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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 , J oe rg WILD and Michael KUENZLE

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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

contents
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

introduction
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

the swiss electric power sector
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

yardstick competition
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

yardstick competition6
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

average cost model
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

regional heterogeneity
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

model specification
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

slide10
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

stochastic frontier model
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

stochastic frontier model12
Stochastic Frontier Model

Efficiency scores:

12

regression results14
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

returns to scale and density16
Returns to Scale and Density

Evaluated at median values of variables:

16

estimated average costs
Estimated Average Costs

Scale expansion paths of the average costs

17

statistics on efficiency scores eff
Statistics on efficiency scores (EFF)

Likelihood ratio test favors Model 2 at 98% significance level

18

summary conclusions
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

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summary conclusions ii
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

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