Does Being a “Top 10” Worst Polluter Affect Environmental Releases?

Evidence from the U.S. Toxic Release Inventory

Wolfram Schlenker♠ and Jason Scorse♣

Preliminary and Incomplete

Abstract

We use the 1998 expansion of the Toxic Release Inventory (TRI) that required newly regulated industry sectors to report facility releases to obtain exogenous changes in a facility’s

relative ranking within a state to test whether being labeled a “Top 10” worst polluter affects a facility’s action. After the expansion of the program some facilities were dropped

from the “Top 10” because newly regulated facilities surpassed their emissions. Facilities

that dropped out of the “Top 10” in 1998 did not significantly alter their emissions in 1999

or 2000, but had 50% higher emissions in 2001 than they would have had if they had stayed

on the “Top 10” list. This timeline is consistent with the fact that EPA does not release emissions until a year and a half after the reporting year, i.e., rankings for 1998 were not released

until the middle of 2000. Facilities responded to rankings that are based on onsite-emissions

used by Scorecard, but less so to total releases published by TRI, which also include offsite

releases, suggesting that Scorecard’s publication had an effect. We find no evidence for a

counterbalancing decrease in offsite transfers for further waste management.

We would like to thank Ann Harrison, Michael Hanemann, Dara O’Rourke, Maximilian Auffhammer, Peter Berck, Matthew Rabin, Sofia Villas-Boas, David Roe, James Hamilton, Mike Toffel, David Levine, Bill

Pease, John List, Christopher Dumas, Madhu Khanna for their extremely helpful comments.

♠University of California at Berkeley and NBER. Email: schlenker@berkeley.edu

♣Monterey Institute of International Studies. Email: jason.scorse@miis.edu
The day it became clear that disclosure was a powerful regulatory tool was June

30, 1988, when Richard J. Mahoney, then head of Monsanto (one of the biggest

chemical manufacturers in the U.S.), made a dramatic claim. Mahoney said

bluntly that he had been astounded by the magnitude of Monsanto’s annual release of 374 million pounds of toxins. He vowed to cut the release of air emissions

90% worldwide by the end of 1992.1

Atlantic Monthly, April 2000

In 1986, on the heels of the 1984 Union Carbide chemical disaster in Bhopal, India, and the

subsequent chemical plant accident in West Virginia, the U.S. Congress passed the Emergency Planning and Community Right-to-Know Act (EPCRA), which required all manufacturing facilities employing more than 10 people and using more than 10,000 pounds of any

of 377 listed toxic chemicals to report their emissions and transfers to the U.S. government

annually for dissemination to the public.2 This information is organized under the heading

of the Toxic Release Inventory (TRI), which represented the first nationally mandated public

Right-to-Know program in U.S. history. Over the years, there has been a push to use more

of such Right-to-Know Acts both in the United States and abroad. These laws no longer

directly tell firms how much to emit, but instead simply requires them to report the emissions they are releasing. The underlying idea is consistent with financial filings of publicly

traded companies. Various stakeholders, e.g., customers, investors, or residents living close

to a facility can use this information to put pressure on firms. Whether they can be seen as

a substitute to environmental regulation that directly limit emissions has been subject to an

intense debate.

Since its first year of operation in 1987, the TRI has been expanded to include almost

650 chemicals as well as several additional industries that were initially exempt. These

include all federal agencies as well as power and mining companies. From 1988 to 1997 total

environmental releases reported under the TRI fell around 60% and from 1998 to 2001 they

fell an additional 20%. It is not possible to compare aggregate emissions reductions across

the entire time period due to the expansion of the program in 1998. The key question for

both policy-makers and academics is whether the publication of the TRI is at least partially

responsible for this dramatic decrease in emissions.3

1Between 1988 and 1992 TRI reported environmental releases from Monsanto facilities dropped almost

94%.

2Transfers refer to waste that is produced on site and then moved to another site.

3One possibility is that firms switched to chemicals that were not covered by the TRI, and therefore,

the decline in TRI releases does not correspond to actual reductions in aggregate levels of emissions, but is

due to the substituting of some chemicals for others. While this may account for part of the reported TRI

1
“Top 10” worst polluter lists have become popular in the television, print, and internet

media, as well as with environmental groups, since they focus attention on a small subset

of the TRI facilities that emit the overwhelming share of toxic chemicals.4 The EPA’s TRI

Explorer website allowed users to rank facilities and provides state reports that highlight

the “Top 10” emitters each year.5 Also, the internet’s most popular environmental website,

Scorecard, which received over 100,000 visits per month, displayed a rotating set of “Top

10” worst polluter lists on its homepage.6 Firms that find themselves in the spotlight will

most likely face significant public pressure to decrease their emissions. An added benefit of

examining only those facilities with the highest environmental releases is that this largely

avoids the problem presented by the reporting thresholds for individual chemicals. Almost

all of the “Top 10” facilities within states report environmental releases for most chemicals

that are much greater than the 10,000 pound limits.

This paper attempts to isolate the extent to which the distinction of being on a “Top 10”

worst polluter list within a given state affects a firm’s environmental releases. Examining

“Top 10” lists at the state level is chosen for two reasons; one policy oriented and one

econometric. From a policy standpoint, it is easier to make changes in state versus national

environmental regulations; therefore, it is reasonable to assume that firms will be more

responsive to how they are perceived by politicians, environmental groups, and citizens at

the state level. In addition, public pressure due to environmental concerns is often localized

since the people who live in the vicinity of highly polluting firms have a direct and immediate

incentive to curb firm emissions. Finally, firms that fall within the “Top 10” list within a

given state may not rank high nationally, and therefore the national rankings will not include

many facilities that likely face significant pressure to reduce their emissions at the state level.7

From an econometric standpoint, the major change in the TRI rules that occurred in

1998, when seven new highly polluting industries were added to the TRI database, provides

a quasi-natural experiment that helps identify causality between changes in facility pollution

rankings within states and subsequent facility emissions. Existing TRI facilities in states that

declines it is important to note that 286 chemicals were added to the TRI list in 1994 and total releases

continued their decline thereafter.

4Another common worst polluter list reported in the media is the “Dirty Dozen”; the top 12 worst

polluters.

5The reporting practice since changed, as did the web interface, but “Top 10” lists were common around

1998.

6The website was subsequently sold by the original creator (Environmental Defense Fund), and has since

shut down.

7Facilities that rank high nationally will by definition also rank high at the state level and therefore

examining the behavior of worst polluters at the state level will includes these facilities.

2
had many new entrants experienced large drops in their pollution rankings, often leading

to their removal from the “Top 10” polluter lists, while facilities in states without many of

the new industries saw little to no change in their rankings. If state pollution rankings do

matter then facilities in the former group had less incentive to reduce emissions after the

rule change, and as a result, likely reduced emissions less than they would have had they not

experienced the unexpected drop in their rankings.

The econometric results confirm that being on a “Top 10” worst polluter list within states

did affect facility emissions in the direction predicted; overall, facilities that had already been

covered under the TRI before 1998, and had been ranked as one of the “Top 10,” had 50%

higher releases compared to a case where they remained on the “Top 10” list. Moreover,

the timing of when new releases and ranking are released to the public, suggests that firms

did not simply change reported releases, but implemented actual changes. EPA made new

releases and rankings public in the middle of 2000. Firms therefore had the possibility to

change reported releases in 2000, while changes in production technology would likely not be

effective until later that year. We find no significant change in either 1999 or 2000 releases.

This finding is important for two reasons. First, it demonstrates that pollution rankings,

and hence the information provided by the TRI does influence facility releases, thereby

bolstering the general case for Right-to-Know programs. Organizations such as the World

Bank are currently investigating whether Right-to-Know programs may be a cost-effective

environmental regulatory tool for developing countries given the (perceived) success of the

TRI and its relatively low cost. The European Union began a program similar to the TRI.8

Moreover, many groups in the U.S. would like to expand the TRI. Second, the econometric

results show how changes to a Right-to-Know program can have unintended consequences;

in this case the expansion of the TRI (with the intent of putting pressure on an additional

set of extremely polluting facilities) decreased the incentives for firms already covered by the

program to reduce their emissions.

The remainder of the paper is organized as follows: Section 1 surveys the TRI literature

as well as work regarding other types of Right-to-Know programs. Section 2 provides an

overview of the TRI data and problems regarding its accuracy. Section 3 presents our

empirical strategy, while Section 4 presents empirical results whether facility releases were

affected by the removal from the “Top 10” lists within states. The policy implications of the

8For a description of the new EU program see, “EU Launches Emissions List” in Chemical and Engineering

News, March 1, 2004, Vol. 82(9). The first reporting year for the EU program was 2003 and the second

dataset will be available in 2006 since the program provides audits every three years, unlike the annual

reports provided by the TRI.

3
results are discussed in Section 5.

