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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

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does self regulation reduce pollution responsible care in the us chemicals industry

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

outline
Outline
  • Why study Self-regulation?
  • Method
  • Data
  • Results
  • Conclusion
self regulation
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?

self regulation4
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?

responsible care
Responsible Care
  • Launched by American Chemical Council in 1989.
  • Union Carbide accident killed 20,000+ people.
  • Stock prices for all chemical firms fell.
responsible care6
Responsible Care
  • Launched by American Chemical Council in 1989.
  • Union Carbide accident killed 20,000+ people.
  • Stock prices for all chemical firms fell.
responsible care7
Responsible Care
  • Adopted worldwide
responsible care8
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
responsible care9
Responsible Care

But no third party verification (until 2002).

No expulsion of errant members (as of 2002).

research question
Research question
  • Did Responsible Care reduce pollution?
  • Our results: No
outline11
Outline
  • Why study Responsible Care?
  • Literature Review
  • Method
  • Data
  • Results
  • Policy conclusion
literature review
Literature Review
  • Can self-regulation achieve stated goals?
  • Maybe yes
  • Maybe no
  • Empirical evaluation is scarce.
supporting view rc create sufficient incentives for plants pollution reduction
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).

supporting view rc can create incentives for plants pollution reduction
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).
supporting view rc create sufficient incentives for plants pollution reduction15
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).
opposing view rc is green wash
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.
empirical study
Empirical study
  • Lenox and King (2000)
  • Pioneering empirical study on self-regulation
lenox and king 2000
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.
lenox and king 200019
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
lenox and king 200020
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.
lenox and king 200021
Lenox and King (2000)
  • Data problem:
    • TRI “Production Ratio” variables to control for output
    • Problematic variable
  • We use # employee, imperfect proxy for output
outline22
Outline

Why study Responsible Care?

Theory

Method

Data

Results

Conclusion

did rc reduce pollution
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?
method
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.

pollution equation
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.

slide26
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

estimation iv gmm
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
instruments for the all plants sample instrument 1
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.
instrument 2 rc participation by firm in previous period
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.
instrument 3 firm s membership in acc pre 1989
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.

instruments for multi plant firms
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.
instruments for multi plant firms33
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.
instrument 4 firm s hap to tri ratio
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.

instrument 5 firm s share of production in dirtier sub industries
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.
instrument 5 firm s share of production in dirtier sub industries36
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.
instrument 5 firm s share of production in dirtier sub industries37
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.
instrument 6 firm s plants neighborhood characteristics
Instrument 6: Firm’s plants’ neighborhood characteristics

Firms face neighborhood pressure to join RC.

% low education

% poor

% white

method39
Method
  • Evidence of heteroskedasticity
  • Use GMM estimator
  • More efficient than standard IV
  • We allow errors to be correlated among plants within the same firm.
control variables
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
control variables41
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)
outline42
Outline
  • Why study Responsible Care?
  • Method
  • Data
  • Results
  • Policy conclusion
slide43

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

slide44

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

slide45

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

slide46

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

outline47
Outline
  • Why study Responsible Care?
  • Method
  • Data
  • Results
  • Policy implication
slide50
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.

impact of rc 95 c i 0 02 0 12 0 05
Impact of RC 95% C.I. (-0.02, 0.12)β=0.05

Most favorable to finding RC caused pollution reduction

Least favorable to finding RC caused pollution reduction

+ 12%

- 2%

Non-RC RC Non-RC RC

Comparison: Average plant-level annual pollution decline 6%

slide54

Instruments do not fail validity tests.

Ho: instruments are not correlated to error in second stage – Fail to Reject

slide55

Are instr. “weak enough to imperil inference,” i.e., the bias in coefficients from the Biv exceeds a specific percent bias in Bols?

Ho: instruments are correlated to RC participation: Fail to Reject

slide56

Are instr. “weak enough to imperil inference,” i.e., the bias in coefficients from the Biv exceeds a specific percent bias in Bols?

Ho: Instruments are not only weakly correlated to RC participation – Fail to Reject

