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Lessons from Quantile Panel Regression Estimation of the Environmental Kuznets Curve

Permanent Link: http://ufdc.ufl.edu/UFE0024319/00001

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Title: Lessons from Quantile Panel Regression Estimation of the Environmental Kuznets Curve
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Kapetanakis, Dimitrios
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

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Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The environmental Kuznets curve (EKC) estimates the income-environmental degradation relationship, typically employing measures of per-capita income and the concentration of pollutants in the air, such as nitrogen oxide and sulfur dioxide. The literature has concentrated on estimation of the EKC at the mean employing longitudinal data on countries or U.S. states. The typical finding is an inverted U-shaped relationship, implying that pollution is increasing in income up to a turning point beyond which pollution decreases. Estimation at the mean, however, likely masks heterogeneities that can be present at higher and/or lower quantiles of the emissions' distribution. This study applies methods for quantile regression estimation of panel fixed effects models to the estimation of the EKC on U.S. state-level data on nitrogen oxide and sulfur dioxide pollutants over the period 1929-1994. Our results indicate that methods that focus on the mean provide too optimistic estimates about emissions reduction of nitrogen oxide, as quantile methods reveal that the rate of reduction is usually smaller; while the opposite holds for sulfur dioxide. The differences arise due to the robustness of quantile regression to outlying observations. These results have implications for policies advocating economic development as a means for improving the environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Dimitrios Kapetanakis.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Flores-lagunes, Alfonso.

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Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024319:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024319/00001

Material Information

Title: Lessons from Quantile Panel Regression Estimation of the Environmental Kuznets Curve
Physical Description: 1 online resource (105 p.)
Language: english
Creator: Kapetanakis, Dimitrios
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The environmental Kuznets curve (EKC) estimates the income-environmental degradation relationship, typically employing measures of per-capita income and the concentration of pollutants in the air, such as nitrogen oxide and sulfur dioxide. The literature has concentrated on estimation of the EKC at the mean employing longitudinal data on countries or U.S. states. The typical finding is an inverted U-shaped relationship, implying that pollution is increasing in income up to a turning point beyond which pollution decreases. Estimation at the mean, however, likely masks heterogeneities that can be present at higher and/or lower quantiles of the emissions' distribution. This study applies methods for quantile regression estimation of panel fixed effects models to the estimation of the EKC on U.S. state-level data on nitrogen oxide and sulfur dioxide pollutants over the period 1929-1994. Our results indicate that methods that focus on the mean provide too optimistic estimates about emissions reduction of nitrogen oxide, as quantile methods reveal that the rate of reduction is usually smaller; while the opposite holds for sulfur dioxide. The differences arise due to the robustness of quantile regression to outlying observations. These results have implications for policies advocating economic development as a means for improving the environment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Dimitrios Kapetanakis.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Flores-lagunes, Alfonso.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024319:00001


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FirstofallIwanttothankmychaircommitteeDr.AlfonsoFlores-Lagunesforhisvaluablehelpanddenitelyforhispatiencebeingmyadvisor.IwouldalsoliketothankmymembercommitteeDr.CarmenCarrion-FloresnotonlyforthethesisbutalsoforthegreatcoursethatwehadinEconometrics.FinallyIwanttothankallFoodandResourceEconomicsDepartment(FRED)familythathelpedmeinmystudiesandmakingmylifebetterinGainesville. 3

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page ACKNOWLEDGMENTS .................................... 3 LISTOFTABLES ....................................... 6 LISTOFFIGURES ....................................... 7 ABSTRACT ........................................... 9 CHAPTER 1INTRODUCTION .................................... 10 2REVIEWOFTHEEKCLITERATURE ......................... 15 2.1Introduction ..................................... 15 2.2TheoreticalEKCDerivation ............................ 17 2.3TheoreticalBackground ............................... 18 2.3.1ArgumentsSupportingEKC ........................ 18 2.3.2CounterargumentsoftheEKC ....................... 20 2.4AlternativeRecentHypotheses ........................... 21 2.4.1Introduction ................................. 21 2.4.2RacetotheBottom ............................. 22 2.4.3NewToxicScenario ............................ 23 2.4.4RevisedEKC ................................ 23 2.5TheDevelopmentofEmpiricalStudies ....................... 23 2.6ResultsSummary .................................. 32 2.7EconometricDrawbacks .............................. 35 3DATA ........................................... 38 3.1DataSelectionDrawbacks ............................. 38 3.2DataSource ..................................... 39 3.3Advantages ..................................... 40 3.4DescriptiveStatistics ................................ 41 3.5TheNatureofEmissionsandRecentActionsAimedatTheirReduction ..... 44 3.5.1NitrogenOxide(NOx) 44 3.5.2SulfurDioxide(SO2) 45 3.5.3AcidRain .................................. 46 3.5.4CleanAirAct ................................ 46 3.5.4.1Acidrainactions ....................... 47 3.5.4.2Cars,trucks,buses,andnon-roadequipment ......... 49 3.5.4.3Permitsandenforcement .................... 52 4

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........................................ 53 4.1ConceptualFramework ............................... 53 4.2PanelDataMethods ................................. 54 4.2.1FixedEffectsModel(FE) .......................... 55 4.2.2RandomEffectsModel(RE) ........................ 56 4.2.3HausmanTest ................................ 59 4.3QuantileRegressionMethods ............................ 61 4.3.1General ................................... 61 4.3.2QuantileRegressionandOptimization ................... 62 4.3.3FixedEffectsforQuantileRegressionandtheApplication ......... 65 5APPLICATIONANDRESULTS ............................. 67 5.1Introduction ..................................... 67 5.2PanelDataMethodsfortheConditionalMean ................... 68 5.3QuantileRegressionMethodsforPanelData .................... 73 5.4Subsamples ..................................... 79 5.4.1Subsample1929-1984 ............................ 79 5.4.2Subsample1985-1994 ............................ 86 5.4.3ResultsDiscussion ............................. 92 5.4.3.1Quantilemethodsvsmethodsforconditionalmean ....... 92 5.4.3.2NOxvsSO2asaresponsevariable .............. 95 6SUMMARYDISCUSSION ................................ 97 6.1Discussion ...................................... 97 6.2FinalRecommendations .............................. 99 REFERENCES ......................................... 100 BIOGRAPHICALSKETCH .................................. 105 5

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Table page 2-1SummaryoftheSulfurSO2EKCstudies ........................ 33 2-2SummaryofEKCstudiesusingdifferentpollutants ................... 34 3-1DescriptiveStatistics ................................... 42 4-1Meanandmedianperformance .............................. 61 5-1FixedandrandomeffectsmodelcoefcientsforNOx 69 5-2FixedandrandomeffectsmodelsummarystatisticsforNOx 69 5-3FixedandrandomeffectsmodelcoefcientsforSO2 71 5-4FixedandrandomeffectsmodelsummarystatisticsforSO2 71 5-5HighquantilesforSO2 72 5-6QuantileregressioncoefcientsforNOx 75 5-7QuantileregressioncoefcientsforSO2 76 5-8FixedandrandomeffectsmodelcoefcientsforNOxusingsubsample1929-1984 ... 80 5-9FixedandrandomeffectsmodelsummarystatisticsforNOxusingsubsample1929-1984 ............................................ 80 5-10QuantileregressioncoefcientsforNOxusingsubsample1929-1984 ......... 81 5-11FixedanfrandomeffectsmodelcoefcientsforSO2usingsubsample1929-1984 ... 82 5-12FixedandrandomeffectsmodelsummarystatisticsforSO2usingsubsample1929-1984 ............................................ 82 5-13QuantileregressioncoefcientsforSO2usingsubsample1929-1984 .......... 84 5-14FixedandrandomeffectsmodelcoefcientsforNOxusingsubsample1985-1994 ... 86 5-15FixedandRandomEffectsModelSummaryStatisticsforNOxusingsubsample1985-1994 ............................................ 87 5-16QuantileregressioncoefcientsforNOxusingsubsample1985-1994 ......... 88 5-17FixedandrandomeffectsmodelcoefcientsforSO2usingsubsample1985-1994 ... 89 5-18FixedandrandomeffectsmodelsummarystatisticsforSO2usingsubsample1985-1994 ............................................ 89 5-19QuantileregressioncoefcientsforSO2usingsubsample1985-1994 .......... 91 6

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Figure page 1-1DifferentIncome-Pollutionrelationshipscenarios .................... 11 2-1EnvironmetalKuznetsCurve:DifferentScenarios .................... 23 3-1Incomeovertime ..................................... 42 3-2NOxconcentrationsovertime .............................. 43 3-3SO2concentationsovertime ............................... 43 5-1EKCxedandrandomeffectsforNOxusingfullsample.A)Fixedeffects.B)Ran-domeffects. ........................................ 70 5-2EKCxedandrandomeffectswithscatterplotforNOxusingfullsample.A)Fixedeffects.B)Randomeffects. ................................ 70 5-3EKCxedandrandomeffectsforSO2usingfullsample.A)Fixedeffects.B)Ran-domeffects. ........................................ 72 5-4EKCxedandrandomeffectswithscatterplotforSO2usingfullsample.A)Fixedeffects.B)Randomeffects. ................................ 72 5-5QuantileregressionforNOxusingfullsample ...................... 74 5-6QuantileregressionforNOxwithscatterplotusingfullsample ............. 74 5-7QuantileregressionforSO2usingfullsample ...................... 77 5-8QuantileregressionforSO2withscatterplotusingfullsample ............. 77 5-9FixedandrandomeffectsforNOxusingsubsample1929-1984.A)Fixedeffects.B)Randomeffects. ...................................... 80 5-10QuantileregressionforNOxusingsubsample1929-1984 ................ 82 5-11FixedandrandomeffectsforSO2usingsubsample1929-1984.A)Fixedeffects.B)Randomeffects. ...................................... 83 5-12QuantileregressionforSO2usingsample1929-1984 .................. 83 5-13FixedandrandomeffectswithscatterplotforNOxusingsample1985-1994.A)Fixedeffects.B)Randomeffects. ................................ 87 5-14QuantileregressionforNOxusingsubsample1985-1994 ................ 89 5-15FixedandrandomeffectswithscatterplotforSO2usingsample1985-1994.A)Fixedeffects.B)Randomeffects. ................................ 90 7

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.................. 90 5-17QuantileregressionsforSO2usingsample1985-1994 .................. 90 5-18CurvesfromquantileregressionandxedeffectsmodelforconditionalmeanforNOx 5-19CurvesfrommedianregressionandxedeffectsmodelforconditionalmeanforNOx 93 5-20CurvesfromquantileregressionandxedeffectsmodelforconditionalmeanforSO2 94 5-21CurvesfrommedianregressionandxedeffectsmodelforconditionalmeanforSO2 95 8

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TheenvironmentalKuznetscurve(EKC)estimatestheincome-environmentaldegradationrelationship,typicallyemployingmeasuresofper-capitaincomeandtheconcentrationofpollutantsintheair,suchasNOx(nitrogenoxide)andSO2(sulfurdioxide).TheliteraturehasconcentratedonestimationoftheEKCatthemeanemployinglongitudinaldataoncountriesorU.S.states.ThetypicalndingisaninvertedU-shapedrelationship,implyingthatpollutionisincreasinginincomeuptoaturningpointbeyondwhichpollutiondecreases.Estimationatthemean,however,likelymasksheterogeneitiesthatcanbepresentathigherand/orlowerquantilesoftheemissions'distribution.ThisstudyappliesmethodsforquantileregressionestimationofpanelxedeffectsmodelstotheestimationoftheEKConU.S.state-leveldataonNOxandSO2pollutantsovertheperiod1929-1994.OurresultsindicatethatmethodsthatfocusonthemeanprovidetoooptimisticestimatesaboutemissionsreductionofNOx,asquantilemethodsrevealthattherateofreductionisusuallysmaller;whiletheoppositeholdsforSO2.Thedifferencesariseduetotherobustnessofquantileregressiontooutlyingobservations.Theseresultshaveimplicationsforpoliciesadvocatingeconomicdevelopmentasameansforimprovingtheenvironment. 9

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Caneconomicdevelopmentleadtoimprovedenvironmentalquality?TheEnvironmentalKuznetsCurve(EKC)hypothesistriestoanswerthisquestionintermsoftheincome-pollutionrelationship.AccordingtoBeckerman(1992)( Beckerman 1992 ),Awaytoattainadecentenvironmentinmostcountriesistobecomerich.ThishypothesisisgraphicallypresentedbytheEKC(seeFigure 1 A),wheretherelationshipbetweenpollutionandincomefollowsaninvertedUshape.Attheearlystagesofacountry'sdevelopmentboththeaverageincomeandthepollutionincrease.However,afteracertainlevelofincomeisattained,thepercapitapollutionstartstodecrease. TheoriginalKuznetscurveexpressedSimonKuznets'stheorythatwhenacountry'sgeneralincomebeginstoincrease,incomeinequalitybeginstodecrease( KuznetsandSimon 1955 ).TheEKCexpressesthesametheorybutreplacesinequalitywithpollution.Despitethefactthatthedebateregardingtherelationshipofeconomicgrowthandenvironmentalqualityhasexistedsincethelatesixties,theEKCrstappearedandwaspopularizedintheearlyninetieswithbothGrossmanandKrueger's(1991)( GrossmanandKrueger 1991 )studyoftheenvironmentalimpactsoftheNorthAmericanFreeTradeAgreement(NAFTA)andShakandBandyopadhyay'sstudy( ShakandBandyopadhyay 1992 ),whichwasastudyuponwhichthe1992WorldBankDevelopmentReportwasbased.However,theEKClabelrstcametolightinPanayotou's(1993)study( Panayotoy 1993 ).Beforethesestudiesthebroadbeliefwasthateconomicgrowthandenvironmentalimprovementweremutuallyexclusive( Meadows,Meadows,Randers,andBehrens 1972 ).Thus,theappearanceoftheEKCdramaticallychangedtheinitialviewpointoftheenvironmentalimpactsofeconomicgrowthandhadimportantinuenceinpolicydecisions. TheEKChypothesisismostlybasedontheassumptionthatpeoplehavetheluxurytocaremoreabouttheenvironmentalqualityoncetheyattainagivenstandardofliving.Forpolicymakers,theEKCisimportantsinceitenablesthemtoestimatethelevelofincomeat 10

