Jun 23, 2008 - policies and environmental quality on the health of state residents. ... The earliest clean air laws in the United States were local ordinances in ...
You Get What You Pay For: Environmental Policy and Public Health Neal D. Woods University of South Carolina David M. Konisky University of Missouri Ann O’M Bowman Texas A&M University June 2008
Abstract In this paper we examine the cumulative impact of state environmental protection policies and environmental quality on the health of state residents. Using a series of path analytic models, we simultaneously analyze the effect of state environmental polices on environmental pollution and health outcomes. Our results indicate that states with stronger environmental programs have lower levels of pollution and better public health. These results are robust across multiple measures and alternative model specifications. We also find some evidence that states that assume authority to implement federal environmental programs have worse health outcomes. Our findings suggest that overall levels of public health may be affected by state choices with respect to environmental policies, and highlight the importance of assessing programmatic consequences across policy areas.
Introduction In 2008, a California plan to conduct aerial spraying of pesticides to rid the state of crop-eating moths ran afoul of environmentalists concerned about the long-term health effects. Previous spraying of the fine chemical mist in urban areas had left some residents complaining of various health problems, including irritated eyes, skin rashes, and respiratory programs (Burke 2008). It was the responsibility of the California Environmental Protection Agency to conduct tests to determine whether aerial spraying posed an unacceptable risk to human health (Kay 2008). In making the determination, California officials would be relying on both state and federal laws regulating allowable exposure rates to toxic substances. Clearly, the state’s decision about whether to allow aerial spraying of pesticides hinged on the consequences for public health. Diminished air quality has been linked to detrimental health outcomes in several community-based studies (Corburn 2007; Maantay 2007; Hansen, Barnett, and Pritchard 2007). As this example suggests, governments enact policies and allocate funds in an effort to prevent, reduce, or mitigate the impact of environmental toxins in large part to prevent its anticipated negative impacts on human health. Yet, there has been little empirical investigation into the relationship between environmental policies and health outcomes. In this paper, we explore the cumulative effect of environmental protection policies and environmental quality on public health. We develop a model explaining health outcomes as a function of environmental conditions, which themselves are, in part, a function of environmental protection policies. We look at three broad indicators of state environmental policies: the overall strength of state environmental programs, the amount states spend on
environmental protection, and the degree of responsibility states assume for implementing federal environmental programs. Our results indicate that governmental policies designed to protect the environment affect not only environmental conditions, they ultimately affect the health of their citizens. All else equal, states with stronger environmental programs and those that spend more on environmental protection have lower levels of pollution, and their citizens enjoy better overall health. These results are robust across multiple measures and model specifications. Our findings regarding state implementation of federal environmental policy are less robust, but suggest the opposite relationship: states with greater authority over federal programs evidence elevated pollution levels and worse health outcomes. The balance of the paper proceeds as follows. In the next section, we present a discussion of the relationship between environmental protection and human health. Subsequently, we consider the issue of federalism and, more specifically, the important role of state governments in environmental policy. We then turn to the data and analytic method, followed by a discussion of the results. The concluding section considers some of the implications of the findings. Linking Environmental Protection and Human Health The earliest clean air laws in the United States were local ordinances in the 1880s that treated air pollution as a public nuisance (Layzer 2006). By the mid-20th century, in the aftermath of several deadly incidents involving toxic smog, the perception of air pollution shifted from that of a nuisance to a public health problem. The initial federal government response, largely an attempt to assist states with funding for air pollution research and training, was located in the Public Health Service of the U.S. Department of
Health, Education and Welfare (HEW). Until the creation of the U.S. Environmental Protection Agency (EPA) in 1970, administrative responsibility for much of the federal anti-pollution effort was located in HEW. Thus organizationally, as well as in public perception, environmental protection and human health have long been inter-connected. A growing body of scholarly literature has reinforced the connection between environmental quality and public health. Extensive research in epidemiology has demonstrated positive associations between fine particulate air pollution and mortality (Laden et al. 2006; Pope et al. 1995; Dockery et al. 1993) and recent research has shown some similar effects of exposure to ozone (Bell et al. 2005). A study of the Bronx area of New York City demonstrated a spatial relationship between the location of land uses that contribute to poor air quality and the location of people hospitalized with asthma (Mantaay 2007). Other research has found that ambient air pollution during pregnancy hinders fetal growth (Hansen, Barnett, and Pritchard 2007). In summarizing extant literature, Kraft (2007, 26) concludes that “…air pollution exacts a heavy toll on public health, causing thousands of premature deaths each year in the United States…” He further notes that major air pollutants “…are associated with a range of health and environmental effects, from eye and throat irritation and respiratory illness to cardiovascular and nervous system damage” (Kraft 2007, 28). Much of the research on the link between pollution and health has focused on air quality. Research on water pollution, while less prevalent, reports similarly harmful health effects. The existence of a leukemia cluster in Woburn, Massachusetts in the 1980s was traced to carcinogens (by-products of past industrial processes) that had leached into the drinking water supply (Brown and Mikkelsen 1990). An outbreak of cryptosporidiosis
