Low Income Job Accessibility to Silver Line Extension (Sle) Job Center, Washington Dc Metro Area

LOW INCOME JOB ACCESSIBILITY TO SILVER LINE EXTENSION (SLE) JOB CENTER, WASHINGTON DC METRO AREA

SUNHYEONG SHIN

CAPSTONE REPORT

Submitted in partial fulfillment of the requirements for the degree of Master of Urban Planning in the Graduate College of the University of Illinois at Urbana-Champaign, 2018

Urbana, Illinois

Adviser: Dr. Jesus Barajas

CONTENTS

1. Introduction ...... 3

2. Literature Review ...... 4

2.1. Job access and unemployment ...... 4

2.2. Accessibility as an indicator of social equity ...... 5

2.3. Theories about job-housing-transport mode mismatch ...... 6

2.4. Other Example Extensions ...... 6

3. Background ...... 7

3.1. Metrorail in Washington DC Metropolitan Area ...... 7

3.2. Phase 1 Silver Line Extension (SLE) ...... 9

3.3. Job description in the SLE area...... 12

4. Methods...... 16

4.1. Identifying disadvantaged area ...... 16

5. Findings...... 18

6. Conclusion ...... 25

7. Sources: ...... 26

Appendix 1. Mapping Area of Transit Demand Modeling by the Metropolitan Washington Council of Governments (MWCOG) ...... 29

Appendix 2. Sequence of Metrorail Openings ...... 30

Appendix 3. Phase 1 Silver Line Ridership in May 2015 ...... 31

Appendix 4. Monthly Ridership of Metro Bus Routes Serving the Silver Line Stations in Comparison with the Total Metro Bus Ridership ...... 32

Appendix 5. Orange Line Ridership Comparison ...... 33

Appendix 6. Median Household Income, 2013 ACS 5 Year Estimate ...... 34

FIGURES AND TABLES

Figure 1 Washington DC Metro Area and Silver Line Extension (SLE) job center ______4 Figure 2 Number of Jobs accessible by Walking and Metrorail in 45 Minutes during the AM Peak, 2013 (upper) base and 2017 (lower) projection. Source: MWCOG TDM Ver. 2.3.39 ______8 Figure 3 Regional Activity Centers by COG in 2013 (Modified to Study Region of this report) Source: MWCOG, 2013 ______9 Figure 4 Silver Line Corridor (Source: MWCOG Place+ Opportunity Report, Figure 3, p.36, 2014) __ 10 Figure 5 Defined Job Center Area of Phase 1 SLE in this analysis ______10 Figure 6 Number of Riders Traveling Westward During AM Peak That Exited at the Five Silver Line Stations (Source: Virginia Transportation Research Council, 2017) ______11 Figure 7 Disadvantaged Block Groups in the Region ______16 Figure 8 Metrorail accessible disadvantaged/ advantaged area ______17 Figure 9 Residence of low income job workers at SLE in 2013 ______22 Figure 10 Residence of low income job workers at SLE in 2015 ______24

Table 1 Profile of Workers at SLE. (LODES WAC data, 2013 and 2015) ______13 Table 2 Job Profile of the region and SLE (LODES WAC data, 2013 and 2015) ______14 Table 3 Number of jobs at SLE from 2002 to 2015 (LODES WAC data, 2002-2015) ______15 Table 4 Workers who live at Metro-accessible area (LODES RAC data, 2013 and 2015) ______19 Table 5 Jobs in SLE of workers from advantaged/disadvantaged neighborhoods (LODES OD data, 2013 and 2015) ______20 Table 6 Residence of workers at SLE in 2013 and 2015 (LODES OD data, 2013 and 2015) ______21

1. INTRODUCTION

Washington D.C. metropolitan area is a region of highly educated jobs, high proportion of public sector jobs, and severe segregation by race and income. The Washington DC Metropolitan area has radially oriented Metrorail system which gives relatively advantageous access to central location of the city. My research question asks whether the Silver Line Extension (SLE) improved or reduced access to jobs for low-income workers in the region. To study job accessibility, I selected geographically specified job centers in my analysis, unlike previous studies which include jobs available in the whole region. I use Longitudinal Employer-Household Dynamics LEHD Origin-Destination Employment Statistics (LODES) data which provides home location and work location of employment data, in Census Block Group level. I compare

3 the 2013 and 2015 LODES datasets to observe the longitudinal changes before and after the opening of SLE in 2014. The study ‘region’ is defined with six Counties (including Fairfax city): District of Columbia; Montgomery, MD; Prince George, MD; Fairfax County, VA; Fairfax City, VA; and Arlington, VA. The scope of study is where Metrorail serves (See Figure 1).

Figure 1 Washington DC Metro Area and Silver Line Extension (SLE) job center

2. LITERATURE REVIEW

2.1. Job access and unemployment Low-income, low skilled, racial minority workers are more likely to commute by transit because owning a car is unaffordable for them. As a result, job accessibility by transit is highly related to their employment outcomes; lack of reliable transportation is a significant obstacle for job seekers to find opportunities they

cannot reach by public transit or somewhere they are not familiar with (Blumenberg, 2017). Access to a car is also related to the intensity of job searching process. Having access to a car increases search intensity for both whites and non-whites, defined as “how many hours per day the unemployed worker spends in searching for a job.” Differences in search activities between whites and nonwhites are correlated with both job access as well as differences in car ownership (Patacchini & Zenou, 2005). In other words, workers who do not drive tend to search for their jobs based on the availability of public transit.

