WEEK 6 S Lenox and Nash (2003)

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Copyright 2003 John Wiley & Sons, Ltd and ERP Environment

In an attempt to avoid costly regulationand liability as a result of externalities,a number of trade associations havepromoted industry self-regulation thevoluntary association of firms to controltheir collective behavior. However,previous studies have found that, withoutexplicit sanctions for malfeasance, suchself-regulatory programs are likely toattract more polluting firms. In this paper,we examine four environmental self-regulatory programs in the chemical,textile, and pulp and paper industries.Using a sample of over 4000 firms withinthese industries, we find evidence that inat least one program more polluting firmstended to join, while in another cleanerfirms were more likely to join. Wepropose that differences in the structureof the programs drive the appearanceof this form of adverse selection. In

INDUSTRY SELF-REGULATION

AND ADVERSE SELECTION:

A COMPARISON

ACROSS FOUR TRADE

ASSOCIATION PROGRAMS

Michael J. Lenox1* and Jennifer Nash2

1 Fuqua School of Business, Duke University, USA2 Kennedy School of Government, Harvard University, USA

Business Strategy and the EnvironmentBus. Strat. Env. 12, 343356 (2003)Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/bse.380

particular, we speculate that only whenself-regulatory programs have explicitsanctions for malfeasance may they avoidadverse selection problems. Copyright 2003 John Wiley & Sons, Ltd and ERPEnvironment.

Received 5 June 2002Revised 7 April 2003Accepted 3 June 2003

INTRODUCTION

In an attempt to avoid costly regulation andliabilities as a result of environmentalexternalities, a number of trade associa-

tions have promoted industry self-regulation the voluntary association of firms to controltheir collective behavior. Due to the collectivenature of government regulation and stake-holder pressures, firms often find their reputa-tions tied together with others within theindustry (King et al., 2001). Fatal accidents,damaging spills and the emission of toxic pol-lutants have consequences not only for theoffending firm but all firms within an industry.

As a consequence, efforts to unilaterally

* Correspondence to: Michael J. Lenox, Associate Professor of Busi-ness, Fuqua School of Business, Duke University, Box 90120,Durham, NC 27708, USA.


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address stakeholder concerns are often insuffi-cient. Industry self-regulation serves as amechanism to facilitate the collective improve-ment of environmental performance within anindustry.

However, previous studies have found thatsuch self-regulatory programs face a numberof challenges (King and Lenox, 2000; Howardet al., 2000). In particular, industry self-regulation is subject to adverse selection; i.e.,lower quality firms will seek to participate.Without mechanisms for measuring andenforcing compliance with program objectives,poor performing firms will seek to join to gainthe signaling and insurance benefits of membership without putting forth the required

effort. Left unchecked, adverse selection willundermine self-regulatory programs as lowquality firms join and reduce the differentia-tion benefits membership may provide.

In this paper, we investigate environmentalself-regulatory programs in the chemicalmanufacturing, chemical distribution, textile,and pulp and paper industries. We examinewhether these programs attract firms withsuperior environmental performance withinthe industry. We propose that differences in thestructure of the programs drive the appearanceof adverse selection. In particular, we speculatethat only when self-regulatory programs haveexplicit sanctions for malfeasance may theyavoid attracting more polluting firms.

Using a sample of over 4000 firms, we findevidence in two of the four programs studiedof more polluting firms joining. Given thedifferences in industry structure and underly-ing production technologies across the indus-

tries studied, it is difficult to confidently assertwhat may be driving this adverse selection.However, our findings are consistent withour hypotheses that the structure of the self-regulatory programs drives selection. Onlywhen members compliance with programgoals is monitored and non-compliantfirms are expelled from the program do weexpect the adverse selection problem to bemitigated.

ENVIRONMENTAL SELF-REGULATORY PROGRAMS

In the last 20 years, environmental self-regulatory programs have proliferated in

both the US and abroad (Nash and Ehrenfeld,1997). Growing environmental regulations inindustrialized nations and increasing environ-mental activism of consumers and the publicin general have driven many industries tolook for alternative strategies to deal withstakeholders. Industries have attempted toavoid costly government regulation and toplacate concerned stakeholders by promisingto reduce their environmental impactsvoluntarily. As explained by Eastman Kodaks

CEO, if industry doesnt take the lead on thisissue, government will (Deavenport, 1993,p. 11).

To facilitate these voluntary reductions, anumber of industry-based trade associationshave initiated self-regulatory programs anddeveloped codes of practice. Codes of envi-ronmental management practice stipulateenvironmental goals for industry membersbeyond those required by government regula-tion. These codes include guidance as to howparticipating firms are to meet these goals andoften require the adoption of specific practicesor management systems. In most cases, thesecodes do not stipulate specific performancestandards or emissions limits. Trade associa-tions rely on a number of mechanisms toensure compliance, including associationoversight, external verification and peerpressure.

