a brief summary what was understood on the aspect of the causes or effects of segregation in city ( any kind of city within US). 1000word. Thank you

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CHAPTER 5 Residential Segregation and Employment Inequality Margery Austin Turner Despite substantial gains during much of the past half-century, employment outcomes for blacks and Hispanics still fall short of outcomes for whites. Blacks and Hispanics are less likely to have jobs and) if they are employed, earn lower wages on average. Differences between minorities and whites in education, skills, and experience explain part of the gap in employment outcomes but not all of it. Even after education and achievement have been controlled for, blacks and Hispanics-particularly men-do not enjoy as much success in the labor market as whites. This chapter reviews current theory and evidence on the contribution of residential segregation to persistent inequality in employment outcomes and presents new exploratory analysis of 2000 census data on the isolation of minority neighborhoods from centers of employment opportunity. Persistence of Employment Inequality During the 1960s and 1970s, disparities between minorities and whites narrowed signicantly, Suggesting that the nation was making real progress toward more equitable employment opportunities. However, during the 1980s the gap between minorities and whites in both employment and wages widened again because of economic restructuring, the loss of manufacturing jobs, and the relative decline in wages for low-skill jobs (Smith 2001; Hol2er 2001 ; Conrad 2001). The prosperity of the 19905 brought increases in employment for virtually all segments of the US. population, and the wage gap between white and minority men narrowed. However, the gap in employment rates between white and minority men continued to widen during the 19905. For women, the opposite pattern prevailed during the past decade, with differences in employment narrowing while differences in wages narrowed. This pattern is probably attributable to the increase in employment among low-skilled women because of welfare reform (Holzer et al 2004). Thus, near the start of the twenty-rst century, blacks and Hispanics are still at a substantial disadvantage relative to whites. As is illustrated in Table 5.1, the share of all blacks who are working (the employment to population ratio) falls substantially below that of either whites or Hispanics. The share of people who are actively seeking work but unable to nd it (the unemployment rate) is roughly twice as high for blacks as for whites, and almost 50 percent higher for Hispanics than for non- Hispanic whites. Considerable research has focused on identifying the causes of these racial and ethnic disparities in employment and wages. Clearly, differences between minorities and whites in education and experience-differences that stem in part from past discrimination-provide part of the explanation. However, these factors explain the wage gap more than the gap in employment rates and the gap Table 5.1 Current Disparities in Employment between white and minority women more b Race and Ethnici mm 5 than between white and minority men (Smith Rm memmpwam 2001 2002 2003 2004 2001;H6126r_2001; Conrad-2001). _ wines 63.7 66.5 66.4 63.2 _ _ _O\"Fght discrmmum 38\"\" Blacks 535 57.6 569 510 minorities 1n the labor market also plays a Hispanics 64.3 63.4 63.0 63.9 role. Paired-testing studies nd that young Ueawm W black and Hispanic men applying for entry- gilt: 3': 15316 15316 {:68 level jobs are less likely than comparably Hi5 -c5 14 19 7.1 65, gualied whites to be invited to apply, SOurce: Seasonally adjusted fourthquarter estimates from the Current IntCIVlEWCd, 01' Offered 3. 1305111011 III PDPHM'D\" Sum? addition, they experience signith levels of unfavorable treatment with respect to information, encouragement, and terms of employment (Turner et al.1991; Cross et al. 1990). More recent employment testing studies have used matched resumes for which the names of the ctitious job applicants were selected to provide explicit cues as to race or ethnicity.' These studies nd that resumes with white-sounding names were 50 percent more likely to generate callbacks from employers than those with black-sounding names. Moreover, having better credentials signicantly improved the rate of call backs for the resumes with white-sounding names but not for the resumes with black-sounding names (Bertrand and Mullainathan 2003). The Spatial Mismatch Hypothesis Beginning in the late 1960s, labor-market analysts have considered the possibility that segregated housing patterns might also contribute to persistent employment inequalities. Specically, John Kain (1968) argued that the concentration of blacks in segregated central-city neighborhoods limited their access to employment, as growing numbers of jobs moved to predominantly white suburban locations. In effect, this spatial mismatch hypothesis posits that demand for labor has shifted away from the neighborhoods where blacks are concentrated, that discrimination in housing and mortgage markets prevents blacks from moving to communities where job growth is occurring, and that information and transportation barriers make it difcult to nd and retain jobs in these distant locations. William Julius Wilson (1987) expanded this basic hypothesis, arguing that the exodus of jobs from central-city locations, combined with the persistence of residential segregation, contributed to rising unemployment among black men during the 1980s, as well as to worsening poverty and distress in black neighborhoods. A recent review nds that most empirical studies support the spatial mismatch hypothesis. Specically, of twenty-eight studies reviewed, twenty-one conrm the hypothesis, and the seven that reject it are methodologically flawed. In fact, the review authors conclude that the impact of residential location on job outcomes may be even greater than most studies estimate. However, these impacts may vary signicantly across metropolitan regions, with the biggest effects occurring in large metropolitan areas that are highly segregated (Ihlanfeldt and Sjoquist 1998). However, over the past decade, conditions in both housing and labor markets have changed quite dramatically in many metropolitan areas. These changes raise questions about whether the traditional formulation of the spatial mismatch hypothesis still applies. Specically, although discrimination in metropolitan housing markets persists, its incidence is declining (Turner etal.2002). As is diSCussed elsewhere in this book, data from the 2000 census indicate that residential segregation is declining, albeit slowly, and that growing numbers of blacks and Hispanics are moving to suburban communities (Pastor 2001). Moreover, the number (and population) of high poverty central city neighborhoods declined during the 1990s (Iargowsky 2003; Kingsley and Pettit 2003), and at least some central cities began to experience economic revitalization, with expanding employment opportunities in previously distressed neighborhoods (Raphael and Stoll 2002). While these changes have by no means eliminated the spatial separation between neighborhoods where minorities live and centers of expanding employment opportunity, they have certainly changed-and complicated- the picture. A recent analysis explores the extent of this change using 1990 and 2000 census data to track the extent of spatial mismatch between people of different races and ethnicities and jobs in all U.S. metropolitan areas (Raphael and Stoll 2002). This analysis constructs dissimilarity indexes, which are Widely used to measure the extent of segregation between racial and ethnic groups, to quantify the spatial separation