The Effect of Competition on Toxic Pollution Releases
Daniel H. Simon and Jeffrey T. Prince
February 2015
Abstract
We examine how competition affects toxic industrial releases, using five years of data from thousands of
facilities across hundreds of industries. Our main result indicates that competition reduces toxic
releases. On average, each percentage-point reduction in the Herfindahl Index (HHI) results in a twopercent reduction in a facility’s toxic releases, and the effect is larger in more concentrated industries. In
addition, we find that competition reduces releases of carcinogenic chemicals, a category of pollutants
that pose a particularly acute public health concern. Our results also shed some light on the
mechanisms through which firms reduce pollution releases. We find that facilities in more competitive
industries engage in more pollution reduction activities. At the same time, we find some evidence that is
consistent with facilities in more competitive industries reducing pollution by reducing output. Taken
together, our results fail to provide support for the hypothesis that competition leads to more socially
undesirable behavior.
Keywords: Competition, Pollution, Toxic Releases Inventory

Daniel H. Simon is at the School of Public and Environmental Affairs at Indiana University and can be reached at
simond@indiana.edu. Jeffrey T. Prince is at the Department of Business Economics and Public Policy in the Kelley
School of Business at Indiana University and can be reached at jeffprin@indiana.edu. We thank…
1. Introduction
Industrial pollution releases are a major societal concern. Every year, US firms release billions of pounds
of toxic pollutants into the air, water, and ground. In developing economies, like China, increasing air
pollution is a major and growing public health issue. Even in the United States, recent research finds a
link between industrial pollution and infant health and mortality (Currie & Schmieder, 2008; Agarwal,
Banternghansa, and Bui, 2010). More generally, research links pollution from all sources with infant
health and mortality (Joyce, Grossman, & Goldman, 1986; Chay and Greenstone, 1999; Currie, Neidell &
Schmieder, 2009), childhood asthma (Neidell, 2004), and most recently, autism (Rzhetsky et al., 2014).
At the same time, as concerns about climate change are increasing, there is a growing interest in
learning about what factors influence the supply of industrial emissions, with the focus on establishing
policies that efficiently reduce emissions. Reflecting these concerns, as this paper was being written,
the EPA proposed new regulations that would require US power plants to reduce CO2 emissions.
This paper explores the influence of competition on firms’ pollution releases. We assume that
environmental regulation, along with reputational concerns and managerial preferences, plays a key
role in creating incentives for firms to engage in some non-zero level of pollution control. In this
context, conventional wisdom drawn from microeconomic theory suggests that competition should
increase socially undesirable behavior like pollution releases. Shleifer (2004) posits that competition
may increase firms’ propensity to engage in unethical behavior (which may or may not be efficient)
because it reduces costs (and competition increases the incentives to reduce costs in order to reduce
prices). He provides examples ranging from child labor to earnings manipulation. This same intuition
applies to pollution control; firms can reduce costs, and therefore reduce prices, by reducing pollution
control activities. When one firm does this, it puts pressure on rivals to follow or else be driven out of
the market. Similarly, Branco and Villas-Boas (2012) argue that competition leads to firms being less
likely to follow the rules of the market because competition reduces the cost of being caught violating
the rules (because profits are lower where competition is higher). Again, extending this argument to the
realm of pollution and compliance with pollution regulation, it suggests that competition should have a
positive effect on firms’ pollution releases as firms have less to lose by exceeding pollution standards.
Little empirical research directly tests the hypothesis that competition increases bad behavior (or
reduces rule following). However, a handful of recent papers testing this hypothesis in very different
contexts all provide evidence that competition increases unethical behavior. Snyder (2010) provides
evidence that liver transplant centers faced with greater competitive pressures may overstate health
problems to gain priority on the liver waiting list. Bennett et al. (2013) provides evidence that increased
competition leads to increased levels of fraud on vehicle releases tests (testers are more likely to pass
customers). Bagnoli and Watts (2003) provide theoretical evidence that firms provide less of a public
good when they face more stringent competition.
This paper extends the competition-bad behavior hypothesis to the realm of pollution, by looking at
how product-market competition affects firms’ polluting behavior. Specifically, we examine whether
firms in more competitive markets emit more toxic pollutants than firms in less competitive markets. To
our knowledge, this is the first paper to empirically examine the impact of competition on industrial
pollution releases.
Although researchers have not examined the impact of competition on industrial releases, a growing
body of research has examined other drivers of industrial releases. Most prominently, many have
argued that states and countries engage in a “race to the bottom,” competing to attract firms by
establishing more lax environmental standards (Konisky, 2007). Researchers have studied this issue both
within the United States (Fredriksson & Millimet, 2002; Konisky, 2007) as well as across nations
(Wheeler, 2001; Prakash & Potoski, 2006).
Several papers have examined the impact of the Clean Air Act (CAA) on a variety of outcomes relating to
firm location decisions and industrial activity. In general, these papers find firms in polluting industries
relocate production and employment from more to less polluted areas (Henderson, 1996; Becker &
Henderson, 2000; Greenstone, 2002). More broadly, numerous papers have examined whether firms
will tend to locate in places with more lax environmental standards, with some studies finding evidence
of a race to the bottom (e.g., List et al., 2003) , while other others do not (Frankel & Rose, 2005).
Underlying the “race to the bottom” perspective is the idea that environmental regulations impose costs
on firms; as a result, industries in locations with more stringent regulations are less competitive. This
view was challenged in a series of papers by Michael Porter and colleagues (Porter, 1991; Porter & van
der Linde, 1995; Esty & Porter, 2005). Known as the Porter hypothesis, Porter and colleagues posit that
rather than increasing firms’ costs, environmental regulations stimulate innovation, such that the gains
from the innovations outweigh the compliance costs, with the result that firms facing more stringent
regulations are expected to be more efficient than those facing less stringent regulations. The Porter
hypothesis has attracted a great deal of attention from environmental economists. The results are far
from clear, with some studies finding support for at least some aspects of the Porter hypothesis
empirically (Jaffe & Palmer, 1997; Greaker, 2006) or theoretically (Xepapadeas & de Zeeuw, 1999; Mohr,
2002), while others fail to find support for the hypothesis (Palmer, Oates, & Portney, 1995).
Another major area of research asks whether there is an “Environmental Kuznets Curve” (Dasgupta et
al., 2002). That is, this research examines whether the relationship between economic growth and
pollution follows an inverted U-shaped relationship, where pollution initially increases with economic
growth, and beyond some level of economic development, further growth is associated with declining
emissions as richer societies are willing to allocate more resources to clean the environment. Again, the
results are not conclusive, but the preponderance of evidence, and in particular the more recent and
rigorous evidence, provides support for the existence of an Environmental Kuznets curve (Grossman &
Krueger, 1995; Dasgupta et al., 2002; Millimet et al., 2003).
