Ž .JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT 32, 233]250 1997ARTICLE NO. EE960967

The Distribution of Pollution: Community Characteristics andExposure to Air ToxicsU

NANCY BROOKS

Department of Economics, Uni ersity of Vermont, 479 Main Street, Burlington, Vermont 05405

AND

RAJIV SETHI

Department of Economics, Barnard College, Columbia Uni ersity, New York, New York 10027

This paper examines the relationship between community-level exposure to air toxics andsocioeconomic, political, and demographic characteristics of the population. An index ofexposure that is sensitive to toxicity differences and to distance from the emission source isconstructed. This index is used to compare exposure levels across communities and to analyzedeterminants of disparities. Cross-sectional regressions on all continental U.S. zip codesreveal greater exposure in black communities even after controlling for a variety of variables.Voter turnout also affects exposure, and local socioeconomic conditions are found toinfluence changes in local exposure levels. Q 1997 Academic Press

1. INTRODUCTION

The publication by the Environmental Protection Agency of its annual ‘‘ToxicReleases Inventory’’ is greeted each year with a flurry of media activity. States areranked in order of total releases, as are the companies reporting the highest levelof releases. Considerable pressure is brought to bear on the targeted companies bytheir local government, press, environmental organizations, and other interestedparties. Representatives of targeted companies respond in a variety of ways. It iscommon for firms to issue a statement of intent to reduce pollution along withspecific targets for the years to come, with a rise in releases commonly attributedto a spurt in production. Alternatively, firms may argue that they are beingtargeted unfairly due to underreporting by other firms, or that their releases are

w x‘‘hazardous by legal definition only’’ 15 . Regardless of their response, firms takethe publication of TRI data very seriously. The resulting impact on consumers islarge enough to have a substantial effect on future earnings, at least in theperception of financial market participants. There is evidence to suggest that firmsnamed in the TRI experience statistically significant stock price declines as a resultw x11 .

* This research has been funded in part by the University of Vermont’s University Committee onResearch and Scholarship, under Grant Number SSCI95-3. We thank Tim Diette for meticulousresearch assistance, Bob Dana for generously providing us with data on zip code areas and centroids,and Ross Thomson for discussions. For helpful comments on an earlier draft we are grateful to JamesHamilton, Ajay Mahal and two anonymous referees.

2330095-0696r97 $25.00

Copyright Q 1997 by Academic PressAll rights of reproduction in any form reserved.

BROOKS AND SETHI234

Clearly the publication of the TRI itself, on account of the response that itgenerates, sets up incentives for firms to engage in a reduction in their releases asmeasured by the TRI itself in order to avoid the unfavorable publicity. Unfortu-nately, the TRI ranking of companies and states is based on a simple unweightedaggregate of total emissions, taking no account of differences in toxicity. Sincethere are over 300 chemicals in the sample, with the most toxic being a million

w xtimes more hazardous than the least by some accounts 13 , this leads to seriousdistortions in the public’s perception of the degree to which they are exposed.

Ž .Residents of regions with low quantities by weight of highly toxic emissions mayremain ignorant of the health effects they face, while residents of areas with largequantities of relatively benign substances may experience an unwarranted degreeof distress. Furthermore, the very incentives that the TRI was designed to set upmay act against its goals of ensuring a safer environment if firms substitute lowquantities of highly toxic substances for large quantities of less toxic ones in thehope of reducing their overall releases by weight. A downward national trend intotal releases may therefore disguise an underlying and unrecognized upward trendin the toxicity of releases, giving rise to a false sense of security.

The practice of aggregating emissions by weight also has implications foracademic studies of the distribution of pollution across demographic and socioeco-nomic lines. Subgroups of the population exposed to the greatest releases by weightmay not be the same subgroups that are exposed to the most damaging healtheffects. Similarly, as with the trend in the nation as a whole, a study may reveal animprovement in the conditions faced by some subgroup of the population whiletheir actual exposure to health hazards has worsened. Paradoxically, it may even bethe case that communities which bring the greatest amount of pressure to bear onlocal companies to reduce emissions may end up with the worst deterioration inexposure to toxins if the distorted incentives described above are in force. Sinceacademic studies themselves play a role in the degree to which communitiesengage in judicial or other action against polluters, it is imperative that they bebased on a more reasonable measure of aggregate exposure than is contained inthe TRI.

This paper has three main objectives. First, we define and generate for each zipcode in the continental U.S. an index of exposure to air toxics that is sensitive bothto differences in toxicity of emissions as well as to the distance of the zip code fromthe source. Second, we use the index to examine the cross-sectional relationshipbetween exposure and various socioeconomic, political, and demographic charac-teristics of the population. This includes income, race, poverty status, and voterturnout, the last of which has been used as a proxy for the propensity of a

w xcommunity to engage in collective action 10 . Third, we try to examine thedeterminants of changes over time in exposure, using the same set of explanatoryvariables.

Section 2 surveys the existing literature on the distribution of exposure to airpollution which, like the federal air pollution policy, has largely neglected air toxicsand focused instead on exposure to the six conventional pollutants identified in the1970 Clean Air Act Amendments. Section 3 describes the construction of an indexof exposure to air toxics and lists the zip codes that rank highest with respect tothis measure on the basis of 1990 emissions data. Descriptive statistics showing thedisparities in exposure faced by different subgroups of the U.S. population are alsoprovided. Section 4 describes the sources of the socioeconomic and political

DISTRIBUTION OF POLLUTION 235

variables that are used in Sections 5 and 6 to explain the existing pattern ofexposure and its changes over the period 1988]1992. Section 7 concludes with adiscussion of the policy implications of our findings.