in Milwaukee in 1997, caused by a water-borne parasite in the city’s public water supply, killed between 50 and 100 people, and sickened several thousand more (Rosenbaum 2005). Studies of the Great Lakes have identified deleterious effects on the reproductive health of individuals who consume large amounts of fish from these polluted waterways (Fields 2005). Clearly, evidence exists linking environmental conditions to various health outcomes in myriad circumstances. All told, approximately one-quarter to one-third of the total burden of disease worldwide can be attributed to environmental hazards, both human-engendered and naturally-occurring (Smith, Corvalan, and Kjellstrom 1999). But, what about environmental policies? Do these policies, be they statutes and regulations or programmatic expenditures, also affect health outcomes? Here the evidence is more limited. Research on Canadian counties found that, after controlling for other factors, environmental expenditures were associated with lower health care costs (Jerrett, Eyles, Dufournaud, and Birch 2003). These researchers interpret their findings to mean that localities that invested in environmental protection experienced a positive return in the form of lower health care expenditures. Governments behave purposively, adopting policies and expending funds in an effort to achieve certain outcomes. Since the 1960s, much environmental protection policy activity has been explicitly justified in terms of public health. For example, the Clean Air Act Amendments of 1990 instructed the EPA that “the increased lifetime cancer risk from exposure to an air toxic must not exceed one in a million for the affected population” (Rosenbaum 2007, 127). The regulatory regime developed by the agency in response to the statutory directive uses this health-based standard to set allowable
emission levels for various toxins. The logic is straightforward: state policies that contribute to reducing toxic emissions will improve air quality, which will lead to salutary public health outcomes.
Federalism, State Governments, and Environmental Policy The bulk of the research studying the link between pollution and human health discussed above has been conducted at the local or community level. Since our focus is on the effects of environmental policy, we choose states as our unit of analysis. States play a central role in formulating, implementing and enforcing environmental policy. By dispersing governmental power, a federal system creates opportunities for states to customize policies to fit conditions and circumstances. States vary widely in their policy choices, and a fairly large body of research seeks to explain why some states adopt relatively pro-environment policies (e.g., Lowry 1992; Ringquist 1993; Hays, Esler, and Hays 1996; Bacot and Dawes 1997; Potoski and Woods 2002; Daley and Garand 2005). Environmental policy is often implemented intergovernmentally, with most major regulatory statutes involving both the national and state governments. Much federal environmental policy is partially preemptive, meaning that the EPA sets national standards but delegates day-to-day programmatic responsibilities to states with approved programs (Scheberle 2004). For example, in water pollution control, the EPA establishes specific discharge standards for facilities, but implementation responsibilities belong to the states. A similar arrangement exists for the regulation of air pollution. States submit an implementation plan to the EPA that details how they will meet federal standards. If
the EPA determines that a state plan is inadequate, the agency can preempt all or part of a state’s air quality program (Woods 2006). Under the partially-preemptive design of most federal environmental protection programs, states are invited (or, in the case of the Clean Air Act [CAA] required) to develop regulatory programs that are at least as stringent as federal standards as a qualification for federal authorization to implement the programs. This authorization, or what is called primacy, reflects a purposive policy choice, and the EPA seldom denies a state’s request (Crotty 1987; Woods 2006). States implementing federal programs have control over several key programmatic activities, including pollution monitoring, source permitting, and most notably, enforcing regulations (Potoski and Woods 2002). This administrative discretion results in extraordinary variation in state environmental policy across the country for a couple of reasons. First, many states choose to adopt policies and programs that are more stringent than required by the federal government.1 For example, many states have adopted more stringent ambient air quality standards for pollutants such as particulate matter, ozone, and lead (Potoski 2001). Second, EPA oversight of the states varies across the country, which means there is not uniformity in how states implement federal programs (e.g., EPA/OIG 1998; GAO 2006; GAO 2000). Moreover, once a state assumes control over a federal program, the EPA’s coercive powers are limited. The ultimate power that the EPA has to rein in recalcitrant states is to revoke a state’s grant of primacy and enforce federal regulations themselves, but this is something that the EPA is
1
Of course, states also put in more stringent policies in areas outside of federal pollution control programs. In recent years, many states have advanced policies to restrict carbon dioxide emissions to address climate change in response to failed policy efforts at the federal level (Rabe 2004).
