examining environmental justice in facility-level regulatory enforcement

Examining Environmental Justice inFacility-Level Regulatory Enforcementn

David M. Konisky, University of Missouri

Tyler S. Schario, University of Missouri

Objective. This article examines claims made by environmental justice advocatesthat government inequitably enforces environmental laws. Methods. We test forrace- and class-based disparities in the regulatory enforcement of the U.S. CleanWater Act from 2000–2005. We estimate pooled logistic regression models, and theanalysis is conducted at the facility-level using an areal apportionment methodologyto measure the composition of populations living near facilities. Results. We findevidence of modest race- and class-based disparities in both government inspectionsand punitive actions taken in response to noncompliant behavior, although thepattern of these disparities depends on model specification. Conclusions. Ourfindings provide some evidence of disparities in government enforcement of thefederal Clean Water Act, lending support to the general claims made by environ-mental justice advocates.

Environmental justice advocates have often contended that disparities inenvironmental risks faced by minority and low-income groups are in partdue to unequal protection provided by government. Robert Bullard(1993:18), for example, has argued that ‘‘[i]nstitutional racism influencesdecisions on local land use, enforcement of environmental regulations, indus-trial facility siting, management of economic vulnerability, and the paths offreeways and highways’’ (emphasis added). Despite these direct claims, thereis only a small empirical literature that systematically examines possibleinequities in government enforcement of environmental laws.

In this article, we consider regulatory actions carried out by the federaland state governments to enforce the federal Clean Water Act (CWA).Specifically, we analyze compliance monitoring and punitive actions di-rected at approximately 6,400 large facilities regulated under the CWA fromthe period of 2000–2005. The central question is whether government

nDirect correspondence to David Konisky, Harry S Truman School of Public Affairs, 105Middlebush Hall, University of Missouri, Columbia, MO 65211, [email protected]. Konisky will provide all data and coding information upon request. The sup-plementary online appendix can be found on Konisky’s personal website at hhttp://web.missouri.edu/�koniskydi. This research was supported by grants from the Russell SageFoundation and the University of Missouri. The authors thank Heather Campbell, JasonGrissom, Douglas Noonan, Craig Thomas, and the anonymous reviewers for helpful com-ments and suggestions. All remaining errors are their own.

SOCIAL SCIENCE QUARTERLY, Volume 91, Number 3, September 2010r 2010 by the Southwestern Social Science Association

mailto:,[email protected]




http://web.missouri.edu/~koniskyd







enforcement behavior is associated with the demographic composition of thepopulations living near facilities—namely: Are governments less likely toenforce the CWA at facilities located in minority and low-income commu-nities? To summarize our results, we find that government is somewhat lesslikely to perform regulatory enforcement of the CWA at facilities in areaswith high levels of poverty. Our findings regarding race are mixed. In gen-eral, we find lower probabilities of inspections for facilities located in largeHispanic areas, but higher probabilities of inspections in large African-American communities. In the case of punitive enforcement actions, ouranalysis suggests that government agencies are more likely to respond tofacilities in noncompliance with punitive measures as the percentages ofAfrican Americans and Hispanics increase. As we show, some of these re-lationships do vary in alternative model specifications, particularly thosedesigned to account for state-level unobserved phenomena.

The article proceeds as follows. In the next sections we review the existingempirical research studying environmental justice and discuss the principaltechniques used by scholars to assess environmental inequity. We then dis-cuss the empirical context of the study and the data and methods used toexamine whether facilities in large minority and/or low-income communi-ties receive less enforcement as claimed by some environmental justice ad-vocates. The subsequent section discusses the results from our statisticaltests, and in the concluding section we discuss the implications of ourfindings.

Research on Environmental Justice and Government Enforcement

There is an extensive scholarly literature that assesses race- and income-based disparities in environmental burdens. Researchers have focused pri-marily on two questions. First, much of the literature examines whethercommercial hazardous waste treatment, disposal, and handling facilities(TSDFs) and other potentially noxious sites are disproportionately locatedin communities with large minority and poor populations. Second, scholarshave studied whether areas with large minority and low-income populationsface disproportionate exposure to pollution. Recent reviews of this literaturehave shown there to be considerable variation in findings, with results beingparticularly sensitive to different empirical contexts and choices about thegeographical scale and scope of the analysis (Baden et al., 2007; Noonan,2008). While there is evidence of race- and class-based disparities, it is by nomeans uniform.

Of more relevance for this study is a third question that has received lessattention in empirical studies: Are there race- and/or class-based disparitiesin government enforcement of environmental laws and regulations? Thereare only a handful of studies that have explicitly considered this question.The first set of studies focused on monetary penalties resulting from civil

836 Social Science Quarterly

judicial decisions. Lavelle and Coyle (1992) studied the pattern of penaltiesin federal district court decisions resulting from litigation in cases wherefacilities were found to be in noncompliance with federal environmentallaws. The authors found that fines given to facilities located in high minorityand poor communities were, on average, about $50,000 lower compared tothose in communities with fewer minorities and more affluence. Lynch,Stretesky, and Burns (2004) also found some evidence that fines leviedagainst petroleum refineries were smaller when they were located in com-munities with large Hispanic and low-income populations. In studies ex-amining a more comprehensive set of judicial decisions, Ringquist (1998)and Atlas (2001) found only minor and generally statistically insignificantdifferences in levels of environmental fines for facilities across different de-mographic and socioeconomic groups.

