Recycling in U.S. Cities
Mitch Carbullido
Creighton University

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I. Research Question
Recycling programs across the United States and across the world have dramatically evolved in the past thirty years, and particularly in the 1990s. The rise of the environmental movement in the 1970s sparked environmental consciousness and began to bring the concepts of conservation and environmental protection into mainstream awareness. It is at this time that the idea of recycling and reusing waste began to materialize (Strong 1997, 3). Initial recycling efforts and programs for the first decade and a half were modest, consisting primarily of businesses and individuals selling used commodities for reprocessing to make a profit. In the late 1980s, however, the recycling movement received a boost when it became apparent that cities across the country were experiencing significant obstacles with trash disposal, specifically rapidly declining landfill space and the climbing cost of garbage disposal services (Strong 1997,
4). Recycling served as a possible and partial solution to the waste management problems that were plaguing parts of the country and threatening others. Over the course of the 1990s, various governments, primarily local city governments, implemented and expanded upon a variety of recycling programs. Recycling rates, both population participation rates and diversion rates comparing the percentage of waste recycled to waste buried or incinerated, skyrocketed. From
1989 to 1996, alone, national recycling rates went from 9% to 28%, and have continued to climb into the twenty-first century. Some have said the dramatic recycling improvements in the 1990s were, “the singular environmental success story of the decade” (Folz 1999).
Recycling and waste management reform, however, has not been uniform across the country. While the majority of major cities in the United States have implemented some form of a recycling program, the extensiveness of these programs and the recycling diversion rates vary

3 greatly across cities. In 2001, for example, Portland, Oregon had a recycling diversion rate of
53.6%, recycling over half of all waste collected in the city. Cleveland, Ohio, on the other hand, had a recycling diversion rate of only 2.0% during the same period, hardly making a dent on waste collection. Cities like New York and Memphis fell somewhere in between with recycling rates around 20%. What factors explain this drastic variation across cities in the United States?
In other words, why do some cities have higher recycling rates than others?
The answer of this question is of great importance both to municipal governments and environmental interest groups. City governments hoping to increase the recycling rate in their city, whether out of necessity due to landfill constraints or a desire for improved environmental performance, can utilize information about what factors contribute to recycling rates to both assess the chances of success in their cities and design better programs to increase recycling participation. The results of this study may also prove to be applicable to governments outside of the United States, municipal, national, and transnational. In addition, environmental interest groups hoping to increase recycling efforts across the country or in specific cities can also find the answer to this question useful by knowing what needs to be done to facilitate increased recycling rates and by knowing which cities to target efforts at for change that have the highest likelihood for success. Determining what factors affect recycling rates will also contribute to a better understanding for politicians, scientists, and sociologists studying city government behavior as well as the progress and effects of the environmental movement in the United States.
Depending on the answer to the question, the study could have greater implications on theories applied to environmental movements, city governments, and bureaucracies.

II. Literature Review
Indeed, in the last decade and a half, significant research has been done on recycling programs in the United States and abroad. Many, however, focus on different units of analysis, whether it is countries or counties. Some studies have addressed the question, “Why do recycling rates change over a period of time?” and others have attempted to explain overall participation rates, that is whether one recycles or not, not how much he recycles. Furthermore, most conclusions of these studies have been contradicted by other studies. As of yet, no one has posited a clear answer as to why some cities have higher recycling rates than others.
Nevertheless, the research that has been done thus far points to some possible explanations that need to be tested.
One analysis done by Anne Scheinberg (2003) examines the recycling movement as a whole from its conception in the 1970s to the present. Scheinberg argues that the recycling movement is a case study example and a manifestation of the Ecological Modernization Theory formulated first by German political scientists Martin Janicke in the 1970s. Janicke’s theory states five transformations that make up ecological modernization: “(1) the changing role of technology and sciences; (2) increasing importance of market dynamics and economic agents;
(3) transformations in the role of the nation-state; (4) modifications in the position, role and ideology of social movements; and (5) changing discursive practices and emerging new ideologies” (Mol 2000)
. Scheinberg applies this theory to the progression of recycling in the
United States, which she categorizes into four periods: “(1) the baseline period, in the era before earth day in 1970; (2) the pre-modern period, 1970-1980, a period in which the conditions for modernization were put into place; (3) the transition period, 1980 to 1984-5-6, a somewhat
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5 truncated period of simple modernization; and (4) the modernization period, 1984 to 1996, a period of reflexive modernization and rapid social and technical change” (Scheinberg 2003, 51).
Through her historical and empirical analysis, Scheinberg successfully applies the
Ecological Modernization Theory to the recycling movement in the United States. Her analysis, however, merely explains the general movement over the course of time. As previously discussed, within the United States there exists a broad discrepancy in recycling rates among cities. Though, overall, recycling has increased dramatically in the United States, which can possibly be explained by Scheinberg’s Ecological Modernization Theory, some cities have not experienced this change, and some have experienced it more than others. While Scheinberg’s analysis helps to explain the collective transformation over time, the variation in the United
States at a single point in time must still be explained.
Another study, done by David Folz (1999), also examines the change in recycling rates over time, but does so using cities as the unit of analysis. His study consists of an analysis of the change of city recycling rates from 1989 to 1996. While national recycling rates during this period increased from 9% to 28%, the variation of change among cities is great. Folz determines several factors that have contributed to increases in recycling rates for cities. His analysis consists primarily of logistical factors, that is, which programs, if implemented, are most successful in increasing recycling rates in cities. In addition, Folz analyzed the various costs associated with different recycling programs. Folz found that recycling rates improved most dramatically in cities that began to mandate recycling. Recycling mandates vary from city to city, and some are at the state level, while others are at the county or city level. Nevertheless, mandatory recycling practices, when implemented, significantly affect recycling rates.

