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AUSTRIAN PUBLIC CHOICE:
AN EMPIRICAL INVESTIGATION
Jean-Philippe Bonardi
University of Lausanne
Faculty of Business and Economics (HEC)
Lausanne – CH1015 - Switzerland
Tel : +41 21 692 3440
E-mail: [email protected]
Rick Vanden Bergh
University of Vermont
School of Business Administration
55 Colchester Avenue, 304 Kalkin Hall
Burlington, Vermont 05405
Tel: (802) 656-8720
Fax: (802) 656-8279
Email: [email protected]
This version: August 2012
JEL categories: B53; D72
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AUSTRIAN PUBLIC CHOICE:
AN EMPIRICAL INVESTIGATION
ABSTRACT
The purpose of this paper is to explore empirically some of the key
propositions that can be derived when one combines insights from Public
Choice and Austrian economics. We call this approach Austrian Public
Choice. These key propositions are (1) that political markets are dynamic
processes driven by the action of suppliers and demanders of public
policies behaving as entrepreneurs, (2) that these political entrepreneurs
are ignorant ex ante of factors that can matter as the regulatory process
unfolds, suggesting that they imperfectly anticipate what will ultimately
matter for policy decisions, and (3) that these political entrepreneurs learn
over time and use local knowledge to discover future opportunities in
political markets. Using a dataset of utility initiated rate reviews in the
U.S. electricity industry, we find support for these propositions, which
suggests that Austrian economics could shed new lights on questions
typically raised by Public Choice scholars.
JEL categories: B53; D72
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In their introduction to a special issue of the Review of Austrian Economics devoted to
Austrian perspectives on Public Choice, Boettke and Lopez wrote: “One of the challenges to
Austrian economics in general is to establish more and broader empirical relevance, to show
that its analytical principles are applicable to standard questions and can provide answers that
standard economics cannot. Such accomplishments are necessary to… “pass the market test”.
… we too emphasize the need for applied-theoretic and empirical work to come from Austrian
economists” (Boettke & Lopez, 2002: 114). The purpose of this paper is to move in this
direction and show the relevance of the Austrian lens for the study of political and regulatory
decisions.
Following Buchanan and Tullock (1965) or Stigler (1971), Public Choice scholars have
showed how private incentives play a considerable role in shaping public policy decisions.
Self-interested politicians or bureaucrats participate in political markets and negotiate with
various interest groups to set policies, therefore questioning views of public officials as
benevolent actors. Public Choice work generally use standard microeconomic tools such as
utility maximization, game theory and thus concentrates on solutions at equilibrium. However,
DiLorenzo (1988), Holcombe (2002) or Ikeda (1997, 2003) have highlighted how the Austrian
focus on dynamic processes –rather then equilibrium–, knowledge imperfections and
entrepreneurship could be used with great benefits to shed new light on the standard Public
Choice research program. In this paper, and for simplicity, we call this combination Austrian
Public Choice.1
Apart from the fact that Austrian researchers usually do not see empirical research as a key
part of their research program –which will not be discussed here– another reason for the lack of
1 Compared to Ikeda (1997) or Boettke and Lopez (2002), who use the term ‘Austrian Political Economy’ to
characterize the study of governments’ decisions and their welfare implications in a broad sense, we adopt a
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empirical studies is certainly related to the data challenge it constitutes. For instance, exploring
whether actors behave as entrepreneurs in political markets, a key aspect in an Austrian
approach (DiLorenzo, 1988), requires observing when actors engage in political action, which
is often quite challenging, especially in a large sample. Another challenge certainly comes from
the difficulty to capture the informational/knowledge problems faced by the actors and the
surprises generated by political processes, as well as their impact on the final public policy
adopted. Last, how can one demonstrate that actors make mistakes or do not make perfect
decisions in the context of political processes, due to the fact that they cannot know in advance
the behaviors that others (either demanders or suppliers of public policies) will adopt?
In this paper, we attempt to address some of these challenges by looking at rate-reviews in
the U.S. electricity sector. Our dataset allows us to identify precisely when a political process
(the rate-review process) starts, why some actors decide to initiate this process and invest in
political activities (utilities initiating the rate-review process and lobbying accordingly), how
some other actors react to it (other potential demanders of public policies, especially rivals such
as consumers or environmental activists, who lobby as well), and how politicians or
bureaucrats, as suppliers of public policies, accommodate these demands.
This empirical set-up also provides an opportunity to compare utilities’ expectations when
they engage in political actions with the factors that, in the end, really drive the final policy
decision (in that case the variation in the allowed rate of return attributed to the utility). This
will allow us to examine empirically the knowledge limitations players face when they first
seize political opportunities, and how much of these limitations come from the dynamic nature
of political markets.
narrower focus here and restrict our analysis to political processes and the dynamics of political markets. Hence
we use the term Austrian Public Choice.
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The rest of the paper is composed of four sections. In the first, we outline the theoretical
pillars of Austrian Public Choice and identify a few propositions to guide our empirical study.
The second presents the empirical context and our strategy to explore Austrian insights in
political environments. The third section discusses the methodology and presents the results.
The last section discusses these results and concludes.
Austrian Public Choice: Key insights
The foundations of what we call Austrian Public Choice have been laid out in several
pieces, in particular Boettke, Coyne and Leeson (2007), DiLorenzo (1988), Ikeda (1997) and
Holcombe (2002). In this section, we review this literature and identify key propositions that
will serve as the basis of our empirical investigation. Compared to typical Public Choice
models (see Mueller (2003) for a review), an Austrian approach to political economy issues has
several characteristics.
