Does Disclosure Reduce Pollution? Evidence from India’s Green Ratings Program***

Nicholas Powers*, Allen Blackman**, Thomas P. Lyon*, and Urvashi Narain**

Abstract

Reluctant to implement conventional command-and-control environmental regulation in less developed countries, governments and other organizations have turned to innovative approaches, including public disclosure programs. However, while these alternative approaches have become increasingly popular, little is known about their effectiveness. We analyze a unique data set covering firms in the Indian pulp and paper industry and assess the impacts of one such public disclosure program, implemented by India’s largest non-governmental organization. Our results offer mixed evidence as to the effectiveness of this program, but shed light on the mechanisms through which disclosure programs may operate, which has previously not been well understood. Disclosure does not uniformly induce costly improvements in environmental performance, but does appear to be effective in reducing pollution from certain plants. This includes plants in communities that are best able to leverage the disclosed information to impose costs on the plants, and those who stand to benefit most from the increased availability of information to managers.

* University of Michigan, Ross School of Business, 710 Tappan St., Ann Arbor, MI 48104** Resources for the Future, 1616 P Street NW, Washington, D.C. 20036*** Preliminary and incomplete. Please do not circulate. Nicholas Powers claims responsibility for all errors and omissions and absolves the other three co-authors from same.

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1 Introduction

Command-and-control environmental regulation has come under increasing fire

from policy-makers and economists alike, as it is prone to inefficiency arguments and can

also face substantial political opposition in both implementation and enforcement. These

problems are further complicated in developing countries, where regulatory institutions

often lack both the political will and finances necessary to enforce such regulation. In

developed and developing countries alike, market-based and other alternatives to

traditional command-and-control regulation have emerged as popular substitutes, for a

variety of reasons. Among these alternatives are disclosure-based programs, in which the

government or some other entity requires all pollution-creating industrial activity to be

measured and recorded, with this information subsequently provided to the general

public.

The case in support of the effectiveness of such programs usually appeals to a

Coasian bargaining rationale. Coase (1960) argued that when property rights are well

defined and transaction costs and other costs associated with bargaining are low, efficient

outcomes can be obtained, yielding either a situation in which polluters compensate

property owners for polluting, or property owners pay would-be polluters to refrain from

doing so. However, this also assumes that both parties are equally informed, which is

very rarely the case. By lowering the costs to the public of informing themselves about

the true levels of pollution being generated, disclosure programs can reduce transactions

costs stemming from this information asymmetry, improving the ability of the market to

internalize the cost of externalities.

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However, empirical work that analyzes how effective these programs are in

practice is rare. García Lopez, Sterner, and Afsah (2004) use a treatment effects model to

estimate the effects of the PROPER disclosure program in Indonesia on pollution levels.

In it, the authors find that the plants that were rated as poor performers showed

immediate improvements (within a month), while both good and bad performers

improved their environmental performance when the participation dummy was lagged 6

months. Beyond this paper, empirical work examining this question is rare1, and it does

little to explain how or why disclosure programs might induce changes in firm behavior.

We employ a unique and incredibly detailed data set to analyze the effectiveness

of one such program, known as the Green Ratings Program (GRP), administered by

India’s largest NGO. The dataset we construct includes not only a large variety of plant-

level indicators of environmental performance, but also information on input prices, and

plant-level data on input choices, production process, product mix, outputs, and plant

characteristics with regards to geographic and socio-economic variables that could impact

the pollution/abatement trade-off.

While we find that GRP did not uniformly induce plants to improve their

environmental performance, inclusion of various interaction terms in our empirical

specifications generate several more nuanced results which yield insights into the

effectiveness of disclosure programs and should inform policy-makers who are

considering this alternative form of regulation. These interaction terms allow us to

identify further effects of disclosure for plants with various characteristics. Namely, our

findings suggest that certain plant and community characteristics can have an effect on

1 Several studies have used the US EPA’s Toxic Release Inventory (TRI) data, but none have directly addressed the impact of disclosure, as TRI data was not collected prior to the formation of the program.

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the impact of disclosure programs. Unsurprisingly, plants who are deemed poor

performers seem to improve more in response to the disclosure program, as do plants

who are in wealthier communities. Finally, single-plant establishments seem to improve

more than those plants who are part of a larger firm.

In section 2, we describe the Green Ratings Program. In section 3, we provide a

conceptual/theoretical framework that informs our econometric work. In section 4, we

present the data and discuss some empirical issues we face before presenting results in

section 5. Finally, we conclude in section 6.

2 The Green Ratings Program

The Green Ratings Program was introduced by India’s Center for Science and

Environment (CSE) in 1999. CSE first conceived of the project in late 1997 and

contacted plants in January 1998, soliciting data on a wide range of indicators for each of

the previous three years.2 All pulp and paper plants with a capacity of more than 100

tons per day as of 1998 were included in the sample; this represents 28 plants. Each plant

was judged in several categories; the largest category, production unit level

environmental performance, includes input management, process management, and waste

management and pollution control. Each of these categories comprises several variables

that receive a score based on the distance to the best available technology (worldwide).

In addition, corporate environmental policy and compliance and community perception

also receive some weight. The indicators are weighted, summed, and each plant receives

two overall indicators that were widely publicized upon the release of the disclosure

2 Following Indian financial convention, each yearly observation is total or average data from April 1 to March 31. Thus, the third (and final pre-disclosure) year was just ending as the plants became aware of the project and were contacted for data.

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program: a score whose range is 0-100 and a corresponding rating of one to five leaves.3

The scores and ratings were released to the general public on July 18, 1999, at a high-

profile event featuring, among others, the former Indian Minister of Finance (and current

Prime Minister), Dr. Manmohan Singh. All major Indian daily newspapers covered the

green ratings in the week following the announcement.4

The pulp and paper sector was the first to be rated by the GRP; based on

anecdotal evidence, CSE India deemed it a success and continued on to other industries,

including the chlor-alkali sector, the automobile industry, and the cement sector. The

pulp and paper sector is, as of today, the only sector to be rated twice (and thus the only

sector for which CSE India has data that allows us to construct a panel with both pre-

disclosure and post-disclosure observations).

The second rating of the pulp and paper industry was released in 2004. Five

subsequent years of data had been collected, and all but one of the first-phase plants with

continuing operations continued their participation in the disclosure program. Note that

in both phases, CSE collect data not only from the plants themselves, but also build a

network of volunteer experts who verify the data collected from the plants, talk to local

stakeholders, and also collect data from the pollution control boards of the states in which

the plants are located. Any discrepancies are investigated, and CSE is able to use robust

data verified from several sources before conducting their analysis.

3 This detailed account of the Green Ratings Program is due to personal communication, Monali Zeya Hazra.4 Some readers may be concerned that the long lag between the plants’ awareness of the disclosure program and the actual disclosure blurs the date when the impact is actually felt. Note that the year ending in 1999 was completed prior to the release of the disclosure, but that plants were aware of the impending institutional change. We assume that the plants, all of whom already had been exposed to some environmental pressure from civil society, were able to anticipate with a fair degree of accuracy the changes in implicit costs that the disclosure program incurred. In slightly more formal terms, the plants took the additional costs and benefits incurred by the disclosure program into their abatement optimization function starting in 1998-1999, rather than waiting for an actual increase in pressure to react. The closure of several of the worst-rated plants, some due to environmental protests, suggests that those plants who failed to anticipate these changes in costs and benefits do not enter into our empirical analysis.

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Figures 1 through 3 track the average levels of various water pollutants

throughout the 8-year program. The graphs clearly suggest that pollution was lower in

the second period (1999-2003) than in the first (1996-1998). Of course, several factors

unrelated to the disclosure program could be responsible for this empirical observation.

