Environmental Justice Through Research-Based Decision-Making

E NVIRONMENTAL J USTICE THROUGH R ESEARCH -B ASED D ECISION -M AKING New Directions in Public Administration Claire L...

Author: William M. Bowen

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E NVIRONMENTAL J USTICE THROUGH R ESEARCH -B ASED D ECISION -M AKING

New Directions in Public Administration Claire L. Felbinger and Sylvester Murray, Series Editors Carolyn Ban, Senior Advisor Does Government Need to Be Involved in Primary and Secondary Education Evaluating Policy Options Using Market Role Assessment by Michael T. Peddle Environmental Justice through Research-Based Decision-Making by William M. Bowen

Environmental Justice through Research-Based Decision-Making William M. Bowen

Garland Publishing N e w Yo r k a n d L o n d o n

Published in 2001 by Garland Publishing 29 West 35th Street New York, NY 10001 This edition published in the Taylor & Francis e-Library, 2002. Garland is an imprint of the Taylor & Francis Group Copyright © 2001 by William M. Bowen All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. Library of Congress Cataloging-in-Publication Data is available from the Library of Congress. Bowen, William M. Environmental justice through research-based decision-making. Includes bibliographical references and index. ISBN 0-8153-3500-8 (Print Edition) ISBN 0-203-90065-0 Master e-book ISBN ISBN 0-203-90069-3 (Glassbook Format)

For Chieh-Chen in gratitude

Contents

Series Editors’s Foreword Acknowledgments

xi xiii

Part I Research-Based Policy and Administrative Decisions for Environmental Justice CHAPTER 1

CHAPTER 2

CHAPTER 3

Background on Environmental Justice Decisions The History and Institutional Context of the Environmental Justice Movement Rhetoric versus Reality in Environmental Justice Discourse Uncertainty and Environmental Justice Advocacy Approach to the Topic

3 4 11 14 17

Policy and Administrative Decisions Problem Definition and Situational Complexity in Environmental Justice The Predicament and Rationale of Environmental Justice Policy and Administrative Decisions “Good” Environmental Justice Policy and Administrative Decisions The Validity of the Empirical Research Foundation Foundational Assumptions

21 22

Major Terms and Concepts in the Discourse The Concept of Environmental Risk The Analytical Geography of Disproportionate Distributions

43 44 47

vii

24 29 35 39

viii

Contents The Pivotal Concept of Exposure Definitions of Race and Minority Neighborhood Definition and Identification of Low-Income Neighborhoods

CHAPTER 4 A Primer on Empirical Research Methods for Environmental Justice A Dualism in Social Scientific Inference Hypothesis Testing Elements of the Conceptual-Theoretical Domain Elements of the Observational-Empirical Domain The Role of Statistics in Environmental Justice Research CHAPTER 5 Design and the Validity of Environmental Justice Research Components and Categories of Environmental Justice Research Design Threats to the Validity of Empirical Statements in Environmental Justice Environmental Justice Research Designs A Recommended Procedure for Selection of Comparison Regions

59 63 68

71 75 76 78 91 99

105 107 110 116 126

Part II Empirical Foundations of Environmental Justice CHAPTER 6 A Critical Review of the Empirical Research Literature Validity and the Peer Review Process The Early Empirical Research: The 1970s The Political Movement Gains Momentum: Research in the 1980s Conflicting Evidence in the Developing Body of Research: The 1990s Summary of the Empirical Research CHAPTER 7 The Impacts of Environmental Hazards: An Overview of Some Ancillary Research Impacts of Proximity to Hazardous Sites on Public Health The Impact of Hazardous Sites on Neighborhood Dynamics

131 132 135 138 142 179 185 186 192

Contents

ix The Impact of Hazardous Sites on Residential Real Estate Prices The Impact of Hazardous Sites on Risk Perception and Environmental Concern

195 200

Part III Environmental Justice Policy and Administrative Decisions CHAPTER 8

Uncertainty and Trade-Offs in Effective Decisions Research-Based Decision-Making in Effective Public Organizations Uncertainty in Policy and Administrative Decisions for Environmental Justice The Costs of Uncertainty Risk Trade-Offs in Environmental Justice Policy and Administrative Decisions

CHAPTER 9 Lessons from Research-Based Environmental Justice Policy and Administrative Decision-Making A Need for Synthesis Strategic Management of the National Political Agenda The Environmental Justice Research Agenda Implications for Policy and Administrative Decisions Notes References Index

207 207 215 220 224

229 230 231 240 244

249 257 273

Series Editors’s Foreword

This volume is another in the Garland series of books designed to examine the significant issues in public sector management. Environmental justice is a new term, less than 30 years old, and is primarily a public policy term. Simply stated, federal and state governments have concluded that a societal problem exists because of disportionate spatial exposure to environmental hazards among minority, low-income, and other susceptible populations. Appropriately, governments have devised several policies and programs to solve or ameliorate the societal problem. In this book, William Bowen questions the efficacy of government policies and decisions related to environmental justice by giving a critical look at how well issues of environmental justice are researched. According to Bowen, the proposition that substantial public health burdens are borne by disadvantaged groups being exposed to environmental hazards has not been adequately proven scientifically. He develops an argument that environmental justice is “largely a provocative political symbol invoked to elicit citizen participation toward greater empowerment for selected social groups, independently of any empirical justification.” The book is divided into three sections. The first contains a plethora in information that contextualizes government’s role to solve societal problems and its requirement to properly identify a problem before trying to solve it. Bowen shows that the situations in which environmental justice concerns arise are inherently complex. He maintains that policy and administrative decision-makers need a scientifically warranted level of concreteness in defining the problem. The second section gives an inclusive critical review of the current research literature that attempts to show an environmental justice problem. Bowen concludes that most current research is descriptive and data are unreliable—the scientific foundation is not there. xi

xii

Series Editor’s Foreword

Finally, Bowen considers the implications of the uncertainty regarding environmental injustice in terms of related policy and administrative decisions. He admits that those currently on the firing line are not apt to be able to devote the time and energy required to secure total knowledge. However, good environmental justice policy should make the fullest possible use of rationality, rather than just a conception of fairness, because rationality alone offers the best chances of success in realizing the positive objectives of policy and decision-makers. Bowen concludes that we must have research-based on policy and administrative decisions about environmental justice, and the scientific research completed to date is insufficient. Claire L. Felbinger American University Washington, D.C.

Sylvester Murray Cleveland State University Cleveland, Ohio

Acknowledgments

Quite a few people have helped me write this book. Without Richard Bingham and Robert Simons I probably would not have undertaken the project at all. Aster Girma provided me with thoughtful research assistance, especially for Chapter 7. The series editors, Claire Felbinger and Sylvester Murray, encouraged me and worked with me closely throughout, making suggestions and helping me to keep on track. Kingsley Haynes, David Elkins, ChiehChen Bowen, Arthur Sementelli, and Dianne Rahm all helped guide me in my thinking with their careful reviews, comments, and suggestions. Susan Petrone helped me by editing the first draft. My discussions with Chris Wallis, in the early conceptual stages of the book, helped me get off to a good start. Li Kang Hua helped me keep myself straight and everything else organized. To all of these people I am grateful.

xiii

PART I

Research-Based Policy and Administrative Decisions for Environmental Justice

2

CHAPTER 1

Background on Environmental Justice Decisions Institia non novit patrem nec matrem; solum veritatem spectat (Justice knows neither father nor mother, it has regard only to truth). —LEGAL MAXIM

A fairly large and growing body of literature today presupposes that there are substantial public health costs borne primarily by minority, low-income, and/or other disadvantaged populations specifically because of their differential exposure to environmental hazards. In the past decade, related questions of justice and equity in environmental policy and administrative decisionmaking have moved to center stage throughout the national government, as well as within numerous state and local governments. This has brought increased demands for policy and administrative decision-makers to solve, or at least ameliorate, related problems. Yet for various reasons there is little agreement on exactly what sorts of actions are likely to have this effect. Are there widespread injustices and inequities caused by the distribution of environmental hazards in America today? Claims that such exist have been used to justify numerous environmental policy and administrative responses throughout the past decade. I tried to approach this question with an open and impartial mind, subject primarily to a deep and abiding respect for the scientific method. By way of analogy, if the question were a religious one about the existence of God, the theist would answer positively, the atheist would answer negatively, and the agnostic would conclude that he or she is essentially uncertain. In this regard, my answer is essentially agnostic on the issue. It is based on recognition that the research needed to establish whether or to what extent minority, low-income, and other disadvantaged neighborhoods are disproportionately exposed to environmental hazards and therefore experience greater public health problems is not anywhere near complete. Although this view conflicts with some of the claims of environmental justice advocates, I have no quarrel with them except insofar as they occasionally use unacceptably poor and sloppy science to justify their positions. Indeed, if the available data could be used to document and expose environmental inequities, I could be found among those leading the effort to do so. 3

4

Environmental Justice through Research-Based Decision-Making

Meanwhile, before a great deal of further scarce attention is given to attempts to solve or ameliorate perceived environmental inequities through policy and administrative responses, more substantial data gathering and empirical research are needed. Until then, it will remain unlikely that policy and administrative decision-makers will be able to determine what specific actions will effectively solve or ameliorate the related problems in a way that avoids inadvertently creating other problems of equal or greater severity. THE HISTORY AND INSTITUTIONAL CONTEXT OF THE ENVIRONMENTAL JUSTICE MOVEMENT Throughout the United States, environmentalists and human rights activists are joining together to affirm and gain legitimacy and political support for the principle that all individuals have a human right to a healthy and clean environment. They argue, in various ways, that for some people the exercise of this right is selectively precluded by environmentally destructive industrial, municipal, and commercial operations. Numerous improperly operated hazardous waste sites, for instance, permit migration of chemicals that can produce cancer, disorders of the central nervous system, reproductive problems, and other illnesses (Johnson and DeRosa 1997). Since some such sites are located in wetlands, in floodplains, or over major aquifers, the resulting contamination of drinking water is apt, in some instances, to lead to public health problems (Marsh and Caplan 1987). Moreover, these activists argue that the full exercise of this right depends on the fair and impartial treatment of all people before the law, and not all people are so treated. In particular, they claim that a disproportionate share of the environmental risks caused by environmentally destructive practices and the responses to these practices by government put groups of people at risk differentially with respect to their race, color, ethnicity, national origin, or income (General Accounting Office [GAO] 1983; Bullard 1983; United Church of Christ 1987; Mohai and Bryant 1992). This argument in effect creates a linkage between environmental activism and concern with distributive justice, known generically as environmental justice. The related concerns raise some of the thorniest environmental and public policy issues on the national political agenda and link them with human rights issues.1 Examples include the “Not in my Backyard” (NIMBY) syndrome and those associated with locally unwanted land uses (LULUs) (Lake 1987). As a consequence, basic questions arise in the social discourse: How much should society expend for these rather than other social problems, and how should this level of expenditure be distributed across people, places, and approaches? Should public policymakers and administrators approach the associated perceived problems by preventing environmental risks before they occur, or should they compensate individuals for the dam-

Background on Environmental Justice Decisions

5

age these risks cause? Or is the appropriate role for government simply to make sure that all relevant information is known? These and related questions are especially difficult to answer in light of the inherent trade-off involved between environmental risk and economic and social opportunity. Greater levels of governmental regulation and control may provide low levels of environmental risk for some people while substantially diminishing opportunities for many others. One of the big reasons that environmental justice raises such concern is that related issues and problems have potentially weighty implications for public resource allocation decisions. Accordingly, it is useful to distinguish at the outset between three broad public policy approaches to resource allocation (Rescher 1983). First is the preventative approach, in which policy and administrative decisions produce or propagate actions that tend to prevent problems in the distribution of environmental risks. While in the idea of preventing environmental risks holds great appeal generally, when specified more clearly in terms of concrete proposals for governmental action, the preventative approach always entails the potentially immense costs of establishing, monitoring, implementing, and enforcing regulations and controls. Second is the compensatory approach, in which policy and administrative decisions produce or propagate actions that tend to compensate individuals for harms associated with maldistributions of environmental risks. While this approach broadly conforms to certain notions of fairness, upon closer scrutiny it always entails potentially mammoth costs associated with public assumption of the burden faced by groups of individual citizens. Moreover, it raises unavoidable and thorny issues and debates about which groups to compensate and by how much. The third and last is an information approach, in which policy and administrative decisions ensure the availability of any and all relevant information regarding the location of certain classes of environmental risks. At first blush the information approach puts less demand on public coffers, but in practical terms it requires definition of the relevant classes of risk and extensive research to identify specific situations in which instances of those classes occur. Furthermore, it puts a great deal of responsibility for their own welfare in the hands of private citizens, at times forcing them to live with the consequences of bad personal decisions in ways that society may not be willing to accept. To make related matters even more difficult, decisions regarding which, if any, of these approaches to take at what overall level of resource commitment cannot be made reasonably on the basis of general principles or abstract considerations alone. In broad terms, the institutional context for environmental justice can be understood in terms of the organizations, institutional arrangements,2 stakeholders, and situations within which these and related concerns are embedded (Torres 1994; Gelobter 1994). The relevant political dimensions include sensitivity to the role of power and conflict in environmental decision-making

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and recognition that environmental decisions are not always simply a tradeoff with the economy in terms of efficiency and jobs, but are often fundamental issues for societal welfare (Taylor 1992). This understanding also acknowledges that society has reason for concern as long as economic activity uses common property resources—often not priced or underpriced—and generates externalities (e.g., negative ones such as pollution and waste).3 In these cases, society has an interest in the levels of production as well as the spatial and social incidence of benefits and costs from such activity. Such an understanding recognizes that because technology is taking on an increasingly important role in society and because social perspectives are highly influential in applications of technology, the public has a responsibility for guiding or at least responding to the adverse consequences of technologyrelated hazard generation. The institutional context for environmental justice refers also in part to widespread discourse concerning environmental equity and environmental racism. Environmental equity is premised on the notion of fairness in the distribution of environmental risks, particularly those of a technological origin (Tarlock 1994). Greenberg and Cidon (1997, 398), define the contrasting term environmental inequality as existing “within a state as a whole when disproportionately many undesirables and disproportionately few desirable characteristics exist in a large number of local governments.” Related notions have attracted the attention of researchers for over two decades (Zeigler, Johnson, and Brunn 1983; Brown 1987; Goldman 1991; Hadden 1989; Lappe 1992). The difficulties of understanding environmental equity include some of the more specific operational problems, such as airborne releases (Cutter 1987), the emergency management of toxic chemical spills by applying spatial search procedures (Gould, Tatham, and Savitsky 1988), and evacuation planning for technological hazards (Johnson and Zeigler 1986). Researchers have also used geographical information systems (GIS) for modeling community vulnerability to hazardous materials (McMaster 1990; Marr and Schoolmaster 1988; Glickman 1994). Federal legislation reinforced these concerns by ensuring that a community’s right to know constitutes a central element in new regulation (Reilly 1992). The new requirements for the collection of data on toxic chemical release in this legislation, combined with GIS applications that inventory these and collateral data, make possible systematic spatial analyses of the distribution of environmental hazards across socioeconomic groups (Stockwell et al. 1993; Burke 1993; Glickman 1994). One of the terms often heard and read about in the related discourse is environmental racism. Pulido (1996) defines this as the idea that people of color and low-income groups are disproportionately exposed to pollution, including biases in natural resource policy and the uneven enforcement of environmental regulations. Bullard defines it as “any policy, practice, or directive that intentionally or unintentionally differentially impacts or disad-


7

vantages individuals, groups, or communities based upon race or color; as well as in the exclusionary and restrictive practices that limit participation by people of color in decision-making boards, commissions, and staffs” (Fisher 1995, 290). Although consideration of environmental racism was propelled to the forefront of the discourse largely by anecdotal and case study evidence of environmental discrimination on a racial basis in the 1980s and early 1990s— much of which had strong political overtones—the roots of the controversy go much deeper. Much of the initial impetus for the current controversy began with popular movements that found support in federal civil rights law, namely, the Civil Rights Act of 1964. This Act requires all federal programs to be nondiscriminatory and thus encompasses federal environmental protection activities. As early as 1971, the President’s Council on Environmental Quality (CEQ) broached the issue of socioeconomic and demographic equity in the distribution of environmental risk (CEQ 1971). This was soon followed by grassroots struggles of local communities (Alston 1990; Harvey 1996; Sarokin et al. 1985). These struggles, involving both local communities of color and a range of progressive coalitions, notably churches and civil rights organizations, sought to oppose the racially discriminatory distribution of hazardous wastes and polluting industries in the United States. These concerns were followed up in the Conservation Foundation’s publication on environmental hazards in urban areas (Smith 1974). These issues were further highlighted by a series of publications and events that occurred in the 1980s and early 1990s. Among the highlights, in 1983 the GAO explored the social, economic, and racial correlates of hazardous landfill siting (GAO 1983). Four years later, the Commission for Racial Justice of the United Church of Christ (UCC) circulated a report on toxic waste patterns that concluded that race was the central determining factor in the distribution of chemical hazard exposure in the United States (UCC 1987).4 This report popularized the term “environmental racism” to describe racial discrimination in hazardous waste siting decisions. The climax of the early controversy came with Robert Bullard’s book Dumping in Dixie: Race, Class, and Environmental Quality (1990), which helped to further politicize the issue of racial discrimination in these decisions. Although protests aimed at stopping a landfill project in the predominantly minority community of Warren County, North Carolina, were successful, Greenpeace had already alleged a similar national bias in the siting of waste incinerators in minority communities (Costner and Thornton 1990). In his sequel to Dumping in Dixie (1990), Confronting Environmental Racism: Voices from the Grassroots (1993), Bullard launched an even more pointed attack on environmental discrimination. The environmental justice movement, as it soon became known, gained force through the 1980s and took a strong hold on the national political agenda by the early 1990s. By then several thousand groups throughout the

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United States had emerged to oppose perceived inequities in the distributions of environmental hazards, based on the assumption that they were threats to the public health of nearby communities (Bullard 1993). In 1991, more than 650 activists from over 300 local grassroots groups attended the First National People of Color Environmental Leadership Summit in Washington, D.C. (Goldman 1996). The summit adopted 17 “principles of environmental justice,” expanding the movement’s focus on race to include other concerns, such as class and non-human species. Cutter (1995) suggested that by the mid-1990s the movement had transcended its original focus but had not abandoned its initial concern with communities of color. Rather it began to include others regardless of race or ethnicity who are deprived of their environmental rights, such as women, children and the poor—a definition endorsed by Hofrichter (1993). This broadened political purpose began to extend the movement’s social and institutional reach from street-level protests to state and federal commissions, corporate strategies, and academic conferences (Goldman 1996, 131). Oakes, Anderton, and Anderson (1996) noted that by the mid-1990s at least twelve states (Arkansas, California, Georgia, Louisiana, Minnesota, Michigan, New York, North Carolina, South Carolina, Tennessee, Texas, and Virginia) had passed environmental equity bills, largely in response to the environmental justice movement. Environmental justice became legitimate as a policy concern at the highest levels of the federal government on February 11, 1994, when President Clinton signed Executive Order 12898, Federal Actions to Address Environmental Justice in Minority Populations and Low Income Populations (President 1994). Executive Order 12898 established a policy framework for federal agencies responsible for addressing environmental justice. The order mandated that each federal agency must make environmental justice an explicit part of its mission. Each federal department and agency was to develop a strategy with which to manage the problem of environmental justice, specifically by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority and low-income populations. The order further mandated that the agencies undertake human health and environmental research, data collection, and analysis to determine whether their programs, policies, or activities have disproportionately high and adverse human health or environmental effects on minority populations and low-income populations. It also mandated research and analysis to assess human health effects from multiple and cumulative exposure to all environmental risks caused by federal government activities and the collection and analysis of information on low-income and minority populations who may be disproportionately at risk, and that appropriate remedial action be taken. The Environmental Protection Agency (EPA) was given the lead role. In 1992, the Office of Environmental Justice (OEJ) was established within the


9

EPA, with a mandate to ensure that communities comprised predominantly of people of color or low-income populations receive equal protection under environmental laws. The official purpose of the OEJ is to identify, evaluate, and mitigate any unfairly distributed environmental risks, specifically without simply shifting them among populations in such a way that a reduction in risks faced by one population creates an increase in risks faced by another. Later, in 1994, the National Environmental Justice Advisory Council (NEJAC) was established within the EPA. Its role is to advise, consult with, and make recommendations on all federal matters related to environmental justice. The NEJAC consists of approximately 25 appointed members from community-based groups, industry and business, academic and educational institutions, government agencies, federally recognized tribes and indigenous peoples, and other interested groups as deemed appropriate. Its mandates specifically include monitoring and evaluating EPA’s progress and adequacy in planning, developing, and implementing environmental justice strategies, projects, and programs; overseeing EPA’s existing and future information management systems, technologies, data collection, and analyses; and overseeing EPA’s direction, criteria, scope, and the adequacy of the agency’s related scientific research and demonstration projects. In April 1995, EPA released a strategy on environmental justice that included agencywide goals and an outline of the approaches by which EPA planned to ensure that any disproportionately high and adverse human health or environmental effects on minority communities and low-income communities were identified and addressed. Although environmental justice concerns have thus now in many ways been institutionalized within government, there is no single agreed upon formal definition of the related central concepts and foci of interest. Bass (1998) defined it as follows: [Environmental justice] refers to the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws. (84)

Stretsky and Hogan (1998) defined it just a bit differently: [Environmental justice] means two things. First, it means that environmental hazards should be distributed equally across society and that no individual, group, or community should bear a disproportionate burden from this type of health threat. Second, and more ideally, it means that no one should be forced to suffer from the adverse effects of environmental hazards. (268)

Accordingly, they argued that the study of environmental justice encompasses more than the analysis of the location or placement of active waste

10


facilities in minority and poor areas. In their view, it also includes the social and economic processes that shape racial, ethnic, economic, and demographic patterns around existing environmentally hazardous sites. In this book I use the term environmental justice in a fairly broad and somewhat fuzzy sense to refer to concern with disproportionate distributions of environmental hazards as well as the entire complex of related concerns, problems, and issues. Related concerns are environmental insofar as they deal with the hazards and risks posed to neighborhoods and individuals largely by nearby environmental chemicals. They have to do with justice insofar as they deal with the question of how fairly the burdens of these environmental risks are distributed throughout society. They are environmental justice issues and problems because, by linking the environment and justice, they bring everyone, regardless of race, color, national origin, or income, to the same level of shared dependence on a healthy environment (Sachs 1995). In this sense, the term can be used to refer to a range of current problems and issues throughout the entire world. On one hand, it can refer to concern over the health effects in minority, low-income, and other disadvantaged neighborhoods caused by disproportionate distributions of environmental hazards in areas throughout the United States. On the other hand, it can refer to international concerns such as those related to the current conflict between the Malaysian logging industry in Belize and the Mayan Indian culture that depends for its survival on the rainforest the industry is cutting down. While the term environmental justice is used here in this broad sense, most of the interest throughout is focused rather more specifically on the hypothesis that environmental hazards are disproportionately distributed in minority, low-income, and/or other disadvantaged neighborhoods, thereby leading to public health problems, especially in the United States. Accordingly, the term environmental justice is reserved to refer to the broader complex of related issues and problems, including the constituent perceptions, conflicts, and institutional responses, and the more specific interest at the heart of the current concern is referred to as disproportionate distributions. This more specific interest is of special concern because the rise of the environmental justice movement in the United States has been based largely on the premise that hazardous environmental facilities are distributed disproportionately in minority, low-income, and other disadvantaged and susceptible neighborhoods. Accordingly, empirical research that documents inequities in the relationship between the location of environmental hazards and the location of these neighborhoods tends to provide scientific warrant for the movement and, conversely, research that finds no such inequities tends to undermine this warrant. As a consequence, heated and occasionally acrimonious debates and issues arise surrounding basic research methods in social science and their application to this premise (see, for example, Social Science Quarterly [September 1996] or Economic Development Quarterly 13


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[1999]). Much debate has focused on assessment of the existing research, particularly the degree to which it provides a credible scientific foundation for making related policy and administrative decisions. The debate rages partially because scientifically acceptable answers to related research questions are at best complex, difficult, and costly to obtain, and partially because the formal knowledge and understanding of basic empirical research methods needed to assess the available answers is relatively scarce. RHETORIC VERSUS REALITY IN ENVIRONMENTAL JUSTICE DISCOURSE To the casual observer, it might seem obvious that a social problem as important as environmental justice would have a great mass of empirical research behind it, justifying related concerns and helping to ensure that any policy or administrative decisions made about it are good ones. Indeed, there is a profusion of articles on the topic that begin by claiming such a body of research exists. In reality, however, as will become clear in the following chapters, one of the most evident characteristics of the related problems and issues is that they have not been thoroughly and systematically researched and documented prior to making relevant decisions. There are a couple of different justifications for the lack of related empirical research. First, there remain high levels of uncertainty regarding the existing socioeconomic patterns of residential proximity to environmental hazards. This is substantiated in Chapter 6. Second, even had socioeconomic patterns of residential proximity to environmental hazards been convincingly demonstrated, they alone would not suffice to scientifically establish an association, much less a causal relationship, with public health problems. This is attributable largely to the difference between proximity and exposure, which is discussed in some detail in Chapter 3. Third, with respect specifically to disproportionate exposure (as opposed to proximity), properly construed, abundant claims to the contrary notwithstanding, the current body of empirical research does not by any stretch of the imagination demonstrate (or even clearly indicate) any broad discernable nationwide pattern. In my view, the existing related empirical research when seen in proper scientific perspective, has a very high level of uncertainty as its most salient feature. Of course, it would be a serious mistake to claim that a scientific view of disproportionate distributions is the only one that matters or has value. In a society that aspires to liberal democratic ideals, the perception of risk alone is important for policy and administrative purposes, regardless of the real risk. For instance, when a Hispanic or African American parent perceives that his or her child’s major health problems have been caused by living near a hazardous site, this view can and probably should matter a great deal in policy and administrative decision processes, regardless of whether there is

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scientific basis for that perception. Similarly, the view of an impoverished individual whose experience has taught her to believe that the poor and those with less access to power are generally exploited, should also matter. Indeed, given liberal democratic values, a broad range of perspectives should matter and have intrinsic worth in societal decision processes regardless of whether they have any sort of scientific basis. However, such views do not constitute scientific arguments, and the concern here is restricted largely to scientific arguments related to the hypothesis of disproportionate distributions and related policy and administrative decisions. While it would be seriously mistaken to claim that a scientific view is the only one that matters, it would be equally mistaken to discount the potential for scientific research to contribute positively to the solution of related problems. Therefore, because it is simply infeasible to focus upon and systematically consider everything relevant that matters, and is of value in society, the emphasis throughout the book is narrowly restricted to related scientific arguments and their use in policy and administrative decisions. Indeed, it can be argued that the scientific basis of the problem of environmental justice had little if anything to do with its movement to the agenda. Foreman (1998), for instance, made a case that, while the political and rhetorical discourse on environmental justice refers often to concern about community health, fear of environmentally caused disease vastly surpasses the available empirical knowledge about it. So emotions and a struggle for political power are the real issues and focus of the movement. He stated: One reason for this focus is that activists and policymakers alike possess a far better understanding of procedural inclusion, of the tools that seem useful for producing it, than they do of ways to reduce risk and enhance health. Moreover, for both activists and policymakers, community involvement speaks in an immediate and direct way to the political challenge at hand. For activists, involvement offers outlets for advocacy, opportunities for dialogue and the casting of blame, and the promise of institutional accountability. Resourceful and well-timed advocacy may even lead to significant material benefits for a community. On the other hand, involvement mechanisms allow policymakers to exhibit responsiveness and deflect criticism. By comparison, channeling health anxieties effectively toward risk reductions and improved health prospects among low income and minority is far more difficult. (64)

Thus, while the prevailing discourse tends to refer to empirical factors related to environmental risk-related public health in low-income and minority neighborhoods, it can be plausibly argued that the real underlying concerns have more to do with access to power, community empowerment, social justice, and anxieties over public health.


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Many individuals in minority and low-income neighborhoods in the United States could be motivated to attempt to redistribute resources and political power by whatever means might be expedient, including exploitation of anxieties over public health. First, the income distribution is heavily skewed (Bureau of the Census 1999a). In 1998 dollars, the household income ratio of the lower limit of the 95th percentile to the upper limit of the 20th percentile income was 8.2 to 1 ($132,199 per year compared to $16,116 per year). The mean of the lowest 20% of yearly household incomes was $9,223, accounting for 3.6% of the country’s total household income, compared to a mean of $127,529 for the top 20%, accounting for 49.2% of the country’s total household income. Second, in terms of the racial and ethic composition of the household in poverty, the percentages were considerably greater among minorities (Bureau of the Census 1999b). When measured by money income less government transfers, although whites made up 82% of the total number of households in poverty, the overall percentages of blacks and Hispanics were much greater. Specifically, 17.9% of white households were in poverty, compared to 34.5% of black households and 30.8% of Hispanic households. Given these realities along with the often seemingly overwhelming obstacles in overcoming them, individuals in minority and low-income neighborhoods often perceive themselves as having little prospect of acquiring material assets or an improved quality of life. More important, life in those neighborhoods is often inextricably linked with human tragedies that result from stigma, stress, poor diet, crime, poor education, low-quality health care, substandard housing, overcrowding, abandonment, and other associated problems. Theirs is not, as they perceive it, a land of opportunity but one of hopelessness amid conspicuous affluence in neighboring wealthier, better educated, and often predominantly white neighborhoods. Moreover, a number of specific redistributive mechanisms are at stake (Foreman 1998). They include employment and promotion opportunities within government, minority set-asides in environmental remediation, facility construction after an environmental cleanup is completed, employment of community members at whatever facility is completed and operating, and any training associated with these. It appears plausible that the terms of the prevailing discourse related to environmental justice effectively mask a nexus of real underlying issues having to do with poverty, housing and job discrimination and, ultimately, the value system that undergirds the free market economic system. Certainly, there seem to be glaring inconsistencies between the political rhetoric in the discourse about environmental risk and its health effects in minority, lowincome, and other disadvantaged neighborhoods, on one hand, and the apparently real underlying concerns with political power on the other. The

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presence of environmental justice on the national political agenda is clearly not attributable to a serious, well thoughtout, and carefully prioritized response to a solid foundation of scientific research. So it must have more to do with clusters of beliefs, values, emotions, feelings and ideals as those just been noted, many of which have very little if any empirically researched justification. In the absence of adequate empirical research, there is no guarantee that the worst fears people have and the dangers they most strenuously seek to avoid through political struggles and community involvement are in any way a reflection of the actual hazards that could harm them the most. A good example is lead paint in older homes that house disproportionate numbers of minority and low-income individuals. Lead is known to be a powerful toxin that attacks the central nervous system, potentially resulting in convulsions, mental retardation, and even death (Brody et al. 1994; Office of Pollution Prevention and Toxics 1995). On the basis of established scientific evidence, the issue of lead paint in minority, low-income, and other disadvantaged neighborhoods should almost certainly have a relatively much higher priority on the national agenda than disproportionate distributions of hazardous sites. So probably should public health issues related to mental well-being, mental anguish, and stress (White 1986; Mann et al. 1999). While the terms of the public discourse continue to refer to disproportionate distributions and their related public health impacts, important questions remain unanswered: To what extent, if any, does the prevailing emphasis on action for community empowerment and broader social justice compete with serious scientific concern over exposure to environmental hazards and related public health effects in these neighborhoods? To what extent do the interests of environmental justice advocates detract from or support serious efforts to base policy and administrative decisions on risk priorities based in empirically established knowledge about the severity or pervasiveness of actual environmental hazards in these neighborhoods? UNCERTAINTY AND ENVIRONMENTAL JUSTICE ADVOCACY The opportunity to get environmental justice on the national policy agenda came not only from envy and anxiety over public health, but also from high levels of related empirical uncertainty. There are literally tens of thousands of synthetic chemicals in the environment, most of which have not been tested for their public health impacts. This creates a prima facie plausibility for the claim that they present a pervasive and serious threat to health and wellbeing. It seems reasonable that the most privileged groups in society would not bear a proportionate share of the associated burden of environmental risk. There is little doubt that public health conditions in minority, poor, and other disadvantaged neighborhoods are not the same as they are in more affluent


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neighborhoods. The mere suggestion, therefore, that various underprivileged groups in society have been involuntarily and disproportionately exposed to environmental risks and as a result experience more health problems, almost automatically takes on powerful political meaning, regardless of the lack of sound empirical justification. Given that so little sound empirical research has been done, there is a great deal of empirical uncertainty surrounding the hypothesis that there are national patterns of disproportionate distributions. The situation is ripe for policy advocacy precisely because there is so much uncertainty. Policy advocates strive to focus public attention on related problems or issues so that policies will be formed and collective actions will be taken to address them. They tend to believe that scientific research should be an instrument for progress toward their conception of the good society (Weimer and Vining 1992; Schneider, Teske, and Mintrom 1995). Superficially, environmental justice policy advocates may appear similar to social scientists, especially in that they have similar academic status and credentials, but their purposes and the activities they do in support of them are entirely different. They do not hold impartial scientific integrity, rationality, and relatively high standards of social scientific validity as their highest value. Some of them are prodigiously skillful at mobilizing attention to perceived problematic conditions, forging agreement on appropriate new public policy directions, and stimulating the formulation and implementation of public policy among the multiple, diverse stakeholder groups and constituencies involved. Some are equipped with the ability to dramatize related problems and issues; to point out their personal relevance to constituents; to make the underlying concerns clear, concrete, and credible; and to generate political pressure for collective actions. They often start with latent concerns and mobilize these to convene the stakeholders needed to address them, forge agreements on appropriate actions, get others to embrace them as priorities for political action, and then sustain action and momentum during implementation (Luke 1998). To this end they often use data and research to help make their concerns clear and concrete and to gain support and legitimization for the policies they advocate. Both environmental justice policy advocates and social scientists at least privately recognize the high levels of uncertainty, but since they have different motivations they respond to it differently. Advocates respond by exploiting it; they seize the opportunities it brings to take action for political (and occasionally personal) gain. They seek to realize their purposes through advocating policy innovations built around political and/or social ideals that they perceive will be associated with significant positive change in society. They essentially use data and research to deduce normative prescriptions for environmental justice policy and administrative decision-makers to select their particular predetermined alternative from choice sets of ways to respond to the related problems. The information and research methods they use are

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selected with respect to these political purposes and/or social ideals. They tend to look only at selected information and knowledge and are most likely to rule out any consideration that could potentially get in the way of achieving these ends. The guise of science gives their representation of the related problems and issues an aura of validity, thus increasing its credibility among those who do not see deeper into the situation. As a rule, any information that conflicts with the political purposes they advocate is apt to be ruled out. In the end, while they cloak their political positions in science, their reason for doing so is primarily to enhance the credibility of their political claims. On the other hand, social scientists expect to find areas of ignorance and uncertainty, and when they do, respond by taking efforts to minimize it. Given a point of uncertainty, social scientists begin their work by bringing sets of established postulates and concepts related to human behavior together with the principles of basic empirical research method to bear in an effort to describe the decision situation itself as a conclusion. Their primary concern is with the relationship between facts, events, and circumstances, and statements made about them. They are concerned primarily with whether or to what extent hypothesis tests and the inferences and conclusions drawn from them are defensible on the basis of reasoning through the relationships between the relevant postulates and concepts on one hand and the information contained in the data on the other. Their fundamental values are relatively high standards of integrity and scientific validity (Weimer and Vining 1992). While certainty is not social scientists’ final objective, uncertainty is nevertheless a central obstacle to be reduced by using established, systematic procedures designed explicitly with self-correction in mind as a means of checking validity. The correct and appropriate use of these procedures is established through the peer review process. This distinction does not imply anything negative about the ethics of the issue advocates. While issue advocacy is without a doubt ethically acceptable in its own time and place, my concern is that its current relative prominence in environmental justice is apt to have the effect of emphasizing the wrong things out of proportion to their merit. Nor does it imply that social scientists cannot have a political agenda. Surely one can be a social scientist with a social agenda and be an advocate for his or her position. But when one’s priority is maintaining a high level of integrity in scientific methods, strict and explicit normative criteria bind and constrain the political uses of one’s research, and the peer review process enforces these criteria. This process ensures that all the evidence and reason used to justify a conclusion fits together in a coherent and effective way that other people can in principle perceive and understand on the basis of reason and rationality. Such constraints are not necessarily binding when one’s primary values are oriented toward one’s responsibility to one’s clients or toward certain political ideals. Thus, if a social scientist takes a political position on the basis of his or her


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Statements of inference about facts and events

Facts and events in the actual world

Social scientist

Decision processes

Policy advocate

Figure 1.1. Social scientists and policy advocates. The social scientist is concerned primarily with the integrity of the inferences or propositions made in relation to facts, events, and circumstances in the actual world. In contrast, the policy advocate is concerned primarily with establishing the credibility of selected propositions for use as premises in policy and administrative decision processes.

research, that position is fortified by the high standards set by these criteria, as determined through peer review. This is probably one of the reasons why social scientific investigation can have such substantive effects on policy deliberations (Bell 1985). The difference between the two roles is illustrated in Figure 1.1. APPROACH TO THE TOPIC With this in mind, my purpose in this book is to develop a relatively coherent, empirical research-based perspective on policy and administrative decisions related to environmental justice, especially disproportionate distributions. This perspective represents a normative standard against which to compare how things actually are, or at least how they are perceived. Specifing a scientific and rational model for related research and decision-making can provide something relatively coherent to strive for, a blueprint against which to understand the real world by comparison. The standard is given policy and administrative relevance by the importance of environmental justice, the need for government to act in response to related concerns, and by the liberal democratic values on which such concerns are based. At the same time, its principal merit is in bringing to bear a value system that places primary value on relatively high standards of intellectual integrity, scientific validity, and rationality in terms of the related debates and decisions. Insofar as possible within the relevant constraints of time and energy, I seek to develop a perspective that is consistent both with empirical research methods and with tenets of good policy and administrative decision-making, as currently taught in various public administration and decision science programs throughout the United States. The focus of interest is not

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on community empowerment, procedural inclusion, or political advocacy (at least not in any way other than paying more serious attention to public health specifically in minority and low-income neighborhoods). My view is that if an advocate’s arguments are congruent with valid scientific evidence, the advocate’s efforts can complement the scientist’s efforts, but the activities of advocates are not and never will suffice as a substitute for good science in creating effective responses. Thus my emphasis herein is on a proper understanding of reasonably high standards of scientific research and decision-making and their use by related policy and administrative decision-makers. This book is addressed primarily to readers who have some capacity to reflect and act on these standards and guidelines in a practical way. This audience consists mostly of young scholars in public policy and administration whose careers are ahead of them. Though it has been written with several interactions with various related policy and administrative decision-makers in mind, those currently on the firing line are not as apt to be able to devote the time and energy required for thinking the matter through in such detail. Their plates are already full of demands to respond to the widespread concerns that substantial public health costs are borne by minority, low-income, and/or other disadvantaged populations specifically because of their differential exposure to environmental hazards. The book contains three sections. Chapters 2 through 5 provide an array of background considerations needed for informed deliberation of environmental justice policy and administrative decisions and their scientific foundations. In Chapter 2 I introduce policy and administrative decisions, rational decision processes, and the contribution of empirical research. With the notable exception of the conception of rationality set forth, most of the discussion is a fairly standard interpretation of intermediate-level policy analysis and administrative decision-making literature. Chapter 2 also lays bare my biases. Chapter 3 is intended for readers who have little or no background in the theory of risk assessment and management and have done little graduate work in analytical geography, public health, or urban studies. I offer some of the relevant concepts and background in these areas. Chapters 4 and 5 I wrote with the past 25 years of my experience in mind. I have been formally trained in natural science as well as in regional analysis, and have spent 5 years as a practicing public administrator and 15 years teaching and researching environmental decision-making and public affairs in universities. Over the course of time, I have become persuaded that the basics of empirical research method are little understood by public policy and administration students as well as practicing policy and administrative decision-makers. Accordingly, I include these two chapters to familiarize the reader with some general principles of empirical research method and design, as they relate to aspects of environmental justice. Points of conventional social scientific and analytical


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method are emphasized, with the focus largely on their relationship to the hypothesis that differential distributions of environmental hazards and risks exist across identifiable groups of people located differentially in geographical space. The underlying idea of these chapters is that, without an adequate prior understanding of these principles, one is not likely to get a proper perspective on the existing body of related empirical research. The reader of the related literature would therefore not have enough understanding of basic scientific method to critically review the research literature or, in some cases, even to understand my review of it. The aim is to develop enough understanding of how social scientific knowledge with respect to this hypothesis is acquired, so that various policy and administrative decision-makers throughout government can effectively help diminish the perceived intensity of the problem. Otherwise, policy and administrative decision-makers are apt to attribute a scientifically unwarranted level of concreteness to the findings and conclusions it contains. The second section, Chapters 6 and 7, gets to the heart of the argument in the book. Here I critically review the related empirical research literature on the basis of the understanding set forth in the first section. In Chapter 6 I provide a systematic critical review and analytical appraisal of the empirical evidence that such distributions form discernable regional and/or national patterns that are unfair to people who live in minority, low-income neighborhoods or to other susceptible populations (e.g., children, the elderly, pregnant women). In Chapter 7 I consider some possible impacts of disproportionate distributions other than those on public health, the idea being that injustice could result from differential distributions of environmental hazards that impact, for example, property values or psychological stress. These chapters demonstrate that empirical uncertainty is an overriding characteristic of all broad-based concerns over discernable regional and/or national patterns of disproportionate distribution today, no matter what impact is considered. The reader who has been thoroughly exposed to the topics covered in the first section might want to go straight to Chapter 6. In the third section, Chapters 8 and 9, I consider the implications of the uncertainty in policy and administrative decisions. In Chapter 8 I examine the policy and administrative decision process with an eye toward a definite concept of rationality and consider the implications of uncertainty in that process. Having set forth a research-based perspective on policy and administrative decisions about environmental justice, I offer in Chapter 9 some of the lessons that may be taken from this careful look at the problem of environmental justice from a research-based perspective. The four main lessons are the need for synthesis, strategic management of the national political agenda, the need for further empirical research related to environmental justice, and related policy and administrative decisions.

CHAPTER 2

Policy and Administrative Decisions To generalize the recognition that the true means the verified and means nothing else places upon men the responsibility for surrendering political and moral dogmas, and subjecting to the test of consequences their most cherished beliefs. Such a change involves a great change in the seat of authority and the methods of decision in society. —DEWEY, Reconstruction in Philosophy

A research-based policy and administrative perspective on decision-making for environmental justice is based on the principle that relevant decisionmakers cannot make good decisions unless they first know what the decision problem is that they are trying to solve. In the private lives of individuals, decisions typically do not involve consequences for society overall, and to know the decision problem one is trying to solve before making a commitment to act is a relatively simple and straightforward matter. But public policy and administrative decisions related to environmental justice potentially have repercussions throughout society, and they tend to involve a great deal more complexity than those made in individual private lives. In such decisions, to know the decision problem can be much more difficult and involved. An underlying premise in this perspective is that the means with which public administrators and policymakers can solve or ameliorate the perceived social problem of environmental justice invariably involve the process of making “good” decisions. Unless they make good decisions—in the sense that appropriate objectives are pursued in an intelligent way—any actions taken by government are more apt to inadvertently lead to government failure. Moreover, since making a good decision generally requires knowing what the decision problem is, valid propositions about environmental justice decision situations are needed in the decision process. If valid descriptions of the decision problem are not available, decision-makers lack the practical means to make good decisions. In turn, if good decisions are not made, poor ones will take their place, and these are apt to create other problems of equal or perhaps even greater magnitude and intensity than the ones they are intended to solve.

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PROBLEM DEFINITION AND SITUATIONAL COMPLEXITY IN ENVIRONMENTAL JUSTICE One coherent way to define a problem is to have a set of statements that prescribe how the world should be in some relevant segment of the world and a set of statements that describe how the world actually is in that segment. Comparing the two sets and demonstrating that they differ in some clear and distinct way, defines the problem; that is, the world that actually exists in this segment is thus shown to differ meaningfully from the way it should be. Statements about how the world should be, normative statements, ultimately refer to standards, such as concepts of fairness or justice (Been 1993). They are often found, for example in laws, statutes, treaties, and various regulatory mandates that constrain social behaviors. Statements about how the world actually is, descriptive statements, refer to facts, events, and circumstances or, more generally, to phenomena that have happened, are happening, or will be happening independent of or anterior to any statements or assertions made about them. Social scientific methods are useful largely in developing the descriptive statements used in problem definition. They are based on explicit inferential processes designed to ensure that any statements about the relevant facts, events, and circumstances correspond with them in ways that can be agreed on within the scientific community. Indeed, the debate over the empirical foundations of disproportionate distributions centers on questions of what sets of statements to accept as descriptive of how the world actually is. Concerns over the locations of environmental hazards, their proximity to identifiable groups in society, and the risks they pose to health and wellbeing, typically arise in situational contexts that involve a huge number of political, economic, social, geographical, and psychological attributes interacting in a complex, indeterminate, interdependent fashion. In such contexts, the specific facts, events, and circumstances that give rise to such concern do not exist in isolation. They cannot be completely factored out from other closely related social problems—including poverty, housing, racial discrimination, unequal opportunities or outcomes, education, and pollution—each explained on its own, and the set of explanations somehow combined to explain the whole. Indeed, one might even question whether the attributes of such situations fall in any meaningful way into specialized political, economic, social, or technological categories: all but the very trivial details are intimately related to corresponding details in other related social problems. The concerns vary from location to location; moreover, the relationships between these concerns and other related problems themselves are not static. Rather they change in both physical and conceptual space, as time brings changes in population, resources, politics, and technology among other factors. In a word, the situations in which environmental justice concerns arise

Policy and Administrative Decisions

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are inherently complex. The nature of this complexity is discussed further in Chapter 9. For now, it suffices to say that because the situations in which environmental justice concerns arise are complex, they cannot be observed without involving arbitrary and subjective judgmental and psychological factors. This presents an obstacle to scientific research, all science being based on observation, because in a very real sense the observation of anything, especially a complex phenomenon, is shaped by factors such as prior knowledge, and the language or notation used to express that knowledge (Hanson 1973). Fallible physical and mental faculties influence observations. The inability of a given observer to repeat an observation again and again in exactly the same way with exactly the same result can become an influence. Humans tend to vary from moment to moment in how they observe the same phenomenon, even when it would seem easy to be objective. Observations can be systematically inaccurate, especially when each observer brings his or her own experiences and perspectives, thus generating biases to their observations. If the situations in which environmental justice concerns arise are difficult to observe accurately, they are far more difficult to describe and exceedingly more difficult to explain. To recognize the complexity of the situations in which environmental justice concerns arise, and the difficulty observing them is implicitly to accede something about their interpretation. Interpretation always mediates between the facts, events, and circumstances that give rise to concern about environmental justice and their description. Descriptions of environmental justice decision situations are therefore always constituted at least in part by human psychological factors in interpretation. These include empirically documented limits to the human span of immediate recall, selective perception, and biases that result from psychological strategies and heuristics. Deeply held human values as well as differences in personality, background, and level of information about the situation tend to make a variety of interpretations (and hence descriptions) plausible, especially when the range of empirical facts is not well established. Thus, it is important to define systematic categories or theoretical concepts that go beyond the understanding of the interpreter. Without them, descriptions of environmental justice decision problems amount to little more than partial representations of poorly understood complex situations and almost certainly are not an adequate or reasonable basis for related policy and administrative decisions. Recognition of the complexity of environmental justice situations reinforces the importance of thorough and precise documentation, sound research design, and accurate analysis. A research-based decision-making approach to environmental justice policy and administrative decisions is founded on a systematically developed and explicit framework that guides and constrains interpretations, descriptions, and explanations. In the absence of such a framework, we are left with

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describing problems and deliberating alternative feasible courses of action with unspecified levels of unaided and potentially arbitrary human judgment filling the interstices between selected bits of information. Such judgment typically is heavily influenced by human psychological strategies and heuristics that have been shown to lead to inferential biases that are large, persistent, and serious in their implications for policy and administrative decision-making (Kahneman, Slovic, and Tversky 1982). Indeed, environmental justice is at once so sufficiently complex, influenced by idiosyncratic interpretation, and potentially important for some individuals in society that any simplistic proposals of easy or comprehensive solutions, no matter the stature of the issuing authority, can be dismissed out of hand. THE PREDICAMENT AND RATIONALE OF ENVIRONMENTAL JUSTICE POLICY AND ADMINISTRATIVE DECISIONS To recognize the inconsistencies between, on one hand, the terms in the prevailing discourse about environmental risk and its health effects in minority, low-income, and/or other disadvantaged neighborhoods and, on the other hand, the apparently real underlying concerns with political power is to raise a broader intellectual and perhaps even moral debate in public administration. It centers on the role of rationality and reason in public policy and administration. How willing or able are policy and administrative decision-makers to become aware of, accept, articulate, and act upon the limitations on the available information, as well as their ability to mentally process it into the form of knowledge adequate to rationally justify the selection of a particular alternative in a decision situation? How intellectually modest are they willing or able to be, particularly when it forces them to recognize how often they as human beings err in forming their beliefs and making their substantive judgments and predictions about the future, and how much they depend on others for their knowledge? How readily are they willing to give up hope that by means such as carefully constructed arguments based on documented experience, together with sound and impartial scientific analysis, they can make good decisions that will substantially improve or at least ameliorate the problem or issue? Basically, how much do and should decision-makers attempt to solve, or at least ameliorate, such social problems and issues by an appeal to rationality, reason, and empirical research, to clear thought about documented experience rather than to enforcement of institutional rules and/or sheer political expediency? Much of this broader debate can be traced to the work of Simon (1976), Lindbloom (1959), and Cohen, March, and Olsen (1972), which focuses on several theoretical models of decision-making and their implications for making “good” decisions. These can be roughly categorized as strictly rational, boundedly rational, or non-rational.


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The strictly rational models imply that, to make good decisions, environmental justice policy and administrative decision-makers must meet a set of normative axioms of rational choice. The axioms in essence state that a decision-maker can be rational if and only if he or she has complete knowledge of the alternatives and their consequences, a perfectly known and consistent preference ordering, and a consistent decision rule for combining knowledge and preferences. However, these axioms are seldom if ever met by conditions in real decision situations, so most decision theorists agree that such models do not adequately describe most policy and administrative decisions. The difference between strictly rational models and boundedly rational models is mostly a matter of degree, not kind. The two hold a similar vision of the decision process, differing primarily in their portrayal of the extent of information search (Simon 1976). More specifically, the boundedly rational models imply that good environmental justice policy and administrative decisions would involve a similar decision process but with less than full knowledge, an imperfectly known and possibly somewhat inconsistent preference ordering, and a possibly somewhat inconsistent decision rule for combining knowledge and preferences. The nonrational models differ fundamentally from the first two, particularly insofar as they portray choices as not being made as a result of rational deliberation about the pros and cons of alternative feasible courses of action (Cohen, March, and Olsen 1988). They portray environmental justice policy and administrative decision processes as a mixture in which various combinations of participants and institutional arrangements combine in largely idiosyncratic, fluid, and often essentially incoherent ways, leading to unpredictable and often clearly suboptimal outcomes. The observed outcomes of policy and administrative decision processes are apt to appear to be irrational largely because, in a sense, they actually are so; for instance, one can find persistent solutions looking for problems (Kingdon 1995). Thus, given the implication that policy and administrative decision-making processes are largely irrational anyhow, the emphasis is not so much on rationality, reason, careful empirical research, and analysis as it is on the use of power and particularly the enforcement of institutional rules to achieve predictable outcomes. Much of the debate among proponents of these models appears to be attributable to the fact that rationality is not a precise term of scientific psychology, but more a rough concept in philosophy and economics. Accordingly, the most prevalent version of the concept asserts rather vaguely that a choice is rational if it is in line with the values and beliefs of the decisionmaker. A more precise form of this version, the one on which the debate in public administration tends to focus, stipulates that a choice is rational if and only if it is consistent with the aforementioned formal axioms of the choice process. These enable the decision-maker to maximize subjectively expected

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utility (Bernoulli 1738; Savage 1954; Edwards 1961). Utility in this sense is the satisfaction, personal value, or ability to give pleasure that the selection of an alternative can bring. Accordingly, when the outcomes of selecting a given alternative are uncertain, policy and administrative decision-makers maximize expected subjective utility through weighing the assessed utility of the outcomes by the subjective probability of their occurrence. Most decision theorists seem to agree that to demand policy and administrative decision-makers to consistently and formally maximize expected utility in their decisions would be to ask more of them than the best they can possibly achieve, given the incomplete information and empirical knowledge they inevitably possess. Most agree that if it were otherwise, if the axioms of rational choice could ever be fulfilled, the world would be very different than it is today. In reality, such decision-makers are ultimately constrained, in any decision of any major consequence at all, to make choices on the basis of incomplete information, imperfect knowledge, changing preference orderings, and an occasionally inconsistent decision rule for combining knowledge and preferences. Therefore, because the concept of rationality defined as maximizing expected utility demands that policy and administrative decision-makers use information and knowledge they do not have and cannot possibly obtain, many individuals and decision theorists conclude that to demand rationality of them is to require more than can be reasonably expected. When combined with observations of seemingly irrational outcomes from policy and administrative decisions, this seems to provide substantial warrant for the non-rational models. If thus defined rational decisions are not feasible, policy and administrative decision-makers seriously interested in environmental risk and its health effects in minority, low-income, and other disadvantaged neighborhoods are in a predicament found in the confluence of two opposing considerations. On one hand, the greatest promise for acting to improve conditions depends on the fullest possible use of rationality, reason, and empirical research to make good policy and administrative decisions. On the other hand, the constraints imposed by political realities, partial information, and limited knowledge and human psychological ability ensure that rationality will always be incomplete and imperfect. So the most promising solution, indeed perhaps the only solution that offers much hope for a better future—increased reliance on rationality, reason, and documented experience—is, by definition, infeasible. In my view, the most suitable way out of this conundrum is to rely on another, different, somewhat obscure yet legitimate, pragmatic, and at least equally coherent concept that defines rationality as the intelligent pursuit of appropriate objectives (Zeleny 1982, 1992; Rescher 1988). Given this definition, rationality cannot be determined unless one refers explicitly to a set of criteria that identify the optimal course of action in the prevailing circum-


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stance; that is, since the specific criteria of rationality in different contexts do not always coincide, they can be adequately specified only within the context of the particular situation in which they occur. Thus rationality does not necessarily refer to the totality of circumstances as seen from every putatively possible point of view or involve the entirety of information and knowledge both present and absent. Rather it is immanent, explicitly contextual, and limited circumstantially: it is a matter of optimizing not in an absolute sense but relative to the prevailing circumstance. It requires a set of criteria with which to identify the optimal course of action, so it is crucial to make concerted efforts at improving available information, clarifying knowledge of the alternatives and their consequences, and using decision-making aids to achieve greater consistency in combining knowledge and preferences. Given this definition, when I speak of rational policy and administrative decision-making, I mean the exercise of rationality within the context of concrete policy and administrative decision situations. It is important to be explicit about how the practical infeasibility or inability to comply with the formal axioms of choice is rationalized. Whereas the more commonly accepted version of rational choice takes such inability to imply that policy and administrative decision-makers cannot make rational decisions, this concept takes it to imply only that decision-makers must be circumstantially limited. That is, even fallible decision-makers can be rational as long as they do their very best, humanly and realistically, given the prevailing circumstances. This view is somewhat more charitable toward policy and administrative decision-makers than are those based on the nonrational models, though it does not logically conflict with them (Henderson 1994). It assumes that the outcomes observed by those who uphold non-rational models are, in a sense, supervened upon the constituent individual decisions (Kincaid 1994);1 that is, they appear as a whole to be irrational. For instance, there may appear to be persistent solutions looking for problems. However, on closer examination, such outcomes are likely to be the aggregated effects of many constituent individual decisions. Their apparent irrationality is likely due to the constituent individual decision-makers each using different criteria of rationality, some of which are not available to the observer. If so, the observer simply does not know the full set of contextually specific constraints binding on the constituent individual decision-maker’s circumstances, nor how the outcomes of constituent individual decisions interact and effect the other, interdependent decision situations. Consequently, it may seem practically impossible to develop a plausible causal schema under which the constituent individual decisions could possibly have been rational; thus the observer may conclude that the non-rational models pertain. Because the observer does not fully and perfectly know the details of each and every particular context in

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which the constituent individual decisions were made, it is not possible to specify the criteria which the decision-makers used to determine rationality within them. If the guidance of rationality is limited circumstantially, then it must not guarantee selection of the unqualifiedly best alternative in a policy and administrative decision. So why follow it? Probably the best answer is twofold, pragmatic and moral. The pragmatic aspect is simple and straightforward. Ultimately there is no way to prove that effort to enhance the degree of rationality in related policy and administrative decision processes will lead to the best possible outcome; that is, there is no guarantee that policy and administrative decisions justified by an appeal to rationality, reason, and empirical research will, in comparison to any other approach, better improve the distribution of environmental risk and its health effects on minority, lowincome, and other disadvantaged neighborhoods. But since proof, by its nature, is a rational sort of thing, a completely rational justification of the benefit of using rationality to justify policy and administrative decisions would beg the question, since it is viciously/virtuously circular. So one is left with an essentially pragmatic justification for following the guidance of rationality—as best as can be determined, it alone offers the best chances for success in realizing the objectives of a decision (Rescher 1988). The moral aspect is rooted in rejection of the morally positive proposition that power and politics rather than reason and rationality are the mainsprings of human action and that persons instead of arguments are therefore the appropriate basis for the resolution of human conflict (Popper 1945). While emotions, passions, power, and politics are undoubtedly a principal influence in many policy and administrative decisions, many people, including me, are unwilling to accept that reason is merely a somewhat superficial manifestation of human nature. The supervening moral obligation of the decision-maker is to do what he or she can to make reason and rationality play as large a part as is humanly possible. To the moral positivist, this may appear hopelessly unrealistic, since it does not consider the obvious weakness of most people, their dependence on emotions and passions, power and politics. Yet it is the bedrock of all political demands of equality before the law. For unless conflicts are resolved first on the basis of reason and rationality, rather than people, they are ultimately and unavoidably resolved on the basis of “might makes right.” This is unacceptable on the grounds of humanitarianism and a belief in the moral equality of people. Indeed, institutions controlled first by reason and rationality are essential to the realization of any liberal democratic values, particularly those at the heart of the discourse on environmental justice. Therefore the only morally acceptable alternative for policy and administrative decision-makers is to appeal as far as humanly possible to rationality and empirical knowledge as the basis for their decisions.


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“GOOD” ENVIRONMENTAL JUSTICE POLICY AND ADMINISTRATIVE DECISIONS It is useful at this point to clarify the concept of a “good” environmental justice policy and administrative decisions. Good environmental justice policy and administrative decisions make the fullest possible use of rationality, but that alone is not enough. What else is needed? Before delving into this question, I should make several prefatory points. First, I use the label “policy and administrative decision” in a generic way to refer to decisions made throughout the various modes of activity within government, including environmental justice planning, policy formulation, administration, and dispute resolution. Further distinctions and refinements are given in Chapter 9. Second, as previously stated, any reasonable conception of good policy and administrative decisions asks no more of decisionmakers than the best they can possibly achieve, given the incomplete information and imperfect empirical knowledge they inevitably possess. It cannot, for example, ask for optimization from a full set of known values that completely describe all the relevant attributes of putative patterns of disproportionate distributions throughout the country. This would require information and knowledge that decision-makers do not have and cannot possibly obtain. Third, such a concept should be understood within the context of and consistent with a broader understanding of the steps of the policy and administrative decision-making process. In Chapter 1 the advancement of environmental justice to the national political agenda was reviewed. This had to happen before the possible alternative feasible courses of action to resolve or ameliorate the problem could be deliberated. It involved a social process of actively selecting between the problems that could potentially be recognized, defined, considered, and eventually acted upon. To understand this process is to understand the justification for the presence of environmental justice on the national policy agenda today (Kingdon 1995). Humans live in a world that is by most reasonable standards less than ideal, and if one had the time and energy, one could find innumerable problems. In reality the time, energy, and attention needed to respond to them are scarce, as individuals or a society feasibly can recognize only a selected subset of them. Many potential problems compete for attention, and the limited time, energy, and attention available to respond to them must be selectively distributed among them. The process of recognizing and focusing on some problems at any given time, to the exclusion of the others, is the agendasetting process. The agenda-setting process legitimately recognizes selected issues and problems and attributes them with enough importance to merit action. Those problems, such as environmental justice, that successfully compete for recognition and attention are said to be on the agenda.

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To get environmental justice on the agenda initially required only a vague, cursory description of both the normative standard and the facts by which it is recognized and defined. The evidence of this is the current lack of a widely agreed upon normative standard or set of facts related to the problem. But once the problem was on the agenda and decision-makers were faced with deliberating alternative feasible courses of action for solving the problem, it became important for policy and administrative decision makers to have a reasonably clear and explicit problem description. In terms of the pertinent normative standards, this much is clear: Environmental justice problems refer, in general terms, to one or more of the canons of distributive justice (Rescher 1966). More specifically, individuals variously envision environmental justice to consist in the egalitarian treatment of people as equals, according to their needs, abilities, efforts, sacrifices, and productive contribution to society. The application of such canons always involves making value judgments regarding how things ideally ought to be, what ought to be sought, what ought to be done, and, in particular, how the burden of environmental risk ought to be distributed throughout society. Some of the current debate over environmental justice stems from the fact that any one or more of these canons, along with their associated and distinctly different conceptions of fairness, may be used to interpret the problem and shape proposals for action. Moreover, each canon in practice leads typically to a very differently described problem as well as to a differently shaped environmental justice policy proposal (Been 1993). As a consequence of the use of different canons, disputes arise over what is fair, particularly what fairness in the distribution of environmental risks should mean. To determine what is ideal, right, fair, or just in society is to some extent always a moral problem, and in this sense concern with improved environmental justice policy and administrative decision processes, and ends sought thereby, is essentially moral. Thus the canons of distributive justice are integral to any meaningful discussion of the problem. Having noted this, it is clearly not sufficient that the definition of problems related to how fairly the burden of environmental risk is distributed throughout society or to what public policy and administrative actions should be taken to solve them refer only to these essentially moral aspects. Even if society were to have reached a clearly articulated and serviceable agreement about what is right or about how things ought to be, a problem still could not be defined by comparison without a corresponding empirical description of how things in fact are and an explanation of how they work. Empirical description is needed because the relevant policy and administrative decision-makers must first know what the decision problem is, so that they can select and commit to actions likely to solve it and not some other vaguely related one. Whatever normative standard of environmental justice is sought through policy and administrative decisions, solutions arguably will come most effectively and efficiently from


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problems defined not only on the basis of a conception of fairness, but also on a foundation of scientifically acceptable knowledge. While from other perspectives debates over different normative standards for environmental justice are certainly germane, from a research-based decision-making perspective, the primary concerns are with how things actually are, how environmental risks are actually distributed, and what distributed them in that way. This is only one aspect of the problem description, the other being the relevant normative standard. The concerns over problem description are highly consequential for policy and administrative decisions because neither any of the various canons of distributive justice nor the particular proposals they lead to can be reduced to empirical facts. As a practical consequence, intelligent deliberation about the relevance or applicability of any of the canons, or which of the feasible alternative courses of actions might best ensure success of realizing them, depends on the availability of a solid foundation of relevant scientific knowledge. The implications of any of the canons of distributive justice for public policy and administrative decisions thus depend tangibly on how the risks are actually distributed, and what made them distribute in that way. If knowledge of these basic facts is lacking, the pursuit of good environmental justice policy and administrative decisions will as a consequence be handicapped. Only when the relevant empirical knowledge is available and rationally applied toward the realization of whatever normative standard of environmental justice is agreed upon by society, can public policy and administrative decisions make right those things currently wrong in society. Having at least cursorily recognized a decision problem enough to get it on the agenda, the next logical step, before rationally deliberating alternative feasible courses of action, is to thoroughly describe it. Problem description is a matter of depicting, characterizing, and specifying the relevant facts, events, and circumstances in relation to the particular normative standard with respect to the problem. Clear and explicit problem description is one of the major steps currently lacking in terms of making good policy and administrative decisions related to environmental justice. This is largely due to the paucity of empirical research on the problem. This is crucial because it can greatly alter the way decision-makers make related choices. Problem description provides the context within which information about the decision situation is anticipated to be useful and serves as a basis from which decisionmakers can draw on previous experience to deliberate and eventually choose a course of action. Problem description includes a range of activities oriented toward selecting and making sense out of decision situations. These include framing the relevant facts, events, and circumstances or putting them into proper context to give them meaning. Even the simplest policy or administrative decision situations of any consequence—those with relatively little uncertainty that are

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substantively similar to situations previously encountered by decision makers—are too complex to enable conditioned responses or the use of standard operating procedures. As a consequence, decision-makers must interpret and diagnose, or frame, the situation by evaluating the states of its most salient features. It is important to note that different frames, which is to say different problem descriptions, emphasize different kinds of information (Wagenaar and Keren 1986). Adequate description and careful framing of thorny and complex problems and issues such as environmental justice can involve massive data and information gathering and empirical research effort; therefore, the process can be very difficult, time-consuming, and resource intensive. The next step in the decision process is an information search. An information search—usually based on descriptive empirical research—is needed to reduce uncertainty about the decision situation. Nearly all policy and administrative decisions of any consequence include some information search. This is another major step currently lacking in terms of making good policy and administrative decisions related to environmental justice. The nature of an information search process can vary tremendously. On one end of the spectrum there are thorough searches and careful analyses. These include not only characterization of the empirical facts, events, and circumstances which constitute the decision problem, but also thorough assessment of the standards, values, and preferences of the relevant stakeholders. Arguably, the failure of past and current public policy and administrative decisions to satisfactorily create widespread perception of environmental justice is attributable specifically to the absence of adequate search and analysis. At the same time, it should be recognized that the time and energy required to conduct a thorough search and careful analysis is often interpreted, especially in the face of political pressure for action, to be mere procrastination. At the other end of the spectrum are decisions that do not benefit from careful or thorough search and analysis. These decisions usually require information that is either too costly to obtain or simply not wanted.2 Only when problems are recognized and described, as further information is gathered, do alternative possible courses of action begin to emerge. This gives rise to the choice process, the process of weighing the alternatives and selecting between them. Success at achieving the goals and objectives of a policy and administrative decision depends heavily on the adequacy of the problem description, as articulated both in terms of the normative standard and the relevant empirical foundations. Because of the paucity of empirical research description of the problem of environmental justice, this step, in any sort of comprehensive problem-solving action, is currently premature. Nevertheless, as the most visible step in decision-making, the choice process represents issues of power and responsibility, and without some understanding of it the chances of successfully solving related problems are substantially diminished.


