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 10th, 2014

Presentation Outline

Introduction Methodology Results Discussions Future Research

http://condrenrails.com/Recent-Trains/images-99/DART-124-Dallas-TX-2-28-12-1.jpg

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

How about jobs?

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Legislation Objective

Intermodal Surface Transportation Efficiency Act of 1991

“provide intermodal connections to jobs and other services for the economically disadvantaged”

Personal Responsibility and Work Opportunity Reconciliation Act of 1996

“primary goal was to move unemployed persons to stable employment”

Balanced Budget Act of 1997

“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) “Job Access and Reverse Commute program in 1998”

Safe, Affordable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU)

“authorized a total of $727 million for JARC grants for fiscal years 2006 through 2009

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)

Transit benefiting job growth?

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)

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?

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Pilot Study: Preliminary Analysis Completed

Pilot Study in Dallas, TX

Corridor Line Miles Number of Stations Opening Year

North Central Red 12.3 9 2002

Northeast Blue 11.2 5 2001-2002

DART Light Rail System

• 61 Stations

• 85 Miles

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

Propensity Score Matching

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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 Neighborhood Design and Walking Trips in Ten U.S. Metropolitan Areas

Account for confounding variables when comparing household walking behavior specific to neighborhood design characteristics.

Cao, 2010 Exploring causal effects of neighborhood type on walking behavior

Matched individuals from suburbs to those residing in traditional neighborhoods and measured differences in travel behavior.

Funderburg et al., 2010

New highways and land use change: Results from a quasi-experimental research design

Utilized to select a control for each spatial unit that received access to new highway infrastructure. Measured difference in growth indicator variables.

MacDonald, et al. 2010

The Effect of Light Rail Transit on Body Mass Index and Physical Activity

Match and compare individuals before/after the construction of a LRT system. Measured change in physical activity of LRT users and non-users.

Billings, 2011 Estimating the value of a new transit option

Matched neighborhoods near transit with similar neighborhoods located elsewhere. Compared housing prices.

Artz and Stone, 2012

Revisiting WalMart’s Impact on Iowa Small-Town Retail: 25 Years Later

Find a match for each host town which represents what would have happened if WalMart had not located there.

Deng, et al., 2012

Private residential price indices in Singapore: A matching approach

Houses sold at the baseline time were matched with those sold at a later time. Sale index was constructed from difference.

Cao and Fan, 2012

Exploring the influences of density on travel behavior using propensity score matching

Matched individuals in low density communities with those in high density communities, Compares travel behavior.

Propensity Score Matching

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Treatment Groups: Census Block Groups whose centroid is within ¼ mile from a LRT station and outside 1 mile of highway on/off ramp

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

Variable Unit Source

Total Population Pop/CBG US Census

Per Capita Income Avg. Income US Census

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

Matching

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)

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Control and Treatment Groups: 40 each

Match Quality

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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)

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

Results - Overall Employment Density

Average treatment effect on the treated (ATT)

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Results - Low Earning Employment Density

Average treatment effect on the treated (ATT)

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< $1,250 a month residents

Results – Medium Earning Employment Density

Average treatment effect on the treated (ATT)

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$1,250 to $3,333 a month

Results – High Earning Employment Density

Average treatment effect on the treated (ATT)

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above $3,333 a month

Results – Service Employment Density

Average treatment effect on the treated (ATT)

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Retail, accommodation and food services, other services

Results – Professional Employment Density

Average treatment effect on the treated (ATT)

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Information, finance, technology, and management

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 2002-2011 Sample size: blocks vs. block groups

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

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