reluctant to do since it does not have the resources to assume complete enforcement responsibilities in lieu of a state program.2 For these reasons, regulatory federalism provides states with considerable discretion to determine how they want to implement federal programs (Sigman 2003). States may therefore choose to assume primacy due either to their commitment to environmental protection, or to use the flexibility they gain by implementing these programs to reduce regulatory stringency so that they can better compete for mobile industrial capital (Crotty 1987; Sigman 2003; Woods 2006).3 The federal government still plays a role in environmental policy, although the role varies across policies (Lowry 1992; Scheberle 2004). Federal grants-in-aid provide one tool by which the EPA can influence state environmental policies. Federal funds have been declining in significance, however. Even though EPA’s budget has experienced modest cuts in recent years, the rescissions have had the greatest impact in programs that transfer dollars to state and local governments (Rabe 2007). By the early 2000s, federal funding accounted for approximately 30 percent of state environmental protection expenditures, down from an estimated 70 percent two decades earlier (Keleman 2004). In sum, the intergovernmental nature of environmental policy means that states play an important role in protecting the public from a range of environmental hazards. Some observers suggest that this role is expanding. Rabe (2007, 425) notes that “Virtually every area of environmental protection has been addressed through some form
2
In 1993 congressional testimony, then EPA Administrator Browner stated that “[t]here are some States that have seriously considered returning primacy to the Federal government. I will be very honest with you, we don’t have the resources to manage even one major State if primacy were to be returned” (quoted in Steinzor and Piermattei 1998). 3 Crotty (1987) suggests both dynamics may be at work, with most states seeking primacy to adopt a proenvironment regulatory strategy while states in the South use it to compete for industry..
of state action in recent years, whether it entailed policy development where no federal role currently exists or direct challenges to federal policy interpretation.” The linkage between state environmental policy choices and the health of their citizenry becomes increasingly important in light of federal actions (and inaction) and intensifying pressure on state policymakers to deal with health care issues. Demands to deal with the problems of access to quality health care, the costs of prescription drugs, the financing of Medicaid, and the extension of insurance coverage to the uninsured continue to vex state governments (Barrett, Greene, and Mariani 2004; Weissert and Weissert 2006). If states with better environmental quality are likely to have a healthier citizenry, then states would be well-advised to take actions that improve environmental conditions. In other words, demonstrating the connections between environmental protection and health outcomes is an important first step.
Data and Method The purpose of our analysis is to evaluate the impact of several indicators of environmental policy on overall levels of environmental pollution, and ultimately on health outcomes. Our analyses are cross-sectional, but include a temporal lag to account for the time necessary for public policy to impact health. We incorporate a time lapse of about a decade between pollution and health outcomes, which allows sufficient time for the health consequences of pollution to materialize, without waiting so long as to potentially attenuate the relationship.4 Accordingly, we use measures of pollution levels
4
Although it clearly takes time for public policies to affect health, there is no firm theoretical reason to choose specific temporal lags. Our choice is partly driven by data availability, and partly driven by what seems a reasonable amount of time for health effects to materialize in this context. . Epidemiological research into the impact of California’s tobacco control program on the incidence of lung cancer used a
from the late 1980s to early 1990s, and health outcomes from the late 1990s to early 2000s. We expect the primary (although perhaps not exclusive) impact of environmental policy to be indirect. That is, we expect environmental policy to affect health through its impact on environmental pollution. Simply estimating the effect of environmental policies on health outcomes is therefore likely lead to incorrect inferences about the relationship between environmental policy and public health. It would also be inappropriate to include environmental outcomes as independent variables alongside environmental policy in a model of health outcomes, since environmental policy choices are obviously causally related to environmental outcomes. To overcome these challenges, we use a system of path analytic equations. This approach allows us to simultaneously assess the effects of environmental policy choices and environmental pollution levels on health outcomes. Path analysis has frequently been used in analyses of environmental programs and outcomes to sort out these complicated relationships (Ringquist 1993, 1995; Hays, Esler, and Hays 1996; Davis and Davis 1999; Abel, Stephan, and Kraft 2007). Dependent Variable Our primary indicator of public health is the average age-adjusted total mortality rate in each state.5 We measure this outcome from 1999-2001, using data reported in the National Vital Statistics System compiled by the National Center for Health Statistics. series of time lags in an effort to determine how much time elapsed before the program impact appeared. The researchers found that effects began materializing as shortly as two years after the policy change, but that the aggregate impact continued to increase for a decade afterwards (Barnoy and Glantz, 2004). 5 Milyo and Mellor (2003) point out that the results of compositional studies may be sensitive to the use of age-adjusted measures as the dependent variable. They recommend using unadjusted measures, with age categories as independent variables. We also ran our models using this approach; it did not affect our results. We report the results using the age-adjusted measure for reasons of model parsimony.
