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01-10-2014

The impact of consumer advocates on regulatory policy in the electric utility sector

Authors: Adam R. Fremeth, Guy L. F. Holburn, Pablo T. Spiller

Published in: Public Choice | Issue 1-2/2014

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Abstract

We examine the effect of consumer advocate participation in administrative procedures on regulatory policy. We use a unique panel database of rate reviews conducted for US electric utilities from 1980 to 2007 to assess how state consumer advocates affect Public Utility Commission decisions on utilities’ allowed financial returns and rate structures. We find first that utilities experience fewer rate reviews in states with consumer advocates, consistent with utilities strategically postponing requests for rate increases. Second, after controlling for observed and unobserved state characteristics, we find that PUCs in states with consumer advocates permit returns on equity that are on average 0.45 percentage points lower than states without advocates—equivalent to a $7.9 million (3.7 %) reduction in average utility operating income, all else equal. Third, consumer advocates are associated with lower residential rates relative to other customer classes. Our findings provide statistical support for the thesis that institutionalizing interest group representation in administrative procedures is one way for legislatures indirectly to influence agency-determined policies.

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The rate structure consists of the different rates charged to residential, industrial and commercial customers.

As the New Mexico Public Utility Commission commented about its discretionary powers, “[there is] a zone of reasonableness between confiscation [of utility assets] and extortion [of consumers] in which the Commission has great discretion in setting just and reasonable rates” (New Mexico PUC Brief, Supreme Court Case No. 24,148, PNM Gas Services v. NMPUC 1998).

See, for example, Hagerman and Ratchford (1978).

By ‘drift’ we mean that by delegating policymaking authority to a regulatory agency, legislators introduce the risk that the policy choice of the agency is different from the policy preference of the legislature.

Arkansas Code 23-4-302(3), Public Utilities and Regulated Industries.

The following four states have implemented legislative changes to the scope of advocate authority: California (2001, 2003, 2006), Kansas (2008), New Jersey (1994, 2005) and South Carolina (2004).

Consumer advocates are appointed by governors in 15 states, by attorneys general in seven states and by other means (e.g., legislative committee) in 11 states.

For example, in Arkansas, the legislation that established the Consumer Utilities Rate Advocacy Division stated that the “Division shall represent the state, its subdivisions, and all classes of Arkansas utility rate payers…to advocate the holding of utility rates to the lowest reasonable level” (Arkansas Code 23-4-302(3), Public Utilities and Regulated Industries; emphasis added).

Utilities tend to trigger rate reviews in response to rising costs (Joskow 1974). Since rates cannot be adjusted otherwise, reviews are an important mechanism by which utilities can restore their profitability after periods of cost inflation. Upon initiation of a rate review, a series of public hearings is held wherein the utility, PUC staff and other intervenors (including consumer advocates), present arguments before the commission or administrative law judge about the appropriate estimate of utility costs and level of profitability. Commissioners, after considering all evidence and testimony presented, make a majority decision on several factors: the allowed rate of return, the allowed rate base and the rate structure. While most rate reviews result in rate increases, utilities typically receive only a fraction of the total increase requested and, for electric utilities during the 1980s, ten percent of all rate cases led to a reduction or no change in rates. See Hyman (2000) for a more detailed description of the rate review process.

The rate base is the level of capital investment expenditures that the PUC deems prudently incurred and on which the utility is allowed to earn a return. Changes in the rate base arise as the PUC formally approves new investments that the utility has recently completed, for example, the completion of new electric generation capacity or the extension of transmission facilities. The allowed rate of return is usually set in reference to the utility’s weighted cost of capital so that it may raise new capital on the debt or equity markets in order to finance future investments. The appropriate rate of return will fluctuate over time as broader capital market conditions and interest rates change, though the official allowed rate of return can only be adjusted accordingly in the context of a rate review. Since rate reviews are costly and lengthy procedures, PUCs may allow utilities to earn actual profits that imply a rate of return somewhat higher than the allowed rate.

See the Biennal Report of the Office of Consumer Advocate, available at http://www.oca.nh.gov/biennialreport.html.

Roberts et al. (1978) also estimate a sample selection model but do not consider the impact of political, institutional or economic factors on the utility’s decision to initiate a review or on the PUC’s allowed ROE.

It is not possible to use observed changes in utility costs as an independent variable in the initiation equation since observed costs reflect managerial effort as well as the impact of exogenous factors. As we assume that managerial effort is chosen by the utility in response to the regulatory climate, including observed costs in the model could yield biased coefficient estimates.

We used the ‘Heckman’ command in Stata followed by the ‘Margins’ command to obtain the marginal effects of variables on Allowed Return on Equity conditional on observing a rate review.

For models including the IV as part of the Heckman procedure we follow the direction of Wooldridge (2002: 567) and Semykina and Wooldridge (2010) for estimating models in the presence of endogeneity and selection.

The data were compiled from a utility rate review report conducted by Regulatory Research Associates (RRA), a subsidiary of SNL Financial. The data are available by subscription from www.snl.com. Rate cases are classified as major if the rate request was $5 million or greater, or if the PUC’s decision resulted in a rate revision of $3 million or more. Interim rate orders or non-rate of return related revenue adjustments owing, for example, to tax revisions or fuel cost changes, are excluded. Our data panel is unbalanced due to industry consolidation during the period, resulting in 4842 utility-year observations.

