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104 APPENDIX A: REFERENCES Sections 1-4 Acemoglu, D., and J. Angrist. 1999. How large are the social returns to education? Evidence from compulsory schooling laws. National Bureau of Economic Research, Inc. Allen, W. B., D. Liu, and S. Singer. 1993. Accessibility measures of US metropolitan areas. Transportation Research Part B: Methodological 27 (6):439-449. Anas, A., R. Arnott, and K. A. Small. 1998. Urban spatial structure. Journal of Economic Literature 36:1426-1464. Annema, J. A., C. Koopmans, and B. Van Wee. 2007. Evaluating transport infrastructure investments: The Dutch experience with a standardized approach. Transport Reviews 27 (2):125-150. Arrow, K. J. 1996 (1962). Economic welfare and the allocation of resources for invention. International Library of Critical Writings in Economics 70:227-244. Aschauer, D. A. 1989. Is public expenditure productive? Journal of Monetary Economics 23 (2):177-200. Audretsch, D. B., and M. P. Feldman. 1996. R&D spillovers and the geography of innovation and production. American Economic Review 86 (3):630-640. Baird, B. A. 2005. Public infrastructure and economic productivity: A transportation-focused review. Transportation Research Record: Journal of the Transportation Research Board 1932:54-60. Banister, D., and J. Berechman. 2000. Transport investment and economic development. London: UCL Press. Baumgardner, J. R. 1988. Physicians services and the division of labor across local markets. Journal of Political Economy 96 (5):948-982. Becker, G. S., and K. M. Murphy. 1992. The division of labor, coordination costs, and knowledge. The Quarterly Journal of Economics:1137-1160. Berechman, J., D. Ozmen, and K. Ozbay. 2006. Empirical analysis of transportation investment and economic development at state, county and municipality levels. Transportation 33 (6):537-551. Berechman, J. 1994. Urban and regional economic impacts of transportation investment: A critical assessment and proposed methodology. Transportation Research Part A: Policy and Practice 28 (4):351-362. Berechman, J., and R. Paaswell. 2001. Accessibility improvements and local employment : an empirical analysis. Journal of Transportation and Statistics. 4 (2/3):/Dec. Bernard, A. B., J. B. Jensen, and P. K. Schott. 2006. Trade costs, firms and productivity. Journal of Monetary Economics 53 (5):917-937. Black, J., and M. Conroy. 1977. Accessibility measures and the social evaluation of urban structure. Environment and Planning A 9:1013-1031. Blundell, R. 1992. Labor supply and taxation: a survey. Fiscal Studies 13 (3):15-40. Boarnet, M. G. 1995. Transportation Infrastructure, Economic Productivity, and Geographic Scale: Aggregate Growth versus Spatial Redistribution. Working Paper 255. Berkeley, CA: The Transportation Center, University of California. Brülhart, M., and N. A. Mathys. 2008. Sectoral agglomeration economies in a panel of European regions. Regional Science and Urban Economics 38 (4):348-362.
105 Brülhart, M., and N. A. Mathys. 2007. Sectoral Agglomeration Economies in a Panel of European Regions. C.E.P.R. Discussion Papers. Cervero, R. 1997. Transit-induced accessibility and agglomeration benefits: A land market evaluation. Berkeley, CA: University of California at Berkeley, Institute of Urban and Regional Development. Chamley, C., and D. Gale. 1994. Information revelation and strategic delay in a model of investment. Econometrica 62 (5):1065-1085. Ciccone, A. 2002. Agglomeration effects in Europe. European Economic Review 46 (2):213. Ciccone, A., and R. E. Hall. 1996. Productivity and the density of economic activity. The American Economic Review. 86 (1):54. Cogan, J. F. 1980. Fixed costs and labor supply. National Bureau of Economic Research, Inc. Commission, European. 2003. Going for Growth: The Economy of the EU. European Commission. Costa, D. L., and M. E. Kahn. 2000. Power couples: Changes in the locational choice of the college educated, 1940-1990. Quarterly Journal of Economics 115 (4):1287-1315. Davies, S. 1998. Review of the evidence on the incidence of imperfect competition in the UK. In The welfare implications of transport improvements in the presence of market failure. London: UK Department for Transport. Deno, K. T. 1988. The effect of public capital on US manufacturing activity: 1970 to 1978. Southern Economic Journal 55 (2):400. DfT. 2003. Fill in Endnote, edited by U. D. f. Transport: UK Department for Transport. âââ. 2003. Wider Economic Impacts. UK Department for Transport. âââ. 2005. Transport, Wider Economic Benefits and Impacts on GDP. London: UK Department for Transport. âââ. 2009. Wider Impacts and Regeneration - FOR CONSULTATION. UK Department for Transport. Diamond, C. A., and C. J. Simon. 1990. Industrial specialization and the returns to labor. Journal of Labor Economics 8 (2):175-201. Duffy-Deno, K. T., and R. W. Eberts. 1991. Public infrastructure and regional economic development: A simultaneous equations approach. Journal of Urban Economics 30 (3):329-343. Duffy-Deno, K. T. 1991. Public infrastructure and regional economic development: A simultaneous equations approach. Journal of Urban Economics 30. Duranton, G., and H. G. Overman. 2002. Testing for localization using micro-geographic data. Centre for Economic Performance, LSE. Duranton, G., and D. Puga. 2004. Micro-foundations of urban agglomeration economies. In Handbook of Regional and Urban Economics, Vol 4: Cities and geography, edited by J. V. Henderson and J.-F. Thisse. Amsterdam, New York: Elsevier. Eberts, R. W. 1986. Estimating the contribution of urban public infrastructure to regional economic growth. In Working Paper No. 8620. Cleveland: Federal Reserve Bank of Cleveland. Eberts, R. W., and D. P. McMillen. 1999. Agglomeration economies and urban public infrastructure. In Handbook of Regional and Urban Economics, Vol. 3: Applied urban economics. Edited by P. Cheshire and E. S. Mills. Amsterdam, New York: North- Holland.
106 Ellison, G., and E. L. Glaeser. 1997. Geographic concentration in US manufacturing industries: A dartboard approach. Journal of Political Economy - Chicago - 105 (5):889-927. Evers, M., D. van Vuuren, and R. de Mooij. 2005. What explains the variation in estimates of labor supply elasticities?. CPB Netherlands Bureau for Economic Policy Analysis. Feldman, O., J. Nicoll, D. C. Simmonds, Caroline Sinclair, and Andrew Skinner. 2008. Use of integrated transportation land use models in wider economic benefit calculations of transport schemes, Transportation Research Board 87th Annual Meeting. Transportation Research Board. Fogarty, M. S., and G. A. Garofalo. 1988. Urban spatial structure and productivity growth in the manufacturing sector of cities. Journal of Urban Economics 23 (1):60-70. FTA. 2008. The Proposed New Starts Economic Development Criterion: Guidance for Project Sponsors and FTA Contractors, edited by USDOT. Washington, DC: Federal Transit Administration. âââ. 2009. Introduction to New Starts. Federal Transit Administration 2009 [cited November 2009]. Available from http://www.fta.dot.gov/planning/newstarts/planning_environment_2608.html. Fujita, M., P. R. Krugman, and A. Venables. 1999. The spatial economy: cities, regions, and international trade. Cambridge, Mass.; London: MIT Press. Gautier, P. A., and C. N. Teulings. 2009. Search and the city. Regional Science and Urban Economics. 39:251-265. George, L., and J. Waldfogel. 2003. Who affects whom in daily newspaper markets? Journal of Political Economy 111 (4):765-784. Glaeser, E. L. 2000. The future of urban research: nonmarket interactions. In Brookings-Wharton papers on urban affairs. Edited by W. G. Gale and J. R. Pack. Washington, DC: Brookings Institution Press. Glaeser, E. L., H. D. Kallal, J. A. Scheinkman, and A. Shleifer. 1992. Growth in cities. Journal of Political Economy 100 (6):1126-1152. Glaeser, E. L., J. Kolko, and A. Saiz. 2001. Consumer city. Journal of Economic Geography 1:27-50. Gonzalez, J.. 2008. Commuting costs and labor force retirement. Instituto Valenciano de Investigaciones EconÃmicas, S.A. (Ivie). Görg, H., and F. Warzynski. 2003. Price cost margins and exporting behavior: Evidence from firm-level data. Nottingham: Leverhulme Centre for Research on Globalisation and Economic Policy. Graham, D. 2005. Wider economic benefits of transport improvements: link between agglomeration and productivity. Stage 1 Report. Department for Transport, London. Graham, D. J. 2007b. Variable returns to agglomeration and the effect of road traffic congestion. Journal of Urban Economics 62 (1):103-120. Graham, D. J. 2007. Variable returns to agglomeration and the effect of road traffic congestion. Journal of Urban Economics 62 (1):103-120. âââ. 2007a. Agglomeration, productivity and transport investment. Journal of Transport Economics and Policy 41:317-343. Graham, D. J., and H. Youn Kim. 2008. An empirical analytical framework for agglomeration economies. Annals of Regional Science 42 (2):267-289. Griffith, R., R. Harrison, and H. W. Simpson. 2006. Product market reform and innovation in the EU. Institute for Fiscal Studies.
107 Hansen, E. R. 1990. Agglomeration economies and industrial decentralization: The wageââ productivity trade-offs. Journal of Urban Economics 28 (2):140-159. Hansen, W. G. 1959. How accessibility shapes land use. Journal of the American Planning Association 25 (2):73-76. Hanson, G. H. 1996. Agglomeration, dispersion, and the pioneer firm. Journal of Urban Economics 39 (3):255-281. âââ. 1997. Increasing returns, trade and the regional structure of wages. Economic Journal 107 (440):113-33. Harris, R. I. D. 1998. Review of the evidence of imperfect competition in the UK. In The welfare implications of transport improvements in the presence of market failure. UK Department for Transport. Haughwout, A. F. 1999. State infrastructure and the geography of employment. Growth and Change 30 (4):549-566. Hausman, J. A., and J. G. Sidak. 2005. Did mandatory unbundling achieve its purpose? Empirical evidence from five countries. Journal of Competition Law and Economics 1 (1):173-245. Helsley, R. W., and W. C. Strange. 1991. Agglomeration economies and urban capital-markets. Journal of Urban Economics 29 (1):96-112. Henderson, J. V.. 1974. The size and type of cities. American Economic Review 64:16. âââ. 1982. The impact of government policies on urban concentration. Journal of Urban Economics 12:280-303. âââ. 1982. Systems of cities in closed and open economies. Regional Science and Urban Economics 12. âââ. 1983. Industrial bases and city sizes. The American Economic Review 73:164-8. âââ. 1988. Urban development: Theory, fact, and illusion. New York u.a: Oxford Univ. Pr. Henderson, J.V. 2003. Marshall's economies. Journal of Urban Economics 53 (1):28. Henderson, V., A. Kuncoro, and M. Turner. 1995. Industrial development in cities. Journal of Political Economy 103 (5):1067-90. Holl, A.. 2006. A review of the firm-level role of transport infrastructure with implications for transport project evaluation. Journal of Planning Literature 21 (1):3-14. âââ. 2007. Twenty years of accessibility improvements. The case of the Spanish motorway building programme. Journal of Transport Geography 15 (4):286-297. Holmes, T. J. 1999. Localization of industry and vertical disintegration. Review of Economics and Statistics 81 (2):314-325. Holtz-Eakin, D. 1994. Public-sector capital and the productivity puzzle. The Review of Economics and Statistics 76 (1):12-21. Holtz-Eakin, D. 1992. State-specific estimates of state and local government capital: Syracuse University. Ingram, D. R. 1971. The concept of accessibility: A search for an operational form. Regional Studies 5 (2):101-107. Isserman, A. M., T. Rephann, and D. J. Sorenson. 1989. Highways and rural economic development: results from quasi-experimental approaches. In Seminar on Transportation Networks and Regional Development. Leningrad, USSR. Jacobs, J. 1969. The economy of cities. New York: Random House.
108 Jaffe, A. B., M. Trajtenberg, and R. Henderson. 1993. Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics 108 (3):577- 598. Jiwattanakulpaisarn, P., R. B. Noland, D. J. Graham, and J. W. Polak. 2005. A critical review of econometric analysis of transport infrastructure investment and its employment effects. London: Imperial College London. Jiwittanakulpaisarn, P., R. B. Noland, D. J. Graham, and J. W. Polak. 2009a. Highway infrastructure and state-level employment: A causal spatial analysis. Journal of Regional Science 88 (1):133-159. âââ. 2009b. Highway infrastructure investment and county employment growth: A dynamic panel regression analysis. Journal of Regional Science 49 (2):263-286. Kerin, P. D. 1992. Efficient bus fares. Transport Reviews 12 (1):33-47. Kolodziejczyk, C. 2006. Retirement and fixed costs to work: an empirical analysis: CAM Working Paper 2006-09, University of Copenhagen. Krugman, P. 1991. Increasing returns and economic geography. Journal of Political Economy 99 (3):483-499. Marshall, A. 1997 (1920). Principles of economics. Amherst, NY: Prometheus Books. Martins, J. O., S. Scarpetta, and D. Pilat. 1996. Mark-up rations in manufacturing industries: OECD. Melo, Patricia C., Daniel J. Graham, and Robert B. Noland, 2009, âA Meta-Analysis of Estimates of Urban Agglomeration Economiesâ, Regional Science and Urban Economics, 39, 332-342. Mills, E. S. 1967. An aggregative model of resource allocation in a metropolitan area. American Economic Review 57. Mohring, H. 1993. Maximizing, measuring, and not double counting transportation-improvement benefits: A primer on closed-economy and open-economy cost-benefit analysis. Transportation Research Part B-Methodological 27 (6):413-424. Mohring, H. 1972. Optimization and scale economies in urban bus transportation. American Economic Review 62 (4):591-604. Mori, T. 1997. A modeling of megalopolis formation: The maturing of city systems. Journal of Urban Economics 42 (1):133-157. Nash, C. A. 1988. Integration of public transport: An economic assessment. In Bus deregulation and privatisation: An international perspective, edited by J. S. Dodgson and N. Topham. Aldershot, UK: Avebury. Nellthorp, J., and P.J. Mackie. 2000. The UK roads review â A hedonic model of decision making. Transport Policy 7:127-138. Newbery, D. M. 1998. Road user charges in Britain. The Economic Journal 98:16. Noland, R. B. 2007. Transport planning and environmental assessment: Implications of induced travel effects. International Journal of Sustainable Transportation 1 (1):1-28. Ottaviano, G. I. P., and D. Puga. 1998. Agglomeration in the global economy: A survey of the 'new economic geography'. World Economy 21 (6):707-731. Ozbay, K., B. Bartin, O. Yanmaz-Tuzel, and J. Berechman. 2007a. Alternative methods for estimating full marginal costs of highway transportation. Transportation Research Part A 41 (8):768-786.
109 Ozbay, K., D. Ozmen-Ertekin, and J. Berechman. 2003. Empirical analysis of relationship between accessibility and economic development. Journal of Urban Planning and Development 129 (2):97-119. âââ. 2007b. Contribution of transportation investments to county output. Transport Policy 14 (4):317-329. Ozbay, K., D. Ozmen, and J. Berechman. 2006. Modeling and analysis of the link between accessibility and employment growth. Journal of Transportation Engineering 132 (5):385-393. Ozbay, K., O. Yanmaz-Tuzel, S. Mudigonda, and B. Bartin. 2008. Evaluating highway capacity investments using a geographic information systems-based tool: Trip-based full marginal cost approach. Transportation Research Record: Journal of the Transportation Research Board 2024 (1):44-53. Pilegaard, N., and M. Fosgerau. 2008. Cost-benefit analysis of a transport improvement in the case of search unemployment. Journal of Transport Economics and Policy 42:23-42. Porter, M. E. 1990. The competitive advantage of nations. New York: MacMillan. Puga, D.. 1998. Urbanization patterns: European versus less developed countries. Journal of Regional Science 38 (2):231-52. Quinet, E., and R. W. Vickerman. 2004. Principles of transport economics. Edward Elgar Publishing. Rauch, J. E. 1993. Productivity gains from geographic concentration of human capital: Evidence from the cities. Journal of Urban Economics 34 (3):380-400. REMI. 2008. Model Equations. Amherst, MA: Regional Economic Models, Inc. Rice, P., A. J. Venables, and E. Patacchini. 2006. Spatial determinants of productivity: Analysis for the regions of Great Britain. Regional Science and Urban Economics 36 (6):727-752. Rodier, C. J. 2007. Beyond Uncertainty: Modeling transportation, land use, and air quality in planning. San José, CA: Mineta Transportation Institute, San José State University. Romer, P. M. 1986. Increasing returns and long-run growth. The Journal of Political Economy 94 (5):1002-1037. Roodman, D. 2009. Estimating fully observed recursive mixed process models with âcmp.â Working Paper Number 168, April. Washington, DC: Center for Global Development. Available at http://www.cgdev.org/content/publications/detail/1421516 Rosenthal, S. S., and W. C. Strange. 2003. Geography, industrial organization, and agglomeration. Review of Economics and Statistics 85 (2):377-393. âââ. 2004. Evidence on the nature and sources of agglomeration economies. In Handbook of Regional and Urban Economics, Vol. 4. Edited by J. V. Henderson and J.-F. Thisse. Amsterdam, New York: Elsevier. Seitz, H. 1993. The impact of the provision of urban infrastructures on the manufacturing industry in cities. In 33rd European Congress of the Regional Science Association. Moscow, Russia. Shefer, D., and H. Aviram. 2005. Incorporating agglomeration economies in transport cost- benefit analysis: The case of the proposed light rail transit in the Tel-Aviv metropolitan area. Papers in Regional Science 84 (3):487-507. Simon, C. J. 1988. Frictional unemployment and the role of industrial diversity. Quarterly Journal of Economics 103 (4):715-728. Small, I. 1997. The cyclicality of mark-ups and profit margins: Some evidence for manufacturing and services. Bank of England.
110 Stephanedes, Y., and D.M. Eagle. 1986. Time-series analysis of interactions between transportation and manufacturing and retail employment. Transportation Research Record: Journal of the Transportation Research Board 1074:16-24. Stock, J.H. and M. Yogo. 2005. Testing for weak instruments in linear IV regression. In Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. Edited by D.W.K. Andrews and J.H. Stock. Cambridge: Cambridge University Tabuchi, T., and A. Yoshida. 2000. Separating urban agglomeration economies in consumption and production. Journal of Urban Economics 48 (1):70-84. van Reeven, P.. 2008. Subsidisation of urban public transport and the Mohring effect. Journal of Transport Economics and Policy 42:349-359. Venables, A. J. 2007. Evaluating urban transport improvements: Cost-benefit analysis in the presence of agglomeration and income taxation. Journal of Transport Economics and Policy 41:173-188. âââ. 2007. Evaluating urban transport improvements: Cost-benefit analysis in the presence of agglomeration and income taxation. Journal of Transport Economics and Policy 41 (2):173. Venables, A. J. 2005. Spatial disparities in developing countries: cities, regions, and international trade. Journal of Economic Geography 5 (1):3-21. Venables, A. J., and M. Gasoriek. 1998. The welfare implications of transport improvements in the presence of market failureâthe incidence of imperfect competition in UK sectors and regions. London: Department of the Environment, Transport and the Regions. Waldfogel, J. 2003. Preference externalities: An empirical study of who benefits whom in differentiated-product markets. Rand Journal of Economics 34 (3):557-568. Walters, A. A. 1982. Externalities in urban buses. Journal of Urban Economics 11 (1):60-72. Williamson, O. E. 1979. Transaction cost economics: The governance of contractual relations. Journal of Law and Economics 22:233-261.
111 Section 6 (Case Studies) 1. 2006-2008 American Community Survey 3-Year Estimates, Means of Transportation to Work for Workers 16 Years and Over. US Census Bureau. 2. Population Estimates. US Census Bureau, 2010. 3. Los Angeles County Metropolitan Transportation Authority. Facts at a Glance, October 13, 2010. http://www.metro.net/news/pages/facts-glance/. 4. Callaghan, L., and W. Vincent. Preliminary evaluation of metro orange line bus rapid transit project. Transportation Research Record, Vol. 2034, 2007, pp. 37-44. 5. Metro Orange Line. Los Angeles County Metropolitan Transportation Authority, 2010. http://www.metro.net/riding_metro/bus_overview/images/901.pdf. 6. Orange Line Map and Station Locations. Los Angeles County Metropolitan Transportation Authority, 2010. http://www.metro.net/around/rail/orange-line/. 7. Southern California Association of Governments. 1994 Regional Mobility Element, Volume 1, Los Angeles, 1994. 8. Southern California Association of Governments. COMPASS Blueprint, 2010. http://www.compassblueprint.org. 9. Los Angeles County Metropolitan Transportation Authority. Joint Development Program, 2010. http://www.metro.net/projects/joint_dev_pgm/. 10. Joint Development Program Fact Sheet. Los Angeles County Metropolitan Transportation Authority, 2010. http://www.metro.net/projects_studies/joint_development/images/joint_dev_project_fact _sheet.pdf. 11. Joint Development Program Completed Projects. Los Angeles County Metropolitan Transportation Authority, 2010. http://www.metro.net/projects_studies/joint_development/images/JDP_completed_projec ts.pdf. 12. Los Angeles County Metropolitan Transportation Authority. 30/10 Initiative Fact Sheet, 2010. http://www.metro.net/projects_studies/30-10_highway/images/10- 2226_ntc_3010_initiative_factsheet_printshop%202.pdf. 13. Texas Transportation Institute. Urban Mobility Report, 2009. http://mobility.tamu.edu/ums/report/. 14. Los Angeles County Economic Development Corporation. http://www.laedc.org/. 15. Chart of Zoning Rules. City of Dallas. http://www.dallascityhall.com/pdf/planning/zonechart.pdf. 16. Transit Ridership Report. American Public Transportation Association, 2010. http://apta.com/resources/statistics/Documents/Ridership/2010_q2_ridership_APTA.pdf. 17. Utah Transit Authority. Homepage, 2010. http://www.rideuta.com/. 18. Utah Transit Authority Profiles,1999-2009. National Transit Database, 2009. http://www.ntdprogram.gov/ntdprogram/data.htm. 19. Parsons Brinckerhoff Quade & Douglas, Inc. I-15/State Street Corridor Study - Transit Salt Lake County, Utah: Final Environmental Impact Statement. US Department of Transportation, Federal Transit Administration, and Utah Transit Authority, 1994. 20. Parsons Transportation Group. Airport to University West-East Light Rail Project: Final Environmental Impact Statement. Federal Transit Administration, Utah Transit Authority, and Wasatch Front Regional Council, 1999. 21. Economic Development Corporation of Utah, 2011. http://www.edcutah.org/.
112 22. Utah Department of Workforce Services, 2010. http://jobs.utah.gov/. 23. Sterling Codifiers, I. Salt Lake City, Utah City Code, 2010. http://www.sterlingcodifiers.com/codebook/index.php?book_id=672. 24. Central Community Zoning Map. 2009. 25. City of South Salt Lake Zoning Map. City of South Salt Lake, 2008. http://www.ssl.state.ut.us/ECON%20DEV/mapsmedia/Current_zoning_22x32final.pdf. 26. Murray City Zoning. City of Murray, 2010. http://www.murray.utah.gov/DocumentView.aspx?DID=992. 27. Midvale Zoning Map. City of Midvale, 2008. http://www.midvalecity.org/files/planning/zoning_map_updated.pdf. 28. 2010 Zoning Map. Community Development Department, City of Sandy, 2010. http://sandy.utah.gov/fileadmin/downloads/comm_dev/maps/zoning/ZoningMap.pdf. 29. About DART. Dallas Area Rapid Transit, 2011. http://www.dart.org/about/aboutdart.asp. 30. Facts: SLRV. Dallas Area Rapid Transit, 2010. http://www.dart.org/factsheet/SLRV/. 31. DART Rail Stations/Park & Rides/Transfer Centers. Dallas Area Rapid Transit. http://www.dart.org/maps/locationslist.asp. 32. Hartzel, T. Dallas Rail Line seen as Catalyst for Transit, Real Estate Cooperation. Dallas Morning News, February 27, 2000. 33. Clower,T. L., B. Weinstein, and M. Seman. Assessment of the Potential Fiscal Impacts of Existing and Proposed Transit-Oriented Development in the Dallas Area Rapid Transit Service Area, 2007. 34. Downtown Dallas Transit Study: Dallas CBD Alternatives Analysis/Draft Environmental Impact Statement. DART Draft Environmental Impact Statement, 2010. 35. Dallas Regional Chamber, 2011. http://www.dallaschamber.org/. 36. Allen, W. B., D. Liu, and S. Singer. Accessibility measures of US metropolitan areas. Transportation Research B, Vol. 27, No. 6, 1993, pp. 439-49.
113 APPENDIX B: MSA MODEL RESULTS TABLE B-1: Total track mile models for employment density (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV APTA track miles -0.774 -1.450 1.879 6.203 (2 year lag) (-2.25) (-3.00) (1.94) (4.44) APTA track miles -0.686 -1.480 1.811 6.197 (4 year lag) (-2.05) (-3.06) (1.93) (4.44) Freeway/arterial miles -0.114 -0.167 0.0774 0.423 (2 year lag) (-1.82) (-2.47) (0.44) (2.17) Freeway/arterial miles -0.0858 -0.145 0.0518 0.386 (4 year lag) (-1.31) (-2.07) (0.28) (1.91) Population, 2006 0.000153 0.000137 0.000202 0.000192 0.000160 0.000174 -0.000157 -0.000131 (4.76) (4.52) (4.98) (4.95) (1.77) (2.05) (-1.34) (-1.17) % of population under 18 -3.377 -3.383 -2.667 -2.609 -73.15 -73.09 -74.42 -74.62 (2005-2007 ACS) (-0.41) (-0.41) (-0.33) (-0.32) (-3.13) (-3.13) (-3.14) (-3.14) % of population over 65 -13.62 -13.55 -13.34 -13.21 -15.49 -15.55 -15.78 -16.14 (2005-2007 ACS) (-2.11) (-2.10) (-2.09) (-2.06) (-0.85) (-0.86) (-0.86) (-0.87) % of population 25+ with -2.584 -2.478 -2.878 -2.937 -42.47 -42.58 -38.29 -38.19 HS diploma (2005-07 ACS) (-0.50) (-0.48) (-0.56) (-0.57) (-2.92) (-2.93) (-2.57) (-2.56) % of population 25+ with 15.55 15.47 15.77 15.73 25.33 25.42 23.95 23.97 coll. Diploma (2005-07 ACS) (4.35) (4.31) (4.46) (4.43) (2.52) (2.52) (2.35) (2.34) % of population white -2.922 -2.883 -3.123 -3.098 2.211 2.151 2.924 2.863 (2005-2007 ACS) (-1.15) (-1.13) (-1.24) (-1.23) (0.31) (0.30) (0.40) (0.39) % of population black -9.042 -9.024 -9.314 -9.349 -10.77 -10.81 -8.878 -8.886 (2005-2007 ACS) (-3.25) (-3.24) (-3.39) (-3.38) (-1.37) (-1.38) (-1.12) (-1.12) % of population Hispanic -0.738 -0.711 -1.175 -1.264 -2.522 -2.569 0.685 0.832 (2005-2007 ACS) (-0.39) (-0.38) (-0.62) (-0.67) (-0.48) (-0.48) (0.13) (0.15) Median household income 0.00497 0.00465 0.00462 0.00418 0.0524 0.0526 0.0535 0.0543 (2005-2007 ACS) (1.53) (1.43) (1.44) (1.30) (5.74) (5.78) (5.76) (5.84) Median value of own-occ. -0.000303 -0.000274 -0.000292 -0.000255 -0.00188 -0.00190 -0.00189 -0.00196 housing unit (2005-07 ACS) (-1.35) (-1.22) (-1.32) (-1.15) (-2.98) (-3.02) (-2.96) (-3.07)
114 Constant 1015.5 1008.1 1049.5 1057.0 4132.7 4143.0 3697.3 3691.9 (2.11) (2.09) (2.20) (2.20) (3.05) (3.05) (2.68) (2.67) Number of observations 354 354 351 351 354 354 351 351 Adjusted R-squared 0.465 0.463 0.460 0.456 0.464 0.464 0.434 0.431 widstat test statistic 109.3 98.85 109.3 98.85 sargan test statistic 10.59 9.789 2.016 1.804 sarganp test statistic 0.00503 0.00749 0.365 0.406
115 Table B-2: Total track mile models for employment density (NYC omitted) (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV APTA track miles -0.315 -0.848 0.503 6.881 (2 year lag) (-0.78) (-1.19) (0.45) (3.28) APTA track miles -0.232 -1.052 0.444 7.279 (4 year lag) (-0.59) (-1.40) (0.40) (3.29) Freeway/arterial miles -0.131 -0.155 0.129 0.436 (2 year lag) (-2.09) (-2.28) (0.73) (2.18) Freeway/arterial miles -0.113 -0.143 0.133 0.393 (4 year lag) (-1.70) (-2.04) (0.72) (1.91) Population, 2006 0.000157 0.000144 0.000184 0.000181 0.000147 0.000152 -0.000177 -0.000158 (4.91) (4.76) (4.21) (4.33) (1.64) (1.79) (-1.38) (-1.28) % of population under 18 -3.187 -3.122 -2.580 -2.523 -73.71 -73.88 -74.32 -74.41 (2005-2007 ACS) (-0.39) (-0.38) (-0.32) (-0.31) (-3.18) (-3.18) (-3.11) (-3.08) % of population over 65 -13.91 -13.84 -13.58 -13.39 -14.61 -14.68 -16.05 -16.58 (2005-2007 ACS) (-2.17) (-2.15) (-2.14) (-2.10) (-0.81) (-0.82) (-0.86) (-0.88) % of population 25+ with -2.157 -2.096 -2.234 -2.493 -43.75 -43.73 -37.57 -37.06 HS diploma (2005-07 ACS) (-0.42) (-0.41) (-0.43) (-0.48) (-3.03) (-3.02) (-2.48) (-2.43) % of population 25+ with 15.40 15.36 15.56 15.60 25.79 25.73 23.71 23.65 coll. Diploma (2005-07 ACS) (4.33) (4.31) (4.44) (4.42) (2.58) (2.57) (2.30) (2.27) % of population white -2.684 -2.674 -2.922 -2.969 1.498 1.522 3.150 3.188 (2005-2007 ACS) (-1.06) (-1.05) (-1.17) (-1.18) (0.21) (0.21) (0.43) (0.43) % of population black -8.951 -8.950 -9.124 -9.222 -11.04 -11.04 -8.665 -8.562 (2005-2007 ACS) (-3.23) (-3.23) (-3.34) (-3.36) (-1.42) (-1.42) (-1.08) (-1.06) % of population Hispanic -0.721 -0.723 -0.940 -1.105 -2.575 -2.532 0.948 1.239 (2005-2007 ACS) (-0.38) (-0.38) (-0.50) (-0.58) (-0.49) (-0.48) (0.17) (0.22) Median household income 0.00443 0.00414 0.00428 0.00397 0.0540 0.0542 0.0531 0.0538 (2005-2007 ACS) (1.37) (1.28) (1.34) (1.24) (5.94) (5.97) (5.64) (5.68) Median value of own-occ. -0.000308 -0.000281 -0.000295 -0.000260 -0.00186 -0.00188 -0.00190 -0.00198 housing unit (2005-07 ACS) (-1.38) (-1.27) (-1.34) (-1.18) (-2.97) (-3.01) (-2.93) (-3.04) Constant 991.7 988.4 1000.0 1023.3 4204.1 4202.3 3641.7 3606.3 (2.07) (2.06) (2.10) (2.14) (3.12) (3.12) (2.60) (2.55)
116 Number of observations 353 353 350 350 353 353 350 350 Adjusted R-squared 0.455 0.453 0.454 0.447 0.375 0.375 0.316 0.304 widstat test statistic 49.60 40.75 49.60 40.75 sargan test statistic 10.72 10.03 2.005 1.739 sarganp test statistic 0.00471 0.00663 0.367 0.419
117 Table B-3: Track miles per square mile of MSA land area, employment density models (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV APTA track miles per sq mi 1151.4 -13607.0 10212.9 49452.0 (2 year lag) (0.65) (-3.12) (2.10) (4.15) APTA track miles per sq mi 885.6 -13199.4 10158.7 48252.0 (4 year lag) (0.50) (-3.14) (2.07) (4.17) Freeway + arterial miles -65.01 23.82 1294.4 1078.8 per sq. mile (2 year lag) (-0.46) (0.15) (3.33) (2.56) Freeway + arterial miles 17.38 137.6 1361.2 1058.4 per sq. mile (4 year lag) (0.11) (0.82) (3.20) (2.29) Population, 2006 0.0000729 0.0000726 0.000183 0.000176 0.000160 0.000163 -0.000134 -0.000117 (4.34) (4.36) (5.32) (5.36) (3.47) (3.57) (-1.42) (-1.30) % of population under 18 -4.374 -3.460 -2.833 -1.789 -58.30 -59.61 -61.25 -63.03 (2005-2007 ACS) (-0.51) (-0.41) (-0.31) (-0.20) (-2.50) (-2.56) (-2.44) (-2.52) % of population over 65 -13.50 -13.81 -11.46 -12.03 -22.29 -22.70 -27.41 -27.30 (2005-2007 ACS) (-2.07) (-2.11) (-1.62) (-1.71) (-1.25) (-1.27) (-1.41) (-1.42) % of population 25+ with -1.708 -1.169 -5.160 -4.103 -31.54 -30.98 -21.44 -22.00 HS diploma (2005-07 ACS) (-0.32) (-0.22) (-0.88) (-0.70) (-2.16) (-2.11) (-1.33) (-1.37) % of population 25+ with 15.00 14.99 16.21 16.04 24.39 23.58 21.18 20.73 coll. Diploma (2005-07 ACS) (4.17) (4.16) (4.17) (4.16) (2.47) (2.39) (1.99) (1.96) % of population white -2.603 -2.674 -3.798 -3.739 1.941 2.329 4.929 5.020 (2005-2007 ACS) (-1.01) (-1.04) (-1.37) (-1.36) (0.28) (0.33) (0.65) (0.66) % of population black -8.468 -8.653 -10.33 -10.37 -12.88 -12.53 -7.904 -7.857 (2005-2007 ACS) (-3.00) (-3.07) (-3.36) (-3.41) (-1.66) (-1.62) (-0.94) (-0.94) % of population Hispanic -0.0198 0.0327 -2.345 -2.051 -0.717 -0.285 5.652 5.567 (2005-2007 ACS) (-0.01) (0.02) (-1.08) (-0.96) (-0.14) (-0.05) (0.95) (0.95) Median household income 0.00519 0.00445 0.00308 0.00236 0.0407 0.0416 0.0458 0.0468 (2005-2007 ACS) (1.50) (1.30) (0.82) (0.64) (4.29) (4.42) (4.44) (4.58) Median value of own-occ. -0.000299 -0.000267 0.00000141 0.0000143 -0.00157 -0.00161 -0.00235 -0.00236 housing unit (2005-07 ACS) (-1.31) (-1.17) (0.01) (0.06) (-2.51) (-2.59) (-3.33) (-3.39) Constant 934.7 889.7 1248.3 1150.7 3225.1 3174.8 2305.9 2376.9 (1.89) (1.79) (2.30) (2.14) (2.38) (2.33) (1.55) (1.61)
118 Number of observations 354 354 351 351 354 354 351 351 Adjusted R-squared 0.457 0.456 0.348 0.358 0.483 0.482 0.385 0.391 widstat test statistic 27.79 30.15 27.79 30.15 sargan test statistic 6.190 7.531 0.0477 0.000937 sarganp test statistic 0.0453 0.0232 0.976 1.000
119 Table B-4: Track miles per square mile of MSA land area, employment density models (NYC omitted) (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV APTA track miles per sq mi 3310.6 -15298.4 5116.5 60021.7 (2 year lag) (1.75) (-2.01) (0.98) (2.81) APTA track miles per sq mi 3118.3 -14783.1 4711.2 58505.1 (4 year lag) (1.62) (-2.00) (0.89) (2.81) Freeway + arterial miles -95.00 36.87 1365.1 1000.4 per sq. mile (2 year lag) (-0.68) (0.23) (3.53) (2.18) Freeway + arterial miles -25.58 155.9 1466.0 947.0 per sq. mile (4 year lag) (-0.17) (0.86) (3.46) (1.85) Population, 2006 0.0000823 0.0000816 0.000190 0.000182 0.000137 0.000141 -0.000181 -0.000160 (4.88) (4.88) (4.10) (4.14) (2.95) (3.06) (-1.39) (-1.30) % of population under 18 -4.648 -3.808 -2.705 -1.635 -57.65 -58.76 -62.03 -63.97 (2005-2007 ACS) (-0.55) (-0.45) (-0.29) (-0.18) (-2.49) (-2.54) (-2.35) (-2.45) % of population over 65 -13.96 -14.22 -11.19 -11.81 -21.21 -21.71 -29.03 -28.71 (2005-2007 ACS) (-2.16) (-2.20) (-1.54) (-1.64) (-1.19) (-1.22) (-1.42) (-1.42) % of population 25+ with -1.240 -0.785 -5.601 -4.464 -32.65 -31.91 -18.66 -19.61 HS diploma (2005-07 ACS) (-0.24) (-0.15) (-0.90) (-0.73) (-2.25) (-2.19) (-1.07) (-1.14) % of population 25+ with 14.57 14.58 16.42 16.22 25.41 24.57 19.96 19.64 coll. diploma (2005-07 ACS) (4.09) (4.09) (4.07) (4.08) (2.59) (2.51) (1.76) (1.75) % of population white -2.102 -2.190 -4.000 -3.925 0.759 1.147 6.132 6.138 (2005-2007 ACS) (-0.83) (-0.86) (-1.37) (-1.37) (0.11) (0.16) (0.75) (0.76) % of population black -8.324 -8.503 -10.52 -10.53 -13.22 -12.89 -6.730 -6.762 (2005-2007 ACS) (-2.98) (-3.05) (-3.27) (-3.32) (-1.72) (-1.68) (-0.74) (-0.76) % of population Hispanic -0.0761 -0.0418 -2.529 -2.197 -0.585 -0.104 6.871 6.622 (2005-2007 ACS) (-0.04) (-0.02) (-1.08) (-0.97) (-0.11) (-0.02) (1.05) (1.03) Median household income 0.00422 0.00355 0.00317 0.00245 0.0430 0.0438 0.0455 0.0465 (2005-2007 ACS) (1.23) (1.04) (0.82) (0.65) (4.55) (4.67) (4.21) (4.37) Median value of own-occ. -0.000300 -0.000270 0.0000246 0.0000347 -0.00157 -0.00161 -0.00250 -0.00250 housing unit (2005-07 ACS) (-1.33) (-1.20) (0.09) (0.13) (-2.52) (-2.60) (-3.19) (-3.25) Constant 924.6 887.7 1280.3 1174.6 3249.0 3179.7 2098.6 2209.4
120 (1.89) (1.81) (2.25) (2.11) (2.42) (2.35) (1.32) (1.41) Number of observations 353 353 350 350 353 353 350 350 Adjusted R-squared 0.453 0.452 0.299 0.313 0.399 0.398 0.203 0.215 widstat test statistic 10.42 11.28 10.42 11.28 sargan test statistic 6.095 7.539 0.0273 0.0135 sarganp test statistic 0.0475 0.0231 0.986 0.993
121 Table B-5: Track miles per capita, population models (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion OLS OLS IV IV OLS OLS IV IV APTA track miles per capita 7.29612e+10 1.92076e+11 5.35190e+10 1.12126e+11 (2 year lag) (11.74) (.) (10.46) (.) APTA track miles per capita 7.22308e+10 1.91069e+11 5.28065e+10 1.11852e+11 (4 year lag) (11.67) (.) (10.34) (.) Freeway + arterial miles -211720502 -132849461 -206582669 -171873095 per capita (2 year lag) (-2.94) (-1.30) (-3.61) (-2.58) Freeway + arterial miles -213482199 -136516366 -207240698 -173488301 per capita (4 year lag) (-2.92) (-1.31) (-3.56) (-2.55) % of population under 18 -9875.3 -6466.6 277.1 3752.7 -9015.8 -5836.3 -4486.3 -1159.3 (2005-2007 ACS) (-0.25) (-0.16) (0.00) (0.07) (-0.29) (-0.18) (-0.12) (-0.03) % of population over 65 -507.4 1030.4 -40205.0 -39099.8 -2587.6 -1046.3 -20256.3 -19034.2 (2005-2007 ACS) (-0.02) (0.03) (-0.93) (-0.90) (-0.11) (-0.04) (-0.72) (-0.67) % of population 25+ with 14093.8 13762.6 69991.9 69746.7 10841.4 10494.1 37139.5 37038.5 HS diploma (2005-07 ACS) (0.57) (0.55) (2.00) (1.99) (0.55) (0.53) (1.63) (1.61) % of population 25+ with 4315.7 5364.6 -26750.5 -25792.8 4885.8 5891.0 -9432.6 -8583.6 coll. diploma (2005-07 ACS) (0.25) (0.32) (-1.12) (-1.08) (0.36) (0.44) (-0.61) (-0.55)
122 % of population white -14492.7 -15082.1 7091.0 5940.3 -13687.2 -14173.8 -3993.5 -4675.6 (2005-2007 ACS) (-1.19) (-1.23) (0.41) (0.34) (-1.41) (-1.46) (-0.36) (-0.42) % of population black 4486.5 3896.5 7976.4 6523.8 3498.3 3068.9 5305.4 4456.8 (2005-2007 ACS) (0.33) (0.29) (0.42) (0.34) (0.33) (0.29) (0.43) (0.36) % of population Hispanic 13910.6 13385.2 22481.8 21608.9 13455.3 13016.5 17488.5 16897.9 (2005-2007 ACS) (1.53) (1.47) (1.75) (1.68) (1.87) (1.80) (2.09) (2.01) Median household income 10.27 9.719 -50.70 -52.20 19.58 19.24 -10.27 -11.37 (2005-2007 ACS) (0.68) (0.64) (-2.43) (-2.49) (1.63) (1.60) (-0.76) (-0.83) Median value of own- occ. -0.313 -0.235 -0.838 -0.696 -0.833 -0.768 -0.965 -0.869 housing unit (2005-07 ACS) (-0.30) (-0.22) (-0.57) (-0.47) (-1.00) (-0.92) (-1.00) (-0.89) Constant 237479.4 189246.5 -2499156.6 -2447215.6 105150.6 45325.9 -1124547.8 -1144615.9 (0.10) (0.08) (-0.75) (-0.74) (0.06) (0.02) (-0.52) (-0.53) Number of observations 364 364 361 361 363 363 360 360 Adjusted R-squared 0.427 0.425 0.202 0.202 0.418 0.415 0.236 0.235 widstat test statistic 55.19 55.09 63.32 62.74 sargan test statistic 154.1 153.3 128.5 127.2 sarganp test statistic 3.47e-34 5.12e-34 1.26e-28 2.36e-28
123 Table B-6: Track miles per square mile of urbanized area, population models (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion Popula tion OLS OLS IV IV OLS OLS IV IV APTA track miles per sq mi 67559530.4 96455407.6 51154923.3 89412557.1 (2 year lag) (19.01) (18.84) (12.62) (11.70) APTA track miles per sq mi 68396610.7 97577299.1 51319128.5 91015000.5 (4 year lag) (18.91) (18.86) (12.34) (11.59) Freeway + arterial miles 1617056.9 67638.4 1663543.8 246591.9 per sq. mile (2 year lag) (3.18) (0.12) (3.50) (0.43) Freeway + arterial miles 1458858.6 -267267.7 1620651.1 -63378.7 per sq. mile (4 year lag) (2.60) (-0.42) (3.08) (-0.10) % of population under 18 18121.5 14263.8 3750.8 797.5 17698.3 14754.9 5143.6 2156.8 (2005-2007 ACS) (0.58) (0.45) (0.11) (0.02) (0.61) (0.50) (0.16) (0.07) % of population over 65 -6682.7 -5916.6 -10869.3 -8923.3 -4330.8 -4128.7 -9953.1 -8340.2 (2005-2007 ACS) (-0.28) (-0.25) (-0.42) (-0.35) (-0.20) (-0.18) (-0.40) (-0.33) % of population 25+ with 30769.5 29779.9 33743.1 31098.7 25615.0 25357.2 32289.3 30103.4 HS diploma (2005-07 ACS) (1.58) (1.51) (1.60) (1.45) (1.40) (1.37) (1.59) (1.46) % of population 25+ with 1022.9 415.6 -5823.5 -5209.7 3641.7 2894.0 -4472.3 -4137.4 coll. diploma (2005-07 ACS) (0.08) (0.03) (-0.41) (-0.36) (0.30) (0.23) (-0.33) (-0.30)
124 % of population white -10236.1 -10126.6 -2855.4 -3234.1 -11972.5 -11866.2 -4065.1 -4280.6 (2005-2007 ACS) (-1.08) (-1.06) (-0.28) (-0.31) (-1.35) (-1.33) (-0.41) (-0.43) % of population black 5727.1 6363.7 10171.2 10381.2 4788.2 5280.6 9469.6 9756.5 (2005-2007 ACS) (0.55) (0.60) (0.90) (0.92) (0.49) (0.54) (0.87) (0.89) % of population Hispanic 18645.0 18911.3 19597.2 19074.7 17918.6 18328.6 19342.2 18952.5 (2005-2007 ACS) (2.67) (2.68) (2.61) (2.51) (2.74) (2.77) (2.67) (2.58) Median household income 7.171 10.10 10.14 12.55 13.67 15.92 11.33 13.54 (2005-2007 ACS) (0.58) (0.81) (0.76) (0.94) (1.18) (1.37) (0.88) (1.04) Median value of own- occ. -0.609 -0.703 -1.588 -1.626 -0.647 -0.718 -1.490 -1.539 housing unit (2005-07 ACS) (-0.73) (-0.84) (-1.75) (-1.79) (-0.83) (-0.91) (-1.70) (-1.75) Constant -2544831.1 -2432755.3 -2505313.7 -2255594.4 -2342057.4 -2296857.9 -2465269.4 -2247183.4 (-1.41) (-1.33) (-1.28) (-1.14) (-1.38) (-1.34) (-1.31) (-1.18) Number of observations 364 364 361 361 363 363 360 360 Adjusted R-squared 0.656 0.650 0.592 0.585 0.515 0.506 0.391 0.377 widstat test statistic 145.9 150.1 60.47 60.58 sargan test statistic 5.782 4.928 6.885 5.837 sarganp test statistic 0.0555 0.0851 0.0320 0.0540
125 Table B-7: Total track miles, heavy rail instrumented dependent variable Employment density, UZA Employment density, UZA Employment density, principal city Employment density, principal city Population Population lag2 lag4 lag2 lag4 lag2 lag4 Comm. Rail Track miles, 2yr lag 1.046355 -8.164 14616.2 (1.00) (-2.92) (7.49) Comm. Rail Track miles, 4yr lag 1.522913 -8.56 13702.4 (1.42) (-3.00) (7.24) Heavy Rail Track miles, 2yr lag -9.97145 46.9 -23408.9 (-2.31) (4.04) (-2.79) Heavy Rail Track miles, 4yr lag -11.6549 46.74 -18888.8 (-2.64) (3.99) (-2.31) Light Rail Track miles, 2yr lag 4.558379 20.13 18181.2 (1.50) (2.47) (3.76) Light Rail Track miles, 4yr lag 5.464875 19.77 19639.1 (1.75) (2.38) (3.92) Freeway/arterial, 2yr lag -5.62E-02 0.3 1643.3 (-0.77) (1.52) (22.42) Freeway/arterial, 4yr lag -3.34E-02 0.26 1768.1 (-0.43) (1.26) (22.40) Population 1.48E-04 1.35E-04 4.74E-05 9.31E-05 (3.67) (3.61) (0.44) (0.93) Constant 854.6961 849.5161 1454.4 1460.3 -206875.3 -200091.1 (35.35) (34.89) (22.41) (22.56) (-5.27) (-5.10) N 351 351 351 351 361 361 Cragg-Donald 29.15 29.86 29.15 29.86 19.12 21.56 Stock-Yogo, 10% 22.3 22.3 22.3 22.3 22.3 22.3 Sargan 3.173 2.845 6.371 6.337 14.45 14.07 Sargan (P) 0.205 0.241 0.0414 0.0421 0.000729 0.000881
126 Table B-8: Track miles per square mile CBSA, heavy rail instrumented dependent variable Employment density, UZA Employment density, UZA Employment density, principal city Employment density, principal city Population Population lag2 lag4 lag2 lag4 lag2 lag4 Comm. rail per sq mi CBSA, 2yr lag 9142.658 -24599.2 -5411201.7 (1.63) (-1.76) (-0.83) Comm. rail per sq mi CBSA, 4yr lag 10929.75 -27465.9 -4604326.6 (1.89) (-1.95) (-0.73) Heavy rail per sq mile CBSA, 2yr lag -68116.7 183687.5 244698977.8 (-2.63) (2.85) (7.17) Heavy rail per sq mile CBSA, 4yr lag -78217.6 193468 245285939.1 (-2.91) (2.94) (7.43) Light rail per sq mile CBSA, 2yr lag 62159.71 -17430.5 10678546.2 (4.48) (-0.51) (0.91) Light rail per sq mile CBSA, 4yr lag 68116.69 -18552.4 8596624.4 (4.51) (-0.50) (0.72) Freeway/arterial per sq mi, 2yr lag -119.139 1715.4 -51615.9 (-0.71) (4.11) (-1.08) Freeway/arterial per sq mi, 4yr lag -66.0447 1826.6 -55199.8 (-0.36) (4.03) (-1.12) Population 1.05E-04 1.07E-04 0.000161 0.000169 (4.71) (4.77) (2.91) (3.09) Constant 872.826 857.2861 1160.2 1167.3 556183.0 554097.7 (21.54) (20.86) (11.54) (11.62) (2.69) (2.81) N 351 351 351 351 351 336 Cragg-Donald 18.03 20.73 18.03 20.73 18.26 19.73 Stock-Yogo, 10% 22.3 22.3 22.3 22.3 22.3 22.3 Sargan 0.924 1.182 2.892 2.526 3.857 3.433 Sargan (P) 0.63 0.554 0.235 0.283 0.145 0.180
127 Table B-9: Seat capacity per capita models (1) (2) (3) (4) (5) (6) (7) (8) Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Population Population Population Population OLS OLS IV IV OLS OLS IV IV Rail seat capacity per capita 42980.1 154556.5 336442415.8 518243585.6 (2 year lag) (2.23) (4.11) (15.85) (17.33) Rail seat capacity per capita 57493.9 147707.0 360863317.7 496550310.4 (4 year lag) (2.81) (4.31) (17.46) (18.82) Bus seat capacity per capita 43033.4 37344.1 40256530.8 6821535.0 (2 year lag) (5.00) (4.08) (3.12) (0.47) Bus seat capacity per capita 39900.6 37392.4 37894720.8 17913563.1 (4 year lag) (5.07) (4.64) (3.33) (1.47) Freeway + arterial miles -56401.7 -81959.3 -196592883 -186019938 per capita (2 year lag) (-1.38) (-1.91) (-3.19) (-2.76) Freeway + arterial miles -63358.4 -85704.3 -194206423 -186161647 per capita (4 year lag) (-1.53) (-2.02) (-3.23) (-2.94) Population, 2006 0.000174 0.000155 0.0000409 0.0000433 (4.94) (4.24) (0.78) (0.87) % of population under 18 -58.78 -56.77 -59.71 -56.61 10780.3 14743.3 2748.8 9640.1 (2005-2007 ACS) (-2.60) (-2.52) (-2.55) (-2.48) (0.31) (0.45) (0.07) (0.28) % of population over 65 -14.98 -14.95 -20.38 -18.33 2103.5 2147.6 -16600.1 -10832.4 (2005-2007 ACS) (-0.85) (-0.86) (-1.12) (-1.03) (0.08) (0.09) (-0.58) (-0.41) % of population 25+ with -41.42 -41.37 -35.80 -37.79 14674.6 14550.1 32316.7 27167.6 HS diploma (2005-07 ACS) (-2.97) (-2.98) (-2.44) (-2.65) (0.69) (0.72) (1.40) (1.27) % of population 25+ with 15.03 16.78 13.81 15.76 -5839.0 -4301.5 -9045.3 -7943.9 coll. diploma (2005-07 ACS) (1.53) (1.73) (1.37) (1.61) (-0.40) (-0.31) (-0.57) (-0.54) % of population white 8.454 7.426 8.128 7.152 -6967.4 -8165.4 -6141.5 -7034.1 (2005-2007 ACS) (1.21) (1.07) (1.13) (1.02) (-0.65) (-0.80) (-0.53) (-0.66) % of population black -6.798 -8.193 -8.031 -8.952 6738.5 4994.1 1096.4 1669.6 (2005-2007 ACS) (-0.89) (-1.08) (-1.02) (-1.17) (0.58) (0.45) (0.09) (0.14) % of population Hispanic -3.876 -4.099 -1.992 -2.718 12167.5 11571.9 14633.4 13384.1 (2005-2007 ACS) (-0.76) (-0.81) (-0.38) (-0.53) (1.57) (1.55) (1.74) (1.71) Median household income 0.0457 0.0443 0.0403 0.0413 5.043 4.932 -13.71 -7.302 (2005-2007 ACS) (5.22) (5.09) (4.37) (4.64) (0.39) (0.40) (-0.97) (-0.56) Median value of own-occ. -0.00168 -0.00166 -0.00174 -0.00172 0.0775 0.0420 -0.0765 -0.108 housing unit (2005-07 ACS) (-2.84) (-2.81) (-2.85) (-2.89) (0.09) (0.05) (-0.08) (-0.12) Constant 3661.8 3736.4 3693.6 3745.4 -670425.5 -684360.5 -685648.0 -789483.4 (2.75) (2.83) (2.69) (2.81) (-0.33) (-0.35) (-0.31) (-0.39) Number of observations 354 354 351 351 364 364 361 361 Adjusted R-squared 0.507 0.512 0.460 0.485 0.582 0.613 0.494 0.565
128 widstat test statistic 45.89 65.86 196.3 263.8 sargan test statistic 0.565 0.465 0.601 1.228 sarganp test statistic 0.754 0.793 0.740 0.541
129 Table B-10: Rail revenue mile models, employment density (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV Rail revenue miles -0.0000134 -0.0000259 0.0000547 0.0000911 (2 year lag) (-2.37) (-3.51) (3.56) (4.50) Rail revenue miles -0.0000137 -0.0000265 0.0000562 0.0000932 (4 year lag) (-2.38) (-3.53) (3.57) (4.52) Freeway + arterial miles -70.20 -68.30 1393.1 1411.2 per sq. mile (2 year lag) (-0.50) (-0.49) (3.63) (3.70) Freeway + arterial miles 14.04 17.22 1480.6 1495.7 per sq. mile (4 year lag) (0.09) (0.11) (3.54) (3.59) Population, 2006 0.000110 0.000107 0.000136 0.000132 0.000121 0.000124 0.0000445 0.0000499 (7.02) (6.92) (7.33) (7.28) (2.84) (2.95) (0.88) (1.00) % of population under 18 -4.551 -3.579 -3.984 -2.972 -56.88 -58.30 -57.12 -58.74 (2005-2007 ACS) (-0.54) (-0.42) (-0.48) (-0.36) (-2.46) (-2.53) (-2.49) (-2.56) % of population over 65 -13.23 -13.57 -12.93 -13.26 -21.60 -22.22 -22.02 -22.77 (2005-2007 ACS) (-2.05) (-2.09) (-2.01) (-2.06) (-1.22) (-1.25) (-1.25) (-1.29) % of population 25+ with -2.566 -1.949 -2.479 -1.848 -32.06 -31.21 -31.31 -30.40 HS diploma (2005-07 ACS) (-0.49) (-0.37) (-0.47) (-0.35) (-2.23) (-2.16) (-2.17) (-2.10) % of population 25+ with 15.11 15.08 15.09 15.07 25.19 24.24 25.26 24.26 coll. diploma (2005-07 ACS) (4.24) (4.23) (4.28) (4.27) (2.59) (2.48) (2.61) (2.50) % of population white -2.519 -2.581 -2.488 -2.563 0.520 1.031 0.203 0.761 (2005-2007 ACS) (-0.99) (-1.01) (-0.99) (-1.02) (0.07) (0.15) (0.03) (0.11) % of population black -8.728 -8.889 -8.830 -8.998 -13.71 -13.25 -13.39 -12.90 (2005-2007 ACS) (-3.13) (-3.19) (-3.20) (-3.26) (-1.80) (-1.74) (-1.77) (-1.70) % of population Hispanic -0.654 -0.565 -0.975 -0.888 -0.561 0.0272 0.577 1.192 (2005-2007 ACS) (-0.35) (-0.30) (-0.52) (-0.47) (-0.11) (0.01) (0.11) (0.23) Median household income 0.00453 0.00380 0.00367 0.00293 0.0416 0.0425 0.0436 0.0445 (2005-2007 ACS) (1.32) (1.12) (1.07) (0.87) (4.44) (4.57) (4.65) (4.78) Median value of own-occ. -0.000243 -0.000216 -0.000197 -0.000169 -0.00151 -0.00156 -0.00162 -0.00168 housing unit (2005-07 ACS) (-1.08) (-0.96) (-0.89) (-0.76) (-2.46) (-2.56) (-2.65) (-2.76) Constant 1014.3 961.1 1010.7 956.3 3268.0 3188.9 3180.7 3101.5 (2.07) (1.96) (2.08) (1.96) (2.45) (2.37) (2.39) (2.32) Number of observations 354 354 351 351 354 354 351 351 Adjusted R-squared 0.465 0.465 0.459 0.459 0.495 0.494 0.487 0.487 widstat test statistic 141.0 146.8 141.0 146.8
130 sargan test statistic 6.928 8.163 0.437 0.262 sarganp test statistic 0.0313 0.0169 0.804 0.877
131 Table B-11: Rail revenue mile models, employment density (NYC omitted) (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV Rail revenue miles -0.0000044 -0.0000483 0.0000463 0.000181 (2 year lag) (-0.41) (-2.29) (1.58) (3.10) Rail revenue miles -0.0000050 -0.0000481 0.0000466 0.000182 (4 year lag) (-0.45) (-2.26) (1.53) (3.09) Freeway + arterial miles -73.51 -59.56 1396.2 1379.0 per sq. mile (2 year lag) (-0.52) (-0.42) (3.64) (3.53) Freeway + arterial miles 6.226 38.34 1489.3 1415.9 per sq. mile (4 year lag) (0.04) (0.25) (3.55) (3.32) Population, 2006 0.000106 0.000103 0.000145 0.000139 0.000125 0.000128 0.00000320 0.0000143 (6.54) (6.49) (6.28) (6.31) (2.83) (2.95) (0.05) (0.23) % of population under 18 -4.497 -3.588 -4.107 -2.928 -56.93 -58.29 -56.60 -58.87 (2005-2007 ACS) (-0.53) (-0.43) (-0.48) (-0.35) (-2.46) (-2.53) (-2.41) (-2.51) % of population over 65 -13.44 -13.75 -12.40 -12.76 -21.41 -22.01 -24.14 -24.77 (2005-2007 ACS) (-2.08) (-2.12) (-1.89) (-1.95) (-1.21) (-1.24) (-1.33) (-1.37) % of population 25+ with -2.266 -1.688 -3.249 -2.501 -32.34 -31.50 -28.21 -27.66 HS diploma (2005-07 ACS) (-0.43) (-0.32) (-0.60) (-0.46) (-2.24) (-2.17) (-1.90) (-1.86) % of population 25+ with 14.95 14.94 15.49 15.46 25.34 24.40 23.71 22.73 coll. diploma (2005-07 ACS) (4.19) (4.18) (4.31) (4.30) (2.59) (2.50) (2.39) (2.29) % of population white -2.433 -2.492 -2.714 -2.808 0.439 0.932 1.056 1.677 (2005-2007 ACS) (-0.95) (-0.98) (-1.06) (-1.10) (0.06) (0.13) (0.15) (0.24) % of population black -8.726 -8.877 -8.830 -9.020 -13.71 -13.26 -13.37 -12.78 (2005-2007 ACS) (-3.12) (-3.18) (-3.15) (-3.22) (-1.80) (-1.74) (-1.72) (-1.65) % of population Hispanic -0.591 -0.504 -1.124 -1.009 -0.621 -0.0399 1.231 1.805 (2005-2007 ACS) (-0.31) (-0.27) (-0.59) (-0.53) (-0.12) (-0.01) (0.23) (0.34) Median household income 0.00417 0.00349 0.00462 0.00380 0.0419 0.0429 0.0400 0.0413 (2005-2007 ACS) (1.21) (1.02) (1.32) (1.10) (4.45) (4.58) (4.13) (4.31) Median value of own-occ. -0.000242 -0.000216 -0.000203 -0.000172 -0.00151 -0.00156 -0.00161 -0.00168 housing unit (2005-07 ACS) (-1.08) (-0.96) (-0.90) (-0.76) (-2.46) (-2.55) (-2.57) (-2.70) Constant 1004.9 954.6 1033.4 969.5 3276.8 3196.2 3083.6 3034.5 (2.05) (1.94) (2.09) (1.96) (2.45) (2.38) (2.26) (2.22) Number of observations 353 353 350 350 353 353 350 350 Adjusted R-squared 0.449 0.448 0.423 0.426 0.401 0.400 0.365 0.366 widstat test statistic 38.46 42.03 38.46 42.03
132 sargan test statistic 7.098 8.663 0.366 0.203 sarganp test statistic 0.0288 0.0131 0.833 0.903
133 Table B-12: Rail revenue mile models, population (1) (2) (3) (4) dependent variable Population Population Population Population OLS OLS IV IV Rail revenue miles 0.276 0.306 (2 year lag) (22.22) (22.07) Rail revenue miles 0.285 0.317 (4 year lag) (22.01) (22.01) Freeway + arterial miles 2397493.4 2098983.4 per sq. mile (2 year lag) (5.32) (4.62) Freeway + arterial miles 2504869.1 2179517.8 per sq. mile (4 year lag) (5.04) (4.36) % of population under 18 24515.8 21028.8 21333.3 17925.5 (2005-2007 ACS) (0.86) (0.73) (0.75) (0.62) % of population over 65 -3393.8 -4607.3 -4705.1 -5997.9 (2005-2007 ACS) (-0.16) (-0.21) (-0.22) (-0.27) % of population 25+ with 23584.0 25234.2 24307.6 25925.7 HS diploma (2005-07 ACS) (1.32) (1.39) (1.35) (1.42) % of population 25+ with 5723.5 3945.7 4420.5 2750.1 coll. diploma (2005-07 ACS) (0.47) (0.32) (0.37) (0.23) % of population white -15346.9 -14696.8 -14046.7 -13398.6 (2005-2007 ACS) (-1.77) (-1.68) (-1.63) (-1.53) % of population black 300.9 1262.2 852.4 1760.7 (2005-2007 ACS) (0.03) (0.13) (0.09) (0.18) % of population Hispanic 16582.9 17890.0 16761.6 17999.7 (2005-2007 ACS) (2.59) (2.75) (2.62) (2.78) Median household income 9.875 12.03 10.62 12.54 (2005-2007 ACS) (0.87) (1.05) (0.93) (1.10) Median value of own-occ. 0.00319 -0.116 -0.171 -0.285 housing unit (2005-07 ACS) (0.00) (-0.15) (-0.22) (-0.37) Constant -2162741.2 -2276710.5 -2148052.8 -2252384.3 (-1.30) (-1.35) (-1.30) (-1.34) Number of observations 364 364 361 361 Adjusted R-squared 0.710 0.703 0.705 0.698 widstat test statistic 452.6 468.5 sargan test statistic 12.77 10.83 sarganp test statistic 0.00169 0.00444
134 Table B-13: Total revenue mile models, employment density (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV Freeway + arterial miles -2.764 49.59 1304.0 1228.7 per sq. mile (2 year lag) (-0.02) (0.28) (2.96) (2.77) Freeway + arterial miles 2.906 123.6 1199.9 1016.0 per sq. mile (4 year lag) (0.02) (0.63) (2.42) (2.02) Total revenue miles 0.000000151 -0.0000131 0.0000199 0.0000389 (2 year lag) (0.08) (-3.50) (4.08) (4.17) Total revenue miles 0.00000118 -0.0000120 0.0000211 0.0000412 (4 year lag) (0.63) (-3.27) (4.15) (4.37) Population, 2006 0.0000761 0.0000593 0.000265 0.000244 -0.0000470 -0.0000563 -0.000319 -0.000338 (2.67) (2.09) (4.85) (4.63) (-0.62) (-0.73) (-2.34) (-2.50) % of population under 18 -5.830 -7.286 -6.439 -5.261 -95.31 -102.2 -94.44 -105.3 (2005-2007 ACS) (-0.50) (-0.65) (-0.52) (-0.44) (-3.11) (-3.36) (-3.07) (-3.44) % of population over 65 -17.80 -20.59 -17.78 -20.14 -34.71 -36.39 -34.74 -37.08 (2005-2007 ACS) (-2.20) (-2.55) (-2.06) (-2.34) (-1.62) (-1.65) (-1.61) (-1.68) % of population 25+ with -0.962 2.411 -1.868 1.614 -47.40 -45.20 -46.09 -43.99 HS diploma (2005-07 ACS) (-0.14) (0.36) (-0.26) (0.23) (-2.67) (-2.49) (-2.58) (-2.41) % of population 25+ with 11.57 9.160 12.62 10.92 19.29 15.16 17.77 12.47 coll. diploma (2005-07 ACS) (2.62) (2.08) (2.68) (2.32) (1.65) (1.26) (1.51) (1.03) % of population white -2.257 -2.787 -3.086 -3.812 6.769 7.085 7.963 8.646 (2005-2007 ACS) (-0.77) (-0.97) (-0.99) (-1.24) (0.88) (0.91) (1.02) (1.10) % of population black -9.313 -9.581 -12.03 -12.74 -9.075 -8.416 -5.169 -3.598 (2005-2007 ACS) (-2.86) (-2.99) (-3.40) (-3.65) (-1.05) (-0.96) (-0.59) (-0.40) % of population Hispanic -0.206 0.598 -2.148 -1.769 1.614 3.459 4.408 7.066 (2005-2007 ACS) (-0.09) (0.26) (-0.87) (-0.71) (0.27) (0.56) (0.71) (1.11) Median household income 0.00644 0.00827 0.00172 0.00223 0.0560 0.0618 0.0628 0.0710 (2005-2007 ACS) (1.54) (1.96) (0.37) (0.47) (5.05) (5.37) (5.47) (5.86) Median value of own-occ. -0.000238 -0.000327 0.0000253 -0.0000054 -0.00218 -0.00248 -0.00256 -0.00297 housing unit (2005-07 ACS) (-0.92) (-1.26) (0.09) (-0.02) (-3.18) (-3.51) (-3.62) (-4.04) Constant 988.7 828.0 1266.6 1067.0 4809.3 4737.9 4409.4 4373.6 (1.50) (1.28) (1.79) (1.55) (2.75) (2.70) (2.50) (2.47) Number of observations 280 275 280 275 280 275 280 275 Adjusted R-squared 0.445 0.463 0.338 0.361 0.523 0.520 0.495 0.492 widstat test statistic 33.88 37.61 33.88 37.61
135 sargan test statistic 4.027 1.962 1.436 0.373 sarganp test statistic 0.133 0.375 0.488 0.830
136 Table B-14: Total revenue mile models, employment density (NYC omitted) (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Urban Area Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City Emp. Dens., Prin. City OLS OLS IV IV OLS OLS IV IV Freeway + arterial miles -89.40 646.8 1313.2 1248.5 per sq. mile (2 year lag) (-0.55) (1.12) (2.95) (1.82) Freeway + arterial miles -161.6 1022.3 1214.5 -24.09 per sq. mile (4 year lag) (-0.90) (1.26) (2.39) (-0.02) Total revenue miles 0.00000820 -0.0000848 0.0000190 0.0000272 (2 year lag) (2.96) (-1.50) (2.52) (0.40) Total revenue miles 0.0000110 -0.0000752 0.0000202 0.000110 (4 year lag) (3.84) (-1.42) (2.50) (1.29) Population, 2006 0.0000111 -0.0000203 0.00100 0.000880 -0.0000401 -0.0000492 -0.000127 -0.000991 (0.34) (-0.62) (1.66) (1.59) (-0.45) (-0.53) (-0.18) (-1.11) % of population under 18 -4.986 -7.491 -12.45 -1.160 -95.40 -102.2 -94.75 -108.8 (2005-2007 ACS) (-0.44) (-0.69) (-0.49) (-0.05) (-3.11) (-3.36) (-3.11) (-2.97) % of population over 65 -17.40 -20.08 -20.01 -21.59 -34.75 -36.43 -34.52 -34.86 (2005-2007 ACS) (-2.21) (-2.57) (-1.13) (-1.34) (-1.62) (-1.65) (-1.64) (-1.33) % of population 25+ with -0.829 2.188 -4.385 1.306 -47.41 -45.18 -47.10 -44.26 HS diploma (2005-07 ACS) (-0.13) (0.34) (-0.30) (0.10) (-2.66) (-2.49) (-2.68) (-2.05) % of population 25+ with 10.88 8.260 18.57 17.60 19.36 15.24 18.69 5.469 coll. diploma (2005-07 ACS) (2.53) (1.94) (1.74) (1.68) (1.65) (1.27) (1.46) (0.32) % of population white -1.162 -1.255 -10.96 -12.03 6.653 6.949 7.513 18.22 (2005-2007 ACS) (-0.41) (-0.45) (-1.26) (-1.38) (0.85) (0.88) (0.72) (1.29) % of population black -8.300 -7.882 -23.09 -25.07 -9.182 -8.566 -7.883 9.415 (2005-2007 ACS) (-2.60) (-2.52) (-2.02) (-2.04) (-1.06) (-0.97) (-0.58) (0.47) % of population Hispanic -0.143 0.855 -6.275 -6.639 1.608 3.437 2.146 11.28 (2005-2007 ACS) (-0.06) (0.39) (-1.01) (-1.03) (0.27) (0.55) (0.29) (1.08) Median household income 0.00609 0.00871 -0.00544 -0.00927 0.0561 0.0618 0.0571 0.0806 (2005-2007 ACS) (1.49) (2.13) (-0.47) (-0.67) (5.05) (5.36) (4.17) (3.59) Median value of own-occ. -0.000290 -0.000418 0.000836 0.000898 -0.00217 -0.00248 -0.00227 -0.00385 housing unit (2005-07 ACS) (-1.15) (-1.66) (0.94) (0.94) (-3.16) (-3.48) (-2.15) (-2.49) Constant 924.1 767.2 2175.6 1708.4 4816.1 4743.3 4706.2 3758.5 (1.44) (1.23) (1.34) (1.21) (2.75) (2.69) (2.43) (1.64) Number of observations 279 274 279 274 279 274 279 274 Adjusted R-squared 0.454 0.481 -1.859 -1.313 0.416 0.412 0.413 0.132 widstat test statistic 2.323 3.330 2.323 3.330
137 sargan test statistic 0.510 0.385 9.154 4.716 sarganp test statistic 0.775 0.825 0.0103 0.0946
138 Table B-15: Total revenue mile models, population (1) (2) (3) (4) dependent variable Population Population Population Population OLS OLS IV IV Freeway + arterial miles 792928.0 714357.3 per sq. mile (2 year lag) (2.36) (2.08) Freeway + arterial miles 518673.0 497496.7 per sq. mile (4 year lag) (1.40) (1.31) Total revenue miles 0.0598 0.0606 (2 year lag) (40.06) (33.41) Total revenue miles 0.0613 0.0615 (4 year lag) (40.85) (34.09) % of population under 18 8545.5 -3183.5 8387.6 -3246.9 (2005-2007 ACS) (0.35) (-0.14) (0.36) (-0.14) % of population over 65 -2796.8 -4448.1 -2416.5 -4353.8 (2005-2007 ACS) (-0.17) (-0.26) (-0.15) (-0.26) % of population 25+ with 7510.9 8388.3 7142.6 8279.6 HS diploma (2005-07 ACS) (0.53) (0.59) (0.52) (0.59) % of population 25+ with -1929.0 -6018.0 -2051.3 -6041.8 coll. diploma (2005-07 ACS) (-0.21) (-0.65) (-0.23) (-0.67) % of population white 626.9 2095.8 1001.7 2186.1 (2005-2007 ACS) (0.10) (0.34) (0.17) (0.36) % of population black 12818.2 15541.3 12954.4 15577.9 (2005-2007 ACS) (1.86) (2.27) (1.92) (2.32) % of population Hispanic 12331.0 14933.1 12131.4 14881.9 (2005-2007 ACS) (2.58) (3.11) (2.59) (3.16) Median household income 22.89 29.42 23.04 29.47 (2005-2007 ACS) (2.73) (3.46) (2.80) (3.54) Median value of own-occ. -0.963 -1.286 -0.993 -1.293 housing unit (2005-07 ACS) (-1.79) (-2.37) (-1.88) (-2.42) Constant -1781499.6 -1760901.3 -1765025.2 -1755179.3 (-1.29) (-1.29) (-1.31) (-1.31) Number of observations 290 285 290 285 Adjusted R-squared 0.903 0.906 0.903 0.906 widstat test statistic 170.1 177.6 sargan test statistic 15.05 13.74 sarganp test statistic 0.000539 0.00104
139 TABLE B-16 Productivity models with two instruments, based on Abel et al. (2010) (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Log of wages Log of wages Log of GDP per capita Log of GDP per capita Log of wages Log of wages Log of GDP per capita Log of GDP per capita pcity pcity pcity pcity pcity pcity Pcity pcity Log of human capital 0.0830 0.0983 0.247 0.263 0.115 0.121 0.226 0.247 (1.71) (2.34) (2.43) (2.90) (2.42) (2.94) (1.97) (2.52) Log of population 0.0339 0.0399 0.0269 0.0334 0.0366 0.0400 0.0284 0.0375 (2.01) (2.80) (0.76) (1.08) (2.29) (2.96) (0.74) (1.17) Log of Employment density, Central City, 2yr lag 0.215 0.387 0.121 0.424 (2.43) (2.10) (1.28) (1.86) Log of Employment density, Central City, 4yr lag 0.184 0.358 0.114 0.385 (2.49) (2.24) (1.46) (2.07) Log of freeway/arterial per capita, 2yr lag 1.304 22.53 (0.17) (1.21) Log of freeway/arterial per capita, 4yr lag 1.745 22.13 (0.23) (1.25) Log of track miles per capita, 2yr lag 3103.9 297.8 (3.34) (0.13) Log of track miles per capita, 4yr lag 2875.3 163.6 (3.30) (0.08) Constant 8.596 8.770 -6.228 -6.064 9.291 9.310 -6.581 -6.368 (16.20) (19.55) (-5.62) (-6.25) (15.67) (18.66) (-4.62) (-5.38) N 351 351 351 351 351 351 351 351 Cragg-Donald 7.155 9.227 7.155 9.227 4.881 6.987 4.881 6.987
140 Stock & Yogo 10% 19.93 19.93 19.93 19.93 19.93 19.93 19.93 19.93 Sargan 0.235 0.524 2.021 1.567 1.209 0.777 3.001 2.432 Sargan (P) 0.628 0.469 0.155 0.211 0.272 0.378 0.0832 0.119
141 TABLE B-17 Productivity model with robust s.e., ordinary and two-stage least squares regressions (3 instruments) Robust SE (1) (2) (3) (4) (5) (6) (7) (8) dependent variable Log of wages Log of wages Log of GDP per capita00 Log of GDP per capita00 Log of wages Log of wages Log of GDP per capita00 Log of GDP per capita00 OLS OLS OLS OLS IV IV IV IV Log of Employment density, Central City, 2yr lag 0.0 55 4 0.152 0.114 0.135 (3. 54) (4.32) (3.10) (1.75) Log of Employment density, Central City, 4yr lag 0.0613 0.154 0.109 0.140 (3.95) (4.41) (3.17) (1.93) Log of human capital 0.1 49 0.149 0.347 0.349 0.123 0.128 0.355 0.356 (6. 24) (6.23) (6.42) (6.47) (3.69) (3.92) (5.32) (5.43) Log of population 0.0 42 0 0.0451 0.0610 0.0652 0.0344 0.0392 0.0633 0.0670 (4. 79) (5.22) (3.08) (3.35) (3.35) (4.04) (2.94) (3.24) Log of freeway/arterial per capita, 2yr lag 1.151 23.69 0.829 23.78 (0.15) (1.37) (0.14) (1.46) Log of freeway/arterial per capita, 4yr lag 1.484 21.81 1.528 21.80 (0.20) (1.29) (0.27) (1.41) Log of track miles per capita, 2yr lag 0.0299 0.0245 0.0259 0.0257 (4.23) (1.54) (3.01) (1.94) Log of track miles per capita, 4yr lag 0.0258 0.0190 0.0224 0.0200 (3.61) (1.18) (2.53) (1.53)
142 Constant 9.7 54 9.674 -4.821 -4.877 9.383 9.370 -4.711 -4.785 (60 .77 ) (60.35) (-13.32) (-13.51) (34.56) (35.71) (-8.22) (-8.66) Number of observations 35 1 351 351 351 351 351 351 351 Adjusted R-squared 0.5 00 0.497 0.375 0.373 0.480 0.484 0.374 0.373
143 TABLE B-18, PART 1: Full sub-sector model results - wages (logged) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Agric. Agric. Mining Mining Utils. Utils. Constr. Constr. Manuf. Manuf. Whole. Whole. Log of sector-specific employment density, principal cities, 2-year lag 0.0021 4 -0.110 0.495 -0.0849 0.0387 0.0251 (0.06) (-1.63) (2.11) (-0.97) (2.33) (0.48) Log of sector-specific employment density, principal cities, 4-year lag -0.00149 -0.113 0.710 0.188 0.0374 0.0172 (-0.04) (-1.22) (1.41) (1.99) (2.33) (0.37) Log of human capital 0.214 0.195 -0.137 -0.228 0.0932 -0.00266 0.160 0.0575 0.148 0.149 0.255 0.256 (1.98) (1.83) (-0.71) (-0.92) (0.72) (-0.01) (3.08) (0.96) (3.79) (3.80) (5.21) (5.26) Log of population 0.0397 0.0440 0.146 0.179 -0.150 -0.249 0.0690 0.00801 0.0106 0.0114 0.0264 0.0292 (1.00) (1.08) (2.90) (3.07) (-1.30) (-1.08) (3.03) (0.33) (0.95) (1.02) (1.23) (1.46) Log of track miles per capita -456.8 -291.6 4341.5 3543.1 2295.6 2450.4 2222.9 1514.3 2466.6 2434.9 3238.8 3259.5 (-0.18) (-0.12) (0.80) (0.67) (0.83) (0.54) (3.07) (1.77) (2.11) (2.04) (2.12) (2.12) Log of freeways/arterials per capita 161.4 172.3 205.7 250.9 -144.9 -222.2 47.81 36.46 -17.22 -16.16 -30.23 -29.88 (2.30) (2.27) (2.22) (1.89) (-1.25) (-1.10) (2.44) (1.67) (-1.16) (-1.07) (-2.07) (-2.03) Constant 10.10 10.000 8.663 8.060 12.40 13.18 10.31 9.839 10.64 10.64 10.75 10.75 (17.35) (16.39) (9.87) (7.20) (10.36) (6.22) (43.39) (39.19) (57.68) (57.08) (48.81) (48.88) N 214 213 180 175 166 167 351 351 350 350 350 350 Adj. R2 0.0151 0.0134 -0.418 -0.403 -1.445 -5.484 0.0652 0.146 0.176 0.175 0.367 0.370 Cragg-Donald 18.86 20.78 7.187 4.979 1.547 0.808 11.73 11.06 49.79 58.06 13.31 17.57 Hansen test 3.857 4.026 0.443 0.256 1.088 0.439 71.51 60.15 2.666 2.634 6.173 6.419 Hansen (P) 0.145 0.134 0.801 0.880 0.580 0.803 2.96e-16 8.68e-14 0.264 0.268 0.0457 0.0404
144 TABLE B-18, PART 2: Full sub-sector model results - wages (logged) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) Logged independent variables: Retail Retail Transp. Transp. Inform. Inform. Fin/Ins Fin/Ins R. Est. R. Est. Prof. S Prof. S Mgmt Sector-specific employment density, principal cities, 2yr lag 0.208 -0.0695 -0.136 0.0992 -0.0541 0.0173 0.0599 (1.94) (-1.53) (-1.46) (1.35) (-0.37) (0.18) (0.83) Sector-specific employment density, principal cities, 4yr lag -0.0617 -0.0524 -0.130 0.0719 -0.0769 0.0093 1 (-0.83) (-1.13) (-1.48) (1.27) (-0.54) (0.11) Log of human capital 0.0171 0.0721 -0.100 -0.0994 0.379 0.375 0.244 0.264 0.158 0.179 0.375 0.384 0.0936 (0.57) (2.78) (-2.37) (-2.36) (4.01) (4.20) (3.76) (4.75) (1.41) (1.51) (3.52) (3.95) (0.99) Log of population 0.0070 0.0240 0.0807 0.0719 0.119 0.120 0.0568 0.0632 0.105 0.112 0.0843 0.0869 0.0615 (0.64) (2.94) (3.26) (2.82) (5.28) (5.36) (2.53) (3.33) (2.66) (2.77) (2.52) (2.78) (1.27) Log of track miles per capita 409.1 2116.6 1782.6 1734.8 5502.3 5428.7 4147.0 4298.9 3450.3 3543.2 2021.0 2090.6 1490.8 (0.44) (2.96) (2.27) (2.17) (2.56) (2.58) (3.43) (3.28) (2.95) (3.24) (1.75) (1.89) (0.74) Log of freeways/arterials per capita -2.529 -2.319 35.11 30.75 -48.52 -44.49 -18.70 -19.73 5.244 6.297 -8.688 -8.821 -44.40 (-0.24) (-0.25) (1.57) (1.42) (-1.84) (-1.77) (-1.24) (-1.25) (0.24) (0.29) (-0.52) (-0.52) (-1.40) Constant 8.881 10.14 9.510 9.570 10.33 10.28 10.07 10.14 9.396 9.412 10.22 10.23 10.40 (17.91) (28.44) (38.98) (39.18) (28.05) (30.00) (46.12) (51.27) (42.56) (44.00) (41.17) (43.32) (22.26) N 351 351 349 349 337 337 350 350 350 350 350 350 274 Adj. R2 -0.258 0.139 -0.0688 -0.0397 0.220 0.225 0.568 0.573 0.319 0.298 0.553 0.549 0.121 Cragg-Donald 3.374 3.677 9.928 8.288 6.569 8.091 14.34 17.34 2.833 2.906 4.510 5.385 5.944 Hansen test 25.52 35.16 3.603 4.488 1.690 1.662 4.446 4.241 7.079 6.852 2.604 2.594 0.613 Hansen (P) 0.0000 0288 2.31e- 08 0.165 0.106 0.430 0.436 0.108 0.120 0.0290 0.0325 0.272 0.273 0.736
145 TABLE B-18, PART 3 â Full sub-sector model results - wages (logged) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) Logged independent variables: Mgmt. Admin. Admin. Educ. Educ. Health Health Art/Ent. Art/Ent. Acc-Fd Acc-Fd Other Other Sector-specific employment density, principal cities, 2- year lag -0.169 -0.0950 -0.0849 - 0.00018 0.242 0.0168 (-1.34) (-1.13) (-3.68) (-0.00) (1.65) (0.46) Sector-specific employment density, principal cities, 4- year lag 0.0545 -0.148 -0.0834 -0.0866 -0.0097 0.136 0.00609 (0.83) (-1.06) (-1.02) (-3.71) (-0.09) (1.08) (0.17) Human capital 0.0918 0.211 0.208 0.196 0.195 0.142 0.139 0.0794 0.0880 0.0465 0.0923 0.190 0.195 (0.98) (4.37) (4.04) (2.85) (2.77) (5.25) (5.10) (0.81) (0.91) (0.62) (1.31) (5.06) (5.24) Population 0.0633 0.114 0.104 0.112 0.110 0.0353 0.0362 0.132 0.134 0.0203 0.0297 0.0448 0.0465 (1.38) (2.57) (2.20) (5.71) (5.85) (4.36) (4.44) (4.22) (4.35) (1.02) (1.74) (4.35) (4.49) Track miles per capita 1470.1 1543.2 1805.3 3719.7 3641.1 1608.5 1608.8 4710.0 4777.6 736.5 1422.8 2133.3 2260.9 (0.75) (2.10) (2.25) (3.17) (3.13) (2.95) (2.94) (2.88) (2.80) (0.66) (1.46) (2.21) (2.30) Freeways/arterials per capita -43.36 -18.53 -15.40 -44.04 -43.26 12.28 13.32 -48.84 -48.54 3.039 2.356 0.663 1.173 (-1.38) (-0.86) (-0.77) (-1.43) (-1.38) (1.06) (1.14) (-1.63) (-1.62) (0.17) (0.14) (0.05) (0.09) Constant 10.39 9.778 9.786 9.443 9.418 10.81 10.80 8.329 8.337 8.109 8.581 9.679 9.707 (22.55) (43.65) (43.08) (23.83) (23.89) (65.77) (66.96) (22.74) (22.57) (12.51) (14.92) (56.04) (55.44) N 273 351 351 317 317 351 351 347 347 351 351 351 351 Adj. R2 0.106 0.155 0.186 0.288 0.301 0.136 0.133 0.277 0.274 0.149 0.260 0.399 0.386 Cragg-Donald 6.710 2.903 2.515 5.368 5.888 30.29 32.76 13.21 11.04 2.772 3.127 20.55 20.96 Hansen test 0.706 2.191 2.344 2.429 2.785 6.242 5.824 0.742 0.781 22.76 22.82 11.61 11.38 Hansen (P) 0.703 0.334 0.310 0.297 0.248 0.0441 0.0544 0.690 0.677 0.00001 0.00001 0.00301 0.00337
146 TABLE B-19 Instrumental variable analysis of sub-sectors, with 3 instruments and robust s.e. Log of wages Log of wages Log of wages Log of wages Log of wages Log of wages dependent variable Manufacturing (31) Manufacturing (31) Finance/Ins (52) Finance/Ins (52) Health (62) Health (62) (9) (10) (19) (20) (31) (32) Log of Sector-specific Employment density, Central City, 2yr lag 0.0387 0.0992 -0.0849 (2.33) (1.35) (-3.68) Log of Sector-specific Employment density, Central City, 4yr lag 0.0374 0.0719 -0.0866 (2.33) (1.27) (-3.71) Log of human capital 0.148 0.149 0.244 0.264 0.142 0.139 (3.79) (3.80) (3.76) (4.75) (5.25) (5.10) Log of population 0.0106 0.0114 0.0568 0.0632 0.0353 0.0362 (0.95) (1.02) (2.53) (3.33) (4.36) (4.44) Log of track miles per capita 2466.6 2434.9 4147.0 4298.9 1608.5 1608.8 (2.11) (2.04) (3.43) (3.28) (2.95) (2.94) Log of freeways/arterials per capita -17.22 -16.16 -18.70 -19.73 12.28 13.32 (-1.16) (-1.07) (-1.24) (-1.25) (1.06) (1.14) Constant 10.64 10.64 10.07 10.14 10.81 10.80 (57.68) (57.08) (46.12) (51.27) (65.77) (66.96) N 350 350 350 350 351 351 Adj. R2 0.176 0.175 0.568 0.573 0.136 0.133 Cragg-Donald 49.79 58.06 14.34 17.34 30.29 32.76 Hansen test 2.666 2.634 4.446 4.241 6.242 5.824 Hansen (P) 0.264 0.268 0.108 0.120 0.0441 0.0544
147 APPENDIX C. REVIEW OF ACADEMIC LITERATURE The purpose of this review of academic literature is to describe and critique theoretical and empirical research on the economic impacts of transit investments. Existing academic literature relevant for these purposes includes literature on broader topics, including how increased accessibility to jobs and other destinations may spur economic growth and development; the economic benefits of road and highway projects; and how agglomerationâ development densification and firm clusteringâmay increase economic productivity. Researchers are still grappling with the complexity of these issues, and we therefore do not find a consensus on the scope and magnitude of the economic impacts of transportation investments; but we do find ample work which informs our framework for analysis. Our focus is on theoretical economic models, and empirical tests of the hypotheses that they suggest. We generally exclude literature on computable general equilibrium models, integrated land-use and transportation models, and regional input-output models. Other work sponsored by FTA is developing such models, which are helpful for some purposes, but not for providing impact estimates, nor for inferring how the effects are transmitted. Larger scale models also tend to rely on numerous assumptions, not all of which are realistic or empirically verifiable. Theoretical Issues Transportation investments are thought to have economic benefits primarily because they improve transportation access, particularly by reducing travel time. Most transit projects do not reduce the monetary cost of travel, and in most cases the monetary cost is a small component of total costs, relative to the value associated with travel time. By reducing travel time, transportation investments may also have indirect effects, both positive and negative. Such effects include increases or decreases in agglomeration economies (particularly due to economies of employment cluster size); travel externalities (such as road congestion and vehicle pollution); and network externalities (transit networks may exhibit increasing economies of scale because of user-side time savings with service frequency increases). In this review of theoretical literature we discuss how reductions in travel time cause almost all impacts of transportation investments, including the less conventionally measured ones; how these in turn may affect the spatial pattern of new development, population and employment; and how agglomeration, or firm clustering, increases economic output and is potentially increased by transportation investments. Accessibility and Development Responses Accessibilityâthe speed and monetary cost of travel to and from various locationsâis closely linked to economic development. Faster and cheaper travel may cause more people to be willing to look for work, to find jobs more quickly when out of work, and to travel farther to find the right job (Berechman 1994). All are potential economic benefits, although they are not likely additional to travel time savings, and they may or may not exceed the real cost of the transportation investment. Faster and cheaper travel may also change where firms locate, how close they try to be to their suppliers, how much inventory they hold, where they warehouse their goods, and so on. These decisions in turn may result in higher or lower productivity in aggregate, additional economic benefits, as we discuss in the next section.
