THE JOURNAL OF INDUSTRIAL ECONOMICS
Volume LIV
June 2006
0022-1821
No. 2
THE ADOPTION OF STATE ELECTRICITY REGULATION:
THE ROLE OF INTEREST GROUPS
Christopher R. Knittelw
This paper examines the adoption of state electricity regulation around
the beginning of the 20th century. I model this decision as a hazard rate
to determine what influenced the adoption of state regulation. I find that
adoption is positively correlated with capacity shortages, greater wealth
and lower residential electricity penetration rates. These results suggest
that state regulation responded to regulatory inefficiencies and
residential consumer interests. In addition, adoption rates were higher
in states that had a strong industrial and coal mining presence. These
results are consistent with the interest group and contracting theories of
regulation.
I. INTRODUCTION
STATES BEGAN REPLACING MUNICIPAL REGULATION of electric utilities with state
regulation in 1907. Despite the long history, surprisingly little is known
about the causes of the regime shift. In this paper, I estimate a hazard model
that defines the probability of adopting state regulation as a function of
variables that measure the potential benefits and costs of a number
of interest groups, the shortage of installed capacity and concurrent levels of
electricity prices and profits. The results shed light on which theories of
regulation are most consistent with the data.
I find evidence that is consistent with the interest group theory of regulation, which began with Peltzman [1976], and the contracting theory of regulation, attributed to Priest [1993]. The interest group theory of regulation
centers around private interest group competition; regulators respond to the
pressures of interest groups. One testable implication of the interest group
theory is that if state regulation benefited a certain class of consumers, such
as residential consumers, at the expense of another class of consumers, the
I would like to thank Severin Borenstein, Jim Bushnell, Thomas Lyon and Victor Stango,
David Greenberg provided excellent research assistance. Financial support from the Boston
University School of Management Junior Faculty Research Grant Program and the University
of California Energy Institute is gratefully acknowledged. All remaining errors are my own.
wAuthor’s affilation: Department of Economics, University of California at Davis;
University of California Energy Institute; and NBER; One Shields Ave, Davis, California
95616, U.S.A.
e-mail: [email protected].
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201
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CHRISTOPHER R. KNITTEL
electric utilities or municipal regulators, then a stronger residential lobby
will increase the probability of adopting state regulation. I find evidence in
support of this. Specifically, I find that greater per capita wealth and lower
residential electricity service penetration are correlated with an increase in
the probability of adopting state regulation. These results suggest that
greater residential consumer lobbying power (measured by wealth) and
greater potential gains (measured by lower penetration) are correlated with
state regulation adoption.
Priest [1993] argues that contracting problems inherent with municipal
regulation may have been an impetus for state regulation. Anecdotal
evidence suggests that contracting problems were present in municipal
regulation for a number of reasons. For one, technological advances during
this time period increased the transmission capabilities of electricity
networks. This increased the minimum efficient scale of the industry, as
fewer generators were needed to serve a given jurisdiction and a single plant
could service multiple cities. Given the increase in the geographical breadth
of utilities, municipal regulation required more and more utilities to contract
with multiple sets of regulators. In contrast, provided the municipalities fell
within the same state, state regulation required only one regulatory contract,
thereby decreasing transaction costs and the uncertainty associated with
generation investments.
Municipal regulator corruption was a second source of contracting
inefficiencies. Municipal corruption caused firms to curtail investments in
generation assets out of a fear that regulators would not allow the firms to
recuperate the investment costs. This created a classic hold-up problem and
inefficiently low levels of investment. Given the dynamic nature of the
industry at the time and the ability for ex post opportunism by municipal
regulators, Priest finds evidence that welfare gains from adopting a more
efficient form of regulation were possible; state regulation filled this role.
I find evidence consistent with Priest. Greater capacity shortage in a state is
correlated with the adoption of state regulation.
The existence of inefficient levels of generation also altered the incentives
of certain interest groups. The probability of adopting state regulation
is greater in states with capacity shortages combined with high levels of
value added, a measure correlated with industrial presence; this result
suggests that when capacity shortages were present, industrial consumers
lobbied for state regulation. Input providers were another interest group
that could have potentially gained from reducing contracting inefficiencies.
Coal was (and is) a major input in the generation of electricity. If contracting
costs reduced output, coal mining firms would find it in their interest to
lobby for a better form of regulation. The results suggest the adoption of
state regulation is more likely in states with greater levels of per capita coal
mining. These results support both the contracting and interest group
theories.
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I find little evidence in support of the capture theory of regulation or the
‘pure’ public interest theory of regulation.1 After controlling for regional
costs differences and cost differences due to hydroelectric output, states with
low prices are more likely to adopt state regulation, while the level of profits
does not appear to be correlated with the decision. These results are at odds
with both the capture and ‘pure’ public interest theories, since the capture
theory predicts that both low prices and profits will lead to state regulation,
whereas the public interest theory predicts that both high prices and profits
lead to state regulation. These results are broadly consistent with the
contracting theory of regulation. If prices were set too low to support
adequate generation and distribution investment levels, then low prices will
be correlated with the adoption of state regulation; profit rates may not be
correlated with adoption since the investments did not occur.
The paper adds to a large historic literature dealing with the political
economy of regulation during the Progressive Era. The results of the paper
parallel research on other industries, most notably the railroad industry.
There is much evidence that the adoption of state-level ‘Granger Laws’ was
the result of consumer interest groups’ ability to lobby for more favorable
pricing (see, for example, Buck [1913], Benson [1969], Miller [1971] and
Galambos and Pratt [1988]). Furthermore, Gilligan, Marshall and Weingast
[1984] find that the passage of the Interstate Commerce Act of 1887 is more
consistent with the interest group theory of regulation than with the public
interest theory. They find evidence that suggests the Act was not passed as a
cartel mechanism for the railroads, nor was it passed to correct market
abuses from railroads. Instead, they find that the Act benefited short-haul
shipping firms and railroads, at the expense of long-haul shipping firms.
