strategic placement of air polluters: an application of...
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Strategic Placement of Air Polluters: An Application ofPoint Pattern Models∗
James E. Monogan IIIAssistant Professor
Univ. of Georgia
David M. KoniskyAssistant Professor
Georgetown Univ.
Neal D. WoodsAssociate Professor
Univ. of South Carolina
May 18, 2013
Abstract
What shapes the geographical placement of U.S. air polluters? We hypothesize thatair polluting facilities are located near downwind borders in order to minimize stateresidents’ exposure to pollutants and to avoid the resulting health and environmentalcosts. To test this hypothesis, we model the location in latitude and longitude of sta-tionary air pollution sources within a given state, using a spatial point pattern analysis.A point pattern analysis treats the location of an observation as the outcome variableitself, asking whether the location of these polluters is random or if it responds to par-ticular covariates. This methodology is frequently used in fields such as epidemiologyto model the coordinate-based location of events, but is novel to political science. Ourresults indicate that (1) air polluting facilities are significantly more likely to be locatednear a state’s downwind border than a control group of other industrial facilities, and(2) this effect is particularly pronounced for facilities with highly toxic air emissions.Collectively, these results suggest that air polluting facilities are strategically locatedin places that export the environmental and health consequences of pollution to states’downwind neighbors.
∗Paper prepared for presentation at the 2013 State Politics and Policy Conference in Iowa City, IA.A previous version of this paper was presented at the 2013 Annual Meeting of the Midwest Political Sci-ence Association in Chicago. For sharing data, we thank Jeffrey Robel. For helpful comments, we thankDavid A.M. Peterson, Edward Weber, Raymond Lodato, Robert Wood, Gina Reynolds, Mike Hanmer, JohnFreeman, Ian Smith, Molly Roberts, Greg McAvoy, Nate Kelly, Peter Enns, and James Honaker.
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1 Introduction
Pollution does not respect jurisdictional boundaries. Thus, a central problem confronting ef-
forts to protect the physical environment concerns interjurisdictional pollution externalities,
which occur when pollution released in one political jurisdiction creates adverse environmen-
tal consequences in another. These “spillover effects” provide a powerful political incentive
for polluting jurisdictions to free ride on their neighbors. This is often clearly evident in the
reluctance of the polluting jurisdiction to shoulder a proportionate share of the burden of
remedying the problem—an issue that has bedeviled policymakers searching for solutions to
problems such as such as climate change, acid rain, and ocean degradation.
Somewhat less obviously, perhaps, jurisdictions have strong incentives to actively promote
spatial pollution externalities, thereby capturing the benefits of economic development within
their own borders while exporting the environmental and health costs to their neighbors
(Hutchinson & Kennedy 2008, Oates 2002, Revesz 1996). These perverse incentives may
confound pollution control efforts in the United States due to the central role that state
governments play in implementing U.S. environmental policy (Lowry 1992, Revesz 1996).
Indeed, there is a long list of instances in which the U.S. states have accused each other of
deliberately exporting pollution to other states. These types of disputes date as far back as a
1907 U.S. Supreme Court case, Georgia v. Tennessee Copper Co., in which Georgia claimed
that sulfur dioxide emissions from Tennessee-based copper smelters were despoiling forests
and orchards and creating health problems for residents of bordering counties in Georgia. In
more recent years, there have been a series of court cases in which state regulators have been
accused of giving preferential treatment to polluters located on or near a state border in order
to protect local industry rather than reduce pollution that largely affected nonresidents.1
1In 2006, for example, New Jersey sued the EPA, claiming it had failed to control the emissions of the
2
Scholars are paying increasing attention to the problem of free riding and its implications
for environmental policy. Formal models of federalism suggest that incentives to free ride
pose a significant threat to environmental quality in nations that decentralize environmen-
tal quality control, such as the U.S. (Hutchinson & Kennedy 2008, Silva & Caplan 1997).
Empirical assessments suggest that pollution levels are systematically elevated near state
borders relative to interior regions (Helland & Whitford 2003, Sigman 2005). In light of the
combined scholarly and real world emphasis that has been placed on the issue, there should
be a strong expectation that states often engage in environmental free riding behavior.
