Does competition reduce costs? : assessing the impact of regulatory restructuring on U.S. electric generation efficiency

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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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1

828

MASSACHUSETTS

INSTITUTE

OF

TECHNOLOGY

DEC

7

200^

Does

Competition

Reduce

Costs?

Assessing the

Impact

of

Regulatory Restructuring

on

U.S. Electric

Generation

Efficiency

Kira

Markiewicz

UC

Berkeley,

Haas

School

of Business

Nancy

L.

Rose

MIT

and

NBER

Catherine

Wolfram

UC

Berkeley

and

NBER*

November

2004

*_{markiewi(a),haas.}

berkeley.edu.nrosefajmit.edu,wolfram@haas.berkelev.edu. Roseacknowledgessupport

fromthe

MIT

CenterforEnergy and EnvironmentalPolicyResearchandtheHooverInstitution.

We

thank

participantsatthe

NBER

ProductivityProgramMeeting,the

NBER

10

Summer

InstituteMeeting,the UniversityofCaliforniaEnergyInstitute

POWER

conference,andthe

MIT

CenterforEnergy and EnvironmentalPolicyResearchconference, as wellasseminarparticipantsatHarvard,MIT,

UC

Berkeley,

UC

Davisand Yalefor theirsuggestions.

We

are particularly grateful forthe detailedcomments onearlier draftsprovided by Al Klevorick,

Mark

Roberts,CharlesRossmanandJohannes

Van

Biesebroeck.

We

alsothank

Tom

Wilkeningforassistanceincodingrestructuringpolicycharacteristicsacrossstates.

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

theallocativeefficiency benefitsof competitionarea tenetof

microeconomic

theory,the

relation

between

competition

and

technical efficiencyis lesswellunderstood. Neoclassical

models

ofprofit-maximization

subsume

staticcost-minimizing behaviorregardless of market

competitiveness, butagency

models

of managerial behaviorsuggest possiblescopefor

competitiontoinfluencecost-reducingeffortchoices. Thispaperexplores the empiricaleffectsof competitionontechnical efficiencyinthecontextofelectricityindustryrestructuring.

Restructuringprograms adopted by

many

U.S.states

made

utilitiesresidualclaimantstocost savings

and

increasedtheirexposuretocompetitive markets.

We

estimate theimpactofthese

changes

on

annualgeneratingplant-levelinput

demand

fornon-fuel operatingexpenses, the

number

of

employees

andfuel use.

We

findthatmunicipally-ownedplants,

whose owners were

forthe

most

partunaffectedbyrestructuring, experiencedthesmallest efficiency gainsoverthe pastdecade. Investor-ownedutilityplantsinstates thatrestructuredtheirwholesaleelectricity markets

had

thelargestreductions innonfueloperatingexpensesand

employment,

while

investor-owned

plants innonrestructuringstates fell

between

these extremes.

The

analysisalsohighlights

thesubstantiveimportanceoftreatingthe simultaneityofinputandoutput decisions,

which

we

do throughaninstrumental variables approach.

JEL

Codes:

L

11,

L43,

L5

₁,

L94,

D24

Keywords:

Efficiency, Production,

Competition,

Electricity restructuring, Electric

Economistshave long arguedthatcompetitiongeneratesimportant efficiencybenefits foran

economy. These

generally focus

on

allocative efficiency;theimplicationsof competitionfor

technical efficiencyare less clear. Neoclassical

models

ofprofit-maximization

subsume

static cost-minimizingbehavior

by

all firms,regardlessof marketcompetitiveness.1

Agency

models, however, inrecognizing the interplayofasymmetricinformationwiththe separationof

management

andcontrol, suggest possible deviations

from

cost-minimization byeffort-averse

managers. These

models

may

imply arole forcompetitioninconstrainingmanagerialbehavior,

by rewardingefficiency gains

and

confrontingless-efficientfirms withthechoiceofcost reductiontotheleveloftheirlower-cost counterparts orexit; see Nickell(1996)fora brief discussionof

some

ofthesetheoreticalarguments. Theiractualrelevanceisultimatelyan

empirical question.

Thispaperassesses theeffectof competition ontechnicalefficiencyusingdata

on

the U.S.

electricgenerationsector.

The

pastdecadehaswitnessedadramatictransformationofthis industry. Until themid-1990s, overninety percentoftheelectricity inthe

US

was

sold

by

vertically-integratedinvestor-ownedutilities(IOUs),

most

operatingasregulatedmonopolists

withintheirserviceareas. Today, non-utilitygenerators

own

roughly aquarterofgeneration capacitynationwide,and

IOUs

in

many

states

own

onlya smallfractionoftotalgenerating capacity andoperateinapartiallyderegulatedstructure thatreliesheavily on market-based

incentives orcompetition.

While

studiesofstate-level electricityrestructuringsuggestpoliticians

may

have been motivatedinlarge part

by

rent-seeking(e.g.,White, 1996, and Joskow, 1997),

many

proponentsofrestructuringarguedthatexposingutilitiestocompetitive,market-based

outcomes

would

yieldefficiency gainsthatcouldultimatelyreduce electricitycosts andretail

prices. Research

on

otherindustriessuggests productivity gains associatedwithderegulation

(e.g., OlleyandPakes, 1996,

on

telecommunications and

Ng

andSeabright,2001,

on

airlines) and withincreased competitive pressurecaused

by

factorsotherthan regulatory change(e.g.,

Galdon-Sanchez

and Schmitz, 2002,

on

ironoremines).2

The

considerable

body

ofacademic

work

on

electricityrestructuring withinthe U.S.and abroad

has thus farfocusedon assessing theperformance ofcompetitivewholesalemarkets, with

1

Theimplicationof competitionfordynamicefficiencythroughinnovationisthesubjectof anextensive

theoreticalandempiricalliterature ineconomics,datingatleastfrom Schumpeter's 1943 classic

particular attention tothe exerciseof market

power

(see for

example

Borenstein,Bushnell

and

Wolak,

2002

and

Joskow

and

Kahn,

2002).

While

many

ofthecostsofelectricityrestructuring

have beenintensively studied, relativelylittleefforthas

been

devotedtoquantifyingany expost operating efficiency gainsofrestructuring, althougha

few

studies(e.g.,Knittel, 2002) have analyzedefficiencyeffectsofvarious incentive regulations in this sector.3 Thisstudyprovides thefirstsubstantialanalysisofearlygeneration efficiency gains ofelectricityrestructuring.

As

such,itcontributestothebroad

economic

debate

on

theroleof competitioninthe

economy

and

isofdirectpolicy relevanceto statescontemplatingthe futureoftheir electricity restructuring programs.

The

resultsofthis

work

indicate thatplant operators

most

affected

by

restructuringreducedlabor

and nonfuelexpenses,holding output constant,

by

roughly

5%

or

more

relativetoother

investor-owned

utility

(IOU)

plants,and

by 15-20%

relativetogovernment-and cooperatively-owned

plants,

which were

largely unaffected

by

restructuring incentives. These

may

beinterpretedas

the

medium-run

efficiency gainsthat

Joskow

(1997, p. 214)posits

"may

beassociatedwith

improvingthe operatingperformance ofthe existing stockofgeneratingfacilitiesandincreasing the productivityoflaboroperating thesefacilities."

Our work

alsohighlightstheimportanceof treatingthe simultaneity ofinput

and

output choice. Failingtorecognizethatshocksto input productivity

may

inducefirmstoadjusttargetedoutput leadstooverstatementofestimated efficiencyeffects, in

some

cases

by

afactorof

two

ormore.

While

endogeneity concernshave beenlongrecognizedinthe productivityliterature,oursisone ofthefirststudiesofelectric

generationtocompensateforthis. Finally,

we

explore thesensitivityoftheestimated efficiency

impacttothechoiceofcontrolgroupto

which

restructuredplants arecompared, anddiscuss the

issuesinvolvedindeterminingtheappropriate counterfactual.

2

Some

hintofthispossibility in electricityisprovidedbyPrimeaux(1977),

who

comparedasample of

municipally

owned

firmsfacingcompetitiontoamatchedsample ofmunicipally

owned

firms inmonopoly

situations and foundasignificantdecreaseincostsper

kWh

forfirmsfacingcompetition.

3

One

exceptionisHiebert (2002),

who

uses stochasticfrontierproductionfunctionstoestimate generation plantefficiencyover 1988-1997.