2. THE DISTRIBUTION OF EXPOSURE TO CRITERIA POLLUTANTS

Federal involvement in the control of air pollution began in earnest with theClean Air Act Amendments of 1970, prior to which legislation had focused mainlyon the funding of state research and training initiatives. The 1970 Act identifiedtwo distinct classes of pollutants: the six conventional or criteria pollutants and theless common but generally more hazardous group of pollutants known as air toxics.The former class of pollutants are now regulated extensively on the basis of

Ž .uniform National Ambient Air Quality Standards NAAQS which provide preciselimits on the average concentration of each of the criteria pollutants that canprevail both annually and over specified short-term periods ranging from 1 to 24

w xhours 17 . In sharp contrast, hundreds of less common but significantly more toxicchemicals have remained essentially unregulated to this day.1 The neglect of airtoxics by regulators has been mirrored in academic research: studies of thedistribution of exposure to air pollution have also tended to focus on exposure tothe criteria pollutants. Furthermore, the studies have for the most part beenregional in scope, with the primary concern being the relationship between incomeand exposure.

w xIn one of the earliest such studies, using 1960 census data, Freeman 8 foundthat in Kansas City, Washington DC, and St. Louis, persons with low incomes andnonwhites faced the highest exposure to both sulfur dioxide and particulates.

w xZupan 23 reported a significant positive correlation between the percentage oflow income households and ambient concentrations of sulfur dioxide and particu-lates in New York City zip code areas and obtained similar results using data on

Ž .emissions rather than ambient concentrations of carbon monoxide, sulfur oxides,w xand particulates for the New York Metropolitan area as a whole. Kruvant 14

investigated exposure to nonpoint source emissions of carbon monoxide andhydrocarbons in the Washington DC area, finding that the most highly pollutedzones were characterized by low incomes, low rents, low proportions of profes-sional and managerial workers, and high proportions of black residents. In a study

w xof 284 cities across 23 states, Asch and Seneca 1 found that exposure toparticulates was highest in larger, more densely populated cities with lowerincomes, lower educational levels, and higher proportions of nonwhite residents.The same pattern was found to characterize intracity variations in Chicago,

w xCleveland, and Nashville. More recently, Brajer and Hall 4 have reported higherexposure to ozone and particulates among black, Hispanic, and low incomeresidents of the South Coast Air Basin of California.2

1 A significant step to alter this was taken with the passage of the 1990 amendments to the Clean AirAct, which now explicitly lists 189 hazardous air pollutants for which the EPA is directed to setstandards over the current decade.

2 In contrast with the paucity of recent work examining the distribution of air pollution, racialdifferences in exposure to hazardous waste treatment, storage, and disposal facilities have been

Ž w x .examined in numerous recent studies see 5 and the references cited therein.

BROOKS AND SETHI236

If pollution is distributed regressively, one might expect uniform national stan-dards such as the NAAQS or mandated automobile performance standards to havea progressive effect, under the assumption that ‘‘any program to improve air quality

Ž w xwill have its greatest impact in those areas with the lower air quality’’ Freeman 8 ,. w xp. 268 . Harrison and Rubinfeld 12 confirm that for the Boston metropolitan area,

the physical benefits of abatement accrue more to the poor than to the rich,although differences in the valuation of environmental amenities across income

w xgroups diminish this effect considerably. Gianessi et al. 7 , taking account of bothcosts and benefits in dollar terms, argue that the net benefits of uniform standards‘‘seem to imply a redistribution of welfare toward a minority who are largely

Ž . w xnonwhite residents of urban areas’’ p. 299 . Asch and Seneca 1 also conclude thatchanges in exposure to particulates over the period 1972]1974 were distributed

w xprogressively. In Zupan’s study of the New York area 23 , percentage reductions inexposure to sulfur dioxide were found to be fairly uniform across income classes sothat the lowest income group, which was previously most exposed, obtained thehighest absolute improvement in air quality even prior to the implementation of

w xuniform standards. Gelobter 9 also finds that nonwhites have experienced arelatively greater reduction in exposure than whites since 1970 but, in contrast withthe other studies, finds that lower income groups have experienced a lower relativedecrease in exposure. We shall return to the issue of the progressiveness ofabatement policies in the context of air toxics in Section 6 below.

All of the above studies refer to some subset of the six criteria pollutants.Additionally, they look only at the simple correlations between air emissions andsocioeconomic characteristics. The present work is an attempt to broaden theempirical analysis of the distribution of exposure to air pollution by considering150 of the most significant air toxics, using data for the entire continental UnitedStates, and taking account of multiple explanatory factors including income, race,educational attainment, housing tenure, and the propensity of communities toengage in collective action. Furthermore, we identify determinants of both thecross-sectional distribution of exposure to air toxics in selected years, as well as thedeterminants of changes in exposure over time.

3. CONSTRUCTING AN INDEX OF EXPOSURE TO TOXICS

The first step in our analysis requires the construction of an index of exposure tohazardous emissions which is sensitive both to differences in toxicity as well as tothe distance of the exposed person from the source. In order to adjust for toxicity,

w xwe follow Horvath et al. 13 in using the weights provided in the AmericanŽ .Conference of Governmental Industrial Hygienists’ ACGIH publication ‘‘1994]

1995 Threshold Limit Values for Chemical Substances and Physical Agents.’’ AŽ . 3Threshold Limit Value TLV is the amount of airborne concentration in mgrm

of a substance to which a worker may be repeatedly exposed for a normal 8-hourworkday and a 40-hour workweek without adverse health effects. For example,sulfuric acid has a TLV of one, while toxic beryllium has a TLV of 0.002 andrelatively benign dichlorotetraflouroethane has a TLV of 6990 mgrm3. TLVs areavailable from ACGIH for 150 of the 286 chemicals for which at least one poundof stack or point air emissions was reported in the EPA’s ‘‘1992 Toxic Releases

Ž .Inventory’’ TRI .

