### Does Being a “Top 10” Worst Polluter Affect Environmental Rel eases? 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) th at required newly regulated industry sectors to report facility releases to obtain exogen ous 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 f acilities were dropped from the “Top 10” because newly regulated facilities surpassed the ir emissions. Facilities that dropped out of the “Top 10” in 1998 did not significantly alter th eir emissions in 1999 or 2000, but had 50% higher emissions in 2001 than they would have ha d if they had stayed on the “Top 10” list. This timeline is consistent with the fact that EPA d oes not release emissions until a year and a half after the reporting year, i.e., rankings f or 1998 were not released until the middle of 2000. Facilities responded to rankings that are ba sed 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. W e find no evidence for a counterbalancing decrease in offsite transfers for further wast e management.

We would like to thank Ann Harrison, Michael Hanemann, Dara O’Rourk e, Maximilian Auffhammer, Peter Berck, Matthew Rabin, Sofia Villas-Boas, David Roe, James Hamilt on, Mike Toffel, David Levine, Bill Pease, John List, Christopher Dumas, Madhu Khanna for their ext remely helpful comments.

♠ University of California at Berkeley and NBER. Email: schlenker@berk eley.edu
♣ Monterey Institute of International Studies. Email: jason.scors e@miis.edu

### The day it became clear that disclosure was a powerful regulatory t ool was June

30, 1988, when Richard J. Mahoney, then head of Monsanto (one o f 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 B hopal, India, and the subsequent chemical plant accident in West Virginia, the U.S. Congre ss passed the Emergency Planning and Community Right-to-Know Act (EPCRA), which re quired all manufacturing facilities employing more than 10 people and using more than 10,0 00 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 firs t 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 t o report the emissions they are releasing. The underlying idea is consistent with financ ial filings of publicly traded companies. Various stakeholders, e.g., customers, invest ors, or residents living close to a facility can use this information to put pressure on firms. Wheth er 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 initia lly exempt. These include all federal agencies as well as power and mining companies. Fr om 1988 to 1997 total environmental releases reported under the TRI fell around 60% a nd from 1998 to 2001 they fell an additional 20%. It is not possible to compare aggregate emiss ions reductions across the entire time period due to the expansion of the program in 1998. T he key question for both policy-makers and academics is whether the publication of the T RI is at least partially responsible for this dramatic decrease in emissions. 3

1 Between 1988 and 1992 TRI reported environmental releases fro m Monsanto facilities dropped almost 94%.

2 Transfers refer to waste that is produced on site and then moved to another site.
3 One possibility is that firms switched to chemicals that were not cover ed by the TRI, and therefore, the decline in TRI releases does not correspond to actual reductio ns in aggregate levels of emissions, but is due to the substituting of some chemicals for others. While this may a ccount for part of the reported TRI



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### “Top 10” worst polluter lists have become popular in the television, pr int, and internet

media, as well as with environmental groups, since they focus atte ntion on a small subset of the TRI facilities that emit the overwhelming share of toxic chemica ls. 4 

### The EPA’s TRI Explorer website allowed users to rank facilities and provides state r eports 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 emission s. An added benefit of examining only those facilities with the highest environmental release s is that this largely avoids the problem presented by the reporting thresholds for indiv idual chemicals. Almost all of the “Top 10” facilities within states report environmental rele ases 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 r eleases. Examining “Top 10” lists at the state level is chosen for two reasons; one polic y 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 c oncerns is often localized since the people who live in the vicinity of highly polluting firms have a direc t 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 nationa l rankings will not include many facilities that likely face significant pressure to reduce their em issions 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 TR I database, provides a quasi-natural experiment that helps identify causality between c hanges in facility pollution rankings within states and subsequent facility emissions. Existing TR I facilities in states that

declines it is important to note that 286 chemicals were added to the T RI list in 1994 and total releases continued their decline thereafter.
4 Another common worst polluter list reported in the media is the “Dirty Dozen”; the top 12 worst polluters.
5 The reporting practice since changed, as did the web interface, bu t “Top 10” lists were common around 1998.
6 The website was subsequently sold by the original creator (Environ mental Defense Fund), and has since shut down.
7 Facilities that rank high nationally will by definition also rank high at the s tate level and therefore examining the behavior of worst polluters at the state level will includ es these facilities.



