The intention of this project is to examine whether suburban decentralization of job markets in

the Philadelphia region has resulted in longer commutes for inner-city workers travelling to employment

sites in the suburbs. The Spatial Mismatch Hypothesis was proposed by John Kain in 1968 and theorized

that the combination of suburban decentralization of jobs along with housing market segregation was

creating negative employment outcomes for inner-city black residents.1 Since then there has been a

wealth of research examining the hypothesis, and no clear consensus has resulted as to whether spatial

mismatch can be considered a primary causal factor in higher levels of black and Hispanic joblessness

and poverty in the inner cities. If spatial barriers are determined to play as large of a role in issues of

poverty as are factors like education, policy mandates breaking these barriers would be justified. This

might mean requiring businesses to locate near public transportation, or requiring builders set aside

affordable units to enhance racial and economic integration.

I was initially interested in public transportation because I felt that the domination of automobiles

among commuters was wasteful and environmentally harmful. After relocating to Philadelphia in 2007, it

occurred to me that there were just as many commuters taking morning rush-hour busses from the central

city to suburbs as there were suburban commuters coming into the city for work. Similarly, I noticed that

night-time bus routes from suburban shopping/retail districts headed towards the city were extremely

crowded. This was the case on SEPTA route 104 on weeknights around 10:00 PM running from West

Chester University towards the 69th Street Terminal, as well as on late night, central city bound-busses

from King of Prussia. Route 104 was extended to West Chester University in 2002 from the West

Chester Transportation Center, and in addition has also been rerouted on a loop through the Edgemont

Square Shopping Center and Newtown Corporate Campus more recently as a result of growing ridership.

Besides being long commutes, these night-time busses run infrequently, and many of the workers

returning to the city transferred to other busses and trains at the trip’s completion, creating a commute

which is time-consuming and strenuous. The Philadelphia region as a whole lost 223,000 jobs between

1 Ilanfeldt, p. 849

1990 and 2000; 41% occurred in Philadelphia proper, and by 2000, only 17% of the region’s jobs were

located in the central city.2 The Delaware Valley typifies what has happened to metropolitan regions in

recent times- edge cities have developed and the proportion of suburb to suburb commuters has increased.

While bus routes can be added or adjusted to accommodate changing mobility needs, other traditional

forms of high speed transit, such as subway and light rail, are difficult to build and frequently cost-

prohibitive. Thus, regional polycentrism creates issues for people who rely on transit. I decided to

explore the relationship between commutes and incomes and race in the city of Philadelphia to gage

whether there was statistical evidence of disadvantage for particular groups. In addition, data was

compared between 1990 and 2000 to see whether disadvantages were growing.

Review of literature

Taylor & Ong

Taylor and Ong (1995) used data from the American Housing Surveys of 1977 and 1985 to

determine whether the length in commute times for workers in minority neighborhoods was increasing at

a greater rate than that of whites. Accepting this eight year time frame as a period of suburban job

dispersal, the longitudinal analysis would show whether the decentralization of low-skilled jobs was

hurting a particular group more than others. The authors did not find an increase in commute time or

commute distance for Blacks or Hispanics relative to whites, and found that existing discrepancies (i.e. a

3 minute longer average commute for blacks in both 1977 and 1985) was likely attributable to mode

choice (an increased reliance on public transit amongst blacks) rather than longer physical distances

between work and home.3 In addition, the authors studied changes in physical distances and commute

times amongst non-moving workers (workers who remained in the same house between 1977 and 1985)

and found that commute distances among white, black, and Hispanic workers who were in the same home

in 1977 and 1985 remained largely unchanged, and that commute times for non-moving Black and

2 Weinberger, p. 23 Taylor and Ong, p. 1459

Hispanic workers had decreased.4 Amongst the non-moving workers it was also found that when

controlling for mode, public transit commuters had both shorter commute distances yet longer commute

times regardless of the racial character of their neighborhoods.5 Thus, the authors faulted policies aiming

to improve public transit as ineffective and suggested policy aimed at improving low-income workers

ability to obtain private vehicles for commuting purposes.

