Abstract
Social scientists have focused some attention on the spatial and temporal patterns of minority suburbanization over the past several decades. However, we argue that no fully comprehensive analysis of suburbanization patterns yet exists along three dimensions simultaneously: the entire suburban landscape, in all metropolitan statistical areas (MSAs), over a long time period. In this study we code the suburban ring of all places in 379 MSAs and use multilevel modeling techniques to investigate patterns of non-Hispanic White, Black, and Asian, and Hispanic suburbanization from 1970 to 2019. We examine group-level disparities in first-ring shares, or the percentage of each group residing in the first ring of suburbs, and in ecological distance gradients, or the rate at which group shares decline across additional suburban rings. We find substantial differences in first-ring shares between non-Hispanic Whites and the three minority groups under consideration, reflecting decades of minority concentration in and non-Hispanic White migration out of principal cities. We also confirm that a larger share of non-Hispanic Whites resided in outer-ring suburbs; however, we do not observe as much variation in suburban diffusion as might be expected from prior research. We conclude that this somewhat null finding results from the fact that minorities have spread radially outward or directly settled in suburban areas that cut across multiple rings, rather than being confined primarily to inner-ring suburbs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
All data and Stata code are available from the first author upon request.
Notes
For example, a resident of Orange Park, FL, a first-ring suburb of Jacksonville, would be approximately 15 miles from downtown Jacksonville. In comparison, a resident of Reading, MA, could also be 15 miles away from downtown Boston. However, Reading is, by our calculation, a fourth-ring suburb, meaning that one would have to travel from Reading through Stoneham, Medford, and Somerville to get to Boston. Thus, to the extent that minorities tend to be concentrated in inner-ring suburbs, we would expect that, ceteris paribus, Orange Park would feature a higher percentage of minorities than would Reading.
To be clear, this hypothesis about the impact of suburban housing stock assumes an overall effect only—more suburban housing should result in greater shares of each group in the suburbs. However, it is likely that the effect of suburban housing growth will vary by the ethnoracial group in question, due to, for example, group differences in the ability to afford suburban housing or the degree of discrimination faced by each group.
In this article we focus on a group-based conceptualization of spatial assimilation. There is certainly interplay between household- and group-level assimilation processes, in that increases in assimilation at the household level will result in observed changes at the group level, and changes at the group level will alter the environment within which individual households behave. Indeed, in their initial discussion of assimilation, Park and Burgess (1921) refer to assimilation as “a process of interpenetration and fusion in which persons and groups acquire the memories, sentiments, and attitudes of other persons and groups and, by sharing their experience and history, are incorporated with them in a common cultural life” (p. 735; emphasis added). In Massey and Mullan’s words (1984), “Social mobility is a group, as well as an individual, phenomenon, particularly when one focuses on the spatial aspects of the process. Groups clearly differ in their rates of assimilation… and residential decisions are often made on group criteria… Assimilation, as defined here, is inherently a group phenomenon, and the ecological processes we consider involve the interaction of populations in space and time…” (p. 859; emphasis in original).
South and Crowder (1997) include the city-to-suburb ratios of variables likely to be correlated with housing supply, such as population size, but these metropolitan area-level variables are not the focus of their analysis.
Some county subdivisions are exactly the same geographic shape as a census place. Other county subdivisions have no census places in them, and still others have one or more census places in them, but also a residual portion without a census place. We treat all three types as place-like entities because they are often townships or other units that function at least residentially as suburban places.
The tract allocation factor indicates what proportion of a tract falls in a suburban place, a principal city, or a full or partial county subdivision. For example, if a suburban place comprises 100% of two tracts and 50% of a third, then the population counts for that place will be the sum of the first two tracts, plus half of the third. This apportioning strategy likely results in some degree of measurement error because it assumes that ethnoracial groups are evenly distributed across census tracts. However, our procedure just as likely reduces the opposite form of measurement error; that is, error resulting from neglecting tracts that partially overlap with place boundaries.
For example, the Carson City, NV MSA is only a principal city, and therefore has no suburban rings to analyze.
We imputed the race/ethnicity of Asians and Hispanics in 1970 using the techniques described in Timberlake and Iceland (2007, pp. 363–365).
