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DOES COMPETITION REDUCE COSTS?
ASSESSING THE IMPACT OF REGULATORY
RESTRUCTURING ON
U.S. ELECTRIC GENERATION EFFICIENCY
Kira Markiewicz
Nancy Rose
Catherine Wolfram
Working Paper 04-37
November 2004
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Does Competition Reduce Costs?
Assessing the Impact of Regulatory Restructuring
on U.S. Electric Generation Efficiency
Kira Markiewicz
UC Berkeley, Haas
Nancy
School of Business
L. Rose
MIT and NBER
Catherine Wolfram
UC Berkeley and NBER*
November 2004
*
markiewi(a),haas. berkeley.edu nrosefajmit.edu wolfram@haas.berkelev.edu
.
from the
MIT
,
.
Rose acknowledges support
We thank
NBER 10 Summer Institute Meeting, the
Center for Energy and Environmental Policy Research and the Hoover Institution.
participants at the
NBER
Productivity Program Meeting, the
POWER conference, and the MIT Center for Energy and
Environmental Policy Research conference, as well as seminar participants at Harvard, MIT, UC Berkeley,
UC Davis and Yale for their suggestions. We are particularly grateful for the detailed comments on earlier
University of California Energy Institute
drafts provided
also thank
Tom
by Al Klevorick, Mark Roberts, Charles Rossman and Johannes Van Biesebroeck.
Wilkening for assistance in coding restructuring policy characteristics across states.
We
Does Competition Reduce Costs?
Assessing the Impact of Regulatory Restructuring
on U.S. Electric Generation Efficiency
Kira Markiewicz
Nancy
L.
Rose
Catherine Wolfram
Abstract
Although the allocative efficiency benefits of competition are a tenet of microeconomic theory, the
relation
between competition and technical efficiency
models of profit-maximization subsume
static
is
less well understood. Neoclassical
cost-minimizing behavior regardless of market
competitiveness, but agency models of managerial behavior suggest possible scope for
competition to influence cost-reducing effort choices. This paper explores the empirical effects of
competition on technical efficiency in the context of electricity industry restructuring.
Restructuring programs adopted by
many
U.S. states
made
utilities residual
savings and increased their exposure to competitive markets.
claimants to cost
We estimate the impact of these
changes on annual generating plant-level input demand for non-fuel operating expenses, the
number of employees and
for the
most
fuel use.
part unaffected
We find that municipally-owned plants,
whose owners were
by restructuring, experienced the smallest efficiency gains over the
past decade. Investor-owned utility plants in states that restructured their wholesale electricity
markets had the largest reductions in nonfuel operating expenses and employment, while investor-
owned
plants in nonrestructuring states fell
between these extremes. The analysis also highlights
the substantive importance of treating the simultaneity of input and output decisions,
which we do
through an instrumental variables approach.
JEL Codes:
L
Keywords:
Efficiency, Production, Competition, Electricity restructuring, Electric
1 1
,
L43, L5
1
,
L94,
D24
Generation, Regulation
Economists have long argued that competition generates important efficiency benefits for an
economy. These generally focus on allocative efficiency; the implications of competition
technical efficiency are less clear. Neoclassical models of profit-maximization
cost-minimizing behavior by
however,
in
all
firms, regardless of market competitiveness.
1
subsume
for
static
Agency models,
recognizing the interplay of asymmetric information with the separation of
management and
control, suggest possible deviations
may imply
managers. These models
from cost-minimization by effort-averse
a role for competition in constraining managerial behavior,
by rewarding efficiency gains and confronting
less-efficient firms with the choice
of cost
reduction to the level of their lower-cost counterparts or exit; see Nickell (1996) for a brief
discussion of some of these theoretical arguments.
Their actual relevance
is
ultimately an
empirical question.
This paper assesses the effect of competition on technical efficiency using data on the U.S.
The
electric generation sector.
past decade has witnessed a dramatic transformation of this
industry. Until the mid-1990s, over ninety percent of the electricity in the
vertically-integrated investor-owned utilities (IOUs),
IOUs
in
many
states
own
sold by
most operating as regulated monopolists
own
within their service areas. Today, non-utility generators
capacity nationwide, and
US was
roughly a quarter of generation
only a small fraction of total generating
capacity and operate in a partially deregulated structure that relies heavily on market-based
incentives or competition.
may have been motivated
While studies of state-level
in large part
by rent-seeking
many proponents of restructuring argued
outcomes would yield efficiency gains
prices.
(e.g.,
electricity restructuring suggest politicians
(e.g.,
White, 1996, and Joskow, 1997),
that exposing utilities to competitive, market-based
that could ultimately reduce electricity costs
and
retail
Research on other industries suggests productivity gains associated with deregulation
Olley and Pakes, 1996, on telecommunications and
Ng
and Seabright, 2001, on
airlines)
and with increased competitive pressure caused by factors other than regulatory change
Galdon-Sanchez and Schmitz, 2002, on iron ore mines).
The considerable body of academic work on
(e.g.,
2
electricity restructuring within the U.S.
and abroad
has thus far focused on assessing the performance of competitive wholesale markets, with
1
The implication of competition
dynamic efficiency through innovation is the subject of an extensive
from Schumpeter's 1943 classic
Capitalism, Socialism, and Democracy.
for
theoretical and empirical literature in economics, dating at least
example Borenstein, Bushnell and
particular attention to the exercise of market
power
Wolak, 2002 and Joskow and Kahn, 2002).
While many of the costs of electricity restructuring
have been intensively studied, relatively
little
(see for
effort has
been devoted to quantifying any ex post
operating efficiency gains of restructuring, although a few studies
(e.g., Knittel,
analyzed efficiency effects of various incentive regulations in this sector.
the
first
such,
is
it
3
2002) have
This study provides
substantial analysis of early generation efficiency gains of electricity restructuring.
contributes to the broad economic debate on the role of competition in the
of direct policy relevance to
states
As
economy and
contemplating the future of their electricity restructuring
programs.
The
results
of this work indicate that plant operators most affected by restructuring reduced labor
5% or more relative to
and nonfuel expenses, holding output constant, by roughly
and by 15-20%
owned
utility
plants,
which were largely unaffected by restructuring
the
(IOU)
plants,
medium-run efficiency gains
that
relative to
Joskow (1997,
government- and cooperatively-owned
p.
incentives.
treating the simultaneity
productivity
may
interpreted as
214) posits "may be associated with
Our work
of input and output choice.
may be
These
improving the operating performance of the existing stock of generating
the productivity of labor operating these facilities."
other investor-
facilities
and increasing
also highlights the importance
of
Failing to recognize that shocks to input
induce firms to adjust targeted output leads to overstatement of estimated
efficiency effects, in
some
cases by a factor of two or more. While endogeneity concerns have
been long recognized in the productivity
generation to compensate for
this.
literature,
Finally,
we
ours
is
one of the
first
studies of electric
explore the sensitivity of the estimated efficiency
impact to the choice of control group to which restructured plants are compared, and discuss the
issues involved in determining the appropriate counterfactual.
2
Some
hint
municipally
situations
3
of this possibility
owned
in electricity is
provided by Primeaux (1977),
and found a significant decrease in costs per
One exception
is
who compared
firms facing competition to a matched sample of municipally
kWh for firms
owned
a sample of
monopoly
firms in
facing competition.
who uses stochastic frontier production functions to estimate generation
One set of independent variables he includes is indicators for regulatory
Hiebert (2002),
plant efficiency over 1988-1997.
orders or legislative enactment of restructuring reforms in 1996 and in 1997. While he finds significant
reductions in
mean
inefficiency associated with restructuring laws in 1996 for coal plants, he finds
effects for gas plants, nor for either fuel type in 1997.
Our work uses a longer time
no
period, richer
characterization of the restructuring environment and dating of reforms consistent with the U.S. Energy
Information Administration, and an alternative technology specification that allows for more complex
Joskow (1997) describes the significant
moved from state-owned
competitive generation market, although these mix restructuring and
productivity shocks and treats possible input endogeneity biases.
labor force reductions that accompanied restructuring in the
monopoly
to a privatized,
privatization effects.
UK,
as the industry
The remainder of the paper
is
organized as follows: Section
competitive effects of efficiency, and discusses
efficiency. Section 2 details our empirical
how
1
describes existing evidence on the
restructuring might alter electric generation
methodology
for testing these predictions,
describes our strategy for identifying restructuring effects.
The data
and
are described in Section 3.
Section 4 reports the results of the empirical analysis, and Section 5 concludes.
Why Might Restructuring Affect Generator Efficiency?
1.
Exit by less-efficient firms
is
a well-understood efficiency benefit of competition: as output shifts
from (innately) higher-cost firms to lower-cost competitors the
production cost for a given
total
output level decline. Olley and Pakes (1996) provide empirical evidence of this
their plant-level analysis
of the magnitude and source of productivity gains
phenomenon
in
in the U.S.
telecommunications equipment industry over 1974-1987. They find substantial increases in
productivity associated with the increased competition that followed the 1984 divestiture and
deregulation in this sector, and identify the primary source of these gains as the re-allocation of
output from less productive to more productive plants across firms. In a similar vein, Syverson
(2004) finds that more competitive local markets
in the
concrete industry are associated with
higher mean, less dispersion, and higher lower-bounds in plant productivity, effects he attributes
to the exit
The
of less-efficient plants
existing evidence
efficiency gains
in
more competitive environments.
on whether competition
also leads to cost reductions through technical
by continuing producers and plants
is
relatively sparse. Nickell
( 1
996) uses a
panel of 670 U.K. manufacturing firms to estimate production functions that include controls for
the competitive environments in
which firms operate. He finds some evidence of reduced
productivity levels associated with market
growth
rates in
power and strong support
for higher productivity
more competitive environments. Concerns about the
ability
analysis to control adequately for unobservable heterogeneity across sectors
specific evidence tighter
and more convincing.
4
Schmitz (2002) study of labor productivity gains
A notable example
at iron
is
the
of cross-industry
may make
sector-
Galdon-Sanchez and
ore mines that faced increased
competitive pressure following the collapse of world steel production in the early 1980s.
They
find unprecedented rates of labor productivity gains associated with this increase in competitive
4
A
number of studies have analyzed
see, for
cases
efficiency gains following regulatory reform in various industries;
(1998) on airlines. Unfortunately, in many
on efficiency (e.g., operating restrictions imposed
those sectors) from the indirect effects of reduced
example, Bailey's (1986) overview and Park
it is
et al.
difficult to disentangle direct regulatory effects
on trucking firms or
competition.
airlines
by regulators
in
pressure, "driven by continuing mines, producing the
same products and using the same
technology as they had before the 1980s" (Galdon-Sanchez and Schmitz, 2002,
Several features of the electric generation sector
competitive effects on technical efficiency.
make
6
First,
it
p. 1233).
5
an attractive subject for testing potential
generation technology
is
reasonably stable and
well-understood and data on production inputs and outputs at the plant-level are readily available
to researchers. This has
made
electric generation a
common
application for
new production and
cost function estimation techniques, dating at least to Nerlove (1963). Second, policy shifts over
a relatively short period have resulted in a dramatic transformation of the market for electric
power. Through the early 1990s, the U.S. electricity industry was dominated by vertically
Most operated
integrated investor-owned utilities (IOUs).
as regulated monopolists over
generation, transmission, and distribution of electricity within their localized geographic market,
though there was some wholesale power traded among
utilities
or purchased from a small but
growing number of non-utility generators. Prices generally were determined by
based on accounting costs of service
at the
firm level.
By
state regulators
1998, every jurisdiction (50 states and
the District of Columbia) had initiated formal hearings to consider restructuring their electricity
sector,
and by 2000, almost half had approved legislation introducing some form of competition
including
retail access.
7
This provides both time series and geographic variation
environments. Third, static and dynamic efficiency claims bolstered
measuring these benefits
is
a vital prerequisite to assessing the
much of the
wisdom of these
in
competitive
policy reform;
policies.
has long been argued that traditional cost-of-service regulation does relatively well in limiting
It
rents but less well in providing incentives for cost-minimizing production; see Laffont
(1993).
