This paper was presented at the annual American Sociological Association meeting on August 23, 2015. Please contact the author for an updated version of this draft.

Assessing Resource-Based Environmental Inequality in Appalachia:

A Case Study of Coal Waste Impoundments

Pierce Greenberg Washington State University

Department of Sociology

Abstract: Coal waste impoundments in Appalachia impose numerous environmental risks

on nearby populations, but have been ignored in studies of environmental inequality.

Coal impoundments are large earthen dams that hold billions of gallons of coal slurry—a

sludge-like waste byproduct of coal extraction. Residents dread the potential of a

disastrous impoundment break and are concerned about potential health impacts.

However, unlike toxic waste facilities in urban settings, no studies have examined the

characteristics and vulnerabilities of neighborhoods near coal impoundments. This gap is

likely due to the separation of sociological literatures on environmental inequality and

resource-dependent communities. I propose the development of a resource-based

environmental inequality (RBEI), which seeks to understand the unequal distribution of

environmental hazards created by resource extraction. This paper explores RBEI by

identifying socioeconomic, mining, and rural predictors of coal impoundment proximity

in 2000. Spatial regression results find that mining employment, historical proximity to

mining, historical intensity of mining in the region, and distance to a county seat are

significant predictors of census tract proximity to coal impoundments. Further, the

relative importance of the separate mining variables indicates that distance to a past

mining site is the strongest predictor of impoundment proximity.

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INTRODUCTION

Coal impoundments threaten the social and environmental health of many

communities throughout Appalachia. These facilities hold billions of gallons of toxic

waste created from coal extraction—and knowledge about the risks they pose is largely

uncertain. Impoundment failures have caused some of the worst environmental and

human disasters in the history of the United States. An impoundment break in 1972 killed

125 people in West Virginia (Erikson 1976). Another break in 2000 poisoned waterways

in Kentucky with more than 300 million gallons of slurry—an amount 30 times larger

than the Exxon Valdez oil spill in Alaska (McSpirit, Hardesty, and Welch 2002).

Communities also face uncertainty about potential health impacts related to

impoundments. Government reports about whether slurry may be seeping into water

sources are largely inconclusive. But despite the potential risks associated with coal

impoundments, no studies have analyzed the characteristics of neighborhoods near coal

impoundments in Appalachia. Instead, environmental inequality research focuses

primarily on racial and socioeconomic disparities in metropolitan areas.

Environmental inequality refers to “any form of environmental hazard that

burdens a particular social group” (Pellow 2000:585). Influential studies in the 1980s

illustrated how minority groups were disproportionately impacted by landfills and toxic

waste dumps (UCCCRJ 1987; Bullard 1994). Since then, quantitative environmental

inequality studies have grown increasingly advanced in how they measure exposure and

proximity to hazards. But most of these studies display an “end of the pipe” bias that

focuses on hazard creation at the end of the production process in urban settings (Pellow

2000:595). By examining industrial landfills or pollution emissions, environmental

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inequality research largely ignores hazards created by resource extraction that impact

rural communities. According to the U.S. Environmental Protection Agency (2013),

mining and mineral processing generates more hazardous waste than any other industry.1

Rural resource-dependent communities also have unique social dynamics that may make

them more vulnerable to extraction-related risks (Flint and Luloff 2005; Bell 2009; Malin

2015).

This case study of coal impoundments in Appalachia illustrates the importance of

resource-based environmental inequality (RBEI): the unequal distribution of

environmental hazards created at the site of natural resource extraction. Rural areas are

often conceived of as both a dumping ground for waste and a repository of natural

resources, but few scholars study these topics in tandem (Lichter and Brown 2011).

Instead, a large portion of the sociological discussion of mining communities centers on

the socioeconomic impacts of extractive industries. Many academics have also

highlighted the stark juxtaposition between abundant natural resources and persistent

poverty (Gaventa 1980; Freudenburg 1992; Rural Sociological Society 1993; Duncan

1999; Freudenburg and Wilson 2002). But no studies analyze environmental hazards

created by extractive industries—and whether they unequally impact a certain social

group. I propose an integrative approach that converges literature on rural and resource

sociology and environmental inequality studies. This approach frames a quantitative

analysis of RBEI that examines the extent to which socioeconomic, mining, and rural

characteristics predict neighborhood-level proximity to coal impoundments in

Appalachia.

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LITERATURE REVIEW

Sociological studies of environmental inequality and resource-dependent

communities rarely engage with one another, perhaps due to a long-standing division

between natural resource sociology and environmental sociology. Natural resource

sociology conceives of the environment as a “supply depot,” while environmental

sociology is more likely to view the environment as a “waste repository” and “living

space” (Dunlap and Catton 2002:245). Field, Luloff, and Krannich (2002) noted that the

study of environmental inequality was a potential area for convergence between

environmental and resource sociology due to it’s “focus on the well-being of specific

populations in specific community contexts” (p. 221). Both literatures combine an

interest in socio-environmental processes and local, community-level outcomes.

However, few scholars have expounded on an opportunity for cohesion between the

fields: the potential presence of environmental inequality in resource-dependent

communities.

Mining Communities and Appalachia

Quantitative studies of mining communities in the natural resource sociology

literature focus on the economic and social impacts of natural resource extraction. Mining

is often seen as a cure-all economic boon for isolated rural communities far from

metropolitan regions (Freudenburg and Wilson 2002). Rural and natural resource

sociologists have long studied how economic dependency on extractive industries

influences rural livelihoods (Landis 1933; Field and Burch 1988, Rural Sociological

Society 1993). For example, Landis (1933) noted in an early study that community

services and social well being often contracted with the booms and busts of iron mining.

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Freudenburg (1992) refers to resource extraction as an “addictive economy.” At first,

rural communities receive an enticing boost from the industry wages, but over time those

benefits wane and the economic dependency on one industry becomes detrimental. For

example, some residents may drop out of high school to get a specialized job in a mine,

but find themselves unemployed when the industry experiences an economic downturn,

technological displacement of workers, or a depletion of natural resources (Freudenburg

1992).