These include exogenous trends of improving environmental performance, exogenous

changes in demand or the prices of inputs, and other factors. Our empirical task is to

identify the effects of the disclosure program itself, as opposed to changes in

environmental performance that were brought about by other exogenous changes to the

conditions faced by the plants. Note that while the data from the second phase is central

to our analysis, we are only analyzing the impact of the first rating. To reduce confusion,

we will refer to first phase and second phase data and observations as pre- and post-

disclosure, respectively.

3 Conceptual Framework

To be determined.

4 Data

As discussed above, the bulk of our raw data was collected and provided by CSE India.

We also employ data from the 2001 Indian census that provides us with proxies for

several community characteristics. We also used data from Prowess to construct some

company-level variables as well as a regional energy price index. Finally, price indices

for other inputs are constructed using data from indiastat.com. The variables used are

summarized below, and summary statistics and correlations are provided in Table 1.

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4.1 Variables

BOD, COD, and TSS These are all various measures of water pollution. Both

biochemical oxygen demand, or BOD, and chemical oxygen demand, or COD, are

indicators of the relative oxygen-depletion effect of a waste contaminant. Total

suspended solids, or TSS, are fibers and other particles suspended in wastewater that

often will reduce productive habitat of the water. All three have been widely adopted as

a measure of pollution effect. All of the firms in our sample have effluent treatment

plants that reduce all three of these measures. Accordingly, the values reported are the

indicators associated with end-of-pipe emissions. All three variables are usually

regulated, in both developed and developing nations, on a mg/L basis. However, in

India, where water is extremely cheap, this provides an incentive for plants to dilute their

emissions by simply consuming more water. Accordingly, we factor the water use and

overall production of the plant into our calculations and instead report the variable in

kg/BDMT (bone-dry metric tons) of product produced. We appeal to intuition in our

choice of scaling; as we expect the cost of abatement to increase at an increasing rate, and

because we are interested in the relative reaction of both “clean” and “dirty” firms, we

use logged values of all three variables.

Information disclosure variables These are the indicators released to the public after the

initial rating (1995-1998), released in July, 1999. Some specifications simply use the

POST-DISC dummy, which is an indicator variable that turns on in the year ending in

1999. Others use a series of dummy variables for the number of leaves the GRP ratings

release classified each plant, based on its environmental management and performance,

as a 1-, 2-, or 3-leaf plant (the scale actually extends to five leaves, but CSE didn’t grant

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any plant a rating higher than three leaves). The different levels should proxy for

different increases in informal regulatory pressure. For example, ONELEAF is a dummy

variable that is equal to 0 in all years for the plants that received a rating of two or three

leaves; for plants that received a rating of one leaf it is equal to 0 for pre-disclosure years

and 1 for post-disclosure years.

Community variables All community variables are based on 2001 Census India data.

LITERACY, which is the percentage of the population that is literate in the municipality

where the plant is located, serves as a proxy for education. WEALTH is the percentage

of households in the corresponding sub-district (similar to a county in the U.S.) who own

a moped, and will proxy for variation in economic levels. CASTE is the percentage of

the municipal population that belongs to either a scheduled caste or scheduled tribe,

which are social groups that are recognized by the government as having been

traditionally disadvantaged groups, and are now guaranteed a proportion of educational

and employment opportunities through the affirmative-action-like system of reservation.

AGLABOR is the proportion of the workers in the municipality who are either cultivators

or agricultural laborers, and should serve as a proxy for the importance of agriculture to

the local economy. We also proxy for population density with URBANPCT, which is the

proportion of the population in the sub-district that lives in municipalities that are

classified as urban. We also proxy for plant visibility within a community with

COMPTOWN, which is the percentage of non-agricultural laborers in a sub-district that

work for the plant. These variables are also employed in several interaction terms, where

we have multiplied the community variable times the POST-DISCLOSURE dummy.

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Thus, WEALTH*POST-DISC, for example, takes a value of zero in the first three years

of the sample and is equal to WEALTH for the latter five years.

Plant characteristics Several specifications include a set of time-variant plant

characteristics, primarily as controls. Unless otherwise indicated, this data was collected

and reported by CSE. SCALE is the log of the amount of final product produced by a

given plant in a given year, in bone-dry metric tons (BDMT is a common unit of

measurement, as moisture content can vary between different classes of products).

FINALGOOD is the proportion of a plants product that is used directly by consumers

(writing/cultural paper or tissue paper). This is meant to proxy for possible consumer

pressure, but its expected sign is ambiguous for bleach, as consumers typically demand

higher levels of brightness for this class of products. VINTAGE is simply the natural log

of the age (in years) of a plant. We also include a variety of raw material input controls,

which are the proportion of fiber inputs that are bamboo, grasses, agro-residues,

recyclables, and market pulp (wood is the omitted category). These are included because

water pollution loads can vary substantially with inputs. For example, a plant could

reduce the pollution load sent to its effluent treatment plant by shifting fiber inputs away

from wood and towards agro-residues. RIVERWATER is a variable between 0 and 1

that indicates what percentage of influent comes from a river (as opposed to groundwater

or rainwater harvesting) in a given year. EFFRIVER is a variable between 0 and 1 that

indicates what portion of the company’s effluent is discharged into a river (as opposed to

discharged on land or in the sea) in a given year. There are also time-invariant plant

characteristics that we code from the CSE reports that accompanied the data. These

include GOV, a dummy variable that equals 1 for government-owned plants,

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COMWATER, a dummy variable that equals 1 if the plant had been the subject of

registered complaints with regards to water pollution prior to disclosure, NGOWATER, a

dummy variable that equals 1 if the plant had been the target of local NGO campaigns

with regards to water pollution prior to disclosure, MEDIAWATER, a dummy variable

that equals 1 if the plant had been the subject of excessive media attention with regards to

water pollution prior to disclosure, and ENFWATER, a dummy variable that equals 1 if

the plant had been fined or faced some other enforcement action from the regional

Pollution Control Board with regards to water pollution prior to disclosure. RAINFALL

is the average amount of rainfall, in millimeters, in the region where the plant is located.

WATERDISTANCE is the distance of the plant to the nearest body of water. SINGLE is

a dummy variable that equals 1 if the plant is the unique establishment owned by the

company, while MULTIPLE is a dummy that equals 1 if the plant is one of several pulp

and paper mills under the same ownership, and CONGLOMERATE is a dummy that

equals 1 if the plant is the unique paper plant owned by a diversified conglomerate.

BATCH is a variable between 0 and 1 that indicates what percentage of the plant’s

pulping process is done in chemical batch processing, while CONTINUOUS represents

what percentage of the plant’s pulping is done in continuous chemical processing.

Continuous processing is a more advanced technology than batch processing, and reduces

the load sent to the effluent treatment plant (the other option is mechanical pulping). We

also use firm-level data from Prowess to control for other various factors that could

impact a firm’s cost of abatement. These include RDSHARE - their R and D to sales

ratio - and FORSHARE - the share of their sales that come from exports. Finally, we

also use CSE data to construct a variable that we refer to as PRODHHI. This is a

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measure of the variation in the product mix produced by a plant, with higher numbers

indicating that production is concentrated on 1 or 2 product classes. The inclusion of this

variable is prompted by anecdotal evidence which suggests that cleaning equipment

during shifts between different production processes can increase the pollution load sent

to the effluent treatment plant.

Exogenous changes to the cost structure faced by plants We also control for changes

in input prices that could affect a plant’s production and abatement decisions.

RELCOALPRICE is an inflation-adjusted region-specific price index for coal,

constructed from Prowess micro-data5. REALNAOH and REALCL are inflation-

adjusted nation-wide price indices for sodium hydroxide, the major chemical used in

pulping, and for chlorine, the primary chemical used in the bleaching process. In some

specifications, we also include RELWOODPRICE, which is an inflation-adjusted price

index for wood. This serves as a proxy for fiber inputs.6,7 RELWAGES is an inflation-

adjusted price index for average daily wage rates for non-agricultural, unskilled workers.

