Modeling
7. CASE STUDY: MODELING ENVIRONMENTAL INEQUITIES
6. PROBLEMS THAT CAN ARISE WITH
7.1 Income effects
Perhaps the fundamental problem is poverty, especially since minority groups in the U.S. disproportionately have low incomes. Income effects can show up in several ways. First, poor people live in areas with low property values (since they cannot afford to live in areas with high property values);
environmental hazards, such as hazardous waste sites, gravitate toward areas with low property values as well, to reduce disposal costs. Secondly, poor people may be more willing to live near facilities that produce environmental risks in order to get jobs associated with those facilities. Third, if people's demand for a high-quality environment increases with income, then poor people will have less resistance than wealthier people to having environmental hazards in their midst.
7.2 Education or information effects
Poor and minority communities frequently have lower educational opportunities and attainment. They may have less access to information about the hazards in their communities, or they may not have the background to understand the risks fully. In either case, they will show less desire to avoid these risks than others with better information or education.
7.3 Political access or power
Poor and minority groups frequently have less ability to influence political processes than wealthier, majority groups. As a result, if government decision- makers influence the siting of these facilities (through zoning or tax programs), the facilities may end up in poor or minority communities because those groups cannot or will not protest as effectively.
7.4 Discrimination
Here, government decision-makers or siters of polluting facilities specifically target poor or minority communities (or specifically avoid other communities) regardless of other factors.
A large number of empirical studies have tried to determine if there is a correlation between poor or minority populations and environmental harms.
The results depend a great deal on how the problem was modeled, and what set of data was used. Finding a correlation may be satisfactory if establishing a discriminatory impact is sufficient to lead to changed policy. However, in other cases discriminatory intent – a deliberate plan to harm some groups while leaving others unaffected – may be required. If so, then correlation is
insufficient for action to result, and a model must be able to distinguish whether environmental inequities result from discrimination or from one or more of the other causes mentioned here.
As is well known in statistics, “correlation is not causality.” Given that there are multiple independent factors influencing the siting of a polluting economic activity, a multi-variable model that lends it self to multiple regression (Chapter 6) is needed.
For purposes of illustration, the variable we wish to predict and explain is the siting of stationary sources of air pollution such as powerplants or oil refineries. It is important to establish the variables that may be influencing the location of these polluting sites. Since we are concerned about environmental equity, race variables are certainly obvious variables. The race variables to be used in this analysis are Asian, Black, Hispanic Origin and Native American. As explained below, the number of polluting sites in a particular zip code area may also be explained by several socioeconomic variables as well such as education levels, income, unemployment rate, and percentage of residents that are renters.
Organizing the variables into equation form for estimation yields the following equation:
The dependent variable (Sitedensity) is the number of newly permitted stationary sources of air pollution in a given zip code divided by the square miles of the zip code area. The number of sources is measured by the number of new sites permitted for construction in years within two years of when the independent variables were collected by the Census Bureau (e.g., 1988 - 1992). This is critical because, by using the characteristics of a community at the time the decision to build a polluting site was made, information regarding the siting process is uncovered.
There are three types of independent variables. The race variables are the key variables for testing whether ethnicity of the population at the time of siting has a significant influence on the density of newly permitted sites in an area. Variables such as College and Income are included for two reasons.
First, including College and Income allows us to control for demographic influences when assessing the significance of the minority variables. If environmental racism is operating, then site density should be high in middle income minority communities as well poor minority communities. Second, inclusion of demographic variables allow us to test whether construction of new point sources of air pollution are being targeted at low income areas in
general, irrespective of the racial composition of these areas. Significance of these variables would suggest "classism" rather than "racism" may be occurring.
%Rent, or the percentage of renter occupied units, is included in the model as a proxy for two influences. First, a higher percentage of renters means a lower percentage of homeowners. Since owner-occupied housing is most individuals’ single largest investment, there is often strong homeowner opposition to polluting sites likely to drive their property values down. In contrast, renters tend to be more mobile, since they often have the ability to move on a month’s notice, or at most have a one year lease. Renters’ frequent mobility provides less attachment to a particular community. Thus a higher percentage of renters in an area would lead us to expect a higher site density.
Home value (Homevalue) would be expected to have a negative effect, as this variable serves as a proxy for land values in the area. Higher land values would make an area less profitable for industrial sites. Unemployment rate is expected to be positive because areas with high unemployment may welcome, rather than oppose, new industrial projects. If an area is receptive to new industrial projects (even if it is polluting industry) it may offer incentives for firms to locate there and would certainly not require expensive mitigation actions of new firms.
With this model we can distinguish between the race variables and other competing explanations of the high correlation of pollution sources and minority population. This equation is in essence a simple model of stationary source siting location decisions within a given metropolitan area. It does not explain other factors such as why that many plants are being built (such as the demand for electricity or oil refining).
As will be described in Chapter 6, collection of data from several sources allows an analyst to statistically estimate the magnitude of each independent explanatory variable on the dependent variable (site density). That is, with data and least squares regression we can compute specific values of the B's (slope coefficients) in equation 1. From the estimated slope coefficients we can tell: (a) whether a particular explanatory variable has a systematic influence on the dependent variable (i.e., is there a statistically significant relationship), and (b) the relative magnitude or contribution of each independent variable to the dependent variable. Thus equation 1 allows us to go beyond the level of correlation saying there is a relationship to explain what factors lead to this relationship. This simple model helps us to analyze whether policies explicitly focusing on racial characteristics in plant siting decisions are the right policy variables, or whether other policy variables such as renters would be more effective to address the concern. You will see the results of this analysis in Chapter 6.