More recently, Konisky (2009) examined state enforcement of the federalClean Air Act (CAA), the CWA, and the Resource Conservation and Re-covery Act (RCRA). Under these laws, most state environmental agencieshave been delegated the authority by the U.S. Environmental ProtectionAgency (U.S. EPA) to enforce program requirements for the regulated en-tities in their state. Konisky estimated a series of event-count models at thecounty level from 1985–2000 to determine if states performed fewer en-forcement actions in counties with low income and large percentages ofminority population. Across these programs, this analysis estimated thatstate governments performed approximately 2 to 5 percent fewer enforce-ment actions for each percentage increase in county poverty. There was not,however, substantial evidence to support claims of racial bias in environ-mental enforcement.

Konisky (2009) studied patterns of regulatory enforcement directed at alarge number of facilities, across multiple pollution control programs, over arelatively long time period. A significant limitation, however, is that thestudy employed county-level analysis. Counties vary significantly in size,which means treating large rural and small urban areas as commensurateunits. Second, there may be important within-county heterogeneity in en-forcement patterns that cannot be disentangled—that is, enforcement di-rected at facilities within a county may vary substantially. Third, county-level analysis neither accounts for the specific location of facilities nor theproximity of the potentially affected population living near the facilities.

One way to address the above limitations is through facility-level analysis,and several studies have examined facility-level enforcement activities. Muchof this work has focused on a small number of plants from particularindustrial sectors such as steel (Deily and Gray, 1991; Gray and Deily,1996) and pulp and paper (Gray and Shadbegian, 2004, Helland, 1998a,1998b; Magat and Viscusi, 1990; Nadeau, 1997), while others have exam-ined municipal wastewater treatment facilities (Earnhart, 2004a, b). Otherwork examines more comprehensive sets of facilities (Decker and Pope,2005; Scholz and Wang, 2006). These studies have not had the explicit

Environmental Justice in Facility-Level Regulatory Enforcement 837

purpose of studying patterns of race- and/or class-based disparities in facilityenforcement, and many do not consider the demographic characteristics ofadjacent populations. The studies that do tend to have inconsistent results.For example, Gray and Shadbegian (2004) found areas with large poorpopulations to be associated with fewer punitive enforcement measures un-der the CAA and the CWA (but more inspections under the CAA), andareas with large minority populations to be related to more punitive actionsunder the CWA. Scholz and Wang (2006) found inspections to be positivelyrelated to per-capita income and negatively associated with the percentage ofAfrican-American and Hispanic residents.

Mennis (2005) more explicitly tested for disparities in regulatory en-forcement in a study analyzing actions conducted to enforce the federal CAAat about 1,200 facilities in New Jersey. He examined the relationship be-tween several types of punitive actions and the characteristics of the com-munity within one kilometer of facilities. He found that feweradministrative orders and lower monetary penalty amounts were given tofacilities in high-percent minority areas compared to low-percent minorityareas, and fewer notices of violations were issued to facilities in high-povertyareas compared to low-poverty areas. Mennis utilized an areal weightingmethod to define the relevant population surrounding the New Jersey fa-cilities, but did not go beyond comparison of means tests, and only includedfacilities from a single state.

Methods of Assessing Environmental Inequity

The most common approach used to evaluate the distribution of envi-ronmental burdens across demographic groups is the method of ‘‘spatialcoincidence.’’ This method begins with an a priori selection of a geograph-ical unit for analysis, such as counties, zipcodes, or Census tracts. Thisselection tends to be based on either general notions of the possible exposurefrom an environmental harm or by data availability. Analysts then determinewhich of the geographical units contain or ‘‘host’’ the facility or unwantedland use of interest and select a relevant group of ‘‘nonhost’’ units forcomparison. To identify inequities, analysts compare the demographic at-tributes of the geographical units containing facilities with those not con-taining facilities.

There are two well-recognized limitations of this approach. First, an as-sumption is made that demographic characteristics are uniformly distributedamong the population of the geographical unit. This is unlikely to be true,particularly when large geographical units such as counties are used to de-scribe the population surrounding the facility. To the extent to which thereis within-unit variation in demographic characteristics, studies may mis-estimate the attributes of the population immediately surrounding the fa-cility. The second limitation is that the spatial coincidence approach assumes


that the relevant population is contained wholly in the unit. This assump-tion is problematic when a facility is located on the border of another unit,in which case part of the relevant population subject to potential environ-mental risks may be in the adjacent unit. To the degree that the units’populations differ in a way meaningful to the study, inferences about en-vironmental inequities may be compromised.

To address the limitations of the spatial coincidence approach, scholarshave recommended the use of distance-based methods to better match pos-sible environmental risks with the potentially affected populations (Mohaiand Saha, 2006).1 The general idea is to, first, precisely map the potentialhazard in space and, second, to use specific distances to identify potentiallyat-risk populations. This is typically done with geographic information sys-tems (GIS) software by drawing a circle with a specific radius around afacility (e.g., one mile, two miles, etc.), and then intersecting the facilityinformation with Census information to estimate population attributes. Aswe describe in detail below, we use an areal apportionment method becauseit best accounts for proximity between possible environmental hazards andsurrounding populations and explicitly allows for differing effects withinanalysis units (Downey, 2006).

Regulatory Enforcement of the Clean Water Act

The empirical context we explore is regulatory enforcement of the CWA.Facilities regulated under the CWA are an important group to study for atleast two reasons. First, according to recent U.S. EPA data, water quality inthe United States continues to be a major problem. Approximately 47percent of U.S. rivers and streams and 59 percent of lakes, reservoirs, andponds are currently rated as ‘‘impaired,’’ which means that they cannotsupport at least one of their designated uses (e.g., drinking water, swimming,aquatic life) (U.S. EPA, 2008). Second, surveys indicate that the publicbelieves that water quality should be a top environmental priority for gov-ernment (Konisky, Milyo, and Richardson, 2008), and that minorities arejust as (if not more) concerned about water-quality problems than are non-minorities (Mohai and Bryant, 1998).