Four of the leading attempts by US indus-

tries to self-regulate their environmental per-formance include Responsible Care, sponsored by the American Chemistry Council, theResponsible Distribution Process of theNational Association of Chemical Distributors,the Sustainable Forestry Initiative of theAmerican Forest and Paper Association andEncouraging Environmental Excellence of theAmerican Textile Manufacturers Institute. Weconsider each in turn.

M. J. LENOX AND J. NASH

Copyright 2003 John Wiley & Sons, Ltd and ERP Environment Bus. Strat. Env. 12, 343356 (2003)

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American Chemistry Council

The American Chemistry Council (formerlyknown as the Chemical Manufacturers Asso-ciation) was the first US trade association to

develop an industry self-regulation initiativeto address environmental performance. TheACCs board of directors adopted theprogram, called Responsible Care, in October1989 in response to growing criticism of theindustry and pervasive negative publicopinion. The program attempted to improvethe reputation of the chemical industry byimproving the environmental performanceof individual chemical firms (Nash andEhrenfeld, 1997). Adoption of Responsible

Care was required, as a condition of member-ship, for all firms that participate in the Amer-ican Chemistry Council (ACC). Adoptionconsisted of two actions. First, the CEO wasrequired to sign a set of ten Responsible Careguiding principles. These principles establish broad environmental objectives for firms.Second, every member was to implement sixcodes of management practice dealing withcommunity awareness and emergencyresponse, pollution prevention, process safety,

employee health and safety, distribution andproduct stewardship. Together, these codescontained more than 100 individual manage-ment practices (Chemical ManufacturersAssociation, 1994).

National Association of Chemical Distributors

In 1991, the National Association of ChemicalDistributors initiated its own code of manage-

ment practice, called the Responsible Distribu-tion Process (RDP). The National Associationof Chemical Distributors (NACD) representsfirms that ship, reformulate and distributechemical products to end-users. All NACDmembers were required to adopt RDP as a con-dition of membership. The Responsible Distri- bution Process contained 32 managementpractices that focused on all aspects of chemi-cal distribution, storage, reformulation and

sale (National Association of Chemical Distrib-utors, 1997). RDP required that members regu-larly review with their suppliers the hazardsassociated with the products they handle andimplement risk reduction measures. Members

were to cease doing business with thosesuppliers whose practices were inconsistentwith RDP principles. Members were furtherrequired to make clear commitments to pollu-tion prevention and resource conservation.

American Textile Manufacturers Institute

A group of American Textile ManufacturersInstitute members launched the EncouragingEnvironmental Excellence (E3) initiative in

March 1992. Founding members hoped to pub-licize their environmental accomplishmentsand to distinguish their products from importsthat might be produced under less environ-mentally responsible conditions (Fleming,1999). About one-third of ATMIs membershipvoluntarily participated in the program (ATMI,2003). Each ATMI member that chose to par-ticipate had to affirm senior managementscommitment to environmental excellence. Inaddition, each member had to establish envi-ronmental objectives and set dates by whichthey would be achieved. Finally, firms wereasked to work with suppliers and customers toaddress environmental issues, build employeeeducation programs and develop emergencyresponse plans.

American Forest and Paper Association

To address low public opinion and campaigns

by environmental advocacy groups, theAmerican Forest and Paper Association(AFPA) adopted the Sustainable Forestry Ini-tiative (SFI) in 1994 (AFPA, 1998a). SFI requiredthat members promote environmentally andeconomically responsible forestry practices,improve forest health and productivity andmanage forests to protect their special quali-ties. In addition to these forestry-related initia-tives, AFPA developed a set of principles

INDUSTRY SELF-REGULATION AND ADVERSE SELECTION


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aimed at improving the environmental healthand safety practices of pulp and paper manu-facturing facilities (AFPA, 1998b).

THEORY AND HYPOTHESES

The success of a self-regulatory program iscontingent on individual members being com-pelled to abide by program requirements. Weassume that compliance is conditional on firms benefiting individually from participation inthe self-regulatory effort. In particular, firmswill participate in self-regulatory programswhen doing so provides a signal to stakehold-ers about a firms quality and when these

stakeholders may subsequently reward firmsfor participation. For example, a number offirms have advertised their participation intrade-association-sponsored programs in anattempt to attract environmentally sensitivecustomers and to appease environmentaladvocacy groups. Some insurance providersoffer lower premiums to firms that adoptcodes. Adherence to a standard set of practicesmay provide evidence of due diligence in legalbattles. Even if a firms environmental perfor-mance is below the norm, its adherence tomanagement practices adopted by other firmsmay demonstrate that it has not been willfullynegligent (King and Baerwald, 1998).

