AN ANALYSIS OF MUNICIPAL FINANCIAL BEHAVIOR USING THE NATIONAL LEAGUE OF CITIES? TYPOLOGY Except where reference is made to the work of others, the work described in this dissertation is my own or was done in collaboration with my advisory committee. This dissertation does not include proprietary or classified information. _________________________ Jeffrey M. Blankenship Certificate of Approval: _________________________ _________________________ Linda F. Dennard, Co-Chair Anne Permaloff, Co-Chair Associate Professor Professor Political Science and Public Political Science and Public Administration Administration _________________________ _________________________ Cynthia J. Bowling Caleb M. Clark Associate Professor Professor Political Science Political Science _________________________ _________________________ Carl Grafton Joe F. Pittman Profesor Interim Dean Political Science and Public Graduate School Administration AN ANALYSIS OF MUNICIPAL FINANCIAL BEHAVIOR USING THE NATIONAL LEAGUE OF CITIES? TYPOLOGY Jeffrey M. Blankenship A Dissertation Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 4, 2007 iii AN ANALYSIS OF MUNICIPAL FINANCIAL BEHAVIOR USING THE NATIONAL LEAGUE OF CITIES? TYPOLOGY Jeffrey M. Blankenship Permission is granted to Auburn University to make copies of this dissertation at its discretion, upon request of individuals or institutions and at their expense. The author reserves all publication rights. ____________________________ Signature of Author ____________________________ Date of Graduation iv VITA Jeffrey M. Blankenship, son of John M. and Mary Sue Blankenship, was born September 15, 1961, in Birmingham, Alabama. He graduated from Bob Jones High School in Madison, Alabama in 1979. He attended the University of Alabama in Huntsville and graduated cum laude with a Bachelor of Arts degree in Political Science in December 1984. He attended the University of Alabama ? School of Law and graduated in May 1988. While working as an attorney, he attended the University of Alabama at Birmingham and graduated with a Master of Public Administration degree in August 1995. After being an attorney for 15 years, he entered the doctoral program in Public Administration and Public Policy at Auburn University in August 2003. v DISSERTATION ABSTRACT AN ANALYSIS OF MUNICIPAL FINANCIAL BEHAVIOR USING THE NATIONAL LEAGUE OF CITIES? TYPOLOGY Jeffrey M. Blankenship Doctor of Philosophy, August 4, 2007 (M.P.A., University of Alabama at Birmingham, 1995) (J.D., University of Alabama, 1988) (B.A., University of Alabama in Huntsville, 1984) 335 Typed Pages Directed by Anne Permaloff and Linda F. Dennard Municipal finance research is an endeavor that requires the classification of cities for comparison purposes. Traditional methods of grouping cities have relied primarily on the form of government (mayor versus manager) or metropolitan status (central versus suburb) of the city. However, with changes in the governing structure and nature of cities, the continued validity of such classification methods has been called into question. The National League of Cities (NLC) recently developed a new typology that consists of six categories of cities: Spread cities; Gold coast cities; Metro centers; Meltingpot cities; Boomtowns; and Centervilles. vi This dissertation uses the NLC typology to analyze the financial behavior of cities and to compare this classification method to other options, specifically classifications based on form of government, metropolitan status, and principal city status. The study focuses on demographic variables previously found to impact municipal financial behavior and several fiscal measures to compare the classification schemes and gauge the relative utility of each. The study tests seven hypotheses predicting significant differences between city types within the NLC typology and that the typology is a better means of classification. The analysis uses institutional, demographic, and financial data from the U.S. Bureau of the Census and the International City/County Management Association. The findings of the study show there are statistically significant differences among the NLC typology categories for all financial measures analyzed based on analysis of variance and t-test methods. The results further confirm that the typology offers a method of classification that has greater explanatory power in predicting financial outputs among cities as shown through comparison of means and multiple-regression analysis. vii ACKNOWLEDGMENTS I would like to recognize some of the people who have helped me while I was completing this dissertation. First, I am most grateful for the help and guidance provided by Dr. Anne Permaloff. Without her assistance and understanding, I would never have been able to produce the work. While I cannot say she made it fun, I am sure it was much more bearable because of her. For this I am beholden. I also thank Dr. Linda Dennard for her advice and support. I am truly indebted to Dr. Cindy Bowling for her encouragement, as well as all the counseling and friendship she provided me throughout my time in the program. Dr. Cal Clark and Dr. Carl Grafton were very encouraging and helpful in offering advice and direction, and I am thankful to them. I would further like to express my appreciation to the many friends and colleagues in Auburn who made the past several years both enjoyable and memorable. I would be remiss if I did not credit Dr. Elizabeth Mueller for all her help, without which I could not even imagine attempting to complete this endeavor. Finally, there are a number of individuals, too many to name here, who throughout my life have encouraged me to follow my dreams and have always had great influence on the paths I have taken. To all these friends, I offer a special thank you. viii Style manual or journal used: Publication manual of the American Psychological Association (5 th Edition, 2001) Computer software used: SPSS 15.0 for Windows (2006) Microsoft Office Word (Professional Edition, 2003) ix TABLE OF CONTENTS LIST OF TABLES............................................................................................................ xii CHAPTER 1: INTRODUCTION........................................................................................1 Importance of Studying the Issue ...................................................................................2 Overview of Past Research .............................................................................................4 Need for Better Classification....................................................................................5 Form of Government..................................................................................................5 Functional Responsibility ..........................................................................................8 Demand Variables......................................................................................................8 Metropolitan Status....................................................................................................9 National League of Cities? Report ................................................................................11 Research Objectives......................................................................................................13 Data and Methodology..................................................................................................16 Summary of Subsequent Chapters................................................................................20 CHAPTER 2: REVIEW OF THE LITERATURE ............................................................21 Systems Theory.............................................................................................................21 Form of Government.....................................................................................................22 Manager Cities Are More Efficient .........................................................................27 Mayor Cities are More Efficient..............................................................................32 x Form of Government Makes No Difference............................................................36 Functional Responsibility .............................................................................................45 Demand Variables.........................................................................................................50 Metropolitan Status.......................................................................................................63 Elasticity .......................................................................................................................71 National League of Cities? Report ................................................................................72 CHAPTER 3: METHODOLOGY AND DATA ...............................................................79 Research Design............................................................................................................80 Hypotheses....................................................................................................................82 Data Sources and Coding Procedures...........................................................................87 CHAPTER 4: DESCRIPTIVE ANALYSIS AND FINDINGS.........................................95 Regional Influence........................................................................................................97 Demographic Factors..................................................................................................105 Comparison of Classifications ....................................................................................115 Fiscal Outputs .............................................................................................................118 Analysis of Variance for Demographic Variables......................................................129 Analysis of Variance for Fiscal Outputs.....................................................................137 Distribution of Cities Within National League of Cities? Typology Based on Designations Used in Prior Classification Schemes ................................144 Student?s t-test Analysis .............................................................................................151 Summary of Student?s t-test Analysis ........................................................................188 CHAPTER 5: MULTIPLE-REGRESSION ANALYSIS AND FINDINGS ..................192 Impact of Demographic Variables on Fiscal Outputs.................................................192 xi Total Expenditure for Different City Types...........................................................194 Common Function Expenditure for Different City Types.....................................201 Police Expenditure for Different City Types.........................................................209 Total Revenue for Different City Types ................................................................216 Property Tax Revenue for Different City Types....................................................223 Sales Tax Revenue for Different City Types.........................................................231 Intergovernmental Revenue for Different City Types...........................................238 Total Debt for Different City Types ......................................................................244 Full Faith and Credit Debt for Different City Types .............................................251 Summary of the Analysis of Demographic Variables ................................................257 Impact of Revenue Sources on Expenditure and Debt Outputs..................................259 Summary of the Analysis of Revenue Source Variables............................................277 Comparison of the Classification Schemes? Utility for Financial Research...............278 CHAPTER 6: CONCLUSION ........................................................................................282 Summary of Hypothesis Testing.................................................................................283 Differences Between the NLC Typology Categories ............................................284 Comparison of Classification Schemes .................................................................284 Strengths and Weaknesses of the Study......................................................................287 Suggestions for Further Research ...............................................................................288 REFERENCES ................................................................................................................290 APPENDIX: LIST OF CITIES ANALYZED.................................................................297 xii LIST OF TABLES Table 1.1 Comparison of Sample Cities to All U.S. Cities............................................17 Table 3.1 Dependent Variables......................................................................................89 Table 3.2 Comparison of Sample Cities Without and With Inclusion of Mega-Metro Centers..................................................................................90 Table 3.3 Independent Variables ...................................................................................92 Table 3.4 Control Variables...........................................................................................93 Table 4.1 Regional Location of City Types Within National League of Cities? Typology ............................................................................................98 Table 4.2 Regional Location Based on Form of Government.....................................100 Table 4.3 Regional Location Based on Metro Status ..................................................101 Table 4.4 Regional Location Based on Principal City Status......................................101 Table 4.5 City Types Within National League of Cities? Typology by Region..........102 Table 4.6 Form of Government by Region..................................................................103 Table 4.7 Metro Status by Region ...............................................................................103 Table 4.8 Principal City Status by Region...................................................................104 Table 4.9 Demographic Characteristics by Region .....................................................105 Table 4.10 Demographic Characteristics of City Types Within National League of Cities? Typology ..........................................................108 Table 4.11 Demographic Characteristics Based on Form of Government ....................110 xiii Table 4.12 Demographic Characteristics Based on Metro Status..................................112 Table 4.13 Demographic Characteristics Based on Principal City Status.....................114 Table 4.14 Fiscal Outputs by Region.............................................................................119 Table 4.15 Fiscal Outputs of City Types Within National League of Cities? Typology......................................................................................................121 Table 4.16 Fiscal Outputs Based on Form of Government ...........................................124 Table 4.17 Fiscal Outputs Based on Metro Status.........................................................126 Table 4.18 Fiscal Outputs Based on Principal City Status ............................................127 Table 4.19 Oneway ANOVA Results for Region and Demographics...........................130 Table 4.20 Oneway ANOVA Results for National League of Cities? Typology and Demographics.......................................................................132 Table 4.21 Oneway ANOVA Results for Form of Government and Demographics .......................................................................................133 Table 4.22 Oneway ANOVA Results for Metro Status and Demographics..................134 Table 4.23 Oneway ANOVA Results for Principal City Status and Demographics .......................................................................................135 Table 4.24 Oneway ANOVA Results for Region and Fiscal Outputs...........................137 Table 4.25 Oneway ANOVA Results for National League of Cities? Typology and Fiscal Outputs.......................................................................139 Table 4.26 Oneway ANOVA Results for Form of Government and Fiscal Outputs........................................................................................140 Table 4.27 Oneway ANOVA Results for Metro Status and Fiscal Outputs..................141 Table 4.28 Oneway ANOVA Results for Principal City Status and Fiscal Outputs........................................................................................142 Table 4.29 Form of Government in National League of Cities? Typology Cities.........144 Table 4.30 Form of Government in National League of Cities? Typology Cities by Region ..........................................................................145 xiv Table 4.31 Metro Status of National League of Cities? Typology Cities ......................147 Table 4.32 Metro Status of National League of Cities? Typology Cities by Region.....................................................................................................148 Table 4.33 Principal City Status of National League of Cities? Typology Cities............................................................................................149 Table 4.34 Principal City Status of National League of Cities? Typology Cities by Region ..........................................................................150 Table 4.35 Comparison of Spread Cities to Other Type Cities Within National League of Cities? Typology on Demographics .............................153 Table 4.36 Comparison of Spread Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................154 Table 4.37 Comparison of Gold Coast Cities to Other Type Cities Within National League of Cities? Typology on Demographics .............................155 Table 4.38 Comparison of Gold Coast Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................156 Table 4.39 Comparison of Metro Centers to Other Type Cities Within National League of Cities? Typology on Demographics .............................157 Table 4.40 Comparison of Metro Centers to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................158 Table 4.41 Comparison of Meltingpot Cities to Other Type Cities Within National League of Cities? Typology on Demographics .............................159 Table 4.42 Comparison of Meltingpot Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................160 Table 4.43 Comparison of Boomtowns to Other Type Cities Within National League of Cities? Typology on Demographics .............................161 Table 4.44 Comparison of Boomtowns to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................162 Table 4.45 Comparison of Centervilles to Other Type Cities Within National League of Cities? Typology on Demographics .............................163 xv Table 4.46 Comparison of Centervilles to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .............................164 Table 4.47 Comparison of Mega-Metro Centers to Other Type Cities Within National League of Cities? Typology on Demographics.................165 Table 4.48 Comparison of Mega-Metro Centers to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs .................166 Table 4.49 Comparison of Northeastern Cities to Cities in Other Regions on Demographics .........................................................................................167 Table 4.50 Comparison of Northeastern Cities to Cities in Other Regions on Fiscal Outputs .........................................................................................168 Table 4.51 Comparison of Midwestern Cities to Cities in Other Regions on Demographics .........................................................................................169 Table 4.52 Comparison of Midwestern Cities to Cities in Other Regions on Fiscal Outputs .........................................................................................170 Table 4.53 Comparison of Southern Cities to Cities in Other Regions on Demographics .........................................................................................171 Table 4.54 Comparison of Southern Cities to Cities in Other Regions on Fiscal Outputs .........................................................................................172 Table 4.55 Comparison of Western Cities to Cities in Other Regions on Demographics .........................................................................................173 Table 4.56 Comparison of Western Cities to Cities in Other Regions on Fiscal Outputs .........................................................................................174 Table 4.57 Comparison of Mayor Cities to Manager and Commission Cities on Demographics .........................................................................................175 Table 4.58 Comparison of Mayor Cities to Manager and Commission Cities on Fiscal Outputs .........................................................................................176 Table 4.59 Comparison of Manager Cities to Mayor and Commission Cities on Demographics .........................................................................................177 Table 4.60 Comparison of Manager Cities to Mayor and Commission Cities on Fiscal Outputs .........................................................................................178 xvi Table 4.61 Comparison of Central Cities to Suburbs and Independent Cities on Demographics .........................................................................................180 Table 4.62 Comparison of Central Cities to Suburbs and Independent Cities on Fiscal Outputs .........................................................................................181 Table 4.63 Comparison of Suburbs to Central and Independent Cities on Demographics .........................................................................................182 Table 4.64 Comparison of Suburbs to Central and Independent Cities on Fiscal Outputs .........................................................................................183 Table 4.65 Comparison of Suburbs to Non-Suburbs on Demographics........................184 Table 4.66 Comparison of Suburbs to Non-Suburbs on Fiscal Outputs........................185 Table 4.67 Comparison of Principal Cities to Non-Principal Cities on Demographics .........................................................................................186 Table 4.68 Comparison of Principal Cities to Non-Principal Cities on Fiscal Outputs .........................................................................................187 Table 4.69 Statistically Significant Differences of City Types .....................................189 Table 5.1 Total Expenditure Regressed on Demographic Variables for City Types Within the National League of Cities? Typology......................194 Table 5.2 Total Expenditure Regressed on Demographic Variables for City Types Based on Form of Government .................................................197 Table 5.3 Total Expenditure Regressed on Demographic Variables for City Types Based on Metro Status...............................................................199 Table 5.4 Total Expenditure Regressed on Demographic Variables for City Types Based on Principal City Status..................................................200 Table 5.5 Common Function Expenditure Regressed on Demographic Variables for City Types Within the National League of Cities? Typology ................202 Table 5.6 Common Function Expenditure Regressed on Demographic Variables of City Types Based on Form of Government.............................................205 Table 5.7 Common Function Expenditure Regressed on Demographic Variables for City Types Based on Metro Status.........................................................206 xvii Table 5.8 Common Function Expenditure Regressed on Demographic Variables of City Types Based on Principal City Status............................................208 Table 5.9 Police Expenditure Regressed on Demographic Variables for City Types Within the National League of Cities? Typology....................209 Table 5.10 Police Expenditure Regressed on Demographic Variables for City Types Based on Form of Government ...............................................212 Table 5.11 Police Expenditure Regressed on Demographic Variables for City Types Based on Metro Status.............................................................213 Table 5.12 Police Expenditure Regressed on Demographic Variables for City Types Based on Principal City Status................................................215 Table 5.13 Total Revenue Regressed on Demographic Variables for City Types Within the National League of Cities? Typology............................216 Table 5.14 Total Revenue Regressed on Demographic Variables for City Types Based on Form of Government.......................................................219 Table 5.15 Total Revenue Regressed on Demographic Variables for City Types Based on Metro Status ....................................................................221 Table 5.16 Total Revenue Regressed on Demographic Variables for City Types Based on Principal City Status........................................................222 Table 5.17 Property Tax Revenue Regressed on Demographic Variables for City Types Within the National League of Cities? Typology....................224 Table 5.18 Property Tax Revenue Regressed on Demographic Variables for City Types Based on Form of Government ...............................................227 Table 5.19 Property Tax Revenue Regressed on Demographic Variables for City Types Based on Metro Status.............................................................228 Table 5.20 Property Tax Revenue Regressed on Demographic Variables for City Types Based on Principal City Status................................................230 Table 5.21 Sales Tax Revenue Regressed on Demographic Variables for City Types Within the National League of Cities? Typology....................231 Table 5.22 Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Form of Government ...............................................234 xviii Table 5.23 Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Metro Status.............................................................235 Table 5.24 Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Principal City Status................................................237 Table 5.25 Intergovernmental Revenue Regressed on Demographic Variables for City Types Within the National League of Cities? Typology ..............238 Table 5.26 Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Form of Government .........................................241 Table 5.27 Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Metro Status.......................................................242 Table 5.28 Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Principal City Status ..........................................243 Table 5.29 Total Debt Regressed on Demographic Variables for City Types Within the National League of Cities? Typology ......................................245 Table 5.30 Total Debt Regressed on Demographic Variables for City Types Based on Form of Government..................................................................248 Table 5.31 Total Debt Regressed on Demographic Variables for City Types Based on Metro Status ...............................................................................249 Table 5.32 Total Debt Regressed on Demographic Variables for City Types Based on Principal City Status...................................................................250 Table 5.33 Full Faith and Credit Debt Regressed on Demographic Variables for City Types Within the National League of Cities? Typology ..............251 Table 5.34 Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Form of Government .........................................254 Table 5.35 Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Metro Status.......................................................255 Table 5.36 Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Principal City Status ..........................................256 Table 5.37 Total Expenditure Regressed on Revenue Sources for City Types Within the National League of Cities? Typology ......................................260 xix Table 5.38 Total Expenditure Regressed on Revenue Sources for City Types Based on Form of Government..................................................................261 Table 5.39 Total Expenditure Regressed on Revenue Sources for City Types Based on Metro Status ...............................................................................262 Table 5.40 Total Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status...................................................................263 Table 5.41 Common Function Expenditure Regressed on Revenue Sources for City Types Within the National League of Cities? Typology ..............264 Table 5.42 Common Function Expenditure Regressed on Revenue Sources for City Types Based on Form of Government .........................................264 Table 5.43 Common Function Expenditure Regressed on Revenue Sources for City Types Based on Metro Status.......................................................265 Table 5.44 Common Function Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status ..........................................266 Table 5.45 Police Expenditure Regressed on Revenue Sources for City Types Within the National League of Cities? Typology ......................................267 Table 5.46 Police Expenditure Regressed on Revenue Sources for City Types Based on Form of Government..................................................................268 Table 5.47 Police Expenditure Regressed on Revenue Sources for City Types Based on Metro Status ...............................................................................268 Table 5.48 Police Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status...................................................................269 Table 5.49 Total Debt Regressed on Revenue Sources for City Types Within the National League of Cities? Typology ......................................270 Table 5.50 Total Debt Regressed on Revenue Sources for City Types Based on Form of Government..................................................................271 Table 5.51 Total Debt Regressed on Revenue Sources for City Types Based on Metro Status ...............................................................................272 Table 5.52 Total Debt Regressed on Revenue Sources for City Types Based on Principal City Status...................................................................272 xx Table 5.53 Full Faith and Credit Debt Regressed on Revenue Sources for City Types Within the National League of Cities? Typology ..............273 Table 5.54 Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Form of Government ...............................................274 Table 5.55 Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Metro Status.............................................................275 Table 5.56 Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Principal City Status................................................276 Table 5.57 Results of Multiple-Regression Analysis on Common Function Expenditure of Different City Classification Schemes..............................279 1 CHAPTER 1 INTRODUCTION Municipal finance is an area that offers the opportunity to study politics at close quarters. By examining and understanding the decisions made by local community leaders concerning the raising and allocation of funds, one is able to gain insight into what is occurring at the level of government that most affects our daily lives. It also offers the chance to have a meaningful impact upon our communities, by providing the means to understand and even influence the policy outputs involving city taxing and spending practices. However, to fully understand the dynamics of municipal finance in a country with over 19,000 cities, the differences between these cities must be taken into consideration. Since the time that researchers first began studying local financial behavior, there has been the need to group differing cities into separate categories to make better sense of their situations and the actions of local leaders. Over the years, the structures and practices of local communities have evolved to meet changing circumstances. Unfortunately, the methods that observers have used to frame the activities they were studying have not been adapted in ways that capture the changing nature of the entities under study. 2 The National League of Cities (NLC) and the Metropolitan Institute at Virginia Polytechnic Institute and State University recently published a study, entitled From Meltingpot Cities to Boomstowns: Redefining How We Talk About America?s Cities, in which they identified a new typology of cities (NLC, 2005). Their primary purpose in developing this typology was to enhance local land use planning, but they noted that the typology should be applicable to other policy areas as well, such as finance. The study utilized factor and cluster analysis to analyze socioeconomic and other variables descriptive of 996 U.S. cities with populations between 25,000 and 500,000. This resulted in the classification of six different types of cities: Spread cities (41%), Gold coast cities (20%), Metro centers (9%), Meltingpot cities (14%), Boomtowns (8%), and Centervilles (8%). (The characteristics of each category will be discussed later in this chapter.) This dissertation examines the new NLC typology to determine what, if any, patterns may be discerned concerning the fiscal behavior of these different types of cities. The purpose of the dissertation is to analyze, using a systems theory model (Easton, 1965a), whether the various types of cities produce different policy outputs manifested by fiscal behaviors involving expenditure, revenue, and debt. Various other policy inputs (political, institutional, and demographic) will be controlled to better measure the effects of the primary independent variable, type of city, upon the fiscal output measures. Importance of Studying the Issue This dissertation is significant in that it is the first such study to examine the new NLC typology as applied to local public fiscal behavior. The study also provides 3 meaningful, up-to-date information about the changing nature of the fiscal behavior of cities. There are many differences among cities, and these differences have a great impact upon their fiscal behaviors (Yinger, 1986). To understand what is occurring in different cities, one must be able to conceptualize the differences among them. Thus, a better means to distinguish between cities will provide more clarity in municipal finance research. In order for municipal finance research to remain meaningful, it must adapt to the changing realities of evolving cities. It has been noted that: A review of the literature about cities suggests that widely-held conceptions about the municipal landscape rely on antiquated notions of city forms and assumptions about the functions cities perform. As such, distinctions between cities, such as central city, suburb, rural and metropolitan and non-metropolitan, and the policy prescriptions based on them are increasingly less useful to decision-makers and others attempting to understand and ameliorate local challenges. Thus, local policy makers need a new frame from which to better address their challenges. (NLC, 2005, p. 2) For these reasons, the NLC typology, if it provides a meaningful framework from which fiscal outputs of cities can be compared, will be a much better classification method than the traditional means of grouping cities for purposes of public finance research. 4 Overview of Past Research David Easton has been credited with bringing systems theory to political science and changing the way we view politics (Greene, 2005, p. 130). Under his systems theory, politics can be viewed as a process whereby environmental events result in inputs, consisting of demands and supports, entering the system and causing the system, through its structures and processes, to produce outputs, which are once again acted upon within the environment resulting in additional inputs through a continuous feedback loop (Easton, 1965a). Or, as Easton more succinctly described his model when he said, ?in its elemental form a political system is just a means whereby certain kinds of inputs are converted into outputs? (p. 112). He further defines outputs as being ?confined to those kinds of occurrences . . . described as authoritative allocations of values or binding decisions and actions implementing and relating to them? (p. 126). Outputs result in outcomes which are the effects that the outputs have upon the environment in which the system is operating. The outputs are the actual decisions, policies, programs, expenditures, etc. that a political system produces. Whereas, outcomes are the effects of those outputs ? or what they accomplish. It is through the feedback cycle that the decision makers become aware of the outcomes that their outputs have created. Many of the studies to date dealing with municipal finance have noted that various factors (inputs) impact a city?s ability to respond to the fiscal demands (inputs) required to meet the needs of its citizens (outputs). A growing amount of the literature on American cities has focused on the fiscal problems faced by the largest central cities in the nation?s metropolitan areas (French, 2004; Ladd & Yinger, 1991; Rusk 2003). 5 Need for Better Classification Classification is a process of grouping things into categories based upon established criteria. In the case of a typology, ?each member of a particular group should be as similar as possible to others in the group, but as distinct as possible from the members of other groups? (NLC, 2005). Much of the existing literature on the fiscal behavior of cities has made comparisons between cities based upon the form of government and/or the metropolitan (metro) status of the cities -- whether a city is a central city, suburb, or independent city (e.g., Booms, 1966; Clark, 1968; Deno & Mehay, 1987; Dye & Garcia, 1978; French, 2004; Hays & Chang, 1990; Lineberry & Fowler, 1967; Morgan & Watson, 1995; Sherbenou, 1961; Stumm & Corrigan, 1998). In order to understand cities, there must be a way to meaningfully group similar cities for comparison purposes, while distinguishing others. It has been noted that the traditional differences between the ways that cities with mayor-council and council- manager forms of government operate have become less distinct (DeSantis & Renner, 2002). Likewise, differences between central cities and suburbs are not as distinct as they once were. NLC argues: ??City? and ?suburb? in this respect are antiquated terms associated with an older economic structure in which central cities were the sole economic engines of metropolitan areas surrounded by residential suburbs? (NLC, 2005, p. 3). Form of Government In 1966, Bernard H. Booms first raised the questions of whether a city?s form of government influenced the level of local public expenditures and whether city-manager forms of government were more efficient than mayor-council forms (Booms, 1966). This 6 question has long been considered important because, ?[i]f reform governments cannot deliver lower expenditures and taxes to their citizens, a major theoretical foundation of the reform movement is void? (Stumm & Corrigan, 1998). Booms (1966) looked at 73 cities in Ohio and Michigan. In addition to measuring the impact of form of government, Booms included variables relating to income, intergovernmental transfers, and the location in which the cities were located. After performing a regression analysis, he found that cities with managers spent, on average, significantly less per capita on common functions than did cities with mayor-council governments. Booms next addressed whether this finding meant that cities with managers were more efficient. To answer this question, he argued that one must first decide whether ?this observed difference is due to demand (preference difference) or supply side (cost difference) phenomenona? (p. 192). Citing Wilson and Banfield (1964), he recognized that prior research suggested that a number of variables impacted the demand for services faced by city officials. It had been previously reported that variables impacting demand included: percent of population of foreign stock (negatively), percentage of home ownership (negatively), and percentage of population that was non-White (positively). However, he argued that it was most likely, in light of the law of large numbers, that individuals in both types of cities had the same desires and demands and, therefore, that it was the supply side factors (efficiency, training, politics, etc.) that resulted in the lower spending in city-manager cities. Since Booms first broached the issue, several scholars have explored the subject of the effects of a city-manager form of government on spending patterns of cities. 7 Despite, or perhaps because of, the fact that there have been a number of studies reported within this literature, there have been conflicting findings concerning how a city?s form of government affects its fiscal behavior. Some research has found that the city-manager form of government results in lower public spending (Booms, 1966; Clark, 1968; Lineberry & Fowler, 1967; Stumm & Corrigan, 1998). Other studies have found the opposite, that city-manager cities have higher levels of spending (Cole, 1971; French, 2004; Nunn, 1996; Sherbenou, 1961). Further confusing the issue are studies concluding that the particular form of government has no impact upon the level of spending (Deno & Mehay, 1987; Dye & Garcia, 1978; Hays & Chang, 1990; Liebert, 1974; Lyons & Morgan, 1977; Morgan & Pelissero, 1980; Morgan & Watson, 1995). Perhaps an explanation for why there are conflicting findings on what effect the form of government has upon municipal spending is that the distinction between cities with managers and those with mayors is not as relevant as many have thought. DeSantis and Renner (2002) identified subcategories within the two broad distinctions of council-manager and mayor-council cities. They found that some cities with elected mayors were employing chief administrative offices to perform the professional management functions traditionally done by managers in council-manager cities. They also noted that managers in council-manager cities were not immune from political concerns. Thus, they concluded that the traditional distinctions based upon form of government structure may no longer be a valid means to describe the reality found it cities, if it ever was one. 8 Functional Responsibility In various studies on the effect of form of government upon municipal spending, one of the main areas of agreement seems to be that functional responsibility is a major determinant of city spending (Dye & Garcia, 1978; Farnham, 1986; Liebert, 1974). After recognizing the problem posed by comparing cities with varying functional responsibilities, Booms (1966) utilized the measure of ?common functions? that included only those expenditures that were most commonly provided by nearly all of the cities. These services consisted of police, fire, interest on local debt, non-capital outlays for highways, sanitation, and public health, as opposed to ?optional functions? the provision of which tended to vary more widely between cities. These optional functions included the provision of services such as education, hospitals, and welfare. He argued that utilizing only common function expenditures would ?eliminate most of the variation . . . between cities? (p. 189). Subsequent researchers have dealt with the issue of differences in functions between cities by measuring and controlling for the levels of functional inclusiveness of a city (Dye & Garcia, 1978; Farnham, 1986; Liebert, 1974). Demand Variables Many of the studies to date have noted that various factors impact a city?s ability to respond to the fiscal demands of its citizens, but usually these studies have only focused on the largest cities ? those with at least 50,000 residents. There is a lot less emphases in the literature on the behavior of smaller cities. One of the reasons for this is that smaller cities tend to be less responsive to surveys concerning their practices and, therefore, it is harder to obtain data. An exception to this general trend is French (2004) who analyzed the effect of form of government in small cities and towns. 9 Concerning the demand for services within a city, various variables have been noted to have an impact upon the level of expenditures demanded in a community. In addition to whether the city is a suburb or not, the regional location, the level of intergovernmental revenue, population density, percent non-White, percent of homeownership, actual population size, percent of population under age 18, percent of population aged 65 and older, and per capita income have all been previously recognized as influencing local governmental spending. It has been noted that cities with the following characteristics tend to spend more per capita: non-suburbs, those that receive greater levels of intergovernmental revenue, those with larger percentages of non-White residents, and those with higher percentages of citizens age 65 and older (Bergstrom & Goodman, 1973; Dye & Garcia, 1978; Farnham, 1986). On the other hand, the variables of population density, percent of homeownership, population size, percent of citizens under age 18, and the level of per capita income all tend to be negatively related to city spending (Bergstrom & Goodman, 1973; Booms, 1966; Dye & Garcia, 1978; Liebert, 1974). Also, it has been suggested that regional location influences demand for services and expenditures in cities. While others suggest it does not. (Dye & Garcia, 1978; Lineberry & Fowler, 1967; Wolfinger & Field, 1966). Metropolitan Status Another way in which cities have been classified in studies is by dividing them into groups depending upon the metro status of the city. That is, whether the city is a central city, suburb, or independent city. More recently the Office of Management and Budget (OMB) has developed a new classification, that of principal city, providing 10 another means by which to compare cities (OMB, 2000). According to the OMB classification, within ?each metropolitan statistical area, micropolitan statistical area, and NECTA [New England city and town areas] the largest place and, in some cases, additional places are designated as ?principal cities? under the official standards? (U.S. Bureau of the Census, 2003). This designation replaces the older central city concept, while allowing for more than one principal city per area (OMB, 2000). It has traditionally been observed that central cities and suburbs differ in many ways. However, there have also been many changes in the composition of central cities and suburbs since they first began being studied. It has recently been noted in the literature that these differences are blurring. In particular, many central cities, long in decline, have began to make a comeback, particularly in the 1990s (Furdell, Wolman & Hill, 1005; Simmons & Lang, 2001; Sohmer & Lang, 2001). Suburbs have also been changing as they grow and become more like traditional central cities in many respects (Frey, 2001; Lang, 2004; Lang & Simmons, 2001; Mikelbank, 2004). For these reasons the old categorization of cities by metro status is not as meaningful as it once was. Elasticity In addition to the impact of forms of government and metro status, the concept of the elasticity of a city has been suggested to have an influence upon local fiscal behavior. Rusk (2003) was one of the first scholars who looked at the effects that a city?s ?elasticity? has upon its ability to maintain fiscal health. In doing so, he focused on the cities? ability or lack of ability to incorporate suburban growth into their borders and how that impacted their growth and welfare. Rusk measured the elasticity of a city by adding the city?s density ranking in 1950 and three times the ranking for its level of expansion as 11 measured through the total increase in land area between 1950 and 2000, to arrive at an elasticity score. Rusk?s study has been criticized for its lack of empirical evidence offered to support his conclusions and its failure to use ?appropriate large-sample statistical tests of the elasticity hypothesis? (p. 348), but rather merely relying upon contrasting a small number of cities with one another (Blair, Stanley & Zhang, 1996). National League of Cities? Report In December 2005, NLC published its report setting out a new typology of cities (NLC, 2005). This new typology was intended to more accurately distinguish cities in an effort to foster better local decision making and provide a more useful ?framework that clarifies the local context in which city officials operate? (p. iii). As noted previously, the authors of the report believe that the currently used methods of distinguishing between cities rely ?on antiquated notions of city forms and assumptions about the functions cities perform? and that their typology provides ?a more accurate reflection of the changing nature of the municipal landscape and the diversity that exists among cities? (p. 2). The sample for the NLC study consisted of 996 cities with populations between 25,000 and 500,000. The study excluded cities with populations below 25,000 due to the difficulty of obtaining necessary data on such small cities. It also excluded all cities with populations above 500,000 to avoid outlier effects. The report noted that these largest cities constituted Mega-metro centers, totaling 30 in all. Following factor and cluster analyses, the study omitted 90 of the 996 cities because they did not fit well within the six identified classifications (C. McFarland, personal communication, October 3, 2006). 12 The factors considered in the analyses included: metropolitan and micropolitan designations; population size, density, and rate of growth; median age of residents and of housing stock; education levels; median household income; and percentages of the population who were under age 18, foreign-born, non-Hispanic Whites, homeowners, and living in an urban area. The analyses also considered region and whether the cities were principal cities or not. The NLC report noted that Spread cities are generally located in metropolitan areas in the Midwest and South. They are characterized as having average population sizes and densities but low percentages of children and foreign-born residents. Gold coast cities have greater percentages of older, wealthy, and educated residents. They are mostly suburbs and located in the West and Midwest, and they have greater percentages of homeowners. Metro centers are larger, more diverse, core cities in the South and West. They are mainly principal cities, as opposed to suburbs, and they have large populations, densities, and older housing. They also have low median incomes and greater percentages of non-White residents. Meltingpot cities are dense, diverse, and mostly located in the West, primarily in California. They have less educated, younger, more diverse residents who are overwhelmingly minorities and have more children. They also have the highest level of foreign-born residents. Boomtowns are identified as having experienced rapid growth and low densities. They have newer housing and their residents tend to be wealthy with large percentages of 13 homeowners and children. Most of these cities are suburbs in the West, South, and parts of the Midwest. Finally, Centervilles are described as being mainly principal cities in micropolitan areas that tend to be located in the Midwest and parts of the South. They have the lowest populations, are the least dense, and have the lowest incomes and percentage of foreign- born residents. Research Objectives This dissertation describes and compares the taxing, spending, and debt levels of nearly one thousand different cities. It also looks at the question of significance of the NLC typology to these areas of local public finance. The unit of analysis for the study is individual cities, both within and outside metropolitan areas, as well as the newly designated micropolitan areas. The data are analyzed using descriptive and linear multiple-regression analyses produced with SPSS software. This study uses Easton?s systems theory model to explain municipal fiscal behavior. It looks at how various input factors impact the ultimate financial outputs of cities as measured by the ways they tax, spend, and borrow. Specifically, it addresses the different policy outputs resulting from the various types of cities utilizing the NLC typology. The principal research question to be address is whether the new NLC typology has any relevance concerning the fiscal output policies of municipalities. The dissertation examines and describes the financial characteristics of various types of cities in the NLC typology and explores how they are similar and different. It also uses 14 multiple-regression analysis to test the significance of city type upon outputs of municipal fiscal behavior, such as levels and composition of expenditure, revenue, and debt. The study compares the level of expenditure (as well as the composition of expenditure on major functional categories such as common versus optional functions) and the revenue and debt structure of the different types of municipalities. To generally address the principal research question of whether the NLC typology results in any significant differences in patterns concerning municipal financial outputs, the study addresses the following specific research questions and tests the related hypotheses: 1. What comparisons can be made about the average expenditure, revenue, and debt outputs between the different city types identified in the NLC report? HYPOTHESIS #1: There are significant differences between the expenditure, revenue, and debt outputs in different city types. 2. Are there differences in expenditure levels between the different city types? HYPOTHESIS #2: There are significant differences in expenditure levels between the different city types. 3. Are there differences in revenue levels and sources between the different city types? 15 HYPOTHESIS #3: There are significant differences in revenue levels between the different city types. HYPOTHESIS #4: There are significant differences in revenue sources between the different city types. 4. Are there differences in total debt levels and type of debt between the different city types? HYPOTHESIS #5: There are significant differences in debt levels between the different city types. HYPOTHESIS #6: There are significant differences in the type of debt incurred by the different city types. 