Evaluation of the Effect of Rail Intra-Urban Transit Stations on Neighborhood Change

Georgia State University ScholarWorks @ Georgia State University Public Management and Policy Dissertations Summer 6-13-2017 Evaluation of the Effect of Rail Intra-Urban Transit Stations on Neighborhood Change Christopher K. Wyczalkowski Georgia State University Follow this and additional works at: https://scholarworks.gsu.edu/pmap_diss Recommended Citation Wyczalkowski, Christopher K., "Evaluation of the Effect of Rail Intra-Urban Transit Stations on Neighborhood Change." Dissertation, Georgia State University, 2017. https://scholarworks.gsu.edu/pmap_diss/66 This Dissertation is brought to you for free and open access by ScholarWorks @ Georgia State University. It has been accepted for inclusion in Public Management and Policy Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. EVALUATION OF THE EFFECT OF RAIL INTRA-URBAN TRANSIT STATIONS ON NEIGHBORHOOD CHANGE A Dissertation Presented to The Academic Faculty By Christopher K. Wyczalkowski In Partial Fulfilment Of the Requirements for the Degree Doctor of Philosophy in Public Policy Georgia State University Georgia Institute of Technology August, 2017 Copyright © Christopher K. Wyczalkowski 2017 EVALUATION OF THE EFFECT OF RAIL INTRA-URBAN TRANSIT STATIONS ON NEIGHBORHOOD CHANGE Dr. Ann-Margaret Esnard, Chair Dr. Joseph Hacker Andrew Young School of Policy Studies Andrew Young School of Policy Studies Georgia State University Georgia State University Dr. Dan Immergluck Dr. Kyle Mangum School of City & Regional Planning Andrew Young School of Policy Studies Georgia Institute of Technology Georgia State University Dr. John Thomas Andrew Young School of Policy Studies Georgia State University Date Approved: 6/13/2017 To Mama i Tata iii ACKNOWLEDGEMENTS There are a great many people to whom I am indebted for their help in the completion of the manuscript, and my doctoral degree. Without help and encouragement, I certainly would not have been able to accomplish this task. More than anyone, I’m grateful to my wife and kids who endured my absence when I was working, and my tiredness when I wasn’t. I want to thank the Dissertation committee for their advice and guidance on this project. Dr. Joseph Hacker was my constant sounding board, friend, and advisor, not just for this project, but throughout the doctoral process. Dr. Dan Immergluck’s interest in my research and advice, particularly in the early stages when he agreed to meet me for coffee in Decatur, provided the confidence I needed to share this topic with the world in a dissertation. Dr. John Thomas provided the framework I needed to form this study as an evaluation. Dr. Kyle Mangum provided the urban economic background that I used to support the framework for this study. I particularly want to acknowledge Dr. Esnard. Dr. Ann-Margaret Esnard has been there from start to finish, providing mentorship, guidance, and most importantly encouragement. I could not have asked for a better mentor. I’d like to thank the faculty for sharing their knowledge, and all of my fellow students (Emmy, Xi, Kelechi, Komla, Obed, Chris, Mike … too many to name) for their steadfast support and advice over the last five years. A special thanks to Rahul Pathak, Sandy Zook, Eric Van Holm, Bryce Farbstein, and the adopted cohort member Mahmoud Elsayed for helping me get through the journey. Lastly, I want to thank my good friends Amir Gorji and Patrik Johnson for being a sounding board for all these years. iv TABLE OF CONTENTS ACKNOLEDGEMENTS iv LIST OF TABLES viii LIST OF FIGURES ix SUMMARY xi INTRODUCTION 1 1.1 What is the Neighborhood Life-Cycle? 3 1.2 Rail Intra-Urban Transit Stations and Neighborhood Change 5 1.3 Study Area and Analytical Approaches 6 1.4 Research Question and Hypotheses 8 1.5 Chapter Contents 11 BACKGROUND AND CONCEPTUAL FRAMEWORK 13 2.1 Neighborhood Change Framework 14 2.1.1 Filtering 16 2.1.2 Gentrification 19 2.1.3 Gentrification and Filtering Cycle 28 2.1.4 Typology 29 2.2 Public Transportation System Effects and Methodological Approaches 32 2.2.1 Hedonic Regression Studies 34 2.2.2 Quasi-Experimental Approaches 37 2.2.3 Other Studies 39 v CASE STUDY: ATALNTA, GEORGIA 42 3.1 The Metro Atlanta Region: Growth and Development 47 3.2 MARTA – Genesis and Evolution 55 3.3 Types of MARTA Stations in Neighborhoods 63 DATA AND METHODOLOGY 70 4.1 Data & Unit of Analysis 72 4.2 Treatment Selection 74 4.3 Control Group Selection 78 4.4 Hypotheses 81 4.5 Operationalization of Neighborhood Change 90 4.5.1 Individual Indicators 92 4.5.2 Neighborhood Change Index 1 93 4.5.3 Neighborhood Change Index 2 96 4.6 Methods 99 4.6.1 Measuring Short-term Effects of Intra-Urban Transit 99 Stations on Neighborhood Change 4.6.2 Measuring Long-term Effects of Intra-Urban Transit 101 Stations on Neighborhood Change RESULTS 105 5.1 Do New MARTA Stations Have Short-Term Effects on 118 Neighborhood Change? 