We also include a linear TREND term to allow for exogenous improvements in

technology that could have affected pollution levels both before and after initiation of the

disclosure program.

4.2 Econometric Issues

5 Coal represents between 80 and 85 percent of the energy generation in our sample in any given year.6 Unfortunately we were unable to find sufficient data to construct similar indices for any of the other fiber inputs. However, these will generally be related to wood prices, especially in India, where the fiber inputs are substitutable (All About Paper, p.40). The inclusion of RELWOODPRICE is also problematic in that pulp and paper mills represent a large enough portion of the market for wood that they cannot be considered to be price-takers. This leads to a possible endogeneity problem. However, we do not have any variables that could serve as valid instruments, and even if we did, the small sample size would render a 2-stage approach virtually infeasible. 7 We do not have a proxy for water costs; the price paid by industrial users of water to the government for water used is so minute that we choose to ignore it. In any case, there does not appear to be any yearly variation in the rates paid by the plants over the time period we analyze.

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There are also several econometric issues we need to take into account as we

proceed with estimation. Chief among these is the fact that with an unbalanced panel

covering at most 22 plants and 8 years of data, we face limitations due to our small

sample size. Accordingly, we strive to be as parsimonious as possible in model selection,

while still controlling for the factors that could explain changes in plant-level

environmental performance. Also, due to the small sample size, we need to make sure

that our results aren’t being driven by an individual plant. To that end, for all results for

which we report significance, we have performed an outlier check and verified that the

results stand if we re-run the regression if all of the observations corresponding to any

one plant is omitted.

Another hurdle to identification is the fact that there is a single start date when all

plants moved from the pre-disclosure to the post-disclosure state of the world.

Accordingly, changes in any number of other factors could explain some of the

improvement in environmental performance. Since our variable of interest would be

perfectly collinear with yearly fixed effects, we can’t control for all the other possible

factors that could influence environmental performance. However, we do make efforts to

control for the most obvious factors that could affect a plant’s propensity for clean

production. These include market pressure as measured by product mix and input prices,

and exogenous technological change, which we proxy with the trend term8

Finally, several of the plant and community variables are time-invariant. This is

actually not an issue that concerns us, but we address it here for the purpose of

clarification. We are not interested in the coefficients on these variables per se; in the 8 Our selection of a linear trend term is based on OLS regressions with plant fixed effects for the pre-disclosure period. As Table A1 in the appendix demonstrates, the addition of a quadratic term affects the results on the linear trend term due to extremely high collinearity (the correlation coefficient of the two variables is .98) and small sample size (N=46 or 47 depending on the dependent variable), but adds little information.

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absence of regulation these might interest us, following Pargal and Wheeler (1996).

However, in the present context, there is certainly unobserved institutional and regulatory

heterogeneity that could overwhelm any relationship between these community variables

and pollution. Rather, we are interested in determining whether these characteristics have

any additional effect once the disclosure program changes the institutional landscape.

Accordingly, we include these variables as interaction terms in order to test, for example,

whether plants in communities with more educated populations respond differently than

plants in less educated communities. The baseline effects of each of these time-invariant

variables are captured by the firm fixed effects.

5 Estimation and Results

Most specifications are estimated using systems OLS with plant fixed effects.9

This yields results that are identical to single-equation OLS, but allows us to test for joint

significance across multiple equations. Accordingly, the null hypothesis of the statistical

tests that we discuss here is that the variable in question has no effect on water pollution

as proxied by our three dependent variables, rather than an individual dependent variable.

As we analyze the results, we only discuss significance of a result if its F-stat is

statistically significant and the individual coefficients share the same sign.10

5.1 Baseline results

9 While the error terms are highly correlated when the dependent variables are BOD, COD, or TSS, we do not use the seemingly unrelated regression (SUR) technique here because we do not have a good reason, a priori, to omit any regressors from any of the three equations. When exactly the same regressors appear in each equation, SUR reduces to OLS (Greene 2003, p.343).10 This is usually the case, as the three dependent variables are highly correlated. However, they are all prone to some measurement error, which is a further reason why our approach is appropriate. Note also that using Systems OLS instead of equation-by-equation OLS causes us to lose a handful of observations (between 1-3) that have observed values for one, but not all, of the dependent variables. Single-equation estimation with these missing observations yields coefficients that vary little from those that we present here.

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We first look to test whether there is sufficient evidence to claim that the GRP

had an impact on water pollution levels, as Figures 1-3 appear to suggest. While, as

columns 1-3 of table 2 suggest, water pollution levels are unambiguously lower after

disclosure than they were before, the probable existence of a pre-existing trend prior to

disclosure forces us to include a linear trend term. Doing so results in a loss of

significance on our variable of interest, and even a sign reversal for 2 of the three

variables when we consider the model with RELWOODPRICE. While we believe this

can be attributed to the high correlation (.815) between the trend variable and the post-

disclosure indicator, it does cast doubt on the impact of the disclosure program for the

average plant.11 The joint test of the significance of the post-disclosure dummy is

significant at the 1% level, but due to the lack of consistency in the signs of the

coefficients being tested, we can make no inference about the direction of the impact of

the disclosure. We conclude that there is insufficient evidence to identify a pollution-

reducing effect of the Green Ratings Project on the average plant.

5.2 Do different types of plants respond differently?

While the data prevent us from making any meaningful inference about the effect

of GRP on the average plant, they still may be able to teach us more about what types of

plant are more likely to modify their behavior in response to disclosure programs.

Accordingly, we modify our empirical approach to allow us to identify different effects

for plants with different characteristics. In order to do this, we create a large set of

11 We also experiment with the possibility that the disclosure program induced a persistent increase in the rate of environmental progress (which would be consistent with a change in the slope of the trend term) rather than in a one-time shift of the equilibrium path (consistent with a one-time decrease in the intercept term). However, this raises further multi-collinearity concerns, as the correlation between the post-disclosure trend term and the original trend term is 0.9685, while that between the former and the post-disclosure dummy is 0.7520. Secondly, results in Table A2 suggest that the one-time drop is more consistent with the data than a change in the trend. Accordingly, we proceed without the post-disclosure trend variable.

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interaction variables that multiply various time-invariant plant or community

characteristics by the post-disclosure indicator variable.12 We must also control for the

possibility that these same characteristics had an effect on the initial slope of the trend

term. To that end, we also create a second set of interaction variables that multiply these

same time-invariant plant or community characteristics by the trend term. Several of the

interaction variables formed are highly collinear (due in part to the fact that all take a

value of 0 for the three pre-disclosure years), and including several sets of these

interaction variables simultaneously would quickly pose a degrees-of-freedom problem.

At this point, we are also more interested in identifying possible channels for the

disclosure program to have an effect rather than identifying the strongest among several

which are undoubtedly closely related.

Thus, we repeat the multivariate regression presented in Section 4.1 and Table 2

several times, adding the two relevant interaction terms corresponding to one community

characteristic in one regression each.13

For each regression, the relevant z-scores for the individual variables and the p-

values corresponding to the joint test of the significance of the interaction between the

post-disclosure dummy and the plant/community characteristic in question are presented

in Table 3. For example, the individual coefficients on the ONELEAF*POST-DISC

variable (the first column of Table 3) are all negative (and two are individually significant

at the 1% level), and the test for joint significance allows us to reject the null of no effect

at the 5% level. This means that, with greater than 95% confidence, the disclosure

12 Some of the characteristics we use to construct these interaction terms, such as EFFRIVER, display minor variation over time. For these, we calculate the pre-disclosure average and interact it with the post-disclosure dummy to create the corresponding interaction term.13 The baseline effects of the time-invariant characteristics are captured by plant-specific fixed effects, which are included in nearly every regression we present in this article.