5. Are the NLC typologies better indicators of financial behavior of cities than prior categorizations? HYPOTHESIS #7: The NLC typology will provide a more statistically significant measure of the financial behaviors of cities than did prior categorizations as form of government and metro status. By using the various output measures to look at the different city types in relationship to these specific research questions, the study provides more insight into the usefulness of the classifications. 16 Data and Methodology The study looks at 936 cities that have been classified using the NLC typology. This includes all 906 of the cities classified in the NLC typology, plus those cities with more than 500,000 populations which were excluded from its analysis to prevent outlier effects. The 30 largest cities were included in this study to have a more complete analysis of the financial behavior of U.S. cities. Because the initial factor and cluster testing has been completed, there is no longer the problem of these cities producing an outlier effect in the creation of the typology. Inclusion of these 30 additional cities only slightly changes the breakdown of the city types as follows: Spread cities (40%), Gold coast cities (20%), Metro centers (9%), Meltingpot cities (13%), Boomtowns (8%), Centervilles (7%), and Mega-metro centers (3%). The study uses both descriptive and regression analyses to illustrate the financial behavior of cities under consideration. The primary independent variable is the city type according to the NLC typology. The dependent variables are output measures involving either expenditure, revenue, or debt levels or the particular composition of these aspects of fiscal behavior. The various statistical tests also use control variables identified in prior studies as impacting the financial behavior of cities. In addition, the study compares the findings of the influence of the NLC typology with an analysis of the same cities using factors previous examined in the existing literature on municipal fiscal behavior, such as the form of governmental structure and metro status, to see if the NLC typology provides a more meaningful classification when studying municipal fiscal behavior. 17 The data for this study include existing financial, institutional, and demographic information relating to the 936 cities, which are those with populations of 25,000 and above (with the year 2000 serving as the base year) included in the NLC typology, as well as the 30 largest cities that were not typed by the NLC study, but which will be included in this study and referred to as Mega-metro centers. This sample (see Table 1.1) provides a good representation of all cities in the United States that have 25,000 or more residents. Table 1.1. Comparison of Sample Cities to All U.S. Cities (Population of 25,000 and higher) Sample cities US cities a Total number of cities 936 1405 Council-manager form 66% 62% Traditional classification: Suburbs 44% 55% Central cities 48% 36% Independent cities 8% 9% Region: Northeast 13% 22% Midwest 27% 26% South 28% 24% West 32% 27% a Source: International City/County Management Association, Municipal Yearbook 2003. Washington, DC: International City/County Management Association, 2003. The primary sources of financial data used in this dissertation are the City Finance Surveys produced by the U.S. Bureau of the Census (Census Bureau). These surveys contain comprehensive information relating to municipal tax, spending, and debt levels and composition. The primary sources of institutional data on the cities are the Forms of 18 Municipal Governments surveys conducted by the International City/County Management Association (ICMA). These surveys provide historical information concerning the form of government structure and the metro status of the cities. Finally, various demographic data come from both Census Bureau and ICMA sources. These data come primarily from the Census Bureau?s 2002 Census of Governments and the 2000 Decennial Census. The 2002 Census of Governments is part of the City Finance Surveys, which are produced by the Census Bureau, every five years, in years ending in 2 and 7. These surveys contain very comprehensive information relating to municipal expenditure levels. The values for the different dependant variables relating to taxing, spending, and debt practices of the cities are the dollar amounts that were listed for each of the cities in the Census Bureau?s City Finance Surveys converted to per capita figures. Replicating the method used by Booms, common functions are defined based upon the Census Bureau?s definition that includes police, fire, health, sanitation (sewerage and solid waste management), highways, and interest on debt (Booms, 1966). The variable ?Common Function? is computed by adding the total expenditures for each of the six categories of services. The primary independent variable used is the type of city according to the NLC?s typology. The variable ?Typology? indicates the classification for each city with Spread cities, Gold coast cities, Metro centers, Meltingpot cities, Boomtowns, and Centervilles coded as 1 through 6, respectively. The 30 largest cities or Mega-metro centers are coded as 7. Additionally, dummy variables have been created for each of the seven types of city with that type coded as 1 and all others as 0. 19 The form of government variable is coded as 1 for city-manager and 0 for all others (mayor-council and commission forms). The metro status is coded as 1 for central cities, 2 for suburbs, and 3 for independent cities. A dummy variable ?Suburb? has been created and coded as 1 if the city is a suburb and 0 for all others. The information used to determine the institutional variables of form of government and metro status comes from the ICMA?s 2001 Municipal Form of Government Survey (ICMA, 2002). The ICMA?s 2001 survey provides historical information on cities throughout the United States. For the relatively small number of the subject cities that did not respond to this survey, the information concerning their form of government and metro status was obtained from the 2003 Municipal Yearbook (ICMA, 2003). Additionally, this study looks at the possible effects of various other independent variables upon the fiscal output behavior of cities based upon findings from previous research concerning demand and expenditure patterns. These variables include: intergovernmental revenue (measured as a percentage in dollars as total intergovernmental revenues divided by total revenues); region of the country where the city is located according to the Census Bureau?s regional classifications (with the West region serving as the control, and using three dummy variables representing the Northeast, Midwest, and South regions, where the identified region is coded as 1 and all others as 0); population density (measured as the actual 2000 population divided by land area in square miles for each city); percentage of the population that is non-White; percentage of owner-occupied housing units; the actual number of the population; percent of the population under the age of 18; percentage of the population age 65 and older; 20 percentage age 25 and over with bachelor?s degree or higher; and the median household income figures, recorded for 1999, that were listed for each of the cities. The alternative classification of cities as principal cities is reflected by the variable ?P-city?, with principal cities coded as 1 and non-principal cities as 0. Summary of Subsequent Chapters The following chapters focus in more detail upon how the NLC typology is relevant to understanding financial decisions of the cities. Chapter two provides a review of the existing literature on the financial behavior of cities, focusing on the literature related to the different methods used for classification of cities and the utility of these classifications for explaining city outputs, including revenues, expenditures, and debt levels. Chapter three explains the basic methodology and data used in this study and how the data were obtained. It also discusses the coding scheme and the rationale behind the coding choices in relation to the regression analyses performed. Chapters four and five contain the findings of the study, relating them to the research questions and hypotheses presented earlier in this chapter. They also explain the significance of these findings. Chapter six consists of a discussion and conclusion of the study and suggestions for future research expanding upon the topics covered within the dissertation. This chapter is followed by a list of references cited within the dissertation. Finally, an appendix is included setting out the list of cities analyzed in the dissertation. 21 CHAPTER 2 REVIEW OF THE LITERATURE The fiscal behavior of cities has long been a measure by which researchers have sought to understand and explain the dynamics of local governmental policies. Examination of expenditure and revenue patterns for local governments has tended to focus on the impacts of the governmental structure or the functional roles performed by local governments. The analyses rely on a combination of systems theory and typology development as their theoretical base. Systems Theory David Easton has been credited with bringing systems theory to political science and changing the way we view politics (Greene, 2005, p. 130). Under his systems theory, politics can be viewed as a process whereby environmental events result in inputs, consisting of demands and supports, entering the system and causing the system, through its structures and processes, to produce outputs, which are once again acted upon within the environment resulting in additional inputs through a continuous feedback loop (Easton, 1957, 1965a). Or, as Easton more succinctly described his model when he said, ?in its elemental form a political system is just a means whereby certain kinds of inputs are converted into outputs? (p. 112). He further defines outputs as being ?confined to 22 those kinds of occurrences . . . described as authoritative allocations of values or binding decisions and actions implementing and relating to them? (p. 126). Outputs result in outcomes which are the effects that the outputs have upon the environment in which the system is operating. The outputs are the actual decisions, policies, programs, expenditures, etc. that a political system produces. Whereas, outcomes are the effects of those outputs ? or what they accomplish. ?In short, an output is the stone tossed into the pond and its first splash; the outcomes are the ever widening and vanishing pattern of concentric ripples? (Easton, 1965b, p. 352). It is through the feedback cycle that the decision makers become aware of the outcomes that their outputs have created. Systems theory has been a model used to explain the fiscal behavior of local governments. Many of the studies to date dealing with municipal finance have noted that various input factors impact a city?s ability to respond to the fiscal demands of its citizens. Variables such as form of government, metropolitan (metro) status of a city, and demographic characteristics of its population have all been examined as input factors that influence the political system of cities. In this regard, the outputs of local political systems include the various taxing, spending, and debt practices of cities. Form of Government Originally most American cities had a structure of government that was similar to the executive models found at the federal and state levels. Mayors were the chief executives, much like presidents and governors (Renner & DeSantis, 1998). However, the Reform Movement of the late 19 th century sought to eliminate many of the abuses 23 associated with partisanship and patronage, and the reformers were most successful at the local level of government. According to Lineberry and Fowler (1967): The reformers? goal was to ?rationalize? and ?democratize? city government by the substitution of ?community oriented? leadership. To the reformers, the most pernicious characteristic of the machine was that it capitalized on socio-economic cleavages in the population, playing on class antagonisms and on racial and religious differences. (p. 701) The reformers were successful in getting many local governments to change the way they elected officials. The adoption of nonpartisan elections and at-large districts were very effective means of changing the nature of local politics and government. However, no reform technique was more effective or any more lasting in changing the nature of city government in America than the switch from mayor-council styles of leadership to the use of first the commission form of government and later the council- manager form, often referred to as the city-manager form (Lineberry & Fowler, 1967, pp. 701-703). The method by which the city-manager form of government was implemented so widely was through the development and adoption of model city charters that called for this type structure (Svara, 1989, 1990). The primary motivation behind the effort to encourage the use of the city-manager form of government was the belief that such systems would be more efficient, less corrupt, and overall better for the cities. It was felt that having a professional city manager responsible for administrative supervision of city government would result in ?a no-nonsense, efficient and business-like regime, where decisions could be implemented 24 by professional administrators rather than by victors in the battle over spoils? (Lineberry & Fowler, 1967, p. 702). By mid century, the city-manager form of government had become ?a dominant system of municipal government in cities with populations under 50,000? (French, 2004, p. 194). This shift had a substantial impact upon city governance in the United States because, as the International City/County Management Association (ICMA) has reported, 95% of all municipalities in 1999 had populations of under 25,000 (French, 2004, p. 194). Thus, it was desirable for those researchers concerned with the fiscal behavior of cities to focus upon the impact that a city?s form of government might have upon its taxing, spending, and debt practices. Booms (1966) was the first scholar to specifically raise the questions of whether a city?s form of government influenced the level of local public expenditures and whether the city-manager form of government was more efficient than the mayor-council form. To compare expenditure levels of cities, Booms used data from the 1962 Census of Governments, along with the 1963 edition of the Municipal Year Book. He noted that a number of cities had changed their form of government by switching to an administrative structure headed by a city manager rather than a mayor, but that there was a lack of research concerning what effect this new form of government had upon a city?s efficiency level and expenditures. He said it was plausible ?to assume that these changes are made with some hope that the switch in form of government will have an impact on the level of local expenditures? (p. 188). He hoped his study would provide a better understanding of city expenditure and efficiency. 25 Booms proposed two hypotheses: 1) that a city?s form of government was an important factor in determining its per capita expenditure level, and 2) that cities with the city-manager form of government were more efficient than those with the mayor-council form. He acknowledged that his hypotheses assumed all cities had the same level of services per capita, both in quality and quantity, and he admitted simply using lower costs in providing services was ?a very crude measure of efficiency? (Booms, 1966, p. 188). Nevertheless, according to Booms, there were no better alternatives then available. Booms recognized that there were variations between different cities in terms of their functional responsibilities. He felt that the majority of this variation was due to ?differences in jurisdictional scope? (p. 189). As an example, he noted that some cities had the sole responsibility for providing public education, while education in most cities was the responsibility of a separate jurisdictional entity. To control for this variation in function, Booms focused his study on the measure of ?common functions? that included only those expenditures that most of the cities in his study provided. Such functions included responsibility for police, fire, interest on local debt, non-capital outlays for highways, sanitation, and public health. He excluded ?optional functions?, such as education, hospitals, and welfare, because there were much wider variations in regard to responsibility for providing these functions among the cities under consideration. He felt that through ?excluding what are optional functions for some city governments, a more accurate comparison of expenditures between cities can be made? (p. 189). (Several of the authors mentioned in this review of the literature dealt with the issue of the functional responsibility of cities. Generally, a discussion of such comments will be reserved for a later section of the review.) 26 Booms also recognized that a study of cities? expenditure levels had to take into account the extent of overlapping jurisdictions within the cities being compared. He assumed that larger cities had more overlapping of jurisdictions and, for this reason, he limited his sample to cities with populations of 100,000 and under. Additionally, he did not include any cities with populations less than 25,000 to make his sample more uniform in terms of city size (p. 189). He also noted that climate and regional differences had been found to influence the level of per capita expenditures in cities. However, he felt this was not a problem for his study, since he only looked at 73 cities in the states of Michigan and Ohio. A number of variables were used to test his first hypothesis ? that a city?s form of government was an important factor in determining its per capita expenditure level. His dependent variable was the amount of public expenditures per capita on common functions. Nine different socio-economic factors were considered as independent variables. Booms utilized a dummy variable to indicate a city?s form of government, with a 1 code representing manager cities and 0 representing mayor cities. He used another dummy variable to represent the state in which the city was located, with Ohio coded as 1 and Michigan as 0. He used a variable to represent the amount of state aid, or intergovernmental transfers per capita. To account for the possible effect of geographical location upon intergovernmental transfers, he used a compound variable composed by multiplying the state dummy variable by the variable for the amount of intergovernmental transfers. Booms found that the variables of density and median family income were insignificant and excluded them from his final analysis. He also found that income 27 distribution had a more significant affect upon city expenditure levels than did median income. Therefore, his final analysis included two income variables: the percentage of families earning less than $3,000 per year and the percentage of families earning more than $10,000 per year. The findings of the study showed that manager cities spent significantly less per capita ($16.49) than did mayor cities. Thus, Booms concluded that, if the assumption that there were equal levels (in terms of quality and quantity) between the two categories of cities was accepted, ?then in some sense manager cities might be considered more ?efficient? in that they supply the same per capita levels of public services at lower costs per capita? (p. 192). However, he noted that a key question still needed to be addressed: ?whether this observed difference is due to demand (preference difference) or supply side (cost difference) phenomenona before any statements concerning the second stated conclusion regarding efficiency can be made? (p. 192). (This issue is discussed more fully later in this chapter under the section headed Demand Variables.) Since 1966 when Booms first addressed the issue of the effects of a city-manager form of government on spending patterns of cities, several studies on this topic have been reported in the literature. These studies reveal conflicting findings, however, about whether and how a city?s form of government affects its fiscal behavior. Manager Cities Are More Efficient In addition to Booms, several scholars have found that the council-manager form of government results in lower spending by cities. Lineberry and Fowler (1967) studied the question of what impact a city?s political structures, including its form of government, had on the policy output measures of taxation and expenditure levels. Their study 28 focused on whether reformed cities (measured by the presence of city managers, nonpartisan elections, and at-large constituencies) produced different taxation and expenditure patterns than did unreformed cities. They found the level of reformism in cities was negatively related to both taxing and spending policy outputs. Lineberry and Fowler looked at a random sample of 200 out of the existing 309 American cities with 1960 populations of 50,000 and above. Their overall thesis was that ?governments which are products of the reform movement behave differently from those which have unreformed institutions, even if the socio-economic composition of their populations may be similar? (p. 703). The dependent variables they examined were expenditure and taxation patterns of cities with differing forms of government, either reformed or unreformed. These were measured as tax/income and expenditure/income ratios, where the total tax of the city was divided by the total personal income and, likewise, the total expenditures of the city were divided by the total personal income. Their primary independent variable was the level of reform of the city. They measured this on a scale that included the form of government of the city (reformed or unreformed), the type of elections (partisan or nonpartisan), and constituency type (district or at-large). Lineberry and Fowler found that cities with managers taxed and spent less than cities with mayors. However, they also noted that their findings revealed not just differences in fiscal outputs, but contrasts between reformed and unreformed cities in terms of their responsiveness to social cleavages within their populations (pp. 707-708). In this regard, the authors stated: 29 Essentially, then, there are two contrasting views about the consequences of municipal reform. One, the reformers? ideal, holds that institutional reforms will mitigate the impact of social cleavages on public policy. The other argues that the elimination of political parties and the introduction of other reforms will make social cleavages more, rather than less, important in political decision-making. (p. 709) The authors hypothesized that there would be less of an impact from socio-economic cleavages on fiscal outputs in reformed (manager) than in unreformed (mayor) cities. As a result, they argued that socio-economic factors should be better at predicting a city?s taxing and spending behavior in those cities with mayors than would be the case in cities with managers. Lineberry and Fowler discovered that their study supported this proposition. Reformed cities were less responsive to cleavages than were the unreformed cities, as illustrated by the fact that the socio-economic variables under consideration explained only 42% of the variation in taxation policy in manager cities, but 52% in cities with mayors. Likewise, in expenditure policy, the socio-economic variables explained 30% of the variation in manager cities, and 42% in cities with mayors (pp. 709-710). The authors concluded that ?when a city adopts reformed structures, it comes to be governed less on the basis of conflict and more on the basis of the rationalistic theory of administration? (p. 710). Finally, Lineberry and Fowler tested the concept of reformism to determine whether it could be conceptualized as a continuous variable. They did this by using a four-point index ranging from least reformed to most reformed, depending upon how 30 many of the indicators of reform the city exhibited. Cities with no reform aspects (city manager, nonpartisan elections, and at-large constituencies) were rated 1. Those with one such indicator were coded 2, those with two were assigned a code of 3, and those with all three were labeled 4. They hypothesized that the ?higher the level of reformism in a city, the lower its responsiveness to socio-economic cleavages in the population? (p. 713). They correlated both taxes and expenditures with the variables ethnicity, private school attendance, owner-occupancy, and median education. In doing so they found ?there is a clear difference between cities which we have labeled ?most reformed? and ?least reformed?? (p. 714). While they noted this was a crude measure of reformism, they concluded that ?some of the relationships we found are strongly suggestive that reformism may in reality be a continuous variable? (p. 713). Clark (1968) analyzed 51 cities with populations between 50,000 and 750,000. His study used data gathered through surveys conducted by the National Opinion Research Center at the University of Chicago. The study also used data from the U.S. Bureau of the Census (Census Bureau) and the Municipal Yearbook. Clark was primarily concerned with the question of the effects that community structural characteristics had on decision-making patterns, but also addressed their effects on overall budget expenditures and urban renewal spending, both standardized by population size. One of the variables in his study of community structure and decision-making was the Index of Governmental Reformism, which he acknowledged was similar to that used by Lineberry and Fowler. This measure consisted of a city?s score involving three reform indicators: professional city manager; nonpartisan elections; and at-large electoral 31 constituencies (p. 582). Each city was assigned a score based upon the number of these characteristics that was present in the community. Thus, the higher a city?s score, the more reformed it was considered. Clark found there was a positive correlation between a city?s score on the Index of Governmental Reformism and the level of centralization of decision-making. In fact, he found that ?reform government has the strongest relationship with centralization of any variable in the model? (p. 586). Stumm and Corrigan (1998) looked at the question of whether cities with professional managers had lower taxing and spending levels than did cities with mayors. They hypothesized that both the per capita property tax and expenditure levels in cities with managers are more likely to be lower than in cities without managers. Their study used detailed financial information obtained through a survey utilizing a random sample of more than 1,300 cities with populations above 10,000. The number of responding cities was only 149 (approximately 11.5%), but the authors argued that because the responding were similar to the overall population?s seven population groups and four regions, survey results were generalizable. They further noted that it was the best data set available. Initially, the authors compared cities with managers with those without and found that the only difference was that cities without managers received substantially larger amounts of state and federal intergovernmental revenues. However, they were unable to determine whether this was because leaders in cities without managers did a better job of locating and obtaining state and federal funds, or whether it was because cities with managers had lower spending and, thus, did not seek such funding (p. 347). 32 In testing their hypotheses, they found that cities with managers had significantly lower property taxes and lower general fund expenditures (as opposed to long-term expenditures such as debt repayment or capital maintenance), but did not have lower overall spending levels, than cities with mayors. They explained the different findings concerning general fund and overall expenditures by noting that a professional city manager?s contributions to lower spending are more likely to show up in short-term expenditures, which is what the general fund is used for. This is attributable to the fact that city managers generally have short tenure in a particular job, and because the most likely area in which any savings might be realized by having a professional city manager would involve routine expenditures of the city, which are paid for from the general fund. Mayor Cities Are More Efficient In contrast to the studies discussed above, a number of published articles have reported research in which unreformed cities (those with a mayor rather than a manager form of government) were found to spend and tax at lower levels than reformed cities. Sherbenou (1961) examined the issue of the relationship between the city-manager form of government and social class. He studied 74 cities with populations of more than 2,500 in the Chicago suburbs. He measured the social class composition of the cities by ranking median housing values for each city. He found that those cities with higher housing values were more likely to have a city manager, whereas those with lower housing values were more likely to have an unreformed style of government. One of the comparisons he made involved the per capita amounts of the cities? expenditures, property taxes, and debt. He found that, on average, cities with managers spent more, had lower debt, and higher property taxes than the cities without managers. 33 He attributed this finding to several factors, including that cities with managers had greater wealth and the idea that the manager form of government tended ?to develop a public confidence in the efficiency and responsibility of municipal government? (p. 134), which leads to demands for more services and an increased willingness on the part of the city to spend. He reported that the comparisons in his study were in conformity with this belief. Cole (1971) studied the relationships that both a city?s region and its form of government had upon fiscal outputs. Concerning the form of government, he looked at whether cities had a manager or mayor. He analyzed all U.S. cities with populations over 50,000 to determine the amount of variance in policies explainable by form of government and region. The dependent variables he considered included the proportion of employees under a civil service plan, per capita planning expenditures, and the number of requests received seeking urban renewal grants (p. 647). One of his hypotheses was that the form of government would measurably influence a city?s policy outputs in terms of the degree to which outputs were considered more in the public interest. He hypothesized that measures of what was in the public interest included greater levels of the following policy outputs: the percentage of city employees covered by merit systems, amount of money requested from urban renewal programs, and the amount of money a city devoted to planning expenditures (p. 649). His findings concluded that cities with mayors had slightly higher proportions of their employees covered by merit systems and had requested substantially more money per capita through urban renewal programs ? contrary to what would be expected based on the idea offered in support of municipal reforms that reformed cities? outputs are more 34 in the public interest. However, he found that, conforming to the expectations of the reformers, the average amount of money spent on city planning was greater in city- manager cities (p. 650). Once he controlled for region, however, this relationship went away. He concluded that political structure alone was not an adequate indicator of a city?s policy. Nunn (1996) examined infrastructure policies of city-manager cities and strong- mayor cities, among other things, to determine whether the capital spending behavior of the different type cities varied. He studied seven cities in Texas that had a city-manager form of government and seven cities in Indiana that had the strong-mayor form. The reason he chose cities in Texas and Indiana, rather than all from the same state, was that Indiana did not have any city-manager cities, and in Texas the only two large cities with strong mayors were Houston and El Paso. Thus, an intrastate match of cities using data from analyses of infrastructure development policies that he had previously conducted was not an option (p. 108). Nunn noted that he intentionally had not selected his sample cities as ?matched? sets, which resulted in the cities varying in many significant ways. However, he argued that his use of regression analysis would minimize the effects of these differences and control for exogenous effects on the cities. His data concerning the cities? policies were obtained from interviews he had conducted with officials in each city, as well as written descriptions of the cities? policies. Data on the capital spending amounts for the cities were obtained from the Census Bureau?s source tapes of Government Finances for the years 1981 through 1991 (p. 97). 35 The dependent variable used by Nunn was the amount of annual capital spending on water, sewer, and street facilities in each city. Annual capital spending consisted of total spending for construction, equipment, and land. He also examined the per capita amounts (p. 101). After analysis of the data, he found manager cities when compared to strong-mayor cities spent substantially more on capital facilities in terms of both total annual spending and per capita spending. Nunn concluded that variations in the cities? policy environments: Seem to affect capital spending for roads, sewers, and water in the cities. The Texas city manager cities spend more per capita on capital facilities than the Indiana strong mayor cities. Capital spending differences exist even after controlling for fundamental economic, fiscal, and demographic differences among the cities. (p. 93) French (2004) examined the effect of form of government in small cities and towns. He looked at 559 cities and towns that had populations between 2,500 and 25,000, and divided the entities into those that had city managers and those that did not, but rather had an elected official responsible for administration. His data were obtained through a mail survey sent to the mayors and managers of 1,000 cities and towns within his population range that were chosen through a random sample. These included all states, except Hawaii which only had two cities with a mayoral form of government and their populations were greater than his range. French hypothesized that those local governments with city managers would have a lower level of per capita spending than those without. After testing with regression analysis and correlation methods, he found that the mean per capita expenditures were 36 greater for cities with managers than for those without ? the opposite of what had been expected. He tested the cities and towns again, divided into regions, and found that the differences in means for per capita expenditures were insignificant for the Northeast and Midwest regions but remained significant for the South and West. Manager entities in the South had average spending of $1,201 per capita, compared with only $749 per capita in those without managers. In the West, he found that those with managers spent $1,575 per capita as opposed to only $1,114 in those without a manager. Form of Government Makes No Difference A number of scholars, in fact a majority of the published studies to date, have concluded that the form of government makes no difference in municipal expenditures. Liebert (1974), while not performing an analysis of municipal fiscal data of his own, discussed the issue of whether or not the governmental structure of a city had much effect upon it financial outputs. His basic conclusion was: Governmental types play no consistent intervening role between population characteristics and expenditure rates, except in the case of nonwhites. By controlling for inclusiveness, we found that middle class cities, especially wealthy ones, make a smaller effort for the public fisc than do poor, blue-collar cities. (p. 782) According to Liebert, it was the functional scope of city government that had the most influence upon a city?s fiscal output. Lyons and Morgan (1977) addressed the issue of whether intergovernmental transfers of revenue to cities from state and federal governments had a stimulative effect 37 or a substitution effect. That is, they wanted to know whether receiving revenues from state or federal governments would stimulate the cities to spend more of their own money, or whether such transfers would led to the cities spending the state or federal money on what otherwise would have been purchased with city money, thereby substituting the intergovernmental revenues for their own without an overall increase in spending. They looked at 285 cities with populations of at least 50,000 over the period from 1950 to 1970. Their data were obtained from the 1952, 1962, and 1972 editions of the County and City Data Book. Their dependent variables were city general expenditure minus aid, in terms of both total per capita spending and per capita spending for public safety functions (police and fire). After performing multiple regression analysis, they found that the variable of intergovernmental revenue had a stimulative effect on expenditures (p. 1091). Dye and Garcia (1978) looked at what role the variation of functional responsibility among cities played in municipal taxing and spending levels. Their study (focused on cities in 1970) looked at all the 243 central cities of Standard Metropolitan Statistical Areas (SMSAs) and 340 selected suburban cities within these metropolitan areas. All the central cities ranged in population from 50,000 to 7 million. The suburban cities were randomly selected from the 1,200 cities with populations of at least 10,000, which were located within SMSAs. They theorized that cities could be either functionally comprehensive (meaning they performed most of 12 identified municipal functions) or else they could be functionally specialized. They concluded that functional responsibility was the main 38 determinant of taxing and spending levels ? with functionally comprehensive cities spending and taxing more than functionally specialized cities without negatively influencing spending patterns on common functions. They also concluded that ?functionally comprehensive cities are more responsive to the social and ethnic character of their populations than functionally specialized cities? (p. 117). In looking at the reformism of cities, Dye and Garcia noted that ?reformed cities are generally less responsive to the social and ethnic character of their populations than unreformed cities? (p. 117). As part of their analysis of functional responsibility, they divided their cities into four groups. One group contained reformed cities that were functionally specialized. Another consisted of unreformed cities that were functionally specialized. Likewise, they had two groupings representing those cities that were reformed and functionally comprehensive and those that were unreformed and functionally comprehensive. They performed additional analyses (controlling for functional responsibility by regressing each of the four groups) and compared the regression coefficients for reformed and unreformed comprehensive cities and then compared reformed and unreformed specialized cities. They found that among functionally specialized cities the unreformed cities were more responsive than reformed cities, and that spending increased in unreformed cities in response to increases in ethnicity and declined with increases in homeownership. However, among functionally comprehensive cities, they found the opposite ? that ?the spending patterns of functionally comprehensive reformed cities are more closely associated with social and ethnic variables (58% explained) than the 39 spending patterns of functionally comprehensive unreformed cities (31% explained)? (pp. 117-118). Morgan and Pelissero (1980) used a quasi-experimental, interrupted time-series design to test what policy effects a change in the form of government would have on cities? fiscal outputs measured by per capita levels of general revenue and various categories of expenditures. The primary independent variable was whether the city was reformed or not (as measured by the presence of a city manager, nonpartisan elections, and at-large representation). They examined 22 cities with populations of 25,000 and above. The cities were divided into two groups ? 11 in an experimental group and 11 in a matched control group. The experimental group was composed of eight cities that were totally unreformed, but changed to totally reformed sometime during the period from 1948 to 1973. The three other cities were totally reformed at the beginning, but dropped at least two of the elements of reformism during the period. The control group consisted of cities that were matched to cities in the experimental group so that each had the same form of government at the beginning of the study. Additionally, the cities were matched in terms of having similar economic bases and similar per capita general revenues, as well as similar functional responsibilities. The cities in the control group did not change their form of government during the period. The dependent variables consisted of per capita measures of seven revenue and expenditure variables including: general revenue, general expenditures, and expenditures on police, fire, highways, sanitation, and parks and recreation. For each of these variables data were obtained on each matched set of cities for a 10 year period 40 surrounding the date of the change in form for the city in the experimental group ? five years before the change and five years after. It was proposed that there would be significant differences over time between the city undergoing a change in its form of government and the one that did not. They hypothesized that, after the changes occurred, cities which became more reformed would spend less and cities that became less reformed would spend more. Morgan and Pelissero concluded from their analysis, however, that changes in form of government ?have almost no impact on changes in taxing and spending levels? (pp. 1001-1005). Deno and Mehay (1987) replicated Booms? 1966 study ? using data from his original sample of cities in Ohio and Michigan, as well as a national sample of cities ? to examine what effects form of government had upon city expenditures for common functions (as well as employee wages and compensation). Noting that Booms had not specified any behavioral model underlying his demand and supply framework to explain differences in expenditures among cities, they used a median voter model to see whether spending patterns were different among cities with manager or elected mayor forms of government. They hypothesized that the cities with different forms of government would have no significant differences in their expenditures because local government expenditure patterns were determined by the preferences of the median voter. Thus, under the median voter model ?the municipal management structure should have no effect on the provision of local public goods? (p. 630). Deno and Mehay further summarized the theoretical basis of their expectations as follows: 41 We assume that local governments are elected by majority rule and that political competition leads to the election of a governing group whose platform is consonant with the preferences of the median voter. If resident-voters are well informed concerning the costs and benefits of local government services, individual preferences are single peaked, and no strategic vote trading occurs, then candidates who are elected will bring the marginal tax price charged the median voter in line with the marginal benefit received from the services provided. Thus, the quantity of the local public good supplied will be equal to the quantity demanded by the median voter. (p. 630) After replicating Booms? study measuring the effects of form of government on different cities? expenditures on common functions, they concluded that the ?empirical results find no statistically significant differences between these two governmental forms, supporting the hypothesis that the median voter is decisive in budget determination? (p. 639). Hays and Chang (1990) offered yet another approach to address the question of the relative efficiency of, and any differences between, the manager and mayor forms of government. According to them, whether there were differences in efficiency between manager and mayor forms of government was an empirical question, which should be answerable. To do so, they modeled municipal government as a multiproduct firm to measure the relative levels of efficiency between manager and mayor cities. In their model, they hypothesized that a city?s objective was to minimize costs of producing outputs, which they measured using expenditures on police protection, fire protection, and garbage collection. These were used because they ?contribute the major portion of the operating budget for most municipalities? (p. 172). 42 They noted that there are basically only three arguments that support the conclusion that there are differences in efficiency between the manager and mayor forms of government: 1) that managers are better trained and will be more efficient; 2) that managers may receive compensation based on efficiency and will have more incentive to achieve greater efficiency; and 3) that managers may get away with being less efficient due to the shortage of qualified individuals for the positions and, therefore, mayor-led cities will be the most efficient. The alternative conclusion is that there are no differences in efficiency levels between managers and mayors (pp. 168-169). After analyzing data on the costs of input and output factors for these services, they concluded that ?there is no apparent difference in the efficiency levels of the two municipal government structures? (p. 176). Morgan and Watson (1995) examined the influence of mayoral strength and found that it was not a significant predictor of municipal expenditures. They analyzed 459 cities and primarily considered the role and influence of strong versus weak mayors in both the manager and mayor forms of government. In comparing cities with differing forms of government, they found that in neither type of city did mayoral power exert a substantial effect on per capita spending (p. 237). Their study was also noteworthy in that it found no relationship between the functional scope of the city government and spending levels. In addition to these conflicting findings over which form of government spends more or less, many scholars have noted how manager and mayor forms of government have been converging and blurring over time. It has been noted that the traditional differences between the ways that cities with mayor-council and council-manager forms 43 of government operate have become less distinct (DeSantis & Renner, 2002; Hansell, 1999a, 1999b). In fact, these original types of governmental structure have morphed into a variety of hybrid forms (Frederickson & Johnson, 2001; Hansell, 1999a, 1999b; Renner & DeSantis, 1998). Noting this evolution, Hansell (1999) has identified at least four variations in the mayor-council-manager forms of government: the classic form where members of the council elect one of themselves to be the mayor; where the mayor is elected by voters to be the leader of the council; where the mayor is elected by the voters with limited powers, such as the veto, nomination of the manager, or the right to review the budget before submission to the council; and where the mayor is elected separately from the council and has his or her own powers, but where there is a manager appointed by the mayor and confirmed or removed only by the council (p. 28). Frederickson and Johnson (2001) note that a city?s governmental structure is often modified as the desires of its citizens change over time. They report the rate at which such change is occurring has increased. These changes include not only cities changing from mayor-council to city-manager form, they also include cities that retain their basic form of government but adapt it with modifications. They also report that the mayor- council cities are changing more than cities with the council-manager form. Based upon their research, they postulate that ?there is much more structural change occurring in American cities than is commonly understood? (p. 876). Noting the change in municipal forms of government over the years, they identify three broad categories of cities. The pure political cities (traditional mayor-council cities) have no chief administrative officer, council members are paid and may have staff, at 44 least one council member is elected by district, elections are partisan, and the mayor is directly elected and is a full-time position. The pure administrative cities (original council-manager cities) select the mayor from within the council, council elections are at large and nonpartisan, members of the council are not paid and do not have a staff, and the mayor is a part-time position paid less than $10,000 per year. Finally, they discuss what they call the adapted cities. These adapted cities have evolved into some combination of the first two types. The adapted cities are further subdivided into three types depending upon which original type city they most resemble: totally adapted cities; adapted political cities; and adapted administrative cities. The authors note that fully adapted cities are by far the most common type. They have a chief administrative officer, an even mix of at-large and district elected council members, and a mayor who may serve either full- or part-time (p. 878). The adapted political and administrative cities fall in between the fully adapted cities and the two original types they were most like (pp. 876-882). Frederickson and Johnson concluded: The formal legal description of a given city as either ?council-manager? or ?mayor-council? is less accurate than the particular structural variations that the citizens of a given community have chosen to adopt to make their government reflect citizens? preferences and values. (p. 882) No studies attempting to tie their typology involving adapted cities to revenue and expenditures patterns were found by the author of this dissertation. Renner and DeSantis (1998) report a 1996 ICMA Municipal Form of Government survey found that ?Regardless of form of government, 77.5% of responding 45 municipalities report an appointed chief administrative officer position of professional management? (p. 30). This included the majority of all responding mayor-council type cities (p. 34). These changes suggest it would be beneficial to utilize a better means of classifying cities for purposes of comparing fiscal outputs. The studies discussed above show there has been a lack of consensus regarding what effect, if any, form of government has on the fiscal output of cities. Many of them also illustrate how the traditional classification of cities based on governmental structure is not as meaningful as it once was due to modifications in the different structural arrangements used by cities. Both of these reasons support the need for a better method of classifying cities. Functional Responsibility In various studies of the effect of form of government on a city?s fiscal behavior, one of the main areas of agreement seems to be that functional responsibility is a major determinant of city spending (Dye & Garcia, 1978; Farnham, 1986; Liebert, 1974). Functional responsibility or functional scope refers to the different type of services provided by a city. There are some common functions, such as police and fire protection, that most all cities provide. However, there are other functions, such as education and hospitals, which are not provided by most cities. Many of these optional functions are very costly. Because of this, comparisons involving the revenue, expenditure, and debt outputs of cities may be misleading if the variation in functional responsibility among cities is not considered. 46 After recognizing the problem posed by comparing cities with varying functional responsibilities, Booms (1966) utilized the measure of ?common functions? that included only those expenditures that were most commonly provided by nearly all of the cities. These services consisted of police, fire, interest on local debt, non-capital outlays for highways, sanitation, and public health, as opposed to ?optional functions? the provision of which tended to vary more widely between cities. These optional functions included the provision of services such as education, hospitals, and welfare. He argued that utilizing only common function expenditures would ?eliminate most of the variation . . . between cities? (p. 189). Subsequent researchers have dealt with the issue of differences among cities in the number and type of services provided by measuring and controlling for the level of functional inclusiveness of a city. Liebert (1974) discussed the issue of whether or not the governmental structure of a city had much effect upon it financial outputs. He concluded that the structure of a city?s government ?plays no consistent intervening role between population characteristics and expenditure rates? (p. 782). Rather it was the functional scope of city government that had the most influence upon a city?s fiscal output. Dye and Garcia (1978) examined variation in functional responsibility among cities and what impact such differences had on municipal taxing and spending levels. They theorized that cities could be either functionally comprehensive (meaning they performed most of 12 identified municipal functions) or else they could be functionally specialized. They concluded that functional responsibility was the main determinant of taxing and spending levels ? with functionally comprehensive cities spending and taxing 47 more than functionally specialized cities. They also concluded that ?functionally comprehensive cities are more responsive to the social and ethnic character of their populations than functionally specialized cities? (p. 117). Dye and Garcia also considered the interplay between functional responsibility and form of government within cities. As noted earlier, they divided their cities into four groups: functionally specialized cities, (1) those which were reformed and (2) those that were unreformed; and functionally comprehensive cities, (3) those which were reformed and (4) those that were unreformed. They analyzed the cities after controlling for functional responsibility by regressing each of the four groups and compared the regression coefficients for reformed and unreformed comprehensive cities, as well as comparing reformed and unreformed specialized cities. They found that, among functionally specialized cities, the unreformed cities were more responsive than reformed cities, and that spending increased in unreformed cities in response to increases in ethnicity and declined with increases in homeownership. However, among functionally comprehensive cities, they found just the opposite. Expenditures in those that were reformed were found to have a stronger relationship to social and ethnic variables than did expenditures in the cities which were unreformed (pp. 117-118). Morgan and Pelissero (1980) compared matched cities to determine whether those that had undergone a change in form of government would have different fiscal outputs than similar cities that had not undergone such a change. They found that the change in form of government did not result in any meaningful differences between the cities in the two groups. 