One

setof independentvariablesheincludesisindicatorsforregulatory ordersor legislativeenactment ofrestructuringreformsin 1996 andin1997. Whilehefindssignificant

reductionsin

mean

inefficiency associatedwithrestructuring lawsin1996forcoalplants,hefindsno effectsforgasplants,norfor either fueltypein1997. Ourworkuses alongertimeperiod,richer

characterizationofthe restructuringenvironmentanddatingof reformsconsistentwiththeU.S. Energy

Information Administration,and analternativetechnologyspecificationthatallowsformore complex productivityshocksandtreatspossible inputendogeneitybiases. Joskow(1997)describes thesignificant

laborforcereductionsthataccompaniedrestructuringinthe

UK,

asthe industry

moved

from state-owned

monopolytoaprivatized,competitive generation market,althoughthesemixrestructuringand

The

remainderofthepaperis organizedasfollows: Section 1 describes existingevidenceonthe

competitiveeffectsofefficiency,anddiscusses

how

restructuringmightalterelectricgeneration

efficiency. Section2 detailsourempirical

methodology

fortestingthesepredictions,and

describes our strategyforidentifying restructuringeffects.

The

dataaredescribedinSection3.

Section4reportstheresultsofthe empiricalanalysis, andSection 5 concludes.

1.

Why

Might

Restructuring Affect

Generator

Efficiency?

Exit

by

less-efficientfirms isawell-understoodefficiencybenefitofcompetition: asoutputshifts from(innately)higher-costfirmstolower-costcompetitors thetotalproductioncostforagiven

outputleveldecline. Olleyand Pakes (1996) provideempirical evidenceofthis

phenomenon

in theirplant-levelanalysisofthemagnitude and source ofproductivitygainsintheU.S.

telecommunications equipmentindustryover 1974-1987.

They

findsubstantial increasesin

productivity associated withthe increasedcompetitionthatfollowedthe 1984divestiture

and

deregulationinthissector,andidentifytheprimary sourceofthesegainsasthere-allocationof

output

from

lessproductiveto

more

productive plants acrossfirms. Ina similarvein, Syverson (2004)finds that

more

competitivelocalmarkets intheconcrete industryareassociatedwith highermean,lessdispersion,

and

higherlower-boundsinplant productivity, effectsheattributes totheexitofless-efficientplantsin

more

competitiveenvironments.

The

existingevidence

on

whether competitionalso leadstocostreductionsthroughtechnical efficiency gains

by

continuingproducersandplantsisrelativelysparse. Nickell(1996)usesa

panel of

670

U.K. manufacturingfirmstoestimateproduction functionsthatinclude controls for

thecompetitiveenvironmentsin

which

firms operate.

He

finds

some

evidenceof reduced

productivitylevelsassociatedwithmarket

power

andstrongsupportforhigher productivity

growthrates in

more

competitive environments. Concerns abouttheability ofcross-industry analysistocontroladequatelyforunobservableheterogeneity acrosssectors

may make

sector-specificevidencetighterand

more

convincing.4

A

notable

example

isthe

Galdon-Sanchez

and

Schmitz (2002)studyoflabor productivity gainsat ironoreminesthatfaced increased

competitive pressurefollowingthe collapseofworldsteelproduction intheearly 1980s.

They

findunprecedentedratesoflabor productivity gains associatedwiththisincreaseincompetitive 4

A

numberofstudieshave analyzedefficiencygainsfollowing regulatoryreforminvariousindustries; see, forexample,Bailey's(1986) overview and Parketal.(1998) onairlines. Unfortunately,in

many

casesitisdifficult todisentangledirectregulatoryeffectsonefficiency(e.g.,operatingrestrictionsimposed

ontrucking firms orairlinesbyregulators inthosesectors)fromtheindirect effectsof reduced

pressure, "drivenbycontinuing mines,producingthe

same

products

and

using the

same

technologyastheyhadbefore the 1980s" (Galdon-Sanchez and Schmitz, 2002,p. 1233).5 Severalfeaturesofthe electricgeneration sector

make

itanattractivesubjectfortestingpotential

competitiveeffectsontechnicalefficiency.6 First,generationtechnologyisreasonablystableand well-understood anddataon productioninputs andoutputsattheplant-levelare readilyavailable

toresearchers. Thishas

made

electricgenerationa

common

applicationfor

new

production

and

costfunction estimation techniques, datingatleasttoNerlove(1963). Second,policyshifts over

arelativelyshortperiodhaveresulted inadramatictransformationofthemarket for electric power.

Through

the early 1990s, theU.S.electricity industry

was

dominatedby vertically

integratedinvestor-ownedutilities(IOUs).

Most

operatedasregulatedmonopolists over

generation,transmission,

and

distribution ofelectricitywithintheirlocalizedgeographicmarket,

thoughthere

was

some

wholesale

power

traded

among

utilities orpurchasedfroma small but

growing

number

ofnon-utilitygenerators. Pricesgenerally

were

determinedby stateregulators

basedon accountingcostsofserviceatthefirmlevel.

By

1998,everyjurisdiction(50 statesand

theDistrictof

Columbia)

had

initiatedformalhearingstoconsiderrestructuring their electricity sector,and

by

2000, almosthalfhad approvedlegislationintroducing

some

form of competition

includingretailaccess.7 Thisprovidesbothtimeseriesand geographicvariation incompetitive

environments. Third, staticand

dynamic

efficiencyclaims bolstered

much

ofthepolicyreform;

measuringthese benefitsisavitalprerequisitetoassessing the

wisdom

ofthesepolicies. Ithaslongbeen arguedthat traditionalcost-of-service regulationdoesrelativelywellinlimiting

rentsbutlesswellinprovidingincentivesforcost-minimizingproduction; seeLaffontandTirole (1993).

Under

pure cost-of-serviceregulation,regulator-approved costs oftheutilitiesarepassed directlythroughtocustomers,andreductionsinthe costofservice yieldat

most

short-term

profits until ratesarerevisedto reflectthe

new

lowercostsatthenextratecase.8

Given

asymmetric information

between

regulatorsandfirms, inefficientbehaviorby

managers

that raises operationscosts

above

minimum

costlevelsgenerally

would

bereflectedinincreasedrates

5

Ng

andSeabright(2001)estimate cost functionsforapanelofU.S.andEuropeanairlinesover

1982-1995,and concludethatpotentialgainsfromfurther privatizationandincreasedcompetition

among

Europeancarriersare substantial,thoughthey point outthatthebest-measuredcomponentofthesegains

relates toownershipratherthanmarketstructure differences.

6

Understandingpossible reallocationofoutput acrossplantsishampered bytheexitofplantsfrommost availabledatabases

when

they are soldtonon-utilityowners.

7

IntheaftermathofCalifornia'selectricitycrisisin2000-2001,restructuringhasbecomelesspopularand

many

stateshave delayedorsuspendedrestructuringactivity,includingsixthathadpreviously approved retailaccesslegislation. See

US

Energy Information Administration(EIA),2003.

and passed throughtocustomers.

Joskow

(1974) and Hendricks (1975) demonstratethat frictions

incost-of-serviceregulation, particularlythosearisingfromregulatorylag(time

between

price-resetting hearings),

may

provide

some

incentivesatthemarginforcost-reducingeffort. Their impactgenerallyis limited,however,apartfromperiodsofrapidnominalcostinflation(see

Joskow, 1974).

This systemledeconomiststoarguethatreplacing cost-of-service regulationwith

higher-powered

regulatory incentive

schemes

orincreasedcompetitioncouldenhanceefficiency.9

Over

the 1980s andearly 1990s,

many

stateutility

commissions

accordinglyadopted

some

form of

incentiveregulation.

The

limitedempiricalevidenceavailableonthesereforms,

which modify

pricesettingwithin the regulated

monopoly

structure,suggests

mixed

results. Knittel (2002) studiesa varietyofincentive regulations inusethrough 1996, andfinds thatthose targetedat

plantperformanceorfuelcost

were

associatedwithgainsinplant-levelgenerationefficiency.10

More

generalreforms, suchas pricecaps, rate freezes,and revenue-decoupling programs, typically

were

associatedwithinsignificantor negative efficiency estimates,allelse equal.

Restructuring,incontrasttoincentiveregulations,fundamentally

changed

the

way

plant

owners

earn revenue.

