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JOURNAL OF REGIONAL SCIENCE, VOL. 42, NO. 1, 2002, pp. 87–105

ENVIRONMENTAL COMPLIANCE COSTS AND THE DISTRIBUTION OF EMISSIONS IN THE U.S.* Daniel L. Millimet and Daniel Slottje Department of Economics, Southern Methodist University, Dallas, TX 75275, U.S.A. E-mail: [email protected]

ABSTRACT. Using the properties of the Gini coefficient, a structural model is developed to assess the impact of uniform changes in environmental compliance costs on the distribution of per capita emissions across U.S. counties and states, a distribution that places a larger burden on minorities. Using data from the U.S. EPA’s Toxic Release Inventory and three state-specific measures of environmental compliance costs, we find that uniform increases in federal environmental standards have little impact on the distribution of environmental hazards, and may actually exacerbate spatial inequality. As a result, Federal standards that target specific high pollution locations are necessary to redress current inequities.



A vast literature has emerged examining the impact of environmental regulation on firm location (e.g. List et al., 2001; Becker and Henderson, 2000; Levinson, 1996; Henderson, 1996; Jaffe et al., 1995). In addition, the spatial distribution of pollution and other environmental hazards has been and continues to be well studied. Numerous studies have found evidence linking race and the distribution of environmental quality (Arora and Carson, 1999; Brooks and Sethi, 1997; Gelobter, 1992; Gianessi, Peskiny, and Wolff 1979; among others). Others have focused on the Coase theorem and the interaction between income and environmental quality (Harrison and Rubinfeld, 1978; Zupan, 1973), whereas others have examined interactions between local political activism and environmental quality (Arora and Carson, 1999; Brooks and Sethi, 1997). The purpose of this paper is to extend these two bodies of research by analyzing the effect of environmental regulation on the spatial distribution of pollution. To assess the impact of stricter environmental compliance costs on per capita emissions inequality across U.S. counties and states, we use a simple structural model along with the properties of the Gini coefficient developed in the income inequality literature.1 From a social welfare perspective there are *We thank Per Fredriksson and John List for helpful comments on an earlier draft and express our gratitude to Arik Levinson for making the data on environmental compliance costs available. Received August 2000; revised March 2001; accepted May 2001. 1 The focus on emissions rather than concentrations, is in line with recent research examining environmental racism that also uses data from the Toxic Release Inventory (see Arora and Cason, © Blackwell Publishing, Inc. 2002. Blackwell Publishing, Inc., 350 Main Street, Malden, MA 02148, USA and 108 Cowley Road, Oxford, OX4 1JF, UK.




(at least) three reasons to be interested in the effects of environmental compliance costs on the distribution of pollution. First, borrowing from the literature on income inequality, equity is important. Although income is a ‘good’ and pollution is a ‘bad’, ceteris paribus, society may wish to distribute the costs of pollution evenly just as it may wish to distribute the benefits of income equally. Moreover, an unequal distribution is more problematic when communities in the upper tail are not randomly drawn, but rather tend to have higher concentrations of minorities. Second, equality in emissions exposure potentially minimizes the health costs associated with pollution for two reasons: (1) threshold effects and (2) nonlinear dose response functions. Threshold effects refer to a minimum level of toxins to which one can be exposed before suffering any adverse effects. Such effects have been shown to be important in terms of the responses by humans to various environmental hazards (e.g., Doull, 1996; Chestnut et al., 1991). Even the Environmental Protection Agency (EPA) and the Food and Drug Administration (FDA) have begun to recognize and incorporate acceptable thresholds into current regulations (U.S. EPA, 1996; Wilson, 1996). Wilson wrote “[T]he weight of evidence favors the view that thresholds exist for all [carcinogenic processes]” (1996, p.3). As a result, disseminating pollutants equally across locations increases the probability the threshold is not crossed in any single location (assuming overall emissions are not sufficiently high). The dose-response function refers to the relationship between harmful outcomes and exposure levels (e.g., the probability of premature death associated with increasing levels of toxic exposure). Such functions are typically assumed to be S-shaped, although in the U.S. individuals are likely confined to the bottom of the S (Chestnut et al., 1991). However, this implies that the health effects from higher pollution may increase exponentially with emission levels.2

1999; Brooks and Sethi, 1997). Although the distinction may be potentially important for air and water pollution, particularly at the county level, using emissions data on land and underground injections is not problematic. Thus, focusing on emissions allows us to aggregate across emission types. In addition, in terms of the structural model presented in the next section, it is more sensible to think about emissions, rather than concentrations. 2 The exact shape of the dose-response function is not known with certainty and varies across pollutants. Most researchers have concentrated on estimating functions of the form H = βX + δEmissions–t + ε where H is some measure of health or illness and Emissions–t refers to the level of air emissions t days prior (e.g., Ostro et al., 1998; Cropper et al., 1997). The limitation of this research is that it implies that an increase in emissions has the same effect on health regardless of the initial level of pollution. However, Chay and Greenstone (1999), Doull (1996), Chestnut et al. (1991), and others document nonlinear and increasing dose-response functions (at least over some ranges of exposure). In addition, Ostro et al. (1998) and Cropper et al. (1997) estimate the effects of air pollution with and without including outliers (defined as days with the highest 5 percent of air emissions). The effects of air pollution are positive in both cases, but lower when the outliers are removed (for ages 2–15 in Ostro et al. and ages 15–64 in Cropper et al.). These results also indicate that the dose-response function for air pollutants increases exponentially over some range of the data. © Blackwell Publishing, Inc. 2002.