between people and jobs. As of 2000, the dissimilarity index between population and employment is highest for blacks (53 on a scale where 100 represents complete segregation), lower for Asians (43) and Hispanics (44), and lowest for whites (33). During the 1990s the index declined for blacks and Hispanics, remained essentially unchanged for Asians, and increased slightly for whites. These improvements appear to result primarily from changes occurring within metropolitan regions and not from people moving between regions. Reductions in spatial mismatch for blacks were biggest in the deest and West, smaller in the South, and smallest in the Northeast. Moreover, improvements were the most dramatic in metropolitan areas where the black share of the population was lowest. The extent of mismatch remains greatest where black-white residential segregation is highest, and reductions in spatial mismatch are associated with declines in segregation levels. There is strong evidence that residential segregation continues to separate minorities from centers of employment opportunity and that this separation contributes to unequal employment outcomes. However, the traditional image of minorities trapped in central city neighborhoods while jobs are located in more and more distant suburban locations is probably too simplistic. Both minority suburbanization and central city revitalization have complicated the reality of spatial mismatch, and there is good reason to suspect that patterns may vary signicantly across metropolitan areas, which necessitates different policy remedies. Current Patterns of Residential and Employment Location To help us understand these emerging patterns better, this chapter presents a new, exploratory analysis of residential and employment locations in six metropolitan areas-Atlanta, Georgia; Boston, Massachusetts; Houston, Texas; Philadelphia, Pennsylvania; Seattle, Washington; and Washington, DC.2 Tables 5.2a and 5.2b present key characteristics of these metropolitan areas, which have very different demographic, housing, and labor market conditions. The six metropolitan regions range in population from about 2 million to just over 5 million, with the central cities accounting for dramatically different shares of the total. At one extreme, Washington, DC is home to only 12 percent of its region's total population, while at the other, Houston is home to 46 percent. In general, these regions are growing faster, in terms of both population and employment, than their central cities. Atlanta grew the fastest during the 1990s, with its population increasing 39 percent in the region but only 6 percent in the central city. The Philadelphia and Boston regions grew much more slowly, and the city of Philadelphia actually lost both population and jobs. TABLE 5.2a DemoEphic and Economic Characteristics for Selected Metros Atlanta Boston Houston City Metro City Metro City Metro Population, 2000 421 ,000 2,223,000 589,000 3,406,000 1,935000 4,178,000 % Black 61% 29% 26% 7% 25% 1 8% % Hispanic 4% 6% 14% 6% 37% 30% % Foreign born 7% 10% 26% 15% 26% 20% % Poor 25% 9% 29% 9% 1 9%: 14% % Unemployed 14% 5% 7% 4% 8% 6% 0'" Aduits \"3' mmge'i'mt/ 26% 25% 26% 30% 20% 22% professional occupations Population change, 1990-2000 6% 39% 3% 6% 15% 26% Employment change 1990-2000 5% 34% -1% 3% 1 4% 1 8% Average family income $73,000 $35,000 $62,000 $89,000 $60,000 $69,000 Average owner-oCCupied house value $241,000 $173,000 $240,000 $239,000 $121,000 $127,000 TABLE 5.2b Demographic and Economic Characteristics for Selected Metros Philadelphia Seattle Washington, D.C. City Metro City Metro City Metro Population, 2000 1,518,000 5,101,000 563,000 2,415,000 572,000 4,923,000 % Black 43% 20% 10% 5% 61% 27% % Hispanic 8% 5% 5% 5% 8% 9% % Foreign born 9% 7% 17% 14% 13% 17% % Poo 23% 11% 12% 8% 20% 7% % Unemployed 11% 6% 5% 4% 11% 4% % Adults in management/ 16% 22% 32% 27% 29% 33% professional occupations Population change, 1990-2000 4% 4% 9% 19% -6% 17% Employment change 1990-2000 -11% 0% 13% 17% -14% 8% Average family income $48,000 $73,000 $84,000 $80,000 $78,000 $91,000 Average owner-occupied house value $72,000 $147,000 $315,000 $273,000 $252,000 $226,000 Source: Urban Institute tabulations of 1990 and 2000 Census data. The racial and ethnic diversity of U.S. metropolitan areas is well represented by these six metropolitan areas. For example, the Atlanta and Washington, DC regions both have quite large black populations and smaller Hispanic populations. In contrast, both blacks and Hispanics account for much smaller shares of the total population in the Boston and Seattle metropolitan areas. In the Houston area, Hispanics account for three of every ten residents. The share of metro area residents who are foreign born also varies widely, from a low of only 7 percent in Philadelphia to a high of 20 percent in Houston. Economic conditions also differ significantly across the six metropolitan areas. Poverty and unemployment were lowest in the Washington, DC, and Seattle regions, and considerably higher in the Houston area. Not surprisingly, both poverty and unemployment were substantially higher in the central cities than in the suburbs, even in the most prosperous metropolitan areas. Finally, average family incomes range from a low of $69,000 in Houston, where only about one in five workers has a managerial or professional position, to a high of $91,000 in the Washington, DC metropolitan area, where one-third of all workers are managers or professionals. Residential Location by Race and Ethnicity Residential segregation is quite high in all six of these metropolitan areas, even among high- wage workers. Overall levels of residential segregation are consistently highest for blacks, ranging from a low of 51in Seattle, where the black population is very small, to a high of 68 in Philadelphia. In Atlanta, Houston, Philadelphia, and Washington, DC, levels of segregation are slightly lower for high- wage blacks than for low-wage blacks. The dissimilarity index in these four metropolitan areas ranges from 59 to 67, even for workers earning over $75,000 annually. Segregation levels are actually higher among high-wage workers than among low-wage workers in Seattle and Boston. Levels of residential segregation for Hispanics are generally lower and more variable across the six metropolitan areas, ranging from a low of 30 in Seattle to a high of 57 in Boston. In Atlanta, Houston, and Washington, DC, levels of segregation generally decline as wage rates increase, but in Boston, Philadelphia, and Seattle the opposite pattern prevails, with substantially higher levels of residential segregation for high-wage Hispanics than for low- wage Hispanics. In all six metropolitan areas, blacks and Hispanics are more likely to live in the central cities, while whites are more likely to live in suburban com- munities (see Table 5.3).3 However, it would be a mistake to think of the suburbs in any of these metropolitan areas as exclusively white. In fact, a majority of blacks live in the suburbs of all but the Houston and Philadelphia metropolitan areas, and a majority of Hispanics live in the suburbs in all but Houston. In general, low-wage blacks areespecially likely to live in central city jurisdictions. Even so, a very substantial share of low-wage blacks and Hispanics now live in suburban jurisdictions. Table 5.3 Residential Location by Race and Ethnicity in Selected Metros (Percentages) All Workers Low-Wage Workers High-Wage Workers C' Inner Outer C' Inner Outer Ci Inner Outer It}, Suburbs Suburbs my Suburbs Suburbs ty Suburbs Suburbs Adanta White 'i' 1 39.2 53.6 5.3 36.6 53.1 12.6 49.2 33.2 18.4 62.4 19.2 24.8 55.8 19.4 17.8 61.5 2.0.?' 