Working Paper The Silver Lining of Price Spikes: How Electricity Price Spikes Can Help Overcome the Energy Efficiency Gap

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1 econstor Der Open-Access-Publikationsserver der ZBW Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW Leibniz Information Centre for Economics Mauritzen, Johannes Working Paper The Silver Lining of Price Spikes: How Electricity Price Spikes Can Help Overcome the Energy Efficiency Gap IFN Working Paper, No Provided in Cooperation with: Research Institute of Industrial Economics (IFN), Stockholm Suggested Citation: Mauritzen, Johannes (2014) : The Silver Lining of Price Spikes: How Electricity Price Spikes Can Help Overcome the Energy Efficiency Gap, IFN Working Paper, No This Version is available at: Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics

2 IFN Working Paper No. 1048, 2014 The Silver Lining of Price Spikes: How Electricity Price Spikes Can Help Overcome the Energy Efficiency Gap Johannes Mauritzen Research Institute of Industrial Economics P.O. Box SE Stockholm, Sweden

3 Iwouldliketothanktwoanonymousrefereesfortheirhelpfulcommentsandsuggestions.Thankyoualsoto LouisMagnusPauchonwhosethoroughandcreativemasterthesisinspiredthisarticle.Ihavereceived financialsupportfromthenorwegiancenterforsustainableenergystudies(censes)andtheeconomicsof ElectricityMarketsresearchprogramatIFN.ThankyoutoAnneBarnwellandEilivFlakneatEnovafor providingdata.thankyoualsotoparticipantsatthe2013europeaniaeeconferenceindüsseldorf,the2013 NordicEconometricsMeetinginBergenandBergenEnvironmentalandEnergyEconomicsResearch Conferenceforvaluablecommentsandsuggestions. TheSilverLiningofPriceSpikes: Howelectricitypricespikescanhelpovercometheenergyefficiencygap November,2014 JohannesMauritzen CenterforSustainableEnergyStudies(CenSES) DepartmentofBusinessandManagementScience NHHNorwegianSchoolofEconomics Bergen,Norway And ResearchInstituteofIndustrialEconomics(IFN) Stockholm,Sweden

4 Abstract Studieshaveshownthatmanyconsumersandbusinessesfailtoinvestinenergyefficiency improvementsdespiteseeminglyamplefinancialincentivestodoso thesowcalledenergy& efficiency&gap&or&paradox.attemptstoexplainthisgapoftenfocusonsearchingcosts, informationfrictionsandbehavioralfactors.usingdataonnorwegianelectricitypricesand Googlesearchesforheatpumps,Isuggestthattheinherentlyspikeynatureofmany electricitymarkethasastrongandsignificantpositiveeffectonsearchingforinformation onenergyefficiencygoods.becauseconsumerspayforelectricitybasedonatleastmonthly averagesofthewholesaleprice,icanidentifytheinformationalandbehavioraleffectby decomposingpricesintosmoothedanddeviationcomponentsusinganovelmethodof measuringspikiness,comparingtheactualpriceserieswitharangeofdeviationsfrom Loesssmoothedseries. JELCodes:Q41;D12;D83 Keywords:Energyefficiency;deregulatedelectricitymarkets;pricespikes;informational frictions; I.Introduction Animportantandoftencontentiousissueinenergymarketresearchhasbeenwhathas beenreferredtoasthe&energy&efficiency&gap.thisisthephenomenonthatbothconsumers andbusinessesdonotseemtoinvestinenergyefficiencydespiteseeminglyamplereturns. Alargeliteraturespanningtheengineering,economicsandpsychologyliteraturehasgrown aroundthequestion.theeconomicsliteraturegoesbacktotheeconometricstudyby Hausman(1979)whofindsalargediscountrateofapproximately20percentfor energyw usingdurables. AstudyofwaterheatersbyDubinandMcFadden(1984)alsofindsan implieddiscountrateofover20percent,thoughtheauthorsconjecturethatthisisdueto creditconstrainedhouseholds.jaffeandstavins(1994)providedanearlymodelofwhat theytermtheenergyefficiencyparadox,&describingthediffusionofnewenergyefficiency technologiesasfollowingagradual s shape.theyclaimthatthisshapecanbeexplained bywayofprivateinformationsearchingcosts:seekingoutinformationonnewtechnologies canbetimeconsumingandexpensive.importantlytheyalsonotethatsinceinformation haspublicwgoodqualities,itmaynotbeprovidedinoptimalquantities.forrecent overviewswithspecialattentionpaidtopolicyimplicationsseegillinghametal.(2009), GillinghamandPalmer(2013)orBrennan(2013). 2

