Sapna Swaroop and Jeffrey D. Morenoff
Building Community: The Neighborhood Context of
Local Social Organization
PSC Research Report
Report No. 04-549
January 2004
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Building Community: The Neighborhood Context of Local Social Organization
Sapna Swaroop
Department of Sociology
University of Michigan
Jeffrey D. Morenoff
Department of Sociology
University of Michigan
Direct all correspondence to Sapna Swaroop, Department of Sociology, University of Michigan, 1225
South University Avenue, Ann Arbor, MI 48104-2590 (sswaroop@umich.edu). We thank Jim House,
Steve Raudenbush, and Al Young for their comments on earlier drafts. We gratefully acknowledge
support from the National Institute of Child and Human Development (NICHD) through a training
grant (T32-HD07339) to the Population Studies Center, University of Michigan.
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Building Community: The Neighborhood Context of Local Social Organization
Abstract
This study explores how neighborhood context influences participation in expressive and instrumental
forms of local social organization. Through a multilevel-spatial analysis of residents in 342 Chicago
neighborhoods, we investigate how neighborhood characteristics (e.g. residential stability, concentrated
disadvantage, and physical and social disorder) are related to local social organization, after adjusting for
potentially confounding individual- level covariates. Residential stability is associated with increased
participation in expressive forms of social organization but not instrumental forms. Concentrated
disadvantage and physical and social disorder are associated with increased participation in instrumental
forms of social organization but are not strongly related to expressive forms. These findings provide
qualified support for the systemic model of local social organization but challenge theories of urban
poverty that predict lower levels of engagement in poor communities. We argue that the positive
association between neighborhood poverty and social organization arises as a result of the heightened
levels of social needs that accompany concentrated disadvantage. We also find that most forms of social
organization are spatially dependent, meaning that they are influenced by the wider spatial context of
surrounding neighborhoods. Thus, the contextual processes that influence social organization “spill over”
the geographic boundaries of local ne ighborhoods.
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Much of the recent research on so-called “neighborhood effects” has focused on whether and how
communities benefit from social resources that are created through social networks, patterns of social
interaction among neighbors, and collective participation in local voluntary associations (Sampson,
Morenoff, and Gannon-Rowley 2002), which we refer to as forms of local social organization. This line
of research focuses on how living in neighborhoods where residents engage in such social processes is
related to better physical and mental health among adults and a lower incidence of problem behaviors
among youth (see reviews by Gephart 1997; Morenoff and Lynch in press; Sampson, Morenoff, and
Gannon-Rowley 2002). However, much less attention has been paid to the question of how
neighborhoods can develop and sustain strong bases of local social organization, particularly the most
disadvantaged neighborhoods, which many scholars characterize as being more socially isolated than
socially organized (Wilson 1996; Rankin and Quane 2000).
Interest in the sources of social organization dates back to the Chicago School of urban sociology
(McKenzie 1923; Park and Burgess 1925), finding its fullest expression in Shaw and McKay’s (1942)
theory of social disorganization, which held that local social organization is fostered by a certain set of
neighborhood structural conditions, namely residential stability, high levels of socioeconomic resources,
and homogeneous ethnic compositions. Although this thesis continues to influence even recent research
(Bursik 1988; Sampson and Groves 1989; Wilson 1996; Furstenburg and Hughes 1997), it blurs
potentially important distinctions between the different types of behaviors that give rise to local social
organization (e.g., social interaction among neighbors, participation in local voluntary associations and
informal neighborhood groups, and actions taken on behalf of neighborhood problems), assuming instead
that a particular set of structural conditions is related to all modes of social organization in more or less
the same way (Guest 2000; Small 2002). It also focuses exclusively on the conditions present in the
immediate neighborhood, neglecting the possibility that the wider spatial environment in which
neighborhoods are embedded may also influence local social organization.
This paper investigates whether and how the social contexts of both the immediate neighborhood
and surrounding areas influence the individual acts of community participation that form the backbone of
local social organization. Our review of previous research begins with classic social disorganization
theory but also identifies three currents of contemporary urban sociological theory—the systemic model,
urban poverty theory, and the social needs perspective—that emphasize different pathways through which
social context influences the development of local social organization. We next discuss the importance of
distinguishing between different forms of local social organization – especially between expressive and
instrumental forms – and of taking into account contextual influences on community engagement that
emanate from beyond the geographic boundaries of the local neighborhood. Finally, our multilevel-spatial
analysis considers how characteristics of both the immediate neighborhood and surrounding areas
influence the likelihood of individual involvement in different modes of social organization.
Contextual Effects on Local Social Organization
Classical social disorganization theory conceives of local social organization as the ability of a
community structure to solve common proble ms and realize its common goals (Shaw and McKay 1942:
Kornhauser 1978; Bursik 1988). This model asserts that residential instability, socioeconomic
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deprivation, and ethnic heterogeneity undermine the development of local social organization in
communities.
The two prevailing contemporary perspectives on contextual sources of local social organization
are close outgrowths of Shaw and McKay’s (1942) original model, in which social organization is the
result of a complex system of friendship and kinship networks and informal and formal associational ties.
The systemic model emphasizes neighborhood residential stability as the key structural feature
influencing local social organization, since limited mobility in and out of a neighborhood enhances
opportunitie s to develop friendships and participate in local organizations (Kasarda and Janowitz 1974;
Sampson 1988; Bursik 1988). Urban poverty theory focuses on how the neighborhood conditions
associated with socioeconomic disadvantage, for example high crime rates, hinder social organization by
promoting fear and limiting resources among residents. In contrast, the less-explored social needs
perspective diverges from social disorganization theory by arguing that adverse structural conditions do
not necessarily weaken local social organization; in fact, they often spur residents to become involved in
neighborhood issues, thus generating social organization. Below we discuss each of these perspectives
and review related empirical research.
Systemic Perspective
Although there has been relatively little empirical research on the relationship between residential
stability and local social organization, previous studies generally support the claims of the systemic
model. Sampson and colleagues have conducted the most extensive research on social organization, using
data from the British Crime Survey (BCS). In a series of studies, they find that residential stability is
associated with the development of residents’ local friendship ties and participation in local social
activities, such as visiting neighbors, seeking entertainment in the neighborhood, and attending local
sporting events, but that it has a weaker effect on organizational participation in the neighborhood
(Sampson 1988; Sampson 1991; Sampson and Groves 1989). Veysey and Messner (1999) replicate and
extend Sampson and Grove’s (1989) work on the BCS using covariance structural modeling, again
demonstrating that residential stability encourages the development of local friendship networks. In the
U.S., Taylor’s (1996) ecological-level study of Baltimore neighborhoods shows that community
residential stability is associated with increased rates of social involvement (a scale that includes
measures of local friendship ties, awareness of neighborhood organizations, and various types of social
interaction among neighbors). Similarly, Guest and Lee’s (1983) ecological study of Seattle shows that
areas characterized by higher rates of residential stability exhibit an “urban village” form of social
organization, based largely on intimate social networks. Moreover, in a multilevel study of Chicago
neighborhoods, Sampson, Morenoff, and Earls (1999) report that neighborhood residential stability is
associated with increases in individual- level reports of neighboring behavior including reciprocated
exchange, intergenerational closure, and child-centered social control.
On balance, this research supports the systemic argument that social ties and interaction among
neighbors flourish in more residentially stable neighborhood environments, but it is much less clear from
this research whether residential stability also promotes participation in neighborhood voluntary
associations or other forms of social organization. Moreover, there are still very few multilevel studies on
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how neighborhood residential stability affects individual-level community participation, especially in
U.S. cities.
Urban Poverty Perspective
William Julius Wilson (1987; 1996) and other urban poverty researchers take up the second
current of classic al social disorganization theory by emphasizing the pernicious consequences of
concentrated socioeconomic disadvantage for social organization. Wilson identifies several reasons why
the concentration of poverty and other dimensions of disadvantage undermine the social and institutional
resource base in poor, inner-city neighborhoods. For example, without a critical mass of working- and
middle-class residents present in poor neighborhoods, community institutions such as businesses, schools,
churches, and other local organizations, which rely heavily on the economic and social support provided
by more advantaged residents, may suffer (Wilson 1996). This weakened institutional resource base
restricts opportunities for residents to become involved in organizational life. Moreover, in neighborhoods
beset by high levels of crime and disorder, fear of victimization can lead to mutual distrust among
neighbors and other community residents, discouraging individuals from interacting with those outside
their immedia te kin and friend networks and compelling them to withdraw from community life
(Furstenburg 1993; Rainwater 1970; Stack 1974; Wilson and Kelling 1982). As a result, these
communities may face diminished levels of local social organization.