At

thewholesale level,plantsselleitherthrough

newly

created spotmarkets or

through long-termcontractsthatarepresumably based

on

expectedspotprices. Inthe spot markets, plant

owners

submitbids indicating theprices at

which

theyarewillingto supply

power

fromtheirplants. Dispatchorderisset

by

thebids, and,in

most

markets, the bidofthemarginal

plantispaidtoallplantsthataredispatched. High-costplants willbeforced

down

inthe dispatch

order, reducinglikelyrevenue. Plant operatorsthatreducecosts

move

higherinthedispatch

order, increasing dispatchprobability, andincrease theprofitmargin

between

own

costs

and

the

expectedmarketprice.

Most

restructuringprogramsalso

changed

the

way

retailrates are determined andthe

way

in

which

retailcustomersareallocated." Retailaccessprogramsin

8

Ratesareconstantbetweenratecases,apartfromcertainspecificautomatic adjustments(suchas fuel

adjustmentclauses),sochangesincostwouldnotbereflected in rates untilthenextratecase.

9

See,forexample,LaffontandTirole, 1993,foratheoretical justification,orJoskowand Schmalensee,

1987,foranappliedargument.

10

Knittel uses

OLS

andstochasticproductionfrontiertechniquestoestimateCobb-Douglasgenerating plantproduction functions incapital,labor,andfuelforapaneloflarge

IOU

plantsover 1981-1996. His

resultsfromfirst-differencedmodels,whichimplicitlyallowfor plant-levelfixedefficiencyeffects,

suggest gainsontheorderof1-2%associatedwiththese reforms.Equationsthatdonotallow forplant fixedeffectssuggest

much

largermagnitudes.

Stateshave useda varietyof approachesto linkretailratesunderrestructuringtowholesaleprices inthe market. Overthe shortterm,moststatesdecoupledutilityrevenuefromcostsbymandatingretailrate freezes,oftenatlevelsdiscountedfrompre-restructuringprices.

Some

states,suchasPennsylvania,are

aggressively tryingtoencourageentrybycompetitiveenergysuppliers,

who may

contractdirectlywith retailcustomers.

combination withthe creationofthe

new

wholesalespotmarkets

may

increase theintensityof

cost-cuttingincentives, leadingtoevengreatereffortto

improve

efficiency.

While

the

most

significantsavings

from

restructuringare likely tobeassociated withefficient

long-runinvestmentsin

new

capacity, there

may

beopportunitiesfor

modest

reductionsin

operatingcostsofexistingplants(seeJoskow, 1997). Thispaperattemptsto

measure

the extent

ofthatpossible

improvement

forthe existingstockofelectricitygenerating plantsintheU.S.

The

implicit null hypothesisisthat,beforerestructuring,operators

were

minimizingtheir costs, giventhecapital stock availableintheindustry.

Under

thenull,thereshouldbe

no

changein plant-levelefficiencymeasures associatedwithrestructuring activity.

We

discuss

below

our

method

forestimating plant efficiencyandidentifying deviationsfromthishypothesis. Assessing

theeffectsofrestructuring requiresspecificationof

how

generatingplants

would

havebeen

operated absent the policy change. Constructingthiscounterfactualiscrucial, butdifficult.

2.

Empirical

Model

Fora single-outputproductionprocess, productive efficiencycan beassessed

by

estimating

whether aplantis

maximizing

outputgivenitsinputs and whetheritisusing the best

mix

of

inputsgiventheirrelative prices. Productionfunctionsdescribe thetechnologicalprocess of transforminginputstooutputs

and

ignore thecosts ofthe inputs;a plantisefficientifitisonthe

production frontier. Cost minimizationassumesthat,giventhe inputcosts,firmschoosethe

mix

ofinputsthatminimizesthe costsof producingagiven levelofoutput.

A

plantcouldbe producingthe

most

output possible fromagiveninputcombination,butnotminimizingcostsif,

for instance, labor

was

cheaprelativetomaterials,yet the plantusedalot ofmaterialsrelativeto labor.

Even

ifthe firm

were

producingthe

maximum

output possiblefrom itsworkersand materials, it

would

notbeefficientifitcouldproducethe

same

levelofoutputlessexpensively bysubstituting laborformaterials.

We

explore theimpactofrestructuringon efficiency

by

specifying a production functionandthen deriving the relevant input

demand

equations implied

bycostminimization.

We

adopttheconventionofrepresenting generating plant output(Q)

by

thenetenergythe

generatingunitsproduce over

some

period(measured

by

annual megawatt-hours,

MWh,

inour data), achoicethatisdiscussedinfurther detail inthedata sectionbelow.

While

amultitudeof

studiesofelectricplantproductivity

model

thisoutputasa functionofcurrentinputs, often using

a

Cobb-Douglas

production or costfunction, the characteristicsofelectricityproductionargue

thatissensitive to importantinstitutional characteristicsofelectricityproductionthathave

been

largelyignoredintheearlierliterature.

First,observedoutputingeneralwillbethelesseroftheoutput the plantiscapable ofproducing, givenits availableinputs,

and

the output calledforbythesystemdispatcher.

Because

thesystem

dispatcher

must

balancetotalproductionwith

demand

ateach

moment,

the

gap between

probable

(Q

p

)andactual

_(Q

A

)outputforagivenplantiwillbe afunctionof

demand

realizations,the setof

otherplantsavailablefordispatch,andplanti's positioninthedispatchorder.12

Second, whilefuel inputsarevariedinresponsetoreal-timedispatching

and

operationalchanges,

other inputstoaplant'sproduction aredeterminedinadvance ofoutputrealizations. Capital

typicallyischosenatthetimeofaunit'sconstruction(orretirement),andatthe plantlevelis

changed

relativelyinfrequently.

From

themanager'sperspective, it

may

be considereda fixed

input. Utilitieshirelaborandsetoperatingandmaterialsexpendituresinadvance, based

on

expected

demand.

While

thesecanbeadjustedoverthe

medium-run,

staffing decisionsaswellas

most

maintenanceexpenditures arenottied toshort-run fluctuationsinoutput.

We

therefore

treattheseas set in advance ofactualproduction, and determiningatarget levelof probable

output,

_Q

p.

Finally,whilelabor, materials,andcapital

may

beto

some

extentsubstitutable toproduce

probableoutput, thegenerationprocess generallydoesnotallowthese inputstosubstitute for fuel intheshort-run.

Given

thisdescriptionofthetechnology,

we

positaLeontief productionprocess

forplantiinyeartofthefollowing form:

Q

lt A

₌

min[g(Eit,

T

E ,eit E ),

Q

it p

(K

i;

L

it,

M

it,

T

p ,£it p )-exp(£it A ))]

where

_Q

A isactualoutputand

_Q

pisprobableoutput; inputsaredenoted

by

E

forenergy(fuel)

input,

K

for capital,

L

for labor,and

M

formaterials;

T

denotesparametervectors,andzdenotes unobserved(totheeconometrician)

mean

zero shocks. See

Van

Biesebroeck (2003)forthe derivationofa similarproduction functionheusesto

model

automobileassembly plant production.

12

Random

shockstoaplant'soperations, suchasunexpected equipmentfailuresorequipmentthat lasts

longer than expected,willcauseittoproducelessormorethanitsprobableoutputfromasetofavailable

As

notedabove,fuelinput decisionsare

made

in realtime, afterthe

manager

hasobservedany shocksassociatedwiththeplant'sprobableoutputproductivity,Ejt

P

,theactualoperationofthe plant,£it

A

,andtheplant's energy-specific productivityinthe current period,£it E

. Probable

output,

_Q

p,is incontrastdeterminedbyinput decisions

made

inadvance ofactualproduction.

We

assume

that capital,

measured

by

thenameplategenerating capacityoftheplant,isfixed.14

Labor

andmaterialsdecisionsare

made

inadvance ofproduction, butafterthelevel

and

productivityoftheplant's capital isobserved. Thisreflectsthequasi-fixityofthese inputs over

time: staffingdecisions and maintenanceplansaredesignedtoensurethatthe plantis available

when

itisdispatched,based

on

the targeted output

_Q

p.

The

errorterm£

it p

incorporates productivityshocksthat

we

assume

are

known

tothe plant

manager

inadvance of scheduling

laborandmaterialsinputs,butarenotobservabletotheeconometrician.