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The image of the policy and administrative choice process can be likened to choosing among items on a menu at a restaurant, except much more serious and consequential. Much as the person ordering food at the restaurant has already selected which restaurant to go to, so the policy or administrative decision-maker at this step has already recognized and described a problem. The person ordering food at the restaurant, faced with a choice between the items on the menu, assimilates the information about the alternatives with his or her preferences and selects between them. Similarly, the policy and administrative decision-maker seeks on the basis of the available empirical knowledge and preferences to select the best of several alternative feasible courses of action achievable, in terms of the goals and objectives of the decision. But the choice is more complicated for the policy or administrative decisionmaker, especially when facing a thorny and complex problem such as environmental justice. This is largely because his or her choice involves elements of what to believe or accept, what preferences to consider, and how to integrate the relevant preferences with the relevant facts, events, and circumstances. It has been suggested that selection of an alternative feasible course of action in the choice process is based among other things on the preferences (e.g., values, subjective utilities, wants, needs, interests, goals) of the decision-makers, along with their expectations regarding the set of alternative possible actions. There are three major contexts of these choices (Rescher 1988). First are those of belief, accepting or endorsing particular facts, events, and circumstances as constituting the decision situation. Second are those of action, selecting and commiting to specific overt actions. Third are those of evaluation, determine what to value and by how much. A good decision requires the decision-maker to offer cogent reasons for, give an adequate account of, or provide a convincing rationale for his or her reasoning within and among all three of these contexts. The ability to make a good policy and administrative decision in a complex situation requires protracted logic and reasoning through the relationships between the myriad of values and stimuli related to the relevant preferences and expectations. At some point along the way—indeed probably at several points along the way—the ability to sustain a chain of protracted logical reasoning depends more specifically on the joint mental processing of multiple empirical facts or bits of information related to each of the alternatives.3 In the process of deliberating alternative feasible courses of action, information associated with multiple stimuli must be combined or integrated to produce a response or to select a preferred course of action. Moreover, the combination or integration process itself can be quite complex, as shown in its logical aspects by Bochenski’s (1970) history of the evolution of formal logic and in its psychological ones by Anderson’s research in information integration theory (Anderson 1981). Large amounts of information and deep

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or long logical chains of reasoning are likely to become involved. As a consequence, reliable and meaningfully communicable interpretations of situations in which environmental justice arises as a concern and good decisions made in response to them are very apt to require the support of systematic and explicit guidelines for deliberation and selection among alternative possible courses of action. Often, the policy and administrative choice process does not proceed on the basis of carefully reasoned judgments about tolerably full and complete information related to the alternative possible courses of action. When the issue of environmental justice was first brought to the Environmental Protection Agency, for instance, chances are that the leadership there was not so much interested in whether the empirical foundations of the problem as presented to them were well established and defined or not. It is likely that they were more interested in creating the appearance of making a response. In any case, oftentimes political realities preclude waiting to gather more information or selecting the course of action that has clearly been identified as optimal, judged on the basis of substantial evidence and information. Especially when multiple stakeholders are involved, a course of action, while clearly suboptimal, is often selected because it represents a mutually satisfactory compromise between competing interests. Sometimes it is selected simply because it is doable, given a restricted set of institutional arrangements, values, and constituents. Other times the cost of a continued information search becomes prohibitive. Consequently, when the empirical aspect of the problem is not well described, the decision-maker faces a lack of clarity and definition with respect to the range of alternative possible courses of action, the relevant values and preferences, and the consequences associated with the alternatives. In such situations, as clarity and definition decrease, so does the likelihood that the outcomes of the decision will achieve the stated goals. Recognizing this, I believe the appropriate criterion of choice for good decision processes related to environmental justice is optimization relative to constraints, that is, good environmental justice policy and administrative decisions consist of making the best possible selection in the prevailing circumstances, or optimizing relative to the constraints binding on the decision process. This requires the decision-maker to marshal enough information and empirical knowledge, adequately measure and represent the relevant preferences, and apply consistent decision rules for combining information with the relevant preferences. Another constraint refers to the satisfaction of stakeholder groups, specifically whether they perceive the outcomes of the choice process as moving in the right direction. Demands for the use of the criterion of optimization relative to clearly articulated constraints seem to offer the most promise for achieving better policy and administrative decisions related to environmental justice.


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The next step, once the choice process has been completed, is implementation. General directives and programs adopted by top policy and administrative decision-makers, boards of directors, or upper-level management are executed, modified, and elaborated by individuals in subordinate administrative organizations. As things stand, the effectiveness of the implementation of policy and administrative actions selected to achieve environmental justice goals is likely to be limited by the lack of empirical research for describing the problem and developing the information bases needed to specify alternative feasible courses of action. The final step, after a choice is made and implemented, is evaluation. This step is particularly critical in the results-oriented institutional environment that policy and administrative decision-makers face today (National Performance Review 1996). It involves further information search guided by the criteria by which the choice is to be judged after the fact. The whole issue of environmental justice as a matter of public sector activity is still not sufficiently formed for much evaluation of environmental justice policy and administrative decisions to occur. But after more time passes, when the issue matures, evaluation is likely to be a tricky matter, largely because of the difficulty of defining the criteria to use in judging the selected courses of action and their consequences. Of course, the ideal criterion against which to evaluate an environmental justice policy or administrative decision ultimately would be whether or not it is beneficial in ameliorating the perceived problem. In practice, however, evaluation of whether a decision or set of decisions reduced disproportionate distributions is at best extremely difficult and costly. Logically, it requires at least a comparison of such distributions at two points in time and then the ability to attribute the cause of any observed change to the particular decision at issue in the evaluation. Given that, now at only one point in time, much less two, the overriding characteristic of the currently available knowledge about disproportionate distributions is a high level of uncertainty, such comparisons seem highly unlikely. So, by implication, this criterion will probably tend to be too indirect and distant from any immediate information or knowledge about the relevant empirical conditions. As a consequence, a surrogate criterion—other than direct observation of changes in the amount of environmental risk at various locations—is or will be needed to evaluate the effects of related decisions. And as it turns out, it can be extremely difficult to come up with a suitable definition for such a criterion. THE VALIDITY OF THE EMPIRICAL RESEARCH FOUNDATION To the extent that the goal of making good policy and administrative decisions related to broad geographical patterns of disproportionate distributions requires an adequate description of the problem, it necessarily rests on infer-

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ring and accepting some empirical statements about the spatial distribution of environmental hazards. Other, more modest problems, such as those related to the public health concerns of individuals, in particular minority and/or low-income neighborhoods located hear a hazardous site, also require empirical statements for their definition. In turn, whatever the nature of the related problem, such empirical statements are logically justified by the methods used to establish them. Generally speaking, empirical research is a matter of using well-established scientific methods to find out from consideration of what is already known something that is not known. In describing the empirical foundations of policy and administrative decision problems, these methods are generally used to obtain answers for a wide range of questions: What are the boundaries of the issue or problem? What variables influence it? Who specifically is affected by it? Who are the participants in the relevant decision-making processes? What are the causes of the issue or problem to which the policy or decision will be addressed? What types of choices and alternatives are available for responding to the issue or problem? What are the parameters of the content of current and related institutional arrangements? What are the most likely linkages between the feasible actions and their corresponding results? What are the most influential variables to use in analyzing the relevant proposals? The answers to such questions form the empirical foundation for the decision problem description. Unless the answers meet certain standards, the decision problem cannot be defined well enough for the decision-maker to know what problem he or she is trying to solve, thus making deliberations of alternative feasible courses of action less effective and decreasing the likelihood of successful outcomes. Empirical research methods are of special value in this regard because they attempt to answer such questions in a way that meets reasonable standards of scientific validity. As examined in considerably more detail in Chapter 4, the concept of validity has to do with whether or not inferences and propositions accurately and reliably represent related facts, events, and circumstances. Historically, validation in science has rested on substantial agreement with a scientific community, based on the replicability of discoveries independently of the investigators. The idea, in essence, is to answer research questions in such a way that any skepticism remaining about the answers refers completely to matters of fact and not the thinking of the researcher in the process of inference. Validation is accomplished in the peer review process by an explicit system of reasoning about factual observations in which constant reference is made to systems of logical rules for observation, inference, generalization, analysis, communication, reasoning, and validation. These rules constitute normative criteria against which claims for empirical information, knowledge, and understanding are evaluated. Sound empirical research assumes that all relevant knowledge is derived ultimately from observation and data. Moreover, it assumes not only that data


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have been acquired but also that they have been systematically organized and analyzed. In particular, the validity of the empirical statements used in defining a problem depends on intersubjective agreements by competent and recognized authorities, specifically regarding the procedures for making and documenting observations as well as the logical system with which the data are organized and analyzed in terms of the statements. One of the first major sets of obstacles to using empirical research to describe environmental justice decision problems stems from the humanness of the observers. It was mentioned earlier that the same person is often unable to repeat an observation again and again in exactly the same way with exactly the same result. Also, observers can be biased and different observers may make very different observations of the same phenomenon. Accordingly, one can imagine how difficult it must be for several people to have the exact same perception, especially of a complex phenomenon such as a hypothesized disproportionate distribution of environmental risk. This makes coming up with a valid description of the decision problem difficult. While the humanness of the observer presents hurdles to obtaining the intersubjective agreements upon which scientific validity has traditionally rested, thanks to carefully conceived scientific methods they are not insurmountable. Indeed, one of the major tasks of the scientific researcher is precisely to overcome these obstacles. Properly understood, there are two separate yet complementary domains to scientific method that together serve a self-correcting function in helping to overcome these difficulties. On one hand, there are the internal aspects that belong in the conceptual-theoretical domain, in which individuals internally experience thoughts, preferences, feelings, valuations, and purposes. This is the domain in which the process known as concept formation begins and gives rise to empirical research. Concept formation is a matter of finding maximum congruence between the attributes of a phenomenon and the categories used to think and communicate about them. Its basic purpose is to define concepts or abstractions formed by generalization from particulars (Conant 1951). Concepts represent discrete attributes of phenomena. All attributes to which a concept applies usually exhibit the attributes in the same constant manner that is also different than is exhibited by those to which it does not apply. Concepts serve functionally as a means of introducing a perspective, a basis for communication, as instruments for generalization, and as elements in theory construction (Nachmias and Nachmias 1992, 28–29). On the other hand, there are external aspects that relate more or less directly to the attributes of the phenomenon, particularly insofar as they are capable of stimulating objective sense perceptions. These belong in the observational–empirical domain. Scientific procedures in this domain begin with the design of experiments and proceed through the acquisition and analysis of data. Data are measurements or recorded observations of particular

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characteristics of the phenomenon under investigation. They serve as the plenary basis from which empirical researchers use reason to attain knowledge through intersubjective agreements. Without adequate data, scientifically valid description of environmental justice policy and administrative decision problems is out of the question. Valid empirical statements obtained by empirical research are based on a clear and explicit inferential structure with which to proceed from phenomena to data to information to proposition. It is useful to note here that the term information refers to a representation of the data, however information is not the same thing as data. While information comes from data, data need not contain information. The difference, strictly speaking, is that data contain information for someone only when they lead to a reduction in the errors that that person makes in response to a situation. More specifically, one may say that data contain information for a person only when they alter the probability of that person’s response to whatever situation or stimuli the information is about.4 More to the point, data contain information related to an environmental justice decision situation only if they lead to a reduction in the errors a decision-maker makes in responding to the problem. The task at hand in empirical research is ultimately to demonstrate that the premises assumed in descriptions of policy and administrative decision problems, as reflected in the data, reflect discernable patterns in the actual world. This is to say that they are more than mere artifacts of biased or even arbitrary determinants of interpretation. This, of course, is a difficult task to achieve when considering anything as complex and emotional as environmental justice. At the same time, because so much rests on the ability to achieve success, it is important to bear in mind the minimum condition for such demonstration. The entire process of inferring the relevant relationships, all the way from foundational assumptions to conclusions must be clear, explicit, and subject to reasonably open and critical examination. If this condition is not fulfilled, it is not possible to discern whether the conclusions are mere artifacts of interpretation, and so scientific validity cannot be achieved. To require that any descriptive statement accepted as a premise in a policy and administrative decision process should meet a reasonable standard of scientific validity puts a premium both on observation and the reasoning process through which individual observations are transformed into descriptive statements about the decision situation (see Chapter 8). It therefore also enhances learning about the problem and puts relatively great value on the use of basic scientific research methods to gather information and knowledge related to the problem. Moreover, the validity of descriptive statements assumed as premises in a related policy and administrative decision can provide, at least in part, the surrogate criterion needed for evaluation. When combined with methods for improving people’s ability to apply whatever knowledge is acquired in the relevant decision processes, valid propositions


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are thus of immense practical value in making good environmental justice policy and administrative decisions. FOUNDATIONAL ASSUMPTIONS The fact that environmental justice issues and problems are on the national political agenda provides evidence of the past failure of public policy and administrative decisions to satisfactorily create widespread perception of success. It thus suggests basic flaws in the way relevant decision situations have been researched and decision-making processes have been understood and conducted to date. Recognizing this, a research-based decision-making perspective is predicated on the assumption that knowledge derived from sound empirical research and structured using suitable decision-aiding methods offers the greatest promise of improving related policy and administrative decisions. The primary mechanisms are improved problem descriptions, improved information search, and more rational information processing procedures. At the same time, research-based decision-making has intrinsic limits. Empirical research alone cannot, for instance, define decision problems, specify exactly what information to search for, or explain how to weight options in the process of making choices. Neither can it define important research questions, select an appropriate research design, or assess the potential value of a research project. In this regard, one of its most important contributions is to help to systematically constrain any such subjective and judgmental aspects, including all biases and assumptions, or, failing that, at least cause them to be explicitly stated and made a part of the articulate repertoire of the researcher or decision-maker. The considerations throughout this section represent an effort to satisfy the implied demand to explicitly state my biases and assumptions. It is most certainly not feasible to get a proper perspective on environmental justice while completely removing all personal views, and such a thing would probably not be desirable even if it were. Rather, to the extent that personal biases enter the appraisal, the attempt is made to state them explicitly up front. That way the reader can get a sense of my biases in perspective and what ends are sought in its formulation. The reader deserves to know what motivates this effort, and what ends are being sought. At issue are foundational assumptions that relate primarily to the reasoning processes through which categories of relevant human experience are characterized according to perceptions and understandings of the attributes shared by their members (Warfield 1990). As such they embody the prior convictions or conjectures on which all thinking and consequent research is, in part, based. They function as a basis for resolution whenever issues arise over a point of theory or method used in the process of making inferences about

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the relevant facts, events, and circumstances. They are composed of ideas that are not necessarily demonstrated, but rather ideas that have been distilled from observation, experiment, experience, and prolonged thought and discussion throughout the academic disciplines and, in this case, the policy communities. Given the situational complexity of environmental justice, not to mention its multidisciplinary foundations in political science, geography, economics, sociology, epidemiology, environmental chemistry and psychology, among others, it is useful for foundational assumptions to be made explicit from the start. Because consciously and explicitly or otherwise, all interpretations of environmental justice situations represent an elaboration of such assumptions, disciplined effort to articulate them helps open up an approach to policy and administrative decision-making of constructive critical examination and appraisal from without.5 One set of relevant foundational assumptions relates to the acceptable sources of descriptive statements for defining environmental justice problems (Nachmias and Nachmias 1992). First, such sources must not be ultimately based or established on the authority or say-so of people who are socially or politically defined as qualified producers of knowledge. While some authoritarian knowledge (e.g., a court’s knowledge of civil law) is undoubtedly relevant to achieving environmental justice, the interest in research-based decision-making is in the knowledge of empirical fact upon which its applicability depends. Second, such sources must not be essentially mystical, based in the end upon revelation, or established from supernatural sources. Whereas mystical knowledge can provide individuals with the inspiration and desire to achieve environmental justice, it is not permissible as a part of any scientifically acceptable statements used in describing the relevant facts, events, and circumstances. Third, such sources must not be rationalistic in the sense that they are based solely on strict adherence to the forms and rules of logic and rationality. While logic and rationality are necessary for achieving environmental justice, largely because they place limits upon scientifically acceptable knowledge, they alone are not sufficient for establishing acceptable statements about the relevant facts, events, and circumstances. Rather the only acceptable source of such statements is sound empirical research, based ultimately on systematically disciplined perception, experience, observation, and analysis. In essence, for empirical research to be a viable source of knowledge one must presuppose the existence of a systematic pattern with a discernable causal structure. Another relevant foundational assumption is that every individual interpreter of a situation in which environmental justice arises as a concern, as well as every policy or administrative decision-maker, is fallible and has bounded rationality (Simon 1957). Each individual’s ability to detect errors in others’ interpretations of the situation is typically much higher than his or her ability to detect self-made errors. Moreover, every individual has a lim-


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ited, somewhat idiosyncratic, and unavoidably rather biased point of view, depending on individual life experiences and circumstances (Kelly 1955). Decision-aiding techniques, such as those discussed in Chapter 8, are used largely to help overcome human fallibility. Still another relevant foundational assumption relates to the nature of language and communication. Specifically, it is assumed that, ultimately, all terms in any language are given arbitrary definition from within a culture or community and ordered according to a grammar that may impart new meaning above and beyond the definitions. Past this point, however, not all language is of equal quality. For example, the high quality of communication associated with the natural sciences stems largely from the existence of and reference to phenomena that have evolved independently of and externally to humans. Phenomena such as nutrient cycles, energy flows, genetic structures of populations, and biochemical processes in the solum have evolved over millions of years prior and external to the development of conscious human knowledge. They are thus “objective” in the sense that they do not depend on human recognition or definition for their existence. A more precise and certainly less controversial way to describe this is to say that clear communication in the natural sciences is enabled by the existence of external referents in nature. Conversely, most communication about environmental justice incorporates components for which no external referents exist. Communications about environmental justice instead refer largely to language-based phenomena, to linguistic constructs such as laws and standards created by human societies. To be sure, in environmental justice there are some discussions, particularly in epidemiology, toxicology, risk assessment, demography, and (to some extent) economics for which external referents do exist. But the bulk of discussion about environmental justice is directed toward referents that exist only in human social reality—institutional rules, concepts of fairness, and social categories of race—none of which have a basis in the natural world. This feature of environmental justice, this lack of external referents, compounds the difficulty of communication, particularly since prose alone is often completely inadequate to systematically present structured information about highly complex situations. As a consequence of this assumption, if high quality communication about environmental justice is to be obtained—by which I mean communication that enables the receiver of the message to trace clearly through the language to the referent—it must necessarily include some measurements, mathematics, or structural graphics. It must also have a clearly defined conceptual basis, so that the sender and receiver of the communication have a common frame of reference with which to interpret the meaning of terms. This necessity of including some level of measurements, mathematics, and structural graphics is in a sense unfortunate. Given the low quality of so much of the educational system, too many people directly affected by environmen-

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tal risks do not have the intellectual discipline or command of language needed to meaningfully communicate about such risks. Another relevant foundational assumption is essentially logical and psychological in nature, relating to the human reasoning process itself, especially reasoning through relationships. Specifically, it is assumed that the unaided human capacity to perform protracted reasoning through relationships is quite limited, especially when considering complex situations in which long, deep logical chains are required to attain coherency.6 Therefore, without the support of systematic and explicit inferential guidelines, people engaged in interpreting the complex situations in which environmental justice arises as a concern or in making decisions related to such situations are prone to make logical and, upon occasion, potentially disastrous errors in reasoning. Indeed, it is very likely that one of the basic flaws in the way relevant decision situations have been researched and decision-making processes have been understood and conducted to date stems from failure to recognize this limitation. The final foundational assumption relates to the use of empirical knowledge in policy and administrative decision-making. It is assumed that improved policy and administrative decisions will necessitate recognition of the limitations on the available knowledge of situations in which environmental justice arises as a concern, as well as the aforementioned human limitations, along with corresponding improvements in both research and decision-making. If not all individuals share a healthy and clean environment and, furthermore, if it is the minorities, low-income, and other disadvantaged people who experience such an environment less often, it does not necessarily follow that it is the responsibility of government to improve the situation. Indeed, given that limitations on the available body of empirical knowledge can be construed as limitations on the scope of government responsibility, there is some reason to doubt whether it does. The fact that something is wrong and is recognized as such, does not necessarily mean that policy and administrative decision-makers know how to make it right or that their efforts to make it right will do more good than harm. For this, specific and detailed considerations of proportion and reference to the facts, events, and circumstances that constitute particular policy and administrative decision situations are needed. Limits on empirical knowledge about the relevant situations are also limits on the practical means for the government to act responsibly. If government decision-makers do not have the practical means to act responsibly, it is arguably inappropriate to hold government responsible.

CHAPTER 3

Major Terms and Concepts in the Discourse Skeptical search for truth is bound before long to collide with established norms and authority. Tension between the pursuit of knowledge and social solidarity is an inherent characteristic of politics and of political man. —MURRAY EDELMAN, Political Language

Before we delve deeper into the research on disproportionate distributions and their public health or other impacts, it will be helpful to have some formal understanding of the major terms and concepts used in the discourse. Many are used in a way that helps advance environmental justice as a political movement by creating postures of political loyalty, while glossing over or even obscuring some of the important considerations related to their proper use (Edelman 1977). Because our purpose is less to grind a political axe than to help policy and administrative decision-makers make better decisions, some degree of formal understanding of these terms can be very useful. Terms and concepts are the basic tools of deliberation on policy and administrative decisions, and as a craftsman must know his or her tools to achieve high quality standards, so must policy and administrative decision-makers know theirs. The specific empirical research question at the heart of the present concern with disproportionate distributions is this: Given two locations having disproportionate levels of environmental risk, yet otherwise identical in every respect except for the minority or low-income status of their residents, is the region surrounding the more environmentally risky location more likely to be occupied by minority and/or low-income residents?1 If the answer is yes, the implication is that disproportionate distributions are present in the study area. In Chapters 4 and 5, I consider some of the basic scientific concepts and empirical procedures needed to answer this question one way or the other in any given study area. But before getting into such considerations of method, let’s examine the substance of the terms and concepts used in asking this question.

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THE CONCEPT OF ENVIRONMENTAL RISK The concept of environmental risk is at the center of much if not most of the discourse on environmental justice. If deliberations over policy and administrative decisions related to environmental justice are to be meaningful in the sense of shared understanding and agreement upon the terms and concepts in the discourse, they thus require definition and specificity beyond vague general reference to environmental risk. Ideally, the specific environmental risks of concern in any particular decision situation would be clearly identified, defined, and dealt with within the context of a set of explicit risk priorities grounded in the estimated severity of the various hazards. Otherwise, any serious attempt by policy and administrative decision-makers to deal with environmental risk straightaway runs up against various inextricably related judgmental and evaluative considerations. In the orthodox view, environmental risk involves two primary components: an eventually harmful or negative event, and the chance the event will occur (Rescher 1983). Life insurance is a paradigm-setting example. In the view of a life insurance salesman, the harmful or negative event is precipitated by a hazard present in the environment. A hazard is a potentially deleterious agent, such as a drunk driver, a synthetic chemical, or a natural agent such as a flood that materially causes the harmful or negative event to occur. The harmful or negative event is, say, the death of an insured person. The value or cost of the negative event is the subsequent payoff of the policy to his or her designated survivors. One can obtain estimates of the chance the event will occur using simple principles of probability theory, with reference to actuarial tables that give the probability of death for a representative person, based upon characteristics of gender, age, and habit. The risk can be calculated by multiplying the cost of the death benefit to the insured person by the appropriate probability from the actuarial tables. A risk estimate is always obtained by multiplying estimates of the value of these two components. Environmental risk, as such, broadly construed, exists whenever an environment contains a hazard and a range of possible outcomes, one of which may occur, none of which will necessarily occur, and some of which are of a distinctly negative character (Rescher 1983). Usually, when considering environmental justice, the hazards of interest are toxic chemicals or a site containing or releasing them. Environmental risk is equivalent with the prospect that such a hazard in the environment may—but will not necessarily—cause things to go wrong. Analogously, the fact that a drunk driver is driving down the road does not necessarily mean that someone will get hurt; the fact that someone lives next to a nuclear power plant does not necessarily mean that they will be irradiated; the presence of a toxic chemical in the environment does not necessarily mean that someone’s health will be impaired. In each case, the negative outcome may occur, or it may not.

Major Terms and Concepts in the Discourse

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Because, in the broad sense of the term, there are so many different environmental hazards present in the daily life of most individuals, there are many different types of things that can go wrong, and so there are many different types of environmental risk. Some of these hazards are natural, such as lightning, earthquakes, volcanoes, hurricanes, tornadoes, hail, and drought. Some of them are attributable to human behavior, such as violent crime in dangerous neighborhoods. Others are technological, a reflection of the dark side of producing the technology which makes possible a high standard of living. Still others entail potential loss of possessions or economic assets, illness or injury or physical impairment, anxiety, worry, psychological or emotional harm, death, harm to loved ones, and harm to valued causes. Many homes contain environmental risks from hazardous products such as paint, insulation, cleaning solvents, or microwave radiation, any of which may, but not necessarily, lead to negative health impacts. Similarly, many workplaces contain environmental risks from synthetic chemicals used in production, insecticides, hazardous wastes, and industrial machinery. In short, environmental risks are ubiquitous. The air we breathe often contains risks that result from naturally occurring residuals of dust, dirt, and soil or from technologically produced particulate residual matter produced by industrial production, combustion engines used in transportation, and the burning of fossil fuels for energy. An important distinction can be made between facing and taking an environmental risk. To be at risk is always to be situated so that something wrong or unpleasant might happen. However, from the point of view of the individual, such a situation itself can come about in different ways. On one hand, one can find oneself in a risky situation by mere circumstance, without having made any choices or taken any deliberate action to get there, such as from natural hazards. When an environmentally risky situation cannot be averted by a choice made by the individual entering into the situation, the individual is said to face the risk. On the other hand, an individual may have the option to choose or take deliberate action that either averts or leads him or her to enter the risky situation. When an individual makes a deliberate choice to enter an environmentally risky situation, he or she is said to take the risk. The distinction is important, partially because people find risks they take to be more acceptable than those they merely face, and partially because it illuminates an elementary principle of fairness implicit in much of the discussion of the problem of environmental justice. To the greatest feasible extent, the environmental risks associated with particular hazards should be borne by those who benefit directly from their production and use, which is to say those who take the risks, not those who do not benefit from them or who merely face them. When this principle is violated, the relevant environmental risks impose indirect costs on those who face them. These indirect costs are formally

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known in the economics literature as negative externalities. Their presence in a situation instantiates a source of market failure commonly accepted as adequate justification for government intervention, since free markets, left alone, will waste resources in their presence. Thus when the principle is violated, there is little doubt that, in general, government policy and administrative decisions are needed to manage the situation. An example can be seen in observed spatial shifts in the distribution of an environmental risk. A study of hazardous-material truck transportation based upon origin-destination and type-of-material data gathered in northeast Indiana by the Indiana State Police in the fall of 1983 demonstrated this (Stough and Bowen 1987). The data indicated clearly that northeast Indiana was serving as a bridge for the transportation of hazardous materials to and from Illinois and Michigan. Origins and destinations in Illinois and Michigan accounted for about 85% of all the hazardous materials flowing through the region. In other words environmental risk was being exported from the major origin and destination states, and imposed upon an intermediate population in northeast Indiana. While people in northeast Indiana benefited indirectly from the region’s bridging role in the sense that the region probably acquired associated net employment and income benefits (albeit small), they also faced costs associated with the disproportionately large number of hazardous-materials loads that passed through the region yet originated outside of it. The net balance of risk was thus taken by people and industries in the importing and exporting states, but faced by people in the bridging state. Another important distinction for environmental justice is between real risk and perceived risk (Slovic, Fischoff, and Lichtenstein 1982; Zeckhauser and Viscusi 1990; Benjamin 1993). People who have illusions about the character or probability of the harm at issue will confront a subjective risk based upon a misconception. The real environmental risk, as an objective matter, has to do with the objective probabilities and harmful or negative events themselves, and not with their recognition.2 Their recognition has to do with the subjective side of risk, which is perceived risk. Research in the field of psychology shows clearly that real risk and perceived risk may bear very little relationship to one another (Lichtenstein et al. 1978). Environmental justice policy and administrative decision-makers must straddle the two dimensions of real and perceived risk. On one hand, in the face of scarce public resources, public expenditures can arguably be expected to mitigate or eliminate only the real risks. On the other hand, all rational citizens must be expected to make every reasonable and proper effort to ensure that the risks they perceive in their environment are mitigated or eliminated. They tend to think the government should go to any length to prevent or compensate them for any harm imposed upon them by the risks they face; they are interested in the level of risk they find to be acceptable, regardless of what the


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experts say. The subjective dimension is thus an inextricable component of environmental risk management and social policy decision-making. The complications of managing environmental risk are compounded by a few of additional noteworthy aspects. One is the ability to recognize and identify a risk when it is present. Some hazards are not visible or readily perceived. Conversely, the fact that there is a perceived risk present in a neighborhood does not necessarily imply that there is an actual one. A second aspect that needs to be recognized and fully appreciated is that risk cannot be totally eliminated. Accordingly, the first step in environmental risk management is to make the relevant risks as explicit as possible by supplying information and helping people consciously make free choices, moving them from being risk facers to risk takers. Third is the availability of data with which to measure the components of risk. In the case of many hazardous chemicals, for example, such data are hard to come by. This third consideration is particularly important in defining problems related to environmental justice. As a consequence of these considerations, the concept of environmental risk is often difficult to manage and operationalize for purposes of doing empirical research. THE ANALYTICAL GEOGRAPHY OF DISPROPORTIONATE DISTRIBUTIONS The terms location, area, and region occupy a central position in environmental justice research because they are integral to the inferential processes that go into establishing disproportionate distributions, defining neighborhoods, and delineating affected regions, among other things. Their importance in this context arises largely from a requirement to identify a unit of analysis in any social scientific research. In the most prevalent discourse, these terms arise, at least implicitly, as a central consideration in the conceptualization and assessment of disproportionate risks, particularly the scientific need to have a frame of reference with which to evaluate them. They are needed to specify, with reference to objectively defined latitude and longitude coordinates, where on the geographical landscape the environmental risks occur, as well as where those exposed to them live.3 In essence they provide a framework with which to enable higherquality communication and make investigation more systematic and coherent. As discussed earlier, empirical research is based upon the acquisition and systematization of observations organized around definite concepts that represent discrete attributes of the relevant phenomena. In order to connect the concepts to the observations, the concepts are converted into variables. A variable is a set of two or more values, each representing a single observation that can be meaningfully associated with the definite concept. The concepts

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are used to guide the observations. The connection between the concepts and the observations are usually made using measurement theory to map the observations into a set of numerical values, each value representing a single observation (Bowen and Bowen 1999). For example, taking the concept of “minority residence,” one might observe a particular residence and, upon doing so, determine whether it maps into this concept. For example, if under one’s concept Chinese are defined as minorities, and the residents in the household are Chinese, then, say, a value of unity may be assigned to that residence; otherwise a value of zero is assigned. It is as variables that the concepts eventually appear as hypotheses to be empirically tested against data. Location, area, and region, are concepts that can be used to evaluate environmental risks and residences against an objective frame of reference specified by sets of latitude and longitude coordinates. They do this because each observation of a particular hazard or residence is locationally unique in that each corresponds to one point only on the landscape in the study area. If repeated, locationally unique observations are made in a way that is consistent with a definite concept, and they make up a variable known as a spatial variable. Spatial variables help mentally organize, make sense out of, and communicate a large number of such observations, by systematically connecting them to latitude and longitude. Spatial variables can be used to help make and test hypotheses about the relationships between risk distributions. The entire set of individual observations of a hazard and an associated residence or neighborhood involved often exceeds the capacity of one individual to make, and the logical connections between them tend to go way beyond the ability of the unaided human mind to discover. The role of statistical analysis is essentially to help discover and justify statements about these connections. Values of spatial variables that are spread out over space and vary from location to location, are said to be distributed. Given the locational information corresponding to each observation, the distributions are specifically known as spatial distributions. Because each spatial variable tends to contain numerous values, each representing an associated observation, such as a hazard or a residence or a neighborhood, spatial distributions are typically described specifically in statistical terms of frequency distributions and probability density functions. Spatial data consist of measurements taken at specific locations or within specific regions. In addition to values for various variables of interest, spatial data sets also include the locations or relative positions of the data values. Figure 3.1 illustrates how spatial variables are usually recorded in data sets. A represents the entire study region divided into seven subregions. In reality, the boundaries for these subregions are defined using one or more political, economic, demographic, or environmental criteria. B represents the recorded data for these subregions in a matrix format. Each intersection of a row and column is referred to as a cell in the matrix format. Each cell is filled with a


49 Variables 1

2 ............... M

1 2 3 1

7 3

Subregions . .

6 2

. 5

4

. . . . N

A

B

Figure 3.1. Data sets for spatial variables. A, the entire study region divided into sever subregions. B, the matrix for the recorded variables, N = number of subregions. See text for discussion.

value for the corresponding subregion and variable. If one observation is made for each subregion on each variable, its value is placed in the corresponding row and column. In many instances, one column in the matrix is reserved for the latitude coordinates of the subregion or neighborhood and another for longitude coordinates. When numerous observations are made for each subregion on each variable, the values can be summarized statistically, and a value representing the summary can be placed in the corresponding row and column. There are a few basic types of spatial data matrices that are relevant to hypothesis tests of disproportionate distributions, each of which, when analyzed appropriately, involves its own particular sets of unique considerations. Cross-sectional data matrices are usually comprised of one or more spatial variables whose values were observed at different locations at the same point in time. An example of cross-sectional data is the population census conducted by the U.S. Census Bureau every 10 years. Time-series data matrices are comprised of one or more variables whose values were observed at the same location but at different points in time. An example of time-series data is the daily levels of carbon monoxide sampled at a monitoring station next to a highway. Pooled data matrices are comprised of a combination of crosssectional and time-series observations. An example of pooled data is the percentage of a minority population in each subregion of a county each year for a

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decade. Running across these categories, point-referenced data are observations recorded at specific fixed locations that might be referenced by latitude and longitude. Areal-referenced data are observations specific to a region, such as the number of hazardous sites occurring in census tracts. Also, location may be regular or irregular, meaning that they may or may not fall on a regularly spaced grid. Furthermore, the spatial variables may be continuous or discrete; an example of a continuous spatial variable is the number of pounds of a toxic chemical released at a particular site; an example of a discrete spatial variable is the number of hazardous sites in a census tract. Regardless of which of type of data are being considered, the important point is that if the distributions of environmental risk between communities are to be compared on the basis of an objective frame of reference, each individual constituent observation must be locationally unique in time and space. There are a couple of basic classes of spatial distribution relevant to the study of disproportionate distributions, the appropriate analysis of each again involves certain considerations. Most of the current analyses of environmental hazards in relation to the locations of socioeconomic and demographic variables are based on one or both of them. The first class is point-pattern data, which occur when the phenomena of interest—the hazardous site or neighborhood—is represented as a set of points on the landscape. Point-pattern data arise when locations themselves are the variable of interest. Spatial point patterns consist of a finite number of observed locations irregularly located within a bounded study region; they are usually analyzed to identify spatial randomness, clustering, or regularity. Pointpattern data may consist of locations only or of a marked point process, with data values for other variables associated with each location. A primary question of interest with spatial point-pattern data is whether the observations appear in clusters or are randomly scattered. For example, the spatial distribution of toxic release sites constitutes a set of points on a map and so may be considered a point pattern. When spatial point patterns exhibit clustering, it is usually interpreted to suggest either a process of contagion or a locally specific causal process. The second class is lattice data. Lattice data are observations associated with spatial regions, such as census tracts, which may be regularly or irregularly spaced. They consist of observations from a random process observed over a collection of spatial regions, supplemented by a neighborhood structure. Typical spatial analyses attempt to find nearest neighbors within them, calculate the level of dependence of one value in the data set upon its neighboring value, and fit spatial regression models. Regions may be delimited by their homogeneity with reference to certain criteria, in which case they are referred to as uniform regions. For example, social areas in a city tend to be relatively homogeneous with respect to the types of people living there, as measured by variables such as income, family size, social status, and level of


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education. Regions may also be delimited functionally, in which case they are referred to as nodal or functional regions. For example, the boundaries of a labor market region may be roughly defined by the distance people are willing and able to travel to work at the market center. Uniform subregions are often used in analysis of the problem of environmental justice within a functional metropolitan region. A basic characteristic of lattice data is that neighboring regions are expected to have correlated values for some of the measured variables. A lattice data set consists of the measured variable or variables at each observation point and a matrix indicating the degree to which pairs of observations are neighbors. For example, levels of stress occurring in each census tract in a county are lattice data. The census tracts are irregularly shaped, but each census tract has a number of neighboring census tracts (adjacent census tracts are neighbors). Appropriate statistical analysis of lattice data has spatial analogies to the temporal analysis of time-series data, insofar as one of the primary goals is to consider the spatial structure of the observations, or the degree to which neighboring observations are related to one another. In analyzing either of these types of spatial data, one typically must make simplifying assumptions in order to fit statistical models, such as regression models, and make inferences. The terms stationarity and isotropy are often used to describe these assumptions. The assumption of stationarity requires that the statistical mean or variance of the process that generated the data does not change over the study region. The assumption of isotropy refers to a spatial process that evolves the same in all directions. More technically, it means that the correlation between any two observations depends only upon their distance from one another and not upon the direction in which they are separated. Since the implications of violating these assumptions in the analysis of spatial data can imply mistaken parameter estimates and unreliable inferences, spatial analytic techniques such as spatial regression models, kriging, and directional variograms have been developed.4 Adequate analysis of spatial data often requires their use. There are essentially two approaches that can withstand scientific scrutiny to evaluate the claim that the distribution of environmental risk is disproportionately high in minority, low-income, and other disadvantaged or susceptible neighborhoods. The first and, in my view, more ideal approach is based upon risk analysis and prioritization. The idea, in essence, is to properly establish, with a reasonable degree of scientific validity, the degree to which the presence of each given set of hazards creates a risk for a local community. Based on these degrees, the next step is to establish a set of risk priorities corresponding to the hazards. In deciding how to make wrong things right, those hazards that create the greatest risk receive highest priority. (An overview of the proper way to evaluate the risk is presented in the next section. While this approach is in many respects ideal, those interested in it face the practical

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obstacle that environmental risks are ubiquitous. Because risk analysis and prioritization techniques are intensively focused on a given and limited set of hazards and require relatively large commitments of time, attention, and resources to study that limited set, only a narrow subset of the entire range of hazards can realistically be examined. The second approach, the one usually taken in the environmental justice literature, is based not on risk but on proximity. The idea in this approach is, in essence, to evaluate how close members of given social groups are to a given hazard. This is most often done first at a conceptual level, selecting a hazard, such as a toxic material release site, for which data are available. Then one counts how many times that hazard can be found located within a given unit area and how many members of the given social groups are located within that area. If both numbers are high, this indicates a high level of proximity of those group members to that hazard. If in relation to a comparison area a significantly higher level of proximity is found for minorities, low-income, and other disadvantaged populations, this is normally taken as empirical substantiation for the claim of disproportionate distributions. Examples of spatial variables potentially useful for evaluating proximity include the number of toxic release sites, number of homes with lead paint, percentage of the total population made up of minorities, number of manufacturing establishments, number of park facilities, and age and price of the housing stock. There are roughly three groups or categories of spatial variables needed to evaluate claims of disproportionate proximity to environmental hazards. The first is made up of variables reflecting environmental hazards. It was noted that hazards are ubiquitous, so one of the challenges faced by the researcher testing the hypothesis of disproportionate distributions is to define a hazard in such a way as to enable observations that represent the overall spatial variation of them all. In other words, one must define a suitable proxy variable. Unless this challenge is successfully met, the researcher may find himself or herself in a position of having demonstrated disproportionate distributions for hazards that, on balance, are minor or even trivial in comparison to others excluded from the study. Regardless of how this challenge is met, many hazards, including most of the technological ones at the heart of environmental justice, are spatially distributed as point patterns. Thus, the question of whether or to what extent the hazards are disproportionately distributed would be answered by systematically comparing the hazards at each point, along with the status of the residents at that point, with the corresponding hazards and status of residences at similar points elsewhere in a comparison region. The locations of environmental risks are closely associated in space with the locations of clusters of some types of industries, particularly those that produce technological hazards; therefore, the second category is made up of variables that explain the location of these industries. The values of these


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variables directly influence the location of the risks, especially when the decisions to locate these industries are made without consideration of the corresponding location of the environmental risks. Unless these variables are controlled, any observed relationships between the location of the environmental risks and minority or low-income neighborhoods may be spurious,5 that is, it may not actually be race or income that motivates the observed relationship with environmental risk, but some other variable, such as proximity to industry or to a transportation network. At times, environmentally risky industry will locate in minority, lowincome, or other disadvantaged neighborhoods. But a single instance of this is not enough to describe the larger distribution of all environmentally risky sites throughout all relevant neighborhoods: a single instance alone cannot suffice as evidence of the existence of patterns of disproportionate distributions. To adequately describe the entire distribution also requires a summary measure of the dispersion of the observed locations throughout the relevant neighborhoods (i.e., the extent to which the observations differ geographically and demographically). Many of the considerations involved in establishing the geographical and demographic dispersion of environmentally risky sites are covered in detail in Chapters 4 and 5. Most of the larger body of existing research literature on industrial location is focused on behavior within single industries, particularly how firms within these industries go about establishing the comparative costs of various locations (Isard et al. 1998). The basic considerations are the geographic distribution of the factors of production, the anticipated patterns of markets, and economies of scale and agglomeration.6 The specific objective in establishing comparative costs is to determine at what location the particular economic activity or set of activities could achieve the lowest total cost of producing and delivering its product to market. Considerations include cost minimization, accessibility to market, the effects of competition, and profit maximization. These considerations have proven to be exceedingly useful in understanding the locations of particular industries. Techniques capable of overcoming the restrictive boundaries of single industry analysis are now being developed in the field of regional science. However, a great deal more must be known before the colocations of environmental risks and minority or low-income residences can be empirically related vis-à-vis the factors that explain industrial location in these clusters. More specifically, the particular industries in the clusters that produce the risks must be identified and the factors that influence their choices of location described, summarized, and evaluated in terms of their relative importance in relation to others and the extent to which they can be expected to persist. Because the numbers of individual industrial location choices made are astronomical, the measures related to industrial location refer to spatial distributions defined in terms of regions (Hoover 1948; Beckmann 1968).

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The third category of spatial variables needed to test the hypothesis of disproportionate distributions is made up of those that influence residential choice decisions and the geographical patterns of residential location that emerge when such choices are made frequently. It is not logically possible for an environmentally risky minority or low-income neighborhood to coincide at a particular location, unless choices were made to situate those residences at that particular location rather than one of the alternatives. At some point, an explanation of any observed disproportionate exposure to risks in minority or low-income neighborhoods logically presupposes some explanation of the geographic structure of the housing markets in the area in which these risks occur. In this regard, the larger body of empirical research indicates that individuals making residential choices are likely to attempt to reduce their search costs and uncertainty by restricting their search to familiar community areas and key nodes such as workplace locations (Huff 1986). Bourne (1981) has identified six constraints that shape the spatial distributions of residential location: supply restrictions; accessibility restrictions; neighborhood restrictions; institutional restrictions; racial, ethnic, and class discrimination; and information restrictions. Housing discrimination and bias within housing markets also restricts housing choices and may thus indirectly lead to disproportionate exposure to risks, particularly in minority neighborhoods when minority individuals face artificially restricted choices. However, there is considerable debate over how important discrimination in housing markets is within the overall context of observed geographical patterns of residential location (Clark 1986, 1989; Galster 1989). In any case, the spatial distributions of residential locations are usually defined in terms of regions, particularly regions whose boundaries coincide with housing submarkets. Adams (1991), for example, found 14 housing submarkets in Minneapolis-St. Paul, Minnesota. One of the major challenges in research on disproportionate distributions is to obtain the right data that contain the appropriate observations in each category. One major concern is that unsuccessful intention to discriminate in housing markets may be bad and is illegal as a process (e.g., steering clients) but may not be identifiable in the resulting distribution of housing; that is, it will not appear in the housing data and so cannot be found by looking at data used to establish the distributions of housing. Furthermore, it may be incorrect to assume that spatially clustered housing submarkets that look ostensibly like the result of discrimination are actually the outcome of a discriminatory process. The only way to establish that they are the specific result of a discriminatory process is to observe the process as contributing evidence, and this is very difficult to do. Thus, on balance, in the absence of any observations of the process, observations of spatial patterns in housing submarkets constitute a necessary but not sufficient condition to demonstrate discriminatory behavior.


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Another concern is that the data must contain an adequate basis for comparison, known as a suitable comparison group. The processes of comparing, finding, and recording differences or making contrasts is elementary to all scientific inference. Each bit of empirical information involves at least one contrast. Accordingly, to establish disproportionate exposure to risks at a minimum requires comparisons of the relevant spatial variables. Normally, these comparisons are made between regions or subregions. One region represents minority or low-income neighborhoods, and the other represents otherwise similar nonminority or not low-income neighborhoods. If the objective is to determine whether minority neighborhoods have disproportionately high environmental risk, one is helpless if, for instance, one only has observations of environmental risk for neighborhoods in which only minority people reside. This idea is illustrated in a simple 2 × 2 contingency table in Table 3.1. Quadrant I represents observations of minority or low-income residences at a location in the presence of a particular environmental risk. Quadrant II represents observations of minority or low-income residences located without the presence of the environmental risk. Quadrant III represents observations of nonminority or not low-income residences at a location in the presence of environmental risk. Quadrant IV represents observations of nonminority or not low-income residences at a location not in the presence of environmental risk. Any one observation in any one quadrant alone provides no empirical information about disproportionate exposure to risks, because it contains no comparison or control. To make valid inferences about the distribution of risk between locations requires comparisons between observations in different quadrants. Comparisons between observations in quadrants I and II are needed to determine specifically whether there are more minorities or lowincome residences with environmental risk present or absent. Comparisons between observations in quadrants III and IV are needed to determine whether there are more nonminority or not low-income residences located with environmental risk present or absent. Comparisons between observations in quadrants I and III are needed to determine whether there are more environmental risks at the locations of residents who are members of minority groups and have low incomes or at those of residents who are not minorities and don’t have low incomes. Comparisons between quadrants II and IV are needed to provide similar information, except with respect to the locations of residences where environmental risk is absent. As a rule, more observations in any quadrant lead to statistically better comparisons. In its simplest meaningful form, the term disproportionate exposure to hazards and or risks at minority or low-income locations can be interpreted using Table 3.1.7 Take, for example, a hypothetical study area in which the values represent the frequency of observed residences in each quadrant. To conduct the research, an operational definition of the concept “minority or low-income” must first be given, and each residence must be classified as

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Table 3.1. Elementary comparisons required for geographically based inferences on environmental justice decision situations Environmental Risk Type of Neighborhood

Present

Absent

Minority or low income

I

II

III

IV

Not minority or low income

either belonging in the category to which this concept applies or not. Similarly, the concept of environmental risk must be operationalized in such a way that enables determination of whether an environmental risk is present or not for each such residence. Then the appropriate data are gathered and used to determine the number of residences in each quadrant. Comparison of the number of observations in quadrant I with those in quarants II–IV can be used to identify a disproportionate environmental risk in the study region, if it exists in the data. It would be identified specifically by comparing the observed numbers in each quadrant with the expected numbers, computed as the average number of residences in all four quadrants. If the number of residences in quadrant I is sufficiently greater than the expected number, on the basis of statistical hypotheses tests, the conclusion would be that there exists a disproportionate exposure to risk at the minority and low-income locations. Since disproportionately high levels of environmental risk at minority and low-income locations probably also means disproportionately low levels at non-minority and not low-income locations, one might also expect fewer than average numbers in quadrant IV. This is known as the interaction between minority and low-income locations and environmental risk, and it can be evaluated by comparing the values in quadrants I and IV with those in quadrants II and III. The appropriate statistical tool to determine this is the chisquare (χ2) test. Use of the concepts of location, area, and region brings up critical choice between levels of spatial aggregation. The choice rests on the definition of boundaries for the areas or regions to be used in an empirical research study. Much as in plane geometry, points without any dimension join to form lines with one dimension, lines with one dimension combine to form planes with two, and planes with two combine to form volumes with three; so it is with spatial data. Geographical areas and regions are, in the abstract, made up of combinations of points; point data combine to form data for areas and regions. Moreover, much as in plane geometry, because there are no inherent


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limits to the numbers of points that can be combined in two dimensions, so in empirical analyses using spatial data the boundaries of the plane are arbitrarily defined. The boundaries of areas and regions used in empirical research are thus, in this sense, arbitrary: they are not to be found inherent in anything in the world. They can be modified to suit the needs of the researcher. Sometimes political boundaries are used. Sometimes there are more or less natural boundaries; for example, the Thornewaite classification system for climatic regimes depends on the spatial distribution of plants. Moreover, the boundaries can be set at different levels of spatial aggregation. They can, for instance, encompass small areas such as individual property lines or census blocks, or they can encompass large areas such as states or multistate regions. The choice of what level of spatial aggregation to use depends on a combination of feasibility (e.g., the availability of data) and the purpose of the research. As a rule of thumb, smaller-sized areas or lower levels of aggregation are preferred units of analysis, because they require proportionally fewer assumptions about causal and statistical variations in local phenomena. Smaller areal units of analysis are also appropriate when the process or mechanism of interest occurs in smaller areas. It is well known among analytical geographers that the results of comparisons between regions can be very sensitive to the levels of aggregation used in the analysis. The sensitivity of analytical results to the definition of the boundaries within which the data are collected or tabulated is sometimes referred to as the modifiable areal unit problem (MAUP). Both the scale and the shape of the areal units can have a substantial influence on the results of an analysis (Fotheringham and Wong 1991). This is important in environmental justice especially because two variables such as race and the location of toxic releases may be related at one scale and not at all related at another scale (Bowen et al. 1995). The appropriate level of spatial aggregation for testing the hypothesis of disproportionate distributions is the one at which discussions of exposure have meaning. In general, the principle is that the appropriate level of spatial aggregation is a reflection of the underlying process or mechanism that gave rise to the data. For example, if one wants to study contagion of a disease such as flu, one would probably prefer to aggregate to census tracts or perhaps counties, not states or nations. The reason for this is that diseases are communicated through the contact of one person and another at a microgeographical level, and the larger levels of spatial aggregation such as states or countries miss this process. Accordingly, the appropriate level of spatial aggregation for the analysis of disproportionate distributions and their effects on public health is a reflection of the processes through which exposure to the hazards might occur. Because exposure to hazards, like exposure to a person with a disease, occurs locally, smaller geographical units are preferable to larger ones. For this reason, in my view, census blocks would be preferred

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and census tracts may be tolerable, but levels of spatial aggregation larger than this, such as counties, altogether miss the process. As a consequence, analyses based upon spatial areas larger than census tracts are of considerably less if any value in this context. Finally, it is useful to reiterate that in assessing proximity it is crucially important to give appropriate and systematic consideration to space, particularly the spatial unit of analysis. First, nothing inherent in the nature of space specifies the unit of analysis, therefore the choice between county, zip code, census tract, census block, or some other area is critical, especially because the hazards or group members of interest might not be distributed evenly within the area. It is entirely possible that the hazards are all in one corner of the area and the people in the groups of concern are all in the other. Moreover, the larger the area, the more likely the possibility that the hazard will be distant from the group members of interest. Second, the boundaries of areas used in related analyses tend to be drawn without regard to the locations of the hazardous sites and with relatively little regard for the social groups of interest. Because neither county nor zip-code boundaries were drawn with environmental justice research in mind, their use in environmental justice research can lead to substantial errors of inference (Monmonier 1994). This is of concern specifically insofar as the characteristics of the group of people located near the boundary of any given area may be more similar to those in the adjacent area than to other groups located within the same area. Hence, the majority of the residents in the area containing the hazard may face less risk than do those who live across the boundary but in closer proximity to the hazard. Because in environmental justice the concern is invariably over whether or to what extent minorities, low-income, or other disadvantaged or susceptible populations face disproportionately high exposure to environmental risk, the logical solution to these problems is to use smaller spatial units that better represent exposure. None of these considerations, by the way, should be taken to imply that proximity to environmental hazards is necessarily an inappropriate proxy to use in research on disproportionate distributions. Rather, they suggest an appropriate level of skepticism. Indeed, quite a lot of evidence indicates that many disadvantaged communities live in areas with poor air quality and that disease and death rates are related to social class and ethnicity/race (Sexton, Olden, and Johnson 1993). Also, in terms of pregnant women, statistically significant evidence shows that maternal proximity to hazardous waste sites may carry a small additional risk of bearing children with congenital malformations (Geschwind et al. 1992). The extent to which observed disproportionately high levels of health problems among more broadly defined populations located in various degrees of proximity to one or more of a wide range of hazards can reasonably be attributed to exposure to those hazards, however, remains uncertain.


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THE PIVOTAL CONCEPT OF EXPOSURE The currently prevalent policy and administrative concerns over environmental justice relate largely to whether and to what extent disproportionately high levels of environmental risk result in high and adverse human health or environmental effects on minority, low-income, and other susceptible populations. Implicit in this concern is a presumption that the relevant populations are exposed to the hazards located within short distances from them and that the hazards are present in sufficient amounts to cause adverse health effects. However, given the dearth of research on the community health effects of proximity to environmental hazards, this presumption is a possible source of error in much of the related discourse. There are several fundamental concepts involved here, all of which when properly construed focus on physical contact between people and pollution. Exposure is normally defined as contact between the body with a specific susceptibility level and a specific pollutant or mixture of pollutants at a specific concentration level. Without contact, the hazard cannot affect the individual’s health. Factors that may be important in describing this contact include the route, magnitude, duration, and frequency of exposure. In a given situation, assessment of exposure to various environmental hazards and its effect on community health can be accomplished with a reasonable degree of scientific validity using community risk assessment (CRA) procedures. The current orthodoxy in environmental management and risk-based decision-making entails a detailed analytical procedure of CRA that includes four phases: hazard identification, doseresponse assessment, human exposure assessment, and risk characterization. Hazard identification answers the question, Does a particular agent in general cause an adverse effect? It identifies the inherent acute or chronic toxicity or possible adverse health effects that may result from exposure to deleterious environmental agents, such as synthetic chemicals. These can include things such as skin disorders; convulsions and other nervous system disorders; defects in the immune system, liver, or kidneys; decreased fertility; detrimental effects on the developing embryo or fetus; and cancer. Documentation of the particular adverse health effects associated with particular agents requires an extensive toxicological database. Dose-response assessment asks the question, What is the relationship between the dose of an agent and the corresponding incidence of adverse response in humans? These relationships are estimated on the basis of an assumption that there is a threshold dose below which no adverse effects occur. This is also the basis for the concept of a “reference dose,” an amount for which long-term daily human exposure among the general human population is likely to be without an appreciable chance of deleterious effects during a lifetime of exposure. Various uncertainty factors are built into reference dose computations, depending on the data sources and type of study used.

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Human exposure assessment asks the question, What exposures are currently experienced or anticipated under current conditions? It is used to identify the magnitude of human exposure to a particular substance, the frequency and duration of that exposure, and the various routes by which exposure may occur. Conceptually, exposure refers to pathway-specific causal mechanisms that propagate discoverable relationships between particular environmental hazards and their associated health and environmental effects in terms of physiological or pathological responses. The magnitude of exposure of a population varies with its size, age structure, gender composition, activity patterns, locations of people, and other factors. Possible sources of exposure occur through groundwater, surface water, soil, air, the food chain, and sediments. Exposure routes include ingestion, inhalation, and dermal contact. Estimates of exposure vary as a function of the concentration of the agent in the source of exposure, as well as its frequency and duration. Risk characterization asks the question, What is the likelihood of the adverse effect in a given population? It integrates the data and analyses involved in hazard evaluation, dose-response evaluation, and human exposure evaluation to determine whether humans will actually experience any of the potential ill effects associated with a particular substance. This process is necessary to determine the real, as opposed to perceived, environmental risks in a situation (National Research Council 1983; Paustenbach 1989). In light of these procedures, it is clear that the term exposure is far more often used in the discourse than is warranted. In principle, CRA procedures can be used to determine exposure and the associated risks faced by a community, but in reality uncertainty and incomplete data tend to preclude the feasibility of doing so. Though in concept the variables and procedures for community risk assessment are known, they are mostly applicable in particular instances and not across wide geographical areas. The amount of empirical work that remains to be done before such assessment would be of practical value, within the context of establishing broad national or even regional-level concerns with the problem of disproportionate distribution, boggles the mind. CRA procedures are limited even in terms of particular communities. Community exposures are usually low level or poorly defined, and frequently include exposure to multiple chemicals, and there is often a long latency period. It is also partially due to the fact that information on the health effects of chemical exposure is very expensive to acquire and is therefore, writ large, very uncertain and incomplete. It tends to involve things such as in vitro extrapolations from animal experiments to predict the nature and magnitude of effects in humans. Also, although the analytic capability and technology is available to detect the health effects of minute concentrations of many chemicals in a community, only seven thousand of the five million substances known have been tested, for example, for carcinogenicity (Kendall 1991).


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Because of limitations on the use of CRA, uncertainty remains in relation to many of the facts, events, and circumstances surrounding alleged and suspected geographical patterns of disproportionate distributions. For example, when considering exposure to hazardous chemicals at a hazardous waste site, there remain myriad uncertainties related to both exposure and health outcome assessment. These may be due to limitations on investigations attributable to a small number of persons affected, a lack of specificity in common clinical indicators of disease, or a lack of toxicological data related to mixtures or combinations of chemicals involved. Chemicals that are not dangerous in isolation may, when mixed together, result in chemical interactions that produce dangerous compounds. They may also involve unknown facts regarding the nature of the chemicals at the site, regional geologic conditions, or disagreements over definitions used to describe residential settlement patterns. Given that the concept of exposure must nonetheless be made operational if empirical research on disproportionate distributions is to take place, proxies for it must be defined. Indeed, the definition of these proxies constitutes a major threat to the validity of much of the currently related research, at least insofar as they are intended to be used for estimating the health risks of potentially susceptible populations in areas of high pollutant emissions. The specific source of the threat is the use of geographical proximity to various toxic or hazardous release, storage, transfer, or handling locations as a proxy variable to represent environmental risk (or potential environmental risk). Proximity is an accepted standard proxy for the concept of risk in a considerable part of the research on disproportionate distributions, probably because proximity to a toxic release or other hazardous site has an intuitive (if not scientifically acceptable) connection with environmental risk. Certainly all the possible exposure routes (ingestion, inhalation, dermal contact) necessitate geographical proximity. Nevertheless, because it is scientifically uncertain whether or to what degree proximity to one of these sites actually (or even potentially) influences the level of environmental risk, this proxy is of questionable adequacy for the task. Indeed, it may be so oversimplified and underconceptualized as to be highly misleading. The proximity of a chemical or some other hazard-producing agent does not necessarily imply that anyone is at risk of exposure. Intervening factors (Kendall 1991) include 1. the activity patterns of persons that result in a given duration of contact with the agent or agents in question; 2. the location of persons, which may range from homes, schools, retirement homes, parks, recreational areas, and transportation routes; 3. age (which influences type/level/frequency of activities); 4. susceptibility (e.g., elderly, pregnant women, infants, hypersensitive individuals, personal habits);

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Environmental Justice through Research-Based Decision-Making 5. management practices; 6. climatic conditions.

Proximity is thus questionable as a proxy for environmental risk, especially when the concern is with estimating health impacts. It is also useful to point out explicitly that proximity to a toxic release site is not altogether convincing as an indicator of potential environmental risk either. The proper way to estimate potential for environmental risk is to use microenvironmental concentration measurements and time-activity pattern data (Sexton et al. 1995). Many factors intervene between a chemical release and the health risk it poses to people. But this should not be allowed to obscure the important point that these factors intervene between a release and the potential for health risk as well. More specifically, the potential for or likelihood of an adverse human health effect from, say, release of a synthetic chemical is a simple function of one’s proximity to it only in a very limited negative sense. If the synthetic chemical is not released at a location, it will not be present in the environment. If it is not present in the environment at that location, one cannot be exposed to it; therefore there is no possibility for it to lead to health problems there. One may say with some confidence that the chemical is not a potential risk in that environment. But because the absence of the chemical in the environment is only one of several factors that can limit the possibilities for harmful effects, the converse problem of making positive statements is considerably more complex. The personal behaviors of individuals in the environment can also limit the possibilities, as can the absence or presence of location-specific factors that determine the availability of particular exposure pathways or points of contact between humans and the chemical. Unless the exposure pathways through which the chemical can physically be transported are present and open within the environment, and an individual exhibits one or more of the behaviors that expose him to that open pathway, he has no potential of being exposed, even if the chemical is present. For example, the chance of an individual encountering an open exposure pathway to air pollution depends upon his or her behavior as well as the variables that determine the plume, including the molecular weight of the chemical involved, wind conditions, and the release mechanism. A central issue in the research and debate over patterns of disproportionate distributions and their health effects in minority, low-income, and other disadvantaged neighborhoods remains whether the proxy of geographical proximity is sufficiently congruent with exposure to adequately represent it for whatever purposes the research conclusions will be put. This could be evaluated empirically to some extent using hypothesis tests to compare the health effects associated with instances of known actual exposure to those associated with instances of geographical proximity. But given the many intervening factors in the relationship, it seems unlikely that significant and convincing results would be found.


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DEFINITIONS OF RACE AND MINORITY NEIGHBORHOOD If meaningful decision rules are to be created to ensure that no neighborhood is to have more than its share of the burden of environmental risk, some definitions are needed. The term community is, in a strict sense, understood to refer to a group of people with a distinctly communal character, not necessarily intimate but enough to contribute to their common self-identification. To constitute a community, to my mind, members must participate extensively in the decisions that govern their common lives, and the group as a whole must take some responsibility for the individual members. (An excellent analysis of the concept of community may be found in Daly and Cobb 1987.) Neighborhood definition is conceptually difficult (Tempkin and Rohe 1997). Neighborhoods can be defined on the basis of housing submarkets, land areas, demographic characteristics, social interactions, political boundaries, public service areas or idiosyncratic combinations of these and other factors. Moreover, there are no known sets of general and implicit rules with which to determine where one neighborhood ends and another begins (Fotheringham and Wong 1991). Yet, as clearly illustrated by Been (1993), in the context of environmental justice, determination of neighborhood borders is fraught with enormous implications for the lives of those who live within them and for policy. The way I use the terms here, some neighborhoods, meaning relatively small geographical areas made up of perhaps several city blocks, constitute communities. But because the present concern is oriented more toward geography than sociology, and because the concept of neighborhood has an explicit geographical context while community is more sociological, the emphasis throughout is on neighborhoods rather than communities. Attention now turns to the definition of neighborhood, specifically the minority neighborhood. The concept of a minority neighborhood refers to a more specific category of neighborhood, especially to the racial composition of the people who live there. The problems of identifying the characteristics of minority neighborhoods merit discussion. These characteristics determine the basic social and geographical categories used to gather and organize the data, so an understanding of the problems involved in their definition is pivotal to any related social scientific research. As it turns out, the concepts of race and minority neighborhood are much less concrete and self-evident than they may seem. Race has different meanings for different people, often depending on the purpose to which the term is applied. (Molnar 1992 lists nine definitions of race.) Anthropologists tend to define a race as a group of people, usually living in a contiguous area, who resemble each other in hereditary characteristics more than they, as a group, resemble any other such group (Weiss and Mann 1985, 526). This definition does not lend itself to ready assignment of people to racial categories on the basis of any sort of physical or otherwise observable features, nor does it go along in any evident way with what most

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people seem to think. Most people when asked on what they base the assignment of race probably say skin color, thus reducing all the ways people differ to one easily noticeable, socially important trait. Others might refer to ancestry or some physical aspects such as type of hair, shape of the nose, or eye color. Still others might refer to blood-type frequencies (Boyd 1950). There is simply no consensus about how to classify people by race: anywhere from four to thirty distinct races may be identified, depending on the system used in the classification (Molnar 1992, 26). Such considerations have led some to conclude that, as a scientific concept, race is useless (Lieberman and Kirk 1997). The standards used by the U.S. government for collecting and organizing racial data were established in 1977, in the Office of Management and Budget (OMB) Statistical Policy Directive #15, which contains Race and Ethnic Standards for Federal Statistics and Administrative Reporting. Prior to this, there were major discrepancies and inconsistencies between categories of race among data sets. These discrepancies were a reflection of the social reality and public policy of the time, as well as the assortment of definitions of broad racial categories used. The OMB standards stemmed largely from increased government responsibility following the ascendancy of civil rights to monitor and enforce civil rights law, including equal access to housing, education, and employment. They pertain widely to the collection and tabulation of data in the U.S. Census, various surveys of the population, federal administration forms (e.g., school registration, mortgage lending applications), statutory requirements associated with civil rights monitoring and enforcement, and medical and other research. The racial categories they contain are defined on a sheer political and social basis, not an anthropological or scientific one, to meet several goals including respect for the dignity of individuals and consistency of reporting. Since the concept of race has no clear biological, natural, or scientifically acceptable definition with which to readily classify people, federal practices give preference wherever feasible to the use of self-identification. There are, however, some conceptual ambiguities as well as practical limitations to this practice. For one, racial identity is fluid, in that self-perception of race and ethnicity change over time and across circumstances for many people. These changes lead to measurement inconsistencies, especially for people with heterogeneous ancestry. Under a regimen of self-reporting, for example, siblings or other blood relatives who happen to be in very different situations may be categorized differently. Moreover, an individual’s self-identified category may change with time and circumstance. Also, some individuals are not satisfied with the range of options presented to them in the questions. They say that government should not limit the range of choices available to them for self-identification. This conflicts with the principle of gathering the data in a way fully consistent with individual self-dignity. Yet unless the categories


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presented to people are decisively limited it can become virtually impossible to give meaningful instructions on how to select the appropriate category. Finally, for many purposes, observer identification is more practical. For example, because racial discrimination is based on the perception of a person’s race, self-identification is not the preferred method of data collection, especially among federal agencies concerned with monitoring and enforcing civil rights laws. These agencies prefer to collect racial and ethnic data by visual observation. The racial category scheme specified in OMB Directive #15 represents a reasoned balance of considerations about a host of social concerns and personal dimensions of racial and ethnic identification. American Indian or Alaskan Native is defined as any person having origins in any of the original peoples of North, South, and Central America who maintains tribal affiliation or community attachment. Asian is a person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippines, Thailand, and Vietnam. Black or African American is defined as a person having origins in any of the black racial groups of Africa. Hispanic or Latino means a person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race. The term Spanish Origin can be used in addition to Hispanic or Latino. Native Hawaiian or Pacific Islander is a person having origins in any of the original people of Hawaii, Guam, Samoa, or other Pacific islands. White is any person having origins in the original people of Europe, the Middle East, or North Africa. In general, the leadership of OMB believes that this classification system should result in as much detailed information on race and ethnicity as possible. Nevertheless, in recent years this system has come under criticism by people who believe that it does not reflect the increasing diversity of the population resulting from factors such as immigration and mixed marriages (Edmonston, Goldstein, and Lott 1996). In a congressional response to these concerns, the House Subcommittee on Census, Statistics, and Postal Personnel held hearings on the measurement of race and ethnicity and OMB announced in July 1993 that it would undertake a comprehensive review of Directive #15. OMB established the Interagency Committee for the Review of Racial and Ethnic Standards, which represented a wide range of government needs for the data. This led to an interagency research initiative to evaluate proposals for revising racial and ethnic categories. The resulting research showed among other things that the percentages reported in the various racial categories are very sensitive to survey design factors. Factors such as the form of the questions (open ended vs. closed ended), the terms in which the questions are asked, and whether or not a category is considered to be a “racial” or an “ethnic” group can have a significant effect on the reported percentages.

