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The Potential Benefits of Hilmer and Related Reforms: Electricity Supply

  • John L. Whiteman
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    This article examines the macroeconomic impact of the elimination of x-inefficiency in the Australian electricity supply industry using a computable general equilibrium (CGE) model of the Australian economy. Data envelopment analysis and a stochastic production frontier model are applied to measure x-inefficiency in the electricity industry. It is assumed that microeconomic reform will eliminate this x-inefficiency. The potential increase in total factor productivity resulting from microeconomic reform is introduced into the CGE model as a Hicksian-neutral factor-augmenting technological change. Two alternative labour market assumptions are utilised in measuring the macroeconomic benefits of the microeconomic reform. The results suggest that even under the most pessimistic labour market assumptions, the potential benefits of microeconomic reform in an industry such as electricity will not be trivial. It therefore follows that the impact of microeconomic reform on economic growth could be substantial, particularly if the Australian labour market is more flexible than hitherto assumed.

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    File URL: http://www.copsmodels.com/elecpapr/g-128.htm
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    Paper provided by Victoria University, Centre of Policy Studies/IMPACT Centre in its series Centre of Policy Studies/IMPACT Centre Working Papers with number g-128.

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    Date of creation: Apr 1998
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    Handle: RePEc:cop:wpaper:g-128
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    1. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    2. Quiggin, J., 1995. "Estimating the Benefits of Hilmer and Related Reforms," CEPR Discussion Papers 338, Centre for Economic Policy Research, Research School of Economics, Australian National University.
    3. Adams, Philip D. & Dixon, Peter B. & McDonald, Daina & Meagher, G. A. & Parmenter, Brian R., 1994. "Forecasts for the Australian economy using the MONASH model," International Journal of Forecasting, Elsevier, vol. 10(4), pages 557-571, December.
    4. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    5. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
    6. 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.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
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