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### had many new entrants experienced large drops in their pollution ran kings, often leading

to their removal from the “Top 10” polluter lists, while facilities in stat es without many of the new industries saw little to no change in their rankings. If state p ollution rankings do matter then facilities in the former group had less incentive to reduc e emissions after the rule change, and as a result, likely reduced emissions less than they w ould have had they not experienced the unexpected drop in their rankings. The econometric results confirm that being on a “Top 10” worst pollu ter 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 o f the “Top 10,” had 50% higher releases compared to a case where they remained on the “To p 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 ch anges. EPA made new releases and rankings public in the middle of 2000. Firms therefore ha d the possibility to change reported releases in 2000, while changes in production tech nology 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 th at pollution rankings, and hence the information provided by the TRI does influence facility releases, thereby bolstering the general case for Right-to-Know programs. Organ izations such as the World Bank are currently investigating whether Right-to-Know program s 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 s imilar to the TRI. 8 

### Moreover, many groups in the U.S. would like to expand the TRI. Seco nd, the econometric results show how changes to a Right-to-Know program can have un intended consequences; in this case the expansion of the TRI (with the intent of putting pres sure on an additional set of extremely polluting facilities) decreased the incentives for fir ms already covered by the program to reduce their emissions. The remainder of the paper is organized as follows: Section 1 survey s 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. Sec tion 3 presents our empirical strategy, while Section 4 presents empirical results whet her facility releases were affected by the removal from the “Top 10” lists within states. The p olicy implications of the

8 For a description of the new EU program see, “EU Launches Emission s List” in Chemical and Engineering News , March 1, 2004, Vol. 82(9). The first reporting year for the EU pr ogram was 2003 and the second dataset will be available in 2006 since the program provides audits eve ry three years, unlike the annual reports provided by the TRI.



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### results are discussed in Section 5.



## 1 Background

Most researchers who have studied the TRI have concluded that a t least part of the decline in TRI-reported chemicals is tied directly to the provision of the infor mation mandated by the TRI legislation. (Cohen 1997, Fung & O’Rourke 2000, Graham 200 0, 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 co mbined the resulting information into a database made increasingly easy for the public to use. The provision of TRI data clearly changed behavior. Case stud ies abound about managers who learned about pollution figures for the first tim e, communities that placed pressures on facilities for reductions, and regula tors that used the data to focus on particular chemicals or facilities. Overall, the TR I become a standard by which actors in the private and public sectors measure d companies’ environmental performance. Environmentalists used the data to develop reports and lists that often focused attention on firms or plants that rank ed the highest on some aspect of the TRI data.

There are a number of channels through which stakeholders can ex ert 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 leg islation to curb firm emissions, and the threat alone may be sufficient to influence firm beh avior. 10 

### Residents have been shown to “vote with their feet” and relocate away from p lants with toxic emissions

9 We do not attempt to quantify the extent to which these different c hannels 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 elemen ts of their production processes that they may have largely neglected. This information, in turn, may help t hem to become more environmentally efficient (this is discussed further in the literature review). In addit ion, if members of the firm include employees with a strong environmental ethic such an internal revie w may also create pressure from within the firm to improve environmental performance even in the absenc e of outside pressure (Gunningham, Kaga & Thornton 2003).
10 Bui (2005) provides evidence that suggests that the use of the T RI information by local and state politicians, which used it to craft additional legislation and focus pres sure on worst polluters, was most responsible for changes in firm behavior brought about by the TRI in the petroleum industry.



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### (Banzhaf & Walsh 2008). Second, green preferences by consume rs, firms, and government

and non-governmental agencies can exert pressure on companie s by purchasing less products from highly polluting firms or by rewarding less polluting firms with increa sed business. Third, the data exposes potential future liabilities. Once pollution da ta is part of the public domain this creates a record for any future liabilities firms may face r egarding 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 pollu ting. Since all of the environmental release data reported under the TR I 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 d ata 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 ev idence 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 mu ch more than commensurate gains. Behavioral economists have conducted numerous la boratory experiments in which they have uncovered evidence of loss aversion, and they poin t to many aspects of contemporary U.S. law that explicitly recognize it (Kahneman, Knets ch & Thaler 1991). For example, in court rulings losses are often treated more much se riously 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 w orking to improve the environment. For this reason, “Top 10” polluter lists have significan t potential to stimulate activism since they highlight firms with the worst environmental perf ormance (in absolute and relative terms), which from a concerned citizen’s standpoint tr anslates into a loss of

11 In order to determine whether, in fact, the public benefits of any e missions 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 Ag ency. However, if the TRI is causing firms to reduce emissions we can be almost certain that members of the pu blic and government at least perceive that the emissions are sufficiently harmful and should be reduced, a nd 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 standpo int, 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).



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### environmental quality.