48 In their study, they recognized the importance of taking into account the functional responsibilities of their cities. Explaining the need to ensure an adequate match between the two groups, they stated: Differences in spending assignments among cities must be considered when expenditures are analyzed. This is particularly true for school and welfare, since these two very expensive activities can account for about half the variation in total general municipal expenditures (Liebert, 1974, pp. 771-72) [in original]. Our experimental and control cities are perfectly congruent on school and welfare responsibilities. (p. 1001) Farnham (1986) examined differences in the functional responsibility of cities and what impact they had on local expenditures. According to him, ?Understanding and controlling for functional inclusiveness is necessary in all studies of local expenditure, revenue, and debt patterns, since these factors can vary solely from differences in the number of functions performed by a community? (p. 151). Farnham studied financial and other data from the Census Bureau and ICMA relating to 2,500 communities with a population of at least 10,000 in 1975. His study compared the functional inclusiveness of the communities to differences in the various communities. He measured the functional responsibility by determining the number of functions over which each community had control. Control was determined by whether a community spent a nominal amount ($5,000) on the function. The functions he considered included education, highways, welfare, hospitals, health, police, fire, sewerage, sanitation, parks and recreation, housing and urban renewal, and libraries. Farnham reported that over 90% of the communities studied had 49 control over the functions of highways, police, fire, and parks and recreation. More than 50% of the communities did not perform any of the least-common functions, which included education, welfare, hospitals, and housing and urban renewal (pp. 153-154). Using a median voter model of local spending practices, he found functional responsibility did indeed account for a large amount of the variation in spending behaviors of the communities. Specifically, he found cities in the Northeast performed more functions than other regions and that the variable for Northeast was reflective of the educational responsibilities of cities in the region (p. 157). Once functional responsibility was considered, cities in the West spent more than those in all other regions (p. 159). He concluded: While controlling for the education function alone is a reasonable proxy for overall functional inclusiveness, it does not account for all of the expenditure variation arising form the performance of the other least-common functions. Moreover, the impact of functional variation does differ among central and suburban cities within SMSAs and independent cities located outside these areas. (p. 163) Deno and Mehay (1987) replicated Booms? study and, like Booms, only used common functions to measure the expenditures in the cities under consideration. Their work was discussed above. Hayes and Chang (1990) recognized the importance of variation in functional responsibility among cities when examining expenditure levels and, therefore, they only looked at the services of police, fire, and garbage collection. They justified the use of only these three services by noting they accounted for more than 40% of total general 50 expenditures in the cities, once the less common expenditures on education, interest payments, and unallocables were excluded (p. 172). Morgan and Watson (1995) examined the influence of mayoral strength and found that it was not a significant predictor of municipal expenditures. They included a measure of functional assignment to account for differences in the types of services provided among the different cities. Cities were rated on a scale of 0 to 3 depending upon how many of the major functions of school, welfare, and hospitals were allocated at least 10% of their direct spending. They found no relationship between the functional scope of the city government and spending levels. The literature discussing functional responsibility points out the variation that exists among cities in terms of the type of services they provide. It also illustrates the need to control for these differences to obtain meaningful comparisons when examining different cities. Demand Variables Various variables have been noted to have an impact on the amount of public services demanded in a community and, thus, the level of municipal taxes, expenditures, and debt. It has been noted that cities with the following characteristics tend to spend more per capita: non-suburbs, those that receive greater levels of intergovernmental revenue, those with larger percentages of non-White residents, and those with higher percentages of citizens age 65 and older (Bergstrom & Goodman, 1973; Dye & Garcia, 1978; Farnham, 1986). On the other hand, the variables of population density, percent of homeownership, population size, percent of citizens under age 18, and the level of per 51 capita income all tend to be negatively related to city spending (Bergstrom & Goodman, 1973; Booms, 1966; Dye & Garcia, 1978; Liebert, 1974). Also, it has been suggested that regional location influences demand for services, expenditures, and structure and function in cities (Cole, 1971; Dye & Garcia, 1978; Farnham, 1986; French, 2004). Others suggest it does not (Lineberry & Fowler, 1967; Stumm & Corrigan, 1998). Citing Wilson and Banfield (1964), Booms (1966) recognized that prior research suggested that a number of variables impacted the demand for services faced by city officials. It had been previously reported that variables impacting demand included: percent of population of foreign stock (negatively), percentage of homeownership (negatively), and percentage of population that was non-White (positively). However, Booms argued that it was most likely, in light of the law of large numbers, that individuals in both types of cities had the same desires and demands and, therefore, that it was the supply side factors (efficiency, training, politics, etc.) that resulted in the lower spending in city-manager cities (p. 193). Lineberry and Fowler (1967) looked at a number of independent variables that the authors believed represented ?a variety of possible social cleavages which divide urban populations ? rich vs. poor, Negro vs. White, ethnic vs. native, newcomers vs. old-times, etc.? (p. 703). These variables were divided into three groups: 1) measures of population size and growth, 2) indicators of social class, and 3) measures of social homogeneity. The specific variables used to measure population size and growth were the actual population figures and the percent of change in the population between 1950 and 1960 (as reported in the 1950 and 1960 censuses). It was noted that larger cities tend to be 52 unreformed and that cities with faster growth rates tend to be reformed in terms of having a manager and both nonpartisan and at-large elections. However, none of these measures of size and growth was found to be important in predicting taxation and expenditure levels (p. 704). The authors utilized the following variables relating to a city's residents as a means to represent social class: median income; percentages with incomes both under $3,000 and over $10,000; percentage of high school graduates; median years of education; percent of homeownership; and percentage of white-collar workers. They found that reformed cities and unreformed cities differed little in terms of these characteristics of social class, noting ?what is striking is not the differences between the cities but the similarities of their class compositions? (pp. 704-706). They also looked at the effect that the degree to which a city was middle class (measured by income, education, and occupation) had upon fiscal outputs. They hypothesized that the more middle class a city was, the higher its taxes and spending would be. However, they found the data did not support this hypothesis and, in fact, showed that ?the relationships between middle class variables and outputs are, if anything, stronger in the reformed cities than in their unreformed counterparts? (p. 711). They referred to prior research that suggested reformed institutions tended to ?maximize the power of the middle class? (p. 712). In addition, they found a negative relationship between the percentage of residents in a city who were homeowners and the fiscal output measures. They also found that unreformed (mayor) cities exhibited this negative correlation more so than did the reformed (manager) cities (p. 712). 53 Additionally, the study used three measures of social homogeneity: percentage of the population who were native born of foreign-born or mixed parentage, utilized for ethnicity; percentage of the population who were non-White, indicating race; and percentage of elementary school children attending private schools, used to measure religious homogeneity (p. 706). No significant difference was found between reformed and unreformed cities in terms of racial composition, but the authors noted the data showed reformed cities ?appear somewhat more homogeneous? (p. 706) in terms of ethnicity and religion. The study revealed a positive relationship between ethnic and religious homogeneity and the level of the fiscal outputs, except for the variable non-White, which tended to have only a very slight correlation to changes in the output measures. However, the other two variables representing homogeneity (ethnicity and private school attendance) had a positive relationship to both taxing and spending behaviors (pp. 712- 713). In summarizing their findings, Lineberry and Fowler noted social class variables had a negative association to outputs, but the class variables failed to show any significant influence. Homogeneity indicators of private school attendance (their indicator for religious homogeneity) and ethnicity were positively correlated to the fiscal behaviors. They stated that ?when we related all twelve of our independent variables to outputs in various city types that the associations were much weaker in cities we have labeled reformed? (p. 712). Clark (1968) addressed the question of the effects that community structural characteristics had on decision-making patterns. He noted that the more reformed the 54 form of government in a community was, the more centralized it was in decision-making. When he looked at the relationship between decision-making patterns and policy output levels, he noted that there was support in the existing literature for the idea that the degree to which decision-making was centralized (i.e., reformed government) was positively associated with higher output levels as measured by city expenditures. However, his study found the opposite ? that more centralized decision-making systems resulted in lower levels of overall budget expenditures and urban renewal spending by cities (p. 587). He explained these seemingly contradictory findings by what he called the ?fragility? of the various output policies being considered. He noted that prior research supporting a positive association between decision-making centralization and expenditures had involved the policy areas of fluoridation, school desegregation, and urban renewal, which he said were, at the time, more fragile decisions than the outcome measures he was studying. This was because issues such as urban renewal and school desegregation become less fragile over time as the newness of, and resistance to, the policy in a community diminishes. Clark maintained urban renewal programs had recently become more accepted by communities and, therefore, they were less fragile policy issues at the time of his study than they had been during the period of the previous studies mentioned above (pp. 587-588). Clark noted that, in a community where decision-making is less centralized, individuals and groups have a better chance of getting policymakers to forego spending on fragile policy areas; whereas, in more centralized decision-making cities, the leadership of the city can more easily overcome or ignore such opposition. Because of 55 this, he opined that cities with centralized decision making may spend more on fragile issues. However, he stated that less fragile policy issues are not as easily thwarted in a community because there is less opposition and, therefore, compromise is more likely to resolve competition between competing interests. He argued that this may actually result in less centralized decision-making cities spending more on non-fragile issues to satisfy various interests. On the other hand, more centralized decision-making cities should be less susceptible to such pressure, and there may be lower spending on these less fragile issues like overall budget expenditures and established urban renewal programs (pp. 587- 588). Clark also looked at the effect of a number of other variables on expenditures. He found the variable that was most strongly associated with the level of expenditures in a community was the percentage of residents who were members of the Roman Catholic Church, which had a positive association. He also found, as noted in prior research, the variables personal income, assessed property values, and level of education were positively associated with expenditure. His study demonstrated two variables were not important in influencing the level of expenditures, even though prior research suggested they were. These were measures of industrial activity and percentage of the area?s population living in the central city. Finally, he found the level of poverty and total size of the population both had a positive association, while economic diversification had a negative association (p. 590). Bergstrom and Goodman (1973) looked at the influence on demand for public goods resulting from various demographic variables. They found that the percentage of homeownership and rate of growth both had a negative association with expenditures on 56 public goods in a community. They reasoned that homeowners may have less demand because renters do not realize they pay property tax for their housing and, therefore, may be more supportive of greater services even if it results in higher property taxes. They speculated that the growth rate may be negatively related to demand because cities undergoing rapid growth may not have reached ?political equilibrium? or consensus to support more services, or that cities with declining populations may have higher spending due to inertial effects (pp. 289-290). They also found that communities with a high employment-residential ratio and a larger percentage of residents over age 65 had a positive influence on public spending. They theorized that communities with larger employment-residential ratios may have to spend more on public goods to attract and retain higher commercial and industrial activity. They also stated that the fact that individuals over age 65 spend a greater share of current income on consumption than younger people might explain why they have a higher demand for public services (pp. 289-290). Liebert (1974) replicated the study of Lineberry and Fowler concerning the impact of various socio-economic variables upon expenditure levels of cities, but Liebert controlled for functional inclusiveness in his analysis. He used ?the presence or absence of city responsibility for public schools? (P. 780) to control for inclusiveness of city services. Liebert noted that Lineberry and Fowler had concluded that ?more middle class cities spent less, and cities with higher proportions of ethnic and religious minorities spent more? (p. 779). After controlling for functional inclusiveness, Liebert used the same measures of middle class as Lineberry and Fowler ? the percentages of the population reflecting homeownership, median income, median education, and white 57 collar occupations. He also found that each of these middle-class measures was negatively related to expenditures. However, Liebert found the opposite of Lineberry and Fowler?s conclusions concerning the effects on public spending of the variables percentage of ethnic and religious minorities. He found a negative relationship between the expenditures of cities and their percentage of these minorities (pp. 779-781). He explained that this finding could be due to the fact that ethnic and religious minorities are no longer the type of minorities whose needs and interests support higher levels of public services and expenditures. He noted they have tended to move out of poverty in the central cities. He further noted that, while the prior study had found only a very slight correlation between the variable non-White and changes in fiscal output measures, his analysis showed there was a positive correlation between the percentage of non-Whites and expenditures (pp. 781-782). He attributed this discrepancy to the fact that the variable non-White in his study was more indicative of Black. Lyons and Morgan (1977), in addition to analyzing the effects of intergovernmental transfers, considered a number of other independent variables in their examination of municipal spending, including: reform structure of the city; median family income; percent employed in manufacturing; population size; percent non-White; median age; percent of homeownership; and whether or not the city government operated schools. They found that the variable of whether or not a city operated schools was the second most influential determinant of city expenditures, behind only the amount of intergovernmental revenue received. In addition to their findings concerning the effects 58 of intergovernmental revenues, operation of schools, and reform status of the government, they found correlations between the level of per capita city expenditure and the variables of median income (positive), manufacturing (negative), percent non-White (positive), age of the population (positive), and homeownership (negative). The remaining variables examined were found to be insignificant in terms of expenditures (pp. 1092-1093). Dye and Garcia (1978) examined the importance of functional responsibility and form of government on city taxing and spending levels. In doing so, they also considered a number of other independent variables and what effect they had upon both revenues and expenditures. The variables they considered included: population size; growth rate; density; percentages of youth, aged, homeownership, non-White, and ethnicity; median property values; median family income; educational attainment; and occupational status. They found, in the case of tax revenues, the variables ethnicity and median family income had a positive correlation, whereas the variable of homeownership had a negative relationship. As far as total expenditures, they found variables of size of the population, density, ethnicity, and property value had positive correlations, and the variable of homeownership had a negative effect. The other variables considered were not statistically significant (p. 113). In their study of the effects of mayoral power on per capita city expenditures in reformed and unreformed cities, Morgan and Watson (1995) analyzed the influence of several other variables, including intergovernmental revenues, and percentages of homeownership, high school graduates, elderly, and non-White. They found that intergovernmental revenue and percentage of high school graduates both had a major 59 effect in both type cities, exhibiting a positive relationship to spending. In the mayor form, the percentage of non-White had a substantial and positive effect on spending, while it was insignificant in manager cities. In manager cities, the percentage of elderly had a positive effect, while homeownership had a negative correlation (pp. 237-238). A final variable that has been noted to have an effect upon city spending is the region of the country in which the city is located. This is especially relevant in national samples of cities. (Most studies of city fiscal behavior have used the regional designations of the Census Bureau: Northeast, Midwest, South, and West.) There have been different conclusions concerning the impact of the regional location of a city, with some researchers claiming findings that geographic region influences a city?s outputs, others have found it does not, and still others maintaining that controlling for region was really controlling for other factors that were in fact the determinants of the outputs. Wolfinger and Field (1966) examined the relationship between the middle-class ethos theory as set out in prior research by Banfield and Wilson (1963). The ethos theory was summarized as a: Theory of ?public-regardingness? and ?private-regardingness? which states that much of what Americans think about the political world can be subsumed under one or the other of these conflicting orientations and that the prevalence of one ethos over the other influences the style, structure, and outcome of local politics. (p. 306) Wolfinger and Field noted that Banfield and Wilson ?attribute these two ethics to different elements in the population and hypothesize that a number of political forms and policies are manifestations of each ethos? (p. 306). These political forms and policies 60 included the middle-class ethos of public regarding views that tended to support the efforts of the municipal reform movement calling for city managers, nonpartisan election, and at-large representation and placed emphasis on efficiency, strong executives, honesty, planning, etc. In contrast, the immigrant ethos was private regarding and tended to oppose such reform efforts. Prior research suggested there were various demographic factors which reflected an individual?s or group?s support for one ethos or the other (pp. 306-307). In their study, Wolfinger and Field sought to test ?the associations between these hypothesized consequences and the demographic characteristics that are said to be the bases of the two ethics? (p. 306). They used the variables form of government, election method, and representation type to indicate whether a city was reformed or not. They utilized the primary independent variable of percentage of residents of foreign stock, along with other independent variables such as social class, income, and education (pp. 310-312). Wolfinger and Field found that the differences between reformed and unreformed cities disappeared once they controlled for region, and they concluded ?one can do a much better job of predicting a city?s political form by knowing what part of the country it is in than by knowing anything about the composition of its population? (p. 320). Lineberry and Fowler (1967) questioned Wolfinger and Field?s findings that differences between reformed and unreformed cities went away once region was controlled for. They based their different view concerning the impact of region by noting: 61 Since regions have had different historical experiences, controls for region are essentially controls for history, and more specifically, historical variation in settlement patterns. The problem with this reasoning, however, is that to ?control? for ?region? is to control not only for history but for demography as well: to know what region a city is in is to know something about the composition of its population. Geographical subdivisions are relevant subjects of political inquiry only because they are differentiated on the basis of attitudinal or socio-economic variables. The South is not a distinctive political region because two surveyors named Mason and Dixon drew a famous line, but because the ?composition of its population? differs from the rest of the county. (p. 706) They noted that regions were ?differentiated on precisely the kinds of demographic variables? (p. 706) which are related to governmental reform. Cities in the Midwest, for example, have a much higher proportion of home ownership (64%) than cities in the Northeast (44%), while northeastern cities have more foreign stock in their population (27%) than the Midwest (16%). Hence, to relate ethnicity to political reformism and then to ?control? for ?region? is in part to relate ethnicity to reformism and then to control for ethnicity. Consequently, we have grave reservations that the substitution of the gross and unrefined variable of ?region? for more refined demographic data adds much to our knowledge of American cities. (pp. 706-707) Based upon this, they argued that form of government and demographic characteristics of cities did make a difference in policy outputs of cities ? regardless of region. 62 Cole (1971) studied the impact of both a city?s region and its form of government on its fiscal outputs. He found that form of government has a slight impact on the policy outputs of cities, although not necessarily in the manner intended by the reform movement. However, after controlling for region this relationship went away. Based upon this, he concluded that political structure alone was not an adequate indicator of a city?s policy. Dye and Garcia (1978) looked at the variation of functional responsibility among cities and what role this played in municipal taxing and spending levels. They also considered the region in which the city was located. They found there were significant differences in functional responsibilities of cities depending up regional location. The cities of the northeastern United States are more functionally comprehensive municipal governments. In contrast, western cities are more specialized municipal governments. . . . Southern and Midwestern cities fall between these extremes. Doubtlessly, variations in the historical development of municipal government in these regions helps explain the regional contrasts.? (p. 108) Farnham (1986) examined the impact of functional responsibility on local expenditure taking geographic region into consideration. He found cities in the Northeast region performed more functions than other regions, and he concluded that ?the Northeast variable largely reflects the educational responsibilities of communities in that region? (p. 157). He also found that cities in the West region spent more than any of the other three regions: It is interesting to note that the ?big spender? image of eastern cities is not supported by the results of this research. Cities in other parts of the country spend 63 less than do communities in the West. This may reflect differences in tastes and attitudes toward government spending among regions of the country or institutional variations. (p. 159) Stumm and Corrigan (1998) found cities with managers had lower taxing and spending levels than did cities with mayors. In their study, they controlled for regional location by using dummy variables for three of the four regions and reported ?region was not a significant factor and inclusion of these variables did not improve the performance of the model? (p. 346). French (2004) examined the effect of form of government in small cities and towns. After testing with regression analysis and correlation methods, he found manager cities had greater expenditures than those with mayors. He tested his model again after dividing the cities into regions and found that the differences in expenditures among the types of cities were insignificant for the Northeast and Midwest regions but remained significant for the South and West. He found that, overall, cities in the Midwest and West spent more than those in the Northeast and South. Prior research has identified a number of variables that have an impact on the fiscal behavior of cities. To have a reliable test of any association between a particular factor and a city?s financial outputs, one must take into account and control for variables believed to have an influence on fiscal policy. Metropolitan Status Another way in which cities have been classified is by dividing them into groups depending upon whether the city is a central city, suburb, or independent city. The term 64 central city has traditionally referred to those larger cities that are the center of urbanized areas. Suburb, on the other hand, referred to what were usually smaller cities in the area surrounding the central cities and were dependent upon the central cities for many of the activities of life, such as work, recreation, retail and other commercial purposes. The term independent city has been used to refer to those cities that are outside metropolitan areas but are central locations of commerce and other social activities for surrounding rural areas. They are somewhat like central cities on a smaller scale. The Office of Management and Budget (OMB) has developed a new classification, that of principal city, providing another means by which to compare cities (OMB, 2000). According to the OMB classification, within ?each metropolitan statistical area, micropolitan statistical area, and NECTA [New England city and town areas] the largest place and, in some cases, additional places are designated as ?principal cities? under the official standards? (U.S. Bureau of the Census, 2003). This designation replaces the older central city concept, while allowing for more than one principal city per area (OMB, 2000). The role and makeup of America?s central cities are continually changing. It has traditionally been observed that central cities and suburbs differ in many ways. However, differences that do exist between central cities and suburbs are not as distinct as they once were (Frey, 2001; Furdell, Wolman & Hill, 2005; Lang, 2004; Lang & Simmons, 2001; Rengert and Lang, 2001; Simmons and Lang, 2001; Sohmer and Lang, 2001). Likewise, the problems and features that central cities share with one another, as well as the notion that all suburbs are alike, have been well publicized and are often assumed to be true without question. 65 While many of these traditional notions may be true, or once were, there have been many changes in the composition of central cities, suburbs, and rural areas across the country since the time cities first began being studied. The National League of Cities (NLC) argues: ??City? and ?suburb? in this respect are antiquated terms associated with an older economic structure in which central cities were the sole economic engines of metropolitan areas surrounded by residential suburbs? (NLC, 2005, p. 3). This suggests that the use of these traditional classifications of central cities, suburbs, and independent cities to group cities should also be re-examined, as the similarities within and differences among each type are blurring. One way in which central cities have changed involves their domination of the metropolitan area in which they are located. No longer are central cities necessarily the core of activity for surrounding communities. It has been noted that, in Chicago, the Loop area downtown has given up its status as the center of the metropolitan area with the development of large commercial, retail, and offices around O?Hare Airport (Greenstein & Wiewel, 2000). Others have noted that some distressed central cities were in worse conditions economically at the end of the century than they were just 20 years earlier (Furdell, Wolman & Hill, 2005). At the same time, many have noted how the central cities ? which for much of the latter part of the 20 th century were in decline ? have recently begun making a comeback and once again now are becoming more vibrant (Simmons & Lang, 2001; Sohmer & Lang, 2001). In a report commenting on results from the 2000 census, Sohmer and Lang (2001) note that the most recent census data show there is wide variation among the downtown areas of America?s central cities. While the Census Bureau does not have a formal 66 definition of what qualifies as the downtown of a city, the authors say there are many shared characteristics that downtowns have in common to allow the authors to identify the downtowns of different cities. They note downtowns tend to have the most expensive rents for office space and are the central business district for the city. As examples of the variation among downtown areas, they compare the downtown section of San Antonio, Texas (at 5.5 square miles it is the nation?s largest) to those in the cities of Norfolk, Virginia; Cincinnati, Ohio; and Lexington, Kentucky (all of which at 0.8 square miles are the smallest). There are also vast differences between the numbers of residents living in the actual downtown areas. The largest is Boston with 80,000 and the smallest is Norfolk with 3,000 (p. 2). Sohmer and Lang conclude from their study of 2000 census data that many downtowns are being rejuvenated. They point out the fact that most downtown areas are gaining population despite the fact that cities as a whole are losing population relative to the entire metropolitan areas in which they are located. They also note that there has been a return of White residents to downtowns. One of the reasons for this is that downtowns are often more convenient to an individual?s place of work and recreation. Also, by marketing the historical nature of the areas, downtowns are attracting more affluent residents and they are offering more of a ?sense of place? (p. 9). Also analyzing 2000 census data, Simmons and Lang (2001) report that older industrial cities are undergoing improvement. No longer are many declining as they have since the end of World War II, especially during the 1970s. Simmons and Lang selected cities to evaluate as ?industrial? by identifying those that were the 50 most populous in the 1950 census. They next eliminated all those that had not undergone decline for at 67 least two decades since 1950. This resulted in 36 cities that contained 20% of the country?s population. Many of the cities cut (e.g., Dallas, Jacksonville, and Los Angeles) were those that had the reputation of being vibrant during times when many other large cities were declining. Most of the cities they finally included in their study were located in the Northeast and Midwest regions of the country; the same areas that had been most hard hit by decline. Their findings included that, in terms of population gain or loss, these cities performed better (lost less or gained) during the decade of the 1990s than anytime since 1950. The authors gave several reasons they believe were likely to have accounted for this turnaround. They noted the historic growth in the economy during the 1990s, as well as increases in immigration and improvements in the accuracy of gathering census data. They also stated this turnaround in population loss may have a number of benefits for these cities, including: increased representation and funding; reclaiming of abandoned housing stock resulting in renewal of neighborhoods; increases in new businesses within the cities; and overall increases in tax revenue (p. 5). Not all the news about central cities has been good. Furdell, Wolman and Hill (2005) utilized a Municipal Distress Index to study the changes in central cities over the 20 year period between 1980 and 2000. They determined distress scores for the year 1980 for all cities that had at least 125,000 residents and were located within metropolitan statistical areas with populations of at least 250,000. Their index was calculated by utilizing standardized values on the following measures of distress: poverty rate; unemployment rate; change in population over the proceeding decade; and median 68 household income (p. 284). They also examined the level of distress in these cities for the year 2000. These parameters resulted in 98 cities being reviewed. The study compared these distressed cities to each other, to central cities that were not distressed, and to the nation as a whole. The authors found that: The cities that were distressed in 1980 were, on average, worse off on each of our indicators of municipal distress in 2000 than they were in 1980. Furthermore, compared to other cities that were not distressed in 1980 (and the nation as a whole), distressed cities, on average, fell further behind in terms of the economic well-being of their residents during the two decades. (p. 301) Like central cities, not all suburbs are alike. Harris (1999) relates that the traditional view of how life in suburbia was lived was not a complete description of what existed outside America?s central cities. He says that ?the ?traditional? view which, like the traditional family, was largely an invention of the 1950s, is that pre-Second World War suburbs were the preserve of the middle and upper classes? (p. 91). He details how during the first half of the 20th century there were at least three types of suburban places in American, which he refers to as the suburbs of suburban mythology, the industrial suburbs, and unincorporated districts on the urban fringe that had extensive development. Harris notes that the mythological suburb ?is often described as gracious, consisting of broad, tree-lined streets that accommodate substantial single-family homes, and the occasional church, school, and park? (p. 93). He acknowledges that suburbs similar to such a place existed. These were the bedroom communities where middle and upper class families moved to experience the good life. However, Harris also recounts that ?there were a growing number of incorporated places which contained a significant 69 number of jobs? (p. 94). These type places he describes as the industrial suburbs, and says they were home to the working class and were very diverse. Finally, he identifies a type of place outside central cities which he feels has mostly been overlooked in the research on suburban America. These are the unincorporated areas where it had been reported about one third of the urban population outside the central cities lived (p. 96). He further notes each of these three types of suburbs developed in different ways. In the mythological suburbs, there were zoning and deed restrictions seeking to protect the value of the homes, and generally much closer control over how the cities were developed. In the industrial suburbs, there were fewer restrictions on development, and the owners of the industries exerted persuasion over city leaders due to the taxes their businesses paid into the cities. He says that the unincorporated places had practically no regulation or control over the type, manner, or timing of development, since there was no city government. Because of this these areas grew in a haphazard fashion. Harris also describes three different ways in which suburbs were built during the early 1900s. Some homes were built by affluent families who hired contractors to build them a house. He also recognizes speculative builders, commercial builders who built more affordable homes and then sold them to lower-income families. Finally, he says there was what he refers to as amateurs, families who built their own homes through their own labor. Frey (2001) analyzed race and ethnicity changes in the 102 most populous SMSAs using census data from the year 2000. He reached a number of revealing findings, including: the racial and ethnic composition of suburbs rose substantially over the preceding decade; ?melting pot metros? (Los Angeles, Chicago, Washington D.C., 70 Houston, and New York) contained the greatest level of minorities that lived in suburbs; the majority of growth in the suburbs in most metropolitan areas was due to increases in minorities; Asian individuals were most likely to be found in major metropolitan suburbs rather than the central cities themselves; and those self-identifying as members of two or more races exhibited different patterns than others relating to where they lived. Recent changes have also been noted in areas outside metropolitan areas. Rengert and Lang (2001) defined rural counties outside metropolitan areas in 12 states of the western U.S. as the ?Rural West?. The Rural West is characterized as hot, dry, highly elevated, sparsely settled, and containing the greatest amount of federal owned land in the country (with the exception of Alaska). Perhaps because of these features, the authors report the area appeals to and attracts a very diverse group of residents. Rengert and Lang divide their Rural West into the Old West, consisting of Cowboy Counties and American Indian Counties, and the Cappuccino Counties of the New West. They note that between 1950 and 2000 the area as a whole experienced much faster growth (103%) than the overall country (86%), but that the Cappuccino Counties grew the fastest (241%). One of the reasons for this, however, was that the area began with a much smaller population in 1950 than the rest of the country (pp. 3-5). Rengert and Lang provide a descriptive examination of the demographics in rural areas of the west. They do not attempt to examine any fiscal output behaviors of their different classifications. However, the authors maintain that understanding these changes in the West region of the United States is important because they will have political and economic implications. They point out that the Old West section has rapidly been losing political influence as the growth has been occurring in the New West region, resulting in 71 the ?yuppification? of that area. This has and will continue to result in conflict between residents of the two regions concerning issues such as water and land management, because the fast growing areas need water (in this arid region where it is in short supply) to support their increasing populations and the fast-growing cities continue to expand out to the more rural areas (pp. 5-6). The status of a city as a central city, suburb, or independent city has declined in importance as cities have changed and evolved over time. Many suburbs now have characteristics traditionally associated with central cities, such as being employment and retail centers for their regions. Likewise, there have been changes in central and independent cities that make it less meaningful to group cities according to these categories. Elasticity In addition to the impact of forms of government and metro status, the concept of the elasticity of a city has been suggested to have an influence upon local fiscal behavior. Rusk (1993, 2003) was one of the first scholars who looked at the effects that a city?s ?elasticity? has upon its ability to maintain fiscal health. In doing so, he focused on the cities? ability or lack of ability to incorporate suburban growth into their borders and how that impacted their growth and welfare. Specifically, he maintained that a city must be elastic to grow because elastic cities absorb suburban growth, whereas inelastic cities foster suburban sprawl; growth patterns have been determined by racial prejudice; cities are constrained by bad state laws; there is a greater income disparity between inelastic 72 cities and the surrounding suburbs; and inelastic cities have more poverty and suffered more from deindustrialization. Rusk measured the elasticity of a city by adding the city?s density ranking in 1950 and three times the ranking for its level of expansion as measured through the total increase in land area between 1950 and 2000, to arrive at an elasticity score. He compared cities based upon their elasticity scores and concluded that ?(1) Elastic central cities outperform inelastic cities on numerous economic development indicators; and (2) central city elasticity enhances the economic welfare of the entire metropolitan area, not just the central city? (Blair, Stanley & Zhang, 1996, p. 346). Rusk?s study has been criticized for its lack of empirical evidence offered to support his conclusions and its failure to use ?appropriate large-sample statistical tests of the elasticity hypothesis? (Blair, Stanley & Zhang, 1996, p. 348), but rather merely relying upon contrasting a small number of cities with one another. In their critic of Rusk?s study, Blair, Stanley and Zhang conclude that Rusk?s call for policies that would strengthen the control of central cities over the entire metropolitan regions in which they are located ?should not be viewed as a powerful tool for the enhancement of metropolitan economic welfare, although it may have modest effects in some circumstances? (p. 351). They point out that Rusk fails to adequately consider other solutions, such as ?tax-base sharing; shared service contracts, and the use of special purpose districts? (p. 346). National League of Cities? Report The National League of Cities (NLC) published a report in December 2005 setting out a new typology of cities (NLC, 2005). It was believed that this new typology 73 could more accurately distinguish among cities in a way that would aid local decision making and provide a more useful ?framework that clarifies the local context in which city officials operate? (p. iii). The report noted ?A typology in a municipal context is very helpful; it can provide a meaningful framework to examine local issues and strategies in reference to others that are occurring in similar places? (p. 2). While the NLC typology was primarily developed to address land use issues, the authors stated it should also be beneficial for consideration of finance, governance, inequality, housing, and transportation policies. The sample for the NLC study consisted of 996 cities with populations between 25,000 and 500,000. The study excluded cities with populations below 25,000 due to the difficulty of obtaining necessary data on such small cities. It also excluded all cities with populations above 500,000 to avoid outlier effects. The report noted that these largest cities constituted Mega-metro centers, totaling 30 in all. Following factor and cluster analyses, the study omitted 90 of the 996 cities because they did not fit well within the six identified classifications (C. McFarland, personal communication, October 3, 2006). The NLC report notes: The cities in this study were examined across as set of social, economic, and demographic variables. Multivariate techniques, including factor and cluster analyses, determined which variables from a broader set of variables were the most important to the analysis and how cities group around these variables. (p. 11) The factors considered in the analyses included: metropolitan and micropolitan designations; population size, density, and rate of growth; median age of residents and of 74 housing stock; education levels; median household income; and percentages of the population who were under age 18, foreign-born, non-Hispanic Whites, homeowners, and living in an urban area. The analyses also considered region and whether the cities were principal cities or not. While NLC acknowledges ?There is no ?typical? city and no practical, realistic, or helpful one-size fits all approach to the varying issues that cities face? (p. 2), its analysis resulted in a typology of six types of cities (seven if you add the excluded Mega-metro center cities). NLC maintains that, while all cities may not be totally represented by any single type of city identified in the typology, some cities may be considered as a combination of the model types (p. iii). Spread cities are generally located in metropolitan areas in the Midwest and South. They are characterized as having average population sizes (61,000) and densities (2,800 residents per square mile) but low percentages of households with children under age 18 (32%) and foreign-born residents (6%). They also have low median household incomes ($36,000). Gold coast cities have greater percentages of older (median age of 38), wealthy ($62,000 median household income), and educated residents (27% with bachelor?s degrees). All are located in metropolitan areas. They are mostly suburbs and located in the West and Midwest, and they have greater percentage of homeowners (69%). Their average population is 57,000 and density is 4,100. Metro centers are larger, more diverse, core cities located mainly in the South and West. They are mainly principal cities, as opposed to suburbs, and they have large populations (259,000) and densities (4,200), and older housing (median age of housing is 75 44 years). They also have low median household incomes ($36,000), low percentage of homeownership (52%), and greater percentage of non-White residents (48%). Meltingpot cities are dense (8,200/square mile), diverse, and mostly located in the West, primarily in California. They have less educated (11% with bachelor?s degrees), younger (median age 31), more diverse residents who are overwhelmingly minorities (70% non-White) and have more children (47% households with children under age 18). They also have the highest level of foreign-born residents (33%) and the lowest percentage of White residents (30%). They are all located in metropolitan areas and two- thirds of them are suburbs. Their average population is 81,000, and median household income is $42,000. Boomtowns are identified as having experienced rapid growth (230% between 1980 and 2000) and low densities (2,400). They have newer housing (median age of housing is 21 years) and their residents tend to be wealthy ($57,000) with large percentages of homeowners (73%) and children (43% households with children under age 18). They primarily have populations between 75,000 and 100,000. Their average population is 80,000. Most of these cities are suburbs in the West, South, and parts of the Midwest. Centervilles are described as being mainly principal cities in micropolitan areas that tend to be located in the Midwest and parts of the South. They have the lowest populations (35,000), are the least dense (1,800/square mile), and have the lowest average income ($33,000) and percentage of foreign-born residents (5%). Their residents have a lower educational level (14% with bachelor?s degrees) and are less diverse (75% White). 76 Finally, Mega-metro centers are the 30 largest U.S. cities which have populations greater than 500,000. These cities are New York, Los Angeles, Chicago, Houston, Philadelphia, Phoenix, San Diego, Dallas, San Antonio, Detroit, San Jose, Honolulu, Indianapolis, San Francisco, Jacksonville, Columbus, Austin, Baltimore, Memphis, Milwaukee, Boston, Washington DC, Nashville, El Paso, Seattle, Denver, Charlotte, Fort Worth, Portland, and Oklahoma City. Mega-metro centers have an average population size of 1,225,000, ranging from 506,000 to 8 million. The average density is 6,102 residents per square mile. Median household income is $40,000, and 28% of their populations over age 25 have bachelor?s degrees. On average, they are 56% non-White and 25% of their households have children under age 18. They have the lowest percentage of homeowners at 50%. To compare and contrast large numbers of cities and obtain meaningful findings about their fiscal outputs, there must be a way to meaningfully group similar cities for comparison purposes, while distinguishing others. Kesselman, Krieger, and Joseph (2007) have noted that ?To ?compare and contrast? is one of the most common human mental exercises? (p. 8). Having a classification that accurately reflects the similarities between cities enhances the researcher?s ability to examine the outputs of those cities. Using comparisons between similar type cities ?refines and systematizes the age-old practice of evaluation some feature of X by comparing it to the same feature of Y in order to learn more about it than isolated study would permit? (p. 8). Classification is a process of grouping things into categories based upon established criteria. In the case of a typology, ?each member of a particular group should be as similar as possible to others in the group, but as distinct as possible from the 77 members of other groups? (NLC, 2005). The authors of the NLC report believe that the currently used methods of distinguishing between cities rely ?on antiquated notions of city forms and assumptions about the functions cities perform? and that their typology provides ?a more accurate reflection of the changing nature of the municipal landscape and the diversity that exists among cities? (p. 2). As previously discussed, much of the existing literature on the fiscal behavior of cities has made comparisons between cities based upon the form of government and/or the metro status of the cities ? whether a city is a central city, suburb, or independent city. However, the changing nature of these traditional classifications raises questions about their continued viability as a meaningful manner of making distinctions among cities. If the purpose of such classification is to illustrate the ways in which cities are alike and different, then it is important that the groupings used accurately reflect the nature of the cities under study. According to NLC (2005), ?These changes occurring in cities in terms of their character, the types of functions they perform, and services they provide prompt a reexamination of the perceptions of uniformity of traditional city types. It is their distinctive qualities that contextualize city problems and policy implications? (p. 3). Municipal decision makers face a number of challenges, not the least of which involves decisions about the city?s fiscal outputs. NLC argues, ?A framework that clarifies the local contexts in which city officials operate can help them more effectively approach and understand these complex challenges? (p. iii). The report ?is intended to be a guiding framework for those seeking to better understand cities, their challenges, and responses to those challenges? (p. iii). 78 Examination of the revenue, expenditure, and debt patterns of cities within the NLC typologies will provide a better understanding of the distinctions and similarities among these new municipal categorizations. Additionally, comparison of fiscal outputs among these new descriptions of cities with prior categorizations used for grouping cities will offer insight into the validity and usefulness of the NLC typology. This dissertation performs such an analysis. The following chapter discusses the methodology and data used in the study. 