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Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

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Author Info
Antonio Alvarez ()
Christine Amsler ()
Luis Orea ()
Peter Schmidt ()

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Abstract

Let u ≥ 0 be technical inefficiency, let z be a set of variables that affect u, and let δ be the parameters of this relationship. The model satisfies the scaling property if u(z, δ) can be written as a scaling function h(z, δ) times a random variable u* that does not depend on z. This property implies that changes in z affect the scale but not the shape of u(z,δ). This paper reviews the existing literature and identifies models that do and do not have the scaling property. It also discusses practical advantages of the scaling property. The paper shows how to test the hypothesis of scaling, and other interesting hypotheses, in the context of the model of Wang, Journal of Productivity Analysis, 2002. Finally, two empirical examples are given. Copyright Springer Science+Business Media, LLC 2006

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File URL: http://hdl.handle.net/10.1007/s11123-006-7639-3
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Publisher Info
Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 25 (2006)
Issue (Month): 3 (06)
Pages: 201-212
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Handle: RePEc:kap:jproda:v:25:y:2006:i:3:p:201-212

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Web page: http://www.springerlink.com/link.asp?id=100296

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Related research
Keywords: Stochastic frontier model; Scaling property; Technical inefficiency; C12; C31; C52;

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  1. Meike Wollni & Bernhard Brümmer, 2009. "Productive efficiency of specialty and conventional coffee farmers in Costa Rica: Accounting for technological heterogeneity and self-selection," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 13, Courant Research Centre PEG. [Downloadable!]
  2. Liu, Yanyan, 2006. "Model Selection in Stochastic Frontier Analysis: Maize Production in Kenya," 2006 Annual meeting, July 23-26, Long Beach, CA 21281, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association). [Downloadable!]
  3. Tiziana Laureti, 2008. "Modelling Exogenous Variables in Human Capital Formation through a Heteroscedastic Stochastic Frontier," International Advances in Economic Research, Springer, vol. 14(1), pages 76-89, February. [Downloadable!] (restricted)
  4. Yanyan Liu & Robert Myers, 2009. "Model selection in stochastic frontier analysis with an application to maize production in Kenya," Journal of Productivity Analysis, Springer, vol. 31(1), pages 33-46, February. [Downloadable!] (restricted)
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