1

THREE ESSAYS ON STRATEGIC BEHAVIOR, INFORMATION REVELATION AND RESTRUCTURING IN THE U.S. ELECTRICITY MARKETS

A dissertation presented

by

Vladlena Sabodash

to The Department of Economics

In partial fulfillment of the requirements for the degree of Doctor of Philosophy

in the field of

Economics

Northeastern University Boston, Massachusetts

September, 2010

2THREE ESSAYS ON STRATEGIC BEHAVIOR, INFORMATION REVELATION AND

RESTRUCTURING IN THE U.S. ELECTRICITY MARKETS

by

Vladlena Sabodash

ABSTRACT OF DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics

in the Graduate School of Arts and Sciences of Northeastern University, September, 2010

3ABSTRACT OF DISSERTATION

This doctoral dissertation analyzes transitioning to restructured electricity markets, drivers

underlying restructuring choice in the U.S., opportunities for suppliers’ strategic behavior created by

restructuring of electricity markets, and effect of restructuring and suppliers’ strategic behavior on

wholesale and retail electricity prices. The following are abstracts of the three chapters of my doctoral

dissertation entitled “Three Essays on Strategic Behavior, Information Revelation and Restructuring in

the U.S. Electricity Markets.”

The first chapter of this dissertation, “Price Spikes in Energy Markets: “Business by Usual

Methods”or Strategic Withholding?” focuses on one phenomenon in restructured electricity markets,

called price spikes. There are two divergent explanations for occurrence of price spikes: ordinary but tight

supply and demand conditions, or abnormal behavior by suppliers. In the latter case, if suppliers alter

their behavior by refusing to bid some of their otherwise-profitable output into the market with the

purpose of raising price sufficiently so that their remaining output earns considerably more than the loss

on the withheld quantity, this represents a perverse supply shift that can cause an abnormal price spike.

This research develops an operational empirical approach for distinguishing between these two sources of

price spikes. The data used in the analysis is hourly bidding data from the New York Day-Ahead

electricity market in the summer of 2001. We use an OLS and non-linear exponential models to estimate

highly non-linear supply curves in the electricity markets. The results indicate that strategic behavior of

several large bidders, as well as two particular generators have contributed to or produced the price spikes

observed in the New York Day-Ahead electricity market in the summer of 2001. These results establish

the legitimacy of concern over strategic withholding and focus policy attention on periods of superpeak

prices and on especially large bidders.

The second chapter of this dissertation, “Effect of Information Revelation on Bidding Strategies

of Marginal Bidders in the New York Day-Ahead Electricity Market,” analyzes the effect of exchange of

and access to rivals’ private information on the pricing strategies of influential suppliers (so-called

4marginal bidders) in the electricity markets and on the probability of those bidders being marginal. In the

majority of competitive markets exchange of cost and demand information between market participants is

not a common practice since it can create opportunities for anticompetitive behavior and cause distortion

of market equilibrium. Rules and regulations of some restructured electricity markets in the U.S.,

however, allow their participants to get access to rivals’ private information by allowing short-term

contractual agreements between bidders of electricity generation and multiple generating units

simultaneously or over time. We provide a simple theoretical model that demonstrates how access to

rivals’ cost information may alter bidding behavior of a marginal, or likely to be marginal, bidder, and

then test this model using our two unique measures of information revelation. This research uses hourly

bidding data from the New York Day-Ahead electricity market in 2006-2008. We use fixed effects panel

data model to investigate the effect of information revelation on price bids and Heckman two-step model

to estimate the probability of bidders being marginal. Estimation results reveal differences between

groups of marginal and non-marginal bidders and indicate that access to rivals’ private cost information

may cause higher marginal price bids by marginal bidders and, therefore, higher market equilibrium price.

The third chapter of this dissertation, “What Drives States’ Choice of Electricity Restructuring:

Understanding the Differences between Restructured and Non-Restructured States,” analyzes the drivers

behind states’ choice to implement restructuring and the differences in performance of restructured and

non-restructured states. Since implementation of electricity restructuring has not been mandatory in the

U.S., there are more than half of U.S. states that still operate their regulated electricity markets. Although

23 states have been restructured up to date, there has been no conclusion reached yet on whether

electricity restructuring achieved its goals of reduced costs and prices to consumers and greater market

efficiency. We use annual state level data for all 51 U.S. states from 1990-2008. The analysis involves

multiple estimations, including GLS model, two-stage least squares estimation and simultaneous

equations model with endogenous switching to account for the heterogeneity in the states’ decision to

implement restructuring or not, and for unobservable states’ characteristics. We find that significant

5heterogeneity is indeed present in the sample between restructured and non-restructured states and that

there are substantial differences in determinants of retail electricity retail rates between the states that

implemented restructuring and those that did not. Our results indicate that as state’s restructuring choice is

not exogenously defined, results from simple OLS or GLS models without correction for endogeneity

bias, should be interpreted with care. These results are particularly important in developing states’

decisions about electricity restructuring and further restructuring reforms that keep in line with the

potential impact of restructuring on electricity prices.

6ACKNOWLEDGEMENTS

This dissertation has been made possible through the assistance and encouragement of many

people. I would like to thank my dissertation advisor, Professor Kwoka, for his continuous inspiration,

enthusiasm and patience. I am very grateful to the other members of my thesis committee, Prof Dadkhah

and Prof Dana, for their support, encouragement and valuable critique and advice at various stages during

my progress. My dissertation committee members, as well as Prof. Wang, Prof. Sum, Prof. Morrison, and

Prof. Alper, have always served as indispensible teachers, mentors, and friends throughout my graduate

experience.

My experience at Northeastern University has been enriched considerably by my classmates, with

whom I shared all these years at graduate school, and who made this journey through graduate school

more enjoyable. I would especially like to mention Megan Gay, who has been the best studying

companion, office mate, and friend I could hope for.

My professional skills and experience were tremendously enhanced during my graduate years by

multiple invaluable opportunities of work with the American Public Power Association on various

projects and their financial support, without which my dissertation work and progress would not be

possible. I also express my gratitude to Synapse Energy Economics and its outstanding staff, in particular

Ezra Hausman, Bruce Biewald and Paul Peterson for their interest in my research, for believing in me,

their continuous support in various aspects of my student and professional life, and for the opportunity to

work with such amazing highly skilled experienced people, experts and just wonderful friendly people.

I have been strengthened by the love and support of my entire family in Russia and Israel. My

profound thanks go to my parents, who have always believed in me whether or not I gave them reason to,

and always supported me in everything even being so far away from me. I also thank my sister Olesya

who always provided me with distant support in all aspects of my life. This work is dedicated to my

family who made my years in graduate school more rewarding and enjoyable. However, with my parents

and my sister being so far away, my friends here became a part of my family and were extremely helpful

7and supportive at all times. I would especially like to thank Julia Kopytova, Evgenia Shumilkina and

Victoria Angelatova for being such wonderful friends, for their continuous support and belief in me.

Without them this journey would not be possible and complete.

8TABLE OF CONTENTS

Abstract 2

Acknowledgements 6

Table of Contents 8

Introduction 9

Chapter 1: Price Spikes in Energy Markets: “Business by Usual Methods”or Strategic Withholding? 12

Chapter 2: Effect of Information Revelation on Bidding Strategies of Marginal Bidders in the New York

Day-Ahead Electricity Market 56

Chapter 3: What Drives States’ Choice of Electricity Restructuring: Understanding the Differences

Between Restructured and Non-Restructured States 98

9INTRODUCTION

In the late 1990s Massachusetts, Rhode Island and California started the process of restructuring

their electric power markets. Since that time more than 20 states began implementation of different

restructuring reforms in the electricity markets, even though some states decided to delay this process or

even completely abandoned the process of deregulation. The primary political driver for restructuring in

those early restructured states was that it would benefit consumers by reducing costs of electricity

generation and electricity prices to consumers both in the short and long run. Later, in the aftermath of

restructuring in the early restructuring adopting states and no evidence of coherent cost and price

reductions, the goal of restructuring has been changed from cost and price reduction to creation of more

efficient electricity markets through increased reliance on market forces. This doctoral dissertation

analyzes the drivers underlying restructuring choice in the U.S., opportunities for suppliers’ strategic

behavior created by restructuring of electricity markets, and effect of restructuring and suppliers’ strategic

behavior on wholesale and retail electricity prices.