Yet there is doubt. Studies that directly assess state government inspection and en-
forcement actions have found limited support for the contention that differences in pollu-
tion levels near state borders are the result of differential state enforcement effort (Gray &
Shadbegian 2004, Konisky & Woods 2010, Konisky & Woods 2012). These results have led
to speculation that, at least within the U.S. context, either top-down efforts by the Envi-
ronmental Protection Agency (Konisky & Woods 2012) or bottom-up efforts by local policy
networks (Scholz & Wang 2006) may be sufficient to overcome state incentives to free ride
on other states’ environmental protection efforts.
We propose an explanation for this puzzling lack of direct empirical evidence for the free
riding hypothesis: that state free riding is largely a function of polluting facility location,
rather than differences in regulatory inspection and enforcement activity. Specifically, we
posit two core hypotheses. First, we hypothesize that air polluting facilities are likely to
locate toward eastern borders (and away from western ones) in order to minimize their
Portland Generating Station, a Pennsylvania coal-fired power plant located just across the Delaware River(Delli Santi 2006). The long-running dispute over this particular power plant was recently addressed by theEPA. In March 2011, the EPA indicated that it would grant a petition by the New Jersey Department ofEnvironmental Protection to require that the Portland Generating Station reduce its emissions by 81 percentover three years (Applebaum 2001).
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residents’ exposure to pollutants and to avoid the resulting health and environmental costs.
We hypothesize that air-polluting facilities are located toward downwind borders (and away
from upwind residents) in ways that minimize state residents’ exposure to pollutants and to
avoid the resulting health and environmental costs. Second, we hypothesize that this effect
will be more pronounced for plants that have greater toxic releases. Although this pattern of
location may be the result of state government policy, we argue that rational firm location
strategies are sufficient to produce this result.
To test these hypotheses we model the location in latitude and longitude of stationary air
pollution sources within a given state, using a spatial point pattern analysis. A point pattern
analysis treats the location of an observation as the outcome variable itself, asking whether
the location of these polluters is random or if it responds to particular covariates. This
methodology is frequently used in fields such as epidemiology to model the coordinate-based
location of events, but is novel to political science.
Our results indicate that (1) air polluting facilities are significantly more likely to be lo-
cated near a state’s downwind border than a control group of other industrial facilities, and
(2) this effect is particularly pronounced for facilities with highly toxic air emissions. Col-
lectively, these results provide strong evidence that the geographic distribution of polluting
facilities allows states to free ride by exporting the environmental and health consequences
of their pollution to their downwind neighbors.
2 Literature and Theory
Effectively managing interjurisdictional externalities is one of the central issues—arguably
the central issue—facing federal systems (Bednar 2009, Oates 1972). In part, this is because
states do not automatically take into account the effect their policies have on residents of
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other states. Decentralized policymaking in the presence of externalities will thus produce
policy that is suboptimal from the point of view of the nation as a whole. Problems of policy
coordination emerge, such as the critical question of how to coordinate the management of
natural resources, such as rivers, that cross state borders (Lubell & Mete 2002, Schlager &
Heikkila 2009, Heikkila & Schlager 2012, Scholz & Wang 2006).
Somewhat less obviously, perhaps, fragmented policymaking also provides states with
strong incentives to actively promote spatial pollution externalities, thereby capturing the
benefits of economic development within their own borders while exporting the environmental
and health costs to their neighbors (Hutchinson & Kennedy 2008, Oates 2002, Revesz 1996).
The existence of interjurisdictional pollution spillover effects, which occur when pollution
released in one political jurisdiction creates adverse environmental consequences in another,
provide a powerful political incentive for polluting jurisdictions to free ride on the pollution
control efforts of their neighbors.
Environmental free riding has been empirically examined in two streams of literature.