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City governments that established a near term recycling rate goal, also experienced greater increases in recycling rates. A near term goal provides an incentive for both the population and the administration implementing the recycling program. Folz found a positive correlation between the cities that established near term recycling rate goals and increases in recycling rates.
Folz also found that convenience is an important factor affecting recycling rates. Those city governments implementing programs that increased the convenience for individuals to recycle usually found that recycling rates increased. This includes increasing the quantity of materials that can be recycled, providing free bins, and same-day pick-up for recyclables and non-recyclable waste. As can be imagined, curbside pickup programs, which several cities have implemented, dramatically increased the convenience of recycling and thus the recycling rates.
In his analysis of the cost of recycling and the change of the cost of recycling, Folz also tried to explain that recycling rates have increased because it has become cheaper to recycle, especially compared to the costs of traditional waste disposal. While overall costs of recycling in the United States have increased 220% from 1989 to 1996, per unit recycling costs have decreased 13% during that same period. In addition, though many believe that recycling costs considerably more than traditional waste disposal, Folz’s study found that the mean net cost per ton for a recycling program at the time of his study was about $85 compared to $131 for traditional waste collection and disposal, and that’s not taking into account the revenues that can be made by selling the recyclable goods, the costs associated with losing land space for landfills, and the value of conserving natural resources. According to Folz, the decrease in costs associated with recycling programs, coupled with the comparative costs of traditional waste

7 disposal programs may have been a factor that increased recycling rates in cities and is also further incentive for cities to expand programs in the future.
Folz’s study does indeed contribute significant findings to explain changes in recycling rates. What his research has primarily done, however, is determine which programs, if implemented, are more successful than others. He found several factors of recycling programs and implementation that will likely produce higher recycling rates. Others have done similar studies with like results. What these findings make evident is that research has already been done showing which programs produce higher recycling rates and that information is readily available to civic leaders. In other words, they know what they have to do and how to do it; it is just a matter of actually doing it. But Folz’s research still begs the question why some cities have adopted these programs that lead to higher recycling rates while others have not.
A third study that analyzes the change of recycling rates over time was done by Richard
C. Feiock and Lesley Graham Kalan (1999). Their study focused on the differences in the change of recycling rates in Florida counties from 1991 to 1996. Building off of Folz’s research,
Feiock and Kalan limited their study to a single state in order to control for specific provisions of state laws that could effect policy decisions and varying environmental values and opinions across the country. Rather than focusing on what programs contribute to higher recycling rates,
Feiock and Kalan focused more on contextual factors that effect recycling rates.
Feiock and Kalan made several key findings in their analysis of Florida counties.
Perhaps just as important as the factors they found which do affect changes in recycling rates are the factors they found which do not. First, they found that program design was not a strong predictor of changes in recycling rates. This finding contradicts the overall basis of Folz’s study.
Whereas Folz found key programs and initiatives that contribute to greater changes in recycling

8 rates in cities, Kalan and Feiock found that the actual program has little to do with recycling rate changes among Florida counties. Whether this concept holds true for cities across the nation, though, is another question. Feiock and Kalan also found that environmental support was not a strong predictor of changes in recycling rates. This finding suggests that higher levels of environmentalism in a community has little effect on changes in the recycling rate. Other studies, however, of which some will be elaborated on later, contradict this finding when applied to other units of analysis.
The two main factors that Feiock and Kalan found are strong predictors in changes in recycling rates are levels of education and income levels in counties. Both of these factors account for more variation in recycling across Florida counties over time than program design or environmental support. Feiock and Kalan found that more educated counties had higher recycling rates over time than less educated counties. “Recycling programs in Florida exhibit much greater success when they are directed to citizens in better educated upper and middleclass counties” (30). This conclusion is supported by others (Van Liere & Dunlap, 1980;
Kinnaman and Fullerton 1997). The level of education in communities can contribute to the awareness of the problems associated with waste management and the solutions available to help solve the problems. In addition, “better educated people may have a greater preference for a clean environment, switching some of the disposal from regular garbage to recycling”
(Kinnaman and Fullerton, 1997).
Counties with higher median incomes also had higher recycling rates over time. This finding is similar to the notion of environmentalism as a “luxury issue,” in which only those who are economically comfortable and have all of their needs met will contribute time and energy to environmental issues. Feiock and Kalan admit, however, that it is not clear whether success of

9 recycling programs in higher income counties was the result of individual preferences of higher income people shaped by their community’s economic position or from the increased fiscal ability and financial resources of the local governments in higher income counties. Regardless of the reason why income levels affect recycling rates, however, the finding is significant.
Feiock and Kalan’s analysis contributes greatly to the study of recycling rates, but leaves questions remaining as to the issue at hand. First, again their analysis consisted of comparison of the changes of recycling rates over a period of time, not the comparison of recycling rates of different entities at a single point in time. In addition, Feiock and Kalan’s study is limited to
Florida counties. While their finding may prove to be empirically applicable to all U.S. cities, this is not necessarily so. Across the United States there exists greater variation in the four factors that they analyzed, which could prove to have an effect on the results. In addition, using
Florida as a microcosm of the United States as a whole may not be entirely accurate. Feiock and
Kalan’s findings, therefore, must be applied to all United States cities.
A study that is related to Feiock and Kalan’s finding that counties with higher median incomes have higher recycling rates is a study done by David Satterthwaite which analyzes the environmental transformation of cities as they become larger and wealthier. Satterthwaite does not focus solely on recycling behavior, however, but on several environmental issues, including pollution and waste management. Satterthwaite found that, over time, cities become more environmental as they develop and become larger and wealthier. He believes that part of the explanation for this phenomenon is that wealthier cities are more capable of transferring their environmental costs to other people, other regions, and future generations. Though his partial explanation for why wealthier cities are more environmental does not have much relevance to recycling rates, since recycling is not associated with a transfer of environmental costs, his