Boardman & Field

Boardman and Field (2002) examined whether black male joblessness in Cleveland and

Milwaukee was attributable to long physical distances and/or long commute times between black

residential location and the location of low-skilled jobs. In this manner they differed from Taylor and

Ong’s study in that they considered both physical distance and commute times as indicators of a spatial

mismatch. They first calculated the physical distances between census tracts in Milwaukee and Cleveland

to all low-skilled jobs, then measured commute times against physical proximities for each census tract.

Their findings indicated that largely white census tracts with extremely low levels of male joblessness had

the farthest average physical distances to low skilled jobs, contrary to the spatial mismatch hypothesis.6

Further, they discovered that minority census tracts with lower rates of male joblessness tended to be

located further from low-skilled jobs, also running contrary to the spatial mismatch hypothesis.7 Thirdly,

similar to Taylor and Ong’s findings, they found that, despite greater proximity to low-skilled jobs, black

census tracts with high levels of male joblessness also had the longest average commute times, indicative

of a restricted mobility in terms of finding and commuting to low-skilled jobs.8 This was specific to the

black census tracts however, as they found that Hispanic and racially heterogeneous census tracts with

similar levels of high male joblessness did not have as long commute times as did the black tracts with

high joblessness.9 Accepting transportation deficiencies as an explanation for the paradox of closer

4 Taylor & Ong, p. 14665 Taylor & Ong, p. 14636 Boardman & field, p. 2467 Boardman & Field, p. 2468 Boardman & Field, p. 2479 ibid

physical proximity and longer commute times, Boardman and Field then examine the issue from a

reduced spatial scale, by, rather than comparing joblessness for tracts to location of jobs throughout the

city, measuring the number of low-skilled jobs contained within predominantly black census tracts, and

comparing this number to levels of joblessness. Predominantly black census tracts in Milwaukee that

were found to have high levels of joblessness also contained the highest number of low-skilled jobs, and

tract joblessness was found to increase with an increase in low-skilled jobs at a 1-mile radius of tract.10

The findings of the study overwhelmingly confirm that the largest concentrations of black joblessness are

in areas of highest low-skill job concentration. The findings counter the Spatial Mismatch Hypothesis,

and the authors attributed high levels of joblessness in areas containing high levels of low-skilled jobs to

low levels of educational attainment. A possible explanation for this might be explained by Parks (2004)

who attributed paradoxically high levels of joblessness and job proximity amongst foreign-born Mexican-

American women in Los Angeles to increased competition for the proximal low-skilled jobs,11 The

Boardman & Field article found little evidence to support mismatch as an explanation for inner city

joblessness.

McLafferty & Preston

McLafferty & Preston (1992) examined commute times for various gender and racial groups

across a ten-county region in Northern New Jersey, taking into consideration previously ignored factors

of labor-market segmentation between racial and gender groups. They wanted to identify whether

minority women, in particular, were being affected by the shift in the labor market from a production-

based economy to a service-based economy. McLafferty and Preston classified workers by industry,

believing that labor market segmentation was significantly determined by certain pre-existing historical

conditions and that labor-market segmentation, race, commute mode, and earnings were interlocking

factors. The researchers identified three independent variables related to labor market segmentation and

broke down the workforce into three groups- those employed in the consumer sector, the producer sector, 10 Boardman & Field, p. 24811 Parks, p. 163

and the manufacturing sector. The researchers found little evidence of spatial mismatch when examining

race without other considerations.12 They found that white men generally had the longest commute times,

but attributed this to a tendency for white male workers in Northern New Jersey to commute to high-

paying jobs in Manhattan; when comparing commute times to white women, both black men and women

were found to have significantly longer commuting times regardless of labor market segmentation.13 The

researchers, however, made several interesting discoveries when taking into consideration labor market

segmentation. They found relatively longer commuting times for both black men and women in the

manufacturing sector when compared to their white and Hispanic counterparts, which they attributed to

an increased reliance on public transportation for blacks, the reasons of which are not entirely explained.14

