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ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

This research examines recent trends in the suburbanization of poor non-Latino Whites, Blacks, and Asians, and Latinos of all races in the United States. The authors find strong associations between a temporally lagged measure of suburban housing supply and poverty suburbanization during the period 2006–2010 for all groups, but these associations are largely attenuated by similarly lagged controls for suburban affordable housing and employment, as well as for other characteristics of metropolitan areas. Findings indicate that poor non-Latino Whites and Asians have higher suburbanization rates in metropolitan areas with higher levels of suburban employment, while the suburbanization of the Black and Latino poor is more strongly related to the availability of affordable suburban housing. Increases in housing supply are associated with change in poverty suburbanization over time for Whites, Blacks, and Latinos. In addition, increases in affordable rental housing are associated with increases in the suburbanization of the Latino poor.

Although the United States has been suburbanizing since the early nineteenth century (Hayden, 2003; Jackson, 1987), the second half of the twentieth century witnessed an especially rapid expansion of suburban America. On the eve of World War II, less than one-third of Americans who lived in metropolitan areas lived in suburbs, to say nothing of the majority of Americans who lived in non-metropolitan areas (Hobbs & Stoops, 2002, p. 33). By the year 2000, about 80% of Americans lived in metropolitan areas, of which over three-fifths lived in suburbs. Hence, by the close of the twentieth century the United States was a majority suburban nation.

Dramatic growth in the suburban population brought with it change in the diversity of suburban dwellers. Rapid increases in suburban housing stock have afforded all Americans more access to suburbs, including groups historically concentrated in central cities such as racial and ethnic minorities (Hall & Lee, 2010; Hanlon, Vicino, & Short, 2006; Timberlake, Howell, & Staight, 2011) and the poor (Berube & Frey, 2005; Hanlon et al., 2006; Holliday & Dwyer, 2009; Madden, 2002, 2003). Increasing suburban diversity has been treated as both a salutary development, signaling a greater openness of American society (Hudnut, 2003; Kalita, 2003; Wiese, 2004), and a warning sign that suburbs are increasingly suffering from problems formerly associated with concentrated inner-city poverty (Francis, Berger, Giardini, Steinman, & Kim, 2009; Holliday & Dwyer, 2009; Orfield, 2002). The bursting of the housing market bubble in 2006 has added increasing urgency to these issues, as many suburbs now face extraordinarily high rates of home foreclosure and bankruptcy (Coulton, Schramm, & Hirsh, 2010; Immergluck, 2010).

Despite some scholarly attention to the increasing suburbanization of poverty, key questions remain unanswered. First, to what extent has the share of the poor population in the suburbs of American metropolitan areas changed over the past three decades? Second, how much racial and ethnic inequality is there in poverty suburbanization? Finally, what accounts for between-metropolitan area variation in suburban poverty growth, and how much do these predictors vary by race and ethnicity? In this article we address these questions with U.S. Census data from 1980 to 2010. We use hierarchical linear modeling techniques to estimate both synchronic and diachronic variation in the suburbanization of poverty across metropolitan areas. We focus on racial and ethnic inequality in the suburbanization of poverty, as well as between-group variation between metropolitan area level predictors of that suburbanization.

We make several contributions to the literature on U.S. urban demographic change. First, we analyze data from 1980 to 2010, enabling the estimation of metropolitan area level growth trajectories in poverty suburbanization rates. This strategy yields more robust estimates of change over time than analyses of change between two censuses (e.g., Kneebone & Garr, 2010). Second, we estimate the relationship between growing suburban housing supply and poverty suburbanization, controlling for two related mechanisms through which the poor suburbanize: affordable housing and employment. Finally, we analyze a large sample of metropolitan areas, an improvement over past research which tends to examine the largest metropolitan areas only (e.g., Kneebone & Garr, 2010; Raphael & Stoll, 2010).

Our findings demonstrate that, although the share of the poor living in suburbs has indeed increased on average over time, there are large differences across racial and ethnic groups and metropolitan areas. Overall, we find that over half of the White poor in metropolitan areas now live in the suburbs, while the Black poor lag far behind their non-Black counterparts. Our analysis indicates that the relationship between suburban employment and poverty suburbanization is stronger for Whites and Asians, while the suburbanization of the Black and Latino poor is more strongly related to the availability of affordable housing in the suburbs. Finally, increases in housing supply are associated with change in poverty suburbanization over time for the three non-Asian groups, and increases in affordable rental housing in the suburbs are associated with increases in the suburbanization of the Latino poor.

BACKGROUND

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

Historical Trends in the Suburbanization of Poverty

While the present analysis focuses on suburban change from 1980 to 2010, suburbanization has been occurring throughout much of the history of the United States. Indeed, Jackson (1987) refers to 1820s-era Brooklyn as the first American commuter suburb. A century later, suburbs had grown in number and population, and were used largely as refuges from the environmental ills associated with industrial production. In this sense, they were not sites of economic exclusivity, in which interests in maximizing the exchange values of suburban land were the primary motivation (Teaford, 2008). Rather, residents primarily derived use values from suburban living arrangements.

Prior to Americans’ widespread reliance on automobiles, access to suburban areas was more egalitarian, in the sense that public transportation was widely available and inexpensive. Most trolley companies during this period had a five-cent fare, making trolley travel affordable for all citizens (Jackson, 1987). As a result, the outskirts of cities in the mid to late nineteenth century often featured jobs and commercial establishments that catered to the poor and working class. For example, slaughterhouses, glue factories, squatter communities, and social reform establishments such as poorhouses, orphanages, contagious disease hospitals, and prisons were all located in suburbs (Hayden, 2003). Moreover, although many suburbs were racially exclusive, it is also true that as late as 1920 Blacks who lived in metropolitan areas were equally or more likely to be suburban as their White counterparts (Timberlake et al., 2011).

Thus, the early development of the suburbs was not driven predominantly by the desire of the well-heeled to distance themselves from poverty; however, this was a core assumption of the dominant early twentieth century theoretical paradigm in urban sociology. The Burgess concentric zone model assumed that income, education, and occupational standing would exhibit an upward sloping gradient with increasing distance from the urban core (Burgess, 1924). Other urban scholarship made similar assumptions about the residential location of the poor (Hauser, 1961). Decades of empirical studies both confirmed hypotheses derived from the Burgess model and challenged its accuracy (Cavan, 1928; Faris & Dunham, 1939; Mowrer, 1927; Shaw & McKay, 1969; Thrasher, 1927). Over time, scholarship found that Burgess's claims regarding the spatial distribution of the poor did not fit in every case. In particular, newer metropolitan areas tended to have less inequality between cities and suburbs, suggesting that the poor were evenly distributed in those places (Berger, 1960; Gans, 1967; Schnore, 1957, 1962, 1963).

