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Environmental Regulations and Technological Change in the Offshore Oil and Gas IndustryAuthor(s): Shunsuke Managi, James J. Opaluch, Di Jin, Thomas A. GrigalunasSource: Land Economics, Vol. 81, No. 2 (May, 2005), pp. 303-319Published by: University of Wisconsin PressStable URL: http://www.jstor.org/stable/4129670

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Environmental Regulations and TechnologicalChange in the Offshore Oil and Gas IndustryShunsukeManagi, James J. Opaluch, Di Jin,and Thomas A. Grigalunas

ABSTRACT: Technological progress can play akey role in raisingstandardsof living while improv-ing environmental quality. Well-designed environ-mental regulations encourage innovation, whilepoorly designed regulations can inhibit progress.The Porter hypothesis goes further to suggest thattougher environmental regulations could spur in-novation, leading to increasedproductivity of mar-ket outputs. We apply frontier production analysisto measure various components of totalfactor pro-ductivity within a joint production model, whichconsiders both market and environmental outputs.We test the causality between technological innova-tion and environmental regulation and find sup-port for a recast version of the Porter hypothesis.(JEL 038, L71)

I. INTRODUCTIONSubstantial efforts have been made toregulate pollution in most industrializedcountries,and thestringency fpollutionreg-ulationshave continued to increase world-wide.Technologicalprogresscanplaya keyroleinmaintainingahighstandardof livingin the face of these increasingly stringentenvironmental regulations. However, theextent of the contribution of technologicalprogressdependson how well environmen-talpolicies are designedandimplemented.Successfulenvironmentalpolicies can con-tribute to technologicalinnovation and dif-fusion(e.g.,Jaffe,Newell, and Stavins2003),while poor policy designs can inhibit inno-vation.Traditionally, conomistshave subscribedto the idea that painful consequences ofenvironmental regulations cannot easilybe avoided, and environmentalregulationnecessarily involves additional cost to in-

dustry (Jaffe et al. 1995; Palmer, Oates,andPortney 1995).Within this context, thekey issue is how to design environmentalregulations to attain environmental goalswhile minimizing productivity loss, andthereby controlling the adverse impact onindustryto the extent feasible.Recently,however,researchershave chal-lengedthis conventionalview with an alter-native hypothesisthat tougherenvironmen-tal regulationscan stimulate nnovationandmotivate increases in x-efficiency, poten-tially increasing productivity and profit-ability.' This is the well-known Porter hy-The authors are, respectively, associate professor of Bio-Applications and Systems Engineering at Tokyo Uni-versity of Agriculture and Technology; professor of En-vironmental and Natural Resource Economics at theUniversity of Rhode Island; associate scientist at theMarine Policy Center, Woods Hole Oceanographic In-stitution; and professor of Environmental and NaturalResource Economics at the University of Rhode Island.The authors thank two anonymous referees, an editor,Rolf Fare, Kristiaan Kerstens, Akira Hibiki, SamuelBwalya, and participants at the Second World Congressof Environmental and Resource Economists for helpfulcomments. This research was funded by the UnitedStates Environmental Protection Agency STAR grantprogram (Grant Number Grant Number R826610-01)and the Rhode Island Agricultural Experiment Station(AES Number 3933), and is Woods Hole ContributionNumber 10704. The results and conclusions of this paperdo not necessary represent the views of the fundingagencies. The usual disclaimers apply.

1 Note that productivity improvement in a compara-tive static sense is not sufficient to guarantee increasedprofits. For example, the presence of short-run, fixedcapital can imply that adoption of new, productivity en-hancing technologies could reduce the present discountedvalue of profits (see Alpay, Buccola, and Kerkvliet2002). In this case, it may be economically rational forincumbent market leaders to resist new technologies,potentially yielding a comparative advantage to newentrants. Additionally, productivity improvements mayshift output supply and/or input demand functions,which might affect prices in some markets, possibly re-sulting in lower profits.

Land Economics* May 2005 * 81 (2): 303-319ISSN 0023-7639; E-ISSN 1543-8325@ 2005 by the Board of Regents of theUniversity of Wisconsin System


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81(2) Managi et al.: Regulations in the Offshore Oil and Gas Industry 305

clearly reduce total factor productivity(TFP) in production of market outputs.With technological change, however, theshort-run costs of regulation could con-ceivably be offset, in part or in full, if theregulations stimulate innovation and in-crease productivity in longer term. Weidentify both the immediate- and longer-term impacts of regulations on productiv-ityby testingthe Porterhypothesiswithin adynamiccontext throughthe period whenimpacts dissipate.An importantchallengefaced in empiri-cal tests of the Porter hypothesis is identi-fying the direction of causality betweentechnologicalinnovation and environmen-tal regulations.New, tougherenvironmen-tal regulations mightspurresearch and de-velopment efforts leading to innovation,as implied by the Porter hypothesis.How-ever, innovation might also precede anddrive tougher new regulations in at leasttwo ways. First, technical innovations, es-pecially those in pollution control technol-ogies, maylead federalagencies to developtougher environmental regulations thatcapitalizeon these new technologies (e.g.,Meyer 1993). For example, U.S. EPA'stechnology-based standards are based onconcepts like Best Conventional Technol-ogy (BCT) or Best Available Technology(BAT), whichimpliesthat the currentstateof technology will tend to drive the strin-gency of environmental regulations. De-velopment of new technologies may thuslead to subsequent increases in the strin-gency of environmentalregulationsdue to"supply ide"effects,such as improvementsin the technicaland/or economic feasibilityof pollutioncontrol.Additionally,economicdevelopment mightlead to an increasedde-mand for environmentalquality,as embod-ied in the environmentalKuznets curve.3