1 Background

Most researchers who have studied the TRI have concluded that at least part of the decline

in TRI-reported chemicals is tied directly to the provision of the information mandated by

the TRI legislation. (Cohen 1997, Fung & O’Rourke 2000, Graham 2000, Jobe 1999, Stephan

2002, Restrepo 1999, Abel, Kraft & Stephan 2005, Hamilton 2005). Hamilton (2005, p. 254)

summarizes the finding as:

Information provision can work. The TRI changed the property rights to information about toxics, forced firms to estimate toxic figures, and combined the

resulting information into a database made increasingly easy for the public to

use. The provision of TRI data clearly changed behavior. Case studies abound

about managers who learned about pollution figures for the first time, communities that placed pressures on facilities for reductions, and regulators that used

the data to focus on particular chemicals or facilities. Overall, the TRI become a

standard by which actors in the private and public sectors measured companies’

environmental performance. Environmentalists used the data to develop reports

and lists that often focused attention on firms or plants that ranked the highest

on some aspect of the TRI data.

There are a number of channels through which stakeholders can exert pressure on firms

to reduce their emissions.9 The first is political pressure: At the behest of environmental

organizations or concerned citizens, politicians may try to enact legislation to curb firm

emissions, and the threat alone may be sufficient to influence firm behavior.10 Residents

have been shown to “vote with their feet” and relocate away from plants with toxic emissions

9We do not attempt to quantify the extent to which these different channels may influence firm behavior;

we are only presenting a summary why rational profit-seeking firms may be influenced by the public disclosure

of toxic release information. Apart from public pressure, firms may in fact benefit from the environmental

audits required by the TRI in that it forces them to scrutinize elements of their production processes that

they may have largely neglected. This information, in turn, may help them to become more environmentally

efficient (this is discussed further in the literature review). In addition, if members of the firm include

employees with a strong environmental ethic such an internal review may also create pressure from within

the firm to improve environmental performance even in the absence of outside pressure (Gunningham, Kaga

& Thornton 2003).

10Bui (2005) provides evidence that suggests that the use of the TRI information by local and state

politicians, which used it to craft additional legislation and focus pressure on worst polluters, was most

responsible for changes in firm behavior brought about by the TRI in the petroleum industry.

4
(Banzhaf & Walsh 2008). Second, green preferences by consumers, firms, and government

and non-governmental agencies can exert pressure on companies by purchasing less products

from highly polluting firms or by rewarding less polluting firms with increased business.

Third, the data exposes potential future liabilities. Once pollution data is part of the public

domain this creates a record for any future liabilities firms may face regarding environmental

or human health claims. Fourth, the data can have effect on future expansion plans of firms.

Firms that want to expand their business, especially in new locations, will find it more

difficult to do so if their current operations are known to be highly polluting.

Since all of the environmental release data reported under the TRI fall within legally

defined limits set by the U.S. Environmental Protection Agency (EPA), any changes in

facility releases that can be traced to the dissemination of the TRI data suggest that the

TRI has provided a public benefit.11

Although prior to the establishment of any environmental Right-to-Know program existing firms had the opportunity to highlight their positive environmental performance (as a

means to attract business or promote a positive image), there is evidence that the costs to

firms of poor environmental performance are much greater than the relative benefits of good

performance (O’Rourke 2005). This may be explained by the theory of loss aversion (Tversky

& Kahneman 1991), which states that people tend to value losses much more than commensurate gains. Behavioral economists have conducted numerous laboratory experiments in

which they have uncovered evidence of loss aversion, and they point to many aspects of

contemporary U.S. law that explicitly recognize it (Kahneman, Knetsch & Thaler 1991).

For example, in court rulings losses are often treated more much seriously than foregone

gains when assessing damages. This heightened sensitivity to losses translates over into the

environmental domain (Shogren 2002); people often expend much greater effort chastising

firms that are highly polluting rather than rewarding firms that are working to improve the

environment. For this reason, “Top 10” polluter lists have significant potential to stimulate

activism since they highlight firms with the worst environmental performance (in absolute

and relative terms), which from a concerned citizen’s standpoint translates into a loss of

11In order to determine whether, in fact, the public benefits of any emissions reductions brought about

by the TRI are greater than the costs would require a detailed cost-benefit analysis and such an analysis

has not yet been undertaken by the Environmental Protection Agency. However, if the TRI is causing firms

to reduce emissions we can be almost certain that members of the public and government at least perceive

that the emissions are sufficiently harmful and should be reduced, and the costs to firms of doing so are

less than the costs imposed on them if they maintain the status quo. This needn’t imply that the chemical

reductions are necessarily optimal from a social benefits standpoint, but that the TRI is providing the public

with information that it finds useful. For a more thorough discussion of the overall benefit of the TRI see

Hamilton (2005).

5
environmental quality.

Fung & O’Rourke (2000) refer to the common practice of using the TRI to create “worst

polluter” rankings as a type of “Maxi-Min” policy instrument, in which maximum attention

is focused on the facilities with the minimal environmental performance (highest levels of

pollution). From a regulatory perspective, these lists offer the potential for continual pressure

on firms to decrease pollution since the lists are generated based on emissions relative to other

firms, and hence there are always worst polluters in every period.

A number of researchers have been able to demonstrate a link between TRI reporting

and stock performance. Hamilton (1995) found that the TRI provided “new” information to

investors and that the stock performance of publicly traded companies was significantly and

negatively correlated with toxic releases on the day after the TRI report was released in 1989,

often translating into decreases in stock valuation of millions of dollars per firm. Khanna,

Quimio & Bojilova (1998) examined the stock returns for major firms in the chemical industry

between 1989-1994 on the day after the TRI data was released and found that from 1990-

1994 firms whose emissions were worse compared to their own past emissions, or relative to

industry trends, suffered significant and negative stock valuations. Konar & Cohen (2001)

found that toxic releases were negatively correlated with stock performance for a sample of

S&P500 manufacturing firms in 1989.

Regarding the composition of emission reductions, Hamilton (1999) found that firms

which emitted more carcinogenic chemicals were more likely to reduce emissions between

1988 and 1991.12 Arora & Cason (1996) and Khanna & Damon (1999) used the TRI data

to explore why firms may have decided to participate in the EPA’s voluntary pollution

reduction program, “33/50.” They found that firms with high public visibility were more

likely to enter the program, and that potential environmental liabilities were also a deciding

factor in their decision to participate.13

The TRI data has also been used extensively to study issues related to Environmental

Justice, such as whether firms site toxic waste facilities disproportionately in poor and minority communities, and whether toxic emissions are influenced by community demographics

(Rhodes 2005, Arora & Cason 1999). Although there is a strong correlation between toxic

releases and concentrations of poor and/or minority populations throughout much of the

U.S., all else equal, the main determinant influencing facility emissions tends to be the level

12This is the only study which attempts to specifically assess the health risk of different TRI chemicals

and determine whether these were targeted for greater reductions than others.

13The 33/50 program ended in 1995 so it does not affect my results, which makes use of data from 1995

onwards.

6
of political participation people exercise in their respective communities.

In addition to external forms of pressure to reduce emissions, before the advent of the TRI

many facilities had never before performed environmental audits, and the detailed analyses

of their own emissions mandated by the TRI may have actually helped some of them uncover

inefficient aspects of their own production processes (Office of Environmental Information,

U.S. Environmental Protection Agency 2003).14

Within a developing country context, Afsah, Blackman & Ratunanda (2000) found that

an environmental Right-to-Know program in Indonesia led firms to reduce their emissions,

while also improving facility efficiency by requiring internal environmental audits. The authors describe the process how Indonesian firms were shamed into reducing emissions after

being highlighted as serious polluters. In Canada, where the government enacted a program

very similar to the TRI, Antweiler & Harrison (2003) found evidence that green consumerism

linked to pollution reporting had a significant effect on toxic emissions reductions.

Jin & Leslie (2003) analyzed the effects of a unique Right-to-Know program in Los

Angeles, CA, which mandated that all restaurants clearly post the results of their health

inspection scores based on a simple letter grade A to F. They found that not only is consumer

demand sensitive to the restaurant health scores (lower health scores resulting in lower

demand), but that after the introduction of the program, the incidence of food-borne illnesses

decreased in the surrounding area, both due to the increased demand for cleaner restaurants,

as well as health improvements (made in response to the Right-to-Know program) in formerly

poorly rated restaurants.

In summary, the TRI has been demonstrated to influence the stock valuation of U.S. firms,

and similar Right-to-Know programs in other countries have influenced firm emissions. In

addition, U.S. firms emitting highly carcinogenic chemicals have been sensitive to the TRI

reporting and decreased these types of emissions more than firms whose emissions are less

toxic. Right-to-Know programs are not limited to environmental data, and a program based

on health inspections has also influenced the behavior of both consumers and restaurant

owners in L.A., resulting in less illness.