5 Behavioralandpsychologicalaspectshaverecentlycometotheforefront.Stern(1992) providesanearlyreviewofthepsychologyliteratureonthesubject,arguingthatstandard economicanalysisfailstoaccountfortheobservedbehaviorinenergyefficiency investment.allcott(2011),inastudyofautomobileefficiency,takesabehavioral economicsapproachandshowsthatusconsumersseemtopaylittleattention( devote verylittlecognitiveattention )tothefuelcostsofautomobilesandtendtobefooledby whatiscalledthe MPGIllusion 1.Anotherrecentpaperrelatedtogasolinepricesis Anderson,Kellogg,andSallee(2011)thatfindconsumersgenerallyusethecurrentpriceas aforecastforfutureprices.allcottandgreenstone(2012)provideareviewofrecent empiricalresearchontheenergyefficiencygap,generallyfindingasmallerenergy efficiencygapthanmanyengineeringandaccountingstudies.buttheytoonotethat [i]mperfectinformationisperhapsthemostimportantformofinvestmentinefficiencythat couldcauseanenergyefficiencygap. Thisarticleattemptstoconnecttheenergyefficiencyliterature,especiallyitsfocuson informationfrictionsandbehavioralconsiderations,tocharacteristicsofderegulated electricitymarket.inparticular,deregulatedelectricitymarketstendtoexperience occasionalpricespikesandhighshortwtermvolatility(weron2006).thiscomesfrom underlyingphysical,engineeringandmarketcharacteristicsofthegenerationand distributionofelectricity.thefocusofthisarticleisontheinformationalconsequencesof pricespikesonenergyefficiency,andiwillnotattempttoprovideadetailedoverviewof theunderlyingcausesandconsequencesofpricespikes.however,afewimportantfactors canbementioned.first,thesupplyanddemandofelectricityinanetworkmustbe preciselyequaltoeachotheratanygivenmoment.imbalancescanleadtoequipment malfunctionsandpoweroutages.inaddition,electricitycannotbestored.energycanbe storedinotherforms chemicalenergyintheformofnaturalgasorpotentialenergyinthe formofwaterinamagazine butelectricitymustflowandbeusednearlyinstantaneously. Finally,demandforelectricityintheshortWruntendstobehighlyinelastic.The combinationofthesefactorswithstrategicbiddingbyauctionparticipantsmeansthat 1 ConsumersunderestimatethefuelsavingsbetweenlowmileWperWgallonvehiclesandoverestimatefuel savingsbetweenhighmilewperwgallonvehicles. 3

6 electricitypricescanjumpinperiodswheretemporarilyhighdemandmustbemetby increasinglyexpensivebackwupgeneration. Pricespikesareoftenseenasanunfortunatebuthardtoavoidaspectofelectricitymarkets. Theinformationalroleofpricesforinvestmentsingenerationandothermarketdecisionsis dilutedwithhighvolatilityanditmaymakemaintainingsystemreliabilitymoredifficultfor thesystemoperators.howeverattemptstocapprices,ascaliforniadidin2000w2001can havesevereconsequences(wolak2003).inthispaperiarguethatwhilepricespikesmay haveseveralnegativeconsequencesfortheoperationofelectricitymarkets,theymayalso haveasilverlining.whenpricespikesoccur,itcangeneratepublicityontv,radioandin newspapers.forexample,infebruaryof2012,oneofthelargestnorwegiantabloidshad anarticlewiththetitle SkyWhighElectricityPricesintheCold. 2 Suchnewscoveragecan sometimesinclude,amongotherthings,informationandestimatesofpricesavingsfrom investinginenergyefficiency.apricespikethencanhavetheeffectofamelioratingthe underwprovisionofinformationonenergyefficiencygoodsaswellasactingasabehavioral nudgeforinattentiveconsumers. Inthepastattemptingtogetanaccuratemeasureofinterestinenergyefficiencygoods wouldhavebeenachallenge.howeveradvancesininformationtechnologyhave dramaticallyreducedthecostandinconvenienceofsearchingoutinformation.in particular,internetsearchenginesprovideeasyandconvenientaccesstoalmostanysource ofinformation.searchdatafromsearchengineshavebeenusedtogaugeinterestin variousissues,forexamplejacobsen(2011)usessearchdataforkeywordsonclimate changetoarguethatinterestinclimatechangewasreducedduringthelastusrecession.i usedataonsearchenginesearchestoestimatetheinformationaleffectofpricespikes.in particular,iusedatafromgoogle,thedominantsearchengineineuropeandnorth America,downloadeddirectlyfromtheirpublicanalyticssite ( 2http:// 4