Thus, causality between environmentalregulations and innovation might go in ei-ther (or both) directions,and it is criticaltoidentify the direction of causalitybetweenenvironmentalregulationsand advancesinenvironmental technology when testingthe Porter hypothesis. These questionsseek empiricalanswers, and our study at-tempts to contribute to the literature,em-pirically and methodologically. We applytwo methods to identify the direction ofcausality between stringency of environ-mental regulationsand innovation, and weidentify the extent to which there is empiri-cal supportfor demand side (environmen-tal Kuznets' curve) and/or "supply side"increases stringencyof environmentalreg-ulations in responseto increases in produc-tivity.

II. LITERATURE REVIEWSeveral empirical investigations haveused indirect data to analyze the relation-ship between the stringency of environ-mental regulationanddevelopmentof newtechnologies. For instance, Lanjouw andMody (1996) find a positive relationshipbetween environmentalcompliancecost (aproxy for environmental regulation strin-gency) and patenting of new environmen-tal technologies. In addition, Jaffe andPalmer (1997) investigate the relationshipbetween environmental compliance ex-penditures andtechnological change usingon two indirect nnovation measures. Theirresults show no significantrelationshipbe-

tween environmentalcompliance cost andpatents. However, they find a significantrelationship between compliance costs andresearch and development expenditures.Jaffe et al. (1995) reviewed empiricalstudies that estimate the influence of envi-ronmentalregulationson productivity.Forexample, Jorgenson and Wilcoxen (1990)have estimated that the long-run cost ofenvironmentalregulationis a reduction of1.91%in the level of the U.S. grossnationalproduct.In contrast, he recentstudyof theU.S. oil refinersby Berman and Bui (2001)suggests that environmental regulation isproductivityenhancing.

3 The environmental Kuznets curve hypothesizes aninverted U-shape relation between pollution intensityand per capita income. At low income levels, economicdevelopment leads to increasing levels of pollution emis-sions. However, as economic growth and income con-tinue to increase beyond a threshold, demand for envi-ronmental quality increases and pollution emissionsdecline (see Tisdell 2001).


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306 Land Economics May 2005

Alpay, Buccola, and Kerkvlet (2002)use the profit function approach to assessthe effects of environmental regulationson profitability and productivity in foodmanufacturingin Mexico and the UnitedStates. They conclude that environmentalregulationshave had no significant ffectonprofitabilityor productivity n the UnitedStates,but regulationshave significantly n-hanced productivity n Mexico, supportingthe Porterhypothesis.

III. MODELING APPROACHWe use Data Envelopment Analysis(DEA) to calculate productivity change(see, for example, Charnes, Cooper, andRhodes 1978;Fare et al. 1994). DEA is aset of nonparametric mathematical pro-gramming echniquesthat estimate the rel-ative efficiency of production units andidentify best practice frontiers.4A princi-pal advantage of DEA is that it can beused to decompose productivitymeasuresand to measure changes over time in the

components. In addition,DEA is not con-ditioned on the assumption of optimizingbehavior on the part of every individualobservation, nor does DEA impose anyparticularfunctional form on productiontechnology. Avoiding these maintainedhy-potheses may be an advantage,particularlyfor analyses with micro-data that extendover a long time series, where productionunits face significantuncertainty,irrevers-ibility,andfixed(and/orsunk)costs. Insuchcases, assumptionsof staticefficiencyof ev-ery productionunitinalltimeperiodswouldlikely be suspect.Decomposition of Productivity Indexes

Malmquist indexes (e.g., Caves, Chris-tensen, and Diewert 1982) are used to4 Other techniques to measure TFP include Solow'sgrowth accounting using input and output indexes (e.g.,Denison 1979) and econometric estimation of the shifts

in production, cost, or profit function (e.g., Ray andSegerson 1990). Both methods require substantial costand/or price data that are unavailable in the offshoreoil and gas industry.

quantify productivity change, and are de-composed into variousconstituents,as de-scribed below. Malmquist Total FactorProductivity (TFP) is an output-basedin-dex of the relative productivityof two ob-servations,measured as the ratioof the twoassociated distance functions (e.g., Caves,Christensen,and Diewert 1982). When ap-plied to observations in different periods,the TFP index is interpretedas a measureof productivity change over time. UnderVariableReturns o Scale,(VRS),theMalm-quist index can be decomposed into mea-sures associated with technologicalchange,efficiency change and scale change:TFPVRs TCvRs ECvRS" CvRS, [1]where TCvRSs technological changeunderVRS,ECVRss efficiency change, andSCVRsis scalechange. Technological changemea-sures shiftsin the productionfrontier.Effi-ciency change measures changes the posi-tion of a production unit relative to thefrontier, so-called "catchingup" (Faireetal. 1994). Scale change measures shifts inproductivitydue to changes in the scale ofoperations relative to the optimal scale.Under the assumptionof Constant Re-turns to Scale, (CRS), technologicalchangemaybe decomposedinto inputbiased tech-nological change, (IB TC), output biasedtechnological change, (OBTC), and mag-nitude change, (MC):TCcRs= IBTCcRs"OBTCcRs"MCcRs. [2]

Here the magnitudecomponent(MCcRs)is a measure of Hicks neutraltechnologicalchange.5 f both IBTC andOBTC areequalto one, then the technological change isHicks neutral.In the endogenous growththeoryframe-work,technological changeis decomposedinto two categories:innovation and learn-'Hicks neutral technological change implies parallelshifts in isoquants on the input side and parallel shifts in

the production frontiers on the output side. In contrast,input-biased technological change implies non-parallelshifts of isoquants,andoutput-biased technological changeimplies non-parallel shifts in production frontiers.


