Spatial Mismatch and Employment in a Decade of Restructuring

Spatial Mismatch and Employment in a Decade of Restructuring *

Sara McLafferty Hunter College

Valerie Preston York University

Spatial barriers to employment limit women’s job opportunities, but their effects differ among racial/ethnic minority groups. This study evaluates the degree of spatial mismatch for minority women and men by comparing the commuting times of African American, Latino, and white workers in the New York metropolitan region. Using Public Use Microdata for 1980 and 1990, we perform a partial decomposition analysis to assess the role of spatial mismatch in lengthening commuting times for minority workers. The results show that African American men and women living in the center of the region have poorer spatial access to employment than their white counterparts. In the suburbs, African American women and Latinas suffer no spatial mismatch; rather, their longer commuting times reflect greater reliance on mass transit. Comparison with 1980 findings reveals little change in spatial mismatch over time despite significant economic and social restructuring in the 1980s. Spatial barriers still limit employment prospects for the majority of minority women living at the core of the region. Key Words: gender, urban labor markets, spatial mismatch, race.

lmost 30 years ago, John Kain proposed A the “spatial mismatch hypothesis” to explain the high rates of poverty and unemployment among African American inner city residents. Residing in segregated areas distant from and poorly connected to major centers of employment growth, African Americans were said to face strong geographic barriers to finding and keeping well-paid jobs (Kain 1968). Kain’s hypothesis stimulated a large body of research on racial differences in unemployment, wages, and commuting in urban areas (Holzer 1991), providing a foundation for de- bates on urban poverty and the urban underclass (Wilson 1987). T h e mismatch hypothesis, however, clearly reflects the time in which it was written. Cities have changed since the 1960s as a result of economic restructuring, changes in family structure, suburbanization, and the growing polarization between rich and poor. These present a continually shifting set of opportunities, conditions, and constraints for minority women and men in large cities; yet the implications for spatial mismatch are poorly understood.

This research has two main objectives. T h e first is to describe the changing context of spatial mismatch in the New York CMSA from 1980 to 1990. Using data from the Public Use

Microdata Sample (PUMS) for both years, we analyze changes in employment, wages, and commuting time by gender and race during the 1980s. T h e second goal is to assess changes in the degree of spatial mismatch for employed African Americans and Latinos in the center and suburbs of the New York region. Differ- ences in commuting time among gender and race groups are analyzed, using a partial decomposition analysis (Blinder 1973), to evalu- ate the importance of spatial barriers compared to the effects of class, labor market segmentation, household structure, and access to transportation. Our findings show a significant and persistent spatial mismatch for African Ameri- can men and women residing in the highly urbanized, central parts of the metropolitan region. In contrast, for African Americans and Latinos in the suburbs, there is no evidence of spatial mismatch; rather, differences in commuting time reflect differences in income, labor market segmentation, and access to transportation. This study expands our previous research on spatial mismatch (McLafferty and Preston 1992) by computing an explicit measure of the degree of spatial mismatch, that controls for other known influences on commuting time. We also explore changes in spatial mismatch during the 1980s.

‘This research was supported hy rhr National Science Foundauon under grant #SES 9012916. We also rhankjim Huff, the anonymous reviewers, and the editor for their thoughtful comments. A Faculty Development grant From York University provided travel support.

Professional Geographcr, 48(4) 1996, pages 42043 1 0 Copyright 1996 by Association of American Geographers. Initial submission, October 1995; revised submission, May 1996; final acccptance, May 1996.

Published by Blackwcll Publishers, 238 Main Strcet, Cambridge, MA 02142, and 108 Cowley Road, Oxford, OX4 IJF, UK.

Spatial Mimatch and Employment 421

Background Anumonwo et al. 1994). Minority women are overrepresented in service occupations like domestic service, clerical work, and health aid, and in certain manufacturing occupations such as textile machine work and assembly. In addition to these occupational differences, many African American and Latina women are single parents, bearing full responsibilities for child care and economic support of their families (Preston et al. 1993).

What are the implications for spatial m i s - match? Minority men and women may have different housing opportunities, reflecting the gendered and racialized nature of residential choice. While both women and men face racial discrimination in housing markets, as sole parents, women also confront discrimination against women with children, or they may need to live near family or day care services for help with child care. Along with these differences in residential location, labor market segmentation means that minority women have different job opportunities than minority men. In the context of economic restructuring, minority women’s concentration in service jobs and low-wage manufacturing may give them better spatial access to employment than minority men. How have residential choice proc- esses and economic restructuring affected spatial mismatch for African American and Latino women and men during the past decade?