53.7Ir 36.3 1I'.'I.'ir 55.2 34.0 9.7 52.0 38.3 Washington, D.C. However, patterns of residential location within the suburbs are dramatically different for blacks and Hispanics than for whites. In general, minorities are overrepresented in the inner ring of suburban communities, while whites are more likely to live in the outer ring. In several metropolitan areas, suburban minorities are clustered in one or two counties. In the Washington, DC region, for example, only 21 percent of blacks and 11 percent of Hispanics live in the central city. The vast majority of those who have located in the suburbs live in the inner suburban communities, while a much larger share of whites live in the outer suburbs. Specically, 37 percent of whites live in outer suburban communities, compared to only 14 percent of blacks and 15 percent of Hispanics. Even among the highest-wage workers, a substantially larger share of whites live in the outer ring of suburbs. Moreover, within the inner suburbs, patterns of residence vary dramatically by race (s ee Annex Table 5A, p.176). Specically, more than two-thirds of all the blacks who live in the inner suburbs of the Washington, DC metropolitan area are clustered in a single county: Prince George's County, Maryland, which was 62 percent black as of 2000. Hispanics living in the inner suburbs are considerably less geographically clustered, but they are still far less likely than non-Hispanic whites to live in the region's outer suburbs. To some extent, variations in residential location by race and ethnicity are attributable to income levels. Even among high-wage workers, blacks and Hispanics are more likely to live in the central city and inner suburbs, while whites are more likely to live in the outer suburbs. For example, in the Washington, DC region, 21 percent of white workers who earn more than $75,000 annually live in outer suburban communities, compared to 11 percent of blacks and 13 percent of Hispanics in the same wage range. Moreover, within the inner suburbs 32 percent of high- wage whites live in Fairfax County, Virginia, compared to only 12 percent of high-wage blacks but 30 percent of high-wage Hispanics (again, Annex Table 5A). Employment Location by Race and Ethnicity Blacks and Hispanics are segregated from whites not only in their places of residence but also in their places of work. The rate of workplace segregation is lower than residential segregation but, as is illustrated in Figures 5.1a and 5.1b, blacks and Hispanics in all six metropolitan areas work in quite separate locations than whites. Specically, black-white dissimilarity indexes by place of work range from 27 in Houston to 39 in Boston. In Atlanta, Houston, and Washington, DC, higher-wage black workers are considerably less segregated from their white counterparts, but in Boston, Philadelphia, and Seattle levels of segregation vary much less by wage level. Levels of workplace segregation for Hispanics from non-Hispanic whites are somewhat lower, ranging from 15 in Seattle to 29 in Philadelphia. For Hispanics, we see a clear pattern of rising levels of segregation as wage rates rise. Consistent with the basic spatial mismatch hypothesis, blacks and Hispanics are more likely to work in the central cities, while whites are more likely to work in suburban areas (see Table 5.4). In all six metropolitan areas the share of blacks working in the central city is substantially higher than the share of whites. This pattern holds regardless of the relative siZe of city and suburban labor markets. In Boston, for example, only 16 percent of whites work in the central city, compared to 46 percent of blacks. In Houston, where about half of non-Hispanic whites work in the central city (51percent) the percentage of blacks is almost 20 percentage points higher (69 percent). Hispanic workers are also more likely than non-Hispanic whites to work in the central city, although the gap is generally not as wide. In Atlanta, Seattle, and Washington, DC, the share of Hispanics working in central-city locations is only slightly higher than the share of whites. Table 5.4 Employment Location by Race and Ethnicity in Selected Metros (Percentages) All Workers Inw-Wage Workers High-Wage Workers C' Inner Outer C' Inner Outer Ci Inner Outer HY Suburbs Suburbs lty Suburbs Suburbs ty Suburbs Suburbs 43,1 413.3 44.7 36.2 17.8 32.2 45.8 19.6 33.1r 45.3 45.6 50.2 24.4 16.3 24.8 White 11.1r Washington, DC. 20.5 55.4 24.0 38.5 25.0 59.5 15.5 Black 39.4 51.9 8.7 32.6 54.4 13.0 45.5 49.2 5.3 Hispanic 23.3 64.3 11.9 19.2 68.0 12.8 33.8 52.1 9.1 [Figures 5.1a and 5.1b about here] Although minorities are more likely than whites to work in central cities, a majority of both blacks and Hispanics hold suburban jobs. The only exception to this rule is Houston, where the central city accounts for 57 percent of the region's total job s. Thus, it would clearly be a mistake to argue that minorities are excluded from suburban employment opportunities. However, we see signicant differences within the suburbs based on race and ethnicity. Just as blacks are more likely to live in the inner-ring suburbs, they are more likely to work there as well; while whites are more likely to work in the outer suburbs. Of the six metropolitan areas, Seattle is the only one in which roughly equal shares of whites, blacks, and Hispanics work in the outer suburbs. In contrast, whites in Boston are more than twice as likely as blacks to work in the outer suburbs and 1.5 times as likely as Hispanics. In Houston, even though the suburbs account for a much smaller share of jobs, whites are 1.7 times more likely than blacks and 1.5times more likely than Hispanics to hold jobs in the outer suburbs. Not only are members of minorities less likely to work in the outer suburbs but blacks are typically underrepresented in white suburban counties that are particularly rich in employment opportunities (Annex Table 5.B). For example, the Atlanta region's Gwinnett County, which is 70 percent white, accounts for about 18 percent of all suburban jobs and 43 percent of jobs in the outer ring. About 15 percent of all white workers are employed in Gwinnett County, compared to only 9 percent of black workers. In contrast, 22 percent of employed blacks in the Atlanta region work in DeKalb County, which is only 35percent white, compared to only 12 percent of employed whites. We see a very similar pattern in the Washington, DC region. Fairfax County, Virginia, which is 67 percent white, accounts for one in five employed, whites in the region compared to only one in ten employed blacks. In contrast, Prince George's County, Mryland, which is 25 percent white, accounts for 19 percent of the region's black employment but only 9 percent of white employment (Annex Table 5.C). The picture is quite different for Hispanic workers in the suburbs. In the Atlanta region, Hispanics are just as likely as non-Hispanic whites to work in predominantly white Gwinnett County and much less likely than blacks to work in DeKalb County. Similarly, in the Washington, DC region, Hispanics are just as likely as non-Hispanic whites to work in Fairfax County and half as likely as blacks to work in Prince George's County. In the Houston region, where Hispanics account for a much larger share of the minority population, only 12 percent of Hispanics work in the predominantly white outer suburbs, compared to 23 percent of non-Hispanic whites (Annex Table 5.D). How do these patterns of employment location vary by wage levels? As we have seen, Houston is the only one of our six metropolitan areas where a majority of all jobs are located in the central city. In Atlanta, Philadelphia, Seattle, and Washington, DC, roughly three-quarters of the region's jobs are located in the suburbs, and in Boston the suburbs account for more than eight of every ten jobs. However, as