Improving performance through graduated conditional ROE incentives.
Frank Cronin (firstname.lastname@example.org) an economic consultant residing in Acton, Mass., is an expert in restructuring, alternative regulation, efficiency and productivity. Judy Kwik (JKwik@ERA-INC.CA) an energy consultant in Toronto, Canada, has been involved in the implementation of PBR for electric utilities in Ontario for more than a decade. Stephen Motluk (email@example.com) an economic consultant in Toronto, Canada, is an expert in alternative regulation and productivity of regulated firms.
Unlike the majority of implemented performance-based rate (PBR) designs, alternative design paradigms are available that minimize data requirements by allowing firms to reveal performance potential. One approach relies on group performance and competition to inform the regulator. The second, while it can, and has been, applied within a peer setting, requires only that the regulator have some information on the range of potential productivity outcomes and the returns that would be associated with such results. The regulated firm is then offered a menu of options representing the productivity factor—return on equity (ROE) choices.
Due to the principal-agent problem between regulators and the monopolies they regulate, regulators don’t have the information necessary to correctly set parameters in exogenously determined PBR plans. In such an asymmetric environment, determining the appropriate inflation escalator and productivity offset can be complicated, confusing, time-consuming, expensive and divisive. Often, the necessary data is as difficult, or more difficult, to obtain than the process of determining the firm’s cost of service. Thus, exogenously determined PBR plans often suffer from the same shortcomings as cost-of-service rate-of-return plans, and in fact might result in worse outcomes, if plan parameters significantly are off the mark.
However, regulated firms do respond to incentives, and this can be used to structure more effective regulation. For example, research on 48 electricity distributors in Ontario, Canada finds that utilities increased their average annual growth in total factor productivity (TFP) by 2.3 percent after the imposition of a variable offset PBR (i.e., price freeze), rising from -.2 percent prior to the freeze to 2.1 percent after its imposition.1 This increase was wide-spread among individual distributors and consistent across size classes. Firms understood the incentives and responded to them.
However, critical design issues such as asymmetric information, allocative inefficiency, initial efficiency differences, and adjustment dynamics have not been resolved, and in some cases, not even addressed. Despite the opportunity provided by group restructurings in network industries in the United States, Canada and the U.K., and the potential for application of endogenously determined PBRs, regulators opted for exogenously imposed, fixed factors based only on estimates of technical efficiency improvements, rather than PBR predicated on inter-firm-based competition.2 Such unresolved issues are the direct legacy of decisions to implement PBR in a data intensive, regulator-imposed, exogenous framework, rather than the minimalist information, endogenous shadow market advocated by some researchers. In some cases (e.g., adjustment dynamics or periods), regulators have opted to impose productivity offsets and performance periods with little to no research upon which to base their parameters.3 In other cases, (e.g., the extent of initial inefficiency), regulators seemingly ignored earlier, critical research findings.
Unlike the majority of implemented PBRs, alternative design paradigms are available that minimize data requirements and allow firms to reveal performance potential. One approach relies on group performance and competition to inform the regulator. The second, while it can, and has been, applied within a peer setting, requires only that the regulator have some information on the range of potential outcomes and the returns that would be associated with such results.