Sociologists have found mixed results when empirically testing the assumption

that mining industries improve socioeconomic outcomes in rural areas. Mining dependent

counties in the 1970s had significantly higher population increases, incomes, and fewer

residents receiving public assistance than similarly situated non-mining counties (Bender

et al. 1985). But negative economic trends in the 1980s disrupted any previous benefits of

extractive industries to communities (Freudenburg and Wilson 2002). A review of more

than 400 mining community studies found no evidence to support the assumption that

mining always improves economic conditions (Freudenburg and Wilson 2002). Instead,

the review found that the adverse-to-favorable ratio for mining community studies was

1.58:1 (Freudenburg and Wilson 2002). One noteworthy trend in these studies is a

“curious anomaly” where incomes are often higher in mining communities, but poverty

and unemployment rates increase or remain the same (Elo and Beale 1985; Freudenburg

and Wilson 2002).

Appalachia entered the national consciousness in the 1960s, when president

Lyndon B. Johnson targeted the impoverished and coal-dependent region in his War on

Poverty policy initiative (Eller 2008). Much of the sociological literature on Appalachia

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focuses on the political economy of the region—emphasizing how coal companies

control local socioeconomic outcomes and ideologies. Gaventa (1980) highlighted how

power dynamics associated with absentee land ownership exacerbated inequalities in the

region. Appalachian residents largely resigned themselves from the political process due

to a history of exploitation at the hands of foreign entities (Gaventa 1980). Further, the

coal industry informally controlled the few non-coal job opportunities available in rural

Appalachia. Residents often hesitated to speak out against mining, out of fear that they

would be excluded from the local workforce (Duncan 1999).

Given the rigidity of rural social stratification systems, many of these local

dynamics still exist in Appalachia. Bell and York (2010) explore how coal companies

continue to control ideology in West Virginia, despite the industry’s decreasing impact

on the state’s economy. Public relations campaigns frame the coal industry as vitally

important to the state, even though coal made up only seven percent of the state’s gross

domestic product in 2004 (Bell and York 2010). Also, Bell (2009) found that coalfield

residents in West Virginia have weaker community ties and social networks than non-

coalfield residents. Those outcomes may stem from an ongoing tension that divides

residents “whose livelihoods depend on coal and those whose livelihoods have directly

threatened by coal (although these categories are not mutually exclusive)” (Bell

2009:634).

The extant literature on mining communities and Appalachia lacks quantitative

studies that examine disparate environmental outcomes created by resource extraction.

Several studies and reviews note the environmental degradation and negative health

impacts caused by strip mining and mountaintop removal (MTR) in Appalachia (Hendryx

6

2013; Scanlan 2013; Talichett 2014). But Perdue and Pavela (2012) found no

significance difference in socioeconomic outcomes in counties with MTR operations

compared to those with other mining methods. Further, Gaventa (1998) acknowledges

that elite landownership erodes the maintenance of environmental capital in communities.

Therefore, absentee control of large tracts of land is conducive to siting locally unwanted

land uses (LULUs) like waste facilities (Gaventa 1998). Despite the potential

connections, few mining community studies have engaged with the vast literature on the

disparate impact of environmental hazards on marginalized social groups.

Environmental Inequality

Environmental inequality studies focus on the unequal distribution of

environmental hazards in poor, minority, and urban communities and neighborhoods.

This line of research proliferated following the emergence of the environmental justice

movement in the 1980s (Szasz and Mesuer 1997). Sociology proved to be a practical

home for environmental inequality studies due to its focus on power and inequality and

the recent maturation of environmental sociology (Pellow and Brehm 2013). Downey

(2005) divides empirical definitions of environmental inequality into several categories,

two of which are useful here: disparate proximity and relative distributional inequality.

Disparate proximity is evident “when members of a specific social group live closer to

some set of hazards than we would expect if group members were randomly distributed

across residential space” (Downey 2005:355). Quantitative studies can prove disparate

proximity by finding statistically significant relationships between close proximity to

hazards and 1) high percentages of disadvantages populations and, 2) negative

socioeconomic indicators (Downey 2005). Relative distributional inequality posits that

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advantaged (white, upper class) groups should bear more of the costs related to

environmental burdens. Therefore, even if disadvantaged groups are not

disproportionately proximate to hazards, relative distributional inequality can exist if the

rich do not share a relative portion of the burden related to those hazards (Downey 2005).

Methodological debates have been pervasive throughout the history of

environmental inequality research. Scholars often find differing results based on the units

of analysis (Glickman, Holding, and Hersh 1995), measures of exposure/proximity

(Mohai and Saha 2007), study areas (Downey et al. 2008), and the types of hazardous

facilities studied (Grant et al. 2010). The adoption of geographic information system

(GIS) technology and other advanced datasets enabled more detailed analyses of

environmental inequality in the 2000s (Mennis 2002; Downey 2006). For example, an

analysis using toxicity-weighted datasets finds that different races are disproportionately

proximate to environmental hazards across 61 U.S. metropolitan areas (Downey et al.

2008). Recent studies, using individuals as the unit of analysis, found that 1) minorities

were more likely than whites to move into hazardous neighborhoods (Crowder and

Downey 2010) and 2) racial and class differences in toxic exposure persist over time

(Pais, Crowder, and Downey 2014). Grant et al. (2010) posit that facility characteristics,

in addition to community makeup, explain patterns of risky pollution emissions.

While researchers have advanced methods of analyzing environmental inequality,

they have rarely applied those techniques to rural areas. Early studies such as Anderton et

al. (1994) excluded rural areas from their analyses entirely, claiming that rural areas

weren’t viable alternatives for large waste facilities (Anderson et al. 1994). Mohai (1995)

points out that, at the time, one of the largest hazardous waste facilities in the country was

8

in rural Alabama. Also, the environmental justice movement arose out of a waste-siting

dispute in rural Warren County, N.C. (Mohai 1995). However, despite rural areas’ role as

a dumping ground, few studies examine environmental inequities in rural settings.