DISTRIBUTION OF POLLUTION 237

Threshold Limit Values have been used by the Occupational Safety and HealthAdministration to set workplace standards in the United States, as well as byregulatory agencies in several other countries, on the presumption that they doindeed provide a measure of the hazards associated with exposure to the relevantsubstances. It has recently been argued, however, that the ‘‘thresholds’’ have beenset at levels that are far too high to ensure the levels of safety claimed by the

w xACGIH 18 . For the purposes of this paper, it is immaterial whether or not thethresholds represent an adequate margin of safety, provided that they provide areasonably good approximation to relati e levels of toxicity across chemicals. Noneof the results presented here are affected if the TLV values are uniformly deflated

w xby ‘‘1r4 to 1r10,’’ as suggested by Roach and Rappaport 18 , or by any constantfraction, however small. It is important therefore to consider the question ofwhether the bias in the TLV data is fairly uniform across substances or whetherthere may be systematic differences in the degree to which the hazards are

3 w xunderstated. Pease 16 observes that the technological feasibility and the costs ofexposure reduction are important determinants of TLV’s, and widespread corpo-rate influence in the setting of TLV’s is documented in detail by Casteleman and

w xZiem 6 . These considerations suggest that the bias in TLV’s will be greatest forhighly toxic chemicals due to the fact that more accurate estimates of their dangers

Ž .would require sharper and therefore costlier and less feasible reductions thanwould be required in the case of less hazardous substances. There is little we cando to correct for this possible bias, other than to keep it in mind when interpretingour results.

The manner in which an index of emissions for each zip code was obtained is asfollows. Let E j denote the emission of substance j from zip code i, and let T j

idenote the threshold limit value associated with substance j. Then the toxicity-weighted aggrezate emission in zip code i is defined as

E ji

E s .Ýi jTj

Since sulfuric acid has a TLV of 1, the units of measurement of toxicity-weightedemissions are ‘‘pounds of sulfuric acid equivalent.’’ For every zip code in our dataset, the weighted sum of the 150 chemicals for which the TLV is known wascalculated. Although only roughly half of the chemical substances reported in theTRI are in our weighted sample, the most widely emitted chemicals are included.In fact, the 150 chemical substances for which we have data comprise 81% of the1,295,606,607 pounds of stack or point air emissions reported to the TRI in 1992.

The above procedure provides us with a measure of emissions for each zip code,but it does not provide us with a measure of exposure. Given that zip codes varygreatly in size and air emissions do not honor zip code boundaries, a reasonablemeasure of air pollution exposure for residents of a particular zip code would be adistance-weighted sum of all air emissions within some distance s of that zip code’scentroid. If d represents the distance between the respective centroids of zipi, k

codes i and k, and a linear weight is applied to distances, then our index of

3 We are grateful to an anonymous referee for pointing out the importance of this issue.

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exposure at zip code i is given by

s y di , kX s E ,Ýi k ž /sŽ .kgN i

Ž .where N i is the set of zip codes whose centroids lie within a distance s of theŽ .centroid of zip code i. Formally, the set N i consists of all zip codes k such that

Ž .d - s. The radius s defines the number of ‘‘neighboring’’ zip codes N i whosei, k

emissions are considered to be relevant to the air quality in zip code i. Largervalues of s will give rise to higher values of exposure for all zip codes i, both

Ž .because more areas will now be included in the set of neighbors N i , and becausethe emissions of each neighbor will receive greater weight. Different values for stherefore lead to different absolute levels of exposure but, within a plausible range,were found to leave relative exposure levels largely unaffected. The results pre-sented below are based on a value of s s 30 kilometers.

The units of measurement of exposure, X, defined above can be referred to as‘‘weighted pounds of sulfuric acid equivalent.’’ It is important to emphasize thatthe emissions of air toxics in the vicinity of a zip code is only one of the numerousfactors that determine the ambient concentrations of the pollutants, and hence theactual damage that such emissions imply for the population that resides there.Meteorological conditions, tree cover, altitude, proximity to surface water, andvarious other physical features of the area play a role in determining ambientconcentrations in affected areas. These other factors, however, can reasonably beassumed to be uncorrelated with the socioeconomic and demographic explanatoryvariables that are used in the econometric analysis below. Differences across areasin the racial composition of communities or in per-capita incomes, for example, areunlikely to be correlated with differences in weather conditions or other physicalfactors that are important in determining the ecological relationship betweenemissions and ambient concentrations in those areas. Consequently, we can assume

Ž .that our index is an unbiased though noisy indicator of the health effectsresulting from exposure to air toxics faced by various communities.

Table I lists the 40 zip codes with the highest levels of exposure, measured in thismanner. The figures confirm a number of case studies and reports in the popularpress regarding conditions in certain areas of the country. The three zip codes withthe highest exposure are all in the vicinity of Tooele, Utah, well known as the

w x‘‘most polluted place in the country’’ 22 . Having lived in the proximity ofrestricted-access military zones where the storage and testing of chemical weaponshas been conducted for half a century, the residents of Tooele attracted nationalattention recently for their policy of aggressively soliciting hazardous waste inciner-ators, landfills, and other heavy polluters in exchange for the generation of localtaxes and jobs. One Tooele polluter, the Magnesium Corporation of America, was

w xsingle-handedly responsible for 87% of the nation’s chlorine emissions in 1992 21 .Many of the other areas which appear in the table are also unsurprising. The 4adjacent counties of Ascension, Assumption, St. James, and Iberville in Louisianaall lie in the industrial corridor along the lower Mississippi river with the highest