Consequently, spreading out pollution over states may mitigate the individual and social costs of pollution.3 Finally, an unequal distribution of pollution contributes to inequalities in the distribution of the quality of life across the U.S. Quality of life differentials have been suspected of affecting internal migration patterns in the U.S., particularly for educated workers (Kahn, 1999; Glaeser, 1998). An influx of highly skilled workers may offer many local externalities, such as smaller lags in adjusting to negative labor market shocks and improved total factor productivity (Rauch, 1993). In addition, cross-sectional studies find that individuals accept lower wages and higher property values in areas offering a better quality of life (Gyourko and Tracy, 1991; Blomquist, Berger, and Hoehn 1988). Thus, an unequal distribution of pollution may have more far-reaching effects than simply affecting the distribution of toxic exposure. Despite these arguments, there has been sparse attention given to the interaction between environmental regulation and the distribution of pollution, especially since the late 1970s. Zupan (1973) showed that the percentage reduction in air pollutants in New York was identical across income groups; thus, the absolute reduction was greatest for the lowest income class. Harrison and Rubinfeld (1978) analyzed the distribution of the benefits from air pollution controls across incomes in the Boston metropolitan area in the 1970s. Asch and Seneca (1978) found that changes from 1972–1974 in exposure to certain air emissions favored the poor. Finally, Gianessi, Peskiny, and Wolff (1979) found greater benefits from air-quality improvements for minorities in nonwhite urban areas. The authors stressed the importance of these analyses “Information of the sort presented in this paper is essential to the evaluation of air pollution policy. Indeed, given the fact that the policy seems to imply a redistribution of welfare toward a minority who are largely nonwhite residents of polluted urban areas, the policy may be judged socially beneficial even if the benefits are not considered commensurate with the costs” (1979, p. 299).

However, since the 1970s research on environmental regulation has shifted away from the distributional consequences.4 Despite the lack of recent attention, Brooks and Sethi conclude that “without [nationwide ambient] standards, the disparities faced by certain subpopulations in the United States will not diminish appreciably in the foreseeable future” (1997, p. 249) Although federal 3 This argument has been applied elsewhere by economists. For example, Fullerton and Stavins (1998, pp. 6) argue that because many environmental damages “may increase non-linearly,” a system of emissions trading permits that reduces overall emissions may increase total damages by exacerbating emissions inequality. 4 Gelobter (1992) is an exception. In addition, List (1999) tests for convergence in emissions across states, but does not explicitly incorporate environmental regulations into the analysis. List and Strazicich (2000) also analyze convergence across states, focusing on the role of centralized versus local control.

© Blackwell Publishing, Inc. 2002.



standards may be necessary to rectify the inequality inherent in the distribution of pollution—as argued in List and Strazicich (2000) also—uniform national standards may not be the solution. In this paper we seek to answer the important question of whether uniform national environmental standards alter the spatial distribution of environmental quality across the U.S.? There are two main avenues through which environmental regulations may alter the distribution of pollution. First, if firms choose locations based on the cost of complying with environmental regulations, then the location of pollutiongenerating activity is directly affected. Initial empirical estimates of the impact of regulations on firm location ranged from positive and significant to negative and significant and led many to regard this relationship as weak at best (see for example, Levinson, 1996; Jaffe et al., 1995). On the other hand, more recent analyses provide strong evidence indicating that environmental regulations do influence the location decisions of pollution-intensive manufacturing firms (Becker and Henderson, 2000; List et al., 2001; Henderson, 1996). Second, even if firms do not relocate, stricter regulations reduce emissions and hence may alter the spatial distribution of relative emissions levels. However, intertwined in any discussion of environmental regulation, firm location, and pollution levels, is the fact that regulations must be enforced to have an effect. Helland (1998a, 1998b) provides empirical evidence that EPA plant inspections are influenced by political and budget considerations. Fredriksson and Millimet (2001) analyze the relationship between corrupt bureaucrats and the enforcement of environmental standards. Becker and Henderson (2000) and Dean, Brown, and Stango (2000) suggest that enforcement is related to firm size. Gray and Deily (1991) find that regulators are less likely to enforce environmental regulations if firms have a high probability of shutting down or are a major source of employment. One might expect the impact of discretionary enforcement on the distribution of pollution to benefit wealthy, nonminority communities. Wealthy residents presumably have greater political clout, and larger plants located in, say, poorer areas inhabited by minorities due to lower land prices are able to avoid enforcement due to their size, resources, and importance in the local labor market. To avoid the complications that arise in the mapping of environmental laws to environmental outcomes, we use measures of actual environmental compliance costs to measure policy stringency in the empirical work. Our findings are striking. Using emissions data from the U.S. EPA’s Toxic Release Inventory (TRI) along with three measures of state-specific environmental compliance costs from 1988–1994, we reach three conclusions. First, the distribution of per capita emissions is far from uniform, particularly at the county level. This inequality is driven primarily by differences in the distribution of toxic air releases (because air emissions constitute the largest share of total pollution); however, air releases are the most equally distributed of the pollution types examined. Second, national uniform increases in environmental compliance costs are found to increase total per capita emissions inequality at the county and state level, although the impacts are not large. Finally, when © Blackwell Publishing, Inc. 2002.



examining individual pollution types, we find significant variation of the effect of more stringent environmental compliance costs. Specifically, greater compliance costs exacerbate inequalities at the county- and state-levels in terms of per capita air and water releases, but have no effect on the distribution of per capita land and underground releases. The remainder of the paper is organized as follows: Section 2 introduces a relatively simple model of emissions determination along with the features of the Gini coefficient that allow an analysis of the impact of environmental changes on inequality; Section 3 discusses the data; Section 4 presents the empirical results; and, Section 5 provides some concluding remarks. 2.


Let pi, i = 1,...,n, represent per capita total pollution in location i. To analyze the effect of regulations on particular types of emissions, total emissions are disaggregated into J distinct categories, pij , j = 1,..., J, where henceforth the location index i is suppressed and p = Σjpj. Four emission types are considered in the empirical section below: air, water, land, and underground releases. Inequality is measured by the environmental Gini coefficient (Heil and Wodon, 1999). The Gini coefficient is the best known and most frequently used measure of inequality. For the present analysis, it is convenient to use the covariance formulation:



a f

2Cov p, F µ

where G is the Gini coefficient, F is the cumulative distribution of per capita emissions, µ is mean per capita emissions, and µ = Σjµj (Lerman and Yitzhaki, 1984, 1985). The question posed in this paper deals with the impact of a uniform change in environmental compliance costs R on total emissions inequality, as well as inequality by emission type. To derive the effect of a change in p on inequality, Equation (1) is differentiated with respect to R, yielding


a f

a fOP Q

∂G 2 ∂ Cov p, F ∂p Cov p, F = 2 µ− ∂R µ ∂R ∂R

Using the properties of covariance and dividing both sides by the Gini coefficient G we obtain the relative change in the environmental Gini coefficient caused by a change in R as


∂G ∂R = Cov δ p , F − E δ p G µ Cov p, F

d i a f

where δp ⬅ ∂p/∂R < 0 and E[⋅] is the expected value operator. © Blackwell Publishing, Inc. 2002.