2.2. Accessibility as an indicator of social equity Historically, mobility-based indicators have been widely used in assessing the success of transportation projects and programs. A mobility improvement is defined as a reduction in the time-plus-money cost of travel per mile. Providing more capacity in highway system to reduce traffic congestion is a typical solution to improve mobility. On the other hand, an accessibility improvement is defined as a reduction in the time- plus-money cost of travel per destination. To be more specific, it is defined as a reduction in the time-plus- money cost of interaction per unit value of destination. Mobility is one means to accessibility (Shen, Levine, Grengs, & Shen, 2012). However, transportation planning based on mobility disproportionately benefits some population groups; namely, those with access to a vehicle. Auto-oriented urban space, disparities in car ownership, and racial segregation are often implicated as the root of high unemployment rates of racial minorities (Kain, 1968). The low car-ownership of low income group creates longer average commuting time for auto-less workers (Grengs, 2010; Kawabata, 2003).

In the transportation planning realm, access to jobs, education, medical services, foods are basic needs of people, and the transportation investments often disproportionately benefit people who drive and burden people who have to use public transit because they do not own a vehicle (E Blumenberg, Ong, & (UCTC), 1997; Evelyn Blumenberg & Pierce, 2014; Kawabata, 2003). Fair and appropriate distribution of benefits and costs is referred as equity. The concept of equity is identified to incorporate fairness in transportation planning. “Horizontal equity” is concerned with the fairness of cost and benefit allocation between individuals and groups who are considered comparable in wealth and ability. This approach is often interpreted to mean that consumers should “get what they pay for and pay for what they get.” “Vertical Equity” intends to give benefits to the underserved group and not those with an ability to pay full price for a given service (Litman, 2002). Accessibility refers to people’s ability to reach desired services and activities. Accessibility-based planning can consider tradeoffs and equity impacts of traditional driver- favoring mobility-based planning (Litman, 2002).

2.3. Theories about job-housing-transport mode mismatch Scholars have studied the intertwined dynamics behind the existing disparities of job access between different social groups. Traditionally, the inner-city population that suffers a high unemployment rate, high poverty concentration with ethnicity characterized as Black has been explained with the spatial mismatch hypothesis. Kain suggested spatial mismatch theory which explains that race-based residential segregation shaped the concentration of African American population in inner city, and second, residential segregation exacerbated unemployment rate. Then both the residential segregation and high unemployment rate are negatively affected by suburbanizing employment (Kain, 1968, 1992).

To make fairer comparison, many scholars look for the suffering of low-income disadvantaged population as ‘modal mismatch’ where job accessibility has huge gap between automobile and public transit in majority of the U.S. cities. Many scholars exhibited the low job accessibility of auto-less workers when compared to those who drive even if the auto-less workers have advantaged central location for accessibility by public transit in a city (Evelyn Blumenberg & Pierce, 2014; Grengs, 2010; Paul M. Ong, 2002).

Numerous factors affect a job seeker’s employment expectation: geographic distance, land-use pattern of housing and jobs, competing workers in applying for a job, car-ownership, and the effectiveness of transportation infrastructure and services (Grengs 2010; Handy and Niemeier, 1997; Hansen, 1959; Wachs and Kumagai, 1973).

2.4. Other example extension The impact studies of public transport infrastructure often focus their analysis on economic aspects. Much of the research studies the benefits of transit-oriented development, property values, and/or economic regeneration. More studies are formed in the interests of the impact, for instance, new local jobs created through the increased local trade that is enabled by the improved accessibility.

Manaugh and El-Geneidy examined the extent of the social benefit that multiple transit projects brought to the disadvantaged population in Montreal, Canada. The projects include the new Light Rail Transit system (LRT or Tram) lines, the rail link to the airport, extensions of the commuter rail and metro systems, the new Bus Rapid Transit (BRT) lines, and increased reliability and travel speeds for existing bus lines due to signal-priority measures and/or reversed lanes. They first identified neighborhoods with both high levels of social disadvantage (based on income, immigration status, and education levels) and transportation disadvantage (low levels of current job access). Then they modeled accessibility to employment opportunities using both existing and new transit networks. By comparing the before and after of the level of access and change in travel time, they identified neighborhoods that will benefit the most from the new plan. They found that many people will have improved accessibility and connections to employment

centers, and many poorer neighborhoods will benefit for the disadvantaged neighborhoods’ geographic location. The authors suggest asking three questions to understand travel behavior of the underserved population: First, where is the underserved population located? Second, where are their places of employment? And last, how can they be better served? (Manaugh & El-Geneidy, 2012)

3. BACKGROUND

3.1. Metrorail in Washington DC Metropolitan Area The Washington Metropolitan Area Transit Authority (WMATA) serves the Washington DC metropolitan area from the central city to outside of the Capital Beltway. WMATA operates the second-largest heavy rail transit system, sixth-largest bus network and fifth-largest paratransit service in the United States according to WMATA Metro Facts. With its radially shaped Metrorail system, the central city area has relatively high job accessibility by Metrorail with high density Metrorail stations. The SLE is formally called Dulles Corridor Metrorail Project, a 23-mile extension of existing Metrorail System and is being built in two phases by the Metropolitan Washington Airports Authority (MWAA). It consists of Phase 1, which initiated its service in 2014, and Phase 2, currently under construction. Phase 1 includes 11.7 miles and five stations. Phase II, which will include connections to Dulles Airport and extend into Loudoun County, is scheduled to open in 2018. With the completion of the whole project, Dulles corridor and the District of Columbia will be seamlessly connected (Metropolitan Washington Airports Authority, 2018).