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Many of the people who have criticized the current standards have done so because they want the federal government to discontinue the collection of racial data altogether. Those in power have often used the concept of race as a means of allocating resources. It can be easily used to set up a conflict between individual and group identity, reinforcing the status quo; that is, in a democratic society, individuals are the ultimate units of value. But historically and politically, social resources have been disproportionately allocated on the basis of the value of race, which refers to characteristics of groups. Those who object to the collection of racial data variously contend that gathering racial data is divisive, racist, archaic, and unscientific, and that the racial categories are often meaningless or overly limited. For example, one of the arguments in support of this view asserts that those definitions of race that assume some sort of natural or biological basis are unavoidably and closely connected with the idea that one’s race determines the stuff that one is made of. Under such a definition, fate rather than reason or choice makes one a member of a particular race. By implication, all rational will and activity of the individual is confined by his or her race. He or she can only achieve a higher destiny insofar as he or she is gripped by superior powers of fate. In other words, things people cannot change determine the shapes of their lives. This is inconsistent with the experience of most people who find that, through applying themselves, they discover capabilities above and beyond those they had previously realized. They find they are capable of self-criticism, of moral or practical judgment, and of assuming responsibility for and learning from their mistakes. Most thoughtful people would agree that the vastly more important aspects of the relationship between people and society are not the parts that cannot be changed but the parts that can be changed. Their hope for the future thus lies in concentrating their critical faculties on the difficult problem of creating one that will be better for generations to come. They hold that the shapes of their lives are by no stretch of the imagination totally dependent on things they cannot change. Because the use of racial categories implies that people’s goals and objectives in life are determined by things they cannot change, and because our experience and hope for the future indicates otherwise, the argument is that racial categories should not be used at all. Complete discontinuance of the practice of gathering racial data, however, is highly unlikely. A number of agencies in the federal governmental are required by statutes such as the Voting Rights Act of 1973, the Civil Rights Act of 1964, and others to collect racial and ethnic data. To end the collection of such data would require repeal of the provisions in these and similar statutes by Congress. Moreover, the practice of collecting such data has some clear advantages. Many of the measurable gains made in advancing the civil rights agenda over the past few decades would have been difficult if not


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impossible if such data had not be available to support race-conscious policies. Ultimately, the concern over race in environmental justice is with racism, which arises because racial categories tend to be automatically associated with certain social stereotypes, regardless of whether they have any natural, behavioral, or otherwise objective basis. Moreover, once created, these stereotypes are defended emotionally and socially by the attribution of superiority to one or another category of person over the others. The emotional aspect of this manifests in prejudice against a group commonly perceived as a race. Racial prejudice tends to clump diverse groups of people into a single category, based upon some real or imaginary trait, ignoring the often vast individual differences between them. Racial discrimination, on the other hand, is the social and political aspect of racism. It manifests in different legal, economic, and social behaviors based upon stereotyped perception of racial category. Racial discrimination often takes the form of employment and residential segregation based on skin color and differences in access to control over limited resources and political power. The social consequences of racism include decreased economic productivity, increased social conflict and violence, psychological and emotional damage to individuals, and sacrifice of the potential contributions of the victims, among other negative things. Its destructiveness may be symbolized and exemplified in its most virulent form by apartheid in South Africa (1948–1994), the institution of slavery in the United States prior to the Civil War, and the Nazi death camps in Europe in the middle of the twentieth century. Accordingly, a minority neighborhood is one that contains an arbitrarily large quantity of people who are deemed to be members of a set comprised of one or more designated racial groups. The quantity is arbitrary in the sense that the decision regarding exactly what value to use is a matter of convention or the judgment of the researcher rather than anything natural or inherent in the people who live in the neighborhoods. The selection of exactly which racial groups to designate as belonging to the set of “minorities” is also a matter of convention or the judgment of the researcher. Social scientific research on environmental justice that can meet reasonable standards of validity requires an explicit and systematic process for identifying minority neighborhoods. To formulate this process involves the creation of rules with which to distinguish the boundaries of the relevant neighborhoods. Given such rules, one way to systematically distinguish minority communities or neighborhoods from others is to use racial data to create an index of the form: Comparison number Index number = ———————— × 100. Base number

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One option is for the base number to be the total number of people in each neighborhood under investigation and for the comparison number to be the corresponding total number of people who belong to whichever racial groups are designated for membership in the “minority” category. The ratio is multiplied by 100 so that, if the comparison number equals the base number, the resulting index value will have a value of 100. Alternatively, the base number can be a benchmark or reference value (e.g., the total number of people designated as minority in a standard region) and the comparison number can be the number of people who belong to the minority groups within the particular neighborhood under investigation. In any case, the resulting index number is likely to be sensitive to the definition of racial categories, as well as the comparison and base numbers selected. Any neighborhood whose index number is greater than some arbitrarily large value determined by convention or the researcher’s judgment can be deemed a minority community or minority neighborhood. Any community or neighborhood whose index number is smaller than that value is deemed otherwise. Definition of the concept of minority community or minority neighborhood is antecedent to any empirical investigation of whether particular neighborhoods or regions have disproportionately high levels of environmental risk, that is, inferences about racial differences in the distribution of environmental risk that stem from racial data represent an elaboration of these concepts. The concepts of race and minority neighborhood give meaning to the measurements used in the associated empirical analysis. Ultimately, these concepts must be a part of the basis of resolution for related disputes arising over the validity of statements used to define related problems. DEFINITION AND IDENTIFICATION OF LOW-INCOME NEIGHBORHOODS The core concern in policy discussions about environmental justice, of course, has less to do with principles of risk or geography or empirical method as with the grievous inequalities and injustices with which minority and low-income neighborhoods are often associated. Nevertheless, while the substance of poverty makes up the larger concern over low-income neighborhoods, the process of making scientifically acceptable statements in relation to it is it fraught with ambiguity and uncertainty. Take, for example, the seemingly simple matter of defining and identifying low-income neighborhoods. Conceptually the first step is to define low income, possibly in terms of the money income needed to meet the needs of households. Such a standard of need is how the U.S. Census Bureau defines poverty. Poverty is a condition of persistent low-income in which a household does not having enough to meet its needs (Fisher 1992). To determine whether a household is low-income, the combined income of the family


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members resident in a household are compared to the standard of income need. If the household resources are above the standard, that household is not low-income. If they are below the standard, the household is low-income. Once low-income households are determined, low-income neighborhoods can be identified with reference to aggregate estimates of the resources of the households in the respective neighborhoods. If a certain (ultimately arbitrary) percentage of such households are low-income, the neighborhood is a lowincome neighborhood. For instance, the U.S. Bureau of the Census defines a poverty area as contiguous census tracts in which at least 20% percent of the households have an income below the official poverty level (Knox 1994, 305). While in concept the definition of a low-income neighborhood is intuitive, in practice it is not so straightforward. The practical difficulty arises because, while household resources are measured in the decennial censuses of population and housing, the same cannot be said for their needs or for anything else to which a definition of low-income might consistently refer as a standard. Consumption needs vary tremendously as a function of the size, age structure, and location of the household, as well as the personal backgrounds and interests of its members. Moreover, these needs are tremendously difficult to define, measure, and document in any sort of comprehensive systematic way. Consequently, conceptually difficult questions arise for which there are no natural or solidly theoretical answers and which preclude meaningful identification of low-income areas. For example, at what points in the continuum of household consumption does a household move from “not having its needs met” to “low-income,” or from there to “comfortable”? How does one meaningfully aggregate across households at various points on this continuum within a neighborhood? Even if these questions could be answered convincingly, the data with which to identify the relevant neighborhoods are very limited. Although changes often occur quickly in society, the decennial censuses are the only nationwide sources of income and poverty statistics for households, families, and individuals for small (substate) geographic areas. The U.S. Bureau of the Census provides estimates of the reliability of their income measures in the decennial censuses and their updates. Both these measures and the related measures of poverty almost certainly differ from the figures that would result from the administration of the census survey to all households and individuals in the nation. These differences are partially attributable to difficulties associated with defining a standard of income need. They are also due partially to random sampling error that arises due to the fact that a sample is surveyed instead of all households. Other contributory nonsampling (response) factors include: inability to obtain information about all cases in the sample; language barriers; definitional difficulties due to mobility or irregular household arrangements; differences in interpretation of questions; respondent inability or unwillingness to provide correct information, for example, due to fear of government and outsiders on the part of

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community residents; respondent inability to recall information; errors made in data collection, such as in recording or coding the data; errors made in processing the data; and errors made in estimating values for missing data in models. As a result, there are uncertainties in the estimates of income, perhaps as large as 26% for the lowest quartile of the income distribution. Conflicts over environmental justice rarely come to blows. They are usually fought with words and actions expressed through them. Words and concepts represented by them are, therefore, central to understanding the situations in which associated problems and issues arise. They are, in large part, either implicitly or explicitly integral to any and all interpretation of these situations, and they are the basis of most of the communication involved in the decision-making process. If policy and administrative decision-makers are to interpret environmental justice problems similarly and communicate about them meaningfully to make good decisions about them, they must at a minimum understand the central terms and concepts in the related discourse.

CHAPTER 4

A Primer on Empirical Research Methods for Environmental Justice What stands out is that there is no field of science that is free from glaring ignorance, even contradiction. —JOHN MADDOX, What Remains to Be Discovered

Social problems related to disproportionate distributions of environmental hazards are now on the national political agenda and policy and administrative decision-makers are now tasked with responding to them. In a sense these problems are similar to all social problems insofar as they are defined by articulating a difference between a standard, in this case a canon of fairness or justice, and a set of statements assumed to reflect related empirical facts, events, and circumstances in the actual world. The tasks of related policy and administrative decision-makers are similar to those faced by decisionmakers attempting to solve any social problem. Normally, decision-makers have goals and objectives that, in general, have to do with solving a problem by taking action to reduce the perceived difference between the facts and the standards, at some time in the future. They do this by deliberating alternative actions and then selecting from among them the one that promises to best bring the future facts more in line with the standard. But they cannot make well-considered decisions if they start from the wrong place, with the wrong decision problem, or use faulty information in deliberating related choices. The way they define and describe their decision problem frames their decision, thus determining the alternatives they consider and the ways they evaluate them. Stated most simply, the statements of empirical fact they accept in their problem description greatly influence the courses of action they choose. If such statements are invalid or otherwise mistaken, the decisions are less apt to succeed at solving the problem. It is important for decision-makers to have valid and relevant statements of facts for describing and deliberating on the problem they aim to solve, and scientific methods are key to obtaining such statements. This chapter considers salient aspects of such methods. As best as can be determined, the greatest chances for success in realizing the objectives of policy and administrative decisions related to perceived 71

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disproportionate distributions of environmental risk rest upon thoroughly and systematically working through the relevant decision processes. Accordingly, there are a number of questions that must be answered prior to actually weighing the alternatives and selecting between them. Foremost among these are; What is the problem description? Why is the problem even being considered? What are its essential elements? What facts, events, and circumstances are to be accepted as constituting the empirical foundation in the problem descriptions? What other decisions impinge on or hinge on this one? The answers frame the decision. They determine how decision-makers conceive of the available alternative possible courses of action, the outcomes and contingencies associated with each, and the way they are evaluated. Sound social scientific research methods are of value in framing related decisions, first insofar as they are useful in avoiding unwarranted statements about the relevant facts, events, and circumstances, and second insofar as they help avoid unwarranted causal schemas.1 Getting the relevant information together for describing a decision problem or for informing deliberations of the choices it presents is a relatively straightforward process of initially organizing one’s thoughts, gathering the appropriate data, and doing statistical analysis to correlate the strength and direction of the sociospatial relationships involved. The resulting conclusions and statements describe the empirical foundation of the decision problem, or help elucidate the connections between alternative feasible courses of action and their associated expected outcomes, specifically by organizing and summarizing the relevant data. Because success at realizing the objectives of a decision depends on reasoning through the relationships between elements of the decision problem and acting to alter the cause or causes of the problem in a way that ameliorates or perhaps even resolves it, more than pure description is involved. In this regard, social scientific methods may also be of value in testing any causal schemas used in framing the problem. People tend to comprehend the world only in terms of causal schemas, and, regardless of how thoroughly things are described, things that are not so comprehended tend to be left out of the reasoning process. Insofar as success at realizing the objectives depends on having a valid causal schema with which to help select one of several alternative possible courses of action, the deliberations prior to making a choice must also include how to explain the decision problem, or what causal schema to accept. Some facts, events, and circumstances must be accepted as relevant to describing the decision problem and others not, and those that are accepted must be conceptually organized in a way that some are understood as causes and others as effects. Unless causal schemas are developed using astute research, a number of obstacles are likely to arise in deliberating the alternative feasible courses of action and selecting between them. These obstacles in turn are apt to increase

A Primer on Empirical Research Methods for Environmental Justice

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the likelihood of errors in related policy and administrative choice processes. It has been clearly demonstrated, for example, that in the absence of empirical research persons tend to be perceived as causes more readily than are impersonal entities or events (Jones and Davis 1965). Similarly, a hidden third factor may account for both the problem and the cause attributed to it. Or there may be several important sets of such causes. Or the line of causation may actually run from the problem to its imputed cause. In any case, to make an effective decision requires some level of causal attribution and to soundly attribute cause requires much further specification and inference regarding the connections between the elements of the problem and the larger social systems in which they are implicated. This chapter contains social scientific, method-oriented background information, concepts, and considerations that are needed to understand perceived environmental justice problems and increase the chances of successfully conducting a related policy and administrative decision. The interpretation and presentation of this are essentially conventional, similar in practically all respects to those in most good empirical research methods textbooks. Social scientific methods are a sequence of procedures in which the relevant theories and concepts are converted into operational definitions, observations are made, hypotheses are tested, and empirical statements are formulated. Each procedure in the sequence is justified on the basis of reason and observation. The advantage of following this sequence is that it places the highest possible premium on the amount of information and the relevance and fidelity of the means used to construct and test hypotheses against data related to the empirical foundation of a decision problem. If the following differs at all from the presentations in contemporary social science research methods textbooks, it is due to its somewhat greater emphasis on the normative dimensions of this sequence and its bearing specifically on environmental justice. The interest here is in how social scientific research methods should be applied, if the objective is to formulate valid empirical statements within the context of environmental justice decision problems. Because human beings are the producers of social science, and the only producers of social science, any social scientific method used in describing an environmental justice decision problem necessarily reflects the human intellect. Social scientific methods are therefore portrayed as a human resource, an instrument of the intellect, that can (and should) come into operation whenever efforts are being made to give valid descriptions and explanations of the facts, events, and circumstances that constitute an environmental justice decision problem. In such a situation, these methods make demands on us; they tell us what we should do and how we ought to go about stating empirical statements about it. They say to us, “These are practical principles with which to best describe and explain the empirical foundation of this decision problem—be governed by them!” Above all they prescribe a systematic

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procedure for making sure that all the evidence and reason used to justify a statement describing the empirical foundation of the problem fits together in a coherent and effective way that other people can, in principle, perceive and understand. When empirical research used in problem description (and explanation) is reasonably well done, it can contribute reliable and clearly communicated summarization of the often large quantities of data involved. For instance, well-conceived and well-constructed social scientific models can in principle be used to clearly indicate patterns in the distributions of locations of residences of racial minorities and toxic chemical releases. This has the palpable value of keeping the problem of environmental justice on the policy agenda. It can also provide an incentive to improve the quality of the available data among the scientists and decision-makers currently inconvenienced by the lack of available data for calibrating the models needed to explain any ostensible associations between race and environmental risk. The research methods used in a given study place logical limitations on the conclusions that can be justifiably obtained from it (Campbell and Stanley 1963). If these limitations are not explicitly recognized in decision problems in which the research conclusions are put to use, decision-makers are apt to attribute an empirically unwarranted level of concreteness to the relevant facts. In turn, when such facts are presupposed as premises in policy and administrative decisions, they are likely to lead to unintended and unforeseen consequences that may be as bad or even worse than the original problem. This chapter illuminates in a general way some of concepts needed to understand the limitations in the current body of research, specifically by emphasizing principles of scientific research methods created for the relatively demanding standards that pertain to research aimed toward scientific explanation. While it is not realistic to expect the strict application of these standards as screening criteria for the admission of research findings in actual policy and administrative decision problems, they nevertheless serve as a clear and explicit reminder of the fact of human fallibility, thereby enabling it to be confronted openly. This is especially important because not knowing the empirical foundation for environmental justice policy and administrative decisions implies that decision-makers need to be cautious and flexible in their approach. The chapter begins by elucidating some basic terms in the lexicon of social scientific research, specifically as they relate to formulating statements for describing the empirical foundation of the problem of environmental justice. These include variables, hypotheses, theories, models, validity, and the relationship between theory and empirical research. A basic understanding of these terms points directly to limited human abilities to systematically tie together several different but related concepts with all of the available information about the relevant decision problems on the basis of sound deep and


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long logic structures. Past failures of policy and administrative decisions indicate that one of the major obstacles to improvement can be found in precisely these limitations. A DUALISM IN SOCIAL SCIENTIFIC INFERENCE An assumption of all social science is that for an empirical statement to be valid it must be derived from the acquisition and systematization of human observation. Because empirical statements are stated in language and because language must accordingly refer to observation, there is no choice but to draw from, at least in part, each of two domains, one internally rooted in human language and one externally rooted in the relevant facts, events, and circumstances. In the following pages, the internal or subjective judgmental and psychological components are referred to as belonging in the conceptual-theoretical domain, in which individuals experience thoughts, preferences, feelings, valuations, and purposes. The main concerns regarding the methods used within this domain have to do with clearly and distinctly defining the relevant terms and concepts, organizing and operationalizing them, formulating hypotheses, designing adequate procedures for testing the hypotheses, and providing systematic guidance for generalizing the results beyond the tests. In contrast, the term observational-empirical domain refers to the external realm of facts, events, and circumstances capable of stimulating objective sense perceptions. The main concerns with respect to the methods used in relation to the empirical-observational domain have to do primarily with gathering the data needed for the tests and analyzing them to extract the relevant information. The elements in both of these domains are integral aspects of obtaining valid social scientific empirical statements about perceived disproportionate distributions. Properly construed, the only real choice is whether to leave the elements drawn from each implicit and unconscious or to make them an explicit part of the articulate repertoire of the researcher and the decisionmaker. This dualism is at the base of a great deal of controversy over social scientific methods, much of which has at least partially found its way into intellectual circles in the discourse of public policy and administration scholarship. The controversy stems largely from obstacles that arise in the creation of language (which occurs in the conceptual-theoretical domain) to use in referring to observations of facts, events, and circumstances (which occur in the observational-empirical domain). Specifically, there can be little doubt that subjective judgmental and psychological factors strongly influence what is observed (Hanson 1958). Moreover, their presence implies that the language used to describe and document observations of facts, events, and cir-

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cumstances cannot be fully reduced to objectively observable terms that refer exclusively to those aspects that manifest or occur objectively within time and space. Accordingly, any terms that must at least in part refer to subjective judgmental and psychological factors for their meaning are conventionally known as theoretical terms. In regards specifically to environmental justice, these include the ones discussed in Chapter 3. Much of the controversy over theoretical terms seems wrongheaded. Granted, in the presence of theoretical terms, it is untenable to argue that observation is ever a solidly neutral and objective foundation upon which to build social scientific empirical statements. But this does not necessarily imply, as some argue, that observations have no definitive role in the warranting of statements of social scientific inference (Kuhn 1970).2 Even though the subjective judgmental elements involved in making observations clearly imply that it is not possible to state empirical statements fully in objective observational terms, this only implies that empirical statements cannot be completely determined on the basis of observation. If one maintains that the validity of empirical statements is instead substantially determined on the basis of observation, the subjective judgmental elements imply merely that social scientific method does not constitute an ultimate or absolute source of authority (Phillips 1987). Indeed, properly construed social science is not so much concerned with the origins of inferences as with the complex of considerations related to whether or not there is good reason to believe that they are based on the most accurate possible observations, sound research design, and logical argumentation. In this regard, there is no sound reason to think that observation has a less than decisive role in validating social scientific empirical statements. HYPOTHESIS TESTING If empirical statements about environmental justice decision problems are to be validated, not only must the methods used within the two domains be well conceived and justified, so must the articulation between them. This is the role of hypothesis testing: it links these two domains together, thereby ensuring the integrity of the articulation between concept and theory, and between observation and data. To meet standard requirements for social scientific validation, empirical statements are tested in the form of hypotheses. Hypothesis testing is essentially a form of evidential reasoning in which the test provides confirming or disconfirming evidence for an empirical statement that may go significantly beyond that evidence in assertive content (Rescher 1970). Hypotheses about disproportionate distributions, for example, go way beyond the ability of any one individual to evaluate with much objectivity, especially given the neces-


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sarily limited experiences, selective perceptions, and limited cognitive abilities of human beings. Recognizing this, to meet standard requirements for social scientific validation, empirical statements are tested in the form of hypotheses. The hypothesis is stated as a clear, specific, testable, tentative answer to the research question. It is tentative because, before it is empirically tested, the investigator may have expectations but does not know whether the test will confirm or disconfirm it. In the spirit of social scientific method, a hypothesis must be judged objectively on the basis of tests against data and must be abandoned or modified if it is shown to be inconsistent with the data. Good hypotheses are, at a minimum, stated in a way that is clear and specific enough to enable substantial agreement on their meaning from within the social scientific community. They are testable using available data. They usually take the form of a declarative statement that specifies an expected relationship between variables in a data set. In Chapter 3, I defined variables as sets of two or more values, each representing a single observation that can be meaningfully associated with a well-defined concept. Now it is useful to go a bit further and recognize that a variable is also, in essence, a symbol that represents that concept; letters typically symbolize variables. For example, x may be a variable; specifically, it is a symbol for a concept to which numerical values representing individual observations made according to the rules of measurement (Bowen and Bowen 1999) are assigned. Variables thus translate concepts into empirical terms, specified in terms of numerical values representing observations in a data set used in testing the hypothesis. The numerical values of the variables in the data set are the points at which mathematical reasoning in the conceptual-theoretical domain most closely and meaningfully links to the observational-empirical domain. The sources of hypotheses can vary. They can be deduced from the conceptual-theoretical domain, induced directly from the observationalempirical domain, or stated in an intuitive combination of these approaches. However, while various sources of hypothesis are feasible, the potential strength of the evidential reasoning varies with the source of the hypothesis. If the hypothesis is deduced from a theory or train of reasoning within the conceptual-theoretical domain, the test can provide conclusive evidence or a demonstration of that theory or train of reasoning. If the hypothesis is induced from the observational-empirical domain, the test can render the empirical statement more tenable or more likely than before (i.e., more probable a posteriori than a priori). Thus hypotheses that are deduced from the conceptual-theoretical domain lead to a mode of evidential reasoning that is logically stronger than are those that are induced from the observationalempirical domain.

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ELEMENTS OF THE CONCEPTUAL-THEORETICAL DOMAIN The conceptual-theoretical domain includes the theories, concepts, models, and principles of research design used in environmental justice research. These basic elements of this domain are now considered. Social Scientific Theory In general, theory enables validity testing by giving discipline to and ensuring the systematicity of the reasoning process behind hypothesis testing. It is helpful in formulating empirical statements related to disproportionate distributions insofar as the validity of many related empirical statements, particularly those that can be sufficiently generalized to bear on policy and administrative decision problems, depends upon shared reasoning about the relevant information. In other words, their validity depends partially upon the way reason is systematically put to use to organize and think through the nexus of relationships between the particular facts, events, and circumstances that constitute and surround the decision problem. Theory provides the systematicity to this reasoning process. It also helps to communicate about it, by ensuring that the way one individual organizes the relevant facts, events, and circumstances in his or her mind is sufficiently similar to the way others organize them, thus enabling the shared understandings upon which social scientific validity depends. Past this point, though there is no one simple definition of theory on which all social scientists would agree, enough can be said about it to keep perspective on it for the purposes at hand. As a good first approximation, a theory is a set of interrelated concepts, definitions, and empirical statements that present a systematic view of a phenomenon by specifying relations among variables, with the purpose of explaining and predicting the phenomena. (Kerlinger 1973, 9)

Theories are conjectural or tentative expressions of what a person or people have seen as regular patterns in their sense perceptions. They are built through logical inference from a set of foundational assumptions. The key element of theories is that they abstract a few characteristics of the phenomenon of interest in an attempt to isolate, describe, and make its central facts and events intelligible, as well as possibly prescribe responses to them.3 Theory enables the placement of facts and events related to a phenomenon of interest in the context of other relevant facts or events, thus enabling scientific explanation of them. It has been mentioned that scientific explanation is conventionally contrasted with description; whereas description is an account of what fact or event occurred, explanation is the giving of reasons why the fact or event at issue occurred:


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In explaining a fact, we place this fact in the context of others in such a way that they illuminate its existence. The theoretical explanation of a fact is an account of it that deals not merely with aspects of what is, but answers the why-question regarding the circumstance that it is, thus rendering its being so intelligible. (Rescher 1970, 2)

To explain a fact is thus essentially to make it understandable and coherent within the context of a broader range of experience and knowledge.4 In the spectrum of types of social scientific theory, the least developed is a classification system. A classification system is the product of a classification process. A classification process categorizes observations into subclasses according to their shared characteristics or attributes. For example, in analyzing disproportionate distributions one must classify neighborhoods into those that are disadvantaged and those that are not. Similarly, one must also classify individuals by race. In any case, in more general operational terms, classification is the act of distinguishing between subclasses of observations. A subclass is formed by a number of observations, each of which exhibits a definite characteristic in a constant manner. In the classical view, the definite characteristic may be any arbitrary division determined culturally and linguistically. The operation involves the mutually exclusive and collectively exhaustive assignment of observations to subclasses, according to this definite characteristic. In other words, each observation is placed into one and only one subclass, such that the definite characteristic is exhibited (1) in a constant manner by all members of the subclass and (2) in a manner that is different from that exhibited by entities not in that class. In the classical view, there may be no overlap between members and nonmembers of a subclass. In essence, the difficulty with defining racial categories is that no such definite characteristic with which to place individuals in subclasses has been identified. A similar problem arises when classifying neighborhoods as belonging or not in the subclass labeled “minority, low-income, or other disadvantaged” neighborhoods. At the other end of the spectrum, the most highly developed type of social scientific theory is axiomatic theory (Nachmias and Nachmias 1992). Axiomatic theory has a postulational-deductive form. To formulate it, one first identifies the relevant set of concepts and definitions, and a set of statements describing the situations in which the theory can be applied. One then formulates a set of axioms, which are assumed to be true, and a set of theorems or empirical statements logically deduced from the axioms and amenable to empirical testing. Good axiomatic theory is either quantitative or at least cleanly qualitative in the sense that it leads toward easily recognized inequalities. It leads to predictions that often are not obvious or even counterintuitive. The important thing is that they exceed the capacity of unaided intuition. Above all, good axiomatic theory leads to testable hypotheses; its predictions can be translated into hypotheses subject to test against data.

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In research oriented toward influencing policy and administrative decisions, no real scientific theorizing is involved. What passes for theory is instead essentially ad hoc definition of arbitrary categories constructed to organize and summarize the available observations. While this process is useful, it can be much more accurately described as concept formation. Indeed, most of what passes for theory about environmental justice is really concept formation. This is unfortunate because as it is theory, not concept formation, that enables genuine scientific explanation of the central facts, events, and circumstances associated with a phenomenon. Theory alone meets the scientific demand to provide a systematic basis from which to guide inductive reasoning beyond sense perceptions, a blueprint from which to determine the appropriate method for generalizing beyond the observational-empirical domain, and a way to corroborate observational-empirical research findings entirely on the basis of abstraction. Without real theory these demands cannot be met adequately, and so scientific explanation, in a strict sense, is not possible. Of key concern here is the failure of the literature to formally theorize about the nature and role of racism within the hazardous materials siting process, and the underlying social and economic processes that would create disproportionate distributions. An adequate theory for scientific explanation of any observed such distributions would, for instance, provide a coherent basis for answers to repeated utterances of the following question: Why is any particular actual (as opposed to hypothetical) hazard (or set of such hazards) located in any particular minority or low-income neighborhood (or set of such neighborhoods) rather than in one (or a set) of the alternative possible locations? As does any explanation, even those based merely in common sense, the answer would subsume these hazards under tentatively established generalizations. The generalizations in this case would probably come from within the established body of knowledge about hazards and risk production and industrial and neighborhood location decisions. Social Scientific Models When social scientific theory related to a set of facts, events, and circumstances surrounding some phenomena is not available, it is sometimes nevertheless possible to put together a model. In general, social scientific models are explicit sets of abstract empirical statements related to some phenomena, from which hypotheses may be deduced. They are simplified representations of the key elements of the phenomenon of interest. In using them, it is important to be sensitive to their limitations. Models are similar to theories in that they lead to testable hypotheses, but models do not in themselves enable scientific explanation the way theories do. Models may account for a fact or event, but they do not in themselves suf-


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fice for giving reasons why the fact or event at issue occurred, the way a theory does. To give scientifically acceptable reasons requires real theory. Yet models can be very useful for organizing the relationships involved in formulating empirical statements related to disproportionate distributions. And at least in principle, under some highly restrictive conditions, models may be used to predict them. A map is a common example of a model. It is an abstract, simplified representation of the salient features of a territory; it is not the territory. Similarly, a model is not the phenomenon it represents but only a simplified representation of the data gathered in relation to its attributes. The map shows explicitly how various locations are related to each other, and it gives an overall perspective. It can therefore be quite useful as a means of describing and helping one know how to navigate the territory, as well as a means of communicating to others how to do so. It does not, however, provide reasons why the locations are related to each other the way they are. Nor can it fully capture all the unique details that characterize any particular location. Thus, strictly speaking, models do not explain things but rather highlight and organize the information related only to those attributes of whatever they represent that are salient for the purposes at hand. Through simplification, less is held in view so that more can be understood of what is retained. One may argue that common sense is a series of concepts and conceptual schemes satisfactory for the practical uses of mankind, and as such good social science models are consistent with common sense (Conant 1951). Models and common sense both selectively reflect the salient features of the reality they represent and, at least to some extent, the relationships between them. But common sense is relatively undefined and imprecise in comparison to social science models; additionally, a great deal of human experience demonstrates that common sense representation of phenomena is often based on an underconceptualized, bland acceptance of fanciful ideas and partial explanations. In contrast, good social scientific models are far more meticulously constructed, systematically tested for internal consistency, and aspects of them are subjected to empirical tests. Thus, common sense can at times conflict with the findings of sound empirical research, and when it does the empirical research findings win virtually every time. Indeed, this is precisely the reason that good models are so important in terms of environmental justice; though common sense may say there are disproportionate distributions of environmental hazards, without good social science one cannot tell for sure whether common sense is mistaken. More specifically, most of the models used in social science and all of the models used to test disproportionate distributions are symbolic models. Symbolic models may be contrasted with iconic models. An iconic model is a physical rendering of whatever it represents. For example, architects use iconic models to represent the buildings they design and engineers use them

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to represent the products they will construct. Symbolic models differ from iconic models in that they tend to be conceptual or mathematical in nature. They are ultimately expressed in language: they do not impinge on the world. At their best they systematize and give discipline to the thought process, thus enabling the formulation of empirically testable empirical statements regarding the nature of the relationships between the facts, events, and circumstances of interest in the actual world. One of the major difficulties in understanding hypothesis tests about disproportionate distributions is attributable to the symbolic nature of the models that must be used in the process. The most abstract (and powerful) models in social science in general and in testing hypotheses related to disproportionate distributions in particular tend to be mathematical and predictive in nature. Mathematical models contain sets of equations that systematically link together the elements of the system they represent. They are often expressed in statements of the following kind: Y = f (X) where Y is a dependent or effect variable and X is a set of one or more independent or explanatory variables. This equation states that the values of variable Y are in some way dependent upon the values of variable X. The precise nature of the relationship is represented by the term f, which indicates the functional relationship between X and Y. The dependent variable is what the researcher wishes to characterize or explain. In most environmental justice research, the dependent variable is proximity to a perceived environmental hazard, a proxy for the spatial distribution of environmental risk. The explanatory or independent variables are expected to account for change in the dependent variable. In most environmental justice research, the independent variables reflect the income and racial status of the relevant locations along with other socioeconomic and locational variables. If a model can be expressed fully in terms of a set of equations, it can be preserved over time without loss of precision and used to facilitate prediction, learning, and communication. This type of model is particularly advantageous insofar as by explicitly indicating which variables are included as independent variables, it clearly and explicitly articulates the ceteris paribus (“other things being equal”) conditions under which a resulting empirical statement may be considered valid. Because holding all else equal can lead to implications profoundly different than ignoring all else, this an enormously important function, especially when considering questions about causation. Three primary questions are often asked about mathematical models: Is there a relationship between the independent and dependent variables? If so, what is the direction and strength of that relationship? What is the functional form of the relationship? The answer to the third involves ascertaining the form of the mathematical function represented by the term f. The answers to all three provide insight into the reality represented by the model. If these questions can be answered clearly and in a way that can be tested against


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future observable or potentially observable facts or events, the model may be predictive. In general, predictive models are those that lead to statements of the form “the model says X, ” such that X goes beyond immediate observation and can be tested against data. The answers to these questions can be tested for their validity, so it is a good idea to further refine the concept of validity to help get a fairly strict understanding of what is meant by the validity of models. Most people recognize that if models are not valid, we cannot depend on them to tell us anything about the world. Fewer understand that the question of a model’s validity is not a black-and-white one. Rather, a model accrues validity through a validation process in which an initial model is tested and refined, then tested and refined again and again under successively more varied and exacting conditions. As it passes more tests, it accrues greater validity. Furthermore, the details of the demands placed upon the models in these tests depend on which of three essential forms of validity is germane for the particular model in question. These are, in ascending order of stringency, (1) face (content) validity, (2) criterion-related validity, and (3) construct validity. The face validity of a model rests solely upon judgment or subjective evaluation. It concerns whether or not, on the face of it, the model relates meaningfully to the situation or phenomenon it represents. For example, it concerns whether or not the variables it includes are reasonable in light of logic, common sense, and previous work on the topic. By virtue of its subjective aspects, face validity does not include replicable procedures for evaluating the validation tests. It is therefore a relatively weak form of validity, usually of more interest in research intended to influence policy and administrative decisions than in research designed to enable scientific explanation. There are two types of the next most stringent form, criterion-related validity. Both are established by using statistical analysis to gauge the strength of the relationship between the model and a criterion to which it should be related, if it is valid. The difference between them is in time dimension, specifically insofar as it relates to when the data are collected. The first type, concurrent validity, is established by correlating a prediction from the model to a criterion measured at about the same time as the data are collected. Concurrent validity is the only type of criterion-related validity permitted by research based on purely cross-sectional data. For the second type, predictive validity, the data for the criterion are collected after the data from which the predictions are made. Models based on pooled data may, under certain explicit and fairly demanding conditions, achieve predictive validity. This is the most widely used form in research designed specifically to facilitate scientific explanation. There is no meaningful sense in which models based solely upon cross-sectional data can strictly be said to have predictive validity. Concurrent validity, on the other hand, which can be used to judge cross-sectional models, is usually measured by the correlation between the

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predicted values from the model and the corresponding observed values. If a model correctly accounts for a very high percentage of the cases, high concurrent validity is indicated. Common statistical goodness of fit measures, such as correlation or R2, may be used to measure this. Conversely, low correlation or goodness of fit measures indicates lesser concurrent validity. In between is a range of acceptable concurrent validity. In my judgment, for example, as a rule, if the estimated percentage of variation in the dependent variable accounted for by the model ranges from 20% to 30%, the concurrent validity would be acceptable but relatively low. Below this, the model as a whole is probably very poor.5 Perhaps relevant variables are omitted, unnecessary or spuriously relevant variables are included, the functional form is wrong, or there are errors of measurement. The most prudent way to judge is to perform extensive residual tests for model misspecification errors. The most stringent form of validity is construct validity. Construct validity formally involves the theory behind the empirical test as well as the empirical elements of the research (Bowen and Bowen 1999). Given the absence of axiomatic theory in relation to the hypothesis of disproportionate distributions, it is therefore of little relevance in this context and is not considered here. It has been mentioned that if a model makes predictions, and it does so in a way that its predictive accuracy can be measured, and its predictive accuracy has been measured and found to be satisfactory, the model can be justifiably said to have predictive validity. The converse of this is that if a model does not produce predictions, or its predictive accuracy for whatever reason cannot be or has not been measured, it cannot be said to have predictive validity. If, for example, the relationships stipulated in the model do not reflect any actual relationships in the system the model represents, the model is unlikely to have predictive validity. The point is that not all models can possibly be validated. Some might lack specificity and others might simply be too far off the mark. To state this idea succinctly, before a model can be validated, it must be validatable (Hodges and Dewer 1992). A validatable model is defined as a model that can possibly be validated. The validatability of a model depends upon four prerequisites, all of which, it is important to note, inhere in the situation being modeled. First, the situation must be observable and measurable. These features are necessary for the empirical testing upon which the validation process for the model depends. Second, the situation must exhibit constancy of pattern through time. Models stipulate sets of relationships between the variables of whatever they represent. These sets of relationships are referred to as patterns or structures. Consistency of pattern or structure through time is required to ensure that the model is predictive for the same or very similar conditions as those used in the validation tests. Third, the situation must exhibit constancy of pattern across a specified range of variation


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not specified in the model. If the model is to be predictive, it must remain valid for a specified range of conditions different from those used in the validation tests. This third prerequisite ensures that the model remains valid for these conditions. Fourth, the situation must permit the collection of ample data. This again, as with the first prerequisite, is necessary to ensure that the model can be empirically tested. Bear in mind that these four prerequisites inhere not in the model, but in the situation it represents. Only if a situation is characterized by these four prerequisites, can a validatable model of it be constructed in principle. If such a model is constructed, and if it survives successively more varied and exacting predictive tests, it will accrue greater and greater validity. Cause-and-Effect Relationships in Environmental Justice Considerable debate in environmental justice discourse arises over the causeand-effect relationships associated with any putatively observed disproportionately distributed environmental risks. One reason for this is that if a particular hazardous site can be shown to have been located prior to the immigration of nearby minority, low-income, or disadvantaged residents, the implied cause-and-effect relationships are limited. Moreover, they are very different from what would have been had the order of events been reversed (Been 1994). Similarly, if the causes of a particular observed instance of disproportionately distributed risk can be attributed to choices made by the nearby residents—such as the choice not to get an education—then policy implications are very different than if they are attributed, to say, racism. Therefore, some explicit consideration must be given to cause-and-effect relationships in general, before considering their bearing on disproportionate distributions in particular. The attribution of causal relationships demands conceptual specification and measurement of the facts, events, and circumstances that underwrite the occurrence of a specific effect. In essence, it calls for tests of hypotheses in the forms “Every time a definite event occurs within some defined class of events E, it will invariably have been preceded by the occurrence of certain events in another defined class C” and “Every time an event in class C occurs, it will be followed by an event in class E.” The first step in testing such a hypothesis is to demonstrate that events in the two classes are statistically related (Salmon 1984). Normally, this requires a valid predictive model that spans at least the interval of time between the imputed cause and its associated effect. Ideally, causal relationships are established on the basis of data measured over some definite period of time using the causal model yi = f (xi1, xi2, . . . , xin; vi1, vi2, . . . , vim; zi1, zi2, . . . , zik), where yi is the observed value of the dependent variable for observation i (probably an environmental risk). The

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symbol xi1 represents the value of the hypothesized causal variable x1 associated with observation i. If, for example, one hypothesizes that the racial characteristics of a neighborhood cause higher levels of risk, x1 would represent these characteristics. The symbols xi2, . . . , xin represent other variables in which there is an interest and that might influence the value of yi during the study period. Examples might include neighborhood income characteristics or measures of neighborhood political organization and activism. The symbols vi1, vi2, . . . , vim refer to influences that acted upon observation i and formed it prior to the study period, but which are of no real interest for the purposes at hand. These can be called prior variables. Differences, for example, in past educational attainment and differences in the age of the household breadwinner can heavily influence current income. The symbols zi1, zi2, . . . , zik represent interfering or confounding variables. These are the variables that specify the ceteris paribus assumptions of the model. Though they are not of real interest for the purposes of the study, they are held constant to avoid mistaken empirical statements. Technically, their main function is to minimize, nullify, isolate, in a word, control extraneous systematic variance in the observations. For example, the amount of environmental risk in a neighborhood is likely to be a function of the amount of industry located within or near the neighborhood, as well as other factors such as zoning, proximity to major transportation routes, and population density. Unless such interfering or confounding variables as these are explicitly held constant, mistaken relationships may be inferred between the hypothesized causal variable and the level of risk in the neighborhood. In designing a study to establish causal relationships, at a minimum, observations of the level of the dependent variable must be made at two or more different times and locations having different levels of x1, each with identical vi1, vi2, . . . , vi,m qualities, ceteris paribus; that is, while all the other z variables are held equal. Technically, the guiding principle is to attempt to (1) maximize the variance in the hypothesized causal variable, (2) control the variance in the prior and confounding or interfering variables, and (3) minimize any variance attributable to errors in observation (Kerlinger 1973). Variance, in this sense, refers to the degree of dissimilarity between observations. The key to maximizing the variance in the hypothesized causal variable is to design, plan, and conduct the research so that the conditions under which observations of it are made are as different as possible. In specific terms of environmental justice, ceteris paribus, the study should thus be conducted under conditions of as much heterogeneity as possible with respect to any hypothesized racial, income, disability, and other hypothesized causal variable. In terms of controlling the variance in the prior variables, there are in principle three basic ways to do it. The first is to eliminate their effect by selecting observations so as to ensure that they are perfectly homogeneous.


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For example, if past educational attainment is the concern, the idea would be to include only observations with the same level of past education. This is very effective in controlling variance, but it also precludes the possibility of generalizing to other levels of the variable. For example, if observations are only made for high school graduates or less, generalization of any findings to postgraduates become questionable. The second way is to use an experiment in which observations are randomly assigned to experimental and control groups. This is in principle the best way, but for practical and ethical reasons it is difficult to see how it could be used in studying the problem of environmental justice. The third way is to build the prior variable right into the model, where it can be controlled statistically. Statistical controls can be used to extract from the total variance in the dependent variable any variance due variables included in the model. Techniques and methods for such statistical controls can be found in most good multivariate statistics book. Minimizing the variance in the observations is primarily a matter of increasing the reliability and accuracy of measurement and otherwise reducing observational error attributable to human fallibility. The strict study of cause and effect relationships in environmental justice would quickly become a seemingly endless maze. Explaining broad geographical patterns of disproportionate distributions is, practically speaking, too complex to accomplish. Most, if not all, relevant observed colocations of environmental risk and minority or low-income residences are the outcomes of countless decisions made by many people, each in somewhat different circumstances and using somewhat different factors in making their decision. Industrialists have an influence, as they decide whether to locate in a particular community, typically on the basis of product-specific assessment of market potentials for the alternative feasible locations. More specifically, their locational considerations are likely to include site-specific transportation costs, production costs, labor costs, quality of life, government incentives and infrastructure, local business climate, site costs, local political climate and stability, and energy costs (Blair 1995). Community decision-makers also have an influence, as they are responsible for a range of related actions such as zoning, environmental restrictions, and tax policies, all of which determine whether or under what conditions a hazardous facility is allowed to locate in their area (Thomas 1986; Scavo 1993; Pecorella 1994). Community residents too have an influence, especially in a democracy, through their influence on their community decision-makers. Indeed, it has been shown empirically that when large numbers of organized residents get involved, they can potentially exercise considerable power over the outcome (Hamilton 1993; Hamilton 1995). Finally, potential and actual community residents also have an influence through individual actions such as not moving into or moving away from the facility. Related decisions tend to be based upon considerations such as neighborhood and housing characteristics, accessibility (travel time, mode, or

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distance to workplace; shopping; recreation; schools; etc.), and environmental amenities (environmental quality), subject to budget and time constraints (Fujita 1989). Past this point, viewed in a proper light, the particular mix of factors and influences that lead to the observed pattern of residences and industries at any given time and place is apt to be an idiosyncratic and indeterminable mix of all these and other factors. Thus there is apt to be as much or more causal variation within relatively small-scale regions as between them.6 Given this complexity, it is currently unreasonable to expect to find any large-scale regions characterized by enough discernable constancy of pattern to make a validatable causal model of disproportionately distributed risks feasible.7 First, given the lack of axiomatic theory, there is too much conceptual ambiguity regarding exactly what causal relationships to expect to exhibit the necessary constancy of pattern over time and space. Second, there is the difficulty of defining a set of observable and measurable attributes of each of the relevant decisions and specifying the relationships between them. Before one can even start to think seriously about building such a model, one would first need at least tentative conceptual answers to the following questions: What specific attributes of the three groups (industry, community decision-makers, residents) and/or their interaction could be observed that could possibly enable predictions to be made as to whether or not a particular location will be selected for a hazard? What independent variables related to the three primary groups of decision-makers (industrial, governmental, residential) could be used to consistently predict whether a particular hazard will get located at a particular site rather than at one of the feasible alternative sites? What, in terms of the set of independent variables, is the pattern or structure of the interactions between the three groups that can enable predictions to be made regarding whether or not a particular location will be selected for a particular hazard? If these questions cannot be at least tentatively answered at a conceptual level, a validatable causal model of environmental justice cannot be formulated conceptually, much less tested empirically. Moreover, even were it somehow possible to specify a suitable conceptual model, the current lack of available data would severely limit the possibilities for testing and validating it. Even if such a model could be formulated conceptually, before it could be tested a set of extremely expensive data that is currently not available would be needed. On balance, therefore, it is safe to say that no one is currently able to decide with much surety, on the basis of scientifically acceptable causal models, whether in any given situation, race or income or any other such variable “causes” an observed disproportionate distribution. In my view, strictly in terms of causal models of disproportionately distributed risk that stand up to reasonable standards of scientific scrutiny, the best one can reasonably hope for are models that exclude possible cause-and-effect relationships. Such models can show that some causal schema is inconsistent with data, that


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events in the hypothesized causal class are not related to events in the hypothesized effect class, or that the time sequence of events in the two classes is inconsistent with a hypothesized cause-and-effect relationship. For example, a causal model that shows a set of hazardous sites in question were located at a particular place prior to the establishment of a nearby minority or lowincome neighborhood could justifiably eliminate an alternative course of action directed at the siting process from the choice set. In other words, causal models can be useful primarily in establishing what is not occurring. They can thus help to exclude associated alternatives in a policy or administrative decision. The best that can be realistically expected is to make empirically informed subjective and psychologically rooted judgments about whether to call a demonstrated relationship causal. But in this respect, when anyone states that anything “causes” a disproportionate distribution, it should be kept in mind that the statement is as much a reflection of the person’s interpretation of the world as it is a reflection of anything actually occurring. Nonscientific Uses of Models in Environmental Justice Decisions If no model of disproportionate risks can be convincingly shown to have predictive validity, thereby meeting reasonable standards of social science in terms of providing causal explanations, then any model that has applied value in terms of making policy and administrative decisions must not fully meet these standards. But this is not a good reason either to raise problems related to the relevance of social science or to insist upon lesser standards. Indeed, a social scientific model with an explicitly recognized unknown level of uncertainty can be very useful in applied policy and administrative decision problems. Moreover, this typifies a pervasive feature of social science. When the impetus for studying a problem and its formulation comes from concern with policy and administrative decisions rather than causal explanation and social scientific validity, and if scientific methods are systematically and carefully brought to bear, problem formulation inevitably loses some of its original power. In reality, the much more demanding standards of research aimed primarily at causal attribution for purposes of scientific explanation are seldom if ever met in research aimed primarily toward improved interpretation, understanding, and policy formulation. From the point of view of those who hold the policy concern, especially among policy advocates, this raises problems related to the relevance of scientific method. But on close analysis, outside the framework of efforts to consciously evaluate the costs and benefits of further and better research, such objections tend to be shortsighted. It has been suggested that to make good policy and administrative decisions—defined as ones that pursue the appropriate objectives in an “intelligent way”—decision-makers must first know what the decision problem is.

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That is, they need a reasonably accurate rendering of not only what is but also what is not known about the decision problem. One of the values of using reasonably high standards of social science is that they help to clarify and make explicit precisely this distinction, thus making the uncertainties explicit. This is of considerable value, particularly since explicitly acknowledging the existence of the uncertainties in a complex decision problem is the first step in systematically thinking them through, understanding the various outcomes that might unfold, their likelihood, their impacts. By clarifying the parts of a problem and leading to an improved understanding of it, even social science models that are not validatable may thus, as a secondary consequence, bring improvements in policy and administrative decision processes. Indeed, one could argue that, in relation to a problem as important as environmental justice, it is particularly important to use nothing but the highest standards of social science method for precisely this reason. Nonvalidatable models may be quite useful in other ways as well. One is that they can aid as stimuli to intuition in other applied research oriented toward affecting decision processes. They can lead to insights that result not directly from the model having said anything about the world—which by definition they have not—but from having clearly articulated their own assumptions. Another is that they can help in summarizing and communicating about large quantities of data. The models used in geographical information systems, for example, can summarize and convey spatial patterns among huge numbers of observations, without in any way having been shown to have predictive validity. Still another stems from the experience of policy and administrative decision-makers who are inconvenienced by data that will not suffice for calibrating a convincing conceptual model, in which case a nonvalidatable model can also provide an incentive to improve the quality of the available data. Finally, nonvalidatable models can be useful in helping to legitimate an idea. Policy advocates, for example, are apt to be less concerned with the validity of their models than with their effectiveness at helping to advance a political agenda—a role in which such a model can be very effective. All of the current models of disproportionate impacts can be roughly categorized as follows: (1) those that by virtue of design, method, and documentation can be recognized as substantially accurate and can furthermore be meaningfully generalized and applied to broad policy and administrative decisions; (2) those that can be thus recognized as substantially accurate but cannot be generalized outside the particular situation in which the research was conducted; (3) those that are judged likely to be approximately accurate, but that have notable flaws in design, method, or documentation; (4) those with flaws of design, method, or documentation that are severe enough to be judged as purely conjectural; and (5) those with substantial enough flaws to be judged as plainly false but convenient. I refer to these categories again in Chapter 6 to classify the current empirical research. For now it suffices to say


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that until the conceptual-theoretical foundations are improved, the models in the currently available research on disproportionate distributions may suggest or indicate the existence of disproportionate risks, but as a rule they do not— and will not in the foreseeable future—reveal things about the actual world. ELEMENTS OF THE OBSERVATIONAL-EMPIRICAL DOMAIN The other broad domain of considerations in social scientific research includes the observational and empirical aspects of hypothesis testing. In general the primary observational-empirical considerations are (1) those that deal with collecting data and (2) those that deal with organizing data into a form suitable for testing hypotheses. Data collection is about making systematic and well-justified connections between the relevant facts, events, and circumstances, the observations they generate, and the archival representation of those experiences in terms of the data. The criteria for success in collecting data have to do with (1) the relevance of the observations vis-à-vis the concepts, (2) the representativeness of the observations with respect to the phenomenon or population of interest, (3) the fidelity of the observer’s responses to the phenomena being observed, and (4) the accuracy with which the responses are recorded. The underlying rationale is formalized largely in the principles of research design (Campbell and Stanley 1963), and in the justification behind the rules of measurement (Stevens 1957; Bowen and Bowen 1999). Organizing data into a form suitable for testing hypotheses has to do with the analysis or sequence of steps through which the data, having been gathered and compiled, are translated into empirical statements in terms of the research question. The appropriate type of data analysis to use depends on the type of research question, the conceptual model and methods used to collect the data, and the formulation of the specific research question or hypothesis. The criteria for success have to do with whether (1) the appropriate type of analysis is used, (2) the analysis is done correctly, (3) any information or meaningful relationships in the data are accurately identified, and (4) the appropriate conclusions are drawn. Data Good data are the lifeblood of social scientific hypothesis tests. They come from repeatedly assigning numerals to the relevant attributes of numerous observations of a phenomenon in accordance with a concept. The numerals reflect these attributes in terms such as how much, how many, how long, how fast, where, and what kind. Empirical research depends on good data in much the same way that a building depends on having a solid foundation. A building will not stand unless the foundation is solid, and social scientific empiri-

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cal statements will not be validatable unless they are based on good data in sufficient quantities. Data are also the first and probably foremost limitation on our empirical knowledge of disproportionate environmental risks. The main challenges in data collection are to make sure, first, that one’s observations are consistent with the relevant concepts; second, that the observations are representative of the relevant phenomenon; and, third, that the numerals accurately represent the observations. In conventional terms of empirical research methods, the first big challenge has to do with operationalizing the concepts, the second with sampling, and the third with measurement. Operational Definitions Historically, the social scientific validity of any empirical statement has rested on substantial agreement within a scientific community, based upon the possibility of its being replicated independently of the investigator. One of the implicit aspects of validation by replication is shared understanding and agreement on the terms and concepts in the related language. Accordingly, in at least one salient respect, the language used in the environmental justice discourse is similar to the language used in everyday conversation. Terms are given arbitrary definition from within a culture or community and ordered according to a grammar and syntax that often imparts new meaning above and beyond the definitions. However, while the arbitrary nature of language does not usually create major problems in ordinary conversation, clear and distinct definitions and unambiguous statements are essential to the integrity of the shared understandings upon which the validity of social scientific empirical statements ultimately depends. This pertains to the meanings not only of the terms used in asking the research question, but also those in the research design and documentation and those used in empirical statements about the relevant facts, events, and circumstances. For research to be replicable it is necessary for all of the major terms and concepts used in asking the research question to have clear and distinct meanings. The conventional way social scientists deal with this is to create operational definitions of all the relevant major terms and concepts in the research question before they make any observations, take any measurements, or collect any data. Operational definitions are sets of explicit procedures describing overt and strictly replicable activities that must be performed to establish empirically the existence of the phenomena represented by the terms and concepts in the research question (Simon and Burstein 1985). Take, for example, the empirical statement that in a specific situation, a low-income community faces a disproportionately high incidence of exposure to a particular environmental risk. One cannot test this empirical statement unless one first defines environmental risk in a way that can be measured or otherwise mean-


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ingfully associated with objective, sensually based observations. Moreover, these observations must be of a sort that can be replicated by other impartial observers. Operational definitions are distinct from other forms of definition, including 1. definition by naming, in which a name is assigned to a concept or object; 2. definition by name and context, in which a name is assigned and employed in a context which is presumed to clarify the meaning attached to the name; 3. definition by extension, in which a category is defined by giving a set of exemplars of the category; 4. definition by intension, in which a set of properties characteristic of the concept or object is given, and 5. definition by structure, in which a concept is defined syntactically (Warfield 1989, 70). Operational definitions are used because they minimize any potential unwanted effects associated with using theoretical terms that generate important beliefs, perceptions, and assumptions that go beyond observations and are uncritically and unconsciously taken for granted. They do this by making explicit agreements on the meanings of the terms used in making and documenting observations. Ensuring that the definitional terms prescribe strictly and overtly replicable activities on the part of the investigator, also ensures that any observations made by one investigator will occur in the same relatively concrete activity-oriented terms as observations made by any other investigator. The keynote is to enable other researchers to repeat exactly what one researcher has done. Data are gathered in accordance with operational definitions that obtain meaning in large part from the concepts to which they refer; that is, the inferential structure of social science is such that the data reflect empirical attributes that are represented by operational definitions which normally refer to concepts that cannot be observed directly for their meaning. In this sense concepts are more fundamental than the operational definitions; operational definitions depend on them for their interpretation.8 Conceptual definitions, in relation to operational definitions, are deeper or further back in the chain of social scientific inference and are necessary to the interpretation of the operational definitions they precede. Empirical statements based on inferential processes that fail to take this directional relationship into account remain ambiguous, imprecise, or underconceptualized (Warfield 1990). Underconceptualization dilutes the coherence of the research as well as potentially precludes other researchers’ meaningfully replicating it.

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Sampling Another data collection concern has to do with sampling. The term universe or population is usually used to refer to the entire category of people, attributes, objects, facts, or events of interest in a scientific research project. A universe can be finite and well defined, such as “everyone listed in the Cleveland telephone book in 1998,” or it can be infinite or hypothetical, such as “all disadvantaged individuals who are exposed to an environmental hazard and as a consequence experience deteriorated health.” A sample is a subgroup or subset of the population that the researcher observes, from which generalizations to the entire universe or population are made. Scarce resources are the major reason for studying a sample instead of the entire universe. Studying the entire universe of interest is almost always cost prohibitive. The concern with sampling is largely about the process of gathering the sample. Process is important because, unless managed well, the sample will not accurately describe the universe, and the hypothesis tests are apt to lead to mistaken inferences. In the abstract, the criteria for success in collecting data refer to the relevance and representativeness of the observations vis-à-vis the major terms and concepts in the hypothesis. The sample is relevant if and only if it contains variables that adequately reflect the relevant terms and concepts. It is representative if and only if it resembles the universe with regard to the variables of interest, almost as if it were a miniature version of the universe. Procedures for drawing samples that ensure that the average sample will resemble the population quite closely can be found in any good research methods textbook. Cost is often the binding constraint on collecting data; therefore, the objective is to meet these abstract criteria at minimum cost, without compromising the validity of any hypothesis tests or empirical statements. Sampling procedures are designed to accomplish exactly this objective. Sampling is a basic cost-reducing tactic. Sampling produces a sample, a relatively few number of observations, from which tests of hypotheses are enabled and generalizations can be made to the entire universe of interest. When the sample accurately represents the attributes of the entire relevant universe, precise inferences on the entire phenomenon can be drawn from observation of a relatively small number of its attributes with the same accuracy as if all of the relevant attributes everywhere and at all times were observed. Specific considerations include the definition of the boundaries of the phenomenon of interest, the design of the sampling procedures, and the size of the sample. Measurement Still another data collection concern has to do with measurement. Measurement is the unique assignment of a range of numerals to a domain of magnitudes according to rules (Stevens 1957). Measurements begin on one hand with an operational definition of a concept (to guide the selection of observa-


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tions) and on the other with the rules of measurement. These rules define the system through which observations related to the concept can be meaningfully translated into numerals. They specifically govern and constrain the process of assigning numerals to observations to ensure that certain psychological and logicomathematical antecedents are fulfilled. If the assignment of a numeral to an observation conforms to the operational definition of the relevant concept and the rules of measurement, the numeral can be logically linked to the concept. In principle, the use of measurement in scientific research has two big advantages. First, in comparison to the use of words to describe the observations, the meanings of numbers in a data set contain relatively less ambiguity. It is one thing, for example, to be told that most toxic waste sites in a region are located in predominantly minority or low-income neighborhoods. It is quite another to be told that a definite percentage, say 75% or 80%, of them are so located. Second, the numbers in data sets are subject to the laws of mathematics. As a consequence, good data enable many times more observations to be systematically considered, so that deep and long logic chains characterized by high levels of integrity can be used to draw inferences from the observations. Because numerals are clearly and logically linked to mathematics and quantitative reasoning, measurement links them to observations made in accordance with a concept. In doing so, it enhances the investigator’s ability to logically and explicitly process information about the relationships between the attributes of the empirical phenomena represented by the concept and other such relationships represented by other concepts. In other words, measurement makes it possible to establish logical relationships between a large number of observations of two or more empirical variables. Measurement, as opposed to observations made in the absence of numerical assignments, thus vastly improves the researcher’s ability to reason through the relationships with reference to which the world is represented by the concepts in a hypothesis. This advantage comes directly and simply from the enhanced ability it provides to how one proceeds in organizing one’s knowledge about the phenomenon. In practice, taking measurements is often fraught with obstacles that compromise the quality of the answer to the research question. The related concepts of measurement reliability and measurement validity are concerned with whether or not certain aspects of the measurement process compromise this quality. The concept of measurement reliability applies to both the operational definitions and to violations of the rules of measurement. Measurements are deemed reliable if they are consistent or repeatable under these rules. If, for whatever reason, the operational definitions do not consistently represent the intended concepts, or if idiosyncratic measurement errors occur in gathering the data, the measurements will not be reliable.