Fung & O’Rourke (2000) refer to the common practice of using the T RI 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 pot ential 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 betw een TRI reporting and stock performance. Hamilton (1995) found that the TRI prov ided “new” information to investors and that the stock performance of publicly traded comp anies was significantly and negatively correlated with toxic releases on the day after the TRI r eport was released in 1989, often translating into decreases in stock valuation of millions of dollar s 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 an d found that from 1990- 1994 firms whose emissions were worse compared to their own past e missions, or relative to industry trends, suffered significant and negative stock valuation s. Konar & Cohen (2001) found that toxic releases were negatively correlated with stock pe rformance for a sample of S&P500 manufacturing firms in 1989. Regarding the composition of emission reductions, Hamilton (1999) f ound that firms which emitted more carcinogenic chemicals were more likely to reduce e missions between
1988 and 1991. 12 

### Arora & Cason (1996) and Khanna & Damon (1999) used the TRI dat a to explore why firms may have decided to participate in the EPA’s volun tary pollution reduction program, “33/50.” They found that firms with high public v isibility were more likely to enter the program, and that potential environmental liabilit ies 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 disproport ionately in poor and minority communities, and whether toxic emissions are influenced by co mmunity demographics (Rhodes 2005, Arora & Cason 1999). Although there is a strong co rrelation between toxic releases and concentrations of poor and/or minority populations t hroughout much of the U.S., all else equal, the main determinant influencing facility emissions te nds to be the level

12 This is the only study which attempts to specifically assess the health risk of different TRI chemicals and determine whether these were targeted for greater reduct ions than others.
13 The 33/50 program ended in 1995 so it does not affect my results, wh ich makes use of data from 1995 onwards.



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### of political participation people exercise in their respective communit ies.

In addition to external forms of pressure to reduce emissions, be fore 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 s ome of them uncover inefficient aspects of their own production processes (Office of Env ironmental 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 r educe their emissions, while also improving facility efficiency by requiring internal environment al audits. The authors describe the process how Indonesian firms were shamed into reducing emissions after being highlighted as serious polluters. In Canada, where the govern ment enacted a program very similar to the TRI, Antweiler & Harrison (2003) found evidence t hat green consumerism linked to pollution reporting had a significant effect on toxic emissions r eductions. Jin & Leslie (2003) analyzed the effects of a unique Right-to-Know pr ogram 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 t hat not only is consumer demand sensitive to the restaurant health scores (lower health sc ores resulting in lower demand), but that after the introduction of the program, the inc idence of food-borne illnesses decreased in the surrounding area, both due to the increased dem and for cleaner restaurants, as well as health improvements (made in response to the Right-to-K now program) in formerly poorly rated restaurants. In summary, the TRI has been demonstrated to influence the stoc k valuation of U.S. firms, and similar Right-to-Know programs in other countries have influenc ed firm emissions. In addition, U.S. firms emitting highly carcinogenic chemicals have been se nsitive 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 da ta, and a program based on health inspections has also influenced the behavior of both consu mers and restaurant owners in L.A., resulting in less illness. The following study adds to the research on Right-to-Know progra ms 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, an d examining whether

14 This can be thought of as a manifestation of the “Porter Hypothes is.” Regulation may actually help firms discover new methods for improving environmental efficiency t hat they may not have in the absence of the regulation (Porter & van der Linde 1995).



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### firms respond to this identification is a logical step in the large and gro wing 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 examp le, facility level releases for reporting year 1998 are made public in the middle of 2000. The time line is shown in Figure 1. Reports include releases for each of 650 chemicals, separ ated by medium: air, surface water, land, and underground injection, which all constit ute 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 da ta 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 (s ee 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 pub lic identifies who received the pressure of being labeled a “Top 10” polluter. We subsequently extracted all facility releases from TRINET 15 

### 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 furth er waste management. TRINET includes onsite releases in their measure of total releases, and hen ce we constructed rankings using the sum of onsite and offsite releases, which is different from Sc orecards definition that only included onsite releases. When we matched the data to the relea se data we obtained from Scorecard, total releases were generally comparable but no t always identical, even if we excluded offsite releases. A possible reason is that TRI data is noisy a nd 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 an d 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 su ch as the facility name, address, zip code, 4-digit SIC code / 6-didigit NAICS code, pr oduction ratio (the

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



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### 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 dist ribution of TRI facilities in 2001. The majority of facilities are concentrated east of the Miss issippi River, with the majority of these 30+ states containing hundreds of facilities. The re 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 t he other hand, the figure shows color-coded total releases in the TRINET data, and s ome of the largest overall
emitters are found in the Western United States. Figure 3 shows total reported environmental releases in TRINET f rom 1988 to 2010. 16 