Figure 2 shows the Job Accessibility Index created by the Metropolitan Washington Council of Governments (MWCOG). The data is retrieved from the MWCOG’s Version 2.3.39 Travel Demand Model. The data for the two maps are the 2013 base year and 2017 future transit system. The darker shade implies larger number of jobs accessible in 45 minutes by walking access to the transit and Metrorail travel mode. For the Silverline extension started service in 2014, the map on the bottom of 2017 projects significantly improved job accessibility by Metrorail on the west side of the region.

Figure 2 Number of Jobs accessible by walking and Metrorail in 45 minutes during the AM Peak, 2013 (upper) base and 2017 (lower) projection. Source: MWCOG TDM Ver. 2.3.39

The Phase 1 of the Silver Line Extension(SLE) includes five stations. Four stations are concentrated at Tysons and the Termination station is at the tip of the Phase 1 rail.

3.2. Phase 1 Silver Line Extension (SLE) Tysons Corner and Reston are identified by the Metropolitan Washington Council of Governments as one of the region’s largest employment centers. The four-station area in Tysons is a vibrant urban market. They have four Metrorail stations and four activity centers on the Silver Line (See Figure 3 and Figure 4). Tysons is one of the region’s most prominent examples of a planned transformation from an auto-oriented employment center to a walkable, mixed-use urban community (Metropolitan Washington Council of Governments, 2014).

SLE is planned to provide both work travel and other purposes such as shopping. The Environmental Impact Statement (EIS) identifies that the purpose of Dulles Corridor Metrorail Project is to provide high connectivity by transit service in the Dulles Corridor. The introduction of the Metrorail would offer an alternative means of travel for the growing number of traffic in the Dulles Corridor and, as a link to the existing WMATA Metrorail system, would improve mobility throughout the region, and help minimize future increases in vehicle miles traveled in the corridor and vehicle emissions (Federal Transit Administration, 2006).

Figure 3 Regional Activity Centers by COG in 2013 (Modified to Study Region of this report) Source: MWCOG, 2013

Figure 4 Silver Line Corridor (Source: MWCOG Place+ Opportunity Report, Figure 3, p.36, 2014)

Figure 5 Defined Phase 1 SLE job center area in this analysis

The shape of Census Block Group is distorted to expand more than a mile away, but the .25 mile radius intersection selection captures the Block Group. In the LODES data, the number of total workers who work at 1-mile radius of the five stations decreased from 123,946 in 2013 to 121,215 in 2015 (U.S. Census Bureau, 2013 and 2015 LODES data).

The ridership of Silver Line extension in 2017 in total is lower than the projected ridership in the EIS in 2006. The termination station, Wiehle-Reston East Station recorded similar amount of the projected ridership. The Tysons- McLean, Greensboro and Spring Hill, however, recorded much smaller ridership than the 2016 ridership projection. To be impartial in analysis, the Metrorail ridership in the whole WMATA region decreased in the same period. Many commuters living further west than the Wiehle- Reston East Station could have transferred to the rail system through bus, park-and-ride, or kiss-and-ride to avoid congestion on the surface network. Intermodal connection is another crucial key especially where the public transit that connects residences to the Metrorail hardly exists. The total ridership of nine Metro Bus routes serving the Silver Line stations dropped gradually. This decrease possibly imply that some bus riders have changed their mode to the Silver Line (Virginia Transportation Research Council, 2017). The Tysons Corner region has numerous office buildings, restaurants, and retail stores. And no additional public parking. The existence of public parking is one of the important factors for people to choose park-and-ride at each station.

Figure 6 Number of Riders Traveling Westward During AM Peak That Exited at the Five Silver Line Stations (Source: Virginia Transportation Research Council, 2017)

SLE’s Impact on Existing Transit System

The existing transit system in the job center area includes the Bus system and the Metrorail Orange line. According to the comparison for the ridership at the Orange Line stations west of Rosslyn between May 2014 weekday and May 2015 weekday, the impact of the Silver Line on the ridership of the Vienna Station (the terminal station of the Orange line) was undetectable. And the ridership at the Dunn Loring Station

dropped slightly. The largest drop in ridership was seen at the West Falls Church Station, which previously was the Metro access point for many commuter buses from Reston and Loudoun County. For other eastward stations where the Orange Line and the Silver Line share track and platforms, the impact of the opening of the Silver Line was moderate. The pattern on weekends was similar to that of the weekdays (Virginia Transportation Research Council, 2017). (See Appendix 5 for details.)