7 IcomparejumpsinthepriceofelectricityandGooglesearchesforheatpumps ( varmepumper ).TheinspirationforchoosingthisparticularcasecamefromPauchon (2012)whonotedacorrelationbetweenperiodsofhighelectricitypricesandinvestmentin energyefficiency.hearguesthatthevariablemarketpricesinnorwayislikelyonereason whyincentivesforenergyefficiencyinvestmentshavebeenrelativelysuccessfulcompared tocountrieslikefrancewhereelectricitypricesvarylessandaremoreheavilyregulated. IchoosetofocusthisstudyonNorwayandtheNordicelectricitymarket.TheNordic marketisbymostaccountsmature,welldevelopedandtransparent(rud2009).dataon pricesandotheraspectsoftheelectricitymarketarepubliclyavailablefromthewebsiteof theexchange. 3 Moreso,thevastmajorityofNorwegianhouseholdshavemarketcontracts fortheirelectricity,givingthemanincentivetopayattentiontomarketmovements. However,thesemarketcontractsarebasedonaveragesofwholesalepricesWnearly60 percentofhouseholdshavecontractswheretheirbillisbasedonanaverageofthemonthly wholesalepriceplusagivenmarkwupwherejustunder40percenthavevariablecontracts thatpartiallyhedgepricemomentsonthewholesalemarket.lessthan5percenthave contractsthatarefixedforayearormore(the&norwegian&energy&agency&2011).inother words,therealpricesthatretailcustomersfacearesmoothedversionsofthewholesale prices.thismakesitpossibletotrytoidentifytheinformationalandbehavioral componentsfromtherealpricemechanismsbydecomposingpricesintosmoothedand spikycomponents. StatisticsNorwayestimatesthattheaverageNorwegianhouseholdusedapproximately 18000kroners(orabout2300Euro)onenergyinthehomein2010,nearly80percentof whichiselectricity(bøeng2011).inturnanestimated75percentofhouseholdelectricity consumptionisforheating(feilberandgrinden2012).oneofthemostsignificantenergy efficiencyimprovementsthatahouseholdcanmakeistoimprovetheefficiencyofelectric heaters.apopularsolutiontothisistoreplaceelectricpanelheaterswithelectricheat pumps.insteadofwarmingtheairdirectlybyrunningelectricitythroughresistantmetal 3 5

8 wiresorceramicplates,electricityisusedtorunacompressorthatineffectdrawsthe latentheatfromoutsideairintothehome.suchaheatpumpcanusesubstantiallyless electricitywhileproducingthesameamountofheatasapanelheater.theefficiencyof suchheatpumpsdependsontheoutsidetemperature coldertemperaturesgenerally meanthattheheatpumpsworklessefficiently.however,theefficiencyofheatpumpshas beengraduallyimprovingandcanbeeffectivelyusedinboththemildercoastalclimateof westernnorwayaswellasthecoldereasternandnorthernregions. Thisstudy,whilelimitedinscope,clearlyillustratesanimportantpointthathassofarbeen lackingfromtheliteratureonenergyefficiencyandelectricitymarkets:thatpricespikes canserveausefulinformationalorbehavioralpurpose.similarresultscanlikelybefound withotherenergyefficiencyinvestmentsandinotherelectricitymarkets.however,i chooseheretofocusonalimitedcasestudythatclearlyillustratesthepointratherthan attemptinganexhaustivestudy. Ontheotherhand,whileheatpumpsinNorwayservesasanarrowcasestudy,itisnotan insignificantone.heatpumpscancostbetween15,000to25,000nok(approximately 2,000to3,500Euro)andcansaveanestimated3,000to7,000NOK( Euro)in yearlyelectricitycostsfornorwegianhouseholds(enova2012).installingaheatpumpis amajorinvestmentthatinturnoffersasubstantialreturn.in2009nearly20percentofall Norwegianhouseholdshadinstalledheatpumps upsharplysince2006whenonly8 percenthadthem(bøeng2011). Onedifficultywhenattemptinganempiricalstudyofpricespikesisthatthetermitselfis vague.nowidelyagreedwupondefinitionexistsofwhatconstitutesaspikeinpricesas opposedtonormalvariation.isidewsteptheissuebypresentingresultsforarangeof spikiness&asdefinedbydeviationsfromaloesssmoothedcurveatvaryinglevelsof smoothness. Mymainfindingisthatpricespikeshavealargeandsignificanteffectonsearchesforheat pumps.adoublingofaveragepricesisestimatedtoincreasegooglesearchesforheat 6