In the majority of the 379 MSAs (231) only one principal city was identified by the OMB. For the remainder, multiple principal cities were listed. In 52 of these, the multiple principal cities were not geographically contiguous (such as Brownsville and Harlingen, TX or Orlando, Kissimmee, and Sanford, FL). In these cases, we coded the principal city as the largest of the options (the one listed first in the OMB’s labeling), treating the other(s) as suburbs of the single principal city. For the remaining MSAs with multiple principal cities, at least one was geographically contiguous to another, forcing us to develop rules to decide whether and how to merge the multiple principal cities into one, shown graphically in Appendix Fig. 5. For instance, Houston, Sugar Land, and Baytown, TX are geographically contiguous. In 1970, Houston was 28 times larger than Sugar Land and 376 times larger than Baytown. Hence, our decision rule treats Houston as a single principal city and Sugar Land and Baytown as suburbs, even though Sugar Land is now over 100,000 and Baytown over 70,000 in population. We believe this reflects part of what we are investigating in this article—places that once functioned as traditional suburbs of central cities have grown quite large over time. As another example, Minneapolis and St. Paul, MN are contiguous. In 1970 the population of Minneapolis was only 1.4 times greater than that of St. Paul. Hence, we merged these into one principal city, treating all other territory in the Minneapolis-St. Paul-Bloomington, MN MSA as “suburban”—including Bloomington, even though it is now relatively large (about 90,000 residents in 2020 [U.S. Census Bureau, 2021]). This strategy enables temporal analysis yet creates the conceptual problem of assigning suburbs to two large, non-contiguous principal cities. Perhaps the clearest example of this is Miami, Ft. Lauderdale, and West Palm Beach, FL. It is reasonable to suggest that the northern suburbs of Miami are really the southern suburbs of Ft. Lauderdale and vice versa, or that the northern suburbs of Ft. Lauderdale are really the southern suburbs of West Palm Beach and vice versa. Ultimately, we treat these problems as cases of measurement error and believe that our comprehensive analysis still has merit, even though there may be a few particularly troublesome metropolitan areas in our data.
In addition, because the United States was not fully tracted in 1970, data for some territory that eventually became part of a metropolitan area does not exist in 1970; hence, we use 1980 as the first period of observation for those areas.
This is achieved by coding the “PC” variable 1 if a place is a principal city, 0 otherwise, and the “ED” variable 0 if a place is either a principal city or in the first suburban ring, 1 if it is a suburban place or county subdivision in ring 2, …, k − 1 if it is a suburban place or county subdivision in ring k. This coding strategy yields the parameters of interest noted above; namely, the share of each group in the first ring of suburbs (or in the principal city, depending on how the “PC” variable is coded) and the ecological distance gradient for each of the MSAs. See Raudenbush and Bryk (2002, p. 179) for a discussion of this coding strategy.
A reviewer asked about the level of measurement of the ecological distance (ED in Eq. 4) variable. We argue that it is best thought of as a discrete ratio variable because the number zero has a meaningful interpretation—the principal city—and the values are properly integers. For example, we do not believe that the number “3.5” would mean anything on this variable—one is either in ring 3 or ring 4.
We restrict our graphical analysis to the principal city and first five rings because of an estimation feature of the HLM software. In carrying out its estimation, HLM precision weights the parameter estimates by the inverse of the variance at levels 2 and 3. This is because the level-2 (suburban rings) and level-3 (MSAs) units have differing numbers of observations (places and rings, respectively), and therefore unequal variances. An example using the overall grand mean is shown below (adapted from Raudenbush and Bryk, 2002, p. 40):
$$\hat{\gamma }_{000} = \mathop \sum \limits_{j = 1}^{J} \Delta_{j}^{ - 1} \overline{Y}_{..j} /\mathop \sum \limits_{j = 1}^{J} \Delta_{j}^{ - 1} ,$$(7)In this example, \({\overline{Y} }_{..j}\) are MSA-level means that contribute to the estimation of the grand mean γ000, and the j are the MSA-level variances. The result of this estimation feature is that the ecological distance gradient estimates are overwhelmingly dominated by the first five rings, within which about 85% of all places and county subdivisions fall (indeed, over half of all places are in just the first two rings). After the fifth ring or so, the observations become so sparse that the linear trends produced by the parameter estimates, like those shown in Figs. 2 and 4, would fall below the 0 line, yielding nonsensical interpretations (i.e., negative suburban shares).