robustness check denominator
Robustness check – Denominator
  • Dependent var: Pollution/ # employee
  • Possible bias against finding RC reduced pollution
  • If plants respond to RC by choosing a production process that is less labor intensive, but that does not raise pollution per unit of production.
  • Should larger plants increase their output at a faster rate than labor, our denominator for large plants may be too small, resulting in too large a measure of pollution intensity
  • Given that RC participants typically have larger plants, this mis-measurement of pollution intensity could bias our estimates of the impact of RC.
  • Alternative dependent var: pollution
robustness check denominator59
Robustness check – Denominator
  • Dependent var: Pollution/ # employee
  • Possible bias against finding RC reduced pollution
  • If plants respond to RC by choosing a production process that is less labor intensive, but that does not raise pollution per unit of production.
  • Should larger plants increase their output at a faster rate than labor, our denominator for large plants may be too small, resulting in too large a measure of pollution intensity.
  • Given that RC participants typically have larger plants, this mis-measurement of pollution intensity could bias our estimates of the impact of RC.
  • Alternative dependent var: pollution
robustness check denominator60
Robustness check – Denominator
  • Dependent var: Pollution/ # employee
  • Possible bias against finding RC reduced pollution
  • If plants respond to RC by choosing a production process that is less labor intensive, but that does not raise pollution per unit of production.
  • Should larger plants increase their output at a faster rate than labor, our denominator for large plants may be too small, resulting in too large a measure of pollution intensity
  • Given that RC participants typically have larger plants, this mis-measurement of pollution intensity could bias our estimates of the impact of RC.
  • Alternative dependent var:
  • pollution & pollution/(employee)2
robustness check add regulatory variables
Robustness check – add regulatory variables
robustness check subsamples
Robustness check: subsamples
  • We cannot differentiate between plants that fail to report pollution and plants that close.
  • It could be possible for participating firms to close their dirty plants and open clean ones.
  • We would not recognize this as improved performance in our estimation method.
  • Subsample - Continuous reporters on pollution.
  • Subsample – Continuous reporters on employees.
robustness check subsamples66
Robustness check: subsamples
  • Our identification of RC’s impact relies on 2 sources of variation:
  • (1) variation in RC status between plants with similar observed characteristics; and
  • (2) the intertemporal variation for plants whose RC status changes within our panel.
  • Limitation – cross section – bias if our instruments and covariates fail to fully control for the systematic differences between plants that were always RC participants and those that were always non-RC participants.
  • Solution - Relating changes in plants' RC status to plants' pollution intensity.
slide68
RC impact for subsets of plants or firms characteristics

Dawson and Segerson (2008)

free-riding problems within voluntary arrangements may be overcome if sub-groups of firms have incentives to participate.

Plants belonging to firms with larger no of plants and firms with larger number of employees.

“Environmental Justice concerns” – plants in poor, low-educated, minority neighborhoods – less likely to reduce pollution.

Robustness check:Heterogeneous program effects

ols vs iv
βOLS vs. βIV

βOLS=0.2** βIV=0.03 or 0.05

Possible explanation

– Firms with plants that face more difficulties in reducing pollution are more likely to self-select and join RC to signal green, but fail to reduce pollution.

– After controlling for self-selection, the pollution increase related to RC participation is less pronounced.

outline71
Outline

Why study Responsible Care?

Method

Data

Results

Conclusion

what have we learned
What have we learned?

RC did not lead to overall waste reductions in the chemicals

industry from 1990-2002.

In some specifications, RC participation caused 7-9%

increase in pollution at the average plant.

Self-regulation without third-party verification or enforceable

penalties may not be an effective substitute for formal

regulation.

what have we learned73
What have we learned?

RC did not lead to overall waste reductions in the chemicals

industry from 1990-2002.

In some specifications, RC participation caused 7-9%

increase in pollution at the average plant.

Self-regulation without third-party verification or enforceable

penalties may not be an effective substitute for formal

regulation.

what have we learned74
What have we learned?

RC did not lead to overall waste reductions in the chemicals

industry from 1990-2002.

In some specifications, RC participation caused 7-13%

increase in pollution at the average plant.

Self-regulation without third-party verification or enforceable

penalties may not be an effective substitute for formal

regulation.

caveats
Caveats
  • We will understate impact of the program.

- If non-members respond to presence of RC by reducing pollution

- Pollution reduction by RC firms cause technological innovations that reduce pollution abatement costs for all firms.

  • We only analyze two of RC's six codes of conduct.
  • RC has mandated third party verification post-2002.
future work why did rc not reduce emissions
Future work: Why did RC not reduce emissions?
  • Future work 1:
  • Did RC participation result in fewer EPA inspections?
  • Soft-pedaling on regulation?
  • Sam and Innes (2008)
  • Future work 2:
  • Pre 2002 – no third party verification.
  • Post 2002 – there was third party verification
  • Test pre-2002 vs. post-2002?
thank you
Thank you!

paper posted at

www.pitt.edu/~shanti1/

pollution equation78
Pollution equation
  • yijt = x1 jt β1 + pjt1 + pjt (x2jt-x2) β2+ µjt
  • Pollution is affected by the plant characteristics (xjt), the observed participation decision (pjt), a subset of plant characteristics which affect the impact of RC (x2jt), and an unobserved component (µjt).
  • The first term (x1 jt β1) accounts for the effect of the covariates on pollution regardless of RC status.
  • The second term (pjt1 ) captures the effect of RC on the average plant,
  • The third term (pjt (x2jt-x2) β2) captures the impact of RC that varies by plant characteristics.
  • We demean the x2variables in the third term in order to consistently estimate the effect of RC on an average plant with the 1coefficient.
slide79
Firm i plant j time t
  • Emissions

yijt = yij(t-1)βy +x1 ijt β1+ pijt1 +pijt (x2ijt-x2) β2+x3it β3+µijt

  • Participation

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