Edwards and Ladd (2000) examine correlations between hog waste, farm loss, and race in

North Carolina. Hooks and Smith (2004) find that unexploded military ordnance is

disproportionately stored near rural Native American reservations. Nonetheless,

quantitative research on unequal environmental outcomes still largely focuses on urban

areas and cities. This could be due to the prominence of urban sociology and its detailed

focus on development, segregation, and spatial processes. Also, environmental inequality

case studies often focus on “end of pipe” pollution associated with industrial

manufacturing, which historically occurred in cities (Hurley 1995; Szasz and Meuser

1997; Pellow 2000; Smith 2009). Nonetheless, this considerable literature gap

necessitates the development of a RBEI to test whether the same unequal environmental

outcomes occur in rural settings.

Resource-Based Environmental Inequality and the Treadmill of Production

This study provides an expansion of existing environmental inequality research

into the realm of environmental degradation and hazard creation at sites of natural

resource extraction. Government agencies, both in the U.S. and globally, have expressed

concerns about the mounting dangers of mining-related waste (ICOLD 2001; NRC 2002;

EPA 2013). Several sources also acknowledge that resource-dependent communities may

be more vulnerable to environmental disasters (Flint and Luloff 2005), but few have

attempted to describe other environmental disparities. RBEI incorporates empirical and

theoretical evidence from both rural/resource sociology and environmental sociology.

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Further, RBEI may illuminate rural vulnerabilities like spatial isolation, access to

healthcare, aging infrastructures, and other variables that go beyond race and class. For

example, Malin (2015) examines environmental health problems exacerbated by uranium

mining in Utah and Colorado. Similar to the case of coal impoundments, uranium

companies stored mine tailings haphazardly and they contaminated nearby communities

for decades (Malin and Petrzelka 2010; Malin 2015). However, despite being burdened

by this risk, communities were divided over a proposal to reopen uranium mines. Malin

(2015) describes the social class differences that underlie the division between “sites of

acceptance,” which advocate for uranium mining renewal, and “sites of resistance,”

which oppose the industry. This case illuminates the connections between resource

dependency, rurality, and unequal environmental impacts.

Treadmill of production theory, largely used in environmental sociology, provides

a useful theoretical framework for analyzing RBEI due to its consideration of both

resource extraction and inequality. Schnaiberg (1980) posits that capitalism emphasizes

ecological disorganization through a never-ending series of environmental “withdrawals”

(resource extraction) and “additions” (pollution, waste). Technological advancement

speeds up the “treadmill” and displaces laborers, prompting corporations and states to

encourage continued production to “offset the substitution of capital for labor in the

production process” (Schnaiberg 1980:229). The treadmill also enables a social and

spatial stratification where upper classes benefit from increased profits, while

corporations locate environmental hazards and risks in powerless communities that

cannot fight siting practices (Brulle and Pellow 2006). Further, the neoliberal agenda of

deregulation leads to fewer economic limits, allowing the treadmill to churn on at

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increasing rates (Schnaiberg and Gould 2000). The mentality of neoliberalism can also

influence communities whom depend on extractive industries. In some cases,

marginalized communities are subject to “economic blackmail,” where they are forced to

accept the opportunity for new jobs in exchange for exposure to environmental hazards

(Bullard 1990; Gould 1991). In other cases, neoliberal ideals become entrenched in

communities and residents openly lobby for potentially harmful resource extraction (Bell

and York 2010; Finewood and Stroop 2012; Malin 2015). While the treadmill of

production is an important tool for understanding RBEI, no studies have empirically

tested whether treadmill dynamics have resulted in the unequal distribution of coal-

related hazards across Appalachia.

THE CASE: COAL IMPOUNDMENTS IN APPALACHIA

Coal impoundments have impacted communities in Appalachia for decades. Prior

to the mechanization of coal mining, workers picked coal off of thick underground seams

(Michalek, Gardner, and Wu 1996). Miners were paid based on how much coal they

handpicked, so they were incentivized to separate out and leave behind waste rock. The

introduction of underground machinery after the 1920s hastened the mining process and

boosted production numbers (Ross 1933). The increased production methods also

extracted far more waste (Michalek et al. 1996). Coal slurry—the refuse mixture of

water, coal and rock created from “washing” coal prior to shipment—became a larger

waste management issue in the 1950s (NRC 2002). The demand for cleaner coal in

energy production led to hydraulic systems for crushing coal and separating out finer

amounts of waste (Michalek et al. 1996). These systems created coal slurry and the need

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for a place to store it. Originally, slurry was dumped into local streams and rivers before

“heightened environmental awareness and public pressure… forced coal operators to

construct storage ponds to contain the slurry” (Michalek et al. 1996:1). Initially, slurry

impoundments were created with no government oversight and little regulation.

“Sporadic failures occurred, primarily in rural areas, yet these failures gave no indication

of the magnitude or seriousness of the coal refuse problem being created” (Michalek et

al. 1996:2).

The problem was eventually realized on February 26, 1972, when an

impoundment in West Virginia collapsed and sent a wall of thick slurry down Buffalo

Creek. At the time of the dam’s break, it had been taking in roughly 1,000 tons of coal

waste per day (Bethell and McAteer 1972). The spill killed 125 people and destroyed

entire communities downstream of the impoundment. Coal companies denied

responsibility for the accident, calling it an “act of God” (Erikson 1976). The emergency

response to the disaster ignored community ties and eroded social cohesion among the

impacted populations (Erikson 1976). Despite major revisions in federal regulations after

Buffalo Creek, numerous impoundment failures occurred in the following decades. For

example, a Harlan County, Kentucky coal impoundment failed in 1981 and damaged 30

houses, killing one person (NRC 2002). An impoundment break in Martin County,

Kentucky in 2000 dumped 300 million gallons of slurry into local waterways, an amount

that was 30 times the size of the Valdez oil spill (McSpirit et al. 2002; Salyer 2005). The

National Research Council (2002) highlighted nine impoundment failures from 1977 to

2000, but acknowledged that their list is not comprehensive.