is illustrated in Figure 5.2, in ve of the six metropolitan areas considered here, high- wage jobs remain more concentrated in central cities than low-wage jobs. More specically, the share of high-wage jobs located in the central city is generally about ten percentage points higher than the share of low-wage jobs. This differential is greatest in the Washington, DC region, where one-third of all high-wage jobs are located in the central city, compared to only 18 percent of low-wage jobs. In the Atlanta, Boston, Houston, Seattle, and Washington, DC regions, high-wage suburban jobs are more likely to be located in clusters than low wage jobs. More than one-third of the Atlanta region's low-wage jobs are located throughout the outer suburbs, while less than a quarter of high- wage jobs are. In the Washington, DC region, fewer than one in ten high-wage jobs are located in the outer suburbs, as compared to more than one in four low wage jobs. The Philadelphia area is different: it has a slightly higher share of high-wage jobs in the suburbs than in the central city and a slightly higher share in the outer suburbs than in the inner suburbs. [Figure 5.2 about here] In some respects it appears that the geographic distribution of high-wage jobs aligns relatively well with minority residential locations. However, the suburban areas where high-wage jobs are concentrated are among the most predominantly white, while suburban areas where blacks and Hispanics are more likely to live offer relatively few high-wage opportunities. For example, in the Atlanta region, predominantly white Gwinnett County accounts for 18 percent of all suburban jobs in the region and 19 percent of high-wage suburban jobs. As was discussed earlier, blacks-including high- wage blacks-are underrepresented in Gwinnett County jobs. Similarly, Fairfax County accounts for 23 percent of the region's high-wage jobs, but only 14 percent of high-wage blacks work there. A person's residential location may play a role in determining access to employment location, but it certainly is not a perfect Predictor. In general, people, regardless of their race or ethnicity, are much more likely to work in the central cities of the metropolitan areas studied here than to live in them. High-wage white workers are especially likely to work in but not live in central cities. However, as illustrated in Table 5.5, the Percentage of racial or ethnic groups who live in the outer suburbs corresponds closely to the percentage who work there. Patterns of residential segregation that limit minority access to some Suburban communities have the potential to limit access to employment opportunities in these communities as well. Table 5.5 Ratio of the Share of Em ulo ees to the Share of Residents b Race and Ethnici in Selected Metros All Workers low-Wage Workers High-Wage Workers 1.... Suburbs Sub Suburbs Suburbs Suburbs Suburbs 2.9 . 1.1 0.8 2.4 0.9 0.5 2 0 0 3 1 0 2.1 0.7 0.8 [Elf . urbs City 0.7 2.5 0 9 1 4 White 1 1 Black . 0.7 . . . . His . . . 'c . 0.3 0.9 2.1 0.3 1.0 2.6 Summary of Findings To summarize, in six very different metropolitan regions we nd minority workers (and especially low-wage black workers) overrepresented in central cities, while jobs (especially low-wage jobs) are situated widely throughout the suburbs. Although many minorities have gained access to suburban residential communities, these are often not the suburban jurisdictions that offer the most promising job opportunities. Correspondingly, black workers in particular are underrepresented in jobs that are located in predominantly white suburban communities. On the other hand, Hispanic workers are generally just as likely as non-Hispanic whites to nd employment in the white suburbs. This evidence conrms that patterns of residential segregation and employment location are far more complex-and varied-than envisioned by Kain's original spatial mismatch concept. It also shows that residential segregation continues to put considerable distance between minority workers, especially blacks, and areas of greatest employment opportunity. To understand these patterns more fully, future analyses should dig deeper, exploring differences by gender, industry, and occupation in these and other metropolitan markets. Job Openings and Neighborhood Composition An analysis by Stoll et al. (2000) provides additional insight on patterns of residential segregation and employment location. This analysis linked census data on the racial composition of subareas within the cities and suburbs of Atlanta, Boston, Chicago, and Los Angeles to a survey of employers that provides inforlnation about recently lled job openings and those hired to ll them. The authors focused on lowskill jobs and on less-educated workers, and grouped contiguous census tracts into seven subareas for each metropolitan area: the central business district, black central city, Hispanic central city, white central city, black suburbs, integrated suburbs, and white suburbs. Overall, the authors nd that the ratio of newly lled jobs to population is approximately the Same in central-city areas (other than the central business district) as in the Suburbs but that the ratio of jobs to population is much higher in white subareas than in minority and integrated subareas. Consistent with the analysis discussed above, the study shows that low-skill jobs are much more decentraJiZed than high-skill jobs, with roughly two- thirds of the low-skill openings located in the white suburbs. In addition, the ratio of low-skill jobs to less-educated people varies dramatically by subarea and is consistently lo\\vest in black and Hispanic central-city areas and highest in the white Suburbs. Moreover, nearly half of all low-skill jobs in the white suburbs are inaccessible by public transportation, making it particularly difcult for minority residents of other subareas to reach them. The authors argue that although jobs in the central business district may be physically accessible, these jobs tend to be highly competitive and may require higher skills (Holzer 1996). The analysis by Stoll et al. (2000) also concludes that the race or ethnicity of new hires for low- skill jobs generally matches the racial composition of the area where the jobs are located. This outcome appears to be explained in part by the racial/ ethnic composition of applicant pools. However, the minority share of applicants for jobs in the white suburbs is signicantly higher than the minority share of population in these areas, suggesting that blacks and Hispanics are trying to get jobs in white suburbs. Racial differences in the probability of success vary considerably across the four metropolitan areas included in the study by Stoll et al., but, in general, blacks appear to be preferred in black central- city areas and black suburbs but disadvantaged in white central-city areas and white and integrated suburbs. Understanding Why Location Matters The fact that residential segregation continues to separate the neighborhoods in which most minority workers live from areas of employment opportunity in both cities and suburbs is important, but it does not fully explain the mechanisms through which spatial mismatch contributes to unequal employment outcomes. Certainly, distance and the resultant commuting costs are part of the explanation. Empirical evidence conrms that blacks are less likely than whites to apply for low-skill jobs in the suburbs (Holzer 1996;Stoll et al. 2000) because they lack information about specic job openings (Hilanfeldt and Sjoquist 1998) and also because they are simply unfamiliar with the locations of major suburban job centers (Hilandfeldt 1997). Not only are suburban job opportunities distant from minority neighborhoods but they are often distant