Ownership selection in the US electric utility industry

Utilities Policy 11 (2003) 203–223 www.socscinet.com/bam/jup

Ownership selection in the US electric utility industry夽 David W. Savitski a,b,∗ b

a Department of Economics and Finance, Northern State University, 1200 South Jay Street, Aberdeen, SD 57401, USA Office of Administrative Litigation, Federal Energy Regulatory Commission, 888 First Street, NE, Washington, DC 20426, USA

Received 19 August 2002; received in revised form 30 June 2003; accepted 17 July 2003

Abstract This paper examines municipal selection of ownership form at retail in the US electric utility industry from 1945 to 1991. Residents are assumed to select a rural electric cooperative (REC), municipally owned utility (MOU), or investor-owned utility (IOU) to maximize net benefits subject to exogenous rate setting constraints. Examination of 233 municipalities that changed ownership form, and 250 that did not, suggests that residents change ownership form to obtain lower rates, other things equal. In addition, access to federal power (MOU case only), growing municipal population, and a larger fraction of Republican-appointed regulatory commissioners increase the probability that residents change to an REC or MOU from an IOU, other things equal.  2003 Elsevier Ltd. All rights reserved. JEL classification: L94; L51; L33 Keywords: Electric utilities; Regulation

1. Introduction The US electric utility industry is dominated by rural electric cooperatives (RECs), municipally owned utilities (MOUs), and investor-owned utilities (IOUs). RECs and MOUs are publicly (communally)-owned, many in number, and typically small; IOUs are privately owned, few in number, and typically large. Electricity itself is a quintessentially homogeneous good.1 Why, then, is this industry served by public and private utilities? Alchian (1965) argues that inalienable ownership makes publicly owned firms less efficient than privately owned firms. Fama and Jensen (1983a) argue that the ∗ Present address: 17014 King James Way, #301, Gaithersburg, MD 20877, USA. Tel.: +1-202-415-9024. E-mail address: [email protected] (D.W. Savitski). 夽 This is based on my dissertation. I am indebted to David Butz, Trudy Cameron, William Comanor, Harold Demsetz, Andrew Dick, and Darrell Williams for their help, and to participants at UCLA’s Bergman Workshop and the 77th annual Western Economic Association International Conference for their comments. The views expressed here are not those of the FERC. All errors are mine. 1 There is some heterogeneity, such as firm vs. interruptible power, slow vs. fast power outage repair, and use of overhead vs. underground lines.

0957-1787/$ - see front matter  2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0957-1787(03)00061-4

efficient ownership structure has the greatest survival probability in a competitive market. Others examining ownership include Alchian and Demsetz (1972), Jensen and Meckling (1976, 1979), Williamson (1981), Demsetz (1983), Fama and Jensen (1983b), Demsetz and Lehn (1985), Mueller (1989), and Eggertsson (1990). Since three ownership forms exist, however, ownership structure provides an incomplete answer. Early on, municipal governments fostered franchise competition among IOUs. Scale economies, no monopoly protection, and corruption of some politicians and utility executives created financial problems. Franchise competition also led to reduced maintenance and investment near the end of the franchise period. IOUs benefited from the subsequent move to state public service (or utility) commission (PSC) regulation coupled with monopoly markets, thereby eliminating franchise competition (Jarrell, 1978).2 This left municipalities dependent on PSCs to discipline IOUs. REC or MOU owner-

2 A 1905 National Civic Federation Commission urged monopoly provision, public or private, with private utilities regulated. The Wisconsin public utility commission was established in 1906 based on commission recommendations, and served as a model for other states.

204

D.W. Savitski / Utilities Policy 11 (2003) 203–223

ship circumvents weak IOU regulation.3 Furthermore, ownership selection is political, so interest groups play a role. The privatization literature, for example, Shapiro and Willig (1990), Ferris and Graddy (1991), and Hart et al. (1997) is thus relevant, as it suggests conditions under which privatization enhances efficiency. Ownership selection is thus determined broadly by internal and external control factors, as well as standard cost factors. Early work on ownership form, for example, Wilcox (1910), Mosher (1929), and Behling (1938), focuses on municipalities struggling to control IOUs with franchise competition, against a backdrop of growing scale economies. Later work continues this theme. Hellman (1972) emphasizes the disciplinary effect of REC and MOU creation (government competition), especially that fostered by state and federal power distribution. Emmons (1993) finds that Franklin D. Roosevelt’s promotion of REC and MOU creation reduced IOU rates. Schap (1986) examines MOU creation using aggregate data, with similar findings. This paper extends their work by endogenizing ownership selection. In addition, firm-level data on all municipalities and utilities are used, regardless of size, as many ownership changes occurred in small towns. Schap examines MOUs, with statistical analysis limited to 1931 Nebraska. In addition, aggregate data mask entries and exits. Emmons focuses on large cities, with populations of at least 50,000, where municipal franchise competition was most intense, and the effect of government competition the least.4 Finally, government competition is tested to see whether its effects are felt when and where federal power is distributed or more widely, as the former undermines its disciplinary effect. The analytical framework has municipal residents selecting an REC, MOU, or IOU at retail to maximize net benefits subject to exogenous rate setting constraints. Rate setters are assumed to maximize political support, consistent with the regulatory models of Stigler (1971), Peltzman (1976), Jarrell (1978), and Becker (1983). This is tested with 233 municipalities that changed ownership form, involving an old and a new utility (the conditional data set), and 250 randomly selected municipalities that

did not change ownership form (the control data set). These observations range from 1945 to 1991. The empirical results suggest that residents change ownership form to obtain lower rates, other things equal. In addition, access to federal power, a growing population, and a larger fraction of Republican-appointed commissioners increase the probability that they change to an REC or MOU from an IOU, other things equal, with the last result unexpected. Changes in municipal labor force were unrelated to ownership selection, except in the REC case (though with mixed results). One result differs by ownership form: access to federal power decreases (increases) the probability that residents select an REC (MOU) relative to an IOU.

2. Theoretical analysis 2.1. Optimization Fig. 1 illustrates the ownership selection process. The upper level indicates the initial ownership selection (for the sample period), and is taken as given. The lower level indicates the observed selection, conditional on initial ownership form. In the left branch, for example, of the municipalities initially served by an REC, 77 retained ownership, 0 changed to an MOU, and 11 changed to an IOU. At each stage, residents select an REC, MOU, or IOU at retail to maximize net benefits within a representative individual model. Hence, distributional issues are ignored, as electricity typically has a small budget share for residents. All ownership forms have access to identical technology and efficient capital markets.5 Table 1 illustrates the dependent variables. The offdiagonal elements constitute the conditional data set. For example, the second nondiagonal element indicates that 11 municipalities served by RECs changed to IOUs. The principal diagonal elements constitute the control data set. Of these 250 municipalities, 77 were served by

3

The experience of the Long Island Lighting Company (LILCO) with the US$ 5.3 billion Shoreham nuclear power station provides an example of potential regulatory problems. The plant was abandoned in 1989 before generating any power, with regulators assessing LILCO’s customers US$ 4 billion of the US$ 5.3 billion through higher rates (The Economist, 1997). LILCO’s average revenue (AR) in 1991 was 13.7 ¢/kWh vs. the IOU average of 9.9 ¢/kWh (Energy Information Administration, Financial Statistics of Selected Investor Owned Utilities, 1993). See Joskow (2001) for a discussion of recent deregulation problems in California. Hence, ownership struggles are likely to continue. 4 This bias against finding a government competition effect on rates enhances his results.

Fig. 1.

5

The ownership selection process.

While RECs and MOUs may employ different technology, this is available to IOUs, and so use of different technology implies higher average cost for RECs and MOUs than for IOUs, other things equal.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

205

Table 1 The dependent variables Old utility

New utility

REC MOU IOU Total a

Total

REC

MOU

IOU

REC (77a) MOU→REC (27) IOU→REC (33) 137

REC→MOU (0) MOU (140) IOU→MOU (36) 176

REC→IOU (11) MOU→IOU (126) IOU (33) 170

88 293 102 483

The number of observations are in parentheses.

Table 2 The dependent variables over time Data set

Data set

Data set

Year

Conditional

Control

Year

Conditional

Control

Year

Conditional

Control

1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960

2 1 2 0 1 0 0 1 0 0 0 1 0 2 1 7

2 5 6 7 1 9 4 8 4 7 5 3 3 2 7 4

1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

12 14 11 8 12 8 10 11 13 9 7 5 2 4 9 3

5 3 4 9 6 2 5 5 8 10 9 5 3 6 9 4

1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 Total

5 0 0 6 10 5 10 4 13 6 6 6 2 2 2 233

7 2 8 5 3 6 3 5 5 4 6 6 6 5 9 250

RECs, 140 by MOUs, and 33 by IOUs. These crosssectional observations range from 1945 to 1991. Table 2 illustrates their time distribution, where table entries are yearly counts. For example, in 1945, four municipalities were observed, two that did and two that did not change ownership form. The conditional observations appear uniform, though in two distinct periods: prior to 1960, relatively few observations exist per year; after 1960, the data sets are roughly similar. This optimization is subject to exogenous rate setting constraints. IOU rates are subject to state and federal jurisdiction; REC and MOU rates are subject to local (and sometimes state and federal) jurisdiction. Regulators and REC and MOU managers maximize political support, consistent with the regulatory models of Stigler (1971), Peltzman (1976), Jarrell (1978), and Becker (1983). Because regulators respond to interest group pressure, and IOUs represent a powerful interest group,

regulation may be weak. Residents confronting weak IOU regulation have little choice but opting for an REC or MOU. RECs are nonprofit utilities serving small communities. They are typically governed by nine-member boards, elected annually.6 For large RECs, the service area is divided into nine regions, each electing one board member. This board sets rates for the service area, though sometimes they come under PSC jurisdiction or are subject to rates set by state or federal producers if they act as distributors, such as for the Tennessee Valley Authority. MOUs are also nonprofit utilities but, in contrast with RECs, their service areas generally coincide with municipal boundaries. Approximately half are

6 Tom Hoy of the National Rural Electric Cooperative Association provided REC information. Any errors in interpretation are my own.

