al., 2017) and more likely to give in the lab when matched with a low-income recipient (Smeets et al., 2015).

By investigating whether high-capacity donors are responsive to the option to direct their gift and whether they are willing to cover administrative costs, our field experiment is designed to contribute to all three of these literatures. Furthermore, because our sample includes many lapsed, high-income donors, our experiment provides insight into the giving behavior of donors who were once active but, at some point, decided to stop their contributions. Previous research (e.g., Bennett, 2009; Nathan & Hallam, 2009) has examined why donors have lapsed, but Sargeant (2001) emphasizes the importance of targeting the right lapsed donors. Arguably, lapsed, high-income donors should be of interest to charities because these individuals have the potential to make large donations. One additional contribution of our experiment is that it tests whether lapsed, high-income donors can be re-engaged through impersonal solicitations and directed giving.

Experiment Design

There are three major fundraising organizations at TAMU: the TAMF, the Association of Former Students (AFS), and the Texas A&M Athletic Foundation. These groups are, for the most part, independent. We partnered with TAMF, a nonprofit organization created by former TAMU students and the Board of Regents to raise capital contributions for the institution. Unlike the other TAMU nonprofit organizations, TAMF focuses their attention on major gifts and does not engage in direct-mail solicitations.

Our goal is to examine whether high-income donors respond to impersonal solicitations in the form of letters and emails. Conditional on responding to the solicitation, we investigate whether they will choose to direct their giving to specific causes or to cover overhead costs for the organization.

TAMF was willing to randomize over a group of Texas A&M alumni who were gauged to have the capacity to give between US$25,000 and US$250,000, but had not been contacted by TAMF since 2010 and were unlikely to be contacted directly as part of the university’s four billion dollar “Lead by Example” capital campaign.⁶ While these alumni had not been recently contacted by TAMF, they were far from “cold list” contacts: 15.7% had made a donation to TAMF in the past, and 77.0% had given to at least one of the three main fundraising units of the University. Among those who had ever made a gift, 45.8% made their last gift in the 3 years prior to our experiment. Cumulative giving to the University by those who had ever given averaged US$2,375, with a median of US$520.

We assigned each mailing address to one of five conditions at random. The control group included 8,766 addresses and did not receive any letters or emails from TAMF (but might still receive solicitations from one of the other fundraising organizations). The other 48,000 mailing addresses were sorted into the remaining equal-sized treatment groups. Table 1 shows the balance across treatments.⁷ Letters were sent out on December 16, 2016, followed by an email on December 27, 2016.⁸

Table 1.

Summary Statistics.

Variable name	Control	Priorities Fund	Specific Priorities	Priorities Fund and Initiatives Fund	Full Choice
Male	65.0%	66.0%	64.8%	65.4%	65.4%
Giving capacity (US$)	77,201 (50,583)	76,014 (49,730)	75,700 (49,579)	76,496 (50,576)	77,012 (50,911)
Ever gave to Texas A&M University	0.774 (0.418)	0.770 (0.420)	0.772 (0.420)	0.773 (0.419)	0.764 (0.425)
Total giving to Texas A&M University (US$)	1,764 (6,046)	1,789 (6,239)	1,825 (6,191)	1,865 (6,461)	1,884 (6,550)
Ever gave to TAMF	0.155 (0.362)	0.156 (0.362)	0.151 (0.358)	0.160 (0.367)	0.162 (0.368)
Total giving to TAMF (US$)	132 (933)	119 (798)	119 (815)	135 (922)	123 (805)
Years since graduation	28.20	28.15	28.09	27.95	28.17
Years since graduation	(10.84)	(10.83)	(10.78)	(10.81)	(10.97)
Texan	82.4%	82.2%	81.9%	81.9%	81.3%
Married	72.7%	72.0%	72.7%	72.2%	72.8%
Observations	8,766	12,000	12,000	12,000	12,000

Note. Standard deviations are in parentheses. Gifts to TAMF, AFS, and Texas A&M Athletic Foundation are counted as gifts to Texas A&M University. TAMF = Texas A&M Foundation; AFS = Association of Former Students.

The first treatment group, Priorities Fund (PF), received a letter that described TAMF’s capital campaign (Figure A.1a of the Supplemental Material). It also described three priorities in which TAMF was interested: supporting student activities (“Transformational Education”), research (“Discovery and Innovation”), and service (“Impact on the State, the Nation, and the World”). Individuals were asked to contribute to the general-purpose fund using reply cards (Figure A.1b of the Supplemental Material); the solicitation lists the priorities, but they were not permitted to select a specific one. In addition to returning the reply card, individuals could donate using a treatment-specific web link. Those with further questions or those who wished to discuss a possible donation could contact TAMF by phone; staff were trained to identify subjects in the experiment and give them the appropriate set of choices. This treatment provides a baseline giving rate relative to the unsolicited control group.