10 confirmation that wealthier communities tend to be more environmental may have implications when comparing recycling rates, since recycling sometimes goes hand in hand with other environmental programs.
Perhaps more pertinent to this study is an analysis done studying variation in recycling participation rates in countries of the European Union. The authors of this analysis observed significant disparities between recycling participation rates among European Union countries.
This study, done by Daniel Guerin, Jean Crete, and Jean Mercier (2001), does a multilevel analysis of the factors that affect recycling participation rates. Guerin et al first analyzed what personal factors make an individual more likely to participate in recycling. The results of this level analysis were not surprising. First, they found that people who participate in local environmental protection programs tend to be more likely to participate in recycling. Basically, those that are concerned enough about environmental protection to take local action are probably also willing to take the extra effort to recycle. Guerin et al also found that those who are more concerned about the global environment tend to be more likely to participate in recycling. This finding is somewhat important because it shows that concern for the global environment is often also translated to care and concern for the individual’s local environment. Both of these findings, however, are fairly intuitive: environmentalists recycle more. What was intriguing about Guerin et al’s study of recycling participation at the personal level was what they found had little effect on participation. Their analysis found that both education and income had only modest impacts on recycling participation, conclusions somewhat contradictory to those made by
Feiock and Kalan in their analysis of Florida counties, though Feiock and Kalan were studying the effects of these factors at the aggregate, contextual level, not the personal level.

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Nevertheless, it shows that there is significant debate about the factors affecting recycling behavior.
The main focus of Guerin et al’s study, however, was the contextual factors that affect recycling participation rates at the country level. These factors are also more relevant to this study since they apply to groups of people rather than individuals. Guerin et al found that the greater percentage of people in a country that were members of ecological associations, the higher the country’s recycling participation rate. This factor alone explained 45% of the variation in recycling participation rates in European Union countries. While this factor is somewhat similar to the conclusion of the personal analysis of their study, it shows that, taken at the aggregate level, environmental participation as a country also affects participation rates of the country. Guerin et al also found that concrete, real environmental problems in countries had a significant effect on recycling rates, even if not directly related to recycling. Their study used the independent variable of deforestation in countries and found that those countries where deforestation was more of a problem tended to have higher recycling participation rates. This conclusion shows a relation to environmental consciousness and recycling participation. One environmental problem that is widely publicized, in this case deforestation, raises the level of concern for the environment, which can translate to environmental action in other areas, in this case recycling.
Guerin et al’s study contributes significant knowledge to the understanding of recycling behavior both in individuals and groups of people. Two factors about their analysis, however, make it important for more research to be done. First of all, their study used only recycling participation rates as the dependent variable. While recycling participation rates are indeed important to analyze, they do not tell the whole story. Many factors may influence actual

12 recycling diversion rates even in entities with identical participation rates, including the effectiveness and convenience of programs, and the factors that this study has analyzed. In addition, in a time when recycling has become fairly common practice, it is not as much of an issue of who is recycling, but how much they are recycling. For these reasons, it is equally important to compare the diversion rates of these groups in order to see if these same factors and others can explain variation in overall diversion rates. In addition, Guerin et al’s study focused on countries, not cities, and only countries that are members of the European Union. Whereas in the European Union recycling may be more of a country issue, handled at the state level, in the
United States, it is more often a local, city government issue and responsibility. No federal legislation exists mandating or providing incentives for any sort of recycling program. What little legislation exists in the United States is either at the state level, or more commonly at the city level. Indeed, at the conclusion of their paper, Guerin et al note that “It would be interesting to extend this kind of comparative analysis to other regions of the world, notably North
America…This kind of research would be a significant step forward in the development of theory about the specific contribution of the contextual and individual bases of environmental consciousness and behavior” (Guerin 2001, 213).
One study that did analyze United States cities was an analysis done by Richard Feiock
(of whom also published the study analyzing recycling rates in Florida counties) and Johathan
West (1993). Specifically, their study analyzed the variation in adoption of curbside recycling program policies in U.S. cities. Basically, their independent variable had two values, either the city had adopted a curbside recycling program policy, or it had not. Feiock and West tested several hypotheses to explain for this variation, of which several showed significant correlation.
Feiock and West first found that the actual need of increased recycling affected adoption of a

13 curbside program in cities. Cities that had limited landfill capacity or rapid population increases tended to be more likely to adopt a curbside program. This shows that necessity or practicality of recycling can have some affect on city governments’ recycling policies. Feiock and West also found that state party competition had a strong influence on the adoption of recycling programs, though the characteristics of the local political institutions had little effect. Those cities with higher state party competition tended to be more likely to adopt a curbside recycling program.
This phenomenon can be explained by understanding that electoral competition can often lead to the expansion of government activity, in this case the adoption of a certain recycling program.
Feiock and West’s study also found that the characteristics of the state the city is in have some effect on recycling. Perhaps not surprisingly, cities located in states that mandate recycling programs, provide financial assistance to local governments, produce waste reduction goals, or have higher state administrative and enforcement expenditures for monitoring were more likely to adopt the curbside program. While recycling is primarily a city responsibility, the state level factors cannot be overlooked.
Another conclusion of Feiock and Wests’s analysis was that wealthier cities were more likely to adopt the curbside recycling policy, a conclusion also found by other studies described previously. Both per capita income and city revenues affected the likelihood of the policy adoption, but per capita income had a much greater affect than city revenues, contrary to what some might think.
The prevalence of environmentalism in the cities had mixed results explaining policy adoption variation. Feiock and West tested this variable by analyzing membership in two environmental organizations. While higher memberships in the National Wildlife Federation had a significantly positive affect on the policy adoption, membership in the National Audobon