The Silver Line could have attracted residents in Arlington and Washington, D.C., to commute westward and work in the Tysons Corner area. When the same periods of 2014 were compared to 2015, the number of riders traveling westward and visiting the five Silver Line stations increased in 2015 (See Figure 6). An analysis of origin and destination stations for riders visiting the five new Silver Line stations showed a significant number of people going from Arlington to the Tysons Corner area via the Silver Line.

3.3. Job description in the SLE area Data

The change between 2013 and 2015 employment is pivotal in this research to observe any increase or decrease after SLE initiated their service. The primary employment data in the analysis is the ‘Longitudinal Employer-Household Dynamics (LEHD)’ Origin-Destination Employment Statistics (LODES) data (U.S. Census Bureau., 2018). LODES datasets constitute three types of files which are Origin-Destination(OD) jobs totals are associated with both a home Census Block and a work Census Block, Residence Area Characteristics (RAC) of which jobs are totaled by home Census Block and Workplace Area Characteristics (WAC) of which jobs are totaled by work Census Block files. Datasets for 2009-2015 contain additional variables (Race, Ethnicity, Education, and Sex) on the RAC and WAC files. The dataset includes all jobs covered under state unemployment insurance law (95 percent of private sector wage and salary employment) plus most civilian federal employment. The data does not cover the following groups: self- employment, military employment, the U.S. Postal Service, and informal employment. (U.S. Census Bureau., 2017)

Table 1 shows the description of jobs at SLE in the LODES Workplace Area Characteristics (WAC) data. The total number of jobs in the selected Block Groups decreased. The number of workers of age 29 or younger and that of workers age 30 to 54 decreased. However, the number of workers age 55 or older increased. For earnings, the number of jobs with middle level income ($1251/month to $3333/month) decreased in the high proportion among jobs with all level of earnings. For race, jobs of white alone decreased in the largest absolute number. Jobs of Black or African American Alone, Native Hawaiian or Other Pacific Islander Alone, and Two or More Race Groups decreased between 2013 and 2015.

Table 1 Profile of workers at SLE. (LODES WAC data, 2013 and 2015)

SLE job center Washington D.C. region

2013 2015 Change % Change 2013 2015 Change % Change Total 2,351,291 2,392,872 41,581 2% Number of jobs for workers age 29 or younger 26,012 25,267 -745 -3% 476,071 472,075 -3,996 -1% Number of jobs for workers age 30 to 54 76,670 73,820 -2850 -4% 1,372,700 1,384,020 11,320 1% Number of jobs for workers age 55 or older 21,264 22,128 864 4% 502,520 536,777 34,257 7% Number of jobs with earnings $1250/month or less 13,297 13,369 72 1% 399,089 395,801 -3,288 -1% Number of jobs with earnings $1251/month to 19,321 17,770 -1551 -8% $3333/month 564,994 558,907 -6,087 -1% Number of jobs with earnings greater than 91,328 90,076 -1252 -1% $3333/month 1,387,208 1,438,164 50,956 4% Race: White Alone 86,480 84,426 -2054 -2% 1,452,486 1,472,369 19,883 1% Race: Black or African American Alone 18,197 17,550 -647 -4% 620,212 635,631 15,419 2% Race: American Indian or Alaska Native Alone 466 491 25 5% 10,880 11,118 238 2% Race: Asian Alone 16,301 16,322 21 0% 221,210 225,258 4,048 2% Race: Native Hawaiian or Other Pacific Islander Alone 187 177 -10 -5% 3,166 3,364 198 6% Race: Two or More Race Groups 2,315 2,249 -66 -3% 43,337 45,132 1,795 4% Ethnicity: Not Hispanic or Latino 113,595 110,777 -2,818 -2% 2,106,482 2,141,889 35,407 2% Ethnicity: Hispanic or Latino 10,351 10,438 87 1% 244,809 250,983 6,174 3%

More than 40 percent of jobs in SLE job center are of Professional, Scientific, and Technical Services sector. The region’s total number of jobs increased 1.7 percent to 2,392,872 between 2013 and 2015. In the same period, the number of SLE job center’s jobs decreased 2.2 percent to 121,215. As a result, in 2013, SLE had 5.3% of the region’s jobs and it slightly decreased in 2015 to 5.1% (See Table 2).

Table 2 Job Profile of the region and SLE (LODES WAC data, 2013 and 2015)