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The concept of measurement validity has to do with whether or not measurements accurately reflect what the investigator intends. This has a couple of aspects. First, a valid measurement must represent the concept it is intended to measure. This requires a well-conceived operational definition. Second, assuming that the operational definition adequately represents the intended concept, measurement validity requires that the correct numerals be assigned to the empirical attributes or characteristics observed. Differences between the correct numeral and the numeral assigned to an empirical entity are known as measurement error. When the measurements on which research is based are not reliable and valid, the research is usually considered too seriously flawed to be credible. Environmental Hazard Data In this regard, the data collection problem in environmental justice research, as reflected in the discourse, is largely a matter of enabling a credible scientific foundation for explanations of socioeconomic differences in health status. The idea is for these differences to be based on identification of neighborhood subgroups in the population whose exposure is disproportionately greater than the average exposure experienced by the remainder of the population.9 Because the general population is a complex mixture of subgroups, each consisting of individuals who experience a wide range of exposures, none of which can ultimately be separated out from the larger social context within which people live and work, a fairly large number of complex issues are involved. Perhaps the most notable concern is that more and better data are sorely needed for several different related areas of inquiry (Calderon et al. 1993; Chase 1993). These include but are not limited to data needed to determine and understand (1) location-specific spatial distributions of demographic and environmental variables associated with environmental risks, (2) social and historical patterns associated with the nature and extent of specific types and dosages of exposure to various chemicals, (3) specific health outcomes associated with various exposure dosages, (4) relationships between exposure and the health and well-being of populations, (5) community values toward the distribution of risk, (6) risk perceptions and attitudes, (7) risk communication, (8) the epidemiological effects of particular risks, as well as (9) the feasibility of public risk compensation and/or insurance programs. Many more data are also needed to adequately prioritize the range of environmental risks that may bear upon health in minority and low-income communities. Indeed, Environmental Equity, a report released by the United States Environmental Protection Agency, concluded that data on the health effects of environmental pollutants by race, ethnicity, and income are generally lacking (Environmental Protection Agency [EPA] 1992). A major recommendation of the report


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was that a research program be established to provide data on health risk by minority status. A significant proportion of the current social scientific research on environmental justice uses Toxic Release Inventory (TRI) data. The TRI is an annual compilation of information on the quantity and location of industrial toxic releases for more than 600 designated toxic chemicals. Authorized under Title III, Section 313 of the Emergency Planning and Community Right to Know Act of 1986 (EPCRA) in the Superfund Amendments and Reauthorization Act of 1986 (P.L. 99–499), these data cover releases by over 20,000 manufacturing facilities and 200 federal facilities located throughout the country. The TRI information includes: 1. what chemicals were released into the environment during the preceding year; 2. how many pounds of each chemical went into the air, water, and land in that year; 3. how many pounds of each chemical were transported away from the reporting facility for disposal, treatment, recycling, or energy recovery; and 4. how chemicals were treated at the reporting facility. The purpose of this information is to reduce risk to communities by helping them to prepare to respond to chemical spills and similar emergencies. For purposes of research on environmental justice, the TRI data, however, are seriously flawed in several respects. There is a major sampling problem insofar as only selected industries are required to report. As a consequence, many releases are exempt from the reporting requirements. Releases exempted from reporting requirements specifically include those by firms or activities with less than 10 employees, firms that manufacture or process less than 25,000 pounds or use less than 10,000 pounds of the chemicals on the TRI, and those not in the industry groups listed between Standard Industrial Classification Codes (SIC) 20 through 39. Indeed, according to Szasz and Meuser (1997), only a fraction of the facilities known to generate wastes is required to participate in the TRI reporting. There are also major measurement problems with the TRI data. First, they contain estimates of releases made by firms without standardized estimation procedures; they are not actual amounts based on monitoring within firms. Second, facilities are not required to monitor their releases and no verification of the firms’ release data is required. As a consequence, well-intentioned errors sometimes occur in firms’ reporting procedures. Moreover, some facilities do not fully comply with the reporting requirements, either by not reporting at all or by reporting only some of the covered chemicals. Szasz and Meuser (1997) cite an unpublished study that estimates 99% of all emissions may be missed by the TRI. In

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any case, while verifying some of the TRI data used in an earlier research project (Bowen et al. 1995), the authors found the single largest reported release to be located in an office complex in the middle of an edge city, miles away from the closest feasible actual release site. As a consequence of such considerations, the reliability of the TRI is doubtful (EPA 1991; ViGYAN 1992). Besides data collection problems, the relevance of the TRI data for researching environmental justice is also a source of concern. First, the TRI reports reflect releases of pounds of chemicals, not exposures of the public to those chemicals. This is a source of considerable potential inferential error, since a small number of pounds of a highly toxic chemical may pose a far greater threat to health and well-being than a large number of pounds of a chemical with lesser toxicity. Again, release estimates alone are not sufficient to determine exposure or to calculate potential adverse effects on human health or the environment. Second, release estimates are aggregated across the entire year and contain no information about the distribution of the releases across the time frame. The chemicals could be released evenly over the course of the year or in one shot; there is no way to tell from the TRI. This can make a huge difference in terms of health implications, especially because the patterns of diffusion of chemicals can vary seasonally with climatic conditions. One way to improve the quality of the TRI is to use sampling procedures to gather the data. Sampling rather than studying the universe of toxic releases is a basic cost-reducing tactic that need not, if done properly, in any way reduce the accuracy of inferences. The savings that would accrue from sampling could then be used to ensure the reliability of the sample, resulting in improved empirical statements related to environmental justice. Other data sets, such as the locations of sites on the National Priorities List (NPL), are also sometimes used. The NPL is a list of about 1400 of the most dangerous abandoned hazardous waste sites in the nation scheduled for cleanup, commonly known as Superfund sites. But, as with the TRI, there are certain inherent limitations. First, since the preponderance of current data were collected without environmental justice concerns specifically in mind, all of them only approximate the type of data needed to determine the health effects of environmental pollutants by race, ethnicity, and income. Second, NPL data have been limited historically by political trends associated, for example, with the reluctance of political leadership to stigmatize their districts by having NPL sites located within them (Rahm 1999). Third, there are problems of availability with some, if not most, of the existing data sets. Assuming that database management skills are available to the user, particularly to a relevant policy and administrative decision-maker, it can take scores of hours to manage and clean and check the data for critical measurement errors. Sometimes the data are not available because the researcher who invested these hours will not make them available. If the researcher is


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employed at a publicly funded university, there may be a legal obligation to make the data available, but as a rule researchers are not required to make their data available to other researchers. Finally, there may be limitations on the existing data that have to do with the quality of the procedures used in data collection. In some data sets, these limitations are dealt with through the use of data collection standards. For example, the Interuniversity Consortium for Political and Social Research at the University of Michigan uses such standards to classify the quality of various data sets (ICPSR 1989). Often, data limitations can flaw research to the point of not being credible. Analysis After data are collected they need to be processed into a form that is amenable to systematic analysis. Not all data contain information, and normally the information contained in those that do can only be obtained through data analysis. Immediately after collection, before anything else is done with them, they are, in a sense, a mass of numerical records of individual observations that have been collected and stored. Further time, energy, and analysis is needed if they are to yield the information they contain in terms of testing hypotheses or helping to make decisions. THE ROLE OF STATISTICS IN ENVIRONMENTAL JUSTICE RESEARCH Typically, data must first, and at a minimum, be described in a summary fashion through the use of descriptive statistics. Descriptive statistics, such as frequency distributions, means, medians, modes, and standard deviations, enable description of collections of data and reduction of the information they contain to understandable form (any basic statistics book will provide definitions for the terms used in this discussion). Furthermore, if the data are to be systematically organized, the information they contain brought logically to bear in terms of the research question, and hypothesis tests used to justify empirical statements taken as premises in policy and administrative decisions, they must be analyzed through the use of inferential statistics. Inferential statistics help enable estimation of the probability that the hypothesis is or is not consistent with the data and aid in making decisions by helping to evaluate which alternative among a range of possibilities is most desirable, with reference to a standard. Many different procedures in social scientific research are called analysis. Some forms of analysis establish relationships between independent variables and dependent variables. When there is one dependent variable, as there often is in environmental justice research, multiple correlation analysis,

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multiple regression analysis, or multiple discriminant analysis may be used. The objective of multiple regression analysis and multiple correlation analysis is to predict changes in a dependent variable (e.g., the amount of toxic release in a census tract) in response to changes in the independent variables (e.g., the number of people living below the poverty line). In a few instances in the environmental justice research, multiple discriminant analysis is used to divide the observations in the data into groups based upon a dependent variable with several known classes. This type of analysis can help to understand group differences and predict the likelihood that an observation will belong to a particular class or group based upon the independent variables. I am somewhat surprised that there is not more multivariate analysis of variance (MANOVA) used in the literature, since it would enable analysis of several dependent variables, each of which could represent an attribute of environmental risk. A MANOVA is generally used to explore simultaneously the relationship between several independent variables that reflect categories, and two or more dependent variables. Another form of analysis seldom if ever used in the environmental justice research, one that could be in principle of considerable value, is canonical correlation analysis. Canonical correlation analysis can be viewed as a logical extension of multiple regression analysis, used when the objective is to simultaneously correlate several independent variables and several dependent variables. Generally speaking, any of these analytic procedures can under certain conditions be used to characterize the form and strength of the relationships, if they seem to exist, and test for the likelihood that they are not simply the outcome of chance. Numerous other analytic procedures are available and of potential value in environmental justice research. For example, if the research question is not about relationships between dependent and independent variables, but rather is about interrelations or interdependence among all the variables without regard to whether they are independent or dependent, the appropriate analytic processes include factor analysis, cluster analysis, and multidimensional scaling. Factor analysis is a statistical procedure used to establish the interrelationships among a large number of variables and then to explain these variables in terms of their common underlying factors. It involves condensing the information contained in a set of original variables and transforming it into a smaller set of factors with a minimum loss of information. Cluster analysis is used to develop meaningful subgroups or clusters of attributes. Specifically, the objective is to classify a group of observations into a small number of mutually exclusive and collectively exhaustive subgroups based on the similarities and differences among the observations. Multidimensional scaling is a set of techniques used to transform similarity or preference judgments into geometric representations that illustrate the relationships between them. The key to success in any empirical analysis is to select the method that appropriately answers the research question in terms of the particular conceptual


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model and data at hand, and to follow the prescribed procedures closely and carefully. The available statistical knowledge generally does not in any way limit what is known about disproportionate distributions. Aggregate numerical data have been gathered, analyzed, and used to understand social phenomena ever since the seventeenth century (Bowen 1998). Many of the concepts needed to analyze the joint distribution of two variables, such as those reflecting the locations of environmental risks and minority residences, have been available since the early twentieth century. Most of the concepts and techniques of linear analysis and the derivations of the sampling distributions needed to draw accurate inferences from samples have been available since the early 1920s. Many of the basic linear modeling concepts such as confidence intervals, statistical hypothesis testing, optimal estimators, efficiency of estimation, and experimental design have been known since the 1930s. Analysis of variance and covariance were conceived before World War II. Soon after that, the theory of hypothesis testing was integrated with societal decision processes. Statistical theory and technique are developed well enough to enable empirical researchers to build models to answer virtually any research question that might be asked about the situations in which environmental justice arises as a concern. Meta-analysis One particular analytical technique of possible interest in environmental justice is meta-analysis. Meta-analysis is a technique that uses statistical procedures to combine two or more empirical studies relating one variable to another. In essence, it systematically synthesizes and enables statistical inferences to be made on the basis of other studies. There are seven steps in a meta-analysis: (1) conceptualizing the relationship under consideration; (2) gathering a set of studies that have tested the specified relationship; (3) designing a coding sheet with which to record the characteristics of the conditions under which each study was conducted; (4) examining each study and, using the coding sheet, recording the conditions under which it was conducted; (5) computing the effect size for each study;10 (6) statistically analyzing the characteristics and effect sizes for all the studies; and (7) writing a summary research report. These steps are described individually in more detail, along with a set of references, in Bowen and Bowen (1999). The reason that meta-analysis is of interest in environmental justice is that it would, in the abstract, be an ideal technique for synthesizing studies of particular subregions in the United States and evaluating the hypothesis that there are national patterns of disproportionate environmental risk. In the abstract, such an approach could be used to systematically establish information at the national level, through aggregation of subregional studies, without

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loss of information pertaining specifically to the constituent subregions. Moreover, given that environmental risk is operationally a rather vague concept, each constituent subregional study could employ a somewhat different proxy for the same concept. For example, some studies might use TRI sites and others NPL sites as a proxy for environmental hazards. Meta-analysis could also be used to establish the relationship between the dependent variable and various moderator variables such as perceptions of people in various groups and various locations around the country. However, while meta-analysis would be ideal in the abstract, in reality it requires all of the constituent studies to have very similar if not the same variables and to use the same units of analysis. To the extent that the aim of a study is to ensure the integrity of the relationship between the facts, events, and circumstances on one hand, and the inferences made about them on the other, these requirements currently make its use in environmental justice research infeasible. For example, it would be nonsense to statistically combine a study that does not find a relationship between race and proximity to toxic sites at the census tract level in Omaha with another that finds such a relationship at the county level in Louisiana. It would perhaps be feasible mathematically to use meta-analytic procedures to combine the statistical findings of the two studies, but the emerging characteristics and effect sizes would have no meaningful interpretation. As shall become clear in Chapter 6, there is such huge variation in the variables and units of analysis in the current research that a meta-analysis might appear superficially to have a meaningful interpretation, but on closer inspection this would surely be found to be illusory. Rather, if a credible meta-analysis is ever to be done, it will probably involve reasonably heavy institutional support for a large-scale project designed specifically to conduct and synthesize local and regional studies, ultimately to enable any possible national-level patterns to be discerned. Selection of a Comparison Region Another major analytic concern in much environmental justice research has to do with the selection of a comparison region. The rationale for the need to select such a region was introduced in Chapter 3. The particular region selected is important because the conclusions from the more common forms of environmental justice research can be very sensitive to these locations (Anderson, Anderton, and Oakes 1994; Anderton 1996; Anderton, Oakes, and Egan 1997). The concern with selection of a comparison region has two basic aspects. One is that, if most of the risky and minority locations are concentrated in particular subregions of the larger study region, the conclusions or empirical statements from the research could be very sensitive to the demographic characteristics of the populations outside the boundaries of the study region, espe-


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cially if those areas contain locations with high concentrations of minorities. Second, the inferences from the study are likely to be sensitive to truncation in the data, resulting from the inclusion of tracts in which the location of the environmental risks was not feasible at the time they were located. For example, in any given metropolitan area, it is not feasible to locate a hazardous industrial site in an area with no industrial land. If none of the subregions without any industrial land at the time of particular siting decisions were potential locations for that facility, it therefore makes very little if any sense to include those subregions as part of the comparison in the analysis. Of course, it is logically possible that the subregions without industrial zones are also the ones with the fewest minorities and were so at the time of the siting decision. If so, insistence on excluding them on conceptual grounds could demand reformulation of the analytical problem in such a way as to take the teeth out of the analysis. In any case, it is important to be sure that the rationale and procedures for selecting and dealing with comparison locations within the study region are conceptually sound, otherwise the inferences are apt to be misleading. Assuming that conceptually sound comparison locations have been identified for which adequate data are available, it is also important to gauge how sensitive the conclusions from an analysis are to the feasible alternatives. Spatial Dependence and Clustering Finally, one analytical point crucial in environmental justice research, particularly research based upon cross-sectional data, relates to spatial dependence and clustering (Anselin 1988). Spatial dependence refers to interdependence among observations in geographic space, such as when the values of a variable at one location are influenced by the values of that variable at a neighboring location. If one considers the frequency of observing clusters of industry or poverty or hazardous waste sites in some areas and not in others, one readily sees that spatial dependence is common in respect to disproportionate distributions. It may occur, for example, when levels of environmental risk in contiguous census tracts within a metropolitan area are positively correlated. The problem with spatial dependence and spatial clustering is that it often gives rise to error in statistical analysis, known as spatial autocorrelation. At root is a concern with the assumptions of various analytical techniques used in researching disproportionate distributions, most especially the standard regression assumption that each observation is independent of every other. When statistical analyses of the relationship between environmental risk and minority or low-income locations are inconsistent with this assumption, and the assumption is made anyhow, the logical implications include seriously biased parameter estimates as well as highly unreliable hypothesis tests. At a minimum, therefore, research that aspires to reasonable standards

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of validity should include a thorough examination of the statistical residuals, including the computation of a Moran’s I and a Geary’s C, as well as a test for the statistical problem known as heteroskedasticity (Odland 1988). If these tests are performed and no spatial autocorrelation is found to be present in the data, simple reports of those statistics suffice to set the concern to rest. If on the other hand, spatial autocorrelation is detected, it can be remedied by fitting spatially autoregressive or lagged models, depending largely on the type of autocorrelation indicated in the diagnostic tests. In the absence of such examination, inferences about disproportionate distributions can be based upon seriously biased parameter estimates as well as highly unreliable hypothesis tests, thus leading indirectly to mistaken premises and misguided policy and administration decisions about the distribution of environmental risks.

CHAPTER 5

Design and the Validity of Environmental Justice Research All man’s disciplined creations have form. Architecture, poetry, music, painting, mathematics, scientific research— all have form. Man puts great stress on the content of his creations, often not realizing that without strong structure, no matter how rich and how significant the content, the creations may be weak and sterile. —FRED N. KERLINGER, Foundations of Behavioral Research

At the heart of the empirical dimensions of concern with environmental justice is a research question about whether and/or to what extent in any given definite study area, minority, low-income, and other disadvantaged communities face disproportionate exposure to environmental risks and, as a consequence, suffer more health problems. The hypothesis is that given any two point locations in a study area having different levels of environmental hazards, yet otherwise identical in every respect except for the minority, lowincome, or disadvantaged status of the nearby residents, those with higher levels of environmental hazards are more likely to be near minority, lowincome, and/or other disadvantaged residences. No causal relationships are stipulated here, no rationalization of or explanation for the hypothesized relationship is offered, so the hypothesis is essentially descriptive in nature. It states that higher levels of risk are expected around minority and low-income residences, so it is said to be directional. Since it is induced from the observational-empirical domain, not deduced from axiomatic theory, the logic of scientific inference dictates that the empirical tests cannot provide conclusive evidence or demonstration of the hypothesized relationship, but can simply render it more or less likely than before. More specifically, empirical tests that find a statistically significant positive correlation between the two variables can render the proposition that these groups are disproportionately located in close proximity to the hazards more tenable or more likely than before. Conversely, empirical tests that find no such correlation, or a negative correlation, render it less tenable or less likely. Before the hypothesis can be tested, the relevant concepts must be operationalized; a study region, a comparison region, and a level of spatial aggregation must be selected; and the necessary data obtained. If causal relationships about racial or income-related factors in the site selection decision process are to be inferred, a host of

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potentially confounding factors such as land use controls, industrial location, and levels of community participation must be controlled in the models. Having stated the hypothesis, the concern with research design is largely in the empirical-observational domain, particularly in generating adequate and proper data to test it. A research design is a plan for collecting, analyzing, synthesizing, communicating, and interpreting the observations in relation to the hypothesis. Failure at any of these components can fatally compromise the empirical validity of the test; therefore, a research design that provides systematic planning and oversight is essential. The function of research design is to formally embed the hypothesis test within the evidential reasoning process, by systematically eliminating possibilities for inferential error, thereby producing data from which sound conclusions may be efficiently derived. A research design tells us, in a sense, what observations to make, how to make them, and how to analyze the quantitative representations of the observations (Kerlinger 1973, 301). In one respect it consists of an explicit logical system for controlling extraneous systematic variation in the observations, or variation not directly related to the relationship of interest, as stated in the hypothesis. It can thus be interpreted as a control mechanism for holding constant everything else that might confound any empirical statements about the relationships between the main concepts under investigation. The significance of this is partially that in serving this control function, research design also explicitly specifies the logical and evidential framework for “adequate” tests of the relationships among the variables. This is known as the internal validity of the research. Unless the extraneous variation is appropriately controlled, its effects are apt to be confounded or confused with the effect of one or the other of the variables in the main relationship under investigation, thus threatening the validity of the conclusions.1 In another respect, the research design influences the degree to which the research can be considered to be representative of a larger universe, so that its results can be generalized from the particular study situation to other situations within that universe. In this respect, by explicitly suggesting and limiting the conditions under which the answer to the empirical research question can be considered valid, the research design enables and limits the external validity of the empirical statements drawn from it. In specifying the logical and evidential framework for adequate tests of the relationships among the variables, the research design also explicitly suggests and limits the conditions under which the answer to the empirical research question can be considered valid. A number of distinct research designs have been clearly identified and their corresponding characteristic strengths, weaknesses, potentials, and logical limitations well established in the literature (Campbell and Stanley 1963). Each employs its own identifiable means to establish the effect of an independent variable on a dependent variable, often through researcher-induced alter-

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ation of the situation, while controlling extraneous variation. The main differences between the different designs are found in the details of their use of three components, as introduced in the following section. Because the means of establishing the effect differ from design to design, designs also differ from one another in the degree to which they are successful at achieving control of extraneous variation. Recognition of the research design used in a given empirical study, and knowledge of the characteristics of that design, is necessary to assess what can and cannot be said with validity on the basis of that study. The validity of the empirical statements that can be supported on the basis of the study will always be limited by the corresponding characteristics of the particular research design it employs. COMPONENTS AND CATEGORIES OF ENVIRONMENTAL JUSTICE RESEARCH DESIGN In general, the distinctions between the various research designs depend on the characteristic patterns found in three components common to all of them. These three components—comparison, manipulation, and control—form the logical basis from which the validity of inferences is determined. For example, take a comparison between two groups used as evidence that, in a particular study area, minorities are disproportionately exposed to an environmental risk. Without an adequate design it would not be possible to ensure that differences between groups exposed to high levels of environmental risk and those not so exposed, are attributable to the minority status of the groups and not to some other variable, say, to their proximity to major transportation routes. In general, research design helps to ensure that any differences in the results shown between groups relate to differences in the exposure to the independent variable rather than to differences in the original composition among the groups. The first component, comparison, is an essential aspect of both descriptive and explanatory research in environmental justice. It involves demonstrating the existence of an association or relationship between two variables by establishing covariation between them. Campbell (1957) points out that at a minimum, finding and documenting relationships between variables in a hypothesis test requires at least one formal comparison between observations. In environmental justice research, the comparisons are typically spatial, and the pertinent comparisons are between the levels of environmental risk in minority and disadvantaged areas or neighborhoods and those in others. This creates the demand for selection of different areas to use as a basis from which to make inferences and raises the importance of the selection process. Without comparison, there is no variation, and since the term disproportionate by definition implies something about the variation, without comparison it is utterly impossible to muster any evidence whatsoever of

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disproportionate risks. Without systematic comparison, it is similarly impossible to muster any scientifically acceptable evidence. The second component, manipulation, is involved primarily in explanatory research. It has to do with the researcher systematically altering one of the independent variables while controlling the others, and observing the associated changes in the dependent variable. This is necessary to establish the time order of the events. This component is also at the root of an important distinction between experiments and other types of empirical research such as surveys or analyses of secondary data. Other types of research take the situation as it comes, without altering it. In contrast, the classical experiment manipulates or systematically alters some aspects of the situation, holding constant as many relevant other aspects as possible, to see what changes follow. Normally, especially when attempting to establish a cause and effect relationship, the researcher manipulates the postulated causal variable and systematically observes its effects on the dependent variable. The third component, control, has to do largely with holding “all else equal” to rule out the possibility of spurious relationships between variables. A spurious relationship is an apparent relationship between two variables that disappears when a neglected third variable is taken into consideration. Control requires that other variables be ruled out as rival ways to view the apparent relationship. Such other variables could invalidate the inference that the two main variables of interest—in this case demographic status of residences and the presence of environmental hazards—are related. Concern with control is germane in both descriptive and explanatory research in environmental justice. It is of concern in descriptive research insofar as it is needed to deal with the effects of moderator variables. These are variables that can be expected to change the direction or magnitude of the relationship between any of the relevant socioeconomic variables and the hazard variable. For instance, when race was the only independent variable, Kriesel, Centner, and Keeler (1996) found it was positively related to environmental hazard and statistically significant at p < .01.2 Yet when variables related to income, population density, education, transportation, and amount of industry were included in the models—as they arguably should be on the basis of industrial and residential location theory—income, education, and transportation factors became significant and race dropped out of the picture. Thus, the relationship between race and environmental hazard that, when viewed narrowly, had appeared significant, appeared instead to be illusory from a broader and more inclusive vantage. Control is of concern in explanatory research especially because in the absence of adequate controls an imputed cause and effect relationship is always open to question on the grounds that some uncontrolled variable other than the imputed causal variable is the actual cause. This concern points directly to two essential considerations in policy and administrative decisions. First, considerations about control point directly to differences in the quality of the scientific evidence that can be used in select-


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ing between alternative feasible courses of action. The quality of scientific evidence varies greatly; as a rule more controls provide higher quality evidence. This is of practical importance in policy and administrative decisions because the chances of realizing environmental justice goals and objectives depend largely on the quality of evidence used in the deliberations conducted prior to selecting between alternatives. Second, it points to the justification for taking a cautious and flexible approach to these decisions. It is possible to know more-or-less exactly how much control is necessary only if axiomatic theory is available from which to deduce the full set of variables that must be controlled. In environmental justice there is no such theory, therefore it is not possible to make this determination solely upon scientific grounds. Because the issue of control remains open to some undetermined extent, policy and administrative decisions in environmental justice must be made largely on the basis of proposed interpretations. For this reason, it is important for policy and administrative decision-makers to remain cautious and flexible. Differences in the procedures used in comparison, manipulation, and control enable particular research designs to be classified as experimental, quasi-experimental or pre-experimental. In experimental designs the units of observation are randomly assigned to two groups, experimental and control, and the independent variable is introduced only to the experimental group. Experimental designs, in comparison to all other designs, are superior in terms of isolating the effect of an independent variable on a dependent variable. A major advantage of experiments is that they help to clarify any causal relationships among the variables. In quasi-experimental designs, it is feasible to manipulate the independent variable but not to do random assignment of units of observation to groups. These designs are used in situations in which participants come into the study with some degree of related preexisting group membership, such as race, gender, or income level, that effectively prohibits the researcher from making random assignments to either the control or experimental groups. If, for example, a person entering into a study of the effect of income on residential proximity to environmental hazards makes $75,000–$100,000 per year, it is not feasible to randomly assign him or her to an income category. This inability to randomly assign observational units to groups effectively precludes the possibility of establishing the nonspuriousness of the variables under consideration. Finally, pre-experimental designs include even fewer procedural precautions than quasi-experimental designs. None of them include provisions for either manipulation or random assignment. Cross-sectional designs, the most common in environmental justice studies, are a special form of preexperimental design in which any control is established statistically rather than experimentally, thereby allowing the researcher to quantitatively isolate or purify any magnitude or significance estimates with regard to the relationship of interest. Other, scientifically weaker preexperimental designs, such as single instance case studies, not only do not include provisions for manipulation

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or random assignment, they do not include provisions for comparison either. When all three of these components are missing, the substantive conclusions or empirical statements that can be made justifiably on the basis of logic and the design of the study are severely limited. In this case, the design usually provides little to no credibility for any findings as to whether or not two variables are related, beyond the good name and authority of the researcher. THREATS TO THE VALIDITY OF EMPIRICAL STATEMENTS IN ENVIRONMENTAL JUSTICE In investigating disproportionate exposure to risks in minority and lowincome neighborhoods, cost and ethical considerations tend to preclude manipulation and control, so it is usually not possible to perform classical experiments. Because establishing cause and effect relationships strictly entails demonstrating the time order of occurrences and eliminating spurious relationships, and because these both require classical experimentation, strictly speaking it is not feasible to establish cause and effect relationships writ large in environmental justice research. The rigid structure of formal experiments simply cannot be easily adapted to the relevant situations.3 Since experiments tend not to be feasible, other and logically weaker research designs must therefore be used. Campbell and Stanley (1963) list a set of factors that jeopardize or threaten the validity of inferences and thus limit the knowledge that can be logically obtained from various research designs. At root are two basic questions about the validity of inferences. The first, related to internal validity, is whether two variables are actually related the way they appear to be, and specifically whether changes in the independent variable actually caused changes in the dependent variable in the specific situation studied. For example, is there a causal relationship between the racial status of a particular neighborhood and the subsequent decision to select a nearby site as a location for toxic material releases? The relevant considerations in answering this question focus primarily upon the correspondence between two sets of things in the research, such as the relevant concepts, variables, measurements, or observations. If the correspondence between any two of them is faulty, the conclusions from the research have no meaningful interpretation and cannot successfully meet the demand for establishing intersubjective agreements in the validation process. The second question, related to external validity asks the extent to which the research findings can be generalized to larger populations and applied to different social or political situations. For example, how much, if at all, does a finding of a correlation between the racial status of neighborhoods and toxic releases in Los Angeles (Sadd et al. 1999) tell us about Detroit or Chicago or Miami? The answer to this question is especially important when the objec-


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tive is to make inferences from local or regional empirical studies to national policy. The threats associated with both of these questions will now be discussed briefly, before introducing the various types of empirical research and generally discuss the strengths and weaknesses of each. Each specific threat is meant as a benchmark or guideline for the interpretation of research. Selection Bias. The first threat to validity results from biases in the process of selecting observations to be included in the study. Ideally, it would be possible to draw random samples of observations from representative sampling frames and randomly assign subjects to one or the other of the comparison groups. A sampling frame is an empirical representation of the population of interest from which the sample is taken. Ideally, a sampling frame would include all of the individuals or sampling units of interest in the population. In reality, however, the sampling frame is the complete list of the possible individuals or sampling units that will be included in a study. For example, in a large national study it is ideal to have a listing of all U.S. citizens. But for all practical purposes it is impossible to get such a list. So a representation of them, such as the U.S. Census, serves as the sampling frame. The most important quality of a sampling frame is that it represents the population of interest in all salient dimensions. But in studying environmental justice, it is often not possible either to draw random samples from a suitable sampling frame or to randomly assign subjects to groups. There are, for instance, no readily available sampling frames from which to select randomly observations of environmental risk. Moreover, it is seldom if ever feasible to assign randomly subjects to experimental and control groups. Consequently, selection bias becomes a serious issue. Whenever the observations are not selected randomly, selection bias is a threat to the validity of the research. This is of special concern because randomness is such a stringent criterion for selection, demanding that each observation in the population to which the conclusions are to be generalized must have an identical probability of being selected. Its importance comes logically from recognition that, when observations are not randomly selected, unidentified and perhaps even unrecognized systematic differences can creep into and confound the comparisons. The results of the research might then be biased, because they are attributable to some unknown extent to systematic differences in the units of observation, other than those associated with the variables of interest. Because selection bias can occur without the researcher’s being aware of it, random selection is particularly important. One form of selection bias occurs when the members of a group being studied self-select at least in part because they possess traits or characteristics extraneous to the research problem, but that possibly influence or are otherwise related to the variables in the research problem. For example, selection bias is likely to occur if a researcher investigating acceptable risk studies the

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members of a neighborhood group voluntarily organized to monitor the local performance of environmental regulatory and funding agencies. Such a group’s reports of perceived risk in the neighborhood are apt to be much higher than the reports of disinterested outsiders. This form of selection bias is a threat to the interpretability of the research. The results may appear to be very interesting, but if the bias is not recognized, they may nevertheless be formally meaningless. History. History threatens the validity of an inference whenever an historical event or events other than the independent variable are associated with, and partially determine, the dependent variable. Especially when considering cause and effect relationships, events outside the research can occur between the observations of the cause and those of the effect, and influence the effect. This is a major factor in environmental justice research. For example, say that a researcher is interested in whether or to what extent living near a toxic release site causes an increase in disease among residents. A survey is sent out. The day after the survey is sent, a sensational newspaper story comes out, suggesting that living near the site may have a substantial negative health effect on nearby residents, though no epidemiological evidence to support the claim exists. As a consequence of reading the story, the residents, who have received the survey, begin to attribute more of their health problems to the site. They then fill out the survey. The results of the research thus partially depend on the newspaper story. A few days later an epidemiologist writes a visible and convincing editorial explaining the lack of empirical evidence, and the effects of the original newspaper story dissipate. When history intervenes, inaccurate conclusions can be reached due to an event that influences the dependent variable. Also changes occurring over a period of time in the units of analysis threaten the validity of inferences due to history. In environmental justice this includes changes in the definitions of census tract boundaries over time. The boundaries of a tract in 1980 may not have been the same in 1970 or 1960. The changes in boundaries may heavily influence the associated observed relationships between socioeconomic variables and risk variables, thus confounding empirical changes in the variables and relationships under investigation. Suppose a study is being done to explain the relationship between changes over a period of several years in terms of percent minority and lowincome households and the levels of environmental risk found in a set of neighborhoods. Also suppose that, unknown to the researcher and unrelated to the demographic changes, over the same period of time there is an increase in the level of political organization in several white neighborhoods in the comparison group. If the increase in political organization influences the amount of environmental risk located there over those years, say by preempt-


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ing the location of a hazardous site (Hamilton 1993, 1995), this could lead to erroneous inferences about racial bias in site selection decision processes. In reality, the change would be attributable to political organization, but the results of the study could attribute it to race. When events that influence the level of the dependent variable occur during a time period included in a study, they can threaten the validity of inferences by changing the individuals or units of observation in ways that cannot be meaningfully distinguished from influences in the relationship of interest. Testing Bias. Testing bias is about the effect of taking a test on the scores on a second test. Take, for example, an evaluation of the effectiveness of public relations campaigns on the acceptability of the perceived risk posed by a hazardous branch plant expansion in a minority and low-income neighborhood. A test is administered before and after the campaign in the same neighborhoods. If the process of testing the first time itself changes the perceived risk, say by heightening awareness of the risky situation, the effect on the responses on the second test may be indistinguishable from the effect of the public relations campaign, thus threatening the internal validity of the inferences. Similarly, testing bias can occur when changes in measurements happen as a result of learning that occurs over the period of the study. If, for example, at the beginning of a study period the observers (or people gathering data) fail to recognize the significance of a hazard, and then learn about it over the course of the study, changes in findings can result as a consequence of the learning. Testing bias can be a serious threat particularly in related studies of risk perception or environmental concern. Instrumentation Bias. Narrowly interpreted, instrumentation bias refers to threats to validity attributable to changes in the observers or the measuring instruments that, in turn, produce changes in the obtained measurements. For example, consider the changes in the reporting requirements for the TRI data since the late 1980s. Several hundred chemicals have since been added to the list. Unless the spatial distribution of the additional chemicals is identical with the spatial distribution of the earlier ones, these additional chemicals stand to change findings about disproportionate distributions based on the newer data. Another example is that recent change in procedures for ensuring the accuracy of the TRI data can bias the findings based on the newer data. Still another is that if the observers in a study doing interviews of health problems in minority and low-income neighborhoods expect to find more health problems there, they might spend more time on the interviews there than in the comparison neighborhoods. As a consequence, the data they collect could fulfill their expectations. A final example is that, if a program to decrease environmental risk were to be evaluated by comparing preprogram and postprogram levels of some hazard, any changes in the community risk

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assessment procedures used to measure the risk over the period of the study could bias the findings. Thus instrumentation bias refers to a host of factors that can influence the data gathering processes. Statistical Regression Bias. Statistical regression bias is a threat to validity that can occur in an environmental justice study when it examines extreme cases of environmental risk. Take, for example, a study of the effect of a program designed to decrease the disproportionate concentration of environmental risk, say from lead paint, within a set of communities that are selected at the beginning because they contain extremely high levels of environmental risk. It is possible that the study will show improvement regardless of the program’s effectiveness, simply because chances are that the extreme cases will get lower, not higher, by virtue of statistical regression to the mean. In general, the effects of statistical regression are that situations characterized by extremely high levels of something are likely to get lower, simply by chance, and those by extremely low levels are likely to get higher. Experimental Mortality Bias. This threat refers to differential loss of observational units from each of the groups being compared. Normally it refers to individuals who drop out of the experimental or control group, so that the final sample on which inferences are made is biased. It may be thought of as any dropout problems that prevent the researcher from getting complete information on any of the observations. For example, lots of people moving out of one neighborhood during the study period because the industrial plant that had employed them closed could influence the results. Take a hypothetical study that looks at a set of hazardous sites and the demographic composition of the surrounding areas during two time periods. Say that, for some reason, some of the sites drop out of the study; perhaps they are closed down, remediated, and turned into recreational areas. One way or another, if the sites are included or excluded from either of the groups used to generate the inferences, their removal from the sample can create a threat to the validity of the study. Bias due to Interaction between Selection Biases and the Dependent Variable. Usually selection bias refers to the nonrandom selection of samples. People who volunteer for a study, for instance, usually have strong feelings one way or the other about a subject. In more specific terms of environmental justice, for the sake of brevity the residences in the minority and/or low-income neighborhoods are referred to as those in the experimental group and the nonminority, not low-income neighborhoods against which they are compared as those in the control group. Because the study area was not randomly selected, the relationship between the experimental group and the control group holds only for that unique situation from which the two


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groups were jointly selected. Take the situation in Cuyahoga County, Ohio. The area with the highest concentrations of minority and low-income families includes at least one major jurisdiction, East Cleveland, in which deliberate urban policy decisions made in the 1930s and 1940s explicitly prohibited industrial location. As a consequence, today one finds a somewhat idiosyncratic situation with direct bearing on the relationship between minority and low-income neighborhoods and environmental risk; that is, high concentrations of minority and low-income residences are located precisely where industry is not, making associated environmental risk there low. For example, there are no TRI releases there, because there is no industry. Due largely to these conditions, the relationship between the locations of these neighborhoods and the location of environmental risks in the county is much weaker than it would likely have been had such decisions not been made. In fact, when compared to the relationship between the locations of nonminority neighborhoods and environmental risks, no significant differences are found (Bowen et al. 1995; Haynes, Lall, and Trice 1999). Yet the lack of significance in this comparison is arguably attributable to the more or less unique situation from which the two groups were jointly selected. As a consequence, the external validity (generalizability) of the findings is questionable. Failure to Control Extraneous Factors. One of the threats to the validity of scientific inferences comes from failure to control extraneous factors. Personal behavior, for example, is eschewed among environmental justice policy advocates as a major variable affecting the health of people in minority, low-income communities, or other disadvantaged communities (Foreman 1998). Yet routine behaviors and practices in terms of exercise, smoking, drinking alcohol, eating habits, and getting routine medical care can have a major impact on health, and may in some instances be characteristically different in minority, low-income, or other disadvantaged communities. If in a study of the health effects of environmental chemicals, variables measuring personal behaviors are not included as controls, the effects of the behaviors may be confounded with the effects of the chemicals. As a consequence, inaccurate conclusions about the causes of health problems in minority and lowincome communities can be reached. Threats to Generalizability. Threats to the generalizability (external validity) of research appear to be different in a fundamental way, as compared to all of the previous ones except the interaction between selection biases and the dependent variable. They have dealt with essentially logical concerns centered on the possibility that other variables influencing the dependent variable might be confounded with the particular relationship of interest. They thus admit to essentially logical solutions, through the application of measurement theory, statistics, and probability.

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In contrast, threats to the generalizability of research refer not to essentially logical concerns, but rather to the nature of the test situation itself, specifically to its representativeness or similarity with other situations to which the research results might possibly be applied. The related questions all have to do with how well and to what extent the research design, the observations, and the conclusions drawn from them can be extrapolated and applied to other situations that include a range of variation not specified in the actual test situation. The rule is that if and only if these other situations are sufficiently similar to the particular test situation can one justifiably extrapolate conclusions from the test situation and apply them to the others. On analysis, this rule, of course, raises a question about the meaning of sufficient similarity, an answer to which can be found in a vast literature in psychology and general recognition theory, in which the concept of similarity is rooted. An overview of that literature suggests that the concept is given meaning with reference to the perception or representation of objects, not to their reality (Ashby and Perrin 1988). Thus the threats to the generalizability of research appear to be fundamentally different from the previous ones insofar as they are psychological rather than logical in nature and so cannot be dealt with on the basis of measurement theory, statistics, and probability alone. Take the accepted practice of using proximity to toxic release sites as a proxy for exposure to all environmental risk. On its face the proxy is reasonable, especially given the intuitive connection between proximity to one of these sites and the concept of environmental risk. Nevertheless, since it is scientifically uncertain whether or to what degree proximity to such a site represents exposure to the entire range of environmental hazards in an area, its use in this context may be misleading. As mentioned earlier, the relative magnitudes of other environmental risks, such as those posed by lead paint, secondhand cigarette smoke, or various indoor air pollutants such as formaldehyde and ozone, may be so great as to obscure by comparison any differences in those attributable directly to such proximity. At root is a question of the similarity of the spatial distributions of the risk posed by hazardous sites and the risk posed by other hazards. If the spatial incidence of risk posed by proximity to hazardous sites is similar enough to that posed by the other hazards, it is reasonable to generalize from studies of proximity to hazardous waste sites to the other risks as well. Conversely, if the spatial distribution of the other risks is sufficiently dissimilar to that of risk attributable to proximity to hazardous sites, one cannot reasonably generalize from research based on proximity to these sites and make conclusions about other forms of environmental risk. In either case, determination is more a matter of perception than of reality. ENVIRONMENTAL JUSTICE RESEARCH DESIGNS Several selected research designs will now be described, representing those that, in my judgment, are more likely to be used in environmental justice


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research. The purpose is not so much to be exhaustive of the various design possibilities as to suggest some things about the reasoning behind their characteristic strengths, weaknesses, potentials, and logical limitations. Detailed description of the various designs can be found in Campbell and Stanley (1963), as well as in many good empirical research methods textbooks. The following conventional symbols are used to describe the various research designs: R ≡ Randomly chosen units of observation O ≡ Observation or measurement of the dependent variable X ≡ Presentation of the treatment Presentation of the treatment includes the presence or the presentation of higher levels of the independent variable. Classic Experimental Design Insofar as the goal of an empirical research project is to find and document relationships between variables, and establish explanations based on causeand-effect relationships, the ideal research design is the classic experiment. It is ideal because compared to all other designs its procedures for comparison, manipulation, and control allow the researcher to isolate the effect of an independent variable on the dependent variable. It does a better job because it offers more insulation from the threats to validity. Accordingly, the classic experimental design is, in a sense, the archetype of inferential proof against which all other research designs are ultimately evaluated. When one understands the logic and nature of the classic experiment, one can understand the limitations of all the other research designs. For an illustrative example of the classic experimental design, one of them, known as the “pretest-posttest with control group” design, is structured as follows: R R

O O

X X

O O

(experimental group) (control group)

In this design, comparison is accomplished in a couple of ways. One is by grouping individuals into two basic groups, an experimental group and a control group. These two groups are made effectively equivalent at the outset by the random assignment of all individuals to one or the other of the two groups. For all practical purposes, the random assignment of individuals to these groups achieves control over extraneous systematic variance associated with any potentially confounding variables, recognized or otherwise. After individuals are assigned to groups, manipulation is accomplished by exposing only the experimental group to the treatment. To evaluate the effect of the

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treatment on the dependent variable, the researcher measures the dependent variable for both groups and compares measurements. As a consequence of the random assignments, the only systematic difference expected between the two groups is attributable to the experimental group’s exposure to the treatment. Comparison is also accomplished in this design by taking measurements of the dependent variable before and after the experimental group is exposed to the treatment. Before-and-after comparison enables the researcher to establish the baseline measurement and to demonstrate the time order of the relationship. In other words, it enables the researcher to establish which variable comes first in time. This is necessary particularly in a cause-andeffect relationship in which the cause (treatment) must, according to most thinking, precede the effect (dependent variable) in time. (A critique of this line of reasoning can be found in Simon and Burstein [1985, 437]). Though the logic of experimental designs enables the researcher to make relatively strong and definite inferences, as a rule their use is not feasible in environmental justice. The rigid experimental requirements for random assignment and manipulation cannot be easily adapted to the relevant situations. Moreover, cost and ethical considerations tend to make experiments impractical. In the few aspects of environmental justice in which they are practical (e.g., studies related to perceived risk), they must be done in laboratory conditions that are so unrealistic that they make generalization of the results to policy and administrative decision situations infeasible. Thus, empirical researchers interested in environmental justice tend to use preexperimental, or quasi-experimental designs that are logically weaker, especially in terms of drawing causal inferences, but more appropriate in other ways to applied problems. Pre-experiments Pre-experiments are logically weak siblings to experiments. With few exceptions, pre-experiments tend to be most appropriate when the researcher will be satisfied with relatively uncontrolled observations of variation in the dependent variable, without much concern for formally establishing its source or relationship with other variables. More specifically, they are suitable for use primarily when the researcher is at the beginning of the study of a phenomenon, knows little to nothing about it, and wants to describe and understand it in a general way before making any specific inquires about it (Simon and Burstein 1985, 37). Because as a rule pre-experiments provide for limited comparisons and no manipulation or control, the results depend to an indeterminate extent upon the observer’s interpretation. Therefore, research based on pre-experimental designs is considered to be of little value for the purposes of establishing valid empirical statements about disproportionate exposure to environmental risk. The three pre-experimental designs


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that shall be considered are the case study, the one-group pretest-posttest, and the static-group comparison. Case Study. A case study design is “a form of empirical inquiry that focuses upon a single, bounded social event within its holistic context” (Elkins 1998, 349). The case study researcher goes to the phenomenon fact, event, or circumstance—and observes it within its real-world context rather than formally abstracting from the context in which it normally appears. The essential feature is that a single group is studied only once, without manipulation or controls. If comparisons are made, they are assumed or imagined. The case study researcher often goes out and makes specific observations and tediously collects specific details about his or her interpretation of this single instance or, at times, a group of such instances. In any case, the situation or situations studied are selected to represent the occurrence of the phenomena of interest. For example, they may represent an instance of supposed disproportionate exposure to environmental risk in a minority or low-income neighborhood. Although the ability to study a phenomenon in its holistic context can be advantageous in terms of realism, single-instance case studies are notoriously weak in a few ways that normally preclude them from yielding valid conclusions about the relationships between variables. First, in return for the ability to make observations in as noncontrived a setting as possible, single-instance case study design precludes manipulation, making it impossible to test for the time ordering of the relevant events. Second, it requires the researcher to relinquish essentially all explicit, formal control of extraneous systematic variance in the independent variables. Any control over the influence of confounding and prior variables in the situation is therefore implicit and informal and is primarily left to the unaided psychological capacities of the researcher. In essence, in relinquishing formal controls, the researcher is left to his or her unaided psychological ability to interpret any observations and document all relevant covariation between variables actually present in the case, without detecting and recording any that is not there. For the purposes of science, this demand is deeply problematic because there is no way to determine how much of what is observed is itself determined by the observer’s background, beliefs, and expectations. Third, single-instance case study design requires the researcher to relinquish the possibility of making any formal comparisons. The fact that single-instance case study designs preclude the possibility of making formal comparisons is of special concern in light of the vulnerability of case study research to researcher bias. Different people are likely to perceive one or more variables in the situation differently, depending on contextual differences associated with factors such as the age, race, and gender of the researcher. The researcher’s unconscious biases and prior disposition can

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also consciously or otherwise come into play, due to level of native intelligence, education, and moral development. Furthermore, temporary conditions such as disease, emotional distress, poor lighting, and high levels of noise may alter the researcher’s perception, bringing about misleading implicit comparisons. Campbell and Stanley (1963) put it this way: Basic to scientific evidence . . . is the process of comparison, of recording differences, or of contrast. Any appearance of absolute knowledge, or intrinsic knowledge about singular isolated objects is found to be illusory upon analysis. Securing scientific evidence involves making at least one comparison. For such a comparison to be useful, both sides of the comparison should be made with similar care and precision. (6)

In other words, the absence of any formal basis of comparison, to implicitly compare a single instance with other more casually observed, remembered, or imagined situations, or even other cases, can preclude scientific validity. Moreover, when the limitations of single-instance case studies are ignored, the findings can be much less precise than claimed.4 Although multiple case studies can potentially overcome some of the limitations of single-instance case studies (Yin 1994), they are still logically weak in terms of providing evidential support for general empirical statements about disproportionate exposures. Even multiple case studies rely much more heavily on the researcher’s interpretation of the situations being studied than do experimental and quasi-experimental research. In turn, since interpretation always represents an elaboration of a set of foundational assumptions that may or may not be a part of the researcher’s articulate repertoire, the resulting indetermination can easily become a limitation on the scientific validity of the findings. Given limited comparisons and the absence of manipulation or control, case study designs (indeed all pre-experimental designs) make it infeasible to systematically separate out the effect of the observer’s assumptions from the reported observations. In general, formally, conclusions from case studies are threatened by selection bias, history, mortality bias, and bias due to the interaction between selection bias and the dependent variable. Nor do they have notable strength in terms of testing biases, instrumentation biases, or statistical regression biases. They are notably weak in terms of generalizing beyond theory, and they can be used only to generalize to theory from which expected observations can be formally deduced. Thus, for the purpose of making any sort of inferences about disproportionate exposure in specific situations, much less national level patterns, the value of case studies is highly limited. Rather, their primary value stems from the fact that environmental justice is a new area of inquiry about which relatively little is known empirically, and they can help to describe and understand the problem in a general way before


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making any specific social scientific inquires about it. They can also be used illustratively to garner insight into the human details of specific situations. But in the end, because of their logical limitations, they have little, if any, scientific value in terms of establishing disproportionate exposure to risk. One-Group Pretest-Posttest. A second pre-experimental design is the one-group pretest-posttest design. One-group pretest-posttest designs can be depicted as follows: O

X

O

In this design, the group is measured on the dependent variable before being introduced to the treatment, and the group is measured again on the dependent variable after the treatment. The essential characteristic is that after the occurrence of the treatment, a group is compared with itself, as it was before the occurrence of the treatment. For example, in research based on a onegroup pretest-posttest design, health indicators might be measured in a particular minority community before and after a toxic release site was located there. One-group pretest-posttest designs are not logically strong, because they include no control group. Consequently there is no way to tell whether selection bias, history, instrumentation bias, statistical regression bias, extraneous factors, or threats to generalizability have affected the dependent variable. Although a number of factors other than the independent variable may have contributed to the change in the dependent variable in this design, Campbell and Stanley (1963, 7) state that the design is “enough better than the [oneshot case study] to be worth doing when nothing better can be done.” Static-Group Comparison. A third pre-experimental design is the static-group comparison. Research based on this design uses observations of two groups, one of which is exposed to the treatment and the other not. The design can be generally depicted as follows: X X

O O

In essence, because only one of the two groups is exposed to the treatment, observations of the dependent variable are made for both groups, and then the two are compared. Any observed differences between the two groups are taken to be attributable to the effect of the treatment. The inclusion of a comparison group, albeit an informal one, is advantageous in that it controls for history, testing bias, instrumentation bias, and statistical regression bias.

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This design has a comparison group used as a control, in the sense that some of the observational units are not exposed to the independent variable. This is a weak sense of control, however, since the comparison group is merely assumed to be similar to the experimental group. The observational units are not assigned to the groups at random so the assumption that the two groups are similar remains doubtful. The fact that randomization is not used implies the logical possibility that other uncontrolled variables could effectuate change in the dependent variable. In more formal terms, because the control group is not based on randomization, the design does not deal satisfactorily with selection bias, extraneous factors, or threats to generalizability. Quasi-Experiments There are trade-offs involved in selecting research designs, largely attributable to the world’s intransigencies. On the one hand, experimental designs cannot be used unless the researcher is able to manipulate the independent variable and randomly assign observational units to either a control or an experimental group. Thus, as has been noted, though experiments are logically the strongest form of research design, they are not often used in research aimed at influencing public policy, primarily because their rigid structure cannot be easily adapted to the relevant problems. Also, experiments are often impractical because they tend to be done in laboratory conditions that are so unrealistic that they limit the generalizability of the results. On the other hand, pre-experimental designs are feasible in many real-world situations but do not allow valid general empirical statements about relationships between variables. Besides, they generally have little, if any, scientific value unless the details of specific observations can be deduced from axiomatic theory, which is largely unavailable in social science. Therefore, other designs are needed that, although logically weaker than experimental designs, are stronger than pre-experimental designs and yet can be used in applied, realworld settings. This gap is filled by quasi-experiments. They include as many features of experimental designs as possible, without creating the need to enter into the laboratory. Contrasting Group. The contrasting group design is used when the researcher cannot randomly assign individuals or other units of observation to groups but can nevertheless classify them into groups or categories that help answer the research question. For instance, this design might be very useful in environmental justice if it were possible to meaningfully categorize people as belonging to various racial groups to be compared. The design can be depicted as follows:


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O1 O2 O3 . . . Ok Differences in measurements of the dependent variable between groups, such as differences in levels of exposure to environmental hazards, are amenable to relatively straightforward statistical analysis, through analysis of variance (ANOVA). However, because membership in the groups is not decided on the basis of randomization, there are corresponding limitations that arise in terms of determining the causes of any observed differences between them. The relationship between the category into which an individual is assigned and the value of the dependent variable may be spurious. For instance, groups may be racial groups, which raises all of the issues of definition and data collection introduced in Chapter 3. Even if the groups are taken as given and differences are shown between them in terms of exposure to an environmental hazard, there is no basis in this design from which to determine whether the observed differences are directly attributable to race (or to some other variable corresponding to race that determines the level of exposure). One way to increase the confidence one can place in the results of a contrasting group design is to gather supplementary evidence. If the same difference between groups is found time and time again in various settings, one can place increasing confidence in the relationship stipulated between the criteria used to define the groups on one hand, and the levels of the dependent variable on the other. To follow through with the racial group example, if in independent and repeated contrasted group tests a hypothesized relationship is seen between race and the level of exposure to environmental hazards, and it is found to be supported by the data, this increases the confirmation that a relationship between the two in fact exists. However, it still does not justify the inference that race is the causal variable. Time Series. Another potentially relevant set of quasi-experimental designs in environmental justice is time series designs. In time series, the researcher makes multiple observations of the dependent variable before and after the introduction of the independent variable. In most social science applications of this design, the independent variable, which is often thought of as a causal variable, is not introduced or otherwise manipulated by the

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researcher. Rather it occurs on its own as a consequence of an action taken by some social agency. A typical time series design can be represented as follows: O1

O2

O3

O4

X

O5

O6

. . . Ok

For example, if a researcher hypothesizes that the presence of a hazardous chemical release site is affecting the rate of a disease in a neighborhood, a time series design might be useful as a means of marshalling evidence, even in the absence of exposure data. In this case, O values reflect observations of the disease rate at various points in time, and X represents the time at which the site was established. If the researcher has historical data on the relevant disease rate from that neighborhood prior to and after the time at which the site was located, and if the disease rate is higher afterwards, this indicates— but does not demonstrate—that the site is the cause of the change. Note that the group is compared to itself repeatedly, over time. Campbell and Stanley (1963) suggest that history is the most serious problem with this design. The idea is that it may not have been X that produced the change in the dependent variable, but rather some other contemporaneous event or combination of events that occurred during the experimental period. To some extent this can be accounted for through modifications of the time series design, such as the addition of control groups, further time observations, and further introductions of X. In any case, one of the major obstacles with time series designs has to do with the troublesome difficulties of the statistical analysis of time measures. One of the major reasons for these difficulties is that time series data tend to move around quite a bit, and it is easy to interpret changes not due to the independent variable as due to it. Moreover, especially because the fact that individual observations in time series data are not independent of one another, as most statistical analyses assume them to be, the usual tests of statistical significance applied to time series can yield highly misleading results. For these reasons, the researcher who employs a time series design should make a special point of learning the nuances of time series analysis and should consult a statistician. Nonequivalent Control Group. Finally, the nonequivalent control group design is similar to the experimental pretest-posttest control group design, except that there is no random assignment of individuals or observational units to groups. It can be depicted as follows: O O

X X

O O

A comparison group is used, but because there is no random assignment, the groups are not necessarily equivalent before the independent variable is intro-


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duced. In this case, the assignment of individuals or units of observations to groups depends on an available categorization scheme, and the introduction of X to one group or the other is not assumed to be the result of a random selection. Of course, the more similar the two groups are before the introduction of X, the stronger the evidence is. Assessment of Research Designs in Environmental Justice It might be tempting to infer from this discussion that pre-experimental designs are worse or that experimental research is better or to make other generalizations about the relative merits of the various designs. In my experience, such statements are almost always so oversimplified as to be substantially misleading. The key to a meritorious proposition or defensible inference from empirical research is not anything inherent in the research design, but whether the design matches the research question, in the sense that whatever design is used allows the researcher to answer the research question by adequately testing the hypothesis. It is not that in general some designs are poor and others are better but that, with respect to particular research questions, some designs are adequate and others are not. All of the various designs have their time and place, depending on the research question being asked. The key is to select one that adequately answers the research question. Two essential problems involved with assessing research designs have been mentioned. One is the matter of internal validity, or establishing cause; this requires adequate comparison, manipulation, and control. The other is the matter of external validity, or generalizing the findings beyond the particular research situation. When the objective is to establish national-level patterns from local- or regional-level studies, or to make general statements about the distribution of all environmental risks based on study of a couple selected from them, the issue of generalizability becomes paramount. Accordingly, one general statement can be made comparing the various designs in terms of these two problems: as a rule, these two problems together pose an intractable dilemma—to improve the ability to establish cause and effect relationships requires sacrifice of generalizability. Because experiments tend to be strong in terms of comparison, manipulation, and control, they are superior to all the other designs for establishing cause and effect relationships. However, experiments are weak insofar as their rigid structure dictates that they be done in laboratories, where it is impossible to replicate reality; so the results of experiments often cannot be convincingly generalized to real-world settings. Also, experiments are of limited value in real-world studies because they examine only a couple variables at a time and keep the other extraneous variables constant. In many instances, quasi-experimental designs offer a suitable resolution of the dilemma, each in its own way mixing the advantages and disadvantages of experiments and pre-experiments.

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Past this point, in terms of making improved policy and administrative decisions, perhaps the most serviceable approach is for the decision-maker to have balanced and reasonable familiarity with the strengths, weaknesses, potentials, and limitations of the various categories and individual designs. This way, he or she can rationally assess the strength and applicability of the evidence. Especially in a politically charged area such as environmental justice, the evidence from empirical research is often used instrumentally, say by policy advocates, as a means of persuasion toward predetermined political ends, without regard to whether the supposed evidence is well supported by the underlying research. The decision-maker’s challenge, therefore, is to know and understand enough about research design to be able to recognize and consciously consider in an informed and reasonable way what can and cannot be claimed about the decision situation, in light of the empirical evidence from research. A RECOMMENDED PROCEDURE FOR SELECTION OF COMPARISON REGIONS The outcome of the selection of comparison regions process is identification of areas to compare, and, as mentioned earlier, it can in large part determine the inferences that are made from a study (Anderson, Anderton, and Oakes 1994; Anderton 1996; Zimmerman 1993). Take, for example, the hypothetical values given in Table 5.1. The top half of the table presents Ohio as a comparison region and the bottom half, Cleveland as the comparison region. Accordingly, a general statement of the concern with selecting comparison regions can be precisely given using the language of probability. Let’s define event A as being a minority, low-income, or otherwise disadvantaged person (with probability P[A]); A′ as not being such a person (with probability P[1 – A]); event B as the condition of living in an environmentally degraded area and being exposed to higher levels of hazards (with probability P[B]); and B′ as not being so exposed. The tabulated numbers of people living near the risky sites are fictitious representations of the number of people in Cleveland who live within, say, a mile of a set of hazardous sites. The same numbers of people living near the sites appear in both the top and bottom halves of the table, since such numbers are a constant in any given study, determined by the particular site and demographic features of the study region. The tabulated numbers for the total and minority populations in Cleveland and Ohio are approximate, based on the 1990 Census and its updates. The purpose of the table is not to allow inferences about the actual numbers of people living near sites in Cleveland, but to illustrate the effect of selecting a comparison region. The concern with disproportionate distributions can be formally stated as P(B | A) > P(B | A′). But whether or


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Table 5.1. Possibly misleading effects attributable to the inappropriate selection of a comparison region B Risky site areas

B′ Not risky site areas

Total

2,500

1,340,000

1,342,500

Ohio A Minority A′ Nonminority

2,500

9,813,000

9,815,500

Total

5,000

11,153,000

11,580,000

Cleveland Metropolitan Area A Minority

2,500

250,000

252,500

A′ Nonminority

2,500

250,000

252,500

Total

5,000

500,000

505,000

not one thus finds disproportionate distributions can depend crucially on the comparison region against which the relevant probabilities are estimated. Specifically, given the numbers in Table 5.1 and using Ohio as a comparison region, P(B | A) = .0018622, and P(B | A′) = .0002547. Because P(B | A) = 7.311 * P(B | A′), the conclusion would be that this is a clear instance of disproportionate distributions. But when Cleveland is used as a comparison region, P(B | A) = .00998, and P(B | A′) = .00998. In this case, although the only difference is the comparison region, because P(B | A) = P(B | A′), the conclusion would be that there are no disproportionate distributions present. The number of people living near the sites did not change at all, but the conclusions drawn about them changed significantly. Thus the selection of comparison regions can heavily influence the findings and should not be done arbitrarily. Rather, it should be done in a way that follows well-reasoned conceptual and statistical guidelines. Accordingly, the appropriate statistical principle for selecting a comparison region is to select one that maximally controls any systematic variance in the dependent variable attributable to extraneous or unwanted independent variables that may have an effect on it. Because the dependent variable in environmental justice studies tends to be a proxy for exposure to environmental hazards, one way to do this is to select study areas that are as homogeneous as possible with respect to any prior or confounding variables that may have influenced the levels of exposure. In conceptual terms, perhaps foremost among these are variables that reflect the industrial composition of locations. Accordingly, for

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any given site the standard region used to establish P(A) should be approximately equal to the size of the labor market within which the site is located. As a rule, this is equal to the size of the county or the metropolitan area within which the site is located. This region also will be large enough to enable the researcher to include plenty of variation in any hypothesized causal variables, such as race or income, that may have affected site location.

PART II

Empirical Foundations of Environmental Justice

CHAPTER 6

A Critical Review of the Empirical Research Literature So my answer to the questions “How do you know? What is the source or the basis of your assertion? What observations led you to it?” Would be: “I do not know: my assertion was merely a guess. Never mind the source . . . If you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can. —KARL POPPER, Conjectures and Refutations

In the preceding chapters I spelled out the significance of having an empirical research foundation in terms of framing policy and administrative decision problems. I also discussed the central terms and concepts used in deliberating alternative feasible actions to solve them, and familiarized the reader with pertinent principles of empirical research method and design. Much of the uncertainty about the empirical foundations of environmental justice is attributable to the failure to fully conceptualize the hypothesis of disproportionate distributions and their public health effects in light of the full range of these considerations. The underlying idea, especially in Chapters 4 and 5, is that, without an adequate prior understanding of these principles, policy and administrative decision-makers cannot get a proper perspective on the existing body of related empirical research. They do not have enough understanding of basic scientific method to critically review the research literature and therefore are apt to attribute a mistaken level of concreteness to claims that geographical patterns of disproportionate distributions have been empirically demonstrated. When such mistakes are made by policymakers and administrators in the decision-making process, they are likely to contribute to further failure to solve related problems. A research-based approach to policy and administrative decision-making provides a partial remedy for such underconceptualization, thus helping to create success at meeting related goals and objectives. In this chapter, I take the argument somewhat deeper into a discussion of some particulars of the underconceptualization of this foundation, by presenting a critical yet insofar as possible impartial review of the relevant body of empirical research literature. I focus specifically on the research testing the hypothesis that disproportionate distributions of environmental hazards are found in minority, low-income, and other disadvantaged neighborhoods. This body of research forms the empirical foundation from which statements used 131

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to define the problem of environmental justice are often taken. A major goal of this chapter is to articulate fundamental research issues—points of scientific research method and design such as those discussed generally in the previous chapters—as they relate specifically to individual research studies that are a part of this body. I hope that this will make some of the main points of uncertainty more explicit and easy to articulate, as well as open up discussion of the research to ongoing logical analysis and critique instead of shielding erroneous research decisions from view. In Chapter 2, I spelled out the foundational assumptions of my perspective on research-based decision-making for environmental justice. They imply that scientific researchers are fallible. This fallibility necessitates the community of scholars to act now and forever to evaluate and reevaluate conclusions stemming from scientific inquiry. VALIDITY AND THE PEER REVIEW PROCESS Perhaps it is ironic, given the common portrayal of scientific validity as consisting in the individual scientist’s impartiality or objectivity, that properly understood it is integrally connected with the social aspects of scientific methods, in particular with expert judgment.1 To characterize it, instead, as consisting in substantial agreement within the scientific community is accurate and has a wide range of applicability, so far as it goes. However, unless more is said there is always the risk of ignoring certain important and implicit aspects of the validation process, thus oversimplifying the matter in a way that could easily lead to the ascription of an unwarranted level of certainty to existing empirical research results. More specifically, scientific validity is established through the peer review process, essentially through cooperative criticism among specialists trained in scientific research methods who have significant background knowledge related to the substantive area of the research. The peer review process is typified by the process conducted by the National Science Foundation (NSF) in determining what research proposals to fund. In it the NSF first carefully seeks out and selects qualified and reputable reviewers and then sends the proposals to them for review. The reviewers are asked to provide detailed critical comments with respect to the merit of the proposed research. Two explicit primary criteria are used. The first is stated, “What is the intellectual merit of the proposed activity?” To judge the proposal on this criterion, the reviewer is asked specifically to answer several supporting questions. How important is the proposed activity to advancing knowledge and understanding? How well qualified is the researcher? To what extent does the proposed activity suggest and explore creative and original concepts? How well conceived and organized is the proposed activity? Is there sufficient access to the necessary resources? The second is stated, “What are the broader impacts of the proposed activity?”