Several papers have also examined the relationship between firm size and pollution abatement, largely
in an effort to determine whether environmental regulations have a disproportionate impact on smaller
firms. Again, the results are inconclusive. Earlier papers used industry data, and generally find
economies of scale in compliance with regulations. Pashigian (1984) finds that compliance with
environmental regulations caused an increase in plant size and a reduction in the number of plants per
industry, suggesting that regulations do impose a greater burden on smaller firms. Dean, Brown, and
Stango (2000) find that industries with high pollution abatement costs had fewer small business
formations (less than 100 employees), while Millimet (2003) finds that the optimal plant size is larger in
high pollution abatement intensive industries (at the two-digit SIC level) in states with more stringent
environmental regulation. An exception is Evans (1986) who finds diseconomies of scale in abatement
in more than half of the 403 industries that he examined. More recently, two papers by Becker and
colleagues use establishment-level data. Becker (2005) finds that air pollution abatement expenditures
under the Clean Air Act were at times disproportionately higher for larger establishments, and
sometimes the reverse, depending on the criteria air pollutant. Looking at all kinds of pollution
abatement, Becker, Pasurka, and Shadbegian (2013) find diseconomies of scale in pollution abatement.
In the paper most similar to this one, Farber and Martin (1986) examine the impact of market
concentration on firms’ pollution control efforts. Using some rough proxies to control for the pollution
levels in an industry, they find that firms in more concentrated industries spend more on both air and
water pollution control efforts than firms in less concentrated industries. Similarly, firms in more
concentrated industries also invest more in pollution abatement equipment than firms in less
concentrated industries. While Farber and Martin find that firms in more concentrated industries devote
more resources to pollution control efforts, they do not report on firms’ polluting behavior. That is the
question that we focus on here.
It is important to note that although the distinction between the Farber and Martin (1986) paper,
focusing on pollution abatement, and this paper looking at pollution releases may sound like two sides
of the same coin, there is an important substantive difference between the two. As noted above, Farber
and Martin (1986) investigate industry pollution control efforts while (crudely) controlling for industry
pollution levels. In other words, they focus on the intensity of pollution reduction efforts, relative to
pollution level, but not on the absolute level of pollution reduction. In contrast, in this paper we focus
on pollution levels directly. In this way, our questions are complementary rather than redundant.
In addition, it is important to note that the relationship between pollution reduction and pollution
releases is uncertain. Intuitively, the two might appear to be substitutes; the more we abate, the less we
pollute, and vice versa. However, this is not necessarily the case. The two might be complements; as
firms pollute more, they also engage in more pollution control. Alternatively, they may be orthogonal to
each other; firms may alter their pollution levels by altering their level of output. Our data will allow us
to examine these different possibilities directly.
To investigate the impact of competition on polluting behavior, we merge data on facilities’ toxic
pollution releases from the Toxic Releases Inventory (TRI) with industry concentration data from the
Census of Manufacturers. Using these data, we estimate panel-data models of toxic releases as a
function of industry concentration. In our baseline analysis, we control for industry demand, and we
include fixed effects to control for unobserved industry and year variation. Additional analyses also
include facility, state-year, and county-year fixed effects as well as some additional controls.
Our primary result indicates that competition reduces industrial pollution releases. As industry
concentration falls, facility releases fall, with each percentage point reduction in the HHI causing a
roughly two percent reduction in toxic releases. This result fails to provide any support for the
hypothesis that competition causes firms to engage in more socially undesirable behavior. Importantly,
this result holds after controlling for industry demand, variation in local regulatory conditions, and new
entry. Additional analysis indicates that the negative effect of competition on releases increases with
the level of concentration in the market, and that competition also causes firms to reduce their releases
of carcinogenic chemicals, a category of pollutants that pose a particularly acute public health concern.
Our results also shed some light on the mechanisms through which firms reduce pollution releases. We
find some tentative evidence that facilities in more competitive industries engage in more pollution
reduction activities. At the same time, we find some evidence that is consistent with facilities in more
competitive industries reducing pollution by reducing output. However, we find no evidence that
consumer environmental preferences create greater pressures for pollution reduction in more
competitive industries.
2. Regulatory Background
An important question is why firms engage in any pollution control. Environmental regulation, along
with reputational concerns and managerial preferences, plays a key role in creating incentives for firms
to engage in some non-zero level of pollution control. As described below, the two most important
pieces of environmental legislation regulating emissions during the past 50 years, the CAA amendments
and the Clean Water Act (CWA) amendments, both provide incentives for firms to reduce emissions
through a combination of technological requirements, permits, and limits on facility emissions.
The CAA was originally passed in 1963, but it was substantially amended in 1970, 1977, and 1990. The
1970 Amendment to the CAA authorized the development of comprehensive federal and state
regulations to limit emissions from both stationary (industrial) sources and mobile sources. The
legislation established national ambient air quality standards (NAAQS) for six air pollutants: carbon
monoxide (CO), ozone, sulfur dioxide (SO2), particular matter, nitrogen dioxide (NO2), and lead. These
standards were to be annually assessed at the county level. Every U.S. county with concentrations of the
relevant pollutant that exceed the national standard is designated a nonattainment county. The
legislation also imposed more stringent requirements on facilities that emitted more pollution
(Greenstone, 2002; Becker et al., 2013) and new facilities, particularly in non-attainment counties. Plants
whose emissions of the regulated pollutant(s) exceed the federal standard in nonattainment counties
are subject to more stringent regulatory oversight than emitters in attainment counties. Similarly, the
legislation also includes a requirement that new sources install controls at least as effective as the best
used by an existing pollution source of the same kind. The level is established in each individual permit
and is called the “lowest achievable emission rate” (LAER). For attainment counties, the 1977
Amendments also included provisions for the Prevention of Significant Deterioration (PSD) of air quality
via a permitting process that established emissions limits for new facilities based on the best available
technology. The 1990 Amendments to the Act greatly expanded the National Emission Standards for
Hazardous Air Pollutants (NESHAPs), identifying 188 air pollutants that are considered hazardous to
human health or the environment. This program requires all existing and new facilities to meet
emissions standards based on the “maximum achievable control technology” (MACT).