Under pure
cost-of-service regulation, regulator-approved costs of the utilities are passed
and reductions
directly through to customers,
profits until rates are revised to reflect the
in the cost
raises operations costs
Ng and
of service yield
new lower costs
asymmetric information between regulators and firms,
5
above minimum cost
at the
at
next rate case.
inefficient behavior
levels generally
most short-term
would be
8
Given
by managers that
reflected in increased rates
Seabright (2001) estimate cost functions for a panel of U.S. and European airlines over 1982-
1995, and conclude that potential gains from further privatization and increased competition
European
and Tirole
carriers are substantial,
among
though they point out that the best-measured component of these gains
ownership rather than market structure differences.
Understanding possible reallocation of output across plants
relates to
6
available databases
7
when they
is
hampered by the
exit
of plants from most
are sold to non-utility owners.
of California's electricity crisis in 2000-2001, restructuring has become less popular and
have delayed or suspended restructuring activity, including six that had previously approved
In the aftermath
many
states
retail
access legislation. See
US
Energy Information Administration (EI A), 2003.
and passed through to customers. Joskow (1974) and Hendricks (1975) demonstrate that
in cost-of-service regulation, particularly those arising
resetting hearings),
may
impact generally
limited,
is
provide some incentives
frictions
from regulatory lag (time between pricemargin for cost-reducing
at the
effort.
Their
however, apart from periods of rapid nominal cost inflation (see
Joskow, 1974).
This system led economists to argue that replacing cost-of-service regulation with higher-
powered regulatory incentive schemes or increased competition could enhance
the
1
980s and early
1
incentive regulation.
990s,
The
many
state utility
monopoly
structure, suggests
studies a variety of incentive regulations in use through 1996,
plant performance or fuel cost
9
Over
commissions accordingly adopted some form of
limited empirical evidence available on these reforms,
price setting within the regulated
More
efficiency.
were associated with gains
mixed
which modify
results. Knittel
(2002)
and finds that those targeted
at
in plant-level generation efficiency.
10
general reforms, such as price caps, rate freezes, and revenue-decoupling programs,
typically
were associated with insignificant or negative efficiency estimates,
all
Restructuring, in contrast to incentive regulations, fundamentally changed the
earn revenue.
At the wholesale
level, plants sell either
else equal.
way
plant
owners
through newly created spot markets or
through long-term contracts that are presumably based on expected spot prices. In the spot
markets, plant owners submit bids indicating the prices at which they are willing to supply power
from
their plants.
plant
is
paid to
all
Dispatch order
is
set
by the
bids, and, in
most markets, the bid of the marginal
plants that are dispatched. High-cost plants will be forced
order, reducing likely revenue. Plant operators that reduce costs
order, increasing dispatch probability,
and increase the
profit
move
8
way
in
which
retail
customers are allocated."
the dispatch
way
own
costs
and the
retail rates are
Retail access
programs
in
Rates are constant between rate cases, apart from certain specific automatic adjustments (such as fuel
adjustment clauses), so changes in cost would not be reflected
9
in
higher in the dispatch
margin between
expected market price. Most restructuring programs also changed the
determined and the
down
in rates until the
See, for example, Laffont and Tirole, 1993, for a theoretical justification, or
next rate case.
Joskow and Schmalensee,
1987, for an applied argument.
10
Knittel uses
OLS
and stochastic production frontier techniques to estimate Cobb-Douglas generating
IOU plants over 1981-1996. His
plant production functions in capital, labor, and fuel for a panel of large
results
from first-differenced models, which implicitly allow for plant-level fixed efficiency effects,
1-2% associated with these reforms. Equations that do not allow for plant
suggest gains on the order of
fixed effects suggest
States
much
larger magnitudes.
have used a variety of approaches
to link retail rates
market. Over the short term, most states decoupled
freezes, often at levels discounted
utility
from pre-restructuring
under restructuring to wholesale prices
revenue from costs by mandating
prices.
Some
aggressively trying to encourage entry by competitive energy suppliers,
retail
customers.
states,
in the
retail rate
such as Pennsylvania, are
who may
contract directly with
combination with the creation of the new wholesale spot markets
may
increase the intensity of
cost-cutting incentives, leading to even greater effort to improve efficiency.
While the most
significant savings
long-run investments in
new
from restructuring are
capacity, there
may be
likely to
be associated with
efficient
opportunities for modest reductions in
operating costs of existing plants (see Joskow, 1997). This paper attempts to measure the extent
of that possible improvement for the existing stock of electricity generating plants
The
implicit null hypothesis
null, there
should be no change in
We discuss below our
plant-level efficiency measures associated with restructuring activity.
for estimating plant efficiency
and identifying deviations from
this hypothesis.
the effects of restructuring requires specification of how generating plants
operated absent the policy change. Constructing this counterfactual
2.
U.S.
before restructuring, operators were minimizing their costs,
is that,
given the capital stock available in the industry. Under the
method
in the
is
Assessing
would have been
crucial, but difficult.
Empirical Model
For a single-output production process, productive efficiency can be assessed by estimating
whether a plant
is
maximizing output given
its
inputs and whether
it is
using the best mix of
inputs given their relative prices. Production functions describe the technological process of
transforming inputs to outputs and ignore the costs of the inputs; a plant
production frontier. Cost minimization assumes
that,
is
efficient if
it is
on the
given the input costs, firms choose the mix
of inputs that minimizes the costs of producing a given level of output.
A plant could be
producing the most output possible from a given input combination, but not minimizing costs
for instance, labor
labor.
Even
materials,
it
if
was cheap
relative to materials, yet the plant used a lot of materials relative to
the firm were producing the
would not be
if,
efficient if
by substituting labor for materials.
it
maximum
output possible from
its
workers and
could produce the same level of output less expensively
We explore the impact of restructuring on
specifying a production function and then deriving the relevant input
efficiency
by
demand equations implied
by cost minimization.
We adopt the convention of representing generating plant output (Q) by the net energy the
generating units produce over
some period (measured by annual megawatt-hours,
data), a choice that is discussed in further detail in the data section below.
studies of electric plant productivity
model
this output as
MWh,
in
our
While a multitude of
a function of current inputs, often using
a Cobb-Douglas production or cost function, the characteristics of electricity production argue
strongly for an alternative specification.
We derive a model of production and cost minimization
that
is
sensitive to important institutional characteristics
of electricity production that have been
largely ignored in the earlier literature.
First,
observed output
given
its
in general will
be the lesser of the output the plant
(Q
)
capable of producing,
available inputs, and the output called for by the system dispatcher. Because the system
demand
dispatcher must balance total production with
p
is
and actual (Q
A
)
output for a given plant
other plants available for dispatch, and plant
i
will
at
each moment, the gap between probable
be a function of demand realizations, the set of
position in the dispatch order.
i's
12
Second, while fuel inputs are varied in response to real-time dispatching and operational changes,
other inputs to a plant's production are determined in advance of output realizations. Capital
typically
changed
chosen
is
at the
time of a unit's construction (or retirement), and at the plant level
From
relatively infrequently.
input. Utilities hire labor
and
the manager's perspective,
set operating
it
may be
and materials expenditures
in
is
considered a fixed
advance, based on
expected demand. While these can be adjusted over the medium-run, staffing decisions as well as
most maintenance expenditures are not tied
Q
We therefore
advance of actual production, and determining a target level of probable
treat these as set in
output,
to short-run fluctuations in output.
p
.
Finally, while labor, materials,
and
capital
may be
to
some
extent substitutable to produce
probable output, the generation process generally does not allow these inputs to substitute for fuel
in the short-run.
for plant
i
Q
Given
in year
A
lt
A
t
= min[ g(E
Q
input,
K for capital,
unobserved
of the technology,
we
posit a Leontief production process
of the following form:
where
is
this description
it ,
TE
,
actual output and
L
e it
E
Q
for labor,
),
p
is
and
(to the econometrician)
Q
p
it
(K L
i;
it ,
M
it ,
Tp
p
,
£ it )-exp(£ it
A
))]
probable output; inputs are denoted by
E
for
energy (fuel)
M for materials; T denotes parameter vectors, and z denotes
mean
zero shocks. See
Van Biesebroeck (2003)
for the
derivation of a similar production function he uses to model automobile assembly plant
production.
12
Random
shocks to a plant's operations, such as unexpected equipment failures or equipment that
longer than expected, will cause
inputs.
it
to produce less or
more than
its
lasts
probable output from a set of available
)
As noted above,
made
fuel input decisions are
in real time, after the
shocks associated with the plant's probable output productivity,
plant,
A
£it
output,
,
Q
and the plant's energy-specific productivity
p
,
is in
P
Ej t
,
manager has observed any
the actual operation of the
in the current period, £ it
contrast determined by input decisions
made
in
productivity of the plant's capital
is
in
.
Probable
advance of actual production.
We assume that capital, measured by the nameplate generating capacity
Labor and materials decisions are made
E
advance of production, but
of the plant,
after the level
it is
dispatched, based on the targeted output
productivity shocks that
we assume
known to
are
Q
p
.
The
the plant
error term £ it
manager
in
p
from probable output by a multiplicative shock exp(£
made
time fuel input choices are
known
but not
shock would be, for example, negative
if
at the
failure, or positive if the plant
the case if a
number of plants ahead of it
realizations
were unexpectedly high.
(PF 1
P
L
Q < Qo(Ki)-(L y -(M
it
lt
In preliminary analysis,
we
in the usual dispatch
)™- exp( £l
to be
is
and material prices
on each measured
S; t ,
at the
determined. This
down due
anticipated, as
to a
might be
demand
order were unavailable or
yields the specification:
p
)
Those
results suggested
The work reported here imposes an
on cost-minimization, to estimate input demand functions, and
restructuring effects
observed
estimated the parameters of the production function, including terms
productivity gains associated with restructuring.
13
assumed
function of labor and materials and
(Q (K)) term. This
it
available
We allow actual output
time probable output
that allowed for differential productivity under restructuring.
constraint, based
),
were run more intensively than
We model probable output (Q p ) with a Cobb-Douglas
capital effects in a constant
A
it
is
incorporates
a generating unit were unexpectedly shut
mechanical
embedding
and
advance of scheduling
labor and materials inputs, but are not observable to the econometrician.
to differ
fixed.
observed. This reflects the quasi-fixity of these inputs over
time: staffing decisions and maintenance plans are designed to ensure that the plant
when
14
is
would solve
input.
additional
isolate possible
A cost-minimizing plant manager, facing wages
for the optimal inputs to
produce probable output Qj
P
t
W
by:
In fact, over a short time period, maintenance and repair expenditures will be inversely related to output
since the boiler needs to be cool and the plant offline for most major work.
We deal with this potential
simultaneity bias below.
14
The empirical
analysis defines a
new
plant-epoch,
so that within each plant-epoch, capacity
is
/',
whenever there
approximately constant.
are significant changes in capacity,
lt
W
,
W, ,-Lj +
min
Lj„
Si
,
M
t
-Mi
demand
(LI)
Lit = (X,YL
(Ml)
Mi^^Q/yS;,
= oo +
ln(Li t)
=
oto
L
ln(X.y
A
it
=
A
P
Qi e
t
it
restructuring,
ln(L
L
is
we expand
irt )
)
t
-
A
£i
t
- ln(
measurement error
L
it
)
A
irt
)-ln(W
irt
)
measures the remaining error
L
;
.
Note
that
L
<p r
L
cp r
in labor
used
many
this
at the plant, this
i
equation will hold
demand
associated with
year
and regulatory
in
/,
as:
+ aL +
i
5t
L
+cp r
L
-
in the labor input equation.
picks up
mean
in the
of the availability constraint (X) or
that
Making
.
