Scale and cost efficiency in the Swiss electricity distribution industry: evidence from a frontier cost approach

1. Scale and cost efficiency in the Swiss electricity distribution industry: evidence from a frontier cost approach Seventh European Workshop on Efficiency and Productivity Analysis University of Oviedo, Spain September 25-27, 2001 Massimo FILIPPINI, Joerg WILD and Michael KUENZLE Center for Energy Policy and Economics Swiss Federal Institutes of Technology ETH Zentrum, WEC CH-8092 Zurich

2. Contents • Introduction • Swiss Electricity Industry • Yardstick Competition • Average Cost Model Specification • Stochastic Frontier Model • Regression Results • Returns to Scale and Density • Efficiency Scores • Summary & Conclusions 2

3. Introduction • Deregulation of the electric power sector but: networks are natural monopolies • Most proposals contain: • third party access (TPA) • unbundling of network and production/sales • Regulation of network access prices • Transmission network • Local distribution networks • Regulatory problem: asymmetric information 3

4. The Swiss electric power sector • Composed of about 1200 firms (public and private) • 940 engaged in distribution (very small utilities!) • 140 engaged in production, transmission and distribution • 90 engaged in production • Great divergence both in terms of size and activities • The municipals and the regional electric utilities purchase most of their power from 10 utilities which form the backbone of the industry. • In Switzerland, the cantons (26) and the municipalities (~3000) regulate the activities of regional private and public electricity utilities. 4

5. Yardstick Competition • Rate-of-return or cost-of-service regulation: • no incentives for efficient production • Price-cap regulation • Yardstick competition (Shleifer, 1985) • Basic idea: regulators typically do not have accurate information on the cost structure of the firms they regulate • In place of such information, yardstick competition instructs regulators to use the costs of similar firms as a benchmark to judge whether the costs of firms they regulate are appropriate 5

6. Yardstick Competition average costs per kWh • Econometric estimation of an average cost function • Benchmarking instrument • Estimation results used to regulate access prices • Heterogeneity factors that cannot be altered by the firms must be included in the cost model and incorporated in setting yardstick prices Company B, low customer density Company A, high customer density output (GWh) 6

7. Average Cost Model • Average cost function for local electricity distribution networks (NOT electricity sales) • Inputs: labor and capital • Three network levels (high, medium, low voltage) • Two customer groups (medium and low voltage) • Capacity utilization => load factor (LF) • Regional heterogeneity 7

8. Regional Heterogeneity Exogenous service area characteristics: • Average consumption per customer • Customer density in settled areas • Share of agricultural land • Share of forest land • Share of unproductive land Factors that can be altered by utilities: • Size (Output) => mergers • Load factor => load management, pricing 8

9. Model Specification AC average cost per kWh Y kWh transported on medium-voltage grid (output) PL, PCprices of labor and capital LVSH share of low-voltage electricity HGRID operation of a high-voltage grid (dummy) AVGL average consumption per low voltage customer LF load factor CD customer density FOSH, AGSH, UPSH share of forest, agricultural and unproductive land OTSH outputs other than electricity distribution 9

10. Data • 59 Swiss electricity distribution utilities • Period 1988-1996 • => Unbalanced panel with 380 observations • Data sources: • (Unpublished) financial statistics on electric utilities (Swiss Federal Office of Energy) • Mail questionnaire • Area statistics (Swiss Federal Office of Statistics) 10

11. Stochastic Frontier Model • Different Specifications of Uiwith Panel Data: • Ui with half-normal, trucated normal or exponential distribution • Uit =Ui(exp(η(T-t)), Ui~N(μ,σu2) truncated • Uit~N(mit,σu2) truncated, mit=zitδ 11

12. Stochastic Frontier Model Efficiency scores: 12

13. Regression Results 13

14. Regression Results • Estimated functions are well behaved • Almost all of the parameter estimates are statistically highly significant and carry the expected sign • Coefficients of OLS and Frontier models are similar • We find economies to scale and significant influence of heterogeneity on costs 14

15. Returns to Scale and Density 15

16. Returns to Scale and Density Evaluated at median values of variables: 16

17. Estimated Average Costs Scale expansion paths of the average costs 17

18. Statistics on efficiency scores (EFF) Likelihood ratio test favors Model 2 at 98% significance level 18

19. Summary & Conclusions • Estimation of an average-cost function for a panel of 59 Swiss electricity distribution utilities • The estimated average cost function can be used as an instrument for yardstick regulation of access prices for local distribution networks • Results indicate the existence of economies of scale • Many utilities are not producing at efficient scale 19

20. Summary & Conclusions II • Regulator might use estimated average costs to judge whether the prices of firms they regulate are appropriate • Heterogeneity factors that cannot be altered by the firms must be incorporated in setting yardstick prices(Customer density, average consumption and shares of different area types) • The estimated average cost function could be also used to calculate individual price-caps 20