What influences the decision to give to charity?

In the US, one in four adults volunteer their time and one in two adults give money to charities.[1] Previous research documents potential motives for such giving: people feel good about themselves when they help others, value appearing nice to others, and desire to conform with social norms.[2] These motives, as well as standard economic models, may easily explain a common finding in charitable giving – individuals give less when there is a greater risk that their donation will have less impact. However, in my job market paper, Exley (2014), I investigate whether an additional mechanism is relevant: Do people use the risk that their donation may have less than the desired impact as an excuse not to give?

Given how often people are asked to give – over 50% of adults report being asked to give more than three times within the past year – it seems reasonable that individuals may desire an excuse not to give.[3] An increasing push for individuals to ‘give wisely’ may also contribute to individuals using risk as an excuse not to give, as the possibility of their donation having no impact may be more salient. Previous research further demonstrates the relevance of motivations broadly related to excuses.[4]

Is there evidence for people using risk as an excuse not to give?

When examining individuals’ charitable giving decisions, it may difficult to distinguish excuse-driven responses from other responses to risk. It may even be difficult to define the level of risk when considering a charitable donation.

Therefore, I began by examining how individuals respond to objective risk in a laboratory study that allowed for the necessary control before moving to risky scenarios more common in charitable giving. In this study, undergraduate students at Stanford University made a series of real decisions that translated into self payoffs or charity payoffs. Funded by research grants, participants could earn cash by choosing self payoffs, and participants could have money donated to the American Red Cross by choosing charity payoffs.

In some decisions, individuals were forced to give – they chose between risky charity payoffs and riskless charity payoffs. While the charity always received a particular dollar amount in the case of riskless charity payoffs, the charity only received a particular dollar amount with some known chance in the case of risky charity payoffs. The dollar amounts and levels of risk (i.e. chance) involved varied across different decisions. Importantly though, since individuals were forced to give, they could not use risk as an excuse not to give. I correspondingly found that:

• When excuses were not relevant, individuals appeared relatively neutral in response to higher charity risk levels.

In other decisions, individuals decided whether or not to give – they chose between risky charity payoffs and riskless self payoffs. The risky charity payoffs were the same as before – the charity only received a particular dollar amount with some known chance. However, by choosing self payoffs, individuals could now receive a particular dollar amount (in cash) for themselves. As such, individuals may have overweighed the charity risk – or used the possibility that choosing risky charity payoffs could result in no actual donation – as a great excuse not to give. Consistent with this excuse-driven response to risk, I found that

• When excuses not to give were relevant, individuals appeared very averse to higher charity risk levels.

Additional results, from other decisions made by the individuals in this study, further limit the scope for non-excuse-driven explanations. First, individuals appeared to use self risk, not just charity risk, as an excuse not to give. Second, individuals with excuse-driven responses to risk behaved in a more excuse-driven way in a separate ‘moral wiggle room’ task, as developed by Dana et al. (2007).

As these results support the existence of excuse-driven ‘types’ of individuals, charities may benefit by exploiting such heterogeneity in types of potential donors. I thus ran an additional applications study that allowed me to consider the relevance of excuse-driven responses in scenarios commonly observed outside of the laboratory. In particular, I found that individuals gave less in response to lower charity performance metrics that indicated a greater risk of donations being used ineffectively or inefficiently. The metrics included:

• Lower college matriculation rates at charter schools,
• Lower success rates at animal shelters, and
• Higher overhead cost rates for charities.

This reduction in giving was even larger when they could use these metrics as excuses not to give.

How may charities benefit in light of these results?

The largest gains for charities in light of these results may involve targeting different types of potential donors with different funding opportunities. For instance, new potential donors, as compared to past donors, may be more susceptible to using excuses not to give. Charities may then benefit from targeting past donors for more risky projects and new donors for less risky projects where excuses not to give are not as salient.[5]

Alternatively, charities may limit excuses not to give by assuring donors about the particular use of their donations. This possibility may contribute to a recent finding in Gneezy et al. (2014) – giving increases when individuals are certain that their donations will not be used for overhead costs.

Finally, a common finding is that individuals are unwilling to invest time or money in learning the ‘best’ ways to donate their money.[6] Previous literature suggests that people may avoid this information because they care about thinking they did good. It may also be the case that some individuals purposely avoid or seek certain performance information on charities in order to find excuses not to give. This possibility raises an interesting question for future research: What is the optimal availability of performance information on charities?

References

Andreoni, J (2006), “Chapter 18 Philanthropy”, in S-C Kolm and J Mercier Ythier (eds.), Handbook on the Economics of Giving, Reciprocity and Altruism, Vol. 2: Applications, Elsevier: 1201–1269.

Dana, J, R A Weber, and J X Kuang (2007), “Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness”, Economic Theory 33: 67–80.

Exley, C (2014), “Excusing Selfishness in Charitable Giving: The Role of Risk”, Working Paper, Stanford University.

Gneezy, U, E A Keenan, and A Gneezy (2014), “Avoiding overhead aversion in charity”, Science346(6209): 632–635.

Haisley, E C and R A Weber (2010), “Self-serving interpretations of ambiguity in other-regarding behavior”, Games and Economic Behavior 68: 614–625.

Hope Consulting (2010), “Money for Good: The US Market for Impact Investments and Charitable Gifts from Individual Donors and Investors”, Technical Report.

Karlan, D and D H Wood (2014), “The effect of effectiveness; donor response to aid effectiveness in a direct mail fundraising experiment”, NBER Working Paper 20047.

Konow, J (2000), “Fair Shares: Accountability and Cognitive Dissonance in Allocation Decisions”,American Economic Review 90(4): 1072–1092.

Niehaus, P (2013), “A Theory of Good Intentions”, Working Paper.

Null, C (2011), “Warm glow, information, and inefficient charitable giving”, Journal of Public Economics 95: 455–465.

Vesterlund, L (2006), “Why do people give?”, in W W Powell and R Steinberg (eds.), The Nonprofit Sector: A Research Handbook, 2nd edition, New Haven, CT: Yale University Press: 568–587.

Endnotes

[1] http://www.volunteeringinamerica.gov/infographic.cfm, http://www.givingusareports.org/.

[2] For an overview of these reasons as well as others, see surveys in Vesterlund (2006) and Andreoni (2006).

[3] Results are from a Google Consumer Survey I ran in September–November 2014. 505 individuals answered “Within the past year, approximately how many times have you been asked – whether in person or via other means, such as emails or social media – to donate money to a charity?” 51% of respondents answered “3 or more times”.

[4] For instance, see Konow (2000), Dana et al. (2007), and Haisley and Weber (2010)

[5] Results from Karlan and Wood (2014) indicate that a similar targeting scheme with large versus small previous donors may be beneficial.

[6] For instance, see Hope Consulting (2010), Null (2011), and Niehaus (2014).

This article is published in collaboration with VoxEU. Publication does not imply endorsement of views by the World Economic Forum.

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Author: Christine L. Exley will complete her Ph.D. in economics at Stanford University in the Spring of 2015, with specialisations in behavioral economics and experimental economics.

Image: An employee counts Indian rupee currency notes inside a private money exchange office in New Delhi July 5, 2013. REUTERS.