Uneven Playing Fields Mapping the Geography of Opportunity in the Baltimore Region URSP601 Research Methods Interim Report 1 March 4, 2014
Uneven Playing Fields •••
Uneven Playing Fields Mapping the Geography of Opportunity in the Baltimore Region
Introduction Baltimore City and the surrounding counties that comprise the Baltimore Metropolitan Council planning region are known for their extremes. Grinding poverty and rising murder rates characterize large swaths of the central city, while sprawling country estates dot the western reaches of Howard County. Does this dichotomy also reflect the geography of opportunity in the region? Do exurbs provide greater opportunities for upward economic mobility than job-rich downtown business districts? This report attempts to identify census tracts in the Baltimore region offering neighborhood assets and social capital that increase economic opportunity. Because the opportunities and resources required to achieve higher economic outcomes or a better quality of life can depend heavily on the age, family status, sex, and race of the individual, this report will focus solely on mapping opportunity for low-income working-age adults.
Methodology One way to measure opportunity is by creating an index that incorporates several variables that are believed to influence the level of opportunity in an area. The index offered here includes variables reflecting both spatial and demographic characteristics that research has shown to promote or detract from the ability of low-income households to move up the economic ladder. All of the variables have been aggregated to the census tract level, and some variables have been weighted to reflect their relative influence on individual economic outcomes. I have included a variety of variables to address
Introduction 1
Uneven Playing Fields ••• Table 1: Variables included in composite opportunity index Relationship to Opportunity
Weight
Mean
Std. Dev.
Min. (location)
Max. (location)
Transit Access (1/4 Buffer from Transit Stops)
Direct
-
43.57%
35.79%
0 (all excl. Balt. city)
100% (Balt. city)
Percent of Occupied Units with No Vehicle
Inverse
-
15.94%
18.37%
0% (Howard)
84.98% (Balt. city)
Commuters: Percent Taking Transit Less Than 30 Minutes
Direct
-
1.8%
3.8%
0% (all)
32% (Balt. city)
Total Job Density (number of jobs/land area)
Direct
-
8.19
21.2
0 (Balt. Co.)
253.87 (Balt. city)
Percent Change in Total Jobs (2002-2010)
Direct
-
37.5%
1.7%
-1% (Balt. city)
35% (Balt. Co.)
Median Income (2011 inflation adjusted dollars)
Direct
3
68,770
31,652
9,412 (Balt. city)
196,250 (Howard)
Percent Population Having Bachelor's Degree or Greater
Direct
2
33%
20%
0% (Balt. city)
89% (Balt. Co.)
Access to Work Force Investment Area Training Programs (Kernel density measure)
Direct
-
186.06
138.78
0% (Harford, Carroll, Howard, Balt. Co.)
440.78 (Balt. City)
Proximity to Community Colleges (meters)
Inverse
0.5
13,527.96
13,182.49
0 (Ann Arundel)
71,436.99 (Carroll)
Proximity to Private Career Schools (miles)
Inverse
0.5
3.71
4.45
0 (Balt. city, Balt. Co)
26 (Harford)
Racial Diversity Index (Percent chance the next person you encounter is of a diff. race than you)
Direct
-
31.85%
18.39%
3% (Balt. city)
69% (Howard)
Access to Combined Civic, Social, Community & Religious Organizations (Kernel density measure)
Direct
0.33
268.18
419.57
.82 (Balt. Co)
2326.47 (Balt. city)
Access to Public Institutions (Kernel density measure)
Direct
0.33
73.70
105.61
467.55 (Carroll)
0.18 (Balt. city)
Access to Social Services (Kernel density measure)
Direct
0.33
64.30
88.99
0 (Ann Arundel, Harford, Carroll, Balt. Co.)
729.43 (Balt. city)
Crime Risk Index: Total Crime
Inverse
-
162.47
142.75
7 (Howard)
639 (Balt. city)
Variable
Methodology 2
Uneven Playing Fields ••• the spatial—or modal—mismatch and access to social capital, two key concepts in the literature on poverty and inequality (see Table 1). Also included are a set of demographic variables to capture significant barriers to opportunity for blacks, the largest minority group in the Baltimore region and the majority racial group in Baltimore City (U.S. Census Bureau, 2010 Census).