Silver Line Extension (SLE) job center Washington D.C. Region DESCRIPTION 2013 2015 2013 2015 2013 2015 2013 2015 Total number of jobs 123,946 121,215 2,351,291 2,392,872 Agriculture, Forestry, Fishing and Hunting 2 2 0.0% 0.0% 957 838 0.0% 0.0% Mining, Quarrying, and Oil and Gas Extraction 20 17 0.0% 0.0% 739 505 0.0% 0.0% Utilities 0 0 0.0% 0.0% 6,703 6,659 0.3% 0.3% Construction 2,359 1,360 1.9% 1.1% 100,097 104,731 4.3% 4.4% Manufacturing 1,566 1,307 1.3% 1.1% 38,048 38,190 1.6% 1.6% Wholesale Trade 3,211 2,477 2.6% 2.0% 48,228 46,167 2.1% 1.9% Retail Trade 10,729 10,193 8.7% 8.4% 187,995 192,248 8.0% 8.0% Transportation and Warehousing 506 293 0.4% 0.2% 56,109 60,594 2.4% 2.5% Information 5,829 6,445 4.7% 5.3% 71,767 74,036 3.1% 3.1% Finance and Insurance 10,350 11,308 8.4% 9.3% 77,645 75,708 3.3% 3.2% Real Estate and Rental and Leasing 2,389 2,435 1.9% 2.0% 43,206 44,740 1.8% 1.9% Professional, Scientific, and Technical Services 52,962 55,952 42.7% 46.2% 438,688 438,773 18.7% 18.3% Management of Companies and Enterprises 8,701 5,488 7.0% 4.5% 39,562 39,250 1.7% 1.6% Administrative and Support and Waste Management and Remediation Services 5,233 6,046 4.2% 5.0% 163,030 167,849 6.9% 7.0% Educational Services 2,371 1,832 1.9% 1.5% 228,311 231,158 9.7% 9.7% Health Care and Social Assistance 4,382 4,247 3.5% 3.5% 227,093 237,863 9.7% 9.9% Arts, Entertainment, and Recreation 1,006 1,206 0.8% 1.0% 32,935 38,431 1.4% 1.6% Accommodation and Food Services 6,807 6,997 5.5% 5.8% 191,894 202,094 8.2% 8.5% Other Services (except Public Administration) 3,959 3,292 3.2% 2.7% 136,879 138,801 5.8% 5.8% Public Administration 1,564 318 1.3% 0.3% 261,405 254,237 11.1% 10.6% Educational Attainment: Less than high school 7,490 8,039 6.0% 6.6% 219,076 236,053 9.3% 9.9% Educational Attainment: High school or equivalent, no college 14,554 14,988 11.7% 12.4% 378,371 398,751 16.1% 16.7% Educational Attainment: Some college or Associate degree 22,994 22,982 18.6% 19.0% 482,461 494,955 20.5% 20.7%

Educational Attainment: Bachelor's degree or advanced degree 52,896 49,939 42.7% 41.2% 795,312 791,038 33.8% 33.1%

Table 3 Number of jobs at SLE from 2002 to 2015 (LODES WAC data, 2002-2015)

Number of workers by wage at SLE (2002-2015) 160000

140000

120000

100000

80000

60000

40000

20000

0 2000 2002 2004 2006 2008 2010 2012 2014 2016

Total number of jobs $1250/month or less $1250/month to $3333/month More than $3333/month

4. METHODS

The purpose of the project’s analysis is to compare commuters from economically disadvantaged area versus non-disadvantaged area. For the job location of the analysis, the scope of analysis here is the area of five stations: Wiehle-Reston East, Greensboro, McLean, Spring Hill, and Tysons Corner (See Figure 5 in Chapter 3).

4.1. Identifying disadvantaged area Step 1: Defining Disadvantaged area The disadvantaged area in the region is identified based on racial minority and median household income level (See Figure 7). The two standards of disadvantaged block groups are: 1) More than 50 percent being racial minority demographics and 2) Median household income being less than 80% of the Washington D.C. Statewide Average Median Household Income. The Median Household Income (in 2013 Inflation Adjusted Dollars, 2009-2013 American Community Survey) is $65,830, and, therefore, areas with median household income less than $52,664 is identified as disadvantaged. I used 2013 data to identify the disadvantaged areas for the classification for both 2013 and 2015. The Median Household Income of other counties in the region is much higher than that of D.C. (See Appendix 5 to see other counties’ median household income).

Figure 7 Disadvantaged Block Groups in the Region

Step 2: Classify Metrorail station 1-mile radius area I defined access to Metrorail to include the census tracts within 1-mile radius of a station. I selected the area in ArcGIS to select Census Block Groups that intersect 1-mile radius of a station. Therefore, the selected areas are bigger than a simple 1-mile radius. I classified the Metrorail station-1mi area into advantaged/disadvantaged groups by the same standards in Step 1. Using the Residence Area Characteristics (RAC) dataset in LODES, I separated workers who live in the Metrorail station 1mile radius area into advantaged and disadvantaged areas using the classification defined above.

Note that I will refer to ‘Metrorail station 1-mile radius area or Metrorail accessible area’ in the analysis. Block Groups located close to one station may constitute both advantaged area and disadvantaged area (See Figure 8).

Figure 8 Metrorail accessible disadvantaged/ advantaged area

5. FINDINGS

In my analysis, I compared 2013 LODES data and 2015 LODES data to analyze the impact of the opening of SLE.

Workers at SLE residing in Metro-accessible area

The profile of workers who live in Metro-accessible area in 2013 and 2015 is in Table 4 below. The number of workers residing in both the advantaged Metro-accessible area and disadvantaged Metro-accessible increased between 2013 and 2015, while the change in workers who lived in disadvantaged areas nearly doubled over the same period. Note that the 2013 statistics include workers living within the one-mile radius of SLE stations. For income level of jobs, low income (1250/month or less) workers and medium income (1250/month to 3330/month) workers decreased in advantaged area. The high income (more than 3330/month) workers increased, and the number is comparable to the whole increase of workers who live in the advantaged area.