Kanazawa and Noll [1994] study the origins of Illinois state regulation of
railroads and find that state regulation benefited large consumers of railroad
shipping (e.g., farmers).
A broader literature on changes in the economy during the Progressive Era
also exists. For example, Weibe [1986] argues that the Progressive Era
represented a general shift in ideology from small-town life to the middleclass. Hofstadtler [1955] echoes these themes. Alfred Chandler, in a number
of works, has argued that the firms that came to dominate during this time
were those best able to maintain high levels of capacity utilizationFthat is,
maintain a high rate of throughput.2 Among other things, high levels of
industrial throughput hinged on the ability of firms to obtain sufficient levels
of electricity. Therefore, an obvious extension of his argument is that the
‘new industries’ had a larger interest in securing reliable electricity generation
1
I define the ‘pure’ public interest theory to be regulation as a response to potential allocative
inefficiencies (e.g., market power). The contracting theory is consistent with a more general
interpretation of the public interest theory that includes all sources of inefficiencies.
2
See, for example, Chandler [1977 and 1990].
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CHRISTOPHER R. KNITTEL
than the old industries of the 19th century. These works are consistent with
the results of the paper: that states with capacity shortages and high levels of
industrial activity were more likely to adopt state regulation.
The remainder of the paper is organized as follows. In section two, I outline
the evolution from municipal regulation to state regulation and discuss the
potential reasons for the change. Section three discusses the empirical model.
Section four discusses the data and results. Further discussion and
concluding remarks are reserved for sections five and six, respectively.
II. THE MOVE TO STATE REGULATION
Electricity regulation began with the use of franchise licenses by
municipalities to control rates and right-of-way as early as 1885. (King
[1912]) These local regulatory authorities often did little to control rates
directly, usually setting a maximum rate above prevailing prices. (Electric
Power and Government Policy [1948]) Instead, municipalities focused on
controlling the number of franchises offered, and thus the level of
competition. Given the high fixed costs associated with electricity generation
and distribution, this likely represented an inefficiency. Indeed, municipal
regulation often did little to limit entry. In Duluth for instance, five firms
provided electricity as early as 1885; New York City had six firms in
operation in 1887; by 1907, forty-five companies provided electricity in
Chicago. (Phillips [1984]) In many cases, the jurisdiction of these firms
overlapped, creating duplicate transmission networks and efficiency
losses.
The migration to state regulation began in 1907, when Wisconsin, New
York and Georgia passed legislation to expand the scope of their railroad
commissions to include gas and electric companies. More states quickly
followed suit. The regulatory bodies were surprisingly similar in scope. The
commissions commonly had the power to set rates, standards, and control
entry and exit. In addition, their authority superseded that of the localities.3
Because very little electricity initially flowed across state boundaries, there
was little need for federal regulation. Not until 1920, with the creation of the
Federal Power Commission, did the federal government regulate a portion
of the electricity industry. Even then, federal involvement was small,
overseeing only interstate transmission; state regulators had most of the
regulatory control.
II(i).
Explanations for Moving to State Regulation
The move from municipal regulation to state regulation could have occurred
for a number of reasons. One explanation is that state regulators acted in the
3
Stigler and Friedland [1962] highlight some differences in regulatory powers across states.
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best interest of consumers in order to curb market power that had been left
unchecked by municipal regulators; I refer to this as the ‘pure’ public interest
theory of regulation. If electric utilities exercised a significant amount of
market power under municipal regulation, consumers (and society) would
benefit from the adoption of state regulation, provided state regulation was
more stringent. A prediction of the ‘pure’ public interest theory is that,
ceteris paribus, high prices and profits will be correlated with the adoption of
state regulation. In support of the public interest theory, Emmons [1997]
finds that state regulation and public ownership are associated with lower
prices during 1930 to 1942.
An alternative explanation for the rise in state regulation is that the
electricity firms sought state regulation and subsequently ‘captured’ state
regulators. The electric utilities, therefore, benefitted from state regulation.4
One testable implication of the capture theory is that low prices and profit
levels will lead to state regulation; firms with low prices and profit levels will
have the greatest incentive to lobby for state regulation since the potential
gains of these firms are largest.
Although their goal is not to test the reasons behind state regulation,
Stigler and Friedland [1962] provide the first supporting evidence for the
capture theory. Stigler and Friedland regress the average revenue of
electricity firms in each state, taken at five year intervals from 1907 to 1922,
on control variables and an indicator variable equal to one if the state had
state regulation. Their results suggest that although state regulation is
associated with lower prices, this effect is not statistically significant. The
statistical insignificance of the coefficient suggests that consumers did not
benefit from state regulation, a result that is consistent with the capture
theory.5 Jarrell [1978] builds on Stigler and Friedland by estimating a system
of equations for demand, average costs and price. He includes an indicator
variable for the time period prior to regulation which allows him to correlate
current price and profit levels with future state regulation.6 His main
conclusions follow from observing that the 1912 prices and profit rates were
lower for firms that became state regulated between 1912 and 1917,
compared to those that did not.
A generalization of the capture theory is Peltzman’s [1976] interest group
theory. The interest group theory holds that, as economic agents, regulators
will respond to the lobbying efforts of both the firms they regulate and other
interested parties, such as consumer groups. Thus, just as electric utilities may
4
The capture theory began with Huntington [1952] and Bernstein [1955] and was formalized
by Stigler [1971] and Noll [1971].
5
One explanation for the result is that the regulation variable is endogenous. For example, if
regulation were passed in a state as a result of high prices, then high prices would be correlated
with regulation even if state regulators sought to lower prices upon taking control.
6
Jarrell defines the period prior to regulation as being the year before the adoption of state
regulation and the first 2 years of state regulation.