In 1985, economist Andrei Schleifer4 introduced a model of yardstick competition (YC) (see sidebar, Yardstick Origins). YC describes the simultaneous regulation of identical or similar firms. Rewards to an individual firm depend on its standing vis-à-vis a shadow firm. Participants in the tournament (the competition with the shadow firm) reveal information about their cost structure and productivity potential to the regulator. With correlated underlying costs, the regulator can use performance information from a group of firms to better define the efficiency frontier and the time path necessary to attain it for inefficient competitors. YC has been effectively employed by European regulators in service quality regulation to identify and benchmark underperforming circuits or districts. Cronin and Motluk (2009) review these efforts as well as a potential application in North America.5
Such YC effectively handles the “principal-agent” problem of regulators with asymmetric information. Indeed, this approach requires a minimum amount of information—only service prices for each competitor. Initial efficiency differences among utilities are revealed by using correlated cost information from like firms. Allocative inefficiency is benchmarked by using total cost comparisons, not just data on changes in output-input ratios. And, rather than regulator-imposed productivity factors and adjustment periods, potential efficiency improvements and the adjustment periods are revealed by endogenizing the process through the tournament. As Scheifer notes: “Yardstick Competition works because it does not let an inefficient cost choice by a firm influence the price and transfer payment that that firm receives. It is essential for the regulator to commit himself not to pay attention to the firms’ complaints.”6
Clearly, the asymmetric information problem presents a challenge, especially when dealing with one regulated firm. When such is the case (one regulated firm), the regulator may not have the option of employing YC7 to induce the firm(s) to endogenously define potential performance. This was noted by the Ontario Energy Board (OEB) in its Implementation Task Force Report on 1st Generation PBR. The report discussed the inherent, asymmetric risk in setting the productivity target:8
Furthermore, it is understandable that regulators may set the productivity target that is embedded in the price cap formula conservatively because the risks associated with over-and underestimating the reasonably achievable productivity gains are asymmetric. The risk associated with overestimating productivity gains is that the incumbents will not earn an adequate return, will have difficulty raising adequate new capital, and may be put financially at risk by the regulatory regime. This risk is far more serious than the risk associated with underestimating the achievable rate of productivity improvement which is simply that shareholders will earn a high return… In designing the overall price or revenue cap mechanism, it is therefore reasonable to base it on the presumption that the productivity offset that is adopted is likely to be conservative. That is, the productivity offset will not fully recognize embedded inefficiency that can be eliminated under a PBR regime or the full extent of productivity improvements that are reasonably achievable. Put simply, the mechanism will be biased, making it far more likely that the regulated companies will earn a higher return under PBR than they would under rate base rate of return regulation than that they will earn a lower return… For the reasons noted above, it is reasonable to anticipate that the PBR regime will not achieve this ideal symmetry. In the interest of caution and conservatism, the PBR regime that is implemented will almost certainly be biased in favour of the incumbents being able to earn an above-normal return because the productivity offset that is adopted will be low.
The regulator still can create an endogenous process through which the firm itself reveals similar behavior as that under YC. With some antecedent information covering productivity performance for such a utility, the regulator can construct a menu that pairs data on a range of probable productivity performances with the associated return on equity that would be permitted with each productivity choice. In addition, such a structure also mitigates the inherent tendency to impose downwardly biased productivity factors discussed above.
Furthermore, the menu is a natural solution to the issue of diversity that exists among the local distribution companies (LDCs). Firms in different circumstances can base their incentive regulation choices to reflect these differences. And, by judicious pairings of the productivity factor (PF) and ROE choices, a menu easily can be structured to reach explicit sharing goals between rate payers and shareholders. As the OEB’s 1st Generation Implementation Task Force noted:9
Given the interplay between the productivity factor and earnings sharing, it is our recommendation that the PBR scheme adopted in Ontario explicitly recognize that there is a trade-off between the productivity offset that is selected and the adoption of an earnings-sharing mechanism. The selection by the muni of a combined X-factors and earnings sharing mechanism from a menu of possible options would allow the company to choose the combination that best meets their PBR expectations at the outset of the PBR period (e.g., there could be several X-factors, some of which would involve earnings sharing, while another, set at a challenging level, would not). This approach deals with the incentive for companies to understate their ability to achieve productivity improvement by providing an incentive to “reveal” their true expectations. In addition, it would allow MEUs to make different choices based on their individual circumstances.
In 1991, the Federal Communications Commission (FCC) instituted a price-cap PBR for local exchange carriers (LECs). The initial LEC price-cap plan was based on two FCC staff studies by Spavins-Lande and Frentrup-Uretsky, which employed the price-dual approach. Similar staff analyses had been relied on for the productivity offsets employed in AT&T’s price-cap plan. Subsequent FCC staff studies using the price-dual approach by Spavins, Belinfante-Uretsky, and Belinfante were used in modifying the LEC price cap plan in 1995. However, significant inconsistencies existed among these studies regarding the historical and recent productivity performance of the LECs.