A
Eirt
+
E irt
L
5,
L
captures year-specific
captures restructuring -specific shifts in labor demand, and
could reflect systematic changes
Note
p
it
I5
a
is
now subsumed
L
Ei rt
in the plant-specific
residual changes in labor input for a plant in a
restructured regime relative to that plant overall and to
15
Q
shadow value of the probable output
or in the
to irt to include plant
and re-write (L2)
r,
= ln(Q
the subscript
differences in labor demand,
It
)
over time, or across regulatory regimes
measures a plant-specific component of labor demand,
demand, a
p
t
)
; ,
are particularly interested in changes in input
restructuring regime
a;
O^-expfe
becomes:
of the production function (y
As we
with error.
A
ln(Qi
constraint (X), or if there
where
-(Mj
rather than probable output,
,
If there are differences across plants,
).
in the coefficients
(L2')
)
equations:
substitution and taking logs of both sides, (LI)
where
YL
t
P
Qit )/Wit
We observe actual output, Q
(L2)
< Qo(K,)-(Li
the Lagrangian on the production constraint.
is
X,
p
t
it
yielding the following factor
where
&
s.t.
all
other plants at the
marginal productivity of labor (y
in optimization errors.
plant-level differences, such as capital stock,
L
),
same point
in the
in time.
shadow value
I6
and many time-varying shocks, such as
technology-neutral productivity shocks, drop out of this equation through the conditioning on output
choice.
16
If there
may
were systematic differences
also reflect the
change
mean
£
i
rt
.
seems plausible that these be mean zero
the restructuring sample we observe.
setting planned output,
could be nonzero in
in
of probable and actual output across restructuring, y
Since e ^ reflects shocks unobservable by the firm when
in the relation
it
in expectation,
L
r
but their realizations
10
(Ml) becomes:
Similarly, equation
ln(M
(M2)
which
is
with equality,
Q
Assuming
in
)
-
ln(S rt
;
t
q> r
component of the Leontief production
A
M -
£
A
irt
+
£ „
M
t
function,
which
will in general hold
= g(E ,YE,£ E )
irt
that g(«)
irt
is
monotonically increasing in E,
we can
simply invert
it
to get
an expression
terms of Q. Note that the price of fuel does not enter into the demand for fuel except
through the level of output the plant
input specifications,
(El)
where
+ aM + 5M +
)
as:
(PF2)
E
A
rt
directly analogous to (L2').
We model the energy
for
= ln(Q
irt )
we
InCEirt)
dispatched to produce. For consistency with the other
is
specify a log-log relationship:
= yQ E -ln(Q
irt )
+
E
cp r
as before, the plant-specific error, Oj
specific term,
E
cp r
,
energy to electricity
E
+
E
a,
+
5
E
+
E
£irt
the year-specific error, 5 t
E
,
and the restructuring-
capture systematic changes in the efficiency with which plants convert
—
that
is,
changes
in plant heat rates
—
across plants, over time, or correlated
with restructuring activity, respectively.
We confront two
(L2'),
(M2) and
equations
may
important endogeneity concerns in estimating the basic input
(El).
The
first is
the possibility that shocks (£
L
irt
£ in
M
E
,
demand
£in ) in
equations,
the input
demand
be correlated with output. If output decisions are made after a plant's manager
observes the plant's efficiency, managers
may
increase planned output in response to positive
shocks to an input's productivity, or reduce planned output
in
response to negative shocks. This
behavior would induce a correlation between the error in the input demand equation and observed
output.
Though one can
control directly for plant-specific efficiency differences and for secular
productivity shocks in a given year, idiosyncratic shocks remain a source of possible bias.
Second, the estimates
may be
subject to selection bias if exit decisions are driven by unobserved
productivity shocks. In this case, negative shocks could lead to plant shutdown, implying that the
11
errors for observations
problems
is
Consider
first
we
unique to our
observe will be drawn from a truncated distribution. Neither of these
setting,
and they have been raised
in
many
earlier papers.
We face a potential simultaneity problem
the simultaneity issue.
for instance, a
if,
malfunctioning piece of equipment reduces the plant's fuel efficiency, leading the
reduce
its
operation of that plant and consequently to use less fuel. There
may be
17
utility to
deviations from
predetermined employment and materials budgets caused by unanticipated breakdowns that
require increased use of labor and repair expenditures and result in lower output.
efficiency shock to an input
may
lead managers to run that plant
concern.
electricity
the
We choose to use an instrumental variables approach, using a measure of state-level
demand
as an instrument for plant output. This
amount of output a plant
efficiently
intensively over the year,
A variety of methods have been used to address this
increasing output as well as input use.
18
more
will
be called
is
likely to
be highly correlated with
to provide, but uncorrelated, for instance,
an individual plant's feedwater pumps are working. This approach
particularly effective for the energy equation, given the responsiveness
demand
A positive
fluctuations in real time, and for identifying
is
likely to
powerful
in identifying variation in
The
of our
exogenous output fluctuations
at
state
demand.
19
stable than those studied in
is
more
many
difficult to address.
non-
It
may be
in part
We therefore explore the
The
plants in our sample
seem more
other contexts (especially see Olley and Pakes, 1996),
suggesting that the selection problem
may
be somewhat less severe for electric generation.
exit increases in restructuring regimes, typically not
because the plant
but because divestitures remove the plants from the reporting database.
divestitures are
to
results to alternative instruments.
potential selection issue
However, plant
be
ex ante labor and maintenance choices, depending
on the extent to which plant managers anticipate
sensitivity
how
of energy input choices
baseload plants, which are more strongly influenced by marginal swings in demand.
less
with
mandated by the restructuring
legislation, this
To
is
retired
the extent that the
should not create selection
problems. But without better information on what determines discretionary divestitures,
we have
17
Nerlove (1963) provides an early discussion of simultaneity bias in production functions. Olley and
Pakes (1996) propose a structural approach to addressing simultaneity, which is compared to alternatives
Griliches and Mairesse (1998). Ackerberg and Caves (2003) discuss this issue and compare treatments
in
proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). While many papers have estimated
production or cost functions for electric generating plants, from the classic analyses
in
Nerlove (1963) and
Christensen and Greene (1976) to very recent work such as Kleit and Terrell (2001) and Knittel (2002),
electricity industry studies typically
18
19
See the references cited
A
in
have not treated either simultaneity or selection problems.
note 17, supra.
drawback to this instrument is that we measure demand only at the state, rather than plant
which means that we do not use the cross-plant variation within a state to identify output coefficients.
further
level,
12
no direct way to assess
their
potential selection effects
work
for
to those for a panel
which
is
impact on the
to
compare
results.
results for the
of plants that continue
may
indirect
way
to assess the significance of
unbalanced panel
to operate
potential selection effects are likely to be
those results
One
we
use in most of our
through the end of our sample period,
most severe. Substantial differences across
suggest the need to more carefully treat potential selection biases.
Identification strategy
There
is
substantial heterogeneity across plants, utilities
and
states,
operate has changed considerably over time. In addition,
environment
in
which
restructuring
is
not randomly assigned across political jurisdictions
is
utilities
and the economic
—
earlier
work suggests
strongly correlated with higher than average electricity prices in the cross-section.
Fortunately,
we have
plants operated
in this sector
own
and
legal
it
20
a database rich in variation. There are thousands of generating
by hundreds of utilities subject
each setting their
that
to regulation
institutional environment.
by dozens of political jurisdictions
Panel data on the costs and operations
of these plants are available, with some recent exceptions, from well before any restructuring
until the present.
21
This allows us to construct benchmarks that
we
believe control for most of the
potentially confounding variation.
The
plant-specific effects,
plants in the sample.
N
{oii
These
},
measure the mean use of input
effects
may be
and vintage, ownership (government
year-specific shock, {5
N
t
},
secular technology trends,
effects
v. private utilities),
macroeconomic
was
initiated in
1993 and the
outcome of those hearings,
and time-invariant
N
{(p r
}.
The
state effects.
shifts
The
over time, such as
The heterogeneity
activity allow the data to distinguish
all states
latest in
in the
timing and
between temporal
held hearings on possible restructuring, the
1998. There
is
considerable variation in the
as well, with just under half the jurisdictions (23 states and the District
of Columbia) enacting restructuring legislation between 1996 and 2000.
20
relative to other
fluctuations or energy price shocks. Restructuring
on plant productivity correspond to a non-zero
shocks and restructuring effects. While
;
associated with differences in plant technology type
measures the efficiency impact of sector-level
outcomes of state-level restructuring
earliest
N at plant
significant role of sunk capital costs in regulatory ratemaking
means
22
The remainder
that high prices
necessarily imply high operating costs for generation facilities within a state, however. See
do not
Joskow (1997)
for a discussion of the contributors to price variation across states.
Cost data are not publicly available for plants owned by exempt wholesale generators, including those
acquired from regulated
22
We collected
utilities.
information on state restructuring legislation from various Energy Information
Administration and National Association of Regulatory Utility Commissioners publications and state
13
:
considered and rejected, or considered and simply did not act on, such legislation. This variation
allows us to use changes in efficiency at plants in states that did not pass restructuring legislation
to identify restructuring separately
from secular changes in efficiency of generation plants over
time.
It is
possible that plants in this control group also altered their behavior over the post- 1 992
period. This could be
due perhaps
to the introduction or intensification
of incentive regulation
within states that did not enact restructuring, to the expectation of potential restructuring that did
not occur, or spillovers from restructuring
their information
movements
in other states (e.g. if regulators
updated
about the costs necessary to run plants of a certain type, or multi-state
utilities
operating under differing regimes improved efficiency of all their plants, not just those in
restructuring states).
To
the extent this occurs, our comparison will understate the magnitude of
any efficiency effect of restructuring.
We therefore consider a second control group, consisting of cooperatively-owned or publiclyowned municipal and
federal plants,
although the group
broader than
is
which
strictly
for convenience
we
will refer to as
An
implied by this label.
"MUNI" plants,
extensive literature has
debated the relative efficiencies of private and public ownership in this sector under traditional
regulation, with
somewhat mixed
effects that absorb
results.
any levels differences
We abstract from that by allowing for plant-specific
in input
use across ownership type. Restructuring
generally altered the competitive environment only for private investor-owned
state,
that
leaving those for publicly- and cooperatively-owned utilities unchanged.
MUNIs may
We adopt a parameterization that measures
publicly-owned plants during the period that investor-owned
1
993 forward.
Using
This suggests
equation
1 1
irt
-
}
relative to
of restructuring,
N to denote input (labor, nonfuel expenses, or fuel), and
to denote the relevant input price (none for the fuel equation),
A
ln(N irt ) = ln(Q )
N
{cp r
utilities are at risk
PRICEN
(1 1 )
within a
provide a second benchmark against which to measure changes in efficiency
associated with restructuring.
defined as
utilities
23
N
\n(PRICE
in )
+ yMUNI +
it
N
a,
+
5t
N
+
we have
N
<p ir
-
e
A
irt
input use
+
e
N
,
r
t
utility commission websites. Since 2000, no additional states have enacted restructuring legislation,
and several have delayed or suspended restructuring activity in response to the California crisis.
public
With
the exception of Arizona
and Arkansas, which included government-owned
utilities in restructuring
programs.
14
This specification implicitly provides two "non-treatment groups" to which investor-owned
plants in restructuring regimes
may
be compared: investor-owned plants in non-restructuring
regimes (with the restructuring effect measured by
N
cp
plants over 1993-1999 (with the restructuring effect
Data
3.
The
r
),
and public- and cooperatively-owned
measured by
N
- y).
cp r
& Summary Statistics
analysis in this paper
is
based on annual generating plant-level data for U.S. electric
Plants are comprised of at least one, but typically several, generating units,
to or retired
from service over the several-decade
would allow
ideal data set
life
utilities.
which may be added
of a typical generating plant. While an
us to explore efficiency at the generating unit level, inputs other than
fuel are not available at the generating unit level,
and some, such as employees, are not even
assigned to the unit level as they are shared across units at the plant.
24
We therefore use a plant-
year as an observation.
The Federal Energy Regulatory Commission (FERC)
plants annually in the
Utilities
Service
FERC Form
(RUS)
1
,
collects data for investor-owned utility
and the Energy Information Administration (EI A) and Rural
collect similar data for
municipally-owned plants and rural
electric
cooperatives, respectively. These data include operating statistics such as size of the plant, fuel
usage, percentage ownership held by the operator and other owners,
capacity factor, operating expense, year built, and
set includes all large
data were reported to
other plant-level statistics.