The second issue concerns the need to analyze changes in spatial mismatch over time. As noted earlier, the mismatch hypothesis reflects the time period in which it was developed-a period in which few African Americans or Lat- inos lived in the suburbs and deindustrialization had just begun. In the years since then, the spatial distributions of jobs and residences have changed significantly. When linked with broad patterns of labor market segmentation, these changes produce an evolving web of job opportunities for African American and Latino men and women. More African Americans and Latinos live in the suburbs, albeit often in a relatively small number of communities located near the city boundary (Schneider and Phelan 1993). At the same time, employment opportunities continue to grow and decline unevenly within metropolitan regions, as manufacturing firms move to the periphery and service jobs in certain sectors expand se- lectively in some central and suburban loca-

The spatial mismatch hypothesis describes the combined effects of residential segregation and economic restructuring on minorities’ spatial access to employment opportunities. The central argument is that African American, and possibly Latino, inner-city residents have poorer spatial access to jobs and employment information because of their concentration in segregated residential areas with few nearby job opportunities (Kain 1968; Ihlanfeldt 1992). Since then, many researchers have tested the spatial mismatch hypothesis by assessing racial differences in unemployment, incomes, and commuting times and distances (Zax and Kain 1991; Ihlanfeldt 1992; Cooke 1993; Rodgers 1994). Although some recent studies question the validity of the spatial mismatch hypothesis, the weight of empirical evidence supports it, a t least in part. As Holzer (1991, 1 18) concludes in a recent review: “Blacks in central-city areas have less access to employment than have blacks or whites in the suburbs.”

Despite the large literature on the spatial mismatch, two important issues have not been adequately addressed, and these serve as general motivations for the research presented here. The first concerns the lack of attention to women’s economic and domestic roles and employment problems in the spatial mismatch literature. With a few recent exceptions (Mad- den and Chiu 1990; Ihlanfeldt 1992; McLaf- ferty and Preston 1992), research has focused on commuting and employment issues for minority men, neglecting women. Yet African American and Latina women’s work is criti- cally important to the economic well-being of households and communities. Their high rates of labor force participation, full-time employment, and single parenthood give women a key economic role (Zinn 1989). Incorporating gender in the analysis of spatial mismatch requires an explicit recognition of differences in labor market experiences, choices, and constraints for minority men and women.

Facing racial and gender discrimination, minority women are “doubly disadvantaged” in urban labor markets (Amott 1992). Their earnings are less, on average, than those of white women and minority men, and they are more likely to work in the low-wage, secondary segment of the labor force (Johnston-

422 Volume 48, Number 4, November 1996

tions (Mollenkopf and Castells 1991). T h e implications of these changes for spatial mismatch are poorly understood. Theoretically, the loss of manufacturing jobs should lead to an increasing spatial mismatch for minorities in the center, especially minority men. On the other hand, for those with appropriate skills and education, the growth of producer service jobs in the cores of large cities may lead to improved spatial access to employment for those resident in the center. In the suburbs, we may see the emergence of a spatial mismatch as minority residents cluster in communities near the city boundary, and jobs tnove to more distant suburban and exurban areas.

To analyze these issues, we present a case study of the spatial mismatch hypothesis for the New York metropolitan regon from 1980 to 1990. T h e next section describes the methods and data used in evaluating the mismatch hypothesis. In the subsequent section, we analyze general trends in residential and workplace location in the metropolitan region and changes in coinmuting times by gender and race. Finally, we discuss the results of the decomposition analysis of changes in spatial mismatch for employed persons over time for the central and suburban parts of the metropolitan area.

Methods and Data

To compare levels of spatial access to employment among race and ethnic groups, many studies have analyzed commuting times or distances for employed workers (Gordon et al. 1989; Holzer 1991). Longer work trips for African Americans or Latinos are thought to indicate mismatch since workers must travel longer, on average, to reach their jobs. Such an approach, however, neglects the many factors that constrain and motivate workers’ commuting decisions. Long commuting trips d o not necessarily signal a spatial mismatch. High-income workers, and those with children, often choose to live in attractive residential areas distant from work to gain other amenities (Brun and Fagnani 1994). In these cases the long worktrip results from a conscious choice rather than a spatial constraint. Although some researchers have analyzed these confounding factors separately in their studies of commuting times (Gordon et al. 1989; Johnston-Anu-

monwo 1992), given the number of possible factors, a multivariate approach is desirable.

To take into the account the many factors affecting commuting times, we employ partial decomposition analysis (Blinder 1973; Oaxaca 1973). this method is widely used in analyzing wage discrimination (Goldin 1990) and has also been used to study racial differences in unemployment (Ihlanfeldt and Sjoquist 1990). T h e method involves a comparison of African Americans’ (or Latinos’) actual average commuting time with the average commuting time they could be expected to have given their earnings, education, access to transportation, and other socio-demographic characteristics. T h e difference between actual and expected commuting times is a measure of spatial mismatch.

For each gendedrace group in the center and suburbs, we estimate the following model:

Time = f(D,E,H,T),

where:

D is a set of demographic variables including age and education. E is a set of economic variables measuring the individual’s earnings, occupation, industry of employment and household income. H is a set of variables describing household characteristics such as marital status and presence of children. T is a set of variables describing the mode of transportation used.

