Do community characteristics influence environmental outcomes? Evidence fro the Toxics Release Inventory.

• Author: Arora, Seema

1. Introduction

   The traditional methods of command and control regulation have been ineffective at worst and costly at best. Recognizing the need to make regulations more flexible, in the past decade, Congress and regulators have started to favor innovative and more market-based approaches to regulation. The use or proposed use of tradable permits for controlling acid rain and more recently for mitigating global warming exemplifies this trend toward more flexible and market-oriented approaches. The use of public information is yet another innovative environmental policy tool. While economists pushed for the adoption of a tradable permits approach by appealing to its cost effectiveness, policy makers adopted public information disclosure without prodding by economists. Congress was inspired by an industrial accident in Bhopal, India, when it passed the Emergency Planning and Community Right-to-Know Act (EPCRA) in 1986. EPCRA requires all manufacturing facilities to make public their releases of over 320 toxic chemicals. The underlying premise of public disclosure as an environmental policy tool is that public knowledge of pollution can engender effective and informed participation by various constituencies to exert pressure on manufacturing facilities to improve their environmental performance.

   Public knowledge of environmental data can be used by consumers to boycott products or by investors to penalize large polluters (Hamilton 1995b; Konar and Cohen 1997). Neighborhood characteristics may also influence enforcement actions by regulators.(1) This paper analyzes the role of communities in influencing environmental outcomes. We examine the potential impact of public disclosure on the environmental performance of facilities by studying how community characteristics such as race and gender, economic status, and variables expected to capture political action influence subsequent toxic releases. A number of studies have concentrated on the relationship between race and environmental outcomes to determine the extent of environmental injustice.(2) In the present paper, we find evidence of environmental injustice and we also examine the effects of other community characteristics in influencing environmental results.

   We combine the Toxics Release Inventory data with demographic data from the 1990 U.S. Census. We use neighborhood characteristics (at the zip code level) to explain toxic releases in 1993, controlling for releases in 1990. Releases in a particular year are determined simultaneously with the demographic characteristics of a neighborhood, and they change over time for a variety of reasons, including facility relocation, expansion, and downsizing, as well as in response to community characteristics. Because the releases in 1993 are determined after the demographic characteristics were determined in 1990, it is reasonable to treat the demographic characteristics as exogenous with respect to these later releases.

   We first analyze the location of manufacturing facilities in a particular neighborhood using a sample selection model. This first stage relates the likelihood that a neighborhood experiences any toxic releases to the characteristics of that neighborhood. We then attribute the level of emissions in 1993 to the demographic and socioeconomic characteristics of the neighborhood in 1990. We conduct the analysis for the entire U.S. as well as specific geographical regions.

   The analysis captures three distinct aspects of the communities to assess the role that each plays in influencing environmental outcomes. First, we consider the racial, immigrant, and gender composition of neighborhoods. Our results indicate that a larger percentage of nonwhite residents may be associated with a higher level of releases in the southeastern states, primarily in nonurban zip codes.(3) We also examine the relationship between economic characteristics and environmental outcomes. Economic factors (such as median income and unemployment rates) have a significant impact on toxic releases, particularly in the southeastern states. Finally, we examine variables expected to be associated with the political activity and preferences of the community and its ability to collectively oppose firms that may harm the local environment. While we use voter turnout data and data on environmental initiative voting for California, for the rest of the U.S., we use demographic variables as proxies to represent a community's propensity for collective action and its political preferences. Our use of demographic variables instead of voter turnout to proxy collective action for the national sample differs from much of the existing literature. These variables appear to influence environmental outcomes mainly in nonurban areas.

2. Theoretical Framework and Hypotheses Construction

   Hamilton (1995a) presents a careful description of three alternative explanations for pollution patterns resulting from capacity expansion plans for commercial hazardous waste facilities, and we adopt his framework to motivate our empirical hypotheses. The three explanations are (i) race/gender related, (ii) the Coase theorem, and (iii) the theory of collective action (Olson 1965). In the first explanation, facility owners and operators consider the race and gender composition of neighborhoods and increase releases in neighborhoods with a greater minority (and perhaps immigrant) population or with a greater fraction of female-headed households. In its pure form, this leads to greater releases in some neighborhoods that otherwise (from a pure profit-maximizing standpoint) would not experience greater releases.

   Alternatively, in a world without transaction costs, the Coase theorem suggests that releases will increase in neighborhoods in which the releases will do the least damage. According to this hypothesis, releases will be greater in neighborhoods with lower rent. Higher incomes may also increase the costs of increased releases in a given neighborhood.(4) Rental values and income levels are correlated with education and race, so releases could increase in minority neighborhoods merely because they affect lower valued property and lower wage earners. Our analysis attempts to sort out these alternative explanations.(5)

   Finally, firms may decide to increase releases in a given neighborhood because they face less (political) collective action in that neighborhood. Residents in different neighborhoods vary in their ability to overcome free-rider problems and engage in collective action. Again, this could result in outcomes that appear similar to the race/gender-related explanation if, for example, minority or immigrant neighborhoods are less politically active. To distinguish between these explanations, we include some variables that are likely to affect incentives to engage in collective action (such as the fraction of households with children); and in a model based on California data only, we include some direct measures of political action and environmental preferences, specifically voter turnout and vote results on an environmental initiative. While we can use voting data for California, due to data limitations for other regions (discussed below), we rely on a combination of demographic variables to proxy for collective action.

   Strong correlations exist between many of our explanatory variables, which creates a classic multicollinearity problem. This problem has the potential to cause incorrect statistical inferences regarding individual coefficient estimates. This potential arises because, although individual coefficient estimates are unbiased, variance estimates are inflated due to the multicollinearity. To sidestep this problem, we focus on joint tests of significance to test the three alternative hypotheses. In particular, we employ the Wald test in a series of hypothesis tests of the form [H.sub.0]: Rb = r, where R is a matrix that creates a joint test that specific elements in the parameter vector b are all equal to zero (r is a vector of zeros). We choose three different R matrices to test each of the three explanations described above.

   To summarize, these alternative theories predict that only certain variables should explain toxic releases. The race/gender hypothesis posits the null that factors such as race, gender, and the foreign-born composition of a neighborhood do not predict releases. Rejection of the null implies that these factors are important and supports the race/gender hypothesis. The economic (Coase theorem) hypothesis postulates the null that economic factors such as income levels, rental values, vacancy rates, unemployment rates, and the proportion of poor households do not explain changing release patterns. Rejection of this null supports what we shall refer to as the economic/Coasian explanation for changing release patterns. Last, the political/collective action hypothesis posits the null that variables related to the political action propensity of local residents do not predict releases. In addition to voter turnout and expressed preferences through environmental initiative voting (for California only), we include variables such as age, education, and the number of households with children.(6) These factors can be reasonably expected to influence the incentives and tendency to engage in political action (e.g., see Filer, Kenney, and Morton 1993).(7) Rejection of this political/collective action null supports the hypothesis that such variables associated with the political activity of local residents influence environmental outcomes.

   We focus on hypothesis tests for these three sets of variables as a group and then also interpret the significant individual variable effects. We recognize that our classification of variables under the different hypotheses is not exact. For example, the proportion of foreign-born residents may be associated primarily with the race/gender hypothesis, but it may also be considered a factor that influences...