The first important feature is that policy-making, from an Austrian lens, cannot be modeled
as a static equilibrium state, but needs to be considered as a dynamic, competitive and
evolutionary process (Benson, 2002). Within this process, each actor involved (both demanders
and suppliers of public policies) search for opportunities to take advantage of the process, and
by doing so differentiate their actions and prevent the equilibrium described in standard Public
Choice models from taking place (Ikeda, 2003). Demanders of public policies might still start
this process in certain cases, but their actions are heterogeneous and often hard to predict for
their rivals. Suppliers of public policy, similarly, are numerous (politicians, bureaucrats,
regulators) and do not passively respond to potential vote support from interest groups (Benson,
2002). They actively seek ways through which to create new opportunities for wealth transfers
(Holcombe, 2002). In other words, all players within the policy-making process act as
entrepreneurs would in the context of economic markets as defined by Hayek (1945) and
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Kirzner (1973 & 1992): they observe, discover and act upon (political) market opportunities
(Rizzo & Driscoll, 1985).
Bureaucrats for instance act as entrepreneurs and discover opportunities to expand their
resources while applying/enforcing the new regulations put forward by politicians and interest
groups. As described in Breton & Wintrobe (1982), this bureaucratic entrepreneurial discovery
process involves competition for budgets, power and influence within regulatory systems.
Bureaucrats, within the regulatory system, often benefit from a fair amount of discretion, in
spite of control by politicians and ultimately by courts. In the short term, maximizing their
utility might mean responding to interest groups’ demands in a way that favors vote
maximization for policy-makers (i.e., by giving groups that can provide the highest voting
support to politicians policy decisions close to what these groups want). This would be a result
compatible with traditional Public Choice insights.
However, there might also be cases where bureaucrats/regulators, because they already
have ample resources (large budgets, wealth of information, experience, credibility, etc.) might
decide to sacrifice short-term obedience to politicians in order to favor longer term objectives
such as the building of a reputation that will allow them to stimulate interest group rivalry in
the future and thus extract higher rents from policy-making processes in the future.2
Viewing policy-making as a process generates another important feature of Austrian Public
Choice: the knowledge problem and the divergence between intended and actual outcomes
(Ikeda, 2003). Because actors have only limited knowledge when they make decisions, errors
regularly occur during the policy-making process and unintended outcomes emerge. Bruce
Benson summarizes this ‘knowledge problem’ in political environments when he states: “The
knowledge problem suggests, among other things, that there are too many uncontrolled margins
2 Rents in that case can be of different kinds, such as extended influence over an industry or even over several
opportunities, better career opportunities, etc.
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and unanticipated responses for a rule designer to recognize and anticipate, in part because the
changes create a new set of opportunities that have not previously been available” (Benson,
2002: 235). In other words, actors in an Austrian approach have to deal with ex ante ignorance
of factors that can affect outcomes. Even after conscientiously scanning their environment and
planning accordingly, actors end up making mistakes, overestimating certain factors,
underestimating other factors, and completely missing others. The implication is that neither
demanders of public policies nor suppliers of public policies will, in most cases, obtain exactly
what they expect from policy-making processes (Shane, 2000). The assumption of rational
ignorance is not, in itself, inconsistent with Austrian political economy for most voters.
However, the idea that actors know enough about the relevant probabilities so that, on average,
errors are only random and cancel each other out, is inconsistent with Austrian political
economy for most voters. An Austrian approach would predict systematic errors made by
actors in the policy process because, as all other actors act in an entrepreneurial way, none has
accurate information about their own real probability of success.
One last important component of an Austrian contribution to the Public Choice research
program is that local knowledge is critical to account for the way actors make decisions in
political markets. As suggested by Hayek (1945), knowledge differs across time and place, but
this does not imply that actors cannot accumulate valuable information and knowledge over
time and therefore learn throughout policy processes. Learning, and the local knowledge that an
actor has accumulated over time within a specific political market setting, will thus be an
important aspect of the future decisions that actor will take. Demanders of public policies will
vary not only in their ability to generate voting support to politicians (as in standard Public
Choice models) but also in the experience and knowledge they hold about how to proceed and
gain an advantage within political processes.
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To summarize, we will try to empirically explore four propositions that define Austrian
Public Choice, i.e.:
- Both demanders and suppliers of public policies act in an entrepreneurial way, looking for
opportunities to change the regulatory status quo to their advantage;
- Political entrepreneurs build on knowledge, learnt over time through previous
experiences, to discover future opportunities in political markets;
- Local knowledge, in particular, is key to generate entrepreneurial discoveries in political
processes;
- Entrepreneurial behaviors within political processes generate a knowledge/ignorance
problem, i.e. an impossibility to know ex ante (even with some probability) all the factors
that influence final policy outcomes and how they will do so. This knowledge problem
pushes actors (both demanders and suppliers) to make prediction errors, i.e., underestimate,
overestimate or even ignore key factors that will ultimately affect the public policy
decision.
Empirical Setting: Rate Reviews in the U.S. Electric Utility Industry
To explore these propositions, we use a dataset of rate-reviews across the fifty states in the
U.S. electric utility industry. In the U.S., profit levels of utilities are regulated under a financial
rate-of-return regime by state (as opposed to federal) agencies. Utilities are able to improve
their financial performance by achieving – through political activities – higher prices and a
higher rate-of-return. State regulatory agencies (Public Utility Commissions, hereafter “PUCs”)
determine the rate-of-return that a utility is allowed to earn, and hence the final rates charged to
consumers, through an administrative process, commonly termed a “rate review”. Utilities are
able to file for rate reviews whenever they wish. Upon initiation of a rate review, a series of
public hearings is held where the utility and competing interest groups present arguments and
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information supporting their positions about justifiable rates-of-return and rate levels. At the
end of this process, PUC commissioners make a final decision on the rate-of-return for the
utility and rates that final consumers pay. Even if they are not supposed to be instrumental in
this type of decision, politicians –i.e., legislators and governors– exercise some monitoring
power and control over regulators throughout the process (Hyman, 2000).
Figure 1 provides an overview of the different stages of the rate-review process.
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Insert Figure 1 about here
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This industry context affords a number of advantages for our empirical investigation.
