Local Opportunity Structures and Intergenerational Neighborhood Income Mobility
Jeremy Pais, Ph.D.
Department of Sociology
University of Connecticut
344 Mansfield Rd. Unit 2068
Storrs, CT 06269-2068
Phone: 860-486-0391
E-mail: j.pais@uconn.edu
July 2014
Word count: 12,145 (excluding tables)
Number of Figures:6
Number of tables: 4
Key words: income mobility; intergenerational elasticity; local labor markets;
multigenerational; residential attainment; social mobility; spatial mobility
Direct correspondence to Jeremy Pais, Department of Sociology, University of Connecticut,
Storrs, CT 06269. E-mail: j.pais@uconn.edu. This research was supported by an internal faculty
grant to the authors from the University of Connecticut.
Local Opportunity Structures and Intergenerational Neighborhood Income Mobility
Abstract:
This study develops an analytical framework for understanding and evaluating the importance of
the local opportunity structure for patterns of intergenerational spatial mobility. Of key interest
are legacy effects: The effects of residential constraints to spatial mobility in one generation that
manifest themselves in future generations. This framework helps us understand the multiple
pathways through which past and current levels of racial residential segregation, racial and ethnic
diversity, economic specialization, suburban sprawl, and urban development affect residential
outcomes and stratification processes more generally. There is considerable metropolitan-area
heterogeneity in the effect of parent’s neighborhood status (first generation) on their adult
children’s neighborhood attainments (second generation). Focal metropolitan-area characteristics
account for about a third of the intergenerational elasticity variation that exists across
metropolitan areas. For black families, the effect of racial residential segregation on
neighborhood income attainment operates almost entirely through the residential constraints
placed on the first generation.
1
Introduction
The connection between social mobility and spatial mobility is a pillar of social
stratification theory (Park, Burgess, and McKenzie 1925; Massey 1985). A comprehensive
understanding of one form of mobility is not complete without the other. Empirical research on
intergenerational social mobility, however, outpaces intergenerational spatial mobility research
by a considerable margin. Cases in point are the many recent studies on the intergenerational
income elasticity—a common indicator intergenerational income mobility (e.g., Solon 1992;
Graham and Sharkey 2013; Hertz 2005; Aaronson, and Mazumder. 200; also see
http://www.equality-of-opportunity.org/). Only recently have researchers set out to quantify in a
similar fashion the strength of the intergenerational elasticity with regard to residential location
(Sharkey 2008; Vartanian, Buck and Gleason 2007). Current estimates using the same data
source and methods have the intergenerational income elasticity in the United States as .534
(Hertz 2005), compared to a slightly more rigid intergenerational elasticity estimate of .640 for
neighborhood income (Sharkey 2008). These estimates suggest that more than half of the
respective income and neighborhood advantages/disadvantages are transmitted from one
generation to the next.
From a conceptual standpoint the lion’s share of recent interest with intergenerational
income mobility is surprising given that the social mechanisms and processes that in large
measure facilitate or hinder income mobility originate in neighborhood environments (discussed
in more detail below). In fact, many of the factors that affect intergenerational spatial mobility
should naturally supersede the causes of income mobility; and thus, intergenerational mobility
research in general faces certain deadlock without a more rigorous understanding of the key
2
issues that affect intergenerational spatial mobility. This study develops an innovative framework
that brings these key issues to light.
The theoretical framework for this study relies on both macro-level conditions and microlevel social processes to explain why local opportunity structures are vital for understanding
intergenerational mobility. The local opportunity structure is essential from both micro and
macro perspectives because it is where residential aspirations and residential constraints are
actualized. To better understand this process, a nuanced temporal approach is taken that (i)
accounts for the way contemporary structural conditions shape the current residential mobility
landscape, but (ii) also acknowledges the different ways historical-structural antecedents can
influence the residential attainments of future generations. The latter possibility assumes the
existence of legacy effects: These are the effects of residential constraints to mobility in one
generation that manifest themselves in future generations primarily through human capital
development or through the cultural conditioning of residential preferences and aspirations. This
legacy perspective will help formulate more comprehensive explanations for why racial
residential segregation, racial and ethnic diversity, economic specialization, suburban sprawl,
and urban development affect neighborhood attainment and mobility outcomes. This perspective
will also provide guidance for intergenerational mobility research more generally.
There are four empirical objectives that will be used to evaluate this framework. First, I
replicate prior estimates of the intergenerational elasticity of neighborhood income using updated
data from the Panel Study of Income Dynamics (PSID). The replicated estimates are equivalent
to those in the literature (Sharkey 2008). These estimates serve as a foundation from which to
build. The second objective involves assessing the extent to which intergenerational
neighborhood income mobility varies across different metropolitan areas. Metropolitan-areas are
3
proxy geographies for local opportunity structures. This objective establishes how much
explanatory power local opportunity structures have for the intergenerational mobility process.
The third and fourth objectives evaluate hypotheses involving specific metropolitan-area
characteristics. The third objective assesses how these structural conditions are mediated by the
intergenerational transmission process. This objective evaluates the legacy effects thesis. The
fourth objective assesses the way intergenerational spatial mobility is moderated by various
structural conditions. This objective provides novel insight into the dynamics that are shaping the
residential outlooks of future generations. Conceptually and empirically this study is a step
forward in the area of intergenerational spatial mobility research.
Social Stratification and the Intergenerational Transmission of Neighborhood Status
The importance of spatial mobility, or the lack of, for the geographic concentration of
affluence and poverty in America widely recognized. The persistence of urban poverty, for
example, is a perennial issue that is sustained in large measure by the perpetual exposure of
children, generation after generation, to impoverished communities. This is because
neighborhood poverty is associated with a dearth of positive role models, higher dropout rates,
lower levels of educational attainment, higher rates of teenage pregnancy, poorer health
outcomes, an increase of youthful involvement in crime and delinquency, and stunted cognitive
development (for reviews see: Diez-Roux 2001; Ellen and Turner 2003; Jencks and Mayer 1990;
Pickett and Pearl 2001; Robert 1999; Sampson, Morenoff, and Gannon-Rowley 2002; Small and
Newman 2001). Often these associations persist apart from parental socioeconomic resources.
Through processes of collective socialization, social and spatial isolation, and the unequal
distribution of institutional resources, neighborhood and community environments come to
facilitate or impede the acquisition of the types of attitudes, skills, and attributes that help
4
determine socioeconomic achievements later in life (Jencks and Mayer 1990). This is why the
intergenerational transmission of neighborhood environment is considered a critical aspect of the
stratification system (Sharkey 2008).
There are several theories that are useful for understanding why childhood residential
environments affect adult outcomes. Human capital theories, for instance, suggest that
advantaged residential locations allow families to more easily transfer instrumental skills and
knowledge from one generation to the next (Borjas 1995). Access to good schools, and to other
children from affluent families, does not guarantee high levels of human capital acquisition in
the next generation, but advantaged locations improve the chances above and beyond parental
resources alone (Sharkey and Elwert 2011). Adult offspring with high levels of human capital
are then able to afford housing in affluent neighborhoods that are of similar stature as their
childhood neighborhoods.
On the opposite end of the stratification hierarchy, families with poorly rewarded job
skills and limited financial means are more likely to live in neighborhoods that subject their
children to inferior educational institutions, few occupationally successful role models, and an
informal street economy with lucrative short-run opportunities through criminal enterprises
(Sampson, Raudenbush, and Earls 1997; Venkatesh 2006). These locational disadvantages make
it more difficult, but not impossible, to acquire the types of skills and attributes that are needed to
gain access to more affluent residential spaces. The overarching implication of this perspective is
that the quality of child’s neighborhood growing up will impact the accumulation of human
capital in ways that affect, to some degree, adult neighborhood outcomes.
An equally pertinent theory comes from the residential satisfaction perspective (Landale
and Guest 1985; Speare et al. 1975), which focuses on personal and lifecourse circumstances that
5
influence residential choices. A variant of the residential satisfaction perspective that is relevant
for the intergenerational transmission process involves cultural conditioning. Cultural
conditioning maintains that adult residential choice is a product of the preferences that are
shaped by childhood neighborhood experiences (Vartanian et al. 2007). For instance, Sampson
and Wilson (1995) maintain that people develop “cognitive landscapes” of the ecological
structure of their communities, which are built on perceptions of racial, ethnic, and/or class based
distinctions. These mental maps then tend to inform people’s residential preferences later in life.
Children come to learn these mental maps, and in the process develop a sense of place in the
larger ecological structure of the community. Wanting, or in some cases needing, to maintain
spatial proximity to friendship networks and family ties also conditions residential preferences
(Bell-McDonald and Richards 2008; Hedman 2013). From this perspective, a socioenvironmental disposition appears to develop in next generation through micro-level cultural and
social processes that are constituted in a child’s milieu.
