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The fundraising efficiency in U.S. non-profit art organizations: an application of a Bayesian estimation approach using the stochastic frontier production model

Abstract

This article examines how efficient art organizations are in raising funds from private giving. We measure fundraising efficiency using a Bayesian estimation approach using the stochastic frontier production model. We show that fundraising efficiencies are generally quite low for art organizations in the U.S. when private giving is only considered as a fundraising output; however, when the effect of fundraising on ticket sales is considered, fundraising efficiencies improve substantially. We also show that government grants have a negative impact on fundraising efficiency and therefore partially crowd out private giving.

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Notes

  1. 1.

    Private giving is labeled as direct public support in the IRS 990 Form. According to Hansmann (1980), non-profit organizations can raise funds through two specific means: (1) through fees for the services that they provide and (2) through private donations and public grants like government subsidies.

  2. 2.

    See Brooks (2003) and Andreoni and Payne (2003) for recent developments in this important issue.

  3. 3.

    Other researchers have measured efficiency of art organizations using a technique called Data Envelopment Analysis (DEA). See Luksetich and Hughes (1997) and Basso and Funari (2004).

  4. 4.

    See citeNknitte02 [electricity], Rezitis et al. (2002) [agriculture], (Kim and Han 2001) and Harris (2001) [manufacturing], Balcombe et al. (2006) [dairy], and Bishop and Brand (2003) [museums].

  5. 5.

    The choice of the exponential distribution assumption is discussed in the following subsection.

  6. 6.

    The deviance is defined as

    $$ D(\theta )=-2log(p(y|\theta))+C $$

    where y is the data, θ are the unknown parameters of the model and p(y|θ) is the likelihood function. C is a constant. DIC is calculated as

    $$ DIC=p_{D}+ED $$

    where p D  = ED − D(Eθ) measures the effective number of parameters of the model and Eθ = E(θ). The second term ED = E θ[D(θ)].

  7. 7.

    See Spiegelhalter et al. (2002) for a discussion.

  8. 8.

    Bishop and Brand (2003), in their study of overall production efficiency of museums, defines capital input as approximated value of the total running and maintenance costs of the museum.

  9. 9.

    These assumptions are consistent with the existing literature, e.g. Griffin and Steel (2007).

  10. 10.

    The initial parameter values assigned for prior distributions in our estimation are a 1 = a 2 = 0.01,  b 1 = b 2 = 0.01, and c 1 = c 2 = 0.01.

  11. 11.

    According to (Koop et al. 1995), MCMC estimation method is the most appropriate method in the presence of the complexity of numerical integrations involved with estimation of stochastic frontier models. Lately, MCMC estimation has been employed in the estimation of stochastic production frontiers and technical efficiency. See van den Broeck et al. (1994), Tsionas (2002), Huang (2004), and Kumbhakara and Tsionas (2005) for the MCMC approach in the study of stochastic frontier model.

  12. 12.

    See footnote 6.

  13. 13.

    56 organizations that reported negative or zero private donations and negative fundraising expenditures are eliminated from the original dataset.

  14. 14.

    Bishop and Brand (2003) also reports fairly low average levels of production efficiency among museums in U.K. estimated to be about 45%. In a study of efficiency of sympony orchestra fundraising efforts using DEA by Luksetich and Hughes (1997)s also reports significant inefficiencies in fundraising efforts.

  15. 15.

    This again points out the fact that simple ratio of fundraising expentitute over private donation may not be an appropriate way of measuring fundraising efficiency for art organizations.

  16. 16.

    Efficiency estimated using private giving as output is used to estimate the effects of grants and visibility.

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Correspondence to David T. Yi.

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Song, S., Yi, D.T. The fundraising efficiency in U.S. non-profit art organizations: an application of a Bayesian estimation approach using the stochastic frontier production model. J Prod Anal 35, 171–180 (2011). https://doi.org/10.1007/s11123-010-0186-y

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Keywords

  • Fundraising efficiency
  • Stochastic frontier models
  • Bayesian estimation
  • Non-profit art organizations
  • Crowding out

JEL Classification

  • C11
  • H32
  • H5
  • L31