The Federal Water Pollution Control Act was originally passed in 1948, but it was amended in 1972 (and
became known as the Clean Water Act), substantially expanding its impact. The CWA set wastewater
standards and water quality standards for all contaminants in surface waters. Congress declared as "the
national goal that the discharge of pollutants into the navigable waters be eliminated by 1985.'' To
achieve this goal, the EPA had to implement successively stricter, technology-based, end-of-the-pipe
effluent limitations. The environmental benefits of the effluent limitations were not to be considered in
setting the standards; only feasibility and costs of control were to be considered in setting these
limitations. Like with the CAA, the CWA uses a permitting process, the National Pollutant Discharge
Elimination System, and effluent limitations based on best–available technology for new facilities to
achieve the zero-discharge goal
Together, these regulations create varying incentives for pollution abatement, depending on the firm’s
location, the types of toxic chemicals released, the form in which they are released, and other firm and
industry characteristics, including competition. Moreover, as we discuss below, competition may also
influence the stringency with which regulations are enforced. In the next section we explore the
mechanisms through which competition may affect releases.
3. Mechanisms Linking Competition and Industrial Pollution Releases
There are two main sets of factors that might cause competition to influence firms’ releases. First, for a
given regulatory regime, competition may influence firms’ incentives to pollute. Second, the regulatory
regime may vary with the level of competition. Each of these mechanisms may result in either a positive
or a negative relationship between competition and pollution. We consider both of these possibilities
below. Initially, we examine how competition may influence firms’ incentives to pollute, and then we
examine how regulatory stringency may vary with competition.
3.1 Competition and incentives to pollute
Holding the regulatory regime fixed, there are several reasons why one might expect that firms in
competitive industries would pollute more than firms in more concentrated industries. The three
primary ones are those cited by Farber and Martin (1986), which increase the propensity of firms with
market power to spend more on pollution control. First, firms in more concentrated industries generally
have higher margins, which provide more funds for pollution abatement efforts. As competition,
increases and margins shrink, firms may look to cut costs by reducing pollution reduction efforts.
Second, their market power enables firms in more concentrated industries to pass higher costs of
pollution abatement on to consumers. Third, firms in more concentrated industries may have greater
incentives to invest in pollution abatement, because reducing pollution via output reduction is more
costly in concentrated industries (because of the higher margins on each unit sold).
At the same time, there are reasons why firms in more competitive industries may pollute less. Namely,
there may be more technological innovation and adoption of new abatement technology in competitive
industries. New entrants may be able to abate pollution more efficiently because they are able to
choose their production and abatement technologies simultaneously (Dean, Brown & Stango, 2000),
which can often lead to more efficient abatement than applying new abatement technologies to existing
production assets (Dean, Brown & Stango, 2000). Incumbents are less likely to choose the two
simultaneously because of differences in the life spans of the different types of technologies as well as
changing pollution abatement requirements (Kemp & Soete, 1992). The added cost of replacing
equipment before the end of its useful life creates a disincentive for incumbents to adopt both types of
technologies simultaneously (Kemp & Soete, 1992). However, pressure from more efficient new
entrants increases the incentives for incumbents to adopt the new technology in order to remain
competitive, despite the disincentives. As a result, in more competitive industries, which have more new
entry, incumbent firms, as well as new entrants, may be more likely to adopt new pollution reduction
technologies.
A second reason why firms in more competitive industries may pollute less is because the cost of
reducing output is smaller (because profit margins tend to be smaller in more competitive markets).
Therefore, firms in competitive markets are more likely to reduce output as a way to reduce releases.
Even in the absence of any environmental regulation, competition may cause firms to reduce pollution
via reductions in output. In general, as competition in an industry increases, we would expect each firm
to reduce output, causing pollution releases to fall as well.
A third reason why firms in more competitive industries may pollute less is because consumers value
pollution reduction; firms facing greater competition will face stronger incentives to respond to
consumer preferences by reducing pollution than firms facing weaker competition. In recent years, with
greater public concern about environmental issues, and about the contribution of firm pollution releases
to global warming, as well as with increasing information about firm pollution releases, it seems
plausible that the link between pollution reduction and profitability may have strengthened.
3.2 Competition and Regulatory Stringency
While competition may directly influence firms’ incentives to pollute, competition may also indirectly
affect firms’ propensity to pollute through its influence on the regulatory regime. Differences in
regulatory stringency resulting from variation in market structure may reduce the propensity to pollute
of firms in more concentrated markets. In particular, environmental concerns, combined with concerns
about efficient use of regulatory resources, may cause regulators to focus more of their efforts, and
impose more stringent regulations on larger firms (Finto, 1990; Dean & Brown, 1995; Pashigian, 1984;
Brock & Evans, 1985) (all else equal, as concentration increases, firm size should increase as well). In
addition, concerns about employment effects may cause regulators to impose more stringent
regulations on larger, more profitable firms, who are more likely to be able withstand the added
regulatory costs.
Just as there are reasons to believe that regulatory standards and enforcement will be more stringent in
concentrated industries, there are also factors that could make regulatory conditions in competitive
industries more stringent. First, firms in more concentrated industries have deeper pockets and greater
political influence to support lobbying efforts to ease regulations (Farber & Martin, 1986). Second,
because of concerns about employment effects, regulations may be enforced more stringently on
smaller firms, than on larger firms (Farber & Martin, 1986). Thus, competition may increase or reduce
releases, through both direct and indirect mechanisms.
As the above discussion indicates, theoretical arguments are ambiguous; some suggest that competition
will increase firms’ pollution releases, while others suggest that competition will reduce firms’ pollution
releases. Despite this ambiguity, the conventional wisdom, embodied in the competition-bad behavior
hypothesis, suggests that firms will engage in a race to the bottom, reducing investments in pollution
reduction and increasing pollution releases in the face of increased competitive pressures.
4. Data
Initiated in 1987, the Toxics Release Inventory (TRI) is one of the two primary datasets used in this
paper. The TRI is a plant-level dataset that includes identifying information about the facility (e.g., name,
county of location, primary industry), and releases of toxic chemicals to the air and water, as well as
transfers to any kind of land disposal. According to the EPA, a chemical is considered toxic if it causes
one or more of the following:



Cancer or other chronic human health effects;
Significant adverse acute human health effects;
Significant adverse environmental effects.
In 1987, the TRI included 275 toxic chemicals. By 2007, this number had nearly doubled, to 500
chemicals, with some of the original chemicals dropped from the reporting requirements. For purposes
of consistency, we restrict our sample to the 234 chemicals that appeared in the 1987 dataset and have
been reported in every year since.
