Abstract
Price cap regulation (PCR) has experienced widespread adoption in the U.S. telecommunications industry, but not in the electricity sector. We suggest that these disparate experiences may reflect in part the manner in which PCR often is implemented in the U.S., relatively limited opportunity for “regulatory bargains” in the electricity sector, and relatively limited competition in the transmission and distribution components of this sector.
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Notes
If the regulated firm is restricted to earn the allowed rate of return on prudent investments but imprudent investments are disallowed, the firm may not be able to realize a return above its cost of capital. Kolbe and Tye (1991) suggest that the allowed rate of return for regulated firms typically does not adequately compensate the firm for this risk of disallowance. Also see Lowry (2007, pp. 7–9).
To illustrate, if I is 3 % and X is 2 %, the regulated firm is permitted to increase its prices, on average, by 1 % (\(=\) 3 %\(-\)2 %) annually. The rate of authorized price increase also can be adjusted (by a “Z factor”) during the price cap regime to account for major, exogenous changes in revenues or costs. Z factor adjustments are discussed in more detail in Sect. 3.
Crew and Kahlon (2014, p. 112) observe that in both the energy and water sectors “there has been a move away from PCR into an extended form of ROR[R].”
This transition is often attributed to the gains that PCR can provide to consumers, producers, and regulators alike (Lehman and Weisman 2000). Abel (2000, pp. 66–68) observes that “Under price-cap regulation, telephone prices have either fallen or remained the same, productivity has generally increased, modern infrastructure has been deployed at a more rapid pace, and firms have performed at least as well financially relative to the other methods of regulation available. ... Therefore, ... it is likely that ... price-cap regulation in the United States telecommunications industry has produced benefits to consumers, producers, and regulators alike.” Price cap regulation also has been employed in the postal sector in advanced economies (e.g., Correia da Silva et al. 2004; Eccles and Kuipers 2006).
PCR has also been implemented in many other telecommunications sectors around the world, including those in Argentina, Australia, Canada, Chile, Columbia, Denmark, Ecuador, France, Germany, Greece, Hungary, Ireland, Mexico, Pakistan, Portugal, Sweden, the United Kingdom, and Venezuela (Sappington and Weisman 2010, pp. 231–234).
Lichtenberg (2015, p. iv) reports that as of July 2015, “36 states had passed legislation deregulating retail telecommunications in all or in part. [Furthermore, the] public utility commissions in Pennsylvania and New Jersey reduced regulation on Verizon where services or geographic areas were found to be competitive. These actions have brought the total number of states eliminating or limiting oversight of retail telecommunications to 38.”
A broad-based PBR plan is one that permits substantial variation in the earnings of the regulated firm and does not link the variation explicitly to particular performance dimensions (e.g., service reliability).
In contrast, 39 U.S. states employed pure PCR in their telecommunications sectors in 2000.
Hemphill et al. (2003, p. 323) report that as of 2003, “Although there has been significant change in the electricity industry over the past two decades, there has been relatively limited application of incentive regulation to the major services provided.”
Electricity regulators in Europe have been implementing alternatives to RORR in recent years (Jamasb and Pollitt 2007; Cambini and Rondi 2010; Perrin 2013). However, these alternatives typically retain some elements of RORR and many are not broad-based. In particular, although some European electricity suppliers often are not automatically reimbursed for realized operating expenses, they continue to be afforded a fair return on capital investment, much as under RORR. Profit sharing also is common. Furthermore, allowed revenues often are linked to such performance measures as the number of new network connections supplied, realized levels of customer satisfaction, the number of network outages, and the fraction of generated electricity that is lost during transmission (Fox-Penner et al. 2013; Perrin 2013).
Technically, as noted above, the X factor measures the extent to which productivity in the regulated industry is expected to increase more rapidly and industry input prices are expected to increase less rapidly than in the economy as a whole. For expositional ease, we will not explicitly mention relevant differences in input price growth rates in the ensuing discussion.
Z factor adjustments typically require that the relevant event: (i) be beyond the control of the regulated firm (i.e., exogenous); (ii) be of sizable financial magnitude; and (iii) affect the regulated firm disproportionately, so that its financial impact is not fully reflected in the inflation index in the prevailing price cap formula. Examples of events that can trigger Z factor adjustments include changes in tax policy, natural disasters, and major changes in regulatory policy. The fuel adjustment clauses that are common in the electricity sector can also be viewed as Z factor adjustments. Fuel adjustment clauses allow the retail prices that a vertically-integrated electricity supplier charges to change automatically to offset a portion of the amount by which costs vary due to changes in key input prices (e.g., the price of natural gas employed to power generating units).
