Does Self-Regulation Reduce Pollution? Responsible Care in the US chemicals industry Shanti...
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Transcript of Does Self-Regulation Reduce Pollution? Responsible Care in the US chemicals industry Shanti...
Does Self-Regulation Reduce Pollution?Responsible Care
in the US chemicals industry
Shanti Gamper-RabindranAssistant Professor
Graduate School of Public & International AffairsUniversity of Pittsburgh
Stephen FingerAssistant Professor
Moore Business SchoolUniversity of South Carolina
Funding: NSF BCS 0351058 U Pitt UCSUR, CRDF, EUCE
Self-regulation• Industry associations mandate their members to
attain environmental goals, beyond that specified by existing regulations.
• Widely used.1) Nuclear power-plants in the US– INPO2) Petroleum industry in the US – STEP
Does self-regulation reduce pollution or environmental risks?
Self-regulation• Industry associations mandate their members to
attain environmental goals, beyond that specified by existing regulations.
• Widely used.1) Nuclear power-plants in the US -INPO2) Petroleum industry in the US – STEP
Does self-regulation reduce pollution or environmental risks?
Responsible Care• Launched by American Chemical Council in 1989.• Union Carbide accident killed 20,000+ people.• Stock prices for all chemical firms fell.
Responsible Care• Launched by American Chemical Council in 1989.• Union Carbide accident killed 20,000+ people.• Stock prices for all chemical firms fell.
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 Care• But no third party verification (until 2002).• No expulsion of errant members (as of 2002).
Outline
• Why study Responsible Care?
• Literature Review
• Method
• Data
• Results
• Policy conclusion
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.• 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.• 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
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
• 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.
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 (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 (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 (2000)
• Data problem: – TRI “Production Ratio” variables to control for
output – Problematic variable
• We use # employee, imperfect proxy for output
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
• “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
• Obs: Plant j (belonging to firm i) at time t.
• yijt = x1 ijt β1+ pijt1 +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.
• Plant j (belonging to firm i) time t
• Pollution Equation
yijt = x1 ijt β1+ pijt1 +x3it β3+µijt
• Participation Equation
Firm: Benefitit* = ji x1 ijt θ1 + x3 itθ3 + z1it θ4
pit = 1 if Benefitit * > 0
Plant-Level Estimating Equation:
Benefitijt* =x1ijtθ1 + -jx1 ijt θ1 + x3it θ3 + z1itθ4 + ijt
Estimation – IV/GMM
yijt = x1 ijt β1+ pijt1 +x3it β3+µijt
• For all plants, use z1it as instrument
• For plants belonging to multi-plant firms, additionally use -jx1 ijt as instrument
What are the instruments?
SampleS1 S2
All plants Plants belonging to Instruments multiplant firms onlyI1: Firm-level variables
I2: Characteristics of other plants belonging to the same firm
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
• 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
• 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
• 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 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
• 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• 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-
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-
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
• Firms face neighborhood pressure to join RC. – % low education– % poor– % white
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
