The Effect of Light Rail Transit on Employment: Evidences from a Longitudinal Quasi- Experimental Design

Transcription

1 The Effect of Light Rail Transit on Employment: Evidences from a Longitudinal Quasi- Experimental Design Presented by Wei Li, Ph.D. Assistant Professor, Department of Landscape Architecture and Urban Planning, Texas A&M University; Assistant Research Scientist, Texas A&M Transportation Institute (TTI) Graduate Student: Joel Mendez April 10 th, 2014

2 Presentation Outline Introduction Methodology Results Discussions Future Research

3 Benefits of public transit Crucial travel mode in the largest and densest cities; Provides the most service at peak travel times in the most congested travel corridors Environmental advantages Mobility for the disadvantaged 3

4 How about jobs? Legislation Intermodal Surface Transportation Efficiency Act of 1991 Objective provide intermodal connections to jobs and other services for the economically disadvantaged Personal Responsibility and Work Opportunity Reconciliation Act of 1996 Balanced Budget Act of 1997 primary goal was to move unemployed persons to stable employment provided a source of funds that could be spent for transportation needs of Temporary Assistance for Needy Families (TANF) participants Transportation Equity Act of 2001 (TEA-21) Safe, Affordable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) Job Access and Reverse Commute program in 1998 authorized a total of $727 million for JARC grants for fiscal years 2006 through

5 Transit benefiting job growth? Portland CBD: light rail system reduced vacancy rate and promoted growth (HDR, 2005) Atlanta: areas within transit service area experienced twice the amount of employment growth (Bollinger et al. 1997) Bay Area, CA: growth in service area accounted for 57% of the total employment growth within the three county area which the system intersects (Cervero et al. 1999) Chicago: Within the past two decades, businesses within the city have migrated to areas in close proximity to transit (Kawamura, 2001)

6 Transit benefiting job growth? Weak relationship between access to jobs and employment participation (Thompson et al. 1997) Six metropolitan areas: Transit accessibility played no significant role in explaining the employment status of TANF recipients. (Sanchez et al. 2004) Boston, MA: Job access had no statistically significant effects on the labor participation (Cooke, 1996) Portland, OR: No causal relationship between increased access to public transit and increased labor participation. (Sanchez, 1999). Chicago, IL: Suggested that unemployment rates were similar among African Americans, regardless of job accessibility from their residences (Ellwood et al. 1986)

7 Research Purpose Investigating the effect of light rail transit on local employment Research Question: Does employment density near light rail transit stations grow faster than the area further away? Does the effects of light rail transit on local employment differentiate by earning levels and industry types? Pilot Study: Preliminary Analysis Completed 7

8 Pilot Study in Dallas, TX Corridor Line Miles Number of Stations Opening Year North Central Red Northeast Blue DART Light Rail System 61 Stations 85 Miles

9 Research Design NR x O1 NR O1 1. Selection of treatment group Selection of Census block groups in proximity to transit ArcGIS i. ¼ mile from light rail station ii. distance from future and previously opened stations 2. Selection of control group Full matching propensity score method based on: i. Employment Characteristics ii. Demographic Characteristics iii. Distance from future and previously opened stations iv. Distance from highway on/off ramp