Crew and Kleindorfer (1996, p. 218) observe that this approach to setting the X factor “is frequently employed in practice” in the U.S. To illustrate, the approach was employed at the federal level by the U.S. Federal Communications Commission (FCC) in 1990 (FCC 1990) and at the state level by the Maine Public Utilities Commission (MPUC) in 1995 (MPUC 1995), the Massachusetts Department of Public Utilities (MDPU) in 1995 (MDPU 1995), and the Illinois Commerce Commission (ICC) in 2002 (ICC 2002). The approach has also been adopted by national telecommunications regulators in other countries (e.g., in Canada in 1996 (CRTC Decision, 97-9) and in Peru in 2004 (Bernstein et al. 2006)).
Beesley and Littlechild (1989) observe that regulators in the UK typically are not required to justify in complete detail every element of their decisions. Indeed, Professor Stephen Littlechild, the original proponent of PCR in the UK, viewed the X factor as “a number to be negotiated” (Littlechild 1983, ¶ 13.17). In contrast, the X factors adopted by the FCC and various U.S. state regulatory commissions have been subject to legal challenges (Vogt 1999). See, for example, USTA v. FCC 188 F.3d 521 (United States Telephone Association v. Federal Communications Commission 1999) and Illinois Bell Tel. Co. v. Illinois Commerce Comm’n, 669 n.e.2d 919, 927 (ill. app. ct. 1996).
The FCC included a stretch factor of 0.5 in its implementation of PCR in 2000 (FCC, 2000, ¶ 74). The Canadian Radio-television and Telecommunications Commission (CRTC) adopted a stretch factor of 1.0 in its implementation of PCR in 1997 (CRTC 1997, CRTC 97-9, ¶ 100). The Alberta Utilities Commission (AUC) instituted a stretch factor of 0.2 in its 2012 PBR plan for electricity and natural gas suppliers (AUC 2012, ¶ 499).
Although the stretch factor often accounts for changes in incentives that are expected to lead to higher levels of realized productivity growth, it typically does not account for likely changes in maximum attainable industry productivity growth. Failure to account for these changes can be problematic for electric utilities because declining demand for electricity and environmental mandates can reduce maximum attainable industry productivity growth rates below historic levels.
The number of wireline local calls in the U.S. increased steadily from 365.3 million in 1985 to 536.5 million in 2000. Wireline calls declined thereafter as consumers substituted wireless calls for wireline calls. The total number of wireline calls (local and toll calls) and the number of billed wireline access minutes exhibited corresponding growth patterns. (FCC 2010, Table 10.2).
Jorgenson (2001) estimates that advances in information technology increased the rate of growth of total factor productivity in the U.S. by 0.5 percentage points between 1995 and 1999. Moore’s Law describes the rapid decline in the cost of computing power, which translates directly into reduced costs of supplying switched telecommunications services. As Nuechterlein and Weiser (2013, p. 149) observe, Moore’s Law roughly states that “the cost of a given amount of computing power halves every 18 months.”
As Mitchell and Vogelsang (1991, p. 9) observe, “In telecommunications networks, production facilities have well-determined capacities, and the costs of operation are nearly independent of the flow of services through those facilities.” Consequently, productivity increases as output increases.
Makholm et al. (2012a) report that the average annual total factor productivity growth rate for the 72 U.S. electricity and gas distribution firms in their sample was 0.85 between 1973 and 2009. The corresponding average annual growth rate between 2000 and 2009 was \(-1.08\).
See Kahn (1966; 1971, pp. 236–241) and Weisman (1988), for example. The utilities incur a large fraction of their costs in providing the option of use, but recover those costs predominantly on the basis of actual use. Lowry et al. (2013, pp. 24–26) discuss recent efforts to recover a larger fraction of fixed costs from fixed, rather than variable, charges on customers.
PBR plans can be designed to foster cost reduction while implementing rate structures that incorporate substantial decoupling. To illustrate, Southern California Edison (SCE) proposed a PBR plan under which authorized revenue increases annually above a base-year revenue by an \(I-X\) factor. Authorized revenue is also linked to the number of customers that SCE serves (CPUC 1996). The plan’s focus on base-year revenue (which includes both fixed and variable charges for electricity) helps to insulate SCE against earnings reductions that might otherwise arise as customers reduce their electricity consumption. The CPUC ultimately implemented a plan along these lines (Weber et al. 2006).
