Nguyen Luu Bao Doan - Evidence of the impacts of urban sprawl on social capital

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Introduction

Sprawl has become one of the most discussed topics that draws the attention of
scholars from different fields such as planning, public health, economics, and
sociol-ogy. Certain practices and planning policies have been based on the perception of the
effects of sprawl. If urban sprawl has negative impacts, planners should implement
strategies to curb leapfrog suburbanization across the country. Otherwise, if urban sprawl
in fact does not have negative impacts, the urban growth versus suburban growth
debate should be reexamined thoroughly.

However, empirical studies of urban sprawl provide opposing evidence of the
effects of sprawl with respect to social capital. Findings from those studies may give
some answers to our question, but due to a lack of social capital and sprawl data, those
answers are not complete. As there is a growing need to build communities that
promote social and health welfare, planners need more rigorous studies to help them
reach agreement on how to achieve it (Freeman, 2001). The purpose of this study is to
reexamine the relationship between sprawl and social capital by improving the method
used in previous studies and taking advantage of the new measures of urban sprawl
and social capital. This analysis, in the context of American metropolitan areas, teases
out impacts of person characteristics and place characteristics on individual social
capital by adopting hierarchical or multilevel modeling techniques.

Background

Urban sprawl, defined as noncontiguous commercial and residential developments
with low density and large separation, has been at the center of a debate among
planning practitioners and scholars in recent years (Burchell et al, 1998; 2002; Ewing,
1997; Ewing et al, 2002; Galster et al, 2001; Weitz and Moore, 1998). Sprawl opponents
believe that owing to sprawl's connection to greater travel distances and increased
automobile dependence, it is responsible for the reduction in quality of life on several
aspects. They have argued that sprawl led to an increase in vehicle-miles traveled and

traffic congestion (Bento et al, 2005; Burchell et al, 1998; 2002; Cervero and Wu,
1998; Ewing, 1994), energy consumption (Anderson et al, 1996; Ewing, 1997), social
segregation (Burchell et al, 1998; Ewing, 1997), and physical inactivity and obesity

Evidence of the impacts of urban sprawl on social capital

Doan Nguyen

School of Architecture, Planning, and Preservation, University of Maryland, College Park,
MD 20742, USA; e-mail:

Received 19 September 2008; in revised form 12 August 2009; published online 17 May 2010

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(Cervero and Duncan, 2003; Ewing et al, 2003), and the depletion of land and air
quality (Anderson et al, 1996; Burchell et al, 1998; 2002).

A number of researchers have contended that sprawl has negative impacts on our
physical resources as well as on our social resources, or social capital. According to
Putnam (Putnam, 2000; Putnam et al, 1993), social networks have value, and the
network or connection among individuals provides members of the network with
actual and potential benefits. He blamed sprawl and suburbanization among other
major causes such as time pressure, technological advances (television and the
Inter-net), and generational change for the decline of social capital in the US. As people live
farther away from each other, sprawl weakens linkages between neighbors (Burchell
et al, 1998). Jacobs (1961) suggested that compact urban areas were likely to promote
social interaction and building compact communities with high density should be
one of the important planning guidelines. New Urbanists have adopted this view
and sought to create high-density and walkable communities as an ideal approach to
develop suburban areas (Freeman, 2001; Hayward, 1998).

A number of empirical studies indicate that compact and walkable neighborhoods

promote some form of social capital. In his 2001 study, Freeman successfully
estab-lished a negative relationship between the level of car usage and the level of social ties
in neighborhoods by using a binomial logistic regression model. Leyden (2003) found
that high neighborhood walkability resulted in residents' higher likelihood of political
participation, of knowing their neighbors, and of social interaction. However, both
studies fail to address the problem of dependence among people residing in the same
neighborhood: they are not random, independent observations. This limitation results
in coefficient biasness in both studies. Meanwhile, a number of scholars argue that
compactness does not alleviate problems caused by sprawl nor is it better than sprawl
in addressing issues such as improving social interaction and reducing commute time
(Audirac et al, 1990; Glaeser and Kahn, 2003; Gordon and Richardson, 1997; 2000;
Hayward, 1998; Kahn, 2000; 2001; 2006). Gordon and Richardson (1997) argued that
changes in land-use policy to favor compactness would violate the principles of the free
market and would be deemed unnecessary. They rejected that urban sprawl would lead
to increased automobile usage and traffic congestion.