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DifferentIncome-Pollutionrelationshipscenarios whichenvironmentalqualityimprovementsappearor,alternatively,thelevelofenvironmentaldegradationthatwouldoccurduringeconomicdevelopment.Incaseswhereenvironmentaldegradationexceedsacertainlevel,theenvironmentalorpublichealthconsequencescouldbeirreversible.Insuchsituations,EKCstudiescombinedwithanenvironmentalstudyforthepollutant'simpactscancontributetoenvironmentalpolicydecisionsthatavoidsuchincidents. However,questionshavebeenraisedaboutthevalidityoftheEKCanditsabilitytoproducevalidestimatesforeverycountryandpollutant.TheEKCisconsideredbysomeasanoverlyoptimisticpointofviewofeconomicdevelopmentanditisthusnotembracedbytheentirescienticcommunity.ManyenvironmentalistsandscientistsarguethattheEKCdoesnotfollowaninvertedUcurveorthatthelevelofincomeatwhichthemaximumemissionslevelisattainedhasnotbeenexperiencedbydevelopedcountriesyet.ThereareatleasttwoothertheoriesabouttheEKCshape.TherstsupportsthattheEKCfollowsanNshape(seeFigure 1 B)andthesecondsupportsthatemissionsisamonotonicallyincreasingfunctionofincome. Giventheoftencontroversialnatureofempiricalstudies,ithasbeenhardtocorroborateorrefutethecompetingtheories.ThereareempiricalstudiessupportingtheinvertedUshape,as 11

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Ontheotherhand,EKChypothesissupportersarguethatthefactthatsomepollutantsdonotfollowaninvertedUcurvemighthappenbecausewehavenotyetreachedthelevelofincomethatwillcauseenvironmentimprovementforeverypollutant,andthustherelationshipwilleventuallybeaninvertedUasincomeincreases.However,therearestudies,e.g.( Cole,Rayner,andBates 1997 ; SuriandChapman 1998 )thatuseasaproxyindicatorofenvironmentalimpactstheenergyconsumptionpercapitatotesttherelationshipbetweenincomeandgeneralenvironmentalquality.Althoughtheirresultsshowamonotonicallyincreaseofenergywithincome,thisfactalonecannotdenitelysupportaninvertedUshapebutitcannotruleoutthepossibilityeither.Accordingtomostoftheempiricalresults,thepollutantsthatappeartohaveaninvertedUshapeincludesulfurdioxide(SO2),nitrogenoxide(NOx),Dichloro-Diphenyl-Trichloroethane(DDT)andleadamongotherpollutants.WhilethepollutantsthatoftenfailtoshowaninvertedUshapearecarbondioxide(CO2)andpollutantsrelatedtolandresourceorenergyusage. OpponentsoftheEKChypothesishaveadifferentconceptionoftherelationshipbetweeneconomicandenvironmentalimprovementandpointtotheeconometricweaknessesoftheEKCapplications.Theirmajorconceptualargumentisthatsincethereisachangeintheallocationofproducionactivitieswhileacountrydevelops,changesinenvironmentaldegradationmightbeduetotheseallocationchanges.Activitiespreviouslytakingplaceindevelopedcountrieshavenowbeentransferredtootherlessdevelopedcountries.Asaresultpollutionindevelopedcountriesfallwhileglobalpollutionincreaseswithincome.Thisprocesscannotcontinueforeversincelessdevelopedcountrieswillnotalwaysbeabletondotherlessdevelopedcountriestowhichtheycanexporttheirpollution. 12

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SeldenandSong 1994 ).WereviewthemajorconceptualargumentsforandagainsttheEKCinthenextchapter. TheeconometriccritiqueoftheEKCempiricalestimationsisbasedonproblemswithheteroskedasticity,simultaneity,omittedbias,andcointegration( Stern 2004 ).Inaddition,itisarguethattherelationshipbetweenincomeandpollutionisspuriousandthereisnocausalitythatcanbeinferred.However,theEKCproponentsdonotclaimtoestimatecausality,butrathertherelationshipbetweenincomeandemissions.Additionally,EKCopponentsarguethatincludingexplanatoryvariablesthatareindirectlyrelatedtoeconomicdevelopment,suchasoutputallocation,technology,education,orenvironmentalregulation,underminetheincome-pollutionrelationship[see,e.g.( Holtz-EakinandSelden 1995 )]. AcommoncharacteristicbetweentheproponentsandthedetractorsoftheEKCisthatbothexaminetherelationshipofincomeandthemeanpollutantlevel.Sofar,thereisnoworkthatexaminestherelationshipbetweenincomeandpollutionquantiles.Inmanycases,pollutionquantilesareofhigherinterestthanthemeanvalue.Therefore,ananalysisofwethertheinvertedUshapeatthemeanalsoappearsatthemedianorotherquantilesprovidesabetterunderstandingoftheincome-pollutionrelationship.Ahighemissionlevel,whichhappensinhighquantiles,mighthaveirreversibleenvironmentalimpactsorcausehealthproblemsthatahighmeanmightnotreveal.Findingtherelationshipbetweenseveralpollutionquantilesandincomewillhelpusexploretheincome-pollutionrelationshipinmoredetail,thuscontributingtomoreefcientenvironmentalpolicy.IftheconvexityofEKCweresensitivetoincomelevelsforexampleifhighincomecontributestohighconvexityinlowquantilesbutnotinhighquantilestheneconomicdevelopmentmightnotbeasbenecialasthemeanrelationshipasserts.Ontheotherhand,iftherelationshipisatterinlowquantilesandthehighquantilesshowahighconvexcurve,thenhighincomelevelsmightleadtoenvironmentalimprovementsevenifatthemean 13

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Thechoiceofdatahasalsoreceivedcriticismintheliterature.Inprinciple,tocapturetheentireEKCweneeddatathatbeginsfromareasonablyearlystageofdevelopmentandcoverawiderangeofincome.Additionally,thedataneedstohaveenoughobservations.Previousstudieshaveusedcross-country,paneldata.However,thesepaneldatasetsareconsideredofquestionablequality( ListandGallet 1999 ).ForthisinvestigationweuseUSpaneldataatthestatelevelcoveringtheyears1929-1994fortheNOxandSO2emissions.Thedatacontains48states.Inadditiontothequalityadvantagethatappearstoexistinthisparticulardataset( Millimet,List,andStengos 2003 ),therearetwoadditionaladvantages.First,itcontainsemissionsfortheentirestateandnotsolelythepollutionofurbanareas.Thisfeatureavoidsunderestimatingpollution,sinceitisempiricalynotedthatalongwithdevelopmentoccursdecentralization,whichreducestheemissionsinurbanareasandallocatesthemtotherestoftheregion.Second,wedonothavetodealwithexchangerateissuessinceweusedataonlyfromonecountry. Thisthesisisorganizedasfollows:Thenextchaptercontainsaliteraturereviewofanumberofstudies,theircontributiontotheliteratureandtheirweaknesses.WecovertheoreticalstudiesthathavebeendevelopedtoexplainthenatureoftheEKCaswellasthoseopposingtheEKC.WealsobrieyreviewpreviousliteraturethatnotesparticulareconometricweaknessesofEKCestimationandpresentasummaryoftheresults.Inthethirdchapterwediscusssomeaspectsaboutdatachoiceaswellassomeofourdataandcharacteristicsofemissions.Thefourthchapterdealswiththeconceptualframeworkandmethods,presentingthecontributionofthequantileregressiontotheEKChypothesis.Thefthchapterreportstheresultsoftheeconometricanalysisandprovidesadiscussiononthem.Finally,thisthesisconcludeswithabriefsummaryandnalrecommendationsforfutureresearch. 14

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BeforetheEKChypothesis,thegeneralbeliefabouteconomicgrowthwasquitedifferent.Therewasaconcernabouttheenvironment'sabilitytosustainthegrowththatmanycountiesexperiencedinthesixtiesandseventies.TheinitialstandpointofeconomicgrowthwassubmittedbytheClubofRome'sLimittoGrowthreport( Meadows,Meadows,Randers,andBehrens 1972 ).Accordingtothisreporttheconsumptionofrawmaterials,energyandnaturalresourceswasgrowingalmostatthesamerateaseconomicgrowth( DeBruynandHeintz 1999 ).Pollutionandconsumptionofrawmaterials,energy,andnaturalresourceswereincreasingsimultaneously.However,thisreportwascriticizedevenbeforethedevelopmentoftheEKChypothesis.Malenbaum( Malenbaum 1978 )showedempiricallythattherateofconsumptionofsomemetalswasdecreasingwiththeriseofincomeinsomedevelopedcountries.Thisempiricalresultcontradictedthemonotonicrelationshipbetweenincomeandresourceusage.Malenbaum'sstudywasfollowedbyotherstudiesthatshowedsimilarresultsforothermaterials( Williams,Larson,andRoss 1987 ; Janike,Monch,Ranneberg,andSimonis 1989 ; Tilton 1990 ). 15

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GrossmanandKrueger 1991 )oftheenvironmentalimpactsofNAFTA.ThisstudyalongwiththeShakandBandyopadhyaystudy( ShakandBandyopadhyay 1992 ),thelatterofwhichprovidedthefoundationfortheWorld'sBankDevelopmentReport1992,resultedinthepopularizationoftheEKC.Followingthosestudies,manyarticles[e.g.( GrossmanandKrueger 1995 ; ListandGallet 1999 ; Millimet,List,andStengos 2003 )]werepublished,supportingempiricallytheEKCtheoryforsomepollutants(mainlyairpollutants).Despitethefactthattherehasneverbeenanempiricalproofthatthisrelationshipholdsforeverypollutant,manyauthors[e.g.( Beckerman 1992 ; Lomborg 2001 )]arguethattheeconomicgrowthwillnallyleadtoenvironmentalqualityimprovement. Ontheotherhand,thereareopponentsoftheEKCtheoryprovidingtheoreticalargumentsagainstit[e.g.( Ansuategi,Barbier,andPerrings 1998 ; Arrow,Bolin,Cosnstanza,Dasgupta,Folke,Holling,Jansson,Levin,Maler,Perrings,andPimentel 1995 ; CopelandandTaylor 2004 ; Ekins 1997 ; Pearson 1994 ; Stern 1998 ; Stern,Common,andBarbier 1996 )],aswellasempiricalresultsthatdonotprovideevidenceofaninvertedUcurveorprovideaturningpointataveryhighincomelevelforsomepollutants[e.g.( SternandCommon 2001 ; TorrasandBoyce 1998 )].AnotherpointregardingtheEKChypothesisthatreceivesharshcriticismisthatstudiesappeartohaveawidevarietyofresultseveniftheyareabletoshowaninvertedUshape.InitsdefenseEKCproponentspostulatethatthedifferencescanbeascribedtothefollowingfactors:(i)somestudiesuseemissionswhileothersuseurbanconcentrationsasindicatorsofenvironmentalpressure,(ii)differentestimationmethodsused,(iii)differentdataused,(iv)differentmethodsemployedtotransferthenationalpercapitaincomedatatocomparablemonetaryunits,(v)theinclusionofdifferentexplanatoryvariablesotherthanincomeinordertoexplainsomepollutant'svariation( DeBruynandHeintz 1999 ). Despiteallthesedisagreements,thegeneralimpressionfromtheexistingliteratureisthattheempiricalstudiesareabletoprovideaninvertedUshapeforsomeairpollutantssuchassus-pendedaerosolparticulates,SO2,NOxandcarbonmonoxide(CO).Bycontrast,pollutantsthat 16

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Lopez'sarticle( Lopez 1994 )TheEnvironmentasafactorofproduction:Theeffectsofeconomicgrowthandtradeliberalizationtheoreticallyexaminestheincome-pollutionrelationshipandwhetherthepollutionimpactsareinternalizedornot.Additionally,heattemptstondtheimpactsoftradeliberalizationsontheenvironment.Lopezdividestheenvironmentalfactorsintotwocategories:onethatprovidesstockfeedbacktotheproducers(suchasshandforeststockandagriculturalsoilquality)andonethatdoesnot(suchasairquality).Heshowsthatincasewheretheresourcesprovidestockfeedback,iftheproducerinternalizestheseeffects(eitherbygovernmentpolicyinducementorprivateinternalization),thecombinationofeconomicgrowthandtradeliberalizationwilldecreasetheenvironmentaldegradation.Inthecasewhereresourcesdonotprovidestockfeedbackoneoftwocasesoccur:intherstcasethepreferencesarehomotheticandinthesecondtheyarenon-homothetic.Homotheticpreferencesimplyaunitincomeelasticityinenvironmentalqualityandsotheeconomicgrowthleadstoenvironmentaldegradation,buthearguesthatenvironmentaldegradationcannotcontinueforever.Thustheenvironmentalqualitywillbecomealuxurygoodandsothehomotheticpreferencesdonotprovideagoodapproximation.Inthesecondcaseheshowsthatnon-homotheticityundersomeconditionsissufcienttoprovidetheinvertedUcurveforthepollution-incomerelationship. Lopez'sarticle( Lopez 1994 )isbasedontheassumptionthatthepollutioniscausedbyproductionactivitiesandnotbyconsumption,whichisalsotheassumptionatworkinSeldenandSong'sarticle( SeldenandSong 1995 )NeoclassicalGrowth:TheJcurveforabatementandtheinvertedUcurveforpollution.SeldenandSongmodifyFoster'sgrowthmodeland 17

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JohnandPecchenino 1994 )theoreticallyexplorestheeffectsofoverlappinggenerations.Itderivesadynamicmodelwheretheyfocusmoreonexternalitiesthatarederivedbyconsumptionratherthanproduction.ThemodelderivesaninvertedUcurveforsomecasesbutitdoesnotexcludeamonotonicrelationshipbetweenincomeandpollutant.McConnell( McConnell 1997 )concentratesontheimpactofincomeelasticityonenvironmentalqualitydemand.Theoutcomeisthathigh-incomeelasticityisnotsufcienttoderiveanEKCbuteitherlow-incomeelasticity(greaterorequaltozero)isnecessaryforpollutionreduction.However,McConnellclaimsthathigh-incomeelasticityforenvironmentalqualitywouldhelpenvironmentalimprovement.Ontheotherhand,Lieb( Lieb 2001 )generalizesStokey'smodelandshowsthathigherincomeelasticitydoesnotprovidenecessarilyamorepositiveormorenegativeslopeinthepollution-incomerelationship.Additionally,Liebshowsthatassumingthatpollutioncomesfromconsumption,anecessaryconditionforenvironmentalimprovementisconsumptionsatiation.FinallyAndreoniandLevinson( AndreoniandLevisnon 2001 )argueaninvertedUcurvecanbeobtainedevenwithouttheassumptionsmadeinpreviouslyliterature( Stern 2004 ). DeBruynandHeintz 1999 ; Stern 2004 )].Sinceinmostempiricalapplicationstheestimationoftheincome-emissionsrelationshipisobtainedfromareduced-formequationthatcontainsonlyincomeasanexplanatoryvariable,theempiricalapplicationsdonotallowmakinganyinferenceaboutincomecausation.Hence,theargumentsabouttheEKChypothesisarebasedprimarilyontheory.InthenexttwosectionswediscusstheargumentsandthecounterargumentsoftheEKChypothesis,respectively. 18