Unfortunately, critical analytical errors were embedded in the FCC’s underlying PF analysis. These errors resulted in the FCC significantly underestimating the current, historical, and potential gains in productivity.10 However, as part of its second-generation framework, the FCC did employ an intriguing construct to infer potential productivity gains on the part of the LECs.
The FCC’s initial review of the three-year old LEC PBR took four years. LECs submitted evidence during the review that their X factors actually had fallen, dropping to only 1.7 percent. The FCC also submitted its updated price dual studies. In its interim decision, the FCC proposed that each firm select its PF from a menu. The higher the PF selected by an LEC, the higher would be its allowed ROE.11 Presumably, this effort attempted to draw out potential productivity gains through revealed preferences. The FCC offered new interim X options ranging from 4.0 to 5.3. Importantly, the latter option allowed the telcos to retain all of their earnings.
As the FCC noted in a subsequent access tariff order:12
In the LEC Price Cap Review Order, the Commission amended its rules to establish a minimum X-Factor of 4.0 percent, with two optional X-Factors of 4.7 and 5.3 percent. LECs selecting the 4.0 and 4.7 percent X-Factors are subject to sharing requirements. The sharing mechanism requires a LEC to return to customers its earnings in excess of certain levels, including interest, through a temporary reduction in the LEC’s PCIs in the next annual access period. The amount of the revenue reduction is determined by a temporary adjustment to the LEC’s PCI, which is calculated in the same manner as other exogenous changes to the price cap formula. When applied to the revised X-Factors, the sharing mechanism effectively permits a LEC selecting the 4.0 percent X-Factor to reach a maximum 12.75 percent rate of return. Likewise, a LEC selecting and outperforming an X-Factor of 4.7 percent can realize at most a 14.25 percent rate of return.
The Commission determined that LECs selecting the 5.3 percent X-Factor under the interim plan should not be subject to a sharing requirement. It reasoned that, although the 5.3 percent X-Factor represents a “major challenge” to most LECs, the most efficient ones will opt to use the higher factor if they are permitted to retain all the profits earned, rather than be subject to a sharing requirement. The Commission determined that this revision to its price cap plan will encourage continued movement away from rate-of-return regulation.
Despite having just argued for a 1.7-percent X factor, the majority of LECs selected the new, no-sharing X option of 5.3 rather than the lower, earnings-constrained X options starting at 4.3 (see Figure 1).
In its follow-on 1997 price-cap decision, the FCC raised the X factor to a single option 6.5, a doubling over the initial X factor set in 1991. Over the course of the 1990’s major price cap, LECs’ ROE also more than doubled, reaching 29 percent in 1999. Over this same period, inter-exchange carriers’ (IXC) ROEs fell to 2.1 percent. LECs’ ROEs would have ranked third highest out of 42 industries and IXCs would have ranked last.13
Recall the FCC set its PF for LECs by averaging its short and long-term estimates to obtain 2.8. However, highly relevant information seems to have been under appreciated or ignored by the FCC. For example, observers in the FCC proceeding noted that the California Public Utilities Commission had begun its incentive regulation of Pacific Bell with an offset of 4.5. Observers also noted that in 1989 OFTEL raised BT’s productivity offset from 3.0 to 4.5 due to excessive profits. In fact, in 1991, the first year of the LEC price-cap plan, BT’s offset was raised to 6.25. Finally, and importantly, in 1993, the year before the FCC’s option menu was implemented, OFTEL increased the offset to 7.5.
Clearly, given the FCC-noted concern for the incentive-blunting effects of earnings sharing, the commission was trying to incent higher performance. In addition, the FCC must have been keenly interested in adding to its understanding of potential productivity performance in the telecommunications industry by resolving the analytical inconsistencies among its studies. While the FCC’s intention was commendable, its implementation was a bit lacking. Critically, the upper end of X factor options only was equal to the recent average LEC productivity performance, even before adjusting for the error associated with an absent input price differential (which would have made the effective value of the 5.3 say, 4.3 or lower). The FCC would have been much better served had the range of options spanned a wider and higher value, say 4.5 to 7.5, with an intermediate option at 6.0.