As part of restructuring reforms, generation component of electricity infrastructure has been

separated from transmission and distribution stages. In the restructured market, consumers still buy

regulated component from the local regulated distribution company, but are free to choose a competitive

retail provider of generation services. Overall, restructuring of the electricity markets is considered to be

one of the most complex due to the unique characteristics of the good produced in the market. At least

until recently, it was considered that electricity cannot be stored, which makes balancing of the supply

and demand in the real-time more complex than for a storable good. This non-storability, or limited

storability, of electricity may lead to significant volatility in the spot market prices. In addition, limited

ability of suppliers and consumers to respond to dramatic changes in the real-time prices may result in

significant price spikes during the hours of peak demand, when even a slight increase in demand produces

market-clearing prices far in excess of the average electricity price. Therefore, in the competitive

electricity market, where market-clearing price reflects marginal cost of the supplier that clears the

10market, consumers face much more price volatility resulting from scarcity conditions and fuel price

fluctuations. In other words, these unique characteristics of electricity create a lot of room for price

manipulations and strategic behavior by electricity producers in unregulated electricity markets.

One such phenomenon in restructured energy markets - a period in which market price rises

suddenly, markedly, unexpectedly, and temporarily, called “price spikes” - is the focus of the first essay

of this dissertation. Some participants and/or observers of electricity markets say that price spikes are

necessary since this is the only way for high-cost “peaking” generating units to recover their costs, which

would not be the case in regulated cost-of-service electricity markets.

However, there are two divergent explanations for occurrence of price spikes. One explanation

emphasizes the role of ordinary demand or supply determinants. When those determining factors take on

unusual values - for example, due to summertime heat or transport limits - equilibrium price may well rise

to unusually, but appropriately, high levels. The alternative explanation involves changes in the suppliers’

behavior itself that underlies the supply function. That is, if suppliers alter their behavior by refusing to

bid some of their otherwise-profitable output into the market with the purpose of raising price sufficiently

so that their remaining output earns considerably more than the loss on the withheld quantity, this

represents a perverse supply shift that can cause an abnormal price spike. This strategy–essentially

unilateral withholding–has consistently been viewed by the FTC as outside the reach of the antitrust laws,

leaving the agency without a remedy for such behavior.

Transition to restructured electricity markets has also been accompanied by creation of day-ahead

and real-time markets for energy where energy is bought and sold by market participants through the

uniform clearing auctions. In these auctions determination of market-clearing price is left to the free

market forces through competitive bids from buyers and sellers of energy. In the majority of competitive

markets exchange of cost and demand information between market participants is not a common practice

since it can create opportunities for anticompetitive behavior and cause distortion of market equilibrium.

Rules and regulations of some restructured electricity markets in the U.S., however, allow their

11participants to get access to rivals’ private information by allowing short-term contractual agreements

between bidders of electricity generation and multiple generating units simultaneously or over time. As a

result of such contractual arrangements bidders can get nearly contemporaneous information about

operations and costs of rivals’ multiple generating units. Such access to private rivals’ cost information,

which became possible as market transitioned to wholesale and retail competition, and its effect on

pricing behavior of market participants is the focus of the second essay in this dissertation.

These opportunities for strategic behavior through price manipulations or acquiring access to

rival’s private information became available as electricity markets transitioned to restructuring and the

prices were no longer determined by the cost of service but were left to the market forces. However, since

implementation of electricity restructuring has not been mandatory in the U.S., there are still more than

half of U.S. states that operate their regulated electricity markets. Drivers behind states’ choice to

implement restructuring and the differences in performance of restructured and non-restructured states are

investigated in the third essay of this dissertation.

Overall, although the primary goals of restructuring were to benefit consumers through reduced

costs and prices and to create efficient electricity markets through greater reliance on market forces,

restructuring experience varied substantially from state to state; there still has been no conclusion reached

on whether electricity restructuring achieved its goals. Together with greater reliance on competitive

market forces, restructuring created greater opportunities for strategic behavior and exercise of market

power, which, if not mitigated, can distort competitive market equilibrium. While moving away from

regulation and market intervention, electricity restructuring still requires a lot of attention and policy

action to prevent anticompetitive outcomes. This doctoral dissertation sheds some light on the effect of

electricity restructuring, opportunities for strategic behavior, and potential anticompetitive outcomes that

result from it.

12CHAPTER 1: PRICE SPIKES IN ENERGY MARKETS: “BUSINESS BY USUAL METHODS”

OR STRATEGIC WITHHOLDING? (Co-authored with John Kwoka)

The authors gratefully acknowledge helpful discussions and comments from Tim Brennan,

Kamran Dadkhah, Diana Moss, Machiel Mulder, Marcel Vermeulen, and personnel from the New York

ISO Market Services. All opinions and remaining errors in this paper are the sole responsibility of the

authors.

I. INTRODUCTION

The antitrust case against Standard Oil case involved a number of allegedly anticompetitive

practices by that company, ranging from industrial espionage to discriminatory rebating. In its opinion

the Supreme Court sought not only to determine Standard Oil’s culpability but also to provide guidance

for the distinction between tough but legally unobjectionable behavior and true anticompetitive practices.

Its criterion for the latter was “acts and dealings wholly inconsistent with the theory that they were made

with the single conception of advancing the development of business power by usual methods.”1 This

standard has been echoed in subsequent judicial rulings and served as the basis for economic analyses of

various business practices.

Several specific practices of Standard Oil, including predatory pricing practices and price

manipulation, were taken as evidence of its antitrust liability. In the century since the Standard Oil

decision, these practices have persisted in energy markets. Crude oil extraction, refining, transport, and

marketing have been the subjects of repeated antitrust inquiries into alleged instances of price distortion

directed at both rivals and customers. Similar allegations have recently arisen in other energy markets,

including natural gas, electricity, and automotive gasoline. The frequency of these allegations suggests

that these industries are either uniquely subject to suspect pricing practices, or uniquely subject to policy

scrutiny for their pricing practices, or perhaps some combination of the two.

1 U.S. v. Standard Oil Company of New Jersey et al., 221 U.S. 76 (1911).

13This paper is concerned with one such phenomenon in energy markets, namely, “price spikes.”

We define a price spike as a period in which market price rises suddenly, markedly, unexpectedly, and

temporarily. That is, in the context of otherwise fairly steady and predictable prices, with little or no

warning or indication, price rises rapidly to very unusual if not unprecedented levels, and then rapidly

reverts to something like its prior level. We develop two arguments: First, we demonstrate that it is not a

coincidence that price spikes occur so frequently in energy markets. We analyze conditions giving rise to

price spikes, and observe that crude oil, natural gas, gasoline, and electricity markets all have

characteristics that make them especially vulnerable to them. Secondly, we develop an operational

empirical approach for distinguishing between a price spike that is in fact the result of abnormal behavior

by suppliers, as opposed to ordinary but tight supply and demand conditions. This latter exercise is in the

spirit of Standard Oil in that we seek to identify “acts and dealings wholly inconsistent

with…business…by usual methods.” We apply this approach to the New York State electricity market.

While other papers have examined strategic behavior and strategic withholding in electricity, ours

differs in two major respects. First, we focus on the reduction of quantity offered to the market under

conditions of strong demand as the defining characteristic of strategic withholding. No economic theory

explains or predicts such a leftward shift in supply under these circumstances. Secondly, we do not treat

all bidders and generators participating in the auction as homogeneous. Rather, we distinguish among

bidders and generators based on certain load and price characteristics that the theory of withholding

predicts should result in different behavior. Our analysis of the data confirms those predictions.