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project had a statistically significant effect on the three indicators of water pollution for

low-rated plants, above and beyond the effects caused by the disclosure on all plants, the

underlying trend terms, and plant-level fixed effects. Since the individual coefficients are

all negative, we are comfortable inferring that this effect is negative. This suggests that

one-leaf plants demonstrated a statistically significant improvement in water pollution

measures following disclosure.

Since our panel is composed of such a small number of plants (22), we are

concerned that several of the results for which we find significance could be driven by

outliers. To that end, for each of the regressions in Table 3 for which we find a

significant result, we have repeated it 22 times, omitting each plant in one regression.

The highest p-value of the F-test of the joint significance of the variable in question is

reported in the right-most column of Table 3.

We find that one-leaf plants (those who received the lowest scores) did exhibit

significant improvement, net of any trend, following disclosure relative to the average

plant. We verify this relationship by adding the full set of controls; these results are

displayed in Table 4. This finding is consistent with the concept of a newly-informed

public imposing higher costs on plants that they learn are dirty, or with plants incurring

additional abatement costs as the outcome of a new Coasian bargaining equilibrium.

Another finding from Table 3 is that plant environmental improvement in

response to the disclosure program is increasing in wealth of the surrounding community,

even after accounting for an overall downward trend.14 This suggests that wealthier

communities are able to better leverage the increased information learned after the

14 Here, we proxy for wealth with the percentage of households that own a moped. Using other measures of wealth, such as the percentage of households that use banking service or the percentage owning a television, produce qualitatively similar results.

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disclosure program and use it to induce change in plant behavior. These results are even

more remarkable when we consider that wealth does not appear to be a predictor of initial

performance. The correlation between WEALTH and any measure of the first-phase

rating (either leaves or SCORE) is less than 0.10 in absolute value.15

The final finding that is significant and robust to our outlier check is that in

response to the disclosure program, stand-alone plants appear to have improved their

environmental performance more than plants that are either part of a larger conglomerate

firm or who are one of several paper plants in an organization. One possible explanation

for this relies on a learning argument. Stand-alone plants can not benefit from the sharing

of best practices that plants who are part of a larger organization are able to do at low

cost. This line of reasoning suggests that not only the surrounding stakeholder

community, but also the managers at some plants, may have been relatively uninformed

and could have benefited from the GRP disclosure.16

Table 3 also indicates that we don’t have compelling evidence here to suggest that

the plant response to the disclosure program varies with the other myriad characteristics

listed in Table 3.

5.3 Robustness checks

One by one, we supplement the full model in Columns 7 through 12 of Table 2

with the three sets of interaction terms discussed in section 5. 1 in order to ensure that

15 However, pre-1998 measures of BOD, COD, and TSS do appear to be weakly positively correlated with wealth levels. We address this issue later in this section.16 An alternative explanation is the possibility of economies of scale in implementing environmental management systems. One remarkable success of the GRP is that of the 21 plants in our sample that did not have certified environmental management systems in the 1st phase, 13 did by the end of the second phase, or were in the process of implementing one. These 13 were comprised of 5 of the 10 stand-alone plants and 8 of the 11 plants owned by larger organizations. While our data limitations prevent us from undertaking a more detailed analysis of this alternative explanation, the above-cited figures certainly suggest that it is plausible.

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those results hold once all time-variant controls are added. The results are included in

Table 4. As can be seen, each of these results does hold.

Some readers might also be concerned that part of the reason for more

improvement by stand-alone plants and by plants in wealthier communities is simply due

to those plants being dirtier to begin with. Simple correlation coefficients suggest that

this may be true in the case of stand-alone plants; the evidence is less clear that dirty

plants were located in wealthier communities. However, we feel that even if this were

the case, this does not affect the validity of our results; if the provision of information

results in an improvement in environmental performance for stand-alone plants, this is

consistent with our conceptual framework in that the disclosure program lowered the

costs of improving environmental performance, and the largest changes are observed in

those plants (stand-alone plants) whose pre-disclosure costs of improving were relatively

high. A similar argument can be made for wealth.

Nonetheless, in table 5, we include the ONELEAF interaction terms

(ONELEAF*TREND and ONELEAF*POST-DISC), which will allow us to disentangle

whether the improvement by the stand-alone plants and the plants in wealthy

communities, respectively, was driven more by these characteristics

or by the fact that they were relatively poor environmental performers prior to disclosure.

In table 5, we re-run models 2 and 3 from table 4, but this time include the ONELEAF

interactions in both specifications. In both cases, the level of significance is attenuated,

but the results are still strong enough to suggest that the channels proxied for by the these

two characteristics facilitate environmental improvement above and beyond that driven

by the simple fact that these plants also tended to be dirtier.

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We also include all three simultaneously in a horse race to see whether any of the

three effects we have identified is more strongly borne out in the data. Given our small

sample size and some multi-collinearity between some of the baseline variables of

interest17, we are agnostic as to whether any effects will “win” the horse race.

Significance on all the variables is lost when included simultaneously; all individual

coefficients remain negative, but none are significant. When we run the joint test for

significance, the results are strongest for ONELEAF, but the p-value of .3199 is still well

above conventional levels of statistical significance.

6 Conclusion

In this paper we have attempted to empirically identify the effect of an innovative

disclosure program on plant-level environmental performance, specifically with regards

to water pollution. While disclosure programs similar to the one analyzed here have

become prevalent, both in the developing and developed worlds, little is known about

their effectiveness. Do such disclosure programs provide incentives for industrial

producers to abate the pollution inherent in their activities? Are there circumstances

under which disclosure programs are more or less effective in providing these incentives?

This paper sheds light on these questions, and can hopefully inform policy analysts who

may be considering similar programs in the future.

All data has limitations, and the data we employ here is no different. While

CSE’s project has yielded a wealth of information on virtually every environmental

indicator of importance, the small sample size hampers our ability to identify all the

possible impacts disclosure programs could have. As such, the familiar idiom that

17 For example, the correlation between WEALTH and SINGLE is 0.68.

19

absence of evidence is not the same as evidence of absence is certainly the case here,

with regards to the effects of the Green Ratings Project. Nonetheless, we are able to

identify some key effects with relatively high confidence. Namely, there is not sufficient

evidence to support the claim that the Green Ratings Project had a significant effect on

the average plant in the study. However, there is statistically significant evidence that

suggests that disclosure programs may have the desired impacts on plants that are

revealed to have poor environmental performance, that are located in wealthier

communities, or that do not enjoy the benefits from sharing best practices that usually

occurs in larger organizations.

Furthermore, the reader should note that of the 6 plants that participated in the

first round of the disclosure project but not in the second, 5 of these (ranked 5th, 17th,

and 24th-26th of 28 plants evaluated in the first round) closed before the second round

was initiated. Environmental protests were at least part of the reason for the closure of

two of these plants18. While our data is not extensive enough to allow us to undertake a

Heckman-type selection process, these closures suggest that, if anything, our empirical

results may understate the impact of the disclosure project. Further research is clearly

necessary, but the findings we present in this paper suggest that while disclosure

programs may not be overwhelming successes, they do elicit changes in behavior from

certain types of industrial actors. We also have shed light on some mechanisms through

which disclosure might elicit these changes.