Models are estimated separately for each group via least squares. To determine expected commuting times, the mean values on all variables for one group (i.e., African American women) are substituted in the equation for a corresponding reference group (i.e., white women). In particular, if b,vf is the vector of parameter values for the reference group equation (white women) and X,f is the vector of mean values on all variables for the comparison group (Af- rican American women), the expected commuting time for African American women (td,Vff) is:

Lf,d = C[bwfX,fI T h e resulting value is the expected commut-

ing time for an “average” African American woman if she were able to make the same

Spatial Mimatch and Employment 423

commuting choices as an equivalent white woman. Because we have controlled all factors except residential location in determining the expected commuting times, the difference between actual and expected times provides a measure of spatial mismatch. The actual time includes the effects of residential location, whereas the expected time does not. Thus, a positive difference between actual and expected times indicates a spatial mismatch. To check the sensitivity of the results, one can also reverse the reference and comparison groups, in which case a negative value would indicate a spatial mismatch.

The above model represents well-known hypotheses about the determinants of commuting times. Economic factors, such as earnings, occupation, and education are expected to have positive effects on commuting time, with workers in high-status, primary occupations, with high earnings and education, hav- ing longer times (Ihlanfeldt 1992). Industry of employment is expected to affect commuting time independent of wages and occupation, insofar as it reflects the geographic concentration of employment in different sectors. In the New York region, jobs in producer services are strongly concentrated in Manhattan, and this clustered spatial pattern should result in longer commuting times.

The effects of household characteristics on commuting times are expected to differ by race and gender. In juggling the competing de- mands of domestic work, child care, and paid employment, women may opt to work near home to free time for domestic responsibilities (Johnston-Anumonwo 1992). In contrast, minority women’s more constrained residential and job opportunity sets may give them less ability to reduce work trips when faced with competing domestic and employment de- mands. Finally, mode of transportation strongly affects commuting time. Workers who rely on mass transit spend more time commuting than do workers who travel by car, regardless of distance (Singell and Lillydahl 1986).

In analyzing these relationships, we utilized data from the 1980 and 1990 Public Use Mi- crodata Samples (PUMS) for the New York metropolitan region. Our samples include only people employed in the paid labor force. Un- employed workers-an important group in the

spatial mismatch debate-are not considered. The sample size for 1980 is approximately 180,000, while for 1990 it is more than double that-400,000. The PUMS data provide all the information needed to estimate the regression models, although several variables had to be simplified to achieve mathematical tractability.

As in all census information, the identifica- tion of raciallethnic groups is problematic. Race typically refers to biological differences of sociocultural significance to a society that “racializes” those differences. Ethnicity refers to cultural differences reflecting religion, national origin, and language. The race variable in the census reports an individual’s self iden- tification as a member of a racial group. We used this variable to identify the African American and white populations. We defined as Latino those people who identified their race as “Spanish,” and those persons regardless of race who stated they were of Hispanic origin. While there are contradictions inherent in any effort to define race and ethnicity, these are among the most significant aspects of social identity. Race as socially constructed in the United States profoundly influences daily ex- perience. Inequality in American society is still rooted in race and ethnicity (Johnson et al. 1994).

The economic variables include household income, weekly earnings, industry and occupation of employment. We combined industries into three groups: “old core” industries, producer services, and consumer services. The “old core” includes manufacturing, wholesal- ing, construction, and transportation (Buck et al. 1992). The producer service sector includes jobs in finance, insurance and real es- tate, and business services; the consumer service sector comprises jobs in personal services, health, education, social services, retailing, and government. To measure occupational segmentation, we created a dummy variable that differentiates high-status primary occupations from low-status secondary occupations accord- ing to the classification described by Amott (1992). Although these two categories greatly simplify occupational differentiation, using more detailed classifications had little impact on the results.

We analyzed household characteristics based on combinations of marital status and the presence of children in the household. Using


dummy variables, four situations were identified: (1) married with children a t home, (2) single (i.e., never married, divorced, separated, or widowed) with children a t home, (3) married with no children at home, and (4) single with no children a t home. Access to transportation is captured by two variables: the number of cars available in the household and a dummy variable equal to one if the person uses mass transit for commuting to work. We singled out mass transit (bus, subway, light rail, and ferry) because it involved significantly longer travel times than other modes of transportation.

The study area consists of the 24 counties that make up the New York Consolidated Metropolitan Statistical Area. It is divided into two parts, the center and the suburbs, that have experienced distinct population and employment trends. The center includes Manhat- tan and the other urbanized counties of New York City and the nearby urban counties in New Jersey, including the cities of Newark and Jersey City. These counties are characterized by high population densities, high reliance on mass transit, and low median household income levels. In contrast, the suburbs include all counties outside the urban core. In this group are older suburban counties like Westchester, NY, and more distant exurban counties in New York, New Jersey, and Con- necticut. The suburban counties typically have moderate population densities, low rates of mass transit use, and relatively high median incomes. However, there is considerable diver- sity among suburban counties, particularly between the older, high-income suburbs near the center where population and employment are stable or declining, and the exurban counties that have seen rapid growth in employment and population in the last decade.