Hence, prior research has shown that the poor have always resided in suburban locations, though the magnitude of poverty suburbanization and its spatial distribution has varied across time and space. More recent scholarship has demonstrated that the poor have been migrating at high rates to suburban areas. By 2009, the suburbs were home to the fastest-growing poor population in the country, outpacing central cities and non-metropolitan communities. Between 2000 and 2008 suburbs in the 95 largest metro areas saw their poor population increase by 25%, five times faster than growth in central city poverty. As a result, suburbs housed the largest share of the nation's poor, approximately one-third (Kneebone & Garr, 2010).

Of course, poverty rates in central cities still remain approximately twice as high as those in the suburbs; nevertheless, the rapid growth of suburban poverty has commanded both popular and scholarly attention because of its putative novelty. In addition, assessments of community need frequently fail to take into account changes in poverty and their impact on the provision of social services. On the basis of this metric, suburban communities may be facing more pressing concerns than central cities, which have experienced stagnant or declining poverty rates over time (Murphy, 2007).

Factors Associated With Poverty Suburbanization

The present study examines the suburbanization of poverty across four racial and ethnic groups. However, overall patterns and trends in suburbanization are important for our purposes, because they suggest clues as to why the poor may have suburbanized at a faster or slower rate across metropolitan areas over time. In the following sections we discuss several major explanations for suburbanization in the United States since 1970, focusing on factors that are especially relevant to poverty suburbanization and to racial and ethnic inequality in that suburbanization.

Suburbanization of Housing

Cross-sectional and temporal variation in the supply of suburban housing may help explain why some metropolitan areas have a higher share of poverty in their suburbs. As suburban rings expand ever outward, native-born affluent and middle-class Whites may increasingly be settling in further-flung suburbs, opening up access to housing opportunities in inner-ring suburbs for poor residents (Dwyer, 2010, 2012; Timberlake et al., 2011; Timberlake, Howell, & Taylor, 2012). In addition, suburban housing prices ought to be relatively inexpensive in metropolitan areas with high relative levels of suburban housing, which may act as a pull factor for the central city poor. The flip side of the suburbanization coin is that many cities have attempted to attract upper income residents as a key part of urban redevelopment approaches. This resulted in newly developed neighborhoods and downtown areas attracting such residents. As a result the poor have been pushed out of some central cities as housing became increasingly unaffordable (Murphy, 2007; Smith, Caris, & Wyly, 2001).

Hence, as housing stock in central cities became more attractive to the affluent, many poor residents migrated to aging suburbs where demand and therefore prices for housing were low. Disinvestment in these suburbs made them areas unattractive to businesses and services that cater to the middle and upper classes (Berube & Frey, 2005; Leigh & Lee, 2005; Madden, 2003; Smith et al., 2001). These suburban locations are in a transition zone between older communities with characteristics attractive to gentrification and the newest suburbs where green field values create a high-end real estate market (Hanlon, 2008, 2009a, 2009b; Holliday & Dwyer, 2009; Smith & Furuseth, 2004). In short, both increases in overall housing supply and the effects of urban (re)development yielded increases in the attractiveness and affordability of the suburbs for the poor.

In terms of racial and ethnic variation, the results of past research suggest that the Black and Latino poor may be less responsive than the White and Asian poor to increases in suburban housing supply. Over two decades of research employing the place stratification model of residential outcomes has demonstrated that Blacks and Latinos are generally less able to convert increases in socioeconomic attainment into “locational attainment,” usually operationalized as residence in suburbs, residence with non-Latino Whites, or residence in low-poverty or high-income neighborhoods (Alba & Logan, 1991, 1993; Crowder & South, 2005; Logan & Alba, 1993, 1995; Logan, Alba, McNulty, & Fisher, 1996; South & Crowder, 1997). Scholars working in this tradition emphasize the role of discrimination in shaping residential patterns. More specifically, negative outgroup sentiments among majority group members become embedded in government, financial, insurance, and real estate institutions, leading to the exclusion of minority group members from neighborhoods dominated by the majority group. This model would predict that when new housing opens up in the suburbs, poor Black and Latino home seekers will be less able to respond than their Asian and White counterparts, thereby producing a weaker association between changes in suburban housing supply and poverty suburbanization for Blacks and Latinos than for Whites and Asians.

Suburbanization of Employment

A second factor in understanding the suburbanization of the poor over time is change in the spatial location of jobs. The increasingly global reach of capitalist markets has had permanent consequences for the American economy and for the geography of employment in metropolitan areas. To a significant degree, manufacturing employers sought cheaper labor costs both overseas and in new domestic locations in what was an emerging world economy (Sassen, 2006). Between the early 1950s and 1980, the American economy transitioned from capital-intensive manufacturing production centrally located in the cities of the Northeast and Midwest to a bifurcated industrial mix of low- and high-skill service jobs located increasingly in the suburbs (Harrington & Campbell, 1997; Kasarda, 1989; Sugrue, 1996). Hence, low-skill work in American cities became increasingly concentrated in the service sector, and increasingly this work was available in the suburbs.1

For example, from 1980 to 2000 manual employment in the ten largest central cities declined at an average annual rate of 1.7%, while their suburbs gained manual employment at an average annual rate of 0.8% (Gobillon, Selod, & Zenou, 2007). Kasarda (1989) shows that between 1970 and 1980 the central cities of Boston, Chicago, Cleveland, Detroit, New York, and Philadelphia lost nearly one million low-skill jobs, while their suburbs gained two million jobs, of which about 40% were low-skill. From 1970 to 1992 in metropolitan Washington, DC, employment growth at all skill levels predominantly occurred in suburban locations with the fastest rates of growth posted in the outer suburban rings. According to 2000 data in Detroit, four out of five jobs were located in the suburbs (Grengs, 2010).