One approach looks for evidence of transboundary pollution—elevated pollution levels near
borders. Higher pollution levels near state borders than in interior areas is taken as ev-
idence that governments deliberately seek to induce pollution externalities. Helland and
Whitford (2003), for instance, find evidence that industrial toxic chemical releases to the
air and water are systematically higher in counties that border other states. For toxic air
emissions they find a particularly strong effect in counties on the eastern edge of states,
where prevailing wind patterns are most likely to carry pollution across state lines. Other
research finds evidence that water pollution levels in rivers are higher downstream from
U.S. state (Sigman 2005), Brazilian county (Lipscomb & Mobarak 2011), and international
(Sigman 2002) borders than in interior locations. Looking at the American states, Kahn
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(2004) finds higher death rates from environmental cancers in border counties relative to
nonborder counties in low regulation states. Not every study of transboundary pollution,
however, provides unequivocal support of the free riding hypothesis, with some reporting
mixed or no evidence of elevated air and water emissions near U.S. state borders (Gray &
Shadbegian 2004, Gray & Shadbegian 2007).
A second literature examines government regulatory behavior directly. These studies
assess whether states inspect polluting facilities or enforce pollution control laws with less
vigor if their pollution is likely to migrate out of state. In general, these studies have
found little evidence of differential inspection and enforcement activity near state borders,
although the results do indicate significantly weaker state air enforcement near international
borders, suggesting that states may strategically allocate their enforcement effort in ways
that serve to export pollution to Canada and Mexico (Gray & Shadbegian 2004, Konisky &
Woods 2010, Konisky & Woods 2012).
2.1 Polluter Location as a Result of Government Behavior
At present there is something of a disjuncture between the transboundary pollution literature
and the literature on regulatory behavior, with the former (largely) finding evidence of
higher pollution levels near borders, and the latter (largely) finding little evidence that this
is the result of government behavior. One possible explanation for this disjuncture is that
environmental free riding is the result of the spatial distribution of polluting facilities, rather
than in differences in how these facilities are treated by regulators.
Facility location itself may be influenced by state government policy. Several scholars
have claimed that the American states can or do encourage polluters to locate near borders.
One asserts that: “states tend to locate landfills and other waste disposal facilities near their
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borders, externalizing the environmental impacts of these facilities on neighboring states.
This occurs so commonly it is termed the ‘state line syndrome.’ Locating waste disposal
facilities near a state line externalizes some of the potential harms form leakage of waste and
water contamination at the facility” (Hall 2008, 55).2 With respect to air pollution, another
observer notes that: “the level of pollution externalities is affected by the location of sources.
In the eastern part of the United States, where the problem of interstate pollution is most
serious, the prevailing winds blow from west to east. Thus, states have an incentive to induce
their sources to locate close to their downwind borders so that the bulk of the effects of the
pollution is externalized” (Revesz 2008, 51).
Broadly speaking, there are two major groups of policy tools that state governments may
employ to induce facilities to locate near borders. The first is environmental regulation. If
states regulate less aggressively in border areas, the reduced cost of regulatory compliance
may be sufficient incentive to locate near borders. Recent analyses, for instance, suggest that
new firms are systematically more likely to locate in regions that currently attain air quality
standards (where they may find less scrutiny and more lenient control measures) than in
non-attainment regions (Henderson 1996, Becker & Henderson 2000, List & McHone 2003).
In addition, intrastate differences in regulatory stringency may be due to differences in
regulatory enforcement (although, as discussed above, studies have not found consistent
evidence for this), or may reflect differences in permit requirements.
Firm location in border areas may not simply be the result of environmental policy, how-
ever. Studies of industry location typically suggest that although firms do take cost differ-
entials into account in deciding where to locate, numerous other factors are more important
than differences in environmental regulation (Jaffe & Stavins 1995). Moreover, because state
2For similar claims, see Mank (1995) and Wiygul and Harrington (1993). Although these claims havebeen repeated several times, none of the literature we reviewed presents any empirical evidence for them.
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EPAs generally have an organizational culture that values environmental protection, states
may find it advisable to use alternative policy tools in order to induce free riding behavior.
States have a wide variety of such tools that could in principle be employed to induce firms
to locate near borders, including tax incentives, subsidies, permitting and zoning decisions
(Revesz 2008).
2.2 Polluter Location as a Result of Firm Behavior
The above logic posits that interstate pollution externalities associated with plant location
are a consequence of strategic state behavior. We argue, however, that strategic state be-
havior is not necessary for firms to locate in areas where their pollution will largely affect
out of state residents. Rather, NIMBY-driven political dynamics may also lead firms to
locate disproportionately in these areas. Thus, this outcome may strictly be the result of a
decentralized process of rational decisionmaking by firms making location choices.