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Despite assurances that the impoundments do not present “imminent danger,”

doubts about their overall safety remain (Ward, Jr. 2013). A Office of Surface Mining

Reclamation and Enforcement (OSMRE) (2011) report found that “quality control

methods used during embankment construction may not be achieving the desired results”

(p. 1). OSMRE engineers reported that only 16 out of 73 embankment compaction tests at

seven high-risk impoundment sites were in compliance with safety regulations (Eilperin

and Mufson 2013). The OSMRE did not release the raw data or the final report related to

their study. While the embankments of impoundments are a concern, most of the major

impoundment failures occurred due to slurry bursting through the bottom of the

impoundment into abandoned mines (NRC 2002). The scarcity of historical mining maps

means some companies may have constructed impoundments on previously mined,

unstable, and hollowed land (NRC 2002).

In addition to the risk of disaster, people living near impoundments are also

subject to ongoing health concerns. However, the scientific evidence about health

problems caused by coal slurry is uncertain. A review of the chemical makeup of coal

slurry from underground injection sites confirms that the substance contains “significant

levels of toxic chemicals” (Ducatman et al. 2013:62). However, the same report could not

prove that coal slurry was leaching into water sources due to the complexity of

groundwater hydrology (Ducatman et al. 2013). Another study concluded that well water

in Williamson, West Virginia was a “public health hazard for the past, present, and

future” due to increased levels of some toxic chemicals (ATSDR 2005:22). Again, the

study could not definitively conclude whether a nearby slurry impoundment was causing

the contamination. This scientific uncertainty is a staple of technological risks that can

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cause dread in nearby communities (Erikson 1994). In a clear connection with the

treadmill of production, companies continue to use impoundments because they remain

the cheapest option, despite safer methods of disposal like a dry-press technique

(Gardner, Houston, and Campoli 2003). European countries have been using dry-press

coal waste disposal methods since the 1940s (Woodruff and Macnamara 2013). Jack

Sparado, a former mine inspector, claims that safer coal waste disposal options would

cost companies one extra dollar per ton of coal (Salyer 2005).

The impoundment disaster in Martin County, Kentucky in 2000 prompted

research on community-level risk perceptions of coal impoundments. Surveys

administered in 2001 and 2005 found that residents in Martin County had greater levels

of risk perception and concern about coal impoundments compared to other counties in

Kentucky (McSpirit et al. 2007). A follow-up study showed that institutional trust levels

and concern slightly recovered in the decade following the impoundment break (Scott et

al. 2012). This increase in trust occurred despite a lack of any additional regulations on

coal impoundments or emergency planning in Kentucky (Scott, McSpirit, and Hardesty

2012). Surveys also found that 58 percent of respondents in two West Virginia counties

agreed or strongly agreed with the statement “people dread living near the impoundment”

(Scott et al. 2012:161). In sum, impoundments clearly pose risks to communities in

Appalachia and warrant an examination of whether certain neighborhoods are

disproportionately proximate to them.

DATA AND METHODS

This study uses spatial analysis to identify significant neighborhood-level

predictors of impoundment proximity. I utilize methods and data sources common to

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environmental inequality research, but examine predictor variables relevant to

Appalachia. These variables capture concepts related to mining presence and intensity,

rurality, and economic deprivation. I chose to use the 2000 long form U.S. Census due to

the survey’s adequate sampling of small-scale geographies.3 GIS Shapefiles and Census

data were obtained from the National Historical Geographic Information System

(Minnesota Population Center 2011).

Units of Analysis

The units of analysis for this study are all U.S. Census tracts in the Appalachian

region in 2000. Tracts are census-designated geographic areas units with an optimal

population of roughly 4,000 (U.S. Census Bureau 2002). Operationalizing

“neighborhoods” or “communities” using census tracts can be problematic, especially in

rural areas, where tracts may span large geographic areas. However, tracts provide full

coverage of the study region as opposed to using census places, which ignore rural

populations outside city limits. Also, tracts conceptualize “neighborhoods” better than the

use of counties, which can often mask meaningful social dynamics due to their large size.

Study Area

While the geographic definition of Appalachia is subject to debate, this project

uses subregions of the Appalachian Regional Commissions’ (ARC) coverage area in

2014. The ARC (2014) divides the 13-state region into five sub-regions that have similar

topography, demographics, and economics. I exclude the Southern region due to the

scarcity of impoundment data in Alabama. While the original database obtained from

MSHA contained 33 coal impoundments in Alabama, only two of those observations

15

included the dates of their creation. An open records request to the Alabama Department

of Environmental Management revealed no additional information about the

impoundments. Therefore, the study area consists of the Northern, North Central, Central,

and South Central regions of Appalachia (Figure 1).

Figure 1. Map of the Appalachian Study Area and Coal Waste Impoundment Locations.

Dependent Variable

The dependent variable measures tract proximity to the nearest coal

impoundment. The impoundment data was obtained by filing a Freedom of Information

Act request with the Mine Safety and Health Administration. In response, MSHA

provided a list of coal impoundments they monitor, as of November 2013. MSHA (2009)

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only monitors impoundments that meet certain size or safety thresholds. Therefore, these

impoundments are the largest and most potentially dangerous.3 The MSHA dataset

included geographic coordinates for each impoundment. However, due to the use of 2000

Census data, I had to identify coal impoundments that were in place by 2000. Additional

public records requests to government agencies in Virginia, Kentucky, Ohio, and West

Virginia obtained impoundment creation dates that were missing from the MSHA

database.4 This discovery process revealed 232 impoundments in the study area, as of

2000. It’s possible that additional impoundments existed in 2000, but have since been

reclaimed or are no longer monitored by MSHA.