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SeldenandSong 1994 ).Hence,itismorelikelythattheexpensesneededtoreducepollutionwilllikelycomefromhigh-incomecountries.Iftheelasticityisgreaterthanunity,thena1%increaseinincomewillincreasetheexpendituresonabetterenvironmentbymorethan1%.Thus,incomeincreasesatalowerratethantheexpensesforenvironmentimprovements.Theexpensescouldbeobtainedeitherthroughdonationstoenvironmentalorganizationsorbyconsumingfewerproductsthatharmtheenvironment( DeBruynandHeintz 1999 ). 2. 3. ShakandBandyopadhyay 1992 )examinedtherelationshipbetweenSO2concentrationsandcivilrightsmovementsandfoundthattheemissionsarehigherinlessdemocraticcountries( DeBruynandHeintz 1999 ).Onthecontrary,TorrasandBoyce( TorrasandBoyce 1998 )foundtheoppositeresultforlow-incomecountries( DeBruynandHeintz 1999 ). 4. Panayotou 1997 )provideevidencethatimposingenvironmentalfriendlyregulationswouldhelpbothlow-incomeanddevelopedcountriesinenvironmentalprotection.Ifenvironmentalfriendlyregulationsareconnectedwithincreasedincome,thenitisreasonabletobelievethathigherincomeleadstoenvironmentalimprovement.AccordingtoDasguptaetal( Dasgputa,Laplante,Wang,andWheeler 2002 ),therearethreereasonsthatcanexplainwhyhigh-incomecountriesimposestricterregulationsforenvironmentalprotection.First,aftermajorinvestmentshavebeenmadeinsectorslikehealthandeducationtheenvironmentbecomesahighprioritytogovernment.Second,adevelopedcountryhasthemeanstomonitorpollutionactivitiesandimposeregulations.Finally,higherincomeandeducationallowlocalcommunitiestoenforcehigherenvironmentalstandards,inadditiontofederalregulations. 19

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Stern 2004 ).Overall,economicdevelopmenthasapositiveimpactontechnology,whichinturnhasanimpactontheenvironmentbyusinglesspollutantinputsperunitofoutput.Anotherwayinwhichtechnologycanbenettheenvironmentisthereductionofemissionsperunitofoutput.TechnologyappearstobeanimportantfactorbehindtheinvertedUshapeoftheEKC. 6. Stern 2004 ).Thisarticlereferstomanypreviousstudiesanditcouldbeconsideredasaliteraturereviewitself.OtherarticlesthathavebeencontributingtothecriticismoftheEKCare( Stern,Common,andBarbier 1996 ; Stern 1998 ; SternandCommon 2001 )amongothers.Besidesthosearticles,thereareothersthatrevealpossibleweaknessesoftheEKCeitherbyprovidinganalternativemethodofestimationorbyusingpollutantsthatdonotconrmtheEKChypothesis.WewilldealwithsomeofthesearticlesintheEconometricDrawbacksandResultsSummarysectionsofthischapter.Forthemoment,welistsomeofthemaincounterargumentshere. 20

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FloresandCarson 1995 ; Komen,Gerkin,andFolmer 1996 ; KristomandRiera 1996 ))haveestimatedelasticitysmallerthanunit.Additionally,astudybyMcConnell( McConnell 1997 )showsthatelasticitygreaterthanunitydoesnotnecessarilyprovideevidenceofanEKC. 2. DeBruynandHeintz 1999 ).Furthermore,thestudiesthathaveinvestigatedthisinuencedohaveinconsistentoutcomes.Forexample,theShakandBandyopaohyay'sstudy( ShakandBandyopadhyay 1992 )whichimplicitlysupportstheideathatopenpoliticalsystemsarebenecialfortheenvironmentfoundthattheSO2concentrationsarehigherinmoredemocraticcountries. 3. 2.4.1Introduction Dasgputa,Laplante,Wang,andWheeler 2002 ),wediscussbrieyinthissectiontheracetothebottomandthenewtoxics(bothpessimistic)andtherevisedEKC(optimistic). 21

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2-1 alongwiththerestofthehypothesesthatwediscussinthissection. Dasguptaetal( Dasgputa,Laplante,Wang,andWheeler 2002 ),though,arguethatglobal-izationinfacthasapositiveimpactontheenvironment.Globalizationhasresultedintransferringconsumerandinvestorinuencetoincludecompaniesindevelopingcountries,pressuringthosecompaniestoadjustthestandardsofproductstheyproduceandthetypesofresourcesthattheyuse( Stern 2004 ).IftheRTBhypotheseswasaccurate,thenwiththeincreasedcapitalmobilityandliberalizationthathaveoccurredinpreviousdecades,thereshouldhavealreadybeenarelax-ationofenvironmentalregulations.Yetinsteadwehaveseentheopposite:ageneraltrendtowardstricterregulations( Dasgputa,Laplante,Wang,andWheeler 2002 ). 22

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EnvironmetalKuznetsCurve:DifferentScenarios Dasgputa,Laplante,Wang,andWheeler 2002 ). 23

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TheEKCdrewattentionaftertheGrossmanandKruegerstudy( GrossmanandKrueger 1995 )fortheenvironmentalimpactsofNAFTA.However,sincewearenotexamininganytradeeffectonpollutionimpacts,weconsideranarticlefrom1995thattheypublishedintheQuarterlyJournalofEconomicsentitled,titleEconomicgrowthandenvironment.InthisarticleGrossmanandKruegertrytocovermanydimensionsofenvironmentalquality.Thustheyusethefollowingfourpollutionindicators:urbanairpollution,thestateoftheoxygeninriverbasins,fecalcontaminationofriverbasinsandcontaminationofriverbasinsbyheavymetals.Foreachindicatortheyuseseveralpollutants.Forexample,instudyingairpollutiontheyestimateEKCforSO2andsuspendedparticleswhichtheydividedintotwocategories,heavyparticles,andsmoke,duetothedifferenthealthproblemsthattheycause.Theyusedifferentemissionsfortherestoftheindicators,thoughtheavailabledatadoesnotallowthemtoexaminemanyaspectsforotherpollutantindicators.ThesourceofthedataistheGlobalEnvironmentalMonitoringSystem(GEMS),whichcontainsairpollutionemissionsonlyforurbanareasfordifferentyearsandcities.Fortheremainingindicatorstheemissionscontainobservationsfordifferentyears.ThemethodologythattheauthorsuseisstraightforwardandsimilartotheoneusedfortheoriginalstudyoftheNAFTAimpacts.Theyestimatethereduced-formfortheincomeemissionsrelationshipequation.Thereduced-formequationisspecicallygivenbyequation( 2 )below.Accordingtotheauthorsanalternativemethodthatcouldbeusedisamodelwithstructuralequationsrelatingtoendogenousofincomevariablessuchasenvironmentalregulations,technologyetc.Theauthorssupportthechoiceofthismodelbasedontwoadvantages.Firstthereduced-formgivesthenetincomeeffectonemissionsconcentration,whilewiththestructuralmodelsomeonewouldberequiredtosolvebackinordertondthenet 24

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whereYitisameasureoftheemissionconcentrationinstationiandyeart,GitistheGDPpercapitainyeartinthestationi,GitistheaverageGDPpercapitaoverthepriorthreeyears,Xitisavectorofothercovariates,anditisanerrorterm.The'saretheparameterstobeestimated.IntheXitcovariatestheyincludealineartimetrendinordertoseparatetheeffectsthatareduetoglobaleffects,suchasglobaladvancesintechnologyandlocalincomeincrease.Othercovariatesthatwereincluded,inordertoreducetheresiduals,werevariablesthatwererelatedtothesiteofthemonitoringstations'locationandthemethodthatwasusedforthemonitoring.TheaverageGDPpercapitaforthepastthreeyearswasaddedbecausepastincomeissupposedtohaveagreatinuenceonenvironmentalbehaviorandsincethelagofGDPishighlycorrelatedwithincome,theauthorspreferredtousetheaverageofthepastthreeyears.AlmostallthepollutantsconrmtheEKChypothesis.TheairpollutionandspeciallytheSO2thatweareinterestedinhaveapeakatfourthousanddollarsbutattheveryhigh-incomeshowsthatitstartstoriseagain,thoughwecannotrelyonregionofincomesincetherewerefewobservationsatthislevelofincome.Similarlymostofthepollutantsshowapeakbetweenfourtoeightthousanddollarsbutsomeofthemshowariseagainatthehigherlevelsofincome. AnotherarticlethatconsiderablycontributedtotheempiricalEKCliteratureisaPanyotou'sarticle( Panayotou 1997 )withthetitle``DemystifyingtheenvironmentalKuznetscurve:turningablackboxintoapolicytool.ThisarticleattemptstoextendtheEKCestimationandidentifytheeffectsofenvironmentalpolicyandeconomicgrowthrate.Whileweexpectanunambiguouslypositiveeffectofenvironmentalpolicyonenvironmentalquality,theeffectofeconomicgrowthdependsonthemagnitudesofdemandandsupply,scaleandcompositionsfactors.Thesefactorscanhavecontradictoryresultsonenvironmentalquality.Therefore,in 25

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2 )belowandthethirdisgivenbytheequationillustratedin( 2 ).ThelatterwasusedtondexplicitlytheeffectofincomeonenvironmentaldegradationsoPanayotoudecomposedtheincomeintothefactorsthatwerepreviouslydescribed(scale,compositions,demandandsupply).Xit=0+YYit+YYY2it+YYYY3it+DDit+DDD2it+DDDD3it+GGit+GYGitYit+PPit+YPYitPit+tt+it 26

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KnackandKeefer 1995 ).Allmodelswereestimatedbyxedandrandomeffects.Totestwhetherthexedortherandomeffectsmodelwasthemostappropriate,theHausmantest( Hausman 1978 )wasemployed,whichultimatelyrejectedthehypothesisoftherandomeffectsinfavorofthexedeffectsmodel.Toavoidanheteroskedasticityproblem,generalizedleastsquares(GLS)wasemployedtoestimatethemodel.Additionallytheauthortriedtocheckthemodelformulticollinearityusingpartialcorrelationcoefcients.Nomulticollinearitywasdetectedfortheexplanatoryvariablesbuttherewasadetectionofsomelower-andhigher-ordertermsforsomevariables.Therstmodelwiththecubicincomeandpopulationdensityasex-planatoryvariablesconrmedtheinvertedUcurvebuttheR2was0.148,eventhoughtheincomecoefcientswhereallsignicantforat1%level.ThesecondmodelalsoconrmedtheinvertedUcurve,hadconsiderablehigherR2(R2=0:238)anddemonstratedthatthepopulationdensitywassignicant.Thecoefcientsforthepolicyandgrowthratewerehighlysignicantandhadtheexpectedsigns,whiletheinteractiontermsandthelineartimetrendwereinsignicantandthustheyweredropped.SimilartoGrossmanandKrueger( GrossmanandKrueger 1995 )thecurvestartstoturnupagainafter$15000(1985prices)butsincetherearefewobservationswiththathighincomeitcannotbeconsideredasasignicantoutcome.ThepeakoftheSO2concen-trationoccursat$5000(1985prices),whichiscomparablewithmanypreviousarticles[e.g.( GrossmanandKrueger 1995 ; Shak 1994 )].Theresultsusingthethirdmodelcharacterizedfromequation( 2 )showthattheemissionshaveanegativerelationwithGDPbutafter$13000itbecomespositive.However,likethepreviousmodelsitcannotbeconsideredsignicantforthesamereasonsandadditionallythecubiccomponentofincomeisinsignicant.TheincreaseoftheR2wassubstantial(itroseto0.502),whilethegrowthrateandthepolicycoefcientskepttheirsignicanceandsign.Discoveringthathighgrowthratesresultinhigherlevelsofenvironmentaldegradationasincomerisesisalsoanimportantnding.Thepolicyinteraction 27

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Anotherapproachtoestimatetheincome-pollutionrelationshipthatdiffersfromtheinitialstudieshasbeenattemptedbyS.M.deBruyn,J.C.J.M.vanBerghandOpschoorintheirarticleEconomicgrowthandemissions:reconsideringtheempiricalbasisofenvironmentalKuznetscurves( DeBruyn,denBergh,andOpschoor 1998 ).TheircritiqueabouttheinitialapproachessuchasthoseofShakandBandyopadhyay(1992),SeldenandSong(1994)andGrossmanandKrueger(1995)isthatincludingalineartimetrendinanequationwithacubictransformationofincomeandusingpaneldatafortheestimationwillnotnecessarilyprovidethecorrectEKCforeveryindividualcountry.Theirargumentsarebasedontheassumptionthatifthelineartimetrendissignicantandnegativeandiftheindividualcountry'stimetrendisdifferentthantheonethatthedataprovides,thentheestimatedEKCwillprovidewronginformationforthecountry'sEKCandthuspointpolicydecisionsinthewrongdirection.InthecasethatthetimetrendisinsignicanttheneveniftheresultsprovideaninvertedUshapeforthepanel,theindividualcountry'sEKCmightevendifferfromaninvertedUshapebecausethetimeeffectmightnotbelinearortheEKCmightnotbeshiftinginauniformdirection.ThususingpaneldatatoprovideanEKCforeverycountrymightprovidemisleadingresults.TotestempiricallytheirmodeltheyestimatedtheEKCforthreepollutants(CO2,NOx,SO2)infourcountries(Netherlands,UK,USAandWesternGermany).FindingmanysimilaritiesbetweenresourcesdemandandEKCtheoreticalbackground,theauthorsdevelopedamodelinspiredbyaresourceeconomicmodelfordemandofresourcesbasedontheintensitytousewhichfollowsaninvertedUcurveandisthussimilartotheEKChypothesis.Themodelbasedontheintensitytouseisrepresentedbythefollowingequation( 2 ): whereemissionspercapitaEitforcountryjandyeartareequaltotheproductofthelevelofGDP,YjtandtheemissionintensityofuseUjt(=Ejt=Yjt).Thuschangesovertimecanbeexplainedbythechangesinintensitytouseandchangesinincome.Becauseintensityofuseis 28