Below, we explain how different aspects of an Austrian Public Choice approach can be
explored empirically using these data.
Identifying political entrepreneurship among demanders of public policies
The rate review process is characterized by an intense information exchange between
policy-makers, the utility and other interest groups (Hyman, 2000). Since the provision of
information regarding policy consequences and alternatives is a central characteristic of actors’
behaviors during public policy processes, the utility’s initiation of a rate review is a rational
strategy driven by some calculation or plan by the firms, and can therefore be viewed, from an
Austrian perspective, as an entrepreneurial behavior.3 Because we are able to identify when the
utility (a demander of public policy) files a formal regulatory request for rate review, we can
identify when it seizes an opportunity to start a policy-making process, i.e. discover an
entrepreneurial opportunity in the political market. Note that the Rate of Return (ROR) is firm-
specific: each ROR applies to a single utility only even though there are multiple utilities
3 Utilities are in fact likely to engage in activities that complement their regulatory filing with the agency, such as
gaining the support of the state governor and legislature (through lobbying, grassroots mobilization, coalition
building and financial campaign contributions). For lack of available data on the period under study here, we will
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operating within a given state. This will allow us to identify individual behaviors, and not just
collective ones as is often the case for political issues.
In the first stage of our empirical analysis, we will therefore account for the decision by
utilities to initiate a rate review at a certain point in time. Using a probit model, we will explore
the political market conditions which affect the firm’s decision (more on this below). We
expect to find that this choice will be driven by a variety of political market conditions, which
include the rivalry potentially created by competing interest groups (residential consumers,
industrial consumers and environmental activists) but also the rivalry faced by legislators and
governors for reelection and the amount of resources held by PUCs for reasons mentioned
earlier. In effect, the rate review process affords the opportunity for us to explore how a
particular Demander (i.e., investor owned utilities) is entrepreneurial in response to
characteristics of both the demand- and supply-sides of the political market.
Characterizing political entrepreneurship among suppliers of public policy
On the supply-side, multiple regulatory and political institutions have a potential role in rate
reviews. Final decisions are in the jurisdiction of state PUCs. However, PUCs are overseen by
state legislatures that determine their budgets, that can conduct hearings on specific decisions
and that can ultimately overturn PUCs through new legislation. PUC commissioners are
additionally typically appointed by state governors, giving a further lever to state politicians to
exert pressure on PUC decisions.4 From an Austrian point of view, political opportunities for
utilities are thus likely to be shaped by characteristics of both the elected state politicians and
the regulatory agency.
Capturing uncertainty, or the difference between intended and actual outcomes
not explore this dimension, but these behaviors are commonplace during the rate review process and, in our
analysis, we will assume they take place (Hyman, 2000). 4 In ten U.S. states PUC commissioners are elected. We control for this variation in our empirical analysis. Below we
discuss how PUC selection rules might influence the choice of a utility to initiate and the overall policy outcome.
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Another advantage of using electric utility rate reviews for our empirical setting is that they
provide a good measure of the final outcome of the political process. As part of their final rate
review rulings, PUCs determine the financial rate-of-return on equity (hereafter ‘ROR’) that the
utility may earn. This ROR, in turn, is used to determine the allowed rate levels (i.e., prices a
utility can charge to its customers). Since, all else being equal, higher RORs lead to higher
profits, utilities are assumed to prefer higher RORs. While PUCs have a statutory duty to set
rates that are “just and reasonable”, in practice they have considerable discretion to set rates
and RORs within some implicit range.5 This will allow us to provide an analysis of the actual
outcome of the policy process, compared to the intended outcome that utilities expect to get.
A key aspect of our empirical approach will be, therefore, to estimate both the determinants
of the rate review initiation with the determinants of the allowed ROR. From a standard Public
Choice perspective, since demanders of public policy have extensive knowledge about the
process and the factors that will ultimately affect the final decision, one might predict that
utilities will initiate when political market conditions are favorable and that nothing else will
affect the final outcome. In other words, the decision made by the utility to initiate a rate review
will have captured and controlled for the key characteristics of the political market and thus any
uncertainty created by the rate review process. On the other hand, from an Austrian point of
view, a utility will plan according to its predictions regarding political market conditions, but
this utility will turn out to have only imperfectly anticipated what will happen during the
political process. Many factors including the entrepreneurial behaviors of rival interest groups
or of politicians and regulators will create unanticipated conditions within the political process
and will therefore generate differences between what the firm expects and the final outcome.
Learning local knowledge and the discovery of future political opportunities
5 Allowed RORs have historically differed significantly across utilities, states and time. For instance, the highest
allowed ROR by a state PUC during 1980 was 16.80% while the lowest was 12.50%.
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Rate-reviews are also phenomena which tend to be repeated over time. We observe and
analyze utility decisions over a period of 10 years. Thus, we can estimate whether utilities
learn from previous periods and whether this learning matters either for their decision to engage
in political activities and for the final determination of the allowed ROR by the regulator. We
assume that when a utility initiates a rate review and goes through the rate review process the
firm is adding to its stock of local knowledge about the political market. We use this to analyze
whether local knowledge affects both the entrepreneurial decision to initiate and the regulatory
outcomes.
Furthermore, using our data, we can identify when a utility observes other utilities initiate
rate reviews. When a given utility observes other utilities engaged in the regulatory process,
we assume the focal utility is adding to their stock of general knowledge about the political
market. Thus, we can compare the effects of learning and accumulating local versus general
knowledge on the entrepreneurial actions of the utility and on the final regulatory outcomes.
Sample
We obtained information on all rate review processes initiated by the population of 190
investor-owned electric utilities during the period 1980 to 1992. These utilities represent those
operating in all fifty U.S. states except Alaska and Nebraska. While the age of our data may
raise concerns, we concentrate on the 1980-1992 period since rate reviews were initiated by
utilities and not by the PUCs. After 1992, PUCs also initiated rate reviews with the aim of
reducing utility rates. Using data after 1992 would have made it difficult to identify whether
rate-reviews were initiated by a utility or by the PUC, thus compromising our analysis of
entrepreneurial actions by Demanders within the political process.