Facilities are required to report their toxic releases to the EPA if they meet three criteria: (1) They had
10 or more full-time employees (or the equivalent); (2) They were in a covered industry (all
manufacturing industries, mining, electricity generation, hazardous waste facilities, along with some
publishing and wholesale trade industries); and (3) They “manufactured" or "processed" more than
25,000 pounds or "otherwise used" more than 10,000 pounds of any listed toxic chemical during a
calendar year.1 Plants that meet the first two criteria must report releases for each toxic chemical that
exceeds the threshold in (3).
Our unit of analysis is the facility-year. We are interested in understanding how average facility releases
vary with industry concentration. Therefore, although each facility reports its releases for each toxic
chemical separately, our general approach is to aggregate all of the facility’s releases reported in the
current year. We also consider some subsets of chemicals, and we similarly aggregate these chemicals
on an annual basis for each facility.
1
Persistent, Bioaccumulative, Toxic chemicals (PBTs) have lower reporting thresholds.
Because each facility must indicate the primary industry in which they operate, we are able to merge
these releases data with national industry concentration ratios from the Census of Manufacturers. The
Census of Manufacturers is only conducted every five years, which limits our sample to five years: 1987,
1992, 1997, 2002, and 2007 (the 2012 Census of Manufacturers has not yet been released). From the
Census of Manufacturers, we take three measures of market structure: HHI is the Herfindahl-Hirschman
index for the 50 largest firms in the industry (measured on a 1-100 scale); CR4 is the four-firm
concentration ratio (measured on a 1-100 scale), and CR8 is the eight-firm concentration ratio
(measured on a 1-100 scale).2,3
After excluding observations for facilities with less than 20 pounds of Releases (and those with missing
values for Releases), 4 our dataset comprises more than 78,000 facility-year observations, including more
than 34,000 facilities, spread across 504 (466) four-digit (six-digit) SIC (NAICS) industry codes, spanning
the five years listed above. We report summary statistics for our key variables in Table 1.5 One concern
is that the median HHI value is only 3.76. This suggests that the median facility is in a very competitive
industry. However, the median values for the two concentration ratios are 31 and 44, respectively,
suggesting a more moderate level of concentration. We address this issue further in our empirical
analysis.
Figure 1 shows median annual facility release levels from 1987-2007. As can be seen, releases have
fallen greatly since 1987. In 1987, the first year that TRI was reported, the median facility released
19300 pounds of toxic chemicals. Median releases fell dramatically from 1987-1994, rose slightly in
1995, and then continued to fall, mostly gradually, over the period 1995-2007 (with a sharp drop from
1999-2001). By 2007, median facility releases had fallen to below 3300 pounds, a roughly 83 percent
reduction.
5. Empirical Analysis
Using these data, we examine how competition affects a facility’s releases. To do so, we estimate the
following basic model:
LnReleasesijt = B1 Concentrationjt + B2lnValue of Shipmentsjt + uj + vt + eijt,
where lnReleases is the logged total releases of toxic chemicals by facility i in industry j during year t.
Concentrationjt is one of our three measures of market structure described above. LnValue of Shipmentsjt
is a (logged) measure of sales for industry j during year t; it roughly controls for the industry’s demand
2
We believe that measuring competition at the national level is appropriate because the vast majority of facilities
in the TRI (more than 99% of our sample) are in manufacturing industries, where generally firms compete on a
national or international scale.
3
The Census of Manufacturers also reports the twenty-firm concentration ratio (CR20). We obtain very similar
results using this measure of market structure instead of the other three described in the text.
4
We exclude observations for facilities emitting fewer than twenty pounds of emissions because at such low levels
of emissions (relative to the median of 11000 pounds), a 100-percent increase in emissions is very small in
absolute terms, and much more likely to be driven by random factors.
5
We report median emissions rather than means because facility emissions are highly skewed, with several
facilities emitting millions of pounds of toxins annually.
conditions. The ui are industry fixed effects that control for differences across industries (e.g.,
differences in technology and chemicals used in the production process),6 and the vt are year fixed
effects which control for any economy-wide changes affecting releases (e.g., overall economic
conditions). We cluster our standard errors by industry to account for correlation in errors, over time
and across facilities, within the same industry.
When interpreting the estimated coefficients in this model, it is important to recognize three points.
First, because our dependent variable (Releases) is logged, our coefficients (approximately) indicate the
percentage change in a facility’s releases associated with a change in the independent variable.7 For our
variable of interest, Concentration, the coefficient (B1) indicates the percentage change in facility
releases associated with a one percentage-point change in concentration. Second, our measures of
market structure are measure of concentration, the inverse of competition. Therefore, if the coefficient
on Concentration (B1) is positive (negative), this would indicate that competition is associated with a
reduction (increase) in industry releases. Third, the presence of the industry and year fixed effects imply
that we are estimating a generalized difference-in-differences model. In this model, B1 indicates the
average change in releases for facilities in industry j relative to the average change in releases for
facilities in industries other than j during the same year. If B1 is positive (negative), then this would
indicate that when concentration increases, facilities in industry j increase (reduce) releases more than
the average facility in other industries.
Despite the fixed effects that we include in our model, one might be concerned that other time-varying
factors, correlated with industry competition, might be biasing our results. Ideally, we would instrument
for concentration to address these issues. However, we are unable to find a suitable instrument.8
Nonetheless, after reporting our baseline results, we address these concerns below with a variety of
additional tests.
6. Results
The first three columns of Table 2 reports the results of our baseline analysis for the model described
above. In column 1, we include HHI as our measure of market structure, while in columns 2 and 3 we
include CR4 and CR8, respectively. Across this set of baseline models, the results indicate that industry
concentration is associated with an increase in facility releases. Specifically, our results indicate that
each percentage-point increase in the HHI is associated with a 2 percent increase in a facility’s releases,
6
Because concentration varies only at the industry level, not at the plant level, facility-industry fixed effects do not
provide any additional control for unobservables that might be correlated with market structure. Nonetheless, we
also consider facility-industry fixed effects in the empirical analysis; our results are qualitatively the same.
7
More precisely, the percentage change in the dependent variable associated with a coefficient b equals
100*(exp(bx)-1), for a given regressor, x. For small values of (bx), this term will provide a close approximation of
the percentage change in the dependent variable. In all of our analyses, the values of bx we consider are all less
than 0.1, and we only discuss unit changes in our regressors, meaning that our coefficient estimates provide very
good approximations of the percentage change in our outcome variables.
8
The best candidate was to use the five-year lagged concentration measure, which has been used in price
regressions (Evans, Froeb, & Werden, 1993), where the simultaneity of price and concentration is the concern.