The first term in Equation (2) is the regression coefficient in the Gini regression of δp on p (Yitzhaki, 1994; Olkin and Yitzhaki, 1992). This term may be interpreted as a weighted average of the effects of p on δp, where the weights are derived from the Gini coefficient. The second term in Equation (2) is the expected change in mean per capita pollution from a unit increase in R. Rearranging terms yields

∂G ∂R = E δ p G µ



E δp µ

LM Cov dδ , F i µ MN Cov a p, F f E δ dη − 1i p


−1 p

r p

where ηrp is the Gini pollution elasticity (that is, the change in emissions inequality associated with a percentage change in total per capita emissions derived through a change in R). Because δp < 0 an elasticity greater than unity implies that a reduction in emissions is inequality-reducing; an elasticity less than unity indicates a reduction is inequality-enhancing. In other words

∂G r ∂R = + if η p < 1 − if ηrp > 1 G

R|S |T

To estimate the impact of environmental compliance costs on inequality, one needs to specify a structural relationship between aggregate pollution P and environmental regulation R. Building on Dean (1999), the supply of emissions is given by








where R/w and r/w are the relative factor prices of emissions and capital, respectively; εh, h = p, k, represent the supply elasticities with respect to relative factor prices; Y is income and, εy is the income elasticity of demand for lower emissions.5 Dividing Equation (4) by population yields an equation for the supply of per capita emissions p and differentiating with respect to R gives

∂p p ≡ δp = εp < 0 ∂R R


The emissions ‘supply’ is part of a larger model where emissions are treated as a factor of production. Thus, Equation (4) gives the aggregate emissions demanded by firms. © Blackwell Publishing, Inc. 2002.



As a result, we are able to obtain an expression for the Gini pollution elasticity with respect to changes in environmental compliance costs

FG p , FIJ HR K µ Cov a p, F f L p O EM P N RQ

Cov ηrp =

Because εp drops out of Equation (5), the emissions supply Equation (4) does not have to be estimated in order to estimate the Gini pollution elasticity with respect to changes in environmental compliance costs. However, because εp remains in Equation (3), we are only able to estimate the impact of a uniform decrease in emissions obtained via an increase in environmental stringency on the distribution of per capita emissions up to a constant of proportionality. Nonetheless, assuming that the supply elasticity of emissions with respect to the relative price of emissions is constant over time, we are able to discover any changes in the magnitude of the effect of compliance costs on pollution inequality that may have occurred. Moreover, as ηrp → 1 , (∂G/∂R/G → 0 ∀εp, implying that compliance costs have no effect on the distribution of emissions, regardless of the value of εp. Thus, if we fail to reject the null hypothesis of ηrp = 1 , then we are able to conclude that uniform changes in environmental compliance costs do not alter the distribution of total per capita emissions. Finally, we can analyze the effects of environmental compliance costs on the distribution of individual emission types by simply replacing per capita total emissions p in Equations (3), (4), and (5) with per capita emissions of type j, pj. 3.


The pollution data are obtained from the EPA’s Toxic Release Inventory (TRI). With the passage of the Emergency Planning and Community Right-toKnow Act (EPCRA) in 1986, all manufacturing facilities are required to release information on the emission of over 650 toxic chemicals and chemical categories to air, water, and land. In addition, facilities are required to report the quantities of chemicals that are recycled, treated, burned, or disposed of in any other manner either on-site or off-site. Any facility that produces or processes more than 25,000 pounds or uses more than 10,000 pounds of any of the listed toxic chemicals must submit a TRI report (U.S. EPA, 1992). The data are currently available from 1988–1998; however, the measures of environmental compliance costs (discussed below) are only available up to 1994. Although data are available at the chemical level, they are aggregated into several broad categories: air, land, water, and underground releases (see Appendix). In the majority of studies using the TRI data, these four pollution categories are aggregated together as well. Although these aggregations give equal weight to each chemical, some studies have been concerned about forming © Blackwell Publishing, Inc. 2002.



new aggregates, weighting each chemical by a measure of toxicity (Brooks and Sethi, 1997; Arora and Cason, 1995). However, as reported by the EPA, most of the widely used chemicals do not vary significantly in their toxicity and many of the less toxic chemicals have not been assigned risk scores by the EPA (Arora and Cason, 1999; U.S. EPA, 1989). Nonetheless, Arora and Cason (1995) perform their analysis weighting each chemical equally as well as weighting chemicals by risk scores (when available). They find their results to be robust to the choice of aggregation scheme. The TRI data are available at the county level. If the primary motivation for this study is to assess the effect of compliance costs on differential exposure by segments of the population, then using data at the county level is advisable. However, because environmental policy is predominantly implemented at the federal and state level and issues of ‘fairness’ (whether real or perceived) are frequently debated, the effect of environmental standards on the distribution of pollution at the state level may be more relevant (Kahn, 1999). For example, Caplan and Silva (1999) analyze important interactions between federal and state governments in policies designed to combat acid rain. Hahn (2000) notes that in some instances firms must garner permission from federal or state officials before altering production processes. Pashigian (1985) shows that the U.S. federal “prevention of significant deterioration” policy favored the regional interests of northern states by imposing relatively more stringent regulations on states in other regions and was therefore opposed by certain states. Thus, understanding the impact of uniform changes in compliance costs at the state level is critical. As a result, we aggregate data to the state level as well. The emissions data are combined with county and state population data obtained from the U.S. Census Bureau along with data on racial composition of counties and states obtained from the U.S. Bureau of Economic Analysis. Table 1 contains the summary statistics and indicates a substantial decline in total per capita emissions as well as for each type. Table 2 presents Spearman correlations between per capita emissions and population shares by race and gender at the county and state level. Consistent with the previous literature on environmental injustice, there is a significant positive correlation between toxic releases and black population share at both the state and county level, as well as a significant positive relationship between pollution and female population share at the state level (data are unavailable at the county level).6 Three measures are used to measure annual environmental compliance costs at the state level. The first index is obtained from Levinson (1999). The index is defined as the ratio of actual PACE in a particular state in a given year to the predicted level of PACE if each industry within the state conformed to the national average for its industry. Thus, a ratio greater than unity indicates compliance costs relatively greater than the national average; a ratio less than unity shows the reverse, see Levinson (1999) for more details. The data on PACE 6

Examining the correlations between aggregate pollution and demographic characteristics yields even stronger evidence in favor of minorities facing greater pollution exposure. © Blackwell Publishing, Inc. 2002.