Despite the theoretical power of this argument linking concentrated disadvantage to lower levels
of community participation, previous empirical analysis has yielded mixed results. In an analysis of data
from the Urban Poverty and Family Life Survey of Chicago, conducted in census tracts where more than
twenty percent of the residents were poor, Sosin (1991) reports no association between neighborhood
poverty and individuals’ participation in community organizations. Using the same data, Fernandez and
Harris (1992) analyze participation across different types of local organizations, including community,
political, school, social, and church groups. They find that neighborhood poverty is associated with lower
rates of participation in a few types of organizations—men’s participation in political and social groups
and women’s participation in church groups—but has no effect on others. Similarly, Stoll (2001) finds no
evidence of a systematic relationship between neighborhood poverty and participation in different types
of local organizations in Los Angeles. Whereas higher poverty is associated with individuals’ decreased
participation in sports and professional organizations, the rate of neighborhood poverty appears unrelated
to participation in neighborhood, political, church, and cultural/ethnic organizations, as well as the PTA.
However, Tigges, Browne, and Green (1998) offer some stronger evidence for the urban poverty thesis,
reporting that neighborhood poverty diminished the size of local social networks in Atlanta. Perhaps the
strongest support for Wilson’s thesis comes from Rankin and Quane’s multilevel (2000) study of social
isolation for a sample of families living in predominantly African-American neighborhoods in Chicago.
They find that neighborhood poverty is associated with families’ diminishing participation in various
community activities—e.g. summer recreation programs, youth groups, organized sports, community
watch organizations, local political organizations, block clubs, neighborhood associations, and tenant
groups—albeit in a nonlinear fashion.
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In sum, the main thesis of the urban poverty perspective—that concentrated poverty diminishes
community participation—garners limited support. As with the systemic model, few studies testing urban
poverty theory use multilevel analysis (for exceptions, see Rankin and Quane 2000; Stoll 2001).
Furthermore, most studies outlined above (except Stoll 2001 and Tigges et. al. 1998) rely on samples of
African Americans living in poor urban neighborhoods, but the rela tionship between socioeconomic
context and participation in activities of social organization might look different when a wider range of
neighborhood contexts is considered. For the most part, the urban poverty literature also lacks empirical
tests of the mechanisms through which concentrated disadvantage influences participation in local social
organization.
Social Needs Perspective
Whereas the systemic and urban poverty perspectives focus on the detrimental effects of
residential instability and socioeconomic disadvantage and thus closely follow the main premises of
classical social disorganization theory, a contrasting view is offered by what we term the social needs
hypothesis. According to this view, the elevated rates of crime, victimization, and physical dilapidation
experienced by residents of poor neighborhoods actually motivate residents to participate in local social
organization in order to help alleviate these problems. In other words, neighborhood conditions that
threaten personal safety and social order may signal the need for social action, and thus induce rather than
discourage participation in local social life and institutions.
The social needs hypothesis draws on a different tradition in urban sociological theory and
research. Janowitz’s (1967) conception of urban neighborhoods as communities of limited liability is one
example of how social needs could affect community participation. He maintains that community
participation is characterized by the intentional, partial, and differentiated involvement of residents in
local social life, and that residents respond to neighborhood conditions to protect personal investments
such as private property and public safety. Thus, residents’ actions may in large part be limited to
addressing relevant social needs. Another way of relating community participation and social needs
emerges from Suttles’ (1972) notion of the defended community. Defended communities are ordinarily
characterized by low levels of social organization (Suttles 1972; Janowitz 1967), but under the particular
circumstances of external threat or extreme social need, resident involvement escalates to ameliorate the
problem. More recent research on disorder also acknowledges that in the presence of social needs,
residents have the option to either resist neighborhood decline through social action or accommodate the
conditions accompanying that decline (Taylor 1996).
Research on the consequences of physical and social disorder for neighborhood social
organization does not generate a clear or consistent set of findings about the social needs hypothesis.
Taylor (1996) finds that Baltimore neighborhoods experiencing high levels of physical deterioration have
higher rates of social participation (having friends in the neighborhood and exchanging favors with
neighbors) than other neighborhoods. Block-level ecological studies of New York City by Perkins and
colleagues (1990; 1996) yield similar results, where the presence of incivilities (the presence of neighbors
who don’t care for their property, poor sanitation services, and litter) was associated with increased
collective participation in block associations and community grassroots organizations. These findings
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support the social needs perspective. However, in other analyses, the presence of physical disorder was
also associated with decreased participation in community organizations (Perkins et. al. 1996), and
individuals’ perceptions of physical disorder were unrelated to participation in formal and informal
problem-solving activities (Woldoff 2002). These inconsistent patterns may be explained by different
ways of measuring disorder: some studies use on-site coders to measure the presence of disorder (Taylor
1996), some use residents’ perceptions of incivilities (Perkins et. al. 1990; Woldoff 2002), and some
combined both observed and perceived disorder into a single scale (Perkins et. al. 1996).
On balance, research on disorder and social organization offers limited insight into the social
needs perspective. First, the existing research consists primarily of ecological-level studies (with the
exception of Woldoff 2002), which cannot investigate the question of how neighborhood context
influences individual participation in social organization. Moreover, previous work uses measures of
physical incivilities as indicators of social needs, neglecting incidences of social incivilities, such as
individuals engaging in public drinking, drug dealing, and causing trouble in the neighborhood. Although
these signs of social disorder are le ss prevalent than physical incivilities, residents may perceive them to
be quite serious and may be even more likely to react to this type of social need by participating in social
organization.
Extending Previous Research
The studies outlined above advance Shaw and McKay’s (1942) ideas by investigating how
characteristics of the local community—particularly its level of residential stability, concentrated poverty,
and social needs—influence various forms of social organization. We now propose two ideas for unifying
and extending this line of research. First, the findings from these studies are difficult to synthesize
because the research as a whole gives little theoretical consideration to the different types of activities
involving neighborhood residents and institutions that constitute community social organization and
instead assumes that specific features of neighborhood context—e.g., residential stability and
concentrated disadvantage—are equally relevant to all forms of community participation. Studies
motivated by systemic theory focus mainly on measures of local social networks, while studies of urban
poverty and disorder favor outcomes related to participation in local social organizations. To bring
coherence to this line of research, we present a conceptual framework for understanding different modes
of participation in social organization and offer theoretical insights into how contextual influences may
vary across dimensions of social organization. Second, we extend previous research on the neighborhood
context of social organization by problematizing the idea that the geographic boundaries of statisticallydefined neighborhoods cleanly delineate the relevant environmental stimuli that influence people to
participate in their community. This issue has both theoretical and methodological implications for
understanding contextual influences on participation in local social organization, which we discuss below.
Conceptualizing Local Social Organization
Urban sociologists have traditionally conceived of local social organization as consisting of three
elements: the prevalence and interdependence of social networks in a community, formal participation in
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neighborhood associations, and the collective supervision that a community directs toward social
problems (Thomas and Znaniecki 1920; Shaw and McKay 1942; Kornhauser 1978; Wilson 1996).
However, most work on local social organization neglects the theoretical distinctions among these
dimensions, either reporting empirical results for various types of community participation with little
substantive discussion of what the outcome variable represents (e.g. Stoll 2001) or combining several
different forms of social organization into a single scale (e.g. Elliott et. al. 1996). Moreover, different
theoretical traditions highlight different outcomes: research on the systemic model stresses the formation
of local social networks and work on urban poverty and disorder focuses on participation in community
organizations.