5.2 Do MARTA Stations Have Long-Term Effects on 123 Neighborhood Change? vi DISCUSSION AND CONCLUSION 134 6.1 Summary of Key Findings 137 6.2 Contributions to Scholarship 138 6.3 Limitations 140 6.4 Future Research 142 APPENDIX A 147 APPENDIX B 173 APPENDIX C 183 APPENDIX D 189 REFERENCES 197 vii LIST OF TABLES Table 3.1 Top 10 urbanized areas in the United States 44 Table 3.2 MARTA Station Names, Type, and Date Opened 60 Table 3.3 TSADS Station Types 67 Table 4.1 Data sources 73 Table 4.2 Variables in the Fixed Effects Model 88 Table 5.1 NCI2 1 Descriptive Statistics by Decade 115 Table 5.2 NCI2 DID Models 125 Table 5.3 Fixed Effects Model output 129 Table 5.4 2010 to 2014 NCI2 DID Models 131 Table 5.5 Post-Match Fixed Effects Model 133 viii LIST OF FIGURES Figure 1.1 Neighborhood Life-Cycle 4 Figure 1.2 Top ten U.S. heavy rail transportation agencies 7 by Unlinked Passenger Trips (UPT) Figure 3.1. Atlanta, GA Rail Intra Urban Rail and Highway Networks 45 Figure 3.2 Atlanta Street Patterns 1864 50 Figure 3.3 City of Atlanta and Ten County population over time 52 Figure 3.4 City of Atlanta black/white demographic change 53 Figure 3.5 Study Area % White residents per census tract 54 Figure 3.6 MARTA Rail Monthly Ridership 62 Figure 3.7 Study Area population density (1980-2010) 66 Figure 3.8 MARTA East Line Construction 1979 68 Figure 3.9 MARTA Rail Map 69 Figure 4.1 Analysis Flow Chart 71 Figure 4.2 MARTA Stations and Census Tracts East Line 78 Figure 4.3 Treatment and Control Groups Around New 83 Neighborhood MARTA Stations Figure 4.4 Factors affecting neighborhood change 84 Figure 5.1 Neighborhood Change Index 1 – Stage 1 109 Figure 5.2 Neighborhood Change Index 1 – Stage 2 110 Figure 5.3 Neighborhood Change Index 1 Count of Tracts for Each Category 111 Figure 5.4 NCI1 Change Gentrification to Filtering/Filtering to Gentrification 112 ix Figure 5.5 NCI1 Stable Tracts 115 Figure 5.6 Neighborhood Change Index 2 Natural Breaks Classification 117 Figure 5.7 DID Models NCI21 128 Figure 6.1 Neighborhood Change Theoretical Models 143 x SUMMARY Development of heavy rail intra-urban public transportation systems is an economically expensive policy tool for State and Local Governments that is often justified with the promise of economic development and neighborhood revitalization around station areas. However, the literature on the effects of rail intra-urban transit stations on neighborhoods is relatively thin, particularly on the socioeconomic effects. This quasi-experimental study evaluated the effect of heavy rail intra-urban transit stations on surrounding neighborhoods, using Atlanta, Georgia and its transit authority, the Metropolitan Atlanta Rapid Transit Authority (MARTA), as a case study. Atlanta is an expansive American city, with a large public transportation system, but low population density and no large-scale policies promoting growth around MARTA rail stations. The study period, 1970 to 2014, covers the entire period of MARTA’s existence – stations opened between 1979 and 2000. Neighborhood change was operationalized with a neighborhood change index (NCI), built on the Neighborhood Life-Cycle framework, with an adaptation that incorporates both the filtering (negative NCI) and gentrification (positive NCI) models of neighborhood change. The study differentiates between an initial effect of new MARTA rail stations, and a long-term effect. Control groups were formed using one and three mile buffers, as well as a matching strategy. Difference-in- difference (DID) models find very little evidence of a positive relationship of NCI with the opening of new MARTA rail stations. The economic recovery that began in 2010 is of special interest for housing research. To address this time-period this study utilized two models, with mixed results. The DID model suggested a negative effect of stations on the xi NCI. To control for selection bias in the 2010 to 2014 economic time-period, this study utilized propensity score matching to balance the treatment and control group on observed characteristics. A time and tract fixed effects model using the matched treatment and control groups found a significant positive effect of stations on neighborhood change. To test the long-term effect, a time and tract fixed effects model (1970-2014) with the NCI as the dependent variable found a positive NCI effect of MARTA stations on neighborhoods. Therefore, overall, positive neighborhood change (on the NCI scale) can be attributed to MARTA transit stations. Since 2002 MARTA ridership has slightly declined; therefore, the study concludes that given stagnant ridership, lack of supporting policy, and the finding of a positive relationship between MARTA transit stations and gentrification, the stations are a positive amenity, and are a significant contributor to neighborhood change.

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