Per Capita Air Releases Per Capita Water Releases Per Capita Land Releases Per Capita Underground Injections Per Capita Total Emissions Levinson Index PACE (per unit of manufacturing) PACE (per capita) Population Share, White Population Share, Black Population Share, Other


12.07 (57.09) 1.43 (55.83) 4.90 (93.03) 5.90 (81.92) 24.31 (193.75) 1.00 (0.22) 0.0034 (0.0020) 0.01 (0.01)


11.09 (57.20) 0.81 (11.88) 1.95 (26.55) 5.01 (60.73) 18.85 (98.92) 1.00 (0.19) 0.0043 (0.0022) 0.01 (0.01)


9.82 (46.58) 0.84 (35.41) 1.86 (28.42) 3.19 (39.43) 15.71 (87.71) 1.00 (0.24) 0.0061 (0.0045) 0.02 (0.01) 0.75 (0.19) 0.12 (0.12) 0.13 (0.15)


8.69 (34.91) 1.02 (58.64) 1.81 (29.53) 2.96 (38.47) 14.48 (93.44) 0.99 (0.26) 0.0072 (0.0057) 0.03 (0.02) 0.75 (0.19) 0.12 (0.12) 0.13 (0.16)


7.91 (36.74) 1.14 (63.94) 1.54 (25.82) 2.99 (36.60) 13.57 (93.24) 0.98 (0.25) 0.0076 (0.0095) 0.03 (0.03) 0.75 (0.20) 0.12 (0.12) 0.13 (0.16)


7.00 (33.97) 1.11 (81.52) 1.34 (28.23) 2.36 (33.26) 11.81 (103.80) 0.99 (0.28) 0.0066 (0.0082) 0.03 (0.03) 0.74 (0.20) 0.12 (0.13) 0.14 (0.16)


6.36 (27.16) 0.26 (5.20)% 1.41 (32.55) 1.42 (30.95) 9.44 (57.53) 0.98 (0.24) 0.0068 (0.0084) 0.03 (0.03) 0.74 (0.20) 0.12 (0.13) 0.14 (0.17)


8.95 (43.17) 0.94 (51.78) 2.10 (43.64) 3.37 (48.70) 15.36 (110.56) 0.99 (0.24) 0.0060 (0.0066) 0.02 (0.02) 0.75 (0.20) 0.12 (0.13) 0.13 (0.16)


© Blackwell Publishing, Inc. 2002.

TABLE 1: Summary Statistics, County Level

Values are weighted by population. Standard deviations in parentheses.




TABLE 2: Spearman Correlations Between Emissions Demographic Characteristics at the State and County Level County Variable









Per Capita Air Releases

–0.00 (p = 0.82)

0.08 (p = 0.00)

–0.11 (p = 0.00)

–0.09 (p = 0.10)

0.31 (p = 0.00)

–0.45 (p = 0.00)

0.36 (p = 0.00)

Per Capita Water Releases

–0.13 (p = 0.00)

0.21 (p = 0.00)

0.02 (p = 0.09)

–0.19 (p = 0.00)

0.36 (p = 0.00)

–0.32 (p = 0.00)

0.34 (p = 0.00)

Per Capita Land Releases

–0.13 (p = 0.00)

0.10 (p = 0.00)

0.16 (p = 0.00)

–0.08 (p = 0.12)

0.09 (p = 0.09)

–0.08 (p = 0.15)

0.05 (p = 0.46)

Per Capita Underground Releases

–0.12 (p = 0.00)

0.09 (p = 0.00)

0.14 (p = 0.00)

–0.35 (p = 0.00)

0.37 (p = 0.00)

–0.12 (p = 0.03)

0.19 (p = 0.00)

Per Capita Total Releases

–0.01 (p = 0.30)

0.07 (p = 0.00)

–0.09 (p = 0.00)

–0.08 (p = 0.11)

0.15 (p = 0.01)

–0.24 (p = 0.00)

0.17 (p = 0.01)

Racial and gender compositions are measured as percentage of total population. Correlations are for 1990–1994 at the county level and 1988–1994 at the state level. P-values associated with the null hypothesis H0 : ρ = 0 in parentheses.

come from the U.S. Census Bureau’s Pollution Abatement and Cost Survey. The survey details the level of pollution abatement and operating costs incurred by manufacturing firms from 1977–1994, and is aggregated to the state level. The benefit of the Levinson index is that it accounts explicitly for the nonuniform distribution of manufacturing activity across states. For robustness, two other measures of environmental compliance costs are used for comparison: PACE per dollar of state manufacturing output and per capita PACE. 4.


Table 3 presents the annual county and state level environmental Gini coefficient (weighted by population) for total per capita emissions, as well as by pollution type. The state environmental Gini coefficient for total per capita releases ranges from 0.464 to 0.557 over the period. At the county level, the environmental Gini coefficient ranges from 0.719 to 0.790. Thus, approximately one-third of pollution inequality over this time period arises from an unequal distribution of pollution within states. In terms of individual emission types, air pollution is distributed most uniformly at both the county and state level and accounts for over half of all toxic releases (Table 1). On the other hand, water, land, and underground releases, are extremely unevenly distributed across the U.S.7

7 The relative ranking of the four emission types is robust to choice of inequality measure. Using the coefficient of variation, standard deviation in logs, Kakwani index, and the Theil index, per capita air releases are the most equally distributed in each year and per capita underground releases are typically the most unequally distributed.

© Blackwell Publishing, Inc. 2002.