### Total releases drop by more than 60 percent between 1988 and 19 98 (from a high of more than seven billion pounds in 1988 to around 2.5 billion pounds in 1997), an d 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 injec tion, 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 r eleases stems from reduction in air emissions, followed by reductions in underground inje ctions. Total offsite releases are a small portion of overall releases. The one exception to the general downward trend are offsite tran sfers 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 tr ansfers 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. environm ental releases. The y-axis shows the percentage share of total U.S. environmental release s (averaged across the years 1988-1997) and the x-axis groups facilities according to their tota l environmental release rankings within their respective states. The sum of the environmen tal releases from facilities that comprised the “Top 10” biggest emitters across the 50 state s accounted for, on average, approximately 58 percent of total U.S. environmental releases ea ch 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 resp ective states (50 times

16 Although the TRI officially began in 1987 the quality of the data in the fir st year is considered unreliable and therefore is omitted.



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### 10) 17 

### were responsible for more than half of the total environmental re leases 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 environmenta l releases, less than one-fifth of the releases of the facilities ranked 1-10. By the time we move bey ond facilities ranked 30 or more, each ranking group accounts for at most only a few perce ntage points of total U.S.
environmental releases. As Fung & O’Rourke (2000) point out, worst polluter lists help to focu s attention on a manageable subset of facilities that are responsible for the greate st 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 sta tes 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 toxic ity of the hundreds of TRI chemicals. For example, some TRI chemicals are several orders of m agnitude more toxic than others, and there are instances where a facility that ranks h igh on total environmental releases may rank low based on some form of “toxic scoring” (and vic e 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 ran kings reports, and is the default used on the Scorecard website. There are literally doze ns 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 reason able 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 req uired to specifically monitor all of their TRI chemical releases, but at minimum, must pres ent reasonable release estimates. The EPA does not employ a comprehensive system for au diting TRI reports and firms do not face regulatory penalties for inaccurate release e stimates. In addition, the text of the EPCRA (the law which established the TRI) makes exp licit that states are not required to expend significant effort in order to ensure accura te TRI reports. However,

17 In some years, not all states had 10 facilities on the TRI list.
18 Such as Scorecard’s cancer risk score which weights all of the TRI c hemicals according to their benzene equivalent.
19 In addition to total environmental releases, Scorecard allows use rs to rank facilities based on 39 different categories ranging from reproductive toxins to kidney toxins to oz one-depleting substances. As one Scorecard employee noted: “We made it so that every facility would be top ranke d on at least one dimension.”



### 10

### regional EPA offices look for large deviations in reported releases fr om 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 v erify the accuracy of
their data. 20 

### In addition, the EPA keeps a close watch on industry trends regard ing the
environmental releases of different chemicals in order to establish b enchmarks 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 re porting requirements, i.e., not filing the necessary TRI reports. Between 199 0 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 viola tions of the Clean Air Act; for example, in 2001 total penalties levied for TRI violations app roached $4 million while fines levied for violations of the Clean Air Act were more than $84 m illion. Unsurprisingly, one of the major shortcomings of studies that see k to uncover evidence
that the TRI has caused firms to reduce their environmental relea ses 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 in centives to misreport
their releases in order to demonstrate reductions that aren’t act ually 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 with in the TRI data, but the extent to which this is driven by purposefully dishonest reporting ve rsus 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 les s to purposeful cheating on the part of firms, than on the outdated estimation techniques u sed for TRI reporting. It has also been observed that in some years the rate of non-complian ce with the TRI has been quite large, up to 1/3 of all covered facilities. However, Brehm & Ham ilton (1996) show that the majority of non-complying facilities were very small, compris ed 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 asse ssed the accuracy of

20 Personal communication of Jason Scorse with EPA employees in Regio n 9.
21 The facilities who are not complying do not comprise “Top 10” worst po lluter lists.



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### the TRI data in two ways: by cross-checking the TRI data with a sou rce outside the control

of firms, and analyzing the TRI chemical reports for peculiar statis tical patterns. First, they compared reported releases of five TRI chemicals with the res ults of EPA regional emissions monitoring. They found that the releases of two of the fiv e chemicals closely matched the monitoring results, two suggest overestimates of th e 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 wa s reported under the TRI (ethylbenzene, which is also highly toxic). They also make use of B enford’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) t he inaccurate release estimates may be due to the fact that in absolute terms the release s per facility of these chemicals are relatively low, and therefore, significant effort is not in vested to ensure precise release figures, and often guesses are made that may skew the ag gregate 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 mist akes in the reported figures). It is worth emphasizing that if firms that are labeled “Top 1 0” 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 r isks 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 fir ms 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 2 000. If a firm where to simply change its numbers, it could do so for the reporting year 20 00, but any change in the production process would likely only show up in 2001.