The next section of this paper sets out some examples of price spikes in energy markets and how

these have been previously evaluated. Section III develops the economics of price spikes, demonstrates

the distinguishing features of abnormal spikes, and explains the special vulnerability of energy markets to

such spikes. The subsequent section describes the New York State wholesale electricity market, sets out

our empirical approach, and tests that approach on periods of alleged price spikes in 2001. Our test finds

evidence of behavior inconsistent with “normal” bidding practices on one of three suspect days in the

14month of August 2001, corroborating concerns about strategic withholding and providing support for this

analytical approach.

II. PRICE SPIKES IN ENERGY MARKETS

While price manipulation of various forms certainly dates back to the days of Standard Oil and

before, price spikes in energy markets began to occur with considerable frequency during the 1990s as

these markets were freed from regulatory constraints. This section recounts a number of these

experiences, which illuminate the key distinctions we later analyze.

One of the first prominent examples of price spikes involved wholesale electricity markets in the

Midwest during the summer of 1998. In late June, the price of wholesale power, normally about $30 to

$50 per MWH, shot up and briefly reached $7,500. A report by the Federal Energy Regulatory

Commission (FERC, 1998) attributed this episode to four exogenous factors--unusually hot weather,

equipment outages, transmission congestion, and retail price inflexibility. It also noted the possible

contribution by traders and buyers inexperienced in the newly formed electricity market,2 although many

other observers suspected price manipulation. Indeed, during the following summer with most of the cited

factors absent, the wholesale price spiked again, briefly reaching $10,000.

Similar price spikes in electricity began to occur in other regions of the country in 1999 and 2000.

The most famous example was California, where in the summer and fall of 2000, prices suddenly rose

from $30-35 per MWH to as much as $750. In that winter, with demand at its seasonal low, persistent

unexplained supply shortages caused price to average $260 to $400 for several months starting in

December, and even at such prices, outright shortages required rolling blackouts throughout the state.

2 A report by the private utility American Electric Power (cited in Michaels and Ellig (1999)) repeated this explanation and sought to allay further concerns by assigning probabilities of such an event ever recurring again. It assessed the probability of comparable weather at 0.3 percent and the probability of similar outages at 1.5 percent, implying the likelihood of both together at less than 1 in 22,000. As the text notes, another spike occurred the following year.

15The persistent chaotic pricing resulted in financial disaster for consumers and distribution companies,

eventually leading to state intervention simply in order to maintain minimal operation of the market.3

The California experience has been analyzed endlessly and countless causes held responsible for

its problems–hot weather, reduced hydro flow, unanticipated outages, fixed retail rates, transmission

congestion, environmental regulations, excessive reliance on the spot market, etc.4 But another important

cause is now widely acknowledged, namely, that the market had been subject to strategic bidding and

trading practices, exploiting weaknesses of the regulatory system to raise price far beyond market

fundamentals. One of the methods involved the refusal of a supplier to bid some of its otherwise-

profitable output into the market with the purpose of raising price sufficiently so that its remaining output

earns considerably more than the loss on the withheld quantity (FERC, 2003; Weaver, 2004). We shall

analyze this conduct - unilateral withholding - in detail below.

Several other examples of price spikes in energy markets also occurred during this period.

Automotive gasoline prices in Chicago increased from $1.85 per gallon on May 30, 2000, to $2.13 on

June 20, before falling back to $1.57 on July 24. In Milwaukee, the price rose from $1.74 to $2.02, then

reverting to $1.48 by late July. A Federal Trade Commission investigation and report on this episode

concluded that there had been no collusion or other antitrust violation by refiners. Rather, it pointed to

high capacity utilization, some unusual supply disruptions, industry miscalculation, and some

independent, profit-maximizing decisions by refiners. The Report did, however, acknowledge that during

the crucial time period, one firm with substantial inventories chose to

limit[] its response because selling extra supply would have pushed down prices and thereby reduced the profitability of its existing ...sales. An executive of this company made clear that he would rather sell less gasoline and earn a higher margin on each gallon sold than sell more gasoline and earn a lower margin.5

3 The persistence of this abnormal price distinguishes this case, which might be termed a price shock rather than a price spike. 4 See, among many other sources, GAO (2002), Borenstein et al (2002), and Puller (2007).

5 FTC, 2001, p. 21.

16 This strategy–essentially unilateral withholding–has consistently been viewed by the FTC as

outside the reach of the antitrust laws, leaving the agency without a remedy for such behavior.

The New York state electricity market experienced price spikes in 2000 and again in 2001.

Unusually hot weather in May of 2000 set the stage for wholesale price to reach $1300 per MWH over

the course of several days. The New York State Department of Public Service staff issued a report on this

experience acknowledging the likely problem of unilateral withholding but not proposing any remedy.6

A 2001 policy paper by the NY State Electric and Gas Corporation cautioned that the “large amounts of

generation available but unscheduled” was a harbinger of trouble for the state ISO.7 And indeed in 2001,

particularly in the month of August, much larger price spikes occurred. We shall postpone further

discussion of this latter experience until Section IV, where it is the focus of our empirical work.

Price spikes continued to appear in various energy markets. In February, 2003, the benchmark

price for natural gas at a major production point climbed from $8 to $16 per MMBtu in a single day and

then to $22 the following day. Price at the New York City consumption point briefly reached $40 per

MMBtu on February 25, then receded, and spiked again on February 28. The Federal Energy Regulatory

Commission concluded that this price increase was due to cold weather, pipeline and storage limits, and

illiquid (i.e., thin) markets, that is, “normal” factors rather than withholding.8

Automotive gasoline markets continued to experience unusual price movements. The Federal

Trade Commission investigated spikes in Arizona, Atlanta, the mid-Atlantic, and the western states in

2003-2004, in each case concluding that the causes were underlying costs or temporary tightness of

supply rather than market manipulation (Kovacic, 2004). Others disagreed. The Consumer Federation of

America cited evidence that refiners were (CFA, p. 32)

6 State of New York Department of Public Service, 2000 7 New York State Electric and Gas Corporation, 2001, p. 14. 8 FERC, 2003, p. 1.

17[r]elying on ... existing plant and equipment to the greatest possible extent, even if that ultimately meant curtailing output of certain refined product...openly questioning the once-universal imperative of a refinery not ‘going short’–that is, not having enough product to meet market demand. The price spikes in these examples differ in many respects from each other. From our perspective

what is important is the fact that there are two divergent explanations for their occurrence. One

explanation emphasizes the role of ordinary demand or supply determinants. When those determining

factors take on unusual values - for example, due to summertime heat or transport limits - equilibrium

price may well rise to unusually, but appropriately, high levels. The alternative explanation involves

changes not in the determinants of ordinary supply (e.g., electricity usage during hot weather), but rather

in the behavior itself that underlies the supply function. That is, if suppliers alter their behavior by

reducing their offer quantities as price rises, this represents a perverse supply shift that can cause an

abnormal price spike.

The analysis that follows is based on this key distinction - whether price spikes result from

normal market behavior in unusual circumstances, or from abnormal behavior designed to exploit market

conditions. The latter, we would argue, is inconsistent with the Standard Oil criterion of “business…by

usual methods” since no ordinary profit-maximizing firm offers less output as market demand shifts to the

right and price is expected to increase.

III. THE THEORY OF PRICE SPIKES

This section sets out a simple theory of price spikes in a fashion that distinguishes between

natural causes and abnormal supplier behavior. We develop the theory in the context of electricity

markets, where this issue has attracted much attention, and also because we shall examine an episode of

price spike in electricity for our empirical test. The issues and analysis, however, are quite general. We

note some alternative methods of analyzing price spikes, and conclude with the key implications for our

approach.

A. A SIMPLE MODEL OF “NATURAL” PRICE SPIKES

18A standard illustration of a wholesale electric power market is depicted in Figure 1. The key

characteristics are (a) a horizontal marginal cost curve up to some fixed capacity where unit costs increase

sharply, and (b) a very inelastic demand. Each of these characteristics deserves comment. Evidence is

overwhelming that electricity demand is quite inelastic in the short run, and not much more elastic in the

long run. Indeed, Independent System Operators and Regional Transmission Organizations, which now

cover approximately one-half of the country, essentially define forward demand as completely inelastic

since they take day-ahead load to be a fixed quantity and then acquire power to meet that load. This

assumption is embodied in the vertical demand D0 in Figure 1.

