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© 2017 Bryan Patrick Grady All rights reserved. PUBLIC POLICY IMPACTS OF JURISDICTIONAL FRAGMENTATION By BRYAN PATRICK GRADY A dissertation submitted to the Graduate School‐New Brunswick Rutgers, The State University of New Jersey In partial fulfillment of the requirements For the degree of Doctor of Philosophy Graduate Program in Planning and Public Policy Written under the direction of Michael Lincoln Lahr And approved by ________________________________________ ________________________________________ ________________________________________ ________________________________________ New Brunswick, New Jersey January 2017 ABSTRACT OF THE DISSERTATION Public Policy Impacts of Jurisdictional Fragmentation By BRYAN PATRICK GRADY Dissertation Director: Michael Lincoln Lahr For decades, there has been substantial debate regarding whether it is preferable for a metropolitan area to be governed by a single local government, or by some number of smaller municipalities. Advocates of the latter, informed by Tiebout (1956) and public choice theory, argue that competitive markets in public goods can and should exist, while those in the former camp believe economies of scale and ability to plan cohesively at the regional level make unitary systems preferable. The literature on this topic, reviewed extensively here, has focused disproportionately on matters of public finance while venturing into other fields of public policy inquiry far less often. Does jurisdictional fragmentation have broader implications? This work will use regression analysis to test whether the structure of local governance affects a variety of outcomes, including ethnic and racial segregation, land use patterns, economic performance and inequality, educational achievement, and housing costs. Overall, results are mixed, though they suggest a future course of research: investigating whether fragmentation has worsened spatial mismatch in the labor market among African‐Americans. ii ACKNOWLEDGMENTS Thank you, first and foremost, to my advisor, Mike Lahr, for his support, insights, and extreme patience as I have (very slowly) completed this dissertation. Further thanks go to the other members of my committee and many other Bloustein School professors who taught, advised, and employed me. Also, thanks to Judd Schechtman, with whom I collaborated on a paper, “Government Growing Wild,” presented at the 2010 American Collegiate Schools of Planning Annual Conference, that informed Chapter V of this work. I also must express my gratitude to Holly Holtzen, my boss at the Ohio Housing Finance Agency for the past three years. She became a second advisor in this process by asking the right questions, supporting my development as a scholar, and providing the flexibility that I needed to write while I worked full‐time. I would be remiss if I did not greatly thank the Lincoln Institute for Land Policy, who awarded me a C. Lowell Harris Dissertation Fellowship in 2008 to help financially support me in the early stages of this work. My parents, Colleen and Michael, have had to put up with a lot. I was rarely the easiest son to raise, but they did so much to help me get to this point that I will never be able to fully appreciate their efforts. Finally, this work is dedicated to my late cousin, Douglas McCraw. He was the closest thing I had to a sibling in my youth; he had many challenges in life that he fought against but ultimately could not overcome. There is a great deal of pain and suffering in the world, and I hope dearly that my efforts will help make the lives of others better. iii TABLE OF CONTENTS ABSTRACT OF THE DISSERTATION . ii ACKNOWLEDGMENTS . iii INTRODUCTION . 1 CHAPTER I: FRAGMENTATION AND ITS DISCONTENTS . 5 CHAPTER II: LITERATURE REVIEW . 25 CHAPTER III: DATA AND METHODS . 51 CHAPTER IV: RESIDENTIAL SEGREGATION . 69 CHAPTER V: LAND USE INEFFICIENCY . 87 CHAPTER VI: ECONOMIC DISTRESS . 103 CHAPTER VII: HOUSING COSTS AND EDUCATION IN OHIO . 117 CHAPTER VIII: CONCLUSIONS AND DISCUSSION . 138 APPENDIX A: SPATIAL DISTRIBUTION OF KEY VARIABLES . 146 APPENDIX B: CORRELATION TABLES FOR NATIONAL MODELS . 156 APPENDIX C: FULL OLS MODEL OUTPUT . 159 APPENDIX D: OUTPUT AND TABLE FOR 3SLS OHIO MODEL . 181 APPENDIX E: RESPONSE TO COMMITTEE FEEDBACK . 183 REFERENCES . 