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2 )wehaveln(Ejt=Ejt1)=ln(Yjt=Yjt1)+ln(Ujt=Ujt1)andtheintensitytouseisnotconsideredasexogenous,theauthorsusedequation( 2 )fortheirmodel Similartothemodelsthatmostofthepreviousstudiesused,equation( 2 )isareduced-formequation.Theintensitytouseisexpressedby1j;2j;3jwhere1jexpressestheconstanttechnologicalandstructuralchanges,2jshowshowtheintensitytouseiscorrelatedwithincome(sinceYjt1isthelagofGDPpercapita),3jshowshowtheintensitytouseiscorrelatedwithenergyprices,sincePjtisanindexofpriceenergyandthecoefcient0jrepresentstheinuenceofeconomicgrowthonemissions.Themodelwasestimatedthreetimesforeachcase,onewithoutrestrictionsandtwobyimposing1j=0and2j=0respectively,andtheregressionwithbettertwasselected.Theresultsshowedagoodtforalmostallcases(excepttheNetherlandsforSO2emissions)sincetheR2wasbetween0.35and0.7,whiletheDurbin-Watson(DW)andLjung-BoxQ-testsat4and8lagswerewithinnormalcriticallevels.Theresultsforthecoefcientsshowedthat0jispositiveineverycase(exceptNetherlandsforSO2emissionswherethetwasprettylow).Thuseconomicgrowthhasapositivecorrelationwithemissionsgrowth.While0jdifferedsubstantiallyamongthecases,mosttimesitwasclosetounity,whichmeansthata1%growthrate,inducesa1%increaseinemissions.Ontheotherhand,theintensitytousehadanoverallnegativeeffectinallcases,counteractingthepositiveeffectofthegrowthrate.Incaseswheretheconstanteffectwasused,WesternGermanywastheonlycountrywithpositivecoefcientforNOxemission,whiletheincomecoefcientwasnegativeinallcaseswhereitwasused.Thepricecoefcientwassignicantat5%levelonlyinonecase. ThefactthatincomemighthaveadifferenteffectindifferentcountriesisatopicthatListandGalletexamineintheirarticleTheEnvironmentalKuznetsCurve:doesonesizetall?( ListandGallet 1999 ).Theauthorswanttodetermineiftheemissionsfollowthefamiliar 29

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wherePjitisthepercapitaemissionofpollutantjforstateiandyeart.Xisavectorofincometransformations(K=4isthecubictransformation),jiisthevectorofpotentiallyheterogeneouscoefcientsofthelineartimetrendTandjitistheerrorterm.Whileallthepreviousstudiesallowedonlyfordifferentintercepts,ListandGallet'sstudyallowsfordifferentcoefcientsacrossthestates,therebyavoidingtheheterogeneitybias.Moreover,byestimatingthemodelasasystemofequationsofthetwoemissions,theygainefciency.Additionally,equation( 2 )allowsfortime-specictimetrends,whichreducesunexplainedvariations.Tondifthecoefcientsshouldbeconsideredasrandomorxedeffects,theyemployedtheHausman( Hausman 1978 )test,whichrejectedtherandomeffectshypothesisinfavorofxedeffects.Thusthemodelwasestimatedasasystemusingtheseeminglyunrelatedregressions(SUR)method.Theresultsconrmedtheirhypothesissincehomogeneitytestsrejectedthetraditionalspecicationsinfavorofthemoregeneralmodelthatallowsthecoefcientstovary.TheEKCwasstillconrmedfromthemoregeneralmodel,buttheturningpointswerequitedifferent.Comparedwiththeresultsfromthetraditionalspecications,ingeneraltheSO2appearstohaveaturningpointatloweralevelofincome,whiletheturningpointforNOxappearstooccuratahigherlevelofincome.Theauthorsexploredpossiblereasonsforthisheterogeneitybytestingthedifferencesinvariablessuchaseducationlevel,populationdensityandnumberofheatingdegreedays.Theydiscoveredthatstateswhichhavelowerturningpointshavehigherpopulationdensitiesandwarmerclimates,thoughtheseresultscanonlybeconsideredaspreliminary. AnotherstudythatarguesthatthetraditionalmodelsemployedtoestimatetheEKCaremisspeciedwaspublishedbyMillimet,List&StengoswithtitleTheenvironmentalKuznets

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Millimet,List,andStengos 2003 ).InordertoestimatetheEKC,theauthorsemployedtwocommonlyusedparametricmodelsandasemiparametricpartiallylinearregression(PLR).Fortheparametricmodelstheyemployedthetraditionalcubictransformationofincomeandfortheotherapproachtheyusedasplineequationofthecubictransformation,inspiredbySchmalenseeetal( Schmalensee,Stoker,andJudson 1998 ).Thesemiparametricmodelhasnospecicfunctionalformofincome.Therefore,thesemiparametricmodelislessrestrictive.Thedisadvantageofthesemiparametricornonparametrictechniquesisthatsincetheymakefewerassumptionsabouttheobjectbeingestimated,theytendtobeslowertoconvergeonobjectsbeingstudiedthancorrectlyspeciedparametricestimators.Inaddition,unliketheirparametriccounterparts,theconvergencerateistypicallyinverselyrelatedtothenumberofvariablesinvolved.Thusnonparametricmethodsaresuitedinsituationswhereweknowlittleaboutthefunctionalform,thenumberofvariablesissmallandthedatasetisreasonablylarge;hencethissituationisidealforasemiparametricestimation.BasedonthosefactstheyappliedthePLRmodelfortheEKCestimation.Totesttheirhypothesis,theyusedaspecicationtestthatwasproposedbyZheng( Zheng 1996 )aswellasbyLiandWang( LiandWang 1998 ).Thetestshowedthatthenullhypothesisoftheparametricmodelwasrejectedinfavorofthesemiparametricmodel.Theresultsfromthisapplicationshowedthatparametricmodelsaremorepessimisticinthesensethattheturningpointhappensforahigherincomecomparedtotheoneestimatedbythesemiparametricmodel.ThegraphsshowedthatthedataseemstobetbetterbythesemiparametricmodelaccordingtotheEKC,especiallyfortheSO2.Though,usingatestforserialcorrelationcomestoaconclusionthatthePLRsuffersfromserialcorrelationwhichmakestheconclusionofthespecicationtestquestionablesincetheidenticallyindependentdistribution(i.i.d.)hypothesisisrejected. Finally,otherstudiesthatemploysemiparametricornonparametricmethodstoestimatetheEKCwasconductedbyFlores( Flores 2007 )andZapata,PaudelandMoss( Zapata,Paudel,andMoss 2008 ).Flores'studyconsidersnonparametricestimationofboththeEKCanditsturning 31

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Stern 2004 ).Thisexplanationisasuppositionthatappearsinmanyexistingstudiesbutthereisnoclearevidenceforit.Inthenextpartofthischapterwewillpresentresultsfromtheliteratureanddiscusssomeofthepracticalreasonsthatmighthaveleadtothem. Abroadlyexaminedpollutant,whichwillbeexaminedaswellasinthisstudy,isSO2.Table 2-1 ,whichwasprovidedbyStern( Stern 2004 ),summarizesseveralstudiesofSO2.ThelowestturningpointisprovidedbythePanayotou( Panayotoy 1993 )studyat$3,137,whilethestudyofSternandCommon( SternandCommon 2001 )projectsthehigherturningat$101,166.Table 2-2 ,whichwasprovidedbydeBruynandHeintz( DeBruynandHeintz 1999 )showstheresultsfromseveralstudiesforseveralemissionslikeNOxthatweexamineinthepresentstudy.Aswecansee,EKCisconrmedinbothstudiesforNOx,whilethathappensforthemajorityoftheotherpollutantsandstudies.Aswealreadymentionedthereisagreatvariationintheresultsasdemonstratedinbothtables.Somereasonsareasfollows. 1. Stern,Common,andBarbier 1996 ),arguingabouttheheteroskedasticityoferrors,usedgeneralizedleastsquares(GLS)inordertoestimatetheEKC.Studieshaveusedifferentmethodsarenotstrictlycomparablesinceitisnaturalforthemtoyielddifferentresults. 32

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SummaryoftheSulfurSO2EKCstudies Panayotou(1993)$3,137EmissionsNo-Ownestimates1987-8855developedanddevel-opingcountriesShak(1994)$4,379ConcentrationsYesTimetrend,loca-tionaldummiesGEMS1972-8847citiesin31countriesTorrasandBoyce(1998)$4,641ConcentrationsYesIncomeinequality,literacy,politicalandcivilrights.urban-ization.locationaldummiesGEMS1977-91Unknownnumberofcitiesin42countriesGrossmanandKrueger(1991)$4,772-5,695ConcentrationsNoLocationaldummies,populationdensity,trendGEMS1977,'82,'88Upto52citiesinupto32countriesPanayotou(1997)$5,695ConcentrationsNoPopulationdensity,policyvariablesGEMS1982-84Citiesin30developedanddevelopingcountriesCole,RaynerandBates(1997)$8,232EmissionsYesCountrydummy,technologylevelOECD1970-9211OECDcountriesSeldenandSong(1994)$10,391-10,620EmissionsYesPopulationdensityWRIprimarilyOECDsource1979-8722OECDand8develop-ingcountriesKaufmann,Davidsdottir,Garnham,andPauly(1998)$14,730ConcentrationsYesGDP/Area,steelexports/GDPUN1974-8913developedand10developingcountriesListandGaller(1999)$22,675EmissionsN/A-USEPA1929-94USStatesSternandCommon(2001)$101,166EmissionsYesTimeandcountryeffectsASL1960-9073developedanddevel-opingcountries Source:Stern(2004)

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SummaryofEKCstudiesusingdifferentpollutants AuthorsMethods(Ef-fects)SO2(peak)(through)type)Part(peak)typeNOxemis.(peak)CO2emis.(peak)FaecalColiform(peak)1/Dissolvedoxygen(peak)DeforestationExch.RatesAdditionalvariables GrossmanandKrueger,1995GLS(re)N(4100)(13000)conc.EKC(6200)conc.EKC(8000)EKC(2700)PPPLaggedincomeShakandBandy-opadhyay,1992OLS(fe)EKC(3700)conc.EKC(3300)conc.MIN(1200)(11400)MIatPPPVarietyofothervariablesPanayotou,1993OLS(pcs)EKC(3000)emis.EKC(4500)emis.EKC(5500)EKC(1200)MERSeldenandSong,1994GLS(re,fe)EKC(10300)emis.EKC(10300)EKC(11200)PPPPopulationdensityTorrasandBoyce,1998OLS(pcs)N(3400)(14000)conc.atatN(5100)(19900)PPPInequalityVariablesHoltz-EakinandSelden,1995OLS(fe)EKC(35400)PPP Source:deBruynandHeintz(1999) Notes: N=N-shapedcurve,U=U-shapedcurve,EKC=invertedU-shapedcurve,MI=monotonicallyincreasingcurve,at=allparame-tersexceptinterceptinsignicant.PeaksroundedatUS$100. GLS=generalizedleastsquares,OLS=ordinaryleastsquares,re=randomeffects,fe=xedeffects,pcs=pooledcrosssection Conc=concentrations,emis=emissions,PPP=purchasingpowerparity,MER=marketexchangerate Particlesdifferwithrespecttohowthesearebeingmeasured Dissolvedoxygenisanindicatorforenvironmentalquality,notdegradation.Andforthesereasonswetaketheinverseofdissolvedoxygen.HenceanEKCinfactreectsaU-.shapedcurveandthemonotonicallydecreasingpatternfoundbyShakandBandy-opadhyayreectscontinuousdeterioration. Turningpointsformodelswithpopulationdensity,forSO2usingrandomeffects,forparticlesandNOxusingxedeffects.

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GrossmanandKrueger 1995 ),theyusedasamplethatwasprovidedbyGEMS,whileShakandBandyopadhyay( ShakandBandyopadhyay 1992 )usedadatabasefromtheCanadianCenterforInlandWaters.TheresultwasthatthelatterstudyfoundanNshapeforfaecalcoliformwhiletheformerstudyfoundaninvertedUshape.Anotherdatasetthatwasbroadlyusedintheempiricalstudies,andisalsothedatasetthatthepresentstudyuses,isthepaneldatafromtheU.S.EnvironmentalProtectionAgency's(EPA)NationalAirPollutantEmissionTrends,whichcontainsobservationsofthe48statesoftheU.S. 3. 4. SeldenandSong 1994 ),laggedincome( GrossmanandKrueger 1995 ),tradevariables( ShakandBandyopadhyay 1992 ; TorrasandBoyce 1998 ),variablesrepresentingthestructureoftheeconomy( Lucas,Wheeler,andHettige 1992 ; SuriandChapman 1998 ; Kaufmann,Davidsdottir,Garnham,andPauly 1998 )andvariablesthatcapturetheeffectsofpoliticalandcivilrights( ShakandBandyopadhyay 1992 ; TorrasandBoyce 1998 ).Includingsuchvariableshascapturedpartoftheincome-relatedeffectthatotherstudieshaveoverlookedandhashenceledtoachangeintheturningpointsandtheincome-pollutionrelationship( DeBruynandHeintz 1999 ).Ontheotherhand,excludingthesevariablesfromthemodelhastheadvantageofestimatingthegrosseffectofincometothepollution.Thesevariableschangeatleasttosomeproportionduetoincome'sincreasethusincludingtheminthemodelwillresultinmissingthateffectasincome'seffect. Stern 2004 )]whilesomeofthemtrytoeliminatetheproblemsthattheEKCapplicationsface[e.g.( Stern,Common,andBarbier 1996 ; Millimet,List,andStengos 2003 )].TheEKCeconometriccriticismsarebasedonheteroskedasticity,omittedvariablebiasandcointegration.Inthispartofthechapterwediscussbrieytheseeconometricproblems,whichhavebeenalsodiscussedbyStern( Stern 2004 ). 35