The shape of supply response S0 is governed by two considerations. First, baseload plants–

nuclear and large fossil fuel–run at low and essentially constant marginal cost set by the price of fuel and

the plants’ thermal efficiency. Such plants account for the large fraction of total power along the

horizontal range of the supply curve in Figure 1. Second, generation plants have relatively fixed

capacities, so that at or near their rated capacity their supply schedules rise rapidly, even becoming

vertical as shown in Figure 1. The small nonlinear range near capacity reflects the fact that capacity is not

quite a point value, but supply response does diminish rapidly as capacity is approached.

Figure 1 captures the intersection of these demand and supply curves, giving rise to a price P0

essentially equal to the marginal cost of baseload units. Demand may shift over the hourly, weekly, and

seasonal cycle, ranging between D1 and D2, but P0 would continue to prevail as a result of normal

competitive forces. It is important to note that a “natural” price spike could occur with this underlying

structure if demand temporarily shifts far to the right, say, to D3. Then a price such as P3 would arise,

simply reflecting unusually strong demand pressing on fixed available capacity.

Such conditions are virtually inevitable in many energy markets. Demand for many forms of

energy is quite volatile, and production capacity is often largely fixed. Random events such as an

unexpectedly severe summer heat wave can shift demand as shown and result in a price spike. Similarly,

an unexpected reduction in supply due to an unanticipated outage of a major generator or sudden

19congestion on a transmission line might cause the supply curve S0 to shift leftward. Then even if demand

remained unchanged, price would rise in that same fashion as described by the previous example of a

price spike.

B. A SIMPLE MODEL OF “ABNORMAL” PRICE SPIKES

In contrast to the “normal” price spikes described above, it may also be the case that high price

results from changes in supplier behavior itself, behavior designed to exploit particular market

circumstances. In electricity markets the relevant circumstances involve periods when demand presses on

available capacity, and so expected price already exceeds normal levels. Generators can then create or

enhance scarcity by bidding into the market amounts that are smaller than their quantity offers when

demand and expected price are lower. Offering smaller quantities when price is higher involves a

divergence from normal supply behavior, and we take such a shift as the defining characteristic of

abnormal price spikes.

The strategy of offering a smaller quantity when demand is expected to be high is termed

unilateral withholding or strategic withholding. This strategy may take several specific forms,9 but for our

purposes the relevant features can be illustrated with a straightforward adaptation of the previous

analysis.10 We begin with that illustration and then provide the more general analytical demonstration.

To begin, suppose one firm supplying the market shown in Figure 2 has two plants of identical

size, denoted X1 and X2. (Their placement in Figure 2 will be explained shortly.) The remainder of the

supply sector can be structured in any manner whatsoever, from a single other firm to a highly fragmented

industry, but output from the other producers is assumed not to respond to the output choice of the firm in

question.11 Market demand is completely inelastic and either intersects the supply curve on its (short)

9 Moss (2006) distinguishes several variants, including physical withholding or simply shutting down a generator, economic withholding by bidding above marginal cost, out of merit order dispatch intended to create transmission congestion, and strategic withholding exploiting market dominance. We return to these distinctions below. 10 This framework is adapted from Kwoka (2000). 11 We shall see the importance of this assumption later on.

20rising segment or lies no farther than the quantity X = X1 = X2 to the left of the point where the marginal

cost schedule ceases to be horizontal. As a result, initial price is given by P1.

For simplicity we begin by assuming that each of the two plants either produces exactly at

capacity or shuts down and produces nothing at all. Thus, the firm decides on plant-level output of either

X or 0, and total two-plant output takes on one of three values: 2X, X, or 0. The question we wish to

address is whether the two-plant firm can profitably withhold output from one of its plants.12 To

determine this, we need only compare its profits with two-plant production vs. profits with only one plant

in operation.

Two-plant profits at initial price P1 are given by [2 X (P1 - C)], where C is marginal cost. These

profits are shown as areas A and B above X1 and X2, respectively. The magnitudes of such profits are

clearly a function of the initial price-cost margin, (and would be zero if the initial position of demand

resembles that in Figure 1). The alternative for this firm is to withhold output from inframarginal

capacity, such as the plant producing X2. This reduction in supply can be represented by a leftward shift of

the overall supply curve in the amount X2, from S1 to S2. This will in turn cause price to rise from P1 to P2,

an amount dependent on the elasticity of supply across the range of withheld output.

For unilateral withholding to be rational for the firm in question, it must gain more in profit on its

remaining plant in service than it loses on the withheld plant. In Figure 2, that gain is shown as area D,

above the quantity X1 from the plant that continues to operate. The loss is given by B, the profit

previously earned by the now-shutdown plant. A comparison of these two areas implies that net profits

will increase when the following inequality is satisfied:

X1 (P2 - P1) > X2 (P1 - C) (1)

12 Obviously, it cannot profitably withhold output from both, since the yet higher price would only benefit other producers. The considerations relevant to this trade-off can be found in Wolfram (1998).

21Since by assumption in this example the two plants are identical, this inequality is satisfied and unilateral

withholding profitable so long as the price increase (P2 - P1) exceeds the initial price-cost margin (P1 - C).

This simple example demonstrates how withholding may be unilaterally profitable. It is, of

course, true that any one firm would prefer that some other firm assume the burden of output reduction so

that it could free ride on the resulting price increase, but it is nonetheless in its own independent interest

and therefore predictably will occur. No conspiracy or cooperation with other firms is necessary. It is also

important to recognize that the size of the withholding firm need not be especially large. In this example

the firm in question needs only to have one plant whose quantity withdrawal is sufficient to produce the

requisite price increase. Clearly, this is a value considerably less than the market share that usually

triggers concern over market power.

C. A GENERAL MODEL OF STRATEGIC WITHHOLDING AND PRICE SPIKES

We can generalize this result and gain further insight as follows. Instead of assuming a two-

identical-plant firm making an all-or-nothing production decision for each plant, we allow the firm to

make a continuous output choice up to its total capacity from all plants. Firm profit is given by

π = P q – c q (2)

where q denotes its output and c is constant unit cost.13 P is the price that results from aggregate quantity

offers by all firms (“supply”) intersecting fixed-quantity demand, or put differently, the price on the offer

curve at exogenously fixed demand quantity. For present purposes the price function P can usefully be

written as P(Qs) = P(q + r), where Qs is the total quantity offers by all firms—the simple sum of this

firm’s quantity offer q and that of remaining suppliers r. Differentiating this expression with respect to q

gives the following profit-maximizing condition:

d π /dq = q (dP/dq) + P - c = 0 (3)

13 The assumption of constant unit cost c is a useful simplification. Unit cost would differ if the firm in question represented marginal capacity, but even then the cost effect is likely to be secondary. For an approach that allows for some cost variation, see Joskow and Kahn (2002)

22Despite the apparent similarity, it should be stressed that this expression is not the usual first-

order condition: dP/dq does not represent movement along the demand curve, since quantity demanded is

by assumption not responsive to price. Rather, dP/dq measures the effect on equilibrium price when this

particular firm reduces its offer quantity q, thereby shifting the supply curve to the left. This term can

usefully be written out as follows:

(dP/dQs) (dQs/dq) = (dP/dQs) (d[q+r]/dq) = (dP/dQs) (1 + dr/dq) (4)

Here dP/dQs denotes the effect on equilibrium price resulting from different possible total offer

quantities Qs. Total Qs changes both with this firm’s supply q and also with any output changes in

response by other firms. We continue to assume dr/dq = 0, that is, other firms hold their offer quantities

constant.