Place stratification theory is the third useful theoretical perspective for understanding the
intergenerational transmission of neighborhood status. Place stratification theory recognizes how
the spatial-ecological structure of a community is controlled and manipulated by powerful group
interests in an effort to maintain physical and social separation from populations they deem
undesirable (Charles 2003; Logan and Molotch 1987). Power and race relations are at the center
of place stratification theory because the residential preferences of the racial majority can
avertedly or inadvertently impede the actualization of minority group residential aspirations.
Although whites’ acceptance of minority neighbors has increased, whites still tend to prefer
predominantly white neighborhoods over integrated neighborhoods, whereas black residents tend
to prefer more ethnically integrated neighborhoods than do whites (Charles 2006; Krysan and
6
Bader 2007). These modest group differences in neighborhood preferences can create enduring
levels of residential separation because the preferences of more powerful groups—powerful in
terms of population size and economic resources—produce macro-level mobility constraints for
less powerful groups (cf. Schelling 1971; Bruch and Mare 2006). From this perspective it is
necessary to examine the effects of the local opportunity structure on the intergenerational
neighborhood attainment separately for majority and minority groups.
There is a longstanding tension in the residential mobility literature between the
residential satisfaction perspective and place stratification theory. Whereas the residential
satisfaction perspective tends emphasize individual preferences in shaping neighborhood
outcomes, place stratification emphasizes structural constraints that limit neighborhood choices
for less powerful groups. The tension in the literature stems from the inability of researchers to
simultaneously account for the long-run effects of preferences and the long-run effects of
constraints on neighborhood attainments (e.g., see Crowder et al. 2012; Sharkey 2008:941).
Arguably, however, the distinction between preferences and constraints is too simple a
dichotomy from an intergenerational vantage point: If neighborhood preferences are largely
formed through cultural conditioning that takes place in spatial environments of racial and class
bifurcation then the next generation’s residential preferences are likely affected by the preceding
generation’s structural constraints. This is the premise behind W. J. Wilson’s (2010) recognition
of how the local opportunity structure provides the context within which cultural responses are
developed. This study evaluates and extends this premise by formulating hypotheses that speak
to the historical and contemporary influences of key structural conditions on the
intergenerational transmission of neighborhood status in the United States.
7
A Framework for Examining the Legacy of the Local Opportunity Structure
The main objective of this study is to examine how the local opportunity structure affects
neighborhood attainment from one generation to the next. The premise guiding this study is of an
essential interplay between the larger opportunity structure and the social processes that occur
within residential settings. Two conceptual dimensions of the intergenerational transmission
process highlight the importance of this interplay.
The first dimension is temporal. The focus is on when exposure to different structural
conditions occurs. For instance, the current metropolitan structure can affect a new generation of
home seekers directly through the immediate opportunities and structural constraints emerging
from local housing markets and local labor markets. This direct pathway is established in the
neighborhood attainment literature, and it is best characterized by the “housing availability
model” of residential mobility (e.g., South and Crowder 1997; Pais et al. 2012; South et al.
2011). However, a second pathway may also exist through the exposure of the first generation to
the constraints of the local opportunity structure when the parents sought appropriate housing for
raising families. A parent’s exposure to the opportunity structure will indirectly affect the status
outcomes of their adult children by determining the social environments in which human capital
is developed and cultural conditioning occurs. It is through these indirect pathways, or legacy
effects, that historical-structural antecedents can have long-term implications for stratification
outcomes.
Despite the clear conceptual significance of both the direct and indirect pathways, there is
little explicit acknowledgement in the neighborhood attainment literature of their co-existence,
and the empirical nature of this co-existence is entirely unexplored. It seems a comparison of
direct and indirect contextual pathways to neighborhood attainment is warranted, as it will
8
provide a window into the relative explanatory power attributed to long-term historical-structural
antecedents versus contemporaneous structural opportunities in affecting neighborhood
outcomes.
The second key distinction stems from a longstanding recognition in the social
stratification literature between status attainment and status mobility (Blau and Duncan 1967).
This distinction is germane here for several reasons. Conceptually, different patterns of mobility
and attainment will likely impart on the next generation different neighborhood aspirations and
preferences. Ideally, the opportunity structure should produce a higher average degree of
neighborhood attainment and a higher rate of intergenerational spatial mobility. This is ideal
because later generations, especially from poorer strata, are more inclined to develop
advantageous aspirations and preferences in areas with these characteristics (cf. Wilson 1959).
Theoretically, however, four different patterns are plausible: (1) High attainment/high mobility;
(2) low attainment/low mobility; (3) high attainment/low mobility; and (4) low attainment/ high
mobility, and specific structural characteristics may affect the average level of neighborhood
attainment as well as the average rate intergenerational neighborhood mobility in different ways.
Racial group heterogeneity can further complicate these patterns.
These two dimensions can now be used to help develop specific hypotheses. Figure 1 is
useful for identifying the different kinds of research hypotheses addressed in this study. It
illustrates the key pathways and effects identified by the proposed framework for understanding
the effects of the local opportunity structure. These key effects include: (a) the intergenerational
elasticity between parent’s and adult child’s neighborhood attainments (i.e., status mobility
between generations); (c) the direct effect of the local opportunity structure on the average level
neighborhood attainment among the second generation; (a * b) the indirect effect of the local
9
opportunity structure on the average level neighborhood attainment among the second generation
that operates through the residential constraints on the parent’s neighborhood attainment; and (d)
the cross-level interactions involving the effects of the local opportunity structure on the strength
of the intergenerational elasticity. Hypotheses concerning each of these pathways and effects are
developed for specific structural conditions (e.g., H1; H1a; H1b; H2). For this study there are
several metropolitan-area characteristics in the spatial stratification literature that capture key
aspects of this larger opportunity environment. Broadly, these metropolitan-area characteristics
include residential segregation, racial and ethnic population composition, economic
specialization, urban development, and suburban sprawl (e.g., Farley and Frey 1994; Jargowsky
1996; Logan et al. 2004; Pais et al. 2012; Sassen 2006; South et al. 2011a).
[Figure 1 about here]
Racial residential segregation is a critical feature of the local opportunity structure
(Galster 1991; Massey and Denton 1993), but it operates differently for majority and minority
groups. In highly segregated metropolitan areas, minorities tend to live in more disadvantaged
neighborhoods (Krivo et al. 2009), while whites tend to live in whiter and wealthier
neighborhoods in these highly segregated areas (Pais et al. 2012). Two intergenerational
transmission pathways can account for these residential patterns. These two pathways are not
mutually exclusive and both are supported by place stratification theory. First, current levels
segregation being highly correlated with past levels of segregation can directly affect an adult
child’s chances of upward/downward spatial mobility in the same fashion it hindered/advanced
the parent’s residential opportunities. That is, the level of residential segregation can impose an
ecological rigidity on the residential geography of U.S. cities in ways that directly recreate each
succeeding generation’s residential opportunities. However, segregation may also operate
10
indirectly by affecting the residential attainments of the parents, which then in turn exposes
young children to areas of varying levels of concentrated disadvantage where micro-level social
and cultural processes condition future generations in ways that make social and spatial mobility
more or less likely. By using first generation’s neighborhood attainment as a mediator of
residential segregation (and other structural characteristics), it is possible to empirically
disentangle the direct and indirect role of residential segregation in shaping neighborhood
outcomes, generation after generation.
Accordingly, we should expect (H1) higher levels of racial segregation to decrease the
neighborhood attainments of black families and increase the neighborhood attainments of white
families, and this should occur because (H1a & H1b) the effects of racial segregation will
operate in an indirect fashion (H1a) and a direct fashion (H1b). We should also expect, racial
residential segregation to (H2) decrease intergenerational spatial mobility for black families (i.e.,
increase the intergenerational elasticity) but increase intergenerational spatial mobility for white
families (i.e., decrease the intergenerational elasticity). Expectations concerning the relative
strength of the indirect and direct effects stemming from residential segregation cannot currently
be developed from the extant literature. It must instead be treated as an empirical question.
A metropolitan area’s racial and ethnic population composition is another structural
characteristic that may affect the intergenerational transmission process of neighborhood
attainment. There are two primary perspectives that guide expectations. One perspective
highlights the longstanding ecological ramifications posited by group-threat theory, which
predicts stronger white avoidance of black neighborhoods, and greater black preference for black
neighborhoods, in highly concentrated black areas (Charles 2000). The housing availability
model, on the other hand, recognizes the demographic reality that affects the probability of
11
having greater or fewer neighborhoods of the opposite race. For example, prior research finds
that large relative shares of black population increases both black and whites exposure to black
neighborhoods (South et al. 2011b). Yet, despite the strong correlation between neighborhood
poverty levels and neighborhood racial composition (Quillian and Pager 2001), racial population
composition at the metropolitan level has little net effect on average neighborhood income for
either white or black households (Pais et al. 2012).