However, in our model, the primary concern is that unobserved regulation may be correlated with market
structure. In this case, the lagged IV does not provide a good alternative.
while each percentage-point increase in the four (eight)-firm concentration ratio is associated with a 0.8
(0.7) percent increase in a facility’s releases. Because the results are qualitatively similar across the
different measures of market structure, we primarily report results for HHI only from this point onward.
Although all three measures of concentration are measured on a 1-100 scale, making the coefficients on
the market structure variables comparable across the first three regressions, the mean values of these
three measures vary, making the interpretation of the relative effects more difficult. In order to better
compare these magnitudes, in columns 4-6 we rerun our baseline models using logged measures of
concentration. Here, the results show that the effect of a one-percent increase in the CR8 has about
twice as great an effect as a one-percent increase in the HHI.
In Table 3 we report some basic robustness checks. In column 1, we include facility-industry fixed
effects in place of industry fixed effects. In column 2, we regress average facility releases on the same
set of right-hand-side variables, weighting each industry-year observation by the number of facilities in
that cell. In column 3, we aggregate our data to the industry level and again regress average facility
releases on the same of set right-hand-side variables. When we aggregate our data to the industry level,
the coefficient on HHI is smaller (0.007) and is no longer statistically significant.9 However, because we
are interested in the impact of competition on the average facility, not the average industry, we favor
the facility-level analyses. Moreover, the industry-level analysis may be distorted by the many industryyears that include only one facility. When we exclude these from our data, in column 4, the coefficient
on HHI is larger (0.013) and statistically significant.
Taken together, the results in Tables 2 and 3 provide solid evidence that competition is associated with
lower facility releases. However, one might question whether competition can be the causal mechanism
given the relatively low median concentration level (HHI=3.8; CR4=31; CR8=44)). At such seemingly low
levels of concentration, it seems unlikely that variation in concentration would influence firm behavior
very much. To address this concern, in Table 4 we rerun our baseline models, restricting our sample to
only those observations with above-the-median levels of concentration. Our results become stronger in
all three cases, providing some support that competition provides the causal mechanism for our
baseline results.10
6.1 Omitted Variables and Alternative Explanations
While competition may influence firms’ releases either directly or indirectly, through its effect on the
regulatory regime, it is also possible that regulatory conditions are correlated with competition, but are
not a function of competition; the two may be correlated as a result of some third factor, or it may be
that regulatory conditions drive competition (rather than the opposite). If regulation is correlated with,
but not a function of, competition, then regulatory conditions could create a spurious correlation
between competition and releases.
9
One might assume that the drop in statistical significance is the result of larger standard errors when we
aggregate our data up to the industry level. However, this is not the case. Because we cluster our data at the
industry level, our standard errors are actually slightly smaller when we aggregate up.
10
We also considered using only those observations with concentration values in the top quartile. Doing so yields
even larger coefficients on our concentration measures.
In light of our results indicating that releases fall with increases in competition, the primary concern
would be that regulatory enforcement becomes more stringent as industries become more competitive,
creating a spurious negative correlation between competition and releases. This seems like an unlikely
scenario, however, as empirical research provides evidence that compliance with environmental
regulations increases sunk costs (Ryan, 2012) and reduces the number of competitors in an industry
(Pashigian, 1984), and entry (Dean & Brown, 1995), particularly by small firms (Dean, Brown & Stango,
2000). However, some more recent research finds that there are diseconomies of scale in pollution
abatement (Becker, Pasurka, & Shadbegian, 2013), which suggests that environmental regulations could
reduce concentration (while reducing releases) in the long run.
Nonetheless, to try and assess this issue empirically, we exploit variation in regulatory stringency
provided by the CAA. As noted above, the 1970 Amendment to the CAA, the Environmental Protection
Agency (EPA) established national ambient air quality standards for four criteria pollutants: carbon
monoxide (CO), tropospheric ozone (O3), sulfur dioxide (SO2), and total suspended particulates (TSPs).
These standards are assessed annually at the county level. Counties with concentrations of the relevant
pollutant that exceed the national standard are designated nonattainment counties. Emitters of the
regulated pollutant(s) that exceeded the federal standard in nonattainment counties are subject to
more stringent regulatory oversight than emitters in attainment counties.
Using Greenstone’s (2002) categorization of emitting and non-emitting industries, along with EPA data
on county annual attainment status, we can assess the impact of HHI on emissions while holding
constant whether the facility faces more stringent regulatory oversight under the CAA. If regulatory
stringency is driving our result, then we should observe a smaller coefficient on HHI when we control for
CAA-mandated regulatory stringency.
We report these results in Table 5. In the first column, we include a dummy that indicates facilities in
industries whose emissions exceed the Federal standard and who are located in counties that were not
in attainment for at least one of the CAA pollution categories. The results show that the effect of HHI is
unchanged by the inclusion of the dummy for facilities in emitting industries in nonattainment counties.
To further assess whether regulatory stringency provides an alternative explanation for our results, we
exploit the fact that CAA regulations apply to only a subset of all TRI chemicals, and only to releases of
those chemicals into the air. In column 2, we run our baseline model, but only for releases into the air of
chemicals regulated by the CAA. The results reveal that HHI has a positive effect on emissions that
nearly achieves conventional levels of statistical significance (p=0.12). Next, to control for the variation
in regulatory stringency, column 3 again includes the dummy indicating facilities facing more stringent
regulation under the CAA. As with all releases, the results show that the effect of HHI is unchanged
when we control for CAA-mandated regulatory stringency. However, one might be concerned that
emissions into the air of CAA chemicals is positively correlated with emissions of non-CAA regulated
chemicals. If HHI increases releases of the non-CAA regulated chemicals, then this would induce a
positive bias in our estimated HHI coefficient. To address this concern, we include the facility’s releases
of non-CAA regulated chemicals into all media (air, water, and ground) in our regression. The results,
which we report in the last column of Table 5, indicate that the effect of HHI is positive and statistically
significant when we control for releases of other non-CAA regulated chemicals. Taken together, the
results in Table 5 provide evidence that the more stringent regulation for emitting facilities in
nonattainment counties, under the CAA, does not provide an explanation for the positive correlation
between HHI and toxic releases that we observe.
Although the above results control for variation in local CAA regulatory stringency, states and counties
may impose other regulations that affect releases. Moreover, there may be variation in the stringency
with which regulations are enforced. Although we would not expect local conditions to be
systematically correlated with national market structure, it is possible that certain industries are
clustered in particular states or counties, allowing room for local conditions to matter. To control for
these unobserved sources of variation in local regulatory stringency, as well as variation in other local
conditions, in Table 6 we report the results from two additional models in which we include state-year
and county-year fixed effects, respectively. The results in both models reveal that the negative effect of
competition on releases is robust to controls for unobserved differences in regulatory stringency and
other local conditions. Interestingly, as we add first state-year, and then county-year, fixed effects, the
negative effect of competition is reduced, suggesting that as competition increases, unobservable local
regulatory conditions become more stringent; when we control for these local conditions, the negative
effect of competition is weakened, but remains economically and statistically significant.
