Editorial Board:
Editor:
George H. Conklin,
 North Carolina
 Central University

Board:
Bob Davis,
 North Carolina
 Agricultural and
 Technical State
 University

Richard Dixon,
 UNC-Wilmington

Ken Land,
 Duke University

Miles Simpson,
 North Carolina
 Central University

Ron Wimberley,
 N.C. State University

Robert Wortham,
 North Carolina
 Central University




Editorial Assistants

Rob Tolliver,
 North Carolina
 Central University

Shannon O'Connor,
 North Carolina
 Central University

® 
Volume 4, Number 2
Fall 2006

Suburban Sprawl, Racial Segregation, and Spatial Mismatch in Metropolitan America  *

by

Charles Jaret
Georgia State University

Robert M. Adelman
University at Buffalo, SUNY

and

Lesley Williams Reid
Georgia State University

Introduction

   Although urban historians and sociologists date the development of early suburbs outside U.S. cities to the late 1800s and early 1900s, only a tiny and relatively privileged portion of the population lived in them (Chudacoff & Smith 2005; Douglas 1925; Fishman 1987).  Massive growth in the suburban population, due to migration and natural increase, took place in the post-WW II era and continued in subsequent decades so that today a majority of the U.S. population is classified as suburban.  Sociologists have created a large literature analyzing the causes, consequences, and implications of suburbanization, and studies of suburbia were especially frequent in the 1950s and 1960s (Berger 1960; Dobriner 1963; Gans 1967; Whyte 1956, 1958). 

    Research interest in the suburbs rose again in the last 15 years, especially with concern about the phenomenon known as "suburban sprawl" (Bruegmann 2005; Squires 2002; Wiewel & Persky 2002).  Sociologists and other researchers have tried to define and measure this form of suburban expansion; they have also sought to uncover its causes and identify its consequences (Bullard, Johnson & Torres 2000; Burchell 1998; Cox & Utt 2004; Downs 1999; Foster-Bey 2002).  Among the negative consequences that suburban sprawl is alleged to produce are inner-city decline and racial segregation (Dreier, Mollenkopf & Swanstrom 2001); income inequality (Yang & Jargowsky 2006); traffic congestion, over-reliance on automobiles, and environmental pollution (Duany, Plater-Zyberk & Speck 2000; Sheehan 2002); obesity and other health problems (Frumkin, Frank & Jackson 2004; Sturm & Cohen 2004); and wasteful expenditures on new infrastructure (Burchell 1998).

   Our goal in this article is to investigate the proposition that extensive suburban growth and/or sprawl is linked to increased residential segregation between blacks and whites, along with the related claim that such suburbanization contributes to more severe job-residence spatial mismatches for blacks.  We examine these issues with a large sample of U.S. metropolitan areas, by using new indexes of suburban sprawl, a recently created measure of blacks' job-residence spatial mismatch (Stoll 2005), and a well established measure of residential segregation (the index of dissimilarity).   To our knowledge, this is the first time these new measures have been used in a comparative analysis to examine the relationship between suburban growth, racial segregation, and spatial mismatch.

Conceptualizing and Measuring Sprawl

   The negative connotation of the term "suburban sprawl" is apparent in most discussions of it and in many definitions (e.g., sprawl is "random unplanned growth characterized by inadequate accessibility to essential land uses such as housing, jobs, and public services like schools, hospitals, and mass transit" [Bullard 2000:1]).  Yet it is a useful term because (unlike more neutral terms such as urban deconcentration or counter-urbanization) it implies differences in existing suburban land use and living patterns.  In other words, "sprawl" is not necessarily the same thing as suburbanization in general (i.e., it is more than just relatively low density living beyond the political boundaries of a city).  As Downs (1997:382) notes, sprawl is "a particular form of suburbanization with several characteristics that differentiate it from other conceivable forms of suburbanization" (Downs 1997: 382).  For example, close-in suburbs on the east side of Cleveland, Ohio (e.g., University Heights, Cleveland Heights, Shaker Heights) do not represent sprawl today whereas the suburban areas on the eastern edge of Cuyahoga County do.

   Although no consensus on a definition of suburban sprawl exists, experts agree on many of its key components.  It refers to spread out, low density development beyond the edge of a city's service and employment centers, where people depend almost exclusively on automobiles because they live far from where they work, shop, go to school, worship, or do leisure activities (Macionis & Parrillo 2001:106).  These and other elements appear in Squires' (2002:2) widely cited definition of sprawl: "a pattern of urban and metropolitan growth that reflects low-density, automobile-dependent, exclusionary new development on the fringe of settled areas often surrounding a deteriorating city."  Besides low-density and automobile dependency, Downs (1997:382-83) stresses additional features of sprawl to distinguish it from other forms of suburbanization: (a)  no limits placed on outward suburban expansion; (b) legal control over land use, property taxes, and local services is divided among many small entities or jurisdictions, with no single central agency responsible for the planning of or control over land use, transportation, and other services or for allocation of fiscal resources for the region as a whole; (c) much noncontiguous "leapfrog" development; (d) relatively fragmented land ownership;  (e) different types of land use are spatially separated or zoned into distinct areas; and (f) extensive strip commercial development along larger suburban roads.