The centerpiece regulatory program of the CWA is the National PollutionDischarge Elimination System (NPDES), which requires that all facilitiesdischarging pollutants directly into U.S. waterways obtain a permit. NPDESpermits are legally binding documents that specify the frequency, quantity,and location of discharges into waterways. Discharge limits contained in

1Several studies have used distance-based methods to test for environmental inequities infacility siting and pollution exposure (e.g., Sicotte and Swanson, 2007; Boer et al., 1997;Pollack and Vittas, 1995; Pastor, Sadd, and Morello-Frosch, 2004; Hamilton and Viscusi,1999; Mohai and Saha, 2006, 2007).


NPDES permits are based on industry-specific, technology-based standards,as well as on state-level water-quality-based limits if the technology standardsare deemed insufficient to meet the specified waterway’s designated use(s).NPDES permits also specify monitoring and reporting requirements,which help the relevant regulating agency determine compliance with permitobligations.

Once a permit has been issued, the enforcement process begins withcompliance-monitoring activities. Large facilities are required to submit pe-riodic discharge reports, which provide information as to whether facilitiesare in compliance with their permits. Notwithstanding these self-monitoringrequirements, government inspections are the principal means by whichgovernment agencies detect violations. In cases where self-monitoring or aninspection detects noncompliance, the state agency (or the EPA regionaloffice in nondelegated states) must determine the severity of the violationsand determine that either no further action is necessary or respond with anenforcement action.

What explains government enforcement decisions? Following the work ofScholz and Wood (1998, 1999), we rely on a political model of regulatorybehavior that uses deterrence theory to connect the behavior of governmentagencies and regulated entities. More specifically, Scholz and Wood arguethat agencies increase enforcement efforts when the marginal returns gen-erate greater deterrence, but that these enforcement efforts also reflect localpolitical conditions. Agencies conduct more enforcement in times and ju-risdictions dominated by policy supporters than in comparable times andjurisdictions controlled by policy opponents (Scholz and Wang, 2006).Based on this political model of regulatory behavior, we model CWAenforcement as a function of facility-level characteristics—including thedemographic composition of the surrounding neighborhood—and a set ofcontextual factors that might also affect agency enforcement decisions.Several studies of regulatory enforcement directed at CWA facilities havebeen based on similar theoretical models (e.g., Earnhart, 2004a, 2004b;Gray and Shadbegian, 2004; Scholz and Wang, 2006). The approach herediffers in a couple of ways. First, we do not simultaneously analyze policyoutputs (agency enforcement) and policy outcomes (firm compliance), so asto focus on the explicit claim in the environmental justice literature ondisparities in government enforcement. Second, as we describe more below,we pay more attention to the measurement of the demographic compositionof the population living around these facilities.

Data and Methods

In this study we test for race- and/or class-based disparities in governmentbehavior by studying enforcement directed at facilities regulated underthe federal CWA. We focus on ‘‘major’’ facilities, which are the largest


water-polluting facilities in the country, and include both industrial andmunicipal dischargers.2 There are currently about 6,400 such facilities ac-tively operating in the contiguous United States.

Dependent Variables

The dependent variables in our analyses are federal and state enforcementactions. We separately consider compliance monitoring actions (i.e., in-spections) and punitive actions taken by government agencies in response tofacility violations, which include informal actions, such as warning lettersand official notices of violation, and formal punitive actions, such as ad-ministrative orders and civil penalties.3 These data were compiled from theU.S. EPA’s Integrated Database for Enforcement Analysis (IDEA). As wediscuss more below, the compliance status of a CWA facility is determinedon a quarterly basis, so our unit of analysis is the facility-quarter, and weconsider the period of 2000–2005. Descriptive statistics for these and allother variables are presented in Table A1 in the online appendix.

Population Characteristics

Data from the U.S. EPA’s Geospatial Data Access Project enable preciselylocating all active CWA facilities holding major NPDES permits. We mea-sure the demographic characteristics of the population living around each ofthese facilities using an areal apportionment method. Specifically, we sep-arately intersected the geospatial map of the facilities with a map of U.S.Census 2000 tracts and block groups. Since we do not have sufficient in-formation to measure the neighborhood around a facility based on hydro-logical criteria or possible exposure to a facility’s pollution, we mustotherwise define the scope of the potentially affected populations. To do so,we first created a one-mile radius circular buffer around each facility. Wethen used a GIS intersect function to merge spatial data from the circularbuffer with that from the Census maps, and used these intersections asweights for each demographic attribute, where the weight was the propor-tion of each Census unit contained within the circular buffers.4 We use this

2A major NPDES facility is one that has been designated as such by an EPA regionaladministrator or in consultation with delegated state environmental agencies, and all mu-nicipal dischargers with design flows of greater than 1 million gallons.

3The results presented here aggregate informal and formal actions. We also consideredeach individually, with similar substantive findings.