9 pumpsbybetween50and200percent.thiseffectappearstobeespeciallystrongforthe narrowestmeasureofspikiness largeandquicklyrevertingdeviationsfromthesmoothed curve,stronglysuggestingthatitistheinformationaleffectatplay.ipresentfurther evidencethatinformationalfactorsaredrivingtheseresultsasopposedtoanormalpricew demandeffect. Acleaneddatasetaswellasthecompletecodeformyanalysiscanbefoundonmywebsite II.Describingtherelationshipbetweenpricesandsearchesforheatpump Googlesearchdataisavailableataweeklyfrequencyandintheformofanindexbetween0 and100.theindexisbasedonasampleofsearchespercountryorregion,whichisthen normalizedbyoverallsearchvolumebycountryandovertime,thustheindexaccountsfor changesintotalsearchvolumeovertimeandacrossregions.thesampledatafortheindex excludesrepeatedsearchesfromthesameuserandincludesonlypopularsearchterms.i specifydatafromsearchesfromwithinnorway. 4 Forsomekeywords,dataonsearchesby subregionarealsoavailable,howeverdatacouldnotbegeneratedforheatpumpsearches duetoalackofsearchvolume.formoreinformationongoogletrendsdatasee ( wellasothervariablesusedintheregressionareintheformofweeklyaverages. Figure1showsaplotoftheweeklyaverageofNorwegianwholesaleelectricityprices againstthegooglesearchindexforheatpumpsfrom2005through2012.eachserieshas beenrescaledtobebetween0and1.bothseriesarequitenoisy,howeverarelationship appearstoexistbetweenjumpsinpriceandsearchesforheatpumps. Figure1:Norwegianwholesaleelectricitypricesandheatpumpsearches 4 GooglecanobservethelocationofthoseusingitssearchservicethroughtheirIPaddresse 7

10 Figure2showsaplotofheatpumpsearchesandheatingdegreeWdaysinOsloobtainedfrom thewebsiteofthenorwegianmeteorologicalinstitute 5.Theeffectthattemperatureand seasonalityhaveonbothelectricitypricesandsearchesforheatpumpsneedtobecarefully considered.becauseheatingisoverwhelminglyelectric,demandforelectricitytendsto increasesubstantiallywhenitiscoldandinturnwillaffectprices.figure3showsheating degreewdaysforosloandthenorwegianwholesalepriceseries.osloisanimperfect measureoftheneedforheatinginnorwayasawhole.norwayisageographicallylarge anddiversecountryandtemperaturesandweathercanvarysubstantiallybetweencities andareas.theosloregionishoweverhometonearlyathirdofthenorwegianpopulation. ThemeasureofheatingdegreeWdaysinOslowillthentendtorepresentmoreoftheshortW run,demandwsideeffectsoftemperatureonprice. Figure2:HeatingDegreeDaysinOsloandheatpumpsearches 5 8

11 Figure3:HeatingDegreeDaysinOsloandNorwegianSpotPrices OnthesupplyWside,hydropowerprovidesthevastmajorityofNorwegianelectricity generation between98 99%Wandmagazinesgetdepletedduringthewintermonths. Moreso,coldweatherduringthewinteralsotendstobecorrelatedwithdryweather. 9

12 Figure3showsthemagazinelevelsofNorwegianhydropowerplantsalongwithsearches forheatpumps,bothrescaledtobebetween0and1.theseasonalityisreadilyapparentin bothseries. Figure4:Heatpumpsearchesandhydropowermagazinelevel III.CalculatingDeviationsfromLoessSmoothedPriceSeries. Insteadofprovidinganarbitrarydefinitionofpricespikes,Iattempttoprovideresultsfora rangeofspikiness.ismooththepriceseriesusingalocallyweightedregression orloess (Cleveland,1979)ofvaryingneighborhoodsizesasshowninfigure5. 10