   Galster et al. (2001) provides an important advance by conceptualizing and measuring suburban sprawl as a multi-dimensional phenomenon.  Their work, plus revision and extension in Cutsinger et al. (2005), distinguishes sprawling areas as places that are relatively low on such dimensions as density/continuity, housing and job proximity, nuclearity, and concentration.  Moreover, they developed quantitative measures that indicate how the largest metropolitan areas in the U.S. compare on each dimension of sprawl (based mainly on 1990 data). 

   In a similar vein, but independently and with more recent data for more metropolitan areas, Ewing et al. (2002) developed a multi-dimensional conceptualization and set of indexes to measure the degree of sprawl in U.S. metropolitan areas.  For Ewing et al. (2002:3) the "common denominator of sprawl"  is land use and development patterns that provide people with difficult accessibility.  They attribute accessibility problems to four traits of sprawl: low density development, segregated land uses, a weak downtown or lack of significant central activity sites, and poorly inter-connected street systems.  For all four of these dimensions (residential density; mix of homes, jobs, and services; centeredness; street network quality) Ewing et al. (2003) provide sprawl index scores for over 80 metropolitan areas in the United States.

   As a result of these advances and others (Fulton et al. 2001; Lopez & Hynes 2003) in the way suburban sprawl is conceptualized and measured, researchers are in a better position to see how different metropolitan areas compare in terms of the extent of sprawl and to investigate the consequences of sprawl.  In the past, researchers had to rely on cruder measures (e.g., those based only on residential density or measures based on the percent of the metropolitan area living in the "urban fringe") that reflect suburbanization in general but are not very sensitive to the more refined aspects of sprawl.  Because it is of both practical and theoretical interest, we examine the empirical relationship between suburban sprawl and residential segregation and spatial mismatch using several different measures of sprawl.

Suburbanization, Sprawl, Segregation, and Spatial Mismatch

   Many urbanists characterize both suburbanization in general and sprawl in particular as processes through which white middle- and upper income households have dispersed ever farther from central cities, while the outward movement of black households has been much more limited, due to policies and practices that are racially biased against blacks and are discriminatory against the less affluent (e.g., zoning ordinances, home sales practices, bank lending policies).  Practices that intentionally excluded blacks from obtaining homes in suburbs as different as Grosse Pointe, Michigan (Baltzell 1964:126), and the Levittowns of New York and Pennsylvania (Baxandall & Ewen 2000; Bressler 1960), along with hundreds of suburbs elsewhere (Jackson 1985; Wiese 2005) have been well documented.  Rusk (1999), evaluating urban and suburban trends from the 1950s through the early 1990s, contends that suburbanization of the middle class, promoted by public policies and private investment decisions, increased the number of impoverished black neighborhoods in cities, thereby contributing to higher levels of black-white residential segregation.  Earlier, Jackson (1976:90) argued that suburbanization produced a racially and economically polarized metropolis and criticized the way it "reduced the degree of concern and responsibility which suburbanites feel for the plight of core areas."

   Research also shows that sprawl brings relocation of jobs to distant suburbs and extensive job creation in those suburbs, but little new job creation in the cities (Dreier, Mollenkopf & Swanstrom 2001; Smith 2003; Wilson 1996).  This implies that as sprawl increases and black-white residential segregation remains high, blacks have greater difficulty finding jobs than whites and/or longer commutes between home and work (Dawkins, Shen & Sanchez 2005; Howell-Moroney 2005; Ihlanfeldt & Sjoquist 2000).  Downs (1997) agrees, arguing that in sprawling metropolitan areas employment sites relocate very far from inner-city black areas, so inner-city residents do not hear about job openings on the suburban periphery and would not be able to get to them efficiently even if they did know of them.  Stoll's (2005) report of a strong positive correlation between job sprawl and black job-residence mismatch in major U.S. metropolitan areas is consistent with this argument.  Raphael (1998) indicates that blacks' and whites' differences in access to areas of high job growth are large and may account for up to half the racial difference in youths' unemployment rates. 

   On the other hand, it is historically inaccurate to depict residential segregation as a "suburban" phenomenon since the roots of segregation lay in the industrial city.  Both Park's (1925:40) and Wirth's (1938:155) classic essays describe the city with a "social mosaic" metaphor alluding to the racially segregated neighborhoods of American industrial cities.  Burgess (1925) explicitly emphasized the segregated city, showing the rings of the inner city comprised of distinct racial-ethnic colonies, and in another article on residential segregation Burgess (1928) provided a map showing different streets in the city of Chicago along which "radial expansion" of each racial and immigrant group was taking place.