148 The implications of accessibility-driven changes in household and firm behavior are quite complex. Our focus here is to explain what is meant by âaccessibilityâ and to emphasize that almost all subsequent economic benefits and costs associated with transportation projects flow from changes in accessibility. Different indexes are used to measure the accessibility of any given location in a region, and to assess changes in accessibility that would be caused by a transportation investment that reduces travel time. A popular measure is Hansenâs accessibility index (1959), which (like other, similar measures) calculates the accessibility of any given neighborhood or âzoneâ by adding up the attractiveness of all other zones to which people in that zone could travel, divided or discounted by the time it takes to get there. The attractiveness, or destination value, of zones is measured in different ways, all of which are intended to represent the value of making a trip to the zone. Different measures of attractiveness are used for different measures of accessibility. A measure of âemployment accessibilityâ might use total employment per zone; a measure of âshopping accessibilityâ might use retail employment as the measure of attractiveness; and so on. The time to travel between zones can be estimated from the distance between zones, from output from a regional travel model, or in other ways. The mathematical formulation of this particular index is: , where Ai = the accessibility of zone i to opportunities in zones 1 to n; Wj = some measure of attractiveness of zone j; cij = the time or distance of travel from zone i to zone j; and β is a parameter typically calibrated to observed zone-to-zone traffic flows from a real-world survey. The β parameter reflects the specific characteristics of the transportation system such as comfort level, safety, and so on, that cannot be directly measured by travel time or distance between zones. A common value used for β in the literature is â2. This default value establishes an inverse relationship between the square of travel time and accessibility; hence the value of this index increases as accessibility improves. Other measures of accessibility have been proposed and implemented (36). Households and firms may respond to increased accessibility provided by transportation investments by traveling more frequently to visit others, purchase goods, and otherwise interact with other firms and households. They may also demand floor space (that is, attempt to relocate) in places where accessibility has increased significantly. This process takes time. Certain places will become more favored than they previously were, while other places in the region may become less desirable. The price of more accessible locations will increase, and this will signal to land developers and redevelopers that investments in further intensification of development are likely to pay off. Locations near network access points such as freeway on- and off-ramps or rail stations may become particularly sought after. But at the same time, properties particularly close to access points may also experience negative impacts such as noise, congestion, pollution, and accidents, because of a concentration of traffic. In addition to the first-stage changes in accessibility, these potential changes in the distribution of population and employment are the immediate reasons for the external economic changes that we discuss later in the paper. We discuss them here despite the fact the FTA j n 1j c. j n 1j W e.W i ij A â â = = βâ =
149 guidelines already exist for estimating development impacts, because these are crucial inputs to estimating those additional economic impacts. The land market will respond to accessibility increases if firms and households value it, and, crucially, if regulations and governments permit intensified development and/or intensified occupancy of existing development. But this is often not the case in the United States. Regulations that hinder development include large lot zoning, density maximums, and minimum parking requirements. Thus greater accessibility is usually a necessary (but not sufficient) condition for significant development-driven economic growth to occur. Without permission to develop, economic impacts will be limited, as will the negative impacts of development. If there were no possibility of in-migration, one might expect property values to increase near rail stations and bus stops while possibly decreasing in less advantaged areas, because improvements in transportation decrease travel costs, and therefore the bid-rent surface for land tends to flatten. If this were to happen, one might see a more concentrated development pattern near stationsâand dispersion or reduced density farther away from stations in places where quick walking, transit, and short-drive access to the stations is no longer possible or feasible. Localized agglomeration mechanisms might increase or decrease in such a situation; this is an empirical question that theory can do little to resolve. The more clustered employment that might be thought typical of effective transit (particularly rail) investments might be better for productivity than highway or road investments. But, crucially, the economic benefits of a transportation investment are not limited to existing workers, firms, and residents. Because the investment reduces the average cost of living and doing business in the area served by the networkâthat is, by reducing the average cost of traveling on the networkâone might expect that this lower cost would result in in-migration from outside the region to take advantage of the lower cost of transportation. This can in turn give rise to agglomeration economies related to city size, also known as urbanization economies. Agglomeration Economies Agglomeration is the clustering of development that is caused by the tendency of firms and households to locate near other firms and households. Firms sometimes agglomerate with other firms in the same industry, and sometimes with firms to which they have some cross- industry or supplier relationship. Agglomeration at a larger scale includes households clustering with those firms, and retailers locating in clusters with good access to those households. Firms and workers are thought to be more productive and efficient when in bigger, denser, and more accessible agglomerations. Reducing travel time to and from existing agglomerations has the potential to cause agglomerations to grow, particularly where demand for growth and densification is pent up. Reducing travel time means reducing various costs of access faced by firmsâby their workers to the workplace, to suppliers of production inputs, and to the markets that purchase their goods. A reduction in transportation cost may make it possible for new and existing firms to join agglomerations or expand within themâfor example, to pay for an expensive multistory building in an industrial district. Even without such changes, the increased accessibility may be itself increase productivity per unit of labor or capital input. Also, transportation facilities, particularly nodal facilities like multimodal transfer centers, are âsharable inputsâ to firm production, like other public infrastructure such as water and sewer systems that might also result in larger cities. Thus it is thought that transport investments (like other public infrastructure investments) increase âthe efficient operation of
150 cities, particularly large cities, and thus also promote the realization of agglomeration economiesâ (Eberts and McMillen 1999). Agglomeration is of particular interest to those who wish to estimate economic benefits of public investments because of âexternalitiesââsignificant costs and benefits that are not directly captured by firms making location and expansion decisions. Externalities also make agglomeration somewhat harder to measure empirically. These external costs and benefits are not captured by conventional benefitâcost analysis, nor by accessibility-based estimates of economic impacts of transportation investments. In particular, firms do not take into account how their presence may benefit other firms and the economy as a whole; those benefits to others may significantly exceed the benefits to the firm itself if the productivity returns to agglomeration are highly nonlinear. Discussion of agglomeration is commonly focused on questions of industrial production and firm location, rather than personal/household utility and residential location, although the latter also play a role in agglomeration, particularly at the regional scale. Anas, Arnott, and Small define âeconomies of agglomerationâ as âa term which refers to the decline in average cost as more production occurs within a specified geographical areaâ (1998: 1427): One class of agglomeration economies is intra-firm economies of scale and scope that take place at a single location. Another class is positive technological and pecuniary externalities that arise between economic agents in close spatial proximity due, for example, to knowledge spillovers, access to a common specialized labor pool, or economies of scale in producing intermediate goods. Agglomeration economies may be dynamic as well as static, and are suspected of giving cities a key role in generating aggregate economic growth. These agglomeration economies may cause higher productivity even without an increase in physical concentration, when travel speeds increase and if the interactions that give rise to agglomeration are facilitated by travel via the affected mode. Another, more conventional and somewhat disfavored explanation for industrial agglomerations is comparative advantage, or what Anas, Arnott, and Small (1998) call âspatial inhomogeneities.â Access to a public good, such as a port or a transportation hub, is an example. Trade Theory Regional industrial agglomerations could develop not because of externalities, but because industries seek advantage in serving their markets. Industrial agglomeration can arguably be largely explained by firms locating their plants centrally in order to maximize internal economies of scale in production while easily accessing their markets: Because of economies of scale, production of each manufactured good will take place at only a limited number of sites. Other things equal, the preferred sites will be those with relatively large nearby demand, since producing near oneâs main market minimizes transportation costs. Other locations will then be served from these main sites. (Krugman 1991: 485-6) When the increasing returns to scale are strong, transportation costs are low, and manufacturing employs a large enough share of the population, a dynamic process of growth and concentration feeding upon itself will end up with population becoming concentrated into a few
151 regions. Thus transportation investment could cause agglomeration (that is, a spatial concentration of firms) without there being the need to calculate additional impacts due to the agglomeration increasing productivity. In the New and Small Starts context, such agglomeration would be accounted for in the capitalization of transportation benefits into the production processâand therefore should be captured by calculating travel time savings and increased ridership. But if agglomeration externalities are importantâif example, if they create significantly increasing external returns to scale for a given industrial agglomeration (see below for more on this point)âthen a reduction in transportation costs, by simply enabling the agglomeration tendency, gives rise to increasing returns that would not be included merely by calculating the firm and household value of travel time reductions. Glaeser (2000) is skeptical that the Krugman model applies in the modern day, because he interprets the fact that transport costs are smaller than ever as meaning that the importance of locating near markets has become less important, and therefore that Krugmanâs model will not yield the observed pattern of agglomeration. This may be a misunderstanding of the model. Using an equilibrium model version of the Krugman theory, Puga (1998) shows that the agglomeration equilibrium is even more centralized under the assumption of decreased transportation costs, which is one explanation for the importance of primate cities in developing countries. Regardless of how important trade theory is to explain modern metropolitan-level agglomerations, lower transportation costs of trade, along with increasing returns to scale, are essential for explaining the geographical distribution of economic activities. Transportation investments, by enabling more efficient trade, can in turn also enable other non-trade-related (production) economies of scale. Externalities The concept of agglomeration economiesâexternal returns to firm and household clusteringâhas long been recognized, being mentioned even by Adam Smith. Alfred Marshall has been credited with the development of the underpinning concepts. He argued that producers within the same industry end up locating near each other to share specialized local input providers, to benefit from a pool of skilled workers, and to have ready access to specialized information that was created by other firms (Marshall 1997 (1920)). For most of the twentieth century, research advances on agglomeration economies were theoretical. Henderson (Henderson 1974, 1983, 1988) embedded agglomeration economies within a general equilibrium framework and urban economics. Later, the theory was incorporated as an integral part of thinking on ânew economic geography,â initially credited to Krugman (1991); see also Ottaviano and Puga (1998) and Fujita, Krugman, and Venables (1999) for surveys of this literature. Two distinct types of agglomeration economies have been identified. âLocalizationâ or âMarshallianâ economies are said to explain the productivity gains from co-location that are external to the firm, but internal to a particular industry. The development of a local, specialized labor market is one example of such effects. âUrbanizationâ or âJacobsianâ economies (after Jacobs 1969) describe economies that are external to the firm and to the industry, but internal to the city or metropolitan area. These economies are caused by the existence of local public goods, economies of scale in the size of markets (e.g. thicker labor markets), and various inter-industry interactions.
152 Why Firms and Households Agglomerate Duranton and Puga (2004) elaborate on motivations for agglomeration. Their categories of âsharing,â âmatching,â and âlearningâ include both within- and across-industry relationships, as well as relationships between retailers and household customers. Sharing mechanisms include sharing of indivisible facilities (like ports) in order to reduce costs, as well as sharing diverse pools of input suppliers to increase productivity, enable narrower specialization, and spread risk. This can also result in an increase in the number of suppliers, rather than an increase in the scale of existing suppliers. While greater division of labor may result in extra coordination costs (Becker and Murphy 1992), agglomeration presumably makes this coordination easier by reducing the cost of such negotiations which are so integral to the vertical disaggregation of production (Williamson 1979). The net effect is likely greater efficiency. An âindivisibleâ good or facility has high cost and large economies of scale. Such a facility is feasible only when it can be shared by many users. Examples include large public transit systems (particularly rail systems), sports arenas, marketplaces, and recreational areas. However, most shared facilities are also subject to crowding, which implies a diseconomy of scale beyond some range. Benefits from fixed and indivisible facilities accrue due to the constant marginal cost of use, at least up to the point at which significant congestion of roads, rails, and/or transit vehicles occurs. Input sharing must demonstrate economies of scale in order to contribute to agglomeration benefits. One strand of research examines the purchased input intensity of various industries relative to the national mean. Purchased input intensity is the amount of purchased inputs divided by sales, which serves as a measure of vertical disintegration. Holmes (1999) finds that the most concentrated industries exhibit the highest degrees of input sharing, consistent with the theory, and furthermore for the ten most concentrated industries the effect is twice as large (Rosenthal and Strange 2004). While a higher degree of supplier specialization would be expected to occur in industrial agglomerations, the manufacturing data is generally organized in a way that precludes a refined analysis of specialization. However, the one exception, the textile industry, does show a higher degree of specialization as the degree of concentration increases, consistent with the theory. Plants may also choose locations with a preferable milieu of input suppliers, but the research generally supports the idea that input sharing is a benefit of (and motivation for) agglomeration (Rosenthal and Strange 2004). Matching mechanisms help buyers and sellers of production inputs to find each other. A larger pool of labor for employers, and a larger pool of firms for workers, lower production costs by reducing the amount of time to match skills and tasks, the time for firms to fill vacated or new positions, and both the travel time and the search time for workers to find jobs. Thus firms and workers are attracted to agglomerations which provide more workplace and worker choices. Easier matching also reduces risk and increases competitiveness. Intermediate suppliers can market to multiple firms rather than relying on just one firm, making production inputs more competitive and reducing the risk of capital investments. When assets are repossessed due to project failure they may be more easily recycled in an urban agglomeration because it is easier to find a match; similarly, when cities get larger, it may be easier for entrepreneurs to find appropriate production machinery (Helsley and Strange 1991). Similarly, workers benefit from being able to move to other firms if one firm fails; they are therefore more likely, when living in large cities or near large industrial clusters with
153 appropriate jobs, to take on riskier positions in more innovative firms. Labor market pooling reduces risk for both employers, who need to respond to positive demand shocks, and employees, who mitigate their risk of unemployment by locating near industrial agglomerations. There is also industry-specific risk, which in a specialized city would increase the risk of unemployment. While most agglomeration research focuses on its effects on productivity, large cities are also thought to benefit consumers via access to specialized goods and services (opera, professional sports); aesthetic charms (attractive architecture, a good climate); specialized public goods (e.g., arts high schools, skateboard parks); and more potential for interaction with other households and firms (Glaeser, Kolko, and Saiz 2001). An increase in households who value such amenities may explain why there was a rise of reverse commuting between 1980 and 1990 (although center city locations also might provide accessibility to many employment opportunities). Tabuchi and Yoshida (2000) find that while the nominal elasticity of wages with respect to city size is 10%, the real (cost-adjusted) elasticity of wages with respect to city size ranges from -7% to -12%. Workers may be willing to pay a wage penalty to live in a large city because of the consumption opportunities. Waldfogel (2003) and George and Waldfogel (2003) argue that the large market of consumers in large cities allows âgoods to be more closely tailored to individual consumersâ tastesâ. Waldfogelâs (2003) study of radio listening habits shows that the average percentage of the population listening to radio increases by 2% for each one million person increase in the population. Learning mechanisms include the generation, diffusion, and accumulation of knowledge. Since learning is a social activity, cities may have an advantage in facilitating learning by bringing large numbers of learners and knowledgeable people together. Chamley and Gale (1994) argue that observing the decisions of other firms is an externality which firms may avail themselves of to make better decisions, following Marshall (1997 (1920)). Firms making risky investments may also benefit from firsthand experience of what others have done. Young firms need time to experiment to find the right combination of inputs and processes for an optimal production process. Diversified cities provide the broad range of suppliers and knowledge that facilitates this phase and makes it more productive for a firm to start up there, rather than in a more specialized city. The fact that diversified cities are more expensive suggests that the benefits of living there may be large; otherwise, firms and households would not be willing to pay more to live there (Duranton and Puga 2004). Transport and Agglomeration Most of the work above conceives of agglomeration as depending on tradeoffs between proximity to various firm amenities. Agglomeration is also affected by the time cost of travel because lower travel time makes the learning, matching, and sharing mechanisms easier for firms, making it more likely for them to benefit from agglomerations. But little of the work outside Krugman (1991) has explicitly discussed how changes in transportation cost may alter agglomerations. In this subsection we focus on such theoretical literature. Changing the cost of transportation by making a transportation investmentâand therefore reducing the costs of transportation for freight, for commuting, for business-to-business travel, for marketing, and/or for any of a number of economic activities that depend on transportationâhas several potential effects on agglomeration and hence productivity, but much depends on context and details of the investment. Reduced transportation cost for firms can allow larger market areas and hence larger and denser agglomerations. Reduced transportation
154 cost for households can make job searching easier and commuting less costly, with effects on labor participation and hence on agglomerations. We paraphrase from Eberts and McMillen (1999) here. Mills (1967) developed a general equilibrium model that included agglomeration economies along with intracity transportation, built on costly land, to bring workers to the central business district (CBD). The amount of land used for transportation limits city size. The amount of land used at a site is proportional to the number of passenger miles at the site. There are decreasing economies of scale in transportation as city size increases, so that doubling population requires more than twice as much land for transportation use. On Hendersonâs account (1974; 1982a; 1982b; 1983, 1988) transportation reduces commute times, freeing labor for housing production, which reduces the price of housing and allows firms to pay lower wages. Thus transportation investment stimulates growth (although whether the value of the growth exceeds the transportation investment cost is a separate question). Similar to Krugman, Mori (1997) modeled firm location as driven by agglomeration economies. In this model, declining transportation costs cause a large city to grow because manufacturers can support a larger market area. These savings more than compensate for higher shipping costs for agricultural products. Methods Measures of agglomeration unrestricted to administrative geographical units (those that consider the attenuation over distance) are another way to incorporate the impact of transport projects on agglomeration and hence the economy. Venables (2005) demonstrates how this could work in practice, in a stylized core-periphery setting. He considers a CBD that draws employment from outside. As travel times are reduced, more workers are willing to commute to the CBD and to work, which leads to increased productivity from agglomeration. Rice et al. (2006) examines the magnitude and geographical reach of agglomeration economies in the UK using a simulation model. They analyze regional differences in productivity, controlling for differences in skills and sectoral composition, and attempt to explain the residual variation using proximity to âurban massâ as measured by journey times. They find a modest agglomeration elasticity of 5%. They also find that firms can benefit from proximity to other firms as far 80 minutes away. They calculate that under their assumptions a 10% increase in the speed of all transport across the UK could deliver a productivity gain of 1.2%. Venables (2007) argues that transport improvement can increase urban center employment on the one hand by increasing links between nearby companies, which increases the âeffective densityâ (accessibility) of employment, and on the other hand increasing the commute catchment area for firms in the agglomeration. Venables argues that the agglomeration benefits of the transport improvement derive from two market imperfections: (1) new employment increases the productivity of existing as well as new urban center workers (a classic externality), and (2) because commuters reach equilibrium between location and commute costs and wages based on after-tax income, whereas the value of extra output produced by a migrant exceeds cost incurred but accrues to the government. He develops a theoretical model with a transport dimension, and shows how a transport improvement, represented by a decrease in the cost per unit distance of travel, can lead to an increase in real income exceeding the decreased cost per unit of travel. He also develops a numerical monocentric city model to estimate the agglomeration benefits, suggesting that agglomeration externalities could increase total benefits by 2.5 to 5 times the amount of the travel cost savings. Pilegaard and Fosgerau (2008) argue that reducing labor search costs by making transport improvements could have substantial external benefits in manufacturing industries, because job
155 turnover in those industries, which is costly, can be in the range of 8% to 12% per year. The authors assume that the turnover leads to unemployment spells, instead of retirements and new employment (e.g. by immigrants and young adults entering the workforce). Employing this assumption in a simple mathematical model with two regions connected by a commuting link, and employing input parameters based on the Danish economy, they suggest that a 10% reduction in inter-region travel times could result in a 29% increase in the net benefits that would calculated in an ordinary benefitâcost analysis. Competition Externalities We move on to effects that are not directly from agglomeration. Improved transportation can also play a role in increasing competition, by allowing geographically limited markets to overlap to a greater extent. Increased competition due to better transportation may increase economic efficiency and consumer surplus when compared to monopolistic or oligopolistic markets. Local competition is also thought to increase productivity, by forcing companies to innovate (Porter 1990). On the other hand, the work of Marshall (1997 (1920)), Arrow (1996 (1962)) and Romer (1986) suggests that local competition may decrease productivity because the gains from innovation cannot be completely captured at the firm level. The ability of increased accessibility to make markets more competitive and efficient depends on the type of firm and industry, the geographic locations, and the size of the markets firms serve. Commercial shops and services tend to serve local markets and will often be dispersed throughout a region, but decreased transportation costs increase the incentives even of these âconvenienceâ land uses to grow larger and serve larger retail markets. Firms marketing their products globally can be subject to competitive pressures at large distances, and those that survive do so via innovation and productivity enhancements. The scale of localized firms is also important. Increasing the market area of a grocery store may lead to less localized competition as the increased productivity due to scale reduces consumer prices and results in fewer but larger grocery stores; on the other hand, the competitive market increases in spatial size due to the reduced transportation cost. Travel savings, both during work activities requiring travel to and from work sites, and on the commute to work, may have a positive economic impact not only because of the taxation wedge but because competition may increase, if the worker uses the freed-up time to do more work. A common example is the time saved by a plumber who can access more jobs in a shorter period of time. The competitive plumber would pass these savings on to the customer; if the market suffers from imperfect competition the plumber can capture this gain for him or herself. If prices are inefficiently high, and productivity suffers as a result, then benefitâcost analyses valuing saved worker travel time at the wage rate would underestimate the economic benefits (e.g. Venables and Gasoriek 1998). Davies (1998) shows that a good approximation to the magnitude of these benefits is dW/dT = 1 + e(m/p)/{1â(m/p)), where dW is the benefit of increasing competition, dT is the benefit to in-work travelers, e is the aggregate price elasticity of demand with respect to price and m/p is the average price-cost mark-up. If the mark-up is about 20% and the aggregate demand price elasticity is 0.50, and the mark-up disappears with a transportation investment, then the additional benefits caused by imperfect competition would amount to about 10% of conventionally measured benefits. However, one may expect a relatively small impact on accessibility, and therefore on regional competition, from the average public transportation infrastructure project in the US, where existing transportation networks are highly connected (e.g. Jiwittanakulpaisarn et al. 2009a). Furthermore, capture of consumer surplus by producers could be just a net transfer
156 without net economic impacts, unless imperfect competition lowers productivity and reduces output. Labor Taxation and Work Travel Transportation improvements likely affect individual decisions about whether to work, how much to work, or where to work. Because workers pay income taxes, which are a disincentive to at least some people in some wage ranges to work more or seek higher-paying (more productive) work, the supply of labor is suboptimal (Venables 2007), although it is unknown by how much. Transport improvements may increase labor supply, which will have an economic value that exceeds the wage net of taxes. Similarly, Cogan (1980) argues that modeled differences between the minimum number of hours women will work are caused by differences in fixed costs of work, such as transportation costs. Even if such changes can be demonstrated, Venables (2007) points out that impacts on individualsâ incomes of starting to work must be exactly offset by their perceived cost of working, leaving only the transport benefits as a real economic welfare gain to the transport users. Network Externalities Transit networks may exhibit increasing economies of scale because of user-side time savings with service frequency increases. The existence of increasing returns to scale in transit network density and size was first demonstrated theoretically by Mohring (1972). This theoretical work has been discussed and refined by others (Nash 1988; Kerin 1992; Walters 1982). Some have argued that there may not be increasing returns to scale depending on the detailsâfor example, whether there is regulation of market entry and exit by private transit operators (van Reeven 2008). There may also be road network increasing returns to scale, despite the fact that there are no reductions in user wait times with greater network density. Holl (2006) argues that benefitâ cost analysis may fail to consider the network benefits of transportation investments because the transport network is typically truncated in such analysis. Empirical Studies Theories and simulation models are not in the end sufficient to give us accurate estimates of the economic impacts of transit beyond the estimated value of travel time savings. Agglomeration economies might attenuate rapidly with distance, might be more important for some economies than others, and might be heavily context-dependent. Transportation investments might even disperse rather than concentrate development, actually reducing productivity and having economic costs rather than benefits. Transit investments could have a net dispersing effect depending on the existing transit network (e.g. Haughwout 1999). Factors include the particular project being added to that system; the transportation technology; the tradeoffs firms make between proximity to other firms in the same industry; suppliers; inputs; labor force; and markets. Therefore, in this section we focus on empirical studies in four areas. We explain but do not provide a complete review of literature on our first two topics, which are studies of how accessibility changes affect economic growth and property values, respectively. These two study approaches are not suited to discriminating between the myriad, often simultaneous, mechanisms that may have economic effects when a transportation investment is made. Our third topic is modeling economic output as a function of transportation investments; we cover this literature in
157 more detail. Finally, we focus on new and more relevant research attempting to discriminate between direct accessibility effects and other additional effects like increasing agglomeration economies. Measuring the net impacts of any or all of the theoretical agglomeration mechanisms described in the previous section is a significant challenge. Accessibility and Economic Change Some empirical studies directly test whether changes in travel time lead to economic growth, and many of these have found a significant positive relationship between accessibility improvements and economic development (see Berechman and Paaswell 2001; Ozbay, Ozmen, and Berechman 2006). We give two examples below. The accessibility index proposed in Allen et al. (1993) was used to capture the overall transportation access level of Philadelphia and other largest US metropolitan areas. Using this index, a regression analysis was performed for data from sixty largest US metropolitan areas in order to investigate the impact of accessibility on employment growth rate. The results showed that accessibility was highly correlated with regional economic growth. Isserman et al. (1989) used a quasi-experimental approach to investigate the effect of highways on smaller communities and rural areas. They examined income growth rates during the period of 1969-1984 for 231 small rural cities, some with highway access, and some without. Cities located near highways had faster economic growth. Accessibility studies do not attempt to distinguish capture or capitalization of travel time savings in firm production processes from additional economic impacts. Property Value Studies Property values offer a potentially fruitful measure of net economic impacts of all kinds of public investments, including transportation, but typically without distinguishing the reasons for, or causes of, net benefits or costs. Modeling property values has the advantage of being empirical, although the approach inevitably lacks the specificity of a simulation model because data are limited. If residents and businesses value a new transit improvement, they will be willing to pay more to locate near it so as to be better able to access it. Cities with good transit systems may have higher rents than those without, because of the improved accessibility enabled by the system. This is the premise of using âhedonic modelingâ techniques to estimate the larger economic impacts of transit investments. It is long established that developers, property owners, and tenants are willing to pay more to purchase or rent real estate that is more accessible to their labor force, employers, commercial opportunities, and other spatially-dependent resources. Observed rents and sales prices can thus reflect part of the value of this accessibility (i.e., part of the benefit of access is capitalized in land prices). On the other hand, land values in the region as a whole could (and arguably, should) go down overall as proximity loses its value, assuming no in-migration (Mohring 1993). Thus it is better thought of as a way to value local impacts, and not regional impacts. Production function studies (see below) are better for understanding regional impacts. Property values may measure several kinds of economic impacts of transportation. First, there is an option value to having transit nearby (e.g., if a car breaks down), even if this rarely translates into ridership. Second, buyers may anticipate the value of being near the line in the future if they believe that attractive destinations may develop near stops, the transit network may become denser, and so on. (Thus investigating development right after a system is developed is seen as an inferior measure to price measures, which can anticipate such changes before they
158 occur (see, e.g., Cervero and Landis 1997)). Third, the economic benefits of transit can only be partly estimated by riders' willingness to pay transit fares; there is a consumer surplus associated with transit consumption just as with other goods, and this may be capitalized into home prices. Fourth, some share of the other benefits discussed aboveâagglomeration benefits and search reduction benefits in particularâmay also be reflected in land values. However, there are several problems using property values as measures of economic impact. First, they amount to double-counting if other measures (e.g., estimates of the value of time savings) are included. Second, land economics predicts that in a somewhat elastic property market, not all benefits are capitalized in property prices; so property values provide a partial measure, or lower bound, of benefits and costs of investments. Third, property values reflect the bids of firms and households, but those bids ignore the external component of agglomeration benefits, as well as other externalities. These caveats are rarely addressed (or stated) in the property value literature. Industry Output and Growth Studies Another means of estimating the economic impacts of transportation investments is to investigate measures of economic productivity in places with different levels of transportation infrastructure, or better yet, in places before and after investments occur. However, like land value studies, studies of economic productivity as a function of transportation investment do not tend to specify a particular theoretical mechanism, such as agglomeration economies (Holl 2007, citing Haughwout 2002 & 1998). The estimated effects are net of all possible causal mechanisms which may have both positive and negative economic impacts. Aschauer (1989) found a strong positive relationship between investment in public infrastructure and output using an aggregate production function model. But there are severe causality problems (e.g., investment may equally lag growth), as well as difficulties in identifying relevant measures of public infrastructure capital and investment. Later studies trying to correct for these problems generally find a much weaker relationship (see Banister and Berechman (2000) for a review). One review found that economic productivity increases somewhere between 5% and 30% for every 100% increase in public capital investment (Quinet and Vickerman 2004). Some estimates are considerably smaller. Jiwattanakulpaisarn et al. (2005) constructed a dynamic model of the private economic output elasticity of additional highway infrastructure, calculating a short-run elasticity of output with respect to highways of 0.007% and a long-run elasticity of output of 0.04% per additional 1% increase in highway density. Although most studies have identified some kind of positive relationship between improved highway accessibility and local economic development, several studies find little or no effect of transportation investment on local economic growth. A common claim of negative- or no-finding studies is that economic growth would have occurred anyway near highways, or that a booming economy leads to more transportation investments, rather than the reverse. If so, estimates of capital investment effects on productivity could be too high. Stephanedes and Eagle (1986) used a time-series approach to investigate the relationship between state highway expenditures and changes in employment levels in 30 non-metropolitan Minnesota counties between 1964 and 1982. The authors found no overall relationship between highway expenditures and changes in employment levels. For a subgroup of regional centers, however, highway expenditures did appear to engender job growth. Duffy-Deno (1991) tested the direction of causation between infrastructure and output using a production function and a demand for public capital function, and found that the direction is in fact from infrastructure capital to output
159 growth, rather than the other way around. Other studies controlling for this potential endogeneity find little bias, on average (Melo, et al. 2009). A recent literature review suggests that the effect of transportation investments (primarily roads) on productivity across the city has declined over time in the US, and is âcurrently indistinguishable from zeroâ (Baird 2005). This could indicate that studied US cities have generally met or exceeded their maximum transportation capacity, given their population before negative externalities (such as congestion) and the opportunity cost of land combine to overwhelm any positive effects of the investments. He cites Fernald, who studied industry data and concluded, âthe evidence suggests that the massive road-building of the 1950s and 1960sâ¦offered a one-time increase in the level of productivity.â Eberts (1986) models the direct effects of public infrastructure on manufacturing output and public capital stock, measured using the perpetual inventory technique. The study includes highways, sewage, and water infrastructure for MSAs, and finds a positive and significant contribution to output. Deno (1988) estimated an industry profit function for 1970 to 1978 data using the same capital stock measures (multiplying capital stock by manufacturingâs share of employment). Calculated elasticities for water, sewer, and highway infrastructure were 0.08, 0.30, and 0.31, implying that doubling highway investment increases firm profits 31%. These relatively high numbers suggest underinvestment in public capital. In Europe, Seitz (1993) used similar methods and found that for each doubling of public capital stock, industrial production costs decrease about 12.7%. But other studies have found smaller effects. In particular, Holtz- Eakin (1992) found that infrastructure investment effects diminished or disappeared once state- specific industrial productivity effects were controlled. Most of the work has been production function-based but some has used aggregate cost functions (ACFs), which allow a better understanding of how economic productivity may be related to specific elements of production (Baird 2005). Holl (2006) investigates the relationship between firm birth and new highway infrastructure in Portugal, demonstrating that areas within 10km of new infrastructure were absolute and relative winners in the rates of new firm birth, partially at the expense of adjacent areas between 10 and 50km from the new infrastructure. Furthermore, she concludes that firm birth rates in areas beyond 50km from new highway infrastructure have not been meaningfully impactedâimplying net growth due to highway infrastructure, rather than merely a redistribution of growth. Agglomeration Estimates In this section we first cover a voluminous empirical literature on the size of agglomeration economies, and then discuss the relatively few entries in the literature that explicitly include measures of travel time or transportation investment. Empirical work on agglomeration economies has typically been focused on manufacturing, as it was there that the most explicit examples of clustering were seen historically. Data is often much more readily available for the manufacturing sector, and on a longer time-series. Notable exceptions are Ciccone and Hall (1996), Ciccone (2002), Graham (2007a; 2007b) and Brülhart and Mathys (2008) who either study the whole economy or subsectors of both manufacturing and services. The empirical literature has generally followed three broad approaches. The longest standing body of research has sought to determine whether variations in labor productivity are explained by variation in concentration. These studies have typically estimated a MSA-wide production function, where measures of concentration are included as a technology-shift
160 parameter within the production function. Often, concentration is measured by total employment, total employment by industry, or total output. The second strand of empirical work has aimed at measuring clustering in individual industries. Following the work of Ellison and Glaeser (1997), the attempt is to verify the existence of âgenuineâ industrial agglomeration. The third strand is the so far small, but growing, body of literature seeking to identify the sources and mechanism through which agglomeration effects lead to productivity gains. We concentrate on this third strand below. Knowledge spillovers or âlearning mechanismsâ are considered one of the most important sources of localization economies, but are thought to be often informal; difficult to relate directly to agglomeration or productivity; and poorly understood, challenging empirical testing (Rosenthal and Strange 2004). Researchers have taken various approaches to measure the degree of agglomeration of informational spillovers. For instance, Jaffe, Trajtenberg, and Henderson (1993), using patents as a proxy for information exchange, find that there is a high degree of concentration in the spatial distribution of patents. Patents were five to ten times more likely to originate from the same SMSA as the control patents in the study. Audretsch and Feldman (1996) find that significant new product introductions (as reported by the Small Business Administration) are spatially concentrated, suggesting that information-intensive industries are beneficiaries of the knowledge spillovers that can occur as a result of industry localization. Empirical studies have provided some evidence on the role that labor pooling, and consequent risk reduction for firm firing decisions and household unemployment spells, plays in agglomerations. Simon (1988) shows that unemployment is higher the more specialized a city is. Diamond and Simon (1990) show that wages are higher in more specialized cities, consistent with the theory that workers will demand higher wages in such cities as compensation for the increased risk of unemployment. In this vein, Costa and Kahn (2000) find that the percentage of dual-bachelor degree couples living in large cities increased from 32% in 1940 to 50% in 1990. The mundane explanation is that such couples met and married in large cities. The more interesting interpretation is that large cities provide an opportunity for both individuals to find suitable employment (Rosenthal and Strange 2004). Baumgardner (1988) shows that physicians in large markets tend to be more specialized (Rosenthal and Strange 2004). Specialization is another measure that may be evidence of labor market pooling. Some labor pooling-related mechanisms associated with urbanization economies have been explicitly studied. One example is urban human capital. Using census data, Rauch (1993) investigates the impact of average level of education on wages and rents. He finds that a one- year increase in average schooling level leads to an increase of 3% in wages and 13% in rents. Other studies use local compulsory schooling laws (Acemoglu and Angrist 1999) or the number of college graduates as instruments to investigate impacts on wages, generally finding a positive effect. Various measures of better labor market matching have been proposed in order to enable empirical analysis; but not all of these represent the theory well. An example is the âtermination rateâ measure. In a thin labor market, employers may be reluctant to fire on the basis of a mediocre match simply because there is no alternative. In a thicker labor market, workers should be able to change jobs more readily, but on the other hand if the match is better they may have less incentive to do so. So this measure is ambiguous. Graham and Kim (2008) suggest measuring agglomeration externalities within an empirical framework which attempts to analyze total factor (labor and capital) productivity, partial factor productivity, factor prices and factor demand, as well as to distinguish
161 agglomeration effects from returns to scale. The framework makes it possible to identify direct (irrespective of labor and capital productivity) and indirect (related to labor and capital) agglomeration externalities. They develop an empirical model using data for small, single- location firms of about 100 employees derived from the British Annual Business Inquiry, a survey of business activity in 10,780 wards (average 21km2 Some studies use estimates of elasticities of production with respect to firm agglomeration or density to simulate the consequences of transport investments. Graham (2007a) assumes a link between agglomeration externalities and transport by employing a density factor that implicitly captures the effect of a transport investment. He uses spatially disaggregated firm- level accounting data available from the British Department of Trade and Industry, from which he draws on employee headcount, revenues (turnover), as well as labor costs and capital inputs from the balance sheet. From this he derives a measure of effective density that relates the employment density of a ward (about 22 km ). The output from this model shows that six of nine industries have positive elasticities of labor productivity with respect to agglomeration. In contrast, they find that six of nine industries have negative elasticities of capital productivity with respect to agglomeration. Service industries show the highest elasticities of labor productivity with respect to agglomeration. 2 Shefer and Aviram (2005) investigate the potential agglomeration benefits of a light rail transit system in the Tel Aviv agglomeration. Their model combines the results of detailed engineering-based studies of the capacity and potential ridership of the system, as well as potential employment gains, with estimates of agglomeration elasticities culled from the literature. They calculate the potential economic benefit due to agglomeration economies using a basic Cobb-Douglas production function as an additional $73 to $355 million (US) in agglomeration benefits. ) to all other wards, normalized by distance; in other words, an employment accessibility measure. He estimates agglomeration elasticities for several primary industries, ranging from negative values for industries such as rubber-related products and medical and precision equipment, to positive elasticities for other industries such as publishing and food manufacturing, the latter of which he attributes to the need for market proximity. He finds average elasticities with respect to total employment density of .129, 0.07 for manufacturing and 0.20 for services. Labor Supply and Market Competition Labor supply effects Despite a considerable body of research studying determinants of labor supply decisions, few studies have considered the role played by transport costs. Kolodziejczyk (2006) finds that there is a link between fixed costs of working and retirement age based on French data. Gonzalez (2008) finds that workers living further away from urban centers are likely to retire earlier, although this did not control for the possibility that individuals change residential location in anticipation of retirement. The simulation model setup suggested by Venables (2007) and put to practice in DfT (2003) requires evidence on labor supply elasticities with respect to (actual or expected) commute travel time or distance. Findings vary significantly between studies. DfT (2003) calculates a mean value of -0.10 for men and -0.40 for women, based on studies for the UK by Blundell (1992) and Ashenfelder et al (1999). Evers et al. (2005) perform a meta-analysis of about 50 studies, and find elasticities of between -0.10 and -0.20 for men and around -0.50 for women. There are few if any studies testing whether job seekers have shorter unemployment spells in larger cities, in clustered industries, or in industries with good transit access.
162 Competition Empirical work on the effects of transportation on increasing competition tends to look at the effect of employment, plant size, and the number of plants, both inside and outside the industry (Rosenthal and Strange 2004, pp. 2141-2142). Some evidence exists on the relationship between trade barriers and productivity, the former including the cost of transport. The European Commission (2003) finds that the introduction of the single market in the European Union in 1992 led its member statesâ GDP in 2002 to be 1.8% higher. It is of course impossible to judge how important increased competition was for this productivity gain. Hausman et al. (2005) estimate the consumer benefits from the increased variation and price effects in the retail food market of the entry of a major supermarket, and find that the additional variety offered to local consumers is worth 20% of expenditures on food, while lower prices are worth only about 5%. Note that this particular change to economic competitiveness is more clearly facilitated by faster and cheaper auto travel, because few households access grocery stores on transit. Most of the empirical literature on transportation costâs effect on competition has focused on improved trade linkages between countries, and not the competitive impact on internal domestic trade. Thus, it is hard to draw definitive conclusions from the empirical evidence. Griffith et al. (2006) find that a reduction in trade barriers led to a reduction in firm price-cost mark-ups. The 5% reduction in the tariff rates experienced by most EU countries over the 15 years to 2000 was found to have decreased mark-ups by 4.5%. DfT (2005) suggests that a 70% to 100% reduction in travel costs would be required to have a similar effect. Bernard et al. (2006) studied trade costs for US manufacturing industries and found evidence that firms in sectors with falling trade costs have higher productivity growth and higher firm death rates. Glaeser et al. (1992) use as a measure of competition the ratio of establishments per employee in a city for a given industry relative to the equivalent ratio for the entire US. They find that an increase in this ratio is positively associated with growth. In a study encompassing the high-tech and machinery industries, Henderson (2003) investigates the relationship of average size of plants in the own industry and county to plant productivity. Henderson finds that the number of plants in the own industry in the county does positively affect productivity, whereas the average employment per plant does not. Rosenthal and Strange (2003) measure the number of new firm births as a function of the average number of establishments per worker in the own industry and other industries. In all six industries, as the number of establishments per worker increased in other industries, the number of firm births in the own industry decreased. On the other hand, for five of the six industries, the average establishment size within the own industry was positively associated with firm births (Rosenthal and Strange 2004, pp. 2141-2142). Transport investments might make markets more competitive; there remains the question of how uncompetitive they are currently. There is limited evidence that they are significantly uncompetitive, based on price-cost margins. Harris (1998) and Davies (1998) find average mark- ups in the UK manufacturing sector of between 15% and 30%, while Gorgâs (2003) findings suggest between 0% and 15%. Other estimates of margins include Small (1997), who find average margins for service sectors typically range between 25% to 40%. Martins, Scarpetta, and Pilat (1996) finds significant mark-ups in most US manufacturing sectors, with most falling between 10% and 30%. The meaning of these mark-ups for competitiveness is unclearâthey could fall under the category of ânormal profitsâ and have no net economic impact.
163 Much harder to come by are widely accepted estimates of aggregate demand elasticities. Harris (1998), Davies (1998), and Newbery (1998) suggest a value of -0.50 for the UK, while Venables et al. (1998) believe the figure should be considerably higher. However, they all admit that their suggestions are based on intuition rather than on empirical work. Discussion and Conclusions The particular hypothesized effects of transit investments, subject to testing or prediction in order to quantify the additional economic benefits or costs, depends very much on the specific details and context. How large is the project? By how much are travel times reduced, and in what parts of the network? Is it an extension of an existing system, or is it stand-alone, integrating only via transfers to other modes? To what extent does it strengthen an already existing network serving major industrial or commercial centers? Are there existing capacity constraints that the investment relieves? Are served areas settled by growing industries? These specific questions are rarely dealt with in the literature but generalization of methods and results likely requires more attention to these specific differences. Diverse Scale of Analysis The relevant scale of analysis in empirical studies will depend on the particular effect one is seeking to testâfor example, neighborhood-level agglomeration effects or land prices, versus citywide urbanization economies of industrial production or increases in network density. It is arguably always necessary to investigate the regional level (or whatever spatial boundary beyond which there is little to no direct effect of the investment) in order to account for the possibility that transport investments simply redistribute development rather than increasing economic growth overall, causing more growth only in spatially targeted areas, possibly along with relative economic declines in areas that do not benefit from greater accessibility. These merely redistributive effects are arguably immaterial, from the federal governmentâs perspective, if not from that of local agencies. Reliance on Simulation Models There is a basic paradox in empirical work versus simulation work when estimating the economic impacts of transit investments. Empirical models are based on real measures of the economy, transportation investments, and other factors, but these models remain limited in their ability to test the complex sets of causal factors that we believe are at work in the spatial economy that reacts to these investmentsâso that it is difficult to know what exactly is happening to yield whatever impacts are found, and therefore it is difficult to know whether the empirical model has included all relevant controls. Simulation models can represent the complexity of any particular hypothesized system of causal relationships, but they rely on assumptions that are subject to error, and the hypothesized sets of causal relationships are themselves not testable except in careful empirical studies of perhaps one or at most two of the relationships. Testing for Concentrating AND Dispersing Impacts of Transportation Labor search is facilitated both by agglomerations and by good transport facilities. But transport investments could also have a net dispersing effect (e.g. Haughwout 1999) depending on the system; the particular project within the system; the transportation technology; and the
164 tradeoffs firms make between proximity to numerous other firms in the same industry; suppliers; inputs; labor force; and markets. Distinguishing by Industry Some but not all literature discriminates by industry when calculating agglomeration economies, but âthe variation across industries suggests that one ought to estimate agglomeration economies separatelyâ (Rosenthal and Strange 2004: 2134). This does not render estimates incorrect for the dataset, but it does at the very least limit the ability to generalize to other locales with different industrial mixes, and it certainly suggests the need for present purposes to have a better understanding of the industrial mix of any particular place in which a new transit system or extension is proposed. Much of the empirical literature focuses on agglomerations of high-tech and traditional machine manufacturing, in large part because of the focus on informational spillovers, which are believed to be most important in these sectors. However, the densest parts of contemporary cities are more typically dominated by other uses, such as professional service firms and front-office functions, while vertically disaggregated manufacturing and cultural production sectors still maintain a foothold. We have less evidence on the importance of agglomeration economies in these types of cities, which is important because such industries will likely account for most urban growth. The research literature has been largely concerned with questions such as comparing the relative strength of within-firm and across-firm agglomeration externalities. Translating this work to estimating the specific impacts of a change to travel times due to a transit investment is a difficult challenge. Distinguishing Transit from Road Investments Transit and highway investments seem likely to cause different development and agglomeration patterns with different economic consequences, though this has not been explored much in the literature. For example, rail systems, with the highest passenger capacity, may enable very high-density industrial and commercial nodes near stations, with few effects farther away from stations (depending in part on parking capacity and cost); while highway investments may enable more relatively modest but spatially broader increases in industrial and commercial density. Rail transit in particular, but shared transportation modes generally, have the potential to allow higher but more localized densities before congestion creates a disincentive for further densification. Road investments have generally much lower capacity for additional travelers to and from any existing concentration of firms, and so localized intense density is not as likely to occur; but citywide density is possibly better supported, because a cityâs road network is almost always substantially larger and denser than is its transit networks. This brings to mind the localization vs. urbanization economies distinction. Congestion costs may be much higher with road-served agglomerations than transit- served agglomerations, depending on density. In sufficiently dense cities, transit improvements may be superior to road improvements when considering agglomeration economies net of transportation costs. The distinction between different transportation investments is crucial. When comparing rail to bus projects, and even comparing among rail projects, the particular characteristics of the system will dictate the market potential and development pattern that can be supported, and consequently the nature of agglomeration benefits and costs that is likely to result.
165 Most production function studies of transport or agglomeration use very large-scale measures (citywide), with a few exceptions as noted above. Citywide measures may be more appropriate for highway and road investments than for transit investments because we expect in some cases very localized impacts from the latter, in addition to effects on overall city size. Whether or not impacts are spatially contained near access points, they should show up in citywide figures if there is a substantial net benefit or cost. While modeling net economic output for cities as a function of citywide measures of accessibility, density, and industrial clustering may not inform us very much about the nature and mechanisms of industrial responses (if any) to transit, it is arguably a better measure than the alternatives. Distribution of Benefits and Costs Who benefits from transit investments, and from the larger cities, larger industrial clusters, or denser downtowns that may be the result of such investments? Industrial agglomerations imply longer-distance commutes, all else equal, because they go hand in hand with higher prices, pushing out residential development. At the same time, in order for transit investments to have any effects on the economy, they must enable faster and/or cheaper travel; it is this that makes it possible for this nonresidential segregation to occur in the first place (on the assumption that wages must reflect commute costs as well as other factors). So if those longer-distance commutes are equal or less in duration, commuters aren't worse off; if they are more productive and therefore get paid more, then they are even better off. But the dynamics of agglomeration growth and of the cost structures, profit structures, and incentives of the firms that employ workers in those agglomerations, may mean, for example, that commuters actually get longer commutes and are paid less. One might expect just one of those effects, but both are simultaneously possible. Theory may in the end be of relatively little use empirically, particularly when data are limited (as is usually the case). Methodological Issues Recently there has been an emphasis on using micro-data for a cross-section of firms. In theory this can better measure the productivity of firms as the specific inputs and outputs of each firm are measured, as opposed to using proxies for citywide or regional productivity. Graham (2007b) did precisely this in estimating models that included a proxy for transport and congestion levels. Although the spatial focus of transit investment is typically localized to city centers, the likely spatial scope of agglomeration effect is likely to extend much further. This suggests the need for cross-metropolitan area studies, not just case studies of individual projects. Undertaking analyses on a cross-MSA level also has the attraction of treating a self-contained regional economy and, in some parts of the world, the availability of longitudinal datasets collected at this level. In the vast amount of literature on agglomeration over recent years, an increasingly common approach to measuring the extent of spatial concentration of activity has been using measures of distance or travel time in its specification (Brülhart and Mathys 2007; Ciccone and Hall 1996; Duranton and Overman 2002; e.g. Fogarty and Garofalo 1988; Graham 2007a, 2007; Hansen 1990; Hanson 1996, 1997; Henderson et al.1995; Rice et al.2006; Rosenthal and Strange 2003; Graham 2007b). These studies take account of the distance over which externalities are present. This introduces two advances: first, it offers an explicit measure of distance decay that allows firms to contribute heterogeneously to an agglomeration depending on their locations
166 relative to it; and second, it enables an analysis of agglomeration economies at a spatial level independent of artificial administrative or statistical boundaries. A more flexible spatial treatment of agglomeration has further attractions. Introducing the cost of movement into the analysis makes explicit the role of transportation. Based on often readily available data on journey times and costs, individual sectors can now be modeled as having different geographical scope. The downside is that one has to make explicit assumptions about the nature and strength of the distance decay. It is possible to construct models that allows for decay parameters to be estimated empirically, but examples are rare (Rice et al. (2006) is an exception). The literature uses a variety of data. Most commonly used are aggregate measures of firm characteristics within a region. Also gaining popularity is firm-level data, typically from commercial providers or from government sources. Both types of data have been used with cross-sectional and panel approaches, the latter allowing for a better accounting of unmeasurable effects and controlling for the possibility that transportation or population density, rather than leading to increased productivity, may instead occur in places where productivity is higher. Melo et al. (2009) provide a recent review and meta-analysis of production function studies of agglomeration. They examine 34 studies and over 700 estimates to determine how the specific characteristics of various studies affect the agglomeration estimates. Their results are useful in providing some context for the large range of estimates found in the literature. One of the key conclusions is that one should not necessarily expect agglomeration elasticities estimated in different regions, for different industrial sectors, and frequently with different methods, to be similar. Those elasticities found in analysis of the service sector tend to be higher than the manufacturing sector, suggesting the need for more focus on how agglomeration affects service sector productivity.
167 APPENDIX D: SUMMARY OF INTERVIEWS Interviews - Sections 1, 2, and 3 The focus of the interviews was on how and whether state and regional agencies in the United States and Britain estimate the economic benefits of their transit projects, either for their internal purposes, to discriminate between potential projects, or to provide additional arguments for a favored project. In this appendix we organize a summary of their responses in the following categories: the use of economic benefit measures, and what types are used; what data sources are used; what documentary sources or documentary guidance is relied upon; and who conducts the analysis. A list of the 18 interview subjects appears below. TABLE D 1 Interview subjects Name Title Organization State/Nation Richard Bickel (with Greg Krykewycz and Karen Morris) Director of Planning Delaware Valley Regional Planning Commission PA Peter Fahrenwald (with staff from Strategic Planning) Manager Chicago Transit Authority IL Rick Gustafson Executive Director Portland Streetcar OR Wil Guzman (with Mark Seaman and others) Senior Program Manager Port Authority of New York and New Jersey NY John Haley Vice President, Infrastructure and Service Development Houston Metro TX Tom Marchwinski Senior Director, Forecasting and Research New Jersey Transit NJ Diana Mendes Senior Vice President DMJM Harris, AECOM David Nelson Director of Transit Planning JACOBS Robert Padgette Director of Policy Development and Research American Public Transit Association Carmine Palombo Director of Transportation Planning Southeast Michigan Council of Governments MI Rich Pereira Project Director, Capital Program Management Miami Dade Transit FL Stephen Salin Vice President, Rail Planning Dallas Area Rapid Transit TX
168 Name Title Organization State/Nation Mark Soronson Vice President HDR/S.R. Beard and Assoc. & Phoenix Metro AZ Andrew Summers (with Mike Salter) Senior Executive East of England Development Agency U.K. David Crockett Director â Public Transport Sector Halcrow U.K. Julian Morison Director and Senior Consultant EconSearch AUS Paul Roberts TIF Technical Manager West Yorkshire Public Transport Executive U.K. Vicky Cadman Economic Adviser U.K. Department for Transport U.K. Accounts of Current Practice Use and Types of Benefit Measures â General Interviewees were asked about the types of benefit measures used in the project development process of major transit projects. Consistent with the benefitâcost measures required by the New Starts process, the most frequently cited benefits measures were forecasted ridership, revenue, and travel time savings derived from travel demand models. Other benefit measures noted included catchment area, accidents, and reliability. Other measures derived from travel demand forecasting include mode shift from auto to transit, vehicle miles traveled (VMT) reduction, and air quality improvement. One respondent stated that, in his opinion, no current benefit measures adequately document the trip reduction benefits of changes in urban form and density that result from job and home relocation. It was noted by one respondent that agency decision makers rely on benefitâcost measures because they are better understood and less âspeculativeâ than environmental, land-use, and economic development measures. One respondent told us that the most successful projects first document the qualitative land-use and community benefits that are most meaningful to the community, in order to build local public and political support, and then initiate the New Starts/Small starts process. Agencies that start with the technical analyses of the New Starts process are more likely to fail because they lack an understanding of local needs that comes from the qualitative analysis. Staff of New Jersey Transit and the Delaware Valley Regional Planning Commission said that the approach they used in developing the âtransit score model,â used for transit investment screening, may also have applicability in creating a model for estimating transit benefits. The transit score model computes a score for a geographic area, using a simple equation with a small number of variables and coefficients. The score is then interpreted, by comparison to a set of point value ranges for different transit modes, to determine how much potential the area has to support different modes of transit. The variables and coefficients are determined through
169 regression analysis, which allowed the model developers to eliminate a large number of non- significant explanatory variables. A number of respondents in the UK said that the types of benefit measures used varied depending on the scale of investment as well as the client or audience of the study. Some investments may have congestion relief as their main objective, while others are aimed at delivering benefits to the wider economy (Gross Value Added or productivity). This may indicate some inconsistency in interpreting the economic benefits of transportation, as congestion relief supports economic growth, which in turn brings increased congestion. However, it also indicates the different scales at which investments are appraised. For example, the impact of congestion at an intersection may have limited economic effects, but nevertheless an investment to improve the intersectionâs performance may be worthwhile. Another example is Transport for London, which often seeks to understand the social benefit from transport investments as this is more heavily weighted in assessment criteria. When working for private transit operators, on the other hand, social impacts are less important than the bottom line. Use and Types of Economic Benefit Measures Interview subjects were asked about their familiarity with different types of economic benefit measures in the context of major transit investments. The consensus among respondents in the US is that there is no accepted best practice methodology for economic benefits estimation in the transit industry. There was a general lack of consensus on how to define economic benefits, beyond the direct employment benefits of project construction and operations, but there is a perception that local decision makers value the economic benefits of transit projects and want them to count towards the New Starts/Small Starts project rating process. Some respondents asserted that economic benefits measures are used by agencies to boost a New Starts/Small Starts project rating when it does not meet cost effectiveness criteria, although others disagree with this assertion. Several respondents made a distinction between localized and regional economic benefits, and argued that different transit modes differ in the geographic scale of their economic effects. A streetcar or BRT will have a much more localized effect than commuter rail, for example, so if the same economic benefit measure is used for all modes, the systems will not compete on an equal footing. New Jersey Transitâs Hudson River rail tunnel project, known as Access to the Regionâs Core, documented both macro and micro benefits. In addition to construction jobs, they estimated long-term benefits from additional jobs and taxes. Property value increases were also forecast. One respondent said that job creation estimates are an economic benefit measure required by the federal government to apply for funding under the American Recovery and Reinvestment Act, and that there is a general lack of guidance on methods. Another respondent suggested that the level of unemployment should be considered in job creation estimates. Some respondents consider travel demand forecasting to include economic benefits because agencies can convert costs and user benefits to dollar amounts. The value of time (wages) is included in travel time estimates, which is essentially an economic benefit. Some agencies have estimated regional economic benefits by taking into account housing price, retail and recreational jobs and sales, retail spending, etc. While estimating economic impacts, some agencies have also gone towards estimating carbon emissions impacts as well. Input-output (I-O) models have been used by some agencies, including New Jersey Transit, the Chicago Transit Authority and one of the consultants with whom we spoke, to estimate economic benefits. Two respondents noted the difficulty in distinguishing job creation
170 from relocation when looking on a regional scale. In the United Kingdom, it was noted that models that are not national in scope do not fully reflect economic reality with regard to movement and trade. Another limitation noted by one respondent is that their I-O analysis did not capture changes in the cost of capital, land values, density, or housing. The same respondent suggested that a benefitâcost analysis may be a better methodology for determining how to allocate resources. Another approach to measuring economic benefit that does not involve estimation is to document real estate investment and changes in real estate value over time. Some agencies conduct this analysis themselves, while others use the services of consulting economists or universities. Work has also been done in the UK to estimate the potential for transportation to enable economic development, focused on how accessibility improvements can encourage job growth in particular areas, subject to access to workforce, available floorspace/land and views, and evidence of local business planning entry or expansion in the area. Interview subjects in the US were generally unfamiliar with agglomeration benefits and were unaware of its use as a factor in estimating overall economic benefits. Those that were familiar with agglomeration, primarily consultants and UK transport agency staff, thought that while it is potentially a valuable measure, in practice it would be too complex and expensive to calculate. US respondents stated that it is more common to rely on property value, tax base, and joint developments as evidence of economic benefits. In the United Kingdom and Australia, respondents were generally aware of agglomeration impacts and methods to analyze this were generally accepted in practice. As discussed in Appendix C in more detail, the UK Department for Transport recently published guidance on the assessment of âwider economic impactsâ of transport investments, which includes a methodology for estimating agglomeration benefits. (One of the interview respondents works developing this guidance.) Respondents in the UK and Australia said the UK approach was generally straightforward to apply and data were generally available, although it can, at times, be a challenge to manipulate the required economic inputs to the necessary format and spatial level needed for modeling. Similarly, US respondents said that regional economic models like REMI have been widely used to estimate economic benefits for major transit and transportation projects, but there is a perception that it is too complex and technical for use by most transit planners. One respondent described attempts to estimate business-to-business impacts of transport improvements as well as impacts on retail areas. In this case agglomeration, land-use changes and access to markets, and attempts at job creation were all estimated. Specifics on Data Respondents generally did not identify issues with data availability and quality for performing typical analyses, which rely on population and employment forecasts. Commonly noted data issues relate to differences between local-level data; such as cross acceptance and conformance of local, county, and regional population and employment forecasts; and differing update schedules of different data collecting agencies. Some respondents said that for economic data, agencies will use a wide variety of sources, including Chamber of Commerce reports, and benefits estimates for sports stadiums. Outside the United States, some respondents felt that the sharing of consistent data between government agencies was very easy. However, one of the problems noted with acquiring economic data is the difficulty which can be encountered when trying to manipulate it to the needed spatial and zonal requirements. One difficulty that was noted was the exclusion of fares from transport modeling and the impact this could have.
171 Guidebooks and Reports Interview subjects in the US were unable to identify an accepted national standard of practice for economic benefit estimation. A few respondents noted that agencies are reluctant to accept methods from other regions. A review of the practice-oriented reports and guidebooks identified through the interviews can be found in Appendix C. In the UK, the Department for Transport publishes standard guidance for the assessment of economic impacts from transport investments. A number of interview respondents noted the guidance in directing their work. Who Conducts the Analysis Interview subjects were asked about the mix of staff and consultant involvement in conducting transit benefits analysis and economic benefits analysis. The level of staffing and the technical expertise of transit agency staff may bear on the economic benefit methods to be developed by this study. There is diversity in the approaches employed by transit agencies reported across all of our interview subjects. Some make extensive use of consultants, while others do almost all work in-house. One agency with an expanding transit system employs long-term contractors who work alongside staff. Travel demand modeling is conducted in-house at some agencies, while others rely on the MPO to maintain these models. Consultants typically play a significant role in the preparation of New Starts/Small Starts documents as well as in a great deal of work in the UK and Australia. One agency representative said that only a small number of consultants are able to do New Starts benefit estimation work. Estimation of economic benefits based on real estate value is often done by a transit agency or MPO, although more sophisticated analysis is sometimes done by economic consultants or universities. Suggestions for Methods and Guidance Interview subjects were asked to provide general suggestions about the data and methods that have been or may be used to estimate the economic benefits of major transit investments, and what kind of guidance they feel would be most useful on these methods. Complexity Respondents expressed concern about the ability of small agencies to complete any new analysis developed through this research. Several respondents requested a how-to document or user guide that would allow a few people at an agency to complete the analysis using available data within a few days. Emphasis was on simple calculations and straightforward methods, so that another analyst could replicate the analysis and get the same result. Training workshops and webinars were suggested. Respondents in the UK expressed less concern about complexity. Standard guidance makes the process of economic evaluation more straightforward. Nevertheless, the transport modeling was highlighted as a very complicated area by some of the respondents, and some stated that good models can take years to build. Some respondents said that the UK appraisal system is complex relative to other European countries. In addition, one respondent said that investment analysis may not be too complex, but politics adds a layer of complexity.
172 Methods There was little consensus on whether identifying modeling variables and procedures, or providing parameter estimates, would be a better approach for estimating economic impacts of transit investments. Some respondents suggested that the outcome of this project should include parameter estimates or multiplier tables for agencies to produce forecasts of economic benefit factors so that agencies should not be required to produce parameters or multipliers themselves. One MPO representative said that agency leadership and politicians may be less likely to use a highly technical analysis in their decision making, therefore the method should use a short list of understandable primary inputs and outputs, with subsidiary factors. Politicians are primarily interested in four benefits measures: real estate value, job creation, mobility, and cost effectiveness. Conversations about methods were invariably about estimation of benefits for existing systems, not prediction, perhaps because prediction is more complex and is rarely conducted as part of transit project planning. While UK guidance covers a number of benefit areas respondents generally noted the benefits that were missing from guidance and expressed the desire for methods to be developed. There were, however, some comments that UK guidance is slightly too onerous and can sometimes create âparalysis by analysis.â Several respondents argued that environmental benefits should be considered part of economic benefits because of the social costs and benefits involved. Transferability For the method to be applicable in all areas, one respondent suggested that researchers could prepare a different standard for each âmegapolitanâ area in the US. Another respondent suggested that the analysis could be separated into parts, and that agencies would be required to complete a subset of those parts based on the mode and regional characteristics. Interview Scripts The interview scripts for both US and international use are provided below for reference. United States Interview Script Purpose of interview : 1. To obtain information about what practitioners expect to be the economic benefits of transit projects, and how they (or their consultants) have calculated those benefits. 2. To obtain reports and other documentary evidence of the above, that we have been unable to obtain from internet searches. 3. To find out what sort of guidance is useful to practitioners. Target of interview: Individuals with direct experience with New Starts, Small Starts, or the development of other major transit investment projects, working at transit agencies, metropolitan planning organizations, state departments of transportation, the Federal Transit Administration, APTA, or consulting firms. (We will focus on transit agencies and their consultants, as we expect these to have the most direct knowledge of relevant transit projects.) Note: The script below is meant as a list of topics, rather than questions (i.e. an interview guide, rather than a questionnaire). Interview discussions will be respondent-driven, and interviewers are directed to alter the order of the questions, skip questions that are obviously irrelevant, etc., as appropriate.
173 ---------------------------------------------------------------------------------------------------------- 1. Have you ever been directly involved in any major transit investment projects such as New Starts, Small Starts, or another major investment? a) Yes b) No ï IF NO, SKIP TO 15 2. How many projects, and in what capacity, were you involved? (e.g., project manager/supervisor, consultant, data analystâ¦) 3. Can you please provide a brief description of these projects? (mode, location, magnitude in miles and cost, ongoing or completed, if completed then outcome, funding source, and if federally funded, funding status, e.g. funding program, applications date, approval date) 4. What kind of benefit measures did you use in your project development process? (e.g., ridership, congestion relief, air quality, economic benefits) 5. Did you use any economic benefit measures, and if you did, what measures did you use? (e.g., land value increase, increase in density, new jobs, new housing, agglomeration economy) 6. Can you provide or tell us how to acquire reports, guidebooks, or other documents describing expected economic development impacts, or discussing methods for calculating economic impacts? 7. What data and methods did you use to estimate expected economic development impacts and what were the data sources? What references documents did you use for the selected methods? 8. Was it difficult to get the necessary data, and if so, what were the difficulties? 9. Would you be willing to share your data with us and consider being a case study for our effort? 10. Who did the analysis? (e.g., staff, consultants,â¦) 11. Did you find the required analysis for the project development process complex, and if so, why? What remedies would you suggest? 12. Do you think that the analysis/method you used misses any important economic benefits, and if so, what are they? 13. What guidance from the FTA do you feel would be helpful in estimating economic impacts of transit investments? How should guidance be presented? (e.g., report, website, spreadsheet, software,â¦) 14. Do you have any general suggestions about the data and methods that have been or can be used for New Starts/other major investments?
174 ï SKIP TO 21 -------------------------------------------------------------------------------------------------- (Note that this section is for those with no direct experience with New Starts, Small Starts or other major transit investments.) 15. Why are you interested in New Starts, Small Starts or other major investment projects? 16. In what capacity do you work and how did you get exposure to the project development process? 17. How familiar are you with benefit estimation for New Starts, Small Starts or other major transit investments? 18. What can you tell us from your experience that will help us improve the methods of analysis for project development, particularly in regard to the estimation of economic benefits? 19. Can you provide or tell us about any documents/reports that are useful for estimating benefits from New Starts or other major investment projects? 20. Is there anyone at your agency or in your region who is involved with New Starts, Small Starts or other major investments, and if so, can we get their contact information? -------------------------------------------------------------------------------------------------- Interviewer states: Now we would like a little information about your agency for our records. If transit agency, ask directly. If consultant, ask if they know the following about the agency they worked for most recently on New Starts or other major investments. 21. What modes does the agency operate? 22. In what area? 23. Annual ridership? 24. Annual revenue? 25. Number of employees?
175 International Interview Script Purpose of interview : 1. To obtain information about what practitioners expect to be the economic benefits of transit projects, and how they (or their consultants) have calculated those benefits. 2. To obtain reports and other documentary evidence of the above, that we have been unable to obtain from internet searches. 3. To find out what sort of guidance is useful to practitioners. Target of interview: Individuals with direct experience with transit investments or evaluation outside the US, including those working at transportation agencies and consulting firms. Note: The script below is meant as a list of topics, rather than questions--an interview guide, rather than a questionnaire. Interview discussions will be respondent-driven, and interviewers are directed to alter the order of the questions, skip questions that are obviously irrelevant, etc., as appropriate.
176 ---------------------------------------------------------------------------------------------------------- 1. Have you ever been directly involved in any major public transport investment projects? c) Yes d) No ï IF NO, SKIP TO 15 2. How many projects, and in what capacity, were you involved? (e.g., project manager/supervisor, consultant, data analystâ¦) 3. Can you please provide a brief description of these projects? (mode, location, magnitude in miles and cost, ongoing or completed, if completed then outcome, funding source, and if federally funded, funding status, e.g. funding program, applications date, approval date) 4. What kind of benefit measures have you used in your project development process? (e.g., ridership, congestion relief, air quality, economic benefits) 5. Did you use any economic benefit measures, and if you did, what measures did you use? (e.g., land value increase, increase in density, new jobs, new housing, agglomeration economy) 6. Can you provide or tell us how to acquire reports, guidebooks, or other documents describing expected economic development impacts, or discussing methods for calculating economic impacts? 7. What data and methods did you use to estimate expected economic development impacts and what were the data sources? What references documents did you use for the selected methods? 8. Was it difficult to get the necessary data, and if so, what were the difficulties? 9. Would you be willing to share your data with us and consider being a case study for our effort? 10. Who did the analysis? (e.g., staff, consultants,â¦) 11. Did you find the required analysis for the project development process complex, and if so, why? What remedies would you suggest? 12. Do you think that the analysis/method you used misses any important economic benefits, and if so, what are they? What were the outcomes of the analysis? Which elements, if any, had any impacts on decision making and to what extent? 13. What additional guidance from the DfT do you feel would have be helpful in estimating economic impacts of public transport investments? 14. Do you have any general suggestions about the data and methods that have been or can be used for public transport investments?
177 -------------------------------------------------------------------------------------------------- (Note that this section is for those with no direct experience with major public transport investments.) 15. What is your interest in public transport investment projects? 16. In what capacity do you work and how did you get exposure to the project development process? 17. How familiar are you with benefit estimation for public transport investments? 18. What can you tell us from your experience that will help us improve the methods of analysis for project development, particularly in regard to the estimation of economic benefits? 19. Can you provide or tell us about any documents/reports that are useful for estimating benefits from public transport investments?
178 Interviews- Section 5 (Case Studies) Interview Script Start by asking about the person's role, their involvement in transit development â let them guide the discussion as much as possible. *Thank you for taking the time to speak with us. We are working on a project funded by the Transit Cooperative Research Program and the Federal Transit Administration that is seeking to develop methods to evaluate the economic productivity impacts of New Starts investments. As part of that work, we are conducting a series of case studies to determine how these considerations were taken into account, as well as to gather information to both inform the methods we are developing and to test them. *We would like to record this call for transcription purposes. Is that okay? *I will start the recording now. Let me know if you wish to pause it or go off the record at any time, okay? Introductions * What is your title? * Have you held any previous positions related to transit? * Walk me through your involvement in the transit development process. * What else does your organization (or past organizations you've worked for) do to support transit development? Interview Themes 1. Evidence of Densification * Have you seen any evidence of densification along the transit routes? 2. Evidence of Firm Clustering * Can you give examples of firms that have opened or expanded along the line? 3. Transit Benefits from Congestion * Is traffic congestion an issue in the corridor? Does transit offer a speed advantage? 4. Types of Economic Development Strategies * How has economic development been pursued around transit stations? Is this different than elsewhere in the region? 5. Industry Development Strategies * Have efforts been made to attract new firms, expand existing firms, or both? By who? 6. Spatial Aspects of Transit Development * Have development plans targeted underdeveloped areas or existing dense areas? Wrapup *Thank you for your time; I've found this very valuable. Are you aware of any other people who might be able to speak with use concerning any of these issues? Do you have any contacts with developers that you could share with us?
179 APPENDIX E: REVIEW OF PRACTICE REPORTS AND GUIDANCE Governments, transportation agencies, and other public and private organizations estimate the economic impacts of proposed transit investments in order to prioritize funding, argue for or against projects, and to evaluate whether investments have been an effective use of public funds. Here we describe how the economic impacts of transit investments are calculated in practice, with examples from the US, the UK and the Netherlands. We cover reports, documented regulations and published administrative guidance. Both the practice of transport modeling, and the ridership forecasts that are sometimes partly or largely based on such models, are important inputs to the estimation of economic impacts. However, we do not cover the practice of transport modeling or ridership forecasting here. The assessment of additional economic benefits goes beyond direct ridership or regional employment effects. Many studies are typically done to justify projects at a regional level and methods vary widely. British practice provides additional insight about how to assess both the overall impacts and the additional economic benefits of transit projects. The UK's "New Approach to Assessment", stemming from changes in policy in 1997, provides an explicit linkage between national goals and specified project outcomes. Our main objective was to find examples in current practice of estimating additional economic benefits of transit in a transparent and theoretically valid manner. However, as we describe below, we found no practice studies in the US that addressed agglomeration economies or related benefits. The UK guidance does offer some insights into how to carry out such estimates, some of which are transferable to US practice. The following sections first summarize US practice at the federal, and then at lower levels of government in the United States. We then provide more detail on specific modeling approaches that may be relevant to the project. This is followed by a description of the basic method used in Britain. United States: Federal Level The FTA New Starts and Small Starts programs are the primary federal funding resource for capital investments in fixed guideway transit systems. SAFETEA-LU identifies specific criteria that FTA must consider. In order to advance a New Start project through the project development process and to enter into a funding agreement, FTA must evaluate each project based on five project justification criteria: mobility improvements, environmental benefits, cost effectiveness, transit-supportive land use, and âother factors.â Measures of environmental benefits are limited to Environmental Protection Agency (EPA) air quality status. Projects located in federally designated ânon-attainmentâ areas for any transportation-related pollutant receive a âhighâ environmental benefit rating, and other projects receive a âmediumâ rating. Economic benefits, or âeconomic development impacts,â are included as optional measures under the âother factorsâ category. This criterion is documented by project sponsors in a âMaking the Caseâ report that is submitted to FTA. Specific reporting guidance is not provided. While SAFETEA-LU required FTA to consider the economic development effects of New Starts projects, this criterion was not required for the FY 2008 and FY 2009 evaluation cycles because FTA (2009) "desires through the rulemaking process to work with the industry on the development of appropriate factors for measuring the economic development effects of candidate projects."
180 FTA (2008) recently published a Proposed New Starts Economic Development Criterion, which lays out a method and reporting requirements for a new, stand-alone economic development criterion that would first apply starting with FY 2011 projects. This criterion is based on the ability to develop land near stations, the presence of transit-supportive plans and policies, and the economic climate. The âdevelopabilityâ criterion is documented through population and employment forecasts, tax assessment data, a build-out analysis of the total additional development that could be accommodated under existing or proposed zoning, and a subjective market assessment by a local analyst. âTransit-supportive plans and policiesâ are defined as those that support pedestrian mobility and accessibility, and include pedestrian network connectivity, building setbacks, parking design, requirements, and regulations, the land- use mix, and residential and commercial densities. These are documented through an inventory of relevant plans, policies, and ordinances as well as a narrative description of potential barriers such as environmental contamination. âEconomic climateâ is documented through long-term metropolitan growth forecasts, recent growth in the station area and project corridor property values, commercial and residential rents, and commercial vacancy rates. United States: State, Regional and Local Level The studies produced by practitioners in the transit-oriented economic development field employ a variety of methods to estimate and compare the economic impacts of transportation investments. Because estimating such impacts has not been part of the FTA application process, these studies are typically done for local purposes. We summarize the reports here and provide more detail below. In many cases, estimated economic benefits are just monetized time savings, not additional economic impacts as defined here. In other cases, the estimates are from multiplier effects arising from time savings. Some reports and documentation did not have sufficient detail to determine whether any additional economic benefits were estimated. Benefitâcost analysis was the predominant methodology used, but not all studies use a formal methodology for making investment decisions. Benefits estimation is accomplished with a variety of formal and ad hoc methods, and commonly accomplished with the use of computerized transportation demand models (including the traditional four-step method) that can be used to estimate direct user benefits. Such estimates are already included in the FTA requirements for funding applications. Other economic benefits can be categorized as either âindirectâ or âinduced.â Indirect benefits may include the employment and intermediate output impacts from construction projects or ongoing maintenance. Induced effects include better access of firms to workers (and vice versa) and recirculation of savings into the economy. Such models use input-output modeling, sometimes in conjunction with land-use modeling, to estimate the economic impact of transportation investments, and explicitly adjust for any double-counting of benefits. A few rail freight studies used ad hoc methods, developing spreadsheet-based rate models in combination with surveys of logistics providers to estimate the market share that could potentially be captured by proposed rail freight improvement alternatives. Occasionally projects eschew formal evaluation methodologies entirelyâfor example, because the project solution entailed a negotiated financial agreement between public and private parties and alternative proposals were not considered. Monetizable benefits that were considered in the various benefitâcost analyses included travel time savings accruing to businesses (especially logistics operations) and consumers;
181 vehicle capital and operating expense savings from modal shift (including pedestrian share); monetized reductions in pollution, greenhouse gases, and accidents from decreased automobile travel; increased business revenue from higher transportation system efficiency and an expanded labor pool; increased retail spending; fiscal impacts from tax revenue increases; project construction-related economic impacts (wages, employment, GRP); long-term wage increases; and property value increases, which may reflect any of a number of the economic impacts above. Input-output models attempt to model the linkages between industries with a matrix that captures the consumption and production dependencies amongst them, and how changes in these factor prices affect economic output. Input-output studies use project construction, maintenance, and travel time savings as inputs to estimate changes in economic output. These are essentially measuring multiplier effects from any construction expenditures plus any structural changes from travel time reductions. While construction impacts are certainly of interest to local areas, from a national perspective they would not be relevant, under the assumption that construction expenditures in other regions would have similar impacts. A few of the studies employ real estate industry methods (in particular, hedonic modeling) to estimate the impact of transportation investments on property values. Since such changes in property values largely reflect travel time improvements, this is double-counting under the current FTA evaluation approach. (Property values might also reflect the marginal internal value of agglomeration, in addition to the value of the greater accessibility. However, relying on property value measures without double-counting would imply entirely replacing measures of travel time with property value estimates, and assuming 100% capitalization, which is unlikely to occur in a competitive market.) Examples of hedonic value or property development studies include the Portland Streetcar study, which uses zoning data, developer surveys, and comparable transit investment programs in the city to estimate the real estate impact of new streetcar investment. The DART Fiscal Impact study uses similar methods as well as employing GIS for visual inspection to estimate the amount of development attributable to light rail access. The Phoenix Metro study also estimated the real estate impact of light rail investment. The DART TOD guidelines provide detailed real estate and physical design guidelines to practitioners in order to evaluate and maximize the development impact near light rail transit stations. These and other specific studies are described in more detail below. Other studies use econometric models, which are multiple regression models that attempt to estimate empirically the contribution of various economic input factors on regional economic output. REMI, the Bureau of Economic Analysis, and TREDIS offer econometric models or frameworks that include econometric modeling components. Input-output models are derived from econometric estimates of multipliers that are then used in input-output models, but the two techniques are not the same. We discuss REMI and TREDIS in more detail below. Economic Modeling Systems There are also modeling systems that attempt to model regional economies and in so doing enable estimates of how alterations to infrastructure and accessibility affect economic development. We discuss input-output methods, regional travel demand models, and econometric methods for estimating production functions. Of these, the last are most applicable to estimating agglomeration effects.