206

D.W. Savitski / Utilities Policy 11 (2003) 203–223

organized with an independent utility board and half with a city governing board. Approximately one-third of the board members are elected and two-thirds are appointed (American Public Power Association, 1987).7 In most cases, MOUs set their own rates, though sometimes they too come under PSC, state, or federal jurisdiction. Ownership is inalienable for both, and restricted to customers (RECs) or municipal residents (MOUs). Because ownership is restricted to customers, the presence and efficacy of regulation should have little impact on the rate structure, as regulators set the rates at which customers serve themselves.8 IOUs, on the other hand, typically serve many municipalities, like RECs, but are typically much larger than the others. Ownership is alienable, in contrast with RECs and MOUs. IOU rates are regulated at retail by PSCs and at wholesale by the Federal Energy Regulatory Commission. IOUs initiate rate cases when seeking rate increases from the PSC. Regulatory efficacy influences the distribution of wealth between owners and customers, affecting municipal benefits in ways not possible for RECs and MOUs. For the latter, the identity of owners as customers means regulatory efficacy influences the distribution of wealth within the community.9 The governance structure of local, state, and federal rate setting institutions, and the behavior of individuals therein, are also exogenous. This is reasonable under an IOU, as residents may not (except at great cost) alter the characteristics of state and federal agencies, but is less so under an REC or MOU. Nevertheless, residents take these institutions as given, with regulators and REC and MOU managers optimizing without regard to ownership selection. In addition, residents know the state of regulation that prevails (or would prevail) under each ownership form.10 This framework departs from Stigler (1971), Peltzman (1976), Jarrell (1978), and Becker (1983) in that residents optimize over ownership form, treating regulators exogenously. This reverses their approach, wherein regulators optimize over interest groups, including municipal residents, treating them exogenously.

7

The survey results are based on 496 responses to 1500 questionnaires. 8 Regulation may offer protection against rent seeking by a customer class, however. 9 Even though PSC rate setting powers vary across RECs and MOUs (by states), this is not controlled for in the empirical work. Any biases should be small given the relatively innocuous nature of regulation for these ownership forms. Because this is not so for IOUs, PSC efficacy is controlled for in this case. 10 Because the governance structure and the behavior of individuals therein are exogenous, it is implicitly assumed that it is cheaper to change ownership form than to change the operation of these institutions.

2.2. Estimation Because the dependent variables are unordered categorical with choice data, the multinomial logit is used. Following Maddala (1983), let Ui = Vi + ei be the representative resident’s utility associated with alternative i, i = 1,2,…,n, where Vi depends on municipal and alternative characteristics and ei is an iid error term with the extreme value distribution. (Municipality subscripts are excluded for clarity.) Let Vi = b⬘i X, where X is a vector of municipal and alternative characteristics. The parameters bi vary across alternatives but not across municipalities. The dependent variable in estimation is the probability that residents choose alternative i, given by

冘 n

Pi ⫽ e

b⬘iX

/

eb⬘kX, i ⫽ 1,2,…,n.

(1)

k⫽1

Since {b1, b2, …, bn} and {b 1 + c, b 2 + c, …, bn + c} yield the same set of probabilities for any constant c, an identifying restriction is needed. Arbitrarily set c = ⫺b 1, so that the first alternative becomes the base case, with the other probabilities defined relative to this case. The normalized probabilities become

冉 冘 冊 n

P1 ⫽ 1 / 1 ⫹

eb⬘kX

(2a)

冉 冘 冊

(2b)

k⫽2

and n

Pi ⫽ e

b⬘iX

/ 1⫹

eb⬘kX , i ⫽ 2,3,…,n.

k⫽2

The IIA property is apparent, as the probability residents select alternative i relative to j is Pi / Pj ⫽ eb⬘iX / eb⬘jX,

(3)

which is independent of the number of alternatives. This weakness is exploited to allow independent estimation of the decisions. For example, conditioning on initial ownership form, say an REC, precludes choices in the right two branches in Fig. 1. A change from an MOU to an IOU, for example, is thus irrelevant (impossible) for residents served by an REC.11 Their introduction (simultaneous estimation of all decisions) would be logically irrelevant for the relative choice probabilities associated with the left branch, as is the case under the IIA

11 While an MOU-to-IOU change is impossible for REC customers, the experience of the former may influence the latter. This effect weakens with distance and time, as IOU performance varies with distance (across IOUs) and time (across personnel). Since the data set is considerably dispersed over both dimensions, this effect is likely to be weak, and is ignored.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

property. Hence, the IIA property is appropriate for this analysis.12

3. Empirical analysis Because of the data-imposed time constraint (discussed below), 1945–1991, prior factors affecting ownership selection, such as the Granger agricultural movement, are taken as given. Some such regional variation continues to change in the sample period, such as the distribution of federal and state hydro power, and this is controlled for. 3.1. Hypotheses Rates are of primary concern to customers. Average revenue, AR, rather than the rate structure, however, is available. AR, nevertheless, suffers from much missing data. To use what is available, the AR difference across the ownership change (old AR less new AR) is used. This captures change over time in that residents begin the change with AR under the old utility and end with AR under the new utility. For nonchangers, the difference is zero. Residents are expected to change ownership to obtain lower rates. H1: Hence, AR differences should be positively associated with the probability that residents change ownership form.13 Deindustrialization and flight of affluent residents into suburbs reduce a community’s ability to exploit scale economies, as typically MOUs sell within city limits and RECs sell to rural areas. Entering RECs and MOUs must then build at a smaller scale, reducing entry likelihood. Existing RECs and MOUs suffer rising average cost due to reduced sales or see retail sales replaced with wholesale sales at reduced rates. IOUs, on the other hand, serve much larger markets on average than they do, so such industrial and demographic changes involve customers moving within their markets to a greater extent, making them less prone to these reallocation effects. H2: Hence, residents in communities experiencing negative industrial and demographic changes are less likely to change to an REC or MOU from an IOU than conversely.14 Municipal employees oppose privatization, as it 12 While relative probabilities are unaffected by additional alternatives, absolute probabilities must be. 13 All predictions implicitly hold other things constant to avoid stating it repetitively. 14 This is true even if municipalities have access to efficient capital markets, as this does not negate the scale disadvantage suffered by small municipalities, which may still opt for an IOU should this disadvantage be significant. This is not an issue, however, for municipalities with access to competitively priced wholesale power (discussed below).

207

reduces wages, benefits, and jobs.15 Their rent seeking effectiveness depends on their goals. For example, senior workers prefer higher wages to more secure employment, whereas junior workers prefer the opposite, as the latter become more likely to be laid off with successful wage demands (Jensen and Meckling, 1979). Privatization unites their interests as it reduces wages and jobs. As municipal workers grow in strength (campaigning, contributions, and votes), their influence over public managers grows, making managers more likely to resist privatization. This is reinforced by the fact that privatization takes from managers, a source of wealth for rewarding supporters. Sympathetic managers can, in return, expect municipal employees’ support. H3: Hence, residents in communities experiencing growth in the strength of municipal workers are more likely to change to an REC or MOU from an IOU than conversely. Stigler and Friedland (1962), Peltzman (1971), Jarrell (1978), and Joskow (1974) examine variation in regulatory efficacy. This influences ownership choice by affecting consumer surplus. Two factors linked to this variation are examined. First, regulator performance depends on institutional incentives. These depend on whether PSC commissioners are popularly elected or governor appointed. Republicans are assumed probusiness and more likely than Democrats to appoint commissioners favoring IOUs over customers, and commercial and industrial customers over residential customers.16 This should lead to higher rates, and to higher residential rates relative to industrial and commercial rates, under an IOU, the more numerous are Republican appointees. H4: Hence, more Republican appointees on the PSC makes residents more likely to change to an REC or MOU from an IOU than conversely. Second, because IOUs have information and resource advantages over PSCs, regulators may favor them over customers. Hellman (1972), Schap (1986), and Emmons (1993) view state regulation of IOUs as failing to produce competitive results, thus advocating an indirect form of competition: “[G]overnment competition is a function of seeking higher standards of performance, in

15 For example, Donahue (1988: p. 15) finds that public prison guards earned approximately 15% more per hour than their private counterparts based on 1980 census data (US Department of Commerce, various years). Ferris and Graddy (1991: p. 549), on the other hand, find that public salaries in the health care industry are approximately 10% below their private and nonprofit counterparts based on 1984 census data. 16 Navarro (1982), for example, finds that this percentage is positively related to a PSC being rated as favorable (in 1978), though the statistical significance is weak.