   Especially for blacks racial segregation in housing was a most severe and bitter aspect of life in the industrial city (Spear 1967; Hirsch 1983).  It was in cities, not suburbs, that whites pioneered mechanisms such as racial zoning ordinances, racially restrictive covenants, urban renewal, and block busting, along with raw violence, as methods of restricting blacks from residing in "white" areas. Of course, suburbs later made extensive use of these (and created other) devices and tactics in attempting to prevent blacks from entering.

   Moreover, data on black-white segregation indicate that many cities reached their maximum level of residential segregation in 1950 or 1960, well before the hey-day of 1960s "white flight" to the suburbs (Massey & Denton 1993:46; Sorenson, Taeuber & Hollingsworth 1975), though some sources indicate that in metropolitan areas with the largest black populations residential segregation peaked in 1970 (Massey 2001:396), which still puts the highest point of black-white residential segregation well before our current era of extensive suburban sprawl.  It is also true that black-white residential segregation (as measured by the index of dissimilarity) in most U.S. metropolitan areas is lower in the suburbs than the in the city.  Adelman (2004:50-51) shows this for middle class blacks and whites, and for the general population segregation data from the Mumford Center show this in most metropolitan areas.  For example, the 2000 black-white dissimilarity index for the suburbs is lower than that of the central city in Chicago (73.4 vs 82.5), Atlanta (61.8 vs 81.6), Boston (45.3 vs 66.4), and Washington, DC (57.8 vs 79.4) (1).   Also, longitudinal data indicate that, except for the largest metropolitan areas (most of which are among the least sprawling metro areas), black-white levels of segregation have made modest to substantial declines over the past 20 years – the time period that the country shifted to majority suburban and has sprawled outward the most.  These facts and trends do not appear to be consistent with the idea that suburbanization is exacerbating residential segregation.  In fact, they seem to suggest that suburbanization since 1970 has been accompanied by a diminishing of black-white residential segregation.

   Regarding the job-residence spatial mismatch that blacks face in U.S. metropolitan areas, clearly suburbanization has exacerbated the problem.  As suburbanization has proceeded, many businesses that once employed large numbers of urban blacks have closed in the cities.  Some have relocated in the suburbs, and similar new jobs in the suburbs have been created, but in most metropolitan areas blacks have not been able to move to suburban areas that provide easy access to those jobs outside the cities (Kasarda 1989, 1995; Wilson 1996).  What is not self evident, however, is whether suburban sprawl (as distinguished from suburbanization in general) is an additionally exacerbating factor on blacks' job-residence spatial mismatch.  Suppose suburbanization in a metropolitan area has transpired in a way that has left the vast majority of black households residing in the central city (e.g., in Akron, Buffalo, Chattanooga, Des Moines, Kansas City, Memphis, Milwaukee, and several other metro areas over 80% of the total black population lives in the central city).  If that is the case, then for most blacks it really does not matter much (in terms of how easy or difficult it is to get from their homes to their jobs) whether the suburbs of that metro area are characterized by sprawl (e.g., one house per acre, dependency on automobiles, long streets and many cul-de-sacs, leapfrog development) or by "new urbanism" or "smart growth" (e.g., ten houses per acre, work sites and shopping within walking distance; short intersecting streets, clustered development).

  We have posed some conflicting ideas about a connection between suburbanization and/or sprawl and two other elements of metropolitan social structure: black-white residential segregation and blacks' spatial mismatch.  Our empirical analysis and discussion should illuminate this matter and improve our understanding of suburbanization and racial dynamics.

   U.S. metropolitan areas vary greatly in their degree of black-white residential segregation and in the degree to which blacks experience a spatial dislocation (mismatch) between areas of residence and employment.  We attempt to learn whether this inter-metropolitan variation in segregation and spatial mismatch is attributable to suburban sprawl.  Our methodological strategy, therefore, is to use metropolitan areas (MAs) as the units of analysis.  The first analytical step examines simple correlations and difference of means tests to discover the pattern of association between MAs' levels of black-white segregation (and black spatial mismatch) and several measures of suburbanization and sprawl.  Subsequent analytical steps use multiple regression analysis to assess if these indicators of suburban growth have an effect on MAs' level of segregation and degree of black spatial mismatch after controlling for other influences on those two dependent variables.

Sample of Metropolitan Areas

   Our analysis is based on a random sample of 150 Metropolitan Statistical Areas (MSAs) and Primary Metropolitan Statistical Areas (PMSAs), as defined by in the 2000 Census, stratified by region and population size.  In this sample, all MAs with a population of one million or more are included, and we chose smaller ones with an equal probability of selection.  The number of MAs in four population size categories is: 80,000 to 500,000 (n=52); 500,001 to one million (n=38); one million and one to two million (n=34); and over two million (n=26).  The sample's regional distribution closely represents that of the actual distribution of all metropolitan areas.