The considerable rate at which employment decentralization occurred throughout the past few decades has important implications for the residential location of the poor and rising poverty rates in many suburbs (Allard, 2004; Raphael & Stoll, 2010).2 Employment decentralization, coupled with declining poverty rates in central cities during the 1990s, declining numbers of central city high poverty neighborhoods, and increasing numbers of high poverty suburban neighborhoods, suggests that the increasing share of the poor residing in suburbs is largely the result of poverty migration, not simply change in the poverty status of suburban families (Berube & Frey, 2005; Jargowsky, 2003; Kingsley & Pettit, 2003).3

Job suburbanization may prove particularly important in explaining the lack of poverty suburbanization for Blacks and Latinos in Northeastern and Midwestern cities, as prior research on spatial mismatch suggests that these groups are especially likely to be disadvantaged in terms of attaining suburban residence in older metropolitan areas relative to dynamic newer ones (Houston, 2005; Martin, 2001; Peck & Godchaux, 2009). Recent research has found that the suburbanization of the Black poor appears to be only weakly related to employment decentralization (Raphael & Stoll, 2010). In addition, there is evidence that employers tend to hold the most negative attitudes towards members of these groups (Moss & Tilly, 2001); hence, suburban jobs may not provide the pull to poor Blacks and Latinos that they might for poor Whites and Asians. However, there is also evidence that recent Latino immigrants are choosing to settle directly in suburbs. For example, Los Angeles and the California Central Valley—major destinations for Latino immigrants—saw increases in the number of high-poverty census tracts located in inner ring suburbs, likely owing to growth in large Latino immigrant barrios of these metropolitan areas (Cooke & Marchant, 2006; Jargowsky, 2003).

We consider the associations of both housing supply and employment demand variables because it is unclear from prior research whether movers were pulled by the suburbanization of jobs or whether the increase in suburban housing supply depressed prices enough to induce more poor people to move into the suburbs.4 Either way, it is clear that both employers and residents seek out market advantages, broadly defined (Bayoh, Irwin, & Haab, 2006; Crozet, 2004; Rhee, 2008); research on the question of whether migration tends to be induced by growth in job opportunities, or if employers follow population migration tends to show that migration and employment growth have reciprocal effects (Blanco, 1963; Borts & Stein, 1964; Lowry, 1966; Muth, 1971; Storper, 2011). Important to our study is that there are racial and ethnic differences in the relationship between population and job suburbanization. Liu and Painter (2011) have shown that job growth tends to occur in areas where native-born Whites concentrate and away from immigrants and other minority populations. This suggests that non-Whites tend to move where jobs locate, not that job growth is induced by the suburbanization of non-Whites. Immigrants relocate to areas of job growth more rapidly than Blacks, but no minority group's spatial concentration appears to cause job growth.

Ecological Context

There is much regional variation in suburban poverty and suburbanization in general, which is largely due to the type of transportation that was dominant at the time of a metropolitan area's greatest growth and the structure of the local economy (Jackson, 1987). Cities in the South and West have experienced rapid immigration for several decades, resulting in population growth and the creation of immigrant enclaves in the suburbs (Jargowsky, 2003). In addition, the location of employment in younger cities, disproportionately in the South and West, has tended to be more suburban, than older cities, primarily of the Northeast and Midwest, as growth in the former happened as the economy transitioned from capital-intensive production to one dominated by labor demand for low- and high-skill service work (Peck & Godchaux, 2009).

Summary

The foregoing discussion suggests that variation in the supply of suburban housing ought to predict both synchronic and diachronic variation in the suburbanization of poverty in U.S. metropolitan areas. In addition, two related factors—lower housing prices in suburban areas with high levels of housing supply and the suburbanization of employment—ought both to attenuate the observed relationship between housing supply and poverty suburbanization and exert independent influences in their own right. Finally, we expect to observe weaker relationships between housing supply, employment demand, and poverty suburbanization among the Black and Latino poor than among their White and Asian counterparts. In the following sections we describe the data and methods we use to test these hypotheses, present findings, and discuss their implications for research on urban demographic change.

DATA

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

The data for this study come from three main sources. First, we analyze data from the 1980, 1990, and 2000 U.S. Decennial Censuses concatenated in the Neighborhood Change Database (NCDB). The NCDB uses the 1999 definitions of metropolitan areas developed by the Office of Management and Budget, and we use these definitions consistently throughout the paper. Second, we merged to the NCDB data from the 2006 to 2010 American Community Survey (ACS), which replaced the decennial census “long form.” Because the ACS is a smaller sample than the long form, five years’ worth of summary file data are needed to produce reliable estimates. Finally, place of work data come from the Department of Housing and Urban Development's State of the Cities Data System (SOCDS) (U.S. Department of Housing and Urban Development, 2009). These data provide counts of suburban jobs, allowing us to examine the relationship between suburban housing supply and labor demand on poverty suburbanization. As discussed below, we predict 2006 to 2010 (hereafter, “2010” for simplicity) poverty suburbanization with data from 2000 in the cross-sectional analyses. We predict change from 1980 to 2010 with decadal rates of change from 1980 to 2000.

The unit of analysis in this paper is the metropolitan area (MA), comprising both Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas (again, using the 1999 OMB definitions).5 We restrict our analyses for each group to MAs with at least 1,000 members of that group in 1990. This criterion and casewise deletions for missing data result in samples of 299 MAs for Whites, 218 for Asians, 261 for Blacks, and 242 for Latinos. Because MAs are the units of analysis, we present unweighted statistics to estimate the correlation of MA-level characteristics with levels of poverty suburbanization, controlling for both overall and group-specific population size in the multilevel models.

MEASURES

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

Dependent Variable

The dependent variable is the percentage of poor residents in the suburbs6 for each of four racial/ethnic groups: non-Latino Whites, Blacks, Asians, and Latinos of all races. For example, if a MA had a score of 40 on the dependent variable, it would mean that 40% of the poor in that MA lived in the suburbs. We occasionally refer to this measure as the “poverty suburbanization rate,” to be distinguished from what might be called the “suburban poverty rate,” which would be the percent poor in each MA's suburban ring. Put differently, our dependent variable indicates not how poor the suburbs are, but how suburban the poor are.

