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Stranded Utilities: How Demographics, Not Management, Caused High Costs and Rates

Fortnightly Magazine - June 1 1997

to customers' long-term interest, owing to inadequate short-term use. If the customers had the opportunity to preserve the service as a viable alternative (em through, say, advance purchase commitments (em then they would have done so. However, the realization of their loss comes too late.

Kahn applies this model to the Ithaca railroad, which was driven out of business by "fair weather" competition from airlines and autos. He also applies it to AT&T. This company's survival in the face of analogous "cream-skimming" by MCI is held to be partly due to its "captive customers being forced to carry a disproportionate share of the back-up capacity costs." This scenario may, as it were, ring a bell.

The combination of product differentiation and possible market failure through "the tyranny of small decisions," complicates the effect of competition on efficiency and on the allocation of electricity generation among different sources. Since the social value of local utility capacity might exceed the revenue from its output, and thus the revenue of competitors displacing that output, a social loss might result. This loss would offset the gain realized through increased competition. All other things being equal, the loss would be proportionate to the sales dislocations produced by deregulation, the subject next addressed.

High Utility Rates:

Underlying Causes

Three factors explain 80 percent of the variation in average electric rates on a state-to-state basis: 1) the proportion of generating capacity, both operable and inoperable, that is nuclear; 2) the level of statewide construction costs; and 3) market concentration, as measured by the percentage of the state's population residing in metropolitan areas. %n8%n Combining these three factors produces a model that can predict average electric revenue per kilowatt-hour for a utility. (See Chart 1, which illustrates this model using 1994 actual and predicted data state by state.)

Apart from a utility's influence on a state's relative investment in nuclear power, the results imply that no more than 20 percent of state-to-state rate variations is attributable to the quality of utility management. %n9%n Similarly, differences in the effectiveness of regulation from state to state appear to be subordinate to circumstantial factors in determining rates.

The findings appear unequivocal, as shown in Chart 1. Even the most superficial inquiry into the source of disparities between predicted and actual values confirms the point: The joint effect of management and regulatory variation upon utility rates is sure to be less than 20 percent (see Chart 1). For example, the lower-than-predicted service rates in Tennessee and Kentucky are readily explained, at least in part, by the importance of sales by the Tennessee Valley Authority in those states. According to Department of Energy's Energy Information Administration, in 1994, the TVA accounted for 50.6 percent of all kilowatt-hour sales in Tennessee, and 30.4 percent of all such sales in Kentucky.

Hydropower and cheap indigenous coal largely explain the singularly low rates in Montana and Wyoming. Roughly one-third of Montana's power is hydro. The delivered cost of coal to utilities there is less than half the national average, according to EIA statistics. Similarly, the delivered cost of