Journal of Productivity Analysis

, Volume 35, Issue 2, pp 171–180 | Cite as

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

  • Seongho Song
  • David T. Yi


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.


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

JEL Classification

C11 H32 H5 L31 


  1. Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37CrossRefGoogle Scholar
  2. Andreoni J (1990) Impure altruism and donations to public goods: a theory of warm-glow giving. Econ J 100:464–477CrossRefGoogle Scholar
  3. Andreoni J (1993) An experimental test of the public goods crowding-out hypothesis. Am Econ Rev 83:1317–1327Google Scholar
  4. Andreoni J, Payne A (2003) Do government grants to private charities crowd out giving or fund-raising? Am Econ Rev 93:792–812CrossRefGoogle Scholar
  5. Balcombe K, Fraser I, Kim J (2006) Estimating technical efficiency of australian dairy farms using alternative frontier methodologies. Appl Econ 38:2221–2236CrossRefGoogle Scholar
  6. Basso A, Funari S (2004) A quantitative approach to evaluate the relative efficiency of museums. J Cult Econ 28:195–216CrossRefGoogle Scholar
  7. Bergstrom T, Blume L, Varian H (1986) On the private provision of public goods. J Public Econ 29:25–49CrossRefGoogle Scholar
  8. Bishop P, Brand S (2003) The efficiency of museums: A stochastic frontier production function approach. Appl Econ 35:1853–1858CrossRefGoogle Scholar
  9. Brooks AC (2003) Do government subsidies to nonprofits crowd out donations or donor? Public Finance Rev 31:166–179CrossRefGoogle Scholar
  10. Duncan B (1999) Modeling charitable contributions of time and money. J Public Econ 72:213–242CrossRefGoogle Scholar
  11. Frumkin P, Kim MT (2001) Strategic positioning and the financing of nonprofit organizations: is efficiency rewarded in the contributions marketplace. Public Admin Rev 61:266–275CrossRefGoogle Scholar
  12. Griffin J, Steel M (2007) Bayesian stochastic frontier analysis using winbugs. J Prod Anal 27:163–176CrossRefGoogle Scholar
  13. Hansmann H (1980) The role of nonprofit enterprise. Yale Law J 89:835–901CrossRefGoogle Scholar
  14. Harris R (2001) Comparing regional technical efficiency in uk manufacturing plants: the case of northern ireland 1974–1995. Reg Stud 35:519–539CrossRefGoogle Scholar
  15. Huang H-C (2004) Estimation of technical inefficiencies with heterogeneous technologies. J Prod Anal 21:277–296CrossRefGoogle Scholar
  16. Jondrow J, Lovell C, Materov I, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econom 19:233–238CrossRefGoogle Scholar
  17. Kim S, Han G (2001) A decomposition of total factor productivity growth in korean manufacturing industries: a stochastic frontier approach. J Prod Anal 16:269–281CrossRefGoogle Scholar
  18. Koop G, Steel M, Osiewalski J (1995) Posterior analysis of stochastic frontier models using gibbs sampling. Comput Stat 10:353–373Google Scholar
  19. Kumbhakara SC, Tsionas EG (2005) Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach. J Econom 126:355–384CrossRefGoogle Scholar
  20. Lee L-F, Tyler WG (1978) The stochastic frontier production function and average efficiency: an empirical analysis. J Econom 7:385–389CrossRefGoogle Scholar
  21. Luksetich W, Hughes P (1997) Efficiency of fund-raising activities: An application of data envelopment analysis. Nonprofit Volunt Sect Q 26:73–84CrossRefGoogle Scholar
  22. Meeusen W, van den Broeck J (1977) Efficiency estimation from cobb–douglas production functions with composed error. International Economic Review 8:435–444CrossRefGoogle Scholar
  23. Rezitis A, Tsiboukas K, Tsoukalas S (2002) Technical efficiency in the greek agricultural sector. Appl Econ 34:1345–1357CrossRefGoogle Scholar
  24. Spiegelhalter D, Best N, Carlin B, der Linde AV (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc 64:583–616CrossRefGoogle Scholar
  25. Tsionas E (2002) Stochastic frontier models with random coefficients. J Appl Econ 17:127–147CrossRefGoogle Scholar
  26. van den Broeck J, Koop G, Osiewalski J, Steel M (1994) Stochastic frontier model: a Bayesian perspective. J Econom 61:273–303CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Mathematical SciencesUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of EconomicsXavier UniversityCincinnatiUSA

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