4As with the spatial coincidence method, areal apportionment assumes demographiccharacteristics are uniformly distributed within a Census unit. This assumption is less prob-lematic here because tracts and block groups are relatively small geographical units. In a smallnumber of cases, the demographic data from the 2000 Census were missing for some units;for these cases, we imputed a value based on the average of the other intersected units.


method to construct the population attributes of central interest to ourstudy: the percentage of the population living around facilities that is poor,African American, and Hispanic, as well as a measure of population densitybecause residential patterns for different minority groups vary along urbanand rural lines.5

By examining both Census tracts and block groups, we check the sen-sitivity of the relationships between demographics and regulatory enforce-ment to the use of different geographical scales.6 In addition, although weonly report and discuss neighborhoods constructed within a one-mile radiusbuffer, we also estimated models using radii of one-half, two, and three miles(presented in tables in the online appendix).7

Control Variables

A potentially important facility-level factor to account for is the politicalcapacity of the population living around a facility. Communities with highlevels of political capacity are more likely to overcome organizational con-straints and collective-action problems and to demand and secure stricterenforcement of environmental laws. Several studies have found that com-munities with higher political capacity were able to stave off environmentallyunfavorable actions (Hamilton, 1993, 1995; Hamilton and Viscusi, 1999;Hird and Reese, 1998). We employed areal apportionment to construct twoindicators of political capacity: percentage of the population with a collegeeducation and the percentage of owner-occupied housing.8 We also includean additional variable to control for residential stability, which several stud-ies have found to be an important indicator of neighborhood-level socialcapital, which is positively associated with a community’s political capacity(Sampson, 1997; Sampson, Morenoff, and Earls, 1999).

Research has shown that government enforcement is often directed atfacilities that are known past violators (e.g., Helland, 1998b; Scholz andWood, 1998; Scholz and Wang, 2006), so we include a variable capturing afacility’s past noncompliance behavior. Facility compliance under the CWAis determined on a quarterly basis, the result of government compliance-monitoring activities and/or facility self-monitoring. To measure noncom-

5In addition to the possibility that without controlling for population density, correlationsbetween enforcement and demographic attributes might be spurious, it is also possible thatagencies enforce facilities more stringently in urban areas because effluent violations mayaffect the drinking water of a large number of residents.

6In the context of facility location, past studies have shown that effects can vary acrossmultiple scales (Anderton et al., 1994; Baden et al., 2007; Cutter et al., 1996; Noonan,2008).

7Recent work on environmental justice in the context of facility location uses distances ofone mile and three miles (Mohai and Saha, 2006, 2007).

8A more direct measure of political resources is voter turnout. Although turnout data areavailable at the precinct level, spatial precinct data are not available for all states.


pliance, we create an indicator variable to denote if a facility is found to bein noncompliance during any one quarter of the past year. We also include ameasure of past inspections. As a predictor of inspections in a given quarter,one might expect a negative relationship if government is allocating itscompliance-monitoring efforts across its jurisdiction. Past inspections, how-ever, may also positively relate to the probability of a current inspection ifgovernment is targeting frequent violators. In the punitive actions model,government inspection of a facility should increase the probability of de-tecting a violation, which in turn increases the likelihood of a sanction. Weinclude lagged inspections measured dichotomously, where 1 means that afacility was inspected at least once during the last year (previous four quar-ters), and 0 not.

We also control for whether the facility is publicly or privately owned and,if privately owned, whether it is in a couple of different industry sectors.9

Nearly 70 percent of the 6,400 facilities examined are publicly owned fa-cilities, mostly municipal wastewater treatment plants. Not much work hasexamined if patterns of regulatory enforcement differ for public and privatefacilities so we do not have a specific directional hypothesis. We also includedummy variables to control for whether the facility is in the pulp and papersector or steel sector of manufacturing. Scholz and Wang (2006) showedthat facilities in these sectors have been targeted for enforcement. There maybe other relevant unobserved facility-level attributes that we can assume arefixed in time given the timeframe of the analysis. Inclusion of a facility-levelfixed effect, however, would not permit the estimation of important, time-invariant facility characteristics, including the neighborhood demographicsof central interest.10

The political model of regulatory behavior also suggests that agency en-forcement decisions directed at facilities are influenced by state political andeconomic conditions (Helland, 1998a; Scholz and Wang, 2006), and weinclude several variables to account for these state-level factors. First, weinclude four measures of state politics. States controlled by Democraticpoliticians are typically assumed to advocate for more stringent enforcementof environmental laws and regulations, although empirical studies have beenmixed. We include two dummy variables to measure partisan control of thestate: a variable coded 1 for Democratic governors and 0 otherwise, and avariable coded 1 for a state legislature under unified Democratic control and0 otherwise (we exclude facilities in Nebraska due to its nonpartisan state

9Another facility factor that some studies have shown to be correlated with regulatoryenforcement decisions is flow capacity (Earnhart, 2004a, 2004b), which is an indicator offacility size. These data were missing for about 15 percent of the facilities in the study but,when included, there was a negative correlation with both inspections and punitive actions;however, the model estimates did not substantively change for the variables of principalinterest.

10We do cluster the standard errors at the facility level to allow for potential correlation inerrors for each facility in the pooled model.


legislature). We also include the average League of Conservation Voters(LCV) voting score for the state’s delegation to the U.S. House of Rep-resentatives to measure elite-level environmental attitudes. To control forcitizen ideology, we include the measure developed by Berry et al. (1998).State economic conditions may also influence the regulatory enforcementbehavior of government agencies. One might expect agencies to curtailenvironmental enforcement effort during tough economic times, and thereis some work suggesting such a relationship (e.g., Helland, 1998c). Tocontrol for economic conditions, we include the state unemployment ratewith data collected from the U.S. Bureau of Labor Statistics.