Dependent Variables

   As a measure of a metropolitan area's level of residential segregation, we use the index of dissimilarity (D) in 2000 for blacks and non-Hispanic whites (obtained from the website of the Lewis Mumford Center for Urban and Comparative Regional Research, at SUNY-Albany).  This index ranges from 0 to 100, with high scores indicating more extreme levels of residential segregation.  This index is sensitive to the evenness or similarity in the way the black and white populations are distributed in a metropolitan area's census tracts – the more that tracts' percentages black and white depart from the total metropolitan area's percentage black and white the higher or more segregated the metropolitan area.

   The measure of blacks' job-residence spatial mismatch used here is an index developed by Michael Stoll (2005).  It does not gauge spatial mismatch in terms of physical distance (e.g., average miles between home and workplace) or time (e.g., average minutes taken to go from home to work), but rather is an index of dissimilarity between blacks’ residential distribution and the spatial distribution of employment sites (using the metropolitan areas’ ZIP- codes as the spatial units).  In other words, it measures the degree to which blacks and jobs are concentrated in different ZIP-codes in each metropolitan area; the larger this disparity, the higher the black job-residence mismatch index.  In metropolitan areas with high index scores the job-residence mismatch faced by blacks is worse than in metro areas with low scores, in that the former have a more severe discrepancy between the ZIP-codes in which blacks live and the ZIP-codes in which jobs are located.

Independent Variables

The explanatory variables of interest in this analysis are measures of suburbanization and suburban sprawl.  To conserve space we do not describe each measure of suburban sprawl in great detail.  However, some technical information is needed to understand them and interpret our results (for more detail, see the cited articles).

   The primary measure of suburban sprawl used here is an index developed by Lopez and Hynes (2003) for all metropolitan areas in the United States using 2000 Census data on residential distribution and population density.  The Lopez-Hynes sprawl index can range from 0 to 100 (a high score indicates a metro area with a great deal of sprawl, a low score indicates a metro area with very little sprawl).  For each metropolitan area the Lopez-Hynes sprawl index tells whether more of the population lives in low-density than high density census tracts (2).   Index scores above 50 indicate most people live in low-density tracts, scores below 50 mean that most people live in high density tracts (e.g., 100 means everyone lives in low-density tracts; 0 means everyone lives in high density tracts; and 50 means that equal percentages live in low-density and high density tracts).  Although the Lopez-Hynes sprawl index is based on population density (i.e., the more a metro area's population is spread out in low density census tracts, the more the area sprawls and the higher its sprawl index is) it does not only reflect residential density.  The Lopez-Hynes sprawl index has correlations of -.498 and -.687 with Ewing et al.'s "mixed land use" and "street pattern" indexes, respectively (discussed in next paragraph), which indicates it is also sensitive to those aspects of sprawl (i.e., extent to which commercial areas are far from residential areas and the areas’ street pattern) and therefore is a very useful index.

   We also take two measures of suburban sprawl from Ewing et al. (2002).  One is their index of mixed land use, which obtains high values in metropolitan areas where many residents live in geographic areas (e.g., blocks, census tracts, or traffic analysis zones) that also contain large numbers of nearby businesses providing retail, entertainment, health, education, professional, and personal services.  The second is Ewing et al.'s street size index, which obtains high values in metropolitan areas that have high percentages of short streets and small blocks, which make for many intersections and good accessibility (low scores indicate sprawling metro areas and occur when there are long parallel streets ending in cul-de-sacs).  Due to data limitations, Ewing et al. were unable to compute sprawl indexes for as many metropolitan areas as were Lopez and Hynes.  As a result, in analyses based on Ewing et al.'s indexes, our sample is comprised of 82 metropolitan areas.  Also note that the Ewing et al. sprawl indexes are coded so that a high score represents a metropolitan area that is low on sprawl (i.e., has a high mixed land use or many short streets), which is the opposite direction as the Lopez-Hynes sprawl index.

   In addition to these measures of sprawl we also include in the analysis two general measures of suburbanization.  The first is simply the percentage of the metropolitan population living outside of the MA's central city(ies) in 2000 (U.S. Bureau of the Census 2003).  This variable indicates roughly how much of each metro area's population is suburban, but does not indicate characteristics relevant to sprawl such as density or distance from the city.  The second is Michael Stoll's (2005) job sprawl index.  This measure focuses on the spatial spread of jobs across a metro area and is a measure of employment decentralization: Stoll's index is simply the percentage of a metro area's jobs that are located beyond a 5 mile radius of the CBD.  Stoll (2005) provides job sprawl indexes for all metro areas with a 2000 population of 500,000 or more (n = 94) and via private communication he provided us with indexes of smaller metro areas, so our analyses that use his index are based on 135 metropolitan areas.  Historically, employment shifted to suburbs after residents moved there so Stoll's job decentralization index reflects a later stage of suburbanization than does the percentage of metro residents living in suburban areas.