Amongst possibilities offered were that manufacturing centers may intentionally locate in non-black areas

where a low-wage workforce can be employed, or that White and Hispanic women may be willing to

sacrifice income levels for the opportunity to work closer to home in order to take care of domestic

responsibilities.15

Raphael

Raphael (1997) examined the link between levels of employment change between 1980 and 1990

for neighborhoods of black and white youth in the San Francisco/Oakland/San Jose CSMA. He assumed

a better measure of accuracy regarding spatial mismatch among teenagers would be found by comparing

rates of employment to proximal net employment growth rather than gross jobs. In order to measure job

accessibility for certain neighborhoods, he broke down neighborhoods by racial composition, then tallied

the number of jobs for 1 minute intervals for a 45 minute commute via private transportation out of the

neighborhood. He found that employment growth between 1980 and 1990 was much greater in the

commuting range of white neighborhoods than in black neighborhoods, and that the loss of proximal

manufacturing jobs for black youth was significantly greater than the loss of manufacturing jobs proximal

12 McLafferty & Preston, p. 41613 McLafferty & Preston, p. 42014 McLafferty & Preston, p. 42015McLafferty & Preston, p. 427

to the neighborhoods of white youth.16 Specifically, he found that, across the region, a gain of 2,000 new

jobs occurred within 2 minutes of the average white youth’s residence, while approximately 2,000 jobs

were lost within 2 minutes of the average black youth’s residence.17 Further, he discovered that within 20

minutes distance of the average black youth, 19,000 manufacturing jobs were lost while only 7,000

manufacturing jobs disappeared in 20 minutes commuting range of the average white youth.18 This is

particularly problematic since manufacturing jobs traditionally provided urban African-Americans with

limited levels of educational attainment with stable, high paying jobs that would not be found in the

service sector. Raphael attributed the disadvantages in employment outcomes to job suburbanization and

restricted mobility of African Americans in terms of housing choices.19

Fernandez

Fernandez (1994) conducted a qualitative study of the results of a food processing plant

relocation from Milwaukee’s Central Business District to the suburban ring. The author initially

examined the travel changes associated with three potential suburban relocation sites, and determined that

the selected location actually offered the least commute difficulties for current workers, regardless of

race.20 Based on this analysis and spoken interviews, the researcher determined that the plant’s owners

were not intentionally relocating to alter the demographics of their workforce; the move was made so a

new factory could be built to accommodate new technologies, but the company intended to keep as much

of its former workforce as it was able to. Surveying company employees six months before and after the

relocation, he determined that Blacks, Hispanics, and women faced the larger percentage changes in

commuting times, longer physical commuting distances, and lower real wages compared to their white

counterparts.21 Of key importance here is that the study established prior to statistical analysis that the

relocation was not intended to rid minority employees; thus, any changes which negatively affect the

16 Raphael, p.8817 Raphael, p. 8818 Raphael, p. 8919 Raphael,p.10620 Fernandez, p. 40221 Fernandez, p.414

travel abilities of the current workforce are attributable to aspects of urban segregation in residential

location. In addition, the authors point out that Milwaukee is a conspicuously segregated city (only 1

percent of Blacks lived outside the city in 1990 and were nearly completely absent from all areas of

growth)22, making it an excellent case for studying the effects on urban workers of urban to suburban

relocation. Pre-relocation white employees were found to have significantly longer trips to the plant

when located in the CBD; post relocation commutes increased for Blacks, Hispanics, and whites 194, 135

and 33 percent collectively;23 a clear reflection of restriction of housing mobility and difficulty accessing

suburban workplaces. In addition, he determined that workers who intended to commute by private

vehicle to the new suburban site would be shortening their commute by 36 minutes if switching to private

vehicle, and that most currently commuting by transit intended to switch to private vehicle upon

relocation.24 In addition, he found that salaried black women actually benefitted from the plant’s

relocation (as opposed to black women at the firm paid hourly) raising the possibility that future studies

would be well off to examine intra racial class differences.25

Sanchez

Looking specifically at public transportation and job access, Sanchez (1999) used the average

annual workforce participation for census blocks based on several variables regarding the relationship

between adequate transportation resources and job access in Portland and Atlanta. He used a simple

linear regression for white and non-white block groups controlling for access to retail and service jobs,

distance from bus and rail stops, frequency of service, white composition of neighborhoods, predicted

levels of car ownership, average commute times, percentage of adults with Bachelor’s degrees,

percentage of single-parent households, and percentage of workers employed in night shift positions to

determine levels of workforce participation measured in weeks actively working per year. Sanchez

determined that one of the strongest indicators of average annual weeks of employment participation was

22 Fernandez, p. 39723 Fernandez, p. 40624 Fernandez, p.40925 Fernandez, p. 413

level of household car ownership,26 as found in Taylor and Ong (1995) and Boardman and Field (2002).