18 Personal communication, Monali Zeya Hazra.

20

Figure 1. Annual average emissions of biochemical oxygen demand (BOD) from 21 pulp and paper plants participating in India’s Green

Ratings Project by performance rating (leaves)

0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003

Year

BO

D (k

g/bd

mt p

rodu

ct)

1 leaf

All2/3 leaves

21

Figure 2. Annual average emissions of chemical oxygen demand (COD) from 21 pulp and paper plants participating in India’s Green Ratings Project

by performance rating (leaves)

0

10

20

30

40

50

60

70

80

90

100

1996 1997 1998 1999 2000 2001 2002 2003

Year

CO

D (k

g/bd

mt p

rodu

ct)

1 leafAll

2/3 leaves

22

Figure 3. Annual average emissions of total suspended solids (TSS) from 21 pulp and paper plants participating in India’s Green Ratings

Project by performance rating (leaves)

0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003

Year

TSS

(kg/

bdm

t pro

duct

)

1 leaf

All2/3 leaves

23

Table 1 – Descriptive statistics and correlation coefficients

VariableM

ean

Stan

dard

De

viat

ion

Min

Max

BOD

COD

TSS

POST

-DIS

C

SCAL

E

FINA

LGOO

D

VINT

AGE

PCTB

AMBO

O

PCTG

RASS

ES

PCTR

ECYC

L

PCTP

ULP

RELW

AGES

FORS

HARE

RDSH

ARE

RELC

OALP

RICE

REAL

NAOH

PROD

HHI

WAT

ERCO

NSEN

T OTHE

RCON

SENT

GRAN

TED

EFFR

IVER

RIVE

RWAT

ER

RELW

OODP

RICE

BOD 1.06 1.09 -3.09

4.671.00

COD 3.37 0.90 0.04 5.43 0.90 1.00TSS 2.10 1.07 -

2.744.08

0.87 0.92 1.00POST-DISC 0.72 0.45 0 1 -

0.20-

0.20-

0.25 1.00SCALE 11.1

90.55 9.84 12.2

1-

0.35-

0.20-

0.15 0.14 1.00FINALGOOD 0.62 0.35 0 1 -

0.03 0.06-

0.06 0.09 0.01 1.00VINTAGE 3.07 0.84 0 4.20 0.46 0.47 0.56 0.11 0.21 0.04 1.00PCTBAMBOO 0.29 0.34 0 1

0.15 0.22 0.26-

0.08 0.02 0.37 0.07 1.00PCTGRASSES 0.02 0.08 0 0.56

0.38 0.34 0.20-

0.05-

0.40 0.14 0.08-

0.21 1.00PCTRECYCL 0.12 0.22 0 1 -

0.08-

0.19-

0.14 0.18-

0.26-

0.53-

0.22-

0.37 0.12 1.00PCTPULP 0.10 0.22 0 1 -

0.58-

0.68-

0.73-

0.01-

0.08-

0.16-

0.51-

0.34-

0.06 0.11 1.00RELWAGES 0.35 0.02 0.32 0.39 -

0.03-

0.03-

0.08 0.59-

0.01 0.05 0.01-

0.02-

0.05 0.12-

0.02 1.00FORSHARE 0.05 0.05 0 0.20 -

0.33-

0.23-

0.27 0.01 0.31 0.15-

0.11-

0.20-

0.19-

0.08 0.09-

0.06 1.00RDSHARE*100 0.10 0.11 0 0.55 -

0.09-

0.02-

0.03 0.01 0.03-

0.12 0.08-

0.27-

0.12-

0.02 0.15 0.01 0.12 1.00RELCOALPRICE 11.6

50.92 9.39 12.8

5 0.02-

0.01 0.08 0.06 0.16-

0.21 0.07-

0.15-

0.13 0.00 0.00 0.31-

0.15 0.11 1.00REALNAOH 0.83 0.13 0.68 1.08

0.24 0.23 0.28-

0.87-

0.16-

0.08-

0.09 0.04 0.06-

0.14-

0.01-

0.55-

0.03 0.01-

0.17 1.00PRODHHI 0.75 0.21 0.24 1 -

0.19-

0.27-

0.27 0.17-

0.03 0.17-

0.33 0.34-

0.02 0.13 0.08 0.10-

0.06-

0.34-

0.11-

0.17 1.00WATERCONSENTGRANTED

0.87 0.34 0 1 -0.37

-0.29

-0.25 0.12 0.18

-0.08

-0.06 0.07

-0.35

-0.08 0.13 0.04 0.02 0.09

-0.05

-0.12 0.00 1.00

OTHERCONSENTGRANTED

0.84 0.37 0 10.09 0.16 0.14 0.10

-0.09 0.06 0.13 0.18

-0.14

-0.23

-0.14 0.09

-0.03 0.06 0.06

-0.09

-0.06 0.54 1.00

EFFRIVER 0.68 0.42 0 10.32 0.34 0.36

-0.05 0.07 0.11 0.42 0.50

-0.27

-0.41

-0.42

-0.03

-0.12

-0.01

-0.13 0.04

-0.07 0.32 0.35 1.00

RIVERWATER 0.73 0.45 0 10.00 0.05 0.22

-0.04 0.17

-0.23

-0.03 0.22

-0.44

-0.05

-0.22 0.00

-0.16

-0.02 0.23 0.03 0.04 0.04 0.06 0.03 1.00

RELWOODPRICE 1.17 0.15 0.96 1.41 -0.03

-0.05

-0.10 0.54

-0.02 0.02

-0.02 0.00

-0.08 0.12 0.01 0.88

-0.14

-0.06 0.44

-0.58 0.07 0.04 0.09

-0.03 0.00 1.00

24

Table 2 – Baseline resultsEach set of three columns presents the results from one Systems OLS regression.

BOD COD TSS BOD COD TSS BOD COD TSS BOD COD TSSPOST-DISC -0.4 -0.274 -0.344 -0.383 -0.258 -0.347 -0.12 -0.031 -0.16 -0.016 0.05 0.009

[0.197]*

[0.136]*

[0.154]*

[0.198]+

[0.136]+

[0.156]* [0.213] [0.144] [0.169] [0.225] [0.151] [0.172]

TREND -0.114 -0.099 -0.075 -0.169 -0.141 -0.164[0.039]

**[0.026]

**[0.031]

*[0.055]

**[0.037]

**[0.042]

**

SCALE -0.6 -0.204 -0.155 -0.554 -0.161 -0.165 -0.568 -0.177 -0.134 -0.637 -0.23 -0.245[0.250]

* [0.173] [0.196][0.254]

* [0.175] [0.200][0.242]

* [0.163] [0.192][0.246]

* [0.165] [0.189]

FINALGOOD -1.077 -0.284 -0.317 -1.034 -0.243 -0.327 -1.034 -0.247 -0.289 -1.094 -0.294 -0.386[0.412]

* [0.285] [0.323][0.414]

* [0.285] [0.326][0.399]

* [0.269] [0.316][0.399]

** [0.268] [0.306]

PCTBAMBOO -1.276 -0.695 -0.446 -1.297 -0.715 -0.441 -1.388 -0.792 -0.52 -1.402 -0.803 -0.543[0.549]

*[0.379]

+ [0.429][0.549]

*[0.377]

+ [0.431][0.531]

*[0.358]

* [0.421][0.529]

**[0.356]

* [0.406]

PCTGRASSES -2.903 -0.228 -0.254 -2.763 -0.095 -0.287 -2.655 -0.013 -0.092 -2.795 -0.122 -0.318[1.062]

** [0.733] [0.831][1.070]

* [0.736] [0.841][1.029]

* [0.694] [0.815][1.029]

** [0.692] [0.789]

PCTRECYCL -2.841 -0.376 -1.226 -2.742 -0.282 -1.249 -2.645 -0.206 -1.098 -2.734 -0.275 -1.242[0.747]

** [0.516][0.585]

*[0.753]

** [0.518][0.592]

*[0.725]

** [0.489][0.574]

+[0.724]