from transit stations. For example, the study discussed earlier that focused on employment opportunities in Atlanta, Boston, Detroit, and Los Angeles found that only about half of all low-skill jobs in the white suburbs were accessible by public transportation, as compared to almost all low-skill jobs in the central business district, and over 80 percent of those in other parts of the central city (Stoll et al. 2000). Proximity to transit stations has been shown to help explain the racial composition of a rm's workforce (Ihlanfeldt and Young 1996). Location per se is not the only source of employment inequality. Recent research highlights three other major factors that put minority workers at a disadvantage in today's labor markets: (1) differences in skills, experience, and credentials; (2) limited information and references for job search and application; and (3) prejudice and discrimination. I suggest that residential segregation exacerbates all three of these problems, and not just for low-skilled members of minority groups, who are most likely to be concentrated in central city neighborhoods, but also for better-educated minority-group members living in the segregated suburbs. Skills, Experience, and Credentials Iabor market experts generally agree that differences between whites and minorities in educational attainment, skills) and experience are major contributors to unequal employment outcomes. Some argue that these differences explain most of the inequality in employment and wages and, consequently, that policies to narrow the gap between whites and minorities should focus primarily on education Uencks and Phillips 1998). Even if education and experience only account for part of the problem, they are clearly important. In today's labor market the importance of key work skills is growing as a determinant of employment success even for low-wage jobs. Historically, manufacturing jobs paid relatively high wages for unskilled labor, but increasingly, even lowskill jobs require higher levels of cognitive skills, including reading and writing ability, computer skills, and the ability to interact effectively with customers and coworkers (Holzer 1996). Analysis of hiring practices indicates that employers increasingly look for candidates with specic skills and certications. The economic boom of the 19905 made it harder for employers to nd qualied workers, and those seeking lower-skilled workers changed their hiring behaviors, becoming more willing to hire minorities, college dropouts, and welfare recipients. Despite the tight labor market, employers continued to impose higher skill requirements, putting more emphasis on the attainment of specic skills (Holzer et al. 2003). Residential segregation clearly contributes to minorities' unequal educational attainment and hence to their disadvantaged position in the evolving labor market. Black high school graduation rates, employment rates, and wages are all negatively affected by the level of black-white segregation in a city. Other things being equal, high levels of segregation have been shown to increase high school dropout rates among blacks, reduce employment among blacks (while increasing the white employment rate), and widen the gap between black and white wages (Cutler and Glaeser 1997). Other analysis indicates that public school desegregation plans of the 1970s reduced high school dropout rates among blacks by between one and three percentage points (half of the total decline achieved during the decade), while having no effect on dropout rates among whites (Guryan 2001). Racial and ethnic segregation undermines the educational attainment, skills, and qualications of minorities in several ways, including School quality. The effects are most obvious and most severe- in distressed central city neighborhoods, where many low-income members of minority groups are concentrated.4 Many high-poverty neighborhoods are served by failing public schools with high dropout rates, low instructional quality, and poor test scores (O'Regan and Quigley 1996). Black and Hispanic children attending these schools are at a tremendous disadvantage even if they stay in school and work hard. In addition, other conditions typical of distressed central city neighbor hoods further undermine their chances of succeeding academically and attaining the skills necessary to compete effectively in today's labor markets. In particular, peer pressure plays a critical role in shaping the choices of young people. Adolescents spend roughly twice as much time with peers as they do with their parents or other adults (Connell and Halpern-Felsher 1997). Thus, young people can be profoundly influenced by their immediate peer groups (Bemdt 1996; Steinberg and Silverberg 1986), which are often composed primarily of neighbors and schoolmates. Youths' peer groups are certainly not determined solely by neighborhood, but neighborhood is likely to have a signicant effect on the choice of peer group. If many young people in a community are uninterested in School or are engaging in crime and other dangerous behaviors, teenagers will be more apt to see these activities as acceptable behavior. Indeed, Andemon (1994:94) reports that in some inner-city communities the \"toughening- up one experiences in prison can actually enhance one's reputation on the street.\" The literature on adolescent educational attainment provides general support for the notion that neighborhood distress plays an important role (Aaronson 1997; BrooksGunn et al. 1993; CaSe and Katz 1991; Clark 1992; Crane 1991; Datcher 1982; Dombusch et al. 1991; Duncan 1994; Duncan et al. 1997; Garner and Raudenbush 1991; Haveman and Wolfe 1994). Young people from high- poverty and distressed neighborhoods are less Successful in school than their counterparts from more afuent communities; they achieve lower grades, are more likely to drop out, and are less likely to go to college. Kids from poor neighborhoods are also less likely to get jobs during and immediately after high school. Finally, young people who live in high crime areas have been found to be more likely to commit crimes themselves (Case and Katz 1991). The effects of residential segregation on educational achievement are not limited to distressed central-city neighborhoods. Growing up in segregated suburbs can also undermine the potential of minority young people, though in more subtle ways. Minority neighborhoods generally have lower house values than comparable white neighborhoods and, consequently, a lower property tax base from which to fund public schools (Oliver and Shapiro 1997). In the inner suburbs of the Washington, DC metropolitan area, the average price for a single-family home in predominantly black Prince George's County was $195,400 in 2003 and in predominantly white Fairfax County was $365,900 (Turner et al. 2004). Public school performance in minority suburban commmin'es typically falls considerably short of the standard expected of schools in the white suburbs (Cashin 2004). In fact, a panel study of Texas public school students nds that the achievement of black students declines signicantly as the percentage of blacks in their schools rises. The negative effect of segregation appears to be the most severe for children in the upper half of the ability distribution, as students of lower ability are less likely to be affected (Hanushek et al. 2005). Another way in which Schools in minority neighborhoods may be disadvantaged stems from teachers' preferences to work close to where they grew up or in areas with similar characteristics to their hometowns. A recent study (Boyd etal.2005) nds that these preferences are widespread and argues that Schools in central cities may have access to pools of less qualied teachers than schools in