208

D.W. Savitski / Utilities Policy 11 (2003) 203–223

return for the extraordinary monopoly powers granted… It is a superseding form of regulation.”17 One disciplinary channel is threatened and actual REC and MOU creation, fostered by state and federal power distribution. A weakness is the inability to repeatedly allocate power: reallocation increases competition in receiving areas but decreases it in losing areas. Furthermore, while competitive pressure bears on IOUs during allocation, once allocated it ceases, as REC and MOU creation so fostered ceases. Hence, the disciplinary effect is not dynamic. If true, government competition may not represent a feasible alternative to regulation.18 A test of whether government competition is static or dynamic derives from the pattern of state and federal power sales. If static, its effects on REC and MOU creation should be felt when and where power is distributed. If dynamic, these effects should be less localized in space and time. H5: Hence, if static (dynamic), residents are more likely to change to an REC or MOU from an IOU than conversely, in states that are (are and are not) receiving state and federal power, during its allocation, but not otherwise (during and after its allocation). 3.2. The data set The conditional data set consists of 233 municipalities that changed ownership form, provided by the trade associations: the National Rural Electric Cooperative Association (RECs), the American Public Power Association (MOUs), and the Edison Electric Institute (IOUs). These observations ranged over 1945–1991, defining the sample period. This was matched with a control group of 250 municipalities that did not change ownership form, selected randomly from an annual list of utilities published in Electrical World’s Directory of Electric Utilities. AR for residential customers is used as they select ownership, but it is missing often. Missing data are treated with a dummy variable technique, where the dummy DAR zeroes out missing observations, the estimated coefficient indicating the effect of AR on ownership selection, conditional on observing data. Since the data span 1945–1991, AR is deflated by the producer price index for fuel, related products, and power. Also, since AR is endogenous, instrumental variables are used 17

Hellman (1972: pp. 66–67). The Granger agricultural movement also advocated the cooperative ownership form, and PSC regulation of railroads and other utilities. 18 Government competition is subject to other criticisms. First, REC and MOU demand for state and federal power may reflect a subsidy implicit in the wholesale price. (Harold Demsetz pointed this out.) Second, state and federal capacity crowds out IOU capacity, reducing their competitive ability. The subsidy interpretation and Hellman’s (1972), Schap’s (1986), and Emmons’ (1993) interpretations are observationally equivalent, however: both predict an increase in the probability of REC or MOU creation.

to generate exogenous AR estimates. The difference in AR is measured by ⌬AR = ARO⫺ARN, where ARO and ARN are the AR of the old and new utilities. ⌬AR = 0 in the control data set. The percentage change in municipal population preceding ownership change for observations on MOUs proxies for municipal economic health. For observations on RECs and IOUs, county population growth where the RECs are headquartered, is used. These are average annual changes over at least the previous 10, but not more than 19 years, derived from the previous two censuses. Many RECs and MOUs are small, with few industrial customers. The effects of deindustrialization will be weak for these communities, but grow as larger cities are examined. Hence, absolute population changes are used (⌬POP). Change in the strength of government workers is measured with the average annual percentage change in the municipal labor force working for local (⌬LF⌬%) or for all levels (⌬LF⌬%) of government over the two census years surrounding the observation year (or the current and previous census years when an observation falls on a census year). The appointing governors’ effect on PSC commissioner incentives is measured with the percentage of Republican governors, for 6, 10, and 15 years preceding ownership change (GOVi%, i = 6, 10, 15). GOVi% = 0 for elected commissioners. The impact of government competition is measured with a dummy variable taking the value one for observations occurring in states receiving federal power, and zero otherwise. Since this power is allocated over time, this is done for lags of 5, 10, and 11+ years after the initial allocation (FEDPWRi, i = 5, 10, 11 +). This is done for federal power only due to data limitations, as there are fewer federal than state producers, and the former account for the bulk of total state and federal wholesale power; for example, it was 71.4% in 1991.19 These data were collected from several sources. AR is from the Directory and the producer price index for fuel, related products, and power is from the Bureau of Labor Statistics (US Department of Labor, 2000). POP, LFL%, and LF⌬% are from the census. Dpsc and GOVi% are from the Council of State Governments’ Book of the States. Finally, FEDPWRi% is from the Energy Information Administration’s Financial Statistics of Selected Investor Owned Electric Utilities (1993) and Inventory of Power Plants in the US 1991 (1992).

19 Energy Information Administration’s Form EIA-861: Annual Electric Utility Report (1991). The Power Authority of the State of New York (PASNY) is included with federal producers as it is more than twice as large as the next largest state producer, is larger than some federal producers, and has about the same wholesale rate as the federal producers.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

Table 3 summarizes these hypotheses. The first two columns list the explanatory variables and their expected impact on the probability that residents change to an REC or MOU from an IOU. The prediction for ⌬AR is silent with respect to type of change (⌬AR is positively related to any ownership change). The next three predictions are straightforward, while those for government competition are less so. If static (indicated), the effect of federal power allocation on REC and MOU creation diminishes over time, so that FEDPWRi should be positively related to the probability that residents change to an REC or MOU from an IOU, with significance diminishing with lag length. If dynamic, all should be positively related to the probability that residents change to an REC or MOU from an IOU. Observation YEAR is included as a crude control for other changes occurring over the sample period. Tables A1, A2, A6, and Table A7 in Appendix A present summary statistics and the correlation matrix. Most correlations are approximately zero, so multicollinearity is not serious. High correlations occur between the labor force variables, and among the governor variables, but one from each set is used at a time. Tables A3– A5, A8 present mean and standard error cross-tabs by ownership form, and test if they differ by ownership change. Most means differ for at least one comparison at 10% or better. Finally, the hypotheses that the means are jointly equal across ownership forms are rejected at 5% or better in all cases for the conditional data set and at 10% for one case for the control data set. Hence, individually and collectively, these variables appear related to ownership selection. 3.3. Estimation results Rather than estimate three cases as illustrated in Fig. 1, a stronger test is had with a different classification. Table 3 Ownership selection hypotheses Hypothesis

Explanatory variable (Xi)

H1: reduced rates H2: deindustrialization H3: rent seeking

⌬AR ⌬POP ⌬LFL%, ⌬LFA% ⌬LFL%2, ⌬LFA%2 GOV6%, GOV10%, GOV15% GOV6%2, GOV10%2, GOV15%2 FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

H4: regulation

H5: government competition (static) Time:

∂P(IOU→REC or MOU) ∂Xi

+ + ⫺ + ⫺ + (strong) + (weak) 0

209

Consider, for example, the left branch: {REC, REC→IOU}. The theory focuses on REC and MOU (relative to IOU) creation and destruction. Since the reverse change {IOU→REC} is excluded, it is not properly tested; a better test is had with {REC, REC→IOU, IOU→REC}, since both REC creation and destruction are observed symmetrically vis-a`-vis the IOU. Similarly, the second branch {MOU→REC, MOU, MOU→IOU} is replaced with {MOU, MOU→IOU, IOU→MOU}. To use the remaining alternatives, {IOU, MOU→REC}, {IOU→MOU} is used again. The choice asymmetry makes interpretation less clear-cut than for the first two sets, but at least MOU creation and destruction are observed. A natural normalization in each set is the nochange selection. To facilitate comparison, the same specifications are used for each case. Predicted signs are indicated in parentheses below each variable, and t statistics are in parentheses below the coefficient estimates. 3.3.1. The REC base case Table 4 presents estimation results for the REC base case. After normalization, the two dependent variables are log[P(REC→IOU) /P(REC)] and log[P(IOU→REC)/ P(REC)]. For the {REC→IOU} change, the predictions in Table 3 are reversed. Consider equations (R1). Globally, the model fits well: the likelihood function has a value of ⫺52.22; the hypothesis that all coefficients (except for the constants) are zero is rejected, with a c2-statistic of 103.68 with 18 degrees of freedom; and the pseudo R2 equals 0.4982.20 ⌬AR is excluded from the first equation in each regression, as there are few observations. It is significant with the expected positive sign in the second equation; the corresponding dummy was also significant, contrary to expectations. ⌬POP is not significant in the first equation, but almost so with the expected sign in the second equation. ⌬LFL% is negative and significant in both equations, with the expected sign in the first but not in the second equation. GOV6% is positive and significant in the first equation, contrary to expectations; it is not significant in the second equation. Of the federal power dummies, only FEDPWR10 is significant (in both equations). The effects of government competition appear to grow and then diminish over time (unexpected sign aside), consistent with the static version. YEAR is consistent with movement towards REC ownership over time. Finally, successful prediction rates (bottom panel) range from 69.7% to 90.9%, with a global rate of 83.5%. A comparison across specifications indicates robust, if not always supportive, results. Globally, all yield similar The pseudo R2 = 1⫺logL(bH⌬)/ logL(bHO), where HA and HO indicate alternative and null hypotheses. If the model has no explanatory power, logL(bH⌬) = logL(bHO), and the pseudo R 2 = 0. If, one the other hand, the model has significant explanatory power, logL(bH⌬) is significantly greater than logL(bHO) and the pseudo R2 is approximately 1. 20