In addition to guaranteeing price reductions for consumers, telecommunications suppliers often have agreed to undertake costly investment projects (e.g., infrastructure expansion and modernization) when they operate under PCR (Sappington and Weisman 1996, p. 74).
These gains include immediate reductions in basic local telephone rates and/or guarantees of limited increases in these rates over time. U.S. regulators have long valued low basic local telephone rates, as evidenced by the fact that they mandated prices for long-distance telephone calls well above cost in order to keep basic local telephone rates low. This rate design was adopted even though it may have failed to promote or perhaps even impeded universal service (Erikson et al. 1998). Sappington and Weisman (1996, p. 30) characterize this rate design as “Reverse Ramsey Pricing” because it implements price-cost margins that vary directly, rather than inversely, with the price elasticity of demand.
The fact that many competitive local exchange carriers ultimately exited regulated markets despite the presence of policies designed to encourage their participation suggests a limited potential for substantial earnings in these markets.
In this sense, ongoing PCR can function much like RORR. As Armstrong et al. (1994, p. 172) observe, “As a rough characterization, under rate-of-return regulation reviews are infrequent, and the regulatory lag is endogenous because either side can request a review, whereas under price caps the lag is relatively long, and the date of the next review is fixed in advance. The difference is one of degree rather than kind.”
A firm’s incentive for efficient operation is dulled to a lesser extent if the X factor the firm faces reflects average industry performance rather than that firm’s individual performance (Littlechild 1988).
Crew and Kahlon (2014) note the recent adoption of policies that insulate regulated firms from earnings risk in the energy and water sectors. The authors attribute this adoption in part to the multiple objectives the firms are being asked to achieve, including energy conservation and environmental protection.
These costs include the political or reputation costs a network failure can impose on regulators. Outages of regulated wireline telecommunications networks also are disruptive. However, the impact of such outages can be limited by the presence of other (e.g., unregulated wireless) communications networks and by the self-healing nature of wireline telecommunications networks, which routinely detect localized network outages and re-route traffic accordingly.
Sappington (2005) reviews the economic literature on the design of service quality regulation for public utilities, which includes the observation by Spence (1975) that price cap regulation can limit incentives for quality provision. Sunderland (2000) reports service quality problems that arose in the telecommunications industry under incentive regulation. Cronin and Motluk (2009) report corresponding problems in the electricity sector. Ter-Martiroysyan and Kwoka (2010) find that although service outages in the electricity sector do not occur more frequently under incentive regulation, the outages that arise tend to persist for longer durations. The authors note that reductions in service quality can be avoided if firms face substantial explicit financial penalties for sub-standard levels of service quality.
To illustrate, electric utilities in Massachusetts are liable for up to 2.5 % of annual revenues should they fail to meet service quality standards (Massachusetts Department of Public Utilities 2014). Electric utilities in Alberta face financial penalties that reflect the financial gain the utility derived from allowing service quality to deteriorate (AUC 2012, ¶¶ 896–8). In practice, regulators may be reluctant to impose the maximum feasible penalties for fear of causing the regulated supplier to experience financial distress.
One additional potential explanation relates to the problems that arose in California in 2000. Some utilities were operating under a form of PCR at this time when the combination of a dramatic increase in wholesale electricity prices and frozen retail prices caused the firms to experience severe financial distress (Borenstein 2002). This fact led some to blame PCR for the firms’ financial troubles, even though the troubles were not limited to utilities that operated under PCR (Hemphill et al. 2003) and many factors other than PCR contributed to the troubles (Jurewitz 2002.) Thus, PCR may have fallen victim to the post hoc ergo propter hoc fallacy, as some may have mistakenly attributed the problems in California’s electricity markets to experimentation with PCR, thereby limiting the adoption of PCR in other electricity sectors.
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Acknowledgments
We thank the editor, Michael Crew, three anonymous referees, Tim Brennan, Robert Brigham, Toby Brown, Carlo Cambini, Ken Costello, John Kwoka, Mark Meitzen, Johannes Pfeifenberger, Michael Pollitt, Laura Rondi, Agustin Ros, Phil Schoech, Timothy Tardiff, and Burcin Unel for helpful comments and suggestions. We also thank Theresa Dinh for excellent research assistance.
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Sappington, D.E.M., Weisman, D.L. The disparate adoption of price cap regulation in the U.S. telecommunications and electricity sectors. J Regul Econ 49, 250–264 (2016). https://doi.org/10.1007/s11149-016-9295-5
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DOI: https://doi.org/10.1007/s11149-016-9295-5