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CHRISTOPHER R. KNITTEL
gain if they are able to capture state regulators, specific consumer groups may
gain from state regulation at the expense of the regulated firm, other consumer
groups, or, in this case, municipal regulators. For example, consumers may
have sought regulation in order to guarantee adequate levels of future
generation capacity and lower prices as the need and use of electric motors and
household appliances increased. Becker [1983] formalizes the interest group
theory within a taxation framework. He shows that the gains to a particular
interest group are proportional to the group’s lobbying ability and the
potential gains from lobbying effort. The interest group theory predicts that if
a certain group expected to gain from state regulation, then the likelihood of
adopting state regulation would be greater in states where this group was
relatively strong and the potential gains large. Conversely, if an interest group
expected to lose from state regulation, then the adoption of state regulation
would be less likely in those states where this group was strong.
Priest [1993] advocates a theory of regulation based on contracting costs;
changes in regulation occur when the existing regulatory framework has
contracting inefficiencies. Municipal regulation had at least two sources of
contracting inefficiencies. First, the introduction of alternating current (AC)
by George Westinghouse in 1893 allowed electricity to travel long distances
more efficiently. This increased the minimum efficient scale of the industry as
it required fewer generators to cover a given jurisdiction. As such, it became
easier and more commonplace for a single firm to service a number of
municipalities. Municipal regulation required these firms to negotiate
multiple regulatory contracts. In contrast, by increasing the geographical
breadth of regulation, state regulation required only one regulatory contract
(provided the municipalities fell within the same state), decreasing the
transaction costs associated with regulation.7
Regulator corruption was a second source of municipal regulation
inefficiency. Anecdotal evidence suggests that local control of electric
utilities was riddled with corruption. The most glaring example was a group
of Chicago City Council members known as the Gray Wolves, who
repeatedly extorted money from utility companies. The motives of the Gray
Wolves are well documented. (see Anderson [1981]) They began their
extortionary activities with the transportation sector by selling votes for
franchise contracts. The Gray Wolves would also open their own electricity
franchises and threaten to engage in predatory pricing if the incumbent
would not buy the Gray Wolves’ operations. Other evidence suggests that
municipal regulation was also corrupt in Wisconsin. McDonald [1957]
argues that local politicians recognized that voters in Milwaukee responded
7
This was less of an issue under Thomas Edison’s direct current (DC) systems, as DC systems
required multiple generators to service a municipality because of the short distances that DC
could travel.
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favorably to attacks on utilities, ‘irrespective of whether the attacks were
justified.’
The increase in the geographical breadth of electricity firms and the ability
for ex post opportunism by corrupt municipal regulators likely led electricity
firms to curb large sunk cost investments, resulting in inefficient levels of
generation.8 If state regulators were less corrupt, or potentially less corrupt,
then state regulators and interested parties would have seen state regulation
as a means of relieving the contracting inefficiencies, thereby spurring
investment in generation capacity. Furthermore, even absent corruption, an
efficiency gain could be realized by allowing firms to serve multiple cities
more efficiently.
The actions of industry leaders support Priest. Statements from the
industry trade group of the time, the National Electric Light Association
(NELA), suggest that the electricity industry saw state regulation as a way to
reduce the hold-up problems present with municipal regulation. In 1907, the
NELA Subcommittee on Public Regulation and Control, as a response to
the impending state regulation in New York and Wisconsin, reached three
major conclusions:
1. That the NELA should favor properly constituted general supervision and regulation of the electric light industry.
2. That if state commissions be constituted, they should be appointed
in that manner which will give them the greatest freedom from local
and political influences, to the end that their rulings shall be without
bias.
3. That state commissions be clothed with ample powers to control the
granting of franchises, to protect users of service against
unreasonable charges or improper discriminations, to enforce a
uniform system of accounting, and to provide for publicity. If the
state provides for publicity on the one hand, on the other hand it
should safeguard investments. Regulation and publicity would be a
grievous wrong unless accompanied by protection. (Anderson
[1981])
Conclusions 2 and 3 suggest that the electric industry sought regulation that
was less corrupt (conclusion 2) and safeguarded investments (conclusion 3).
II(i)(i). Testing Among Theories It is important to note that while the
public interest and capture theories are at odds with each other, many of the
other theories are not mutually exclusive. Indeed, a broader interpretation
8
Indeed, Priest [1993] reviews a number of municipal franchise contracts that provided
municipalities the ability to ‘renegotiate’ the contracts over time, creating commitment
problems on the part of the regulators.
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CHRISTOPHER R. KNITTEL
of the public interest theory, one that interprets regulation as a response to
market failures, is entirely consistent with the contracting theory. In this
sense, a finding that state regulation represented a response to inefficient
municipal regulation also supports the public interest theory. This is largely
a semantic point, but I refer to the ‘pure’ public interest theory to describe
regulation as the response to market power levels, and the contracting
theory to refer to regulation as the response to municipal regulatory
inefficiencies.
The contracting and interest group theories may also coexist. In
fact, the greater the contracting inefficiencies inherent in municipal
regulation, the greater the potential gains of many of the interested parties.
In this case, the two theories will positively interact with each other, as
well as increase the number of relevant interest groups. For example if
contracting problems reduced the amount of electricity consumed, input
providers had an interest in lobbying for the adoption of state regulation.
The potential gains from state regulation would also be larger for consumers
that sought a reliable source of electricity if municipal regulation reduced
investment.
II(ii).
Testable Implications
The strategy in this paper is to correlate the likelihood of adopting state
regulation with variables that measure the relative strength and potential
gains of the relevant players across states, as well as the level of contracting
inefficiencies present. I summarize the testable implications as follows:
Testable Implication 1: If the interest group theory is correct and industrial
consumers stood to gain (lose) from state regulation, state regulation
adoption is more (less) likely in states with a strong manufacturing presence.