13 Figure5.Loesssmoothedpriceseriesatvaryinglevelsofsmoothness. Moreformally,definetheweightsasinequation1. = 1 # < 1 0 # 1 1 where = andhisthehalfwwidthofthewindowcontainingtheobservations.this meansthatforeachpriceattime,,observationscloseintimeareweightedheavierthan thosefartheraway.foreach aquadraticregressionwithweightsascalculatedaboveis runtogivethefittedprice,.thelevelofsmoothingcanbeadjustedbyincludingafixed proportionorspanofthedata,s. Takingthedifferenceofthepriceseriesfromthesmoothedcounterparts,atvaryinglevels ofspan,s,igetasetofseriesrepresentingarangeofdeviancesasrepresentedbyequation 2., = 2 11

14 Attheoneextremewheresislarge,pricespikesaredefinedasthedifferencebetweenthe priceseriesandaquadraticregressionwhereallobservationsareweightedequally.inthis regressionpricespikesarethendefinedasthedifferencesbetweentheactualpriceandthe quadratictrendoftheentiredataset.attheotherextreme,onlydatapointsclosetoeach otheraffectthelocalregression,thusthedeviancesfromthesmoothedseriesrepresents onlyimmediateandshortwlivedjumpsinprice. IV.TheeffectofpricespikesonGooglesearchesforheatpumps. Havingcalculatedthedeviancesofpricesfromasetofsmoothedseries,Ithenrunasimple regressionrepeatedlyoverthevariousspans,s.theregressioncanbewrittenasin equation3. = +, +, + ##$ ##$h, +, 3 representsthegooglesearchindexforheatpumpsinnorwayattimet,while, and, representpositiveandnegativedeviancesfromthesmoothedseries,h, at varyinglevelsofthesmoothnessparameter,s. representstheintercepttermwhile, representstheerrorterm.iseparatethepositiveandnegativedeviationsbecausethe effectsofpositivedeviationsarelikelytobedifferentfromtheeffectsofnegativedeviations iftheresultsarereflectinganinformationalorbehavioraleffect.positivepricejumpsare morelikelytoleadtonewscoverageandincreasedattentionthanpricefalls.ifthe hypothesisiscorrect,thentheestimatedcoefficientsonthepositivepricedeviationsshould besubstantiallylargerthanthenegativecoefficients. ThesmoothedseriesisalsoincludedintheregressiontocontrolfortherealpriceWdemand effect.presumably,ifconsumersareonlyreactingtotheeffectofincreasedpricesthenthe smoothedseriesshouldbettercapturetheeffectsincethepricesthatconsumerspayarein effectalsosmoothedsincetheypayapricethatisbasedon,ataminimum,themonthly averageofwholesaleprices. 12

15 Thevariableofinterestisthenthepositivedeviancefromthesmoothedseries,,.Figure 5showstheestimatedcoefficients,,onthisvariableforarangeofregressionswherethe span,s,ofthesmoothingalgorithmisallowedtovarybetween0and1in.01increments. Thefigureshowsacomparisonwiththeestimatedcoefficientsonthesmoothedseries whileresultsforcoefficientsofalltheincludedvariablescanbefoundinfigure11inthe appendix.thebandsrepresentplusandminusoneandtwostandarderrorsonthe coefficients,andareadjustedforserialcorrelationandheteroskedasticityintheerrorterm. Plusorminustwostandarderrorscanbeinterpretedasanapproximately95%confidence banandplusorminusonestandarderrorcanbeinterpretedasanapproximately70% confidenceban. Figure5.Coefficientsonpricedeviationsfromsmoothedseries Theresultsofthissimplemodelappearconsistentwiththeideathatpricespikesare providinganinformationalorbehavioraleffect.evenwithverysmallspan whereprice spikesaredefinedinthenarrowestsenseandthesmoothedseriesincludesmostofthe variation thecoefficientisestimatedtobelargeandsignificant.infactthepointestimate actuallybecomeslargeratthelowerlevelsofspan.incontrast,apricewdemandeffect 13

16 wouldimplythatthecoefficientonthedeviationswouldgotowardszeroasthespan approachedzerosincetheaveragedpriceeffectofthepricedeviationsbecomesnegligible. Oneissuewiththeregressionpresentedaboveisthattherelationshipbetweenprice deviationsandgooglesearchesisassumedtobelinear.figure11intheappendixshows scatterplotsofpricedeviationsagainstgooglesearcheswhilefigure12showsscatterplots ofthelogwtransformeddata.thelogwtransformeddataappearstofitthelinearity assumptionsubstantiallybetter.logwtransformingthedataalsohastheaddedadvantageof givingthecoefficientsaconvenientinterpretationintermsofelasticities. Figure6showstheestimatedcoefficientsonpositivedeviationsandsmoothedpriceswith logwtransformeddata.intheseregressionstheeffectofpositivedeviationsfromthe smoothedpricecurveremainlargeandsignificantatalllevelsofspan.notably,the estimatedeffectatverysmallspanisevenmoreprominent.theestimatescanbe interpretedtomeanthatfora25%increaseinpricesgooglesearchesincreasebybetween %. 6 6Atspanscloseto1thecoefficientisapproximately1,e.25.7.Atspanscloseto0,theestimated coefficientiscloseto2,