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81(2) Managi et al.: Regulations in the Offshore Oil and Gas Industry 307TABLE 1

MODEL SPECIFICATIONSModel 4

Model 1 Model 3 Innovation LBDBase Model: Model 2 Innovation LBD and and DiffusionIndex Calculated Total TFP Production TFP Diffusion (Total) (Oil and Gas)OutputVariablesOil production (bbl) X X X XGas production (Mcf) X X X XWater pollution X XOil spill X XInputVariablesNumber of platforms X X X XAverage platform size (#slot/ X X X X#platform)Number of exploration wells X X X XNumber of development wells X X X XAverage drilling distance for X X X Xexploratory wellsAverage drilling distance for X X X Xdevelopment wellsProduced water X X X XWeighted innovation index X XHorizontal and directional X X

drilling (exploratory)Horizontal and directional X Xdrilling (development)

Environmental compliance cost X XAttributeVariablesWater depth X X X XDepletion effects (oil) X X X XDepletion effects (gas) X X X XOil reserves in the field X X X XGas reserves in the field X X X XPorosity (field type) X X X X

ing-by-doing (e.g., Young 1993). This re-lates to the two models of technologicalchange--innovation (e.g., Romer 1990),that focuses on the creationof distinct newtechnologies, and learning-by-doing (e.g.,Arrow 1962), that looks at incrementalim-provements in productivity with existingtechnologies.We use differentmodels to measure andto decompose productivitychangein termsof market outputs, environmental (pollu-tion) outputs, and joint production (so-called "green"productivity) (see Table 1).A detailed discussionof the decomposition

methodsis contained nManagiet al. (2004).Here we briefly describe the general logicof the approach.We decompose TFPchangeinto changesin productivityor market and environmen-tal outputs:TFPTotal= TFPMarket TFPE,,,, [3]where TFPTotal is the total measure of TFPchange, includingboth changes in produc-tivity of market outputs, (TFPMarke,,)andchanges in productivity of environmental


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outputs, (TFPEnv). A base model (Model 1in Table 1) is used to calculate an overallmeasureof TFPchange of joint outputs.Asecond model (Model 2) is created whichexcludes variables associated with non-market outputs. When applied to Model2, DEA calculates TFP change of marketoutputs only. The TFP change associatedwith environmental outputs is then calcu-lated asTFPEnv= TFPTotalITFPMarket. [4]

Thus,dividingthe totalmeasureof produc-tivity change from Model 1 by the produc-tivity change measure of market outputsfrom Model 2 provides the residual mea-sureof productivitychangein the environ-mental sector.We use a similar methodology for de-composing total TFP change into that as-sociated with innovation (TFPi,,o,), andlearning-by-doing (TFPLBD). In this case,the components of TFP are defined asTFProtal= TFPinno,, TFPLBD, [5]We developed Model 3 in Table 1 to carryout this decomposition. Model 3 includesan inputvariable that measuresspecificallyidentifiable new technologicaldiscoveries,discussed n detail below.ApplyingDEA toModel 3 provides a measure of residualTFP change beyond that which can be ex-plained by changes in the input of specifi-cally identifiable new technologies. TheDEA result with Model 3 is interpretedasTFPassociated with non-structuraleffects,including learning-by-doing. TFP associ-ated with specifically identifiable newtechnologies is then calculated asTFPInno = TFPTotat/TFPLBD. [6]

Similarly,we decomposeboth technologicalchange, (TC,) and efficiencychange, (EC,)into indexes representing that associatedwith identifiablenew technologiesand a re-

sidual that is not explainedby identifiabletechnologies.Productivityndexesand DirectionalDistanceFunctions

Chung, Fire, and Grosskopf (1997) de-fine an output-oriented Malmquist-Luen-berger productivityindex that is compara-ble to the Malmquist productivityindex,but that includesproductivitychangeswithrespect to both desirable and undesirableoutputs. In contrast to the Shephardout-put distance function that measures effi-ciency by expandingall outputs simultane-ously, the directional distance functionmeasures efficiency due to increasingde-sirable outputs (market goods) while de-creasing undesirable outputs (e.g., pollu-tion emissions). Using the directionaldistance function specification, our prob-lem can be formulated as follows. Let x =(Xl,...,xM) E R+, b = (bl,...,bL) E R, y =(y1,...,YN) E Ru be row vectors of inputs,pollution outputs (undesirable outputs),and market outputs, respectively. Definethe technology set (Q) byQ' = ((x', b', y'):x' canproduce(y',b')}. [7]Thus, Q' represents the set of all outputvectors, y' and b', that can be producedusing the input vector, x'. The directionaldistance function at time t is defined asdo(yt,x',b';gt)supl{:(y',x',b')+ OgtEQt', [8]where g' is defined as the vector (y', 0,-bt), that is, desirableoutputs arepropor-tionately increased, inputs are held fixedand pollution outputs are proportion-ately decreased,Sincewe use a vintage model, the DEAformulation differs from that in Chung,Fare,andGrosskopf(1997). Our DEA for-mulation is as follows. Let k be the indexof an oil and gas field, t be time (i.e., year),ikbe the discovery year for field k, and jkbe the number of years since discoveryoffield k (i.e., field year).Thus,foreachfield,jk = t - ik. In the vintage model, we con-