182 Input-Output Models Input-output models focus on the interrelationships of sales and purchases among sectors of the economy by using multiplier effects. Regional input-output models can be used to estimate the impacts of transportation investments on the economy. This is done by changing input assumptions on travel costs and accessibility, and by allowing the inter-industry interactions to determine the outputs using multiplier effects. These models require extensive data that might not always be available at the regional level. For any region, a survey of a representative sample of firms for each industry included in the IO model is needed to develop an accurate region-specific technology matrix. This can be a very expensive and time-consuming endeavor. Moreover, multipliers used in these models are assumed to be temporally and spatially invariant, and so might not accurately capture long-term spatial changes due to mechanisms such as agglomeration and other changes in land use. We examined documentation for REMI and TREDIS to confirm whether any components of the model could adequately capture agglomeration externalities. As documentation on the details of both models is not fully available, our review is limited to documentation on their respective websites and in supporting papers. REMI combines travel demand, input-output, and econometric modeling components in their framework. The REMI model addresses the connection between transport costs and productivity via the ability of firms to access labor markets and the potential variety and concentration of those labor markets. Commuting time and expenses are input within equations that are stated in the documentation as providing productivity measures based on the location of where employees live and work for each occupational sector. The time dynamics within the equations allow for simulated forecasts and dynamic linkages to other parts of the model structure. The models include an elasticity of substitution among product inputs, with respect to costs, which is based on estimates from an analysis of traffic analysis zones in Chicago. These were based on cross-commuting patterns of workers between the various zones. The derived elasticity appears to be occupation-specific, although details are vague within the documentation (REMI 2008). This is a good example of the difficulty of replicating this sort of modeling system, which is not fully documented. Despite this drawback, REMI has been used by many agencies. Another example of a modeling system is TREDIS (Transportation Economic Development Impact System), designed for passenger and freight transportation economic impact modeling. It offers components for transportation demand and economic impact estimation. It can be used by itself or in conjunction with other modeling packages such as REMI, and is compatible with other transportation demand modeling packages. TREDIS uses input-output and economic geography modeling techniques to estimate the economic impact of transportation investments. The Chicago Metropolis 2020 study used the TREDIS framework, but only used the input-output component to estimate indirect benefits. TREDIS is integrated with the LEAP model to estimate market access benefits, which closely resemble the additional economic benefits that we are concerned with here. The database and resulting estimates derived to evaluate sensitivity of responses from each industrial sector to changes in market access are proprietary, and we are unable to provide further information on this. Econometric Estimation of Production Functions Another approach is to develop econometric models of production functions. The resulting relationship between economic output and transportation inputs (measured via
183 accessibility) is the agglomeration elasticity used in the UK guidance on wider economic benefits. Most research in this area has focused on highway capital or infrastructure and typically does not consider transit. The empirical work conducted by Ozbay et al. focuses on highway capital and investments for several reasons. First, quite often highway capital is the major component of regional transportation infrastructure (Holtz-Eakin 1994; Boarnet 1995). Second, as shown by Ozbay et al. (2003), employment growth clusters mainly near highways. Third, in New Jersey, highways are the predominant mode of travel. A series of production function models for the NY/NJ metropolitan area using a time- series dataset for the decade of 1990-2000 have been estimated by Ozbay et al.(2003; 2006; 2007a; 2007b; 2008). Three basic models were developed for different cases. The first case considered the effect of private capital on the gross county product (GCP). The second case included the effects of both private and public highway capital stocks. The third case tests the hypothesis that the output within a metropolitan area depends, in part, on highway capital stocks within the area. This third case also examines the question of whether or not the economic benefit from a particular transportation corridor is mainly a redistribution of economic activity from nearby areas. Ozbay et al. (2003; 2006; 2007a; 2007b) have considered important issues such as âlagged variable effects,â âspillover effects,â and the âdynamic nature of the investment- output relationshipâ. Jiwattanakulpaisarn et al. (2009a, 2009b) also examine how infrastructure affects employment levels using similar techniques. The models estimated in Ozbay et al. (2007a; 2007b) and Berechman et al. (2006) implicitly assume that county economic growth is caused by investments made in transportation. However, it is also conceivable to hypothesize that high economic growth creates the need for transportation services and thus investment. Disregarding such causality might result in problems of simultaneity bias in the empirical analysis, which in turn will generate incorrect estimates. These methods might be applicable to our research in that they provide a technique for linking various infrastructure features to economic output, while controlling for various other economic factors. They also suggest that any model that examines the impact of transit must not omit highway infrastructure effects, which might have large impacts on output. The output elasticity of highway capital investment was found by Ozbay et al. (2003; 2006; 2007a; 2007b; 2008) to range between 0.135 and 0.206 (depending on the time lags) suggesting that a 1% increase in highway capital leads to approximately a 0.171% average increase in county economic output. The magnitude of this elasticity falls toward the lower end of the range of elasticities reported in the literature (Duffy-Deno and Eberts 1991). These methods are quite similar to those used in the UK to estimate agglomeration impacts. The main difference is that they use regional data (i.e., county or state level) as opposed to firm-level data. As discussed in our framework, we hope to conduct a mix of different estimates to tease out how infrastructure affects productivity. Sample US Studies This section describes the sample of economic studies that we were able to obtain. These were found through web searches but also via the interviews conducted with practitioners. In no way should this be seen as a comprehensive list, but it does demonstrate the difficulty in obtaining what are not typically widely circulated studies. None of the studies provide sufficient detail to fully understand all of the analysis that underlies them. They also do not address the key issue of additional economic benefits.
184 Chicago Metropolis 2020 The Chicago Metropolis 2020 study analyzed the potential economic impact of four public transit investment planning scenarios: decline (stable capital and operating funding), maintain (stable level of service with attendant capital and operating support), expand (significant increases in public transit funding) and expand and plan (expand scenario, with land- use reform to stimulate redevelopment). The research methodology was based on two computer models. The first was a regional transportation model, which was used to estimate the change in transportation demand under the alternative transit investment scenarios. The second was an input-output model of the Chicago region economy using the TREDIS framework, which translated changes in transportation demand to increased business production as well as the effect of reinvestment of those savings into the local economy. The study only included the direct and indirect benefits related to travel time savings in the model, and excluded the effect of construction spending, fiscal impacts and property values. Scarborough The Scarborough Rapid Transit study commissioned by Toronto Metrolynx evaluated the impact of five alternative improvement programs to a semi-automated rapid transit line. The methodology employed consisted of a 30-year discounted benefitâcost analysis considering five categories of impacts: transportation user benefits; financial impact on Metrolynx; land value appreciation; environmental impacts; and direct and indirect economic impacts and socio- community impacts such as noise, health, and aesthetics. Impacts were monetized where possible. The socio-economic and environmental impacts, as well as user benefits such as comfort and accessibility, were not monetized. The study modeled both short-term economic impacts, primarily from construction-related employment and wages, and longer-term impacts on wages and GRP, reflecting enhanced regional competitiveness derived from more efficient transportation, using Ontario-specific input-output multipliers. The origin of the input-output model was not specified in the study report. Access to the Regionâs Core (ARC) The ARC study analyzed the economic, fiscal, and real estate impact of the planned trans-Hudson tunnel and extension connecting New Jersey Transit rail lines between Newark and Manhattan. The study used the REMI framework to evaluate the short-term economic impact of tunnel construction on jobs as well as long-run economy-wide impacts. Within the REMI framework, long-term economic impacts are driven by NJ Transit maintenance and operations, improved quality of life (lower emissions, reduced transportation-related accidents, and increased leisure time), travel time savings and lower expected regional housing costs. The study estimated the permanent employment impact at an additional 74,000 jobs, which is expected to generate significant office space growth, primarily in Manhattan. The study also analyzed the fiscal impacts of the project on New York and New Jersey based on the outputs of the REMI model, regional tax rates, household size, and regional home-ownership trends. Although a benefitâcost ratio was not explicitly calculated, the methodology otherwise conforms to a benefitâcost analysis. DART Fiscal The authors performed a study of the fiscal impacts of existing and proposed development attributable to DART station-area development. Tax revenues accruing to state, local and special districts were considered. The researchers developed criteria to identify development which was partly or entirely motivated by access to a DART station, while filtering
185 out those developments that could not be attributable to DART station proximity (e.g. drive- through fast food restaurants). The researchers used both quantitative and qualitative methods to evaluate development value and tax assessments. Sources included tax rolls for existing developments, interviews with real estate developers, DART officials, local chambers of commerce, periodicals, aerial photography to identify new developments, and field observations. Return on Investment in Rail Freight Capacity Improvement This study examines ten case studies of projects that seek to improve or expand rail freight capacity. The benefits captured by the projects fall under economic, environmental, safety/security, transportation, and other categories. Likewise, the benefits are evaluated using methods appropriate to each category of benefit. Not all the projects use all of these methods and some do not use any formal methods, so the presentation is divided into the description of formal and informal methods and a brief listing of projects using some of these methods. Formal methods employed by the project evaluators included benefitâcost analysis, input-output models, regional economic simulation models (REMI was used in three of the projects), and the Bureau of Economic Analysis model, as well as reliance on domain experts in transportation and real estate to estimate impacts that were fed into the models. Even in cases in which benefits were not strictly monetized, such as emissions reduction, benefitâcost analysis was used to compare the efficiency of alternatives in domain-specific terms (e.g. $/kg of emissions reduced). Some projects that evaluated the impact of rail improvements versus highway improvements for truck freight employed the FHWAâs HERS model to calculate effects of alternative highway investments. Projects employing formal decision methods include the Chicago Region Environmental and Transportation Efficiency Project, the Iowa CMAQ rail projects, the New York Cross Harbor Goods Movement Environmental Impact Statement, the Mid-Atlantic Rail Operations Study (MAROps), and the Palouse River and Coulee City Railroad (PCC) study. Informal methods include internally developed models and processes to estimate project impacts. Some projects sought to evaluate the competitive requirements of rail freight versus truck freight. They developed rate models derived from surveys and interviews of shippers and carriers to estimate the required performance goals and investments needed to achieve them. A couple of projects evaluated the impacts of local freight facilities improvements with informal methods. Projects employing informal methods included the Alameda Corridor transportation project in the Los Angeles port district, the Rail Freight Bottom Line Report, the I-81 Marketing Analysis for Virginia, the Northern Ohio Corridor Study, and the Shellpot Bridge project. Portland Streetcar The Portland Streetcar report analyzed the economic, environmental, and fiscal impacts of the new westside streetcar line in Portland Oregon. The report is oriented towards the linkage between transportation and land use, and promotes streetcar expansion and higher density development as a way to efficiently improve environmental and economic performance. The analysis methods consisted of real estate-based projections derived from previous experience with streetcar alignments, and the policy changes in terms of FAR and attendant land uses negotiated with developers to make high-density development feasible.
186 NJ Transit Retail and Recreational New Jersey Transit conducted a rider survey-based evaluation of the retail and recreational spending attributable to ridership on New Jersey Transit lines. The study aggregated the responses to these surveys to estimate the total economic impact of transit-based spending on local revenues and taxes. The study also calculated a benefitâcost ratio for this incremental spending with respect to transit operating costs. DART TOD Guidelines and Policy The DART TOD guidelines describe the recommended physical design of stations and station areas that lead to successful transit-oriented development. Phoenix Metro Phoenix Metro published a summary of the economic impacts of their light rail investment strategy, describing the value of planned and executed real estate development in the light rail station areas. Howland Hook The Howland Hook study used a benefitâcost analysis to determine the preferred investment program for transportation investments to improve truck freight traffic out of the Howland Hook terminal. The Netherlands Annema, Koopmans, and Van Wee (2007) investigate benefitâcost analysis practice for infrastructure investments in the Netherlands, where a standardized approach has been mandated by law since 2000. The Dutch Ministries of Transport, Public Works, and Water Management and Economic Affairs developed a standard CBA practice guide, which would serve as the basis for future CBA. The researchers developed a benchmark system to measure the quality, transparency (i.e. accessibility for a non-expert reader), correctness, completeness, and risk analysis of benefitâcost analyses conducted for 13 major infrastructure projects ranging in size from 300 million to 12 billion euros. Their evaluation of these CBAs found 10 out of 13 inadequate in terms of transparency, 12 out of 13 were considered âfairly complete,â and only 6 of 13 received positive marks for quality. The authors concluded that while the Dutch standardized CBA approach has improved ex ante project evaluations and provide fairly complete information for policymakers, they suffer from a lack of quality in their methods and assumptions. Overview of UK Appraisal Procedures The Department for Transport in the UK publishes and regularly updates guidance to be used by consultants and agencies conducting appraisal and benefitâcost analysis of transport investments. The guidance is designed to allow the department to make a standard comparison of transport investments across the country in a balanced way, providing a linkage to national goals and objectives. This process provides a framework for multi-attribute assessment and was originally an outcome of changes in assessment policy in 1997. Originally known as the New Approach to Assessment (NATA), the overall process is focused around achieving national objectives in five areas: environmental impacts, safety impacts, economic (welfare) impacts, integration, and accessibility.
187 Within each of these areas there are a number of sub-objectives about which the UK guidance provides detail on how to assess and quantify. Some sub-objectives are qualitatively assessed, but this does not imply they are less important than those that receive a quantitative analysis. One outcome of these procedures is to diminish the importance of standard benefitâcost analysis, which is one of many line items within the list of sub-objectives. Nellthorp and Mackie (2000) found that NATA initially resulted in decision makers placing a greater emphasis on environmental outcomes than in past practice. Noland (2007) provides a discussion of NATA and its integration with Strategic Environmental Assessment with a brief review of how ultimate decisions are strongly influenced by public input and political considerations, regardless of what the assessment determines is the âbestâ solution. âWider economic benefitsâ is also a specific sub-objective, which is of the greatest relevance for this project because it corresponds to the idea of truly additional economic benefitsâthat is, benefits beyond the monetization or capitalization of decreases in travel time (increases in travel speed). The sections below explain the details of the guidance on estimating these âwider economic benefitsâ. TABLE E 1 Objectives and sub-objectives in UK transport appraisal Objectives Sub-objectives Environment Noise Local air quality Greenhouse gases Landscape Townscape Heritage of Historic Resources Biodiversity Water environment Physical fitness Journey ambience Safety Accidents Security Economy Transport Economic Efficiency Reliability Wider Economic Impacts Accessibility Option values Severance Access to the Transport System Integration Transport interchange Land-use policy Other government policies The assessment of economic development impacts in UK appraisal includes two distinct elements. The first is âregeneration,â or supporting the economy of deprived areas, as measured by the employment rate. This is calculated using a relatively straightforward accessibility
188 analysis. Formal guidance already exists for the assessment of regeneration benefits in DfT (2003). The second is âwider impacts,â which includes agglomeration externalities, competition effects and labor market externalities. Guidance on the calculation of âwider impactsâ is in the final stages of development, but a draft methodology was recently published DfT (2009). This methodology has been applied extensively over the last two years and consists of four elements: ⢠Agglomeration economies: âEffective densityâ (that is, employment accessibility) is calculated with and without the project, based on official employment data and journey costs for work and commuting travel. The transport data are extracted from standard transport models. The proportional change in effective density by location from the base case to the intervention scenario drives productivity growth using Grahamâs elasticities. In practice, these elasticities may or may not be adjusted for local conditions, but should be. ⢠Changes in accessibility: The main impact that transport has on productivity via agglomeration in this model is via changes in accessibility to employment, rather than increased concentration of employment in space. The DfT discussion paper recommends including an assessment of the impact of land-use changes where evidence is available. Some argue that these typically only account for a small proportion of the total agglomeration benefits (Feldman et al. 2008). But much may be dependent on local regulatory context. In the US, land use may change faster than in the UK, where land use is more strongly regulated. However, in the UK density restrictions may be easier to remove. ⢠Imperfect competition: Based on Davies (1998), the discussion paper recommends adding 10% of work-related travel user benefits to appraisal benefits (10% is based on Davies (1998) relationship between priceâcost margins, the elasticity of aggregated demand, and the magnitude of the additional benefits occurring under imperfect competition. As Davies (1998) shows, the âmissingâ user benefits are equal to a proportion of the conventionally measured benefits, where this proportion is the product of the average priceâ cost marking in the economy and the aggregate demand elasticity. The discussion paper finds the average priceâcost margin to be 20% and the demand elasticity -0.5 and hence the missing benefits to equal 10%). This is certainly also subject to local economic conditions. It assumes that the transport investment perfects price competition, and that mark-ups are entirely uncompetitive; and is therefore unlikely to represent a quality estimate of competition benefits. ⢠Labor supply: The two impacts described in âcommuting costs and taxationâ in Chapter 2 are part of the DfTâs methodology. One of the first applications of this type of agglomeration analysis was for the CrossRail project in London. An underground EastâWest rail link connecting two of the major rail stations in the city was estimated to deliver significant capacity and accessibility benefits to the capital, worth about £12.8bn to transport users (NPV). It was estimated that these improvements would attract an additional 26,000 jobs to the CBD by 2026, delivering agglomeration benefits of around £3bn. Detailed Guidance on Assessing Wider Economic Benefits Although the UK guidance on assessing âwider economic benefitsâ is still in draft form (DfT 2009), consultants have begun using this for the assessment of various transport projects and plans, most notably the CrossRail project in London. Although the guidance is designed for
189 all transportation projects, two of the measures are most important for public transportation projects: the agglomeration benefits associated with increased employment density, and the potential increase in employment (and reductions in time to find employment) from greater accessibility to jobs. Since these two measures are related, the guidance attempts to assure that no double-counting occurs. In both cases the chief input used in calculating the effects is the change in time and money costs of travel due to the transportation project. Some of the measures are not relevant to transit, in particular those focused on benefits of reducing âbusinessâ travel, the preponderance of which is via private vehicle. Road congestion might also be affected if a public transit project is significant enough to permanently reduce road traffic. We do not address potential road congestion effects in this report that would be accounted for in a traditional benefitâcost analysis. In the UK the benefits associated with any estimated congestion reduction would be input into standard benefitâcost analysis based on travel demand modeling outputs and variable demand matrices. Method for Estimating Agglomeration Benefits Agglomeration benefits are estimated based on how changes in travel time between firms may increase their productivity by making interactions among more firms possible. In theory, this kind of effect is more likely to occur with increases in localized interactions; thus the scale is important, particularly for transit effects which would be partially based on localized walking distances. Effective employment density, as the concept is defined in the UK guidance, can be calculated in several ways, but essentially represents the employment accessibility of a given spatial unit, such as a county, municipality, or even a census tract. The measure is intended to represent reductions in travel time that lead to easier interactions among firms, or among workers who are employed in firms. These interactions are thought to cause higher productivity. The scale used is dependent on data availability and the details of the study being undertaken. It is convenient for it to be consistent with required outputs from a transportation model to match the generalized costs (usually travel times) associated with the spatial unit (in this case a transport analysis zone for a transport model). Thus, changes in generalized cost of travel associated with the project are a required input to the calculation of agglomeration benefits. Effective density of employment is essentially a gravity-based employment accessibility measure, representing the size of employment clusters divided by a function of the time to travel between them. It is calculated as: Ek T = workplace-based employment in zone k in year t. jk α = is a parameter which can be estimated, different decay parameters may be used for different sectors (DfT 2009). = generalized cost of travel between areas j and k in year t. As travel speed increases, the value of far-away zones similarly increases. DfT (2009) suggests breaking down the various components of generalized cost. For example, this can be done by trip purpose, time-of-day, and mode. One critical assumption made in the UK methodology is that residential and employment locations are static. Thus, agglomeration benefits are only calculated on the basis of direct â= k kjtktj TEED α ,,,
190 changes in accessibility via transport improvements. The guidance suggests that an integrated land-use/transport model could be used to further investigate endogenous changes to residential and employment location, as a sensitivity analysis of the static case. A second major input is estimates of percent increases in industrial productivity as a function of employment accessibility. These were estimated for the UK by Graham (2005) for each of the major sectors of the economy and for each ward (similar to a census tract). The estimates can be aggregated to larger spatial units if needed. The formula used to calculate agglomeration benefits (simplified from DfT 2003 and modified according to DfT 2009) is: Where, WB1 = âwider benefit,â type 1 ED = effective density of employment (âED is change in ED), for alternative case, A, and base case, B Ïk GDP = GDP per worker = productivity elasticity with respect to effective density for sector k E = workplace-based employment i and j denote the disaggregate sectors and spatial units in the analysis. In the United Kingdom, method productivity effects are discounted over a 60-year time horizon from the project start with standard assumptions used about demand growth. These are compared to a base case scenario in which the project is not constructed. The growth in GDP takes into account the effect of agglomeration-related productivity improvements. Results are clearly dependent on the time frame and discount rates used, suggesting that some sensitivity analysis would be beneficial. The difference in GDP between the base case and the âdo somethingâ case is then aggregated over all spatial units and each year of calculations, with appropriate discounting to determine the overall agglomeration benefit. Considerable thought was given to the appropriate definition of âeffective densityâ (employment accessibility). However, the chosen relationship between distance and the decay of agglomeration effects was selected for convenience rather than reflecting empirical evidence. Essentially, the importance of activity further away is assumed to decay at a rate equal to the inverse of the generalized cost of travel (so activity twice as far away has half the impact on agglomeration). Whether or not the chosen relationship is correct, there is a conceptual problem with using a distance decay function based on generalized cost when estimating the relationship between density and productivity (the agglomeration elasticities). Although perceived cost of movement may be the intuitively correct measure of distance, there is a dual causality between the accessibility, productivity and effective density measured in this way. The overall pattern of results by industry based on either measure of effective density is very similar. However, generalized cost based estimates tend to be of higher magnitude than the distance based measures. This reflects the fact that distance based measures of agglomeration do not account for the fact that speeds may vary systematically with city sizeâthat is, they do not recognize congestion diseconomies. In effect, the exclusion of travel time information in the â         ÃÃ      ï£ ï£« â= ji ijij jB jA EGDP ED ED WB k , 11 Ï
191 definition of effective density induces a downwards bias on the agglomeration elasticity values for the most urbanized wards. Graham notes that from the point of view of transport appraisal, the use of the generalized cost based elasticity estimates may actually be less appropriate than those based on straight-line distance. This is because the benefits to business and freight users from congestion reductions are already included in standard costâbenefit analysis and so inclusion of the congestion effect implied by the generalized cost agglomeration estimates could risk some double-counting of these benefits. If we choose to measure density in a way that recognizes differences in transport networks (i.e. time or generalized cost), there will be two routes through which differences in density can contribute to the measured productivity differences across locations: First, there are agglomeration economies (e.g. input and output market sharing and knowledge spillovers). Second, locations with better transport networks will have lower input costs (e.g. cheaper to transport goods in and out). For the first impact, agglomeration economies result indirectly from generalized cost savings which in turn allow greater interactions and therefore greater productivity benefits. The second set of impacts are those that result more directly from the generalized cost saving where for example the cost saving allows greater output from firms - increased output that is not associated with agglomeration economies. In respect of the second impact, the use of elasticities calculated on the basis of generalized cost effective densities would be problematic as it would risk double-counting some of the user benefits already captured in appraisal. Effects of Increased Competition and Reductions in Market Imperfections Two of the additional measures considered in the UK guidance are 1) how transport can increase competition between economic agents, and 2) reductions in market imperfections that can lead to increased output. Increased competition is considered not to be a measurable effect, due to the mature nature of existing transport networks. It is unlikely that small changes would affect the relative level of competition, except perhaps in very isolated areas. The reduction in market imperfections is how businesses can reduce costs when travel costs are lower, and the increased output that they may be able to achieve. UK guidance recommends the use of an âuprate factorâ, estimated to be about 10% of business travel time savings plus reliability gains. This is derived from differences between price and marginal costs as well as the demand elasticity of the specific market under consideration. These effects would likely be mainly associated with reductions in road traffic that might occur due to a transit investment, and in most cases would be swamped by additional induced traffic. Therefore, when evaluating transit projects, we believe this benefit would be trivial, if present at all. Labor Market Effects Various labor market effects also can be estimated related to the commute to work. These include 1) increased employment due to lower transport costs, 2) increased work hours due to shorter commutes, and 3) increased employment in more productive jobs. The second of these is considered minor and not elaborated upon in UK guidance. Methods for the first and third are elaborated on below. The increase in labor supply, that is more people choosing to work, is spurred by the effective reduction in the costs of working by reducing transport costs. This is an important
192 consideration, especially when considering the distributional impacts of investment and how transport costs disproportionately affect lower income employees. One critical input necessary for evaluating this effect is an estimate of the elasticity of labor supply with respect to wages, on the assumption that reduced travel times have the same effect as an increase in wages (of course, wages may decrease to reflect decreased cost of access; which is a problem with this assumption). UK guidance recommends using a range between 0.05 and 0.15 with a best estimate of 0.1, based on reviews of the literature. In theory these should be sector-specific and also based on household demographics. This effect is calculated as: C ij = Commuters that live in area i and work in area j. dT ij GDP = Change in generalized cost of commuting from i to j. j W = GDP per worker entering the labor market in area j. j El = Elasticity of labor supply = Average wage from working in j. Given the need for transport generalized cost data, the level of aggregation should again correspond to travel demand model outputs. The key unknown, in our view, is how to estimate the elasticity of labor supply. The other labor market effect to consider is the relocation of employees to more productive regions. This is distinct from any agglomeration benefits associated with co-location of firms. UK guidance assumes that different industries have variations in productivity according to which region they are located in. This is done by defining a productivity index for a given industry and region that is also dependent on specific worker characteristics (e.g., skills, educational level and age). The formula used is: Where, âE AI = Change in employment in area A and industry i. PI AI = Index of productivity per worker in area A and industry I, where the base is average national productivity per worker. GDP = National average industry GDP per worker. All estimates are also forecast over the 60 year time horizon and discounted back to the present. ElCGDP CW CdT GP t tit j tj j tittj j tijtij t à           Ãà à à â= â ââ â ,, ,, ,, 1 ââ ÃÃâ= A I tItAItAIt GDPPIEGP ,,,3
193 Deficiencies in Existing Practice It may be desirable that FTA guidelines for determining land development impacts be combined with the additional economic impacts related to agglomeration and labor search. Most of these effects, however, are highly dependent on how the land market responds. But how the land market responds is contingent upon development policies, so it is not accurate to assume that land development can be treated as an assumed input to calculations of economic impact. UK guidance, in fact, assumes land use does not change, but allows sensitivity analysis if one can use an integrated transport/land-use model. We do not believe this level of modeling detail is necessary for assessing localized access around transport stations. The UK experience with calculating economic impacts is based on guidance developed by the UK Department for Transport, which is based on a limited amount of commissioned research. It is likely that procedures in the US would demand more numerous, robust examples of research evidence and a more established consensus on effects, before adopting a given approach. To estimate wider economic benefits, UK practice is currently relying on one approach to estimate a set of agglomeration elasticities to determine benefits. One potential problem is endogeneity, that is, whether more productive firms locate in agglomerations as opposed to the agglomerations resulting in more productive firms. The procedure used also suggests using straight-line distances (ostensibly to avoid double-counting) rather than actual travel costs; again it is unclear whether this is a robust technique (see discussion in section 6.4.2.1 above). A further problem is the reliance on large-scale transportation (and land-use models). Some of these tend to be black boxes that are difficult to explain to the public and whose workings may not be well understood by anyone except model developers. While these are used in transportation and air quality conformity analysis in the US, there are many uncertainties associated with the results that they generate (Rodier 2007). In any case, direct transference of UK estimates to the US would be questionable. At the very least, estimates of agglomeration effects focused specifically on transit impacts are needed. UK practice in terms of assessing projects focuses on multi-attribute assessment, where economic benefits are one of many attributes, some of which are expressed qualitatively and in non-monetary terms. This type of procedure is not used in the United States, but could provide a means of considering other attributes associated with transit, both positive and negative. For example, it would provide a non-monetized value for emissions reductions that might be achievable. It could likewise provide a qualitative assessment of how urban amenities could be improved with development around a transit station.
194 APPENDIX F: ELASTICITY ESTIMATES TABLE F 1 Elasticities of employment density and population w.r.t. transit, specific to each MSA Elasticities of employment density Population Total track miles Track miles per sqm CBSA area Total track miles per capita Track miles per sqm UZA area Albuquerque, NM 0.0359 - 0.1228 0.0217 - 0.1055 4.6664 - 10.2513 4.3644 - 8.1623 Atlanta-Sandy Springs-Marietta, GA 0.0347 - 0.1187 0.0232 - 0.1131 0.1359 - 0.2985 0.107 - 0.2002 Baltimore-Towson, MD 0.0523 - 0.1789 0.1124 - 0.5472 1.1883 - 2.6105 1.1516 - 2.1538 Buffalo-Niagara Falls, NY 0.0054 - 0.0184 0.0192 - 0.0934 0.4033 - 0.8861 0.3557 - 0.6653 Charlotte-Gastonia-Concord, NC-SC 0.0251 - 0.0857 0.0454 - 0.2208 0.2625 - 0.5767 0.1982 - 0.3707 Chicago-Naperville-Joliet, IL-IN-WI 0.3691 - 1.2632 0.2871 - 1.3977 0.6079 - 1.3355 0.7484 - 1.3996 Cleveland-Elyria-Mentor, OH 0.0262 - 0.0896 0.0733 - 0.3568 0.6265 - 1.3763 0.5673 - 1.0609 Dallas-Fort Worth-Arlington, TX 0.082 - 0.2805 0.0511 - 0.249 0.1697 - 0.3727 0.1953 - 0.3653 Denver-Aurora, CO 0.024 - 0.0821 0.0161 - 0.0784 0.4407 - 0.9681 0.63 - 1.1783 Houston-Baytown-Sugar Land, TX 0.0106 - 0.0364 0.0067 - 0.0326 0.0347 - 0.0763 0.0395 - 0.0739 Little Rock-North Little Rock, AR 0.0039 - 0.0135 0.0054 - 0.0263 0.5926 - 1.3018 0.5035 - 0.9417 Los Angeles-Long Beach-Santa Ana, CA 0.244 - 0.835 0.2822 - 1.3738 0.2351 - 0.5164 0.5024 - 0.9397 Memphis, TN-MS-AR 0.0082 - 0.0282 0.0101 - 0.0492 0.3306 - 0.7262 0.3058 - 0.572 Miami-Fort Lauderdale-Miami Beach, FL 0.0547 - 0.187 0.0598 - 0.2912 0.252 - 0.5536 0.3779 - 0.7068 Minneapolis-St. Paul-Bloomington, MN-WI 0.0079 - 0.027 0.0073 - 0.0356 0.0919 - 0.2019 0.0905 - 0.1692 Nashville-Davidson--Murfreesboro, TN 0.0401 - 0.1371 0.0395 - 0.1924 1.052 - 2.311 0.8629 - 1.6137 New Orleans-Metairie-Kenner, LA 0.0111 - 0.0381 0.0198 - 0.0965 0.7523 - 1.6526 0.7713 - 1.4424 New York-Northern New Jersey-Long Island, NY- NJ-PA 0.2554 - 0.8741 0.213 - 1.0371 0.2751 - 0.6044 0.4706 - 0.8801 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.1434 - 0.4908 0.1738 - 0.846 0.7586 - 1.6666 0.7191 - 1.3449 Pittsburgh, PA 0.0099 - 0.034 0.0105 - 0.0514 0.3158 - 0.6937 0.2264 - 0.4234 Portland-Vancouver-Beaverton, OR-WA 0.0338 - 0.1155 0.0283 - 0.1379 0.7936 - 1.7434 1.0308 - 1.9277 Providence-New Bedford-Fall River, RI-MA 0.005 - 0.0171 0.0175 - 0.0851 0.2117 - 0.4651 0.1609 - 0.3009 Sacramento--Arden-Arcade--Roseville, CA 0.0315 - 0.1076 0.0346 - 0.1686 0.6656 - 1.4621 0.9212 - 1.7228
195 St. Louis, MO-IL 0.0284 - 0.097 0.0184 - 0.0895 0.46 - 1.0106 0.4033 - 0.7543 Salt Lake City, UT 0.0101 - 0.0347 0.006 - 0.0291 1.2244 - 2.6898 1.6626 - 3.1093 San Diego-Carlsbad-San Marcos, CA 0.0702 - 0.2402 0.0938 - 0.4564 0.8115 - 1.7827 0.9443 - 1.7661 San Francisco-Oakland-Fremont, CA 0.0822 - 0.2812 0.1864 - 0.9072 0.8182 - 1.7975 1.3531 - 2.5306 San Jose-Sunnyvale-Santa Clara, CA 0.0947 - 0.3239 0.1982 - 0.9646 3.8473 - 8.4519 6.813 - 12.7416 Seattle-Tacoma-Bellevue, WA 0.0456 - 0.156 0.0434 - 0.2111 0.6135 - 1.3477 0.6078 - 1.1368 Tampa-St. Petersburg-Clearwater, FL 0.0018 - 0.006 0.0039 - 0.0188 0.0256 - 0.0563 0.0231 - 0.0431 Trenton-Ewing, NJ 0.0044 - 0.0152 0.1102 - 0.5366 4.1339 - 9.0814 3.6953 - 6.911 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.1131 - 0.3869 0.1421 - 0.6919 0.9003 - 1.9778 1.0271 - 1.9208
196 TABLE F 2 Productivity elasticities (average payroll and GDP per capita) w.r.t principal city employment density, for two track mile measures Employment agglomeration elasticities Total track miles Track miles per sqm CBSA area Average payroll GDP per capita Average payroll GDP per capita Albuquerque, NM 0.002 - 0.014 0.0055 - 0.0166 0.0012 - 0.012 0.0033 - 0.0142 Atlanta-Sandy Springs-Marietta, GA 0.0019 - 0.0135 0.0053 - 0.016 0.0013 - 0.0129 0.0035 - 0.0153 Baltimore-Towson, MD 0.0029 - 0.0204 0.0079 - 0.0242 0.0062 - 0.0624 0.0171 - 0.0739 Buffalo-Niagara Falls, NY 0.0003 - 0.0021 0.0008 - 0.0025 0.0011 - 0.0107 0.0029 - 0.0126 Charlotte-Gastonia-Concord, NC-SC 0.0014 - 0.0098 0.0038 - 0.0116 0.0025 - 0.0252 0.0069 - 0.0298 Chicago-Naperville-Joliet, IL-IN-WI 0.0205 - 0.144 0.0561 - 0.1705 0.0159 - 0.1593 0.0436 - 0.1887 Cleveland-Elyria-Mentor, OH 0.0015 - 0.0102 0.004 - 0.0121 0.0041 - 0.0407 0.0111 - 0.0482 Dallas-Fort Worth-Arlington, TX 0.0045 - 0.032 0.0125 - 0.0379 0.0028 - 0.0284 0.0078 - 0.0336 Denver-Aurora, CO 0.0013 - 0.0094 0.0036 - 0.0111 0.0009 - 0.0089 0.0024 - 0.0106 Houston-Baytown-Sugar Land, TX 0.0006 - 0.0042 0.0016 - 0.0049 0.0004 - 0.0037 0.001 - 0.0044 Little Rock-North Little Rock, AR 0.0002 - 0.0015 0.0006 - 0.0018 0.0003 - 0.003 0.0008 - 0.0036 Los Angeles-Long Beach-Santa Ana, CA 0.0135 - 0.0952 0.0371 - 0.1127 0.0156 - 0.1566 0.0429 - 0.1855 Memphis, TN-MS-AR 0.0005 - 0.0032 0.0013 - 0.0038 0.0006 - 0.0056 0.0015 - 0.0066 Miami-Fort Lauderdale-Miami Beach, FL 0.003 - 0.0213 0.0083 - 0.0252 0.0033 - 0.0332 0.0091 - 0.0393 Minneapolis-St. Paul-Bloomington, MN-WI 0.0004 - 0.0031 0.0012 - 0.0036 0.0004 - 0.0041 0.0011 - 0.0048 Nashville-Davidson--Murfreesboro, TN 0.0022 - 0.0156 0.0061 - 0.0185 0.0022 - 0.0219 0.006 - 0.026 New Orleans-Metairie-Kenner, LA 0.0006 - 0.0043 0.0017 - 0.0051 0.0011 - 0.011 0.003 - 0.013 New York-Northern New Jersey-Long Island, NY- NJ-PA 0.0142 - 0.0996 0.0388 - 0.118 0.0118 - 0.1182 0.0324 - 0.14 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.0079 - 0.056 0.0218 - 0.0663 0.0096 - 0.0964 0.0264 - 0.1142 Pittsburgh, PA 0.0006 - 0.0039 0.0015 - 0.0046 0.0006 - 0.0059 0.0016 - 0.0069 Portland-Vancouver-Beaverton, OR-WA 0.0019 - 0.0132 0.0051 - 0.0156 0.0016 - 0.0157 0.0043 - 0.0186 Providence-New Bedford-Fall River, RI-MA 0.0003 - 0.0019 0.0008 - 0.0023 0.001 - 0.0097 0.0027 - 0.0115 Sacramento--Arden-Arcade--Roseville, CA 0.0017 - 0.0123 0.0048 - 0.0145 0.0019 - 0.0192 0.0053 - 0.0228 St. Louis, MO-IL 0.0016 - 0.0111 0.0043 - 0.0131 0.001 - 0.0102 0.0028 - 0.0121
197 Salt Lake City, UT 0.0006 - 0.004 0.0015 - 0.0047 0.0003 - 0.0033 0.0009 - 0.0039 San Diego-Carlsbad-San Marcos, CA 0.0039 - 0.0274 0.0107 - 0.0324 0.0052 - 0.052 0.0143 - 0.0616 San Francisco-Oakland-Fremont, CA 0.0046 - 0.0321 0.0125 - 0.038 0.0103 - 0.1034 0.0283 - 0.1225 San Jose-Sunnyvale-Santa Clara, CA 0.0052 - 0.0369 0.0144 - 0.0437 0.011 - 0.11 0.0301 - 0.1302 Seattle-Tacoma-Bellevue, WA 0.0025 - 0.0178 0.0069 - 0.0211 0.0024 - 0.0241 0.0066 - 0.0285 Tampa-St. Petersburg-Clearwater, FL 0.0001 - 0.0007 0.0003 - 0.0008 0.0002 - 0.0021 0.0006 - 0.0025 Trenton-Ewing, NJ 0.0002 - 0.0017 0.0007 - 0.0021 0.0061 - 0.0612 0.0168 - 0.0724 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.0063 - 0.0441 0.0172 - 0.0522 0.0079 - 0.0789 0.0216 - 0.0934
198 TABLE F 3 Productivity elasticities (average payroll and GDP per capita) w.r.t population, for two track mile measures Population agglomeration elasticities Total track miles per capita Track miles per sqm UZA area Average payroll GDP per capita Average payroll GDP per capita Albuquerque, NM 0.196 - 0.3526 0.2847 - 0.6489 0.1833 - 0.2808 0.2662 - 0.5167 Atlanta-Sandy Springs-Marietta, GA 0.0057 - 0.0103 0.0083 - 0.0189 0.0045 - 0.0069 0.0065 - 0.0127 Baltimore-Towson, MD 0.0499 - 0.0898 0.0725 - 0.1652 0.0484 - 0.0741 0.0702 - 0.1363 Buffalo-Niagara Falls, NY 0.0169 - 0.0305 0.0246 - 0.0561 0.0149 - 0.0229 0.0217 - 0.0421 Charlotte-Gastonia-Concord, NC-SC 0.011 - 0.0198 0.016 - 0.0365 0.0083 - 0.0128 0.0121 - 0.0235 Chicago-Naperville-Joliet, IL-IN-WI 0.0255 - 0.0459 0.0371 - 0.0845 0.0314 - 0.0481 0.0457 - 0.0886 Cleveland-Elyria-Mentor, OH 0.0263 - 0.0473 0.0382 - 0.0871 0.0238 - 0.0365 0.0346 - 0.0672 Dallas-Fort Worth-Arlington, TX 0.0071 - 0.0128 0.0104 - 0.0236 0.0082 - 0.0126 0.0119 - 0.0231 Denver-Aurora, CO 0.0185 - 0.0333 0.0269 - 0.0613 0.0265 - 0.0405 0.0384 - 0.0746 Houston-Baytown-Sugar Land, TX 0.0015 - 0.0026 0.0021 - 0.0048 0.0017 - 0.0025 0.0024 - 0.0047 Little Rock-North Little Rock, AR 0.0249 - 0.0448 0.0361 - 0.0824 0.0211 - 0.0324 0.0307 - 0.0596 Los Angeles-Long Beach-Santa Ana, CA 0.0099 - 0.0178 0.0143 - 0.0327 0.0211 - 0.0323 0.0306 - 0.0595 Memphis, TN-MS-AR 0.0139 - 0.025 0.0202 - 0.046 0.0128 - 0.0197 0.0187 - 0.0362 Miami-Fort Lauderdale-Miami Beach, FL 0.0106 - 0.019 0.0154 - 0.035 0.0159 - 0.0243 0.0231 - 0.0447 Minneapolis-St. Paul-Bloomington, MN-WI 0.0039 - 0.0069 0.0056 - 0.0128 0.0038 - 0.0058 0.0055 - 0.0107 Nashville-Davidson--Murfreesboro, TN 0.0442 - 0.0795 0.0642 - 0.1463 0.0362 - 0.0555 0.0526 - 0.1021 New Orleans-Metairie-Kenner, LA 0.0316 - 0.0568 0.0459 - 0.1046 0.0324 - 0.0496 0.047 - 0.0913 New York-Northern New Jersey-Long Island, NY- NJ-PA 0.0116 - 0.0208 0.0168 - 0.0383 0.0198 - 0.0303 0.0287 - 0.0557 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.0319 - 0.0573 0.0463 - 0.1055 0.0302 - 0.0463 0.0439 - 0.0851 Pittsburgh, PA 0.0133 - 0.0239 0.0193 - 0.0439 0.0095 - 0.0146 0.0138 - 0.0268 Portland-Vancouver-Beaverton, OR-WA 0.0333 - 0.06 0.0484 - 0.1104 0.0433 - 0.0663 0.0629 - 0.122 Providence-New Bedford-Fall River, RI-MA 0.0089 - 0.016 0.0129 - 0.0294 0.0068 - 0.0104 0.0098 - 0.019 Sacramento--Arden-Arcade--Roseville, CA 0.028 - 0.0503 0.0406 - 0.0926 0.0387 - 0.0593 0.0562 - 0.1091 St. Louis, MO-IL 0.0193 - 0.0348 0.0281 - 0.064 0.0169 - 0.0259 0.0246 - 0.0477 Salt Lake City, UT 0.0514 - 0.0925 0.0747 - 0.1703 0.0698 - 0.107 0.1014 - 0.1968
199 San Diego-Carlsbad-San Marcos, CA 0.0341 - 0.0613 0.0495 - 0.1128 0.0397 - 0.0608 0.0576 - 0.1118 San Francisco-Oakland-Fremont, CA 0.0344 - 0.0618 0.0499 - 0.1138 0.0568 - 0.0871 0.0825 - 0.1602 San Jose-Sunnyvale-Santa Clara, CA 0.1616 - 0.2907 0.2347 - 0.535 0.2861 - 0.4383 0.4156 - 0.8065 Seattle-Tacoma-Bellevue, WA 0.0258 - 0.0464 0.0374 - 0.0853 0.0255 - 0.0391 0.0371 - 0.072 Tampa-St. Petersburg-Clearwater, FL 0.0011 - 0.0019 0.0016 - 0.0036 0.001 - 0.0015 0.0014 - 0.0027 Trenton-Ewing, NJ 0.1736 - 0.3124 0.2522 - 0.5749 0.1552 - 0.2377 0.2254 - 0.4375 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.0378 - 0.068 0.0549 - 0.1252 0.0431 - 0.0661 0.0627 - 0.1216
200 TABLE F 4 Elasticities of employment density and population w.r.t. transit, specific to each MSA Rail revenue miles Elasticities of employment density Population Atlanta-Sandy Springs-Marietta, GA 0.0946 - 0.1569 0.2302 - 0.256 Baltimore-Towson, MD 0.0537 - 0.089 0.3768 - 0.4192 Buffalo-Niagara Falls, NY 0.0098 - 0.0163 0.0959 - 0.1067 Charlotte-Gastonia-Concord, NC-SC 0.0282 - 0.0467 0.0581 - 0.0646 Chicago-Naperville-Joliet, IL-IN-WI 0.6827 - 1.1322 1.234 - 1.3725 Cleveland-Elyria-Mentor, OH 0.0464 - 0.077 0.2681 - 0.2982 Dallas-Fort Worth-Arlington, TX 0.0923 - 0.1531 0.1389 - 0.1545 Denver-Aurora, CO 0.0795 - 0.1318 0.4211 - 0.4684 Houston-Baytown-Sugar Land, TX 0.0178 - 0.0295 0.0383 - 0.0427 Little Rock-North Little Rock, AR 0.0019 - 0.0032 0.0223 - 0.0249 Los Angeles-Long Beach-Santa Ana, CA 0.5003 - 0.8297 0.7098 - 0.7895 Memphis, TN-MS-AR 0.0163 - 0.0271 0.0982 - 0.1093 Miami-Fort Lauderdale-Miami Beach, FL 0.0403 - 0.0668 0.1178 - 0.131 Minneapolis-St. Paul-Bloomington, MN-WI 0.0212 - 0.0351 0.0921 - 0.1025 Nashville-Davidson--Murfreesboro, TN 0.0037 - 0.0062 0.0175 - 0.0195 New Orleans-Metairie-Kenner, LA 0.0203 - 0.0336 0.1846 - 0.2053 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.4483 - 0.7434 1.0563 - 1.1748 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.1933 - 0.3206 0.7004 - 0.7791 Pittsburgh, PA 0.017 - 0.0283 0.1473 - 0.1638 Portland-Vancouver-Beaverton, OR-WA 0.0831 - 0.1378 0.4964 - 0.5521 Sacramento--Arden-Arcade--Roseville, CA 0.0426 - 0.0706 0.2184 - 0.2429 St. Louis, MO-IL 0.0648 - 0.1075 0.342 - 0.3804 Salt Lake City, UT 0.027 - 0.0447 0.417 - 0.4638 San Diego-Carlsbad-San Marcos, CA 0.0859 - 0.1424 0.3457 - 0.3845 San Francisco-Oakland-Fremont, CA 0.4553 - 0.755 2.2277 - 2.4778 San Jose-Sunnyvale-Santa Clara, CA 0.0392 - 0.0649 0.3323 - 0.3696
201 Seattle-Tacoma-Bellevue, WA 0.0056 - 0.0093 0.0291 - 0.0324 Tampa-St. Petersburg-Clearwater, FL 0.0019 - 0.0031 0.0085 - 0.0095 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.1749 - 0.2901 0.8638 - 0.9608
202 TABLE F 5 Agglomeration elasticities (employment density and population) w.r.t. rail revenue miles Agglomeration elasticities, rail revenue miles Principal city employment density agglomeration elasticity Population agglomeration elasticities Average payroll GDP per capita Average payroll GDP per capita Atlanta-Sandy Springs-Marietta, GA 0.0052 - 0.0179 0.0144 - 0.0212 0.0128 - 0.0292 0.035 - 0.0346 Baltimore-Towson, MD 0.003 - 0.0101 0.0082 - 0.012 0.0209 - 0.0478 0.0573 - 0.0566 Buffalo-Niagara Falls, NY 0.0005 - 0.0019 0.0015 - 0.0022 0.0053 - 0.0122 0.0146 - 0.0144 Charlotte-Gastonia-Concord, NC-SC 0.0016 - 0.0053 0.0043 - 0.0063 0.0032 - 0.0074 0.0088 - 0.0087 Chicago-Naperville-Joliet, IL-IN-WI 0.0378 - 0.1291 0.1038 - 0.1528 0.0684 - 0.1565 0.1876 - 0.1853 Cleveland-Elyria-Mentor, OH 0.0026 - 0.0088 0.0071 - 0.0104 0.0149 - 0.034 0.0408 - 0.0403 Dallas-Fort Worth-Arlington, TX 0.0051 - 0.0175 0.014 - 0.0207 0.0077 - 0.0176 0.0211 - 0.0209 Denver-Aurora, CO 0.0044 - 0.015 0.0121 - 0.0178 0.0233 - 0.0534 0.064 - 0.0632 Houston-Baytown-Sugar Land, TX 0.001 - 0.0034 0.0027 - 0.004 0.0021 - 0.0049 0.0058 - 0.0058 Little Rock-North Little Rock, AR 0.0001 - 0.0004 0.0003 - 0.0004 0.0012 - 0.0028 0.0034 - 0.0034 Los Angeles-Long Beach-Santa Ana, CA 0.0277 - 0.0946 0.076 - 0.112 0.0393 - 0.09 0.1079 - 0.1066 Memphis, TN-MS-AR 0.0009 - 0.0031 0.0025 - 0.0037 0.0054 - 0.0125 0.0149 - 0.0147 Miami-Fort Lauderdale-Miami Beach, FL 0.0022 - 0.0076 0.0061 - 0.009 0.0065 - 0.0149 0.0179 - 0.0177 Minneapolis-St. Paul-Bloomington, MN- WI 0.0012 - 0.004 0.0032 - 0.0047 0.0051 - 0.0117 0.014 - 0.0138 Nashville-Davidson--Murfreesboro, TN 0.0002 - 0.0007 0.0006 - 0.0008 0.001 - 0.0022 0.0027 - 0.0026 New Orleans-Metairie-Kenner, LA 0.0011 - 0.0038 0.0031 - 0.0045 0.0102 - 0.0234 0.0281 - 0.0277 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.0248 - 0.0847 0.0681 - 0.1004 0.0585 - 0.1339 0.1606 - 0.1586 Philadelphia-Camden-Wilmington, PA- NJ-DE-MD 0.0107 - 0.0365 0.0294 - 0.0433 0.0388 - 0.0888 0.1065 - 0.1052 Pittsburgh, PA 0.0009 - 0.0032 0.0026 - 0.0038 0.0082 - 0.0187 0.0224 - 0.0221 Portland-Vancouver-Beaverton, OR-WA 0.0046 - 0.0157 0.0126 - 0.0186 0.0275 - 0.0629 0.0754 - 0.0745 Sacramento--Arden-Arcade--Roseville, CA 0.0024 - 0.0081 0.0065 - 0.0095 0.0121 - 0.0277 0.0332 - 0.0328 St. Louis, MO-IL 0.0036 - 0.0123 0.0099 - 0.0145 0.0189 - 0.0434 0.052 - 0.0514 Salt Lake City, UT 0.0015 - 0.0051 0.0041 - 0.006 0.0231 - 0.0529 0.0634 - 0.0626
203 San Diego-Carlsbad-San Marcos, CA 0.0048 - 0.0162 0.0131 - 0.0192 0.0192 - 0.0438 0.0525 - 0.0519 San Francisco-Oakland-Fremont, CA 0.0252 - 0.0861 0.0692 - 0.1019 0.1234 - 0.2825 0.3386 - 0.3345 San Jose-Sunnyvale-Santa Clara, CA 0.0022 - 0.0074 0.006 - 0.0088 0.0184 - 0.0421 0.0505 - 0.0499 Seattle-Tacoma-Bellevue, WA 0.0003 - 0.0011 0.0008 - 0.0013 0.0016 - 0.0037 0.0044 - 0.0044 Tampa-St. Petersburg-Clearwater, FL 0.0001 - 0.0004 0.0003 - 0.0004 0.0005 - 0.0011 0.0013 - 0.0013 Washington-Arlington-Alexandria, DC- VA-MD-WV 0.0097 - 0.0331 0.0266 - 0.0392 0.0479 - 0.1095 0.1313 - 0.1297
204 TABLE F 6 Total revenue miles, MSA specific elasticities (employment density and population) Total revenue mile elasticities Principal city employment density Population Abilene, TX 0.0096 - 0.0188 0.1628 - 0.1633 Albany, GA 0.0148 - 0.0289 0.1983 - 0.1989 Albany-Schenectady-Troy, NY 0.0422 - 0.0825 0.5231 - 0.5249 Albuquerque, NM 0.0505 - 0.0985 0.367 - 0.3682 Alexandria, LA 0.0088 - 0.0172 0.2079 - 0.2086 Allentown-Bethlehem-Easton, PA-NJ 0.0214 - 0.0419 0.179 - 0.1795 Altoona, PA 0.0038 - 0.0074 0.217 - 0.2177 Amarillo, TX 0.0121 - 0.0237 0.1648 - 0.1653 Ames, IA 0.0149 - 0.0291 0.7773 - 0.7798 Anchorage, AK 0.0333 - 0.0649 0.3604 - 0.3615 Anderson, IN 0.0105 - 0.0206 0.1377 - 0.1382 Ann Arbor, MI 0.0201 - 0.0392 0.5872 - 0.5891 Appleton, WI 0.0085 - 0.0167 0.286 - 0.2869 Athens-Clarke County, GA 0.018 - 0.0352 0.249 - 0.2498 Atlanta-Sandy Springs-Marietta, GA 0.33 - 0.6443 0.4598 - 0.4613 Auburn-Opelika, AL 0.002 - 0.004 0.0366 - 0.0368 Augusta-Richmond County, GA-SC 0.0168 - 0.0327 0.0654 - 0.0656 Bakersfield, CA 0.0443 - 0.0864 0.2644 - 0.2652 Bangor, ME 0.0071 - 0.0139 0.2221 - 0.2228 Baton Rouge, LA 0.0271 - 0.0529 0.1836 - 0.1842 Battle Creek, MI 0.0075 - 0.0146 0.1696 - 0.1702 Bay City, MI 0.0167 - 0.0326 0.5833 - 0.5852 Beaumont-Port Arthur, TX 0.0173 - 0.0338 0.148 - 0.1484 Bellingham, WA 0.0241 - 0.0471 0.5923 - 0.5942 Bend, OR 0.003 - 0.0059 0.0764 - 0.0767 Billings, MT 0.009 - 0.0175 0.2343 - 0.2351
205 Binghamton, NY 0.0136 - 0.0265 0.4591 - 0.4606 Birmingham-Hoover, AL 0.0411 - 0.0803 0.1524 - 0.1529 Bismarck, ND 0.0035 - 0.0068 0.1787 - 0.1793 Blacksburg-Christiansburg-Radford, VA 0.0147 - 0.0287 0.2749 - 0.2758 Bloomington, IN 0.0114 - 0.0223 0.325 - 0.326 Bloomington-Normal, IL 0.0134 - 0.0262 0.401 - 0.4023 Boise City-Nampa, ID 0.0163 - 0.0318 0.1408 - 0.1413 Bradenton-Sarasota-Venice, FL 0.0364 - 0.0711 0.3287 - 0.3297 Bremerton-Silverdale, WA 0.0332 - 0.0649 0.6071 - 0.609 Brownsville-Harlingen, TX 0.0141 - 0.0276 0.1247 - 0.1252 Brunswick, GA 0.142 - 0.2773 6.0693 - 6.0891 Canton-Massillon, OH 0.042 - 0.0821 0.3776 - 0.3789 Cape Coral-Fort Myers, FL 0.0649 - 0.1267 0.313 - 0.314 Casper, WY 0.0042 - 0.0082 0.1755 - 0.1761 Cedar Rapids, IA 0.0135 - 0.0263 0.2504 - 0.2512 Champaign-Urbana, IL 0.0374 - 0.0731 0.9794 - 0.9826 Charleston, WV 0.0226 - 0.0441 0.4688 - 0.4703 Charleston-North Charleston, SC 0.0442 - 0.0862 0.2577 - 0.2585 Charlotte-Gastonia-Concord, NC-SC 0.3679 - 0.7184 0.4347 - 0.4361 Chattanooga, TN-GA 0.0337 - 0.0658 0.2325 - 0.2333 Cheyenne, WY 0.0053 - 0.0103 0.2672 - 0.268 Chicago-Naperville-Joliet, IL-IN-WI 0.8275 - 1.6157 0.8568 - 0.8596 Chico, CA 0.0154 - 0.03 0.2818 - 0.2827 Cincinnati-Middletown, OH-KY-IN 0.1267 - 0.2474 0.3976 - 0.3989 Clarksville, TN-KY 0.0376 - 0.0735 0.1975 - 0.1982 Cleveland-Elyria-Mentor, OH 0.2388 - 0.4663 0.7904 - 0.793 College Station-Bryan, TX 0.0429 - 0.0838 0.5564 - 0.5582 Columbia, MO 0.0098 - 0.0191 0.2263 - 0.227 Columbia, SC 0.016 - 0.0313 0.1311 - 0.1316 Columbus, GA-AL 0.0199 - 0.0389 0.1963 - 0.1969
206 Columbus, OH 0.0818 - 0.1597 0.2628 - 0.2636 Corpus Christi, TX 0.0494 - 0.0965 0.4121 - 0.4135 Cumberland, MD-WV 0.0042 - 0.0082 0.1641 - 0.1647 Dallas-Fort Worth-Arlington, TX 0.412 - 0.8044 0.3551 - 0.3562 Davenport-Moline-Rock Island, IA-IL 0.0496 - 0.0968 0.526 - 0.5277 Dayton, OH 0.0709 - 0.1385 0.4179 - 0.4193 Decatur, AL 0 - 0 0 - 0 Decatur, IL 0.0187 - 0.0364 0.5275 - 0.5292 Deltona-Daytona Beach-Ormond Beach, FL 0.0643 - 0.1255 0.3057 - 0.3067 Denver-Aurora, CO 0.3707 - 0.7239 1.1253 - 1.129 Des Moines, IA 0.0233 - 0.0455 0.2565 - 0.2573 Detroit-Warren-Livonia, MI 0.2704 - 0.528 0.3686 - 0.3698 Dothan, AL 0 - 0 0 - 0 Dubuque, IA 0.0042 - 0.0083 0.1995 - 0.2002 Duluth, MN-WI 0.031 - 0.0606 0.3934 - 0.3947 Eau Claire, WI 0.0093 - 0.0182 0.2623 - 0.2631 El Centro, CA 0.0078 - 0.0153 0.2447 - 0.2455 El Paso, TX 0.0806 - 0.1573 0.5416 - 0.5433 Elkhart-Goshen, IN 0.0061 - 0.0119 0.1321 - 0.1325 Elmira, NY 0.0064 - 0.0126 0.5087 - 0.5103 Erie, PA 0.014 - 0.0273 0.3639 - 0.3651 Eugene-Springfield, OR 0.0422 - 0.0823 0.5866 - 0.5885 Evansville, IN-KY 0.0132 - 0.0258 0.2083 - 0.209 Fairbanks, AK 0.0078 - 0.0152 0.2341 - 0.2349 Fayetteville-Springdale-Rogers, AR-MO 0.0112 - 0.022 0.0831 - 0.0834 Flagstaff, AZ 0.0104 - 0.0202 0.3103 - 0.3113 Flint, MI 0.0491 - 0.0958 0.4586 - 0.4601 Florence, SC 0.0031 - 0.006 0.0443 - 0.0445 Fond du Lac, WI 0.0018 - 0.0036 0.1038 - 0.1041 Fort Collins-Loveland, CO 0.015 - 0.0292 0.1958 - 0.1965
