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and conditions. It also is difficult to measure. Based on the results of this analysis, we propose that quantitative measures of relative efficiency derived from formal statistical and optimization models are good proxies for the more subjective indicators used by the financial markets in evaluating a utility's business and financial prospects.
Hossein Haeri, Ph.D. is director of energy information services at PG&E Energy Services, in Portland, Ore. Matei Perussi is a statistical analyst at PG&E Energy Services. The views presented in this article do not necessarily represent those of PG&E Energy Services. M. Sami Khawaja, Ph.D. is the president of Quantec, an economic consulting firm in Portland.
Editor's Note: Last fall, when Public Utilities Fortnightly published a study ranking electric utilities on the basis of productive efficiency, known as The Fortnightly 100, some readers took umbrage. They argued that any survey ranking electric companies for efficiency in the generation sector ignores a host of potential biases, such as differences in asset mix, geographical location, population density, regional trading patterns and, more fundamentally, the fact that certain restructuring policies involve a sell-off of generating assets.
Here, two authors from that article return with the same data set to explore whether operational efficiency - as defined and analyzed in last fall's article - can show any positive relationship with a utility's performance in financial markets. They are joined here by a third author who worked with them on a previous article, published in 1997, that also ranked utility operational efficiency.
In the final analysis, as the authors show so well, productive efficiency still matters. It matters a lot. - B.W.R.
1 See "The Fortnightly 100" by Janice Forrester, M. Sami Khawaja, Hossein Haeri and Michael Carter in Public Utilities Fortnightly, Sept. 1, 1998, p. 26. See also "Competitive Efficiency: A Ranking of U.S. Electric Utilities" by Hossein Haeri, M. Sami Khawaja and Matei Perussi in Public Utilities Fortnightly, June 15, 1997, p. 26.
2 Although we have used "productive efficiency" and "productivity" interchangeably, the two terms are not synonymous. "Efficiency" describes how well a production process is run; "productivity" measures its result in terms of utilization of resources.
3 This approach is also consistent with the Standard and Poor's general evaluation framework and rating criteria outlined in its Rating Methodology for industrials and utilities.
4 Analytically, the regression model is defined as:
Where y is the dependent variable, a is the intercept of the regression line with the vertical axis, xi represents the independent variables (i = 1¼ k), k is the number of independent variables, bi is the estimated coefficient measuring the effect on y resulting from a one-unit change in xi and e is the error term, i.e., the unexplained portion of the variations in y.
5 As mentioned above, operational efficiencies measure how well a utility uses capital, labor, O&M and fuel in generating electricity. As such, operational efficiencies simply measure how well inputs are converted to output, and the various other variables used in this study are not reflected in the calculation of efficiency.
6 This index is a variant