We

allowactualoutput

to differ

from

probableoutputbya multiplicativeshock exp(£it

A

),

assumed

tobeobservedatthe

timefuelinputchoices are

made

but not

known

atthetime probable outputisdetermined. This shock

would

be, forexample, negativeifageneratingunit

were

unexpectedlyshut

down

duetoa

mechanical failure,or positiveifthe plant

were

run

more

intensivelythan anticipated, asmight be

thecaseifa

number

ofplantsahead ofitintheusual dispatchorder

were

unavailable or

demand

realizations

were

unexpectedlyhigh.

We

model

probableoutput

_(Q

p)witha

Cobb-Douglas

functionoflaborandmaterialsand

embedding

capital effects inaconstant

_(Q

(K)) term. Thisyieldsthe specification:

(PF1 )

Q

lt P

<

Qo(Ki)-(Lit

y

L -(Mit)™- exp(£l p )

Inpreliminaryanalysis,

we

estimated theparametersoftheproductionfunction,includingterms thatallowedfor differential productivityunderrestructuring.

Those

resultssuggested

productivity gains associatedwithrestructuring.

The

work

reportedhereimposes anadditional

constraint,based oncost-minimization,toestimate input

demand

functions,andisolatepossible

restructuring effects

on

each

measured

input.

A

cost-minimizingplantmanager,facing

wages

W

lt andmaterial pricesS;t ,

would

solvefortheoptimal inputstoproduce probableoutputQjt

P by:

13

Infact,overa shorttimeperiod,maintenance andrepairexpenditureswillbeinverselyrelatedtooutput sincetheboilerneedstobecoolandthe plantofflineformost major work.

We

dealwiththispotential

simultaneitybiasbelow.

14

Theempirical analysis defines a

new

plant-epoch,/',wheneverthereare significantchangesincapacity, sothatwithineachplant-epoch, capacityisapproximatelyconstant.

min

W,

,-Lj,

+

_Si t-Mi , s.t.

&

t p

<

Qo(K,)-(Lit) YL -(Mj

O^-expfe

t p ) Lj„

M

it

yielding the followingfactor

demand

equations:

(LI)

L

it

=

(X,Y L Qit P

)/W

it

(Ml)

Mi^^Q/yS;,

where

X,is theLagrangianontheproductionconstraint.

We

observeactualoutput,

_Q

it

A

=

Qit P

eit

A

,ratherthan probable output,

Q

it

p

.

Making

this

substitution andtaking logsof bothsides,(LI)becomes:

(L2) ln(Lit)

=

oo

+

ln(Qit A )

-

£it A

-ln(

W

;, )

where

oto

=

ln(X.yL). Iftherearedifferences acrossplants,overtime,or across regulatoryregimes

inthecoefficientsoftheproductionfunction_(yL)orinthe

shadow

valueoftheprobableoutput constraint (X),orifthereis

measurement

error inlaborusedattheplant,thisequationwillhold witherror.

As

we

areparticularly interested inchangesininput

demand

associatedwith restructuring,

we

expand

the subscriptitto irttoinclude plantiinyear/,

and

regulatory

restructuringregimer, andre-write(L2)as:

I5 (L2') ln(Lirt)

=

ln(Qirt A

)-ln(W

irt)

+

ai L

+

5t L +cpr L

-

Eirt A

+

Eirt L

where

a; L

measures aplant-specific

component

oflabor

demand,

5,

L

captures year-specific differencesinlabor

demand,

cpr

L

capturesrestructuring -specific shiftsinlabor

demand,

andEirt

L measurestheremainingerrorinthelabor inputequation.

a

is

now

subsumed

intheplant-specific

demand,

a;

L

.

Note

that<pr

L

picks

up

mean

residualchangesin labor input fora plantina restructuredregimerelative to that plant overallandtoallotherplantsatthe

same

pointintime. Itcouldreflectsystematicchangesinthemarginalproductivity oflabor_(yL), inthe

shadow

value

oftheavailabilityconstraint(X)orinoptimizationerrors.I6

15

Notethat

many

plant-leveldifferences,suchas capital stock,and

many

time-varyingshocks,suchas

technology-neutral productivity shocks,dropoutofthisequationthroughthe conditioningonoutput choice.

16

Ifthereweresystematic differencesinthe relationof probable andactualoutput acrossrestructuring,yr L

may

also reflectthechangein

mean

£irt . Since e

^

reflectsshocks unobservablebythefirm

when

settingplannedoutput, itseemsplausiblethatthesebe

mean

zeroinexpectation,buttheirrealizations

could be nonzerointhe restructuringsample

we

observe.

Similarly, equation

(Ml)

becomes:

(M2)

ln(M

irt)

=

ln(Qrt A )

-

ln(Srt)

+

a;

M

+

5t

M

+

q>r

M

-£irt A

+

£ „t

M

which

isdirectlyanalogousto(L2').

We

model

theenergy

component

oftheLeontief productionfunction,

which

will ingeneralhold

withequality, as: (PF2)

_Q

irt A

=

g(Eirt,Y E ,£ E )

Assuming

that g(«)ismonotonicallyincreasinginE,

we

can simplyinvertittogetanexpression

for

E

intermsof Q.

Note

thatthe priceoffueldoesnot enterintothe

demand

forfuelexcept

through thelevelofoutput the plantisdispatchedtoproduce. Forconsistencywiththe other inputspecifications,

we

specifya log-logrelationship:

(El) InCEirt)

=

_yQ E -ln(Qirt)

+

cpr E

+

a, E

+

5E

+

£irt E

where

asbefore,theplant-specificerror, OjE the year-specificerror, 5t

E

,andthe restructuring-specific term,cpr

E

,capture systematicchangesinthe efficiencywith

which

plantsconvert energyto electricity

—

thatis,changesinplantheatrates

—

across plants,overtime,or correlated

withrestructuringactivity,respectively.

We

confront

two

importantendogeneity concernsinestimating the basic input

demand

equations,

(L2'),

(M2)

and(El).

The

firstisthepossibility thatshocks(£irt

L

£in

M

,£in E

) inthe input

demand

equations

may

becorrelatedwithoutput. Ifoutput decisionsare

made

afteraplant's

manager

observestheplant's efficiency,

managers

may

increaseplannedoutputin responseto positive shocksto aninput'sproductivity, orreduceplannedoutputinresponsetonegative shocks. This behavior

would

inducea correlation

between

the errorinthe input

demand

equationand observed

output.

Though

one cancontroldirectly forplant-specificefficiency differencesandforsecular productivityshocksinagivenyear,idiosyncratic shocksremain asourceofpossiblebias. Second,theestimates

may

besubjecttoselection biasifexitdecisionsaredriven

by

unobserved

productivity shocks. Inthiscase,negativeshocks couldleadtoplantshutdown, implyingthatthe

errors forobservations

we

observewillbe

drawn from

a truncateddistribution. Neitherofthese

problems isuniquetooursetting, andtheyhave beenraisedin

many

earlierpapers.17

Considerfirstthesimultaneity issue.

We

faceapotentialsimultaneityproblem if,for instance,a

malfunctioningpieceof equipment reducestheplant's fuel efficiency, leading theutilityto reduceitsoperationofthatplantand consequentlytouselessfuel. There

may

bedeviationsfrom predetermined

employment

andmaterials budgetscaused

by

unanticipated

breakdowns

that

requireincreaseduseoflaborandrepairexpendituresandresult inloweroutput.

A

positive efficiency shocktoaninput

may

lead

managers

torunthatplant

more

intensivelyovertheyear,

increasingoutputaswellasinputuse.

A

varietyof

methods

havebeen usedtoaddressthis

concern.18

We

choosetouse aninstrumental variablesapproach,using a

measure

ofstate-level electricity

demand

asaninstrumentforplant output. Thisis likelytobehighly correlatedwith

the

amount

ofoutput a plantwillbecalledto provide,but uncorrelated,for instance,with

how

efficientlyanindividualplant'sfeedwater

pumps

areworking. This approachislikely tobe particularlyeffective fortheenergy equation,giventheresponsivenessof energyinputchoicesto

demand

fluctuations in realtime,andforidentifying

exogenous

output fluctuationsat

non-baseloadplants,

which

are

more

stronglyinfluencedby marginal swings in

demand.

It

may

be lesspowerfulinidentifying variationinexantelaborand maintenancechoices, dependinginpart onthe extentto

which

plant

managers

anticipate state

demand.

19

We

thereforeexplore the

sensitivityof ourresultstoalternativeinstruments.

The

potentialselectionissueis

more

difficulttoaddress.