TABLE 3: Per Capita Emissions Inequality at the State and County Level, 1988–1994 Environmental Gini Coefficient (G) County





Land Underground Total

1988 1989 1990 1991 1992 1993 1994

0.589 0.598 0.604 0.610 0.635 0.658 0.673

0.736 0.745 0.727 0.755 0.785 0.784 0.745

0.691 0.729 0.716 0.737 0.746 0.745 0.775

0.834 0.890 0.716 0.922 0.943 0.903 0.943

0.740 0.724 0.719 0.740 0.772 0.790 0.771



Land Underground Total

0.369 0.379 0.386 0.398 0.421 0.434 0.448

0.696 0.553 0.687 0.753 0.739 0.886 0.561

0.657 0.577 0.637 0.633 0.606 0.663 0.692

0.639 0.663 0.673 0.661 0.671 0.651 0.641

0.528 0.481 0.470 0.483 0.528 0.557 0.464

Weighted by population. Calculated using Equation (1) from the text. The Gini coefficient ranges from zero to unity, with higher values indicating greater inequality.

Tables 4 (county level) and 5 (state level) report the Gini pollution elasticities with respect to changes in environmental compliance costs ηrp using Equation (5) for the three different measures of environmental compliance costs. In addition, standard errors obtained through 250 bootstrap repetitions are also provided.8 If the elasticity is positive and significantly less than unity, then increases in environmental stringency are inequality-enhancing; if the elasticity is significantly greater than unity, then stricter environmental standards are inequality-reducing. Elasticities not significantly different from unity indicate that uniform increases in environmental compliance costs do not alter the spatial distribution of per capita pollution. In addition, ceteris paribus, the greater the deviation of the Gini pollution elasticity from unity, the greater the impact of environmental compliance costs on the environmental Gini coefficient. Finally, although the exact effect on the Gini coefficient, given in Equation (3), is not estimable (without prior estimation of εp, the emissions supply elasticity with respect to environmental compliance costs), changes in the Gini pollution elasticity over time are informative. Assuming εp is fairly constant over time, changes in the deviation between ηrp and unity indicate whether environmental compliance costs are having a more substantial impact on the distribution of pollution. The top panel of Table 4 presents the county-level results using the Levinson index to measure environmental compliance costs. In terms of total per capita emissions, the elasticities range from 0.945 to 0.980. The elasticities are less than and statistically different from unity at at least the 95 percent level of significance, therefore, uniform increases in environmental compliance costs (as measured by the Levinson index) are inequality-enhancing. However, the 8

Mills and Zandvakili (1997) highlight the usefulness of bootstrap techniques in obtaining standard errors of measures of inequality. © Blackwell Publishing, Inc. 2002.



TABLE 4: The Impact of A Uniform Increase in Environmental Compliance Costs, County Level

d i

Elasticity ηrp Year






0.991 (0.002) 0.997 (0.001) 0.995 (0.001) 0.996 (0.001) 0.991 (0.003) 0.991 (0.003) 0.990 (0.004)

0.995 (0.003) 0.996 (0.002) 0.996 (0.002) 0.998 (0.001) 1.000 (0.002) 1.001 (0.003) 0.998 (0.002)

0.999 (3.0*10–04) 1.000 (2.0*10–04) 1.000 (2.0*10–04) 1.000 (3.0*10–04) 0.999 (4.0*10–04) 1.000 (5.0*10–04) 1.000 (5.0*10–04)

0.975 (0.007) 0.980 (0.004) 0.978 (0.005) 0.970 (0.006) 0.952 (0.008) 0.945 (0.008) 0.963 (0.007)

PACE (Per Unit of Manufacturing Output) 1988 0.917 (0.015) 0.967 (0.009) 1989 0.929 (0.017) 0.988 (0.006) 1990 0.905 (0.016) 0.976 (0.010) 1991 0.884 (0.022) 0.960 (0.013) 1992 0.830 (0.024) 0.947 (0.015) 1993 0.881 (0.019) 0.943 (0.019) 1994 0.945 (0.019) 0.969 (0.017)

0.987 (0.006) 0.999 (0.003) 1.001 (0.005) 1.002 (0.004) 1.001 (0.003) 0.997 (0.005) 0.996 (0.004)

0.998 (0.001) 0.999 (0.001) 0.998 (0.001) 0.999 (0.001) 0.998 (0.002) 0.999 (0.002) 1.001 (0.001)

0.904 (0.016) 0.915 (0.018) 0.894 (0.019) 0.880 (0.026) 0.820 (0.025) 0.855 (0.028) 0.923 (0.017)

PACE (Per Capita) 1988 0.918 (0.019) 1989 0.928 (0.027) 1990 0.896 (0.022) 1991 0.853 (0.023) 1992 0.792 (0.025) 1993 0.838 (0.025) 1994 0.918 (0.032)

0.993 (0.008) 1.004 (0.006) 1.006 (0.006) 1.006 (0.006) 1.007 (0.005) 1.001 (0.006) 0.993 (0.007)

0.998 (0.001) 0.998 (0.001) 0.997 (0.001) 0.998 (0.002) 0.997 (0.002) 0.997 (0.002) 0.999 (0.001)

0.925 (0.019) 0.932 (0.023) 0.912 (0.026) 0.888 (0.036) 0.831 (0.034) 0.852 (0.032) 0.913 (0.028)

Levinson Index 1988 0.992 (0.009) 1989 0.989 (0.006) 1990 0.992 (0.006) 1991 0.979 (0.008) 1992 0.956 (0.006) 1993 0.947 (0.006) 1994 0.964 (0.007)

0.973 (0.009) 0.991 (0.007) 0.976 (0.010) 0.962 (0.014) 0.951 (0.015) 0.945 (0.018) 0.967 (0.016)

Notes: Weighted by county population. Calculated using Equation (5) from the text. Standard errors in parentheses generated from 250 bootstrap repetitions. An elasticity greater than one implies that stricter uniform environmental standards reduce the environmental Gini coefficient (and hence environmental inequality); an elasticity less than one, the reverse.

magnitude of the effect is not large. For example, assuming that the supply of emissions is unit elastic, then using the data from 1993 (where the elasticity is furthest from unity) and Equation (3), the county-level environmental Gini coefficient will increase by 1.7 percent (from 0.790 to 0.803) in response to a uniform one standard deviation increase in abatement costs. Although the result is small, the inequality-enhancing effect of regulation seems surprising at first glance. However, upon careful consideration of what the Gini elasticity measures, this result becomes more clear. The elasticity yields the distributional response to a uniform increase in environmental compliance costs. Moreover, because the Levinson index (as well as our other measures) is based on actual abatement expenditures, the experiment presumes a uniform policy that is also uniformly enforced across all locations. Thus, the elasticity © Blackwell Publishing, Inc. 2002.