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sider all fields discovered in the same yearto be a vintage group, and we calculateseparate distance functions for each fieldin each vintage group. We then calculatethe Malmquistproductivityindex by com-paring distance functions in two differentvintages (i and i + 1).For discoveryyear i, the distance func-tion for field k' in field year j' is calcu-lated asdo(y>, ,xki,,bj,,;gil,VRS)]

= max k'' . [9]0kkj'o4j9subjecto 1(k)

(1 + kj')y,,n -Xinkjqjn 0, n = 1 ... N,kEK(i) j=01(k)(1 - k'J')bi,,j, Xjb,= 0, = 1 L.. ,kEK(i) j=OJ(k)

XkJ,'m Y kXikjm O0, m = 1,..... M,kEK(i) j=O1(k)ak - Xkja > 0, g = 11.....G,EK(i) j=OJ(k)

k (hkj = 1, k E K(i), j = 1, . . . , J(k),kEK(i)j=OXkJ>-0O, k E K(i), j = 1...1 J(k).where a representsfield attribute,K(i) in-cludes all fields discoveredin i andJ(k) isthe last field year for field k. Note that theabove linear programmingproblem [9] isused to estimate the distance functionfor asingle field in a particularperiod (i.e., yeari). To estimatethe productivity hangeovertime and its components (e.g., equation[1]), we need to calculateseveral differentdistance functions including both the sin-gle period andmixed period distancefunc-tions for each field and time period. Forthe mixedperioddistance unction,we havetwo vintage years,i and i + 1. For example,the outputconstraint n [9] becomes

1(k)(1 + k'j')ykj-n k- ni+1 ? 0,kEK(i+l) j=On = 1 ..., N, [10]

This constraint dentifies the maximumfea-sible radial expansion (0) of outputs fromfield k' of vintage i that does not exceed alinearcombinationof efficient output vec-tors from fields of vintagei + 1. The larger

the maximum feasible value of 0, thegreaterthe productivity change in fields ofvintage i + 1 relative to vintage i.In our study, time (t) and vintage year(i) extend from 1968through1995; he vec-tors of outputs (y and b), inputs (x), andattributes (a) for each model are listed inTable 1. A weighted innovation index, de-tailed below, is assigned to each vintage,and held constant across time for fields ofthat vintage. Other than the two depletionvariables, all attribute variables (e.g., wa-ter depth) vary across fields, but are con-stant over time for a given field.

Assessment of the Porter HypothesisWe test the Porterhypothesis by exam-ining whether levels of the stringency ofenvironmental regulations are associatedwith subsequent increases in productivity.The DEA methods discussed above areused to measure productivity change andthe various constituents of productivitychange over time. We then use two ap-proaches:the Almon distributed agmodel

(Almon 1965) and the Granger causalitytests (Granger1969), to identify causalre-lationshipsbetween the stringencyof envi-ronmentalregulationsand the variouspro-ductivity measures.The Almon lag model relates the pro-ductivity indexes (TFP change and TC)to lags in the stringencyof environmentalregulations using the functional specifi-cation:N

Pt = ot + P iEti +- t, [11]iwhere P, denotes the productivityindex attime t, at is a constant term, E,-i denoteslagged values of the regulation stringencyindex, Pi is the coefficient of the ith lagand e, is a stochastic term. In general, theAlmon polynomial distributed lag modelallows coefficients to follow a variety ofpatternsas the length of the lags increases.Typically, an inverted "U" pattern is ex-pected, and a second-degree polynomial isoften considered adequate to characterizethe lag structure.Following common prac-tice (e.g., Harvey 1990), we choose the lag


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lengththatminimizes the Akaike Informa-tion Criteria (AIC).6We also applythe Grangercausalitytest(Granger1969;JohansenandJuselius1990)to examine the direction of causality be-tween environmentalregulationsand pro-ductivity indexes. The Granger test pro-vides a simplemeans to identifycause-and-effect relationshipswhen the structure ofthe relationship is not clear.' In our case,the Porter hypothesis is consistent with afinding that lags on the stringencyof envi-ronmental regulations have positive andstatistically significant coefficients whenadded to a distributed lag model for pro-ductivity change.Application

We applythe above methods to oil andgas productionin the Gulf of Mexico, oneof the first areasin the worldto beginlarge-scale offshore oil and gas production.Off-shore operations in the Gulf of Mexicohave played an importantrole in domesticenergy production and supply stabiliza-tion. Federal offshore oil and gas produc-tion accounted for 26.3% and 24.3% oftotal U.S. production, respectively (U.S.Department of Interior2001), and the off-shore fraction of production has been in-creasingover time. Oil and gas productionin Gulf of Mexico accounted for 88% and99%, respectively, of total U.S. offshoreoil and gas production through 1997 (U.S.Department of Interior 2001).