A second concern one might raise with our baseline results is that new entrants enter with new
technology that both reduces releases and increases competition. This may be due to the greater
incentives for innovation and adoption of new technology by entrants, or it may be due to the more
stringent regulations faced by new facilities under both the Clean Air and the Clean Water Amendments.
Again, new technology provides a potential mechanism to explain our results. However, one might also
consider new entrants entering with better technology, and more stringent regulations for new
facilities, to be omitted variables that create a spurious correlation between competition and facility
releases. In particular, both the new technology the entrants bring, as well as the more stringent
regulations they face, may have a direct effect on releases, in addition to any effect of new entry on the
behavior of incumbents. In this case, we would like to purge the direct effect of the new entrants’
technology and the more stringent regulations they face, so that we can isolate the impact of changes in
competition on facilities’ releases.
To address this concern, we rerun our baseline analysis, excluding new entrants in their first year that
they appear in the sample. We define a facility as a new entrant if it enters our sample after 1987
(because 1987 is the first year in which the TRI data were collected).11 For example, a facility that does
not appear in our data in 1987, but does appear in 1992, would be treated as a new entrant in 1992. By
excluding new entrants from our sample, we are able to isolate the effect of changes in competition on
11
The TRI data do not include the date that the facility was established. Therefore, we cannot distinguish new
entrants from facilities that are only new to TRI. Therefore, we also tried excluding only those facilities that appear
in the data for the first time after 1987, if other facilities in the same industry appear in the data in prior years.
Facilities in industries that are new to TRI are less likely to be new facilities. Instead, it is likely that TRI reporting
requirements were changed, requiring facilities in the industry to begin reporting to TRI. Doing so does not change
our results.
incumbent firms. The results are very similar to our baseline results, again indicating that a one
percentage point increase in HHI leads to a roughly two percent increase in releases. This approach
ignores any facilities that were new in 1987. To address this, in column 2 we treat all facilities as if they
were entrants in 1987, excluding all observations from 1987. Although weaker, the results still reveal a
positive relationship between HHI and toxic releases. The results in Table 7 indicate that it is not new
entrants armed with better technology, nor is it more stringent regulations imposed on new facilities,
which is driving our results.12
An additional concern is that if competition influences firms’ releases, then regulators would respond
with more stringent regulation to counterbalance this effect. For example, if competition increases
firms’ incentives to pollute, then regulators might monitor firms more closely and enforce regulations
more stringently in more competitive industries. It is worth noting, however, that in order for this
scenario to explain our results, it would have to be the case that the effect of competition on releases is
actually positive, but that it is more than offset by the (spurious) negative correlation between releases
and regulatory stringency. This seems unlikely, and we are not aware of any evidence, either theoretical
or empirical to support such a view of regulatory stringency responding to market structure in this way.
Finally, it is possible that industry concentration may be correlated with demand conditions;
competition and firm output tend to rise as demand increases, creating a spurious positive correlation
between competition and releases (through the increase in output). We do not believe that this is a
major concern for two reasons. First, we control for industry demand with Value of Shipments.
Nonetheless, even if this variable does not fully capture industry demand, industry concentration and
demand should be negatively correlated, which would negatively bias our estimate of the coefficient on
concentration, suggesting that we are actually underestimating the positive effect of concentration on
releases (i.e., we are underestimating the negative effect of competition on releases).
Taken together, the above results provide strong evidence that releases fall with competition. However,
they only provide limited insight into the mechanism underlying this relationship. In the next section we
explore some possible mechanisms to explain the observed negative relationship between competition
and releases.
6.2 Mechanisms
The most intuitive explanation for the negative relationship between competition and plant-level
releases is that facilities in more competitive industries engage in more pollution reduction activities
(other than output reduction). This may include recycling toxic chemicals, treating waste water and
other releases, trapping releases and recovering the energy, and other similar kinds of pollution
reduction efforts.
To consider this possibility, we aggregate the total pounds of toxic chemicals that each facility treated,
recycled, and recovered. We use this as our measure of pollution reduction in Table 8. We first rerun our
12
This approach also addresses the concern that incumbent firms may be grandfathered in when new, more
stringent regulations are introduced.
baseline model, using pollution reduction as our left-hand-side variable. The results in column 1 indicate
that competition has no effect on pollution reduction activities; the coefficient on HHI is small, negative,
and statistically insignificant. However, from our results above, we know that releases and HHI are
positively correlated. Therefore, if pollution reduction activities and releases are complements (e.g.,
when firms pollute more they also do more pollution reduction), then our estimated HHI coefficient
would be biased upwards by the correlation between HHI and releases. Alternatively, pollution
reduction and releases may be substitutes; as facilities increase pollution reduction activities they may
reduce releases. If so, then our HHI coefficient estimate would be biased downward. To address this, in
column 2, we include the facility’s total releases as a control variable. When we do so, the HHI
coefficient becomes more negative, approaching conventional levels of statistical significance (p<.12).
These results provide tentative evidence that competition increases facilities’ pollution reduction
activities, and suggest that pollution reduction may provide one mechanism through which competition
reduces industrial pollution.
Because the results in column 2 only provide tentative support for the positive effect of competition on
pollution reduction, we further explore this result by considering our other measures of concentration.
When we do so, we find that both CR4 and CR8 have a negative and statistically significant (p<.03) effect
on pollution reduction.13 Taken together, these results provide robust evidence that competition
increases pollution reduction, and that pollution reduction provides a mechanism to explain the
negative effect of competition on releases.
Another possible explanation for the observed negative relationship between competition and releases
is that firms reduce output as competition increases, because marginal profit is lower in more
competitive industries; as output falls, plant releases fall. However, as the number of plants in an
industry increases, total industry releases should increase, even if plant-level behavior does not change
at all. Indeed, even if average releases fall with competition, due to facilities reducing output, total
releases could rise with an increase in the number of emitting facilities. Therefore, the expected effect
of competition on total industry releases is ambiguous. However, if we find a negative relationship
between competition and total industry releases, this would indicate that there is some additional
mechanism, beyond output reduction driving our results, because in general, competition should
increase, not reduce total output.