(CCGT)
steam and combined cycle gas turbine
FERC or EIA
plants, defined as those for
our sample years.
many
number of employees,
generating plants for which
over the 1981 through 1999 period.
which gross capacity exceeded
We also excluded approximately
25
undue influence. Further
We follow the
year,
1,500 observations where data were missing,
on the data are provided
literature in characterizing output
Some
labor
may be
is,
in reality,
One
utility
total
screen for
Appendix.
energy output of the plant over the
NET MWhs.
This
is
shared across multiple plants, though assigned to one particular plant in our data.
unfortunate consequence of restructuring
is
in the plant
employment
that available data
variable.
on plants sold by
utilities to
non-
generators are extremely limited after the sale, due to changed reporting requirements. This
that plants will
an
multidimensional, although most dimensions are not
This will lead induce measurement error, particularly
25
by the
tests to
in the
measured by annual net megawatt-hours of electricity generation,
imperfect choice. Output
24
details
We excluded smaller
00 megawatts for fewer than three of
1
and dropped several hundred observations based on regression diagnostic
outliers or
Our base data
be excluded from the dataset
after
such
means
sales.
15
recorded in the plant data. For example, generating plants
(such as spinning reserves,
when
may
also provide reliability services
the plant stands ready to increase output at short notice), voltage
support and frequency control. While the production process varies considerably across these
different outputs, only net generation
is
not a
the
homogenous product. Because
Friday in July
first
Sunday
down
is
in
is
well measured in the data.
26
Moreover,
electricity is non-storable, electricity
how
produced
5PM
on
with taking their plant
to balance the costs associated
do maintenance against the probability that a poorly maintained plant will
to
at
5AM on the second
a separate output from electricity produced at
March. Firms must decide
electricity output
during
fail
peak demand hours, and the availability of the plant may be an important modifier of output
Changes
quality.
in incentives associated
with restructuring
may have
of these tradeoffs, although the expected direction of the effects
is
altered firms' assessments
theoretically ambiguous.
Hourly output prices and output from individual plants might allow us to better assess
Lacking such data,
We have
we rely on a single
output dimension, but acknowledge
information on three variable inputs. The
employees
The second,
at the plant.
first,
EMPLOYEES,
NONFUEL EXPENSE,
includes
is
all
its
27
this.
limitations.
a count of full-time
non-fuel operations and
maintenance expenses, such as expenses for coolants, maintenance supervision and engineering
expenses. This variable
less
is
than ideal as a measure of materials, both because
expenditures rather than quantities, and because
counted
in
EMPLOYEES,
although that expense
NONFUEL EXPENSES includes
EMPLOYEES will
coal, barrels
reflect
changes in
Btu content of each
Input prices pose a challenge.
includes the
wage
bill
for the
reflects
employees
not separately delineated in our data.
is
As
payroll costs (not separately identified), both this and
28
staffing.
of oil, and mcf of natural gas).
plant-specific
it
it
The
We convert fuel into BTUs
fuel to obtain total
Wages
perceived regulatory environment.
third input is fuel use
in particular
by type of fuel (tons of
using the reported annual
BTU input at the plant for each year.
may
be endogenous to the firm and
Hendricks (1975) suggests that
utilities
may
its
bargain less
26
The inputs required to produce a given level of energy (MWh) from a specific plant also will depend on
whether the plant runs continuously or intermittently and on its average capacity utilization. Starting a
plant frequently and running
per
27
mwh generated than does
it
at
low capacity
For instance, under traditional regulation,
utilities
blackouts or brownouts, leading to investment
maintenance resources to increase plant
restructured wholesale markets
because
28
The
this is
when
elasticity
may
in
more
inputs (particularly fuel)
at its rated capacity.
may have
faced strong political incentives to avoid
greater capacity to increase reserve margins and in greater
reliability.
On
the other hand, competitive firms producing in
face even stronger incentives to be available
when demand peaks
prices are highest.
of NONFUEL
consistent with our back
total
utilization rates typically use
running a plant continuously
EXPENSES with
respect to
EMPLOYEES is
of the envelope calculations suggesting
about
.5 in
our data, broadly
that labor costs are roughly half of the
nonfuel operating budget.
16
aggressively over input prices such as wages during periods in which higher costs could be
readily passed
firm
was
on
customers through higher regulated prices, and more aggressively when the
to
likely to
be the residual claimant to cost savings. In other industries, regulatory reform
has sometimes been associated with substantial reductions in wages, suggesting rent-sharing
under regulation (see Rose,
may
1
not be exogenous to the firm, and
workers of similar
may
not reflect the opportunity cost to managers of the
We address this by using state-level
marginal unit of labor.
skills
These suggest that observed wages
987, on the trucking industry).
average wages from industries with
and training to power plant operators, including natural gas
petroleum refining and hazardous waste treatment
facilities,
denoted as
WAGE.
distribution,
This reflects
opportunity wages, and avoids confounding the employee price measurement with any changes in
recorded wages due to changes in labor force composition
or changes in
wage
bargaining.
materials prices that comprise
EXPENSES therefore
We do not have plant- or even firm-specific indices
NONFUEL EXPENSES.
final input is the capital stock
Our
data record the plant capacity in megawatts.
Our empirical model of NONFUEL
capacity of the plant
we
retirement,
is
create a
changes
we measure by
of the plant, which
retirements to define plant-epochs.
Each
plant
is
identifier
for
in input use.
two other
The
is
and associated new plant-specific
plant
may
vary within plant-epoch and lead
may become
The second
is
FGD affects the environmental output, unmeasured by
state,
we have
less efficient or
the addition of a flue-gas
in
some
\n{NET MWhs).
plant data with information on state-level restructuring activity.
identified the date at
which formal hearings on restructuring began, the
enactment date for legislation restructuring the
state's utility sector, if any, the
date for retail access under that legislation, and
some
rate freezes
This allows
FGD (also called scrubber), to reduce sulfur-dioxide emissions
We supplement the operational
For each
effect.
AGE in years, dated from the installation date of the
require additional inputs for a given level of output.
coal plants.
time the
of the plant.
oldest operating generating unit at the plant: as plants age they
desulfurization system, or
Any
an identifiable unit addition or unit
plant characteristics that
first is
information on unit
assigned a unique identifier.
significantly changed, or there
new
plant capacity and vintage.
We combine this with
to alter the underlying input efficiency
We include controls
changes
for the
corresponds to an input demand equation with constant prices and a price
The
to
associated with restructuring
of one.
coefficient
capital
at utilities
implementation
associated aspects of restructuring such as
and mandatory divestiture of generation. Testing
for restructuring-specific
shocks
17
requires a determination of how to
match
this
when were
information with firm decisions:
plant
operators in a given state likely to have begun responding to a policy change? Consultations with
industry participants and readings of these events suggest that utilities often acted in advance of
final
outcomes. The legislative and regulatory process leading up to state restructuring typically
lasted a
number of years, allowing
utilities to anticipate
the
coming change, and
alter their
behavior in advance. For example, Boston Edison's 10-K filed in March 1994 discussed
Massachusetts' consideration of restructuring, stating "The
Company
is
responding to the current
and anticipated competitive pressure with a commitment to cost control and increased operating
efficiency without sacrificing quality of service or profitability" (p. 6).
begun
to
phase
in input
29
Utilities
changes, especially those involving labor and particularly unionized
workers. Moreover, as policy changes were discussed, rates were frozen in
explicitly
enabling
by policy makers or
utilities to
In this work,
restructuring.
turns
we
31
in effect
by
implicit
PUC
decisions not to hear
capture the savings from incremental cost reductions.
The primary
start
RESTRUCTURED,
variable of interest,
of formal proceedings
is
A second variable, RETAIL ACCESS,
number of plants
PECO's
CEO Joseph Paquette discussed
have been focusing on our
all
PECO
operations.
an indicator variable that
indicates the start of retail access for plants in
32
Table
1
reports the
cost saving accomplishments and strategies for the future,
restructuring of the utility industry and
strategic plans to
becoming more competitive."
effectiveness of
rate cases,
30
each year that were in states that had RESTRUCTURED and the
in our database
In a 1993 article outlining
and
new
states, either
a state that eventually passed restructuring
in
the four states that implemented retail competition during the sample.
29
many
allow restructuring effects to begin with the opening of formal hearings on
on with the
legislation.
may have
One
enhance our
abilities to satisfy
initiatives cited in the article included
particular
was quoted
Chairman
as stating,
"we
our customer needs by
improving the cost
accomplishment noted was the reduction
in total
employment from 18,700 to 12,900.
30
As noted earlier, some of these changes may have also affected utilities in non-restructuring states. For
example, the number of utility rate cases dropped dramatically in the 1990s, implying that many or most
utilities may have been short- or medium-run residual claimants to cost reductions. Knittel (2002)
identifies a number of incentive regulations adopted in various jurisdictions during the 1990s. Many of the
fuel-related regulations (modified pass-through clauses, heat rate
and equivalent availability factor
incentive programs) were strongly correlated with ultimate restructuring.
(e.g., price
31
The
RESTRUCTURED variable
is
some delays or suspension of planned
While
regulations
based on whether a state had passed legislation as of mid-2001,
although in the aftermath of the California electricity
32
Some of the broader
caps and revenue decoupling programs) were almost orthogonal to eventual restructuring.
RESTRUCTURED indicates
approval of retail access legislation, the specified phase-in of retail
access was often slow. Only five states implemented
in 1997, California, Massachusetts,
there has been no additional restructuring, and
crisis,
restructuring activity.
and
New York
in
retail
access during our sample period:
Rhode
Island
1998, and Pennsylvania in 1999 (U.S. EIA, 2003).
Because we have no valid observations on Rhode Island plants in 1997 or beyond, retail access effects will
be determined by the 4 states implementing in 1998 or 1999. Divestiture requirements in California and
Massachusetts further reduces the post-retail access sample of plants, as investor-owned plants in those
states were largely divested by 1999.
18
number of plants
in states that
had started
restructuring legislation or regulation
retail
access by 1999.
33
was enacted and the policy uncertainty
RESTRUCTURED will underestimate the true effect by averaging
evaluate this possibility
beginning
in the
we
If utilities did not
introduce a third variable,
in
34
Similarly if actual
will be reflected in an incremental effect of RETAIL
municipally-owned plants over the restructuring time period,
1992, equal to one for
all
we
1999.
The
final
column
is
important to
ACCESS. To examine
define the variable
municipally-owned plants from 1993, the
RESTRUCTURED is one, through
resolved,
non-response years. To
implementation of retail access and the associated wholesale market reforms
it
until
LAW PASSED, an indicator equal to one
year the state passes restructuring legislation.
efficiency gains,
respond
first
reports the
MUNI*POST
year for which
number of plants
in this
category.
Details on the data sources and
2b report summary
summary
statistics are
statistics for plant-level
provided in the appendix. Tables 2a and
data in 1985 across three categories: investor-owned
plants in states that later restructure, investor-owned plants in states that do not restructure,
non-IOU
plants.
changes across
initiatives.
We choose this date to ensure that comparisons are made prior to any significant
states in the competitive or regulatory
From
and
these tables,
it
from the same population. The
environment, even prior to restructuring
appears that the plants from these groups are not random draws
first
three variables measure employees and non-fuel operating
expenses, scaled by the plant's capacity, and fuel use in millions of British thermal units
(mmBtus), scaled by the plant's output. In 1985, before
state-level restructuring initiatives
were
considered, plants in states that eventually restructured had higher intensities of employees and
non-fuel operating expenses, although the difference
The
first
two rows
in
is
significant only for non-fuel expenses.
Table 2b show that municipally-owned plants had significantly higher
employment and non-fuel input use than
plants in non-restructuring states.
heat rates and capacity factors are not significant in either of the tables.
The
The
last
differences in
four variables in
both tables describe the stock of plants in the two types of states. Although plants are very
similar in size across IOUs,
33
MUNIs
plants are considerably smaller.