Individuals in the second treatment group, Specific Priorities (SP), received letters (Figure A.2a of the Supplemental Material) that were nearly identical to those in the PF treatment. The only difference was that individuals were asked to give to the general-purpose fund for all of the priorities, or one or more of the specific priorities, at the end of the letter. In other words, individuals could direct their giving to a specific priority that they wished to support, give to one account that combined all of the priorities together, or both. Comparing this treatment with the PF treatment provides the incremental effect of the opportunity to direct one’s gift.

Those in the third treatment group, Priorities Fund and Initiatives Fund (PFIF), received a letter (Figure A.3a of the Supplemental Material) that was similar to the PF letter. However, their letter also described three initiatives for TAMF: to improve TAMF office space (“Center Expansion Fund”), to hire students to help TAMF with their fundraising (“Maroon Coats”), and to develop fundraising strategies (“Texas A&M Foundation President’s Excellence Fund”). Contributions to these funds would help cover overhead and administrative costs. Individuals in this treatment were only given the option to donate to two funds: the priorities general-purpose fund or the initiatives general-purpose fund.

Finally, individuals in the Full Choice (FC) treatment received a letter (Figure A.4a of the Supplemental Material) much like those sent to those in the PFIF treatment. Their letter detailed the three priorities and three initiatives, but it allowed individuals to donate to the general-purpose funds and/or any combination of the specific priorities and initiatives. Individuals in this treatment, then, had all possible donation options. They could direct their giving to one or more specific priority or initiative as well as contribute to one or both of the general-purpose funds. Emails were sent to individuals in our treatment groups 10 days after the letters. These emails matched the content of the treatment letters and included a hyperlink to the treatment-specific website.

TAMF provided us with information on donations received from individuals in the experiment, as well as phone calls and emails from the targeted sample, between December 16, 2016, and January 19, 2017. These data also included donations to another of the University’s fundraising arms, the AFS (the university’s alumni organization), whose responsibilities include raising the annual fund.

Results

We begin by discussing the direct contact received by TAMF during our experiment. There were four emails, two of which said that they had already given and two of which said that they would not be giving. Seven phone calls were handled by TAMF staff. Three were interested in learning more about giving, two asked to be removed from the solicitation list, and another said that they thought they had already given.⁹ Another caller explained that TAMU had rejected all three of that alumnus’s children and that they would not be giving, a response consistent with research on the relationship between alumni children’s age and application status and donations by Meer and Rosen (2009).

Turning to actual donations, Table 2 reports giving rates. Column 1 includes responses only to the mailers we created or to the web links created exclusively for the experiment. By definition, the response rate in the control group is zero, but the responses for the other treatments are also essentially zero—there were a total of 19 donations, with no more than six in any one treatment arm. The differences among the control and treatment arms are not statistically significant (p = .33, chi-square test). Four donors directed to a specific priority, two in the SP treatment and two in the FC treatment. Two donors gave to the initiatives fund, one in PFIF and one in FC; one donor gave to a specific initiative. We note that with such low response rates, it would be irresponsible to draw any conclusions, regardless of statistical significance.

Table 2.

Probability of Giving.

Treatment group	Observations	(1) Giving in experiment	(2) Other subject giving to TAMF	(3) Giving to AFS
Control	8,766	0	0.00137(0.0370)	0.047(0.2117)
Priorities Fund	12,000	0.00025(0.0158)	0.00092(0.0303)	0.049(0.2154)
Specific Priorities	12,000	0.00033(0.0183)	0.00133(0.0365)	0.048(0.2131)
Priorities Fund and Initiatives Fund	12,000	0.00050(0.0224)	0.00158(0.0398)	0.049(0.2152)
Full Choice	12,000	0.00042(0.020)	0.00125(0.0353)	0.046(0.2099)

Note. Column 1 is the probability of a positive donation to TAMF in the experiment. Column 2 contains the probability of giving by the subjects to TAMF during the same time period, but outside the experiment. Column 3 contains the probability of giving to the University’s alumni association (AFS), outside of TAMF. Standard deviations are in parentheses. TAMF = Texas A&M Foundation; AFS = Association of Former Students.