17 Figure6.Coefficientsonlogpricedeviationsfromsmoothedseries Themodelssofarhavenottakenintoaccounttheeffectsoftemperatureandseasonality, andtheabsenceofthesefactorscouldintroduceabiasintotheestimation.thereasonis becauseheatpumpsarenotonlyenergyefficiencygoodsbutalsoheatinggoods. Presumablycoldweatherandseasonalchangecouldbothleadtohigherpricesaswellas increasedinterestinheatpumpswithouttherenecessarilybeinganycausalconnection betweenthetwofactors.todealwiththisiincludemeasuresoftemperatureand seasonalityintheregression,asinequation4. log( ) = + log(, )+ log(, ) + ##$ log(##$h, ) + #$%& + # #$%& +, 4 Here#$%& representsheatingdegreedaysinosloinweektwhile#$%& representsthefilllevelinpercentofnorwegianhydropowerplantmagazinesinweekt. Thefullresultsfromtheseregressionsareshowninfigure7. 15

18 Figure7.Coefficientsonmodelwithheatingdegreedaysandmagazinelevel Withthismodel,thecoefficientsonthelogpositivedeviationsareslightlysmallerthan thoseinthesimplermodelshowninfigure6,buttheyremainsignificantandmuchlarger thantheestimatedcoefficientsonthesmoothedseries.moreso,noticethatthecoefficient onthepositivedeviationsisestimatedtobesubstantiallylargerthanthecoefficienton negativedeviationsandremainssignificantlylargerthanzeroforawiderrangeof spikiness. Asnotedearlier,heatingdegreeWdaysinOsloisanimperfectmeasureforheatingdemandin geographicallydiversenorway.however,theresultsarenotsubstantiallychangedby addingmeasuresofheatingdemandfromotherpartsofthecountry(seefigure14inthe appendix). Afurtherrobustnesscheckistorunaregressionwheresearchesforheatpumpsare replacedbysearchesonkeywordsthatcanbeexpectedtobeseasonalandcorrelatedwith weatherbuthavenootherdirectconnectionwithelectricityprices.infigure15inthe appendixishowtheresultswhentheleftwhandsidevariableissearchesforwintertires ( vinterdekk ),whicharerequiredbylawinnorwayduringthewinter.nosignificant 16

19 effectofpricespikesonsearchesforwintertiresisestimated.thesameholdswhen searchesforwinterjacket( vinterjakke )areused. Sincethetimeperiodinthedataisweekly,theinformationaleffectismostlikely concurrent apricespikeinacertainweekleadstonewscoverageandincreased awarenessandinturngooglesearchesinthesameweek.butthisdependsonwheninthe weekthepricespikeoccursandalagofaweekormoreseemspossible.thereforialsorun regressionswhereiincludetwolaggedtermsforthepositivepricedeviations.the estimatedcoefficientsfortheconcurrentpositivedeviations,thetwolags,aswellasthe smoothedseriesareshowninfigure8below,whilethefullresultsareshowninfigure16in theappendix. Figure8.Coefficientsonmodelwithlags. Thelargestandmostconsistenteffectisstillontheconcurrentpricedeviations,butthere appearstobealaggedeffect atbothoneandtwoweeks atnarrowlydefinedprice spikes. 17

20 Ingeneral,theresultsarerobusttospecification.Forexample,includingautoregressive andmovingaverageterms(arma)intheregressiontomodelthedynamicsofthegoogle searchseriesalsodoesnotsubstantiallychangetheresults(seefigure17intheappendix). Onefeaturethathasappearedinalltheresultssofarhasbeenaspikeintheestimated coefficientonpricedeviationsatspanvaluesclosetozero.atthesespanvaluesjustafew pricespikesdominatethetotalvariationinthedeviationseries,asthetoppanelinfigure9 shows. 18

21 Figure9.Pricedeviationsatverysmallspanandfrequencyofnewsarticles Withonlyafewlargespikesdominatingthetotalamountofvariationintheseseries,one mightquestionwhetherthejumpinthecoefficientseriesshouldbetakenasrealorsimply asnoise.totrytoanswerthisaswellastoaddsupporttotheinformationalandbehavioral hypothesisihavepresented,iwentthroughthearchiveofthelargestnorwegian 19

