Big City Bias: The Problem with Simple Rate Comparisons
December 2002
By Johannes P. Pfeifenberger and Mark W. Jenkins
Looking beyond ranking utilities on price.
It's tempting to compare rates between utilities- to use those simple rankings as regulatory carrots and sticks-but those who do may play a dangerous game. While such rankings may appear compelling, they can add an inappropriate bias to the regulatory process and penalize well-performing electric utilities that operate in high-cost service territories, such as large metropolitan areas.
In fact, the cost disadvantage faced by utilities serving our large cities is quite pronounced. As a result, the average electric rates paid by consumers in large metro areas consistently exceed the average rates of small metro areas by at least 10 to 20 percent, throughout the country.
To make matters worse, this bias is often magnified for high-profile utilities-firms that are highly visible to the public and state politics, closely scrutinized by local media, and supplying power to a large portion of the state's economy.
As it happens, the utilities that furnish service to major metropolitan areas often face both of these challenges: A perception of high rates, and the intense public and political scrutiny associated with being a high-profile utility.
The Appeal of Simple Comparisons
In today's world, state public utility commissions (PUCs) and their staffs generally monitor the rates of utilities under their jurisdiction and employ within-state, regional, or nation-wide comparisons and rankings. Such comparisons and rankings often are featured prominently by the media, and can become important factors in state politics and in public relations efforts by various interest groups. They offer a seemingly quick and easy tool to evaluate the performance of utilities, the PUCs themselves and, for that matter, the effectiveness of consumer advocates and interest groups. Needless to say, however, such rate comparisons can easily lead to bias in the regulatory process.
Even where PUCs succeed in keeping politics at bay, there can be little doubt that rate comparisons often play an important implicit role in regulation, since parties on all sides have reason to introduce such comparisons to serve their own motives.
Utilities themselves may use simple rate comparisons to document superior operational performance in hopes of gaining favorable treatment. Likewise, intervenors and consumer advocates may present rate comparisons to support demands for rate reductions. Even the regulators themselves may unwillingly succumb to the allure of simplistic comparisons and rankings. If the commissioners perceive a utility's rates as low, PUCs or their staffs may delay in opening up a rate case. And once begun, a case may turn in favor of the utility at the top of the rankings. If rates are perceived to be high, quite the opposite may result.
From an economic perspective, setting rates fully or partially based on rate comparisons is often appealing because, at least in theory, such comparisons can help identify efficient utilities, reward superior performance, and create incentives more akin to those found in competitive markets. However, the general proposition that regulation should take into consideration rate comparison and other performance benchmarks is sound only if it can accurately reflect relative company efficiencies and management performance. That requires explicit consideration of the costs that different service territories impose on utilities.
The Big City Bias
Providing utility service to low-density rural areas can be costly. The rural electrification programs and perceived continuing need for universal service funds in telecommunications demonstrate this fact quite clearly.1 Yet the opposite is also true. Big cities can also prove costly to serve.
In fact, little systematic research exists on how the size of metropolitan areas affects the cost of supplying electricity to consumers. While there is generally no doubt in people's minds that most goods and services are more expensive in large cities, there often is little guidance as to how the cost of supplying electricity varies with city size.2 Certainly, very few rate comparisons compiled by regulatory agencies or presented in regulatory proceedings explicitly take into account the size of the metropolitan area that utilities serve.
The U.S. Bureau of Labor Statistics (BLS), the government agency that compiles consumer price information, reports the regional average of end-use electricity prices for large, mid-sized, and small metro areas based on numerous monthly surveys that include 26 large metropolitan areas. (A metro area is defined as "large" if its population exceeds 1.5 million; "small" metro areas are those with a population of less than 50,000.) This consumer price data, averaged over 1998 to 2001, is shown in Figure 1. The figure shows quite clearly that: (1) average electricity prices in mid-sized metropolitan areas significantly exceed electricity prices in small metropolitan areas; and (2) electricity prices in large metropolitan areas on average exceed those of mid-sized metropolitan areas. This amounts to a significant difference of average utility rates in large and small metro areas. In the Midwest, for example, average rates in large metropolitan areas are approximately 20 percent higher than the average rates of small metro areas.
Importantly, the consistent pattern of electricity rates across the individual regions clearly suggests that serving major metropolitan areas imposes on utilities direct and indirect costs not faced by the average utility in a state, region, or the country as a whole. Any credible electricity rate comparison and performance benchmarking analyses thus needs to be either limited to an "apples-to-apples" comparison of utilities operating in similar service territories, or corrected for differences in operating characteristics and costs that utilities face in their service territory.
The additional costs of serving a large metropolitan area can be quite substantial. For example, add-on taxes (such as gross receipt taxes) can exceed 10 percent in large metro areas, which may be double and triple the level of such taxes in more outlying, less urban areas. Statistics on consumer expenditures also show that property taxes in urban areas on average are approximately 50 percent higher than property taxes in rural areas. As Figure 2 shows, utilities serving large metro areas also face labor costs that exceed statewide averages by at least 10 percent. This labor cost disadvantage is, again, consistent across the various regions of the country.
Other important operating characteristics also tend to increase the average costs of utilities serving large metro areas. Such factors include the high cost of underground distribution facilities, a high proportion of small customers, relatively poor load factors (due to disadvantageous customer mix and higher air conditioning loads), stricter environmental standards, and higher costs of transmitting power from distant generation sources.
