Abstract
Free, subsidized, or cost-covering? The decision on how much to charge for a good or service is fundamental in social business planning. The higher the fee paid by the recipient, the more people in need can be served by the additional revenues. However, charging a fee simultaneously excludes the very poor from consumption. This paper argues that the entrepreneur’s trade-off between both effects is governed by her level of poverty aversion, i.e., her preference intensity for the service of needy people with different incomes. Additionally, we account for the possibility of excess demand for the provided good and assume that applicants are rationed by non-price-allocation mechanisms. We therefore contribute to the extensive literature on the pricing and rationing behavior of nonprofit firms. Within our theoretical model, we find ambiguous reactions of the entrepreneur to a cut in donations. Given a sufficiently low level of status-quo donations, entrepreneurs with relatively high poverty aversion tend to increase the project volume, whereas those with relatively low poverty aversion do the opposite.
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Notes
Aside from user fees, nonprofit organizations typically generate income from additional sources, which can be clustered into donations and unrelated business income (Steinberg and Weisbrod 1998). A large body of literature exists that deals with aspects of each of the sources and the interactions between them. For example, contributions to the field of public or private donations highlight the role of lead donors (Andreoni 1998, 2006), fundraising strategies (List and Lucking-Reiley 2002), united charities (Fisher 1977; Bilodeau 1992), and the interaction between government grants and fundraising success (Rose-Ackerman 1987; Andreoni and Payne 2003). Work on unrelated business income points to disutility from engaging in commercial activities (Schiff and Weisbrod 1991; Weisbrod 1998) and agency problems within the organization (Du Bois et al. 2004). In Sect. 4 we analyze the effect of a variation in those funds on the entrepreneur’s choice of user fees.
Throughout the paper, \(R_+ \) is used to denote the set of nonnegative real numbers, and \(R_{++} \) to denote the set of positive real numbers.
In practice, non-price-rationing instruments are often not sufficiently precise to identify all untargeted individuals. For reasons of simplicity, we refrain from modeling this uncertainty.
See Food for Survival (2000).
Equation (2) is not defined for \(f=c\); in this case, as proposition 1 will show, it holds that \(F=c\cdot \bar{{n}}\left( c \right)\).
In the maximization problem and subsequent derivations we simplify the explicit notation \(\bar{{n}}\!\left(\! {{R}^{-1}\left( F \right)} \right)\) and \(\underline{n}\!\left(\!{{R}^{-1}\left( F \right)} \right)\) by use of \(\bar{{n}}\) and \(\underline{n}\).
The elasticity of marginal utility is defined as \(\varepsilon =\frac{d{u}^{\prime }\left( n \right)}{dn}\cdot \frac{n}{{u}^{\prime }\left( n \right)}\).
With \(\alpha =0\), the value of serving individual \(n=0\) is not defined. We simplify this case and set \(u\left( 0 \right)=1\).
Depending on the model’s parameter values, \(F^{{*}}>0\) can also result for \(\alpha \in [\hat{{\alpha }},\bar{{\alpha }})\), with \(\hat{{\alpha }}<\bar{{\alpha }}\). Such entrepreneurs perceive a dominant replacement effect for the first unit of user fees, i.e., \(\left. {\left[ {\underline{n}^{\alpha }\cdot (1/c)} \right]} \right|_{F=0} \le \left. {\left[ {-(\bar{{n}}^{\alpha }-\underline{n}^{\alpha })\cdot \bar{{n}}_F } \right]} \right|_{F=0} \). The increase of fees initially decreases utility to some minimum before the revenue effect overcompensates the utility loss and induces a global maximum. Because all important results can be proved without an extension to these special cases, we simplify the analysis by ignoring them.
For notational clarity, the terms \(\underline{n}\left( f \right)\) in Fig. 3 and \(F=R\left( f \right)\) are expanded to \(\underline{n}\left( {f;D} \right)\) and \(F=R\left( {f;D} \right)\) to emphasize the influence of donations.
This result is shown in the next proof.