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The 1980 to 1992 panel of data creates a potential sample of 2470 utility-year observations.
After eliminating observations due to missing data, we are left with 1651 utility-year
observations.6 The sample includes 499 rate reviews initiated by utilities.
Variables
Dependent variables
As explained earlier, we will consider various models to isolate the specificities of an
Austrian Public Choice approach. To model utilities’ entrepreneurial actions, we will consider
probit models capturing the conditions in which these utilities decide to initiate a rate review. In
these models, the dependent variable Firm initiated rate review takes a value of 1 if the utility
decides to initiate a rate review in a specific year and 0 if it does not.
To measure the outcome of the political process (related to a utility firm’s specific action)
we calculate the change in the allowed ROR ( Allowed ROR) since the utility’s previous rate
review. We use the change in ROR rather than the absolute level since this allows us to control
for constant firm-level factors that influence the absolute ROR. We obtained the rate review
data from a private firm, Regulatory Research Associates, that tracks PUC decisions and cross-
checked a sample of rate review results with data available in annual volumes of the National
Association of Regulatory Utility Commissions (NARUC) for accuracy.
Independent variables
Interest group activities: We use three variables to capture interest groups’ competitive
activities and thus important characteristics of the Demanders in the political market. Consumer
Advocate is a measure of the degree of residential utility consumer organization per state. In the
U.S. utilities sector, 30 states have created consumer advocacy offices to represent residential
6 To measure our dependent variable (change in allowed ROR) we need a baseline measure of allowed ROR.
Thus, we eliminate observations on utilities until they initiate their first rate review in the data. We also eliminate
observations if we are missing information on the allowed rate of return for a firm since this makes it impossible to
calculate the change in allowed ROR. The need for a baseline and the missing data on allowed ROR resulted in a
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utility consumer interests before state regulatory agencies and courts (Holburn and Vanden
Bergh, 2006). Consumer advocates, which are publicly funded and which have statutory power
to participate in rate review procedures, can provide strong opposition to utility requests for rate
increases (Holburn and Spiller, 2002). The variable Consumer Advocate equals one if a
consumer advocacy office existed in a given state in a particular year and zero otherwise.
Rivalry can also come from industrial consumers who, due to higher average levels of
consumption than residential consumers, have stronger incentives to organize. Industrial
Consumers, a time-varying variable, is equal to the industrial percentage share of electricity
consumption in each state. Data on electricity consumption by consumer sector was obtained
from the Energy Information Administration. Finally, we use Environmental activists to capture
the extent to which state populations participate in environmental and other non-governmental
activist organizations. We use data from the Sierra Club which is the largest environmental
NGO in the U.S. Such groups have historically been particularly active against utilities
regarding the citing of new power generation plants and the environmental impacts of existing
facilities. The variable Environmental activist is equal to the total number of Sierra Club
members in the state divided by state population (in thousands). Annual information on state
membership was provided directly to us by the Sierra Club.
Voting support generated by the utility. To capture the idea that a utility will be more
likely to influence policy in a favorable direction when it can provide voting support for policy-
makers, we also include Market Share for a utility, defined as the total megawatt hours (MWh)
of electricity provided by the utility divided the total MWh provided by all utilities in the state.
This measure captures both the idea of firm size and its importance as electricity provider
within the state, i.e. important political variables for politicians and regulators. The assumption
reduction of 311 observations. We also eliminated 475 observations arising from missing data to measure market
share. Finally, we have missing data on three utilities resulting in 33 additional observations being eliminated.
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is that if a utility is a major player within a PUC’s jurisdiction, then that utility is more likely to
provide strong political support to suppliers of public policies.
Politicians’ activities: To attempt to capture conditions under which elected politicians may
extend pressure on the PUC, we develop proxies for the degree of rivalry among elected
politicians. We construct two dummy variables based on the winning vote margin in the most
recent state gubernatorial and legislative elections. For the executive branch (governors), we
consider rivalry intense if the margin of electoral victory between the winning and second-place
candidates is less than 5%. In this case there is likely to be intense political competition during
the next electoral cycle. For the legislative branch, given the importance of party control of the
legislature, we consider rivalry intense if the margin of control by the majority party (measured
by the number of seats in the combined upper and lower chambers) is less than 5%. Thus, we
create dummy variables for Governor rivalry and for Legislature rivalry which are equal to one
if rivalry is intense and zero otherwise. We use dummy rather than continuous variables since
the underlying distributions of governor vote and legislature party majorities are not normal but
highly skewed. We collected this information from annual volumes of The Book of the States.
Bureaucrats’ entrepreneurial activities: As argued earlier in this paper, a dynamic theory
of regulators’ behaviors could suggest that PUCs with greater resources will be more likely to
diverge from the pure vote-maximizing approach of politicians to try to build long-term capital
such as reputation. Therefore, regulators with greater resources are also more likely to take
risks and make decisions against a strong political actor like a utility. Again, we use several
measures of resources. Our first, PUC Budget per state capita, is a measure of financial
resources. Second, we construct a measure of PUC commissioner experience since experience
may partially substitute for financial resources: Tenure PUC is equal to the sum of each
commissioner’s tenure in years divided by the total number of commissioners on the PUC. We
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expect that more experienced commissioners will have better information/local knowledge
regarding utility rate review requests. We obtained annual information on PUC budgets and the
identities of PUC commissioners from annual reports of the National Association of Regulatory
Utility Commissioners, annual volumes of The Book of the States and the websites of individual
PUCs. We also include a variable that may influence the weight that PUCs put on utility versus
consumer interests in their ROR decisions. Elected PUC is a dummy variable equal to one in
states where PUC commissioners are elected and zero otherwise. PUC commissioners are
elected by the voting population in 10 states and are appointed by the governor in other states.