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Stern,Common,andBarbier 1996 ).ThustheresidualisnegativelycorrelatedwiththetotalGDP.Evidenceofheteroskedasticityap-pearedinstudy( Schmalensee,Stoker,andJudson 1998 ).Inordertocorrectthisproblem,Stern( Stern 2002 )estimatedadecompositionusingfeasibleGLS.Inthisapplication,thegoodnessoftwassignicantlyimproved. 2. SternandCommon 2001 ).Theauthorsbasedtheirargumentsonthefollowingthreefacts:(a)differencesbetweenthexedandrandomeffectsmodelsusingtheHausmantest;(b)differentestimatesindifferentsubsamples;(c)existenceofserialcorrelation( Stern 2004 ).TheHausmantestconrmsthatthereisasignicantdifferencebetweenrandomandxedeffects.Thisindicatesthatthetermsofincomearecorrelatedwiththexedeffects.Consideringthatthexedeffectsexpressthemeaneffectoftheomittedvariables,itisverylikelythatincometermswillbecorrelatedwithomittedvariables,yieldingbiasedestimators.Theauthorsusedtwosubsamplesinordertoseeiftheyproducedifferentresults.Onesamplecontainednon-OECDcountriesandtheotherOECDcountries.Theestimatesfromthenon-OECDsampleprovidedaveryhighturningpoint,whiletheOECDsampleprovidedaturningpointwithintherangeofthesample.ThefactthatthedatacannotbepooledtogetherisconrmedfromaChowF-test,whichrejectsthishypothesis.Additionally,thearticleprovidesevidencethatthemodelsuffersfromserialcorrelation,indicatingmisspecicationofomittedvariablesormissingdynamics( Stern 2004 ).Anarticlethattriedtodealwithmisspecicationproblemis( Millimet,List,andStengos 2003 ),tobethatdiscussedpreviouslyinthesectionThedevelopmentofempiricalstudies. 3. Stern 2004 ).ThoughtherearesomestudiesthathavetriedtoidentifyifthereiscointegrationinEKCmodels,theresultsfromthesestudiesdonotprovideaclearcutpictureofthecointegration.Forexample,PermanandStern( PermanandStern 2003 )testforunitrootandcointegrationasdoSternandCommon's( SternandCommon 2001 )dataandmodelsrespectively.Thetestsforunitrootdemonstratethattheserieshavestochastictrends.Ontheotherhand,thetestsforcointegrationshowthataroundhalfoftheindividualcountriesregressionscointegrate,thoughsomeofthemhaveawrongsign.Usingsubsamples,theresultscontinuetogiveavaguepicture.Somerejectandsomefailtorejectthenoncointegrationhypothesis.However,incaseswherecointegrationisfound,besidestheshapeofEKCdifferingacrosscountries,thehypothesisofacommonintegratedvectorwasstronglyrejected.Inordertocorrectforthelackofcointegrationtheyusedtherstdifference.TheresultsshowedthattheEKCpredictedlargeturningpointsbutstilltheparameterswerestatisticallydifferentbetweenthegroupsofcountries.Additionally,DayandGrafton( DayandGrafton 2003 )usingCanadiantimeseriesdatafailedtorejectthehypothesisofnocointegrationinalmosteverycase.AnotherstudythatfailstogiveaclearansweraboutthepresenceofcointegrationisdeBruyn's( DeBruyn 2000 ).Usingthedata 36

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Thepicturethatwegetfromtheliteraturedoesnotallowustoarriveatanalconclusionabouttheappropriatenessofthemethodsusedthusfarandtheirabilitytoprovidetrustworthyresults.IntheEKCempiricalstudies,likealltheempiricalapplications,thereareproblemsthatwehavetoconsiderbeforemakinganalconclusionabouttheEKChypothesisaswellasthepositionoftheturningpoints(iftheyexist).DespitethenumerousstudiesthathaveexploredtheEKC,wearenotinapositiontosayiftheinvertedUcurveexists,atleastforeverypollutant.Thedataqualityandpollutantindicatorthathavebeenusedtoderivetheincome-pollutionrelationshipseemtobethemainreasonforthedifferentresults.Inthenextchapter,wediscussthemethodstobeusedinourstudyandthereasonsthatledustothisapproach. 37

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InEKCapplications,animportantissueisdataselection.WestartthischapterbydescribingthedrawbacksthatappearindataselectionofEKCstudiesandtheadvantagesofpaneldata,astheoverwhelmingmajorityoftheexistentEKCliteratureusespaneldata.Thissectionisfollowedbyadiscussionofthesourcesanddetailsofthedatathatisusedinthepresentstudy.Wecontinuewiththedescriptivestatisticssectionwherewepresentedsomediagrams,andwenishwithsomebasicinformationaboutthepollutantsthatareusedinthisstudy. 1. 2-1 .Becauseinmostcasestheprovinceorstateleveldatasetisnotavailable,andthetimeseriesareusuallynotlargeenoughforvaluableestimates1,researchersareconstrainedtousingcrosscountryorpaneldatainordertocollectenoughobservations. 2. DeBruyn,denBergh,andOpschoor 1998 )whichusestimeseriesforEKCestimation,including20to30observationsforeachtimeseriestheyuse. 38

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4. Stern 2004 ).Anotherdrawbackinthechoiceofthepollutantisthatnotallofthemfollowthesamepath.ThereisnospecicpollutantthatcanbeusedtotesttheEKChypothesis,yetusingdifferentpollutantleadstodifferentresults.Whilesomeattemptshavebeenmadetocaptureallenvironmentalimpacts( SuriandChapman 1998 ; Cole,Rayner,andBates 1997 ),theindicatorsusedinthesestudiesarebasedonenergyusewhichdoesnotnecessarilyfollowsaninvertedUcurvewithincome( Galli 1998 ).Thus,tosomeextent,theselectionofpollutantsdependsonthehypothesiswewanttotest.Butthetestresultsfromonepollutantcannotconrmthatotherpollutantswouldhavethesameresults. ListandGallet 1999 )andMillimet,ListandStengos( Millimet,List,andStengos 2003 ).TheEPA'sreportcontainsannualobservationsfrom1929to1994for48statesintheU.S..Forthepollutants,weuseemissionspercapitaofNOxandSO2in 39

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Hsiao 2007 ).Belowwepresenttwooftheadvantagesthatourstudygainsbyusingpaneldata. 1. Itcontainsmoresamplevariabilitysincebothcross-sectiondataandtimeseriesconstitutepaneldata.Thelargenumberofcross-sectionalunitsalongwiththesignicantrangeintimeallowforgreatervariability.Thusinferenceusingpaneldataismoreprecise. 2. Becausedifferentquantitativetechniquescanbeappliedtopaneldata,thistypeofdataofferstheopportunitytocapturemorecomplexeffects.Forexample,wecanusexedeffectstocontrolforsomeoftheimpactoftheomittedvariablesbysimplyaddingdummyvariablesforindividualsandtime. 40

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1. 2. 3. 4. Aswecanseefromgure 3-1 ,apartfromthetheeconomiccrisisoftheearly'30s,incomerisessteadily,reachingitsmaximumlevelinthelastyearofthestudy.Atthesametime,NOxandSO2donotseemtofollowtheoppositepathandreduceovertime.WhileNOxconcentrationwasdecreasingovertime,apartfromthesecondhalfofthe30'swhenitincreasedsubstantially,SO2wasincreasingovertimeuntilthelastdecadesinwhichitseemstohave 41

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DescriptiveStatistics VariableMin1stQuartileMedianMean2ndQuartileMaxStdDev Income1,1625,8498,4369,08912,38022,4604,241.5NOx0.02300.05140.07590.09280.10661.13600.0735SO20.00210.05920.09680.16470.18401.61800.2060 Incomeovertime stabilizedapartfromtheyear1988.FromthediagramswecaninferthatU.S.SO2concentrationcontainsmoreoutliersthanNOx.Fromtable 3-1 ,wecanseethatincomeobservationscontainawiderangefrom$1,162to$22,460in1987dollarswhichhelpsusmakesafeinferencesforthatrangeofincome.Inaddition,wecanseethatthereislargevariationnotonlyofincomebutofemissionstoo,whichwillhelpourmodelprovidepreciseinderence.Missingobservationsregardingincomelevelslowerthan$1162resultinlossofimportantinformationabouttheincome-emissionrelationshipsinceonethirdoftheworld'scountrieshavelowerthan$1100incomepercapita( Millimet,List,andStengos 2003 ) 42

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Figure3-3. 43

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Theprimaryhumanactivitysourcesofnitrogenoxidearemotorvehicles,electricalutilitiesandotherindustrial,commercialandresidentialsourcesthatburnfuels.Nitrogenoxidesformwhenfuelburnsathightemperatures,inthesourcesmentioned.MobilesourcesareresponsibleformorethanhalfofallnitrogenoxideemissionsintheUnitedStates.About40percentofNOxemissionsarefrompowerplants.Therestisemittedfromvarioussourceslikeindustrialandcommercialboilers.Bothon-roadandnonroadmobilesourcesaremajornitrogenoxidepolluters. Nitrogenoxidescantravellongdistances,causingavarietyofhealthandenvironmentalproblemsinlocationsfarfromtheiremissionssource.Theseproblemsincludeozoneandsmog,whicharecreatedintheatmospherefromnitrogenoxides,hydrocarbons,andsunlight.Onsmoggydays,asymptomisdifcultybreathingortroublingseeingobjectsinthedistance.Nitrogenoxideemissionsalsocontributetotheformationofparticulatematterthroughchemicalreactionsintheatmosphere.NOxcanharmtheenvironmentinseveralways,suchasformatingground-levelozoneandacidaerosols,deterioratingwaterquality,andreactingwithotherelementstoformtoxicchemicals.MoredetailedtheproblemstheNOxcancausearethrough: 44

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Moreover,oneofthemostseriousimpactsisacidrain,whichwillbediscussedinaseparatesubsectionsinceitiscausedbyacombinationofSO2andNOx. 45

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TheCleanAirActAmendmentsof1990createanew,balancedstrategyfortheNationtoattacktheproblemofurbansmog.Overall,thenewlawrevealstheCongress'shighexpectations 46

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TheCleanAirActrequiresEPAtosetNationalAmbientAirQualityStandardsforsixcommonairpollutants.ThesecommonlyfoundairpollutantsarefoundallovertheUnitedStates.Theyareparticlepollution(oftenreferredtoasparticulatematter),ground-levelozone,carbonmonoxide,sulfuroxides,nitrogenoxides,andlead.Thesepollutantscanharmourhealthandtheenvironment,andcausepropertydamage.Ofthesixpollutants,particlepollutionandground-levelozonearethemostwidespreadhealththreats.BecausethemaininterestofthisstudyistoconcentrateonthepollutionproblemsthatarecausedfromNOxandSO2emissions,wewillonlyexaminethepartofCleanAirActthatconcernthesepollutants.TheCleanAirActcanbeseparatedinsomeactionsaccordinglytothepurposeoftheaction.Thus,weonlydiscussactionsthataffectstheNOxandSO2emissions. TheinitialphaseofEPA'sAcidRainProgramwentintoeffectin1995.Thelawrequiredthehighestemittingunitsat110powerplantsin21Midwest,Appalachian,andNortheasternstatestoreduceemissionsofSO2.Thesecondphaseoftheprogramwentintoeffectin2000,further 47

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EachallowanceisworthonetonofSO2emissionsreleasedfromtheplant'ssmokestack.PlantsmayonlyreleasetheamountofSO2equaltotheallowancestheyhavebeenissued.IfaplantexpectstoreleasemoreSO2thanithasallowances,ithastopurchasemoreallowancesorusetechnologyandothermethodstocontrolemissions.Aplantcanbuyallowancesfromanotherpowerplantthathasmoreallowancesthanitneedstocoveritsemissions. Thereisanallowancesmarketthatoperateslikethestockmarket,inwhichbrokersoranyonewhowantstotakepartinbuyingorsellingallowancescanparticipate.Allowancesaretradednationwide. EPA'sAcidRainProgramhasprovidedbonusallowancestopowerplantsforinstallingcleancoaltechnologythatreducesSO2releases,usingrenewableenergysources(solar,wind,etc.),orencouragingenergyconservationbycustomerssothatlesspowerneedstobeproduced.EPAhasalsoawardedallowancestoindustrialsourcesvoluntarilyenteringtheAcidRainProgram. The1990CleanAirActhasstiffmonetarypenaltiesforplantsthatreleasemorepollutantsthanarecoveredbytheirallowances.AllpowerplantscoveredbytheAcidRainProgramhavetoinstallcontinuousemissionmonitoringsystems,andinstrumentsthatkeeptrackofhowmuchSO2andNOxtheplant'sindividualunitsarereleasing.PowerplantoperatorskeeptrackofthisinformationhourlyandreportitelectronicallytoEPAfourtimeseachyear.EPAusesthisinformationtomakesurethattheplantisnotreleasingquantitiesofpollutantsexceedingtheplant'sallowances.Apowerplant'sprogramformeetingitsSO2andNOxlimitswillappearontheplant'spermit,whichisledwiththestateandEPAandisavailableforpublicreview. Besidestheground-breakingfeaturesintheAcidRainProgram,the1990CleanAirActencouragedotherinnovativeapproachesthatspurtechnology.Theseapproachesallowbusinesses 48

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Thesereductionswouldnotbepossiblewithoutcleaner,verylowsulfurgasolineanddieselfuel.Inadditiontotheirdirectemissionsbenets,cleanerfuelsenablesophisticatedemissioncontroldevicestoeffectivelycontrolpollution.CongressrecognizedtheimportanceofcleanerfuelstoreducingmotorvehicleemissionsandgaveEPAauthoritytoregulatefuelsintheCleanAirAct. TheCleanAirActrequirescertainmetropolitanareaswiththeworstground-levelozonepollutiontousegasolinethathasbeenreformulatedtoreduceairpollution.Otherareas,includ-ingtheDistrictofColumbiaand17states,withground-levelozonelevelsexceedingthepublichealthstandards,havevoluntarilychosentousereformulatedgasoline.Reformulatedgasolinereducesemissionsoftoxicairpollutants,suchasbenzene,aswellaspollutantsthatcontributetosmog. 49