w xconcentration of petrochemical companies in the country 5 . Other zip codes

DISTRIBUTION OF POLLUTION 239

TABLE IZip Codes with the Highest Levels of Exposure, 1990

ZIP Exposure County ZIP Exposure County

1. 84074 58954952 Tooele, UT 21. 70341 1605833 Assumption, LA2. 84006 19507868 Salt Lake, UT 22. 59635 1568863 Lewis and Clark, MT3. 84029 17352346 Tooele, UT 23. 36505 1527043 Mobile, AL4. 63048 3299578 Jefferson, MO 24. 61910 1512544 Douglas, IL5. 84044 3296631 Salt Lake, UT 25. 62256 1481618 Monroe, IL6. 63019 2933659 Jefferson, MO 26. 36460 1451603 Monroe, IL7. 03570 2801958 Coos, NH 27. 61956 1447901 Douglas, IL8. 63070 2789288 Jefferson, MO 28. 61919 1436453 Douglas, IL9. 61953 2591467 Douglas, IL 29. 61913 1403855 Douglas, IL

10. 62295 2434872 Monroe, IL 30. 70792 1399071 St. James, LA11. 63012 2311566 Jefferson, MO 31. 70778 1394798 Ascension, LA12. 70346 1990893 Ascension, LA 32. 70721 1337199 Iberville, LA13. 63028 1941588 Jefferson, MO 33. 04694 1294510 Washington, ME14. 70725 1925730 Ascension, LA 34. 70763 1291197 St. James, LA15. 72723 1751632 St. James, LA 35. 79922 1273654 El Paso, TX16. 82501 1750947 Fremont, WY 36. 63010 1233077 Jefferson, MO17. 70086 1739687 St. James, LA 37. 61930 1224763 Douglas, IL18. 63052 1623971 Jefferson, MO 38. 29536 1203699 Dillon, SC19. 61863 1622542 Champaign, IL 39. 70737 1174336 Ascension, LA20. 70734 1611515 Ascension, LA 40. 03582 1170615 Coos, NH

appearing in the table can be traced to specific facilities. The Doe Run Company’sŽ .Herculaneum Smelter, located in Herculaneum, Missouri zip code 63048 , the

Ž .Cabot Corporation in Tuscola, Illinois zip code 61953 , the Georgia-Pacific Corpo-Ž .ration in Woodland, Maine zip code 04694 , the James River Corporation’s millsŽ .in Berlin, New Hampshire zip code 03570 , and Asarco Inc. in East Helena,

Ž .Montana zip code 59635 have all been identified as the most significant pollutersw xin their respective states 21 . When multiple sources are located in close proximity

to each other, or when facilities are located in relatively densely populated areascomprising numerous zip codes, ‘‘clusters’’ of zip codes with high exposure will tendto appear in the data. Hence the table lists eight distinct zip codes in JeffersonCounty, Missouri and 12 in the 4 adjacent Louisiana counties.

While the zip codes with the highest indexes of exposure can be traced in mostcases to specific facilities identified by the EPA, it is important to emphasize thatthe converse is not true. Many of the facilities identified in the TRI as being themost significant polluters are located in areas which are not among the mostexposed in terms of our index. The main reason for the discrepancy is the processof unweighted aggregation used by the Agency, which makes no distinctionbetween relatively benign and highly toxic releases in arriving at its list of worstpolluters. This illustrates the need for a more refined index such as the one used inthe present analysis.

Since our index allocates to each zip code a level of exposure to air toxics, it canbe combined with census data to provide a measure of exposure for each individualin the United States, making the approximation that each resident of a zip code isexposed to the same level of air quality. It is then possible to find the mean level ofexposure in the nation as a whole and to track movements over time in this

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magnitude. Figure 1 depicts changes in mean exposure over the period 1988]1992.4

Although there has been a general decline over this period, there was a slightincrease in exposure from 1990 to 1991. The EPA on the other hand, reported

Ž .declines in total emissions by weight over the entire period. What accounts forthe discrepancy? Two distinct factors could be responsible. First, there may havebeen a decline of emissions overall but a rise in more densely populated areas,giving rise to an increase in mean exposure. Alternatively, there may have been adecline in emissions by weight but an increase in the relative share of the mosttoxic pollutants. Either of these scenarios could lead to greater exposure with feweremissions, and illustrates the importance of moving beyond the EPA aggregates.

Since census data provides information on race, ethnicity, poverty status, andeducational attainment at the zip code level, it is a straightforward matter toexamine how different subgroups in the U.S. population fare with regard toexposure. Instead of computing the population weighted mean across zip codes, wecan compute subpopulation weighted means. Table II provides the mean exposurelevels for various subgroups in the U.S. population, based on race, poverty status,income, educational attainment, and housing status, measured in thousands ofdistance-weighted pounds of sulfuric acid equivalent.

The descriptive statistics in Table II may be summarized as follows. The twolargest groups in the population, whites and blacks, experienced a consistentdisparity in exposure over the entire period. The rural location of much of theNative American population accounts for the low exposure of this subgroup since,as shall be argued below, a disproportionate share of total emissions occurs in theproximity of urban areas of the country. Persons below the poverty line wereconsistently more highly exposed relative to those above the poverty line over theentire period, though the level of exposure has fallen steadily for both groups.Individuals with no high school degree have faced somewhat higher average levelsof exposure relative to high school and college graduates. Individuals in renteroccupied housing experienced higher levels of exposure relative to those who owntheir homes, though the gap appears to be closing. In order to explore therelationship between exposure and income, mean levels of exposure were calcu-lated for seven different income categories.5 For the two highest income groups,exposure declined in every year. The remaining categories experienced declines inexposure in every year except 1991, in which there was a slight increase. It is likelythat the U-shaped relationship between income and exposure is due to the factthat extreme levels of poverty and wealth can be found side by side in large,densely populated cities with poor air quality, while the residents of suburbs withlower exposure tend to be persons with intermediate levels of income. Support forthis hypothesis is provided by the fact, reported below, that urban zip codes areindeed more highly exposed to air toxics according to the measure adopted here.