Substituting, we can rewrite (2) as follows:

-q (dP/dQs) = P –c. (5)

Dividing by P, we obtain

m = s W (6)

In this expression, m is the initial price-cost margin (P – c)/P, s is the post-withholding market

share of this firm (q/Q), and W is the elasticity of price from output withholding (dP/P)/(dq/q). It is

readily apparent that W is the reciprocal of the supply elasticity. The reason for this is simply that the

price effect of a quantity shift is zero when supply elasticity is infinite, but the price effect increases as

supply elasticity falls. Mathematically, too, W = 1/E, and so equation (5) can be written

s = m E (7)

Condition (7) states that unilateral withholding is profitable up to the point where the firm’s

market share equals the product of the price-cost margin and the elasticity of supply.14 Thus, in the

extreme case where pre-withholding margin is zero, there are no lost profits from withholding so that any

14 It is most natural to interpret this as pre-withholding margin and post-withholding share. Pre-withholding output matters as well, but its role is subsumed in supply elasticity.

23post-withholding output yields a profit gain. On the other hand, if the market is on, or moves to, the zero-

elasticity portion of the supply curve, the induced price rise becomes limitless and withholding is

profitable at any level of final output.

Table 1 provides some sample calculations of the necessary post-withholding market shares s2,

based on various possible values of m and E. Margins from 0 to .50 are examined against a range of

supply elasticities from zero to 50.15 Table 1 indicates that if m = 0 or E = 0, the necessary levels of post-

withholding output and share are trivial. But if pre-withholding margins are positive so that withholding

entails a profit sacrifice, the post-withholding share required for profitability involves a trade-off. For

market operation at elasticities such as 0.1, the necessary post-withholding share is only 5 percent even

for a fairly large pre-withholding margin of .50. Even with somewhat larger supply elasticities,

withholding often remains profitable. For example, if E = 0.5 and pre-withholding margin is 20 percent, a

10 percent post-withholding share suffices. And if E = 1.0, a 10 percent pre-withholding margin still

requires only a 10 percent post-withholding share.

The table does indicate, however, that where supply elasticity is greater, profitable withholding is

considerably more difficult. Indeed, withholding is unlikely to be a profitable strategy where E takes on

values of 5.0 or greater. As noted previously, however, such supply elasticities characterize the interval

well short of capacity.

D. OTHER ANALYSES OF PRICE SPIKES

A few other studies have examined price spikes, typically with a methodology oriented toward a

broad analysis of market power in electricity. Prominent among such studies are Borenstein et al (2002)

and Joskow and Kahn (2002), both of which focus on the 2001 California experience. The basic

methodology for each of these is similar and involves a comparison of actual price to a counterfactual

15 With respect to E, one study reports data implying that elasticity of supply to California’s Power Exchange is about 50.0 for output up to about 70 percent of capacity. For 70 – 80 percent, elasticity falls to just over 1.0. For 80-90 percent of capacity, elasticity equals 0.3, and above 90 percent, it is 0.1. See Joskow and Kahn (2002), Wolfram (1998) and Puller (2007).

24competitive price based on calculated marginal costs of the marginal generation plant. After allowing for

other factors, both studies find evidence of market power, especially during peak periods, although by

itself this does not establish whether the mechanism was withholding. Joskow and Kahn cast some light

on this latter question in their examination of what they term the “output gap,” the difference between

generators’ production capacity that would be profitable to use and that which in fact they did use. Again

allowing for other factors, they find significant amounts of undispatched, and unexplained, power during

peak hours.16

Boddeus (2008) extends and applies this approach to the Dutch electricity market. He calculates

“unloaded but profitable capacity” per firm, a concept similar to the output gap, and then regresses

variable on two explanatory variables --the residual supply index (a measure of the pivotalness of the

firm’s supply) and an index of “cheap” capacity held by the firm. The hypotheses are that the more

crucial is the firm’s supply to price determination, and also the more cheap inframarginal capacity it

holds, the more likely it is to withhold output. His statistical tests confirm that is the case. He then tests

for a relationship between the wholesale market price and this unloaded but profitable capacity, and finds

confirmation that firms indeed do influence price by holding their output off the market.

Other work on strategic withholding and on price spikes exists, including that based on supply

function equilibria and that emphasizing the purely statistical properties of price movements (Knittel and

Roberts (2005), Siefert and Uhrig-Homburg (2006)).17 Most of these approaches to the problem face

methodological challenges. Those that require determination of marginal costs encounter either

accounting or econometric issues. Generator unit comparisons must resolve questions of seasonal outages 16 See Harvey and Hogan (2001) for comments and criticisms of Joskow and Kahn. The previously-cited New York State DPS Report had performed a similar comparison of offer quantities under different load conditions, relying upon nonpublic cost data. 17 Boddeus provides a convenient summary of much of this work. Mention should also be made of an effort by the Dutch Competition Authority to identify withholding by examining the dispatch inefficiency of the market, that is, the extent to which higher cost units are dispatched while lower cost units are available. After this paper was completed, we became aware of work by Zhang et al (2007), which examines the same price experience in the New York ISO but focuses on the price choices of small, presumably marginal generators. Our theory guides us elsewhere, but with results that are not inconsistent.

25for maintenance. Counterfactual pricing models require assumptions about demand elasticity. Models

dependent on Cournot or other particular behavior seem suspect. The need for nonpublic data makes any

of these approaches problematic. Below, we offer a methodology that, while itself imperfect, avoids many

of these problems.

E. CONCLUDING OBSERVATIONS ON WITHHOLDING

We conclude this section with two observations. First, unilateral withholding has been shown to

be profitable when demand presses on available fixed supply. Under these conditions an abrupt shift in

supplier behavior can create price spikes that differ from the price - no matter how high - that results from

ordinary supply and demand equilibrium. It is this abrupt behavioral shift by suppliers that constitutes the

distinguishing characteristic of strategic withholding and price spikes, and it is this characteristic that

forms the basis for our empirical test below.

Secondly, this analysis also provides an explanation as to why electricity, natural gas, and

automotive gasoline markets exhibit price spikes with unusual frequency. The reason is simply that each

of these is characterized by relatively fixed capacity and demand that is both inelastic and subject to large

shifts, periodically approaching or reaching that capacity. In electricity, generation capacity is very close

to a fixed quantity: No amount of additional fuel (the variable input) can elicit more output once capacity

has been reached. In petroleum refining, the refineries serving a region establish an essentially fixed

maximum output defined by their rated capacities. Natural gas transport is limited by pipeline capacity

constraints, which when reached can fragment markets and make supply exceedingly inelastic. Since

these energy markets are generally characterized by low demand elasticity and considerable volatility in

demand, the preconditions for profitable withholding are often met.

We now examine one of these price spike experiences, with a view to distinguishing strategic

behavior vs. normal price volatility as the cause.

IV. PRICE SPIKES IN NEW YORK

26This section analyzes the New York State wholesale electricity market, and in particular, its price

spike experience in 2001. We begin by describing that market, followed by our data, methodology, and

results.

A. THE NEW YORK WHOLESALE ELECTRICITY MARKET

The New York Independent System Operator grew out of the New York Power Pool and began

formal operation of its electricity market in late 1999. The ISO administers a day-ahead market as well as

a real-time market, both operating as uniform price auctions of offers (bids) necessary to satisfy

administratively determined requirements. The day-ahead market encompasses approximately 95 percent

of trades. The remaining real-time trades resolve any supply-demand discrepancies at the actual hour. The

entire state is divided into 11 geographic load zones, within each of which the same price obtains. Price

may on occasion differ between zones due to transmission constraints that prevent a common price from

emerging.

The day-ahead market–the focus of our attention–operates as follows: The New York ISO

announces its load forecast, in the form of a fixed MW quantity for each hour, one day in advance. That

forecast is based on known patterns of use (day-night, day of week, etc.), adjusted for such factors as

expected weather. Bidders, which either own or have under contract one or more generating units, submit

bids consisting of up to six price-quantity combinations for each generation unit, so that, for example, a

particular generating unit might be offered at $10 per MW up to 50 MW, and also at $50 up to 100 MW,

etc., for a specific day-ahead hour. 18 The NYISO uses a sophisticated computer algorithm with

information about transmission conditions and constraints to create an aggregate offer curve and

determine a uniform market-clearing price for each hour for the entire ISO or if transmission and other

18 Bidders can submit their offers in either a block or curve format. We use a modified form of the technique described in Siddiqui et al (2004) to convert block offers to the smooth curve format. Neither the contracts nor the identity of the bidder is available to the public, although the bids themselves are available with a six-month lag. In each hour, no generator can be offered by more than one bidder, but any generator can be offered by different bidders over time.