Consider a firm choosing where to locate a polluting plant. Any firm interested in siting
a facility whose environmental effects may be perceived as being noxious can usually expect
to face significant opposition from concerned local residents. This may lead to substantial
political and legal costs, including costs of participating in extensive regulatory proceedings
and court battles or the opportunity costs imposed by a delay in construction. Moreover,
concerted local NIMBY opposition can at times derail a proposed facility completely (Rabe
1994).
A rational firm will take these potential costs into consideration ex ante in making location
decisions. Generally, the amount of opposition to a facility may be thought of as reflecting
two things (1) the value that residents place on environmental amenities, and (2) their ability
to overcome free rider problems in organizing for collective action (Lubell & Zahran 2006,
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Hamilton 1995). Prior research has found, for instance, that hazardous waste facilities tend
to be located in places where there is an ex ante expectation of relatively limited organized
opposition (Hamilton 1993, Hamilton 1995).
There is a third important factor influencing the expected efficacy of local opposition
to industrial plant siting, however: access to the appropriate political, regulatory, and legal
channels necessary to voice effective opposition. If a large percentage of those threatened
by a firm’s location are out-of-state residents, then access to these avenues for expressing
opposition are significantly diminished. This is because out-of-state residents largely lack
political representation and may have reduced opportunities to contest the site in regulatory
and legal arenas as well. Thus, a rational firm seeking to minimize organized political
opposition (and maximize political support) will seek to locate in places where pollution
largely affects nonresidents.3
An important implication of this logic is that strategic state behavior is not necessary to
produce locational sorting that leads to environmental free riding outcomes. Although strong
state incentives to encourage environmental free riding remain, the above logic suggests that
a process of rational firm behavior is sufficient to produce such outcomes independent of
state location incentives to do so. In fact, the stronger these firm-level incentives are, the
less a state would need to use the levers of government policy to achieve this result. In the
limiting case, these incentives will be sufficient to lead to environmental free riding outcomes
without any state government involvement at all.
3This possibility also was suggested to us in personal correspondence with an economic developmentofficial in North Carolina.
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3 Constructing an Empirical Test
One possible avenue for testing the free riding hypothesis would be to directly assess the
mechanism by which states encourage polluting facilities to locate near state borders. As
the above discussion suggests, however, this is a difficult endeavor because (1) there are a
wide variety of mechanisms that could lead to this result; (2) different states may employ
different policy tools for this purpose; and (3) there is a paucity of systematic, comparable
data regarding many of these processes. In bemoaning the lack of empirical evidence on this
issue one observer notes: “it is difficult to find direct evidence concerning whether states
also provided incentives for sources to locate close to their downwind borders, because such
incentives are unlikely to be reflected in regulatory documents” (Revesz 2008, 53). Moreover,
rational firms may calculate the political, regulatory, and legal costs of various possible
location sites in such a way that leads to locational sorting that produces enviornmental free
riding even in the absence of overt state policy.
We thus approach the question by assessing whether polluting facilities are spatially
located in a way that is consistent with the free riding hypothesis. By focusing on actual firm
location, our approach enables us to bypass the issue of mechanism. If we find no evidence
that plants are located disproportionally near borders, this provides strong evidence that
states are not using facility location in order to free ride (or at least they are not doing so
successfully). If, on the other hand, we find such evidence, future research may attempt to
isolate the mechanisms by which free riding occurs, confident that the phenomenon they are
explaining does have an empirical foundation.
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3.1 Point Pattern Analysis
We test the free riding hypothesis by modeling the location in latitude and longitude of
stationary air pollution sources within a given state, using a spatial point pattern analysis.
Intuitively, this method seeks to gain a sense of the risk for an observation (such as a major air
polluter) to occur relative to a broader underlying population. For instance, epidemiologists
often use this methodology to model where cases of disease occur relative to the location of
the population at risk. The classic example of this methodology focuses on John Snow’s map
of cholera deaths in London in 1854, showing that residents nearest to a particular well were
most suspect to cholera (Ward & Gleditsch 2008, 11-12). While many researchers since then
have effectively used this technique to discern how patterns in disease can lend themselves
to causes of public health concerns, we also believe that this technique can be applied to
political questions as well.