• 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 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)
CHEMICAL SECTOR
TRI-RSEI
Plants in the US chemicals industry
Plant-level toxicity-weighted air emissions
RSEI EPA IDEACENSUS
Toxicity weights for emissions
Clean Air Act EPA Inspection at plants
DATABASE CONSTRUCTION
CHEMICAL SECTOR
TRI-RSEI
Dun & Bradstreet
Plants in the US chemicals industry
Plant-level Employment
RSEI
DATABASE CONSTRUCTION
Firm-plant linkages
Mergents & Corporate Affiliations
ACC
RCC membership
Plant-level toxicity-weighted air emissions
CHEMICAL SECTOR
TRI-RSEI
Dun & Bradstreet
Plants in the US chemicals industry
Plant-level toxicity-weighted air emissions
Plant-level Employment
RSEI EPA IDEACENSUS
Toxicity weights for emissions
Demographics% poor % minority % low educ % urban at the census tract-level
Clean Air Act EPA Inspection at plants
DATABASE CONSTRUCTION
Firm-plant linkages
Mergents & Corporate Affiliations
ACC
RCC membership
CHEMICAL SECTOR
TRI-RSEI
Dun & Bradstreet
Plants in the US chemicals industry
Plant-level toxicity-weighted air emissions
Plant-level Employment
RSEI EPA IDEACENSUS
Toxicity weights for emissions
Demographics% poor % minority % low educ % urban at the census tract-level
Clean Air Act EPA Inspection at plants
DATABASE CONSTRUCTION
Firm-plant linkages
Mergents & Corporate Affiliations
Firms 1,500+ Plants 2,700+ Time 1988-2001
ACC
RCC membership
1988 2001Plants # RC participants 804 1199
% participants 24 28Firms # RC participants 126 142
% participants 6.1 6.4
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 on probability of participation from 1 std dev change in variables
Variable Instruments†ACC membership in 1988 & 1989 0.4 ** 0.5 **†RC participation dummy (t-1) 0.9 ** 0.9 **% RC participation in SIC-4 0.8 ** 0.3 **Firm's HAP/TRI -0.01Firm's SIC pollution index -0.01Firms' plants' average neighborhood pressure % white 0.004 % low education -0.06 ** % poverty 0.01 % urban -0.04 ** Non-attainment county dummy -0.03Other co-variatesPlant's pollution (t-1) 0.01 0.01Plant's HAP/TRI (t-1) 0.01 0.02 *Plant's SIC pollution index 0.01 ** 0.02 *
All plants Multiplant firms
[1] [2] [3] [4] All plantsMain spec Vary instruments
RC-status 0.05 0.05 0.05 0.05 (0.03) (0.03) (0.04) (0.03)
[5] [6] [7] [8] Only Plants Owned by Multi-Plant FirmsMain spec Vary instruments
RC-status 0.03 0.03 0.03 0.02 (0.03) (0.05) (0.03) (0.04)
RC did not reduce pollution
Impact of RC
95% C.I. (-0.02, 0.12) β=0.05
+ 12%
- 2%
Non-RC RC Non-RC RC
Comparison: Average plant-level annual pollution decline 6%
Most favorable to finding RC caused pollution reduction
Least favorable to finding RC caused pollution reduction
All plants Multiplant Firms
[1] [2]RC-status 0.05 0.03(i.e. impact of RC on pollution) (0.03) (0.03)Test - statisticsUnder-ID: Kleibergen-Paap 192 135 LM rk statisticsWeak-ID: Kleibergen-Paap 2025 726 Wald rk statistics Stock-Yogo Critical Values 5% Relative Bias 11 19 20% Relative Bias 6 6Hansen J statistic 0.1 7p-value for Hansen J stat 1 0.6Emissions equation: R-squared 0.8 0.8Observations 18,850 12,705
Ho: instruments are not correlated to error in second stage – Fail to Reject
Instruments do not fail validity tests.
All plants Multiplant Firms
[1] [2]RC-status 0.05 0.03(i.e. impact of RC on pollution) (0.03) (0.03)Test - statisticsUnder-ID: Kleibergen-Paap 192 135 LM rk statisticsWeak-ID: Kleibergen-Paap 2025 726 Wald rk statistics Stock-Yogo Critical Values 5% Relative Bias 11 19 20% Relative Bias 6 6Hansen J statistic 0.1 7p-value for Hansen J stat 1 0.6Emissions equation: R-squared 0.8 0.8Observations 18,850 12,705
Ho: instruments are correlated to RC participation: Fail to Reject
Are instr. “weak enough to imperil inference,” i.e., the bias in
coefficients from the Biv exceeds a
specific percent bias in Bols?