⌬AR ( /+) DAR ( /0) ⌬POP (⫺/+) ⌬LFL% (⫺/+) ⌬LFL%2 (+/⫺) GOV6% (⫺/+) GOV6%2 (+/⫺) FEDPWR5 (⫺/+) FEDPWR10 (⫺/+) FEDPWR11+ (⫺/+) YEAR

CONSTANT

Dependent variables

Independent variables

⫺0.001 (⫺0.164) ⫺0.858 (⫺0.681) ⫺2.878∗∗ (⫺1.762) ⫺0.230 (⫺0.361) 0.107∗∗∗ (3.282)

0.032∗∗ (1.969)

0.004 (0.002) 4.442∗∗∗ (2.283) 2.726∗ (1.741) ⫺0.211 (⫺1.606)

7.776 (1.102)

2.180 (0.183) ⫺0.616∗∗ (⫺2.442)

Log P(IOU→REC) P(REC) (R1.b) ⫺6.488∗∗∗ (⫺3.154) 389.4∗∗∗ (2.672) ⫺4.659∗∗∗ (⫺4.873) 3.150 (1.626) ⫺0.169∗∗ (⫺2.026)

Log P(REC→IOU) P(REC) (R1.a)

⫺52.216 c218 = 103.68 0.0000 0.4982

Log-likelihood: c2df: Prob ⬎ c 2: Pseudo R2:

Table 4 Multinomial logit regression results for the REC base case

11.200 (0.421) ⫺1.424 (⫺1.319) ⫺0.009 (⫺0.182) 0.502 (1.645) ⫺0.004 (⫺1.572) 1.962 (0.329) 8.136 (1.489) 7.949 (1.540) ⫺0.479 (⫺1.419)

15.005 (1.012)

Log P(REC→IOU) P(REC) (R2.a)

⫺45.504 c222 = 117.11 0.0000 0.5627

⫺6.624∗∗∗ (⫺3.123) 429.1∗∗∗ (2.803) ⫺5.038∗∗∗ (⫺4.675) 3.530∗ (1.738) ⫺0.242∗∗ (⫺2.066) 0.006 (0.637) ⫺0.002 (⫺0.054) 13.7e⫺06 (0.036) ⫺0.736 (⫺0.556) ⫺3.050∗∗ (⫺1.842) ⫺0.140 (⫺0.202) 0.109∗∗∗ (3.183)

Log P(IOU→REC) P(REC) (R2.b)

⫺0.177∗ (⫺1.810)

2.805 (1.537)

3.26e⫺06 (0.312) ⫺0.563∗∗ (⫺2.448) 0.019 (0.640) 0.166∗∗ (2.485) ⫺0.001∗∗ (⫺2.121)

5.769 (1.121)

Log P(REC→IOU) P(REC) (R3.a)

⫺49.579 c218 = 108.95 0.0000 0.5235

0.094∗∗∗ (2.738) (continued on next page)

⫺2.186 (⫺1.417)

⫺6.257∗∗∗ (⫺2.937) 355.6∗∗ (2.441) ⫺5.014∗∗∗ (⫺4.526) 3.48e⫺06∗ (1.724) ⫺0.246∗ (⫺2.244) 0.003 (0.333) 0.068∗∗ (2.043) ⫺0.001∗∗∗ (⫺1.821)

Log P(IOU→REC) P(REC) (R3.b)

210 D.W. Savitski / Utilities Policy 11 (2003) 203–223

77 11 33 121

90.9 72.7 69.7 83.5

Prediction success rates (%) 93.5 81.8 66.7 85.1

93.5 63.6 60.6 81.8

Expected signs are in parentheses below the variables, and t statistics are in parentheses below the estimates. The prediction success rate is the percent of correct predictions, where a correct prediction occurs when the largest predicted probability (in excess of 0.5) is correct. GOV15% is used in Eq. (3). ∗ Significant at 10%. ∗∗ Significant at 5%. ∗∗∗ Significant at 1%.

REC REC→IOU IOU→REC Global

n

Table 4 (continued)

D.W. Savitski / Utilities Policy 11 (2003) 203–223 211

⌬AR (+/+) DAR (0/0) ⌬POP (⫺/+) ⌬LFL% (⫺/+) ⌬LFL%2 (+/⫺) GOV6% (⫺/+) GOV6%2 (+/⫺) FEDPWR5 (⫺/+) FEDPWR10 (⫺/+) FEDPWR11+ (⫺/+) YEAR

CONSTANT

⫺0.008 (⫺1.179) ⫺0.159 (⫺0.184) 0.878 (1.191) 0.703 (1.445) 0.072∗∗∗ (3.260)

⫺1.213∗∗ (⫺2.287) ⫺0.738 (⫺1.285) ⫺1.172∗∗∗ (⫺3.316) 0.088∗∗∗ (5.102)

⫺5.748∗∗∗ (⫺3.883) 137.0∗ (1.787) ⫺3.440∗∗∗ (⫺4.476) 3.47e⫺08 (0.013) 0.028 (0.498)

⫺5.267∗∗∗ (⫺4.613) 117.9∗∗∗ (3.405) ⫺1.728∗∗∗ (⫺4.820) ⫺0.014∗ (⫺1.688) ⫺0.024 (⫺0.534)

0.007∗ (1.835)

Log P(IOU→MOU) P(MOU) (M1.b)

Dependent variables

Independent variables

Log P(MOU→IOU) P(MOU) (M1.a)

⫺238.22 c218 = 112.23 0.0000 0.1907

Log-likelihood: c2df: Prob ⬎ c 2: Pseudo R2:

Table 5 Multinomial logit regression results for the MOU base case

⫺5.071∗∗∗ (⫺4.346) 117.7∗∗∗ (3.409) ⫺1.683∗∗∗ (⫺4.660) ⫺0.013 (⫺1.616) ⫺0.041 (⫺0.825) 0.008 (1.247) 0.007 (0.475) 6.49e⫺06 (0.044) ⫺1.326∗∗ (⫺2.445) ⫺0.834 (⫺1.430) ⫺1.230∗∗∗ (⫺3.425) 0.084∗∗∗ (4.812)

Log P(MOU→IOU) P(MOU) (M2.a)

⫺237.04 c222 = 114.60 0.0000 0.1947

⫺5.894∗∗∗ (⫺3.782) 140.0∗ (1.817) –3.478∗∗∗ (⫺4.486) 7.37e⫺08 (0.029) 0.027 (0.386) 0.004 (0.510) ⫺0.014 (⫺0.639) 80.2e⫺06 (0.309) ⫺0.192 (⫺0.221) 0.834 (1.120) 0.688 (1.403) 0.074∗∗∗ (3.191)

Log P(IOU→MOU) P(MOU) (M2.b)

0.070∗∗∗ (4.260)

⫺0.024 (⫺0.043)

⫺4.923∗∗∗ (⫺4.356) 141.1∗∗∗ (4.198) ⫺1.794∗∗∗ (⫺5.061) ⫺0.012 (⫺1.480) ⫺0.033 (⫺0.693) 0.006 (0.940) 0.032∗∗ (2.351) ⫺0.266 (⫺1.600)

Log P(MOU→IOU) P(MOU) (M3.a)

⫺247.80 c218 = 93.08 0.0000 0.1581

0.081∗∗∗ (3.678) (continued on next page)

0.364 (0.527)

⫺5.991∗∗∗ (⫺3.979) 128.6∗ (1.791) ⫺3.464∗∗∗ (⫺4.563) ⫺1.66e⫺08 (⫺0.007) 0.027 (0.404) 0.003 (0.351) ⫺0.013 (⫺0.619) 0.069 (0.252)

Log P(IOU→MOU) P(MOU) (M3.b)

212 D.W. Savitski / Utilities Policy 11 (2003) 203–223

140 126 36 302

69.3 61.1 2.8 57.9

Prediction success rates (%) 70.0 62.7 2.8 58.9

68.6 57.1 8.3 56.6

Expected signs are in parentheses below the variables, and t statistics are in parentheses below the estimates. The prediction success rate is the percent of correct predictions, where a correct prediction occurs when the largest predicted probability (in excess of 0.5) is correct. GOV15% is used in Eq. (3). ∗ Significant at 10%. ∗∗ Significant at 5%. ∗∗∗ Significant at 1%.