Thus, firms will be attracted to self-regulatory programs as a way to differentiatethemselves from others within the industry.Troubling, though, is that if there is no mecha-nism for screening members, these programsmay be subject to adverse selection bad firms

will join to receive the insurance and signalingbenefits of membership. In other words, indus-try self-regulatory programs will attract poorperformers that benefit from participationwithout putting forth any real effort. Whilesome firms will join with the intent of meetingprogram objectives, others may join to masktheir poor performance.

King and Lenox (2000) argue that, withoutmechanisms for penalizing malfeasance, self-

regulatory programs are likely to be subject toadverse selection. Trade associations haveemployed a variety of informal mechanismsto encourage compliance with program re-quirements. For example, a number of trade

associations convene meetings to shareimplementation experiences among members.Such meetings offer opportunities formembers to compare their commitments andimpose pressure on managers of firms that arefalling behind. ACC organizes hundreds ofmeetings annually where progress towardResponsible Care goals is discussed (Rees,1997). NACD, AFPA and ATMI also organizeconferences and workshops where membersaddress their progress in environmental audit-

ing, environmental management systems andcrisis management.

Such interpersonal communication canserve as a basic ingredient of sustained indi-vidual accountability (Furger, 1997, p. 449).Neo-institutional sociologists claim that com-pliance may be achieved through informalmechanisms such as shaming and public expo-sure (Braithwaite, 1989) and the emergence ofnew norms and values that change memberspreferences for collectively valued actions(Gunningham, 1995; Hoffman, 1997; Rees,1997; Jennings and Zandbergen, 1995; Furger,1997). The conferences and meetings convenedby the trade associations provide a venue forindividuals from firms to exert peer pressureon one another for compliance.

Previous empirical research casts doubt onthe power of such informal sanctions toprevent adverse selection into industry self-regulatory programs, however (King and

Lenox, 2000). In a study of ACCs ResponsibleCare program, King and Lenox (2000) foundthat the program attracted firms whose emis-sions of toxic chemicals were greater thanthose of other firms of similar size and type(King and Lenox, 2000). In other words, morepolluting firms on average were more likelyto be members. This behavior persisted overa seven-year period (19901996), suggestingthat the problem was not attributable to an



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initial shake-out period following theprograms founding. Based on their findings,King and Lenox (2000) speculate that industryself-regulation will fail without explicitsanctions.

Self-regulatory programs are limited in thepunishments they may administer, however. Inthe United States, monetary penalties such asfines are likely to raise antitrust concernsamong government regulators. United Statesantitrust law does not allow firms collectivelyto boycott or to raise production costs forcompetitors, for fear that this may lead to pricecollusion (Maitland, 1985). Ultimately, partici-pation in self-regulatory programs is volun-tary. Any punishment must be willinglyaccepted by negligent members since thosein violation may simply leave the program.Thus, the only explicit sanction available isexpulsion.

We propose that the most viable way to

avoid adverse selection is simply to expel non-compliant firms from the program. This meansthat self-regulatory organizations must (i)establish structures to monitor individual firmcompliance with program objectives and (ii)establish procedures for removal of firms fromthe program. Only when rigid monitoring andsanctioning mechanisms are in place may poorperformers be dissuaded from joining the self-regulatory program in the first place.

Proposition 1. Rigid monitoring and sanction-ing mechanisms are needed to avoid adverse selec-tion in industry self-regulatory programs.

Let us consider each of the four self-regulatory programs described earlier indetail. Table 1 summarizes the authority struc-tures used by each trade association to ensurecode compliance. The structures are similar inmany ways. Program adoption is required fortrade association membership in every caseexcept E3. In terms of monitoring, all fourtrade associations required members to self-audit compliance with code requirements. Theprimary differences arise in the mechanisms inplace for assessing these self-audits and ensur-ing compliance.

ACC members self-audits were provided toa consultant hired by the trade association. Theconsultant compiled and reported informationon the association as a whole but not on indi-

vidual firm compliance (Nash, 2002). Startingin 1994, ACC members could voluntarilysubmit to an external certification of theirenvironmental management systems. ACCcontracted Verrico Associates, a private con-sultant, to develop a procedure to assessmembers Responsible Care practices.Verricos verification program was based onpeer, as opposed to third party, review. Chem-ical industry managers from other firms came



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Table 1. Authority structures established by trade association codes (Nash and Ehrenfeld, 2001)

ACCs NACDs ATMIs AFPAsResponsible Responsible Encouraging Sustainable

Care Distribution Environmental ForestryProcess Excellence Initiative

Code adoption required as condition of membership

Members submit self-audited reports on progress

Voluntary third party audits available Individual compliance revealed

to association membersHistory of expulsion of members for

non-compliance


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into the plants of the member seeking reviewand offered their advice for improving Respon-sible Care practices (Nash, 2002).