The

plantsinoursample

seem more

stablethan those studiedin

many

othercontexts (especially seeOlley andPakes, 1996), suggestingthatthe selection

problem

may

be

somewhat

lesssevere for electricgeneration.

However,

plantexitincreases inrestructuringregimes, typically notbecausethe plantisretired

butbecausedivestitures

remove

theplantsfromthereporting database.

To

theextentthatthe

divestituresare

mandated by

the restructuringlegislation,thisshouldnot createselection

problems. But withoutbetterinformation

on what

determinesdiscretionarydivestitures,

we

have

17

Nerlove (1963)providesanearlydiscussionofsimultaneity biasinproductionfunctions. Olleyand Pakes (1996) propose astructuralapproachtoaddressing simultaneity,whichiscomparedto alternatives in

Grilichesand Mairesse(1998). Ackerberg andCaves(2003)discussthisissueandcomparetreatments

proposed by Olley and Pakes (1996) and Levinsohn andPetrin (2003). While

many

papershaveestimated productionorcostfunctionsfor electricgeneratingplants,fromtheclassicanalysesinNerlove(1963)and

Christensen andGreene(1976)toveryrecentworksuchasKleitandTerrell(2001) andKnittel (2002),

electricity industrystudies typicallyhavenottreated eithersimultaneityorselectionproblems.

18

Seethereferencescited innote 17, supra.

19

A

furtherdrawbackto thisinstrumentisthat

we

measure

demand

onlyatthestate, ratherthan plant

level,whichmeansthat

we

donotuse the cross-plant variation within astatetoidentifyoutputcoefficients.

no direct

way

toassesstheirimpact

on

theresults.

One

indirect

way

toassess thesignificanceof potential selectioneffectsisto

compare

resultsfortheunbalancedpanel

we

usein

most

of our

work

tothosefora panelofplants thatcontinuetooperatethroughthe

end

of oursampleperiod,

for

which

potentialselectioneffects are likely tobe

most

severe. Substantial differences across

thoseresults

may

suggest theneedto

more

carefullytreatpotentialselection biases.

Identificationstrategy

There issubstantialheterogeneity acrossplants,utilitiesandstates,

and

the

economic

environmentin

which

utilitiesoperatehas

changed

considerablyovertime. Inaddition,

restructuring isnot

randomly

assigned acrosspoliticaljurisdictions

—

earlier

work

suggeststhatit

isstrongly correlatedwithhigher thanaverageelectricityprices inthecross-section.20

Fortunately,

we

haveinthissectoradatabaserich in variation. Therearethousands ofgenerating plantsoperated

by

hundreds ofutilitiessubjecttoregulation

by

dozensofpoliticaljurisdictions eachsetting their

own

legal

and

institutionalenvironment. Paneldata

on

thecosts

and

operations

ofthese plantsare available,with

some

recentexceptions,fromwellbeforeanyrestructuring

untilthepresent.21 Thisallows ustoconstruct

benchmarks

that

we

believe controlfor

most

ofthe

potentially confoundingvariation.

The

plant-specific effects, {oii

N

},measurethe

mean

useofinput

N

atplant;relativetoother plantsinthe sample.

These

effects

may

beassociatedwithdifferencesinplanttechnology type andvintage,ownership(governmentv.privateutilities),andtime-invariantstateeffects.

The

year-specificshock, {5t

N

},measuresthe efficiency impactofsector-levelshiftsovertime,suchas

seculartechnologytrends,

macroeconomic

fluctuationsorenergypriceshocks. Restructuring

effectson plantproductivitycorrespondtoa non-zero{(pr

N

}.

The

heterogeneity inthetimingand

outcomes

ofstate-levelrestructuringactivity allowthe datatodistinguish

between

temporal

shocks andrestructuring effects.

While

allstates held hearings

on

possiblerestructuring,the

earliest

was

initiated in 1993andthelatestin 1998. Thereisconsiderablevariationinthe

outcome

ofthose hearings,as well,withjustunderhalf thejurisdictions(23 statesandtheDistrict

of

Columbia)

enactingrestructuring legislation

between

1996and 2000.22

The

remainder

20

Thesignificant roleofsunkcapital costs inregulatoryratemakingmeansthathighpricesdonot necessarily imply highoperatingcosts forgenerationfacilitieswithin astate,however. SeeJoskow(1997)

foradiscussionofthe contributorstopricevariationacrossstates.

Costdata are not publicly availableforplants

owned

byexemptwholesalegenerators,includingthose acquiredfromregulatedutilities.

22

We

collectedinformationonstaterestructuringlegislationfromvariousEnergyInformation

AdministrationandNational Associationof RegulatoryUtilityCommissionerspublicationsandstate

considered

and

rejected,orconsidered and simplydidnotacton, suchlegislation. Thisvariation allows ustouse changesinefficiencyatplants in states thatdidnot pass restructuringlegislation to identifyrestructuring separately

from

secularchangesinefficiencyofgenerationplantsover

time.

Itispossiblethatplantsinthiscontrolgroupalsoaltered theirbehavior overthe post-1

992

period. Thiscouldbe

due

perhapstothe introductionorintensificationofincentive regulation withinstatesthatdid notenactrestructuring, tothe expectationofpotentialrestructuringthatdid not occur, orspilloversfromrestructuring

movements

inotherstates(e.g. ifregulators updated theirinformationaboutthecostsnecessarytorunplantsofacertain type, ormulti-stateutilities

operatingunderdifferingregimes

improved

efficiencyofalltheir plants,notjustthosein

restructuringstates).

To

theextentthisoccurs,our comparisonwillunderstate themagnitude of any efficiencyeffectofrestructuring.

We

thereforeconsider asecondcontrolgroup, consistingof cooperatively-ownedor

publicly-owned

municipaland federal plants,

which

forconvenience

we

will refer toas

"MUNI"

plants,

althoughthegroupisbroaderthanstrictly implied

by

this label.

An

extensiveliteraturehas

debatedtherelativeefficienciesofprivate andpublicownership in thissectorundertraditional regulation,with

somewhat mixed

results.

We

abstract

from

thatby allowingforplant-specific effects thatabsorbanylevelsdifferencesininputuse acrossownershiptype. Restructuring generallyalteredthe competitiveenvironmentonly forprivateinvestor-ownedutilitieswithin a

state,leaving thoseforpublicly-and cooperatively-ownedutilitiesunchanged.23 Thissuggests

that

MUNIs

may

provideasecond

benchmark

against

which

to

measure

changesinefficiency associatedwithrestructuring.

We

adopta parameterizationthatmeasures {cpr

N

} relativeto publicly-ownedplantsduring the periodthatinvestor-ownedutilitiesareatriskofrestructuring,

definedas 1993 forward.

Using

N

todenoteinput(labor,nonfuelexpenses, orfuel),and

PRICE

N todenotethe relevant input price(noneforthefuel equation),

we

haveinput use equation11 : (1₁) ln(Nirt)

=

ln(Qirt A )-

\n(PRICE

N in)

+

yMUNI

it

+

a, N

+

5t N

+

<pir N

-eirt A

+

e,rt N

publicutilitycommissionwebsites. Since 2000,noadditionalstateshave enactedrestructuringlegislation,

andseveralhave delayedorsuspendedrestructuringactivity inresponsetotheCaliforniacrisis.

Withtheexception ofArizonaand Arkansas,whichincludedgovernment-ownedutilities inrestructuring

programs.

This specificationimplicitlyprovides

two

"non-treatmentgroups"to

which

investor-owned plantsinrestructuringregimes

may

be compared: investor-ownedplants innon-restructuring

regimes (with the restructuringeffect

measured

by

cpr

N

),andpublic-and cooperatively-owned

plantsover 1993-1999(with the restructuringeffect

measured

by

cp_r

N -_y).

3.

Data

&

Summary

Statistics

The

analysisin thispaperisbased

on

annualgeneratingplant-level dataforU.S.electricutilities.

Plantsarecomprised ofatleastone,buttypically several, generatingunits,

which

may

be

added

toorretiredfromserviceoverthe several-decadelifeofatypical generatingplant.

While

an idealdataset

would

allowustoexplore efficiency atthegeneratingunitlevel, inputsotherthan fuelarenot availableatthe generatingunitlevel,and some, suchasemployees,arenoteven

assignedtothe unitlevelastheyaresharedacrossunitsattheplant.24

We

thereforeuse a

plant-year asanobservation.