22 newspaper, Aftenposten 7 andcountedthenumberofarticlesthatmentionedelectricity prices,differentiatingbetweenthosethatspokeofhighandlowprices.theresultsare showninthelowerpaneloffigure9. Fromthefigureitisapparentthatafewpricejumps inparticularthoseinthewinterof 2006W2007andthosein2010generatedalargepartofthenewscoverage.Thusthejump inthecoefficientseriesonthepricedeviationsatverylowspandoesappeartoberealin thesensethatitreflectsthatthelargestinformationaleffectscomefromthefewbutlarge andsharppricedeviations. V.TheSilverLiningofPriceSpikes Theargumentsandmethodsofthisarticlearerelativelystraightforward,yettheresults demonstrateanimportantbutlargelyoverlookedpoint.bydrawingattentiontoelectricity prices forexamplethroughnewsreports spikeyelectricitymarketscanplayan importantinformationalrole.theresultsofthisarticleindicateaclearconnectionbetween pricespikesandsearchesongoogleforheatpumpsinnorway.thiscorrelationis especiallystrongwherepricespikesaredefinedmostnarrowlyassharpdeviationsfrom theoverallmovementofprices. ThroughoutthisarticleIhavesuggestedtwodistinctmechanismsfortheobservedresults thatareinpracticedifficulttopullapart.thefirstisamechanismbasedonunderw provisionofinformationandotherinformationfrictions.becauseinformationhaspublic goodpropertiesthen,bydefinition,itisnotoptimallyprovidedundernormalmarket conditions.pricespikesthenhavetheeffectofincreasinginformationprovidedonprices throughnewscoverageand,potentially,otheravenues. Theothermechanismisbehavioral.Consumersmaybeawareofelectricityprices,butit takessomementalefforttomakethenecessarycalculationsinvolvingyearlyconsumption 7http://a.aftenposten.no/kjop/article2853.ece 20

23 andinturnenergysavingsfromappliances.pricespikesmaythenbeinterpretedasgiving anudgetoconsumerstoundertakethismentalexertion.inthisstudyidonotattemptto separatelyidentifytheseeffects.seperatingtheinformationalmechanismfromthe behavioralmechanismwouldlikelyinvolveanexperimentalorfieldwexperimental approachandiswelloutsidethescopeofthispaper. Anaturalextensionofthisarticlewouldbetoexplorehowpricespikesaffectactual investmentsinheatpumps.unfortunately,tomyknoweldge,dataonactualpurchasesof heatpumpsisnotavailable.however,enova,thenorwegianstateagencyforrenewable energyandenergyefficiencywasabletoprovidemewithdataonthenumberof applicationstheyreceivedforsupportforconsumerairwtowwaterandwaterwtowwaterheat pumpsonamonthlyfrequency.figure10showsthenumberofapplicationsaswellas monthlyaveragesofthegooglesearchindexandelectricitypriceseries.unfortunatly, EnovadoesnothavedataonthemostpopularairWtoWairheatpumps.Still,thedatashown infigure10suggeststhattherecouldbeareallinkbetweenpricespikesontheelectricity marketandactualinvestmentsinheatpumps.ofcourse,interepretationofthese correlationsatamonthlyfrequencyshouldbemadecarefully ahostofotherfactors, notablyweather couldalsopotentiallyexplainthepattern. 21

24 Figure10.HeatPumpSearchesandApplicationsforHeatPumps Thearticleisnotmeantasacomprehensivenormativestudyofthewelfarebenefitsof energyefficiencyorpricespikesinelectricitymarkets.still,thearticlehasimplicationsfor economicwelfareandefficiencyofelectricitymarkets.thoughnorway selectricity generationwcomingoverwhelminglyfromhydropowerwisnearlycarbonwfree,norwayis fullyintegratedinthenordicelectricitymarketandincreasinglytoothereuropean countriesthroughinterconnectors.thusgainsinenergyefficiencyhaveimplicationsfor greenhousegasemissionsattheeuropeanlevel. Moregenerally,thefailureofconsumerstoadoptenergyefficiencygoodsdespitefinancial incentivestodosopresentsanoveralllossofeconomicefficiencyandwelfare.inthe economicslitterature,severalarticlesexistontheconsumerwelfareofintroducingrealw 22