79 CHAPTER 3 METHODOLOGY AND DATA This dissertation describes and compares the taxing, spending, and debt practices of nearly one thousand different cities. The principal research question to be addressed is whether the new National League of Cities (NLC) typology has any relevance concerning the fiscal output policies of municipalities. Both descriptive and multiple-regression analyses are used to illustrate the financial behavior of cities under consideration. The dissertation initially examines and describes the characteristics of the various types of cities in the NLC typology and explores how they are similar and different, as well as how they compare to other classification schemes. It then uses descriptive statistics to look at the cities in terms of various fiscal measures, again comparing and contrasting these outputs among cities. Comparison of means analysis is employed through t-tests and ANOVA testing to determine the significance of the typology. Multiple-regression analysis is applied to test the significance of city type upon outputs of fiscal behavior, such as levels and composition of expenditure, revenue, and debt, and to compare the findings to similar analysis using traditional categorizations of cities. Systems theory has often been a model used to explain the fiscal behavior of local governments. Under Easton?s (1957, 1965a) systems theory, politics can be viewed as a process whereby environmental events result in inputs, consisting of demands and 80 supports, entering the system and causing the system, through its structures and processes, to produce outputs, which are once again acted upon within the environment resulting in additional inputs through a continuous feedback loop. Many of the studies to date dealing with municipal finance have noted that various input factors impact a city?s ability to respond to the fiscal demands of its citizens. Variables such as form of government, metropolitan (metro) status of a city, and demographic characteristics of its population have all been examined as input factors that influence the political system of cities. In this regard, the outputs of local political systems include the various taxing, spending, and debt practices of cities. Research Design The unit of analysis for the study is individual cities, both within and outside metropolitan areas, as well as the newly designated micropolitan areas. The data are analyzed using descriptive and linear regression analysis produced with SPSS software. The theoretical population for the study is all U.S. cities with populations of 25,000 and above. According to the International City/County Management Association (ICMA), there were 1,405 U.S. municipalities in 2003 that had 25,000 or more residents (IMCA, 2003). The study looks at 936 cities that have been classified using the NLC typology. The sample cities include all 906 of the cities classified in the NLC typology, plus those cities with more than 500,000 populations, which were excluded from its analysis to prevent outlier effects. The 30 largest cities were included in this study to have a more complete analysis of the financial behavior of U.S. cities. Because the initial factor and 81 cluster testing has been completed, there is no longer the problem of these cities producing an outlier effect in the creation of the typology. Initially, the study describes and compares characteristics of the different city types in terms of their financial policy outputs and various institutional and demographic variables. It is expected that such an analysis will provide a better understanding of the different types of cities by profiling the actual taxing, spending, and debt related behaviors associated with a particular city type. It also tests the significance of any differences noted between categories. It compares the percentages of traditional central cities with those of the new designation of principal cities. According to the U.S. Bureau of the Census (Census Bureau), principal cities are defined by the U.S. Office of Management and Budget (OMB) as the largest city in Metropolitan or Micropolitan Statistical Areas, plus other cities within the areas that meet certain requirements concerning population size and employment patterns (Census Bureau, 2003). The study then uses multiple-regression analysis to look at the influences that city types have on fiscal outputs, controlling for a number of factors shown in past research to have effects on the financial behavior of cities. Specifically, the dependent variables are output measures involving the total levels of expenditure, as well as the amounts spent on different functional categories. The study also examines the revenue and debt levels (both as totals and in terms of major categories) of the different types of municipalities. While the primary independent variable is the city type according to the NLC typology, the study also compares findings about the influence of city type with analysis of the same cities using categorizations previously examined in the literature on municipal fiscal behavior, specifically the form of governmental structure and metro 82 status of a city. This is done to see if the NLC typology provides a more meaningful classification when studying municipal fiscal behavior than do these prior groupings. The study employs control variables identified in prior studies as impacting the financial behavior of cities, including form of government, metro status, principal city designation, the percentage of intergovernmental revenue (IGR), region of the country, educational level, median household income, population size, density, and growth, and the percentages of residents who are homeowners, children, elderly, and non-White. As with any statistical analysis, there is the possibility that any findings will be due to chance rather than an accurate reflection of the association between variables under consideration. A two-tailed test of significance is used because the testing performed does not hypothesize the type of association (either positive or negative) expected. To ensure an acceptable degree of reliance, relationships between variables are only considered significant if they are at or below the .05 level. Hypotheses The principal research question of this study is whether the NLC typology results in any significant differences in patterns concerning municipal financial outputs. To generally address this issue, the study examines five more specific research questions and tests seven related hypotheses. The first six of these hypotheses predict significant differences exist among the fiscal outputs of cities according to a city?s classification within the NLC typology. These hypotheses are examined through the use of descriptive statistics and tested using t-test and one-way ANOVA methods to determine the significance of the typology in 83 terms of various expenditure, revenue, and debt measures. The final hypothesis suggests that the NLC typology provides a better way to categorize cities and, therefore, that it will result in a better explanation of financial behavior than did prior categorizations. This hypothesis is tested with multiple-regression analysis and compared to similar regressions utilizing form of government and metro status as the primary independent variables. The research questions and hypotheses are as follows: 1. What comparisons can be made about the average expenditure, revenue, and debt outputs between the different city types identified in the NLC report? HYPOTHESIS #1: There are significant differences between the expenditure, revenue, and debt outputs in different city types. 2. Are there differences in expenditure levels between the different city types? HYPOTHESIS #2: There are significant differences in expenditure levels between the different city types. 3. Are there differences in revenue levels and sources between the different city types? HYPOTHESIS #3: There are significant differences in revenue levels between the different city types. 84 HYPOTHESIS #4: There are significant differences in revenue sources between the different city types. 4. Are there differences in total debt levels and type of debt between the different city types? HYPOTHESIS #5: There are significant differences in debt levels between the different city types. HYPOTHESIS #6: There are significant differences in the type of debt incurred by the different city types. 5. Are the NLC typologies better indicators of financial behavior of cities than prior categorizations? HYPOTHESIS #7: The NLC typology will provide a more statistically significant measure of the financial behaviors of cities than did prior categorizations as form of government and metro status. By using the assorted output measures to look at the various city types in relationship to these specific research questions, the study provides more insight into the usefulness of the classifications. As noted above, the final hypothesis argues that the NLC typology provides a better way to categorize cities for purposes of evaluating their fiscal outputs. It postulates 85 that the type of city (independent variable) influences the city?s financial behavior (dependent variable). While this assertion may be true, there are undoubtedly other factors that also impact the taxing, spending, and debt practices of cities. In order for an analysis to test the merit of the claim that a city?s type influences its fiscal outputs accurately, those other variables believed to have an influence must be taken into account and controlled. For this reason, the study utilizes a number of control variables. In previous studies examining the fiscal behavior of cities, several scholars have recognized the importance of taking into account the variation among cities in the types of the services they provide when comparing fiscal output measures among the cities (Booms, 1964; Dye & Garcia, 1978; Farnham, 1986; Liebert, 1974). This is because functional responsibility has been shown to be a major determinant of city spending. Functional responsibility or functional scope refers to the different types of services or functions provided by a city. There are some common functions, such as police and fire protection, that most all cities provide. However, there are other functions, such as education and hospitals, which are not provided by most cities. Because many of these optional functions are very costly, comparisons involving the revenue, expenditure, and debt outputs of cities may be misleading if the variation in functional responsibility among cities is not considered. Dye and Garcia (1978) found that functional responsibility was the main determinant of taxing and spending levels ? with functionally comprehensive cities spending and taxing more than functionally specialized cities. Cities in the Northeast were found to be more functionally comprehensive, meaning they perform more functions that other regions; cities in the West were the most functionally specialized. 86 However, after functional responsibility was controlled, cities in the West spent more than those in other regions (Farnham, 1986, p. 159). Recognizing the differences in functional responsibilities among cities and to control for this factor, this study uses the measure of common functions consisting of only those expenditures for services that most of the cities provide (Booms, 1964; Deno & Mehay, 1987). Common functions include expenditures for police, fire, interest on debt, highways, sanitation (consisting of sewerage and solid waste management), and public health. It has been recognized that police service is the most commonly provided municipal function, whereas education is one of the least common functions provided by cities (Farnham, 1986). While the provision of education is not as commonly provided, it is a very expensive undertaking and accounts for a large percent of the total expenditures in cities that operate schools (Dye & Garcia, 1978; Farnham, 1986; Lyons & Morgan, 1977). To allow a more comprehensive comparison among cities, separate analysis will be performed on expenditures for police services and education. Various variables have been noted to have an impact on the amount of public services demanded and, thus, the level of municipal taxes, expenditures, and debt. Cities with the following characteristics tend to spend more per capita: non-suburbs, those that receive greater levels of intergovernmental revenue, those with larger percentages of non- White residents, and those with higher percentages of citizens age 65 and older (Bergstrom & Goodman, 1973; Clark, 1968; Dye & Garcia, 1978; Farnham, 1986). On the other hand, the variables of population size, density, growth rate, percent of homeownership, percent of citizens under age 18, and the levels of income and education 87 all tend to be negatively related to city spending (Bergstrom & Goodman, 1973; Booms, 1966; Dye & Garcia, 1978; Liebert, 1974). Also, some researchers suggest that regional location influences demand for services and expenditures in cities (Cole, 1971; Dye & Garcia, 1978; Farnham, 1986; French, 2004). Others suggest it does not (Lineberry & Fowler, 1967; Stumm & Corrigan, 1998). Since it is a major purpose of this study to evaluate the usefulness of the NLC typology for financial inquiry, it is important that the effect which a city?s classification within the typology has upon its fiscal outputs be isolated as much as possible from other influences. The dependent variable common function is used, along with control variables, to provide a more accurate analysis in this study. Data Sources and Coding Procedures The data for this study include existing financial, institutional, and demographic information relating to 936 cities, which are those with year 2000 populations of 25,000 and above that also are included in the NLC typology, as well as the 30 largest cities that were not typed by the NLC study but were referred to as Mega-metro centers. The sample of cities utilized by NLC in the development of its typology provides a good representation of all cities in the United States that have 25,000 or more residents as previously shown in Table 1.1, with the exception of those cities with populations of 500,000 and above. The appendix consists of a listing of all 936 cities included in the current study arranged by type according to the NLC typology. The primary source of financial data used in this dissertation is the report of the City Finance Surveys produced by the Census Bureau as part of the 2002 Census of 88 Governments. The Census of Governments is conducted every five years in years ending in 2 and 7. These surveys contain comprehensive information relating to municipal tax, spending, and debt levels and composition (Census Bureau, 2002). The primary sources of institutional data on the cities are the 2001 Forms of Municipal Governments survey conducted by ICMA and the Municipal Yearbook 2003 (ICMA, 2001, 2003). This survey and the annual yearbook provide information about the form of government structure and the metro status of U.S. cities. Finally, various demographic data come from both Census Bureau and ICMA sources, primarily the 2000 Decennial Census, Summary File 3 (Census Bureau, 2000). The values for the different dependant variables relating to taxing, spending, and debt practices of the cities are the dollar amounts listed for each of the cities in the Census Bureau?s 2002 City Finance Surveys converted to per capita figures. Replicating the method used by Booms, common functions are defined in this dissertation based upon the Census Bureau?s definition that includes police, fire, health, sanitation (sewerage and solid waste management), highways, and interest on debt (Booms, 1966). The variable common function is computed by adding the total expenditures for each of these six categories of services. The study also examines total expenditures and expenditures on police and education for each city. Table 3.1 shows coding and data information for the dependent variables used in this study. 89 Table 3.1. Dependent Variables Name Label Coding Expenditure: Total expenditures TlExpend Dollar amount per capita Common functions (police, fire, health, sanitation, highways, and interest on debt combined) ComFct Dollar amount per capita Police Police Dollar amount per capita Education TtlEdu Dollar amount per capita Revenue: Total revenue TtlRev Dollar amount per capita Property tax PrptTax Dollar amount per capita Sales tax SaleTax Dollar amount per capita Income tax IncTax Dollar amount per capita Intergovernmental IGRev Dollar amount per capita Percentage intergovernmental IGRprct Actual percentage amount (Computed by the author as the amount of total intergovernmental revenues received divided by the amount of total revenues Debt: Total outstanding TlDebtO Dollar amount per capita Long-term, full faith and credit LTDoFFC Dollar amount per capita Note. Data source for fiscal outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 90 The primary independent variable used is the type of city according to the NLC?s typology: Spread cities, Gold coast cities, Metro centers, Meltingpot cities, Boomtowns, and Centervilles plus the 30 largest cities or Mega-metro centers. Inclusion of the 30 additional cities only slightly changes the percentages for the breakdown of the city types, as shown in Table 3.2. Data for the variable typology are obtained from a listing of all cities used in the NLC analysis, arranged by type, which was provided by one of the authors of the report (C. McFarland, personal communication, June 14, 2006). Additional analysis are performed using form of government and metro status as the independent variables to test the fiscal outputs of cities based upon these traditional classifications. These analyses are conducted for purposes of comparison with the findings obtained from testing the NLC typology as the primary independent variable. Table 3.2. Comparison of Sample Cities Without and With Inclusion of Mega-Metro Centers Without Mega-metro cities a With Mega-metro cities Total cities 906 936 Spread cities 41% 40% Gold coast cities 20% 20% Metro centers 9% 9% Meltingpot cities 14% 13% Boomtowns 8% 8% Centervilles 8% 7% Mega-metro centers --- 3% a Source for figures without Mega-metro cities: National League of Cities (2005), From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities. Retrieved from: http://www.nlc.org/content/Files/RMPtypologiesrpt06.pdf. 91 The value assigned for the variable Typology indicates NLC?s classification for each city represented nominal measurement with the categories assigned scores of 1 to 7 respectively. Additionally, dummy variables have been created for each of the seven types of cities with that type coded as 1 and all others as 0. The variable Typology is examined through comparison of means using t-tests and one-way ANOVA testing. These dummy variables are used to generate descriptive statistics about each type of city and in the multiple regression analysis to represent the different city types. The variables form of government and metro status were used as independent variables in separate analyses involving the effects of these institutional factors on municipal fiscal behavior. The values of these variables for each city come from ICMA?s 2001 Municipal Form of Government survey (ICMA, 2002). The form of government surveys provide historical information on cities throughout the United States. For the relatively small number of the subject cities that did not respond to this survey, the information concerning their form of government and metro status was obtained from the Municipal Yearbook 2003 (ICMA, 2003). ICMA uses five categories to describe form of government: mayor, manager, commission, town meeting, and representative town meeting. Because none of the cities in this study have town meeting nor representative town meeting forms, the variable form of government only uses the first three ICMA categories. Table 3.3 shows coding and other information for the independent variables used in the study. Here, too the variable categories are coded as a nominal measure and dummy variables created for each category. 92 Table 3.3. Independent Variables Name Label Coding Typology a Typology Spread cities = 1; Gold coast cities = 2; Metro centers =3; Meltingpot cities = 4; Boomtowns = 5; Centervilles = 6; Mega- metro = 7 City type dummy variables Name of city type City type = 1; all others 0 Form of government b Formgvt Mayor-council = 1; Council-manager = 2; Commission = 3 Manager Manager Manager = 1; all others 0 Metro status b Metro Central = 1; Suburb = 2; Independent = 3 Suburb Suburb Suburb = 1; all others 0 a Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. b Data Source for Form of Government: International City/County Management Association, Municipal Form of Government Survey 2001. Washington, DC: International City/County Management Association, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: International City/County Management Association, 2003. Additionally, this study looks at the possible effects of various control variables upon the fiscal output behavior of cities based upon findings from previous research concerning demand and expenditure patterns. Data and coding information on these control variables are shown in Table 3.4. The form of government and metro status for each city are coded, and data gathered, in the same manner when they are used as control variables as when they were used as independent variables as discussed above. 93 Table 3.4. Control Variables Name Label Coding Form of government a Formgvt Mayor-council = 1; Council-manager = 2; Commission = 3; Manager a Manager Manager = 1; all others 0 Metro status a Metro Central = 1; Suburb = 2; Independent = 3 Suburb a Suburb Suburb = 1; all others 0 Principal city designation b Pcity Pcity = 1; all others 0 Percentage IGR c IGRrate Actual percentage amount (Computed by the author as the amount of total intergovernmental revenues received divided by the amount of total revenues) Population size d Pop2000 Amount of population Population density d Densty00 Amount of population divided by land area (Computed by author) Growth rate 1980-2000 e Growth Actual percentage change Non-White d Non-White Percent non-White Black d Black Percent Black Hispanic d Hispanic Percent Hispanic Home ownership d Homeownr Percent owner-occupied housing units Children d Children Percent under age 18 Elderly d Elderly Percent age 65 and over Education d EduBach Percent age 25 and older with bachelor?s degree or higher Median household income d medINC Actual dollar amount a Data Sources: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. b Definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan 94 and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. c Data Source: U.S. Bureau of the Census, 2002 Census of Governments? City Finance Surveys Census of Governments, 2002. d Data Source: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. e Data Source: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. Region of the country where the city is located is coded according to the Census Bureau?s regional classifications where Northeast is coded as 1, Midwest as 2, South as 3, and West as 4. Additionally, with the West region serving as the control, three dummy variables representing the Northeast, Midwest, and South regions were created. The regional dummy variables are coded as 1 for the identified region and 0 for all others. Population size and density are measured as the actual 2000 population and that population divided by land area in square miles for each city. The growth rate for a city is determined by calculating a percentage figure equal to the change in population for a city between 1980 and 2000, divided by its 1980 population, with the resulting amount multiplied by 100. The median household income of a city is measured using the actual dollar amount figures of median household income, recorded for 1999, that are listed for each of the cities. All other demographic figures represent year 2000 census data. Examination of the NLC typology is performed following the research design set out in this chapter. The following two chapters describe this analysis and contain the findings of the study, relating them to the research questions and hypotheses presented earlier in this chapter. They also explain the significance of these findings. Chapter four focuses on the descriptive analysis and chapter five discusses the multiple-regression analysis performed in the study. 95 CHAPTER 4 DESCRIPTIVE ANALYSIS AND FINDINGS This chapter summarizes the descriptive analysis performed in the study and reports the findings of the various testing on demographic and financial data for the 936 sample cities. The objectives of the study include describing and comparing spending, taxing, and debt practices of the cities, as well as various demographic characteristics, to better understand the cities being studied. In addition, the significance of the National League of Cities? (NLC) typology to fiscal output behaviors of the cities is analyzed and compared with other classification systems to determine whether the NLC typology provides a better gauge by which such financial behaviors can be measured and understood. The principal research question of the study is whether the NLC typology results in significant differences in patterns among the different city types concerning municipal financial outputs. To generally address this issue, the study examines and tests seven related hypotheses. The research hypotheses are as follows: 1. There are significant differences between the expenditure, revenue, and debt outputs in different city types. 2. There are significant differences in expenditure levels between the different city types. 96 3. There are significant differences in revenue levels between the different city types. 4. There are significant differences in revenue sources between the different city types. 5. There are significant differences in debt levels between the different city types. 6. There are significant differences in the type of debt incurred between the different city types. 7. The NLC typology will provide a more statistically significant measure of the financial behaviors of cities than did prior categorizations. The first six of these hypotheses predict significant differences among the fiscal outputs of cities according to a city?s classification within the NLC typology. These hypotheses are examined through the use of descriptive statistics and tested using t-test and one-way analysis of variance (ANOVA) methods to determine the significance of the typology in terms of various fiscal measures. The final hypothesis suggests that the NLC typology provides a better way to categorize cities and, therefore, that it will result in a better explanation of financial behavior than did prior categorizations. This hypothesis is tested with multiple-regression analysis and compared to similar regressions utilizing form of government, metropolitan (metro) status, and principal city status as the primary independent variables. This portion of the analysis is reported in chapter five. Prior research has suggested that various factors have an impact on the financial behavior of cities, although the studies have not been consistent on the influences resulting from many of the factors. Under systems theory, these are the input factors that act upon the city?s political system and affect the outputs produced. The current study 97 analyzes several of these factors identified in prior research. These factors include regional location, form of government, metro status, and principal city status of the cities included in NLC?s typology. The analysis also looks at the demographic factors that have been noted to impact fiscal behavior. These include: population size and density; growth rate; levels of income, home ownership, and education; and the age and racial makeup of the residents of the cities. The outputs of the system are the ultimate financial decisions made in terms of its spending, taxing, and debt practices. The various classification schemes are compared to see which are best at distinguishing among cities in terms of these demographic factors, as well as fiscal outputs. The analysis initially examines and describes the structural and demographic characteristics of the various types of cities in the different classification schemes and explores how they are similar and different. Additionally, descriptive statistics are utilized to look at the cities in terms of various fiscal output measures, again comparing and contrasting these outputs among cities. Comparison of means analysis is employed through t-tests and ANOVA testing to determine the statistical significance of any noted differences among the cities within each classification. The next chapter reports how multiple-regression analysis is applied to test the significance and influence of city types within the NLC typology on fiscal outputs and to compare the findings to similar analysis using traditional categorizations of cities. Regional Influence Because it has long been noted that differences exist in financial behavior based on the region of the country in which the city is located, the analysis begins with an 98 examination of differences among cities within the various classifications based on the U.S. Bureau of the Census (Census Bureau) region in which they are located. As set out in Chapter 1, Table 1.1 shows the regional location of the cities in the study compared to all U.S. cities with populations of 25,000 and above. The sample is under representative of the cities in the Northeast by 9%. Cities in the West and South are over represented by 4 and 5% respectively. Table 4.1 shows the regional location of cities that comprise each of the city types within the NLC classification. The association between typology and region is statistically significant, as are all the other classifications? association with region. Table 4.1. Regional Location of City Types Within National League of Cities? Typology (Percentages) Region Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Northeast 16.4 11.9 20.0 12.8 0.0 8.8 10.0 Midwest 33.3 34.1 18.8 6.4 23.7 32.4 16.7 South 34.1 13.0 33.8 15.2 36.8 36.8 43.3 West 16.1 41.1 27.5 65.6 39.5 22.1 30.0 N 372 185 80 125 76 68 30 Note. X 2 (18, N = 936) = 167.76, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Notable are the facts that there are no Boomtowns in the Northeast and Meltingpot cities are predominately located in the West (66%). Two thirds of the Spread cities are about evenly divided between the Midwest and South, with the remaining third 99 about evenly divided between the Northeast and West. Gold coast cities are mainly located in the Midwest and West, with a total of about 75% of this type city located there. Metro centers are the most evenly dispersed type of city in each of the four regions. Meltingpot cities are least likely to be found in the Midwest, while two thirds of them are in the West. Boomtowns are largely located in the South and West, with less than one fourth in the Midwest and none in the Northeast. Centervilles are predominately located in the Midwest and South with very few in the Northeast. Mega-metro centers are mainly found in the South, with relatively few in the Northeast and Midwest. One of the purposes of this study is to compare the typology classifications to other methods of classification including form of government, metro status, and principal city status. Thus, an examination of the regional location of cities based on these other classifications enables one to begin seeing how they compare to the NLC classification. Table 4.2 shows the regional breakdown of cities classified by form of government. The greatest percentage of mayor-council cities is located in the Midwest, followed by the Northeast. The two regions account for nearly 75% of all cities with the mayor-council form of government. The Western region has only 10% of the cities with the mayor-council form of government. City-manager cities account for around two thirds of all the cities in the study. Manager cities are primarily located in the West, followed by the South. These two regions account for almost 74% of all manager cities in the study. Just under 5% of the manager cities are located in the Northeast. Table 4.2 also illustrates that less than 1.5% of the sample cities have the commission form of government. Of these commission cities, an equal number (31%) are located in the Midwest and South. The West has the 100 least cities with the commission form of government. (Commission cities are not expected to have much influence in the analysis due to their low number.) Table 4.2. Regional Location Based on Form of Government (Percentages) Region Mayor Manager Commission Northeast 30.2 4.6 23.1 Midwest 38.0 21.8 30.8 South 21.4 31.4 30.8 West 10.4 42.3 15.4 N 308 615 13 Note. X 2 (6, N = 936) = 197.42, p = .01. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Table 4.3 depicts the regional location of cities based on their metropolitan (metro) status. It shows that central cities and suburbs are about evenly divided in the sample, with 48% being central cities and 44% suburbs. Independent cities account for only 8% of the total cities. The central cities are mainly in the South (37%) and are least likely to be located in the Northeast (16%). The remainder of the central cities is about evenly divided between the Midwest and West. Most of the suburbs can be found in the West (41%) and Midwest (31%). Independent cities are fairly evenly divided among the regions, with the exception of the Northeast. 101 Table 4.3. Regional Location Based on Metro Status (Percentages) Region Central Suburb Independent Northeast 16.2 11.2 6.8 Midwest 23.3 30.6 32.4 South 36.9 17.7 32.4 West 23.6 40.5 28.4 N 450 412 74 Note. X 2 (6, N = 936) = 60.29, p = .01. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Table 4.4 reports the regional location of cities based on whether or not they are principal cities. As can be seen, the sample cities are about evenly divided between those that are principal cities and those that are not. Over 65% of principal cities are found in the South and West. Just over 10% are in the Northeast. Table 4.4. Regional Location Based on Principal City Status (Percentages) Region Principal Non-Principal Northeast 12.0 14.7 Midwest 22.7 32.3 South 34.3 21.2 West 31.0 31.8 N 493 443 Note. X 2 (3, N = 936) = 23.33, p = .01. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. 102 Another way to understand the import of region is to consider the makeup of the cities within each region in terms of the classification categories. Table 4.5 shows the percentages of the different types of cities within the NLC typology for each of the four Census Bureau regions. Spread cities, which account for 40% of all cities in the study, constitute just under 50% in each of the regions, with the exception of the West, where they account for only around 20%, and where Gold coast and Meltingpot cities are both more numerous. Table 4.5. City Types Within National League of Cities? Typology by Region (Percentages) Typology (percent of total) Northeast Midwest South West Spread cities (40%) 49.2 48.6 48.3 20.4 Gold coast cities (20%) 17.7 24.7 9.1 25.9 Metro centers (9%) 12.9 5.9 10.3 7.5 Meltingpot cities (13%) 12.9 3.1 7.2 27.9 Boomtowns (8%) 0.0 7.1 10.6 10.2 Centervilles (7%) 4.8 8.6 9.5 5.1 Mega-metro centers (3%) 2.4 2.0 4.9 3.1 N 124 255 263 294 Note. X 2 (18, N = 936) = 167.76, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Table 4.6 shows that 75% of all cities in the Northeast have mayoral forms of government, whereas almost 75% of all cities in the South are of the city-manager form. Cities in the Midwest are relatively evenly divided between mayors and managers. The West is predominately comprised of manager cities. Because of the small number of commission cities, they do not comprise even 2.5% of any of the four regions. 103 Table 4.6. Form of Government by Region (Percentages) Form of Government Northeast Midwest South West Mayor 75.0 45.9 25.1 10.9 Manager 22.6 52.5 73.4 88.4 Commission 2.4 1.6 1.5 0.7 N 124 255 263 294 Note. X 2 (6, N = 936) = 197.42, p = .01. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. A breakdown of cities in each region based on their metro status is presented in Table 4.7. Central cities constitute the majority of cities in the Northeast and South, with suburbs leading in the West. The division is more even between central and suburban cities in the Midwest, with just under half being suburbs. Independent cities make up a larger percentage of the cities in the Midwest and South than in the other regions, but even in these regions they account for less than 10% of the total cities. Table 4.7. Metro Status by Region (Percentages) Metro Status Northeast Midwest South West Central 58.9 41.2 63.1 36.1 Suburb 37.1 49.4 27.8 56.8 Independent 4.0 9.4 9.1 7.1 N 124 255 263 294 Note. X 2 (6, N = 936) = 60.29, p = .01. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. 104 Table 4.8 sets out the distribution of cities in each region based on whether they are principal cities. Cities in the Northeast and West are about evenly divided, with principal cities having slightly more in each region. The West is also fairly evenly divided, but non-principal cities are somewhat more numerous. In the South, almost two thirds of the cities are non-principal cities. Table 4.8. Principal City Status by Region (Percentages) City Status Northeast Midwest South West Principal 52.4 56.1 35.7 48.0 Non-principal 47.6 43.9 64.3 52.0 N 124 255 263 294 Note. X 2 (3, N = 936) = 23.33, p = .01. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. These initial tables show there are a number of differences among the regions in terms of the various classification schemes examined. However, as Lineberry and Fowler (1967) note, ?The South is not a distinctive political region because two surveyors named Mason and Dixon drew a famous line, but because the ?composition of its population? differs from the rest of the county? (p. 706). Therefore, additional analysis of specific demographic characteristics of cities within the different classifications is necessary to understand what influence region may have on a city?s fiscal outputs. 105 Demographic Factors Table 4.9 shows the mean values on various demographic characteristics within each census region and for the total sample. The high and low values for each variable as well as the standard deviation are presented as well. Table 4.9. Demographic Characteristics by Region (Mean values) Variable Northeast Midwest South West Total N Population 149,387 90,416 119,806 123,296 116,814 936 SD (726,605) (208,422) (191,141) (259,405) (336,043) High 8,008,278 2,896,016 1,953,631 3,694,820 8,008,278 Low 25,671 26,009 25,514 26,128 25,514 Density 6,578 3,226 2,456 4,710 3,920 936 SD (7,383) (1,838) (1,820) (3,437) (3,819) High 52,825 13,886 12,508 23,799 52,825 Low 466 781 159 11 11 Growth Rate for 1980-2000 a 2.6% 18.9% 51.6% 73.9% 43.2% 936 SD (13.1) (42.5) (80.7) (139.6) (95.6) High 40.0% 237.6% 592.8% 1,819% 1,819% Low -22.1% -42.9% -27.6% -53.2% -53.2% Median Income $40,101 $45,254 $37,631 $49,032 $43,616 936 SD (12,685) (14,419) (11,677) (16,472) (14,918) High $98,390 $106,773 $94,609 $139,895 $139,895 Low $21,186 $20,542 $17,206 $25,849 $17,206 Home Ownership 50.2% 65.4% 57.7% 58.2% 59.0% 936 SD (14.2) (12.2) (11.4) (11.7) (13.0) High 81.8% 92.7% 92.2% 90.0% 92.7% Low 18.2% 21.0% 27.2% 23.8% 18.2% Bachelor?s Degree or Higher 23.0% 27.8% 26.1% 27.4% 26.6% 936 SD (12.4) (14.8) (11.2) (14.4) (13.5) High 69.2% 72.6% 73.7% 74.4% 74.4% Low 8.2% 6.9% 6.7% 2.3% 2.3% Children 23.6% 24.6% 24.4% 26.6% 25.0% 936 SD (3.9) (3.7) (4.4) (5.1) (4.6) High 33.2% 32.8% 35.5% 39.5% 39.5% Low 5.8% 9.0% 9.7% 14.1% 5.8% 106 Variable Northeast Midwest South West Total N Elderly 13.8% 12.8% 12.9% 11.0% 12.4% 936 SD (2.9) (3.9) (5.3) (3.9) (4.4) High 20.7% 27.7% 37.8% 33.1% 37.8% Low 5.8% 4.1% 3.6% 3.2% 3.2% Non-White 31.6% 21.4% 40.1% 44.1% 35.1% 936 SD (24.1) (17.5) (19.1) (24.3) (23.1) High 88.5% 98.8% 95.0% 99.0% 99.0% Low 2.9% 3.2% 4.2% 5.9% 2.9% Black 12.5% 11.4% 23.7% 4.7% 12.9% 935 SD (14.1) (16.1) (18.8) (6.4) (16.2) High 61.8% 97.7% 84.5% 47.1% 97.7% Low 0.3% 0.2% 0.4% 0.2% 0.2% Hispanic 14.3% 5.2% 12.1% 26.3% 15.0% 936 SD (16.2) (6.8) (16.3) (20.8) (18.1) High 82.3% 51.6% 94.1% 96.3% 96.3% Low 0.5% 0.6% 0.6% 1.8% 0.5% N 124 255 263 294 936 Note. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other data are from the 2000 Decennial Census. Average populations of the cities range from 90,000 in the Midwest to 149,000 in the Northeast. Population density is the greatest in the Northeast and the least in the South. The mean growth rates for the cities range considerably, from a low of only 2.6% in the Northeast to a high of 74% in the West. Each region?s median household income is within an $11,000 range. Home ownership rates go from around 50% to just over 65%. Educational levels vary within a range of 5%, and the percentages of children and elderly for the regions all fall within 3% ranges. The percentages of non-White residents in the South and West are twice as much as in the Midwest. The South has the largest percentage of Blacks, with nearly twice as 107 many as the next highest region, the Northeast, and the West has the least. In contrast, the West has the largest percent of Hispanics and the Midwest the least. Table 4.9 also shows that there are large ranges between the individual values reported by cities for many of these variables. The variables population size and growth rate, among others, have mean values for each of the four regions which are exceeded by the standard deviations of values within the region. Thus, it may be misleading to rely simply on a city?s regional location to infer much about its demographic makeup. On the other hand, the variables home ownership and children have much less variation in values within the regional categories, but there is also less difference between the regions. The mean values for demographic characteristics of the cities within each of the classifications of the NLC typology are shown in Table 4.10. In 5 of the 7 city types the population mean ranges in size from 35,000 to 81,000. Metro centers and Mega-metro centers are substantially larger, at 259,000 and 1.2 million respectively. Population density figures vary more widely with Meltingpot cities being the densest and Centervilles the least dense. Growth rates between 1980 and 2000 were all within 15 to 30% of one another, with the exception of Meltingpot cities, which grew at an average rate nearly twice that of most of the other cities, and Boomtowns that increased at the exceptionally higher rate of 230%. The Boomtown grouping not only has the largest standard deviation, it is the only group where the low end of the data range (51.2%) is positive, not negative. Table 4.10 further shows that the median household incomes vary from $33,000 to $62,000 per year. The measure of home ownership shows rates are generally around 50 to 60% with the exception of Gold coast cities (68%) and Boomtowns (73%). 108 Educational levels (percentage with a bachelor?s degree or higher) range from a low of 17% in Meltingpot cities to 40% in Gold coast cities. Table 4.10. Demographic Characteristics of City Types Within National League of Cities? Typology (Mean values) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N Population 60,798 57,421 259,364 81,064 80,113 34,753 1,225,480 936 SD (30,598) (30,595) (108,592) (50,182) (42,379) (7,133) (1,469,732) High 205,727 203,413 486,699 337,977 222,030 63,677 8,008,278 Low 25,514 26,940 85,403 26,992 31,880 25,575 506,132 Density 2,823 4,119 4,242 8,256 2,384 1,783 6,102 936 SD (1,721) (2,255) (2,650) (7,268) (1,013) (1,084) (5,556) High 11,254 16,571 11,494 52,825 4,916 6,217 26,403 Low 32 454 153 727 771 11 834 Growth Rate 1980- 2000 a 18.3% 30.1% 24.4% 53.5% 230.9% 15.7% 26.9% 936 SD (31.3) (35.5) (41.3) (52.8) (240.6) (29.7) (33.7) High 173.3% 148.9% 190.5% 291.2% 1,819% 111.9% 140.0% Low -42.9% -17.2% -23.2% -53.2% 51.2% -21.8% -21.0% Median Income $35,982 $62,085 $36,260 $42,021 $57,089 $33,363 $39,763 936 SD (6,929) (15,841) (7,057) (10,901) (11,512) (6,465) (8,001) High $51,969 $139,895 $56,054 $84,429 $88,771 $62,034 $70,243 Low $17,206 $28,266 $23,483 $24,468 $34,758 $22,700 $29,536 Home Ownership 56.4% 68.1% 51.6% 51.5% 72.6% 58.9% 50.4% 936 SD (10.1) (12.7) (9.6) (13.9) (9.2) (8.8) (9.1) High 86.2% 91.1% 74.9% 88.9% 92.7% 72.2% 63.2% Low 21.7% 21.0% 23.8% 18.2% 48.8% 30.2% 30.2% Bachelor?s Degree or Higher 23.4% 39.9% 24.4% 17.1% 30.9% 21.6% 28.1% 936 SD (11.3) (13.8) (8.2) (9.5) (12.7) (9.4) (8.5) High 73.7% 74.4% 54.3% 65.1% 60.7% 48.2% 47.2% Low 6.7% 9.3% 9.0% 2.3% 10.2% 8.2% 11.0% Children 23.8% 23.1% 25.9% 29.3% 29.1% 24.5% 24.7% 936 SD (3.9) (3.6) (2.9) (5.0) (3.5) (4.2) (4.0) High 32.8% 29.7% 32.9% 39.5% 38.0% 34.6% 31.1% Low 5.8% 13.2% 16.8% 10.5% 18.0% 13.1% 14.5% 109 Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N Elderly 13.5% 14.1% 11.5% 9.2% 8.5% 14.2% 10.8% 936 SD (3.7) (5.3) (2.2) (2.9) (4.2) (3.9) (2.1) High 33.1% 37.8% 17.4% 19.2% 26.5% 27.2% 17.8% Low 3.6% 5.3% 5.5% 3.9% 3.2% 6.1% 6.7% Non-White 27.3% 25.2% 48.0% 70.8% 25.8% 26.2% 55.6% 936 SD (17.2) (15.5) (16.4) (17.0) (15.5) (18.4) (16.8) High 98.8% 78.6% 88.2% 99.0% 78.4% 73.9% 89.5% Low 2.9% 3.2% 12.1% 27.3% 5.9% 3.2% 24.5% Black 15.0% 5.1% 25.1% 12.0% 6.8% 11.4% 24.8% 935 SD (17.5) (7.9) (18.6) (15.5) (8.6) (14.6) (20.5) High 97.7% 54.2% 73.5% 78.2% 45.5% 69.6% 81.6% Low 0.3% 0.2% 1.3% 0.4% 0.2% 0.2% 1.6% Hispanic 7.8% 9.7% 15.9% 46.4% 12.6% 10.4% 20.2% 936 SD (8.7) (8.7) (14.1) (22.6) (9.9) (15.5) (18.5) High 51.7% 47.8% 65.8% 96.3% 37.7% 71.2% 76.6% Low 0.6% 0.5% 0.8% 6.9% 1.1% 0.7% 1.7% N 372 185 80 125 76 68 30 Note. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other data are from the 2000 Decennial Census. The mean percentages of children age 18 and younger are all between 23 and 29%. Likewise, the percentages of those ages 65 and older vary from around 9 to 14%. Racial makeup of the cities varies considerably overall with a range of 25 to 71% non- White. However, excluding the two most populous city types and Meltingpot cities, the others only range from 25 to 27% non-White. Gold coast cities and Boomtowns have the lowest percentages of Blacks, whereas Meltingpot cities have by far the greatest concentration of Hispanics, at 46%. Table 4.11 reveals the same demographic information for cities based on their form of government. The average populations range from a low of 92,000 in manager 110 cities to 165,000 for mayor cities. Cities with mayor-council and council-manager forms of government have relatively similar densities at around 4,000. Commission cities have the greatest density, being over twice the amount of the other two types of cities. Growth rates are the highest in manager cities at nearly three time that of mayor cities. Median income is lowest in commission cities and highest in cities with managers. Home ownership rates vary with the range being around a 5% difference. Likewise, the differences in educational levels among all three categories are within about 5%. The age categories of children and elderly are the least dispersed with both ranging around a difference of merely 2%. The racial compositions of the cities based on their form of government vary more with a difference of about 5% for non-White, 6% for Black, and 7.5% for Hispanic. Table 4.11. Demographic Characteristics Based on Forms of Government (Mean values) Variable Mayor Manager Commission N Population 165,213 92,255 131,972 936 SD (554,399) (126,762) (148,376) High 8,008,278 1,321,045 529,121 Low 26,156 25,514 26,186 Density 4,065 3,734 9,241 936 SD (3,686) (2,941) (17,699) High 30,138 23,799 52,825 Low 32 11 48 Growth Rate for 1980-2000 a 18.8% 56.1% 13.0% 936 SD (45.5) (111.3) (21.4) High 337.6% 1,818.8% 52.4% Low -42.9% -53.2% -18.1% Median Income $39,685 $45,768 $34,967 936 SD (11,851) (15,930) (6,872) High $98,390 $139,895 $45,130 Low $19,544 $17,206 $23,066 111 Variable Mayor Manager Commission N Home Ownership 56.7% 60.2% 54.7% 936 SD (12.9) (12.8) (16.6) High 92.2% 92.7% 70.1% Low 21.0% 22.8% 18.2% Bachelor?s Degree or Higher 23.7% 28.1% 22.9% 936 SD (11.7) (14.2) (8.7) High 69.7% 74.4% 38.9% Low 6.7% 2.3% 9.2% Children 24.7% 25.3% 23.4% 936 SD (3.6) (5.0) (2.2) High 35.2% 39.5% 26.8% Low 10.4% 5.8% 19.4% Elderly 13.0% 12.1% 14.3% 936 SD (3.3) (4.8) (2.8) High 25.4% 37.8% 20.1% Low 4.6% 3.2% 10.0% Non-White 31.9% 36.7% 35.4% 936 SD (22.5) (23.1) (29.9) High 98.8% 99.0% 86.7% Low 2.9% 3.2% 5.7% Black 17.0% 10.8% 15.7% 935 SD (19.5) (13.8) (20.3) High 97.7% 78.2% 69.6% Low 0.0% 0.2% 0.2% Hispanic 10.0% 17.5% 16.1% 936 SD (13.6) (19.2) (29.0) High 90.3% 96.3% 82.3% Low 0.6% 0.5% 0.7% N 308 615 13 Note. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other data are from the 2000 Decennial Census. Table 4.12 contains the demographic measures for cities based on their metro status. Population means range from 37,000 for independent cities to central cities at 179,000. The mean density is greatest in suburbs, where it is nearly three times as much 112 as in independent cities. Suburbs have the highest growth rate at just over twice that of central cities and nearly three times the rate reported for independent cities. The mean values for the variable median household income range from $34,000 to $53,000. Home ownership rates vary within a 10% range. The difference in educational levels is within 5%, and percentages of children and elderly are both around 2%. The percentage of non- White is lowest in independent cities, and it is exactly the same for central cities and suburbs. The percent of Blacks is lowest in suburbs and nearly twice as much in central cities, whereas suburbs have the largest percent of Hispanics. Table 4.12. Demographic Characteristics Based on Metro Status (Mean values) Variable Central Suburb Independent N Population 178,651 63,661 36,711 936 SD (475,265) (44,133) (8,919) High 8,008,278 478,403 80,537 Low 25,514 26,156 25,575 Density 3,255 5,034 1,757 936 SD (2,618) (4,795) (847) High 26,403 52,825 4,144 Low 153 366 11 Growth Rate for 1980-2000 a 28.8% 62.8% 21.9% 936 SD (50.2) (130.4) (45.5) High 504.3% 1,818.8% 337.6% Low -42.9% -53.2% -21.8% Median Income $36,736 $52,947 $33,503 936 SD (8,903) (15,947) (6,655) High $90,377 $139,895 $62,034 Low $17,206 $19,544 $20,649 Home Ownership 54.6% 64.0% 57.3% 936 SD (9.9) (14.7) (9.6) High 85.4% 92.7% 70.2% Low 21.7% 18.2% 30.4% 113 Variable Central Suburb Independent N Bachelor?s Degree or Higher 25.1% 28.7% 23.7% 936 SD (12.0) (15.1) (11.0) High 74.4% 72.6% 64.2% Low 7.1% 2.3% 8.8% Children 24.6% 25.8% 23.8% 936 SD (4.4) (4.6) (4.8) High 35.5% 39.5% 33.6% Low 5.8% 10.4% 9.7% Elderly 12.7% 11.9% 13.7% 936 SD (3.8) (5.0) (3.9) High 33.1% 37.8% 26.8% Low 3.6% 3.2% 4.9% Non-White 36.0% 36.0% 25.4% 936 SD (20.6) (26.0) (17.7) High 98.8% 99.0% 81.9% Low 3.2% 2.9% 5.4% Black 16.8% 8.9% 11.6% 935 SD (17.1) (14.4) (14.7) High 97.7% 94.3% 69.6% Low 0.2% 0.2% 0.2% Hispanic 13.3% 17.9% 9.0% 936 SD (16.1) (20.2) (13.4) High 94.1% 96.3% 74.6% Low 0.6% 0.5% 0.7% N 450 412 74 Note. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other data are from the 2000 Decennial Census. Figures for demographic characteristics of principal cities and non-principal cities are found in Table 4.13. Mean population is over three times as high in principal cities, but density is greater in non-principal cities. Growth rate, median income, and home ownership are all greater in non-principal cities. The percentage with a bachelor?s degree or higher is 1% larger in principal cities. Non-principal cities have a percentage of 114 children that is 1.3% higher, and their percentage of elderly is 0.2% less. Principal cities have a higher percentage of non-Whites and Blacks, but a lower percentage of Hispanics. Table 4.13. Demographic Characteristics Based on Principal City Status (Mean values) Variable Principal Non-Principal N Population 173,840 53,351 936 SD (454,656) (33,460) High 8,008,278 226,419 Low 25,671 25,514 Density 3,486 4,403 936 SD (3,392) (4,196) High 52,825 44,871 Low 32 11 Growth Rate for 1980-2000 a 37.6% 49.5% 936 SD (63.6) (121.5) High 504.3% 1,818.8% Low -53.2% -42.9% Median Income $39,854 $47,804 936 SD (12,694) (16,064) High $100,411 $139,895 Low $17,206 $19,544 Home Ownership 55.7% 62.6% 936 SD (10.9) (14.2) High 87.5% 92.7% Low 18.2% 19.9% Bachelor?s Degree or Higher 27.0% 26.0% 936 SD (12.8) (14.2) High 74.4% 73.7% Low 5.9% 2.3% Children 24.4% 25.7% 936 SD (4.4) (4.6) High 38.5% 39.5% Low 5.8% 10.4% Elderly 12.5% 12.3% 936 SD (4.0) (4.7) High 33.1% 37.8% Low 3.6% 3.2% 115 Variable Principal Non-Principal N Non-White 36.5% 33.6% 936 SD (21.2) (24.9) High 99.0% 98.8% Low 3.2% 2.9% Black 15.2% 10.3% 935 SD (16.4) (15.7) High 81.6% 97.7% Low 0.2% 0.2% Hispanic 13.9% 16.1% 936 SD (16.7) (19.4) High 94.1% 96.3% Low 0.6% 0.5% N 493 443 Note. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other data are from the 2000 Decennial Census. Comparison of Classifications A careful review of these demographic tables suggests that the NLC typology provides a better distinction among cities based on the variables that influence cities? financial behaviors. One reason for this is that the NLC typology (with the inclusion of Mega-metro centers) provides seven categories of cities for comparison purposes, while the other methods have less, with region, form of government, metro status, and principal city status having only four, three, three, and two categories respectively. If the NLC classifications result in significant differences between categories of cities in terms of fiscal outputs, then the greater number of categories will be an advantage in comparing cities. (The statistical significance of the differences between the various categories is discussed later in this chapter.) 116 An illustration of how a classification system with more categories can give a better understanding of the differences between cities is available by examining the data for the variables reflecting racial composition under different classification methods. When cities are grouped based on their metro status, as shown in Table 4.12, both central cities and suburbs have the same percentage of residents who are non-White, whereas central cities have nearly twice as many Blacks and suburbs have a larger share of Hispanics. As depicted in Table 4.10, when the same data are considered under the NLC typology, two categories that generally correspond to the central city designation, Metro and Mega-metro centers, have 50% or more of their residents who are non-White, whereas in another, Spread cities, the percent non-White is only about half that amount. When suburban-type cities are considered, Gold coast cities and Boomtowns have about 25% of their residents who are non-White. However, Meltingpot cities have almost 71% of their residents who are non-White, which is the largest percentage of non-Whites for any of the typology classifications. Similarly, the data show that there is considerable variation among suburban-type cities in terms of mean percentages for Blacks and Hispanics. Another reason why the NLC classifications may provide a better means of grouping cities for comparison purposes is that, in general, the city types within NLC?s typology exhibit less variation among the cities? individual values on the variables being considered. A classification method more accurately represents the true nature of cities within its divisions when there is less dispersion, or variation, among the individual values assigned on variables for the different cities that make up the category. 117 To meaningfully compare the degree of dispersion of the values within categories with differing means, the standard deviations must be considered in relation to the means. One method of accomplishing this is to compare the coefficient of variation (CV) for the different categories. This figure is the ratio of the standard deviation to the mean. By comparing the coefficients of variation for the various classification schemes, one can see that the NLC typology offers a method of classification having less variation among the cities in each category for most of the variables under consideration. For example, the variation in population sizes for the cities in the NLC classifications are: Spread (CV = .50); Gold coast (CV = .53); Metro centers (CV = .42); Meltingpot (CV = .62); Boomtowns (CV = .53); Centervilles (CV = .21); and Mega-metro centers (CV = 1.20). In comparison, variations in population for the different form of government classifications are: Mayor (CV = 3.36); Manager (CV = 1.37); and Commission (CV = 1.12). Variations in population based on the metro status of cities are: Central (CV = 2.66); Suburb (CV = .69); and Independent (CV = .24). Finally, classifying cities based on whether they are principal cities results in variations of: principal cities (CV = 2.62) and non-principal (CV = .63). Only for the variable elderly does any other classification method result in categories having less variation, on average, than the NLC typology and here the difference is very slight. The average coefficient of variation for the NLC typology categories for elderly is 30%, whereas the average for the form of government classifications is 28%. All other classification methods have larger average variations than the NLC?s typology on this variable. 