Building on research by Guest and colleagues (2002; 1983) and others (Marcus 1960; Gordon and
Babchuk 1959), we propose that closer theoretical attention should be given to differences in the activities
that comprise social organization, particularly the distinctions between expressive and instrumental
motivations for community participation. Expressive acts are motivated by one’s sense of identity and
obligation as a “neighbor,” and include behaviors such as social exchange with neighbors and
participation in community groups designed to promote social interaction, neighborliness, and friendship
among residents. This type of social organization recalls Toennies’ ([1887] 1963) conception of
gemeinschaft, or emotional and sentimental social relationships. Instrumental participation is instead
motivated by functional and political concerns of neighborhood residents, such as the desire to protect
personal investments and promote local businesses in the neighborhood. Examples of instrumental
participation include membership in organizations designed to protect community interests, for example
neighborhood watch programs, and participation in other types of informal problem-solving groups.
Some theorists suggest that instrumental participation has become the modal form of community
involvement, whereas expressive participation has declined over time (Greer 1962). Others argue that
both expressive and instrumental forms of organization continue to pervade urban communities (Guest
and Lee 1983).1
This categorization of local social organization must be fluid because community participation
may take on a more expressive or instrumental bent depending on the situation. For example, a social
group may form for expressive purposes but mobilize for instrumental purposes when the neighborhood's
interests are threatened. Similarly, a community improvement group may primarily be concerned with
neighborhood problems, but also serve a secondary function as a venue through which people interact
with their neighbors socially. Still, distinguishing between the principal functions of these two modes of
participation is valuable, particularly for assessing the effectiveness of social organization. Some research
suggests that communities characterized by high levels of instrumental organizational participation are
successful in protecting their political and social interests, perhaps much more so than communities that
rely personal and social networks to solve problems (Guest and Lee 1983; Guest and Oropesa 1984). For
example, Pattillo-McCoy’s (1999) ethnographic research in Chicago shows that strong personal networks
among residents in “Groveland” sometimes detracts from efforts to reduce crime since residents hesitate
to report offenses committed by their neighbors’ and friends’ family members. As a result, friendship
networks, a form of expressive social organization, work against the common neighborhood goal of
reducing crime. More instrumental forms of social organization, such as membership in a neighborhood
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watch group designed specifically to control crime, might be more effective agents of social control in
these contexts.
Contextual factors may differentially influence expressive versus instrumental forms of local
social organization. For example, the systemic model makes a clear prediction about contextual
influences on expressive forms of social organization, such as the formation and maintenance of local
social networks: the more stable a community and the longer one has lived there, the more salient an
individual’s identity as a neighbor, and the more opportunities residents have to interact and maintain
sustained contact, leading to the development of social networks. However, empirical studies of the
systemic model show weaker or non-significant effects of residential stability on more instrumental forms
of social organization, such as organizational participation (Shaw and McKay 1942; Sampson and Groves
1989; Veysey and Messner 1999). Instrumental forms of participation may be more motivated by
neighborhood social needs that threaten personal investments and motivate residents to participate in
problem-solving activities. In fact, most research on the relation between neighborhood disorder and
social organization has emphasized participation in instrumental organizations, including block
associations and community grassroots organizations (Perkins et. al. 1990;1996). In this study, we
develop a typology of local social organization to address the differences between expressive and
instrumental forms of participation (see below).
Spatial Dynamics of Lo cal Social Organization
We also advance prior research by analyzing whether participation in neighborhood social
organization is a spatially dependent process. In this case, spatial dependence means that a person’s
decision to participate in some form of social organization may be influenced not only by what happens
within her own neighborhood, but also by what is happening in neighborhoods that are geographically
close to hers. Theoretically, there are two general processes through which such spatial dependence can
arise. The first involves a spatial diffusion process through which participation in a particular form of
social organization spreads across space, from one neighborhood to another, through social networks.
Studies of recruitment and participation in voluntary associations and social movements suggest that
social networks are one of the primary pathways through which people become involved in local
organizations (Booth and Babchuk 1969; McPherson et. al. 1992; Snow et. al. 1980), and there is no a
priori reason to assume that networks map onto the geographic boundaries of statistical neighborhoods
(e.g., census tracts) in a clean fashion.
A second possibility is that even without diffusion, contextual influences can spread from one
neighborhood to another through spatial externalities (Anselin 2003; Morenoff 2003). For example,
according to the social needs hypothesis, residents are more likely to get involved in neighborhood
organizations if they perceive the need for community action, and these perceptions may be triggered in
part by signs of physical and social disorder. Perhaps residents of one neighborhood respond to social
need not only on the basis of the disorder they perceive in their own neighborhood, but also on the basis
of disor der that they see in surrounding neighborhoods, which can trigger a sense of urgency for what
may happen to their own neighborhood if they do not take action.
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In either case, the main point is that residents’ participation in social organization in a given
neighborhood can be influenced by what is happening in other nearby locations. To account for this
possibility, we integrate both multilevel and spatial modeling techniques to account for a wider
geographic scope of contextual effects on community partic ipation.
In this study, we address the limitations of past research by examining the contextual sources of
both expressive and instrumental forms of local social organization. Through analysis of multilevel
models, we evaluate how well the systemic, urban poverty, and social needs models apply to these
distinct forms of participation. We also use multilevel spatial models to assess whether participation in
various forms of local social organization is a spatially dependent activity, or whether conditions in
surrounding neighborhoods influence participation in a particular focal neighborhood.
Data
This study utilizes multilevel data drawn from the 1995 Project on Human Development in
Chicago Neighborhoods (PHDCN) and the 1990 United States Census. The PHDCN Community Survey,
explicitly designed to investigate community social life, offers measures of social organization and
perceptions of neighborhood problems, and the Census provides contextual information regarding
neighborhood structural characteristics. We rely on 342 geographically contiguous, statistically
constructed “neighborhood clusters” (NCs) to approximate Chicago’s local neighborhoods. The PHDCN
team defined the NCs by aggregating the City’s 865 inhabited census tracts, and decisions of which tracts
to aggregate were informed by local geographic knowledge (e.g., ecological boundaries such as parks,
railroad tracks, and freeways) and a cluster analysis of census data. The resulting NCs are relatively
homogeneous with respect to racial-ethnic mix, socioeconomic status, housing density, and family
structure. 2
Social Organization Measures
In Figure 1, we present a typology that describes our measures of local social organization based
on whether the motivation for community participation is expressive or instrumental, and whether the
type of participation is informal or formal. Table 1 displays descriptive statistics for each of the four
outcome measures.
Informal expressive participation is measured by a two-item scale of local social ties, which is the
combined average of two measures3 capturing the number of friends and relatives that respondents
reported living in the neighborhood. 4 We classify social ties as expressive because they are the product of
sociability and neighborliness rather than purposeful decisions to protect neighborhood interests and
personal investment. Our measure of formal expressive participation is a count of how many of the
following expressive organizations the respondent, or any member of the respondent’s household,
belongs to: neighborhood religious organizations, ethnic/nationality groups, and civic groups such as the
Elks and Masons. Although these groups may also serve instrumental functions from time to time, we
classify them as expressive because their primary function is to build social networks and promote a sense
of community among participants.
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Our measure of informal instrumental participation is a count of the number of problem-solving
actions the respondent (or any member of the respondent’s household) exhibited in the past twelve
months which was aimed at taking care of a local problem or making the neighborhood a better place to
live, including speaking with a politician or a minister, attending a meeting, or getting together with
neighbors to take action about a problem. We classify this measure as instrumental because all of the
above actions aim to address neighborhood problems, and we consider the actions to be informal because
they do not involve participation in a formal organization established to deal with such problems. We
measure formal instrumental participation as the number of instrumental organizations to which the
respondent (or any member of the household) belongs, including neighborhood watch programs, block
groups (or tenant associations or community councils), and neighborhood ward groups (or local political
organizations). These associations are instrumental in that they are explicitly dedicated to maintaining or
improving conditions within a neighborhood.
Independent Variables
Neighborhood Characteristics
Building on previous research (Sampson et al. 1997; Sampson et al. 1999; Morenoff 2003;
Morenoff et al. 2001), we construct four Census-based measures of neighborhood sociodemographic
structure, each of which is based on the summation of equally weighted z-scores divided by the number of
items.5 We include a measure of residential stability to test the claims of the social disorganizationsystemic models, using a scale which incorporates the percentage of residents five years old and older
who resided in the same house five years earlier and the percentage of owner-occupied homes. To assess
the urban poverty thesis, we use an index of concentrated disadvantage, based on the proportion of
neighborhood residents living below the poverty line, the proportion of female -headed households, the
proportion of families receiving public assistance, and the unemployment rate. Other structural variables
include concentrated immigration (the average of percent Latino and percent foreign-born) and
population density, which measures persons per square kilometer.