Prior research suggests that elected PUCs place greater weight on consumer welfare (Besley
and Coate, 2003). Details on commissioner selection were obtained from the Book of the States.
Learning local political knowledge: The Austrian Public Choice framework stresses the
role of local knowledge in the ability of political entrepreneurs to discover opportunities. We
introduce three measures that will help us distinguish local from more general types of
knowledge. We create the variable Local knowledge to capture the stock of knowledge that the
utilities has potentially learned about the PUC and the local politicians. Local knowledge is
equal to the total number of rate reviews the utility has experienced in the past (our sample for
this variable started in 1970).
To estimate the real impact of this local knowledge on political entrepreneurial discovery,
we also compute General knowledge which is a proxy for the utility’s stock of general
knowledge about the political market. General knowledge (state) and General knowledge
(region) are equal to the number of rate reviews for other utilities operating in the same
state/region that the focal utility has observed in the past. The knowledge associated with these
measures are arguably of a much more general nature since it does not take into account how
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the firm’s own characteristics are considered by the local regulators and politicians nor the
firm’s own past interactions with the political market.
Control variables
We control for a number of factors that may affect a utility’s performance in the rate review
process as well as the decision to initiate a rate review. These variables can be interpreted by
the fact that regulators and politicians, when they allocate public policies in order to get
reelected or reappointed, have to take into account some public interest dimensions and not
only the private interests related to interest groups lobbying and highlighted by the Public
Choice approach. Interest rates on treasury securities enter into a PUC’s decision on the
allowed ROR since these are a benchmark to measure the cost of capital. Interest rate change,
measured in percentage points, is the difference between the interest rate on ten year Treasury
bills at the given time minus the interest rate at the time of the last rate review. Change fuel cost
is the percentage change in a utility’s average fuel costs (on a per Btu basis) since the last rate
review, and is driven mainly by external market forces. Increases in the cost of utilities’ fuel
purchases, as occurred during the early 1980s, directly reduce utility profits, thereby increasing
the probability that utilities will initiate rate reviews. In the selection equation, we also control
for the absolute level of fuel costs. Since absolute costs are inversely related to profits, we
expect a positive relationship between absolute costs and the probability that utilities initiate.
We measure Average fuel cost as the average price of fuel per Btu purchased by electric
utilities within a state. Fuel cost data is published by the Energy Information Administration.
To control for varying economic conditions across the States, we include a measure of the
Change per capita income (lagged one year) which is equal to the annual percentage change in
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per capita income in the state. Voter pressure on utility rates may be inversely correlated with
recent economic growth trends. We gathered this data from the Bureau of Economic Analysis.
A summary of the variables and descriptive statistics can be found in Tables 1 and 2.
-------------------------------------------
Insert Tables 1 and 2 about here
-------------------------------------------
Table 1 provides statistics for variables included in the full sample of utility-year
observations used in the probit models (firms that choose to initiate a rate review) while Table
2 provides statistics for variables included in the Allowed ROR (OLS regression) model.
Methodology
Empirically, our investigation will move in several stages with the purpose of exploring
step by step how the different aspects of an Austrian Public Choice approach improve our
understanding of public policy decisions and guide our empirical analysis. First, we will use an
ordinary least squares (OLS) regression model accounting for a baseline of what might be seen
as a traditional Public Choice approach. Variables capturing the impact of various interest
groups and of various policy-makers on the Allowed ROR will be included. We will then
explore entrepreneurial political behaviors of utilities by exploring their decision to initiate rate
review processes. We will therefore introduce and discuss a set of probit models (Maddala
1983) which allow us to analyze this choice. Within these probit models, and as a third step, we
will then add variables considering the role of knowledge, both general and local, in political
entrepreneurial behaviors. Last, we will consider a two-stage Heckman model (Heckman,
1979) which allows us to analyze how the utilities’ entrepreneurial decision to initiate a rate-
review in the first stage has an effect on the second stage PUC decision regarding the
Allowed ROR. We use this two-stage model to help estimate the total marginal impact of
political entrepreneurial behaviors on public policies. Furthermore, this two-stage model will
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allow us to consider which dimensions create the ignorance problem we introduced in the
previous section, by comparing the factors a firm considers relevant to the PUC decision when
the utility initiates a rate-review with the factors that still affect the ultimate outcome of the
policy-making process after the firm initiates. If firms do not face the ignorance problem, then
we would expect that the decision to initiate to account entirely for the observed variation in the
PUC decision over allowed ROR.
RESULTS
The first model we consider to start our discussion is a model capturing what might be
considered as a straightforward ‘traditional’ Public Choice analysis, i.e. a model that does not
include utilities’ political entrepreneurship in the Austrian sense. Table 3 presents the results
for an OLS regression with changes in the rate-of-return as the dependent variable and
variables characterizing the demand (interest group rivalry) and the supply (politicians and
regulators) sides of political markets as independent variables. To account for unobserved
variations related to firms’ characteristics, we also include firm fixed effects in the regression.
-----------------------------------
Insert Table 3 about here
-----------------------------------
Results indicate that, beyond economic control variables, the variables having a statistically
significant impact on public policy decisions are variables related to the supply side of political
markets, and in particular the variables characterizing the regulator’s behaviors. Elected PUC
and Tenure PUC both have a negative and significant impact on the rate-of-return allocated to
each utility firm. From a Public Choice perspective, this seems to indicate that regulators that
have the most knowledge and consumer connection occupy the best position for creating strong
bargaining power vis-à-vis utilities. This fits with arguments indicating that players that truly
control the policy-making process are the most likely to extract rent from such a process.