Changes in the New York Region: 1980-1 990

Three aspects of New York‘s changing geography are relevant for our analysis: continued deindustrialization and the relocation of manufacturing jobs to the periphery of the region, continuing growth and differentiation of service sector employment, and the persistence of residential segregation. New York is one of several cities transformed by the globalization

of capital that stimulated rapid expansion in financial services and related producer services while failing to arrest the loss of manufacturing jobs (Sassen 1991; Mollenkopf and Castells 1991). In the center, between 1980 and 1990, the old core’s share of total employment increased 1.7%, largely because of the revival of small-scale manufacturing in the inner ring of counties surrounding Manhattan (Table 1). In the suburbs, the old core’s employment share fell 1.8%.

Service industries in the New York region are increasingly differentiated spatially and functionally. Manhattan remains the preemi- nent location for producer services in the region, with the highest concentrations of employment and the most sophisticated financial and business activities. In 1990, 19.9% of jobs at the center of the New York region were in producer services (Table 1). However, employment in producer services has increased steadily in the suburbs. In addition to back-office activities, producer service firms oriented to a local or regional market have located in the suburbs where by 1990, producer services accounted for 18.7% of jobs (Table 1).

Many argue that these sectoral shifts have polarized the labor market between well-paid professionals and managers employed in primary occupations and poorly paid service, sales, and clerical workers in secondary occupations (Sassen 1991). Suburban residents are still more likely than are central residents to work in primary occupations (Table 1). The high proportion of suburban residents in primary occupations results in large wage differentials between central and suburban residents. In 1990, suburban workers still earned substantially more than central workers despite large wage increases throughout the region (Table 1).

The New York region is typical of older and larger cities of the Northeast and Midwest, where segregation is most enduring (Massey and Denton 1993; Farley and Frey 1994). Per- sistent segregation is readily apparent from our analysis of workers living in different parts of the region. African American and Latino workers are still concentrated a t the center of the region where, in 1990, they accounted for 38% of the resident workforce while in the suburbs their share was 10% (Table 1). For

Spatial Mismatch and Employment 425

Table 7 Characteristics of Workers in the New York CMSA, 1980-1990 (percentages? Location

Central Suburbs

1980 1990 Changeb 1980 1990 Change

Industry Old Core Consumer Service Producer Service

Race African American Latino White

Men Women

Occupation Primary Secondary

Automobile Transit Other

(dollars)c

Iminutesl

Sex

Means of Transportation

Mean weekly earnings

Mean cornmuting time

38.1 41.7 20.3

192 14 4 62 2

54 7 45 3

47 1 52 9

41 5 45 4 13 1

$284 88

34 0

39 8 40 3 19 9

19 5 18.5 53.7

52 6 47 4

48 1 51 9

45 7 41 0 13 3

$623 84

32 3

1.7 -1.4 -0 4

0 3 4 1

-8 5

-2 1 2 1

1 0 -1 0

4 2 -4 4

0 2

40 3 43 9 158

5 2 3 4

88.6

58 2 41 8

54 9 45 1

81 8 11 4 6 8

$323 09

28 5

38 5 42 8 18.7

5 2 4 9

86 4

55 2 44 8

58 7 41 3

84 5 9 4 6 1

$713 21

27 6

-1 8 -1 1

2 9

0 0 1 5

-2 2

-3 0 3 0

3 8 -3 8

-2 7 -2 0

0 7

"All statistics are reported for workers disaggregated by place of residence. 'Change.s in shares do not sum to zero because of roundzng exceptfor race where the list of raciakhnic groups is not exhaustive. 'Mean weekly earnings are reported in 1979 and 1989 dollars, respectively.

white workers living in the center, their share of the resident workforce declined by more than 8%, largely as a result of a growing Asian population.

The changing spatial patterns of employment and residence in New York mean that women's work experiences have unfolded dif- ferently in each part of the region. Mirroring national trends, women's participation in the labor market increased steadily during the 1980s. By 1990, women accounted for 47.4% of workers living at the center of the region and 44.8% of those in the suburbs (Table 1). The biggest increase was in the suburbs where women's share of the resident labor force increased by 3.0 percentage points. At the center of the region where women have historically had higher-than-average participation in paid employment, the increase was much smaller, only 1.7 percentage points.

Despite substantial investments in public transit during the 1980s, workers in both parts of the region were less likely to commute by transit in 1990 than in 1980. The decline was most precipitous at the center of the region, where transit use decreased by 4.4 percentage

points, replaced by driving and other means of transportation, mainly walking to work. In the suburbs, transit use also declined but only by 2.0 percentage points. Increased reliance on the automobile has contributed to slightly shorter commuting times in both the center and the suburbs. Between I980 and 1990, mean commuting times fell by 1.6 minutes for central workers and by 0.4 minutes for suburban workers.

The 1980s saw considerable movement of women into primary occupations. white women, in particular, made substantial ad- vances. In the center, almost half of white female workers, 47.7%, were employed in primary occupations by 1990, an increase of 11.9 percentage points. Progress has been slower for minority women living a t the center of the region. The proportions of minority women in primary occupations are lower (28.6% and 23.1% for African American and Latina women, respectively), and the magnitudes of change are smaller (5.4 and 3.5%). Occupa- tional change was faster in the suburbs, where the small number of minority women have made relatively more progress in moving into


primary occupations. For example, the share of African American women in primary occupations increased by 7.8% in the suburbs compared with a slightly smaller increase of 5.4% in the center. In both the center and the suburbs, Latinas have made the least progress moving into primary occupations.