Reducing the environmental impact ofoffshore operations is among the mostpressing challenges facing the oil and gasindustryin United States today. In recentdecades,compliancewithenvironmentaleg-ulationshas become increasinglycostlyandcomplex. For example, in 1996, the petro-leum industry, ncluding refining, spent anestimated$8.2billion on environmentalpro-tection;approximatelyhe same amount hatit spent exploring ornew domesticsupplies(AmericanPetroleumInstitute2001).Data used in this analysis are obtainedfrom the U.S. Departmentof the Interior,MineralsManagementService(MMS),Gulfof Mexico OCS Regional Office. Specifi-cally,we developourprojectdatabaseusingfive MMS data sets:1. Production data, including monthly oil,gas, and produced water outputs fromevery well in the Gulf of Mexico overthe period from 1947 to 1998. The datainclude a total of 5,064,843observationsfrom 28,946 productionwells.2. Borehole datadescribingdrillingactivity

of each of 37,075wells drilled from 1947to 1998.3. Platform data with informationon eachof 5,997 platforms, including substruc-tures, from 1947 to 1998.4. Field reserve data includingoil and gasreserve sizes anddiscoveryyearfor eachof 957 fields from 1947 to 1997.5. Reservoir-level porosity informationfrom 1974-2000. This data includes a to-tal of 15,939 porosity measurementsfrom 390 fields.Because the earlydata did not includeenvi-ronmentalreporting,we use the data for theperiodfrom 1968 to 1998.Thus,the projectdatabase is comprisedof well-level data foroil output, gas output, producedwaterout-put, and field-level data for the number ofexplorationwells, total drillingdistance ofexploration wells, total vertical distanceof exploration wells, number of develop-ment wells, total drillingdistance of devel-opment wells, total vertical distance of de-velopment wells, number of platforms, totalnumber of slots, total number of slots drilled,water depth, oil reserves, gas reserves, origi-

6 Note that for our case, this same lag also maximizesadjusted R2, which is an alternative recommended cri-terion.7 The Granger causality test is a more rigorous testthan the Almon lag test in two ways. First, the Grangertest identifies whether the lagged independent variable(stringency of environmental regulations) adds explana-tory power, relative to a model based on lagged depen-dent variable (productivity change). This allows theGranger test to distinguish between an instance of twovariables independently following time trends, versus acausal relationship among the two variables. In contrast,the Almon lag test considers the whether the laggedindependent variable has any explanatory power, not

considering lags on the dependent variable. Secondly,the Granger test examines causality in both directions,so that it can potentially distinguish between modelswhere causality goes in either or both directions.


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nal proved oil and gas combined reserves(in BOE), discoveryyear and porosity.Although we have well-level productiondata,the well is not a good unitformeasur-ing technological efficiency due to spillo-ver effects acrosswells withina given field.Rather, the field level is a more appro-priate unit for measuringtechnological ef-ficiency.For this reason, the relevant vari-ables were extracted from the MMS datafiles and merged by year and field, so thatthe final data set wascomprisedof 28yearsof annual data at the field level. On aver-age there are 406 fields operating in anyparticularyear, anda total of 10,964obser-vations.We use the cost of complyingwith envi-ronmental regulations as the measure ofenvironmental stringency. Our environ-mentalcompliance cost is based on ex anteestimates from U.S. Environmental Pro-tection Agency sources, since we do nothave the ex post cost studies. We compileddata for water pollution and oil spill pre-vention costs from Federal Register andEPA documents, which contain cost esti-mates foreachset ofregulations.Theseenvi-ronmentalregulations equirephased mple-mentation over a period of years andregulationsare occasionallyrevised, whichimpliesa variation n stringencyover time.The outputvariables n our model are oilproduction,gasproduction,waterpollution,and oil spills (see Table 1). Our inputvari-ables include the following:the number ofplatforms,platformsize,numberof develop-ment wells,numberof explorationwells,av-erage distance drilledfor exploratorywells,average distance drilled for developmentwells, and environmentalcompliance cost.Field attributesare the waterdepth, initialoil reserves, nitialgas reserves,field poros-ity, and an aggregatemeasure of resourcedepletion.A more complete descriptionofthe data is availableupon request.One goal of the studyis to measurepro-ductivityeffects anddecomposeproductiv-ity into that associated with specificallyidentifiable new technologies versus lessstructural ffects,suchas learning-by-doing.To do so,we adaptMoss's(1993)methodol-ogy to focus on technological innovation,

and we extend the index for our full studyperiod. Next we refined the Moss innova-tion count to consider the relative impor-tance of innovations using a study by theNational Petroleum Council (NPC). Weconstructa cumulativeweighted technologyinnovation index at time t, calculated as

t IInnovW" wi,tX InnovNW [12]t=to i=lwhere Innov, is the cumulative weightedtechnology innovation index at time t; wi,is the weight for technology category i attime t;

Innovi,is the non-weighted tech-

nology innovationcountadaptedfrom Mossin category i at time t.One important innovation of the recentdecades is the extent of horizontaldrilling,which refers to the ability to guide a drill-stringat any angle.This allows the wellboreto intersect he reservoir romthe sideratherfrom above, allowinga much more efficientextraction of resourcesfrom thin or partlydepleted formations.Horizontal drilling isalso advantagousor formationswith certaintypes of naturalfractures,ow permiability,a gap cap, bottom water, and for some lay-ered formations.A measureof horizontaldrillingand ourweighted innovation variable are used asinput variables in Model 3 (see Table 1).When appliedto Model 3, DEA calculatesthe residual fractionof TFP change whichcannot be explained by the inputof specifi-cally identifiable new technologies, and istherefore attributed to less structural ef-fects. The fraction of TFP change associ-ated with identifiable technological inno-vations is calculated by dividing total TFPchangefrom Model 1, by the residual mea-sure of TPFchangefromModel 3. We alsoapply the same approach to decomposeboth technological change and efficiencychange into the portions associated withspecifically identifiable new technologiesand the residual portions that are attrib-uted to less structural effects.