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Society had no relationship. Perhaps these mixed results were due to the natures of the two organizations, but it does leave some question as to the overall effects of environmentalism on recycling. Overall, Feiock and West five most important single variables for explaining the curbside recycling policy adoption were landfill capacity, population, party competition, income, and National Wildlife Federation membership.
Feiock and West’s study makes significant grounds in the search to explain recycling variation in U.S. cities. Their analysis identifies several factors that have significant effects on recycling behavior in cities. Two limitations of this specific analysis, though, necessitate more research. First of all, the study was done in 1993 with information collected from 1990. This was at the very beginning of the recycling movement and the wave of the establishment of municipal recycling programs in U.S cities. Eleven years later, there has been significant development in recycling programs and dramatic increases in recycling rates, which could greatly affect the conclusions that Feiock and West make from their analysis in 1993. In addition, Feiock and West’s study analyzes only the adoption of a recycling program, and only one program, curbside pickup. First, curbside pickup has become the hallmark of recycling programs today and most cities that have recycling programs have some sort of curbside pickup, even though the recycling rates of these cities vary greatly. Second, there are several different variables even within curbside pickup programs, such as whether participation is mandatory or voluntary, the extent of the program, when it is provided, etc. These different variables, as discussed previously, can have a significant effect on recycling rates. But what still remains to be done is an in depth analysis of the variation in recycling diversion rates of U.S. cities at a single point in time.

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III. Hypothesis
I hypothesize that cities that have more environmentally conscious citizens, have higher median incomes, and are more Democratic (with the big D) are more likely to have higher recycling diversion rates. Two of these independent variables, environmentally conscious citizens and median income, are derived from the aforementioned research done using different units of analysis or different, but related, dependent variables. Though some studies have found that these two variables have some effect on recycling behavior, other studies have found that they do not (Feiock and Kalan, Satterthwaite, Guerin et al). It is necessary to extend the research of these variables to United States cities at a single point in time to find if they can help explain the wide variation in recycling diversion rates across the United States. The third independent variable, how Democratic a city is, is a new variable that I will introduce based on theories of party politics and assumptions about the political party system of the United States.
First, I hypothesize that the level of environmental consciousness of the city’s population will have an effect on recycling diversion rates. Environmental consciousness can be defined as the degree to which the citizens of the city are aware of the current problems associated with the environment, are concerned about the state of the environment, are active in environmental protection, and are willing to take the necessary steps to protect the environment. The most environmentally conscious cities will most likely have a high percentage of their populations involved in environmental organizations and environmental protection and cleanup programs.
Environmental activism will be prevalent throughout the city and its citizens. The least environmentally conscious cities will have just the opposite, low percentages of citizens involved in environmental organizations and environmental programs and an absence of environmental

16 activism. Most cities fall somewhere in between, but I contend that the more environmentally conscious the citizens of a city are, the higher the recycling diversion rate will be for the city.
This prediction is based on two fundamental assumptions. First, it is assumed that environmental consciousness has a relation to recycling. Recycling, though sometimes thought to be a more practical and structural function, is indeed often associated with environmentalism.
Recycling is a key part of the three pronged environmental approach often advocated throughout the 1990s: reuse, renew, and recycle. Environmentalists are generally concerned about a cleaner environment and the preservation of natural areas. Recycling can be linked to support both of these interests. First, recycling eliminates some of the pollution caused by trash disposal both in cities that bury their trash and cities that incinerate their trash. Landfills can often have drastic effects on the environment and incinerators contribute to air pollution. Second, recycling can aid in the effort to preserve the natural environment. By reusing materials, recycling cuts back on the need to extract natural resources and harm the natural environment in the process. In addition, recycling can help reduce the need to use land for landfills. Recycling, therefore, can be and has been closely linked to recycling in the United States. The other assumption involved when causally linking environmental consciousness with recycling rates is that the values of a city’s citizens will affect the policies and practices of its government. Without this assumption, even cities with very environmentally conscious citizens may have low recycling rates if the city government fails to respond to the values of its citizens and provide recycling programs.
Some may contend that the existence of recycling programs and high recycling rates may actually be fueling environmental consciousness, instead of the other way around. While a few citizens may be inclined to think environmentally more often because of the existence of a recycling program, this connection as a rule is not well founded. The environmental movement

17 in the United States was ignited in the 1970s, followed by a series of reforms and legislation aimed at protecting, cleaning, and preserving the environment. Recycling, then, came in the
1990s, only after the environmental movement had reached mainstream thinking and local politics. Because recycling, at least, initially comes with associated additional costs and efforts, recycling programs would be unlikely without at least some value in the positive effects that recycling will have on the environment. Recycling was indeed a consequence of the introduction of environmentalism in American society, not vice versa. In addition, it is unlikely that another factor is contributing to increased environmental consciousness and recycling rates simultaneously. Environmental consciousness is a broad concept that describes a movement and a way of thinking; recycling is an action that is one effect of this phenomenon.
Median income of cities is the second variable I intend to analyze in relation to recycling diversion rates. I predict that the higher the median income of a city, the higher the recycling rate of that city will be. Indeed, some prior studies have tested this variable in relation to recycling behavior, though in a different way, and have been met with mixed results (Feiock and
Kalan, Guerin et al). When analyzing recycling diversion rates of U.S. cities, though, I believe that median income will show a strong relationship with recycling rates for two reasons, one as a function of income in individuals, and the other as a function of income at the city level. First, many environmental issues, like recycling, have been characterized as “luxury issues,” that is, individuals tend only to care about them when all of their needs are met and they are living comfortably. In other words, a family that is living below the poverty line and has no food to eat is less likely to be concerned about recycling and make the extra effort to recycle than a family that is comfortably living in a million dollar home. In addition, in poorer communities, issues like recycling are likely to take a back burner to economic and social service issues that are