The

Federal

Energy

Regulatory

Commission

(FERC)

collectsdataforinvestor-ownedutility

plantsannually inthe

FERC

Form

1, andthe

Energy

InformationAdministration(EI

A)

and Rural

Utilities Service

(RUS)

collectsimilardataformunicipally-ownedplantsandrural electric

cooperatives,respectively.

These

dataincludeoperatingstatisticssuchas sizeoftheplant, fuel

usage,percentageownershipheld

by

theoperatorandotherowners,

number

of employees,

capacityfactor, operatingexpense,yearbuilt, and

many

otherplant-level statistics.

Our

basedata

setincludesalllargesteam and

combined

cyclegas turbine

(CCGT)

generating plants for

which

data

were

reportedto

FERC

or

EIA

overthe 1981 through 1999period.25

We

excludedsmaller

plants,definedasthosefor

which

gross capacityexceeded 100

megawatts

forfewerthan threeof

oursampleyears.

We

alsoexcluded approximately 1,500 observations

where

data

were

missing,

anddroppedseveralhundredobservationsbasedonregression diagnosticteststoscreenfor outliersor

undue

influence. FurtherdetailsonthedataareprovidedintheAppendix.

We

follow the literature incharacterizingoutputbythetotal energyoutputofthe plantoverthe

year, measured

by

annualnetmegawatt-hours ofelectricitygeneration,

NET

MWhs.

Thisisan

imperfect choice. Outputis, in reality,multidimensional,although

most

dimensions arenot

24

Some

labor

may

beshared across multipleplants,thoughassignedtooneparticularplantinourdata.

Thiswillleadinducemeasurementerror,particularly intheplantemploymentvariable.

25

One

unfortunateconsequence ofrestructuringisthatavailabledataonplantssoldbyutilitiesto non-utilitygenerators areextremelylimitedafterthesale,duetochangedreportingrequirements. Thismeans thatplantswill be excludedfromthe datasetaftersuchsales.

recorded inthe plantdata. For example,generating plants

may

alsoprovidereliabilityservices (such asspinningreserves,

when

the plant standsreadytoincrease outputatshortnotice), voltage support

and

frequencycontrol.

While

theproductionprocess varies considerably across these

differentoutputs,onlynet generationiswell

measured

inthedata.26 Moreover,electricityoutput isnot a

homogenous

product.

Because

electricityisnon-storable,electricityproducedat

5PM

on

thefirstFridayinJulyisa separateoutputfromelectricityproducedat

5AM

onthesecond

Sunday

inMarch. Firms

must

decide

how

tobalancethecostsassociatedwithtakingtheirplant

down

todo maintenanceagainstthe probabilitythata poorlymaintainedplantwillfail during

peak

demand

hours,

and

theavailabilityoftheplant

may

be an important modifier ofoutput

quality.

Changes

in incentivesassociatedwithrestructuring

may

have alteredfirms' assessments ofthesetradeoffs, althoughtheexpecteddirectionoftheeffectsistheoreticallyambiguous.27 Hourlyoutput prices

and

output

from

individualplantsmight allowustobetterassessthis. Lacking suchdata,

we

rely

on

asingleoutputdimension,but

acknowledge

its limitations.

We

haveinformation

on

threevariableinputs.

The

first,

EMPLOYEES,

isacountoffull-time

employees

attheplant.

The

second,

NONFUEL

EXPENSE,

includesallnon-fuel operationsand

maintenanceexpenses,suchasexpenses forcoolants, maintenancesupervision

and

engineering

expenses. Thisvariableis lessthanidealasa

measure

ofmaterials,bothbecauseitreflects

expendituresratherthanquantities,

and

becauseitincludes the

wage

bill forthe

employees

countedin

EMPLOYEES,

althoughthatexpenseisnot separately delineatedinourdata.

As

NONFUEL

EXPENSES

includes payrollcosts(notseparatelyidentified),boththisand

EMPLOYEES

will reflectchangesin staffing. 28

The

thirdinputisfueluseby type offuel(tonsof coal,barrelsofoil,and

mcf

ofnaturalgas).

We

convertfuel into

BTUs

using the reportedannual plant-specific

Btu

contentof eachfuel toobtaintotal

BTU

inputatthe plantforeachyear.

Inputpricespose achallenge.

Wages

inparticular

may

be

endogenous

tothefirmandits perceived regulatoryenvironment. Hendricks (1975) suggeststhatutilities

may

bargain less 26

Theinputsrequiredtoproduceagivenlevelof energy

(MWh)

from aspecificplantalso willdepend on whethertheplantrunscontinuouslyor intermittentlyandonitsaveragecapacityutilization. Startinga

plant frequently and runningitatlowcapacityutilizationratestypicallyusemoreinputs(particularly fuel)

per

mwh

generated thandoes running aplantcontinuouslyatitsratedcapacity.

27

Forinstance,undertraditionalregulation,utilities

may

havefaced strongpoliticalincentivestoavoid

blackouts orbrownouts,leadingto investmentingreatercapacityto increase reservemarginsandingreater

maintenanceresourcesto increase plantreliability.

On

theotherhand,competitive firmsproducing in

restructuredwholesale markets

may

faceevenstronger incentivestobeavailable

when demand

peaks becausethisis

when

pricesare highest.

28

Theelasticityof

NONFUEL

EXPENSES

withrespectto

EMPLOYEES

isabout.5inourdata,broadly consistentwithourbackoftheenvelopecalculationssuggestingthatlabor costs areroughlyhalfofthe totalnonfuel operating budget.

aggressivelyoverinputpricessuchas

wages

during periods in

which

highercostscould be

readilypassed

on

to customers throughhigher regulatedprices,and

more

aggressively

when

the firm

was

likelytobethe residualclaimanttocost savings. Inotherindustries,regulatoryreform

hassometimes beenassociatedwithsubstantialreductionsinwages,suggesting rent-sharing

underregulation(seeRose, 1987,

on

thetruckingindustry). Thesesuggestthatobserved

wages

may

notbe

exogenous

tothefirm,and

may

notreflectthe opportunity costto

managers

ofthe

marginalunitoflabor.

We

addressthis

by

usingstate-levelaverage

wages from

industrieswith workersofsimilarskillsandtraining to

power

plant operators, including natural gasdistribution, petroleumrefiningand hazardous wastetreatmentfacilities,denotedas

WAGE.

Thisreflects

opportunitywages, andavoidsconfoundingthe

employee

price

measurement

withany changesin recorded

wages

duetochanges inlabor force compositionat utilities associatedwithrestructuring orchangesin

wage

bargaining.

We

do nothave plant-oreven firm-specificindicesforthe materials pricesthatcomprise

NONFUEL

EXPENSES.

Our

empirical

model

of

NONFUEL

EXPENSES

thereforecorrespondstoaninput

demand

equationwithconstantpricesanda price

coefficientofone.

The

finalinputisthecapitalstockoftheplant,

which

we

measure byplantcapacityandvintage.

Our

datarecord the plant capacityinmegawatts.

We

combine

thiswith information

on

unit retirementstodefine plant-epochs.

Each

plantis assigned auniqueidentifier.

Any

timethe capacityofthe plantissignificantlychanged,or thereisanidentifiable unitadditionorunit

retirement,

we

createa

new

identifierandassociated

new

plant-specificeffect. Thisallows

capitalchangestoaltertheunderlyinginput efficiencyoftheplant.

We

include controlsfor

two

other plantcharacteristics that

may

varywithinplant-epochand lead

tochangesin inputuse.

The

firstisplant

AGE

inyears,datedfromtheinstallationdateofthe oldestoperatinggenerating unitattheplant: as plantsagethey

may

become

lessefficientor require additional inputsforagivenlevel ofoutput.

The

secondisthe additionofa flue-gas desulfurizationsystem, or

FGD

(also calledscrubber),toreducesulfur-dioxideemissionsin

some

coal plants.

FGD

affectstheenvironmentaloutput,

unmeasured

by

\n{NET

MWhs).

We

supplementthe operational plant datawith information onstate-levelrestructuringactivity. For each state,

we

have identifiedthedateat

which

formal hearings

on

restructuringbegan, the

enactmentdatefor legislationrestructuringthe state'sutilitysector, ifany, theimplementation

dateforretailaccess underthat legislation,and

some

associated aspectsofrestructuringsuchas

ratefreezes

and

mandatorydivestitureofgeneration. Testingforrestructuring-specificshocks

requiresa determinationof

how

to

match

thisinformationwithfirm decisions:

when

were

plant operators inagivenstatelikelytohave

begun

respondingtoapolicychange? Consultationswith

industryparticipants

and

readingsoftheseeventssuggestthatutilitiesoftenactedinadvance of finaloutcomes.