   The second step in this analysis uses multiple regression analysis to examine the influence of our sprawl and suburbanization variables controlling for other variables known or expected to influence black-white residential segregation and/or black spatial mismatch (3).   These variables represent the MA's demographic (total population size; percentage of population that is black) and economic structure (percentage of workforce employed in manufacturing industries; percentage of workforce employed in business services).  We also include a variable measuring black-white socioeconomic inequality (difference between the percentages of blacks and non-Hispanic whites who have a bachelor's degree or higher).  In addition, we control for region of the country the MA is located in (Northeast is the reference category).  All of these variables are derived from the 2000 U.S. Census (to conserve space we omit further details on these variables, but they are available on request from the first author). 

This section uses the measures described above to investigate two alleged consequences of suburban sprawl.  The hypothesis under scrutiny is that suburban sprawl promotes higher levels of residential segregation between blacks and whites and that it also exacerbates the spatial mismatch that blacks face between residence and jobs.

Correlations of Sprawl, Suburbanization, Segregation, and Mismatch

   Table 1 shows zero-order correlations between our dependent variables (metropolitan areas' black-white index of dissimilarity and their index of black job-residence spatial mismatch) and the measures of suburbanization and sprawl (metropolitan areas' percentage suburban, Stoll's job sprawl index, Lopez-Hynes' sprawl index, and Ewing et al.'s mixed use and street size indexes). 

   Table 1 shows weak but significant positive correlations between percentage of a metro area's population that is suburban and both the dissimilarity index (r = .300) and the black job-residence spatial mismatch index (r = .175).  Although the strength of this relationship is not very impressive, these data are consistent with the contention of many works cited above that implicate suburbanization in negative outcomes for blacks.  Specifically, in highly suburbanized metro areas blacks are more residentially segregated from whites than in less suburbanized metro areas, and in more suburbanized metropolitan areas blacks live at greater distance from employment sites than they do in less suburbanized metropolitan areas.  Stronger support for these relationships is evident in the correlations between Stoll's job sprawl index and both dependent variables (r = .420 with black-white residential segregation and r = .437 with blacks' spatial mismatch).  In metropolitan areas where more employment is located far from the downtown area, blacks and whites are more highly segregated and blacks’ face farther commutes between their homes and workplaces.  Thus the two measures that best reflect suburbanization in general (i.e., the outward and decentralizing movement of people and jobs) do support the idea that suburban growth has had a negative impact on blacks.

   However, the picture changes when we move from indicators of general suburbanization to indicators that specifically and more precisely measure suburban sprawl.  In Table 1, Lopez-Hynes' sprawl index is negatively related to the dissimilarity index (r = -.116) and the black spatial mismatch index (r = -.361).  In contrast to the findings given in the previous paragraph, this implies that more sprawling metro areas have less segregation and spatial mismatch.  Ewing et al.'s mixed land use index is positively related to the two dependent variables (r = .188 and .369, respectively), but (given the direction in which this index is coded) this means on this dimension of sprawl the less sprawling metropolitan areas (i.e., those with more mixed land use) have more black-white residential segregation and more black spatial mismatch than do the more sprawling metropolitan areas (i.e., places with less mixed land use).  Finally, the correlations between Ewing et al.'s street size index and the two dependent variables are quite weak.  Therefore, the contention that high levels of suburban sprawl are associated with elevated black-white residential segregation and more severe black spatial mismatch receives no support in the correlations based on the more refined sprawl indexes; if anything, the data suggest the opposite (i.e., less segregation and spatial mismatch in places characterized by some traits of sprawl).

   To illuminate this rather unanticipated result – more sprawling metropolitan areas seem to have less segregation and spatial mismatch – we contrast, in Tables 2 A and B, the segregation and black spatial mismatch indexes of the 30 metropolitan areas with the highest and lowest Lopez-Hynes sprawl indexes.  Among the 30 most sprawling metropolitan areas, the index of dissimilarity (53.33) is 5.60 points lower than it is among the 30 least sprawling metro areas (58.93).  The differential is even larger with regard to blacks' spatial mismatch: in the 30 more sprawling metropolitan areas the black spatial mismatch index is 36.11 compared to 51.99 in the 30 least sprawling metropolitan areas (a differential of 15.88 points).  We found a similar pattern when we tested the Ewing et al. mixed land use index.  The thirty least sprawling metro areas had higher black-white residential segregation and more black spatial mismatch than did the thirty most sprawling metro areas (table not shown to conserve space). 

   However, in examining the metropolitan areas listed in Tables 2 A and B, it is obvious that the most severely sprawling metropolitan areas are disproportionately located in the South, are small or medium in size, and have economies with relatively small manufacturing sectors and large service and trade sectors.  This raises the possibility that the difference just shown in the contrast between high and low sprawl metropolitan areas may be spurious, since it is known that black-white segregation is higher in large metropolitan areas, in the Midwest and Northeast, and in metropolitan areas with a large manufacturing base (Logan, Stults & Farley 2004).  It may be those factors rather than sprawl that are responsible for the pattern found in Tables 1 and 2 A and B.