In support of the group-threat perspective, we should expect (H1) higher levels of racial
population concentration to decrease the neighborhood attainments of black families and
increase the neighborhood attainments of white families, and this should primarily occur by
(H1a) the indirect effects of racial concentration on the second generation’s neighborhoods
attainments and (H2) the decreasing of black’s and the increasing of white’s intergenerational
spatial mobility. Conversely, and in support of the housing availability model, we should also
expect (H1) higher levels of racial population concentration to decrease the neighborhood
attainments of black and white families, but this should primarily occur by (H1b) the direct
effect of racial concentration on the second generation’s neighborhoods attainments and (H2) the
decreasing of black’s and white’s intergenerational spatial mobility.
For similar reasons, the relative size of the foreign-born population in the metropolitan
area may influence the intergenerational transmission process. A large local presence of foreignborn population may foster, or conversely weaken, neighborhood class divisions by increasing
residential exposure to racial and ethnic diversity (Fischer and Tienda 2006; Logan and Zhang
2010). This could have three consequences. First, according to the housing availability model,
increasing numbers of foreign-born residents will facilitate greater class integration among
whites and blacks, which will be reflected in greater levels of intergenerational spatial mobility,
12
but a general decline in neighborhood income status for all groups. Place stratification, on the
other hand, posits that levels of immigrant population concentration may further precipitate
white flight and white avoidance of minority neighborhoods (e.g., Crowder, Hall, and Tolnay
2011; Wilson and Taub 2007), thereby exposing black households to declining neighborhood
incomes but having the opposite effect for white households. A third possibility suggests that the
new residential opportunities for class integration created by high levels of immigration will
primarily benefit black households as the residential dichotomies between white and black
neighborhoods weakens through added diversity. This is often referred to as the buffering
hypothesis (Frey and Farley 1996).
In support of the housing availability model, we should expect (H1) higher levels of
foreign-born population concentration to decrease the neighborhood attainments of black and
white families, and this should primarily occur by (H1b) the direct effect of foreign-born
concentration on the second generation’s neighborhoods attainments (given the rapid increase in
immigration beginning in the late 1980s) and (H2) the increasing of black’s and white’s
intergenerational spatial mobility (i.e., lower attainments/higher mobility). According to the
place stratification perspective, we should expect (H1) higher levels of foreign-born population
concentration to decrease the neighborhood attainments of black families and increase the
neighborhood attainments of white families, and this should primarily occur by (H1b) the direct
effects of foreign-born concentration on the second generation’s neighborhoods attainments, and
(H2) the decreasing of black’s and the increasing of white’s intergenerational spatial mobility.
Finally, the buffering perspective anticipates (H1) higher levels of foreign-born population
concentration to increase the neighborhood attainments of black families and decrease the
neighborhood attainments of white families, and this should primarily occur by (H1b) the direct
13
effect of foreign-born concentration on the second generation’s neighborhoods attainments, and
(H2) the increasing of black’s and the decreasing of white’s intergenerational spatial mobility.
A metropolitan area’s employment specialization is another factor that can shape the
residential landscape (e.g., Farley and Frey 1994; Logan et al. 2004). From the housing
availability perspective, areas with many highly educated workers and many high paying jobs
will produce better kinds of residential opportunities than areas with many low paying jobs.
Major industrial shifts towards high finance and information technology, on the one end, and
poor paying service jobs, on the other end, coupled with deindustrialization and the hollowing
out of the manufacturing core in many U.S. cities have helped to spatially bifurcated the rich and
poor (Jargowsky 1996; Sassen 2006). The emergence of the public sector as a source of stable
mid-tier employment, especially among urban blacks (Waldinger 1996), seems to have provided
at least a partial stopgap against these macro-level trends. Thus, economic transformations over
the last half a century involving shifting employment concentrations in key industries, such as,
high finance, insurance and real estate (FIRE), government, manufacturing, and the low-skilled
service sector have helped to create today’s residential hierarchies.
Accordingly, we should expect (H1) higher paying industries (e.g., FIRE) to be
associated with higher levels of neighborhood status attainment for black and white families than
mid-tier paying industries (e.g., government and manufacturing) and low paying industries (low
skilled service sector). However, it is difficult to anticipate a priori (H1a & H1b) the relative
strength of the indirect and direct effects stemming from these labor market characteristics.
Therefore, the expectation is that the sub-hypotheses should operate in the same direction as H1,
all else equal. However, there is sound reason to anticipate (H2) industry effects on
intergenerational spatial mobility: Employment specializations associated with increasing
14
income inequality and residential bifurcation (e.g., higher levels of FIRE and low-wage serve)
are expected to reduce prospects for intergenerational spatial mobility; whereas industries that
provide stable mid-tier employment (e.g., higher levels of manufacturing and government
employment) should facilitate greater levels of intergenerational spatial mobility.
Urban development and suburbanization may affect the intergenerational transmission
process as well. Large supplies of new housing could create improved opportunities for
neighborhood attainment, but this could largely depend on what kinds of units are being built
(single family vs. multiunit rental properties), and where the new units are being built. New units
are often built as a result of affordable housing initiatives, suburban expansion, and urban
renewal, and these development projects generate new units in poor and nonpoor areas alike (cf.
Dwyer 2007; Galster et al. 2001; Massey et al. 2013). Of course, this will have different
structural implications for neighborhood attainment. For instance, South (et al. 2011a) finds new
housing built in nonpoor census tracts increases the probability of moving into nonpoor
neighborhoods for whites and blacks. Yet, there is little indication that new housing in general
improves neighborhood income status (Pais et al. 2012). On the other hand, large supplies of new
housing could create opportunities for more spatial mobility regardless of where the housing is
built. New housing creates short-term vacancies in other parts of the city, and this spurs
increased opportunities for residential migration.
Accordingly, it is difficult to expect in advance whether (H1) higher levels of new
housing will increase or decrease neighborhood income status, and more difficult still to
anticipate the legacy of new housing on the second generation’s neighborhoods attainments (H1a
& H1b). But, there is adequate reason to expect (H2) new housing to the increase
intergenerational spatial mobility for black and white families.
15
The level of suburbanization in a metropolitan area might also affect neighborhood
attainment and spatial mobility across generations. In metropolitan areas with large suburban
areas there could be greater neighborhood heterogeneity, and therefore, more opportunity for
upward and/or downward spatial mobility. On the other hand, highly suburbanized metropolitan
areas tend to contain numerous independent municipalities, which can use zoning policies and
other strategies to prevent the in-migration of poor populations (cf. Yang and Jargowsky 2006).
Place stratification perspectives maintain that these barriers to suburban entry are particularly
pronounced for minority households (Alba and Logan 1991). Current research suggests,
however, that urban renewal and suburban sprawl are linked to significant increases in suburban
poverty; as better-off households vacate housing in older inner-ring suburban areas—either to
return to gentrifying neighborhoods or to attain greater housing amenities in exurban areas
(Knox 2008)—the resulting effect is has been to increase affordable housing in large suburban
areas (Howell and Timberlake 2013). These contemporary patterns suggest that large suburban
areas may actually lack the type exclusivity found in smaller suburban areas.
Therefore, there is relatively weak reason to expect (H1) higher levels of suburbanization
to simply increase average neighborhood attainments, as smaller suburban areas are likely more
exclusive, particularly for white households. However, given the fairly recent novelty of
suburban poverty, it is reasonable to expect the direct and indirect effects of suburbanization to
operate differently. More specifically, (H1b) whites should be more affected by current direct
levels of suburbanization, possibility in a more negative direction; whereas given the legacy of
suburban exclusion of minorities, we should expect (H1a) the indirect effect of suburbanization
to be stronger and negative for black families. Lastly, we should expect (H2) large suburban
areas to increase intergenerational spatial mobility for both whites and blacks.
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Data and Methods
Following Sharkey (2008), this study estimates intergenerational neighborhood income
elasticities using four additional waves of the Panel Study of Income Dynamics (PSID). The
PSID started in 1968 with an initial panel of approximately 4,800 families that were interviewed
annually until 1995 and biennially up to the latest round in 2011. The PSID follows the
progression of multiple generations over time because new families are added to the panel as
children in the original panel split-off to form their own households. With the PSID-Geocode
Match File, researchers can merge geographic information (e.g., census-tract and metropolitanlevel census data) to individual PSID records. The PSID is the only multigenerational data
source in the U.S. with enough geographic coverage to study the structural determinants of
intergenerational spatial mobility.1
Sample Selection and Neighborhood Status: The sample selection and core variable
construction follows the same design as Sharkey (2008:945). Two PSID files are created: a
parent file (aka first generation), including all PSID respondents that are ever parents (adoptive
parents included); and a child file (aka second generation), including all PSID respondents that
were ever children of a PSID parent. Pairs of parents and children are then matched using family
IDs provided by the PSID. Matched family pairs are included in the sample if they meet the
following criteria: (1) the “parent” is observed as the head of household at age 26 or older in at
least one year where the “child” was under 18 years old; (2) the “child” is later observed as an
adult head of household or a spouse/partner of a head of household at age 26 or older. Data
1
The PSID, and the specific use of the data here and in Sharkey, are not without limitation and complication. Of
specific concern here is the geographic representativeness of the PSID sample for estimating MSA-level effects, and
the sampling weight adjustments using a multilevel estimator. Sensitivity checks that address these issues are
provided below. For further discussion and sensitively checks dealing with general issues of PSID
representativeness, attrition bias, and sampling weight adjustments see Sharkey (2008, footnotes 5, 8, 16). The
combined information here, and in Sharkey (2008), should minimize (not eliminate) concern about biased estimates
and type-one error. The geographic limitations of the data do likely produce higher (i.e., more conservative) typetwo error rates at the MSA-level.