18 important to the people. Therefore, cities with higher median incomes will be made up of more people likely to take environmental action like recycling. In addition, income can affect recycling rates because of the tax revenue that income generates. Cities with higher median incomes will most likely have more tax revenue per capita. While some scholars contend that today recycling costs are similar or less than traditional waste management, most cities experience increased costs for recycling, especially when the programs begin. Therefore, cities with higher tax revenue per capita are better equipped to take on effective and efficient recycling programs than cities with less tax revenue per capita. For these reasons, I predict that cities with higher median incomes will have higher recycling rates.
The causal connection between these two variables is hard to refute. The assertion that recycling rates are actually increasing median income is completely implausible. No evidence exists suggesting that recycling has any effect on income or the economy. In addition, the contention that a third variable is causing the variation in both higher income and recycling rates at the same time holds little weight. Again, there is little that recycling rates and median income have in common that would lead one to believe that they are both side effects of another phenomenon. If it is shown that there is, indeed, a relationship between median income and recycling diversion rates in U.S. cities, it is only logical that higher median income has an effect on recycling rates.
The third variable that I hypothesize affects recycling rates is how Democratic the city is.
I contend that cities that are more Democratic will have higher recycling rates. Cities that tend to vote for Democratic candidates and have Democratic elected officials are more likely to have higher recycling rates. This is a function, primarily, of the political party system of the United
States. Environmental issues, in general, have been associated with the Democratic Party.

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Again, this relies on the assumption that recycling is associated with environmentalism. I believe that Democratic city officials are more likely to enact laws, policies, and programs that will lead to higher recycling rates. In addition, cities in which Democrats hold a majority are more likely to hold their officials accountable for environmental issues, like recycling.
Recycling is often considered a liberal issue of concern to liberal citizens more than conservative citizens. Indeed, if recycling involves greater costs, and greater use of tax resources in order to achieve an environmental goal, it would be less in line with conservative and Republican values of smaller government and lower taxes. Therefore, I believe that recycling is a Democratic issue, and I predict cities that are more Democratic will tend to have higher recycling rates.
It would be difficult to argue that higher recycling rates create a more Democratic city; that is just implausible. The political environment of a community is rooted more in values and culture, not in the existence or effectiveness of a certain program. One may argue that both how
Democratic a city is and the recycling rates could be affected by the first variable discussed, environmental consciousness. Indeed, it may seem likely that higher levels of environmental consciousness will cause both more Democrats to be elected and higher recycling rates. I contend, however, that analyzing how Democratic a city is as in individual variable is still useful and valid. First of all, environmentalism is only one of many issues that affect party affiliation.
To assert that environmental consciousness is what causes cities to be more Democratic would be ignoring the complexity of the American political party system. In addition, in a system of elected representation, the mere existence of environmentalism alone cannot fully explain recycling rates. While environmental consciousness may cause individuals to recycle more with the programs that are made available to them, the actual recycling program is decided by the elected and appointed officials of the city. If a city provides no recycling program, the city will

20 have a low recycling rate no matter how environmentally conscious its citizens are. In other words, the political environment and elected officials of a city matter, not merely the values of the community. They can be separated as two different issues and two different functions. For this reason, I believe it is important to assess both variables.
Overall, I predict that cities that have more environmentally conscious citizens, have higher median incomes, and are more Democratic will have higher recycling rates. The relation between these independent variables and recycling rates is logical, and there appears to be a causal relationship between each variable and the phenomenon this study seeks to explain.
IV. Methodology
To test my hypothesis, I have gathered information on the thirty largest United States cities with indicators to analyze recycling rates, environmental consciousness, income, and political partisan environments. By compiling this data into a regression analysis, I can study the impact of each of the three independent variables—environmental consciousness, income, and level of Democratic support—on recycling rates in U.S. cities, and determine a formula that can be used to explain and predict recycling rates based on these three variables.
The data on recycling diversion rates of the thirty largest cities come directly from a recycling study published in Waste News in February of 2001 (Appendix 1). Among other data, this study reported the recycling diversion rates of U.S. cities based on current data in 2001. The recycling diversion rates are a reflection of the total amount of waste generated by the city that is recycled as opposed to buried or incinerated, and includes all forms and types of recycling including paper, plastics, and yard waste. The Waste News study then ranked the thirty largest

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U.S. cities based on this data. I have used these recycling diversion rates as an indicator for my dependent variable.
As an indicator for the first independent variable of my study, environmental consciousness of the city’s citizens, I have gathered information about the percentage of the population that are Sierra Club members, one of the largest environmental non-governmental organizations in the United States (Appendix 2). Membership in the Sierra Club indicates a certain level of environmental consciousness and activism. If a city has a relatively high percentage of its citizens having membership in the Sierra Club, the city would most likely have a relatively environmentally conscious citizenry. On the other hand, a city with relatively few citizens having membership in the Sierra Club would most likely have a relatively environmentally “unconscious” citizenry. Unfortunately, the Sierra Club does not publish nor distribute data about membership numbers by city, and only publishes the information by state.
Therefore, for this variable, I have calculated the percentage of the state’s population that the city is located in that are members of the Sierra Club. While all cities analyzed in this study were located in states with less than one percent of the population having membership in the Sierra
Club, there is a significant variation among the states. For example, while Oklahoma, the state in which Oklahoma City is located, has .09% of its population registered as members of the Sierra
Club, Oregon, the state in with Portland is located, has .65% of its population registered as members of the Sierra Club, over a 600% difference. The variation across the states that the cities are located in is particularly important for using this indicator as an assessment of environmental consciousness.
It should be noted, however, that using a characteristic of the states to analyze cities, comes with potential drawbacks. Indeed, some states have great diversity within the state and an