A Critical Review of the Empirical Research Literature

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While similar criteria and questions are typically asked in the peer review process prior to the publication of an article in refereed journals throughout the entire range of scholarly fields, not all such journals have the same standards. Some are more literary in orientation, some more philosophical, some more ideological, and others are more scientific and empirical. Some are oriented more toward practitioners, some toward more purely mathematical and abstract thinking. Moreover, there is a great deal of variation in the stringency of the standards demanded by different peer-review journals within these and other categories. (A journal’s standards can be gauged roughly by its rejection rate.) Thus the fact that research findings are published in a peer-reviewed journal does not necessarily imply that they meet reasonable standards of science. Indeed, highly reputable peer-reviewed social science, administration, and management journals that insist upon such standards are relatively few. In any case, though the peer review process is arguably the best of all feasible ways to ensure that the answers to research questions are “good” ones, it is nevertheless imperfect. This is aptly illustrated by the persecution of Galileo Galilei for grave suspicion of heresy (Butterfield 1957). His persecution resulted from his publication of empirical evidence supporting the Copernican as opposed to the Ptolemaic hypothesis in relation to the physics of tides. It illustrates the point that, empirical statements that pass through the peer review process and so are considered to be well established or even certain, sometimes turn out to be recognized as mistaken and in need of correction. Of course, given the view of science as a product of the human intellect, this imperfection is no surprise. Scientific validity is a product of human social activity, and since all human activity is subject to the vagaries and biases of human fallibility, the peer review process is not exempt from error. While the fallibility of the peer review process precludes the possibility that scientific validation can provide absolute certainty on any question, specifically including those related to disproportionate distributions, one finds a bright prospect stemming from the possibility of scientific advancements associated with the discovery of error; that is, scientific knowledge advances through the discovery of error. For example, it is now virtually certain that Ptolemy was in error and Galileo’s conclusions about the physics of tides were correct. Indeed, the peer review process, properly understood, is based on the insight that scientific knowledge advances most quickly when mistakes in epistemology and method are systematically sought and found. Criticism, it seems, is the only way humans have of detecting and learning from our mistakes in a systematic way. In the peer review process, regardless of what standards are applied, the reviewer’s obligation is to provide critical, impersonal, and (insofar as is possible) impartial judgments of the intellectual merits of the research. The following discussion provides such a review of the empirical research on

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environmental justice, with an eye toward reasonable standards of science as outlined Chapters 4 and 5. Of course, complete impartiality is not possible, so it is important to state explicitly one’s assumptions and biases up front as done in Chapter 2. While personally sympathetic with the spirit of many of the claims of the environmental justice policy advocates, I make a continual and conscious effort throughout to avoid any partiality other than insisting on sound empirical research methods. Thus, some articles that are being taken seriously in the realm of policy but that have serious conceptual and statistical flaws are sharply criticized. The limitations and weaknesses of some articles based on anecdotes or case studies, which therefore do not lend themselves logically to establishing valid relationships between race or income and environmental risk, are pointed out. So are the limitations and weaknesses of research based on simple correlation analyses that fail to hold important considerations constant. It will become clear, that in a couple instances, later research partially or completely repudiated some of the earlier findings. Furthermore, while some research utilized ostensibly systematic analytic procedures, it nevertheless could not meet reasonable standards of scientific scrutiny. Criticisms are offered in the spirit of the scientific attitude of free and respectful criticism, in the belief that they are necessary for the advancement of knowledge about the factual components of environmental justice. Finally, while the following does not represent an exhaustive literature review, it attempts to cover most of the significant research available at this time. A concerted effort has been made to be comprehensive in identifying the relevant literature, but there are too many research projects already conducted, and too many new ones being published, to presume to have found and included them all. The specific criteria and principles for the criticisms, again insofar as possible, either have been spelled out in previous chapters or will be provided. The approach is to go through the pertinent articles one by one in moreor-less chronological order, generally sorted by decade, quality, and year, starting with lower quality and earlier years. A more ideal approach would have been to conduct a formal meta-analysis of the existing studies, however, for reasons discussed earlier, there is far too little consistency in the design and methods among the existing research for this to occur and yield meaningful, cognitively significant conclusions. Each article is therefore reviewed in summary fashion, following a few conventions. First, each study is summarized descriptively, with a minimum of evaluation. Second, a critique of it is provided, in light of the principles of empirical research methods covered in the previous chapters. Third, contribution of the study to scientific knowledge and understanding of environmental justice is summarily assessed with stars that roughly correspond to the categories introduced in Chapter 4. One star (*) indicates that the study design and methods have enough substantial flaws to be judged useless in terms of contributing anything to scientific knowledge


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and that the study conclusions should therefore not be considered as having any merit whatsoever for public and administrative decisions. Two stars (**) indicate that the research was not designed, conducted, or documented well enough to know with confidence whether it contains any scientifically meritorious conclusions, and so should be considered purely conjectural. Three stars (***) indicate that the data, design, and methods are likely to lead to approximately accurate findings but have enough notable flaws to conclude only that the research may have some scientific merit. Four stars (****) indicate that the research is sufficiently well designed and documented to be likely substantially accurate and to have scientific merit up to the limits pointed out in the comments. I could recommend considering research with four stars as having at least the minimum level of scientific merit to use as input into policy and administrative decisions; however I would not recommend generalizing the findings outside the particular situation in which the research was conducted. Five stars (*****) indicate that, by virtue of its design, method, and documentation, the research can be recognized as substantially accurate and can furthermore be meaningfully generalized and applied to broad policy and administrative decisions. Research with five stars should accordingly be considered as essential to informed policy and administrative decision-making related to broad geographical patterns of disproportionate distributions. After all the studies have been reviewed, a brief summary statement of the current state of related scientific knowledge is provided. THE EARLY EMPIRICAL RESEARCH: THE 1970S The earliest research on the hypothesis of disproportionately distributed hazards in the 1970s more tended to be conducted by social scientists than policy advocates. At the same time, since environmental justice had not yet become a major legitimate political issue, the research methods tended to be simpler, more suitable for making suggestions about and providing limited insights into the relationship between environmental risks and socioeconomic variables. They were not, as a rule, designed to withstand the sort of scientific scrutiny that would later be required as a result of the political movement and its demands for weighty national-level resource allocation decisions. Freeman (1972).*** In this groundbreaking article, Freeman set out to “find out what can be said now about the patterns of distribution of benefits and costs associated with environmental pollution and pollution abatement programs” (243). To this end, he explicitly included two aspects, one conceptual-theoretical and the other observational-empirical. In the conceptualtheoretical piece, he proposed that environmental quality considerations, including environmental risk, could be integrated into the framework of the conventional theory of individual choice and welfare by considering them to

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be final products that enter directly into individual utility functions. This led him theoretically to the expectation that environmental quality will be positively associated with wealth or money income. In the empirical piece, he examined census tract data for 1960 on income, race, and housing tenure from Washington, D.C., St. Louis, and Kansas City in an attempt to determine whether the theoretically expected pattern of air pollution was in fact observed. These variables were cross-tabulated with indices of annual average pollution exposure per family, specifically suspended particulates and rates of sulfation. Simple bivariate cross-tabs clearly confirmed the theoretically predicted systematic inverse relationship between pollution exposure and income (as well as race). Freeman argued that since the distribution of environmental quality was theoretically the result of interaction between the distribution of wealth and income and market forces, the most effective way to improve the distribution of environmental quality was to improve the basic distribution of wealth. A couple points can and should be made about this article. First, solidly based on theory, the inferences and policy suggestions fit coherently within a larger, established body of knowledge. In this regard, had subsequent research about environmental justice been guided by Freeman’s example, the current body of knowledge would far more successfully stand up to a reasonable degree of scientific scrutiny. Second, as Freeman noted, one must avoid making hasty generalizations on the basis of this evidence. Only two pollutants out of a range of thousands of chemicals emitted into the environment were examined. Pollutant levels at various locations within the three cities studied were indicated by indices that did not in any way equate with exposure. Again, only three cities out of hundreds in the country were examined. The data used in the study obscured variation within socioeconomic variables within census tracts as well as variation in exposure within income and social groups, such as could be associated with occupational differences. Finally, in no way do the observed relationships establish any sort of causation. Asch and Seneca (1978).*** This was one of the earliest research articles and is often cited in the literature. The research examined exposure to air pollution, particularly the question of whether it is related to socioeconomic variables. Two levels of analysis were conducted. The first was based upon a nonrandom sample of 284 cities from 23 states. Using this sample, levels of particulate matter in the air were examined using simple bivariate correlation with socioeconomic variables within each state. The results indicated that exposure to particulate matter in the air was relatively higher in cities with low-income characteristics, and that as a rule, low-income families were exposed to poorer air quality conditions. A statistically significant correlation with race was not found at this level (cities), but the authors argued that aggregation to the city level was too gross. So they investigated intracity vari-


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ation at the census tract level for particulate matter, sulfur dioxide, and nitrogen dioxide in Chicago, Cleveland, and Nashville. Accordingly, income consistently showed an inverse relationship to levels of all three pollutants in all three cities. The results related to percent nonwhite were inconsistent, ranging from a positive relationship between percent nonwhite for particulate matter and sulfur dioxide in Chicago, to a negative relationship for particulate matter and nitrogen dioxide in Cleveland. Indices of exposure followed a similar pattern. None of the samples were drawn randomly, and only a few cities and pollutants were looked at, therefore it is at best difficult to determine the range of cities and types of pollution to which the results can reasonably be generalized. Moreover, since all of the reported correlation coefficients were of a simple bivariate type, the possibility of spurious relationships between variables remained a serious threat to validity. In consideration of these limitations and in light of the range of results, it is difficult to draw any clear-cut general conclusions of a substantive nature that might be of meaningful use in a policy or administrative decision related to environmental justice. This is not to say that the research is poor; indeed it meets reasonably high standards of validity. But if any summary conclusion were to be drawn from it, it would be that family income tends to be related to air pollution and that the evidence on race was inconsistent and inconclusive. Gianessi, Peskin, and Wolff (1979).*** The overall goal of this research was to determine whether an attempt to apply the same air pollution policy (Clean Air Act of 1970) uniformly throughout the United States would differentially impact populations in particular income groups or geographical areas. The geographical level of analysis was census county groups and standard metropolitan statistical areas (SMSAs) throughout the United States. The costs and benefits of the policy to each area were estimated on the basis of data reflecting the population, industrial structure, levels of air pollution emissions, and amounts of damage aggregated by racial group and income class. Assuming that the policies were fully implemented as designed, and that all establishments in an industry had essentially the same technical response to the provisions of the policy, the results indicated that those who would get the largest net gain were lower income and nonwhite groups. Given the research goals and the limitations on the available data, this research was remarkably well done. The methods of analysis were clearly articulated and justified. The results were presented and interpreted prudently and with apparent accuracy. The bearing of the research on environmental justice policy or administrative decisions, specifically as it relates to disproportionate exposure to environmental risk and associated health impacts is, however, at best vague and indirect. While it suggested that minorities and low-income populations would benefit disproportionately from uniformly

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applied national-level air pollution policy, this pertained at a much larger level of spatial aggregation than is relevant in terms of concern over disproportionate exposure to environmental hazards among minorities, lowincome, and other susceptible populations. Moreover, the relevant costs and benefits were estimated without regard to the impacts of environmental risk on community health in particular, which again limits the relevance of the research within the context of the current debate. THE POLITICAL MOVEMENT GAINS MOMENTUM: RESEARCH IN THE 1980S The research in the 1980s was far more advocacy oriented. All of it, for one reason or another, was of fairly low quality in terms of providing a scientifically valid foundation for describing related policy and administrative decision problems. Due to limitations or flaws of design, method, or documentation, all are judged to be either purely conjectural or plainly false but convenient. While the research helped to propel environmental justice onto the national policy agenda, it contributed little in terms of scientifically acceptable empirical knowledge about the relationships of interest. Bullard (1983).** This was an early and politically influential case study of Houston’s solid waste disposal system, using quantitative data on solid waste sites and demographics in their vicinity. Its stated goal was to determine whether black Houston residents were more likely to live near waste disposal sites than their nonblack Houston counterparts. The conclusion, which was stated in very general if not universal terms related to patterns of disproportionate exposure affecting entire groups of people and social trends far beyond Houston, was affirmative. Comparisons of the percentage of black and nonblack residences in the neighborhoods in the vicinity of the sites reportedly indicated that although blacks made up only 28% of the Houston population in 1980, 6 of the 8 incinerators and 15 of 17 landfills were in predominately black neighborhoods. These findings were established by identifying 25 hazardous sites and then collecting and tabulating data on percentage black in their vicinity. Had Been (1994) not attempted to carefully recreate and extend this research to determine whether the host locations were disproportionately minority or poor at the time the sites were selected, this research would appear to have somewhat greater scientific merit. However, given that (according to Been) some of the sites referred to by Bullard were selected as long ago as 1920 and had ceased operation by the 1970s, and that some of the sites in the research were double counted, the scientific foundations of the study completely lack credibility, much less validity. Specifically, once the sites that were no longer operative or hazardous were removed, and double-


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counted sites were removed, 10 rather than the 25 sites upon which Bullard based his conclusions remained in the data set. Past this point, Bullard’s research provided no description of how the neighborhoods surrounding the sites were defined, making replication impossible. Nor were any cross-sectional controls included for income, industrial composition, proximity to transportation routes, or other potentially relevant factors. For these reasons, in combination with the fact that this was a case study of Houston—which bears all the threats to validity and limitations associated with case studies— this research had little to no scientific merit. It should be pointed out that this study was and remains very influential politically. It is often cited as proof of environmental racism. Moreover, any critique of research design and implementation notwithstanding, the statement that black Houston residents are more likely to live near waste disposal sites than nonblacks may well be more-or-less accurate. If so, one cannot tell on the basis of this research. The only sound conclusion one can reach on the basis of the research design and methods is that this study does not constitute scientifically credible evidence one way or the other, because the data, design, and implementation do not pass even relatively minimal scientific standards of scrutiny. General Accounting Office (GAO, 1983).** This research is probably best categorized as measurement research, although in some respects it is a fairly crude form of it. It is similar to case study description in that there are no formal comparisons with other areas, no manipulations, and no controls, and to this extent it has all the same threats to validity. At the same time, it differs from case study description by focusing more explicitly on a couple of dimensions of the situations and by measuring them systematically and in great detail, using numbers, rather than measuring in a rough and ad hoc way. At the suggestion of a congressman advocating for civil rights, the researchers arbitrarily selected a convenience sample of four large offsite hazardous waste landfills in eight states in the southeastern United States and described the racial and ethnic data for census tracts within 4 miles of each. For three of the four sites, the majority of the residents in the nearby census tracts were black. More specifically, in three of the four communities studied, blacks made up 52%, 66%, and 90% of the nearby population. In contrast, blacks made up between 22% and 30% of the host states’ overall populations. Moreover, between 26% and 42% of the population in the vicinity of the four sites had income below the poverty level, while the host state figures ranged from 14% to 19%. This research was apparently well done but did not go anywhere near far enough to constitute any sort of scientific contribution that would withstand enough reasonable scrutiny for the conclusions to be used descriptively for related policy and administrative decisions beyond these four sites. It did not

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attempt to establish why these particular locations were selected for the sites, the population mix of the area when the site was established, nor how the communities’ racial and economic status compared at the time of the siting decision to others in the respective states. While it did provide a believable observation or a snapshot of an apparent disproportionately distributed risk at a couple apparently targeted locations, it is a misnomer to claim, as is occasionally done in the literature, that it demonstrates any sort of a generalizable disproportionate risk in the spatial relationship between minority and lowincome residences and environmental hazards. Indeed, given the limitations of research design and purpose, it does not establish general or universal relationships of any sort, but precisely provides measurements only in four arbitrarily selected from thousands of similar sites throughout the country. As a consequence of these research design limitations, the results cannot logically be generalized beyond the few sites studied in a way that will meet reasonable scientific scrutiny. Even for the few sites to which the conclusions apply, it was not shown or indicated in any way that there were any resulting public health problems. As far as it goes, the research is undoubtedly well done, but given the design limitations it contributes little if any knowledge of any scientific merit. United Church of Christ (UCC, 1987).** This was a politically influential, national-level, cross-sectional analysis of 415 operating commercial waste facilities listed on the EPA’s Hazardous Waste Data Management System. The study related the locations of these sites using various statistical techniques at the zip-code level to socioeconomic data from the 1980 census. It compared the socioeconomic data for the areas nearby the sites with similar socioeconomic data from all other zip codes in the United States. The researchers came to the conclusions that national patterns could be identified in which race was the most significant among variables entering into association with the location of these facilities, and that areas with communities hosting the greatest number of the facilities also had the highest composition of minority residents. Since this study has evidently remained in the “gray” literature (publications whose accuracy has not been ascertained through scientific peer review), in a strict sense of the term it lacks all formal scientific validity. Past this point, however, there are several subtle but nevertheless grave reasons to doubt the accuracy of its conclusions. First, zip-code areas are in general geographical areas clearly too large for such a study and are apt to lead to erroneous inferences (Monmonier 1994). It turned out that, in this specific case, the use of overly large geographical areas was a seriously misleading error (Anderton et al. 1994a, 1994b). Second, the logic and construction of the comparison group was arguably inappropriate; that is, as illustrated in Chapter 5, there is good reason to limit the comparison group to those geographical areas that could arguably have been selected as feasible alternatives for


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the particular facilities in question. Third, the study neglected to employ the proper spatial statistical models, including diagnostic tests for violations of statistical assumptions (Anselin 1988), a concern that is especially important given the clustering of hazardous facilities around urban areas. Since statistical analysis in the presence of these violations can lead to biased and inconsistent estimates, they at least should have been diagnostically tested for and then appropriately remedied if found. The primary contribution of the UCC study was its political ideals, not its scientific content. It attracted political attention to the possibility of discerning national patterns in terms of disproportionate numbers of hazardous facilities in minority neighborhoods. In attracting this attention, it also brought currency to the political term “environmental racism.” Yet, scientifically its contribution was somewhere between negligible and naught, as evidenced first by not having been peer reviewed and second by the reasonably subtle but potentially grave sources of inaccuracy in the analysis and subsequent conclusions. Earickson and Billick (1988).** The research question in this article asked whether there was a relationship between the socioeconomic characteristics of a population in a given set of census tracts in Louisville and Detroit and the amount of air pollution (particulates, sulphur dioxide, lead) there. The analysis showed clearly that the highest concentrations of pollutants tended to be found in or near the inner core of these cities, where the neighborhoods tended to be home to poor, mostly black, and working-class families. The results suggested that the most important factors related to the distribution of these pollutants in these two cities, in order of rank, were “economic well being,” “black community,” and “working class community.” This research is probably most appropriately classified as a case study of air pollution in Louisville and Detroit using quantitative data. Though multivariate statistical analysis was used, strictly speaking the particular technique, factor analysis, is not one that enables statistical hypothesis tests. Factor analysis can be used to test hypotheses regarding the number of factors in the data but not to determine whether two or more variables are related. Moreover, the case study design seriously limits the degree to which the research can be used with any validity to infer anything about conditions in the hundreds of other cities. At the same time, however, the research does clearly and convincingly suggest that, at least in these two cities for these particular few pollutants at the time of the study, inner-city residents bore a heavy and very possibly inequitable burden. Assuming that these air pollutants potentially cause ill health in the concentrations found in these two cities (a point which is not addressed empirically in the research), the research thus hints that poor inner-city residents there, particularly children in mostly black and working-class families, were at higher risk of ill health because of air pollutants.

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CONFLICTING EVIDENCE IN THE DEVELOPING BODY OF RESEARCH: THE 1990S Given the attention garnered by the environmental justice policy advocates during the 1980s and the associated policy and administrative demands, the 1990s brought a more scientifically focused cadre of researchers to the issue. Advocacy did not stop; in fact the movement gained still further momentum. Some of the research was still focused primarily toward the accomplishment of preconceived political ideals and secondarily toward improvement of the scientific knowledge base about the issue. But as a rule the research from both scientists and policy advocates became more sophisticated and convincing. Nevertheless, some poor-quality research remained and the results from even the best research remained mixed and inconclusive. Poor-Quality Research The following research is all politically relevant but, due to limitations or flaws in conception, design, method, or documentation, does not have scientific merit. The limitations or flaws in the studies in this category are serious enough to make the conclusions drawn from them purely conjectural or plainly false. Brajer and Hall (1992).** This study examined the distribution of a couple of air pollutants in the South Coastal Air Basin of California, specifically ozone and respirable dust emissions (PM10). The levels of these pollutants were analyzed to determine their spatial association with socioeconomic variables including median income level and income distribution, education level, age composition, racial composition, and population density. While the basin was divided into different “districts,” the article did not specify how or at what level of spatial aggregation these districts were defined. Relationships were identified using simple bivariate correlation. The findings were that relatively younger, poorer, black, or Hispanic residents lived in districts with more of these pollutants than those with incomes in the top 40%, and Anglo or Asian residents. This was the first of several related studies in southern California, all of which came to similar conclusions. Had subsequent studies not provided a form of criterion-related validity to the conclusions of this one, however, would be no veracity in these conclusions. First, the conclusions from spatial analytic studies can be very sensitive to the level of spatial aggregation used (Fotheringham and Wong 1991). Given the lack of documentation of the spatial units used in this particular study, the research must be said to have contained unspecified levels of uncertainty. Second, there was no justification given for why these two pollutants were selected out of the entire range of possibilities. Third, the analyses lacked all statistical controls for spurious


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relationships, some of which (such as land use or proximity to industry or transportation networks) may be of considerable consequence in terms of explaining the relationships between these two pollutant categories and the socioeconomic variables. On balance, the shortcoming with this research was not so much that the conclusions were demonstrably wrong, but that the documentation was so poor and the design so limited that one could not be sure one way or the other. This is crucial because, in the process of drawing scientific research conclusions, the burden of proof is on the researcher. Lavelle and Coyle (1992).** This nationwide study used data aggregated to the zip-code level to evaluate the geographic location of 1177 hazardous sites on the National Priorities List (NPL), or Superfund sites. These were examined in relation to the socioeconomic characteristics of the residents in nearby zip-code areas and their associated levels of enforcement. On the basis of percentile rankings, Lavelle and Coyle argued that the length of NPL prioritization and remediation time varied positively with the percentage of minority and poor persons residing in Superfund site neighborhoods. They concluded that predominantly white neighborhoods had higher quality cleanup actions and experienced more prompt and vigorous environmental law enforcement. The study suffered from several methodological errors, including reliance upon aggregated zip-code data (Lazarus 1993; Monmonier 1994). It neglected to recognize the logical constraints to generalization implicated by the passage of time and the fact that to make historically oriented conclusions requires appropriate time-referenced data. Yandle and Burton (1996) also noted that Lavelle and Coyle’s use of 1990 census data was problematic because “the report studied all actions taken since CERCLA was enacted in 1980, and thus drew historical conclusions from data that were not historically accurate (481).” None of the appropriate statistical controls were used to hold the numerous potentially confounding or spurious relationships (e.g., proximity to industry or transportation networks) constant. No effort was made to evaluate the extent to which the observed relationships were attributable to the concentration of minority and low-income populations in a few large urban areas with NPL sites. Moreover, when one additional variable, income, was considered, the results became altogether mixed and making simple summary conclusions difficult to draw. For example, evidently, on average, while wealthier areas appear to have been more rapidly placed on the NPL and to have experienced more vigorous law enforcement, lower-income areas appear to have experienced better hazardous waste enforcement. On balance, this article is best described as having hinted at the possibility that there may have been some racial discrimination occurring with respect to the public management of these sites. It demonstrated nothing that

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could accurately be said to have any scientific validity. The evidence rendered the empirical statement of disproportionately distributed enforcement only slightly more tenable or more likely and said absolutely nothing about any associated health impacts. While it implied that further related research using appropriately defined geographical areas, with a suitable comparison group and appropriate control variables, might be worthwhile, it did not come close to providing adequate scientific justification for any potentially weighty public resource allocation decisions. Mohai and Bryant (1992).** Mohai and Bryant were coprincipal investigators in the University of Michigan’s Detroit area study. The objective of that study was to examine the relationship between race and income in terms of the distribution of 14 commercial hazardous waste facilities in the Detroit, Michigan area, plus 2 planned facilities. The research was based upon information about the locations of the sites for these facilities, together with information from surveys administered to a socially stratified, randomly selected sample of households (n=793) located at various predetermined distances from the sites. Part of the analysis was based upon χ2 tests of percentages of minority group or low-income residents living within arbitrary fixed distances from the sites. Part was based upon multiple regression analysis. The authors claim to have shown, on the basis of these analyses, that the percentage of minorities and the percentage of people living below the poverty line are overrepresented closer to the sites. They also claim to have shown that racial variables account for the locations of the sites better than income variables. Mohai and Bryant present data tables of percentages by groups along with the 2 values from which their conclusions were evidently drawn. I attempted to reproduce the χ2 tests from the data in these tables, but was unable to do so. While the data tables present percentages by groups, χ2 statistics can only be meaningfully calculated from simple frequency distributions or simple total percentages, the sum of which add up to 100%. The data in the χ2 tables they present are not of this type, therefore it is hard to determine whether the flaw is poor documentation or erroneous statistical computations. Nevertheless, because no other variables were used as controls, the results from the χ2 tests are questionable on the grounds of (1) failure to control for extraneous factors and (2) bias due to the interaction between selection biases and the dependent variable. Also, because all observations are from only one city, this study is, in a sense, a case study; therefore, any potential relationships found in it cannot be generalized beyond this city. Past this point, the most intellectually compelling aspect of the study was a multiple regression of the racial and income characteristics of the residents at particular places in relation to their distance from the commercial hazardous waste facilities. In the regression models, the slope coefficients for race were positive, statistically significant, and larger than those for income. Mohai and Bryant (as well as countless others following them) misinter-


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preted this as evidence that race rather than income is the primary determinant of variation in the locations of environmental risks. Much as a chain is no stronger than its weakest link, the “evidence” from their model was at best extremely weak and at worst implied an almost completely unwarranted level of precision and accuracy. Specifically, the adjusted R2 value on their best-fitting model was .06, meaning that 94% of the variation in their dependent variable remained unaccounted for. (R2 gives the percentage of total variation in the dependent variable that is accounted for by variation in the full set of independent variables in the model.) This alone would be no reason to reject a model, especially because a great deal of random variation in the dependent variable may be present; indeed many highly persuasive models have a low R2 value. However, to be at all persuasive, a model with a low R2 value would at least include an adequate defense against charges of specification bias (Gujarati 1995). For example, if theory stipulates the complete set of variables that should be included in the model, such defense would take a theoretical form. Since in this situation the model was induced from experience rather than deduced from theory, another defense could have been found in statistical tests of specification errors, such as tests for omitted variables and incorrect functional form. However, because no such tests were conducted (certainly none were presented), the most likely explanation for the low R2 values is that the models were misspecified, more specifically underfitted. The reason misspecification would seem likely is not only based upon general knowledge of basic statistical modeling, though such is certainly a consideration. The results of multiple regression analysis can be highly sensitive to violations of the regression assumptions such as no perfect linear relationship between independent variables, no dependence of the value of given observations on the values of their neighbors, and correct specification. The greater concern is that most of environmental justice research literature indicates clearly that, unless relevant variables related to the industrial and residential location decisions are included in models such as these, the results can be misleading (e.g., Kriesel, Centner, and Keeler 1996). As a consequence, it seems safe to bet that the slope coefficients are biased estimates of the population parameters, the standard hypothesis testing procedures give misleading conclusions about the statistical significance of the estimated parameters, and the variance of the disturbance term is incorrectly estimated. On balance therefore, this research did not, as it was presented, meet even minimal standards of scientific acceptability. It was simply too cursory and insufficiently rigorous in its design, execution, and documentation to stand up to even minimal levels of scientific scrutiny. White (1992).* This article contended that minorities should be petrified about hazardous waste incineration, especially since a disproportionately large percentage of hazardous sites in Baton Rouge, Louisiana, were located

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in minority communities. It purported to contain empirical evidence based upon the amount of waste in the 10 largest white communities and 10 largest black communities in the region. Indeed, on the surface, the evidence made it appear that there were almost certainly more sites located and greater volumes of waste per capita was larger in the black communities. But the evidence presented in this article will not stand up to the slightest scientific scrutiny, largely because of the underlying research design. It is perhaps best characterized as a pre-experimental variant of the static group comparison, except in this case the comparison groups are not assumed to be similar. One of the groups is made up of the 10 largest black communities in the region and the other of the 10 largest white communities. Certainly, both were equivalent before the law, but beyond this point there were many other uncontrolled variables that could have effectuated differences between them in terms of the suitability of their neighborhoods for waste site location. There were no controls reported, for example, for variables such as income, education, proximity to transportation, land value, or size of the relevant areas. Moreover, there was no justification, indeed no explicit consideration at all, given to the selection of comparison regions. One wonders, for example, how inclusion of the next 10 largest communities in each group would have affected the results, or the next 10 largest after that. Nor was there any consideration given to what sort of wastes were processed at the sites, a point of particular concern given the tremendous variation in the levels of hazard associated with various forms of waste. In more formal terms, especially because the comparisons groups were not selected randomly, the alleged evidence was severely threatened by selection bias, failure to control for extraneous factors, statistical regression bias, and bias due to the interaction between selection biases and the dependent variable. The article was published in the gray literature and so has less scientific legitimacy than most of the others considered in this chapter. On balance, the most generous and reasonable evaluation of its contribution is that it weakly hints at the possibility of a disproportionate concentration of sites in some selected minority neighborhoods around Baton Rouge. It says nothing at all about associated health effects. Nor does it say anything about whether the sites came first or the neighborhoods came first. Moreover, the findings cannot logically be generalized. Because of the prominence of this article in the literature, the evidence contained in it probably should not go unnoticed, but it should not receive any weight at all in scientific assessment, because its conclusions could so easily turn out to be illusory with closer analysis. Burke (1993).** Burke did a case study of Los Angeles using census data at the census-tract level and TRI data on more than 700 toxic chemical releases there in 1989. The main socioeconomic variables were median per capita income, population density, and race/ethnicity. Several visual tests


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along with exploratory spatial analytic techniques were used, all within a geographical information system (GIS). Though the article was not clear on the details of the techniques used to establish particular results, it reported that, on average, the number of TRI facilities in a census tract increased with a decrease in population density, an increase in minority percentage, or a decrease in per capita income. Race was reported as having proved consistently to have a statistically significant relationship with TRI releases; most TRI sites are located in Hispanic-dominated tracts. These results were consistent with the hypothesis that minority residences were disproportionately located near TRI facilities in Los Angeles. This was the second of several research projects that indicated the presence of statistically significant relationships between racial variables and hazardous sites in the southwest United States in general (Anderson et al. 1994) and southern California in particular (Brajer and Hall 1992; Boer et al. 1997; Sadd et al. 1999). The operative word here is “indicated” rather than “established” or “demonstrated.” This is because the data were cross-sectional only, and no controls were used for industrial location, the location of transportation systems, or educational levels, among others. No reports were provided on the regression residual analyses. Also the design was, in essence, a case study and is therefore subject to all the validity threats of any case study. However, the fact that several subsequent and independent studies arrived at a similar conclusion provides a degree of criterion validity to the conclusions. Indeed, as will become clear in the reviews of further research on this study area, this is one of the areas in the country where considerably more research is apt to convincingly establish geographical patterns of disproportionate distribution. If this could then be properly related somehow to public health, it could go a long way toward confirming the claims of the environmental justice advocates. Indeed, based on the existing research, minority residences there, particularly among Hispanics, are very likely, for whatever reason, to be disproportionately located near hazardous sites. Downey (1998).** Following the Bowen et al. (1995) investigation of Ohio and Cuyahoga County, Downey investigated the distribution of TRI emissions in Michigan first at the postal zip code level for the entire state, then for the Detroit metropolitan area. Regression analyses were used to relate the total weight of all chemical releases reported in the 1989 TRI with race and income. Statistically significant relationships were found in the multiple regression analyses of the larger geography, but race dropped out and only income remained significant when examining the smaller geographic area. No residual analyses for model specification were included in the reported results, so the quality of the models cannot be determined. The smaller geographic area was, of course, a better representation of the concept of

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environmental risk, especially for purposes of considering public health impacts, because the chances of exposure are greater in smaller geographical areas, and without exposure there can be no negative public health impacts. Past this point, a reasonably complete critique of the interpretation and contribution of Downey’s research may be found elsewhere (Bowen and Haynes, forthcoming). Krieg (1998a).** Krieg examined 1990 hazardous waste site data from the Massachusetts Department of Environmental Protection, demographic data from the U.S. Census, and tax information from 44 town annual reports. The objective was to find the most viable indicators of the distribution of environmental hazards. The unit of geography was the town. The variables included (1) the number of waste sites in the town, (2) the percentage nonwhite in the town’s population, (3) the percentage of the population that has a bachelor’s degree or higher, (4) median household income, and (5) the percentage of the town’s tax base that comes from commercial and industrial taxes. A dummy variable was also included to indicate whether the town was in the Boston metropolitan area. Two types of analyses were used: (1) comparisons of mean values and (2) correlation. The statistical findings were that waste sites were related to commercial and industrial taxes, even when race and class variables were held constant. Otherwise, percent nonwhite yielded the strongest partial correlation with waste sites after controlling for taxes, education, and income. The primary conclusion was that when the larger portion of a community’s tax base derived from commercial and industrial taxes, the industry held greater power to influence local government decisionmaking and to externalize environmental hazards. Without doubting the accuracy of the primary conclusion, this research has relatively little of any merit to say about whether minority, low-income, and other disadvantaged and susceptible populations are disproportionately exposed to environmental hazards, thereby creating greater health problems for them. Though a significant correlation between percentage nonwhite and the numbers of hazardous waste sites was found, because the unit of analysis was the town little or nothing scientifically credible can be said about the various groups’ exposure to the hazards. Moreover, the research was essentially a cross-sectional case study in which it was never stated how the towns were defined or selected, and so the correlation cannot be generalized through time or space. Past this point, three things should be noted. First, the literature review contained only a small selected portion of the relevant available research and by no means was representative of the larger body. Second, a great deal of the variation in the location of the sites could probably be accounted for by proximity to transportation routes, though this is not accounted for in the analysis. Third, on the basis of the information contained


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in the article, either the documentation was poor or the interpretation of the evidence was technically and substantively in error.2 Calderon et al. (1993). This article contributed no new research. I include it here because of the thoroughness of its review of other research, largely case studies, that examined the impact of contaminants in water on minorities and economically disadvantaged persons. The article was a centerpiece for discussion at a 2-day workshop sponsored by the EPA, the National Institute for Environmental Health Sciences, and the Agency for Toxic Substances and Disease Registries and held in Durham, North Carolina, in August 1992. The group at the workshop reportedly considered the evidence and came to the following conclusions: 1. Despite the sparseness and limitations of the data, the existing data suggest that environmental inequities exist. 2. Corrective action is possible under some circumstances. 3. The impact of polluted water on minority and/or economically disadvantaged populations not covered by the Safe Water Drinking Act is not known. 4. The status of infrastructure for drinking water and wastewater treatment is unknown. 5. Characteristics such as differences in lifestyle, socioeconomic status, and culture that may affect exposure to water contaminants are unknown. 6. Education and communication need improvement. GAO (1995). In this report, the GAO summarized 10 studies on the race and income of people living rear a variety of waste facilities, primarily hazardous waste sites. All studies were national or regional in scope. None examined the changes in demographics near the specific waste facilities over time. Nor did any of them examine the extent to which people living near the facilities were exposed to harmful materials. Taken as a whole, the results were very mixed. Of those included in the 20 and reported elsewhere in this chapter, 1 found a relationship between race and environmental hazard (UCC 1987); 2 found mixed results, depending upon the assumptions and methods used (Hird 1993; Zimmerman 1993); 1 found no relationship between the locations of either race or economic groups and the locations of commercial hazardous waste facilities (Anderton et al. 1994a; Anderton et al. 1994b). The UCC found that in 1993 people of color throughout the nation were even more likely to live in communities with hazardous waste than they were in 1980 (Goldman and Fitton 1994). Zimmerman, in looking at NPL sites primarily in New York and New Jersey

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for the EPA, found that these sites were located in areas with population densities above the state average but lower than in the urban areas. Few other socioeconomic characteristics differed, on average, between the areas surrounding the sites and what was typical of the state. A study by ViGYAN, Inc., for the Office of Solid Waste at the EPA found no conclusive evidence of racial discrimination in siting. One by Claritus Inc. found that of the 132 disposal facilities examined, 76% were located in communities that had a white population not of Hispanic origin that was equal to or greater than the average for that population in the state. Finally, a study by ICF Incorporated and ViGYAN, Inc., prepared for the Regulatory Analysis Branch of the EPA Office of Solid Waste found no significant socioeconomic differences when looking at the proximity of minority and low-income populations to a sample of 41 cement plants. Medium-Quality Research The design, method, and documentation of the following research led me to believe that the findings and conclusions were approximately accurate. However, the flaws and limitations were substantial enough to judge that the research was not of sufficient quality, say, to recommend using it in a court of law or in an actual policy or administrative decision related to environmental justice. Napton and Day (1992).*** Napton and Day examined air pollution among five groups of air polluting industries in metropolitan statistical areas (MSAs) in Texas and its relationship to nearby populations. Census tract areas within 1 mile of each spatial concentration of polluting industries were designated as the study group, and these were compared with randomly selected census tracts within the same MSAs. Data were gathered on variables reflecting the demographic characteristics of the persons living in both sets of tracts, the polluted and the nonpolluted ones, as well as variables that described the tracts, such as homeownership, age, and value of housing. Discriminant function analysis was used to determine which of the variables best distinguished between the two groups of tracts. The reported results, which were statistically significant at α = .05, indicated that mean rent in the tract was, in a manner of speaking, the most important variable distinguishing between polluted tracts and nonpolluted tracts. Past this point, when compared to the control tracts, the population in the study tracts was made up of younger transient working families with a higher per capita income, living in newer homes. No indication at all was found of any significant racial or income differences between the populations living in the two groups of tracts. The most immediately evident potential source of bias in this research was found in the selection of industries to study. Given the ubiquity of envi-


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ronmental hazards, as recognized by the researchers, there is little reason to think that the set of five industries studied provide an accurate representation of the entire larger range of industrially produced hazards. Otherwise, it appears to have been well designed and conducted. Certainly, the procedures for selecting comparison tracts was refreshingly well conceived. The use of a 1-mile radius was convincingly justified through reported discussions with Texas Air Control Board toxicologists. Also, the selection of discriminant analysis as a way to obtain the estimates was appropriate. Besides contributing evidence contrary to the prevailing claims of broad geographic patterns of disproportionate distributions, the main contribution of the study was in suggesting and testing the use of housing variables as a way to distinguish between polluted and nonpolluted areas. Indeed, given that housing variables have been shown to be significant determinants of the same social and demographic characteristics of residents that pertain to many disproportionate distribution questions (Cadwallader 1981), it is somewhat surprising that the subsequent research has not picked up on and followed this contribution much further. Hird (1993).*** Hird examined the equity implications of the EPA’s Superfund program by examining the geographic distribution of Superfund (NPL) sites, who pays for the cleanup, and the speed at which they are cleaned up. To do this, he used data on the number of proposed and final NPL sites in each county in the United States as of January 1, 1989, as well as census data on their socioeconomic characteristics. He began by using regression models to predict the number of NPL sites in any given county, taking the socioeconomic characteristics of that county as independent variables. The results indicated that more economically advantaged counties with more manufacturing, a higher than average educated workforce, higher levels of owner-occupied homes, and higher percentage nonwhite, all contained more NPL sites. Next, apparently using two-sample t-tests and simple bivariate regression analysis, he analyzed specifically whether or not counties that are disproportionately represented by racial minorities, high levels of poverty, and unemployed persons had more than their share of sites. The results indicated that NPL sites were located predominantly in affluent counties and generally without regard to race or, depending on how the data are cut, with a lower percentage of racial minorities. Past this point, analysis of the pace of Superfund cleanups indicated that there was virtually no relationship between a site reaching a particular cleanup stage and the county’s socioeconomic characteristics; that is, socioeconomic characteristics of counties were not significant indicators of the pace of federal cleanup effort. On balance, the conclusion was that Superfund sites were not predominately located in poorer counties, although nonwhite counties may have been disproportionally affected. Furthermore, sites in poor and nonwhite counties were not cleaned

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up at pace different than others. This means that most of the cleanup effort benefited people in wealthier counties, and in this sense the Superfund program was seen to be inequitable. This was a closely reasoned and thoughtful article. It is singularly unfortunate, therefore, that Hird used counties as the units of analysis, because the concern with disproportionate distributions and consequent negative health impacts in disadvantaged communities is meaningful only in terms of much smaller geographical units. Indeed, this discrepancy is evident in Hird’s use of language. For example, throughout the article he used the terms “neighborhood” and “community” synonymously to refer to all of the residents in any given county. To me, such terminology is reasonable only if living in a particular county contributed to self-identification, residents there participated extensively in the decisions by which their common lives are governed, and the county as a whole took responsibility for the residents. Such a term might be meaningfully applied to a very small and homogeneous rural county, but in my experience it almost certainly has no meaningful application in even a moderately sized urban area, much less a major city. Thus, the research had no meaningful bearing on the issue of disproportionate distributions and their health effects on minority, low-income, or other disadvantaged communities. Neither did it indicate that the otherwise apparently correct and fitting statistical analyses were adjusted in a way that appropriately accounted for the spatial clustering of sites around urban areas. As a consequence, while the research convincingly made the case that the Superfund program was inequitable in relation to county-level socioeconomic characteristics throughout the country, it had little to say about environmental hazards and their public health effects in minority, low-income, and other disadvantaged neighborhoods. Zimmerman (1993).*** Zimmerman attempted to assess whether over 800 inactive hazardous sites on the NPL were disproportionately located in communities that had fewer financial and political resources and how cleanup activities were distributed across such communities. This was accomplished by comparing the spatial distribution of NPL sites against the distribution of population and industry within Census Places. (The places Zimmerman analyzed are large areas of an average of 39.4 square miles and have a mean of 83,000 to 90,000 inhabitants, depending on the year.) With the exception of a modestly documented and largely inclusive regression model designed to help understand the existence of a site cleanup plan, all conclusions throughout the paper were based upon informal comparisons of descriptive statistics. They indicated that the NPL sites were located in census places containing almost every social and economic makeup. Beyond this, two ways to represent average percentages in various socioeconomic groups were used. The first was a simple population averaging of means and the other implicitly weighted the communities according to their population size. Using intuitive


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comparisons of simple averages, the percentage of minorities in census places containing NPL sites was found to be comparable to that of the regions within which the communities were located. In contrast, when averages weighted by minority percentage across all census places with NPL sites were used, it appeared that minorities were more likely to live in a census place with a hazardous site than was the population at large. Zimmerman attributed the difference in results to the effect of a relatively few large minority communities with NPL sites. A similar procedure used to examine income indicated that the association of severe poverty with NPL site location was less pronounced than with minority status. She concluded that the analysis revealed that the average percentage of minorities and persons living below the poverty level was comparable to percentages in the nation and the larger regions in which the sites are located. In essence, minorities were overrepresented in those census places with NPL sites located specifically in relatively large urban areas. In terms of cleanup activities, communities with relatively higher percentages of minorities were found to have fewer cleanup plans. But this reportedly appeared to be more a function of the nature of the process of designating NPL sites in the early 1980s than a result of actions connected with cleanup plans per se. In this regard she concluded that “potential equity issues appear with respect to the location of NPL sites and their designation for cleanup for Black populations and to a lesser extent for Hispanic populations in areas of larger population concentration, but are not apparent for populations in poverty” (664). While these results indicate the possibility that minorities are disproportionately located in the proximity of environmental hazards, the analysis was limited to a subset of NPL sites, did not provide statistical tests for differences, and relied on spatial units that are way too large to be of interest when considering health effects. Moreover, it appeared on the basis of this study that the problem was associated with spatial clustering of NPL sites around a relatively few large population centers, probably with high concentrations of industrial and blue-collar jobs. This is especially noteworthy because the clustering of industrial production and the NPL sites that contain the concomitant industrial wastes would be highly predictable on the basis of industrial location theory. A corollary question would concern how the affected individuals in these industrial/population/hazardous site clusters would earn a living in the absence of jobs generated by production processes that produce the materials found in these sites. The spatial clustering of sites in a few areas would also make reliable statistical hypothesis tests impossible without the appropriate adjustments. Perlin et al. (1995).*** This research used nationwide 1990 demographic and TRI data, aggregated to the county level, to examine relationships between airborne emissions from certain industrial facilities and three

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socioeconomic variables, ethnicity/race, household income, and urban versus rural residence. It descriptively documented the uneven distribution in the United States and between EPA regions with respect to ethnic and racial populations, household incomes, population groups living in urban vs. rural counties, industrial facilities, and industrial air emissions. Intuitive comparisons of descriptive statistics clearly indicated that, on a national basis, members of several racial/ethnic groups were more likely, on average, to live in a county with higher TRI air releases than the median value for whites. Similarly, counties characterized by a higher than average annual household income, on average, had more such emissions. Since the county was the smallest unit of geography in the research, it contained no scientific evidence documenting differences by ethnicity and race or socioeconomic status in terms of exposures to environmental hazards and related health effects. Rather, its primary contribution was in showing that regional variations were substantial and in suggesting that analyses that look only at the national level are apt to miss important information and reach erroneous conclusions unless regions are included as controls. Pollock and Vittas (1995).*** This research was essentially a quantitative case study of the extent to which various minority groups resided near TRI facilities in Florida during the early 1990s. Subject to the potentially serious inaccuracies in the TRI data, the researchers estimated the exposure of residences using precise distances between 534 TRI facilities and 9093 census-block groups. The analysis was based upon regression models that included the levels of urbanization, population density, manufacturing employment, wholesale employment, median rent, median housing value, and median housing age in the respective census blocks. When all of these factors were statistically held constant in the analysis, percent low-income white, low-income African American, and low-income Hispanic remained statistically significant, leading to the conclusion that this was still another manifestation of an enduring legacy of racial inequity. This research was innovative in that it is one of the few that I have seen that represented exposure directly as a function of distance from the site. Conceptually, this is a definite improvement over the more frequently taken approach. Also, the way the relationship between race and distance to a TRI site was established was more convincing than in a number of other research projects, because it was found to hold even after the aforementioned variables were held constant. At the same time, the research had several shortcomings that limit its relevance for policy and administrative decisions and cast doubt on its conclusions. First, it was, in essence, a case study of Florida. Second, the hypotheses were not deduced from axiomatic theory. There is no reason to think that Florida is representative of the United States as a whole, and, even if the study’s conclusions were correct, there would be no reason to


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think that they could be convincingly generalized outside the state of Florida. Third, the comparison between census-block groups near TRI sites and all other census-block groups poses a validity threat because the use of all other census-block groups is probably not a very reasonable comparison region. It would imply, for example, that a site in Miami would have been a possible substitute for a site in Tallahassee, which is probably not reasonable. Nor could the sites have been located in Everglades National Park. Accordingly, a more reasonable comparison would have been to compare the demographic characteristics in census-block groups near TRI sites with all block groups that were appropriately zoned at the time of the siting decision within perhaps 50 miles of the site. Fourth, no tests for model specification or violation of the assumptions of the regression model were reported, an omission of considerable importance given the likely spatial clustering of TRI sites and the likely levels of heteroskedasticity and collinearity in the data. Finally, though one of the novel aspects of this article was explicitly using distance from a TRI site as a proxy for exposure and pointing out that potential exposure is probably not a linear function of distance, this point is not dealt with adequately. It is not recognized that this function is apt to vary from chemical to chemical and site to site (due to differences in distance decay characteristics and site management practices, respectively). Indeed, on the margin, the use of different but uniform distance functions for all chemicals and sites may even imply a misleading level of precision because they do not begin to account for the far more significant differences in exposure attributable to differences between chemicals and management practices from site to site. On balance, this article provides one of the more convincing arguments that a disproportionate number of especially low-income blacks were located closer to these sites in Florida. However, as was pointed out in the previous paragraph, there is solid reason to doubt the external validity of the conclusions in the research. Therefore, based upon this research alone, there is reason to doubt the judiciousness of taking the existence of a pattern of disproportionate exposure as a premise in a policy and administrative decision in Florida, much less in the United States. Tomboulian et al. (1995).*** This research focused upon census tracts in the Tri-County Detroit area. The purpose was to investigate whether the presence of environmental disamenities was related to socioeconomic characteristics of people in the corresponding census tracts. Environmental disamenities were measured using six data sets reflecting vapor emissions, respirable dust emissions (PM10), daily traffic density, top-ranked contaminated sites, active hazardous waste management facilities, and extensive industrial land use. Throughout, two-sample t-tests were used to compare demographic variables in tracts with an environmental disamenity to those without that disamenity. Lower economic status was consistently related with

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poorer environmental quality. Extensive industrial land use and high daily traffic density were found to be disproportionately centered in the city of Detroit, where the population was about 75% African American. Otherwise, no disproportionate minority associations with environmental disamenities were found. The authors concluded that (1) environmental quality indicators were more consistently tied to economic status than to racial or ethnic groups in the area, and (2) the distribution of environmental disamenities was related to the pervasive racial segregation in the area. While this research was based upon a lush data set and was rich in detailed documentation, the analytical procedures lacked the systematicity and level of control that would normally characterize fully convincing scientific research that attempts to determine an association between variables. The research would not be useful in aiding prediction or as a prelude to determining a causal relationship. Rather, the analytic procedures place it squarely in the category of descriptive research for measurement and estimation rather than research to find relationships. It should be noted that it was based upon cross-sectional data only, and the conclusions pertained only to the Detroit area. Nor did it include any information about the health status of residents in the study area, much less the relationship between health status and environmental hazards. Past this point, one of its more significant aspects was its inconsistency with the earlier study of Detroit by Mohai and Bryant (1992). Though Tomboulian et al. looked at a broader geographical area and used a wider array of variables, the stark inconsistency of the conclusions is notable. In this regard, the quality of the data and the thoroughness of the documentation in this study far surpassed those in Mohai and Bryant. The researchers in this study avoided creating a similarly unwarranted level of precision and accuracy in their findings. Evidently, the matter of whether or not environmental hazards pose disproportionate health risks on minority communities in the area remains essentially in doubt and an open question. Cutter and Solecki (1996).*** Cutter and Solecki compared the spatial distribution and character of airborne toxic releases at the county level for the southeastern United States (EPA Region IV) for the period 1987–1990. Their stated purpose was to determine whether low-income minority communities were disproportionately at risk from these releases. The toxic release data came from two sources. The first was the TRI. The second was the Airborne Chemical Release Inventory Data (ACRID), developed by the authors, which provided measures of about 11,000 stationary and transportation-related releases that occurred between 1980 and 1990. Any of the releases recorded in the ACRID data could reportedly have potentially caused acute bodily harm, injury or even death within hours of exposure. The variables included residential population; population density; percentage urban population; percentage in various racial, age, and gender categories; income per capita; per-


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centage of the population below poverty; and mean value of housing. Pearson product moment correlation analyses between these variables and releases were computed. In general, the correlation analysis failed to indicate any substantial association between the racial composition of the county and the frequency of airborne toxic releases. Rather, they indicated that the frequency of TRI releases tended to be higher in heavily populated counties with high population densities, higher per capita incomes, and higher housing values. In contrast, the frequency of ACRID releases, including those that occurred as a result of truck transportation accidents, tended to be higher in less urbanized counties with lower populations and population densities, and lower per capita incomes. Specifically, with the qualified exception of a couple of high-release-level nonmetropolitan counties along the Gulf Atlantic coast, such as Greene, Alabama, and Washington Counties in Mississippi, each of which has a large pulp and paper mill industrial base, the variable “percentage African American” showed no association with the number of releases. To their great credit, the authors went to considerable lengths to improve on the theoretical basis behind their analysis. Also, their analyses were relatively well documented. Yet, the study was limited primarily in two ways. First, the fact that the data were aggregated to county level limits it in terms of the degree to which the findings reflect actual exposures; indeed the analysis had essentially no tangible bearing upon the question of disproportionate distributions specifically in relation to associated adverse public health impacts. Second, it would have helped create a more forceful argument had the researchers thoroughly considered spatial dependence and spatial heterogeneity by doing the appropriate statistical tests and making adjustments as indicated. Otherwise, the study was fastidiously thought out and meticulously presented. The researchers did a good job of justifying their statement that “the story of environmental justice in the southeast United States is more complicated than simple correlations between race, income and toxic exposures” (395). Furthermore, their concluding suggestion that more research is needed on the underlying sociospatial processes that give rise to the production of hazardous places and their populations is forcefully argued and supported by the analysis. Kriesel, Centner, and Keeler (1996).*** This research explored various analytical models that could potentially be used to determine whether the exposure of minorities to environmental risks constitutes a form of racism. The empirical data were from census-block groups in Georgia and Ohio. The conclusion from the analysis was that the significance of racial variables depended on the number of independent variables other than race and income that were used as controls in the model. Race was found to be a significant variable when only race and poverty were the only independent variables, but

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not when a broader set of variables on education, transportation, and industrial location were included. The empirical results clearly would have been stronger had the researchers reported on thorough residual analyses of their models and had they made any needed adjustments for spatial dependence. However, the primary conclusions from this research were not so much relevant to empirical conditions in Georgia and Ohio as they were to the empirical research methods used to establish and evaluate evidence of disproportionate distributions more generally. In this respect, their research was excellent; it clearly and convincingly demonstrated that different statistical techniques and model specifications can provide different answers about the existence (and possibly causes) of disproportionate exposure to environmental risk. The primary contribution of this research is in demonstrating that different empirical findings about the same situation are feasible, depending upon the breadth of the analytical framework used. What can appear from one point of view to be a statistically significant relationship between race and the location of a hazardous site (Mohai and Bryant 1992) may not look like any relationship at all once the broader range of variables are considered. Thus to avoid making weighty public resource allocation decisions based on an overly narrow view, it is important for any research efforts aspiring to conclusions that are taken seriously as premises in policy and administrative decisions to include the full range of relevant variables. Liu (1996).*** This research considered the socioeconomic characteristics of downwind areas affected by ozone plumes in New York and Philadelphia. The first step was to use past research on plume studies and statistical analysis of ozone trends to delineate the areas most probably affected by the urban ozone plumes from the core areas of these two cities. The second was to select comparison regions, which included the nation, the census-defined geographic region (New England and Middle Atlantic), and the states or their combinations involved in the urban plumes. The third, in essence, was to summarize and compare the 1990 census data by income and race for the source regions (the urban cores), the regions affected by the plumes, and the comparison regions. The comparison was accomplished through summary statistics, primarily on an intuitive basis, since no statistical hypothesis tests were evidently conducted. The results on household income indicated that the largest differences between the downwind and source areas were that the downwind areas had percentages of high-income households that were almost three times greater than for the Philadelphia source area and one and one-half times greater than for the New York source area. Conversely, downwind areas had percentages of low-income households that were approximately half of those for the New York and Philadelphia source areas. In terms of race, the percentages of nonwhite populations for the source areas were


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respectively almost twice as large as that for the Philadelphia downwind area and more than three times as large as that for the Philadelphia downwind area. Furthermore, population dynamics data for these areas did not provide any evidence that the high-income residences tended to vote with their feet. This research was based on a case study design of only one from an array of thousands of chemicals released in only two of hundreds of cities and therefore had very limited external validity, and it did not directly deal with the issue of disproportionate health impacts from environmental hazards in minority, low-income, and other disadvantaged communities. Nevertheless, it raised an important point. Especially when considering air pollution, the environmental hazard and therefore risk is not always located at the source of the release. Thus, when looking at and attempting to establish disproportionate health impacts with respect to air releases, the demographic profiles of the affected populations may be very different than those of the populations in the area immediately surrounding the releases. Yandle and Burton (1996).*** The purpose of this study was to determine whether poor and nonwhite communities hosted a disproportionately large number of waste disposal facilities in metropolitan Texas. Seventy-two historical sites and 69 current sites were examined in relation to demographic information for each of the historical and current nearby census tracts, including median family income, total population, white population, and Hispanic population. All data were taken from the census period closest to the siting decision for the particular facility in question. Following Lavelle and Coyle (1992), the inferences were based on a quartile ranking method known as percentile ranking. The statistical significance of the results was evaluated using χ2 and Cramer’s V. The analyses indicated that there was a statistically significant relationship between relative poverty and hazardous waste landfill siting in metropolitan Texas, but no positive correlation between the proximity of nonwhite populations and the siting of hazardous waste landfills at the time of the siting. Instead, relative to the metropolitan area as a whole, the landfills were located more often in white than in nonwhite host communities. This research was excellent insofar as it was one of the few quantitative longitudinal studies of environmental justice. This was exactly the sort of research needed to help distinguish whether residences or hazardous sites came first, thus providing the insight needed for policy and administrative decision situations. The computational basis for the research, however, has come under reasoned and fair yet severe criticism (Anderton 1996). First, the sites in the study were heterogeneous with respect to the activities conducted at them and the hazards they pose for surrounding populations. This point is crucial when considering their public health consequences for nearby populations. Yet, it is completely missed by the preponderance of research such as

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this. Second, as is characteristic of much environmental justice research, the data were of questionable accuracy. Third, the research apparently neglected to consider changes in both tract and metropolitan area delineations over time. Fourth, the use of quartiles from the distribution of metropolitan areas was likely to build biases into the comparison group against which landfill tracts were compared. Finally, the design did not adequately deal with competing and spurious effects. The primary contributions of the research were to raise several significant points of research method and render the proposition slightly more tenable or more likely than before that the hazardous sites in metropolitan Texas tended to be located before the nearby disadvantaged residences. Been and Gupta (1997).*** This was a national-level longitudinal study covering the period 1970–1994, based upon census tracts, that looked at the same types of hazardous facilities as were examined in studies by UCC (1987), Anderson et al. (1994), and Anderton et al. (1994a; 1994b). The types of analysis included bivariate t-tests and multivariate regression analyses (logits). The findings indicated that the facilities that began operating between 1970 and 1990 were not sited in areas that were disproportionately African American. Contrary to much of the environmental justice literature, they indicated that high poverty rates were negatively correlated with the probability that a tract will be selected to host a facility. They did, however, support the claims of environmental justice policy advocates in relation to Hispanic populations. Specifically, they indicated that the percentage of Hispanics in a tract in 1990 affected the probability that the tract would host such a facility. They also indicated that the percentage of Hispanics at the beginning of a decade increased the probably that the tract would be selected to host a facility in that decade. The primary contribution of the research was in taking a longitudinal approach to the question of disproportionate proximity to hazardous sites. The statistical analyses were more-or-less standard and generally appropriate aside from their neglect of appropriate spatial statistical procedures. The researchers went to considerable lengths to ensure that the spatial units used in the analyses were defined in a consistent way through time. It contained an insightful and convincing justification for using census tracts as the unit of analysis. Also, the researchers went to painstaking lengths to interpret their findings in a meaningful and relevant way. There are two primary criticisms of the research. First, again, the researchers evidently did not do and certainly did not report on a thorough examination of the residuals of their analyses. This is a serious concern especially because the data are spatial and are therefore quite likely to reflect spatial clustering, interdependence, and heterogeneity of industry and/or population. Accordingly, the conclusions include any and all errors attribut-


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able to failure to adequately consider these matters. Moreover, they can be especially serious given factors such as the very high concentration of heavily polluting petrochemical and plastics firms in Texas, as well as the relatively high concentrations of Hispanics in the Southwest. The consequences of failure to deal adequately with such considerations include biased parameter estimates and unreliable hypothesis tests. At the very least, one would think the researchers would have included dummy variables to capture regional effects. Second, and not unrelated, is that the comparison group included all tracts throughout the country that were nonhost sites. Though the authors asserted that this was the appropriate approach, they did not adequately justify this assertion on conceptual (or any other) grounds. Indeed, they seem to have altogether missed the geographical rationale for using a comparison group for any given site that includes only those tracts located in the same metro area (see Chapter 5). The effect of this oversight in this particular research was unfortunate, especially because during the 1970s and 1980s (the study period) new industry was developing most rapidly in the Southwest, where considerably above average percentages of Hispanics reside. Therefore, if the probability of a new site locating in a given tract was anywhere near proportionate to the probability of a new industrial plant locating nearby, the relationships between Hispanic residential location and hazardous sites identified in the study were likely to have been artifacts of industrial development. Moreover, any negative connotations related to the findings of disproportionate percentages of host tracts in Hispanic neighborhoods were thus questionable on the substantive grounds that they also implied that the affected Hispanic population in the region disproportionately benefited from jobs and other advantages associated with nearby industrial development. While the concept behind the research was excellent, for these two reasons, in implementation, the research findings are not believable. Cowart and Holmes (1999).*** This research used fairly sophisticated regression analysis to examine whether 1990 census data on the racial composition of host census tracts for TRI sites could account for their 1992 geographical distribution in Birmingham, Alabama, as well as the number of pounds of pollution emitted. The independent variables included, for each census tract in the study region, percentage black, median age of the housing stock, median household income, median value of a single-family home, and number of persons per acre. An independent variable was also included to indicate whether a pollution-producing facility from the TRI was located in an adjoining tract. In terms of the analysis of site location, both income and population density were reported to be highly significant, but percentage black was not statistically significant, contradicting the hypothesis of disproportionate distributions in minority neighborhoods. In terms of the analysis of number of pounds of pollution per tract, holding all else equal, the results

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indicated a statistically significant relationship with percentage black. On balance, more pounds of pollution were apparently emitted in areas with a slightly higher than average percentage of black people, with slightly lower than average income, living in homes valued slightly higher than average in tracts with somewhat less than average population density. Though the results from this research were mixed, one would not want to put too much stock in any evidence it contained regarding the hypothesis that minorities and low-income individuals are disproportionately exposed to environmental hazards (and therefore possibly experienced greater negative health impacts). First, with the exception of reported tests for multicollinearity, no residual analyses of the models were reported. Given the lack of an axiomatic theory from which to correctly specify the model and in the one model that showed a relationship with percentage black, a reasonably low R2 value (.22), it is fairly likely that the model would not pass statistical specification tests. While in the absence of a fully documented residual analysis one cannot be sure, an incorrectly specified model would, in turn, likely have incorrect parameter estimates and unreliable hypothesis tests. Also, as mentioned numerous times, residual analysis would have been particularly worthwhile, especially because in cross-sectional data involving spatial clusters of sites, regression assumptions about the independence of the residual errors are likely to be violated. Before anyone would be well advised to bank on the results of this research, the question of these violations should be cleared up. Second, the analyses included no control for amount of manufacturing, zoning, or proximity to transportation routes. Third, the comparison group of tracts was not well justified. This could be important since the results are likely to be sensitive to truncation in the data resulting from inclusion of tracts with no industrial land among other things. Finally, strictly in terms of the central concern with public health in minority communities, the number of pounds of pollution released at a site says little if anything about the levels of toxicity of those releases or about exposure and how public health might be affected. High-Quality Research The design, method, and documentation of the following research enable it to be recognized as substantially accurate enough to be worthy of consideration in actual policy and administrative decisions. The results of the studies with five stars, can be meaningfully generalized and applied broadly within the context of these decisions, and the results of those with four stars cannot be justifiably generalized beyond the particular study region in which the research was conducted. Hamilton (1993).**** This research tested whether the degree to which firms internalize their hazards depends on the abilities of affected communi-


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ties to engage in collective action. It was theoretically based around private firms’ decisions to expand capacity at existing hazardous facilities. The empirical aspect of the research used county-level demographic data from 1980 and planned capacity-change data from a 1987 EPA survey of hazardous waste treatment, storage, disposal, and recycling facilities. Seventytwo (72) counties were identified where net planned expansion was positive and 84 counties without positive expansion plans. Regression models of capacity expansion were constructed for all 156 of these counties, using the following variables: current capacity surplus, hazardous waste generation, manufacturing value added, median housing value, percentage voter turnout in 1980 presidential election, percentage of adults with 4 or more years of college, percentage of nonwhite population, urban population percentage, population density, and value of land and buildings per farm acre. Voter turnout was consistently the only statistically significant variable at the 1% level. Specifically, the higher the turnout, the less likely the county was to be chosen as a site for future expansion of commercial hazardous waste processing capacity. Controlling for other factors, nonwhite population percentage was evidently not a statistically significant factor in the expansion decisions, though the higher the nonwhite population percentage, the less likely it was that there would be a reduction in capacity. As with some of the previously reviewed research, this study did not deal directly with the question of whether minorities, low-income, and other disadvantaged or susceptible communities were disproportionately exposed to environmental hazards and therefore experienced more health problems.3 The fact that the unit of analysis was at county level precludes the possibility of the research dealing directly with this question. Tests of the regression residuals are not reported, leaving the critical reader in doubt as to whether the parameter estimates were unbiased and the statistical hypothesis tests accurate. It should be noted, however, that the specification of the models has some systematic theoretical basis (in Coase’s Theorem) and in this sense is quite advanced from much of the research on environmental justice. The most important conclusion to draw from this research is that community mobilization and potential to engage in collective action evidently systematically influenced the capacity expansion decisions, at least at the county level. Anderson et al. (1994).***** This was a national-level study that used census-tract data as the basic unit of analysis as well as a very carefully constructed comparison group. The data were from two primary sources. The first was the 1992 Environmental Services Directory, a national list of commercial transfer, storage, and disposal facilities (TSDF) in operation in 1992. The second source was the U.S. Census from 1970, 1980, and 1990. The comparison group consisted of all tracts that do not have a TSDF, given that the tracts are in larger areas that have at least one TSDF. Various hypotheses

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about the relationships between the census variables and the TSDF sites were tested using bivariate statistics and regression analysis. All had to do with the census variables and their relationship to the TSDF census tracts. The 1980 results indicated that TSDF sites nationwide tend to be located in predominately white, industrial, working-class neighborhoods. Throughout the nation, the average percentage black in the TSDF tracts was found to be lower than in the non-TSDF tracts (though not statistically significant). When looking only at the 25 largest SMSAs, the results showed that the average percent black in TSDF tracts is over 4% lower than in the comparison tracts, and this difference was statistically significant. At the same time the percent Hispanic was found to be somewhat higher at both the national and SMSA level, though in neither case was it statistically significant. The 1990 results indicated smaller differences with respect to Hispanic populations. The most significant difference between the 1980 results and the 1990 results was that economic disadvantage tended to be significantly higher in 1990 within the tracts containing the sites. There appeared to be significantly more blacks located in TSDF tracts in the South Atlantic region of the country only, though there were significantly less in the North. Similarly, in the North and South Atlantic regions, the percent Hispanic was found to be significantly less in the TSDF tracts, and in the Southwest there were significantly more Hispanics. Otherwise, no consistent, statistically significant national pattern of racial or ethnic discrimination in the location of the sites was found. This study was similar in many respects to the UCC (1987) study, at least in that it was a national study looking at very similar data, except for two main differences: (1) the comparison group was conceptualized better and (2) a more appropriate level of geography was used—census tracts instead of zip-code regions. It also used somewhat different and well-advised analytical procedures, in that they allowed the researchers to systematically consider industrial location by statistically controlling for percent industrial and manufacturing employment in a census tract. These were decided improvements over the previous study. First, as discussed in earlier chapters, the matter of comparing whether areas containing a waste facility are different with respect to racial and economic composition always logically presupposes categories from which to draw comparisons. This study defined these categories on the basis of the assumption that the choice of where to locate a TSDF was constrained by various rules and market forces, which restricted the comparison group of tracts to those that could potentially serve as alternative locations. Thus the comparison group for any given existing site was restricted to the city or market area for that site, rather than the entire nation. Second, it is well established that census-tract areas are more appropriate than zip-code areas for this sort of analysis (Monmonier 1994). Thus, while these results were inconsistent with those of the previous UCC study, because the research methods were arguably much better, the findings are much more convincing


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and should be much more highly regarded in terms of their use in making policy and administrative decisions.4 Anderton et al. (1994a; 1994b).***** The research for the two articles reviewed here was evidently based on much the same data as the research for Anderson et al. (1994). Similar to that study, these were national in scope, using census tracts. They can be fairly characterized as systematic, controlled, empirical, and critical investigations of the spatial relationship between environmental hazards and the locations of minority and lowincome neighborhoods. As is the case with essentially all related research, there was not a clearly defined theoretical or conceptual model behind either analysis; the models were induced on the basis of experience and not deduced from theory. The concept of environmental risk was operationalized, as in the Anderson study, using TSDF facilities. The statistical tests used included ttests and regression analysis, though no reports of residual analysis were given. Industrial location variables were controlled using percent industrial employment in the tract. The other independent variables included percent black, percent Hispanic, percent families in poverty, percent households on public assistance, percent males employed, mean value of owned occupied housing and percent houses built before 1960. The research found that percent males employed, mean value of owner occupied homes, and percent housing built before 1960 were all significantly and negatively related to the locations of the TSDFs, and percent industrial employment was significant and positively related. This indicated that race did not distinguish between census tracts with or without TSDFs, but instead the TSDFs seemed to be located in working-class neighborhoods with residents heavily concentrated in industrial occupations, living in somewhat cheaper and newer homes. Past this point, the studies examined the sensitivity of related findings with respect to the size of the area analyzed. This was done by comparing the results of analyses of the same data aggregated to the zip-code level with the results of aggregating it to the census-tract level. On this basis, any conclusions about whether or not disproportionate exposure occurred were found to depend heavily on how the areas of potential impact were defined. With the exception of the same subregional patterns reported in Anderson et al. (1994), no consistent national-level patterns supportive of the claims of disproportionate distribution by race were found. Rather, the most significant relationships were found in the unemployment and industrial variables. Other than the countervailing substantive findings, the most noteworthy contribution of this research was in showing clearly that the appearance of the problem depends on how the study areas are defined. This is most strikingly illustrated by examination of the 25 largest SMSAs. To visualize the findings, one can imagine concentric circles, with the inner circle (call it circle A) containing a TSDF, the next circle (B) containing areas adjacent to the TSDF

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area, and the next circle (C) containing all areas within 2.5 miles of the TSDF. The results from these SMSAs indicated that the demographic characteristics of residents within circles A and B were statistically similar, containing in particular no differences between minority and nonminority; but circle C contained a higher percentage minority. Thus if one used zip-code or other larger geographical areas—which are more similar to C insofar as they are about the same size and include the entire area in all circles, as if the demographics are uniform throughout—one would mistakenly conclude that minorities live closer to the sites, when in fact the demographics closest to the site show no patterns whatsoever.5 It should be noted that this research was funded in part by a grant from Waste Management Incorporated, as sponsored by the Institute for Chemical Waste Management. While this has been the source of some expressed or implied doubt as to the integrity of the research (Goldman 1996), I cannot find any major fault with the design, method, or documentation other than failure to report on a thorough examination of the residuals, especially spatial dependence. Such failure may, of course, be a major substantive flaw, but one cannot tell without actually analyzing their data. While residual analysis is of major importance generally, this is one of the most methodologically sound of the current research studies. Been (1994).**** Been was evidently the first person to note and bring attention in the literature to the fact that the research existing at the time did not establish whether communities that were host to hazardous sites were disproportionately minority or poor at the time the sites were selected. This of course is significant because the causal process and appropriate policy responses can be substantially different, depending upon the sequence of events. If the host communities were disproportionately minority at the time the sites were selected, it implies that the cause of any current observations of disproportionate distributions is to be found in the siting process, and the appropriate policy response is to focus on that process. If, however, the host communities were not disproportionately minority at the time the sites were selected, the appropriate policy response is to address causes that lie in market failures and widespread social values that may have little or nothing to do with the siting process. This research extended GAO (1983) and Bullard (1983) by analyzing data about the demographic characteristics of the same host communities that were examined in those studies. The main difference is that Been began looking at the demographic characteristics of nearby residences at the time the sites were selected, then traced demographic changes in the neighborhoods after the siting decisions. In terms of the four sites studied by the GAO, the locations for the three with disproportionately high percentages of minorities were reported to have been selected in the early or mid-1970s. Associated


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demographic data revealed that the host community for each site was disproportionately populated by African Americans at the time of the siting (1.6–to 3.3 times greater than that of the host state’s population). In terms of extending Bullard’s study of Houston, Been judiciously omitted sites that Bullard included that were no longer operative and hazardous as well as sites that Bullard had double-counted, thus reducing the number of sites from 25 to 10. Whereas Bullard did not make his definitions of neighborhoods explicit, thus precluding the possibility of replication, Been employed census tracts. Of the 10 remaining sites, 7 were landfills and 3 were mini-incinerators. All of the mini-incinerators and 4 of the landfills were located in the early 1970s, and for these sites the 1970 census data were used. Two (2) of the remaining sites were located in the 1950s, and for these 1960 data were used, primarily for the sake of comparability with later data. The definitions of the tracts were changed in the 1950s, making the pre-1960 census data not comparable with the post-1960 data. The remaining landfill was located at its current site in 1980 and for that one, the 1980 census data were used. Been found that at the time of siting, four of the seven host communities to landfills had about the same or a lower percentage of African Americans as did Houston as a whole, while three had percentages above Houston. Of the three host communities to mini-incinerators, one was in an almost exclusively white neighborhood and two were located in areas that were more populated by African Americans. Moreover, after the sites were located the percentages of African Americans tended to increase, so that by the time of the 1990 census, all of the neighborhoods hosting landfills and two of the three hosting mini-incinerators had become home to a relatively high percentage of African Americans. A similar trend was found when looking at the host neighborhood’s economic characteristics. Been’s study was an extension of two case studies and therefore belongs in the category of case studies. As discussed in Chapter 5, this implies certain limits on the scientific value of the research. The substantive findings cannot logically be generalized to other places; the evidence suggested that market dynamics played a role in creating the current situation only in Houston (but not in the USGAO study in the Southeast). Also, the research methods did not enable causal attributions to be made in a positive sense, but rather only enabled particular causal processes to be eliminated from consideration by virtue of excluding possible sequences of events. Yet, the case study clearly established on empirical grounds, in a way that can be generalized, evidence that the historical sequencing of site and residential location decisions can potentially have an important bearing on the proper interpretation of current situations. Specifically, the findings clearly suggested the possibility that proposed policy solutions can run a substantial risk of addressing the wrong cause, if the relevant sequences of events are not adequately considered in the deliberations prior to making a choice in a policy or administrative decision.

