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Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2012. Methodology for Determining the Economic Development Impacts of Transit Projects. Washington, DC: The National Academies Press. doi: 10.17226/22765.
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Page 11
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2012. Methodology for Determining the Economic Development Impacts of Transit Projects. Washington, DC: The National Academies Press. doi: 10.17226/22765.
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Page 11
Page 12
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2012. Methodology for Determining the Economic Development Impacts of Transit Projects. Washington, DC: The National Academies Press. doi: 10.17226/22765.
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Page 12
Page 13
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2012. Methodology for Determining the Economic Development Impacts of Transit Projects. Washington, DC: The National Academies Press. doi: 10.17226/22765.
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Page 13
Page 14
Suggested Citation:"Executive Summary." National Academies of Sciences, Engineering, and Medicine. 2012. Methodology for Determining the Economic Development Impacts of Transit Projects. Washington, DC: The National Academies Press. doi: 10.17226/22765.
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Page 14

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1 EXECUTIVE SUMMARY Transit Cooperative Research Program (TCRP) project H-39, “Methodology for Determining the Economic Development Impacts of Transit Projects,” was aimed at developing a method for transit agencies to assess whether and under what circumstances transit investments have economic benefits that are in addition to land development stimulated by travel time savings. The method was intended for possible use by transit agencies proposing new transit systems as well as major capital investments in existing transit systems. Current evaluation procedures include estimates of travel time savings, costs of construction, environmental impacts, and effects on land development. This TCRP study addresses an additional type of impact: the productivity increases associated with agglomeration economies—economies of scale in density—that may be caused by transit improvements. We reviewed existing evaluation practices and academic research, and then carried out a wide- ranging empirical study on metropolitan-level data from cities across the US, firm-level data from two metropolitan areas, and case studies of three recent transit projects. A study of this question had never been carried out in the US to our knowledge. Recent research in the UK has been the basis for the formal evaluation of such impacts there and has suggested that agglomeration-related benefits are substantial. The measures of agglomeration used for the empirical estimates in this report are at the metropolitan area level: employment density in the urbanized area and the principal cities of the metropolitan area, and the size of the metropolitan area as measured by its population. While we investigate local level measures of density in our firm-level analysis, we are unable with the available data to investigate how clustering of activities in certain economic sectors in close proximity to one another or to other businesses such as business services or suppliers, within a defined radius of transit stations, might affect productivity. However, we find evidence that there is little such activity occurring in the two regions for which we have firm-level data, or for the three case study regions. This report does not address the development impacts of opening new transit lines or transit stations. Although such transit-induced development may have local benefits, this type of impact is already addressed in current guidance. From a regional or national perspective such development impacts may be primarily redistributive rather than a net addition to economic growth, and therefore there is reason to discount them as additive effects. Review of literature and practice We reviewed academic literature, conducted interviews with professional practitioners, and studied practice reports from the US as well as the written guidance adopted by the United Kingdom to evaluate wider economic impacts of transit investments. It has long been argued in the academic literature that improvements to transportation could lead to easier interactions between firms, more centralized and higher-density employment clusters, and larger cities. These changes could increase the productivity of firms and workers by making labor markets more accessible, increasing information exchanges between firms, enabling more specialization, and in other ways. Despite this well-established theory, empirical research on the link between transportation investments, agglomeration, and productivity increases is limited. This is particularly true for transit projects, which are likely to have markedly different effects on agglomeration than roads or highways. Those few estimates

2 available suggest that the wider economic benefits of transportation projects can add as much as a 25 percent increment to the benefits calculated in a conventional benefit-cost analysis. Our interviews of US practitioners revealed little awareness of or interest in the possibility of these additional economic impacts. In discussing the possible addition of a “wider economic benefits” criterion in evaluating transit project proposals, there were significant concerns, including worry that the additional complexity and reporting requirements would be burdensome; that any requirement might not be consistently applied or evaluated; and that the new criterion could put some agency applications at a disadvantage. We also interviewed practitioners in the United Kingdom and Australia, where wider economic impacts are routinely calculated. With some effort, agencies there have been able to provide the inputs needed for analysis, but the process and outputs are complex and not well understood. We also reviewed agency reports that addressed the economic impacts of prospective rail investments. None of these estimated agglomeration impacts or other wider economic impacts. The most relevant reports described analysis using input-output models or integrated land use and transportation models. Most of these focused on multiplier effects of cost savings in firm production processes, which double-count travel time savings in some cases, and in others would distinguish transit projects from each other only insofar as different regions have different intrinsic economic multipliers. Finally, we reviewed the approach used in the United Kingdom and promulgated by the Department for Transport there, which avoids the problems of US practitioner approaches. The UK approach is based on estimates of how productivity varies intra-regionally as a function of employment accessibility (or “effective density”), which in turn is increased by transit investments that reduce travel time. Though innovative, this method ignores the potential impacts of transit investments on employment densification and urban growth, and it also relies on firm-level revenue and capital data that are generally unavailable in the US. Empirical study We developed a three-part study approach suitable for evaluating how transit investments affect agglomeration economies in the US. Our goals were: first, to explore the relationship between transit and agglomeration from different perspectives; and second, to estimate how increases in transit capacity affect agglomeration-related productivity, with the most accuracy possible given data constraints and funding limitations. We sought to ensure that our estimates did not include economic benefits of capitalizing travel time savings so as to avoid “double- counting.” In the first part of the empirical study, we compiled productivity, agglomeration, and transit capacity data for all of the metropolitan areas in the United States, and analyzed the data exhaustively using a variety of methods, measures, and model specifications, producing MSA- specific estimates of how wages and GDP are correlated with transit capacity due to agglomeration. In the second part, we conducted a spatially fine analysis of firm clustering near transit stops in two metro areas. In the third part, we conducted case studies of transit projects in three metro areas. Each part is described below. Nationwide analysis For our analysis of all metropolitan areas in the US, we gathered a time-series of data from the National Transit Database, the American Public Transportation Association, the

