Abstract
We examine how taxes impact charitable giving and how this relationship is affected by the degree of wasteful government spending. In our model, individuals make donations to charities knowing that the government collects a flat-rate tax on income (net of charitable donations) and redistributes part of the tax revenue. The rest of the tax revenue is wasted. The model predicts that a higher tax rate increases charitable donations. Surprisingly, the model shows that a higher degree of waste decreases donations (when the elasticity of marginal utility with respect to consumption is high enough). We test the model’s predictions using a laboratory experiment with actual donations to charities and find that the tax rate has an insignificant effect on giving. The degree of waste, however, has a large, negative and highly significant effect on giving.
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Notes
See http://www.gallup.com/poll/176102/americans-say-federal-gov-wastes-cents-dollar.aspx. The estimated rate of waste differs across Republicans and Democrats, with Republicans estimating 59 cents and Democrats estimating 42 cents per dollar. To isolate the effect of waste on giving, we consider a simple model with individuals being homogeneous with respect to their perceptions of the rate of waste.
See http://www.huffingtonpost.com/2013/03/18/wasteful-spending-poll_n_2886081.html. Based on the survey responses the article argues that “for many, waste is indeed defined as ‘money spent on some government program I don’t like’.” Note that these perceptions may exogenously change over time depending on government actions or even through simple debates (e.g., discussions of wasteful government spending during elections may heighten individuals’ perceptions about waste).
To our knowledge, previous theoretical and empirical models on the impact of tax rates on donations assume one of the two extreme cases. For example, when calculating the price of giving, empirical studies typically make the simplifying assumption that individuals do not receive any return from their paid taxes. Previous models are, therefore, special cases of our model.
Field data suffer from measurement and identification challenges (e.g., Andreoni 2006; List 2011; Andreoni and Payne 2013; Vesterlund 2016; Duquette 2016). Itemizers have an incentive to overstate their donations to evade taxes, while non-itemizers have no incentive to report any donations at all. The price of giving is correlated with taxable income and might endogenously change with the donated amount. It is hard to disentangle the permanent impact of taxes on donations from the transitory impact. Wasteful government spending may provoke tax evasion which might in turn affect charitable donations (Barone and Mocetti 2011; Alm et al. 2016). There is also the possibility that tax rates affect labor supply decisions; see Saez et al. (2012) for a survey of this literature. Our design eliminates these types of measurement and identification challenges by: (1) exogenously varying the price of giving and the level of waste, (2) using actual donations data from our controlled laboratory experiment, (3) automatically taxing all participants in the experiment and (4) by assigning income to participants prior to informing them about the specifics of income taxation and the tax rate.
One important example of public goods in which individuals make donations, but the government does not, is giving to religious organizations.
See Sect. 2 for a discussion of the major findings of the empirical literature on this topic.
Some of these papers study a related (but different) question. They ask, for a given tax rate, how responsive total giving is to tax-deductibility and focus on estimates of price-elasticity only. Theoretically, it is easy to show that the impact of tax-deductibility on donations is (unambiguously) positive, since tax-deductions make it cheaper to donate and individuals enjoy higher levels of spendable income for a given level of donation. In other words, there are no opposing forces when it comes to the effect of tax-deductibility on donations.
This result would not hold if individuals are impure-altruists (Andreoni 1990). Impure altruism models explain why crowding-out is not complete when government provides public funds to charities. Interestingly, Hungerman (2014) shows that when individuals hide income, this creates a deadweight loss and leads to a surprising finding: warm-glow implies more crowding-out in a setting where individuals can evade taxes.
Duquette and Hargaden (2019), on the other hand, find a positive relationship between the match rate and donations even for very large values of the match rate.
Auten et al. (2000) argue that the relationship between income and donations is U-shaped.
Theoretically, assuming a concave consumption utility, when considering rebates or matches offered by a third party, an increase in the rebate or match rate always leads to higher total donations received by the charity (including the matches). To see why increasing the rebate/match rate does not lead to ambiguous effects on donations received by the charity the way increasing the rate of tax or waste does, consider the following examples. When the rebate (or match) rate increases, an individual might still choose to donate nothing and consume his/her available wealth. When a tax rate increases, however, the individual’s net income strictly decreases even when he/she does not donate anything to the charity. Alternatively, when the rebate (or match rate) increases, an individual can donate the same (total) level as before and enjoy a higher net income. In contrast, when the tax rate increases, if the individual donates the same level as before, then he/she will definitely have a lower net income.
One can think of the “waste” as either the government funding programs that the individuals do not care for, or inefficient spending.