Commuting times suggest that spatial mismatch is a continuing problem. In 1980 and 1990, minority workers commuted longer times than white workers, although gender and race differences have diminished slightly (Ta- ble 2). Commuting times are still significantly shorter for minority workers in the suburbs than for those in the central city. African American women and Latinas living in the suburbs have average commuting times that are 11.1 minutes and 9.2 minutes less than those of their counterparts in the central city.

The gender gap in commuting time varies significantly between center and suburb. At the center of the New York region, minority men and women commute equally long times, while in the suburbs, there is a modest gender difference in commuting times for Latinos and African Americans as well as a large gender difference for whites. On the one hand, the existence of gender differences in commuting time among minority workers is an encourag- ing indication that, in New York's suburbs, Latina and African American women may be wealthy enough that they can accommodate household and employment responsibilities by working close to home. On the other hand, Latina and African American women in the suburbs still commute longer than white suburban women.

Spatial Mismatch Results ~~

The longer average commuting times for Af- rican Americans and Latinos suggest a persistence of the spatial mismatch during the 1980s. However, as noted earlier, those comparisons fail to account for class, economic status, and other factors that affect commuting time. We incorporated these factors by estimating the regression equations described earlier for each genderhace group in the center and suburbs. Table 3 shows coefficients of determination and sample sizes for the regression models for 1980 and 1990, respectively. Given the large sample sizes, the models fit moderately well, with R2 values ranging from .20 to .40. All values were significantly different from zero (p = 0.05).

T h e parameters of the regression models are not shown here because of the exceptionally large number of values. Instead, we identify the most significant and consistent results to provide a context for the discussion of spatial mismatch. (For a more detailed discussion of gender and racial differences in the determinants of commuting time, see McLafferty and Preston, forthcoming.) In all models, mode of transportation has the most significant impact on commuting times. Regardless of gender, race, or residential location, workers relying on mass transit spend from 20 to 40 minutes longer commuting than do comparable workers who use other modes. Economic and class variables are also significant. For all groups in all contexts, the log of weekly earnings has a strong positive association with time. High earnings both enable and encourage workers

Table 2 Mean Commuting Times by Gender and Race, 1980-1990 (minutes) 1980 1990 Change

Central African American men 39 1 37 3 -1 8 African American women 40 4 37 8 -2 6 Latino men 34 8 33 3 -1 5 Latina women 35 7 33 8 -1 9 White men 32 5 31 9 -0 8 White women 30 8 28 8 -2 0

African American men 28 7 29 5 0 8 African American women 25 6 26 7 1 1 Latino men 31 2 28 3 -2 9 Latina women 25 0 24 6 -0 4 White men 33 1 32 1 -1 0 White women 22 0 23 0 1 0

Suburbs

Spatial Mismatch and Employment 42 7

Table 3 Goodness of Fjr Statistics 1980

African American Latino White

Centei Men

Women

Suburbs Men

Women

R? i a 22 27 N 6799 6386 24920 R’ 23 28 36 N 7859 4555 20552

R 2 31 34 38 N 2074 1623 40109 R2 29 36 40 N 221 1 1182 29944

1990 African

American Latino White

Center Men R’ 20 23 25

N 7735 9886 30364 Women R2 25 30 34

N 10684 7873 26623 Suburbs

Men R’ 21 23 24

Women RZ 29 29 32 N 3524 4113 77726

N 4260 3218 63436

from all race and ethnic groups to lengthen their commuting trips. Other economic and class variables have smaller and less consistent effects on commuting time. Older workers, and workers with more years of education, often have longer work trips. In many cases, workers in producer services have significantly longer commutes, reflecting the continued spatial concentration of employment in this sector in lower Manhattan.

While the impacts of transportation, class, and economic variables are similar for men and women, the effects of household variables are strikingly different. Among women, only white women appear to reduce their commuting times to accommodate domestic responsibilities. In both the center and suburbs, married white women with children have shorter commutes than other white women, indicating that they are able or choose to reduce commuting time in order to free time for child care and other domestic chores. In contrast, African American and Latina women have no such opportunity, and their commuting times vary little with marital status and the presence of children. For all race/ethnic groups in the center and suburbs, the effects of household variables are substantially larger for men than for women. Marriage is the key dimension of household structure for men. Married men

commute from two to five minutes longer, on average, than their unmarried counterparts, ir- respective of the presence of children. Mar- riage, then, appears to either encourage or enable men to commute longer. This result may reflect the unequal gender division of labor in many married couple households, or the ten- dency to purchase a suburban residence upon marriage. It may explain, in part, the very large gender disparity in commuting time in suburban areas where married couples predominate.