More formally, a spatial point process can be thought of as an inhomogenous Poisson
count process where events can occur in an arbitrarily small space (Cressie 1993, 650-657).
In other words, if the larger area where events could occur was divided into a grid, each cell
could contain a certain count of observations based on the spatially-varying Poisson process.
By allowing the cells to shrink arbitrarily (to zero-space, in the limit), the spatial Poisson
process becomes a smoothed process where at any given point an event may be observed or
not. In certain applications of biostatistics, an isolated point process may be of interest, as
it would merely describe where observations (such as the presence of rarely-observed plants
or animals) were more likely to occur.
However, in most applications in epidemiology, and in this application of polluter location,
the spatial propensity for a case of cholera or the construction of a major air polluter is not
in itself interesting without reference to the population of interest. Cases of diseases are most
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likely to occur in places with the highest population density and major air polluters are most
likely to emerge in regions with the most industrial development. Hence, the relative risk of
a disease or a polluter being observed given the spatially-dependent underlying population
is of greater interest. Therefore, models of the relative risk choose a well-selected control
group, such as the number of doctor visits for non-threatening diseases, and model the point
process underlying the control group. With a model of the cases (e.g., the disease of interest)
and controls (e.g., non-threatening diseases), the relative risk can be thought of as:
r(x) = log{f1(x)/f2(x)}.
Where f1 is the spatial density of cases (such as a disease), f2 is the spatial density of the
control group (reflecting the broader population), and x is the location of a case or control
in space. Kelsall & Diggle (1995) approach this problem by estimating Poisson processes
with spatially-varying intensity parameters and then calculating the ratio of the parameters
to get the relative risk:
ρ(x) = r(x) + c1 = log{λ1(x)/λ2(x)}.
Where c1 is an additive constant, λ1(x) is the Poisson parameter for the spatial distribution
of cases, and λ2(x) is the Poisson parameter for the spatial distribution of controls.
In a later article, Kelsall & Diggle (1998) present an alternative estimator after observing
that pooling cases and controls and using a binary estimator yields another valid estimate
of relative risk:
logit{p(x)} = ρ(x) + c2 = r(x) + c1 + c2.
Where p(x) is the probability that the observation at location x is a case rather than a
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control, c2 is another additive constant, and all other terms are the same as before. In other
words, by modeling whether an observation in space is a case or control, we can effectively
model the relative risk of a case emerging. While a constant offsets the results, this does not
interfere with our ability to capture spatial variation in the relative risk.
3.2 Model Specification
Following Kelsall & Diggle (1998), we pool cases and controls in a binary estimator. Also, as
they suggest, we fit a logistic generalized additive model. This means that we can nonpara-
metrically estimate the baseline relative risk of a major air polluter emerging at a particular
place, yet we can also incorporate covariate terms into the relative risk. Equation 1 presents
the formalized model:
P (yi = 1) = p(xi,ui) (1)
logit{p(xi,ui)} = u′iβ + g(xi)
In this equation: yi is a dichotomous variable coded 1 for a case observation (major air
polluter) and 0 for a control observation (hazardous waste facility), xi refers to a location
in latitude and longitude, ui is a vector of covariates observed at location xi, β is a vector
of coefficients for the covariates, g(xi) is location xi’s value of a smooth function over space
that is not dependent on the covariates, and p(xi,ui) is the probability a major air polluter
(a case observation) will be placed at location xi given covariates ui.
In our case, ui contains a constant and the covariate of distance from the leeward border.
Our primary hypothesis is that the coefficient for this covariate will be negative: In other
words, the farther a site is from the downwind border, the less likely it should be to serve as
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the site of a major air polluter. We are subjecting this hypothesis to a tough test because
the smoothed term g(xi) is allowed to vary in any way that will capture spatial changes
in the relative risk, which could emerge for a variety of reasons. Therefore, distance from
the downwind border has to offer additional explanation above and beyond this data-driven
term that can control for a wide array of processes.