The above descriptive statistics reveal some systematic patterns in differentialexposure across subpopulations. Individuals belonging to racial and ethnic minori-ties, those living in rented accommodation, those having fewer years of schooling,

4 We do not report results based on TRI data for 1987, the first year for which establishments wererequired to report their releases. These data are regarded by the EPA and others as being largelyunreliable on account of the unfamiliarity of firms with the newly instituted reporting requirements.

5 w xTo facilitate comparison with the work of Asch and Seneca 1 , the seven categories used by themwere adjusted for inflation to obtain our categories.

DISTRIBUTION OF POLLUTION 241

FIG. 1. Mean exposure to air toxics, all persons.

and those living below the poverty line are liable to be most highly exposed. Thereare a number of competing hypotheses that could be advanced as explanations forsuch findings. It could be that specific groups are targeted when firms emittinghazardous waste make decisions to locate, for instance because of the perceptionthat certain types of communities will be less willing and able to engage in costly

TABLE IIŽ .Mean Exposure for Different Subgroups, 1988]1992 1000s of Pounds

1988 1989 1990 1991 1992

All persons 104.59 97.09 79.45 83.55 65.23Below poverty 121.16 114.45 90.77 94.37 74.22Above poverty 102.83 95.08 78.27 82.06 64.22White 93.06 87.01 70.60 80.77 59.23Black 154.31 137.92 119.21 107.73 101.84Asian 126.71 118.23 96.93 66.19 60.74Native American 64.89 57.80 51.98 43.99 36.85No high school 106.89 98.34 81.12 87.58 66.63High school 99.29 92.35 75.51 78.28 62.11College 95.47 89.53 73.38 77.65 60.32Renter occupied 17.75 109.42 90.12 89.58 70.62Owner occupied 94.49 86.81 71.49 81.02 61.10

Ž .Income F 9999 109.82 101.02 82.60 94.71 68.62Ž .Income 10,000]14,999 102.45 94.74 76.80 88.38 63.74Ž .Income 15,000]22,499 101.28 93.23 76.36 86.39 63.13Ž .Income 22,500]32,499 102.03 94.02 77.49 84.03 63.86Ž .Income 32,500]47,499 103.30 94.99 78.86 83.04 65.13Ž .Income 47,500]74,999 101.89 94.16 78.16 78.76 64.66Ž .Income G 75,000 95.31 89.49 73.85 69.94 59.49

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collective action against the firms. Alternatively, it could be the case that firmsignore the demographic and socioeconomic characteristics of the communities inwhich they locate, but that the desire for proximity to markets or cheap land drivesthem to sites in which certain populations are overrepresented. A third explanationis that the siting decisions of firms trigger a change in property values and aresulting pattern of migration which causes community demographics to change

w xover time in systematic ways 3 . It is possible to distinguish among some of thesecompeting hypothesis econometrically, and we attempt to do so in Section 5 below.

With regard to changes over time, it appears that all groups have benefited since1988. In absolute terms it is broadly true that those groups with the worst levels ofexposure to begin with have benefited the most. This includes racial and ethnicminorities as well as those at extreme ends of the income distribution spectrum.There are a number of factors which could account for this, since the collectionand release of the information contained in the TRI triggers at least three distinctkind of reactions. Anticipating the negative publicity associated with high reportedreleases, firms have an incentive to abate even prior to the release of theinformation to the public. Firms with the highest levels of emissions clearly havethe most to fear from adverse publicity; they also have the greatest scope forabatement. Hence it should not be surprising that those suffering the greatestlevels of exposure are liable to benefit most from a uniform contraction in toxicemissions nationwide. However, there are reasons to expect that abatement in-duced by the threat of community opposition would not be uniform. After therelease of the information, public pressure may be brought to bear on selectedfirms either through local organizations or through the behavior of consumers in

Ž .regional or national markets. To the extent that the threat of local collectiveaction differs from one community to the next, depending on community resourcesand characteristics, the incentive to abate would be nonuniform across firms. Withfirms in some communities under greater pressure to abate than those in others,benefits may be expected to differ across subpopulations. This observation mayhelp account for the fact that the exposure of those in the highest income categoryrelative to those in the lowest declined sharply over the period 1988]1992. Theseissues are explored further in Section 6.

4. DATA SOURCES

As described above, the main data sources for the dependent variable are theEPA’s ‘‘Toxic Releases Inventory’’ for emissions data and the ACGIH’s ‘‘1994]1995Threshold Limit Values for Chemical Substances and Physical Agents’’ for data onrelative toxicity. In order to construct the index of exposure described above, dataon zip code centroids and areas was also required. For the zip code centroids, thesource was the Geographic Data Technology, Inc.’s 1993 5-digit Zipcode CentroidDataset. For the zip code areas, the data set was the Geographic Data Technology,Inc.’s U.S. Zipcode Areas.