We also consider several neighborhood factors that might mediate the effects of neighborhood
structural characteristics on participation in local social organization. For example, one reason
neighborhood structure may matter for community participation is that it influences the local
organizational infrastructure and, as a result, the opportunity to participate in formal organizations (which,
in turn, may inf luence informal ties if people meet through these organizations). Our measure of
community institutions is based on PHDCN Community Survey respondents’ reports of how many of the
following institutions are present in the neighborhood: a block group (or other group dealing with local
issues, such as a tenant association), crime prevention program (e.g., neighborhood watch), or community
newspaper (or newsletter or bulletin). Because it is possible that individuals within a neighborhood differ
in their awareness of local institutions, we include in our analysis both an individual- and neighborhoodlevel version of this measure. The individual- level scale is defined as the mean number of institutions that
the respondent reports (a=.69), while the neighborhood-level measure is constructed by averaging the
scale scores of all respondents within each NC.
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Another set of neighborhood mediators tap the presence of physical and social incivilities in a
neighborhood that may function as visual indications of social need. Physical disorder is a three-item
scale indicating how much of a problem (where responses are coded as 1 for “not a problem,” 2
“somewhat of a problem,” and 3 “a big problem”) the respondent perceives each of the following to be in
her/his neighborhood: (1) litter, broken glass, or trash on the sidewalk or streets, (2) graffiti on the
buildings and walls, and (3) vacant and deserted houses or storefronts (a= .76). Social disorder is also a
three-item scale consisting of respondents’ assessments of how much of a neighborhood problem is (1)
drinking in public, (2) people selling or using drug, and (3) groups of teenagers or adults hanging out and
causing trouble (a=.86). The same response scale described above is used for this measure. We include
both individual- and neighborhood-level versions of this variable —where the neighborhood-level variable
is computed by averaging across respondents who live in the same NC—so that our estimates of the
contextual effects of disorder are adjusted for individual differences on perceptions of disorder. In other
words, we recognize that each individual may have a different way of perceiving disorder, but we also
estimate the effects of shared perceptions of disorder across all respondents who live in the same
neighborhood. All the neighborhood-level variables have been standardized around a mean of zero and a
standard deviation of one to place them on a common metric.
Individual-Level Controls
Although our focus is on the neighborhood sources of local social organization, we include
individual-level controls in our analysis to adjust for compositional effects. We consider a number of
demographic control variables, including gender (female = 1) and age (in years). We code race/ethnicity
with four dummy variables: Non-Hispanic white (omitted), African American, Hispanic , and nonHispanic other races. We also use dummy variables to indicate marital status as married,
separated/divorced, or widowed. Our measures of socioeconomic status include education (years of
education completed), family income, and the respondent’s occupational prestige (higher scores
correspond to more prestigious jobs). We control for the individual’s level of investment in the
community with a dummy variable indicating whether the respondent is a homeowner, a count variable
indicating the number of residential moves the respondent has made within the past five years, and a
measure of the respondent’s number of years in the neighborhood (living at the same address). We also
include respondents’ perceptions of their neighborhood size (the logged number of blocks a respondent
considers the neighborhood) to control for the possibility that participation in a neighborhood activity is
more likely among people with more expansive geographic definit ions of their neighborhood. Descriptive
statistics for both the neighborhood- and individual- level independent variables are provided in Table 2.
Methods
Multilevel analyses of the four measures of local social organization were conducted using
hierarchical modeling techniques (Raudenbush and Bryk, 2002). Hierarchical models for multilevel data
consist of two equations estimated simultaneously: a level-1 (individual-level) model and level-2
(neighborhood-level) model. The level-1 model is either a linear model (for social ties) or a generalized
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linear model (for count variables, including participation in expressive and instrumental organizations and
problem-solving actions).6 The linear model is written as Yij = β 0 j + ∑q β q X qij + ε ij , where Yij is
the size of the local social network for respondent i in neighborhood j; β 0 j is the intercept; X qij is the
value of covariate q; and β q is the partial effect of that covariate on network size. The person-specific
error term, ε ij , is assumed to be independently, normally distributed with constant variance σ2. The
generalized linear model views the count variable, Yij for person i in neighborhood j, as sampled from an
over-dispersed Poisson distribution. It replaces Yij with the natural log link, η ij = log( λij ) , where λij is
the event rate, assuming constant exposure across persons, ηij is the log of the event rate (Raudenbush and
Bryk 2002). 7 We can obtain the percent change in Y associated with a one-unit change in X, holding all
other variables constant, by calculating 100 x [exp(βq ) – 1] (Long 1997).
The level-2 model is the same in both cases. The intercept from level-1, β0j , is allowed to vary
randomly across NCs: β 0 j = γ 00 + ∑s γ 0sW sj + µ 0 j , where γ 00 is the average value of the outcome
across all neighborhoods, γ 0s are the neighborhood-level regression coefficients, Wsj are the
neighborhood-level predictors, and µ 0 j is the unique increment to the intercept associated with
neighborhood j (i.e., the random effect), assumed to be normally distributed with variance τ.
Spatial Models
Spatial regression models are estimated through an autoregressive process in the dependent
variable known as a “spatial lag” model,8 Y = ρWY + Xβ + ε , where ρ is the spatial autoregressive
parameter, W is a weights matrix that expresses a form of spatial association among each pair of
neighborhoods (in the analysis below it is a binary contiguity matrix), X is a matrix of exogenous
explanatory variables with an associated vector of regression coefficients β , and ε is a vector of
normally distributed, random error terms. The spatial autoregressive parameter, ρ , can be interpreted as
the effect of a one-unit change in WY on Y. 9
Results
The results of the multilevel analyses for the four social organization outcome variables are
presented in Tables 3-6. The first model in each table contains neighborhood-level structural variables but
no individual- level controls. The reason for showing estimates of neighborhood effects before adjusting
for individual-level covariates is that many of the individual- level controls may actually be reflecting the
effects of prior neighborhood conditions. The full set of individual-level controls are added in the second
model. In subsequent models we introduce neighborhood measures from the PHDCN Community
Survey, namely community institutions, physical disorder, and social disorder (along with their
individual-level analogues).10
Table 3 presents results from models of local social ties. Model 1 shows that people living in
more stable neighborhoods and neighborhoods with higher concentrations of immigrants/Hispanics tend
to have more local ties to friends and family members.11 These findings are consistent with systemic
theory and classic social disorganization theory, both of which hypothesizes that more residentially stable
and ethnically homogeneous environments encourage the spread of social networks. These effects are
13
reduced to non-significance by the introduction of individual-level characteristics in model 2 (although
residential stability effect remains marginally significant, p<.10), but they regain significance in
subsequent models. Neighborhood disadvantage, although not predictive of social ties in model 1,
becomes significantly associated with more social ties after controlling for individual characteristics.12
Supplemental analysis revealed that controlling for individual- level race, in particular, suppresses the
effect of neighborhood disadvantage, because African Americans, who are heavily represented in
disadvantaged neighborhoods, tend to have fewer local social ties. Thus, poor neighborhoods are not
havens of social isolation in the sense that residents have many social connections to their neighbors.
Model 3 shows that local social ties are more prevalent in neighborhoods where there are more
community organizations and also that individuals who are aware of more organizations in their
neighborhoods also have more social ties. Neither physical nor social disorder is associated with social
ties in models 4 and 5, respectively.
Results for participation in expressive organizations are reported in Table 4. As was the case with
local social ties, residential stability and immigrant concentration are both associated with higher rates of
participation in expressive organizations, and these results are robust across model specifications.
Although concentrated disadvantage is associated with lower rates of expressive participation in model 1,
this effect becomes non-significant after controlling for individual-level covariates in model 2. 13 None of
the other neighborhood-level variables are significantly related to participation in expressive
organizations, including community institutions, social disorder, and physical disorder. Thus, residential
stability and immigrant concentration are the strongest predictors of both formal and informal forms of
expressive social organization—local social ties and participation in expressive organizations.