** [0.487][0.556]

*

PCTPULP -1.691 0.7 -0.115 -1.734 0.659 -0.105 -1.547 0.824 -0.021 -1.398 0.941 0.221[1.174] [0.811] [0.918] [1.174] [0.807] [0.923] [1.135] [0.766] [0.899] [1.135] [0.763] [0.870]

RELWAGES 6.103 4.884 3.735 3.626 2.531 4.31 1.675 1.047 0.829 4.13 2.953 4.799[1.816]

**[1.255]

**[1.421]

** [2.987] [2.054][2.348]

+ [2.314] [1.561] [1.832] [2.878] [1.935][2.208]

*

FORSHARE -4.306 -1.137 -2.08 -3.938 -0.788 -2.165 -3.773 -0.676 -1.731 -4.199 -1.007 -2.419

25

[1.171]** [0.809]

[0.916]*

[1.223]** [0.841]

[0.961]*

[1.146]** [0.773]

[0.907]+

[1.179]** [0.793]

[0.905]**

RDSHARE -6.957 -2.201 9.867 0.032 4.436 8.245 -4.406 0.009 11.541 -16.146 -9.107 -7.441[30.557

][21.106

][23.905

][31.269

][21.499

][24.576

][29.537

][19.921

][23.384

][30.533

][20.528

][23.422

]

RELCOALPRICE 0.039 0.031 0.022 0.022 0.015 0.026 0.018 0.013 0.009 0.039 0.03 0.043[0.046] [0.032] [0.036] [0.048] [0.033] [0.038] [0.045] [0.030] [0.035] [0.047] [0.032] [0.036]

REALNAOH 0.995 0.788 0.746 1.173 0.957 0.705 0.122 0.031 0.173 -0.631 -0.553 -1.044[0.486]

*[0.336]

*[0.380]

+[0.515]

*[0.354]

**[0.405]

+ [0.556] [0.375] [0.440] [0.766] [0.515][0.587]

+

REALCL -1.067 -0.418 -0.542 -1.018 -0.371 -0.553 -1.107 -0.452 -0.568 -1.217 -0.538 -0.747[0.433]

* [0.299] [0.339][0.435]

* [0.299] [0.342][0.419]

** [0.282][0.331]

+[0.424]

**[0.285]

+[0.325]

*

PRODHHI 0.605 0.751 0.566 0.596 0.742 0.568 0.582 0.731 0.551 0.587 0.735 0.56[0.349]

+[0.241]

**[0.273]

*[0.349]

+[0.240]

**[0.274]

*[0.337]

+[0.228]

**[0.267]

*[0.336]

+[0.226]

**[0.258]

*

VINTAGE 0.219 -0.245 -0.363 0.207 -0.256 -0.36 0.369 -0.115 -0.264 0.463 -0.042 -0.112[0.292] [0.202] [0.228] [0.292] [0.201] [0.229] [0.287] [0.193] [0.227] [0.293] [0.197] [0.225]

WATERCONSENT -0.372 -0.096 -0.087 -0.333 -0.059 -0.096 -0.381 -0.104 -0.094 -0.457 -0.163 -0.216

GRANTED [0.253] [0.175] [0.198] [0.256] [0.176] [0.201] [0.245] [0.165] [0.194][0.249]

+ [0.168] [0.191]

OTHERCONSENT 0.232 -0.085 -0.041 0.214 -0.102 -0.037 0.263 -0.058 -0.021 0.312 -0.02 0.058GRANTED [0.222] [0.154] [0.174] [0.223] [0.153] [0.175] [0.215] [0.145] [0.170] [0.217] [0.146] [0.166]

EFFRIVER -7.084 0.658 -0.751 -6.986 0.751 -0.774 -8.445 -0.521 -1.644 -9.285 -1.174 -3.003[4.100]

+ [2.832] [3.208][4.100]

+ [2.819] [3.222][3.989]

* [2.690] [3.158][4.014]

* [2.698] [3.079]

RIVERWATER -0.012 -0.505 0.279 0.034 -0.461 0.269 0.007 -0.488 0.292 -0.068 -0.547 0.17

[0.387][0.267]

+ [0.303] [0.390][0.268]

+ [0.306] [0.374][0.252]

+ [0.296] [0.376][0.253]

* [0.289]

RELWOODPRICE 0.53 0.503 -0.123 -0.983 -0.764 -1.59

26

[0.507] [0.349] [0.399] [0.691] [0.465][0.530]

**

Constant 12.668 4.178 3.593 12.234 3.766 3.694 15.934 7.008 5.737 18.32 8.861 9.595[4.854]

* [3.353] [3.797][4.870]

* [3.348] [3.828][4.820]

**[3.251]

* [3.816][5.082]

**[3.417]

*[3.899]

*Observations 148 148 148 148 148 148 148 148 148 148 148 148R-squared 0.94 0.95 0.96 0.94 0.96 0.96 0.94 0.96 0.96 0.94 0.96 0.96Plant-fixed effects are included in all regressions.Standard errors in brackets+ significant at 10%; * significant at 5%; ** significant at 1%

27

Table 3 – Results of regressions including interaction termsEstimated with Systems OLS. Each row presents the results from one system of three regressions, where the three DV’s are BOD, COD, and TSS. The independent variables included in each regression are TREND, POST-DISC, the two interaction terms in each column, and plant fixed effects.

Variable of interest (second interaction variable included)

Z-score on variable of interest in

BOD regression

Z-score on variable of interest in

COD regression

Z-score on variable of interest in

TSS regression

p-value on joint test

of significanc

e

Highest p-value

on outlier check

ONELEAF*POST-DISC(ONELEAF*TREND) -0.96 -2.52 -2.58 0.0324 0.0961

TWOTHREE*POST-DISC(TWOTHREE*TREND) 0.64 2.45 2.08 0.0595 0.1437WEALTH*POST-DISC(WEALTH*TREND) -1.29 -2.31 -2.99 0.0240 0.1040

LITERACY*POST-DISC(LITERACY*TREND) -0.69 1.07 0.36 0.3723CASTE*POST-DISC(CASTE*TREND) -0.58 -0.54 -0.15 0.8873AG*POST-DISC(AG*TREND) -1.05 -1.38 -1.11 0.5374

URBAN*POST-DISC(URBAN*TREND) 0.74 -1.02 0.06 0.4057

GOVOWN*POST-DISC(GOVOWN*TREND) -0.45 0.50 -0.34 0.7839

COMPTOWN*POST-DISC(COMPTOWN*TREND) -0.87 -0.29 -1.05 0.7090

COMWATER*POST-DISC(COMWATER*TREND) 0.36 0.04 0.55 0.9321

NGOWATER*POST-DISC(NGOWATER*TREND) 0.27 1.80 1.37 0.2543

MEDIAWATER*POST-DISC(MEDIAWATER*TREND) -0.17 1.42 0.68 0.4231ENFWATER*POST-DISC(ENFWATER*TREND) 0.04 2.51 1.35 0.0526 0.1609RAINFALL*POST-DISC(RAINFALL*TREND) 0.61 1.08 1.64 0.4174

WATERDIST*POST-DISC(WATERDIST*TREND) -2.20 -0.32 -2.13 0.0536 0.1832

SCALE*POST-DISC(SCALE*TREND) 0.39 1.63 1.66 0.2457

FINALGOOD*POST-DISC(FINALGOOD*TREND) 0.84 0.35 -0.32 0.5842

28

FORSHARE*POST-DISC(FORSHARE*TREND) 1.24 0.71 0.73 0.6603

RIVERWATER*POST-DISC(RIVERWATER*TREND) 0.64 0.26 0.33 0.9384EFFRIVER*POST-DISC(EFFRIVER*TREND) 0.31 0.05 1.27 0.4612SINGLE*POST-DISC(SINGLE*TREND) -0.73 -2.77 -3.13 0.0043 0.0140

CONGOLMERATE*POST-DISC(CONGLOMERATE*TREND) -0.58 0.29 -0.12 0.8783

MULTIPLE*POST-DISC(MULTIPLE*TREND) 0.91 2.31 2.98 0.0147 0.0536BATCH*POST-DISC(BATCH*TREND) 0.14 -0.25 0.48 0.8733

CONTINUOUS*POST-DISC(CONTINUOUS*TREND) 0.55 1.46 0.21 0.4003

CMP*POST-DISC(CMP*TREND) -0.91 -1.36 -1.12 0.5675

RGP*POST-DISC(RGP*TREND) -0.64 -1.00 -0.85 0.7758

WASTEPULP*POST-DISC(WASTEPULP*TREND) -0.69 -0.19 -0.75 0.8529

CRS*POST-DISC(CRS*TREND) 1.38 0.74 1.22 0.5367

HHI*POST-DISC(HHI*TREND) 0.01 -0.80 -0.75 0.7623

29

Table 4 – Systems OLS estimation resultsSystems OLS regressions with all controls, and significant interaction terms, included one-at-a-time.