To examine this issue, in Table 9, we regress total industry releases on the same set of right-hand-side
variables. Here, our results show a positive, but statistically insignificant effect of concentration on total
industry releases. Moreover, the coefficient on HHI is substantially smaller than it was in Table 3, where
we looked at average industry releases. Therefore, we cannot exclude plants reducing output in
response to greater competition as a mechanism underlying our results. To further explore this issue, we
include a control for the (logged) number of plants in the industry emitting toxic releases during the
year. Not surprisingly, the results in column 2 indicate that the number of emitting facilities has a
positive effect on total releases, as expected. More importantly, when we include the number of
13
Moreover, in additional analyses we find that all three logged measures of concentration have a negative and
statistically significant effect (p<.01) on pollution reduction.
emitting facilities, the coefficient on HHI increases greatly in size (it becomes roughly equal to the
coefficient on HHI in the average releases regression) and becomes statistically significant.14 This result
provides additional evidence that output reduction provides at least part of the mechanism for our
result. Taken together, these results provide further evidence consistent with output reduction
providing at least one mechanism for the negative competition-releases relationship that we observe.
A final possible mechanism to explain our results is that consumers value pollution reduction, and that
competition causes firms to be more responsive to consumer preferences. To assess this mechanism, we
first allow the effect of competition to vary by year, interacting the HHI with the year dummy variables.
If the consumer preferences mechanism is correct, we would expect the effect of HHI to become more
positive over time, as concerns, as well as information, about industrial pollution have increased.
However, the results in column 1 of Table 10 reveal that, if anything, the effect of competition is
weakening over time; the coefficient on HHI becomes less positive in each succeeding year.
Although these results fail to provide support for the consumer preferences mechanism, this could be
because there are other factors varying over time that weaken the effect of competition on releases.
Therefore, we consider an alternative test of this mechanism. To do so, we use the average score from
the League of Conservation Voters (LCV) for all congressional representatives and senators in the state.
The LCV annually assigns all federal congressional representatives and senators a score based on their
voting record on key environmental policies during that year. Using the LCV average score as a proxy for
public sentiment regarding the environment, we include both the LCV average score and the interaction
of the LCV average with the HHI to examine whether the effect of competition is stronger in state-years
where the LCV average score is higher. The results in column 2 of Table 10 again fail to provide any
support for the consumer preferences mechanism. We find no evidence that the effect of competition is
greater in state-years where LCV data suggests greater public sentiment for environmental concerns;
the HHI-LCV average score interaction is negative and statistically insignificant. Taken together, the
results in Table 10 fail to provide any support for consumer preferences as a mechanism to explain the
negative effect of competition on releases.
6.3 Carcinogens
In this section we examine whether releases of carcinogens increase with competition. Carcinogens pose
a serious threat to mortality and therefore are a particular social concern. We investigate the impact of
competition on releases of carcinogens in Table 11.
The results in column 1 indicate that facilities release more carcinogens as concentration increases.
However, this may simply reflect the effect of competition on all releases. To explore whether
competition disproportionately influences carcinogenic releases, we include releases of noncarcinogens
as a control variable. The results in column 2 show that even after controlling for non-carcinogenic
releases, concentration has a positive effect on carcinogenic releases. These results suggest that not
14
We obtain similar results when we conduct this analysis at the industry-year level (i.e., when we do not weight
observations by the number of facilities in each industry-year cell).
only do releases fall as competition increases, but the most dangerous releases, carcinogens, fall at an
even faster rate when competition increases.
7. Conclusion
The conventional wisdom from microeconomic theory suggests that competition should increase
socially undesirable behavior, like polluting. This paper tests the competition-bad behavior hypothesis,
using five years of toxic emissions data from thousands of factories across hundreds of different
industries to assess the impact of product market competition on firms’ toxic pollution emissions. In
doing so, this paper provides the first systematic evidence regarding the effect of competition on firms’
pollution emissions.
Our results fail to provide support for the competition-bad behavior hypothesis. Our baseline analysis
indicates that competition reduces plant-level emissions. On average, each percentage-point increase in
the HHI results in a two-percent reduction in a facility’s toxic pollution emissions. We find similar effects
using four- and eight-firm concentration ratios, and the effect is larger in more concentrated industries.
We also find that emissions of carcinogenic chemicals fall with competition, and this result holds, even
after controlling for non-carcinogenic releases. Our results also shed some light on the mechanisms
through which firms reduce pollution emissions. We find some tentative evidence that facilities in more
competitive industries engage in more pollution reduction activities. We also find some evidence
consistent with facilities in more competitive industries reducing output as a means of reducing
pollution. We do not find evidence that consumer environmental preferences create greater pressures
to reduce pollution in more competitive industries.
A natural concern with our analysis is that there may be other factors, correlated with competition,
which are responsible for the relationship between competition and emissions that we observe. We
address this concern, as we show that variation in regulatory stringency created by the CAA cannot
explain our results, nor can unobserved local conditions or incumbents entering with new, more
efficient technology. In addition, while unobserved industry demand conditions may be correlated with
competition, this should only increase the magnitude of the effect that we report, because demand
should be positively correlated with both competition and emissions.
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FIGURES
Figure 1
Median Facility Releases: 1987-2007
25000
Pounds
20000
15000
10000
5000
0
TABLES
Variable
Total facility releases (pounds)
HHI (1-100)
CR4 (1-100)
CR8 (1-100)
Value of shipments ($ 000s)
HHI
CR4
CR8
Ln(HHI)
Ln(CR4)
Ln(CR8)
Table 1
Descriptive Statistics
Median
11102
3.76
31
44
7823492
Table 2
The impact of concentration on facility emissions
Ln(Facility
Ln(Facility
Ln(Facility
Ln(Facility
Releases)
Releases)
Releases)
Releases)
0.020**
(0.006)
0.008**
(0.002)
0.007**
(0.002)
0.116**
(0.036)
Standard Deviation
2489143
5.63
19.32
22.07
35014610
Ln(Facility
Releases)
Ln(Facility
Releases)
0.197**
(0.066)
0.229**
(0.076)
0.117*
(0.046)
78618
Ln(Value of
0.099*
0.105*
0.117*
0.109*
0.108*
Shipments)
(0.046)
(0.045)
(0.045)
(0.047)
(0.046)
N (Facility77794
78374
78743
77794
78374
Years)
All models only include facility-years with releases>20 pounds. All models include industry and year fixed
effects. Standard errors, clustered by industry, are reported in parentheses. †p<.10 *p<.05 **p<.01
HHI
Table 3
The impact of concentration on facility emissions: Robustness checks
Ln(Facility
Ln(Average
Ln(Average Ln(Average
Releases)
Facility
Facility
Facility
a
a
Releases)
Releases)
Releases)b
0.019**
0.013*
0.007
0.013†
(0.006)
(0.006)
(0.006)
(0.008)
0.080†
0.070†
0.034
0.020
(0.041)
(0.042)
(0.035)
(0.049)
FacilityIndustry FE
Industry FE Industry FE
Industry FE
77794
77742
9248
4432
Ln(Value of
Shipments)
Unit Fixed
Effects
N (FacilityYears)
a
Only includes industry-years with average emissions>20 pounds. b Only includes industry-years with
average emissions>20 pounds and more than one emitting facility. All models only include facility-years
with releases>20 pounds. All models include year fixed effects. Standard errors, clustered by industry,
are reported in parentheses. †p<.10 *p<.05 **p<.01
Table 4
The impact of concentration on facility emissions: More concentrated industries
Ln(Facility
Ln(Facility
Ln(Facility
Releases)a
Releases)b
Releases)c
HHI
0.021**
(0.007)
CR4
0.014**
(0.004)
CR8
0.013**
(0.004)
Ln(Value of
0.187*
0.159*
0.182*
Shipments)
(0.0079)
(0.075)
(0.078)
N (Facility38550
38467
39310
Years)
a
Only includes industry-years where HHI>3.76. b Only includes industry-years where CR4>31. c Only
includes industry-years where CR8>44. All models only include facility-years with releases>20 pounds.