22 Benford’s Law states that the first digits of self-reported data s hould follow a monotonically decreasing distribution; i.e. 1s should appear more frequently than 2s, which sh ould 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 respec t to the TRI, Benford’s Law suggests that the first digits of the self-reported pollution figures should follow th e same monotonically declining pattern. For the first digits of the TRI reported emissions data, 1s should ap pear 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 r eleases would be to randomly create and disclose rankings in different states and the n observe the changes in releases between the control and treatment groups, utilizing a d ifference in difference approach. In the absence of such an experiment, the most credib le way to identify the effects of state rankings on releases is within the context of a quas i-natural experiment in which there is an exogenous shock to facility rankings that is differen t across different states, thereby creating a quasi-control and treatment group. Such a s hock occurred in 1998 when Congress changed the TRI rules and required seven additional indu stries to disclose their
emissions data. 23 

### This rule change instantly added approximately 2,000 facilities to the T RI, spread out across all 50 states. These new industries were (and a re) some of the countries largest polluters, and therefore, their addition lowered the rankin gs for the facilities that were already under the jurisdiction of the TRI. This expansion of th e 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 pollute rs, 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 releas ed more toxic chemicals than an existing facility resulted in a lower ranking for the existing fac ility, 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 w as 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 im proved to number 3. However, if the new facility emitted 9,000 pounds (a quantity less t han the number 2 facility) then the existing facility remained ranked number 2 with one m ore facility added

23 These seven new industries are: (i) metal mining (SIC 1021, 1031, 1 041, 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 wa ste 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).
24 Some people have suggested that in fact many of the existing firms lo bbied for the inclusion of these additional firms, and therefore, that the rule change was not com pletely exogenous. If this were true it would only strengthen the case presented here since it would demon strate that these firms cared about their rankings and that they wanted other larger firms to enter the jur isdiction of the TRI to make their releases appear relatively less polluting.
25 Sanders (2011) examines the effect of changes in releases due to t he 1998 expansion on housing prices.



### 13

### below them in the rankings. The number of newly covered facilities tha t 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 t hat 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 wh ere the number of newly covered facilities that ended up in the “Top 10” in each state is linked t o various Census characteristics, one at a time. The two sectors that show a statis tically significant link are manufacturing and health care. To incorporate possible baseline ch anges, we hence include facility-level time trends to account for the fact that facility relea ses have been trending. The effect of a change in ranking should be seen on top of the preexis ting trends. We use the following instrumental variable approach:

y i,t + τ 

### = α i 

### + β i 

### t + γ t 

### + δI ∆( top 10)
, 1998 

### + r i, 1998 

### + ǫ it 

### I ∆( top 10)
, 1998 

### = a i 

### + b i 

### t + c t 

### + dN s, 1998 

### + r i, 1998 

### + μ it



### where y i,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 ∆( top 10)
, 1998 

### that captures whether a facility drops out of the “Top 10” list in 1998, i.e., the variable is one if the facilit y drops out of the “Top 10” in t = 1998, and 0 otherwise. The regression also included facility fixed eff ects

α i 

### , facility-specific time trends β i 

### to adjust for overall release patterns, year fixed effects γ t 

### , and the initial facility rank r i, 1998 

### in 1998 without counting newly regulated facilities. It is important to adjust for the initial rank as facility with a lower rank (h igher releases) are less likely to be dropped from the “Top 10” list. Since changes in the “Top 1 0” list I ∆( top 10)
, 1998 

### are endogenous, we instrument with the number of new entrants in the top 10 list from newly regulated sectors N s, 1998 

### . The variable N s, 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 le vel. 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 Env ironmental 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 mod el 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 mad e public in late spring

of 2000, it would have been difficult to implement changes to the produ ction process in the same year. On the other hand, simply adjusting the reported n umbers 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 t o 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) rep licate the analysis but also include facilities from US territories (American Samoa, District of Colu mbia, Puerto Rico, and the Virgin Islands). In each of the two sets of regressions, co lumns (a) accounts for facility fixed effects, columns (b) additionally controls for facility-sp ecific time trends, and columns (c) also account for year fixed effects. Errors are cluste red at the state level, since the variation (number of newly regulated facilities in the “Top 10”) va ries at the state level. Column (1a) give very imprecise estimate that are not significantly diff erent from zero, but also include a very wide range of possible estimates. There is trem endous variation in emissions over time, both on average (picked up by the facility fixed e ffect), but also across time. Some facilities are expanding, while others are contracting. Co lumn (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 sign ificant: Firms that were dropped from the “Top 10” list after the expansion of the pro gram on average have a staggering 50% higher emissions, although this effect is relative to th e 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 -0.3 7. 26 