   Since the timing, history, and degree of suburbanization and sprawl differ considerably across regions of the U.S. and since regional differences in black-white residential segregation are large, we checked the correlations between the dependent variables and the Lopez-Hynes sprawl index within each U.S. region.  In each region the negative correlation persists and usually is stronger than in Table 1 (where region is not controlled).  In the Northeast the correlation between the sprawl index and black-white segregation is -.660 and with black spatial mismatch is -.552.  In the South, correlations between the same variables, respectively, are -.271 and -.582; in the Midwest they are -.121 and -.120; and in the West they are -.505 and -.567. 

   The overall pattern is perhaps best illustrated in the Northeast.  In this region, the twelve MAs that sprawl the most (as measured by the Lopez-Hynes index) have a mean of 54.76 on residential segregation and a mean of 43.87 on black spatial mismatch, but in the twelve MAs that sprawl the least the means on those variables are significantly higher: 71.07 and 62.89, respectively (p < .01 in both cases) (4).   Clearly, and somewhat counter intuitively, in the most sprawling metropolitan areas there is less, not more, black-white residential segregation and black spatial mismatch.  However, as is true for all regions, in the Northeast the most sprawling metro areas on the Lopez-Hynes index (see footnote 4) also are metro areas that are smaller in size and that have smaller black populations than do the least sprawling metro areas.  Thus, controlling for region does not eliminate the possibility that the negative correlation between sprawl and the dependent variables is spurious.  The multiple regression analyses in Tables 3 and 4 allow us to test whether or not metropolitan area size, percent black, or other variables are more important influences on black-white residential segregation and spatial mismatch than is suburban sprawl.

Multivariate Results

   Tables 3A and B show how our measures of suburbanization (models A and B), and sprawl (models C and D), in conjunction with other relevant variables, account for variation in black-white residential segregation.  In model A, the independent variable of primary interest is the percent of the metropolitan area's population that is "suburban" (lives outside the central city).  Even after controlling for other variables, the positive association shown in the zero-order correlation between it and black-white residential segregation is maintained (b = .191, p < .001).  The more suburbanized metropolitan areas are, in fact, more highly segregated than the less suburbanized metropolitan areas, even after taking into account the real effects of black population size, differences in black and white educational levels, economic structure of the metro area, overall population size, and region of the country.

   Model B shows a nearly identical result when the indicator of suburbanization is the job decentralization.  Blacks and whites are more residentially separate in metropolitan areas with decentralized employment than they are in metro areas with centralized employment locations, even after controlling for other variables that affect residential segregation.  The multivariate results in models A and B confirm the pattern shown in Table 1's correlations: higher suburbanization in general leads to more black-white segregation.

   Models C and D, which show the effect of suburban sprawl (measured by the Lopez-Hynes index and the Ewing et al. mixed land use index, respectively), tell a different story.  In model C, net of other factors, the sprawl index is negatively related to black-white residential segregation, but its unstandardized coefficient is of marginal significance (b = -.089; p = .077), and its small standardized regression coefficient also indicates that this measure of sprawl has very limited impact on black-white residential segregation.  Thus, the results in model C are different than in the preceding models.  In models A and B suburbanization is clearly linked with higher black-white segregation, but model C shows no such relationship between the Lopez-Hynes suburban sprawl index and black-white segregation (if anything, it has a weak negative impact on residential segregation).  Model D examines the effect of sprawl (measured as lack of a mixture of residential and commercial land uses).  It indicates a statistically insignificant b (.029).  This means the significant relationship shown in the zero-order correlation (r = .188; metros with more mixed land use are more racially segregated) is spurious and disappears when other variables are taken into account.

   The regression analysis shown in model E is perhaps the most interesting, since it includes the suburbanization variable used in model A (percentage of metro population that is suburban) and the sprawl variable used in model C (Lopez-Hynes sprawl index).  It clearly shows the inconsistent and contradictory pattern noted above.  Percent suburban is positively related to black-white residential segregation (b = .224, p < .001), but suburban sprawl is negatively related to black-white residential segregation (b = -.155, p < .01).  Of the two effects, the former is a little stronger than the latter (as indicated by its higher standardized coefficient), but the fact that they link with segregation in opposite directions is important.  It means that in metro areas with equal percentages of the population living in suburbs (and with other variables controlled), the ones with more sprawl (i.e., lower densities, long unconnected streets) have less black-white residential segregation.  Conversely, in metro areas with equal Lopez-Hynes sprawl indexes, those with higher percentage suburban have higher black-white residential segregation.

   Considering all regression analyses, Tables 3 A and B give a two-part story about black-white residential segregation.  First and most unambiguously, the more highly suburbanized metropolitan areas show up as more segregated, and metro areas with larger percentages of their population living in central cities have less black-white residential segregation.  Second, beyond that, teasing out effects of the dimensions of sprawl complicates the picture.  While mixed land use appears to have no effect on black-white residential segregation, sprawl as measured by the Lopez-Hynes index (indicating low density and long unconnected streets) seems linked to diminished black-white residential segregation. 