22 overall assessment of a state may differ greatly from a particular city in that state. In addition, rural areas tend to have different characteristics than urban areas, and the state characteristics include both areas. This study, though, is focused solely on urban areas. Nevertheless, the state characteristic can be used as a general characteristic of the cities within the state for the purpose of comparison.
For the second independent variable, median income, I have gathered the information on the median household income of these thirty cities from the United States Census Bureau
(Appendix 3). These numbers are based on median household yearly income in the year 2003 and vary from $22,978 in Cleveland, Ohio, to $70,240 in San Jose, California. Because this data is from the United States Census Bureau, the level of reliability and accuracy of the information is very high and can be counted on as a reliable indicator of median income in these cities.
For the third variable, how Democratic a city is, I have used the percentage of the vote that the Democratic mayoral candidate received in the last mayoral election of the city
(Appendix 4). Because most election information is compiled by counties rather than cities, the mayoral race is one of the few races that is an indicator of voting behavior in cities, as opposed to counties or states. Since all cities have different mayoral election cycles and terms, it was necessary to find the information pertaining to the most recent mayoral election, which may have been as far back as six years ago. While most cities had two main candidates for the mayoral race with one being a Democrat, and one being a Republican, some cities had two candidates from the same party. In these cases, one hundred percent of the votes would be considered cast for the party that the two candidates share. In San Francisco, for example, in which both candidates for the last mayoral election were Democrats, the vote was recorded as one hundred percent to the Democratic candidate. The fact that both candidates are from the same party is

23 usually an indicator that the city heavily leans to that party. I am assuming that cities in which the Democratic mayor candidate received a higher percentage of the votes are cities that are more Democratic.
After collecting this data on the thirty largest U.S. cities, I have calculated a multivariate regression using recycling rates as the dependent variable, and percentage of citizens in the state in which the city is located that are registered as members of the Sierra Club, median household income, and the percentage of the vote received by the Democratic mayor candidate in the last mayoral election as the three independent variables.
V. Analysis
I hypothesized that cities that have more environmentally conscious citizens, have higher median incomes, and are more Democratic, will have higher recycling diversion rates.
Therefore, I expect to find in my regression analysis a strong, positive relationship with these three independent variables and recycling rates, and a model that explains a significant amount of the variation of recycling rates in U.S. cities. My regression analysis produced the following statistics:
Constant
Variable Coefficient Standard
Error
-10.264 12.811
% of State Sierra Club
Members (X
1
)
Median Household Income of City (X
2
)
% Vote for Democratic
Mayor Candidate (X
3
)
R
.612 .375
25.397
.001
.073
R-Square
16.071
.000
.106
Adjusted R-Square
.303
Standardized
Coefficient
.308
.338
.112
Std. Error of Estimate
12.7469
T
Value
-.801 .430
1.580
1.784
.690
Significance
.126
.086
.496

24
Equation of the Model: Y= -10.264 + 25.397(X
1
) + .001(X
2
) + .073(X
3
)
This regression shows that there is, indeed, a positive relationship between the three independent variables and recycling rates. The Pearson’s R of .612 indicates that there is a moderate positive association between the independent variables and recycling rates. The adjusted R-Square of .303 indicates that 30.3% of the variation in recycling rates can be explained using the three independent variables of state Sierra Club membership, median household income, and vote percentage for the Democratic mayor candidate. While this number is not extremely high, it does indicate that these three independent variables are useful for explaining the variation in recycling rates. A closer look at the statistics, however, reveals that the significance of each of the three variables is low. In other words, the probability of achieving the shown relationships even if the null were true and there was no relationship between the variables, is too high to be statistically relevant using a 5% significance test.
The Constant of –10.264 is not a significant number for the purpose of this study.
Basically, it tells us that a city located in a state with 0% of its citizens registered as members of the Sierra Club, having a $0.00 median household income, and 0% of the vote of the last mayoral election going to the Democratic mayor candidate would have a –10.264% recycling rate, which is impossible to have. A city with only an $11,000 median household income would surpass this constant. What it does tell us is that there is a certain minimum at which these factors begin to affect recycling rates. Because there are few cities with median incomes of less than $11,000, however, this number can be ignored. I will now analyze the three independent variables, each highlighted in the following repeated multivariate regression tables.

25
Variable (1): Percent of State Registered as Members of the Sierra Club:
Constant
Variable
% of State Sierra Club
Members (X
1
)
Median Household Income of City (X
2
)
% Vote for Democratic
Mayor Candidate (X
3
)
Coefficient
-10.264
25.397
.001
.073
Standard
Error
12.811
16.071
.000
.106
Standardized
Coefficient
.308
.338
.112
T
Value
Significance
-.801 .430
1.580
1.784
.690
.126
.086
.496
The first independent variable, the percent of the state’s population in which the city is located that are members of the Sierra Club, shows a positive causal relationship with recycling diversion rates. As the percentage of the population that are Sierra Club members increases by
1%, the recycling diversion rate is predicted to increase by 25.397%, when all other variables are held constant. Since all cases in this study were located in states with the percent of the population that are Sierra Club members less than 1%, a more understandable conclusion from these statistics is that as the percentage that are Sierra Club members increases by .10%, the recycling diversion rate of the city is predicted to increase by 2.5397%.
The significance of this variable, however, indicates that it may not be statistically significant. The significance of .126, indicates that there is a 12.6% chance of getting a relationship of this magnitude if the null were true and there was no actual relationship between these variables. Nevertheless, it must be remembered that this variable is based on statistics of the state that the city is located in, not the city itself. If, as it may well be the case, Sierra Club membership is concentrated in urban areas, as opposed to rural areas, this relationship could be strengthened. Indeed a .126 significance level is not extremely high, indicating that there is an
87.4% that this relationship is relevant. For the purpose of this study, however, the effect that