Independent Variables

Housing Supply and Employment Demand

We measured 2000 levels of, and 1980 to 2000 change in, suburban housing supply by calculating the ratio of the total number of housing units in the suburban rings of each MA to the total number of housing units in the central city. For example, in 2000 San Antonio had a score of .334, indicating that there were three times as many housing units in the central city as in the suburbs. At the other end of the distribution is Fort Lauderdale, Florida—its score of 8.05 indicates that for every housing unit in the central city there were about eight in the suburbs.

As a control for the relationship of suburban housing supply to poverty suburbanization, we included measures of affordable renter- and owner-occupied housing. These variables are operationalized as the percentage of housing units located in suburban tracts with average housing prices that are below the median for each MA. For example, if a MA scored 20 on this variable, it would mean that 20% of the housing units in the MA were in suburban tracts that had below-median average housing prices. This is a somewhat crude operationalization of housing affordability; however, it is fairly highly correlated with, say, the percentage of tracts with average housing prices in the lowest quartile.7

As a further control for overall housing supply, we included the percentage of all jobs in a MA that are suburban. We considered controlling for the types of jobs that the poor might be especially drawn to, principally service sector jobs; however, the correlations between total jobs and those broken down by sector were extremely high (between about 0.82 and 0.97 in 2000). Furthermore, these job counts are only broken down by broad industry sector, not skill level, reducing the utility of including a sectoral job count in the models.

Ecological Context

In accordance with much prior research, we estimate the relationship of several ecological variables on levels of and changes in poverty suburbanization. First, we control for the natural log of population size and the percentage of each group to account for two well-known ecological relationships—larger cities, and also cities with larger minority populations, tend to be more segregated, therefore potentially influencing levels of suburbanization for poor members of minority groups. Region and age associations were controlled for by including dummy variables for each MA's census region and period in which the central city passed 50,000 in population.

Decadal Rates of Change

In models predicting change from 1980 to 2010, we include measures of MA-level decadal rates of change in several time-varying variables. For each MA, these rates of change r follow this formula:

  • math image(1)

where z is a MA level characteristic measured in census years t and t + 10, PYt, t + 10 are person years lived between census years t and t + 10, and PY1980, 2000 are person-years lived between 1980 and 2000 (Preston, Heuveline, & Guillot, 2001, p. 12). Person-years are estimated with the following formula:

  • math image(2)

where T is the length of the intercensal period (10 or 20 years), and Nt and Nt + T are the populations of each MA in census year t and t + T, respectively (Preston, Heuveline, & Guillot, 2001, p. 15). Equation 1 yields decadal rates of change in MA characteristic z (in percent per decade), weighted by decade specific rates of population growth to account for variations in the timing of that growth over the 10- to 20-year period.

METHODS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

We use Hierarchical Linear Modeling (HLM) techniques to investigate change in MA-level poverty suburbanization over time. The HLM linear growth model treats multiple observations of MAs as nested within MAs, yielding estimates of average decadal change in poverty suburbanization from 1980 to 2000. We estimate linear growth models because of the small number of observations (four) on each MA. With more observations it would be advantageous to model nonlinear change; however, as noted by Raudenbush and Bryk (2002, p. 163), the linear growth model “can provide a good approximation for more complex processes that cannot be fully modeled because of the sparse number of observations.”

The growth models used in this analysis are “intercepts- and slopes-as-outcomes” models, in which poverty suburbanization in 2010 (intercepts) and MA-level change in poverty suburbanization (slopes) are estimated for each MA using level 1 data (repeated observations of MAs). In this analysis the level 1 model is specified as

  • display math(3)

where ytj is the observed level of poverty suburbanization for each group in census year t in MA j. The CENSUS variable is coded −3 for the 1980 census, −2 for the 1990 census, −1 for the 2000 census and 0 for the 2006 to 2010 ACS. Therefore, the intercepts (β0j) are interpreted as the predicted level of poverty suburbanization for city j in 2010 and the CENSUS slopes (β1j) are interpreted as estimated growth from 1980 to 2010 in poverty suburbanization per decade. For example, if β0j were 30.0 and β1j were 3.0, it would mean that MA j had increased its share of suburban poverty by an average of 3 percentage points per decade from a predicted level of 21% in 1980 to a predicted level of 30% in 2010.

At level 2, the β0j and β1j become outcomes to be predicted by MA level characteristics. HLM regresses the intercepts (the predicted level of group specific poverty suburbanization in 2010) and the slopes (the predicted per decade change in group specific poverty suburbanization) on MA-level covariates, as in the following examples:

  • math image(4a)
  • math image(4b)

Because the covariates zk are grand mean centered (each MA-level covariate is centered around the group-specific sample mean), γ00 in equation 4a is interpreted as the covariate-adjusted average level of poverty suburbanization in 2010 for the group-specific sample of MAs and the γ01 to γ0k are associations between MA-level characteristics measured in 2000 and poverty suburbanization in 2010. In equation 4b, γ10 is the covariate adjusted average decadal change in poverty suburbanization from 1980 to 2010 for the sample of MAs and the γ11 to γ1k are estimates of change in MA-level characteristics from 1980 to 2000 (except for the time invariant measures) on change in poverty suburbanization.

FINDINGS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

Descriptive Statistics

Dependent Variables

Figure 1 shows average growth trajectories from 1980 to 2010 in rates of poverty suburbanization for the four racial/ethnic groups included in this study. The data come from random coefficient models estimated in HLM (Raudenbush & Bryk, 2002, pp. 75–80), in which the models depicted in equation 3 above are estimated. At level 2, no covariates are included; hence, the models provide estimates of average poverty suburbanization in 2010 and change from 1980 to 2010, unadjusted for MA-level factors. Figure 1 shows that the suburban share of poverty has been increasing for all four groups since 1980. In 1980, the HLM estimate for Blacks was 21.7, indicating that about one-fifth of the Black poor lived in the suburbs in the average MA. For Asians and Latinos this same figure was over 30%, while it was considerably higher for Whites (nearly 50%).

image

Figure 1. Trends in Poverty Suburbanization, by Racial/Ethnic Group, 1980 to 2006/2010

Download figure to PowerPoint

Over the next three decades the share of the Black poor in the suburbs increased by approximately 1.8% per decade. The share of Latino poor in suburbs increased over the same time period by about 2.3% per decade. For Asians this figure was 2.0% per decade and Whites had the lowest rate of change at 1.5% per decade. The relatively low starting position for Blacks in 1980 has led to continuing inequality in terms of poverty suburbanization. The low growth in the suburbanization of the White poor is likely due to the fact that by 1980 nearly half of this population had already achieved suburban status. Hence, despite some recent hand-wringing about the dramatic rise in poverty in America's suburbs, the story our data tell is one of rather durable racial and ethnic inequality. That is, although poverty has been shifting to the suburbs over the past three decades, the most current estimates do not represent a dramatic break from the recent past.