Last, we account for whether a state has been delegated authority, or whatis often called primacy, to enforce the federal CWA (coded 1 for primacystates and 0 for nonprimacy states). The CWA is a partially preemptiveprogram in which interested states can petition (and receive partial fundingfrom) the EPA for authorization to implement and enforce the CWA if theyhave enacted at least as stringent a program into state law and have dem-onstrated that they have sufficient legal and administrative capacity to runthe program. The federal government maintains oversight responsibility tomonitor state implementation, and operates the program in states that areeither not awarded primacy or do not seek such authorization. In the periodstudied in this analysis, all but four states (Idaho, Massachusetts, NewHampshire, and New Mexico) had been delegated authority by the U.S.EPA to enforce the NDPES program.11 We do not have a strong theoreticalprediction about the direction of its relationship with regulatory enforce-ment, and past work suggests mixed findings. In state-level analysis, Hunterand Waterman (1996) found that delegated states performed fewer CWAregulatory enforcement actions than did federal EPA personnel, while Scholzand Wang (2006) found that a facility was more likely to receive aninspection when in a state with primacy.

Model Specifications

To examine the relationship between the racial and class composition ofthe populations living around these CWA facilities and government reg-ulatory enforcement,12 we estimate pooled logistic regression models topredict the likelihood that a government agency performed an inspection orresponded to a violation with a punitive enforcement action at a given

11Alaska has not been delegated authority either, but it is excluded from the analysis. TheEPA delegated NPDES program authority to Maine in January 2001 and to Arizona inDecember 2002.

12In our reduced form approach, it is not possible to rule out the possibility that thecommunity characteristics we examine do not affect regulators directly, but instead affectfacility compliance decisions, which in turn affect the enforcement decisions.


facility in a given quarter.13 The explanatory variables of primary interest arethe percentages of African Americans, Hispanics, and individuals living inpoverty. Consistent with the political model of regulatory behavior, wecontrol for facility-level attributes and state political conditions that shouldaffect agency enforcement decisions.

In subsequent analysis, we consider models that take into account possibleregional differences and unobserved state-level factors. An alternative spec-ification that includes regional indicator variables will account for the non-random sorting of the demographic groups of interest across the country,which is potentially important because African Americans and Hispanicstend to be concentrated in different parts of the country. Addressing theeffect of unobserved state-level factors is also important. If the state-levelcontrols discussed above do not fully capture variation in state regulatoryregimes, the models may suffer from omitted variable bias. Examples mightinclude variation in how state environmental agencies use their discretion toenforce the CWA, as well as the effects of differences in the structure andinternal operation of these agencies. A state fixed-effects specificationswitches the identification of the effects of the environmental justice mea-sures using variation within states (rather than across states), and allows forunobserved state-specific phenomena.14

Empirical Findings

The model estimates in Table 1 suggest mixed findings regarding therelationship between government inspections and the extent of the minoritypopulations residing near CWA facilities. The positive coefficients on theAfrican American variables indicate that government was, on average, morelikely to conduct an inspection at a facility in a community with a largeblack population. However, the opposite was the case for facilities in com-munities with a large percentage of Hispanics, as indicated by the negativecoefficients on the Hispanic variable in each of the models. The divergentresults for facilities in large African-American and Hispanic areas highlightthe importance of considering these minority groups separately, somethingthat is not always done in the extant literature. Turning to the results forpoverty, there is a negative relationship between poverty and inspections,although the coefficients are only marginally statistically significant in themodels using block groups to measure neighborhood characteristics. These

13Because of the pooled nature of the data, the models may suffer from serial correlation.We use a time cubic polynomial (i.e., t, t2, and t3) as recommended by Carter and Signorino(2007) to model temporal dependence in binary data. It is also possible that spatial au-tocorrelation among the defined populations is leading to an underestimation of standarderrors.

14A Hausman test indicates that the fixed-effects model is preferred to the random-effectsmodel.


TABLE 1

Government Enforcement at CWA Facilities (2000–2005)

Variables

Inspections Punitive Actions

Tracts Block Groups Tracts Block Groups

1 2 3 4

Race and Income Measures% African American 0.0041 nn n 0.0038 nn n 0.0100 nn n 0.0086 nn n

(0.0007) (0.0007) (0.0016) (0.0015)% Hispanic � 0.0086 nn n � 0.0091 nn n 0.0176 nn n 0.0168 nn n

(0.0009) (0.0009) (0.0015) (0.0014)% Poverty � 0.0032 � 0.0029 n � 0.0066 � 0.0056 n

Facility-Level Controls (0.0021) (0.0017) (0.0041) (0.0003)Population density 0.0339 nn n 0.0356 nn n 0.0120 0.0128

(0.0046) (0.0051) (0.0103) (0.0099)% Home ownership 0.0028 nn n 0.0031 nn n 0.0030 0.0034 n

(0.0010) (0.0009) (0.0021) (0.0018)% College educated � 0.0016 � 0.0024 nn 0.0026 � 0.0004

(0.0012) (0.0011) (0.0023) (0.0021)Residential stability 0.0057 nn n 0.0029 nn n 0.0024 � 0.0027 n

(0.0011) (0.0009) (0.0022) (0.0016)Inspected last year 0.4153 nn n 0.4184 nn n 0.3023 nn n 0.3053 nn n

(0.0234) (0.0233) (0.0357) (0.0359)Noncompliance last year 0.0068 0.0088 0.6385 nn n 0.6375 nn n

(0.0169) (0.0169) (0.0365) (0.0366)Public facility 0.2177 nn n 0.2105 nn n 0.1714 nn n 0.1654 nn n

(0.0259) (0.0260) (0.0534) (0.0538)Paper industry � 0.0798 n � 0.0756 � 0.6803 nn n � 0.6674 nn n

(0.0461) (0.0464) (0.1550) (0.1546)Steel industry � 0.0018 � 0.0017 � 0.0485 � 0.0690