more afuent and predominantly white communities. Finally, there is some evidence to suggest that community norms and expectations about attending college (or attending an elite college or university) are lower for young people growing up in predominantly minority com- munities (Cashin 2004), and the lower house values in these communities translate into lower levels of wealth accumulation, which may limit the ability of minority parents to nance an elite college education (Oliver and Shapiro 1997). Even for middle- and upper- income minority families who have eScaped the distress of inner-city neighborhoods, residential segregation may restrict young people's educational achievement, perpetuating their disadvantage in today's increasingly demanding labor markets. Information and References A second important set of factors that help determine labor market success involves information-not only a job seeker's access to information about possible job openings but also an employer's information about a candidate's likely performance. When job openings are advertised using only community newspapers, bulletin boards, or help-wanted signs, job seekers living in distant neighborhoods may never nd out about them (Holzer et al.1994). The spatial separation of employment centers (especially suburban job centers) from minority neighborhoods may prevent a minority job seeker from nding out about potential opportunities. In addition, however, many employers rely heavily on referrals from their current employees to ll open positions, in part because so many jobs (even low-skilled jobs) require cognitive and social skills. References provide some assurance to an employer that a candidate has the skills necessary to Succeed (Hoher 1996; HOIZer and Ihlanfeldt 1996}. Consequently, social networks may determine the openings a job seeker learns about and whether he or she receives credible references for these positions. Research on the role of networks in the job search process has documented a high and rising reliance on friends, relatives, and acquaintances. Moreover, informal job search methods tend to be effective and increase the likelihood of both nding and keeping a job. In particular, recommendations from in-house workers have been shown to be critical to nding jobs (Kasinitz and Rosenberg 1996; Sullivan 1989; Wial 1991; Ioannides and Loury 2004). The effectiveness of these informal job search methods is not equal for all racial and ethnic groups. In general, minority job seekers are less successful in using their networks of family and friends than whites (Ioannides and Loury 2004). Again, although residential segregation is not the only reason why minorities have less effective networks, it certainly is a factor, particularly for minorities living in high-poverty central city neighborhoods and also for those in the segregated suburbs. A substantial body of research indicates that residents of high-poverty central-city neighborhoods are often isolated from networks of people who can provide information about jobs or references to prospective employers (O'Regan 1993; Pastor and Adams, 1996). Not only are employment rates low in these segregated connnunities, but relatively few neighbors work in the expanding employment centers of the suburbs. Thus, neighbors cannot provide either information or referrals for the full range of job opportunities available in a metropolitan region. Neighbors certainly are not the only information source to which job Seekers can turn, but the available evidence suggests that poor people's social ties are more localized than those of middle-class people (Briggs 1997) and are at least partially dependent on networks within the neighborhood (Pastor 2001). Little research has focused on the issue of job networks for members of minorities living in segregated suburban areas. It seems likely that the problem is less severe than in central-city neighborhoods because a much higher share of residents work. For example, in 2000 the unemployment rate among black men was 7.6 percent in Prince George's County-almost half that of Washington, DC, at 15.7 percent. As was diScussed earlier, few blacks in the Washington, DC region work in the high-wage employment centers of suburban Fairfax County. Even a high-wage worker living in predominantly black Prince George's County is likely to have a network of neighbors, family, and friends who work mostly in Washington, DC, and offer little or no access to the expanding employment opportunities of Fairfax County. Indeed, empirical evidence conrms that, even after distance and transportation access have been controlled for, the ability of minorities to get jobs in an area depends signicantly on the ethnic composition of that area (Stoll et al 2000). Prejudice and Discrimination Although white prejudices about the intelligence, diligence, and reliability of blacks and Hispanics have been waning over the past several decades, negative stereotypes still persist (Pew Research Center 2003). There is strong evidence that prejudiced attitudes on the part of employers result indiscrimination against qualied minority job applicants (Turner et al.1991; Ihlanfeldt and Sjoquist 1998). Blacks are particularly unlikely to be hired for jobs that require higher cognitive skills, especially daily computer use, arithmetic, or customer interaction (Holzer 1996). Many analysts have Suggested that customer prejudice may also be a factor, since the racial composition of a rm's workforce has been found to be related both to the race of the manager and to the racial composition of the rm's customers (Ihlanfeldt and Young 1996). Residential segregation helps fuel the Persistence of racial and ethnic prejudice and discrimination in a number of ways. One of the most direct links involves the racial composition of a rm's customers. In communities that are racially and ethnically mixed, a rm's customer base is likely to be more diverse, eliminating one of the incentives for managers to diSCr'iminate against minority job applicants. Additionally, residents of minority neighborhoods may be disadvantaged by employer stereotypes about these communities. Recent research has shown that people's perception of disorder in a neighborhood is influenced by the neighborhood's racial composition as well as by objective indicators of disorder (Sampson and Raudenbush 2004). Thus, minority neighborhoods are likely to be stigmatized by outsiders who simply assume that residents are less likely to succeed in school, work hard, or obey the law. Residential segregation also may sustain minorities' misperceptions and fears about whites. Even in the segregated Suburbs, where employment, incomes, and education levels are high, young People have only limited experience with whites and may therefore avoid whites and predominantly white neighborhoods (Sjoquist 1996). Indeed, Braddock (1980) argues that patterns of social interactions at a young age may shape a child's patterns of behavior and interactions for the long run. In particular, he argues that minority students who attend racially segregated schools and who have not interacted with students of different races tend to overestimate the degree of hostility that they Will experience in interracial situations. As adults, these students tend to make choices that maintain their separation from whites when they become adults, which could limit their access to economic and social opportunities. Implications for Policy What can policy makers do to address the multiple ways in which the persistence of residential segregation contributes to employment inequality? The most obvious and direct response has been to reduce the information and transportation barriers created by physical distance through programs that help central-city