17
constraints limit our sample to only black and white families. This procedure produces 4,633
parent-child pairs with valid geo-identifiers, which is slightly more than Sharkey’s 4,464 family
pairs because of the additional four waves of PSID data used here.
The key measure of neighborhood status is average neighborhood income. Data for this
measure comes from the Neighborhood Change Data Base (NCDB), which provides decennial
U.S. Census data from 1970 to 2010 normalized to the census tract boundaries in 2010
(GeoLytics 2013). Linear interpolation and extrapolation is used to estimate average
neighborhood income for non-census years (1968, 1971-1979 1981-1989, 1991-1999, 20012009, 2011). Average neighborhood income is converted into real 2000 dollars using the
Consumer Price Index. The census tract data are then merged with the parent’s file and the
child’s file in long format (i.e., person-year). The next step is to adjust neighborhood income by
using four auxiliary regressions, where neighborhood income is regressed on age, age-squared,
and family-level fixed effects. That is, one regression model for (1) white first generation; (2)
black first generation; (3) white second generation; and (4) black second generation. The
predicted values of neighborhood income at age 40 are derived from these regressions including
family-fixed effects. These predicted values are then averaged over the duration of the study
period for each respondent according to the same criteria above: (a) the parent was older than 26
with children under age 18 and (b) the child was out of the parent’s home and older than 26.
These average exposure measures are then logged. Hertz (2005) explains how these adjustments
help minimize measurement error and provide equal footing for first and second generation
comparisons at age 40.
Local Opportunity Structure: The focal independent variables for this study are a series
of metropolitan-area characteristics. Metropolitan areas are typically used as geographic
18
delineators of local labor markets. Metropolitan-level data come from the U.S. Census of
Population and Housing Summary Files (U.S. Department of Commerce) unless noted otherwise.
Crosswalks are used to convert historic MSA codes to current Office of Management and Budget
CBSA codes(see https://www.census.gov/population/metro/; and http://www.nber.org/data/cbsamsa-fips-ssa-county-crosswalk.html). The construction of these MSA variables follows the same
averaging procedure as the neighborhood income variables: MSA-level values are interpolated
and extrapolated for non-census years; these values are then merged via the PSID retro-coded
CBSA geo-identifiers to the parent file and child file in long format; then the metropolitan area
characteristics are averaged over the duration the respondents met the sample criteria set above.
No auxiliary regressions are used. These values are then linked to the match parent-child data file
to provide distinct MSA-level exposures for the first generation and the second generation.
Metropolitan-area characteristics include: Black-white residential segregation measured
using the dissimilarity index (Logan and Stults, Project US2010); the percentage of the
population that is non-Hispanic black; the percentage of the population that is foreign-born; the
percentage of the civilian labor force that is employed in either finance; insurance or real estate
industries; the percentage employed in public sector jobs (i.e., either federal, state, or local
government); the percentage in manufacturing industries; and the percentage of the civilian labor
force that is employed in the low-wage service sector (i.e., entertainment and recreation services,
hotels and lodging places, private household services, and other miscellaneous personal
services). MSA-development characteristics include the percentage of new housing units built in
the last ten years, and the percentage of the population living outside central cities constitutes the
relative size of the suburban population. In addition to these key explanatory variables, this study
also controls for MSA median income and population size, both in natural log form.
19
Control Variables: All the statistically significant independent variables predicting
intergenerational neighborhood income elasticity in Sharkey’s regression model (Table 6,
2008:956) are selected as control variables at the child-parent level. These variables include the
first generation’s family income constructed in the same fashion as neighborhood income above.
The race of the first generation’s head of household (1=black; 0=white). The gender of both the
first and second generation respondents (1=men; 0=women). The educational attainment of both
the first and second generation, which is coded categorically as less than a high school degree;
high school degree; some college; college degree or more (less than high school degree is the
reference group). Home ownership, measured as the proportion of years the first generation is
observed owning their home. And finally, a measure of parental aspiration, a scale that captures
how ambitions the respondent is with regard to improving their economic wellbeing.
Analytic Approach: This study has four primary empirical objectives. The first objective
is to replicate Sharkey’s (2008) previous analysis with more recent waves of PSID data using
OLS regression with robust standard errors and with, and without, sampling weights.
Construction of the sampling weights follows Sharkey (2008:943, footnote #6). This step simply
establishes baseline validity, and arguably improves the reliability of the intergenerational
elasticities given the increased amount of data on the second generation.
The second objective is to assess the amount of metropolitan-area variability in the
intergenerational transmission process. A series of null multilevel regression models provide the
estimates of variance that are needed to make these assessments. There are a two null regression
models that allow a random intercept for the second generation’s neighborhood income status
and a random slope for the intergenerational elasticity (i.e., the effect of the first generation’s
neighborhood status attainment on the second generation’s neighborhood status attainment).
20
Version one includes the full sample. Version two includes only those families that remained in
the same metropolitan area for the study period and that live in a metropolitan area that had at
least ten other PSID families. The more restrictive sample provides a sensitivity check of two
data issues. One issue is the potential bias caused by intermetropolitan migration. The second
issue involves geographically sparse PSID data coverage across a full range of metropolitan
areas (Clarke 2008). In the full sample, the PSID families’ modal metropolitan- area is used as
the geoID needed to calculate the metropolitan-level variance components. Relying on a modal
geoID will to some degree bias the metropolitan variance estimates because families may have
moved to different areas over the course of the study. By restricting the sample to non-movers,
we can evaluate the extent of this bias. Restricting the sample of metropolitan areas to only those
MSAs with ten or more PSID families provides robustness check against (a) the potentially
unduly influence of shrinkage in the multilevel estimator, and against (b) the fact that measures
of metropolitan characteristics are less precise in smaller areas (e.g., the dissimilarity index). The
restriction to ten or more families means that the analysis only includes the largest metropolitan
areas. These same sensitivity checks are implemented on the full cross-level interaction model
discussed below.
The third objective is to estimate the total, direct, and indirect metropolitan influences on
the second generation’s neighborhood attainment. This is accomplished via a 2-1-1 multilevel
mediation analysis (Krull and MacKinnon 2001) with one modification. Ordinary the total effect
should equal the direct effect plus the indirect effect when doing path analysis [i.e., cT = c + a*b
(see Figure 1)]. In this study, the additive combination of direct and indirect effects are not
exactly equal to the total effect because MSA exposure is different for first and second
21
generation respondents (although highly correlated).2 The direct effects of the second
generation’s MSA exposure reported here reflect current structural constraints on the second
generation’s residential choices, which includes whatever structural consistency that persists
from the first generation to the second generation (e.g., highly segregated areas tend to remain
highly segregated).
The mediation estimates are based on three regression models. The first model estimates
the total effect (cT) which regresses second generation neighborhood status on first generation
MSA measures and all family level control variables. The total effects models omit the first
generations neighborhood status and the second generation’s MSA measures. The total effects
incorporate the indirect effects that are transmitted via intergenerational elasticity and the direct
effects that influence the second generation’s exposure to MSA characteristics. The second
model estimates the proxy direct effects (c’) by regressing the second generation’s neighborhood
income status on the first generation’s neighborhood income status, family controls, and the first
generation’s MSA measures. The proxy direct effect is use to calculate the indirect effect, which
is the difference in the first generation MSA effects between model 1 and model 2 (a*b = cT –
c’). Model 3 regresses the second generation’s neighborhood status on the first generations
neighborhood status, family controls, and the second generation’s MSA measures. The direct
effects (c) come from this model. The null hypotheses are whether these effects (cT, a*b, and c)
are statistically significant from zero. The standard errors are estimated from a 1000 bootstrap
replications. Mediation analyses are done separately for white and black families.
The fourth objective seeks to explain why rates of intergenerational spatial mobility vary
across metropolitan areas. This analysis relies on a pooled multilevel model with cross-level
2
If we base the analysis only on first generation MSA exposure then the total effect does equal the direct effect plus
the indirect effect. This approach, however, would give imprecise estimates of the direct effect we are interested in
because it would not fully capture the second generation’s level of MSA exposure.