All models include industry and year fixed effects. Standard errors, clustered by industry, are reported in
parentheses. †p<.10 *p<.05 **p<.01
Table 5
The impact of concentration on facility releases: Controlling for regulatory stringency
Ln(Facility Ln(Facility Ln(Facility Ln(Facility
Releases)a
CAA
CAA
CAA
b
b
Releases)
Releases)
Releases)b
HHI
0.020**
0.011
0.011
0.014†
(0.006)
(0.007)
(0.007)
(0.007)
Ln(Value of
0.103*
0.085†
0.090*
0.055
Shipments)
(0.046)
(0.044)
(0.044)
(0.060)
Emitting
-0.306**
-0.295**
-0.164*
industry in
(0.038)
(0.046)
(0.077)
nonattainment
county
Ln(non-CAA
0.290**
releases)
(0.014)
N (Facility77794
56502
56502
24898
Years)
a
Only includes facility-years with releases>20 pounds. b Only includes facility-years with CAA
releases>20 pounds. All models include industry and year fixed effects. Standard errors, clustered by
industry, are reported in parentheses. †p<.10 *p<.05 **p<.01
Table 6
The impact of concentration on facility releases: Controlling for unobservable local conditions
Ln(Facility
Ln(Facility
Releases)
Releases)
HHI
0.016**
0.013*
(0.006)
(0.005)
Ln(Value of
0.088†
0.047
Shipments)
(0.046)
(0.036)
Local Area
State-Year
County-Year
Fixed Effects
N (Facility77794
76102
Years)
All models only include facility-years with releases>20 pounds. All models include industry fixed effects.
Standard errors, clustered by industry, are reported in parentheses. †p<.10 *p<.05 **p<.01
Table 7
The impact of concentration on facility emissions: Incumbent facilities only
Ln(Facility
Ln(Facility
Releases)a
Releases)a,b
HHI
0.026**
0.020**
(0.007)
(0.007)
Ln(Value of
0.088
0.118†
Shipments)
(0.056)
(0.061)
N (Facility55770
40843
Years)
a
Excludes the first observation for a facility if it occurs after 1987. b Only includes observations from
2007. All models only include facility-years with releases>20 pounds. All models include industry and
year fixed effects. Standard errors, clustered by industry, are reported in parentheses. †p<.10 *p<.05
**p<.01
HHI
CR4
CR8
Table 8
The impact of concentration on pollution reduction activities
Ln(Pollution Ln(Pollution Ln(Pollution Ln(Pollution
Reduction)
Reduction) Reduction) Reduction)
-0.006
-0.013
(0.008)
(0.008)
-0.007*
(0.003)
-0.006*
(0.002)
0.153**
0.123*
0.119*
0.119*
(0.053)
(0.054)
(0.053)
(0.053)
0.191**
0.192**
0.192**
(0.016)
(0.016)
(0.016)
46316
42521
42003
42115
Ln(Value of
Shipments)
Ln(facility
releases)
N (FacilityYears)
All models only include facility-years with pollution reduction>20 pounds. All models include industry
and year fixed effects. Standard errors, clustered by industry, are reported in parentheses.
†p<.10 *p<.05 **p<.01
HHI
Table 9
The impact of concentration on aggregate industry emissions
Ln(Aggregate Ln(Aggregate
Industry
Industry
Releases)
Releases)
0.002
0.015*
(0.007)
(0.006)
0.072
0.071
(0.054)
(0.041)
1.163**
(0.048)
77656
77656
Ln(Value of
Shipments)
Ln(Number
of Facilities)
N (FacilityYears)
All models only include facility-years with releases>20 pounds in industry-years with aggregate
emissions>50 pounds. All models include industry and year fixed effects. Standard errors, clustered by
industry, are reported in parentheses. †p<.10 *p<.05 **p<.01
Table 10
The impact of concentration on facility emissions: The influence of consumer sentiment
Ln(Facility
Ln(Facility
Releases)
Releases)
HHI
0.030**
0.021**
(0.011)
(0.008)
HHI_1992
0.025**
(0.007)
HHI_1997
0.016**
(0.006)
HHI_2002
0.015*
(0.007)
HHI_2007
0.014
(0.009)
Ln(Value of
0.093*
0.099*
Shipments)
(0.046)
(0.046)
LCV Score
-0.755
(0.107)
HHI*LCV Score
-0.000
(0.000)
N (Facility77794
77286
Years)
All models only include facility-years with releases>20 pounds. All models include industry and year fixed
effects. Standard errors, clustered by industry, are reported in parentheses. †p<.10 *p<.05 **p<.01
Table 11
The impact of concentration on carcinogenic emissions
Ln(Facility
Ln(Facility
Carcinogenic Carcinogenic
Releases)
Releases)
HHI
0.018*
0.019**
(0.008)
(0.007)
Ln(Value of
0.145*
0.116*
Shipments)
(0.056)
(0.059)
Ln(Noncarcinogenic
0.331**
releases)
(0.017)
N (Facility-Years)
29414
19654
All models only include facility-years with carcinogenic releases>20 pounds. All models include industry
and year fixed effects. Standard errors, clustered by industry, are reported in parentheses. †p<.10
*p<.05 **p<.01.