residents nd jobs in suburban areas and provide assistance with the reverse commute.5 Unfortunately, the most recent effort to rigorously evaluate the effectiveness of programs that help inner-city workers nd and commute to suburban jobs found no signith benets with respect to either employment levels or wages among participants. Specically, the Bridges -toWork (BtW) demonstration was designed and launched in the mid- 1990s by Public / Private Ventures with support from the Department of Housing and Urban Development and a consortium of foundation funders to connect the presumed surplus of work- ready applicants in central-city neighborhoods to existing employment opportunities in the suburbs (Roder and Scrivner 2005). The BtW design included three program elements: (1) a suburban job placement mechanism, including the recruitment of suburban employers to hire central-city residents; (2} targeted commming services for inner-city residents; and (3) limited supportive services intended to directly address the challenges of the suburban commute. This third element, which attempted to help participants Sustain their connection to the suburban labor market by addressing problems created or exacerbated by the suburban commute, distinguished BtW from earlier reverse commute experiments.6 Four sites-Baltimore, Denver, Milwaukee, and St. Iouis-implemented the BtW intervention as controlled experiments, with random assignment of applicants to treatment and control groups. All four sites encountered serious problems with recruitment and retention of eligible participants. Although thousands of people inquired about the BtW program or applied to participate, only about 10 percent were actually enrolled. This drop-off resulted from program managers screening out people who did not meet standards for work readiness and from applicants withdrawing from consideration once they understood what the program offered. Across the four sites, the BtW interventions produced no statistically signicant effects on employment or income overall. Participant retention proved to be a major problem; two-thirds of participants were placed in a BtW job, but most used the program services for only a short time. Focus groups and interviews with program staff suggest that the low retention was due to participants, personal struggles, dissatisfaction with certain aspects of their suburban jobs, and the long commute. Moreover, the economy was booming during the second half of the 1990s, and jobs were opening up throughout the metropolitan areas, making the BtW program less attractive or essential for the targeted work-ready population. The disappointing ndings from the BtW demonstration suggest that trying to solve the spatial mismatch problem through transportation strategies alone may be ineffective; other employment barriers, many of which are caused or exacerbated by residential segregation, must also be addressed. In other words, transportation strategies may play a role, but they are unlikely to be effective unless they are linked to help with training and job retention strategies. Specically, helping central-city residents nd and keep suburban jobs may require substantial investments in job readiness training (to compensate for the poor performance of local schools and the absence of role models in high- poverty neighborhoods), ongoing mentoring (to help minority workers overcome their inexperience and uncertainties about interacting with whites), and more intensive outreach (to inform central-city residents about the availability of job opportunities in unfamiliar suburban locations). Even so, in some metropolitan areas the distance between minority neighborhoods and Suburban employment locations may simply make it unfeasible to design reverse commuting strategies that are both effective and affordable (Turner and Rawlings 2005). This leads to the consideration of strategies for tackling residential Segregation more directly and expanding opportunities for minorities to live closer to suburban employment centers. Other chapters in this volume diScuss fair housing and inclusionary zoning strategies for opening up suburban jurisdictions to minorities. The evidence and analysis presented in this chapter suggest that these strategies should focus on suburban areas where job opportunities are clustered. In recent decades, black and Hispanic households have begun to gain access to suburban residential communities, but, in many cases, the suburban jurisdictions where they live are distant from the areas of greatest job growth and opportunity. Fair housing and inclusionary zoning initiatives need to be more targeted to jurisdictions like Fairfax County in the Washington, DC region, where jobs are plentiful and schools are high-performing. In addition to strategies aimed at opening suburban housing markets to greater diversity, housing policies can provide direct assistance to members of minorities who want to move to more opportunity-rich neighborhoods. Evidence from two demonstration initiatives suggest that assisted housing mobility strategies can improve employment outcomes for lowincome minority-group members. Long-term research on black families that moved from segregated neighborhoods in Chicago to white or racially mixed suburban neighborhoods as part of the Gautreaux demonstration nds signicant increases in employment and reductions in welfare recipiency (Rosenbaum and Deluca 2000). More recent evidence from the Moving to Opportunity (MTO) demonstration has not detected statistically signicant employment or earnings effects for participants who received housing vouchers to relocate from high-poverty public and assisted housing developments to low-poverty neighborhoods throughout their metro areas (Orr et al. 2004). Analysis of data for individual MTO sites, however, does reveal signith inuence on employment and earnings in New York City and Los Angeles; multivariate analysis of differences among MTO movers suggests that those who relocated to stable, predominantly white suburban neighbor- hoods did experience substantial earnings gains (Cove 2006). Future policy conversations should look for opportunities to influence employment locations as well as residential locations. Incentives to encourage employers to locate in more central locations (to which residents from a variety of neighborhoods can reasonably commute) near transit hubs (to provide easier access for workers who rely on public transportation), and in racially mixed or minority jurisdictions, could help reduce imbalances between where minorities live and where job opportunities are greatest. These types of strategies are often promoted by smart growth advocates because of their potential to counteract urban sprawl, and, if intelligently implemented, they may also help counteract the employment effects of residential segregation. Moreover, state and regional economic development strategies should focus on attracting more employment opportunities to areas where minorities predominate. For example, in the Washington, DC metropolitan area, the region's Transportation Planning Board is exploring landuse scenarios in which more economic growth occurs in Prince George's County and other eastern suburbs, where blacks are concentrated (Turner et al. 2004). Finally, although most efforts to address the employment barriers created and sustained by residential segregation have focused on low-wage workers and distressed central-city neighborhoods, the barriers facing middle- and upper-income minorities living in segregated suburbs also, warrant attention. Possible strategies include information referrals to help minority professionals learn about and apply for jobs in predominantly white suburban employment centers; in-person or Web-based networks to expand minority job seekers' circle of family, neighbors, and friends; mentoring programs to help minority workers joining a predominantly white workforce for the first time; and carpooling or ride-sharing programs for intersuburban commuters. Residential segregation creates more daunting barriers to employment for low-skilled workers than for more highly educated and qualified workers. Even for skilled professionals, patterns of residential segregation have the potential to limit access to areas of employment opportunity and growth in many metropolitan areas. NOTES 1. The assignment of names was based on empirical analysis of data from birth certificates of all babies born in Massachusetts over a five-year period. Examples of the names used are Emily Walsh and Lakisha Washington or Brendan Balcer and Jamal Jones. 