26
Sierra Club membership, and thus environmental consciousness, has on recycling rates is inconclusive. It does appear that there is a relationship, but the actual strength of the relationship should be studied further for more conclusive findings.
Variable (2): Median Household Income:
Constant
Variable Coefficient
-10.264
Standard
Error
12.811
% of State Sierra Club
Members (X
1
)
Median Household Income of City (X
2
)
% Vote for Democratic
Mayor Candidate (X
3
)
25.397
.001
.073
16.071
.000
.106
Standardized
Coefficient
T
Value
Significance
-.801 .430
.308
.338
.112
1.580
1.784
.690
.126
.086
.496
The second variable, median household income, also shows a positive causal relationship with recycling diversion rates. As the median household income of a city increases by $1.00, the recycling rate is predicted to increase by .001%, when all other variables are held constant.
Because household income is consistently in the thousands, however, a more useful conclusion would be that as median household income increases by $1000.00, recycling rates are predicted to increase by 1%. This is a fairly useful statistic and does indicate that cities with higher median household incomes are more likely to have higher recycling rates. The standardized coefficient of .338 is the highest of the three variables, indicating that median household income has the most impact on the model of the three variables tested. The significance of this statistic of .086, however, calls into question the significance of this relationship. There is an 8.6% chance of getting a relationship of this magnitude if the null were true and there was not an actual relationship between median household income and recycling rates. With a 5%, one-sided

27 significance standard, this variable would not be significant. If, however, the significance standard were increased to 10%, this statistic would be significant. With these considerations, it would be safe to conclude that there is most likely a relationship between median income and recycling rates, though it may not be to the magnitude that this study has found.
Variable (3): Percent Vote Received by Democratic Mayoral Candidate:
Constant
Variable
% of State Sierra Club
Members (X
1
)
Median Household Income of City (X
2
)
% Vote for Democratic
Mayor Candidate (X
3
)
Coefficient
-10.264
25.397
.001
.073
Standard
Error
12.811
16.071
.000
.106
Standardized
Coefficient
.308
.338
.112
T
Value
Significance
-.801 .430
1.580
1.784
.690
.126
.086
.496
The third variable, percent of vote that the democratic mayoral candidate received in the last mayor election, also shows a positive causal relationship with recycling diversion rates. As the percent of the vote received by the Democratic mayor candidate increases by 1%, the recycling diversion rate is predicted to increase by .073%. The impact of this variable, however, is not significant. The standardized coefficient of .112 indicates that this variable has a minimal effect on the model compared to the other two variables. In addition, the significance level of this variable of .496 is extremely high, indicating that there is nearly a 50% chance of getting a relationship of this magnitude if the null were true and there was no actual relationship between these variables, making the numbers statistically invalid. The high significance indicator of this variable and the low impact that the variable has on the model rule the variable out as a reliable indicator of explaining and predicting recycling diversion rates in cities.

28
VI. Conclusion
In my study I have tested three variables and their relationships with recycling diversion rates in U.S. cities. I hypothesized that there is a direct causal relationship between environmental consciousness, median income, and how Democratic a city is, and recycling rates.
What I have found, however, is that there is most likely such a relationship with median income and recycling rates, the relationship between environmental consciousness and recycling rates is inconclusive, and the relationship between how Democratic a city is, or the “blueness” of city, and recycling rates is nonexistent.
Finding that median income is a reliable indicator of recycling rates in U.S. cities is a significant finding in itself. This finding is consistent with other studies, which found correlations between wealth and environmentalism or recycling (Feiock and Kalan,
Satterthwaite, Feiock and West). These studies, however, were based on different units of analysis or different dependent variables, and what this study has done is confirmed that such a correlation between wealth and recycling is also evident in city recycling diversion rates at a single point in time. This conclusion further contributes to the notion of environmentalism as a luxury issue, and is significant for the development and implementation of environmental programs in cities and regions, recycling being just one of those programs. In order for a city or region to successfully implement environmental programs, the city or region must be economically viable. The better off financially that a city’s citizens are, the better chance an environmental program will be successfully implemented. Because many of the cities and regions with the most severe environmental problems also happen to be some of the poorest places on earth, this is worrisome to environmentalists. Those hoping for changes in

29 environmental policies and implementations of environmental programs must either find a way to improve the economy of the targeted city or region, or find another means around this problem. Several studies, including this one, have found a direct causal relationship between income and the successful implementation of environmental policies and programs, and this fact cannot be ignored.
The inconclusiveness of the relationship between environmental consciousness and recycling rates in U.S. cities is perhaps more of a result of the indicator used in this study as opposed to a lack of a relationship. Other studies have found a relationship between environmentalism and recycling rates using other units of analysis (Guerin et al), and others have found little relationship between these two variables (Feiock and Kalan). This study reflects the inconclusiveness of this relationship when applied to U.S. cities. While the statistics found do indicate that there is fairly high probability that such a relationship does exist, more research must be done in order to confirm or refute such a finding. A more reliable and accurate indicator of environmental consciousness, such as surveys, could possibly lead to a more decisive conclusion about the relationship between environmental consciousness and recycling rates.
Intuitively, it would seem likely that such a relationship exists, but dynamics of American and local politics may limit the ability for environmental consciousness to translate into concrete environmental initiatives. Again, however, more research must be done to make a decisive conclusion either way.
The final finding, that there is no relationship between how Democratic a city is and recycling rates, is also significant. While the Democratic Party has traditionally been hailed as the more environmental of the two main parties of the United States, this apparently is not evident in local politics, assuming that recycling rates are a reflection of environmentalism.