The number of workers age 29 or younger residing in disadvantaged area increased 9.7 percent whereas the number of those residing in advantaged area increased .1 percent. Both areas showed increase in workers age 55 or older highest among three worker age categories. White workers living in disadvantaged areas had the largest percentage change among all racial and ethnic groups, increasing 17.2% to 130,986 workers.

Table 4 Workers who live at Metro-accessible area (LODES RAC data, 2013 and 2015)

Advantaged Disadvantaged Advantaged Disadvantaged

2013 2015 2013 2015 Change Percent Change Total number of Workers 380,452 395,006 311,510 340,285 14,554 28,775 3.8% 9.2% Age Workers age 29 or younger 90,955 91,072 74,964 82,224 117 7,260 0.1% 9.7% Workers age 30 to 54 212,965 222,764 177,794 191,963 9,799 14,169 4.6% 8.0% Workers age 55 or older 76,532 81,170 58,752 66,098 4,638 7,346 6.1% 12.5% Income Earnings $1250/month or 54,262 54,191 65,357 69,252 -71 3,895 -0.1% 6.0% less Earnings $1251/month to 69,663 68,822 101,799 107,159 -841 5,360 -1.2% 5.3% $3333/month Earnings greater than 256,527 271,993 144,354 163,874 15,466 19,520 6.0% 13.5% $3333/month Race and Ethnicity White Alone 288,423 299,625 111,901 130,986 11,202 19,085 3.9% 17.1% Black or African American 45,796 47,776 173,046 181,101 1,980 8,055 4.3% 4.7% Alone American Indian or Alaska 1,753 1,764 1,563 1,715 11 152 0.6% 9.7% Native Alone Asian Alone 36,549 37,371 18,435 19,576 822 1,141 2.2% 6.2% Native Hawaiian or Other 503 590 459 488 87 29 17.3% 6.3% Pacific Islander Alone Two or More Race Groups 7,428 7,880 6,106 6,419 452 313 6.1% 5.1% Not Hispanic or Latino 344,138 358,301 272,065 298,512 14,163 26,447 4.1% 9.7% Hispanic or Latino 36,314 36,705 39,445 41,773 391 2,328 1.1% 5.9%

Educational Attainment Less than high school 28,822 31,855 39,980 43,786 3,033 3,806 10.5% 9.5% High school or equivalent, no 51,564 56,488 58,418 63,945 4,924 5,527 9.5% 9.5% college Some college or Associate 69,121 72,427 65,529 71,462 3,306 5,933 4.8% 9.1% degree Bachelor's degree or 139,990 143,164 72,619 78,868 3,174 6,249 2.3% 8.6% advanced degree Sex Male 190,503 196,303 145,584 159,570 5,800 13,986 3.0% 9.6% Female 189,949 198,703 165,926 180,715 8,754 14,789 4.6% 8.9%

Table 5 shows the number of the workers commute to SLE job center before and after the opening of SLE in 2014. The number of all types of jobs from disadvantaged area slightly increased. The number of workers from advantaged area decreased 6 percent. Large portion of the decrease possibly matches decrease of the number of workers reside in advantaged area earning $3333/month or more. The number of all jobs decreased in advantaged area. Notably, the number of jobs in all other services industry sectors significantly increased when compared to the number of all other sectors from both advantaged and disadvantaged area.

Table 5 Jobs in SLE of workers from advantaged/disadvantaged neighborhoods (LODES OD data, 2013 and 2015)

Description Advantaged Disadvantaged 2013 2015 Change 2013 2015 Change Total number of jobs 50,107 47,133 -2974 73,839 74,082 243 Number of jobs for workers age 29 or younger 9,230 8,164 -1066 16,782 17,103 321 Number of jobs for workers age 30 to 54 32,510 30,125 -2385 44,160 43,695 -465 Number of jobs for workers age 55 or older14 8,367 8,844 477 12,897 13,284 387 Number of jobs with earnings $1250/month or less 5,608 5,465 -143 7,689 7,904 215 Number of jobs with earnings $1251/month to $3333/month 7,929 7,001 -928 11,392 10,769 -623 Number of jobs with earnings greater than $3333/month 36,570 34,667 -1903 54,758 55,409 651 Number of jobs in Goods, Producing industries sectors 2,105 1,434 -671 1,842 1,252 -590 Number of jobs in Trade, Transportation, and Utilities industry sectors 6,006 5,379 -627 8,440 7,584 -856 Number of jobs in All Other Services industry sectors 41,996 40,320 -1676 63,557 65,246 1689

Proportional Comparison

For all types of jobs in SLE area, workers that live in the neighborhoods within the Metro-accessible area increased when compared to the whole region, 4.4 percent increase in advantaged area and 9.0 percent in disadvantaged area, a statistically significant difference (95 percent confidence interval: 0.038 0.054, p <0.001). (See Table 6-2). Workers in the SLE area living in Metro-accessible disadvantaged neighborhoods increased by 10.5 percent, while workers from other disadvantaged neighborhoods in the region increased by 2.8 percent, a statistically significant difference (p < 0.001). Low income job workers live in advantaged neighborhoods in the region and Metrorail accessible area both decreased. Metrorail accessible area showed 2.5 percent decrease and the region showed 2.3 percent decrease.