The following study adds to the research on Right-to-Know programs by examining

whether the TRI has had effects on the emissions of facilities in the U.S. through “Top

10” pollution rankings within states. “Top 10” lists are an efficient way of both presenting

emissions data to the public and for targeting regulatory action, and examining whether

14This can be thought of as a manifestation of the “Porter Hypothesis.” Regulation may actually help

firms discover new methods for improving environmental efficiency that they may not have in the absence

of the regulation (Porter & van der Linde 1995).

7
firms respond to this identification is a logical step in the large and growing TRI literature.

2 Data

The Environmental Protection Agency (EPA) administers the Toxic Release Inventory. EPA

compiles all facility reports and jointly releases them on one day in late spring / early

summer a year and half after the reporting year ended. For example, facility level releases

for reporting year 1998 are made public in the middle of 2000. The timeline is shown in

Figure 1. Reports include releases for each of 650 chemicals, separated by medium: air,

surface water, land, and underground injection, which all constitute onsite releases. The

report also includes offsite releases as well as offsite transfers for further waste management,

where the latter category is not included in TRI’s definition of total releases.

While all data sets originally come from EPA, we obtained two distinct data sets: Scorecard provided us total onsite environmental releases, sometimes simply called total releases.

Scorecard is the website that at the time listed “Top 10” polluters (see discussion above).

Scorecard only added onsite releases, which are shown in Figure A1 in the appendix for the

years 1998-2001, the years for which they provided us the data. If firms react to public

pressure, Scorecard’s ranking at the time the data were made public identifies who received

the pressure of being labeled a “Top 10” polluter.

We subsequently extracted all facility releases from TRINET15 for the years 1988-2010.

These data report releases by medium: onsite (air, surface water, land, and underground

injection), offsite releases, as well as offsite transfers for further waste management. TRINET

includes onsite releases in their measure of total releases, and hence we constructed rankings

using the sum of onsite and offsite releases, which is different from Scorecards definition that

only included onsite releases. When we matched the data to the release data we obtained

from Scorecard, total releases were generally comparable but not always identical, even if we

excluded offsite releases. A possible reason is that TRI data is noisy and needs to be cleaned.

EPA revises past estimates and cleans them, as did Scorecard, and they might use different

algorithm. Our baseline estimates use the releases of Scorecard and the rankings obtained

from Scorecard’s releases. In sensitivity check, we also use total releases from TRINET, as

well as ranking based on these emissions.

The facility level data sets included other descriptive information such as the facility

name, address, zip code, 4-digit SIC code / 6-didigit NAICS code, production ratio (the

15Accessed in June 2010 on http://www.epa.gov/tri/tridotnet/index.html

8
ratio of total output in one year to the next) and parent company name. In 2001, roughly

20,000 facilities reported emissions. Figure 2 shows the national distribution of TRI facilities

in 2001. The majority of facilities are concentrated east of the Mississippi River, with the

majority of these 30+ states containing hundreds of facilities. There are relatively few TRI

facilities scattered throughout the West except for the coastal states, which also contain

significant concentrations of facilities, particularly in California. On the other hand, the

figure shows color-coded total releases in the TRINET data, and some of the largest overall

emitters are found in the Western United States.

Figure 3 shows total reported environmental releases in TRINET from 1988 to 2010.16

Total releases drop by more than 60 percent between 1988 and 1998 (from a high of more

than seven billion pounds in 1988 to around 2.5 billion pounds in 1997), and show a general

downward trend. Figure 4 further disaggregates total releases. The top four panels show

releases into the air, surface water, land, and underground injection, respectively, the sum

of which are total onsite releases as shown in the bottom left panel. Total offsite releases

are shown in the bottom right panel. Most of the reduction in onsite releases stems from

reduction in air emissions, followed by reductions in underground injections. Total offsite

releases are a small portion of overall releases.

The one exception to the general downward trend are offsite transfers for further waste

treatment, as shown in Figure 5. Especially in the early 1990s, there is a significant increase in

total transfers, and the absolute size of these transfers is big. Transfers have since stabilized

and most recently declined. It should be noted that offsite waste transfers are not included

in total releases either in the Scorecard or TRINET data set.

Total releases are extremely concentrated within a relatively small number of facilities.

Figure 6 uses pollution rankings to highlight how the “Top 10” polluters across states accounted for a widely disproportionate share of total U.S. environmental releases. The y-axis

shows the percentage share of total U.S. environmental releases (averaged across the years

1988-1997) and the x-axis groups facilities according to their total environmental release

rankings within their respective states. The sum of the environmental releases from facilities

that comprised the “Top 10” biggest emitters across the 50 states accounted for, on average,

approximately 58 percent of total U.S. environmental releases each year (with a minimum

of 52% and a maximum of 62% in any given year). Put another way, the approximately 500

facilities that comprised the “Top 10” worst polluter lists in their respective states (50 times

16Although the TRI officially began in 1987 the quality of the data in the first year is considered unreliable

and therefore is omitted.

9
10)17 were responsible for more than half of the total environmental releases emitted by the

more than 20,000 TRI facilities in the entire U.S.; approximately 1/40 of the TRI facilities

released more than 1/2 of the total reported toxic chemicals. This high concentration of total environmental releases dips dramatically as the next 10 biggest emitters (ranked 11-20)

accounted for on average only 11 percent of total environmental releases, less than one-fifth

of the releases of the facilities ranked 1-10. By the time we move beyond facilities ranked 30

or more, each ranking group accounts for at most only a few percentage points of total U.S.

environmental releases.

As Fung & O’Rourke (2000) point out, worst polluter lists help to focus attention on a

manageable subset of facilities that are responsible for the greatest environmental pollution,

and hence may be an efficient way means of prioritizing environmental activism. As Figure 6

shows, in the case of the TRI, “Top 10” worst polluter lists within states target the facilities

that are responsible for the majority of environmental releases. It is important to note,

however, that total environmental releases may be a poor proxy for actual levels of toxicity

and environmental damage because of the heterogeneity of toxicity of the hundreds of TRI

chemicals. For example, some TRI chemicals are several orders of magnitude more toxic

than others, and there are instances where a facility that ranks high on total environmental

releases may rank low based on some form of “toxic scoring” (and vice versa).18 Despite this

complication, this study focuses on total environmental releases because this has been the

category most cited by media sources, used by the EPA in its own rankings reports, and is

the default used on the Scorecard website. There are literally dozens of toxicity categories

and weighting schemes to choose from and for the purposes of this analysis, which focuses on

overall media exposure, total environmental releases is a reasonable choice.19 In a sensitivity

check, we use toxicity-weighted releases given in TRINET.

All of the TRI data are self-reported by firms and firms are not required to specifically

monitor all of their TRI chemical releases, but at minimum, must present reasonable release

estimates. The EPA does not employ a comprehensive system for auditing TRI reports

and firms do not face regulatory penalties for inaccurate release estimates. In addition,

the text of the EPCRA (the law which established the TRI) makes explicit that states are

not required to expend significant effort in order to ensure accurate TRI reports. However,

17In some years, not all states had 10 facilities on the TRI list.

18Such as Scorecard’s cancer risk score which weights all of the TRI chemicals according to their benzene

equivalent.

19In addition to total environmental releases, Scorecard allows users to rank facilities based on 39 different

categories ranging from reproductive toxins to kidney toxins to ozone-depleting substances. As one Scorecard

employee noted: “We made it so that every facility would be top ranked on at least one dimension.”

10
regional EPA offices look for large deviations in reported releases from one year to the next

and routinely audit facilities that report the 10 greatest changes in environmental releases

(both positive and negative) by SIC code, and request that they verify the accuracy of

their data.20 In addition, the EPA keeps a close watch on industry trends regarding the

environmental releases of different chemicals in order to establish benchmarks with which to

judge the accuracy of changes in releases in individual facilities.

The EPCRA permits levying fines of up to $25,000 per violation of TRI reporting requirements, i.e., not filing the necessary TRI reports. Between 1990 and 1999 the EPA

brought 2,309 administrative actions against facilities under EPCRA (Office of Enforcement

and Compliance Assurance, U.S. Environmental Protection Agency 2000). These fines (both

in relative and absolute terms) are much lower than the fines for violations of the Clean Air

Act; for example, in 2001 total penalties levied for TRI violations approached $4 million

while fines levied for violations of the Clean Air Act were more than $84 million.

Unsurprisingly, one of the major shortcomings of studies that seek to uncover evidence

that the TRI has caused firms to reduce their environmental releases is that reductions

in releases may be due to non-truthful or inaccurate reporting by facilities. Firms that

face significant pressure to reduce their toxic emissions may have incentives to misreport

their releases in order to demonstrate reductions that aren’t actually occurring, even if the

reputational costs and any increased regulatory scrutiny may be great if they are caught. A

few studies have uncovered significant evidence of inaccuracy within the TRI data, but the

extent to which this is driven by purposefully dishonest reporting versus measurement error

in estimating releases is unknown.