We believe that this research design provides a powerful test of the environmental free
riding hypothesis as it pertains to facility siting. It is important to emphasize that with
this design, it is unnecessary to control for the multitude of factors that have been shown to
be correlated with facility location (e.g., market demand, industry agglomeration, natural
resources, labor supply, infrastructure, etc.). There are two reasons why such controls are
unnecessary. First, there is little reason to believe that these factors systematically differ
in their distribution in upwind and downwind parts of states. As an example, major air
polluters often prefer to locate near water features such as lakes and navigable waterways.
Many manufacturing processes are water-intensive, and being located near a major waterway
provides a means for transporting inputs to a facility, and finished products to downstream
markets. Although major water features tend to form the borders of states, they do so on
each side. Population centers serving as markets and labor pools are similar.
Second, any observed pattern of siting is identified relative to a carefully selected con-
trol group: major hazardous waste facilities. The factors noted above that are potentially
important in site selection of major air polluters also apply to the siting of hazardous waste
facilities. However, unlike major air polluters, there is no strong reason to locate a hazardous
waste site either upwind or downwind. The pollution from these sites is contained (assum-
ing they are compliant with relevant statutes) and not subject to the dispersion through
airsheds. For this reason, there is little reason to expect that the placement of hazardous
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waste facilities is subject to the same kind of free-riding motivation we suggest is present for
major air polluters.
3.3 Data and Measurement
To specify the model presented in Equation 1, we gathered extensive polluter site information
from the Environmental Protection Agency’s Geospatial Data Access Project (http://www.
epa.gov/enviro/geo data.html). These data identified all of the major air polluters registered
with the EPA (forming our case group). We also were able to find the EPA’s comprehensive
list of hazardous waste treatment, storage and disposal facilities. We use these facilities
as our control group because facilities like this should reflect the larger distribution of
where polluters would be located given population concentration and relative industrial
development. However, states have lower incentives for free riding with hazardous waste
facilities because they do not emit airborne pollutants carried by the wind. (In the cases
where they do, the sites are also listed as major air polluters and are counted as a case
observation, not a control.) The Geospatial Data Access Project also includes the latitude
and longitude coordinates for each site as part of its comprehensive list, thereby allowing
us to place our cases and controls in space. Our outcome variable in all of this is the
probability that a particular site in latitude and longitude hosts a major air polluter rather
than a hazardous waste facility.
Again, our primary hypothesis is that the farther a site is from the leeward border,
the lower the relative risk of a major air polluter. To measure distance to the downwind
border at all 36,972 sites we analyze (16,211 cases and 20,761 controls), we use a two-step
process. In the first step, we estimate the wind direction at each site. Figure 1 displays the
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prevailing wind direction for 299 weather stations across the United States.4 To interpolate
the prevailing wind direction at all of the other sites, we fit a circular kriging model to forecast
at new locations. With any data for which the outcome variable should be treated as an
angle, the model needs to account for the unique features that angular data can pose (Gill &
Hangartner 2010). For instance, if the wind is blowing due North, that is 360◦ on a compass.
Slightly deviating values might be 359◦ or 1◦, and such values need to be recognized as similar
(a feature many models would miss). We follow the novel approach developed by Morphet
(2009) that addresses the needs of a circular outcome and also uses spatial smoothing to
krige a forecast of the error term based on the errors of nearby observations.5 Intuitively,
then, we use broad trends in wind direction as well as unique features of local patterns to
interpolate the prevailing winds at each site we study.
The second step in calculating distance to the downwind border is to find the latitude and
longitude coordinate of the spot on the border that is directly downwind. By simply drawing
a long line based on the interpolated wind angle at each site, GIS software can find where
this line and a state border intersect.6 Finally, with the latitude and longitude coordinates
of the polluter’s location and the coordinates for the downwind point on the border, we can
compute the distance from the leeward border. Simple Euclidean distance, however, will not
suffice because the distance must account for the fact that the Earth is spherically shaped.
Therefore, we compute the distance using the haversine formula presented in Equation 2
(Banerjee, Carlin & Gelfand 2004, 17-18):
d = R arccos{sin(y1) sin(y2) + cos(y1) cos(y2) cos(x1 − x2)} (2)
4These data were provided by Jeffrey Robel of the National Climatic Data Center at the National Oceanicand Atmospheric Administration upon personal communication. The document he provided is entitledClimatic Wind Data for the United States and is dated November 1998.