22
interactions between the intergenerational elasticity and the second generation’s MSA measures.
The pooled multilevel model also assesses the black-white difference in MSA-level effects. The
cross-level interactions are estimates for whether a metropolitan-area characteristic facilitates or
hinders intergenerational spatial mobility. Cross-level interactions also tell us, to a greater or
lesser extent, why rates of intergenerational spatial mobility vary across metropolitan areas. The
following composite equation is the pooled cross-level interaction model:
y 2ij = β 0 + β1 (y1ij ) + β 2 (black ij ) + β3 (y1ij* black ij ) + β 4 (Xij ) +
fixed effects, level one
β6 (Wj ) + β7 (Wj * black ij ) + β 7 (y1ij*Wj ) + β8 (y1ij *Wj * black ij ) +
fixed effects, level two
v0j + v1j (y1ij ) + ε ij
random effects
Formally, y2ij is the average neighborhood income among second generation respondents, y1ij is
the average neighborhood income among first generation respondents, blackij is a dummy
variable for the race of the PSID family, Xij is a matrix of family-level covariates, Wj is a matrix
of second generation MSA-level exposure variables, β0 is the average level of neighborhood
income among the second generation (holding all family and MSA-level covariates at their
grand means), which varies across metropolitan areas by v0j. β1 is the average intergenerational
elasticity which varies across metropolitan areas by v1j. (v0j and v1j are allowed co-vary). εij is
the level-one idiosyncratic error. The main effects for this model [β4(Xij), β6(Wj), β7(y1ij*Wj)]
are the main effects for white families, and the black interactions captures the racial difference in
the effects from whites [β7(Wj*blackij), β8(y1ij*Wj*blackij)] . The focus in on the β7s and β8s in
the cross-level interaction models because the main effects by race are provided in the mediation
analysis.
23
Results
Replication: Table 1 provides the descriptive statistics for white and black PSID families
using aggregate sampling weights. This sample is representative of the families created in the
post-civil rights era by black and white individuals alive in the United States in 1968. The
family-level descriptive statistics are very similar to those reported by Sharkey (2008: Table 1).
For example, the mean first generation age-adjusted log neighborhood income is 10.78 for
Sharkey and 10.79 here. The mean second generation age-adjusted log neighborhood income is
10.97 for Sharkey and 11.11 here. The slightly higher average reflects the fact that the second
generation has had more time to mature financially. The same pattern holds for education where
31 percent of the second generation had college degrees by 2003 compared to 38 percent of the
second generation with college degrees by 2011. The descriptive statistics in Table 1 also
provide insight into how metropolitan areas have changed between first and second generation
respondents. There has been a modest decline in residential segregation exposure between
generations going from an average of .69 to .60; a substantial decline in manufacturing
employment from 21% to 14%; a 7 percentage point decline in new housing but an offsetting
increase in suburbanization between generations. There has also been a 3 percentage point
increase in foreign-born population concentration and more than a doubling of employment in
the low-wage service sector. These generational differences largely mirror the population trends
over the last several decades.
[Table 1 about here]
Table 2 presents the replication analysis of Sharkey (2008: Table 6). The actual point
estimates are very similar between the two studies and the pattern of effects across model
specifications is nearly identical. First, the average intergenerational neighborhood elasticity is
24
.609, meaning a one percent change in parent’s neighborhood income status is associated with a
.61 percent change in the second generations neighborhood status (slightly less than the Sharkey
estimate of .641). Parent’s neighborhood income status exerts a large effect that accounts for 74
percent of neighborhood income variation in the second generation (R2 = .74). The remaining 26
percent variation of the second generation’s neighborhood income status stems from spatial
mobility and neighborhood change (and perhaps idiosyncratic error). Model 2 presents the
unadjusted racial disparity among second generation households, which is slightly larger here (.411) than the Sharkey estimate (-.369). Comparing these unconditional effects across studies
suggests that as more data is collected over time (i.e., as the second generation matures), the
amount of intergenerational spatial mobility increases slightly and the racial neighborhood
income gap increases slightly. Estimates here suggest the neighborhood income status among
black families in the second generation in the post civil-rights era is approximately 33 percent
less than that of second generation white families [(e-.411-1)*100 = 33%]. Neighborhood status of
the first generation in Model 3 accounts for nearly half (-.411 vs. -.215) of this racial
neighborhood income gap among the second generation.
[Table 2 about here]
Model 4 (with PSID sample weights) and Model 5 (without PSID sample weights)
introduce family-level covariates. The PSID sampling weights have little effect on the results and
virtually no effect on the substantive conclusions: A substantial intergenerational elasticity and a
substantial racial neighborhood income gap remain after controlling for relevant family-level
characteristics. The family covariate point-estimates are similar between studies as well, with the
exception of parental aspiration being non-significant, and a positive effect for homeownership
instead of a negative effect (see Sharkey 2008:957, footnote #19). The regression model without
25
weights tends to produce more efficient standard errors, generally larger family-level point
estimates, and correspondingly, a larger R-square (.83 vs. .88). The sensitivity check for PSID
weights is important here because the sampling weights as constructed produce nonsensical
variance estimates in the multilevel models. For these reasons the multilevel models omit the
PSID sampling weights (cf. Lee and Forthofer 2006:21).
Metropolitan Heterogeneity: The next empirical objective uses a multilevel estimator to
examine the degree of metropolitan-area heterogeneity in the intergenerational neighborhood
attainment process. This analysis answers two basic but important questions: (1) How different
are rates of intergenerational neighborhood mobility across U.S. metropolitan areas; and (2) Is
there more or less intergenerational mobility in richer or poorer metropolitan areas? The
multilevel specification for this analysis includes a random intercept capturing the difference
across metropolitan-areas in the average neighborhood income attainment of second generation
respondents; a random slope capturing the difference in the average effect of the first
generation’s neighborhood status on the second generation’s neighborhood status (i.e.,
intergenerational elasticity); and a covariance between the random intercept and the random
slope. This covariance accounts for the relationship between the average level of neighborhood
income in an MSA and the strength of the intergenerational elasticity in that MSA. Based on the
full sample, the metropolitan-area estimates for the intergenerational neighborhood income
elasticity is as low as .449 at the 5th percentile and as high as .758 at the 95th percentile. The
correlation between the random intercept and the random slope is -.170. Based on the restricted
sample, the corresponding estimates are very comparable: .447 and .749, respectively, and the
correlation is -.127 (results not shown). The negative correlation means that metropolitan areas
26
with higher average neighbored income levels experience greater rates of intergenerational
neighborhood mobility (i.e., a flatter slope for the intergenerational elasticity).3
To contextualize the varying degree of these intergenerational elasticity estimates, it is
revealing to forecast how long of an influence the first generation’s neighborhood income status
will have on future generations. Assuming a constant level intergenerational change between
generations (e.g., no exogenous shocks)4, and ignoring metropolitan heterogeneity for the
moment, the first generation will still exert approximately an 8 percent influence on
neighborhood attainment five generations later (.6095 = .08; also see Sharkey 2008:951). Stated
differently, among families that began 60 percent (above or below) the mean neighborhood
income level, it will be 5 generations before the neighborhood income of the descendents is
within 10 percent of the long-run mean. Accounting for metropolitan heterogeneity gives us a
much broader perspective of this process. For example, among metropolitan areas with the
highest intergenerational elasticity estimates (e.g., New York, NY; Baltimore, MD; Philadelphia,
PA; Cleveland, OH; Chicago, IL), the influence may still be felt in 9 generations (e.g., .7589
=.08). Among metropolitan areas with the lowest intergenerational elasticity estimates (e.g.,
Phoenix, AZ; Jackson MS; Nashville, TN), the first generation’s influence will be felt 3
generations out (e.g., .4493 = .09). The range between 3 to 9 generations of long-term influence
clearly highlights the importance of geographic location in understanding rates of
intergenerational neighborhood mobility.
Multilevel Mediation Analysis: The next step in the analysis is to evaluate the hypotheses
involving specific metropolitan area characteristics. Together, Table 3 and Table 4 provide the
3
This negative intercept-slope correlation is not driven by the log transformation as supplemental models without
log transformed neighborhood income also produce negative correlations.
4
This is a strong and perhaps unreasonable assumption. The estimates of MSA heterogeneity are for expressing the
effect size; not for prediction. The assumption of no exogenous change over nine generations is too strong for us to
reasonably use these estimates for prediction.
27
results that are needed to evaluate these hypotheses. A 2-1-1 multilevel mediation analysis
evaluates the legacy effects of metropolitan-area characteristics on the neighborhood attainment
of the second generation. The results of this analysis are presented in Table 3 and discussed in
this section.