Independent Variables

Table 1 presents MA-level means and standard deviations for the independent variables used in this analysis, broken down by racial/ethnic group. For the time-varying variables we present descriptive statistics for both the year 2000 and average decadal rates for change from 1980 to 2000 (see equation 1). The housing supply measure shows that in 2000 the average MA had between 2.01 and 2.16 housing units in the suburbs for every unit in the central city. This ratio had increased, on average, between 14.1% and 14.7% per decade. The share of affordable owner-occupied housing in the suburbs averaged between 21.5% and 22.1% and had declined between 3.3% and 4.6% per decade over the study period. In contrast, affordable suburban rental housing increased during the period between 6.1% and 8.1%, reaching a 2000 level of between 21.5% and 22.1%. The suburban share of total employment averaged between 45.4% and 48% and increased from 1980 to 2000 between 4.7% and 6.1% per decade. Growth in suburban employment and suburban housing are highly correlated, which has been the norm in U.S. cities for several decades as urban areas have decentralized along each of these dimensions.

Table 1. MA-Level Means and Standard Deviations of the Independent Variables Used in the Analysis
 WhiteAsianBlackLatino
 (n = 299)(n = 218)(n = 261)(n = 242)
 MeanSDMeanSDMeanSDMeanSD
  1. a

    These are person-year weighted rates of change (in% per decade) from 1980 to 2000.

  2. b

    MA population logged in Table 2.

Housing and employment variables        
Suburban:central city housing stock        
20002.011.952.162.122.122.012.082.06
Change from 1980 to 2000a14.110.414.510.914.710.614.710.8
Suburban affordable owner-occupied        
200021.510.222.110.121.810.221.610.2
Change from 1980 to 2000a−4.619.7−4.318.2−3.519.1-4.020.1
Suburban affordable rental        
200021.59.422.19.822.09.522.09.6
Change from 1980 to 2000a8.120.46.116.37.618.77.018.6
Suburban total employment        
200045.419.548.019.746.919.646.819.7
Change from 1980 to 2000a4.712.46.111.25.512.05.511.1
Control variables        
% own group        
200076.016.53.65.612.410.611.414.7
Change from 1980 to 2000a−5.55.752.520.511.115.139.824.2
MA population in 000sb        
2000730.61432.8945.41626.6809.91517.1869.51560.9
Change from 1980 to 2000a11.710.313.09.811.610.413.110.3
Region (2000 only)        
Northeast0.180.190.180.18
Midwest0.240.240.230.22
South0.380.350.430.36
West0.200.220.160.24
Age (2000 only)        
Prior to 19000.160.220.180.19
1900 to 19390.280.340.310.31
1940 to 19690.240.220.240.24
1970 or later0.320.220.270.26

Regarding the ecological context variables, it is important to note the striking growth in the Asian and Latino populations. The Asian population grew at a rate of 52.5% per decade in the 218 MAs under analysis for Asians, over four times the growth rate in those MAs as a whole, and the Latino population grew at a rate of 39.8% per decade, over three times the overall growth rate in the 242 MAs under consideration for Latinos. The Black population grew more slowly, an average of 11.1% per decade in the 261 MAs analyzed for Blacks. While Whites continued to be the largest group in U.S. MAs (76% of the 299 MAs analyzed for Whites), they experienced negative growth during the study period (−5.5% per decade). This is due largely to growth in the minority population, but may also indicate some outmigration of Whites from the MAs included in the analysis to either newer MAs not covered in our sample, or out of metropolitan areas altogether.

MA-Level Relationships

Interpretation of Parameter Estimates

The 2010 intercepts and 1980 to 2010 slopes shown in Figure 1 represent averages for the group-specific samples of MAs. However, there is considerable variation across MAs around these average intercepts and slopes. Tables 2 and 3 examine the extent to which this variation is related to housing supply, affordable housing, suburban employment, and ecological context measures. Because all covariates have been grand mean centered, the intercepts can be interpreted as covariate-adjusted averages for all MAs. The coefficients in Table 2 are interpreted as variation in 2010 levels of poverty suburbanization associated with one-unit changes in the independent variables (measured in 2000). The log of MA population coefficient is interpreted as the expected change in the dependent variable associated with a one percent change in MA population.

Table 2. Coefficients From HLM Regression of 2006/2010 Levels of Poverty Suburbanization on Measures of Housing Supply, Affordable Housing, Suburban Employment, and Ecological Context
 12
 WhiteAsianBlackLatinoWhiteAsianBlackLatino
ParameterCoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SE
Note
  1. All covariates have been grand mean centered.

  2. *indicates coefficient is at least twice the size of its standard error.

Intercept52.160.89*38.621.39*27.061.05*40.531.32*52.160.60*38.571.22*27.030.74*40.530.93*
Housing and employment variables                
Suburban:central city housing stock7.700.85*7.790.94*5.050.84*6.070.83*1.590.44*2.880.79*0.650.711.270.70
Suburban affordable owner-occupied    0.210.11−0.260.180.530.14*0.490.16*
Suburban affordable rental    0.650.11*0.400.200.640.15*0.740.17*
Suburban total employment    0.470.05*0.680.11*0.170.08*0.290.09*
Control variables                
% own group    −0.060.050.070.140.340.08*0.010.06
Log MA population    −0.760.760.971.581.360.920.151.24
Northeast    0.492.13−10.204.15*−12.173.13*−22.663.68*
Midwest    4.672.04*−3.354.30−6.873.06*−1.803.13
South    13.201.76*7.763.38*−2.963.326.582.76*
1900 to 1939    −1.431.79−4.413.741.291.90−2.602.76
1940 to 1969    −1.672.292.594.567.492.69*2.963.94
1970 or later    −3.352.40−1.095.439.443.34*1.214.28
Level 2 unconditional variance442.7 579.9 386.4 568.2 442.7 579.9 386.4 568.2 
Model residual variance230.8 310.9 261.3 391.1 106.9 222.5 119.2 184.4 
% of level 2 variance explained47.9 46.4 32.4 31.2 75.8 61.6 69.2 67.5 
Table 3. Coefficients From HLM Regression of 1980 to 2006/2010 Change in Levels of Poverty Suburbanization on Measures of Housing Supply, Affordable Housing, Suburban Employment, and Ecological Context
 12
 WhiteAsianBlackLatinoWhiteAsianBlackLatino
ParameterCoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SECoeff.SE
Note
  1. All covariates have been grand mean centered. Covariates in italics are person-year adjusted decadal rates of change.