A list of the main explanatory variables used in our econometric analysis is givenin Table III. The source of the first 10 variables listed in the table is the 1990 U.S.Census of Population and Housing. The population density of the zip codes wasconstructed using the census data on population along with the zip code areas usedin the construction of the dependent variable. The one variable that is not from the

DISTRIBUTION OF POLLUTION 243

TABLE IIIVariable Names and Definitions

Name Variable description

Urban Proportion of persons who live inside urbanized areaBlack Proportion of persons who are blackPoverty Proportion of persons with incomes below poverty levelSchool Proportion of persons 25 and over who are high school graduates onlyCollege Proportion of persons 25 and over who are college graduatesManfac Proportion of persons in labor force who are employed in manufacturingRentoc Proportion of housing units which are renter occupied

Ž .Medval Median value of specified owner occupied housing in $10,000sŽ .Medinc Median household income in $10,000s

Popden Population divided by zip code areaVoters Proportion of persons 18 and over voting in 1992 presidential election

census is the proportion of the voting age population that voted in the lastpresidential election. This is used as a proxy for the propensity of the community toparticipate in collective action. The voting variable is a much better proxy forcollective action participation than an actual measure of community involvementin, for instance, environmental organizing since it is more likely to be exogenous.The level of community participation in environmental issues would, in general, beendogenous. For instance, the location of a firm emitting hazardous air pollutantsmay trigger a vigorous response from the host community. Supposing the responseto be most vigorous, on average, in those communities which have the greatestexposure, we would find a positive correlation between community activism andexposure. If the latter is used as an explanatory variable in a study of thedeterminants of exposure, it would appear, incorrectly, that firms locate in commu-nities in which the propensity for collective action is greatest. Since voter turnoutin presidential elections is not likely to be significantly influenced by changes inlocal exposure, such a variable is a better proxy for the propensity of a communityto engage in collective action. The source for the voting data was the Departmentof Commerce’s statistical abstract supplement ‘‘U.S.A. Counties, 1994.’’ A problemwith the voting data is that it is only available at the county level. To use the votingdata at the zip code level, each zip code was assigned the voting data for the countyin which it was contained. Unfortunately, there are many zip codes that overlaptwo or more counties. In this case, the U.S. Postal Directory was used to find thename of the municipality where the zip code post office was located. The countycode for that municipality was then assigned to the zip code. Using the county leveldata in our zip code level analysis will introduce measurement error which will biasthe coefficients on the voting variables toward zero. Despite this bias, we reportsignificant effects of this variable below.

5. DETERMINANTS OF EXPOSURE

The objective in this section is to investigate the impact of various socioeconomicand political determinants on the level of exposure to toxic point-source airemissions. The distance-weighted sum of sulfuric acid equivalent pounds of emis-sions was regressed on the set of explanatory variables using OLS, in accordance

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with the log-linear specification

ln 1 q E s b q b x q ??? qb x q « ,Ž .j 1 2 2 j n n j j

where the subscript j refers to the zip code, « is the error term associated with zipjcode j, and the x are observations of the explanatory variables listed in Table IIIiabove. The use of a log-linear model is preferred to that of a linear model in thepresent context because it is less sensitive to the presence of huge outliers such asTooele, Utah, with 59 million pounds of distance-weighted sulfuric acid equivalent

Ž . Ž .emissions see Table I . The use of ln 1 q E rather than ln E as the dependentvariable is to allow inclusion of zip codes for which the distance-weighted sum ofexposure is zero. In this case the dependent variable takes the value 1. For highvalues of E the transformation has negligible effect.

One advantage of the log-linear specification is that the coefficients of theregression can be interpreted in a meaningful way in terms of growth in thedependent variable. Most of the explanatory variables refer to the proportion of

Ž .some subpopulation such as voters, college graduates, or urban residents in thezip code population; these variables take values between 0 and 1. An increase of1% in subpopulation i corresponds to an increase of 0.01 in the correspondingexplanatory variable x , and hence an increase in the dependent variable of 0.01b .i iIn percentage terms, this amounts to an increase by b %. Hence the coefficient ofia variable which represents a population proportion can be interpreted as theŽ .approximate growth in the dependent variable associated with a 1% increase inthe presence of that particular subpopulation. Since the dependent variable used in

Ž .our regressions is ln 1 q E rather than ln E, this interpretation is valid only forzip codes which have large values of E to begin with. For these areas, any givengrowth in 1 q E is virtually identical to the corresponding growth in E. For zipcodes with zero initial exposure, the interpretation in terms of growth in exposureis clearly invalid.

Data from the Toxic Releases Inventory on point-source air emissions is avail-able for the years 1988]1992 so it is possible to run a cross-sectional regression foreach of these years. The main reason for running the cross-section regression ineach year is to check for structural changes in the relationships between thevariables. Note that the values of the explanatory variables do not change from oneyear to the next, being set at their 1990 values for the entire period. This isreasonable with regard to the socioeconomic and demographic variables whichchange gradually over time in response to residential dynamics. It is very unlikelythat the 1990 values of these variables were affected in any significant way by theemissions of previous years, particularly since the TRI data is made public onlyafter a two-year lag. With regard to the median value of housing, however,interpretation of the coefficients over the period 1988]1990 requires caution. The1990 median housing value is certain to have been affected by the release of TRIdata in 1989, particularly in those zip codes which received a great deal of publicitydue to a high degree of exposure. Hence this variable is endogenous for the period1988]1990, and will yield inconsistent estimates of the coefficients for theseperiods.