Mortality rates are a widely-used general indicator of public health (e.g., Dockery et al. 1993; Pope et al. 1995; Idler and Benyamini 1997; Kawachi et al. 1997; Lochner et al. 2001; Mellor and Milyo 2001; McLaughlin and Stokes 2002; Jerret et al. 2003; Bell, Dominici and Samet 2005; Laden et al. 2006). Independent Variables We include three measures of state environmental policy choices. First, we measure environmental program strength using Hall and Kerr’s (1991) Green Policy Index, a composite score that represents 67 state policy initiatives in a variety of environmental arenas, including air, water, and hazardous waste. The index includes indicators such as the sanctions available to the appropriate agencies in each state, the size of the state’s pollution monitoring program, and a variety of specific policy indicators. As with similar, but somewhat earlier indices published by the Conservation Foundation (Duerksen 1983) and the Fund for Renewable Energy and the Environment (FREE 1988), scholars have often used the Green Policy Index to measure the strength of state environmental programs (Hays, Esler, and Hays 1996; Daley and Garand 2005; Woods 2006).6 Second, we measure environmental spending as average real per capita environmental and natural resource expenditures between 1986 and 1991. These data reflect all state expenditures, including state, federal, and other monies (fees, fines, licenses, et cetera) that pass through the state budgetary process, and were obtained from
6
The Green Policy Index is an additive index of dichotomous indicators reflecting the presence or absence of policies in some areas, and scales representing the stringency of state programs in others. The original index also contained several indicators of state implementation of federal regulatory programs. Because our analyses incorporate a second variable to account for this, we have recalculated the index with these indicators removed. Prior work factor analyzing the index has shown that the indicators of implementation of federal programs load on a different factor than indicators of state program strength (Woods 2006).
various issues of the Resource Guide to State Environmental Management, published by the Council of State Governments.7 Prior studies have used similar data to represent a state’s commitment to environmental protection (Williams and Matheny 1984; Lowry 1992; Bacot and Dawes 1997; List and McHone 2000; Newmark and Witko 2007). Spending and environmental program strength each capture elements of state environmental protection effort, but they represent different dimensions. Although many items in the Green Policy Index require expenditures, others represent the strength of the legal powers that state agencies possess. Prior research indicates that environmental spending and indexes of environmental program strength behave differently (Bacot and Dawes 1997), thus we include each separately. Finally, we include a measure of state primacy to capture the intergovernmental dimension of state environmental policy. States that assume primacy implement federal programs themselves, obtaining greater authority and flexibility in the process. Prior studies have come to conflicting conclusions regarding the determinants of primacy assumption. While some researchers have found slight evidence that primacy is adopted by relatively pro-environment states (Sigman 2003), others have found a negative association between primacy assumption and state environmentalism (Woods 2006). In light of these conflicting results, we do not offer a hypothesis about the effect that environmental primacy assumption will have on pollution and human health. We measure primacy in the states as the total number of sections of the Clean Air Act and Clean Water Act that a state had assumed administrative responsibility for as of 1992, which we obtained from Ringquist (1993). 7
During this time period, the Council of State Governments collected expenditure data for fiscal years 1986, 1988, and 1991. Our figures are the average per capita expenditures over those three years, expressed in real (2000) dollars.
Our indicator of environmental pollution is a composite measure of 70 environmental conditions calculated from information contained in the Green Index, the most comprehensive set of pollution indicators of which we are aware. The measure was created by taking the Green Index’s composite air pollution score, composite water pollution score, and hazardous and solid waste score, standardizing each, and summing them.8 Higher values indicate greater pollution, and thus lower environmental quality. Control Variables In addition to the policy indicators described above, we expect manufacturing activity to be a major determinant of pollution levels. Prior studies have found manufacturing to be a strong correlate of both state-level emissions and environmental conditions (Ringquist 1993, 1995). We include 1992 manufacturing gross state product as an indicator of the level of industrial activity in the state, which is roughly contemporaneous with our measure of state pollution.9 We expect states with higher levels of industrial activity to evidence higher levels of pollution. We also control for a variety of state-level factors that should impact aggregate mortality rates. One control variable is a composite index of overall state healthiness, compiled by the United Health Group (2000). This index includes components measuring lifestyle, access to health care, occupational safety and disability, and rates of
8
The composite air pollution score contains 15 indicators of acid rain, carbon dioxide emissions, air quality violations, and so on. The composite water pollution score contains 25 indicators of water pollution including such items as pesticide contaminated groundwater, safe drinking water act violations, percentage of lakes and rivers contaminated, and others. The composite solid and hazardous waste score contains 30 indicators, including the amount of municipal solid waste generated, number of Superfund sites, hazardous waste generation and treatment, and releases of cancer causing chemicals and other solid waste toxins. Each individual score is an additive index of state rankings on each indicator. We created our composite measure by converting each individual score into z or standardized scores and summing them. The scale reliability coefficient (Cronbach’s alpha) of the individual components is .85. 9 We obtained these data from the Bureau of Economic Analysis website: http://www.bea.gov/regional/gsp/ (accessed January 24, 2008).