While ACC chose a voluntary system,NACD required third party review of

members RDP policies as a condition of mem-bership in the association. In 1994, the NACDboard of directors voted to require members tomail their environmental self-audits to Under-writers Laboratories, a third party verifier, toensure consistency with code requirements(Nash, 2002). These reports were not providedto the NACD, but the association wasinformed of non-compliance by UnderwritersLaboratories.

In contrast, ATMI and AFPA did not require

third party review or certification. ATMImembers that participated in E3 were requiredto submit an annual report to the trade associ-ation that outlined the firms environmentalpolicy, objectives and plan of action. AFPArequired members to submit annual reports tothe association on their progress implementingSFI and environmental health and safety prin-ciples. In both cases, member companies self-audits were reviewed by trade association staffmembers (Cantrell, 1999; Fleming, 1999). Sim-ilarly to ACC, AFPA did offer firms the oppor-tunity to voluntarily submit to third partycertification.

None of the trade associations published per-formance profiles of individual members. ACCand AFPA published performance reports based on information aggregated from allmembers that they shared with external advi-sory committees and concerned stakeholderssuch as environmental advocacy groups. In

1996, ACC began the practice of revealing therelative performance of lagging firms to a com-mittee of Responsible Care members.

In terms of sanctions, two trade associations,NACD and AFPA, reported having expelledmembers for non-compliance with coderequirements by 1996. After the first year of theprogram, NACD canceled the memberships ofseveral companies that failed to submit theirfirst self-assessments. The majority of these

firms later rejoined NACD after complying(Soriano, 1999). In 1994, NACD cancelled thememberships of three additional firms thatfailed to send their RDP audits to Underwrit-ers Laboratories for review. AFPA has expelled

17 members for failing to implement the Sus-tainable Forestry Initiative (AFPA, 2003). WhileACC had not officially asked any of itsmembers to leave the association for failure toimplement Responsible Care by 1996, it hadtargeted poor performers and required them todevelop action plans.

To summarize, ATMI did not adopt explicitsanctioning mechanisms for non-compliantfirms and participation in E3 was voluntary forassociation members. The ACC revealed indi-

vidual member performance to a committee ofassociation members and relied on informalpeer pressure to insure compliance withResponsible Care. In contrast, AFPA andNACD relied on expulsion from the associa-tion. While all trade associations reserved theright to remove members, only AFPA andNACD had removed members as of 1996.

Based on our earlier proposition, we hypoth-esize that ACCs Responsible Care and ATMIsE3 programs were subject to adverse selectionwhile AFPAs Sustainable Forestry Initiativeand NACD Responsible Distribution Processprograms were not.

Hypothesis 1. ACCs Responsible Care programwas likely to attract more polluting firms.

Hypothesis 2. ATMIs Encouraging Environ-mental Excellence program was likely to attractmore polluting firms.

Hypothesis 3. AFPAs Sustainable ForestryInitiative was likely to attract less polluting firms.

Hypothesis 4. NACDs Responsible Distribu-tion Process program was likely to attract less pol-luting firms.

DATA AND MEASURES

To explore whether these industry self-regulatory programs were subject to adverse



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selection, we gathered data on firms in the USchemicals, textiles, and pulp and paper manu-facturing sectors. These three sectors werechosen because they include the four industryself-regulatory programs that we are examin-

ing: the Responsible Care and ResponsibleDistribution Process codes in the chemicalindustry, the E3 program in the textiles indus-try and the Sustainable Forestry Initiativeinvolving the pulp and paper industry. Datawere collected for 1996, the first year for whichdata are available for each of the codes.

Using data from the Dun and BradstreetMillion Dollar Disk and the EPAs ToxicRelease Inventory (TRI), we identified a popu-lation of 4090 firms of which 2562 were chem-

ical firms, 541 were textile firms and 1108 werepulp and paper firms. A handful of firms (~200)manufacture in more than one of these seg-ments. Firms were coded as to whether theyparticipated in each of the industry self-regulatory programs in 1996. Responsible Carewas assigned a value of one for participantsin the Responsible Care Program and zerootherwise. Responsible Distribution Process,E3 and Sustainable Forestry Initiative partici-pants were coded in the same way. We alsocreated a binary variable to indicate that a firmparticipates in an industry self-regulatoryprogram. ISR Participation was assigned avalue of one if and only if the firm was amember of one of the four codes under study.All data on participation were gathered fromthe sponsoring trade associations.