The effects of racial residential segregation on the intergenerational transmission of
neighborhood status are expected to operate differently for white and black families. As
expected, the total effect for black families is negative and statistically significant: A unit
increase in segregation exposure among the first generation decreases the neighborhood income
attainment of the second generation by .3 percent (Table 3). Importantly, this total effect for
black families is then almost entirely mediated (87 % mediated = -.271/-.312) once the first
generation’s neighborhood income status is controlled. The indirect effect is 7.7 times larger than
the direct effect (7.7 = -.271/.035). This key finding suggests that the effects of residential
segregation on neighborhood income attainment operates primarily through a transgenerational
process that conditions young black children’s experiences in ways that make upward mobility
more difficult. For white families, the pattern of effects is different. The total effect is
nonsignificant but this is because a small negative indirect effect is offset by a small positive
direct effect. There is little theoretical guidance to anticipate these divergent effects.5
[Table 3 about here]
According to the housing availability perspective, place stratification theory and the
buffering hypothesis, neighborhood attainment is also influenced by the racial and ethnic
population concentration of an area. However, the effects from the mediation analysis in Table 3
provide little support for any one perspective. There are no significant effects total, direct,
5
As conjecture, I suspect that highly segregated areas have negative implications of varying degrees for the
development of all children. However, as adults, whites may work harder to spatially segregate themselves from
blacks in highly segregated areas and this produces a positive direct effect for white families.
28
indirect or otherwise for share of black population in a metropolitan-area (net of other MSA
characteristics).6 For foreign-born population concentration, there are positive total and direct
effects for blacks and whites, and a positive indirect effect for whites. Higher levels of
neighborhood attainment among white families living in established immigrant gateway cities
appears to have been channeled in part (via human capital development and/or cultural
conditioning) to the second generation through the neighborhood status of the parents. The
positive direct effects for whites and blacks are counter-intuitive to the expectations developed
from place stratification theory and from the buffering hypothesis. It could be that foreign-born
population concentration does facilitate greater racial neighborhood integration, but it occurs not
through residential buffering—e.g., decreasing the neighborhood income status of whites while
increasing the neighborhood status of blacks—but rather through a residential queuing process
where blacks, and even more so for whites, are finding housing and job opportunities in highly
settled immigrant areas that translate into higher levels of neighborhood attainment. Whether this
queuing process enhances black-white residential contact is yet to be determined.
The local structure of economic specialization in several core employment areas impacts
neighborhood income attainment in several ways. As expected, high employment concentrations
in finance, real estate and insurance are associated with higher levels of neighborhood income
attainment, and the effect is positive for both whites and blacks. Interestingly, the FIRE effect
operates almost entirely through the effects FIRE has on the previous generation’s neighborhood
attainment (i.e., indirectly). It appears that local resources generated in areas of high FIRE
employment help white and black families to develop the levels of human, cultural, and social
6
As noted earlier, the main concern with the PSID data for this study is the potential for an inflated type-two error
rate. Thus, the effects for black population concentration may not be exactly zero, but its effects are likely not as
large as the statistically significant effects observed here. The same interpretation applies to all non-significant
findings.
29
capital in children that ultimately translate into high levels of neighborhood income attainment.
Counter to expectations, neither public-sector employment nor manufacturing employment have
positive effects on neighborhood income attainment. Instead, there is a modest negative direct
effect of the public sector for whites, and a negative direct effect of manufacturing also for white
families. Employment concentrations in the low-wage service sector do, however, produce the
expected negative effects. The negative effects are large and universal for whites and are
primarily direct and large for blacks.
Urban development and suburbanization are expected to influence neighborhood
attainment, but the reasons are more nuanced and complex than other structural conditions.
In terms of the total, direct, and indirect effects of new housing on the second generation’s
neighborhood attainment, the expectations depend on where exactly the new housing is being
built and what kind of housing units are being built. The mediation analysis suggests that new
housing primarily has a negative indirect effect for white families. This negative effect for whites
is likely reflecting affordable housing initiatives in the 1970s that emphasized multi-unit rental
complexes (cf. Massey et al. 2013). Although the pattern of effects for suburbanization behave
according to expectations—larger negative effects for blacks—none of the effects are
statistically significant.
Cross-level Interactions: Table 4 presents the results from the cross-level interaction
models estimating the effect of metropolitan-area characteristics on the varying rate of
intergenerational neighborhood mobility across MSAs. Plots of the statistically significant crosslevel interactions with 95% confidence intervals are provided. These plots are based on the
coefficients from the full models, where all the covariates are held constant at their race specific
30
grand means and where the MSA characteristics vary between the minimum and maximum
values in Table 1.
[Table 4 about here]
The effect of racial residential segregation on intergenerational neighborhood mobility
is positive for white families (β = -.219), and the effect is less positive for blacks (β = -.219 +
.190 = -.029) but not statistically different from that of whites. Note that a negative effect on the
intergenerational elasticity is a positive effect on spatial mobility. The interaction plot illustrates
these modest interaction effects: Whites in highly segregated areas have the flattest slope (i.e.,
the least rigid elasticity), so that whites from relatively poor neighborhoods growing up tend to
experience greater neighborhood mobility than other whites in poor neighborhoods and from
black families in poor neighborhoods regardless of the level of residential segregation in the
area. There is hardly any difference in the rate of intergenerational neighborhood mobility
between black families in high versus low segregated areas. The important effect of racial
segregation on intergenerational neighborhood mobility appears to be primarily in the residential
choices and opportunities of whites, especially whites from poor neighborhoods in highly
segregated areas. This pattern of effects is even stronger when based on the restricted PSID
sample of nonmovers in large metropolitan areas (Model 2, Table 4).
[Figure 2 about here: Segregation]
There is a great deal of consistency between the results in Model 1 and Model 2 of Table
4. Where the effect is statistically significant in Model 1, the effect is of a similar magnitude and
statistically significant under the more restrictive sample in Model 2. There are three exceptions.
There is a larger and statistically significant positive effect of foreign-born population on
neighborhood mobility for white families (β = -.287) using the more restrictive sample, and the
31
large difference in effect of manufacturing for black families is not reproduced using the more
restrictive sample. Also, the large effects for FIRE employment are not reproduced in the more
restricted model. In the case of FIRE employment this makes sense in that FIRE is concentrated
among the largest cities and there is perhaps not enough variation among large cities in this type
of employment to reproduce the same effect size. However, the patterns of effects are the same
in all three instances.
[Figure 3 about here: FIRE]
Looking at the interaction plot to get more insight into the significance of these
interaction effects; the negative effect of FIRE for whites and the positive effect for blacks on
intergenerational mobility means that in areas of high FIRE employment it is easier for whites
from better-off neighborhoods to maintain their position than it is for blacks from better-off
neighborhoods. In fact, the widest neighborhood gap among white and black second generation
members is between those from good neighborhoods that live in areas of high FIRE
employment. It appears more difficult for upper-middle class black families in these cities to
transfer their neighborhood status to their children. There are also significant interaction effects
stemming from the low-wage service sector. High levels of employment in the low-wage service
sector reduce prospects for intergenerational mobility for white and black families alike.
[Figure 4 about here: Low Wage]
As expected there are positive effects of new housing and suburbanization on rates of
intergenerational neighborhood mobility, but the effects differ by race. New housing clearly
effects neighborhood mobility for whites (β = -.517), but the effect is significantly less positive
for black families (-.517 + .467 = -.05), and is not statistically different from zero (not shown).
The interaction plot suggests that the positive effect of new housing on neighborhood mobility
32
hinders the neighborhood status attainment of whites from better-off backgrounds more than it
helps the neighborhood attainment of whites from poor neighborhoods. This finding may reflect
recent urban gentrification trends and/or trends toward housing growth in exurban areas with
higher levels of rural poverty. A similar pattern emerges for suburbanization but for black
families: Suburbanization has a positive effect on intergenerational neighborhood mobility for
black families (β = -.024 + -.146 = -.170) which is statistically different from whites and from
zero. Like white families, however, there is relatively little difference between black families
from poor neighborhoods in high versus low areas of suburbanization; the effect of
suburbanization seems to primarily lower the multigenerational prospects of stable neighborhood
status attainment among black families from better-off neighborhoods.
[Figure 5 about here: New Housing]
Finally, a comparison of the null multilevel models with only family level controls to the
models that include all metropolitan characteristics finds the proportional reduction of level-two
variance is 50 percent for the random intercept and 33 percent of the random slope (not shown).
In other words, the metropolitan characteristics included in this study account for about half of
all the MSA-level variation in neighborhood income attainment and about a third of the MSAlevel variation in intergenerational neighborhood income mobility.
