Shanti Gamper-Rabindran Assistant Professor Graduate School of Public & International Affairs

1. Does Self-Regulation Reduce Pollution?Responsible Care in the US chemicals industry Shanti Gamper-Rabindran Assistant Professor Graduate School of Public & International Affairs University of Pittsburgh Stephen Finger Assistant Professor Moore Business School University of South Carolina Funding: NSF BCS 0351058 U Pitt UCSUR, CRDF, EUCE

2. Outline • Why study Self-regulation? • Method • Data • Results • Conclusion

3. Self-regulation • Industry associations mandate their members to attain environmental goals, beyond that specified by existing regulations. • Widely used. • Nuclear power-plants in the US– INPO 2) Petroleum industry in the US – STEP Does self-regulation reduce pollution or environmental risks?

4. Self-regulation Industry associations mandate their members to attain environmental goals, beyond that specified by existing regulations. Widely used. Nuclear power-plants in the US -INPO 2) Petroleum industry in the US – STEP Does self-regulation reduce pollution or environmental risks?

5. Responsible Care • Launched by American Chemical Council in 1989. • Union Carbide accident killed 20,000+ people. • Stock prices for all chemical firms fell.

6. Responsible Care • Launched by American Chemical Council in 1989. • Union Carbide accident killed 20,000+ people. • Stock prices for all chemical firms fell.

7. Responsible Care • Adopted worldwide

8. Responsible Care • Stated goal – self-regulation to improve environmental performance of the chemical industry. • Codes of Conduct – waste minimization & pollution prevention. • Firms submit self-assessment to ACC

9. Responsible Care But no third party verification (until 2002). No expulsion of errant members (as of 2002).

10. Research question • Did Responsible Care reduce pollution? • Our results: No

11. Outline • Why study Responsible Care? • Literature Review • Method • Data • Results • Policy conclusion

12. Literature Review • Can self-regulation achieve stated goals? • Maybe yes • Maybe no • Empirical evaluation is scarce.

13. Supporting View: RC create sufficient incentives for plants’ pollution reduction. • Incentive 1 : Industry self-regulation can pre-empt stricter government regulation. • Coordination problem for firms in industry. • Comply & reduce pollution? Or Shirk? • Critical number of members will reduce pollution, even if others free-ride, to maintain overall credibility of the RC program. • Costs to these firms of reducing pollution under RC < Costs of government regulation if self-regulation fails. Dawson and Segerson (2008).

14. Supporting View: RC can create incentives for plants’ pollution reduction. • Incentive 2: Benefits from Green Reputation. • The RC program, by limiting its membership to firms that commit to RC’s goals, including pollution prevention, allows member firms to benefit from the positive reputational effect of being socially responsible. • These firms can benefit from consumers who choose to purchase from, and investors who choose to invest in, firms that establish the reputation of being responsible (Hay, Stavins, and Vietor, 2005).

15. Supporting View: RC create sufficient incentives for plants’ pollution reduction. Participation in a program that signals green - reduces inspections or enforcement actions by the regulatory agencies (Maxwell & Decker, 2006; Innes & Sam, 2008) • discourages boycotts by environmental groups or pre-empt their lobbying for stricter regulations (Maxwell et al., 2000; Baron, 2001).

16. Opposing View: RC is green-wash • Firms join RC for positive publicity but in reality they do not incur the costs to reduce their pollution. • Firms have no incentive to reduce pollution. • Firms not subject to sanctions if fail to achieve code of conduct • Firms not subject to third party verification.

17. Empirical study • Lenox and King (2000) • Pioneering empirical study on self-regulation

18. Lenox and King (2000) • Method problem: Ignore self-selection. • Overstate RC impact on reducing pollution • If the firms that self-select into RC are those that will reduce their pollution regardless of RC participation. • Understate RC impact on reducing pollution • If firms that self-select are those are those that face more difficulties in reducing pollution, and join to benefit from best practices.