25 timepricing(seeforexampleborenstein(2006),lijesen(2007)andallcott(2011)).this articlesuggeststhatvolatilityonthewholesalemarketcanhaveadirectimpacton consumerbehaviorevenwithoutrealwtimepricing. Finally,thepurposeofthisarticlehasbeentosuggestthatvolatilityinwholesaleelectricity marketsmayhavebeneficialinformationalandbehavioraleffectsonenergyefficiency investments.however,theintentionisnottosuggestthatpricevolatilityinelectricity marketisnecessarilybeneficialonthewhole.volatilityinelectricitymarketspresentsa hostofchallengesforsystemreliability,generationandnetworkinvestmentthatmay outweighthebenefitsforenergyefficiency. VI.WorksCited Allcott,Hunt Consumers PerceptionsandMisperceptionsofEnergyCosts. The& American&Economic&Review, Allcott,Hunt RethinkingRealWTimeElectricityPricing. Resource&and&Energy& Economics,Specialsection:SustainableResourceUseandEconomicDynamics,33 (4): doi: /j.reseneeco Allcott,Hunt,andMichaelGreenstone IsThereanEnergyEfficiencyGap? Journal&of& Economic&Perspectives,no.26(1):3 28. Anderson,ST,RKellogg,andJMSallee WhatDoConsumersBelieveAboutFuture GasolinePrices? Annual&Report&2011&The&Norwegian&Energy&Regulator&Contents Bøeng,AnnChristin EnergibrukIHusholdningene, industri/statistikker/husenergi/hvertw3waar/2011w04w19#content. Borenstein,Severen TheLongWRunEfficiencyofRealWTimeElectricityPricing. The& Energy&Journal26(3). Brennan,Timothy EnergyEfficiencyPolicyPuzzles. The&Energy&Journal34(2):1 26.doi: / Dubin,JA,andDLMcFadden AnEconometricAnalysisofResidentialElectric ApplianceHoldingsandConsumption. Econometrica:&Journal&of&the&Econometric& Society52(2): ENOVA.2012.Luft/luftHVarmepumpe&(AirHtoHAir&Heat&Pump). v/luftluftwvarmepumpew/112/0/. Feilber,Nicolai,andBjørnGrinden Nykunnskapomfordelingavstrømforbruket. kunnskapwomwfordelingwavwstromforbruket/. Gillingham,Kenneth,RichardGNewell,JamesSweeney,TimothyBrennan,Maximilian Auffhammer,RichardHowarth,andDannyCullenward EnergyEfficiency EconomicsandPolicy. 23

26 Gillingham,Kenneth,andKarenPalmer BridgingtheEnergyEfficiencyGap:Insights forpolicyfromecononmictheoryandempiricalanalysis. Resources&for&the&Future& Discussion&Paper. Hausman,JA IndividualDiscountRatesandthePurchaseandUtilizationofEnergyW UsingDurables. The&Bell&Journal&of&Economics10(1): Jacobsen,GD TheAlGoreEffect :AnInconvenientTruthandVoluntaryCarbon Offsets. Journal&of&Environmental&Economics&and&Management,no.August2010. Jaffe,AB,andRNStavins TheEnergyParadoxandtheDiffusionofConservation Technology. Resource&and&Energy&Economics16(2). Lijesen,MarkG TheRealWTimePriceElasticityofElectricity. Energy&Economics29 (2): doi: /j.eneco Pauchon,LouisMagnus StrategiestoReduceEnergyConsumptionofHouseholdsin NorwayandFrance. Rud,Linda.2009.Essays&on&Electricity&Markets.10/09.Research&in&Economics. Stern,PaulC WhatPsychologyKnowsAboutEnergy. American&Psychologist47 (10). Weron,Rafal.2006.Modeling&and&Forecasting&Electricity&Loads&and&Prices.JohnWiley& Sons,Ltd. Wolak,Franka DiagnosingtheCaliforniaElectricityCrisis. The&Electricity&Journal 16(7):11 37.doi: /S1040W6190(03)00099WX. 24

27 Appendix Figure11.Fullresultsfromregressionofequation3 Figure12.RelationshipbetweendeviationsfromsmoothedpricesandSearches 25

28 Figure13.RelationshipbetweendeviationsfromsmoothedpricesandSearches,log transformed Figure14.Fullresultsforregressionwithaddedregionaltemperaturevariables 26

29 Figure15.Resultsforregressiononsearchesfor WinterTires Figure16.Fullresultsforregressionwithlagvariablesonpositivepricedeviations 27

30 Figure17.Fullresultsforregressionwitharmaterms. 28