IOU
plants in restructuring
For the tables and the regression analysis that follows, plants are assigned to the state in which they are
A plant located in one state may be owned by a company with exclusive service territory in a
regulated.
and that second state is the state by which the regulatory policy is measured. Some plants
company with service territory in more than one state and some plants are owned by
several companies that are regulated by different states. In the regression analysis, we found that separately
different state,
are
owned by
a
characterizing "mixed" regulation and "shared" plants had very
little impact on our results.
There is on average about a 2.6-year lag between the initiation of hearings and the passage of the law.
We have experimented with a number of alternative measures of restructuring activity, including variables
34
that begin with hearings regardless
of restructuring outcomes, those that measure years since hearings were
19
states
tended to be older, more likely to use gas, and less likely to use coal, than
non-restructuring states.
likely to use gas, than
MUNI plants tended to be younger,
IOU plants
in
non-restructuring states.
less likely to
IOU
plants in
use coal, and more
The regression
analysis will control
for these differences directly or with the use of plant-epoch effects.
If
investor-owned
utilities
achieved efficiency improvements
restructuring of the generation sector, one
generation for affected companies, and
distribution costs
would expect
little
when
facing impending
to see a relative decrease in the cost
of
difference in the change in transmission and
between the affected and not affected
states since restructuring
programs leave
transmission and distribution comparatively untouched. If restructuring did not affect operating
efficiency in the generation sector,
we might
would not be
between restructuring and non-restructuring companies, or (2)
we would
statistically different
same
see the
expect either (1) the change in generation expenses
pattern of change in costs for the transmission and distribution sectors as
for the generation sector.
35
Table 3a and 3b display the difference in mean
and non-restructuring
states for a
change
tests for
in costs
investor-owned
utilities in restructuring
between 1990 and 1996. Table 3a reports the
percentage change in total costs for each category of cost, and Table 3b reports the percentage
change
in costs
per
significant at the
level.
The
MWh. The larger decrease in generation costs
1%
level,
and the
result for generation costs per
at restructuring
MWh
is
companies
significant at the
difference in costs for companies in restructuring and non-restructuring states
significant for either the transmission or distribution costs.
These aggregate
preliminary support for the expectation that the portion of the
utility
statistics
is
is
6%
not
provide
company faced with
competition (the generating sector) responded with a decrease in costs, while other sectors and
companies not faced with competition did not share
4.
The
Effects of Restructuring on Input
this response.
Use
and the presence of restructuring-associated rate freezes.
change materially to the conclusions we draw below.
35
For the analysis comparing costs of generation, transmission, and distribution services, we rely on data
reported annually by utility companies to the Federal Energy Regulatory Commission (FERC) in the FERC
Form 1, page 320, 321, and 322 respectively. We use a balanced sample composed of all companies with
data reported for all three sectors in both 1990 and 1996. This amounts to 49 companies in states that did
not deregulate and 74 in states that did deregulate for the comparison of costs, and 48 and 72 respectively
for the comparison of costs per MWh. Using costs per
necessitates the exclusion of a few companies
for which
data was not available in one of the two years.
initiated for states that eventually restructured,
None of these seem
to
MWh
MWh
20
Following equation
(II),
we
(EMPLOYEES, NONFUEL EXPENSE, and BTUs)
ln(N irt ) =
(Rl )
with the following basic regression model:
n
^ \n(NETMWh M) + tfhiQPRICE"*
)
+
&A GE
N
IOU*RESTRUCTURED + yMUNI1993 m9 +
where we allow for non-unity coefficients on the output term
input price term (p 2
Oj
.
r
N
on
WAGE)
in the
EMPLOYEES
important plant characteristics that vary over time:
invariant fixed effect for input
N at plant-epoch
i,
N
)
(Pi
equation,
36
k
+
ilt
N
(p
N
estimate the influence of restructuring on the use of input
FGD
fa
+5
N
+
t
ilt
+
N
p\ s
A
lrt
+
e
N
irt
and on the
for all equations
and include controls for two
AGE and FGD (scrubber).
which may contain a
N
is
otj
a time-
state-specific
and
ownership-specific error that will not be separately identified. These plant-specific effects
control for
much of the expected
variation in input use across plants arising from heterogeneous
They
technologies, state or regional fixed factors, and basic efficiency differences.
mix between
for differences in the plant
each plant to
rejects the
is
itself
restructuring and non-restructuring states
over time, removing any time-invariant plant effects.
exogeneity of plant effects,
an industry-level effect
in
year
t,
all
As
a
also control
by comparing
Hausman
test
reported results include plant-epoch fixed-effects.
which controls
for systematic changes in input
demand
37
5
N
t
across
plants over time.
all
N
£irt
time
is
t.
assumed
to be a time varying
This shock
is
mean
zero shock for input
N at plant-epoch
i
in
unlikely to be independent over time for a given plant. There
regime
is
r at
likely to
be
persistence in input shocks, particularly for labor, from year to year. Indeed, estimated rhos
based on assumed first-order
range for non-fuel expenses.
serial correlation are in the .65
It is
autogressive process, however.
range for labor inputs and
unlikely that the correlation
The
is
in the .33
as simple as a first-order
physical operation of power plants
is
correlation at longer differences. For example, routine maintenance cycles
likely to induce
may
involve
scheduled shutdowns but increased labor and nonfuel expenses every three or four years.
have explored
GLS
specifications based
some
on an assumption of first-order autoregressive
We
errors,
and
the results are quite similar to those reported in the tables below. Rather than impose this
assumption, however,
36
Recall that
we do
we choose
to estimate the
model without a
GLS
and simply
not have a price associated with nonfuel expenses, and that according to equation (El),
fuel prices should not enter into the fuel input function.
We experimented with
the price of a given plant's fuel relative to the prices of other fuels in the
output but the variable had no
37
correction,
This use of fixed effects
is
power
using a variable measuring
same region
as an instrument for
in the first stage.
work of Joskow and Schmalensee (1987), who used generating
between operating performance and unit characteristics. Our
similar to the
unit-level data to explore the relationship
21
report standard errors that are corrected for a general pattern of correlation over time within a
given plant.
38
Input use over time will vary with the level of plant operation, which
specifications as the net generation
by the
plant in megawatt-hours
is
measured
in these
(NET MWh). We
treat the
endogeneity and measurement problems described earlier by instrumenting for output with state
demand
(the log
of total
state electricity sales, a
consumption rather than production measure).
This instrument affects the likelihood that a given plant in the state will be dispatched more over
the year, but
not influenced by the characteristics of the plant or the choices of individual plant
is
operators.
We consider specifications that include interactions
of IOU ownership with the three primary
restructuring indicator variables described in section 3:
RETAIL ACCESS.
and
In the input regressions, a negative coefficient on the restructuring variables
would imply increased input
efficiency associated with the regulatory reform.
for the input analysis are presented in Tables 4 for
and 6 for BTU.
RESTRUCTURED, LA W PASSED,
We first discuss the results
for
EMPLOYEES,
5 for
The core
results
NONFUEL EXPENSES
employment and nonfuel expenses, and then
discuss the results for fuel use.
Column
1
of tables 4 and 5 reports a simple
output. In this column,
OLS
formulation that excludes any control for
IOU*RESTRUCTURED captures the mean
differential in input use for
investor-owned plants in states that eventually pass restructuring legislation, measured over the
period following the
first
This corresponds to the
restructuring hearings, relative to
mean
IOU
plants in non-restructuring states.
within-plant shift in input use, independent of output.
The
results
suggest statistically and economically significant declines in inputs during restructuring.
Employment
relative to
declines by almost
IOU
6%
(2%) and nonfuel expenses decline by almost 13% (2%),
plants in regimes that have not restructured. Controlling for plant output reduces
the estimated impact of restructuring by
and
statistically distinguishable
employment and -10% (2%)
more than one-quarter, though
from zero (see column 2 of each
for nonfuel expenses.
fixed effects are actually finer than plant-level, as
we
40
We
use [exp(<p r
N
the effects remain large
table), at
-4% (2%)
Measuring restructuring
permit
a,
to
and other significant changes in rated plant capacity.
38
Reported standard errors are calculated using the cluster option
39
39
effects
for
from the
change with unit additions or retirements
in Stata.
)-l]*100 to approximate the implied percentage effect of
IOU* RESTRUCTURED on
input use.
40
Note
that the
Cobb-Douglas functional form assumption suggests
that the coefficient
on output should be
one, substantially larger than the coefficients estimated in these regressions. If we impose this constraint,
the effect of restructuring
is
estimated to be positive and significant.
We have estimated
production
22
enactment date of legislation does
each
little
to
change the qualitative conclusions (see column 3 in
table.).
This finding hints
at
an interesting pattern in the data: restructuring
is
associated with same-plant
output reductions relative to same-plant output in non-restructured regimes
is
why
at the
same
date. This
estimated input use reductions associated with restructuring are smaller in the presence of
output controls.
Some of this might be
restructuring, as the
less in a
raw input and
expected: if these plants were less efficient prior to
more competitive environment.
exempt generators,
this
output reduction also
consumption
the case, they
may
be used
encourages entry by non-utility or
If restructuring
might further reduce output
may
may have been
price data suggest
at less efficient plants.
41
Some
plant-level
be generated by the apparent lower growth in state-level electricity
in restructuring states, relative to electricity
consumption growth
in
non-
42
restructuring states.
This result
is
not only of interest economically, but also
estimation of the input
demand
relationship.
The
is
important for the econometric
inverse correlation of output and restructuring
policy attaches great importance to obtaining a consistent estimate of the relationship between
output and input demand. Understating the response of input use to output changes will load
more of observed
input reductions onto the restructuring variable, overstating the responsiveness
will lead to underestimates
of the restructuring
The second notable
is
While
group.
pattern
IOU
the dependence of the implied restructuring effect on the control
plants in restructuring states exhibit
nonfuel expenses relative to
than twice as large
IOUs
when compared
MUNI*POST 1992 coefficients
21-23 percent (columns 2 and
in
modest reductions
in
employment and
in non-restructuring states, the implied reductions are
to public
Employment drops by 13-15 percent
functions in
effect.
more
and cooperative plants over the restructuring period.
(see the difference in the
columns 2 and
3 in table 4)
3 in table 5), relative to
IOU*RESTRUCTURED
and
and nonfuel expenses decline by
municipal plants over the 1993-1999
EMPLOYEES and NON-FUEL EXPENSES using more
flexible functional forms than
Douglas, and the results also suggest efficiency gains associated with restructuring.
We
Cobb-
have also
estimated instrumental variables versions of equations (L2) and (M2) that include the other input instead of
output and obtained very similar results to those reported here.
41
Total state-level electricity consumption, while increasing on average during the 1993-1999 period in
restructuring states, appears to
42
This
is
grow
less fast
than consumption
in
non-restructuring states.
unlikely to be causally related to restructuring, as restructuring effects
state electricity
electricity
on
retail rates
were
neutral
however, a negative correlation between
consumption and restructuring, conditioning on state means and national growth rates in
or negative (due to rate freezes) during this period.
There
is,
consumption.
23
period. This suggests that even
IOU
plants in non-restructuring regimes
were reducing
their input
use to a significant extent during the 1993-1999 period, perhaps in response to latent threats of
increased competition and restructuring.
Given the importance of controlling
We return to this
issue in greater detail below.
for output in assessing restructuring effects,
we
next turn to
instrumental variables estimates of the input equations that treat potential measurement error and
simultaneity bias with respect to output.
Column 4 of these
tables instrument for the log of
current plant output with the log of total electricity sales in the state.
on the estimated output coefficients for the two input demands. For
43
This has opposite effects
EMPLOYEES,
instrumental variables (IV) estimates of the output coefficient are below the
although the imprecision of the IV estimates
make
consistent with there being a positive relationship
although as
we
Both
respect to output are quite small (at
OLS
estimates,
difficult to reject equivalence.
between shocks
discuss below, the direction of this relationship
specifications of the instrument.
to efficiency
8.1% (1% standard
is
error)
largely
is
is
not robust to alternative
is
elasticity
with
and 6.5% (7.5%), respectively for
Consistent with
relatively fixed.
estimated coefficient on IOU*RESTRUCTURED
This
and input use,
and IV estimates of the labor demand
the basic specification), suggesting that labor
at
it
OLS
the
unchanged
this,
the
in the basic specification,
-4.5% (2.3%).