Environmental performance data were col-lected from the EPAs Toxic Release Inventory(TRI). The TRI contains data on the annual

release, transfer and treatment of over 300 toxicchemicals from US manufacturing facilities.We measure total facility emissions as the sumof all chemical releases, weighting each chem-ical by its toxicity. Following King and Lenox(2000), we then create a measure of relativefacility emissions to correct for expected dif-ferences in emissions due to facility size andthe product being manufactured (see theAppendix). We create a firm-level measure of

environmental performance (Relative Emis-sions) by calculating the weighted average ofthese facility-level scores using the percentageof total production that each facility repre-sented for the company as the weight.

where sit is facility i size in year t, snt is firm nsize in year t and REit is the measure of relativefacility emissions described in the Appendix.

In essence, this variable measures the degreeto which a firm emits more toxic chemicalsthan expected given the size and the specificindustry segments of its facilities. In thisway, this measure captures whether a firm is

more polluting relative to other firms in itsindustry.

In addition to Relative Emissions, we controlfor a number of additional factors that proba-bly influence the decision to participate in anindustry self-regulatory effort. Firms thatoperate in more polluting industry segmentsare likely to be under greater scrutiny regard-less of their performance within their segment.Thus, high-performing firms that operate indirtier segments of the broader industry willhave more to gain from differentiating them-selves from dirtier firms within their segment.All else being equal, we therefore expect firmsin more polluting segments to be more likelyto join self-regulatory efforts. Following Kingand Lenox (2000), we calculate the dirtiness ofthe sectors a firm operates in (Sector Emissions)as the average toxicity-weighted emissions forthe sectors in which the firm operates dividedby the average number of employees for these

sectors.Other controls include whether the firm isforeign owned, the degree to which the firm isfocused within one industry segment, and thenumber of manufacturing plants a firm owns.

Foreign is coded as one when the firm has anon-US owner and zero otherwise. Previousresearch has proposed that foreign-ownedfacilities are placed under greater scrutinyin domestic markets and therefore would

Relative Emissions s sm il nti n it

( ) = ( )"log in RE



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gain more from positively differentiatingthemselves from others by joining the self-regulatory programs (King and Shaver, 2001).Firm ownership data were gathered from theDun and Bradstreet Million Dollar Disk.

Focus is calculated as the percentage of afirms plants that are chemical, textile, orpulp and paper plants, respectively. Previousresearch has proposed that firms that generatemost of their production in a target industryare more likely to be associated with thatindustry and are therefore more likely to par-ticipate in that industrys self-regulatory effort(King and Lenox, 2000).

Finally, Plants is included to control for firmsize. It is likely that larger firms with moremanufacturing plants are more likely to par-ticipate in self-regulatory efforts due to thegreater attention they attract and the leader-ship role they often take within the industry.Plants is measured simply as the number ofmanufacturing facilities that a firm owns in theUS. See Tables 2 and 3 for description statisticsand correlations for our measures.

ANALYSIS AND RESULTS

Our first model specifies the likelihood that afirm will participate in a trade-association-sponsored industry self-regulatory program.We estimate the following Probit model:

We include the industry-level dummy vari-ables (Chemical Industry, Textile Industry andPulp and Paper Industry) to control for industryeffects. In Table 4, we present two specifica-tions of our Probit model. In Model 1, weexamine the overall effect ofRelative Emissionson participation in industry self-regulatoryprograms. We find no evidence of general sys-temic adverse selection, i.e. that more pollutingfirms are more likely to participate. This is not

Pr ISR Participant Relative

Emissions Sector Emissions Foreign

Focus Plants Chemical Industry

Textile Industry Pulp and Paper

Industry

=( ) = +{

+ +

+ + +

+ +

}

1 0 1

2 3

4 5 6

7 8

F b b

b b

b b b

b b



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Table 2. Descriptive statistics

Variable Description Mean Standard Minimum Maximumdeviation

ISR participant Whether or not a firm participates in an 0.08 0.27 0 1

industry sponsored self-regulatory programResponsible Care Whether or not a firm participates in ACCs 0.05 0.21 0 1

Responsible CareResponsible Whether or not a firm participates in NACDs 0.01 0.09 0 1

Distributors Responsible Distribution ProcessE3 Whether or not a firm participates in ATMIs 0.01 0.09 0 1

E3 programSFI Whether or not a firm participates in AFPAs 0.02 0.13 0 1

Sustainable Forestry InitiativeRelative emissions Average relative emissions of facilities based -0.03 0.65 -4.56 3.47

on sector and sizeSector emissions Average total emissions of sector 4.04 14.78 0 404.92Foreign Whether or not a firm is foreign owned 0.03 0.18 0 1

Focus Ratio of industry specific production to total 0.90 0.24 0.01 1production

Plants Number of a firms manufacturing facilities 3.92 11.09 1 315

n = 4090.


