provides descriptive statistics for all the variables included in our regression analyses, disaggregated by race/ethnicity. These statistics show that there are fairly large differences in residential mobility levels across the four race/ethnic groups: over one-third of both black and Asian householders moved to a different tract during the average two-year mobility interval, but just over one-quarter of Latino and white householders experienced this type of inter-neighborhood migration. Group differences in other characteristics also reinforce well-known patterns. For example, the number of years of completed schooling is lowest among Latino householders, with an average just slightly below that among black householders, and highest among Asian householders, followed by non-Latino whites. The family incomes for members of these racial and ethnic groups follow a similar ranking, although the average family income for blacks is slightly lower than for Latinos in the sample. Non-Latino black households are also more likely than other households to be headed by unmarried individuals and women. Only about 40% of non-Latino blacks own their own homes, compared to 48% of Latinos, 59% of Asians, and 71% of whites, and Latino and black households tend to contain more children and have more people per room than those of other racial and ethnic groups.
Descriptive Statistics for Variables in Models of Residential Mobility between Census Tracts by Race/Ethnicity: PSID Householders, 1990–2003.
Most importantly, however, reveals sharp group differences in proximity to industrial pollution. graphically illustrates these differences, which provide a baseline description of racial and ethnic disparities in residential proximity to industrial pollution. Specifically, the figure shows the average proximate pollution level in and around tracts occupied by all members of each of the racial/ethnic groups represented in our data at both the beginning and the end of the average observation period (times t and t+2). Here it is important to reiterate that these hazard data are based on distance- and emissions-weighted estimates of TRI facility activity within 1.5 miles of census tracts occupied by individual householders distributed across over 300 metropolitan areas.
Observed Proximate Industrial Pollution by Race/Ethnicity
Consistent with the results of at least some past aggregate-level studies, the descriptive statistics in point to pronounced racial and ethnic differences in residential proximity to industrial pollution. Specifically, at the end of the average observation period, the average level of proximate industrial pollution in and around tracts occupied by Latino respondents (84,013) was almost twice the level experienced by non-Latino whites (43,556), while the level for non-Latino black respondents (106,947) was almost 2.5 times the level experienced by non-Latino whites. Not surprisingly, these differences are statistically significant. In contrast, Asian householders experienced, on average, less proximate industrial pollution in their tract of origin (25,180) than did non-Latino whites, a difference that is also statistically significant.
Also noteworthy is the fact that the four groups differed in terms of their short term hazard trajectories. For example, white, Asian, and Latino householders experienced, on average, slightly lower levels of proximate industrial pollution at the end of the mobility interval (time t+2) than at the beginning (time t), although only the change for Latinos was statistically significant. In contrast, the average level of industrial pollution experienced by black householders actually increased significantly from the beginning to the end of the average observation period.
These observed group differences in pollution proximity confirm the existence of high levels of environmental racial inequality at the individual level. However, these descriptive statistics provide little information about the source of this inequality. Most importantly, it is unclear whether these group differences in residential context reflect group differences in socioeconomic resources and other characteristics that affect residential attainment. Moreover, while group differences in hazard proximity and hazard trajectory likely reflect, at least in part, the effect of group-differentiated mobility patterns, the precise nature of these mobility differences is currently unknown. For example, group differences in hazard proximity, and changes in hazard proximity over time, could reflect group differences in the likelihood of leaving hazardous neighborhoods or group differences in the hazard levels in and around the neighborhoods to which movers relocate. In addition, racial and ethnic differences in mobility patterns are likely themselves influenced by group differences in sociodemographic resources and other factors that shape migration behaviors more generally.