TABLE 5: The Impact of A Uniform Increase in Environmental Compliance Costs, State Level

d i

Elasticity ηrp

Year Air





0.931 (0.020) 0.951 (0.018) 0.950 (0.018) 0.952 (0.031) 0.903 (0.044) 0.928 (0.032) 0.857 (0.050)

0.936 (0.029) 0.944 (0.029) 0.953 (0.027) 0.981 (0.022) 1.009 (0.029) 1.043 (0.049) 0.978 (0.028)

0.987 (0.016) 0.991 (0.010) 0.989 (0.007) 0.991 (0.011) 0.979 (0.017) 0.974 (0.013) 0.993 (0.016)

0.921 (0.040) 0.927 (0.026) 0.919 (0.032) 0.892 (0.034) 0.855 (0.041) 0.835 (0.044) 0.871 (0.045)

PACE (Per Unit of Manufacturing Output) 1988 0.673 (0.125) 0.748 (0.076) 1989 0.789 (0.095) 0.789 (0.071) 1990 0.706 (0.092) 0.738 (0.119) 1991 0.629 (0.126) 0.612 (0.108) 1992 0.475 (0.144) 0.483 (0.143) 1993 0.625 (0.135) 0.587 (0.192) 1994 0.800 (0.114) 0.638 (0.198)

0.847 (0.067) 0.976 (0.042) 0.994 (0.079) 0.993 (0.064) 1.011 (0.065) 0.934 (0.092) 0.925 (0.076)

0.949 (0.041) 0.951 (0.038) 0.949 (0.040) 0.908 (0.052) 0.864 (0.056) 0.899 (0.057) 0.965 (0.040)

0.653 (0.094) 0.712 (0.095) 0.631 (0.091) 0.548 (0.128) 0.400 (0.140) 0.498 (0.141) 0.681 (0.131)

PACE (Per Capita) 1988 0.591 (0.141) 1989 0.694 (0.130) 1990 0.607 (0.119) 1991 0.455 (0.144) 1992 0.278 (0.156) 1993 0.412 (0.172) 1994 0.623 (0.165)

0.902 (0.099) 1.107 (0.072) 1.096 (0.085) 1.060 (0.072) 1.112 (0.096) 1.030 (0.105) 0.970 (0.091)

0.952 (0.048) 0.957 (0.043) 0.946 (0.046) 0.895 (0.062) 0.866 (0.060) 0.874 (0.068) 0.947 (0.047)

0.659 (0.111) 0.705 (0.124) 0.607 (0.113) 0.476 (0.145) 0.338 (0.158) 0.372 (0.152) 0.532 (0.170)

Levinson Index 1988 0.966 (0.050) 1989 0.959 (0.035) 1990 0.959 (0.040) 1991 0.930 (0.037) 1992 0.880 (0.038) 1993 0.866 (0.039) 1994 0.900 (0.042)

0.755 (0.071) 0.768 (0.069) 0.692 (0.093) 0.566 (0.098) 0.442 (0.124) 0.555 (0.155) 0.567 (0.172)

Notes: See Table 4.

informs one of the change in pollution inequality if the federal government imposes an equivalent increase in abatement costs across all counties. Posed in this light, one might expect the distributional effects to be minimal because such uniformity may not affect firm location decisions. However, more careful consideration reveals that location decisions may still be affected. For example, the inequality-enhancing effect would result if firms located in clean areas with relatively strict regulations prior to the uniform increase are unable to incur the higher costs. As a result, these firms may shut down or move to dirty areas with less stringent regulation initially. Consequently, inequalities in the distribution of pollution will rise. Seen in this light, the fact that uniformly applied increases in regulation increase environmental inequality is consistent with recent findings that firms choose locations based on environmental standards. Furthermore, the results suggest that the marginal firms affected by uniformly higher abatement costs © Blackwell Publishing, Inc. 2002.



are more likely to be located in areas with initially more stringent regulations. Consequently, these marginal firms tend to be located in areas heavily population by nonminorities. Our results are consistent with the findings of Brooks and Sethi (1997), who find that firms located in areas inhabited by minorities are less responsive to changes in pollution costs. Examining the individual emission types indicates that the impact of a uniform increase in compliance costs varies across emission types. In terms of air emissions—the largest component of total emissions—the elasticities are very similar to those for total emissions; however, the elasticity is significantly different from unity at the 95 percent significance level only in the post-1990 time period. For water releases, the elasticities are significantly different from unity in every year of the sample, although the point estimates are always larger than 0.99, implying little actual effect. Finally, for land and underground releases, the elasticities are never significantly different from unity at the 95 percent significance level. Thus, federal legislation raising compliance costs, uniformly across the U.S. as measured by the Levinson index, will not redress current inequities in the distribution of pollution. The bottom two panels of Table 4 explore the robustness of these findings with respect to alternative measures of abatement costs. The results are qualitatively similar to those obtained using the Levinson index. Specifically, the elasticities are significantly less than unity for air, water, and total emissions and not significantly different from unity for land and underground releases. However, the actual magnitudes implied by these estimates are much larger. For example, again assuming a unit elasticity of supply for emissions, a one standard deviation increase in PACE per unit of manufacturing (per capita PACE) applied uniformly across all counties in 1992 (where the point estimates are furthest from one) raises the environmental Gini coefficient for per capita total emissions by 25 percent (21 percent), from 0.774 to 0.967 (0.938). Although these effects are quite large, it should be noted that a one standard deviation increase amounts to more than a doubling of mean abatement expenditures (Table 1). Table 5 presents the analogous results at the state level. Overall, the findings are very similar to those obtained using county-level data. The elasticities using all three measures of abatement costs are predominantly significantly less than unity for per capita total, air, and water emissions; and almost never significantly different from unity for land and underground releases. In general, the point estimates for the Gini elasticity tend to be smaller than those reported in Table 4, but the standard errors are much larger, reflecting the smaller sample size. Performing the same simulation exercise as previously, we find that a uniform one standard deviation increase in the Levinson index in 1993 would have increased the environmental Gini coefficient by 4.3 percent, from 0.552 to 0.576. Simulations involving the other abatement measures yield changes of similar magnitude to those found at the county level (an approximate increase in the Gini coefficient of 20 percent). In general, the results presented in this study have three important implications. First, the fact that the analysis implies similar distributional effects of © Blackwell Publishing, Inc. 2002.