A recent report by the Environmental Integrity Project (2004) analyzed TRI emissions

data in 2001 in Texas and found underreporting in the range of 15%, with greater disparities

for some highly carcinogenic chemicals. The study attributes this less to purposeful cheating

on the part of firms, than on the outdated estimation techniques used for TRI reporting. It

has also been observed that in some years the rate of non-compliance with the TRI has been

quite large, up to 1/3 of all covered facilities. However, Brehm & Hamilton (1996) show

that the majority of non-complying facilities were very small, comprised a small percentage

of total environmental releases, and often their non-compliance was the result of ignorance

of the law rather than evasion.21

In a recent study by DeMarchi & Hamilton (2006) the authors assessed the accuracy of

20Personal communication of Jason Scorse with EPA employees in Region 9.

21The facilities who are not complying do not comprise “Top 10” worst polluter lists.

11
the TRI data in two ways: by cross-checking the TRI data with a source outside the control

of firms, and analyzing the TRI chemical reports for peculiar statistical patterns. First,

they compared reported releases of five TRI chemicals with the results of EPA regional

emissions monitoring. They found that the releases of two of the five chemicals closely

matched the monitoring results, two suggest overestimates of the reductions reported under

the TRI (lead and benzene, which are highly toxic; benzene is a known carcinogen), and

one which actually decreased more in the emissions monitoring than was reported under the

TRI (ethylbenzene, which is also highly toxic). They also make use of Benford’s Law to

assess whether the reported releases for a larger subset of TRI chemicals are distributed in

a manner that suggests accurate reporting.22 They found that for lead and nitric acid (two

highly regulated chemicals) the reported releases did not adhere to the expected distribution.

The authors posit that (in addition to the incentives to misreport) the inaccurate release

estimates may be due to the fact that in absolute terms the releases per facility of these

chemicals are relatively low, and therefore, significant effort is not invested to ensure precise

release figures, and often guesses are made that may skew the aggregate distributions.

In summary, the while the TRI data represents the most extensive toxic release database

in the U.S., there exists both the incentive to misreport releases and a relatively weak

legal structure to monitor and punish such deviations (including mistakes in the reported

figures). It is worth emphasizing that if firms that are labeled “Top 10” worst polluters

incur significant costs because of this stigma there exist significant incentives to misreport

emissions to the extent that the costs of breaking the law and the risks associated with it

are lower than the costs of being a “Top 10” polluter in the first place. Below, we can utilize

the timing of the release of information to indirectly test whether firms simply misreporting

releases in response to changes in rankings. EPA publishes releases a year and half after the

reporting year is over, e.g., releases for 1998 are made public in May 2000. If a firm where

to simply change its numbers, it could do so for the reporting year 2000, but any change in

the production process would likely only show up in 2001.

22Benford’s Law states that the first digits of self-reported data should follow a monotonically decreasing

distribution; i.e. 1s should appear more frequently than 2s, which should appear more frequently than 3s,

etc. This pattern has been verified in numerous types of datasets and is used by accountants as a way to

detect discrepancies in balance sheets and tax forms. With respect to the TRI, Benford’s Law suggests that

the first digits of the self-reported pollution figures should follow the same monotonically declining pattern.

For the first digits of the TRI reported emissions data, 1s should appear more frequently than 2s, which

should appear more frequently than 3s, etc.

12
3 Model

The ideal way to test for the effects of pollution rankings on facility releases would be

to randomly create and disclose rankings in different states and then observe the changes

in releases between the control and treatment groups, utilizing a difference in difference

approach. In the absence of such an experiment, the most credible way to identify the

effects of state rankings on releases is within the context of a quasi-natural experiment in

which there is an exogenous shock to facility rankings that is different across different states,

thereby creating a quasi-control and treatment group. Such a shock occurred in 1998 when

Congress changed the TRI rules and required seven additional industries to disclose their

emissions data.23 This rule change instantly added approximately 2,000 facilities to the TRI,

spread out across all 50 states. These new industries were (and are) some of the countries

largest polluters, and therefore, their addition lowered the rankings for the facilities that

were already under the jurisdiction of the TRI. This expansion of the TRI program had

been fought and stalled in Congress for many years and its passage could not have been

easily anticipated by firms.24

Our analysis focuses on facilities that make the “Top 10” list of polluters, and specifically

firms that were dropped from the list in 1998 because newly covered facilities had higher

emissions.25 The addition of the seven new industries in 1998 only had the effect of potentially

lowering the rankings of existing facilities. New entrants that released more toxic chemicals

than an existing facility resulted in a lower ranking for the existing facility, while those that

emitted less were ranked below them on the list and did not change the ranking of an existing

facility. For example, if an existing facility emitted 10,000 pounds and was ranked number

2 and one of the new facilities in their respective state emitted 11,000 pounds (a quantity

greater than the number 2 facility) than the existing facility’s rank improved to number

3. However, if the new facility emitted 9,000 pounds (a quantity less than the number 2

facility) then the existing facility remained ranked number 2 with one more facility added

23These seven new industries are: (i) metal mining (SIC 1021, 1031, 1041, 1044, 1061, and 1099); (ii) coal

mining (SIC 1221 and 1222); (iii) electrical utilities that combust coal / oil (SIC 4911, 4931, and 4939); (iv)

Resource Conservation and Recovery Act Subtitle C hazardous waste treatment and disposal facility (SIC

4953); (v) chemicals and allied products wholesale distributions (SIC 5169); (vi) petroleum bulk plants and

terminals (SIC 5171); (vii) solvent recovery services (SIC 7389).

24Some people have suggested that in fact many of the existing firms lobbied for the inclusion of these

additional firms, and therefore, that the rule change was not completely exogenous. If this were true it

would only strengthen the case presented here since it would demonstrate that these firms cared about their

rankings and that they wanted other larger firms to enter the jurisdiction of the TRI to make their releases

appear relatively less polluting.

25Sanders (2011) examines the effect of changes in releases due to the 1998 expansion on housing prices.

13
below them in the rankings. The number of newly covered facilities that made the “Top 10”

list in each state is shown in Figure 7. While there is no strong apparent spatial pattern,

one might be concerned that the number of newly covered facilities that made the “Top

10” list are correlated with other factors that determine releases, e.g., the demographic or

industrial composition of a state. Table 1 presents regressions where the number of newly

covered facilities that ended up in the “Top 10” in each state is linked to various Census

characteristics, one at a time. The two sectors that show a statistically significant link are

manufacturing and health care. To incorporate possible baseline changes, we hence include

facility-level time trends to account for the fact that facility releases have been trending.

The effect of a change in ranking should be seen on top of the preexisting trends.

We use the following instrumental variable approach:

yi,t+τ = αi + βit + γt + δI∆(top10),1998 + ri,1998 + ǫit

I∆(top10),1998 = ai + bit + ct + dNs,1998 + ri,1998 + μit

where yi,t+τ are the log of total environmental releases for facility i at time t + τ . They are

modeled as a function of an indicator variable I∆(top10),1998 that captures whether a facility

drops out of the “Top 10” list in 1998, i.e., the variable is one if the facility drops out of

the “Top 10” in t = 1998, and 0 otherwise. The regression also included facility fixed effects

αi, facility-specific time trends βi to adjust for overall release patterns, year fixed effects γt,

and the initial facility rank ri,1998 in 1998 without counting newly regulated facilities. It is

important to adjust for the initial rank as facility with a lower rank (higher releases) are less

likely to be dropped from the “Top 10” list. Since changes in the “Top 10” list I∆(top10),1998

are endogenous, we instrument with the number of new entrants in the top 10 list from newly

regulated sectors Ns,1998. The variable Ns,1998 is displayed in Figure 7 for 1998, the year the

program expansion occurred, and zero for all other years. Since our source of variation comes

at the state level, all regression cluster the errors at the state level.

One specific feature of this analysis is the timing of the data as shown in Figure 1.

Releases for reporting year 1998 had to be transmitted to the Environmental Protection

Agency, who releases data for all facilities in the late spring / early summer two years later,

i.e., 2000. The new state rankings where hence made public in the middle of 2000. If firms

wanted to implement measures to reduce their emissions and relative ranking, the first full

year to do so would be in 2001, or three years later. The baseline model hence uses τ = 3.

By the same token, examining the effect for τ = 2 gives us an indirect test whether

14
facilities shirk on their reports. Since the new rankings were only made public in late spring

of 2000, it would have been difficult to implement changes to the production process in

the same year. On the other hand, simply adjusting the reported numbers would easily be

feasible. Finding an effect for τ = 3, but not τ = 2 hence makes it less likely that the effect

is only driven by fake changes in the reported numbers in response to changes in rankings,

as they would have been available in τ = 2.

4 Empirical Analysis

The baseline results are shown in Table 2. The first three columns (1a)-(1c) show the results

for facilities in the 50 states. The last three columns (2a)-(2c) replicate the analysis but also

include facilities from US territories (American Samoa, District of Columbia, Puerto Rico,

and the Virgin Islands). In each of the two sets of regressions, columns (a) accounts for

facility fixed effects, columns (b) additionally controls for facility-specific time trends, and

columns (c) also account for year fixed effects. Errors are clustered at the state level, since

the variation (number of newly regulated facilities in the “Top 10”) varies at the state level.