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To consider how an Austrian Public Choice perspective changes the traditional Public
Choice perspective, we now introduce utilities’ behaviors as political entrepreneurs. The OLS
analysis above does not take into account that the subset of utilities that engage in rate reviews
may have done so due to differential opportunities in the political market. As mentioned
earlier, utilities can, but do not have to initiate rate reviews. If utilities are behaving
entrepreneurially, they are likely to initiate a rate review when they have identified an
opportunity to turn the public policy process to their advantage. We thus consider two probit
models with the dependent variable Firm initiated rate review. Results of the probit models can
be seen in Table 4. The independent variables in the first model we consider are the same
demand and supply dimensions presented in the baseline OLS model presented in Table 3.
Column 1 in Table 4 provides the raw coefficient estimates from the probit model, whereas
column 2 calculates the marginal effects related to the different independent variables on the
probability of initiating. We present the marginal effects separately due to nonlinearities in the
probit estimation (Maddalla 1983).
------------------------------------
Insert Table 4 about here
------------------------------------
The results presented in Model 1 do provide support for an Austrian-style hypothesis that
utilities seek entrepreneurial opportunities in the political market. It appears that the utility is
concerned not only with the supply side characteristics, but also with the structure of the
demand side of the political market to determine when to initiate a rate review. On the supply
side, when we observe an Elected PUC or when the PUC Budget increases we see a reduced
likelihood that a utility initiates a rate review by nearly 17% or 3% respectively. These results
are consistent with the notion that a utility is less likely to engage with a political actor as the
attractiveness of the supply side of the political market declines.
21
We see similar effects for the demand side of the market. First, those utilities that possess
attractive features for the policy makers (i.e., Market share is larger) are more likely to initiate
by about 15.6%. However when the rivalry with other Demanders increases, utilities are less
likely to engage with the PUC. An increase in the strength of Environmental Activists or
Industrial Consumers the likelihood that a utility will initiate a rate review declines by nearly
2.9% or 31.4% respectively.
In Model 2 (columns 3 and 4) of the same Table 4, we present estimated coefficients and
marginal effects when we add our knowledge variables. Comparing the statistical significance
of the Local knowledge with the General knowledge variables is consistent with an Austrian
perspective. When a firm’s own knowledge increases, they are more likely to initiate a rate
review. However, gaining knowledge by observing others does not appear to have a significant
effect on political entrepreneurship. We interpret this result as consistent with an Austrian view
of policy-making processes, in which the knowledge that matters for entrepreneurs is mainly
local knowledge rather than the general knowledge gained from observing other utilities going
through rate-reviews). While the significance of some of the other political market variables
changes when we introduce knowledge, there appears to be a robust relationship between
characteristics of the supply and demand side of the political market and a utility incentive to
act entrepreneurially. The main interpretation is that utilities are more likely to act
entrepreneurially when the political market is more attractive. The political market
attractiveness improves when PUC budgets decline, PUC commissioners are appointed, and
when environmental activists are not as strong.
In the last part of our empirical analysis, we turn to a model taking into account both the
utilities’ political entrepreneurial behaviors (initiation of the rate-review process) and the final
outcome regarding public policy (the rate-review decision by the PUC). We therefore estimate
22
a Heckman two-stage sample selection model which incorporates the utility’s first-stage
decision to initiate a rate review (Heckman, 1979) into the second-stage decision of the PUC to
change allowed ROR. In table 5, we present the estimated coefficients along with the estimated
total marginal effects that different characteristics have on the final PUC decision. The first
stage results are identical to Model 2 presented in Table 4. The second stage coefficient
estimates now take into consideration the entrepreneurial decision of the utility. Given
nonlinearities in the estimation of the two-stage model (Heckman, 1979), we estimate the
marginal effects separately and present these estimates in the third column of Table 5. The
significance of the Inverse Mills Ratio shows the importance of taking into account political
entrepreneurial behaviors and their impact on policy-making processes. Failing to take the
entrepreneurial behaviors into account would create a bias in the coefficient estimates on the
factors that affect the PUC decision. Significance also suggests that we can reject the null
hypothesis at the 5% level of confidence that the decision to initiate has no effect on the final
policy outcome. This provides support for the Austrian perspective that utilities are
entrepreneurial in their decision to engage with the PUC and that this decision is not
independent of the PUC rate review process.
-------------------------------------
Insert Table 5 about here
------------------------------------
Turning to our key variables, we find good statistical support for some of the predictions
related by the Austrian Public Choice approach. The analysis of the results in Table 5 provides
several interesting insights. First, several political market variables appear to be significant in
the first stage (selection equation) but not in the second stage (regression): PUC budget,
Environmental Activists, and Consumer Advocate. Our interpretation of these joint results is
that the factors associated with these variables were correctly predicted by the firms when they
23
decided to initiate. In other words, the utilities understood how these factors could affect PUC
decisions negatively and managed these in its entrepreneurial choice to engage with the
political market. Through an understanding of when to be entrepreneurial, the utilities did not
experience any added uncertainty associated with these political market factors. On the other
hand, the uncertainty created by longer Tenure PUC and when operating in a state with an
Elected PUC seems to have been underappreciated by utilities: the coefficient associated with
both Tenure PUC and Elected PUC are significant in the first stage, but also in the second stage
(both with a negative sign).
Interestingly for an Austrian argument, the variable Legislature Rivalry displays a
coefficient estimate that was not significant in the first stage but that became significant in the
second stage. Again, our interpretation of these results is that the impact of this dimension,
related to the behaviors of regulators and politicians, was not understood ex ante by utilities. In
other words, the behaviors of regulators and politicians in the political process created
conditions that were largely ignored and not anticipated by utilities. These results related to the
supply side of political markets are thus quite consistent with the Austrian view.