Similar to Bergstrom et al. (1986), this result holds only when the set of contributors does not change as the initial income distribution changes. If the set of contributors changes when the initial income distribution changes, then the model would predict higher contributions when income inequality increases. Hence, similar to Bergstrom et al. (1986) and Uler (2009), there is a trade-off between contributions and (initial) income equality.
This can be seen by using Eq. (1) and the fact that each individual consumes the same amount of the private good.
Note that \(\frac{{u^{{\prime \prime }} \left( x \right)x}}{{u^{{\prime }} \left( x \right)}} = \frac{{\frac{{du^{{\prime }} \left( x \right)}}{dx}x}}{{u^{{\prime }} \left( x \right)}} = \frac{{\frac{{du^{{\prime }} \left( x \right)}}{{u^{{\prime }} \left( x \right)}}}}{{\frac{dx}{x}}}\). The elasticity of marginal utility with respect to consumption can also be interpreted as the sensitivity of the marginal rate of substitution between private consumption and public good consumption to price changes: the derivative of the marginal rate of substitution with respect to the price of private consumption (see Mirrlees 1971). We are grateful to Daniel Hungerman for this insight.
For example, if \(\frac{\partial G}{\partial t} = 0\) and \(\frac{\partial G}{\partial w} = - 4\), then the partial derivative of G with respect to t is greater than the partial derivative of G with respect to w in the relative sense.
In the experiment, we have three people in our experimental society. If this condition does not hold for \(n = 3\), then we do not expect it to hold with \(n > 3\).
Note that \(\frac{1}{n} \le \frac{{\left( {1 - wt} \right)}}{{\left( {1 - at} \right)n}} < 1\) for any \(0 \le w \le 1\), and \(0 \le t \le 1\). \(\frac{{\left( {1 - wt} \right)}}{{\left( {1 - at} \right)n}}\) equals \(\frac{1}{n}\) when \(w = 1\).
We assume individual \(i\)’s utility function is given by: \(\frac{{c_{i}^{1 - \theta } }}{1 - \theta } + \frac{{g_{i}^{1 - \theta } }}{1 - \theta }\) for \(0 < \theta \ne 1\) and \(\ln \left( {c_{i} } \right) + { \ln }\left( {g_{i} } \right)\) for \(\theta = 1\). We find that total public goods provision, \(G\), is equal to \(\frac{{\left( {1 - wt} \right)Y}}{{(1 - at)^{{\frac{1}{\theta }}} + \left( {1 - wt} \right)}}\). It is not difficult to calculate \(\frac{\partial G}{\partial t}\) and \(\frac{\partial G}{\partial w}\), and confirm that all of our results continue to hold. Future research could generalize our findings beyond a CRRA formulation.
Our main results do not change when we allow for non-contributors. In particular, the sufficient conditions provided in Theorems 1–3 stay the same. For example, take Theorem 1. Under the sufficient condition provided in Theorem 1, for a given degree of waste, it is possible to show that (1) as t increases, the set of contributors (weakly) increases and (2) \(\frac{\partial G}{\partial t} > 0\) for any t. Similarly, all of our corollaries continue to hold. The only adjustment that needs to be done is for Theorem 4. While the threshold provided in Theorem 4 becomes larger when we allow for non-contributors, it can be shown that it is still strictly less than 1. In addition, it is not difficult to show that, for a given rate of tax, (1) as w increases, the set of contributors (weakly) decreases, and (2) \(\frac{\partial G}{\partial w} < 0\) for any \(w\).
This can be easily seen from Eq. (1) and the fact that, in equilibrium, all individuals enjoy the same net consumption. In the case of corner solutions, total donations and average individual donations move in the same direction.
Recall that the condition provided in Theorem 1 is very mild and that only three percent of subjects did not satisfy this condition.
When the parameters of our experiment are used, \(\frac{{\left( {1 - wt} \right)}}{{\left( {1 - at} \right)n}}\) varies between 0.33 and 0.67. Therefore, if \(\theta > 0.67\), then giving is a strictly decreasing function of w. If \(0.33 < \theta < 0.67,\) then giving may sometimes increase and sometimes decrease depending on the parameters (see Tables C.1 and C.2 in Appendix C).
The elasticity of marginal utility with respect to consumption may not be captured by the estimated relative risk aversion coefficient from the lottery task if agents are not expected utility maximizers or if the risk elicitation task used in the study does not correctly capture risk preferences. Nevertheless, in the absence of a better tool to capture the elasticity coefficient, approximating the curvature of the consumption utility based on our risk elicitation task allows us to test the model in a stronger fashion. Since, in our experiment, donation decisions and risk elicitation tasks are performed under very similar conditions (including payoff levels for a given subject), it is not unreasonable for the behavior in the risk elicitation task to shed light on subjects’ behavior regarding donation decisions.