PBR in Ontario
By the 1990s, more than 300 municipal electric utilities (MEUs) varying in size from several hundred to over 200,000 customers operated in the province of Ontario. Some critics maintained that mergers among the publicly-owned MEUs would create efficiencies due to privatization and increased scale. In 1998, Bill 35, the Energy Competition Act, 1998, was enacted. While the Energy Competition Act affected the electric sector broadly, restructuring Ontario Hydro and enabling the IESO (the Ontario ISO and power pool), transferring regulatory authority to the Ontario Energy Board and charging the board to examine performance-based regulation, it also undertook a fundamental restructuring of the MEUs.
Under the Act, MEUs were to be corporatized and recapitalized, placed under municipal shareholder control for possible sale, and placed under the regulatory oversight of the OEB and its yet-to-be-determined PBR. Not only was ownership and capital structure up in the air, but MEUs were subject to a new regulator and its unknown regulations. These policies made the restructuring of the MEU sector alone arguably one of the most complex regulatory restructurings in the world.14
Faced with the recent transfer of almost 300 electricity distributors to the OEB’s authority, the OEB instituted a process to structure a suitable regulatory framework. These LDCs represented a highly diverse collection, ranging in size from several hundred to hundreds of thousands of customers. Some stakeholders, including utilities, held that initial levels of efficiency varied significantly, due primarily to overcapitalized rate bases.15 Some participants pointed to the YCs being implemented in the U.K., Europe, and Australia and argued that Ontario should adopt such models.16 Due to the government’s tight deadline (which envisioned the market opening in November 2000, although subsequently this was delayed to May 2002), these critical issues could not be analyzed and addressed within the time permitted.17
The OEB’s stakeholder task force on implementation issues noted the dilemma involved in moving to a PBR.18 While it was clear that the utility would face notably greater incentives to eliminate the significant embedded inefficiencies likely accumulated under cost of service, the regulator couldn’t easily quantify the potential level of inefficiency.
[T]he move towards a PBR regulatory system is in part motivated by the belief that, in general, PBR regulation allows for more efficient and effective regulation than rate of return regulation. It follows that the essence of the rationale for moving from rate base-rate of return (RB-ROR) regulation to any other form of Performance Based Regulation (PBR) is to remove embedded, non-quantifiable inefficiencies… It seems difficult to conceive that any party that supports the adoption of PBR could deny that the differences in incentives under the alternate regimes affect efficiency… [T]here is a significant amount of inefficiency embedded in the existing cost structure of the regulated companies. If there were no significant embedded inefficiencies, there would be no justification for incurring the regulatory and other transitional costs and risks that are involved in moving from RB-ROR regulation to a PBR mechanism.
Taking into consideration the extremely short period allowed by the government to establish the PBR, the task force went on to say:
…the adoption of the PBR mechanism would not be necessary if it were not for the practical reality that the amount of embedded inefficiency cannot be quantified with an acceptable level of confidence or precision… In these circumstances… the setting of appropriate productivity factors is that much more difficult, and the risk, therefore, of “getting it wrong” that much higher. In short: historical productivity gains … underestimate the expected, and reasonable, productivity gains that should be realized under Performance Based Regulation; achievable productivity gains cannot be accurately estimated because the amount of embedded inefficiency cannot be determined; and the appropriate productivity offset cannot be quantified with a reasonable level of confidence and precision.
The intent of moving to PBR is to set an offset that reflects the gains that can be reasonably expected… The incentive to improve productivity comes both from the danger of under earning and the opportunity to earn an above-normal return—the carrot and the stick.
The task force noted that a PF that better balanced the interests of ratepayers with shareholders could be structured if the utility’s interest in achieving “above normal” returns could be leveraged by providing the firm a choice in the PF under which it would operate. This would mitigate the likely downward bias in exogenously imposed PFs.