In the 379 MSAs analyzed in this article, 85% were majority White and 90% were a plurality White. In the suburbs of those MSAs, 89% were majority White and 93% were a plurality White (authors’ calculations).
More precisely, in these figures we show the three highest-population counties in the Chicago MSA: Cook (containing Chicago), DuPage (due west of the city), and Will (to the south and southwest of Cook County. The Chicago MSA also includes, counterclockwise from the north of Cook County, Lake, McHenry, DeKalb, Kane, Kendall, and Grundy counties. Each of these counties is included in the analysis and the full Chicago MSA is shown in Fig. 1.
The robust standard error estimates provided by the HLM software assume a probability sample, though not necessarily a simple random sample. In this article, however, we analyzed repeated measures of all places and county subdivisions from all MSAs that were defined in each census from 1970 to 2019. Hence, there is no sampling error in the data in the conventional meaning of that term. We follow Grodsky and Pager (2001, p. 552) in recommending that the standard errors be interpreted as “estimates of parameter dispersion contaminated by measurement error.” In other words, smaller standard errors indicate more consistently-measured relationships between the independent variables and group suburbanization measures. Coefficients marked with asterisks indicate that they are at least twice the size of their standard error estimates.
References
Airgood-Obrycki, W. A. (2019). Suburban status and neighborhood change. Urban Studies, 56, 2935–2952.
Airgood-Obrycki, W. A., Hanlon, B., & Rieger, S. (2021). Delineate the U.S. suburb: An examination of how different definitions of the suburbs matter. Journal of Urban Affairs, 43, 1263–1284.
Alba, R. D., & Logan, J. (1991). Variations on two themes: Racial and ethnic patterns in the attainment of suburban residence. Demography, 28, 431–453.
Alba, R., Logan, J., Lutz, A., & Stults, B. (2002). Only English by the third generation? Loss and preservation of the mother tongue among the grandchildren of contemporary immigrants. Demography, 39, 467–484.
Alba, R. D., Logan, J., Stults, B. J., Marzan, G., & Zhang, W. (1999). Immigrant groups in the suburbs: A reexamination of suburbanization and spatial assimilation. American Sociological Review, 64, 446–460.
Anacker, K. (2015). Analyzing census tract foreclosure risk rates in mature and developing suburbs in the United States. Urban Geography, 36, 1221–1240.
Anacker, K., Niedt, C., & Kwon, C. (2017). Analyzing segregation in mature and developing suburbs in the United States. Journal of Urban Affairs, 39, 819–832.
Breymaier, J. R. (2017). The social and economic value of intentional integration programs in Oak Park, IL. Harvard University.
Charles, S. L. (2014). The spatio-temporal pattern of housing redevelopment in suburban Chicago, 2000–2010. Urban Studies, 51, 2646–2664.
Cooke, T. J. (2010). Residential mobility of the poor and the growth of poverty in inner-ring suburbs. Urban Geography, 31, 179–193.
Drake, S. C., & Cayton, H. R. (1993). Black metropolis: A study of Negro life in a northern city. Chicago: University of Chicago Press. (Original work published in 1945).
Ehrenhalt, A. (2013). The great inversion and the future of the American city. Vintage.
Farley, R., & Frey, W. H. (1994). Changes in the segregation of whites from blacks during the 1980s: Small steps toward a more integrated society. American Sociological Review, 59, 23–45.
Farley, R., Schuman, H., Bianchi, S., Colasanto, D., & Hatchett, S. (1978). “Chocolate city, vanilla suburbs:” Will the trend toward racially separate communities continue? Social Science Research, 7, 319–344.
Farrell, C., & Firebaugh, G. (2016). Is immigrant neighborhood inequality less pronounced in suburban areas? Social Science Research, 57, 161–176.
Fischer, M. J. (2008). Shifting geographies: Examining the role of suburbanization in blacks’ declining segregation. Urban Affairs Review, 43, 475–496.