Some studies of environmental inequality use dichotomous variables that measure

whether 1) a hazardous facility falls within a geographic unit, or 2) a unit of analysis falls

within a distance buffer of a facility (Mohai and Saha 2007). But Downey (2006) claims

that spatial relationships should be conceptualized as a “continuous, unbounded surface

in which variable values can vary continuously rather than being tied to specific analysis

units” (p. 570). Therefore, the dependent variable in this study is the distance in meters

between the centroid of a tract and the coordinates of the nearest impoundment. The

natural log of the variable reduced the skewness score from .942 to -.541, so it was used

in the analysis. The average distance from a tract to a coal impoundment was roughly 63

kilometers in 2000. While distance buffers aren’t used as part of this analysis, it is worth

noting that 602,064 people lived in tracts whose centroid was within six kilometers of an

impoundment, as of 2000.

Predictor Variables

Economic deprivation factor

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Due to collinearity between relevant socioeconomic variables, I calculated a

principal component factor score that combined four U.S. Census variables: percent

below poverty, percent unemployed, percent without a high school diploma over the age

of 25, and percent of households receiving public assistance. These variables are

consistent with other economic deprivation indices used in environmental inequality

studies (Sicotte and Swanson 2007; Smith 2009). Percent below poverty and percent

households receiving public assistance were the most relevant in defining economic

deprivation with factor loadings of .886 and .842, respectively. The factor loadings for

percent unemployed and percent without a high school diploma were .748 and .721

respectively. A high eigenvalue (2.577) and the percent of the variance explained

between the variables (64.43) indicate that the economic deprivation measure is reliable

and valid.

Mining

The mining variables operationalize tract-level mining economic impact and

historical presence of mining both locally and regionally. I include the percent of the

workforce employed in mining from the U.S. Census Bureau in order to capture whether

a neighborhood benefits from jobs related to the industry. Two additional mining-based

variables were constructed in GIS: 1) the distance from the centroid of the tract to the

nearest abandoned and sealed mine and 2) a mine count of the number of all abandoned

and sealed mines within a 150-kilometer buffer of the centroid of a tract (mine count).

Mine locations were obtained from MSHA’s Mine Data Retrieval System. Distance to

the nearest abandoned and sealed mine indicates a historical presence of mining near the

tract. The “mine count” functions as a control for tracts in geographic regions like Central

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Appalachia that have a history of extensive mining in the region.

Rural

This analysis utilizes several measures of rurality to determine whether rural

places are more likely to be proximate to coal impoundments. County seats play an

Table 1. Summary Statistics

Mean Std. Deviation Minimum Maximum

Dependent variable

Distance to nearest impoundment (meters)

62,897.47 52,108.30 296.971 256,637.90

Natural log of distance to nearest impoundment

10.630 1.012 5.693 12.455

Independent variables

Economic deprivation

Economic deprivation factor 1.06e-07 .999 -1.867 7.613

Mining

Percent employed in mining 1.135 2.931 0 32.881

Distance to nearest abandoned and sealed mine (meters)

28,405.06 30,420.13 94.006 199,272

Mine count within 150 kilometers

1017.23 1453.14 0 5,443

Rural

Distance to nearest county seat (meters)

11,564.57 7,655.154 .930 43,210.6

Percent rural non-farm 41.921 41.574 0 100

Percent rural farm 1.556 3.029 0 29.679

N = 4,203.

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important role in rural regions as the centralized place of jobs, resources, and politics

(Fuguitt 1965). Additional research in Appalachia speaks to the important spatial

stratification between the powerful (and more populated) county seat cities and isolated

small towns (Duncan 1999). Therefore, I measured the distance from the centroid of each

tract to the centroid of the nearest county seat. The rural variables also include percent

rural non-farm and percent rural farm population. The U.S. Census defines rural farm

population as residents who live on farms that produce more than $1,000 of agricultural

production (U.S. Census 2002). The rural non-farm population is calculated by

subtracting the rural farm population from the total rural population (defined as any place

with less than 2,500 residents). Rural non-farm communities and neighborhoods in

Appalachia may face unique challenges due to historical patterns of elite absentee land

ownership (Gaventa 1980, 1998).

Analytical Strategy

This study utilizes spatial regression to control for spatial dependence between

observations. Ordinary least squares (OLS) regression assumes that all of the

observations are independent. But spatial autocorrelation in cases where “location

matters” can violate this basic assumption (Dubin 1998:304). Spatial autocorrelation

occurs when observations impact one another based on their spatial proximity (Tobler

1970; Dubin 1998). This spatial dependency is often ignored in social science analyses,

leading to an underestimation of the actual variance in the data (Ward and Gleditsch

2008). In an environmental inequality assessment of transportation-related emissions in

Florida, Chakraborty (2009) found that the OLS coefficients for several socioeconomic

indicators substantially weaken when spatial dependence is controlled for through spatial

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regression. Chakraborty (2009) notes that the model-fit diagnostics also improved in the

spatial regression models, indicating that spatial regression provided a “more correct or

valid model” (p. 689).

In order to remove spatial dependence, I assigned distance-based, row-

standardized weights to the observations using GeoDa software. The software draws a

Euclidean distance radius around the centroid of each tract to determine “neighbors”

(Anselin, Syabri, and Kho 2006). These weight parameters set a maximum distance at

which observations influence one another spatially. Extensive testing of different

distances indicated that 16-kilometer distance-based weights adequately controlled for

spatial dependence. These weights resulted in only 64 neighborless tracts out of 4,203.

The average number of neighbors for a place was roughly 42 and the median was 14.