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Since2006,renershavebegunsupplyingdieselfuelwithverylowsulfurlevelsforhighwaydieselvehicles.Aswithgasolinevehicles,efcientnewemissioncontrolsondieselenginesrequirethisUltra-LowSulfurDiesel(ULSD)fueltofunctionproperly.Highwaydieselfuelsulfurlevelsare97percentcleanerthandieselpriorto2006.In2007,renersbeganreducingsulfurindieselfuelusedfornonroaddieselengines,suchasconstructionequipment. TheCleanAirActencouragesdevelopmentandsaleofalternativefuels.Alternativefuelsaretransportationfuelsotherthangasolineanddiesel,includingnaturalgas,propane,methanol,ethanol,electricity,andbiodiesel.Thesefuelscanbecleanerthangasolineordieselandcanreduceemissionsofharmfulpollutants.Renewablealternativefuelsaremadefrombiomassmaterialslikewood,wastepaper,grasses,vegetableoils,andcorn.Theyarebiodegradableandreducecarbondioxideemissions.Inaddition,mostalternativefuelsareproduceddomestically,whichisbetterforoureconomy,energysecurityandhelpsoffsetthecostofimportedoil. TheCleanAirActalsorequiresEPAtoestablishanationalrenewablefuel(RF)program.Thisprogramisdesignedtosignicantlyincreasethevolumeofrenewablefuelthatisblendedintogasolineanddiesel. Dieselenginesaremoredurableandaremorefuelefcientthangasolineengines,butcanpollutesignicantlymore.Heavy-dutytrucksandbusesaccountforaboutone-thirdofnitrogenoxidesemissionsandone-quarterofparticlepollutionemissionsfromtransportationsources.Insomelargecities,thecontributionisevengreater.Similarly,nonroaddieselenginessuchas 50

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EPAhasissuedrulestocutemissionsfromonroadandnonroadvehiclesbymorethan90percentbycombiningstringentemissionsstandardsfordieselenginesandclean,ultra-lowsulfurdieselfuel.UndertheCleanAirAct,EPAisalsoaddressingpollutionfromarangeofnonroadsources,includinglocomotivesandmarinevessels,recreationalvehicles,andlawnandgardenequipment.Together,thesesourcescompriseasignicantportionofemissionsfromthetransportationsector. CongressrequiredconformityintheCleanAirActAmendmentsof1990.Inotherwords,transportationprojectssuchasconstructionofhighwaysandtransitraillinescannotbefederallyfundedorapprovedunlesstheyareconsistentwithstateairqualitygoals.Inaddition,transportationprojectsmustnotcauseorcontributetonewviolationsoftheairqualitystandards,worsenexistingviolations,ordelayattainmentofairqualitystandards. Theconformityprovisionsrequireareasthathavepoorairqualitynow,orhaditinthepast,toexaminethelong-termairqualityimpactsoftheirtransportationsystemandensurethatitiscompatiblewiththearea'scleanairgoals.Indoingso,thoseareasmustassesstheimpactsofgrowthonairpollutionanddecidehowtomanagegrowth. Propermaintenanceofacar'sengineandpollutioncontrolequipmentiscriticaltoreduceexcessiveairpollution.Tohelpensurethatsuchmaintenanceoccurs,theCleanAirActrequirescertainareaswithairpollutionproblemstoruninspectionandmaintenance(I/M)programs.The1990Actalsoestablishedtherequirementthatpassengervehiclesbeequippedwithonboarddiagnostics.Thediagnosticssystemisdesignedtotriggeradashboardcheckenginelightalertingthedriverofapossiblepollutioncontroldevicemalfunction.Tohelpensurethatmotoristsrespondtothecheckenginelightinatimelymanner,theActrequiresthatI/Mprogramsincludeaninspectionoftheonboarddiagnosticsystem. 51

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OperatingpermitsareespeciallyusefulforbusinessescoveredbymorethanonepartoftheCleanAirActandadditionalstateorlocalrequirements,sinceinformationaboutallofasource'sairpollutionisinoneplace.Thepermitprogramsimpliesandclariesbusinesses'obligationsforcleaningupairpollutionandcanreducepaperwork.Forinstance,anelectricpowerplantmaybecoveredbytheacidrain,toxicairpollutant,andsmog(ground-levelozone)sectionsoftheCleanAirAct.Thedetailedinformationrequiredbythoseseparatesectionsisconsolidatedintooneplaceinanoperatingpermit. Businessesseekingpermitshavetopaypermitfees,muchlikecarownerspayingforcarregistrations.Thesefeespayfortheairpollutioncontrolactivitiesrelatedtooperatingpermits. 52

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Inthischapterwewilldiscussthemethodsthatwereemployedtoconductthisstudyaswellastheconceptualframeworkthatledustousethesemethods.Webeginwiththeconceptualframework,thenwemovetoadiscussionofthemethodsthatwereusedtoanalyzepaneldatasetsandnallywedescribethequantileregressionmethodsthatarethemainfocusofourapplication.Firstweillustratequantileregressionmethodsforcross-sectionaldatasetsandthenwecenteronpaneldatamethodsaswellastheprecisealgorithmsusedbythesoftware(Rversion2.7.2)toestimatethecoefcientsandtheirstandarderrors. 53

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Wooldridge 2005 )andGreene's( Greene 2002 ). Usingpaneldataresultsinobservationsnotbeingidenticallyorindependentlydistributed.Forexample,inourcase,wheretheincomeandtheemissionsaremeasuredforeverystateforaseriesofyears,measurementscorrelateovertimeorcorrelatebetweentheyearsinvariousstates.Therefore,observationsarenotindependentlydistributed,resultinginbiasedandinconsistentestimatesincaseswheretimeseriesorcross-sectionmethodsareused.Thiscanbeeasilyshowninequation( 4 ),wherez0iaretheunobservedindividualheterogeneitywhichcontainsallthecharacteristicsthatarenotincludedintheregressorsandportraystheindividual(ortime)attributessuchasability,geographicalcharacteristics,etc.Incaseswhereweusemethodsofcross-sectionalanalysisandpooltheobservations,equation( 4 )becomesequation( 4 ) wherevit=z0i+"itisanerroroftencalledcompositeerror.Weknowfromtheassumptionsoftheordinaryleastsquares(OLS)thattheerrortermshouldbeuncorrelatedwiththeregressors. 54

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4 )willprovidebiasedandinconsistentestimatesincaseswheretheindividualeffectsz0iarecorrelatedwiththeregressorsxit. 4 )andcanbeestimatedbyOLS.Individualicanrepresentindividuals,states,nations,families,rms,cities,etc.acrosstime.Incaseswherethereisalargeamountoftimeobservationsortheobservationsaresupposedtovaryacrosstime,andwewanttocapturethisvariation,theLSDVmodelisdescribedbyequation( 4 )whereadditionaldummyvariablesareincludedforeverytimeperiod(weexcludeoneyearinordertoavoidcollinearity).Thexedeffectsmodelwheretimeandindividualeffectsareincludedisoftencalledatwo-wayxedeffectsmodel.Thenamexedeffectsdoesnotindicatenon-stochasticpropertiesbutinsteadindicatesthattheindividualortimeeffectsremainconstantovertime( Greene 2002 ). WhereyitandXitaretheobservationoftheithindividualinthetimeperiodt. Thetwo-wayxedeffectsmodelisgivenbytheequation: InorderforthexedeffectsmodeltoprovideunbiasedandconsistentestimatorswehavetomakesomeassumptionsasillustratedinWooldridge( Wooldridge 2005 ). Foramodeloftheformofequation( 4 )wehavethefollowing: AssumptionFE.1

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AssumptionFE.4 AssumptionFE.6 4 )canbereformulatedas 56

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wherethereareKregressorsincludingaconstanttermwhichisthemeanoftheunobservedindividualheterogeneity.Theuiistherandomheterogeneitycomponentspecictotheithindividualandisconstantthroughtime.Fromequation( 4 )wecaneasilyseethatui=z0iE[z0i].TobeREmodelconsistent,wehavetomakesomeadditionstothepreviouslydescribedassumptionsfortheFEmodel.Thus,followingWooldrige( Wooldridge 2005 )wereplaceFE.3,FE.4andFE.5withRE.3,RE.4andRE.5respectively,thereforewehave: AssumptionRE.3 E["2itjX]=2"; E[u2itjX]=2u;

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E[uiujjX]=0; 4 ),theerrortermofequation( 4 )canbewrittenasfollows: Thereforeusingequations( 4 )wehave: E[itisjX]=2+2u; E[2itjX]=2u;t6=s E[2itjsjX]=08tandsifi6=j 58

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4 )wecandeducethaterrortermsareseriallycorrelated.Hence,theusageofOLSwouldresultininefcientestimators.Consequently,toestimateourmodelefcientlywehavetousegeneralizedleastsquares(GLS). Usingmatrixalgebra,Greene( Greene 2002 )showsthatthetransformationforGLSofyiandXiis: SwamyandArora 1972 ). 59

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Hausman 1978 )providesaspecicationtestbetweentheFEandREmodelinordertotestwhichoneismoreappropriate.TheHausmantestisbasedonthehypothesisthattheestimatesfromFEandREshouldnotdiffersignicantlyifindividualeffectsareuncorrelatedwithregressorswhiletheREmodelismoreefcient.FollowingGreene( Greene 2002 )theHausmantestisachi-squaredtestwhichisbasedonthefollowingWaldcriterion: Hausman 1978 )wehavethefollowingresult: sinceHausman'sessentialresultisthatthecovarianceofanefcientestimatorwithitsdifferencefromaninefcientestimatoriszero,wehavethefollowing: Therefore,usingequations( 4 )and( 4 )wehave: 60

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4.3.1General 4-1 ),ifthesampledistributionisnotnormalorhasthickertails,themedianperformsbetterthanthemean.Ontheotherhand,ifthesampleapproximatesanormaldistribution,themeanappearstoperformbetter. Table4-1. Meanandmedianperformance DistributionDensityMeanMedian Normal1 2exp(z2 2exp(jzj)21.57Cauchy1 Imbens 2005 ) 4 )itestimatesequation( 4 ). whereistheselectedquantile. Themedian'sandmean'spropertiesusuallyplayacrucialroleinthemethodsthatareusedtoestimateregressionrelationships.Therefore,similartothepropertiesofmedianandmean,amodelthatestimatestheconditionalmeaniseasiertocollapsethanamodelthatestimatesthe 61

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Koenker 2005 ). Employingdecisiontheoryasanalternativewayofestimatingthequantileofasampleinsteadofrankingtheobservations,leadstominimizingtheexpectedloss.Ifweconsiderthatthelossisgivenbythepiecewisefunction( 4 ) thenwehavetondthe^xwhichminimizestheexpectedlossforsome2(0;1).Therefore,inordertondthequantilewehavetominimize:

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Followingthisminimizationproblem,KoenkerandBasset( KoenkerandBasset 1978 )providedanestimationmethodoftheconditionalquantileQy(jx)asafunctionofx's.Knowingthatthemeancanbeestimatedbysolvingtheminimizationprobleminequation( 4 ),theestimationoftheconditionalmeanofyonxas(x)=xTcanbeestimatedbyminimizing( 4 ). 63

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4 ),theconditionalquantileQy(jx)asafunctionofx'scanbegivenby( 4 ) Aswedescribedpreviously,oneadvantageofquantileregressionisthatitismorerobustanditdoesnothavetoassumeGaussianerrorsinordertoprovideconsistency,likeleastsquares.Nowthatwehavedescribedhowitispossibletoestimatequantileregression,wehavetodiscussthetheconditionsunderwhichtheestimatorsareconsistent.Supposethataconditionalquantilefunctionisgivenbyequation( 4 ): andthattheconditionaldistributionfunctionsFniofYi,i=1;2;:::;satisfythecondition: Koenker 2005 ),ElBantiandHallin( ElBantliandM 1999 )showedthattheaboveconditionisnecessaryandsufcientforconsistency,providedweassumethatthesequenceofdesignmatricesXsatisesthefollowingconditions

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Ontheotherhand,thereexistpenalizedmethods( Koenker 2004 )thatareusedforlongi-tudinaldatathatimprovetheefciencyoftheFEmodel.However,theymostlysuitedforcaseswheretherearejustfewobservations(usuallylessthan5)ofoneofthetwo-wayeffects,timeorindividuals.Nevertheless,notheoreticalbackgroundhasbeendevelopedyettousetwo-waypenalizedmethods.Therefore,evenifitwasmoreappropriate,itwouldbeinfeasibletouse.IncaseswhereweneededtoimprovetheFEmodel'sefciencywecouldestimateamodelwherequantileregressionsareestimatedsimultaneouslybyimposingidenticalindividualeffectsforeveryquantileandminimizingtheequation( 4 )( Koenker 2004 ) wherewkisaweighttocontroltherelativeinuenceoftheqquantilesf1;:::;qgontheesti-mationoftheithparameter.Nevertheless,usingthismodelweimposeonemoreassumption:individualeffectsdonotdependonthequantile. Therefore,sinceinourcasetherearesufcientdegreesoffreedomtoprovidesignicantresults,themoreappropriatemodeltouseistheFEbyestimatingeachquantileregression 65

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4 )havingeachindividualeffectidependonthequantile Qy(jxi)=i()+t()+x()+ui Therefore,theestimationmethodofthismodelisgoingtobethesameastheonethatwedescribedintheprevioussubsectionforthequantileregressionandissynopsizedinequation( 4 ).Inorderforsoftwaretosolvethisminimizationproblemandobtainestimators,weusethemethoddescribedindetailbyKoenkerandd'Orey( Koenkerandd'Orey 1987,1994 ).ThismethodisamodiedversionofBarrodaleandRobertsalgorithm( BarrodaleandRoberts 1974 )forl1-regressionandisquiteefcientforproblemsthatincludeuptoseveralthousandobservations.Additionally,italsoimplementsaschemeforcomputingcondenceintervalsfortheestimatedparameters,basedonaninversionofaranktestdescribedbyKoenker( Koenker 1994 ).Inadditiontothatmethod,thesoftwareprovidesseveralotheroptionstouseliketheFrisch-Newtoninteriorpointmethodwhichisusedforproblemswithverylargenumberofobservations. The,softwareemployedseveraloptionsfortheestimationofthestandarderrors.Althoughasymptoticestimationofthestandarderrorsispossible,inmanycasesitissomewhatproblem-atic.Weusebootstrapmethodsinordertoestimatethestandarderrors.ForourapplicationweusedthebootstrapoptionthatisbasedontheMarkovchainmarginalbootstrapproposedbyHeandHu( HeandHu 2002 )andKocherginsky,HeandMu( Kocherginsky,He,andMu 2005 ).ThesoftwareweusewasdevelopedinRbyKoenkeranditisdiscussedindetailinQuantileregressioninR:Avignette( Koenker 2005 ) 66