Friedman, S. (2019). Muslim-Non Muslim locational attainment in Philadelphia: A new fault line in residential inequality? Demography, 56, 1327–1348.
Friedman, S., Gibbons, J., & Galvan, C. (2014). Declining segregation through the lens of neighborhood quality: Does middle-class and affluent status bring equality? Social Science Research, 46, 155–168.
Friedman, S., Tsao, H., & Chen, C. (2013). Housing tenure and residential segregation in metropolitan America. Demography, 50, 1477–1498.
GeoLytics, Inc. (2015). CensusCD neighborhood change database 1970–2000 tract data [machine-readable database]. East Brunswick, NJ.
Gordon, M. (1964). Assimilation in American life. Oxford University Press.
Grodsky, E., & Pager, D. (2001). The structure of disadvantage: Individual and occupational determinants of the black-white wage gap. American Sociological Review, 66, 542–567.
Hanlon, B. (2009a). A typology of inner-ring suburbs: Class, race, and ethnicity in U.S. suburbia. City & Community, 8, 221–246.
Hanlon, B. (2009b). Once the American dream: Inner-ring suburbs of the metropolitan United States. Temple University Press.
Hess, C. (2021). Residential segregation by race and ethnicity and the changing geography of neighborhood poverty. Spatial Demography, 9, 57–106.
Holliday, A. L., & Dwyer, R. E. (2009). Suburban neighborhood poverty in U.S. metropolitan areas in 2000. City & Community, 8, 155–176.
Howell, A. J., & Timberlake, J. M. (2013). Racial and ethnic trends in the suburbanization of poverty in U.S. metropolitan areas, 1980–2010. Journal of Urban Affairs, 36, 79–98.
Hoyt, H. (1939). The structure and growth of residential neighborhoods in American cities. U.S. Federal Housing Administration.
Iceland, J. (2009). Where we live now: Immigration and race in the United States. University of California Press.
Iceland, J., Sharp, G., & Timberlake, J. M. (2013). Sun belt rising: Regional population change and the decline in black residential segregation, 1970–2009. Demography, 50, 97–123.
Jargowsky, P. (2003). Stunning progress, hidden problems: The dramatic decline in concentrated poverty in the 1990s. Brookings Institution Press.
Keating, W. D., & Bier, T. (2008). Greater Cleveland’s first suburbs consortium: Fighting sprawl and suburban decline. Housing Policy Debate, 19, 457–477.
Kneebone, E., & Berube, A. (2013). Confronting suburban poverty in America. Brookings Institution Press.
Lee, S. (2011). Analyzing intra-metropolitan poverty differentiation: Causes and consequences of poverty expansion to suburbs in the metropolitan Atlanta region. The Annals of Regional Science, 46, 37–57.
Lee, S., & Leigh, N. G. (2007). Intrametropolitan spatial differentiation and decline of inner ring suburbs: A comparison of four U.S. metropolitan areas. Journal of Planning Education and Research, 27, 146–164.
Lichter, D. T., Parisi, D., & Taquino, M. C. (2015). Toward a new macro-segregation? Decomposing segregation within and between metropolitan cities and suburbs. American Sociological Review, 80, 843–873.
Logan, J. R., & Alba, R. D. (1993). Locational returns to human capital: Minority access to suburban community resources. Demography, 30, 243–267.
Logan, J. R., Alba, R. D., McNulty, T., & Fisher, B. (1996). Making a place in the metropolis: Locational attainment in cities and suburbs. Demography, 33, 443–453.
Logan, J. R., Alba, R. D., & Zhang, W. (2002). Immigrant enclaves and ethnic communities in New York and Los Angeles. American Sociological Review, 67, 299–322.
Logan, J. R., Stults, B. J., & Farley, R. (2004). Segregation of minorities in the metropolis: Two decades of change. Demography, 41, 1–22.
Logan, J. R., & Zhang, C. (2010). Global neighborhoods: New pathways to diversity and separation. American Journal of Sociology, 115, 1069–1109.
Massey, D. S. (1985). Ethnic residential segregation: A theoretical synthesis and empirical review. Sociology and Social Research, 69, 315–350.