207 Fort Smith, AR-OK 0.0041 - 0.0081 0.0593 - 0.0595 Fort Walton Beach-Crestview-Destin, FL 0.0062 - 0.0121 0.151 - 0.1515 Fort Wayne, IN 0.0279 - 0.0544 0.2273 - 0.2281 Fresno, CA 0.0552 - 0.1079 0.3164 - 0.3174 Gainesville, FL 0.0516 - 0.1008 0.6733 - 0.6755 Glens Falls, NY 0.0019 - 0.0038 0.1457 - 0.1461 Grand Forks, ND-MN 0.005 - 0.0098 0.2362 - 0.2369 Grand Junction, CO 0.0149 - 0.0291 0.3212 - 0.3223 Grand Rapids-Wyoming, MI 0.037 - 0.0722 0.3248 - 0.3258 Great Falls, MT 0.0071 - 0.0139 0.3294 - 0.3305 Greeley, CO 0.0054 - 0.0105 0.0967 - 0.097 Green Bay, WI 0.0194 - 0.0379 0.2629 - 0.2637 Greenville, SC 0.0065 - 0.0127 0.0562 - 0.0564 Hagerstown-Martinsburg, MD-WV 0.0044 - 0.0086 0.0951 - 0.0954 Hanford-Corcoran, CA 0.0166 - 0.0324 0.3162 - 0.3172 Harrisburg-Carlisle, PA 0.0063 - 0.0124 0.1997 - 0.2004 Holland-Grand Haven, MI 0.0039 - 0.0076 0.074 - 0.0742 Honolulu, HI 0.1149 - 0.2243 1.2426 - 1.2467 Houston-Baytown-Sugar Land, TX 0.3529 - 0.6892 0.4357 - 0.4371 Huntington-Ashland, WV-KY-OH 0.0087 - 0.017 0.1683 - 0.1688 Huntsville, AL 0.0142 - 0.0278 0.0945 - 0.0948 Indianapolis, IN 0.0876 - 0.171 0.2422 - 0.243 Iowa City, IA 0.0158 - 0.0309 0.634 - 0.636 Ithaca, NY 0.0116 - 0.0226 0.9865 - 0.9897 Jackson, MI 0.0028 - 0.0054 0.1345 - 0.135 Jackson, MS 0.0165 - 0.0322 0.1088 - 0.1091 Jackson, TN 0.0098 - 0.019 0.3013 - 0.3023 Jacksonville, FL 0.1616 - 0.3154 0.4626 - 0.4641 Janesville, WI 0.021 - 0.0411 0.4744 - 0.476 Jefferson City, MO 0.0046 - 0.0089 0.1575 - 0.158
208 Johnson City, TN 0.0089 - 0.0174 0.1307 - 0.1311 Johnstown, PA 0.0061 - 0.012 0.2873 - 0.2882 Kalamazoo-Portage, MI 0.0241 - 0.0471 0.2866 - 0.2875 Kankakee-Bradley, IL 0.0101 - 0.0196 0.35 - 0.3511 Kansas City, MO-KS 0.148 - 0.2889 0.2903 - 0.2912 Kennewick-Richland-Pasco, WA 0.0627 - 0.1225 0.6557 - 0.6579 Killeen-Temple-Fort Hood, TX 0.0193 - 0.0376 0.1285 - 0.1289 Knoxville, TN 0.038 - 0.0743 0.2366 - 0.2374 Kokomo, IN 0 - 0 0 - 0 La Crosse, WI-MN 0.0077 - 0.0151 0.3492 - 0.3503 Lafayette, IN 0.0169 - 0.033 0.4729 - 0.4745 Lafayette, LA 0.0075 - 0.0147 0.1476 - 0.1481 Lakeland, FL 0.0315 - 0.0614 0.221 - 0.2218 Lancaster, PA 0.0061 - 0.0119 0.1801 - 0.1807 Lansing-East Lansing, MI 0.0227 - 0.0443 0.4246 - 0.426 Laredo, TX 0.0255 - 0.0499 0.4275 - 0.4288 Las Cruces, NM 0.0078 - 0.0152 0.1346 - 0.135 Las Vegas-Paradise, NV 0.0934 - 0.1823 0.5415 - 0.5433 Lawrence, KS 0.0083 - 0.0161 0.3438 - 0.3449 Lawton, OK 0.0295 - 0.0577 0.3158 - 0.3168 Lebanon, PA 0.0047 - 0.0093 0.2105 - 0.2112 Lewiston-Auburn, ME 0.0029 - 0.0058 0.1218 - 0.1222 Lexington-Fayette, KY 0.0235 - 0.0459 0.2833 - 0.2842 Lincoln, NE 0.019 - 0.0371 0.308 - 0.309 Little Rock-North Little Rock, AR 0.0314 - 0.0613 0.2107 - 0.2114 Logan, UT-ID 0.0108 - 0.021 0.4275 - 0.4289 Longview, WA 0.0032 - 0.0063 0.1296 - 0.1301 Los Angeles-Long Beach-Santa Ana, CA 1.3054 - 2.549 1.061 - 1.0645 Louisville, KY-IN 0.1249 - 0.2439 0.3808 - 0.3821 Lubbock, TX 0.0226 - 0.0442 0.3552 - 0.3564
209 Lynchburg, VA 0.0221 - 0.0431 0.2734 - 0.2743 Macon, GA 0.0199 - 0.0389 0.2822 - 0.2831 Madison, WI 0.0327 - 0.0639 0.5133 - 0.5149 Mansfield, OH 0.0045 - 0.0089 0.1138 - 0.1142 McAllen-Edinburg-Pharr, TX 0.0191 - 0.0374 0.0963 - 0.0966 Medford, OR 0.0067 - 0.0131 0.1797 - 0.1803 Memphis, TN-MS-AR 0.0912 - 0.178 0.3142 - 0.3153 Merced, CA 0.0373 - 0.0728 0.6986 - 0.7009 Miami-Fort Lauderdale-Miami Beach, FL 0.4015 - 0.784 0.673 - 0.6752 Milwaukee-Waukesha-West Allis, WI 0.1367 - 0.2669 0.7077 - 0.71 Minneapolis-St. Paul-Bloomington, MN-WI 0.2411 - 0.4707 0.6002 - 0.6022 Missoula, MT 0.0092 - 0.018 0.355 - 0.3561 Mobile, AL 0.0263 - 0.0513 0.1881 - 0.1887 Modesto, CA 0.023 - 0.045 0.2348 - 0.2356 Monroe, LA 0.0085 - 0.0167 0.221 - 0.2218 Morgantown, WV 0.0167 - 0.0325 0.4875 - 0.4891 Muncie, IN 0.015 - 0.0294 0.4602 - 0.4617 Muskegon-Norton Shores, MI 0.0089 - 0.0174 0.146 - 0.1465 Myrtle Beach-Conway-North Myrtle Beach, SC 0.0122 - 0.0237 0.189 - 0.1896 Naples-Marco Island, FL 0.0181 - 0.0354 0.236 - 0.2368 Nashville-Davidson--Murfreesboro, TN 0.0653 - 0.1275 0.1763 - 0.1769 New Orleans-Metairie-Kenner, LA 0.0516 - 0.1008 0.2692 - 0.2701 New York-Northern New Jersey-Long Island, NY- NJ-PA 0.9863 - 1.9259 1.3314 - 1.3358 Niles-Benton Harbor, MI 0.0012 - 0.0024 0.0205 - 0.0206 Odessa, TX 0.0119 - 0.0233 0.3446 - 0.3457 Oklahoma City, OK 0.0451 - 0.088 0.1583 - 0.1588 Olympia, WA 0.0219 - 0.0427 0.6439 - 0.646 Omaha-Council Bluffs, NE-IA 0.0454 - 0.0887 0.2919 - 0.2928 Orlando, FL 0.128 - 0.2499 0.461 - 0.4625
210 Oshkosh-Neenah, WI 0.006 - 0.0118 0.2066 - 0.2073 Oxnard-Thousand Oaks-Ventura, CA 0.047 - 0.0918 0.2403 - 0.2411 Palm Bay-Melbourne-Titusville, FL 0.0367 - 0.0716 0.151 - 0.1515 Panama City-Lynn Haven, FL 0.0164 - 0.032 0.3632 - 0.3644 Pensacola-Ferry Pass-Brent, FL 0.0202 - 0.0395 0.2837 - 0.2846 Peoria, IL 0.0247 - 0.0483 0.302 - 0.303 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.3012 - 0.5882 0.6253 - 0.6274 Phoenix-Mesa-Scottsdale, AZ 0.3648 - 0.7122 0.549 - 0.5508 Pittsburgh, PA 0.1462 - 0.2854 0.7235 - 0.7259 Pocatello, ID 0.0058 - 0.0114 0.2045 - 0.2051 Port St. Lucie-Fort Pierce, FL 0.0185 - 0.036 0.0374 - 0.0375 Portland-Vancouver-Beaverton, OR-WA 0.2568 - 0.5015 0.879 - 0.8819 Poughkeepsie-Newburgh-Middletown, NY 0.0296 - 0.0577 0.2509 - 0.2517 Providence-New Bedford-Fall River, RI-MA 0.0792 - 0.1546 0.3551 - 0.3562 Pueblo, CO 0.0098 - 0.0191 0.2169 - 0.2176 Punta Gorda, FL 0 - 0 0 - 0 Racine, WI 0.0125 - 0.0243 0.3555 - 0.3566 Rapid City, SD 0.0038 - 0.0073 0.1052 - 0.1056 Reading, PA 0.0096 - 0.0187 0.2292 - 0.2299 Redding, CA 0.0165 - 0.0322 0.2336 - 0.2344 Reno-Sparks, NV 0.0892 - 0.1742 1.0622 - 1.0657 Richmond, VA 0.0036 - 0.007 0.0209 - 0.021 Riverside-San Bernardino-Ontario, CA 0.1758 - 0.3432 0.1782 - 0.1788 Roanoke, VA 0.0388 - 0.0757 0.6183 - 0.6203 Rochester, MN 0.0121 - 0.0235 0.3282 - 0.3293 Rochester, NY 0.0289 - 0.0564 0.3039 - 0.3049 Rockford, IL 0.0188 - 0.0368 0.2232 - 0.224 Rome, GA 0.0102 - 0.02 0.3026 - 0.3036 Sacramento--Arden-Arcade--Roseville, CA 0.1798 - 0.351 0.5281 - 0.5298 Saginaw-Saginaw Township North, MI 0.0111 - 0.0218 0.2154 - 0.2161
211 Salem, OR 0.0254 - 0.0495 0.3376 - 0.3387 Salt Lake City, UT 0.1145 - 0.2236 1.0147 - 1.018 San Angelo, TX 0.0104 - 0.0203 0.2044 - 0.2051 San Antonio, TX 0.2459 - 0.4801 0.5917 - 0.5936 San Diego-Carlsbad-San Marcos, CA 0.2668 - 0.5209 0.6152 - 0.6172 San Francisco-Oakland-Fremont, CA 0.5441 - 1.0624 1.5252 - 1.5302 San Jose-Sunnyvale-Santa Clara, CA 0.1608 - 0.3141 0.7821 - 0.7846 San Luis Obispo-Paso Robles, CA 0.005 - 0.0097 0.0882 - 0.0885 Sandusky, OH 0 - 0 0 - 0 Santa Barbara-Santa Maria-Goleta, CA 0.0695 - 0.1357 1.0318 - 1.0352 Santa Cruz-Watsonville, CA 0.0576 - 0.1125 1.5831 - 1.5883 Santa Fe, NM 0.0129 - 0.0251 0.3662 - 0.3674 Santa Rosa-Petaluma, CA 0.071 - 0.1386 0.7192 - 0.7215 Savannah, GA 0.0313 - 0.0612 0.4654 - 0.4669 Scranton--Wilkes-Barre, PA 0.0175 - 0.0341 0.2313 - 0.2321 Seattle-Tacoma-Bellevue, WA 0.3707 - 0.7239 1.1067 - 1.1103 Sebastian-Vero Beach, FL 0.0074 - 0.0145 0.1355 - 0.136 Sheboygan, WI 0.0086 - 0.0167 0.3156 - 0.3166 Sherman-Denison, TX 0.0065 - 0.0126 0.1184 - 0.1188 Shreveport-Bossier City, LA 0.0547 - 0.1068 0.3678 - 0.369 Sioux City, IA-NE-SD 0.0107 - 0.0208 0.2177 - 0.2184 Sioux Falls, SD 0.0085 - 0.0166 0.1888 - 0.1894 South Bend-Mishawaka, IN-MI 0.0292 - 0.0571 0.3392 - 0.3403 Spartanburg, SC 0.0044 - 0.0086 0.0593 - 0.0594 Spokane, WA 0.0582 - 0.1137 0.758 - 0.7605 Springfield, IL 0.0145 - 0.0282 0.3559 - 0.357 Springfield, MO 0.0134 - 0.0263 0.1534 - 0.1539 Springfield, OH 0.0045 - 0.0088 0.1024 - 0.1028 St. Cloud, MN 0.0125 - 0.0244 0.3494 - 0.3506 St. Joseph, MO-KS 0.0143 - 0.0279 0.375 - 0.3762
212 St. Louis, MO-IL 0.1727 - 0.3372 0.5219 - 0.5236 State College, PA 0.013 - 0.0253 0.5468 - 0.5486 Stockton, CA 0.0485 - 0.0948 0.3783 - 0.3796 Sumter, SC 0.0144 - 0.0281 0.3178 - 0.3188 Syracuse, NY 0.0333 - 0.0651 0.5763 - 0.5782 Tallahassee, FL 0.0221 - 0.0431 0.3202 - 0.3212 Tampa-St. Petersburg-Clearwater, FL 0.1538 - 0.3004 0.4042 - 0.4055 Terre Haute, IN 0.008 - 0.0155 0.1472 - 0.1477 Toledo, OH 0.052 - 0.1016 0.3248 - 0.3258 Topeka, KS 0.0142 - 0.0277 0.2608 - 0.2617 Tucson, AZ 0.1029 - 0.2008 0.4589 - 0.4604 Tulsa, OK 0.0298 - 0.0582 0.1747 - 0.1753 Tuscaloosa, AL 0.0049 - 0.0096 0.0693 - 0.0695 Utica-Rome, NY 0.0127 - 0.0248 0.2232 - 0.2239 Victoria, TX 0.0142 - 0.0276 0.1928 - 0.1935 Virginia Beach-Norfolk-Newport News, VA-NC 0.1922 - 0.3753 0.4441 - 0.4455 Visalia-Porterville, CA 0.022 - 0.0429 0.2155 - 0.2162 Waco, TX 0.0125 - 0.0243 0.2057 - 0.2063 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.4834 - 0.944 1.3678 - 1.3723 Waterloo-Cedar Falls, IA 0.0124 - 0.0241 0.2327 - 0.2335 Wausau, WI 0.006 - 0.0118 0.2665 - 0.2674 Wenatchee, WA 0.0229 - 0.0448 0.9606 - 0.9638 Wheeling, WV-OH 0.0077 - 0.015 0.2841 - 0.2851 Wichita, KS 0.0242 - 0.0473 0.1623 - 0.1628 Williamsport, PA 0.0073 - 0.0142 0.4299 - 0.4313 Yakima, WA 0.0134 - 0.0262 0.2169 - 0.2176 York-Hanover, PA 0.007 - 0.0136 0.1956 - 0.1962 Youngstown-Warren-Boardman, OH-PA 0.013 - 0.0255 0.0747 - 0.075 Yuba City, CA 0.0166 - 0.0324 0.3041 - 0.3051 Yuma, AZ 0.0108 - 0.021 0.1728 - 0.1733
213 TABLE F 7 Agglomeration elasticies w.r.t. total revenue miles Principal city employment density Population Average wages GDP per capita Average wages GDP per capita Abilene, TX 0.0005 - 0.0021 0.0015 - 0.0025 0.009 - 0.0186 0.0247 - 0.022 Albany, GA 0.0008 - 0.0033 0.0023 - 0.0039 0.011 - 0.0227 0.0301 - 0.0269 Albany-Schenectady-Troy, NY 0.0023 - 0.0094 0.0064 - 0.0111 0.029 - 0.0598 0.0795 - 0.0709 Albuquerque, NM 0.0028 - 0.0112 0.0077 - 0.0133 0.0203 - 0.042 0.0558 - 0.0497 Alexandria, LA 0.0005 - 0.002 0.0013 - 0.0023 0.0115 - 0.0238 0.0316 - 0.0282 Allentown-Bethlehem-Easton, PA-NJ 0.0012 - 0.0048 0.0033 - 0.0057 0.0099 - 0.0205 0.0272 - 0.0242 Altoona, PA 0.0002 - 0.0008 0.0006 - 0.001 0.012 - 0.0248 0.033 - 0.0294 Amarillo, TX 0.0007 - 0.0027 0.0018 - 0.0032 0.0091 - 0.0188 0.025 - 0.0223 Ames, IA 0.0008 - 0.0033 0.0023 - 0.0039 0.0431 - 0.0889 0.1181 - 0.1053 Anchorage, AK 0.0018 - 0.0074 0.0051 - 0.0088 0.02 - 0.0412 0.0548 - 0.0488 Anderson, IN 0.0006 - 0.0023 0.0016 - 0.0028 0.0076 - 0.0158 0.0209 - 0.0187 Ann Arbor, MI 0.0011 - 0.0045 0.003 - 0.0053 0.0325 - 0.0672 0.0893 - 0.0795 Appleton, WI 0.0005 - 0.0019 0.0013 - 0.0023 0.0158 - 0.0327 0.0435 - 0.0387 Athens-Clarke County, GA 0.001 - 0.004 0.0027 - 0.0048 0.0138 - 0.0285 0.0379 - 0.0337 Atlanta-Sandy Springs-Marietta, GA 0.0183 - 0.0735 0.0502 - 0.087 0.0255 - 0.0526 0.0699 - 0.0623 Auburn-Opelika, AL 0.0001 - 0.0005 0.0003 - 0.0005 0.002 - 0.0042 0.0056 - 0.005 Augusta-Richmond County, GA-SC 0.0009 - 0.0037 0.0025 - 0.0044 0.0036 - 0.0075 0.0099 - 0.0089 Bakersfield, CA 0.0025 - 0.0099 0.0067 - 0.0117 0.0146 - 0.0302 0.0402 - 0.0358 Bangor, ME 0.0004 - 0.0016 0.0011 - 0.0019 0.0123 - 0.0254 0.0338 - 0.0301 Baton Rouge, LA 0.0015 - 0.006 0.0041 - 0.0071 0.0102 - 0.021 0.0279 - 0.0249 Battle Creek, MI 0.0004 - 0.0017 0.0011 - 0.002 0.0094 - 0.0194 0.0258 - 0.023 Bay City, MI 0.0009 - 0.0037 0.0025 - 0.0044 0.0323 - 0.0667 0.0887 - 0.079 Beaumont-Port Arthur, TX 0.001 - 0.0039 0.0026 - 0.0046 0.0082 - 0.0169 0.0225 - 0.02 Bellingham, WA 0.0013 - 0.0054 0.0037 - 0.0064 0.0328 - 0.0677 0.09 - 0.0802 Bend, OR 0.0002 - 0.0007 0.0005 - 0.0008 0.0042 - 0.0087 0.0116 - 0.0104 Billings, MT 0.0005 - 0.002 0.0014 - 0.0024 0.013 - 0.0268 0.0356 - 0.0317
214 Binghamton, NY 0.0008 - 0.003 0.0021 - 0.0036 0.0254 - 0.0525 0.0698 - 0.0622 Birmingham-Hoover, AL 0.0023 - 0.0092 0.0063 - 0.0108 0.0084 - 0.0174 0.0232 - 0.0206 Bismarck, ND 0.0002 - 0.0008 0.0005 - 0.0009 0.0099 - 0.0204 0.0272 - 0.0242 Blacksburg-Christiansburg-Radford, VA 0.0008 - 0.0033 0.0022 - 0.0039 0.0152 - 0.0314 0.0418 - 0.0372 Bloomington, IN 0.0006 - 0.0025 0.0017 - 0.003 0.018 - 0.0372 0.0494 - 0.044 Bloomington-Normal, IL 0.0007 - 0.003 0.002 - 0.0035 0.0222 - 0.0459 0.0609 - 0.0543 Boise City-Nampa, ID 0.0009 - 0.0036 0.0025 - 0.0043 0.0078 - 0.0161 0.0214 - 0.0191 Bradenton-Sarasota-Venice, FL 0.002 - 0.0081 0.0055 - 0.0096 0.0182 - 0.0376 0.05 - 0.0445 Bremerton-Silverdale, WA 0.0018 - 0.0074 0.0051 - 0.0088 0.0336 - 0.0694 0.0923 - 0.0822 Brownsville-Harlingen, TX 0.0008 - 0.0031 0.0021 - 0.0037 0.0069 - 0.0143 0.019 - 0.0169 Brunswick, GA 0.0079 - 0.0316 0.0216 - 0.0374 0.3362 - 0.6942 0.9225 - 0.822 Canton-Massillon, OH 0.0023 - 0.0094 0.0064 - 0.0111 0.0209 - 0.0432 0.0574 - 0.0511 Cape Coral-Fort Myers, FL 0.0036 - 0.0144 0.0099 - 0.0171 0.0173 - 0.0358 0.0476 - 0.0424 Casper, WY 0.0002 - 0.0009 0.0006 - 0.0011 0.0097 - 0.0201 0.0267 - 0.0238 Cedar Rapids, IA 0.0007 - 0.003 0.0021 - 0.0036 0.0139 - 0.0286 0.0381 - 0.0339 Champaign-Urbana, IL 0.0021 - 0.0083 0.0057 - 0.0099 0.0543 - 0.112 0.1489 - 0.1326 Charleston, WV 0.0012 - 0.005 0.0034 - 0.0059 0.026 - 0.0536 0.0713 - 0.0635 Charleston-North Charleston, SC 0.0024 - 0.0098 0.0067 - 0.0116 0.0143 - 0.0295 0.0392 - 0.0349 Charlotte-Gastonia-Concord, NC-SC 0.0204 - 0.0819 0.0559 - 0.097 0.0241 - 0.0497 0.0661 - 0.0589 Chattanooga, TN-GA 0.0019 - 0.0075 0.0051 - 0.0089 0.0129 - 0.0266 0.0353 - 0.0315 Cheyenne, WY 0.0003 - 0.0012 0.0008 - 0.0014 0.0148 - 0.0306 0.0406 - 0.0362 Chicago-Naperville-Joliet, IL-IN-WI 0.0458 - 0.1842 0.1258 - 0.2181 0.0475 - 0.098 0.1302 - 0.116 Chico, CA 0.0009 - 0.0034 0.0023 - 0.0041 0.0156 - 0.0322 0.0428 - 0.0382 Cincinnati-Middletown, OH-KY-IN 0.007 - 0.0282 0.0193 - 0.0334 0.022 - 0.0455 0.0604 - 0.0538 Clarksville, TN-KY 0.0021 - 0.0084 0.0057 - 0.0099 0.0109 - 0.0226 0.03 - 0.0268 Cleveland-Elyria-Mentor, OH 0.0132 - 0.0532 0.0363 - 0.063 0.0438 - 0.0904 0.1201 - 0.107 College Station-Bryan, TX 0.0024 - 0.0096 0.0065 - 0.0113 0.0308 - 0.0636 0.0846 - 0.0754 Columbia, MO 0.0005 - 0.0022 0.0015 - 0.0026 0.0125 - 0.0259 0.0344 - 0.0307 Columbia, SC 0.0009 - 0.0036 0.0024 - 0.0042 0.0073 - 0.015 0.0199 - 0.0178 Columbus, GA-AL 0.0011 - 0.0044 0.003 - 0.0053 0.0109 - 0.0224 0.0298 - 0.0266
215 Columbus, OH 0.0045 - 0.0182 0.0124 - 0.0216 0.0146 - 0.0301 0.0399 - 0.0356 Corpus Christi, TX 0.0027 - 0.011 0.0075 - 0.013 0.0228 - 0.0471 0.0626 - 0.0558 Cumberland, MD-WV 0.0002 - 0.0009 0.0006 - 0.0011 0.0091 - 0.0188 0.0249 - 0.0222 Dallas-Fort Worth-Arlington, TX 0.0228 - 0.0917 0.0626 - 0.1086 0.0197 - 0.0406 0.054 - 0.0481 Davenport-Moline-Rock Island, IA-IL 0.0027 - 0.011 0.0075 - 0.0131 0.0291 - 0.0602 0.08 - 0.0712 Dayton, OH 0.0039 - 0.0158 0.0108 - 0.0187 0.0232 - 0.0478 0.0635 - 0.0566 Decatur, IL 0 - 0 0 - 0 0 - 0 0 - 0 Deltona-Daytona Beach-Ormond Beach, FL 0.001 - 0.0042 0.0028 - 0.0049 0.0292 - 0.0603 0.0802 - 0.0714 Denver-Aurora, CO 0.0036 - 0.0143 0.0098 - 0.0169 0.0169 - 0.035 0.0465 - 0.0414 Des Moines, IA 0.0205 - 0.0825 0.0564 - 0.0977 0.0623 - 0.1287 0.171 - 0.1524 Detroit-Warren-Livonia, MI 0.0013 - 0.0052 0.0035 - 0.0061 0.0142 - 0.0293 0.039 - 0.0347 Dubuque, IA 0.015 - 0.0602 0.0411 - 0.0713 0.0204 - 0.0422 0.056 - 0.0499 Duluth, MN-WI 0 - 0 0 - 0 0 - 0 0 - 0 Eau Claire, WI 0.0002 - 0.0009 0.0006 - 0.0011 0.0111 - 0.0228 0.0303 - 0.027 El Centro, CA 0.0017 - 0.0069 0.0047 - 0.0082 0.0218 - 0.045 0.0598 - 0.0533 El Paso, TX 0.0005 - 0.0021 0.0014 - 0.0025 0.0145 - 0.03 0.0399 - 0.0355 Elkhart-Goshen, IN 0.0004 - 0.0017 0.0012 - 0.0021 0.0136 - 0.028 0.0372 - 0.0331 Elmira, NY 0.0045 - 0.0179 0.0122 - 0.0212 0.03 - 0.0619 0.0823 - 0.0733 Erie, PA 0.0003 - 0.0014 0.0009 - 0.0016 0.0073 - 0.0151 0.0201 - 0.0179 Eugene-Springfield, OR 0.0004 - 0.0014 0.001 - 0.0017 0.0282 - 0.0582 0.0773 - 0.0689 Evansville, IN-KY 0.0008 - 0.0031 0.0021 - 0.0037 0.0202 - 0.0416 0.0553 - 0.0493 Fairbanks, AK 0.0023 - 0.0094 0.0064 - 0.0111 0.0325 - 0.0671 0.0892 - 0.0794 Fayetteville-Springdale-Rogers, AR-MO 0.0007 - 0.0029 0.002 - 0.0035 0.0115 - 0.0238 0.0317 - 0.0282 Flagstaff, AZ 0.0004 - 0.0017 0.0012 - 0.002 0.013 - 0.0268 0.0356 - 0.0317 Flint, MI 0.0006 - 0.0025 0.0017 - 0.003 0.0046 - 0.0095 0.0126 - 0.0113 Florence, SC 0.0006 - 0.0023 0.0016 - 0.0027 0.0172 - 0.0355 0.0472 - 0.042 Fond du Lac, WI 0.0027 - 0.0109 0.0075 - 0.0129 0.0254 - 0.0525 0.0697 - 0.0621 Fort Collins-Loveland, CO 0.0002 - 0.0007 0.0005 - 0.0008 0.0025 - 0.0051 0.0067 - 0.006 Fort Smith, AR-OK 0.0001 - 0.0004 0.0003 - 0.0005 0.0057 - 0.0119 0.0158 - 0.0141 Fort Walton Beach-Crestview-Destin, FL 0.0008 - 0.0033 0.0023 - 0.0039 0.0109 - 0.0224 0.0298 - 0.0265
216 Fort Wayne, IN 0.0002 - 0.0009 0.0006 - 0.0011 0.0033 - 0.0068 0.009 - 0.008 Fresno, CA 0.0003 - 0.0014 0.0009 - 0.0016 0.0084 - 0.0173 0.023 - 0.0205 Gainesville, FL 0.0015 - 0.0062 0.0042 - 0.0073 0.0126 - 0.026 0.0346 - 0.0308 Glens Falls, NY 0.0031 - 0.0123 0.0084 - 0.0146 0.0175 - 0.0362 0.0481 - 0.0429 Grand Forks, ND-MN 0.0029 - 0.0115 0.0078 - 0.0136 0.0373 - 0.077 0.1023 - 0.0912 Grand Junction, CO 0.0001 - 0.0004 0.0003 - 0.0005 0.0081 - 0.0167 0.0221 - 0.0197 Grand Rapids-Wyoming, MI 0.0003 - 0.0011 0.0008 - 0.0013 0.0131 - 0.027 0.0359 - 0.032 Great Falls, MT 0.0008 - 0.0033 0.0023 - 0.0039 0.0178 - 0.0367 0.0488 - 0.0435 Greeley, CO 0.002 - 0.0082 0.0056 - 0.0097 0.018 - 0.0371 0.0494 - 0.044 Green Bay, WI 0.0004 - 0.0016 0.0011 - 0.0019 0.0183 - 0.0377 0.0501 - 0.0446 Greenville, SC 0.0003 - 0.0012 0.0008 - 0.0014 0.0054 - 0.0111 0.0147 - 0.0131 Hagerstown-Martinsburg, MD-WV 0.0011 - 0.0043 0.003 - 0.0051 0.0146 - 0.0301 0.04 - 0.0356 Hanford-Corcoran, CA 0.0004 - 0.0015 0.001 - 0.0017 0.0031 - 0.0064 0.0085 - 0.0076 Harrisburg-Carlisle, PA 0.0002 - 0.001 0.0007 - 0.0012 0.0053 - 0.0109 0.0145 - 0.0129 Holland-Grand Haven, MI 0.0009 - 0.0037 0.0025 - 0.0044 0.0175 - 0.0362 0.0481 - 0.0428 Honolulu, HI 0.0004 - 0.0014 0.001 - 0.0017 0.0111 - 0.0228 0.0304 - 0.027 Houston-Baytown-Sugar Land, TX 0.0002 - 0.0009 0.0006 - 0.001 0.0041 - 0.0085 0.0112 - 0.01 Huntington-Ashland, WV-KY-OH 0.0064 - 0.0256 0.0175 - 0.0303 0.0688 - 0.1421 0.1889 - 0.1683 Huntsville, AL 0.0196 - 0.0786 0.0536 - 0.093 0.0241 - 0.0498 0.0662 - 0.059 Indianapolis, IN 0.0005 - 0.0019 0.0013 - 0.0023 0.0093 - 0.0192 0.0256 - 0.0228 Iowa City, IA 0.0008 - 0.0032 0.0022 - 0.0037 0.0052 - 0.0108 0.0144 - 0.0128 Ithaca, NY 0.0049 - 0.0195 0.0133 - 0.0231 0.0134 - 0.0277 0.0368 - 0.0328 Jackson, MI 0.0009 - 0.0035 0.0024 - 0.0042 0.0351 - 0.0725 0.0964 - 0.0859 Jackson, MS 0.0006 - 0.0026 0.0018 - 0.003 0.0547 - 0.1128 0.15 - 0.1336 Jackson, TN 0.0002 - 0.0006 0.0004 - 0.0007 0.0075 - 0.0154 0.0204 - 0.0182 Jacksonville, FL 0.0009 - 0.0037 0.0025 - 0.0044 0.006 - 0.0124 0.0165 - 0.0147 Janesville, WI 0.0005 - 0.0022 0.0015 - 0.0026 0.0167 - 0.0345 0.0458 - 0.0408 Jefferson City, MO 0.0089 - 0.036 0.0246 - 0.0426 0.0256 - 0.0529 0.0703 - 0.0627 Johnson City, TN 0.0012 - 0.0047 0.0032 - 0.0055 0.0263 - 0.0543 0.0721 - 0.0643 Johnstown, PA 0.0003 - 0.001 0.0007 - 0.0012 0.0087 - 0.018 0.0239 - 0.0213
217 Kalamazoo-Portage, MI 0.0005 - 0.002 0.0014 - 0.0023 0.0072 - 0.0149 0.0199 - 0.0177 Kankakee-Bradley, IL 0.0003 - 0.0014 0.0009 - 0.0016 0.0159 - 0.0329 0.0437 - 0.0389 Kansas City, MO-KS 0.0013 - 0.0054 0.0037 - 0.0064 0.0159 - 0.0328 0.0436 - 0.0388 Kennewick-Richland-Pasco, WA 0.0006 - 0.0022 0.0015 - 0.0026 0.0194 - 0.04 0.0532 - 0.0474 Killeen-Temple-Fort Hood, TX 0.0082 - 0.0329 0.0225 - 0.039 0.0161 - 0.0332 0.0441 - 0.0393 Knoxville, TN 0.0035 - 0.014 0.0095 - 0.0165 0.0363 - 0.075 0.0997 - 0.0888 La Crosse, WI-MN 0.0011 - 0.0043 0.0029 - 0.0051 0.0071 - 0.0147 0.0195 - 0.0174 Lafayette, IN 0.0021 - 0.0085 0.0058 - 0.01 0.0131 - 0.0271 0.036 - 0.0321 Lafayette, LA 0 - 0 0 - 0 0 - 0 0 - 0 Lakeland, FL 0.0004 - 0.0017 0.0012 - 0.002 0.0193 - 0.0399 0.0531 - 0.0473 Lancaster, PA 0.0009 - 0.0038 0.0026 - 0.0045 0.0262 - 0.0541 0.0719 - 0.0641 Lansing-East Lansing, MI 0.0004 - 0.0017 0.0011 - 0.002 0.0082 - 0.0169 0.0224 - 0.02 Laredo, TX 0.0017 - 0.007 0.0048 - 0.0083 0.0122 - 0.0253 0.0336 - 0.0299 Las Cruces, NM 0.0003 - 0.0014 0.0009 - 0.0016 0.01 - 0.0206 0.0274 - 0.0244 Las Vegas-Paradise, NV 0.0013 - 0.0051 0.0035 - 0.006 0.0235 - 0.0486 0.0645 - 0.0575 Lawrence, KS 0.0014 - 0.0057 0.0039 - 0.0067 0.0237 - 0.0489 0.065 - 0.0579 Lawton, OK 0.0004 - 0.0017 0.0012 - 0.0021 0.0075 - 0.0154 0.0205 - 0.0182 Lebanon, PA 0.0052 - 0.0208 0.0142 - 0.0246 0.03 - 0.0619 0.0823 - 0.0733 Lewiston-Auburn, ME 0.0005 - 0.0018 0.0013 - 0.0022 0.019 - 0.0393 0.0523 - 0.0466 Lexington-Fayette, KY 0.0016 - 0.0066 0.0045 - 0.0078 0.0175 - 0.0361 0.048 - 0.0428 Lincoln, NE 0.0003 - 0.0011 0.0007 - 0.0013 0.0117 - 0.0241 0.032 - 0.0285 Little Rock-North Little Rock, AR 0.0002 - 0.0007 0.0004 - 0.0008 0.0067 - 0.0139 0.0185 - 0.0165 Logan, UT-ID 0.0013 - 0.0052 0.0036 - 0.0062 0.0157 - 0.0324 0.0431 - 0.0384 Longview, WA 0.0011 - 0.0042 0.0029 - 0.005 0.0171 - 0.0352 0.0468 - 0.0417 Los Angeles-Long Beach-Santa Ana, CA 0.0017 - 0.007 0.0048 - 0.0083 0.0117 - 0.0241 0.032 - 0.0285 Louisville, KY-IN 0.0006 - 0.0024 0.0016 - 0.0028 0.0237 - 0.0489 0.065 - 0.0579 Lubbock, TX 0.0002 - 0.0007 0.0005 - 0.0008 0.0072 - 0.0148 0.0197 - 0.0176 Lynchburg, VA 0.0723 - 0.2906 0.1984 - 0.3441 0.0588 - 0.1213 0.1613 - 0.1437 Macon, GA 0.0069 - 0.0278 0.019 - 0.0329 0.0211 - 0.0436 0.0579 - 0.0516 Madison, WI 0.0013 - 0.005 0.0034 - 0.006 0.0197 - 0.0406 0.054 - 0.0481
218 Mansfield, OH 0.0012 - 0.0049 0.0034 - 0.0058 0.0151 - 0.0313 0.0416 - 0.037 McAllen-Edinburg-Pharr, TX 0.0011 - 0.0044 0.003 - 0.0053 0.0156 - 0.0323 0.0429 - 0.0382 Medford, OR 0.0018 - 0.0073 0.005 - 0.0086 0.0284 - 0.0587 0.078 - 0.0695 Memphis, TN-MS-AR 0.0003 - 0.001 0.0007 - 0.0012 0.0063 - 0.013 0.0173 - 0.0154 Merced, CA 0.0011 - 0.0043 0.0029 - 0.005 0.0053 - 0.011 0.0146 - 0.013 Miami-Fort Lauderdale-Miami Beach, FL 0.0004 - 0.0015 0.001 - 0.0018 0.01 - 0.0206 0.0273 - 0.0243 Milwaukee-Waukesha-West Allis, WI 0.0051 - 0.0203 0.0139 - 0.024 0.0174 - 0.0359 0.0478 - 0.0426 Minneapolis-St. Paul-Bloomington, MN-WI 0.0021 - 0.0083 0.0057 - 0.0098 0.0387 - 0.0799 0.1062 - 0.0946 Missoula, MT 0.0222 - 0.0894 0.061 - 0.1058 0.0373 - 0.077 0.1023 - 0.0911 Mobile, AL 0.0076 - 0.0304 0.0208 - 0.036 0.0392 - 0.0809 0.1076 - 0.0959 Modesto, CA 0.0134 - 0.0537 0.0366 - 0.0635 0.0333 - 0.0686 0.0912 - 0.0813 Monroe, LA 0.0005 - 0.002 0.0014 - 0.0024 0.0197 - 0.0406 0.054 - 0.0481 Morgantown, WV 0.0015 - 0.0058 0.004 - 0.0069 0.0104 - 0.0215 0.0286 - 0.0255 Muncie, IN 0.0013 - 0.0051 0.0035 - 0.0061 0.013 - 0.0269 0.0357 - 0.0318 Muskegon-Norton Shores, MI 0.0005 - 0.0019 0.0013 - 0.0023 0.0122 - 0.0253 0.0336 - 0.0299 Myrtle Beach-Conway-North Myrtle Beach, SC 0.0009 - 0.0037 0.0025 - 0.0044 0.027 - 0.0558 0.0741 - 0.066 Naples-Marco Island, FL 0.0008 - 0.0033 0.0023 - 0.004 0.0255 - 0.0526 0.0699 - 0.0623 Nashville-Davidson--Murfreesboro, TN 0.0005 - 0.002 0.0014 - 0.0023 0.0081 - 0.0167 0.0222 - 0.0198 New Orleans-Metairie-Kenner, LA 0.0007 - 0.0027 0.0018 - 0.0032 0.0105 - 0.0216 0.0287 - 0.0256 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.001 - 0.004 0.0028 - 0.0048 0.0131 - 0.027 0.0359 - 0.032 Niles-Benton Harbor, MI 0.0036 - 0.0145 0.0099 - 0.0172 0.0098 - 0.0202 0.0268 - 0.0239 Odessa, TX 0.0029 - 0.0115 0.0078 - 0.0136 0.0149 - 0.0308 0.0409 - 0.0365 Oklahoma City, OK 0.0546 - 0.2196 0.1499 - 0.26 0.0738 - 0.1523 0.2024 - 0.1803 Olympia, WA 0.0001 - 0.0003 0.0002 - 0.0003 0.0011 - 0.0023 0.0031 - 0.0028 Omaha-Council Bluffs, NE-IA 0.0007 - 0.0027 0.0018 - 0.0031 0.0191 - 0.0394 0.0524 - 0.0467 Orlando, FL 0.0025 - 0.01 0.0068 - 0.0119 0.0088 - 0.0181 0.0241 - 0.0214 Oshkosh-Neenah, WI 0.0012 - 0.0049 0.0033 - 0.0058 0.0357 - 0.0736 0.0979 - 0.0872 Oxnard-Thousand Oaks-Ventura, CA 0.0025 - 0.0101 0.0069 - 0.012 0.0162 - 0.0334 0.0444 - 0.0395 Palm Bay-Melbourne-Titusville, FL 0.0071 - 0.0285 0.0194 - 0.0337 0.0255 - 0.0527 0.0701 - 0.0624 Panama City-Lynn Haven, FL 0.0003 - 0.0013 0.0009 - 0.0016 0.0114 - 0.0236 0.0314 - 0.028
219 Pensacola-Ferry Pass-Brent, FL 0.0026 - 0.0105 0.0071 - 0.0124 0.0133 - 0.0275 0.0365 - 0.0325 Peoria, IL 0.002 - 0.0082 0.0056 - 0.0097 0.0084 - 0.0173 0.023 - 0.0205 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 0.0009 - 0.0036 0.0025 - 0.0043 0.0201 - 0.0415 0.0552 - 0.0492 Phoenix-Mesa-Scottsdale, AZ 0.0011 - 0.0045 0.0031 - 0.0053 0.0157 - 0.0324 0.0431 - 0.0384 Pittsburgh, PA 0.0014 - 0.0055 0.0038 - 0.0065 0.0167 - 0.0345 0.0459 - 0.0409 Pocatello, ID 0.0167 - 0.0671 0.0458 - 0.0794 0.0346 - 0.0715 0.0951 - 0.0847 Port St. Lucie-Fort Pierce, FL 0.0202 - 0.0812 0.0554 - 0.0962 0.0304 - 0.0628 0.0835 - 0.0744 Portland-Vancouver-Beaverton, OR-WA 0.0081 - 0.0325 0.0222 - 0.0385 0.0401 - 0.0827 0.11 - 0.098 Poughkeepsie-Newburgh-Middletown, NY 0.0003 - 0.0013 0.0009 - 0.0015 0.0113 - 0.0234 0.0311 - 0.0277 Providence-New Bedford-Fall River, RI-MA 0.001 - 0.0041 0.0028 - 0.0049 0.0021 - 0.0043 0.0057 - 0.0051 Pueblo, CO 0.0142 - 0.0572 0.039 - 0.0677 0.0487 - 0.1005 0.1336 - 0.1191 Racine, WI 0.0016 - 0.0066 0.0045 - 0.0078 0.0139 - 0.0287 0.0381 - 0.034 Rapid City, SD 0.0044 - 0.0176 0.012 - 0.0209 0.0197 - 0.0406 0.054 - 0.0481 Reading, PA 0.0005 - 0.0022 0.0015 - 0.0026 0.012 - 0.0248 0.033 - 0.0294 Redding, CA 0 - 0 0 - 0 0 - 0 0 - 0 Reno-Sparks, NV 0.0007 - 0.0028 0.0019 - 0.0033 0.0197 - 0.0407 0.054 - 0.0481 Richmond, VA 0.0002 - 0.0008 0.0006 - 0.001 0.0058 - 0.012 0.016 - 0.0143 Riverside-San Bernardino-Ontario, CA 0.0005 - 0.0021 0.0015 - 0.0025 0.0127 - 0.0262 0.0348 - 0.031 Roanoke, VA 0.0009 - 0.0037 0.0025 - 0.0043 0.0129 - 0.0267 0.0355 - 0.0316 Rochester, MN 0.0049 - 0.0199 0.0136 - 0.0235 0.0588 - 0.1215 0.1615 - 0.1439 Rochester, NY 0.0002 - 0.0008 0.0005 - 0.001 0.0012 - 0.0024 0.0032 - 0.0028 Rockford, IL 0.0097 - 0.0391 0.0267 - 0.0463 0.0099 - 0.0204 0.0271 - 0.0241 Rome, GA 0.0021 - 0.0086 0.0059 - 0.0102 0.0343 - 0.0707 0.094 - 0.0837 Sacramento--Arden-Arcade--Roseville, CA 0.0007 - 0.0027 0.0018 - 0.0032 0.0182 - 0.0375 0.0499 - 0.0445 Saginaw-Saginaw Township North, MI 0.0016 - 0.0064 0.0044 - 0.0076 0.0168 - 0.0348 0.0462 - 0.0412 Salem, OR 0.001 - 0.0042 0.0029 - 0.005 0.0124 - 0.0255 0.0339 - 0.0302 Salt Lake City, UT 0.0006 - 0.0023 0.0016 - 0.0027 0.0168 - 0.0346 0.046 - 0.041 San Angelo, TX 0.01 - 0.04 0.0273 - 0.0474 0.0293 - 0.0604 0.0803 - 0.0715 San Antonio, TX 0.0006 - 0.0025 0.0017 - 0.0029 0.0119 - 0.0246 0.0327 - 0.0292 San Diego-Carlsbad-San Marcos, CA 0.0014 - 0.0056 0.0039 - 0.0067 0.0187 - 0.0386 0.0513 - 0.0457
220 San Francisco-Oakland-Fremont, CA 0.0063 - 0.0255 0.0174 - 0.0302 0.0562 - 0.1161 0.1542 - 0.1374 San Jose-Sunnyvale-Santa Clara, CA 0.0006 - 0.0023 0.0016 - 0.0027 0.0113 - 0.0234 0.0311 - 0.0277 San Luis Obispo-Paso Robles, CA 0.0136 - 0.0547 0.0374 - 0.0648 0.0328 - 0.0677 0.0899 - 0.0801 Santa Barbara-Santa Maria-Goleta, CA 0.0148 - 0.0594 0.0406 - 0.0703 0.0341 - 0.0704 0.0935 - 0.0833 Santa Cruz-Watsonville, CA 0.0301 - 0.1211 0.0827 - 0.1434 0.0845 - 0.1744 0.2318 - 0.2066 Santa Fe, NM 0.0089 - 0.0358 0.0244 - 0.0424 0.0433 - 0.0894 0.1189 - 0.1059 Santa Rosa-Petaluma, CA 0.0003 - 0.0011 0.0008 - 0.0013 0.0049 - 0.0101 0.0134 - 0.0119 Savannah, GA 0 - 0 0 - 0 0 - 0 0 - 0 Scranton--Wilkes-Barre, PA 0.0039 - 0.0155 0.0106 - 0.0183 0.0572 - 0.118 0.1568 - 0.1398 Seattle-Tacoma-Bellevue, WA 0.0032 - 0.0128 0.0088 - 0.0152 0.0877 - 0.1811 0.2406 - 0.2144 Sebastian-Vero Beach, FL 0.0007 - 0.0029 0.002 - 0.0034 0.0203 - 0.0419 0.0557 - 0.0496 Sheboygan, WI 0.0039 - 0.0158 0.0108 - 0.0187 0.0398 - 0.0823 0.1093 - 0.0974 Sherman-Denison, TX 0.0017 - 0.007 0.0048 - 0.0083 0.0258 - 0.0532 0.0707 - 0.063 Shreveport-Bossier City, LA 0.001 - 0.0039 0.0027 - 0.0046 0.0128 - 0.0265 0.0352 - 0.0313 Sioux City, IA-NE-SD 0.0205 - 0.0825 0.0563 - 0.0977 0.0613 - 0.1266 0.1682 - 0.1499 Sioux Falls, SD 0.0004 - 0.0016 0.0011 - 0.002 0.0075 - 0.0155 0.0206 - 0.0184 South Bend-Mishawaka, IN-MI 0.0005 - 0.0019 0.0013 - 0.0023 0.0175 - 0.0361 0.048 - 0.0427 Spartanburg, SC 0.0004 - 0.0014 0.001 - 0.0017 0.0066 - 0.0135 0.018 - 0.016 Spokane, WA 0.003 - 0.0122 0.0083 - 0.0144 0.0204 - 0.0421 0.0559 - 0.0498 Springfield, IL 0.0006 - 0.0024 0.0016 - 0.0028 0.0121 - 0.0249 0.0331 - 0.0295 Springfield, MO 0.0005 - 0.0019 0.0013 - 0.0022 0.0105 - 0.0216 0.0287 - 0.0256 Springfield, OH 0.0016 - 0.0065 0.0044 - 0.0077 0.0188 - 0.0388 0.0516 - 0.0459 St. Cloud, MN 0.0002 - 0.001 0.0007 - 0.0012 0.0033 - 0.0068 0.009 - 0.008 St. Joseph, MO-KS 0.0032 - 0.013 0.0089 - 0.0154 0.042 - 0.0867 0.1152 - 0.1027 St. Louis, MO-IL 0.0008 - 0.0032 0.0022 - 0.0038 0.0197 - 0.0407 0.0541 - 0.0482 State College, PA 0.0007 - 0.003 0.002 - 0.0035 0.0085 - 0.0175 0.0233 - 0.0208 Stockton, CA 0.0002 - 0.001 0.0007 - 0.0012 0.0057 - 0.0117 0.0156 - 0.0139 Sumter, SC 0.0007 - 0.0028 0.0019 - 0.0033 0.0194 - 0.04 0.0531 - 0.0473 Syracuse, NY 0.0008 - 0.0032 0.0022 - 0.0038 0.0208 - 0.0429 0.057 - 0.0508 Tallahassee, FL 0.0096 - 0.0384 0.0263 - 0.0455 0.0289 - 0.0597 0.0793 - 0.0707
221 Tampa-St. Petersburg-Clearwater, FL 0.0007 - 0.0029 0.002 - 0.0034 0.0303 - 0.0625 0.0831 - 0.0741 Terre Haute, IN 0.0027 - 0.0108 0.0074 - 0.0128 0.021 - 0.0433 0.0575 - 0.0512 Toledo, OH 0.0008 - 0.0032 0.0022 - 0.0038 0.0176 - 0.0363 0.0483 - 0.043 Topeka, KS 0.0018 - 0.0074 0.0051 - 0.0088 0.0319 - 0.0659 0.0876 - 0.0781 Tucson, AZ 0.0012 - 0.0049 0.0034 - 0.0058 0.0177 - 0.0366 0.0487 - 0.0434 Tulsa, OK 0.0085 - 0.0342 0.0234 - 0.0406 0.0224 - 0.0462 0.0614 - 0.0547 Tuscaloosa, AL 0.0004 - 0.0018 0.0012 - 0.0021 0.0082 - 0.0168 0.0224 - 0.0199 Utica-Rome, NY 0.0029 - 0.0116 0.0079 - 0.0137 0.018 - 0.0371 0.0494 - 0.044 Victoria, TX 0.0008 - 0.0032 0.0022 - 0.0037 0.0144 - 0.0298 0.0396 - 0.0353 Virginia Beach-Norfolk-Newport News, VA-NC 0.0057 - 0.0229 0.0156 - 0.0271 0.0254 - 0.0525 0.0698 - 0.0622 Visalia-Porterville, CA 0.0016 - 0.0066 0.0045 - 0.0079 0.0097 - 0.02 0.0266 - 0.0237 Waco, TX 0.0003 - 0.0011 0.0007 - 0.0013 0.0038 - 0.0079 0.0105 - 0.0094 Washington-Arlington-Alexandria, DC-VA-MD-WV 0.0007 - 0.0028 0.0019 - 0.0034 0.0124 - 0.0255 0.0339 - 0.0302 Waterloo-Cedar Falls, IA 0.0008 - 0.0032 0.0022 - 0.0037 0.0107 - 0.0221 0.0293 - 0.0261 Wausau, WI 0.0106 - 0.0428 0.0292 - 0.0507 0.0246 - 0.0508 0.0675 - 0.0601 Wenatchee, WA 0.0012 - 0.0049 0.0033 - 0.0058 0.0119 - 0.0246 0.0327 - 0.0292 Wheeling, WV-OH 0.0007 - 0.0028 0.0019 - 0.0033 0.0114 - 0.0235 0.0313 - 0.0279 Wichita, KS 0.0268 - 0.1076 0.0735 - 0.1274 0.0758 - 0.1564 0.2079 - 0.1853 Williamsport, PA 0.0007 - 0.0028 0.0019 - 0.0033 0.0129 - 0.0266 0.0354 - 0.0315 Yakima, WA 0.0003 - 0.0013 0.0009 - 0.0016 0.0148 - 0.0305 0.0405 - 0.0361 York-Hanover, PA 0.0013 - 0.0051 0.0035 - 0.006 0.0532 - 0.1099 0.146 - 0.1301 Youngstown-Warren-Boardman, OH-PA 0.0004 - 0.0017 0.0012 - 0.002 0.0157 - 0.0325 0.0432 - 0.0385 Yuba City, CA 0.0013 - 0.0054 0.0037 - 0.0064 0.009 - 0.0186 0.0247 - 0.022 Yuma, AZ 0.0004 - 0.0016 0.0011 - 0.0019 0.0238 - 0.0492 0.0653 - 0.0582
222 TABLE F 8 Seat capacity (rail and motor bus) elasticities, employment density and population Employment density Population Rail seat capacity per capita Motor bus seat capacity per capita Rail seat capacity per capita Motor bus seat capacity per capita Abilene, TX - 0.1553 - 0.1455 - 0.8586 - 0.4059 Akron, OH - 0.1686 - 0.158 - 0.3947 - 0.1866 Albany, GA - 0.1513 - 0.1418 - 0.6617 - 0.3128 Albany-Schenectady-Troy, NY - 0.1001 - 0.0938 - 0.4054 - 0.1916 Albuquerque, NM - 0.1291 - 0.121 - 0.307 - 0.1451 Alexandria, LA - 0.0979 - 0.0918 - 0.7535 - 0.3562 Allentown-Bethlehem-Easton, PA-NJ - 0.064 - 0.0599 - 0.1746 - 0.0825 Altoona, PA - 0.1448 - 0.1357 - 2.6967 - 1.2748 Amarillo, TX - 0.0669 - 0.0626 - 0.2964 - 0.1401 Ames, IA - 0.6619 - 0.6203 - 11.2932 - 5.3385 Anchorage, AK - 0.1682 - 0.1576 - 0.5956 - 0.2816 Anderson, IN - 0.1967 - 0.1844 - 0.8402 - 0.3972 Ann Arbor, MI - 0.13 - 0.1218 - 1.2439 - 0.588 Anniston-Oxford, AL - 0.1218 - 0.1142 - 0.6664 - 0.315 Appleton, WI - 0.082 - 0.0768 - 0.8969 - 0.424 Athens-Clarke County, GA - 0.222 - 0.208 - 1.0024 - 0.4738 Atlanta-Sandy Springs-Marietta, GA 0.0788 - 0.2025 0.1067 - 0.1 0.2373 - 0.3265 0.0486 - 0.023 Auburn-Opelika, AL - 0.0941 - 0.0882 - 0.5551 - 0.2624 Augusta-Richmond County, GA-SC - 0.0749 - 0.0702 - 0.0955 - 0.0452 Austin-Round Rock, TX - 0.1422 - 0.1333 - 0.1901 - 0.0898 Bakersfield, CA - 0.0931 - 0.0873 - 0.1818 - 0.0859 Baltimore-Towson, MD 0.156 - 0.4008 0.1666 - 0.1561 1.3554 - 1.865 0.219 - 0.1035 Bangor, ME - 0.0857 - 0.0804 - 0.8747 - 0.4135 Baton Rouge, LA - 0.0661 - 0.0619 - 0.1463 - 0.0692 Battle Creek, MI - 0.1956 - 0.1833 - 1.4471 - 0.6841
223 Bay City, MI - 0.353 - 0.3308 - 4.0312 - 1.9056 Beaumont-Port Arthur, TX - 0.0913 - 0.0856 - 0.2554 - 0.1207 Bellingham, WA - 0.2543 - 0.2383 - 2.0403 - 0.9645 Bend, OR - 0.0474 - 0.0444 - 0.3915 - 0.1851 Billings, MT - 0.1679 - 0.1573 - 1.4316 - 0.6767 Binghamton, NY - 0.0959 - 0.0898 - 1.0608 - 0.5015 Birmingham-Hoover, AL - 0.0741 - 0.0695 - 0.0897 - 0.0424 Bismarck, ND - 0.116 - 0.1088 - 1.9476 - 0.9207 Blacksburg-Christiansburg-Radford, VA - 0.852 - 0.7984 - 5.2016 - 2.4589 Bloomington, IN - 0.1473 - 0.1381 - 1.3722 - 0.6487 Bloomington-Normal, IL - 0.1204 - 0.1128 - 1.1738 - 0.5549 Boise City-Nampa, ID - 0.0444 - 0.0416 - 0.1257 - 0.0594 Bradenton-Sarasota-Venice, FL - 0.08 - 0.0749 - 0.2361 - 0.1116 Bremerton-Silverdale, WA - 0.4757 - 0.4458 - 2.84 - 1.3425 Brownsville-Harlingen, TX - 0.0743 - 0.0697 - 0.2148 - 0.1015 Buffalo-Niagara Falls, NY 0.0325 - 0.0836 0.248 - 0.2324 0.3926 - 0.5403 0.4529 - 0.2141 Burlington-South Burlington, VT - 0.1625 - 0.1522 - 1.6621 - 0.7857 Canton-Massillon, OH - 0.1426 - 0.1337 - 0.419 - 0.1981 Cape Coral-Fort Myers, FL - 0.1222 - 0.1145 - 0.1927 - 0.0911 Carson City, NV - 0.1028 - 0.0963 - 2.044 - 0.9662 Casper, WY - 0.0885 - 0.0829 - 1.2062 - 0.5702 Cedar Rapids, IA - 0.1599 - 0.1498 - 0.9701 - 0.4586 Champaign-Urbana, IL - 0.3116 - 0.292 - 2.6661 - 1.2603 Charleston, WV - 0.0966 - 0.0906 - 0.6563 - 0.3103 Charleston-North Charleston, SC - 0.1239 - 0.1161 - 0.2363 - 0.1117 Charlotte-Gastonia-Concord, NC-SC - 0.4258 - 0.3991 - 0.1644 - 0.0777 Charlottesville, VA - 0.2241 - 0.2101 - 2.7074 - 1.2798 Chattanooga, TN-GA - 0.1489 - 0.1395 - 0.3361 - 0.1589 Cheyenne, WY - 0.1493 - 0.14 - 2.4682 - 1.1667 Chicago-Naperville-Joliet, IL-IN-WI 0.455 - 1.1691 0.1468 - 0.1376 1.018 - 1.4007 0.0497 - 0.0235
224 Chico, CA - 0.1431 - 0.1341 - 0.8571 - 0.4052 Cincinnati-Middletown, OH-KY-IN - 0.1638 - 0.1535 - 0.168 - 0.0794 Clarksville, TN-KY - 0.1584 - 0.1484 - 0.2717 - 0.1284 Cleveland-Elyria-Mentor, OH 0.0988 - 0.2539 0.205 - 0.1921 0.7066 - 0.9724 0.2218 - 0.1048 College Station-Bryan, TX - 0.1972 - 0.1848 - 0.8356 - 0.395 Colorado Springs, CO - 0.161 - 0.1509 - 0.3725 - 0.1761 Columbia, MO - 0.1689 - 0.1583 - 1.2755 - 0.603 Columbia, SC - 0.046 - 0.0431 - 0.1231 - 0.0582 Columbus, GA-AL - 0.15 - 0.1406 - 0.4833 - 0.2284 Columbus, IN - 0.035 - 0.0328 - 0.5915 - 0.2796 Columbus, OH - 0.1022 - 0.0958 - 0.1074 - 0.0508 Corpus Christi, TX - 0.22 - 0.2061 - 0.5999 - 0.2836 Corvallis, OR - 0.0922 - 0.0864 - 1.8688 - 0.8834 Cumberland, MD-WV - 0.0434 - 0.0407 - 0.5531 - 0.2615 Dallas-Fort Worth-Arlington, TX 0.069 - 0.1773 0.1263 - 0.1184 0.1285 - 0.1769 0.0356 - 0.0168 Danville, IL - 0.189 - 0.1771 - 2.1889 - 1.0347 Davenport-Moline-Rock Island, IA-IL - 0.2487 - 0.2331 - 0.8626 - 0.4078 Dayton, OH - 0.1197 - 0.1122 - 0.2305 - 0.1089 Decatur, IL - 0.259 - 0.2428 - 2.3939 - 1.1316 Deltona-Daytona Beach-Ormond Beach, FL - 0.1735 - 0.1626 - 0.2698 - 0.1275 Denver-Aurora, CO 0.0529 - 0.1359 0.2797 - 0.2621 0.3469 - 0.4774 0.2775 - 0.1312 Des Moines, IA - 0.1687 - 0.1581 - 0.6063 - 0.2866 Detroit-Warren-Livonia, MI - 0.1385 - 0.1298 - 0.0617 - 0.0292 Dubuque, IA - 0.1322 - 0.1239 - 2.0319 - 0.9605 Duluth, MN-WI - 0.3324 - 0.3115 - 1.3776 - 0.6512 Eau Claire, WI - 0.1054 - 0.0988 - 0.9722 - 0.4596 El Centro, CA - 0.1018 - 0.0954 - 1.0374 - 0.4904 El Paso, TX - 0.1897 - 0.1777 - 0.4169 - 0.1971 Elkhart-Goshen, IN - 0.0333 - 0.0312 - 0.2367 - 0.1119
225 Elmira, NY - 0.1153 - 0.108 - 2.9815 - 1.4094 Erie, PA - 0.1243 - 0.1164 - 1.0563 - 0.4993 Eugene-Springfield, OR - 0.3151 - 0.2953 - 1.4329 - 0.6774 Evansville, IN-KY - 0.0646 - 0.0606 - 0.3334 - 0.1576 Fairbanks, AK - 0.1113 - 0.1043 - 1.0975 - 0.5188 Fargo, ND-MN - 0.0838 - 0.0785 - 0.8737 - 0.413 Farmington, NM - 0.0211 - 0.0198 - 0.2228 - 0.1053 Fayetteville-Springdale-Rogers, AR-MO - 0.1096 - 0.1027 - 0.2647 - 0.1251 Flagstaff, AZ - 0.0755 - 0.0707 - 0.7385 - 0.3491 Flint, MI - 0.3632 - 0.3404 - 1.11 - 0.5247 Florence, SC - 0.0379 - 0.0355 - 0.1797 - 0.085 Fond du Lac, WI - 0.0511 - 0.0479 - 0.947 - 0.4477 Fort Collins-Loveland, CO - 0.12 - 0.1124 - 0.5127 - 0.2424 Fort Smith, AR-OK - 0.0384 - 0.036 - 0.1804 - 0.0853 Fort Walton Beach-Crestview-Destin, FL - 0.0422 - 0.0396 - 0.336 - 0.1588 Fort Wayne, IN - 0.0921 - 0.0863 - 0.2458 - 0.1162 Fresno, CA - 0.1285 - 0.1204 - 0.2405 - 0.1137 Gadsden, AL - 0.0982 - 0.092 - 0.575 - 0.2718 Gainesville, FL - 0.5868 - 0.5499 - 2.5027 - 1.1831 Gainesville, GA - 0.016 - 0.015 - 0.1032 - 0.0488 Glens Falls, NY - 0.0406 - 0.038 - 0.9912 - 0.4685 Grand Forks, ND-MN - 0.0744 - 0.0697 - 1.1392 - 0.5385 Grand Junction, CO - 0.1255 - 0.1176 - 0.8844 - 0.4181 Grand Rapids-Wyoming, MI - 0.095 - 0.0891 - 0.2729 - 0.129 Great Falls, MT - 0.2434 - 0.2281 - 3.6834 - 1.7412 Greeley, CO - 0.04 - 0.0374 - 0.2357 - 0.1114 Green Bay, WI - 0.1314 - 0.1231 - 0.5811 - 0.2747 Greenville, SC - 0.0187 - 0.0175 - 0.0526 - 0.0249 Gulfport-Biloxi, MS - 0.0894 - 0.0838 - 0.3319 - 0.1569 Hagerstown-Martinsburg, MD-WV - 0.0464 - 0.0435 - 0.3271 - 0.1546
226 Hanford-Corcoran, CA - 0.1668 - 0.1563 - 1.0389 - 0.4911 Harrisburg-Carlisle, PA - 0.0345 - 0.0323 - 0.3552 - 0.1679 Hattiesburg, MS - 0.0288 - 0.027 - 0.2632 - 0.1244 Holland-Grand Haven, MI - 0.0442 - 0.0414 - 0.2761 - 0.1305 Honolulu, HI - 0.307 - 0.2877 - 1.0856 - 0.5132 Hot Springs, AR - 0.0942 - 0.0883 - 0.7476 - 0.3534 Houston-Baytown-Sugar Land, TX 0.0061 - 0.0158 0.1624 - 0.1522 0.0164 - 0.0226 0.0655 - 0.031 Huntington-Ashland, WV-KY-OH - 0.086 - 0.0806 - 0.5423 - 0.2564 Huntsville, AL - 0.0517 - 0.0484 - 0.1122 - 0.053 Idaho Falls, ID - 0.0471 - 0.0441 - 0.6067 - 0.2868 Indianapolis, IN - 0.0813 - 0.0762 - 0.0735 - 0.0348 Iowa City, IA - 0.3347 - 0.3136 - 4.3898 - 2.0751 Ithaca, NY - 0.307 - 0.2877 - 8.5656 - 4.0491 Jackson, MI - 0.031 - 0.0291 - 0.4956 - 0.2343 Jackson, MS - 0.0597 - 0.0559 - 0.1284 - 0.0607 Jackson, TN - 0.1176 - 0.1102 - 1.1873 - 0.5612 Jacksonville, FL - 0.1764 - 0.1654 - 0.1652 - 0.0781 Janesville, WI - 0.2109 - 0.1977 - 1.5549 - 0.735 Jefferson City, MO - 0.0852 - 0.0798 - 0.9618 - 0.4546 Johnson City, TN - 0.0969 - 0.0908 - 0.4645 - 0.2196 Johnstown, PA - 0.1474 - 0.1382 - 2.2618 - 1.0692 Jonesboro, AR - 0.0383 - 0.0359 - 0.2207 - 0.1043 Kalamazoo-Portage, MI - 0.1277 - 0.1196 - 0.4962 - 0.2346 Kankakee-Bradley, IL - 0.1169 - 0.1095 - 1.33 - 0.6287 Kansas City, MO-KS - 0.1556 - 0.1458 - 0.0998 - 0.0472 Kennewick-Richland-Pasco, WA - 0.504 - 0.4723 - 1.722 - 0.814 Killeen-Temple-Fort Hood, TX - 0.0632 - 0.0592 - 0.1378 - 0.0651 Kingsport-Bristol-Bristol, TN-VA - 0.0741 - 0.0694 - 0.1999 - 0.0945 Kingston, NY - 0.1985 - 0.186 - 1.8862 - 0.8917 Knoxville, TN - 0.1132 - 0.106 - 0.2301 - 0.1088
227 La Crosse, WI-MN - 0.1233 - 0.1155 - 1.8203 - 0.8605 Lafayette, IN - 0.2725 - 0.2554 - 2.4936 - 1.1788 Lafayette, LA - 0.0718 - 0.0673 - 0.4612 - 0.218 Lake Charles, LA - 0.0404 - 0.0379 - 0.2542 - 0.1202 Lakeland, FL - 0.0963 - 0.0902 - 0.2213 - 0.1046 Lancaster, PA - 0.0211 - 0.0197 - 0.2039 - 0.0964 Lansing-East Lansing, MI - 0.1114 - 0.1044 - 0.6811 - 0.322 Laredo, TX - 0.2343 - 0.2196 - 1.2823 - 0.6062 Las Cruces, NM - 0.0912 - 0.0854 - 0.5154 - 0.2436 Las Vegas-Paradise, NV - 0.1115 - 0.1045 - 0.2114 - 0.0999 Lawrence, KS - 0.0974 - 0.0912 - 1.3249 - 0.6263 Lawton, OK - 0.2481 - 0.2325 - 0.8668 - 0.4098 Lebanon, PA - 0.0539 - 0.0505 - 0.7819 - 0.3696 Lewiston, ID-WA - 0.0233 - 0.0218 - 0.3882 - 0.1835 Lewiston-Auburn, ME - 0.0695 - 0.0651 - 0.9388 - 0.4438 Lexington-Fayette, KY - 0.0805 - 0.0754 - 0.3173 - 0.15 Lima, OH - 0.0629 - 0.0589 - 0.7823 - 0.3698 Lincoln, NE - 0.1663 - 0.1559 - 0.8822 - 0.4171 Little Rock-North Little Rock, AR 0.0122 - 0.0312 0.0755 - 0.0707 0.1762 - 0.2425 0.1656 - 0.0783 Logan, UT-ID - 0.2893 - 0.2711 - 3.7573 - 1.7762 Longview, TX - 0.0316 - 0.0296 - 0.1571 - 0.0743 Longview, WA - 0.0673 - 0.0631 - 0.887 - 0.4193 Los Angeles-Long Beach-Santa Ana, CA 0.0475 - 0.1221 0.1817 - 0.1702 0.0835 - 0.1149 0.0483 - 0.0228 Louisville, KY-IN - 0.2404 - 0.2253 - 0.2397 - 0.1133 Lubbock, TX - 0.2401 - 0.225 - 1.2313 - 0.5821 Lynchburg, VA - 0.7731 - 0.7245 - 3.131 - 1.4801 Macon, GA - 0.1015 - 0.0951 - 0.4699 - 0.2221 Madera, CA - 0.066 - 0.0619 - 0.3819 - 0.1805 Madison, WI - 0.1942 - 0.182 - 0.996 - 0.4708 Mansfield, OH - 0.0888 - 0.0832 - 0.7283 - 0.3443
228 McAllen-Edinburg-Pharr, TX - 0.0292 - 0.0273 - 0.048 - 0.0227 Medford, OR - 0.0919 - 0.0862 - 0.805 - 0.3806 Memphis, TN-MS-AR 0.0194 - 0.0499 0.1091 - 0.1023 0.1447 - 0.1991 0.1229 - 0.0581 Merced, CA - 0.1656 - 0.1552 - 1.015 - 0.4798 Miami-Fort Lauderdale-Miami Beach, FL 0.0461 - 0.1183 0.1373 - 0.1286 0.1668 - 0.2295 0.0752 - 0.0356 Michigan City-La Porte, IN - 0.0571 - 0.0535 - 0.5332 - 0.2521 Milwaukee-Waukesha-West Allis, WI - 0.1952 - 0.1829 - 0.3303 - 0.1561 Minneapolis-St. Paul-Bloomington, MN- WI 0.0115 - 0.0296 0.2493 - 0.2337 0.062 - 0.0853 0.2029 - 0.0959 Missoula, MT - 0.1612 - 0.1511 - 2.0338 - 0.9614 Mobile, AL - 0.1039 - 0.0974 - 0.2432 - 0.115 Modesto, CA - 0.0966 - 0.0905 - 0.3216 - 0.152 Monroe, LA - 0.0948 - 0.0888 - 0.8022 - 0.3792 Montgomery, AL - 0.0759 - 0.0711 - 0.2186 - 0.1033 Morgantown, WV - 0.1946 - 0.1824 - 1.8616 - 0.88 Mount Vernon-Anacortes, WA - 0.2449 - 0.2295 - 2.0128 - 0.9515 Muncie, IN - 0.2968 - 0.2782 - 2.9694 - 1.4037 Muskegon-Norton Shores, MI - 0.1536 - 0.144 - 0.8246 - 0.3898 Myrtle Beach-Conway-North Myrtle Beach, SC - 0.0537 - 0.0504 - 0.2731 - 0.1291 Napa, CA - 0.1745 - 0.1635 - 1.9348 - 0.9146 Naples-Marco Island, FL - 0.0405 - 0.0379 - 0.1724 - 0.0815 Nashville-Davidson--Murfreesboro, TN 0.0274 - 0.0705 0.101 - 0.0947 0.16 - 0.2201 0.0892 - 0.0422 New Orleans-Metairie-Kenner, LA 0.045 - 0.1155 0.146 - 0.1368 0.5066 - 0.6971 0.2489 - 0.1177 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.2092 - 0.5374 0.1173 - 0.1099 0.61 - 0.8394 0.0518 - 0.0245 Niles-Benton Harbor, MI - 0.0126 - 0.0118 - 0.0695 - 0.0329 Ocala, FL - 0.0478 - 0.0448 - 0.1606 - 0.0759 Odessa, TX - 0.105 - 0.0984 - 0.9908 - 0.4683 Oklahoma City, OK - 0.0779 - 0.073 - 0.0894 - 0.0423 Olympia, WA - 0.1787 - 0.1675 - 1.7208 - 0.8134
229 Omaha-Council Bluffs, NE-IA - 0.1485 - 0.1391 - 0.312 - 0.1475 Orlando, FL - 0.0904 - 0.0847 - 0.1065 - 0.0503 Oshkosh-Neenah, WI - 0.0845 - 0.0792 - 0.9454 - 0.4469 Owensboro, KY - 0.0487 - 0.0456 - 0.5738 - 0.2712 Oxnard-Thousand Oaks-Ventura, CA - 0.1399 - 0.1311 - 0.2337 - 0.1105 Palm Bay-Melbourne-Titusville, FL - 0.0835 - 0.0782 - 0.1124 - 0.0531 Panama City-Lynn Haven, FL - 0.0639 - 0.0599 - 0.4632 - 0.219 Parkersburg-Marietta, WV-OH - 0.0488 - 0.0457 - 0.3849 - 0.1819 Pensacola-Ferry Pass-Brent, FL - 0.0562 - 0.0526 - 0.2578 - 0.1218 Peoria, IL - 0.1223 - 0.1146 - 0.4884 - 0.2309 Philadelphia-Camden-Wilmington, PA-NJ- DE-MD 0.1862 - 0.4784 0.1046 - 0.098 0.8351 - 1.1492 0.071 - 0.0336 Phoenix-Mesa-Scottsdale, AZ - 0.1291 - 0.121 - 0.0635 - 0.03 Pine Bluff, AR - 0.1184 - 0.111 - 0.7382 - 0.349 Pittsburgh, PA 0.0268 - 0.0687 0.1622 - 0.152 0.2861 - 0.3937 0.2624 - 0.1241 Pocatello, ID - 0.1474 - 0.1381 - 1.6864 - 0.7972 Port St. Lucie-Fort Pierce, FL - 0.0966 - 0.0905 - 0.064 - 0.0303 Portland-South Portland-Biddeford, ME 0.0277 - 0.0711 0.0448 - 0.042 0.8057 - 1.1087 0.1977 - 0.0934 Portland-Vancouver-Beaverton, OR-WA 0.0762 - 0.1957 0.2047 - 0.1918 0.5633 - 0.775 0.229 - 0.1083 Poughkeepsie-Newburgh-Middletown, NY - 0.0999 - 0.0937 - 0.2772 - 0.131 Providence-New Bedford-Fall River, RI- MA - 0.1475 - 0.1382 - 0.2162 - 0.1022 Pueblo, CO - 0.1198 - 0.1122 - 0.8703 - 0.4114 Racine, WI - 0.1216 - 0.114 - 1.1347 - 0.5364 Rapid City, SD - 0.0677 - 0.0635 - 0.6198 - 0.293 Reading, PA - 0.0591 - 0.0554 - 0.4629 - 0.2188 Redding, CA - 0.1588 - 0.1488 - 0.7351 - 0.3475 Reno-Sparks, NV - 0.1796 - 0.1683 - 0.699 - 0.3304 Richmond, VA - 0.1058 - 0.0991 - 0.2004 - 0.0947 Riverside-San Bernardino-Ontario, CA - 0.1083 - 0.1014 - 0.0359 - 0.017
230 Roanoke, VA - 0.2035 - 0.1908 - 1.061 - 0.5016 Rochester, NY - 0.1129 - 0.1058 - 0.3884 - 0.1836 Rockford, IL - 0.0911 - 0.0853 - 0.3528 - 0.1668 Rome, GA - 0.6693 - 0.6273 - 6.4738 - 3.0603 Sacramento--Arden-Arcade--Roseville, CA 0.0634 - 0.163 0.1541 - 0.1444 0.4026 - 0.5539 0.148 - 0.07 Saginaw-Saginaw Township North, MI - 0.179 - 0.1677 - 1.131 - 0.5347 Salem, OR - 0.1491 - 0.1397 - 0.6486 - 0.3066 Salinas, CA - 0.1252 - 0.1173 - 0.732 - 0.346 Salisbury, MD - 0.0939 - 0.088 - 1.2731 - 0.6018 Salt Lake City, UT 0.0544 - 0.1398 0.2051 - 0.1922 1.0417 - 1.4334 0.5939 - 0.2808 San Angelo, TX - 0.0519 - 0.0486 - 0.3336 - 0.1577 San Antonio, TX - 0.1951 - 0.1829 - 0.1535 - 0.0726 San Diego-Carlsbad-San Marcos, CA 0.102 - 0.262 0.1549 - 0.1451 0.5081 - 0.6992 0.1167 - 0.0552 San Francisco-Oakland-Fremont, CA 0.2327 - 0.5979 0.2064 - 0.1934 1.4095 - 1.9395 0.1891 - 0.0894 San Jose-Sunnyvale-Santa Clara, CA 0.0751 - 0.1929 0.1679 - 0.1574 0.7889 - 1.0856 0.2669 - 0.1262 San Luis Obispo-Paso Robles, CA - 0.1363 - 0.1277 - 0.7881 - 0.3725 Santa Barbara-Santa Maria-Goleta, CA - 0.2508 - 0.235 - 1.2168 - 0.5752 Santa Cruz-Watsonville, CA - 0.2591 - 0.2428 - 2.3277 - 1.1003 Santa Fe, NM - 0.1513 - 0.1418 - 1.4074 - 0.6653 Santa Rosa-Petaluma, CA - 0.1754 - 0.1644 - 0.5809 - 0.2746 Savannah, GA - 0.127 - 0.119 - 0.6167 - 0.2915 Scranton--Wilkes-Barre, PA - 0.0837 - 0.0784 - 0.3617 - 0.171 Seattle-Tacoma-Bellevue, WA 0.0284 - 0.0729 0.3109 - 0.2914 0.183 - 0.2519 0.3034 - 0.1434 Sebastian-Vero Beach, FL - 0.0834 - 0.0782 - 0.4992 - 0.236 Sheboygan, WI - 0.1686 - 0.158 - 2.028 - 0.9587 Sherman-Denison, TX - 0.0515 - 0.0483 - 0.3077 - 0.1455 Shreveport-Bossier City, LA - 0.2068 - 0.1938 - 0.4546 - 0.2149 Sioux City, IA-NE-SD - 0.1939 - 0.1817 - 1.294 - 0.6117 Sioux Falls, SD - 0.0801 - 0.0751 - 0.5816 - 0.2749 South Bend-Mishawaka, IN-MI - 0.1714 - 0.1606 - 0.6502 - 0.3074
231 Spartanburg, SC - 0.0427 - 0.04 - 0.1866 - 0.0882 Spokane, WA - 0.2592 - 0.2429 - 1.1026 - 0.5212 Springfield, IL - 0.1928 - 0.1806 - 1.5502 - 0.7328 Springfield, MO - 0.0358 - 0.0336 - 0.1336 - 0.0632 Springfield, OH - 0.1039 - 0.0973 - 0.7733 - 0.3656 St. Cloud, MN - 0.1325 - 0.1242 - 1.2121 - 0.573 St. George, UT - 0.0314 - 0.0294 - 0.2829 - 0.1337 St. Joseph, MO-KS - 0.1393 - 0.1305 - 1.1955 - 0.5651 St. Louis, MO-IL 0.0438 - 0.1124 0.0872 - 0.0818 0.2858 - 0.3932 0.0862 - 0.0407 State College, PA - 0.2431 - 0.2279 - 3.3536 - 1.5853 Stockton, CA - 0.1677 - 0.1571 - 0.4272 - 0.2019 Sumter, SC - 0.3402 - 0.3188 - 2.4538 - 1.16 Syracuse, NY - 0.1332 - 0.1248 - 0.7524 - 0.3557 Tallahassee, FL - 0.1394 - 0.1306 - 0.6614 - 0.3127 Tampa-St. Petersburg-Clearwater, FL 0.0041 - 0.0105 0.0903 - 0.0846 0.0231 - 0.0318 0.0775 - 0.0367 Terre Haute, IN - 0.0504 - 0.0472 - 0.3046 - 0.144 Texarkana, TX-Texarkana, AR - 0.046 - 0.0431 - 0.3046 - 0.144 Topeka, KS - 0.1548 - 0.1451 - 0.9299 - 0.4396 Tucson, AZ - 0.2124 - 0.199 - 0.3098 - 0.1464 Tulsa, OK - 0.047 - 0.0441 - 0.0902 - 0.0426 Tuscaloosa, AL - 0.0583 - 0.0546 - 0.2675 - 0.1264 Tyler, TX - 0.0278 - 0.0261 - 0.1903 - 0.0899 Utica-Rome, NY - 0.1147 - 0.1075 - 0.6581 - 0.3111 Vallejo-Fairfield, CA - 0.4954 - 0.4643 - 1.3274 - 0.6275 Victoria, TX - 0.1903 - 0.1784 - 0.8478 - 0.4008 Virginia Beach-Norfolk-Newport News, VA-NC - 0.2755 - 0.2581 - 0.208 - 0.0983 Visalia-Porterville, CA - 0.1154 - 0.1081 - 0.3697 - 0.1748 Waco, TX - 0.1009 - 0.0946 - 0.5444 - 0.2574 Washington-Arlington-Alexandria, DC-VA- 0.2508 - 0.6444 0.1868 - 0.175 1.5331 - 2.1096 0.1727 - 0.0817
232 MD-WV Waterloo-Cedar Falls, IA - 0.1185 - 0.1111 - 0.7297 - 0.345 Wausau, WI - 0.1753 - 0.1643 - 2.5249 - 1.1936 Weirton-Steubenville, WV-OH - 0.095 - 0.089 - 0.5576 - 0.2636 Wenatchee, WA - 0.2886 - 0.2704 - 3.9503 - 1.8674 Wheeling, WV-OH - 0.0803 - 0.0753 - 0.9688 - 0.458 Wichita Falls, TX - 0.1105 - 0.1035 - 0.5854 - 0.2767 Wichita, KS - 0.0846 - 0.0793 - 0.1853 - 0.0876 Williamsport, PA - 0.147 - 0.1378 - 2.8329 - 1.3391 Winchester, VA-WV - 0.1196 - 0.1121 - 2.5682 - 1.214 Yakima, WA - 0.1149 - 0.1077 - 0.6077 - 0.2873 York-Hanover, PA - 0.0238 - 0.0223 - 0.2174 - 0.1028 Youngstown-Warren-Boardman, OH-PA - 0.1067 - 0.1 - 0.2001 - 0.0946 Yuba City, CA - 0.2579 - 0.2417 - 1.5439 - 0.7298 Yuma, AZ - 0.0698 - 0.0654 - 0.3658 - 0.1729
233 TABLE F 9 Productivity elasticities (based on employment density) rail and motor bus seat capacity per capita Rail seat capacity per capita Motor bus seat capacity per capita Average payroll GDP per capita Average payroll GDP per capita Abilene, TX - - 0.0086 - 0.0166 0.0236 - 0.0196 Akron, OH - - 0.0093 - 0.018 0.0256 - 0.0213 Albany, GA - - 0.0084 - 0.0162 0.023 - 0.0191 Albany-Schenectady-Troy, NY - - 0.0055 - 0.0107 0.0152 - 0.0127 Albuquerque, NM - - 0.0072 - 0.0138 0.0196 - 0.0163 Alexandria, LA - - 0.0054 - 0.0105 0.0149 - 0.0124 Allentown-Bethlehem-Easton, PA-NJ - - 0.0035 - 0.0068 0.0097 - 0.0081 Altoona, PA - - 0.008 - 0.0155 0.022 - 0.0183 Amarillo, TX - - 0.0037 - 0.0071 0.0102 - 0.0085 Ames, IA - - 0.0367 - 0.0707 0.1006 - 0.0837 Anchorage, AK - - 0.0093 - 0.018 0.0256 - 0.0213 Anderson, IN - - 0.0109 - 0.021 0.0299 - 0.0249 Ann Arbor, MI - - 0.0072 - 0.0139 0.0198 - 0.0164 Anniston-Oxford, AL - - 0.0068 - 0.013 0.0185 - 0.0154 Appleton, WI - - 0.0045 - 0.0088 0.0125 - 0.0104 Athens-Clarke County, GA - - 0.0123 - 0.0237 0.0337 - 0.0281 Atlanta-Sandy Springs-Marietta, GA 0.0044 - 0.0231 0.012 - 0.0273 0.0059 - 0.0114 0.0162 - 0.0135 Auburn-Opelika, AL - - 0.0052 - 0.0101 0.0143 - 0.0119 Augusta-Richmond County, GA-SC - - 0.0042 - 0.008 0.0114 - 0.0095 Austin-Round Rock, TX - - 0.0079 - 0.0152 0.0216 - 0.018 Bakersfield, CA - - 0.0052 - 0.0099 0.0142 - 0.0118 Baltimore-Towson, MD 0.0086 - 0.0457 0.0237 - 0.0541 0.0092 - 0.0178 0.0253 - 0.0211 Bangor, ME - - 0.0048 - 0.0092 0.013 - 0.0108 Baton Rouge, LA - - 0.0037 - 0.0071 0.01 - 0.0084 Battle Creek, MI - - 0.0108 - 0.0209 0.0297 - 0.0247 Bay City, MI - - 0.0196 - 0.0377 0.0537 - 0.0447
234 Beaumont-Port Arthur, TX - - 0.0051 - 0.0098 0.0139 - 0.0116 Bellingham, WA - - 0.0141 - 0.0272 0.0386 - 0.0322 Bend, OR - - 0.0026 - 0.0051 0.0072 - 0.006 Billings, MT - - 0.0093 - 0.0179 0.0255 - 0.0212 Binghamton, NY - - 0.0053 - 0.0102 0.0146 - 0.0121 Birmingham-Hoover, AL - - 0.0041 - 0.0079 0.0113 - 0.0094 Bismarck, ND - - 0.0064 - 0.0124 0.0176 - 0.0147 Blacksburg-Christiansburg-Radford, VA - - 0.0472 - 0.091 0.1295 - 0.1078 Bloomington, IN - - 0.0082 - 0.0157 0.0224 - 0.0186 Bloomington-Normal, IL - - 0.0067 - 0.0129 0.0183 - 0.0152 Boise City-Nampa, ID - - 0.0025 - 0.0047 0.0068 - 0.0056 Bradenton-Sarasota-Venice, FL - - 0.0044 - 0.0085 0.0122 - 0.0101 Bremerton-Silverdale, WA - - 0.0264 - 0.0508 0.0723 - 0.0602 Brownsville-Harlingen, TX - - 0.0041 - 0.0079 0.0113 - 0.0094 Buffalo-Niagara Falls, NY 0.0018 - 0.0095 0.0049 - 0.0113 0.0137 - 0.0265 0.0377 - 0.0314 Burlington-South Burlington, VT - - 0.009 - 0.0174 0.0247 - 0.0206 Canton-Massillon, OH - - 0.0079 - 0.0152 0.0217 - 0.018 Cape Coral-Fort Myers, FL - - 0.0068 - 0.0131 0.0186 - 0.0155 Carson City, NV - - 0.0057 - 0.011 0.0156 - 0.013 Casper, WY - - 0.0049 - 0.0095 0.0134 - 0.0112 Cedar Rapids, IA - - 0.0089 - 0.0171 0.0243 - 0.0202 Champaign-Urbana, IL - - 0.0173 - 0.0333 0.0474 - 0.0394 Charleston, WV - - 0.0054 - 0.0103 0.0147 - 0.0122 Charleston-North Charleston, SC - - 0.0069 - 0.0132 0.0188 - 0.0157 Charlotte-Gastonia-Concord, NC-SC - - 0.0236 - 0.0455 0.0647 - 0.0539 Charlottesville, VA - - 0.0124 - 0.0239 0.0341 - 0.0284 Chattanooga, TN-GA - - 0.0082 - 0.0159 0.0226 - 0.0188 Cheyenne, WY - - 0.0083 - 0.016 0.0227 - 0.0189 Chicago-Naperville-Joliet, IL-IN-WI 0.0252 - 0.1333 0.0692 - 0.1578 0.0081 - 0.0157 0.0223 - 0.0186 Chico, CA - - 0.0079 - 0.0153 0.0218 - 0.0181
235 Cincinnati-Middletown, OH-KY-IN - - 0.0091 - 0.0175 0.0249 - 0.0207 Clarksville, TN-KY - - 0.0088 - 0.0169 0.0241 - 0.02 Cleveland-Elyria-Mentor, OH 0.0055 - 0.0289 0.015 - 0.0343 0.0114 - 0.0219 0.0312 - 0.0259 College Station-Bryan, TX - - 0.0109 - 0.0211 0.03 - 0.0249 Colorado Springs, CO - - 0.0089 - 0.0172 0.0245 - 0.0204 Columbia, MO - - 0.0094 - 0.018 0.0257 - 0.0214 Columbia, SC - - 0.0026 - 0.0049 0.007 - 0.0058 Columbus, GA-AL - - 0.0083 - 0.016 0.0228 - 0.019 Columbus, IN - - 0.0019 - 0.0037 0.0053 - 0.0044 Columbus, OH - - 0.0057 - 0.0109 0.0155 - 0.0129 Corpus Christi, TX - - 0.0122 - 0.0235 0.0334 - 0.0278 Corvallis, OR - - 0.0051 - 0.0098 0.014 - 0.0117 Cumberland, MD-WV - - 0.0024 - 0.0046 0.0066 - 0.0055 Dallas-Fort Worth-Arlington, TX 0.0038 - 0.0202 0.0105 - 0.0239 0.007 - 0.0135 0.0192 - 0.016 Danville, IL - - 0.0105 - 0.0202 0.0287 - 0.0239 Davenport-Moline-Rock Island, IA-IL - - 0.0138 - 0.0266 0.0378 - 0.0315 Dayton, OH - - 0.0066 - 0.0128 0.0182 - 0.0151 Decatur, IL - - 0.0144 - 0.0277 0.0394 - 0.0328 Deltona-Daytona Beach-Ormond Beach, FL - - 0.0096 - 0.0185 0.0264 - 0.0219 Denver-Aurora, CO 0.0029 - 0.0155 0.008 - 0.0183 0.0155 - 0.0299 0.0425 - 0.0354 Des Moines, IA - - 0.0093 - 0.018 0.0256 - 0.0213 Detroit-Warren-Livonia, MI - - 0.0077 - 0.0148 0.021 - 0.0175 Dubuque, IA - - 0.0073 - 0.0141 0.0201 - 0.0167 Duluth, MN-WI - - 0.0184 - 0.0355 0.0505 - 0.0421 Eau Claire, WI - - 0.0058 - 0.0113 0.016 - 0.0133 El Centro, CA - - 0.0056 - 0.0109 0.0155 - 0.0129 El Paso, TX - - 0.0105 - 0.0203 0.0288 - 0.024 Elkhart-Goshen, IN - - 0.0018 - 0.0036 0.0051 - 0.0042 Elmira, NY - - 0.0064 - 0.0123 0.0175 - 0.0146
236 Erie, PA - - 0.0069 - 0.0133 0.0189 - 0.0157 Eugene-Springfield, OR - - 0.0175 - 0.0337 0.0479 - 0.0399 Evansville, IN-KY - - 0.0036 - 0.0069 0.0098 - 0.0082 Fairbanks, AK - - 0.0062 - 0.0119 0.0169 - 0.0141 Fargo, ND-MN - - 0.0046 - 0.0089 0.0127 - 0.0106 Farmington, NM - - 0.0012 - 0.0023 0.0032 - 0.0027 Fayetteville-Springdale-Rogers, AR-MO - - 0.0061 - 0.0117 0.0167 - 0.0139 Flagstaff, AZ - - 0.0042 - 0.0081 0.0115 - 0.0095 Flint, MI - - 0.0201 - 0.0388 0.0552 - 0.046 Florence, SC - - 0.0021 - 0.004 0.0058 - 0.0048 Fond du Lac, WI - - 0.0028 - 0.0055 0.0078 - 0.0065 Fort Collins-Loveland, CO - - 0.0066 - 0.0128 0.0182 - 0.0152 Fort Smith, AR-OK - - 0.0021 - 0.0041 0.0058 - 0.0049 Fort Walton Beach-Crestview-Destin, FL - - 0.0023 - 0.0045 0.0064 - 0.0053 Fort Wayne, IN - - 0.0051 - 0.0098 0.014 - 0.0117 Fresno, CA - - 0.0071 - 0.0137 0.0195 - 0.0163 Gadsden, AL - - 0.0054 - 0.0105 0.0149 - 0.0124 Gainesville, FL - - 0.0325 - 0.0627 0.0892 - 0.0742 Gainesville, GA - - 0.0009 - 0.0017 0.0024 - 0.002 Glens Falls, NY - - 0.0022 - 0.0043 0.0062 - 0.0051 Grand Forks, ND-MN - - 0.0041 - 0.0079 0.0113 - 0.0094 Grand Junction, CO - - 0.007 - 0.0134 0.0191 - 0.0159 Grand Rapids-Wyoming, MI - - 0.0053 - 0.0102 0.0144 - 0.012 Great Falls, MT - - 0.0135 - 0.026 0.037 - 0.0308 Greeley, CO - - 0.0022 - 0.0043 0.0061 - 0.0051 Green Bay, WI - - 0.0073 - 0.014 0.02 - 0.0166 Greenville, SC - - 0.001 - 0.002 0.0028 - 0.0024 Gulfport-Biloxi, MS - - 0.005 - 0.0095 0.0136 - 0.0113 Hagerstown-Martinsburg, MD-WV - - 0.0026 - 0.005 0.0071 - 0.0059 Hanford-Corcoran, CA - - 0.0092 - 0.0178 0.0254 - 0.0211
237 Harrisburg-Carlisle, PA - - 0.0019 - 0.0037 0.0052 - 0.0044 Hattiesburg, MS - - 0.0016 - 0.0031 0.0044 - 0.0036 Holland-Grand Haven, MI - - 0.0024 - 0.0047 0.0067 - 0.0056 Honolulu, HI - - 0.017 - 0.0328 0.0467 - 0.0388 Hot Springs, AR - - 0.0052 - 0.0101 0.0143 - 0.0119 Houston-Baytown-Sugar Land, TX 0.0003 - 0.0018 0.0009 - 0.0021 0.009 - 0.0173 0.0247 - 0.0205 Huntington-Ashland, WV-KY-OH - - 0.0048 - 0.0092 0.0131 - 0.0109 Huntsville, AL - - 0.0029 - 0.0055 0.0079 - 0.0065 Idaho Falls, ID - - 0.0026 - 0.005 0.0072 - 0.006 Indianapolis, IN - - 0.0045 - 0.0087 0.0124 - 0.0103 Iowa City, IA - - 0.0185 - 0.0358 0.0509 - 0.0423 Ithaca, NY - - 0.017 - 0.0328 0.0467 - 0.0388 Jackson, MI - - 0.0017 - 0.0033 0.0047 - 0.0039 Jackson, MS - - 0.0033 - 0.0064 0.0091 - 0.0075 Jackson, TN - - 0.0065 - 0.0126 0.0179 - 0.0149 Jacksonville, FL - - 0.0098 - 0.0189 0.0268 - 0.0223 Janesville, WI - - 0.0117 - 0.0225 0.0321 - 0.0267 Jefferson City, MO - - 0.0047 - 0.0091 0.013 - 0.0108 Johnson City, TN - - 0.0054 - 0.0104 0.0147 - 0.0123 Johnstown, PA - - 0.0082 - 0.0158 0.0224 - 0.0187 Jonesboro, AR - - 0.0021 - 0.0041 0.0058 - 0.0048 Kalamazoo-Portage, MI - - 0.0071 - 0.0136 0.0194 - 0.0161 Kankakee-Bradley, IL - - 0.0065 - 0.0125 0.0178 - 0.0148 Kansas City, MO-KS - - 0.0086 - 0.0166 0.0236 - 0.0197 Kennewick-Richland-Pasco, WA - - 0.0279 - 0.0538 0.0766 - 0.0638 Killeen-Temple-Fort Hood, TX - - 0.0035 - 0.0068 0.0096 - 0.008 Kingsport-Bristol-Bristol, TN-VA - - 0.0041 - 0.0079 0.0113 - 0.0094 Kingston, NY - - 0.011 - 0.0212 0.0302 - 0.0251 Knoxville, TN - - 0.0063 - 0.0121 0.0172 - 0.0143 La Crosse, WI-MN - - 0.0068 - 0.0132 0.0187 - 0.0156
238 Lafayette, IN - - 0.0151 - 0.0291 0.0414 - 0.0345 Lafayette, LA - - 0.004 - 0.0077 0.0109 - 0.0091 Lake Charles, LA - - 0.0022 - 0.0043 0.0061 - 0.0051 Lakeland, FL - - 0.0053 - 0.0103 0.0146 - 0.0122 Lancaster, PA - - 0.0012 - 0.0023 0.0032 - 0.0027 Lansing-East Lansing, MI - - 0.0062 - 0.0119 0.0169 - 0.0141 Laredo, TX - - 0.013 - 0.025 0.0356 - 0.0296 Las Cruces, NM - - 0.0051 - 0.0097 0.0139 - 0.0115 Las Vegas-Paradise, NV - - 0.0062 - 0.0119 0.017 - 0.0141 Lawrence, KS - - 0.0054 - 0.0104 0.0148 - 0.0123 Lawton, OK - - 0.0137 - 0.0265 0.0377 - 0.0314 Lebanon, PA - - 0.003 - 0.0058 0.0082 - 0.0068 Lewiston, ID-WA - - 0.0013 - 0.0025 0.0035 - 0.0029 Lewiston-Auburn, ME - - 0.0038 - 0.0074 0.0106 - 0.0088 Lexington-Fayette, KY - - 0.0045 - 0.0086 0.0122 - 0.0102 Lima, OH - - 0.0035 - 0.0067 0.0096 - 0.008 Lincoln, NE - - 0.0092 - 0.0178 0.0253 - 0.021 Little Rock-North Little Rock, AR 0.0007 - 0.0036 0.0018 - 0.0042 0.0042 - 0.0081 0.0115 - 0.0096 Logan, UT-ID - - 0.016 - 0.0309 0.044 - 0.0366 Longview, TX - - 0.0018 - 0.0034 0.0048 - 0.004 Longview, WA - - 0.0037 - 0.0072 0.0102 - 0.0085 Los Angeles-Long Beach-Santa Ana, CA 0.0026 - 0.0139 0.0072 - 0.0165 0.0101 - 0.0194 0.0276 - 0.023 Louisville, KY-IN - - 0.0133 - 0.0257 0.0365 - 0.0304 Lubbock, TX - - 0.0133 - 0.0256 0.0365 - 0.0304 Lynchburg, VA - - 0.0428 - 0.0826 0.1175 - 0.0978 Macon, GA - - 0.0056 - 0.0108 0.0154 - 0.0128 Madera, CA - - 0.0037 - 0.0071 0.01 - 0.0084 Madison, WI - - 0.0108 - 0.0208 0.0295 - 0.0246 Mansfield, OH - - 0.0049 - 0.0095 0.0135 - 0.0112 McAllen-Edinburg-Pharr, TX - - 0.0016 - 0.0031 0.0044 - 0.0037