The

legislativeandregulatoryprocess leading

up

to staterestructuring typically

lasteda

number

ofyears,allowingutilitiesto anticipatethe

coming

change,

and

altertheir behaviorinadvance. For example, Boston Edison's

10-K

filedin

March

1994discussed

Massachusetts'considerationofrestructuring, stating

"The

Company

isrespondingtothecurrent

andanticipatedcompetitive pressurewitha

commitment

tocostcontrol andincreased operating efficiencywithoutsacrificingqualityofserviceorprofitability"(p. 6).

29

Utilities

may

have

begun

tophaseininputchanges, especially those involving laborandparticularly unionized

workers. Moreover,aspolicychanges

were

discussed,rates

were

frozenin

many

states,either explicitly

by

policy

makers

orineffect

by

implicit

PUC

decisionsnottohear

new

rate cases,

enablingutilitiestocapture thesavingsfromincremental cost reductions.30

In thiswork,

we

allowrestructuring effects tobeginwiththeopening of formalhearings

on

restructuring.

The

primaryvariableofinterest,

RESTRUCTURED,

isanindicatorvariablethat

turns

on

withthestartofformalproceedingsina statethateventuallypassedrestructuring

legislation.

31

A

secondvariable,

RETAIL

ACCESS,

indicatesthestartofretailaccessforplants in

the fourstates that

implemented

retailcompetitionduringthesample.32 Table 1 reportsthe

number

ofplants inour databaseeach yearthat

were

in states that

had

RESTRUCTURED

and

the

29

Ina 1993articleoutlining

PECO's

costsavingaccomplishmentsandstrategiesforthefuture,Chairman

and

CEO

Joseph Paquettediscussed restructuringoftheutilityindustryandwasquotedas stating,

"we

have beenfocusingonourstrategicplanstoenhance ourabilitiesto satisfyourcustomer needs by

becomingmorecompetitive."

PECO

initiativescited inthearticleincludedimprovingthe cost effectivenessofalloperations.

One

particularaccomplishment notedwasthereductionintotal employment from 18,700to 12,900.

30

As

notedearlier,someofthese changes

may

havealsoaffectedutilitiesinnon-restructuringstates. For example,thenumberofutilityratecasesdroppeddramaticallyinthe 1990s,implyingthat

many

ormost utilities

may

have beenshort- ormedium-runresidual claimantstocostreductions. Knittel(2002)

identifiesanumberofincentive regulationsadoptedinvarious jurisdictions during the 1990s.

Many

ofthe

fuel-relatedregulations(modifiedpass-throughclauses,heatrateandequivalentavailabilityfactor

incentiveprograms)werestrongly correlatedwithultimaterestructuring.

Some

ofthebroaderregulations (e.g.,pricecapsand revenue decoupling programs)werealmostorthogonaltoeventualrestructuring.

31

The

RESTRUCTURED

variableisbasedon whetherastatehad passedlegislationasofmid-2001, althoughintheaftermathoftheCaliforniaelectricitycrisis,therehasbeennoadditionalrestructuring,and

somedelays orsuspensionof plannedrestructuringactivity.

32

While

RESTRUCTURED

indicatesapprovalofretailaccesslegislation,thespecifiedphase-inofretail accesswasoftenslow. Onlyfive statesimplementedretailaccessduringoursampleperiod: RhodeIsland

in1997,California,Massachusetts,and

New

Yorkin 1998,and Pennsylvaniain 1999(U.S.EIA,2003). Because

we

have novalidobservationson

Rhode

Island plants in 1997orbeyond,retailaccesseffectswill

bedeterminedbythe4states implementingin 1998or 1999. Divestiturerequirementsin Californiaand

Massachusettsfurtherreducesthepost-retailaccesssample ofplants, asinvestor-ownedplantsinthose

stateswerelargelydivestedby 1999.

number

ofplantsinstatesthathadstartedretailaccess by 1999.33 Ifutilitiesdidnotresponduntil

restructuringlegislationor regulation

was

enactedandthepolicy uncertainty resolved,

RESTRUCTURED

willunderestimatethetrue effect

by

averaginginnon-responseyears.

To

evaluatethispossibility

we

introduceathirdvariable,

LAW

PASSED,

anindicatorequaltoone beginningintheyearthestatepasses restructuringlegislation.34 Similarly ifactual

implementation ofretailaccess andtheassociatedwholesalemarket reforms isimportantto

efficiencygains,itwillbereflectedinanincrementaleffectof

RETAIL ACCESS. To

examine

municipally-ownedplantsoverthe restructuringtimeperiod,

we

define the variable

MUNI*POST

1992,equaltooneforallmunicipally-ownedplants

from

1993, thefirstyearfor

which

RESTRUCTURED

isone,through 1999.

The

final

column

reportsthe

number

ofplants in this

category.

Detailsonthedatasources

and

summary

statisticsareprovidedinthe appendix. Tables2a

and

2b

report

summary

statistics forplant-leveldatain 1985across threecategories: investor-owned

plants instatesthatlaterrestructure,investor-ownedplantsin states thatdonotrestructure,

and

non-IOU

plants.

We

choosethisdatetoensurethatcomparisonsare

made

prior to

any

significant

changesacrossstates inthecompetitive or regulatoryenvironment,evenprior torestructuring

initiatives.

From

thesetables,itappearsthattheplants fromthesegroupsarenot

random

draws

from

the

same

population.

The

firstthree variables

measure employees

andnon-fuel operating expenses, scaled

by

theplant'scapacity,

and

fuel useinmillionsofBritishthermalunits

(mmBtus),

scaledbytheplant'soutput. In 1985, beforestate-levelrestructuring initiatives

were

considered,plants in states thateventually restructuredhadhigherintensitiesof

employees

and

non-fueloperatingexpenses,althoughthe differenceissignificantonlyfornon-fuelexpenses.

The

first

two rows

inTable

2b

show

thatmunicipally-ownedplantshadsignificantlyhigher

employment

andnon-fuel input use thanplants innon-restructuring states.

The

differences in

heatrates andcapacityfactors arenotsignificantin eitherofthe tables.

The

lastfour variables in bothtablesdescribe the stockofplants inthe

two

typesofstates.

Although

plants arevery

similarinsizeacross IOUs,

MUNIs

plants areconsiderablysmaller.

IOU

plantsinrestructuring

33

Forthetablesandthe regression analysisthatfollows, plants areassignedtothestate inwhichthey are regulated.

A

plantlocatedinonestate

may

be

owned

byacompanywithexclusive serviceterritory ina

differentstate,andthatsecondstateisthestatebywhichtheregulatorypolicyismeasured.

Some

plants

are

owned

byacompanywithserviceterritoryinmorethanonestateand

some

plants are

owned

by

severalcompaniesthatareregulatedbydifferentstates.Intheregression analysis,

we

foundthatseparately characterizing"mixed"regulationand "shared"plantshad very littleimpactonourresults.

34

Thereisonaverageabouta 2.6-yearlagbetweentheinitiationofhearingsandthepassageofthe law.

We

have experimented withanumberofalternativemeasures ofrestructuringactivity,including variables

thatbegin withhearings regardlessofrestructuringoutcomes,thosethatmeasureyears since hearingswere

statestendedtobeolder,

more

likelytousegas, andless likely tousecoal,than

IOU

plants in

non-restructuringstates.

MUNI

plantstendedtobe younger, less likely tousecoal,and

more

likely tousegas,than

IOU

plantsin non-restructuringstates.

The

regression analysiswillcontrol

forthese differencesdirectly orwiththeuseof plant-epocheffects.

Ifinvestor-ownedutilitiesachievedefficiency

improvements

when

facing

impending

restructuringofthe generationsector,one

would

expecttoseearelativedecreaseinthe costof

generationforaffectedcompanies, andlittledifferenceinthechangeintransmissionand distributioncosts

between

the affectedandnot affectedstatessince restructuringprogramsleave transmission

and

distribution comparatively untouched. Ifrestructuringdidnotaffectoperating efficiencyinthegenerationsector,

we

mightexpecteither (1)thechangeingenerationexpenses

would

notbestatisticallydifferent

between

restructuring

and

non-restructuringcompanies,or(2)

we

would

see the

same

patternof changeincostsforthetransmissionanddistributionsectors as

forthegenerationsector.35

Table3a

and

3b

display the differencein

mean

tests forinvestor-ownedutilitiesinrestructuring

andnon-restructuring statesforachange incosts

between

1990and 1996.Table 3areportsthe

percentage

change

intotalcosts foreachcategory ofcost,and Table

3b

reportsthepercentage changeincostsper

MWh.