All plants Multiplant Firms
[1] [2]RC-status 0.05 0.03(i.e. impact of RC on pollution) (0.03) (0.03)Test - statisticsUnder-ID: Kleibergen-Paap 192 135 LM rk statisticsWeak-ID: Kleibergen-Paap 2025 726 Wald rk statistics Stock-Yogo Critical Values 5% Relative Bias 11 19 20% Relative Bias 6 6Hansen J statistic 0.1 7p-value for Hansen J stat 1 0.6Emissions equation: R-squared 0.8 0.8Observations 18,850 12,705
Ho: Instruments are not only weakly correlated to RC participation – Fail to Reject
Are instr. “weak enough to imperil inference,” i.e., the bias in
coefficients from the Biv exceeds a
specific percent bias in Bols?
Yearly effect of participation
[1] [2] [3]Main spec. 6 year blocks 3 year blocksRC 0.05 RC x 0.07 * RC x 0.09 *
(0.03) I(yr<='95) (0.04) I(yr='90-92) (0.05)RC x 0.05 RC x 0.05 I(yr>'95) (0.04) I(yr='93-95) (0.05)
RC x 0.02 I(yr='96-98) (0.04)RC x 0.09 * I(yr='99-01) (0.05)
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 – 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 – 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: Alternative denominators
[1] [2] [3]Media air air air
Denominator empl empl2
RC 0.05 0.05 0.05(0.03) (0.03) (0.04)
Observations 18,850 18,850 18,850
Robustness check – Alternative media
[4] [5] [6] [7] [8] [9]Media all all water land onsite offsiteDenominator empl empl empl empl emplRC 0.02 0.02 0.02 -0.01 0.05 0.01
(0.03) (0.02) (0.02) (0.02) (0.04) (0.05)
Robustness check – add regulatory variables
[1] [2] [3] [4] [5] All plants Multiplant firms
Main Inspection Main InspectionSpec Vars spec Vars
added addedFirst stage participation equationFirm's average lagged plant-level 0.3 Clean Air Act inspection (0.2)Dummy for firms with no plants 0.5 * subject to Clean Air Act inspection (0.3)Dummy for plant inspection under the 0.06 -0.05 -0.05 Clean Air Act (t-1) (0.1) (0.1) (0.1)GMM estimationRC participation dummy 0.05 0.06 0.03 0.03 0.03
(0.03) (0.03) (0.03) (0.04) (0.04)No obs. 18850 13564 12705 9702 9702
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: subsamples
[1] [2] [3] [4]Sample Main Continuous Continuous RC status spec. reporters reporters for no. changes for pollution of employees RC 0.05 0.03 0.07 * 0.04
(0.03) (0.04) (0.04) (0.06)
Obs 18,850 9,399 13,598 7,747
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.
Robustness check: subsamples
[1] [2] [3] [4]Sample Main Continuous Continuous RC status spec. reporters reporters for no. changes for pollution of employees RC 0.05 0.03 0.07 * 0.04
(0.03) (0.04) (0.04) (0.06)
Obs 18,850 9,399 13,598 7,747
• 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
[1] [2] [3] [4]RC 0.04 0.04 0.05 0.05
(0.05) (0.04) (0.03) (0.03)RC x -0.0007 no plants (0.003)RC x dummy for 0.2 0.2 single-plant Firm (0.1) (0.1)RC x firm's # -0.001 employees (t-1) (0.02)RC x plant's # 0.02 employees (t-1) (0.02)RC x % poor in plant's 0.1 neighborhood (0.1)RC x % urban in plant's -0.03 neighborhood (0.04)
Heterogeneous program effects
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.
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 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 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
• 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 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?
Pollution equation• yijt = x1 jt β1 + pjt1 + pjt (x2jt-xx2) 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 (pjt1 ) captures the effect of RC on the average plant,
• The third term (pjt (x2jt-xx2) β2) captures the impact of RC that varies by plant characteristics.
• We demean the x2 variables in the third term in order to consistently
estimate the effect of RC on an average plant with the 1 coefficient.