The Spatial/Modal Mismatch The term “spatial mismatch” has been used to describe the condition in which low-income households are geographically distant from jobs that would typically be held by low-income individuals. The concept originated in John Kain’s 1968 paper analyzing the effects of housing discrimination on employment among blacks. He concluded that the resulting spatial segregation of black households from employment centers exacerbated black inner-city poverty (as cited in Ong and Blumenberg, 1998, p. 184). “Modal mismatch” has been used to describe the similar condition in which low-income households are separated from job-rich areas by long commute times (Ong and Blumenberg, 1998). Although home and work may be relatively close spatially, the time it takes to commute using public transportation may be unreasonably long. Both of these conditions have a negative effect on individual employment outcomes among low-income populations (Blumenberg and Ong, 2001). The three main policy responses to the spatial/modal mismatch that have been advocated in the literature are economic development in job-poor neighborhoods, promotion of automobile ownership among low-income households through removal of value limits on vehicles owned by welfare recipients, and improved intra-city and “reverse commute” public transportation options (Ong and Blumenberg, 1998; Ong, 1996; Blumenberg and Manville, 2004). Evidence of a spatial/modal mismatch and the presence of resources mitigating the mismatch are captured in this opportunity
Methodology 3
Uneven Playing Fields ••• index with variables measuring job density, percent change in total jobs, transit access, percentage of commuters taking transit for less than thirty minutes, and percent of occupied units with no vehicle.
Access to Social Capital Social capital can be defined as the resources that can help an individual escape poverty made available through social networks based upon trust, shared norms, and reciprocity (Curley, 2010). Therefore, a lack of social capital, as is the case in many high-poverty neighborhoods, can hamper upward mobility. The opportunity index suggested here includes a wide range of variables measuring neighborhood resources and feelings of safety, which Curley found to be important factors for building social capital (2010). Measures for access to workforce investment area training programs; proximity to community colleges and private career schools; access to civic, social, community, and religious organizations; access to public institutions; and access to social services capture the level of neighborhood resources in each census tract. A crime risk index is used as a proxy for feelings of safety. To avoid over-emphasizing the importance of neighborhood resources in the index, the community college and private career school variables were both weighted by 0.5. The three variables measuring access to various organizations, institutions, and social services were each weighted by .33.
Barriers to Employment for Blacks Racial segregation not only acts as a barrier to employment through the effects of spatial mismatch, but also results in lower educational outcomes for blacks, which then result in higher probabilities of unemployment (Howell-Moroney, 2005). The same effect is not found for whites. Segregation and educational attainment are captured in the index with variables for percent population having a bachelor’s degree or greater and a racial diversity index. Because lower educational attainment is the direct mechanism through which segregation reduces employment probabilities and
Methodology 4
Uneven Playing Fields ••• to compensate for the use of multiple variables to capture other concepts, I have weighted the education variable by 2. Lastly, median income was included in the opportunity index to account for the wide opportunity gap between low and high income groups. Including this variable highlights where the target population of the opportunity mapping exercise is located, but is also a signal that these areas have limited means to overcome the spatial/modal mismatch or a dearth of social capital. This variable has been weighted by 3 to account for its strength as an indicator of low opportunity areas and the ability of high income individuals to overcome virtually all other barriers to employment.
Findings The resulting opportunity map shows a concentration of high opportunity census tracts in Howard County, mostly because of the high incomes in that area (see Figure 1). High opportunity census tracts are also found in Baltimore City, Anne Arundel County, and Baltimore County although they account for a smaller land area and are more scattered. The rural northern regions of Carroll County, Baltimore County, and Harford County have very low opportunity index scores, likely because of the low job density, lack of public transportation, and absence of neighborhood resources. Baltimore City exhibits a stark contrast between the high opportunity north-south central corridor and the very low opportunity census tracts to the east and west away from the harbor.