### The sum of the average time trend and average 1998 rank fixed effect is -0.53, whic h 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 t hat remain on the “Top

26 Note that the fixed effects for firms ranked 9 and 10 is largest in mag nitude, 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 e stimate from column (1b). Finally, column (1c) also includes year fixed effects, which has lit tle effect on the results. Columns (2a)-(2c) replicate the analysis by including facilities in US ter ritories. 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 n umber of new facilities that enter in the “Top 10” is highly significant in predicting the probab ility of a facility dropping out of the top 10. All regressions adjust for the initial ra nk of a facility without counting newly regulated firms. This is crucial as a facility ranked 10t h is more likely to drop out of the top 10 than one that is ranked first, and relative re ductions might differ by
initial releases. Given that we include facility fixed effects as well as facility-specific tim e trends, we need to include at least three years of releases for each facility. Our bas eline 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 p aired 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 per iod. Columns (a) use facility fixed effects as well as facility-level time trends that wer e shown to be crucial in Table 2. Columns (b) add year fixed effects. The results are similar if w e start the analysis in 1993 (columns 1a-1b), 1994 (columns 2a-2b), but drop in magnitu de 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 p ublic. 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 publishe d in late spring of 2000. Firms that are simply gaming the system and misrepresenting their re leases 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 ha ve 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 relea ses are not made public. Columns (a) again use facility fixed effects and facility-specific time tr ends, 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 f or τ = 1 (before new rankings are released), but also suggests to us that they cha nges in releases were not only on the books, as firms had time to change the reported release s 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 describe d in the data section, Scorecard provided us with their ranking of total releases, which o nly included onsite releases. On the other hand, total releases in TRINET also include offs ite releases. Moreover, onsite and offsite releases are different for some facilities, likely refle cting different cleaning algorithms. The table hence presents all four possible combinations : The dependent variable

y i,t + τ 

### is either taken from Scorecard or TRINET and the number of new en trants in the “Top 10” ( N

s, 1998 

### ) is either taken from Scorecard or TRINET. We obtain the largest a nd 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 t o 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 a nalysis for releases into air, surface water, land, and underground injections in column s (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 f urther waste management. Results by medium appear noisier, and only land emissions are significan t 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 f urther waste management, although the error terms are again wide, but the point estimates ar e 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 s ubsequent years, and the instrument we are using in 1998 does not ensure that a facility is still o ff the “Top 10” list in subsequent years. We find no evidence for continued impacts, as p oint estimates for τ = 4

17

### or τ = 5 are insignificant. This makes it again unlikely that our instrument is s imply picking

up some confounder, as these cofounders would have to impact fa cility emissions in 2001, but not 1999, 2000, 2002, or 2003. While not impossible, it would be un likely to be only showing up in the one year (2001) where firms should respond. Finally, TRINET also provides a toxicity-weighted estimate of facility r eleases. Facility rankings are still determined by summing the pounds emitted of all ch emicals, but the dependent variable is changes in toxicity-weighted emissions. One wo rry is that firms respond by cutting emissions, but cut the less harmful ones. Our regressio n 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 d epend on a few facilities that emit highly toxic substances, and it becomes crucial how the tr end of these particular
facilities is modeled.



## 5 Conclusions

The results from this study provide evidence that the TRI databas e did influence facility emissions through a list of “Top 10” polluters as compiled by Scorecar d. Facilities that experienced exogenous drops in their pollution rankings, which resu lted in their removal from “Top 10” polluter lists within their states, subsequently had higher e missions. The results suggest that overall the more than 160 facilities within the industrie s originally covered by the TRI who were removed from “Top 10” worst polluter lists release d a staggering 50% more toxins into the atmosphere than they otherwise would have if t hey were not dropped
from the list. These results are relative to a downward trend. Our results have significant policy implications. Although changes to t he TRI reporting rules led to less emissions reductions among already existing firms, th e finding that firms do respond to pollution rankings should bolster the overall case for Rig ht-to-Know programs. The TRI appears to be providing members of the public and policy make rs with information that they act upon, which in turn, creates pressure on the most p olluting firms to change their behavior. The costs (real or perceived) are significant enou gh that firms respond. Since environmental releases are heavily concentrated amongst the wo rst polluters, influencing their behavior is a very efficient way to reduce overall emissions, esp ecially 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. governme nt have remained

extremely low, at approximately $25 million a year, and the cost to indu stry of providing the information has dropped from approximately $550 million in the firs t year to $300 million a year since (Fung & O’Rourke 2000), the TRI may be a potentially cos t-effective means of better enabling the public to express its environmental preferenc es. 27 

