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Resource Rent Taxation and Benchmarking.A New Perspective for the Swiss Hydropower Sector

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Author Info
Silvia Banfi () (Centre for Energy Policy and Economics, ETH)
Massimo Filippini () (Università della Svizzera italiana, Centre for Energy Policy and Economics, ETH)

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Abstract

The electricity generation in Switzerland is mainly based on hydropower (55% of total production). The exploitation of water in the hydropower sector can generate significant so-called resource rents. These are defined by the surplus return above the value of capital, labor, materials and energy used to exploit hydropower. In Switzerland, hydropower producers pay to the State a fixed fee per kW gross capacity. With this system the substantial differences in costs, revenues and in the production characteristics of the hydropower plants are not taken into account. In this context, the following paper has two main goals: 1) To discuss the introduction in the Swiss hydropower sector of a new payment system based on a resource rent tax; 2) To propose a combination of a RRT system with a benchmarking analysis of the production cost obtained through the estimation of a stochastic frontier variable cost function. We estimate a true random effects stochastic frontier variable cost function using panel data in order to overcome the asymmetric information problem. In addition, using the information on cost efficiency of the single companies, we show how to introduce in the RRT scheme a benchmark system which gives incentives to minimize the production costs.

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Publisher Info
Paper provided by Biblioteca universitaria di Lugano (University Library of Lugano) in its series Quaderni della facoltà di Scienze economiche dell'Università di Lugano with number 0906.

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Length: 23 pages
Date of creation: Aug 2009
Date of revision:
Handle: RePEc:lug:wpaper:0906

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Related research
Keywords: Resource Rent Taxation; Hydropower; Efficiency;

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Find related papers by JEL classification:
D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity
H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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  1. David W. Gillen & Jean-François Wen, 2000. "Taxing Hydroelectricity in Ontario," Canadian Public Policy, University of Toronto Press, vol. 26(1), pages 35-49, March. [Downloadable!] (restricted)
  2. Banfi, Silvia & Filippini, Massimo & Mueller, Adrian, 2005. "An estimation of the Swiss hydropower rent," Energy Policy, Elsevier, vol. 33(7), pages 927-937, May. [Downloadable!] (restricted)
  3. Eirik S. Amundsen & Christian Andersen & Jan Gaute Sannarnes, 1992. "Rent Taxes on Norwegian Hydropower Generation," The Energy Journal, International Association for Energy Economics, vol. 13(1), pages 97-116.
  4. Amundsen, Eirik S., 1997. "Gas power production, surplus concepts and the transformation of hydro electric rent into resource rent," Resource and Energy Economics, Elsevier, vol. 19(3), pages 241-259, August. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
  6. Massimo Filippini & Cornelia Luchsinger, 2007. "Economies of scale in the Swiss hydropower sector," Applied Economics Letters, Taylor and Francis Journals, vol. 14(15), pages 1109-1113. [Downloadable!] (restricted)
  7. Amundsen, Eirik S. & Tjotta, Sigve, 1993. "Hydroelectric rent and precipitation variability : The case of Norway," Energy Economics, Elsevier, vol. 15(2), pages 81-91, April. [Downloadable!] (restricted)
  8. Mehdi Farsi & Massimo Filippini & William Greene, 2005. "Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies," Journal of Regulatory Economics, Springer, vol. 28(1), pages 69-90, 07. [Downloadable!] (restricted)
  9. Ricardo, David, 1821. "On the Principles of Political Economy and Taxation," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, edition 3, number ricardo1821. [Downloadable!]
  10. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June. [Downloadable!] (restricted)
  11. Banfi, Silvia & Filippini, Massimo & Luchsinger, Cornelia, 2002. "Deregulation of the Swiss Electricity Industry: Short-Run Implications for the Hydropower Sector," The Electricity Journal, Elsevier, vol. 15(6), pages 69-77, July. [Downloadable!] (restricted)
  12. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2006. "Cost Efficiency in Regional Bus Companies: An Application of Alternative Stochastic Frontier Models," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 40(1), pages 95-118, January. [Downloadable!] (restricted)
  13. Battese, George E., 1992. "Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics," Agricultural Economics, Blackwell, vol. 7(3-4), pages 185-208, October. [Downloadable!] (restricted)
  14. Garnaut, Ross & Clunies Ross, Anthony, 1975. "Uncertainty, Risk Aversion and the Taxing of Natural Resource Projects," Economic Journal, Royal Economic Society, vol. 85(338), pages 272-87, June. [Downloadable!] (restricted)
  15. Filippini, Massimo, 1996. "Economies of Scale and Utilization in the Swiss Electric Power Distribution Industry," Applied Economics, Taylor and Francis Journals, vol. 28(5), pages 543-50, May. [Downloadable!] (restricted)
  16. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August. [Downloadable!] (restricted)
  17. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
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