27constraints bind for individual zones. That price in turn determines which units will be called upon and

what price will obtain.

Like all ISOs, the NYISO has had in place measures to mitigate market power, defined (in

language reminiscent of Standard Oil) as “conduct that…is significantly inconsistent with competitive

behavior” (NYISO, 2008b, p. 12-15). These measures are triggered by various types of possible conduct

by bidders and generators, including physical withholding (failure to offer output), economic withholding

(offering output at unjustifiably high prices), and uneconomic production (over-producing so as to create

system constraints). Criteria for each are set out in considerable detail.

In its first full year of operation, the New York Day-Ahead market experienced significant price

spikes. As shown in Figure 3, price was relatively well-behaved until August, when on three successive

high-demand days it rose from its normal $40-45 per MWH to $194, $917, and $180 per MWH. It is this

experience that is analyzed further below.

B. ASSUMPTIONS AND DATA FOR THIS ANALYSIS

Our method of analysis is intended to permit judgments about price spikes without reliance upon

nonpublic cost or other data. It depends simply on the difference between the quantity offered when

demand is very strong and that under more typical demand conditions. Our key proposition is that a

reduction in offer quantity under peak forecast conditions represents a divergence from normal profit-

maximizing business behavior. Such behavior is presumed suspect and warrants further investigation.

This criterion and approach require both some simple and likely unobjectionable assumptions, together

with others that may be more questionable. The latter will require further analysis before final judgment

can be passed on the overall merits of this approach, and so the present exercise should be interpreted as

an initial effort. The simple assumptions are as follows:

(1) Load forecast is common knowledge, represents the market’s ex ante best estimate of day-

ahead price, and is the basis of bid decisions. This assumption seems unobjectionable since load forecast

28is publicized by the ISO, and does indeed represent the quantity the ISO is committed to acquiring in the

day-ahead market, even though the real-time market serves to reconcile actual demand with supply offers.

(2) There are no generator outages due to scheduled maintenance during the summer months. In

fact, the New York ISO requires generators to submit their outage schedules for approval, and will permit

outages only if the otherwise available capacity meets forecasted load.19 As a practical matter, no

scheduled outages are approved for summer months, so by focusing on summertime data we avoid

difficult determinations about the causes of outages that other studies have had to contend with.

More debatable assumptions that we also make in this preliminary analysis include the following:

(3) New York State constitutes a single integrated market. This assumes away any transmission

constraints that might subdivide the overall market into load zones with substantially different prices.

Consistent with this assumption, the ISO compiles a statewide price that we take as indicative of, if not a

precise measure of, all zonal prices. The more disaggregated zonal case would require analysis beyond

the scope of this inquiry.

(4) Supply arises from generators within the state of New York. Put differently, imports of power

either must be de minimus or do not vary greatly over the relevant time. Data show that imports constitute

less than 5 percent of total consumption, an amount that does not seem so large as to undermine our

approach.

The core data for this analysis consist of bid price data by hour by bidder and by generating unit

for the months of July and August, 2001. This focus allows us to examine supply behavior in the overall

market, by groups of bidders, by individual bidders, and by individual generators under a range of

demand conditions up to and including the spike periods in early August. These two months cover the

period of summertime demand and hence operation of the full set of generators, but it avoids the period of

scheduled outages. Relative to the approach of inferring or estimating the frequency of planned outages,

simply focusing on a period with no such outages seems preferable. 19 Based on email exchange with representative of NYISO. Data on scheduled maintenance for 2008 confirm that no such maintenance occurred in the months of June, July, and August (NYISO, 2008a).

29These bid data are publicly available on the NYISO website with a six-month lag. There are a

total of 125 active bidders and 307 generating units–each identified by ID numbers but not names—that

participated in the day-ahead auction during this two-month period. Although the ultimate owners of

these units cannot be determined, that does not matter for most of our analysis. The actual data consist of

one or more price/quantity bids per generation unit as described above. As the first stage of our analysis,

we compile these into the actual offer curves of bidder and each generator as well as the overall market

offer curve, for each of 1488 hours in July and August, 2001.

C. ANALYZING PRICE SPIKES IN NEW YORK

Our methodology involves examination of differences in the quantity offers in periods when

demand is expected to approach capacity vs. more normal periods during the two summer months of

2001. We identify high-demand periods ex ante as those when the ISO load forecast is at or near its

maximum, and ex post as those when prices spiked. Inspection of the data indicates a maximum load

forecast of 32,008 MW at 2 PM, August 9, which is also the hour when price reached $917 per MWH.

Table 2 reports the hours, load forecasts, and prices for all of what we shall term the peak hours in 2001.

We begin by analyzing aggregate market offers and then focus on large generators.

1. Aggregate Analysis

We construct aggregate offer curves for each of the 1488 hours. Figure 4 displays one such offer

curve, that for an hour that was “normal” in all respects–both load and price were squarely within the

range of summertime experience in 2001. This hour, 2 pm on July 19, will henceforth be treated as the

benchmark hour.20 As is evident, generators’ overall price/quantity bids map out a textbook supply

curve.21 We are interested in comparing this offer curve to that for peak hours. If at peak hours aggregate

20 Other possible benchmark hours give exactly the same results. We shall discuss choice of this hour as a benchmark further below. 21 Note that since bidders offer price-quantities based on the generation units they own or control, aggregating bidders would result in the same curve as this aggregation of bids by generating units.

30quantity offers are smaller, or equivalently if price is higher for any quantity, that would represent a shift

of the offer curve in a direction consistent with strategic withholding.

To test this formally, we employ a two-step statistical procedure. The first step consists of fitting

the nonlinear relationship between price bid P and bid quantity Q for each hour. After examining various

functional forms, we find that the nonlinear exponential model provides the best overall fit:22

P = a · bQ (8)

In this nonlinear equation the parameter a is the vertical intercept–effectively, the bid price of baseload

plants. This parameter will reflect their low marginal costs, but also perhaps their must-run status or even

generating units’ determination that they be called upon to run at any market price.23 Our hypothesis

focuses on the second parameter, b, which captures the curvature of the offer curve. A larger value of b

denotes a more sharply rising curve, that is, a higher offer price at any quantity, as would be expected

under strategic withholding.

Using nonlinear least squares regression, we estimate equation (8) for each of 1488 hours.

Typical of the results are those for our benchmark hour, as follows (standard errors in parentheses):

P = (1.92·10-7) · 1.001Q (9) (4.97·10-8) (7.73·10-6)

Both parameters a and b are estimated quite precisely, and the overall relationship displays a

very high R2 = .98, implying a very good overall fit. Inspection reveals, however, that the fit is less good

along the upper tail of the offer curve, where prediction errors are more sizeable (though not systematic).

This is unfortunate since it is in this region that withholding behavior should be most apparent. We

22 Our choice of this model over, say, the log-linear model, is grounded in the fact that about one third of our observations on price/quantity bid pairs have zero price values. These are more readily handled in the exponential than the log-linear model. 23 That is, some generating units bid their output in at very low, even zero, price. While this ensures they will not be the marginal generation unit, it also guarantees they will be called upon to produce their offer quantity at whatever (higher) price arises.

31proceed nonetheless with our intended statistical test, and then develop some alternative methods of

examining the data.

We compile the estimates of the curvature coefficient b for all 1488 hours. Next we regress those

estimated values against two measures of peak demand–hourly forecasted loads, and the actual market-

clearing price. We hypothesize a higher value of b when load and/or price is greater, which would

indicate a leftward/upward shift of the offer curve under the conditions of high load and/or price. The

results of these regressions are reported in Table 3. Column (a), however, indicates a negative relationship

between LOAD and b, implying that total offer quantity at any price in fact increases with load forecast.

This indicates a normal supply response by generators to expected demand shifts. Additional regressions

testing for breaks or nonlinearities fail to detect evidence of withholding behavior based on load forecast.