The results, however, regarding transit and mobility access in relation to annual workforce participation

were mixed; access and mobility variables in Atlanta (access to retail and service positions, distance from

bus and rail stops, frequency of service, and average commute times) were found to be strongly correlated

to workforce participation, although minority block-groups in Portland were unaffected, not satisfying the

hypothesis that minority groups are particularly disadvantaged in terms of adequate transit resources,

although it was determined that distance from bus stops was significantly more determinant of workforce

participation than distance from rail stops27, important because reconfiguration of bus routes is easier than

that of trains.

Stoll

Finally, in his publication Job Sprawl and the Spatial Mismatch between blacks and jobs, Stoll

(2005) uses data from the 2000 U.S. Census and 1999 U.S. Department of Commerce’s Zip Code

Business Patterns Files and comes up a broad, aggregate analysis of the relationship between black

residence and job sprawl. Using a sprawl index based on the total number of jobs located outside a five-

mile radius of central business districts and a mismatch index based the proportion of metropolitan blacks

who would have to relocate in order to accurately reflect the distribution of jobs, Stoll found a significant

positive correlation between job sprawl and urban residential segregation of blacks.28 His data determined

that a 10 percentage point increase in sprawl index correlated with a 3.1% increase in mismatch index

(residential segregation). In addition, Stoll determined that the correlation between residential

segregation and job sprawl was more significant in cities in the Midwest and northeast than in cities in the

south and west29, an indication spatial mismatch’s greater significance in cities that have gone through

what McLafferty and Preston identified as ‘economic restructuring’30- the shift from domestic economies

26 Sanchez, p. 29427 Sanchez, p. 29128 Stoll, p. 22029 Stoll, p. 730 McLafferty & Preston, p. 407

from ‘producing’ ones (i.e. manufacturing) to service and information based. Philadelphia, then as a city

which has gone from 300,000 manufacturing jobs in 1960 to 85,000 in 1990,31 qualifies as an excellent

example of how shifting economies and increasing sprawl maybe negatively affecting the commuting

abilities and employment outcomes of urban residents.

Summary of literature

Reviews of spatial mismatch literature often call results ‘mixed’. One issue is the variation in

possible methodologies for studying the hypothesis- one can interpret ‘distance’ between work and home

as either a measure of time or physical distance, and use any number of dependent variables to control for

demographic characteristics. With no underlying formula there is no possibility for consensus. What is

clear, however, is that policy recommendations aiming to improve access in the 1970’s and 1980’s were

created before current understanding of global issues were fully understood. To recommend subsidies for

private vehicle ownership, as Taylor and Ong suggest, seem inappropriate today- urban congestion,

sprawl, and climate change are enough to delegitamize the idea of putting more cars on the road, inducing

the demand for freeway construction, intruding on existing open space, and opening up new suburban

land for development. The research, whether concluding in support of SMH or not, confirms

unanimously that urban residential segregation is persistent (Fernandez, p. 397, McLafferty & Preston, p.

412, Stoll, p. 1), that blacks unanimously rely on public transportation more than whites (Fernandez p.

408, McLafferty & Preston, p. 429, Taylor & Ong p. 1459, Boardman & Field p. 250, Parks, p. 153), and

that urban areas continue to lose jobs to the suburbs and overseas (Fernandez, p. 392, Stoll, p. 8, Parks, p.