  2. *indicates coefficient is at least twice the size of its standard error.

Intercept1.450.12*2.000.60*1.790.32*2.340.34*1.450.12*2.000.60*1.780.31*2.340.33*
Housing and employment variables                
Suburban:central city housing stock0.140.01*0.070.070.120.03*0.190.03*0.150.01*0.070.070.100.03*0.130.04*
Suburban affordable owner-occupied    0.000.01−0.040.020.010.020.000.01
Suburban affordable rental    0.000.00−0.040.040.010.010.030.02*
Suburban total employment    −0.010.010.040.070.010.020.000.04
Control variables                
% own group    −0.030.030.000.040.000.03−0.020.02
MA population    −0.020.010.060.070.070.040.020.04
Northeast    0.560.42−1.332.080.541.600.491.15
Midwest    0.560.420.461.830.491.161.501.19
South    0.610.361.021.69−1.000.891.720.89
1900 to 1939    −0.460.33−3.611.49*−1.440.73−1.500.91
1940 to 1969    −0.780.37*−3.371.71−2.140.85*−2.391.29
1970 or later    −0.940.37*−4.511.90*−1.951.20−3.001.18*
Level 2 unconditional variance5.2 40.8 18.6 23.3 5.2 40.8 18.6 23.3 
Model residual variance2.8 39.2 16.7 18.4 2.7 39.5 15.9 17.4 
% of level 2 variance explained45.5 4.0 10.6 21.3 48.7 3.0 14.7 25.6 

In Table 3, the coefficients on the time-varying variables are interpreted as effects of one percent per decade changes in the independent variables on average decadal change in poverty suburbanization for each group. So, for example, a “suburb:central city housing stock” coefficient of 0.20 would indicate that for every one percent per decade increase in the ratio of suburban to central city housing supply, a group's poverty suburbanization increased an average of two-tenths of a percent per decade across all MAs. The region and age dummies are interpreted as increments or decrements to the intercepts for the included region and age categories, relative to the omitted categories (the West and older than 1900, respectively). For example, if the “South” coefficient were 1.0, this would mean that poverty suburbanization increased one percent per decade faster in the South than in the West.

Interpretation of Standard Error Estimates

The robust standard error estimates provided by the HLM software assume some kind of probability sample, though not necessarily a simple random sample. In this article, however, we analyzed repeated measures from all MAs that met the criterion of at least 1,000 members of a racial or ethnic group in 1990. Hence, the standard errors should be interpreted cautiously, and more as “estimates of parameter dispersion contaminated by measurement error” (Grodsky & Pager, 2001, p. 552) rather than sampling variability per se. In other words, smaller standard errors indicate more consistent correlations of the independent variables on minority suburbanization. We include the standard error estimates in Tables 2 and 3, and mark coefficients with an asterisk to indicate that they are at least twice the size of their associated standard errors.

Cross-Sectional Results

Table 2 presents coefficients from HLM regressions of MA-level variation in poverty suburbanization in 2010 on the independent variables described above, measured in 2000. Model 1 estimates the relationship of housing supply only, allowing for a baseline estimate of the extent to which higher levels of suburban housing stock were associated with the suburbanization of the poor of each racial/ethnic group. We find that suburban housing supply is associated with the suburbanization of the poor for all four groups in 2000, although the relationship varies in magnitude. For Whites and Asians, on average, MAs that are one unit higher on the ratio of suburban to central city housing stock (about half of a standard deviation in 2000—see Table 1) have 7.7 and 7.8 point higher shares of the White and Asian poor in the suburbs, respectively. For Blacks and Latinos this association is somewhat lower, at 5.1 and 6.1, respectively.8 This finding suggests that, on average, the White and Asian poor have been more successful at responding to higher levels of suburban housing stock than have the Black and Latino poor. Whether this indicates less discrimination against the White and Asian poor, or more effective social networks enabling these groups to find out about suburban housing opportunities, or some other explanation, we cannot say with our data.

Model 2 in Table 2 introduces a host of controls for the association between suburban housing supply and the suburbanization of the poor. First, we include measures of housing affordability to assess the degree to which affordable housing in particular is associated with the suburbanization of the poor. Second, we control for the supply of jobs in the suburbs to enable us to assess the independent relationship of housing supply to poverty suburbanization, net of the correlation between housing supply and suburban labor demand.

In terms of housing affordability, we find that the supply of affordable owner-occupied and rental housing is positively and significantly associated with the suburbanization of the Black and Latino poor, with coefficient magnitudes ranging from about 0.49 to about 0.74. For instance, we find that the Latino poverty suburbanization rate varies with respect to affordable rental housing at a rate of 0.74% per one percent change in the supply of affordable rental housing. For Whites, only the rental housing coefficient is significant at conventional levels, and for Asians neither coefficient is significant.9 Hence, our findings suggest that, net of total housing supply, the Black and Latino poor are more sensitive to the supply of affordable housing than are the White and Asian poor.

In contrast, our findings for the association of labor demand with poverty suburbanization suggest that the White and Asian poor tend to live in suburbs at a higher rate when there are more suburban jobs available. For example, for every one percent change in suburban employment the expected poor White and Asian suburbanization rates increase between 0.47% and 0.68%, respectively.10 It is difficult to know the precise mechanism for this association; however, it indicates the ease with which the White and Asian poor have responded to employment pull factors from the suburbs, relative to their poor Black and Latino counterparts.11 It is possible that job suburbanization has occurred in suburban areas that are either more preferred by Whites and Asians, or more discriminatory toward Blacks and Latinos. Indeed, some research on the spatial mismatch between areas of Black settlement and employment has argued that the cause of low rates of Black suburban employment is “race, not space” (Cohn & Fossett, 1996, 1998; Ellwood, 1986).12

The findings in model 2 also indicate that the association between housing supply and poverty suburbanization rates is heavily attenuated by the presence of controls. Indeed, the housing supply ratio coefficients were reduced from model 1 to model 2 by 60% to 80% for each group. This indicates that cities with large supplies of suburban housing also had large supplies of affordable housing and jobs. This is unsurprising, of course; however, the findings in model 2 indicate that a residual relationship between suburban housing supply and the presence of poor in the suburbs remains after controlling for these confounding factors, especially for Asians.