186 iv LIST OF TABLES CHAPTER IV: RESIDENTIAL SEGREGATION Table 4‐1: Descriptive Statistics of Dissimilarity Indices and Population Shares 76 Table 4‐2: Descriptive Statistics of Control Variables 77 Table 4‐3: 2010 Asian‐White Dissimilarity Index Regression Coefficients 77 Table 4‐4: 2010 Black‐White Dissimilarity Index Regression Coefficients 78 Table 4‐5: 2010 Hispanic‐White Dissimilarity Index Regression Coefficients 78 Table 4‐6: Poverty Dissimilarity Index Regression Coefficients 81 Table 4‐7: Revised Black‐White Dissimilarity Index Regression Coefficients 82 CHAPTER V: LAND USE INEFFICIENCY Table 5‐1: Descriptive Statistics of Ewing & Hamidi (2014) Sprawl Indices 93 Table 5‐2: Ewing & Hamidi (2014) Density Index Regression Coefficients 94 Table 5‐3: Ewing & Hamidi (2014) Mixed Use Index Regression Coefficients 94 Table 5‐4: Ewing & Hamidi (2014) Centering Index Regression Coefficients 94 Table 5‐5: Ewing & Hamidi (2014) Streets Index Regression Coefficients 95 Table 5‐6: Ewing & Hamidi (2014) Composite Index Regression Coefficients 95 Table 5‐7: Summary of OLS Regression Model Correlations 95 Table 5‐8: Ewing & Hamidi (2014) Factor Loadings for MSA Sprawl Analysis 99 CHAPTER VI: ECONOMIC DISTRESS Table 6‐1: Descriptive Statistics of Dependent and New Control Variables 112 Table 6‐2: Percent Change in Nominal Household Income Regression Coefficients 113 Table 6‐3: Percent Change in Gross Metropolitan Product Regression Coefficients 113 Table 6‐4: Change in Poverty Rate Regression Coefficients 113 Table 6‐5: Change in Gini Coefficient Regression Coefficients 114 CHAPTER VII: HOUSING COSTS AND EDUCATION IN OHIO Table 7‐1: Descriptive Statistics of Ohio School District Variables 131 Table 7‐2: Ohio School District Three‐Stage Least Squares Model Results 132 APPENDIX B: CORRELATION TABLES FOR NATIONAL MODELS Table B‐1: Correlation Table for National Fragmentation Variables 157 Table B‐2: Correlation Table for Dissimilarity Indices (Chapter IV) 157 Table B‐3: Correlation Table for Sprawl Variables (Chapter V) 158 Table B‐4: Correlation Table for Economic Variables (Chapter VI) 158 Table B‐5: Correlation Table for OLS Control Variables (Chapters IV‐VI) 158 APPENDIX D: OUTPUT AND TABLE FOR 3SLS OHIO MODEL Table D‐1: Correlation Table for Ohio School District 3SLS Model 182 v LIST OF ILLUSTRATIONS APPENDIX A: SPATIAL DISTRIBUTION OF KEY VARIABLES Metropolitan Power Diffusion Index by Metropolitan Statistical Area, 2007 146 Governments per Million Residents by Metropolitan Statistical Area, 2007 146 Modified Rusk Index for Primary City in Metropolitan Statistical Area, 2010 147 Asian‐White Dissimilarity Index by Metropolitan Statistical Area, 2010 147 Black‐White Dissimilarity Index by Metropolitan Statistical Area, 2010 148 Hispanic‐White Dissimilarity Index by Metropolitan Statistical Area, 2010 148 Poverty Dissimilarity Index by Metropolitan Statistical Area, 2010 149 Ewing and Hamidi (2014) Density Index by Metropolitan Statistical Area, 2010 149 Ewing and Hamidi (2014) Mixed Use Index by Metropolitan Statistical Area, 2010 150 Ewing and Hamidi (2014) Centering Index by Metropolitan Statistical Area, 2010 150 Ewing and Hamidi (2014) Streets Index by Metropolitan Statistical Area, 2010 151 Ewing and Hamidi (2014) Composite Index by Metropolitan Statistical Area, 2010 151 Change in Nominal Household Income by Metropolitan Statistical Area, 2007‐13 152 Change in Gross Metropolitan Product by Metropolitan Statistical Area, 2007‐13 152 Change in Poverty Rate by Metropolitan Statistical Area, 2007‐13 153 Change in Gini Coefficient by Metropolitan Statistical Area, 2007‐13 153 School Districts per 100,000 County Residents, Ohio, 2010 154 Performance Index Percentage by School District, Ohio, 2014 155 Median Monthly Housing Cost by School District, Ohio, 2010‐2014 156 vi 1 INTRODUCTION Last October, the two major party nominees for President of the United States, Democrat Hillary Clinton and Republican Donald Trump, met in St. Louis, Missouri, at an event the Commission on Presidential Debates (2016) described as a “town meeting” and widely labeled by the media as a “town hall.” A group of undecided voters was convened on stage; some had the chance to ask questions of the candidates directly. These labels were applied to the event despite the proceedings being held in a large urban area and viewed by tens of millions of people across the country, the vast majority of whom could not directly participate. Earlier in the year, two unsuccessful candidates for the Republican nomination, Governors John Kasich and Chris Christie, traded claims about who had held more “town hall meetings” in New Hampshire in advance of the state’s primary (Ward, 2016). One can infer that this was not an argument about which campaign had superior event planners, but rather took place because candidates wished to point to the number of such events as a proxy for engagement with the electorate, regardless of whether they led a candidate to adapt his or her policies or messaging. This terminology is derived from the New England town meeting, where citizens convene to serve as a legislative body (Crawford, 2013; Mabry, 2016). The town meeting is only viable as a system of governance in communities small enough for all interested residents to meet