The conditions specifying \(\mathop {\alpha }\limits ^{\smile }\) are presented in the next proof. For the current argumentation it suffices to set \(\mathop {\alpha }\limits ^{\smile }\) >1.
See McCord et al. (2001).
This technical result does not imply that non-poverty-averse entrepreneurs do not change their project sizes if donations increase. Rather, in line with proposition 4, non-poverty-averse entrepreneurs maximize their project sizes; consequently, their project volumes increase with higher donations. The zero-value of Eq. (13) emanates from the fact that a replacement effect does not exist for \(\alpha =0\) and, hence, does not change if user-fee revenues are increased.
Rearranging the first-order condition (setting Eq. (8) to zero) yields \(\underline{n}=\bar{{n}}\cdot \left[ {{1-1}/{\left( {\bar{{n}}_F \cdot c} \right)}} \right]^{-1/\alpha }\) with \(\begin{array}{l} \lim \limits _{\alpha \rightarrow 0}\ \bar{{n}}\cdot \left[ {{1-1}/{\left( {\bar{{n}}_F \cdot c} \right)}} \right]^{-1/\alpha }=0 \end{array}\).
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Acknowledgments
This paper benefited greatly from extensive discussions with Matthias G. Raith and Steffen Burchhardt. Comments by Anne Chwolka, university colleagues, and conference participants were also extremely helpful. I would also like to thank co-editor Amihai Glazer and an anonymous referee for their valuable feedback.
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Appendix
Appendix
Proof of Proposition 5
Let \(D\in \left( {0,\bar{{n}}\left( 0 \right)\cdot c} \right)\) and \(\alpha <\hat{{\alpha }}\) with
Then, according to proposition 3, \(F^{{*}}>0\). Now, consider Eq. (11). With \(f=f^{{*}}>0\), an increase in donations enlarges the entrepreneur’s total income by \(c/{\left( {c-f^{{*}}} \right)}\). Consequently, an increase in donations leads to a decrease in the optimal project volume if \({dF^{{*}}}/{dD}<-c/{\left( {c-f^{{*}}} \right)}\). Applying the implicit function theorem to the first-order condition yields
which can be rearranged to
\({\partial ^{2}U\left( {F;D} \right)}/{\partial F^{2}}\) is given by Eq. (9) and \(\left. {\left( {{\partial ^{2}U\left( {F;D} \right)}/{\partial F\partial D}} \right)} \right|_{F=F^{{*}}} \) by Eq. (12). Hence, the optimal project volume decreases if
The two terms of condition (13) characterize the change in the replacement effect resulting from an increase in user-fee revenues. The first term is positive by definition and the second term is nonnegative for all \(\alpha \ge {\alpha }^{\prime }\), with
Next, we show that a unique \(\mathop {\alpha }\limits ^{\smile } \in \left( {0,{\alpha }^{\prime }} \right)\) exists for which \(\Omega \) is zero. Hence, \(\Omega \) is positive for all \(\alpha >\mathop {\alpha }\limits ^{\smile } \) and negative for all \(\alpha <\mathop {\alpha }\limits ^{\smile } \).
Rearranging Eq. (13) yields
with
and
With \(\bar{{n}}>\underline{n}\), \(\bar{{n}}_F <0\) and \(\bar{{n}}_{FF} >0\), \(y\left( {\alpha ,\underline{n}} \right)\) is the product of a linear and a convex increasing function of \(\alpha \). Hence, \(y\left( {\alpha ,\underline{n}} \right)\) is also increasing and convex in \(\alpha \). On the other hand, \(z\left( {\alpha ,\underline{n}} \right)\) is linearly increasing in \(\alpha \). Consequently, the difference of both terms, \(\tilde{\Omega }\), has maximally two roots. Apparently, one is given for \(\alpha =0\).Footnote 15 There exists a second root for \(\alpha =\mathop {\alpha }\limits ^{\smile } >0\) if and only if \(\left. {\left( {{dy\left( {\alpha ,\underline{n}} \right)}/{d\alpha }} \right)} \right|_{\alpha =0} <\left. {\left( {{dz\left( {\alpha ,\underline{n}} \right)}/{d\alpha }} \right)} \right|_{\alpha =0} \), i.e.,
This condition holds since \(\alpha \rightarrow 0\) implies that \(F^{{*}}\rightarrow F_{\max } \) and \(\underline{n}\rightarrow 0\).Footnote 16 Although the limits of \(y_\alpha \) and \(z_\alpha \) are infinity for \(\underline{n}\rightarrow 0\), the application of l’Hôpital’s rule shows that \(y_\alpha \) and \(z_\alpha \) diverge and \(z_\alpha >y_\alpha \) results:
Consequently, there exists a unique \(\mathop {\alpha }\limits ^{\smile } \in \left( {0,{\alpha }^{\prime }} \right)\) for which the value of \(\tilde{\Omega }\), or respectively \(\Omega \), is zero.