Column (1a) give very imprecise estimate that are not significantly different from zero,

but also include a very wide range of possible estimates. There is tremendous variation in

emissions over time, both on average (picked up by the facility fixed effect), but also across

time. Some facilities are expanding, while others are contracting. Column (1b) therefore

adds facility-specific time trends. Since we are estimating a log model of releases, these

are firm-specific growth rates in emissions. The results are now significant: Firms that

were dropped from the “Top 10” list after the expansion of the program on average have a

staggering 50% higher emissions, although this effect is relative to the trend. The top panel

of Figure 8 shows the distribution of the facility-specific time trends corresponding to the

regression results of column (1b) in Table 2. The average is -0.16, i.e., on average facility

decrease emissions by 16% per year. Appendix Table A1 lists the rank fixed effects in 1998,

and the average fixed effect for facilities in the “Top 10” in 1998 is -0.37.26 The sum of the

average time trend and average 1998 rank fixed effect is -0.53, which is roughly equivalent

to the estimated treatment effect. In other words, if the effect is not measured relative to

expected declines, but relative to the total emissions in 1998, facilities that drop out of the

“Top 10” have small reduction in releases, while releases of facilities that remain on the “Top

26Note that the fixed effects for firms ranked 9 and 10 is largest in magnitude, i.e., these are the facilities

just below the threshold, and they reduce releases the most.

15
10” decrease significantly.

Note that the confidence interval in column (1a) includes the point estimate from column

(1b). Finally, column (1c) also includes year fixed effects, which has little effect on the results.

Columns (2a)-(2c) replicate the analysis by including facilities in US territories. The results

remain qualitatively similar, but confidence levels drop.

F-statistics of the first-stage instrument are given in the footer of the table. The firststage results are shown in Appendix Table A2. Not surprisingly, the number of new facilities

that enter in the “Top 10” is highly significant in predicting the probability of a facility

dropping out of the top 10. All regressions adjust for the initial rank of a facility without

counting newly regulated firms. This is crucial as a facility ranked 10th is more likely to

drop out of the top 10 than one that is ranked first, and relative reductions might differ by

initial releases.

Given that we include facility fixed effects as well as facility-specific time trends, we need

to include at least three years of releases for each facility. Our baseline regressions include

data for the years 1995-1998. Recall that releases ar recorded with a time lag of τ = 3 in

the baseline regression, i.e., the change in “Top 10” status in 1998 is paired with emissions

in 2001, ande hence dat from 1995-1998 includes release data from 1998-2001.

Table 3 examines the sensitivity of the results to the chosen time period. Columns (a)

use facility fixed effects as well as facility-level time trends that were shown to be crucial in

Table 2. Columns (b) add year fixed effects. The results are similar if we start the analysis

in 1993 (columns 1a-1b), 1994 (columns 2a-2b), but drop in magnitude and significance if

we limit the analysis to the minimum number of three years (1996-1998) in columns 3a-3b.

Table 4 examines the timing of the publication of facility rankings in more detail. Recall

Figure 1, which outlined when facility releases and rankings are made public. EPA collects

releases from firms for calendar year t and publishes them jointly for all facilities in late spring

a year and a half later. For example, the 1998 releases were published in late spring of 2000.

Firms that are simply gaming the system and misrepresenting their releases hence had ample

time to simply report lower numbers at the end of 2000. On the other hand, implementing

production changes in response to changes in the ranking will likely have limited effect in

2000 and only show up in 2001. Columns (3a-3b) in Table 4 replicate the analysis if the

time lag τ is three periods, while columns (2a-2b) report releases two periods later, and

columns (1a-1c) report the results one period later when new releases are not made public.

Columns (a) again use facility fixed effects and facility-specific time trends, while columns

(b) additionally add year fixed effects.

16
While we find the large and significant effect for τ = 3, no effect is found for τ = 1 or

τ = 2. We find this reassuring as it not only provides a falsification test for τ = 1 (before

new rankings are released), but also suggests to us that they changes in releases were not

only on the books, as firms had time to change the reported releases in τ = 2, the year

new rankings are reported. Moreover, if our instrument was highly correlated with other

statewide trends, these confounding results should also show up in τ = 1 and τ = 2.

Table 5 differentiates further between data sources. As described in the data section,

Scorecard provided us with their ranking of total releases, which only included onsite releases. On the other hand, total releases in TRINET also include offsite releases. Moreover,

onsite and offsite releases are different for some facilities, likely reflecting different cleaning

algorithms. The table hence presents all four possible combinations: The dependent variable

yi,t+τ is either taken from Scorecard or TRINET and the number of new entrants in the “Top

10” (Ns,1998) is either taken from Scorecard or TRINET. We obtain the largest and most

significant impact if we use Scorecard’s ranking on the right hand side, while it makes little

difference in which data source we use for the left hand side variable. On the other hand,

using TRINET’s ranking as right hand side variable results in lower point estimates that are

only marginally significant. This suggests that the public responded to Scorecard’s ranking

based on onsite releases, and not TRI’s total releases that include offsite releases.

Since TRINET separates emissions by medium, Table 6 replicates the analysis for releases

into air, surface water, land, and underground injections in columns (1a)-(1d), all of which

are considered onsite releases. Column (2a) sums onsite releases, while column (2b) considers

offsite releases. Finally, column (3) examines offsite transfers for further waste management.

Results by medium appear noisier, and only land emissions are significant at the 5% level

although the magnitude of the coefficients is large for all of the four onsite releases. The point

estimate for total onsite releases is again comparable to column (1a) in Table 5 where we used

Scorecard’s instead of TRINET’s estimate of facility onsite releases. There is no evidence

that firms reduced releases by decreasing offsite transfers for further waste management,

although the error terms are again wide, but the point estimates are of the opposite sign as

expected.

Scorecard’s data ends in 2001, but TRINET has data through 2010 at the time of writing.

Table 7 replicates the analysis for time lags τ ≥ 3. It tests whether the facility releases

remained different after 2001. Note that rankings are updated in subsequent years, and the

instrument we are using in 1998 does not ensure that a facility is still off the “Top 10” list in

subsequent years. We find no evidence for continued impacts, as point estimates for τ = 4

17
or τ = 5 are insignificant. This makes it again unlikely that our instrument is simply picking

up some confounder, as these cofounders would have to impact facility emissions in 2001,

but not 1999, 2000, 2002, or 2003. While not impossible, it would be unlikely to be only

showing up in the one year (2001) where firms should respond.

Finally, TRINET also provides a toxicity-weighted estimate of facility releases. Facility

rankings are still determined by summing the pounds emitted of all chemicals, but the

dependent variable is changes in toxicity-weighted emissions. One worry is that firms respond

by cutting emissions, but cut the less harmful ones. Our regression results are mixed:

while results in previous tables were insensitive to the number periods that were included

to estimate preexisting time trends in releases, this is no longer true: estimated effects are

significant if we chose t ∈ [1993, 1998], but not if we chose t ∈ [1994, 1998] or t ∈ [1995, 1998].

This is likely due to fact that the toxicity-weighted averages heavily depend on a few facilities

that emit highly toxic substances, and it becomes crucial how the trend of these particular

facilities is modeled.

5 Conclusions

The results from this study provide evidence that the TRI database did influence facility

emissions through a list of “Top 10” polluters as compiled by Scorecard. Facilities that

experienced exogenous drops in their pollution rankings, which resulted in their removal from

“Top 10” polluter lists within their states, subsequently had higher emissions. The results

suggest that overall the more than 160 facilities within the industries originally covered by

the TRI who were removed from “Top 10” worst polluter lists released a staggering 50%

more toxins into the atmosphere than they otherwise would have if they were not dropped

from the list. These results are relative to a downward trend.

Our results have significant policy implications. Although changes to the TRI reporting

rules led to less emissions reductions among already existing firms, the finding that firms do

respond to pollution rankings should bolster the overall case for Right-to-Know programs.

The TRI appears to be providing members of the public and policy makers with information

that they act upon, which in turn, creates pressure on the most polluting firms to change

their behavior. The costs (real or perceived) are significant enough that firms respond. Since

environmental releases are heavily concentrated amongst the worst polluters, influencing

their behavior is a very efficient way to reduce overall emissions, especially since pollution

rankings are relative and every year there are always top polluters.

18
Given that the maintenance costs of the TRI for the U.S. government have remained

extremely low, at approximately $25 million a year, and the cost to industry of providing

the information has dropped from approximately $550 million in the first year to $300 million

a year since (Fung & O’Rourke 2000), the TRI may be a potentially cost-effective means of

better enabling the public to express its environmental preferences.27

These results also highlight the potential unintended consequences of bringing new entrants under the jurisdiction of the TRI. While the inclusion of new industries in 1998 shifted

the focus to facilities that polluted significantly more than already existing facilities, at the

same time, this expansion decreased incentives for the latter group of facilities to reduce

their emissions. Depending on the relative susceptibility of the new and existing firms to

public pressure as well as their abatement costs, it is open question whether the change in

the TRI rules will lead to long-term increases or decreases in total environmental releases

across the United States.28

Despite the likelihood that the facility-level data provided by the TRI is not entirely

precise, the results of the current study are still of interest and policy-relevant because they

indicate that at minimum firms are concerned with the public perception of their emissions.