Massey, D. S., & Denton, N. A. (1985). Spatial assimilation as a socioeconomic outcome. American Sociological Review, 50, 94–106.
Massey, D. S., & Denton, N. A. (1988). Suburbanization and segregation in U.S. metropolitan areas. American Journal of Sociology, 94, 592–626.
Massey, D. S., & Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Harvard University Press.
Massey, D. S., & Mullan, B. P. (1984). Processes of Hispanic and black spatial assimilation. American Journal of Sociology, 89, 836–873.
Massey, D. S., & Tannen, J. (2018). Suburbanization and segregation in the United States: 1970–2010. Ethnic and Racial Studies, 41, 1594–1611.
Missouri Census Data Center. (2021). Geocorr 2014: Geographic correspondence engine. Retrieved Feb 27, 2021 from https://mcdc.missouri.edu/applications/geocorr2014.html.
Park, R. E., & Burgess, E. W. (1921). Introduction to the science of sociology. University of Chicago Press.
Pfeiffer, D. (2012). Has exurban growth enabled greater racial equity in neighborhood quality? Evidence from the Los Angeles region. Journal of Urban Affairs, 34, 347–371.
Pfeiffer, D. (2016). Racial equity in the post-civil rights suburbs? Evidence from US regions 2000–2012. Urban Studies, 53, 799–817.
Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and modeling population processes. Blackwell Publishers.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage Publications.
Raudenbush, S. W., Bryk, A. S., & Congdon, R. (2020). HLM 8.1 for windows [Computer software]. Scientific Software International, Inc.
Rothstein, R. (2017). The color of law: A forgotten history of how our government segregated America. Liveright Publishing Corporation.
Schneider, M., & Phelan, T. (1993). Black suburbanization in the 1980s. Demography, 30, 269–279.
South, S. J., & Crowder, K. D. (1997). Residential mobility between cities and suburbs: Race, suburbanization, and back-to-the-city moves. Demography, 34, 525–538.
Stahura, J. M. (1987). Characteristics of black suburbs, 1950–1980. Sociology & Social Research, 71, 135–138.
Sugrue, T. J. (1996). The origins of the urban crisis: Race and inequality in postwar Detroit. Princeton University Press.
Taylor, K. Y. (2019). Race for profit: How banks and the real estate industry undermined black homeownership. The University of North Carolina Press.
Timberlake, J. M., Howell, A. J., & Staight, A. J. (2011). Trends in the suburbanization of racial/ethnic groups in U.S. metropolitan areas, 1970–2000. Urban Affairs Review, 47, 218–255.
Timberlake, J. M., & Iceland, J. (2007). Change in racial and ethnic residential inequality in American cities, 1970–2000. City & Community, 6, 335–365.
United States Census Bureau. (2021). QuickFacts: Bloomington city, Minnesota. Retrieved Feb 2, 2021 from https://www.census.gov/quickfacts/fact/table/bloomingtoncityminnesota/PST045219.
Vicino, T. J. (2008). The spatial transformation of first-tier suburbs, 1970 to 2000: The case of metropolitan Baltimore. Housing Policy Debate, 19, 479–518.
Warner, W. L., & Srole, L. (1945). The social systems of American ethnic groups. Yale University Press.
Zhang, W., & Logan, J. R. (2017). The emerging spatial organization of the metropolis: Zones of diversity and minority enclaves in Chicago. Spatial Demography, 5, 99–122.
Acknowledgements
We gratefully acknowledge research support from the Charles Phelps Taft Research Center at the University of Cincinnati. We also thank Rachel Dwyer, Matthew Hall, John Iceland, and two anonymous Spatial Demography reviewers for comments on an earlier draft of this paper.
Funding
Funding provided by the Charles Phelps Taft Research Center at the University of Cincinnati.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1
Appendix 1
See Fig.
Decision rules for merging principal cities (PCs) in MSAs with multiple contiguous PCs
5.
Rights and permissions
About this article
Cite this article
Timberlake, J.M., Howell, A.J. Trends in the “Ecological Distance” of Ethnoracial Group Suburbanization in U.S. Metropolitan Areas, 1970–2019. Spat Demogr 10, 413–446 (2022). https://doi.org/10.1007/s40980-022-00106-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40980-022-00106-0