MOU MOU→IOU IOU→MOU Global

n

Table 5 (continued)

D.W. Savitski / Utilities Policy 11 (2003) 203–223 213

⌬AR ( /+) DAR (0/0) ⌬POP ( /+) ⌬LFL% ( /+) ⌬LFL%2 ( /⫺) GOV6% ( /+) GOV6%2 ( /⫺) FEDPWR5 ( /+) FEDPWR10 ( /+) FEDPWR11+ ( /+) YEAR

CONSTANT

Dependent variables

Independent variables

⫺0.018 (⫺1.518) ⫺1.185 (⫺0.714) ⫺1.611 (⫺1.034) ⫺0.204 (⫺0.245) 0.163∗∗∗ (3.607)

⫺2.035 (⫺1.080) ⫺4.230∗∗ (⫺2.134) ⫺1.901∗ (⫺1.816) 0.219∗∗∗ (3.896)

⫺9.018∗∗∗ (⫺3.114) 714.4∗∗ (2.368) ⫺5.908∗∗∗ (⫺3.681) ⫺1.72e⫺07 (⫺0.074) 0.232 (0.565)

Log P(IOU→MOU) P(IOU) (I1.b)

0.008 (0.648)

⫺13.316∗∗∗ (⫺3.574) ⫺3037∗∗ (⫺2.482) ⫺9.359∗∗∗ (⫺3.002) ⫺1.65e⫺06 (⫺0.664) 0.083 (0.585)

Log P(MOU→REC) P(IOU) (I1.a)

⫺50.197 c218 = 109.20 0.0000 0.5210

Log-likelihood: c2df: Prob ⬎ c 2: Pseudo R2:

Table 6 Multinomial logit regression results for the IOU base case

⫺12.632∗∗∗ (⫺3.052) ⫺2898∗∗ (⫺2.247) ⫺8.726∗∗∗ (⫺2.894) ⫺1.40e⫺06 (⫺0.543) ⫺0.120 (⫺0.354) 0.092 (0.956) 0.089∗ (1.816) ⫺0.001∗ (⫺1.747) ⫺2.644 (⫺1.155) ⫺4.514∗∗ (⫺2.099) ⫺1.606 (⫺1.461) 0.198∗∗∗ (3.223)

Log P(MOU→REC) P(IOU) (I2.a)

⫺47.285 c222 = 115.03 0.0000 0.5488

⫺7.559∗∗ (⫺2.480) 638.283∗∗ (2.323) ⫺5.631∗∗∗ (⫺3.584) 1.99e⫺07 (0.069) 0.013 (0.039) 0.094 (0.981) 0.021 (0.525) ⫺0.00048 (⫺1.046) ⫺1.585 (⫺0.809) ⫺1.719 (⫺1.022) ⫺0.037 (⫺0.044) 0.134∗∗∗ (2.848)

Log P(IOU→MOU) P(IOU) (I2.b)

0.192∗∗∗ (3.470)

⫺3.306 (⫺1.622)

⫺13.027∗∗∗ (⫺3.456) ⫺2521∗∗ (⫺2.079) ⫺8.511∗∗∗ (⫺2.862) ⫺1.55e⫺06 (⫺0.587) ⫺0.034 (⫺0.137) 0.042 (0.709) 0.081 (1.477) ⫺0.001 (⫺1.489)

Log P(MOU→REC) P(IOU) (I3.a)

⫺52.134 c218 = 105.33 0.0000 0.5025

0.159∗∗∗ (3.376) (continued on next page)

⫺1.582 (⫺0.943)

⫺9.041∗∗∗ (⫺2.961) 734.0∗∗ (2.479) ⫺5.929∗∗∗ (⫺3.625) ⫺8.03e⫺08 (⫺0.040) 0.113 (0.471) 0.040 (0.671) ⫺0.013 (⫺0.308) ⫺0.00001 (⫺0.027)

Log P(IOU→MOU) P(IOU) (I3.b)

214 D.W. Savitski / Utilities Policy 11 (2003) 203–223

33 27 36 96

n

90.9 66.7 83.3 81.3

Prediction success rates (%) 87.9 70.4 80.6 80.2

93.9 63.0 75.0 78.1

Expected signs are in parentheses below the variables, and t statistics are in parentheses below the estimates. The prediction success rate is the percent of correct predictions, where a correct prediction occurs when the largest predicted probability (in excess of 0.5) is correct. GOV15% is used in Eq. (3). ∗ Significant at 10%. ∗∗ Significant at 5%. ∗∗∗ Significant at 1%.

REC MOU→REC IOU→MOU Global

Table 6 (continued)

D.W. Savitski / Utilities Policy 11 (2003) 203–223 215

216

D.W. Savitski / Utilities Policy 11 (2003) 203–223

support. ⌬AR is consistently positive and significant as expected for the {IOU→REC} case. The corresponding dummy was consistently significant, contrary to expectations. ⌬POP is not significant in the first equation, but is positive in the second equation, as expected. ⌬LFL% is significant and negative in each equation (except (R2.a)), expected for the first but not for the second equation. ⌬LFL%2 is not significant. LF⌬% (not shown) tends to be weaker, with ⌬LF⌬%2 never significant. GOV6% is positive in the first equation (almost in (R2.a)), contrary to expectation; it is zero in the others. GOV15% is used in equations (R3), and is positive in both, the latter as expected. The squared terms have the expected signs relative to the linear terms. In other specifications, GOV6%, GOV10%, and GOV15% generally have positive coefficients in both equations, while their squared terms generally have negative coefficients. They also tend to be weaker in the second equation. FEDPWR5 is never significant. FEDPWR10 is significant in three of six equations, and almost significant in the others, though of the unexpected signs. FEDPWR11+ was significant in the first equation (and almost in (R3.a)). YEAR is consistently negative and positive in the first and second equations, though significant in one of three of the first equations. Finally, the model generates similar successful prediction rates across specifications.

success rate of 57.9%. The model underperforms for the {IOU→MOU} changes. A comparison across specifications again indicates robust results. Globally, all specifications yield similar support. ⌬AR is consistently positive and significant as expected for both equations. The corresponding dummy was consistently significant, contrary to expectations. ⌬POP is negative in each of the first equations, as expected, significant in the first and almost significant at 10% in the second two; it is never significant in the second equation. ⌬LFL% and ⌬LFL%2 are not significant in any equation. ⌬LF⌬% and ⌬LF⌬%2 (not shown) also were never significant. GOV6% is positive in (M1.a), contrary to expectation; it is zero otherwise. GOV15% is used in equations (M3), with similar results. In other specifications, GOV6%, GOV10%, and GOV15% sometimes have positive coefficients in the first equation, and (while weaker) negative coefficients in the second equation, while their squared terms generally have negative coefficients in the first equation, and insignificant ones in the second equation. FEDPWR5 and FEDPWR11+ are significant in equations (M1.a) and (M2.a). FEDPWR10 was not significant. YEAR is consistently positive. Finally, the model generates similar successful prediction rates across specifications.

3.3.2. The MOU base case Table 5 presents estimation results for the MOU base case. The two normalized dependent variables are log[P(MOU→IOU) / P(MOU)] and log[P(IOU→MOU) / P(MOU)]. For the {MOU→IOU} changes, the predictions in Table 3 are again reversed. Consider equations (M1). Globally, the model again fits well: the likelihood function has a value of ⫺238.22; the hypothesis that all coefficients except the constants are zero is rejected, with a c2-statistic of 112.23 with 18 degrees of freedom; and the pseudo R2 equals 0.1907. ⌬AR is significant with the expected positive sign in both equations; the corresponding dummy was also significant, contrary to expectations. ⌬POP is significant, with the expected negative sign in the first equation; it is not significant in the second equation. ⌬LFL% is not significant. GOV6% is positive and significant in the first equation, contrary to expectations; it is not significant in the second equation. FEDPWR5 and FEDPWR10 are significant in the first equation as expected, consistent with the static version of the hypothesis (with FEDPWR11+ not significant). None are significant in the second equation. This is consistent with the power being distributed to existing MOUs, as it appears unrelated to MOU creation, and thus undermines the argument for government competition. YEAR is positive and significant in both equations, suggesting increasing likelihood of ownership changes over time. Finally, successful prediction rates range from 2.8% to 69.3%, with a global

3.3.3. The IOU base case Table 6 presents estimation results for the IOU base case. The two normalized dependent variables are log[P(MOU→REC) / P(IOU)] and log[P(IOU→MOU) / P(IOU)]. For the {MOU→REC} changes, the predictions are absent, as the theory is silent on public–public conversions. Consider equations (I1). Globally, the model fits well: the likelihood function has a value of ⫺50.20; the hypothesis that all coefficients except the constants are zero is rejected, with a c2-statistic of 109.20 with 18 degrees of freedom; and the pseudo R2 equals 0.5210. ⌬AR is significant in both equations, with the expected positive sign in the second; the corresponding dummy was also significant, contrary to expectations. ⌬POP, ⌬LFL%, and GOV6% are not significant. GOV6% is almost significant in the second equation at 10%, but its sign is contrary to expectations. FEDPWR10 and FEDPWR11+ are significant in the first equation. Consistent with the static version, the effects of government competition appear to grow and then diminish over time, at least insofar as power generates changes in ownership form. This change is unlikely to translate into much pressure on IOUs to lower their rates, however, as they are not significant in the second equation. The results from (I1.a) are also consistent with power being distributed to existing rather than new MOUs. YEAR is again positive and significant in both equations, suggesting increasing likelihood of ownership changes over time.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