239 Medford, OR - - 0.0051 - 0.0098 0.014 - 0.0116 Memphis, TN-MS-AR 0.0011 - 0.0057 0.003 - 0.0067 0.006 - 0.0117 0.0166 - 0.0138 Merced, CA - - 0.0092 - 0.0177 0.0252 - 0.0209 Miami-Fort Lauderdale-Miami Beach, FL 0.0026 - 0.0135 0.007 - 0.016 0.0076 - 0.0147 0.0209 - 0.0174 Michigan City-La Porte, IN - - 0.0032 - 0.0061 0.0087 - 0.0072 Milwaukee-Waukesha-West Allis, WI - - 0.0108 - 0.0209 0.0297 - 0.0247 Minneapolis-St. Paul-Bloomington, MN- WI 0.0006 - 0.0034 0.0018 - 0.004 0.0138 - 0.0266 0.0379 - 0.0315 Missoula, MT - - 0.0089 - 0.0172 0.0245 - 0.0204 Mobile, AL - - 0.0058 - 0.0111 0.0158 - 0.0131 Modesto, CA - - 0.0054 - 0.0103 0.0147 - 0.0122 Monroe, LA - - 0.0053 - 0.0101 0.0144 - 0.012 Montgomery, AL - - 0.0042 - 0.0081 0.0115 - 0.0096 Morgantown, WV - - 0.0108 - 0.0208 0.0296 - 0.0246 Mount Vernon-Anacortes, WA - - 0.0136 - 0.0262 0.0372 - 0.031 Muncie, IN - - 0.0164 - 0.0317 0.0451 - 0.0376 Muskegon-Norton Shores, MI - - 0.0085 - 0.0164 0.0234 - 0.0194 Myrtle Beach-Conway-North Myrtle Beach, SC - - 0.003 - 0.0057 0.0082 - 0.0068 Napa, CA - - 0.0097 - 0.0186 0.0265 - 0.0221 Naples-Marco Island, FL - - 0.0022 - 0.0043 0.0062 - 0.0051 Nashville-Davidson--Murfreesboro, TN 0.0015 - 0.008 0.0042 - 0.0095 0.0056 - 0.0108 0.0154 - 0.0128 New Orleans-Metairie-Kenner, LA 0.0025 - 0.0132 0.0068 - 0.0156 0.0081 - 0.0156 0.0222 - 0.0185 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.0116 - 0.0613 0.0318 - 0.0725 0.0065 - 0.0125 0.0178 - 0.0148 Niles-Benton Harbor, MI - - 0.0007 - 0.0013 0.0019 - 0.0016 Ocala, FL - - 0.0026 - 0.0051 0.0073 - 0.006 Odessa, TX - - 0.0058 - 0.0112 0.016 - 0.0133 Oklahoma City, OK - - 0.0043 - 0.0083 0.0118 - 0.0098 Olympia, WA - - 0.0099 - 0.0191 0.0272 - 0.0226 Omaha-Council Bluffs, NE-IA - - 0.0082 - 0.0159 0.0226 - 0.0188
240 Orlando, FL - - 0.005 - 0.0097 0.0137 - 0.0114 Oshkosh-Neenah, WI - - 0.0047 - 0.009 0.0128 - 0.0107 Owensboro, KY - - 0.0027 - 0.0052 0.0074 - 0.0062 Oxnard-Thousand Oaks-Ventura, CA - - 0.0078 - 0.0149 0.0213 - 0.0177 Palm Bay-Melbourne-Titusville, FL - - 0.0046 - 0.0089 0.0127 - 0.0106 Panama City-Lynn Haven, FL - - 0.0035 - 0.0068 0.0097 - 0.0081 Parkersburg-Marietta, WV-OH - - 0.0027 - 0.0052 0.0074 - 0.0062 Pensacola-Ferry Pass-Brent, FL - - 0.0031 - 0.006 0.0085 - 0.0071 Peoria, IL - - 0.0068 - 0.0131 0.0186 - 0.0155 Philadelphia-Camden-Wilmington, PA-NJ- DE-MD 0.0103 - 0.0545 0.0283 - 0.0646 0.0058 - 0.0112 0.0159 - 0.0132 Phoenix-Mesa-Scottsdale, AZ - - 0.0072 - 0.0138 0.0196 - 0.0163 Pine Bluff, AR - - 0.0066 - 0.0127 0.018 - 0.015 Pittsburgh, PA 0.0015 - 0.0078 0.0041 - 0.0093 0.009 - 0.0173 0.0247 - 0.0205 Pocatello, ID - - 0.0082 - 0.0157 0.0224 - 0.0186 Port St. Lucie-Fort Pierce, FL - - 0.0054 - 0.0103 0.0147 - 0.0122 Portland-South Portland-Biddeford, ME 0.0015 - 0.0081 0.0042 - 0.0096 0.0025 - 0.0048 0.0068 - 0.0057 Portland-Vancouver-Beaverton, OR-WA 0.0042 - 0.0223 0.0116 - 0.0264 0.0113 - 0.0219 0.0311 - 0.0259 Poughkeepsie-Newburgh-Middletown, NY - - 0.0055 - 0.0107 0.0152 - 0.0126 Providence-New Bedford-Fall River, RI- MA - - 0.0082 - 0.0158 0.0224 - 0.0187 Pueblo, CO - - 0.0066 - 0.0128 0.0182 - 0.0152 Racine, WI - - 0.0067 - 0.013 0.0185 - 0.0154 Rapid City, SD - - 0.0038 - 0.0072 0.0103 - 0.0086 Reading, PA - - 0.0033 - 0.0063 0.009 - 0.0075 Redding, CA - - 0.0088 - 0.017 0.0241 - 0.0201 Reno-Sparks, NV - - 0.0099 - 0.0192 0.0273 - 0.0227 Richmond, VA - - 0.0059 - 0.0113 0.0161 - 0.0134 Riverside-San Bernardino-Ontario, CA - - 0.006 - 0.0116 0.0165 - 0.0137 Roanoke, VA - - 0.0113 - 0.0217 0.0309 - 0.0258
241 Rochester, NY - - 0.0063 - 0.0121 0.0172 - 0.0143 Rockford, IL - - 0.005 - 0.0097 0.0138 - 0.0115 Rome, GA - - 0.0371 - 0.0715 0.1017 - 0.0847 Sacramento--Arden-Arcade--Roseville, CA 0.0035 - 0.0186 0.0096 - 0.022 0.0085 - 0.0165 0.0234 - 0.0195 Saginaw-Saginaw Township North, MI - - 0.0099 - 0.0191 0.0272 - 0.0226 Salem, OR - - 0.0083 - 0.0159 0.0227 - 0.0189 Salinas, CA - - 0.0069 - 0.0134 0.019 - 0.0158 Salisbury, MD - - 0.0052 - 0.01 0.0143 - 0.0119 Salt Lake City, UT 0.003 - 0.0159 0.0083 - 0.0189 0.0114 - 0.0219 0.0312 - 0.0259 San Angelo, TX - - 0.0029 - 0.0055 0.0079 - 0.0066 San Antonio, TX - - 0.0108 - 0.0208 0.0297 - 0.0247 San Diego-Carlsbad-San Marcos, CA 0.0057 - 0.0299 0.0155 - 0.0354 0.0086 - 0.0165 0.0235 - 0.0196 San Francisco-Oakland-Fremont, CA 0.0129 - 0.0682 0.0354 - 0.0807 0.0114 - 0.022 0.0314 - 0.0261 San Jose-Sunnyvale-Santa Clara, CA 0.0042 - 0.022 0.0114 - 0.026 0.0093 - 0.0179 0.0255 - 0.0212 San Luis Obispo-Paso Robles, CA - - 0.0075 - 0.0146 0.0207 - 0.0172 Santa Barbara-Santa Maria-Goleta, CA - - 0.0139 - 0.0268 0.0381 - 0.0317 Santa Cruz-Watsonville, CA - - 0.0144 - 0.0277 0.0394 - 0.0328 Santa Fe, NM - - 0.0084 - 0.0162 0.023 - 0.0191 Santa Rosa-Petaluma, CA - - 0.0097 - 0.0187 0.0267 - 0.0222 Savannah, GA - - 0.007 - 0.0136 0.0193 - 0.0161 Scranton--Wilkes-Barre, PA - - 0.0046 - 0.0089 0.0127 - 0.0106 Seattle-Tacoma-Bellevue, WA 0.0016 - 0.0083 0.0043 - 0.0098 0.0172 - 0.0332 0.0473 - 0.0393 Sebastian-Vero Beach, FL - - 0.0046 - 0.0089 0.0127 - 0.0106 Sheboygan, WI - - 0.0093 - 0.018 0.0256 - 0.0213 Sherman-Denison, TX - - 0.0029 - 0.0055 0.0078 - 0.0065 Shreveport-Bossier City, LA - - 0.0115 - 0.0221 0.0314 - 0.0262 Sioux City, IA-NE-SD - - 0.0107 - 0.0207 0.0295 - 0.0245 Sioux Falls, SD - - 0.0044 - 0.0086 0.0122 - 0.0101 South Bend-Mishawaka, IN-MI - - 0.0095 - 0.0183 0.026 - 0.0217 Spartanburg, SC - - 0.0024 - 0.0046 0.0065 - 0.0054
242 Spokane, WA - - 0.0144 - 0.0277 0.0394 - 0.0328 Springfield, IL - - 0.0107 - 0.0206 0.0293 - 0.0244 Springfield, MO - - 0.002 - 0.0038 0.0054 - 0.0045 Springfield, OH - - 0.0058 - 0.0111 0.0158 - 0.0131 St. Cloud, MN - - 0.0073 - 0.0142 0.0201 - 0.0168 St. George, UT - - 0.0017 - 0.0034 0.0048 - 0.004 St. Joseph, MO-KS - - 0.0077 - 0.0149 0.0212 - 0.0176 St. Louis, MO-IL 0.0024 - 0.0128 0.0067 - 0.0152 0.0048 - 0.0093 0.0133 - 0.011 State College, PA - - 0.0135 - 0.026 0.037 - 0.0308 Stockton, CA - - 0.0093 - 0.0179 0.0255 - 0.0212 Sumter, SC - - 0.0188 - 0.0363 0.0517 - 0.043 Syracuse, NY - - 0.0074 - 0.0142 0.0202 - 0.0168 Tallahassee, FL - - 0.0077 - 0.0149 0.0212 - 0.0176 Tampa-St. Petersburg-Clearwater, FL 0.0002 - 0.0012 0.0006 - 0.0014 0.005 - 0.0096 0.0137 - 0.0114 Terre Haute, IN - - 0.0028 - 0.0054 0.0077 - 0.0064 Texarkana, TX-Texarkana, AR - - 0.0025 - 0.0049 0.007 - 0.0058 Topeka, KS - - 0.0086 - 0.0165 0.0235 - 0.0196 Tucson, AZ - - 0.0118 - 0.0227 0.0323 - 0.0269 Tulsa, OK - - 0.0026 - 0.005 0.0071 - 0.0059 Tuscaloosa, AL - - 0.0032 - 0.0062 0.0089 - 0.0074 Tyler, TX - - 0.0015 - 0.003 0.0042 - 0.0035 Utica-Rome, NY - - 0.0064 - 0.0123 0.0174 - 0.0145 Vallejo-Fairfield, CA - - 0.0274 - 0.0529 0.0753 - 0.0627 Victoria, TX - - 0.0105 - 0.0203 0.0289 - 0.0241 Virginia Beach-Norfolk-Newport News, VA-NC - - 0.0153 - 0.0294 0.0419 - 0.0348 Visalia-Porterville, CA - - 0.0064 - 0.0123 0.0175 - 0.0146 Waco, TX - - 0.0056 - 0.0108 0.0153 - 0.0128 Washington-Arlington-Alexandria, DC-VA- MD-WV 0.0139 - 0.0735 0.0381 - 0.087 0.0103 - 0.02 0.0284 - 0.0236
243 Waterloo-Cedar Falls, IA - - 0.0066 - 0.0127 0.018 - 0.015 Wausau, WI - - 0.0097 - 0.0187 0.0266 - 0.0222 Weirton-Steubenville, WV-OH - - 0.0053 - 0.0101 0.0144 - 0.012 Wenatchee, WA - - 0.016 - 0.0308 0.0439 - 0.0365 Wheeling, WV-OH - - 0.0045 - 0.0086 0.0122 - 0.0102 Wichita Falls, TX - - 0.0061 - 0.0118 0.0168 - 0.014 Wichita, KS - - 0.0047 - 0.009 0.0129 - 0.0107 Williamsport, PA - - 0.0081 - 0.0157 0.0223 - 0.0186 Winchester, VA-WV - - 0.0066 - 0.0128 0.0182 - 0.0151 Yakima, WA - - 0.0064 - 0.0123 0.0175 - 0.0145 York-Hanover, PA - - 0.0013 - 0.0025 0.0036 - 0.003 Youngstown-Warren-Boardman, OH-PA - - 0.0059 - 0.0114 0.0162 - 0.0135 Yuba City, CA - - 0.0143 - 0.0275 0.0392 - 0.0326 Yuma, AZ - - 0.0039 - 0.0075 0.0106 - 0.0088
244 TABLE F 10 Productivity elasticities (based on population) rail and motor bus seat capacity per capita Rail seat capacity per capita Motor bus seat capacity per capita Average payroll GDP per capita Average payroll GDP per capita Abilene, TX - - 0.0476 - 0.0463 0.1305 - 0.0548 Akron, OH - - 0.0219 - 0.0213 0.06 - 0.0252 Albany, GA - - 0.0367 - 0.0357 0.1006 - 0.0422 Albany-Schenectady-Troy, NY - - 0.0225 - 0.0218 0.0616 - 0.0259 Albuquerque, NM - - 0.017 - 0.0165 0.0467 - 0.0196 Alexandria, LA - - 0.0417 - 0.0406 0.1145 - 0.0481 Allentown-Bethlehem-Easton, PA-NJ - - 0.0097 - 0.0094 0.0265 - 0.0111 Altoona, PA - - 0.1494 - 0.1453 0.4099 - 0.1721 Amarillo, TX - - 0.0164 - 0.016 0.0451 - 0.0189 Ames, IA - - 0.6256 - 0.6086 1.7166 - 0.7207 Anchorage, AK - - 0.033 - 0.0321 0.0905 - 0.038 Anderson, IN - - 0.0465 - 0.0453 0.1277 - 0.0536 Ann Arbor, MI - - 0.0689 - 0.067 0.1891 - 0.0794 Anniston-Oxford, AL - - 0.0369 - 0.0359 0.1013 - 0.0425 Appleton, WI - - 0.0497 - 0.0483 0.1363 - 0.0572 Athens-Clarke County, GA - - 0.0555 - 0.054 0.1524 - 0.064 Atlanta-Sandy Springs-Marietta, GA 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Auburn-Opelika, AL - - 0.0308 - 0.0299 0.0844 - 0.0354 Augusta-Richmond County, GA-SC - - 0.0053 - 0.0051 0.0145 - 0.0061 Austin-Round Rock, TX - - 0.0105 - 0.0102 0.0289 - 0.0121 Bakersfield, CA - - 0.0101 - 0.0098 0.0276 - 0.0116 Baltimore-Towson, MD 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Bangor, ME - - 0.0485 - 0.0471 0.133 - 0.0558 Baton Rouge, LA - - 0.0081 - 0.0079 0.0222 - 0.0093 Battle Creek, MI - - 0.0802 - 0.078 0.22 - 0.0923 Bay City, MI - - 0.2233 - 0.2172 0.6127 - 0.2573
245 Beaumont-Port Arthur, TX - - 0.0142 - 0.0138 0.0388 - 0.0163 Bellingham, WA - - 0.113 - 0.11 0.3101 - 0.1302 Bend, OR - - 0.0476 - 0.0463 0.1305 - 0.0548 Billings, MT - - 0.0219 - 0.0213 0.06 - 0.0252 Binghamton, NY - - 0.0367 - 0.0357 0.1006 - 0.0422 Birmingham-Hoover, AL - - 0.0225 - 0.0218 0.0616 - 0.0259 Bismarck, ND - - 0.017 - 0.0165 0.0467 - 0.0196 Blacksburg-Christiansburg-Radford, VA - - 0.0417 - 0.0406 0.1145 - 0.0481 Bloomington, IN - - 0.0097 - 0.0094 0.0265 - 0.0111 Bloomington-Normal, IL - - 0.1494 - 0.1453 0.4099 - 0.1721 Boise City-Nampa, ID - - 0.0164 - 0.016 0.0451 - 0.0189 Bradenton-Sarasota-Venice, FL - - 0.6256 - 0.6086 1.7166 - 0.7207 Bremerton-Silverdale, WA - - 0.033 - 0.0321 0.0905 - 0.038 Brownsville-Harlingen, TX - - 0.0465 - 0.0453 0.1277 - 0.0536 Buffalo-Niagara Falls, NY - - 0.0689 - 0.067 0.1891 - 0.0794 Burlington-South Burlington, VT - - 0.0369 - 0.0359 0.1013 - 0.0425 Canton-Massillon, OH - - 0.0497 - 0.0483 0.1363 - 0.0572 Cape Coral-Fort Myers, FL - - 0.0555 - 0.054 0.1524 - 0.064 Carson City, NV 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Casper, WY - - 0.0308 - 0.0299 0.0844 - 0.0354 Cedar Rapids, IA - - 0.0053 - 0.0051 0.0145 - 0.0061 Champaign-Urbana, IL - - 0.0105 - 0.0102 0.0289 - 0.0121 Charleston, WV - - 0.0101 - 0.0098 0.0276 - 0.0116 Charleston-North Charleston, SC 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Charlotte-Gastonia-Concord, NC-SC - - 0.0485 - 0.0471 0.133 - 0.0558 Charlottesville, VA - - 0.0081 - 0.0079 0.0222 - 0.0093 Chattanooga, TN-GA - - 0.0802 - 0.078 0.22 - 0.0923 Cheyenne, WY - - 0.2233 - 0.2172 0.6127 - 0.2573 Chicago-Naperville-Joliet, IL-IN-WI - - 0.0142 - 0.0138 0.0388 - 0.0163 Chico, CA - - 0.113 - 0.11 0.3101 - 0.1302
246 Cincinnati-Middletown, OH-KY-IN - - 0.0476 - 0.0463 0.1305 - 0.0548 Clarksville, TN-KY - - 0.0219 - 0.0213 0.06 - 0.0252 Cleveland-Elyria-Mentor, OH - - 0.0367 - 0.0357 0.1006 - 0.0422 College Station-Bryan, TX - - 0.0225 - 0.0218 0.0616 - 0.0259 Colorado Springs, CO - - 0.017 - 0.0165 0.0467 - 0.0196 Columbia, MO - - 0.0417 - 0.0406 0.1145 - 0.0481 Columbia, SC - - 0.0097 - 0.0094 0.0265 - 0.0111 Columbus, GA-AL - - 0.1494 - 0.1453 0.4099 - 0.1721 Columbus, IN - - 0.0164 - 0.016 0.0451 - 0.0189 Columbus, OH - - 0.6256 - 0.6086 1.7166 - 0.7207 Corpus Christi, TX - - 0.033 - 0.0321 0.0905 - 0.038 Corvallis, OR - - 0.0465 - 0.0453 0.1277 - 0.0536 Cumberland, MD-WV - - 0.0689 - 0.067 0.1891 - 0.0794 Dallas-Fort Worth-Arlington, TX - - 0.0369 - 0.0359 0.1013 - 0.0425 Danville, IL - - 0.0497 - 0.0483 0.1363 - 0.0572 Davenport-Moline-Rock Island, IA-IL - - 0.0555 - 0.054 0.1524 - 0.064 Dayton, OH 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Decatur, IL - - 0.0308 - 0.0299 0.0844 - 0.0354 Deltona-Daytona Beach-Ormond Beach, FL - - 0.0053 - 0.0051 0.0145 - 0.0061 Denver-Aurora, CO - - 0.0105 - 0.0102 0.0289 - 0.0121 Des Moines, IA - - 0.0101 - 0.0098 0.0276 - 0.0116 Detroit-Warren-Livonia, MI 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Dubuque, IA - - 0.0485 - 0.0471 0.133 - 0.0558 Duluth, MN-WI - - 0.0081 - 0.0079 0.0222 - 0.0093 Eau Claire, WI - - 0.0802 - 0.078 0.22 - 0.0923 El Centro, CA - - 0.2233 - 0.2172 0.6127 - 0.2573 El Paso, TX - - 0.0142 - 0.0138 0.0388 - 0.0163 Elkhart-Goshen, IN - - 0.113 - 0.11 0.3101 - 0.1302 Elmira, NY - - 0.0476 - 0.0463 0.1305 - 0.0548
247 Erie, PA - - 0.0219 - 0.0213 0.06 - 0.0252 Eugene-Springfield, OR - - 0.0367 - 0.0357 0.1006 - 0.0422 Evansville, IN-KY - - 0.0225 - 0.0218 0.0616 - 0.0259 Fairbanks, AK - - 0.017 - 0.0165 0.0467 - 0.0196 Fargo, ND-MN - - 0.0417 - 0.0406 0.1145 - 0.0481 Farmington, NM - - 0.0097 - 0.0094 0.0265 - 0.0111 Fayetteville-Springdale-Rogers, AR-MO - - 0.1494 - 0.1453 0.4099 - 0.1721 Flagstaff, AZ - - 0.0164 - 0.016 0.0451 - 0.0189 Flint, MI - - 0.6256 - 0.6086 1.7166 - 0.7207 Florence, SC - - 0.033 - 0.0321 0.0905 - 0.038 Fond du Lac, WI - - 0.0465 - 0.0453 0.1277 - 0.0536 Fort Collins-Loveland, CO - - 0.0689 - 0.067 0.1891 - 0.0794 Fort Smith, AR-OK - - 0.0369 - 0.0359 0.1013 - 0.0425 Fort Walton Beach-Crestview-Destin, FL - - 0.0497 - 0.0483 0.1363 - 0.0572 Fort Wayne, IN - - 0.0555 - 0.054 0.1524 - 0.064 Fresno, CA 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Gadsden, AL - - 0.0308 - 0.0299 0.0844 - 0.0354 Gainesville, FL - - 0.0053 - 0.0051 0.0145 - 0.0061 Gainesville, GA - - 0.0105 - 0.0102 0.0289 - 0.0121 Glens Falls, NY - - 0.0101 - 0.0098 0.0276 - 0.0116 Grand Forks, ND-MN 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Grand Junction, CO - - 0.0485 - 0.0471 0.133 - 0.0558 Grand Rapids-Wyoming, MI - - 0.0081 - 0.0079 0.0222 - 0.0093 Great Falls, MT - - 0.0802 - 0.078 0.22 - 0.0923 Greeley, CO - - 0.2233 - 0.2172 0.6127 - 0.2573 Green Bay, WI - - 0.0142 - 0.0138 0.0388 - 0.0163 Greenville, SC - - 0.113 - 0.11 0.3101 - 0.1302 Gulfport-Biloxi, MS - - 0.0476 - 0.0463 0.1305 - 0.0548 Hagerstown-Martinsburg, MD-WV - - 0.0219 - 0.0213 0.06 - 0.0252 Hanford-Corcoran, CA - - 0.0367 - 0.0357 0.1006 - 0.0422
248 Harrisburg-Carlisle, PA - - 0.0225 - 0.0218 0.0616 - 0.0259 Hattiesburg, MS - - 0.017 - 0.0165 0.0467 - 0.0196 Holland-Grand Haven, MI - - 0.0417 - 0.0406 0.1145 - 0.0481 Honolulu, HI - - 0.0097 - 0.0094 0.0265 - 0.0111 Hot Springs, AR - - 0.1494 - 0.1453 0.4099 - 0.1721 Houston-Baytown-Sugar Land, TX - - 0.0164 - 0.016 0.0451 - 0.0189 Huntington-Ashland, WV-KY-OH - - 0.6256 - 0.6086 1.7166 - 0.7207 Huntsville, AL - - 0.033 - 0.0321 0.0905 - 0.038 Idaho Falls, ID - - 0.0465 - 0.0453 0.1277 - 0.0536 Indianapolis, IN - - 0.0689 - 0.067 0.1891 - 0.0794 Iowa City, IA - - 0.0369 - 0.0359 0.1013 - 0.0425 Ithaca, NY - - 0.0497 - 0.0483 0.1363 - 0.0572 Jackson, MI - - 0.0555 - 0.054 0.1524 - 0.064 Jackson, MS 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Jackson, TN - - 0.0308 - 0.0299 0.0844 - 0.0354 Jacksonville, FL - - 0.0053 - 0.0051 0.0145 - 0.0061 Janesville, WI - - 0.0105 - 0.0102 0.0289 - 0.0121 Jefferson City, MO - - 0.0101 - 0.0098 0.0276 - 0.0116 Johnson City, TN 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Johnstown, PA - - 0.0485 - 0.0471 0.133 - 0.0558 Jonesboro, AR - - 0.0081 - 0.0079 0.0222 - 0.0093 Kalamazoo-Portage, MI - - 0.0802 - 0.078 0.22 - 0.0923 Kankakee-Bradley, IL - - 0.2233 - 0.2172 0.6127 - 0.2573 Kansas City, MO-KS - - 0.0142 - 0.0138 0.0388 - 0.0163 Kennewick-Richland-Pasco, WA - - 0.113 - 0.11 0.3101 - 0.1302 Killeen-Temple-Fort Hood, TX - - 0.0476 - 0.0463 0.1305 - 0.0548 Kingsport-Bristol-Bristol, TN-VA - - 0.0219 - 0.0213 0.06 - 0.0252 Kingston, NY - - 0.0367 - 0.0357 0.1006 - 0.0422 Knoxville, TN - - 0.0225 - 0.0218 0.0616 - 0.0259 La Crosse, WI-MN - - 0.017 - 0.0165 0.0467 - 0.0196
249 Lafayette, IN - - 0.0417 - 0.0406 0.1145 - 0.0481 Lafayette, LA - - 0.0097 - 0.0094 0.0265 - 0.0111 Lake Charles, LA - - 0.1494 - 0.1453 0.4099 - 0.1721 Lakeland, FL - - 0.0164 - 0.016 0.0451 - 0.0189 Lancaster, PA - - 0.6256 - 0.6086 1.7166 - 0.7207 Lansing-East Lansing, MI - - 0.033 - 0.0321 0.0905 - 0.038 Laredo, TX - - 0.0465 - 0.0453 0.1277 - 0.0536 Las Cruces, NM - - 0.0689 - 0.067 0.1891 - 0.0794 Las Vegas-Paradise, NV - - 0.0369 - 0.0359 0.1013 - 0.0425 Lawrence, KS - - 0.0497 - 0.0483 0.1363 - 0.0572 Lawton, OK - - 0.0555 - 0.054 0.1524 - 0.064 Lebanon, PA 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Lewiston, ID-WA - - 0.0308 - 0.0299 0.0844 - 0.0354 Lewiston-Auburn, ME - - 0.0053 - 0.0051 0.0145 - 0.0061 Lexington-Fayette, KY - - 0.0105 - 0.0102 0.0289 - 0.0121 Lima, OH - - 0.0101 - 0.0098 0.0276 - 0.0116 Lincoln, NE 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Little Rock-North Little Rock, AR - - 0.0485 - 0.0471 0.133 - 0.0558 Logan, UT-ID - - 0.0081 - 0.0079 0.0222 - 0.0093 Longview, TX - - 0.0802 - 0.078 0.22 - 0.0923 Longview, WA - - 0.2233 - 0.2172 0.6127 - 0.2573 Los Angeles-Long Beach-Santa Ana, CA - - 0.0142 - 0.0138 0.0388 - 0.0163 Louisville, KY-IN - - 0.113 - 0.11 0.3101 - 0.1302 Lubbock, TX - - 0.0476 - 0.0463 0.1305 - 0.0548 Lynchburg, VA - - 0.0219 - 0.0213 0.06 - 0.0252 Macon, GA - - 0.0367 - 0.0357 0.1006 - 0.0422 Madera, CA - - 0.0225 - 0.0218 0.0616 - 0.0259 Madison, WI - - 0.017 - 0.0165 0.0467 - 0.0196 Mansfield, OH - - 0.0417 - 0.0406 0.1145 - 0.0481 McAllen-Edinburg-Pharr, TX - - 0.0097 - 0.0094 0.0265 - 0.0111
250 Medford, OR - - 0.1494 - 0.1453 0.4099 - 0.1721 Memphis, TN-MS-AR - - 0.0164 - 0.016 0.0451 - 0.0189 Merced, CA - - 0.6256 - 0.6086 1.7166 - 0.7207 Miami-Fort Lauderdale-Miami Beach, FL - - 0.033 - 0.0321 0.0905 - 0.038 Michigan City-La Porte, IN - - 0.0465 - 0.0453 0.1277 - 0.0536 Milwaukee-Waukesha-West Allis, WI - - 0.0689 - 0.067 0.1891 - 0.0794 Minneapolis-St. Paul-Bloomington, MN- WI - - 0.0369 - 0.0359 0.1013 - 0.0425 Missoula, MT - - 0.0497 - 0.0483 0.1363 - 0.0572 Mobile, AL - - 0.0555 - 0.054 0.1524 - 0.064 Modesto, CA 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Monroe, LA - - 0.0308 - 0.0299 0.0844 - 0.0354 Montgomery, AL - - 0.0053 - 0.0051 0.0145 - 0.0061 Morgantown, WV - - 0.0105 - 0.0102 0.0289 - 0.0121 Mount Vernon-Anacortes, WA - - 0.0101 - 0.0098 0.0276 - 0.0116 Muncie, IN 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Muskegon-Norton Shores, MI - - 0.0485 - 0.0471 0.133 - 0.0558 Myrtle Beach-Conway-North Myrtle Beach, SC - - 0.0081 - 0.0079 0.0222 - 0.0093 Napa, CA - - 0.0802 - 0.078 0.22 - 0.0923 Naples-Marco Island, FL - - 0.2233 - 0.2172 0.6127 - 0.2573 Nashville-Davidson-Murfreesboro, TN - - 0.0142 - 0.0138 0.0388 - 0.0163 New Orleans-Metairie-Kenner, LA - - 0.113 - 0.11 0.3101 - 0.1302 New York-Northern New Jersey-Long Island, NY-NJ-PA - - 0.0476 - 0.0463 0.1305 - 0.0548 Niles-Benton Harbor, MI - - 0.0219 - 0.0213 0.06 - 0.0252 Ocala, FL - - 0.0367 - 0.0357 0.1006 - 0.0422 Odessa, TX - - 0.0225 - 0.0218 0.0616 - 0.0259 Oklahoma City, OK - - 0.017 - 0.0165 0.0467 - 0.0196 Olympia, WA - - 0.0417 - 0.0406 0.1145 - 0.0481 Omaha-Council Bluffs, NE-IA - - 0.0097 - 0.0094 0.0265 - 0.0111
251 Orlando, FL - - 0.1494 - 0.1453 0.4099 - 0.1721 Oshkosh-Neenah, WI - - 0.0164 - 0.016 0.0451 - 0.0189 Owensboro, KY - - 0.6256 - 0.6086 1.7166 - 0.7207 Oxnard-Thousand Oaks-Ventura, CA - - 0.033 - 0.0321 0.0905 - 0.038 Palm Bay-Melbourne-Titusville, FL - - 0.0465 - 0.0453 0.1277 - 0.0536 Panama City-Lynn Haven, FL - - 0.0689 - 0.067 0.1891 - 0.0794 Parkersburg-Marietta, WV-OH - - 0.0369 - 0.0359 0.1013 - 0.0425 Pensacola-Ferry Pass-Brent, FL - - 0.0497 - 0.0483 0.1363 - 0.0572 Peoria, IL - - 0.0555 - 0.054 0.1524 - 0.064 Philadelphia-Camden-Wilmington, PA-NJ- DE-MD 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Phoenix-Mesa-Scottsdale, AZ - - 0.0308 - 0.0299 0.0844 - 0.0354 Pine Bluff, AR - - 0.0053 - 0.0051 0.0145 - 0.0061 Pittsburgh, PA - - 0.0105 - 0.0102 0.0289 - 0.0121 Pocatello, ID - - 0.0101 - 0.0098 0.0276 - 0.0116 Port St. Lucie-Fort Pierce, FL 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Portland-South Portland-Biddeford, ME - - 0.0485 - 0.0471 0.133 - 0.0558 Portland-Vancouver-Beaverton, OR-WA - - 0.0081 - 0.0079 0.0222 - 0.0093 Poughkeepsie-Newburgh-Middletown, NY - - 0.0802 - 0.078 0.22 - 0.0923 Providence-New Bedford-Fall River, RI- MA - - 0.2233 - 0.2172 0.6127 - 0.2573 Pueblo, CO - - 0.0142 - 0.0138 0.0388 - 0.0163 Racine, WI - - 0.113 - 0.11 0.3101 - 0.1302 Rapid City, SD - - 0.0476 - 0.0463 0.1305 - 0.0548 Reading, PA - - 0.0219 - 0.0213 0.06 - 0.0252 Redding, CA - - 0.0367 - 0.0357 0.1006 - 0.0422 Reno-Sparks, NV - - 0.0225 - 0.0218 0.0616 - 0.0259 Richmond, VA - - 0.017 - 0.0165 0.0467 - 0.0196 Riverside-San Bernardino-Ontario, CA - - 0.0417 - 0.0406 0.1145 - 0.0481 Roanoke, VA - - 0.0097 - 0.0094 0.0265 - 0.0111
252 Rochester, NY - - 0.1494 - 0.1453 0.4099 - 0.1721 Rockford, IL - - 0.0164 - 0.016 0.0451 - 0.0189 Rome, GA - - 0.6256 - 0.6086 1.7166 - 0.7207 Sacramento-Arden-Arcade-Roseville, CA - - 0.033 - 0.0321 0.0905 - 0.038 Saginaw-Saginaw Township North, MI - - 0.0465 - 0.0453 0.1277 - 0.0536 Salem, OR - - 0.0689 - 0.067 0.1891 - 0.0794 Salinas, CA - - 0.0369 - 0.0359 0.1013 - 0.0425 Salisbury, MD - - 0.0497 - 0.0483 0.1363 - 0.0572 Salt Lake City, UT - - 0.0555 - 0.054 0.1524 - 0.064 San Angelo, TX 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 San Antonio, TX - - 0.0308 - 0.0299 0.0844 - 0.0354 San Diego-Carlsbad-San Marcos, CA - - 0.0053 - 0.0051 0.0145 - 0.0061 San Francisco-Oakland-Fremont, CA - - 0.0105 - 0.0102 0.0289 - 0.0121 San Jose-Sunnyvale-Santa Clara, CA - - 0.0101 - 0.0098 0.0276 - 0.0116 San Luis Obispo-Paso Robles, CA 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Santa Barbara-Santa Maria-Goleta, CA - - 0.0485 - 0.0471 0.133 - 0.0558 Santa Cruz-Watsonville, CA - - 0.0081 - 0.0079 0.0222 - 0.0093 Santa Fe, NM - - 0.0802 - 0.078 0.22 - 0.0923 Santa Rosa-Petaluma, CA - - 0.2233 - 0.2172 0.6127 - 0.2573 Savannah, GA - - 0.0142 - 0.0138 0.0388 - 0.0163 Scranton--Wilkes-Barre, PA - - 0.113 - 0.11 0.3101 - 0.1302 Seattle-Tacoma-Bellevue, WA - - 0.0476 - 0.0463 0.1305 - 0.0548 Sebastian-Vero Beach, FL - - 0.0219 - 0.0213 0.06 - 0.0252 Sheboygan, WI - - 0.0367 - 0.0357 0.1006 - 0.0422 Sherman-Denison, TX - - 0.0225 - 0.0218 0.0616 - 0.0259 Shreveport-Bossier City, LA - - 0.017 - 0.0165 0.0467 - 0.0196 Sioux City, IA-NE-SD - - 0.0417 - 0.0406 0.1145 - 0.0481 Sioux Falls, SD - - 0.0097 - 0.0094 0.0265 - 0.0111 South Bend-Mishawaka, IN-MI - - 0.1494 - 0.1453 0.4099 - 0.1721 Spartanburg, SC - - 0.0164 - 0.016 0.0451 - 0.0189
253 Spokane, WA - - 0.6256 - 0.6086 1.7166 - 0.7207 Springfield, IL - - 0.033 - 0.0321 0.0905 - 0.038 Springfield, MO - - 0.0465 - 0.0453 0.1277 - 0.0536 Springfield, OH - - 0.0689 - 0.067 0.1891 - 0.0794 St. Cloud, MN - - 0.0369 - 0.0359 0.1013 - 0.0425 St. George, UT - - 0.0497 - 0.0483 0.1363 - 0.0572 St. Joseph, MO-KS - - 0.0555 - 0.054 0.1524 - 0.064 St. Louis, MO-IL 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 State College, PA - - 0.0308 - 0.0299 0.0844 - 0.0354 Stockton, CA - - 0.0053 - 0.0051 0.0145 - 0.0061 Sumter, SC - - 0.0105 - 0.0102 0.0289 - 0.0121 Syracuse, NY - - 0.0101 - 0.0098 0.0276 - 0.0116 Tallahassee, FL 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Tampa-St. Petersburg-Clearwater, FL - - 0.0485 - 0.0471 0.133 - 0.0558 Terre Haute, IN - - 0.0081 - 0.0079 0.0222 - 0.0093 Texarkana, TX-Texarkana, AR - - 0.0802 - 0.078 0.22 - 0.0923 Topeka, KS - - 0.2233 - 0.2172 0.6127 - 0.2573 Tucson, AZ - - 0.0142 - 0.0138 0.0388 - 0.0163 Tulsa, OK - - 0.113 - 0.11 0.3101 - 0.1302 Tuscaloosa, AL - - 0.0476 - 0.0463 0.1305 - 0.0548 Tyler, TX - - 0.0219 - 0.0213 0.06 - 0.0252 Utica-Rome, NY - - 0.0367 - 0.0357 0.1006 - 0.0422 Vallejo-Fairfield, CA - - 0.0225 - 0.0218 0.0616 - 0.0259 Victoria, TX - - 0.017 - 0.0165 0.0467 - 0.0196 Virginia Beach-Norfolk-Newport News, VA-NC - - 0.0417 - 0.0406 0.1145 - 0.0481 Visalia-Porterville, CA - - 0.0097 - 0.0094 0.0265 - 0.0111 Waco, TX - - 0.1494 - 0.1453 0.4099 - 0.1721 Washington-Arlington-Alexandria, DC-VA- MD-WV - - 0.0164 - 0.016 0.0451 - 0.0189
254 Waterloo-Cedar Falls, IA - - 0.6256 - 0.6086 1.7166 - 0.7207 Wausau, WI - - 0.033 - 0.0321 0.0905 - 0.038 Weirton-Steubenville, WV-OH - - 0.0465 - 0.0453 0.1277 - 0.0536 Wenatchee, WA - - 0.0689 - 0.067 0.1891 - 0.0794 Wheeling, WV-OH - - 0.0369 - 0.0359 0.1013 - 0.0425 Wichita Falls, TX - - 0.0497 - 0.0483 0.1363 - 0.0572 Wichita, KS - - 0.0555 - 0.054 0.1524 - 0.064 Williamsport, PA 0.0131 - 0.0372 0.0361 - 0.0441 0.0027 - 0.0026 0.0074 - 0.0031 Winchester, VA-WV - - 0.0308 - 0.0299 0.0844 - 0.0354 Yakima, WA - - 0.0053 - 0.0051 0.0145 - 0.0061 York-Hanover, PA - - 0.0105 - 0.0102 0.0289 - 0.0121 Youngstown-Warren-Boardman, OH-PA - - 0.0101 - 0.0098 0.0276 - 0.0116 Yuba City, CA 0.0751 - 0.2126 0.206 - 0.2518 0.0121 - 0.0118 0.0333 - 0.014 Yuma, AZ - - 0.0485 - 0.0471 0.133 - 0.0558
255 APPENDIX G: MARGINAL PRODUCTIVITY CHANGES (FOR A 1% INCREASE IN TRANSIT INVESTMENT) TABLE G 1 Average change per 1% change in track miles/track miles per capita Average wage changes Average GDP per capita changes MSA name OLS-emp OLS-pop OLS- total IV-emp IV-pop IV-total OLS-emp OLS-pop OLS- total IV-emp IV-pop IV-total Albuquerque, NM $0.71 $70.09 $70.80 $5.01 $126.11 $131.11 $3.06 $159.47 $162.53 $9.29 $363.54 $372.83 Atlanta-Sandy Springs-Marietta, GA $0.86 $2.55 $3.41 $6.05 $4.59 $10.65 $3.98 $6.26 $10.24 $12.11 $14.27 $26.38 Baltimore-Towson, MD $1.29 $22.25 $23.54 $9.09 $40.03 $49.12 $5.06 $46.13 $51.19 $15.37 $105.16 $120.53 Buffalo-Niagara Falls, NY $0.11 $6.07 $6.18 $0.75 $10.93 $11.68 $0.46 $14.03 $14.49 $1.41 $31.98 $33.39 Charlotte-Gastonia-Concord, NC-SC $0.62 $4.89 $5.50 $4.33 $8.80 $13.13 $3.79 $15.95 $19.75 $11.53 $36.37 $47.90 Chicago-Naperville-Joliet, IL-IN-WI $10.16 $12.69 $22.85 $71.56 $22.83 $94.39 $46.32 $30.61 $76.93 $140.76 $69.78 $210.55 Cleveland-Elyria-Mentor, OH $0.60 $10.88 $11.48 $4.22 $19.58 $23.80 $2.84 $27.23 $30.06 $8.62 $62.07 $70.68 Dallas-Fort Worth-Arlington, TX $2.14 $3.36 $5.50 $15.08 $6.05 $21.13 $10.94 $9.09 $20.03 $33.25 $20.72 $53.96 Denver-Aurora, CO $0.63 $8.71 $9.34 $4.41 $15.68 $20.08 $2.93 $21.58 $24.51 $8.89 $49.19 $58.09 Houston-Baytown-Sugar Land, TX $0.31 $0.76 $1.07 $2.18 $1.38 $3.55 $1.75 $2.29 $4.04 $5.32 $5.22 $10.54 Little Rock-North Little Rock, AR $0.08 $9.19 $9.28 $0.57 $16.54 $17.11 $0.35 $21.19 $21.54 $1.07 $48.30 $49.37 Los Angeles-Long Beach-Santa Ana, CA $6.39 $4.67 $11.06 $45.00 $8.40 $53.40 $31.07 $12.01 $43.08 $94.43 $27.38 $121.82 Memphis, TN-MS-AR $0.18 $5.48 $5.66 $1.27 $9.86 $11.13 $0.86 $13.93 $14.79 $2.63 $31.75 $34.38 Miami-Fort Lauderdale-Miami Beach, FL $1.22 $4.26 $5.48 $8.59 $7.67 $16.26 $5.94 $10.99 $16.92 $18.04 $25.05 $43.09 Minneapolis-St. Paul-Bloomington, MN-WI $0.21 $1.89 $2.10 $1.51 $3.39 $4.90 $0.92 $4.32 $5.24 $2.81 $9.84 $12.65 Nashville-Davidson--Murfreesboro, TN $0.90 $17.96 $18.86 $6.35 $32.31 $38.67 $4.22 $44.47 $48.69 $12.83 $101.37 $114.20 New Orleans-Metairie-Kenner, LA $0.26 $13.18 $13.44 $1.81 $23.72 $25.53 $1.57 $42.56 $44.14 $4.77 $97.03 $101.80 New York-Northern New Jersey-Long Island, NY- NJ-PA $8.96 $7.32 $16.28 $63.12 $13.17 $76.29 $40.26 $17.40 $57.66 $122.35 $39.67 $162.02 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $3.87 $15.52 $19.39 $27.26 $27.93 $55.19 $18.72 $39.73 $58.44 $56.88 $90.56 $147.44 Pittsburgh, PA $0.23 $5.43 $5.66 $1.59 $9.77 $11.36 $1.10 $14.02 $15.12 $3.34 $31.97 $35.31 Portland-Vancouver-Beaverton, OR-WA $0.80 $14.34 $15.14 $5.67 $25.80 $31.47 $3.74 $35.30 $39.05 $11.37 $80.48 $91.86 Providence-New Bedford-Fall River, RI-MA $0.11 $3.49 $3.60 $0.76 $6.29 $7.05 $0.49 $8.27 $8.75 $1.48 $18.85 $20.32 Sacramento--Arden-Arcade--Roseville, CA $0.73 $11.79 $12.52 $5.17 $21.21 $26.38 $2.81 $23.90 $26.71 $8.55 $54.48 $63.03
256 St. Louis, MO-IL $0.67 $8.23 $8.90 $4.71 $14.80 $19.51 $2.89 $18.80 $21.69 $8.78 $42.87 $51.64 Salt Lake City, UT $0.22 $20.17 $20.39 $1.55 $36.29 $37.84 $1.02 $49.49 $50.51 $3.11 $112.81 $115.92 San Diego-Carlsbad-San Marcos, CA $1.73 $15.19 $16.92 $12.20 $27.33 $39.53 $7.81 $36.23 $44.04 $23.74 $82.59 $106.33 San Francisco-Oakland-Fremont, CA $2.78 $21.01 $23.80 $19.60 $37.81 $57.41 $12.40 $49.57 $61.98 $37.70 $113.01 $150.71 San Jose-Sunnyvale-Santa Clara, CA $4.00 $123.39 $127.39 $28.20 $222.02 $250.21 $16.44 $268.09 $284.53 $49.95 $611.15 $661.11 Seattle-Tacoma-Bellevue, WA $1.30 $13.30 $14.60 $9.17 $23.92 $33.10 $5.83 $31.48 $37.31 $17.71 $71.77 $89.48 Tampa-St. Petersburg-Clearwater, FL $0.04 $0.41 $0.45 $0.26 $0.74 $1.00 $0.17 $0.98 $1.15 $0.51 $2.23 $2.74 Trenton-Ewing, NJ $0.13 $94.13 $94.26 $0.94 $169.36 $170.30 $0.50 $185.19 $185.68 $1.51 $422.16 $423.67 Washington-Arlington-Alexandria, DC-VA-MD-WV $3.45 $20.82 $24.26 $24.28 $37.45 $61.73 $13.76 $43.98 $57.74 $41.83 $100.25 $142.08
257 TABLE G 2 Average change per 1 mile change in track miles/track miles per capita Average wage changes Average GDP per capita changes MSA name percent associat ed with 1 mile change OLS-emp OLS-pop OLS-total IV-emp IV-pop IV-total OLS-emp OLS-pop OLS-total IV-emp IV-pop IV-total Albuquerque, NM 2.38% $1.69 $166.87 $168.56 $11.92 $300.25 $312.17 $7.28 $379.70 $386.98 $22.12 $865.58 $887.70 Atlanta-Sandy Springs-Marietta, GA 2.02% $1.74 $5.16 $6.89 $12.23 $9.28 $21.51 $8.05 $12.65 $20.69 $24.46 $28.83 $53.29 Baltimore-Towson, MD 0.93% $1.21 $20.79 $22.00 $8.50 $37.41 $45.90 $4.73 $43.11 $47.84 $14.36 $98.28 $112.65 Buffalo-Niagara Falls, NY 15.63% $1.66 $94.89 $96.56 $11.72 $170.74 $182.46 $7.26 $219.17 $226.43 $22.07 $499.63 $521.70 Charlotte-Gastonia-Concord, NC-SC 10.42% $6.41 $50.93 $57.34 $45.14 $91.63 $136.78 $39.52 $166.18 $205.71 $120.12 $378.84 $498.96 Chicago-Naperville-Joliet, IL-IN-WI 0.14% $1.47 $1.84 $3.31 $10.35 $3.30 $13.65 $6.70 $4.43 $11.13 $20.36 $10.09 $30.46 Cleveland-Elyria-Mentor, OH 2.90% $1.74 $31.54 $33.27 $12.24 $56.74 $68.99 $8.22 $78.92 $87.14 $24.98 $179.90 $204.88 Dallas-Fort Worth-Arlington, TX 1.18% $2.53 $3.97 $6.51 $17.83 $7.15 $24.98 $12.93 $10.74 $23.67 $39.30 $24.49 $63.79 Denver-Aurora, CO 2.89% $1.81 $25.19 $26.99 $12.73 $45.32 $58.05 $8.46 $62.37 $70.82 $25.71 $142.17 $167.88 Houston-Baytown-Sugar Land, TX 6.99% $2.16 $5.35 $7.51 $15.22 $9.62 $24.84 $12.24 $16.02 $28.26 $37.19 $36.53 $73.72 Little Rock-North Little Rock, AR 29.41% $2.37 $270.44 $272.81 $16.71 $486.60 $503.31 $10.33 $623.14 $633.47 $31.40 $1,420.53 $1,451.93 Los Angeles-Long Beach-Santa Ana, CA 0.21% $1.33 $0.97 $2.30 $9.35 $1.75 $11.10 $6.46 $2.50 $8.95 $19.62 $5.69 $25.31 Memphis, TN-MS-AR 14.29% $2.57 $78.32 $80.89 $18.11 $140.92 $159.03 $12.34 $198.99 $211.33 $37.52 $453.62 $491.14 Miami-Fort Lauderdale-Miami Beach, FL 1.04% $1.27 $4.45 $5.73 $8.97 $8.01 $16.98 $6.20 $11.47 $17.67 $18.83 $26.14 $44.98 Minneapolis-St. Paul-Bloomington, MN-WI 8.26% $1.77 $15.59 $17.36 $12.44 $28.05 $40.50 $7.64 $35.67 $43.31 $23.21 $81.33 $104.54 Nashville-Davidson--Murfreesboro, TN 3.13% $2.82 $56.12 $58.94 $19.86 $100.98 $120.84 $13.19 $138.96 $152.15 $40.09 $316.78 $356.86 New Orleans-Metairie-Kenner, LA 7.75% $2.00 $102.19 $104.19 $14.05 $183.88 $197.93 $12.17 $329.96 $342.13 $37.00 $752.19 $789.19 New York-Northern New Jersey-Long Island, NY-NJ-PA 0.08% $0.72 $0.59 $1.31 $5.08 $1.06 $6.14 $3.24 $1.40 $4.64 $9.84 $3.19 $13.03 Philadelphia-Camden-Wilmington, PA- NJ-DE-MD 0.30% $1.15 $4.62 $5.77 $8.11 $8.31 $16.41 $5.57 $11.82 $17.38 $16.92 $26.94 $43.86 Pittsburgh, PA 4.55% $1.02 $24.68 $25.71 $7.21 $44.41 $51.62 $4.99 $63.74 $68.74 $15.18 $145.31 $160.49 Portland-Vancouver-Beaverton, OR- WA 2.07% $1.66 $29.63 $31.29 $11.71 $53.31 $65.02 $7.73 $72.94 $80.68 $23.50 $166.28 $189.78 Providence-New Bedford-Fall River, RI-MA 14.71% $1.60 $51.37 $52.97 $11.24 $92.43 $103.67 $7.14 $121.60 $128.74 $21.70 $277.20 $298.89
258 Sacramento--Arden-Arcade--Roseville, CA 2.71% $1.99 $31.94 $33.93 $14.02 $57.47 $71.49 $7.63 $64.76 $72.39 $23.18 $147.64 $170.82 St. Louis, MO-IL 2.18% $1.46 $17.92 $19.38 $10.26 $32.25 $42.51 $6.29 $40.97 $47.26 $19.12 $93.39 $112.51 Salt Lake City, UT 5.26% $1.16 $106.15 $107.31 $8.17 $190.99 $199.16 $5.38 $260.45 $265.83 $16.35 $593.73 $610.08 San Diego-Carlsbad-San Marcos, CA 1.08% $1.87 $16.35 $18.21 $13.14 $29.42 $42.55 $8.41 $39.00 $47.41 $25.55 $88.90 $114.45 San Francisco-Oakland-Fremont, CA 0.54% $1.49 $11.27 $12.76 $10.51 $20.27 $30.78 $6.65 $26.58 $33.23 $20.21 $60.59 $80.81 San Jose-Sunnyvale-Santa Clara, CA 0.63% $2.53 $77.90 $80.43 $17.80 $140.16 $157.96 $10.38 $169.25 $179.63 $31.54 $385.83 $417.36 Seattle-Tacoma-Bellevue, WA 1.15% $1.50 $15.32 $16.82 $10.57 $27.56 $38.13 $6.71 $36.27 $42.98 $20.41 $82.68 $103.09 Tampa-St. Petersburg-Clearwater, FL 41.67% $1.55 $17.07 $18.61 $10.88 $30.71 $41.59 $6.98 $40.80 $47.78 $21.21 $93.01 $114.22 Trenton-Ewing, NJ 14.49% $1.93 $1,364.14 $1,366.07 $13.61 $2,454.49 $2,468.10 $7.18 $2,683.88 $2,691.06 $21.83 $6,118.29 $6,140.12 Washington-Arlington-Alexandria, DC- VA-MD-WV 0.31% $1.05 $6.37 $7.42 $7.43 $11.45 $18.88 $4.21 $13.45 $17.66 $12.79 $30.66 $43.45 TABLE G 3 Average change per 1% change in rail revenue miles Average wage changes Average GDP per capita changes MSA name OLS- emp OLS-pop OLS- total IV-emp IV-pop IV-total OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total Atlanta-Sandy Springs-Marietta, GA $2.35 $5.70 $8.05 $8.00 $13.06 $21.06 $10.86 $26.42 $37.29 $16.00 $26.10 $42.10 Baltimore-Towson, MD $1.33 $9.31 $10.63 $4.52 $21.30 $25.82 $5.19 $36.45 $41.65 $7.65 $36.01 $43.66 Buffalo-Niagara Falls, NY $0.20 $1.91 $2.10 $0.67 $4.36 $5.03 $0.85 $8.31 $9.17 $1.26 $8.21 $9.47 Charlotte-Gastonia-Concord, NC-SC $0.69 $1.43 $2.12 $2.36 $3.26 $5.63 $4.26 $8.79 $13.06 $6.28 $8.69 $14.97 Chicago-Naperville-Joliet, IL-IN-WI $18.79 $33.97 $52.77 $64.14 $77.75 $141.89 $85.66 $154.83 $240.49 $126.17 $152.95 $279.12 Cleveland-Elyria-Mentor, OH $1.06 $6.14 $7.20 $3.63 $14.06 $17.68 $5.03 $29.03 $34.06 $7.40 $28.68 $36.08 Dallas-Fort Worth-Arlington, TX $2.41 $3.63 $6.05 $8.24 $8.31 $16.55 $12.33 $18.54 $30.87 $18.15 $18.32 $36.47 Denver-Aurora, CO $2.07 $10.98 $13.06 $7.07 $25.14 $32.21 $9.70 $51.38 $61.08 $14.28 $50.76 $65.04 Houston-Baytown-Sugar Land, TX $0.52 $1.11 $1.63 $1.76 $2.55 $4.31 $2.93 $6.31 $9.23 $4.31 $6.23 $10.54 Little Rock-North Little Rock, AR $0.04 $0.46 $0.50 $0.13 $1.05 $1.18 $0.17 $1.99 $2.16 $0.25 $1.97 $2.22 Los Angeles-Long Beach-Santa Ana, CA $13.10 $18.59 $31.70 $44.72 $42.55 $87.27 $63.71 $90.38 $154.09 $93.83 $89.29 $183.12 Memphis, TN-MS-AR $0.36 $2.15 $2.51 $1.22 $4.92 $6.14 $1.71 $10.31 $12.03 $2.53 $10.19 $12.71 Miami-Fort Lauderdale-Miami Beach, FL $0.90 $2.63 $3.53 $3.07 $6.02 $9.09 $4.37 $12.80 $17.17 $6.44 $12.64 $19.09 Minneapolis-St. Paul-Bloomington, MN-WI $0.57 $2.49 $3.07 $1.96 $5.71 $7.67 $2.48 $10.78 $13.26 $3.65 $10.65 $14.30 Nashville-Davidson--Murfreesboro, TN $0.08 $0.39 $0.48 $0.29 $0.90 $1.19 $0.39 $1.85 $2.24 $0.58 $1.82 $2.40
259 New Orleans-Metairie-Kenner, LA $0.47 $4.27 $4.74 $1.60 $9.77 $11.37 $2.86 $26.02 $28.88 $4.21 $25.71 $29.92 New York-Northern New Jersey-Long Island, NY-NJ-PA $15.73 $37.07 $52.80 $53.68 $84.84 $138.52 $70.65 $166.47 $237.12 $104.06 $164.45 $268.51 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $5.22 $18.90 $24.12 $17.80 $43.27 $61.07 $25.22 $91.40 $116.62 $37.15 $90.29 $127.44 Pittsburgh, PA $0.39 $3.34 $3.73 $1.32 $7.65 $8.97 $1.89 $16.30 $18.19 $2.78 $16.10 $18.88 Portland-Vancouver-Beaverton, OR-WA $1.98 $11.83 $13.81 $6.76 $27.08 $33.84 $9.21 $55.02 $64.24 $13.57 $54.36 $67.92 Sacramento--Arden-Arcade--Roseville, CA $0.99 $5.10 $6.10 $3.40 $11.68 $15.07 $3.81 $19.54 $23.35 $5.61 $19.30 $24.92 St. Louis, MO-IL $1.53 $8.07 $9.60 $5.22 $18.46 $23.68 $6.60 $34.84 $41.44 $9.73 $34.41 $44.14 Salt Lake City, UT $0.59 $9.06 $9.64 $2.00 $20.73 $22.73 $2.72 $41.99 $44.70 $4.00 $41.48 $45.48 San Diego-Carlsbad-San Marcos, CA $2.12 $8.53 $10.65 $7.24 $19.53 $26.77 $9.55 $38.46 $48.01 $14.07 $37.99 $52.06 San Francisco-Oakland-Fremont, CA $15.42 $75.46 $90.88 $52.63 $172.72 $225.35 $68.73 $336.30 $405.03 $101.23 $332.23 $433.45 San Jose-Sunnyvale-Santa Clara, CA $1.66 $14.06 $15.71 $5.65 $32.18 $37.83 $6.80 $57.70 $64.50 $10.01 $57.00 $67.01 Seattle-Tacoma-Bellevue, WA $0.16 $0.83 $0.99 $0.54 $1.90 $2.45 $0.71 $3.72 $4.44 $1.05 $3.68 $4.73 Tampa-St. Petersburg-Clearwater, FL $0.04 $0.18 $0.22 $0.13 $0.41 $0.55 $0.18 $0.81 $0.99 $0.26 $0.80 $1.06 Washington-Arlington-Alexandria, DC-VA-MD-WV $5.33 $26.34 $31.68 $18.20 $60.30 $78.50 $21.29 $105.14 $126.43 $31.36 $103.87 $135.22
260 TABLE G 4 Average change per 1% change in total revenue miles Average wage changes Average GDP per capita changes name OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total Abilene, TX $0.16 $2.72 $2.88 $0.65 $5.62 $6.27 $0.64 $10.74 $11.38 $1.10 $9.57 $10.67 Albany, GA $0.25 $3.29 $3.53 $0.99 $6.78 $7.77 $1.01 $13.55 $14.56 $1.76 $12.07 $13.83 Albany-Schenectady-Troy, NY $0.92 $11.42 $12.34 $3.71 $23.57 $27.28 $3.62 $44.81 $48.43 $6.28 $39.93 $46.20 Albuquerque, NM $1.00 $7.27 $8.27 $4.02 $15.01 $19.03 $4.30 $31.25 $35.55 $7.45 $27.85 $35.30 Alexandria, LA $0.15 $3.56 $3.71 $0.61 $7.35 $7.96 $0.64 $15.04 $15.68 $1.11 $13.40 $14.51 Allentown-Bethlehem-Easton, PA-NJ $0.49 $4.07 $4.56 $1.96 $8.41 $10.37 $2.03 $16.94 $18.97 $3.52 $15.09 $18.61 Altoona, PA $0.06 $3.68 $3.74 $0.26 $7.59 $7.85 $0.28 $16.09 $16.37 $0.49 $14.34 $14.83 Amarillo, TX $0.23 $3.12 $3.35 $0.92 $6.44 $7.36 $1.00 $13.61 $14.61 $1.74 $12.12 $13.86 Ames, IA $0.25 $13.16 $13.41 $1.01 $27.17 $28.18 $1.04 $54.31 $55.35 $1.80 $48.39 $50.19 Anchorage, AK $0.97 $10.48 $11.45 $3.89 $21.64 $25.52 $4.75 $51.47 $56.22 $8.24 $45.87 $54.10 Anderson, IN $0.17 $2.24 $2.41 $0.69 $4.62 $5.31 $0.85 $11.05 $11.90 $1.47 $9.85 $11.32 Ann Arbor, MI $0.55 $15.96 $16.50 $2.19 $32.94 $35.13 $1.68 $49.24 $50.92 $2.92 $43.88 $46.79 Appleton, WI $0.18 $5.92 $6.10 $0.71 $12.22 $12.93 $0.73 $24.59 $25.32 $1.27 $21.91 $23.18 Athens-Clarke County, GA $0.31 $4.23 $4.54 $1.23 $8.74 $9.97 $1.08 $14.88 $15.96 $1.87 $13.26 $15.13 Atlanta-Sandy Springs-Marietta, GA $8.18 $11.40 $19.57 $32.85 $23.52 $56.38 $37.88 $52.78 $90.66 $65.69 $47.03 $112.72 Auburn-Opelika, AL $0.03 $0.54 $0.57 $0.12 $1.12 $1.24 $0.11 $2.06 $2.17 $0.20 $1.84 $2.03 Augusta-Richmond County, GA-SC $0.32 $1.26 $1.58 $1.30 $2.60 $3.90 $1.12 $4.36 $5.48 $1.94 $3.89 $5.82 Bakersfield, CA $0.93 $5.54 $6.47 $3.73 $11.44 $15.17 $4.17 $24.91 $29.08 $7.23 $22.19 $29.42 Bangor, ME $0.13 $3.99 $4.12 $0.51 $8.23 $8.75 $0.50 $15.70 $16.20 $0.87 $13.99 $14.86 Baton Rouge, LA $0.58 $3.92 $4.50 $2.33 $8.09 $10.42 $2.92 $19.75 $22.66 $5.06 $17.60 $22.65 Battle Creek, MI $0.16 $3.71 $3.87 $0.66 $7.65 $8.31 $0.69 $15.70 $16.40 $1.20 $13.99 $15.20 Bay City, MI $0.33 $11.66 $12.00 $1.34 $24.08 $25.42 $1.24 $43.27 $44.51 $2.15 $38.56 $40.71 Beaumont-Port Arthur, TX $0.40 $3.40 $3.79 $1.60 $7.01 $8.61 $1.68 $14.34 $16.01 $2.91 $12.78 $15.68 Bellingham, WA $0.48 $11.80 $12.28 $1.93 $24.35 $26.28 $2.03 $49.92 $51.95 $3.53 $44.48 $48.01 Bend, OR $0.05 $1.37 $1.43 $0.22 $2.84 $3.06 $0.26 $6.47 $6.73 $0.44 $5.77 $6.21 Billings, MT $0.17 $4.41 $4.58 $0.68 $9.11 $9.79 $0.75 $19.44 $20.18 $1.29 $17.32 $18.61 Binghamton, NY $0.27 $9.26 $9.54 $1.10 $19.12 $20.22 $0.91 $30.90 $31.81 $1.58 $27.53 $29.11
261 Birmingham-Hoover, AL $0.93 $3.45 $4.38 $3.74 $7.12 $10.86 $4.43 $16.40 $20.83 $7.68 $14.62 $22.30 Bismarck, ND $0.07 $3.37 $3.44 $0.26 $6.96 $7.22 $0.25 $12.67 $12.92 $0.43 $11.29 $11.72 Blacksburg-Christiansburg-Radford, VA $0.26 $4.94 $5.20 $1.06 $10.19 $11.25 $0.99 $18.48 $19.47 $1.72 $16.47 $18.19 Bloomington, IN $0.20 $5.63 $5.83 $0.79 $11.62 $12.42 $0.75 $21.34 $22.09 $1.30 $19.01 $20.31 Bloomington-Normal, IL $0.30 $8.96 $9.26 $1.21 $18.50 $19.70 $1.36 $40.61 $41.97 $2.36 $36.19 $38.55 Boise City-Nampa, ID $0.33 $2.83 $3.15 $1.31 $5.83 $7.15 $1.39 $11.98 $13.37 $2.40 $10.68 $13.08 Bradenton-Sarasota-Venice, FL $0.67 $6.04 $6.71 $2.69 $12.48 $15.17 $3.08 $27.81 $30.89 $5.34 $24.78 $30.12 Bremerton-Silverdale, WA $0.61 $11.20 $11.81 $2.46 $23.13 $25.59 $1.88 $34.29 $36.16 $3.26 $30.55 $33.81 Brownsville-Harlingen, TX $0.18 $1.61 $1.79 $0.73 $3.32 $4.05 $0.72 $6.40 $7.13 $1.26 $5.71 $6.96 Brunswick, GA $2.31 $98.80 $101.11 $9.29 $203.97 $213.25 $8.95 $382.35 $391.29 $15.52 $340.69 $356.21 Canton-Massillon, OH $0.76 $6.79 $7.54 $3.03 $14.01 $17.04 $3.37 $30.29 $33.67 $5.85 $26.99 $32.84 Cape Coral-Fort Myers, FL $1.18 $5.71 $6.90 $4.76 $11.79 $16.55 $6.04 $29.15 $35.19 $10.48 $25.97 $36.46 Casper, WY $0.09 $3.88 $3.98 $0.37 $8.02 $8.39 $0.69 $28.91 $29.61 $1.20 $25.76 $26.97 Cedar Rapids, IA $0.29 $5.43 $5.72 $1.18 $11.21 $12.39 $1.34 $24.81 $26.15 $2.32 $22.11 $24.42 Champaign-Urbana, IL $0.67 $17.45 $18.12 $2.68 $36.03 $38.71 $2.42 $63.39 $65.81 $4.20 $56.48 $60.68 Charleston, WV $0.48 $9.90 $10.37 $1.91 $20.43 $22.35 $2.36 $48.95 $51.30 $4.09 $43.62 $47.70 Charleston-North Charleston, SC $0.85 $4.97 $5.82 $3.42 $10.26 $13.68 $3.46 $20.20 $23.66 $6.00 $18.00 $24.00 Charlotte-Gastonia-Concord, NC-SC $9.04 $10.68 $19.72 $36.32 $22.04 $58.36 $55.72 $65.82 $121.54 $96.63 $58.65 $155.28 Chattanooga, TN-GA $0.63 $4.33 $4.95 $2.52 $8.93 $11.45 $2.90 $19.99 $22.89 $5.02 $17.81 $22.84 Cheyenne, WY $0.10 $4.95 $5.04 $0.39 $10.21 $10.60 $0.38 $19.29 $19.68 $0.66 $17.19 $17.85 Chicago-Naperville-Joliet, IL-IN-WI $22.78 $23.59 $46.37 $91.53 $48.69 $140.22 $103.82 $107.50 $211.32 $180.05 $95.79 $275.84 Chico, CA $0.26 $4.72 $4.98 $1.04 $9.75 $10.79 $1.10 $20.10 $21.20 $1.90 $17.91 $19.82 Cincinnati-Middletown, OH-KY-IN $2.95 $9.26 $12.21 $11.86 $19.12 $30.97 $13.19 $41.39 $54.58 $22.88 $36.88 $59.76 Clarksville, TN-KY $0.58 $3.07 $3.65 $2.35 $6.33 $8.68 $1.93 $10.14 $12.07 $3.35 $9.03 $12.38 Cleveland-Elyria-Mentor, OH $5.47 $18.11 $23.58 $21.98 $37.38 $59.36 $25.86 $85.59 $111.45 $44.85 $76.26 $121.11 College Station-Bryan, TX $0.70 $9.09 $9.80 $2.82 $18.77 $21.59 $2.44 $31.59 $34.03 $4.23 $28.15 $32.38 Columbia, MO $0.17 $3.88 $4.04 $0.67 $8.00 $8.68 $0.53 $12.31 $12.84 $0.92 $10.97 $11.89 Columbia, SC $0.31 $2.50 $2.81 $1.23 $5.17 $6.39 $1.20 $9.80 $11.00 $2.08 $8.73 $10.81 Columbus, GA-AL $0.36 $3.58 $3.94 $1.46 $7.39 $8.84 $1.29 $12.71 $14.00 $2.24 $11.32 $13.56 Columbus, OH $1.89 $6.08 $7.97 $7.60 $12.55 $20.15 $8.16 $26.22 $34.38 $14.16 $23.37 $37.52 Corpus Christi, TX $0.93 $7.80 $8.73 $3.75 $16.09 $19.85 $4.08 $34.01 $38.09 $7.07 $30.30 $37.37
262 Cumberland, MD-WV $0.07 $2.61 $2.68 $0.27 $5.39 $5.65 $0.25 $9.64 $9.89 $0.43 $8.59 $9.02 Dallas-Fort Worth-Arlington, TX $10.77 $9.28 $20.05 $43.27 $19.16 $62.43 $54.99 $47.39 $102.38 $95.36 $42.23 $137.59 Davenport-Moline-Rock Island, IA-IL $1.02 $10.87 $11.89 $4.12 $22.43 $26.55 $4.67 $49.54 $54.21 $8.10 $44.15 $52.25 Dayton, OH $1.50 $8.85 $10.35 $6.04 $18.27 $24.30 $6.12 $36.05 $42.17 $10.61 $32.12 $42.74 Decatur, AL $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 Decatur, IL $0.39 $11.01 $11.40 $1.56 $22.72 $24.29 $2.01 $56.88 $58.90 $3.49 $50.69 $54.18 Deltona-Daytona Beach-Ormond Beach, FL $1.02 $4.84 $5.86 $4.09 $9.99 $14.08 $4.89 $23.25 $28.14 $8.47 $20.72 $29.19 Denver-Aurora, CO $9.67 $29.35 $39.02 $38.85 $60.59 $99.45 $45.23 $137.30 $182.53 $78.45 $122.34 $200.79 Des Moines, IA $0.54 $5.91 $6.44 $2.16 $12.19 $14.35 $2.71 $29.76 $32.46 $4.69 $26.52 $31.21 Detroit-Warren-Livonia, MI $6.90 $9.40 $16.29 $27.71 $19.40 $47.11 $31.20 $42.53 $73.73 $54.11 $37.90 $92.01 Dothan, AL $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 Dubuque, IA $0.08 $3.65 $3.73 $0.31 $7.54 $7.86 $0.39 $18.17 $18.56 $0.67 $16.19 $16.86 Duluth, MN-WI $0.58 $7.38 $7.97 $2.34 $15.25 $17.59 $2.36 $29.91 $32.27 $4.09 $26.65 $30.74 Eau Claire, WI $0.17 $4.82 $4.99 $0.69 $9.95 $10.64 $0.69 $19.59 $20.28 $1.20 $17.45 $18.66 El Centro, CA $0.11 $3.56 $3.67 $0.46 $7.35 $7.81 $0.46 $14.49 $14.96 $0.81 $12.91 $13.72 El Paso, TX $1.22 $8.23 $9.46 $4.92 $17.00 $21.92 $6.49 $43.61 $50.10 $11.25 $38.86 $50.11 Elkhart-Goshen, IN $0.11 $2.37 $2.48 $0.44 $4.89 $5.32 $0.55 $11.89 $12.44 $0.95 $10.60 $11.55 Elmira, NY $0.12 $9.30 $9.42 $0.47 $19.21 $19.68 $0.45 $35.79 $36.25 $0.78 $31.89 $32.68 Erie, PA $0.25 $6.61 $6.86 $1.02 $13.64 $14.66 $1.09 $28.31 $29.40 $1.89 $25.23 $27.12 Eugene-Springfield, OR $0.76 $10.62 $11.38 $3.07 $21.92 $24.99 $3.00 $41.68 $44.68 $5.20 $37.14 $42.34 Evansville, IN-KY $0.26 $4.08 $4.34 $1.04 $8.43 $9.47 $1.38 $21.85 $23.24 $2.40 $19.47 $21.87 Fairbanks, AK $0.19 $5.68 $5.87 $0.76 $11.73 $12.49 $0.60 $18.00 $18.59 $1.03 $16.04 $17.07 Fayetteville-Springdale-Rogers, AR-MO $0.23 $1.71 $1.94 $0.93 $3.54 $4.47 $1.00 $7.42 $8.42 $1.74 $6.61 $8.35 Flagstaff, AZ $0.18 $5.41 $5.59 $0.73 $11.17 $11.89 $0.61 $18.28 $18.89 $1.06 $16.28 $17.34 Flint, MI $0.99 $9.22 $10.21 $3.96 $19.03 $23.00 $3.65 $34.15 $37.80 $6.34 $30.43 $36.77 Florence, SC $0.06 $0.84 $0.89 $0.23 $1.73 $1.96 $0.23 $3.27 $3.50 $0.39 $2.92 $3.31 Fond du Lac, WI $0.03 $1.94 $1.98 $0.14 $4.01 $4.15 $0.16 $8.93 $9.08 $0.27 $7.95 $8.23 Fort Collins-Loveland, CO $0.30 $3.94 $4.24 $1.21 $8.13 $9.34 $1.08 $14.11 $15.19 $1.87 $12.57 $14.44 Fort Smith, AR-OK $0.07 $1.03 $1.10 $0.29 $2.12 $2.40 $0.34 $4.92 $5.26 $0.59 $4.39 $4.98 Fort Walton Beach-Crestview-Destin, FL $0.11 $2.69 $2.80 $0.44 $5.55 $5.99 $0.48 $11.69 $12.17 $0.83 $10.42 $11.25 Fort Wayne, IN $0.55 $4.47 $5.01 $2.20 $9.22 $11.42 $2.50 $20.43 $22.93 $4.34 $18.20 $22.54
263 Fresno, CA $1.08 $6.17 $7.25 $4.33 $12.74 $17.07 $4.50 $25.75 $30.24 $7.80 $22.94 $30.74 Gainesville, FL $0.94 $12.28 $13.22 $3.78 $25.36 $29.14 $3.12 $40.65 $43.77 $5.40 $36.22 $41.63 Glens Falls, NY $0.04 $2.68 $2.71 $0.14 $5.53 $5.67 $0.13 $9.74 $9.87 $0.23 $8.68 $8.91 Grand Forks, ND-MN $0.08 $3.95 $4.04 $0.34 $8.16 $8.50 $0.31 $14.43 $14.74 $0.53 $12.86 $13.39 Grand Junction, CO $0.29 $6.32 $6.62 $1.18 $13.06 $14.24 $1.13 $24.35 $25.48 $1.96 $21.70 $23.66 Grand Rapids-Wyoming, MI $0.78 $6.83 $7.60 $3.12 $14.09 $17.21 $3.54 $31.06 $34.59 $6.13 $27.67 $33.80 Great Falls, MT $0.11 $5.25 $5.36 $0.46 $10.83 $11.29 $0.43 $20.11 $20.54 $0.75 $17.92 $18.67 Greeley, CO $0.11 $2.03 $2.14 $0.45 $4.19 $4.64 $0.43 $7.76 $8.19 $0.75 $6.91 $7.66 Green Bay, WI $0.42 $5.68 $6.10 $1.69 $11.72 $13.41 $1.86 $25.11 $26.97 $3.22 $22.37 $25.59 Greenville, SC $0.13 $1.11 $1.24 $0.52 $2.30 $2.82 $0.54 $4.63 $5.16 $0.93 $4.12 $5.05 Hagerstown-Martinsburg, MD-WV $0.08 $1.71 $1.79 $0.32 $3.53 $3.85 $0.33 $7.22 $7.55 $0.58 $6.43 $7.01 Hanford-Corcoran, CA $0.30 $5.76 $6.06 $1.21 $11.89 $13.11 $1.07 $20.39 $21.46 $1.86 $18.17 $20.03 Harrisburg-Carlisle, PA $0.14 $4.32 $4.46 $0.55 $8.92 $9.47 $0.56 $17.52 $18.08 $0.96 $15.61 $16.58 Holland-Grand Haven, MI $0.07 $1.41 $1.49 $0.30 $2.92 $3.21 $0.35 $6.76 $7.12 $0.61 $6.02 $6.64 Honolulu, HI $2.38 $25.74 $28.12 $9.56 $53.14 $62.70 $9.79 $105.90 $115.69 $16.98 $94.37 $111.34 Houston-Baytown-Sugar Land, TX $10.25 $12.65 $22.90 $41.19 $26.12 $67.31 $58.03 $71.64 $129.67 $100.64 $63.83 $164.48 Huntington-Ashland, WV-KY-OH $0.16 $3.15 $3.31 $0.66 $6.50 $7.16 $0.73 $14.00 $14.73 $1.26 $12.47 $13.73 Huntsville, AL $0.33 $2.18 $2.51 $1.32 $4.50 $5.82 $1.18 $7.83 $9.01 $2.04 $6.98 $9.02 Indianapolis, IN $2.01 $5.57 $7.59 $8.09 $11.50 $19.60 $10.37 $28.68 $39.05 $17.98 $25.55 $43.53 Iowa City, IA $0.28 $11.32 $11.60 $1.13 $23.36 $24.49 $0.99 $39.87 $40.87 $1.72 $35.53 $37.25 Ithaca, NY $0.21 $18.32 $18.53 $0.86 $37.82 $38.68 $0.86 $73.54 $74.40 $1.49 $65.53 $67.02 Jackson, MI $0.06 $2.79 $2.85 $0.23 $5.77 $6.00 $0.24 $11.52 $11.76 $0.41 $10.27 $10.68 Jackson, MS $0.32 $2.09 $2.40 $1.27 $4.31 $5.58 $1.43 $9.42 $10.85 $2.48 $8.39 $10.87 Jackson, TN $0.18 $5.52 $5.70 $0.72 $11.40 $12.12 $0.74 $22.74 $23.47 $1.28 $20.26 $21.54 Jacksonville, FL $3.45 $9.87 $13.31 $13.84 $20.37 $34.21 $15.37 $44.01 $59.38 $26.65 $39.21 $65.87 Janesville, WI $0.42 $9.48 $9.90 $1.69 $19.57 $21.26 $1.66 $37.32 $38.98 $2.87 $33.26 $36.13 Jefferson City, MO $0.09 $2.95 $3.03 $0.34 $6.08 $6.42 $0.28 $9.55 $9.82 $0.48 $8.51 $8.99 Johnson City, TN $0.14 $2.11 $2.25 $0.58 $4.36 $4.93 $0.61 $8.92 $9.53 $1.06 $7.95 $9.01 Johnstown, PA $0.10 $4.73 $4.83 $0.40 $9.76 $10.16 $0.40 $18.69 $19.09 $0.69 $16.65 $17.34 Kalamazoo-Portage, MI $0.49 $5.86 $6.36 $1.98 $12.11 $14.09 $2.12 $25.27 $27.39 $3.69 $22.51 $26.20 Kankakee-Bradley, IL $0.17 $6.06 $6.24 $0.70 $12.51 $13.21 $0.74 $25.87 $26.61 $1.29 $23.05 $24.34
264 Kansas City, MO-KS $3.43 $6.72 $10.15 $13.76 $13.87 $27.64 $15.28 $29.97 $45.24 $26.49 $26.70 $53.19 Kennewick-Richland-Pasco, WA $1.46 $15.22 $16.67 $5.85 $31.41 $37.26 $5.66 $59.20 $64.87 $9.82 $52.75 $62.58 Killeen-Temple-Fort Hood, TX $0.34 $2.25 $2.59 $1.36 $4.64 $6.00 $0.87 $5.82 $6.69 $1.51 $5.18 $6.69 Knoxville, TN $0.77 $4.79 $5.56 $3.10 $9.89 $12.99 $3.30 $20.56 $23.86 $5.73 $18.32 $24.05 Kokomo, IN $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 La Crosse, WI-MN $0.14 $6.15 $6.29 $0.55 $12.70 $13.25 $0.57 $25.56 $26.12 $0.98 $22.77 $23.75 Lafayette, IN $0.32 $9.03 $9.35 $1.30 $18.65 $19.94 $1.30 $36.40 $37.70 $2.26 $32.43 $34.69 Lafayette, LA $0.16 $3.15 $3.31 $0.64 $6.50 $7.15 $1.03 $20.27 $21.30 $1.79 $18.06 $19.85 Lakeland, FL $0.58 $4.10 $4.68 $2.34 $8.47 $10.81 $2.51 $17.61 $20.12 $4.35 $15.69 $20.04 Lancaster, PA $0.12 $3.53 $3.65 $0.48 $7.29 $7.77 $0.54 $15.89 $16.43 $0.93 $14.16 $15.09 Lansing-East Lansing, MI $0.46 $8.58 $9.04 $1.84 $17.71 $19.55 $1.73 $32.27 $33.99 $2.99 $28.75 $31.75 Laredo, TX $0.35 $5.78 $6.13 $1.39 $11.93 $13.32 $1.61 $26.95 $28.56 $2.79 $24.01 $26.80 Las Cruces, NM $0.12 $1.99 $2.11 $0.46 $4.12 $4.58 $0.47 $8.07 $8.53 $0.81 $7.19 $8.00 Las Vegas-Paradise, NV $1.96 $11.38 $13.35 $7.89 $23.50 $31.39 $10.73 $62.25 $72.98 $18.62 $55.46 $74.08 Lawrence, KS $0.12 $5.16 $5.28 $0.50 $10.65 $11.15 $0.48 $20.04 $20.52 $0.83 $17.85 $18.69 Lawton, OK $0.46 $4.96 $5.43 $1.87 $10.25 $12.12 $1.38 $14.71 $16.08 $2.39 $13.10 $15.49 Lebanon, PA $0.08 $3.64 $3.72 $0.33 $7.51 $7.84 $0.34 $15.29 $15.63 $0.60 $13.62 $14.22 Lewiston-Auburn, ME $0.05 $2.25 $2.30 $0.22 $4.64 $4.86 $0.23 $9.33 $9.56 $0.39 $8.31 $8.70 Lexington-Fayette, KY $0.48 $5.81 $6.29 $1.94 $11.99 $13.93 $2.14 $25.86 $28.00 $3.72 $23.04 $26.76 Lincoln, NE $0.35 $5.75 $6.11 $1.42 $11.87 $13.30 $1.48 $23.97 $25.45 $2.56 $21.36 $23.92 Little Rock-North Little Rock, AR $0.64 $4.31 $4.95 $2.58 $8.90 $11.48 $2.80 $18.77 $21.57 $4.85 $16.73 $21.58 Logan, UT-ID $0.17 $6.65 $6.82 $0.67 $13.73 $14.41 $0.56 $22.34 $22.90 $0.98 $19.90 $20.88 Longview, WA $0.07 $2.69 $2.76 $0.27 $5.55 $5.82 $0.25 $10.27 $10.52 $0.44 $9.15 $9.59 Los Angeles-Long Beach-Santa Ana, CA $34.19 $27.79 $61.98 $137.39 $57.37 $194.76 $166.23 $135.10 $301.33 $288.27 $120.38 $408.66 Louisville, KY-IN $2.54 $7.74 $10.27 $10.19 $15.97 $26.16 $12.35 $37.66 $50.02 $21.42 $33.56 $54.98 Lubbock, TX $0.37 $5.81 $6.19 $1.49 $12.00 $13.49 $1.55 $24.29 $25.84 $2.69 $21.65 $24.33 Lynchburg, VA $0.42 $5.23 $5.66 $1.70 $10.81 $12.50 $1.78 $22.04 $23.82 $3.08 $19.64 $22.72 Macon, GA $0.38 $5.34 $5.72 $1.52 $11.03 $12.54 $1.46 $20.64 $22.10 $2.53 $18.39 $20.92 Madison, WI $0.73 $11.43 $12.16 $2.93 $23.59 $26.52 $3.07 $48.08 $51.14 $5.32 $42.84 $48.16 Mansfield, OH $0.08 $1.95 $2.02 $0.31 $4.02 $4.33 $0.33 $8.19 $8.51 $0.57 $7.30 $7.86 McAllen-Edinburg-Pharr, TX $0.26 $1.30 $1.56 $1.04 $2.68 $3.72 $1.02 $5.13 $6.14 $1.77 $4.57 $6.33
265 Medford, OR $0.12 $3.12 $3.24 $0.47 $6.45 $6.92 $0.46 $12.37 $12.83 $0.80 $11.02 $11.82 Memphis, TN-MS-AR $1.99 $6.87 $8.87 $8.01 $14.19 $22.21 $9.57 $32.99 $42.57 $16.60 $29.40 $46.00 Merced, CA $0.64 $12.06 $12.71 $2.58 $24.90 $27.49 $3.14 $58.94 $62.08 $5.45 $52.52 $57.97 Miami-Fort Lauderdale-Miami Beach, FL $8.96 $15.02 $23.98 $36.01 $31.01 $67.02 $43.61 $73.10 $116.71 $75.63 $65.14 $140.77 Milwaukee-Waukesha-West Allis, WI $3.28 $16.98 $20.26 $13.18 $35.06 $48.24 $15.31 $79.28 $94.59 $26.56 $70.64 $97.20 Minneapolis-St. Paul-Bloomington, MN-WI $6.53 $16.25 $22.78 $26.22 $33.54 $59.77 $28.21 $70.23 $98.44 $48.92 $62.58 $111.50 Missoula, MT $0.15 $5.78 $5.93 $0.60 $11.93 $12.53 $0.70 $26.97 $27.67 $1.21 $24.03 $25.24 Mobile, AL $0.52 $3.71 $4.22 $2.08 $7.65 $9.73 $2.17 $15.55 $17.72 $3.77 $13.86 $17.62 Modesto, CA $0.46 $4.70 $5.16 $1.85 $9.70 $11.56 $2.10 $21.43 $23.54 $3.65 $19.10 $22.75 Monroe, LA $0.14 $3.72 $3.87 $0.58 $7.69 $8.26 $0.71 $18.49 $19.20 $1.24 $16.48 $17.71 Morgantown, WV $0.30 $8.85 $9.15 $1.21 $18.26 $19.48 $1.36 $39.75 $41.11 $2.36 $35.42 $37.77 Muncie, IN $0.25 $7.56 $7.80 $0.99 $15.60 $16.59 $0.97 $29.72 $30.69 $1.68 $26.48 $28.16 Muskegon-Norton Shores, MI $0.17 $2.72 $2.89 $0.67 $5.62 $6.29 $0.67 $10.98 $11.65 $1.16 $9.79 $10.95 Myrtle Beach-Conway-North Myrtle Beach, SC $0.17 $2.68 $2.85 $0.69 $5.53 $6.22 $1.00 $15.54 $16.54 $1.73 $13.84 $15.58 Naples-Marco Island, FL $0.37 $4.79 $5.16 $1.48 $9.89 $11.37 $1.93 $25.21 $27.15 $3.36 $22.47 $25.82 Nashville-Davidson--Murfreesboro, TN $1.47 $3.97 $5.44 $5.91 $8.20 $14.11 $6.88 $18.57 $25.45 $11.93 $16.55 $28.48 New Orleans-Metairie-Kenner, LA $1.19 $6.22 $7.42 $4.79 $12.85 $17.64 $7.28 $37.95 $45.23 $12.62 $33.82 $46.44 New York-Northern New Jersey-Long Island, NY-NJ-PA $34.61 $46.73 $81.34 $139.08 $96.46 $235.54 $155.45 $209.84 $365.29 $269.58 $186.98 $456.56 Niles-Benton Harbor, MI $0.02 $0.40 $0.42 $0.09 $0.82 $0.92 $0.10 $1.75 $1.85 $0.18 $1.56 $1.74 Odessa, TX $0.28 $8.02 $8.30 $1.12 $16.56 $17.68 $1.28 $37.03 $38.31 $2.22 $32.99 $35.22 Oklahoma City, OK $0.92 $3.22 $4.14 $3.68 $6.65 $10.33 $4.21 $14.78 $18.99 $7.29 $13.17 $20.46 Olympia, WA $0.40 $11.88 $12.28 $1.62 $24.52 $26.14 $1.40 $41.27 $42.67 $2.43 $36.77 $39.20 Omaha-Council Bluffs, NE-IA $1.00 $6.42 $7.41 $4.01 $13.25 $17.26 $4.65 $29.87 $34.52 $8.06 $26.62 $34.68 Orlando, FL $2.57 $9.25 $11.82 $10.32 $19.10 $29.41 $14.19 $51.11 $65.30 $24.60 $45.55 $70.15 Oshkosh-Neenah, WI $0.14 $4.89 $5.03 $0.57 $10.09 $10.66 $0.56 $19.32 $19.89 $0.98 $17.22 $18.20 Oxnard-Thousand Oaks-Ventura, CA $1.17 $5.99 $7.16 $4.71 $12.36 $17.07 $5.01 $25.62 $30.63 $8.70 $22.83 $31.52 Palm Bay-Melbourne-Titusville, FL $0.77 $3.18 $3.95 $3.10 $6.56 $9.67 $2.96 $12.21 $15.17 $5.14 $10.88 $16.02 Panama City-Lynn Haven, FL $0.29 $6.47 $6.76 $1.17 $13.36 $14.53 $1.23 $27.17 $28.40 $2.13 $24.21 $26.34 Pensacola-Ferry Pass-Brent, FL $0.35 $4.98 $5.34 $1.43 $10.29 $11.71 $1.32 $18.57 $19.89 $2.29 $16.54 $18.84 Peoria, IL $0.59 $7.17 $7.76 $2.36 $14.80 $17.16 $2.57 $31.45 $34.02 $4.46 $28.02 $32.49 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $8.13 $16.88 $25.01 $32.66 $34.84 $67.51 $39.31 $81.60 $120.90 $68.17 $72.71 $140.87
266 Phoenix-Mesa-Scottsdale, AZ $8.27 $12.44 $20.71 $33.21 $25.69 $58.90 $39.34 $59.21 $98.55 $68.22 $52.76 $120.98 Pittsburgh, PA $3.32 $16.41 $19.73 $13.32 $33.88 $47.21 $16.18 $80.07 $96.24 $28.06 $71.34 $99.40 Pocatello, ID $0.09 $3.26 $3.35 $0.37 $6.73 $7.10 $0.34 $11.82 $12.15 $0.59 $10.53 $11.11 Port St. Lucie-Fort Pierce, FL $0.32 $0.65 $0.97 $1.29 $1.34 $2.63 $1.48 $3.00 $4.48 $2.57 $2.67 $5.24 Portland-Vancouver-Beaverton, OR-WA $6.12 $20.95 $27.07 $24.60 $43.25 $67.85 $28.47 $97.44 $125.91 $49.37 $86.82 $136.20 Poughkeepsie-Newburgh-Middletown, NY $0.62 $5.27 $5.89 $2.50 $10.88 $13.38 $2.18 $18.52 $20.70 $3.79 $16.50 $20.29 Providence-New Bedford-Fall River, RI-MA $1.72 $7.73 $9.45 $6.92 $15.96 $22.88 $7.71 $34.56 $42.26 $13.36 $30.79 $44.16 Pueblo, CO $0.16 $3.65 $3.81 $0.66 $7.53 $8.19 $0.61 $13.54 $14.15 $1.06 $12.06 $13.12 Punta Gorda, FL $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 Racine, WI $0.28 $8.01 $8.29 $1.13 $16.53 $17.66 $1.29 $36.83 $38.12 $2.24 $32.82 $35.05 Rapid City, SD $0.07 $1.82 $1.89 $0.26 $3.77 $4.03 $0.27 $7.58 $7.85 $0.47 $6.75 $7.22 Reading, PA $0.21 $4.96 $5.16 $0.83 $10.23 $11.06 $0.85 $20.36 $21.21 $1.47 $18.14 $19.62 Redding, CA $0.30 $4.27 $4.57 $1.21 $8.81 $10.03 $1.21 $17.14 $18.35 $2.10 $15.27 $17.37 Reno-Sparks, NV $1.90 $22.57 $24.47 $7.62 $46.60 $54.22 $8.80 $104.78 $113.58 $15.26 $93.36 $108.63 Richmond, VA $0.09 $0.50 $0.58 $0.35 $1.03 $1.38 $0.37 $2.13 $2.50 $0.64 $1.90 $2.54 Riverside-San Bernardino-Ontario, CA $3.28 $3.32 $6.60 $13.17 $6.86 $20.03 $14.30 $14.50 $28.80 $24.80 $12.92 $37.72 Roanoke, VA $0.74 $11.87 $12.61 $2.99 $24.50 $27.49 $3.13 $49.91 $53.04 $5.43 $44.47 $49.90 Rochester, MN $0.28 $7.63 $7.91 $1.13 $15.76 $16.88 $1.10 $30.08 $31.18 $1.92 $26.80 $28.72 Rochester, NY $0.62 $6.57 $7.19 $2.51 $13.56 $16.07 $2.79 $29.34 $32.13 $4.83 $26.14 $30.98 Rockford, IL $0.39 $4.57 $4.96 $1.55 $9.44 $10.98 $1.67 $19.75 $21.42 $2.89 $17.60 $20.49 Rome, GA $0.18 $5.24 $5.42 $0.71 $10.82 $11.54 $0.73 $21.54 $22.27 $1.26 $19.20 $20.46 Sacramento--Arden-Arcade--Roseville, CA $4.20 $12.34 $16.53 $16.87 $25.47 $42.34 $16.08 $47.25 $63.33 $27.89 $42.10 $69.99 Saginaw-Saginaw Township North, MI $0.21 $4.10 $4.31 $0.85 $8.46 $9.32 $0.87 $16.89 $17.76 $1.51 $15.05 $16.56 Salem, OR $0.44 $5.86 $6.30 $1.77 $12.09 $13.86 $1.68 $22.31 $23.98 $2.91 $19.88 $22.78 Salt Lake City, UT $2.49 $22.05 $24.53 $10.00 $45.51 $55.51 $11.53 $102.19 $113.72 $20.00 $91.05 $111.05 San Angelo, TX $0.17 $3.30 $3.47 $0.68 $6.82 $7.49 $0.68 $13.31 $13.99 $1.17 $11.86 $13.03 San Antonio, TX $4.93 $11.87 $16.80 $19.82 $24.51 $44.33 $21.05 $50.65 $71.70 $36.50 $45.13 $81.63 San Diego-Carlsbad-San Marcos, CA $6.59 $15.19 $21.77 $26.46 $31.35 $57.82 $29.68 $68.44 $98.12 $51.47 $60.98 $112.45 San Francisco-Oakland-Fremont, CA $18.43 $51.67 $70.10 $74.06 $106.66 $180.72 $82.14 $230.25 $312.39 $142.44 $205.17 $347.61 San Jose-Sunnyvale-Santa Clara, CA $6.80 $33.08 $39.89 $27.34 $68.30 $95.64 $27.93 $135.79 $163.72 $48.43 $121.00 $169.43 San Luis Obispo-Paso Robles, CA $0.09 $1.67 $1.76 $0.38 $3.44 $3.82 $0.44 $7.76 $8.20 $0.76 $6.91 $7.67