IV. EMPIRICALRESULTSData Envelopment AnalysisThe DEA framework s used to measureproductivity change and to carry out the


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312 Land Economics May 20051.7

W 1.6 --------- -Total TFP?.5 --------- --2- MarketTFP

" 1.4 ---- Env.TFP0

-I 1.2u .111

0.91965 1970 1975 1980 1985 1990 1995

FIGURE 1DECOMPOSITION OF TFP CHANGE BY SECTOR

various decompositions described above,contributingto a better understandingofnature of technologicalchange for our ap-plication. Figure 1 presents the results fortotal TFPchange,and TFPchange decom-posed into the market and environmentalsectors. Overall, total TFP increases byabout 65% from 1968through1995,whichimpliesa geometricmeanof about 1.9%peryear. Over the first 16 years (1968 through1984) total TFPincreasesby about17%,ora rate of about 1.0% per year. In the next10 years (1985 through 1995) total TFPincreasesby about 34%, or a rate of about3.2% per year. This is consistent with theincreasing rate of technological progressthat has been observed in the industry(e.g., Bohi 1998). TFP change in the mar-ket sector accounts for about 75% of thetotal, while TFP change in the environ-mental sector accounts for about 25%.The decomposition of technologicalchange, (TC,) into the total, market, andenvironmental sectors are presented inFigure2. Total TCaccounts for an increasein productivityof about 48% over the studyperiod.Again,we findthat the largestshareof TC occurs in the market sector, whichaccounts for about 80% of the total TC.In contrast, the environmental sector ac-counts for about 20% of TC.

Thus, there has been a considerablein-crease in productivity in market outputsdespite increasingly rigorousenvironmen-tal regulations.In contrast,the rateof pro-ductivitychange in the environmental sec-tor has lagged behind that in the marketsector. These results confirm the concep-tual literature, which finds that becausecommand-and-controlbased environmen-tal policies provide little latitude for inno-vation,theyarelikelyto inhibitproductivitygrowth.Nevertheless,despite these institu-tionalbarriers,ndustryhasbeen able to in-crease productivityof environmental ech-nologies to some extent, hence moderatingcompliancecosts.Figures3 through5 depicttrendsof TFPchange,decomposed ntolearning-by-doing,(LBD), innovation, (INNOV), and diffu-sion, (DIFF). Overall effects of joint pro-duction are presented in Figure 5. We findTFP increases by approximately20% forinnovation, 25% for learning-by-doing,and 30% for diffusion over the study pe-riod. Note that diffusion plays the mostimportant role until the end of the timehorizon, when there is a clear trend to-wardsLBD and innovation. The increasedproductivity due to innovation mainlycomes from the market sector (oil and gasproduction), rather than the environmen-


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81(2) Managiet al.:Regulationsn the OffshoreOil and GasIndustry 3131.71 . 6 - - - - - - - T o t a l TC

= 1.5 ---- Market TC,,o 1.4-.& 1.3

1965 1970 1975 1980 1985 1990995---------------------------------------------------------------- - - - - - - - -

0.91965 1970 1975 1980 1985 1990 1995

FIGURE 2DECOMPOSITION OF TECHNOLOGICAL CHANGE BY SECTOR

tal sector,while the increasedproductivitydue to LBD came frommore equally fromthe market and environmental sectors.Again, this is consistent withprior expecta-tions. Technology-based regulations arelikely to restrict firms' ncentives for devel-oping andimplementing new environmen-tal technologies. However, firms may re-tain some latitude for cost savings throughless structural innovations (e.g., learning-by-doing), such as more careful manage-ment of the technologies upon which theregulations are based.

Additional insights into the nature oftechnological change can be obtained byidentifyingthe extent to which it conformsto Hicks neutrality.DEA is capable of de-composing technological change into out-put-biased technological change, (OBTC),input-biasedtechnologicalchange, (IBTC),and magnitude change, (MC) (see equa-tion [2]). When the DEA measures ofOBTC and IBTC simultaneously equal 1,productivity change is Hicks neutral.How-ever, DEA only provides overall measuresof bias, which are aggregated over all in-

1.3--. Learning-by-doingSInnovation

S1.2x Diffusion --------------------------------------------------------

-M1------ -- ----------------------------------------------------------------0.9

1965 1970 1975 1980 1985 1990 1995FIGURE 3DECOMPOSITION OF TFP CHANGE FOR MARKET OUTPUTS


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1 . 3 e a m i n g - b y - d o i n g- Innovation1.2 --- - Diffusion