Another way to mitigate the bias would be to establish a productivity offset that better balances the interests of shareholders and ratepayers… Imposing this risk on the company would be more acceptable if the company is given a choice in adopting an aggressive productivity target.
Given the varying circumstances facing LDCs, the uncertainty regarding initial efficiency, and a desire to maximize cost savings for rate payers, OEB staff and consultants designed a response to the task force analysis and recommendation. In the 2000 Draft Rate Handbook,19 they proposed a PF–ROE menu (see Figure 2). Distributors could choose a combination of productivity growth and ROE: Higher productivity growth would permit higher returns. The menu offered combinations from 1.25 percent productivity growth and 10-percent ROE up to 2.5 percent productivity and 15-percent ROE.
Due to the varying circumstances facing utilities and differences across utilities in their potential for efficiency gains, the plan allows utilities to select the particular productivity factor from a set of six that it believes best reflects the combination of circumstances, opportunities, risks and rewards facing the utility… Note that the default value for Option A is a productivity factor of 1.25 which is about 25 basis points above the distributors’ average from 1988 to 1997. This means that, on average, ratepayers of distributors in the default option would experience a decline of 1.25 percent in real rates after which the distributor then has the opportunity for higher ROE above the target market-based rate of return. Associated with the 1.25 percent productivity factor is a ceiling of 10 percent on a distributor’s ROE. Utilities selecting Option A whose actual productivity change falls below the target (i.e., 1.25 percent) would experience ROE below the target ROE (i.e., the market-based rate of return).
How realistic are the elements of the menu? Recall that over the 10-year period from 1988 to 1997, Ontario LDCs had a growth in total factor productivity (TFP) of approximately one percent. Substantial variation existed. While the government’s schedule allowed only a few months for specification, collection, and analyses of LDC data, a substantial effort was made to profile a large number of LDC’s cost and productivity performance. The associated research found that across the 48 distributors, representing the vast majority of electricity customers, half of the distributors had productivity exceeding the average, with many of these also exceeding 2.0 and even 2.5 percent. Many of the utilities with above-average TFP growth were also utilities found to have total costs per customer around or below the mean. Finally, this research found that growth of TFP was about 2 percent during the earlier, de facto PBR from 1994 to 1997.20
Clearly, the PF choices offered in the menu represented performance levels that had been observed over the preceding decade by these same utilities. Individually, a number of utilities had operated at the upper tier options over the entire decade from 1988 to 1997. As a group, they had averaged 2 percent TFP growth (option D) during the higher incented 1994 to 1997 period.
Furthermore, during the mid 1990s, the Norwegian regulator, NVE, had begun work to examine and potentially establish a PBR for its several hundred electric distribution utilities. This research involved two sets of information: first, a times-series analysis to examine long-run productivity potential, and second, a cross-section benchmarking to determine each LDC’s relative efficiency. In the former instance, NVE found that the most efficient firms had a growth in productivity of 1.5 to 2 percent. In the latter instance, NVE found that among the roughly 80 percent of firms found to be off the efficiency frontier, many were found to be 30, 40 percent (or more) less efficient than the best. The regulator, NVE, instituted a two-part PF: a long-run element equal to 1.5 and a relative inefficiency factor that ranged from zero to 3 depending on the individual LDC’s efficiency. This meant that the PF imposed by NVE for its first term PBR ranged from 1.5 to 4.5 over the 1997 to 2002 period.21
In 2008, the OEB commenced a proceeding to examine choices for its “third-generation PBR” for electric distributors. Board staff issued a discussion document that covered a wide range of design issues. Among these issues, board staff broached the question of implementing a PF-ROE menu as developed in the first-generation PBR process.22
Stakeholders seemed to be fairly consistent in their view that the repeated changes to government policy toward electric distributors and in the regulatory oversight by the OEB had wreaked havoc among the LDCs’ operations and investments. Indeed, the authors commented on this unfortunate development in 2005:
Electric distribution utilities have undergone significant regulatory reforms over the past decade including changes in: governance and ownership, regulatory oversight, horizontal and vertical integration, and retail market competition. Yet, we question if the costs associated with these restructurings are yielding sufficient benefits? In Ontario, decision makers embarked upon new policies in an information void, relying upon anecdotal or ideological beliefs. Critical empirical research was either ignored or never undertaken. Almost immediately, political implications of “unforseen” consequences (e.g., distribution rates would rise as municipals were privatized) drove politicians to shift positions: even the “independent” status of the regulator would not forestall overt interference. Government policy and regulatory missteps, inconsistencies, and contradictions have left the distribution sector worse than before restructuring, burdened with sizeable and largely unnecessary costs, but without correcting the one notable deficiency among some distributors, excessive allocative inefficiency.23
For example, the provincial government had voided its own legislated mandate to recapitalize the MEUs with market-based capital costs, had forced utility mergers when no savings where apparent, and ultimately overturned the newly implemented PBR plan put in place by the “independent” regulator. Subsequently, the OEB adopted a “second-term” PBR with a macro price index. Furthermore, the OEB did not have, or could not use, LDC capital data specified and collected during (and after) the first-generation implementation process, and so opted for an O&M-based efficiency comparison (that ignored capital usage, reliability, or capitalization policies). Because of these shortcomings, the OEB used a literature-reviewed X factor.
While lacking critical data on LDC capital, stakeholders put forward evidence that these governance vagaries substantially reduced LDC productivity over the decade since 1997. Indeed, subsequent research, which included both O&M and monetized values of capital similar to that employed in the OEB’s first generation, found a widespread negative rate of productivity growth over the 2000 to 2006 period.
Based on this information, data and research from the first-generation PBR, and subsequent work using this data examining efficiency and utility performance, a menu proposal was developed to address the OEB staff request (see Figure 3). Due to the legacy implications of ambiguous and contradictory government and regulatory direction, two options were presented that fell below the lowest option in Figure 1. The new menu provides a baseline ROE of 8.5 percent and PF of 0.8 with an ROE of 8.5, i.e., option A. Option B also provides a below-market rate of return of 9.5; it would require a PF of 1.0. Option C is nearly identical to Option A in the 2000 menu. The ceiling ROE is now 12.5 percent with a PF of 1.6.
Following the first-generation process, research examined some of the issues raised by stakeholders but not initially examined due to time pressures: first, the status of efficient versus inefficient LDCs; second, the relationship between third-party financing and allocative inefficiency; and third, productivity potential including the performance of frontier (most efficient) versus interior (less efficient) LDCs.24 Looking at the period 1988 to 1997, a TFP framework similar to that used in first-generation PBR was used: one output, four input, fixed weight calculation of TFP. It was found that the LDCs that were judged to be most efficient, after examining both technical and allocative efficiency, at the start of the period had consistently higher subsequent growth in TFP than did less efficient LDCs. This was true over both the 1988 to 1993 and the 1993 to 1997 periods. Over the full 10-year period, the average annual growth in TFP for these frontier firms is about 1.6 percent.
That is, during the earlier decade the most efficient firms had been able to improve TFP by 1.6 percent. On this basis, the recommendation for the menu was that a ceiling be set for PF at 1.6 percent for the three-year term of the next PBR. Increments of 0.2 in the PF would be associated with 100-basis point increments in the allowed ROE. This would set the baseline TFP at .8; slightly below the ten-year growth among Ontario LDCs in the first generation.25
Furthermore, it was noted that before starting its second-term PBR, NVE raised the upper range of its PF from the 4.5 set in the first term. Presumably, the institutional and operating changes implemented by the regulator were not done in such a way as to render the LDCs unable to continue expectations of on-going productivity improvements.