We now turn to the results for instrumental social organization, beginning with problem-solving
actions in Table 5. Model 1 shows that problem-solving behaviors are more prevalent in residentially
stable neighborhoods and less common in Hispanic/immigrant neighborhoods, but both of these
associations weaken and become non-significant with the introduction of individual- and neighborhoodlevel controls in subsequent models. The relationship between neighborhood disadvantage and problemsolving behavior is nonlinear, becoming weaker (i.e., less positive) at higher levels of disadvantage, but
these effects only become significant after the introduction of individual-level controls in model 2. 14
Supplemental analysis revealed that individual- level indicators of socioeconomic status (SES) (e.g.,
education, income, and occupational prestige) are the key suppressor variables because many residents of
disadvantaged neighborhoods tend to be of low SES, and low SES is associated with less problem-solving
behavior at the individual level. That is to say, residents of more disadvantaged neighborhoods are
actually more likely to be involved in problem-solving behavior, notwithstanding their own individual
socioeconomic characteristics. The effects of disadvantage become even stronger after controlling for
community institutions in model 3, which is significant as an individual- level variable but not at the
neighborhood level—individuals who are aware of more community organizations are more likely to
engage in neighborhood problem-solving behavior.
The association between neighborhood disadvantage and problem-solving behavior is largely
mediated by the presence of physical and social disorder in the neighborhood, as illustrated in models 4
and 5. The significant contextual effects of disorder, even after controlling for their individual-level
14
analogues, indicates that people who live in neighborhoods where residents perceive more disorder are
more likely to participate in problem-solving action, even after adjusting for differences in the varying
ways that individuals perceive disorder. That disorder mediates the positive association between
neighborhood disadvantage and participation in informal instrumental activities supports the social needs
perspective, which argues that poor neighborhoods stimulate social action because they present more
visible signs of social need.
In Table 6, we estimate models predicting participation in instrumental organizations. The rate of
participation in instrumental organizations is lower in Hispanic/immigrant neighborhoods in model 1, and
this holds after controlling for individual-level covariates in model 2. This finding is consistent with the
results from instrumental problem-solving behavior (see Table 5). Here again, concentrated disadvantage
becomes significantly associated with instrumental participation, in a nonlinear fashion, after the
introduction of individual- level controls, and the effects (both linear and quadratic) become stronger after
adjusting for community institutions. As was the case with problem-solving actions, neighborhood
disorder is associated with higher rates of participation in instrumental organizations, only in this case the
finding is specific to social disorder. Moreover, introducing social disorder in model 5 reduces the size of
the disadvantage coefficient by about half. Thus, neighborhood social disorder appears to promote both
formal and informal forms of instrumental participation.
In the final stage of our analysis, we consider the wider spatial context of local social
organization. Table 7 presents results from spatial models for each of the social organization outcomes. In
the interest of parsimony, we present only one model per outcome, in which we combine social and
physical disorder into a measure of overall perceived disorder.15 Each of the outcomes has been adjusted
for within-neighborhood differences on individual- level covariates, following the procedure outlined
above.16 In all cases but one, the spatial dependence term is significant and positive. This means that
neighborhood residents are more likely to form local social ties and participate in expressive and
instrumental organizations when they are surrounded by people in other neighborhoods who are engaged
in similar behaviors. The finding of no significant spatial dependence in the case of problem-solving
action is revealing, for it suggests that in cases where we do find spatial dependence, the underlying
mechanism may be related to social networks. That is to say, some problem-solving behaviors, such as
contacting a politician or minister, are very individualistic actions that do not rely on strong social
networks to make them happen. However, social networks may be more directly implicated in the
formation of social ties within the neighborhood and participation in local formal organizations, all of
which do display significant positive spatial dependence.
Conclusion
Our findings offer several important new insights into prevailing theories of social organization.
First, contrary to the image of disadvantaged neighborhoods as socially isolated places where residents
withdraw from community life out of fear or apathy, our results indicate that neighborhood residents
respond to adverse ecological conditions by taking actions intended to alleviate neighborhood problems
and getting involved in organizations that address instrumental needs of the community. Moreover,
residents of disadvantaged neighborhoods also tend to have strong personal networks connecting them to
15
friends and family members in their neighborhoods. Second, the findings suggest that collective
perceptions of disorder represent a mechanism through which neighborhood disadvantage translates into
community participation on behalf of social needs. In other words, signs of physical and social incivilities
appear to function as signals of community distress that motivate residents to become engaged in
instrumental activities, either through formal or informal channels. We note, however, that these
inferences are based on cross-sectional data, and that further research is needed on the connection
between disadvantaged neighborhood contexts and community participation, preferably using
longitudinal data that can link individuals’ perceptions of neighborhood conditions to their subsequent
participation in community activities.
A third set of findings adds an important qualification to social disorganization theory and the
systemic perspective in particular. Although residential stability appears to encourage the formation of
local social ties and participation in expressive organizations, it is not predictive of instrumental forms of
community participation. Similarly, residents of ethnically homogeneous neighborhoods (those
characterized by high concentrations of Hispanics and immigrants) have social networks connecting them
to many friends and family in the neighborhood and are actively involved in expressive organizations, but
they are less likely to participate in instrumental organizations. Our results do not address why
Hispanic/immigrant neighborhoods vary so greatly on levels of expressive vs. instrumental social
organization, which is another question that warrants future research.
A final important finding is that individuals’ decisions about whether and how to participate in
community organization appear to be contingent not only on the social environment of their immediate
neighborhood, but also on the wider spatial context of surrounding areas. One explanation for the spatial
dependence we find in social ties and participation in expressive and instrumental organizations is that
statistically-defined neighborhood boundaries are permeable, and that the contextual factors that predict
community participation in one place simply “spill over” these boundaries. A related explanation for
spatial effects, particularly those involving participation in formal organizations, is that many
“neighborhood” organizations provide services for areas beyond the neighborhood in which they are
located (McRoberts 2003), and as a consequence may draw in members from surrounding areas. It is also
important to note that such spatial dynamics do not apply to the case of engagement in problem-solving
actions. This exception to the rule provides another interesting question for future research: is it possible
that the wider spatial environment is most salient for forms of community participation in which
individuals are more influenced by the behavior of others in their social networks, many of whom may
live outside their local neighborhood? That is to say, individuals may engage in problem-solving activities
for very individualistic reasons, whereas they may be more likely to participate in formal organizations
because they already have ties to others in those organizations.
In sum, our results have signficant implications for those interested in harnessing the power of
communities to address neighborhood problems. Residents appear to respond to the detrimental
conditions associated with disadvantage, including social needs, by getting involved in activities designed
to alleviate neighborhood problems. In addition, our analysis of spatial dependence suggests that
improving community participation in one neighborhood will enhance social organization in surrounding
16
neighborhoods as well, meaning that even small, localized efforts to improve community participation
will have effects that reach beyond the immediate neighborhood.
Endnotes
1
Although recent research has acknowledged the importance of investigating extra-local participation, we lack
the data appropriate for doing so. For a review, see Guest (2002).
2
More details about the PHDCN sample design are available in previous publications (e.g. Sampson et. al.
1997).
3
The response categories for each measure were 0 (no ties), 1-2, 3-5, 6-9, and 10 or more. We recoded each
measure using the mean of the response category (10 in the case of the highest category) so that the resulting
measure can be interpreted as the number of ties to friends and family living in the neighborhood.
4
Whenever a respondent is asked a question which refers to his or her “neighborhood,” survey protocol states
“By 'neighborhood,' we mean the area around where you live and around your house. It may include places you
shop, religious or public institutions, or a local business district. It is the general area around your house where
you might perform routine tasks, such as shopping, going to the park, or visiting neighbors.”
5
Previous research has shown that factor-weighted scales yield the same results as the z-scores (Sampson et.
al. 1997).
6
An alternative modeling strategy would be to estimate generalized linear models for dichotomous variables,
predicting participation/non-participation in expressive and instrumental organizations and problem-solving
actions. Our strategy is superior for three reasons. First, we utilize all available information rather than
collapsing existing data into two categories. Second, we follow modeling strategies used in extant research.