SYSTEM ESTIMATED 1 2 3BOD COD TSS BOD COD TSS BOD COD TSS

TREND -0.193 -0.147 -0.184 -0.11 -0.143 -0.147 -0.158 -0.154 -0.186[0.056]** [0.037]** [0.043]** [0.069] [0.048]** [0.051]** [0.059]** [0.039]** [0.043]**

POSTDISC 0.033 0.146 0.126 0.297 0.321 0.438 0.135 0.201 0.237[0.233] [0.154] [0.177] [0.287] [0.197] [0.213]* [0.241] [0.160] [0.178]

SCALE -0.665 -0.29 -0.317 -0.68 -0.294 -0.337 -0.733 -0.351 -0.431[0.248]** [0.164]+ [0.188]+ [0.242]** [0.167]+ [0.180]+ [0.255]** [0.169]* [0.189]*

FINALGOOD -1.112 -0.323 -0.423 -0.708 -0.149 -0.084 -0.99 -0.31 -0.43[0.398]** [0.263] [0.302] [0.399]+ [0.275] [0.297] [0.411]* [0.273] [0.304]

VINTAGE 0.465 0.071 -0.009 0.393 -0.021 -0.111 0.386 -0.013 -0.051[0.299] [0.198] [0.227] [0.290] [0.200] [0.215] [0.306] [0.204] [0.227]

PCTBAMBOO -1.508 -0.798 -0.603 -0.889 -0.631 -0.168 -1.179 -0.825 -0.615[0.531]** [0.351]* [0.402] [0.535]+ [0.368]+ [0.397] [0.570]* [0.379]* [0.422]

PCTGRASSES -3.502 -0.086 -0.721 -1.625 0.115 0.352 -2.524 -0.358 -0.756[1.107]** [0.732] [0.839] [1.131] [0.779] [0.840] [1.127]* [0.749] [0.835]

PCTRECYCL -2.863 -0.142 -1.201 -1.91 0.137 -0.475 -2.482 -0.118 -1.021[0.729]** [0.482] [0.553]* [0.728]* [0.501] [0.541] [0.731]** [0.486] [0.541]+

PCTPULP -1.105 1.291 0.718 -0.229 1.643 1.451 -0.834 1.402 0.899[1.165] [0.770]+ [0.883] [1.147] [0.790]* [0.852]+ [1.165] [0.774]+ [0.862]

RELWAGES 4.04 2.965 4.753 3.376 2.722 4.272 3.696 2.769 4.563[2.867] [1.896] [2.173]* [2.757] [1.898] [2.049]* [2.867] [1.905] [2.123]*

FORSHARE -4.146 -0.934 -2.32 -3.497 -0.79 -1.927 -3.63 -0.754 -2.091[1.176]** [0.778] [0.891]* [1.153]** [0.794] [0.857]* [1.217]** [0.809] [0.901]*

RDSHARE -16.008 -5.52 -4.116 -15.724 -11.036 -9.622 -15.265 -12.442 -13.148[30.485] [20.156] [23.105] [29.243] [20.137] [21.733] [30.582] [20.323] [22.645]

RELCOALPRICE 0.038 0.031 0.043 0.057 0.039 0.06 0.046 0.032 0.047[0.047] [0.031] [0.035] [0.045] [0.031] [0.033]+ [0.047] [0.031] [0.035]

REALNAOH -0.671 -0.567 -1.081 -0.563 -0.569 -1.04 -0.61 -0.585 -1.1[0.763] [0.505] [0.578]+ [0.733] [0.505] [0.545]+ [0.761] [0.506] [0.564]+

30

REALCL -1.251 -0.533 -0.763 -1.121 -0.512 -0.683 -1.188 -0.522 -0.724[0.423]** [0.279]+ [0.320]* [0.406]** [0.279]+ [0.301]* [0.421]** [0.280]+ [0.312]*

PRODHHI 0.664 0.472 0.369 0.336 0.593 0.306 0.343 0.574 0.331[0.371]+ [0.245]+ [0.281] [0.329] [0.227]* [0.245] [0.357] [0.237]* [0.264]

WATERCONSENT -0.444 -0.22 -0.261 -0.44 -0.146 -0.19 -0.394 -0.09 -0.106GRANTED [0.251]+ [0.166] [0.190] [0.238]+ [0.164] [0.177] [0.251] [0.167] [0.186]

OTHERCONSENT 0.322 0.022 0.103 0.368 0.006 0.108 0.268 -0.06 0GRANTED [0.218] [0.144] [0.165] [0.207]+ [0.143] [0.154] [0.216] [0.144] [0.160]

EFFRIVER -8.359 -2.902 -3.994 -8.29 -0.759 -2.177 -10.617 -1.944 -4.071[4.154]* [2.747] [3.148] [3.835]* [2.641] [2.850] [4.045]** [2.688] [2.995]

RIVERWATER -0.063 -0.529 0.189 0.001 -0.497 0.253 0.108 -0.388 0.406[0.375] [0.248]* [0.284] [0.360] [0.248]* [0.267] [0.386] [0.256] [0.286]

RELWOODPRICE -0.962 -0.734 -1.55 -0.923 -0.759 -1.565 -0.943 -0.727 -1.535[0.689] [0.456] [0.522]** [0.660] [0.454]+ [0.490]** [0.686] [0.456] [0.508]**

ONELEAF*TREND 0.077 0.024 0.069[0.049] [0.032] [0.037]+

ONELEAF -0.201 -0.373 -0.461[0.261] [0.173]* [0.198]*

MOPED*TREND -0.291 0.027 -0.063[0.248] [0.171] [0.185]

MOPED*POST-DISC -1.661 -1.59 -2.459[1.201] [0.827]+ [0.893]**

SINGLE*TREND -0.008 0.04 0.069[0.049] [0.033] [0.036]+

SINGLE*POST-DISC -0.292 -0.38 -0.587[0.239] [0.159]* [0.177]**

Constant 17.888 10.916 11.184 17.166 8.796 9.137 20.598 10.76 12.392[5.210]** [3.445]** [3.949]** [4.901]** [3.375]* [3.643]* [5.186]** [3.446]** [3.840]**

Observations 148 148 148 148 148 148 148 148 148R-squared 0.94 0.96 0.97 0.95 0.96 0.97 0.94 0.96 0.97p-value on joint tests of:ONELEAF 0.0744MOPED*POST-DISC 0.0570SINGLE*POST-DISC 0.0094

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Table 5 – Full model resultsSystems OLS regressions with all controls and significant interaction terms.