3 National Transportation Atlas Database, the Bureau of Economic Analysis, the American Community Survey, and the Longitudinal Employer-Household Dynamics (LEHD) database. We carried out metropolitan area-level estimates in two stages: first, estimating how transit capacity is associated with agglomeration; and second, how agglomeration is associated with wages and GDP. We tested a variety of measures of transit capacity and agglomeration. There are strong statistical associations between transit capacity and two measures of agglomeration: the employment density of the principal cities within the metropolitan area, and the total population of the metropolitan area. Our second-stage models linked these agglomeration measures to metropolitan area productivity, measured with wages and GDP. We applied both model stages in estimating the changes in productivity associated with adding additional transit capacity. There was substantial variation in agglomeration-related economic benefits, depending on levels of transit, population, and employment density in the metropolitan area. Larger metropolitan areas with larger transit systems are associated with stronger relationships between additional transit investments and productivity. The estimates ranged between $1 and $50 per capita per year, depending on the metropolitan area. Among metropolitan areas with existing rail systems, the net agglomeration benefit of one additional track mile ranges from $10 million to $500 million per year. We view these estimates with caution. Our study is both the first US study and the first transit-specific empirical study of the link between transportation investment, agglomeration, and productivity. There is a need for continued research with more complete data enabling methodological improvements. The benefits likely take quite some time to be realized, lagging full ridership levels by several years or more. The estimates are best suited for categorical comparisons between rail projects, rather than figures to be compared directly with the value of travel time savings. Firm-level analysis In the second stage of empirical analysis, we investigated firm-level spatial data in two regions, Dallas-Fort Worth and Portland, Oregon, to look for evidence of growth or relocation near rail stations. We chose these two metropolitan areas based on initial analysis of worker-by- industry data at the census block level for a number of metropolitan areas with recent transit investments. We had initially intended to include New Jersey’s recent transit investments with firm-level productivity data (i.e., payroll and revenue) from the state, but this proved impossible due to confidentiality issues and budget cuts in the state department of labor. Instead, we purchased data from the National Establishment Time-Series database from a private vendor, Walls & Associates, consisting of a twenty-year time-series of geo-coded firm establishment locations with number of workers, industry classification, and retail sales. Unfortunately, this database includes no payroll or revenue information. We focused on how the location of newly opened light rail stations influenced the location and growth of firms, as well as their retail sales. These spatial effects are of interest because they address the nature of the transit-to-firm-agglomeration link, which is the precursor to some of the expected productivity effects of transit capacity investments. There were substantial differences between the two regions when analyzing block-level data about employment changes over time. Unexpectedly, proximity to CBD-based transit stations in the Dallas region was associated with employment density reductions. In the case of Dallas, perhaps residential development near CBD-based stations could account for lower