In the case of nonfuel expenses, instrumenting for output substantially increases the estimated
output elasticity, from
15% (1%)
to
40% (9%)
in the basic specification.
negative correlations of input shocks and output, as for example,
if
This
is
consistent with
large maintenance
expenditures are associated with outages at the plant. Given the strong link between output and
nonfuel expenses implied by these results, and the fact that both declined with restructuring,
instrumenting also has a substantial effect on the estimated effect of restructuring. The estimated
coefficient
on
IOU*RESTRUCTURED coefficient drops by more than
range of the estimated labor input effect
Columns
5
This
is
SALES)
Table
44
it
into the
about -4.8% (2.8%).
^ Measuring
restructuring by
an important determinant of plant-level output.
is
bringing
and 6 of the tables explore robustness to alternative measures of restructuring,
maintaining the use of IV estimates.
43
at
half,
zero in the
first
stage for
Column
(4)
is
The
F-statistic
LAW PASSED
from the
test that
in
column
5
\n(STATE
F(l,9674)=18.13 for Table 4 and F(l,9686)=16.68 for
5).
Results are broadly similar in unreported regressions that measure restructuring from the date of
legislation, although the effect
number of years
since hearings
of RETAIL ACCESS is further tempered, and in regressions that add the
were begun, which suggest that employment and nonfuel expense
reductions begin relatively small and increase over time.
24
yields slightly larger coefficients
on restructuring
in the
employment regressions and
slightly
smaller (and statistically indistinguishable from zero) coefficients in the nonfuel expense
Column
regressions.
identified
1999, and
RETAIL ACCESS variable. We
by no more than two years of data
it is
note that
in the four states that
-5% (5%
labor
demand
is
coefficient
its
implement
not particularly stable across alternative instrument sets.
RETAIL ACCESS in
additional
6 adds the
The
is
access as of
retail
coefficient
on
imprecisely estimated, though the point estimate suggests an
when
standard error) change in employment
The estimated impact of retail access on nonfuel expenses
is
states
implement
retail access.
-15%
substantially larger, at
(7.8%).
In the instrumental variables results, as in the
statistically
IOU plant
larger,
restructuring
and economically
in a restructured
on the order of
1
results, the
upon the chosen benchmark or
restructuring effect depends
demand between
OLS
and non-restructuring
regime relative to
in
control group.
states, conditional
The gap
on output,
in
is
IOU
input
generally
though relatively modest. The performance gain of an
significant,
5% reductions
implied magnitude of the
MUNI plants over the same period is
employees and
20%
substantially
reductions in nonfuel expenses.
We therefore experimented with a number of more flexible specifications of the MUNI control
group.
Column 7 of tables
MUNI
plants over the
4 and 5 reports one variant, in which
1993-1999 period and
(MUNI*RESTRUCTURED).
MUNI
In neither case can
independent of restructuring (point estimates for
we
we
include separate controls for
plants in restructuring regimes
reject the null that
MUNI plant input use is
MUNPRESTRUCTURED are positive for
employees and negative for nonfuel expenses). In unreported specifications we have allowed for
more
flexible specifications
coefficients.
The
of the
MUNI controls over time and for differential MUNI output
pattern of results
is
consistent with input performance gains for
restructured regimes relative to nonrestructured
IOU plants,
IOU
plants in
and even greater gains when
measured against MUNI performance.
To provide
further insight into the question
of column (4) without the
of benchmark group,
we
re-estimate the basic
IOU*RESTRUCTURED and MUNI*POST 1992 variables, but
allowing for separate year effects for each of three categories of plants:
eventually restructure,
model
IOU
plants in states that do not restructure,
IOU plants
in states that
and MUNI plants. Figures
1
(employees) and 2 (nonfuel expenses) plot the estimated year effects for each plant group. Figure
1
suggests that
period.
The
MUNIs
use more labor than do
IOU plants,
figures suggest greater divergence
measures as the 1990s progress. As
this is a
all
else equal, throughout the
between MUNI and
IOU plants
in
sample
both input
period of increasing competitive pressures and
25
substantial
movement toward
information in the
restructuring, these patterns suggest to us that there is considerable
MUNI benchmark comparisons.
Table 6 reports results from variants of our basic specification for fuel inputs. In column
output coefficient
is
constrained to unity (effectively estimating
IOU
the right-hand side variables). In this specification,
IOU plants
use slightly more fuel than
effect
is
ln(BTU/MWh)
the
1,
as a function
of
plants in restructured regimes appear to
in nonrestructured regimes, all else equal,
Column 2
imprecisely estimated (1.2%, standard error 0.8%).
reports
though the
OLS
results
relaxing the output coefficient constraint, which generates a lower estimated output elasticity
standard error
regimes
(-1
.1),
and an implied reduction
in fuel use associated with
IOU
plants in restructuring
.2%, again relatively imprecise with a standard error 0.7%). The remaining columns
report results instrumenting for output with state electricity consumption.
in all four
(.9,
—
columns are very similar
fuel
use
is
The
qualitative results
very highly correlated with the megawatt-hours a
plant produces, with an estimated output elasticity that
is
slightly
below one though
statistically
indistinguishable from unity. This could reflect slight increasing returns to scale with respect to
fuel, consistent
with the well-understood engineering relation between more efficient fuel burn
and higher levels of output
at
a given plant, though the data are not precise enough to establish
this.
In all
and
on
IV
specifications, the coefficient estimates for
statistically indistinguishable
IOU
fuel efficiency relative to
RETAIL ACCESS in column
operating under retail access
fuel use in restructured
IOU*RESTRUCTURED are small,
positive,
from zero. These provide no evidence of restructuring effects
plants in non-restructuring states. Indeed, the coefficient
5 implies
5% (3%) higher fuel
—an enormous
use,
all
on
else equal, for plants
increase in economic terms. While
somewhat higher
wholesale markets could be consistent with more startup and ramping
costs as plants adjust their output to follow hourly changes in marginal revenue, the estimated
magnitude of this effect
is
The
IOU
difference between
the other inputs.
The
too large to be explained even by this behavior.
and
MUNI plants
is
more
difficult to pin
MUNI coefficient point estimates are slightly
distinguished from zero, are not generally distinguishable from the
estimates.
We find little evidence of gains
though a caveat
is
in order.
in fuel efficiency
down
larger,
and though they can be
IOU*RESTRUCTURED point
over our horizon from restructuring,
While variations on the order of even 0.5%- 1%
are economically significant,
it
may be
difficult to
aggregated data. Because fuel efficiency
at
a plant
for fuel use than for
in fuel productivity
measure these sufficiently precisely with our
is
heavily influenced by factors such as the
26
allocation of output across units at a plant, the
and for
number of times
units are stopped
and
started,
long the units were running below their capacity, our inability to measure or control
how
for possible changes in these operational characteristics
capture fuel efficiency changes.
Sample
its
may make
it
particularly difficult to
We continue to explore this aspect of utility input choice.
selection
As noted
earlier,
our results could be affected by sample
ownership transfers that eliminated data reporting.
attrition
Plant sales are particularly an issue in retail
access states, as exempt wholesale generators
(EWGs) purchased
required to divest during restructuring. Since
EWGs
companies are not required
sense, these
due to plant retirements and
plants that incumbents
are not regulated
to report operating data
by
FERC
were
in the traditional
on their plants, and hence drop
out of the database. If the plants dropping out of the sample had been systematically higher or
lower
in efficiency relative to their plant-epoch effect
contaminate the restructuring estimates.
for purchase
were very
efficient
One could
and to surviving
construct plausible stories that plants selected
and thus good acquisition
the database due to retirements or sales that offered the
substantial gain if efficiency could be improved.
The
targets, or that inefficient plants left
new owners
the opportunity for
correlation coefficient of the last year of
data on the plant with the efficiency measures used in the regression
MW, -0.05
employees per
plants, selection bias could
MW),
for non-fuel expenses per
is
negative (-0.17 for
suggesting that less efficient plants
tend to drop out of the database, either because they are sold or shutdown.
of attrition,
we
1
999.
The
in
each case restricting the sample to plants that were in the dataset
results for this
The one exception
is statistically
is
the coefficient on
IOU*RETAIL, which
indistinguishable from zero. This
suggesting that
utilities
may
in the last
which could be picked up with the
coefficient
simply due to the difficulty of estimating the
is
not significant in our
declines in magnitude and
be consistent with anecdotal evidence
deferred maintenance on plants they
Testing robustness of the
RETAIL
sample (available from the authors) are for the most part very
similar to the results for the full sample, suggesting that selection bias
results.
assess the impact
estimated both basic IV specifications and specifications that included the
ACCESS variable,
year,
To
knew they were going
on IOU*RETAIL
until
we
RETAIL ACCESS effect
to divest,
limit the sample, or
with so
45
little
data.
RESTRUCTURED effect
45
Imposing this selection rule eliminates, for example, almost
due to required divestitures before 1999.
all
Massachusetts and California
IOU
plants
27
We next consider the robustness of our results
input
demand
to a variety
of alternative specifications of the
equations. Given the null results in our basic fuel use regressions,
we
focus on
labor and nonfuel expense input choices in this analysis.
In Table 7,
we
indicator that turns on in 1993 if the plant
utility
generation as of 1993
to higher intensity
is
(HIGHNUGS).
above median penetration of non-
in a state that has
This measure should capture any
to the policy variable in
columns 2 and
in addition to
4,
which include
larger than the estimated effect of
larger in
column
2,
RESTRUCTURED
3%
IOU
3,
responses
We
and as a complement
of RESTRUCTURED and
estimated impact of
plants (columns 1)
-6%
(3%).
for restructuring, at
standard error).
are close substitutes, however, as the presence of
RESTRUCTURED (with a positive though
and
BOTH. The
in table 4, at
which includes controls
estimated direct effect of restructuring (-6%,
mechanisms
at
1
direct effects
an interaction of these variables,
high levels of non-utility generation on employment
somewhat
utility
of actual generation competition from unregulated market participants.
consider this as a replacement for the policy variables in columns
HIGHNUGS
on an
report results that use an alternative measure of competitive pressure, based
It
is
negative and
The
effect
-8% (3%),
as
is
is
the
appears that these
BOTH largely offsets the effect of
insignificant effect estimated at
6%, standard
error
NUG penetration appears to have no detectable direct effect on
IOU plant input use, and the results in column 4 suggest that controlling for NUG penetration
3%).
For nonfuel expenses, high
eliminates the direct measured restructured effect:
Only IOU
plants in regimes with both high
NUG penetration and restructuring reduce nonfuel expenses relative to other IOU plants (see the
coefficient
on BOTH
MUNIS and IOUS is
in
column
4).
In all four specifications, the estimated difference
around 15%, very similar to
this difference in tables
In tables 8 (employees) and 9 (nonfuel expenses),
large v. small plants (columns
1
and
2),
and old
v.
we
IOU
new
plants (columns 3 and 4). Recall that
IOU
plants.
plants in restructured regimes exhibit lower input use than
nonrestructured regimes (see the coefficients on
5.
explore results for two sampling strata:
these are dimensions on which municipal plants appear to differ from
specifications,
4 and
between
IOU*RESTRUCTURED),
For
all
do IOU plants
in
though the magnitude
of the estimated effect varies with the subsample. Employee reductions appear to be largest
at
small and old plants; nonfuel expense reductions appear to be largest at large plants, and similar
across old and
coefficient
IOUs
new
plants.
More
interesting, perhaps,
is
the comparison to
MUNI plants.
The
on MUNPPOST 1992 measures the difference between publicly-owned plants and
in nonrestructuring
positive, consistent with
regimes over the 1993-1999 period.