SYSTEM ESTIMATED 1 2 3BOD COD TSS BOD COD TSS BOD COD TSS

TREND -0.139 -0.155 -0.176 -0.168 -0.158 -0.195 -0.14 -0.145 -0.157[0.071]+ [0.048]** [0.052]** [0.058]** [0.039]** [0.044]** [0.073]+ [0.050]** [0.054]**

POSTDISC 0.362 0.416 0.567 0.19 0.223 0.281 0.384 0.356 0.459[0.293] [0.200]* [0.216]** [0.239] [0.161] [0.181] [0.309] [0.211]+ [0.226]*

SCALE -0.71 -0.355 -0.412 -0.747 -0.362 -0.448 -0.734 -0.358 -0.428[0.244]** [0.166]* [0.179]* [0.250]** [0.169]* [0.189]* [0.247]** [0.168]* [0.180]*

FINALGOOD -0.722 -0.195 -0.136 -0.873 -0.354 -0.415 -0.677 -0.239 -0.201[0.397]+ [0.271] [0.292] [0.405]* [0.274] [0.306] [0.404]+ [0.275] [0.295]

VINTAGE 0.386 0.094 -0.007 0.211 0.088 -0.043 0.285 0.136 0.028[0.296] [0.201] [0.217] [0.313] [0.212] [0.237] [0.312] [0.212] [0.228]

PCTBAMBOO -1.004 -0.657 -0.263 -1.084 -0.863 -0.606 -0.89 -0.751 -0.392[0.534]+ [0.364]+ [0.393] [0.559]+ [0.378]* [0.423] [0.553] [0.377]* [0.404]

PCTGRASSES -2.412 0.037 -0.206 -3.122 -0.324 -1 -2.431 0.05 -0.19[1.199]* [0.817] [0.882] [1.131]** [0.765] [0.856] [1.205]* [0.821] [0.880]

PCTRECYCL -2.045 0.229 -0.472 -2.608 -0.08 -1.045 -2.078 0.236 -0.474[0.727]** [0.495] [0.535] [0.716]** [0.484] [0.542]+ [0.731]** [0.498] [0.534]

PCTPULP 0.079 1.966 1.942 -0.379 1.476 1.173 0.205 1.845 1.764[1.170] [0.797]* [0.860]* [1.166] [0.788]+ [0.882] [1.187] [0.809]* [0.867]*

RELWAGES 3.285 2.767 4.258 3.229 2.9 4.465 3.065 2.82 4.259[2.735] [1.862] [2.010]* [2.810] [1.900] [2.126]* [2.755] [1.877] [2.012]*

FORSHARE -3.457 -0.753 -1.868 -3.18 -0.856 -1.975 -3.153 -0.853 -1.923[1.144]** [0.779] [0.841]* [1.203]** [0.813] [0.910]* [1.185]** [0.807] [0.866]*

RDSHARE -16.494 -7.827 -7.108 -13.908 -9.739 -10.141 -14.901 -10.321 -11.226[29.070] [19.798] [21.370] [30.062] [20.325] [22.742] [29.414] [20.035] [21.478]

RELCOALPRICE 0.056 0.039 0.06 0.047 0.032 0.047 0.057 0.038 0.057[0.045] [0.030] [0.033]+ [0.046] [0.031] [0.035] [0.045] [0.031] [0.033]+

REALNAOH -0.61 -0.591 -1.089 -0.612 -0.595 -1.11 -0.595 -0.59 -1.081[0.728] [0.496] [0.535]* [0.745] [0.503] [0.563]+ [0.731] [0.498] [0.534]*

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REALCL -1.159 -0.514 -0.708 -1.222 -0.523 -0.741 -1.167 -0.494 -0.674[0.403]** [0.274]+ [0.296]* [0.412]** [0.278]+ [0.312]* [0.406]** [0.276]+ [0.296]*

PRODHHI 0.448 0.353 0.151 0.489 0.44 0.279 0.405 0.386 0.196[0.358] [0.244] [0.263] [0.367] [0.248]+ [0.278] [0.363] [0.247] [0.265]

WATERCONSENT -0.416 -0.2 -0.226 -0.326 -0.154 -0.131 -0.377 -0.187 -0.182GRANTED [0.238]+ [0.162] [0.175] [0.252] [0.170] [0.190] [0.248] [0.169] [0.181]

OTHERCONSENT 0.374 0.045 0.148 0.238 -0.015 0.026 0.332 0.042 0.124GRANTED [0.207]+ [0.141] [0.152] [0.215] [0.145] [0.163] [0.215] [0.146] [0.157]

EFFRIVER -7.038 -2.392 -2.926 -8.962 -2.939 -4.191 -7.561 -2.125 -2.649[3.959]+ [2.697] [2.911] [4.053]* [2.740] [3.066] [4.008]+ [2.730] [2.926]

RIVERWATER 0.009 -0.481 0.272 0.174 -0.428 0.4 0.092 -0.478 0.315[0.357] [0.243]+ [0.262] [0.379] [0.256]+ [0.287] [0.373] [0.254]+ [0.272]

RELWOODPRICE -0.908 -0.734 -1.532 -0.916 -0.718 -1.515 -0.902 -0.715 -1.492[0.655] [0.446] [0.481]** [0.671] [0.454] [0.508]** [0.659] [0.449] [0.481]**

ONELEAF*TREND 0.078 0.027 0.073 0.098 0.012 0.055 0.09 0.009 0.044[0.047]+ [0.032] [0.034]* [0.051]+ [0.034] [0.038] [0.053]+ [0.036] [0.039]

ONELEAF -0.156 -0.367 -0.438 -0.053 -0.264 -0.257 -0.118 -0.311 -0.311[0.249] [0.170]* [0.183]* [0.272] [0.184] [0.206] [0.284] [0.193] [0.207]

MOPED*TREND -0.268 0.053 -0.024 -0.194 -0.086 -0.26[0.247] [0.168] [0.182] [0.322] [0.219] [0.235]

MOPED*POST-DISC -1.841 -1.606 -2.585 -1.643 -1.113 -1.534[1.195] [0.814]+ [0.879]** [1.593] [1.085] [1.163]

SINGLE*TREND -0.047 0.039 0.05 -0.03 0.045 0.075[0.051] [0.035] [0.039] [0.065] [0.044] [0.048]

SINGLE*POST-DISC -0.315 -0.284 -0.513 -0.094 -0.135 -0.306[0.250] [0.169]+ [0.189]** [0.324] [0.221] [0.236]

Constant 16.461 10.812 10.58 19.613 11.657 12.732 17.469 10.441 10.323[5.012]** [3.413]** [3.684]** [5.129]** [3.468]** [3.880]** [5.122]** [3.489]** [3.740]**

Observations 148 148 148 148 148 148 148 148 148R-squared 0.95 0.97 0.97 0.95 0.96 0.97 0.95 0.97 0.97p-value on joint tests of:ONELEAF 0.0631 0.3975 0.3199MOPED*POST-DISC 0.0371 0.6018SINGLE*POST-DISC 0.0648 0.5804

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35

Table A1 – regressions to determine functional form of the trend included

Estimated with Systems OLS. These regressions include only pre-disclosure observations.

BOD COD TSS BOD COD TSSTREND -0.178 -0.133 -0.2 -0.287 -0.131 -0.174

[0.064]** [0.044]** [0.039]** [0.316] [0.218] [0.191]

TRENDSQ 0.028 0 -0.007[0.079] [0.055] [0.048]

Constant 1.424 3.52 1.984 1.5 3.519 1.967[0.377]** [0.259]** [0.227]** [0.439]** [0.302]** [0.265]**

Observations 44

44

Plant fixed effects? Yes YesR-squared 0.94 0.95 0.97 0.94 0.95 0.97Standard errors in brackets+ significant at 10%; * significant at 5%; ** significant at 1%

36