federal uniform changes in abatement expenditures at the state and county level is significant in terms of policymaking. As mentioned earlier, federal environmental legislation is typically the product of federal and state negotiations (Caplan and Silva, 1999; Pashigian, 1985). To the extent that the distributional effects of policy changes at the county level are also reflected at the state level, there is no conflict of interest in letting the ‘state’ speak on behalf of its counties. Second, the findings may interpreted in light of the Coase theorem. The Coase theorem states that—in the absence of transaction costs—polluting firms should locate where the willingness-to-pay to avoid pollution damages is minimized. Typically, this is assumed to imply that firms should locate in poorer areas, inhabited by a larger share of minorities. However, for whatever reason (e.g., historical inertia), firms may not be ‘optimally’ located. As environmental standards increase uniformly across all locations, firms may find it more imperative to minimize overall production costs. This may involve improving efficiency by relocating to areas where the costs associated with pollution are the lowest. In response to a uniform increase in abatement costs, our results imply that firms relocate from relatively clean to relatively dirty locations, and thus our analysis may be reconciled with the Coase theorem. Third, List and Strazicich (2000) find that emissions spatially converged during the post-1969 era of centralized environmental control. List and Gerking (2000) document that environmental quality did not deteriorate when Reagan returned many environmental controls back to the states during the 1980s. Although the present analysis is not directly related to the issue of regulatory federalism and whether decentralized policymaking leads to a ‘race to the bottom,’ it does highlight the fact that (unless appropriately specified) federal legislation is not a panacea for an environmental distribution that places a larger burden on minorities and females. Although the arguments in favor of federal environmental control are based on the idea of uniformity and the prevention of negative competition by jurisdictions, the analysis in this paper shows that simple, uniform, federal regulations will not diminish current racial and gender inequities in the spatial distribution of pollution. If environmental controls are to be implemented at the federal level, policies analogous to the Clean Air Act that levy regulations nonuniformly are needed. In other words, rather than imposing equivalent regulations on all locations, the government needs to target areas with high relative pollution levels specifically. 5.


The interaction between environmental regulation and the distribution of emissions has important social welfare ramifications. However, empirical studies with this emphasis have all but disappeared since the 1970s. To this end, we present a simple structural model of emissions determination which, when combined with the properties of the Gini coefficient, allows one to assess the impact of changes in emissions and regulatory compliance costs on the © Blackwell Publishing, Inc. 2002.



distribution of per capita emissions across U.S. counties and states. With this model, we are able to examine the extent to which uniform federal environmental regulations may help ameliorate inequities in the distribution of pollution. Combining data from the U.S. EPA’s Toxic Release Inventory and three different state-specific measures of environmental compliance costs, we obtain several illuminating results. First, we find that per capita emissions are distributed extremely unequally, particularly at the county level, and that locations in the upper tail of the distribution are heavily populated with minorities and females. Second, although uniform increases in environmental compliance costs may reduce total per capita emissions, such increases may exacerbate the level of per capita total pollution inequality in the U.S. This outcome is consistent with uniform increases in abatement expenditures reducing emissions at a faster rate in locations with initially more stringent regulations, as well as firms relocating to areas with initially more lax environmental standards. Thus, it appears that without stricter environmental controls specifically targeting relatively polluted locations, racial discrepancies in the spatial distribution of pollution will only continue. REFERENCES Arora, Seema and Timothy N. Cason. 1995. “An Experiment in Voluntary Environmental Regulation: Participation in EPA’s 33/50 Program,” Journal of Environmental Economics and Management, 28, 271–286. ———. 1999. “Do Community Characteristics Influence Environmental Outcomes? Evidence from the Toxic Release Inventory,” Southern Economic Journal, 65, 691–716. Asch, Peter and Joseph J. Seneca. 1978. “Some Evidence on the Distribution of Air Quality,” Land Economics, 54, 279–297. Becker, Randy and Vernon Henderson. 2000. “Effects of Air Quality Regulations on Polluting Industries,” Journal of Political Economy, 108, 379–421. Blomquist, Glenn C., Mark C. Berger, and John P. Hoehn. 1988. “New Estimates of Quality of Life in Urban Areas,” American Economic Review, 78, 89–107. Brooks, Nancy and Rajiv Sethi. 1997. “The Distribution of Pollution: Community Characteristics and Exposure to Air Toxics,” Journal of Environmental Economics and Management, 32, 233–250. Caplan, Arthur J. and Emilson C.D. Silva. 1999. “Federal Acid Rain Games,” Journal of Urban Economics, 46, 25-52. Chay, Kenneth Y. and Michael Greenstone. 1999. “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession,” NBER Working Paper No. 7442. Chestnut, Lauraine G., Joel Schwartz, David A. Savitz, and Cecil M. Burchfield. 1991. “Pulmonary Function and Ambient Particulate Matter: Epidemiological Evidence from NHANES I,” Archives of Environmental Health, 46, 135–144. Cropper, Maureen L., Natalie B. Simon, Anna Alberini, and P.K. Sharma. 1997. “The Health Effects of Air Pollution in Delhi, India,” Policy Research Working Paper No. 1860, Washington DC: The World Bank. Dean, Judith M. 1999. “Testing the Impact of Trade Liberalization on the Environment,” in P.G. Fredriksson (ed.), Trade, Global Policy, and the Environment, World Bank Discussion Paper No. 402, Washington DC: The World Bank. Dean, Thomas J., Robert L. Brown, and Victor Stango. 2000. “Environmental Regulation as a Barrier to the Formation of Small Manufacturing Establishments: A Longitudinal Examination,” Journal of Environmental Economics and Management, 40, 56–75. © Blackwell Publishing, Inc. 2002.