10 Propensity Score Matching Author Study Propensity Score Use Diaz and Handa, 2004 An Assessment of Propensity Score Matching as a Non-Experimental Impact Estimator: Evidence from a Mexican Poverty Program PSM was used to find comparable households varying in program participation and evaluate impacts of the program. Boer et al., 2007 Cao, 2010 Funderburg et al., 2010 MacDonald, et al Billings, 2011 Artz and Stone, 2012 Deng, et al., 2012 Cao and Fan, 2012 Neighborhood Design and Walking Trips in Ten U.S. Metropolitan Areas Exploring causal effects of neighborhood type on walking behavior New highways and land use change: Results from a quasi-experimental research design The Effect of Light Rail Transit on Body Mass Index and Physical Activity Estimating the value of a new transit option Revisiting WalMart s Impact on Iowa Small-Town Retail: 25 Years Later Private residential price indices in Singapore: A matching approach Exploring the influences of density on travel behavior using propensity score matching Account for confounding variables when comparing household walking behavior specific to neighborhood design characteristics. Matched individuals from suburbs to those residing in traditional neighborhoods and measured differences in travel behavior. Utilized to select a control for each spatial unit that received access to new highway infrastructure. Measured difference in growth indicator variables. Match and compare individuals before/after the construction of a LRT system. Measured change in physical activity of LRT users and non-users. Matched neighborhoods near transit with similar neighborhoods located elsewhere. Compared housing prices. Find a match for each host town which represents what would have happened if WalMart had not located there. Houses sold at the baseline time were matched with those sold at a later time. Sale index was constructed from difference. Matched individuals in low density communities with those in high density communities, Compares travel 10 behavior.

11 Propensity Score Matching Treatment Groups: Census Block Groups whose centroid is within ¼ mile from a LRT station and outside 1 mile of highway on/off ramp Variable Unit Source Total Population Pop/CBG US Census Per Capita Income Avg. Income US Census Matching Labor Force Pop age 16-64/CBG US Census Education Attainment % with B.S % with M.S US Census Employment Density Jobs/sq. mi LODES Total Area sq. miles US Census Vacancy Rate % vacant US Census Control Groups: Census Block Groups whose centroid is more than ¼ mile from a LRT station and more than 1 mile from highway on/off ramp 11

12 Identifying Control and Treatment Groups Propensity scores are calculated to identify an appropriate control group. Determined through a logistic regression Measures the probability of receiving a treatment based on observed baseline covariates (Heinze and Juni, 2011; Rosenbaum and Rubin, 1983) Ideal in situations when there is a treatment group and need for control group. (Caliendo and Kopeinig, 2008) It is then possible to compare the outcome variable amongst groups. This outcome can be more accurately attributed to the effect of the treatment. (Dehejia and Wahba, 2002) 12

13 Control and Treatment Groups: 40 each 13

14 Match Quality

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16 Calculating the treatment effects Determining the average treatment effect on the treated (ATT) Estimating the difference between the mean outcome of the treated census block groups with the mean outcome of the matched control census block groups (Rosenbaum and Ruben, 1983) This difference in means acts as an unbiased estimate of the treatment effect on each outcome (Stuart, 2010; Heinze and Juni, 2011)

17 Employment Variables Total Employment Density Employment Density by Earnings $1,250 a month - low income residents $1,250 to $3,333 a month- median income residents above $3,333 a month - high income residents Employment Density by Industry Retail, accommodation and food services, other services opportunities for low income employees Information, finance, technology, and management - opportunities for high income residents

18 Results - Overall Employment Density Average treatment effect on the treated (ATT) 18

19 Results - Low Earning Employment Density Average treatment effect on the treated (ATT) < $1,250 a month residents 19

20 Results Medium Earning Employment Density Average treatment effect on the treated (ATT) $1,250 to $3,333 a month 20

21 Results High Earning Employment Density Average treatment effect on the treated (ATT) above $3,333 a month 21

22 Results Service Employment Density Average treatment effect on the treated (ATT) Retail, accommodation and food services, other services 22

23 Results Professional Employment Density Average treatment effect on the treated (ATT) Information, finance, technology, and management 23

24 Discussions Evidences from the Dallas pilot study are inconclusive regarding the effects of light rail transit on employment but provide useful guidance for future research. Near station areas are more resilient to economic recession? Overall job growth and low-medium earnings job growth in near station areas are truly faster than the rest of city? The small Mama-Papa shops near LRT stations are not hiring champions? It takes many years to see significant employment benefits of LRT? Limitations Pre-treatment conditions: public LODES data available Sample size: blocks vs. block groups

25 Future Research Case studies in other cities Carry out analysis on the restricted-access employment data 25