The evidence in this study suggests that once firms experience improvements in their pollution rankings they actually report lower emissions reductions, which is the opposite direction

expected from untruthful reporting. This indicates that firms are thinking strategically about

their pollution rankings. If anything, untruthful reporting is more likely to occur in instances

where firms report greater emissions reductions (i.e., relatively lower emissions). Moreover,

the timing of the changes in facility releases makes it less likely that is simply due to changes

in reported emissions, as firms could have reacted and changed their emissions a year earlier.

Given that the TRI currently covers only a small fraction of the toxic chemicals emitted

in the U.S. (approximately 5%) there is significant room to expand the scope of the program,

but as this paper has demonstrated, there may be unintended consequences that should be

taken into account.

27This does not include the actual costs of emissions reductions, but simply the cost of providing the

information to the government that the TRI legislation requires. The estimated cost of all U.S. environmental

regulation is in the hundreds of billions per year.

28In addition, if the chemicals emitted by the original set of TRI facilities are significantly more toxic than

the new entrants then changes in the composition of the TRI may have shifted attention to facilities that

actually pose less of a health and environmental risk.

19
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22
Figure 1: Timeline of Publication of Facility Releases and Rankings

 Year t Year t+1 Year t+2 Year t+3 

Emissions

for calendar

year t

Late spring:

EPA publishes

releases in t

First full year

to implement

changes

Notes: Figures illustrates the time line when information about the toxic releases is made public and when

firms can respond.

23
Figure 2: Location and Releases of TRI Facilities in 2001

Notes: Figure displays location of TRI facilities in the contiguous United Sates in 2001 (Source: TRINET). Tota

color-coded using a nonlinear scale to better illustrate the range of releases.

24
Figure 3: Total TRI Releases 1988-2010

0 1 2

3

4

5

6

7

8 Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Original Industries New Industries in 1998 All Industries

Notes: Figures displays total releases of all facilities by year. Industries who had to report releases in the

TRI expanded in 1998. Source: TRINET.

25
Figure 4: TRI Releases By Medium (1988-2010)

0 1 2 3 4 5

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Onsite Air Releases

0 1 2 3 4 5

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Onsite Surface Water Discharge

0 1 2 3 4 5

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Onsite Releases to Land

0 1 2 3 4 5

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Onsite Underground Injection

0 1 2 3 4 5 6 7

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Onsite Releases

0 1 2 3 4 5 6 7

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Offsite Releases

Notes: Figures displays releases of all facilities by year and medium. Total releases in Figure 3 that determine the ranking with a state are the sum of all online releases, which are further separated into air, water,

land, and underground injections in the first two rows) as well as offsite releases. The legend is the same as

in Figure 3. Source: TRINET.

26
Figure 5: TRI Offsite Transfers (1988-2010)

0 1 2

3

4

5

Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Total Offsite Transfers for Further Waste Management

Notes: Figures displays total offsite transfers for further waste management that are not included in total

releases. The legend is the same as in Figure 3. Source: TRINET.

27
Figure 6: Share of Releases

0 20 40 60

Percent of Total Releases

0 10 20 30 40 50 60 70 80 90 100

Facility Ranking Within State

Notes: Figures shows the average percentage of releases of the “Top 10” polluters for the years 1988-1997.

Source: TRINET.

28
Figure 7: Number of Newly Covered Facilities in 1998 in the Top 10 of Each Sta

Notes: Figure displays the number of facilities that were forced to report for the first time after the program expanded 

the “Top 10” polluters in a state. The corresponding numbers for the states / territories not displayed are: Alaska 

American Samoa (0), Puerto Rico (5), and Virgin Islands (3).

29
Figure 8: Distribution of Facility-Specific Time Trends

0 .5 1 1.5 2

Density

−4 −3 −2 −1 0 1

Facility Time Trends for Years 1995−1998 (Mean: −0.16 / SDev: 0.42)

0 .5 1 1.5 2 2.5

Density

−4 −3 −2 −1 0 1

Facility Time Trends for Years 1994−1998 (Mean: −0.09 / SDev: 0.35)

0 1 2 3

Density

−4 −3 −2 −1 0 1

Facility Time Trends for Years 1993−1998 (Mean: −0.04 / SDev: 0.31)

Notes: Figure displays the distribution of the facility-specific timer trends. The three panels use time periods

1995-1998, 1994-1998, and 1993-1998, respectively. Regression results are given in column (1b) of Table A1,

and columns (2a) and (1a) of Table A3, respectively. The average of the 1998 state rank fixed effects are:

-0.37, -0.52, and -0.60, respectively.

30
Table 1: Explaining Number of Newly Regulated Facilities in “Top 10”

Coefficient (Standard Error)

Variables in 2000 Census of Population

Fraction of population that is White -1.34 (2.81)

Fraction of population that is Black / African American -1.70 (3.80)

Fraction of population that is Hispanic 5.52 (4.01)

Fraction of population that is below 5 years old 51.47 (57.08)

Fraction of population that is 65 or older 0.03 (19.13)

Housing units per capita -12.84 (13.14)

Vacant housing units per capita 33.77 (22.50)

Median Age -0.07 (0.19)

Variables in 2002 Economic Census

Utilities: Establishments per 1000 People -8.77 (6.65)

Utilities: Employees per 1000 People 0.20 (0.46)

Mining: Establishments per 1000 People 0.83 (1.51)

Mining: Employees per 1000 People 0.04 (0.06)

Construction: Establishments per 1000 People -0.68 (0.45)

Construction: Employees per 1000 People 0.07 (0.06)

Manufacturing: Establishments per 1000 People -3.58∗∗ (1.66)

Manufacturing: Employees per 1000 People -0.04 (0.04)

Finance: Establishments per 1000 People -0.25 (1.18)

Finance: Employees per 1000 People -0.02 (0.04)

Health Care and Services: Establishments per 1000 People -2.39∗∗ (1.09)

Health Care: Employees per 1000 People -0.07∗(0.04)

Notes: Table presents regresson results. Each row is a separate regression, where we regress the number

of newly regulated facilities in 1998 in each of the 50 states that were among the “Top 10” polluters on

characteristics from the Census. The regressions have 50 observations (one fore each state). Significance

levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

31
Table 2: The Effect of Being a “Top 10” Facility

Variable (1a) (1b) (1c) (2a) (2b) (2c)

Out of Top 10 0.147 0.535∗∗ 0.534∗∗ 0.044 0.422∗ 0.422∗

(0.236) (0.216) (0.216) (0.249) (0.245) (0.245)

F-stat (1st stage) 322.20 239.25 239.10 316.68 237.12 236.98

R-squared 0.1038 0.7371 0.7373 0.0937 0.6969 0.6969

Observations 1874 1874 1874 1922 1922 1922

Facilities 473 473 473 486 486 486

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends No Yes Yes No Yes Yes

Year Fixed Effects No No Yes No No Yes

Include Territories No No No Yes Yes Yes

Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998

Time Diff τ 3 3 3 3 3 3

Notes: Table regresses log releases on whether the facility dropped out of the “Top 10” list, which is

instrumented by the 1998 program expansion. The first three columns use facilities in the United States,

while the last three columns also include facilities from US territories. Appendix Table A1 reports rank and

year fixed effects, while Table A2 reports first stage results. Facility releases as well as state rankings are

taken from Scorecard. Errors are clustered at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗

5%, ∗ 10%.