Finally, successful prediction rates range from 66.7% to 90.9%, with a global success rate of 81.3%. A comparison across specifications indicates robust results. Globally, all specifications yield similar support. ⌬AR is consistently negative in the first equation and positive in the second equation, consistent with expectations (in the second equation). The corresponding dummy was consistently significant, contrary to expectations. ⌬POP, ⌬LFL%, ⌬LFL%2 are insignificant in each case. ⌬LF⌬% (not shown) tends to be weaker; ⌬LF⌬%2 was never significant. GOV6% is positive and its squared term is negative (I2.a). Neither is significant, in general, in the second equation. GOV15% is used in equation (I3), with similar results. In other specifications, GOV6%, GOV10%, and GOV15% generally have positive and negative coefficients in the first and second equations, while their squared terms generally have the opposite signs. They also tend to be weaker in the second equation. FEDPWR5 is never significant. FEDPWR10 is significant and negative in the first equation (almost in (I3.a)), but not in the second equation. FEDPWR11+ was significant in (I1.a) and almost so in (I2.a), but not in the second equation. YEAR is positive in each equation. Finally, the model generates similar prediction rates across specifications. Now compare results across the base cases. Globally, the model performed similarly for each base case. The MOU base case was relatively weak, however, driven by poor performance for {IOU→MOU} changes. Some variable seems unaccounted for, possibly state restrictions on MOU creation. Of the specific variables, while not always of the expected signs, none changed signs across specifications, given the base case. ⌬AR was significant with the expected sign in every regression (except for the {REC→MOU} case), consistent with residents changing utilities to obtain lower rates. ⌬POP was significant in almost half of the cases with the expected sign, but does not seem to matter in the IOU base case. ⌬LFL% and ⌬LF⌬% made the weakest showing, significant only in the REC base case. GOV6% was significant in almost half of the cases and mostly of the unexpected sign. This suggests that while Republican governors may appoint PSC commissioners sympathetic to IOUs and business customers, they may also hinder residents’ ability to create an REC. If the latter dominates the former, negative signs on the linear terms would be expected. The federal power variables were significant in almost half of the cases, mostly as expected, with FEDPWR5 making the weakest showing. Finally, YEAR is, with two exceptions, significant, and generally consistent with increased likelihood of ownership change over time. Several differences across base cases emerged. ⌬LFL% and ⌬LF⌬% were significant only in the REC base case. The negative sign suggests that government employees resist any ownership change. FEDPWRi had

217

the unexpected sign in the {IOU→REC} conversion but was not significant in the {IOU→MOU} conversion. This suggests that federal power facilitates movement away from REC ownership. This is surprising, given that federal producers wholesale much of their power with preference to RECs and MOUs, which buy much of theirs. Perhaps power was distributed largely to existing RECs and MOUs. If true, this weakens the argument for government competition. Finally, what can be inferred from the {MOU→REC} changes in the IOU base case? It appears that such changes result in higher AR. Perhaps these MOUs were inefficiently run, using tax revenue to subsidize rates, so that MOU AR plus the average subsidy exceeded REC AR. Also, federal power distribution reduced the probability that residents switch from an MOU to an REC. This is consistent with the power being distributed to existing MOUs, which would reduce the gain from converting to an REC.

4. Conclusions The paper has examined municipal selection of ownership form at retail in the US electric utility industry during 1945–1991. Municipal residents appear to change ownership form to obtain lower rates. Furthermore, a growing population and a larger fraction of Republicanappointed commissioners increase the probability that residents change to an REC or MOU from an IOU, other things equal, with the last result unexpected. Municipal labor force rent seeking appears unrelated to ownership selection. Federal power appears to be distributed to existing rather than to new MOUs, however, which undermines arguments for government competition and suggests rent seeking. One result differs by ownership form: access to federal power decreases (increases) the probability that residents select an REC (MOU) relative to an IOU. Several research extensions naturally follow. First, rate setting institutions are treated exogenously. One avenue for further research would thus be to endogenize institutional structure. For example, what governs PSC structure? Why do some PSCs have appointed and some have elected commissioners? Why did some PSCs change commissioner selection methods? Similarly, what governs MOU structure, and that of municipal governments generally? Why do some MOUs have independent governing boards whereas others have city governing boards? Second, RECs and MOUs are treated symmetrically, but empirical differences emerged. Another avenue would be to examine how institutional differences affect ownership choice. For example, why did municipalities switch from an MOU to an REC despite higher AR—do municipalities subsidize rates with tax revenue? Third, as the industry moves to more

218

D.W. Savitski / Utilities Policy 11 (2003) 203–223

Table A1 Summary statistics: the conditional data set

ARN DARN ARO DARO ⌬POP (000) ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

n

Mean Standard Minimum error

Maximum

175 233 97 233 233 233 233 233 233 233 233 233 233 233

0.090 0.751 0.090 0.416 14.159 0.327 0.660 32.189 33.069 34.464 0.073 0.094 0.300 71.691

0.288 1.000 0.311 1.000 376.135 20.354 45.951 100.000 100.000 100.000 1.000 1.000 1.000 91.000

0.036 0.025 0.433 0.000 0.050 0.020 0.494 0.000 50.266 ⫺36.936 4.798 ⫺18.263 9.183 ⫺42.371 36.199 0.000 30.462 0.000 28.315 0.000 0.261 0.000 0.293 0.000 0.459 0.000 10.127 45.000

competitive wholesale markets, do we see more ownership changes as residents are presented with more competitive wholesale power alternatives? Fourth, was the distribution of state and federal wholesale power in fact distributed to existing RECs and MOUs? This would have significantly, if not completely, undermined government competition as a disciplinary policy. Several policy implications are also suggested. First, government competition, and the current restructuring efforts, has significant policy implications for subsidized programs. Posner (1971) argues that regulation often involves cross subsidization. Since this requires entry

barriers to protect the subsidy source, government competition and restructuring that forces IOUs to lower rates or causes them to lose customers undermines the ability of regulators to do so. For example, how are subsidized programs, such as low income assistance, research and development, and subsidies for efficient appliances, to be funded in a competitive wholesale market? One possibility is that they be funded through a nonbypassable distribution charge. A related issue concerns ensuring that the technically demanding equality between quantity supplied and demanded be maintained continuously, as electricity storage is not feasible. Who should be responsible for ensuring reliability? Successful restructuring will put pressure on private efforts to ensure reliability, such as swapping power and maintaining excess reserves, which were naturally dealt with under regulation, or were voluntary. Maintaining excess reserves in a competitive environment puts generators at a cost disadvantage relative to those with smaller reserves. Second, the various ownership structures, such as RECs, MOUs, IOUs, plus others such as irrigation and public power districts, and state and federal producers, complicate restructuring. If done at the state level, it means heterogeneous restructuring, since states start restructuring at different times under different conditions. This may create problems for ensuring reliability of the grid. Hence, should restructuring be initiated at the state or federal level? The former preserves flexibility while the latter results in a more homogenous restructuring. The latter may also undo some successful

Table A2 The correlation matrix: the conditional data set ARN ARN ARO ⌬POP ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

ARO

⌬POP

1.000 ⫺0.155 ⫺0.036 0.080 0.120 0.035 0.125 0.152 ⫺0.172 ⫺0.187 ⫺0.209 ⫺0.198

1.000 0.071 0.102 0.109 ⫺0.161 ⫺0.172 ⫺0.154 ⫺0.057 0.005 0.059 ⫺0.009

1.000 0.076 0.128 ⫺0.065 ⫺0.044 ⫺0.030 0.012 ⫺0.047 0.014 ⫺0.083

GOV15%

FEDPWR5

FEDPWR10

1.000 0.044 0.020 ⫺0.097 ⫺0.055

1.000 ⫺0.091 ⫺0.184 0.188

1.000 ⫺0.212 0.245

⌬LFL%

⌬LFA%

GOV6%

GOV10%

1.000 0.833 0.062 0.110 0.099 ⫺0.101 0.099 ⫺0.013 ⫺0.245

1.000 ⫺0.030 0.017 0.002 ⫺0.094 ⫺0.04 ⫺0.005 ⫺0.240

1.000 0.849 0.699 0.029 0.105 ⫺0.057 0.041

1.000 0.904 0.036 0.065 ⫺0.076 ⫺0.028

FEDPWR11+

YEAR

1.000 0.078

1.000

D.W. Savitski / Utilities Policy 11 (2003) 203–223

state experiments. Also, tax advantages of some ownership forms may distort restructuring, and REC and MOU ownership of much of the grid raises access issues, such as using a publicly owned grid for private transmission. Third, will restructuring increase air pollution and decrease the use of renewable energy sources? These are likely to depend more on policy than on market forces. A final policy note. IOU control has evolved from franchise competition to state PSC regulation to government competition. Moral hazard, rent seeking, weak regulator incentives, etc., undermine regulation, suggesting a different tack: develop alternatives rather than control rates. This brings us full circle, to Demsetz (1968) and Baumol (1982). As of February 2001, 24 states have enacted restructuring legislation, 18 have ongoing legislative investigations, and 8 have no restructuring activity (Energy Information Administration (2001)).21 The evidence from electricity deregulation in California suggests that this may not be the closing chapter in this evolution. See, for example, Joskow (2001).