[Figure 6 about here: Suburbanization]
Conclusion
Intergenerational neighborhood status attainment and mobility are important for general
processes of social stratification. Access to opportunities, resources, and knowledge that
determine status outcomes largely occur within specific locational contexts, like neighborhoods
and local labor markets. Neighborhood location is especially key because it is the site where
33
micro-level social and cultural mechanisms, historical-structural antecedents, and contemporary
structural constraints intersect to shape the next generation’s life-chances. Therefore,
stratification theories that omit the causes and consequences of intergenerational spatial mobility
will fail to fully appreciate the geographic pervasiveness of concentrated affluence and poverty
in American society. This study builds on these ideas to further close the conceptual and
empirical gap between intergenerational spatial mobility research and other forms of
intergenerational mobility research.
The academic components that are needed to understand the causes and consequences of
intergenerational spatial mobility come from several established areas of research. First, human,
social, and cultural capital theories, which often reference peer group affiliation and role model
emulation as important forms of local social interaction (Jencks and Mayer 1990), explain why
neighborhood environments for children affect skill formation (Sharkey and Elwert 2011) and
the development of aspirations, expectations, and preferences (Sampson and Wilson 1995;
Vartanian et al. 2007). Second, we know primarily through place stratification theory (Charles
2003; Logan and Molotch 1989) and the housing availability model (South and Crowder 1997)
that there are structural constraints placed on many families that hinder their ability to attain
housing in residential spaces that would conceivably maximize their children’s long-run
prospects. The high degree of importance these perspectives give neighborhood environments,
especially for child development, begs the question as to whether the residential constraints of
preceding generations affect the status attainments of subsequent generations. This long-run view
forces us to think more keenly about the historical aspects of the local opportunity structure, and
their legacy effects on future generations. This legacy effects perspective produces novel insights
into how racial residential segregation, racial and ethnic diversity, economic specialization,
34
suburban sprawl, and urban development initiatives are capable of affecting intergenerational
mobility patterns and status attainments well into the future.
Four empirical objectives help to establish the validity of these combined perspectives.
First, replication of previous intergenerational elasticity estimates is an important step here for
assessing the reliability of the data, measures, and methods. Replication is generally an
underappreciated exercise, but this is likely to change, especially as contemporary social science
comes to rely on increasingly complex datasets and methodological approaches. Second, it was
important to establish the extent of metropolitan-area variation in the intergenerational
neighborhood income elasticity as this indicates how relevant local opportunity structures are for
intergenerational transmission processes. The variation is quite noteworthy: Some local
opportunity structures produce intergenerational elasticities that could resonate up to nine
generations, whereas in other areas the effect of parent’s neighborhood status only extends three
generations. This clearly highlights the importance of local opportunity structure in
understanding rates of intergenerational spatial mobility.
Third, a multilevel mediation analysis decomposes the total, direct, and indirect effects of
specific metropolitan-area characteristics on the second generation’s neighborhood income
attainments. Several important findings that support the legacy effects perspective emerge.
Foremost, among black families the effect of racial residential segregation on neighborhood
income attainment operates almost entirely through the residential constraints placed on the first
generation. This means that the prior generation’s exposure to residential segregation helps
explain the difficulties of the current generation. According to the legacy effects perspective, the
residential constraints faced by the parent’s likely affected the child’s skill development and
general disposition in ways that influence adult status outcomes. Areas with high employment
35
concentrations in better paying sectors of the economy—like finance, real estate, and
insurance—provides another example. Black and white families alike in these areas are better
able to attain the kinds of neighborhoods that facilitate advantageous human, cultural, and social
capital development in children, which in turn improves the status outcomes of the next
generation. New housing in an area also indirectly effects the neighborhood attainments of later
generations, but in a negative way and only for white families. Without more specific
information on the siting of new housing, it is difficult to be certain, but this finding likely
reflects the affordable housing initiatives in the 1970s that emphasized multi-unit rental
complexes in whiter and wealthier areas.
The fourth empirical objective evaluates the effects of the local opportunity structure on
varying rates of intergenerational neighborhood income mobility. The ideal opportunity structure
should produce high levels of neighborhood income attainment and higher rates of
intergenerational spatial mobility. Both absolute levels of neighborhood attainment (which was
the primary focus of the mediation analysis) and neighborhood status mobility are essential
components of the generational transmission process. A cross-level moderation analysis deals
with the latter, and several noteworthy findings emerge: Racial residential segregation increases
intergenerational spatial mobility among white families, which primary benefits whites from
poor neighborhoods; high level of employment concentration in finance, real estate, and
insurance industries increases intergenerational spatial mobility for blacks, but this mobility
comes primarily at the expense of black middle-class families having more difficulty maintaining
their neighborhood income status over the course of generations in these areas; high
concentrations of low-wage service work reduce prospects for intergenerational spatial mobility
for white and black families alike; new housing increases spatial mobility for white families, and
36
this benefits white families from poor neighborhoods more than whites from better-off
neighborhoods; high levels of suburbanization increases intergenerational spatial mobility among
black families, which primarily makes it more difficult for blacks from better-off neighborhoods
to maintain their neighborhood income status. Together these focal opportunity structure
characteristics account for about a third of the variation in intergenerational spatial mobility that
exists across metropolitan areas.
Having a multigenerational longitudinal panel for a nationally representative sample with
geographic identifiers is invaluable to the study of intergenerational mobility research. However,
the data, and this study in particular, is not without limitation. One primary concern is with the
potential geographic sparseness of the PSID data and the amount observational clustering within
metropolitan areas that is available for analysis. To determine whether this was an issue that
would bias point-estimates and/or variance components, the analyses were run on a restricted
sample which included only the metropolitan areas that had at least 10 PSID families of which
had remained in those areas for the duration of the study period. The results between the full
sample and restricted sample are quite similar, especially with regard to the amount variation for
the intergenerational elasticity across metropolitan areas. If the amount of observational
clustering, the size of the metropolitan area, and/or intermetropolitan migration had a substantial
impact then we would expect the unadjusted random slope variance to be substantially different
between samples, and it is not.
Regardless of the sample used, there is still a possibility that the estimated structural
effects are smaller, and the standard errors larger, then would be the case with ample data. As a
result, we should be cautious to conclude that any of the non-significant effects are entirely
unimportant for the intergenerational transmission process. Instead, it is best to view these non-
37
significant effects as of secondary importance to the effects that are statically significant. The
focal structural characteristics in this study are prominent in the residential mobility literature,
but the list of focal characteristics is by no means exhaustive. However, given a finite amount of
statistical power, especially for the cross-level interaction analysis, future research should only
examine additional structural characteristics that have significant potential to break new
theoretical ground.
This study is a step forward in the area of intergenerational spatial mobility research, but
more work is needed. Future research should examine the intergenerational transmission process
for alternative neighborhood outcomes. Racial and ethnic neighborhood composition is
potentially an important outcome that could shed light on the relative salience of race and
ethnicity for intergenerational spatial mobility. It would be interesting to compare the strength
intergenerational elasticities for neighborhood income alongside neighborhood racial
composition. Future research could also study the reciprocal linkage between neighborhood
outcomes and other stratification outcomes over the course of generations. This would be useful
for examining the explanatory power of specific mechanisms that ultimately support the
generational persistence of concentrated affluence and poverty. Finally, when sufficient
multigenerational data becomes available it will be interesting to see how rates of
intergenerational spatial mobility actually effect the residential expectations and aspirations of
future generations. This could be a key factor in understanding why the “mental maps” of large
cities, and perhaps specific neighborhood reputations, persist despite significant physical and
social change.
38
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Figure 1: A Conceptual Model of the Intergenerational Spatial Mobility Process involving
key Local Opportunity Structure Pathways and Effects
______________________________________________________________________________
First Gen.
Neighborhood
Status
(a)
(b)
(d)
Local Opp.
Structure
First Gen.
(cT)
Local Opp.
Structure
Second Gen.
(c)
Second Gen.
Neighborhood
Status
Key Effects
H0: a = intergenerational elasticity (null hypothesis: no different across local labor markets)
H1: cT = total structural effect of first gen. opportunity structure on second gen. neighborhood attainment
[i.e., not conditional on first gen. neighborhood status (a) or second gen. opportunity structure (c)]
H1a: a*b = indirect structural [i.e., legacy] effect on second gen. neighborhood attainment
H1b: c = direct structural effect on second gen. neighborhood attainment
H2: d = moderating structural effect on the intergenerational elasticity
______________________________________________________________________________
44
10.5
11
11.5
Figure 2: Metropolitan Area Black-White Residential Segregation
10.5
11
First Gen. Ave. Neighborhood Income (ln)
Low Seg. White
High Seg. White
11.5
Low Seg. Black
High Seg. Black
10.5
11
11.5
12
Figure 3: Metropolitan Area Employment in Finance, Real Estate, and Insurance
10.5
11
First Gen. Ave. Neighborhood Income (ln)
Low % FIRE, White
High % FIRE, White
11.5
Low % FIRE, Black
High % FIRE, Black
45
10.5
11
11.5
12
Figure 4: Metropolitan Area Employment in the Low Wage Service Sector
10.5
11
First Gen. Ave. Neighborhood Income (ln)
Low % Low Serv., White
High % Low Serv., White
11.5
Low % Low Serv., Black
High % Low Serv., Black
10.5
11
11.5
Figure 5: Metropolitan Area Percent New Housing
10.5
11
First Gen. Ave. Neighborhood Income (ln)
Low New Housing, White
High New Housing, White
11.5
Low New Housing, Black
High New Housing, Black
46
10.5
11
11.5
Figure 6: Metropolitan Area Percent Suburban Population
10.5
11
First Gen. Ave. Neighborhood Income (ln)
Low Suburb, White
High Suburb, White
11.5
Low Suburb, Black
High Suburb, Black
47
Table 1: Descriptive Statistics for a Study of Intergenerational Spatial Mobility: Panel
Study of Income Dynamics, 1968-2011 (n = 4633 parent-child pairs)
First Generation
Characteristics
Mean
S.D.
