Portland, Ore., and Dallas Tex., were compared “because each one has relatively new light-rail systems,” said authors Robert Noland, Ph.D.; Daniel Chatman, Ph.D.; and Nicholas Klein, Ph.D. “The report found that new firms did cluster around transit stations in Portland but less so in Dallas, likely because of different planning and zoning criteria.”
The research findings “indicate that newly formed firms do tend to cluster around rail stations in the Portland region, but new firm startups in the Dallas region are not nearly as correlated with rail station access,” Noland noted. “The difference between the two regions holds for different firm sizes and different industry sectors. While both the Dallas-Fort Worth and Portland regions have relatively new light rail and commuter rail systems, there are substantial differences in how these systems are associated with the birth of new firms.”
“Portland has adopted more stringent policies than Dallas-Fort Worth in focusing development near rail stations and within the central business district (CBD),” said Noland. “These include restrictions on off-street parking for new development and an urban growth boundary that restricts development on the metropolitan fringe. These policies have led to more infill development, some of which naturally occurs near rail stations, both in the CBD and elsewhere.
“By contrast, Dallas has no comprehensive planning around transit, and there is ample parking in the CBD. Portland’s transit system also provides relatively better access than does the Dallas system, with a much higher mode share for all transit ridership and a much higher modal share of rail. Both factors likely increase the attractiveness of rail station areas to startup firms.”
Data regarding startups came from the National Establishment Time-Series (NETS) dataset, which is derived from Dun & Bradstreet records of firms. It includes data on firm size, industrial category, dates of firm startup and closure, and location at the block level. The NETS data were used to develop a geographically specific dataset, including firm location relative to the rail transit networks in the two regions.
“Because the NETS data are a time series over 18 years, it was possible to evaluate from 1991-2008 how firm startups within the regions have changed over time and how they may be influenced by proximity to the rail network, with attention to firms of various sizes and in specific industry sectors,” said Noland. “A random-effects, negative binomial regression model was used to examine associations between proximity to rail stations and to control for a large set of other spatially correlated variables, such as distance to downtown, access to freeways, and socioeconomic characteristics of census tracts.”
The full report includes 28 tables and figures, such as (for each city) Number of Firms by Year; Negative Binomial Model Coefficients; Density of Firm Births per Square Mile; Predicted Effects of Station Distance Variables; and more.
Robert B. Noland is a professor at the Edward J. Bloustein School of Planning and Public Policy at Rutgers University and director of the Alan M. Voorhees Transportation Center. He received his PhD at the University of Pennsylvania in energy management and environmental policy. The Mineta National Transit Research Consortium is composed of nine university transportation centers led by the Mineta Transportation Institute (MTI) at San Jose State University. MTI conducts research, education, and information transfer programs regarding surface transportation policy and management issues, especially related to transit.