Model 2 also controls for several measures of ecological context, including MA size, percent of each racial/ethnic group, region, and the period in which the central city of each MA surpassed 50,000. For Blacks, higher percentages of Blacks are associated with higher percentages of poor Blacks in the suburbs. We did not observe a similar relationship for the other groups. MA size was not significantly correlated with poverty suburbanization, controlling for all other variables in the model. We did observe substantial regional variation by race and ethnicity. For Whites, Asians, and Latinos, the poor are more suburbanized in the South compared to the West, whereas for Blacks, suburbanization rates in the South and West are statistically indistinguishable. Poverty suburbanization for Blacks is somewhat lower in the Midwest than the West, and poverty suburbanization for all three minority groups is much lower in the Northeast. For example, the share of the Latino poor in the suburbs of the average northeastern MA is fully 22.66 percentage points lower than the statistically equivalent Western MA. This is likely due to the historical concentration of Puerto Rican and Dominican immigrants in northeastern central cities, whereas western MAs have historically been more evenly populated by Mexican and other Latin American immigrants. Finally, we observe higher rates of poverty suburbanization for Blacks in newer metropolitan areas relative to the oldest category, those whose central city reached 50,000 prior to 1900. This is likely due to newer MAs having less of an entrenched history of racialized residential patterns (Farley & Frey, 1994; Timberlake & Iceland, 2007).

Change in Poverty Suburbanization Over Time

Table 3 presents results from our analysis of change over time in the share of the poor who live in suburbs in U.S. metropolitan areas. The “intercept” row in these models indicates the average percent per decade increase for MAs that are average on all included covariates. As indicated above in our discussion of Figure 1, the rate of increase in poverty suburbanization was higher for Asians and Latinos than for Whites and Blacks. We interpret the relatively low rate of increase for Whites as reflecting the already-high rate of poverty suburbanization of the White poor. Because the Black rate was so low in 1980, it suggests that the Black poor have experienced disproportionate difficulty in attaining suburban residence relative to their Asian and Latino counterparts.

Model 1 of Table 3 only includes the measure of per-decade change in the suburban:central city housing stock ratio (see equations (1) and 2). We find that although the poor of all groups were able to convert increases in suburban housing stock into increases in suburban location, this conversion occurred more efficiently for Whites and Latinos than for Asians and Blacks. However, statistical tests indicate that we cannot reject the null hypothesis that the between-group differences are statistically indistinguishable from zero. Hence, although the point estimates for Whites and Latinos are more than double that for Asians, the relatively inconsistently measured association for Asians renders the differences not statistically significant.

In Model 2 we control for the relationship between changes in the supply of affordable housing and jobs in the suburbs. In contrast to Table 2, the presence of these controls and the ecological context variables did not attenuate the correlations of changes in housing supply. In addition, only changes in affordable rental housing were associated with changes in the suburbanization of the Latino poor. This indicates that changes in overall housing supply were independently related to changes in the rate at which the White, Black, and Latino poor suburbanized; that is, net of correlations between changes in overall housing supply, affordable housing supply, and suburban employment demand.

Model 2 also controls for changes in MA population size, percent racial/ethnic group, plus region and age. For the first two variables we observe no consistently measured associations; hence, it does not appear that changes in MA size or racial/ethnic distributions were significantly related to change in poverty suburbanization, net of all other variables in the models. Similarly, we found no significant differences by region in the rate at which the poor of these four racial/ethnic groups suburbanized. Finally, we observed relatively uniform associations of MA age, with the poor in older MAs suburbanizing more rapidly than the poor in newer MAs. For example, compared to the oldest MAs, those whose central city passed 50,000 prior to 1900, the Asian poor in the newest MAs were suburbanizing at a 4.51% per decade lower rate. The combination of the cross-sectional and temporal correlations suggests that older MAs had lower minority suburbanization rates to begin with, and therefore had more “room” to increase over time.

CONCLUSIONS

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies

In this article, we examined racial and ethnic differences in levels of and changes in the suburbanization of the poor from 1980 to 2010 in U.S. metropolitan areas. We hypothesized that the suburbanization of the poor would be influenced in part by growth in the relative supply of housing in the suburbs of MAs, controlling for housing affordability and labor demand. We found support for this hypothesis, though the associations were much stronger for Whites and Asians than for Blacks and Latinos. For Blacks and Latinos, the availability of affordable housing in the suburbs predicts an increasing suburban presence of the poor. For Latinos, increases over time in the supply of affordable rental housing were associated with increased poverty suburbanization.

We also found that the suburbanization of jobs impacts racial and ethnic groups in different ways. We found much stronger relationships between suburban employment and suburban residence of the White and Asian poor compared to their Black and Latino counterparts. Our results cannot definitively explain this relationship, but it likely issues from a variety of contextual factors that either induce or block the suburbanization of the poor, independent of the effects of housing supply (Storper, 2011).

In examining change from 1980 to 2000 in the suburbanization of poverty, we found mixed results. Changes in suburban housing supply positively influenced the rate of poverty suburbanization for Whites, Blacks, and Latinos. The age of MAs was more strongly related to change in poverty suburbanization than was region, likely owing to differences in the historical concentration of the poor in central cities. In younger cities, rates of increase in poverty suburbanization were lower, as these MAs likely had higher shares of the poor already residing in the suburbs by 1980.

This article demonstrates that the suburbanization of the poor is not a recent development, nor has it radically changed over the past three decades. By 1980, approximately half of the metropolitan White poor already lived in the suburbs, and this percentage has changed little in the ensuing 30 years. Although the White poor are much more likely than their minority counterparts to live in the suburbs, substantial fractions of the poor of each of the other groups lived in the suburbs by 1980, and these percentages have also been growing slowly over time. Hence, suburbs overall have neither been exclusive sites of White affluence nor have they undergone a radical compositional transformation in the 30 years under consideration. They have always been more socioeconomically diverse than is sometimes presumed (Berger, 1960; Gans, 1967; Schnore, 1957). Academic research suggests that suburbs are not simply bedroom communities for the affluent, nor are they racially and socioeconomically homogenous (Mikelbank, 2004). Our findings support this line of scholarship.