267 Sandusky, OH $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 Santa Barbara-Santa Maria-Goleta, CA $1.57 $23.37 $24.94 $6.33 $48.25 $54.57 $6.69 $99.32 $106.01 $11.60 $88.50 $100.11 Santa Cruz-Watsonville, CA $1.26 $34.67 $35.93 $5.07 $71.58 $76.65 $5.14 $141.39 $146.54 $8.92 $125.99 $134.91 Santa Fe, NM $0.25 $7.01 $7.26 $0.99 $14.47 $15.46 $1.14 $32.43 $33.57 $1.98 $28.90 $30.88 Santa Rosa-Petaluma, CA $1.65 $16.75 $18.41 $6.64 $34.59 $41.23 $6.95 $70.41 $77.36 $12.05 $62.74 $74.80 Savannah, GA $0.60 $8.91 $9.51 $2.41 $18.39 $20.79 $2.44 $36.21 $38.65 $4.23 $32.26 $36.49 Scranton--Wilkes-Barre, PA $0.31 $4.05 $4.36 $1.23 $8.36 $9.59 $1.40 $18.50 $19.90 $2.42 $16.48 $18.91 Seattle-Tacoma-Bellevue, WA $10.60 $31.64 $42.24 $42.59 $65.32 $107.91 $47.41 $141.52 $188.93 $82.21 $126.10 $208.32 Sebastian-Vero Beach, FL $0.14 $2.57 $2.71 $0.56 $5.31 $5.87 $0.67 $12.18 $12.84 $1.15 $10.85 $12.00 Sheboygan, WI $0.17 $6.12 $6.29 $0.67 $12.64 $13.31 $0.80 $29.48 $30.28 $1.39 $26.27 $27.66 Sherman-Denison, TX $0.12 $2.13 $2.24 $0.47 $4.39 $4.86 $0.47 $8.61 $9.08 $0.82 $7.67 $8.49 Shreveport-Bossier City, LA $1.00 $6.74 $7.75 $4.03 $13.92 $17.95 $6.79 $45.66 $52.45 $11.78 $40.69 $52.47 Sioux City, IA-NE-SD $0.19 $3.90 $4.09 $0.77 $8.05 $8.82 $0.99 $20.24 $21.23 $1.72 $18.03 $19.75 Sioux Falls, SD $0.17 $3.68 $3.85 $0.67 $7.61 $8.27 $0.98 $21.71 $22.68 $1.70 $19.34 $21.04 South Bend-Mishawaka, IN-MI $0.55 $6.44 $7.00 $2.23 $13.30 $15.53 $2.90 $33.66 $36.56 $5.03 $30.00 $35.03 Spartanburg, SC $0.09 $1.22 $1.31 $0.37 $2.51 $2.88 $0.38 $5.08 $5.45 $0.66 $4.52 $5.18 Spokane, WA $1.17 $15.24 $16.41 $4.70 $31.45 $36.16 $4.68 $60.86 $65.53 $8.11 $54.23 $62.34 Springfield, IL $0.28 $6.84 $7.12 $1.12 $14.12 $15.24 $1.01 $24.88 $25.89 $1.75 $22.17 $23.92 Springfield, MO $0.23 $2.63 $2.86 $0.93 $5.44 $6.36 $0.96 $10.90 $11.85 $1.66 $9.71 $11.37 Springfield, OH $0.08 $1.77 $1.84 $0.31 $3.64 $3.96 $0.31 $7.10 $7.41 $0.54 $6.32 $6.86 St. Cloud, MN $0.23 $6.52 $6.75 $0.94 $13.46 $14.40 $0.96 $26.76 $27.72 $1.66 $23.84 $25.50 St. Joseph, MO-KS $0.26 $6.71 $6.96 $1.03 $13.85 $14.87 $1.04 $27.27 $28.30 $1.80 $24.30 $26.10 St. Louis, MO-IL $4.07 $12.31 $16.39 $16.37 $25.42 $41.79 $17.59 $53.16 $70.76 $30.51 $47.37 $77.88 State College, PA $0.23 $9.57 $9.80 $0.91 $19.75 $20.67 $0.63 $26.37 $27.00 $1.08 $23.50 $24.58 Stockton, CA #REF! $7.54 #REF! #REF! $15.57 #REF! #REF! $33.01 #REF! #REF! $29.42 #REF! Sumter, SC $0.23 $5.09 $5.32 $0.93 $10.50 $11.43 $0.81 $17.97 $18.78 $1.41 $16.01 $17.42 Syracuse, NY $0.69 $11.89 $12.57 $2.76 $24.54 $27.30 $3.03 $52.30 $55.33 $5.25 $46.60 $51.85 Tallahassee, FL $0.41 $5.95 $6.36 $1.65 $12.28 $13.92 $1.33 $19.36 $20.69 $2.31 $17.25 $19.56 Tampa-St. Petersburg-Clearwater, FL $3.24 $8.52 $11.76 $13.02 $17.58 $30.61 $14.64 $38.46 $53.10 $25.39 $34.27 $59.66 Terre Haute, IN $0.14 $2.62 $2.76 $0.57 $5.41 $5.98 $0.62 $11.42 $12.04 $1.07 $10.18 $11.25 Toledo, OH $1.07 $6.66 $7.72 $4.28 $13.74 $18.02 $4.58 $28.61 $33.19 $7.95 $25.49 $33.44
268 Topeka, KS $0.27 $4.95 $5.22 $1.08 $10.22 $11.30 $1.03 $18.90 $19.93 $1.78 $16.84 $18.62 Tucson, AZ $2.02 $9.00 $11.01 $8.10 $18.58 $26.68 $7.69 $34.31 $41.99 $13.33 $30.57 $43.90 Tulsa, OK $0.67 $3.91 $4.58 $2.68 $8.08 $10.76 $3.23 $18.96 $22.20 $5.61 $16.90 $22.50 Tuscaloosa, AL $0.10 $1.35 $1.44 $0.38 $2.78 $3.16 $0.40 $5.63 $6.03 $0.70 $5.02 $5.71 Utica-Rome, NY $0.23 $3.95 $4.18 $0.90 $8.16 $9.06 $0.79 $13.80 $14.59 $1.36 $12.30 $13.66 Victoria, TX $0.29 $3.93 $4.22 $1.16 $8.12 $9.28 $1.54 $21.00 $22.54 $2.67 $18.71 $21.38 Virginia Beach-Norfolk-Newport News, VA-NC $3.72 $8.59 $12.30 $14.93 $17.73 $32.66 $15.24 $35.21 $50.45 $26.43 $31.37 $57.81 Visalia-Porterville, CA $0.39 $3.82 $4.21 $1.56 $7.88 $9.44 $1.66 $16.23 $17.89 $2.87 $14.46 $17.34 Waco, TX $0.21 $3.53 $3.75 $0.86 $7.30 $8.16 $0.94 $15.51 $16.45 $1.63 $13.82 $15.45 Washington-Arlington-Alexandria, DC-VA-MD-WV $14.74 $41.71 $56.46 $59.24 $86.12 $145.36 $58.84 $166.49 $225.33 $102.05 $148.35 $250.40 Waterloo-Cedar Falls, IA $0.24 $4.45 $4.69 $0.95 $9.19 $10.13 $1.11 $20.95 $22.06 $1.93 $18.67 $20.60 Wausau, WI $0.12 $5.21 $5.33 $0.48 $10.76 $11.24 $0.51 $22.38 $22.89 $0.88 $19.94 $20.82 Wenatchee, WA $0.43 $17.86 $18.28 $1.71 $36.86 $38.58 $1.70 $71.11 $72.81 $2.94 $63.36 $66.30 Wheeling, WV-OH $0.13 $4.69 $4.82 $0.51 $9.69 $10.20 $0.61 $22.47 $23.08 $1.06 $20.02 $21.08 Wichita, KS $0.53 $3.54 $4.07 $2.12 $7.31 $9.43 $2.39 $16.01 $18.39 $4.14 $14.26 $18.40 Williamsport, PA $0.12 $7.04 $7.16 $0.48 $14.53 $15.01 $0.52 $30.65 $31.17 $0.90 $27.32 $28.22 Yakima, WA $0.24 $3.89 $4.13 $0.97 $8.03 $8.99 $1.06 $17.10 $18.16 $1.83 $15.24 $17.07 York-Hanover, PA $0.14 $3.91 $4.05 $0.56 $8.07 $8.63 $0.63 $17.52 $18.15 $1.09 $15.61 $16.70 Youngstown-Warren-Boardman, OH-PA $0.23 $1.33 $1.56 $0.93 $2.75 $3.68 $1.00 $5.75 $6.76 $1.74 $5.13 $6.87 Yuba City, CA $0.30 $5.50 $5.80 $1.21 $11.35 $12.56 $1.14 $20.80 $21.94 $1.97 $18.54 $20.51 Yuma, AZ $0.17 $2.69 $2.85 $0.67 $5.55 $6.22 $0.70 $11.28 $11.98 $1.22 $10.05 $11.27
269 TABLE G 5 Average change per 1% change in rail seat capacity per capita Average wage changes Average GDP per capita changes name OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total Atlanta-Sandy Springs-Marietta, GA $1.95 $5.88 $7.83 $10.32 $16.65 $26.97 $9.05 $27.24 $36.28 $20.64 $33.29 $53.93 Baltimore-Towson, MD $3.85 $33.47 $37.32 $20.37 $94.77 $115.13 $15.09 $131.11 $146.20 $34.43 $160.23 $194.66 Buffalo-Niagara Falls, NY $0.65 $7.80 $8.44 $3.42 $22.08 $25.50 $2.82 $34.02 $36.84 $6.43 $41.58 $48.01 Chicago-Naperville-Joliet, IL-IN-WI $12.53 $28.02 $40.55 $66.23 $79.35 $145.58 $57.10 $127.73 $184.82 $130.28 $156.09 $286.37 Cleveland-Elyria-Mentor, OH $2.26 $16.19 $18.45 $11.97 $45.83 $57.80 $10.70 $76.52 $87.22 $24.42 $93.52 $117.94 Dallas-Fort Worth-Arlington, TX $1.80 $3.36 $5.16 $9.54 $9.51 $19.05 $9.21 $17.16 $26.37 $21.02 $20.97 $41.99 Denver-Aurora, CO $1.38 $9.05 $10.43 $7.30 $25.62 $32.92 $6.45 $42.33 $48.78 $14.73 $51.73 $66.46 Houston-Baytown-Sugar Land, TX $0.18 $0.48 $0.65 $0.94 $1.35 $2.29 $1.01 $2.70 $3.71 $2.31 $3.29 $5.60 Little Rock-North Little Rock, AR $0.25 $3.61 $3.86 $1.32 $10.21 $11.53 $1.08 $15.70 $16.78 $2.47 $19.19 $21.66 Los Angeles-Long Beach-Santa Ana, CA $1.25 $2.19 $3.43 $6.58 $6.19 $12.77 $6.05 $10.63 $16.68 $13.81 $12.99 $26.80 Memphis, TN-MS-AR $0.43 $3.17 $3.59 $2.25 $8.96 $11.21 $2.04 $15.19 $17.23 $4.66 $18.57 $23.22 Miami-Fort Lauderdale-Miami Beach, FL $1.03 $3.72 $4.75 $5.44 $10.54 $15.98 $5.00 $18.12 $23.12 $11.42 $22.14 $33.56 Minneapolis-St. Paul-Bloomington, MN-WI $0.31 $1.68 $1.99 $1.65 $4.75 $6.40 $1.35 $7.25 $8.60 $3.08 $8.86 $11.94 Nashville-Davidson--Murfreesboro, TN $0.62 $3.60 $4.22 $3.27 $10.20 $13.46 $2.89 $16.85 $19.74 $6.59 $20.59 $27.18 New Orleans-Metairie-Kenner, LA $1.04 $11.71 $12.75 $5.50 $33.16 $38.65 $6.34 $71.43 $77.77 $14.47 $87.29 $101.76 New York-Northern New Jersey-Long Island, NY-NJ-PA $7.34 $21.41 $28.75 $38.81 $60.61 $99.42 $32.96 $96.14 $129.10 $75.22 $117.49 $192.71 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $5.03 $22.54 $27.56 $26.57 $63.82 $90.39 $24.30 $108.98 $133.27 $55.44 $133.18 $188.62 Pittsburgh, PA $0.61 $6.49 $7.10 $3.21 $18.38 $21.59 $2.96 $31.66 $34.62 $6.76 $38.70 $45.45 Portland-South Portland-Biddeford, ME $0.58 $16.96 $17.55 $3.08 $48.03 $51.11 $2.57 $74.79 $77.35 $5.86 $91.40 $97.26 Portland-Vancouver-Beaverton, OR-WA $1.82 $13.42 $15.24 $9.60 $38.01 $47.61 $8.44 $62.44 $70.88 $19.27 $76.31 $95.57 Sacramento--Arden-Arcade--Roseville, CA $1.48 $9.40 $10.89 $7.83 $26.63 $34.46 $5.68 $36.02 $41.69 $12.95 $44.02 $56.97 Salt Lake City, UT $1.18 $22.63 $23.82 $6.25 $64.08 $70.34 $5.48 $104.91 $110.39 $12.51 $128.21 $140.71 San Diego-Carlsbad-San Marcos, CA $2.52 $12.54 $15.06 $13.31 $35.52 $48.83 $11.35 $56.53 $67.87 $25.89 $69.08 $94.97 San Francisco-Oakland-Fremont, CA $7.88 $47.75 $55.63 $41.68 $135.19 $176.87 $35.13 $212.78 $247.91 $80.17 $260.04 $340.21 San Jose-Sunnyvale-Santa Clara, CA $3.18 $33.37 $36.55 $16.80 $94.50 $111.30 $13.04 $136.99 $150.03 $29.75 $167.41 $197.17 Seattle-Tacoma-Bellevue, WA $0.81 $5.23 $6.04 $4.29 $14.82 $19.11 $3.63 $23.41 $27.04 $8.28 $28.61 $36.89 St. Louis, MO-IL $1.03 $6.74 $7.77 $5.46 $19.09 $24.54 $4.46 $29.11 $33.57 $10.17 $35.57 $45.74
270 Tampa-St. Petersburg-Clearwater, FL $0.09 $0.49 $0.57 $0.45 $1.38 $1.83 $0.39 $2.20 $2.59 $0.88 $2.69 $3.57 Washington-Arlington-Alexandria, DC-VA-MD-WV $7.65 $46.76 $54.40 $40.44 $132.39 $172.82 $30.53 $186.61 $217.14 $69.66 $228.06 $297.71
271 TABLE G 6 Average change per 1% change in motor bus seat capacity per capita Average wage changes Average GDP per capita changes Name OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total OLS- emp OLS- pop OLS- total IV-emp IV-pop IV-total Abilene, TX $2.60 $14.36 $16.96 $5.01 $13.97 $18.98 $10.25 $56.67 $66.92 $8.53 $23.79 $32.32 Akron, OH $3.62 $8.46 $12.08 $6.97 $8.23 $15.21 $15.07 $35.28 $50.34 $12.54 $14.81 $27.35 Albany, GA $2.51 $10.97 $13.47 $4.84 $10.67 $15.50 $10.34 $45.22 $55.56 $8.61 $18.98 $27.59 Albany-Schenectady-Troy, NY $2.19 $8.85 $11.03 $4.22 $8.61 $12.82 $8.58 $34.72 $43.30 $7.14 $14.58 $21.72 Albuquerque, NM $2.56 $6.08 $8.64 $4.93 $5.92 $10.85 $11.00 $26.14 $37.14 $9.15 $10.98 $20.13 Alexandria, LA $1.68 $12.90 $14.58 $3.23 $12.55 $15.79 $7.09 $54.51 $61.60 $5.90 $22.89 $28.78 Allentown-Bethlehem-Easton, PA-NJ $1.46 $3.97 $5.43 $2.81 $3.86 $6.67 $6.05 $16.52 $22.57 $5.04 $6.94 $11.97 Altoona, PA $2.45 $45.69 $48.14 $4.73 $44.44 $49.17 $10.74 $199.97 $210.71 $8.94 $83.96 $92.89 Amarillo, TX $1.27 $5.61 $6.88 $2.44 $5.46 $7.90 $5.52 $24.47 $29.99 $4.59 $10.28 $14.87 Ames, IA $11.21 $191.18 $202.39 $21.61 $185.97 $207.58 $46.25 $789.01 $835.26 $38.49 $331.27 $369.76 Anchorage, AK $4.89 $17.32 $22.21 $9.43 $16.85 $26.28 $24.02 $85.08 $109.10 $19.99 $35.72 $55.71 Anderson, IN $3.20 $13.66 $16.85 $6.17 $13.28 $19.45 $15.79 $67.43 $83.22 $13.14 $28.31 $41.45 Ann Arbor, MI $3.53 $33.80 $37.33 $6.81 $32.88 $39.69 $10.90 $104.30 $115.20 $9.07 $43.79 $52.86 Anniston-Oxford, AL $1.99 $10.87 $12.86 $3.83 $10.57 $14.41 $7.82 $42.77 $50.59 $6.51 $17.96 $24.47 Appleton, WI $1.70 $18.57 $20.27 $3.27 $18.06 $21.34 $7.05 $77.11 $84.16 $5.87 $32.38 $38.24 Athens-Clarke County, GA $3.77 $17.04 $20.82 $7.28 $16.58 $23.86 $13.27 $59.91 $73.18 $11.04 $25.15 $36.20 Atlanta-Sandy Springs-Marietta, GA $2.64 $1.20 $3.85 $5.10 $1.17 $6.27 $12.25 $5.58 $17.83 $10.20 $2.34 $12.54 Auburn-Opelika, AL $1.39 $8.19 $9.57 $2.68 $7.96 $10.64 $5.29 $31.21 $36.50 $4.40 $13.10 $17.51 Augusta-Richmond County, GA-SC $1.44 $1.84 $3.28 $2.78 $1.79 $4.57 $5.00 $6.37 $11.37 $4.16 $2.68 $6.84 Austin-Round Rock, TX $3.52 $4.70 $8.21 $6.78 $4.57 $11.35 $14.04 $18.77 $32.81 $11.69 $7.88 $19.57 Bakersfield, CA $1.95 $3.81 $5.76 $3.76 $3.71 $7.47 $8.77 $17.13 $25.90 $7.30 $7.19 $14.49 Baltimore-Towson, MD $4.11 $5.41 $9.52 $7.93 $5.26 $13.20 $16.12 $21.19 $37.31 $13.42 $8.90 $22.31 Bangor, ME $1.54 $15.71 $17.25 $2.97 $15.28 $18.25 $6.06 $61.83 $67.89 $5.04 $25.96 $31.00 Baton Rouge, LA $1.41 $3.12 $4.53 $2.72 $3.04 $5.76 $7.11 $15.74 $22.85 $5.92 $6.61 $12.53 Battle Creek, MI $4.27 $31.63 $35.90 $8.24 $30.77 $39.01 $18.10 $133.97 $152.07 $15.07 $56.25 $71.31 Bay City, MI $7.06 $80.60 $87.66 $13.61 $78.41 $92.02 $26.19 $299.08 $325.27 $21.80 $125.57 $147.37 Beaumont-Port Arthur, TX $2.10 $5.86 $7.96 $4.04 $5.70 $9.75 $8.85 $24.75 $33.60 $7.37 $10.39 $17.76
272 Bellingham, WA $5.06 $40.63 $45.70 $9.77 $39.53 $49.29 $21.43 $171.97 $193.40 $17.84 $72.20 $90.04 Bend, OR $0.85 $7.04 $7.89 $1.64 $6.85 $8.49 $4.01 $33.15 $37.16 $3.34 $13.92 $17.26 Billings, MT $3.16 $26.96 $30.12 $6.10 $26.23 $32.32 $13.93 $118.75 $132.67 $11.59 $49.86 $61.45 Binghamton, NY $1.93 $21.40 $23.34 $3.73 $20.82 $24.55 $6.45 $71.38 $77.83 $5.37 $29.97 $35.34 Birmingham-Hoover, AL $1.68 $2.03 $3.71 $3.23 $1.98 $5.21 $7.98 $9.66 $17.64 $6.64 $4.06 $10.70 Bismarck, ND $2.19 $36.73 $38.92 $4.22 $35.73 $39.95 $8.23 $138.11 $146.34 $6.85 $57.99 $64.84 Blacksburg-Christiansburg-Radford, VA $15.30 $93.42 $108.72 $29.51 $90.87 $120.38 $57.29 $349.76 $407.05 $47.68 $146.85 $194.53 Bloomington, IN $2.55 $23.78 $26.33 $4.92 $23.13 $28.05 $9.67 $90.09 $99.77 $8.05 $37.82 $45.88 Bloomington-Normal, IL $2.69 $26.23 $28.92 $5.19 $25.51 $30.70 $12.19 $118.89 $131.08 $10.15 $49.92 $60.06 Boise City-Nampa, ID $0.89 $2.52 $3.41 $1.72 $2.45 $4.17 $3.78 $10.69 $14.47 $3.15 $4.49 $7.64 Bradenton-Sarasota-Venice, FL $1.47 $4.34 $5.81 $2.84 $4.22 $7.06 $6.77 $19.97 $26.74 $5.63 $8.39 $14.02 Bremerton-Silverdale, WA $8.78 $52.40 $61.18 $16.93 $50.97 $67.90 $26.87 $160.40 $187.27 $22.36 $67.34 $89.71 Brownsville-Harlingen, TX $0.96 $2.77 $3.72 $1.85 $2.69 $4.54 $3.82 $11.02 $14.84 $3.18 $4.63 $7.80 Buffalo-Niagara Falls, NY $4.93 $8.99 $13.92 $9.50 $8.75 $18.25 $21.49 $39.25 $60.74 $17.89 $16.48 $34.36 Burlington-South Burlington, VT $3.75 $38.41 $42.17 $7.24 $37.37 $44.61 $14.16 $144.90 $159.07 $11.79 $60.84 $72.63 Canton-Massillon, OH $2.56 $7.53 $10.09 $4.94 $7.32 $12.27 $11.44 $33.62 $45.06 $9.52 $14.11 $23.64 Cape Coral-Fort Myers, FL $2.23 $3.52 $5.75 $4.30 $3.42 $7.72 $11.38 $17.94 $29.33 $9.47 $7.53 $17.01 Carson City, NV $2.15 $42.85 $45.00 $4.15 $41.68 $45.84 $8.21 $163.30 $171.51 $6.83 $68.56 $75.40 Casper, WY $1.96 $26.69 $28.64 $3.77 $25.96 $29.73 $14.57 $198.68 $213.25 $12.13 $83.41 $95.54 Cedar Rapids, IA $3.47 $21.04 $24.51 $6.69 $20.47 $27.16 $15.84 $96.11 $111.95 $13.19 $40.35 $53.54 Champaign-Urbana, IL $5.55 $47.51 $53.06 $10.71 $46.21 $56.92 $20.17 $172.55 $192.72 $16.79 $72.45 $89.23 Charleston, WV $2.04 $13.86 $15.90 $3.94 $13.48 $17.42 $10.09 $68.54 $78.63 $8.40 $28.78 $37.17 Charleston-North Charleston, SC $2.39 $4.56 $6.95 $4.61 $4.43 $9.04 $9.71 $18.53 $28.24 $8.08 $7.78 $15.86 Charlotte-Gastonia-Concord, NC-SC $10.46 $4.04 $14.50 $20.17 $3.93 $24.10 $64.48 $24.90 $89.39 $53.67 $10.46 $64.13 Charlottesville, VA $4.80 $58.01 $62.81 $9.26 $56.42 $65.68 $16.16 $195.22 $211.38 $13.45 $81.96 $95.41 Chattanooga, TN-GA $2.77 $6.26 $9.03 $5.35 $6.09 $11.43 $12.80 $28.90 $41.70 $10.66 $12.13 $22.79 Cheyenne, WY $2.76 $45.69 $48.45 $5.33 $44.44 $49.77 $10.79 $178.24 $189.03 $8.98 $74.83 $83.81 Chicago-Naperville-Joliet, IL-IN-WI $4.04 $1.37 $5.41 $7.79 $1.33 $9.13 $18.42 $6.24 $24.66 $15.33 $2.62 $17.95 Chico, CA $2.40 $14.37 $16.77 $4.63 $13.98 $18.60 $10.21 $61.14 $71.35 $8.50 $25.67 $34.17 Cincinnati-Middletown, OH-KY-IN $3.82 $3.91 $7.73 $7.36 $3.81 $11.16 $17.05 $17.49 $34.54 $14.19 $7.34 $21.54 Clarksville, TN-KY $2.46 $4.22 $6.67 $4.74 $4.10 $8.84 $8.13 $13.94 $22.07 $6.77 $5.85 $12.62
273 Cleveland-Elyria-Mentor, OH $4.70 $5.08 $9.78 $9.05 $4.94 $14.00 $22.20 $24.02 $46.21 $18.47 $10.08 $28.56 College Station-Bryan, TX $3.22 $13.66 $16.88 $6.22 $13.29 $19.50 $11.20 $47.45 $58.65 $9.32 $19.92 $29.24 Colorado Springs, CO $3.39 $7.84 $11.23 $6.53 $7.62 $14.16 $11.28 $26.10 $37.38 $9.39 $10.96 $20.34 Columbia, MO $2.89 $21.85 $24.75 $5.58 $21.26 $26.84 $9.19 $69.38 $78.57 $7.65 $29.13 $36.78 Columbia, SC $0.88 $2.35 $3.23 $1.69 $2.29 $3.98 $3.44 $9.20 $12.64 $2.86 $3.86 $6.73 Columbus, GA-AL $2.73 $8.81 $11.54 $5.27 $8.57 $13.84 $9.71 $31.29 $41.00 $8.08 $13.14 $21.22 Columbus, IN $0.80 $13.44 $14.24 $1.54 $13.07 $14.61 $3.58 $60.51 $64.09 $2.98 $25.40 $28.39 Columbus, OH $2.36 $2.48 $4.85 $4.56 $2.42 $6.98 $10.20 $10.72 $20.92 $8.49 $4.50 $12.99 Corpus Christi, TX $4.16 $11.35 $15.51 $8.02 $11.04 $19.06 $18.15 $49.50 $67.66 $15.11 $20.78 $35.89 Corvallis, OR $2.07 $41.97 $44.04 $3.99 $40.83 $44.82 $7.73 $156.71 $164.45 $6.43 $65.80 $72.23 Cumberland, MD-WV $0.69 $8.79 $9.48 $1.33 $8.55 $9.88 $2.55 $32.48 $35.03 $2.12 $13.64 $15.76 Dallas-Fort Worth-Arlington, TX $3.30 $0.93 $4.23 $6.37 $0.90 $7.27 $16.86 $4.75 $21.61 $14.03 $1.99 $16.02 Danville, IL $3.54 $41.04 $44.58 $6.83 $39.92 $46.75 $13.53 $156.70 $170.23 $11.26 $65.79 $77.05 Davenport-Moline-Rock Island, IA-IL $5.14 $17.82 $22.96 $9.91 $17.33 $27.24 $23.43 $81.24 $104.67 $19.50 $34.11 $53.61 Dayton, OH $2.53 $4.88 $7.41 $4.89 $4.75 $9.63 $10.32 $19.88 $30.20 $8.59 $8.35 $16.94 Decatur, IL $5.41 $49.95 $55.36 $10.42 $48.59 $59.01 $27.94 $258.17 $286.11 $23.25 $108.39 $131.65 Deltona-Daytona Beach-Ormond Beach, FL $2.75 $4.27 $7.02 $5.30 $4.15 $9.45 $13.19 $20.52 $33.71 $10.98 $8.61 $19.59 Denver-Aurora, CO $7.30 $7.24 $14.53 $14.07 $7.04 $21.11 $34.13 $33.86 $67.99 $28.40 $14.22 $42.62 Des Moines, IA $3.88 $13.96 $17.84 $7.49 $13.58 $21.07 $19.57 $70.35 $89.92 $16.29 $29.54 $45.83 Detroit-Warren-Livonia, MI $3.53 $1.57 $5.10 $6.81 $1.53 $8.34 $15.98 $7.12 $23.10 $13.30 $2.99 $16.29 Dubuque, IA $2.42 $37.21 $39.63 $4.67 $36.19 $40.86 $12.04 $185.03 $197.07 $10.02 $77.68 $87.70 Duluth, MN-WI $6.24 $25.86 $32.10 $12.03 $25.16 $37.19 $25.27 $104.74 $130.01 $21.03 $43.97 $65.01 Eau Claire, WI $1.94 $17.87 $19.81 $3.74 $17.38 $21.12 $7.88 $72.61 $80.49 $6.55 $30.49 $37.04 El Centro, CA $1.48 $15.09 $16.57 $2.85 $14.67 $17.53 $6.03 $61.43 $67.46 $5.02 $25.79 $30.81 El Paso, TX $2.88 $6.34 $9.22 $5.56 $6.16 $11.72 $15.27 $33.57 $48.85 $12.71 $14.09 $26.81 Elkhart-Goshen, IN $0.60 $4.24 $4.84 $1.15 $4.13 $5.28 $3.00 $21.31 $24.31 $2.50 $8.95 $11.44 Elmira, NY $2.11 $54.53 $56.64 $4.07 $53.05 $57.11 $8.11 $209.80 $217.91 $6.75 $88.08 $94.83 Erie, PA $2.26 $19.18 $21.44 $4.35 $18.66 $23.01 $9.67 $82.17 $91.84 $8.05 $34.50 $42.55 Eugene-Springfield, OR $5.70 $25.94 $31.64 $11.00 $25.23 $36.23 $22.39 $101.82 $124.21 $18.64 $42.75 $61.39 Evansville, IN-KY $1.27 $6.54 $7.80 $2.44 $6.36 $8.80 $6.78 $34.98 $41.76 $5.64 $14.69 $20.33 Fairbanks, AK $2.70 $26.64 $29.34 $5.21 $25.91 $31.12 $8.55 $84.34 $92.90 $7.12 $35.41 $42.53
274 Fargo, ND-MN $1.54 $16.11 $17.66 $2.98 $15.67 $18.65 $7.57 $78.93 $86.50 $6.30 $33.14 $39.44 Farmington, NM $0.47 $5.01 $5.48 $0.92 $4.87 $5.79 $3.10 $32.73 $35.83 $2.58 $13.74 $16.32 Fayetteville-Springdale-Rogers, AR-MO $2.26 $5.46 $7.71 $4.35 $5.31 $9.66 $9.78 $23.63 $33.41 $8.14 $9.92 $18.06 Flagstaff, AZ $1.32 $12.87 $14.19 $2.54 $12.52 $15.06 $4.44 $43.50 $47.94 $3.70 $18.26 $21.96 Flint, MI $7.30 $22.31 $29.62 $14.08 $21.71 $35.79 $27.05 $82.66 $109.71 $22.51 $34.70 $57.22 Florence, SC $0.72 $3.39 $4.11 $1.38 $3.30 $4.68 $2.80 $13.27 $16.07 $2.33 $5.57 $7.90 Fond du Lac, WI $0.96 $17.73 $18.69 $1.84 $17.25 $19.09 $4.39 $81.45 $85.85 $3.66 $34.20 $37.86 Fort Collins-Loveland, CO $2.41 $10.31 $12.72 $4.65 $10.03 $14.68 $8.64 $36.94 $45.58 $7.19 $15.51 $22.70 Fort Smith, AR-OK $0.66 $3.12 $3.79 $1.28 $3.04 $4.32 $3.19 $14.97 $18.16 $2.65 $6.29 $8.94 Fort Walton Beach-Crestview-Destin, FL $0.75 $5.98 $6.73 $1.45 $5.82 $7.26 $3.27 $26.00 $29.27 $2.72 $10.92 $13.64 Fort Wayne, IN $1.81 $4.83 $6.64 $3.49 $4.70 $8.19 $8.28 $22.09 $30.37 $6.89 $9.27 $16.16 Fresno, CA $2.51 $4.69 $7.20 $4.83 $4.56 $9.40 $10.46 $19.57 $30.03 $8.70 $8.22 $16.92 Gadsden, AL $1.53 $8.93 $10.46 $2.94 $8.69 $11.63 $6.69 $39.20 $45.89 $5.57 $16.46 $22.03 Gainesville, FL $10.70 $45.65 $56.36 $20.64 $44.41 $65.05 $35.43 $151.10 $186.53 $29.49 $63.44 $92.93 Gainesville, GA $0.32 $2.04 $2.36 $0.61 $1.99 $2.60 $1.38 $8.92 $10.30 $1.15 $3.74 $4.90 Glens Falls, NY $0.75 $18.23 $18.98 $1.44 $17.73 $19.17 $2.71 $66.30 $69.01 $2.26 $27.83 $30.09 Grand Forks, ND-MN $1.25 $19.08 $20.32 $2.40 $18.56 $20.96 $4.55 $69.62 $74.17 $3.78 $29.23 $33.02 Grand Junction, CO $2.47 $17.41 $19.88 $4.76 $16.94 $21.70 $9.51 $67.05 $76.56 $7.92 $28.15 $36.07 Grand Rapids-Wyoming, MI $2.00 $5.73 $7.73 $3.85 $5.58 $9.43 $9.09 $26.09 $35.18 $7.56 $10.95 $18.52 Great Falls, MT $3.88 $58.65 $62.53 $7.48 $57.06 $64.53 $14.86 $224.82 $239.68 $12.37 $94.39 $106.76 Greeley, CO $0.84 $4.95 $5.79 $1.62 $4.81 $6.43 $3.21 $18.91 $22.12 $2.67 $7.94 $10.61 Green Bay, WI $2.84 $12.55 $15.39 $5.47 $12.21 $17.68 $12.55 $55.51 $68.05 $10.44 $23.30 $33.75 Greenville, SC $0.37 $1.04 $1.41 $0.71 $1.02 $1.73 $1.54 $4.33 $5.87 $1.28 $1.82 $3.10 Gulfport-Biloxi, MS $1.64 $6.10 $7.75 $3.17 $5.94 $9.11 $6.32 $23.48 $29.81 $5.26 $9.86 $15.12 Hagerstown-Martinsburg, MD-WV $0.83 $5.88 $6.72 $1.61 $5.72 $7.33 $3.52 $24.82 $28.34 $2.93 $10.42 $13.35 Hanford-Corcoran, CA $3.04 $18.92 $21.96 $5.86 $18.41 $24.27 $10.76 $67.01 $77.77 $8.96 $28.13 $37.09 Harrisburg-Carlisle, PA $0.75 $7.69 $8.43 $1.44 $7.48 $8.92 $3.03 $31.16 $34.19 $2.52 $13.08 $15.60 Hattiesburg, MS $0.46 $4.21 $4.68 $0.89 $4.10 $4.99 $1.93 $17.61 $19.54 $1.60 $7.39 $9.00 Holland-Grand Haven, MI $0.84 $5.27 $6.12 $1.63 $5.13 $6.76 $4.04 $25.24 $29.28 $3.36 $10.60 $13.96 Honolulu, HI $6.36 $22.48 $28.84 $12.26 $21.87 $34.13 $26.16 $92.52 $118.68 $21.77 $38.84 $60.62 Hot Springs, AR $1.39 $11.05 $12.44 $2.68 $10.75 $13.43 $6.05 $48.04 $54.09 $5.04 $20.17 $25.21
275 Houston-Baytown-Sugar Land, TX $4.72 $1.90 $6.62 $9.10 $1.85 $10.95 $26.70 $10.78 $37.48 $22.23 $4.52 $26.75 Huntington-Ashland, WV-KY-OH $1.61 $10.15 $11.76 $3.10 $9.87 $12.98 $7.16 $45.12 $52.28 $5.96 $18.94 $24.90 Huntsville, AL $1.19 $2.59 $3.78 $2.30 $2.52 $4.82 $4.28 $9.30 $13.58 $3.56 $3.91 $7.47 Idaho Falls, ID $0.90 $11.63 $12.53 $1.74 $11.31 $13.06 $3.13 $40.32 $43.45 $2.60 $16.93 $19.53 Indianapolis, IN $1.87 $1.69 $3.56 $3.61 $1.65 $5.25 $9.63 $8.71 $18.34 $8.02 $3.66 $11.67 Iowa City, IA $5.97 $78.35 $84.32 $11.52 $76.21 $87.73 $21.05 $276.09 $297.14 $17.52 $115.92 $133.44 Ithaca, NY $5.70 $159.05 $164.75 $10.99 $154.71 $165.70 $22.88 $638.51 $661.39 $19.05 $268.08 $287.13 Jackson, MI $0.64 $10.29 $10.94 $1.24 $10.01 $11.26 $2.66 $42.45 $45.11 $2.21 $17.82 $20.04 Jackson, MS $1.14 $2.47 $3.61 $2.21 $2.40 $4.61 $5.16 $11.12 $16.29 $4.30 $4.67 $8.97 Jackson, TN $2.15 $21.76 $23.91 $4.16 $21.16 $25.32 $8.87 $89.60 $98.47 $7.39 $37.62 $45.00 Jacksonville, FL $3.76 $3.52 $7.29 $7.26 $3.43 $10.68 $16.79 $15.71 $32.50 $13.97 $6.60 $20.57 Janesville, WI $4.21 $31.06 $35.27 $8.13 $30.21 $38.34 $16.59 $122.31 $138.91 $13.81 $51.35 $65.16 Jefferson City, MO $1.59 $18.00 $19.59 $3.07 $17.50 $20.58 $5.17 $58.31 $63.47 $4.30 $24.48 $28.78 Johnson City, TN $1.57 $7.50 $9.07 $3.02 $7.30 $10.32 $6.62 $31.72 $38.34 $5.51 $13.32 $18.83 Johnstown, PA $2.43 $37.22 $39.64 $4.68 $36.20 $40.88 $9.59 $147.13 $156.72 $7.98 $61.77 $69.76 Jonesboro, AR $0.62 $3.59 $4.21 $1.20 $3.49 $4.69 $2.86 $16.45 $19.31 $2.38 $6.91 $9.29 Kalamazoo-Portage, MI $2.61 $10.16 $12.77 $5.04 $9.88 $14.92 $11.26 $43.75 $55.00 $9.37 $18.37 $27.74 Kankakee-Bradley, IL $2.02 $23.04 $25.06 $3.90 $22.41 $26.31 $8.64 $98.31 $106.95 $7.19 $41.28 $48.46 Kansas City, MO-KS $3.60 $2.31 $5.91 $6.94 $2.25 $9.19 $16.06 $10.30 $26.36 $13.37 $4.32 $17.69 Kennewick-Richland-Pasco, WA $11.70 $39.96 $51.65 $22.55 $38.87 $61.42 $45.51 $155.48 $200.99 $37.88 $65.28 $103.16 Killeen-Temple-Fort Hood, TX $1.11 $2.41 $3.52 $2.13 $2.35 $4.48 $2.86 $6.24 $9.10 $2.38 $2.62 $5.00 Kingsport-Bristol-Bristol, TN-VA $1.44 $3.89 $5.34 $2.78 $3.79 $6.57 $6.05 $16.33 $22.39 $5.04 $6.86 $11.90 Kingston, NY $3.44 $32.68 $36.12 $6.63 $31.79 $38.43 $12.14 $115.33 $127.46 $10.10 $48.42 $58.52 Knoxville, TN $2.29 $4.66 $6.95 $4.42 $4.53 $8.95 $9.83 $19.99 $29.82 $8.18 $8.39 $16.57 La Crosse, WI-MN $2.17 $32.07 $34.25 $4.19 $31.20 $35.39 $9.02 $133.22 $142.24 $7.51 $55.93 $63.44 Lafayette, IN $5.20 $47.62 $52.82 $10.04 $46.32 $56.36 $20.97 $191.91 $212.88 $17.46 $80.57 $98.03 Lafayette, LA $1.53 $9.85 $11.38 $2.96 $9.58 $12.53 $9.86 $63.34 $73.20 $8.21 $26.59 $34.80 Lake Charles, LA $0.81 $5.12 $5.94 $1.57 $4.98 $6.56 $6.20 $38.99 $45.19 $5.16 $16.37 $21.53 Lakeland, FL $1.79 $4.10 $5.89 $3.45 $3.99 $7.44 $7.67 $17.63 $25.30 $6.39 $7.40 $13.79 Lancaster, PA $0.41 $4.00 $4.41 $0.80 $3.89 $4.69 $1.86 $18.00 $19.86 $1.55 $7.56 $9.11 Lansing-East Lansing, MI $2.25 $13.76 $16.01 $4.34 $13.38 $17.72 $8.47 $51.76 $60.23 $7.05 $21.73 $28.78
276 Laredo, TX $3.17 $17.34 $20.51 $6.11 $16.87 $22.98 $14.77 $80.83 $95.60 $12.29 $33.94 $46.23 Las Cruces, NM $1.35 $7.64 $8.99 $2.60 $7.43 $10.03 $5.47 $30.90 $36.37 $4.55 $12.97 $17.52 Las Vegas-Paradise, NV $2.34 $4.44 $6.79 $4.52 $4.32 $8.84 $12.82 $24.30 $37.12 $10.67 $10.20 $20.87 Lawrence, KS $1.46 $19.89 $21.35 $2.82 $19.34 $22.16 $5.68 $77.22 $82.90 $4.72 $32.42 $37.15 Lawton, OK $3.90 $13.63 $17.53 $7.52 $13.26 $20.78 $11.55 $40.37 $51.92 $9.62 $16.95 $26.57 Lebanon, PA $0.93 $13.51 $14.44 $1.80 $13.15 $14.94 $3.91 $56.78 $60.69 $3.26 $23.84 $27.09 Lewiston, ID-WA $0.41 $6.90 $7.32 $0.80 $6.72 $7.52 $1.50 $24.93 $26.43 $1.25 $10.47 $11.71 Lewiston-Auburn, ME $1.28 $17.32 $18.61 $2.47 $16.85 $19.32 $5.32 $71.91 $77.23 $4.43 $30.19 $34.62 Lexington-Fayette, KY $1.65 $6.51 $8.16 $3.18 $6.33 $9.51 $7.35 $28.96 $36.31 $6.11 $12.16 $18.27 Lima, OH $1.17 $14.53 $15.70 $2.25 $14.14 $16.39 $5.32 $66.15 $71.47 $4.43 $27.77 $32.20 Lincoln, NE $3.11 $16.48 $19.58 $5.99 $16.03 $22.02 $12.95 $68.67 $81.62 $10.78 $28.83 $39.61 Little Rock-North Little Rock, AR $1.55 $3.39 $4.93 $2.98 $3.30 $6.28 $6.73 $14.76 $21.48 $5.60 $6.20 $11.79 Logan, UT-ID $4.50 $58.47 $62.98 $8.68 $56.88 $65.56 $15.12 $196.31 $211.43 $12.58 $82.42 $95.00 Longview, TX $0.62 $3.08 $3.70 $1.20 $3.00 $4.20 $3.23 $16.05 $19.28 $2.69 $6.74 $9.42 Longview, WA $1.40 $18.40 $19.79 $2.69 $17.90 $20.59 $5.33 $70.27 $75.60 $4.44 $29.50 $33.94 Los Angeles-Long Beach-Santa Ana, CA $4.76 $1.26 $6.02 $9.18 $1.23 $10.41 $23.13 $6.15 $29.28 $19.25 $2.58 $21.83 Louisville, KY-IN $4.88 $4.87 $9.75 $9.42 $4.74 $14.15 $23.78 $23.70 $47.48 $19.79 $9.95 $29.74 Lubbock, TX $3.93 $20.16 $24.09 $7.58 $19.61 $27.19 $16.42 $84.20 $100.62 $13.67 $35.35 $49.02 Lynchburg, VA $14.80 $59.94 $74.74 $28.54 $58.30 $86.84 $62.31 $252.32 $314.63 $51.86 $105.94 $157.80 Macon, GA $1.92 $8.89 $10.81 $3.70 $8.65 $12.35 $7.42 $34.36 $41.78 $6.18 $14.43 $20.60 Madera, CA $1.27 $7.35 $8.62 $2.45 $7.15 $9.60 $4.85 $28.08 $32.93 $4.04 $11.79 $15.83 Madison, WI $4.32 $22.17 $26.50 $8.34 $21.57 $29.91 $18.19 $93.29 $111.49 $15.14 $39.17 $54.31 Mansfield, OH $1.52 $12.46 $13.98 $2.93 $12.12 $15.05 $6.38 $52.39 $58.77 $5.31 $21.99 $27.31 McAllen-Edinburg-Pharr, TX $0.39 $0.65 $1.04 $0.76 $0.63 $1.39 $1.55 $2.55 $4.11 $1.29 $1.07 $2.36 Medford, OR $1.60 $14.00 $15.60 $3.08 $13.62 $16.70 $6.33 $55.42 $61.75 $5.27 $23.27 $28.53 Memphis, TN-MS-AR $2.39 $2.69 $5.08 $4.60 $2.62 $7.22 $11.46 $12.91 $24.37 $9.54 $5.42 $14.96 Merced, CA $2.86 $17.53 $20.39 $5.51 $17.05 $22.56 $13.97 $85.63 $99.60 $11.63 $35.95 $47.58 Miami-Fort Lauderdale-Miami Beach, FL $3.06 $1.68 $4.74 $5.91 $1.63 $7.54 $14.91 $8.17 $23.08 $12.41 $3.43 $15.84 Michigan City-La Porte, IN $1.01 $9.43 $10.44 $1.95 $9.18 $11.12 $4.27 $39.89 $44.15 $3.55 $16.75 $20.30 Milwaukee-Waukesha-West Allis, WI $4.68 $7.93 $12.61 $9.03 $7.71 $16.74 $21.86 $37.00 $58.87 $18.20 $15.54 $33.73 Minneapolis-St. Paul-Bloomington, MN-WI $6.75 $5.49 $12.24 $13.02 $5.34 $18.36 $29.18 $23.75 $52.92 $24.29 $9.97 $34.26
277 Missoula, MT $2.62 $33.10 $35.73 $5.06 $32.20 $37.26 $12.25 $154.53 $166.78 $10.19 $64.88 $75.07 Mobile, AL $2.05 $4.79 $6.84 $3.95 $4.66 $8.61 $8.59 $20.11 $28.70 $7.15 $8.44 $15.59 Modesto, CA $1.93 $6.44 $8.37 $3.73 $6.26 $9.99 $8.81 $29.36 $38.17 $7.34 $12.32 $19.66 Monroe, LA $1.60 $13.51 $15.11 $3.08 $13.14 $16.22 $7.93 $67.11 $75.03 $6.60 $28.17 $34.77 Montgomery, AL $1.38 $3.98 $5.36 $2.66 $3.87 $6.53 $5.50 $15.85 $21.35 $4.58 $6.65 $11.23 Morgantown, WV $3.53 $33.78 $37.31 $6.81 $32.86 $39.67 $15.86 $151.77 $167.63 $13.20 $63.72 $76.92 Mount Vernon-Anacortes, WA $4.77 $39.20 $43.97 $9.20 $38.13 $47.33 $22.19 $182.41 $204.60 $18.47 $76.59 $95.06 Muncie, IN $4.87 $48.76 $53.63 $9.40 $47.43 $56.83 $19.17 $191.74 $210.91 $15.95 $80.50 $96.46 Muskegon-Norton Shores, MI $2.86 $15.37 $18.24 $5.52 $14.96 $20.48 $11.56 $62.03 $73.59 $9.62 $26.04 $35.66 Myrtle Beach-Conway-North Myrtle Beach, SC $0.76 $3.87 $4.63 $1.47 $3.76 $5.23 $4.42 $22.46 $26.88 $3.68 $9.43 $13.11 Napa, CA $4.24 $46.97 $51.21 $8.17 $45.69 $53.86 $19.00 $210.62 $229.61 $15.81 $88.43 $104.24 Naples-Marco Island, FL $0.82 $3.50 $4.32 $1.58 $3.41 $4.99 $4.32 $18.42 $22.74 $3.60 $7.73 $11.33 Nashville-Davidson--Murfreesboro, TN $2.28 $2.01 $4.28 $4.39 $1.95 $6.34 $10.64 $9.39 $20.04 $8.86 $3.94 $12.80 New Orleans-Metairie-Kenner, LA $3.38 $5.75 $9.13 $6.51 $5.60 $12.11 $20.59 $35.10 $55.68 $17.14 $14.74 $31.87 New York-Northern New Jersey-Long Island, NY-NJ-PA $4.12 $1.82 $5.93 $7.94 $1.77 $9.71 $18.49 $8.16 $26.65 $15.39 $3.43 $18.82 Niles-Benton Harbor, MI $0.24 $1.35 $1.59 $0.47 $1.31 $1.78 $1.07 $5.93 $7.00 $0.89 $2.49 $3.38 Ocala, FL $0.80 $2.70 $3.51 $1.55 $2.63 $4.18 $3.26 $10.95 $14.21 $2.71 $4.60 $7.31 Odessa, TX $2.44 $23.06 $25.51 $4.71 $22.44 $27.15 $11.28 $106.47 $117.75 $9.39 $44.70 $54.09 Oklahoma City, OK $1.58 $1.82 $3.40 $3.05 $1.77 $4.82 $7.27 $8.35 $15.62 $6.05 $3.51 $9.56 Olympia, WA $3.30 $31.74 $35.04 $6.36 $30.88 $37.23 $11.45 $110.28 $121.74 $9.53 $46.30 $55.84 Omaha-Council Bluffs, NE-IA $3.26 $6.86 $10.12 $6.29 $6.67 $12.97 $15.20 $31.93 $47.12 $12.65 $13.40 $26.05 Orlando, FL $1.81 $2.14 $3.95 $3.50 $2.08 $5.58 $10.02 $11.81 $21.83 $8.34 $4.96 $13.30 Oshkosh-Neenah, WI $2.00 $22.36 $24.35 $3.85 $21.75 $25.60 $7.91 $88.42 $96.32 $6.58 $37.12 $43.70 Owensboro, KY $0.88 $10.34 $11.22 $1.69 $10.06 $11.75 $4.08 $48.10 $52.18 $3.40 $20.19 $23.59 Oxnard-Thousand Oaks-Ventura, CA $3.49 $5.82 $9.31 $6.73 $5.67 $12.39 $14.92 $24.92 $39.84 $12.42 $10.46 $22.88 Palm Bay-Melbourne-Titusville, FL $1.76 $2.37 $4.12 $3.39 $2.30 $5.69 $6.75 $9.08 $15.83 $5.62 $3.81 $9.43 Panama City-Lynn Haven, FL $1.14 $8.25 $9.39 $2.20 $8.03 $10.23 $4.78 $34.66 $39.44 $3.98 $14.55 $18.53 Parkersburg-Marietta, WV-OH $0.86 $6.80 $7.66 $1.66 $6.61 $8.27 $3.97 $31.30 $35.27 $3.30 $13.14 $16.44 Pensacola-Ferry Pass-Brent, FL $0.99 $4.53 $5.51 $1.90 $4.40 $6.31 $3.68 $16.87 $20.54 $3.06 $7.08 $10.14 Peoria, IL $2.90 $11.60 $14.50 $5.60 $11.28 $16.88 $12.73 $50.86 $63.59 $10.60 $21.35 $31.95 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $2.82 $1.92 $4.74 $5.44 $1.86 $7.31 $13.65 $9.26 $22.91 $11.36 $3.89 $15.25
278 Phoenix-Mesa-Scottsdale, AZ $2.93 $1.44 $4.37 $5.64 $1.40 $7.04 $13.92 $6.85 $20.78 $11.59 $2.88 $14.47 Pine Bluff, AR $2.06 $12.82 $14.88 $3.97 $12.47 $16.44 $8.25 $51.43 $59.68 $6.87 $21.59 $28.46 Pittsburgh, PA $3.68 $5.95 $9.63 $7.10 $5.79 $12.89 $17.95 $29.04 $46.99 $14.94 $12.19 $27.14 Pocatello, ID $2.35 $26.89 $29.24 $4.53 $26.15 $30.68 $8.52 $97.46 $105.97 $7.09 $40.92 $48.01 Port St. Lucie-Fort Pierce, FL $1.68 $1.11 $2.79 $3.24 $1.08 $4.32 $7.74 $5.13 $12.87 $6.44 $2.15 $8.60 Portland-South Portland-Biddeford, ME $0.94 $4.16 $5.11 $1.82 $4.05 $5.87 $4.16 $18.35 $22.51 $3.46 $7.70 $11.17 Portland-Vancouver-Beaverton, OR-WA $4.88 $5.46 $10.34 $9.41 $5.31 $14.72 $22.69 $25.39 $48.08 $18.89 $10.66 $29.54 Poughkeepsie-Newburgh-Middletown, NY $2.10 $5.83 $7.93 $4.05 $5.67 $9.72 $7.38 $20.46 $27.84 $6.14 $8.59 $14.73 Providence-New Bedford-Fall River, RI-MA $3.21 $4.71 $7.92 $6.19 $4.58 $10.77 $14.35 $21.05 $35.40 $11.95 $8.84 $20.78 Pueblo, CO $2.01 $14.63 $16.64 $3.88 $14.23 $18.11 $7.48 $54.32 $61.80 $6.22 $22.81 $29.03 Racine, WI $2.74 $25.56 $28.30 $5.28 $24.87 $30.15 $12.60 $117.56 $130.16 $10.49 $49.36 $59.84 Rapid City, SD $1.17 $10.74 $11.92 $2.26 $10.45 $12.72 $4.88 $44.64 $49.52 $4.06 $18.74 $22.80 Reading, PA $1.28 $10.01 $11.29 $2.46 $9.74 $12.20 $5.25 $41.12 $46.37 $4.37 $17.27 $21.63 Redding, CA $2.90 $13.44 $16.34 $5.60 $13.07 $18.67 $11.66 $53.94 $65.60 $9.70 $22.65 $32.35 Reno-Sparks, NV $3.82 $14.86 $18.67 $7.36 $14.45 $21.81 $17.71 $68.95 $86.67 $14.74 $28.95 $43.70 Richmond, VA $2.52 $4.78 $7.31 $4.87 $4.65 $9.52 $10.79 $20.44 $31.23 $8.98 $8.58 $17.56 Riverside-San Bernardino-Ontario, CA $2.02 $0.67 $2.69 $3.89 $0.65 $4.54 $8.81 $2.92 $11.73 $7.33 $1.23 $8.56 Roanoke, VA $3.91 $20.36 $24.27 $7.53 $19.81 $27.34 $16.43 $85.65 $102.08 $13.68 $35.96 $49.63 Rochester, NY $2.44 $8.39 $10.83 $4.70 $8.17 $12.87 $10.90 $37.50 $48.40 $9.07 $15.74 $24.81 Rockford, IL $1.86 $7.22 $9.09 $3.60 $7.03 $10.62 $8.06 $31.22 $39.28 $6.71 $13.11 $19.81 Rome, GA $11.60 $112.17 $123.77 $22.37 $109.12 $131.48 $47.66 $460.93 $508.59 $39.67 $193.52 $233.19 Sacramento--Arden-Arcade--Roseville, CA $3.60 $3.46 $7.06 $6.94 $3.36 $10.30 $13.79 $13.24 $27.03 $11.48 $5.56 $17.03 Saginaw-Saginaw Township North, MI $3.41 $21.53 $24.94 $6.57 $20.94 $27.51 $14.03 $88.67 $102.71 $11.68 $37.23 $48.91 Salem, OR $2.59 $11.25 $13.84 $4.99 $10.95 $15.94 $9.85 $42.86 $52.71 $8.20 $17.99 $26.19 Salinas, CA $2.72 $15.90 $18.61 $5.24 $15.46 $20.70 $12.94 $75.69 $88.63 $10.77 $31.78 $42.55 Salisbury, MD $1.71 $23.17 $24.87 $3.29 $22.53 $25.83 $6.65 $90.25 $96.91 $5.54 $37.89 $43.43 Salt Lake City, UT $4.46 $12.90 $17.36 $8.59 $12.55 $21.14 $20.65 $59.81 $80.46 $17.19 $25.11 $42.30 San Angelo, TX $0.84 $5.39 $6.23 $1.62 $5.24 $6.86 $3.38 $21.72 $25.10 $2.81 $9.12 $11.93 San Antonio, TX $3.92 $3.08 $7.00 $7.55 $3.00 $10.55 $16.71 $13.14 $29.85 $13.90 $5.52 $19.42 San Diego-Carlsbad-San Marcos, CA $3.82 $2.88 $6.71 $7.37 $2.80 $10.18 $17.23 $12.99 $30.22 $14.34 $5.45 $19.79 San Francisco-Oakland-Fremont, CA $6.99 $6.41 $13.40 $13.48 $6.23 $19.71 $31.15 $28.55 $59.70 $25.93 $11.99 $37.92
279 San Jose-Sunnyvale-Santa Clara, CA $7.10 $11.29 $18.40 $13.70 $10.99 $24.69 $29.16 $46.35 $75.51 $24.27 $19.46 $43.73 San Luis Obispo-Paso Robles, CA $2.57 $14.89 $17.46 $4.96 $14.48 $19.44 $11.98 $69.29 $81.27 $9.97 $29.09 $39.06 Santa Barbara-Santa Maria-Goleta, CA $5.68 $27.56 $33.24 $10.95 $26.81 $37.76 $24.14 $117.13 $141.26 $20.09 $49.18 $69.27 Santa Cruz-Watsonville, CA $5.67 $50.98 $56.65 $10.94 $49.59 $60.53 $23.14 $207.89 $231.03 $19.26 $87.28 $106.54 Santa Fe, NM $2.90 $26.94 $29.84 $5.58 $26.21 $31.79 $13.40 $124.66 $138.05 $11.15 $52.34 $63.49 Santa Rosa-Petaluma, CA $4.09 $13.53 $17.62 $7.88 $13.16 $21.04 $17.17 $56.87 $74.04 $14.29 $23.88 $38.17 Savannah, GA $2.43 $11.80 $14.23 $4.69 $11.48 $16.16 $9.88 $47.98 $57.86 $8.22 $20.14 $28.37 Scranton--Wilkes-Barre, PA $1.46 $6.33 $7.80 $2.82 $6.16 $8.99 $6.69 $28.92 $35.61 $5.57 $12.14 $17.71 Seattle-Tacoma-Bellevue, WA $8.89 $8.67 $17.56 $17.14 $8.44 $25.58 $39.76 $38.80 $78.56 $33.09 $16.29 $49.38 Sebastian-Vero Beach, FL $1.58 $9.47 $11.06 $3.05 $9.21 $12.27 $7.49 $44.85 $52.34 $6.24 $18.83 $25.07 Sheboygan, WI $3.27 $39.33 $42.60 $6.31 $38.26 $44.57 $15.75 $189.45 $205.20 $13.11 $79.54 $92.65 Sherman-Denison, TX $0.93 $5.53 $6.46 $1.78 $5.38 $7.17 $3.75 $22.38 $26.13 $3.12 $9.40 $12.51 Shreveport-Bossier City, LA $3.79 $8.34 $12.13 $7.31 $8.11 $15.42 $25.68 $56.45 $82.12 $21.37 $23.70 $45.07 Sioux City, IA-NE-SD $3.47 $23.17 $26.64 $6.70 $22.54 $29.24 $18.03 $120.29 $138.32 $15.01 $50.50 $65.51 Sioux Falls, SD $1.56 $11.35 $12.92 $3.02 $11.04 $14.06 $9.21 $66.88 $76.09 $7.67 $28.08 $35.75 South Bend-Mishawaka, IN-MI $3.25 $12.34 $15.60 $6.27 $12.01 $18.28 $17.01 $64.52 $81.53 $14.15 $27.09 $41.24 Spartanburg, SC $0.88 $3.83 $4.71 $1.69 $3.73 $5.42 $3.66 $15.98 $19.64 $3.04 $6.71 $9.75 Spokane, WA $5.21 $22.16 $27.37 $10.04 $21.56 $31.60 $20.81 $88.52 $109.33 $17.32 $37.17 $54.49 Springfield, IL $3.71 $29.79 $33.50 $7.14 $28.98 $36.13 $13.47 $108.36 $121.83 $11.22 $45.49 $56.71 Springfield, MO $0.61 $2.29 $2.91 $1.19 $2.23 $3.42 $2.55 $9.49 $12.04 $2.12 $3.99 $6.10 Springfield, OH $1.79 $13.32 $15.11 $3.45 $12.96 $16.41 $7.19 $53.56 $60.76 $5.99 $22.49 $28.48 St. Cloud, MN $2.47 $22.62 $25.09 $4.77 $22.00 $26.77 $10.15 $92.82 $102.97 $8.44 $38.97 $47.42 St. George, UT $0.47 $4.21 $4.67 $0.90 $4.09 $4.99 $1.96 $17.65 $19.61 $1.63 $7.41 $9.04 St. Joseph, MO-KS $2.49 $21.38 $23.87 $4.80 $20.80 $25.60 $10.13 $86.92 $97.05 $8.43 $36.50 $44.92 St. Louis, MO-IL $2.06 $2.03 $4.09 $3.97 $1.98 $5.95 $8.89 $8.78 $17.67 $7.40 $3.69 $11.08 State College, PA $4.25 $58.68 $62.94 $8.20 $57.08 $65.29 $11.73 $161.75 $173.48 $9.76 $67.91 $77.67 Stockton, CA $3.34 $8.51 $11.86 $6.44 $8.28 $14.73 $14.63 $37.27 $51.90 $12.18 $15.65 $27.83 Sumter, SC $5.45 $39.29 $44.73 $10.50 $38.22 $48.72 $19.23 $138.73 $157.96 $16.01 $58.25 $74.25 Syracuse, NY $2.75 $15.52 $18.26 $5.30 $15.09 $20.39 $12.08 $68.28 $80.36 $10.06 $28.67 $38.72 Tallahassee, FL $2.59 $12.28 $14.87 $4.99 $11.95 $16.94 $8.43 $39.99 $48.41 $7.01 $16.79 $23.80 Tampa-St. Petersburg-Clearwater, FL $1.90 $1.63 $3.54 $3.67 $1.59 $5.26 $8.59 $7.38 $15.97 $7.15 $3.10 $10.25
280 Terre Haute, IN $0.90 $5.42 $6.31 $1.73 $5.27 $7.00 $3.91 $23.62 $27.53 $3.25 $9.92 $13.17 Texarkana, TX-Texarkana, AR $0.77 $5.09 $5.85 $1.48 $4.95 $6.43 $3.10 $20.51 $23.61 $2.58 $8.61 $11.19 Topeka, KS $2.94 $17.65 $20.59 $5.67 $17.17 $22.84 $11.22 $67.38 $78.60 $9.34 $28.29 $37.63 Tucson, AZ $4.16 $6.07 $10.24 $8.03 $5.91 $13.94 $15.88 $23.16 $39.03 $13.21 $9.72 $22.94 Tulsa, OK $1.05 $2.02 $3.07 $2.03 $1.96 $4.00 $5.10 $9.79 $14.89 $4.25 $4.11 $8.36 Tuscaloosa, AL $1.13 $5.19 $6.33 $2.18 $5.05 $7.23 $4.73 $21.73 $26.47 $3.94 $9.12 $13.07 Tyler, TX $0.57 $3.90 $4.47 $1.10 $3.79 $4.89 $2.52 $17.26 $19.78 $2.10 $7.24 $9.34 Utica-Rome, NY $2.03 $11.65 $13.68 $3.92 $11.33 $15.25 $7.10 $40.70 $47.80 $5.91 $17.09 $23.00 Vallejo-Fairfield, CA $10.82 $28.98 $39.80 $20.86 $28.19 $49.05 $45.94 $123.10 $169.05 $38.24 $51.68 $89.92 Victoria, TX $3.88 $17.29 $21.17 $7.49 $16.82 $24.30 $20.72 $92.30 $113.03 $17.25 $38.75 $56.00 Virginia Beach-Norfolk-Newport News, VA-NC $5.33 $4.02 $9.35 $10.27 $3.91 $14.18 $21.84 $16.49 $38.34 $18.18 $6.93 $25.10 Visalia-Porterville, CA $2.04 $6.55 $8.59 $3.94 $6.37 $10.31 $8.69 $27.85 $36.55 $7.23 $11.69 $18.93 Waco, TX $1.73 $9.36 $11.09 $3.34 $9.10 $12.44 $7.61 $41.07 $48.69 $6.34 $17.24 $23.58 Washington-Arlington-Alexandria, DC-VA-MD-WV $5.70 $5.27 $10.96 $10.98 $5.12 $16.11 $22.73 $21.03 $43.76 $18.92 $8.83 $27.75 Waterloo-Cedar Falls, IA $2.27 $13.95 $16.22 $4.37 $13.57 $17.94 $10.67 $65.69 $76.36 $8.88 $27.58 $36.46 Wausau, WI $3.43 $49.38 $52.81 $6.61 $48.04 $54.65 $14.72 $212.04 $226.76 $12.25 $89.02 $101.28 Weirton-Steubenville, WV-OH $1.62 $9.49 $11.10 $3.12 $9.23 $12.34 $8.21 $48.18 $56.38 $6.83 $20.23 $27.06 Wenatchee, WA $5.36 $73.42 $78.79 $10.34 $71.42 $81.77 $21.36 $292.41 $313.76 $17.78 $122.77 $140.54 Wheeling, WV-OH $1.33 $16.00 $17.32 $2.56 $15.56 $18.12 $6.35 $76.61 $82.96 $5.29 $32.16 $37.45 Wichita Falls, TX $1.89 $9.99 $11.88 $3.64 $9.72 $13.35 $8.47 $44.87 $53.34 $7.05 $18.84 $25.89 Wichita, KS $1.84 $4.04 $5.89 $3.56 $3.93 $7.49 $8.34 $18.27 $26.61 $6.94 $7.67 $14.61 Williamsport, PA $2.41 $46.37 $48.77 $4.64 $45.10 $49.74 $10.48 $202.00 $212.48 $8.72 $84.81 $93.54 Winchester, VA-WV $2.34 $50.34 $52.69 $4.52 $48.97 $53.49 $10.27 $220.69 $230.96 $8.55 $92.65 $101.21 Yakima, WA $2.06 $10.89 $12.95 $3.97 $10.60 $14.57 $9.06 $47.93 $56.99 $7.54 $20.12 $27.66 York-Hanover, PA $0.48 $4.35 $4.82 $0.92 $4.23 $5.15 $2.13 $19.48 $21.60 $1.77 $8.18 $9.95 Youngstown-Warren-Boardman, OH-PA $1.90 $3.56 $5.46 $3.66 $3.46 $7.13 $8.22 $15.40 $23.62 $6.84 $6.47 $13.30 Yuba City, CA $4.66 $27.91 $32.57 $8.99 $27.15 $36.14 $17.64 $105.62 $123.26 $14.68 $44.34 $59.03 Yuma, AZ $1.09 $5.69 $6.78 $2.09 $5.54 $7.63 $4.55 $23.87 $28.43 $3.79 $10.02 $13.81
281 APPENDIX H: MARGINAL CHANGES IN TOTAL WAGES AND GDP TABLE H 1 Marginal change in total payroll for a 1% increase in track mileage Employment density model based on total track miles, Population model based on track miles per capita MSA name WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employment density change Percent due to population change Albuquerque, NM $33,991,645 1.07% 98.93% $54,077,252 4.75% 95.25% Atlanta-Sandy Springs-Marietta, GA $9,287,398 29.96% 70.04% $29,554,280 66.28% 33.72% Baltimore-Towson, MD $39,100,410 5.65% 94.35% $72,064,169 21.59% 78.41% Buffalo-Niagara Falls, NY $3,598,341 1.95% 98.05% $5,898,381 8.38% 91.62% Charlotte-Gastonia-Concord, NC-SC $4,786,750 14.29% 85.71% $11,101,410 43.39% 56.61% Chicago-Naperville-Joliet, IL-IN-WI $148,945,106 39.42% 60.58% $551,621,709 74.94% 25.06% Cleveland-Elyria-Mentor, OH $13,761,642 5.74% 94.26% $25,432,050 21.87% 78.13% Dallas-Fort Worth-Arlington, TX $24,130,052 35.63% 64.37% $84,330,923 71.79% 28.21% Denver-Aurora, CO $22,266,386 4.78% 95.22% $39,973,326 18.76% 81.24% Houston-Baytown-Sugar Land, TX $4,111,935 26.22% 73.78% $12,237,727 62.02% 37.98% Little Rock-North Little Rock, AR $3,442,277 1.02% 98.98% $5,466,692 4.53% 95.47% Los Angeles-Long Beach-Santa Ana, CA $128,052,642 39.05% 60.95% $471,630,807 74.65% 25.35% Memphis, TN-MS-AR $4,303,294 3.43% 96.57% $7,404,392 14.03% 85.97% Miami-Fort Lauderdale-Miami Beach, FL $24,928,344 16.02% 83.98% $60,187,210 46.72% 53.28% Minneapolis-St. Paul-Bloomington, MN-WI $4,732,561 10.33% 89.67% $9,942,037 34.62% 65.38% Nashville-Davidson--Murfreesboro, TN $16,236,196 5.77% 94.23% $30,033,476 21.97% 78.03% New Orleans-Metairie-Kenner, LA $9,782,702 1.87% 98.13% $15,992,353 8.05% 91.95% New York-Northern New Jersey-Long Island, NY- NJ-PA $239,922,529 41.72% 58.28% $919,047,805 76.70% 23.30% Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $65,657,218 20.83% 79.17% $175,919,501 54.74% 45.26% Pittsburgh, PA $5,982,045 5.47% 94.53% $10,965,662 21.01% 78.99% Portland-Vancouver-Beaverton, OR-WA $26,970,381 4.14% 95.86% $47,466,618 16.57% 83.43% Providence-New Bedford-Fall River, RI-MA $2,461,640 3.93% 96.07% $4,303,392 15.82% 84.18%
282 Sacramento--Arden-Arcade--Roseville, CA $20,919,011 4.31% 95.69% $37,010,488 17.15% 82.85% St. Louis, MO-IL $13,650,355 8.49% 91.51% $27,290,744 29.88% 70.12% Salt Lake City, UT $23,028,380 0.80% 99.20% $36,287,753 3.57% 96.43% San Diego-Carlsbad-San Marcos, CA $37,266,491 8.93% 91.07% $75,420,278 31.07% 68.93% San Francisco-Oakland-Fremont, CA $106,951,042 7.42% 92.58% $207,521,142 26.91% 73.09% San Jose-Sunnyvale-Santa Clara, CA $268,942,069 1.80% 98.20% $438,625,573 7.77% 92.23% Seattle-Tacoma-Bellevue, WA $33,391,174 9.00% 91.00% $67,705,044 31.25% 68.75% Tampa-St. Petersburg-Clearwater, FL $639,029 9.15% 90.85% $1,300,981 31.64% 68.36% Trenton-Ewing, NJ $23,033,425 0.16% 99.84% $35,482,726 0.72% 99.28% Washington-Arlington-Alexandria, DC-VA-MD- WV $106,168,437 12.68% 87.32% $236,797,624 40.03% 59.97%
283 TABLE H 2 Marginal change in total GDP for a 1% increase in track mileage Employment density model based on total track miles, Population model based on track miles per capita MSA name GDP-OLS Percent due to employment density change Percent due to population change GDP-IV Percent due to employment density change Percent due to population change Albuquerque, NM $78,082,482 2.01% 97.99% $153,257,118 3.11% 96.89% Atlanta-Sandy Springs-Marietta, GA $28,838,588 44.69% 55.31% $70,122,943 55.85% 44.15% Baltimore-Towson, MD $85,152,535 10.16% 89.84% $174,761,250 15.05% 84.95% Buffalo-Niagara Falls, NY $8,454,953 3.62% 96.38% $16,744,783 5.56% 94.44% Charlotte-Gastonia-Concord, NC-SC $17,605,163 23.95% 76.05% $38,798,839 33.03% 66.97% Chicago-Naperville-Joliet, IL-IN-WI $485,286,573 55.14% 44.86% $1,235,725,062 65.81% 34.19% Cleveland-Elyria-Mentor, OH $36,194,691 10.32% 89.68% $74,345,075 15.27% 84.73% Dallas-Fort Worth-Arlington, TX $85,888,738 51.12% 48.88% $214,909,584 62.09% 37.91% Denver-Aurora, CO $57,480,771 8.66% 91.34% $117,024,029 12.94% 87.06% Houston-Baytown-Sugar Land, TX $15,194,890 40.16% 59.84% $36,192,233 51.25% 48.75% Little Rock-North Little Rock, AR $8,003,666 1.91% 98.09% $15,700,999 2.97% 97.03% Los Angeles-Long Beach-Santa Ana, CA $443,934,479 54.75% 45.25% $1,128,549,916 65.46% 34.54% Memphis, TN-MS-AR $11,266,756 6.28% 93.72% $22,643,118 9.50% 90.50% Miami-Fort Lauderdale-Miami Beach, FL $73,361,806 26.49% 73.51% $163,720,316 36.07% 63.93% Minneapolis-St. Paul-Bloomington, MN- WI $11,822,754 17.87% 82.13% $25,265,005 25.41% 74.59% Nashville-Davidson--Murfreesboro, TN $42,262,930 10.37% 89.63% $86,834,592 15.34% 84.66% New Orleans-Metairie-Kenner, LA $32,111,059 3.47% 96.53% $63,543,119 5.33% 94.67% New York-Northern New Jersey-Long Island, NY-NJ-PA $781,950,804 57.49% 42.51% $2,011,359,930 67.93% 32.07% Philadelphia-Camden-Wilmington, PA-NJ- DE-MD $199,162,452 33.20% 66.80% $459,145,804 43.77% 56.23% Pittsburgh, PA $16,199,213 9.85% 90.15% $33,190,991 14.61% 85.39% Portland-Vancouver-Beaverton, OR-WA $68,845,843 7.55% 92.45% $139,315,687 11.33% 88.67%
284 Providence-New Bedford-Fall River, RI- MA $6,030,444 7.17% 92.83% $12,178,221 10.79% 89.21% Sacramento--Arden-Arcade--Roseville, CA $44,039,379 7.84% 92.16% $89,260,482 11.76% 88.24% St. Louis, MO-IL $33,555,921 14.90% 85.10% $70,615,868 21.53% 78.47% Salt Lake City, UT $56,905,320 1.50% 98.50% $111,372,836 2.33% 97.67% San Diego-Carlsbad-San Marcos, CA $95,951,935 15.63% 84.37% $202,687,453 22.49% 77.51% San Francisco-Oakland-Fremont, CA $268,939,794 13.14% 86.86% $560,755,972 19.16% 80.84% San Jose-Sunnyvale-Santa Clara, CA $593,682,535 3.35% 96.65% $1,173,982,610 5.14% 94.86% Seattle-Tacoma-Bellevue, WA $85,387,014 15.74% 84.26% $180,475,745 22.64% 77.36% Tampa-St. Petersburg-Clearwater, FL $1,651,899 15.98% 84.02% $3,495,877 22.95% 77.05% Trenton-Ewing, NJ $45,380,901 0.30% 99.70% $88,219,532 0.47% 99.53% Washington-Arlington-Alexandria, DC-VA- MD-WV $249,594,338 21.53% 78.47% $543,408,118 30.05% 69.95%
285 TABLE H 3 Marginal change in total payroll for a 1 mile increase in track mileage Employment density model based on total track miles, Population model based on track miles per capita MSA name Percent change in track miles WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employment density change Percent due to population change Albuquerque, NM 2.38% $86,472,671 1.00% 99.00% $160,142,862 3.82% 96.18% Atlanta-Sandy Springs-Marietta, GA 2.02% $22,307,107 25.20% 74.80% $69,599,386 56.86% 43.14% Baltimore-Towson, MD 0.93% $37,640,530 5.48% 94.52% $78,549,156 18.51% 81.49% Buffalo-Niagara Falls, NY 15.63% $63,604,786 1.72% 98.28% $120,190,345 6.42% 93.58% Charlotte-Gastonia-Concord, NC-SC 10.42% $63,731,060 11.18% 88.82% $152,027,143 33.01% 66.99% Chicago-Naperville-Joliet, IL-IN-WI 0.14% $19,096,510 44.47% 55.53% $78,880,073 75.81% 24.19% Cleveland-Elyria-Mentor, OH 2.90% $43,813,617 5.23% 94.77% $90,836,641 17.75% 82.25% Dallas-Fort Worth-Arlington, TX 1.18% $26,112,657 38.92% 61.08% $100,257,612 71.37% 28.63% Denver-Aurora, CO 2.89% $45,939,589 6.70% 93.30% $98,790,335 21.93% 78.07% Houston-Baytown-Sugar Land, TX 6.99% $26,181,930 28.79% 71.21% $86,625,122 61.28% 38.72% Little Rock-North Little Rock, AR 29.41% $118,974,647 0.87% 99.13% $219,496,910 3.32% 96.68% Los Angeles-Long Beach-Santa Ana, CA 0.21% $17,976,104 57.79% 42.21% $86,802,485 84.27% 15.73% Memphis, TN-MS-AR 14.29% $66,279,838 3.18% 96.82% $130,303,706 11.39% 88.61% Miami-Fort Lauderdale-Miami Beach, FL 1.04% $18,740,639 22.24% 77.76% $55,572,073 52.82% 47.18% Minneapolis-St. Paul-Bloomington, MN-WI 8.26% $39,679,135 10.18% 89.82% $92,568,735 30.73% 69.27% Nashville-Davidson--Murfreesboro, TN 3.13% $61,214,695 4.78% 95.22% $125,495,300 16.43% 83.57% New Orleans-Metairie-Kenner, LA 7.75% $74,000,307 1.92% 98.08% $140,579,235 7.10% 92.90% New York-Northern New Jersey-Long Island, NY-NJ-PA 0.08% $14,627,364 55.05% 44.95% $68,529,442 82.74% 17.26% Philadelphia-Camden-Wilmington, PA-NJ-DE- MD 0.30% $20,378,041 19.96% 80.04% $57,989,468 49.39% 50.61% Pittsburgh, PA 4.55% $37,340,437 3.98% 96.02% $74,982,004 13.97% 86.03% Portland-Vancouver-Beaverton, OR-WA 2.07% $43,432,592 5.31% 94.69% $90,245,900 18.01% 81.99%
286 Providence-New Bedford-Fall River, RI-MA 14.71% $47,176,904 3.01% 96.99% $92,338,961 10.84% 89.16% Sacramento-Arden-Arcade-Roseville, CA 2.71% $41,638,274 5.87% 94.13% $87,727,581 19.61% 80.39% St. Louis, MO-IL 2.18% $33,565,940 7.52% 92.48% $73,623,076 24.13% 75.87% Salt Lake City, UT 5.26% $89,516,432 1.08% 98.92% $166,141,969 4.10% 95.90% San Diego-Carlsbad-San Marcos, CA 1.08% $34,975,938 10.24% 89.76% $81,711,600 30.87% 69.13% San Francisco-Oakland-Fremont, CA 0.54% $36,359,316 11.70% 88.30% $87,713,333 34.14% 65.86% San Jose-Sunnyvale-Santa Clara, CA 0.63% $97,209,419 3.14% 96.86% $190,926,807 11.27% 88.73% Seattle-Tacoma-Bellevue, WA 1.15% $38,792,883 8.92% 91.08% $87,949,001 27.72% 72.28% Tampa-St. Petersburg-Clearwater, FL 41.67% $29,335,742 8.30% 91.70% $65,554,205 26.17% 73.83% Trenton-Ewing, NJ 14.49% $373,370,860 0.14% 99.86% $674,573,285 0.55% 99.45% Washington-Arlington-Alexandria, DC-VA-MD- WV 0.31% $28,968,290 14.21% 85.79% $73,703,681 39.33% 60.67%
287 TABLE H 4 Marginal change in total GDP for a 1 mile increase in track mileage Employment density model based on total track miles, Population model based on track miles per capita MSA name Percent change in track miles GDP-OLS Percent due to employment density change Percent due to population change GDP-IV Percent due to employment density change Percent due to population change Albuquerque, NM 2.38% $198,516,791 1.88% 98.12% $455,383,344 2.49% 97.51% Atlanta-Sandy Springs-Marietta, GA 2.02% $66,951,597 38.89% 61.11% $172,399,371 45.90% 54.10% Baltimore-Towson, MD 0.93% $81,858,842 9.88% 90.12% $192,751,680 12.75% 87.25% Buffalo-Niagara Falls, NY 15.63% $149,155,458 3.21% 96.79% $343,654,232 4.23% 95.77% Charlotte-Gastonia-Concord, NC-SC 10.42% $228,645,304 19.21% 80.79% $554,595,417 24.07% 75.93% Chicago-Naperville-Joliet, IL-IN-WI 0.14% $64,290,247 60.21% 39.79% $175,957,629 66.86% 33.14% Cleveland-Elyria-Mentor, OH 2.90% $114,733,569 9.43% 90.57% $269,772,907 12.19% 87.81% Dallas-Fort Worth-Arlington, TX 1.18% $95,009,209 54.62% 45.38% $256,001,771 61.61% 38.39% Denver-Aurora, CO 2.89% $120,531,826 11.94% 88.06% $285,702,973 15.31% 84.69% Houston-Baytown-Sugar Land, TX 6.99% $98,547,580 43.30% 56.70% $257,066,372 50.45% 49.55% Little Rock-North Little Rock, AR 29.41% $276,257,476 1.63% 98.37% $633,191,163 2.16% 97.84% Los Angeles-Long Beach-Santa Ana, CA 0.21% $70,029,302 72.12% 27.88% $198,000,049 77.52% 22.48% Memphis, TN-MS-AR 14.29% $173,159,880 5.84% 94.16% $402,425,070 7.64% 92.36% Miami-Fort Lauderdale-Miami Beach, FL 1.04% $57,823,257 35.08% 64.92% $147,223,691 41.88% 58.12% Minneapolis-St. Paul-Bloomington, MN-WI 8.26% $99,006,181 17.63% 82.37% $238,959,534 22.21% 77.79% Nashville-Davidson--Murfreesboro, TN 3.13% $158,011,105 8.67% 91.33% $370,612,948 11.23% 88.77% New Orleans-Metairie-Kenner, LA 7.75% $242,999,463 3.56% 96.44% $560,519,361 4.69% 95.31% New York-Northern New Jersey-Long Island, NY-NJ-PA 0.08% $51,792,905 69.82% 30.18% $145,534,856 75.52% 24.48% Philadelphia-Camden-Wilmington, PA-NJ-DE- MD 0.30% $61,411,763 32.03% 67.97% $154,934,481 38.58% 61.42% Pittsburgh, PA 4.55% $99,842,426 7.27% 92.73% $233,115,300 9.46% 90.54% Portland-Vancouver-Beaverton, OR-WA 2.07% $111,982,325 9.59% 90.41% $263,432,198 12.38% 87.62%
288 Providence-New Bedford-Fall River, RI-MA 14.71% $114,665,052 5.55% 94.45% $266,224,825 7.26% 92.74% Sacramento--Arden-Arcade--Roseville, CA 2.71% $88,827,731 10.54% 89.46% $209,603,489 13.57% 86.43% St. Louis, MO-IL 2.18% $81,853,446 13.31% 86.69% $194,872,744 16.99% 83.01% Salt Lake City, UT 5.26% $221,755,815 2.02% 97.98% $508,933,112 2.68% 97.32% San Diego-Carlsbad-San Marcos, CA 1.08% $91,027,595 17.73% 82.27% $219,771,474 22.32% 77.68% San Francisco-Oakland-Fremont, CA 0.54% $94,693,615 20.01% 79.99% $230,261,684 25.01% 74.99% San Jose-Sunnyvale-Santa Clara, CA 0.63% $217,110,470 5.78% 94.22% $504,460,609 7.56% 92.44% Seattle-Tacoma-Bellevue, WA 1.15% $99,138,776 15.62% 84.38% $237,762,884 19.79% 80.21% Tampa-St. Petersburg-Clearwater, FL 41.67% $75,306,377 14.61% 85.39% $180,027,652 18.57% 81.43% Trenton-Ewing, NJ 14.49% $735,513,043 0.27% 99.73% $1,678,199,587 0.36% 99.64% Washington-Arlington-Alexandria, DC-VA-MD- WV 0.31% $68,935,803 23.84% 76.16% $169,628,886 29.44% 70.56%
289 TABLE H 5 Marginal change in total payroll for a 1% increase in rail revenue miles MSA name WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employment density change Percent due to population change Atlanta-Sandy Springs-Marietta, GA $26,042,083 29.13% 70.87% $68,130,757 38.00% 62.00% Baltimore-Towson, MD $18,191,884 12.47% 87.53% $44,186,842 17.52% 82.48% Buffalo-Niagara Falls, NY $1,383,711 9.30% 90.70% $3,311,679 13.26% 86.74% Charlotte-Gastonia-Concord, NC-SC $2,354,229 32.66% 67.34% $6,252,320 41.96% 58.04% Chicago-Naperville-Joliet, IL-IN-WI $304,841,090 35.62% 64.38% $819,739,008 45.20% 54.80% Cleveland-Elyria-Mentor, OH $9,486,575 14.76% 85.24% $23,285,906 20.51% 79.49% Dallas-Fort Worth-Arlington, TX $24,262,381 39.93% 60.07% $66,417,951 49.77% 50.23% Denver-Aurora, CO $22,221,228 15.88% 84.12% $54,824,795 21.96% 78.04% Houston-Baytown-Sugar Land, TX $5,686,135 31.70% 68.30% $15,039,900 40.90% 59.10% Little Rock-North Little Rock, AR $216,489 7.86% 92.14% $514,633 11.29% 88.71% Los Angeles-Long Beach-Santa Ana, CA $247,961,604 41.34% 58.66% $682,737,774 51.24% 48.76% Memphis, TN-MS-AR $2,053,363 14.25% 85.75% $5,028,643 19.86% 80.14% Miami-Fort Lauderdale-Miami Beach, FL $11,550,388 25.47% 74.53% $29,742,760 33.76% 66.24% Minneapolis-St. Paul-Bloomington, MN-WI $7,011,804 18.71% 81.29% $17,522,581 25.54% 74.46% Nashville-Davidson--Murfreesboro, TN $497,080 17.51% 82.49% $1,235,505 24.04% 75.96% New Orleans-Metairie-Kenner, LA $3,363,330 9.90% 90.10% $8,072,123 14.07% 85.93% New York-Northern New Jersey-Long Island, NY-NJ-PA $589,594,879 29.79% 70.21% $1,546,867,660 38.75% 61.25% Philadelphia-Camden-Wilmington, PA-NJ-DE- MD $85,213,469 21.63% 78.37% $215,747,077 29.15% 70.85% Pittsburgh, PA $5,414,659 10.37% 89.63% $13,024,351 14.72% 85.28% Portland-Vancouver-Beaverton, OR-WA $19,170,952 14.34% 85.66% $46,967,556 19.97% 80.03% Sacramento--Arden-Arcade--Roseville, CA $7,480,432 16.32% 83.68% $18,493,054 22.53% 77.47% St. Louis, MO-IL $16,621,241 15.94% 84.06% $41,019,293 22.04% 77.96% Salt Lake City, UT $8,045,613 6.07% 93.93% $18,963,941 8.79% 91.21% San Diego-Carlsbad-San Marcos, CA $20,458,408 19.90% 80.10% $51,400,279 27.03% 72.97%
290 San Francisco-Oakland-Fremont, CA $258,978,768 16.97% 83.03% $642,134,240 23.35% 76.65% San Jose-Sunnyvale-Santa Clara, CA $18,993,817 10.54% 89.46% $45,722,892 14.94% 85.06% Seattle-Tacoma-Bellevue, WA $2,287,447 16.10% 83.90% $5,649,379 22.25% 77.75% Tampa-St. Petersburg-Clearwater, FL $345,670 17.90% 82.10% $860,711 24.54% 75.46% Washington-Arlington-Alexandria, DC-VA-MD- WV $123,670,183 16.84% 83.16% $306,458,098 23.19% 76.81%
291 TABLE H 6 Marginal change in total GDP for a 1% increase in rail revenue miles MSA name GDP-OLS Percent due to employment density change Percent due to population change GDP-IV Percent due to employment density change Percent due to population change Atlanta-Sandy Springs-Marietta, GA $120,631,840 29.13% 70.87% $136,214,879 38.00% 62.00% Baltimore-Towson, MD $71,262,646 12.47% 87.53% $74,708,895 17.52% 82.48% Buffalo-Niagara Falls, NY $6,037,309 9.30% 90.70% $6,236,508 13.26% 86.74% Charlotte-Gastonia-Concord, NC-SC $14,512,806 32.66% 67.34% $16,635,617 41.96% 58.04% Chicago-Naperville-Joliet, IL-IN-WI $1,389,369,258 35.62% 64.38% $1,612,556,257 45.20% 54.80% Cleveland-Elyria-Mentor, OH $44,845,568 14.76% 85.24% $47,511,454 20.51% 79.49% Dallas-Fort Worth-Arlington, TX $123,893,919 39.93% 60.07% $146,385,192 49.77% 50.23% Denver-Aurora, CO $103,946,980 15.88% 84.12% $110,691,922 21.96% 78.04% Houston-Baytown-Sugar Land, TX $32,190,687 31.70% 68.30% $36,749,641 40.90% 59.10% Little Rock-North Little Rock, AR $942,327 7.86% 92.14% $966,850 11.29% 88.71% Los Angeles-Long Beach-Santa Ana, CA $1,205,446,794 41.34% 58.66% $1,432,559,389 51.24% 48.76% Memphis, TN-MS-AR $9,855,499 14.25% 85.75% $10,417,386 19.86% 80.14% Miami-Fort Lauderdale-Miami Beach, FL $56,207,721 25.47% 74.53% $62,470,613 33.76% 66.24% Minneapolis-St. Paul-Bloomington, MN-WI $30,307,784 18.71% 81.29% $32,690,200 25.54% 74.46% Nashville-Davidson--Murfreesboro, TN $2,324,988 17.51% 82.49% $2,494,216 24.04% 75.96% New Orleans-Metairie-Kenner, LA $20,514,468 9.90% 90.10% $21,250,720 14.07% 85.93% New York-Northern New Jersey-Long Island, NY-NJ-PA $2,647,823,347 29.79% 70.21% $2,998,358,502 38.75% 61.25% Philadelphia-Camden-Wilmington, PA-NJ-DE- MD $412,001,883 21.63% 78.37% $450,226,413 29.15% 70.85% Pittsburgh, PA $26,414,861 10.37% 89.63% $27,423,851 14.72% 85.28% Portland-Vancouver-Beaverton, OR-WA $89,162,731 14.34% 85.66% $94,282,858 19.97% 80.03% Sacramento--Arden-Arcade--Roseville, CA $28,651,630 16.32% 83.68% $30,572,179 22.53% 77.47% St. Louis, MO-IL $71,772,064 15.94% 84.06% $76,449,606 22.04% 77.96%
292 Salt Lake City, UT $37,293,079 6.07% 93.93% $37,939,605 8.79% 91.21% San Diego-Carlsbad-San Marcos, CA $92,188,682 19.90% 80.10% $99,969,229 27.03% 72.97% San Francisco-Oakland-Fremont, CA $1,154,139,859 16.97% 83.03% $1,235,137,198 23.35% 76.65% San Jose-Sunnyvale-Santa Clara, CA $77,959,092 10.54% 89.46% $80,999,682 14.94% 85.06% Seattle-Tacoma-Bellevue, WA $10,231,363 16.10% 83.90% $10,906,322 22.25% 77.75% Tampa-St. Petersburg-Clearwater, FL $1,561,034 17.90% 82.10% $1,677,657 24.54% 75.46% Washington-Arlington-Alexandria, DC-VA- MD-WV $493,588,144 16.84% 83.16% $527,917,323 23.19% 76.81%
293 TABLE H 7 Marginal change in total GDP for a 1% increase in total revenue miles name WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employmen t density change Percent due to population change Abilene, TX $291,922 5.58% 94.42% $634,495 10.32% 89.68% Albany, GA $290,464 6.96% 93.04% $639,121 12.70% 87.30% Albany-Schenectady-Troy, NY $6,837,112 7.47% 92.53% $15,113,081 13.58% 86.42% Albuquerque, NM $4,242,532 12.09% 87.91% $9,760,519 21.11% 78.89% Alexandria, LA $318,704 4.08% 95.92% $683,331 7.64% 92.36% Allentown-Bethlehem-Easton, PA-NJ $1,980,748 10.70% 89.30% $4,503,158 18.91% 81.09% Altoona, PA $282,607 1.72% 98.28% $592,957 3.30% 96.70% Amarillo, TX $529,240 6.87% 93.13% $1,163,592 12.55% 87.45% Ames, IA $767,350 1.88% 98.12% $1,612,358 3.60% 96.40% Anchorage, AK $2,704,765 8.45% 91.55% $6,030,373 15.23% 84.77% Anderson, IN $128,853 7.11% 92.89% $283,910 12.96% 87.04% Ann Arbor, MI $3,971,860 3.30% 96.70% $8,456,085 6.23% 93.77% Appleton, WI $922,115 2.90% 97.10% $1,955,950 5.50% 94.50% Athens-Clarke County, GA $526,148 6.75% 93.25% $1,155,606 12.35% 87.65% Atlanta-Sandy Springs-Marietta, GA $63,321,683 41.78% 58.22% $182,406,521 58.27% 41.73% Auburn-Opelika, AL $39,342 5.25% 94.75% $85,257 9.74% 90.26% Augusta-Richmond County, GA-SC $484,059 20.41% 79.59% $1,192,326 33.29% 66.71% Bakersfield, CA $2,398,611 14.34% 85.66% $5,623,851 24.58% 75.42% Bangor, ME $393,778 3.11% 96.89% $836,831 5.87% 94.13% Baton Rouge, LA $2,174,753 12.87% 87.13% $5,036,366 22.32% 77.68% Battle Creek, MI $265,609 4.23% 95.77% $570,297 7.92% 92.08% Bay City, MI $601,919 2.78% 97.22% $1,275,373 5.28% 94.72% Beaumont-Port Arthur, TX $811,927 10.47% 89.53% $1,842,208 18.53% 81.47%