(0.0771) (0.0770) (0.1462) (0.1460)State-Level ControlsDemocratic governor 0.1570 nn n 0.1569 nn n � 0.2191 nn n � 0.2251 nn n

(0.0156) (0.0156) (0.0439) (0.0438)Democratic legislature � 0.0616 nn n � 0.0606 nn n 0.3901 nn n 0.4009 nn n

(0.0190) (0.0190) (0.0517) (0.0517)U.S. House LCV score � 0.0032 nn n � 0.0032 nn n � 0.0195 nn n � 0.0195 nn n

(0.0008) (0.0008) (0.0019) (0.0019)Citizen ideology 0.0202 nn n 0.0205 nn n � 0.0006 0.0001

(0.0017) (0.0017) (0.0033) (0.0033)Unemployment 0.1125 nn n 0.1104 nn n 0.2334 nn n 0.2379 nn n

(0.0132) (0.0132) (0.0259) (0.0258)Delegated state 0.5070 nn n 0.5084 nn n 0.4609 nn n 0.4583 nn n

(0.0389) (0.0389) (0.1180) (0.1188)Constant � 3.2697 nn n � 3.1300 nn n � 3.7136 nn n � 3.4300 nn n

(0.1362) (0.1271) (0.2415) (0.2198)Observations 152,208 152,208 75,600 75,600LR (DF 5 21) 4315.3 4254.3 4019.8 3980.7Prob4LR 0.00 0.00 0.00 0.00% Correctly predicted 73.3 73.4 89.2 89.3

NOTE: Cells contain logistic regression coefficients with facility clustered standard errors inparentheses. All models include a time cubic polynomial. Significance levels: n n n0.01; n n0.05;n0.10.


results provide some indication that governments were less likely to performan inspection at facilities surrounded by poor populations. The estimates donot substantially differ depending on the Census unit used to construct thedemographic composition of the areas adjacent to the facilities. Moreover,the results are not sensitive to the use of alternative distances to approxi-mate population characteristics, as shown in Tables A2–A4 in the onlineappendix.

The magnitude of these disparities is small. We computed the predictedprobabilities of a CWA facility receiving an inspection in a given quarterover the entire distribution of the African American, Hispanic, and povertyvariables, holding the other variables in the model constant at their means.The left column of graphs in Figure A1 in the online appendix present thesepredicted probabilities from the model estimates using Census blocks toconstruct population characteristics. As an example, the change in the pre-dicted probability of an inspection for a CWA facility in a community witha poor population of 6.28 percent (25th percentile) is 0.26, compared to0.25 for a facility in an area with a surrounding population of 16.8 percent(75th percentile). The difference of 0.01 reflects less than a 5 percentdifference in the probability of an inspection. The sizes of the effects are ofsimilar magnitude for the percentages of the neighborhood population thatare African American or Hispanic.

Many control variables are suggestive of systematic relationships withcompliance-monitoring activities. As anticipated, percentage of home own-ership is associated with a greater likelihood of inspection although, un-expectedly, there is a weakly negative relationship with the education level ofthe population. Government agencies were also more likely to inspect CWAfacilities in densely populated areas and in areas with more residential sta-bility. Past compliance monitoring positively predicted a government in-spection, and publicly owned facilities were more likely to receive aninspection. Facilities in states with Democratic governors and more liberalcitizenries were more likely to receive an inspection at a CWA facility,although facilities in states with Democratically controlled state legislaturesand more pro-environmental-leaning elites were, curiously, less likely toreceive an inspection. Counter to expectations, government inspections werepositively associated with unemployment, suggesting that economic condi-tions did not lead to a curtailment of compliance-monitoring activities.Finally, facilities in states in which the U.S. EPA had delegated the authorityto administer the federal NPDES program were more likely to have receivedan inspection, suggesting that states were more aggressive in compliancemonitoring than was the federal government.

To examine the relationship between race, poverty, and punitive enforce-ment actions, we consider the same model as above with one difference.Since punitive actions logically only follow discoveries of violations, werestrict the sample to facilities designated by the federal or a state govern-ment as being in any kind of noncompliance in at least one quarter over the


past year. Because a designation of noncompliance can mean anything froma paperwork violation to a serious discharge violation, in these models wealso control for whether the facility was designated as being in ‘‘significant’’noncompliance (CWA regulations include criteria that specify the duration,severity, and type of violations that rise to the level of significant noncom-pliance). We expect that the federal and state governments would be morelikely to punish a violating facility when the infractions reach the level ofsignificant noncompliance.

There are positive coefficients on the African American and Hispanicvariables in each model, as presented in the last two columns of Table 1.This differs from the case of inspections, for which we found a negativerelationship between the probability of an inspection and the percentage ofHispanics in the area surrounding the facilities. These findings suggest thatgovernment agencies were more likely to pursue a punitive sanction against aCWA facility in violation of its permit obligations when the facility waslocated in a large minority community. With respect to class-based dispar-ities, the parameter estimates again suggest a weakly negative association—CWA facilities in areas with large poor populations were somewhat lesslikely to face a punitive action in response to a violation.

This pattern of relationships mirrors that found by Gray and Shadbegian(2004) in their facility-level study of CWA regulatory enforcement at 400pulp and paper mills, but differs from that found by Mennis (2005) in hisstudy of air pollution facilities in New Jersey. Moreover, as was the case forinspections, the estimated coefficients are of about the same magnituderegardless of whether Census tracts or block groups were used to estimate thedemographic attributes of the population living adjacent to the facilities.