   Turning to our second dependent variable, Tables 4A and B show the regression results for blacks' job-residence spatial mismatch using the same format as Tables 3 A and B.  In all models the influence of one variable – black-white residential segregation – clearly overshadows all the others.  Its standardized coefficient in model A (.876) is over three times larger than that of the next largest (percent employed in business services) and indicates that the most segregated metropolitan areas have the most severe job-residence spatial mismatches for blacks.  Compared to the strength of residential segregation, in model A, the influence of percentage of the metro population that is suburban is modest and marginally significant (b = -.073, p = .053).  The negative sign for percent suburban is unexpected (given its correlation of .175 in Table 1), implying that after controlling for other variables, more suburbanized metropolitan areas have slightly less black spatial mismatch than do the more suburbanized metropolitan areas.

   Model B shows, surprisingly, that job sprawl (b = .011, p = .767) has no direct relationship with black spatial mismatch.  This suggests that its zero-order correlation with black spatial mismatch (r = .437) is spurious and runs through the more potent effect of residential segregation. 

   The results in model C, where the key independent variable is the Lopez-Hynes sprawl index, closely resemble model A.  Here suburban sprawl has a weak marginally significant negative effect on black spatial mismatch (b = -.081, p = .055), but as in the other models it is dwarfed by the impact of black-white residential segregation. 

   In model D, the suburban sprawl independent variable is the Ewing et al. mixed land use index, and it is not close to being statistically significant.  This is actually an interesting result, as it means that whether or not people live in areas in which shopping and jobs are located near their residences makes no difference in terms of the degree of job-residence spatial mismatch that blacks face. 

   Finally, model E shows that when both a general suburbanization variable (percent suburban) and a sprawl variable (Lopez-Hynes index) are included in the regression analysis together neither one reaches statistical significance.  Thus this and the other models do not support the idea that suburbanization and sprawl exacerbate black spatial mismatch.   In model E, as in all the other models, black-white residential segregation has by far the strongest impact on blacks' job-residence spatial mismatch.  Beyond that, and net of other variables, having a relatively large portion of the labor force employed in business services worsens the black job-residence spatial mismatch and having a large black population reduces the mismatch.

   This research examines two important structural realities of black life in contemporary metropolitan America – residential segregation and job-residence spatial mismatch – and focuses on the impact of suburbanization and sprawl.  With regard to segregation, our findings do show that suburbanization is associated with greater residential separation of blacks and whites.  Whether suburbanization is measured by percentage of the metropolitan living outside the central city or by extent of employment decentralization, our results indicate that more suburbanized metro areas have higher levels of black-white residential segregation than do less suburbanized metropolitan areas.  However, when we sharpen the analysis to investigate whether certain characteristics related to the concept of suburban sprawl (i.e., density, land use, street patterns ) promote racial residential segregation, our answer seems to contradict the previous sentence; that is, more sprawling metropolitan areas do not have higher black-white residential segregation, if anything they have lower levels of segregation, than do less sprawling metropolitan areas.

   We can explain this apparent paradox by noting that many of the metropolitan areas with the highest levels of general suburbanization are located in two parts of the country: the Northeast (especially the New York area) and Florida (5).  Those in the Northeast represent, historically, some of this country's earliest and most exclusive suburban development and they exemplify the "inelastic" growth pattern that Rusk (1995, 1999) criticizes for, among other things, enabling suburban towns to exclude black residents.  By 2000, most of these Northeastern metro areas still maintained relatively high levels of black-white residential segregation, but their suburbs had "filled in" considerably in terms of their population density and the presence of nearby shopping and employment sites, which, on the measures of sprawl analyzed here, places these highly suburbanized MAs near the "low sprawl" end of the continuum.  The situation in parts of Florida is quite different, but produces a similar "high suburbanization but low sprawl MA" outcome linked to high black-white residential segregation.  Suburban growth around Florida's main cities is of more recent origin, but sprawl in south Florida has been discouraged by geography.  The narrow land mass between some of the major coastal cities and the Everglades limits how far the suburbs can extend.  Encouraged by land use regulations and the nature of the target markets, much of Florida's suburban growth consists of condominiums and single family homes on small lots, with shopping and other services nearby.  A substantial portion of the housing is for elderly or retirement communities, which researchers note sharply increases black-white residential segregation (Logan, Stults & Farley 2004).  The many expensive gated communities for the affluent have a similar effect.  The end result is that Miami, Ft. Lauderdale, West Palm Beach, and Tampa-St. Petersburg are metro areas with high percentages of suburban residents but low sprawl indexes, and relatively high black-white residential segregation.