In order to test competing theoretical explanations for these group differences in residential proximity to industrial hazards, presents a series of models that regress proximate industrial pollution at the end of the observation interval (time t+2) on a set of individual- and household-level indicators measured at the beginning of the observation interval. Model 1 examines overall group differences in pollution proximity while controlling for the year of observation. As noted above, this control helps to account for group differences in the distribution of person-period observations across the years of the study and possible temporal trends in levels of neighborhood pollution. The coefficient for year of observation indicates that individual-level proximity to industrial pollution is, on average, lower in later observation periods than in early periods. Net of this effect, substantial and statistically significant racial/ethnic differences in residential proximity to pollution persist. Specifically, average levels of proximate industrial pollution are about 34,000 points higher for Latino householders and almost 63,000 points higher for black householders than for whites (the reference category). Similarly, the effect of Asian race remains significant after controlling for year of observation, with the average pollution level experienced by Asian householders about 14,000 points lower than the average for white householders. Although these results point to important group differences in exposure to neighborhood pollution, it is worth noting that the contrast between Latinos and whites is somewhat sensitive to the thresholds used in the construction of the pollution measure. While pollution measures using both larger (600-foot) grid cells and alternative distances (0.5 and 2.5 miles) at which decay functions reach zero consistently point to greater exposure to pollution for Latinos than for whites, this difference is statistically significant only when using the 400-foot grid cell size and 1.5-mile threshold distance, as in the analysis described here.
Coefficients for Linear Regression of Proximate Industrial Pollution in Census Tract of Residence: PSID Householders, 1990–2003.
Model 2 provides a crucial test of the central tenet of the income-inequality/assimilation perspective by adding controls for the education of the household head and the total income of the family. Both of these variables exert significant negative effects on pollution proximity in the neighborhood of residence: proximate industrial pollution is 3,000 points lower for each additional year of education and about 82 points lower for each additional $1,000 in income. Thus, as predicted by the income-inequality/assimilation perspective, householders with greater socioeconomic resources are better able to avoid high-pollution areas.
Including householder education and family income in the regression model also results in the attenuation of the Asian race coefficient, providing further support for the income-inequality/assimilation perspective. In fact, the Asian race coefficient in Model 2 is statistically non-significant and half the size of the coefficient in Model 1. Thus, in these data, it appears that the lower level of proximate industrial pollution experienced by Asian householders, relative to that experienced by white householders, is largely attributable to Asian’s slightly greater socioeconomic resources. In sharp contrast, however, disparities in education and income explain relatively little of the higher proximate pollution levels experienced by Latino and African American householders. Controlling for these socioeconomic resources does reduce the Latino coefficient by about 30% (from 34.11 in Model 1 to 23.83 in Model 2) and the black coefficient by just under 12% (from 62.86 in Model 1 to 55.42 in Model 2). However, both of these coefficients remain sizable and statistically significant in Model 2, lending support to the central argument of the discrimination/stratification perspective. Moreover, the results in Model 3 show that controls for age, gender, and other individual- and household-level characteristics reduce these significant black and Latino disadvantages only slightly.11
In other words, comparing householders with similar education, income, and other household- and individual-level characteristics, Latino and especially African American householders face levels of proximate industrial pollution that are substantially higher than those faced by white householders.12
Model 4 of highlights group differences in the effect of socioeconomic resources on residential proximity to pollution, thereby providing a test of additional tenets of the stratification perspective. Here group differences are assessed by interacting family income with the dummy variables for race/ethnicity. The coefficients for these interaction terms provide some evidence that the negative effect of income on residential proximity to pollution is stronger for black and Latino householders than for white householders.13
Specifically, the coefficients for the interactions between income and both Latino ethnicity and black race are negative and statistically significant. The fact that the negative effect of income is especially strong among members of these minority groups is consistent with the argument presented in Logan and Alba’s (1993)
weak version of the stratification perspective, which assumes that white respondents of virtually all socioeconomic strata are able to avoid disadvantageous residential areas, but high levels of economic resources are requisite for minority householders to improve their residential lot..
This dynamic is further illustrated in which presents predicted levels of proximate industrial pollution for householders from different racial and ethnic groups at three distinct income levels. These predicted values are based on the coefficients in Model 4 of and assume mean values from the pooled sample of movers for all variables except income which is altered to represent low-income ($11,000, about the 25th percentile), middle-income ($28,000, about the 50th percentile), and high-income ($52,000, about the 75th percentile) householders.
Predicted Level of Proximate Industrial Pollution in Tract of Residence by Race/Ethnicity and Household Income
Again, consistent with the weak version of the stratification perspective, the differences in neighborhood pollution between low-, middle-, and high-income black respondents and between Latino householders of different income levels is more pronounced than the stratification across income categories for whites (and Asians). In fact, for white householders of all incomes, the level of neighborhood pollution tends to be uniformly low whereas even high-income black and Latino householders still tend to reside in neighborhoods with higher levels of proximate industrial pollution than do even low-income white householders.