5See Banerjee, Carlin & Gelfand (2004, Chapter 2) for more details on kriging methodology.6Specifically, we used the spatstat library in R 3.0.0 to do this.
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Figure 1: Average wind direction from 1930-1996 at 299 weather stations aroundthe United States.
In this equation: x1 refers to the longitude coordinate of the site, x2 refers to the longitude
coordinate of the downwind point on the state border, y1 refers to the latitude coordinate
of the site, y2 refers to the latitude coordinate of the downwind point on the state border,
R = 6371 is the radius of the Earth in kilometers, and d is the distance from a site to
the border in kilometers. Thus, in a two-step process we compute our primary covariate of
interest, distance to a site’s downwind border.
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4 Results
Table 1 presents the results from estimating the model presented in Equation 1. As can be
seen, we obtain a negative and discernible coefficient on the input of distance from leeward
border. This fits exactly with our hypothesis: For every additional kilometer away from
a downwind border, the relative risk that a site will host a major air polluter decreases.
Therefore, the concentration of major air polluters relative to toxic waste facilities is higher
closer to downwind borders. In fact, moving from a site on a state border to one that is 177
kilometers upwind diminishes the odds of a major air polluter relative to a toxic waste facility
by 5.7%.7 Overall, then, this is a powerful result: Rather than look at the concentration
of air polluters alone, we look at how preponderant air polluters are given an area’s level
of industrialization. Beyond that, we also allow the relative concentration to be modeled
by a nonparameteric baseline relative risk, meaning that even in a model of the relative
preponderance of major air polluters any number of unobserved factors shaping local trends
is controlled for. Therefore, this negative and discernible effect speaks volumes about states’
motivation to free ride on air pollution.8
Additionally, Figure 3 presents additional information for the model presented in Table 1.
This figure visually presents the nonparametric, smoothed generalized additive intercept that
was included in the model. The horizontal axis of this figure presents longitude coordinates,
and the vertical axis presents latitude coordinates. The map shows a point for every site of a
major air polluter or hazardous waste facility in our data. The lighter-shaded areas indicate
7177 kilometers (approximately 110 miles) is chosen because it is the standard deviation of our distancemeasure. Mathematically, this odds ratio is: exp(−0.00033× 177) = 0.943.
8We also estimated a version of this model that ignored variation in wind direction and simply calculatedthe distance to the border due east of the site. This alternate specification yielded a discernibly negativeresult as well. As another robustness check, we estimated a model that excluded power plants from the groupof major air polluters, in case the location process for these sites remarkably differed from other polluters.This model without power plants yielded findings nearly identical to those reported in Table 1.
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Table 1: Model of Spatial Variation in Risk of Major Air Polluter Placement(Generalized Additive Model, Logit Link)
Covariate Estimate Std. Error z-ratio p-valueDistance from leeward border -0.00033 0.00008 -4.06967 0.00005Intercept -0.23557 0.01960 -12.01650 0.00000
Notes: Estimates computed with R 3.0.0. Approximate significance test for intercept smoothed over latitude
and longitude: χ228.72 = 4795.416 (p < .001). N = 36972, AIC= 45110.
a higher baseline relative risk for hosting a major air polluter, and the darker-shaded areas
indicated a lower baseline relative risk. The contour lines on the plot also serve to indicate
depth, marking areas having the same value of the smoothed intercept. Again, higher values
do not indicate more numerous major air polluters in raw counts, but that air polluters are
likely to make up a higher proportion of the sites relative to hazardous waste facilities. As
Table 1 reports, this smoothed term easily explains a discernible amount of variance beyond
the flat intercept model (as the significant chi-squared test indicates), and distance from the
downwind border continues to have explanatory power even with the smoothed intercept in
the model.
4.1 The Conditioning Effect of Toxicity
As a final analysis, we also considered that states may have an increased incentive to free
ride for major air polluters that release toxic emissions into the air. Toxic emissions are likely
to have adverse health consequences for those downwind of the pollution, so decisionmakers
have extra incentive to see that state residents are at least windward, or upwind, of such
sites. Therefore, we would expect that the coefficient on distance from leeward border would
exhibit a stronger negative effect when analyzing the relative risk of placement of toxic major
air polluters.