Observations with the largest numbers of neighbors were located in metropolitan areas of

Pittsburgh, Pennsylvania and Youngstown, Ohio. Compared to studies of urban areas, 16

kilometers is a large distance for assigning weights. For example, Chakraborty (2009)

uses 2-kilometer weights in his analysis of Tampa, Florida. But the larger distance

threshold is justified given the study area and unit of analysis. Appalachia is a rural

region where some census tracts cover larger geographic areas due to lower population

density. Further, the 16-kilometer distance is not so large that it obscures the spatial

relationship between tracts. In other words, it’s reasonable to assert that rural tracts

within 16 kilometers influence one another socially and economically.

The Lagrange Multiplier diagnostic from the OLS models suggested that spatial

dependence was not fully explained by the independent variables and thus violated “the

assumption that the error terms are independent of one another” (Ward and Gleditsch

21

2008:66, Anselin 2005). Therefore, following Anselin’s (2005) instructions, I utilized a

spatial error model for the spatial regression. The equation for the spatial error model is

summarized as:

y = Xβ + λWξ + ε

where y is the natural log of the distance to the nearest impoundment, X indicates the

matrix of values of the predictor variables, β represents the vector of coefficients for each

predictor variable, λ is the coefficient for the errors that are spatially autocorrelated

(spatial autoregressive coefficient), W is the term that denotes the extent of the spatial

weights matrix, ξ indicates the spatial component of the error term, and ε represents the

independent error that is not spatially autocorrelated (Anselin 2005; Ward and Gleditsch

2008). Therefore, the term λWξ represents the error terms (ξ) that are weighted and

controlled for utilizing the distance-based matrix (W) and the spatial autoregressive

coefficient (λ) (Chakraborty 2009).

RESULTS

Bivariate Correlations

The bivariate correlations between distance to the nearest coal impoundment and

the independent variables are mostly significant in the expected directions. Economic

deprivation is negatively correlated with distance to impoundment, indicating that a

decrease in impoundment proximity results in increased economic deprivation. The

mining-related variables are significantly and strongly correlated with distance to an

impoundment, as expected: close proximity to an impoundment is associated with high

percent employed in mining, higher historical intensity of mining, and closer distance to

22

the nearest abandoned and sealed mine. Distance to a county seat is not significantly

correlated with distance to an impoundment. Percent rural non-farm population has a

weak, but significant, positive correlation with the dependent variable. Tracts with higher

percent farm population are associated with a greater distance from the nearest coal

impoundment.

Correlations between the independent variables do not signal high levels

collinearity, but reveal several interesting relationships. Economic deprivation is

negatively correlated with all measurements of “high” mining characteristics (i.e., close

proximity to an abandoned and sealed mine). Increased distance from a county seat is

associated with higher mining employment and closer distance to an abandoned and

sealed mine, but is not significantly correlated to mine count. The Appalachian literature

on persistent poverty suggests that rurality is expected to be associated with economic

deprivation, but the bivariate correlations show that tracts closer to a county seat are

associated with higher economic deprivation. Percent rural non-farm is not significantly

correlated with economic deprivation, but percent rural farm is positively and weakly

associated with the economic deprivation factor.

23

Tabl

e 2.

Biv

aria

te C

orre

latio

ns B

etw

een

All

Pair

s of D

epen

dent

and

Inde

pend

ent V

aria

bles

1.

2.

3.

4.

5.

6.

7.

8.

1. N

atur

al lo

g of

di

stan

ce to

nea

rest

im

poun

dmen

t

1.00

0

2. E

cono

mic

de

priv

atio

n fa

ctor

-.1

71**

* 1.

000

3. P

erce

nt e

mpl

oyed

in

min

ing

-.430

**

.312

***

1.00

0

4. D

ista

nce

to

near

est a

band

oned

an

d se

aled

min

e

.680

***

-.143

***

-.244

***

1.00

0

5. M

ine

coun

t with

in

150

kilo

met

ers

-.331

***

.341

***

.525

***

-.351

***

1.00

0

6. D

ista

nce

to

near

est c

ount

y se

at

-.009

-.1

57**

* .1

23**

* .1

04**

* .0

01

1.00

0

7. P

erce

nt ru

ral

non-

farm

.069

***

.028

.3

19**

* .0

50**

.2

32**

* .4

03**

* 1.

000

8. P

erce

nt ru

ral f

arm

.1

50**

* .0

63**

* .0

19

.049

***

.134

***

.174

***

.524

***

1.00

0

N =

4,2

03.

* =

p <

.05,

**

= p

24

Spatial Regression Results

The OLS regressions of the models had significant Moran’s I statistics, which

indicated spatial autocorrelation in the models. Thus, those results violate the assumption

of independence between observations and are not presented. The incorporation of 16-

kilometer distance-based weights adequately controlled for space as indicated by the

insignificant Moran’s I of the residuals and significant spatial autoregressive coefficients

(Lambda) across all models. I present the spatial regression results in a process of model

building that separates the explanatory power of each category of variables: economic

deprivation, mining, and rurality.

Model 1 shows a significant (p = .052) negative relationship between economic

deprivation and distance to an impoundment. However, the strength of the relationship is

slight: a 1-factor increase in the economic deprivation factor is, on average, associated

with a .9 percent decrease in distance to an impoundment.5 Model 2 displays the strength

of the mining-related variables, which are all strongly and significantly correlated with

distance to an impoundment. As distance to an impoundment increases, 1) distance to an

abandoned and sealed mine increases, 2) percent employed in mining decreases, and 3)

the number of abandoned and sealed mines within a 150-kilometer buffer decreases.

Model 2 shows that a 1,000-meter increase in distance to an abandoned and sealed mine

results in a 1.1 percent increase in the distance to the nearest impoundment. A one

percent rise in mining employment results in a 3.7 percent decrease in distance to an

impoundment. Meanwhile, a 9.8 percent decrease in distance to an impoundment is

associated with a 1,000-mine increase in the mine count variable.