Neighborhood income (ln)
Individual Controls
Black family (1=yes; 0=no)
Family income (ln)
Male head of household
Education
Less than high school (ref.)
High school
Some college
College degree
Home ownership
(proportion of years owned)
Parental aspirations
Metropolitan Area Variables
Black-white segregation
(dissimilarity index)
% Black pop.
% Foreign-born pop.
% FIRE employment
% Public sector employment
% Manufacturing
% Low wage service
% New housing (last ten years)
% Suburban pop.
Population size (ln)
Median income (ln)
10.79
.39
.16
10.79
.83
.36
.66
.37
.16
.32
.22
.30
.33
.47
.41
.46
.73
5.25
.39
1.73
.69
.14
.07
.06
.16
.21
.05
.23
.56
13.96
9.74
.14
.11
.06
.03
.05
.07
.04
.11
.19
1.29
.84
Second Generation
Characteristics
Mean
S.D.
11.11
Range
Min
Max
.27
8.57
12.26
.50
.50
.00
6.80
.00
1.00
12.84
1.00
.05
.28
.29
.38
.25
.45
.45
.49
.00
.00
.00
.00
1.00
1.00
1.00
1.00
.00
1.00
1.00
9.00
.14
.00
.01
.02
.02
.06
.02
.04
.00
10.92
9.63
.88
.56
.48
.20
.41
.36
.33
.56
.90
16.80
11.60
.60
.15
.10
.07
.13
.14
.11
.16
.60
14.24
10.84
.14
.11
.08
.02
.05
.04
.03
.08
.18
1.25
.27
48
Table 2, Replication of Sharkey (2008, Table 6): A Decomposition of Neighborhood Income
Elasticity using the Panel Study of Income Dynamics, 1968-2011 (robust standard errors)
Dependent Variable:
Second Generation Neighborhood Income
Model 5
Model 1
Model 2
Model 3
Model 4 (unweighted)
β/(se)
β/(se)
β/(se)
β/(se)
β/(se)
First Gen. Characteristics
Neighborhood income (ln)
.609 ***
.540 ***
.481 ***
.472 ***
(.013)
(.013)
(.014)
(.011)
Black family (1=yes; 0=no)
-.411 *** -.215 *** -.198 ***
-.193 ***
(.010)
(.007)
(.007)
(.005)
Family income (ln)
.014 *
.015 ***
(.005)
(.003)
Male head of household
.009
.019 ***
(.009)
(.005)
a
High school degree
.011
.012 ***
(.006)
(.004)
Some collegea
.024 ***
.027 ***
(.007)
(.005)
a
College degree
.051 ***
.054 ***
(.008)
(.006)
Home ownership
(proportion of years owned)
.006
.017 ***
(.008)
(.005)
Parental aspirations
-.002
.000
(.001)
(.001)
Second Gen. Characteristics
Male head of household
.006
.007 *
(.004)
(.003)
a
High school degree
.031 **
.022 **
(.010)
(.006)
Some collegea
.047 ***
.036 ***
(.011)
(.006)
a
College degree
.082 ***
.062 ***
(.011)
(.007)
Intercept
4.535 *** 11.170 *** 5.312 *** 5.716 ***
5.773 ***
(.142)
(.005)
(.137)
(.143)
(.110)
R2
.74
.30
N child-parent pairs
4633
4633
* p < .05; ** p < .01; *** p < .001
a. Reference group are those without high school degrees
.81
4633
.83
4633
.88
4633
49
Table 3: A 2-1-1 Multilevel Mediation Analysis providing the Total, Direct and Indirect
Effects of Metropolitan-level Characteristics on the Intergenerational Neighborhood
Attainment Process by Race: Panel Study of Income Dynamics, 1968-2011
Metropolitan-Area Characteristics
Black-White segregation
Effects for Blacks
Effects for Whites
% Black population
Effects for Blacks
Effects for Whites
% Foreign-born population
Effects for Blacks
Effects for Whites
% FIRE employment
Effects for Blacks
Effects for Whites
% Public sector employment
Effects for Blacks
Effects for Whites
% Manufacturing employment
Effects for Blacks
Effects for Whites
% Low-wage service employment
Effects for Blacks
Effects for Whites
% New housing (last ten years)
Effects for Blacks
Effects for Whites
% Suburban population
Effects for Blacks
Effects for Whites
Total Effect
-.312 ***
-.042
Indirect Effect
-.271 ***
-.084 **
Direct Effect
-.035
.083 *
.027
.051
.029
.040
-.004
-.026
.229 *
.307 ***
.163
.230 ***
.571 **
.543 **
.418 **
.392 **
-.064
-.297
.230
-.036
.147
.098
.031
-.083 **
.054
-.012
-.026
.017
.038
-.294 **
.018
-.601 ***
-.135
-.384 ***
-.383 *
-.571 ***
-.028
-.229 ***
-.047
-.169 ***
-.064
-.028
-.040
.018
-.046
.006
-.022
-.002
.154 **
.191 **
* p < .05; ** p < .01; *** p < .001: Two-tail probabilities based on a 1000 bootstrap replications.
Note: All models control for family-level covariates. The total and indirect effects are based on the first generation’s
MSA exposure. The direct effects are based on the second generation’s exposure and thus the direct and the indirect
do not sum to the total effect (see page 11 for more information).
50
Table 4: Cross-Level Interactions predicting the Effects of Metropolitan-Level
Characteristics on the Intergenerational Neighborhood Income Elasticities for White and
Black Families from the Panel Study of Income Dynamics, 1968-2011a
Model 2:
Model 1:
Non-mover families
Full sample
in large MSAsb
BlackBlackEffects for White dif.
Effects for
White dif.
Whites
in Effects
Whites
in Effects
β/(se)
β/(se)
β/(se)
β/(se)
First Gen. Neighborhood Income (ln)
.438 ***
.060 ***
.453 ***
.045 **
(.011)
(.013)
(.011)
(.016)
Cross-Level Interactions w/
First Gen. Neighborhood Income (ln)
Black-white segregation
-.219 *
.190
-.355 *
.096
(.108)
(.181)
(.142)
(.224)
% Black pop.
.028
-.228
-.109
-.018
(.088)
(.124)
(.097)
(.148)
% Foreign-born pop.
-.092
-.033
-.287 *
-.048
(.128)
(.153)
(.136)
(.174)
% FIRE employment
1.654 **
-2.702 **
.319
-1.359
(.623)
(.944)
(.767)
(1.119)
% Public sector employment
.056
.027
.524
.141
(.255)
(.401)
(.360)
(.493)
% Manufacturing
.214
-.928 *
.191
-.335
(.226)
(.403)
(.304)
(.483)
% Low wage service
1.486 ***
-.161
1.765 ***
.408
(.341)
(.705)
(.440)
(.849)
% New housing (last ten years)
-.517 ***
.467 *
-.668 ***
.510 *
(.118)
(.208)
(.140)
(.246)
% Suburban pop.
-.024
-.146 *
-.020
-.201 *
(.051)
(.072)
(.058)
(.082)
Population size (ln)
.015
-.005
.073 ***
-.032
(.012)
(.018)
(.016)
(.024)
Median income (ln)
.037
.002
-.069
.047
(.038)
(.062)
(.044)
(.074)
Intercept
11.040 ***
-.188 *** 11.034 ***
-.183 ***
(.004)
(.005)
(.004)
(.006)
Variance Components
Level-one residual (s.d.)
.092
.090
Level-two random intercept (s.d.)
.021
.016
Level-two random slope (s.d.)
.070
.048
Level-two correlation (int. slope)
-.638
-.582
51
N level-one (child-parent pairs)
N level-two (second gen. MSA)
4633
260
3247
78
* p < .05; ** p < .01; *** p < .001
a. The models also control for all family-level characteristics in Table 2.
b. The sample is restricted to only those families that remained in the same MSA for the duration of the study period
and lived in a MSA that had a minimum of 10 PSID families.
52