The results of the OLS regressions are reported in Table IV. The regressionresults are robust in that there is very little change in the size of most coefficientsor in their significance across years. With the exception of the coefficient for the

DISTRIBUTION OF POLLUTION 245

TABLE IVOLS Results

Variable 1988 1989 1990 1991 1992

1.01* 1.04* 0.79* 0.92* 0.92*Intercept Ž . Ž . Ž . Ž . Ž .4.18 4.46 3.36 3.90 3.92

4.11* 4.05* 4.01* 4.02* 4.04*Urban Ž . Ž . Ž . Ž . Ž .67.16 68.07 68.01 67.91 68.16

2.82* 2.91* 2.78* 2.78* 2.73*Black Ž . Ž . Ž . Ž . Ž .20.92 22.22 21.37 21.33 20.94

13.84* 13.78* 13.40* 13.65* 13.34*Manfac Ž . Ž . Ž . Ž . Ž .70.81 72.63 71.21 72.21 70.59

y3.76* y3.69* y3.29* y3.55* y3.37*Voters Ž . Ž . Ž . Ž . Ž .y17.02 y17.19 y15.47 y16.62 y15.74

25.06* 24.56* 23.96* 23.82* 23.08*Medinc Ž . Ž . Ž . Ž . Ž .34.55 34.89 34.32 33.96 32.90

y17.36* y16.77* y16.28* y16.26* y15.49*2Medinc Ž . Ž . Ž . Ž . Ž .y26.23 y26.11 y25.57 y25.41 y24.20y3.18* y3.42* y2.99* y3.69* y3.43*

Poverty Ž . Ž . Ž . Ž . Ž .y5.48 y6.08 y5.37 y6.58 y6.135.35* 5.36* 4.20* 5.30* 4.68*2Poverty Ž . Ž . Ž . Ž . Ž .6.20 6.40 5.06 6.35 5.61

y0.21* y0.20* y0.27* y0.42* y0.44*Medval Ž . Ž . Ž . Ž . Ž .y4.50 y4.35 y5.84 y9.21 y9.68

y4.58* y4.38* y3.82* y3.67* y3.47*School Ž . Ž . Ž . Ž . Ž .y16.81 y16.59 y14.60 y13.95 y13.20

y1.02* y1.16* y1.17* y0.90* y0.96*College Ž . Ž . Ž . Ž . Ž .y4.03 y4.73 y4.78 y3.66 y3.92

2.39* 2.41* 2.40* 2.28* 2.17*Rentoc Ž . Ž . Ž . Ž . Ž .15.19 15.77 15.83 14.99 14.22

0.007* 0.007* 0.008* 0.008* 0.008*Popden Ž . Ž . Ž . Ž . Ž .2.96 3.12 3.71 3.60 3.56

Observations 28575 28575 28575 28575 28575

2R 0.49 0.50 0.49 0.49 0.48

Note. * denotes significance at 1%. t statistics are in parentheses.

median value of housing, almost all coefficients in all years are statisticallysignificant at the 1% level. Given this consistency, the 1990 regression will be usedto discuss the results. Despite controlling for a wide variety of variables, there is asignificant and positive relationship between the proportion of blacks in thecommunity and the level of exposure. A 1% greater proportion of this subpopula-tion in a zip code area is, on average, correlated with a value of exposure that is2.8% larger. The collective action proxy variable is negative and significant despitethe measurement error issue discussed in the previous section. A 1% greater voterturnout corresponds, on average, with a value of exposure that is 3% lower. Urbanzip codes are, other things equal, more highly exposed, as are zip codes in which ahigher proportion of employed residents work in manufacturing. Education issignificantly and negatively related to exposure. A 1% greater proportion ofŽ .noncollege high school graduates corresponds to about 4% lower exposure, whilea similarly greater proportion of college graduates corresponds to about 1% lowerexposure. The higher the percentage of renter-occupied housing in a zip code area,

BROOKS AND SETHI246

the higher is the level of exposure. The effect on the dependent variable of theexplanatory variables ‘‘poverty’’ and ‘‘medinc’’ was found to fit a quadratic specifi-cation far better than a linear one. For the first of these variables, the relationship

Žis U-shaped the coefficient of the variable is negative while the coefficient of its.square is positive , while the effect of medinc is inverse U-shaped. It is straightfor-

ward matter to calculate the threshold values of these variables at which theireffect on the dependent variable changes sign. The effect of the proportion of thepopulation that is below the poverty line on exposure is positive for values of this

Ž .variable exceeding 0.36 i.e., 36% and negative otherwise. In other words, once thepoverty level exceeds this threshold, a further increase corresponds to significantlyhigher levels of exposure. This threshold appears too high to be plausible as themean poverty proportion is 15% with a standard deviation of 11% and may becapturing the effect of some omitted or unobservable variable. With regard tomedian income, the effect of higher median incomes on exposure is negative forvalues of ‘‘medinc’’ in excess of $67,000 and positive otherwise. Hence, according toour results, it is only the very highest income groups for which higher incomeimplies lower exposure. This inverse U-shaped relationship is exactly the oppositeof that discussed in Section 3 above, and shows that high income groups are morehighly exposed not because of their high incomes but despite them. Once otherfactors such as urban residence are accounted for, the effect of high incomes onexposure is, as one would expect, strongly negative. Again, however, the threshold

Žappears too high to be plausible mean median income is $27,000 with a standard.deviation of $12,000 and the effect of unobserved variables cannot be ruled out.

In general, the results of the cross-section regressions confirm that there doesappear to be greater exposure to air toxics in black communities even aftercontrolling for a variety of economic and political variables. This may arise frominequitable siting practices which specifically target politically vulnerable communi-ties. Alternatively, it may arise from discrimination in the housing market whichselectively prevents members of such communities from escaping exposure throughmigration to less-exposed areas. Our cross-sectional analysis cannot distinguishbetween these two competing hypotheses, but in either case, it does appear to bethe case that race plays a direct explanatory role in the degree to which Americansare exposed to air toxics.

6. DETERMINANTS OF CHANGES OVER TIME

The analysis in Section 5 above identified communities with lower educationalattainment, high poverty levels, high minority concentrations, and higher renteroccupied housing as being more highly exposed. One might expect, then, thatabatement induced by the publication of TRI data would serve to improveconditions most in precisely these kinds of communities. This would certainly bethe case if abatement was uniform throughout the nation, and independent of localconditions. However, if local community characteristics are important in determin-ing the degree of which firms respond to the release of emissions data, then anonuniform pattern of abatement might result which does not necessarily favor thecommunities that were initially the most highly exposed. We address this issueempirically in the present section.