The spatial mismatch results are shown in Figure 1 for 1980 and 1990, respectively. For each gender and race/ethnic group, we present the actual and expected average commuting times. The difference (actual-expected) indicates spatial mismatch. All results use the white population as a reference group; however, we found that reversing the reference group had little impact on the findings. As is evident in Figure 1, in both 1980 and 1990, a significant spatial mismatch existed for African American men and women living in the center of the metropolitan region. In 1980, African Ameri- can women’s actual commuting times were six minutes higher than expected and men’s were five minutes higher. These differences are large in comparison with eight- to ten-minute gap in average commuting times between Af- rican Americans and whites, indicating that poor spatial access to employment, as reflected in long commuting times, accounts for a siz- able portion of observed racial disparities. By 1990, spatial mismatch had reduced slightly, to five minutes for women and almost four minutes for men. Given the overall decline in commuting times for all groups, however, the spatial mismatch is still large and significant.

For Latinos in the center, our results provide less evidence of spatial mismatch. In 1980, the difference between actual and expected times was 1.98 minutes for Latina women and 0.8 minutes for men; by 1990 the differences had changed to 2.16 and 0.60 minutes, respectively. The greater mismatch for women than for men indicates Latinas’ poorer geographical access to the types of jobs in which they nor- mally work. Many Latina women work in pri- vate household, health care, and service jobs that are concentrated in congested Manhattan. Although our model controls for occupation, the very broad categories used here may not


A

i

Spatial Mismatch: Center

0 i A I

‘ A

I I

AActual

-Expected

20 J 1980 1990 1980 1990 1980 1990 1980 1990

Black Women Latina Women Black Men Latino Men

Spatial Mismatch: Suburbs

E 35 i=

25 P 5 < >

h X E X A Actual

-Expected - I -1

20 1 1980 1990 1980 1990 1980 1990 1980 1990

Black Women Latina Women Black Men Latino Men

Figure 1: Spatial mismatch by gender, race, and residentla/ location, 1980 and 1990.

capture these detailed, gendered, occupational effects.

In sharp contrast to the results for workers living in the center, for employed suburban workers we find no signs of spatial mismatch. After taking into account socioeconomic, geographic, and household factors, African Ameri- cans’ and Latinos’ average commuting times are approximately equal to the expected values and thus are similar to those of white suburban women with the same characteristics. This suggests that the longer commuting times for African Americans and Latinos in the suburbs are largely the result of differences in mode of transportation, earnings, education, and household structure, rather than an explicitly spatial mismatch. Theoretically, minorities’ lower average earnings and education levels should lead to shorter commuting times; therefore, the

longer average commuting times in the suburbs are likely the result of mass transit use. Our data indicate that even in the suburbs, African Americans and Latinos are much more reliant on mass transit than are white men and women. Minority suburban women, in particular, are two to three times more likely to commute by mass transit than are their white counterparts.

These findings confirm past studies that indicate that spatial mismatch is a more severe problem for urban than suburban residents (Zax and Kain 1991). African American workers living in central areas face a spatial mismatch that necessitates longer commuting times than those of white workers with similar social and economic characteristics and access to transportation. This spatial mismatch is not less for African American women than for Af-

Spatial Mismatch and Employment 429

rican American men; indeed it is greater. Inso- far as our spatial mismatch measure shows the effects of residential segregation, these results suggest that in the center, African American women’s residential choices are even more geographically constrained than those of Afri- can American men. Also, for both men and women, spatial mismatch has remained re- markably stable during the 1980s. Although the degree of spatial mismatch decreased slightly over time, it is still large in both abso- lute and relative terms.

Conclusions

What are the implications of these findings for urban policy making? Our results confirm what many others have argued-that African Americans in central cities continue to face significant spatial barriers in traveling to work. These lead both women and men to spend more time commuting, time that is unavailable for other purposes. While we have not looked a t the impacts on employment and work per- formance, longer commuting times could cer- tainly pose a significant barrier to finding and holding a job (Rodgers 1994). Reducing these barriers will require highly focused job information and placement policies that are sensi- tive to the employment needs of African American men and women in central city neighborhoods. Community-based economic development is also essential for generating job opportunities and local business ownership within a reasonable commuting range. Along with these job-focused strategies, policies to reduce racial segregation and discrimination in housing markets will also have important benefits.

The situation in the suburbs differs significantly, with little evidence of an explicitly spatial mismatch. For the fraction of African Americans and Latinos who now reside in suburban areas, living in the suburbs carries no spatial penalty, at least in terms of commuting time. Despite this, African American women and Latinas in the suburbs continue to spend more time commuting, on average, than white women, mainly because of their much greater reliance on mass transit. Although not a purely spatial mismatch, in the sense of the term used in this paper, access to transportation is a prob-

lem with strong and obvious geographic dimensions.