The menu can be structured to meet sharing goals between ratepayers and shareholders. In Figure 3, the split between customers and ratepayers is about 60 to 40 percent. How was the stakeholder sharing split for menu increments derived? As indicated, if the LDC selects an incremental PF of 0.2, it’s allowed ROE increases by 100-basis points. Assuming total costs per customer, the share of capital in costs, the share of equity in capital, and the time path of the LDC’s operational savings, the savings to the ratepayer and increased earnings to shareholders can be calculated. Assuming that in a three-year incentive regulation (IR) the LDC would need two years to reach full incremental ROE, a 57-percent share for customers and 43-percent share to shareholders was calculated. Ratepayers likely would experience long-term benefits as well.
Indeed, the issue of self-directed choice would seem to be very appealing. On the one hand, it would allow the regulator to explore the feasible set of PF much more assiduously than could be done through a multi-generation IR framework that could take a decade to fill out. On the other hand, it permits the regulator to recognize the existence of diversity among the LDCs and to embed this reality into the PF options.
From the customer stakeholders’ perspective, the menu also would appear appealing. Such a structure mitigates the inherent tendency for risk-averse regulators in an asymmetric environment to impose downwardly biased productivity factors. Instead, the selection risk would be transferred to the party best informed to assess the options and with the greatest alignment of information, action and reward. LDCs would be incented to more aggressively review productivity improvements and undertake those that it could manage. Within the plan’s term (and presumably after the term), ratepayers most likely would experience greater reductions in rates than would have happened otherwise. Shareholders would experience concomitant increases in ROE and earnings.
1. Cronin, F.J., et al., 1999, “Productivity and Price Performance for Electric Distributors in Ontario,” Ontario Energy Board Staff Report.
2. For a discussion of peer-based, endogenously determined PBR, see “The Road Not Taken,” By Francis J. Cronin and Stephen A. Motluk, Public Utilities Fortnightly, March 2004.
3. Both the British regulator, Ofgem, and the Dutch regulator, Dte, have imposed what some participants viewed as relatively sizeable efficiency improvements without any empirical basis for these dynamics/periods. In the case of Dte, some LDCs had X factors of 9-percent per year imposed.
4. Shleifer, A., “A Theory of Yardstick Competition,” The Rand Journal of Economics, Vol. 16, No. 3, Autumn 1985, pp.319-327.
5. See F. J. Cronin and S. Motluk, “Yardstick Competition in Service Quality Regulation” (in process).
6. Ibid, p.323.
7. Although artificially-created competitions still may be possible by employing out-of-jurisdiction comparisons.
8. See Report Of the OEB, PBR Implementation Task Force, May 1999, p. 70 - 71.
9. See Report Of the OEB, PBR Implementation Task Force, May 1999.
10. In its 1990 order establishing price caps for the LECs, the FCC based its 2.8 productivity offset on the average of two staff reports that employed an output price dual approach to estimate an LEC long-term productivity differential (i.e., telecommunications vs. aggregate productivity growth) of 2.1 and an LEC short-term differential (i.e., 1984-1989) of 3.5. Variants of these approaches for AT&T and LECs appeared in earlier FCC notices. Cronin and Gold find growth in TFP increasing almost monotonically from 2.7 percent in the mid-1960s to around 5 in the late 80s. Since aggregate TFP was about zero in the late 80s and early 90s, the LEC X factor of 2.8 would have been 2.2 percent below actual LEC TFP growth. This error was compounded by a further mistake regarding the non inclusion of an input price differential that would have lowered the effective value of the X factor 1-2 percent per year.
11. The policy implications of this work are discussed in Cronin and Motluk, “The Road Not Taken: PBR with Endogenous Market Designs,” Public Utilities Fortnightly, March 2004. An earlier version of this paper, Restructuring Monopoly Regulation with Endogenous Market Designs, was presented at the Michigan State University, Institute for Public Utilities, Annual Regulatory Conference, Charleston, S.C., December 2003. Results from this research also have been used as the basis for an invited seminar on improving utility benchmarking at Camp NARUC, “Restructuring Monopoly Providers or Regulation through Revelation,” 46th Annual Regulatory Studies Program MSU, IPU, Regulatory Studies Program, August 2004.