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Followingthemethodusedinmoststudiestoestimatetherelationshipofenvironmentaldegradation,weusedathirddegreepolynomialofincome.Thereasonforthisisbasedonthetheoryregardingtheshapethattheincomeandenvironmentalrelationshiphas.Aswediscussedintheliteraturereviewchapter,accordingtothistheorythepossiblecurvesareamonotonicincreasingcurve,aninvertedUcurveliketheEKChypothesizesandanNcurve.Inordertobeabletocapturethesecurves,weuseathirddegreepolynomialofincome.Henceourmodelisexpressedinequation( 5 ) whereE=capisemissionspercapita,inourapplicationNOxorSO2shorttonespercapita,whichindicatetheenvironmentaldegradation.Incomepercapitaismeasuredin1987dollars.Ourresultsarecomparabletotheexistentliteraturesincethemajorityofstudiesalsouseathirddegreepolynomial.Becausethedatasetweusehasbeenusedbyotherresearchers( Millimet,List,andStengos 2003 ; ListandGallet 1999 ),wereplicatethexedandrandomeffectsmodelinordertohaveallthestatisticalresultsandbeabletomakeadequatecomparisonswiththeresultsfromquantileregression. Inthefollowinganalysisweprovidediagramswiththecurvesthateachmodelprovidesaswellastableswithcoefcientsandtheirsignicancelevels.Thetablesdonotincludethe 67

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ReplicatingthestudiesthatusedFEweprovideidenticalresultswiththeestimatesfromstudiesthatusedthesamedataset( Millimet,List,andStengos 2003 ; ListandGallet 1999 ).Fromtable 5-1 wecanseethatintheFEmodelallcoefcientsarehighlysignicant,whiletheREmodelprovidesaninsignicantsquareincome.Sincetheestimatedequationisareducedform,wehavetorefrainfrommakingcausalconjecturesfromthevaluesoftheestimatedcoefcients.Additionally,sinceitisdifculttorealizewhatashortton(theunitinwhichresponsevariables,NOxandSO2,aremeasured)cancausetoenvironment.Nevertheless,thecoefcients'estimatedvaluesareimportantbecausetheyareusedtoconstructthecurvesoftheincome-pollutionrelationshipandtheturningpointsofthecurveiftheyexist. Fromtable 5-2 wecanseethattheHausmantestindicatesthatthenullhypothesisisrejectedinfavoroftheFEmodel(soxedandrandomeffectsdonotprovidesimilarresults).Thus,incomeislikelycorrelatedwithindividualortimeeffects,sotheREmodelisinconsistent.Table 5-2 alsoprovidessomeadditionalstatisticslikeR2andDurbin-Watson.R2intheFEappearstohaveaveryhighvalue(0.63)atleastincomparisontotheexistentliterature.Thisfactisprobablyduetotheuseofthetwo-wayFEmodelbutautocorrelationcouldalsocontributeto 68

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FixedandrandomeffectsmodelcoefcientsforNOx CoefcientValueStd.errort-valuep-value Randomeffectsmodel FixedandrandomeffectsmodelsummarystatisticsforNOx Sumofsquaredresiduals6.26115.44R2.6342380.2458Durbin-Watson0.2975p-value<2.2e-160.3002p-value<2.2e-16F-statistic88.3068(3052,3)343.711(3164,3) HausmantestofH0:REvs.FE:2=195,df=3p-value=<2.2e-16] Giventhenatureofourstudy,weconcentrateontheshapeoftheestimatedEKCcurve.Fromgure 5-1 wecanseethatbothrandomandxedeffectsconrmtheEKChypothesisusingNOx.Nevertheless,theresultsfortheturningpointoftheFEandREmodeldiffer.TheturningpointofFEisapproximatelyat$8,600,whiletheREmodelshowsaturningpointat$14,000.However,sincetheHausmantestindicatedinconsistencyontheREestimates,theFEresultsaredeemedmoretrustworthy.Figure 5-2 containsthecurvesfromtheestimatedequationsaswellastheobservationsinthesample.Wecanseethatthereisnotalargenumberofoutlierscomparedtothenumberofobservations(3168). 69

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EKCxedandrandomeffectsforNOxusingfullsample.A)Fixedeffects.B)Randomeffects. Figure5-2. EKCxedandrandomeffectswithscatterplotforNOxusingfullsample.A)Fixedeffects.B)Randomeffects. Table 5-3 showsthatallcoefcientsofthexedandtherandomeffectsmodelusingSO2asthedependentvariablearehighlysignicant.TheHausmantestfromtable 5-4 showsthattheREmodelwasrejectedinfavorofFEmodelasinthecaseofNOx.Therefore,thereislikelycorrelationbetweenindividualsortimeeffectsandincomepercapita.TheDurbin-WatsonpointoutthattheREandFEmodelssufferfromautocorrelation.R2isagainhighforFEandisrelativelylowintheREmodel. Figure 5-3 showsthecurvesfromtheFEandREmodelsforSO2.Aswecanseethereisatremendousdifferencenotonlyintheshapeofthecurvesbutalsointhescaleoftheresponse 70

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FixedandrandomeffectsmodelcoefcientsforSO2 CoefcientValueStd.Errort-valuep-value RandomEffectsModel FixedandrandomeffectsmodelsummarystatisticsforSO2 Sumofsquaredresiduals43.5820128.605R-squared.7006.061195Durbin-Watson.0102,p-value<2.2e-16.0898,p-value<2.2e-16F-statistic62.11(115,3052)68.7478(3164,3) HausmantestofH0:REvs.FE:2=154df=3p-value<2.2e-16 5-3 clearlyshowsthatthecurveisshiftedupwards.especiallyintheFEmodel.ThisfactcanbeinferredalsofromthedescriptivestatisticsoftheSO2emissionvariable.Wecanseefromtable 5-5 thatathighquantilesofSO2andincomethatafterthe80thpercentile,emissionsincreasedramaticallywhichresultsintheFEmodelpredictingunrealisticallyhighemissionsincomparisontowhathasbeenobserved,whileincome'squantilesobviouslydonotfollowthatincrease.Thiscanalsoseenfromthedifferencebetweenmeanandmedianwherethemedianisapproximately0.09whilemeanis0.16. 71

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HighquantilesforSO2 Figure5-3. EKCxedandrandomeffectsforSO2usingfullsample.A)Fixedeffects.B)Randomeffects. Figure5-4. EKCxedandrandomeffectswithscatterplotforSO2usingfullsample.A)Fixedeffects.B)Randomeffects. 72

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Panelquantileregressionresultsrevealhighlysignicantincomecoefcientsforallquantiles,asshownintable 5-6 5-5 theestimatedEKCcurvesfromthequantileregressionareplacedinthesamediagramfortworeasons:(1)sothatthecurvesfromdifferentquantilescanbecomparedtoseeiftherearedifferentslopesbetweenthemand(2)tobeabletoseeiftheycrossed.Theoretically,allcurvesshouldnotcrosseachotherbecause,forexample,ifthecurveof90thpercentilecrossedthecurveofthe95thpercentile,thenthatwouldmeanthatitispossibleforthe90thpercentilevaluetobelargerthanthe95thpercentilevalue.However,inpracticeitiscommonforcurvestocrossatsomepoints,butthatshouldnothappentoagreatextent( Koenker 2005 ).Aswecanseefromgure 5-5 thequantileregressionconrmstheEKCinthecaseofNOxbutitevidencesadifferentcurvefromboththepreviousREandFEmodels.BecausetheFEandREdiagramshavethesamescalewithquantileregressions'diagram,thecurvesfromthegures 5-2 and 5-5 canbedirectlycompared.Inviewofthefactthatquantileregressionprovidessimilarcurvesforallquantileswithonlydifferencethathigherquantilescurvesareupwardshifted,thepathofpollution'squantileswillbesimilaracrossincome.Atthebeginningofdevelopment,NOxemissionsincreaseatahigherratecomparedtotherateofdecreasewhentheturningpointisattained.Inaddition,thequantileregressions'turningpointsareapproximately$11,000.Athigherincomes,closetothemaximumvalueofoursample,emissionsseemtostabilizeandceasetodecreaseafterwards.InthecaseofNOxtheEKCdoesnotseemtocrosseachothertoagreatextent,apartfromthe25thpercentileandmedianwhich 73

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QuantileregressionforNOxusingfullsample Figure5-6. QuantileregressionforNOxwithscatterplotusingfullsample crosseachotherapproximatelyat$15,000.Wecanseefromgure 5-5 thatthecurvesaremuchcloserforlowerquantiles.Higherquantilesresultsinhigherdistancebetweenthemanditspreviousestimatedquantilewithanexceptionofthe95thand90thquantilethathavealmostidenticalcurves.ThisfactcanleadtoaconclusionthattheNOxdistributionispositiveskewed.Figure 5-6 revealsthatquantileregressiondidnotaffected,atleastinhighextent,fromoutlierssincethecurvesarecrossingorareclosetothemajorityoftheactualobservations. 74

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QuantileregressioncoefcientsforNOx 5thPercentile 10thPercentile 25thPercentile 75thPercentile 90thPercentile 95thPercentile 5-7 .Thecurves,however,donotconvincinglyconrmtheEKC.Nevertheless,itisinterestingtonotethattheFEmodelshowingamonotonicallyincreasingrelationshipchangesdramaticallyinthequantileregression 75

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QuantileregressioncoefcientsforSO2 5thPercentile 10thPercentile 25thPercentile 75thPercentile 90thPercentile 95thPercentile 76

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QuantileregressionforSO2usingfullsample Figure5-8. QuantileregressionforSO2withscatterplotusingfullsample Curvesfromthequantileregressionstillseemtobehavewellintermsofcrossingeachotherandalsoseemtohavesimilarslopesforeveryquantile.Asclearlydemonstratedingure 5-8 ,thequantileregressionisnotaffectedmuchbyoutliers,atleastnottotheextentseeninmodelsfortheconditionalmean.DifferentlyfromthecaseofNOx,SO2'squantilecurvesarenotcloseratlowerquantilescomparetothehigher.Besidesmedianand75thquantileregressioncurvethatareclose,therestcurvesseemtobesymmetricdistance,forinstance,5thand10thquantilecurve 77

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Millimet,List,andStengos 2003 ).Inordertoseeifthosetwomethodologiesyielddifferentresults,weestimatethepreviousmodelsfortherstsubsampleof1929-1984andthesecond1985-1994.Wepresenttheresultsforthosetwosubsamplestoseeifthereisadifferenceintheestimatedcurves. 5-8 showsthatallcoefcientsarestatisticallysignicantforboththeFEandREmodels.IntheREusingthefullsamplethesquareofincomecoefcientwashighlyinsignicant(pvalue=0:76),whileinthesmallersampletheincomecoefcientissignicant.However,theHausmanteststillindicatesthattheREmodelestimatesareinconsistent.R2washigherinthesmallersamplethaninbothofthefullsamplemodels.Evenso,testsforserialcorrelationstillprovidethesameoutcomeasinthefullsample.Ingure 5-9 wecanseethatbothmodelsstillconrmtheEKCbuttheyprovidemorepessimisticresultsinthesensethattheturningpointoccursatahigherincome.Sinceourincomerangeissmallerinthissample,weproduceacurveonlyforthatrangeofincomewhichexplainswhythecurvedoesnotshowthedecreaseitshowedusingthefullsampleshows. Usingthequantileregression,wheremoreobservationsareneededtoprovidepreciseresults,wecanseethatthereisadifferenceinthestatisticalsignicanceofthecoefcients.Thereareveinsignicantcoefcientswherethreeofthemareofthecubeincometermandtwoofthemconcernthe95thpercentileregression.Ingure 5-10 wecanseethatthe95thpercentile'scurvecrossesthe90thpercentilecurvetoagreatextentbutalsothemedian'scurvecrossesthe 79

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FixedandrandomeffectsmodelcoefcientsforNOxusingsubsample1929-1984 Fixedeffectsmodel CoefcientValueStd.errort-valuep-value Randomeffectsmodel FixedandrandomeffectsmodelsummarystatisticsforNOxusingsubsample1929-1984 StatisticFERE Sumofsquaredresiduals0.0416180.046085R20.65910.26134Durbin-Watson0.2616,p-value<2.2e-160.2665,p-value<2.2e-16F-statistic48(105,2582),p-value:<2.2e-16316(2684,3),p-value=2.45e-5 HausmantestofH0:REvs.FE:2=204,df=3,p-value<2.2e-16 FixedandrandomeffectsforNOxusingsubsample1929-1984.A)Fixedeffects.B)Randomeffects. 80

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QuantileregressioncoefcientsforNOxusingsubsample1929-1984 CoefcientValueStd.errortvaluePr(>jtj) 5thPercentile 10thPercentile 25thPercentile 75thPercentile 90thPercentile 95thPercentile 25th'scurveforawiderangeofincomes.Ingeneral,thoughthecurves,aswellastheturningpoints,aresimilartothoseusingfullsample. EstimatesforSO2usingthe1929-1984sampleandtheFEandREmodelshaveexactlythesamecharacteristicsastheestimatesforthefullsample(tables 5-11 and 5-12 ).Allcoefcients 81

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QuantileregressionforNOxusingsubsample1929-1984 Table5-11. FixedanfrandomeffectsmodelcoefcientsforSO2usingsubsample1929-1984 FixedEffectsModel CoefcientValueStd.Errort-valuep-value FixedandrandomeffectsmodelsummarystatisticsforSO2usingsubsample1929-1984 StatisticFERE Sumofsquaredresiduals0.2670.267R20.700.05Durbin-Watson0.13p-value<2.2e-160.14p-value<2.2e-16F-statistic62(115,3052),p-value<2.2e-1649(2582,3),p-value=0.004 HausmantestofH0:REvs.FE:2=68.3023,df=3p-value=9.857e-15

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FixedandrandomeffectsforSO2usingsubsample1929-1984.A)Fixedeffects.B)Randomeffects. Figure5-12. QuantileregressionforSO2usingsample1929-1984 aresignicant,themodelssufferfromautocorrelation,andtheHausmantestrejectsthenullhypothesisinfavoroftheFEmodel.Nevertheless,theshapeofthecurve(gure 5-11 )issomehowdifferentfromthoseusingfullsample.TheFEestimatesseemtobeaffectedlesssinceitsmaximumvalueisabout0.6shortton,whereasitwasabout0.8forthefullsample.Additionally,thecurve'sslopeseemsmuchatteratahighincomelevel(thoughitisstillmonotonicallyincreasing). ContrarytothemethodsestimatingtheconditionalmeanofSO2,quantilemethodshavedifferentresultswhenusingtherstpartialsample,atleastintermsofthestatisticalsignicance 83