But prospective donors could have gone directly to the TAMF website rather than through our web links or reply cards. Column 2 examines other gifts made to TAMF from subjects in the experiment over this time period. Response rates remain infinitesimal, though they rise to about 0.1%. But the response rate for those in the control group—who were not solicited in any way by TAMF—is about the same as that in any of the actual treatments. Finally, Column 3 examines gifts made to the AFS, in the event that donors were induced to give to the university, but did so through a different organization. There are no differences in giving rates across treatments (p = .87, chi-square test). Taken together, it is evident that the experiment did not change prospective donors’ behavior in any meaningful way.

For completeness, Table 3 reports amounts given conditional on giving. We do not attempt to draw any conclusions from these results, given the small sample sizes, though we note that the dearth of differences across treatments for the gifts including those to AFS (in Column 3) further suggests that our experiment did not alter donor behavior.

Table 3.

Average Gift Conditional on Giving.

Treatment group	(1) Giving in experiment	(2) Other subject giving to TAMF	(3) Giving to AFS
Control	—	403.24(826.38) [12]	144.31(181.03) [412]
Priorities Fund	216.67(246.64) [3]	1,822.84(5,041.75) [11]	141.18(167.25) [585]
Specific Priorities	200.00(183.71) [5]	656.88(1,208.87) [16]	168.44(339.08) [577]
Priorities Fund and Initiatives Fund	100.00(54.77) [6]	407.00 (363.90) [19]	157.34(188.59) [584]
Full Choice	70.00(41.08) [5]	544.17(740.45) [15]	148.69(266.90) [554]

Note. Column 1 is the average donation to TAMF in the experiment. Column 2 contains the average gift by the subjects to TAMF during the same time period, but outside the experiment. Column 3 contains average giving to the University’s alumni association (AFS), outside of TAMF. Standard deviations are in parentheses. Number of gifts is in brackets. TAMF = Texas A&M Foundation; AFS = Association of Former Students.

Conclusion

We began by asking whether high-capacity donors could be solicited through impersonal means like direct mail and emails. At least in the context of giving to TAMU, we find no evidence that this approach was successful. Our other research questions—the effect of directed giving opportunities and high-income donors’ willingness to give to administrative costs—cannot be answered to any meaningful degree given our low response rates. Given that these prospective donors had either lapsed in their giving to TAMF or chosen never to give at all, these response rates may not be surprising. Impersonal solicitations of the sort in our experiment may not move the margin of giving behavior very much, especially for those who are not already inclined to give.

These are still important questions, and they should be examined with other groups, including with lower capacity donors.¹⁰ The structure of this experiment also provides a framework for follow-on work, conditional on the creation of exogenous variation in giving.¹¹ First, if donors form a habit of giving, then the overall payoff to these interventions may be larger; researchers should examine follow-on giving probabilities (see Landry et al., 2010; Meer, 2013; Rosen & Sims, 2011). The somewhat unusual overlapping nature of TAMU’s fundraising organizations also provides an opportunity to investigate competition and spillovers in giving (Gee & Meer, 2020; Meer, 2017). If donors are induced to give to one organization and reduce giving to a related one, then the overall benefit to society from the production of public goods may be greatly reduced. Experimental variation in giving enables researchers to answer these questions more effectively.

This particular experiment did not yield answers to the full slate of research questions. But the insights on the solicitation of high-income donors are useful for nonprofits considering different strategies. And its framework can be applied to other nonprofit organizations to investigate these important questions in charitable fundraising.

Supplemental Material

sj-pdf-1-nvs-10.1177_08997640211003251 – Supplemental material for High-Capacity Donors’ Preferences for Charitable Giving

Supplemental material, sj-pdf-1-nvs-10.1177_08997640211003251 for High-Capacity Donors’ Preferences for Charitable Giving by Mackenzie Alston, Catherine Eckel, Jonathan Meer and Wei Zhan in Nonprofit and Voluntary Sector Quarterly

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jonathan Meer

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Mackenzie Alston is an assistant professor at the Department of Economics at Florida State University. Her research focuses on understanding the role of stereotypes and discrimination in settings like the labor market and classrooms using a combination of experiments and surveys.

Catherine Eckel is University Distinguished Professor and Sara and John Lindsey Professor in the Department of Economics at Texas A&M University, where she directs the Behavioral Economics and Policy Program. She uses the methodology of lab and field experiments to study charitable giving, gender differences, worker motivation, and economic preferences.

Jonathan Meer is the Mary Julia and George R. Jordan Professor of Public Policy at Texas A&M University. His research focuses on altruism, the economics of education, and the minimum wage. He teaches an online principles of microeconomics course that reaches over 2,500 students per year.

Wei Zhan is an assistant professor at the Department of Economics at Hamilton College. She primarily uses laboratory and field experiments to study charitable giving, gender differences, and prosocial behavior.

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Supplementary Material

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