"Back-to-basics" strategies challenge enterprise-risk philosophies.
Nearly a year ago, cover story announced the rise of the chief risk officer (CRO). "Utility...
Competitive Efficiency: A Ranking of U.S. Electric Utilities
without breaking it into components. %n1%n
Methods for measuring efficiency can be divided into two families, each comprising several specific techniques. One group of measurement techniques relies on mathematical programming. Using observed outputs and inputs for a group of firms, the algorithm calculates a measure of how efficient each firm is in converting inputs into outputs. This calculation is done by constructing a production "frontier" and measuring each firm's distance from it. %n2%n The other family is econometric. This family involves applying regression techniques to calibrate a production function that compiles information on inputs, outputs and other production characteristics of a group of firms over one or more periods. Each firm's efficiency is measured by comparing it with other firms in the group.
In general, efficiency is almost always measured in relative terms, comparing one firm with another firm or with an industry average (benchmarking). A firm can also be compared with itself at different times (trend analysis), or its performance can be evaluated against its goals (goal or "gap" analysis). The difference between efficiency levels under the operationally best possible resource
allocation and the actual resource allocation is the degree of x-inefficiency (em the familiar concept introduced by Harvey Leibenstein in 1966.
Utilities use technology to transform capital, labor, energy and materials into electricity. The physical relationship between the amounts of each input and electricity produced can be expressed as a production function. In our analysis, we used a simple formulation of the production function known as the Cobb-Douglas. Under this formulation, output, measured in MWh, depends on capital, labor, fuels and materials used by utilities. A load factor variable was included to account for idle capacity. A trend variable was used to capture the time-varying effect of technology. %n3%n
Except for the Producer Price Index, which came from the Bureau of Labor Statistics, all other data came from Edison Electric Institute's Uniform Statistical Reports. Data were gathered on each variable from 1990 through 1995. We chose the holding company as the analysis unit rather than the operating company. Mergers during the data period were aggregated into single holding-company level. The analysis began with the complete database for all EEI member utilities. Only utilities with complete data for all variables in all six years were kept. This criterion left 94 observations for use in the analysis.
Output was measured as total physical production in MWh sold to all accounts (Schedule 14). Input variables were capital, labor, fuel, operating expenses and load factors. Fuel inputs were total outlays for all fuels in real dollars (Schedule 14). Operating expenses were the sum of all expense accounts and included operation, maintenance, depreciation, depletion, amortization and property losses, excluding local taxes (Schedule 2). Annual load factors were obtained from Schedule 17. All monetary variables were expressed in real terms, deflated by the PPI.
Leaders and Laggards
The statistical results from calibrating the production function showed that all included variables affected output and, together, explained more than 99 percent of its variations. %n4%n Estimated efficiency rankings and percentage changes in overall relative efficiency from