Lessons for Regulators
Rate comparisons play an increasingly prominent, though usually implicit, role in utility regulation. And, in theory, rate comparisons can help identify efficient utilities and reward superior performance. Overall, several approaches alternative regulation, such as yardstick regulation or modified price cap regulation, have been developed specifically for that purpose.3 Even in the traditional regulatory model, overall firm performance can (and should) be taken into account as a "non-cost factor" in the determination of "just and reasonable rates" and a utility's allowed earnings.4 Still, due to the well-known difficulties with direct rate comparisons, their applicability and practicality in rate regulation is limited. While applied somewhat more frequently overseas and in other industries,5 there are only a few U.S. examples in which a utility's retail rates are determined explicitly with reference to benchmarks such as industry averages or a selected group of comparable companies.
These observations suggest a word of caution for regulators. If constructed appropriately, comparisons of retail rates between utilities can be used to enhance the efficiency of the regulatory process, by identifying and rewarding superior utility performance. However, simplistic comparisons can systematically disadvantage utilities operating in high-cost service territories, such as major metropolitan areas. A more accurate picture of relative utility performance can be achieved by either selecting an appropriate standard for comparison (e.g., a truly comparable group of companies) or by controlling for the differences in operating conditions faced by the utility relative to the costs faced by a broad-based sample of companies. Simple rate comparisons-standing alone-can greatly distort the true picture.
If the additional costs of serving customers in large metro areas are not considered explicitly in rate comparisons or other benchmark analyses, the regulatory process will deny due recognition for superior performance to utilities serving our large cities. This would not only be unfortunate for the utilities and their shareholders, but undermine the very incentives able to elicit such performance. It would also, unavoidably, lead to higher costs and higher rates for consumers.
Johannes P. Pfeifenberger is a principal and Mark W. Jenkins a research analyst at The Brattle Group, an economic and management consulting firm with offices in Cambridge, Mass., Washington, San Francisco, and London. The authors can be contacted at www.brattle.com.
References
- See, for example, Robert W. Crandall and Leonard Waverman, "Who Pays for Universal Service?" Brookings Institute, 2000.
- In fact, some may anticipate that the increased population density might lower average costs, such as is the well-documented case in telecommunications.
- Note, however, that the common practice in regulated industries has not been to employ a "yardstick" analysis for rate levels, but rather for changes in rate levels. This practice better reflects the inherent complexities associated with making comparisons across regulated firms that are not identical in all respects. For a summary of performance-based in U.S. utility regulation, see Sappington, Pfeifenberger, Hanser, and Basheda, "The State of Performance-Based Regulation in the U.S. Electric Utility Industry," The Electricity Journal, October 2001, pp. 71-79.
- Precedent of the Federal Energy Regulatory Commission, for example, provides that "the Commission will not lower a pipeline's ROE if its lower risk is the result of the pipeline's own efficiency" (Order 414-A, 1998).
- One example of a non-utility application in the United States is Medicare: If a hospital can treat a patient for less than what it costs other hospitals to treat a similarly diagnosed patient, it can keep the difference and realize a profit; otherwise it incurs a loss. See also, for example, Andrei Schleifer, "A Theory of Yardstick Competition," Rand Journal of Economics, vol. 16, no. 3, 1985, pp. 319-327.
The Trouble with Averages
Why comparing rates is not so simple.
A key ruling from the California Public Utilities Commission (CPUC) shows just how difficult it can be to assemble a meaningful comparison of costs and rates between utilities.
The case involved Pacific Gas and Electric Co. (PG&E), which filed a formal application with the CPUC on Dec. 12, 1997, seeking an increase in retail charges for electric distribution service. (Application 97-12-020)
To support its request, PG&E performed and submitted a unit cost study, in which it compared its 1996 operating costs across a sample consisting of the 100 largest electric utilities in the United States.
The study included costs for categories such as distribution plant per mile, distribution operations and maintenance per mile, and customer account costs per customer. PG&E compared its own costs against figures representing industry averages and industry medians. But the process became bogged down after Enron jumped into the fray and argued that the averages were skewed by outlier data drawn from just a few utilities. For instance, PG&E included Consolidated Edison of New York in its study, even though ConEd operated in Manhattan, an atypical environment.
With ConEd in the study, PG&E's cost profile looked fine. But average costs fell with ConEd excluded, making PG&E look worse in comparison. That was Enron's point, and the CPUC tended to agree:
- "We are troubled that the approach to the study may have been more results-oriented than is reasonable or appropriate for a study of this nature and purpose. …
[T]he inclusion of ConEd in the peer group, with its associated outlier values, strikes us as problematic. Exclusion of this single firm significantly alters the outcome of the analysis, resulting in PG&E's aggregate costs falling at 6 percent rather than 1 percent above the composite. …
[W]e cannot conclude that PG&E's costs are similar to the peer group that excludes ConEd."
See CPUC Decision 00-02-046, Feb. 17, 2000, reported at 199 PUR4th 177, p. 227.
-Bruce Radford, Editor-in-Chief
This case clearly illustrates the importance of looking beyond simple rate comparisons. However, the exclusion from the study of ConEd, a utility serving the largest metropolitan area in the country, while leaving in many utilities that operate in much lower-cost service territories than that of PG&E, is at best problematic.
-J.P.P. and M.W.J.
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