disease.10 We also include control variables for health care availability and utilization. First, we employ physicians per capita as a measure of the availability of health care. Second, we include total spending on health care (from 2000 to 2002) as an indicator of health care utilization.11 Last, we include a set of demographic control variables.12 We expect both income and education to be highly related to health status. Because the two variables are highly correlated, we combine them into a single indicator of socioeconomic status by standardizing each variable and summing them. In addition to overall levels of income, some prior research suggests that health is affected by income distribution (Kawachi et al. 1997; Lochner et al. 2002; McLaughlin and Stokes 2002; Lopez 2004), so we control for the percent of the state population in poverty. Finally, we include a variable measuring the percent of the state population that is African-American, which too may help explain aggregate health outcomes. These variables represent factors that should influence overall levels of public health, and closely mirror those used in other compositional (as opposed to individual-level) studies (e.g., Kawachi et al. 1997; Kawachi, Kennedy, and
10
Lifestyle indicators include the prevalence of smoking, motor vehicle deaths and rates of violent crime. Health care access indicators include adequacy of prenatal care, health insurance coverage, and state and local support for health care. Occupational safety and disability indicators include occupational fatalities and limited activity days. Disease indicators include heart disease, cancer cases, and infectious disease. The original measure also included indicators of mortality. Because mortality is our dependent variable in some models, we have recalculated the index with these measures excluded. 11 The data on state health care expenditures were collected from the Centers for Medicare & Medicaid Services. Because health care spending may reflect either preventative care or treatment, we do not make a prediction regarding the direction of this variable. The state health score contains a component that measures government support for health care, which is defined as the ratio of the percentage state and local expenditures for welfare, health, and hospitals divided by the percentage of the state’s population with an annual household income below $15,000 (United Health Group 2000). This measure is analytically distinct from our measures of availability and utilization, and is not highly correlated with them (corr = .30 with health care spending per capita, and .62 with physicians per capita). Therefore we retain both measures. Recalculating the health score excluding the public support indicator does not alter our results. 12 All of the demographic data, as well as the physicians per capita data, come from the Statistical Abstract of the United States. Each variable is measured as of the year 2000, except for physicians per capita, which was measured as of 1997 (the closest year for which the Census Bureau provides data).
Glass 1999; Lochner et al. 2001; Mellor and Milyo 2001; McLaughlin and Stokes 2002). Descriptive statistics for all variables are presented in Table A in the Appendix. Analytic Strategy To determine the effects of state environmental policy choices on public health, we integrate the determinants of pollution levels and health outcomes into a single causal model. Our hypothesis suggests that the effects of environmental policy should largely be indirect, operating through pollution outcomes. Environmental pollution is thus properly viewed as an intervening variable between environmental policy and health outcomes. It is also possible that some environmental policies have a direct effect on public health. State environmental agencies may aggressively use permitting powers to require polluters to locate far away from residential areas. Informational policies such as community right-to-know laws may affect where citizen live or work, thus lowering their exposure to pollutants. Policies such as these may improve public health by altering the level of exposure to a given amount of pollution, rather than lowering pollution levels. Similarly, workplace safety policies, seat belt use laws, and other factors included in the programmatic index may influence mortality rates irrespective of their impact on the environment. To account for each of these possibilities, we use a system of path analytic equations, which allows us to simultaneously assess: (1) the effect of environmental policy on pollution, (2) the effect of pollution on public health, and (3) the effects of environmental policy on public health. This approach is suited to our research question because it accounts for endogeneity between environmental policy and pollution levels.
It also allows us to separately examine the direct and indirect effects of environmental programs on human health.
Results The results of the analysis are presented in Table 1. The first column reports the unstandardized parameter estimates and their associated robust standard errors, while the second column reports the standardized beta-weights.13 The model estimates two sets of regression equations: the first considers the effect of environmental policy on pollution and the second considers the effect of pollution on health. Figure 1 shows the schematic representation of these results, with standardized coefficients represented on the paths. Table 1 about here Figure 1 about here The findings indicate a strong pattern of relationships between state environmental policy indicators and levels of environmental pollution. Controlling for manufacturing activity – a strong predictor of state pollution levels – each of the three state environmental policy variables is statistically significant. Ceteris paribus, states with stronger environmental programs and states that spend more on environmental protection have lower overall levels of pollution. The parameter estimate on the primacy variable suggests the opposite relationship. Because we do not have a directional prediction for this variable, we use a
13
A Breusch-Pagan test could not reject the null hypothesis of heteroskedastic errors, therefore the models are estimated using heteroskedasticity-corrected robust standard errors (White 1980). There is evidence of moderate levels of multicollinearity in some models, although variance inflation factors (VIFs) remain below the value of 10, at which multicollinearity is often mentioned as a cause for concern (Kutner, Nachtsheim, and Neter 2004), with our highest VIFs reaching 6. A Ramsey RESET test revealed no evidence of model misspecification.