Now that we control for the entrepreneurial choice of utilities to initiate when political
markets are more favorable, we can estimate the overall marginal effect that different political
market characteristics have on a PUC decision to change the allowed ROR. The estimated
marginal effects are largely consistent with an Austrian Public Choice perspective. When
political markets are less favorable, the marginal effect on the change in allowed ROR is
negative. We see statistically significant negative marginal effects for PUC Budget, Tenure
PUC, Environmental Activists, Consumer Advocate, and Elected PUC. The magnitude of these
effects are economically significant as they range from about a 3% to about a 20% effect on the
change in allowed ROR relative to the mean of -0.43. Additionally when a firm increases its
24
own knowledge, it has a statistically significant positive marginal effect of close to 10% on the
change in allowed ROR (marginal effect of .04 while average allowed ROR = -0.43)
CONCLUSION
This article sets out to empirically explore some of the key propositions of Austrian Public
Choice. Overall, results are supportive and suggest that Austrians have something to bring to
the study of how political markets work. Key findings can be summarized by the following
points. First, firms involved in public policy processes (in this case, electric utilities) behave as
political entrepreneurs in the sense that they scan the competitive environment in the demand
side as well as the characteristics of the supply side of the political market, looking for
opportunities to change regulations in their favor. This fits with Kirzner’s definition of
entrepreneurship in economic markets (Kirzner, 1992).
Second, our study also uncovers the important role played by knowledge, especially local
knowledge, on entrepreneurship in political markets. Compared to general knowledge
accumulated by observing other firms going through rate-reviews, the knowledge that firms
have learnt through their own experiences with their state regulator is much more likely to
impact their decision to engage in political activities and also affect favorably the final policy
decision. This is in line with the role of knowledge in markets as characterized by Hayek
(1945).
Third, other demanders, competing against the initiating firm, also act in an entrepreneurial
way, introducing surprises into the process. Our empirical analysis suggests that suppliers of
public policies, both regulators and politicians, are the ones that are particularly likely to trigger
these surprises and uncertainty within political processes. Other demanders of public policies
(residential consumers, activists, industrial consumers) seem to matter much less. This result is
25
probably specific to the U.S. electricity sector. However, this result is also broadly compatible
with the Austrian view of entrepreneurship, starting with Mises (1949) or Rothbard (1962 &
1985), who stress the importance of property rights in entrepreneurial decisions: entrepreneurial
decisions, characterized by profits and losses, matter really for firm owners. In the case of
political decisions, a parallel can be made there. Regulators or politicians all have some form of
property rights over public policies, whereas consumers or activists do not.
In a similar vein, our analysis shows that politicians’ and regulators’ behaviors are the ones
that create true uncertainty that is hard for utilities to predict and plan for. When considered
from a perspective that is ‘out of the equilibrium’, suppliers of public policies create more
uncertainty than is generally assumed in most Public Choice models.
As in any empirical work, this paper has many limitations that should be acknowledged.
First, the effort is focused on decisions made by one type of actor (utilities). The decisions and
the uncertainty created by other actors’ entrepreneurial behaviors (consumers, activists,
politicians, regulators, etc.) are assumed rather than observed. Second, the firms’ political
activities during the rate-review process are assumed but not measured. For instance, we do not
have data on lobbying expenditures or campaign contributions. Adding observations on these
data would certainly strengthen the approach presented here.
Third, and more importantly, real implications of the Austrian approach are in the nature of
the regulation process, and on its implications in terms of welfare and institutional
comparisons. As this article only attempts to empirically explore some foundations of Austrian
Political Economy, it is limited in its ability to participate in important debates on these
questions, such as that of Virginia and the Chicago schools regarding the costs and harm
generated by the political market process (Benson, 2002). We leave that for future research.
26
Finally, in addition to showing relevance compared to mainstream approaches, another
potential benefit from empirical investigations in Austrian political economy might be to
generate questions. The results presented here can be considered from this point of view as
well. For instance, it seems that certain aspects of the political process, or rather the behaviors
of certain actors within that process, can be better predicted than others. It seems for instance
that the rivalry generated by consumer advocates can be fairly well assessed ex ante by utilities,
whereas other behaviors such as those of Sierra Club activists or of legislators generate
systematic errors by utilities. In other words, are there certain factors in political markets that
generate a higher level of uncertainty, and therefore clusters of errors, among actors than
others? In the Austrian Business Cycle Theory, manipulated interest rates play that role of
generating clusters of errors among entrepreneurs; are there similar mechanisms in political
markets? Future work on Austrian Public Choice probably needs to provide some answers to
these questions.