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As a consequence of this study, one can confidently say that, in some instances, the siting process bears some responsibility for any observed disproportionate burdens of environmental risk faced by minorities and the poor. Similarly, one can also say that in some instances market dynamics play a significant role as well. Glickman (1994).**** The purpose of Glickman’s research was to demonstrate a way to use GIS to analyze environmental equity. He did this using both proximity-based and risk-based measurements in Allegheny County, Pennsylvania. Data from the TRI were used to divide Allegheny County’s industrial facilities into those that may pose chronic hazards and those that by virtue of containing extremely hazardous substances may pose acute hazards. For the proximity-based analysis, GIS was used to construct circles with radii of 0.5 mile, 1 mile, and 2 miles around each hazardous facility. On the basis of census data, the proportion of nonwhite residents and poor residents in each circle were calculated. The results indicated that the percentage of poor and the percentage of nonwhites were slightly higher in close proximity to the sites. For the risk-based analysis, the researchers considered the probability of accidental release of chemicals, their toxicity, the size of the area affected by such a release, the quantity released, the nature of the release, the release rate, and the weather at the time of the release. On the basis of these considerations, the risk posed by a release was estimated by the expected annual number of persons exposed to accidental chemical releases. The reported results indicated that people who were not white and were poor actually bore proportionally slightly less of the risk than they would have if equity existed. Other than to note that this research was subject to the same validity threats as pertain to all case studies, the procedures used to obtain these particular results cannot be evaluated on the basis of the information provided in the article. Bowen et al. (1995).**** This research focused on toxic chemical releases in Ohio and Cuyahoga County from 1987–1990, as reported in the TRI. It took a two-pronged approach to examine the issue of spatial scale of analysis, first by using all the counties in Ohio as the units of analysis and then all the census tracts in Cuyahoga County (including the Cleveland metropolitan area). The toxic releases were weighted by their inherent toxicity. Socioeconomic data were from the 1990 U.S. Census. Zero order and partial correlation analyses were used, and all data were adjusted as appropriate for spatial dependence and clustering problems. The statewide assessment of data aggregated to the county level indicated a strong positive relationship between the locations of releases and the locations of minority populations in Ohio. Further examination using analysis of variance showed, however, that


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the more highly populated urban counties were among those with the greatest release amounts. This gave support to the speculation that the positive relationships identified in the statewide assessment of county-level data may, on closer scrutiny, be attributable to differences between urban-industrial counties and rural-agricultural counties. Furthermore, the intrametropolitan analysis of Cuyahoga County indicated that minority concentration is greatest in those tracts without toxic releases and least in those tracts with them. At the same time the poverty rate was slightly greater in tracts with toxic release sites, and housing values and rents were lower. This research suggested that one’s conclusions about whether or to what degree minority, low-income, and other disadvantaged populations reside closer to toxic releases critically depend on the geographical scale of analysis one assumes. Its greatest strengths were in using the simplest possible yet appropriate statistical analyses. For example, because regression analysis is often taken to imply cause and effect, and correlation does not imply causation, regression analyses were deliberately avoided. Its greatest weaknesses were in the reliability of the TRI data and the case-study nature of the analysis. Though the researchers went to considerable lengths to clean and verify the data, the TRI data are notoriously inaccurate (see Chapter 4). As the research was a case study, the results lacked external validity. Hamilton (1995).**** Hamilton used EPA capacity expansion data and U.S. Census data to examine the relationship between the demographic characteristics of zip-code areas with TSDFs and the capacity expansion plans of those TSDFs for the years 1987–1992. The data consisted of records for all zip code areas that were reported to have had commercial hazardous waste facilities operating in 1986. The data reflected voting records, income, education, housing value, race, urban vs. rural, value of built land, hazardous waste capacity, transportation, and storage characteristics, and were analyzed on the basis of descriptive statistics, t-tests, and regression models. The t-test results indicated that, when compared to those facilities with no reported plans to expand, the average percentage of nonwhite persons was greater in the areas with facilities that planned to expand (25% versus 18% nonwhite persons). Similarly, the percentage of persons living in poverty was higher in the zipcode areas where expansion was planned (14% in poverty as opposed to 11% in zip-code areas with no expansion plans). When the more sophisticated regression models were built, the significant determinants of further capacity expansion were percentage of residents who voted (–), total population in the zip-code area (–), percent of the population who rented their home (+), median household income (+), and the number of tons of hazardous waste imported into the state (–). The percent nonwhite population was not significant. Statistical tests on the residuals from the regression models were not reported, so, as far as spatial clustering and dependence is concerned, there is

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reason to doubt whether the estimates were unbiased and the statistical hypothesis tests accurate. Past this, although this research clearly provided some meaningful results, it did not explicitly attempt to test broad hypotheses about disparities among racial groups in terms of exposure to pollution and resulting incidence of disease. Rather, it was an internal comparison of capacity expansion in TSDF areas and, as such, it addressed a question different than the one about disproportionate distributions. Thus the results bear more directly on which of three theories best helps to explain observed variation in hazardous capacity expansion than upon whether or to what extent disproportionate distributions and corresponding public health problems were found. Because the research used zip-code areas, and because these are too large to make any meaningful inferences about exposure, it has little if any bearing upon the question of the existence of racial or other disparities in public health. Heitgerd, Burg, and Strictland (1995).**** This research examined proximity to 1200 Superfund sites at the census-block level, using a GIS to compare the racial and ethnic characteristics of the population within 1 mile of the site to all other areas in the host counties. This included 670, or 22%, of the counties in the contiguous United States. Multiple analysis of variance was used, including a statistical control for which of the states and counties contained each particular site. The racial and Hispanic origin distributions around the sites were found to be generally in line with regional population differences. Overall, while about three times as many whites as all other racial and ethnic groups combined were found to live within 1 mile of the sites, the results support the hypothesis of disproportionate distributions. That is, a statistically significant difference was found in the mean percentage for each racial and ethnic group, except whites, between the population living within 1 mile of a site and the population living in the remainder of the county. This was a very well-conceived study and is probably one of the strongest pieces of evidence supportive of the hypothesis of disproportionate distributions in terms of race and ethnicity. At the same time, it has a number of limitations, several of which are noted by the authors. One of the foremost among these is that the data were purely cross-sectional, so no causal relationships could be indicated. And no statistical controls were included for income, zoning, industrial location, or any other variables. As a consequence, there is no indication of how much, if any, of the variation in the observed relationships is a mere artifact of other factors (Kriesel, Centner, and Keeler 1996), some of which would lead logically to a very different interpretation. For example, if an observed relationship between race and site location turned out to be spurious with income, the most direct implications would have less to do with race than with the values behind the free market system


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in the United States. Also, to include zoning controls would likely have altered the statistics by further restricting the areas included in the analysis— and data on industrial location would have gone a long way towards explaining the observed relationships with race. No historical data were included, so no inferences could be made regarding whether the sites or the populations came first in time. Furthermore, to make any inferences from the results of the analysis of NPL sites to other environmental hazards, one must make the implausible assumption that the observed distribution of NPL sites was representative of all environmental hazards. Finally, the analysis contained no exposure data, and it is almost certain that the data include many observations of people living near sites who are not at risk of being exposed to environmental hazards and exclude many observations of people who are. Glickman and Hersh (1995).**** The primary purpose of this research was to develop and implement procedures with which to estimate any environmental inequities associated with selected industrial hazardous sites on selected social groups in Allegheny County, Pennsylvania. The tools and techniques used were largely centered around GIS. The variables of interest were proximity to and levels of risk posed by the hazardous industrial sites. Demographic variables included minority status (black), income, and age. Considerable variation in the level of risk and the demographic characteristics of the nearby residents was found from one hazardous industrial site to another. While the authors therefore recommended that generalizations about inequities should be avoided, they nevertheless found them with regard to blacks, poor blacks, poor whites and the elderly, both in terms of proximity and risk. The research clearly demonstrated the potential influence of the choice of a geographical unit of analysis on the conclusions from a research study. It also established that risk-based evaluations of equity differ from proximity-based evaluations, even with regard to a particular given set of hazardous sites. This research was more-or-less descriptive and more oriented toward development and demonstration of techniques than establishment of empirical results. All of the inferences were based on simple univariate comparisons of the variables in the area immediately surrounding the hazardous site with the rest of the area in the county, so each was similar to a static group comparison. Accordingly, the results of this study were all subject to the same validity threats that threaten all static group comparisons. The contribution of Glickman and Hersh’s research was far more a matter of tools and techniques than of definitive empirical findings for Allegheny County. In this regard, it was clearly outstanding work; it showed the power and potential of GIS in the environmental justice context. In terms of empirical findings, however, it was of limited value, especially because all the included tests of inequity were univariate in nature, and all of the empirical

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findings were so highly contingent and variegated that no broad and simple generalizations were possible. Oakes, Anderton, and Anderson (1996).***** This was the first published national-level longitudinal study of the residential characteristics of census tracts that have one or more of 476 commercial transfer, storage, and disposal facilities (TSDFs). The data on residential communities, which came from the 1970, 1980, and 1990 Censuses, were aggregated to the census-tract level. The data on TSDF locations came primarily from the 1992 Environmental Services Directory and were supplemented by a telephone survey. The comparison group, with which the residential characteristics of tracts with TSDFs were compared, was adjusted to minimize the effects of changes in census-tract definition over time and also restricted to metropolitan areas or rural communities that contained at least one TSDF. The variables used to summarize the racial and ethnic composition of the communities were percent black persons and percent Hispanic persons. The variables used to represent disadvantaged or lower-status populations were percentage families at or below the poverty line for nonfarm families of four and percentage of households receiving public assistance. Other variables included percentage of males in the civilian labor force who are not employed, percentage of persons employed in manufacturing and industrial occupations, mean value of the housing stock, and total persons. These data enabled the researchers to look at neighborhood characteristics before and after new TSDFs were sited. It also enabled them to control for community characteristics in other communities that are similar in terms of these characteristics but have no TSDF sites. The results (based on two sample t-tests) of a “before and after” longitudinal comparison of TSDF tracts in the 1970, 1980, and 1990 censuses to tracts without TSDFs in the decade the TSDF began operation indicated that, compared to other industrial areas, TSDFs were not systematically sited in poor or minority communities. Rather, they tended to be sited in areas that subsequently followed general population trends in tracts without TSDFs, and they tended to become somewhat more similar over time to communities with above-average industrial employment. The results of a “before and after” longitudinal comparison of TSDF tracts to tracts without TSDFs specifically in terms of persons employed in manufacturing or industrial occupations indicated that TSDFs were sited in tracts that were initially more industrial, compared to other tracts, and which remained more industrial after siting. In summary, when consistent areas and comparison models were used, no evidence was found supporting the hypothesis of socially discriminatory siting. Nor did the evidence suggest that the siting of a TSDF differentially altered migration processes with respect to race or ethnicity. Rather, on balance, this research indicated that the tracts containing TSDF sites were best


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characterized as largely white and disproportionately industrial, workingclass residential areas. Anderton, Oakes, and Egan (1997).**** These researchers examined whether national demographic patterns could be discerned at a census-tract level around abandoned toxic waste sites, specifically those regulated under the Comprehensive Environmental Response Compensation and Liability Act of 1980. Both the locations of the sites and their placement on the NPL were examined. The site data were from the EPA’s CERCLA files. These were analyzed in relation to the 1990 tract-level census data, including 61,258 tracts and more than 200 variables measuring neighborhood characteristics. Two different comparison groups were used, one included every other neighborhood tract in the country, and the other included all tracts in the same metropolitan area as the particular site. (In other words, only census tracts from metropolitan areas were included in the second group, but if a metro area did not have CERCLIS sites, the census tracts in that area were not included.) Independent sample t-tests and Poisson regression analyses were used. The results strongly contradicted the hypothesis that the sites were more likely to be found in minority and disadvantaged communities. At the same time, however, they indicated that, as the percentage of minorities and socioeconomically disadvantaged people in a neighborhood with a CERCLA site increased, the probability that it would be placed promptly on the NPL decreased. The analysis and methods used in this research met standards considerably beyond those that characterize much of the larger body of environmental justice research, but this research too had its limitations. Failure to account for spatial dependence could have substantially influenced the findings. Especially given a nationwide view, in which CERCLA sites cluster densely around heavily urbanized industrial areas, this could be a major consideration. Also, the article acknowledged that the research did not enable one to determine the cause of the observed lower probability that CERCLA sites in minority and disadvantaged neighborhoods will be placed promptly on the NPL. One possible inference consistent with the research findings is that there is a slight bias in the prioritization process. Another is that minority and disadvantaged communities are by chance slightly less likely to live near the most hazardous sites. Boer et al. (1997).**** This research was based on the analysis of demographic data in relation to the locations of hazardous waste TSDFs in Los Angeles County census tracts. Both univariate and multivariate analyses were conducted, including controls for land use zones. The reported results stated, in essence, that TSDF facilities in this county tended to be located in working-class communities of color located near industrial areas.

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This research was well done, and as such it contributed a fairly reliable and clearly communicated summarization of the large quantities of data involved. The models clearly indicated the possibility that the locations of the residences of lower-income minority populations, largely Hispanic, were correlated with the locations of TSDFs in the study region. Past this point, however, several concerns arise in relation to the research methods, some of which can lead to reasonable doubt as to the accuracy and relevance of the findings in relation to the question of whether certain populations are disproportionately exposed to environmental hazards. First, as the authors acknowledged, the research was in essence a case study of Los Angeles. Second, the fact that it was a cross-sectional study implies that it did not contain any information about changes in the study region over time and therefore could not provide any explanation of why the observed proximity relationships occurred. Third, the relationships established in the research were proximity relationships, and proximity relationships are not conceptually or epidemiologically the same as exposure or risk relationships. Fourth, the authors did not provide an adequate rationale for their selection of comparison locations. This is important because the conclusions from environmental justice research can be very sensitive to these locations (Anderson, Anderton, and Oakes 1994; Anderton 1996; Anderton, Oakes, and Egan 1997). Their statistical inferences were thus likely to be sensitive to truncation in their data, resulting from the evident inclusion of tracts with no industrial land; that is, none of the census tracts without industrial land at the time of particular siting decisions were potential locations for that TSDF facility. Conceptually, it therefore made little sense to include those tracts in the comparison tracts in the analysis. Of course, it is logically possible that the tracts without industrial zones are also the ones with the fewest minorities and were so at the time of the siting decision. If so, to insist on excluding them on conceptual grounds could be to demand reformulation of the problem in such a way as to take the teeth out of the analysis. In any case, one wonders about their rationale and procedures for selecting and dealing with comparison tracts within the study region and how sensitive the conclusions were to the feasible alternatives. Finally, the authors did not provide reports of adequate residual analysis, a concern that is especially crucial in light of the fact that the figures and maps in the research report indicate that the data may contain high levels of spatial clustering. While all of their models assumed that each observation was spatially independent, the clumping of sites strongly suggested that they were not. In this regard, one wonders whether the lack of documented residual analysis indicates faulty analytical procedures or merely poor documentation, and, if faulty analytical procedures were used, one wonders how significantly they influenced their conclusions. Liu (1997).**** Liu based this research on a well thought out analytical framework centered on existing theories of neighborhood change. The null


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hypothesis was that there was no significant difference between the socioeconomic characteristics (percent black and median family income per the U.S. Census) of Houston communities “before” and “after” hazardous sites were located there. The research was quasi-experimental, based on a two-group pretest-posttest design. The design can be described as follows: N N

O O

X X

O O

where O represents an observation, X represents a treatment, and N stands for “not randomly selected.” The site data, being from Houston, were similar to those used in Been (1994), including nine mini-incinerators and landfills, with the addition of presiting data. “Communities” were defined using censustract boundaries. The comparison group was selected on the basis of similarity of the tract in terms of minority composition, similar demographic change during the presiting decade, and proximity to the nine hazardous sites. Statistical inferences were based upon two-sample t-tests and Wilcoxon rank-sum tests of tracts before and after the hazardous sites were located. The results indicated no significant difference between the socioeconomic characteristics of the tracts in the test group and the control group, meaning that the tracts with the sites were not significantly different than those without. The forces stimulating neighborhood change were thus evidently similar for the neighborhoods with and without hazardous sites. This research did not exclude the possibility that the hazardous sites negatively effect public health in the nearby communities, but it did demonstrate that it can be misleading to make inferences on the socioeconomic effect of hazardous sites on nearby communities by analyzing the postsiting changes alone. While a number of potentially confounding variables such as industrial location remained uncontrolled in the design, in the absence of having found any statistically significant relationships between race or income and the location of the hazardous sites, these omissions hardly seem relevant. Also, since this was in essence a case study of Houston, the results have no bearing in any substantive way on any other locations in the country. Stretesky and Hogan (1998).**** This research was primarily a crosssectional analysis of the racial, ethnic, and economic characteristics in census tracts around Superfund sites in Florida, peripherally fortified by some additional comparisons over three 10-year time periods dating back to 1970. The cross-sectional findings indicated that minorities (blacks and Hispanics) were more likely to live near the sites, but not residents with low income or those living in poverty. Analysis of the locations of minority residences and Superfund sites from one time period to the next indicated that minorities tended to be moving into the areas around the sites, suggesting that the current observed

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relationships were attributable more to housing and employment discrimination than discriminatory siting practices. Though this research was clearly one of the most convincing sources of evidence of disproportionately high numbers of minority residences in close proximity to hazardous waste sites, it too had its weaknesses. First, the conclusions would have been much stronger had the comparison tracts only included the alternative possible sites. For instance, tracts zoned exclusively residential at the time of siting could not possibly have provided an alternative site. Depending on their demographic composition relative to the feasible alternative tracts, including them in the comparison tracts could have heavily influenced the parameter estimates provided by the analysis. Second, Stretesky and Hogan reported on having thoroughly tested for multicollinearity in their model, but then did not mention the likely far more relevant spatial considerations of spatial dependence and clustering. Third, the interpretation of the comparison of demographic data in Superfund tracts over time presupposes that these sites were located in these tracts in 1970. Yet, it is not stated for certain that they were so sited. Finally, though their models controlled for housing values, income, percentage manufacturing employment, and urbanization, given the results in Kriesel, Centner, and Keeler (1996), it would have been better to have controlled for proximity to transportation routes, too. Any questions about the analytic methods aside, this is one of the better conceived, conducted, and reported research articles available, suggestive of disproportionate proximity to Superfund sites, specifically among minorities in Florida. Neumann, Forman, and Rothlein (1998).**** This research employed several health-related databases to establish “hazard potential rankings” for TRI release sites in Oregon and related these descriptively to demographic and socioeconomic data at the census-block level using a geographical information system. The results were mixed. It turned out that almost 90% of the population living within 1 mile of a TRI site were white, but a disproportionately high percentage of the total racial and ethnic population of the state were found there. As the distance increased from the sites, the percentages of each minority group (blacks, Hispanics, Native Americans, and Asians) were found to decrease, indicating that there is a disproportionate location of TRI facilities in minority neighborhoods. The TRI sites were also evidently located in areas with lower incomes as compared to those in the surrounding county. In contrast, there was no evidence of a correlation between the hazard potential of a site overall and the socioeconomic characteristics of the surrounding communities. Though Neumann, Forman, and Rothlein’s analyses were clearly very thorough, all were limited by their exclusively bivariate nature. This is of concern especially given the evidence found in Kriesel, Centner, and Keeler (1996)


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that observed bivariate relationships between race and the location of hazardous sites can sometimes be shown to be spurious in the light of fuller, multivariate analyses. Moreover, because Neumann, Forman and Rothlein’s analyses were entirely cross-sectional and descriptive they provide no sense of underlying explanation for the observed relationships. The results from this study are probably most aptly characterized as mixed, since they necessitate another decision regarding whether to refer to the proximity measures as opposed to the hazard potential measures as a basis for diagnosing disproportionate exposure. On the one hand, the analysis of proximity to sites provides clear bivariate evidence that minorities were more closely located to the sites. On the other hand, the fact that there was no evidence of a relationship between the hazard potential of a site overall and the socioeconomic characteristics of the surrounding communities is entirely inconsistent with the hypothesis of disproportionate exposure. In any case, on the basis of the results reported in this study, it would seem worthwhile to pursue a fuller multivariate study in Oregon, complete with the appropriate spatial statistics. Haynes, Lall and Trice (1999).**** This research examined censusblock–level data from Cuyahoga County, Ohio, to search for relationships between the location of TRI releases and the socioeconomic characteristics of nearby residences. The core concern in the research was to assess the effect of statistical adjustments for spatial clustering and dependence specifically in terms of the parameter estimates from regression models of disproportionately high concentrations of minority, low-income, and other disadvantaged populations nearby environmental hazards. Locations of TRI sites were used to represent the environmental hazards. The socioeconomic variables, from the 1990 census, included number of families, number of households, median income, median housing value, percent black population, percent Hispanic population, and percent of the population in various age groups. Before any statistical adjustments were made, the regression analysis found two variables significantly related to the locations of TRI releases at the census-block level, percent Hispanic (positive) and median housing value (negative). The levels of spatial dependence were then diagnosed and remedied as appropriate, by screening for spatial dependence (Getis 1995), and both of the relationships became statistically insignificant. The conclusion was that it is crucial to use the appropriate spatial statistics, otherwise incorrect conclusions about disproportionate distributions of environmental hazards may be drawn. While the substantive conclusions concerning the absence of any relationship between TRI sites and socioeconomic variables in Cuyahoga County simply confirmed the results established in Bowen et al. (1995), potentially providing some criterion validity, this study contributed a more

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refined analysis, including smaller geographic units and more advanced spatial statistics. More importantly, however, the study illustrated the importance of using the appropriate spatial statistics, specifically within the context of environmental justice research. Again, this was in essence a case study of Cuyahoga County, Ohio, and the study has not yet been subjected to the peer review process. So the results are not strictly generalizable to counties other than that one and have not been validated by the peer review process. Nor is there any reason to think that the spatial distributions of TRI sites and nearby neighborhoods in Cuyahoga County are representative of the broader distributions of hazards and nearby communities at the state or national level. Indeed, TRI sites represent only one of numerous hazards in the environment to which populations can possibly be disproportionately exposed, and the absence of a relationship between the spatial distribution of these sites and the spatial distribution of certain populations does not imply anything at all about other hazards. Sadd et al. (1999).**** This study, the most recent of several studies that looked at southern California, dealt exclusively with air pollution. The pollution data reflected stack and fugitive air emissions in 1992 as recorded in the TRI. The data for the demographic variables, which were generally the same as those used in prior studies evaluating disproportionate proximity to environmental hazards, were from the 1990 Census. These were analyzed using geographical information systems together with a variety of fairly sophisticated statistical analyses (including univariate comparisons and regression models). The conclusion was that, even controlling for other factors such as income and the extent of manufacturing employment and industrial land use, minority residents (primarily Hispanics) tended to be disproportionately located in neighborhoods surrounding these toxic air emissions. The statistical models in this research were reasonably well done, so the research undoubtedly contributed a fairly reliable and clearly communicated summarization of the large quantities of data involved. Moreover, the results clearly indicated the possibility that the locations of the residences of racial minorities, primarily Hispanics, were correlated with toxic chemical releases into the air in the study region. Past this point, however, the models are based solely upon cross-sectional data. Consequently, they provided no insight into the nature of the causal processes involved, so the results are largely irrelevant in terms of informing policy and administrative decisions. Any causal schemas used to reason about them remain entirely within the domain of unaided perception, hunch, intuition, or common sense, none of which can constitute an adequate basis for the sort of complex and highly consequential policy and administrative decisions involved. Finally, the researchers also neglected to systematically consider and deal with the issue of spatial dependence, a point that could possibly have very significant bearings on the


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results, especially in light of the clustering of sites evidenced on the maps in the article. SUMMARY OF THE EMPIRICAL RESEARCH Since organizations and individuals throughout the federal and state government systems are mandated to make environmental justice a part of their mission, it is virtually inevitable that some empirical statements will serve to help define related policy and administrative decisions. In turn, the success of these decisions at reaching their stated goals is likely to rest in large part on whether or not these empirical statements are valid, or at least could potentially accrue validity through successively more stringent tests. If the decision problems are described primarily on the basis of poorly established empirical statements, they are far more apt to lead to incompletely justified and ultimately inadequate outcomes. To use invalid empirical statements in describing a decision problem is to underconceptualize it, and history is replete with instances demonstrating that underconceptualized ways of grappling with societal problems are partially successful at best and, at worst, have disastrous side effects (Warfield 1990). Literally dozens of published articles on environmental justice begin by stating that a large body of empirical research demonstrates that minorities, low-income, and otherwise disadvantaged and susceptible neighborhoods are disproportionately exposed to environmental hazards. The implication is usually that the activities of society overall have imposed correspondingly greater health risks on the people living in these neighborhoods and that, in turn, these are unjust or unfair. The residents neither chose to take the risks nor to benefit proportionally from the activities from which they came. However, in light of the preceding review of the research, a much more accurate and fitting statement goes something like this: A fairly small and largely heterogeneous body of research hints or perhaps even indicates (but by no means demonstrates) that in some specific areas, some ostensibly identifiable groups in the population may in some instances live closer to some selected environmental hazards. Simply stated, on balance the evidence regarding disproportionate distributions is mixed and inconclusive. Moreover, even assuming that the conclusions from it were strong, clear, and distinct—suggesting that patterns of disproportionate exposure have been systematically identified throughout the country—essentially none of the research is meaningfully linked to actual exposure and associated public health effects. As a consequence, little to nothing can be said with scientific authority regarding the existence of geographical patterns of disproportionate distributions and their heath effects on minority, low-income, and other disadvantaged communities. Indeed, the empirical foundations of the entire issue, as cast in the political discourse, are very weak.

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It is argued in Chapters 8 and 9 that it therefore is not feasible for policy and administrative decision-makers to generate clear and adequately definite problem descriptions to make choices in response to these putative patterns and expect outcomes that contribute a remedy or a solution. Indeed, it also therefore appears that the entire set of problems and issues related to widespread geographical patterns of disproportionate distributions, as reflected in the related discourse, misses (and possibly even diverts attention from) any serious consideration of public health in minority and low-income neighborhoods. Past this point, the most salient feature of the considerably smaller subset of this body that could withstand reasonable scientific scrutiny is a high level of uncertainty. Seen in proper perspective, one is led not so much to any clear stance one way or another on the hypothesis of broad geographical patterns of disproportionate distributions as to a view that the related issues and problems have not been thoroughly and systematically researched and documented. The research needed to develop a well-documented understanding of the empirical basis of disproportionately distributed environmental risk is, in this perspective, way too close to infancy to constitute anything like a well-developed body of knowledge, as some environmental justice policy advocates would have one believe. The first set of results to summarize contain the subset for which the research was sufficiently well designed, conducted, and documented to be substantially accurate and have scientific merit. These are the results I could recommend considering as having the minimal level of scientific merit to use as input into policy and administrative decisions with enormous resource allocation implications. With respect to only these results, the overall summary is entirely mixed and inconclusive. In general terms of national trends, on the basis of only this subset, contrary to prevailing opinion, there does not appear to be any consistent, statistically significant pattern of racial or ethnic discrimination in the location of hazardous sites. If anything, as a rule, the hazardous sites seem to be located in white working-class neighborhoods with residents heavily concentrated in industrial occupations, living in somewhat less expensive neighborhoods. As a whole, the body of research on disproportionate distributions does not unambiguously support the constituent perceptions and intuitions of broad-scale systematic inequity, as many of the environmental justice advocates and others who have not thoroughly and properly reviewed the entire body of relevant research would insist.6 To the extent that any meaningful patterns can be discerned in this subset, they occur at a subnational level. Even Heitgerd et al. (1995), who present some of the better evidence supportive of the hypothesis at the national level, point out that different regions in the country exhibit strikingly different patterns. For example, heavily Hispanic California counted for almost one-quarter of the total number of people in the analysis and the sites there were located in relatively more densely pop-


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ulated areas. This differs from the Southeast, where the percentage of blacks around NPL sites is largest. This is consistent with Anderton et al. (1994a, 1994b), who provided a fairly solid indication that in the southwestern part of the country there are more Hispanics living near the sites, especially in southern California (Boer et al. 1997; Sadd et al. 1999). However, to be consistent with the entire range of related findings in this subset, any generalized and reliable statements must be heavily qualified. For instance, there are firm indications that overall in the South Atlantic region (EPA Region IV), especially Florida (Stretesky and Hogan 1998), there tends to be significantly more black people located near hazardous sites (Anderson et al. 1994). However, differences in spatial aggregation and analytic procedures between research projects preclude determination of the extent to which any of the observed relationships are attributable to a couple of highrelease-level nonmetropolitan counties, such as Greene, Alabama, and Washington, Mississippi (Cutter and Soleki 1996). Also, it would be incorrect to say in general that minorities in that region are disproportionately proximate to the sites, since in the same region there is evidently a significantly less than average percentage of Hispanics located near them (Anderson et al. 1994). In terms of the northwestern, midwestern, and northern parts of the country, no general statements about the relationship between hazardous sites and socioeconomic variables can be supported by the totality of methodologically sound research results. The results are mixed (Glickman 1994; Neumann, Forman, and Rothlein 1998), show no relationship (Bowen et al. 1995; Haynes, Lall, and Trice 1999), or show the opposite relationship than expected (Anderson et al. 1994 found significantly fewer blacks living near hazardous sites in the North). In any case, given the mode of evidential reasoning in all these studies, none logically proves or even demonstrates the existence of the putative pattern, but rather more accurately indicates its existence, specifically by making the hypothesis of disproportionate distributions more likely than before. A couple of other apparently methodologically sound research projects should be mentioned in this context, though neither deals directly with the central question of whether or to what extent disproportionate distributions exist and negatively affect public health in minority communities. For one, Been (1994) showed convincingly that the causal processes involved in observed instances of disproportionate distributions can be complex in ways that preclude the possibility of remedy via simple government policy and administrative actions. While her research did not positively establish what cause was actually operative in Houston, her close scrutiny of the time order of events there, though essentially a negative reasoning process, did render unsatisfactory simple single-cause explanations for the observed disproportionate distributions. Moreover, since each of the operative causes that appeared possible after her research implies a different policy and administrative

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response, this makes it much more difficult to determine the appropriate governmental actions for ameliorating or remedying any associated problems. Also, Hamilton (1993, 1995) provided solid evidence that collective political action can have a strong effect in terms of keeping hazardous sites out of counties (and presumably neighborhoods) where location and expansion decisions are being deliberated, at least at the county level. Thus, in the face of uncertainty as to the empirical existence of current patterns of disproportionate exposure, there appears to be sound reason for those living in minority, low-income, and other disadvantaged communities to orchestrate efforts designed to preempt any possible future problems. The next subset of research projects to summarize ought rationally to receive less weight in deliberations for policy and administrative decisions. It contains findings that could potentially accrue enough validity to justify secondary consideration in policy and administrative decisions but nevertheless have flaws in data, design, or methods that cast reasonable doubt on their scientific merit. Again, these include spatial aggregation problems, incompletely specified models, lack of documentation, improper comparison groups, design for purposes other than the pertinent one, or a mixture of these. Notwithstanding these considerations, taken as a whole, one either simply cannot generalize from these projects or cannot generalize the findings from some of them without ignoring inconsistent or even contradictory findings from others. In terms of nationwide patterns in the proximity of hazardous sites to minority, low-income, or otherwise disadvantaged communities, no simple generalizations can be made on their basis. Taken as a whole, the relevant results are again either mixed or inconclusive, or the project was conducted at an inappropriate level of spatial aggregation to draw conclusions related to public health in minority communities (Hird 1993; Zimmerman 1993; Perlin et al. 1995). The study regions for all of the other studies in this category were states or smaller. In this research again, in Florida, there was indication of disproportionate proximity to hazardous sites, especially among low-income black and Hispanic residences (Pollock and Vittas 1995). In Texas, the results were again mixed and inconclusive. One study there found a relationship between hazardous sites and lowincome residences (Yandle and Burton 1996) and one found no such relationship (Napton and Day 1992). In terms of race and ethnicity, neither of these studies found a discernable relationship. Similarly, the findings in Birmingham, Alabama were mixed (Cowart and Holmes 1999). The findings in Ohio and Georgia both depended on how complete the model was, so no firm general conclusions regarding the hypothesis of disproportionate exposure can be reached (Kriesel, Centner, and Keeler 1996). In the Tri-county area around Detroit, the data seem to have been more consistently tied to economic status than to racial status, but at the same time the researchers reported that the distribution of environmental disamenities seems to have been related to racial


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segregation (Tomboulain et al. 1995). Indeed, there is only one region where simple clear-cut generalizations can be made on the basis of these projects. Both race and income seem to be related to air pollution in New York and Philadelphia (Liu 1996). The third and final subset of research includes those projects in which the study design and methods have enough substantial flaws to be judged not to contain any information with scientific merit substantial enough to contribute anything to knowledge about the relationship between hazardous site location and socioeconomic variables. Much of it is anecdotal, circumstantial, or based on case studies more appropriate for qualitative description than establishing relationships between variables. In any case, as a rule it was not designed, conducted, or documented well enough to determine with any confidence whether it contains any scientifically meritorious conclusions and so should be considered purely conjectural or simply false. While the results of these projects have been reviewed here, on the basis of flawed methods and consequent limitations on the potential for them to accrue validity, these results ought not to receive any consideration in policy and administrative decisions, and they receive no further consideration here. Accordingly, on balance, scientifically valid empirical statements about environmental justice that meet with substantial agreement within the scientific community—especially those with relevance for policy and administrative decisions—are hard to come by. When the body of related empirical research is seen in its proper scientific perspective, there is above all a great deal of uncertainty surrounding the related issues and problems. Most of the available research is essentially descriptive, not explanatory, in nature. Most of it is based on inadequate statistical analyses, especially as it relates to spatial clustering and dependency. Essentially all of it is inadequately designed for the purposes of explicitly establishing relationships between particular environmental hazards and public health in minority communities. Much of it is based on unreliable data. A relatively large subset of it uses inappropriate comparison groups. Some of it fails to recognize the crucial difference between cross-sectional correlation and causation. As a consequence, in general, there is a scarcity of scientifically acceptable evidence documenting differences by ethnicity/race and socioeconomic status for proximity to environmental agents, much less associated dose rates and related health effects. Current information and sound empirical knowledge is lacking, among many other things, on location-specific spatial distributions of demographic and environmental variables associated with environmental risks. Similarly, social and historical patterns associated with the nature and extent of specific types and dosages of exposure to various chemicals have not been anywhere near conclusively established. Specific health outcomes associated with various exposure dosages are often unknown, and as a rule the relationships between exposure and the health and well-being of populations in minority,

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low-income, and other disadvantaged neighborhoods have not been determined. A great deal of uncertainty remains with respect to the relative magnitudes of various environmental risks. The costs and benefits of alternative approaches to risk reduction and management remain largely unknown. Little is known scientifically about community values toward the distribution of risk, risk perceptions and attitudes, risk communication, the epidemiological effects of particular risks, or the feasibility of public risk compensation and/or insurance programs. Until the research needed to sufficiently reduce this uncertainty is completed, whether or to what extent minorities, lowincome, and otherwise disadvantaged and susceptible populations disproportionately experience exposure to environmental hazards, and therefore face greater health problems, is a question that will remain largely unanswered.

CHAPTER 7

The Impacts of Environmental Hazards: An Overview of Some Ancillary Research with Aster Girma If we are to solve effectively the problems which confront us, and at the same time, to protect and promote such values as freedom and representative government, we need to create and maintain policy dialogues, supported by research, concerning problems, causes and options in order to clarify the perceptions of policymakers and the public and to broaden and strengthen the areas of agreement. —R. W. LAMSON, 93 Congressional Record E7277-E7279, DECEMBER 18, 1974

The discourse about environmental justice refers continually to disproportionate distributions of environmental hazards and, specifically their impacts in terms of public health in minority, low-income, and other disadvantaged neighborhoods. The review of the empirical research in Chapter 6 reveals that in reality little is currently known empirically about disproportionate distributions. Because this knowledge is logically antecedent to any knowledge of associated public health effects it implies that next to nothing with any generality can possibly be currently known empirically about any effects associated with disproportionate distributions of environmental hazards. Indeed, any general propositions about any of the impacts of disproportionate distributions that would stand up to reasonable scientific scrutiny must be stated in broad terms that do not translate easily into meaningful deliberations over policy and administrative choices. For instance, it is known that environmental amenities and disamenities, many of which are produced by what society does collectively, are distributed across geographical space throughout communities, nations, and the globe. Moreover, since what society does collectively affects environmental quality, and environmental quality affects the health and well-being of individuals and communities, the geographical distribution of environmental quality is inextricably bound up with the geographical dimensions of distributive justice. Beyond this, if one wants to I heartily thank Aster Grima, Ph.D. student at Cleveland State University, for initiating and doing the bulk of the research for this chapter.

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make any more specific empirical statements of a sort that can meaningfully inform deliberations over policy and administrative choices, the matter quickly becomes very vague and difficult to think clearly about. When distributions of environmental hazards are perceived to be inequitable (i.e., to violate social standards of fairness), especially when socially produced environmental disamenities preclude the environmental conditions in which people can be physically, mentally, and socially healthy, they become a common concern having social, cultural, economic, and psychological dimensions. The purpose of this chapter is to review some of the relevant empirical research and other literature focused more narrowly on a few of these dimensions. Particular consideration is given to public health, that is, what society does collectively to ensure the conditions in which people can be healthy (International Federation of the Red Cross 1999). This— the indirect costs of collective social actions on minority, low-income, and other disadvantaged neighborhoods—is key. It intellectually justifies government involvement and action on the basis of widely accepted and practiced economic theories, specifically related to external costs. While, according to the environmental justice discourse, the basic concern is with the indirect impacts of environmental hazards in particular on public health, other impacts of collective action, such as those on real estate values, can also be constructed to be of related concern. IMPACTS OF PROXIMITY TO HAZARDOUS SITES ON PUBLIC HEALTH Considerations over proximity to environmental hazards, particularly hazardous sites, arise in the political and social discourse and appeal to people’s passions largely because of implied public health risks. Some agencies, such as the Environmental Defense Fund, have helped in some ways to inform this discourse by generating and making available lists of environmental chemicals and their known and suspected health effects. This information almost certainly helps in terms of forming broad, essentially intuitive yet informed views of a given situation in broad areas such as counties. But in the larger context of making good policy and administrative decisions related to environmental justice generally and disproportionate distributions specifically, the available information is only a small part of what is needed for decisions to be made on sound scientific foundations. The rest is difficult (if not practically impossible) to obtain. Demands for reliable information about the public health effects posed cumulatively by the entire array of environmental hazards affecting any one particular set of microgeographical locations, such as groups of neighborhoods, go way beyond the reach of the currently best available data and empirical knowledge. In general, chemicals are identified, classified, and assessed as toxicants in the hazard identification stages of risk assessment (see Chapter 3), often by

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authoritative national and international scientific studies. Their health effects are typically classified as related to cancer, cardiovascular or blood toxicity, developmental toxicity, endocrine toxicity, gastrointestinal or liver toxicity, immunotoxicity, kidney toxicity, musculoskeletal toxicity, neurotoxicity, reproductive toxicity, respiratory toxicity, and skin or sense organ toxicity. To date, most identification, classification, and assessment research has focused on three of these categories: cancer, reproductive toxicity, and developmental toxicity. No authoritative scientific or regulatory agencies routinely categorize toxicological evidence in an attempt to identify and assess other chemicals such as suspected neurotoxicants or endocrine disruptors. A small subset of environmental chemicals and chemical categories evidently accounts for the majority of public health risks associated with hazardous air pollutants. Nationally, just six pollutants account for over 90% of the estimated lifetime cancer risk associated with outdoor hazardous air pollutant exposures. Just two pollutants were known to be the main contributors to noncancer hazards. The measurements needed to fully describe the geographic distribution of these are not particularly accurate, especially in the microgeograpical terms needed to meaningfully study disproportionate public health impacts by race and income. The risks to public health posed by many of the thousands of chemicals released on land and in the water have simply not been evaluated. There are several limitations and obstacles that lead to the difficulty with assessing the specific environmental risks that affect any one particular microgeographical location. One is the problem of extrapolating across scales of space, time, and social organization; that is, for the most part, scientific data related to specific hazards, such as particular chemicals, are limited by what can be obtained in a controlled laboratory setting or in a limited field study. Outside these settings, there are substantial obstacles involved in generalizing from individual laboratory studies of particular chemicals to entire arrays of chemicals in real-world neighborhoods. Another set of obstacles concerns measurement uncertainties; conditions of observation; and inadequacy of the models, methods, and procedures that affect all risk analyses. Imperfect knowledge of human behavior, the variety of possible exposure pathways for any given chemical, differences in duration and extent of exposure, and the fact that the same disease may have been caused by numerous different agents are all confounding factors in estimating exposures and their public health effects (Spivey 1994). Still another obstacle arises as a consequence of limits on monitoring facilities with which to acquire data. For instance, there are fewer than 50 monitoring stations measuring outdoor levels of hazardous air pollutants in the entire United States, and those measure only a few pollutants. An obstacle specifically pertaining to related policy and administrative decisions arises from ill-informed public postures toward environmental chemical releases; that is, if a chemical is allowed by law to be released into

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the environment, most people tend to assume that it must have been tested and evaluated for its potential risks. In reality, however, the process of obtaining knowledge about chemical hazards requires extensive and often very expensive empirical research on the kinds of specific health impacts each chemical can produce. If the basic tests to check on a chemical’s toxicity have not been conducted, or if the results aren’t publicly available, current laws tend to treat that chemical as if it were perfectly safe. As a consequence, it is difficult if not impossible to easily obtain reliable information about the cumulative risks associated with exposure to the array of environmental hazards that affect any given neighborhood, much less the public health effects attributable to proximity to specific locations. Another major obstacle in assessing the public health risk from environmental hazards has to do with estimating their marginal effects in the presence of other environmental hazards. There is tremendous variation from place to place in the amounts of other hazards, besides hazardous sites, present in many locations, many of which can have similar health effects. For example, while some air pollution comes from point sources of emissions, such as manufacturing facilities, chemical plants, steel mills, oil refineries, power plants, and hazardous waste incinerators, there are also many mobile sources, sometimes of the same toxic chemical. These include cars, trucks, and buses, ships, airplanes, and agricultural and construction equipment. Indeed, in some places the mobile sources together contribute a huge percentage of the pollution (e.g., overall in the United States about 75% of carbon monoxide pollution, and more oxides of nitrogen come from mobile sources rather than area or point sources (Environmental Defense Fund 2000). The existence of such a range of different sources creates research difficulties when it comes to identifying the particular source of the toxicant with the particular detrimental health effect. While it may be known in general, on the basis of laboratory research, that a particular chemical released into the environment has a particular health effect, it can still be difficult to determine in a particular neighborhood whether the residents’ exposure to that chemical was attributable specifically to point or mobile sources. Moreover, the same health effect may have been caused by a relatively small activity such as a dry cleaner, gas station, or an auto-body paint shop (known generally as area sources). Or it may have come from indoor sources. Thus to establish with any validity that the source of a chemical that led to a given disease or health problem is a particular hazardous site, rather than one or more of these other possibilities, requires holding these others constant, either through experimental or statistical controls. Accordingly, even if in an effort to empirically establish the public health effects of disproportionate distributions one counterfactually assumes that such distributions have been demonstrated, associated research exclusively emphasizing proximity to hazardous sites ignores potentially crucial variation in terms of these other sources of hazards.


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Still another major set of obstacles to obtaining empirical knowledge about disproportionate distributions and their public health effects in minority, low-income, and other disadvantaged communities arises from the fact that proximity to hazardous sites is only one, and not necessarily the most important or highest, priority of the broader range of related hazards. The broader range includes access to and willingness to use medical services, other aspects of the physical environment (e.g., natural hazards or aesthetics), the biological environment (e.g., microbial pathogens) and the social environment (e.g., economic and political conditions). One of the major reasons that the environmental justice movement tends not to focus specifically on the issue of public health in these communities has largely to do with the fact that such a focus would necessarily include considerations throughout this broader range. Socioeconomic status, particularly education, is linked to the prevalence and course of cigarette smoking, physical inactivity, poor diet, substance abuse, and their associated health outcomes (Adler et al. 1999). For the movement to focus on public health in minority and low-income communities could thus alter the prevalent portrayal of individual residents in the relevant communities, possibly implying a degree of self-responsibility for health-related decisions that could decrease political sympathies toward their plight. All of the aforementioned obstacles notwithstanding, failure to deal with possible disproportionate distributions of environmental hazards specifically in the context of public health in minority and low-income communities is apt to lead to substantial social costs in terms of public health. Some of these can potentially be discerned in empirical research studies. Others are more a matter of individual human tragedies. Take, for example, a susceptible young child born into a socioeconomically disadvantaged family who happens to live in a neighborhood near a chemical manufacturer who recently accidentally leaked some actinomycin D into the aquifer from which she draws drinking water. Actinomycin D is known to be a developmental toxicant. As a tragic consequence of drinking the water, the child may thus be precluded from the enjoyment of normal development and a reasonable standard of physical and mental health, and may possibly be precluded from becoming a happy and productive member of society. Lead paint in many of the older houses that are probably disproportionately located in minority and low-income neighborhoods undoubtedly often causes similar tragic effects. Moreover, the cumulative effect of many children having similar experiences—perhaps with different chemicals and different health impacts—is not only a personal and familial tragedy but also a gross waste of human potential. Because the knowledge base with which to identify and prevent such situations is far and away most highly developed within the field of public health, such problems and potential problems can be most effectively and efficiently dealt with by people trained in that field.

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Under the International Bill of Human Rights, when what society does collectively precludes individuals from the enjoyment of normal development and a reasonable standard of physical and mental health, it is quite possibly a violation of human rights (Mann et al. 1999). Specifically, such violations can occur under the International Covenant on Economic, Social and Cultural Rights, adopted and opened for signature, ratification, and accession by United Nations General Assembly Resolution 2200 A (XXI) on 16 December 1966 and entered into force on 3 January 1976 in accordance with Article 27. Article 12 of the Covenant reads that the signatories recognize the right of everyone to the enjoyment of the highest attainable standard of physical and mental health. Accordingly, if and when as a consequence of proximity and exposure to an environmental hazard individuals in the relevant neighborhoods and communities face public health risks, and if and when that risk thereby precludes them from attaining this standard, their human rights are violated under this Article.1 Failure to recognize or acknowledge any public health problems that preferentially affect marginalized or stigmatized communities may violate the human right to nondiscrimination by leading to neglect of necessary health services. Such considerations notwithstanding, it has been stated that relatively few empirical studies have examined environmental justice specifically in terms of the public health issues involved. Of those that have been conducted, a few have found a significant relationship between residential proximity to environmental hazards and increased health risk and incidence of disease, especially among pregnant women and infants (Nordstrom, Beckman, and Nordstrom 1978; Goldman et al. 1985; Guthe et al. 1992; Berry and Bove 1997; Croen et al. 1997; Knox and Gilman 1997). These and similar studies form the relatively sparse empirical basis for concern about potentially enormous health problems related, for instance, to the migration of chemicals from hazardous waste sites (Marsh and Caplan 1987; Johnson and DeRosa 1997). Yet, other studies using slightly different research methods, looking at somewhat different health hazards and in somewhat different situations, have not found such a relationship (Ozonoff, Colten, and Cupples 1983; Bell et al. 1991; Shaw et al. 1992). Again, as with broader research into the hypothesis of disproportionate residential proximity to hazardous waste sites, researchbased knowledge about residential proximity and health is evidently in very limited supply. To mention a couple of related studies in a bit more depth, Sexton et al. (1995) examined the central role of environmental health research in defining the dimensions of environmental justice, understanding its causes, and identifying solutions. Their research was designed to improve the ability to identify, evaluate, prevent, and /or reduce risks for all members of society. They provided a conceptual model for postulated causal relationships between environmental health sciences research to determine the extent to which soci-


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ety has achieved “equity” and “justice” in the health and safety of its citizens. They postulated causal relationships between class, race, and environmental factors on one hand (explanatory variables) and environmental health risks (outcome variable) on the other. Their emphasis was on characterizing health risks for the general population and population subgroups defined by socioeconomic status and race or ethnicity, as well as improving the quality and quantity of data available and the capacity for interpreting it within the context of environmental justice. Along somewhat different lines, Guthe et al. (1992) used a geographic information system (GIS) to identify areas within Newark, East Orange, and Irvington, New Jersey, where there may be greater environmental exposure specifically to lead. Heavy industrial, residential, and vehicular sources of lead contamination were found in these areas, and a large number of children there were found to have elevated levels of lead in their blood. Use of the GIS increased the likelihood of matching blood lead screening records to specific locations, thus helping to identify the spatial patterns of reported blood lead levels relative to potential sources of exposures and the population at risk. That research found a correlation between children’s elevated blood lead levels and the various sources of lead in Newark. In a still different vein, Croen et al. (1997) used survey research to examine a hypothesized relationship between residential proximity to waste sites and congenital malformation. They found no increased risk for maternal residence in the same census tract as NPL sites, but found elevated risks for maternal residence within .25 mile of such sites. Finally, Neumann, Forman, and Rothlein (1998) used several health-related databases to establish “hazard potential rankings” for TRI release sites in Oregon and related these descriptively to demographic and socioeconomic data at the census-block level, using a geographical information system. As reported in detail in Chapter 6, the results were mixed. On balance, many if not most of the relationships between residential proximity to the currently existing environmental hazards and public health have not been empirically established, but, to the extent possible policy and administrative decision-makers should assume and act as if these relationships involve significant public health threats. This judgment is based ultimately on consideration of the relative costs of two possible types of error. First, decision-makers could assume and act as if residential proximity to hazardous sites poses significant public health threats when in reality it does not. In this case, the costs of error are primarily opportunity costs of options forgone; that is, the scarce resources used in support of any corresponding government actions or programs would thus not be available for a wide range of alternative uses such as national security, human capital development programs, medical research, or infrastructure maintenance. Second, decisionmakers could assume and act as if residential proximity to hazardous sites

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does not pose significant health threats, when in reality it does. In this case, the costs of error are personal and familial tragedy, gross wastes of human potential, and human rights violations. Because the employment of otherwise wasted human potential is clearly not only in the relevant individual’s best interest but also society’s, and because modern human rights are a civilizational achievement that represent some of the highest social ideals yet conceived, the latter costs are arguably far greater. Thus, the most rational approach for policy and administrative decision-makers is arguably to invest heavily in empirical research, while thinking and acting as if proximity to hazardous sites poses a public health problem. The existence and extent of these relationships remain highly uncertain; therefore, it would be fallacious for policy and administrative decisionmakers to think that investments to protect the public health of residents who live near the sites are well justified on empirical grounds. The current research base does not clearly support such investments. Indeed, the only way to justify them on such grounds would be to invest far more heavily in empirical research. Meanwhile, it is useful to bear in mind that, based on the available empirical evidence, proximity to hazardous sites is not likely to be the most significant environmental hazard in minority, low-income, or other disadvantaged communities. Lead paint, for example, or indoor air pollution attributable to things such as secondhand cigarette smoke are apt to be every bit as risky, if not more. THE IMPACT OF HAZARDOUS SITES ON NEIGHBORHOOD DYNAMICS A major category of potential impacts from environmentally hazardous sites contains their neighborhood effects. Conceptually, at least, the presence of a hazardous site, in a minority, low-income, or otherwise disadvantaged neighborhood can “bring it down” or at least accelerate the speed of decline. This, of course, does not imply that such sites necessarily will bring down a neighborhood, but it is certainly plausible that they might. In any case, if the residents in a neighborhood that is in fact brought down by a hazardous site do not benefit proportionally from the collective social activities that made such a site necessary in the first place, those residents face indirect costs or externalities. This would occur, for example, if they do not proportionally consume or benefit from goods and services that could not have been produced without the hazardous byproducts contained at the site. The obstacles to estimating the neighborhood impacts of a hazardous site stem largely from the fact that U.S. urban neighborhoods contain immense variation. All are the complex, dynamic, and somewhat idiosyncratic outcomes of the interrelated processes of demographic, political, cultural, economic, social, technological, and environmental changes mixed with locally


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and politically contingent factors. People and families move. Real incomes change. Government regimes and activities change. Changes occur in the technologies of manufacturing and merchandising. Houses deteriorate with age. In some neighborhoods, most of the houses are maintained, in other neighborhoods not. In some neighborhoods, the schools are funded heavily and the standards are high, others not. Transportation systems change, bringing corresponding changes in the relative value of particular neighborhood locations. Nevertheless, despite the obstacles to conducting empirical research in the dynamic and complex systems of urban neighborhoods, related research has been done. For instance, it is widely accepted among urban researchers of any ilk that such neighborhood dynamics such as these largely determine the socioeconomic status of the residents, as measured usually by their income, education, occupation, and (unfortunately) racial characteristics (Cadwallader 1996). It is also well established that higher socioeconomic status neighborhoods tend to have greater environmental quality, more environmental amenities, more space, greater levels of cleanliness, and more appealing aesthetics. They also tend to enjoy better health (Adler et al. 1999). In more specific terms of the adverse public health effects of environmental hazards in minority, low-income, and other disadvantaged neighborhoods, however, the obscurity of the broader mechanisms by which socioeconomic status affects health remains a major obstacle to scientific understanding. The implicit hypothesis in much of the rhetorical discourse is that lower socioeconomic status affects biological functions, vis-à-vis the influence of inequitably distributed environmental chemicals that influence public health. However, assuming for the moment that the distribution of such hazards is empirically linked to socioeconomic status, to test this hypothesis would require isolating their influence from other determinants of public health that are also related to socioeconomic status. For instance, it is known that health behaviors (e.g., cigarette smoking, physical inactivity, poor diet, substance abuse), psychological characteristics (e.g., depression, hostility), psychological stress, and low self-perceptions of hierarchical position are all related to socioeconomic status. Thus, while there is little doubt that neighborhood dynamics largely determine the socioeconomic status of the residents or that the socioeconomic status of the residents influences the quality of the neighborhood environment, relatively little is known specifically about the relationship between neighborhood dynamics and health. A few researchers have focused on various aspects of neighborhood dynamics within the specific context of environmental justice. Been (1994) was the first in the literature to empirically examine neighborhood dynamics in terms of environmental justice, specifically by examining the changes in neighborhood socioeconomic characteristics between the time of facility siting and the time of the study. She suggested that externalities associated with

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hazardous sites could make host neighborhoods less desirable and, in doing so, encourage the immigration of low-income households by reducing property values. Her study was based on data reflecting the socioeconomic characteristics of host neighborhoods in Houston, using 1970, 1980, and 1990 Census data. She found generally that in some instances the presence of hazardous waste sites in a neighborhood sets up negative dynamics associated with neighborhood decline, but the empirical basis of this association, and particularly the associated causal mechanisms have not been thoroughly researched and so remain largely speculative. Extending Been’s study, Liu (1997) suggested a framework with which to study hazardous waste siting specifically in terms of its contribution to the dynamic of neighborhood decline. To isolate and establish the effects of a hazardous waste site on its host neighborhood, he examined alternative hypotheses for explaining observed neighborhood changes, including invasion succession, other push forces, and neighborhood life cycle. His study first examined the dynamics of locally unwanted land use (LULU) neighborhoods in both presiting and postsiting time periods and tested the differences between them. Second, he tested some possible hypotheses for explaining the observed differences, based on the various aforementioned ideas about neighborhood change. The units of analysis were the census tracts in which the nine observations for the same solid waste facilities Been had studied in Houston were located. Differences between 1960 and 1970 were taken as presiting changes, while differences between 1970 and 1980 were taken as postsiting changes. A comparison group of socioeconomically similar tracts was used to observe whether the changes in the host tracts over the period differed from changes in tracts that were similar at the time of the siting, and in which no hazardous facility was subsequently located. The findings indicated that changes in the relative median family income (a partial measure of socioeconomic status) in the host tracts did not differ significantly from those in the comparison group, thus suggesting that the hazardous waste sites did not contribute significantly to overall neighborhood decline. Krieg (1998a; 1998b) also suggested that the environmental justice research and associated discourse too often tends to ignore the broader scope of neighborhood dynamics, particularly related commercial and industrial taxes. Instead of using census tract as a unit of analysis, he divided the Boston region into two areas, the City of Boston and the Route 128 region, hypothesizing that commercial and industrial taxes remained the strongest indicator of the location of environmental hazards. To test this hypothesis, he compared differences in the number of hazardous waste sites between incorporated areas within each region and then between the two regions. He used six variables—toxic hazards, education level, race, income, taxes, and region. He found that hazardous waste sites were most likely to be found in communities that get a large percentage of their total tax base from commercial and indus-


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trial sources. He interpreted this to indicate that the higher the portion of a community’s tax base derived from commercial and industrial taxes, the greater the power industry used to influence local government decisionmaking, and the more likely industry externalized environmental hazards. Also, it is useful to mention in this context that Weinberg (1998) has taken a broad interest in the question of how certain groups come to be exposed to toxic wastes and, in particular, the causality rather than the fact of their exposure. The more specific questions he is interested in include, for instance, How do decisions get made in organizations about production processes that lead to toxic products or byproducts? What types of criteria are used in the decision-making process? Are the concerns of communities ever taken into account? How do organizations talk about the toxic waste? Meanwhile, until such questions get answered and further empirical research is done to establish the impacts of environmentally hazardous sites on minority, low-income, and other disadvantaged neighborhoods, the status quo is likely to continue. But many obstacles are implied by this. Even if more were known about disproportionate distributions, a great deal of further research, largely conditioned on presuppositions made about such distributions, would be needed before it would be possible to empirically establish their overall effects on minority, low-income, or other disadvantaged neighborhoods. THE IMPACT OF HAZARDOUS SITES ON RESIDENTIAL REAL ESTATE PRICES While most of the rhetorical discourse related to environmental justice has to do with the effect of environmental hazards on health and environmental disamentities, hazardous sites can also impose indirect costs on residences in host neighborhoods, specifically through their impacts on property values. These too can potentially be the source of concern for environmental justice. Accordingly, the effects of hazardous sites on residential real estate values can be evaluated. If property values are found to come down most heavily in disadvantaged neighborhoods, the evidence can be used to substantiate claims of inequity related to disproportionate distributions. Conceptually, property values near hazardous sites decline because, given a choice between two sites offered for the same price and identical in every respect except proximity to an environmental hazard, home buyers will tend to choose the site that is further away (Nelson et al. 1992; Nelson et al. 1997). When proximity to such a hazard involves activities such as trucking, dumping, filling, sorting, spraying, operation of heavy equipment, noise, or odor, prospective housing buyers tend to equate it with a diminished environmental quality or quality of life, especially if they perceive a health risk, regardless of whether their perception is backed by valid scientific evidence.