294 Bellingham, WA $1,426,129 3.91% 96.09% $3,053,265 7.35% 92.65% Bend, OR $144,521 3.81% 96.19% $309,106 7.15% 92.85% Billings, MT $506,593 3.69% 96.31% $1,082,392 6.94% 93.06% Binghamton, NY $1,343,806 2.87% 97.13% $2,849,574 5.44% 94.56% Birmingham-Hoover, AL $2,995,317 21.26% 78.74% $7,427,722 34.45% 65.55% Bismarck, ND $274,214 1.91% 98.09% $576,343 3.65% 96.35% Blacksburg-Christiansburg-Radford, VA $439,850 5.08% 94.92% $951,731 9.44% 90.56% Bloomington, IN $613,030 3.39% 96.61% $1,306,196 6.39% 93.61% Bloomington-Normal, IL $1,040,882 3.24% 96.76% $2,214,826 6.12% 93.88% Boise City-Nampa, ID $1,194,558 10.36% 89.64% $2,707,935 18.37% 81.63% Bradenton-Sarasota-Venice, FL $2,621,663 9.97% 90.03% $5,922,904 17.73% 82.27% Bremerton-Silverdale, WA $1,537,388 5.19% 94.81% $3,329,823 9.63% 90.37% Brownsville-Harlingen, TX $305,886 10.17% 89.83% $692,248 18.05% 81.95% Brunswick, GA $6,064,858 2.29% 97.71% $12,791,645 4.36% 95.64% Canton-Massillon, OH $1,695,525 10.01% 89.99% $3,832,043 17.80% 82.20% Cape Coral-Fort Myers, FL $2,066,976 17.18% 82.82% $4,960,750 28.75% 71.25% Casper, WY $222,389 2.34% 97.66% $469,289 4.46% 95.54% Cedar Rapids, IA $1,008,027 5.11% 94.89% $2,181,731 9.49% 90.51% Champaign-Urbana, IL $2,593,377 3.68% 96.32% $5,540,406 6.92% 93.08% Charleston, WV $1,896,962 4.59% 95.41% $4,086,404 8.57% 91.43% Charleston-North Charleston, SC $2,373,717 14.63% 85.37% $5,578,848 25.01% 74.99% Charlotte-Gastonia-Concord, NC-SC $21,914,522 45.84% 54.16% $64,867,708 62.23% 37.77% Chattanooga, TN-GA $1,591,460 12.65% 87.35% $3,678,885 21.99% 78.01% Cheyenne, WY $320,127 1.94% 98.06% $673,026 3.71% 96.29% Chicago-Naperville-Joliet, IL-IN-WI $267,871,408 49.13% 50.87% $810,102,746 65.27% 34.73% Chico, CA $540,627 5.18% 94.82% $1,170,773 9.60% 90.40% Cincinnati-Middletown, OH-KY-IN $15,988,468 24.17% 75.83% $40,556,853 38.28% 61.72% Clarksville, TN-KY $537,114 16.01% 83.99% $1,276,822 27.06% 72.94% Cleveland-Elyria-Mentor, OH $31,042,457 23.20% 76.80% $78,157,608 37.03% 62.97% College Station-Bryan, TX $1,193,223 7.16% 92.84% $2,630,311 13.05% 86.95%
295 Columbia, MO $475,217 4.15% 95.85% $1,019,603 7.77% 92.23% Columbia, SC $1,340,148 10.89% 89.11% $3,051,848 19.22% 80.78% Columbus, GA-AL $686,598 9.21% 90.79% $1,541,056 16.50% 83.50% Columbus, OH $9,546,009 23.74% 76.26% $24,134,311 37.73% 62.27% Corpus Christi, TX $2,163,828 10.70% 89.30% $4,919,628 18.92% 81.08% Cumberland, MD-WV $132,626 2.50% 97.50% $280,286 4.76% 95.24% Dallas-Fort Worth-Arlington, TX $80,466,977 53.71% 46.29% $250,548,849 69.31% 30.69% Davenport-Moline-Rock Island, IA-IL $2,830,835 8.61% 91.39% $6,320,574 15.50% 84.50% Dayton, OH $5,103,956 14.51% 85.49% $11,983,980 24.83% 75.17% Decatur, IL $0 0.00% 0.00% $0 0.00% 0.00% Deltona-Daytona Beach-Ormond Beach, FL $773,983 3.42% 96.58% $1,649,529 6.44% 93.56% Denver-Aurora, CO $1,264,089 17.37% 82.63% $3,038,578 29.03% 70.97% Des Moines, IA $66,407,587 24.78% 75.22% $169,245,579 39.07% 60.93% Detroit-Warren-Livonia, MI $2,634,021 8.34% 91.66% $5,866,890 15.04% 84.96% Dubuque, IA $39,377,246 42.32% 57.68% $113,846,324 58.81% 41.19% Duluth, MN-WI $0 0.00% 0.00% $0 0.00% 0.00% Eau Claire, WI $251,457 2.08% 97.92% $529,357 3.98% 96.02% El Centro, CA $1,316,041 7.31% 92.69% $2,904,901 13.31% 86.69% El Paso, TX $534,424 3.42% 96.58% $1,139,053 6.45% 93.55% Elkhart-Goshen, IN $260,097 3.11% 96.89% $552,754 5.88% 94.12% Elmira, NY $3,618,059 12.95% 87.05% $8,384,562 22.45% 77.55% Erie, PA $360,797 4.40% 95.60% $775,856 8.22% 91.78% Eugene-Springfield, OR $468,696 1.25% 98.75% $979,041 2.40% 97.60% Evansville, IN-KY $1,132,100 3.70% 96.30% $2,419,096 6.96% 93.04% Fairbanks, AK $2,343,795 6.71% 93.29% $5,145,814 12.28% 87.72% Fayetteville-Springdale-Rogers, AR-MO $920,566 5.96% 94.04% $2,007,674 10.98% 89.02% Flagstaff, AZ $353,382 3.21% 96.79% $751,697 6.06% 93.94% Flint, MI $518,803 11.92% 88.08% $1,191,827 20.84% 79.16% Florence, SC $481,702 3.23% 96.77% $1,024,878 6.10% 93.90% Fond du Lac, WI $2,035,516 9.66% 90.34% $4,586,537 17.23% 82.77%
296 Fort Collins-Loveland, CO $105,398 6.45% 93.55% $230,867 11.83% 88.17% Fort Smith, AR-OK $120,452 1.73% 98.27% $252,747 3.32% 96.68% Fort Walton Beach-Crestview-Destin, FL $829,453 7.11% 92.89% $1,827,512 12.96% 87.04% Fort Wayne, IN $179,884 6.51% 93.49% $394,240 11.93% 88.07% Fresno, CA $356,738 3.95% 96.05% $763,981 7.40% 92.60% Gainesville, FL $1,310,907 10.91% 89.09% $2,985,840 19.25% 80.75% Glens Falls, NY $3,262,745 14.87% 85.13% $7,683,392 25.36% 74.64% Grand Forks, ND-MN $2,193,901 7.12% 92.88% $4,834,380 12.98% 87.02% Grand Junction, CO $190,196 1.32% 98.68% $397,559 2.54% 97.46% Grand Rapids-Wyoming, MI $285,903 2.09% 97.91% $601,916 3.99% 96.01% Great Falls, MT $607,067 4.43% 95.57% $1,305,841 8.28% 91.72% Greeley, CO $3,706,375 10.22% 89.78% $8,391,856 18.14% 81.86% Green Bay, WI $280,275 2.11% 97.89% $590,200 4.04% 95.96% Greenville, SC $257,859 5.25% 94.75% $558,790 9.73% 90.27% Hagerstown-Martinsburg, MD-WV $1,275,112 6.88% 93.12% $2,803,846 12.57% 87.43% Hanford-Corcoran, CA $499,089 10.40% 89.60% $1,131,736 18.42% 81.58% Harrisburg-Carlisle, PA $227,183 4.43% 95.57% $488,677 8.28% 91.72% Holland-Grand Haven, MI $351,427 4.99% 95.01% $759,751 9.27% 90.73% Honolulu, HI $1,796,533 3.08% 96.92% $3,816,914 5.82% 94.18% Houston-Baytown-Sugar Land, TX $207,212 4.97% 95.03% $447,915 9.24% 90.76% Huntington-Ashland, WV-KY-OH $17,605,291 8.46% 91.54% $39,255,747 15.25% 84.75% Huntsville, AL $79,870,435 44.76% 55.24% $234,721,292 61.19% 38.81% Indianapolis, IN $487,019 4.93% 95.07% $1,052,332 9.17% 90.83% Iowa City, IA $677,673 13.08% 86.92% $1,572,214 22.66% 77.34% Ithaca, NY $8,575,161 26.56% 73.44% $22,151,860 41.31% 58.69% Jackson, MI $1,338,018 2.43% 97.57% $2,825,861 4.63% 95.37% Jackson, MS $1,265,157 1.16% 98.84% $2,640,507 2.23% 97.77% Jackson, TN $213,423 2.01% 97.99% $448,973 3.83% 96.17% Jacksonville, FL $823,975 13.18% 86.82% $1,913,235 22.81% 77.19% Janesville, WI $441,304 3.14% 96.86% $938,096 5.93% 94.07%
297 Jefferson City, MO $11,003,673 25.88% 74.12% $28,280,712 40.47% 59.53% Johnson City, TN $835,022 4.25% 95.75% $1,793,151 7.95% 92.05% Johnstown, PA $310,476 2.81% 97.19% $658,041 5.34% 94.66% Kalamazoo-Portage, MI $245,699 6.39% 93.61% $537,895 11.72% 88.28% Kankakee-Bradley, IL $377,033 2.09% 97.91% $793,742 3.98% 96.02% Kansas City, MO-KS $1,148,989 7.76% 92.24% $2,546,191 14.07% 85.93% Kennewick-Richland-Pasco, WA $348,647 2.79% 97.21% $738,790 5.29% 94.71% Killeen-Temple-Fort Hood, TX $13,381,476 33.76% 66.24% $36,451,644 49.80% 50.20% Knoxville, TN $2,107,452 8.73% 91.27% $4,710,313 15.70% 84.30% La Crosse, WI-MN $582,726 13.04% 86.96% $1,351,467 22.59% 77.41% Lafayette, IN $2,544,684 13.85% 86.15% $5,941,944 23.83% 76.17% Lafayette, LA $0 0.00% 0.00% $0 0.00% 0.00% Lakeland, FL $597,443 2.17% 97.83% $1,258,683 4.13% 95.87% Lancaster, PA $1,091,288 3.45% 96.55% $2,326,477 6.50% 93.50% Lansing-East Lansing, MI $638,524 4.84% 95.16% $1,378,627 9.01% 90.99% Laredo, TX $1,280,156 12.46% 87.54% $2,954,360 21.69% 78.31% Las Cruces, NM $1,133,407 3.27% 96.73% $2,412,239 6.17% 93.83% Las Vegas-Paradise, NV $2,558,467 5.08% 94.92% $5,535,586 9.43% 90.57% Lawrence, KS $706,115 5.64% 94.36% $1,535,506 10.41% 89.59% Lawton, OK $194,002 5.47% 94.53% $421,226 10.11% 89.89% Lebanon, PA $15,554,063 14.71% 85.29% $36,580,183 25.13% 74.87% Lewiston-Auburn, ME $347,499 2.35% 97.65% $733,329 4.47% 95.53% Lexington-Fayette, KY $363,942 8.56% 91.44% $812,177 15.40% 84.60% Lincoln, NE $238,174 2.20% 97.80% $501,954 4.20% 95.80% Little Rock-North Little Rock, AR $151,485 2.36% 97.64% $319,728 4.50% 95.50% Logan, UT-ID $2,039,403 7.66% 92.34% $4,515,331 13.89% 86.11% Longview, WA $1,324,452 5.81% 94.19% $2,884,496 10.71% 89.29% Los Angeles-Long Beach-Santa Ana, CA $2,160,798 12.97% 87.03% $5,008,285 22.48% 77.52% Louisville, KY-IN $490,879 2.46% 97.54% $1,036,953 4.67% 95.33% Lubbock, TX $133,207 2.42% 97.58% $281,301 4.61% 95.39%
298 Lynchburg, VA $484,901,200 55.16% 44.84% $1,523,623,384 70.54% 29.46% Macon, GA $8,019,645 24.69% 75.31% $20,425,163 38.96% 61.04% Madison, WI $1,057,660 5.99% 94.01% $2,307,326 11.04% 88.96% Mansfield, OH $804,177 7.47% 92.53% $1,777,544 13.58% 86.42% McAllen-Edinburg-Pharr, TX $781,329 6.59% 93.41% $1,713,691 12.08% 87.92% Medford, OR $5,402,461 5.99% 94.01% $11,785,789 11.04% 88.96% Memphis, TN-MS-AR $140,096 3.83% 96.17% $299,710 7.20% 92.80% Merced, CA $477,021 16.58% 83.42% $1,139,304 27.89% 72.11% Miami-Fort Lauderdale-Miami Beach, FL $389,316 3.60% 96.40% $831,103 6.77% 93.23% Milwaukee-Waukesha-West Allis, WI $7,266,546 22.49% 77.51% $18,194,212 36.09% 63.91% Minneapolis-St. Paul-Bloomington, MN-WI $1,185,723 5.06% 94.94% $2,565,172 9.40% 90.60% Missoula, MT $78,505,938 37.37% 62.63% $219,380,737 53.73% 46.27% Mobile, AL $20,811,367 16.19% 83.81% $49,546,351 27.32% 72.68% Modesto, CA $52,060,749 28.66% 71.34% $136,621,891 43.87% 56.13% Monroe, LA $466,503 2.53% 97.47% $986,101 4.80% 95.20% Morgantown, WV $1,012,506 12.26% 87.74% $2,332,717 21.37% 78.63% Muncie, IN $1,155,304 8.94% 91.06% $2,586,837 16.04% 83.96% Muskegon-Norton Shores, MI $389,306 3.72% 96.28% $831,996 6.99% 93.01% Myrtle Beach-Conway-North Myrtle Beach, SC $688,433 3.30% 96.70% $1,465,689 6.24% 93.76% Naples-Marco Island, FL $491,366 3.16% 96.84% $1,044,786 5.98% 94.02% Nashville-Davidson--Murfreesboro, TN $242,765 5.74% 94.26% $528,408 10.60% 89.40% New Orleans-Metairie-Kenner, LA $441,590 6.04% 93.96% $963,789 11.13% 88.87% New York-Northern New Jersey-Long Island, NY-NJ-PA $963,341 7.13% 92.87% $2,122,903 12.99% 87.01% Niles-Benton Harbor, MI $5,651,823 27.03% 72.97% $14,652,410 41.89% 58.11% Odessa, TX $5,267,090 16.09% 83.91% $12,529,424 27.18% 72.82% Oklahoma City, OK $908,298,850 42.55% 57.45% $2,630,245,695 59.05% 40.95% Olympia, WA $37,079 5.59% 94.41% $80,601 10.34% 89.66% Omaha-Council Bluffs, NE-IA $667,142 3.35% 96.65% $1,420,940 6.32% 93.68% Orlando, FL $3,218,221 22.15% 77.85% $8,036,624 35.64% 64.36% Oshkosh-Neenah, WI $1,671,225 3.28% 96.72% $3,557,402 6.20% 93.80%
299 Oxnard-Thousand Oaks-Ventura, CA $4,351,030 13.46% 86.54% $10,127,032 23.24% 76.76% Palm Bay-Melbourne-Titusville, FL $15,500,195 21.73% 78.27% $38,578,275 35.07% 64.93% Panama City-Lynn Haven, FL $553,865 2.84% 97.16% $1,174,166 5.38% 94.62% Pensacola-Ferry Pass-Brent, FL $3,157,073 16.37% 83.63% $7,527,221 27.58% 72.42% Peoria, IL $1,111,335 19.54% 80.46% $2,718,576 32.10% 67.90% Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $688,359 4.32% 95.68% $1,479,134 8.07% 91.93% Phoenix-Mesa-Scottsdale, AZ $1,230,483 6.65% 93.35% $2,700,147 12.18% 87.82% Pittsburgh, PA $1,816,054 7.56% 92.44% $4,017,573 13.74% 86.26% Pocatello, ID $88,342,048 32.51% 67.49% $238,485,602 48.39% 51.61% Port St. Lucie-Fort Pierce, FL $49,331,664 39.92% 60.08% $140,312,158 56.39% 43.61% Portland-Vancouver-Beaverton, OR-WA $28,655,527 16.81% 83.19% $68,568,170 28.23% 71.77% Poughkeepsie-Newburgh-Middletown, NY $177,598 2.78% 97.22% $376,284 5.27% 94.73% Providence-New Bedford-Fall River, RI-MA $183,373 33.04% 66.96% $496,909 48.98% 51.02% Pueblo, CO $37,577,109 22.61% 77.39% $94,175,677 36.25% 63.75% Racine, WI $2,008,316 10.54% 89.46% $4,559,750 18.66% 81.34% Rapid City, SD $8,418,430 18.23% 81.77% $20,377,808 30.26% 69.74% Reading, PA $294,441 4.31% 95.69% $632,633 8.05% 91.95% Redding, CA $0 0.00% 0.00% $0 0.00% 0.00% Reno-Sparks, NV $786,435 3.39% 96.61% $1,675,589 6.38% 93.62% Richmond, VA $156,856 3.45% 96.55% $334,396 6.50% 93.50% Riverside-San Bernardino-Ontario, CA $1,183,446 4.01% 95.99% $2,535,786 7.51% 92.49% Roanoke, VA $427,505 6.60% 93.40% $937,670 12.09% 87.91% Rochester, MN $6,880,296 7.75% 92.25% $15,245,557 14.05% 85.95% Rochester, NY $458,174 14.71% 85.29% $1,077,590 25.14% 74.86% Rockford, IL $11,513,063 49.65% 50.35% $34,935,216 65.74% 34.26% Rome, GA $2,599,912 5.90% 94.10% $5,667,184 10.88% 89.12% Sacramento--Arden-Arcade--Roseville, CA $1,049,519 3.54% 96.46% $2,239,321 6.67% 93.33% Saginaw-Saginaw Township North, MI $4,572,029 8.68% 91.32% $10,213,756 15.60% 84.40% Salem, OR $968,398 7.78% 92.22% $2,146,445 14.11% 85.89% Salt Lake City, UT $295,061 3.27% 96.73% $627,991 6.17% 93.83%
300 San Angelo, TX $20,288,521 25.40% 74.60% $51,950,736 39.85% 60.15% San Antonio, TX $462,601 4.92% 95.08% $999,481 9.15% 90.85% San Diego-Carlsbad-San Marcos, CA $1,315,549 6.99% 93.01% $2,895,549 12.76% 87.24% San Francisco-Oakland-Fremont, CA $20,466,149 10.14% 89.86% $46,306,679 18.01% 81.99% San Jose-Sunnyvale-Santa Clara, CA $234,025 4.84% 95.16% $505,264 9.01% 90.99% San Luis Obispo-Paso Robles, CA $19,934,576 29.36% 70.64% $52,586,254 44.71% 55.29% Santa Barbara-Santa Maria-Goleta, CA $41,809,994 30.25% 69.75% $111,021,914 45.77% 54.23% Santa Cruz-Watsonville, CA $199,744,927 26.29% 73.71% $514,965,965 40.98% 59.02% Santa Fe, NM $48,212,181 17.06% 82.94% $115,598,864 28.59% 71.41% Santa Rosa-Petaluma, CA $276,053 5.35% 94.65% $598,755 9.91% 90.09% Savannah, GA $0 0.00% 0.00% $0 0.00% 0.00% Scranton--Wilkes-Barre, PA $6,611,275 6.31% 93.69% $14,464,014 11.59% 88.41% Seattle-Tacoma-Bellevue, WA $5,239,179 3.51% 96.49% $11,175,504 6.61% 93.39% Sebastian-Vero Beach, FL $686,656 3.39% 96.61% $1,463,118 6.40% 93.60% Sheboygan, WI $5,193,388 8.98% 91.02% $11,633,126 16.12% 83.88% Sherman-Denison, TX $2,023,569 6.31% 93.69% $4,426,901 11.58% 88.42% Shreveport-Bossier City, LA $1,404,435 7.03% 92.97% $3,092,243 12.83% 87.17% Sioux City, IA-NE-SD $97,420,318 25.09% 74.91% $248,875,545 39.47% 60.53% Sioux Falls, SD $187,221 5.18% 94.82% $405,459 9.61% 90.39% South Bend-Mishawaka, IN-MI $498,022 2.65% 97.35% $1,053,897 5.02% 94.98% Spartanburg, SC $133,650 5.19% 94.81% $289,459 9.62% 90.38% Spokane, WA $1,858,990 12.95% 87.05% $4,307,950 22.44% 77.56% Springfield, IL $388,948 4.67% 95.33% $838,462 8.71% 91.29% Springfield, MO $680,338 4.31% 95.69% $1,461,827 8.06% 91.94% Springfield, OH $1,216,191 7.93% 92.07% $2,699,263 14.36% 85.64% St. Cloud, MN $206,490 6.96% 93.04% $454,354 12.70% 87.30% St. Joseph, MO-KS $4,631,778 7.14% 92.86% $10,207,816 13.01% 86.99% St. Louis, MO-IL $986,551 3.91% 96.09% $2,111,994 7.33% 92.67% State College, PA $775,897 8.06% 91.94% $1,723,978 14.57% 85.43% Stockton, CA $124,216 4.21% 95.79% $266,647 7.87% 92.13%
301 Sumter, SC $883,221 3.45% 96.55% $1,882,912 6.50% 93.50% Syracuse, NY $532,216 3.67% 96.33% $1,136,887 6.90% 93.10% Tallahassee, FL $28,379,824 24.86% 75.14% $72,373,750 39.17% 60.83% Tampa-St. Petersburg-Clearwater, FL $1,081,064 2.32% 97.68% $2,280,724 4.41% 95.59% Terre Haute, IN $2,461,039 11.37% 88.63% $5,627,513 19.98% 80.02% Toledo, OH $294,003 4.34% 95.66% $631,861 8.11% 91.89% Topeka, KS $4,894,852 5.47% 94.53% $10,628,268 10.12% 89.88% Tucson, AZ $1,396,008 6.44% 93.56% $3,057,761 11.82% 88.18% Tulsa, OK $18,531,438 27.57% 72.43% $48,238,375 42.56% 57.44% Tuscaloosa, AL $249,065 5.13% 94.87% $539,153 9.52% 90.48% Utica-Rome, NY $3,053,161 13.80% 86.20% $7,126,495 23.76% 76.24% Victoria, TX $756,021 5.16% 94.84% $1,637,037 9.58% 90.42% Virginia Beach-Norfolk-Newport News, VA-NC $5,732,550 18.31% 81.69% $13,885,071 30.37% 69.63% Visalia-Porterville, CA $2,681,766 14.56% 85.44% $6,299,363 24.91% 75.09% Waco, TX $175,194 6.65% 93.35% $384,434 12.17% 87.83% Washington-Arlington-Alexandria, DC-VA-MD-WV $682,698 5.39% 94.61% $1,481,333 9.99% 90.01% Waterloo-Cedar Falls, IA $297,982 6.84% 93.16% $654,979 12.50% 87.50% Wausau, WI $12,870,935 30.21% 69.79% $34,167,429 45.72% 54.28% Wenatchee, WA $808,952 9.26% 90.74% $1,816,350 16.57% 83.43% Wheeling, WV-OH $517,725 5.71% 94.29% $1,126,609 10.55% 89.45% Wichita, KS $220,412,058 26.11% 73.89% $567,478,792 40.75% 59.25% Williamsport, PA $532,006 5.04% 94.96% $1,150,723 9.37% 90.63% Yakima, WA $491,947 2.22% 97.78% $1,036,938 4.23% 95.77% York-Hanover, PA $1,202,447 2.33% 97.67% $2,537,206 4.44% 95.56% Youngstown-Warren-Boardman, OH-PA $406,562 2.64% 97.36% $860,299 5.01% 94.99% Yuba City, CA $1,617,444 12.98% 87.02% $3,749,407 22.50% 77.50% Yuma, AZ $494,372 1.67% 98.33% $1,036,729 3.20% 96.80%
302 TABLE H 8 Marginal change in total GDP for a 1% increase in total revenue miles name GDP-OLS Percent due to employmen t density change Percent due to population change GDP-IV Percent due to employme nt density change Percent due to population change Abilene, TX $1,151,836 5.58% 94.42% $1,080,556 10.32% 89.68% Albany, GA $1,197,747 6.96% 93.04% $1,137,500 12.70% 87.30% Albany-Schenectady-Troy, NY $26,828,592 7.47% 92.53% $25,596,071 13.58% 86.42% Albuquerque, NM $18,236,255 12.09% 87.91% $18,108,336 21.11% 78.89% Alexandria, LA $1,346,323 4.08% 95.92% $1,245,915 7.64% 92.36% Allentown-Bethlehem-Easton, PA-NJ $8,236,931 10.70% 89.30% $8,082,549 18.91% 81.09% Altoona, PA $1,236,929 1.72% 98.28% $1,120,160 3.30% 96.70% Amarillo, TX $2,308,814 6.87% 93.13% $2,190,947 12.55% 87.45% Ames, IA $3,166,865 1.88% 98.12% $2,872,055 3.60% 96.40% Anchorage, AK $13,283,459 8.45% 91.55% $12,782,651 15.23% 84.77% Anderson, IN $636,224 7.11% 92.89% $605,050 12.96% 87.04% Ann Arbor, MI $12,257,101 3.30% 96.70% $11,263,108 6.23% 93.77% Appleton, WI $3,829,493 2.90% 97.10% $3,505,978 5.50% 94.50% Athens-Clarke County, GA $1,849,310 6.75% 93.25% $1,753,100 12.35% 87.65% Atlanta-Sandy Springs-Marietta, GA $293,317,983 41.78% 58.22% $364,688,186 58.27% 41.73% Auburn-Opelika, AL $149,988 5.25% 94.75% $140,290 9.74% 90.26% Augusta-Richmond County, GA-SC $1,676,571 20.41% 79.59% $1,782,438 33.29% 66.71% Bakersfield, CA $10,782,446 14.34% 85.66% $10,911,548 24.58% 75.42% Bangor, ME $1,550,171 3.11% 96.89% $1,421,874 5.87% 94.13% Baton Rouge, LA $10,959,292 12.87% 87.13% $10,954,310 22.32% 77.68% Battle Creek, MI $1,125,049 4.23% 95.77% $1,042,615 7.92% 92.08% Bay City, MI $2,233,370 2.78% 97.22% $2,042,466 5.28% 94.72% Beaumont-Port Arthur, TX $3,427,860 10.47% 89.53% $3,356,913 18.53% 81.47%
303 Bellingham, WA $6,035,578 3.91% 96.09% $5,577,242 7.35% 92.65% Bend, OR $680,734 3.81% 96.19% $628,417 7.15% 92.85% Billings, MT $2,231,179 3.69% 96.31% $2,057,571 6.94% 93.06% Binghamton, NY $4,481,954 2.87% 97.13% $4,102,095 5.44% 94.56% Birmingham-Hoover, AL $14,249,669 21.26% 78.74% $15,251,504 34.45% 65.55% Bismarck, ND $1,031,192 1.91% 98.09% $935,462 3.65% 96.35% Blacksburg-Christiansburg-Radford, VA $1,646,787 5.08% 94.92% $1,537,949 9.44% 90.56% Bloomington, IN $2,322,845 3.39% 96.61% $2,136,201 6.39% 93.61% Bloomington-Normal, IL $4,718,097 3.24% 96.76% $4,333,112 6.12% 93.88% Boise City-Nampa, ID $5,065,899 10.36% 89.64% $4,956,586 18.37% 81.63% Bradenton-Sarasota-Venice, FL $12,060,755 9.97% 90.03% $11,760,543 17.73% 82.27% Bremerton-Silverdale, WA $4,705,776 5.19% 94.81% $4,399,103 9.63% 90.37% Brownsville-Harlingen, TX $1,218,714 10.17% 89.83% $1,190,416 18.05% 81.95% Brunswick, GA $23,470,861 2.29% 97.71% $21,366,329 4.36% 95.64% Canton-Massillon, OH $7,569,576 10.01% 89.99% $7,384,018 17.80% 82.20% Cape Coral-Fort Myers, FL $10,547,122 17.18% 82.82% $10,925,489 28.75% 71.25% Casper, WY $1,655,621 2.34% 97.66% $1,507,940 4.46% 95.54% Cedar Rapids, IA $4,604,276 5.11% 94.89% $4,301,160 9.49% 90.51% Champaign-Urbana, IL $9,419,828 3.68% 96.32% $8,685,883 6.92% 93.08% Charleston, WV $9,380,568 4.59% 95.41% $8,721,814 8.57% 91.43% Charleston-North Charleston, SC $9,650,477 14.63% 85.37% $9,789,475 25.01% 74.99% Charlotte-Gastonia-Concord, NC-SC $135,093,553 45.84% 54.16% $172,594,233 62.23% 37.77% Chattanooga, TN-GA $7,351,733 12.65% 87.35% $7,335,088 21.99% 78.01% Cheyenne, WY $1,248,988 1.94% 98.06% $1,133,345 3.71% 96.29% Chicago-Naperville-Joliet, IL-IN-WI $1,220,873,141 49.13% 50.87% $1,593,600,205 65.27% 34.73% Chico, CA $2,300,688 5.18% 94.82% $2,150,443 9.60% 90.40% Cincinnati-Middletown, OH-KY-IN $71,464,938 24.17% 75.83% $78,243,001 38.28% 61.72% Clarksville, TN-KY $1,776,174 16.01% 83.99% $1,822,402 27.06% 72.94% Cleveland-Elyria-Mentor, OH $146,745,964 23.20% 76.80% $159,469,062 37.03% 62.97% College Station-Bryan, TX $4,145,460 7.16% 92.84% $3,944,148 13.05% 86.95%
304 Columbia, MO $1,508,754 4.15% 95.85% $1,397,180 7.77% 92.23% Columbia, SC $5,248,046 10.89% 89.11% $5,158,255 19.22% 80.78% Columbus, GA-AL $2,438,764 9.21% 90.79% $2,362,550 16.50% 83.50% Columbus, OH $41,183,895 23.74% 76.26% $44,940,257 37.73% 62.27% Corpus Christi, TX $9,439,934 10.70% 89.30% $9,263,470 18.92% 81.08% Cumberland, MD-WV $489,991 2.50% 97.50% $446,947 4.76% 95.24% Dallas-Fort Worth-Arlington, TX $410,898,222 53.71% 46.29% $552,209,768 69.31% 30.69% Davenport-Moline-Rock Island, IA-IL $12,906,065 8.61% 91.39% $12,437,439 15.50% 84.50% Dayton, OH $20,795,030 14.51% 85.49% $21,074,091 24.83% 75.17% Decatur, IL $0 0.00% 0.00% $0 0.00% 0.00% Deltona-Daytona Beach-Ormond Beach, FL $4,000,224 3.42% 96.58% $3,679,661 6.44% 93.56% Denver-Aurora, CO $6,072,311 17.37% 82.63% $6,300,018 29.03% 70.97% Des Moines, IA $310,642,960 24.78% 75.22% $341,708,865 39.07% 60.93% Detroit-Warren-Livonia, MI $13,273,535 8.34% 91.66% $12,760,578 15.04% 84.96% Dubuque, IA $178,187,649 42.32% 57.68% $222,354,725 58.81% 41.19% Duluth, MN-WI $0 0.00% 0.00% $0 0.00% 0.00% Eau Claire, WI $1,250,490 2.08% 97.92% $1,136,216 3.98% 96.02% El Centro, CA $5,330,007 7.31% 92.69% $5,077,908 13.31% 86.69% El Paso, TX $2,171,359 3.42% 96.58% $1,997,490 6.45% 93.55% Elkhart-Goshen, IN $1,059,157 3.11% 96.89% $971,521 5.88% 94.12% Elmira, NY $19,168,296 12.95% 87.05% $19,172,704 22.45% 77.55% Erie, PA $1,812,760 4.40% 95.60% $1,682,492 8.22% 91.78% Eugene-Springfield, OR $1,803,118 1.25% 98.75% $1,625,656 2.40% 97.60% Evansville, IN-KY $4,849,867 3.70% 96.30% $4,472,941 6.96% 93.04% Fairbanks, AK $9,201,410 6.71% 93.29% $8,719,347 12.28% 87.72% Fayetteville-Springdale-Rogers, AR-MO $4,925,953 5.96% 94.04% $4,636,853 10.98% 89.02% Flagstaff, AZ $1,118,927 3.21% 96.79% $1,027,296 6.06% 93.94% Flint, MI $2,247,244 11.92% 88.08% $2,228,209 20.84% 79.16% Florence, SC $1,627,441 3.23% 96.77% $1,494,495 6.10% 93.90% Fond du Lac, WI $7,539,885 9.66% 90.34% $7,332,808 17.23% 82.77%
305 Fort Collins-Loveland, CO $412,313 6.45% 93.55% $389,809 11.83% 88.17% Fort Smith, AR-OK $553,386 1.73% 98.27% $501,184 3.32% 96.68% Fort Walton Beach-Crestview-Destin, FL $2,972,272 7.11% 92.89% $2,826,520 12.96% 87.04% Fort Wayne, IN $863,178 6.51% 93.49% $816,513 11.93% 88.07% Fresno, CA $1,551,708 3.95% 96.05% $1,434,297 7.40% 92.60% Gainesville, FL $5,996,235 10.91% 89.09% $5,894,791 19.25% 80.75% Glens Falls, NY $13,610,155 14.87% 85.13% $13,833,370 25.36% 74.64% Grand Forks, ND-MN $7,261,252 7.12% 92.88% $6,906,060 12.98% 87.02% Grand Junction, CO $691,682 1.32% 98.68% $624,026 2.54% 97.46% Grand Rapids-Wyoming, MI $1,043,397 2.09% 97.91% $948,116 3.99% 96.01% Great Falls, MT $2,337,578 4.43% 95.57% $2,170,274 8.28% 91.72% Greeley, CO $16,864,412 10.22% 89.78% $16,480,674 18.14% 81.86% Green Bay, WI $1,074,299 2.11% 97.89% $976,415 4.04% 95.96% Greenville, SC $985,770 5.25% 94.75% $922,014 9.73% 90.27% Hagerstown-Martinsburg, MD-WV $5,639,718 6.88% 93.12% $5,352,520 12.57% 87.43% Hanford-Corcoran, CA $2,072,310 10.40% 89.60% $2,028,229 18.42% 81.58% Harrisburg-Carlisle, PA $958,534 4.43% 95.57% $889,915 8.28% 91.72% Holland-Grand Haven, MI $1,244,372 4.99% 95.01% $1,161,133 9.27% 90.73% Honolulu, HI $7,283,279 3.08% 96.92% $6,678,810 5.82% 94.18% Houston-Baytown-Sugar Land, TX $991,604 4.97% 95.03% $925,152 9.24% 90.76% Huntington-Ashland, WV-KY-OH $72,440,135 8.46% 91.54% $69,716,308 15.25% 84.75% Huntsville, AL $452,167,252 44.76% 55.24% $573,535,937 61.19% 38.81% Indianapolis, IN $2,164,766 4.93% 95.07% $2,018,892 9.17% 90.83% Iowa City, IA $2,433,633 13.08% 86.92% $2,436,922 22.66% 77.34% Ithaca, NY $44,138,537 26.56% 73.44% $49,213,128 41.31% 58.69% Jackson, MI $4,715,079 2.43% 97.57% $4,298,066 4.63% 95.37% Jackson, MS $5,079,148 1.16% 98.84% $4,575,399 2.23% 97.77% Jackson, TN $880,222 2.01% 97.99% $799,221 3.83% 96.17% Jacksonville, FL $3,717,312 13.18% 86.82% $3,725,449 22.81% 77.19% Janesville, WI $1,817,360 3.14% 96.86% $1,667,423 5.93% 94.07%
306 Jefferson City, MO $49,078,715 25.88% 74.12% $54,442,861 40.47% 59.53% Johnson City, TN $3,288,211 4.25% 95.75% $3,047,712 7.95% 92.05% Johnstown, PA $1,005,985 2.81% 97.19% $920,262 5.34% 94.66% Kalamazoo-Portage, MI $1,038,847 6.39% 93.61% $981,616 11.72% 88.28% Kankakee-Bradley, IL $1,490,540 2.09% 97.91% $1,354,374 3.98% 96.02% Kansas City, MO-KS $4,950,018 7.76% 92.24% $4,734,532 14.07% 85.93% Kennewick-Richland-Pasco, WA $1,487,871 2.79% 97.21% $1,360,804 5.29% 94.71% Killeen-Temple-Fort Hood, TX $59,675,184 33.76% 66.24% $70,161,999 49.80% 50.20% Knoxville, TN $8,200,275 8.73% 91.27% $7,910,713 15.70% 84.30% La Crosse, WI-MN $1,506,091 13.04% 86.96% $1,507,603 22.59% 77.41% Lafayette, IN $10,915,633 13.85% 86.15% $11,001,166 23.83% 76.17% Lafayette, LA $0 0.00% 0.00% $0 0.00% 0.00% Lakeland, FL $2,481,547 2.17% 97.83% $2,256,513 4.13% 95.87% Lancaster, PA $4,397,881 3.45% 96.55% $4,046,671 6.50% 93.50% Lansing-East Lansing, MI $4,107,209 4.84% 95.16% $3,827,469 9.01% 90.99% Laredo, TX $5,497,537 12.46% 87.54% $5,476,004 21.69% 78.31% Las Cruces, NM $5,100,306 3.27% 96.73% $4,685,174 6.17% 93.83% Las Vegas-Paradise, NV $9,625,952 5.08% 94.92% $8,989,233 9.43% 90.57% Lawrence, KS $3,291,888 5.64% 94.36% $3,089,697 10.41% 89.59% Lawton, OK $784,995 5.47% 94.53% $735,651 10.11% 89.89% Lebanon, PA $85,060,140 14.71% 85.29% $86,342,223 25.13% 74.87% Lewiston-Auburn, ME $1,349,427 2.35% 97.65% $1,229,109 4.47% 95.53% Lexington-Fayette, KY $1,078,148 8.56% 91.44% $1,038,467 15.40% 84.60% Lincoln, NE $1,000,714 2.20% 97.80% $910,282 4.20% 95.80% Little Rock-North Little Rock, AR $628,797 2.36% 97.64% $572,817 4.50% 95.50% Logan, UT-ID $9,076,308 7.66% 92.34% $8,673,432 13.89% 86.11% Longview, WA $5,520,550 5.81% 94.19% $5,189,331 10.71% 89.29% Los Angeles-Long Beach-Santa Ana, CA $9,405,469 12.97% 87.03% $9,409,150 22.48% 77.52% Louisville, KY-IN $1,648,063 2.46% 97.54% $1,502,635 4.67% 95.33% Lubbock, TX $508,718 2.42% 97.58% $463,680 4.61% 95.39%
307 Lynchburg, VA $2,357,310,925 55.16% 44.84% $3,196,953,600 70.54% 29.46% Macon, GA $39,045,019 24.69% 75.31% $42,921,129 38.96% 61.04% Madison, WI $4,418,600 5.99% 94.01% $4,160,474 11.04% 88.96% Mansfield, OH $3,385,473 7.47% 92.53% $3,229,856 13.58% 86.42% McAllen-Edinburg-Pharr, TX $3,018,722 6.59% 93.41% $2,857,701 12.08% 87.92% Medford, OR $22,730,314 5.99% 94.01% $21,402,652 11.04% 88.96% Memphis, TN-MS-AR $589,115 3.83% 96.17% $543,966 7.20% 92.80% Merced, CA $1,880,370 16.58% 83.42% $1,938,388 27.89% 72.11% Miami-Fort Lauderdale-Miami Beach, FL $1,541,073 3.60% 96.40% $1,419,945 6.77% 93.23% Milwaukee-Waukesha-West Allis, WI $34,877,140 22.49% 77.51% $37,691,305 36.09% 63.91% Minneapolis-St. Paul-Bloomington, MN-WI $5,793,042 5.06% 94.94% $5,409,225 9.40% 90.60% Missoula, MT $382,033,911 37.37% 62.63% $460,779,331 53.73% 46.27% Mobile, AL $97,156,512 16.19% 83.81% $99,833,908 27.32% 72.68% Modesto, CA $225,027,104 28.66% 71.34% $254,882,378 43.87% 56.13% Monroe, LA $2,177,629 2.53% 97.47% $1,986,763 4.80% 95.20% Morgantown, WV $4,247,599 12.26% 87.74% $4,223,797 21.37% 78.63% Muncie, IN $5,269,076 8.94% 91.06% $5,092,161 16.04% 83.96% Muskegon-Norton Shores, MI $1,933,510 3.72% 96.28% $1,783,493 6.99% 93.01% Myrtle Beach-Conway-North Myrtle Beach, SC $3,093,130 3.30% 96.70% $2,842,323 6.24% 93.76% Naples-Marco Island, FL $1,932,279 3.16% 96.84% $1,773,321 5.98% 94.02% Nashville-Davidson--Murfreesboro, TN $979,510 5.74% 94.26% $920,208 10.60% 89.40% New Orleans-Metairie-Kenner, LA $2,562,640 6.04% 93.96% $2,414,045 11.13% 88.87% New York-Northern New Jersey-Long Island, NY-NJ-PA $5,067,689 7.13% 92.87% $4,820,092 12.99% 87.01% Niles-Benton Harbor, MI $26,435,217 27.03% 72.97% $29,580,023 41.89% 58.11% Odessa, TX $32,126,358 16.09% 83.91% $32,985,039 27.18% 72.82% Oklahoma City, OK $4,079,097,339 42.55% 57.45% $5,098,315,613 59.05% 40.95% Olympia, WA $162,935 5.59% 94.41% $152,867 10.34% 89.66% Omaha-Council Bluffs, NE-IA $3,079,491 3.35% 96.65% $2,830,945 6.32% 93.68% Orlando, FL $14,775,185 22.15% 77.85% $15,925,231 35.64% 64.36% Oshkosh-Neenah, WI $5,806,727 3.28% 96.72% $5,334,881 6.20% 93.80%
308 Oxnard-Thousand Oaks-Ventura, CA $20,255,566 13.46% 86.54% $20,348,368 23.24% 76.76% Palm Bay-Melbourne-Titusville, FL $85,652,200 21.73% 78.27% $92,010,895 35.07% 64.93% Panama City-Lynn Haven, FL $2,190,625 2.84% 97.16% $2,004,420 5.38% 94.62% Pensacola-Ferry Pass-Brent, FL $13,505,403 16.37% 83.63% $13,898,011 27.58% 72.42% Peoria, IL $4,265,882 19.54% 80.46% $4,504,020 32.10% 67.90% Philadelphia-Camden-Wilmington, PA-NJ-DE-MD $2,890,275 4.32% 95.68% $2,680,569 8.07% 91.93% Phoenix-Mesa-Scottsdale, AZ $4,584,983 6.65% 93.35% $4,342,547 12.18% 87.82% Pittsburgh, PA $7,964,151 7.56% 92.44% $7,604,484 13.74% 86.26% Pocatello, ID $427,128,369 32.51% 67.49% $497,677,737 48.39% 51.61% Port St. Lucie-Fort Pierce, FL $234,775,535 39.92% 60.08% $288,215,592 56.39% 43.61% Portland-Vancouver-Beaverton, OR-WA $139,793,069 16.81% 83.19% $144,375,965 28.23% 71.77% Poughkeepsie-Newburgh-Middletown, NY $643,770 2.78% 97.22% $588,712 5.27% 94.73% Providence-New Bedford-Fall River, RI-MA $845,908 33.04% 66.96% $989,372 48.98% 51.02% Pueblo, CO $174,768,456 22.61% 77.39% $189,048,626 36.25% 63.75% Racine, WI $7,054,783 10.54% 89.46% $6,913,337 18.66% 81.34% Rapid City, SD $37,644,482 18.23% 81.77% $39,329,897 30.26% 69.74% Reading, PA $1,093,565 4.31% 95.69% $1,014,127 8.05% 91.95% Redding, CA $0 0.00% 0.00% $0 0.00% 0.00% Reno-Sparks, NV $3,616,709 3.39% 96.61% $3,325,929 6.38% 93.62% Richmond, VA $651,665 3.45% 96.55% $599,623 6.50% 93.50% Riverside-San Bernardino-Ontario, CA $4,862,260 4.01% 95.99% $4,496,738 7.51% 92.49% Roanoke, VA $1,716,334 6.60% 93.40% $1,624,823 12.09% 87.91% Rochester, MN $31,937,102 7.75% 92.25% $30,544,062 14.05% 85.95% Rochester, NY $1,957,727 14.71% 85.29% $1,987,333 25.14% 74.86% Rockford, IL $50,241,452 49.65% 50.35% $65,800,598 65.74% 34.26% Rome, GA $10,934,287 5.90% 94.10% $10,287,131 10.88% 89.12% Sacramento--Arden-Arcade--Roseville, CA $4,135,793 3.54% 96.46% $3,808,728 6.67% 93.33% Saginaw-Saginaw Township North, MI $20,423,814 8.68% 91.32% $19,692,842 15.60% 84.40% Salem, OR $4,185,277 7.78% 92.22% $4,003,920 14.11% 85.89% Salt Lake City, UT $1,212,436 3.27% 96.73% $1,113,771 6.17% 93.83%
309 San Angelo, TX $77,709,311 25.40% 74.60% $85,883,446 39.85% 60.15% San Antonio, TX $1,905,367 4.92% 95.08% $1,776,812 9.15% 90.85% San Diego-Carlsbad-San Marcos, CA $5,009,636 6.99% 93.01% $4,759,103 12.76% 87.24% San Francisco-Oakland-Fremont, CA $94,864,833 10.14% 89.86% $92,641,983 18.01% 81.99% San Jose-Sunnyvale-Santa Clara, CA $942,915 4.84% 95.16% $878,667 9.01% 90.99% San Luis Obispo-Paso Robles, CA $85,058,622 29.36% 70.64% $96,845,327 44.71% 55.29% Santa Barbara-Santa Maria-Goleta, CA $188,402,159 30.25% 69.75% $215,928,305 45.77% 54.23% Santa Cruz-Watsonville, CA $890,164,023 26.29% 73.71% $990,530,607 40.98% 59.02% Santa Fe, NM $197,884,286 17.06% 82.94% $204,787,379 28.59% 71.41% Santa Rosa-Petaluma, CA $1,284,975 5.35% 94.65% $1,202,948 9.91% 90.09% Savannah, GA $0 0.00% 0.00% $0 0.00% 0.00% Scranton--Wilkes-Barre, PA $28,097,289 6.31% 93.69% $26,531,582 11.59% 88.41% Seattle-Tacoma-Bellevue, WA $21,365,099 3.51% 96.49% $19,669,978 6.61% 93.39% Sebastian-Vero Beach, FL $3,176,862 3.39% 96.61% $2,921,684 6.40% 93.60% Sheboygan, WI $21,827,333 8.98% 91.02% $21,102,865 16.12% 83.88% Sherman-Denison, TX $8,227,311 6.31% 93.69% $7,768,460 11.58% 88.42% Shreveport-Bossier City, LA $6,412,925 7.03% 92.97% $6,094,292 12.83% 87.17% Sioux City, IA-NE-SD $435,744,626 25.09% 74.91% $480,462,855 39.47% 60.53% Sioux Falls, SD $886,333 5.18% 94.82% $828,485 9.61% 90.39% South Bend-Mishawaka, IN-MI $2,398,685 2.65% 97.35% $2,190,875 5.02% 94.98% Spartanburg, SC $540,831 5.19% 94.81% $505,561 9.62% 90.38% Spokane, WA $12,588,805 12.95% 87.05% $12,591,371 22.44% 77.56% Springfield, IL $2,019,212 4.67% 95.33% $1,878,749 8.71% 91.29% Springfield, MO $4,007,969 4.31% 95.69% $3,716,984 8.06% 91.94% Springfield, OH $6,357,009 7.93% 92.07% $6,089,635 14.36% 85.64% St. Cloud, MN $861,512 6.96% 93.04% $818,185 12.70% 87.30% St. Joseph, MO-KS $18,501,573 7.14% 92.86% $17,599,035 13.01% 86.99% St. Louis, MO-IL $3,587,987 3.91% 96.09% $3,315,273 7.33% 92.67% State College, PA $3,211,979 8.06% 91.94% $3,080,316 14.57% 85.43% Stockton, CA $499,332 4.21% 95.79% $462,640 7.87% 92.13%
310 Sumter, SC $3,624,889 3.45% 96.55% $3,335,418 6.50% 93.50% Syracuse, NY $2,163,874 3.67% 96.33% $1,995,064 6.90% 93.10% Tallahassee, FL $122,546,716 24.86% 75.14% $134,886,396 39.17% 60.83% Tampa-St. Petersburg-Clearwater, FL $2,979,771 2.32% 97.68% $2,713,311 4.41% 95.59% Terre Haute, IN $10,773,861 11.37% 88.63% $10,633,212 19.98% 80.02% Toledo, OH $1,038,201 4.34% 95.66% $963,046 8.11% 91.89% Topeka, KS $21,537,713 5.47% 94.53% $20,184,482 10.12% 89.88% Tucson, AZ $4,544,354 6.44% 93.56% $4,296,186 11.82% 88.18% Tulsa, OK $83,687,396 27.57% 72.43% $94,023,992 42.56% 57.44% Tuscaloosa, AL $1,085,971 5.13% 94.87% $1,014,640 9.52% 90.48% Utica-Rome, NY $13,123,295 13.80% 86.20% $13,220,999 23.76% 76.24% Victoria, TX $2,886,272 5.16% 94.84% $2,697,471 9.58% 90.42% Virginia Beach-Norfolk-Newport News, VA-NC $21,855,926 18.31% 81.69% $22,848,862 30.37% 69.63% Visalia-Porterville, CA $12,992,276 14.56% 85.44% $13,172,134 24.91% 75.09% Waco, TX $733,109 6.65% 93.35% $694,332 12.17% 87.83% Washington-Arlington-Alexandria, DC-VA-MD-WV $2,385,398 5.39% 94.61% $2,233,983 9.99% 90.01% Waterloo-Cedar Falls, IA $1,590,778 6.84% 93.16% $1,509,185 12.50% 87.50% Wausau, WI $52,778,155 30.21% 69.79% $60,471,605 45.72% 54.28% Wenatchee, WA $3,441,224 9.26% 90.74% $3,334,916 16.57% 83.43% Wheeling, WV-OH $2,273,013 5.71% 94.29% $2,134,866 10.55% 89.45% Wichita, KS $879,700,961 26.11% 73.89% $977,562,304 40.75% 59.25% Williamsport, PA $2,504,839 5.04% 94.96% $2,338,454 9.37% 90.63% Yakima, WA $2,112,379 2.22% 97.78% $1,921,771 4.23% 95.77% York-Hanover, PA $4,788,604 2.33% 97.67% $4,361,078 4.44% 95.56% Youngstown-Warren-Boardman, OH-PA $1,946,888 2.64% 97.36% $1,778,109 5.01% 94.99% Yuba City, CA $7,314,315 12.98% 87.02% $7,318,166 22.50% 77.50% Yuma, AZ $2,153,737 1.67% 98.33% $1,949,390 3.20% 96.80%
311 TABLE H 9 Marginal change in total payroll for a 1% increase in rail seat capacity per capita name WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employment density change Percent due to population change Atlanta-Sandy Springs-Marietta, GA $25,342,530 24.93% 75.07% $87,270,157 38.28% 61.72% Baltimore-Towson, MD $63,862,566 10.32% 89.68% $197,010,331 17.69% 82.31% Buffalo-Niagara Falls, NY $5,562,318 7.65% 92.35% $16,794,625 13.40% 86.60% Chicago-Naperville-Joliet, IL-IN-WI $234,276,566 30.89% 69.11% $841,035,603 45.49% 54.51% Cleveland-Elyria-Mentor, OH $24,294,695 12.27% 87.73% $76,108,435 20.71% 79.29% Dallas-Fort Worth-Arlington, TX $20,724,820 34.94% 65.06% $76,459,831 50.07% 49.93% Denver-Aurora, CO $17,748,019 13.23% 86.77% $56,018,895 22.16% 77.84% Houston-Baytown-Sugar Land, TX $2,282,458 27.27% 72.73% $7,990,962 41.18% 58.82% Little Rock-North Little Rock, AR $1,681,487 6.45% 93.55% $5,027,450 11.41% 88.59% Los Angeles-Long Beach-Santa Ana, CA $26,847,711 36.29% 63.71% $99,936,239 51.53% 48.47% Memphis, TN-MS-AR $2,942,043 11.84% 88.16% $9,185,609 20.05% 79.95% Miami-Fort Lauderdale-Miami Beach, FL $15,551,221 21.64% 78.36% $52,294,906 34.02% 65.98% Minneapolis-St. Paul-Bloomington, MN-WI $4,548,805 15.68% 84.32% $14,630,668 25.77% 74.23% Nashville-Davidson--Murfreesboro, TN $4,382,477 14.64% 85.36% $13,983,719 24.25% 75.75% New Orleans-Metairie-Kenner, LA $9,055,629 8.15% 91.85% $27,453,456 14.22% 85.78% New York-Northern New Jersey-Long Island, NY-NJ-PA $321,017,756 25.53% 74.47% $1,110,196,323 39.03% 60.97% Philadelphia-Camden-Wilmington, PA- NJ-DE-MD $97,380,542 18.23% 81.77% $319,319,982 29.39% 70.61% Pittsburgh, PA $10,308,872 8.55% 91.45% $31,354,072 14.87% 85.13% Portland-South Portland-Biddeford, ME $6,252,413 3.32% 96.68% $18,213,160 6.02% 93.98% Portland-Vancouver-Beaverton, OR- WA $21,154,541 11.91% 88.09% $66,086,131 20.16% 79.84% Sacramento--Arden-Arcade--Roseville, CA $13,357,296 13.61% 86.39% $42,284,897 22.73% 77.27%
312 Salt Lake City, UT $19,866,884 4.96% 95.04% $58,674,523 8.89% 91.11% San Diego-Carlsbad-San Marcos, CA $28,922,025 16.72% 83.28% $93,762,826 27.26% 72.74% San Francisco-Oakland-Fremont, CA $158,518,640 14.17% 85.83% $503,997,386 23.56% 76.44% San Jose-Sunnyvale-Santa Clara, CA $44,179,719 8.69% 91.31% $134,522,453 15.09% 84.91% Seattle-Tacoma-Bellevue, WA $13,941,170 13.42% 86.58% $44,068,786 22.45% 77.55% St. Louis, MO-IL $13,462,908 13.28% 86.72% $42,510,230 22.24% 77.76% Tampa-St. Petersburg-Clearwater, FL $902,519 14.98% 85.02% $2,887,382 24.75% 75.25% Washington-Arlington-Alexandria, DC- VA-MD-WV $212,394,557 14.06% 85.94% $674,707,368 23.40% 76.60%
313 TABLE H 10 Marginal change in total GDP for a 1% increase in rail seat capacity per capita Name GDP-OLS Percent due to employment density change Percent due to population change GDP-IV Percent due to employment density change Percent due to population change Atlanta-Sandy Springs-Marietta, GA $117,391,379 24.93% 75.07% $174,480,579 38.28% 61.72% Baltimore-Towson, MD $250,167,344 10.32% 89.68% $333,095,180 17.69% 82.31% Buffalo-Niagara Falls, NY $24,269,119 7.65% 92.35% $31,627,406 13.40% 86.60% Chicago-Naperville-Joliet, IL-IN-WI $1,067,758,480 30.89% 69.11% $1,654,450,026 45.49% 54.51% Cleveland-Elyria-Mentor, OH $114,847,496 12.27% 87.73% $155,288,026 20.71% 79.29% Dallas-Fort Worth-Arlington, TX $105,829,646 34.94% 65.06% $168,517,500 50.07% 49.93% Denver-Aurora, CO $83,022,100 13.23% 86.77% $113,102,825 22.16% 77.84% Houston-Baytown-Sugar Land, TX $12,921,588 27.27% 72.73% $19,525,728 41.18% 58.82% Little Rock-North Little Rock, AR $7,319,137 6.45% 93.55% $9,445,157 11.41% 88.59% Los Angeles-Long Beach-Santa Ana, CA $130,518,139 36.29% 63.71% $209,691,924 51.53% 48.47% Memphis, TN-MS-AR $14,120,883 11.84% 88.16% $19,028,998 20.05% 79.95% Miami-Fort Lauderdale-Miami Beach, FL $75,676,997 21.64% 78.36% $109,838,321 34.02% 65.98% Minneapolis-St. Paul-Bloomington, MN-WI $19,661,729 15.68% 84.32% $27,295,036 25.77% 74.23% Nashville-Davidson--Murfreesboro, TN $20,498,119 14.64% 85.36% $28,230,083 24.25% 75.75% New Orleans-Metairie-Kenner, LA $55,234,372 8.15% 91.85% $72,274,135 14.22% 85.78% New York-Northern New Jersey-Long Island, NY-NJ-PA $1,441,665,015 25.53% 74.47% $2,151,940,124 39.03% 60.97% Philadelphia-Camden-Wilmington, PA-NJ- DE-MD $470,828,931 18.23% 81.77% $666,364,950 29.39% 70.61% Pittsburgh, PA $50,290,784 8.55% 91.45% $66,018,598 14.87% 85.13% Portland-South Portland-Biddeford, ME $27,563,920 3.32% 96.68% $34,655,625 6.02% 93.98% Portland-Vancouver-Beaverton, OR-WA $98,388,260 11.91% 88.09% $132,661,562 20.16% 79.84% Sacramento--Arden-Arcade--Roseville, CA $51,161,258 13.61% 86.39% $69,904,162 22.73% 77.27% Salt Lake City, UT $92,087,116 4.96% 95.04% $117,385,315 8.89% 91.11% San Diego-Carlsbad-San Marcos, CA $130,327,020 16.72% 83.28% $182,360,828 27.26% 72.74%
314 San Francisco-Oakland-Fremont, CA $706,438,918 14.17% 85.83% $969,432,683 23.56% 76.44% San Jose-Sunnyvale-Santa Clara, CA $181,333,264 8.69% 91.31% $238,311,170 15.09% 84.91% Seattle-Tacoma-Bellevue, WA $62,356,501 13.42% 86.58% $85,076,316 22.45% 77.55% St. Louis, MO-IL $58,134,086 13.28% 86.72% $79,228,336 22.24% 77.76% Tampa-St. Petersburg-Clearwater, FL $4,075,746 14.98% 85.02% $5,627,951 24.75% 75.25% Washington-Arlington-Alexandria, DC-VA- MD-WV $847,701,790 14.06% 85.94% $1,162,278,658 23.40% 76.60%
315 TABLE H 11 Marginal change in total payroll for a 1% increase in bus seat capacity per capita name WAGES-OLS Percent due to employment density change Percent due to population change WAGES-IV Percent due to employment density change Percent due to population change Abilene, TX $1,716,852 15.31% 84.69% $1,921,312 26.39% 73.61% Akron, OH $5,126,487 29.93% 70.07% $6,453,059 45.85% 54.15% Albany, GA $1,108,189 18.61% 81.39% $1,275,081 31.19% 68.81% Albany-Schenectady-Troy, NY $6,112,735 19.81% 80.19% $7,103,359 32.87% 67.13% Albuquerque, NM $4,432,732 29.61% 70.39% $5,566,242 45.47% 54.53% Alexandria, LA $1,252,166 11.50% 88.50% $1,355,679 20.49% 79.51% Allentown-Bethlehem-Easton, PA-NJ $2,357,466 26.82% 73.18% $2,897,402 42.08% 57.92% Altoona, PA $3,637,371 5.09% 94.91% $3,715,333 9.62% 90.38% Amarillo, TX $1,086,529 18.40% 81.60% $1,247,993 30.90% 69.10% Ames, IA $11,580,260 5.54% 94.46% $11,877,380 10.41% 89.59% Anchorage, AK $5,248,322 22.02% 77.98% $6,209,537 35.88% 64.12% Anderson, IN $901,169 18.97% 81.03% $1,039,990 31.70% 68.30% Ann Arbor, MI $8,985,552 9.46% 90.54% $9,553,004 17.16% 82.84% Anniston-Oxford, AL $857,153 15.46% 84.54% $960,416 26.60% 73.40% Appleton, WI $3,065,001 8.38% 91.62% $3,226,782 15.34% 84.66% Athens-Clarke County, GA $2,412,283 18.13% 81.87% $2,764,494 30.51% 69.49% Atlanta-Sandy Springs-Marietta, GA $12,454,486 68.70% 31.30% $20,292,146 81.31% 18.69% Auburn-Opelika, AL $660,547 14.50% 85.50% $734,068 25.16% 74.84% Augusta-Richmond County, GA-SC $1,004,611 43.96% 56.04% $1,399,276 60.86% 39.14% Austin-Round Rock, TX $8,740,041 42.80% 57.20% $12,076,452 59.73% 40.27% Bakersfield, CA $2,136,664 33.87% 66.13% $2,769,992 50.38% 49.62% Baltimore-Towson, MD $16,295,881 43.20% 56.80% $22,580,049 60.13% 39.87% Bangor, ME $1,649,845 8.93% 91.07% $1,745,639 16.27% 83.73% Baton Rouge, LA $2,192,461 31.12% 68.88% $2,784,668 47.24% 52.76% Battle Creek, MI $2,463,396 11.91% 88.09% $2,676,518 21.13% 78.87% Bay City, MI $4,398,498 8.05% 91.95% $4,617,083 14.79% 85.21%
316 Beaumont-Port Arthur, TX $1,703,661 26.34% 73.66% $2,086,071 41.48% 58.52% Bellingham, WA $5,308,864 11.08% 88.92% $5,726,366 19.81% 80.19% Bend, OR $798,286 10.80% 89.20% $858,892 19.35% 80.65% Billings, MT $3,330,274 10.50% 89.50% $3,573,592 18.86% 81.14% Binghamton, NY $3,288,206 8.29% 91.71% $3,458,994 15.19% 84.81% Birmingham-Hoover, AL $2,536,222 45.23% 54.77% $3,563,424 62.08% 37.92% Bismarck, ND $3,106,260 5.62% 94.38% $3,188,529 10.56% 89.44% Blacksburg-Christiansburg-Radford, VA $9,194,247 14.07% 85.93% $10,180,296 24.51% 75.49% Bloomington, IN $2,769,249 9.70% 90.30% $2,950,382 17.55% 82.45% Bloomington-Normal, IL $3,250,481 9.30% 90.70% $3,450,786 16.89% 83.11% Boise City-Nampa, ID $1,293,153 26.12% 73.88% $1,580,739 41.21% 58.79% Bradenton-Sarasota-Venice, FL $2,269,581 25.30% 74.70% $2,756,489 40.17% 59.83% Bremerton-Silverdale, WA $7,961,091 14.35% 85.65% $8,835,693 24.93% 75.07% Brownsville-Harlingen, TX $636,832 25.72% 74.28% $775,989 40.70% 59.30% Buffalo-Niagara Falls, NY $9,169,650 35.38% 64.62% $12,020,473 52.05% 47.95% Burlington-South Burlington, VT $6,474,295 8.90% 91.10% $6,848,748 16.23% 83.77% Canton-Massillon, OH $2,269,421 25.40% 74.60% $2,758,340 40.29% 59.71% Cape Coral-Fort Myers, FL $1,722,440 38.81% 61.19% $2,314,394 55.70% 44.30% Carson City, NV $1,898,259 4.79% 95.21% $1,933,367 9.06% 90.94% Casper, WY $1,601,855 6.83% 93.17% $1,662,807 12.70% 87.30% Cedar Rapids, IA $4,316,130 14.15% 85.85% $4,782,173 24.63% 75.37% Champaign-Urbana, IL $7,594,799 10.46% 89.54% $8,147,315 18.81% 81.19% Charleston, WV $2,907,263 12.83% 87.17% $3,184,590 22.59% 77.41% Charleston-North Charleston, SC $2,833,180 34.39% 65.61% $3,687,124 50.96% 49.04% Charlotte-Gastonia-Concord, NC-SC $16,116,971 72.14% 27.86% $26,789,025 83.70% 16.30% Charlottesville, VA $8,589,960 7.65% 92.35% $8,983,497 14.10% 85.90% Chattanooga, TN-GA $2,899,563 30.70% 69.30% $3,671,357 46.76% 53.24% Cheyenne, WY $3,075,571 5.71% 94.29% $3,159,445 10.71% 89.29% Chicago-Naperville-Joliet, IL-IN-WI $31,255,573 74.71% 25.29% $52,719,640 85.42% 14.58% Chico, CA $1,819,444 14.31% 85.69% $2,018,634 24.87% 75.13%
317 Cincinnati-Middletown, OH-KY-IN $10,118,877 49.37% 50.63% $14,616,978 65.90% 34.10% Clarksville, TN-KY $982,259 36.83% 63.17% $1,301,201 53.61% 46.39% Cleveland-Elyria-Mentor, OH $12,871,692 48.03% 51.97% $18,429,286 64.69% 35.31% College Station-Bryan, TX $2,056,492 19.09% 80.91% $2,375,647 31.87% 68.13% Colorado Springs, CO $4,290,636 30.18% 69.82% $5,411,042 46.14% 53.86% Columbia, MO $2,907,333 11.70% 88.30% $3,153,053 20.80% 79.20% Columbia, SC $1,540,458 27.21% 72.79% $1,899,112 42.57% 57.43% Columbus, GA-AL $2,011,158 23.69% 76.31% $2,411,699 38.10% 61.90% Columbus, IN $793,876 5.59% 94.41% $814,660 10.51% 89.49% Columbus, OH $5,807,337 48.78% 51.22% $8,356,021 65.37% 34.63% Corpus Christi, TX $3,843,870 26.83% 73.17% $4,724,680 42.09% 57.91% Corvallis, OR $2,473,915 4.70% 95.30% $2,517,637 8.91% 91.09% Cumberland, MD-WV $469,997 7.28% 92.72% $489,881 13.47% 86.53% Dallas-Fort Worth-Arlington, TX $16,980,539 78.02% 21.98% $29,178,202 87.56% 12.44% Danville, IL $1,794,693 7.95% 92.05% $1,882,089 14.61% 85.39% Davenport-Moline-Rock Island, IA-IL $5,465,430 22.38% 77.62% $6,485,480 36.37% 63.63% Dayton, OH $3,655,572 34.18% 65.82% $4,750,015 50.73% 49.27% Decatur, IL $3,759,841 9.76% 90.24% $4,008,217 17.66% 82.34% Deltona-Daytona Beach-Ormond Beach, FL $1,514,434 39.13% 60.87% $2,039,546 56.04% 43.96% Denver-Aurora, CO $24,734,362 50.19% 49.81% $35,925,085 66.64% 33.36% Des Moines, IA $7,296,175 21.77% 78.23% $8,615,096 35.55% 64.45% Detroit-Warren-Livonia, MI $12,334,147 69.18% 30.82% $20,151,949 81.65% 18.35% Dubuque, IA $2,670,332 6.11% 93.89% $2,753,448 11.43% 88.57% Duluth, MN-WI $5,302,395 19.44% 80.56% $6,142,898 32.36% 67.64% Eau Claire, WI $2,120,680 9.78% 90.22% $2,261,174 17.70% 82.30% El Centro, CA $1,173,175 8.93% 91.07% $1,241,372 16.28% 83.72% El Paso, TX $3,527,410 31.27% 68.73% $4,485,427 47.42% 52.58% Elkhart-Goshen, IN $705,011 12.34% 87.66% $768,912 21.81% 78.19% Elmira, NY $2,817,794 3.72% 96.28% $2,841,236 7.12% 92.88%
318 Erie, PA $3,536,440 10.53% 89.47% $3,795,791 18.91% 81.09% Eugene-Springfield, OR $6,516,425 18.03% 81.97% $7,461,509 30.36% 69.64% Evansville, IN-KY $1,654,370 16.24% 83.76% $1,866,028 27.76% 72.24% Fairbanks, AK $1,765,705 9.21% 90.79% $1,872,928 16.74% 83.26% Fargo, ND-MN $2,691,321 8.75% 91.25% $2,842,999 15.97% 84.03% Farmington, NM $370,479 8.66% 91.34% $391,028 15.82% 84.18% Fayetteville-Springdale-Rogers, AR-MO $2,057,735 29.27% 70.73% $2,577,248 45.07% 54.93% Flagstaff, AZ $1,222,797 9.27% 90.73% $1,297,803 16.84% 83.16% Flint, MI $5,906,927 24.66% 75.34% $7,137,719 39.35% 60.65% Florence, SC $484,222 17.41% 82.59% $551,596 29.48% 70.52% Fond du Lac, WI $1,138,236 5.12% 94.88% $1,162,888 9.66% 90.34% Fort Collins-Loveland, CO $2,489,033 18.96% 81.04% $2,872,200 31.69% 68.31% Fort Smith, AR-OK $620,682 17.56% 82.44% $707,912 29.69% 70.31% Fort Walton Beach-Crestview-Destin, FL $858,106 11.17% 88.83% $926,285 19.95% 80.05% Fort Wayne, IN $1,736,278 27.26% 72.74% $2,141,292 42.63% 57.37% Fresno, CA $3,239,595 34.82% 65.18% $4,229,237 51.43% 48.57% Gadsden, AL $528,724 14.58% 85.42% $588,002 25.29% 74.71% Gainesville, FL $9,350,199 18.99% 81.01% $10,792,575 31.73% 68.27% Gainesville, GA $238,616 13.43% 86.57% $262,741 23.52% 76.48% Glens Falls, NY $1,329,457 3.93% 96.07% $1,343,172 7.50% 92.50% Grand Forks, ND-MN $1,438,560 6.13% 93.87% $1,483,636 11.46% 88.54% Grand Junction, CO $1,823,824 12.42% 87.58% $1,990,675 21.95% 78.05% Grand Rapids-Wyoming, MI $3,769,453 25.83% 74.17% $4,597,284 40.85% 59.15% Great Falls, MT $3,270,118 6.20% 93.80% $3,374,712 11.58% 88.42% Greeley, CO $696,698 14.49% 85.51% $774,209 25.15% 74.85% Green Bay, WI $3,217,960 18.44% 81.56% $3,697,252 30.95% 69.05% Greenville, SC $567,582 26.20% 73.80% $694,218 41.31% 58.69% Gulfport-Biloxi, MS $1,178,703 21.22% 78.78% $1,385,563 34.81% 65.19% Hagerstown-Martinsburg, MD-WV $852,348 12.42% 87.58% $930,287 21.94% 78.06%
319 Hanford-Corcoran, CA $1,273,257 13.84% 86.16% $1,406,905 24.15% 75.85% Harrisburg-Carlisle, PA $3,398,101 8.85% 91.15% $3,592,999 16.15% 83.85% Hattiesburg, MS $382,208 9.86% 90.14% $407,794 17.82% 82.18% Holland-Grand Haven, MI $852,619 13.80% 86.20% $941,818 24.09% 75.91% Honolulu, HI $18,059,315 22.04% 77.96% $21,371,393 35.92% 64.08% Hot Springs, AR $674,117 11.19% 88.81% $727,821 19.98% 80.02% Houston-Baytown-Sugar Land, TX $23,085,296 71.25% 28.75% $38,174,909 83.09% 16.91% Huntington-Ashland, WV-KY-OH $1,729,051 13.69% 86.31% $1,908,108 23.92% 76.08% Huntsville, AL $1,021,912 31.52% 68.48% $1,301,927 47.72% 52.28% Idaho Falls, ID $924,831 7.20% 92.80% $963,263 13.33% 86.67% Indianapolis, IN $4,026,989 52.52% 47.48% $5,938,384 68.68% 31.32% Iowa City, IA $9,728,579 7.08% 92.92% $10,121,978 13.13% 86.87% Ithaca, NY $11,246,855 3.46% 96.54% $11,312,201 6.63% 93.37% Jackson, MI $818,642 5.90% 94.10% $842,449 11.05% 88.95% Jackson, MS $1,236,900 31.71% 68.29% $1,578,049 47.93% 52.07% Jackson, TN $1,851,320 9.01% 90.99% $1,960,286 16.41% 83.59% Jacksonville, FL $6,022,569 51.65% 48.35% $8,831,202 67.93% 32.07% Janesville, WI $2,975,808 11.95% 88.05% $3,234,410 21.19% 78.81% Jefferson City, MO $2,006,363 8.14% 91.86% $2,107,719 14.94% 85.06% Johnson City, TN $988,342 17.27% 82.73% $1,124,496 29.27% 70.73% Johnstown, PA $3,095,742 6.12% 93.88% $3,192,402 11.44% 88.56% Jonesboro, AR $277,417 14.79% 85.21% $309,071 25.60% 74.40% Kalamazoo-Portage, MI $2,307,310 20.46% 79.54% $2,695,628 33.78% 66.22% Kankakee-Bradley, IL $1,401,190 8.08% 91.92% $1,471,152 14.83% 85.17% Kansas City, MO-KS $7,796,148 60.93% 39.07% $12,122,940 75.56% 24.44% Kennewick-Richland-Pasco, WA $6,529,629 22.64% 77.36% $7,764,583 36.72% 63.28% Killeen-Temple-Fort Hood, TX $792,951 31.45% 68.55% $1,009,635 47.63% 52.37% Kingsport-Bristol-Bristol, TN-VA $883,231 27.04% 72.96% $1,087,420 42.36% 57.64% Kingston, NY $3,217,608 9.52% 90.48% $3,422,726 17.26% 82.74% Knoxville, TN $3,179,788 32.97% 67.03% $4,094,906 49.37% 50.63%
320 La Crosse, WI-MN $3,253,295 6.34% 93.66% $3,361,810 11.84% 88.16% Lafayette, IN $6,162,813 9.85% 90.15% $6,575,099 17.81% 82.19% Lafayette, LA $2,194,536 13.47% 86.53% $2,417,249 23.58% 76.42% Lake Charles, LA $694,648 13.72% 86.28% $766,790 23.97% 76.03% Lakeland, FL $1,610,005 30.33% 69.67% $2,032,725 46.32% 53.68% Lancaster, PA $1,370,081 9.37% 90.63% $1,455,362 17.00% 83.00% Lansing-East Lansing, MI $4,532,780 14.06% 85.94% $5,018,195 24.49% 75.51% Laredo, TX $2,364,033 15.45% 84.55% $2,648,616 26.59% 73.41% Las Cruces, NM $826,652 15.03% 84.97% $922,846 25.96% 74.04% Las Vegas-Paradise, NV $7,912,084 34.53% 65.47% $10,307,684 51.12% 48.88% Lawrence, KS $1,404,013 6.85% 93.15% $1,457,604 12.72% 87.28% Lawton, OK $1,174,984 22.25% 77.75% $1,392,825 36.20% 63.80% Lebanon, PA $924,558 6.45% 93.55% $956,322 12.02% 87.98% Lewiston, ID-WA $263,769 5.66% 94.34% $270,847 10.63% 89.37% Lewiston-Auburn, ME $1,224,367 6.89% 93.11% $1,271,623 12.79% 87.21% Lexington-Fayette, KY $2,644,413 20.23% 79.77% $3,083,623 33.46% 66.54% Lima, OH $1,055,630 7.44% 92.56% $1,101,929 13.75% 86.25% Lincoln, NE $4,247,825 15.86% 84.14% $4,775,958 27.21% 72.79% Little Rock-North Little Rock, AR $2,152,127 31.31% 68.69% $2,737,379 47.47% 52.53% Logan, UT-ID $4,532,501 7.15% 92.85% $4,718,642 13.24% 86.76% Longview, TX $497,982 16.75% 83.25% $564,106 28.51% 71.49% Longview, WA $956,828 7.05% 92.95% $995,246 13.08% 86.92% Los Angeles-Long Beach-Santa Ana, CA $47,115,855 79.01% 20.99% $81,406,603 88.18% 11.82% Louisville, KY-IN $7,613,162 50.08% 49.92% $11,049,148 66.54% 33.46% Lubbock, TX $4,118,357 16.32% 83.68% $4,648,324 27.88% 72.12% Lynchburg, VA $10,624,190 19.80% 80.20% $12,345,230 32.86% 67.14% Macon, GA $1,477,439 17.76% 82.24% $1,687,947 29.98% 70.02% Madera, CA $518,997 14.74% 85.26% $577,956 25.52% 74.48% Madison, WI $11,776,810 16.32% 83.68% $13,292,584 27.88% 72.12% Mansfield, OH $966,971 10.86% 89.14% $1,041,004 19.46% 80.54%
321 McAllen-Edinburg-Pharr, TX $318,761 37.81% 62.19% $425,254 54.65% 45.35% Medford, OR $1,873,383 10.25% 89.75% $2,005,820 18.46% 81.54% Memphis, TN-MS-AR $4,159,483 47.02% 52.98% $5,915,307 63.76% 36.24% Merced, CA $1,902,255 14.03% 85.97% $2,105,379 24.44% 75.56% Miami-Fort Lauderdale-Miami Beach, FL $15,525,455 64.60% 35.40% $24,687,334 78.35% 21.65% Michigan City-La Porte, IN $619,397 9.67% 90.33% $659,728 17.50% 82.50% Milwaukee-Waukesha-West Allis, WI $12,951,431 37.14% 62.86% $17,195,563 53.95% 46.05% Minneapolis-St. Paul-Bloomington, MN-WI $27,988,866 55.13% 44.87% $41,972,239 70.89% 29.11% Missoula, MT $2,811,801 7.34% 92.66% $2,932,508 13.58% 86.42% Mobile, AL $1,639,699 29.94% 70.06% $2,064,121 45.86% 54.14% Modesto, CA $1,873,602 23.09% 76.91% $2,236,027 37.32% 62.68% Monroe, LA $1,521,126 10.57% 89.43% $1,633,274 18.98% 81.02% Montgomery, AL $1,261,385 25.77% 74.23% $1,537,646 40.77% 59.23% Morgantown, WV $2,807,527 9.46% 90.54% $2,984,912 17.16% 82.84% Mount Vernon-Anacortes, WA $2,994,956 10.85% 89.15% $3,223,751 19.43% 80.57% Muncie, IN $3,377,075 9.09% 90.91% $3,578,310 16.54% 83.46% Muskegon-Norton Shores, MI $1,533,127 15.70% 84.30% $1,721,430 26.97% 73.03% Myrtle Beach-Conway-North Myrtle Beach, SC $717,738 16.44% 83.56% $810,953 28.06% 71.94% Napa, CA $4,762,130 8.27% 91.73% $5,008,829 15.17% 84.83% Naples-Marco Island, FL $807,017 19.01% 80.99% $931,632 31.76% 68.24% Nashville-Davidson--Murfreesboro, TN $4,448,739 53.12% 46.88% $6,585,898 69.20% 30.80% New Orleans-Metairie-Kenner, LA $6,484,189 36.97% 63.03% $8,598,528 53.77% 46.23% New York-Northern New Jersey-Long Island, NY-NJ-PA $66,267,218 69.38% 30.62% $108,400,208 81.79% 18.21% Niles-Benton Harbor, MI $140,154 15.34% 84.66% $156,882 26.43% 73.57% Ocala, FL $507,643 22.93% 77.07% $605,061 37.10% 62.90% Odessa, TX $2,050,577 9.58% 90.42% $2,182,472 17.36% 82.64% Oklahoma City, OK $2,647,319 46.54% 53.46% $3,752,533 63.31% 36.69%
322 Olympia, WA $4,768,107 9.41% 90.59% $5,066,853 17.07% 82.93% Omaha-Council Bluffs, NE-IA $5,939,889 32.25% 67.75% $7,608,514 48.55% 51.45% Orlando, FL $5,182,261 45.92% 54.08% $7,315,102 62.73% 37.27% Oshkosh-Neenah, WI $2,682,420 8.21% 91.79% $2,819,688 15.06% 84.94% Owensboro, KY $742,423 7.82% 92.18% $777,662 14.39% 85.61% Oxnard-Thousand Oaks-Ventura, CA $4,105,499 37.45% 62.55% $5,462,935 54.27% 45.73% Palm Bay-Melbourne-Titusville, FL $1,159,584 42.63% 57.37% $1,600,352 59.56% 40.44% Panama City-Lynn Haven, FL $955,899 12.12% 87.88% $1,040,603 21.48% 78.52% Parkersburg-Marietta, WV-OH $699,745 11.25% 88.75% $755,892 20.08% 79.92% Pensacola-Ferry Pass-Brent, FL $1,270,985 17.89% 82.11% $1,453,663 30.17% 69.83% Peoria, IL $3,394,393 20.02% 79.98% $3,951,367 33.17% 66.83% Philadelphia-Camden-Wilmington, PA- NJ-DE-MD $16,739,841 59.57% 40.43% $25,813,718 74.50% 25.50% Phoenix-Mesa-Scottsdale, AZ $10,400,214 67.02% 32.98% $16,778,087 80.11% 19.89% Pine Bluff, AR $714,760 13.83% 86.17% $789,717 24.13% 75.87% Pittsburgh, PA $13,992,040 38.20% 61.80% $18,718,427 55.06% 44.94% Pocatello, ID $1,548,637 8.04% 91.96% $1,625,378 14.77% 85.23% Port St. Lucie-Fort Pierce, FL $527,175 60.14% 39.86% $815,817 74.95% 25.05% Portland-South Portland-Biddeford, ME $1,819,461 18.49% 81.51% $2,091,420 31.02% 68.98% Portland-Vancouver-Beaverton, OR- WA $14,348,085 47.20% 52.80% $20,428,466 63.92% 36.08% Poughkeepsie-Newburgh-Middletown, NY $2,700,644 26.50% 73.50% $3,310,937 41.68% 58.32% Providence-New Bedford-Fall River, RI- MA $7,051,423 40.55% 59.45% $9,591,645 57.48% 42.52% Pueblo, CO $1,286,053 12.10% 87.90% $1,399,691 21.44% 78.56% Racine, WI $2,685,314 9.68% 90.32% $2,860,571 17.53% 82.47% Rapid City, SD $989,525 9.85% 90.15% $1,055,721 17.81% 82.19% Reading, PA $2,587,460 11.32% 88.68% $2,796,801 20.19% 79.81% Redding, CA $1,528,109 17.77% 82.23% $1,745,930 29.99% 70.01% Reno-Sparks, NV $5,250,175 20.44% 79.56% $6,132,610 33.74% 66.26%
323 Richmond, VA $5,725,327 34.55% 65.45% $7,459,411 51.13% 48.87% Riverside-San Bernardino-Ontario, CA $4,688,137 75.10% 24.90% $7,925,160 85.67% 14.33% Roanoke, VA $5,003,616 16.10% 83.90% $5,636,929 27.55% 72.45% Rochester, NY $6,887,120 22.52% 77.48% $8,181,461 36.55% 63.45% Rockford, IL $1,775,744 20.52% 79.48% $2,075,516 33.85% 66.15% Rome, GA $6,737,890 9.37% 90.63% $7,157,616 17.01% 82.99% Sacramento-Arden-Arcade-Roseville, CA $8,658,808 51.01% 48.99% $12,644,026 67.37% 32.63% Saginaw-Saginaw Township North, MI $2,675,381 13.66% 86.34% $2,951,792 23.88% 76.12% Salem, OR $2,891,386 18.69% 81.31% $3,329,053 31.31% 68.69% Salinas, CA $4,265,624 14.60% 85.40% $4,744,603 25.32% 74.68% Salisbury, MD $1,765,253 6.87% 93.13% $1,832,973 12.75% 87.25% Salt Lake City, UT $14,480,752 25.66% 74.34% $17,637,709 40.63% 59.37% San Angelo, TX $419,961 13.46% 86.54% $462,548 23.57% 76.43% San Antonio, TX $8,298,795 55.97% 44.03% $12,511,429 71.59% 28.41% San Diego-Carlsbad-San Marcos, CA $12,876,358 57.02% 42.98% $19,541,794 72.45% 27.55% San Francisco-Oakland-Fremont, CA $38,175,609 52.18% 47.82% $56,172,337 68.39% 31.61% San Jose-Sunnyvale-Santa Clara, CA $22,236,945 38.62% 61.38% $29,837,448 55.50% 44.50% San Luis Obispo-Paso Robles, CA $2,737,331 14.74% 85.26% $3,048,358 25.53% 74.47% Santa Barbara-Santa Maria-Goleta, CA $8,809,596 17.09% 82.91% $10,008,066 29.01% 70.99% Santa Cruz-Watsonville, CA $8,260,204 10.02% 89.98% $8,825,729 18.08% 81.92% Santa Fe, NM $2,823,614 9.71% 90.29% $3,008,539 17.57% 82.43% Santa Rosa-Petaluma, CA $4,970,477 23.19% 76.81% $5,936,684 37.45% 62.55% Savannah, GA $3,029,450 17.07% 82.93% $3,441,192 28.99% 71.01% Scranton--Wilkes-Barre, PA $2,513,832 18.78% 81.22% $2,896,506 31.43% 68.57% Seattle-Tacoma-Bellevue, WA $40,506,669 50.61% 49.39% $58,993,980 67.01% 32.99% Sebastian-Vero Beach, FL $763,039 14.32% 85.68% $846,655 24.89% 75.11% Sheboygan, WI $3,374,643 7.68% 92.32% $3,530,206 14.15% 85.85% Sherman-Denison, TX $384,509 14.33% 85.67% $426,703 24.91% 75.09% Shreveport-Bossier City, LA $2,910,490 31.27% 68.73% $3,700,792 47.42% 52.58%
324 Sioux City, IA-NE-SD $2,534,180 13.03% 86.97% $2,780,766 22.91% 77.09% Sioux Falls, SD $2,282,179 12.11% 87.89% $2,484,101 21.46% 78.54% South Bend-Mishawaka, IN-MI $2,711,844 20.86% 79.14% $3,178,508 34.32% 65.68% Spartanburg, SC $743,476 18.61% 81.39% $855,460 31.19% 68.81% Spokane, WA $7,727,457 19.03% 80.97% $8,922,243 31.78% 68.22% Springfield, IL $4,643,031 11.06% 88.94% $5,007,226 19.78% 80.22% Springfield, MO $787,971 21.14% 78.86% $925,719 34.71% 65.29% Springfield, OH $1,018,937 11.84% 88.16% $1,106,463 21.03% 78.97% St. Cloud, MN $3,281,215 9.85% 90.15% $3,500,749 17.81% 82.19% St. George, UT $347,493 10.00% 90.00% $371,214 18.04% 81.96% St. Joseph, MO-KS $1,824,839 10.43% 89.57% $1,957,060 18.76% 81.24% St. Louis, MO-IL $7,085,675 50.30% 49.70% $10,298,900 66.74% 33.26% State College, PA $6,946,061 6.76% 93.24% $7,205,472 12.57% 87.43% Stockton, CA $3,429,339 28.19% 71.81% $4,259,675 43.76% 56.24% Sumter, SC $2,472,665 12.18% 87.82% $2,692,971 21.56% 78.44% Syracuse, NY $7,109,605 15.04% 84.96% $7,937,474 25.97% 74.03% Tallahassee, FL $3,266,411 17.40% 82.60% $3,720,637 29.46% 70.54% Tampa-St. Petersburg-Clearwater, FL $5,574,033 53.80% 46.20% $8,287,828 69.77% 30.23% Terre Haute, IN $569,694 14.20% 85.80% $631,451 24.70% 75.30% Texarkana, TX-Texarkana, AR $441,207 13.12% 86.88% $484,492 23.04% 76.96% Topeka, KS $2,982,118 14.28% 85.72% $3,307,672 24.82% 75.18% Tucson, AZ $5,328,229 40.67% 59.33% $7,254,111 57.61% 42.39% Tulsa, OK $1,798,873 34.27% 65.73% $2,339,004 50.83% 49.17% Tuscaloosa, AL $768,796 17.89% 82.11% $879,257 30.16% 69.84% Tyler, TX $588,891 12.75% 87.25% $644,597 22.46% 77.54% Utica-Rome, NY $2,236,779 14.85% 85.15% $2,493,190 25.69% 74.31% Vallejo-Fairfield, CA $6,912,256 27.18% 72.82% $8,519,176 42.52% 57.48% Victoria, TX $1,494,418 18.34% 81.66% $1,715,540 30.80% 69.20% Virginia Beach-Norfolk-Newport News, VA-NC $9,779,940 56.97% 43.03% $14,838,221 72.41% 27.59%