Despite the growing presence of the poor in suburbs, we stress that achieving suburban residence is not necessarily a path to dramatic improvements in quality of life. Suburbs are not only areas with larger lawns and bigger houses; they are municipally independent political units with the power to mold their own destinies (Teaford, 2008). Therefore, the economic profile of individual suburbs is vitally important to understanding the quality of life of its citizens. For example, some evidence suggests that the suburban poor face more barriers to much needed services than the poor in central cities. In many suburbs the low-income health care infrastructure is underdeveloped and the spatial location of service providers requires significant travel, both of which can be a burden on the poor (Andrulis, Duchon, & Reid, 2004). More generally, Harris (1999) shows that many low-status suburbs are more disadvantaged than central cities.

There is a tragic irony here, in that the poor may be increasingly moving to suburban areas to escape social isolation and concentrated inner-city poverty precisely during the period when the nonpoor are moving further outward, in addition to reclaiming choice central city locations. When the affluent segregate themselves in suburban enclaves, they sever the social relations that provide low and moderate income communities with job networks, role models, and political clout. That is, the damaging effects of what Reich (1991) calls the “secession of the successful” are more pronounced when the affluent move not just into separate neighborhoods, but separate municipalities, thereby siphoning off fiscal and political resources. To the extent that poverty suburbanization is occurring in conjunction with the simultaneous exurban and central city concentration of affluence (Dwyer, 2007; Reardon & Bischoff, 2011), then the suburbanization of the poor may be seen more as a reconstitution of old spatial inequalities than a step toward their amelioration.

ENDNOTES
  1. 1

    At least relative to the entry level work available in central cities. Raphael and Stoll (2010) find that in the 50 largest U.S. metropolitan areas most employment (72%) is located at least five miles from the central business district.

  2. 2

    The relationship between employment decentralization and population suburbanization appears to be strongest for the nonpoor, in that their suburbanization is more strongly associated with employment decentralization. This suggests other factors may be at play in the suburbanization of the poor, such as available affordable housing or the presence of social networks.

  3. 3

    Clearly, rising poverty rates in particular suburbs are due to a combination of changes in the poverty status of resident families, in-migration of poor families, and out-migration of nonpoor families. Assessing the precise contributions of each of these mechanisms to overall changes in poverty suburbanization is beyond the scope of this article.

  4. 4

    Because the causal relationships are unclear from this analysis, we cannot say with confidence whether housing supply increases and then job growth occurs or whether job growth happens first, with increases in housing supply to follow. We are most interested in the overall relationship between suburbanization of housing supply and the suburbanization of the poor. Our baseline model reflects this interest. We control for jobs and affordable housing to see what independent effects of housing supply remain.

  5. 5

    A reviewer was concerned about the nonindependence of observations of Primary Metropolitan Statistical Areas clustered within Consolidated Metropolitan Statistical Areas. The HLM software does not assume that observations are independent and identically distributed at either level 1 or level 2, so we are confident that the standard error estimates are not downwardly biased.

  6. 6

    We define suburbs as census tracts that are located primarily or entirely not in the central cities of metropolitan areas. So in order for a MA to have “suburbs” it could neither have only a central city (like Anchorage, AK) nor no central city (like Nassau County, NY).

  7. 7

    In addition to the quartile version, we experimented with a 30% and 40% cutoff, and the results do not change appreciably. There is also a technical reason for not using a percentile much lower than the median—in many MAs the share of suburban housing in the lowest quartile or quintile is zero. This would not be a problem for the models estimated in equation 4a, but would make it impossible to calculate per-decade change in the suburban share of affordable housing as shown in equation 1, because the natural log of zero (which would be obtained if the numerator of equation 1 were zero) is undefined. Thus, using a definition of housing affordability much lower than the median would result in the loss of many level 2 cases.

  8. 8

    The White-Black and Asian-Black differences in these coefficients are significant at the .05 level. The White-Latino and Asian-Latino differences are significant at the .10 level, though we again urge caution in interpreting the standard errors as conventional estimates of sampling variability.

  9. 9

    For owner-occupied housing, the White-Black difference is significant at the .05 level, the White-Latino difference is significant at .10, and the Asian-Black and Asian-Latino differences are significant at the .001 level.

  10. 10

    Each of the White or Asian vs. Black or Latino differences is significant at at least at the .05 level.

  11. 11

    An alternative explanation is that employer relocation to the suburbs occurs after the White and Asian poor move to the suburbs, relative to their Black and Latino counterparts. Due to data limitations we cannot rule out this possibility. Recent work suggests, however, that employment growth occurs in areas of concentrated White affluence and that poor minorities tend to follow jobs rather than jobs locating where poor minorities settle (Liu & Painter, 2011).

  12. 12

    An alternative explanation not able to be ruled out by our analysis is that poverty suburbanization is largely driven by racial and ethnic differences in residential preferences. If jobs suburbanized in areas that fit the residential preferences of poor Asians and Whites, for example, then the result would be poverty suburbanization driven by residential preferences rather than job suburbanization.

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  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies
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Biographies

  1. Top of page
  2. ABSTRACT
  3. BACKGROUND
  4. DATA
  5. MEASURES
  6. METHODS
  7. FINDINGS
  8. CONCLUSIONS
  9. REFERENCES
  10. Biographies
  • Aaron J. Howell is Visiting Instructor of Sociology at Oberlin College and is a Doctoral Candidate in the Department of Sociology at the University of Cincinnati. His research examines urban inequality, with an emphasis on the relationship between residential choice, race and ethnicity, and neighborhood stratification.

  • Jeffrey M. Timberlake is Associate Professor of Sociology at the University of Cincinnati. His research interests are in the sociology of population, urban sociology, race and ethnicity, and quantitative research methods. His current research focuses on causes and consequences of urban inequality. Recent projects include analyses of racial and ethnic residential segregation, exposure of children to neighborhood poverty and violence, attitudes of Ohioans toward immigrants and immigration, and urban demographic change from 1970 to 2010. He has recently published papers in Demography, Urban Affairs Review, and Social Science Quarterly.