I--w 1 ----------- -- -

0.91965 1970 1975 1980 1985 1990 1995

FIGURE 4DECOMPOSITION OF TFP CHANGE FOR ENVIRONMENTAL OUTPUTS

puts, (IBTC), or outputs, (OBTC) (FireandGrosskopf1996).Incontrast,the para-metric measurements of technologicalchange (e.g., Antle and Capalbo 1988) canprovide measures of bias for each individ-ual input and output.With consideringmarket outputs only(Model2 in Table 1) we findan IBTC mea-sure of 1.29 and OBTC measure of 1.81.Therefore,the overalltechnologicalchangebias index, which is the product of IBTCand OBTC,is 2.33. Unfortunately,DEA isnot a statistical technique, and thereforedoes not allow one to test for statistical ig-

nificance.However,the overallbias index issufficiently far from one to suggest thatHicks neutral echnologicalchange maynothold in Model 2. In comparison,with thejoint productionmodel (Model 1), which n-cludes market and environmentaloutputs,we find a largeroverall echnological hangebias index (IBTC = 1.96, OBTC = 1.60,and the overall bias index = 3.14).TestinghePorterHypothesis

As discussedabove,we conducttwo testsof the Porter hypothesis.First,we use Al-

1 . 3- . - -

L e a r n i n g - b y - d o i n g -------------------------- Innovation

UU1.2 "- -- Diffusion--

o 1.1F--

0.91965 1970 1975 1980 1985 1990 1995FIGURE 5

DECOMPOSITION OF TFP CHANGE FOR JOINT OUTPUTS


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81(2) Managi et al.: Regulations in the Offshore Oil and Gas Industry 315TABLE 2

ALMON DISTRIBUTED AGS FORIMPACTOFENVIRONMENTALEGULATIONSON ALTERNATIVERODUCTIVITYEASURESJoint Production ofEnvironmental andMarket Outputs Market Outputs Only

Technological Total Factor Technological Total FactorChange Productivity Change ProductivityTime lag 0 -0.185* -0.376*** 0.008 -0.003(-2.04) (-3.52) (0.06) (-0.02)1 0.022*** -0.110* -0.030 -0.013(3.55) (1.88) (-0.39) (-0.13)2 0.122** 0.100*** -0.030 0.081(2.59) (4.94) (-0.23) (0.08)3 0.115** 0.254*** 0.008 0.060

(2.46) (18.72) (0.11) (0.48)4 0.352***(10.60)5 0.394***

(8.79)6 0.38***(8.02)7 0.31***(7.59)8 0.18***(7.32)Sum of lags 0.075*** .484*** 0.040 0.053(3.86) (28.46) (0.42) (0.84)Adjusted R2 0.361 0.976 0.111 0.112

AIC 88.4 95.1 200.8 172.9Note: t-statistics rereported n parentheses.* Significant t the 10% evel;** significant t the 5%level;***significant t the 1% level.

mon distributed agmodels to test whetherincreases in the stringencyof environmen-tal regulations are associated with futurechanges in productivity indexes (TFPchangeand TC).The results for the Almonlag model are reported in Table 2. Theresults show statistically significant rela-tionships between the stringency of envi-ronmentalregulationsandjoint productiv-ity for both TC and TFP change. Mostindividual lag coefficients are statisticallysignificantat the 5% or 1% level for bothproductivitymeasures, and the aggregateeffects are significant at better than the1% level. The initial lags are negative insign, indicating that the immediate effectof environmental regulation is to reduceboth productivityindexes. But the longerterm lags and the sum of all lags are posi-tive for TC and TFP change. The resultindicates that joint productivityincreases

in the longer term, and that longer termincreases more than offset the short termdecreases.Thus, we find empiricalsupportfor the hypothesis that more stringenten-vironmental regulations induce both TFPchange and TC of the joint productionmodel (Model 1).However, we find no support for in-duced productivity change or technologi-cal change when considering of marketoutputs only (Model 2). As indicated inTable 2, the individual lags and the sumof all lags are not statistically significantat standard levels. Hence, our Almon lagresultsdo not supportthe standardversionof the Porter hypothesis,which states thatincreases in the stringencyof environmen-tal regulationscan induce increases in pro-ductivity of market outputs, potentiallyleading to increased profits in the longterm.


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316 Land Economics May 2005TABLE 3

GRANGER CAUSALITY TEST FOR PRODUCTIVITYAND ENVIRONMENTAL STRINGENCYNull Hypothesis 1 Null Hypothesis 2Environmental Productivity EnvironmentalStringency Productivity Stringency

Productivity measure X2 Prob. > X2 X2 Prob. > X2Joint production of TC 11.72 0.0388 4.27 0.5132environmental and TFP 2.51 0.2855 2.00 0.3528market outputsMarket outputs only TC 0.97 0.6142 10.55 0.0378TFP 0.23 0.8912 1.07 0.5848

Next we use Grangercausality tests toexplorecausal directionsbetween the strin-gency of environmental regulations andchanges nthe relevantproductivitymeasure(TFP change or TC). When TC is the de-pendentvariable,the model providesa testof shifts in the productionfrontier. WhenTFPchange is the dependent variable,themodel provides a test forchangesinoverallproductivity (technological change, effi-ciency change and scale change).First, we test one direction of causality,where the relevantproductivitymeasure isthe dependentvariable and environmentalstringencyis the independent variable. Inthis case, we regress the current level ofproductivity on lags of productivity andlags of environmental stringency,and testfor statisticalsignificance of lags on envi-ronmental stringency. Next, we test forcausality in the reverse direction with amodel where environmental stringency isthe dependent variable and productivitychange is the independent variable. Wealso carryout separate analysesfor marketoutputs (oil andgas), andforjoint produc-tion of market and environmentaloutputs.In all cases, the null hypotheses of theGrangertests are for non-causality.Thus,rejecting a null hypothesis is consistentwith a finding of causality.The optimalnumber of lagsis also a criti-cal issueinGrangercausality est.We testedseveral informationcriteria: he Akaike In-formation criteria, (AIC), the SchwarzBayesian criteria,(SC), and the Akaike fi-nal prediction error criterion, (FPE). We