2. This analysis uses data from the 2000 census Transportation Planning Package on the place of residence and place of employment of all workers where a worker is defined as an individual who was employed for at least one week during 1999. The analysis was conducted by the Urban Institute, with support from the Fannie Mae Foundation, for inclusion in this book. It is intended to explore differential patterns of residential and workplace location as a starting point for further analysis. Future studies might involve multivariate analysis, controlling for gender and other worker attributes, as well as industry and other workplace attributes. 3 . The residential patterns of workers reported here correspond very closely to patterns for total population and households. See Massey and Denton (1993) to understand how residential segregation led to the concentration of minority poverty. 5. To date, the policy discussion has focused on the mismatch between minority neighborhoods in the central-city and suburban job centers, not the mismatch discussed earlier between minority neighborhoods in the suburbs and suburban employment centers. 6. Among the services included in the programs' original plans, were a guaranteed ride home in an emergency, payment of day-care costs to cover the extra time required to commute to the suburbs, and staff assistance dealing with diversity issues in the workplace. References Aaronson, Daniel. 1997."Sibling Estimates of Neighborhood Effects." In Jeanne Brooks- Gunn, Greg J. Duncan,and J. Lawrence Aber (eds.) Neighborhood Poverty II: Policy Implications in Studying Neighborhoods. New York: Russell Sage Foundation Press. Anderson, Elijah. 1994. "The Code of the Streets." Atlantic Monthly, May, pp.81-94. Berndt, Thomas J. 1996. "Transitions in Friendship and Friends' Influence." In Julia Graber, Jeanne Brooks-Gunn, and Anne C. Petersen (eds.) Transitions through Adolescence: Interpersonal Domains and Contexts. Mahwah, NJ: Erlbaum. Bertrand, Marianne and Sendhil Mullainathan. 2003. "Are Emily and Brendan More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination." NBER Working Paper No.9873. Cambridge, MA:National Bureau of Economic Research. Boyd, Donald, Hamilton Lankford, Susanna Loeb, and James Wyckoff. 2005. "The Draw of Home: How Teachers' Preferences for Proximity Disadvantage Urban Schools." Journal of Policy Analysis and Management 24(1): 113-132.Annex 5.A Percentage of Workers by Wage Level, Race, and Residence Location in the Washington, DC Metropolitan Area, 2000 All Workers All Races White Black Asian Hispanic Other Total Region 100.0 100.C 100.0 100.0 00.0 District of Columbia 6.7 20.9 4.9 10.8 10.3 Inner Core 8.0 4.1 7.5 13.3 9,8 Arlington County, VA 4.5 5.0 1.6 5.2 8.8 5.1 Alexandria city, VA 3.0 3.1 2.5 2.3 4.5 4.7 InnerSuburbs 48.8 61.1 77.9 61.4 58.1 Montgomery County, MD 17.8 18.2 11.4 30.2 22.9 19.1 Prince George's County, MD 15.6 6.7 42.4 9.3 11.8 14.9 Fairfax County, VA 20.6 23.1 7.2 37.3 25.7 23.5 Fairfax city, VA 0.5 0.5 0.1 0.8 0.7 0.4 Falls Church city, VA 0.2 0.3 0.0 0.3 0.2 0.2 Outer Suburbs 26.0 11.2 8.8 12.6 17.1 Calvert County, MD 1.5 2.1 0.8 0.2 0.2 0.9 Charles County, MD 2.4 2.8 2.5 0.5 0.6 2.5 Frederick County, MD 4.0 6.0 0.9 0.9 1.1 2.0 Loudoun County, VA 3.6 4.9 1.0 2.8 2.4 2.8 Prince William County, VA 5.9 6.6 4.6 3.3 6.0 6.4 Stafford County, VA 1.9 2.6 0.9 0.5 0.7 1.7 Manassas city, VA 0.7 0.8 0.4 0.4 1.1 0.6 Manassas Park city, VA 0.2 0.3 0.1 0.1 0.3 0.3 Far Suburbs 10.5 2.7 0.9 2.0 4.7 Clarke County, VA 0.3 0.4 0.1 0.0 0.0 0.1 Culpeper County, VA 0.6 0.8 0.4 0.1 0.3 0.4 Fauquier County, VA 1.1 1.6 0.4 0.2 0.3 0.6 King George County, VA 0.3 0.4 0.2 0.0 0.1 0.2 Spotsylvania County, VA 1.8 2.4 0.9 0.3 0.6 1.6 Warren, VA 0.6 0.9 0.1 0.0 0.1 0.2 Fredericksburg city, VA 0.4 0.5 0.3 0.0 0.2 0.4 Berkeley County, WV 1.4 2.2 0.2 0.1 0.2 0.7 Jefferson County, WV 0.8 1.2 0.2 0.1 0.2 0.5 Annex 5.A (cont.) Percentage of Workers by Wage Level, Race, and Residence Location in the Washington, DC Metropolitan Area, 2000 Low-Wage Workers All Races White Black Asian Hispanic Other Total Region 100.0 100.0 100.0 100.0 100.0 District of Columbia 6.2 25.5 6.2 12.4 11.4 Inner Core 5.5 5.2 7.7 14.0 10.5 Arlington County, VA 4.2 3.5 2.2 5.5 9.4 5.3 Alexandria city, VA 2.7 2.0 3.0 2.3 4.6 5.2 InnerSuburbs 43.7 54.2 75.3 61.0 55.7 Montgomery County, MD 16.7 16.0 11.7 27.1 22.1 17.8 Prince George's County, MD 15.7 8.1 35.8 11.7 12.4 13.9 Fairfax County, VA 18.3 18.9 6.5 35.3 25.5 23.3 Fairfax city, VA 0.5 0.5 0.1 1.0 0.8 0.4 Falls Church city, VA 0.2 0.2 0.0 0.2 0.2 0.2 Outer Suburbs 28.7 11.0 9.4 10.6 16.4 Calvert County, MD 1.5 2.3 1.1 0.2 0.2 0.8 Charles County, MD 2.4 3.2 2.4 0.8 0.4 2.3 Frederick County, MD 4.4 7.4 1.1 1.2 0.7 1.8 Loudoun County, VA 3.0 4.3 0.9 2.0 2.1 2.2Prince William County, VA 5.9 7.2 4.2 4.1 5.1 6.4 Stafford County, VA 2.1 3.2 0.9 0.7 0.6 1.8 Manassas city, VA 0.8 0.9 0.4 0.4 1.2 0.7 Manassas Park city, VA 0.2 0.3 0.1 0.1 0.3 0.4 Far Suburbs 15.9 4.1 1.3 2.1 6.0 Clarke County, VA 0.3 0.5 0.1 0.0 0.0 0.1 Culpeper County, VA 0.9 1.3 0.7 0.2 0.4 0.6 Fauquier County, VA 1.2 2.0 0.6 0.2 0.2 0.7 King George County, VA 0.4 0.6 0.4 0.0 0.1 0.2 Spotsylvania County, VA 2.1 3.3 1.1 0.4 0.5 2.1 Warren, VA 0.8 1.5 0.2 0.1 0.1 0.2 Fredericksburg city, VA 0.7 0.9 0.6 0,1 0.3 0.5 Berkeley County, WV 2.0 3.7 0.3 0.1 0.2 0.9 Jefferson County, WV 1.2 2.1 0.3 0.2 0.2 0.8 Annex 5.A (cont.) Percentage of Workers by Wage Level, Race, and Residence Location in the Washington, DC Metropolitan Area, 2000 High-Wage Workers All Races White Black Asian Hispanic Other Total Region 100.0 100.0 100.0 100.C 00.0 District of Columbia 8.9 16.2 5.1 12.2 11.6 Inner Core 10.0 3.4 5.4 11.4 7.9 Arlington County, VA 5.7 6.2 1.6 4.4 6.9 5.4 Alexandria city, VA 3.5 3.8 1.9 1.0 4.5 2.5 InnerSuburbs 60.2 69.0 32.5 63.1 64.9 Montgomery County, MD 23.7 23.7 16.5 34.7 26.8 21.6 Prince George's County, MD 7.2 3.6 40. 4.8 5.5 8.7 Fairfax County, VA 30.7 32.0 12.3 42.1 29.9 33.7 Fairfax city, VA 0.4 0.5 0.1 0.6 0.7 0.5 Falls Church city, VA 0.4 0.4 0.0 0.3 0.2 0.4 Outer Suburb 17.1 10.3 6.2 11.9 13.1 Calvert County, MD 0.9 1.0 0.4 0.6 0.4 0.2 Charles County, MD 1.3 1.3 1.8 0.4 0.9 1.2 Frederick County, MD 2.6 3.0 0.6 0.4 1.9 2.3 Loudoun County, VA 4.9 5.5 1.9 3.1 2.9 3.7 Prince William County, VA 4.1 4.3 4.5 1.5 4.1 4.2 Stafford County, VA 1.3 1.4 0.7 0.1 0.8 1.1 Manassas city, VA 0.4 0.5 0.3 0.1 0.7 0.3 Manassas Park city, VA 0.1 0.1 0.1 0.0 0.1 0.2 Far Suburbs 3.8 1.1 0.8 1.4 2.5 Clarke County, VA 0.1 0.1 0.0 0.0 0.0 0.0 Culpeper County, VA 0.3 0.3 0.0 0.0 0.0 0.0 Fauquier County, VA 1.0 1.1 0.3 0.2 0.5 0.6 King George County, VA 0.2 0.2 0.0 0.0 0.0 0.1 Spotsylvania County, VA 0.8 0.9 0.5 0.0 0.4 0.8 Warren, VA 0.2 0.2 0.0 0.1 0.0 0.0 Fredericksburg city, VA 0.2 0.2 0.0 0.0 0.2 0.3 Berkeley County, WV 0.3 0.4 0.0 0.3 0.2 0.3 Jefferson County, WV 0.3 0.4 0.1 0.1 0.1 0.5Annex 5.B Percentage of Workers by Wage Level, Race, and Workplace