118 With the exception of form of government, the NLC classification results in one of the lowest levels of variation within categories. In some areas, the NLC typology is similar to other methods of classifying cities in terms of the degree of variation within categories. The NLC typology and form of government classifications both have average coefficients of variation of 15% for the variable children. For the variable home ownership, the NLC typology (CV = .18) has slightly less variation than that for metro status (CV = .19). For the variable percent Black, the NLC typology (CV = 1.16) again has slightly less variation than in the regional classification (CV = 1.17). On the other demographic variables, the NLC typology has much less variation than the other classification methods. This overall lower level of variation within categories further suggests that the NLC typology is a better method of classifying cities for comparison purposes. Fiscal Outputs The fiscal outputs of the cities are also analyzed under the various classification methods. In comparing fiscal outputs of the cities, it should be noted that not all cities reported figures for all the financial variables. The Census Bureau data do not differentiate among cities that reported an amount of zero for a category and those who failed to report anything. The data simply lists zero for the value. The current study treats all zero values as missing data. Thus, the figures analyzed only include reported amounts greater than zero. This results in several of the financial variables having lower numbers of responses for some of the variables than others. Results reported for variables with low numbers of responses should be considered accordingly. 119 Table 4.14 reports the per capita levels of fiscal outputs for cities by region, as well as for the sample as a whole. Total expenditures and total revenues are the highest in the Northeast. Additionally, the Northeast spends the most on education, nearly three times the amounts reported in the Midwest and West. The Northeast also has the highest property tax revenue, but it has the lowest revenue from sales tax. The Northeast has income tax revenues that are around half the amount for the Midwest and a third as much as the South. No cities in the West report any revenue from income taxes. The Northeast reports the largest per capita amount of intergovernmental revenues, which are three times more than in any other region. It has the greatest percentage of intergovernmental revenues, with the South having the lowest. The South has the highest total debt, but the Northeast has the greatest level of total full faith and credit debt. Table 4.14. Fiscal Outputs by Region (Mean values expressed in per capita dollar amounts) Variable Northeast Midwest South West Total N EXPENDITURE: Total 2,500 1,377 1,843 1,441 1,677 936 SD (1,374) (621) (1,198) (991) (1,097) High 8,354 5,757 11,635 8,306 11,635 Low 322 507 85 231 85 Common Functions 633 663 687 661 665 936 SD (197) (220) (241) (313) (256) High 1,501 1,579 1,889 2,377 2,377 Low 172 220 136 129 129 Police 197 179 194 201 193 934 SD (78) (57) (80) (91) (78) High 470 396 672 896 896 Low 5 75 14 67 5 Education 1,481 528 841 524 1,208 123 SD (444) (1,055) (648) (846) (658) High 2,717 2,110 2,054 1,808 2,717 Low 1 0 0 0 0 120 Variable Northeast Midwest South West Total N REVENUE: Total 2,380 1,307 1,783 1,455 1,630 936 SD (1,285) (567) (1,157) (977) (1,048) High 7,561 4,588 10,888 9,054 10,888 Low 298 408 54 277 54 Property Tax 803 261 271 210 320 930 SD (521) (147) (228) (153) (317) High 2,283 700 1,599 1,106 2,283 Low 53 12 0 16 0 Sales Tax 57 122 229 290 210 812 SD (114) (135) (189) (183) (187) High 693 629 1,636 1,045 1,636 Low 0 0 1 6 0 Income Tax a 162 306 445 --- 292 103 SD (213) (172) (498) --- (255) High 929 771 2,029 --- 2,029 Low 47 19 111 --- 19 Intergovernmental 960 283 297 219 357 934 SD (776) (201) (429) (198) (459) High 3,647 2,527 3,828 2,222 3,828 Low 46 44 3 39 3 Percentage Intergovernmental 36.1% 22.4% 15.1% 16.6% 20.4% 936 SD (17.0) (10.3) (12.3) (9.2) (13.5) High 78.0% 64.0% 58.0% 57.0% 78.0% Low 5.0% 4.0% 1.0% 2.0% 1.0% DEBT: Total 1,717 1,467 1,990 1,632 1,698 926 SD (1,203) (1,530) (1,726) (1,891) (1,680) High 7,984 14,300 10,036 22,511 22,511 Low 26 9 44 6 6 Total Full Faith & Credit 1,239 670 770 649 782 770 SD (876) (551) (786) (848) (784) High 5,334 3,175 6,850 6,992 6,992 Low 96 3 1 1 1 N 124 255 263 294 936 Note. Values are rounded to the nearest dollar amount; 0 represents values less than $0.50. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a No Western cities reported any values for income tax. 121 Mean per capital values of financial outputs for the various city types within NLC?s typology are shown in Table 14.15. Metro centers and Mega-metro centers spend the most overall ? with total expenditures of approximately $2,400 and $3,400 respectively. The other city types spend from $1,200 to $1,700. Table 4.15. Fiscal Outputs of City Types Within National League of Cities? Typology (Mean values expressed in per capita dollar amounts) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N EXPENDITURE: Total 1,709 1,506 2,392 1,233 1,295 1,593 3,436 936 SD (915) (951) (1,157) (937) (684) (949) (2,368) High 5,966 8,306 6,088 7,806 4,371 5,110 11,635 Low 85 322 757 231 545 415 1,046 Common Functions 666 668 802 538 629 622 997 936 SD (216) (298) (241) (214) (270) (168) (313) High 2,339 2,377 1,753 1,545 2,033 1,042 1,889 Low 220 136 390 129 251 252 417 Police 182 203 236 196 168 154 284 934 SD (58) (98) (72) (66) (95) (38) (116) High 488 848 528 578 896 256 672 Low 75 5 101 85 80 73 146 Education 1,072 1,286 1,629 1,773 404 1,096 951 123 SD (561) (426) (629) (478) (762) (611) (862) High 2,213 1,755 2,717 2,352 1,546 1,711 2,110 Low 0 1 41 1,056 5 1 0 REVENUE: Total 1,664 1,477 2,257 1,249 1,308 1,569 3,005 936 SD (920) (967) (1,073) (919) (645) (959) (2,144) High 6,077 9,054 5,344 7,561 4,400 5,507 10,888 Low 54 298 875 277 511 401 935 Property Tax 318 381 420 236 243 217 481 930 SD (304) (380) (376) (236) (167) (240) (388) High 1,728 2,283 1,599 1,893 1,233 1,426 1,661 Low 5 22 49 16 12 0 51 122 Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N Sales Tax 187 233 225 190 230 211 316 812 SD (180) (213) (168) (107) (181) (197) (328) High 872 1,045 667 585 745 996 1,636 Low 0 0 0 1 2 1 23 Income Tax a 216 354 396 --- 158 282 706 103 SD (139) (169) (236) --- (190) (165) (648) High 644 652 771 --- 292 503 2,029 Low 37 95 47 --- 23 19 81 Intergovernmental 362 231 754 301 176 285 855 934 SD (366) (160) (824) (461) (165) (288) (990) High 2,348 805 3,647 2,510 1,099 1,488 3,828 Low 7 10 57 15 3 22 70 Percentage Intergovernmental 21.1% 17.0% 28.2% 21.8% 13.8% 19.4% 23.3% 936 SD (13.4) (8.9) (19.1) (14.1) (9.0) (13.2) (15.6) High 66.0% 48.0% 77.0% 78.0% 37.0% 57.0% 58.0% Low 1.0% 1.0% 4.0% 1.0% 1.0% 1.0% 5.0% DEBT: Total 1,547 1,481 2,778 1,315 1,677 1,352 4,413 926 SD (1,680) (1,609) (1,769) (1,135) (1,207) (832) (2,562) High 22,511 14,300 10,036 7,609 7,016 4,074 10,510 Low 14 6 450 24 9 69 1,238 Total Full Faith & Credit 681 782 1,128 706 725 664 1,493 770 SD (611) (948) (1,002) (641) (544) (544) (1,316) High 3,184 6,992 5,334 3,293 2,492 2,317 6,850 Low 1 3 6 5 1 24 207 N 372 185 80 125 76 68 30 Note. Values are rounded to the nearest dollar amount; 0 represents values less than $0.50. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a No Meltingpot cities reported any values for income tax. Spending on common functions (which includes police, fire, health, sanitation, highways, and interest on debt combined) varies much less, with Mega-metro centers spending the most ($997) followed by Metro centers ($802). Meltingpot cities spend the 123 least at around half the amount spent by Mega-metro centers. The remaining cities range from $622 to $688 on common function spending. There is a similar trend shown among cities in terms of their spending on police. Education spending varies widely, with Metro centers and Mega-metro centers spending four times as much as most of the other cities. Boomtowns spend the least at $21 per capita, being outspent by the two largest type cities by around 20 times as much. Many cities did not report educational spending levels or are not the responsible governing authority for education. Table 4.15 also shows that the different types of cities have total revenues per capita slightly less than their total expenditures, with the exception of Meltingpot cities and Boomtowns which have overall revenues slightly higher than their expenditures. Gold coast cities depend on property tax more than other cities, raising around 26% of their revenue from that source. The other city types average from 14 to 19% of revenues coming from property taxes. The percentage of intergovernmental revenue is highest in Metro centers (28%) and lowest in Boomtowns (14%). Total debt is greatest in Mega- metro centers and Metro centers, at $4,400 and $2,800 per capita, while the other cities range from $1,300 to $1,700. Cities with a council-manager form of government have the lowest total expenditures and revenues, as shown in Table 4.16. They receive the highest amounts of sales and income taxes, but get the lowest amount from property tax. Manager cities also have the lowest amount of intergovernmental revenues, as well as percentage of intergovernmental revenues. Commission cities have the lowest amount of total debt and mayor-council cities have the highest. Mayor cities also have the largest amount of full faith and credit debt. 124 Table 4.16. Fiscal Outputs Based on Forms of Government (Mean values expressed in per capita dollar amounts) Variable Mayor Manager Commission N EXPENDITURE: Total 1,926 1,550 1,801 936 SD (1,314) (956) (645) High 11,635 8,306 3,038 Low 85 231 1,168 Common Functions 688 652 729 936 SD (261) (254) (204) High 1,889 2,377 1,109 Low 172 129 465 Police 197 190 215 934 SD (80) (78) (77) High 672 896 431 Low 5 14 125 Education 1,319 1,042 949 123 SD (655) (593) (1,071) High 2,717 1,878 1,989 Low 0 1 17 REVENUE: Total 1,836 1,524 1,757 936 SD (1,211) (948) (618) High 10,888 9,054 3,027 Low 54 277 1,206 Property Tax 429 265 350 930 SD (417) (237) (198) High 2,283 1,893 709 Low 20 0 49 Sales Tax 162 231 180 812 SD (214) (171) (164) High 1,636 1,045 490 Low 0 0 0 Income Tax 278 324 289 103 SD (285) (177) (186) High 2,029 771 481 Low 19 89 111 Intergovernmental 524 270 513 934 SD (626) (306) (691) High 3,828 2,268 2,331 Low 22 3 79 Percentage Intergovernmental 25.6% 17.7% 24.6% 936 SD (15.5) (11.2) (22.3) High 78.0% 66.0% 77.0% Low 2.0% 1.0% 6.0% 125 Variable Mayor Manager Commission N DEBT: Total 1,739 1,680 1,607 926 SD (1,754) (1,656) (953) High 14,300 22,511 3,763 Low 9 6 331 Total Full Faith & Credit 894 724 644 770 SD (844) (748) (614) High 6,850 6,992 2,025 Low 9 1 6 N 308 615 13 Note. Values are rounded to the nearest dollar amount; 0 represents values less than $0.50. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As can be seen in Table 4.17, suburbs have the lowest total expenditures, as well as expenditures on common functions and police of the three metro status categories. Independent cities have the lowest spending on education. Total revenues and intergovernmental revenues are lowest in suburbs and highest in central cities. Independent cities have the lowest levels of revenue from property, sales, and income taxes. Total debt and full faith and credit debt are both highest in central cities. Total debt is the lowest in suburbs, but independent cities have the lowest full faith and credit debt. 126 Table 4.17. Fiscal Outputs Based on Metro Status (Mean values expressed in per capita dollar amounts) Variable Central Suburb Independent N EXPENDITURE: Total 1,991 1,318 1,766 936 SD (1,221) (806) (1,114) High 11,635 8,306 5,966 Low 85 231 610 Common Functions 731 591 680 936 SD (242) (248) (272) High 1,889 2,377 2,339 Low 247 129 252 Police 202 189 158 934 SD (77) (82) (46) High 672 896 408 Low 75 5 73 Education 1,229 1,209 953 123 SD (671) (638) (622) High 2,717 2,352 1,711 Low 0 0 1 REVENUE: Total 1,907 1,307 1,741 936 SD (1,149) (795) (1,148) High 10,888 9,054 5,793 Low 54 277 444 Property Tax 349 304 233 930 SD (352) (277) (275) High 2,283 2,050 1,630 Low 2 5 0 Sales Tax 221 201 197 812 SD (199) (173) (190) High 1,636 1,045 996 Low 0 0 1 Income Tax 301 289 248 103 SD (305) (164) (168) High 2,029 652 503 Low 37 23 19 Intergovernmental 465 250 287 934 SD (571) (297) (230) High 3,828 2,331 1,173 Low 8 3 22 Percentage Intergovernmental 22.0% 18.9% 19.0% 936 SD (14.9) (11.7) (12.7) High 78.0% 77.0% 57.0% Low 1.0% 1.0% 1.0% 127 Variable Central Suburb Independent N DEBT: Total 2,068 1,297 1,667 926 SD (1,758) (1,188) (2,710) High 14,300 7,609 22,511 Low 14 6 33 Total Full Faith & Credit 882 682 662 770 SD (854) (721) (518) High 6,850 6,992 2,638 Low 3 1 7 N 450 412 74 Note. Values are rounded to the nearest dollar amount; 0 represents values less than $0.50. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 4.18 contains the fiscal output levels of cities based on whether or not they are principal cities. Principal cities have more total expenditures and they spend more on common functions and police, but less on education. All categories of revenues are higher in principal cities, but the percentage of intergovernmental revenues is slightly lower. Both total debt and full faith and credit debt are higher in principal cities. Table 4.18. Fiscal Outputs Based on Principal City Status (Mean values expressed in per capita dollar amounts) Variable Principal Non-Principal N EXPENDITURE: Total 1,917 1,410 936 SD (1,234) (845) High 11,635 5,759 Low 85 231 Common Functions 734 589 936 SD (261) (228) High 2,339 2,377 Low 136 129 128 Variable Principal Non-Principal N Police 202 182 934 SD (76) (80) High 672 896 Low 67 5 Education 1,137 1,302 123 SD (725) (549) High 2,717 2,352 Low 0 1 REVENUE: Total 1,841 1,395 936 SD (1,168) (837) High 10,888 6,077 Low 54 277 Property Tax 330 309 930 SD (327) (304) High 2,283 1,702 Low 15 0 Sales Tax 236 180 812 SD (198) (169) High 1,636 1,021 Low 0 0 Income Tax 302 280 103 SD (321) (144) High 2,029 652 Low 49 19 Intergovernmental 399 309 934 SD (515) (382) High 3,828 2,510 Low 3 7 Percentage Intergovernmental 20.0% 20.7% 936 SD (13.6) (13.4) High 77.0% 78.0% Low 1.0% 1.0% DEBT: Total 2,041 1,313 926 SD (1,991) (1,125) High 22,511 7,016 Low 14 6 Total Full Faith & Credit 825 731 770 SD (814) (746) High 6,850 6,992 Low 3 1 N 493 443 129 Note. Values are rounded to the nearest dollar amount; 0 represents values less than $0.50. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As seen with the demographic variables, the city types within the NLC typology tend to exhibit less variation among the cities? individual values than do most of the other classification methods on the fiscal output variables being considered. Comparing the coefficients of variation (CV) for the different categories, the NLC typology ranks lowest on five of the variables: common function expenditure, sales tax, income tax (tied with form of government), total debt, and full faith and credit debt. It has the second lowest coefficient of variation for police expenditure, intergovernmental revenue, and percentage intergovernmental revenue. On the variable of police expenditure, the NLC typology category (CV = .38) was essentially tied with metro status (CV = .37). On the variables total expenditure, education expenditure, total revenue, and property tax revenue, the NLC typology was in the middle of the classification schemes in terms of variation within categories. This low variation among the categories within the NLC typology further supports the hypothesis that it provides a better means of classifying cities. Analysis of Variance for Demographic Variables These first tables set out descriptive figures that suggest definite differences in the classification schemes, both in terms of demographics and government finance outputs. To test these indicated findings, comparison of means through oneway ANOVA testing 130 was performed on each of the classifications of cities. The following tables set out the results of such testing. The effect sizes (eta 2 ) are reported for those variables showing statistical significance. Eta 2 is a measure that reflects the usefulness of predicting variation in an interval variable (Y) by knowing the category the case falls in on a categorical variable (X). In this regard, ?The magnitude of eta 2 equals the proportion of the variation in Y that can be attributed to differences in X? (Bernstein & Dyer, 1984, p. 221). Table 4.19 shows that all the demographic variables have statistically significant differences (p <.01) among the census regions, with the exception of population. The effect sizes range from a low of 1% for educational level to a high of 21% for both Black and Hispanic. Table 4.19. Oneway ANOVA Results for Region and Demographics Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Population Btwn groups 3.2E+011 3 1.080E+011 .96 .41 --- W/in groups 1.1E+014 932 1.129E+011 Total 1.1E+014 935 Density Btwn groups 1.7E+009 3 581834649.22 45.6 .00 .13 W/in groups 1.2E+010 932 12759445.69 Total 1.4E+010 935 Growth Btwn groups 651752.23 3 217250.75 25.64 .00 .08 Rate for W/in groups 7898617.8 932 8474.91 1980-2000 a Total 8550370.1 935 Median Btwn groups 2.0E+010 3 6753702168.2 33.51 .00 .10 Income W/in groups 1.9E+011 932 201522036.14 Total 2.1E+011 935 Home Btwn groups 20604.46 3 6868.15 46.56 .00 .13 Ownership W/in groups 137474.71 932 147.51 Total 158079.17 935 Bachelor?s Btwn groups 2210.02 3 736.67 4.08 .01 .01 Degree or W/in groups 168420.82 932 180.71 Higher Total 170630.83 935 131 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Children Btwn groups 1128.18 3 376.06 19.17 .00 .06 W/in groups 18284.02 932 19.62 Total 19412.20 935 Elderly Btwn groups 943.34 3 314.45 17.31 .00 .05 W/in groups 16927.59 932 18.16 Total 17870.94 935 Non-White Btwn groups 80092.2 3 26697.4 59.44 .00 .16 W/in groups 418606.03 932 449.15 Total 498698.22 935 Black Btwn groups 51004.44 3 17001.48 81.18 .00 .21 W/in groups 195196.28 932 209.44 Total 246200.72 935 Hispanic Btwn groups 63906.13 3 21302.04 82.43 .00 .21 W/in groups 240860.42 932 258.43 Total 304766.55 935 Note. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 4.20 shows the results of similar testing of the different city types within the NLC typology categories for the demographic variables. All demographic variables in Table 4.20 are found to be statistically significantly different among the seven typology categories (p <.01). The strengths of these relationships span from around 50% for median income and the percentages of the population that are Hispanic and non- White to a low of 13% for the percent of the population that is Black. 132 Table 4.20. Oneway ANOVA Results for National League of Cities? Typology and Demographics Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Population Btwn groups 4.1E+013 6 6.840E+012 98.45 .00 .39 W/in groups 6.5E+013 929 69477669883 Total 1.1E+014 935 Density Btwn groups 3.4E+009 6 574371880.15 52.36 .00 .25 W/in groups 1.0E+010 929 10969941.92 Total 1.4E+010 935 Growth Btwn groups 3040875.7 6 506812.62 85.46 .00 .36 Rate for W/in groups 5509494.4 929 5930.56 1980-2000 a Total 8550370.1 935 Median Btwn groups 1.1E+011 6 18471364513 176.45 .00 .53 Income W/in groups 9.7E+010 929 104684022.72 Total 2.1E+011 935 Home Btwn groups 45550.15 6 7591.69 62.67 .00 .29 Ownership W/in groups 112529.03 929 121.13 Total 158079.17 935 Bachelor?s Btwn groups 51197.52 6 8532.92 66.37 .00 .30 Degree or W/in groups 119433.31 929 128.56 Higher Total 170630.83 935 Children Btwn groups 4901.94 6 816.99 52.31 .00 .25 W/in groups 14510.25 929 15.62 Total 19412.20 935 Elderly Btwn groups 3793.59 6 632.27 41.73 .00 .21 W/in groups 14077.35 929 15.15 Total 17870.94 935 Non-White Btwn groups 238161.41 6 39693.57 141.54 .00 .48 W/in groups 260536.81 929 280.45 Total 498698.22 935 Black Btwn groups 32199.13 6 5366.52 23.30 .00 .13 W/in groups 214001.59 929 230.36 Total 246200.72 935 Hispanic Btwn groups 150479.56 6 25079.93 151.01 .00 .49 W/in groups 154286.99 929 166.08 Total 304766.55 935 Note. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 133 In Table 4.21, it can be seen that oneway ANOVA testing finds all of the demographic variables, with the exception of children, have statistically significant differences (p <.01) between the categories of city type based on form of government. However, the largest effect sizes are only 4%. Table 4.21. Oneway ANOVA Results for Form of Government and Demographics Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Population Btwn groups 1.1E+012 2 5.477E+011 4.89 .01 .01 W/in groups 1.0E+014 933 1.120E+011 Total 1.1E+014 935 Density Btwn groups 4.0E+008 2 197919698.49 13.95 .00 .03 W/in groups 1.3E+010 933 14192355.77 Total 1.4E+010 935 Growth Btwn groups 298102.83 2 149051.41 16.85 .00 .04 Rate for W/in groups 8252267.2 933 8844.87 1980-2000 a Total 8550370.1 935 Median Btwn groups 8.6E+009 2 4289797502.8 20.06 .00 .04 Income W/in groups 2.0E+011 933 213826419.27 Total 2.1E+011 935 Home Btwn groups 2834.33 2 1417.17 8.52 .00 .02 Ownership W/in groups 155244.84 933 166.39 Total 158079.17 935 Bachelor?s Btwn groups 4166.63 2 2083.32 11.68 .00 .02 Degree or W/in groups 166464.2 933 178.42 Higher Total 170630.83 935 Children Btwn groups 109.38 2 54.69 2.64 .07 --- W/in groups 19302.82 933 20.69 Total 19412.2 935 Elderly Btwn groups 204.42 2 102.21 5.4 .01 .01 W/in groups 17666.52 933 18.94 Total 17870.94 935 Non-White Btwn groups 4756.24 2 2378.12 4.49 .01 .01 W/in groups 493941.98 933 529.41 Total 498698.22 935 Black Btwn groups 8065.96 2 4032.98 15.8 .00 .03 W/in groups 238134.76 933 255.24 Total 246200.72 935 Hispanic Btwn groups 11423.38 2 5711.69 18.17 .00 .04 W/in groups 293343.17 933 314.41 Total 304766.55 935 134 Note. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a Data Source for Growth Rate:U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 4.22 shows there are significant differences (p <.01) between categories of cities based on metro status and all the demographic variables, with the strongest relationship being median income at 31%. Table 4.22. Oneway ANOVA Results for Metro Status and Demographics Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Population Btwn groups 3.4E+012 2 1.680E+012 15.33 .00 .03 W/in groups 1.0E+014 933 1.096E+011 Total 1.1E+014 935 Density Btwn groups 1.1E+009 2 528461075.11 39.19 .00 .08 W/in groups 1.3E+010 933 13483799.76 Total 1.4E+010 935 Growth Btwn groups 284378.33 2 142189.16 16.05 .00 .03 Rate for W/in groups 8265991.7 933 8859.58 1980-2000 a Total 8550370.1 935 Median Btwn groups 6.5E+010 2 32369804690 210.7 .00 .31 Income W/in groups 1.4E+011 933 153633477.82 Total 2.1E+011 935 Home Btwn groups 19223.01 2 9611.51 64.58 .00 .12 Ownership W/in groups 138856.16 933 148.83 Total 158079.17 935 Bachelor?s Btwn groups 3408.43 2 1704.21 9.51 .00 .02 Degree or W/in groups 167222.41 933 179.23 Higher Total 170630.83 935 Children Btwn groups 460.84 2 230.42 11.34 .00 .02 W/in groups 18951.35 933 20.31 Total 19412.2 935 Elderly Btwn groups 291.84 2 145.92 7.75 .00 .02 W/in groups 17579.1 933 18.84 Total 17870.94 935 Non-White Btwn groups 7642.8 2 3821.4 7.26 .00 .02 W/in groups 491055.42 933 526.32 Total 498698.22 935 Black Btwn groups 13568.31 2 6784.16 27.21 .00 .06 W/in groups 232632.41 933 249.34 Total 246200.72 935 135 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Hispanic Btwn groups 7314.71 2 3657.36 11.47 .00 .02 W/in groups 297451.84 933 318.81 Total 304766.55 935 Note. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As seen in Table 4.23, there are statistically significant differences (p <.01) between principal cities and non-principal cities for six demographic variables: population, density, median income, home ownership, children, and percent Black. Non- White is significant at the .05 level. The two variables with the highest effect sizes (7%) are median income and home ownership. Table 4.23. Oneway ANOVA Results for Principal City Status and Demographics Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Population Btwn groups 3.4E+012 1 3.387E+012 30.96 .00 .03 W/in groups 1.0E+014 934 1.094E+011 Total 1.1E+014 935 Density Btwn groups 2.0E+008 1 196210119.75 13.63 .00 .01 W/in groups 1.3E+010 934 14390896.37 Total 1.4E+010 935 Growth Btwn groups 32896.24 1 32896.24 3.61 .06 --- Rate for W/in groups 8517473.8 934 9119.35 1980-2000 a Total 8550370.1 935 Median Btwn groups 1.5E+010 1 14747700063 71.25 .00 .07 Income W/in groups 1.9E+011 934 206993516.19 Total 2.1E+011 935 Home Btwn groups 11305.59 1 11305.59 71.94 .00 .07 Ownership W/in groups 146773.58 934 157.15 Total 158079.17 935 Bachelor?s Btwn groups 251.65 1 251.65 1.38 .24 --- Degree or W/in groups 170379.19 934 182.42 Higher Total 170630.83 935 136 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Children Btwn groups 373.70 1 373.70 18.33 .00 .02 W/in groups 19038.49 934 20.38 Total 19412.2 935 Elderly Btwn groups 20.28 1 20.28 1.06 .30 --- W/in groups 17850.65 934 19.11 Total 17870.94 935 Non-White Btwn groups 2014.21 1 2014.21 3.79 .05 --- W/in groups 496684.01 934 531.78 Total 498698.22 935 Black Btwn groups 5573.69 1 5573.69 21.63 .00 .02 W/in groups 240627.03 934 257.63 Total 246200.72 935 Hispanic Btwn groups 1134.33 1 1134.33 3.49 .06 --- W/in groups 303632.23 934 325.09 Total 304766.55 935 Note. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. a Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As the results of the ANOVA testing show, there are statistically significant differences between the city-type categories in the NLC typology and the metro status classifications for all of the demographic variables. In the region and form of government classifications, the categories have significant differences on all demographic variables except one, whereas classification based on principal city status has significant differences on only 6 out of the 11 variables. However, while most of the classification schemes have statistically significant differences between categories of cities on most of the demographic variables, the strength of the relationships are clearly greater in the NLC typology than in any of the others. This supports the hypothesis that, in terms of demographic variables, the typology provides a better method of classifying cities for comparison purposes. 137 Analysis of Variance for Fiscal Outputs Table 4.24 shows the results of oneway ANOVA testing of cities classified by census region in terms of fiscal output levels. All financial variables except expenditures on common functions have statistically significant differences at the level of <.01 between regions. The strongest relationship is with the amount of property tax revenue and the weakest are police expenditures and total debt levels. Table 4.24. Oneway ANOVA Results for Region and Fiscal Outputs Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Btwn groups 1.3E+008 3 43541615.59 40.81 .00 .12 Expenditure W/in groups 9.9E+008 932 1066935.25 Total 1.1E+009 935 Common Btwn groups 230230.27 3 76743.43 1.17 .32 --- Functions W/in groups 61362722 932 65839.83 Total 61592952 935 Police Btwn groups 72425.16 3 24141.72 3.97 .01 .01 W/in groups 5660406.7 930 6086.46 Total 5732831.9 933 Education Btwn groups 15641497 3 5213832.48 16.71 .00 .30 W/in groups 37135165 119 312060.21 Total 52776663 122 Total Btwn groups 1.1E+008 3 37162392.31 37.84 .00 .11 Revenue W/in groups 9.2E+008 932 982088.78 Total 1.0E+009 935 Property Btwn groups 34018967 3 11339655.67 177.64 .00 .37 Tax W/in groups 59111157 926 63834.94 Total 93130124 929 Sales Tax Btwn groups 5151604 3 1717201.35 59.57 .00 .18 W/in groups 23292011 808 28826.75 Total 28443615 811 Income Tax Btwn groups 705447.81 2 352723.90 5.96 .00 .11 W/in groups 5919091.7 100 59190.92 Total 6624539.5 102 Intergovern- Btwn groups 53010987 3 17670328.95 114.43 .00 .27 mental W/in groups 1.4E+008 930 154415.56 Revenue Total 2.0E+008 933 138 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Percent Btwn groups 4.37 3 1.46 106.73 .00 .26 Intergovern- W/in groups 12.73 932 .01 mental Total 17.11 935 Total Debt Btwn groups 36873329 3 12291109.61 4.4 .00 .01 Outstanding W/in groups 2.6E+009 922 2791436.41 Total 2.6E+009 925 Full Faith Btwn groups 32131045 3 10710348.21 18.6 .00 .07 & Credit W/in groups 4.4E+008 766 575749.11 Debt Total 4.7E+008 769 Note. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The stated hypotheses all predict there will be significant differences (at the level of <.05) among the categories in the NLC typology in terms of the fiscal output measures relating to expenditure, revenue, and debt levels, as well as sources of revenue and types of debt. To test these hypotheses, a comparison of means for the fiscal outputs among different NLC typology categories is also performed through oneway ANOVA testing. Table 4.25 shows the results of this testing. All fiscal output variables are found to be significantly different (p <.01) among the categories of city types, further supporting the hypotheses. The effect size (eta 2 ) for each of the fiscal output variables is also reported; overall, they are lower than those for the demographic variables. These findings show that the strength of the relationship is greatest between the NLC typology and income tax (27%), education expenditures (21%), and total expenditures (16%). The relationships are weakest between typology and per capita amounts of sales tax (2%), property tax (5%), full faith and credit debt (6%), and percent intergovernmental revenue (7%). The strengths of the remaining relationships range from 11 to 14%. 139 Table 4.25. Oneway ANOVA Results for National League of Cities? Typology and Fiscal Outputs Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Btwn groups 1.8E+008 6 29284875.50 28.66 .00 .16 Expenditure W/in groups 9.5E+008 929 1021850.65 Total 1.1E+009 935 Common Btwn groups 7049838.0 6 1174973.00 20.01 .00 .11 Functions W/in groups 54543114 929 58711.64 Total 61592952 935 Police Btwn groups 617993.33 6 102998.89 18.67 .00 .11 W/in groups 5114838.5 927 5517.63 Total 5732831.9 933 Education Btwn groups 10911668 6 1818611.27 5.04 .00 .21 W/in groups 41864995 116 360905.13 Total 52776663 122 Total Btwn groups 1.2E+008 6 19855383.09 20.32 .00 .12 Revenue W/in groups 9.1E+008 929 977030.81 Total 1.0E+009 935 Property Btwn groups 4312379.7 6 718729.95 7.47 .00 .05 Tax W/in groups 88817745 923 96227.24 Total 93130124 929 Sales Tax Btwn groups 653026.69 6 108837.78 3.15 .01 .02 W/in groups 27790589 805 34522.47 Total 28443615 811 Income Tax Btwn groups 1760294.8 5 352058.95 7.02 .00 .27 W/in groups 4864244.7 97 50146.85 Total 6624539.5 102 Intergovern- Btwn groups 26263187 6 4377197.79 23.82 .00 .13 mental W/in groups 1.7E+008 927 183769.44 Revenue Total 2.0E+008 933 Percent Btwn groups 1.14 6 .19 11.07 .00 .07 Intergovern- W/in groups 15.97 929 .02 mental Total 17.11 935 Total Debt Btwn groups 3.6E+008 6 59621447.67 24.32 .00 .14 Outstanding W/in groups 2.3E+009 919 2451413.50 Total 2.6E+009 925 Full Faith Btwn groups 29021092 6 4836848.67 8.31 .00 .06 & Credit W/in groups 4.4E+008 763 582088.82 Debt Total 4.7E+008 769 Note. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 140 Table 4.26 shows that the different forms of government have statistically significant differences (at the level <.01) for the variables total expenditures, total revenues, property and sales tax revenues, intergovernmental revenues, percentage of intergovernmental revenues, and full faith and credit debt. The strongest of these relationships is between form of government and percent intergovernmental revenues at just 8%. Table 4.26. Oneway ANOVA Results for Form of Government and Fiscal Outputs Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Btwn groups 29310832 2 14655415.97 12.48 .00 .03 Expenditure W/in groups 1.1E+009 933 1174381.21 Total 1.1E+009 935 Common Btwn groups 281916.96 2 140958.48 2.15 .12 --- Functions W/in groups 61311035 933 65713.86 Total 61592952 935 Police Btwn groups 17272.01 2 8636.01 1.41 .25 --- W/in groups 5715559.8 931 6139.16 Total 5732831.9 933 Education Btwn groups 2411232.6 2 1205616.32 2.87 .06 --- W/in groups 50365430 120 419711.92 Total 52776663 122 Total Btwn groups 20202620 2 10101309.89 9.36 .00 .02 Revenue W/in groups 1.0E+009 933 1078875.99 Total 1.0E+009 935 Property Btwn groups 5495464.6 2 2747732.32 29.07 .00 .06 Tax W/in groups 87634660 927 94535.77 Total 93130124 929 Sales Tax Btwn groups 822489.67 2 411244.84 12.05 .00 .03 W/in groups 27621126 809 34142.31 Total 28443615 811 Income Tax Btwn groups 44830.16 2 22415.08 .34 .71 --- W/in groups 6579709.3 100 65797.09 Total 6624539.5 102 Intergovern- Btwn groups 13558065 2 6779032.55 34.48 .00 .07 mental W/in groups 1.8E+008 931 196626.63 Revenue Total 2.0E+008 933 Percent Btwn groups 1.31 2 0.66 38.81 .00 .08 Intergovern- W/in groups 15.79 933 0.02 mental Total 17.11 935 141 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Debt Btwn groups 793849.98 2 396924.99 .14 .87 --- Outstanding W/in groups 2.6E+009 923 2827501.46 Total 2.6E+009 925 Full Faith Btwn groups 5283728.7 2 2641864.33 4.33 .01 .01 & Credit W/in groups 4.7E+008 767 610001.48 Debt Total 4.7E+008 769 Note. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 4.27 shows there are statistically significant differences (p <.01) among cities with different metro statuses for all the fiscal output variables, except education spending and sales and income tax revenues. The strongest relationships are found in total expenditures (9%) and total revenues (8%). Table 4.27. Oneway ANOVA Results for Metro Status and Fiscal Outputs Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Btwn groups 97798061 2 48899030.55 44.41 .00 .09 Expenditure W/in groups 1.0E+009 933 1100975.82 Total 1.1E+009 935 Common Btwn groups 4172929.1 2 2086464.57 33.9 .00 .07 Functions W/in groups 57420023 933 61543.43 Total 61592952 935 Police Btwn groups 132217.22 2 66108.61 10.99 .00 .02 W/in groups 5600614.6 931 6015.7 Total 5732831.9 933 Education Btwn groups 491760.77 2 245880.39 .56 .57 --- W/in groups 52284902 120 435707.52 Total 52776663 122 Total Btwn groups 78310408 2 39155204.07 38.52 .00 .08 Revenue W/in groups 9.5E+008 933 1016595.41 Total 1.0E+009 935 Property Btwn groups 1048532.7 2 524266.38 5.28 .01 .01 Tax W/in groups 92081592 927 99332.89 Total 93130124 929 Sales Tax Btwn groups 84568.84 2 42284.42 1.21 .30 --- W/in groups 28359046 809 35054.45 Total 28443615 811 142 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Income Tax Btwn groups 23763.23 2 11881.62 .18 .84 --- W/in groups 6600776.2 100 66007.76 Total 6624539.5 102 Intergovern- Btwn groups 10335631 2 5167815.48 25.83 .00 .05 mental W/in groups 1.9E+008 931 200087.89 Revenue Total 2.0E+008 933 Percent Btwn groups 0.23 2 0.11 6.33 .00 .01 Intergovern- W/in groups 16.88 933 0.02 mental Total 17.11 935 Total Debt Btwn groups 1.3E+008 2 63187571.72 23.48 .00 .05 Outstanding W/in groups 2.5E+009 923 2691443.72 Total 2.6E+009 925 Full Faith Btwn groups 7975169.4 2 3987584.69 6.58 .00 .02 & Credit W/in groups 4.7E+008 767 606492.43 Debt Total 4.7E+008 769 Note. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As can be seen in Table 4.28, statistically significant differences (p <.01) are found between principal cities and non-principal cities on all financial variables other than education spending, property and income tax revenues, percentage intergovernmental revenues, and full faith and credit debt. The strongest relationship is with expenditures on common functions at 8%. Table 4.28. Oneway ANOVA Results for Principal City Status and Fiscal Outputs Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Total Btwn groups 59862708 1 59862707.55 52.49 .00 .05 Expenditure W/in groups 1.1E+009 934 1140413.06 Total 1.1E+009 935 Common Btwn groups 4759580.6 1 4759580.61 78.22 .00 .08 Functions W/in groups 56833371 934 60849.43 Total 61592952 935 Police Btwn groups 98858.40 1 98858.40 16.35 .00 .02 W/in groups 5633973.5 932 6045.04 Total 5732831.9 933 143 Dep. Var. Sum of Squares df Mean square F Sig. Eta 2 Education Btwn groups 823223.36 1 823223.36 1.92 .17 --- W/in groups 51953439 121 429367.27 Total 52776663 122 Total Btwn groups 46444735 1 46444734.68 44.25 .00 .05 Revenue W/in groups 9.8E+008 934 1049624.4 Total 1.0E+009 935 Property Btwn groups 93543.32 1 93543.32 0.93 .33 --- Tax W/in groups 93036581 928 100254.94 Total 93130124 929 Sales Tax Btwn groups 611811.43 1 611811.43 17.81 .00 .02 W/in groups 27831804 810 34360.25 Total 28443615 811 Income Tax Btwn groups 12497.06 1 12497.06 0.19 .66 --- W/in groups 6612042.4 101 65465.77 Total 6624539.5 102 Intergovern- Btwn groups 1917114.4 1 1917114.45 9.18 .00 .01 mental W/in groups 1.9E+008 932 208905.95 Revenue Total 2.0E+008 933 Percent Btwn groups 0.01 1 0.01 0.6 .44 --- Intergovern- W/in groups 17.1 934 0.02 mental Total 17.11 935 Total Debt Btwn groups 1.2E+008 1 122099533.41 45.34 .00 .05 Outstanding W/in groups 2.5E+009 924 2693158.18 Total 2.6E+009 925 Full Faith Btwn groups 1683024.7 1 1683024.73 2.74 .10 --- & Credit W/in groups 4.7E+008 768 613895.62 Debt Total 4.7E+008 769 Note. Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data source for Fiscal Outputs: U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The ANOVA testing results for the fiscal outputs of the different classification methods show that only the NLC typology has statistically significant differences between its city types and all of the fiscal output measures. The strength of the relationships ranges from 2 to 27%. Only the regional classification has any relationships that are stronger -- property and sale taxes, percent intergovernmental revenue, and education expenditures. These findings further support the hypothesis that the NLC typology is a better means of classification than other methods. 144 Distribution of Cities Within National League of Cities? Typology Based on Designations Used in Prior Classification Schemes Thus far, the analysis has compared demographic and fiscal variables for cities that have been grouped according to the various classification methods. To gain an even better understanding of the differences among the NLC typology categories, the different NLC typology categories are examined in terms of their form of government, metro status, and principal city makeup. Table 4.29 shows that Spread cities and Metro centers are the typology categories most evenly divided between mayor and manager cities. Gold coast and Meltingpot cities and Boomtowns all have a 4:1 or greater ratio of manager to mayor cities. Among Centervilles, there are more than twice as many cities with managers than mayors. Mega-metro centers have the opposite composition, with twice as many mayor cities. Table 4.29. Form of Government in National League of Cities? Typology Cities (Percentages) Form of Government Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Mayor 44.1 21.1 40.0 16.0 15.8 30.9 66.7 Manager 54.0 78.9 57.5 82.4 84.2 67.6 30.0 Commission 1.9 0.0 2.5 1.6 0.0 1.5 3.3 N 372 185 80 125 76 68 30 Note. X 2 (12, N = 936) = 85.74, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. 145 After controlling for region, Table 4.30 shows that the distribution of cities within the NLC typology based on forms of government changes drastically in some categories. Among Spread cities, the percentage of mayor cities increases by more than 30% in the Northeast and 13% in the Midwest, whereas it decreases by around 20% in both the Table 4.30. Forms of Government of National League of Cities? Typology Cities by Region (Percentages) Location and Form of Government Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers NORTHEAST Mayor 77.0 72.7 81.3 62.5 0.0 66.7 100.0 Manager 21.3 27.3 18.8 25.0 0.0 33.3 0.0 Commission 1.6 0.0 0.0 12.5 0.0 0.0 0.0 MIDWEST Mayor 57.3 23.8 40.0 75.0 22.2 45.5 100.0 Manager 40.3 76.2 53.3 25.0 77.8 54.5 0.0 Commission 2.4 0.0 6.7 0.0 0.0 0.0 0.0 SOUTH Mayor 23.6 20.8 37.0 10.5 17.9 28.0 53.8 Manager 74.0 79.2 59.3 89.5 82.1 72.0 46.2 Commission 2.4 0.0 3.7 0.0 0.0 0.0 0.0 WEST Mayor 26.7 3.9 13.6 2.4 10.0 0.0 55.6 Manager 73.3 96.1 86.4 97.6 90.0 93.3 33.3 Commission 0.0 0.0 0.0 0.0 0.0 6.7 11.1 N 372 185 80 125 76 68 30 Note. Northeast: X 2 (10, N = 124) = 10.17, p = .43; Midwest: X 2 (12, N = 255) = 38.25, p = .01; South: X 2 (12, N = 263) = 14.60, p = .26; West: X 2 (12, N = 294) = 70.87, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. 146 South and West. The proportion of Gold coast cities with mayors is more than three times as great in the Northeast region, while the number of manager cities increases by around 15% in the West. The percent of Metro centers with mayors in the Northeast region more than doubles, and the percentage of managers increases by nearly 30% in the West. The percent of mayors in Meltingpot cities increases nearly four fold in the Northeast and even more than that in the Midwest. There are no Boomtowns in the Northeast, but their distribution changes the least of any type city in the other three regions. The share of Centervilles with mayors doubles in the Northeast and increases by half in the Midwest, while dropping to zero in the West. All Mega-metro centers in the Northeast and Midwest are mayor cities, whereas the number of mayor cities is just over 50% in the South and West. In terms of metro status, Table 4.31 shows that Spread cities have over two and a half times as many central cities as suburbs. Metro centers are almost all central cities and Mega-metro centers are entirely composed of central cities. Centervilles are more than two thirds independent cities, with less than 3% suburbs. The remaining city types are mostly suburbs. 147 Table 4.31. Metro Status of National League of Cities? Typology Cities (Percentages) Metro Status Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Central 68.0 15.1 93.8 25.6 17.1 27.9 100.0 Suburb 25.8 83.8 6.3 73.6 81.6 2.9 0.0 Independent 6.2 1.1 0.0 0.8 1.3 69.1 0.0 N 372 185 80 125 76 68 30 Note. X 2 (12, N = 936) = 722.39, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. After controlling for region, the distribution of cities based on their metro status remains much more stable than with form of government. As shown in Table 4.32, Mega-metro centers remain all central cities, while Metro centers change the least of the other types. Among the Metro centers, there is a 2% increase in central cities in the South and a 3% increase in the West. The greatest change for Spread cities occurs in the South, where there is a 12% decline in suburbs. Gold coast cities change the most in the Northeast, with increases in central cities of 8% and independent cities of 4%, and a reduction in suburbs of 11%. Among Meltingpot cities, there is a change from suburbs to central cities of 16% in the South and 12% in the Midwest. There are no Boomtowns in the Northeast, and the largest change is in the Midwest where the percentage of suburbs increases by 12%. Centervilles undergo the most widespread change after region is controlled. There is a 22% increase in central cities in the Northeast, with a 3% reduction in suburbs and 19% 148 fewer independent cities. Independent cities increase by 13% in the Midwest, with 3% shifting from suburbs and 10% from central cities. In the South, independent cities undergo a reduction of 9%, while central cities increase by 8% and suburbs by 1%. Central cities in the West are reduced by 8%, with the loss shifting evenly to suburbs and independent cities. Table 4.32. Metro Status of National League of Cities? Typology Cities by Region (Percentages) Location and Form of Government Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers NORTHEAST Central 72.1 22.7 93.8 18.8 0.0 50.0 100.0 Suburb 26.2 72.7 6.3 81.3 0.0 0.0 0.0 Independent 1.6 4.5 0.0 0.0 0.0 50.0 0.0 MIDWEST Central 58.9 7.9 93.3 37.5 5.6 18.2 100.0 Suburb 36.3 92.1 6.7 62.5 94.4 0.0 0.0 Independent 4.8 0.0 0.0 0.0 0.0 81.8 0.0 SOUTH Central 78.7 12.5 96.3 42.1 25.0 36.0 100.0 Suburb 14.2 87.5 3.7 57.9 75.0 4.0 0.0 Independent 7.1 0.0 0.0 0.0 0.0 60.0 0.0 WEST Central 60.0 19.7 90.9 22.0 16.7 20.0 100.0 Suburb 28.3 78.9 9.1 76.8 80.0 6.7 0.0 Independent 11.7 1.3 0.0 1.2 3.3 73.3 0.0 N 372 185 80 125 76 68 30 Note. Northeast: X 2 (10, N = 124) = 74.38, p = .01; Midwest: X 2 (12, N = 255) = 238.47, p = .01; South: X 2 (12, N = 263) = 196.82, p = .01; West: X 2 (12, N = 294) = 200.96, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for Metro Status: International City/County Management Association 149 (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Table 4.33 shows the principal city status of the typology cities. Metro centers are almost all principal cities, whereas Mega-metro centers are all principal cities. In contrast, almost none of the Centervilles are principal cities. Spread cities have a little more than twice as many principal cities, whereas Gold coast and Meltingpot cities and Boomtowns have about twice as many non-principal cities. Table 4.33. Principal City Status of National League of Cities? Typology Cities (Percentages) City Status Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Principal 68.3 35.7 93.8 32.8 31.6 4.4 100.0 Non-Principal 31.7 64.3 6.3 67.2 68.4 95.6 0.0 N 372 185 80 125 76 68 30 Note. X 2 (6, N = 936) = 235.79, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Table 4.34 reports the percentages of principal cities in each NLC typology category after controlling for region. Because they are all principal cities, Mega-metro centers have no change. There is a 14% reduction in principal cities in the Northeast, and a 10% reduction in the Midwest. Principal cities increase by 14% in the South and by 7% in the West. Principal cities decrease by 4% in the Northeast, 11% in the Midwest, and 150 3% in the South, but they increase by 10% in the West. For Metro centers, there is a 19% reduction in the percentage of principal cities in the Northeast, while they increase by 2% in the South. All Metro centers in the Midwest and West are principal cities. The percentage of principal cities among both Meltingpot cities and Boomtowns decreases in the Northeast and Midwest, but goes up in the South and West. All Centervilles in the Midwest and South are principal cities, whereas there is a 13% increase in the share of principal cities in the Northeast and a 9% decline in the West. Table 4.34. Principal City Status of National League of Cities? Typology Cities by Region (Percentages) City Status Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers NORTHEAST Principal 54.1 31.8 75.0 18.8 0.0 16.7 100.0 Non-Principal 45.9 68.2 25.0 81.3 0.0 83.3 0.0 MIDWEST Principal 58.1 25.4 100.0 12.5 16.7 0.0 100.0 Non-Principal 41.9 74.6 0.0 87.5 83.3 100.0 0.0 SOUTH Principal 81.9 33.3 96.3 42.1 35.7 0.0 100.0 Non-Principal 18.1 66.7 3.7 57.9 64.3 100.0 0.0 WEST Principal 75.0 46.1 100.0 35.4 36.7 13.3 100.0 Non-Principal 25.0 53.9 0.0 64.6 63.3 86.7 0.0 N 372 185 80 125 76 68 30 Note. Northeast: X 2 (5, N = 124) = 18.99, p = .01; Midwest: X 2 (6, N = 255) = 70.25, p = .01; South: X 2 (6, N = 263) = 105.42, p = .01; West: X 2 (6, N = 294) = 63.31, p = .01. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of 151 Commerce, 2000. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. These tables show that the city types within NLC?s typology vary in their composition in terms of the traditional classifications of form of government and metro status, as well as the new designation of principal city. The analysis now turns to an examination of the specific designations within the different classification schemes to see whether the differences that exist between individual types of cities are statistically significant. Student?s t-test Analysis The analysis of variance testing discussed previously compares the means of different categories of cities and shows, overall, there are statistically significant differences between the categories within a particular classification scheme. The analysis in this study also tests to see whether there are statistically significance differences between any two individual categories. This is done through comparison of means testing by performing Student?s t-test analysis on each of the various city types against all others in the same classification scheme. The Student?s t-test is a method of comparing the means of two different groups when they are independent samples. For example, the mean value of each demographic and fiscal variable for Spread cities is individually compared with the mean value on the same variable for each of the other six types of cities within the typology. The results of 152 this testing show whether there is a statistically significant difference between the two type cities on each variable. To provide a clear, visual impression of all the statistical differences that exist between city types, the results are presented in separate tables reporting the level of significance (either <.01 or <.05), if any, for the city type being tested. Blank cells represent no statistically significant differences between the two categories on the variable reported upon. Each table contains the results of testing for either demographic factors or fiscal outputs for a particular city type. Because the main interest of this study is the NLC typology, this portion of the analysis begins by reporting the results of comparisons among the different type cities within the typology. Table 4.35 shows the results of the comparisons of mean values performed on demographic variables between Spread cities and each of the other cities within the NLC typology. The testing shows that Spread cities have the most differences with Meltingpot cities and Boomtowns. Spread and Meltingpot cities have statistically significant differences (all at the <.01 level) on all demographic variables except percent Black. Spread cities and Boomtowns have differences, which are statistically significant at the <.01 level, on all variables except percent non-White. The table also shows that Spread cities are most similar to Centervilles, having statistically significant differences between only three demographic variables: population, density, and median income. The table also shows that the vast majority of the differences between city types are at <.01. 153 Table 4.35. Comparison of Spread Cities to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 936 Median Income <.01 <.01 <.01 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.05 936 Children <.05 <.01 <.01 <.01 936 Elderly <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 <.01 <.01 935 Hispanic <.05 <.01 <.01 <.01 <.01 936 N 185 80 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Table 4.36 contains the results of testing for Spread cities and all other type cities on the fiscal outputs. Again, Spread cities are most like Centervilles, having significant differences only on two of the financial outputs: the amount of expenditures on police and revenue from property tax. The Spread cities are least like Metro centers in terms of fiscal outputs. The two city types differ on all fiscal outputs except revenue from sales tax. 154 Table 4.36. Comparison of Spread Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N a EXPENDITURES: Total <.05 <.01 <.01 <.01 <.01 936 Common Functions <.01 <.01 <.01 935 Police <.01 <.01 <.05 <.01 <.01 934 Education <.01 <.01 <.05 123 REVENUE: Total <.05 <.01 <.01 <.01 <.01 936 Property Tax <.05 <.05 <.01 <.01 <.01 <.01 930 Sales Tax <.05 <.05 812 Income Tax b <.01 <.05 --- 103 Intergovernmental <.01 <.01 <.01 <.05 934 Percentage Intergovernmental <.01 <.01 <.01 933 DEBT: Total <.01 <.01 925 Total Full Faith & Credit <.01 <.01 770 N 185 80 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. Table 4.37 contains the results of comparisons involving Gold coast cities. It shows that the most differences are between Gold coast and Meltingpot cities, which differ on all of the demographic variables. The type cities that are most similar to Gold coast cities are Spread cities and Centervilles, but these types nevertheless differ from Gold coast cities on eight of the variables. Spread cities have significant differences in 155 all areas other than population and percentages of elderly and non-White. Centervilles differ in all factors except percentages of elderly, non-White, and Hispanic. Table 4.37. Comparison of Gold Coast Cities to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 <.01 936 Median Income <.01 <.01 <.01 <.05 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.01 <.01 <.01 936 Children <.05 <.01 <.01 <.01 <.01 <.05 936 Elderly <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 <.01 <.01 <.01 935 Hispanic <.05 <.01 <.01 <.05 <.01 936 N 372 80 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. In terms of fiscal outputs, Gold coast cities are most similar to Centervilles, as shown in Table 4.38. These two type cities only differ in terms of expenditures on police and revenues from property tax. 156 Table 4.38. Comparison of Gold Coast Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers N a EXPENDITURES: Total <.05 <.01 <.05 <.01 936 Common Functions <.01 <.01 <.01 935 Police <.01 <.01 <.01 <.01 <.01 934 Education <.05 <.01 123 REVENUE: Total <.05 <.01 <.05 <.01 936 Property Tax <.05 <.01 <.01 <.01 930 Sales Tax <.05 <.05 812 Income Tax b <.01 --- 103 Intergovernmental <.01 <.01 <.05 <.01 934 Percentage Intergovernmental <.01 <.01 <.01 <.01 <.05 933 DEBT: Total <.01 <.01 925 Total Full Faith & Credit <.05 <.01 770 N 372 80 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. Spread cities, Metro centers, and Mega-metro centers, at eight each, have the most statistical differences with Gold coast cities. Spread cities differ in terms of expenditures on common functions and education and on both measures of debt. Both Metro and Mega-metro centers differ in education spending, and in revenues from property, sales, and income taxes. 157 Table 4.39 shows the results of testing for Metro centers on demographic factors. As can be seen, they are most like Mega-metro centers, differing only in terms of population, median income, educational level, and percent non-White. The Metro centers differ more from all other types of cities with Spread cities having the next lowest number of statistical differences at eight. Table 4.39. Comparison of Metro Centers to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Melting- pot cities Boom- towns Center- villes Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 936 Median Income <.01 <.01 <.01 <.05 <.05 936 Home Ownership <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.05 936 Children <.01 <.01 <.01 <.01 <.05 936 Elderly <.01 <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 <.01 <.01 <.05 936 Black <.01 <.01 <.01 <.01 <.01 935 Hispanic <.01 <.01 <.01 <.05 936 N 372 185 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 158 Table 4.40 sets out the results of fiscal output comparisons for Metro centers. Once again, they are most like Mega-metro centers, but they differ in terms of all the expenditure measures and total debt. They differ from all other types on eight or more outputs. Table 4.40. Comparison of Metro Centers to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Melting- pot cities Boom- towns Center- villes Mega- metro centers N a EXPENDITURES: Total <.01 <.01 <.01 <.01 <.01 <.05 936 Common Functions <.01 <.01 <.01 <.01 <.01 <.01 935 Police <.01 <.01 <.01 <.01 <.01 <.05 934 Education <.01 <.01 <.05 123 REVENUE: Total <.01 <.01 <.01 <.01 <.01 936 Property Tax <.05 <.01 <.01 <.01 930 Sales Tax 812 Income Tax b <.05 --- 103 Intergovernmental <.01 <.01 <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.01 <.05 <.01 <.01 933 DEBT: Total <.01 <.01 <.01 <.01 <.01 <.01 925 Total Full Faith & Credit <.01 <.05 <.01 <.01 <.01 770 N 372 185 125 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. 