### These results also highlight the potential unintended consequence s of bringing new entrants under the jurisdiction of the TRI. While the inclusion of new ind ustries in 1998 shifted the focus to facilities that polluted significantly more than already ex isting facilities, at the same time, this expansion decreased incentives for the latter grou p of facilities to reduce their emissions. Depending on the relative susceptibility of the new an d 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 e nvironmental 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 impr ovements in their pollution rankings they actually report lower emissions reductions, which is the opposite direction expected from untruthful reporting. This indicates that firms ar e 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 sim ply due to changes in reported emissions, as firms could have reacted and changed the ir emissions a year earlier. Given that the TRI currently covers only a small fraction of the tox ic 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 con sequences that should be
taken into account.

27 This 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. Th e estimated cost of all U.S. environmental regulation is in the hundreds of billions per year.
28 In 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 ha ve 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 relea ses is made public and when firms can respond.



### 23Figure 2: Location and Releases of TRI Facilities in 200104.53e−060.004640.2674.7544.22741.28e+034.86e+031.58e+044.53e+041.17e+052.8e+056.24e+051.31e+062.61e+064.98e+069.13e+061.62e+072.78e+074.64e+077.56e+071.2e+081.88e+082.87e+084.32e+08 Total Releases (Pounds) By Facility in 2001Notes:Figure displays location of TRI facilities in the contiguous United Satesin 2001 (Source: TRINET). Total releases in pounds arecolor-coded using a nonlinear scale to better illustrate the range ofreleases.



### 24

### Figure 3: Total TRI Releases 1988-2010012345678Total 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 w ho had to report releases in the TRI expanded in 1998. Source: TRINET.



### 25

### Figure 4: TRI Releases By Medium (1988-2010)012345Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year Total Onsite Air Releases012345Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year Total Onsite Surface Water Discharge012345Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year Total Onsite Releases to Land012345Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year Total Onsite Underground Injection01234567Total Releases (Billion Pounds)

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year Total Onsite Releases01234567Total 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 re leases in Figure 3 that determine the ranking with a state are the sum of all online releases, which a re further separated into air, water, land, and underground injections in the first two rows) as well as off site releases. The legend is the same as in Figure 3. Source: TRINET.



### 26

### Figure 5: TRI Offsite Transfers (1988-2010)012345Total 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 manag ement that are not included in total releases. The legend is the same as in Figure 3. Source: TRINET.



### 27

### Figure 6: Share of Releases0204060Percent 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.



### 28Figure 7: Number of Newly Covered Facilities in 1998 in the Top 10 of Each State0123456789

Number of Newly Covered Facilities in 1998 in Top 10Notes:Figure displays the number of facilities that were forced to report for the first time after the program expanded in 1998 and were amongthe “Top 10” polluters in a state. The corresponding numbers for the states / territories not displayed are: Alaska (5), DC (1), Hawaii (6),American Samoa (0), Puerto Rico (5), and Virgin Islands (3).



### 29

### Figure 8: Distribution of Facility-Specific Time Trends0.511.52Density

−4 −3 −2 −1 0 1 Facility Time Trends for Years 1995−1998 (Mean: −0.16 / SDev: 0.42)0.511.522.5Density

−4 −3 −2 −1 0 1 Facility Time Trends for Years 1994−1998 (Mean: −0.09 / SDev: 0.35)0123Density

−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. Regress ion results are given in column (1b) of Table A1, and columns (2a) and (1a) of Table A3, respectively. The average o f 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 -1.7 0 (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 regres sion, 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 obser vations (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) 322.20 239.25 239.10 316.68 237.12 236.9 8 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 19 95-1998 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 colu mns use facilities in the United States, while the last three columns also include facilities from US territories. A ppendix Table A1 reports rank and year fixed effects, while Table A2 reports first stage results. Facilit y releases as well as state rankings are taken from Scorecard. Errors are clustered at the state level. S ignificance 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) 266.92 266.75 247.32 247.19 217.49 217.4 9 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 19 96-1998 1996-1998 Time Diff τ 3 3 3 3 3 3
Notes: Table replicates columns (1b) and (1c) of Table 2, but varies how man y years are included prior to the program expansion in 1998. These years are used in the identific ation of the facility-level fixed effects and time trends. Facility releases as well as state rankings are take n 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) 488.87 488.48 248.18 247.96 239.25 239.1 0 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 19 95-1998 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 co lumn (1b) and (1c) in Table 2: They use a time lag of three years, i.e., evaluate releases in 2001. Columns (1) a nd (2) examine releases in 1999 and 2000, respectively. Facility releases as well as state rankings are t aken 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 Sco recard. 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%.