Alternatively, we examine the relationship between the supply response measure b and actual

market-clearing price MCP. This approach diverges from our effort to relate bid behavior to ex ante

demand forecasts, but focuses instead on bid behavior based on the realization of price as the best

estimate of the predicted price in future hours. The results of the regression of the supply curvature

parameter on MCP for all 1488 observations are reported in column (b) of Table 3. Again, a negative

overall relationship between the two emerges. In contrast to LOAD, however, the data reveal a different

relationship between b and MCP above and below a breakpoint for MCP. The results of dividing the

sample at a MCP of $200/MWH that point and estimating the relationship for MCP < 200 and then for

MCP > 200 are shown in columns (c) and (d), respectively. The regression coefficient continues to be

negative for the large majority of observations on market-clearing price below $200/MWH (column (c)),

but for the nine hours with very high prices there is in fact a positive and significant association (column

(d)). This latter result implies an inward shift of the offer curve under peak conditions, the kind of supply

response indicative of strategic withholding.

These observations for MCP > 200 are nine consecutive hours on the single day August 9. As

previously shown in Table 2, market-clearing prices for those hours exceeded the previous maximum

32value of $184/MWH, which had been set on the prior day. There is no evidence of offer-reductions on

either August 8 or 10, despite quite high loads and prices on both of those days. Withholding appears to

be limited to the singularly unusual circumstances of August 9.24

2. Individual Bidder Analysis of Prices and Quantities

The theory underlying withholding behavior involves overall output changes by bidders offering

a set of generating units at peak demand. Since each bidder makes price and output bids on behalf of one

or more generating units, theory predicts that strategic withholding is most likely a decision made by a

bidder seeking to maximize its total profits from all the generation capacity it holds, rather than from a

single generating unit. Our next step is therefore to compare the supply behavior of individual bidders at

peak vs. nonpeak hours. We hypothesize that some of these bidders, most likely large ones, offer a lesser

quantity under peak demand conditions in order to cause price to spike. This section analyzes the data to

determine whether this is the case.

Analysis of data on bidding practices is made difficult by the fact that the rights to represent a

generator in the ISO auction can transfer from one bidder to another on a short-term basis. This makes

comparisons over time of supply behavior for many individual bidders difficult since they do not offer the

same units at all hours. We can nonetheless cast some light on our hypothesis by examining the subset of

bidders that have multiple generating units (typically, more than 5) while offering a relatively consistent

set of units over time (typically, at most one or two units changing between peak and nonpeak hours). Of

the 125 bidders in our data set, about fifteen meet these criteria. For each of these bidders, we construct

aggregate offer curves for the peak hour of 2 pm on August 9, 2001, and compare to these same bidders’

offer curves for several nonpeak hours one, two, and three weeks earlier.

What we observe is illustrated in Figures 5.a and 5.b. There are in fact two seemingly different

types of supply behavior at peak hours in the data. Figure 5.b depicts a leftward-shift of the offer curve

24 The load forecasts for the peak hours on August 9 were extremely high, although hours on other days also had very high loads. The correlation between forecasted load and actual market-clearing price is .48, which is significant but hardly overwhelming.

33relative to nonpeak supply, much like the theoretical construct of strategic withholding discussed above.

But the data also reveal an alternative strategy involving a similar shift in the offer curve (leftward or,

equivalently, upward) for most quantities, but prices that are actually less than in non-withholding hours

at very large quantities. This depiction, shown in Figure 5.a, is commonly called “economic

withholding,” and suggests that offer quantities are repriced (upward) in contrast to the quantity reduction

behavior central to our analysis of withholding. Thus, with the possible exception of the highest

quantities in one bidding strategy, we can conclude that indeed there is evidence of reductions in offer

quantities at peak hours.25

Next we seek to understand how the strategy involving a lower price for some quantities may

nonetheless be profit-maximizing to the firm. We note preliminarily that most bidders develop a daily -

or sometimes even weekly - pricing strategy for their entire set of generating units. Relative to hourly

pricing, this presumably conserves on transactions costs while seeking maximum expected profit over the

daily load cycle. Demand fluctuates over the day in a fairly predictable manner, so that each bidder can

project its residual load across the cycle and chooses its overall - and fixed - supply accordingly. A

correctly chosen set of offer quantities and prices will result in high (if not maximum) profits as demand

shifts, achieving substantial profits without the need to alter the offer hour by hour.

The result of this strategy can be illustrated with the data in Table 2, which shows that the

market-clearing price exceeded $200/MWH during nine hours of August 9, 2001, and was below

$200/MWH the rest of the time. Knowing this, a bidder with a withholding strategy over the 24-hour

period similar to that illustrated in Figure 5.a can maximize profits during fifteen hours when the market-

clearing price is below $200/MWH. On the other hand, a bidder with a persistent strategy similar to the

one depicted in Figure 5.b chooses to maximize its profits during the nine hours with the market-clearing

price above $200.

25 Given that these bidders are only a subset, this exercise should be interpreted as preliminary.

34While this explains how each strategy can maximize expected profit, the question remains: what

determines whether a bidder adopts the strategy described in Figure 5.a or 5.b? A closer look at the

generation portfolios of bidders of both types sheds light on this issue. The key is to recall that any bidder

not only needs to reduce quantity in order to raise price, but also to continue to supply sufficient quantity

at the elevated price to increase its total profit. The need to continue to supply generation requires that all

the bidders’ low-cost base-load generation is dispatched. Bidders, however, have very different amounts

of base-load generation. A bidder with mostly base-load generation may withhold only some production

at the end of the supply curve, corresponding to the output with highest level of marginal costs and

resulting in a “leftward” shift of its offer curve. On the other hand, a company with a little or no base-

load generation (and therefore an offer curve that rises almost immediately) may find it profitable to

withhold production from the early ranges of the supply curve, causing an apparent “upward” shift. This

is precisely what the data on the generation portfolios of two types of bidders and their price-quantity bid

pairs reveal.

What still remains to be explained is why a bidder like that shown in Figure 5.a offers its largest

quantities at lower prices at peak load times. An example suggests the reason. On August 9, 2001, there

were 9 hours with market-clearing prices above $200/MWH and 15 hours with market-clearing prices

below this value. The strategy of the bidder in question was to raise its price during the hours with lower

market-clearing prices, but where price was expected to be higher, it lowered its peak offer price in order

to ensure dispatch of all its generation and thereby earn high profit. Bidders of this type avoid

withholding during the expected super-peak hours and instead free-ride on other bidders whose lower-

cost generation portfolios will induce them to withhold.

These observations tie together two considerations: different types of bidders (based on their

generation portfolio) and differences in bidding strategies. Each bidder chooses its price-quantity offers

based on its mix of generation and marginal costs, as well as on transactions and information costs.

Although there may seem to be two types of price-quantity offers, each in fact promotes high prices while

35ensuring dispatch of the bidder’s generation units. This explanation contrasts with the literature that has

generally treated “physical withholding” and “economic withholding” as two different strategies,

somehow exogenously determined or chosen. Our view is that these are simply different manifestations

of the same strategy, adapted to different exogenous conditions.26

Finally, it is noteworthy that there is a great deal of consistency within each of these two types of

bidders with respect to their individual generating units. Unit offer curves for each bidder at peak hour

vs. nonpeak hours closely resemble each other, and are necessarily mirrored in the aggregate offer curve

for each type of bidder. Figures 6.a and 6.b illustrate these supply behaviors. Figure 6.a shows that most

generators represented in the auction by a bidder of the type illustrated in Figure 5.a offer higher price

bids at peak hours for the majority, or even all, of their output. Figure 6.b demonstrates that generators of

the type shown in Figure 5.b offer the same price bids on peak and nonpeak hours for the majority of the

output range and raise price bids at peak hours only for the highest output offers. Clearly there is

considerably consistency among generating units controlled by a single bidder, even though bidders

pursue different withholding strategies to manipulate price to their advantage.