143). Philadelphia, ranked 4th on Michael Stoll’s rankings of cities with high levels of job sprawl and

spatial mismatch32, and longitudinal studies of mismatch cited in the literature review-Taylor & Ong, and

Raphael, examined changes between 1977- 1985 and 1980-1990 respectively. This paper looks studies

31 Kim, 200632 Stoll, p. 6

the more recent a more recent period (1990-2000) in an appropriate city, highly segregated and plagued

by high job sprawl.33

Data and Methods

The goal of the current paper is to determine whether average incomes play a role in the lengths

of commutes for residents in the city of Philadelphia. Tract-level data from the American Community

Surveys from the 1990 and 2000 United States Census will be used to analyze whether an indirect

relationship exists between income levels and lengths of commutes for commuters in the city of

Philadelphia. Secondly, the results from the 1990 and 2000 census will be compared to determine

whether conditions are changing over a ten year period, since a central tenet of the mismatch hypothesis

is that jobs will continue to disperse over time. Commute times will serve as the dependent variable

while controlling for per capita incomes, percentage of tract residents with college level education or

higher, percentage of black and Hispanic residents, and percentage of residents with access to private

vehicles for commuting.

The following tables were used for data collection from the 1990 American Community Survey

of Philadelphia County: P008. Race, P011 Hispanic Origin, P049 Means of transportation to work, P050

Travel Time to work, and P114A. per capita income in 1989 (dollars). For 2000, data was collected from

ACS table surveys P6-Race, P7-Hispanic or Latino by race, P30- Means of Transportation to work for

workers 16+ years old, P31-Travel time to work for workers 16+ years old, and P82- Per capita income

(dollars). For race, per capita income, means of transportation, and private vehicle occupancy, the desired

categories were selected and then divided by total number of occupants- an example is illustrated below

of how percentage black was calculated for census tract 1 in 1990:

Tract 1- Race

White- 1754

Black- 26333 Stoll, p. 6

American Indian- 8

Asian- 34

Other- 8…………………….% black= 263/2067, which equals 13 %.

The mean travel times for 1990 and 2000 (P050 and P31, respectively) were slightly more difficult to

compute, since tallies were broken up into ranges as shown below.

1990 ACS Table P050: Travel Time to work, Census Tract 1:

Did not work at home: 

Less than 5 minutes 67

5 to 9 minutes 138

10 to 14 minutes 256

15 to 19 minutes 312

20 to 24 minutes 319

25 to 29 minutes 81

30 to 34 minutes 179

35 to 39 minutes 40

40 to 44 minutes 17

45 to 59 minutes 78

60 to 89 minutes 48

90 or more minutes 8

Worked at home 48

In order to calculate mean travel times, I take a median for each category- 5 to 9 minutes for example

would be 7 minutes. I am then going to multiply 7 by 138, the number of commuters falling in that

category. I will create a product for each category, then divide this sum by total number of people and

come up with a mean commute time for each tract. Thus, mean travel time for the tract 1:

2.5x67= 167.5

7x138= 966

12x256= 3072

17x312= 5304

22x319= 7018

27x81= 2187

32x179= 5728

37x40= 1480

42x17= 714

52x78= 4056

75x48= 3600

90x8= 720

35012.5/1543= 22.69 minutes mean travel time.

Means were thus created for these five variables for all 367 census tracts in the city of

Philadelphia. Census tracts with less than 100 residents were disregarded. The means of these variables

were then entered into SPSS for statistical analysis, with commute time in minutes serving as the

dependent variable and Per Capita income, race, and access to private vehicles serving as dependent

variables. Coefficients of determinations (R squared) and Beta coefficients were then used to determine

the degree of variability owed to the predictor and the statistical relationship respectively.

Results

Results indicate that dependent variables measured (income, race, and access to private

vehicles) accounted for a large part of the variability in commute times.

1990 Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .852a .726 .722 4.8488862

a. Predictors: (Constant), percapita, Hispanic, Black, Private Vehicle, B.A.

2000 Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .882a .778 .775 5.02811

a. Predictors: (Constant), private, Hispanic, black, B.A., income

The coefficient of determination (R squared) determined by SPSS was 73 and 78 percent for 1990

and 2000 respectively, indicating that nearly three quarters of variability in length of commutes could be

accounted for by variables included in the study. Beta coefficients indicate the strength in relationships

between individual variables measured. The results were mixed; in affirmation of the spatial mismatch

hypothesis, standardized coefficients indicate that race is a strong predictor of length of commute times in

both 1990 and 2000- particularly the relationship between black composition and commute time.

