Analysing the effectiveness of public service producers with endogenous resourcing
One of the main motivations for productivity analysis is to assess the scope for overall improvements in the output possibilities of individual producers. At times of fiscal and government budgetary pressures, attention focuses particularly on the output potential of public service providers and its relationship to the inputs provided by government funding. Public services, such as education and healthcare, are themselves an important form of economic activity whose performance is of wide public interest, and which merit an adequate recognition of the richness of the additional considerations which may arise in making effectiveness assessments using frontier techniques such as Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). The interesting example of university Departments illustrates one such additional consideration, namely endogeneity of the available resource levels through their dependence on the Departmentâ€™s achieved outputs of teaching and research. Fortunately progress can be made in the presence of such endogeneity through the application of SFA to the assessments of the overall effectiveness and performance of the public service provider, and their decomposition into both technical and allocative components, using the notion of an Achievement Possibility Set that includes the multiplier effects which such resource endogeneity generates.
|Date of creation:||Nov 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom|
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Web page: http://www.york.ac.uk/economics/
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- Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990.
"Production frontiers with cross-sectional and time-series variation in efficiency levels,"
Journal of Econometrics,
Elsevier, vol. 46(1-2), pages 185-200.
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- Atkinson, Scott E. & Primont, Daniel, 2002. "Stochastic estimation of firm technology, inefficiency, and productivity growth using shadow cost and distance functions," Journal of Econometrics, Elsevier, vol. 108(2), pages 203-225, June.
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- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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