Table 6 Residence of workers at SLE in 2013 and 2015 (LODES OD data, 2013 and 2015)

6-1. The distribution of total workers who work at SLE from the region 2013 2015 Change From Disadvantaged area 73,839 74,082 243 From Advantaged Area 50,107 47,133 -2,974 6-2. Number of workers from the Metrorail accessible area 2013 2015 Change From Metrorail 1mi Disadvantaged area 5,741 6,256 515 From Metrorail 1mi Advantaged Area 19,706 20,573 867 6-3. Number of $1250/month or less workers from the Metrorail accessible area 2013 2015 Change From Metrorail 1mi Disadvantaged area 806 891 85 From Metrorail 1mi Advantaged Area 5,608 5,465 -143 6-4. Number of $1250/month or less workers from the region 2013 2015 Change From Disadvantaged area 7,689 7,904 215 From Advantaged Area 1,729 1,689 -40

Table 7 and Table 8 show that low income job workers live in disadvantaged area started to work in SLE job center after 2014 SLE transit service comparing the 2.8 percent increase in the regional and 10.5 percent increase in Metrorail accessible area.

For Low income jobs ($1,250/month or less), people who work from disadvantaged Metrorail station 1mile radius area to SLE increased 10.5 percent (806 to 891). People who work from advantaged Metrorail station 1mile radius area to SLE job center decreased 2.3 percent (1729 to 1689). The two proportions are statistically significant with X-squared = 77.73, df = 1, p-value < 0.001, 95 percent confidence interval: ‘- 0.106 -0.0591’.

Table 7 Number of low income job ($1,250/month or less) workers from Metrorail accessible area Left Axis: Number of workers live in disadvantaged area. Right Axis: Number of workers live in advantaged area

Table 8 Number of low income job ($1,250/month or less) workers from the region Left Axis: Number of workers live in disadvantaged area. Right Axis: Number of workers live in advantaged area

Change in low income job ($1250/month or less) access from the region

Figure 9 and 10 show the home location of low income workers at SLE job center in 2013 and 2015. In the analysis of the number of low income workers, the commutes from the Southeast of the region visibly increased. Understandingly, the southwest portion of the region shows higher density of workers.

Figure 9 Residence of low income job workers at SLE in 2013

Figure 10 Residence of low income job workers at SLE in 2015

6. CONCLUSION

Comments Transit-dependent people tend to shape their job seeking boundaries based on the availability of public transit (Patacchini & Zenou, 2005) The results of analysis on SLE low income jobs are another case of improved job accessibility for low-income workers by new public transit service. The infrastructure investment on SLE has revealed propensity to provide better accessibility to low income workers by transit to SLE job center in one year of opening. The decrease in travel cost, both time and money, may increase low income labor supply as observed in 2015 data. With increased labor supply, the industries may be more willing to purchase the low-income level labor, prices of final goods and services may decrease, and sales may increase. Therefore, demand for low-income labor in SLE job center will increase too. The low- ridership of SLE is problematized in recent years. The latent socioeconomic benefit, however, may compensate the investment cost in the long run.

I suggest further investigation specifically targeting the transit-dependent population who Silver Line as their commute mode. Who are the target population, when is the population’s peak hour-where more frequent service is needed, how the transfer schedule should be designed to connect low-car ownership area. Comparing change of low income workers’ home location between SLE job center and other job centers which are located at Metrorail extension area in the same period will improve the analysis. I described the SLE job center’s change of jobs, but the change may be endogenous to the job changes in the region. Additional analyses as these may test possible factors of job change in SLE other than the opening SLE transit service in 2014.

7. SOURCES:

Bernick, M., & Cervero, R. (1997). Transit villages in the 21st century.

Blumenberg, E., Ong, P., & (UCTC), U. of C. T. C. (1997). CAN WELFARE RECIPIENTS AFFORD TO WORK FAR FROM HOME? Access, (10), 15–19. Retrieved from https://trid.trb.org/view/575338

Blumenberg, E., & Pierce, G. (2014). A Driving Factor in Mobility? Transportation’s Role in Connecting Subsidized Housing and Employment Outcomes in the Moving to Opportunity (MTO) Program. Journal of the American Planning Association, 80(1), 52–66. Retrieved from http://10.0.4.56/01944363.2014.935267

Cervero, R., & Tsai, Y.-H. (2003). Job Access and Reverse Commuting Initiatives in California: Review and Assessment. Transportation Research Record, 1859(November), 78–86. https://doi.org/10.3141/1859-10

Grengs, J. (2010). Job accessibility and the modal mismatch in Detroit. Journal of Transport Geography, 18(1), 42–54. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2009.01.012

Kain, J. F. (1968). Housing Segregation, Negro Employment, and Metropolitan Decentralization*. The Quarterly Journal of Economics, 82(2), 175–197. Retrieved from http://dx.doi.org/10.2307/1885893

Kain, J. F. (1992). The Spatial Mismatch Hypothesis: Three Decades Later. Housing Policy Debate, 3(2), 371–460. https://doi.org/10.1080/10511482.1992.9521100