25

Model 3 shows that, despite the lack of a bivariate correlation, greater distance

from a county seat is a significant predictor of impoundment proximity when controlling

for the other rural variables: a 1,000-meter decrease in the distance to a county seat

results in a .4 percent increase in distance to an impoundment. Also, an increase in rural

farm population is associated with an increase in distance to an impoundment. Rural non-

farm population is not a significant predictor of impoundment proximity in Model 3. The

log likelihood and Akaike information criterion statistics show that Model 2 has the best

model fit of the variable subsets.

Model 4 shows little change in the direction and strength of the coefficients when

combining the rural and mining variables. The log likelihood increase from Model 2 (-

707.819) to Model 4 (-692.463) indicates that the incorporation of rural controls

improves the model fit. Further, rural non-farm population gains significance when

controlling for mining variables, but in the unexpected direction—an increase in rural

non-farm population is associated with an increase in distance to an impoundment. The

full model, Model 5, adds the economic deprivation factor, which is significant at the .10

level (p = .067). The model fit improves only slightly with the incorporation of economic

deprivation, indicating that it doesn’t explain much of the variance between the

observations. There are no major changes in the strength or significance of the mining

and rural variables between Model 4 and Model 5.

The standardized coefficients are presented to help determine the relative

importance of each variable in the models. I calculated the standardized coefficients by

subtracting the mean of the variable from each observation, dividing by the standard

deviation, and running the regression models again. These coefficients, in tandem with

26

Tab

le 3

. Coe

ffic

ient

s fro

m S

patia

l Err

or R

egre

ssio

n of

Tra

ct P

roxi

mity

to a

Coa

l Im

poun

dmen

t on

Inde

pend

ent V

aria

bles

, App

alac

hia,

200

0.

M

odel

1

Mod

el 2

M

odel

3

Mod

el 4

M

odel

5

Eco

nom

ic D

epri

vatio

n

Econ

omic

dep

rivat

ion

fact

or

-.009

(-.0

09)†

-

- -

-.008 (-

.008

)†

Min

ing

Dis

tanc

e to

nea

rest

ab

ando

ned

and

seal

ed m

ine

- 1.

10e-

005

(.330

)***

-

1.08

e-00

5 (.3

26)*

**

1.05

e-00

5 (.3

26)*

**

Perc

ent e

mpl

oyed

in m

inin

g

- -.0

37 (-

.108

)***

-

-0.0

37 (-

.109

)***

-.0

34 (-

.108

)***

Min

e co

unt w

ithin

150

ki

lom

eter

s -

-9.8

0e-0

05 (-

.140

)***

-

-1.1

5e-0

04 (-

.167

)***

-3

.13e

-004

(-.1

62)*

**

Rur

al

Dis

tanc

e to

nea

rest

coun

ty s

eat

- -

-4.0

8e-0

06 (-

.031

)***

-3

.04e

-006

(-.0

23)*

**

-3.1

9e-0

06 (-

.025

)***

Perc

ent r

ural

non

-far

m

- -

1.21

e-00

5 (.0

00)

4.03

e-00

4 (.0

16)*

* 4.

50e-

004

(.015

)**

Perc

ent r

ural

farm

-

- .0

06 (.

019)

***

.006

(.01

9)**

* .0

06 (.

019)

***

Con

stan

t 11

.047

***

10.7

59**

* 11

.101

***

10.7

78**

* 10

.784

***

Lam

bda

0.96

0***

.9

45**

* 0.

960*

**

.944

***

.944

***

Mul

ticol

linea

rity

num

ber

1.00

0 3.

454

4.29

7 5.

563

5.57

4

Log

likel

ihoo

d -9

77.0

4 -7

07.8

1 -9

64.8

7 -6

92.4

6 -6

90.7

9

Log

likel

ihoo

d ch

ange

from

O

LS m

odel

s 49

77.0

1 3,

682.

50

5,00

1 3,

556.

29

3,55

7.11

Aka

ike

info

crit

erio

n 19

58.0

8 14

23.6

4 19

37.7

5 13

98.9

3 13

97.5

9

Mor

an’s

I of

resi

dual

sa

.002

-.0

03

.001

.0

02

-.003

Not

e: S

tand

ardi

zed

coef

ficie

nts

are

pres

ente

d in

par

enth

eses

.

N =

4,2

03 tr

acts

.a Si

gnifi

canc

e of

Mor

an’s

I of

resi

dual

s was

bas

ed o

n 99

9 pe

rmut

atio

ns.

† p

< .1

0, *

p <

.05

, **

p

27

the bivariate correlations and model fit diagnostics, reveal that the mining variables are

the strongest predictors. However, there is variation among the mining variables: distance

to abandoned and sealed mine has the largest standardized coefficient (.326) in Model 5,

followed by mine count (-.162) and percent employed in mining (-.108).

DISCUSSION

These results illuminate several interesting findings related to RBEI in

Appalachia. First, mining-related variables are the most significant tract-level predictors

for proximity to an impoundment. Thus, proximity to the historical presence of mining

(distance to abandoned and sealed mine), historical intensity of mining in the region

(mine count), and mining employment in 2000 explains the most variation between the

observations. Since coal companies build impoundments near mines for convenience

purposes, this relationship is expected. However, this study marks the first time that these

basic assumptions have been empirically tested and supported. Previous literature

illustrates that 1) residents with families employed in mining downplayed the risks of

coal impoundments (McSpirit et al. 2007), 2) mining communities have lower levels of

social capital (Bell 2009), and 3) people dread living near an impoundment (Scott et al.

2012). Therefore, it’s clear that mining communities are unique social groups that are

vulnerable to the risks posed by impoundments.