159 Table 4.41 shows comparisons of demographic variables for Meltingpot cities. They differ from all other type cities on at least eight factors, and differ from Gold coast cities on all of them. Table 4.41. Comparison of Meltingpot Cities to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Boom- towns Center- villes Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 <.01 <.01 <.01 936 Median Income <.01 <.01 <.01 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.01 <.01 <.01 936 Children <.01 <.01 <.01 <.01 <.01 936 Elderly <.01 <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 <.01 <.01 <.01 936 Black <.01 <.01 <.01 <.01 935 Hispanic <.01 <.01 <.01 <.01 <.01 <.01 936 N 372 185 80 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. In terms of fiscal outputs Table 4.42 shows that Meltingpot cities are most different from Metro and Mega-metro centers. They differ from the first on all outputs except education spending and sales tax revenue. They differ from the latter in all areas 160 other than revenue from sales taxes and percentage of intergovernmental revenues. Because no Meltingpot cities reported any revenue from income tax, no comparisons can be calculated for this area. Table 4.42. Comparison of Meltingpot Cities to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Boom- towns Center- villes Mega- metro centers N a EXPENDITURES: Total <.01 <.05 <.01 <.05 <.01 936 Common Functions <.01 <.01 <.01 <.01 <.01 <.01 935 Police <.05 <.01 <.05 <.01 <.01 934 Education <.01 <.05 <.01 <.05 <.05 123 REVENUE: Total <.01 <.05 <.01 <.05 <.01 936 Property Tax <.01 <.01 <.01 <.01 930 Sales Tax <.05 812 Income Tax b --- --- --- --- --- --- 103 Intergovernmental <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.05 <.01 933 DEBT: Total <.01 <.05 <.01 925 Total Full Faith & Credit <.01 <.01 770 N 372 185 80 76 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. 161 As shown in Table 4.43, Boomtowns differ from all other city types on eight or more demographic variables. They are most like Meltingpots in that they do not differ on the variables of population, children, and elderly. Table 4.43. Comparison of Boomtowns to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Center- villes Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 <.01 <.01 <.01 936 Median Income <.01 <.05 <.01 <.01 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.01 <.01 936 Children <.01 <.01 <.01 <.01 <.01 936 Elderly <.01 <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 <.01 <.05 <.01 935 Hispanic <.01 <.05 <.01 <.05 936 N 372 185 80 125 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. The testing of fiscal outputs for Boomtowns is shown in Table 4.44. Boomtowns are most like Centervilles, differing only in total expenditures, intergovernmental revenues and percentage of intergovernmental revenues. They are least like Metro centers, with which they differ in all areas except sales and income taxes. They also 162 differ from Mega-metro centers on all outputs other than education spending and sales and income taxes. Table 4.44. Comparison of Boomtowns to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Center- villes Mega- metro centers N a EXPENDITURES: Total <.01 <.01 <.05 <.01 936 Common Functions <.01 <.01 <.01 935 Police <.01 <.01 <.05 <.01 934 Education <.05 <.01 <.01 <.01 123 REVENUE: Total <.01 <.01 <.01 936 Property Tax <.01 <.01 <.01 <.01 930 Sales Tax 812 Income Tax b --- 103 Intergovernmental <.01 <.05 <.01 <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.01 <.01 <.01 <.01 <.01 933 DEBT: Total <.01 <.05 <.01 925 Total Full Faith & Credit <.01 <.01 770 N 372 185 80 125 68 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. Table 4.45 shows that Centervilles are most like Spread cities in terms of demographic factors. They differ only in terms of population, density, and income levels. 163 Meltingpot cities are the most different from Centervilles, differing on all factors except percent Black. Table 4.45. Comparison of Centervilles to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Mega- metro centers N a Population <.01 <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 936 Median Income <.01 <.01 <.05 <.01 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 <.01 <.01 936 Children <.01 <.05 <.01 <.01 936 Elderly <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 <.05 <.01 935 Hispanic <.05 <.01 <.01 936 N 372 185 80 125 76 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. The fiscal output comparisons for Centervilles are shown in Table 4.46. They are most similar to Spread and Gold coast cities, differing only in police expenditures and property tax revenues. They differ from Boomtowns on total expenditures and the two intergovernmental revenue variables. They are most unlike the Metro centers, showing 164 statistical differences on all outputs other than education spending and sales and income taxes. They also have these three differences with Mega-metro centers, plus percentage of intergovernmental revenues. Table 4.46. Comparison of Centervilles to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Mega- metro centers N a EXPENDITURES: Total <.01 <.05 <.05 <.01 936 Common Functions <.01 <.01 <.01 935 Police <.01 <.01 <.01 <.01 <.01 934 Education <.05 123 REVENUE: Total <.01 <.05 <.01 936 Property Tax <.01 <.01 <.01 <.01 930 Sales Tax 812 Income Tax b --- 103 Intergovernmental <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.01 933 DEBT: Total <.01 <.01 925 Total Full Faith & Credit <.01 <.01 770 N 372 185 80 125 76 30 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. 165 As previously noted, Mega-metro centers are most similar to Metro centers. Mega-metro centers are most different from Boomtowns, with which they differ in all demographic areas except educational level. Table 4.47. Comparison of Mega-Metro Centers to Other Type Cities Within National League of Cities? Typology on Demographics (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes N a Population <.01 <.01 <.01 <.01 <.01 <.01 936 Density <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 936 Median Income <.01 <.01 <.05 <.01 <.01 936 Home Ownership <.01 <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.05 <.01 <.05 <.01 <.01 936 Children <.05 <.01 <.01 936 Elderly <.01 <.01 <.01 <.01 <.01 936 Non-White <.01 <.01 <.05 <.01 <.01 <.01 936 Black <.01 <.01 <.01 <.01 <.01 935 Hispanic <.01 <.01 <.01 <.05 <.01 936 N 372 185 80 125 76 68 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. a N values for dependent variables represent the number of cities reporting a value for that variable. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Table 4.48 shows Mega-metro centers differ least with Metro centers on fiscal outputs. They differ from Spread and Meltingpot cities on nine financial variables. Spread cities differ on all fiscal outputs except education spending, income tax, and 166 percentage intergovernmental revenue. Meltingpot cities differ on all except percent intergovernmental revenues and sales taxes. Table 4.48. Comparison of Mega-Metro Centers to Other Type Cities Within National League of Cities? Typology on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes N a EXPENDITURES: Total <.01 <.01 <.05 <.01 <.01 <.01 936 Common Functions <.01 <.01 <.01 <.01 <.01 <.01 935 Police <.01 <.01 <.05 <.01 <.01 <.01 934 Education <.05 <.05 123 REVENUE: Total <.01 <.01 <.01 <.01 <.01 936 Property Tax <.01 <.01 <.01 <.01 930 Sales Tax <.05 812 Income Tax b --- 103 Intergovernmental <.05 <.01 <.01 <.01 <.01 934 Percentage Intergovernmental <.05 <.01 933 DEBT: Total <.01 <.01 <.01 <.01 <.01 <.01 925 Total Full Faith & Credit <.01 <.01 <.01 <.01 <.01 770 N 372 185 80 125 76 68 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Meltingpot cities reported any values for income tax. Comparisons were also made among the different regional classifications of the cities. Table 4.49 shows cities in the Northeast are most like those in the South, but they 167 have statistical differences in all demographic variables except population, income, children, and percent Hispanic. They differ from cities in the West in all areas except population and from those in the Midwest on all except population and percent Black. Table 4.49. Comparison of Northeastern Cities to Cities in Other Regions on Demographics (Significant differences based on t-test comparisons) Variable Midwest South West N a Population 936 Density <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 936 Median Income <.01 <.01 936 Home Ownership <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.05 <.01 936 Children <.05 <.01 936 Elderly <.01 <.05 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 935 Hispanic <.01 <.01 936 N 255 263 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. Table 4.50 illustrates the statistical differences between cities in the Northeast and other regions in terms of fiscal outputs. It shows that Northeastern cities differ most with those in the South, lacking statistical significance only on police spending and total debt. 168 Northeastern cities also have statistical differences from cities in the Midwest on all areas except spending on common functions and education and their total amount of debt. They differ from those in the West on all outputs other than expenditures on common functions and police and their total debt. Table 4.50. Comparison of Northeastern Cities to Cities in Other Regions on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Midwest South West N a EXPENDITURES: Total <.01 <.01 <.01 936 Common Functions <.05 935 Police <.05 934 Education <.01 <.05 123 REVENUE: Total <.01 <.01 <.01 936 Property Tax <.01 <.01 <.01 930 Sales Tax <.01 <.01 <.01 812 Income Tax b <.01 <.05 --- 103 Intergovernmental <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.01 <.01 933 DEBT: Total 925 Total Full Faith & Credit <.01 <.01 <.01 770 N 255 263 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Western cities reported any values for income tax. 169 Table 4.51 shows cities in the Midwest are most like those in the South. The two regions do not have statistical differences on four variables: population, education, children, or elderly. There are significant differences with the Northeast on all except population and percent Black, and with the West on all except population and educational level. Table 4.51. Comparison of Midwestern Cities to Cities in Other Regions on Demographics (Significant differences based on t-test comparisons) Variable Northeast South West N a Population 936 Density <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.01 936 Median Income <.01 <.01 <.01 936 Home Ownership <.01 <.01 <.01 936 Bachelor?s Degree or Higher <.01 936 Children <.05 <.01 936 Elderly <.01 <.01 936 Non-White <.01 <.01 <.01 936 Black <.01 <.01 935 Hispanic <.01 <.01 <.01 936 N 124 263 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 170 Table 4.52 shows the fiscal outputs of cities in the Midwest differ from those in the Northeast in all financial categories other than expenditures on common functions and education and level of total debt. They are more similar to cities in the South and West, but still differ from each of these regions on half of the fiscal output measures. Table 4.52. Comparison of Midwestern Cities to Cities in Other Regions on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Northeast South West N a EXPENDITURES: Total <.01 <.01 936 Common Functions 935 Police <.05 <.05 <.01 934 Education 123 REVENUE: Total <.01 <.01 <.05 936 Property Tax <.01 <.01 930 Sales Tax <.01 <.01 <.01 812 Income Tax b <.01 --- 103 Intergovernmental <.01 <.01 934 Percentage Intergovernmental <.01 <.01 <.01 933 DEBT: Total <.01 925 Total Full Faith & Credit <.01 770 N 124 263 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Western cities reported any values for income tax. 171 As shown in Table 4.53, Southern cities have about the same amount of statistical differences with each of the other regions on the demographic variables. They differ from those in the West on eight and those in the Northeast and Midwest on seven. Table 4.53. Comparison of Southern Cities to Cities in Other Regions on Demographics (Significant differences based on t-test comparisons) Variable Northeast Midwest West N a Population 936 Density <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.05 936 Median Income <.01 <.01 936 Home Ownership <.01 <.01 936 Bachelor?s Degree or Higher <.05 936 Children <.01 936 Elderly <.05 <.01 936 Non-White <.01 <.01 <.05 936 Black <.01 <.01 <.01 935 Hispanic <.01 <.01 936 N 124 255 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. The fiscal outputs of Southern cities differ statistically with Northeastern cities more than any other region. (See Table 4.54.) They differ on all outputs except police spending and total debt. The South differs from the Midwest and West on six of the 172 fiscal variables. The South does not differ with either in spending on common functions and education and full faith and credit debt. It also does not differ with the Midwest on property and income taxes and intergovernmental revenues, or with the West on police spending and percent intergovernmental revenue. Table 4.54. Comparison of Southern Cities to Cities in Other Regions on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Northeast Midwest West N a EXPENDITURES: Total <.01 <.01 <.01 936 Common Functions <.05 935 Police <.05 934 Education <.01 123 REVENUE: Total <.01 <.01 <.01 936 Property Tax <.01 <.01 930 Sales Tax <.01 <.01 <.01 812 Income Tax b <.05 --- 103 Intergovernmental <.01 <.01 934 Percentage Intergovernmental <.01 <.01 933 DEBT: Total <.01 <.05 925 Total Full Faith & Credit <.01 770 N 124 255 294 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Western cities reported any values for income tax. 173 Table 4.55 shows that cities in the West have statistically significant differences with the other regions? cities on most all the demographic variable except population. That is the only area in which it does not differ from the Northeast. In the Midwest, it differs on all except population and education. All of the differences between the West and the Northeast and Midwest are statistically significant at a level of <.01. The West differs from the South in terms of all factors except, population, home ownership and education. Table 4.55. Comparison of Western Cities to Cities in Other Regions on Demographics (Significant differences based on t-test comparisons) Variable Northeast Midwest South N a Population 936 Density <.01 <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 <.05 936 Median Income <.01 <.01 <.01 936 Home Ownership <.01 <.01 936 Bachelor?s Degree or Higher <.01 936 Children <.01 <.01 <.01 936 Elderly <.01 <.01 <.01 936 Non-White <.01 <.01 <.05 936 Black <.01 <.01 <.01 935 Hispanic <.01 <.01 <.01 936 N 124 255 263 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 174 The West differs less with the other regions in fiscal outputs than demographics, as shown in Table 4.56. The most statistical differences are with the Northeast, where it differs in all outputs except spending on common functions and police and total debt. There were no comparisons on income tax for the West. Table 4.56. Comparison of Western Cities to Cities in Other Regions on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Northeast Midwest South N a EXPENDITURES: Total <.01 <.01 936 Common Functions 935 Police <.01 934 Education <.05 123 REVENUE: Total <.01 <.05 <.01 936 Property Tax <.01 <.01 <.01 930 Sales Tax <.01 <.01 <.01 812 Income Tax b --- --- --- 103 Intergovernmental <.01 <.01 <.01 934 Percentage Intergovernmental <.01 <.01 933 DEBT: Total <.05 925 Total Full Faith & Credit <.01 770 N 124 255 263 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. b No Western cities reported any values for income tax. 175 The study also compares the means of cities on demographic and financial variables based on their form of government. Table 4.57 shows that mayor cities differ from those with managers in all areas except density. There are no statistically significant demographic differences between mayor and commissioner cities. Table 4.57. Comparison of Mayor Cities to Manager and Commissioner Cities on Demographics (Significant differences based on t-test comparisons) Variable Manager Commissioner N a Population <.05 936 Density 936 Growth Rate for 1980-2000 b <.01 936 Median Income <.01 936 Home Ownership <.01 936 Bachelor?s Degree or Higher <.01 936 Children <.05 936 Elderly <.01 936 Non-White <.01 936 Black <.01 935 Hispanic <.01 936 N 615 13 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 176 Table 4.58 shows that mayor cities have significant differences with manager cities on all fiscal outputs except spending on common functions and police, income tax revenues, and total debt. There are no statistically significant differences in fiscal characteristics between mayor and commission cities. Table 4.58. Comparison of Mayor Cities to Manager and Commissioner Cities on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Manager Commissioner N a EXPENDITURES: Total <.01 936 Common Functions 935 Police 934 Education <.05 123 REVENUE: Total <.01 936 Property Tax <.01 930 Sales Tax <.01 812 Income Tax 103 Intergovernmental <.01 934 Percentage Intergovernmental <.01 933 DEBT: Total 925 Total Full Faith & Credit <.01 770 N 615 13 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 177 Table 4.59 reports the differences manager cities have with the other forms of government. In addition to the comparison with mayor cities already discussed, they differ statistically from commissioner cities only on median income and percent children. Table 4.59. Comparison of Manager Cities to Mayor and Commissioner Cities on Demographics (Significant differences based on t-test comparisons) Variable Mayor Commissioner N a Population <.05 936 Density 936 Growth Rate for 1980-2000 b <.01 936 Median Income <.01 <.01 936 Home Ownership <.01 936 Bachelor?s Degree or Higher <.01 936 Children <.05 <.01 936 Elderly <.01 936 Non-White <.01 936 Black <.01 935 Hispanic <.01 936 N 308 13 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 178 Manager cities and commission cities have no statistically significant differences in terms of the fiscal outputs. (See Table 4.60) All differences between manager and mayor cities have already been noted. Table 4.60. Comparison of Manager Cities to Mayor and Commissioner Cities on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Mayor Commissioner N a EXPENDITURES: Total <.01 936 Common Functions 935 Police 934 Education <.05 123 REVENUE: Total <.01 936 Property Tax <.01 930 Sales Tax <.01 812 Income Tax 103 Intergovernmental <.01 934 Percentage Intergovernmental <.01 933 DEBT: Total 925 Total Full Faith & Credit <.01 770 N 308 13 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 179 Because of the lack of differences between the mayor and commission cities, analyses were run with these categories combined. When manager cities were compared to all non-manager cities demographic results were the same as when manager cities were compared to mayor cities alone. The inclusion of commissioner cities did not change either the areas of statistical differences or the levels of significance. Comparison between fiscal outputs for manager to non-manager cities resulted in the same differences as with manager and mayor, with the addition of a statistical difference in expenditures on common functions at the level of <.05. Cities are also compared on the basis of metro status. Table 4.61 shows that central cities and suburbs differ at a level of <.01 for all demographic factors except percent non-White. Central and independent cities differ on all except the rates of growth, levels of education, and percentage of children. The differences with independent cities are also at <.01, except for the variables home ownership, elderly, and Hispanic that are at <.05. 180 Table 4.61. Comparison of Central Cities to Suburbs and Independent Cities on Demographics (Significant differences based on t-test comparisons) Variable Suburb Independent N a Population <.01 <.01 936 Density <.01 <.01 936 Growth Rate for 1980-2000 b <.01 936 Median Income <.01 <.01 936 Home Ownership <.01 <.05 936 Bachelor?s Degree or Higher <.01 936 Children <.01 936 Elderly <.01 <.05 936 Non-White <.01 936 Black <.01 <.01 935 Hispanic <.01 <.05 936 N 412 74 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 181 Table 4.62 shows central cities and suburbs differ statistically on education spending and revenue from sales and income taxes. Independent cities differ only on police spending, property tax, intergovernmental revenue, and full faith in credit debt. Table 4.62. Comparison of Central Cities to Suburbs and Independent Cities on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Suburb Independent N a EXPENDITURES: Total <.01 936 Common Functions <.01 935 Police <.05 <.01 934 Education 123 REVENUE: Total <.01 936 Property Tax <.05 <.01 930 Sales Tax 812 Income Tax 103 Intergovernmental <.01 <.01 934 Percentage Intergovernmental <.01 933 DEBT: Total <.01 925 Total Full Faith & Credit <.01 <.01 770 N 412 74 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 182 Table 4.63 reports the results of demographic comparisons of suburbs to both central and independent cities. The results concerning central cities and suburbs have already been discussed. The table provides additional findings showing suburbs and independent cities differ statistically on all demographic variables (at <.01), with the exception of the percentage Black. Table 4.63. Comparison of Suburbs to Central and Independent Cities on Demographics (Significant differences based on t-test comparisons) Variable Central Independent N a Population <.01 <.01 936 Density <.01 <.01 936 Growth Rate for 1980-2000 b <.01 <.01 936 Median Income <.01 <.01 936 Home Ownership <.01 <.01 936 Bachelor?s Degree or Higher <.01 <.01 936 Children <.01 <.01 936 Elderly <.01 <.01 936 Non-White <.01 936 Black <.01 935 Hispanic <.01 <.01 936 N 450 74 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 183 Table 4.64 shows differences between suburbs and central and independent cities on fiscal outputs. Central cities have already been discussed. Independent cities differ on total, police, and education spending, as well as total and property tax revenues. Table 4.64. Comparison of Suburbs to Central and Independent Cities on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Central Independent N a EXPENDITURES: Total <.01 <.01 936 Common Functions <.01 <.01 935 Police <.05 <.01 934 Education 123 REVENUE: Total <.01 <.01 936 Property Tax <.05 <.05 930 Sales Tax 812 Income Tax 103 Intergovernmental <.01 934 Percentage Intergovernmental <.01 933 DEBT: Total <.01 925 Total Full Faith & Credit <.01 770 N 450 74 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 184 Table 4.65 contains the results of comparison between the demographic variables for suburbs and non-suburb, differing only on percent non-White. The findings are the same as those between central cities and suburbs. The combination of independent cities and central cities as non-suburbs failed to change either the statistical differences or the level of significance found when suburbs and central cities were compared alone. Table 4.65. Comparison of Suburbs to Non-Suburbs on Demographics (Significant differences based on t-test comparisons) Variable Non-Suburb N a Population <.01 936 Density <.01 936 Growth Rate for 1980-2000 b <.01 936 Median Income <.01 936 Home Ownership <.01 936 Bachelor?s Degree or Higher <.01 936 Children <.01 936 Elderly <.01 936 Non-White Black <.01 935 Hispanic <.01 936 N 524 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 185 Table 4.66 shows suburbs compared to non-suburbs on fiscal outputs. The category non-suburbs differs from suburbs much like central cities alone, except differences on police spending and property taxes at <.05 are no longer significant. Table 4.66. Comparison of Suburbs to Non-Suburbs on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Non-Suburb N a EXPENDITURES: Total <.01 936 Common Functions <.01 935 Police 934 Education 123 REVENUE: Total <.01 936 Property Tax 930 Sales Tax 812 Income Tax 103 Intergovernmental <.01 934 Percentage Intergovernmental <.01 933 DEBT: Total <.01 925 Total Full Faith & Credit <.01 770 N 524 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 186 Table 4.67 reports results from demographic comparisons based on principal city status. Cities that are principal cities differ from those that are not on just over half the variables. They differ statistically at the level of <.01 for the variables of population, density, income, percent of home ownership, children, and Blacks. The remaining demographic variables were not statistically different between the two type cities. Table 4.67. Comparison of Principal Cities to Non-Principal Cities on Demographics (Significant differences based on t-test comparisons) Variable Non-Prinicpal N a Population <.01 936 Density <.01 936 Growth Rate for 1980-2000 b 936 Median Income <.01 936 Home Ownership <.01 936 Bachelor?s Degree or Higher 936 Children <.01 936 Elderly Non-White 936 Black <.01 935 Hispanic 936 N 443 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. a N values for dependent variables represent number of cities reporting. b Data Source for Growth Rate: U.S. Bureau of the Census, 1980 and 2000 Decennial Censuses, Washington, DC: U.S. Department of Commerce, 1980 and 2000. All other demographic data are from the 2000 Decennial Census. 187 Table 4.68 shows principal cities differ from non-principal cities (at <.01) for all fiscal outputs variables except for education spending, property and income taxes, percent intergovernmental revenues, and full faith and credit debt. Table 4.68. Comparison of Principal Cities to Non-Principal Cities on Fiscal Outputs (Significant differences based on t-test comparisons) Variable Non-Prinicpal N a EXPENDITURES: Total <.01 936 Common Functions <.01 935 Police <.01 934 Education 123 REVENUE: Total <.01 936 Property Tax 930 Sales Tax <.01 812 Income Tax 103 Intergovernmental <.01 934 Percentage Intergovernmental 933 DEBT: Total <.01 925 Total Full Faith & Credit 770 N 443 Note. The figures indicate the level of significance, if any, with <.05 italicized in blue and <.01 bold in red. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a N values for dependent variables represent the number of cities reporting a value for that variable. 188 Summary of Student?s t-test Analysis The findings resulting from the t-test analysis show that many of the categories of cities within the various classification schemes differ substantially from the others, while yet other categories of cities differ much less. The tables pertaining to the NLC typology classification each have six columns for the results of whether the city being tested differs from the other six type cities in the typology. On the tables for demographic variables there is a row representing each of the 11 variables on which the cities are compared. Thus, each of these tables could potentially report 66 statistically significant differences. The financial tables have 12 rows and 72 possible statistical differences. To summarize the findings a ratio of the number of significant differences shown on the table for each city to the number of potential differences for that table was calculated and transformed into a percentage. The percentage figure is used to measure the percent of possible differentiation a city has to all other cities in its category in the classification scheme. It is recognized that any difference between two cities shows up twice within all the tables for each categorization scheme, since there is a table to reflect the difference for each of the two cities being compared. However, this is the same for all the categories being analyzed and does not alter the percentage of differences being reported for each city type. These percentages are reported for each type city utilized by the different classification schemes on both demographic and financial variables. Table 4.69 sets out the percentages, on both demographic and fiscal variables, for each type city within the NLC, regional, form of government, metro status, and principal city status classifications. The cities within the NLC typology have average ranging from 189 73% to 86% among the demographic variables and from 40% to 72% for the financial variables. The values average to 78% and 57% respectively. The regional classifications range from 67% to 82% and average 76% for demographic variables. Fiscal variables ranges are from 56% to 75% with an average of 63%. The categories of form of government are much lower averaging 36% for demographic variables (range from 9% to 55 %) and 22% for financial variables (range from 0% to 33%) with the commissioner form of government pulling these numbers down. The metro status groupings for demographics range from 82% to 91% for an average of 85% while the financial variable averages range between 38% and 58% for an average of 50%. Finally, the averages for the designations based on principal city status are 55% and 58% for demographic and financial variables respectively. Table 4.69. Statistically Significant Differences of City Types (Based on t-test comparisons) City type Demographic variables Fiscal outputs Spread 73% 58% Gold coast 82% 53% Metro center 76% 72% Meltingpot 86% 58% Boomtown 85% 54% Centerville 73% 40% Mega-metro center 74% 67% Northeast 79% 75% Midwest 76% 58% South 67% 61% West 82% 56% 190 City type Demographic variables Fiscal outputs Mayor 45% 33% Manager 55% 33% Commissioner 9% 0% Central 82% 54% Suburb 91% 58% Independent 82% 38% Principal 55% 58% Non-Principal 55% 58% Note: Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Region: 2000 Decennial Census. Washington, DC: U.S. Department of Commerce, 2000. Data Source for Form of Government and Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. These data show that metro status categories exhibit the highest percentages of statistical differences for demographic variables, but only around 50% for fiscal outputs. The regional descriptions have the highest average differences for fiscal outputs. However, the NLC typology cities have the second highest percentages for both types of variables. Form of government and principal city designations are low on both. It appears that the typology, region, and metro status classification schemes provide methods to classify cities that result in similarly high levels of statistical differences on the variables analyzed. However, because the typology has seven separate groupings of cities with relatively high numbers of significant differences between the city categories for both demographic and fiscal variables, the findings provide further 191 evidence to support the hypothesis that the NLC typology provides a better method of classification than the other categorizations. The foregoing analysis shows the NLC typology appears to have practical utility in providing a better manner of city classification for purposes of financial research. In comparison to the other methods examined, the typology has a greater number of classification categories and lower variation within these categories. Overall there are statistically significant differences between the categories and the strengths of the relationships are high in terms of both demographic and fiscal variables. The NLC typology also has a comparatively large percentage of statistically significant differences between individual city types. The next chapter discusses the regression analysis performed to test this apparent utility by exploring the impact of these differences, with an emphasis on whether the NLC typology provides a better measure of fiscal behavior as hypothesized. 192 CHAPTER 5 MULTIPLE-REGRESSION ANALYSIS AND FINDINGS The analysis discussed in the proceeding chapter shows the National League of Cities (NLC) typology provides a method of classifying cities with more categories and lower variation between the cities within each category than the traditional schemes used for relegation. The analysis now looks more specifically at the effects that different variables have on the fiscal behaviors of cities within the various classification schemes. It begins by examining the influence of the demographic variables on fiscal behavior and the impact of revenue sources on expenditure and debt outputs. The analysis concludes by comparing the overall effect of the independent variables on common function spending for different city types to test the utility of the classification methods. Impact of Demographic Variables on Fiscal Outputs Since there are statistically significant differences demographically between the different categories of the NLC typology, further examination of the effects of these demographic variations on fiscal behavior is warranted. To measure the relative impact of the different demographic factors, multiple-regression analysis is performed for each category of city type within the NLC typology, form of government, metropolitan (metro) status, and principal city classifications. A separate regression analysis is performed on 193 each of the following fiscal output measures: total, common function, and police expenditures; total, property tax, sales tax, and intergovernmental revenues; and total and full faith and credit debt. The variables for education expenditure and income tax revenue are not used due to the low number of cases reported for each. All demographic variables previously discussed are utilized in the regression analysis, except for educational level and percent non-White which are excluded because they are found to be too highly correlated to other explanatory variables. The variable percentage of intergovernmental revenue is included as an independent variable. To test the correlation between the independent variables, each of the variables was used as the dependent variable and regressed against the others in separate tests. After performing these procedures, no R 2 value greater than .640 was found in any of the individual regressions and it is concluded that multicollinearity is not a problem. Results of the regression analysis are reported in tables that include a number of findings. The adjusted R 2 value represents the proportion of variation in the dependent variable that can be explained by the regression model. Because it takes into account the number of independent variables, it is more accurate than R 2 . Thus, it provides a means of comparing different classification schemes? usefulness in financial analysis of cities. The tables also include unstandarized coefficient b values for each explanatory variable. These values depict the amount and direction of change in the dependent variable for each unit increase in an explanatory variable. The standardized coefficient Beta is used to measure the relative predictive power of independent variables in the regression model. It shows the amount that the dependent variable changes (in terms of standard deviation) for each standard deviation increase in the explanatory variable. 194 Total Expenditure for Different City Types Table 5.1 shows the results of multiple-regression analysis on total expenditure for each of the categories within the NLC typology. Initially, it is noted that the adjusted R 2 values vary from a low of -.033 for Boomtowns to a high of .608 for Mega-metro centers. This indicates that the independent variables in the regression model do not explain variation in total spending for Boomtowns, but they do account for about 61% of the variation in total spending in Mega-metro centers. The second highest adjusted R 2 value (.518) is found with Metro Centers. Table 5.1. Total Expenditure Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .126 Population .00 .00 .118 2.356 .019 Density -.14 .03 -.272 -5.123 .000 Growth Rate -.45 1.75 -.016 -.259 .796 Median Income .03 .01 .223 3.240 .001 Home Ownership -33.98 6.99 -.377 -4.858 .000 Children -9.17 15.63 -.040 -.587 .558 Elderly 44.49 14.90 .179 2.986 .003 Black 2.03 3.24 .039 .624 .533 Hispanic -2.53 6.24 -.024 -.405 .686 Percentage Intergovernmental 768.12 377.89 .113 2.033 .043 GOLD COAST .233 Population .00 .00 .073 1.033 .303 Density -.05 .03 -.117 -1.570 .118 Growth Rate -4.93 1.91 -.184 -2.583 .011 Median Income .01 .01 .096 1.256 .211 Home Ownership -24.93 7.79 -.332 -3.200 .002 Children -58.73 30.52 -.221 -1.924 .056 195 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Elderly -4.26 18.76 -.024 -.227 .821 Black -4.03 8.40 -.033 -.479 .632 Hispanic -3.57 7.77 -.033 -.460 .646 Percentage Intergovernmental -1,204.78 750.99 -.113 -1.604 .110 METRO CENTERS .518 Population .00 .00 -.020 -.224 .823 Density -.06 .06 -.141 -1.056 .295 Growth Rate -11.82 3.31 -.422 -3.576 .001 Median Income .07 .03 .404 2.666 .010 Home Ownership -.41.92 18.49 -.349 -2.267 .027 Children 21.64 45.63 .054 .474 .637 Elderly -23.10 66.08 -.044 -.350 .728 Black 6.24 6.98 .100 .894 .374 Hispanic -1.99 9.91 -.024 -.201 .842 Percentage Intergovernmental 3,125.85 602.97 .515 5.184 .000 MELTINGPOTS .245 Population .00 .00 -.012 -.152 .879 Density -.01 .02 -.079 -.611 .543 Growth Rate 1.06 1.67 .060 .636 .526 Median Income -.02 .01 -.265 -1.749 .083 Home Ownership 2.26 10.19 .033 .222 .825 Children -144.68 28.90 -.776 -5.006 .000 Elderly -72.51 38.21 -.228 -1.898 .060 Black 3.44 6.66 .057 .516 .607 Hispanic 8.22 6.39 .198 1.286 .201 Percentage Intergovernmental 580.29 699.06 .087 .830 .408 BOOMTOWNS -.033 Population -.00 .00 -.034 -.251 .803 Density -.06 .09 -.082 -.604 .548 Growth Rate -.09 .37 -.031 -.238 .812 Median Income .02 .01 .356 1.976 .052 Home Ownership -17.88 11.65 -.239 -1.534 .130 Children -6.68 38.45 -.034 -.174 .863 Elderly 35.13 35.77 .216 .982 .330 Black -7.77 10.21 -.098 -.761 .449 Hispanic 14.49 11.06 .209 1.310 .195 196 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Percentage Intergovernmental 505.10 972.50 .066 .519 .605 CENTERVILLES .209 Population -.01 .02 -.099 -.750 .456 Density -.18 .11 -.211 -1.670 .100 Growth Rate -1.48 5.27 -.046 -.281 .780 Median Income .08 .02 .511 3.283 .002 Home Ownership -33.77 23.17 -.315 -1.458 .150 Children 6.14 42.31 .027 .145 .885 Elderly 25.22 44.30 .105 .569 .571 Black 11.78 10.33 .182 1.140 .259 Hispanic -10.13 10.36 -.165 -.978 .332 Percentage Intergovernmental -1,004.29 841.02 -.140 -1.194 .237 MEGA-METRO CENTERS .608 Population .00 .00 .405 1.404 .176 Density -.03 .19 -.079 -.179 .860 Growth Rate -4.23 12.02 -.060 -.352 .729 Median Income .04 .06 .138 .731 .474 Home Ownership 57.71 63.44 .221 .910 .374 Children -598.61 183.72 -1.020 -3.258 .004 Elderly -18.36 232.66 -.017 -.079 .938 Black 106.04 29.86 .916 3.551 .002 Hispanic 66.59 29.02 .520 2.295 .033 Percentage Intergovernmental 2,405.03 3,679.25 .159 .654 .521 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Spread cities show the greatest number of statistically significant regression coefficients, with 6 of the 10 variables being significant at the level of < .05. Of these, 197 home ownership has the greatest relative impact with a Beta of -.377. This is followed by density (-.272) and median income (.223). None of the regression coefficients for Boomtowns is found to be statistically significant. Metro centers and Mega-metro centers have adjusted R 2 values of just over 50% and 60% respectively. In Metro centers, percent intergovernmental revenue, growth rate, median income, and home ownership are all found to have relatively high predictive power. For Mega-metro centers, the percentages of children and Blacks are the strongest predictors followed by Hispanics. Overall, within the NLC typology categories, each of the demographic variables are found to be significant in at least one of the categories, with median income and home ownership showing statistical significant in three of the city types. Table 5.2 sets out the findings of regression analysis on total expenditure based on form of government. The regressions have an adjusted R 2 of .195 for manager cities and .275 for non-manager cities. Population, median income, home ownership, and percent Black are statistically significant for each category, with all but homeownership in a positive relationship with total expenditure. Density is significant only for manager cities, and percent intergovernmental revenue is significant only for non-manager cities. Home ownership is the strongest predictor in each type city. Table 5.2. Total Expenditure Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .195 Population .00 .00 .113 3.009 .003 198 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Density -.05 .02 -.157 -3.181 .002 Growth Rate .12 .34 .014 .348 .728 Median Income .01 .00 .170 3.217 .001 Home Ownership -28.80 4.70 -.386 -6.124 .000 Children -22.42 11.78 -.117 -1.903 .057 Elderly 17.31 9.91 .087 1.747 .081 Black 9.19 2.98 .133 3.084 .002 Hispanic -5.68 3.04 -.114 -1.865 .063 Percentage Intergovernmental 422.77 325.44 .050 1.299 .194 NON-MANAGER a .275 Population .00 .00 .337 6.700 .000 Density -.03 .02 -.123 -1.781 .076 Growth Rate -.72 1.67 -.025 -.428 .669 Median Income .02 .01 .219 3.283 .001 Home Ownership -29.50 7.70 -.298 -3.832 .000 Children -44.56 24.15 -.123 -1.845 .066 Elderly 23.13 24.04 .058 .962 .337 Black 8.31 4.13 .125 2.013 .045 Hispanic 1.39 6.07 .016 .230 .818 Percentage Intergovernmental 2,065.66 467.40 .252 4.419 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Table 5.3 shows that, for the metro status classification, the regression model is best at predicting total spending in central cities (adjusted R 2 = .313). Central cities also have the most statistically significant coefficients at 6, compared to 3 for suburbs, and 2 for independent cities. Median income is the strongest predictor variable for central and 199 independent cities and in both cases is in a positive relationship. Home ownership is the strongest predictor in suburbs followed by children, both showing a negative relationship. Table 5.3. Total Expenditure Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .313 Population .00 .00 .214 4.608 .000 Density .03 .03 .060 1.067 .286 Growth Rate -1.62 1.08 -.067 -1.500 .134 Median Income .04 .01 .286 5.801 .000 Home Ownership -23.66 7.77 -.192 -3.045 .002 Children -45.75 16.25 -.164 -2.816 .005 Elderly 29.00 16.40 .089 1.769 .078 Black 15.88 3.41 .223 4.654 .000 Hispanic .83 4.21 .011 .196 .845 Percentage Intergovernmental 1,747.63 366.12 .214 4.773 .000 SUBURB .168 Population .00 .00 .133 2.805 .005 Density -.01 .012 -.063 -.917 .360 Growth Rate .34 .31 .055 1.120 .263 Median Income .01 .00 .130 2.154 .032 Home Ownership -20.33 4.13 -.369 -4.928 .000 Children -40.88 15.13 -.234 -2.702 .007 Elderly 3.02 11.75 .019 .257 .798 Black -.22 2.96 -.004 -.075 .940 Hispanic -5.31 3.20 -.133 -1.659 .098 Percentage Intergovernmental 563.00 358.59 .081 1.570 .117 INDEPENDENT .221 Population .03 .02 .199 1.641 .106 Density -.21 .15 -.156 -1.338 .186 Growth Rate -2.12 3.00 -.086 -.705 .483 Median Income .06 .03 .353 2.403 .019 Home Ownership 11.61 24.90 .100 .466 .643 Children -21.46 42.20 -.092 -.509 .613 200 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Elderly -28.74 49.04 -.101 -.586 .560 Black 10.49 11.64 .139 .901 .371 Hispanic 2.70 11.75 .032 .230 .819 Percentage Intergovernmental -2,848.02 964.30 -.324 -2.953 .004 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.4 sets out the findings based on principal city status. The model is better at predicting total expenditure for principal cities than for non-principal cities. Each category has six significant variables within that group, with median income, home ownership, and percentage intergovernmental common to both. Home ownership in non- principal cities is the strongest predictor while percent Black and population are the stronger predictors of the principal city?s independent variables. Table 5.4. Total Expenditure Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .259 Population .00 .00 .231 5.471 .000 Density .02 .02 .050 .957 .339 Growth Rate -.21 .86 -.011 -.249 .803 Median Income .02 .01 .202 3.870 .000 Home Ownership -21.99 7.17 -.194 -3.067 .002 201 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children -49.77 16.70 -.178 -2.980 .003 Elderly 10.41 15.65 .034 .666 .506 Black 18.38 3.67 .244 5.006 .000 Hispanic -1.60 4.24 -.022 -.378 .705 Percentage Intergovernmental 1,222.92 410.83 .135 2.977 .003 NON-PRINCIPAL .187 Population .00 .00 .077 1.608 .108 Density -.05 .01 -.232 -3.782 .000 Growth Rate .06 .34 .008 .163 .871 Median Income .01 .00 .142 2.369 .018 Home Ownership -29.78 4.24 -.498 -7.024 .000 Children -1.57 12.85 -.009 -.122 .903 Elderly 28.16 10.81 .157 2.604 .010 Black -3.84 2.7 -.071 -1.411 .159 Hispanic -7.46 3.17 -.171 -2.351 .019 Percentage Intergovernmental 1,241.64 292.73 .197 4.242 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Common Function Expenditure for Different City Types Regression results performed on common function expenditure for the NLC typology categories are presented in Table 5.5. Mega-metro centers again have the highest adjusted R 2 value at 55%, but none of the variables in this category is statistically significant. (It should be recognized that there are only 30 Mega-metro centers and having 10 independent variables with such a low number of cases results in an inflated 202 adjusted R 2 value. Also, the variable for children just misses reaching statistical significance.) There are no variables reaching significance for the Centerville category. Meltingpot and Gold coast cities have the highest predictive values at 26% and 23% respectively. The percentage of children (a negative relationship) is the strongest predictor for Meltingpot cities and home ownership (a negative relationship) has the highest Beta value for Gold coast cities. Home ownership and growth rate (both negative relationships) are the best predictors for Metro centers, and median income (positive) and home ownership (negative) have the greatest impact for Boomtowns. Table 5.5. Common Function Expenditure Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .019 Population .00 .00 .060 1.130 .259 Density -.02 -.01 -.132 -2.337 .020 Growth Rate .12 .44 .017 .271 .786 Median Income .00 .00 .031 .431 .667 Home Ownership -3.31 1.75 -.155 -1.886 .060 Children -2.22 3.92 -.041 -.567 .571 Elderly 5.40 3.73 .092 1.446 .149 Black .79 .81 .064 .970 .333 Hispanic -.15 1.57 -.006 -.097 .923 Percentage Intergovernmental -66.94 94.69 -.042 -.707 .480 GOLD COAST .226 Population .00 .00 -.026 -.364 .716 Density -.01 .01 -.053 -.708 .480 Growth Rate -.23 .60 -.027 -.383 .702 Median Income .00 .00 .074 .953 .342 Home Ownership -.9.46 2.46 -.402 -3.852 .000 Children -8.58 9.62 -.103 -.892 .374 203 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Elderly 4.07 5.91 .072 .688 .493 Black -1.81 2.65 -.048 -.683 .496 Hispanic .94 2.45 .027 .384 .701 Percentage Intergovernmental -628.51 236.73 -.187 -2.655 .009 METRO CENTERS .057 Population .00 .00 .139 1.126 .264 Density -.03 .02 -.306 -1.636 .106 Growth Rate -1.93 .96 -.331 -2.003 .049 Median Income .01 .01 .359 1.693 .095 Home Ownership -12.43 5.39 -.496 -2.305 .024 Children 8.75 13.31 .105 .657 .513 Elderly 2.56 19.28 .023 .133 .895 Black .95 2.04 .073 .468 .641 Hispanic -1.83 2.89 -.106 -.631 .530 Percentage Intergovernmental -47.31 175.90 -.037 -.269 .789 MELTINGPOTS .258 Population .00 .00 .055 .648 .518 Density -.01 .00 -.303 -2.252 .026 Growth Rate .26 .39 .065 .664 .508 Median Income .00 .00 .139 .884 .378 Home Ownership -3.56 2.41 -.231 -1.477 .142 Children -19.38 6.83 -.455 -2.837 .005 Elderly 13.95 9.03 .192 1.544 .125 Black 3.30 1.58 .239 2.092 .039 Hispanic 3.30 1.51 .349 2.186 .031 Percentage Intergovernmental -279.16 165.21 -.184 -1.690 .094 BOOMTOWNS .007 Population .00 .00 -.055 -.420 .676 Density -.02 .04 -.085 -.641 .524 Growth Rate -.04 .14 -.036 -.278 .782 Median Income .01 .00 .469 2.654 .010 Home Ownership -9.43 4.51 -.319 -2.089 .041 Children 3.26 14.89 .042 .219 .827 Elderly 18.67 13.85 .290 1.348 .182 Black -2.30 3.95 -.073 -.581 .563 Hispanic 4.70 4.28 .171 1.097 .277 204 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Percentage Intergovernmental 74.00 376.62 .025 .196 .845 CENTERVILLES -.007 Population -.00 .00 -.116 -.775 .441 Density -.01 .02 -.088 -.619 .538 Growth Rate -.25 1.05 -.044 -.237 .813 Median Income .01 .01 .249 1.415 .163 Home Ownership -3.76 4.63 -.198 -.813 .420 Children 1.41 8.45 .035 .166 .868 Elderly -1.56 8.85 -.036 -.176 .861 Black .35 2.06 .030 .170 .866 Hispanic -2.77 2.07 -.254 -1.336 .187 Percentage Intergovernmental -104.99 168.04 -.083 -.625 .535 MEGA-METRO CENTERS .553 Population -1.57E-006 .00 -.007 -.024 .981 Density .03 .03 .584 1.244 .229 Growth Rate -1.64 1.70 -.176 -.965 .347 Median Income .00 .01 .019 .094 .926 Home Ownership 6.61 8.97 .191 .737 .470 Children -52.96 25.98 -.682 -2.038 .056 Elderly -40.70 32.91 -.277 -1.237 .231 Black 7.32 4.22 .478 1.733 .099 Hispanic -.58 4.10 -.034 -.142 .889 Percentage Intergovernmental -438.60 520.38 -.219 -.843 .410 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The results for manager and non-manager cities are reported in Table 5.6. The adjusted R 2 values are 15% and 17% respectively. (Because of the low number of cases 205 for commission cities, they are combined with mayor cities as non-manager cities.) For manager cities, the strongest predictors are home ownership and median income, and for non-manager cities they are home ownership and population. Table 5.6. Common Function Expenditure Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .152 Population .00 .00 .135 3.511 .000 Density -.02 .00 -.174 -3.428 .001 Growth Rate .05 .09 .023 .572 .568 Median Income .00 .00 .241 4.438 .000 Home Ownership -6.83 1.28 -.345 -5.335 .000 Children -4.42 3.21 -.087 -1.377 .169 Elderly 9.26 2.70 .176 3.432 .001 Black 1.32 .81 .072 1.625 .105 Hispanic -.91 .83 -.069 -1.092 .275 Percentage Intergovernmental -197.00 88.62 -.087 -2.223 .027 NON-MANAGER a .167 Population .00 .00 .285 5.278 .000 Density -.01 .00 -.164 -2.207 .028 Growth Rate -.08 .36 -.014 -.221 .825 Median Income .00 .00 .113 1.586 .114 Home Ownership -5.68 1.65 -.286 -3.437 .001 Children -9.49 5.19 -.131 -1.830 .068 Elderly -1.63 5.16 -.021 -.315 .753 Black 2.26 .89 .170 2.550 .011 Hispanic .22 1.30 .012 .165 .869 Percentage Intergovernmental -74.02 100.40 -.045 -.737 .462 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management 206 Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Table 5.7 shows that adjusted R 2 values for regressions on common function spending involving metro status are also low. Independent cities have no statistically significant variables with none even approaching significance. Median income is the strongest predictor for central cities, followed by percent Black. For suburban cities, home ownership, followed by percent children and percentage of intergovernmental revenue, is the best predictors in the model. Table 5.7. Common Function Expenditure Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .225 Population 8.65E-005 .00 .170 3.455 .001 Density .01 .01 .059 .981 .327 Growth Rate -.37 .23 -.077 -1.636 .103 Median Income .01 .00 .341 6.532 .000 Home Ownership -4.03 1.64 -.165 -2.464 .014 Children -9.46 3.42 -.171 -2.764 .006 Elderly 8.93 3.45 .139 2.587 .010 Black 3.98 .72 .281 5.535 .000 Hispanic .89 .89 .060 1.003 .317 Percentage Intergovernmental -73.69 77.12 -.046 -.956 .340 SUBURB .151 Population .00 .00 .101 .2116 .035 Density -.01 .00 -.090 -1.292 .197 Growth Rate .13 .10 .069 1.389 .165 Median Income .00 .00 .160 2.629 .009 207 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Home Ownership -5.14 1.28 -.303 -4.009 .000 Children -11.12 4.70 -.207 -2.367 .018 Elderly .67 3.65 .013 .183 .855 Black .18 .92 .010 .190 .849 Hispanic -1.20 .99 -.097 -1.205 .229 Percentage Intergovernmental -381.82 111.39 -.179 -3.428 .001 INDEPENDENT -.024 Population .00 .00 .007 .052 .959 Density -.04 .04 -.135 -1.009 .317 Growth Rate -.40 .84 -.066 -.471 .639 Median Income .01 .01 .197 1.168 .247 Home Ownership 5.66 6.97 .201 .812 .420 Children -7.25 11.81 -.127 -.614 .541 Elderly -3.82 13.72 -.055 -.279 .782 Black 1.25 3.26 .068 .384 .703 Hispanic 1.04 3.29 .051 .315 .753 Percentage Intergovernmental -447.84 269.78 -.209 -1.660 .102 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Regression results based on principal city status are depicted in Table 5.8. The analysis of principal cities has an adjusted R 2 value of .124. Population, median income, and percent Black, all have approximately equal strength in predicting the amount of common function spending, with home ownership slightly lower. For non-principal cities, the adjusted R 2 is .142, with home ownership having the most impact, followed by median income and density. 