19. Lenox and King (2000) • Method problem: Ignore self-selection. • Overstate RC impact on reducing pollution • If the firms that self-select into RC are those that will reduce their pollution regardless of RC participation • Understate RC impact on reducing pollution • If firms that self-select are those are those that face more difficulties in reducing pollution, and join in order to from shared best practices

20. Lenox and King (2000) • Method problem: Ignore self-selection. • Overstate RC impact on reducing pollution • If the firms that self-select into RC are those that will reduce their pollution regardless of RC participation. • Understate RC impact on reducing pollution • If firms that self-select are those are those that face more difficulties in reducing pollution, and join to benefit from best practices.

21. Lenox and King (2000) • Data problem: • TRI “Production Ratio” variables to control for output • Problematic variable • We use # employee, imperfect proxy for output

22. Outline Why study Responsible Care? Theory Method Data Results Conclusion

23. Did RC reduce pollution? • Do plants that belong to RC participating firms reduce their pollution relative to statistically equivalent plants that belong to non-RC participating firms?

24. Method “Treatment” groups – plants belonging to RC firms. “Control” groups – statistically equivalent plants belonging to non-RC firms. Use Instrumental Variables (IV) to address self-selection into program Limitation Non-RC firms reduce their pollution in response to RC.

25. Pollution Equation Obs: Plant j (belonging to firm i) at time t. yijt = x1 ijt β1+ pijt1 +x3it β3+µijt yijt = Log (toxicity weighted air pollution/ # employee) pijt = 1 if plant j is owned by firm which participates in RC at time t; 0 otherwise. x1 ijt = plant factors that directly affect plant’s pollution. x3 it = firm factors that directly affect plant’s pollution. 1 negative => impact of RC.

26. Plant j (belonging to firm i) time t • Pollution Equation yijt = x1 ijt β1+ pijt1 +x3it β3+µijt • Participation Equation Firm: Benefitit*=  jix1 ijt θ1 + x3 itθ3 + z1itθ4 pit = 1 if Benefitit* > 0 Plant-Level Estimating Equation: Benefitijt* =x1ijtθ1 + -jx1 ijt θ1 + x3itθ3 + z1itθ4 + ijt

27. Estimation – IV/GMM yijt = x1 ijt β1+ pijt1 +x3it β3+µijt • For all plants, use z1it as instrument • For plants belonging to multi-plant firms, additionally use -jx1 ijt as instrument

28. What are the instruments?

29. Instruments for the ‘all plants’ sampleInstrument 1 • Average RC participation within the same sub-industry • If the average is high: • there may be features of the sub-industry that make RC appealing.

30. Instrument 2: RC participation by firm in previous period • Persistence in RC participation • The cost of continuing participation less than the cost of a new member joining • members may have already implemented new systems and procedures to adhere RC’s standards. • costly to switch out of the program as it may send a negative signal to their consumers or to regulators about their conduct.

31. Instrument 3: Firm’s membership in ACC pre-1989 Firms that were ACC members prior to RC were more likely to receive a positive net benefit from the trade association. After RC, they continue in ACC if RC benefits > RC costs Firms that choose not to be members of the ACC prior to RC costs of membership exceeded the trade association benefits of the program. For these firms to join RC after its inception, they need to: offset their negative trade association costs and. generate positive net benefits from RC.

32. Instruments for multi-plant firms • If Dow Chemical needs to reduce pollution at a plant in New Jersey due to neighborhood pressure, that factor: • reduces the additional cost for Dow to join RC and thus affect the likelihood of all Dow plants being in the program. • does not directly cause Dow to reduce pollution at a plant in Louisiana. • Caveat – technological spillovers across plants in the same firm. • Must check if instruments invalid using over-identification test.

33. Instruments for multi-plant firms • Firm f owns plant j, k, l, m. • As instrument for plant j, use characteristics of other plants owned by same firm. • Nevo (2000) uses the average prices of the same product in other cities in the region as instruments for a product’s price in a given city. • Berry, Levinsohn and Pakes (1995) use characteristics of other products by same producers as instruments for unobserved characteristics of a given product.