Overall, these results highlight the fact that, at a given point in time, Latino and black householders face substantially higher levels of proximate industrial pollution than do whites, and that these racial/ethnic disparities cannot be explained by group differences in economic resources or other individual characteristics. However, these results provide us with no information about the group-differentiated mobility patterns through which environmental racial inequality is likely to be shaped and maintained. presents a series of logistic regression models designed to assess the possibility that racial and ethnic differences in pollution proximity arise out of group differences in the likelihood of moving away from environmentally hazardous neighborhoods. Here the dependent variable represents the log-odds that the PSID householder moved out of their census tract of origin between successive interviews. Model 1 shows the effectof the level of proximate industrial pollution in the tract of origin on out-mobility for a pooled sample of all respondents, controlling only for the year of observation. The positive logit coefficient indicates that the likelihood of moving out of the tract increases with the level of industrial pollution in the area. However, the coefficient is very small and does not approach statistical significance (p = .354).
Logistic Coefficients for Regression Analyses of Residential Mobility Out of Census Tract of Origin: PSID Householders, 1990–2003
While the overall effect of local industrial pollution on the likelihood of leaving the neighborhood appears to be weak, any racial and ethnic differences in this effect could help to produce the large group differences in proximate industrial pollution observed in the previous analyses. To investigate this possibility, the second model adds dummy variables indicating the race/ethnicity of the respondent along with a set of product terms representing the interactions between race/ethnicity and proximate industrial pollution in the tract of origin. The results point to a number of important differences in the mobility patterns of the four racial and ethnic groups. First, the coefficients for the group dummies indicate that when proximate industrial pollution is at zero, the likelihood of changing tracts is significantly higher for black and Latino householders than for whites.
More importantly, there are significant differences in the effects of local industrial pollution on this mobility. In this interactive model, the coefficient for proximate industrial pollution in the tract of origin (b=.0003) indicates that for white respondents, the odds of leaving the tract increase modestly but significantly as levels of local industrial pollution increase. In contrast, the statistically significant negative coefficient for the interaction between black race and proximate industrial pollution (b=−.0003) indicates that the effect of local industrial pollution on out-migration is weaker for black householders than for white householders. In fact, the combination of the baseline effect of proximate industrial pollution and the interaction between black race and proximate industrial pollution indicates that local hazard levels have no effect on the probability of out-mobility for black respondents [.0003+(−.0003)=0]. Overall, the fact that black householders are less likely than white householders to leave environmentally hazardous neighborhoods likely contributes modestly to their relatively high and persistent level of exposure to environmental hazards. There is also some evidence that Hispanic householders are less likely than whites to move away from high levels of pollution, although the negative coefficient for the interaction between Hispanic ethnicity and proximate industrial pollution is small and statistically non-significant (b=−.0002, p=.18). Similarly, the relatively large positive interaction term for Asian race and local pollution (b = .0041) in Model 3 indicates that the lower level of pollution experienced by Asians may be due, in part, to a greater likelihood of leaving highly polluted neighborhoods. However, given the small size of the Asian subsample, this difference is not statistically significant (p=.13).14
The remainder of the models in attempt to explain the source of these group differences in the effects of proximate industrial pollution on out-mobility. Model 3 tests the argument, drawn from the income-inequality/assimilation perspective, that these modest differences in the probability of moving away from hazardous areas are due to group differences in socioeconomic resources. This argument is tested by adding controls for family income and the education of the householder. The results indicate that education significantly increases and income significantly decreases the likelihood of inter-tract mobility. However, controlling for these resource characteristics does little to attenuate either the effect of local industrial pollution on out-mobility or racial differences in this effect. Specifically, the results are consistent with the Model 2 finding that the likelihood of inter-tract mobility among whites increases with neighborhood hazard levels, and that this effect is not significantly different for Latino or Asian householders. Most importantly, Model 3 provides evidence that the weaker effect of proximate industrial pollution on black householder out-migration is not due to a deficit in socioeconomic resources among blacks.