two-tailed hypothesis test in evaluating this relationship. Even with this more conservative test, the results suggest that states with more autonomy to administer federal clean air and water laws evidence higher levels of pollution. This finding is consistent with some prior research, which suggests that states that pursue primacy are not necessarily doing so in order to pursue “green” environmental policies (Woods 2006). Because of the different scales of the dependent and independent variables, it is difficult to get a clear sense of the magnitude of these effects. To interpret the relative impact of each of these variables, the effects can be expressed in terms of standard deviations, as indicated by the standardized coefficients in the final column of Table 1 and on the path diagram presented in Figure 1. A one standard deviation increase in environmental program strength is associated with a .30 standard deviation decrease in environmental pollution, ceteris paribus, while a one standard deviation increase in environmental spending decreases environmental pollution by about half that amount, a .16 standard deviation. A one standard deviation increase in primacy assumption, on the other hand, is associated with a .20 standard deviation increase in environmental pollution. The second equation looks at health outcomes. These results indicate that levels of environmental pollution are positively associated with overall levels of mortality. All else constant, a one standard deviation increase in environmental pollution is associated with a .21 standard deviation increase in the age-adjusted mortality rate. We do not find that environmental program strength has a direct effect on health outcomes. Some of the control variables do not demonstrate the anticipated level of importance in explaining health outcomes. Neither state health care spending nor the
number of physicians per capita is significantly associated with state mortality rates. However, one of the health-related control variables, state health score, does perform as expected: states with better overall health ratings evidence sharply lower mortality rates, with a one standard deviation increase in the health score leading to a .47 standard deviation decrease in age-adjusted mortality. Last, some of the demographic measures also help explain state variation in mortality rates. As is often found in studies of health outcomes (e.g., Lochner et al. 2001), individuals of higher socioeconomic status tend to have lower mortality rates. In addition, larger state minority populations are associated with higher mortality rates. Collectively, this evidence suggests that a variety of environmental policies affect pollution outcomes, and ultimately health outcomes as well. While environmental programs do not evidence a statistically significant direct effect, they do have an indirect effect through this variable. The magnitude of the indirect effect can be calculated by multiplying the path coefficient (or beta weight) from the variable to the intervening variable with the path coefficient from the intervening variable to the dependent variable. Thus environmental program strength has an indirect effect of (-.30 x .21) = -.06, state primacy has an indirect effect of .04, and environmental spending has an indirect effect of -.03. These indirect effects suggest that stronger commitments to environmental protection are associated with public health improvements. Robustness Checks The results presented above indicate that a variety of state environmental policy choices have a statistically significant and substantively meaningful indirect effect on
health outcomes. To ascertain whether these results are driven by our modeling choices, we estimate several alternative specifications. The first alternative employs a different indicator of environmental conditions: the average total pounds (in thousands) of toxic pollution emitted in air, water, and land from 1988 to 1990. These data come from the EPA’s Toxic Release Inventory (TRI).14 Although a narrower measure than the Green Index, toxic releases themselves are widely employed in the environmental literature as an indicator of environmental problem severity (Bacot and Dawes 1997; Daley and Garand 2005; Newmark and Witko 2007). Our second robustness check uses an alternative indicator of public health: average age-adjusted self-reported health status. These data come from the Behavioral Risk Factor Surveillance System, which is a state-representative survey conducted annually by the U.S. Centers for Disease Control and Prevention. The survey asks: “Would you say that in general your health is excellent, very good, good, fair, or poor?” This perceptual measure provides a different type of indicator of the overall health of the population than mortality levels, and it too has been widely-used in public health studies (e.g., Idler and Benyamini. 1997; Kawachi, Kennedy, and Glass 1999; Subramanian, Kawachi, and Kennedy. 2001; Lopez 2004). Previous research has demonstrated that self-rated health is a good predictor of mortality and other evaluations of overall health (Idler and Benyamini 1997), and these survey data have previously been aggregated to the national (Nelson et al. 2003), state (Subramanian et al 2001) and metropolitan (Lopez
14
Although TRI data are a component of the environmental conditions in the Green Index, toxic releases constitute a very small portion of that measure.
2004) level. Our dependent variable is the average percentage of respondents who describe their health as “good,” “very good,” or “excellent” from 2001 to 2003.15 Finally, we estimate a model that includes a set of policy indicators from an earlier time period, the late 1970s. This specification allows us to look at the effect of inserting a lag between our policy variables and environmental conditions. Although our original model incorporates about ten year gap between policy variables and health outcomes, our environmental policy indicators were measured roughly contemporaneously with our environmental pollution variable. As an alternative, we run the model using policy indicators drawn from roughly a decade before the measured included in the composite environmental pollution index. In lieu of the Green Policy Index, we employ the Conservation Foundation’s index of state environmental policy Duerksen (1983).16 Our spending figures are state averages from 1977-1980, as reported in the annual Census Bureau publication Environmental Quality Control. Finally, we use a primacy indicator developed by Crotty (1984), which is an additive index of the total number of sections a state had primacy under the Clean Air Act, Clean Water Act, Safe Drinking Water Act (SDWA), and Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) in 1980.17 For reasons of space, we present our alternative models in a table, but do not provide separate figures for the path models. Conceptually, the path model remains the same as that presented in Figure 1, although the operationalization of some of the 15
The other response categories were “fair” and “poor.” We collapse the categories because this is the norm in public health studies which use these data (e.g., Lopez 2004; Nelson et al. 2003; Subramanian et al. 2001). Using the five category response does not substantively affect our results. 16 We have recalculated this index excluding state spending for environmental programs (for which we have a separate variable) and scores for a state’s congressional delegation’s pro-environment voting. 17 Crotty’s index thus includes more federal acts than the one we employ in Model 1, which only includes the Clean Air and Water Acts. By the early 1990s, however, there is little variation in state primacy assumption under SDWA and FIFRA.