Analyzing these issues calls for additional research in several areas. First, the measurement of spatial mismatch needs to be further refined and the sensitivity of results explored to assess the generality and robustness of these findings. The models estimated in this paper may have measurement and specification er- rors that can affect the estimates of spatial mismatch (Hashimoto and Kochin 1980). Spe- cifically, the occupation and transportation variables were highly simplified and thus may fail to represent finer-grained effects. Using a single dummy variable to measure transit use, for example, assumes that mass transit adds a fixed amount to individuals’ commute times. In reality, the effect of mass transit is apt to vary with travel distance. More detailed measures of economic, household, and transportation factors should be incorporated to estimate more accurately workers’ commuting times. How- ever, given the variability of individual and household circumstances and the complexity of residential and workplace location decision making, it is unlikely that any aggregate model can account for more than a fraction of the variation in workers’ commuting times (Han- son and Pratt 1995).

Second, we have only analyzed spatial mismatch for employed workers and thus are omitting one of the most important groups in the spatial mismatch debate, the unemployed. Recent research on the links between spatial access to jobs and unemployment gives con- flicting results (Ihlanfeldt 1992; Cooke 1993). Further work is needed to unravel the impacts of poor geographical access to potential employment opportunities on job information, job search, and unemployment for African American and Latino women and men (Rodg- ers 1994).

Finally, further research on the spatial mismatch of minority women is sorely needed. We have emphasized gender differences in labor market segmentation as a basis for expect- ing gender disparities in spatial mismatch, but gender affects minority women’s decisions and circumstances well beyond the economic sphere. Factors such as access to child care and other services, proximity to family, and discrimination in housing against women with children influence women’s and men’s residen-


tial and employment decisions in profoundly different ways. Qualitative and quantitative studies are needed to explore how these proc- esses unfold in inner-city and suburban set- tings. Understanding women's decisions and responsibilities in geographically, socially, and economically constrained environments is a significant topic for future spatial mismatch research. W

Literature Review

Amon, T . 1992. Women and the US Economy Today. New York: Basic books.

Blinder, A. 1973. Wage discrimination: Reduced form and structural estimates. Human Resources 8:43 6-55.

Bnin, J. and J. Fagnani. 1994. Lifestyles and locational choices-Trade-offs and compromises: A case study of middle-class couples living in the Ile-de-France region. Urban Studies 3 1:92 1-34.

Buck, N., M. Drennan, and K. Newton. 1992. Dy- namics of the metropolitan economy. In Divided Cities, New York and London in the Contemporary World, ed. S. Fainstein, I. Gordon, and M. Harloe, 68-104. Cambridge, MA: Blackwell.

Cooke, T . 1993. Proximity to job opportunities and African-American inale job opportunities: A test of the spatial mismatch hypothesis in Indianapolis. Profissional Geographer 4S :407- 1 5.

Farlcy, R. and W. Frey. 1994. Changes in the segregation of whites from blacks during the 1980s: Small steps toward a more integrated society. American Sociological Review 5 9 2 3 4 5 .

Goldin, C. 1990. Understanding the Gender Gap. New York: Oxford University Press.

Gordon, P., A. Kumar, and H. hchardson. 1989. 'The spatial mismatch hypothesis: Some new evidence. Urban Studies 26:3 15-26.

Hanson, S., and C;. Pratt. 1995. Gender, Work and Space. New York: Routlcdge.

Hashimoto, M., and L. Kochin. 1980. A bias in the statistical estimation of the effects of discrimination. Economic Inquiiy 18:478-86.

Ilolzer, H. 1991. The spatial mismatch hypothesis: What has the evidence shown? Urban Studies 28:105-22.

Ihlanfeldt, K. 1992. Job Accessibility and the Employ- ment and School Enrollment of Teenagers. Kalama- zoo: W. E. Upjohn.

Ihlanfeldt, K., and D. Sjoquist. 1990. Job accessibility and racial differences in youth employment rates. American Economic Review 80:267-76.

Johnson, J., Jr., M. Oliver, and L. Bobo. 1994. Un- derstanding the contours of deepening urban inequality: Theoretical underpinnings and research

design of a multi-city study. Urban Geography 15377-89.

Johnston-Anumonwo, I. 1992. The influence of household type on gender differences in work trip distance. Profissional Geographer 44:161-69.

Johnston-Anumonwo, I., S. McLafferty, and V. Preston. 1994. Gender, race and the spatial context of women's employment. In Gender in Urban Research, ed. J. Garber and R. Turner, 236-55. Thousand O a k , CA: Sage Publications.

Kain, J. 1968. Housing segregation, Negro employment, and metropolitan decentralization. Quar- terly 'journal of Economics 82:175-97.

Madden, J., and L. Chui. 1990. The wage effects of residential location and unemployment on employed married women. Urban Studies 27:353-69.

Massey, D. and N . Denton. 1993. American Apart- heid: SeFegation arid the Making of the Underclass. Cambridge, MA: IIarvard University.

McLafferty, S . , and V. Preston. 1992. Spatial mismatch and labor market segmentation for African- American and Latina women. Economic Gcogruphy 68:406-3 1.

-. Forthcoming. Gender, race and the determinants of commuting: New York in 1990. Urban Geop-aphy .

Mollenkopf, J., and M. Castells. 1991. Dual City, Resmcturing New York. New York: Kussell Sage Foundation.