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Families will be likely to consider the closer site as a suitable alternative only if it is offered for less money. In the aggregate, this tends to decrease land value in the neighborhood, and one of the net effects tends to be an increase in low-income, lower socioeconomic status residents. It becomes a concern specifically in the environmental justice discourse when hazardous waste facilities thus depress nearby property values, which then serves to attract poorer families to the neighborhood, many of whom are minorities (Stretesky and Hogan 1998). The standard and seemingly most appropriate approach for empirically researching the effect of environmental hazards on nearby property values makes use of multivariate statistical models called hedonic price models. Hedonic price models of sales price determinants have been widely used to infer the influence on sales price from residential and neighborhood attributes such as land use, residential quality and accessibility, new residential construction, neighborhood disinvestment, and various externalities in the local surrounding environment. Their relevance in research related to environmental justice is due to the estimates they provide of the marginal effects of attributes such as environmental hazards on housing values. Ideally, they could be used to assess changes in the marginal effects of particular environmental hazards over time, and then these could be compared to changes in equivalent comparison neighborhoods, thus estimating the marginal loss in property value attributable specifically to those hazards. Hedonic price models tend to use classic regression analyses in which a neighborhood’s housing units’ sales prices are regressed on measures of their attributes. If two realms of theoretical conditions are satisfied, the estimated regression coefficients can, in the judgment of many theorists, be interpreted as the implicit marginal prices of the attributes. The first, being rooted primarily in human perception, are phenomenological in their nature. Their aim is to organize masses of housing attribute data around sets of concepts. They are as follows: The utility of a housing unit is a function of its attributes. For any one household, the market price of each housing unit on the market is known and determined by market demand. Each household maximizes its utility subject to a budget constraint. And an explicit linear function, called the hedonic price function, known to all households, relates the households’ preferred mix of attributes with the available housing units on the market. It is necessary to meet these phenomenological conditions because they specify the range of circumstances to which the models apply. The second realm of theoretical conditions, being rooted in mathematics and quantitative reasoning, are fundamental and statistical in their nature. Their aim is not so much to confront directly the housing attribute data as it is to organize the relatively few parameters of the phenomenological theory in terms of how they are gathered. These include the statistical conditions that specify the range of circumstances to which the regression coefficients provide unbiased and statisti-


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cally sound estimates of the marginal attribute prices. When linear regression models are used to estimate the parameters, these statistical conditions include the standard assumptions of the classic linear regression model. The phenomenological theory behind hedonic housing price models stipulates the relationships between several salient concepts—household utility, household preferences, limited resources, and housing attributes. Likewise, conformity to fundamental theory and procedure ensures the integrity of the logical linkages between the phenomenological theory and the empirical price estimates. Together, these two realms of theory ensure that the integrity of the marginal attribute price estimates, as part of a larger body of coherent knowledge, becomes plausible, thus fortifying them and helping to ensure their validity. When hedonic price models are used to estimate the implicit marginal attribute prices in a housing market, attributes in three basic categories are widely considered to be relevant. The first category includes the structural characteristics (S) of the housing units on the market, such as square footage and the age of the building. The second includes characteristics of the immediately surrounding social and natural environment (E), such as the quality of the neighborhood or the presence of environmental disamenities. The third includes locational characteristics (L), such as distance from the CBD and proximity to major transportation routes. Given appropriately measured variables in these three categories, it is generally agreed that proper specification of the hedonic price function accordingly expresses the market prices of housing units as: P = f (S, E, L). When aggregates of households make expenditures on houses with known levels of a full set of attributes in each category, the equation represents the market-clearing function. If the theoretical conditions for this function are met, the partial derivative with respect to any attribute theoretically provides an estimate of the implicit marginal price of that attribute. Econometric regression models, such as P = β1 + βS ΧS + βE ΧE + βL ΧL + µ, are typically used to empirically estimate these attribute prices. Here P represents observed cross-sectional sales prices of the housing units on the market; ΧS represents their corresponding structural attributes; ΧE represents the salient features of their immediately surrounding environment (which might include the presence of environmental hazards); ΧL represents a set of corresponding variables reflecting their locational attributes (such as socioeconomic status and housing type); and µ represents the stochastic disturbance term from classic regression theory. This disturbance term represents all those factors that affect the sales price but are not taken into account explicitly, for instance as a consequence of vague theory, unavailable data, sampling error, or intrinsic randomness in human behavior. The values of βL, βS, and βE estimate the corresponding implicit marginal prices. Nelson et al. (1992) used hedonic price models and data previously collected by Genereux (1989) to study the effects of landfills on the value of

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single-family residential lots in Minnesota and Illinois. The particular landfills were selected because they were set among rural, residential, and suburbanizing landscapes near a major urban area with relatively flat terrain. Their models isolated the influence of landfill proximity on property values, controlling for the influence of lot size, location, and other attributes on price. They found that land values rise significantly with increased distance from landfills. They also extended their research (Nelson et al. 1997) to study the price effects of landfills on different housing value strata, finding that landfill price effects clearly fell differently on different house price strata. The price effects were proportionally more than three times greater on homes that sold for more than $150,000, when compared to homes that sold for less than $100,000, and nearly twice as high as for homes selling between $100,000 and $150,000. Thus, evidently the houses occupied by higher social status households are more price-sensitive to the proximity of environmental hazards than lower income households. Kohlase (1991) and Ketkar (1992) both looked at how the cleanup of hazardous waste sites affected the median price of residential property. Kohlase used 1976-1985 housing sales in Houston’s Harris County to study the impact of toxic waste sites on housing values, with a focus on how prices changed after the Environmental Protection Agency (EPA) announced that toxic waste sites were cleaned. A discernable premium to be located farther from a toxic waste site appeared after the EPA announced that a site was designated a Superfund site. Specifically, the regression models indicated that, ceteris paribus, the marginal value of each additional mile of distance from the nearest toxic waste site for a house in 1976 and 1980 were $880 and $1180, respectively. Then, in 1985, a year after the EPA announced that the cleanup of one of the sites had been completed, no such depressive effect was observed. Thus, the evidence suggested that the houses located near the sites lost value until the sites were cleaned up, at which point the efforts to clean up the sites enhanced the housing values. Similarly, Ketkar (1992) used hedonic price models to examine 64 municipalities in seven urban counties in the state of New Jersey, finding that the cleanup of one or more sites would increase the median price of nearby property in the range of $1300 at the 1980 price level. As can be seen in Table 7.1, which summarizes some of the related empirical research on the effects of hazardous facilities on nearby property values, the evidence that hazardous facilities depress housing prices is mixed. On the basis of these studies it appears that, although conceptually the mechanism through which property values decrease is clear, the empirical evidence as to whether hazardous facilities actually reduce nearby property values does not clearly and unambiguously support claims that this conceptual mechanism is always operative for each facility.


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Table 7.1. Empirical research on the impact of hazardous facilities on nearby property values Author

Hazard

Findings

Ridker and Henning (1967)

Air quality (emissions)

Reduces nearby residential property value.

Adler et al. (1982)

Hazardous waste sites, contaminated local water supplies

The study evaluated before and after contamination (1974). It found no statistical relationship between property sale prices and proximity to landfills before 1974. Between 1.5 and 2.25 miles, there was about a 10% difference in price change between homes sold before and after 1974.

Epp (1982)

Landfill

Negative price effect of $4266 per mile or 5% to 7 % (the results were not statistically significant because the area is limited to 1 mile).

Planning Research Associates (1983)

Landfill

No effect on the property values. Instead attracts residential development. Not mentioned what stage of the life of the landfill.

Genereux (1989)

Landfill

Distance to the landfill was found to have a significant relationship to changes in property values. Within 0.48 km of the center of a landfill, residential property suffered a loss in value ranging from 10.2% to 21.6%. Price reduction also observed above 5% at 3.22–4.03 km from landfills.

Petit & Johnson (1987)

Landfill in remote areas

No effect on the property values. Instead attracts residential development.

Cartee (1989)

Landfill

Little empirical evidence to suggest that landfills impose negative price effect.

Kohlase (1991)

Toxic waste site

The marginal price to avoid a toxic waste site disappears after a site has been cleaned.

Nelson et al. (1992)

Landfill

Negative price effects on property values, but infrastructure newly installed by landfill may induce development.

Nelson et al. (1997)

Landfill

Negative house price associated with landfill proximity.

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THE IMPACT OF HAZARDOUS SITES ON RISK PERCEPTION AND ENVIRONMENTAL CONCERN A final set of impacts from environmental hazards examined here can be found in the subjective, psychological, and cultural attributes of minority, low-income, and other disadvantaged neighborhoods. Regardless of any geographical and empirical relationships between industrial pollution and environmental hazards and associated public health risks, adverse neighborhood effects, or adverse effects on real estate values, if differential levels of perceived risk and associated concern lead to mental anguish and stress, this alone could potentially help justify an environmental justice related demand for policy and administrative action. To have some understanding of these attributes and their influence on the environmental justice discourse is useful because public risk acceptance and the success of any related risk-management policies are likely to hang on how people think about environmental risk at both the individual and collective levels. Moreover, such understanding has important policy implications (Burby and Strong 1995). The theory behind research on risk perception has come variously from geography, sociology, economics, political science, anthropology, and psychology. Each field has emphasized somewhat different aspects of the topic, depending on the larger interests of the particular field. In broad terms, geographical research on risk perception focused originally on understanding human behavior in the face of natural hazards, but it has since broadened to include technological hazards. In sociology, research has been based on the basic idea that people perceive technologies (and other things) to be dangerous because they know them to be dangerous. In economics, theory suggests that affluent people are more willing to take risks stemming from technology because they benefit more, and the consequent negative externalities tend not to affect them directly. Political scientists tend to view the controversies over environmental risk as struggles over interests such as holding office or party advantage. The corresponding searches for explanations of risk perception have thus placed emphasis on social and demographic characteristics such as gender, age, social class, liberal or conservative ratings, and/or adherence to political parties. In anthropology, studies have shown that perception and acceptance of risk have their roots in social and cultural factors. Some cultural studies in particular begin by assuming that a culture is a community of persons holding one another mutually accountable. Accordingly, a person tries to live at some level of being held acountable which is bearable and which matches the level at which that person wants to hold others accountable. Viewing individuals as the active organizers of their own perceptions, cultural theorists have thus proposed that individuals choose what to fear (and how much to fear it) in order to support their way of life (Douglas and Wildavsky 1982; Bowen 1989). From this perspective, selective attention to risk


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and preferences among different types of risk taking (or avoiding) correspond to cultural biases—that is, to worldviews or ideologies entailing deeply held values and beliefs defending different patterns of social relations. In psychology, research has tended to focus on various mental heuristics and biases used in perceiving risk, such as the salience of recent events, that can distort decision-making about more complex, underlying risks (Noll and Krier 1990). Within the context of public policy and risk, much of the leading empirical research into risk perception has been oriented around the following line of reasoning. People respond to the risks they perceive, and, if their perceptions are faulty and public policy responds to their perceptions, the policy is likely to be misdirected. Indeed, such a line of reasoning is woven implicitly throughout this book. The underlying argument is that, for many hazards, it is possible for experts to give objective and relatively accurate descriptions, using various data. For instance, the value of risks from fireworks, firearms, breast cancer, poisoning, electrocution, venomous snake bite, and lightning, among many others can be objectively stated in terms of the number of corresponding additional deaths per year, based on government records. However, research has shown that the objective value of many risks often has little to do with how they are perceived or how people respond to them (Slovic, Fischhoff, and Lichtenstein 1982).2 For example, Lichtenstein et al. (1978) presented two separate groups of college students with annual death tolls from motor vehicle accidents and electrocution in the United States, and then asked them to estimate the frequency of 40 other causes, ranging from small pox to heart disease. The student responses were biased—that is, they differed systematically from the corresponding objective values—specifically in that they markedly overestimated the frequency of death for infrequent causes, such as botulism, and underestimated frequencies for common causes, such as heart disease. While people generally are evidently not very good at estimating risks, trained experts often do not always know the value of the risks either. For example, the hazards of nuclear power, recombinant DNA research, or residential proximity to Superfund sites cannot be objectively and accurately described. Indeed, a large component of judgment is always included in the expert assessments of a wide range of the risks faced by various components of society. In this light, because the values of many of the risks are simply not known by anyone, at some point the matter of making decisions regarding public policy toward risk management becomes one of how safe is safe enough to satisfy the public and the experts. Accordingly, society will make additional investments in risk reduction on the basis of a trial and error process in which the value of the remaining perceived risk from any given hazard is, in the judgment of all participants, jointly weighed against its corresponding benefits. Some hazards correspond to highly beneficial activities and the bearer, chooses to accept the risk, such as driving an automobile. In

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these cases, the risks will tend to be perceived as less than they really are, and high risks will tend to be found acceptable. In other cases, such as are more typical in concerns with environmental justice, the affected individuals tend to perceive themselves either as not benefiting at all from the hazard or else benefiting in an amount that is disproportionately small in comparison to the corresponding expected or perceived loss. In these cases, the risk will tend to be perceived as greater than it really is, and relatively low risks will be found to be unacceptable. As a consequence, in public policy and administrative decisions regarding them, this puts a premium on risk-communication programs, buttressed by extensive empirical research designed to indicate accurately what experts do and do not know about the corresponding risks. Because the information from such programs can potentially influence perception and behavior toward acceptable risk, lest they be viewed as mere propaganda campaigns, it is useful to select knowledgeable and trustworthy program designers and coordinators. A competent and credible risk-communication program staff would probably have to be put together in consultation with representatives of the affected individuals, since if people did not trust their informants, there would be little point in pursuing the program. But at the same time, in the absence of such programs, social decisions regarding additional investments in risk reduction, based on balancing perceived risk and corresponding benefits, will tend to be made on a less than adequate basis of information. A related but slightly different consideration has to do with environmental concern. The difference is that environmental concern has more of an emotional emphasis; one can perceive a risk without feeling much concern about it, but if one is concerned about it, one’s emotions are necessarily involved. Several related and leading research projects are summarized in Table 7.2. Mohai (1990) was interested in what he called black environmentalism. He suggested that although blacks and other minorities appear to be disproportionately burdened by environmental hazards, little is known about the relative extent of their concern, awareness, and political activity regarding environmental quality issues. He therefore set out to assess the extent to which blacks and whites differ in their levels of concern for environmental quality and to evaluate alternative explanations for any observed differences. He utilized survey data from a sample of 559 blacks and 6035 whites and employed factor analysis to identify those items that could be used to construct reliable indicators of environmental concern. One such indicator was perception of the seriousness of environmental problems. Another was perception of the likelihood of future shortages in the range of environmental amenities. The third was the relative importance placed on environmental protection. Data on education, income, and occupational status were also included. On the basis of these three indicators, no statistical differences were


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Table 7.2. Empirical research on environmental concern Author

Research description

Findings

Kellert (1984)

National survey of 1392 urban dwellers; 1217 whites, 150 blacks

Large and significant differential in the scores of blacks and whites. Black adults tended to be substantially less interested, concerned, and informed about the natural environment.

Taylor (1982)

Survey of 50 Northeastern Illinois University students (30 whites, 14 blacks, 6 others)

Whites were more likely to list environmental problems as top world problems than were nonwhites.

Mohai (1990)

Survey of the public’s attitudes toward soil, water, and renewable resource conservation policy based on responses of 559 blacks and 6035 whites

The degree of concern among blacks was found to be identical to that of whites. However, the rate of environmental participation was found to be significantly lower for blacks.

Burby and Strong (1995)

Survey of 750 lowand moderate-income households

Blacks’ heightened concern stems primarily from cultural factors as tendencies to attribute illness to pollution and distrust of government efforts to ensure their health.

Klineberg, Mckeever, and Rothenbach (1998)

Survey data from four waves of the biennial Texas Environmental Survey; sample sizes ranged from 1000 to 1004 people aged 18 or older; total four samples (4005) households

The determinants of environmental concern vary in predictable ways, depending upon the trade-offs reflected in the questionnaire items. People differ importantly in the presources available to them and in the kinds of trade-off they are willing to accept.

found between blacks and whites, and few differences were found when socioeconomic categories were considered. Yet when differences between blacks and whites were examined in terms of participation in environmental action, it was found that rates of participation among blacks were only about one-third of those among whites. Thus there appeared on the basis of this work to be differences in level of participation between blacks and whites but no differences in levels of concern. Accordingly, as shown in a different way

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by Hamilton (1993, 1995), one of the keys to resolution of perceived disproportionate distributions of environmental hazards is evidently mobilization against the siting of environmental hazards in black communities. Burby and Strong (1995) conducted empirical research into hypothesized racial differentials in concerns with pollution and industrial hazards, and particularly why any observed such differences occur. They interviewed over 750 low- and moderate-income households. Their results indicate that blacks’ heightened concern stems primarily from cultural factors such as tendencies to more readily attribute illness to pollution and to distrust government efforts to ensure their safety. This perception turned into the perception that quality of community life is deteriorating. Klineberg et al. (1998) focused on how environmental concern has been measured. They argued that the determinants of environmental concern vary in a predictable way depending on the trade-off reflected in the questionnaire items. Their data came from the first four waves of the Biennial Taxes Environmental Survey, conducted during November and December of 1990, 1992, 1994, and 1996. The analysis was run with all four samples combined (n = 4005). Using several measures of environmental concern, they concluded that it matters greatly how environmental concern is measured. They found only two demographic variables consistently correlated with environmental concern across all the different measures, age and education. Though, in comparison to factors such as geographical distance from a residence to a hazardous waste site, mental anguish and stress are intangible and difficult to measure objectively, individuals are protected from undue mental anguish or stress under the International Bill of Human Rights. If it could be established empirically that minorities and/or low-income people who live near environmental hazards experience a disproportionate amount of these maladies, such could constitute evidence of violations of their human rights. Of course, a great deal more empirical research needs to be done before any such argument could be made coherently. But according to the Constitution of the World Health Organization, the mental well-being of individuals is an explicit part of their health (International Federation of Red Cross et al. 1999).3 Under the International Bill of Human Rights, health is protected as a human right, and the mental well-being of individuals is an explicit part of their health; therfore, individuals are protected as a human right from undue mental anguish or stress. Thus, if what society does collectively, unduly causes individuals in minority, low-income, or other disadvantaged communities to experience mental anguish or stress, the government can be held accountable (Mann et al. 1999). In this regard, the relevant empirical question for policy and administrative decision-makers is not so much one of geographical proximity or exposure, but one of risk perception and environmental concern.

PART III

Environmental Justice Policy and Administrative Decisions

CHAPTER 8

Uncertainty and Trade-Offs in Effective Decisions The true purpose of knowledge resides in the consequences of directed action. —DEWEY, The Quest for Certainty

RESEARCH-BASED DECISION-MAKING IN EFFECTIVE PUBLIC ORGANIZATIONS A basic if often implicit assumption woven throughout the rhetorical discourse related to the role of government in environmental justice is that public organizations serve purposes and that the purposes they serve are specifically those stated in official pronouncements such as mandates, missions, goals, and objectives. Accordingly, especially given the recent high levels of concern with government performance (National Performance Review 1996), the effectiveness of organizations tasked with remedying problems related to environmental justice are likely to be evaluated. At some point the aggregated results of individual decisions made within such organizations are apt to be compared with these and other related official statements of purpose (Georgiou 1973). What specific outcomes are being generated in return for the tax dollars being spent to accomplish environmental justice goals? What measures of performance toward the broad goals of environmental justice may be established? What specifically is involved in setting strategies, measuring outcomes, and defining specific targets against which they can be compared? How can actual performance be compared to the targets? How can government officials benchmark the progress made toward these goals? Before too long, questions such as these, all of which demand an accounting in terms of related decision processes, are likely to loom large in the experience of public administrators and policy-makers responsible for making environmental justice decisions. Moreover, if later evaluation determines that the aggregated results have attained the stated goals and objectives, the organizations will be considered to have been effective, and otherwise not. Organizational purposes thus impart organizations with legitimacy, definition, a frame of reference for rationalizing the various decisions

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made and actions taken by their individual members, and a conceptually simple starting point for assessing their effectiveness.1 An implication of this assumption is that individuals in decision situations make choices on the basis of their understanding of how the actions they choose and the outcomes they expect to follow from them link up with the organization’s stated goals and objectives. Accordingly, factors such as the decision-maker’s level of information about the decision situation, valuation of potential outcomes and of alternative actions possible within the decision situation, and calculation process for selecting among alternative actions are all directly related. More specifically, while it is usually not feasible to explain organizational effectiveness positively in terms of individual decisions, it is nevertheless clear that organizational effectiveness is constrained by them in a negative sense; that is, good individual decisions are necessary but not sufficient for effective organizations. The organization is not likely to achieve its goals and objectives without them, but they are not enough to ensure that it will.2 Thus the ability of organizations to attain their goals and objectives is limited by the ability of their individual decision-makers to rationally link individual-level choices to the organization’s goals and objectives. In general, a research-based conception of the decision process, as outlined here, is a conventional one, consistent with experience and based in an explicit recognition of the obstacles and difficulties that must be overcome, specifically in the intelligent pursuit of organizational goals and objectives related to environmental justice. In addition to the foundational assumptions specified in Chapter 2, it is based on the further assumption that thought and action are goal oriented. The decision-maker is thus considered to be an active processor of information, with a conscious purpose, in pursuit of ever changing goals and objectives. While this additional assumption entails a notable simplification of human experience and is therefore the subject of considerable debate, it is nevertheless useful for the purpose at hand; that is, it helps to analyze and illuminate the policy and administrative choice processes related to environmental justice, as seen from the point of view of the decision-maker. Research-based decision making for environmental justice is oriented specifically toward the factors that help assure good decision-making. It is conceptually distinguished partially by its emphasis on valid empirical knowledge and partially by how it uses such knowledge in making choices. It assumes that to make a good decision—one in which appropriate objectives are pursued in an intelligent way—a decision-maker must first know what the decision problem is and then must respond to it judiciously. Thus, first, before a choice is made, a reasonably accurate interpretation of what is as well as what is not known empirically about the decision situation is needed. Accordingly, the interpretation must be more or less coherent from the decision-

Uncertainty and Trade-Offs in Effective Decisions

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maker’s point of view, not from a point of view that might, in the abstract, be available to others in more favorable or perhaps even ideal circumstances. The concept of research-based decision-making stipulates that, having thoroughly and accurately described the decision situation on the basis of sound empirical research, good decisions depend also on the framework the decision-maker uses to transform the available information and empirical knowledge into the selection of an alternative course of action. Indeed, the framework used in making choices in policy and administrative decisions related to environmental justice can potentially influence organizational effectiveness every bit as much as do the facts, events, and circumstances assumed as premises. Accordingly, a suitable framework is one that can provide guidance on how to use the available information and empirical knowledge in a way that is focused, logical, and consistent with the broader context; includes subjective judgment as well as information and empirical knowledge; and is straightforward, reliable, and easy to use. More specifically such a framework encourages deliberating choices from a clear and distinct problem description and statement of the objectives, a suitable range of alternatives, a well-defined listing of specific outcomes, and an explicit consideration and systematic treatment of uncertainties and trade-offs (Hammond, Keeney, and Raiffa 1999). In general, decision problems are defined by a perceived difference between the way things should be, according to a proposed standard, and the way they are accepted to be empirically in fact. More specifically, in problems related to environmental justice, the standard is typically one of fairness, justice, or equality and is usually stated in, or implied by, an organizational goal or objective. The way things are accepted to be empirically is given in propositions about the existence of disproportionate distributions of environmental hazards in minority, low-income, and other disadvantaged neighborhoods. Accordingly, on intuitive and commonsensical grounds, if not on the grounds of the overall body of empirical research, many if not most of these propositions tend to be highly plausible. Yet, no combination of unaided perception, hunch, intuition, or common sense can ever constitute an entirely adequate factual basis for complex and highly consequential policy and administrative decisions. Since the body of empirical research does not clearly substantiate widespread systematic and disproportionate distributions, much less the public health impacts so often allegedly associated with them, it must be said that the problem itself is poorly defined. As a consequence, policy and administrative decision-makers dealing with environmental justice are highly likely to err. They cannot know clearly what decision problem they face. So they are more likely to be influenced by psychological factors such as the human tendency to subconsciously decide what to do before figuring out possible alternative choices of action. Accordingly they can, indeed must, at least to some extent see what they want to see in related

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decision situations and find confirming evidence for just about whatever preconceived political agenda they consciously or otherwise hold. Given the political and institutional mandates to act many are thus forced by lack of a sound empirical foundation to put the proverbial cart before the horse. Insofar as they aim to make a “good” decision, to ensure organizational effectiveness at improving environmental justice, they face the double bind of having to act on the basis of a poorly defined problem which is inconsistent with the intelligent pursuit of appropriate objectives. To reiterate and follow through a bit further with some of the terms and concepts introduced in Chapter 2, when decision-makers are faced with a mandate to select and commit to actions designed to solve, ameliorate, or otherwise remedy a problem then they have a decision situation. Facing a decision situation, a decision process is set up, beginning with problem description and ending with evaluation. At some point in the process, between problem description and evaluation, they encounter another component, the more finely grained process of choice.3 The choice process entails deliberately selecting and committing to one or more actions from among a wider choice set of alternative possibilities. In general, its purpose is to bring the way things are accepted in fact to be closer to the way it is accepted that they should be. The overall effects or consequences of the decision depend on the way the entire process is structured and conducted all the way from problem recognition to evaluation, but the choices that are made constitute the real crux of how individual policy and administrative decisions ensure organizational effectiveness. Whether policy and administrative decisions are good or bad ones, with net positive or net negative consequences, the choice process represents the fundamental tool with which government organizations respond to challenges and uncertainties and create opportunities. In some decision situations, the best alternatives are fairly obvious, but especially in complex policy and administrative situations related to environmental justice these tend to be the exception. Given the high levels of uncertainty surrounding the hypothesis of disproportionate distributions, policy and administrative decision-makers in the environmental justice arena typically do not have a clear definition of their decision problem. As a consequence, related decisions tend to bring considerable anxiety, confusion, doubt, and, more importantly, the specter of poor or erroneous choices. The concepts and techniques of research-based policy and administrative decisions are not a panacea for this. However, they do provide the basis for at least a partial remedy, based on years of experience, common sense, and research explicitly into how to make good decisions. This remedy can be more-or-less fully justified in terms of the principles of social scientific method, together with widely accepted results of research in economics, statistics and operations research, and their applications to problem solving in psychology and, more recently, artificial intelligence. Its essence is to break decisions down


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into their key elements, identify those most relevant, apply some hard systematic thinking, and then select and commit to an alternative course of action. In a research-based decision framework, the choice process is understood to involve deliberations resulting in the selection of and commitment to one alternative course of action from among a choice set containing a number of possible alternatives. The choice set is a listing of two or more mutually exclusive alternative courses of action possible, which may be formally symbolized by A1, A2, . . . Ai. One of these will be selected in the choice process, subsequently carried out or implemented, and eventually, in the final stages of the decision process, the outcomes (resulting facts, events, and circumstances) will be evaluated in terms of the particular standard used to define the problem. Of course, combinations of A1, A2, . . . Ai. are possible, but for conceptual purposes, to maintain clarity of thought, such combinations can be reconsidered and restated so as to constitute a set of mutually exclusive actions, one of which will be selected. Without such restatement, the sorts of choices that typify complex policy and administrative decisions related to environmental justice quickly become fuzzy at best and unintelligible at worst. Thus, for the conceptual and analytical purposes at hand, it is considered that only one course of action will be taken in any given decision situation. Each discrete alternative action is seen to correspond with a range of expected outcomes. These can be expressed symbolically by means of the notation Yk | Ai, which means the range of outcomes 1, 2, . . . k, given that action Ai is selected. In turn, this range of outcomes corresponds with a fixed set of future facts, events, and circumstances. Accordingly, by selecting one of the courses of action, the decision-maker thereby makes a commitment to one out of a set of alternative possible future sets of facts, events, and circumstances. This set is referred to as the range of outcomes with which the action is associated. In the abstract, the ideal sought is that the future that actually happens will be one in which the facts, events, and circumstances are closer to the standard used to define the decision problem. Overall, this idea of selecting an action that determines an alternative future can be represented diagrammatically as in Figure 8.1.4 Assuming that the appropriate objectives have been identified in pronouncements of organizational goals and objectives, their intelligent pursuit, as seen from the point of view of the decision-maker, thus presupposes answers to only four basic questions: What are the alternative possible courses of action? What are the outcomes that might follow from each such action? What is the nature of the linkage between each action and the associated outcomes, and specifically what is the likelihood of each outcome given the action? How satisfactory or agreeable is each set of outcomes specifically in terms of the objectives of the decision? If these can be answered clearly and coherently, given the specific decision situation at hand, a good decision

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Environmental Justice through Research-Based Decision-Making Present deliberations

X

? →

Actions

Future outcome

 A1 →

Yk | A1

 B2 →  A3 →  ................  ................

Yj | A2

.................. ..................

 An →

Yl | An

Figure 8.1. Elementary structure of a choice situation.

can be made, thus removing a potential impediment in terms of the organization attaining its environmental justice goals and objectives. Note that, thus conceived, time is a logically necessary ingredient in the choice process. The purpose of policy and administrative decision is assumed to be to effectuate an outcome in which future empirical facts, events, and circumstances are closer to the standard used to specify the decision problem, therefore the choice must anticipate the future; that is, logically, to make a policy and administrative decision in which the outcome solves or ameliorates the problem stated in the problem description, the decision-maker must make reasonably accurate predictions about the future facts, events, and circumstances likely to follow from selection of each alternative action. More specifically, the concept of choice, thus conceived, logically presupposes that the future outcomes depend on which of the alternative feasible actions is presently selected, and in turn that the present selection of an alternative course of action depends on the decision-maker’s expectations regarding the corresponding future outcomes. The decision-maker deliberates the alternatives at some definite time, say X, and this is followed in time by which of the actions A1, A2, . . . An is selected. The deliberations, which end in identification of the preferred alternative course of action, occur in the present, resulting in the selection of that action. Then time passes on, in some cases only instant and in others a large amount, but one way or another the action previously selected at X is now taken. Eventually, the outcomes associated with the action actually taken occur. These outcomes may or may not be the ones that were expected at X. Moreover, they may or may not be agreeable, depending on whether the facts, events, and circumstances that now exist are closer to the way things should be after time has passed, according to the relevant standard stated in the problem description. Simply stated, deliberations


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in the choice process come in time before actions and actions come before outcomes. This is consistent with human experience, in that policy and administrative decision-makers operate in the context of the real world in which the passage of time provides the ultimate bond in all relationships. Because the intelligent pursuit of appropriate objectives logically presupposes knowledge of the decision problem, to make a good policy or administrative decision the decision-maker must first have a description of it. This is a matter of depicting, characterizing, and specifying the facts, events, and circumstances in relation to the normative standard, values, and preferences with respect to which the need to make a decision is recognized. The problem description provides and specifies the context within which information about the decision situation is anticipated to be useful as a means of allowing the decision-maker(s) to draw on previous experience in deliberating and eventually choosing what to do. Accordingly, the problem description is an especially important step in the decision process because it frames the decision, thus determining the alternatives that are considered and the way they are evaluated. Ideally, it includes a description of why the problem is being considered, what the relevant constraints and essential elements of the problem are, and what other decisions impinge on or hinge on the current one. Widespread experience suggests that success at resolving or ameliorating a decision problem depends on which alternative course of action is selected. In turn, the course of action is selected on the basis of the objectives stated in the problem description. Explicit and clear statements of the objectives are thus essential. By enabling identification of which particular action is expected to bring the way things are accepted to be in fact closest to the way they should be, according to whatever standard is used, they form the basis for deliberating the alternative courses of action. More specifically, they help to determine what information to seek, how to explain the choice to others, and how to determine how much time and effort the decision deserves (Hammond, Keeney, and Raiffa 1999). If the objectives are stated too narrowly or are not articulated with sufficient fullness and clarity, the decision process will be less likely to solve or ameliorate the problem and more likely to entail unanticipated and unintended consequences. A large part of the key to making good policy and administrative decisions must be in the specification of alternative courses of action and the outcomes associated with each. There are at least three aspects to this (the full range of considerations goes considerably beyond this book). First, the outcome of the decision process can be no better than the one associated with the best feasible course of action, and logically this cannot be selected unless it is first considered in the deliberations. Thus, the payoff from using a high standard for specifying a reasonably full range of alternatives can be high. Second, reasonably full description of the expected outcomes associated with each alternative course of action is necessary before the choice is made,

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because without it intelligent assessment of the relative merits of each such action in terms of solving or ameliorating the decision problem is not possible. This is especially important given the substantial resource allocation implications in many environmental justice decisions. Furthermore, high levels of complexity and interdependence are involved, so expected outcomes not fully specified prior to the selection of a course of action may never be recognized or understood, thus precluding meaningful evaluation. Third, the actions cannot be coherently connected to their corresponding outcomes in the absence of some understanding of the statistical relationships between them, if not (preferably) the causal mechanisms involved. Indeed, one of the major sources of concern with the high levels of uncertainty in current environmental justice policy and administrative decisions is that it precludes adequate understanding of these relationships and their mechanisms. Without this understanding, even when decision-makers proceed in the most “rational” way, they can readily be misled into making suboptimal decisions, perhaps ones for which the proverbial cure is worse than the disease. Typically, none of the alternative choices of action in policy and administrative choice sets is clearly and distinctly dominant. This is partially attributable to the typical presence of several different and often competing objectives. In environmental justice these might include improved health as well as decreased risks to property values, and less noise, odors, pollution, congestion, and stigma in minority, low-income, and other disadvantaged communities. Since usually none of the alternative choices of action can be recognized and agreed upon as being unequivocally the best in terms of all of the objectives, the matter of trade-offs between conflicting objectives arises. The idea in essence is that once the clearly subdominant alternatives have been eliminated and only a few remain, say two of them, typically, each will be better than another in terms of some objectives and worse in others. This presents an obstacle, because the decision-maker needs to give up something for one objective in order to get more in terms of another. For instance, by virtue of the criteria for evaluating potential sites in terms of their physical geography, some neighborhoods have less land suitable for siting a hazardous site. Unyielding adherence to the objective of distributing the location of such sites proportionally throughout all neighborhoods is thus apt to necessitate putting some sites in neighborhoods with land that is less suitable than that found in others, thus increasing the overall risk to society. Fortunately, in terms of efforts to make good decisions, techniques are available for systematically helping to deal with the obstacle of multiple conflicting objectives, even in highly complex decision situations. A good example is the “even swap method” (Hammond, Keeney, and Raiffa 1999). If the trade-offs are not made explicit and are not systematically considered through some such method, the unanticipated outcomes are apt to include other problems of equal or perhaps even greater magnitude and intensity than the original problem.


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UNCERTAINTY IN POLICY AND ADMINISTRATIVE DECISIONS FOR ENVIRONMENTAL JUSTICE To intelligently deliberate over which of the alternative expected future outcomes comes closest to the standard used to define the decision problem, as specified in the problem description, the decision-maker must, at the time of the deliberations, have some way of specifying the outcomes for each of the alternative actions. Since decision-makers cannot peer directly into the future to see the full range of specific outcomes expected to follow from each possible course of action, some other method must be used to make sufficiently accurate and complete predictions to evaluate which of the outcomes comes closest to this standard. Ideally, these predictions would take the form of inferences made on the basis of scientific methods, but in reality the potential for making them on this basis is restricted by the pertinent limitations in the conceptual-theoretical and observational-empirical domains (see Chapter 4). Accordingly, uncertainty, generally the indetermination or underdetermination of some of the characterizing elements of the decision situation, is a major if not the primary obstacle that must be overcome in making these predictions. There are, in essence, three basically different modes of uncertainty that can arise in the choice process. One, which directly relates to making the predictions, can be referred to as process (or predictive) uncertainty. It occurs when some of the characterizing elements of the process through which particular actions effectuate their outcomes remain undetermined or underdetermined. Process uncertainty affects the choice process because in it, ignorance of the way actions are transformed or transmitted into outcomes precludes sufficient specification of the outcomes likely to follow from selection of and commitment to particular courses of action. When sufficient numbers of critical characterizing elements remain undetermined or underdetermined, the decision-maker consequently commits to a course of action without knowing quite what outcomes to expect. Decisions made with such uncertainty might therefore effectuate adjustments only at the margin. Thus process uncertainty readily lends itself to unpredicted outcomes and unanticipated consequences, some of which are apt to be highly undesirable. To elaborate a bit on and clarify the concept of process uncertainty, take the conceptually most straightforward case in which human ignorance precludes the possibility of predicting outcomes that operate through strict causal relationships. The simplest examples of such relationships occur when the course of action being considered has led invariably, numerous times in the past, in a known and lawlike fashion to a single, specific outcome. For instance, when one turns the key in the ignition in one’s car, it leads in this way directly to one specific outcome—the ignition circuit is closed and the battery sends a charge that starts the engine. Whenever such a simple cause and effect relationship characterizes an action and its corresponding outcome

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in a choice situation, the outcome can be predicted with near certainty by an informed decision-maker, on the basis of knowledge of that relationship. Of course, in complex policy and administrative decision situations there tend to be few if any such simple cause and effect relationships known, so it is normally infeasible to thus predict specific outcomes of actions. As a consequence, decision-makers are constrained to think about the linkages between actions and their outcomes in terms of the probability that particular actions will result in particular outcomes. Accordingly, in the abstract, the probability that a particular action will result in a particular outcome is obtained by first identifying many past situations or cases, all of which are similar in salient respects to the decision situation at hand. Having identified and selected these cases, ideally with an eye toward sampling theory, one records the outcomes that have occurred for each case and then counts the number of the particular outcomes of interest and the total number of cases. The probability can be estimated by dividing the number of occurrences of the outcome of interest by the total number of cases. In the abstract, at least, very little could be simpler. However, in complex policy and administrative decision situations not only are no known and simple causal processes usually operative in terms of the actions being considered, the information (and time and energy) needed to obtain the relevant probabilities tends to be unavailable also. So the selection of a course of action tends to be based largely on unaided perception, hunch, intuition, or common sense. As a consequence, while probabilistic reasoning logically makes it possible to make outcome predictions on a more-or-less solid empirical foundation, in practice some level of process uncertainty will always be present in any complex policy and administrative decision. Another, largely different mode of uncertainty can be referred to as evaluative uncertainty. It occurs when the standard used to define the decision problem remains undetermined or underdetermined, thus precluding the possibility of adequately evaluating the outcomes expected to occur upon committing to and taking any of the available courses of action. In terms of environmental justice, as suggested earlier, this standard is usually one of the canons of distributive justice. Evaluative uncertainty is of special concern in related policy and administrative decisions because most of the rhetorical discourse remains (intentionally?) vague about which particular canon of distributive justice is used to define the problem. The practical significance of evaluative uncertainty can be seen in consideration of how the evaluation of the same definite outcome can vary tremendously, depending on the particular canon used to define the problem. Been (1993), for example, carefully examined seven different standards of fairness, any of which could be used to define the problem of disproportionate siting, all of which either assume a different canon or else emphasize different aspects of the same canon. Some assume that distributive justice is a


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matter of the treatment of people as equals, others find it in the treatment of people according to their needs, abilities, efforts and sacrifices, or productive contribution to society. Specifically, she examined standards in which fairness meant 1. evenly apportioning environmental hazardous land uses among all neighborhoods; 2. that neighborhoods in which no environmentally hazardous land uses are found must compensate those neighborhoods in which they are found for damages; 3. that more affluent neighborhoods receive a greater number of hazardous land uses, or pay a greater share of the damages than minority or low-income neighborhoods; 4. that neighborhoods all receive an equal number of vetoes that they could use against other neighborhoods for the privilege of excluding a hazardous land use; 5. that those who benefit from the production processes through which environmental hazards are produced as byproducts bear their entire costs; 6. no intentional discrimination against any identifiable socioeconomic group; 7. “equal concern and respect” for all neighborhoods. She made a compelling case that evaluation of the expected outcomes of several of the courses of action that have actually been proposed for consideration in policy and administrative decision processes would be largely different, depending on the standard used. When evaluative uncertainty occurs in a policy or administrative decision, the decision-maker cannot meaningfully evaluate one or more of the outcomes in terms of the standard used to define the problem, making it difficult if not practically impossible to intelligently deliberate which of the outcomes is preferable. In the abstract, there are two possible ways for the decision-maker to proceed in his or her deliberations. One is to evaluate the expected outcomes of particular actions in terms of every feasible standard, using sensitivity analysis, and deal with any conflicting objectives they imply. The other is to select a course of action on the basis of unaided perception, hunch, intuition, or common sense, while remaining essentially uncertain as to which action is to be preferred on a rational basis. Evaluative uncertainty often mixes with process uncertainty, further confounding the difficulty of making good decisions. The third mode, which can be referred to as outcome (or descriptive) uncertainty, is the most serious. Outcome uncertainty occurs when enough of the characterizing elements of the decision situation remain undetermined or

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underdetermined so that the very outcomes at issue cannot be specified. When it occurs, no matter how much time and energy the decision-maker expends deliberating the choice, the outcomes of one or more of the available courses of action will not be known until after the action is taken. The decision-maker, thus insufficiently informed as to the nature of decision situation, is thereby precluded from sensible deliberation based on adequate information and knowledge. A good and reasonably familiar example of outcome uncertainty can be found in consideration of a decision faced in relation to an ecological system. Say a particular species in the system is creating a problem by causing a disease in the nearby human populations, and a decision-maker is tasked with taking action to ameliorate or solve it. The decision-maker may contemplate alternative actions such as introducing a predator of the problem species into the system, removing one of its prey species, removing another crucial species in its lifecycle, or introducing a deleterious agent into its genome through genetic engineering. Unless the decision-maker fully understands the larger system with respect to the decision that is being made, including all of its interdependencies and nonlinearities, it will not be possible to adequately describe the decision situation. The expected outcomes of any of the available courses of action will thus remain undetermined or underdetermined until after they occur. Of course, when high levels of situational complexity are involved in a decision, such as in most ecosystems as well as in human societies, such understanding is at best rare. Moreover, if an action is taken regardless of such uncertainty, and highly undesirable and unintended consequences eventually result, it may be to late to reverse the damage done. In specific terms of environmental justice, a good example of outcome uncertainty is the indetermination that arises when considering whether minority neighborhoods or hazardous sites came first in observed instances of disproportionate distributions. Policy implications, or implications for action, are entirely different depending on which is assumed to come first, and the correct assumption—the one that squares most closely with empirical reality—has not been determined systematically, especially in any way that can readily be generalized to entire jurisdictions. At the same time, unless the process is specified, especially including this sequence, outcome uncertainty will remain present and is apt to influence any related policy or administrative decision. The relevant decision-makers will remain unsure of whether to select and commit to actions with outcomes that are expected to affect industry through the siting process or residential choice, say through enhanced enforcement of fair housing and other antidiscrimination laws. Moreover, to the extent that commitments to action are made under an incorrect assumption, one can expect that the most likely outcomes will at best involve a waste of scarce resources, and at worst inadvertent catalysis of other problems that are as bad or worse than the original one. Many other outcome uncertainties


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remain, such as the secondary and tertiary outcomes of related actions in terms of poverty, housing, racial discrimination, education, and pollution, as well as public health, risk perception, and environmental concern. In decision situations in which high levels of outcome uncertainty arise due to an inadequate basis of information and knowledge, deliberation to select an alternative course of action is premature. Indeed such is arguably the situation regarding most major policy or administrative decisions related to environmental justice today. While the body of empirical research indicates that there may in some instances be disproportionate distribution problems for which government intervention can be justified in broad terms of market failure, the widespread presence of high levels of outcome uncertainty tends to make good decisions to intervene in specific instances risky. Rather than trying to solve or ameliorate any of the current inadequately defined problems, it makes good sense to initiate further empirical research and problem definition so that in the future, once an adequate empirical basis of information and knowledge is available, good policy and administrative decisionmaking can get underway. While the remedy for evaluative uncertainty is to more clearly specify the standard by which the problem and decision objectives are defined, the most widely recommended framework for decision making under uncertainty for the other two modes is to maximize subjectively expected utility (SEU). In its simplest form, the SEU framework defines the conditions of perfect utility-maximizing choice in a world in which all of the relevant information and knowledge about the problem frame, objectives, alternative feasible actions and corresponding outcomes is had with certainty. In it, the value of each outcome is stated numerically in terms of its utility, or its prima facie desirability, according to the standard used to define the problem. This is evaluated specifically in terms of expected value calculations, as originally formulated by John Bernoulli in the early 1700s and subsequently systematized by countless theoreticians (Savage 1954). The framework assumes that decisionmakers possess a utility function, an ordering by preference among all the possible outcomes, and that all the alternative choices of action and the range of outcomes associated with such actions are known. The premise is that decision-makers will choose whichever course of action maximizes subjectively expected utility, or the satisfaction expected to occur coincident with the outcome, with respect to the objectives of the decision. In the version of the SEU framework that deals specifically with uncertainty, it is assumed that there is a probability distribution of outcomes associated with each alternative course of action.5 When the outcomes are uncertain, the subjectively expected utility associated with an alternative course of action is the sum of the utilities of its outcomes each weighted by the probability of its occurrence. In other words, faced with a set of alternative actions, the decision-maker calculates the expected value of each such

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action by multiplying the probability of each of its associated outcomes by the corresponding utility and then summing across all outcomes. Whichever action has the highest expected utility is then selected. The probabilities may be either objective or subjective in nature, depending on the information available in the particular situation. Because individual differences in perspective may cause the same situation to be perceived somewhat differently by different people, they are apt to estimate the probability distributions differently, especially when the data needed to obtain objective probabilities are not available. Thus the relevant utilities are referred to as subjectively expected. When limited information and knowledge precludes pinpointing the relevant utilities or probabilities, specific expected values cannot be computed, and the SEU framework calls instead for bracketing them to determine a range of expectation for each alternative; that is, the highest and lowest feasible values are identified for each, and the expected value used to represent the outcomes of the action is taken to fall somewhere in between, such as at the median or average of the two. The decision-maker can thus choose on the basis of the assumption that the expected value of an alternative will be somewhere between the values that would obtain “if worst comes to worst” and “if best comes to best.” Process uncertainty can always be treated on the basis of expected value bracketing. Outcome uncertainty can usually be thus treated, as long as ranges can be specified. In both cases, some level of uncertainty is bound to remain within any major policy and administrative decision process. In extreme situations, when the limits on information and knowledge preclude even the possibility of bracketing, the decision-maker faces a case of total uncertainty, at which point intelligent deliberation of the choice becomes simply infeasible. THE COSTS OF UNCERTAINTY Generally speaking, anyone who has paid attention to the effect of, say, uncertain interest rate changes on investment behaviors, is well aware that uncertainty can be extremely costly. Many examples of the costs of uncertainty can be found. Some of these costs are intangible, manifesting in loss of confidence and its implications, and others tangible, manifesting in changes in the very character of the decisions themselves. In specific terms of environmental justice, the intangible costs of uncertainty can be found in its erosive effects on social capital. For instance, given that decision situations in environmental justice are typically highly uncertain, it follows that when deliberating the available courses of action, policy and administrative decision-makers tend to perceive only a weak relationship between the courses of action and the outcomes likely to follow from each. From the point of view of individual decision-makers, this creates a double bind (Bateson 1972). Given increasing demands for performance, account-


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ability and results, such as those contained in the National Performance Review (1996), decision-makers can be expected to feel the need to show they are doing something to accomplish their objectives. They are the ones most likely to be held accountable for achieving related goals and objectives. Yet, given the high level of uncertainty, they are unlikely to be able to meaningfully deliberate alternative courses of action in terms of their promise for meeting the stated objectives or to determine whether particular choices they made helped to meet the stated objectives. Because they have to show that they are doing something, the demand must at some point arise for them to claim credit for any actions that might be perceived as having made any related improvement, regardless of whether their choices actually lead to any positive discernable change. In the long run, such credit-claiming behaviors are apt to result in decreased levels of trust in the organization, increased labor-management conflict, and lessened levels of social solidarity, commitment, motivation, and job satisfaction. The tangible costs of uncertainty can go beyond this to affect the very character of the decisions themselves. One way this can happen is illustrated in Table 8.1. This table represents a choice between two possible courses of action made under a mixture of all three modes of uncertainty. The actual

Table 8.1. Choice of two actions given a mixture of the modes of uncertainty The underconceptualized situation with uncertainty (apparent)

The fully conceptualized situation (actual) Action 1 (A1)* Outcome

Probability

Value

Outcome

Probability

O1 O2 O3

Value

.4

50

O1

.8

x

.4

–50

O2

.2

–10

.2

–10 Action 2 (A2 )**

O1

.4

50

O1

.4

50

O2

.4

–50

O2

.6

x

O3

.2

–20

–50 ≤ × ≤ 50. **Where –50 ≤ × ≤ –20. *Where

This example is based on one in Rescher (1983, 110).

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situation, described in the left-hand column, differs from the apparent situation, described in the right-hand column, because of uncertainty. Process uncertainty can be observed by comparing the difference between the actual situation and the apparent situation specifically in terms of the estimated probability of each outcome. Evaluative uncertainty can be observed in the indefinite value (x) associated with the first outcome (O1) in the apparent situation. Outcome uncertainty can be observed by comparing the two outcomes specified in the apparent situation with the three outcomes in the actual situation. Given such a choice, the expected value computations are: Action I (Actual): EV = (.4)(50) – (.4)(50) – 2 = –2 Action I (Apparent): EV = .8x – 2 (EV ranges from –42 to 38; midpoint is –2) Action II (Actual): EV = (.4)(50) – (.4)(50) –4 = –4 Action II (Apparent): EV = (.8)(50) + .6X (EV ranges from –10 to 8; midpoint is –1) In terms of the actual hazards, the SEU framework would clearly indicate selection of Action I (since –2 > – 4). But given the choice under uncertainty, as in the apparent situation, the best choice would be Action II (both because the midpoint is higher and because the range is lower). It is thus clear, at least in the abstract, that uncertainty can exact a price, particularly insofar as it can lead to the selection of suboptimal (underconceptualized) courses of action. Regardless of whether one is interested in intangible or tangible costs, as an empirical matter, in complex policy and administrative decisions the costs of uncertainty remain at best extremely difficult to assess. Even order of magnitude estimates of the costs associated with specific instances of uncertainty tend to present daunting challenges (Friedman 1984). Perhaps the best general approach, based on economic theory, relates to people’s willingness to pay to avoid it. In essence the idea is that since most people dislike uncertainty and are willing to pay to avoid it, one can estimate its costs by finding out how much people are willing to pay not to face it. In more formal terms of economic theory, the willingness to pay to avoid uncertainty can be considered as a measure of the marginal benefit associated with another unit of its reduction. The underlying assumption is standard in economic reasoning; the more people are willing to pay to avoid it the greater its costs. In terms of implementing this assumption in a research project, however, a major problem, of course, is that people’s statements of their willingness to pay as a hypothetical matter often differ substantially from their willingness to pay as a practical one. Moreover, especially in relation to concerns such as environmental justice, which involve externalities, this approach would tend to


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severely underestimate the associated costs; that is, whereas the concern with disproportionate distributions involve the indirect social costs associated with the imposition of environmental disamenities on minority and lowincome neighborhoods, people’s willingness to pay for the reduction of uncertainty only extends to include the direct private benefits they can expect to receive. As a consequence of such considerations, empirical estimates of the tangible costs of the uncertainty involved in environmental justice policy and administrative decisions based on expressed willingness to pay would be understated. But absent some such approach, the costs remain essentially if not totally uncertain. On balance, given that high levels of uncertainty characterize policy and administrative decisions related to environmental justice, and that it is generally agreed that uncertainty is costly, it follows that such uncertainty can be expected to impose costs. More specifically, given that the costs of either process or outcome uncertainty are apt to manifest in unanticipated outcomes—even if they were somehow anticipated, methods are not available with which to estimate them—their magnitude in policy decisions related to environmental justice cannot be readily estimated. Given that doing research and gathering information to reduce the uncertainty can itself be costly in time, effort, and financial resources, the decision of how much to invest in research to reduce the uncertainty involves a rationally intractable problem. While it would be rational to invest in further reduction of uncertainty up to the point at which the value of additional research costs becomes larger than the value of the corresponding reduction, because the costs of further reductions cannot be estimated, rational computation is of limited usefulness. Finally, in this context, it is useful to mention that frameworks other than SEU are also available that can at times be of use in aiding the decision process throughout the modes through which the public sector responds to environmental justice problems (Zeleny 1982; Warfield 1990; Bowen 1995). These frameworks can improve decision outcomes by helping clarify related decision processes; avoid various distortions; manage the information, criteria, and uncertainties; and evaluate the importance of the criteria. They provide a storage mechanism for the relevant problem information and help to make the requirements for more information explicit. They can also be of use in helping facilitate mutual learning between policy and administrative decision-makers and members of various stakeholder groups, especially by increasing focused discussion between various stakeholders. One of the tangible benefits of using them is that all participants can help to understand the decision problem better; it becomes immediately clearer after it has been formalized in terms of definite alternative courses of action and the criteria used to choose between them. These other frameworks are likely to be of value especially when there is relatively little time pressure for making a decision, the problem is not well defined, and the decision-makers are well-rounded

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generalists with the larger picture in mind rather than exponents of narrowly focused specialist interests (Zeleny 1992). Because, given the current high levels of uncertainty, decision-makers cannot adequately predict and specify the future outcomes to expect on taking any given course of action, they must tend to perceive only a weak relationship between the available courses of action and the outcomes likely to follow from each. Whereas such conditions bring abundant opportunities for policy advocates who make their living from the exploitation of uncertainty, they do not portend well for the prospects of actually solving or even substantially ameliorating any of the related problems. RISK TRADE-OFFS IN ENVIRONMENTAL JUSTICE POLICY AND ADMINISTRATIVE DECISIONS Ideally, the goal in just about any policy and administrative decision about environmental risk is to select and commit only to “risk superior” alternative courses of action in which, rather than merely offsetting, substituting, or shifting environmental risks, there is an overall reduction in them. Indeed, I believe that policy and administrative decision-makers using research-based decision-making will increase the likelihood of taking such courses of action. But these are most likely to arise in the choice process, in the form of coherent alternatives for action, only after the decision problem has been clearly defined and thoroughly described, the trade-offs assessed, and the outcomes of each alternative fully specified. Choices aimed to solve one problem can inadvertently lead to outcomes that are as severe or worse than the one that the attempt is made to solve. These inadvertent outcomes are known variously as side effects (medicine), collateral damage (military), or unintended consequences (public policy). Examples include the Clean Air Act of 1977, insofar as it required all coalburning power plants to install scrubbers to remove sulfur dioxide from their smokestacks. A then unanticipated and now widely recognized outcome was the formation of tons of solid sulfur sludge that must be disposed of on land. In essence, by failing to anticipate and consider trade-offs between mediums (air, water, land), the overall effect was merely to shift pollution from one medium to the other, thus substituting one environmental risk for another. Similarly, the ban on ocean disposal of industrial wastes, single-mindedly intended to protect marine ecosystems, may have inadvertently encouraged their disposal closer to human populations and fragile freshwater ecosystems (Schneider 1993), thus merely transferring the environmental risk from one set of locations to another. Still another example, the ban on the fungicide EDB (1,2 = Dibromoethane) removed the target environmental hazard but may have merely offset it with another by inadvertently leaving a fungus on some grains and nuts with effects that are more carcinogenic than the


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fungicide (Ames et al. 1987; Office of Management and Budget 1990– 1991). In these examples and many more (Graham and Wiener 1995), the net effect of failure by decision-makers to explicitly and systematically consider the full range of trade-offs was to either shift the incidence of risk or increase the overall level of risk faced by members of society. Accordingly, failure to consider the full range of trade-offs involved in policy and administrative decisions related to environmental justice is apt to either directly or indirectly lead to or exacerbate a number of connected social problems. One such trade-off, mentioned earlier, arises when considering proposals that mandate specific physical distributions of environmental hazards, specifically equal proportions of hazardous sites in all neighborhoods. This is the trade-off between fairly distributed risk and overall level of risk, and it arises largely because of spatial variation in the geographical suitability of neighborhoods as hosts for such sites. Not all neighborhoods have land or location that is equally suited as a site for an environmental hazard. Accordingly there is good reason to locate a hazardous site where geographical conditions ensure that its potential for damage is at a minimum. For example, it makes little sense to locate a hazardous site close to an aquifer or lake that serves as the primary water source for a nearby city, since doing so could unduly expose everyone drinking from it to environmental hazards. If, in pursuit of the goal of proportional distributions, an affluent neighborhood located next to the water source was forced to host such a site, rather than a poor neighborhood located farther away, the net effect could be an increase in the level of risk faced by society overall. Similarly, because the probability of a hazardous materials transportation accident increases with distance traveled, similar neighborhoods further away from major hazardous materials transportation routes are less suitable as host neighborhoods for hazardous waste repository destinations than are those located closer to them. If, in pursuit of proportional distributions, an affluent neighborhood located far from a major transportation route is forced to host a site, the overall effect could be to increase the overall level of environmental risk faced by society. In essence, due to spatial variation in the suitability of neighborhoods as hosts in the site selection process, some environmental justice decisions are bound to involve trade-offs between equitable distributions of environmental hazards and the overall level of risk posed to society. To not recognize and deal with them explicitly may be tantamount to jumping out of the frying pan and into the fire. A closely related trade-off involved in essentially any remedy to environmental justice problems has to do with the cost of health care and environmental protection on one hand and equity on the other. Regardless of what concept of fairness is used to define an environmental justice problem, remedies that go the farthest towards achieving equity also tend to require the

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greatest expenditure of public revenues. This is exemplified by a study of the trade-off between cost and equity in Albany, New York, which revealed that selection of the minimum-cost site would increase the level of inequity by 110% (as measured by minimizing the maximum risk faced by any given neighborhood) [List and Mirchandani 1992]. Moreover, huge public investments in health care and environmental protection are already in place. According to Graham and Weiner (1995): The fraction of national income devoted to health care and environmental protection has doubled in the past two decades, from about 8 percent in 1970 to about 16 percent today, and is projected to exceed 20 percent by the year 2000 (U.S. HHS 1992; U.S. EPA 1990). In 1990 over $115 billion was expended in the United States on various pollution control programs, and these expenditures are increasing each year at a rate twice the rate of economic growth (U.S. EPA 1990). In the same year the United States spent $600 billion on the delivery of health care services; these expenditures are also increasing much faster than the growth of the economy. (6)

Given these already massive investments, trade-offs are bound to arise between the additional cost of government programs and policies designed specifically to remedy environmental justice problems, and the level of equity that results from policy and administrative decisions. The basic question is this: At what point should the goal of equity outweigh the goal of controlling the cost of health care and environmental protection? In this context it is worth noting that a wide range of decision situations are feasible in which the only way to avoid this trade-off is to specify alternative courses of action whereby some segments of society benefit but none lose, often known as Pareto-optimal solutions. Unless concrete programs and policies to implement such solutions can be identified, any assertions that environmental justice goals can be achieved without the reduction of investments in other policy and administrative goals will remain essentially a matter of fanciful rhetoric; writ large, the problem of environmental justice can be roughly interpreted as a concern that the costs attributable to environmental risk are distributed regressively, in such a way that the lower tiers of America’s split-level social structure bear a disproportionate burden. Accordingly, any solution must involve a reduction in related costs borne specifically by the lower tiers without a proportionate reduction in those born by the upper tiers. This would be relatively easy to accomplish if somehow these costs could be reduced without additional public investments. Where this is feasible, pursuant to the goals of environmental justice, the costs of environmental risk borne by lower tiers could be reduced while those born by the upper tiers remains constant, in a Pareto-optimal fashion, simply by shifting the locus of investment. But unless such Pareto-optimal reductions are feasible, and tangible alternative actions can be identified through which they can be imple-


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mented, reason dictates that the goals of environmental justice can be accomplished in only two ways. The first, which is contrary to the explicit language of Executive Order 12898, is to shift the burden of environmental risk from the lower tier to the upper tier, in a manner that in many ways resembles a transfer payment. The other is to make still further and additional investments in environmental risk reduction, above and beyond the current ones, in which case the trade-off between equity and the control of expenditures for health care and environmental risk reduction will remain as a consideration in related policy and administrative decisions. Another trade-off that can potentially arise, especially when considering proposals that require proportional distributions between neighborhoods, might be termed the tradeoff between neighborhood rights, and individual rights; that is, such proposals tend to overlook potentially vast differences in the circumstances and situations of individuals within neighborhoods (Been 1993). When such differences are overlooked, it opens the possibility that the rights of some individuals within the affected neighborhoods will be violated while others in the same neighborhoods are possibly enabled to become freeriders on whatever investments are made in implementing the proposal. Still another likely set of trade-offs to arise is between environmental risk and local benefits, specifically jobs and tax revenues. Many if not most of the environmental hazards of concern in environmental justice are physically necessary byproducts of the very same industrial processes that provide local jobs and tax revenues needed by government to provide public services. Of course, when the hazards are transported after they are produced, spatial shifts can occur in their locations, in which case the coincident risks can impose indirect costs on the locations to which they are transported (Stough and Bowen 1985). But often, especially in terms of TRI releases, the location of the environmental hazard coincides with the location of the benefits, including the housing occupied by those minorities and low-income individuals that labor in the concomitant production process. In this case, the tradeoffs can be most clearly seen in considering ways to decrease residents’ exposure to the environmental hazard. One way is to move the production process, say, from an inner city to a peripheral area, but this is likely to exacerbate any existing spatial mismatches between the locations of minority and low-income residences and the locations of the jobs for which they are qualified. Another is not to move the production process but to improve it in such a way that the primary product is produced without producing the hazardous byproduct. The problem with this latter approach, of course, is that it is often not realistic. Even when it is, as a rule, innovations in production processes entail the substitution of capital for labor, which tends to decrease the number of available routine jobs, often those for which local minority and lowincome individuals are qualified. Thus, decision-makers deliberating alternative remedies for environmental justice problems need to carefully consider

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the outcomes they expect to be associated with particular alternatives, specifically in terms of the possibility of any local benefits that might be foregone with implementation. Alternative courses of action that decrease environmental risk at a location are also likely to decrease the number of jobs and the quality of public services nearby. Finally, decisions related to environmental justice can involve a trade-off between devoting attention to health effects attributable to proximity to the distribution of hazardous sites and health problems attributable to other causes in minority and low-income communities (Foreman 1998). While there is little doubt that many significant health challenges disproportionately bedevil minority and low-income neighborhoods, fear of environmentally induced disease undoubtedly goes way beyond the extent of verifiable disease causation. Here, a trade-off arises. On one hand, the limited available attention can be concentrated on implementing remedies to problems caused by scientifically well-established disease-causing agents, such as lead and some pesticides, as well as stimulating a broad range of behaviors conducive to health promotion and disease prevention. On the other hand, it can be expended on the far less well-defined and established problems associated with residential proximity to hazardous sites.