variables changes. The results from the three alternative models are presented in Table 2. The table reports unstandardized coefficient estimates, but in order to aid in interpreting them the text will refer to the effects in terms of standard deviation changes. Table 2 about here The first column presents the results when measuring pollution with the TRI data instead of the index of environmental conditions. All other variables are the same as those we included in the original model. With this alternative measure of environmental conditions, environmental program strength and environmental spending continue to be associated with lower levels of pollution at statistically significant levels. Primacy over federal environmental programs, however, is no longer significant. Toxic releases are also positively associated with mortality levels. A one standard deviation increase in toxic releases is associated with a .11 standard deviation increase in mortality rates, reaffirming the prior finding linking pollution to health outcomes. This model provides further support for the contention that spending and program strength reduce levels of pollution, and indirectly reduce state mortality rates. We continue to find no evidence of a direct connection between environmental policy and public health outcomes. The second column presents the results of a model replacing mortality as the measure of health outcomes with self-reported health status. Because higher values of self reported health indicate better health outcomes, the expected signs on the coefficient estimates are reversed. These results largely replicate those of the mortality models. Stronger environmental programs and greater environmental spending are associated with lower levels of pollution, while higher pollution levels are in turn associated with lower aggregate self-reported health. The effect is large: a one standard deviation increase in
environmental pollution is associated with a .49 standard deviation decrease in selfreported health status. These results again suggest that environmental policy has only an indirect effect on public health. The third column presents the results of the model in which we have included a roughly ten year lag between our policy variables, our measure of environmental conditions, and mortality rates. These results are partially consistent with the previous findings: states with stronger environmental programs evidence lower future levels of pollution. A one standard deviation increase in environmental program strength is associated with a .19 standard deviation decrease in environmental pollution. Although the variable for environmental spending does not reach traditional levels of statistical significance, it is correctly signed and fairly close (p= .13). The primacy variable is not statistically significant when measured in the earlier time period. These results suggest that some of the demonstrated relationships hold when considering different time periods for the policy variables. Overall, the control variables perform largely as expected, with the exception of percent poverty in models 2 and 4. Somewhat anomalously, these results suggest that states with a higher percentage of the population living in poverty have lower mortality rates. Other significant variables indicate that better health outcomes tend to be associated with healthier lifestyles, higher average socioeconomic status, and a lower percentage of African-Americans in the population. Conclusion The empirical analyses in this paper have attempted to disentangle the complicated inter-relationships between environmental policy choices, environmental
conditions, and public health outcomes in an intergovernmental arena. Studying these relationships at the state level, we found a strong association between several environmental policies and pollution outcomes. We further showed that while state environmental policies do not directly affect health outcomes, they do have an indirect effect through their influence on pollution levels. These results are robust across multiple measures and model specifications. The existence of a relationship between environmental quality and health outcomes beyond the local level is noteworthy for two important reasons. First, although exposure to particular chemicals or pollutants at the community level has long been thought to produce locally-specific health consequences, these analyses document a relationship between aggregate levels of pollution and overall health outcomes. Second, state governments make important environmental policy decisions, and our analysis suggests that these decisions may have significant consequences for the health of the state populace. More generally, by suggesting a link between environmental policy and public health this research underscores the importance of assessing cross-policy consequences. The literature in public policy often treats specific policy areas as if they are independent of one another. Our results suggest that a more comprehensive way of understanding policy impact is to look beyond the confines of a particular policy to assess its ramifications for other policy areas. Our findings have important implications for policymakers as well. Persistent health care concerns such as access to and affordability of care continue to vex state governments. One popular policy prescription is prevention, such as encouraging the public to adopt behavioral and lifestyle changes that lessen the need for care. As states
seek to improve health outcomes (and limit health care costs) our analysis suggests that, in addition to the standard menu of health policy options, policymakers would do well to consider bolstering state environmental programs. Although the analyses presented here cannot establish the comparative impact of environmental protection versus other options, our results indicate that state spending and programmatic initiatives designed to mitigate pollution may significantly improve overall levels of public health. State environmental policy is embedded within a partially preemptive intergovernmental framework, which allows states to implement and enforce federal regulations. From the federal perspective, as long as state environmental programs are sufficiently congruent with regulatory objectives, then the burden of implementation can be shifted to states. From the states’ perspective, primacy provides the authority to design and operate environmental programs that fit their conditions and circumstances. The typical assumption is that primacy assumption is an indicator of state commitment to the environment, thus primacy will produce a “better” environmental outcome (e.g., Lowry 1992). Our results provide some evidence that this assumption may be erroneous. In some of our models, we find that states which have greater authority over federal programs have elevated levels of environmental pollution and worse health outcomes than those states in which the federal government implements these programs. These results vary depending on the time period under consideration and the measure of environmental pollution employed. Moreover, our cross-sectional analyses are incapable of determining the direction of this relationship. Thus, the results may be interpreted as indicating that states with higher pollution levels are more likely to seek federal authorization to implement their own programs. For all of these reasons, future
research is necessary to untangle these relationships. If the negative relationship between state primacy assumption and pollution levels holds, it may indicate that some states are insincere in their motivation regarding primacy; or that some are unable to do an effective job of implementation. Either way, the implications strike at the heart of devolutionary federalism.