Yet, we assumed that \(\alpha <\hat{{\alpha }}\) and derived the requirement that \(\alpha >\mathop {\alpha }\limits ^{\smile } \). Consequently, an increase in donations leads to a reduction in the optimal project volume if \(\mathop {\alpha }\limits ^{\smile } <\hat{{\alpha }}\) and \(\alpha \in \left( \mathop {\alpha }\limits ^{\smile } ,\hat{{\alpha }} \right)\). However, \(\mathop {\alpha }\limits ^{\smile } <\hat{{\alpha }}\) requires a sufficiently low level of donations. For \(D\rightarrow \bar{{n}}\left( 0 \right)\cdot c\), \(\ln \bar{{n}}-\ln \underline{n}\), which determines \(\hat{{\alpha }}\) and \({\alpha }^{\prime }\), is infinitely large, such that \(\hat{{\alpha }}\rightarrow 0\) and \({\alpha }^{\prime }\rightarrow 1\). Since \(\mathop {\alpha }\limits ^{\smile } <{\alpha }^{\prime }\), it must hold that \(\mathop {\alpha }\limits ^{\smile } \in \left( {0,1} \right)\) and, consequently, \(\mathop {\alpha }\limits ^{\smile } >\hat{{\alpha }}\). In other words, given that the amount of donations is relatively high, all entrepreneurs react by enlarging their project volumes when they experience an increase in donations. In contrast, for \(D\rightarrow 0\), \(\left. {\left( {\ln \bar{{n}}-\ln \underline{n}} \right)} \right|_{F=0} \rightarrow 0\) and, hence, \(\hat{{\alpha }}\rightarrow \infty \). According to proposition 3, all entrepreneurs with \(\alpha <\hat{{\alpha }}\) choose \(F^{{*}}>\)0. Consequently, \(\left. {\left( {\ln \bar{{n}}-\ln \underline{n}} \right)} \right|_{F=F^{{*}}} >0\) and \({\alpha }^{\prime }\in \left( {1,\infty } \right)\). Since \(\mathop {\alpha }\limits ^{\smile } <{\alpha }^{\prime }\), it holds that \(\mathop {\alpha }\limits ^{\smile } <\hat{{\alpha }}\). As a result, there exists a specific level of donations \(D^{\prime }\) such that \(\mathop {\alpha }\limits ^{\smile } =\hat{{\alpha }}\), if \(D={D}^{\prime }\), and \(\mathop {\alpha }\limits ^{\smile } <\hat{{\alpha }}\), if \(D\in \left( {0,D^{\prime }} \right)\). Hence, for all \(D\in \left( {0,D^{\prime }} \right)\), an increase in donations leads to a reduction in the optimal project volume \(F^{{*}}+D\), if \(\alpha \in \left( {\mathop {\alpha }\limits ^{\smile } ,\hat{{\alpha }}} \right)\). \(\square \)
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Starke, C. Serving the many or serving the most needy?. Econ Gov 13, 365–386 (2012). https://doi.org/10.1007/s10101-012-0116-8
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DOI: https://doi.org/10.1007/s10101-012-0116-8