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surprising, since we hypothesize that the pres-ence of adverse selection should vary acrossindustry programs.

We do find that firms in more polluting seg-ments, foreign-owned firms, larger firms andfirms more focused within the industry tend tobe more likely to join. As we suggested earlier,firms in industry segments that are more pol-luting are likely to feel greater pressure to joinself-regulatory efforts. Larger firms with moremanufacturing facilities may find themselvesunder great scrutiny from advocacy groupsand other stakeholders. Larger firms may alsorealize economies of scale in complying withprogram objectives. Firms that produce morein the given target industry may seek mem-bership since they are more closely identifiedwith the industry and its environmentalrecord.

The foreign-owned firm effect is particularly

interesting. The magnitude of the effect isrelatively large and significant. Thus foreignfirms likely gain more from participating inself-regulatory programs than domestic firms.This suggests that participation likely serves,at least in part, as a signal for good behavior.Information asymmetries between stakehold-ers and firms are likely more pronounced inthe case of foreign-owned firms. As a conse-quence, there is less opportunity for foreign



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Table 3. Correlations

1 2 3 4 5 6 7 8 9 10

1. ISR participant 1.002. Responsible Care 0.75* 1.00

3. Responsible Distributors 0.31* -0.02 1.004. E3 0.30* 0.01 -0.01 1.005. SFI 0.47* 0.00 -0.01 -0.01 1.006. Relative emissions 0.01 0.03* 0.00 -0.01 -0.03* 1.007. Sector emissions 0.15* 0.16* 0.05* -0.02 0.03* -0.02 1.008. Foreign 0.10* 0.12* -0.02 0.00 0.02 0.00 0.02 1.009. Focus -0.07* -0.10* 0.02 -0.01 0.00 -0.01 -0.04* -0.15* 1.00

10. Plants 0.31* 0.35* -0.01 0.05* 0.09* 0.01 0.05* 0.13* -0.45* 1.00

n = 4090, * p < 0.01.

Table 4. Probit estimates of ISR participation

1 2

Intercept -2.846*** -2.858***(0.234) (0.235)

Relative emissions 0.001(0.047)

Relative emissions 0.112*chemical industry (0.061)

Relative emissions -0.198

textile industry (0.163)Relative emissions -0.165*pulp and paper (0.089)industry

Sector emissions 0.010*** 0.010***(0.002) (0.002)

Foreign 0.490*** 0.471***(0.139) (0.140)

Focus 0.741*** 0.744***(0.161) (0.161)

Plants 0.038*** 0.038***(0.003) (0.003)

Chemical industry 0.458** 0.471**

(0.151) (0.151)Textile industry 0.356* 0.354*

(0.153) (0.154)Pulp and paper industry 0.478*** 0.478***

(0.150) (0.150)

n 4090 4090c2 (df) 310.06 (8)*** 318.13 (10)***R2 (pseudo) 0.1375 0.1412

Standard errors in parentheses.* p < 0.05, **p < 0.01, *** p < 0.001 (two-tailed test).


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firms to differentiate themselves. Industry self-regulation may be their best way to placateconcerned stakeholders. Previous empiricalfindings are consistent with this effect (Kingand Shaver, 2001).

In Model 2, we relate Relative Emissions toour industry dummies. We begin to see aninteresting story emerge. More polluting firmsin the chemical industry are found to be sig-nificantly (p < 0.05) more likely to participatein industry self-regulation. We should notethat the chemical industry includes both man-ufacturers and distributors. There may verywell be a difference in these two groups that is

not captured in this particular model. Con-versely, cleaner firms in the pulp and paperindustry are found to be significantly (p < 0.05)more likely to participate. In the textile indus-try, we also find cleaner firms joining, thoughwe are not confident in this estimate at the 95%confidence level. These findings suggest vari-ance in the rise of adverse selection in self-regulatory programs. In the chemical industry,more polluting firms are joining, suggesting

adverse selection. In the pulp and paper indus-try, cleaner firms are joining.

To provide further evidence of the differ-ences between programs, we split the sampleby industry and look directly at participationin individual programs. In Table 5, we presentthe estimates for each self-regulatory programregressed against our set of independent vari-ables. Our models for Responsible Distributionand E3 suggest that dirtier and cleaner firms join respectively, though we are not confidentin these estimates at a 95% confidence level. Inthe case of Responsible Care, consistent withKing and Lenox (2000), we find significant evi-

dence that more polluting firms were morelikely to join. In the case of the SustainableForestry Initiative, we find that cleaner firmswere more likely to participate. In addition, weonce again find that firms in dirtier segmentsof the industry, foreign-owned firms, largefirms and firms focused in the industry weremore likely to join. Hence, we find further evi-dence of variance with respect to the presenceof adverse selection.



