Theoretically, areas near transit stations should have much better accessibility. By reducing the effects of congestion, transit stations should abet both the preservation of existing agglomeration economies and the creation of new ones. Without the diseconomies of congestion, existing employment clusters should continue to grow, and the relative concentration of employment within clusters served by transit systems should grow and continue to increase.
Based on our theory, transit oriented development (TOD) areas serving such fixed route transit systems as LRT should retain if not capture a higher share of workers than their regions during an economic
downturn and afterward. Our first research question is simple:
Do FRT station areas capture proportionately more workers than their regions over time and are there variations by transit type and age of systems?
We mean the term “capture” as the share of total workers and workers within two-digit NAICS sectors that are within census blocks whose edges are with 0.125 (one-eighth) mile distance bands of FRT stations as described in our data below.
We use a pre-post design with an interrupted time period to address the research question. Next, we review our data, study period, the FRT systems selected for analysis, and analytic approach. This is followed by results and implications.
3.3.1
Data
Because we evaluate the change in the distribution of total workers over time, we use employment data. The source of data is the Longitudinal Employer-Household Dynamics
88
(LEHD) program which is part of the Center for Economic Studies at the U.S. Census Bureau.6 For all FRT systems studied, two-digit NAICS data are available annually from 2004 through 2015 at the census block level. We include every block whose closest point is within 0.125 mile of the nearest LRT station point.
3.3.2
Study Periods
We evaluate shift in shares of workers over three discrete time periods extending from before the Great Recession of the late 2000s, through the Great Recession itself, and afterward:
• 2004-2007 covers the period of relatively constant growth from the early 2000s to the end of 2007. This is the pre-shock period that we call “pre-recession”.
• 2008-2011 covers the period of the Great Recession. According to Nelson, Stoker and Hibberd (2018), FRT station areas should retain if not capture a higher share of the shift of regional workers than their regions as a whole. This is the “interrupted period”. • 2012-2015 covers the period after the Great Recession which we call “post-recession”.
This is the post-test period. Based on our theory, FRT station areas should capture a higher share of the shift of regional workers than their metropolitan areas as a whole. Whether this share in the shift would be higher than predicted during the Great Recession we cannot say, but we can predict it should be higher than the pre-recession period.
3.3.3
Fixed Route Transit Systems Studied
Table 3.1 shows the FRT systems we include in our analysis. We excluded systems in the largest metropolitan areas (such as New York City, Chicago, Los Angeles) or metropolitan areas with a complex web of public transit (such as Boston, Philadelphia, San Francisco-Oakland) because we wanted to estimate outcomes associated with individual systems independent of the influences of multiple systems.
3.3.4
Analytic Approach
Given that change in employment share over time is our principal interest, we choose descriptive analysis as our analytic approach to assess change in the distribution of workers within and between time periods before, during and after the Great Recession with respect to transit station proximity.
We analyze those workers that normally occupy space in urban settings. This excludes the North American Industrial Classification System (NAICS) sectors of agriculture, forestry, mining, and construction. We also exclude the industrial sectors (utilities, manufacturing, transportation and warehousing, and wholesale trade) because of their land-extensive nature, making them usually
89
unsuitable per se for locating near transit stations.7 We further assemble other sectors into roughly comparable space-consuming land uses based on Nelson et al. (2015) that are reported in Table 3.2. This allows us to detect differences in the nature of changes in the distribution of workers over time by comparable land use categories. As noted earlier, we evaluate employment performance within 0.125 (one-eighth) mile of FRT stations.
Our analysis considered only those systems operating during any part of each study period. For instance, as there were only three BRT systems in the US operating before the GR, our analysis considered only those systems (see Table 3.1). This increased to 11 systems during the GR and 17 after. This achieves internal analytic consistency within time periods. Generally, we assigned FRT systems to periods as follows. Systems were organized by decade of initial operations including:
• 1980s (including the 1970s for the first Pittsburgh BRT lines) which covers the period both before the Great Recession and the decade before LEHD data were available; • 1990s which covers the period both before the Great Recession and also the decade
before LEHD data were available;
• 2000s which covers the period when systems were substantially being planned,
constructed and operation mostly before the Great Recession and for which LEHD data became available (being 2002 for most systems outside Arizona for which data are available since 2004); and
• 2010s which covers those systems launched either during or after the Great Recession. In some cases, such as LRT systems for which only one was launched in the 2010s, we assigned it to the decade of the 2000s. We note FRT system assignments below.
Assigning systems to decades helps accomplish two things. First, for the older and more mature systems, analysts should know that those systems were already operating a decade or more before LEHD data became available. In effect, for those systems, LEHD data can be interpreted as outcomes associated with mature systems, unlike for instance systems launched in the 2000s when LEHD data became available.
Second, age of operations also reflect transit and land use planning approaches at the time. For instance, in the 1980s, LRT systems were often designed to connect nodes, such as suburban centers to downtowns, without much thought that station areas could themselves evolve into discrete sub centers. LRT systems in Portland, Sacramento, San Diego and San Jose, for instance, followed freeway corridors, sometimes being placed in freeway medians, and often elevated so that passengers needed to elevate, escalate, or amble up to platforms and then back down (Cervero et al. 2004). As Chapter 2 shows, these station locations add little to real estate value and can actually be viewed as a negative externality in the market, thereby dampening
3 Urban microbreweries and small-scale crafts shops are the kinds of exceptions that support our
90
value. Chapter 8 explores these concerns through visual examples and correlations between real estate rents, accessibility, and station location and design. As experience grows, modern LRT (and others) are located and designed to create positive user and real estate benefits.
Our method includes calculation of z-scores (p < 0.05) to assess whether differences between periods of time are statistically significant. They were, in all cases. Although this step may not be necessary because the analysis is of the total universe of workers and locations, we do so
nonetheless.
We note that our methods only creates an association between FRT station proximity and change in the distribution of workers over time. We do not derive causal relationships.
Table 3.1: Fixed Route Transit (FRT) Systems Studies Using Economic Base—Shift-Share Analysis Light Rail
Transit
Year Bus Rapid Transit
Year Streetcar
Transit
Year Commuter Rail Transit
Year
Buffalo 1984 Cleveland 2008 Atlanta 2014 Albuquerque-Santa
Fe
2006
Charlotte 2007 Eugene-
Springfield
2007 Cincinnati 2016 Austin 2010
Cleveland 1980 Kansas City 2005 Dallas 2015 Dallas-Fort Worth 1996
Dallas 1996 Las Vegas 2004 Kansas City 2016 Miami Tri-Rail 1989
Denver 1994 Nashville 2009 Little Rock 2004 Minneapolis 1997
Houston 2004 Phoenix 2009 New Orleans 2016 Nashville 2006
Minneapolis-St. Paul
2004 Pittsburgh 1977 Portland 2001 Orlando 2014
Norfolk 2011 Reno 2010 Salt Lake City 2013 Portland 2009
Phoenix 2008 Salt Lake City 2008 Seattle 2007 Salt Lake City 2008
Pittsburgh 1984 San Antonio 2012 Tacoma 2003 San Diego 1995
Portland 1986 San Diego 2014 Tampa 2002 San Jose-Stockton 1998
Sacramento 1987 Seattle 2010 Tucson 2014 Seattle-Tacoma 2000
Salt Lake City 1999 Stockton 2007 Washington, DC 2016
San Diego 1981
San Jose 1987
Seattle 2003
91