Appendix A. Data appendix Tables A1–A5 describe the conditional data set. Tables A1 and A2 present summary statistics and correlation matrix. Tables A3–A5 present variable cross-tabulations by ownership form, conditional on initial ownership form. For the REC base case (Table A3), only three observations on AR are not missing, so AR tests are not done (AR tests are done for the other cases). For the REC case, mean ⌬POP is 31.222 (thousands) with a standard error of 127.964. The hypothesis that mean ⌬POP is the same for RECs and RECs that switched to IOUs is not rejected, with a t statistic of ⫺0.110 and 10 degrees of freedom. Four of 10 means differ by ownership form at 10% or better. Finally, for the REC– REC→IOU comparison, the hypothesis that all means are jointly equal is rejected at 1% with an F statistic of F(10,77) = 5.590. Tables A4 and A5 present results for the MOU and IOU base cases. Table A6–A8 describe the control data set. Tables A6 and A7 present summary statistics and correlation matrix. Table A8 presents variable cross-tabulations by ownership form. The first set of columns presents conditional means and standard errors. For RECs, for

21

The 24 are: Arizona, Arkansas, California, Connecticut, Delaware, Illinois, Maine, Maryland, Massachusetts, Michigan, Montana, Nevada, New Hampshire, New Jersey, New Mexico, New York, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, Texas, Virginia, and West Virginia. The 18 are: Alaska, Colorado, Florida, Indiana, Iowa, Kentucky, Louisiana, Minnesota, Mississippi, Missouri, North Carolina, North Dakota, South Carolina, Utah, Vermont, Washington, Wisconsin, and Wyoming. The eight are: Alabama, Georgia, Hawaii, Idaho, Kansas, Nebraska, South Dakota, and Tennessee.

219

example, mean AR is 0.053 (5.3 ¢/kWh) with a standard error of 0.048. The second set of columns tests the hypotheses that the means do not differ by ownership form. For example, the hypothesis that mean AR is the same for RECs and MOUs is rejected, with a t statistic of 2.367 and 76 degrees of freedom. Most means differ by ownership form for at least one comparison. At the bottom, the hypothesis that all means are jointly equal is tested. For the REC–MOU comparison, this is not rejected at 10% with an F statistic of F(12,204) = 0.920. Overall, the results suggest that the variables are related to ownership selection.

Table A3 Cross-tabulations: the conditional data set—REC base case. Variable

Ownership selection

Hypothesis: mi = mj

REC

REC vs. REC→IOU

ARN

REC→IOU

0.082 (0.035) 0.649 DARN (0.480) 0.082 ARO (0.035) 0.649 DARO (0.480) ⌬POP (000) 31.222 (127.964) 0.956 ⌬LFL% (3.955) 2.460 ⌬LFA% (9.285) 24.023 GOV6% (36.221) 25.195 GOV10% (33.427) 25.195 GOV15% (31.319) FEDPWR5 0.078 (0.270) FEDPWR10 0.065 (0.248) FEDPWR11+ 0.494 (0.503) YEAR 71.325 (13.507)

0.098 (0.019) 0.182 (0.405) 0.311 (0.000) 0.091 (0.302) 34.282 (78.491) ⫺0.722 (7.086) ⫺2.815 (15.487) 50.000 (39.441) 47.273 (33.494) 47.576 (32.114) 0.091 (0.302) 0.273 (0.467) 0.455 (0.522) 54.000 (8.331)

n

11

77

⫺0.110 0.768 1.102 ⫺2.064∗∗∗ ⫺2.046∗∗∗ ⫺2.169∗∗∗ ⫺0.135 ⫺1.448 0.233 5.881∗ Hypothesis: all means equal F(10,77) = 5.590∗

t Statistics are in parentheses below the estimates. Assuming that these means are from independent samples, a t statistic is given by t = (mi⫺mj) / (si 2 /ni + sj 2 / nj)1 / 2, where m is the mean of the variable in question, s is the standard error, and n is the sample size. This is conservative with degrees of freedom (df) min{ni⫺1,nj⫺1}. The critical t values are ±1.812, ±2.228, and ±3.169 at 10%, 5%, and 1% significance with 10 degrees of freedom with a two-tailed test. ∗∗ Indicates significance at 5%. ∗ Significant at 10%. ∗∗∗ Significant at 1%.

220

D.W. Savitski / Utilities Policy 11 (2003) 203–223

Table A4 Cross-tabulations: the conditional data set—MOU base case Variable

ARN DARN ARO DARO ⌬POP (000) ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR n

Hypothesis: mi = mj

Ownership selection MOU

MOU→REC

MOU→IOU

MOU vs. MOU→REC

MOU vs. MOU→IOU

0.037 (0.047) 0.486 (0.502) 0.037 (0.047) 0.486 (0.502) 34.781 (291.688) 1.041 (3.194) 2.027 (7.079) 28.094 (35.486) 29.643 (31.243) 29.715 (29.742) 0.150 (0.358) 0.079 (0.270) 0.343 (0.476) 68.371 (12.988) 140

0.067 (0.046) 0.889 (0.320) 0.022 (0.029) 0.444 (0.506) ⫺0.157 (5.323) ⫺1.929 (6.208) ⫺1.609 (13.768) 34.568 (33.629) 32.222 (28.734) 28.642 (24.794) 0.148 (0.362) 0.111 (0.320) 0.222 (0.424) 77.000 (8.949) 27

0.090 (0.032) 0.992 (0.089) 0.027 (0.049) 0.317 (0.467) 4.108 (17.236) 0.579 (4.150) 0.905 (6.917) 35.648 (36.843) 36.429 (30.547) 37.646 (27.851) 0.063 (0.245) 0.079 (0.271) 0.198 (0.400) 71.921 (9.066) 126

⫺3.463∗∗∗

⫺10.840∗∗∗

⫺5.389∗∗∗

⫺11.085∗∗∗

2.190∗∗

1.694

0.395

2.844∗∗∗

1.416

1.242

2.425∗∗

1.009

1.339

1.306

⫺0.908

⫺1.699∗

⫺0.421

⫺1.790∗

0.199

⫺2.246∗∗

0.026

2.332∗∗

⫺0.487

0.000

1.330

2.698∗∗∗

⫺2.605∗∗ ⫺4.225∗∗∗ Hypothesis: all means equal F(14,152) = 10.481∗∗∗ F(14,251) = 30.263∗∗∗

See Table A3 for notes. The critical t values are ±1.706 (±1.658), ±2.056 (±1.980), and ±2.779 (±2.617) at 10%, 5%, and 1% significance with 26 (120) degrees of freedom with a two-tailed test.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

221

Table A5 Cross-tabulations: the conditional data set—IOU base case Variable

ARN DARN ARO DARO ⌬POP (000) ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR n

Hypothesis: mi = mj

Ownership selection IOU

IOU→REC

IOU→IOU

0.048 (0.053) 0.515 (0.508) 0.048 (0.053) 0.515 (0.508) ⫺24.936 (436.435) 1.445 (1.901) 3.750 (4.459) 47.979 (40.347) 43.030 (30.871) 43.639 (29.196) 0.152 (0.364) 0.091 (0.292) 0.333 (0.479) 64.485 (14.618) 33

0.055 (0.055) 0.606 (0.496) 0.039 (0.053) 0.455 (0.506) 61.791 (107.079) 0.394 (5.423) 2.095 (11.684) 29.040 (37.623) 33.182 (28.881) 37.071 (27.711) 0.061 (0.242) 0.03 (0.174) 0.455 (0.506) 73.182 (10.489) 33

0.015 (0.049) 0.111 (0.319) 0.085 (0.052) 0.806 (0.401) 10.262 (24.876) 1.396 (3.942) 1.252 (6.602) 15.741 (28.714) 17.500 (27.710) 21.296 (28.096) 0.056 (0.232) 0.139 (0.351) 0.528 (0.506) 70.944 (9.131) 36

IOU vs. IOU→REC

IOU vs. IOU→MOU

⫺0.526

2.678∗∗

⫺0.736

3.915∗

0.690

⫺2.923∗

0.481

⫺2.625∗∗

⫺1.109

⫺0.463

1.051

0.067

0.760

1.855∗∗∗

1.972∗

3.793∗

1.338

3.603∗

0.937

3.233∗

1.196

1.293

1.031

⫺0.619

⫺1.006

⫺1.644

⫺2.178∗∗ ⫺2.777∗ Hypothesis: all means equal F(14,54) = 7.542∗ F(14,51) = 2.008∗∗

See Table A3 for notes. The critical t values are ±1.697, ±2.042, and ±2.750 at 10%, 5%, and 1% significance with 30 degrees of freedom with a two-tailed test.

Table A6 Summary statistics: the control data set

AR DAR ⌬POP (000) ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

n

Mean

135 250 250 250 250 250 250 250 250 250 250 250

0.080 0.540 1.478 1.068 2.388 29.465 30.040 30.161 0.108 0.068 0.384 68.768

Standard error 0.037 0.499 3.582 3.310 7.561 36.990 32.220 30.571 0.311 0.252 0.487 13.485

Minimum

Maximum

0.013 0.000 ⫺2.300 ⫺8.390 ⫺31.170 0.000 0.000 0.000 0.000 0.000 0.000 45.000

0.219 1.000 34.900 18.120 40.730 100 100 100 1.000 1.000 1.000 91.000

222

D.W. Savitski / Utilities Policy 11 (2003) 203–223

Table A7 Correlation matrix: the control data set AR AR DAR ⌬POP ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR

DAR

⌬POP

⌬LFL%

⌬LFA%

1.000 0.831 ⫺0.046 ⫺0.092 0.028 ⫺0.024 ⫺0.017 0.025 ⫺0.184 ⫺0.115 0.135 0.410

1.000 ⫺0.089 ⫺0.256 ⫺0.151 ⫺0.024 ⫺0.011 0.015 ⫺0.118 ⫺0.101 0.234 0.688

1.000 0.057 0.05 ⫺0.092 ⫺0.083 ⫺0.094 0.071 0.015 ⫺0.034 ⫺0.187

GOV15%

FEDPWR5

FEDPWR10

FEDPWR11

1.000 ⫺0.133 ⫺0.104 ⫺0.175 ⫺0.002

1.000 ⫺0.094 ⫺0.275 ⫺0.131

1.000 ⫺0.213 0.002

1.000 0.303

1.000 0.79 0.071 0.082 0.094 0.012 ⫺0.088 ⫺0.080 ⫺0.373

1.000 0.043 0.047 0.078 ⫺0.042 ⫺0.074 ⫺0.051 ⫺0.318

GOV6%

GOV10%

1.000 0.895 0.814 ⫺0.086 ⫺0.022 ⫺0.185 ⫺0.081

1.000 0.933 ⫺0.121 ⫺0.055 ⫺0.152 ⫺0.047

YEAR

1.000

Table A8 Cross-tabulations: the control data set Variable

REC

MOU

IOU

Hypothesis: mi = mj REC vs. MOU

AR DAR ⌬POP (000) ⌬LFL% ⌬LFA% GOV6% GOV10% GOV15% FEDPWR5 FEDPWR10 FEDPWR11+ YEAR n

0.053 (0.048) 0.649 (0.480) 1.212 (2.838) 0.956 (3.955) 2.460 (9.285) 24.023 (36.221) 25.195 (33.427) 25.195 (31.319) 0.078 (0.270) 0.065 (0.248) 0.494 (0.503) 71.325 (13.507) 77

0.037 (0.047) 0.486 (0.502) 1.696 (4.158) 1.041 (3.194) 2.027 (7.079) 28.094 (35.486) 29.643 (31.243) 29.715 (29.742) 0.129 (0.336) 0.064 (0.246) 0.336 (0.474) 68.371 (12.987) 140

0.048 (0.053) 0.515 (0.508) 1.173 (2.2666) 1.445 (1.901) 3.750 (4.459) 47.979 (40.347) 43.030 (30.871) 43.639 (29.196) 0.091 (0.292) 0.091 (0.292) 0.333 (0.479) 64.485 (14.618) 33

REC vs. IOU

MOU vs. IOU

2.367∗∗

0.466

⫺1.095

2.355∗∗

1.289

⫺0.296

⫺1.010

0.076

0.987

⫺0.162

⫺0.875

⫺0.946

0.356

⫺0.983

⫺1.758∗

⫺0.798

⫺2.941∗∗∗

⫺0.960

⫺2.708∗∗

⫺2.236∗∗

⫺1.035

⫺2.970∗∗∗

⫺2.456∗∗

⫺1.218

⫺0.219

0.653

0.029

⫺0.447

⫺0.492

1.591

0.032

2.259∗∗

1.560 2.300∗∗ Hypothesis: all means equal F(12,204) = 0.920 F(12,97) = 1.646∗

2.604∗∗

1.403 F(12,160) = 1.491

See Table A3 for notes. The critical t values are ±1.70 (±1.67), ±2.04 (±2.00), and ±2.75 (±2.60) at 10%, 5%, and 1% significance with 30 (60) degrees of freedom with a two-tailed test. The 1% critical F(10,60) is 2.63.

D.W. Savitski / Utilities Policy 11 (2003) 203–223

References Alchian, A.A., 1965. Some economics of property rights. Il Politico (December), 816–829. Alchian, A.A., Demsetz, H., 1972. Production, information costs, and economic organization. American Economic Review 62 (December), 777–795 (In: Demsetz, H., 1988. Ownership, Control, and the Firm. The Organization of Economic Activity, vol. 1. Basil Blackwell Inc., New York, pp. 119–143). American Public Power Association 1987. Survey of Administrative and Policy-Making Organization of Publicly Owned Electric Utilities. Baumol, W.J., 1982. Contestable markets: an uprising in the theory of industry structure. American Economic Review 72 (1), 1–15. Becker, G.S., 1983. A theory of competition among pressure groups for political influence. The Quarterly Journal of Economics XCVIII (3), 371–400. Behling, B.N., 1938. Competition and monopoly in public utility industries. University of Illinois Bulletin XXXV (100), 1–187. Council of State Governments, various years. The Book of the States. Council of State Governments, Lexington, KY, various years. Demsetz, H., 1968. Why regulate utilities? The Journal of Law and Economics II (April), 55–65. Demsetz, H., 1983. The structure of ownership and the theory of the firm. Journal of Law and Economics 26 (2), 375–390 (In: Demsetz, H., 1988. Ownership, Control, and the Firm. The Organization of Economic Activity, vol. 1. Basil Blackwell Inc., New York, pp. 187–201). Demsetz, H., Lehn, K., 1985. The structure of corporate ownership: causes and consequences. Journal of Political Economy 93 (6), 1155–1177 (In: Demsetz, H., 1988. Ownership, Control, and the Firm. The Organization of Economic Activity, vol. 1. Basil Blackwell Inc., New York, pp. 202–222). Donahue, J.D., 1988. Prisons for Profit: Public Justice, Private Interests. Economic Policy Institute, Washington, DC. Eggertsson, T., 1990. Economic Behavior and Institutions. Cambridge University Press, New York. Electrical World, various years. Directory of Electric Utilities. New York, McGraw Hill. Emmons, W.M. III, 1993. Franklin D. Roosevelt, electric utilities, and the power of competition 53 (4), 880–907. Energy Information Administration, 1991. Form EIA-861: Annual Electric Utility Report. Energy Information Administration 1992. Inventory of Power Plants in the United States 1991. GPO, Washington, DC. Energy Information Administration 1993. Financial Statistics of Selected Investor Owned Electric Utilities 1991. GPO, Washington, DC. Energy Information Administration, 2001. Status of state electric industry restructuring activity as of February 2001. Available from ⬍http://www.eia.doe.gov/cneaf/electricity/chg str/regmap.html⬎, February. Fama, E.F., Jensen, M.C., 1983a. Separation of ownership and control. The Journal of Law and Economics XXVI (2), 301–325. Fama, E.F., Jensen, M.C., 1983b. Agency problems and residual claims. The Journal of Law and Economics XXVI (2), 327–349. Ferris, J.M., Graddy, E., 1991. Production costs, transaction costs, and local government contractor choice. Economic Inquiry 29 (3), 541–554. Hart, O., Shleifer, A., Vishny, R.W., 1997. The proper scope of

223

government: theory and an application to prisons. Quarterly Journal of Economics 112 (4), 1127–1161. Hellman, R., 1972. Government Competition in the Electric Utility Industry: A Theoretical and Empirical Study. Praeger Publishers, New York. Jarrell, G.A., 1978. The demand for state regulation of the electric utility industry. The Journal of Law and Economics XXI (2), 269–295. Jensen, M.C., Meckling, W.H., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. The Journal of Financial Economics 4 (3), 305–360. Jensen, M.C., Meckling, W.H., 1979. Rights and production functions: an application to labor-managed firms and codetermination. Journal of Business 52 (4), 469–506. Joskow, P.L., 1974. Inflation and environmental concern: structural change in the process of public utility. The Journal of Law and Economics XVII (2), 291–327. Joskow, P.L., 2001. California’s electricity crises. National Bureau of Economic Research, Working Paper 8442. Maddala, G.S., 1983. Limited-dependent and Qualitative Variables in Econometrics. Cambridge University Press, New York. Mosher, W.E., 1929. Electric Utilities: The Crises in Public Control. Harper & Brothers, New York. Mueller, D.C., 1989. Public Choice II. Cambridge University Press, New York. Navarro, P., 1982. Public utility commission regulation: performance, determinants, and energy policy impacts. Energy Journal 3 (2), 119–139. Peltzman, S., 1971. Pricing in public and private enterprises: electric utilities in the United States. The Journal of Law and Economics XIV (1), 109–147. Peltzman, S., 1976. Toward a more general theory of regulation. The Journal of Law and Economics XIX (2), 221–240. Posner, R.A., 1971. Taxation by regulation. The Bell Journal of Economics and Management Science 2 (1), 22–50. Schap, D., 1986. Municipal Ownership in the Electric Utility Industry: A Centennial View. Praeger Publishers, New York. Shapiro, C., Willig, R.D., 1990. Economic rationales for the scope of privatization. In: Suleiman, E.S., Waterbury, J. (Eds.), The Political Economy of Public Sector Reform and Privatization. Westview Press, Boulder and London, pp. 55–87. Stigler, G.J., 1971. The theory of regulation. The Bell Journal of Economics and Management Science 2 (1), 3–21. Stigler, G.J., Friedland, C., 1962. What can regulators regulate? The case of electricity. The Journal of Law and Economics V (October), 1–16. The Economist, 1997. America’s power companies: LILCO’s nuclear waste. pp. 58–59, January 4. US Department of Commerce, Bureau of the Census, various years. Number of inhabitants. In: Characteristics of the Population. Census of Population, vol. 1. GPO, Washington, DC (various states) (Chapter A). US Department of Labor, Bureau of Labor Statistics, 2000. Producer price index—fuel, related products, and power. Available from ⬍http://www.bls.gov/ppihome.htm⬎, October. Wilcox, D.F., 1910. Municipal Franchises, vol. 1. McGraw-Hill Book Company, New York. Williamson, O.E., 1981. The economics of organization: the transaction cost approach. The American Journal of Sociology 87 (3), 548–572.