Testable Implication 2: If the interest group theory is correct and residential
consumers stood to gain (lose) from state regulation, state regulation
adoption is more (less) likely in states with greater wealth.
Testable Implication 3: If the contracting theory drove state regulation, state
regulation adoption is more likely in states with large capacity shortages,
representing regulatory inefficiencies.
Testable Implication 4: If both the interest group and contracting theories of
regulation are correct, then in states with inefficient levels of capacity, a
strong coal mining and industrial consumer presence will increase the
likelihood of state regulation adoption.
Testable Implication 5: If the ‘pure’ public interest theory is correct, state
regulation adoption is more likely when we observe high prices and profit
levels.
Testable Implication 6: If the capture theory is correct, when we observe low
prices and profit levels the adoption of state regulation is more likely.
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Table I
State Regulation Adoption
Year
States Adopting Regulation
1907
1908
1909
1910
1911
1912
1913
Georgia, New York, Wisconsin
Vermont
Michigan
Maryland, New Jersey
California, Connecticut, Kansas, Nevada, New Hampshire, Ohio, Washington
Oregon, Rhode Island
Colorado, Idaho, Illinois, Indiana, Maine, Missouri, Montana, North Carolina, Oklahoma,
West Virginia
Pennsylvania, Virginia
Alabama, Nebraska, Wyoming
Utah
Arkansas, Massachusetts, North Dakota, Tennessee
South Carolina
Kentucky, Louisiana
Arizona
New Mexico
Delaware
Florida
Mississippi
Iowa, Minnesota
South Dakota, Texas
1914
1915
1917
1919
1922
1934
1935
1941
1949
1951
1956
1960
1975
Source: Stigler and Friedland (1967).
III. MODELING THE DECISION TO REGULATE
To test which theories are most consistent with the data, I make use of the
Census Bureau’s 1907, 1912 and 1917 editions of the Central Electric Light
and Power Stations. Table I lists the year in which each state adopted state
regulation. The sample was chosen because, as the table indicates, most of
the states adopted state regulation prior to 1919. For the purpose of
estimation, the relevant states are those states with municipal regulation
during each of these years; this yields 97 state/year observations. Once a state
adopts state regulation, it is no longer in the sample.
Given the courseness of the data and the presence of a number of ‘failure
ties,’ I estimate the discrete-time grouped binary response model proposed
by Prentice and Gloeckler [1978]. Specifically, I estimate the probability a
state adopts state regulation between time tj and tj þ 5 (e.g., the probability
adopting state regulation between 1907 and 1912), conditional on the state
not having adopted state regulation in the past. I allow this probability to be
a function of variables that capture each of the testable implications above,
denoted by Xit.
I assume a proportional hazard model for the underlying continuous time
probability of implementation.9 This implies that the instantaneous hazard
9
This discussion follows Jenkins [2004].
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CHRISTOPHER R. KNITTEL
for state i at time t is given as:
ð1Þ
lit ðt; Xit ; bÞ ¼ l0 ðtÞ exp ðXit bÞ
where l0(t) is the baseline hazard rate at time t. The survivor functionF
that is the probability of not adopting state regulation by time tFis given
as:
ð2Þ
Z t
lit ðt; Xit ; bÞdt
Sðt; Xit ; bÞ ¼ exp 0
¼ exp½exp½Xit b þ logHt where Ht ¼
Rt
0
l0 ðtÞdt.
While the underlying probability of adoption is continuous, identification
of the parameters makes use of adoption occurring in disjoint time intervals,
given as:
½a0 ; a1 Þ; ½a1 ; a2 Þ; ½a2 ; a3 Þ; ½a3 ; 1Þ
¼ ½1907; 1912Þ; ½1912; 1917Þ; ½1917; 1922Þ; ½1922; 1Þ:
The Xit vector is assumed to be constant within these intervals. Letting Ti be
the time state i adopts state regulation, given the underlying continuous time
process, the probability state i adopts state regulation between 1917 and
1922 is:
ð3Þ
Pr Ti 2 ½aj1 ; aj Þ ¼ S aj1 ; Xit ; b S aj ; Xit ; b
The hazard function associated with the jth interval is:
ð4Þ
S aj ; Xit ; b
hj ðXit ; bÞ ¼ Pr Ti 2 ½aj1 ; aj ÞjTi aj1 ¼ 1 S aj1 ; Xit ; b
Z aj
l0 ðtÞdt:
¼ 1 exp exp Xij b þ gj with gj ¼
aj1
Defining an indicator variable, yij, as equal to one if state i adopts
state regulation in interval j, the log-likelihood function can be
written as:
ð5Þ
log L ¼
Ti N X
X
yij log hj ðXit ; bÞ þ 1 yij log 1 hj ðXit ; bÞ :
i¼1 j¼1
The presence of the gj terms can be accounted for non-parametrically by
including fixed year effects in the analysis. Writing the log-likelihood
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
211
function in this way, it can be seen that the model takes a similar form to
estimating a probit or logit on the pooled data. Indeed, Sueyoshi [1995]
shows that estimating a logit model with fixed year effects yields results very
similar to the proportional hazard model described above.10
The standard errors I report are clustered at the state level since the error
terms of an individual state are likely to be correlated across time.
III(i). Independent Variables
In this section I discuss the elements of Xit. The sources of the data are
described in Appendix B.
III(i)(i). Electricity Firm, Consumer Group and Input Provider Variables
Profit Rate: The profit rate is defined as the return on sales across all
privately-owned electric utilities within the state. For data availability
reasons, revenues omit interest income and costs do not include taxes.11
Price: The price is the log of the average revenue per KWh across all firms
within the state. Unfortunately, data on the relative prices for industrial and
residential consumers are unavailable.