325 Visalia-Porterville, CA $1,652,766 23.78% 76.22% $1,983,381 38.22% 61.78% Waco, TX $1,531,831 15.64% 84.36% $1,718,974 26.87% 73.13% Washington-Arlington-Alexandria, DC- VA-MD-WV $42,804,405 51.95% 48.05% $62,888,889 68.19% 31.81% Waterloo-Cedar Falls, IA $1,841,418 13.97% 86.03% $2,037,152 24.36% 75.64% Wausau, WI $4,873,746 6.49% 93.51% $5,043,259 12.10% 87.90% Weirton-Steubenville, WV-OH $649,504 14.56% 85.44% $722,145 25.24% 74.76% Wenatchee, WA $5,182,129 6.81% 93.19% $5,378,008 12.65% 87.35% Wheeling, WV-OH $1,461,572 7.66% 92.34% $1,528,688 14.12% 85.88% Wichita Falls, TX $1,104,944 15.87% 84.13% $1,242,460 27.23% 72.77% Wichita, KS $2,340,261 31.34% 68.66% $2,977,357 47.50% 52.50% Williamsport, PA $3,369,611 4.93% 95.07% $3,436,619 9.33% 90.67% Winchester, VA-WV $3,935,579 4.45% 95.55% $3,995,631 8.45% 91.55% Yakima, WA $1,599,737 15.90% 84.10% $1,799,245 27.27% 72.73% York-Hanover, PA $1,114,873 9.85% 90.15% $1,189,428 17.80% 82.20% Youngstown-Warren-Boardman, OH- PA $1,634,763 34.79% 65.21% $2,133,686 51.40% 48.60% Yuba City, CA $2,324,715 14.31% 85.69% $2,579,309 24.87% 75.13% Yuma, AZ $559,557 16.02% 83.98% $629,965 27.44% 72.56%
326 TABLE H 12 Marginal change in total GDP for a 1% increase in bus seat capacity per capita GDP-OLS Percent due to employment density change Percent due to population change GDP-IV Percent due to employment density change Percent due to population change Abilene, TX $6,774,180 15.31% 84.69% $3,272,028 26.39% 73.61% Akron, OH $21,365,346 29.93% 70.07% $11,607,824 45.85% 54.15% Albany, GA $4,569,692 18.61% 81.39% $2,269,374 31.19% 68.81% Albany-Schenectady-Troy, NY $23,986,160 19.81% 80.19% $12,030,510 32.87% 67.13% Albuquerque, NM $19,053,819 29.61% 70.39% $10,326,847 45.47% 54.53% Alexandria, LA $5,289,617 11.50% 88.50% $2,471,804 20.49% 79.51% Allentown-Bethlehem-Easton, PA-NJ $9,803,508 26.82% 73.18% $5,200,438 42.08% 57.92% Altoona, PA $15,920,220 5.09% 94.91% $7,018,660 9.62% 90.38% Amarillo, TX $4,739,992 18.40% 81.60% $2,349,868 30.90% 69.10% Ames, IA $47,791,929 5.54% 94.46% $21,156,897 10.41% 89.59% Anchorage, AK $25,775,207 22.02% 77.98% $13,162,428 35.88% 64.12% Anderson, IN $4,449,616 18.97% 81.03% $2,216,360 31.70% 68.30% Ann Arbor, MI $27,729,277 9.46% 90.54% $12,724,152 17.16% 82.84% Anniston-Oxford, AL $3,372,605 15.46% 84.54% $1,631,027 26.60% 73.40% Appleton, WI $12,728,784 8.38% 91.62% $5,783,903 15.34% 84.66% Athens-Clarke County, GA $8,478,716 18.13% 81.87% $4,193,847 30.51% 69.49% Atlanta-Sandy Springs-Marietta, GA $57,691,531 68.70% 31.30% $40,570,402 81.31% 18.69% Auburn-Opelika, AL $2,518,280 14.50% 85.50% $1,207,902 25.16% 74.84% Augusta-Richmond County, GA-SC $3,479,542 43.96% 56.04% $2,091,811 60.86% 39.14% Austin-Round Rock, TX $34,913,231 42.80% 57.20% $20,821,453 59.73% 40.27% Bakersfield, CA $9,604,918 33.87% 66.13% $5,374,413 50.38% 49.62% Baltimore-Towson, MD $63,835,475 43.20% 56.80% $38,177,214 60.13% 39.87% Bangor, ME $6,494,881 8.93% 91.07% $2,966,043 16.27% 83.73% Baton Rouge, LA $11,048,529 31.12% 68.88% $6,056,771 47.24% 52.76% Battle Creek, MI $10,434,280 11.91% 88.09% $4,893,207 21.13% 78.87%
327 Bay City, MI $16,320,258 8.05% 91.95% $7,394,101 14.79% 85.21% Beaumont-Port Arthur, TX $7,192,660 26.34% 73.66% $3,801,285 41.48% 58.52% Bellingham, WA $22,467,851 11.08% 88.92% $10,460,061 19.81% 80.19% Bend, OR $3,760,140 10.80% 89.20% $1,746,140 19.35% 80.65% Billings, MT $14,667,467 10.50% 89.50% $6,793,214 18.86% 81.14% Binghamton, NY $10,967,048 8.29% 91.71% $4,979,384 15.19% 84.81% Birmingham-Hoover, AL $12,065,606 45.23% 54.77% $7,316,856 62.08% 37.92% Bismarck, ND $11,681,215 5.62% 94.38% $5,175,301 10.56% 89.44% Blacksburg-Christiansburg-Radford, VA $34,423,014 14.07% 85.93% $16,450,847 24.51% 75.49% Bloomington, IN $10,493,020 9.70% 90.30% $4,825,161 17.55% 82.45% Bloomington-Normal, IL $14,733,735 9.30% 90.70% $6,751,160 16.89% 83.11% Boise City-Nampa, ID $5,484,021 26.12% 73.88% $2,893,374 41.21% 58.79% Bradenton-Sarasota-Venice, FL $10,441,027 25.30% 74.70% $5,473,295 40.17% 59.83% Bremerton-Silverdale, WA $24,368,034 14.35% 85.65% $11,673,030 24.93% 75.07% Brownsville-Harlingen, TX $2,537,269 25.72% 74.28% $1,334,420 40.70% 59.30% Buffalo-Niagara Falls, NY $40,008,377 35.38% 64.62% $22,636,789 52.05% 47.95% Burlington-South Burlington, VT $24,422,088 8.90% 91.10% $11,150,557 16.23% 83.77% Canton-Massillon, OH $10,131,702 25.40% 74.60% $5,315,085 40.29% 59.71% Cape Coral-Fort Myers, FL $8,789,063 38.81% 61.19% $5,097,191 55.70% 44.30% Carson City, NV $7,234,237 4.79% 95.21% $3,180,142 9.06% 90.94% Casper, WY $11,925,369 6.83% 93.17% $5,343,004 12.70% 87.30% Cedar Rapids, IA $19,714,407 14.15% 85.85% $9,427,782 24.63% 75.37% Champaign-Urbana, IL $27,586,308 10.46% 89.54% $12,772,822 18.81% 81.19% Charleston, WV $14,376,554 12.83% 87.17% $6,797,028 22.59% 77.41% Charleston-North Charleston, SC $11,518,447 34.39% 65.61% $6,469,975 50.96% 49.04% Charlotte-Gastonia-Concord, NC-SC $99,354,159 72.14% 27.86% $71,277,857 83.70% 16.30% Charlottesville, VA $28,910,007 7.65% 92.35% $13,049,611 14.10% 85.90% Chattanooga, TN-GA $13,394,503 30.70% 69.30% $7,320,078 46.76% 53.24% Cheyenne, WY $11,999,448 5.71% 94.29% $5,320,366 10.71% 89.29% Chicago-Naperville-Joliet, IL-IN-WI $142,453,013 74.71% 25.29% $103,707,869 85.42% 14.58%
328 Chico, CA $7,742,811 14.31% 85.69% $3,707,769 24.87% 75.13% Cincinnati-Middletown, OH-KY-IN $45,229,155 49.37% 50.63% $28,199,333 65.90% 34.10% Clarksville, TN-KY $3,248,214 36.83% 63.17% $1,857,197 53.61% 46.39% Cleveland-Elyria-Mentor, OH $60,847,917 48.03% 51.97% $37,602,238 64.69% 35.31% College Station-Bryan, TX $7,144,602 19.09% 80.91% $3,562,281 31.87% 68.13% Colorado Springs, CO $14,285,093 30.18% 69.82% $7,775,662 46.14% 53.86% Columbia, MO $9,230,411 11.70% 88.30% $4,320,685 20.80% 79.20% Columbia, SC $6,032,463 27.21% 72.79% $3,209,892 42.57% 57.43% Columbus, GA-AL $7,143,544 23.69% 76.31% $3,697,309 38.10% 61.90% Columbus, IN $3,574,131 5.59% 94.41% $1,583,031 10.51% 89.49% Columbus, OH $25,054,317 48.78% 51.22% $15,559,662 65.37% 34.63% Corpus Christi, TX $16,769,300 26.83% 73.17% $8,896,389 42.09% 57.91% Corvallis, OR $9,237,041 4.70% 95.30% $4,057,293 8.91% 91.09% Cumberland, MD-WV $1,736,422 7.28% 92.72% $781,171 13.47% 86.53% Dallas-Fort Worth-Arlington, TX $86,709,771 78.02% 21.98% $64,308,770 87.56% 12.44% Danville, IL $6,852,766 7.95% 92.05% $3,101,781 14.61% 85.39% Davenport-Moline-Rock Island, IA-IL $24,917,457 22.38% 77.62% $12,761,936 36.37% 63.63% Dayton, OH $14,893,885 34.18% 65.82% $8,353,005 50.73% 49.27% Decatur, IL $19,432,217 9.76% 90.24% $8,941,269 17.66% 82.34% Deltona-Daytona Beach-Ormond Beach, FL $7,274,894 39.13% 60.87% $4,228,681 56.04% 43.96% Denver-Aurora, CO $115,702,976 50.19% 49.81% $72,533,180 66.64% 33.36% Des Moines, IA $36,767,368 21.77% 78.23% $18,737,969 35.55% 64.45% Detroit-Warren-Livonia, MI $55,813,774 69.18% 30.82% $39,359,032 81.65% 18.35% Dubuque, IA $13,279,515 6.11% 93.89% $5,910,019 11.43% 88.57% Duluth, MN-WI $21,474,863 19.44% 80.56% $10,738,082 32.36% 67.64% Eau Claire, WI $8,616,300 9.78% 90.22% $3,965,288 17.70% 82.30% El Centro, CA $4,777,362 8.93% 91.07% $2,181,837 16.28% 83.72% El Paso, TX $18,688,044 31.27% 68.73% $10,256,679 47.42% 52.58% Elkhart-Goshen, IN $3,542,200 12.34% 87.66% $1,667,436 21.81% 78.19% Elmira, NY $10,840,315 3.72% 96.28% $4,717,752 7.12% 92.88%
329 Erie, PA $15,149,955 10.53% 89.47% $7,018,468 18.91% 81.09% Eugene-Springfield, OR $25,582,571 18.03% 81.97% $12,643,186 30.36% 69.64% Evansville, IN-KY $8,852,535 16.24% 83.76% $4,309,714 27.76% 72.24% Fairbanks, AK $5,590,822 9.21% 90.79% $2,559,610 16.74% 83.26% Fargo, ND-MN $13,185,047 8.75% 91.25% $6,011,572 15.97% 84.03% Farmington, NM $2,421,071 8.66% 91.34% $1,102,929 15.82% 84.18% Fayetteville-Springdale-Rogers, AR-MO $8,913,266 29.27% 70.73% $4,818,354 45.07% 54.93% Flagstaff, AZ $4,131,250 9.27% 90.73% $1,892,479 16.84% 83.16% Flint, MI $21,880,232 24.66% 75.34% $11,411,554 39.35% 60.65% Florence, SC $1,894,261 17.41% 82.59% $931,348 29.48% 70.52% Fond du Lac, WI $5,229,347 5.12% 94.88% $2,305,940 9.66% 90.34% Fort Collins-Loveland, CO $8,919,229 18.96% 81.04% $4,442,284 31.69% 68.31% Fort Smith, AR-OK $2,978,352 17.56% 82.44% $1,466,160 29.69% 70.31% Fort Walton Beach-Crestview-Destin, FL $3,732,521 11.17% 88.83% $1,739,005 19.95% 80.05% Fort Wayne, IN $7,941,931 27.26% 72.74% $4,227,443 42.63% 57.37% Fresno, CA $13,513,585 34.82% 65.18% $7,614,422 51.43% 48.57% Gadsden, AL $2,319,675 14.58% 85.42% $1,113,453 25.29% 74.71% Gainesville, FL $30,946,768 18.99% 81.01% $15,417,523 31.73% 68.27% Gainesville, GA $1,041,068 13.43% 86.57% $494,770 23.52% 76.48% Glens Falls, NY $4,834,821 3.93% 96.07% $2,108,302 7.50% 92.50% Grand Forks, ND-MN $5,249,988 6.13% 93.87% $2,336,968 11.46% 88.54% Grand Junction, CO $7,022,830 12.42% 87.58% $3,308,450 21.95% 78.05% Grand Rapids-Wyoming, MI $17,151,424 25.83% 74.17% $9,028,556 40.85% 59.15% Great Falls, MT $12,534,398 6.20% 93.80% $5,583,053 11.58% 88.42% Greeley, CO $2,663,412 14.49% 85.51% $1,277,460 25.15% 74.85% Green Bay, WI $14,232,779 18.44% 81.56% $7,058,024 30.95% 69.05% Greenville, SC $2,356,707 26.20% 73.80% $1,244,135 41.31% 58.69% Gulfport-Biloxi, MS $4,534,368 21.22% 78.78% $2,300,561 34.81% 65.19% Hagerstown-Martinsburg, MD-WV $3,596,239 12.42% 87.58% $1,694,116 21.94% 78.06% Hanford-Corcoran, CA $4,508,496 13.84% 86.16% $2,150,184 24.15% 75.85%
330 Harrisburg-Carlisle, PA $13,776,153 8.85% 91.15% $6,287,006 16.15% 83.85% Hattiesburg, MS $1,597,235 9.86% 90.14% $735,538 17.82% 82.18% Holland-Grand Haven, MI $4,080,168 13.80% 86.20% $1,945,293 24.09% 75.91% Honolulu, HI $74,308,299 22.04% 77.96% $37,954,561 35.92% 64.08% Hot Springs, AR $2,931,410 11.19% 88.81% $1,366,032 19.98% 80.02% Houston-Baytown-Sugar Land, TX $130,691,848 71.25% 28.75% $93,279,490 83.09% 16.91% Huntington-Ashland, WV-KY-OH $7,685,515 13.69% 86.31% $3,660,692 23.92% 76.08% Huntsville, AL $3,669,852 31.52% 68.48% $2,017,980 47.72% 52.28% Idaho Falls, ID $3,205,934 7.20% 92.80% $1,441,227 13.33% 86.67% Indianapolis, IN $20,727,937 52.52% 47.48% $13,192,863 68.68% 31.32% Iowa City, IA $34,282,819 7.08% 92.92% $15,395,281 13.13% 86.87% Ithaca, NY $45,152,067 3.46% 96.54% $19,601,475 6.63% 93.37% Jackson, MI $3,376,338 5.90% 94.10% $1,499,652 11.05% 88.95% Jackson, MS $5,580,201 31.71% 68.29% $3,072,776 47.93% 52.07% Jackson, TN $7,624,030 9.01% 90.99% $3,484,317 16.41% 83.59% Jacksonville, FL $26,861,934 51.65% 48.35% $17,000,842 67.93% 32.07% Janesville, WI $11,718,358 11.95% 88.05% $5,497,334 21.19% 78.81% Jefferson City, MO $6,500,893 8.14% 91.86% $2,947,618 14.94% 85.06% Johnson City, TN $4,178,844 17.27% 82.73% $2,052,115 29.27% 70.73% Johnstown, PA $12,238,533 6.12% 93.88% $5,447,249 11.44% 88.56% Jonesboro, AR $1,271,789 14.79% 85.21% $611,554 25.60% 74.40% Kalamazoo-Portage, MI $9,940,243 20.46% 79.54% $5,012,404 33.78% 66.22% Kankakee-Bradley, IL $5,979,661 8.08% 91.92% $2,709,770 14.83% 85.17% Kansas City, MO-KS $34,767,209 60.93% 39.07% $23,334,193 75.56% 24.44% Kennewick-Richland-Pasco, WA $25,407,345 22.64% 77.36% $13,040,192 36.72% 63.28% Killeen-Temple-Fort Hood, TX $2,049,428 31.45% 68.55% $1,126,279 47.63% 52.37% Kingsport-Bristol-Bristol, TN-VA $3,705,050 27.04% 72.96% $1,968,847 42.36% 57.64% Kingston, NY $11,353,894 9.52% 90.48% $5,212,894 17.26% 82.74% Knoxville, TN $13,639,965 32.97% 67.03% $7,581,483 49.37% 50.63% La Crosse, WI-MN $13,512,942 6.34% 93.66% $6,026,909 11.84% 88.16%
331 Lafayette, IN $24,836,074 9.85% 90.15% $11,436,716 17.81% 82.19% Lafayette, LA $14,116,029 13.47% 86.53% $6,710,988 23.58% 76.42% Lake Charles, LA $5,285,251 13.72% 86.28% $2,518,097 23.97% 76.03% Lakeland, FL $6,914,050 30.33% 69.67% $3,767,722 46.32% 53.68% Lancaster, PA $6,165,336 9.37% 90.63% $2,826,678 17.00% 83.00% Lansing-East Lansing, MI $17,054,090 14.06% 85.94% $8,149,041 24.49% 75.51% Laredo, TX $11,021,048 15.45% 84.55% $5,329,463 26.59% 73.41% Las Cruces, NM $3,344,908 15.03% 84.97% $1,611,707 25.96% 74.04% Las Vegas-Paradise, NV $43,268,628 34.53% 65.47% $24,329,795 51.12% 48.88% Lawrence, KS $5,452,143 6.85% 93.15% $2,443,041 12.72% 87.28% Lawton, OK $3,480,797 22.25% 77.75% $1,780,895 36.20% 63.80% Lebanon, PA $3,884,637 6.45% 93.55% $1,734,266 12.02% 87.98% Lewiston, ID-WA $952,565 5.66% 94.34% $422,173 10.63% 89.37% Lewiston-Auburn, ME $5,082,202 6.89% 93.11% $2,278,210 12.79% 87.21% Lexington-Fayette, KY $11,768,891 20.23% 79.77% $5,923,285 33.46% 66.54% Lima, OH $4,804,911 7.44% 92.56% $2,164,821 13.75% 86.25% Lincoln, NE $17,705,689 15.86% 84.14% $8,592,149 27.21% 72.79% Little Rock-North Little Rock, AR $9,367,727 31.31% 68.69% $5,142,760 47.47% 52.53% Logan, UT-ID $15,217,297 7.15% 92.85% $6,837,728 13.24% 86.76% Longview, TX $2,592,089 16.75% 83.25% $1,267,337 28.51% 71.49% Longview, WA $3,654,143 7.05% 92.95% $1,640,503 13.08% 86.92% Los Angeles-Long Beach-Santa Ana, CA $229,050,206 79.01% 20.99% $170,811,983 88.18% 11.82% Louisville, KY-IN $37,065,988 50.08% 49.92% $23,218,512 66.54% 33.46% Lubbock, TX $17,205,315 16.32% 83.68% $8,381,662 27.88% 72.12% Lynchburg, VA $44,726,356 19.80% 80.20% $22,431,689 32.86% 67.14% Macon, GA $5,708,195 17.76% 82.24% $2,814,772 29.98% 70.02% Madera, CA $1,982,097 14.74% 85.26% $952,687 25.52% 74.48% Madison, WI $49,549,753 16.32% 83.68% $24,138,947 27.88% 72.12% Mansfield, OH $4,066,199 10.86% 89.14% $1,889,396 19.46% 80.54% McAllen-Edinburg-Pharr, TX $1,256,523 37.81% 62.19% $723,518 54.65% 45.35%
332 Medford, OR $7,415,627 10.25% 89.75% $3,426,956 18.46% 81.54% Memphis, TN-MS-AR $19,964,215 47.02% 52.98% $12,254,207 63.76% 36.24% Merced, CA $9,293,775 14.03% 85.97% $4,439,652 24.44% 75.56% Miami-Fort Lauderdale-Miami Beach, FL $75,551,612 64.60% 35.40% $51,852,380 78.35% 21.65% Michigan City-La Porte, IN $2,618,921 9.67% 90.33% $1,203,964 17.50% 82.50% Milwaukee-Waukesha-West Allis, WI $60,462,913 37.14% 62.86% $34,648,370 53.95% 46.05% Minneapolis-St. Paul-Bloomington, MN-WI $120,978,927 55.13% 44.87% $78,303,586 70.89% 29.11% Missoula, MT $13,125,446 7.34% 92.66% $5,908,317 13.58% 86.42% Mobile, AL $6,878,757 29.94% 70.06% $3,737,456 45.86% 54.14% Modesto, CA $8,545,066 23.09% 76.91% $4,401,596 37.32% 62.68% Monroe, LA $7,554,749 10.57% 89.43% $3,501,137 18.98% 81.02% Montgomery, AL $5,026,813 25.77% 74.23% $2,644,823 40.77% 59.23% Morgantown, WV $12,614,219 9.46% 90.54% $5,788,460 17.16% 82.84% Mount Vernon-Anacortes, WA $13,936,245 10.85% 89.15% $6,474,583 19.43% 80.57% Muncie, IN $13,280,226 9.09% 90.91% $6,073,484 16.54% 83.46% Muskegon-Norton Shores, MI $6,185,861 15.70% 84.30% $2,997,826 26.97% 73.03% Myrtle Beach-Conway-North Myrtle Beach, SC $4,165,185 16.44% 83.56% $2,031,230 28.06% 71.94% Napa, CA $21,352,896 8.27% 91.73% $9,693,639 15.17% 84.83% Naples-Marco Island, FL $4,245,344 19.01% 80.99% $2,115,289 31.76% 68.24% Nashville-Davidson-Murfreesboro, TN $20,808,045 53.12% 46.88% $13,295,493 69.20% 30.80% New Orleans-Metairie-Kenner, LA $39,549,995 36.97% 63.03% $22,636,537 53.77% 46.23% New York-Northern New Jersey-Long Island, NY-NJ-PA $297,600,766 69.38% 30.62% $210,116,672 81.79% 18.21% Niles-Benton Harbor, MI $615,867 15.34% 84.66% $297,542 26.43% 73.57% Ocala, FL $2,054,614 22.93% 77.07% $1,056,977 37.10% 62.90% Odessa, TX $9,465,346 9.58% 90.42% $4,348,147 17.36% 82.64% Oklahoma City, OK $12,154,112 46.54% 53.46% $7,435,953 63.31% 36.69% Olympia, WA $16,566,954 9.41% 90.59% $7,598,538 17.07% 82.93% Omaha-Council Bluffs, NE-IA $27,652,259 32.25% 67.75% $15,287,878 48.55% 51.45% Orlando, FL $28,636,549 45.92% 54.08% $17,446,842 62.73% 37.27%
333 Oshkosh-Neenah, WI $10,609,399 8.21% 91.79% $4,813,490 15.06% 84.94% Owensboro, KY $3,452,503 7.82% 92.18% $1,560,878 14.39% 85.61% Oxnard-Thousand Oaks-Ventura, CA $17,562,602 37.45% 62.55% $10,086,582 54.27% 45.73% Palm Bay-Melbourne-Titusville, FL $4,451,085 42.63% 57.37% $2,651,394 59.56% 40.44% Panama City-Lynn Haven, FL $4,013,622 12.12% 87.88% $1,885,839 21.48% 78.52% Parkersburg-Marietta, WV-OH $3,223,092 11.25% 88.75% $1,502,752 20.08% 79.92% Pensacola-Ferry Pass-Brent, FL $4,735,899 17.89% 82.11% $2,337,872 30.17% 69.83% Peoria, IL $14,885,829 20.02% 79.98% $7,479,168 33.17% 66.83% Philadelphia-Camden-Wilmington, PA-NJ-DE- MD $80,936,103 59.57% 40.43% $53,868,714 74.50% 25.50% Phoenix-Mesa-Scottsdale, AZ $49,495,912 67.02% 32.98% $34,463,914 80.11% 19.89% Pine Bluff, AR $2,867,353 13.83% 86.17% $1,367,375 24.13% 75.87% Pittsburgh, PA $68,258,741 38.20% 61.80% $39,413,200 55.06% 44.94% Pocatello, ID $5,613,615 8.04% 91.96% $2,542,977 14.77% 85.23% Port St. Lucie-Fort Pierce, FL $2,431,885 60.14% 39.86% $1,624,335 74.95% 25.05% Portland-South Portland-Biddeford, ME $8,021,141 18.49% 81.51% $3,979,511 31.02% 68.98% Portland-Vancouver-Beaverton, OR-WA $66,731,919 47.20% 52.80% $41,008,183 63.92% 36.08% Poughkeepsie-Newburgh-Middletown, NY $9,486,780 26.50% 73.50% $5,019,929 41.68% 58.32% Providence-New Bedford-Fall River, RI-MA $31,531,675 40.55% 59.45% $18,512,216 57.48% 42.52% Pueblo, CO $4,776,441 12.10% 87.90% $2,243,741 21.44% 78.56% Racine, WI $12,349,395 9.68% 90.32% $5,678,039 17.53% 82.47% Rapid City, SD $4,111,019 9.85% 90.15% $1,893,071 17.81% 82.19% Reading, PA $10,630,741 11.32% 88.68% $4,959,599 20.19% 79.81% Redding, CA $6,135,013 17.77% 82.23% $3,025,402 29.99% 70.01% Reno-Sparks, NV $24,370,372 20.44% 79.56% $12,286,519 33.74% 66.26% Richmond, VA $24,463,720 34.55% 65.45% $13,756,940 51.13% 48.87% Riverside-San Bernardino-Ontario, CA $20,458,399 75.10% 24.90% $14,927,066 85.67% 14.33% Roanoke, VA $21,043,395 16.10% 83.90% $10,232,212 27.55% 72.45% Rochester, NY $30,765,605 22.52% 77.48% $15,774,434 36.55% 63.45% Rockford, IL $7,674,512 20.52% 79.48% $3,871,610 33.85% 66.15%
334 Rome, GA $27,686,710 9.37% 90.63% $12,694,364 17.01% 82.99% Sacramento--Arden-Arcade--Roseville, CA $33,165,058 51.01% 48.99% $20,902,735 67.37% 32.63% Saginaw-Saginaw Township North, MI $11,019,388 13.66% 86.34% $5,247,502 23.88% 76.12% Salem, OR $11,010,455 18.69% 81.31% $5,471,608 31.31% 68.69% Salinas, CA $20,310,671 14.60% 85.40% $9,750,720 25.32% 74.68% Salisbury, MD $6,877,706 6.87% 93.13% $3,082,391 12.75% 87.25% Salt Lake City, UT $67,121,280 25.66% 74.34% $35,286,320 40.63% 59.37% San Angelo, TX $1,692,076 13.46% 86.54% $804,383 23.57% 76.43% San Antonio, TX $35,410,039 55.97% 44.03% $23,041,639 71.59% 28.41% San Diego-Carlsbad-San Marcos, CA $58,022,819 57.02% 42.98% $38,007,149 72.45% 27.55% San Francisco-Oakland-Fremont, CA $170,129,748 52.18% 47.82% $108,046,789 68.39% 31.61% San Jose-Sunnyvale-Santa Clara, CA $91,270,334 38.62% 61.38% $52,858,069 55.50% 44.50% San Luis Obispo-Paso Robles, CA $12,741,786 14.74% 85.26% $6,124,407 25.53% 74.47% Santa Barbara-Santa Maria-Goleta, CA $37,439,948 17.09% 82.91% $18,357,963 29.01% 70.99% Santa Cruz-Watsonville, CA $33,684,680 10.02% 89.98% $15,534,145 18.08% 81.92% Santa Fe, NM $13,063,643 9.71% 90.29% $6,007,719 17.57% 82.43% Santa Rosa-Petaluma, CA $20,890,459 23.19% 76.81% $10,769,336 37.45% 62.55% Savannah, GA $12,316,966 17.07% 82.93% $6,038,708 28.99% 71.01% Scranton--Wilkes-Barre, PA $11,478,650 18.78% 81.22% $5,708,527 31.43% 68.57% Seattle-Tacoma-Bellevue, WA $181,179,488 50.61% 49.39% $113,889,921 67.01% 32.99% Sebastian-Vero Beach, FL $3,612,345 14.32% 85.68% $1,729,992 24.89% 75.11% Sheboygan, WI $16,253,705 7.68% 92.32% $7,338,710 14.15% 85.85% Sherman-Denison, TX $1,555,962 14.33% 85.67% $745,269 24.91% 75.09% Shreveport-Bossier City, LA $19,709,401 31.27% 68.73% $10,816,757 47.42% 52.58% Sioux City, IA-NE-SD $13,156,108 13.03% 86.97% $6,230,882 22.91% 77.09% Sioux Falls, SD $13,444,646 12.11% 87.89% $6,316,319 21.46% 78.54% South Bend-Mishawaka, IN-MI $14,174,762 20.86% 79.14% $7,170,830 34.32% 65.68% Spartanburg, SC $3,101,907 18.61% 81.39% $1,540,481 31.19% 68.81% Spokane, WA $30,867,221 19.03% 80.97% $15,382,612 31.78% 68.22% Springfield, IL $16,886,241 11.06% 88.94% $7,860,020 19.78% 80.22%
335 Springfield, MO $3,261,959 21.14% 78.86% $1,654,028 34.71% 65.29% Springfield, OH $4,095,991 11.84% 88.16% $1,919,746 21.03% 78.97% St. Cloud, MN $13,466,659 9.85% 90.15% $6,201,277 17.81% 82.19% St. George, UT $1,458,135 10.00% 90.00% $672,312 18.04% 81.96% St. Joseph, MO-KS $7,419,401 10.43% 89.57% $3,434,341 18.76% 81.24% St. Louis, MO-IL $30,596,605 50.30% 49.70% $19,194,549 66.74% 33.26% State College, PA $19,145,655 6.76% 93.24% $8,572,139 12.57% 87.43% Stockton, CA $15,012,858 28.19% 71.81% $8,048,676 43.76% 56.24% Sumter, SC $8,731,631 12.18% 87.82% $4,104,469 21.56% 78.44% Syracuse, NY $31,282,793 15.04% 84.96% $15,074,310 25.97% 74.03% Tallahassee, FL $10,632,986 17.40% 82.60% $5,227,533 29.46% 70.54% Tampa-St. Petersburg-Clearwater, FL $25,172,157 53.80% 46.20% $16,154,249 69.77% 30.23% Terre Haute, IN $2,483,974 14.20% 85.80% $1,188,337 24.70% 75.30% Texarkana, TX-Texarkana, AR $1,779,631 13.12% 86.88% $843,470 23.04% 76.96% Topeka, KS $11,384,869 14.28% 85.72% $5,450,305 24.82% 75.18% Tucson, AZ $20,314,412 40.67% 59.33% $11,937,151 57.61% 42.39% Tulsa, OK $8,714,948 34.27% 65.73% $4,890,918 50.83% 49.17% Tuscaloosa, AL $3,217,074 17.89% 82.11% $1,588,037 30.16% 69.84% Tyler, TX $2,607,309 12.75% 87.25% $1,231,802 22.46% 77.54% Utica-Rome, NY $7,815,472 14.85% 85.15% $3,759,955 25.69% 74.31% Vallejo-Fairfield, CA $29,360,267 27.18% 72.82% $15,618,268 42.52% 57.48% Victoria, TX $7,977,970 18.34% 81.66% $3,952,903 30.80% 69.20% Virginia Beach-Norfolk-Newport News, VA-NC $40,103,318 56.97% 43.03% $26,261,591 72.41% 27.59% Visalia-Porterville, CA $7,030,750 23.78% 76.22% $3,641,594 38.22% 61.78% Waco, TX $6,725,324 15.64% 84.36% $3,257,367 26.87% 73.13% Washington-Arlington-Alexandria, DC-VA-MD- WV $170,839,456 51.95% 48.05% $108,334,986 68.19% 31.81% Waterloo-Cedar Falls, IA $8,669,924 13.97% 86.03% $4,139,820 24.36% 75.64% Wausau, WI $20,927,459 6.49% 93.51% $9,346,736 12.10% 87.90% Weirton-Steubenville, WV-OH $3,298,372 14.56% 85.44% $1,582,841 25.24% 74.76%
336 Wenatchee, WA $20,637,225 6.81% 93.19% $9,243,992 12.65% 87.35% Wheeling, WV-OH $6,998,972 7.66% 92.34% $3,159,570 14.12% 85.88% Wichita Falls, TX $4,962,517 15.87% 84.13% $2,408,459 27.23% 72.77% Wichita, KS $10,583,001 31.34% 68.66% $5,811,262 47.50% 52.50% Williamsport, PA $14,679,736 4.93% 95.07% $6,461,971 9.33% 90.67% Winchester, VA-WV $17,252,500 4.45% 95.55% $7,560,037 8.45% 91.55% Yakima, WA $7,037,644 15.90% 84.10% $3,416,363 27.27% 72.73% York-Hanover, PA $4,994,569 9.85% 90.15% $2,299,885 17.80% 82.20% Youngstown-Warren-Boardman, OH-PA $7,069,097 34.79% 65.21% $3,982,308 51.40% 48.60% Yuba City, CA $8,796,550 14.31% 85.69% $4,212,511 24.87% 75.13% Yuma, AZ $2,347,673 16.02% 83.98% $1,140,789 27.44% 72.56%
337 APPENDIX I: GLOSSARY OF TERMS Agglomeration effect: The economic gain that accumulates to firms by locating in a region with a large quantity of other firms. Cross-sectional analysis: Regression analysis where observations are based on one year of data across a sample of observations (e.g., a cross-section of MSAs). Deadweight loss: The economic inefficiency that is generated by a government intervention in the free market. Normally associated with the costs of tax policy. Decadal census: The constitutionally-mandated counting of Americans that takes place in years ending in zero, i.e., every ten years. Dependent variable: The object of a regression analysis, or the left-hand side of the regression equation. Elasticity: A measure of how a change in a given variable (e.g. the independent variable) will affect another variable (e.g. the dependent variable). Often expressed as the percent change. Endogeneity: In econometrics this refers to a parameter in a regression that is correlated with the modelâs error term. Practically speaking, it is a bidirectional cause-and-effect relationship between the dependent variable and one or more of the independent variables. For example, the productivity of an MSA may cause it to agglomerate, rather than agglomeration causing an MSA to be more productive. Firm formation: The establishment of a new private enterprise. GDP: Short for gross domestic product, GDP is the most widely cited statistic that measures economic activity. In this study, we use data provided by the Bureau of EconomicAnalysis. GDP is reported both overall and for each of the nineteen economic sectors as defined by the North American Industrial Classification System (NAICS). It is the sum of consumer purchases of new goods, capital investment, government expenditures, net exports (i.e. export value minus import value), and net change in business inventory. It can also be thought of as the monetary value of all contributions to the economy in a given year, commonly called âvalue added.â Independent variable: The variables on the right-hand side of a regression equation, i.e., those that are hypothesized to have an effect on the dependent variable. Instrumental variable (IV): A variable that, instead of being directly inserted into a regression as an independent variable, that is used to estimate a predicted value of an independent variable in a regression. It should not be associated with the dependent variable that is the object of the regression. Regressions that include an instrumental variable are known as âtwo-stage least squares regressionsâ or, simply, âinstrumental variable regressions.â This is a technique that controls for endogeneity in a regression.
338 LEHD: Short for Longitudinal Employer-Household Dynamics, LEHD is a data set developed by the Census Bureau in conjunction with state governments. Using a variety of federal and state data sources, LEHD is able to impute, among other statistics, the number and characteristics of individuals employed within a given geographic area. These areas can be as minuscule as a census block, a level of spatial precision not previously available. LEHD statistics were the basis for computing employment density of metropolitan areas and principal cities from 2002 on. Log-log specification: A regression model in which the dependent variable and one or more independent variables are âlogged,â i.e. the parameter specified in the model is the logarithm of the underlying model. The effect is to transform coefficients into estimates not of a one-unit change in a given independent variable, but a one-percent change, making computation of economic elasticities much easier. Longitudinal analysis: Research in which data are collected at multiple points in time and are considered as separate observations. This can consist of cohort analysis, where a sample of individuals is tracked over time, or panel analysis, wherein a random sample is taken each time, i.e. repeated cross-sections. Ordinary least squares (OLS): The most common form of linear regression analysis, which allows one to estimate how each independent variable is associated with the dependent variable. Overidentification: A condition when, in an instrumental variable regression, there are more instruments than variables suspected of being endogenous. Spline regression: A series of regression models wherein the relationships between the independent and dependent variables are assumed to change over the range of the dependent variable. In this study, it is at one point hypothesized that the relationship between transit infrastructure and employment density is different when metropolitan population is greater than two million. Time lag: When two variables in a single cross-sectional observation are not measured at the same time. In this study, an independent variable is often lagged two or four years to assist in the establishment of causality and to reduce endogeneity. Wages and salaries (payroll): As one would expect, this refers to income earned by employees as compensation for employment, including incidental earnings (bonuses, commissions, tips). Payroll data used herein were obtained from the Census Bureauâs County Business Patterns, with these figures being aggregated to the metropolitan statistical area level such that previous yearsâ figures correspond to current geographies. Payroll figures were divided by employee totals from the same data set to generate payroll per worker figures. This is a gross measure, in that it includes money that is paid in income taxes and social insurance contributions, even if the funds are withheld by the firm and are never actually paid to the employee. It also includes the value of in-kind benefits, such as health insurance, that constitute non-monetary compensation. As computed by federal agencies, however, wages and salaries include only âcoveredâ employment, i.e. individuals that are eligible for unemployment insurance. This includes 98% of workers in the United States, but does exclude âself-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected
339 officials in most states, most employees of railroads, some domestic workers, most student workers at schools, and employees of certain small nonprofit organizations,â according to the Bureau of Labor Statistics. With respect to (w.r.t.): A phrase used to indicate that a rate of change is being calculated per unit of another variable, holding all other variables constant.
340 APPENDIX J: DOCUMENTATION AND RESULTS FOR TASK 6B Description The data set used here is a complete set of Dun & Bradstreet (D&B) reports for every firm that is or was located within the case study areas between 1990 and 2009. These areas are the three counties in each region served by light or commuter rail. Specifically, these are Collin, Dallas, and Tarrant Counties in Texas; and Clackamas, Multnomah, and Washington Counties in Oregon. D&B time-series used herein are the estimates of each firmâs annual number of employees and the dollar value of its sales. These data may be directly reported by the firm or estimated based on economic census data or proprietary modeling by D&B or data vendor Walls & Associates, for every year that the company is located within the case study area. Along with these data are a geocode and a six-digit NAICS code, again updated annually, allowing the firm to be located both within the region and the national economy. After data cleaning, records showed that the set included 1,025,441 firms in the Dallas-Fort Worth area and 336,158 in the Portland area. From there, geocodes were mapped using ArcGIS and spatially joined to the appropriate block, as defined by Census 2000. These data were then aggregated at the block level while being disaggregated by two-digit NAICS sector. The result was industry-specific employment and sales counts for each census block by year. From there, the distances from the centroid of each block to the central business district of the respective metropolitan area and to the nearest rail station that was open in the corresponding year were calculated for all blocks, of which there were 60,923 in the Dallas-Fort Worth area (meaning that, as the data is a 20-year panel in long form, there are 1,218,460 observations) and 28,270 (565,400 observations) in the Portland area. Using block-level data, four ratios were computed: employees per acre, employees per firm, sales per firm, and sales per employee. The first of these was computed for each NAICS sector as well as across all sectors, while the other three were only computed for all firms. These ratios became the dependent variables in panel regressions. Fixed-effects and random-effects models were specified with independent variables consisting of dummy variables representing each year of the data (except 1990, the reference year) and rail station distance. With regard to the latter, six variables were specified, identifying blocks whose centroid is located: [1] within ¼ mile of a station situated in the central business district, [2] within ½ mile of a CBD station, [3] within one mile of a CBD station, [4] within ¼ mile of a non-CBD station, [5] within ½ mile of a non-CBD station, and [6] within one mile of a non-CBD station. (Naturally, the reference category is all blocks not within one mile of a rail station.) These terms are mutually exclusive, in that blocks are assigned to the âclosestâ range that applies to it and that only the station closest to the centroid is included in the analysis. Central business districts are defined according to the transit agencies themselves; Dallas Area Rapid Transit (DART) identifies a âDowntown Dallasâ area on its route map, while Tri-Met designates a city center âFree Ride Zoneâ in Portlandâs urban core. Straight-line distance to the central business district in miles was also included in random-effects models only. A summary of findings is provided on the next page, followed by statistical outputs in the appendices.
341 Results In the first set of analyses using the variables above, we specified a panel model that uses time-series econometric techniques to better compute correlations over the course of the study period. This was done first using what is known as a fixed-effects model, which imposes statistical constraints on the model by assuming the independent variables are non-random, and a generalized least squares or random-effects model that does not have this constraint. Using a Hausman test, it was determined that the data are in fact non-random in all cases and, therefore, that the fixed-effects model is more appropriate. Results for Dallas and Portland contrast substantially. In Dallas, the presence of transit stations was found to be largely negatively correlated with three of the four dependent variables: employees per acre, employees per firm, and sales per firm. For instance, being located within a quarter mile of a CBD transit station in Dallas reduced the number of employees per acre by 20 and the sales per firm by over $900,000. Contrarily, however, there was a positive impact on sales per employee for businesses located within a quarter mile of a non-CBD rail station, increasing said value by $11,768, all else being equal. In Portland, however, results were far more ambiguous, with most coefficients found not to be statistically significant, though the positive effect on sales per employee persisted. Detailed output is available in Appendix 1. From here, it was decided that sector-specific analysis would be appropriate, specifically as it pertained to employees per acre. Hence, the ratio was computed for workers employed in the 20 two-digit NAICS categories, again in both Dallas and Portland. There was indeed substantial variation across industries, as detailed below and in the regression outputs in Appendix 2. Clearly, there is no single decisive trend; overall, a majority of coefficients reported an absence of a statistically significant relationship. This was especially true in Portland, where none of the sectors indicated a strong correlation in either direction. (A correlation is considered to be âstrongâ if a majority of dummy variables indicate the same directional relationship.) Further, the only consistent finding across regions is that rail access has no impact on manufacturing employees per acre. Overall, it is difficult to ascertain any sort of conclusions from these data. Table: Impacts of Rail Stations on Employees per Acre by Two-Digit NAICS Sector, 1990-2009 NAICS Sector Dallas Portland NAICS Sector Dallas Portland Agriculture Ambiguous Slightly Neg. Real Estate Positive Slightly Neg. Mining Positive None Prof. Services Positive Slightly Neg. Utilities Negative None Management Ambiguous None Construction Positive Slightly Neg. Administration Ambiguous None Manufacturing None None Education Ambiguous None Wh. Trade Slightly Neg. Slightly Pos. Health Care Positive Ambiguous Retail Trade Slightly Neg. Ambiguous Arts & Ent. Positive None Transportation Slightly Neg. None Hotels/Dining Positive None Information Slightly Neg. None Other Services Positive None Fin. & Ins. Ambiguous None Public Admin. Slightly Neg. None Finally, it was decided to do some regressions cross-sectionally, rather than as a panel, to measure change over time as a single phenomenon rather than a year-to-year one. Hence, the dependent variables of the ordinary least squares regressions were the change in each of the
342 original measurements between 1996, the year Dallas inaugurated its rail system, and 2009, the most recent year in the data set; independent variables were the six rail dummies plus the straight-line distance to the central business district (in miles). Findings indicated that, in Dallas, presence of rail stations depressed (in increasing order of magnitude) employees per acre, employees per firm, and sales per firm. Across all three, however, being located within ¼ mile of a CBD rail station had strong negative impacts. There appeared to be no impact on sales per employee in Dallas. Meanwhile, in Portland, the effect on employees per acre was positive within ½ mile of CBD stations, while the effect on employees per firm was consistently negative and no impact was found with regard to sales. Again, no strong findings are to be found.
343 Non-Sector-Specific Panel Models Fixed-Effects Models, Dallas Dep. Variable EmpPerAcre EmpPerFirm SalesPerFirm SalesPerEmp underqtrCBD -20.365â¡ (0.98) -13.527â¡ (0.74) -913626â¡ (173482) 9120 (7361) underhalfCBD -1.296 (1.07) -0.422 (0.81) -1488424 (213344) 8021 (9053) underoneCBD 6.387â¡ (0.86) -5.421â¡ (0.65) -1240234â¡ (147299) 2282 (6250) underqtrelse 0.764 (0.48) -2.423â¡ (0.36) -677369â¡ (80364) 11769â¡ (3410) underhalfelse -1.181â (0.37) -0.416 (0.28) -383930â¡ (65395) 608 (2775) underoneelse -0.500* (0.24) -0.568â (0.18) -52101 (39794) 209 (1689) R2 0.000524 0.000587 0.00106 0.00257 Ï 0.839 0.621 0.625 0.606 N 1218460 1218460 673308 673308 Fixed-Effects Models, Portland Dep. Variable EmpPerAcre EmpPerFirm SalesPerFirm SalesPerEmp uhalfCBDrev -1.813 (3.19) 1.023 (2.12) -887958 (573592) 17146 (12024) underoneCBD -3.965â (1.26) 0.395 (0.84) -308214 (261369) 2566 (5479) underqtrelse 0.172 (0.47) -0.104 (0.31) 90061 (100757) 11683â¡ (2112) underhalfelse -0.528 (0.44) 0.794â (0.29) 23114 (97139) 4351* (2036) underoneelse -0.367 (0.34) 0.188 (0.23) 30361 (74171) 1265 (1555) R2 0.000896 0.000271 0.000568 0.00912 Ï 0.846 0.645 0.542 0.694 N 565400 565400 289064 289064 Note 1: All tables report coefficients with standard errors in parentheses. An asterisk denotes a finding of statistical significance at the 95% level; a dagger, 99%; a double dagger, 99.9%. For sake of presentation, coefficients and standard errors for annual dummy variables and the constant term have been omitted from panel results, though they were included in the model. Note 2: The variable underqtrCBD was found to be collinear with other variables in the panel data for Portland. Therefore, underqtrCBD and underhalfCBD were combined into uhalfCBDrev, which is equal to one if either of its constituent variables is equal to one, and zero otherwise. This change did not appreciably alter the results.
344 Employees per Acre by Two-Digit NAICS Sector Results Fixed-Effects Models, Dallas Dep. Variable EmpPerAcre11 EmpPerAcre21 EmpPerAcre22 EmpPerAcre23 underqtrCBD -0.187â¡ (0.01) 2.149â¡ (0.19) -4.118â¡ (0.13) -2.904â¡ (0.12) underhalfCBD 0.001 (0.01) 0.720â¡ (0.21) -4.899â¡ (0.14) 0.152 (0.13) underoneCBD 0.102â¡ (0.01) -0.219 (0.17) -0.011 (0.11) 1.224â¡ (0.10) underqtrelse -0.000 (0.01) 0.029 (0.10) -0.077 (0.06) 0.149â (0.06) underhalfelse -0.015â¡ (0.00) 0.305â¡ (0.07) -0.297â¡ (0.05) 0.404â¡ (0.04) underoneelse 0.004 (0.00) -0.096* (0.05) 0.010 (0.03) 0.023 (0.03) R2 0.000348 0.000143 0.00198 0.000921 Ï 0.435 0.546 0.534 0.713 N 1218460 1218460 1218460 1218460 Dep. Variable EmpPerAcre31 EmpPerAcre42 EmpPerAcre44 EmpPerAcre48 underqtrCBD 0.470 (0.30) -1.245â¡ (0.11) 0.299 (0.38) -0.347* (0.15) underhalfCBD 0.063 (0.33) 0.191 (0.13) 0.094 (0.42) -0.032 (0.16) underoneCBD 0.049 (0.26) -0.094 (0.10) 0.111 (0.34) 0.119 (0.13) underqtrelse 0.070 (0.15) 0.062 (0.06) -0.146 (0.19) 0.110 (0.07) underhalfelse -0.031 (0.11) -0.231â¡ (0.04) -0.010 (0.15) -0.804â¡ (0.06) underoneelse 0.049 (0.07) -0.015 (0.03) -0.986â¡ (0.09) 0.022 (0.04) R2 0.0000525 0.000238 0.000137 0.000240 Ï 0.691 0.648 0.496 0.835 N 1218460 1218460 1218460 1218460 Dep. Variable EmpPerAcre51 EmpPerAcre52 EmpPerAcre53 EmpPerAcre54 underqtrCBD -5.746â¡ (0.34) -2.362â¡ (0.26) 2.642â¡ (0.14) 8.517â¡ (0.21) underhalfCBD 0.874* (0.37) 0.158 (0.29) 0.113 (0.15) 1.207â¡ (0.24) underoneCBD 0.431 (0.30) 1.102â¡ (0.23) 0.405â (0.12) 0.828â¡ (0.19) underqtrelse 0.173 (0.17) 0.210 (0.13) 0.046 (0.07) 0.099 (0.11) underhalfelse 0.044 (0.13) 0.015 (0.10) 0.022 (0.05) -0.024 (0.08) underoneelse 0.117 (0.08) 0.624â¡ (0.06) 0.164â¡ (0.03) -0.176â¡ (0.05) R2 0.000297 0.000252 0.000425 0.00162 Ï 0.698 0.735 0.487 0.754 N 1218460 1218460 1218460 1218460 NAICS Sector Code Key: 11 = Agriculture, 21 = Mining, 22 = Utilities, 23 = Construction, 31 = Manufacturing, 42 = Wholesale Trade, 44 = Retail Trade, 48 = Transportation and Warehousing, 51 = Information, 52 = Finance and Insurance, 53 = Real Estate, 54 = Professional Services.
345 Fixed-Effects Models, Dallas (contâd) Dep. Variable EmpPerAcre55 EmpPerAcre56 EmpPerAcre61 EmpPerAcre62 underqtrCBD 0.222â¡ (0.05) -2.529â¡ (0.11) -5.189â¡ (0.20) 1.660â¡ (0.10) underhalfCBD -0.010 (0.05) 0.383â (0.12) -0.145 (0.22) 0.274* (0.11) underoneCBD 0.405â¡ (0.04) 0.426â¡ (0.10) 0.069 (0.17) 0.534â¡ (0.09) underqtrelse 0.033 (0.02) 0.183â¡ (0.05) 0.307â (0.10) 0.169â¡ (0.05) underhalfelse 0.009 (0.02) -0.258â¡ (0.04) 0.053 (0.07) 0.235â¡ (0.04) underoneelse -0.027* (0.01) -0.068* (0.03) -0.041 (0.05) 0.081â (0.03) R2 0.000127 0.000792 0.000631 0.000709 Ï 0.284 0.727 0.766 0.720 N 1218460 1218460 1218460 1218460 Dep. Variable EmpPerAcre71 EmpPerAcre72 EmpPerAcre81 EmpPerAcre92 underqtrCBD 0.284â¡ (0.02) 1.269â¡ (0.06) 0.273 (0.14) 0.379 (0.25) underhalfCBD 0.256â¡ (0.03) 0.646â¡ (0.07) 0.145 (0.15) 0.370 (0.27) underoneCBD 0.601â¡ (0.02) 0.781â¡ (0.05) 0.734â¡ (0.12) 0.054 (0.22) underqtrelse 0.014 (0.01) 0.139â¡ (0.03) 0.031 (0.07) -0.505â¡ (0.12) underhalfelse 0.044â¡ (0.01) 0.081â¡ (0.02) -0.032 (0.05) -0.000 (0.09) underoneelse 0.006 (0.01) 0.076â¡ (0.02) 0.117â¡ (0.03) 0.049 (0.06) R2 0.00121 0.00155 0.000207 0.0000399 Ï 0.731 0.744 0.637 0.567 N 1218460 1218460 1218460 1218460 NAICS Sector Code Key: 55 = Management, 56 = Administration, 61 = Educational Services, 62 = Health Care and Social Assistance, 71 = Arts and Entertainment, 72 = Accommodation and Food Services, 81 = Other Services, 92 = Public Administration.
346 Fixed-Effects Models, Portland Dep. Variable EmpPerAcre11 EmpPerAcre21 EmpPerAcre22 EmpPerAcre23 uhalfCBDrev -0.019 (0.17) 0.000 (0.04) -0.014 (0.52) -0.020 (0.25) underoneCBD -0.017 (0.07) 0.000 (0.02) -0.014 (0.21) -0.174 (0.10) underqtrelse 0.001 (0.03) 0.003 (0.01) 0.016 (0.08) 0.023 (0.04) underhalfelse -0.049* (0.02) 0.002 (0.01) 0.010 (0.07) -0.097* (0.03) underoneelse -0.002 (0.02) 0.000 (0.00) 0.011 (0.06) -0.025 (0.03) R2 0.0000347 0.0000545 0.0000409 0.00101 Ï 0.450 0.635 0.687 0.737 N 565400 565400 565400 565400 Dep. Variable EmpPerAcre31 EmpPerAcre42 EmpPerAcre44 EmpPerAcre48 uhalfCBDrev -0.329 (0.79) -0.013 (0.37) -0.760* (0.39) -0.436 (2.03) underoneCBD -0.034 (0.31) -0.146 (0.15) -0.116 (0.15) -0.604 (0.80) underqtrelse -0.151 (0.12) -0.062 (0.06) 0.261â¡ (0.06) 0.024 (0.30) underhalfelse -0.055 (0.11) -0.069 (0.05) -0.213â¡ (0.05) 0.006 (0.28) underoneelse -0.132 (0.09) 0.130â (0.04) -0.025 (0.04) -0.015 (0.22) R2 0.000150 0.000332 0.00109 0.0000453 Ï 0.533 0.708 0.808 0.721 N 565400 565400 565400 565400 Dep. Variable EmpPerAcre51 EmpPerAcre52 EmpPerAcre53 EmpPerAcre54 uhalfCBDrev -0.022 (0.66) 0.178 (0.98) -.0411* (0.21) -0.516 (0.99) underoneCBD -0.013 (0.26) 0.073 (0.39) -0.016 (0.08) -1.674â¡ (0.39) underqtrelse -0.102 (0.10) 0.246 (0.15) 0.020 (0.03) -0.293* (0.15) underhalfelse -0.010 (0.09) 0.015 (0.13) -0.041 (0.03) 0.008 (0.14) underoneelse -0.025 (0.07) 0.018 (0.11) -0.007 (0.02) -0.147 (0.11) R2 0.000115 0.0000443 0.000561 0.000463 Ï 0.639 0.631 0.714 0.678 N 565400 565400 565400 565400 NAICS Sector Code Key: 11 = Agriculture, 21 = Mining, 22 = Utilities, 23 = Construction, 31 = Manufacturing, 42 = Wholesale Trade, 44 = Retail Trade, 48 = Transportation and Warehousing, 51 = Information, 52 = Finance and Insurance, 53 = Real Estate, 54 = Professional Services.
347 Fixed-Effects Models, Portland (contâd) Dep. Variable EmpPerAcre55 EmpPerAcre56 EmpPerAcre61 EmpPerAcre62 uhalfCBDrev -0.000 (0.18) -0.001 (0.82) 0.584 (0.50) 0.307 (0.67) underoneCBD -0.000 (0.07) -0.083 (0.33) -0.057 (0.20) -1.927â¡ (0.26) underqtrelse -0.009 (0.03) -0.152 (0.12) -0.057 (0.07) 0.213* (0.10) underhalfelse -0.016 (0.02) -0.063 (0.11) 0.022 (0.07) 0.161 (0.09) underoneelse -0.011 (0.02) -0.065 (0.09) -0.033 (0.05) -0.082 (0.07) R2 0.0000369 0.000296 0.000162 0.000503 Ï 0.453 0.451 0.903 0.771 N 565400 565400 565400 565400 Dep. Variable EmpPerAcre71 EmpPerAcre72 EmpPerAcre81 EmpPerAcre92 uhalfCBDrev 0.042 (0.21) 0.079 (0.33) -0.550 (0.29) 0.031 (0.65) underoneCBD -0.041 (0.08) -0.013 (0.13) -0.151 (0.11) 0.057 (0.26) underqtrelse 0.021 (0.03) 0.037 (0.05) -0.019 (0.04) 0.153 (0.10) underhalfelse -0.036 (0.03) -0.037 (0.04) -0.062 (0.04) -0.045 (0.09) underoneelse -0.020 (0.02) -0.004 (0.04) -0.038 (0.03) -0.042 (0.07) R2 0.000290 0.000793 0.000959 0.000163 Ï 0.757 0.754 0.714 0.746 N 565400 565400 565400 565400 NAICS Sector Code Key: 55 = Management, 56 = Administration, 61 = Educational Services, 62 = Health Care and Social Assistance, 71 = Arts and Entertainment, 72 = Accommodation and Food Services, 81 = Other Services, 92 = Public Administration.
348 Cross-Sectional Results Results for Dallas, 1996-2009 Dep. Variable ÎEmpPerAcre ÎEmpPerFirm ÎSalesPerFirm ÎSalesPerEmp underqtrCBD -55.862â¡ (4.43) -20.693â¡ (2.59) -2525731â¡ (663397) 51159 (28114) underhalfCBD -1.686 (4.85) -3.980 (2.84) -1795638* (799778) 11766 (33894) underoneCBD 2.164 (4.08) -3.827 (2.39) -740483 (586785) 19266 (24868) underqtrelse 0.620 (2.27) -4.744â¡ (1.33) -962725â¡ (326788) 13673 (13849) underhalfelse -2.228 (1.78) -0.759 (1.04) -584572* (268895) 2350 (11396) underoneelse -2.526* (1.18) -1.294 (0.69) -204780 (169212) -3399 (7171) Distance_CBD -0.051 (0.03) 0.014 (0.02) -821 (4429) 325 (188) Constant 1.542* (0.63) -0.809* (0.37) -168318 (95409) -8410* (4043) R2 0.003 0.001 0.001 0.000 F 23.313 12.650 4.855 1.052 N 60923 60923 26894 26894 Results for Portland, 1996-2009 Dep. Variable ÎEmpPerAcre ÎEmpPerFirm ÎSalesPerFirm ÎSalesPerEmp underqtrCBD 32.427â¡ (2.13) -6.601â¡ (1.76) -570195 (581070) 7503 (13299) underhalfCBD 13.931â¡ (2.56) -13.458â¡ (2.12) 35840 (703536) -28208 (16101) underoneCBD -1.104 (2.78) -0.102 (2.30) -258082 (804377) 9500 (18409) underqtrelse -0.878 (0.89) -1.978â (0.74) -508750 (276813) 9367 (6335) underhalfelse -1.236 (0.79) -1.545* (0.66) -393174 (253370) 4493 (5799) underoneelse -0.648 (0.61) -1.437â (0.50) -173461 (198498) 6733 (4543) Distance_CBD -0.034 (0.02) 0.010 (0.02) -2141 (10654) 644â (244) Constant 1.323â¡ (0.33) -0.224 (0.28) -16642 (131520) -5962* (3010) R2 0.010 0.003 0.001 0.001 F 39.956 10.401 0.894 1.940 N 28270 28270 12065 12065
349 APPENDIX K: MSA DATA REALLOCATION ASSUMPTIONS 2010-09-15 APTA Transit Mileage Error Checking and Adjustments The following adjustments were made to data obtained from APTA used in the nationwide MSA analysis. Albuquerque / Santa Fe - Commuter rail opened in 2006 - Two segments o Phase 1 â Belem to Bernalillo o Phase 2 â Ext to Santa Fe - Some service connects Belem to ABQ, other service ABQ to Santa Fe o Service patterns ABQ â Santa Fe include NB and SB trains in bot AM and PM - Decisions o Service to Santa Fe didnât commence until mid-December 2008. Since the commuter rail really only served the ABQ area during the period of the dataset, all mileage for NM CR remains associated with ABQ for 2007 and 2008. Anchorage - 479 mile rail under ZZapta is intercity rail and should not be considered - Remove from ZZapta to avoid misleading results if ZZapta is used in future analysis Baltimore - Light rail and metro okay - MARC CR needs to be split between DC and Balt - Service features by route: o Brunswick line â 100% to DC, no service to DC o Penn line â assume that Baltimore to Perryville serves Balt only, assume Balt to DC is split by service orientation. Approx 40% of trains serve commutes to Balt and 60% serve commutes to DC o Camden line â 6 of 9 daily RT are DC commute direction - Mileage by line (from NTAD 2009 GIS shapefiles â total of 196.8 is slightly less than APTA number of 200.2 â possibly due to double-counting converged lines near union station by APTA) o Brunswick Line â 86.9 miles (2002 extension was on this line â branch line from points of rock to Frederick, md) o Penn Line â South of Balt â 39.1 miles o Penn Line â North of Balt â 36.1 miles o Camden Line â 34.7 miles
350 - Allocate Brunswick 100% to DC, Penn north of Baltimore 100% to Baltimore, Penn south of Baltimore and Camden to be split on the basis of commute direction train ratios - Notes: Ideally would split based on ridership not train frequency, but ridership by station is not immediately available Boston - HR and LR seem okay - CR mostly serves Boston area only, but one line serves Providence and has several runs that operate in Providence commute direction, however ridership from Providence is about 2k PAX / day, a very small number relative to MBTA system and lower than nearby MA stations (from MBTA 2009 Blue Book) - Allocation â reduce Boston CR total by 6.8 miles, which is the distance from last MA station to Providence Chattanooga, TN - 1 mile tourist incline under ZZapta - Serves tourists only and is appropriately excluded in the regression script (may want to exclude if future analysis includes ZZapta) Chicago - HR and CR look okay, CR does not appear to serve other CBSAs Detroit - Downtown people mover under ZZapta category is excluded in the regression script - Decision â leave as is, but an argument could be made that this line serves a legitimate transit role - Note Kenosha, WI, below Eugene, OR - 4 mi BRT opened in 2008, included within ZZapta and thus excluded from regression analysis Galveston - Concern: âGalveston, TX - 6.8 mi - service suspendedâ - Response: This line is featured under the Houston CBSA. See explanation under Houston.
351 Houston - Galveston Island Trolley was suspended after Hurricane Ike in late 2008, but that change does not show up in APTA figures as they appear to be reported for first full year after service changes (i.e. 2009 would be first year lacking service). Irving, TX - Concern: â1.4 mi automated system from urban - internal circulator for a developmentâ - Response: Doesnât seem to be included in Dallas figures (which is the appropriate CBSA), the system serves internal circulation during limited hours only, and in a development that is only partially built. Decision: do not add to dataset. Jacksonville, FL - Downtown people mover, included in ZZapta and thus excluded from regression analysis Johnstown, PA - Features 0.1 mile incline under ZZapta, used for both tourism and local commuting, excluded from regression analysis Kenosha, WI - Concern: âKenosha, WI - 2 mile loop from Kenosha Metro stationâ - Response: Kenosha is part of Chicago CBSA and Kenosha streetcar is already represented in Chicago APTA LR figures Los Angeles - Orange line BRT included in ZZapta / excluded from regression - Concern: âSan Pedro - Port of LA Waterfront Red Carâ - Response: The waterfront Red Car is operated on weekends only and as such cannot play a real transit role, not included (same rationale as Tucson) Miami - Busway included under ZZapta
352 Morgantown WV - Concern: â8.2 mi PRT - 16k daily ridersâ - Response: 8.2 miles should be added to ZZapta as it serves downtown in addition to campus. However, it will not be included in regression analysis as long as the script continues to remove ZZapta from allapta variable Nashville TN - Concern: â32 mi to Lebanon - Music City Star - morning into Nashville, evening from Nashvilleâ - Response: Should appear in 2007 and 2008 â 32 mi. (although ridership is approx. 1k only). Added to dataset. New Haven, CT - 50 mi CR âShore Line East â OK to leave in New Haven, links into Metro North, but Metro North also serves New Haven and is remaining under 100% allocation to NYC CBSA (so balanced), also no data on Shore Line Eastâs ridersâ final destinations, so any other split would be purely speculative. Philadelphia - Concern: âCheck the following for Philadelphia: Girard Ave trolley accounts for change from 2004 to 2005? Of 8.5 milesâ - Response: This is correct. The Girard trolley was reactivated (after over a decade of bus operations). No alterations necessary - NJ Transit RiverLine â Partial allocation switched from Trenton CBSA to Philadelphia CBSA Phoenix - Concern: Should Phoenix LRT be added to dataset? - Response: No. The Phoenix LRT opened in December 2008. Unless a system is open for most or all of a given year APTA does not include that mileage for the opening year. In the case of Phoenix that mileage would appear for the first time in 2009 data. Portland, ME - Amtrak Downeaster is listed as commuter rail. This route runs 5 RT trains / day which are evenly distributed and not concentrated in commuting hours. Given that the total trip time is nearly 2.5 hours this service definitely seems to fall under intercity rail category - Decision â Remove from data and replace with zero
353 Providence, RI - Does not have own CR system, but is served by one line of the MBTA CR system, however ridership at 2k / day is very small compared to overall Boston CR system - Allocation â 6.8mi CR (distance from last MA station to Providence) San Francisco - BART â Okay as is â 100% within SF/Oakland CBSA - Caltrain o length 77.4 miles o According to Feb 2010 Caltrain annual counts (source: caltrain.com), approx. 60% of total boardings and deboardings occur at SF County and San Mateo County stations (i.e. in SF CBSA) and approx. 40% occur at Santa Clara County stations (i.e. in San Jose CBSA) o For lack of a better methodology follow ridership and apply 60% of mileage to SF CBSA and 40% to San Jose CBSA - Amtrak Capital Corridor o Also functions somewhat like CR, but since it connects 3 CBSAs, and is neither metro or core-oriented CR, do not add to SF, San Jose, or Sacramento CBSAs San Jose - Caltrain â See SF description above - ACE â Switch 100% allocation from Stockton, CA CBSA to San Jose CBSA Savannah - Concern: âSavannah 2009 street carâ - Response: The Savannah streetcar started operations after 2008 and it operates on a tourist-oriented weekends-schedule â not added to dataset Stockton - ACE o This commuter system is currently fully allocated to the Stockton CBSA o However service is oriented towards San Jose and all trips are for San Jose / Silicon Valley commutes o Decision: Fully allocate ACE mileage to San Jose CBSA
354 Tampa - Concern: âTeco line - october 2002 - replica vehicles - serves tourists â ridership. MAYBE DELETE?â - Response: Yes, mainly serves tourists, but the same is true of several other systems that remain in the dataset. Unless the system runs only on a limited schedule (i.e. Friday to Sunday) it may also serve as a functional local connector. New Orleansâ St Charles Ave streetcar and the Memphis historic streetcar routes are both tourist-oriented services that also play important local transit roles. Decision â leave this route in dataset Trenton - NJ Transit Riverline o Currently 100% allocated to Trenton o Only 5.25 miles are actually in Trenton CBSA (approx. 15% of total), the rest are in Philadelphiaâs CBSA o 17% of system-wide boardings occur at Trenton (Avg boardings FY 2008) and more boardings are made in Camden than in Trenton o Decision: Allocate 20% of mileage to Trenton and 80% to Philadelphia, none to NYC (the difference for NYC would be negligible anyway, no non-arbitrary justification to allocate some share to NYC without info on RiverLine/NEC transfers) Tucson AZ - Concern: â1 mile on weekends - serving U of AZ - sports events?â - Response: Dataset includes tourist trolleys that act as function transit, but a weekend only trolley does not fit that description, no rationale for inclusion. Washington DC - Partial reallocation of MARC route mileage from Baltimore to DC â see Baltimore for details Bus Data Reallocation Bus seating capacity data was reallocated for New Jersey to correspond to population for the New York-Newark, Trenton-Ewing, and Philadelphia MSAs. New Jersey is the only state to our knowledge that runs a statewide bus agency where this reallocation is needed.
355 APPENDIX L: AUTHORSHIP The executive summary, synthesis and framework were written by Chatman, Noland, Rognlien, and Tulach with contributions from Graham and Ozbay. Data analysis sections 4 and 5 were written by Chatman, Noland, and Grady. The case studies were written by Chatman, Tulach, Desautels, Alexander, Rognlien, and Noland. Tulach and Grady conducted data processing and analysis throughout. Appendix A was written by Chatman, Noland, Rognlien, Voorhoeve, Ozbay, and Bilton with contributions from Berechman, Deka, and Graham. Appendix B was written by Bilton, Noland, Rognlien, Chatman, and Deka. Appendix C was written by Noland, Chatman, Voorhoeve, Rognlien, and Ozbay with contributions from Graham.