Appendix Table A. Descriptive Statistics Variable Age-adjusted mortality rate (per, 100,000 people) Environmental spending per capita (1986-1991) Green Policy Index State primacy (1992) Environmental pollution Health care spending per capita ($1,000) Physicians per capita State health score Percent poverty Socioeconomic status Percent Black Manufacturing gross state product Toxic emissions (millions of pounds) Self-reported health status Conservation Foundation Policy Index State primacy (1980) Environmental spending per capita (1977-80) n = 50
Mean
Minimum
Maximum
857.5
Standard Deviation 81.49
660.1
1,035
48.76
59.76
11.13
378.7
17.6 5.24 0 8.819
8.226 1.82 2.640 0.979
4 2 -4.771 6.522
37 7 5.276 11.01
228.3 1.4 12.07 0 12.48 216.01
55.34 13.33 3.110 1.688 1.799 232.15
150 -25 7 -3.80 6 5.74
402 28 20 3.56 17 1153.19
60.73
67.89
1
318.6
84.9 21.28
3.3 6.71
77 8
9 37
6.08 14.82
1.7 19.21
3 1.12
10 104.66
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Table 1. Effect of Environmental Policy on Environmental Pollution and Public Health Environmental Pollution (Green Index)
Environmental program strength Environmental spending State primacy Manufacturing activity Constant F Adjusted R2
Parameter Estimate (Robust Std. Error) -0.098* (.033) -0.007† (.004) 0.296‡ (.152) 0.006* (.002) -0.840 (1.304) 10.66* .36
Standardized Beta -.30 -.16 .20 .55
Health Outcome (Age-Adjusted Mortality Rate) Environmental pollution Environmental program strength Health care spending Physicians per capita State health score Percent poverty Percent Black Socioeconomic status Constant F Adjusted R2
6.351* (2.594) -0.706 (1.050) 7196.051 (7609.252) -0.093 (.141) -2.868* (1.455) -4.631 (3.941) 1.256‡ (.837) -17.321† (5.512) 874.966* (79.235) 28.18* .76
.21 -.07 .09 -.06 -.47 -.18 .15 -.36
N = 50 * p < .01 † p < .05 ‡ p < .10 One-tailed tests for all variables except state primacy and health care spending which are two-tailed.
Table 2. Effect of Environmental Policy on Environmental Pollution and Public Health: Alternative Models Environmental Pollution
Environmental program strength Environmental spending State primacy Manufacturing activity Constant F Adjusted R2
TRI (1) -2.001† (.629) -0.192* (.074) -3.268 (5.380) 0.201* (.052) 84.980 † (38.971) 12.48* .44
Green Index (2) (3) -0.098* -0.074† (.033) (.044) -0.007‡ -0.032 (.004) (.028) 0.296 † -0.017 (.152) (.186) 0.006* 0.006 † (.002) (.002) -0.840 0.954 (1.304) (1.38) 10.66* 4.21* .36 .26 Health Outcome
Age-Adjusted Mortality Rate Environmental pollution Environmental program strength Health care spending Physicians per capita State health score Percent poverty Percent Black Socioeconomic status Constant F Adjusted R2
0.134‡ (.080) -0.735 (1.174) 11386.860 (7850.984) -0.076 (.144) -3.264* (1.360) -7.739† (3.727) 1.469‡ (.867) -22.229* (5.953) 861.737* (78.503) 23.55* .75
Age-Adjusted Self-Reported Health Status -0.224‡ (.134) -0.039 (.038) 80.66 (301.66) -.0006 (.006) 0.121† (.056) -0.285 (.239) 0.022 (.046) 0.300 (.321) 88.13* (3.97) 16.80* .69
Age-Adjusted Mortality Rate 6.917* (2.747) -1.159 (.922) 5475.983 (7814.878) -0.070 (.139) -2.953† (1.25) -4.924‡ (3.507) 1.181‡ (.831) -16.822* (5.887) 901.560* (76.522) 33.98* .77
Cells contain parameter estimates, with robust standard errors in parentheses. N = 50 * p < .01; † p < .05; ‡ p < .10. One-tailed tests for all variables except state primacy and health care spending which are two-tailed.
Figure 1. Health Outcomes Path Model
Environmental program strength
State primacy
Environmental spending Manufacturing activity
-.07
.09
-.30*
-.06
.20‡ -.47*
.21* -.16†
Environmental Pollution
Mortality Rate
Health care spending Physicians per capita State health score
-.18
Percent poverty .55*
.15‡ -.36†
Percent Black Socioeconomic status
* p < .01 † p < .05 ‡ p < .10