Oaxaca, R. 1973. Male-female wage differentials in urban labor markets. Ivitcmational Economic Review 143693-709.

Preston, V., S. McLafferty, and E. Hamilton. 1993. The impact of family status on black, white and Hispanic women's commuting. Urban Geography 14:2 2 8-50.

Rodgers, C. 1994. Job search and unemployment duration: Implications for the spatial mismatch hypothesis. Research Paper No. 9437. Morgan- town, WV: West Virginia University, Regional Research Institute.

Sassen, S. 1991. The Global City: New York, London, Tokyo. Princeton, NJ: Princeton University Press.

Schneider, M., and T. Phelan. 1993. Black suburbanization in the 1980s. Demography 30:269-79.

Singell: L., and J. Lillydahl. 1986. An empirical analysis of the commute to work patterns of males and females in two-earner households. Urban Studies 23:1119-29.

Waldinger, R., and T. Bailey. 1991. The changing ethnichacia1 division of labor. In Dual City, Rc- structuring New York, ed. J. Mollenkopf and M. Castells, 43-78. New York: Russell Sage Founda- tion.

Wilson, W. J. 1987. The Truly Disadvantaged. Chi- cago: University of Chicago Press.

Zax, J., and J. Kain. 1991. Commutes, quits and moves. Journal of Urban Economics 293153-65.

Race, Gender, and Spatial Segmentation 43 1

Zinn, M. 1989. Family, race and poverty in the eighties. Signs: Journal of Women in Culture and Sociery 14:856-74.

markets, spatial analysis, and the geography of health and health care.

VALERIE P R E S T O N is Associate Professor in the Department of Geography, York University, North York, Ontario M3J 1P3, Canada. H e r research interest include gender and urban labor markets, im- migration, and urban social policy.

SARA McLAFFERTY is Associate Professor in the Department of Geography, Hunter College, 695 Park Avenue, N e w York, NY 10021. H e r research interests include gender and race in urban labor

Race, Gender, and Spatial Segmentation in the Twin Cities+

Elvin K. Wyly Rutgers University This study analyzes commuting trends in a relatively vibrant setting during the 1980s to determine (a) how labor market segmentation correlates with differences in the spatial dimensions of local labor markets, and (b) whether this link represents a direct spatial effect, independent of earnings, travel mode, and part-time work. I use 1980 and 1990 PUMS data to analyze changes in racial and gender divisions in the workforce, and I develop an estimate of work trip distance to adjust for different travel modes. For all groups except white men, employment in a job “typical” of one’s gender and racial group is associated with more localized commutes, hut this effect is strongly mediated by variations in earnings and part-time work. Using a covariance structure model to control for these effects, I find no independent link between segmentation and longer comniutes among African Americans. Earnings and commute distances remained unchanged over the decade for African Americans, providing no evidence of a purely spatial mismatch manifest in lengthening work trips without corresponding wage gains. The spatial dimensions of an employment mismatch for inner-city minorities are concealed through the replacement of production jobs by poorly paid service work in the expanding downtown economy of a vihrant regional center. Key Words: spatial mismatch, labor market segmentation, commuting.

Introduction

esearch on the economic restructuring of FL ecent decades highlights a deepening ra- cia1 polarization of the North American city, as evidenced by rising unemployment and poverty among urban African Americans (Wilson 1987; Kasarda 1989; Galster 1991; Massey and Denton 1993). This research has reopened debate over John Kain’s (1968) celebrated “spatial mismatch” hypothesis, which correlates worsening inner-city unemployment and poverty with residential segregation and intraur- ban locational shifts in employment growth. Yet with few exceptions (McLafferty and Pre- ston 1992), analysts overlook the relations be-

tween labor market segmentation and mismatch, and the resurgence of interest in the spatial mismatch largely ignores the circumstances of minority women-despite a growing body of evidence on the durability of gender divisions in urban labor markets (Hanson and Pratt 1988b, 1995; Tomaskovic-Devey 1993). Consequently, the relations between racial and gender divisions in the workforce and urban spatial restructuring remain unclear. This study analyzes changes in the spatial dimensions of labor market segmentation in a relatively vibrant setting, focusing on the commuting patterns of male and female African American and white workers in the Minneapo- lis-St. Paul region during the 1980s.

‘I thank John S . Adams and three anrinymour reviewers for vdluabk criticisms and suggestions on earlier versions of h s manuscript. This paper is a suhstannally revised and expanded version of research presented a t the 1995 annual meeting of thc Asaociaoon of American Geogra- phers.

Professional Geographer, 48(4) 1996, p a g e 43 1- 0 Copyright 1996 by Association of American Geographers. Initial submission, August 1995; revised submissions, April 1996, May 1996; final acceptance, May 1996.

Published by Blackwell Publishers, 238 Main Street, Cambridge, M A 02142, and 108 Cowley Road, Oxford, OX4 IJF, UK.

Spatial Mismatch and Employment in a Decade of Restructuring

Documents

Transcript of Spatial Mismatch and Employment in a Decade of Restructuring