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−120 −110 −100 −90 −80 −70
2530
3540
45
Longitude
Latit
ude
−6
−5
−4
−3
−2
−1
−1
0
0 1
1
1
Figure 2: Smoothed baseline risk surface for major air polluters in the UnitedStates, prior to accounting for distance. Lighter colors indicate a higher baselinepropensity that a site will host a major air polluter. Points represent the locationof observed sites.
To assess this, we consulted the EPA’s 2010 Toxics Release Inventory, which reports how
many pounds of toxic emissions each major air polluter releases. We used these data to
subset our air polluter data into three groups: air polluters that released no toxins into the
air, polluters that released toxins but were below the median level for toxic polluters, and air
polluters that were above the median level for toxic polluters. For the sake of comparison,
20
we also bundled all toxic polluters into one group (regardless of whether they were above
or below the median) and contrasted these groups to the results from the full data. With
the subsetted data, we re-estimated the model of Equation 1. Each model included all of
the hazardous waste sites as controls, but the case observations were limited to the relevant
subset.
●
●
●
●
●
Coefficients for Leeward Distance
−0.0008 −0.0004 0.0000 0.0004
No toxic
Low toxic
Full data
Any toxic
High toxic
Figure 3: Coefficient estimates and 90% confidence intervals for the effect ofdistance from the leeward state border on relative risk for a major air polluter.Coefficients are presented for subsets of major air polluter data based on levelof toxic emissions.
21
Figure 3 presents a forest plot of the coefficient for distance to leeward border from each
of five models. The horizontal axis presents possible values of the coefficient. The vertical
axis lists the subset of the data, moving from the set with the lowest theoretical incentive
for free riding at the bottom of the axis to the highest theoretical incentive at the top of
the axis. The points represent the estimate of the coefficient, and the lines represent the
90% confidence intervals for the estimate. As the figure shows, all of these coefficients are
negative and discernible from zero. We also see that the coefficients that are nearest to zero
arise when air polluters that do not release toxic pollutants are compared with the control
group or when polluters with low levels of toxic emissions are compared with hazardous
waste sites. While we would expect a slightly stronger effect for low-level emitters than
non-emitters, both of these groups show weaker results than the model using the full data.9
Also, relative to the model using the full data, the group of air polluters emitting any toxins
into the air shows a greater sensitivity to distance from the downwind border than the full
data, which pool non-emitters of toxins in as well. Not surprisingly, the biggest effect comes
with the heavy emitters of toxins. Therefore, it appears that the threat toxins pose to a
state’s citizens shapes the degree to which we observe free-riding behavior.10
5 Implications
Our results provide evidence that air polluting facilities are significantly more likely to be
located near a state’s downwind border relative to a control group of other industrial facilities
9One possible reason the low-emitters may have a weaker effect than non-emitters is that some of thenon-emitters were simply not active in 2010. It is possible that many of these polluters, when they were inoperation, emitted more toxics than the active low-emitters.
10In fact, for a one-tailed test at the 90% confidence level, the coefficient for distance for high emittersof toxins is significantly less than the coefficients for the full data, the low emitters of toxins, and thenon-emitters of toxins.
22
that should reflect the distribution of industry and population within the state. With each
additional kilometer farther from a downwind border, the probability that a geographic site
hosts a major air polluter decreases. This effect is particularly pronounced for facilities with
highly toxic air emissions. Collectively, these results suggest that air polluting facilities are
strategically located in places that export the environmental and health consequences of
pollution to other states.
One obvious question these results raise pertains to mechanism. While our results would
seem to clearly imply that states induce facilities to locate in particular locations in order
to free ride on their neighbors, we have not directly tested this assertion. Moreover, we have
argued that the political dynamics that accompany industrial site selection may be enough
to generate this result even in the absence of state government inducements. Clearly, this
phenomenon is ripe for future research.
Irrespective of mechanism, however, the result poses a profound challenge to federalism.
The phenomenon of environmental free riding observed here suggests that the widely recog-
nized challenge of managing pollution externalities in federal systems may be even greater
than many observers have suggested.
23
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