3. Generator Group Analysis

The analysis of generation portfolios of individual bidders revealed different behavior patterns

among generators of different sizes. Thus, we next examine more closely the supply behavior of different

size segments of the population of 307 generators. Theory suggests that it is large generators that offer a

lesser quantity under peak demand conditions. We have no hypothesis regarding small and medium size

generators: In principle they might aid the withholding strategy by contracting, but it seems more likely

that they hold output constant or actually increase their quantity offers in response to others’ contraction

and the prospects of higher price.27

26 Indeed, to the extent that the common distinction between physical and economic withholding seems to rest on whether the supply curve is said to shift leftward or upward, the difference seems unpersuasive.

27 Recall our earlier assumption that smaller producers’ output remained unchanged. While common to such analyses, there is no reason to accept that assumption.

36To test these predictions, we compute each generator’s maximum quantity offer for each hour in

July and August, calling this result their “size.”28 We then rank all generators by their average size

across all hours and identify a breakpoint that distinguishes “large” from “small and medium” generators.

The breakpoint that clearly emerges is at 485 MW. Above this point there are 19 large generators, with

the next considerably smaller at 390 MW. The remaining 288 generators will simply be called “others” or

“small and medium size generators.” Each of these two groups accounts for approximately one-half of

total offer quantity.29

Table 4 reports the supply responses of these two groups according to market clearing price MCP.

These data reveal that the 19 large generators collectively offered 15,760 MW for prices in the range of

$150-175 and 15,726 MW for MCP between $175 and $200, but only 15,527 MW for MCP > $200. This

decrease of 233 MW relative to their maximum represents a 1.5 % supply reduction. While hardly

overwhelming, it is not consistent with normal business practices, which would involve either an increase

in offer quantity as price rises (as is the case at lower prices for these generators), or at least a fixed

quantity if these generators faced a capacity constraint at 15,726-15,760 MW. The fact that large

generators offered smaller quantities to the ISO as peak price was approached is behavior consistent with

strategic withholding.

These data also allow testing the behavior of small and medium size bidders, which the literature

generally assumes is unchanged in the face of price spikes. We find, however, that across this same range

of prices, small and medium generators actually expand their offer quantities much as they did at lower

prices. For MCP between $150 and $175, they offered 17,394 MW, whereas for prices above $200, their

28 There are in fact two methods of calculating such an average–across all 1488 hours, and across hours where the generator bid any positive quantity. While the former is in some sense a better measure of their average size, the complete absence of a generator during periods of high demand must be the result of some factor not relevant for purposes of determining its typical size in the NYISO, for example, delivery of power to another market, etc. Henceforth we shall focus on the set of hours with positive quantity offers. 29 We have also examined the data distinguishing between small and medium size generators, but without important differences.

37quantity offers rose to 17,765 MW. This behavior implies that small and medium size generators behave

more like competitive firms, increasing their output in response to the prospect of higher price. Indeed,

their 371 MW increase more than fully offsets the decrease by large generators, resulting in a small

positive change in total offer quantity at peak hours. This helps to explain our earlier result indicating a

total quantity offer no smaller at peak loads, but it would seem that that result occurs despite efforts by the

large generators to rein in quantity. Without the output reduction by the large generators, total output

would presumably have been larger yet, implying that large generator withholding indeed did contribute

to the price spike episode.

4. Individual Generator Analysis of Prices and Quantities

Our final approach to the issue of price spikes and strategic withholding is to examine the

behavior of a few large individual generating units within bidders’ generation sets. The largest

generators’ offer quantities for the peak price hour of 2 pm on August 9 are shown in Table 5, together

with the corresponding quantities for the benchmark hour (which is typical of their summertime offer

quantities). The striking fact that emerges is that among the group of nineteen large generators, ten are

found to hold output constant. Seven others alter their offers in the narrow range +16 to -10 MW, which

are trivial relative to their own or the market’s total offer quantities. Essentially all of the aggregate output

reduction derives from two large generators, those labeled generator K and generator O.

Generator K offered at least 670 MW for the most of the summer of 2001, and between 690 and

697 MW consistently for all hours of August 7 and August 8, and for hours up until 2 am on August 9. At

that point its quantity offer dropped to 350 MW for the following 24 consecutive hours–a decline of 340-

347 MW. It then rose to the range of 500-530 MW up until August 20 at which time it resumed its

previous level of about 700 MW. Its offer prices cast further light on its strategy. On August 8 up until 12

noon, it bid 530 MW at price bid $0 (ensuring it would be called upon), 645 at $58 per MW, and 690 at

$160. Starting at noon and for the next twelve hours, it offered only two blocks—645 MW at price bid

$0, and 690 MW at $160. This ensured that a minimum of 645 MW would operate, and more if price

38exceeded $160 (which it did for four hours). Starting at 4 am on August 9, it bid its lower quantity of

350 MW in at a price bid of $0 and continued to bid a price of zero for the next 48 hours (half of which

involved quantities of 350 MW and half 530 MW). Its low bids ensured that its reduced output would be

called upon and receive the high market-clearing price.

The other generator - Generator O - also employed a strategy involving an initially greater

quantity at a very high bid price. Its quantity offer averaged about 600 MW for July and early August,

but at 4 am on August 8 it reduced its offer to 400 MW and 24 hours later to 350 MW, for a total decline

of 250 MW. It was not until August 15 that it resumed its offer of 600 MW to the market. Its pricing

strategy differed from that pursued by Generator K. Up until 4 am on August 8, Generator O offered six

blocks of output at the following sequence of prices: (34, 37, 77,30 500, 700, 1000). Only the first three

blocks were likely to be called upon; the latter were being made available only if prices happened to

spike.

Beginning at 4 am on August 8, this generator dropped the last three price-quantity blocks

altogether, reducing its total quantity offer to 400 MW and then to 350 MW. It simultaneously repriced

the three initial blocks to (42,31 100, 200), up from (34, 37, 77), and held to that strategy even as market-

clearing prices fluctuated between $33 and $917/MWH. The effect was that it was called upon for

varying amounts of output, ranging from none (for early morning hours on August 8 through 11) to 350

MW when that was its maximum offer (on August 9), but never the full 400 MW (which was offered at a

price of $200 at times where market-clearing price was lower than $200/MWH). By contrast, recall that

Generator K dropped its offer price to zero when it truncated output so as to ensure it would be called

upon.

D. CONCLUSIONS REGARDING NEW YORK PRICE SPIKES

30 This number varied in eight hours.

31 Or $43.

39The New York ISO experienced very unusual price movements in August 2001. We have found

that several large bidders, as well as two particular generators behaved in ways that appear to have

contributed to or produced the price spike that occurred on August 9. We have focused on bidders and

generators that might have engaged in physical withholding - a reduction in offer quantity - but economic

withholding - repricing output - also appears to be at work. The use of this latter strategy helps explain

the modest reduction in total offer quantity in the market even at the peak price. Repricing reinforces

physical withholding by helping to ensure a higher market-clearing price, but as we have seen the

generator using economic withholding risks that some of its output is not called upon and hence does not

benefit from that higher price.

V. SUMMARY OBSERVATIONS

In the Standard Oil case, the Supreme Court sought to identify anticompetitive and illegal

behavior by articulating a standard of consistency with “business by usual methods.” While imprecise,

this standard does prove helpful in analyzing energy markets that have experienced price spikes: It

directs attention to supply reductions in the face of expected high demand and price - a phenomenon that

differs from normal market behavior and is thus competitively suspect. The 2001 experience of the

NYISO provides support for this interpretation. It illustrates how price spikes due to strategic

withholding by some suppliers can be distinguished from large price increases resulting from ordinary

demand and supply behavior, albeit under conditions of extreme volatility.

While our test is as yet imperfect, taken at face value the results are of considerable substantive

interest. They document the fact that strategic withholding did indeed occur in the NYISO during the

summer of 2001. We also find that strategic withholding was responsible for unusually high prices only

during the superpeak period consisting of a few hours on one day. Other hours on that day as well as high

prices on the surrounding days all seem to be consistent with equilibrium prices under conditions of very

tight supply. Moreover, the analysis focuses attention on a very small number of large suppliers that were

40uniquely responsible for the withholding. Other suppliers either held their offer quantities constant or, in

the case of less sizeable bidders, actually increased their supply.

These results have implications for both regulatory and antitrust analysis of electricity markets.

They es