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QuantileregressioncoefcientsforSO2usingsubsample1929-1984 CoefcientValueStd.errortvaluePr(>jtj) 5thPercentile 10thPercentile 25thPercentile 75thPercentile 90thPercentile 95thPercentile 5-13 showsthat11outof21coefcientsareinsignicant.Allthecubeincometermsareinsignicantbutnoneoftherstpowerofincome.Althoughitisremarkablethatthecurvesbarelycrosseachotheringure 5-12 ,theyseemalmostatandparalleltoeachother,apartfromthe$15000levelofincomewherepollutionstartstodecrease.Whilethis 84

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Koenker 2004 )orusingthesamexedeffectsforeveryquantileandestimatingallthequantileregressionssimultaneously.Nevertheless,weleavetheseexercisesforfuturework. TheFEandREmodelscollapseforthissampleinthecaseofNOx.(table 5-14 )Tosomeextentthatwasexpectedduetothelackofdegreesoffreedom,especiallyintheFEmodel.Allthecoefcientsareinsignicantinbothmodels.However,itshouldbepointedoutthatintheFEmodeltheyarejointlysignicant,whileintheREmodeltheyarejointlyinsignicant.AlsotheHausmantestdoesnotrejecttheREinfavoroftheFEmodel,thoughthep-value(0.057)wasreallycloseto0.05,i.e.itrejectsfor6%level.Sinceallcoefcientsareinsignicant,theshapeandpositionofthecurvesarenotverymeaningful,excepttoshowthatthecoefcientsareaffectedbyoutliersasshowningure 5-2 .Itisalsonoteworthythatthecurvesdonotshowthesamerateofpollutionreductionliketheydidinthefullsample. Table5-14. FixedandrandomeffectsmodelcoefcientsforNOxusingsubsample1985-1994 Fixedeffectsmodel CoefcientValueStd.errort-valuep-value

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FixedandRandomEffectsModelSummaryStatisticsforNOxusingsubsample1985-1994 StatisticFERE Sumofsquaredresiduals0.288630.33228R20.9370.014Durbin-Watson2.2,p-value=0.97681.9,p-value=0.2262F-statistic105(59,420),p-value<2.2e-162.3(476,3),p-value0.28 HausmantestofH0:REvs.FE:2=7.5,df=3,p-value=0.057 FixedandrandomeffectswithscatterplotforNOxusingsample1985-1994.A)Fixedeffects.B)Randomeffects. Forquantileregression,theresultsintable 5-16 seembetterthantheFEandREmodels.However,therearestillmanyinsignicantcoefcients,especiallyatthe90thand95th,per-centiles,whereallcoefcientsareinsignicant.Figure 5-14 conrmsthecollapseofatleastsomeoftheconditionalquantilessincemanycurvescrosseachotherextensively.Additionally,theyfailtoconvincinglyconrmthepollutiondecreaseseeninboththerstsubsampleandthefullsample. InthecaseofSO2thereisacontradictionbetweenthetablesandthegraphs.InboththeFEandREmodels(table 5-17 andtable 5-18 ),allcoefcientsarehighlysignicantbutthecurves 87

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QuantileregressioncoefcientsforNOxusingsubsample1985-1994 CoefcientValueStd.errortvaluePr(>jtj) 5thPercentile Income4:8203e051:7521e052:751186:1951e03Income22:3770e091:0385e092:289012:2574e02Income33:8158e142:0159e141:892895:9060e02 10thPercentile Income4:8203e051:6449e052:930453:5694e03Income22:3770e099:8291e102:418351:6015e02Income33:8158e141:9167e141:990874:7143e02 25thPercentile Income4:1707e051:4612e052:85433494:5262e03Income22:1416e098:6721e102:46954291:3925e02Income33:5732e141:6952e142:10787863:5633e02 Income3:4614e051:2673e052:731286:5747e03Income21:7281e097:7934e102:217392:7131e02Income32:8050e141:5743e141:781807:5504e02 75thPercentile Income3:4857e051:2418e052:80695:2351e03Income21:7416e097:5952e102:29302:2343e02Income32:8945e141:5132e141:91295:6446e02 90thPercentile Income2:1402e051:7125e051:249742:1209e01Income21:1226e091:0025e091:119832:6343e01Income31:9050e141:9200e140:992183:2168e01 95thPercentile Income2:1402e051:6674e051:283522:0002e01Income21:1226e099:7655e101:149592:5097e01Income31:9050e141:8704e141:018473:0904e01 5-15 obviouslydonotprovideagoodt,despitethefactthatR2ishighintheFEmodel.TheHausmantestindicatesthatFEperformsbetterandthusREprovidesinconsistentresults. 88

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QuantileregressionforNOxusingsubsample1985-1994 Table5-17. FixedandrandomeffectsmodelcoefcientsforSO2usingsubsample1985-1994 Fixedeffectsmodel CoefcientValueStd.Errort-valuep-value Randomeffectsmodel FixedandrandomeffectsmodelsummarystatisticsforSO2usingsubsample1985-1994 StatisticFERE Sumofsquaredresiduals9.2164e-060.11083R20.98440.09Durbin-Watson1.2,p-value<2.2e-161.1,p-value<2.2e-16F-statistic20.4(420,3),p-value0.01416.4(476,3),p-value:0.02 HausmantestofH0:REvs.FE:2=24.3576,df=3p-value=2.103e-05

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FixedandrandomeffectswithscatterplotforSO2usingsample1985-1994.A)Fixedeffects.B)Randomeffects. Figure5-16. QuantileregressionsforSO2usingsample1985-1994 Figure5-17. QuantileregressionsforSO2usingsample1985-1994 90

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QuantileregressioncoefcientsforSO2usingsubsample1985-1994 CoefcientValueStd.errortvaluePr(>jtj) 5thPercentile Income1:0982e043:1375e053:5001555:1475e04Income26:2864e091:9290e093:2588751:2095e03Income31:1853e133:9111e143:0305982:5916e03 10thPercentile Income1:0982e043:2556e053:3732318:1185e04Income26:2864e091:9924e093:1550991:7198e03Income31:1853e134:0220e142:9470483:3870e03 25thPercentile Income9:3764e053:1512e052:975483:0942e03Income25:2530e091:9357e092:713686:9279e03Income39:5942e143:8824e142:471211:3862e02 Income7:2118e053:1872e052:262742:4162e02Income23:9430e091:9458e092:026384:3357e02Income37:2773e143:8277e141:901265:7953e02 75thPercentile Income7:3487e052:9164e052:51971:2114e02Income24:0908e091:7721e092:30852:1457e02Income37:6014e143:5071e142:16743:0764e02 90thPercentile Income7:4006e053:7258e051:986294:7651e02Income24:2652e092:2685e091:880166:0778e02Income38:2738e144:4992e141:838966:6627e02 95thPercentile Income7:4006e053:9180e051:8888865:9596e02Income24:2652e092:3968e091:7795757:5869e02Income38:2738e144:7597e141:7382908:2893e02 5-19 )butalsointhegraphs(gure 5-16 ).Despitethefactthat6coefcientsareinsignicant,theirp-valuesareverycloseto0.05.Additionally,curvesdonotcrosseachothertoagreatextentand,aswecansee 91

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5-17 wherethescaleismuchclosertothefullsamplegraphs,thecurvehasasimilarshapetothatofonefromthefullsample. Overall,theresultsfromthesubsamplesshowthat,whiletherstsubsamplecloselyfollowstheresultsofthefullsample,inthesecondsubsamplethatanyhappensforthequantileregres-sionofSO2.Theseresultsmightbeexpectedsincethesecondsubsampleisrelativelysmallandmostobservationscomefromtherstsubsample.Inanycase,theresultsforthesubsamplesarenotdramaticallydifferent,whichmaybeanindicationthattheswitchinmethodologiestomeasureemissionsbyEPAhaslittleimpactonthefullsampleresults. InthecaseofNOx,modelsforbothquantilesandthemeanconrmtheEKChypothesis,althoughinthecaseofquantileregressionthereisnotactuallyaninvertedUshapesincetherateofdecreaseafterthepeak(turningpoint)ofthefunctionisslowerthantherateofincreasebeforethepeak,atleastinthesample.Ontheotherhand,inboththeREandFEforthe 92

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CurvesfromquantileregressionandxedeffectsmodelforconditionalmeanforNOx CurvesfrommedianregressionandxedeffectsmodelforconditionalmeanforNOx 5-18 whereboththecurvesfromquantilesregressionandtheFEmodelforconditionalmeanarepresented.Alsoingure 5-19 wecandirectlycomparethecurvesofthetwomomentsthatmeasurethecentraltendency,themean(fromFEmodel)andthemedian.Inthisgurethetwoverticallinesshowstheturningpointsofthecurves. Devisingaprecisemethodologyiscrucialbecausegovernmentsmaybasetheirenvironmen-talpoliciesonresultsfromtheconditionalmeanmethodsandthereforebelievethatpollution 93

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CurvesfromquantileregressionandxedeffectsmodelforconditionalmeanforSO2 OurresultsfromquantileregressionarenotveryoptimistictowardtheEKChypothesis.Therefore,economistsshouldbemorecarefulaboutanalyzingthebenetsfromeconomicdevelopmentsinceafterthepeakofenvironmentaldegradationoccurs,itisnotfollowedbyaconsiderablereductioninpollution.Sinceourresultsarefromareduced-formequation,wehavetorefrainfrommakingcausalinferences.Furthermore,wecannotbesureaboutthecausesofthereductionaftertheturningpoint,whichcouldbetheresultoftechnologicalprogressorstricterenvironmentalpolicies.Inthatregard,apossiblewaytoincreasethereductionofpollutionaftertheturningpointisthroughtechnologyincentivesand/orstricterenvironmentalpolicies. InthecaseofSO2theresultsaremoreheterogeneousNOx.SincetheHausmantestindicatesthattheREmodelisinconsistent,weonlyconsidertheFEmodelfortheestimationoftheconditionalmeanequation.Inthatcase,itisclearfromgure 5-4 thattheFEequationfortheSO2hasbeenshiftedbecauseoftheoutliers.Additionally,itsshapemonotonicallyincreases, 94

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CurvesfrommedianregressionandxedeffectsmodelforconditionalmeanforSO2 5-7 ).Thesecomparisonsaregraphicallypresentedingures 5-20 and 5-21 .BothcasesarenotoptimisticintermsoftheEKChypothesisandtheresultsfromtheestimationofNOxequation.Thequantileregressionimpliesthatadditionalenvironmentaldegradationwillnotbeproducedbyeconomicgrowth,while,insteadtheFEconditionalmeanresultsindicatethatpollutionwillcontinuetoincreasewitheconomicgrowth.Therefore,basedontheseresults,animplicationisthatstricterenvironmentalregulationshouldbeimposedtodecreaseSO2pollution.Incaseswherethepollutionlevelisatacceptablelevels,accordingtothequantileregressionresultsnofurtherpolicyrestrictionsshouldbeimposedthatcouldaffecteconomicactivity.Ontheotherhand,iflesspollutionisdesired,stricterenvironmentalregulationsarenecessary. 95

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GrossmanandKrueger 1991 )intheirstudyoftheenvironmentalimpactsoftheNorthAmericanFreeTradeAgreement(NAFTA).ThisstudywasbaptizedastheEKCinPanayotou'sstudy( Panayotoy 1993 ).However,despiteitsmanysupportersandsomeempiricalevidencethatsupportsthevalidityoftheEKC,manyresearchersopposethisconceptandhaveproducedstudiesthatcontradictit.Duetothesecontradictionsothertheorieswerecreatedtodescribetheincome-pollutionrelationship,suchasthenewtoxicstheoryandtheracetothebottomscenario.InadditiontothetheoreticalargumentsopposingtheEKChypothesis,EKCstudiesreceiveeconometriccritiques.Someoftheproblemsthattheseapplicationstendtofaceareomittedvariablebiases,autocorrelationandcointegration.TheEKCstudiesintheliteraturehavegreatdisparitiesintermsoftheirresults,butsomeofthereasonsforthiscanbeattributedtotheuseofdifferentdata,differentmodels,differentpollutants,differentvariables,anddifferentmeasurementsofincome. Inthisstudywefocusonadifferentaspectoftheincome-pollutionrelationshipbyconcen-tratingondifferentaspectsofthedistribution.Namely,thepanelquantileregressionmethodisemployedtoestimatetherelationshipatdifferentpointsofemission'sdistributionasrelatedtoincome.Quantileregressionprovidessomeadvantagescomparedtotheusualmethodsemployedtoestimatetheconditionalmean.Forexample,theestimatesaremorerobustandinferencesareunencumberedbythemorerestrictiveassumptionsthatunderpinotherestimationmethods.Ad-ditionally,quantileregressionestimatesotherconditionalquantilesthatmightrevealadifferent 97

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Theestimatesfortheconditionalmeanandtheconditionalquantilesprovidedifferentresults.WhileinNOxemissionsbothFEmodelsconrmtheEKChypothesis,theFEmodelforthequantilesresultedinamuchlowerpollutionreductionthantheFEmodelforthemean.Inthatsense,theestimatesforthequantilescanbeconsideredaslessoptimistic.Inadditiontoadifferentshapeofthecurve,resultsshowadifferentturningpointsinceareductionforthemeanpollutionisnoticedafter$8600,whereasallquantilesshowtheturningpointatapproximately$11000.TheoppositehappenedwhenSO2isusedasaresponsevariable.InthecaseofFEfortheconditionalmean,itshowsapessimisticestimateinwhichtheseisamonotonicallyincreasingrelationshipwhileFEforquantileregressionshowedarelationshipthatasymptoticallyapproachesamaximum.Anotherndingisthatthemodelfortheconditionalmeanishighlyaffectedbyoutliers.Therefore,estimatesfortheconditionalmedianappearmorevaluablethantheusualmethods. PossibleexplanationsforthedifferentresultsthatthetwoemissionsprovideisthateithertheEKChypothesiscannotdescriberealisticallytheincome-pollutionrelationship,soitis 98

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DimitriosKapetanakisreceivedhisMasterofScienceatUniversityofFloridainFoodandResourceEconomicsDepartment.HereceivedhisBachelordegreeatAgriculturalUniversityofAthensmajoredinruraleconomicsandeconomicdevelopment. 105