Value Added per Capita: To measure the strength of industrial interests, I
use the log of per capita value added on manufacturing goods in the given
year. The use of electric motors was expanding during this time and industry
expectations likely pointed to an increase in their use. In 1899, the ratio of
installed horsepower of electric motors to other types of motors was 4.9 per
cent, while in 1925 this figure rose to 73 per cent. (Ruggles [1929]) The
primary sticking point for investing in electric motors was a fear of service
interruption. Therefore, manufacturing firms had an increasing interest in
the price of electricity, as well as the ability of electricity firms to invest in new
capacity.
Coal Produced per Capita: If municipal regulation decreased the incentive
for electricity firms to commit to new capacity, then generation levels would
be inefficiently low. This implies a loss to firms that provide inputs for
electricity generation. The interest group theory predicts that in states with a
high level of coal mining, the adoption of state regulation will be more likely.
To capture this, I include the log of per capita tonnage of coal mined in the
state in the given year.12
It is also true that coal mining firms from outside the state had incentive
to lobby policy-makers. Unfortunately, the extent of this is difficult to
10
This is corroborated in unreported results.
A second measure of profitability would be return on assets; however these data are not
available. The use of return on sales is consistent with Jarrell [1978] and Emmons [1997].
12
One is added to each state’s tonnage to allow for logarithms to be taken.
11
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CHRISTOPHER R. KNITTEL
quantify; however, it is likely that policy-makers responded more to pressure
from firms operating within their own state since these firms also represented
a tax base. In addition, the costs associated with lobbying within your own
state may have been lower than the costs associated with out-of-state efforts.
Wealth per Capita: The wealth of the state is likely to be correlated
with the degree of residential interest group activity. If the adoption
of state regulation benefitted residential consumers, we expect those
states with higher levels of wealth to be more likely to adopt state
regulation.
Percentage of Population Served: This variable is the percentage of the
population that had access to electricity and is likely to be inversely
correlated with the potential gains from residential interest group activity. If
the percentage of population served is high, then the gains from expanding
the residential electricity distribution network are small. Therefore the
interest group theory would predict that if residential consumers expected to
gain from state regulation, the lower the penetration rate the more likely
state regulation would be adopted.
Capacity Shortage: As noted, a by-product of municipal contracting
inefficiencies is that firms were less likely to incur large sunk cost
investments, since the uncertainty associated with these investments were
larger. This implies that in states with a high degree of corruption or firms
facing multiple regulatory contracts, capacity levels would have been below
socially optimal levels. To measure the degree to which this is true, I regress
the level of installed electric capacity held by both private and municipal
utilities on the population size, the degree of industrialization and the wealth
of state i at time t.13 Specifically, I estimate:
ð6Þ
lnðTotal Capacityit Þ ¼ b0 þ b1 lnðPopulationit Þ þ b2 lnðValueAddedit Þ
þ b3 lnðWealthit Þ þ eit :
The fitted value of this regression for state i measures the amount of utility
capacity that is installed in an average state with the same population, level
of value added and level of wealth. The residual from this regression
measures the degree in which state i differs from this average. A negative
residual implies that state i has less installed capacity compared to a typical
state with the same population, level of industrial activity and wealth. Thus,
the negative of the residual measures capacity shortages and is likely to be
positively correlated with contracting problems inherent in municipal
13
Because the left hand side variable is total capacity, not capacity per capita, the right hand
side variables are also in their levels.
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
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Table II
First Stage Regression for‘Optimal’Capacity
Level
Variable
Coefficient
Constant
1.110
(1.570)
0.237
(0.105)
0.677
(0.077)
0.865
(0.068)
0.79
ln(Wealth)
ln(ValueAdded)
ln (Population)
R2
regulation.14, 15 The contracting theory of regulation predicts that the larger
the capacity shortage, the more likely state regulation would be adopted.16
Table II presents the results of this first stage regression. All of the
variables have the expected sign and are significant at the .05 level and
explain 79 per cent of the variation in the log of capacity levels.17
Percentage of Output Generated by Municipal Utilities: Public ownership
of the electricity rights overcomes the potential for regulatory corruption;
therefore if regulatory corruption prompted state regulation, the greater the
level of municipal generation the smaller the gains from moving to state
regulation under the contracting theory. This effect would result in a
negative correlation between public ownership and state regulation
14
A negative residual does not necessarily imply that manufacturing firms are doing without
needed electricity. Many manufacturing firms during this time generated their own electricity.
This was likely because of utility capacity shortages, signifying contracting problems. If
economies of scale were present, then self-generation also signified an inefficiency;
manufacturing firms that were generating their own electricity could benefit from state
regulation. In both scenarios, this variable is correlated with contractual inefficiencies arising
from municipal regulation.
15
Regulatory corruption and technological changes are just two sources for municipal
regulatory inefficiencies. There may have been some states that, on average, simply had ‘bad’
management at the regulatory level. This would also be captured by the capacity shortage
variable.
16
Because Xt includes population, value added and wealth, an equivalent way to test the
effect of capacity shortages would be simply to include capacity in Xt. Using the residual from
the first-stage regression, however, aids in interpreting the coefficients and allows for the
interaction terms discussed below.
17
In addition, state fixed effects were also included to control for other unobserved state level
influences, but they did not qualitatively change the results, although the coefficient on the
shortage variable was less precisely estimated. They were not included because they cloud the
interpretation of the capacity shortage variable. Identification of the shortage variable when
state fixed effects are included relies on within state changes in contracting inefficiencies. It is
likely that there is very little variation in contracting problems within a state during the sample
period, since the sample is drawn from all states that have not adopted state regulation.
Furthermore, inclusion of fixed effects reduces the sample size since states that adopted state
regulation between 1907 and 1911 must be dropped as they are in the data set only once
(11 states).