The graphs in the right column of Figure A1 (online appendix) show thepredicted probabilities of a punitive action taken toward a noncompliantCWA facility for different values of the race and poverty measures, againholding the remaining variables constant at their mean levels. The change inthe predicted probability that government takes a punitive action against anoncompliant facility in a community with a poor population of 6.28percent (25th percentile) is about 0.09 compared to 0.08 for a facility in anarea with a surrounding population of 16.8 percent (75th percentile). Thedifference in the predicted probability is similarly about 0.01 for a facilitylocated in an area with approximately 1 percent of the population beingAfrican American (about the 25th percentile) compared to an area with 13percent of the population being African American (about the 75th percen-tile). These predicted probabilities reflect about a 10 percent difference inthe probability that government responds to noncompliant behavior with apunitive enforcement measure.

In terms of the control variables, past noncompliance was positivelyrelated to the likelihood that a facility received a punitive action. Facilitiesin the paper (but not steel) industry were associated with a decreasedprobability of punitive enforcement. Dissimilar to the case of inspections,


facilities in states with Democratic governors were associated with a lowerlikelihood of inspections, while facilities in states with Democratically con-trolled state legislatures were associated with greater likelihoods. The prob-ability of a punitive action was again positively associated withunemployment and states delegated the authority to administer the federalNPDES program.

To evaluate the robustness of these findings, we also considered severalalternative models (reported in Tables A5 and A6 in the online appendix).First, we estimated negative binomial regression models using the totalnumber of inspections or punitive actions directed at a facility in a givenquarter as the dependent variables. The results of these models weregenerally similar to those from the previously reported logistic regressions.Second, we consider the possibility that the parameters of interest varydepending on whether the CWA facility in question is publicly owned, suchas a municipal wastewater treatment facility, or privately owned, such as anindustrial discharger. Theoretically, it is not clear what to expect and moststudies in the literature on regulatory enforcement have considered onlyprivately owned facilities, with Earnhart’s (2004a, 2004b) analysis of en-forcement directed at publicly owned treatment facilities in Kansas being anexception. With regard to the findings for the racial composition of theadjacent populations, the results are largely consistent with our baselinespecifications. The same is true for poverty, but the results are considerablymore robust for private facilities.

Region and State Effects

To account for possible regional differences and unobserved state-levelfactors, we also considered a couple of additional specifications, which arepresented in Table 2. Columns 1 and 3 show model estimates for inspec-tions and punitive actions, respectively, when adding four regional indicatorvariables (Northeast, South, Midwest, and West) to the previous models (forsuccinctness, we only report the results when using Census tracts to measurepopulation characteristics). The coefficients suggest the same general patternof disparities in regulatory enforcement for each type of regulatory action.We also estimated models using eight (U.S. BEA) region dummies (notreported), with largely similar results. The only exception is that the positivecoefficient for percent Hispanic does not reach statistical significance in thepunitive action models.

We also estimated state fixed-effects models to capture possible state-levelunobserved factors related to regulatory enforcement decisions. The resultsfor the inspections and punitive actions equations are presented in Columns2 and 4 of Table 2, respectively. Incorporating state fixed effects yields somesubstantively different results. In the case of inspections, the coefficientssuggest that as the percentage of African Americans living near a facility


TABLE 2

Government Enforcement at CWA Facilities (2000–2005), Region and StateEffects Models

Variables


1 2 3 4

Race and Income Measures% African American 0.0052 n n n � 0.0029 n n n 0.0049 n n n � 0.0016

(0.0008) (0.0008) (0.0016) (0.0015)% Hispanic � 0.0047 n n n � 0.0017 0.0098 n n n 0.0007

(0.0009) (0.0010) (0.0016) (0.0016)% Poverty � 0.0022 0.0042 n n � 0.0058 � 0.0012

(0.0021) (0.0018) (0.0041) (0.0035)Facility-Level ControlsPopulation density 0.0248 n n n 0.0244 n n n 0.0269 n n n 0.0357 n n n

(0.0040) (0.0038) (0.0093) (0.0090)% Home ownership 0.0029 n n n 0.0005 0.0015 � 0.0008

(0.0010) (0.0009) (0.0020) (0.0018)% College educated � 0.0007 � 0.0018 n 0.0017 � 0.0049 n n

(0.0012) (0.0010) (0.0023) (0.0020)Residential stability 0.0032 n n n 0.0024 n n 0.0061 n n n 0.0066 n n n

(0.0011) (0.0010) (0.0022) (0.0020)Inspected last year 0.4125 n n n � 0.0789 n n n 0.3436 n n n 0.2446 n n n

(0.0238) (0.0222) (0.0362) (0.0388)Noncompliance last

year� 0.0248 0.1225 n n n 0.5766 n n n 0.5916 n n n

(0.0172) (0.0157) (0.0363) (0.0355)Public facility 0.1994 n n n 0.2206 n n n 0.1998 n n n 0.3127 n n n

(0.0258) (0.0234) (0.0522) (0.0462)Paper industry � 0.0697 0.0992 n n � 0.6732 n n n � 0.5437 n n n

(0.0457) (0.0461) (0.1507) (0.1323)Steel industry � 0.0604 0.0325 0.1074 0.4902 n n n

(0.0765) (0.0795) (0.1350) (0.1182)State-Level ControlsDemocratic governor 0.1414 n n n � 0.0009 � 0.1084 n n 0.0116

(0.0154) (0.0160) (0.0439) (0.0454)Democratic legislature 0.0581 n n n � 0.1002 n n n 0.1075 n n � 0.2954 n n n

(0.0216) (0.0291) (0.0548) (0.0703)U.S. House LCV score � 0.0049 n n n � 0.0030 n n n � 0.0087 n n n 0.0062 n n