Third, this strategy substantively addresses the question of how much individuals participate, rather than
simply whether they participate, enhancing face validity.
7
A problem with assuming constant exposure is that the questions on organizational membership ask whether
the respondent or anyone in the respondent’s family is a member of the organization in question, and thus,
exposure to organizational membership should vary by family size.
8
There is also a spatial error model, in which the autocorrelation process is modeled in the error term, as
follows: Y = Xβ + λε + ξ , where X is a matrix of exogenous explanatory variables with an associated vector of
regression coefficients β , λ is the autoregressive coefficient, ε is a vector of error terms, and ξ is a random
error term Anselin (1988; 1995) has developed regression diagnostic tests to determine whether spatial
dependence is better captured by a lag or error process.
9
However, rewriting the spatial lag model in its reduced form (where all endogenous variables are expressed
as functions of the exogenous variables) shows that the ρ coefficient not only captures the effects of spatial
proximity to Y in other locations, but also spatial proximity to the observed and unobserved predictors of Y
(Anselin 2003, Morenoff 2003). Thus, a statistically significant spatial lag coefficient is statistically consistent
could signal the operation of either a diffusion process in Y or spatial externalities in the X variables.
10
The high bivariate correlation between neighborhood-level measures of physical and social disorder (r = .90)
prevents us from disentangling the effects of both types of disorder in a single regression model.
11
All of our findings on the contextual effects of concentrated immigration must be interpreted with caution,
because the PHDCN data set does not include an individual-level control for immigrant status. Thus, the
17
apparent contextual effects of neighborhood Hispanic/immigrant composition could actually be artifacts of
individual-level associations between immigrant status and participation in local social organization.
12
We tested for quadratic effects on all individual- and neighborhood-level continuous variables and we
include quadratic terms when they are statistically significant. In this model, individual-level years in
neighborhood has a significant negative quadratic effect, meaning that the association between living in the
neighborhood for more years and having more social ties dissipates at higher levels of residential tenure. In
fact, both the linear and quadratic effects of the years in neighborhood variable are significantly associated
with all forms of local social organization.
13
Supplemental analysis again revealed that racial group membership (specifically the African American
variable) appears to be driving the change in the association between concentrated disadvantaged and
participation in expressive organizations. As was the case with social ties, after controlling for individual-level
race, the effect of neighborhood-level disadvantage becomes more positive, although in this case it does not
become significantly positive.
14
There is no quadratic term for conc entrated disadvantage in model 1 because supplemental analysis revealed
it was non-significant.
15
To create the overall disorder measure we calculated the mean of all six disorder items at the individuallevel, and then aggregated to the neighborhood level by calculating the mean for each NC.
16
This two-stage process of first using HLM to adjust for the potentially confounding effects of individuallevel covariates and then running a spatial regression model on the adjusted dependent variable appears to
work better with linear models than with nonlinear models. Thus, the results of our spatial models for social
ties in Table 7 are closely comparable to those from the hierarchical linear models of social ties in Table 3.
However, spatial model results for the count variables (participation in expressive organizations, problemsolving actions, and instrumental organizations) are not as consistent with their corresponding hierarchical
generalized linear models.
18
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21
Figure 1. Typology of Local Social Organization
Motivation
Expressive
Informal
Social Ties
-Number of friends in
neighborhood
-Number of relatives in
neighborhood
Problem-Solving Actions
-Talking with politician
Instrumental -Talking with minister
-Attending problemsolving meeting
-Getting together with
neighbors to take action
Formal
Expressive Organizations
-Religious group/organization
-Ethnic/nationality group
-Civic group
Instrumental Organizations
-Neighborhood watch
-Block Group, tenant
association, or community
council
-Neighborhood ward group or
local political organization
Table 1. Local Social Organization Descriptive Statistics: 1995 PHDCN Community Survey
Mean
SD
Minimum
Maximum
Sample
Size
Social Organization Outcomes
Social Networks
3.45
(2.68)
0
10
7,328
Expressive Organizations
Religious group
Ethnic group
Business/Civic group
0.36
0.32
0.02
0.02
(0.55)
(0.47)
(0.13)
(0.14)
0
0
0
0
3
1
1
1
7,128
7,128
7,128
7,128
Problem-Solving Behaviors
Spoke with politician
Spoke with minister
Attended a meeting
Got together with neighbors
0.96
0.29
0.15
0.27
0.26
(1.30)
(0.45)
(0.36)
(0.44)
(0.44)
0
0
0
0
0
4
1
1
1
1
7,297
7,297
7,297
7,297
7,297
Instrumental Organization
Neighborhood watch
Block/Community group
Ward/Political group
0.22
0.08
0.11
0.03
(0.53)
(0.27)
(0.31)
(0.17)
0
0
0
0
3
1
1
1
7,168
7,168
7,168
7,168
Table 2. Neighborhood- and Person-Level Descriptive Statistics: 1990 Census and 1995 PHDCN Community Survey
Mean
SD
Minimum
Maximum
Sample
Size
Independent Variable
Neighborhood
Stability
Disadvantage
Immigration
Density
Community Institutions
Physical Disorder
Social Disorder
0
0
0
0
0
0
0
(1.00)
(1.00)
(1.00)
(1.00)
(1.00)
(1.00)
(1.00)
-2.07
-1.16
-1.92
-1.66
-2.55
-1.97
-1.85
2.32
3.89
3.52
5.65
2.09
2.74
2.04
342
342
342
342
342
342
342
Individual
Male
Female
Age
Non-Hispanic White
African American
Latino
Other
Education
Income (in thousands)
Prestige
Single
Married
Divorced
Widowed
Homeowner
Years in Neighborhood
No. of Moves
Neighborhood Size
Community Institutions
Physical Disorder
Social Disorder
0.41
0.59
42.6
0.28
0.4
0.25
0.07
12.31
30.72
40.57
0.32
0.37
0.17
0.14
0.45
10.39
0.95
31.76
0
0
0
(0.49)
(0.49)
(16.73)
(0.45)
(0.49)
(0.43)
(0.27)
(3.12)
(29.53)
(13.32)
(0.47)
(0.48)
(0.37)
(0.35)
(0.50)
(11.97)
(1.38)
(29.67)
(1.00)
(1.00)
(1.00)
0
0
17
0
0
0
0
0
2.5
17
0
0
0
0
0
0
0
0
-1.59
-1.17
-1.19
1
1
100
1
1
1
1
17
150
86
1
1
1
1
1
81.5
11
95
1.34
1.99
1.58
7,739
7,739
7,739
7,739
7,739
7,739
7,739
7,739
7,716
7,739
7,739
7,739
7,739
7,739
7,739
7,739
7,739
7,716
7,729
7,729
7,729
Table 3. Neighborhood- and Person-Level Predictors of Social Ties from Hierarchical Linear Models: 1990 Census and 1995 PHDCN Community Survey
Independent Variable
Neighborhood
Stability
Disadvantage
Immigration
Density
Community Institutions
Physical Disorder
Coef.
(1)
Std. Err.
(2)
Coef. Std. Err.
(3)
Coef. Std. Err.
0.211
0.001
0.239
-0.142
(0.056) **
(0.050)
(0.044) **
(0.046) **
0.094
0.224
0.053
-0.118
0.160
0.402
0.160
-0.096
0.171
(0.057)
(0.059) **
(0.050)
(0.046) **
(0.056)
(0.073)
(0.055)
(0.043)
(0.068)
**
**
**
*
*
Coef.
(4)
Std. Err.