However, it is important to note that neighborhood proximity to coal

impoundments is not solely explained by mining employment. In fact, only 25 tracts in

Appalachia had 20 percent or more of the workforce employed in mining in 2000.6

28

Further, distance to an abandoned and sealed mine is likely the most important predictor

of impoundment proximity, due to the strong bivariate correlation (.680) and the highest

standardized regression coefficient in Model 5 (.326). Further, the bivariate analysis

reveals a significant, but not incredibly strong correlation (-.244) between mining

employment and proximity to abandoned and sealed mine (Table 2). This indicates that

communities benefiting from mining employment are not always most proximate to

impoundments. Instead, proximity to past mining is the most important predictor of a

neighborhood’s current impoundment proximity, as of 2000. This may reveal another

community-level impact of “addictive” economies—environmental hazards that remain

following the decline of extractive industries (and jobs). Another explanation could relate

to the residential location of mine workers, who may not live in tracts extremely

proximate to impoundments and mines. Longitudinal research could examine whether the

decline in the coal industry over the last several decades weakened the relationship

between tract-level mining employment and impoundment proximity.

Measures of rurality were also significant, but only improved the model fit

slightly. The results do find that tract distance away from a county seat is correlated with

close proximity to an impoundment, when controlling for other variables in the model.

The county seat is often the epicenter of local power relations in rural areas due to larger

populations, more job opportunities, and the proximity of local government services.

Therefore, tracts farther from county seats may be marginalized, especially in rural

regions. Rural non-farm population is a significant predictor in an unexpected direction:

increased rural non-farm population is correlated with being more distant from

impoundments. One explanation may be due to the broad study area. In addition to rural

29

Central Appalachia, there are also rural areas in Northern and South Central Appalachia

that are far from mining regions and impoundments. But the alternative approach—

examining separate subregions—runs the risk of selecting on the dependent variable and

losing variation between the observations. The models also find that rural farm

population is a significant predictor of being farther from an impoundment. As expected,

tracts with larger farm populations, on average, are “buffered” from the impact of

impoundments.

Economic deprivation, while statistically significance at the .10 level, is not a

very strong predictor of impoundment proximity. The variable provided only a marginal

improvement in the model fit in Model 5. As the bivariate correlations show, there is a

statistically significant negative correlation between economic deprivation and distance

to the nearest impoundment. However, that relationship may be mediated by the mining

variables and spatial dependence between observations. Table 2 shows that the

correlations between economic deprivation and all of the mining variables are significant

in the expected direction: increased mining prevalence is correlated with increased

economic deprivation. This finding is similar to urban environmental inequality studies,

which attempt to untangle the relationship between industrial/manufacturing employment

and race and class (Anderton et al. 1994; Bowen 2002).

Lastly, the importance of space in this study should not be overlooked. The log

likelihood between the OLS and spatial error regressions increased dramatically across

all models. Therefore, it is imperative that future studies of both environmental inequality

and resource-dependent communities utilize spatial analysis. It is unlikely that many of

the mining community studies in Freudenburg and Wilson’s (2002) considered spatial

30

dependence, since social scientists were slow to adopt spatial methods (Goodchild et al.

2000). However, this study proves that consideration should be given to spatial

relationships between observations in both mining and environmental inequality studies.

Analyses that ignore spatial dependence run the risk of inflating the significance of

explanatory variables and presenting invalid models (Chakraborty 2009).

CONCLUSION

The findings presented here mark the first time that hypotheses about

environmental inequality have been applied to hazardous coal waste facilities in

Appalachia. This study finds evidence for both disparate proximity and relative

distributional inequality, as defined by Downey (2005). Mining communities—especially

those marked by mining employment, proximity to abandoned and sealed mines,

intensity of historical mining in the region, and increased distance from a county seat—

are disparately proximate to coal impoundments. Also, distance to an abandoned and

sealed mine is the most important predictor of impoundment proximity, indicating a

strong connection between past mining presence and present hazard proximity. Further,

there is no evidence that upper class groups bore more of the risks related to coal

impoundments and thus relative distributional inequality exists. In fact, economic

deprivation is a significant, albeit weak, predictor of impoundment proximity.

A key aspect of advocating for environmental equity and justice is recognizing

that environmental inequality exists. Influential early studies of urban environmental

inequality fueled the development of the environmental justice movement (Szasz and

31

Meuser 1997). However, the same attention has not been directed towards rural and

resource-dependent communities. The development of RBEI will require additional

empirical findings to bring attention to the hazards created by resource extraction. While

this study is largely descriptive, an integrative approach between natural resource, rural,

and environmental sociology allows for further investigation of this case and other cases

of RBEI. For example, this case brings up several interesting questions for future

research: While communities may “accept” environmental hazards in exchange for jobs,

what happens to those hazards when the jobs disappear? How do the fluctuations of

extractive industries impact social dynamics in communities near hazards? The answers

to those questions may help inform future discussions on natural resource use and

extraction. This paper takes a necessary first step towards describing the relationship

between resource extraction, environmental hazards, and neighborhood-level outcomes.

32

ENDNOTES 1The EPA does not regulate coal waste impoundments. The Mine Safety and Health Administration and Office of Surface Mining and Reclamation, and state-level environmental protection agencies are responsible for monitoring coal waste impoundments. 2Following the 2000 Census, the U.S. Census Bureau switched to the American Community Survey (ACS) to collect data in more frequent intervals. While the ACS has adequate sampling at the metropolitan areas and large city level, there are large margins of error and other reliability concerns at the tract-level (Spielman, Folch, and Nagle 2014). 3The Kentucky Department of Environmental Protection acknowledged that their 2014 database included 44,618 freshwater impoundments (mostly small silt ponds for water storage) and 675 slurry impoundments. However, only 138 met MSHA’s monitoring requirements. 4Names of impoundments without date of creation data were subject to an Internet search to see if they existed prior to 2000. This search revealed documents that confirmed the existence of four additional impoundments prior to 2000 (sources available upon request). 5The following interpretations are approximations calculated by multiplying the coefficients by 100 to determine the percent increase in y. This interpretation is valid when dealing with small coefficients like the ones in this study (Gordon 2010:320). 6Bender et al. (1985) defines “mining dependent” counties as those counties with 20 percent or more of the workforce employed in mining.

33

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