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The Impact of the Washington Metro on Development Patterns

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The Impact of the Washington Metro on Development Patterns ABSTRACT Title of dissertation: THE IMPACT OF THE WASHINGTON METRO ON DEVELOPMENT PATTERNS Katja Pauliina Vinha, Doctor of Philosophy, 2005 Dissertation directed by: Professor Nancy Bockstael Department of Agricultural and Resource Economics Professor Maureen Cropper Department of Economics It is a tenet of urban planning that transportation networks help shape the spatial configuration of cities. In the case of heavy rail systems, a common belief is that building a subway system will promote employment and population density, thereby discouraging urban sprawl and its negative consequences. This dissertation examines the impact of the Washington Metro rail system in 1990 and 2000 on the distribution of employment and population in two counties in the Washington, DC metropolitan area— Montgomery County and Prince Georges County. It asks whether employment and residential construction increased more rapidly near Metro rail stations than in other parts of the metropolitan area. It also examines the impact of the Metro on the socio- demographic composition of population near Metro stations. Evaluating the impact of the Metro system on employment and population density is complicated by the fact that stations along the Metro line may be located in areas of high population and/or employment density to begin with, or in areas with significant amounts of developable land available. To deal with this issue I use a propensity score matching estimator. The technique is an improvement over the traditional methods of evaluation as it acknowledges the endogeneity of the location of Metro stations. Furthermore, matching estimators relax the functional form assumptions of OLS estimators. The research finds statistically significant impacts on employment and overall development density from proximity to a Metro station and does not find consistent impacts on population or dwelling unit densities. However, for Prince George’s County a negative impact on the percentage of the population belonging to a minority is found. The results also suggest that impacts on development are greater closer to the station than farther away and that they are greater the longer the stations have been in operation. THE IMPACT OF THE WASHINGTON METRO ON DEVELOPMENT PATTERNS by Katja Pauliina Vinha Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2005 Advisory Committee: Professor Nancy Bockstael, Co-chair Professor Maureen Cropper, Co-chair Professor Antonio Bento Professor Lori Lynch Professor Mahlon Straszheim © Copyright by Katja Pauliina Vinha 2005 DEDICATION To Elvi Kulmakorpi and in memory of Jenny Vinha ii ACKNOWLEDGEMENTS The completion of this dissertation would not have been possible without the help, support and encouragement of many people. First I would like to thank my dissertation committee members. My two main advisors were exceptional. Nancy Bockstael and Maureen Cropper provided detailed feedback on my work, nudged me in the right direction when I was lost and were willing to submit to long teleconference calls to discuss my progress. I thank you for patiently waiting as I tried to find my topic. Antonio Bento always showed enthusiasm for what I was doing and gave feedback on the work that significantly improved the dissertation. The comments of both Marlon Straszheim and Lori Lynch helped to better focus the research. Also, I would like to specially thank Jeffrey Smith whose comments on propensity score matching estimators greatly clarified my thinking. I am also endebted to Rodney Green (Howard University), Wayne Koemple (MNCPPC-Montgomery County), Philip Taylor (MNCPPC-Prince George’s County) and Robert Stedman (WMATA) for sharing data that was crucial in the development of the research. Many people provided me a home away from home while commuting from Colombia to Washington. The hospitality of the Galindo-Carvajal family, of the Gonzalez-Krivonos family and of Maria Lucia Guerra made my trips not only productive but fun. iii This endeavor would not have been possible without the support of my family. My parents, Irja and Matti, provided me with a stimulating environment to question and supported me through the long dissertation process. They always believed that I would finish. My husband, Felipe Barrera, was my number one fan throughout and gave me the emotional support that kept me going during the many low points. He was willing to read and comment on my work (and sometimes explain what was it that I was doing) even if my responses were not always the best. For sure, without him this dissertation would not exist. And finally to Bebe-bebe who gave me an ultimatum to finish and literally kicked me to get done. I love you. iv TABLE OF CONTENTS List of Tables…………………………………………..………………………………..vii List of Figures……………………………………………………………………..…...viii Chapter 1: Introduction and Literature Review…………………………………..…...1 1.1 Statement of the problem........................................................................................ 4 1.2 Literature review – Land use and transportation interactions................................. 7 1.2.1 Hedonic price studies...................................................................................... 8 1.2.2 Intensity and distribution of land use............................................................ 10 1.3 Contribution of the Dissertation ........................................................................... 14 Chapter 2: Propensity Score Matching Estimators…………………………………. 16 2.1 Binary propensity score matching estimator ........................................................ 17 2.2 Multiple treatment matching propensity score estimator ..................................... 32 Chapter 3: Application of the Matching Estimator…………………………………. 39 3.1 Endogeneity of station location decisions ............................................................ 39 3.2 Considerations in the implementation of propensity score matching................... 45 3.2.1 Differences in the two Counties.................................................................... 46 3.2.2 Differences among station areas ................................................................... 50 3.2.3 Temporal Impacts ......................................................................................... 61 3.2.4 Spatial Impacts.............................................................................................. 63 3.3 Outcome measures................................................................................................ 66 3.3.1 Employment.................................................................................................. 67 3.3.2 Population and socio-economic characteristics ............................................ 67 3.3.3 Overall Density ............................................................................................. 69 3.4 Explanatory variables in the propensity score estimation .................................... 70 3.4.1 Location of the TAZ ..................................................................................... 71 3.4.2 Population and socio-economic characteristics ............................................ 72 3.4.3 Employment.................................................................................................. 74 3.4.4 Land use and zoning ..................................................................................... 75 3.4.5 Past growth.................................................................................................... 76 3.4.6 Accessibility.................................................................................................. 76 Chapter 4: Case Study - Montgomery County………………………………………. 78 4.1 Initial Conditions in Montgomery County............................................................ 78 4.2 Propensity scores .................................................................................................. 90 4.3 Construction of the counterfactual........................................................................ 98 4.4 Impacts of Metro stations in Montgomery County............................................. 107 4.4.1 Impacts in 2000........................................................................................... 108 4.4.1.1 One-mile treatment common support sample......................................... 108 v 4.4.1.2 One-mile treatment thick support sample............................................... 112 4.4.1.3 Half-mile treatment thick support sample............................................... 118 4.4.1.4 One-mile multiple treatment analysis ..................................................... 121 4.4.2 Impacts in 1990........................................................................................... 124 4.4.3 Western branch of Red Line ....................................................................... 126 4.5 Conclusions......................................................................................................... 129 Chapter 5: Case Study - Prince George's County………………………………….. 131 5.1 Initial Conditions in Prince George’s County..................................................... 131 5.2 Propensity Scores................................................................................................ 143 5.3 Construction of the counterfactual...................................................................... 150
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