### 35Table 6: Results by MediumAir Water Land Underground Onsite Offsite Waste TransferVariable (1a) (1b) (1c) (1d) (2a) (2b) (3)**Out of Top 10**0.401∗1.328∗∗2.241∗3.633 0.557∗∗∗1.597 -0.006(0.235) (0.651) (1.248) (3.426) (0.211) (1.598) (0.705)F-stat (1st stage) 222.09 14.69 16.54 4.17 239.07 5.03 14.67R-squared 0.7415 0.7283 0.5840 0.5554 0.7387 0.5678 0.4657Observations 1873 1544 1193 186 1881 1482 1726Facilities 472 392 307 48 474 384 439Facility Fixed Effects Yes Yes Yes Yes Yes Yes YesFacility Time Trends Yes Yes Yes Yes Yes Yes YesYear Fixed Effects No No No No No No NoInclude Territories No No No No No No NoYears in Sample 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998Time Diffτ3 3 3 3 3 3 3Release Estimates TRINET TRINET TRINET TRINET TRINET TRINET TRINETFacility State Rankings Scorecard Scorecard Scorecard Scorecard Scorecard Scorecard ScorecardNotes:Table replicates column (2) of Table 5, but separates releases by medium. Total onsite releases (column 2a) are the sum of the first fourcolumns (1a-1d). Total releases in Table 5 are the sum of onsite andoffsite releases (2a)-(2b). The evolution of releases by medium over timeare shown in Figure 4 for the TRINET data. Errors are clustered atthe state level. Significance levels are indicated by∗∗∗1%,∗∗5%,∗10%.



### 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) 239.07 306.57 124.80 248.75 296.58 142.1 5 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 19 94-1998 1994-1998 Time Diff τ 3 4 5 3 4 5 Release Estimates TRINET TRINET TRINET TRINET TRINET TRINE T Facility State Rankings Scorecard Scorecard Scorecard Sco recard Scorecard Scorecard
Notes: Table replicates Table 4, but includes time lags longer than three perio ds to see whether differences persist over time. The first three columns use years 1995-1998, w hile the last three columns use 1994-1998 to estimate facility fixed effects and time trends. Errors are cluste red 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) 239.81 249.33 267.79 263.07 307.38 325.8 4 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 19 94-1998 1993-1998 Time Diff τ 3 3 3 3 3 3 Release Estimates TRINET TRINET TRINET TRINET TRINET TRINE T Facility State Rankings Scorecard Scorecard Scorecard TRI NET 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. Signific ance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.



### 38

## A1 Online Appendix

Figure A1: Total TRI Releases 1988-2001 (Scorecard)012345678Total 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 w ho had to report releases in the TRI expanded in 1998. Source: Scorecard.



### A1

### Figure A2: Share of Releases (Scorecard)0204060Percent 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” polluter s 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I releases in 1999 -1.050 ∗∗∗ -1.051 ∗∗∗ (0.066) (0.065) I releases in 2000 -2.139 ∗∗∗ -2.087 ∗∗∗ (0.115) (0.123) F-stat (1st stage) 322.20 239.25 239.10 316.68 237.12 236.9 8 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 19 95-1998 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 a re 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I releases in 1999 0.183 ∗∗∗ 0.185 ∗∗∗ (0.017) (0.017) I releases 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 19 95-1998 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 Eff ects 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I rank 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) I releases in 1997 -0.285 ∗∗∗ (0.038) I releases in 1998 -0.565 ∗∗∗ -0.452 ∗∗∗ (0.058) (0.043) I releases in 1999 -1.089 ∗∗∗ -1.149 ∗∗∗ (0.084) (0.081) I releases in 2000 -1.648 ∗∗∗ -1.881 ∗∗∗ -1.877 ∗∗∗ (0.105) (0.114) (0.098) F-stat (1st stage) 266.92 266.75 247.32 247.19 217.49 217.4 9 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 19 96-1998 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 fi xed effects in the second stage. Errors are clustered at the state level. Significance levels a re indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.



### A5