MUNI plants
using more inputs than
While the point estimates remain
IOUs during
this time, the relative
performance
plants (see
at
ofMUNIs
column
old plants.
3
of both
Changes
They appear
differs across subsamples.
tables),
and are indistinguishable from IOUs
employment
in
in general less inefficient for old
at
in
employment changes
MUNI plants are more in line with IOUs
and for nonfuel expenses, the difference does not appear to vary with the size of the
these differences are of some interest,
with respect to the
In table
1
0,
we
it
small plants,
at
While
plant.
does not appear that the conclusions of the earlier tables
MUNI benchmark are substantially affected by these sample plant differences.
consider whether the results are explained by a regression to the
phenomenon among IOU
plants: is the gain in efficiency
among
mean
plants in restructuring regimes
because they had on average low productivity draws prior to restructuring, and simply return to
mean
investigate the extent to
low cost
losses at
plants.
mean
this,
we
which efficiency gains
To
input use from a regression
the
To examine
efficiency over time?
at the
higher cost plants are offset by efficiency
on data
for the pre-restructuring period, 1981
"expensive" and those below zero as "cheap."
indicators with the restructuring variables,
more
IOU
detailed restructuring variables.
plants, at
and
residuals
1992.
We calculate
above zero as
We then interact these expensive and cheap
IOU
roughly
46
The
results in
columns
1
and 2
plants in non-restructured regimes are
1
0%
reductions in both labor and nonfuel
expenses for expensive plants. The coefficients on the
interactions are economically
mean
-
predict
which are post- 1992 and re-run the basic regression
suggest most of the input declines relative to
associated with expensive
we
separate plants into "cheap" and "expensive" categories,
residual for each plant and classify plants with
specification using these
and low cost plants and
identify high
IOU*RESTRUCTURED* CHEAP
statistically indistinguishable
from
zero, contrary to
mean
reversion predictions, and perhaps suggesting that the form of efficiency improvement was to
bring less efficient plants into line with
we have
more
efficient plants. This
received from several industry participants,
who
is
consistent with
comments
claimed that restructuring led their
firms to identify high-cost plants as those disadvantaged in the dispatch order, and to focus
attention
on bringing the costs of those plants closer
MUNI benchmark implies
to an efficient
larger efficiency gains for both
cheap and expensive
both categories of MUNI plants experienced higher input use than
states,
We
though the increase was largest
at the
cheap
benchmark
IOU
plant.
IOU
plants, as
plants in nonrestructured
MUNI plants.
have implemented a number of additional robustness checks, including alternative
instruments and instrument strategies and more flexible dynamics in input choice.
complete discussion and example results are available
46
Using the
The
direct effects
of cheap and expensive are absorbed
in a technical appendix.
A more
Of particular note
in the plant fixed effects.
29
were specifications
of fixed costs of input adjustments.
that allow for the possibility
We find that
the restructuring estimates are robust to allowing for differential effects of output on input use
depending on whether current output
to
is
above or below plant mean output, and to allowing inputs
respond to future as well as current output
Labor use estimates were
levels.
using lagged values of output as instruments, following Blundell and
although the estimated difference between
remained economically important.
IOU
Bond
less robust to
(1998, 2000),
plants in restructuring regimes and
MUNIs
47
Conclusion
5.
This research provides some of the
first
estimates of the impact of electricity generation sector
The
restructuring in the United States on plant-level efficiency.
yield substantive
medium-run efficiency
restructuring regimes reduced their labor
gains.
The
results suggest restructuring
estimates suggests that
to a
The estimated
benchmark based on municipal,
reductions in labor use and
plants over the
same time
20%
plants in
and nonfuel operating expenses by about
anticipation of increased competition in electricity generation relative to
did not restructure their markets.
IOU
federal,
IOU
plants,
5%
when compared
on the order of 15%
reductions in nonfuel operating expenses relative to
period.
There
evidence of increases
is little
to plants in non-restructuring regimes, although the
power of these
in
plants in states that
efficiency gains are even larger
and cooperative
may
non-IOU
in fuel efficiency relative
tests is limited
given the
plausible range of possible fuel use improvements.
These same-plant reductions
in input
use suggest an important role for competition in promoting
technical efficiency, buttressing the findings of Nickell (1996),
Galdon-Sanchez and Schmitz (2002), among
Ng
others. This finding
the industry context. Generating plant technology
is
48
is
particularly interesting given
reasonably well understood by engineers,
and the pre-restructuring industry was remarkably open
operations and input use across plants and firms.
and Seabright (2001), and
in
sharing detailed information on plant
Presumably, external benchmarks were more
accessible in this setting than in most industries. This could suggest that competition induced
greater effort
effort, rather
on cost reduction by increasing the
sensitivity
of returns
to
managerial and worker
than by reducing informational asymmetries over managerial effort (see Nickell,
1996).
47
48
Whether lagged output is a valid instrument for this application is, moreover, a debatable proposition.
Evidenced, for example, by our use of publicly available, plant-identifiable, data in our analysis.
Additional
work remains
to
be done
electricity industry efficiency.
49
to
fill
out the picture of the overall effects of restructuring on
We have started by
existing utility plants both because this
is
looking
at
operating efficiency within
one of the few places where gains are
likely to
before restructured wholesale markets open up and because rich data are available on
owned
plants.
As our results
show up
utility-
suggest, even these data are inadequate for the fine-level analysis
required to estimate within and across-plant changes in fuel efficiency. This analysis will require
datasets with both cleaner measures of fuel efficiency and richer information
factors that affect fuel use. Finally, assessing
efficiently after restructuring requires
power
plants are so long-lived, very
more
whether investment decisions are made more
time, and access to better non-utility data. Since
few new additions are made each
no more than a handful of anecdotes about investment
It is
on independent
year,
and currently
we have
after restructuring.
important to recognize that these efficiency estimates are, however, only one input to judging
the ultimate benefit of restructuring policies.
realized magnitude of potential
dynamic
The
overall assessment depends as well
efficiencies,
on the
and possible offsetting effects from higher
investment expenditures, restructuring costs, the loss of coordination and network economies
within vertically integrated systems, and the exercise of market
markets.
Dynamic
costs could
be higher
productivity growth over time.
It is
striking as firms respond to the
new
human and
if restructuring
power
in
unregulated generation
reduces knowledge sharing that affects
possible, however, that longer run effects will be
incentives created
more
by restructuring with investments
in
both
physical capital that further enhance efficiency. If California's crisis does not induce
reversals of the restructuring
movement, and regulators do not shut down data reporting and
researcher access to detailed plant-level data, time
may
enable us to distinguish
among
these
possibilities.
See Wolfram (2004) for a discussion of the general issues involved
efficiency changes
accompany
in assessing different types
of
electricity restructuring.
31
Table
Note:
1:
Number
of Plants Affected by Restructuring in
Each Year
Total
IOU*
IOU*RETAIL
MUNI*
Year
Plants
RESTRUCTURED
ACCESS
POST 1992
1981
465
1982
490
1983
511
1984
527
1985
546
1986
547
1987
582
1988
588
1989
592
1990
597
1991
600
1992
591
1993
602
27
119
1994
598
103
129
1995
608
166
129
1996
589
187
119
1997
569
239
101
1998
557
236
47
101
1999
486
183
30
91
TOTAL
10645
1,141
77
789
RESTRUCTURED indicated
those that have started formal hearings that
eventually go on to pass legislation, and
states that have started
retail
RETAIL
ACCESS indicates
plants
in
access.
32
Table 2a: Summary of Data for Plants Larger Than 100
Non-Restructured lOUs
Variable
NON-RESTRUCTURED
(N = 244)
(N = 196)
AGE
%COAL
%GAS
0.29
0.21
19780
14354
0.26
0.14
.03
16490
8496
3291
11
2.0
11
3.1
0.21
0.41
0.20
-.01
806
28
52
36
662
645
.8
50
806
24
80
48
16
11
Mean
(TJ
MW
%GAS
-.54
-.54
13
3.4
40
37
-28
-6.48**
21
5.11**
1985: Munis versus
= 196)
Difference
in
Means
T-statistic
for
Difference
Std.
Dev.
AGE
%COAL
MW,
-.1
1.79
2.99**
0.01
2.95**
NON-RESTRUCTURED
MUNIS
T-statistic
for
Std. Dev.
0.39
(N=106)
EMPLOYEES /MW
NONFUEL EXPENSE/ MW
HEAT RATE
CAPACITY FACTOR
Means
Difference
Mean
Table 2b: Summary of Data for Plants Larger Than 100
Non-Restructured lOUs
Variable
Difference
in
Std.
Dev.
MW
1985: Restructured versus
RESTRUCTURED
Mean
EMPLOYEES /MW
NONFUEL EXPENSE/ MW
HEAT RATE
CAPACITY FACTOR
MW,
Mean
Std. Dev.
0.27
0.13
0.26
0.14
.01
.45
15220
9292
16490
8496
-1269
-1.17
12
5.9
11
3.1
.4
.68
0.40
0.23
0.41
0.20
-.01
-.23
703
608
-102
-1.36
11
-5
-4.8
-3.53**
68
27
47
45
806
24
80
645
19
40
37
-12
-2.16**
12
2.27**
16
Significant at .05 level or better
33
Table 3a: Percentage Change
Difference of
lOUs
in
in Costs From 1990
Means Tests
to 1996
Non-Restructuring Versus Restructuring States
N
Distribution
Transmission
Generation
74
12.3%
22.7%
-17.0%
49
17.3%
33.8%
4.2%
Difference
-5.0%
-11.1%
-21.3%
tstat
-0.99
-1.34
-3.65**
Restructuring
Mean
NonRestructuring
Mean
*significant at
All
5%
measures are
Table 3b: Percentage Change
in
in
in
1
% level
nominal dollars.
Costs Per
Difference of
lOUs
"significant at
level
MWh* From
1990 to 1996
Means Tests
Non-Restructuring Versus Restructuring States
N
Distribution
Transmission
Generation
72
1.5%
13.1%
-13.5%
48
-1.6%
12.6%
-5.1%
Difference
3.1%
0.4%
-8.3%
tstat
0.70
0.06
Restructuring
;
Mean
NonRestructuring
Mean
*significant at
All
5%
level
measures are
"significant at
in
-1.87
1
% level
nominal dollars.
Transmission and Distribution costs per MWh are costs per MWh sales to ultimate
customers, while Generation costs per MWh are costs per MWhs generated at company
*
plants.
Note: Three observations dropped
due
to lack of
MWh
data.
34
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Table 10:
Test for
Mean Reversion
Estimates
Effect, IV
In(EMPLOYEES)
ln(NON-FUEL
EXPENSES)
IOU*RESTRUCTURED_cheap
-0.011
IOU*RESTRUCTURED_expen
(0.028)
-0.106***
(0.022)
-0.102***
MUNI*POST 1992 cheap
(0.031)
0.143***
(0.022)
0.193***
(0.041)
MUNI*POST 1992 expen
0.056**
(0.022)
0.095***
-0.004
(0.021)
(0.027)
-0.115***
ln(WAGE)
(0.045)
ln(NET MWH)
0.409***
0.037
(0.084)
AGE
(0.061)
-0.003
0.003
(0.002)
FGD
0.028
j
(0.123)
(0.056)
10222
842 plant-epoch and 19 year fixed
Robust clustered standard errors
significant at
1
0%
**
;
significant at
Instruments for
5%
(0.003)
0.235*
10258
effects included.
in
parentheses,
***
;
significant at
1
%
MWh: \n{STATESALES)
41
Figure
1:
Year-Effects by
Group from Basic
IV Specification:
Employment
0.2
0.1
^--^•«^
"•^Munis
•
*
- lOUs: Non-Restructuring States
lOUs: Restructuring States
-0.5
42
Figure
2:
Year-Effects by
Group from Basic
IV Specification:
Non-fuel Expenses
— •—
HI
-a
a
x
-
-*
ra
°P N$> Nc£>
#>
N
£p
£}>
$>
,$>
Nc£>
^>
^
,d? ,d?
c$s
s
^ ^ ^ ^>
,dp
Munis
-
lOUs: Non-Restructruing States
lOUs: Restructuring States
Sources
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.