Hispanic composition also has a direct relationship with commute time, although it is not as strong a

predictor as the African-American variable. Income was found to have a negative relationship with

commute length in 1990, although it was statistically insignificant. In 2000, income had a neutral effect

on length of commute. Levels of educational attainment had a negative relationship with length of

commute time, although the relationship was statistically insignificant for both 1990 and 2000. The

results determined by access to private vehicle were slightly confounding- in contradiction to previous

pieces of research, access to private vehicles had the strongest direct relationship to length of commute-

tracts where people drove to work the most instead of using public transportation generally had

significantly longer commute times, both in 1990 and 2000.

1990

Model

Unstandardized Coefficients

Standardized

coefficients

t Sig.B Std. Error Beta

1 Constant 6.744 .760 8.879 .000

B.A. -1.289 .893 -.045 -1.444 .150

Black 15.462 .668 .668 23.157 .000

Hispanic 7.431 2.138 .098 3.476 .001

Private Vehicle 27.094 1.238 .685 21.894 .000

Income 6.586E-5 .000 .067 1.907 .057

a. Dependent Variable: commute

2000

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 Constant 6.847 .781 8.772 .000

Income .000 .000 .171 3.993 .000

B.A. -.814 2.164 -.015 -.376 .707

Black 18.724 .711 .670 26.337 .000

Hispanic 14.694 1.867 .199 7.870 .000

Private 24.948 1.248 .558 19.987 .000

a. Dependent Variable: commute34

34 Data accessed from factfinder.census.gov

Mean length of Commute 1990-

Mean commute time (in minutes)-19900.00 - 11.10

11.10 - 24.97

24.97 - 29.88

29.88 - 34.41

34.41 - 53.70

Percentage of residents with Bachelor’s degrees or higher- 1990.

% of residents with college degrees- 19900.000000 - 0.090000

0.090001 - 0.210000

0.210001 - 0.370000

0.370001 - 0.580000

0.580001 - 0.870000

0.870001 - 3.770000

Mean Per Capita Income- 1990.

Per Capita Income (Dollars)- 19900.00 - 4367.00

4367.00- 10976.00

10976.00 - 18604.00

18604.00 - 32661.00

32661.00 - 58144.00

58144.00 - 109946.00

% Black- 1990.

Black0.000000 - 0.080000

0.080001 - 0.240000

0.240001 - 0.400000

0.400001 - 0.570000

0.570001 - 0.800000

0.800001 - 1.000000

% Hispanic- 1990.

Hispanic0.000000 - 0.020000

0.020001 - 0.090000

0.090001 - 0.200000

0.200001 - 0.340000

0.340001 - 0.560000

0.560001 - 0.770000

% of residents commuting by private vehicle- 1990.

PrivateVehicle0.000000 - 0.150000

0.150001 - 0.350000

0.350001 - 0.480000

0.480001 - 0.620000

0.620001 - 0.760000

0.760001 - 0.940000

Mean Length of Commute- 1990

Mean length of commute (in minutes)0.00 - 12.06

12.06 - 28.57

28.57 - 34.45

34.45 - 40.00

40.00 - 52.82

Per Capita Income- 2000

Per Capita Income (in dollars)0.00 - 5283.00

5283.00 - 15383.00

15383.00 - 29771.00

29771.00 - 59035.00

59035.00 - 109633.00

% of Residents holding Bachelor’s degrees or higher- 2000

% of residents with Bachelor's degrees or higher0.000000 - 0.100000

0.100001 - 0.230000

0.230001 - 0.400000

0.400001 - 0.600000

0.600001 - 0.920000

% Black- 2000.

0.000000 - 0.120000

0.120001 - 0.320000

0.320001 - 0.540000

0.540001 - 0.780000

0.780001 - 1.000000

% Hispanic- 2000.

Hispanic0.000000 - 0.060000

0.060001 - 0.190000

0.190001 - 0.340000

0.340001 - 0.580000

0.580001 - 0.890000

% of residents commuting by private vehicle- 2000

private0.000000 - 0.190000

0.190001 - 0.420000

0.420001 - 0.570000

0.570001 - 0.730000

0.730001 - 0.950000

Discussion

Statistical analysis shows a strong relationship between minority census tracts and length of

commute in time, and an increase in the length of commute times for both blacks and Hispanics in over

the ten year period between 1990 and 2000. The maps depict racial segregation in the city quite clearly;

black concentrations are found in Western census tracts and the Western portion of North Philadelphia.