CHAPTER 9

Lessons from Research-Based Environmental Justice Policy and Administrative Decision-Making [Q]uite apart from the decision itself, the very process of using research seems to have a beneficial effect on the bureaucratic structures with which democracies govern. Recognizing and confronting areas of ignorance tends to reduce rigidity. Making policies and programs smaller scale, more iterative, and more dependent upon the acquisition of knowledge induces modesty rather than grandiloquence. And incorporating the evaluation mechanisms that allow policies, programs, and performance to be assessed promotes prudence and responsiveness on the part of agency officials. So using research tends to push decision-makers toward moderation, but also toward policies and programs that are more likely to work. —ELEANOR CHELIMSKY, “On the Social Science Contribution to Govenmental Decision Making”

There appears to be a major gap between the political rhetoric and the empirical reality of environmental justice. The rhetoric refers primarily to concern with public health impacts in minority and low-income neighborhoods, but the reality is that relatively little serious research has been done on the issue. It seems, as Foreman (1998) suggested, that fear of environmentally caused disease greatly surpasses the empirical knowledge about it. Very little is evidently known about the distribution of environmentally hazardous sites in terms of the socioeconomic characteristics of nearby neighborhoods. Similarly, little is known about the public health risks posed by such sites, and even less is known about differences in these risks in terms of socioeconomic categories. So the environmental justice movement must in reality be more about concern with procedural inclusion, community empowerment, the focusing of anger, and the casting of blame, and generation of pressure against targeted institutions and economic interests than about the public health effects of environmental hazards. On this basis, “environmental justice” is largely a provocative political symbol, invoked to elicit citizen participation toward greater empowerment for selected social groups, independently of any empirical justification. It is invoked as a means of appealing to selected groups by the promise of gaining 229

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for them greater control over resources and greater voice in policy and administrative decision processes. It represents an emotionally powerful normative standard against which to compare the way things are perceived to be, and in doing so helps to establish and label a more widely spread perception of a social problem.1 Regardless of any empirical foundations, or more correctly the lack thereof, the environmental justice movement thus apparently promises to bolster the prospects for disenfranchised communities, primarily through enhanced political power. Given the wide disparity between the political rhetoric and corresponding assertions about the disproportionate distributions and their health impacts on one hand, and the associated high levels of empirical uncertainty on the other, how can policy and administrative decisions be made more effectively to actually solve related problems? A NEED FOR SYNTHESIS The first lesson from this perspective has to do with the need for synthesis of words and actions, of thought, policies, and institutions. It becomes more and more apparent that the root causes of social problems such as environmental justice all have to do with the quality of human thought. More specifically, especially when dealing with complex and multidimensional problems, the salient dimensions tend not to be aligned in the way knowledge about them is taught and learned in institutions of learning; that is, they cannot be easily separated out into their political, economic, geographical, epidemiological, and other aspects, and approached on the basis of one aspect or another. Yet, organization of the relevant knowledge in institutions of learning tends to be divided along disciplinary lines that separate out these aspects. So the organization of knowledge, as presented in these institutions, works against its contribution to the understanding of and solution for the more general overall problem. As a consequence, participants in the process of defining a problem or responding to it, who have been through formal systems of learning, have to mentally process the relevant knowledge and values in frameworks they have not encountered in formal learning environments. When they are called upon to make decisions, they lack a framework with which to abstract from, simplify, and systematize the salient dimensions of the problem so to bring them meaningfully within the purview of the human mind. The need for synthesis is the need to put the various parts and dimensions of decisions together, within, and between various academic disciplines and viewpoints in a way that is systematic and reasonably coherent. It means developing conceptual frameworks with which to integrate the huge number of political, economic, social, geographical, epidemiological, chemical, and psychological attributes interacting in a complex, indeterminate, interdependent fashion, and make them coherent (e.g., Mainzer 1994). It means letting

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go of efforts to categorize the salient dimensions of the problems into specialized “political,” “economic,” “social,” “geographical,” “technological” or other categories, and recognizing that all but the very trivial details are intimately related to corresponding details in other related social problems. It thus means thinking about environmental justice not only in terms of disproportionate distributions and public health, but also in terms of other closely related social problems including poverty, housing, racial discrimination, unequal opportunities or outcomes, education, and pollution, and putting these together in terms of policies and institutions. The gap between the rhetoric and reality of environmental justice is attributable largely to the lack of such a framework. It is allowed to persist probably because it masks what boils down to a struggle for political power, one that in the end is likely to be lost by those who are less fortunate in society. But especially in a society that aspires to the impartial application of the rule of law, that is not good enough. As discussed in Chapter 2, to accept the sort of irrational social decision process in which the end sought is redistributed political power is implicitly to accept the morally positive principle that might makes right. Instead, if society is to have reasonable hope of living in a liberal system ruled through juridical democracy, the appropriate approach is to use reasoned arguments that are as consistent as possible with sound empirical knowledge and rationality, in an effort to determine what ought to be preferred. This is a matter of the use of science and rationality, not of power politics. Accordingly, environmental justice suggests the need for better integration of thought, policies, and institutions that enables policy and administrative decision-makers to intelligently pursue appropriate goals and objectives. STRATEGIC MANAGEMENT OF THE NATIONAL POLITICAL AGENDA The second lesson stems from having recognized the way environmental justice surfaced as an item on the national political agenda, especially the corresponding implications for decisions aimed toward solving or ameliorating related problems. As described in Chapter 1, environmental justice was initially propelled to the national political agenda largely by grassroots activists and policy advocates in the 1980s and early 1990s, not by scientists or planners who had studied environmental hazards and their effects on public health. The related perceived problems of disproportionate distributions were then fortified at least at the outset, until the early to mid-1990s, through research produced primarily by advocates, not impartial social scientists. The process culminated in 1994 when President Clinton signed Executive Order 12898, quite possibly on the basis of a mistaken assumption about the certitude of the related empirical foundation. This, at least for the time being,

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solidified environmental justice as an issue on the national political agenda by giving it executive authority. Without a doubt, the way environmental justice surfaced on the national political agenda speaks highly of the responsible participants in the process, especially in terms of their respect for the exercise of the grassroots liberties and values for which liberal democratic institutions were established. But it is readily apparent that related developments were not strategically managed to ensure consistency between the various political and institutional mandates laid upon policy and administrative decision-makers on one hand, and sound empirical research and careful risk assessment and prioritization procedures on the other. Thus, there is no reason to be at all surprised by the lack of a strong scientific foundation for the concerns expressed in the prevailing political discourse. Nor is it surprising that related policy and administrative decision-makers find themselves in the double bind of having no way of knowing whether they know enough to know that they do not know all that they need to know to make good decisions. Kingdon (1995) generally describes the ways government agendas are set as a matter of linkages between problems, policies, and politics; the events that lead to the presence of an item on the agenda do not always proceed neatly and rationally. Unpredictable occurrences and seemingly random events play an important part in the process. Certainly such a description squares closely with the path on which environmental justice rose to prominence. But even granting that the agenda setting operates as Kingdon describes, if participants do not first identify problems and then seek solutions for them as the rational model would lead one to expect, this does not in any way preclude the circumstantial rationality of efforts to manage the process strategically. All it implies is that one should not expect such efforts to comprehensively alter the ways that government agendas are set. Indeed, a number of advantages would be likely to follow from administrative efforts to strategically manage the national political agenda. They can improve the chances of bringing problems to the agenda with enough clarity of definition to make meaningful deliberation of solutions feasible. They can help minimize the effect of seemingly random elements and events in the process. They can also help ensure that the problems that do surface on the national political agenda are adequately defined on the basis of scientific research, thus enabling more rational policy and administrative responses, and presumably leading to less government failure. And once strategically managed problems arise on the agenda, such efforts can improve their chances of being recognized as ones for which meaningful deliberation of actions that stand to exert major influence on the future of society are feasible. This they can do by helping identify and distinguish between scientifically well-justified and well-established problems and those brought forward largely because they are associated with symbols that are salient in the politi-


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cal sphere or that meet with the ambitions of advocates. While such efforts cannot change the recent history of environmental justice and its rise in its current essentially rhetorical form to prominence on the agenda, they can ensure that related problems will be brought forth on a more solid scientific foundation in the future. Several distinct kinds of related problems can potentially arise in efforts to strategically manage the political agenda. The ability to distinguish between them on the basis of their salient characteristics and strategically manage them accordingly can help develop a broader and more coherent foundation upon which government action can be based.2 One possible conceptual framework with which to do so is based upon recognition of different dimensions of complexity in various related problems, and subsequent assignment of the problems to various modes of response on the basis of a corresponding classification system (Wang, Fang, and Bowen 2000). Two essentially distinct dimensions of complexity can be distinguished on the basis of some general characteristics of environmental justice problems. Such problems typically are essentially geographical in nature, involve multiple interdependent resources, high levels of uncertainty, and multidisciplinary foundations. These characteristics relate to their external complexity. Their external complexity increases along with increases in their geographical scale, the number and salience of the particular interdependent resources at stake, the level of uncertainty, and the number of disciplines involved. The decision situations in which high levels of these characteristics arise are externally complex in the sense that the related facts, events, and circumstances tend to interact in a complex, indeterminate fashion. Thus their scientific basis in geography, economics, operations research, medicine, epidemiology, and public health, among other areas, involves multiple interdependent considerations, some of which cannot be made coherent outside the particular context in which they occur. Accordingly, to the extent that they are externally complex, the principal avenues to their resolution all involve the participation of specialists trained in these and related areas, experienced specifically in their relationship to related facets of environmental management. Environmental justice problems typically attract attention from people in a wide variety of circumstances and institutions, involve multiple stakeholders, multiple conflicting objectives, and competing values. Thus they can also be considered to be internally complex; they are internally complex insofar as these characteristics make interpretation of the relevant facts, events, and circumstances that constitute the decision situations difficult. In psychological terms, internal complexity in this sense arises because those who are studying the situation or making decisions about it are, by definition, not a part of it. In terms of politics, internal complexity arises largely out of concern with the ideals of liberal democracy, particularly with the associated moral tenet of

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respect for a wide variety of possibly even inconsistent values and perspectives. Internal complexity ensures that efforts to manage environmental justice problems will be determined in large part through the bargaining, compromise, and negotiating of various stakeholder groups in society. As the number of participating organizations, stakeholders with different objectives, and heterogeneous subjective value dimensions increases, so does the level of internal complexity of the problem. An increase in internal complexity, in turn, ensures that the effectiveness of policy and administrative decisions to resolve or ameliorate the problem will be more difficult. Recognition of different levels of both external and internal complexity enables one to categorize environmental justice problems in a way that can be used by those strategically managing the national political agenda to help assign related responsibilities to various modes of activity within government organizations. To recognize these two different types of complexity and then shepherd the government response through the various modes of activity, as appropriate, is arguably to enhance the chance of attaining related goals and objectives. Before elaborating any further on this idea, four distinct if somewhat interrelated modes of activity that get involved in government responses to environmental justice problems can be identified. These are (1) environmental justice planning, (2) environmental justice policy formulation, (3) environmental justice administration, and (4) environmental justice dispute resolution. Environmental justice planning consists largely of using disciplined human intelligence, scientific analysis, and synthesis to help fully conceptualize and describe environmental justice problems. Environmental justice planning thus brings together information, specialized scientific and technical knowledge, and understanding based upon social scientific postulates of human behavior, in an effort to conceptualize, illuminate, and describe related problems. It is often appropriate for environmental justice planners initially to describe and document the problem. Because of the highly abstract and conceptually oriented activities involved in processes of integrating information and knowledge from a wide range of input areas, interdisciplinary specialists and professionals typically play key roles. Thus, environmental justice planning helps avoid tunnel vision by integrating information and knowledge from various disciplines into proposed alternative courses of action taken in response to environmental justice problems. Environmental justice policy formulation is the mode through which high degrees of internal complexity, particularly that of a political nature, can be resolved on the basis of communication and negotiation between various stakeholders and stakeholder groups. This is the mode through which effort is made to determine the “best” systematic plan of action, as articulated by combinations of scientific specialists, policy and administrative decision-makers,


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and citizens. Citizen participants, representatives of the various stakeholders, and interest groups all influence environmental policy formulation. The legitimacy of a proposed environmental justice policy is established by linking public decision and mass opinion in the process of policy formulation. During the policy formulation process, the key is to find a balance between citizen participation in terms of communication of interests and negotiation of outcomes, determination of a fair and equitable distribution of environmental risks and benefits, and the fullest possible use of the available scientific knowledge. Environmental justice administration is necessary to implement the results of the activities in the environmental justice planning and environmental justice policy formulation modes and to work cooperatively with environmental justice planners to strategically manage the policy agenda. Implementation is carried out primarily through the cooperation of relevant government organizations and activities. The implementation of environmental justice policies in many ways entails the most concrete of the activities in any of the modes. Because it involves degrees of administrative discretion, however, it is not altogether as concrete as it may appear to be at first blush. Furthermore, failures in environmental justice planning or environmental justice policy formulation often guarantee the contingent failure of environmental justice administrative activities. It would be incorrect to portray environmental justice administration as a matter of general, routine, and mechanical implementation devoid of discretion. Public sector environmental justice administrators sometimes face crises and unanticipated problems. Moreover since the public at large may not understand the underlying reasons for the specific decisions that are made by these key actors, it is sometimes necessary for them to promote understanding of the reasons behind their decisions and persuade the public. In these situations, environmental justice administrators need not only the ability to perform administrative routines and standard operating procedures, but also the general ability to manage crises and promote policy. Environmental justice dispute resolution. During the implementation of an environmental justice plan or policy, especially given the high levels of emotion that can so easily get involved, some extent of conflict is almost inevitable. Conflict may be attributed to factors such as complexity and uncertainty within environmental and socioeconomic systems, defects in past or current environmental policy and administrative processes and systems, lack of information and experience, or negligence to consider the diversity of values and perceptions of stakeholders. Whatever the underlying justification, environmental justice conflicts are apt to arise and bear upon policy and administrative decisions, thus diverting attention and limiting the effectiveness of public organizations in terms of improving the overall situation.

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The practice of environmental dispute resolution in the past couple decades has given rise to a number of dispute resolution mechanisms. In broad terms, the alternatives include dispute avoidance, collaboration through negotiation or mediation, reliance on higher authority, or the use of power (Slaikeu and Hasson 1998). Although different mechanisms may be appropriate depending on the particular circumstances, all have potential impediments associated with them. Rational selection among them depends primarily on considerations about the particular circumstance and wishes of the relevant stakeholders that go beyond any systematic institutional implications stemming from recognition of the distinction between external and internal complexity (Rabe 1991). Unless specific problems are assigned to modes on the basis of some sort of rationale, the chances of successfully solving them are lessened. This can be done on the basis of the distinction between external and internal complexity. The distinction enables one to differentiate between four categories of related problems such that each category corresponds to its own particular appropriate starting point and sequence of activities in terms of the four modes of public sector activity. First, one can characterize the preponderance of environmental justice problems by high levels of both external and internal complexity. Given the highly political nature of such problems, few if any can be characterized in the second way, by high levels of external complexity and low levels of internal complexity. Perhaps one can characterize a few of them in the third way, by low levels of external complexity and high levels of internal complexity; an example is lead paint in homes, in which the causal pathways and associated diseases have been almost completely determined scientifically. But the associated level of internal complexity is high because of the wide range of political and social interests involved. The fourth way is by low levels of both external and internal complexity. Each of these four categories suggests a characteristically different sort of public sector response. Accordingly, the assignment of a particular problem to its befitting category can help simplify and rationalize the assignment of responsibilities to the appropriate mode, thus helping to attain environmental justice goals and objectives. Environmental justice problems are typically classified as having a high degree of both external and internal complexity; that is, they tend to involve a great deal of uncertainty in terms of the relevant facts, events, and circumstances, as well as large variation in terms of political interest groups. Therefore, the government response to them should be formulated and implemented with cooperation from among interested public administrative agencies, quasi-public and private business enterprises, and other organizations or citizen groups. Especially when high levels of internal complexity are involved, decision makers are well advised to seriously consider the philosophy, procedures, and techniques oriented toward multiactor, multivalue, and


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multi-interest situations associated with high levels of citizen participation (Renn et al. 1993; Selin and Chavez 1995). It is useful to briefly make a distinction between top-down and bottomup approaches to environmental justice policy and administration. This distinction is based on the direction of influence among different strata within public sector organizational systems. Top-down and bottom-up approaches to environmental justice policy and administration differ not only in terms of the interfaces between different modes, they also come from different philosophies of the role of government in society. Generally speaking, the top-down approach comes from a more paternalistic, centrally planned, or authoritarian social philosophy that emphasizes centralized and integrated government planning and management. This emphasis can often facilitate relatively effective and efficient implementation of policy and administrative decisions designed to attain related goals and objectives. It puts primary faith in the ability of scientists and other specialists to understand and respond appropriately to the broader picture. It assumes that these scientists and specialists employ methods of wide scope, have significant control over events, and can generate the most comprehensive, anticipatory, designed approach to problems. Under a top-down approach, potential environmental justice problems would come up for discussion first within the context of environmental justice planning rather than any of the other modes, which would save time, energy, and resources. It was noted that problems with environmental justice characteristically exhibit high levels of both external and internal complexity. Accordingly, before they can be dealt with in any sort of a comprehensive way that solves or ameliorates the problem without inadvertently creating others of equal or greater intensity, they must be analyzed and fully conceptualized. This places a premium on strong abstract reasoning and conceptualization skills. In this respect it is befitting that the initial responsibilities go to specialists and others trained with advanced degrees that prepared them specifically to deal with high levels of abstraction and conceptualization. Accordingly, to the extent that a top-down approach is to be taken to an environmental justice problem, the appropriate starting point is usually with environmental justice planning. This provides a way to initially sort through the external complexity in light of current research and to formulate the results in terms of a few pertinent and practical alternative courses of action to solve or ameliorate the problem. In any case, environmental justice planning is the mode most likely to come up with a few logical, well-conceptualized plans for action, based upon current state-of-the-art knowledge. Without such plans, the subsequent processes of environmental justice policy formulation and environmental justice administration are less likely to have an adequately developed conceptual basis and are therefore less likely to lead to broadly satisfactory social responses.

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The top-down approach also has some disadvantages. Among others, government planners are not usually the first people to experience directly an environmental justice problem. Indeed, the problem may not personally or directly affect them at all. Nor do they necessarily have the fullest understanding of how field regulators and street-level bureaucrats perceive and perform their tasks, and the institutional support they have. So their plans may be underconceptualized and not sufficiently realistic to support the attainment of stated goals and objectives. Also, the unilateral control method that typifies top-down government practices may easily cause discontent at lower levels, damaging the necessary cooperative atmosphere, both within the institution of government and between public authorities and private citizens. The bottom-up approach avoids some of the disadvantages of the topdown approach and has some advantages of its own. It provides the lower levels with greater power to guide and constrain the decision processes through which environmental justice discovery and problem-solving activities are conducted. It places greater responsibility and discretion on citizens as well as those in the mode of environmental justice administration. Thus it realistically relieves the people in the modes of environmental justice planning and environmental justice policy formulation of some of the responsibility for actively discovering, describing, and conceptualizing related problems. It also increases the responsiveness of environmental justice planners, by putting them on alert that citizens are very aware of and paying attention to environmental justice problems. Another advantage of the bottom-up approach comes directly from the way it puts the power to identify, describe, and legitimate the problems in lower levels. This enhances the abilities for lower-level self-determination and helps avoid unsatisfactory procedures coming from central authority. The bottom-up approach has its disadvantages, especially when environmental justice administrators cannot solve a particular problem on their own. In these cases, the environmental justice planning and environmental justice policy formulation activities tend not to get involved until help is requested. Often, as a consequence, either the time to solve the problem is much longer or the actions taken on the basis of an underconceptualized problem produce unanticipated consequences. Also, the bottom-up approach is less likely to have a preventive, anticipatory orientation and is more likely to grapple with problems that have already occurred. This is a distinct disadvantage insofar as it tends to overemphasize current problems that are perceived as urgent, and this too often at the expense of underemphasizing important issues that require long-term vision and broad, comprehensive consideration. Among the four modes, either environmental justice planning or environmental justice policy formulation is an appropriate starting point for the top-down approach. In contrast, environmental justice administration is the appropriate organizational starting point for the bottom-up approach. Dis-


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putes can be ongoing and impede progress, and in any case, dispute resolution is often a mechanism that passively reacts to disputes that have already happened. As for environmental dispute resolution, it should not usually be regarded as an appropriate starting point for either the top-down or the bottom-up approach. Though much of the initial impetus for concern with environmental justice has come from policy advocates and grassroots activists, a bottom-up approach to government responses to environmental justice problems is generally not recommended. If such a problem occurs most directly within the purview of a particular environmental justice administrative activity, it may be appropriate for this activity to take the responsibility to lead the response. However, since these problems typically involve high levels of internal complexity, often of a political nature, they entail costs and benefits for a relatively broad range of social groups. They are therefore not likely to be manageable by a single administrative unit designed primarily to implement policies and procedures. In such cases, it is appropriate that the lead roles in any broader government responses be advanced beyond the single administrative unit to the mode of environmental justice policy formulation. This is the mode in which policy can be formulated with a broader view of the interests of the various stakeholder groups, and where the high level of internal complexity can be dealt with most successfully. On balance, it would be most appropriate for environmental justice problems to enter the system of public institutions in the mode of environmental justice planning. This is where a carefully integrated and designed approach to conceptualizing and understanding the dynamics of the related complex social systems is most feasible. It is also likely to be where the participants have the best training and opportunity to objectively search the situation for the full and detailed set of information about the particular problem’s outstanding characteristics. Finally, it is where deductions can be made from this information and formulated into input for the environmental justice policy formulation stage. Environmental justice problems have a high level of both external and internal complexity, so if initial discussions bypass the environmental justice planning process, social responses are more likely to neglect some important abstract considerations. These include economic considerations regarding the allocation of limited attention and resources. In summary, the way environmental justice surfaced as an item on the national political agenda, starting with grassroots, getting fortified by research produced largely by advocates, then finding its way into an executive order, is not consistent with this framework. Indeed, related problems have not been strategically managed at all to ensure consistency between the various political and institutional mandates laid upon policy and administrative decision-makers in various modes on one hand, and sound empirical research and careful risk assessment and prioritization procedures on the other. This is

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one of the reasons for the wide disparity between abundant rhetorical and political discourse, which presupposes that disproportionate distributions have been scientifically established and shown to affect public health, and the deeper empirical reality of enormous related uncertainty. Because related problems have not been strategically managed, they have not been appropriately matched with modes of response, and the likelihood of solving the problems without creating others of equal or greater severity thus have been diminished. Meanwhile, until improvements are made in terms of strategically managing the agenda, it is a good idea to continue to provide environmental justice programs that educate the public and raise their political aspirations and competence so that they can contribute to setting an appropriate political agenda. THE ENVIRONMENTAL JUSTICE RESEARCH AGENDA The third set of lessons has to do with the quality of the scientific foundation for claims about disproportionate distributions as well as the effects of exposure to environmental hazards on public health, particularly its current weakness. The review of the related research in Chapter 6 substantiates the view that, on balance, there is a great deal of empirical uncertainty surrounding the pertinent issues and problems. Most of the available research is essentially descriptive; little to none is explanatory in nature. Indeed, it is not even clear what a validatable predictive model of disproportionate distributions would look like. A great deal of the existing research is based on inadequate statistical analyses, especially as it relates to spatial clustering and dependency. Essentially, all is inappropriately designed for the specific purposes of establishing relationships between particular environmental hazards and public health in minority, low-income, or other disadvantaged neighborhoods. Much of the related data is unreliable. Only a relatively small subset of the current body of research uses conceptually appropriate comparison groups. Much of it is based on anecdote and case study and some of it fails to recognize the crucial difference between cross sectional correlation and causation. Thus, overall the related scientific foundation tends to be mixed, inconclusive, or simply weak. There is little doubt that, in relatively broad and general terms, exposure to pollutants and chemical agents such as lead or some forms of indoor air pollution or pesticides can, in sufficient quantities for sufficient periods of time, adversely affect human health. But in the far more specific terms of whether or to what extent discernable patterns of residential proximity correspond with exposure to definite subsets of the ubiquitous array of environmental hazards, or exposure to such subsets corresponds with adverse public health impacts is quite another matter. Moreover, though it is clear that the


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related uncertainty is vast, its magnitude is essentially inestimable. Very little can be said on the basis of the current body of sound scientific research about location-specific spatial distributions of demographic and environmental variables associated with even relatively few of this array. Social and historical patterns associated with the nature and extent of specific types and dosages of exposure to various chemicals remain essentially uncertain. For all but a relatively few chemicals, specific health outcomes associated with various exposure dosages have not been established in the laboratory, much less in the real world of minority, low-income, or other disadvantaged neighborhoods. Until the research needed to sufficiently reduce all the uncertainty is completed, the question will remain essentially unanswered as to whether or to what extent minorities, low-income, or otherwise disadvantaged and susceptible populations are disproportionately exposed to environmental hazards and therefore face greater health problems. Though the answers to such questions are uncertain, they are potentially important in several respects. First, the moral tenets of liberal democracy are predicated upon an assumption of the inherent worth of the individual (Redford 1969). If and when the principle that all individuals have a human right to a healthy and clean environment is not respected for some individuals, it thus not only potentially creates tragedy in individual lives but also reflects negatively upon the national character. Second, in economic terms, if and when environmental hazards erode public health conditions the potential effects include not only the decreased social welfare that results from removing the affected individuals from the productive labor force, but also the increased public burden in terms of health care costs and other related payments for those who, as a result, are less able to take care of themselves. Third, valid scientific research tends to decrease uncertainty, thus constraining the range of feasible interpretations of a situation, some of which may be wildly unsubstantiated or even incoherent. In terms of environmental justice, the uncertainty that stems from lack of scientifically established answers thus leaves open a much broader range of ostensibly feasible perceptions of injustice than would otherwise be possible and is likely to foment social unrest and instability. Finally, as was mentioned in Chapter 8, there are several ways in which failure to deal appropriately with perceived or actual disproportionate distributions can violate international human rights agreements. Thus there is justification for considerable further related research. At a minimum the implied research would require considerable investment in further data acquisition and analysis. It would for instance require the creation of new databases that specifically enable researchers to assess the exposure of populations to environmental hazards, the health effects of those exposures, and the demographics of the populations exposed. The specific goal would be to compare demographic and socioeconomic groups in terms

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of differences in exposure and subsequent health effects. This is a daunting challenge, especially for those in the fields of public health and epidemiology, who would have the best training for this particular body of research. Because the results of research are only as good as the quality of the data, more and better data are key. Data are useless if they are not accurate and reliable. To be of value, in terms of testing hypotheses related to disproportionate distributions and their health effects on communities, they have to meet several specific criteria. First, they must accurately operationalize the essential concepts in the research. If they are to be used in testing hypotheses about the relationship between exposure and public health, they must contain variables that measure exposure (not proximity) and various related diseases. Second, they must contain minimum levels of measurement error. Measurement error is systematic and can greatly distort the results of a study. Third, they must fit the necessary parameters of the question; for example, if the researcher is interested in the effects of a range of environmental hazards on public health, the data must include measurements of the entire pertinent range of hazards at the appropriate level of geographical specificity. Beyond this, if the data are to support serious policy and administrative decision processes directed to solve or ameliorate perceived environmental justice problems, there are also several other relevant considerations. First, especially insofar as the purpose of the research is to investigate the health effects of environmental hazards in minority, low-income, and other disadvantaged communities, the necessary data are primary data. There is generally an inevitable gap between primary data collected by a researcher with a specific hypothesis in mind, and data collected by others with other hypotheses in mind. Secondary or previously existing data sets tend only to approximate the kind of data that researchers would like to have for testing specific hypotheses, including those related to serious environmental hazard–public health issues in minority, low-income, and other disadvantaged neighborhoods. Thus, related analyses based on secondary data are not apt to withstand scientific scrutiny. Second, it would be useful to find and take steps to improve the accessibility of the currently available data. The currently accepted practices in which the researcher who collects data does not make them available are contrary to the spirit of scientific replicability. If researchers interested in disproportionate distributions and associated health effects would openly share their data, it would greatly improve the quality of available knowledge, in terms of both what is and is not known about the topic. Third, it would help a great deal if the collection procedures for some of the secondary data frequently used for assessing disproportionate distributions, such as the Toxic Release Inventory (TRI), were improved to reduce bias and error. The current practice for the TRI, for example, is to require reporting of all releases by all firms not subject to a definite set of exclusions, but without strict enforcement of standard procedures through the use of stiff


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penalties for noncompliance. Because data gathering is such a costly undertaking, one way to reform this practice without incurring a great deal of additional expenditure of resources would be to get rid of the exclusions and use scientific sampling procedures. Valid research depends not only on good and appropriate data but also on suitable research design. In this regard, case studies and anecdotes do not provide an adequate basis from which to infer larger patterns of disproportionate distribution or related health effects, particularly when the generalizations go from single cities to the entire country. At a minimum, the appropriate designs for answering questions about spatial patterns in the distribution of environmental hazards are correlational in nature, based on small-scale geographical areas that can serve as plausible proxies for exposure. Also, the selection of comparison regions in such designs is crucial. Past this point, when the boundaries of the jurisdiction in which a policy or administrative decision is to be made exceed the boundaries of the study region, probably the most suitable way to determine patterns at the larger jurisdictional level is through metaanalysis of constituent regional studies. For example, national patterns could be tested for and inferred through a metaanalysis of numerous metropolitan level studies (Bowen et al. 1995), based on one similar study for each Standard metropolitan statistical area (SMSA) in the country. Additonal variables could be included. Such an approach would not only enable the researcher to test hypotheses about national-level patterns based on local-level studies, but also might influence policy and administrative decisions, by including moderator variables other than proximity. Such variables might include, for example, ones used to determine the effect of the tendency of blacks to suffer more mental anguish as a result of exposure to industrial pollution and other environmental hazards (Burby and Strong 1995). It also would help improve the scientific foundations of relevant knowledge if more researchers would perform, document, and report on residual analyses, testing for statistical pathologies in their models. Without such analyses it is not possible to determine whether the estimates produced by the models are unbiased with respect to the parameters nor whether the statistical tests of significance are reliable. This is of especial concern in relation to the explicitly spatial aspects in the typically cross-sectional data used in establishing disproportionate distributions. Unless the appropriate spatial statistical tests are done and documented, there will always remain potent reason for doubt as to the believability of the conclusions drawn from the study. Finally, in terms of institutional responses, I recommend that the Environmental Protection Agency (EPA) and the National Institutes of Health take a more active lead in ensuring that more empirical research is done, especially epidemiologically oriented research focused specifically on minority and low-income communities. The key is to put the responsibility in activities

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with a strong capacity for sound environmental and health related research. Second, the I recommend that the EPA be required to formally evaluate the results of all national-level government policies and programs related to environmental justice. Such evaluations are consistent with the National Performance Review. Moreover, because such policies and programs entail either the potentially immense costs of establishing, monitoring, implementing, and enforcing regulations and controls or the potentially mammoth costs associated with public assumption of the burden faced by groups of individual citizens, such evaluation is simply good management practice. It is logical that more formal evaluation research should be devoted to determining effectiveness of policies and programs. Third, policy and administrative decisionmakers should actively seek to engage researchers in a continuing dialogue, based upon free and open recognition of the vast uncertainty surrounding related problems and issues. This will help focus the research, making it more useful and increasing the likelihood that the findings will be used appropriately. IMPLICATIONS FOR POLICY AND ADMINISTRATIVE DECISIONS The fourth and final lesson has to do with environmental justice policy and administrative decisions. In this regard, a research-based approach to related policy and administrative decisions does not suggest that, if only there were more rational mechanisms in place, society would have an altogether consistent and effective approach to the management of environmental risks. While vastly increased data gathering, improved empirical research, and information dissemination are necessary for improved policy and administrative decision-making in the area, they are clearly not sufficient. Thus anyone who thinks good policy and administrative decisions can dispense with a solid foundation of empirical research and a suitable framework with which to systematically integrate it into the choice process is seriously mistaken. But so is anyone who thinks that environmental justice could be reduced to purely scientific and value-free terms. Many of the solutions to related problems will necessarily involve essentially political matters of values, of beliefs that guide speech and conduct and make people who they are, not matters of empirical research or the framework with which it is used in deliberating choices. Examples include questions such as how far to go in curtailing individual liberty in the interest of risk reduction, how much to pay for the reduction of perceived as opposed to actual risk, and what principle of fairness to use as a standard against which to evaluate the justness of a given distribution. Improved empirical research and the use of rational decision-making frameworks are oriented almost exclusively toward improving the means rather than the ends of policy and administrative decisions, and appropriate


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objectives are essentially matters of the evaluative and political judgment of ends. Research-based decision-making can help inform these political judgments, but is not and never will be a substitute for them. Thus in some important respects the bottom line is and always will remain not so much a matter of empirical research and rational decision-making, as of human and social values. Having said this, it would nevertheless be a serious mistake to think that one cannot reason about values, that they are simply a matter of tastes and preferences for which there is no accounting. Some values, for example, those that approve the preemption of one person’s basic human needs in favor of the satisfaction of another’s merely trivial wants or that set aside important objectives in favor of trivial inconveniences, are clearly inappropriate. By implication, because not all values are appropriate, to make good policy and administrative decisions, ones in which the decision-makers intelligently pursue appropriate objectives, not only can policy and administrative decisionmakers reason about values, they must. For if inappropriate goals and objectives are sought, good policy and administrative decisions are not possible, no matter how complete the related research foundation or how efficient and effective the means with which it is employed. Such recognition implies that a research-based decision-making approach for environmental justice cannot determine the appropriate course of action for policy and administrative responses to related problems, not that the selection process cannot be helped by such an approach. On the contrary, whenever policy and administrative decision-makers are faced with a related decision, such an approach can arguably increase the likelihood of realizing the goals and objectives, of finding successful solutions. For only with a reasonably clear, distinct, and reliable problem description can the decisionmaker know what the decision is all about. And only adequate research can make such a problem description available. This line of reasoning puts a premium on problem description and information search. It suggests that unless problems are reasonably well described, and the information used to connect feasible courses of action with associated outcomes well established, good decisions are not feasible. But the empirical research foundations needed to provide a reasonably clear and distinct description of national-level patterns of disproportionate distributions and their effects on minority and low-income communities has not been anywhere near completed. It follows that it is not feasible to make good related policy and administrative decisions, at least not in the sense that they presuppose any sort of widespread pattern of injustice. Such has simply not, on a proper reading of the research, been demonstrated. But, given that there are very real public health and human rights considerations involved, this line of reasoning does lead logically to a solution in terms of the appropriate government approach to related problems.

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The solution begins with a recognition that, to the extent environmental hazards impose costs indirectly upon individuals, particularly those in lowincome and minority neighborhoods, there is an incontestable role for government to intervene to solve the problem. In terms of economic theory, this is a matter of negative externalities, or indirect costs associated with risks that are imposed on third parties. This is a form of market failure, and when the market fails there is a justifiable if not necessary role for government. Thus, to the extent that environmental hazards are disproportionately located in minority and low-income areas and impose costs there, the only way for the problem to get solved is for the government to act. At the same time, unless the net benefits of such intervention outweigh the corresponding costs, to intervene would be to create other problems, some of which could be of equal or greater severity than the one that gave rise to the need for intervention. The matter of estimating the expected costs and benefits of government intervention is thus a major one that merits serious consideration prior to undertaking any sort of decision process that leads to any sort of commitment to act. One consideration has to do with the recognition that such estimates always entail some level of uncertainty. In turn, such recognition raises the question of how much uncertainty is tolerable as a basis from which to take government action or how much must be reduced prior to making such commitments. In this regard, at one extreme, one could demand as a prerequisite for government action such a high level of scientific certitude in estimating all the costs and benefits of intervention that action would effectively be stultified. To meet such a demand would require waiting until the related decision situations are all fully described on the basis of sound empirical research, and the connections between each alternative action and its corresponding outcome known and evaluated. At the other extreme, one could advocate active intervention without much empirical knowledge about the situation. This latter tactic is apparently the one preferred by many environmental justice advocates. Both extremes should be avoided. On one hand, a foundation of empirical research and knowledge is essential, and it should arguably far exceed the current one. On the other hand, one cannot wait for near certitude to act, especially when the consequences of inaction potentially include adverse impacts upon public health and human rights. On balance, therefore, the key is not for the government to make broad policy and administrative decisions—designed, for example, to comprehensively alter siting procedures so as to preempt further disproportionate distributions—nor is it to attempt to influence the values that undergird the free market system in any way. Instead, it is for the government to be prepared to begin not with putative national patterns of disproportionate distributions, but with specific public health concerns at particular sites. Accordingly, the place to concentrate time, energy, and resources for problem description is not primarily with hypothesized geographical patterns of disproportionate distribu-


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tions but with specific instances of reported public health problems that occur in close proximity to a particular site. For instance, if in a particular minority or low-income neighborhood the residents raise a concern about the public health effects of a nearby hazardous site, a government agency should be prepared to conduct a thorough community risk assessment prior to making any decisions to act. If the case that the site is imposing risk on the neighborhood is sufficiently compelling, epidemiologists might get involved, along with physicians, biostatisticians, environmental chemists, and various other scientists trained in aspects of community health assessment and environmental justice. Upon report of a public health problem, these investigators would deploy to the site. There they would do just enough empirical research to establish reasonable confidence in whatever inferences about the public health effects of the site are to be taken as premises in any future decisions to act or not. Their goal should be to use scientific methods to help describe the problem. Part of the description would be a determination of whether the perceived health problems are real and, if so, whether or to what extent the cause can be plausibly attributed to the site. Their goal would not be anything like scientific certitude, and their focus would not be on national patterns. Rather, their goal would specifically be to establish the plausibility of a public health problem at that location having been caused by the site. If plausibility is established, it would constitute probable cause of a negative externality of the type associated with environmental justice, so it would be appropriate for government to intervene further. Such an approach would assuage many of the concerns and anxieties of affected parties, protect public health and human rights, and provide an approach for government involvement not predicated on any broad empirically oriented assumptions about any sort of national patterns as a basis for action. The lesson to be learned here is that policy and administrative decisions related to geographical patterns of disproportionate distributions are apt to bring perils associated with having an inadequate of related research, which precludes keeping the factors that help assure good decision-making in mind. Related decision processes in which choices are essentially intuitive deductions from partial representations of poorly understood and incompletely documented systems of interacting problems, many of which are connected in unspecified ways to the real issue or problem at hand, are unlikely to lead to meaningful and satisfactory results. In such decisions, any deliberations made in the related choice processes tend to be insufficiently articulated to serve as adequate bases for appropriate allocation or investment of limited time and resources. Rather, choices predicated on empirically incomplete or mistaken statements of fact are likely to overemphasize some interests at the expense of others that are no less important. Such decisions would also seem to increase the likelihood of incorrect assessment of ends, resulting in the adoption of unsuitable ends for public policy and administrative agencies, as well as the

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inadequate management of means to ends, resulting in the adoption of acts geared toward means unsuited to legitimate ends. For instance, by focusing interest on aspects of minority and low-income communities in which evidence of serious and continuing harm to human health is weak and ambiguous, attention is thereby drawn away from some of the more substantive, serious, and well-established related public health issues and problems. These issues and problems specifically include adult lead exposure in the workplace, child lead exposure in the home, tobacco use and alcohol abuse, asthma induced by indoor air pollution, and pesticide exposure among largely Hispanic farmworkers. The concepts, language, and procedures of community risk assessment were conceived and designed specifically with determination of the public health impacts of environmental hazards in mind. So logically, in my view, the appropriate language for discussing environmental justice should be that of community risk-based decision-making. This language provides a rich vocabulary, complete with a highly variegated set of terms and concepts to enable a maximum congruence of categories. It provides a means of rationalizing and talking about the nature of risks and risk taking, risk management, and the ramifications of risk. It thus pro-offers indispensably valuable clarity and distinctiveness of thought in terms of the factual side of the processes through which environmental justice problems are described and related decisions are deliberated. Finally, there is a crucial sense in which all of the empirical uncertainty implies some limits to the appropriate scope of related government responsibility. The potential for government to improve the lot of any community depends in large part upon the availability of the practical means with which to do so. It is clearly not sufficient that the criteria with which to determine the appropriate scope of government responsibility with respect to the problem tell us only about conditions in terms of the standards used to define the problem. No such criteria is adequate if it merely depicts an abstract ideal that policy and administrative decision makers cannot apply in practice to determine by comparison to current empirical facts, events, and circumstances which of several putative possible policy options is preferable. Rather, specific, detailed, and repeated considerations of proportion and reference to the facts, events, and circumstances that constitute particular policy and administrative decision situations are also needed. The limitations on government responsibility arise because models of the problem that meet high standards of social scientific validity are not available. In turn, if government decisionmakers do not have the practical means to act responsibly, it is arguably inappropriate to hold government responsible. Accordingly, absent research guided by reasonably high standards of social scientific method, regardless of laudable concepts of fairness contained in government policy or administrative decisions, efforts to solve the problem or improve the situation are likely to be counterproductive if not massively destructive.

Notes

CHAPTER 1 1. The political agenda is the list of items to which policy and administrative decision-makers give serious attention at any given time (Kingdon 1995). 2. The institutional arrangements for environmental justice are the related laws, traditions, statutes, orders, legal precepts, and other formal or informal rules of conduct that govern and constrain the relevant policy and administrative decisions (Kiser and Ostrom 1985). They are the source of the legitimate authority and discretion held by policy and administrative decision-makers, as well as a source of restrictions on the alternative feasible courses of action available to them. 3. Common property resources are those owned in common by people everywhere, such as the earth’s atmosphere and water in the water cycle. In general, the problem with common property resources is that access to them is free or open to everybody, and costs arising from their use and abuse can be passed on indirectly to others (Hardin 1968). In general, because common property resources belong to everyone, nobody is motivated to take responsibility for them, and nobody protects them. In more specific terms of environmental justice, many environmental hazards are produced as byproducts of production processes and put into the air or water or onto the land. In many instances these repositories are common property resources, unless they are either managed through public policy and administrative decisions or privatized, and people behave towards them in ways that are ultimately in neither society’s best interest nor their own. They are of special concern when considering policy and administrative decisions because, in terms of prevailing economic theory, they involve externalities or indirect costs and benefits that accrue to third parties to an economic transaction. 4. The broad findings of the report were later reported as having been confirmed (e.g., Adeola 1994; Mohai and Bryant 1992).

CHAPTER 2 1. Kincaid (1994) argues, in essence, that if one’s interpretation of the aggregated outcomes of a set of individual decisions refers only to the aggregated outcomes and

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not to the constituent individual decisions, it will remain incomplete. At the same time, he denies that the aggregated outcomes must be reduced to claims about the constituent individual decisions in order to create complete interpretations. 2. While the statement that important information and empirical knowledge related to a major social issue or problem would be deliberately ignored may at first blush seem peculiar, evidence indicates otherwise. For example, Chelimsky (1991) notes that the ignorance of U.S. decision-makers during the Vietnam War may be attributable to deliberate disregard of empirical information. A similar explanation is likely with respect to both the D.A.R.E Program and boot camps for juvenile offenders, both of which continue to get funding despite considerable empirical evidence attesting to their ineffectiveness (Dukes, Ullman, and Stein 1995; National Institute of Justice 1996). Somewhat more generally, especially when operating in emotionally charged spheres of concern such as environmental justice, policy and administrative decision-makers can at times be interested less in reason and rationality as applied to the larger long-term picture than they are in their own short-term power or position. Under such conditions, to continue to get by in the system, they occasionally scheme or plan to achieve certain objectives, occasionally tacit ones, either in concert with others or, failing that, through Machiavellian control or domination. Notwithstanding official pronouncements to the contrary, their real concerns may be more with things like image projection and face-saving rather than gathering clear and reliable information. These concerns have little or nothing to do with standards of justice or fairness, liberty and equality, or what ought to be the ends and means of political action, as the discourse would suggest. When such political realities dominate or heavily influence the information search process, it is likely that important empirical knowledge is either not obtained or deliberately ignored. 3. The reasoning process involved in making a policy and administrative decision is made more difficult by the fact that single causes are seldom sufficient for understanding or prediction even in relatively simple situations. In complex situations, such as those in which environmental justice arises as a concern, multiple causation is, in my view, probably much more the rule than the exception. 4. According to information theory, information is measured in binary digits or bits. One bit is the amount of information required to control, without error, which of two equiprobable responses is to be chosen by a person to whom it is presented as a stimulus. The point made here is that data contain information only if the conditional probability of one of the responses, on its examination, is not equal to the probability of the alternative response in the absence of such examination. In mathematical terms, assume data set A, two possible responses to it, X1 and X2, and the probabilities for each of the two responses, P(X1) and P(X2). Given that prior to examining data set A, P(X1) = P(X2), A contains information if and only if after examining it P(X1| A) = P(X2| A). 5. Thomas Sowell (1987) has done an excellent job of showing how different foundational assumptions can lead to different interpretations of a situation. He distinguishes between two basic, more-or-less persistent and discernible sets of them, which he calls the constrained view and the unconstrained view. These two views are closely reflected in the discourse over environmental justice, in the views of the skeptics and the advocates. One of the curious things about this discourse is how often the same people line up on opposite sides of debates. We presume that a closer, more systematic and in-depth look at the thinking of people on both sides would show that they

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are reasoning from very different foundational assumptions and that these assumptions, often implicit, are what provide much of the consistency behind the opposition. Yet disagreements over foundational assumptions cannot be resolved by empirical evidence because they are in a sense prior to any evidence. They are what people sense or feel before they reason in any way that could meaningfully be called theoretical, much less deduce any specific consequences as hypotheses to be tested against evidence. When different foundational assumptions are used in interpreting a complex situation, there is no common framework with which to resolve disagreements. One must instead assume that the foundations on one side or both are lacking in some way and need to be upgraded. 6. This assumption is supported by a large and growing literature in psychology and the decision sciences. According to this literature, if the policy and administrative decision process is to succeed in ameliorating problems, the logic patterns through which all of the information related to the decision situation is integrated and formed into preferences and commitments to particular alternatives should exhibit certain definite properties. For example, the property of transitivity should be exhibited in the relationships between stimuli; that is, if stimulus D implies A, and A implies B, stimulus D implies B. Similarly, if A is preferred to B, and B is preferred to C, then logically A should be preferred to C. While the properties of logical reasoning processes are fairly clear, a great deal of the literature shows empirically that human performance typically exhibits systematic logical errors in processing information, even in simple inferential and decision-making tasks that involve only short, shallow, local logic. Moreover, it has been shown that these errors can have substantial and serious implications for decisions in social policy, economics, law, interpersonal conflict, and medicine, among other areas (Arkes and Hammond 1986).

CHAPTER 3 1. Although this question misses much of the larger political concern with assessing, reducing, and avoiding threats to public health in minority and low-income neighborhoods, it nevertheless has direct bearing on the health effects of environmental risk in one clear but limited and essentially negative sense. If a particular environmental hazard is not present in the environment at a particular location, one cannot be exposed to it there, and it therefore cannot possibility lead to health problems there. So, unless the answer the question is yes, at least insofar as public health effects go, concern with environmental justice tends not to arise in the first place. 2. Objective probabilities are defined by relative frequencies. The principles used to obtain relative frequencies can be found in any good elementary statistics book (e.g., Kerlinger 1973). 3. Latitude and longitude coordinates are objective in the sense that they do not have multiple definitions. For example, 41’49.17” N and –81’96.78” E refers to one and only one location on the face of the earth. Anyone who understands the system will identify exactly the same location. 4. Kriging is a linear interpolation method that allows predictions of unknown values of a random function from observations at known locations. Variograms provide a measure of spatial correlation by describing how sample data are related with distance and direction.

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5. A spurious relationship between two variables, A and B, is one that is statistically significant until another variable, C, enters the picture. When C enters the picture, the relationship between A and B disappears. 6. Agglomeration economies are unit cost savings in production that accrue to individual firms as a result of a location that is in close geographical proximity to other firms. 7. “Meaningfulness” has a definite technical meaning in measurement theory (Bowen and Bowen 1999). In this sense, if the term is not meaningful, it is subject to numerous alternative interpretations and thus is inadequate as a basis for establishing the intersubjective agreements on which scientific validity depends.

CHAPTER 4 1. Causal schemas were defined by Heider (1944) as conceptual organizations of events in which some are understood to be causes and others to be effects. Conceptual schemas are one of the primary means by which individuals strive to achieve a coherent interpretation of the events that surround them. 2. The reasoning behind such conclusions, in essence, is that observation logically has a determinative role in validation only if the terms used in the empirical statements can be reduced to or made logically equivalent to statements expressible fully in objective observational terms. But since the subjective judgmental element of observation prevents the possibility of creating such terms, an impasse is reached in which the rationality of the entire social scientific enterprise can be brought into question. 3. Properly construed, a theory is not determined by the facts and events themselves, but by the superordinating point of view of the theorist. It is a creation of the human intellect bound and determined by the facts and events it subsumes (Rescher 1970), yet it must conform to the facts and events in order to predict them 4. An explanation is furthermore scientific if reasons are adduced why this fact or event obtains rather than one among its possible alternatives. Ideally, the strongest forms of scientific explanation work by subsuming or placing the concrete item to be explained as somehow a special case of a natural law taken to state how things must operate within a certain range of reality. An example is the law of supply and demand. Within the realm of free market exchange relationships in society, any particular set of transactions must comply with this law. When, as is usually the case in social science, laws are not known within the range of reality related to the phenomenon being explained, the stronger the chain of reasoning used in the adduction, the more convincing the scientific explanation. 5. Of course, as is true with virtually all generalizations, it is not always the case that a model with a low goodness of fit statistic is a poor one. Sometimes models with very low goodness of fit measures are very good ones. But these tend to have a great mass of axiomatic theory behind them, to justify the specification of the model. As a rule, when in doubt, the prudent way to proceed is to do thorough testing of the model’s residuals, especially for model specification. 6. Many of the historical, economic, and geographical conditions in, say, Los Angeles are very different from those in other cities, such as, Chicago. If cause-andeffect relationships could be established through constructing a model of Los Angeles,

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they may nevertheless be very different from those that would be determined by a similar model using similar data from other places. 7. This judgment is consistent with these thoughts of Bertrand Russell (1953): “The word ‘cause’ is so inextricably bound up with misleading associations as to make its complete extrusion from the philosophical vocabulary desirable . . . the reason physics has ceased to look for causes is that, in fact, there are no such things. The law of causality . . . is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no harm” (393). 8. Concepts are necessary to give meaning to operational definitions especially because the replicable activities performed during the process of making observations in accordance with an operational definition have no meaning in and of themselves. More specifically, the meaning of an operational definition originates, as does all meaning, in the abstract replacement of one symbol (or set of symbols) by another. In creating an operational definition, the word used to label the concept to be defined, a symbol, is replaced by another set of words, also symbols, this time representing the operational definition. Thus operational definitions acquire their meaning, at least in part, from the concepts to which they refer. If the operational definition conforms logically to the conceptual definition, the meaning of the activities performed during the process of making observations under that operational definition may be logically linked to the meaning of the concept. 9. A conceptual model for collecting the data needed to enable researchers to establish causal relationships between exposures and adverse health outcomes in identifiable subgroups can be found in Wagener, Williams, and Wilson (1993). 10. An effect size computation is a standardization process through which the strength of the X:Y relationship in an individual study is expressed as standard deviation units. These units are defined with reference to the summary statistics used to describe the X and Y variables in that particular study. They may be computed in different ways depending on the particular summary statistics provided in the individual study source report. The goal of effect size computation is to convert the summary statistics provided in the individual source report into standard deviation units that may be statistically integrated across studies. The direction and number of standard deviation units computed for a particular study are called its “effect size.” When the effect sizes are properly computed, they may be used to aggregate or compare the studies for the purpose of overall summary description and statistical inference.

CHAPTER 5 1. For example, unless factors such as land use controls, industrial location, and levels of community participation are controlled in a research design, it is not possible to assess whether an apparent causal relationship between, say, an environmental risk and the demographic status of residences is an illusion attributable on closer analysis to one or more of these other factors. 2. This p value indicates that the probability of the observed relationship occurring at random is less than 1 in 100. Thus, if one concludes that a relationship exists, the chances that one is wrong are less than 1 in 100. 3. Of course, by the same line of reasoning, it is normally not feasible to strictly establish cause-and-effect relationships in most, if not all, research conducted with the

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intention of influencing policy and administrative decisions. While this can be interpreted to suggest that conventional thinking about cause and effect is irrelevant for these decisions, since the outcomes of the decisions depend on the quality of the thought that goes into deliberating the alternatives, it is not a point to be dismissed lightly. 4. Yin (1994) points out that insofar as the research objective is to expand and generalize theories, a case study can provide a sound basis for scientific inference. A good example of this logic is a story I heard in a college physics class, about Albert Einstein’s test of whether gravity would bend rays of light. The story went that Einstein made some deductions from his theory. Specifically, if the theory was correct, he deduced that gravity would bend rays of light, and the planet Mercury would be visible from a particular location on the surface of the earth, at a certain time, even though geometrically it was known to be located behind the sun. He went out to sea to the appropriate location, looked in the sky at the appropriate time, and saw the planet, thereby confirming his theory with a single instance. While a single instance of an observation can thus logically be used to confirm a theory, there are few (if any) theories of sufficient specificity in social science from which such clear, distinct and unambiguous tests can be deduced, especially in environmental justice.

CHAPTER 6 1. Scientific validation rests specifically on expert judgments regarding the replicability of the research independent of the investigator. In essence, research is determined to be replicable if, in the opinions of several qualified reviewers, anyone who knows the techniques used in the research and has adequate background knowledge could repeat the research and obtain a highly similar conclusion. 2. The first hypothesis was that towns in the more recently developed region receive, on average, a larger percentage of their tax base from commercial and industrial sources than do towns in the older, previously developed region. This was the basis for the reported “trend” that the “toxic waste crisis” is becoming suburbanized. However, upon inspection, the data do not appear to support the hypothesis. For the first region, the reported mean was 22%, the standard deviation 13%, and the number of observations 25. For the second region, the reported mean was 18%, the standard deviation 13%, and the number of observations 19. These data were interpreted in the article to support the hypothesis that there is a difference between the two regions. However, I did a simple t-test of the difference between two means and found that t = 1.011, indicating that the difference between regions is statistically insignificant. So either the documentation was poor or there is more variation within regions than between regions. If the latter is the case, the evidence for the “trend” reported in the article appears to be illusory. 3. While this research did not bear directly on the central question in this book, it has very important indirect bearing on the larger political issues involved. Since on the basis of this research political mobilization seemed to be a major determinant of the level of hazardous waste capacity in a community, USEPA environmental justice policies designed to stimulate collective action in minority and low-income communities are likely to help reduce future levels of nearby hazards there.

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4. My major criticism of the methods is that the authors do not report on the residuals from the models. This is important specifically because they fail to consider the important statistical issues related to spatial clustering and spatial dependence, and these could significantly influence the findings. 5. At least one legal conflict has arisen in a situation where the percent minority in an area was disputed on the basis of definitional differences in the extent of area included within the boundaries (Tsao 1992). In East Bibb Twiggs Neighborhood Assn. v. Macon-Bibb County Planning and Zoning Commission, the plaintiff evidently argued that an area encompassing both an existing landfill and a proposed new site was predominantly black. The court, using smaller census-tract areas, ruled that a predominantly white community surrounded the landfill. 6. The conclusions in this section are drawn from the research done in the 1990s. Prior to this time, research in the 1980s was primarily advocacy oriented in nature, based on substandard design, method, and documentation, and is therefore not to be given any weight in an effort to draw sound conclusions about what is and is not known scientifically. While some good related research from the 1970s does exist, it is largely outdated and does not significantly affect the conclusions drawn herein.

CHAPTER 7 1. This human right is not properly categorized as a civil right, such as the right to life, liberty, security of persons, freedom of movement, and not to be arbitrarily detained. Rather it is an economic, social and cultural right, such as rights to the highest attainable standard of health, to work, to social security, to adequate food, to clothing and housing, and to education. It is the obligation of governments to ensure that such rights are progressively realized, unlike civil rights, which are guaranteed immediately. 2. This has been brought under question by more recent research which suggests that as the consequences of a hazard become more significant, from the point of view of its perceiver, assessment of the associated risk becomes better and better (Benjamin 1993). 3. Some reactions to stress are psychological and others physiological. Psychological reactions often involve a feeling of shock or increased anxiety. Some people develop apathy and depressive mental states while others experience irritability, resentment, or a feeling of being trapped or helpless. Physiological responses are often associated with increased levels of adrenaline and other catecholamines. This tends to increase heart rates, blood pressure, rates of respiration, perspiration, and other physiological functions. It is also associated with increased muscular tension, memory lapse, headache, insomnia, tiredness, and overall weakness (Sorensen et al. 1987).

CHAPTER 8 1. This assumption has been the target of strident criticism on both practical and conceptual grounds (Palumbo and Maynard-Moody 1991). In the extreme, some such criticisms imply that pronouncements of organizational purposes amount to little more than illusions created to conceal the deeper power struggles and pursuit of self-

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interest that actually motivates most organizational behavior. Though these criticisms are clearly meritorious in some ways, any reasonably full discussion of them would go considerably beyond the scope of the current work. It suffices to say that, in my view, if the effectiveness of public organizations is to be assessed on the basis of goal attainment then the highest feasible degree of integrity and skill at employing rationality and empirical knowledge should be used in any constituent decision processes. For not to appeal as far as humanly possible to rationality and empirical knowledge as a basis for policy and administrative decisions is to abandon the moral obligations that correspond with a rejection of moral positivism (see Chapter 2). 2. Other necessary factors include a good organizational design and good collective choice mechanisms within the organization. 3. Traditionally, actions have been considered as following from antecedent mental and intellectual processes, wishes, desires or wills for them to occur. Choice is used here to mean the mental and intellectual processes of deliberation and volition used in forming judgments, making opinions, and discerning and comparing alternatives. 4. Note that a different subscript is used on each Y. This is intended to represent two ideas. First, there is a range of possible specific outcomes associated with each specific alternative action (Ai). Second, each specific action corresponds to a definite set of specific outcomes, some of which are likely to be different than those that correspond to the alternatives. Of course, there may be several specific outcomes with joint membership in the set of outcomes associated with each of two or more alternative actions. A different subscript is used for each because, normally, choices are made on the differences between members of the outcome sets rather than their similarities. 5. Presentations of the relevant concepts of probability and expected value can be found in many good statistics books. An excellent presentation of these concepts in relation to public policy can be found in Friedman (1984).

CHAPTER 9 1. A good example is found in the case of a neighborhood in South Phoenix, Arizona, where latent feelings of inequity in a community were heightened by a serious contamination event. While government studies indicated that the health risks were not significant, significant property value losses occurred as a consequence of amplification of perceived risk (Pijawka et al. 1998). 2. For a much more detailed and elaborated discussion of the main concept in this section, see my forthcoming paper with Mingshen Wang and Fang (2000).

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Index

causal schema, 72–73 cause (and effect), 72, 73, 85–89, 105, 108, 109, 110, 112 census-tract-level analysis, 57–58, 136, 137, 139, 141, 146, 150, 155, 159, 160, 161, 163, 164, 165, 167, 168, 172, 173, 175 choice, 25–26, 32, 33, 34, 185, 208, 211–215 choice set, 210–212 citizen participation, 229, 234, 237 Civil Rights Act of 1964, 7, 66 classification, 57, 64, 65, 79, 97, 187, 233 Cleveland, 137, 168, 177 clustering, spatial, 50, 54, 103–104, 140, 152, 153, 155, 160, 162, 169, 173, 174, 176, 178, 183 common property resources, 6, 249 community, 63, 68 empowerment, 12, 14, 17, 18, 106, 229 health, 12, 59, 247 risk assessment, 59–61, 114, 186, 232, 239, 248 comparison in research design, 107, 108, 111, 117, 118, 119, 120, 125 comparison region, 52, 102–103, 105, 126–128, 146, 155, 158 compensatory approach, 5 complexity, 22–24, 233–234, 236–237 concept formation, 37, 80

access to market, 53, 87 advocacy, 3, 12, 14–17, 18, 89, 90, 115, 126, 134, 135, 138, 142, 180, 224, 231, 239, 246 agenda, 16, 90 national political, 4, 7, 12, 14, 39 research, 240–244 setting processes, 29, 30 strategic management of, 231–240 agglomeration, 53 air pollution, 136, 141, 150, 158, 178, 183, 188 Alabama, 157, 161 alternative courses of action, 24, 25, 32–34, 208, 210–214 analysis, 47, 48, 51, 57–58, 91, 99 area, 47–48, 57, 58 authority, 40, 76 axiomatic theory, 79, 84, 88, 105, 109, 122 axioms of rational choice, 25–27 black environmentalism, 202 block-group-level analysis, 57–58, 154, 157, 170, 176, 177 bottom-up approach, 237–239 bracketing, 220 California, 142, 146, 173, 178 case study design, 119–121, 138, 139, 141, 144, 146, 148, 154, 159, 164, 169, 174, 175, 178, 240

273

274 conceptual-theoretical domain, 37, 75–76, 77, 78 congenital malformation, 191 control in research design, 107, 108 county-level analysis, 58, 137, 151, 153, 156, 163, 168 criteria for rationality, 26–27, 34, 35, 219–220 for scientific research, 16, 36, 38, 83, 91, 111, 132, 134–135, 242 cross-sectional research, 49, 83, 103, 109, 139, 140, 147, 148, 156, 162, 170, 174, 175, 177, 178, 183 data, 4, 36–38, 55, 85, 91–92, 106, 240, 241, 242 area referenced, 50, 57 environmental hazard, 96–101 lattice, 50–51 point pattern, 50 pooled, 49, 83 racial, 64, 66–68 time series, 69, 124 definition of community, 63 of environmental inequity, 6 of environmental justice, 9–10 of enviromental racism, 6 of exposure, 59 of a “good” decision, 19, 21, 34 of low-income neighborhood, 68–70 of minority neighborhood, 63, 67 of neighborhood, 63 of rationality, 26–28 of social problem, 22–24, 209 of race, 63 of theory, 78 operational, 92–93 types of, 93 democratic ideals, 11, 17, 28, 66, 231, 232, 241 descriptive statements, 22, 38, 40, 48, 53, 71–74, 78 Detroit, 141, 144, 155, 182

Index discernable patterns, 7, 11, 19, 35, 38, 40, 54, 61, 62, 74, 78, 84, 90, 96, 98, 102, 131, 240, 242, 245, 246 disproportionate distributions, 10, 11, 12, 14, 15, 16, 19, 22, 29, 35, 43, 47, 49, 50, 52, 53, 54, 57, 58, 61, 62, 91, 101, 103, 104, 113, 126, 127, 230, 231, 240, 241, 243 dispute resolution, 29, 234, 235–236, 239 distributive justice, 4, 30, 31, 185, 216 documentation, 23, 59, 90, 92 dose-response assessment, 59 early research (1970s), 135–138 education, 13, 22, 41, 51, 64, 85, 87, 108, 120 effective organizations, 207–214 elementary principle of fairness, 45 emotion, 12, 14, 28, 38, 45, 67, 202, 230 environmental chemicals, 10, 61, 115, 186–187 concern, 200–204 hazard, 45, 52, 58, 59, 185, 186, 188, 189, 190, 193, 240, 246, 248 risk, 44–47 facing vs. taking, 45–46 real vs. perceived, 11–12, 46, 47, 60 spatial shifts in, 46 Environmental Protection Agency, 6, 9, 34, 96, 98 errors in decisions, 191 evaluation, 33, 35, 38, 60, 83, 113, 207, 216, 217, 244 Executive Order 12898, 8, 227, 231 expectations, 33, 77, 113, 119 expected value calculations, 219–220 experimental design, 117–118 experimental mortality bias, 114 exposure, 59–62 externalities, 6, 46, 192, 193, 196, 222, 246, 247 external validity, 106, 110, 115, 125 failure of past policy, 32, 39 Florida, 154, 175

Index foundational assumptions, 39–42 framing, 23, 31, 32, 41, 47, 48, 71, 72, 207, 209, 211, 213, 219, 230–231, 244 “good” decisions, 21, 24, 25, 33, 34, 70, 208–209, 211, 213, 214, 232, 244 Georgia, 157 goal orientation, 9, 32, 33, 35, 207–208 governmental responsibility, scope of, 248 government failure, 21 grassroots struggles, 7, 8 hazard identification, 59 hedonic price models, 196 heteroskedasticity, 104 heuristics, psychological, 23, 24 high-quality research, 162–179 Hispanic populations, 65, 142, 146–147, 153, 154, 159, 160, 164, 165, 170, 172, 174, 175, 176, 177, 180, 182 history as threat to validity, 112–113 Houston, 138, 166, 174, 181 human exposure assessment, 59, 60 human rights, 4, 190, 191, 204, 241, 245, 246, 247 hypothesis, 10, 12, 16, 37, 48, 49, 54, 57, 76–77, 105 imperfect information, 25, 26, 29 impartiality, 3, 4, 15, 24, 132, 231 implementation, 35 inconclusive results, 137, 142, 179, 180, 182 industrial location, 53, 106, 115 inferential biases, 23, 24, 37, 38, 39, 111–112, 113–115, 201 information approach to policy, 5 search, 25, 32, 34, 35, 39 institutional context, 4–11 instrumentation bias, 113–114 internal validity, 106, 110, 113, 125

275 interpretation, 23, 34, 38, 40, 89, 93, 208, 233, 241 isotropy, 51 lead and lead paint, 14, 52, 189, 191, 236, 240, 248 lessons of environmental justice, 229–248 location, 10, 43, 47, 48 Louisville, 141 longitudinal studies, 159, 160, 172 low-income neighborhood, definition, 68–70 manipulation in research design, 107, 108, 109 market failure, 46, 246 Massachusetts, 148 measurement, 41, 48, 77, 94–96 medium-quality research, 150–162 mental anguish, 200, 204, 243 meta-analysis, 101–103, 134 Michigan, 144, 147, 155 mobile sources of pollution, 188 models, in research, 80–85, 89–91 moderator variables, 108 modifiable areal unit problem, 57 moral debate, 24, 28, 30, 66, 233, 241 National Environmental Justice Advisory Council (NEJAC), 9 national-level analysis, 140, 143, 152, 153, 160, 163, 165, 169, 172, 173 National Priorities List (NPL), 98–99 neighborhoods, 63 New England, 158 New Jersey, 149 New York, 149, 158 nonequivalent control group design, 124–125 normative standards, 17, 22, 29, 30, 31, 32, 36, 38, 41, 43, 64, 67, 71, 74, 76, 99, 186, 189, 190, 208, 211, 213, 215, 216, 217, 230, 248

276 objectives, 16, 21, 26, 28, 53, 71–72, 207, 211, 231, 233, 245 objectivity, 23, 37, 41, 46, 47, 48, 75, 76 observation, 23, 35, 36, 37, 75, 76 observational-empirical domain, 37–38, 91–99 obstacles in research, 16, 23, 37, 52, 187–189 Office of Environmental Justice, 8–9 Ohio, 157–158, 168–169, 177–178 one-group pretest-posttest design, 121 operational definition, 92–93 Oregon, 176–177 organizational effectiveness, 207–208 peer review, 132–135 Pennsylvania, 168, 171 personal behavior in public health, 61–62, 187, 193 Philadelphia, 158 planning for environmental justice, 234 policy advocates, 14–17 and administrative decisions, 29 formulation, 234–235 framework, 8 power, 12, 13, 24, 67, 230, 231 rhetoric, 11–14, 229–230, 231, 233, 240 poor-quality research, 142–150 potential environmental risk, 62 poverty and poverty area, 68–70 pre-experimental design, 118–122 preventative approach, 5 problem definition, 22–23, 29, 31–32, 209 procedural inclusion, 12, 18, 229 property values, 195–199 proximity, 52 proxy variables, 52, 58, 61–62, 82, 102 public health, 3, 4, 8, 11, 12, 14–15, 185, 186–192 quasi-experimental design, 122–125

Index race, racism, and racial discrimination, 63–67 range of expectations, 209, 213, 215 of outcomes, 211, 219 rationality, 24–28 real estate prices, 195–199 reasoning through relationships, 42 regions, 47–48, 50, 51. See also comparison region research design definition of, 106 function of, 106 research quality rating criteria, 134–135 residential choice, 54 residual analysis, 84, 104, 177–178 risk analysis and prioritization, 51 characterization, 60 perception, 46, 200–204 trade-offs, 224–228 sampling, 94 scientific explanation, 78–79 scope of governmental responsibility, 248 selection bias, 111–112 siting decisions, criteria for, 87 South Atlantic, 164, 181 spatial aggregation, level of, 56–58 autocorrelation, 103–104 clustering and dependence, 103–104 data analysis, 51 distributions, 48 variable, 48 spurious relationships, 53 static-group comparison design, 121–122 stationarity, 51 statistical regression bias, 114 statistics, role of, 99–104 strategic management, 231–240 stress, 200

Index subjectively expected utility, 26, 219–220 synthesis, 230–231 synthetic chemicals, 14, 44, 59 testing bias, 113 Texas, 150, 159, 161, 182 theoretical terms, 76 theory, 78–80 time and decision-making, 212–213 time series, 49 top-down approach, 237–238 Toxic Release Inventory (TRI), 97–98 trade-offs, 224–228 uncertainty, 215–223 costs of, 220–224

277 evaluative, 216–217 outcome, 217–219 process, 215–216 underconceptualization, 61, 93, 222, 238 United Church of Christ, 4, 7, 190 United Nations, 190 utility, 26, 196, 197, 219 validatability, criterion for, 84–85 validation and validity, 35–39, 83–84, 90, 110–116, 132 variables, 47–48, 77 Warren County, North Carolina, 7 zip-code-level analysis, 58, 140, 147, 169

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