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CHRISTOPHER R. KNITTEL
Table III
Summary Statistics
Variable
Mean
Min
Max
Std Dev
Became
Regulated
Did not
Became Regulated
Price ($/Kwh)
Profit Rate
Coal Mined (100 tons)
Value Added per Capita
Capacity Shortage
Wealth per Capita (1907$)
Percentage of Pop Served
Muni Percentage
Hydro Percentage
Population (1000s)
0.416
3.315
0.230
5.227
0.093
0
1, 826
6.267
11.165
0.313
1, 866
0
0.483
0.048
0
0.008
1.323
659
1.384
0
0
102
1
9.014
0.047
44.70
1.002
1.963
4, 454
48.670
62.955
3.061
9, 604
F
1.631
0.079
10.24
0.126
0.491
738
7.951
12.111
0.491
1, 739
1
2.565
0.226
5.072
0.096
0.033
1, 928
5.753
6.501
0.406
2, 115
0
3.868
0.234
5.336
0.090
0.024
1, 736
6.643
14.247
0.247
1, 690
adoption. In contrast, contracting inefficiencies could still exist with
municipal generation since the minimum efficient scale during this time
period began to supplant demand at the municipal level. This effect would
result in a positive correlation between municipal generation and the
adoption of state regulation.
III(i)(ii). Cost Variables Regional Indicator Variables: To control for
unobserved costs that vary by region, I partition the U.S. into five regions
and include a set of regional indicator variables.18
Hydroelectric Generation: The marginal cost of electricity produced by
hydroelectric generation is lower than that produced from other sources.
Therefore, to control for the availability of low cost power, I include the
amount of hydroelectric horsepower in the state, both privately and publicly
owned, divided by the total amount of generation capacity in the state.
Because hydroelectric horsepower is not identical to KW’s (the measure of
capacity for thermal units), this variable is not a percentage; however it is
proportional to the percentage of capacity that comes from hydroelectric
resources.
Table III lists the summary statistics for these variables. In addition, the
table reports the means of the variables for states that adopted state
regulation and those that remained municipally regulated. The means
foreshadow many of the results. The average price in states that became state
regulated in the given year is lower than in states that remained under
municipal control. This difference is statistically significant at the .01 level.19
The relative profits of the two groups do not appear to be different (p-value
18
The regions are defined as: Region 1: CT, ME, MA, NH, NJ, NY, PA, RI, VT; Region 2:
DE, FL, GA, MD, NC, SC, VA, WV; Region 3: IL, IN, IA, KA, MI, MN, MO, NE, ND, OH,
SD, WI; Region 4: AL, AR, KE, LA, MI, OK, TN, TX; Region 5: AZ, CA, CO, ID, MO, NV,
NM, OR, UT, WA, WY.
19
This assumes a two-tailed test and equal variances across the two groups.
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
215
Table IV
Duration Model Estimates of the Decision to Regulate
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
ProfitRat
1.368
(2.534)
2.056
(0.592)
0.140
(0.083)
1.660
(1.001)
0.128
(0.053)
0.258
(0.128)
0.301
(0.172)
0.288
(2.797)
0.052
(0.701)
0.059
(0.362)
1.494
(2.616)
2.286
(0.752)
0.014
(0.039)
1.915
(1.049)
0.011
(0.006)
0.271
(0.127)
2.123
(1.296)
0.284
(2.781)
0.335
(1.119)
0.085
(0.410)
0.613
(0.298)
0.874
(2.598)
2.149
(0.589)
0.296
(0.238)
1.156
(1.010)
0.012
(0.005)
0.217
(0.127)
0.984
(0.447)
2.069
(3.740)
0.134
(0.762)
0.042
(0.352)
2.175
(2.642)
1.825
(0.567)
0.305
(0.188)
1.696
(0.988)
0.014
(0.005)
0.194
(0.115)
0.638
(0.478)
2.561
(3.130)
0.060
(0.634)
0.001
(0.357)
1.774
(2.854)
2.374
(0.737)
0.273
(0.236)
1.101
(1.156)
0.010
(0.006)
0.152
(0.212)
1.824
(1.492)
5.769
(4.059)
0.429
(1.149)
0.048
(0.416)
1.011
(0.590)
1.937
(1.859)
0.747
(0.409)
1.405
(0.534)
0.243
(0.725)
97
ln (Price)
ln (ValueAdded/Capita)
ln (Wealth/Capita)
%PopServed
ln (CoalTon/Capita)
CapShortage
Muni%
Hydro%
ln (Population)
ln (ValueAdded/Capita) CapShortage
ln (Wealth/Capita) CapShortage
1.033
(1.096)
ln (CoalTon/Capita) CapShortage
I(year 5 1912)
I(year 5 1917)
–N–
2.062
(0.667)
1.295
(0.811)
97
1.890
(0.691)
1.017
(0.828)
97
2.066
(0.671)
1.282
(0.811)
97
0.608
(0.413)
1.380
(0.502)
0.335
(0.777)
97
denotes significance at the .1 level,
at the .05 level and
at the .01 level. Standard errors clustered at the state level.
Regional indicator variables also included.
of .62). Value added per capita, capacity shortage levels and wealth per
capita levels are higher in states that adopted state regulation, although these
differences are not statistically significant (p-values of .81, .57 and .23,
respectively). While not statistically significant (p-value of .45), the
prevalence of coal mining is larger in states that did not become state
regulated (this inequality is reversed when logs are taken, however). Finally,
residential penetration and municipal generation rates are lower in states
that adopted state regulation (p-values of .58 and .00, respectively).
IV. RESULTS
Column 1 of Table IV presents the maximum likelihood results for the basecase model. The results suggest that higher levels of per capita wealth are
associated with an increase in the probability of adopting state regulation. In
particular, a one standard deviation increase in the log of wealth is
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CHRISTOPHER R. KNITTEL
associated with a 73 per cent increase in the hazard rate.20 Similarly, lower
levels of residential service penetration are associated with increases in the
probability of adoption. A one standard deviation decrease in the residential
penetration rate is associated with a doubling of the hazard rate. Combined,
these results suggest that states with stronger residential interests and greater
potential residential gains were more likely to adopt state regulation.