DISTRIBUTION OF POLLUTION 247

In order to investigate the impact of socioeconomic and political determinantson changes in the level of exposure over time, a logistic model was used. Thedependent variable took a value of 1 if there was an increase in the level ofexposure from 1990 to 1992 and a 0 otherwise. The results of the logistic regressionare reported in Table V. The explanatory variables are all as defined in Section 6above, with the exception of the variable ‘‘Chem90’’ which refers to the initialexposure level in the corresponding zip code. The coefficients in the table are themarginal effects evaluated at the explanatory variable means. This implies that thecoefficients can be directly interpreted as a change in the probability that exposurewill increase. The reason that the time period 1990 to 1992 was chosen wasprimarily because of changes in the TRI’s reporting requirements initiated in 1990,

w xwhich caused large changes in reported emissions 20 .The results of the logistic regression suggest that local conditions are an

important determinant of community exposure levels. For a ‘‘representative’’ zip

TABLE VLogistic Regression Marginal Effects

Variable Marginal effects at the mean

y0.16*Intercept Ž .y4.79

y.001*Chem90 Ž .y3.89

0.04*Urban Ž .4.72

0.18*Black Ž .9.65

0.55*Manfac Ž .19.62

y0.25*Voters Ž .y7.67

0.50*Medinc Ž .4.41

y0.35*2Medinc Ž .y3.05y0.17*

Poverty Ž .y3.75y0.09*

Medval Ž .y11.60y0.07

School Ž .y1.610.18*

College Ž .4.84y0.05

Rentoc Ž .y1.91y0.004*

Popden Ž .4.24

Observations 28575

Note. Dependent variable: change in exposure1990]1992.

*Denotes significance at 1%. t statistics are inparentheses.

BROOKS AND SETHI248

code in which all values of the dependent variables are equal to their populationmeans, a 1% greater percentage of blacks corresponds to a roughly 0.002 greater

Ž .probability that the community exposure levels will increase rather than decrease .The proxy for local collective action was also significant with a coefficient ofy0.25, implying that a 1% higher voter turnout corresponds to a decrease in theprobability of an increase in exposure by 0.0025. These results suggest that bothlocal demographics as well as local propensities to apply public pressure areimportant determinants of changes in exposure.

The relationship between zip code level median income and exposure levels ispositive but decreasing. At the mean, a $1000 increase in median income corre-sponds to a 0.004 increase in the probability that exposure will increase. As statedin the previous section, we cannot rule out omitted variable bias as an explanationfor the positive coefficient on this variable. Only when median income is roughly$143,000 will the coefficient have the predicted negative sign. Additionally, we findthat a 1% increase in poverty corresponds to a 0.0017 decrease in the probability ofan increase in exposure while an increase of 1% in the proportion of collegegraduates is associated with a 0.0018 increase in probability of an increase inexposure. This somewhat implausible finding could reflect the fact that pollution isa residual of economic activity, which in turn is correlated with higher incomes andeducational levels. Despite our attempts to control for this possibility through themanufacturing, urban, and population density variables, which are all significant inthe regression with the predicted signs on their respective coefficients, it isconceivable that the effect of economic activity has not been fully captured. A$10,000 increase in the median value of housing property corresponds to a 0.009decrease in the probability of an increase in exposure.

Despite our conclusion that local community characteristics are important inexplaining changes over time in exposure levels, we do find some evidence tosupport the hypothesis that the publication of the TRI and its subsequent mediaattention will induce the heaviest polluters to reduce their emissions. A 10,000

Žpound increase in the 1990 level of exposure the mean level of exposure was.39,968 pounds is associated with a 0.001 decrease in the probability that exposure

will increase over the subsequent two years. As discussed in Section 2 above, thiseffect will be progressive if communities with higher levels of poverty and higherminority concentrations are the most highly exposed communities to begin with.Nevertheless, this effect has to be weighed against the regressive effect that localcommunity characteristics have on changes in exposure. While national attentiondoes lead some firms to reduce exposure, this public pressure does not appear toimpact high exposure communities uniformly. In particular, communities with ahigher proportion of people of color are not as likely to experience decreases inexposure as other communities which are comparable with regard to their initialexposure levels.

7. CONCLUSIONS

The nationwide attention which the publication of the TRI elicits undoubtedlyprovides a strong inducement for emitters of air toxics to take measures to abate.The overall decline in emissions nationally over the years since reporting wasinitiated provides clear evidence of this effect. To the extent that a national

DISTRIBUTION OF POLLUTION 249

response from environmentally conscious consumers affect firms uniformly regard-less of where they happen to be located, the publication of the TRI may beexpected to lead to the greatest reductions in those communities which suffer thehighest exposure levels to begin with. This progressive impact is complicated,however, by the possibility that firms take into account not only the environmentalpreferences of their customers but also the degree of opposition they face from thecommunities in which they are located. In attempting to find the lowest-costresponse to the publication of the TRI, it appears that firms will attain the mostsubstantial degrees of abatement when local community opposition is expected tobe high. The importance of race as a determinant of abatement could arise from aperception on the part of firms that local collective action will be either lessintense or less effective when it comes from predominantly minority communities.If the attainment of greater equality across subpopulations with regard to theirexposure to hazardous pollutants remains a policy goal, more stringent measuresmay ultimately be required than the requirement that firms simply report theirreleases to the EPA. One possibility is the imposition of nationwide ambientstandards with regard to the most hazardous of the air toxics. The process ofdefining such standards is already underway following the Clean Air Act Amend-ments of 1990. Our analysis leads us to the conclusion that without such standards,the disparities in exposure faced by certain subpopulations in the United States willnot diminish appreciably in the foreseeable future.

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