The initiation of rate reviews in the State of Wisconsin follows a slightly different path than in other states since the Wisconsin Public Service Commission requires utilities to file for rate reviews automatically every two years. For the purposes of our analysis we have included the five Wisconsin utilities and their associated rate cases. Excluding Wisconsin from our sample reduces the sample size by 140 observations and does not make either a quantitative or qualitative difference in the results of our analysis.

In unreported analyses we include only those reviews initiated by utilities and find quantitatively and qualitatively similar results. Information on which party initiated a rate review was identified from media sources, private consulting company reports, and Public Utility Commission documents.

Data on utility rate increase requests are available from Regulatory Research Associates.

An allowed Return on Equity was formally specified in 1,095 out of the 1,349 rate cases in the sample. In the other 254 cases, utility rates were adjusted but no allowed ROE was determined.

Utility-specific data on the proportion of industrial consumers are unavailable for the 1980–1989 period. We thus use the state-level variable, Industry, as a proxy for the organized industrial consumer competition that utilities may confront.

Some states adopted automatic fuel adjustment clauses (FACs) during the 1980s that allowed utilities to pass through fuel costs without requiring a formal rate review. However, since such clauses rarely allowed utilities to pass through 100 % of the cost increases, fuel-cost-triggered rate reviews were not eliminated completely.

Although $\mathit{Interest\ Rate}_{t}$ is included in the second stage of the Heckman model it does not enter the first stage initiation model for several reasons. First, rate review initiation decisions are triggered by changes in utility costs and earnings since the last rate review rather than the level of costs per se (Bonardi et al. 2006; Fremeth and Holburn 2012). Second, since Interest Rate is highly correlated (ρ=0.6) with $\Delta\mathit{Interest\ Rate}_{ijt}$ including it creates statistical problems of multicollinearity. Third, no prior literature that models rate review initiation decisions empirically includes the level of the interest rate as a variable, just the change since the last rate review (Bonardi et al. 2006; Fremeth and Holburn 2012). Our approach is thus consistent with prior research. Including $\mathit{Interest\ Rate}_{t}$ in the first stage does not change our primary results qualitatively although statistical significance levels are weaker owing to multicollinearity.

This report is available at: http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html.

We gathered utility-specific rate data from FERC Form EIA-861. Form EIA-861 identifies rates by electricity product-type, including Bundled, Energy, and Delivery. Our analysis focuses on Bundled products (which includes costs for generation, transmission, and distribution) to ensure comparability across states and time.

The condition number is an alternative measure of multicollinearity and is calculated as the square root of the ratio of the largest eigenvalue to individual ith eigenvalues of a matrix. A large condition number indicates a nearly singular matrix; condition numbers greater than 30 typically indicate significant multicollinearity.

The marginal effects for the Allowed ROE equation are calculated using the margins post-estimation command in Stata (Cameron and Trivedi 2010: 347).

To assess the conditional effect of an interaction term (i.e., how the effect of Advocate on Allowed Return on Equity changes as market share changes) we take the partial derivative of one variable (i.e., Advocate) with respect to the dependent variable (i.e., Allowed Return on Equity) and assess the result at levels of interest for the other independent variable (i.e., when market share is set to its average of 30.85 % for rate case observations). The calculation is based on the results from the marginal effect on Allowed Return on Equity conditional on it being observed from model 3 in Table 4b is −0.387+(−0.002)(30.85)=−0.45.

In 2007, the average electricity service revenue of the utilities in our sample was $1.395 billion.

The calculation, based on the results from the marginal effect on Allowed Return on Equity conditional on it being observed from model 3 in Table 4b, is −0.387+(−0.002)(100)=−0.59.

This calculation is based on average rates in 2007 of $0.102/kW h for residential customers and $0.068/kW h for non-residential customers. We assume average residential consumption of 11,186 kW h per year. We restrict the weighted average of residential and non-residential rates to be the same in states with and without consumer advocates when estimating the impact of an advocate on residential rates.

An illustrative example of the duties of a state ombudsman can be found in the legislation that established this office in Iowa in 1972. Iowa Code Chapter 2C established this independent office to “serve as an independent and impartial agency to which citizens can air their grievances about government. By facilitating communications between citizens and government and making recommendations to improve administrative practices and procedures, the Ombudsman is to promote responsiveness and quality in government.”

Staiger and Stock (1997) suggest that an F-statistic less than 10 indicates a weak instrument.

This calculation is based on the average utility in our sample and uses the results from the marginal effect on allowed ROE conditional on it being observed from model 7 in Table 4b. The calculation is −0.766+(−0.002)(30.8)=−0.83.

The following example is illustrative of credit-claiming: “The Office of the Ohio Consumers’ Counsel had several successes in 2011 reducing charges to residential customers’ electrical bills. American Electric Power customers benefited from the return of $43 million in significantly excessive earnings and $78 million of unjustified charges related to its 2009-11 electric security plan”, available at http://www.pickocc.org/annualreports/2011/pdfs/electric.pdf.

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Title: The impact of consumer advocates on regulatory policy in the electric utility sector
Authors: Adam R. Fremeth
Guy L. F. Holburn
Pablo T. Spiller
Publication date: 01-10-2014
Publisher: Springer US
Published in: Public Choice / Issue 1-2/2014
Print ISSN: 0048-5829
Electronic ISSN: 1573-7101
DOI: https://doi.org/10.1007/s11127-013-0145-z

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