0.170
0.368
0.139
-0.100
0.180
0.047
(0.058)
(0.082)
(0.062)
(0.044)
(0.068)
(0.083)
**
**
*
*
**
Social Disorder
Individual
Female
Age
African American
Latino
Other
Education
a
Income
Prestige
Married
Divorced
Widowed
Homeowner
Years in Neighborhood
Years in Neighborhood Squared a
No. of Moves
Neighborhood Size (Logged)
Community Institutions
Physical Disorder
Social Disorder
-0.204
-0.024
-0.563
0.178
-0.160
-0.039
0.091
(0.066)
(0.003)
(0.118)
(0.122)
(0.142)
(0.013)
(0.127)
**
**
**
**
-0.001
0.285
0.036
0.221
0.088
0.077
-0.217
-0.026
-0.464
0.318
-0.060
-0.054
-0.018
(0.066)
(0.003)
(0.111)
(0.120)
(0.141)
(0.013)
(0.127)
**
**
**
**
**
(0.003)
(0.081) **
(0.094)
(0.139)
(0.082)
(0.007) **
-0.091 0.013 **
-0.002
0.288
0.014
0.242
0.070
0.078
(0.003)
(0.081) **
(0.094)
(0.137)
(0.082)
(0.007) **
-0.094 0.013 **
-0.085 (0.026) **
0.180 (0.030) **
-0.082 (0.026) **
0.161 (0.030) **
0.367 (0.038) **
-0.217
-0.026
-0.462
0.324
-0.054
-0.054
-0.012
(0.066)
(0.003)
(0.112)
(0.120)
(0.141)
(0.013)
(0.127)
**
**
**
**
-0.002
0.287
0.011
0.242
0.069
0.078
-0.093
(0.003)
(0.081) **
(0.094)
(0.137)
(0.082)
(0.007) **
(0.013) **
-0.082
0.159
0.370
0.036
(0.026) **
(0.030) **
(0.038) **
(0.042)
**
Coef.
(5)
Std. Err.
0.165
0.375
0.145
-0.105
0.179
(0.057)
(0.082)
(0.061)
(0.046)
(0.069)
0.029
(0.094)
-0.218
-0.026
-0.480
0.313
-0.067
-0.053
-0.012
(0.066)
(0.003)
(0.119)
(0.120)
(0.143)
(0.013)
(0.127)
-0.002
0.287
0.010
0.245
0.072
0.078
-0.093
(0.003)
(0.080) **
(0.095)
(0.137)
(0.082)
(0.007) **
(0.013) **
-0.083
0.159
0.371
(0.026) **
(0.030) **
(0.038) **
0.051
(0.039)
**
**
*
*
*
**
**
**
**
**
Table 3. Neighborhood- and Person-Level Predictors of Social Ties from Hierarchical Linear Models: 1990 Census and 1995 PHDCN Community Survey
(Continued)
(1)
Independent Variable
Coef.
Intercept
3.455
Std. Err.
N
7,328
Coefficients and standard errors multiplied by 100.
a
Notes:** p<.01, *p<.05
(2)
(3)
(4)
Std. Err.
(5)
Coef. Std. Err.
Coef. Std. Err.
Coef.
Coef.
3.467
3.460
3.461
3.462
7,328
7,328
7,328
7,328
Std. Err.
Table 4. Neighb.- and Person-Level Predictors of Expressive Organizations from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(1)
(2)
(3)
(4)
(5)
Independent Variable
Neighborhood
Stability
Disadvantage
Immigration
Density
Community Institutions
Physical Disorder
Coef.
Std. Err.
Coef. Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
0.175
-0.290
0.165
-0.035
(0.030) **
(0.034) **
(0.020) **
(0.028)
0.122
0.019
0.079
-0.005
0.129
0.045
0.092
-0.001
0.006
(0.029) **
(0.040)
(0.026) **
(0.026)
(0.038)
0.137
0.016
0.073
-0.003
0.013
0.073
(0.030) **
(0.045)
(0.031) *
(0.026)
(0.038)
(0.043)
0.133
0.025
0.081
-0.005
0.011
(0.029) **
(0.045)
(0.028) **
(0.026)
(0.038)
0.034
(0.044)
(0.028) **
(0.035)
(0.022) **
(0.026)
Social Disorder
Individual
Female
Age
African American
Latino
Other
Education
Incomea
Prestige
Married
Divorced
Widowed
Homeowner
Years in Neighborhood
Years in Neighborhood Squared a
No. of Moves
Neighborhood Size (Logged)
Community Institutions
Physical Disorder
Social Disorder
0.128
0.005
-0.771
0.041
-0.159
0.001
0.059
(0.039) **
(0.001) **
(0.068) **
(0.063)
(0.084)
(0.006)
(0.062)
0.126
0.004
-0.743
0.077
-0.133
-0.003
0.033
(0.039) **
(0.001) **
(0.069) **
(0.063)
(0.084)
(0.006)
(0.063)
0.128
0.004
-0.751
0.073
-0.135
-0.003
0.031
(0.039) **
(0.001) **
(0.069) **
(0.063)
(0.084)
(0.006)
(0.063)
0.126
0.004
-0.757
0.073
-0.139
-0.003
0.035
(0.039) **
(0.001) **
(0.071) **
(0.064)
(0.084)
(0.006)
(0.063)
0.003
0.159
0.045
0.078
0.185
0.021
(0.001)
(0.047) **
(0.065)
(0.073)
(0.046) **
(0.004) **
-0.020 (0.007) **
0.002
0.160
0.041
0.083
0.180
0.021
0.002
0.163
0.045
0.083
0.183
0.021
-0.022
(0.002)
(0.047) **
(0.065)
(0.073)
(0.046) **
(0.004) **
(0.007) **
0.002
0.160
0.040
0.082
0.180
0.021
-0.021
(0.002)
(0.047) **
(0.065)
(0.073)
(0.046) **
(0.004) **
(0.007) **
-0.059 (0.022) **
0.054 (0.016) **
-0.058
0.050
0.099
(0.022) **
(0.016) **
(0.021) **
-0.058
0.051
0.097
-0.031
(0.022) **
(0.016) **
(0.021) **
(0.020)
-0.021
(0.002)
(0.047) **
(0.065)
(0.073)
(0.046) **
(0.004) **
(0.007) **
-0.059
0.050
0.099
(0.022) **
(0.016) **
(0.021) **
0.011
(0.022)
Table 4. Neighb.- and Person-Level Predictors of Expressive Organizations from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(Continued)
(1)
(2)
(3)
(4)
(5)
Independent Variable
Coef.
Intercept
-1.084
Std. Err.
N
7,128
Coefficients and standard errors multiplied by 100.
a
Notes:** p<.01, *p<.05
Coef. Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
-1.179
-1.184
-1.185
-1.184
7,128
7,128
7,128
7,128
Std. Err.
Table 5. Neighb.- and Person-Level Predictors of Problem-Solving Actions from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(1)
Independent Variable
Neighborhood
Stability
Disadvantage
Disadvantage Squared
Immigration
Density
Community Institutions
Physical Disorder
(2)
(3)
(4)
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
0.075
-0.021
(0.026) **
(0.022)
-0.097
-0.054
(0.023) **
(0.031)
-0.054
0.138
-0.041
-0.024
-0.032
(0.024) *
(0.043) **
(0.019) *
(0.027)
(0.029)
-0.018
0.257
-0.062
0.015
-0.023
0.006
(0.022)
(0.047) **
(0.020) **
(0.026)
(0.025)
(0.031)
0.009
0.129
-0.045
-0.046
-0.035
0.027
0.135
(0.022)
(0.057) *
(0.020) *
(0.026)
(0.025)
(0.031)
(0.043) **
-0.006
0.108
-0.028
-0.030
-0.049
0.018
(0.021)
(0.059)
(0.021)
(0.027)
(0.023) *
(0.030)
0.118
(0.045) **
Social Disorder
Individual
Female
Age
Age Squareda
African American
Latino
Other
Education
Incomea
Prestige
Married
Divorced
Widowed
Homeowner
Years in Neighborhood
Years in Neighborhood Squared a
No. of Moves
Neighborhood Size (Logged)
(5)
0.013
0.035
-0.036
(0.031)
(0.006) **
(0.006) **
0.002
0.030
-0.030
(0.030)
(0.006) **
(0.006) **
-0.002
0.028
-0.029
(0.030)
(0.006) **
(0.006) **
-0.005
0.029
-0.029
(0.030)
(0.006) **
(0.006) **
-0.080
-0.064
-0.072
0.057
0.148
(0.063)
(0.057)
(0.064)
(0.006) **
(0.044) **
-0.034
0.029
-0.014
0.043
0.079
(0.057)
(0.054)
(0.062)
(0.006) **
(0.045)
-0.006
0.053
0.016
0.043
0.000
(0.058)
(0.054)
(0.062)
(0.006) **
(0.046)
-0.053
0.022
-0.019
0.044
0.089
(0.056)
(0.053)
(0.063)
(0.006) **
(0.045) *
0.002
0.201
0.074
0.030
0.470
0.025
(0.001)
(0.039)
(0.050)
(0.065)
(0.042)
(0.004)
0.002
0.210
0.064
0.056
0.441
0.027
0.002
0.209
0.060
0.047
0.437
0.025
0.002
0.210
0.059
0.055
0.446
0.025
-0.028
(0.001)
(0.039) **
(0.051)
(0.064)
(0.041) **
(0.003) **
(0.006) **
-0.027
(0.001)
(0.039) **
(0.050)
(0.064)
(0.040) **
(0.003) **
(0.006) **
-0.065
0.044
(0.019) **
(0.016) **
-0.066
0.044
(0.019) **
(0.016) **
*
**
-0.024
**
**
(0.007) **
-0.029
(0.001)
(0.039) **
(0.050)
(0.064)
(0.041) **
(0.003) **
(0.007) **
-0.068
0.064
(0.020) **
(0.017) **
-0.062
0.051
(0.019) **
(0.016) **
Table 5. Neighb.- and Person-Level Predictors of Problem-Solving Actions from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(Continued)
(1)
Independent Variable
Community Institutions
Physical Disorder
Social Disorder
Intercept
Coef.