Findings 5
Uneven Playing Fields ••• Figure 1: Map of Composite Opportunity Index Scores by Census Tract
The average level of opportunity for the region is .77 and the median is .29, indicating opportunity is skewed to the right. The highest opportunity census tract, with a score of 22.39, is found in Baltimore County along the northwest border of Baltimore City (see Table 2). The tract experienced a 35% increase in total number of jobs between 2002 and 2010, the highest of all tracts. The second highest opportunity area was in central Baltimore City. Its high rating results from a combination of job density, job growth (11%), and high scores for transportation access. The census tract with the
Findings 6
Uneven Playing Fields ••• lowest score is in extreme northwestern Carroll County primarily because of its low scores on access to transportation, all social capital indicators, job density, and college education. On average Howard County has the highest opportunity with an index of 6.76, while Carroll County has the lowest opportunity with an index of -2.93. All counties combined have an average index of .96, while Baltimore City has an index of .33. The level of opportunity varies more widely outside of the city, ranging from -9.92 to 22.39, than within Baltimore City where it ranges from -7.23 to 15.33. Comparing the top and bottom quintiles of census tracts reveals a high level of inequality of opportunity in the region. The bottom twenty percent of the region’s census tracts have a mean of -4.51, while the top 20% of census tracts have a mean of 7.26. Table 2: Composite opportunity index descriptive statistics Mean
Std. Dev.
Median
Min.
Max
Total
0.77
4.28
.29
-9.92
22.39
Counties
.96
4.18
.55
-9.92
22.39
Ann Arundel County
1.27
2.97
.96
-6.21
7.6
Baltimore County
.72
3.82
.44
-7.11
22.39
Carroll County
-2.93
2.44
-2.82
-9.92
1.76
Harford County
-1.77
3.14
-0.94
-9.86
3.75
Howard County
6.76
2.86
7.01
-1.32
14.46
Baltimore City
.33
4.48
-0.60
-7.23
15.33
Top quintile
7.26
2.81
6.97
3.95
22.39
Bottom quintile
-4.51
1.39
-4.36
-9.92
-2.81
We can also examine the locations of the census tracts in the top and bottom quintiles. Figure 2 shows the composition of the top and bottom quintiles by geographic area. While 38% of the census tracts in the bottom quintile are in Baltimore City, over a quarter of the top quintile tracts are also in the city. Baltimore County is similar in that it is well-represented in both the top and bottom quintiles.
Findings 7
Uneven Playing Fields ••• Harford and Carroll Counties are not represented in the top quintile, while Howard County is not represented in the bottom quintile.
Figure 2: Top and Bottom Composite Opportunity Index Quintile Breakdown by Location
Bottom Quintile Top Quintile 0%
Baltimore city
10%
20%
Ann Arundel
30%
40%
50%
Baltimore Co.
60%
Carroll
70%
80%
Harford
90%
100%
Howard
In conclusion, Baltimore City, Baltimore County, and Ann Arundel County to lesser extent can be described as areas of uneven opportunity for low-income working-age adults, while Howard, Harford, and Carroll Counties have a more even geography of opportunity. In the case of Howard it is evenly high opportunity, while it is evenly low opportunity for the other two counties. At the regional level, the geography of opportunity is extremely varied and can change drastically over very short distances, particularly within Baltimore City.
Findings 8
Uneven Playing Fields •••
References Blumenberg, Evelyn and Paul Ong. 2001. “Cars, Buses, and Jobs: Welfare Participants and Employment Access in Los Angeles.” Journal of the Transportation Research Board. 1756: 22-31. Blumenberg, Evelyn, and Michael Manville. 2004. "Beyond the Spatial Mismatch: Welfare Recipients and Transportation Policy". Journal of Planning Literature. 19 (2): 182-205. Curley, Alexandra M. 2010. “Relocating the Poor: Social Capital And Neighborhood Resources.” Journal of Urban Affairs. 32 (1): 79-103. Howell-Moroney, Michael. 2005. “The Geography of Opportunity and Unemployment: an Integrated Model of Residential Segregation and Spatial Mismatch.” Journal of Urban Affairs. 27 (4): 353-377. Kain, John F. (1968) “Housing segregation, negro employment, and metropolitan decentralization,” in Ong, Paul, and Evelyn Blumenberg. 1999. “Job access, commute and travel burden among welfare recipients.” Journal of Planning Literature. 13 (3). Ong, Paul M. 2002. “Car ownership and welfare-to-work.” Journal of Policy Analysis and Management. 21 (2): 239-252. Ong, Paul and Evelyn Blumenberg. 1999. “Job access, commute and travel burden among welfare recipients.” Journal of Planning Literature. 13 (3).
Findings 9
Uneven Playing Fields •••
Appendix Figure 3: Frequency Chart of Opportunity Index Scores
Number of Census Tracts
300 250 200 150 100 50 0 -10 - -5
-5 - 0
5 - 10
Opportunity Index
Findings 10
10 - 15
15 - 20
More than 20