In order to test whether the effect of local industrial pollution on out-mobility, and group differences therein, are attributable to, or suppressed by, the effects of other mobility predictors, Model 4 adds measures of other basic respondent sociodemographic characteristics. Most of the effects of these characteristics are consistent with theory and prior research. Net of other effects, educational attainment and family income are both significantly and positively associated with the likelihood of moving out of the origin tract.15
The likelihood of moving decreases significantly with age but this decline tapers off at older ages. Married respondents are less likely than the unmarried to change tracts, and the number of children in the household is inversely associated with inter-tract migration. In addition, the likelihood of moving to a different tract increases significantly with household crowding and is significantly lower for those who own their own home and those who have been in their home for at least three years.16
Most importantly, the positive effect of proximate industrial pollution on the log-odds of out-mobility among white householders becomes statistically non-significant after controlling for these significant micro-level predictors of mobility, as does the interaction coefficient indicating the difference in this effect between black and white householders. Supplemental models (not shown) indicate that controlling for the age of the respondents is primarily responsible for these changes to the coefficients. Specifically, adding controls for age and its polynomial without controlling for the other sociodemographic characteristics drops the coefficients for proximate industrial pollution and the interaction involving black race to non-significance. This reflects the fact that while age is not significantly correlated with hazard levels in the neighborhood of origin among most groups, the correlation is actually negative (r = −.05) and statistically significant among white householders, indicating that younger white householders tend to originate in neighborhoods with somewhat higher hazard levels than those in which older white householders originate. Combined with the generally negative influence of age on residential mobility observed in our research and most other studies, this higher concentration of younger, more mobile whites in more polluted areas and older, less mobile whites in less polluted areas produces the relatively higher risk of out-mobility from polluted neighborhoods among white householders observed in the preceding models. Thus, controlling for this age effect brings the effect of pollution among whites closer to zero, more in line with the non-effect among black householders.
Overall, the results presented in provide modest support for the argument that dramatic racial and ethnic differences in proximity to industrial hazards are due to differential propensities to escape polluted neighborhoods, with the likelihood of leaving hazardous areas slightly higher among white householders than among black householders. Somewhat contrary to the income-inequality thesis, this differential cannot be explained by racial differences in income or education. Instead, the greater tendency for whites to leave highly polluted areas appears to be primarily due to the concentration of younger whites in neighborhoods that are more polluted than those in which older whites live. Thus, there is little evidence to suggest that minority householders are less responsive than whites to high levels of pollution. In fact, comparing white and minority individuals with similar characteristics reveals similar mobility responses to the level of pollution in and around the neighborhood of residence.
Of course, the gross racial differences in mobility away from neighborhood pollution have important implications for overall patterns of environmental inequality, especially in the context of profound differences in the types of neighborhoods in which members of different groups originate. The fact that Latino and black householders are much more likely than white householders to originate in highly polluted areas (see ), and also slightly less likely than white householders to escape high levels of pollution in and around their neighborhoods of origin, helps to maintain profound racial and ethnic differences in the levels of proximate industrial pollution experienced by these groups.
However, group differences in mobility away from polluted areas represent just one of the ways that mobility dynamics might shape existing patterns of environmental inequality. assesses the extent to which the influence of out-mobility on environmental inequality is complemented or contradicted by racial/ethnic differences in mobility destinations. Specifically, presents the results of a series of Heckman-corrected linear regression models designed to examine the effects of race, ethnicity, and the other explanatory variables on industrial pollution levels in and around the tracts to which mobile PSID householders relocate, adjusting for the non-random selection of respondents into the mover category.17
Here it is important to note that many of the mobility predictors included in the preceding analysis (e.g., age, gender, marital status, etc.) are included only in the selection model since they are assumed to affect the likelihood of moving, but not necessarily the choice of destinations.18
Coefficients for Regression of Proximate Industrial Pollution in Census Tract of Destination: Mobile PSID Householders, 1990–2003.