32
Table 3: Sensitivity of Results to Years Used in the Analysis

Variable (1a) (1b) (2a) (2b) (3a) (3b)

Out of Top 10 0.532∗∗ 0.524∗∗ 0.553∗∗ 0.550∗∗ 0.388 0.388

(0.223) (0.223) (0.223) (0.223) (0.236) (0.236)

F-stat (1st stage) 266.92 266.75 247.32 247.19 217.49 217.49

R-squared 0.6276 0.6451 0.6805 0.6909 0.7902 0.7902

Observations 2764 2764 2323 2323 1383 1383

Facilities 475 475 475 475 461 461

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes Yes Yes

Year Fixed Effects No Yes No Yes No Yes

Include Territories No No No No No No

Years in Sample 1993-1998 1993-1998 1994-1998 1994-1998 1996-1998 1996-1998

Time Diff τ 3 3 3 3 3 3

Notes: Table replicates columns (1b) and (1c) of Table 2, but varies how many years are included prior to

the program expansion in 1998. These years are used in the identification of the facility-level fixed effects

and time trends. Facility releases as well as state rankings are taken from Scorecard. Errors are clustered

at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

33
Table 4: Results for Different Time Lags After the Expansion

Variable (1a) (1b) (2a) (2b) (3a) (3b)

Out of Top 10 -0.029 -0.029 0.001 -0.002 0.535∗∗ 0.534∗∗

(0.157) (0.157) (0.107) (0.107) (0.216) (0.216)

F-stat (1st stage) 488.87 488.48 248.18 247.96 239.25 239.10

R-squared 0.6396 0.6399 0.6975 0.7070 0.7371 0.7373

Observations 1863 1863 1863 1863 1874 1874

Facilities 468 468 469 469 473 473

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes Yes Yes

Year Fixed Effects No Yes No Yes No Yes

Include Territories No No No No No No

Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998

Time Diff τ 1 1 2 2 3 3

Notes: Table replicates columns (1b) and (1c) of Table 2, but varies at what time the impacts of the 1998

expansion are evaluated. Columns (3a) and (3b) are the same as column (1b) and (1c) in Table 2: They use

a time lag of three years, i.e., evaluate releases in 2001. Columns (1) and (2) examine releases in 1999 and

2000, respectively. Facility releases as well as state rankings are taken from Scorecard. Errors are clustered

at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

34
Table 5: Results by Data Source (Scorecard versus TRINET)

Variable (1) (2) (3) (4)

Out of Top 10 0.535∗∗ 0.438∗∗ 0.365∗ 0.229

(0.216) (0.171) (0.215) (0.301)

F-stat (1st stage) 239.25 239.07 263.07 278.02

R-squared 0.7371 0.7095 0.7142 0.6776

Observations 1874 1881 1917 1853

Facilities 473 474 481 468

Facility Fixed Effects Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes

Year Fixed Effects No No No No

Include Territories No No No No

Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998

Time Diff τ 3 3 3 3

Release Estimates Scorecard TRINET TRINET Scorecard

Facility State Rankings Scorecard Scorecard TRINET TRINET

Notes: Table replicates column (1b) of Table 2, but now uses both data from Scorecard as well as EPA’s

data engine TRINET. Previous tables exclusively used data from Scorecard. Both facility releases as well as

facility state rankings are either taken from Scorecard or TRINET. Errors are clustered at the state level.

Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

35
Table 6: Results by Medium

Air Water Land Underground Onsite Offsite 

Variable (1a) (1b) (1c) (1d) (2a) (2b) 

Out of Top 10 0.401∗ 1.328∗∗ 2.241∗ 3.633 0.557∗∗∗ 1.597 

(0.235) (0.651) (1.248) (3.426) (0.211) (1.598) 

F-stat (1st stage) 222.09 14.69 16.54 4.17 239.07 5.03 R-squared 0.7415 0.7283 0.5840 0.5554 0.7387 0.5678 Observations 1873 1544 1193 186 1881 1482 Facilities 472 392 307 48 474 384 Facility Fixed Effects Yes Yes Yes Yes Yes Yes Facility Time Trends Yes Yes Yes Yes Yes Yes Year Fixed Effects No No No No No No Include Territories No No No No No No Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 Time Diff τ 3 3 3 3 3 3 

Release Estimates TRINET TRINET TRINET TRINET TRINET TRINET 

Facility State Rankings Scorecard Scorecard Scorecard Scorecard Scorecard Scorecard 

Notes: Table replicates column (2) of Table 5, but separates releases by medium. Total onsite releases (column 2a) are 

columns (1a-1d). Total releases in Table 5 are the sum of onsite and offsite releases (2a)-(2b). The evolution of releasare shown in Figure 4 for the TRINET data. Errors are clustered at the state level. Significance levels are indicated b

36
Table 7: Results Beyond 2001

Variable (1a) (1b) (1c) (2a) (2b) (2c)

Out of Top 10 0.438∗∗ 0.036 -0.350 0.514∗∗∗ 0.207 -0.167

(0.171) (0.210) (0.320) (0.157) (0.222) (0.271)

F-stat (1st stage) 239.07 306.57 124.80 248.75 296.58 142.15

R-squared 0.7095 0.7124 0.5203 0.6634 0.6769 0.5748

Observations 1881 1874 1868 2342 2337 2326

Facilities 474 473 472 478 478 476

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes Yes Yes

Year Fixed Effects No No No No No No

Include Territories No No No No No No

Years in Sample 1995-1998 1995-1998 1995-1998 1994-1998 1994-1998 1994-1998

Time Diff τ 3 4 5 3 4 5

Release Estimates TRINET TRINET TRINET TRINET TRINET TRINET

Facility State Rankings Scorecard Scorecard Scorecard Scorecard Scorecard Scorecard

Notes: Table replicates Table 4, but includes time lags longer than three periods to see whether differences

persist over time. The first three columns use years 1995-1998, while the last three columns use 1994-1998

to estimate facility fixed effects and time trends. Errors are clustered at the state level. Significance levels

are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

37
Table 8: Toxicity-Weighted Releases

Variable (1a) (1b) (1c) (2a) (2b) (2c)

Out of Top 10 0.214 0.366 0.571∗ 0.232 0.262 0.579∗∗

(0.221) (0.306) (0.342) (0.303) (0.243) (0.276)

F-stat (1st stage) 239.81 249.33 267.79 263.07 307.38 325.84

R-squared 0.5929 0.5498 0.5304 0.6101 0.5337 0.4918

Observations 1873 2331 2773 1917 2387 2837

Facilities 472 476 477 481 484 484

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes Yes Yes

Year Fixed Effects No No No No No No

Include Territories No No No No No No

Years in Sample 1995-1998 1994-1998 1993-1998 1995-1998 1994-1998 1993-1998

Time Diff τ 3 3 3 3 3 3

Release Estimates TRINET TRINET TRINET TRINET TRINET TRINET

Facility State Rankings Scorecard Scorecard Scorecard TRINET TRINET TRINET

Notes: Table replicates Table 2 except that it uses toxicity-weighted sum of releases instead of the weightbased sum of releases. Columns (a), (b), and (c) use different time periods and results differ by the time

chosen time period. Errors are clustered at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗

5%, ∗ 10%.

38
A1 Online Appendix

Figure A1: Total TRI Releases 1988-2001 (Scorecard)

0 1 2

3

4

5

6

7

8 Total Releases (Billion Pounds)

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Original Industries New Industries in 1998 All Industries

Notes: Figures displays total releases of all facilities by year. Industries who had to report releases in the

TRI expanded in 1998. Source: Scorecard.

A1
Figure A2: Share of Releases (Scorecard)

0 20 40 60

Percent of Total Releases

0 10 20 30 40 50 60 70 80 90 100

Facility Ranking Within State

Notes: Figures shows the percentage of releases of the “Top 10” polluters for the years 1988-2001. Source:

Scorecard.

A2
Table A1: The Effect of Being a “Top 10” Facility - Fixed Effects

Variable (1a) (1b) (1c) (2a) (2b) (2c)

Out of Top 10 0.147 0.535∗∗ 0.534∗∗ 0.044 0.422∗ 0.422∗

(0.236) (0.216) (0.216) (0.249) (0.245) (0.245)

Irank 1 in 1998 -0.611∗∗ -0.349 -3.551∗∗∗ -0.517∗∗ -0.251 -3.383∗∗∗

(0.264) (0.236) (0.354) (0.254) (0.242) (0.375)

Irank 2 in 1998 -0.370∗∗∗ -0.062 -3.265∗∗∗ -0.295∗∗∗ 0.027 -3.105∗∗∗

(0.076) (0.116) (0.204) (0.105) (0.146) (0.252)

Irank 3 in 1998 -0.613∗∗∗ -0.273∗∗ -3.476∗∗∗ -0.605∗∗∗ -0.249∗∗ -3.381∗∗∗

(0.138) (0.113) (0.230) (0.136) (0.112) (0.244)

Irank 4 in 1998 -0.672∗∗∗ -0.432∗∗ -3.634∗∗∗ -0.646∗∗∗ -0.408∗∗ -3.540∗∗∗

(0.186) (0.189) (0.207) (0.185) (0.189) (0.229)

Irank 5 in 1998 -0.404∗∗ -0.409∗∗ -3.611∗∗∗ -0.356∗-0.353∗∗ -3.485∗∗∗

(0.193) (0.168) (0.279) (0.194) (0.174) (0.302)

Irank 6 in 1998 -0.395∗∗ -0.451∗∗∗ -3.654∗∗∗ -0.346∗∗ -0.400∗∗ -3.532∗∗∗

(0.163) (0.153) (0.276) (0.173) (0.169) (0.307)

Irank 7 in 1998 -0.288 -0.296 -3.498∗∗∗ -0.221 -0.228 -3.360∗∗∗

(0.186) (0.208) (0.334) (0.195) (0.221) (0.362)

Irank 8 in 1998 -0.564∗∗ -0.297 -3.499∗∗∗ -0.487∗∗ -0.187 -3.319∗∗∗

(0.232) (0.213) (0.336) (0.241) (0.234) (0.372)