27
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29
Table 1: Descriptive statistics
Full sample
Variable Mean S.D. MIN MAX
Firm initiated rate review 0.30 0.46 0 1
PUC budget 2.02 1.48 0.23 10.58
Tenure PUC 3.63 2.62 0 17
Governor Rivalry 0.21 0.41 0 1
Legislature Rivalry 0.09 0.29 0 1
Market share 0.29 0.26 0 1
Environmental Activists 1.50 1.08 0.29 6.97
Industrial Consumers 0.27 0.08 0.08 0.53
Consumer Advocate 0.58 0.49 0 1
Elected PUC 0.14 0.34 0 1
Change per capita income 0.06 0.03 (0.06) 0.28
Interest rate change (0.85) 2.28 (6.25) 4.49
Change fuel cost (1.54) 20.46 (54.84) 147.14
Average Fuel Cost 1.68 0.82 0.45 6.33
Local knowledge 6.24 2.78 1 17
General knowledge (state) 17.13 12.23 0.00 51
General knowledge (region) 72.83 31.03 13 133
n=1,651
Table 2: Descriptive statistics
Subsample of firms that initiate rate reviews
Variable Mean S.D. MIN MAX
Allowed ROR -0.43 1.37 -4.25 3.25
PUC budget 1.77 1.25 0.23 10.58
Tenure PUC 3.33 2.61 0 17
Governor Rivalry 0.23 0.42 0 1
Legislature Rivalry 0.08 0.28 0 1
Market share 0.30 0.25 0.00 1
Environmental Activists 1.46 1.00 0.29 6.36
Industrial Consumers 0.27 0.08 0.08 0.49
Consumer Advocate 0.58 0.49 0 1
Elected PUC 0.08 0.28 0 1
Change per capita income 0.07 0.03 0.00 0.28
Interest rate change -0.29 2.37 -6.25 4.49
Change fuel cost 2.08 20.34 -52.90 147.14
Average Fuel Cost 1.82 0.93 0.45 6.33
Local knowledge 7.00 2.87 2 17
General knowledge (state) 18.36 12.73 0 49
General knowledge (region) 73.03 31.44 13 133
n=499
30
Table 3: Regression model
Dependent Variable: Allowed ROE
OLS
PUC budget -0.180
(0.126)
Tenure PUC -0.115***
(0.042)
Governor Rivalry -0.070
(0.162)
Legislature Rivalry 0.449
(0.346)
Market share -0.371
(1.959)
Environmental Activists -0.130
(0.223)
Industrial Consumers 1.851
(2.991)
Consumer Advocate 0.183
(0.392)
Elected PUC -3.010**
(1.467)
Change per capita 0.976
income (2.652)
Interest rate change 0.339***
(0.038)
Change fuel cost -0.007
(0.005)
Average Fuel Cost 0.572***
(0.184)
Constant 0.465
(1.181)
Observations 499
Adjusted R2
Firm Fixed Effects
0.372
YES Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
31
Table 4: Probit models (coefficient estimates and marginal effects)
Dependent Variable: Firm initiated rate review
Model 1 Model 2
Probit Marginal Probit Marginal Standard Effects Knowledge Effects
PUC budget -0.086*** -0.029*** -0.091*** -0.031***
(0.027) (0.009) (0.028) (0.010)
Tenure PUC -0.010 -0.003 -0.042*** -0.014***
(0.015) (0.005) (0.016) (0.005)
Governor Rivalry 0.119 0.041 0.123 0.042
(0.085) (0.029) (0.087) (0.029)
Legislature Rivalry -0.081 -0.028 -0.199 -0.067
(0.123) (0.042) (0.128) (0.043)
Market share 0.455*** 0.156*** 0.247 0.084
(0.133) (0.046) (0.155) (0.052)
Environmental Activists -0.086** -0.029** -0.116*** -0.039***
(0.036) (0.012) (0.041) (0.014)
Industrial Consumers -0.913** -0.314** 0.457 0.155
(0.463) (0.159) (0.508) (0.172)
Consumer Advocate -0.101 -0.035 -0.157** -0.053**
(0.073) (0.025) (0.079) (0.027)
Elected PUC -0.566*** -0.169*** -0.266* -0.085**
(0.127) (0.032) (0.143) (0.042)
Change per capita 2.029 0.697 2.877*** 0.312***
Income (1.293) (0.444) (1.055) (0.118)
Interest rate change 0.052*** 0.018*** 0.036* 0.012*
(0.019) (0.006) (0.020) (0.007)
Change fuel cost 0.005** 0.002** 0.005*** 0.002***
(0.002) (0.001) (0.002) (0.001)
Average Fuel Cost 0.050 0.017 0.094** 0.032**
(0.044) (0.015) (0.046) (0.016)
Local knowledge 0.125*** 0.042***
(0.015) (0.005)
General knowledge (state) 0.000 0.000
(0.004) (0.001)
General knowledge (region) -0.001 -0.000
(0.001) (0.000)
Constant -0.135 -1.203***
(0.229) (0.324)
Observations 1,651 1,651 1,651 1,651 Log likelihood -959.78 -921.26
Standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
32
Table 5: Heckman model (coefficient estimates and marginal effects)
Dependent Variable -- Allowed ROR
2nd Stage 1st Stage Total Marginal Effect on
Allowed ROE Firm initiated rate review Allowed ROR
PUC budget -0.161 -0.091*** -0.029***
(0.104) (0.028) (0.009)
Tenure PUC -0.082** -0.042*** -0.013***
(0.040) (0.016) (0.005)
Governor Rivalry -0.165 0.123 0.039
(0.156) (0.087) (0.028)
Legislature Rivalry 0.532* -0.199 -0.063
(0.299) (0.128) (0.040)
Market share -0.550 0.247 0.078
(1.572) (0.155) (0.049)
Environmental Activists -0.321 -0.116*** -0.037***
(0.204) (0.041) (0.013)
Industrial Consumers 1.330 0.457 0.145
(2.447) (0.508) (0.161)
Consumer Advocate 0.310 -0.157** -0.050**
(0.343) (0.079) (0.025)
Elected PUC -2.476** -0.266* -0.084*
(1.113) (0.143) (0.045)
Change per capita -1.347 2.877*** 0.228***
Income (2.644) (1.055) (0.106)
Interest rate change 0.314*** 0.036* 0.012*
(0.037) (0.020) (0.006)
Change fuel cost -0.010** 0.005*** 0.002***
(0.005) (0.002) (0.001)
Average Fuel Cost 0.543*** 0.094** 0.030**
(0.155) (0.046) (0.015)
Local knowledge 0.125*** 0.040***
(0.015) (0.005)
General knowledge (state) 0.000 0.000
(0.004) (0.001)
General knowledge (region) -0.001 -0.000
(0.001) (0.000)
Constant 1.344 -1.696***
(0.997) (0.300)
Observations 499 1,651
Firm Fixed Effects YES
Inverse Mills Ratio Significant YES**
Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
33
Figure 1: Utility rate reviews as a political process
The utility firm
initiates the process
and asks for a rate
review
The Public Utility
Commission opens
up an investigation,
evaluation and holds
hearings
Demanders of Public
Policy (e.g., the utility,
industrial consumers,
environmental
activists, etc.) lobby
and express their
views in hearings
Potential
intervention from
other Suppliers of
Public Policy
PUCs make a
decision regarding
the firm’s rate-of-
return (increase,
decrease or no
change)