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-
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Where
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.
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46
Data Appendix - Table Al: Summary of Variables
Variable
Source
Definition
Output and Input Variables
LN_NONFUEL EXPENSE
Log of the annual non
LNEMPLOYEES
production expenses ($)
Log of the average number of
LN_BTU
employees
at the plant.
Log of the
total btus
burned by the
+
UDI
UDI
of fuel
plant. Calculated
as (tons of coal *
btu/lb)
UDI
fuel
(barrels
2000 lbs/ton*
of oil*42
+ (Mcf
gas* 1000 cf/mcf btu/cf). The
gal/barrell*btu/gal)
values of the btu content of each
fuel are provided in the
UDI
data
for each plant each year.
LN_NETMWH
Log of the
of the
net
MWh generation
UDI
plant.
Restructuring Variables
IOU
Dummy equal to
classified as
1
for plants
IOU, holding, or
1997 UDI Utility
Datapak Book
private companies.
MUNI
Dummy equal to
owned by
1
for plants
utilities classified as
1997 UDI Utility
Datapak Book
government or cooperative
utilities.
IOU*RESTRUCTURED
Dummy equal to one
for
IOU
plants in states that restructured
in years
MUNI*RESTR UCTURED
following the advent of
EIA
"Status of
State Electric Industry
Dummy equal to one for MUNI
Restructuring
plants in states that restructured
Activity" Timeline as
following the advent of
formal hearings
Dummy equal
to
one for
IOU
of July 2002, EIA
"The Changing
Structure of the
Power
plants in states that restructured,
Electric
beginning with year that
Industry:
legislation
IOU*RETAIL ACCESS
information compiled
from:
formal hearings.
in years
IOU*LAW PASSED
Restructuring
was enacted
Dummy equal to one
for
12/96,"
IOU
plants in states that restructured
in years
following the advent of
retail access.
An
EEI
Competition
Update,
"Electric
in
the
States" February,
EIA "The
Changing Structure of
2001,
47
MUNI*POST 1992
Dummy equal to one for MUNI
the Electric
plants in years 1993-1999.
Industry:
2000
Update,"
NARUC
Power
An
"Utility Regulatory
Policy in the United
States
and Canada,
Compilation" 19941995 and 1995 -96,
"Restructuring the
Electricity Industry,"
The Council of State
Governments, 1999,
and State
PUC
websites, relevant
legislation
IOU*HIGHNUGS
Dummy equal to one beginning
in
1993 for
IOU
plants in states
EIA
and
Electric
reports.
Power
Annual.
with above median penetration
ofNUG plants
in 1993.
BOTH
Interaction of
CHEAP (EXPENSIVE)
RESTRUCTURED and HIGH
NUGS dummy variables.
Dummy indicating whether plant
average residual from input use
regression for 1981-1992 period
is
below (above)
INDEPENDENT7 OTHER VARIABLES
PLANT AGE
Number of years
unit at the plant
FGD
zero.
since the oldest
came on
UDI
line.
Dummy equal to indicating the
operation of a FGD scrubber at
1
UDI
the plant.
CAPACITY FACTOR
calculated as
(NET MWH)
(GROSS MW *
LNJVAGE
UDI
Capacity factor of the plant,
hours
1
in year)
wage bill
employment
BLS
State-level annual
divided by total
at
plants in the following
industries:
SIC 4923-4925
4953
(natural gas distribution),
(hazardous waste treatment),
2911 (petroleum refining).
STATE SALES
State-level utility sales in
Sales: Sales to
gigawatthours, not including
Ultimate Customers
non-utility sales.
from EIA's "Electric
Sales and Revenue"
Table 6 and EIA's
48
"Electric
Power
Annual" Tables 117
and 90.
PLANT CHARACTERISTIC VARIABLES
Based on the load type reported
LOAD TYPE
in FERC Form
through 1992.
UDI
1
We assign plants to the last load
type reported
if they
change load
type no more than twice,
otherwise
we do not
assign load
types to missing data years.
We
use this cutoff to include plants
had a "stray" change—one peak,
cycle or shutdown type in the
middle of a string of "base"
types, for example.
Based on the primary fuel burned
FUEL TYPE
UDI
at the plant
Dummy variable equal to one if
BIG
the plant capacity (Gross
greater than
OLD
MW) is
550MW.
Dummy equal to one if the
youngest unit
in service
ECONOMIC AND WEATHER VARIABLES
ANNUAL UNEMPLOYMENT
Annual
ANNUAL HDDAYS
at the plant
UDI
came
before 1960.
average unemployment
Bureau of Labor
in the state.
Statistics
Population weighted heating
U.S. Dept. of
degree days for each state-year.
Commerce National
(use
ANNUAL CDDAYS
UDI
MD for DC)
Oceanic and
Atmospheric
Population weighted cooling
degree days for each state-year.
(use
MD for DC)
Administration
Historical Climatology
"Monthly State,
Regional, and National
Heating Degree Days
Weighted By
Series,
Population"
Note:
UDI
stands for the Utility Dal a Institute
O&M Production Cost D atabase. All data in the U
production cost data bases are extracted from the
EIA Form 412
electric
FERC Form
by municipal and other government
cooperatives). These are annual reports.
(filed
1
by investor-owned utilities),
and RUS Form 7 & 12 (filed by
(filed
utilities),
49
Data Appendix
-
Table A2:
Variable
Summary Statistics
Mean
Obs
LN NONFUELEXP
LN EMPLOYEES
LN BTU
LN NETMWH
PLANTAGE
FGD
LN
WAGE
IOU
MUNI
IOU*RESTRUCTURED
MUNI*RESTRUCTURED
IOU*LAW PASSED
IOU*RETAIL ACCESS
MUNI* POST 1992
IOU*HI
NUGS
BIG
OLD
LN STATE SALES
ANNUAL UNEMP
ANNUAL CDDAYS
ANNUAL HDDAYS
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10645
10608
10608
15.880
4.562
30.304
14.075
27.925
0.124
10.543
0.805
0.195
0.107
0.013
Std.Dev.
1.137
1.108
1.631
1.760
12.940
0.330
0.255
0.396
0.396
Max
Min
9.674
18.694
19.839
5.476
7.099
33.067
16.905
76.000
1
9.815
1 1
.488
0.007
0.309
0.113
0.272
0.085
0.074
0.103
0.500
0.262
0.304
0.500
0.476
0.499
0.850
8.285
12.627
0.081
11.171
0.064
1439.436
4382.568
0.021
0.022
0.180
931.661
81
2144.882
414
3879
10758
50
Technical Appendix: Sensitivity Analysis
Robustness of results
The conclusion
to alternative
instruments
that same-plant input use declined after restructuring for
IOU
plants appears robust to a
variety of alternative specifications, although the precise magnitude of the estimated efficiency gain depends
crucially
on the estimated relationship between inputs and output. In tables Tl and T2, we analyze the
sensitivity
of the results to alternative output specifications and instrument
specification of the regression
In the
first
column of each
model
we
table,
(similar to
low output
low output
levels based
on the 60
th
although specifications using cutoffs ranging from the
results.
1
relative to
th
its
IV
using the basic
5).
on output with variables
report results that interact the coefficient
indicating whether the plant achieved a high or
cut-off between high and
column 4 of tables 4 and
sets,
We use a
historical performance.
percentile of the plant's historical output,
to the 80
th
percentile yielded qualitatively similar
Introducing this flexibility could be important if fixed labor and materials costs cannot be scaled
back when the plant operates
relationship
at historically
low output
between output and employment
(table
levels.
The
results in
column
(1) suggest that the
Tl) or output and nonfuel expense
(table
T2)
is
not
materially different across output levels, however.
Columns 2 through 4 of tables Tl and T2 introduce an
column
state
(2),
we add
the square of
\n{STATESALES)
as
array of nonlinearities in the instrument
In
an instrument. This allows extreme departures from
average sales to have an additional predictive effect on plant output.
50
Column
(3)
term between \n(STATE SALES) and RETAIL ACCESS, allowing the relation between
output to vary after implementation of retail access.
set.
51
Column
(4) interacts
adds an interaction
state
demand and
\n(STATESALES) with
plant
fuel
type indicator variables, allowing the plant output to respond differentially to state demand depending upon
fuel type.
For employment, reported
in table
Tl, these instrument sets almost
magnitude of the coefficient on \n(NET MWhs)
all
increase the estimated
relative to the specifications reported in
Table
4.
The
restructuring effect remains negative and statistically significant, although smaller than reported in Table 4.
For non-fuel expenses, reported
in table
T2, these changes in the instrument set have varying effects on the
estimated magnitude of the output elasticity as well as the impact of restructuring, relative to results in Table
5.
50
Efficiency effects remain large relative to municipal plants in
In the first stage, the coefficient
on
state sales
\n(STATESALES) and \n(STATESALES)
uses \n(STATESALES).
2
is
all
specifications,
significant, although the F-statistic
are equal to zero
is
and roughly the
on the hypothesis
that both
smaller than the F-statistic from a specification that just
Technical Appendix
- page
1
magnitude of those found
in table 5 with respect to
exception of results in column
The
final
IOU
plants in non-restructuring regimes, with the
(3).
two columns of tables Tl and T2 use lagged values of output
discussions in Blundell and
applications to input
(1998, 2000) on the use of lagged values of endogenous variables in
demand and production
exogenous instruments.
specifications,
Bond
52
as instruments. This follows
function specifications, particularly in the case of weak
In our case, lagged values of output are very highly correlated with output in
even those estimated using
first
differences, although first differences
may
all
not be appropriate
given that most plants have scheduled major outages for equipment maintenance and overhauls on a two to
We explore two variations.
four year cycle.
instrument.
We report results
from
this as
The
simplest, in
a base, though this
column
(5),
uses the prior year output as an
valid only if there
is
is
no
first-order serial
correlation in the time-varying efficiency shock, unlikely given the estimated rho in our data.
variation uses lags from years
two through four years before the
set substantially reduces, or eliminates (Table
relative to nonrestructuring
column
IOU
plants,
1 1
,
column
current.
For employment,
instrument
6) the estimated efficiency gain of restructuring
though the performance gain relative to
(6) results also suggest a coefficient
this
The second
on output that
is
much
MUNIs
remains large. The
higher than in any other specifications.
In the case of non-fuel expenses, these instruments suggest results for restructuring effects that are broadly
consistent with our other results.
Robustness of results
to
including forward-looking output
We have explored specifications that allow
employment.
53
for a fixed cost
would appear especially unproductive
employment
prior to an
in
a discrete change. If this were the case, the
employment
following the reduction until the lower output levels are realized.
considerations on our results,
forward looking changes
and
51
52
levels, particularly
This could cause a plant that foresaw declining output over a several year period, for
instance accompanying restructuring, to ratchet
plant
of changing input
we
estimated input
in output.
statistically significant in the
In the first stage, the coefficient
54
OLS
The
To
demand equations
coefficient
reduction, and especially productive
investigate the effect of dynamic
that included variables
on the forward looking changes
specifications similar to those in
on the interaction term
is
columns
(3)
in
measuring the
output were positive
of tables 4 and
5,
but
highly significant.
and Bond describe instrumenting with lagged values in a common factor model that allows for a
first-order auto-regressive term and is first-differenced to eliminate firm-specific effects. We have estimated this form
of the model and the results seem to yield an AR(1) term that is over 1 .6. For the reasons discussed in the text, we
don't think our data have a simple first-order autoregressive error structure.
53
There is an extensive labor literature on the presence of fixed costs and other non-convexities in employment
In fact, Blundell
decisions.
Technical Appendix - page 2
negative and statistically indistinguishable from zero once
changes
54
in output.
This input
In both cases, the coefficients
demand equation
is
we
instrumented for both output and forward
on our restructuring variables were
based on one derived by Rota (2004),
face a fixed cost to changing their
employment
who
specifies a
virtually
unchanged.
dynamic model where firms
levels.
Technical Appendix - page 3
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