4 employment density growth there in comparison to elsewhere in the metropolitan area. Meanwhile, in Portland there was no statistically significant association between station proximity and employment growth, although an alternative model with cross-sectional data found substantial employment density increases near transit stations, along with a reduction in firm size. This combined pattern in Portland—more clustering of smaller firms near transit stations, resulting in higher density there—is in line with agglomeration theory. Case studies For the third and final stage in our empirical analysis, we carried out case studies of the Dallas Area Rapid Transit (DART) light rail system in Dallas-Fort Worth; the TRAX light rail line in Salt Lake City; and the Los Angeles Metro’s Orange Line, a 14-mile fixed-guideway bus rapid transit (BRT) line that began serving passengers in 2005. The case studies included a review of all relevant public documents and reports, a spatial analysis of industry growth data, a description of regulatory constraints, and interviews with more than a dozen knowledgeable local officials and developers in the regions. The spatial analysis revealed some densification effects along the transit corridors in all three study regions, but these changes did not appear to be largely or even partly attributable to the transit lines. Our mapping of zoning and other regulatory constraints did not suggest a strong causal role in shaping or hindering non-residential development responses to rail station access, although Dallas-Fort Worth and Salt Lake City appear to have relatively weak regulatory environments in comparison to Los Angeles. The strongest emergent theme from interviews with key stakeholders in economic development, planning, and real estate was the lack of emphasis on transit’s potential role in fostering or attracting industry or affecting industrial clustering, though access to transit was marketed by residential and mixed-use developers. Developers of all types, including commercial and industrial developers, were seen as generally indifferent about transit access, believing it plays at best a minor role in development. Interviewees reported that the regulatory environment was relatively flexible in adjusting to the demands of developers, and was not usually seen as posing substantial hurdles to development, particularly in Salt Lake City and Dallas-Ft. Worth. While height and bulk restrictions were not viewed as impediments, parking requirements and procedural hurdles were sometimes seen as problematic. The Orange Line Bus Rapid Transit service in the San Fernando Valley of Los Angeles is the only recently-opened fixed-guideway bus rapid transit system in the US. Fairly significant densification has occurred along the Orange Line corridor during the last decade, although it is difficult to attribute this to the presence of the BRT line in particular. While zoning regulations along the corridor do not differ substantially from those outside the corridor, zoning plans and economic development initiatives elsewhere in the Metro system—particularly those focused on entertainment industries—could encourage increases in population and employment densities near transit stations that enable the Orange Line to increase agglomeration across the metropolitan area. The denseness of the network to which the Orange Line connects increases the potential for it to contribute to agglomeration economies. In Salt Lake City, a much smaller metropolitan region, we found little empirical evidence that recent transit investment has resulted in agglomeration. Professional planners and developers reported that non-residential development near rail stations is still in the early stages, and that there are signs of nascent agglomeration in the downtown area. Finance and technology companies were cited as recent examples of firms interested in transit access as a part of their location strategy.

5 In Dallas, an emerging world city and center for finance, energy, and insurance, a rise in road congestion increases the potential for transit investment to increase agglomeration by improving access to labor markets. But there is limited evidence of this potential being realized. Even in the relatively permissive regulatory environment in Dallas, increasing density around transit is difficult. Infrastructure (including parking) and lengthy procedural hurdles are viewed as the most significant challenges faced by developers in the region. Spreadsheet tool We produced a spreadsheet tool that could be used by transit agencies and others to estimate the agglomeration-related economic benefits of rail investments in the forms of new systems or additions to existing systems. With information about any of five possible measures of the proposed investment—track miles, total seating capacity, rail-specific seating capacity, rail revenue miles, or total revenue miles—the spreadsheet provides a range of possible wage or GDP impacts. The tool is best used to compare the agglomeration impacts of transit investments in different metropolitan areas to each other. It is less well suited to within-region comparisons of systems because it cannot take into account variations in alignment or other factors beyond the five inputs listed above. Conclusions Our metropolitan area-level estimates provide a reasonable starting point for assessing how the GDP and wage benefits of agglomeration caused by transit investments can be expected to vary depending on characteristics of the existing network, the population of the metropolitan area which it serves, and the employment density of its principal cities. City size, employment density, and transit network size at the time of the proposal are all highly predictive of the size of the agglomeration benefit. The case studies reinforce the idea that city size and transit network maturity are important. Our calculations, summarized in the spreadsheet tool, are expressed in terms of increases to average wage and per capita GDP. In practice, these increases could take the form of more jobs, higher wages for existing jobs, shorter unemployment spells, and greater firm profits— likely some combination of all four. At the same time, both the firm-level analysis and the case studies suggest that there are large differences from city to city in how benefits arise, due to differences between cities in development patterns, regulatory environments, and institutional support. Agglomeration benefits likely take a substantial amount of time to be realized. Our US metro-level data did not allow us to directly quantify the time dimension. Further research is certainly needed on this topic. The obvious next research steps are to collect and analyze historical data on transit, agglomeration, and productivity over several decades, and to use more advanced statistical methods to better understand mutual causality. Other research requires testing other measures of transit capacity, such as examining how firm formation may occur in response to transit investments, and investigating whether the residential focus of TOD efforts may dampen employment-related agglomeration impacts of transit.

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TRB’s Transit Cooperative Research Program (TCRP) Web-Only Document 56: Methodology for Determining the Economic Development Impacts of Transit Projects explores development of a method for transit agencies to assess whether and under what circumstances transit investments have economic benefits that are in addition to land development stimulated by travel time savings.

As part of the project a spreadsheet tool was developed that may be used to help estimate the agglomeration-related economic benefits of rail investments in the form of new systems or additions to existing systems.

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