(0.0008) (0.0010) (0.0020) (0.0028)Citizen ideology 0.0136 n n n � 0.0003 0.0113 n n n � 0.0005

(0.0015) (0.0016) (0.0035) (0.0046)Unemployment 0.1512 n n n 0.0515 n n n 0.1724 n n n � 0.0795 n n n

(0.0131) (0.0125) (0.0256) (0.0308)Delegated state 0.5379 n n n 0.3043 n n n 0.2864 n n n � 0.2522

(0.0387) (0.0837) (0.1100) (0.7222)RegionsSouth 0.0704 n n 0.8909 n n n

(0.0340) (0.0993)Northeast 0.5171 n n n � 0.5170 n n n

(0.0458) (0.1183)


increases, the probability of an inspection decreases, a finding more con-sistent with the claims of many environmental justice advocates. Althoughthere is a negative association between the percentage of Hispanics residingnear the facility and inspections, the coefficient is not statistically significant.In contrast with the previous models, there is a positive relationship betweenpercent poor and the likelihood of a government inspection. Turning to thepunitive action model, none of the coefficients on the environmental justicevariables reach statistical significance, suggesting a lack of disparities ingovernment sanctioning decisions.

These divergent results are interesting. Not only does the evidence ofinequities disappear in the punitive action models, there is an altogetherdifferent pattern of disparities in the inspections model. Moreover, to theextent that time-invariant, unobserved factors are influencing state-level en-forcement behavior (i.e., factors not accounted for in the previous models),this suggests a different implication about the disparities since their source isnot only unobserved, but not known. The magnitude of the effects is stillsmall, however. Given the large sample size, the models have the statisticalpower to capture even small deviations from zero, so even though the di-rection of the effects is different, the evidence of disparities remains modest.

Conclusion

The purpose of this article was to test for class- and race-based disparitiesin the regulatory enforcement behavior of government agencies, payingspecial attention to carefully measure the demographic characteristics of thepopulations living near facilities. Studying enforcement directed at largeCWA facilities, we find evidence of modest race- and class-based disparitiesin both government inspections and punitive actions taken in response to

TABLE 2—continued

Variables


1 2 3 4

Midwest 0.4394n n n � 0.2971 n n n

(0.0491) (0.1089)Constant � 3.2698n n n � 1.5014 n n n � 4.5216 n n n � 2.4039n n n

(0.1446) (0.1360) (0.2571) (0.7701)State Fixed Effects yes yesObservations 152,208 152,208 75,600 75,569LR (DF) 4890.0 (24) 18,217.1 (67) 5049.0 (24) 9318.0 (66)Prob4LR 0.00 0.00 0.00 0.00% Correctly predicted 73.3 76.7 89.2 89.2

NOTE: Cells contain logistic regression coefficients with facility clustered standard errors inparentheses. All models include a time cubic polynomial. Significance levels: nn n0.01; nn0.05;n0.10.


noncompliance. The findings were generally consistent across various waysof measuring the characteristics of the populations living around these fa-cilities. The pattern of inequities does vary depending on model specifica-tion. When exploiting variation across states to identify disparities, we findthat the federal and states governments were, on average, (1) less likely toinspect facilities in large poor and Hispanic areas, but more likely to inspectthese facilities in large African-American areas; and (2) less likely to imposesanctions on violating facilities in large poor areas, but more likely to imposesuch measures on facilities in large minority areas. However, when iden-tifying the effects using variation within states to account for state-levelunobserved phenomena, we detect a different pattern of disparities; in thesemodels, we estimate a negative relationship with the percent of the adjacentpopulation that is African American and a positive relationship with thepercent that is poor, but no differences in punitive actions.

The analysis does have a couple of limitations that are important to note.First, we only considered a single pollution control program. Future workshould examine other programs to determine if the relationships found herehold in other policy areas. Of particular interest is government enforcementdirected at commercial hazardous waste TSDFs, since these facilities havegarnered so much attention in the environmental justice literature. In ad-dition, the analysis focused on the relatively short time period of 2000 to2005, and many of the phenomena explored could vary temporally. As theU.S. EPA continues to make geospatial data available, this is an area ripe forfuture research.

Second, the analysis cannot sort out the reasons for the observed dispar-ities in government regulatory enforcement. Among the causes often pos-tulated to explain disparities in facility location, two seem most applicable tothe case of regulatory enforcement: weak political mobilization among low-income and minority communities and intentional discrimination. We didconsider the former by including two measures of political mobilizationcapacity in our models as alternative explanations of regulatory enforcement,but the small disparities for class and race still consistently emerged.Examining the latter would require information about public officials’ mo-tivations and decision making that is unobservable in a large-scale study.

Sorting out the causes of enforcement inequities is further complicatedgiven the results when incorporating state fixed effects. While intentional orinstitutional discrimination is one possible unobserved factor that couldinfluence state-level enforcement regimes, it is not the only one. Otherpossibilities include varying degrees of discretion afforded to (and exploitedby) state environmental agencies to implement federal pollution programssuch as the CWA, and variation in the organizational structures and de-cision-making systems of these administrative agencies. Additional researchshould consider these and other potential explanations.

One more caution is important to note. Evidence of disparities in reg-ulatory enforcement—whatever their pattern—does not itself mean that


poor and minority groups face disproportionate environmental risks. We donot have sufficient information to link the enforcement behavior studied inthis article with specific environmental outcomes, including differences inexposure to pollution resulting from agencies’ enforcement decisions. Nev-ertheless, our findings do provide some support for claims made by some inthe environmental justice community that enforcement of environmentallaws and regulations is uneven across income and minority groups.

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