Std. Err.
-0.026
N
7,297
Coefficients and standard errors multiplied by 100.
a
Notes:** p<.01, *p<.05
(2)
Coef.
Std. Err.
(3)
Coef.
0.372
Std. Err.
(0.022) **
(4)
Coef.
0.000
0.103
Std. Err.
(0.022)
(0.018) **
(5)
Coef.
0.381
Std. Err.
(0.022) **
(0.019) **
-0.170
-0.236
-0.237
0.116
-0.239
7,297
7,297
7,297
7,297
Table 6. Neighb.- and Person-Level Predictors of Instrumental Organizations from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(1)
Independent Variable
Neighborhood
Stability
Disadvantage
Disadvantage Squared
Immigration
Density
Community Institutions
Physical Disorder
(2)
(3)
(4)
(5)
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
0.072
-0.072
(0.045)
(0.039)
-0.102
0.221
-0.121
(0.049) *
(0.071) **
(0.049) *
-0.031
0.382
-0.141
(0.046)
(0.076) **
(0.048) **
-0.028
0.364
-0.139
(0.047)
(0.086) **
(0.048) **
-0.015
0.187
-0.093
(0.045)
(0.091) *
(0.047) *
-0.252
-0.029
(0.036) **
(0.043)
-0.161
-0.014
(0.043) **
(0.041)
-0.100
0.009
-0.099
(0.044) *
(0.039)
(0.053)
-0.108
0.007
-0.096
0.023
(0.046) *
(0.039)
(0.054)
(0.061)
-0.149
-0.024
-0.079
(0.046) **
(0.038)
(0.053)
0.211
(0.063) **
Social Disorder
Individual
Female
Age
Age Squareda
African American
Latino
Other
Education
Incomea
Prestige
Married
Divorced
Widowed
Homeowner
Years in Neighborhood
Years in Neighborhood Squared a
Mobility
Neighborhood Size (Logged)
0.099
0.056
-0.052
(0.057)
(0.012) **
(0.012) **
0.072
0.042
-0.039
(0.056)
(0.012) **
(0.012) **
0.072
0.042
-0.039
(0.056)
(0.012) **
(0.012) **
0.071
0.041
-0.038
(0.056)
(0.012) **
(0.012) **
-0.115
-0.037
-0.012
0.080
0.177
(0.102)
(0.099)
(0.105)
(0.014) **
(0.078) *
-0.033
0.130
0.075
0.050
0.038
(0.090)
(0.095)
(0.103)
(0.014) **
(0.082)
-0.029
0.132
0.077
0.050
0.039
(0.090)
(0.096)
(0.103)
(0.014) **
(0.082)
-0.044
0.124
0.060
0.051
0.043
(0.090)
(0.094)
(0.102)
(0.014) **
(0.082)
0.007
0.209
0.174
-0.138
0.827
0.024
(0.002)
(0.076)
(0.091)
(0.120)
(0.084)
(0.007)
0.006
0.211
0.162
-0.090
0.760
0.029
(0.002)
(0.073)
(0.090)
(0.115)
(0.080)
(0.007)
0.006
0.211
0.162
-0.090
0.760
0.029
(0.002)
(0.073)
(0.090)
(0.115)
(0.080)
(0.007)
0.006
0.212
0.161
-0.092
0.764
0.028
(0.002)
(0.073)
(0.090)
(0.115)
(0.080)
(0.007)
**
**
-0.029
**
**
(0.014) *
-0.081
0.050
(0.034) *
(0.026)
**
**
-0.040
**
**
(0.014) **
-0.066
0.021
(0.032) *
(0.027)
**
**
**
**
-0.040
**
**
(0.014) **
-0.039
**
**
(0.014) **
-0.066
0.020
(0.032) *
(0.027)
-0.069
0.019
(0.032) *
(0.027)
Table 6. Neighb.- and Person-Level Predictors of Instrumental Organizations from Hierarchical Generalized Linear Models: 1990 Census and 1995 PHDCN CS
(Continued)
(1)
Independent Variable
Community Institutions
Physical Disorder
Social Disorder
Intercept
Coef.
Std. Err.
-1.540
N
7,168
Coefficients and standard errors multiplied by 100.
a
Notes:** p<.01, *p<.05
(2)
Coef.
Std. Err.
(3)
Coef.
0.880
Std. Err.
(0.042) **
(4)
Coef.
0.880
0.006
Std. Err.
(0.042) **
(0.033)
(5)
Coef.
0.880
Std. Err.
(0.042) **
(0.035)
-1.833
-2.133
-2.132
0.031
-2.134
7,168
7,168
7,168
7,168
Table 7. Coefficients from Spatial Lag Regression Models of HLM-Adjusted Social Organization Outcomes: 1990 Census and 1995 PHDCN Community Survey
Independent Variable
Intercept
Stability
Disadvantage
Disadvantage Squared
Immigration
Density
Community Institutions
Perceived Disorder
Spatial dependence
Notes: ** p<.01, *p<.05
Social Ties
Coef.
Std. Err.
-0.053
(0.159)
0.047
(0.022)
0.109
(0.029)
*
**
0.059
-0.022
0.124
0.031
(0.022)
(0.021)
(0.026)
(0.089)
**
0.363
(0.071)
**
**
Expressive Organizations
Coef.
Std. Err.
-0.279
(0.127) *
0.042
(0.017) *
0.053
(0.023) *
-0.005
0.003
0.039
0.159
(0.018)
(0.017)
(0.020)
(0.072)
*
0.332
(0.073)
**
Problem-Solving Actions
Coef.
Std. Err.
-0.654
(0.107) **
-0.070
(0.014) **
0.077
(0.027) **
-0.019
(0.009) *
0.008
(0.013)
-0.020
(0.013)
0.102
(0.016) **
0.380
(0.058) **
0.082
(0.071)
Instrumental Organizations
Coef.
Std. Err.
-0.492
(0.187) **
-0.079
(0.024) **
0.218
(0.047) **
-0.058
(0.016) **
-0.005
(0.024)
0.006
(0.022)
0.144
(0.028) **
0.311
(0.102) **
0.343
(0.064)
**
Appendix A. Correlations among Neighborhood-Level Covariates: 1990 Census and 1995 PHDCN Community Survey
Variable
Stability
Disadvantage
Immigration
Density
Community Institutions
Physical Disorder
Social Disorder
Notes: ** p<.01, *p<.05
Stability
1.00
-0.23
-0.25
-0.59
0.10
-0.39
-0.36
Disadv.
**
**
**
**
**
1.00
0.24
0.09
-0.63
0.72
0.75
Immigration
**
**
**
**
1.00
0.23
-0.51
0.57
0.51
Community
Instit.
Density
**
**
**
**
1.00
-0.11
0.29
0.32
*
**
**
1.00
-0.67
-0.68
Physical
Disorder
**
**
1.00
0.90
Social
Disorder
**
1.00