The first model in presents the gross differences in proximate industrial pollution in destination tracts among the four racial/ethnic groups (non-Latino whites define the reference category). The results indicate that conditional upon moving, Latino householders enter neighborhoods characterized by a level of proximate industrial pollution that is over 32,000 points greater than that experienced by white movers. This hazard proximity disadvantage is even more pronounced for black householders who, on average, enter neighborhoods in which the level of local industrial pollution is almost 75,000 points higher than in neighborhoods entered by white movers.19
In sharp contrast, Asian householders tend to move to tracts with slightly lower levels of proximate industrial pollution than do whites, complementing their somewhat stronger reaction to local hazard levels in making the decision to move (see ). All of these group contrasts in destination hazard levels are statistically significant and provide support for the argument that the overall level of environmental inequality revealed in is shaped substantially by differences in the types of neighborhoods to which members of different racial/ethnic groups move.20
Key to the racial income-inequality and related assimilation theses is the assumption that these racial/ethnic differences in destination outcomes are due primarily to group differences in socioeconomic resources. Accordingly, the residual effects of race/ethnicity on destination hazard levels should be largely attenuated when the resource characteristics of respondents are controlled. In contrast, the residential discrimination/stratification thesis suggests that even after controlling for socioeconomic resources, significant group differences in destinations will persist as minority-group members are blocked from accessing the best quality neighborhoods.
Model 2 of provides a test of these competing theoretical arguments by incorporating two primary measures of socioeconomic resources, the education of the householder and total taxable income of the family. Not surprisingly, the coefficients for both of these characteristics are negative, although only the net effect of income is statistically significant. Thus, higher-income movers are apparently better able than lower-income movers to gain access to less hazardous neighborhoods: after controlling for respondents’ race/ethnicity and education, and conditional on mobility, a $1,000 increment in income is associated with a reduction of just over 204 points in the dependent variable (−.2044*1000=−204.4).
Providing further support for the income-inequality/assimilation perspective, controlling for the significant effect of family income helps to attenuate some of the gross racial/ethnic differences in destination outcomes. Specifically, from Model 1 to Model 2, the positive coefficient for Hispanic ethnicity is reduced by 24% (from 32.2852 to 24.4690) and the negative coefficient for Asian race is reduced by over 40% (from −19.5604 to −11.6381), and both of these coefficients become statistically non-significant. Thus, a sizable portion of the higher level of destination pollution experienced by Hispanic movers, relative to that experienced by white movers, is explained by their relatively lower incomes, and the relatively lower level of destination pollution experienced by Asian householders in comparison to whites largely reflects their relatively higher socioeconomic standing.
However, in a finding that supports the basic assumptions of the residential-discrimination/stratification thesis, controls for socioeconomic resources do little to attenuate the black disadvantage in the level of pollution experienced in destination tracts. While this disadvantage is reduced by about 11% from Model 1 (74.8391) to Model 2 (66.3391), the coefficient for black race remains statistically significant net of the effects of education and income.21
Thus, even among those with similar socioeconomic resources, mobile black householders enter neighborhoods that are substantially more polluted than those accessed by white movers.
Model 3 adds a series of interaction terms to test for racial/ethnic differences in the benefits of income for avoiding highly polluted destinations.22
One of the interesting repercussions of adding these interaction terms is that the positive coefficient for Latino ethnicity increases and becomes statistically significant at the .10 level (p= .051) with the addition of the interaction terms, suggesting a relatively pronounced contrast in destination pollution between white and Latino movers at the bottom of the income distribution. Similarly, the negative coefficient for Asian race becomes larger and statistically significant in Model 3. Thus, among mobile householders with no income, Asian householders tend to enter neighborhoods with significantly lower levels of pollution than those entered by white householders. However, this difference dissipates at higher levels of income, as indicated by the (non-significant) positive interaction between Asian race and income.
More marked are the significant negative interactions between income and both Latino ethnicity and black race. These interactions suggest that, in contrast to the small and non-significant effect of income among white householders (b = −.0611), income is significantly more important in determining the residential destinations of black and Latino householders. Once again, these significant differences in the effects of income are consistent with the existence of discriminatory barriers in the housing market as summarized in the weak version of the discrimination/stratification perspective. Regardless of their level of income, whites are able to avoid moving into highly hazardous neighborhoods, while black and Latino householders must attain high levels of income in order to improve their destination outcomes. Even so, predicted values based on the coefficients in Model 3 confirm that even the lowest-income white movers tend to enter neighborhoods with pollution proximity levels far below those entered by the highest-income black and Latino movers. These patterns highlight the significant disadvantages faced by black and Latino householders in the effort to avoid neighborhood pollution and suggest that disparities in mobility destinations play an important role in shaping overall differences in proximity to local industrial pollution.