The

largerdecreaseingenerationcostsatrestructuring

companies

is significantatthe

1%

level,andtheresult forgenerationcostsper

MWh

issignificantatthe

6%

level.

The

difference incosts forcompaniesinrestructuringandnon-restructuringstatesisnot

significant for eitherthetransmission ordistribution costs.

These

aggregatestatisticsprovide

preliminarysupportfortheexpectationthattheportionoftheutility

company

facedwith competition(thegeneratingsector)responded witha decreasein costs,whileother sectors

and

companies

notfacedwith competitiondid notsharethisresponse.

4.

The

EffectsofRestructuring

on

Input

Use

initiated for statesthateventuallyrestructured,andthepresenceofrestructuring-associatedratefreezes.

None

oftheseseemtochangemateriallytotheconclusions

we

drawbelow.

35

Fortheanalysiscomparingcostsofgeneration,transmission,anddistributionservices,

we

relyondata reportedannuallybyutilitycompaniestotheFederalEnergy RegulatoryCommission

(FERC)

inthe

FERC

Form

1,page320,321,and322respectively.

We

use abalancedsamplecomposedofallcompanies with data reportedforallthree sectorsinboth 1990 and1996. Thisamountsto49companiesin statesthatdid notderegulateand74instatesthatdidderegulateforthecomparisonofcosts,and 48and 72respectively

forthecomparison ofcostsper

MWh.

Usingcostsper

MWh

necessitatestheexclusionofafewcompanies

forwhich

MWh

datawasnot availableinone ofthetwoyears.

Following equation(II),

we

estimatetheinfluenceofrestructuring

on

theuseofinput

N

(EMPLOYEES,

NONFUEL

EXPENSE,

and

BTUs)

withthefollowing basic regressionmodel:

(Rl) ln(Nirt)

=

^

n

\n(NETMWh

M

)

+

tfhiQPRICE"*

)

+

&A

GE

ilt

+

fa

k

FGD

ilt

+

(pr N

IOU*RESTRUCTURED

+

yMUNI

1993.

m9

+

Oj N

+

5t N

+

p\ N slrt A

+

eirt N

where

we

allowfornon-unitycoefficientsontheoutputterm(Pi

N

)forallequationsand

on

the input priceterm_(p2

N

on

WAGE)

inthe

EMPLOYEES

equation,36 andinclude controlsfor

two

important plantcharacteristics thatvaryovertime:

AGE

and

FGD

(scrubber). otj

N

isa

time-invariantfixedeffect forinput

N

atplant-epochi,

which

may

containastate-specificand

ownership-specificerror that willnotbeseparately identified. Theseplant-specificeffects

controlfor

much

oftheexpectedvariation ininputuseacrossplants arisingfrom heterogeneous

technologies,stateorregional fixedfactors,andbasic efficiencydifferences.

They

alsocontrol

fordifferencesinthe plant

mix

betweenrestructuringandnon-restructuringstates

by comparing

eachplanttoitselfovertime,

removing

anytime-invariant planteffects.

As

a

Hausman

test

rejectstheexogeneityofplanteffects, allreportedresultsincludeplant-epochfixed-effects.37 5t N

isanindustry-leveleffectinyeart,

which

controlsforsystematicchangesininput

demand

across allplantsovertime.

£irt

N

is

assumed

tobeatime varying

mean

zeroshock forinput

N

atplant-epochi inregimer at

timet. Thisshockis unlikelytobe independent over timeforagivenplant. Thereis likely tobe

persistencein inputshocks,particularly for labor,

from

yeartoyear. Indeed,estimated rhos

basedon

assumed

first-order serialcorrelationareinthe .65rangeforlabor inputsandinthe.33 rangefornon-fuel expenses. Itisunlikelythatthe correlation isassimpleasafirst-order

autogressive process,however.

The

physicaloperationof

power

plants islikelytoinduce

some

correlationatlongerdifferences. For example,routinemaintenancecycles

may

involve

scheduled shutdownsbutincreased laborandnonfuelexpenses everythreeorfouryears.

We

have explored

GLS

specificationsbasedon an assumption offirst-orderautoregressiveerrors, and

theresultsarequite similartothose reportedinthetablesbelow. Ratherthan

impose

this

assumption, however,

we

choosetoestimatethe

model

withouta

GLS

correction, and simply 36

Recallthat

we

donothave apriceassociatedwithnonfuel expenses,andthataccordingtoequation (El),

fuelpricesshouldnot enterintothefuelinput function.

We

experimented withusingavariablemeasuring

thepriceofagiven plant's fuel relative tothepricesofotherfuels inthesameregionasaninstrumentfor

output but the variablehadno powerinthefirststage.

37

ThisuseoffixedeffectsissimilartotheworkofJoskowand Schmalensee(1987),

who

usedgenerating

unit-leveldatatoexplore the relationshipbetweenoperatingperformanceandunit characteristics. Our

report standarderrors thatare correctedfora general patternofcorrelationover timewithina

givenplant.38

Inputuseover timewillvary withthe levelofplantoperation,

which

is

measured

inthese specificationsasthe net generation

by

theplant inmegawatt-hours

(NET

MWh).

We

treatthe

endogeneityand

measurement

problems describedearlierby instrumentingforoutput withstate

demand

(thelog oftotalstateelectricitysales, aconsumptionratherthanproductionmeasure).

Thisinstrumentaffectsthe likelihoodthatagivenplantinthestate willbedispatched

more

over

theyear,butis not influenced

by

the characteristicsofthe plantor thechoicesofindividual plant operators.

We

consider specificationsthatincludeinteractionsof

IOU

ownership withthe threeprimary

restructuring indicatorvariablesdescribedinsection3:

RESTRUCTURED,

LA

W

PASSED,

and

RETAIL ACCESS.

Inthe input regressions,a negativecoefficientonthe restructuring variables

would

implyincreased input efficiency associatedwiththeregulatory reform.

The

coreresults

fortheinput analysis are presentedinTables4for

EMPLOYEES,

5for

NONFUEL

EXPENSES

and

6for

BTU.

We

firstdiscuss theresultsfor

employment

and nonfuelexpenses,andthen discuss theresultsfor fuel use.

Column

1 of tables4and5reportsasimple

OLS

formulationthatexcludesanycontrolfor

output. In thiscolumn,

IOU*RESTRUCTURED

captures the

mean

differentialininputusefor investor-ownedplants in states thateventuallypass restructuringlegislation,

measured

overthe periodfollowing thefirstrestructuringhearings,relative to

IOU

plants innon-restructuringstates. This correspondstothe

mean

within-plantshiftin inputuse, independentofoutput.

The

results

suggeststatistically and economicallysignificantdeclinesininputs duringrestructuring.

Employment

declinesby almost

6%

(2%)

andnonfuelexpensesdecline

by

almost

13%

(2%),39

relative to

IOU

plantsinregimesthathavenotrestructured. Controlling forplant outputreduces

theestimatedimpactofrestructuringby

more

than one-quarter,thoughtheeffectsremainlarge

andstatisticallydistinguishablefromzero (see

column

2 of eachtable),at

-4%

(2%)

for

employment

and

-10%

(2%)

fornonfuel expenses.40

Measuring

restructuring effectsfromthe

fixedeffectsareactuallyfinerthanplant-level, as

we

permita,tochange withunitadditions or retirements

andothersignificantchangesin ratedplantcapacity.

38

Reportedstandarderrorsarecalculatedusingthe clusteroptioninStata.

39

We

use [exp(<pr

N

)-l]*100toapproximatetheimpliedpercentageeffectof

IOU*

RESTRUCTURED

on

inputuse.

40

NotethattheCobb-Douglasfunctionalform assumptionsuggeststhatthe coefficientonoutputshouldbe

one,substantially largerthan thecoefficientsestimatedinthese regressions. If

we

imposethisconstraint,

theeffectofrestructuringisestimatedtobepositiveandsignificant.

We

haveestimated production 22