34. Instrument 4: Firm’s HAP to TRI ratio Hazardous air pollutants (HAPs) are subject to stricter pollution abatement regulations (the “MACT-hammer”). Parent firm w/ plants with high HAP/TRI must reduce pollution, regardless of RC, face lower additional costs in joining RC. HAP/TRI of other plants belonging to the same firm affects plant j’s participation in RC, through their effect on the parent firm.

35. Instrument 5: Firm’s share of production in dirtier sub-industries • Poll inten for SIC28xx= Pollution/empl in SIC 28xx Pollution/empl SIC-28 • Less likely to join RC • More costly to reduce pollution when rely on pollution-intensive production technologies. • More likely to join RC • Less costly for dirtier firms to reduce pollution if diminishing return to pollution abatement.

36. Instrument 5: Firm’s share of production in dirtier sub-industries • Pollution/empl in SIC 28xx Pollution/empl SIC-28 • Less likely to join RC • More costly to reduce pollution when rely on pollution-intensive production technologies. • More likely to join RC • Less costly for dirtier firms to reduce pollution if diminishing return to pollution abatement.

37. Instrument 5: Firm’s share of production in dirtier sub-industries • Pollution/empl in SIC 28xx Pollution/empl SIC-28 • Less likely to join RC • More costly to reduce pollution when rely on pollution-intensive production technologies. • More likely to join RC • Less costly for dirtier firms to reduce pollution if diminishing return to pollution abatement.

38. Instrument 6: Firm’s plants’ neighborhood characteristics Firms face neighborhood pressure to join RC. % low education % poor % white

39. Method • Evidence of heteroskedasticity • Use GMM estimator • More efficient than standard IV • We allow errors to be correlated among plants within the same firm.

40. Control variables • Larger firms may have greater financial resources to invest in pollution abatement. • Plant’s size [lagged plants’ employees] • Firm’s size [lagged firms’ employees] • The number of plants owned by the firm. • Dummy for single-plant firms

41. Control variables • Industry-level variables at SIC-4 • Producer price index, shipment quantity index, the Herfindahl-Hirschman index and SIC-4 dummies. • Year dummies • changes in federal regulations and available technologies. • Neighborhood pressure on plants • the median income, share white, share < high school education. • Lagged emissions (instrumented by t-2)

42. Outline • Why study Responsible Care? • Method • Data • Results • Policy conclusion

43. DATABASE CONSTRUCTION TRI-RSEI CHEMICAL SECTOR Plant-level toxicity-weighted air emissions Plants in the US chemicals industry CENSUS EPA IDEA RSEI Toxicity weights for emissions Clean Air Act EPA Inspection at plants

44. DATABASE CONSTRUCTION TRI-RSEI Dun & Bradstreet CHEMICAL SECTOR Plant-level Employment Mergents & Corporate Affiliations ACC RCC membership Firm-plant linkages Plant-level toxicity-weighted air emissions Plants in the US chemicals industry RSEI

45. DATABASE CONSTRUCTION TRI-RSEI Dun & Bradstreet CHEMICAL SECTOR Plant-level Employment Mergents & Corporate Affiliations ACC RCC membership Firm-plant linkages Plant-level toxicity-weighted air emissions Plants in the US chemicals industry CENSUS EPA IDEA RSEI Demographics% poor % minority % low educ % urban at the census tract-level Toxicity weights for emissions Clean Air Act EPA Inspection at plants

46. DATABASE CONSTRUCTION TRI-RSEI Dun & Bradstreet CHEMICAL SECTOR Plant-level Employment Mergents & Corporate Affiliations ACC RCC membership Firm-plant linkages Plant-level toxicity-weighted air emissions Plants in the US chemicals industry CENSUS Firms 1,500+ Plants 2,700+ Time 1988-2001 EPA IDEA RSEI Demographics% poor % minority % low educ % urban at the census tract-level Toxicity weights for emissions Clean Air Act EPA Inspection at plants

47. Outline • Why study Responsible Care? • Method • Data • Results • Policy implication

50. Participation equation Are instruments correlated with participation? Probability of RC participation with values of covariates set at the sample mean is 0.13 for all plants. 0.54 for plants owned by multi-plant firms.