Specifically, participants were told that the amount earned “may be the same for everyone in this room, or each participant’s earnings may depend on their relative performance in the test.” We used this language to facilitate comparison between our two treatments: Equal versus Unequal. In addition, before the experiment started, subjects were told that they may lose part of the money they earn in part 1. They received explicit instructions for part 2 after they finished part 1 (see Appendix B).
We used the following charities: American Cancer Society, American Red Cross, Doctors Without Borders, Feeding America, Food for the Poor, and Save the Children.
Participants were told that, if requested, they would receive a confirmation email from the charity to verify that the experimenters sent their donations to the charity.
As an alternative design, we could have used the collected money to fund a cause that would be perceived as wasteful by most subjects. Due to the difficulty of identifying a cause that would simultaneously be perceived as wasteful by subjects, while also not being a waste of limited research funds, we chose to avoid this alternative design.
Tables C1 and C2 in Appendix C give theoretical predictions of giving for each t and w assuming specific utility functions.
Seven participants (3 in the Equal and 4 in the Unequal treatment) received a score of zero in part 1. These subjects might have failed to submit their answers on time or simply chose not to work on the task. Our results are robust to inclusion or exclusion of these 7 participants and are available upon request from the authors.
We choose to present Tobit regression analyses in the main text, since roughly half of the participants gave $0. Our qualitative results are robust to using OLS regressions (see Appendix D).
Appendix D also provides an analysis of heterogeneity among individuals.
The neutrality result relies on the assumption that the set of contributors stays the same as income inequality increases. Otherwise, Bergstrom et al. (1986), as well as our model, predict that total giving increases as initial income inequality increases. Therefore, a stronger test of neutrality result requires to have a within-subject variation of income (see Duquette and Hargaden 2019), but since this is not the main aim of the paper, our research design does not incorporate such variation.
One participant in our experiment has missing data after part 2 due to health reasons. Therefore, when we add variables from parts 3 and 4 and the questionnaires to the regression analysis, this participant gets automatically dropped from the regression analysis.
The negative impact of w on giving is robust to including the subjects with seven safe choices.
As a robustness check (not shown here for brevity), we have repeated our analysis by eliminating the 14 subjects that made less than six safe choices. Our qualitative results do not change.
We thank Charles Noussair, who suggested that studying repeated decisions with feedback is essential to understanding whether the failure of donations to respond to the tax rate is a transitory phenomenon that would go away with better comprehension of the task or whether it is a durable and solid pattern of behavior.
Hence, we can effectively control for order effects and guarantee that the level of experience and different tax rates cannot be correlated.
Note that there are 30 independent groups.
To isolate the effects of tax and waste on donations, our paper focuses on the demand side of giving. A related literature studies the factors that affect the supply side (e.g., Krasteva and Yildirim 2016). Future research could incorporate how the supply side might also be affected from changes in the tax rate and the rate of waste.
For each income distribution, we have run regressions (both Tobit and OLS) to study whether the impact of tax rate on donations is statistically significant when \(w = 1\). None of the regressions show a statistically significant effect of tax rate on donations at a 5% significance level. When the data from different income distributions are pooled, then a significant impact can be seen when using an OLS regression (p value = 0.04), but not when using a Tobit regression (p value = 0.79).
Our experiment was not designed to differentiate between these alternative explanations. Future research could address this research question.
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Acknowledgements
We thank two anonymous referees and the Editor of this journal for their valuable suggestions. We thank James Alm, Yan Chen, Rachel Croson, Emel Filiz-Ozbay, Enda Hargaden, Charles Holt, Rowell Huesmann, Daniel Hungerman, Steve Leider, John List, Charles Longfield, Yusufcan Masatlioglu, Yesim Orhun, Laura Razzolini, Anya Samek, Joel Slemrod, Nat Wilcox, seminar participants at the University of Chicago, the University of Michigan, the 2015 Economic Science Association Conference, and the 2015 Science of Philanthropy Initiative Annual Conference. We thank the John Templeton Foundation, the Science of Philanthropy Initiative, and the University of Maryland for financial support. Andrew Card, Yulia Chhabra, Juyeon Ha, Zedekiah Higgs, and Ethan McCall provided excellent research assistance. We also thank John Jensenius and Pak Ho Shen for their help with programming the experiment.
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Sheremeta, R.M., Uler, N. The impact of taxes and wasteful government spending on giving. Exp Econ 24, 355–386 (2021). https://doi.org/10.1007/s10683-020-09673-9
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DOI: https://doi.org/10.1007/s10683-020-09673-9