The results suggest that the adoption of state regulation was more likely in
states with low capacity levels, relative to their population, wealth and
industrial presence. This is consistent with the view that contracting
problems inherent in municipal regulation led to state regulation, whether it
be because of corruption, changes in the geographical breadth of utilities or
other inefficiencies. A one standard deviation increase in capacity shortages
is associated with a 17 per cent increase in the hazard rate. Furthermore,
there is evidence that the level of per capita coal mining and value added are
positively correlated with the adoption of state regulation. Increasing per
capita coal mining and value added by one standard deviation is correlated
with a 30 and 13 per cent increase in the hazard rate, respectively.
The results with respect to the percentage of demand serviced by
municipal generation are mixed. While negative, suggesting that municipal
generation internalized the contracting inefficiencies due to corruption, the
coefficient is imprecisely estimated. This is consistent with inefficiencies
arising from both regulatory corruption and increases economies of scale
being important since these effects would tend to counteract each other.
The results suggest that low prices led to state regulation; profit rates do
not appear to have influenced the adoption of state regulation. These results
do not provide much support for the capture theory, since the capture theory
predicts that the coefficients on both price and profit rates will be negative;
they are also at odds with the ‘pure’ public interest theory. They are broadly
consistent with the more general public interest and contracting theories of
regulation. If prices are set too low to support generation and distribution
investment, state regulation will replace municipal regulation, and this will
be socially beneficial. Profit rates may not be correlated with adoption rates
since the investment does not occur under municipal regulation.
Together, the results suggest that the adoption of state regulation was more
likely in states that had high wealth levels, low penetration rates and low
capacity levels, suggesting that state regulation benefited residential consumers
and increased investment. These results are most consistent with the theory put
forth by Priest [1993] and the interest group theory of regulation. While the
evidence with respect to industrial consumers and coal mining is weak, this
may be due to the restrictive nature in which these variables enter the
specification. Below, I allow for more flexible functional forms.
20
Intepretation of the parameter estimates uses the fact that ln lit (t,Xit,b) 5 lnl0(t) þ Xitb.
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
IV(i).
217
Alternative Specifications
IV(i)(i). Capacity Shortage and Value Added One implication of Model
1 is that while capacity shortage levels were associated with adoption rates,
the level of industrial demand does not appear to have influenced adoption.
However, it may have been the case that industrial consumers lobbied for
state regulation only when capacity levels were inefficiently low. To test this,
I interact the Capacity Shortage variable with the log of per capita value
added. Table IV reports these results as Model 2.
The results suggest that a manufacturing presence only seems to
matter when it is combined with capacity shortages; only the interaction
term is significant. In addition, the coefficient associated with only
the log of value added is small in economic terms. Particularly, the
relationship is:
@ lnlit
¼ :014 þ :613CapacityShortage:
@ ln ðValueAdded=CapitaÞ
One explanation for these results is that in states with adequate capacity
levels, industrial consumers did not lobby for state regulation (and may have
lobbied against), perhaps because they were able to lobby municipal
regulators for preferential prices (relative to residential consumers).
However, if industrial consumers viewed adequate levels of capacity as
being more important than preferential prices, then when capacity levels
were low they would seek regulation. A second explanation is that low
capacity levels may have reflected the inability of industrial consumers to
obtain preferential prices, possible because municipal regulators captured
all of the rents. Therefore, in states with corrupt municipal regulators, both
industrial and residential consumers sought state regulation.
IV(i)(ii). Capacity Shortage and Wealth The potential benefits to
residential consumers from state regulation may also increase with the level
of capacity shortage. If investment in capacity led to greater residential
service, then we would expect the effect of wealth to be greater, the greater
the capacity shortage. Model 3 interacts the capacity shortage variable with
per capita wealth. Although the coefficient on the interaction term is
positive, it is not precisely estimated.
IV(i)(iii). Capacity Shortage and Coal Mining As with value added, the
incentive for coal mining firms to lobby for state regulation may only arise if
capacity levels were inefficiently low. Provided generation levels were near
their social optimum the demand for coal would not increase under state
regulation. This would imply that the interest group pressures from coal
mining firms would be stronger, the larger the capacity shortage in the state.
To test this, I interact the Capacity Shortage variable with the level of coal
mining in the state. Model 4 reports these results. The parameter estimates
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CHRISTOPHER R. KNITTEL
suggest that the larger the capacity shortage, the larger the effect from
increases in coal mining interests; however, this effect is not precisely
estimated.21 The point estimates imply that the relationship between coal
mining and the hazard rate is:
@ lnlit
¼ :194 þ :608CapacityShortage:
@ lnðCoal=CapitaÞ
At the mean level of capacity shortage (zero), a one standard deviation
increase in the log of the per capita amount of coal mined in the state is
correlated with a 23 per cent increase in the hazard rate; this is significant at
the .10 level. If we increase the level of capacity shortage by one standard
deviation, then a one standard deviation increase in the log of per capita coal
mining is associated with an increase in the hazard rate of 64 per cent.
Model 5 adds all of the interactions terms. The results are robust to the
inclusion of all three interaction terms.
IV(ii).
Robustness Checks
As a robustness check, I estimated a Cox proportional hazard model. The
results are also consistent with the grouped data results. In addition, a
Grambsch and Therneau [1994] test could not reject the proportional hazard
assumption.22 The Cox proportional model is semi-parametric in the sense
that it does not parametrically model the baseline hazard rate; the model
yields only estimates of relative hazard rates. Given that the Cox
proportional hazard results are consistent with the grouped data results,
I report the grouped data results for ease of interpretation.