Hispanics are clustered in the eastern section of North Philadelphia. Coincidentally, the commute maps

correlate with the race maps, with high concentrations of high commute tracts in minority neighborhoods,

particularly North and West Philadelphia. High income districts are concentrated in center city and the

far northwest portions of the city, and visibly lack the kind of concentration of long commutes present in

the largely black sections of North and West Philadelphia. Private vehicle ownership is strongest in the

Northwest and Northeast sections of the city, both areas where there is no subway. Statistical analysis

showed a strong positive correlation between the length of commute and vehicle ownership; although

unable to identify a cause (we don’t know where these people are going) it may be explained by

northwestern residents having jobs in the booming northwestern suburbs and Northeast residents having

local jobs rather than commuting downtown. The results counter the findings of Taylor & Ong (2002),

who found that national commute times between whites and minorities were converging between 1977

and 1985.35 Since minority neighborhoods in urban areas tend to be poorer, one would expect that, as

race and length of commute time had a positive correlation, income and commute time might as well.

Unexpectedly, the results did not show significant relationships between income levels and lengths of

commutes. This maybe owed to the fact, as discussed in the Boardman & Field and Fernandez studies,

that low-income or blue-collar whites tend to be less reliant on mass transit than are African Americans36,

and public transit speeds are significantly slower than private auto use, although the fact that private auto

commuters had such longer commutes confounds this even further.

35 Taylor & Ong, p. 145436 Boardman & Field, p.

That commute times for residents in minority neighborhoods are higher than others and are

increasing over time indicates, however, that job sprawl continues to occur, and that for either spatial

reasons, commute mode, or skill reasons, inner city blacks and Hispanics are having to commit more time

to commuting than white counterparts. Auto subsidies, as suggested by Taylor & Ong, have never been

used and are unlikely to occur, and emphasis on education and job readiness, as suggested by Boardman

& Field, seem worthwhile, but lack immediacy- only 18% of Philadelphians hold Bachelor’s degrees;37 it

is impossible to immediately educate a city of undereducated people. While job readiness programs and

access to adequate schools are desired ends, the fact remains that the working-age adults without

specialized training face few options in the city, as reflected, I feel, by the longer commutes exhibited by

minority populations in the city of Philadelphia, especially as low-skilled high wage jobs found in

manufacturing leave the city and are replaced by service sector jobs.38 The simultaneous existence of

longer commutes for transit-reliant minorities and longer commutes for people using automobiles

suggests that job growth in the region is occurring increasingly in further to reach suburban zones.

Weinberger (2007) identified a growth pattern in Philadelphia common to regions with sprawl-like

growth; an extension of roads to accommodate areas of new growth, and subsequent development of

housing, which quickly creates additional congestion and demands for even newer outlying

communities.39Despite zero population growth, land consumption continues to increase, creating ever

more job sprawl, and greater commutes for city residents in the urban core, an inability for regional transit

to cover costs of running transit to low-density outlying development, and a longer commutes or greater

auto dependency for people trying to access jobs.40 A most appropriate solution is to encourage

development in the traditional CBD, enabling high capacity transit systems to operate functionally, which

in turn would relieve commuting hardships on urban minority and poor populations.

Sources

37 The Brookings Institute: Philadelphia in Focus: A profile from Census 200038 Weinberg, p. 1739 Weinberg, p.1840 Weinberg, p. 18

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Weinberger. R. (2007). Men, Women, Job Sprawl, and the Journey to Work in the Philadelphia Region.

Public Works Management and Policy, Volume 11, Issue 3, pp.177-193.

Taylor, B., Ong P. (1995). Spatial Mismatch or Automobile Mismatch? An examination of race,

residence, and commuting in U.S. Metropolitan Areas. Journal of Urban Studies, Volume 32, No. 9, pp.

1453- 1473.

Boardman, J., Field, S. (2002). Spatial Mismatch and Race Differentials in Male Joblessness: Cleveland

and Milwaukee- 1990. The Sociological Quarterly, Volume 43, No. 2, pp. 237-255

Parks, V. (2004). The Effects of Spatial and Social Accessibility on Unemployment for Native-Born

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