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Table 5. Probit estimates of participation in individual self-regulatory programs

ACCs NACDs ATMIs AFPAsResponsible Care RDPa E3 SFI

Intercept -2.155*** -2.539*** -2.020*** -2.552***

(0.178) (0.420) (0.436) (0.321)Relative emissions 0.134* 0.009 -0.195 -0.140

(0.067) (0.098) (0.164) (0.092)Sector emissions 0.008*** 0.004* -0.094 0.055***

(0.001) (0.002) (0.077) (0.008)Foreign 0.618*** 0.147 0.334

(0.158) (0.524) (0.326)Focus 0.379* 0.356 0.418 0.835**

(0.183) (0.420) (0.448) (0.326)Plants 0.035*** -0.007 0.017** 0.020***

(0.003) (0.014) (0.006) (0.005)

n 2562 2562 541 1108c2

(df) 232.36 (5)*** 5.05 (5) 8.27 (5) 74.40 (5)***R2 (pseudo) 0.1769 0.0141 0.0348 0.1356

Standard errors in parentheses. p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed test).aForeign did not vary across adopters of the chemical distributors code and thus wasdropped.


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DISCUSSION

In support of Hypothesis 1, we find evidencethat, in 1996, participants in the AmericanChemistry Councils Responsible Care pro-

gram were more polluting on average thanother chemical firms in the United States. Insupport of Hypothesis 3, we find evidence that,in 1996, participants in the American Forestand Paper Associations Sustainable ForestryInitiative were less polluting on average thanother pulp and paper companies in the UnitedStates. We did not find conclusive support forHypotheses 2 and 4. We were not confident (ata 90% level) in our estimates of the effect of rel-ative emissions on program membership.

Thus, we conclude that as of 1996 adverseselection had occurred in the Responsible Careprogram but not the Sustainable ForestryInitiative. What may explain its presence inResponsible Care and its absence from the Sus-tainable Forestry Initiative? We have proposedthat the existence of mechanisms to screenmembers compliance with program objectivesand to eject members that fail to comply is thekey difference. ACC to that point had relied onthe velvet glove of informal peer pressure bymember firms, while the AFPA had a history ofremoving non-compliant members. We believethis subtle but important difference mayexplain our findings.

We should be cautious in our conclusions.We present evidence of a statistically sig-nificant difference in the environmentalperformance of participants in differentself-regulatory programs. We have no way totest directly which program characteristics or

aspects of industry structure may be drivingthis difference. We, in essence, have a sampleof four the four self-regulatory programsunder study. Other factors may explain thisoutcome. For example, the relatively smallnumber of pulp and paper firms participatingin the Sustainable Forestry Initiative may makeinformal mechanisms more effective than inthe much larger Responsible Care program. Wecannot rule out such alternative hypotheses

without examining a larger number of self-regulatory programs across a comparably setof industries and contexts. The small numberof environmental self-regulatory programsthat have been established to date make such

an analysis infeasible at this time.

CONCLUSION

In this paper, we explore the conditions underwhich industry self-regulation may be subjectto adverse selection. We present evidence thatcirca 1996 relatively more polluting firmstended to participate in the American Chem-istry Councils Responsible Care program,

while relatively cleaner firms participated inthe American Forest and Paper AssociationsSustainable Forestry Initiative. We hypo-thesized that differences in the structure ofthe two programs are driving our findings. Inparticular, we propose that the failure of ACCto expel members for non-compliance duringthe period under study invited under-performing firms to join and to not invest inimprovements.

We should recognize that the programsstudied are evolving. In 1998 NACD voted torequire that each members implementation ofRDP be verified by an approved third party(NACD, 2003). ACC will soon require thirdparty verification of Responsible Care com-pliance as a condition of membership. It willbegin raking its members based on their envi-ronmental performance and publish the results(Nash, 2003). By December 2007, member firmswill be required to have facilities externally

certified to a new Responsible Care man-agement system that builds on the ISO 14001international environmental managementstandard.

By our own arguments, we believe that theattraction of more polluting firms to theResponsible Care program will be attenuatedas the ACC is more diligent in monitoringmember firms and expelling non-compliantmembers. Through its recent changes, the ACC



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may very well have solved the adverse selec-tion problem. The changes in program struc-ture in Responsible Care and other programsprovide a valuable laboratory to explore theeffectiveness of various monitoring and sanc-

tioning schemes. The environmental perfor-mance data necessary to test the impact ofthese changes, using the approach taken in thispaper, will not be available for a number ofyears.