12. In the Matter of Annual 1995 Access Tariff Filings, Pacific Bell and Nevada Bell Petition Regarding Election of 5.3 X-Factor for Application Back to January 1, 1995, Memorandum Opinion and Order Adopted: July 25, 1995; Released: July 25, 1995.
13. Cronin, F.J. and M. Gold, “Prices, Profits and Productivity: Analytical Miscues and their Effects on Telecommunications Deregulation.”
14. Unfortunately, as discussed below, later inconsistent and ever-changing regulatory policies substantially undermined utilities’ operational performance for the next decade. These productivity losses wiped out the widespread gains observed over the decade from 1988 to 1997.
15. Some participants contended that high-use, contributed capital MEUs had distorted their factor-input mix, causing an over reliance on capital. In subsequent research we did find that about 20 percent of firms were on both the technical as well as the allocative efficiency frontiers. Furthermore, we found that the average MEU was about 13-percent less efficient technically than the best practice MEUs, but about 30-percent less efficient in terms of allocative efficiency, i.e., having the right mix of inputs given relative prices. F. J. Cronin, S.A. Motluk, “Agency Costs of Third-Party Financing and the Effects of Regulatory Change on Utility Costs and Factor Choices,” Annals of Public and Cooperative Economics, 78, No.4, 2007.
16. Subsequently, we examined some of the PBRs implemented in the U.K., Australia and Europe. These PBRs generally benchmark on partial costs and examine only a minority of inefficiency. They create sizeable distortions in efficiency rankings: Individual utilities could experience errors in rankings of 20, 30 or even 40 percent. See F.J. Cronin & S.A. Motluk, “Flawed Competition Policies: Designing Markets with Biased Costs and Efficiency Benchmarks,” Review of Industrial Organization, Vol.31, No. 1, Aug 2007.
17. Ironically, had the Government’s original schedule been even a bit more conservative, sufficient time would have been available to deal with these and other analytical challenges.
18. See Report Of the OEB, PBR Implementation Task Force, May 1999.
19. See Chapter 4, “Rate Adjustment Mechanism,” 2000 Draft Rate Handbook.
20. As noted below, in subsequent research we found that over the 1988-1997 decade, annual TFP growth among the most efficient utilities averaged about 1.6 percent. During the higher incented 1994 to 1997 period, TFP growth among these firms on the efficiency frontier averaged about 2.8 percent.
21. This information was communicated by NVE to the OEB staff in private correspondence. In 1997, the range was 2 to 4.5.
22. For a discussion, see OEB, Staff Discussion Paper on 3rd Generation Incentive Regulation for Ontario’s Electric Distributors.
23. See, Cronin, F.J. and Motluk, S., “Reviewing Electric Distribution Restructuring in Ontario: Policy without Substance or Commitment.” Utility Policy, March, 2006.
24. This research has its genesis in a paper originally prepared as a kickoff to a potential research program for the OEB for a yardstick regulation regime for Ontario LDCs, presented at the Canadian Economics Association 35th Annual Meeting at McGill University, Montreal, Quebec in June 2001: Cronin, F. J., Motluk, S. A., Inter-Utility Differences in Efficiency, presented at the Canadian Economics Association Meeting, McGill University, Montreal, 2001. Follow-on research was presented at the North American Productivity Workshop II, Union College, N.Y., 2002.
25. This recommendation assumed that the LDC budgets going into IR had been in a steady-state mode and provided sufficient funds for capital refurbishment, growth, and necessary additions induced by wholesale price increases or conservation, and that the operational side of OM&A is receiving a similarly sufficient budget. However, this seems not to be the case for some LDCs. Information on declining reliability, budget shortfalls and falling ROE would all seem to indicate that there may be an operational budget gap. No doubt, many LDCs have seen increases in OM&A but our expectation is that the LDCs have had to substantially increase the “A” (i.e., administration) portion of that to meet the substantial increase in regulatory/operating burdens (third party billing, etc.) imposed on them over the past 10 years.