30
While the Democratic Party may have the ability to affect national environmental initiatives, their ability to do so with local environmental initiatives, however, has not been demonstrated.
This finding is also a reflection of local partisan politics. This study would support the notion that, at the local level, partisan politics is very different than at the national level. The ability for local politicians to incorporate the national party platform into their agenda may be difficult.
How Democratic a city is, however, is also a reflection of it’s citizens. Though Democratic citizens may attest to being more environmental, this is not demonstrated in the recycling rates of a city. Even cities with equal recycling programs will have variations in recycling rates based on citizen participation. What this study has found is that Democrats are not more likely to participate in recycling than Republicans. I still, however, believe that more research must be done on this subject. Using the percent of the vote that the Democratic mayoral candidate received as the indicator for how Democratic a city is, is subject to error. Indeed many would argue that a Republican from New York is more Democratic than a Democrat from Omaha. In addition, whether or not a candidate is an incumbent could also affect the vote percentages.
Because election data is compiled by county and elections are subject to local political discrepancies, a survey, again, may be the only reliable alternative to find a more accurate indicator for this variable. While this study has found that there is no relationship between how
Democratic a city is and recycling rates, this is, indeed, the first study that has tested such a relationship, and more research should be done, possibly using different indicators, before making a final conclusion about this relationship.
Ultimately, the purpose of this study was to find why some cities have higher recycling rates than others. What this study has concluded is that part of the reason is due to income; cities with higher mean incomes tend to have higher recycling rates. Part of the reason is not how

31
Democratic a city is, but part of the reason could be how environmentally conscious a city is.
While this study does contribute to the research related to recycling and environmentalism, it also shows that more research must be done in this field. Many of the previous studies done on recycling have been conflicting or inconclusive, and this study reflects that. We must further probe into the factors that affect recycling rates, such knowledge cannot only help cities, regions, and environmental groups implement successful recycling programs, but it could also add to the understanding of environmental action and what factors help bring about environmental policies and programs. This study, like many before, leaves more questions than answers, but nevertheless is a contribution to the field of research.

Appendix 1
Portland
Seattle
Chicago
San Jose
San Diego
San Francisco
Los Angeles
Jacksonville
Baltimore
Philadelphia
Austin
Milwaukee
San Antonio
Indianapolis
Charlotte
Oklahoma City
Memphis
New York
Dallas
Phoenix
Washington
Houston
Boston
Columbus
Nashville
Denver
Fort worth
Detroit
El passé
Cleveland
53.6%
52.0%
47.9%
47.0%
46.0%
42.0%
40.9%
39.0%
35.3%
32.5%
28.5%
28.0%
26.3%
24.0%
24.0%
23.6%
19.9%
19.7%
19.0%
18.0%
17.0%
16.0%
14.0%
9.7%
8.0%
7.5%
7.2%
7.2%
4.0%
2.0%
32

Appendix 2
City
Portland
Seattle
Chicago
San Jose
San Diego
San Francisco
Los Angeles
Jacksonville
Baltimore
Philadelphia
Austin
Milwaukee
San Antonio
Indianapolis
Charlotte
Oklahoma City
Memphis
New York
Dallas
Phoenix
Washington
Houston
Boston
Columbus
Nashville
Denver
Fort worth
Detroit
El passé
Cleveland
% Sierra Club
Members
0.65%
0.51%
0.21%
0.56%
0.56%
0.56%
0.56%
0.19%
0.30%
0.23%
0.12%
0.27%
0.12%
0.13%
0.22%
0.09%
0.12%
0.24%
0.12%
0.23%
0.62%
0.12%
0.43%
0.19%
0.12%
0.50%
0.12%
0.22%
0.12%
0.19%
33

Appendix 3
City
Portland
Seattle
Median
Income
$40,885.00
$49,469.00
Chicago
San Jose
$40,879.00
$70,240.00
San Diego $47,631.00
San Francisco $57,833.00
Los Angeles $40,733.00
Jacksonville $41,167.00
Baltimore $32,452.00
Philadelphia $33,062.00
Austin $40,921.00
Milwaukee $32,291.00
San Antonio $36,994.00
Indianapolis
Charlotte
$41,349.00
$44,129.00
Oklahoma City $35,694.00
Memphis $32,315.00
New York
Dallas
$39,937.00
$36,678.00
Phoenix
Washington
Houston
$40,919.00
$42,118.00
$35,597.00
Boston
Columbus
Nashville
Denver
Fort worth
Detroit
El passé
Cleveland
$42,567.00
$40,042.00
$39,794.00
$43,978.00
$39,729.00
$39,729.00
$32,495.00
$22,978.00
34

Appendix 4
City
Portland
Seattle
Chicago
San Jose
San Diego
San Francisco
Los Angeles
Jacksonville
Baltimore
Philadelphia
Austin
Milwaukee
San Antonio
Indianapolis
Charlotte
Oklahoma City
Memphis
New York
Dallas
Phoenix
Washington
Houston
Boston
Columbus
Nashville
Denver
Fort worth
Detroit
El passé
Cleveland
% Democratic
Vote- Mayor
Race
100.00%
100.00%
78.00%
80.50%
.00%
100.00%
100.00%
42.00%
88.00%
63.00%
58.26%
100.00%
68.25%
62.62%
40.00%
38.07%
70.00%
44.51%
56.07%
72.00%
60.00%
63.00%
72.00%
95.46%
84.20%
65.00%
61.00%
54.00%
39.38%
54.00%
35

36
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