Our findings have important implicationsfor firm managers. On one hand, more pollut-ing firms may seek to undermine programenforcement so as to allow their own entry.However, any benefit they receive will proba- bly be temporary as their presence drives

down the value of participation. On the otherhand, firms with superior environmentalperformance should look to establish strictmonitoring and sanctioning mechanisms toensure that self-regulatory programs do notattract polluting firms that will dilute the valueof membership. Less polluting firms gainfrom preventing adverse selection into self-regulatory programs.

The state may create incentives for firmsto adopt strict governance structures by re-warding firms that participate in effective self-regulatory programs. For example, memberfirms could receive favorable consideration inpermitting from regulatory agencies or besubject to less frequent inspections. The statecould give preferential consideration toprogram members in procurement decisions.Regulators may ease the monitoring respon-sibilities of self-regulatory programs byrequiring and verifying firm reports on envi-

ronmental behavior. Of course, we only rec-ommend that the state provide such benefits ifthe self-regulatory program has the gover-nance structures in place to prevent adverseselection. The simple promise of these benefitsmay be sufficient to encourage high perform-ing firms to seek strict monitoring and enforce-ment mechanisms.

As the popularity of industry self-regulationgrows as an alternative or supplement to

traditional forms of government regulation,research is needed to explore the conditionsunder which effective industry self-regulationis possible. In this paper, we further under-standing of industry self-regulation by explor-

ing whether the inclusion of sanctioningmechanisms is critical for the functioning ofindustry self-regulation. We find evidencesupporting this proposition. We believe thatthe prospects for trade associations to leadindustry self-regulation are contingent on thenature of the monitoring and compliancemechanisms brought to bear on non-compliantparticipants.

APPENDIX RELATIVEENVIRONMENTAL PERFORMANCE

MEASURE1

To correct for differences in toxicity betweenemitted chemicals, we follow King and Lenox(2000) and weight each chemical by its toxicityusing the reportable quantities (RQ) databasein the CERCLA statute. We construct aggregatereleases for a given facility in a given year (Eit)by summing the weighted releases of the 246

chemicals that have been consistently a part ofthe TRI database.

(1)

where Eit is aggregate emissions for facility i inyear t, wc is the toxicity weight for chemical cin year t and eci is the pounds of emissions ofchemical c.

Following King and Lenox (2000), wemeasure relative environmental performance

at the facility level by estimating a productionfunction relationship between facility size andaggregate toxic emissions for each four-digitStandard Industrial Classification (SIC) codewithin each year using standard OLS regres-sion. We use employees to measure facility size because we have no measure of production

E w eit c citc= "



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1 Portions of this appendix have previously appeared in print (seeKing and Lenox, 2001).


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units or sales at the facility level. We estimatethe following relationship for each industry:

(2)

where Eit is actual emissions for facility i inyear t, sit is facility size and ajt, b1jt and b2jt arethe estimated coefficients for sectorj in year t.

The relative environmental performance of afacility (REit) is given by the standardizedresidual, or deviation, between observed andpredicted emissions given the facilitys sizeand industry sector. Thus, if a facility emitsmore than predicted given its size and SICcode, it will have a positive residual and a pos-itive score for environmental impact.

ACKNOWLEDGEMENTS

We are indebted to the many people who have con-tributed to this project. The authors would especiallylike to thank Andrew King for his contributions. JohnEhrenfeld, Jennifer Howard-Grenville and Juan Alcacergenerously provided their expertise and insight. Wewould also like to thank seminar participants at DukeUniversity, New York University and Oxford University.Finally, we would like to thank the editor and two anony-

mous reviewers for their detailed and insightful com-ments. This research was supported in part by theNational Science Foundation and the US EnvironmentalProtection Agency, Performance Incentives Division (No.R827918).

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American Forest and Paper Association (AFPA). 2003.What is the Sustainable Forestry Initiative Program?http://www.afandpa.org/Content/NavigationMenu/Environment_and_Recycling/SFI/What_is_the_SFI_Program_/What_is_the_SFI_Program_.htm. [16

June 2003].American Textile Manufacturers Institute (ATMI). 2003.

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National Association of Chemical Distributors (NACD).1997. The National Association of Chemical Distributors:the Responsible Distribution Process. National Associa-tion of Chemical Distributors: Arlington, VA.

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BIOGRAPHY

Michael J. Lenox (corresponding author) isAssociate Professor of Business at the FuquaSchool of Business, Duke University, Box90120, Durham, NC 27708, USA.Tel.: +1 919 660-8025Fax: +1 919 684-2818E-mail: [email protected]

Jennifer Nash is at the Kennedy School ofGovernment, Harvard University, 79 JFKStreet, Cambridge, MA 02138, USA.

Tel.: +1 617 384-7325Fax: +1 617 496-0036E-mail: [email protected]



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WEEK 6 S Lenox and Nash (2003)