Doull, John. 1996. “Specificity and Dosimetry of Toxicologic Responses,” Regulatory Toxicology and Pharmacology, 24, S55–S57. Fredriksson, Per G. and Daniel L. Millimet. 2001. “Bureaucratic Corruption and Environmental Policy: Theory and Evidence from the United States,” Department of Economics, Southern Methodist University. Fullerton, Don and Robert N. Stavins. 1998. “How Economists See the Environment,” in R.N. Stavins (ed.), Economics of the Environment. New York: W.W. Norton. Gelobter, M. 1992. “Toward a Model of ‘Environmental Discrimination’,” in B. Bryant and P. Mohai (eds.), Race and the Incidence of Environmental Hazards. Boulder, CO: Westview. Gianessi, Leonard P., Henry M. Peskiny, and Edward Wolff. 1979. “The Distributional Effects of Uniform Air Pollution Policy in the United States,” Quarterly Journal of Economics, 93, 281–301. Glaeser, Edward. 1998. “Are Cities Dying?” Journal of Economic Perspectives, 12, 139–160. Gray, Wayne B. and Mary E. Deily. 1991. “Enforcement of Pollution Regulations in a Declining Industry,” Journal of Environmental Economics and Management, 21, 260–274. Gyourko, Joseph and Joseph Tracy. 1991. “The Structure of Local Public Finance and the Quality of Life,” Journal of Political Economy, 99, 774–806. Hahn, Robert W. 2000. “The Impact of Economics on Environmental Policy,” Journal of Environmental Economics and Management, 39, 375–399. Harrison Jr., David and Daniel L. Rubinfeld. 1978. “The Distribution of Benefits from Improvements in Urban Air Quality,” Journal of Environmental Economics and Management, 5, 313–332. Heil, Mark T. and Quentin T. Wodon. 1999. “Future Inequality in CO2 Emissions and the Projected Impact of Abatement Proposals,” Policy Research Working Paper No. 2084, Washington DC: The World Bank. Helland, Eric. 1998a. “The Revealed Preferences of State EPAs: Stringency, Enforcement, and Substitution,” Journal of Environmental Economics and Management, 35, 242–261. ———. 1998b. “The Enforcement of Pollution Control Laws: Inspections, Violations, and SelfReporting,” Review of Economics and Statistics, 80, 141–153. Henderson, J. Vernon. 1996. “Effects of Air Quality Regulation,” American Economic Review, 86, 789–813. Jaffe, Adam B., Steven R. Peterson, Paul R. Portney, and Robert N. Stavins. 1995. “Environmental Regulations and the Competitiveness of U.S. Manufacturing: What does the Evidence Tell Us?” Journal of Economic Literature, 33, 132–163. Kahn, Matthew. 1999. “The Silver Lining of Rust Belt Manufacturing Decline,” Journal of Urban Economics, 46, 360–376. Lerman, Robert I. and Shlomo Yitzhaki. 1984. “A Note on the Calculation and Interpretation of the Gini Index,” Economic Letters, 15, 363–368. ———. 1985. “Income Inequality Effects by Income Source: A New Approach and Applications to the United States,” Review of Economics and Statistics, 67, 151–156. Levinson, Arik. 1996. “Environmental Regulations and Manufacturers’ Location Choices: Evidence from the Census of Manufactures,” Journal of Public Economics, 61, 5–29. ———. 1999. “An Industry-Adjusted Index of State Environmental Compliance Costs,” NBER Working Paper No. 7297. List, John A. 1999. “Have Air Pollutant Emissions Converged? Evidence From Unit Root Tests,” Southern Economic Journal, 66, 144–155. List, John A. and Shelby Gerking. 2000. “Regulatory Federalism and Environmental Protection in the United States,” Journal of Regional Science, 40, 453–471. List, John A., Daniel L. Millimet, Per G. Fredriksson, and Warren W. McHone. 2001. “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator,” Department of Agricultural and Resource Economics, The University of Arizona, Tucson, Arizona, U.S.A. List, John A. and Mark C. Strazicich. 2000. “Regulatory Federalism and the Distribution of Air Pollutant Emissions,” Department of Agricultural and Resource Economics, The University of Arizona, Tucson, Arizona, U.S.A. © Blackwell Publishing, Inc. 2002.



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APPENDIX Definitions of the various pollution categories (available at http://www.scorecard. org). Air Releases Total releases to air include all TRI chemicals emitted by a plant from both its smoke stack(s) as well “fugitive” sources (such as leaking valves). Stack Air Releases. Releases to air that occur through confined air streams, such as stacks, vents, ducts or pipes. Sometimes called releases from a point source. Fugitive Air Releases. Releases to air that do not occur through a confined air stream, including equipment leaks, evaporative losses from surface impoundments and spills, and releases from building ventilation systems. Sometimes called releases from nonpoint sources. Water Releases Releases to water include discharges to streams, rivers, lakes, oceans and other bodies of water. This includes releases from both point sources, such as industrial discharge pipes, and nonpoint sources, such as stormwater runoff, but not releases to sewers or other off-site wastewater treatment facilities. It includes releases to surface waters, but not ground water.

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Land Releases Land releases include all the chemicals disposed on land within the boundaries of the reporting facility, and can include any of the following types of on-site disposal: RCRA Subtitle C Landfills. Wastes which are buried on-site in landfills regulated by RCRA Subtitle C. Other On-site Landfills. Wastes which are buried on-site in landfills that are not regulated by RCRA. Land Treatment/Application Farming. Wastes which are applied or incorporated into soil. Surface Impoundments. Surface impoundments are uncovered holding ponds used to volatilize (evaporate wastes into the surrounding atmosphere) or settle waste materials. Other Land Disposal. Other forms of land disposal, including accidental spills or leaks. Underground Injection Underground injection releases fluids into a subsurface well for the purpose of waste disposal. Wastes containing TRI chemicals are injected into either Class I wells or Class V wells: Class I Injection Wells. Class I industrial, municipal, and manufacturing wells inject liquid wastes into deep, confined, and isolated formations below potable water supplies. Other Injection Wells. Include Class II, III, IV, and V wells. Class II oil- and gas-related wells re-inject produced fluids for disposal, enhanced recovery of oil, or hydrocarbon storage. Class III wells are associated with the solution mining of minerals. Class IV wells may inject hazardous or radioactive fluids directly or indirectly into underground sources of drinking water (USDW), only if the injection is part of an authorized CERCLA/RCRA clean-up operation. Class V wells are generally used to inject non-hazardous wastes into or above an underground source of drinking water. Class V wells include all types of injection wells that do not fall under I – IV. They are generally shallow drainage wells, such as floor drains connected to dry wells or drain fields.

© Blackwell Publishing, Inc. 2002.

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