Kawabata, M. (2003). Job Access and Employment among Low-Skilled Autoless Workers in US Metropolitan Areas. Environment and Planning A, 35(9), 1651–1668. https://doi.org/10.1068/a35209

Litman, T. (2002). Evaluating Transportation Equity. World Transport Policy & Practice. https://doi.org/10.3141/1756-04

Manaugh, K., & El-Geneidy, A. M. (2012). Who benefits from new transportation infrastructure? Using accessibility measures to evaluate social equity in public transport provision. In Accessibility Analysis and Transport Planning (pp. 211–227). https://doi.org/10.4337/9781781000113.00021

Patacchini, E., & Zenou, Y. (2005). Spatial mismatch, transport mode and search decisions in England. Journal of Urban Economics, 58(1), 62–90. https://doi.org/https://doi.org/10.1016/j.jue.2005.01.005

Paul M. Ong, author. (2002). Car Ownership and Welfare-to-Work. Journal of Policy Analysis and Management VO - 21, (2), 239. Retrieved from http://www.library.illinois.edu/proxy/go.php?url=http://search.ebscohost.com/login.aspx?direct=true

&db=edsjsr&AN=edsjsr.3325633&site=eds-live&scope=site

Shen, Q., Levine, J., Grengs, J., & Shen, Q. (2012). Does accessibility require density or speed? Journal of the American Planning Association, 78(2), 157–172. https://doi.org/10.1080/01944363.2012.677119

U.S. Census Bureau. (2018). LEHD Origin-Destination Employment Statistics Data (2002-2015) [computer file]. Washington, DC: U.S. Census Bureau, Longitudinal-Employer Household Dynamics Program [distributor], accessed on {April 22, 2018} at https://lehd.ces.census.gov/data/#lodes. LODES 7.3 [version]

U.S. Census Bureau. (2017). LEHD Origin-Destination Employment Statistics (LODES) Dataset Structure Format Version 7.3, accessed on {April 22, 2018} at https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.3.pdf

Metropolitan Washington Council of Governments. (2014). Place+ Opportunity Report: Strategies for Creating Great Communities and a Stronger Region.

Virginia Transportation Research Council. (2017). The Impact of Phase 1 of the Silver Line on the Northern Virginia Transportation System 2017. VTRC 17-R28. Data retrieved by: http://www.virginiadot.org/vtrc/main/online_reports/pdf/17-r28.pdf

Data source: transit agencies and the Virginia Department of Transportation (VDOT).

Federal Transit Administration, Virginia Department of Rail and Public Transportation, Washington Metropolitan Area Transit Authority. (2006). Environmental Impact Statement. Retrieved by: http://www.dullesmetro.com/about-dulles-rail/environment/ https://www.washingtonpost.com/local/trafficandcommuting/silver-line-turns-1-and-is-celebrated-as-a- catalyst-for-change-in-tysons/2015/07/27/7de05ec6-3466-11e5-8e66- 07b4603ec92a_story.html?noredirect=on&utm_term=.a6026a799e8d

Metropolitan Washington Airports Authority, (2018). http://www.dullesmetro.com/about-dulles-rail/what- is-dulles-Metrorail/

Social Explorer Tables: ACS 2013 (5-Year Estimates)(SE), ACS 2013 (5-Year Estimates), Social Explorer; U.S. Census Bureau

U.S. Department of Transportation (1998) Federal Transit Administration. Access to jobs. A guide to innovative practices in welfare to work transportation. Version III,

Rep. No. 0030 at 91 (2013).

WMATA 2016 historical rail ridership, retrieved by https://www.wmata.com/initiatives/plans/upload/2016_historical_rail_ridership.pdf

Lazo, L. (2015, July 27). Year-old Silver Line is celebrated as a catalyst for change in Tysons Corner. Retrieved April 24, 2018, from https://www.washingtonpost.com/local/trafficandcommuting/silverline-turns-1-and-is-celebrated-as-a-catalyst-for-change-in-tysons/2015/07/27/7de05ec6-3466-11e5- 8e66-07b4603ec92a_story.html?utm_term=.86efe5f2b636

Appendix 1. Mapping Area of Transit Demand Modeling by the Metropolitan Washington Council of Governments (MWCOG)

Appendix 2. Sequence of Metrorail Openings

Appendix 3. Phase 1 Silver Line Ridership in May 2015 Report 2017 (p.18 in pdf) Table 2, Line Average Weekday Ridership in May 2015

Appendix 4. Monthly Ridership of Metro Bus Routes Serving the Silver Line Stations in Comparison with the Total Metro Bus Ridership

Appendix 5. Orange Line Ridership Comparison

Orange Line Ridership Comparison for Weekdays of May 2014

Orange Line Ridership Comparison for Weekdays of May 2015

Appendix 6. Median Household Income, 2013 ACS 5 Year Estimate

Municipality Median Household Income(MHI) 80 % of MHI District of Columbia, District of Columbia 65,830 52,664 Montgomery County, Maryland 98,221 78,577 Prince George's County, Maryland 73,623 58,898 Fairfax County, Virginia 110,292 88,234 Fairfax city, Virginia 97,242 77,794 Falls Church city, Virginia 120,000 96,000