A typical problem with hazard models is the presence of unobserved
heterogeneity. While the fixed region effects will capture regional heterogeneity, state heterogeneity may still exist. To account for this, I estimated
the model proposed by Meyer [1990] that allows for unobserved heterogeneity through the presence of a gamma-distributed random effect. The
parameter estimates were similar to those reported here.
As noted above, after 1922, a number of years passed before another state
adopted state regulation. One concern is that these, states systematically
differed from the later previous ones, and their inclusion biases the results.
For example, they might have had more effective municipal regulation.
I estimated the model omitting these states from the sample and the results
did not qualitatively change, although the standard errors increased.
As a final robustness check, I created a new dependent variable that equals
the year a state adopted regulation minus 1907 (the first year of state
21
The p-value is .14 and the coefficient is marginally significant in Model 5.
More specifically, I employ the Stata test that is based on Grambsch and Therneau’s test.
The p-value for the global test is 0.78.
22
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
219
regulation). I then regressed this variable on the independent variables
defined above, using the final year the state was in the data set. Because of the
censoring of states that did not adopt state regulation by 1917, I
estimated a Tobit model under the assumption that the errors were normally
distributed. The signs of the coefficients are consistent with the hazard model.
Not surprisingly, the coefficients are not as precisely estimated as in the
hazard model, since we have only one observation per state.
V. DISCUSSION
The analysis implies that stronger residential interests are associated with an
increase in the probability of adopting state regulation. Furthermore,
stronger industrial and coal mining interests coupled with capacity
shortages are correlated the adoption of state regulation. These results
suggest that residents expected to gain from state regulation, while industrial
consumers and coal mining interests expected to gain when capacity levels
were inefficiently low, that is, municipal regulation was sufficiently
inefficient. Recent work by Lyon [2003] corroborates these results. Lyon
undertakes an ex post analysis of state regulation adoption. He estimates the
effect of state regulation on generation investment and prices, thus looking
at how the industry changes after state regulation is in place. He finds that
generation levels increased and more closely tracked population growth
under state regulation; these results suggest that the expectations of
consumer groups and coal mining firms were fulfilled.
A limitation of the present analysis is that I have not identified the player
or players that lost as a result of state regulation. The gains of consumer
groups and coal mining firms could have been at the expense of either the
utilities, municipal regulators, or both. If the contracting inefficiencies were
the result of corruption, municipal regulators clearly lost from the adoption
of state regulation, since extortion from electric utilities was no longer
possible. Alternatively, if the contracting inefficiencies predominantly came
from unrealized economies of scale, then a Pareto improvement was
possible.
Whether the electric utilities experienced gains is not clear. It is certainly
possible that, given the inefficiencies of municipal regulation, both
consumers and utilities gained from state regulation. It is most likely that
the effect on electricity firms was mixed. State regulation brought a
tremendous amount of consolidation, possibly a by-product of the
unrealized economies of scale under municipal regulation. The advent of
the electric utility holding company in the 1920’s led to further consolidation. Likely not a coincidence, one of the biggest proponents of state
regulation within the electricity industry was Samuel Insull, the first to
establish a holding company after the adoption of state regulation in Illinois.
In a 1898 speech delivered to the National Electric Light Association
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CHRISTOPHER R. KNITTEL
(NELA), Insull, then controller of Chicago Edison, advocated the
establishment of state agency control over rates and service.23 He argued
that competition did not lower rates, but instead increased the riskiness of
investment thereby increasing costs. State regulation, he stated, would allow
the electricity industry to acquire investment capital at a lower interest rate,
reducing the costs of production. (NELA [1905])
Consolidation was extensive. Dewey [1931] reports that as of 1929, 32
holding companies controlled 92.5 per cent of total generation, with the four
largest holding companies controlling 52 per cent. While the firms that
survived the consolidation movement potentially gained from the adoption of
state regulation, the firms that did not survive potentially lost. Furthermore,
the analysis in this paper deals with expectations; it is unclear whether certain
utilities foresaw the consolidation and thus pushed for state regulation.24
VI. CONCLUSION
This paper revisits the emergence of state regulation during the beginning of
the 20th century. The results support both the interest group theory of
regulation, as well as the theory put forth by Priest [1993] that municipal
regulation led to an inefficiently low level of investment because of hold up
problems and contracting inefficiencies.
The results have a variety of implications for the present-day changes in
the electricity industry and other industries, as they imply that regulators
respond to the lobbying efforts of interest groups and to inefficiencies
inherent in current regulatory systems. The regulatory structure of the
electricity industry recently underwent another change, as many electricity
markets now rely more on competitive forces to discipline pricing behavior.
One potential explanation for this regime change is that technological
changes in the industry have altered the degree of economies of scale present
and regulation is no longer needed. A second explanation is that the relative
powers of interest groups have changed, and restructuring is likely to benefit
a particular set of interest groups at the expense of another. In fact, Ando
and Palmer [1998] analyze electricity restructuring during the late 1990’s.
They also find evidence that is consistent with the interest group theory of
regulation. In addition, White [1997] suggests that restructuring in
California may have been because of the high retail rates in California,
relative to other regional states. This is broadly consistent with the
contracting theory, if these high rates were due to regulatory inefficiencies.
23
Insull was president of NELA at the time of the address.
Consolidation also did not go unnoticed by policy makers. As a response to the
consolidation movement, Congress passed the Public Utility Holding Company Act (PUCHA)
in 1935. Among other things, PUCHA required holding companies to be geographically
integrated, placed restrictions on merger activity and limited diversification into other
industries. PUCHA ultimately led to a number of divestitures.
24
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ELECTRICITY REGULATION AND THE ROLE OF INTEREST GROUPS
221
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DATA SOURCES
The price data were collected from the appendix of Stigler and Friedland
[1962]. The level of hydroelectric generation, output, revenues and costs
were collected from the 1907, 1912 and 1917 editions of the Census Bureau’s
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