208 Table 5.8. Common Function Expenditure Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .124 Population .00 .00 .202 4.406 .000 Density -.00 .00 -.037 -.653 .514 Growth Rate -.06 .20 -.015 -.311 .756 Median Income .01 .00 .232 4.087 .000 Home Ownership -4.17 1.65 -.174 -2.526 .012 Children -6.64 3.84 -.112 -1.727 .085 Elderly 5.32 3.60 .082 1.478 .140 Black 3.33 .85 .209 3.948 .000 Hispanic -.41 .98 -.026 -.422 .673 Percentage Intergovernmental -32.85 94.52 -.017 -.348 .728 NON-PRINCIPAL .142 Population .00 .00 .081 1.634 .103 Density -.01 .00 -.186 -2.923 .004 Growth Rate .05 .09 .027 .528 .597 Median Income .00 .00 .210 3.365 .001 Home Ownership -6.14 1.19 -.381 -5.173 .000 Children -5.27 3.60 -.107 -1.466 .143 Elderly 6.98 3.02 .145 2.309 .021 Black .24 .76 .017 .315 .753 Hispanic -.86 .89 -.074 -.971 .332 Percentage Intergovernmental -227.19 81.89 -.134 -2.774 .006 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 209 Police Expenditure for Different City Types Regression analysis is also conducted on police spending. The results for the typology cities are shown in Table 5.9. Centervilles have no significant variables. Boomtowns and Mega-metro centers have only one each ? children and Black, respectively. Spread and Meltingpot cities have the most with five, whereas Gold coast cities have three. The adjusted R 2 values range from lows of -.010 for Boomtowns and .079 for Centervilles to a high of .725 for Mega-metro centers. The remaining city types? adjusted R 2 values range from 17 to 27%. Table 5.9. Police Expenditure Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .207 Population .00 .00 .093 1.962 .050 Density .00 .00 .026 .510 .610 Growth Rate -.07 .11 -.039 -.678 .498 Median Income .00 .00 .365 5.557 .000 Home Ownership -1.83 .43 -.318 -4.305 .000 Children -.58 .95 -.039 -.611 .542 Elderly 4.97 .91 .313 5.479 .000 Black 1.46 .20 .438 7.385 .000 Hispanic 1.02 .38 .153 2.675 .008 Percentage Intergovernmental -26.84 23.02 -.062 -1.166 .244 GOLD COAST .271 Population .00 .00 -.042 -.599 .550 Density .00 .00 .067 .918 .360 Growth Rate -.22 .19 -.079 -1.134 .258 Median Income .00 .00 .182 2.419 .017 Home Ownership -3.03 .78 -.393 -3.878 .000 Children -2.60 3.06 -.095 -.848 .397 210 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Elderly 2.50 1.88 .135 1.327 .186 Black .20 .84 .016 .237 .813 Hispanic 2.36 .78 .210 3.026 .003 Percentage Intergovernmental -19.70 75.31 -.018 -.262 .794 METRO CENTERS .165 Population .00 .00 .223 1.922 .059 Density .00 .01 -.010 -.056 .955 Growth Rate -.41 .27 -.234 -1.507 .137 Median Income .00 .00 .430 2.154 .035 Home Ownership -3.52 1.52 -.470 -2.322 .023 Children 5.09 3.74 .204 1.360 .178 Elderly 4.46 5.42 .135 .824 .413 Black 1.10 .57 .283 1.923 .059 Hispanic .32 .81 .061 .388 .700 Percentage Intergovernmental 1.08 49.45 .003 .022 .983 MELTINGPOTS .256 Population 4.07E-005 .00 .031 .383 .702 Density -.00 .00 -.233 -1.804 .074 Growth Rate -.09 .12 -.075 -.807 .421 Median Income .00 .00 .335 2.221 .028 Home Ownership -1.90 .71 -.402 -2.685 .008 Children -5.64 2.01 -.431 -2.804 .006 Elderly 2.97 2.66 .134 1.119 .266 Black 1.53 .46 .362 3.309 .001 Hispanic 1.30 .45 .447 2.924 .004 Percentage Intergovernmental -76.53 48.61 -.164 -1.574 .118 BOOMTOWNS -.010 Population 2.15E-005 .00 .010 .073 .942 Density .01 .01 .110 .819 .416 Growth Rate -.01 .05 -.035 -.269 .789 Median Income .00 .00 -.032 -.182 .856 Home Ownership .48 1.60 .047 .304 .762 Children -11.78 5.26 -.435 -2.239 .029 Elderly -6.04 4.90 -.268 -1.234 .222 Black -.86 1.40 -.078 -.613 .542 Hispanic 2.43 1.51 .253 1.605 .113 211 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Percentage Intergovernmental -23.91 133.09 -.023 -.180 .858 CENTERVILLES .079 Population -.00 .00 -.266 -1.867 .067 Density -.00 .01 -.096 -.707 .483 Growth Rate -.17 .23 -.135 -.757 .452 Median Income .00 .00 .203 1.210 .231 Home Ownership -1.38 1.01 -.318 -1.365 .178 Children 1.81 1.85 .198 .978 .332 Elderly -2.96 1.94 -.303 -1.527 .132 Black .02 .45 .009 .051 .959 Hispanic -.20 .45 -.080 -.438 .663 Percentage Intergovernmental 39.25 36.79 .135 1.067 .290 MEGA-METRO CENTERS .725 Population -8.83E-006 .00 -.112 -.463 .649 Density .01 .01 .424 1.151 .264 Growth Rate -.57 .49 -.164 -1.147 .266 Median Income .00 .00 .078 .491 .629 Home Ownership -1.64 2.61 -.129 -.631 .536 Children -11.69 7.55 -.406 -1.548 .138 Elderly -5.35 9.56 -.098 -.559 .583 Black 2.78 1.23 .489 2.262 .036 Hispanic 1.30 1.19 .206 1.087 .291 Percentage Intergovernmental 96.69 151.19 .130 .640 .530 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The variables median income and home ownership are statistically significant in four of the categories of cities, while Black and Hispanic are significant in three. The 212 percentage of children and elderly are only significant in two and one categories, respectively. Table 5.10 shows adjusted R 2 values of .348 for non-manager cities and .184 for manager cities in regressions on police expenditure. Manager cities have seven significant variables and non-manager have five. The greatest predictors for manager cities are median income and home ownership, and for non-manager cities percent Black has the most impact, with median income and home ownership having the next two highest Beta weights. Table 5.10. Police Expenditure Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .184 Population 5.90E-005 .00 .096 2.555 .011 Density .00 .00 .058 1.170 .243 Growth Rate -.00 .03 -.002 -.059 .953 Median Income .00 .00 .363 6.817 .000 Home Ownership -1.86 .38 -.307 -4.833 .000 Children -2.37 .96 -.152 -2.459 .014 Elderly 3.62 .81 .225 4.468 .000 Black 1.20 .24 .213 4.910 .000 Hispanic .66 .25 .165 2.668 .008 Percentage Intergovernmental -40.87 26.61 -.059 -1.536 .125 NON-MANAGER a .348 Population 3.68E-005 .00 .252 5.270 .000 Density -.00 .00 -.100 -1.514 .131 Growth Rate -.10 .10 -.058 -1.047 .296 Median Income .00 .00 .348 5.535 .000 Home Ownership -1.86 .45 -.302 -4.113 .000 Children -2.19 1.41 -.099 -1.551 .122 Elderly 1.20 1.41 .049 .851 .395 213 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Black 1.78 .24 .436 7.369 .000 Hispanic 1.08 .36 .196 3.035 .003 Percentage Intergovernmental 36.61 27.35 .073 1.339 .182 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. As shown in Table 5.11, central and independent cities have adjusted R 2 values of .430 and .344, while suburbs are lower at .163. Central cities have seven significant variables, with percent Black and median income having the most impact. Suburban cities also have seven variables shown to be significant, with percentage of children, home ownership, and median income impacting the most. Independent cities only have four variables showing significant, but home ownership and children are both very strong predictors, followed by population and density. Table 5.11. Police Expenditure Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .430 Population 1.25E-005 .00 .077 1.823 .069 Density .01 .00 .234 4.562 .000 Growth Rate -.08 .06 -.052 -1.291 .197 214 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Median Income .00 .00 .354 7.887 .000 Home Ownership -1.42 .45 -.182 -3.176 .002 Children -2.99 .94 -.169 -3.182 .002 Elderly 5.32 .95 .258 5.617 .000 Black 2.10 .20 .465 10.668 .000 Hispanic 1.32 .24 .277 5.436 .000 Percentage Intergovernmental -2.32 21.13 -.004 -.110 .913 SUBURB .163 Population .00 .00 .115 2.417 .016 Density -.00 .00 -.050 -.724 .469 Growth Rate -.02 .03 -.025 -.513 .608 Median Income .00 .00 .226 3.731 .000 Home Ownership -1.55 .42 -.276 -3.668 .000 Children -5.41 1.55 -.304 -3.496 .001 Elderly .09 1.20 .006 .076 .939 Black 10.2 .30 .178 3.353 .001 Hispanic .74 .33 .182 2.263 .024 Percentage Intergovernmental -72.61 36.68 -.103 -1.979 .048 INDEPENDENT .344 Population -.00 .00 -.236 -2.119 .038 Density -.01 .01 -.219 -2.046 .045 Growth Rate -.15 .11 -.149 -1.329 .189 Median Income .00 .00 .236 1.753 .084 Home Ownership -3.43 .94 -.720 -3.644 .001 Children 6.22 1.60 .646 3.898 .000 Elderly .87 1.85 .075 .471 .639 Black -.49 .44 -.159 -1.123 .266 Hispanic -.59 .44 -.173 -1.330 .188 Percentage Intergovernmental 60.92 36.45 .168 1.671 .100 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 215 Regression results in Table 5.12 show principal cities have an adjusted R 2 value of .351 compared to .146 for the non-principal cities. Median income is the strongest predictor for both type cities. Black has almost as much impact as median income in principal cities while in non-principal cities home ownership?s impact is next most important. Table 5.12. Police Expenditure Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .351 Population 2.82E-005 .00 .170 4.298 .000 Density .00 .00 .064 1.292 .197 Growth Rate -.09 .05 -.074 -1.780 .076 Median Income .00 .00 .408 8.354 .000 Home Ownership -2.23 .41 -.321 -5.416 .000 Children -1.09 .96 -.064 -1.139 .255 Elderly 4.74 .90 .252 5.268 .000 Black 1.78 .21 .384 8.420 .000 Hispanic .83 .24 .183 3.405 .001 Percentage Intergovernmental 26.17 23.60 .047 1.109 .268 NON-PRINCIPAL .146 Population .00 .00 .083 1.685 .093 Density -.00 .00 -.061 -.972 .331 Growth Rate -.01 .03 -.015 -.312 .755 Median Income .00 .00 .349 5.678 .000 Home Ownership -1.62 .41 -.285 -3.919 .000 Children -3.80 1.25 -.220 -3.051 .002 Elderly 1.84 1.05 .108 1.751 .081 Black 1.14 .26 .224 4.333 .000 Hispanic .99 .31 .239 3.199 .001 Percentage Intergovernmental -46.66 28.40 -.078 -1.643 .101 216 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Total Revenue for Different City Types Table 5.13 reports the results of regression analysis on total revenue for the NLC typology cities. The adjusted R 2 values are similar overall to what they were for the total expenditure regressions. Mega-metro and Metro centers have the highest adjusted R 2 values, followed by Centervilles, Meltingpot cities, Gold coast cities, Spread cities, and Boomtowns. Spread cities and Metro centers each have four significant variables. Gold coast and Meltingpot cities and Centervilles only have one each. Median income is statistically significant in four categories of cities followed by home ownership in three. Table 5.13. Total Revenue Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .118 Population .00 .00 .088 1.750 .081 Density -.16 .03 -.302 -5.653 .000 Growth Rate -.22 1.76 -.007 -.122 .903 Median Income .03 .01 .225 3.247 .001 Home Ownership -33.26 7.06 -.367 -4.709 .000 Children -5.13 15.78 -.022 -.325 .745 Elderly 43.69 15.05 .175 2.904 .004 Black 2.19 3.28 .042 .669 .504 Hispanic .76 6.31 .007 .120 .904 217 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Percentage Intergovernmental 573.86 381.61 .084 1.504 .134 GOLD COAST .245 Population .00 .00 .045 .639 .523 Density -.05 .03 -.110 -1.492 .138 Growth Rate -3.67 1.93 -.135 -1.902 .059 Median Income .01 .01 .109 1.431 .154 Home Ownership -29.95 7.86 -.392 -3.811 .000 Children -48.44 30.80 -.179 -1.573 .118 Elderly 3.07 18.93 .017 .162 .871 Black -8.53 8.48 -.069 -1.006 .316 Hispanic -2.13 7.84 -.019 -.272 .786 Percentage Intergovernmental -1,384.06 757.90 -.127 -1.826 .070 METRO CENTERS .506 Population -.00 .00 -.068 -.763 .448 Density -.04 .06 -.091 -.669 .505 Growth Rate -10.81 3.10 -.416 -3.484 .001 Median Income .07 .02 .455 2.967 .004 Home Ownership -.41.10 17.35 -.369 -2.368 .021 Children 40.61 42.83 .109 .948 .346 Elderly -7.59 62.03 -.015 -.122 .903 Black 4.98 6.55 .086 .760 .450 Hispanic -3.78 9.30 -.050 -.407 .685 Percentage Intergovernmental 2,564.81 565.95 .456 4.532 .000 MELTINGPOTS .225 Population .00 .00 -.009 -.105 .916 Density -.01 .02 -.111 -.843 .401 Growth Rate 1.11 1.66 .064 .670 .504 Median Income -.02 .01 -.234 -1.520 .131 Home Ownership .32 10.13 .005 .032 .975 Children -136.17 28.72 -.744 -4.741 .000 Elderly -57.10 37.97 -.183 -1.504 .135 Black 3.63 6.62 .061 .548 .585 Hispanic 8.28 6.36 .203 1.302 .195 Percentage Intergovernmental 550.04 694.72 .084 .792 .430 218 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 BOOMTOWNS .039 Population -.00 .00 -.084 -.651 .517 Density -.09 .08 -.147 -1.126 .264 Growth Rate -.13 .34 -.047 -.377 .708 Median Income .03 .01 .456 2.624 .011 Home Ownership -18.16 10.60 -.258 -1.713 .092 Children -8.25 34.98 -.045 -.236 .814 Elderly 40.11 32.54 .261 1.232 .222 Black -12.13 9.29 -.161 -1.306 .196 Hispanic 20.78 10.06 .317 2.065 .043 Percentage Intergovernmental 551.06 884.78 .076 .623 .536 CENTERVILLES .266 Population -.02 .02 -.124 -.974 .334 Density -.21 .11 -.239 -1.967 .054 Growth Rate -2.64 5.13 -.082 -.514 .609 Median Income .08 .02 .558 3.721 .000 Home Ownership -36.64 22.56 -.338 -1.624 .110 Children 14.89 41.20 .065 .362 .719 Elderly 22.17 43.13 .091 .514 .609 Black 12.75 10.06 .195 -1.267 .210 Hispanic -9.78 10.09 -.158 -.970 .336 Percentage Intergovernmental -1,083.99 818.93 -.150 -1.324 .191 MEGA-METRO CENTERS .593 Population .00 .00 .488 1.661 .113 Density -.12 .17 -.301 -.671 .510 Growth Rate -2.58 11.09 -.041 -.233 .818 Median Income .04 .05 .131 .681 .504 Home Ownership 58.91 58.55 .249 1.006 .327 Children -624.08 169.56 -1.175 -3.681 .002 Elderly -11.99 214.73 -.012 -.056 .956 Black 101.38 27.56 .968 3.678 .002 Hispanic 69.29 26.78 .598 2.588 .018 Percentage Intergovernmental 3,719.61 3,395.70 .271 1.095 .287 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. 219 Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.14 shows regression results on total revenue for manager and non- manager cities. Non-manager cities have an adjusted R 2 value of .228, and the manager category?s is .165. Each category has six significant variables, with home ownership the strongest predictor for both. Population, density, median income, and percent Black are also significant in both categories. Elderly is only significant for manager cities and percentage intergovernmental is significant only for the non-manager group. Table 5.14. Total Revenue Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .165 Population .00 .00 .079 2.082 .038 Density -.06 .02 -.175 -3.474 .001 Growth Rate .20 .34 .024 .597 .551 Median Income .01 .00 .203 3.770 .000 Home Ownership -31.33 4.75 -.424 -6.600 .000 Children -14.21 11.89 -.075 -1.195 .233 Elderly 22.81 10.00 .116 2.280 .023 Black 7.22 3.01 .105 2.399 .017 Hispanic -4.96 3.07 -.101 -1.613 .107 Percentage Intergovernmental 180.90 328.50 .021 .551 .582 NON-MANAGER a .228 Population .00 .00 .270 5.194 .000 Density -.04 .02 -.155 -2.165 .031 Growth Rate -.57 1.59 -.021 -.359 .720 Median Income .02 .01 .218 3.175 .002 220 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Home Ownership -28.81 7.32 -.315 -3.934 .000 Children -40.88 22.97 -.123 -1.779 .076 Elderly 20.74 22.87 .057 .907 .365 Black 8.11 3.93 .133 2.065 .040 Hispanic 2.88 5.77 .035 .500 .618 Percentage Intergovernmental 1,857.13 444.64 .246 4.177 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Regression results on total revenue for cities based on metro status (Table 5.15) show central cities have the highest adjusted R 2 value (.255) and the greatest number of significant variables (7). The major predictors of total revenue are median income for central and independent cities and home ownership for suburbs. The next strongest predictors are: home ownership, percent Black, and percentage intergovernmental for central cities; percent children and median income for suburbs; and percentage intergovernmental for independent cities. 221 Table 5.15. Total Revenue Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .255 Population .00 .00 .156 3.232 .001 Density .01 .03 .021 .362 .718 Growth Rate -1.65 1.06 -.072 -1.558 .120 Median Income .04 .01 .317 6.188 .000 Home Ownership -25.48 7.61 -.219 -3.347 .001 Children -37.13 15.92 -.141 -2.332 .020 Elderly 34.82 16.06 .114 2.167 .031 Black 14.72 3.34 .220 4.404 .000 Hispanic 2.04 4.13 .029 .494 .622 Percentage Intergovernmental 1,486.16 358.72 .194 4.143 .000 SUBURB .170 Population .00 .00 .129 2.726 .007 Density -.02 .01 -.091 -1.319 .188 Growth Rate .42 .30 .070 1.411 .159 Median Income .01 .00 .147 2.428 .016 Home Ownership -22.79 4.06 -.420 -5.613 .000 Children -36.28 14.89 -.211 -2.436 .015 Elderly 4.32 11.57 .027 .374 .709 Black -1.06 2.91 -.019 -.365 .715 Hispanic -4.67 3.15 -.118 -1.483 .139 Percentage Intergovernmental 369.34 352.97 .054 1.046 .296 INDEPENDENT .219 Population .02 .02 .160 1.313 .194 Density -.22 .16 -.161 -1.376 .174 Growth Rate -1.65 3.10 -.065 -.532 .597 Median Income .06 .03 .366 2.486 .016 Home Ownership 19.13 25.71 .160 .744 .460 Children -33.45 43.57 -.138 -.765 .447 Elderly -36.07 50.63 -.124 -.712 .479 Black 14.45 12.02 .186 1.202 .234 Hispanic 3.31 12.14 .039 .272 .786 Percentage Intergovernmental -2,944.32 995.53 -.325 -2.958 .004 222 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The variable total revenue in principal cities is impacted by median income, percent Black, and home ownership, as shown in Table 5.16. Home ownership, followed by density, is the strongest predictor for non-principal cities. Growth rate is the only variable that is not statistically significant for either of these type cities. Table 5.16. Total Revenue Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .201 Population .00 .00 .167 3.820 .000 Density .01 .02 .040 .726 .468 Growth Rate -.09 .84 -.005 -.101 .919 Median Income .02 .01 .228 4.214 .000 Home Ownership -22.82 7.05 -.213 -3.239 .001 Children -42.77 16.41 -.162 -2.606 .009 Elderly 15.39 15.38 .053 1.001 .317 Black 16.71 3.61 .235 4.633 .000 Hispanic -.92 4.17 -.013 -.220 .826 Percentage Intergovernmental 888.05 403.75 .103 2.199 .028 NON-PRINCIPAL .187 Population .00 .00 .061 1.269 .205 Density -.05 .01 -.273 -4.453 .000 Growth Rate .13 .33 .019 .387 .699 Median Income .01 .00 .162 2.693 .007 Home Ownership -32.11 4.20 -.543 -7.650 .000 223 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children 3.84 12.72 .021 .302 .763 Elderly 29.93 10.70 .169 2.797 .005 Black -3.51 2.69 -.066 -1.304 .193 Hispanic -6.24 3.14 -.145 -1.988 .047 Percentage Intergovernmental 1,121.53 289.79 .180 3.870 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Property Tax Revenue for Different City Types The results of regression testing of the NLC typology in terms of property tax revenue are set out in Table 5.17. Spread cities have the most (6) significant variables influencing property tax revenue. Of these, median income (with a positive influence) and home ownership (with a negative effect) are the variables with the greatest impact. Density and growth rate are the main influences in Gold coast cities, and they both have a negative relationship to property tax revenue. Percentage intergovernmental revenue and median income have the highest Beta values in the Metro center regression and both positively impact property tax revenue; whereas the percentage of children, with a negative impact, has the strongest impact for Meltingpot cities. Median income is the only significant variable shown for Boomtowns, and Mega-metro centers have no statistically significant independent variables. Median income is the greatest indicator of property tax revenue for Centervilles, with a Beta of 224 .722 (over twice the impact of the only other significant variable, density). Overall median income has the greatest impact on property tax revenue in 4 of the 7 categories. Table 5.17. Property Tax Revenue Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .367 Population .00 .00 .048 1.132 .258 Density -.02 .01 -.137 -3.024 .003 Growth Rate -1.51 .50 -.156 -3.039 .003 Median Income .02 .00 .503 8.557 .000 Home Ownership -12.41 2.01 -.411 -6.186 .000 Children -2.52 4.47 -.033 -.563 .574 Elderly 21.57 4.23 .260 5.097 .000 Black -1.06 .92 -.061 -1.152 .250 Hispanic .51 1.78 .015 .287 .775 Percentage Intergovernmental 840.02 109.05 .366 7.703 .000 GOLD COAST .108 Population .00 .00 .022 .286 .775 Density -.03 .01 -.175 -2.182 .030 Growth Rate -2.50 .83 -.233 -3.028 .003 Median Income .00 .00 .099 1.191 .235 Home Ownership -5.79 3.36 -.193 -1.721 .087 Children -22.35 13.18 -.210 -1.696 .092 Elderly -4.02 81.00 -.055 -.496 .621 Black 4.47 3.63 .093 1.234 .219 Hispanic -3.07 3.35 -.070 -.915 .361 Percentage Intergovernmental 399.00 324.17 .093 1.231 .220 METRO CENTERS .520 Population -.00 .00 -.213 -2.422 .018 Density -.03 .02 -.188 -1.396 .167 Growth Rate -2.95 1.17 -.303 -2.531 .014 Median Income .03 .01 .491 3.240 .002 Home Ownership -10.21 6.01 -.258 -1.697 .094 225 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children -10.83 14.83 -.084 -.730 .468 Elderly -8.96 21.88 -.052 -.409 .683 Black 3.01 2.27 .148 1.326 .189 Hispanic 5.68 3.23 .213 1.760 .083 Percentage Intergovernmental 1,029.79 196.08 .525 5.252 .000 MELTINGPOTS .328 Population .00 .00 .027 .346 .730 Density .00 .00 .029 .240 .811 Growth Rate .12 .40 .026 .289 .773 Median Income .00 .00 .113 .786 .433 Home Ownership -1.10 2.42 -.065 -.454 .651 Children -30.23 6.86 -.645 -4.411 .000 Elderly -9.75 9.06 -.122 -1.075 .284 Black 3.29 1.58 .216 2.080 .040 Hispanic 2.14 1.52 .205 1.412 .161 Percentage Intergovernmental 174.08 165.81 .104 1.050 .296 BOOMTOWNS .102 Population -.00 .00 -.243 -1.934 .058 Density -.03 .02 -.162 -1.280 .205 Growth Rate -.06 .09 -.085 -.695 .490 Median Income .01 .00 .354 2.086 .041 Home Ownership -4.10 2.66 -.226 -1.542 .128 Children -13.96 8.78 -.293 -1.590 .117 Elderly -5.61 8.18 -.142 -.685 .496 Black 1.06 2.33 .055 .456 .650 Hispanic 3.88 2.53 .228 1.534 .130 Percentage Intergovernmental 176.06 223.51 .094 .788 .434 CENTERVILLES .469 Population .01 .00 .190 1.742 .087 Density -.07 .02 -.320 -2.988 .004 Growth Rate -1.99 1.09 -.246 -1.823 .074 Median Income .03 .01 .722 5.636 .000 Home Ownership -9.73 4.98 -.360 -1.955 .056 Children -1.36 8.85 -.024 -.153 .879 Elderly 12.10 9.24 .200 1.310 .196 Black .15 2.16 .009 .069 .946 Hispanic 1.01 2.14 .065 .470 .640 226 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Percentage Intergovernmental 552.13 183.53 .300 3.008 .004 MEGA-METRO CENTERS .381 Population 5.75E-005 .00 .218 .600 .555 Density -.02 .04 -.322 -.584 .566 Growth Rate .23 2.48 .020 .092 .928 Median Income .01 .01 .118 .497 .625 Home Ownership -8.354 13.08 -.195 -.639 .531 Children -71.71 37.88 -.745 -1.893 .074 Elderly -.70 47.98 -.004 -.015 .988 Black 8.00 6.16 .421 1.299 .209 Hispanic 6.25 5.98 .298 1.045 .309 Percentage Intergovernmental 1,158.33 758.68 .466 1.527 .143 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Regressions on property tax revenue for manager and non-manager cities are reported in Table 5.18. The adjusted R 2 value for non-manager cities is over twice as large as for manager cities. The biggest predictor in non-manager cities is median income with a Beta weight value of .616, with the next largest Beta weight -.352 for home ownership. Home ownership is followed by percentage intergovernmental revenue and density. For manager cities, the Beta for median income is .324, with percentage intergovernmental revenue at .289 and home ownership at .253. 227 Table 5.18. Property Tax Revenue Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .176 Population 8.50E-007 .00 .000 .012 .990 Density -.01 .00 -.079 -1.570 .117 Growth Rate -.03 .08 -.014 -.357 .721 Median Income .01 .00 .324 6.029 .000 Home Ownership -4.67 1.18 -.253 -3.954 .000 Children -5.24 2.95 -.111 -1.776 .076 Elderly 3.56 2.48 .073 1.435 .152 Black 1.87 .75 .109 2.503 .013 Hispanic -.90 .76 -.073 -1.173 .241 Percentage Intergovernmental 611.74 82.16 .289 7.446 .000 NON-MANAGER a .379 Population 6.39E-005 .00 .085 1.818 .070 Density -.02 .01 -.245 -3.813 .000 Growth Rate -1.44 .49 -.158 -2.913 .004 Median Income .02 .00 .616 9.970 .000 Home Ownership -11.13 2.29 -.352 -4.861 .000 Children -17.41 7.16 -.152 -2.431 .016 Elderly 25.49 7.10 .202 3.590 .000 Black .39 1.22 .019 .320 .749 Hispanic 4.03 1.79 .142 2.249 .025 Percentage Intergovernmental 835.04 140.12 .318 5.959 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. 228 Table 5.19 shows the regression results for metro status. The regression model is best at predicting property tax revenue in independent (adjusted R 2 = .445) and central cities (adjusted R 2 = .323). The largest percentage of variation in central cities is due to median income and percentage intergovernmental revenue. In independent cities, the major predictor is median income, with a Beta of .806. The only other statistically significant variable, percentage intergovernmental, has a Beta of .200. For suburbs, the Beta for percentage intergovernmental is only slightly higher than that of home ownership which is followed by median income. Table 5.19. Property Tax Revenue Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .323 Population 2.47E-005 .00 .033 .724 .470 Density -.01 .01 -.047 -.839 .402 Growth Rate -.65 .31 -.092 -2.088 .037 Median Income .02 .00 .428 8.742 .000 Home Ownership -8.69 2.24 -.244 -3.890 .000 Children -10.43 4.68 -.129 -2.228 .026 Elderly 14.31 4.70 .153 3.044 .002 Black 1.14 .98 .055 1.160 .247 Hispanic 1.49 1.21 .068 1.225 .221 Percentage Intergovernmental 917.04 105.39 .390 8.701 .000 SUBURB .184 Population 5.41E-005 .00 .009 .183 .855 Density -.01 .00 -.104 -1.510 .132 Growth Rate -.07 .10 -.035 -.715 .475 Median Income .00 .00 .203 3.388 .001 Home Ownership -4.80 1.40 -.254 -3.422 .001 Children -9.76 5.15 -.163 -1.896 .059 229 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Elderly 3.81 4.00 .068 .952 .342 Black -.21 1.01 -.011 -.210 .834 Hispanic -1.78 1.09 -.130 -1.635 .103 Percentage Intergovernmental 706.71 124.35 .293 5.683 .000 INDEPENDENT .445 Population .00 .00 .034 .332 .741 Density -.05 .03 -.138 -1.385 .171 Growth Rate -.86 .63 -.143 -1.374 .174 Median Income .03 .01 .806 6.462 .000 Home Ownership -6.67 5.29 -.234 -1.261 .212 Children -11.68 8.83 -.203 -1.323 .191 Elderly 13.96 10.30 .201 1.356 .180 Black .68 2.44 .037 .279 .781 Hispanic 2.01 2.46 .099 .818 .417 Percentage Intergovernmental 442.78 209.23 .200 2.116 .038 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.20 contains the results of regressions on property tax revenue based on principal city status. The adjusted R 2 values show the model is about equally predictive for each type city, with median income, home ownership, and percent intergovernmental revenue having the most impact for each type. 230 Table 5.20. Property Tax Revenue Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .241 Population 4.45E-005 .00 .062 1.449 .148 Density -.00 .01 -.044 -.820 .413 Growth Rate -.41 .23 -.080 -1.792 .074 Median Income .01 .00 .402 7.604 .000 Home Ownership -6.71 1.93 -.223 -3.485 .001 Children -10.19 4.49 -.138 -2.272 .024 Elderly 8.85 4.20 .109 2.104 .036 Black 1.24 .99 .062 1.255 .210 Hispanic .42 1.14 .022 .372 .710 Percentage Intergovernmental 799.54 110.51 .332 7.235 .000 NON-PRINCIPAL .236 Population .00 .00 .030 .645 .520 Density -.01 .00 -.154 -2.572 .010 Growth Rate -.07 .12 -.030 -.629 .530 Median Income .01 .00 .308 5.273 .000 Home Ownership -6.74 1.49 -.313 -4.528 .000 Children -4.99 4.50 -.076 -1.109 .268 Elderly 8.52 3.78 .132 2.255 .025 Black -.78 .95 -.040 -.817 .415 Hispanic -1.28 1.11 -.082 -1.154 .249 Percentage Intergovernmental 902.92 104.02 .393 8.680 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 231 Sales Tax Revenue for Different City Types Table 5.21 shows regression results for the NLC typology categories in terms of sales tax revenue. Percentage of intergovernmental revenue is statistically significant in all categories, except for Mega-metro centers where percent Black is the only significant factor shown and percent Hispanic is just above significance at .051. The model best predicts sales tax revenue for Meltingpot and Gold coast cities and Centervilles. In all these instances population density and percentage intergovernmental revenue are impacting variables with a variety of variables also impacting in the first two categories and only Black in Centervilles. Percent Black is statistically significant in Gold coast cities as well, and in both categories this variable has a negative relationship with sales tax revenue. Table 5.21. Sales Tax Revenue Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .157 Population .00 .00 .058 1.054 .293 Density -.01 .01 -.107 -1.799 .073 Growth Rate -.02 .36 -.004 -.059 .953 Median Income .00 .00 .022 .280 .780 Home Ownership -.38 1.76 -.020 -.217 .828 Children -2.53 3.47 -.055 -.730 .466 Elderly .97 3.31 .021 .291 .771 Black .70 .73 .069 .968 .334 Hispanic 3.08 1.32 .156 2.336 .020 Percentage Intergovernmental -460.11 80.57 -.347 -5.711 .000 232 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 GOLD COAST .381 Population .00 .00 .048 .700 .485 Density -.00 .01 -.048 -.656 .513 Growth Rate .09 .42 .014 .207 .836 Median Income .00 .00 .106 1.400 .163 Home Ownership -4.24 1.65 -.255 -2.566 .011 Children -17.89 6.56 -.306 -2.727 .007 Elderly -3.06 4.04 -.078 -.759 .449 Black -4.73 2.00 -.162 -2.368 .019 Hispanic 5.45 1.63 .226 3.341 .001 Percentage Intergovernmental -519.40 156.42 -.224 -3.320 .001 METRO CENTERS .251 Population .00 .00 .170 1.514 .135 Density -.01 .01 -.180 -1.009 .317 Growth Rate -1.01 .60 -.254 -1.677 .098 Median Income .01 .01 .521 2.633 .011 Home Ownership -2.48 3.64 -.133 -.682 .498 Children 9.72 8.56 .170 1.136 .260 Elderly 20.21 12.68 .266 1.595 .116 Black 2.37 1.27 .268 1.860 .068 Hispanic 1.97 1.83 .165 1.080 .284 Percentage Intergovernmental -366.12 113.34 -.409 -3.23 .002 MELTINGPOTS .399 Population .00 .00 .121 1.616 .109 Density -.01 .00 -.419 -3.691 .000 Growth Rate -.38 .17 -.190 -2.235 .027 Median Income .00 .00 .367 2.633 .010 Home Ownership -2.90 1.04 -.371 -2.779 .006 Children 5.79 2.95 .274 1.962 .052 Elderly 11.89 3.92 .331 3.036 .003 Black -.30 .73 -.042 -.411 .682 Hispanic .84 .67 .178 1.256 .212 Percentage Intergovernmental -284.72 73.86 -.355 -3.855 .000 BOOMTOWNS .113 Population .00 .00 .143 1.133 .262 Density -.02 .02 -.082 -.636 .527 233 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Growth Rate .07 .09 .091 .732 .467 Median Income .00 .00 .092 .549 .585 Home Ownership -3.89 2.95 -.193 -1.319 .192 Children -3.83 9.47 -.076 -.405 .687 Elderly -.84 8.82 -.020 -.096 .924 Black -2.80 2.53 -.136 -1.106 .273 Hispanic 2.52 2.71 .137 .930 .356 Percentage Intergovernmental -705.66 251.25 -.339 -2.809 .007 CENTERVILLES .344 Population .00 .00 .086 .649 .519 Density -.08 .02 -.477 -3.738 .001 Growth Rate -.59 1.04 -.093 -.567 .573 Median Income .00 .01 .087 .554 .582 Home Ownership -2.08 4.80 -.096 -.433 .667 Children 10.81 8.27 .247 1.307 .198 Elderly -12.67 8.80 -.264 -1.439 .157 Black -5.39 2.07 -.422 -2.601 .012 Hispanic .16 2.00 .013 .079 .938 Percentage Intergovernmental -493.62 166.70 -.344 -2.961 .005 MEGA-METRO CENTERS .116 Population 7.70E-005 .00 .350 .774 .449 Density -.01 .04 -.169 -.251 .804 Growth Rate -1.56 2.59 -.160 -.603 .554 Median Income .01 .012 .303 1.065 .301 Home Ownership 9.18 15.18 .257 .605 .553 Children -82.07 44.35 -1.011 -1.851 .081 Elderly 15.98 49.10 .106 .326 .749 Black 15.39 7.23 .971 2.129 .047 Hispanic 13.81 6.61 .790 2.091 .051 Percentage Intergovernmental -704.76 788.51 -.323 -.894 .383 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the 234 Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Findings for cities based upon form of government are found in Table 5.22. It shows that manager cities have seven significant variables compared to non-manager cities? four. The model accounts for around 20% of the variation in sales tax revenue for both type cities. Table 5.22. Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .179 Population 9.83E-005 .00 .076 1.913 .056 Density -.01 .00 -.175 -3.287 .001 Growth Rate .02 .06 .013 .309 .757 Median Income .00 .00 .217 3.814 .000 Home Ownership -3.83 .91 -.283 -4.219 .000 Children -4.55 2.27 -.132 -2.000 .046 Elderly 2.61 1.91 .075 1.371 .171 Black -1.63 .58 -.128 -2.804 .005 Hispanic -1.39 .57 .159 2.453 .014 Percentage Intergovernmental -393.49 61.92 -.257 -6.355 .000 NON-MANAGER a .209 Population 5.97E-005 .00 .172 2.848 .005 Density -.00 .00 -.100 -1.212 .227 Growth Rate .71 .30 .162 2.321 .021 Median Income .00 .00 .028 .349 .728 Home Ownership -2.71 1.58 -.158 -1.723 .086 Children -3.38 4.53 -.061 -.744 .457 Elderly 3.24 4.51 .050 .719 .473 Black 2.95 .80 .282 3.702 .000 Hispanic 1.61 1.14 .110 1.415 .158 Percentage Intergovernmental -440.58 88.71 -.338 -4.967 .000 235 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Table 5.23 sets out regression results for metro status. Sales tax revenue is best predicted for suburbs. Percentage of intergovernmental revenue is the largest predictor for both central cities and suburbs, while density is the only significant indicator in independent cities. Table 5.23. Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .188 Population 4.98E-005 .00 .127 2.303 .022 Density -.01 .01 -.072 -1.039 .300 Growth Rate -.34 .20 -.088 -1.711 .088 Median Income .01 .00 .219 3.648 .000 Home Ownership -1.06 1.58 -.050 -.670 .503 Children -7.86 3.19 -.170 -2.467 .014 Elderly 2.84 3.17 .055 .896 .371 Black 1.92 .64 .169 2.993 .003 Hispanic 3.10 .78 .262 3.946 .000 Percentage Intergovernmental -428.90 69.31 -.323 -6.188 .000 SUBURB .238 Population .00 .00 .099 2.039 .042 Density -.00 .00 -.090 -1.270 .205 Growth Rate .10 .06 .083 1.633 .103 Median Income .00 .00 .147 2.307 .022 236 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Home Ownership -3.52 .91 -.296 -3.854 .000 Children -4.22 3.23 -.117 -1.306 .192 Elderly .33 2.53 .010 .129 .898 Black -.68 .70 -.053 -.960 .338 Hispanic .73 .70 .086 1.047 .296 Percentage Intergovernmental -620.26 80.65 -.406 -7.691 .000 INDEPENDENT .122 Population -.00 .00 -.085 -.621 .537 Density -.07 .03 -.304 -2.229 .030 Growth Rate .33 .55 .082 .595 .554 Median Income .01 .01 .175 1.023 .311 Home Ownership .29 4.79 .015 .061 .952 Children -7.32 7.79 -.196 -.939 .352 Elderly .84 9.36 .018 .090 .929 Black -1.58 2.22 -.128 -.714 .478 Hispanic 3.34 2.16 .248 1.548 .128 Percentage Intergovernmental -360.83 181.28 -.248 -1.990 .052 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.24 reveals that the principal city model predicts differences in sales tax revenue slightly better for non-principal cities. The major predictors in these cities are percentage intergovernmental revenue, home ownership, and density ? all of which have a negative impact upon sales tax revenue. In principal cities, percentage intergovernmental revenue and median income are the strongest indicators, with the former having a negative and the latter a positive influence. 237 Table 5.24. Sales Tax Revenue Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .162 Population 4.41E-005 .00 .107 2.240 .026 Density -.00 .00 -.048 -.778 .437 Growth Rate -.01 .15 -.002 -.045 .964 Median Income .00 .00 .240 3.982 .000 Home Ownership -1.83 1.36 -.097 -1.347 .179 Children -7.95 3.08 -.176 -2.584 .010 Elderly 2.84 2.87 .059 .992 .322 Black 1.93 .66 .162 2.915 .004 Hispanic 2.50 .76 .218 3.307 .001 Percentage Intergovernmental -427.02 74.70 -.293 -5.716 .000 NON-PRINCIPAL .215 Population .00 .00 .076 1.491 .137 Density -.01 .00 -.223 -3.411 .001 Growth Rate .04 .07 .033 .632 .528 Median Income .00 .00 .145 2.213 .028 Home Ownership -3.60 .93 -.298 -3.888 .000 Children -.05 2.69 -.001 -.017 .987 Elderly 2.69 2.26 .077 1.187 .236 Black -.77 .60 -.071 -1.283 .200 Hispanic 1.16 .67 .136 1.730 .085 Percentage Intergovernmental -505.70 62.16 -.395 -8.135 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 238 Intergovernmental Revenue for Different City Types The results of multiple-regression analysis on intergovernmental revenue for NLC typology cities are shown in Table 5.25. The adjusted R 2 values for the different categories of cities are the highest for intergovernmental revenue than for any other financial output tested. However, the variable percentage of intergovernmental revenue is highly correlated with this output and accounts for the high values. The Beta weight for this variable is very high relative to the other statistically significant variables in all categories except Mega-metro where the value for percent Black and children are higher. Spread cities have the greatest number of statistically significant variables and Boomtowns have the lowest. Table 5.25. Intergovernmental Revenue Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .619 Population .00 .00 .039 1.191 .234 Density -.04 .01 -.169 -4.816 .000 Growth Rate -.41 .46 -.035 -.892 .373 Median Income .01 .00 .160 3.514 .000 Home Ownership -9.98 1.85 -.275 -5.383 .000 Children 1.51 4.14 .016 .364 .716 Elderly 14.51 3.95 .145 3.674 .000 Black -.62 .86 -.030 -.720 .472 Hispanic 3.31 1.66 .079 2.001 .046 Percentage Intergovernmental 2,053.96 100.19 .750 20.500 .000 GOLD COAST .518 Population .00 .00 .040 .713 .477 Density -.01 .00 -.141 -2.385 .018 239 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Growth Rate -.61 .25 -.136 -2.402 .017 Median Income .00 .00 .013 .209 .834 Home Ownership -3.68 1.04 -.292 -3.554 .000 Children -7.87 4.05 -.176 -1.940 .054 Elderly 2.00 2.49 .066 .802 .424 Black -.70 1.12 -.035 -.631 .529 Hispanic .33 1.03 .018 .319 .750 Percentage Intergovernmental 1,261.69 99.75 .705 12.649 .000 METRO CENTERS .875 Population -.00 .00 -.094 -2.092 .040 Density .00 .02 .005 .073 .942 Growth Rate -3.08 1.20 -.154 -2.563 .013 Median Income .01 .01 .098 1.266 .210 Home Ownership -4.86 6.71 -.057 -.724 .472 Children 12.31 16.57 .043 .743 .460 Elderly -19.25 23.99 -.051 -.802 .425 Black 2.22 2.53 .050 .877 .383 Hispanic .64 3.60 .011 .177 .860 Percentage Intergovernmental 3,686.51 218.91 .853 16.840 .000 MELTINGPOTS .607 Population .00 .00 .026 .445 .657 Density .00 .01 .014 .153 .879 Growth Rate .16 .59 .018 .265 .791 Median Income -.01 .01 -.142 -1.301 .196 Home Ownership -.14 3.61 -.004 -.037 .970 Children -31.13 10.25 -.339 -3.037 .003 Elderly -15.67 13.55 -.100 -1.157 .250 Black -1.14 2.36 -.038 -.482 .631 Hispanic .27 2.27 .013 .118 .906 Percentage Intergovernmental 2,148.31 247.90 .657 8.666 .000 BOOMTOWNS .377 Population -7.23E-005 .00 -.019 -.179 .858 Density -.01 .02 -.059 -.565 .574 Growth Rate -.01 .07 -.010 -.098 .922 Median Income .00 .00 .199 1.422 .160 Home Ownership -2.08 2.18 -.115 -.952 .344 240 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children -.16 7.19 -.003 -.022 .983 Elderly 4.30 6.69 .110 .643 .522 Black -.36 1.91 -.019 -.191 .849 Hispanic 1.93 2.07 .115 .931 .355 Percentage Intergovernmental 1,279.34 181.87 .695 7.034 .000 CENTERVILLES .517 Population -.00 .00 -.015 -.147 .884 Density -.05 .03 -.200 -2.030 .047 Growth Rate -.99 1.25 -.102 -.791 .432 Median Income .02 .01 .476 3.913 .000 Home Ownership -2.64 5.50 -.081 -.481 .632 Children -5.64 10.04 -.082 -.562 .577 Elderly -1.45 10.51 -.020 -.138 .891 Black 3.20 2.45 .163 1.307 .196 Hispanic -.11 2.46 -.006 -.046 .964 Percentage Intergovernmental 1,269.44 199.48 .583 6.364 .000 MEGA-METRO CENTERS .765 Population .00 .00 .255 1.141 .268 Density -.01 .06 -.057 -.168 .868 Growth Rate .67 3.89 .023 .173 .864 Median Income .01 .02 .106 .726 .477 Home Ownership 14.08 20.53 .129 .686 .501 Children -170.68 59.46 -.696 -2.871 .010 Elderly 7.06 75.30 .015 .094 .926 Black 37.36 9.66 .772 3.865 .001 Hispanic 24.16 9.39 .451 2.573 .019 Percentage Intergovernmental 3,289.99 1,190.73 .519 2.763 .012 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 241 Regression results for intergovernmental revenue for cities with managers and those without are shown in Table 5.26. With the exception of the variable percentage intergovernmental revenue, the factors tested exhibit low predictive power. Table 5.26. Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .549 Population .00 .00 .056 1.985 .048 Density -.01 .00 -.120 -3.249 .001 Growth Rate .04 .08 .013 .437 .662 Median Income .00 .00 .092 2.320 .021 Home Ownership -5.42 1.13 -.227 -4.810 .000 Children -1.80 2.83 -.029 -.635 .525 Elderly 3.91 2.38 .061 1.643 .101 Black 1.87 .72 .084 2.615 .009 Hispanic -1.28 .73 -.080 -1.751 .080 Percentage Intergovernmental 1,919.76 78.04 .705 24.601 .000 NON-MANAGER a .645 Population .00 .00 .173 4.919 .000 Density -.00 .01 -.036 -.738 .461 Growth Rate .34 .57 .025 .605 .546 Median Income .01 .00 .109 2.341 .020 Home Ownership -9.17 2.62 -.190 -3.500 .001 Children -6.41 8.22 -.037 -.779 .436 Elderly 7.25 8.18 .038 .886 .376 Black 2.58 1.41 .080 1.833 .068 Hispanic 1.29 2.06 .030 .624 .533 Percentage Intergovernmental 2,870.17 159.04 .721 18.046 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for 242 demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Table 5.27 also shows that the demographic variables, with the exception of percentage intergovernmental revenue, account for low variation in both central cities and suburbs. In independent cities, however, median income has nearly as much influence. Table 5.27. Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .671 Population .00 .00 .086 2.674 .008 Density .02 .01 .101 2.594 .010 Growth Rate -.15 .35 -.013 -.426 .671 Median Income .01 .00 .110 3.219 .001 Home Ownership -4.74 2.52 -.082 -1.882 .061 Children -7.08 5.27 -.054 -1.343 .180 Elderly 5.90 5.32 .039 1.108 .269 Black 4.22 1.11 .126 3.817 .000 Hispanic 1.43 1.37 .040 1.045 .297 Percentage Intergovernmental 2,772.56 118.77 .725 23.345 .000 SUBURB .604 Population .00 .00 .083 2.536 .012 Density .00 .00 .068 1.420 .156 Growth Rate .13 .08 .056 1.654 .099 Median Income .00 .00 .027 .637 .524 Home Ownership -4.67 1.05 -.230 -4.457 .000 Children -4.89 3.84 -.076 -1.274 .204 Elderly 2.01 2.98 .034 .673 .501 Black -.97 .75 -.047 -1.297 .195 Hispanic -2.28 .81 -.155 -2.801 .005 Percentage Intergovernmental 1,774.84 91.02 .697 19.499 .000 243 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 INDEPENDENT .492 Population .00 .00 .006 .059 .954 Density -.03 .03 -.125 -1.325 .190 Growth Rate -.13 .50 -.026 -.260 .796 Median Income .02 .00 .512 4.317 .000 Home Ownership 3.65 4.16 .153 .878 .384 Children -13.17 7.06 -.272 -1.867 .067 Elderly -6.57 8.20 -.112 -.802 .426 Black 2.69 1.95 .172 1.381 .172 Hispanic 1.64 1.97 .095 .832 .408 Percentage Intergovernmental 973.99 161.20 .536 6.042 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.28 reveals that the variables other than percentage intergovernmental revenue have little effect on predicting total intergovernmental revenue in principal and non-principal cities. The strongest indicator is home ownership in non-principal cities (Beta = -.338). Table 5.28. Intergovernmental Revenue Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .657 Population .00 .00 .125 4.364 .000 244 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Density .02 .01 .100 2.788 .006 Growth Rate .22 .24 .027 .901 .368 Median Income .00 .00 .088 2.481 .013 Home Ownership -3.41 2.04 -.072 -1.673 .095 Children -9.84 4.75 -.084 -2.071 .039 Elderly 2.84 4.45 .022 .638 .524 Black 5.27 1.04 .167 5.043 .000 Hispanic .50 1.21 .016 .417 .677 Percentage Intergovernmental 2,639.90 116.88 .696 22.587 .000 NON-PRINCIPAL .612 Population .00 .00 .076 2.297 .022 Density -.01 .00 -.139 -3.290 .001 Growth Rate .03 .11 .008 .240 .811 Median Income .00 .00 .099 2.397 .017 Home Ownership -9.13 1.32 -.338 -6.902 .000 Children 3.62 4.01 .044 .904 .367 Elderly 8.43 3.37 .104 2.497 .013 Black -1.83 .85 -.075 -2.151 .032 Hispanic -1.95 .99 -.099 -1.964 .050 Percentage Intergovernmental 2,113.91 91.37 .741 23.136 .000 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Total Debt for Different City Types Multiple-regression analyses on total debt shows the demographic variables have the lowest explanatory power for this fiscal output than for another other in each of the classification schemes. The findings for cities within the NLC typology are shown in 245 Table 5.29. The highest adjusted R 2 value is exhibited in the regression on Mega-metro centers where it is .248. The most significant variables shown for any group of cities are three. Spread and Gold coast cities, Metro centers, and Centervilles have only one apiece. Table 5.29. Total Debt Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .015 Population .00 .00 .060 1.116 .265 Density -.15 .06 -.151 -2.653 .008 Growth Rate .45 3.45 .008 .129 .897 Median Income -.00 .02 -.017 -.238 .812 Home Ownership -12.87 13.77 -.076 -.935 .350 Children -23.75 30.95 -.055 -.767 .443 Elderly 9.05 29.32 .020 .309 .758 Black -.13 6.42 -.001 -.020 .984 Hispanic 6.75 12.26 .035 .550 .582 Percentage Intergovernmental -584.32 741.28 -.047 -.788 .431 .057 GOLD COAST Population .00 .00 .057 .715 .476 Density -.08 .06 -.105 -1.264 .208 Growth Rate 1.06 3.65 .023 .291 .771 Median Income -.01 .01 -.117 -1.377 .170 Home Ownership -14.51 14.76 -.114 -.984 .327 Children -4.98 57.68 -.011 -.086 .931 Elderly -27.78 35.35 -.091 -.786 .433 Black -15.54 15.82 -.076 -.982 .327 Hispanic -6.14 14.64 -.033 -.419 .676 Percentage Intergovernmental -3,895.97 1,428.68 -.215 -2.727 .007 METRO CENTERS .103 Population .00 .00 .239 1.988 .051 246 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Density -.06 .12 -.085 -.465 .644 Growth Rate -22.28 6.90 -.520 -3.230 .002 Median Income .09 .05 .373 1.804 .076 Home Ownership -16.29 38.58 -.089 -.422 .674 Children 64.17 95.20 .105 .674 .503 Elderly -19.20 137.87 -.024 -.139 .890 Black 20.43 14.56 .214 1.403 .165 Hispanic 7.56 20.67 .060 .366 .716 Percentage Intergovernmental -443.45 1,257.97 -.048 -.353 .726 MELTINGPOTS .118 Population .00 .00 .006 .071 .943 Density -.04 .02 -.251 -1.770 .079 Growth Rate 6.77 2.19 .316 3.100 .002 Median Income -.01 .02 -.051 -.314 .754 Home Ownership 4.32 13.34 .053 .324 .747 Children -135.02 37.87 -.596 -3.565 .001 Elderly -80.54 50.19 -.209 -1.604 .111 Black 13.33 8.72 .183 1.528 .129 Hispanic 21.34 8.38 .425 2.545 .012 Percentage Intergovernmental -401.88 916.38 -.050 -.439 .662 BOOMTOWNS .096 Population .00 .00 .014 .113 .910 Density -.32 .15 -.271 -2.136 .036 Growth Rate .20 .61 .039 .319 .751 Median Income .04 .02 .365 2.164 .034 Home Ownership -15.69 19.24 -.119 -.815 .418 Children -60.18 63.49 -.174 -.948 .347 Elderly 16.66 59.07 .058 .282 .779 Black -19.71 16.86 -.140 -1.169 .247 Hispanic 46.53 18.27 .380 2.547 .013 Percentage Intergovernmental -1,474.80 1,605.87 -.109 -.918 .362 CENTERVILLES .107 Population .00 .02 .028 .196 .845 Density -.11 .10 -.149 -1.104 .275 Growth Rate 11.40 5.30 .409 2.149 .036 Median Income .02 .02 .115 .693 .491 Home Ownership -1.27 21.62 -.013 -.059 .953 247 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children -76.35 41.00 -.385 -1.862 .068 Elderly 36.01 46.57 .157 .773 .443 Black 10.23 9.70 .180 1.055 .296 Hispanic -2.61 9.82 -.049 -.266 .792 Percentage Intergovernmental 943.47 798.70 .148 1.181 .242 MEGA-METRO CENTERS .248 Population .00 .00 .313 .784 .443 Density .12 .28 .258 .424 .677 Growth Rate .75 18.01 .010 .042 .967 Median Income .03 .08 .096 .366 .719 Home Ownership 144.98 95.08 .513 1.525 .144 Children -720.45 275.36 -1.135 -2.616 .017 Elderly -346.92 348.72 -.289 -.995 .332 Black 100.15 44.76 .800 2.238 .037 Hispanic 64.36 43.49 .464 1.480 .155 Percentage Intergovernmental -4,349.58 5,514.51 -.265 -.789 .440 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Tables 5.30 through 5.32 set out the findings for regressions on total debt for cities based on form of government, metro status, and principal city status. As with the NLC typology groups, the regressions for these categories all show low adjusted R 2 values and have relatively few statistically significant variables. The variable population is the most commonly occurring in terms of statistical significance being shown significant in all regressions except the one for independent cities. 248 Table 5.30. Total Debt Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .076 Population .00 .00 .199 4.956 .000 Density -.08 .03 -.136 -2.549 .011 Growth Rate .63 .62 .042 1.007 .314 Median Income .00 .01 .022 .380 .704 Home Ownership -12.26 8.75 -.095 -1.401 .162 Children -24.57 21.88 -.074 -1.123 .262 Elderly -10.93 18.49 -.032 -.591 .555 Black 9.92 5.54 .083 1.791 .074 Hispanic 1.47 5.66 .017 .259 .795 Percentage Intergovernmental -1,204.32 607.563 -.082 -1.982 .048 NON-MANAGER a .173 Population .00 .00 .356 6.554 .000 Density -.05 .03 -.154 -2.055 .041 Growth Rate .98 2.43 .025 .403 .687 Median Income .01 .01 .044 .597 .551 Home Ownership -11.78 11.15 -.088 -1.056 .292 Children -111.95 36.04 -.226 -3.107 .002 Elderly -54.38 35.21 -.102 -1.545 .123 Black 14.98 5.97 .166 2.511 .013 Hispanic 7.99 8.71 .067 .917 .360 Percentage Intergovernmental 305.35 672.50 .028 .454 .650 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. 249 Table 5.31. Total Debt Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .134 Population .00 .00 .285 5.455 .000 Density -.01 .04 -.013 -.207 .836 Growth Rate .40 1.75 .011 .229 .819 Median Income .03 .01 .142 2.564 .011 Home Ownership 5.80 12.58 .033 .461 .645 Children -84.14 26.32 -.209 -3.197 .001 Elderly -3.79 26.77 -.008 -.142 .887 Black 23.46 5.53 .228 4.244 .000 Hispanic 10.82 6.83 .100 1.584 .114 Percentage Intergovernmental -15.68 594.80 -.001 -.026 .979 SUBURB .080 Population .00 .00 .119 2.360 .019 Density -.03 .02 -.140 -1.898 .058 Growth Rate .95 .47 .105 2.009 .045 Median Income .01 .01 .085 1.328 .185 Home Ownership -12.95 6.46 -.158 -2.006 .046 Children -41.56 24.55 -.160 -1.693 .091 Elderly -33.05 18.93 -.138 -1.747 .082 Black 1.77 4.69 .021 .377 .706 Hispanic 4.45 4.99 .076 .891 .374 Percentage Intergovernmental -1,283.37 558.35 -.127 -2.299 .022 INDEPENDENT -.022 Population .03 .04 .089 .642 .524 Density -.38 .43 -.120 -.895 .374 Growth Rate 2.19 8.36 .037 .262 .794 Median Income -.05 .07 -.122 -.724 .472 Home Ownership 107.69 69.44 .383 1.551 .126 Children -35.62 117.67 -.063 -.303 .763 Elderly -250.59 136.75 -.364 -1.833 .072 Black -11.89 32.46 -.065 -.366 .715 Hispanic 4.69 32.78 -.023 -.143 .887 Percentage Intergovernmental -3,679.29 2,688.75 -.172 -1.368 .176 250 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.32. Total Debt Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .088 Population .00 .00 .248 5.290 .000 Density -.01 .03 -.018 -.308 .758 Growth Rate .91 1.54 .029 .590 .555 Median Income .00 .01 .008 .129 .897 Home Ownership 8.01 12.85 .044 .624 .533 Children -70.55 29.92 -.157 -2.358 .019 Elderly -50.06 28.04 -.101 -1.785 .075 Black 23.38 6.58 .192 3.550 .000 Hispanic 6.13 7.60 .051 .806 .421 Percentage Intergovernmental -799.06 737.43 -.055 -1.084 .279 NON-PRINCIPAL .053 Population .00 .00 .114 2.187 .029 Density -.06 .02 -.234 -3.502 .001 Growth Rate .69 .48 .074 1.414 .158 Median Income .01 .01 .113 1.730 .084 Home Ownership -18.77 6.13 -.234 -3.062 .002 Children -20.69 18.92 -.084 -1.093 .275 Elderly -5.74 15.93 -.024 -.360 .719 Black -2.53 3.98 -.034 -.635 .525 Hispanic 2.90 4.58 .050 .632 .528 Percentage Intergovernmental -281.12 422.82 -.034 -.665 .506 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. 251 Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Full Faith and Credit Debt for Different City Types Table 5.33 contains the findings of regressions on full faith and credit debt for the categories of cities in the NLC typology. Metro centers, Centervilles, and Mega-metro centers have the largest adjusted R 2 values between 20 ? 29%. All the categories except Spread cities and Centervilles show only one (but not the same) significant variable. Table 5.33. Full Faith and Credit Debt Regressed on Demographic Variables for City Types Within the National Leagues of Cities? Typology Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 SPREAD .091 Population .00 .00 .044 .773 .440 Density -.02 .02 -.058 -.982 .327 Growth Rate -1.07 1.35 -.053 -.790 .430 Median Income .02 .01 .166 2.203 .028 Home Ownership -13.11 5.15 -.215 -2.545 .011 Children -9.63 12.25 -.058 -.786 .432 Elderly 6.64 11.79 .038 .563 .574 Black -.82 2.46 -.022 -.333 .739 Hispanic 4.14 4.75 .059 .871 .384 Percentage Intergovernmental 1,148.17 283.87 .256 4.045 .000 GOLD COAST .031 Population -.00 .00 -.033 -.361 .719 Density -.01 .04 -.029 -.299 .765 Growth Rate .64 2.33 .024 .273 .785 Median Income .01 .01 .101 1.057 .292 Home Ownership -27.10 9.45 -.362 -2.867 .005 252 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Children 36.06 38.72 .134 .931 .353 Elderly 17.78 23.33 .099 .762 .447 Black -4.33 12.10 -.032 -.358 .721 Hispanic 8.06 10.51 .069 .767 .445 Percentage Intergovernmental -373.96 938.65 -.035 -.398 .691 METRO CENTERS .288 Population .00 .00 -.017 -.153 .879 Density -.04 .06 -.101 -.606 .547 Growth Rate -4.54 3.50 -.190 -1.298 .199 Median Income .02 .03 .137 .728 .469 Home Ownership 9.25 19.60 .090 .472 .638 Children -36.44 48.49 -.105 -.752 .455 Elderly -47.39 69.84 -.104 -.679 .500 Black 9.69 7.36 .181 1.317 .192 Hispanic -1.54 10.61 -.022 -.145 .885 Percentage Intergovernmental 3,051.70 636.63 .587 4.794 .000 MELTINGPOTS .036 Population -.00 .00 -.138 -1.272 .207 Density -.02 .01 -.194 -1.144 .256 Growth Rate .54 1.92 .035 .283 .778 Median Income -.01 .01 -.145 -.787 .433 Home Ownership 5.40 8.66 .119 .624 .534 Children -71.85 25.45 -.588 -2.823 .006 Elderly -51.34 33.87 -.236 -1.516 .133 Black 1.08 5.70 .027 .190 .850 Hispanic 7.01 5.48 .251 1.280 .204 Percentage Intergovernmental 756.67 568.79 .178 1.330 .187 BOOMTOWNS .135 Population .00 .00 .017 .117 .907 Density -.15 .08 -.263 -1.848 .071 Growth Rate -.39 .56 -.106 -.692 .493 Median Income .03 .01 .623 2.916 .005 Home Ownership -11.91 9.70 -.212 -1.228 .226 Children -21.46 33.01 -.131 -.650 .519 Elderly 22.70 31.91 .183 .711 .480 Black 8.83 8.62 .133 1.024 .311 253 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 Hispanic 14.42 9.21 .251 1.566 .124 Percentage Intergovernmental 425.19 802.26 .073 .530 .599 CENTERVILLES .256 Population .01 .01 .182 1.218 .230 Density -.03 .07 -.057 -.397 .693 Growth Rate -.06 3.32 -.003 -.018 .985 Median Income .04 .01 .456 2.551 .014 Home Ownership -31.94 13.88 -.539 -2.301 .026 Children -10.65 25.58 -.084 -.416 .679 Elderly 29.00 31.71 .199 .915 .365 Black -5.04 6.29 -.145 -.801 .427 Hispanic -3.53 6.11 -.101 -.578 .566 Percentage Intergovernmental 2,128.23 679.43 .458 3.132 .003 MEGA-METRO CENTERS .199 Population .00 .00 .406 .984 .337 Density .00 .15 .011 .018 .986 Growth Rate 8.78 9.55 .225 .920 .369 Median Income .04 .05 .264 .977 .341 Home Ownership 14.56 50.40 .100 .289 .776 Children -197.39 145.95 -.605 -1.352 .192 Elderly -10.95 184.83 -.018 -.059 .953 Black 55.78 23.72 .867 2.351 .030 Hispanic 16.57 23.05 .233 .719 .481 Percentage Intergovernmental -283.12 2,922.91 -.034 -.097 .924 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Tables 5.34 through 5.36 show the results of regressions on full faith and credit debt for cities grouped based on their form of government, metro status, and principal 254 city status. These findings also show the models to have low predictive value for this fiscal output measure. Median income was the only variable shown as statistically significant in all the categories in these last three classification schemes. In the regressions of NLC typology cities, it was only significant in Spread cities, Boomtown, and Centervilles. Table 5.34. Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Form of Government Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 MANAGER .066 Population 6.68E-005 .00 .012 .273 .785 Density -.03 .02 -.134 -2.194 .029 Growth Rate .39 .43 .043 .897 .370 Median Income .01 .00 .264 4.100 .000 Home Ownership -15.42 4.49 -.271 -3.438 .001 Children -10.65 11.20 -.070 -.951 .342 Elderly .26 9.36 .002 .028 .978 Black .47 2.81 .009 .169 .866 Hispanic .48 2.92 .012 .165 .869 Percentage Intergovernmental 1,358.57 307.25 .207 4.422 .000 NON-MANAGER a .207 Population .00 .00 .276 4.901 .000 Density -.02 .01 -.131 -1.692 .092 Growth Rate -.18 1.23 -.009 -.147 .884 Median Income .02 .01 .228 3.007 .003 Home Ownership -11.83 5.48 -.186 -2.160 .032 Children -30.97 18.52 -.127 -1.672 .096 Elderly .52 18.05 .002 .029 .977 Black 8.44 3.01 .191 2.803 .005 Hispanic 3.45 4.26 .062 .810 .418 Percentage Intergovernmental 1,450.89 336.10 .275 4.317 .000 255 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. a Mayor and commission cities were combined as non-manager cities. Table 5.35. Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Metro Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 CENTRAL .236 Population .00 .00 .171 3.245 .001 Density .01 .02 .041 .640 .523 Growth Rate .28 .92 .016 .307 .759 Median Income .03 .01 .263 4.576 .000 Home Ownership -2.53 6.15 -.030 -.411 .681 Children -29.24 12.86 -.149 -2.274 .024 Elderly 9.72 13.24 .042 .734 .463 Black 8.12 2.69 .160 3.016 .003 Hispanic 2.01 3.42 .037 .589 .556 Percentage Intergovernmental 1,977.71 291.43 .352 6.786 .000 SUBURB .036 Population .00 .00 -.026 -.449 .654 Density -.02 .01 -.106 -1.224 .222 Growth Rate .19 .47 .025 .407 .684 Median Income .01 .00 .234 3.183 .002 Home Ownership -13.98 4.40 -.295 -3.175 .002 Children -10.63 17.92 -.065 -.593 .553 Elderly -5.49 13.37 -.038 -.411 .681 Black 1.88 3.29 .037 .573 .567 Hispanic 1.20 3.50 .035 .342 .733 Percentage Intergovernmental 259.48 372.92 .044 .696 .487 256 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 INDEPENDENT .178 Population .01 .01 .131 .949 .347 Density .10 .09 .157 1.152 .255 Growth Rate .93 1.50 .088 .619 .539 Median Income .04 .01 .505 2.863 .006 Home Ownership -5.03 12.64 -.089 -.398 .692 Children -60.13 22.36 -.485 -2.690 .010 Elderly 4.88 26.33 .037 .185 .854 Black 4.36 6.01 .128 .724 .472 Hispanic 8.08 6.00 .211 1.347 .184 Percentage Intergovernmental 519.21 655.01 .111 .793 .432 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.36. Full Faith and Credit Debt Regressed on Demographic Variables for City Types Based on Principal City Status Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 PRINCIPAL .191 Population .00 .00 .218 4.538 .000 Density -.01 .01 -.034 -.557 .578 Growth Rate .69 .66 .053 1.045 .297 Median Income .01 .00 .145 2.396 .017 Home Ownership -2.19 5.38 -.030 -.408 .684 Children -33.96 12.66 -.180 -2.684 .008 Elderly -13.94 12.04 -.068 -1.158 .248 Black 8.46 2.69 .173 3.151 .002 Hispanic 2.06 3.21 .041 .640 .522 Percentage Intergovernmental 1,870.94 301.55 .323 6.204 .000 257 Unstandardized Coefficients Standardized Coefficients Variable B Std. error Beta t score Sig. Adj?d R 2 NON-PRINCIPAL Population .00 .00 -.018 -.314 .754 .074 Density -.03 .01 -.156 -2.116 .035 Growth Rate .27 .54 .030 .500 .617 Median Income .01 .00 .309 4.409 .000 Home Ownership -18.84 4.37 -.369 -4.315 .000 Children -4.13 13.75 -.025 -.300 .764 Elderly 8.99 11.39 .057 .790 .430 Black -1.33 2.94 -.027 -.454 .650 Hispanic 1.19 3.34 .032 .356 .722 Percentage Intergovernmental 827.96 308.75 .152 2.682 .008 Note: Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients are expressed in per capita dollar amounts. Shaded rows show statistical significance. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Data Source for demographic data: U.S. Bureau of the Census, 2000 Decennial Census, Washington, DC: U.S. Department of Commerce, 2000. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Summary of the Analysis of Demographic Variables The multiple-regression testing discussed shows that the categories within each classification scheme differ in terms of the extent to which the demographic variables are able to explain variation in the dependent variables. It appears, however, that the NLC typology categories tend to have higher adjusted R 2 values for most of the models examined, which supports the hypothesis that NLC categorizations are better for studying the financial behavior of cities. As noted previously, the NLC typology also has more categories with which to segregate cities. 258 To the extent that the adjusted R 2 values can be averaged for comparison purposes, the NLC typology has the highest or next to the highest average adjusted R 2 value on each of the fiscal output measures, with the exception of police spending where the form of government classification has an average adjusted R 2 value of .266, principal city status has an average of .249, and the NLC typology is third with .242. The typology classification is second in four financial areas. The average adjusted R 2 values for common function expenditure are .160 for form of government cities and .159 for the NLC typology. The principal city classification has an average adjusted R 2 value of .635 and the NLC typology?s is .611 for intergovernmental revenue. The form of government classification has an average adjusted R 2 value for total debt of .125 compared to NLC?s average of .106. In terms of full faith and credit debt, the metro status categories have an average adjusted R 2 value of .150, whereas the NLC typology?s is .148. In contrast, the NLC classification scheme is the highest in four areas: total expenditure (adjusted R 2 value of .272 to form of government?s .235), total revenue (adjusted R 2 value of .285 to metro status? .215), property tax (adjusted R 2 value of .325 to metro status? .317), and sales tax (adjusted R 2 value of .252 to form of government?s .194). These rankings show that the NLC typology has the most predictive capacity in terms of the fiscal variables analyzed. No other classification method has over two financial measures for which it has the highest average adjusted R 2 value. These findings support the hypothesis that the NLC typology provides a better means of comparing cities in terms of financial outputs. 259 Impact of Revenue Sources on Expenditure and Debt Outputs In addition to the effect of the demographic variables on the fiscal outputs of cities, there is also an impact caused by revenue sources on the expenditure and debt outputs. These are shown in tables that set out the results of multiple-regression analysis on the expenditure and debt variables using the three revenue sources (property and sales tax and intergovernmental revenues) as explanatory variables. These tables show which of the variables are statistically significant in each regression and the coefficient b values for each of the explanatory variables, along with the adjusted R 2 values for each regression. As noted before, the adjusted R 2 value shows the proportion of change in the dependent variable that can be accounted for by the explanatory variables included in the regression model. Because the variables included in these regression models are all expressed in terms of per capita dollar amounts, the actual amount of change they predict in the dependent variable can be compared using the coefficient b values rather than Betas. To test the correlation between the independent variables, each of the variables was used as the dependent variable and regressed against the others in separate tests. After performing these procedures, no R 2 value greater than .342 was found in any of the individual regressions and it is concluded that multicollinearity is not a problem. Table 5.37 shows the results of regression analysis on total expenditure for the NLC typology cities. The amount of variation in total spending that is explained by the revenue sources is shown by the adjusted R 2 values, which are highest for Mega-metro centers, Meltingpot cities, and Metro centers. The remaining city types also have adjusted R 2 values that are higher than typically found in the regressions utilizing 260 demographic variables. All of the revenue variables are statistically significant for all city types, with the exception of property tax revenue which is not significant for Centervilles and Mega-metro centers. Table 5.37. Total Expenditure Regressed on Revenue Sources for City Types Within the National Leagues of Cities? Typology Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Property tax .70** 1.41*** 1.06*** 2.13*** 1.26*** -.11 1.05 (.23) (.21) (.27) (.26) (.32) (.87) (.56) Sales tax 1.10*** 2.20*** 1.66*** 1.19** 2.02*** 1.32* 2.32*** (.25) (.27) (.44) (.45) (.27) (.50) (.49) Intergovern- mental 1.37*** .89* .95*** .94*** 2.24*** 2.12*** 1.50*** (.16) (.42) (.12) (.14) (.32) (.51) (.23) Adjusted R 2 .386 .463 .715 .728 .683 .426 .901 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Mega-metro centers have the largest adjusted R 2 value and total spending for these cities is impacted the most by sales tax revenue. The influence of sales tax revenue is more than twice as much as property tax and 65% more than intergovernmental revenue. Sales tax revenue also has the greatest influence in Gold coast cities and Metro centers. Property tax revenue has the most effect on spending only in Meltingpot cities. 261 The three remaining city types have their spending influenced most by intergovernmental revenue. Table 5.38 shows the effects on total spending for manager and non-manager cities. The model better explains spending in non-manager cities. Sales tax revenue has the greatest effect in both type cities, but all variables are significant for both. Table 5.38. Total Expenditure Regressed on Revenue Sources for City Types Based on Form of Government Variable Manager Non-Manager Property tax 1.34*** 1.08*** (.18) (.14) Sales tax 1.67*** 2.14*** (.17) (.21) Intergovernmental 1.42*** 1.30*** (.12) (.08) Adjusted R 2 .483 .737 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The regression findings for cities based on metro status are set out in Table 5.39. All the revenue variables are statistically significant in all city types other than property tax revenue in independent cities. Total spending is more fully explained by the model for central cities, where the adjusted R 2 value is more than twice as large as in the independent cities. 262 Sales tax revenue has the greatest impact in central cities and suburbs, while intergovernmental revenue has a somewhat larger effect in independent cities. There is not as much variation in the amount of the changes caused by the different revenue sources as is shown in the regressions for the NLC typology categories. Table 5.39. Total Expenditure Regressed on Revenue Sources for City Types Based on Metro Status Variable Central Suburb Independent Property tax 1.25*** 1.47*** .34 (.16) (.14) (.60) Sales tax 1.76*** 1.87*** 1.78** (.18) (.18) (.65) Intergovernmental 1.26*** 1.10*** 2.07** (.08) (.13) (.64) Adjusted R 2 .670 .542 .277 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.40 shows all the revenue variables are statistically significant in regressions based on whether or not a city is a principal city, but non-principal cities have a somewhat larger adjusted R 2 value. The sales tax variable has the greatest impact on spending in both type cities. However, both property tax and intergovernmental revenues predict changes nearly as great as those caused by sales tax revenue. For example, in non-principal cities the impact of a one dollar change in the amount of property tax, sales 263 tax, and intergovernmental revenues result in $1.17, $1.53, and $1.24 increases in the amount of total spending. Table 5.40. Total Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status Variable Principal Non-Principal Property tax 1.24*** 1.17*** (.17) (.12) Sales tax 1.83*** 1.53*** (.20) (.16) Intergovernmental 1.32*** 1.24*** (.09) (.09) Adjusted R 2 .587 .649 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.41 depicts the change in expenditures on common functions in NLC typology cities resulting from increases in the level of property tax, sales tax, and intergovernmental revenues. Metro centers and Centervilles show no statistically significant impacts, but the other categories of cities all show the greatest change resulting from sales tax revenue. 264 Table 5.41. Common Function Expenditure Regressed on Revenue Sources for City Types Within the National Leagues of Cities? Typology Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Property tax -.04 .22** .16 .50*** .18 .02 .17 (.07) (.07) (.12) (.09) (.17) (.19) (.15) Sales tax .27*** .88*** .09 .86*** .82*** .04 .43** (.07) (.09) (.19) (.16) (.14) (.11) (.13) Intergovern- mental .13** .11 -.00 -.00 .59** .18 .11 (.05) (.14) (.05) (.05) (.17) (.11) (.06) Adjusted R 2 .058 .385 -.003 .375 .404 .062 .607 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.42 shows that the revenue source variables are all statistically significant except for property tax revenue in non-manager cities. The model predicts the amount of change in common function spending equally well in both type cities. Sales tax revenue has the greatest effect in both categories. Table 5.42. Common Function Expenditure Regressed on Revenue Sources for City Types Based on Form of Government Variable Manager Non-Manager Property tax .33*** .07 (.06) (.05) 265 Variable Manager Non-Manager Sales tax .60*** .52*** (.06) (.07) Intergovernmental .08* .10*** (.04) (.03) Adjusted R 2 .244 .248 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.43 shows that the model predicts change in spending best in suburbs, where it is twice as good at explaining variation in common function spending than in central and independent cities. Sales tax revenue has the greatest effect in central cities and suburb, while intergovernmental revenue is slightly stronger in independent cities. Table 5.43. Common Function Expenditure Regressed on Revenue Sources for City Types Based on Metro Status Variable Central Suburb Independent Property tax .16** .31*** -.17 (.05) (.05) (.16) Sales tax .36*** .83*** .42* (.06) (.07) (.17) Intergovernmental .08** .08 .46** (.03) (.05) (.16) Adjusted R 2 .169 .352 .174 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Metro Status: International City/County Management 266 Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Similar results are found for principal and non-principal cities (See Table 5.44). Both have about equal adjusted R 2 values, with sales tax having the greatest impact. Table 5.44. Common Function Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status Variable Principal Non-Principal Property tax .12* .29*** (.05) (.05) Sales tax .44*** .62*** (.06) (.07) Intergovernmental .13*** .03 (.03) (.04) Adjusted R 2 .196 .258 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.45 reports the results of regressions on police expenditure for NLC typology cities. Metro centers and Centervilles show no significant revenue variables and the model is much less successful at explaining change in spending in most of the other categories than in the other expenditure regressions. However, sales tax revenue again has the most impact in every type city with statistically significant results. 267 Table 5.45. Police Expenditure Regressed on Revenue Sources for City Types Within the National Leagues of Cities? Typology Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Property tax .01 .07** .02 .12*** -.02 -.05 .03 (.02) (.02) (.03) (.03) (.08) (.05) (.04) Sales tax .05* .29*** .05 .24*** .18** .04 .11** (.02) (.03) (.05) (.05) (.06) (.03) (.03) Intergovern- mental -.00 .04 .02 .01 .10 .06* .08*** (.01) (.05) (.02) (.02) (.07) (.03) (.02) Adjusted R 2 .006 .379 .026 .281 .104 .086 .800 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. The regressions results in Table 5.46 are about equally successful in explaining variation in police expenditure for cities based on their form of government. Sales tax revenue has the biggest impact in both manager and non-manager cities. Property tax revenue is not significant for manager cities and intergovernmental revenue does not show significance for the non-manager category. 268 Table 5.46. Police Expenditure Regressed on Revenue Sources for City Types Based on Form of Government Variable Manager Non-Manager Property tax .11*** .01 (.02) (.02) Sales tax .18*** .13*** (.02) (.02) Intergovernmental -.01 .05*** (.01) (.01) Adjusted R 2 .219 .263 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.47 sets out the results for police expenditure for cities according to their metro status. The model explains 21% and 26% of the variation in spending for central cities and suburbs respectively, but only 9% for independent cities. The only statistically significant variable for independent cities is intergovernmental revenue, while sales tax revenue is the main predictor for both the others. Table 5.47. Police Expenditure Regressed on Revenue Sources for City Types Based on Metro Status Variable Central Suburb Independent Property tax .05** .05** -.02 (.02) (.02) (.03) 269 Variable Central Suburb Independent Sales tax .12*** .25*** .04 (.02) (.02) (.03) Intergovernmental .03*** .04* .08* (.01) (.02) (.03) Adjusted R 2 .211 .264 .092 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.48 shows sales tax revenue is the major explanation for increases in police expenditure in cities regardless of whether or not they are principal cities. The model is about equally effective in both type cities. Table 5.48. Police Expenditure Regressed on Revenue Sources for City Types Based on Principal City Status Variable Principal Non-Principal Property tax .04** .06** (.01) (.02) Sales tax .12*** .22*** (.02) (.02) Intergovernmental .05*** .01 (.01) (.01) Adjusted R 2 .253 .196 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan 270 Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.49 shows the results for regressions on total debt in NLC typology cities. Metro centers are the only cities that do not show any significant findings. Property tax revenue is the main explanation for increases in total debt in Meltingpot cities, whereas intergovernmental revenue is the indicator for Centervilles. In all other categories, sales tax revenue has the most influence. Table 5.49. Total Debt Regressed on Revenue Sources for City Types Within the National Leagues of Cities? Typology Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Property tax -.08 .97* 1.20 1.37* 2.20* .33 2.06 (.56) (.47) (.81) (.57) (.91) (.91) (1.49) Sales tax 1.51* 2.33*** 2.60 1.13 2.99*** -.05 4.31** (.60) (.59) (1.30) (1.00) (.76) (.53) (1.30) Intergovern- mental 1.07** -.16 -.06 .25 .24 1.08* -.23 (.38) (.93) (.35) (.31) (.89) (.53) (.62) Adjusted R 2 .041 .092 .047 .095 .195 .154 .372 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 271 Sales tax revenue is the biggest influencer of total debt in both manager and non- manager cities (Table 5.50). The adjusted R 2 value is twice as large for non-manager cities as it is for manager cities. Table 5.50. Total Debt Regressed on Revenue Sources for City Types Based on Form of Government Variable Manager Non-Manager Property tax 1.36** .52 (.42) (.34) Sales tax 1.90*** 3.21*** (.39) (.49) Intergovernmental .83** .72*** (.28) (.19) Adjusted R 2 .106 .221 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.51 includes the results of regressions on total debt based on the metro status of cites. Again, the adjusted R 2 values are lower than for other regression involving metro status. Sales tax revenue has the biggest impact on total debt in both central cities and suburbs. In independent cities, intergovernmental revenue is the only significant variable, and it predicts increases in debt. 272 Table 5.51. Total Debt Regressed on Revenue Sources for City Types Based on Metro Status Variable Central Suburb Independent Property tax 1.08** 1.34*** -2.55 (.38) (.28) (1.70) Sales tax 2.17*** 2.33*** 3.18 (.43) (.35) (1.83) Intergovernmental .52** .35 4.18* (.19) (.25) (1.79) Adjusted R 2 .131 .175 .098 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.52 shows the results of regressions on total debt for principal and non- principal cities. Sales tax revenue has the biggest influence on debt in both type cities. Property tax revenue is not shown to be statistically significant for principal cities, and intergovernmental revenue is not significant for non-principal cities. The model explains more of the variation in debt in non-principal cities. Table 5.52. Total Debt Regressed on Revenue Sources for City Types Based on Principal City Status Variable Principal Non-Principal Property tax .48 1.47*** (.42) (.25) 273 Variable Principal Non-Principal Sales tax 2.36*** 1.78*** (.47) (.32) Intergovernmental .91*** .19 (.22) (.18) Adjusted R 2 .113 .186 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.53 reports the results of regressions on full faith and credit debt in cities grouped according to the NLC typology. In these regressions, sales tax revenue is only statistically significant in Gold coast cities, Boomtowns, and Mega-metro centers. Sales tax results in the largest change in full faith and credit debt in Gold coast cities and Mega-metro centers. All the other city types are influenced the most by the property tax variable. Intergovernmental revenue is only significant in Spread cities and Metro centers. Table 5.53. Full Faith and Credit Debt Regressed on Revenue Sources for City Types Within the National Leagues of Cities? Typology Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Property tax .61*** .59* 1.00** 1.02** 1.41** 1.48* 1.08 (.17) (.29) (.32) (.33) (.45) (.63) (.66) 274 Revenue Variable Spread cities Gold coast cities Metro centers Melting- pot cities Boom- towns Center- villes Mega- metro centers Sales tax .15 1.62*** .19 -.38 .93* -.49 1.52* (.19) (.38) (.52) (.60) (.39) (.34) (.57) Intergovern- mental .37** -.13 .32* .19 .01 .21 .36 (.11) (.61) (.14) (.17) (.45) (.36) (.27) Adjusted R 2 .209 .126 .338 .249 .196 .325 .562 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.54 has results from regressions on full faith and credit debt for manager and non-manager cities. It shows property tax revenue has the largest effect in manager cities, being twice as influential as the next closest (sales tax) in impacting the debt. The variable sales tax is strongest in non-manager cities, resulting in an increase of $0.88 in debt for every dollar change in sales tax revenue. All the revenue variables are statistically significant in each city type, but the model is more predictive in non-manager cities. Table 5.54. Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Form of Government Variable Manager Non-Manager Property tax 1.41*** .34* (.19) (.14) 275 Variable Manager Non-Manager Sales tax .74*** .88*** (.18) (.21) Intergovernmental .25* .54*** (.13) (.08) Adjusted R 2 .248 .338 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Form of Government: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Table 5.55 shows results for regressions on full faith and credit debt for cities based on their metro status. The model best explains debt amounts for central cities, where property tax revenue is the largest indicator. In suburbs, sales tax revenue results in more change in debt level. None of the revenue variables are statistically significant in the regression for independent cities. Table 5.55. Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Metro Status Variable Central Suburb Independent Property tax .72*** .82*** .37 (.16) (.18) (.32) Sales tax .70*** 1.14*** .20 (.18) (.24) (.36) Intergovernmental .50*** .28 .46 (.08) (.16) (.35) Adjusted R 2 .334 .161 .080 276 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. As seen in Table 5.56, the regression model predicted full faith and credit debt better for principal cities, where all of the revenue variables were significant. The variable intergovernmental revenue was not significant for non-principal cities. Sales tax revenue had the largest impact in both categories. Table 5.56. Full Faith and Credit Debt Regressed on Revenue Sources for City Types Based on Principal City Status Variable Principal Non-Principal Property tax .52*** 1.00*** (.15) (.17) Sales tax .67*** 1.01*** (.17) (.23) Intergovernmental .58*** .24 (.08) (.12) Adjusted R 2 .305 .223 Note. Level of significance: *p <.05; **p <.01; ***p <.001. Variables tested are property tax, sales tax, and intergovernmental revenue. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. 277 Summary of the Analysis of Revenue Source Variables The above tables show there is much variation in the impact of revenue sources on expenditure and debt outputs for the various city types. However, it is clear that the revenue source variable that has the most impact on spending and debt is sales tax revenue. Sales tax revenue is the biggest predictor of expenditure and debt in almost all categories of cities. Property tax revenue is the main influence in the following situations: Meltingpot cities when regressions are performed on total expenditure and total debt and Metro centers when regressions are performed on common function expenditure; for the categories of Spread cities, Metro centers, Meltingpot cities, Boomtowns, Centervilles, manager cities, and central cities in regressions on full faith and credit debt. Intergovernmental revenue has the greatest impact only in the following situations: Independent cities in regressions on total, common function, and police expenditures and total debt; for the categories of Spread cities, Boomtowns, and Centervilles when regressions are done on total expenditure; and for Centervilles in regressions on police expenditure and total debt. The regression analysis of revenue sources also supports the enhanced utility of the NLC typology scheme for classifying cities for purposes of financial research. The NLC typology classifications have the highest or next to highest average adjusted R 2 values in regressions on most of the fiscal outputs. The typology has the highest average explanatory score for common function expenditure (adjusted R 2 value of .271 to form of government?s .246). It has the second highest average for total expenditure (principal city classification has an average adjusted R 2 value of .618 compared to NLC?s average 278 of .615), police expenditure (form of government has an average adjusted R 2 value of .241 compared to NLC?s average of .240), and full faith and credit debt (form of government has an average adjusted R 2 value of .293 compared to NLC?s average of .286). It is third in the regression on total debt, where it has an average score of .142 compared to .164 for form of government and .150 for principal city. Comparison of the Classification Schemes? Utility for Financial Research In addition to the analysis already discussed, multiple-regression analysis is performed to measure the various classification schemes in terms of their relative usefulness in examining fiscal outputs of cities. One way to do this is to compare separate regression models using the different classification methods and see which is best at explaining the fiscal output measures. To have a more accurate comparison of the fiscal output of the different cities, common function expenditure is used as the dependent variable to control for variation in functional inclusiveness among the cities. Regressions are conducted on common function expenditure using the demographic variables discussed earlier in this chapter and variables for different categories in the various classification schemes. The results are presented in Table 5.57. 279 Table 5.57. Results of Multiple-Regression Analysis on Common Function Expenditures of Different City Classification Schemes Variable Model #1 Model #2 Model #3 Model #4 Model #5 Model #6 Population .00*** 5.86E-005* .00*** .00*** .00*** 4.46E-005 (.00) (.00) (.00) (.00) (.00) (.00) Density -.01*** -.01** -.01*** -.00 -.01** -.00 (.00) (.00) (.00) (.00) (.00) (.00) Growth Rate .06 -.04 .07 .10 .05 -.03 (.09) (.10) (.09) (.09) (.09) (.10) Median Income .00*** .00*** .00*** .01*** .00*** .00*** (.00) (.00) (.00) (.00) (.00) (.00) Home Ownership -6.49*** -6.54*** -6.45*** -5.16*** -5.48*** -5.36*** (.99) (.98) (.99) (.99) (.99) (.99) Children -6.26* -5.54*** -6.51* -6.38* -5.55* -5.36* (2.66) (2.66) (2.68) (2.61) (2.62) (2.63) Elderly 6.88** 8.10* 6.82** 6.49** 6.49** 7.58** (2.35) (2.38) (2.35) (2.30) (2.31) (2.35) Black 1.96** 1.40* 1.96** 2.13*** 1.84** 1.58** (.58) (.59) (.58) (.57) (.57) (.58) Hispanic -.70 -.03 -.56 -.42 -.72 .02 (.67) (.76) (.69) (.66) (.66) (.76) Percentage Intergovernmental -119.65 -163.95** -130.17* -159.57* -107.93 -172.88** (63.14) (62.61) (64.20) (62.27) (62.16) (63.08) Spread --- -129.06*** --- --- --- -103.16** --- (30.38) --- --- --- (30.44) Gold coast --- -121.93** --- --- --- -71.89 --- (38.84) --- --- --- (39.40) Meltingpot --- -200.00*** --- --- --- -142.23*** --- (39.76) --- --- --- (40.64) Boomtown --- -52.15 --- --- --- .34 --- (46.54) --- --- --- (46.84) Centerville --- -155.21*** --- --- --- -145.64** --- (40.25) --- --- --- (50.22) Mega-metro center --- 125.46* --- --- --- 116.44* --- (56.73) --- --- --- (56.11) Manager --- --- -16.27 --- --- -10.29 --- --- (17.86) --- --- (17.36) 280 Variable Model #1 Model #2 Model #3 Model #4 Model #5 Model #6 Central --- --- --- 129.40*** --- 85.36*** --- --- --- (20.81) --- (24.34) Independent --- --- --- 114.18** --- 134.59** --- --- --- (33.44) --- (41.65) Principal city --- --- --- --- 93.52*** 43.78* --- --- --- --- (16.70) (20.69) Adjusted R 2 .157 .197 .157 .190 .184 .223 Note: Level of significance: *p <.05; **p <.01; ***p <.00. Variables tested include: population, density, growth, median income, homeowner, children, elderly, Black, Hispanic, and percentage intergovernmental revenues. Values of unstandardized beta coefficients and standard errors are expressed in per capita dollar amounts. Data Source for Typology: C. McFarland, personal communication, June 14, 2006. C. McFarland coauthored the NLC report, From Meltingpot Cities to Boomtowns: Redefining How We Talk About America?s Cities, under the name of C. Brennan. Data Source for Form of Government and Metro Status: International City/County Management Association (ICMA), Municipal Form of Government Survey 2001. Washington, DC: ICMA, 2002 and International City/County Management Association, Municipal Yearbook 2003. Washington, DC: ICMA, 2003. Data Source for Principal City definition based on material in Office of Management and Budget, Standards for Defining Metropolitan and Micropolitan Statistical Areas, 65 Fed. Reg. 82,238 (December 27, 2000). Data source: U.S. Bureau of the Census, Metropolitan and Micropolitan Statistical Areas and Components, Washington, DC: U.S. Department of Commerce, 2004. Fiscal Output data are from the U.S. Bureau of the Census (2002), Census of Governments, spreadsheet of financial data. Model #1 utilizes only the demographic variables and is found to explain 15.7% of the cities? variation in spending on common functions. Model #2 includes the demographic variables and dummy variables for the NLC typology categories (using Metro centers as the reference category) and is shown to account for 19.7% of variation in spending. Model #3 is a regression of the demographic variables and a dummy variable for the manager form of government. The model explains 15.7% of the variation in spending, no better than the demographic variables alone. Model #4 uses dummy variables for central and independent cities (with suburbs as the reference category) and 281 results in explanation for 19.0% of the variation in spending. Model #5 uses the demographic variables, along with a dummy variable representing principal cities, and results in an 18.4% explanation of spending. Finally, Model #6 includes all of the independent variables from the other regression models and the adjusted R 2 value is .223. These regression results show that use of the NLC typology classification provides a better means of categorizing cities to explain common function expenditure levels. This finding, along with the greater specificity offered by the typology?s seven categories, further supports the hypothesis that it is a better method of classifying cities than the others analyzed. The following chapter concludes the dissertation with a summary of the hypothesis testing conducted, along with discussion of strengths and weaknesses of the study. It also includes suggestions for additional research expanding upon the topics covered in the dissertation. 282 CHAPTER 6 CONCLUSION In evaluating the National League of Cities? (NLC) typology and comparing it to other classifications, the most obvious distinction is that it contains more categories (seven with the inclusion of Mega-metro centers) into which cities are grouped than the other classification schemes analyzed. Thus, provided the NLC categories offer meaningful distinctions among the city types, they may result in a better understanding of municipal financial behavior. This chapter concludes the dissertation by summarizing the findings of the study and addressing related issues. The study initially looks at the regional and demographic makeup of the different city types within the NLC typology to gain a better understanding of the characteristics of the cities within each category. The differences between the NLC typology categories are also examined by comparing the mean values for various fiscal outputs of the city types. This terminus a quo indicates there are indeed differences between the categories of cities within the NLC typology, in terms of both demographic factors that impact city finances and actual fiscal output measures, but it does not address the statistical significance of the apparent variations. To measure statistical differences, seven hypotheses are tested through different techniques. 283 Summary of Hypothesis Testing The seven hypotheses tested in this dissertation relate to the financial behavior and practices of cities. The fiscal measures analyzed relate either to the level of expenditure, revenue, and debt or to their composition (sources of revenue and types of debt). Specifically, the research hypotheses are as follows: 1. There are significant differences between the expenditure, revenue, and debt outputs in different city types. 2. There are significant differences in expenditure levels between the different city types. 3. There are significant differences in revenue levels between the different city types. 4. There are significant differences in revenue sources between the different city types. 5. There are significant differences in debt levels between the different city types. 6. There are significant differences in the type of debt incurred between the different city types. 7. The NLC typology will provide a more statistically significant measure of the financial behaviors of cities than did prior categorizations. The first six of the hypotheses all predict there are significant differences between the city types within the NLC typology in terms of the fiscal outputs. The final hypothesis posits that the NLC typology will provide a more statistically significant measure of fiscal outputs than do other classifications based on form of government, metropolitan (metro) status, and principal city status. Several of the statistical tests performed show 284 support in terms of both these types of hypotheses. However, the hypotheses predicting differences and the one comparing usefulness are addressed separately to emphasize the different inferences that can be drawn from the results. Differences Between the NLC Typology Categories The main tests the study uses for detecting statistically significant differences between the NLC groupings are analysis of variance (ANOVA) and Student?s t-test procedures. Oneway ANOVA testing of differences between the categories within the NLC typology in terms of both the mean values of demographic variables (those noted in prior studies to influence municipal finance) and the measures of fiscal outputs for the cities shows that overall there are indeed statistically significant differences between the different typology categories. The only demographic variable found not to be significant is population. All of the financial measures show statistical significance at the specified level of < .05, confirming hypotheses 1 through 6. The t-test analysis shows that, in terms of demographic variables that influence city finance, 78% of the possible differences analyzed exhibit statistical significance. Among fiscal outputs examined, 57% of the potential differences show statistical significance. All of the city types within the NLC typology have some difference in each of the fiscal outputs that are significant at the level of < .05. Thus, the t-test analysis further confirms hypotheses 1 through 6. Comparison of Classification Schemes Hypothesis #7 postulates that the NLC typology will provide a more statistically significant measure of the financial behaviors of cities when compared to the other classification schemes being analyzed. To test the relative utility of the NLC typology in 285 this regard, several methods of comparison are conducted. Along with the typology categories, the categories within the classification methods based on form of government, metro status, and principal city status are examined in terms of both their demographic characteristics and fiscal outputs. Such analysis shows the NLC typology has the lowest average variation, as measured by coefficients of variation, for all of the demographic variables impacting city finance ? other than percent elderly. Comparison of the fiscal output measures using coefficients of variation shows that, on eight of the fiscal output variables, the NLC typology is the lowest (common function expenditure, sales tax revenue, income tax revenue, total debt, and full faith and credit debt) or the second lowest (police expenditure, total revenue, and percentage intergovernmental revenue) of the classification schemes. On the remaining four fiscal measures, it is in the mid range of the schemes. None of the other classification schemes, however, scores as low on as many variables. This low variation supports the premise (as maintained by hypothesis #7) that the NLC typology is a better classification method for examining fiscal behaviors. As noted previously, oneway ANOVA testing shows that the NLC typology has statistically significant differences on each of the demographic variables, except population. While the metro status classification also has significant differences on all (and the other classification methods have significant differences on most) demographic variables, the strength of the relationships (measured by the eta 2 value) is clearly greater for the NLC typology. More importantly, the NLC typology is the only classification scheme found through ANOVA testing to have statistically significant differences on all fiscal outputs. Again, the effect sizes of these differences in the NLC categories are 286 greater overall than those of the other classification methods. Thus, ANOVA testing confirms hypothesis #7. Student?s t-test analysis further substantiates the hypothesis that the NLC categorizations are best for financial comparisons. In terms of possible differences between categories within each classification scheme on the demographic variables, the NLC typology shows statistical significance on 78% of the potential differences. For fiscal outputs, the average for the NLC categories is 57%. Only the metro status classification has a greater average for demographic factors (85%), but its average on fiscal outputs is lower (only 50%). The principal city classification averages more fiscal output differences (58%), but only about an equal amount (58%) on demographic factors. These findings suggest that the NLC classification is at least as good as the other classification schemes in terms of the overall average number of significant differences exhibited among its categories. Thus, hypothesis #7 is partially supported by this testing procedure. Multiple-regression analysis is one of the most common means of measuring the utility of several variables in predicting variation in a dependent variable. For this reason, separate regressions are performed for each fiscal output to measure the predictive impact of the demographic variables within each of the different city categorization methods. Regressions are also performed on revenue sources to determine the effects these have on fiscal outputs involving expenditure and debt for each category in the different classification schemes. These regressions show the models tend to have higher predictive power, as determined by adjusted R 2 values, when they are performed for the NLC typology categories. This analysis also supports hypothesis #7. 287 To assess the overall effectiveness of different classification schemes in predicting fiscal outputs, regressions are performed testing the influence of the different demographic variables on common function expenditure. Separate regressions, each using a different classification scheme, are performed and compared to the others to measure any variation in the predictive capacity of the models. This analysis shows that the NLC classification model has the greatest predictive ability among the different classification schemes, further confirming that the NLC typology is a better means of categorizing cities, as hypothesized. Strengths and Weaknesses of the Study There are a number of strengths to the study conducted in this dissertation. It is the first such analysis focusing on the usefulness of the NLC typology in conducting local public finance research. As such, it provides empirical support showing the utility of the typology in an area that heretofore was unexamined. The study also provides a detailed examination of recent financial data involving a large sample of US cities, and it considers extant demographic characteristics of the cities. This not only results in a better understanding of the new typology, but it offers fresh insight into fiscal behavior of cities within the previous classifications as well. The study also has several weaknesses that should be noted. First, the breadth of the study exploring the demographic and financial variables associated with the different city types precludes a more concentrated examination, at this time, of important areas concerning fiscal practices of cities. Also, some governmental data fails to differentiate between cities without a revenue source or expenditure category and cities that fail to 288 report such data. This limits inquiry into some policy outputs among cities. Examples include income tax revenue and school expenditure. Suggestions for Further Research While the findings of this study offer initial insights into the usefulness of the NLC typology for municipal finance research, there are a number of additional areas that warrant further exploration. One area involves a closer examination of the effects that regional location may have on the behavior of cities within the different NLC typology categories. Do particular city types behave differently in terms of fiscal outputs according to their region of the county? Are Meltingpot cities in the Northeast the same as those in the West? If not, do factors attributable to time of settlement impact their fiscal behavior? Or do economic conditions impact different city types? Does a particular type city in the Sun Belt have different financial outputs than the same type cities in the Rust Belt? Another area for inquiry involves consideration of how the characteristics associated with other classification schemes impact the typology city types. For example, do particular NLC city types governed by city managers have fiscal outputs that vary from similar NLC city types with mayors? Or does the metro status of Spread cities have greater impact than for Centervilles? Also, restrictions on a city?s ability to fully determine its own outputs can be examined. Differing state laws that limit cities? financial options are likely to influence behaviors within city types. How do restrictions on a city?s ability to impose property or sales taxes influence fiscal behavior among the different typology classifications? How 289 does the distribution of intergovernmental revenue change spending by particular type cities? Are there demographic influences on fiscal outputs that are masked by intervening variables such as the structure of revenue sources? 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Journal of Urban Economics, 19, 316-337. 297 APPENDIX LIST OF CITIES ANALYZED 298 SPREAD CITIES ALABAMA Auburn Bessemer Decatur Dothan Gadsden Huntsville Prichard Tuscaloosa ALASKA Fairbanks ARIZONA Flagstaff Tempe ARKANSAS Fayetteville Ft Smith Jacksonville Jonesboro North Little Rock Pine Bluff West Memphis CALIFORNIA Chico Davis El Cajon Hemet La Mesa Lodi Manteca Monrovia Napa Redding Redlands Rohnert Park San Luis Obispo Santa Cruz Upland Visalia (California) Yuba City COLORADO Englewood Ft Collins Grand Junction Greeley Loveland Northglenn Pueblo Wheat Ridge CONNECTICUT Bristol Meriden Middletown Naugatuck New Britain New London Norwich DELAWARE Dover Newark Wilmington DISTRICT COLUMBIA None FLORIDA Altamonte Springs Bradenton Clearwater Daytona Beach Ft Lauderdale Ft Myers Ft Pierce Gainesville Hollywood Lakeland Largo (Florida) Melbourne Oakland Park Ocala Panama City Pensacola Pinellas Park Pompano Beach Riviera Beach Sanford Sarasota Tallahassee Titusville West Palm Beach GEORGIA Albany Macon Marietta Rome Savannah Valdosta Warner Robins HAWAII None IDAHO Boise Idaho Falls Lewiston Pocatello ILLINOIS Alton Belleville Bloomington Calumet Chicago Heights Danville Decatur East St Louis Joliet 299 (Illinois) Kankakee Lansing Moline Normal Pekin Peoria Rock Island Rockford Springfield Urbana INDIANA Anderson Bloomington Columbus Elkhart Evansville Ft Wayne Greenwood Hammond Kokomo Lafayette Merrillville Michigan City Mishawaka Muncie New Albany Portage Terre Haute West Lafayette IOWA Ames Cedar Falls Cedar Rapids Council Bluffs Davenport Dubuque Iowa City Sioux City Waterloo KANSAS Lawrence (Kansas) Topeka KENTUCKY Bowling Green Covington Henderson Owensboro LOUISIANA Alexandria Bossier City Kenner Lafayette Consol Govt Lake Charles Monroe MAINE Bangor Lewiston Portland MARYLAND Frederick Hagerstown MASSACHUSETTS Attleboro Brockton Chicopee Everett Fall River Fitchburg Gloucester Haverhill Holyoke Leominster Malden New Bedford Northampton Pittsfield Revere Salem Taunton Westfield MICHIGAN Battle Creek Bay City Burton East Lansing Garden City Inkster Jackson Kalamazoo Kentwood Lincoln Park Madison Heights Muskegon Oak Park Pontiac Roseville Southgate Taylor Warren Westland Wyoming MINNESOTA Brooklyn Center Duluth Fridley Moorhead Richfield St Cloud MISSISSIPPI Biloxi Gulfport Hattiesburg Pascagoula MISSOURI Columbia Florissant Gladstone Independence Jefferson City Joplin Raytown Springfield 300 (Missouri) St Charles St Joseph University City MONTANA Billings Great Falls Missoula NEBRASKA Bellevue NEVADA Carson City Sparks NEW HAMPSHIRE Dover Manchester Nashua Rochester NEW JERSEY Vineland NEW MEXICO Farmington Las Cruces NEW YORK Albany Binghamton Elmira Niagara Falls North Tonawanda Saratoga Springs Schenectady Troy Utica NORTH CAROLINA Asheville Burlington (North Carolina) Chapel Hill Fayetteville Gastonia Goldsboro Greenville Hickory High Point Rocky Mount Wilmington NORTH DAKOTA Bismarck Fargo Grand Forks OHIO Barberton Canton Cuyahoga Falls East Cleveland Elyria Euclid Fairborn Fairfield Garfield Heights Hamilton Huber Heights Kent Kettering Lakewood Lima Lorain Mansfield Maple Heights Parma Reynoldsburg Sandusky Warren Youngstown OKLAHOMA Lawton Midwest City Moore (Oklahoma) Norman OREGON Corvallis Eugene Gresham Medford Salem PENNSYLVANIA Allentown Altoona Bethlehem Easton Erie Harrisburg Lancaster Norristown Reading Scranton State College Wilkes-Barre Williamsport York RHODE ISLAND Cranston East Providence Pawtucket Warwick Woonsocket SOUTH CAROLINA Anderson Charleston Columbia Florence Greenville North Charleston Rock Hill Spartanburg Sumter 301 SOUTH DAKOTA Rapid City Sioux Falls TENNESSEE Clarksville Johnson City Kingsport Knoxville Murfreesboro TEXAS Abilene Amarillo Baytown Beaumont Bryan College Station Denton Euless Galveston Haltom City Hurst Longview Midland Odessa San Angelo Sherman Temple Tyler Victoria Waco Wichita Falls UTAH Logan Murray Ogden Provo VERMONT None VIRGINIA Blacksburg (Virginia) Charlottesville Danville Harrisonburg Lynchburg Portsmouth Roanoke WASHINGTON Auburn Bellingham Bremerton Everett Kennewick Longview Lynnwood Olympia Renton Yakima WEST VIRGINIA Charleston Huntington Morgantown Parkersburg Wheeling WISCONSIN Appleton Eau Claire Greenfield Janesville Kenosha La Crosse Oshkosh Racine Sheboygan West Allis WYOMING Casper Cheyenne 302 GOLD COAST CITIES ALABAMA None ALASKA None ARIZONA Prescott ARKANSAS None CALIFORNIA Alameda Arcadia Berkeley Beverly Hills Brea Burbank Burlingame Camarillo Carlsbad Cerritos Claremont Concord Culver Cupertino Cypress Foster Fountain Valley Fremont Glendora Huntington Beach Irvine La Mirada La Verne Lakewood Livermore Los Altos Los Gatos Town Manhattan Beach Martinez (California) Menlo Park Monterey Mountain View Newport Beach Novato Pacifica Palm Springs Palo Alto Petaluma Placentia Pleasant Hill Pleasanton Rancho Palos Verdes Redondo Beach Redwood City San Bruno San Buenaventura San Carlos San Clemente San Dimas San Juan Capist San Leandro San Mateo San Rafael Santa Barbara Santa Clara Santa Monica Santa Rosa Saratoga Simi Valley Sunnyvale Thousand Oaks Torrance Walnut Creek Yorba Linda COLORADO Arvada Boulder CONNECTICUT Danbury Norwalk Shelton Stamford DELAWARE None DISTRICT COLUMBIA None FLORIDA Boca Raton Boynton Beach Coral Gables Deerfield Beach Delray Beach Dunedin Hallandale Beach Margate Ormond Beach Plantation Port Orange Sunrise Tamarac GEORGIA Smyrna HAWAII None IDAHO None ILLINOIS Arlington Heights Buffalo Grove Burbank Des Plaines 303 (Illinois) Downers Grove Elk Grove Elmhurst Evanston Glenview Highland Park Hoffman Estates Lombard Niles Oak Forest Oak Lawn Oak Park Orland Park Palatine Park Ridge Schaumburg Skokie Streamwood Wheaton Wheeling Wilmette Woodridge INDIANA None IOWA Bettendorf KANSAS Lenexa Overland Park Shawnee KENTUCKY None LOUISIANA None MAINE None MARYLAND Annapolis Bowie Gaithersburg Rockville MASSACHUSETTS Beverly Marlborough Medford Melrose Newton Peabody Quincy Waltham Woburn MICHIGAN Allen Park Ann Arbor Dearborn Heights Farmington Hills Portage Royal Oak Southfield St Clair Shores Sterling Heights MINNESOTA Bloomington Brooklyn Park Burnsville Edina Village Maplewood Minnetonka Plymouth Rochester Roseville St Louis Park MISSISSIPPI None MISSOURI Kirkwood MONTANA None NEBRASKA None NEVADA None NEW HAMPSHIRE None NEW JERSEY Westfield NEW MEXICO Santa Fe NEW YORK Lindenhurst Long Beach New Rochelle Valley Stream White Plains NORTH CAROLINA None NORTH DAKOTA None OHIO Beavercreek Brunswick Cleveland Heights Mentor North Olmsted Shaker Heights Stow Upper Arlington Westerville Westlake 304 OKLAHOMA None OREGON Beaverton Lake Oswego PENNSYLVANIA Bethel Park Monroeville Plum RHODE ISLAND None SOUTH CAROLINA None SOUTH DAKOTA None TENNESSEE Germantown Hendersonville TEXAS Deer Park Duncanville No Richland Hills Richardson UTAH Bountiful VERMONT None VIRGINIA None WASHINGTON Bellevue Edmonds Kirkland (Washington) Redmond Richland WEST VIRGINIA None WISCONSIN Menomonee Falls New Berlin Wauwatosa WYOMING None 305 METRO CENTERS ALABAMA Birmingham Mobile Montgomery ALASKA Anchorage ARIZONA Mesa Tucson ARKANSAS Little Rock CALIFORNIA Anaheim Bakersfield Fresno Glendale Long Beach Modesto Oakland Pasadena Riverside Sacramento Stockton COLORADO Aurora Colorado Springs CONNECTICUT Bridgeport Hartford New Haven Waterbury DELAWARE None DISTRICT COLUMBIA None FLORIDA Miami Orlando St Petersburg Tampa GEORGIA Atlanta HAWAII None IDAHO None ILLINOIS None INDIANA None IOWA Des Moines KANSAS Wichita Wyand County & Kansas City KENTUCKY None LOUISIANA New Orleans Shreveport MAINE None MARYLAND None MASSACHUSETTS Lowell Lynn Springfield Worcester MICHIGAN Grand Rapids MINNESOTA Minneapolis St Paul MISSISSIPPI Jackson MISSOURI Kansas City St Louis MONTANA None NEBRASKA Lincoln Omaha NEVADA Las Vegas Reno NEW HAMPSHIRE None NEW JERSEY Newark Trenton 306 NEW MEXICO Albuquerque NEW YORK Buffalo Rochester Syracuse Yonkers NORTH CAROLINA Durham Greensboro Raleigh Winston-Salem NORTH DAKOTA None OHIO Akron Cincinnati Cleveland Dayton Toledo OKLAHOMA Tulsa OREGON None PENNSYLVANIA Pittsburgh RHODE ISLAND Providence SOUTH CAROLINA None SOUTH DAKOTA None TENNESSEE None TEXAS Arlington Corpus Christi Lubbock UTAH Salt Lake City VERMONT None VIRGINIA Alexandria Chesapeake Hampton Newport News Norfolk Richmond Virginia Beach WASHINGTON Spokane Tacoma WEST VIRGINIA None WISCONSIN None WYOMING None 307 MELTINGPOT CITIES ALABAMA None ALASKA None ARIZONA Yuma ARKANSAS None CALIFORNIA Alhambra Azusa Baldwin Park Bell Bell Gardens Bellflower Buena Park Carson Ceres Chino Chula Vista Colton Compton Costa Mesa Covina Daly City Downey El Centro El Monte Escondido Fairfield Fontana Fullerton Garden Grove Gardena Gilroy Hawthorne Hayward Huntington Park (California) Imperial Beach Indio Inglewood La Habra La Puente Lancaster Lawndale Lompoc Lynwood Madera Maywood Merced Milpitas Montclair Montebello Monterey Park National City Newark Norwalk Oceanside Ontario Orange Oxnard Paramount Pico Rivera Pittsburg Pomona Porterville Rialto Richmond Rosemead Salinas San Bernardino San Gabriel San Pablo Santa Ana Santa Maria Seaside South Gate So San Francisco Stanton (California) Temple Turlock Tustin Union City Vallejo Vista Walnut Watsonville West Covina Westminster Whittier COLORADO None CONNECTICUT None DELAWARE None DISTRICT COLUMBIA None FLORIDA Hialeah Kissimmee Lake Worth Lauderhill Miami Beach North Lauderdale North Miami Beach North Miami GEORGIA East Point HAWAII None 308 IDAHO None ILLINOIS Addison Berwyn Elgin Glendale Heights Hanover Park North Chicago Waukegan INDIANA East Chicago IOWA None KANSAS None KENTUCKY None LOUISIANA None MAINE None MARYLAND None MASSACHUSETTS Cambridge Chelsea Lawrence Somerville MICHIGAN None MINNESOTA None MISSISSIPPI None MISSOURI None MONTANA None NEBRASKA None NEVADA None NEW HAMPSHIRE None NEW JERSEY Elizabeth Hoboken New Brunswick Paterson Perth Amboy Plainfield Union West New York NEW MEXICO None NEW YORK Freeport Hempstead Mt Vernon Newburgh NORTH CAROLINA Jacksonville NORTH DAKOTA None OHIO None OKLAHOMA None OREGON None PENNSYLVANIA None RHODE ISLAND None SOUTH CAROLINA None SOUTH DAKOTA None TENNESSEE None TEXAS Brownsville Garland Grand Prairie Harlingen Irving Killeen Laredo McAllen Pasadena UTAH None VERMONT None VIRGINIA None WASHINGTON None 309 WEST VIRGINIA None WISCONSIN None WYOMING None 310 BOOMTOWNS ALABAMA Hoover ALASKA None ARIZONA Chandler Gilbert Glendale Peoria Scottsdale ARKANSAS Springdale CALIFORNIA Antioch Clovis Corona Folsom Palmdale Rancho Cucamonga Roseville San Marcos Tracy Vacaville Victorville COLORADO Longmont Thornton Westminster CONNECTICUT None DELAWARE None DISTRICT COLUMBIA None FLORIDA Cape Coral Coconut Creek Coral Springs Davie Miramar Palm Bay Pembroke Pines Port St Lucie GEORGIA Roswell HAWAII None IDAHO Nampa ILLINOIS Bolingbrook Carol Stream Naperville Tinley Park INDIANA Carmel Lawrence IOWA West Des Moines KANSAS Olathe KENTUCKY None LOUISIANA None MAINE None MARYLAND None MASSACHUSETTS None MICHIGAN Novi MINNESOTA Apple Valley Blaine Coon Rapids Eagan Eden Prairie Maple Grove MISSISSIPPI None MISSOURI Blue Springs Lees Summit St Peters MONTANA None NEBRASKA None NEVADA Henderson North Las Vegas NEW HAMPSHIRE None NEW JERSEY None NEW MEXICO None 311 NEW YORK None NORTH CAROLINA Cary Concord NORTH DAKOTA None OHIO None OKLAHOMA Broken Arrow Edmond OREGON Hillsboro Tigard PENNSYLVANIA None RHODE ISLAND None SOUTH CAROLINA Mt Pleasant SOUTH DAKOTA None TENNESSEE Bartlett TEXAS Carrollton Desoto Grapevine La Porte League City Lewisville (Texas) Mesquite Missouri City Plano Round Rock UTAH Layton Orem St George West Jordan VERMONT None VIRGINIA Manassas WASHINGTON Kent Vancouver WEST VIRGINIA None WISCONSIN None WYOMING None 312 CENTERVILLES ALABAMA None ALASKA Juneau ARIZONA Sierra Vista ARKANSAS Conway CALIFORNIA Eureka Hanford Paradise Santa Paula Tulare Woodland COLORADO None CONNECTICUT Torrington DELAWARE None DISTRICT COLUMBIA None FLORIDA None GEORGIA La Grange HAWAII None IDAHO None ILLINOIS Freeport Galesburg Quincy INDIANA Marion Richmond IOWA Burlington Clinton Marshalltown Mason KANSAS Hutchinson Leavenworth Manhattan Salina KENTUCKY Frankfort Hopkinsville Paducah LOUISIANA New Iberia MAINE None MARYLAND None MASSACHUSETTS None MICHIGAN Midland MINNESOTA Mankato MISSISSIPPI Greenville Meridian Tupelo MISSOURI Cape Girardeau MONTANA Butte-Silver Bow NEBRASKA Grand Island NEVADA None NEW HAMPSHIRE Concord NEW JERSEY None NEW MEXICO Alamogordo Clovis Hobbs NEW YORK Auburn Jamestown Rome NORTH CAROLINA Wilson 313 NORTH DAKOTA None OHIO Bowling Green Findlay Lancaster Marion OKLAHOMA Bartlesville Enid Muskogee Ponca City Shawnee Stillwater OREGON Albany PENNSYLVANIA New Castle RHODE ISLAND None SOUTH CAROLINA None SOUTH DAKOTA None TENNESSEE Columbia Oak Ridge TEXAS Conroe Huntsville Kingsville Lufkin Nacogdoches San Marcos UTAH None VERMONT None VIRGINIA Suffolk WASHINGTON Walla Walla WEST VIRGINIA None WISCONSIN Manitowoc WYOMING Laramie 314 MEGA-METRO CENTERS ALABAMA None ALASKA None ARIZONA Phoenix ARKANSAS None CALIFORNIA Los Angeles San Diego San Francisco San Jose COLORADO Denver CONNECTICUT None DELAWARE None DISTRICT COLUMBIA Washington DC FLORIDA Jacksonville GEORGIA None HAWAII Honolulu IDAHO None ILLINOIS Chicago INDIANA Indianapolis IOWA None KANSAS None KENTUCKY None LOUISIANA None MAINE None MARYLAND Baltimore MASSACHUSETTS Boston MICHIGAN Detroit MINNESOTA None MISSISSIPPI None MISSOURI None MONTANA None NEBRASKA None NEVADA None NEW HAMPSHIRE None NEW JERSEY None NEW MEXICO None NEW YORK New York City NORTH CAROLINA Charlotte NORTH DAKOTA None OHIO Columbus OKLAHOMA Oklahoma City OREGON Portland PENNSYLVANIA Philadelphia RHODE ISLAND None SOUTH CAROLINA None 315 SOUTH DAKOTA None TENNESSEE Memphis Nashville-Davidson County TEXAS Austin Dallas El Paso Ft Worth Houston San Antonio UTAH None VERMONT None VIRGINIA None WASHINGTON Seattle WEST VIRGINIA None WISCONSIN Milwaukee WYOMING None