Irank 9 in 1998 -0.614∗∗ -0.605∗∗ -3.807∗∗∗ -0.510∗-0.493∗-3.626∗∗∗

(0.283) (0.252) (0.371) (0.290) (0.274) (0.409)

Irank 10 in 1998 -0.349 -0.532∗∗ -3.734∗∗∗ -0.324 -0.428∗-3.560∗∗∗

(0.239) (0.227) (0.366) (0.260) (0.256) (0.407)

Ireleases in 1999 -1.050∗∗∗ -1.051∗∗∗

(0.066) (0.065)

Ireleases in 2000 -2.139∗∗∗ -2.087∗∗∗

(0.115) (0.123)

F-stat (1st stage) 322.20 239.25 239.10 316.68 237.12 236.98

R-squared 0.1038 0.7371 0.7373 0.0937 0.6969 0.6969

Observations 1874 1874 1874 1922 1922 1922

Facilities 473 473 473 486 486 486

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends No Yes Yes No Yes Yes

Year Fixed Effects No No Yes No No Yes

Include Territories No No No Yes Yes Yes

Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998

Time Diff τ 3 3 3 3 3 3

Notes: Table replicates Table A1 in the main paper except that it also reports fixed effects in the second

stage. Errors are clustered at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

A3
Table A2: The Effect of Being a “Top 10” Facility - First Stage

Variable (1a) (1b) (1c) (2a) (2b) (2c)

No. New Top 10 0.092∗∗∗ 0.091∗∗∗ 0.091∗∗∗ 0.090∗∗∗ 0.090∗∗∗ 0.090∗∗∗

(0.005) (0.006) (0.006) (0.005) (0.006) (0.006)

Irank 1 in 1998 -0.420∗∗∗ -0.416∗∗∗ 0.129 -0.399∗∗∗ -0.401∗∗∗ 0.152∗

(0.042) (0.048) (0.080) (0.040) (0.046) (0.078)

Irank 2 in 1998 -0.364∗∗∗ -0.360∗∗∗ 0.186∗∗ -0.353∗∗∗ -0.354∗∗∗ 0.199∗∗∗

(0.041) (0.048) (0.074) (0.040) (0.047) (0.073)

Irank 3 in 1998 -0.233∗∗∗ -0.231∗∗∗ 0.314∗∗∗ -0.231∗∗∗ -0.231∗∗∗ 0.322∗∗∗

(0.051) (0.059) (0.066) (0.050) (0.058) (0.064)

Irank 4 in 1998 -0.064 -0.061 0.484∗∗∗ -0.066 -0.065 0.488∗∗∗

(0.055) (0.064) (0.030) (0.054) (0.063) (0.030)

Irank 5 in 1998 0.066 0.066 0.612∗∗∗ 0.062 0.060 0.613∗∗∗

(0.057) (0.066) (0.066) (0.057) (0.066) (0.065)

Irank 6 in 1998 0.220∗∗∗ 0.223∗∗∗ 0.769∗∗∗ 0.233∗∗∗ 0.235∗∗∗ 0.787∗∗∗

(0.052) (0.060) (0.083) (0.053) (0.061) (0.084)

Irank 7 in 1998 0.302∗∗∗ 0.305∗∗∗ 0.851∗∗∗ 0.313∗∗∗ 0.314∗∗∗ 0.867∗∗∗

(0.060) (0.069) (0.093) (0.060) (0.069) (0.092)

Irank 8 in 1998 0.393∗∗∗ 0.404∗∗∗ 0.950∗∗∗ 0.402∗∗∗ 0.412∗∗∗ 0.965∗∗∗

(0.058) (0.067) (0.093) (0.058) (0.066) (0.093)

Irank 9 in 1998 0.502∗∗∗ 0.505∗∗∗ 1.050∗∗∗ 0.509∗∗∗ 0.510∗∗∗ 1.063∗∗∗

(0.050) (0.059) (0.089) (0.049) (0.058) (0.088)

Irank 10 in 1998 0.571∗∗∗ 0.571∗∗∗ 1.117∗∗∗ 0.576∗∗∗ 0.575∗∗∗ 1.128∗∗∗

(0.045) (0.053) (0.084) (0.043) (0.051) (0.083)

Ireleases in 1999 0.183∗∗∗ 0.185∗∗∗

(0.017) (0.017)

Ireleases in 2000 0.363∗∗∗ 0.368∗∗∗

(0.035) (0.034)

R-squared 0.8387 0.9365 0.9365 0.8380 0.9364 0.9364

Observations 1874 1874 1874 1922 1922 1922

Facilities 473 473 473 486 486 486

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends No Yes Yes No Yes Yes

Year Fixed Effects No No Yes No No Yes

Include Territories No No No Yes Yes Yes

Years in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998

Time Diff τ 3 3 3 3 3 3

Notes: Table presents the first stage results for Table A1 in the main paper. Errors are clustered at the

state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

A4
Table A3: Sensitivity of Results to Years Used in the Analysis - Fixed Effects

Variable (1a) (1b) (2a) (2b) (3a) (3b)

Out of Top 10 0.532∗∗ 0.524∗∗ 0.553∗∗ 0.550∗∗ 0.388 0.388

(0.223) (0.223) (0.223) (0.223) (0.236) (0.236)

Irank 1 in 1998 -0.592∗∗ -2.542∗∗∗ -0.531∗∗ -2.989∗∗∗ -0.274 -4.027∗∗∗

(0.256) (0.345) (0.251) (0.356) (0.258) (0.386)

Irank 2 in 1998 -0.313∗∗∗ -2.272∗∗∗ -0.201∗-2.664∗∗∗ -0.101 -3.854∗∗∗

(0.115) (0.186) (0.115) (0.201) (0.108) (0.228)

Irank 3 in 1998 -0.544∗∗∗ -2.505∗∗∗ -0.414∗∗∗ -2.882∗∗∗ -0.156 -3.909∗∗∗

(0.134) (0.211) (0.118) (0.217) (0.139) (0.272)

Irank 4 in 1998 -0.753∗∗∗ -2.705∗∗∗ -0.616∗∗∗ -3.074∗∗∗ -0.547∗∗∗ -4.300∗∗∗

(0.188) (0.196) (0.183) (0.201) (0.190) (0.236)

Irank 5 in 1998 -0.464∗∗∗ -2.412∗∗∗ -0.429∗∗ -2.886∗∗∗ -0.301∗-4.055∗∗∗

(0.175) (0.260) (0.173) (0.274) (0.166) (0.313)

Irank 6 in 1998 -0.539∗∗∗ -2.487∗∗∗ -0.486∗∗∗ -2.944∗∗∗ -0.373∗∗ -4.126∗∗∗

(0.156) (0.248) (0.156) (0.269) (0.172) (0.322)

Irank 7 in 1998 -0.481∗∗ -2.424∗∗∗ -0.397∗∗ -2.855∗∗∗ -0.122 -3.875∗∗∗

(0.194) (0.287) (0.197) (0.310) (0.247) (0.394)

Irank 8 in 1998 -0.641∗∗∗ -2.586∗∗∗ -0.501∗∗ -2.956∗∗∗ -0.037 -3.790∗∗∗

(0.202) (0.293) (0.203) (0.314) (0.267) (0.407)

Irank 9 in 1998 -0.873∗∗∗ -2.815∗∗∗ -0.846∗∗∗ -3.303∗∗∗ -0.468 -4.221∗∗∗

(0.273) (0.361) (0.273) (0.383) (0.294) (0.441)

Irank 10 in 1998 -0.847∗∗∗ -2.795∗∗∗ -0.731∗∗∗ -3.190∗∗∗ -0.283 -4.036∗∗∗

(0.268) (0.364) (0.248) (0.366) (0.283) (0.438)

Ireleases in 1997 -0.285∗∗∗

(0.038)

Ireleases in 1998 -0.565∗∗∗ -0.452∗∗∗

(0.058) (0.043)

Ireleases in 1999 -1.089∗∗∗ -1.149∗∗∗

(0.084) (0.081)

Ireleases in 2000 -1.648∗∗∗ -1.881∗∗∗ -1.877∗∗∗

(0.105) (0.114) (0.098)

F-stat (1st stage) 266.92 266.75 247.32 247.19 217.49 217.49

R-squared 0.6276 0.6451 0.6805 0.6909 0.7902 0.7902

Observations 2764 2764 2323 2323 1383 1383

Facilities 475 475 475 475 461 461

Facility Fixed Effects Yes Yes Yes Yes Yes Yes

Facility Time Trends Yes Yes Yes Yes Yes Yes

Year Fixed Effects No Yes No Yes No Yes

Include Territories No No No No No No

Years in Sample 1993-1998 1993-1998 1994-1998 1994-1998 1996-1998 1996-1998

Time Diff τ 3 3 3 3 3 3

Notes: Table replicates Table 3 in the main paper except that it also reports fixed effects in the second

stage. Errors are clustered at the state level. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

A5