find identical results for the appropriatenumber of lagswith each of the three crite-ria and, therefore,we reportresultsfor theAIC only.As indicated in Table 3, the Grangertests indicate that environmental strin-gency causes TC in the joint productionmodel, which is consistent with the re-stated version of the Porter hypothesis.The finding is consistent with the notionthat increases in environmentalstringencyshift the jointproduction rontierof marketand environmentaloutputs.In contrast,wefind no significantcausalitybetween strin-gencyof environmental egulationsandpro-ductivityof marketgoods,thusrejecting hestandard form of Porter hypothesis. Ofcourse,it shouldbe emphasized hatthe re-sult is for ourapplicationonly,and thatspe-cial circumstancesmight lead to this result.For example, environmentalregulations nthe offshore industry are command-and-control oriented, so that there is not muchflexibility to develop new technologies tocomply with environmental regulations.The resultscould differin a context whereregulations were more flexible, such aswhen financial incentives are employed.Finally, we test whether higher produc-tivity leads to more stringentenvironmen-tal regulations. As indicated in Table 3,we find a causal link from technologicalchange of market outputs to environmen-tal stringency,but not of joint production.This findingsupportsthe case for causalityon the demand side, whereby increasesinreal income stimulate demand for environ-


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mental goods. However, the finding doesnot support causality on the supply side,where lower costs of environmentalcontroldrive adoption of tougher environmentalstandards.

V. DISCUSSIONAND CONCLUSIONTechnological progress plays an impor-tant role in addressingenvironmentalprob-lems while simultaneously mprovingstan-dardsof living. Over the past 50 years, ourprofessionhas greatly improvedour under-standing heprocessof technologicalnnova-tion.We haveprogressed rom"confessionsof ignorance,"where time is the only "ex-planatory"variable in technologicalprog-ress, towardsa better understandingof themechanisms hat driveproductivitychange,and improved measurements of variouscomponentsof productivitychange.This paper contributes to the literatureon productivity change in several ways.First, we applyData Envelopment Analy-sis to a uniquemicro-leveldata set to mea-surevariouscomponentsof total factorpro-ductivitywithin a joint productionmodel,which considers both market and environ-mentaloutputs.This contributes o our un-derstandingof the impactenvironmentcon-trols have had on various components oftotal factor productivity in this industry,andtherebythe potential for technologicalchange to maintainproductivity n the faceof increasingly tringentenvironmentalreg-ulations.The results show an upward trend in

productivity n the Gulf of Mexico offshoreoil andgas industry,despiteresourcedeple-tion and increasingly stringent environ-mental regulations. Our findings indicateimproved productivity of environmentaltechnologies, but environmental produc-tivity change has lagged behind that formarket outputs. Over the 28-year studyperiod, technological change can be parti-tioned into approximately80%in the mar-ket sector (oil and gas production), andabout 20% in the environmental sector.This result may be due in part to the com-mand-and-controlnatureof most environ-mental regulations, which allow much less

flexibility for innovation in the environ-mental sector, as compared to the level offlexibility for innovation in production ofmarket outputs.We also analyzed the contribution oftechnological change and efficiencychangefor both market and environmental out-puts. We developed an index for decom-posing technological change into techno-logical innovation, which is associated withdiscoveryof identifiablenew technologies,andlearning-by-doing,which embodies theless structuralcomponents of productivitychange. The results indicate that diffusionhas had a significantly larger impact onTFP than technological innovation andlearning-by-doing. his is important orpro-viding an improved understanding of theprocess of technological change, and couldcontribute to designof effective policy.Forexample, the significanceof technologicaldiffusion as a determinantof productivitychange suggests that it is very importantfor policies to encourage the sharing ofnew technologies in this industry.Next, we apply two models to under-stand the dynamics of the causal relation-ships between the stringency of environ-mental regulations and productivity, andthereby test the Porter hypothesis. ThePorter hypothesis states that environmen-tal regulationscould spurinnovation,lead-ing to long run increases in productivityand potentially to increasedprofits for theregulated industry. We recast the Porterhypothesis to explore the relationship be-tween environmentalregulations and pro-ductivity more fully. Specifically, we testwhether environmental regulations en-hance joint productivityof environmentalandmarketoutputs,in addition to the stan-dard Porter hypothesis, which applies toproductivityof market outputs only.Our results support the recast versionof Porter hypothesis,which examines pro-ductivityof joint productionof market andenvironmental outputs. But we find noevidence supporting the standard formu-lation of the Porter hypothesis regardingincreased productivityof market outputs.This finding could be due in part to thecommand-and-controldesign of environ-


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318 Land Economics May 2005mental regulationsin offshore oil and gas,which historically has not provided muchlatitude for innovation in achieving envi-ronmental goals.Our result suggests we must be carefulto maintaina realistic view of the potentialfor environmental regulations. An overlynaive conviction that there exists a nearuniversal potential for win-win solutionsin environmentalproblems could be usedto justify poorly conceived environmen-tal policies.

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