   Our results point to the conclusion that although something about suburbanization promotes black-white residential segregation, that "something" does not appear to be low population density, the lack of mixed land use, or the suburban street pattern.  Indeed, we find many cases where high black-white residential segregation persists in metro areas with high density, considerable mixed land use, and a more urban street pattern.  Conversely, some of the least segregated metro areas rank high on sprawl, and that association persists even after controlling for other factors.  Perhaps a critical factor is how large a number of distinct suburban town governments exist in a metropolitan area, each of which controlling local zoning or building codes and the degree of enforcement of anti-discrimination laws in ways that affect blacks' access to these areas.  Yang and Jargowsky (2006) find that the number of suburban governments has a negative effect on economic segregation, so researchers may want to assess whether or not it has a similar effect on black-white segregation.

   We briefly add two important comments on suburbanization and black-white residential segregation.  First, suburbanization is a multi-faceted process, and while our results show it produces residential segregation this is still a vague conclusion.  What are the specific reasons and mechanisms currently producing black-white residential segregation (e.g., discriminatory steering or marketing, group preferences) and do they differ from those identified as causing residential segregation in city neighborhoods or in the suburbs of earlier decades?  Second, although throughout this analysis we have focused on suburbanization, sprawl, and segregation, we would be remiss in not noting that several other variables in our models have comparable or stronger effects.  As others have noted, segregation is more severe in large metropolitan areas, in those with large black populations, and in the Midwest and Northeast.  It also is worse where the inter-racial gap in education is larger and in metro areas that have relatively more manufacturing and less business services.

   With regard to blacks' job-residence spatial mismatch, we find no convincing evidence that suburbanization or sprawl has a significant direct impact.  Once a metro area's degree of black-white residential segregation is taken into account (which, as noted above, is a variable affected by percent suburban and job decentralization), neither percent suburban, job decentralization, nor the sprawl indexes make much additional difference on Stoll's black spatial mismatch index (and what little difference they do make is in reducing spatial mismatch).  However, it is possible that this result is an artifact of the way this index measures black spatial mismatch.  Stoll's black spatial mismatch index measures the disparity in the spatial distribution of blacks and jobs across ZIP-code areas, but is insensitive to either the physical distance or amount of time needed to make job commutes, so researchers may want to test this with alternative measures (e.g., average travel time from home to work for blacks).  For now though, we are left with the conclusion that substantiates a point we made earlier – if blacks and whites are highly segregated from each other (especially if the former are largely contained in the central cities), then it makes little difference whether density is high, moderate, or low, if land uses are mixed or homogeneous, or what the street pattern looks like, the vast majority of jobs are located in places in which the largest concentrations of blacks do not live.

   The analytical use of new measures that tap different dimensions of sprawl has enabled us to refine our understanding of the connection between suburban growth and both residential segregation and spatial mismatch.  It enables scholars to be more precise in specifying which aspects of suburban development are important and how.  Researchers should learn more about these sprawl indexes, develop new and better measures, and use them as tools in their work.

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*Please direct correspondence to Charles Jaret, Department of Sociology, Georgia State University, Atlanta, GA 30303 or email to <cjaret@gsu.edu>.  We thank Melissa Hayes for research assistance and Professor Michael Stoll for sharing unpublished data.
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 (l)This pattern of lower levels of black-white residential segregation in the suburbs than in the city of a metropolitan area holds for most U.S. metropolitan areas.  However, the opposite pattern – higher black-white segregation in the suburbs than in the city – is found fairly often in the medium-size metropolitan areas of the West and in the small metropolitan areas of the Midwest (based on data from the Mumford Center for Urban and Regional Research at the State University of New York at Albany).
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(2) Low-density tracts are those with a population density of between 200 and 3,500 persons per square mile.  High-density tracts have more than 3,500 persons per square mile.  Tracts with less than 200 persons per square mile are considered rural tracts and are excluded from consideration in this measure of sprawl.  For the rationale for these cut-off points, see Lopez & Hynes (2003).
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(3) The results shown are from ordinary least squares (OLS) regression analyses.  However, we checked these results against Long and Ervin's (2000) HC3 correction for known and unknown sources of heteroskedasticity.  This procedure calculates robust standard errors to protect against the possibility of mis-specifying the mean square error because of the aggregated data used in our analyses.  The results are virtually identical and we thus present the OLS results.
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(4) Based on the Lopez-Hynes index, the 12 most sprawling Northeast metro areas in our sample are:  Portsmouth, New London, Duchess, Pittsfield, Harrisburg, Burlington, Worcester, Hartford, Albany, Monmouth-Ocean, Atlantic City, and Springfield.  The twelve least sprawling Northeast metro areas in our sample are:  Jersey City, New York City, Bergen-Passaic, Nassau-Suffolk, Newark, Philadelphia, Boston, Buffalo, Bridgeport, Rochester, Trenton, and Providence.
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(5) In our sample of 150 metropolitan areas, 19 of the 25 with the highest percent suburban (i.e., living outside the central city) were in the Northeast or in Florida.
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