Along this line, scholars criticizing compact development have shown empirical
evidence to demonstrate that urban sprawl does not undermine social capital. By using
DDB Needham Lifestyle Survey data, Glaeser and Gottlieb (2006) found that central
city residence decreases four types of social-capital activity such as attending church,
working on a community project, contacting a public official, and being a registered
voter. In a study using the Social Capital Benchmark Survey data (The Roper Center,
2005), Brueckner and Largey (2006) regressed individuals' social-interaction variables
on census-tract density among other variables; they found that high density was
negatively related to all friendship and group-involvement variables. Even though those
scholars used different approaches and different datasets, neither of those studies
addressed the issues of the hierarchical nature of their data and both used population
density as a proxy for urban form. Nevertheless, their findings apparently support the
notion that compactness, not sprawl, is the culprit of the decline of social capital that
Putnam (2000) lamented in his book.

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fail to account for the effect of poor street connectivity, which also distinguishes
compactness from sprawl.

Most empirical studies that look at the effects of sprawl on social capital examine
some of its factors such as trust and neighborhood ties (Brueckner and Largey, 2006;
Freeman, 2001; Glaeser and Gottlieb, 2006; Leyden, 2003). A few studies in this group
are likely to overlook nonneighborhood social ties. As Guest and Wierzbicki (1999)
indicated, social interactions with other nonlocal groups of people could substitute for
neighborhood social ties. In his book, Putnam suggested that the range of activities
embodying social capital includes civic participation, religious participation, and
polit-ical participation. More importantly, those studies that use regression analysis of
individual social capital and a sprawl variable measured at some geographical scale
do not take into account the hierarchical nature of the data and thus might produce
biased estimates.

This study improves the common limitation of previous studies by using
hierarch-ical modeling to address the problem of dependence among people residing in the
same neighborhoods and by using Ewing's sprawl index to tease out spatial impacts
on a variety of social-capital factors.

As a result of increasing sprawl, space between residential and commercial
devel-opment increases, forcing people to drive more and to divert their time away from
social-interaction activities. This study tests the hypothesis that urban sprawl plays
certain roles in the relationship between individual characteristics and social capital.
Some scholars suggested that there is a positive relationship between income and
certain aspects of social capital such as social interaction or voting habits (Brueckner
and Largey, 2006; Glaeser and Gottlieb, 2006). In the light of this, urban sprawl can
amplify the effects of income on social capital such that in more sprawled areas, the
effects of income on social capital are greater as income increases due to the fewer

choices of transportation for the lower-income groups. There is a possible interaction
between urban form and race such that urban sprawl lessens the effects of different
racial groups on social capital since longer commutes reduce interaction opportunities
for all racial groups.

Data

Social-capital variables came from the 2000 Social Capital Community Benchmark
survey data (The Roper Center, 2005). The survey was conducted by the Saguaro
Seminar at Harvard University's Kennedy School of Government between July and
November 2000.(1)_{The effective sample size for the analysis is 22191.}

The survey provides nine social-capital variables to capture civic engagement,
political participation, and social interaction on the basis of a person's answers to the
survey questionnaire. Those variables include social trust, diversity of friendship,
the number of group involvements, informal social interaction, organized-group
inter-action, faith-based social capital, giving and volunteering, nonelectoral participation,
and electoral politics (see table 1 for a detailed description of dependent variables).

The survey data also provide individual weight and socioeconomic and
demo-graphic information of respondents. The weight accounts for the population distribution
in the sample and for the odds of selection for any household in the sample. Some or
all of those person characteristics have been included in prior studies on social capital
by Brueckner and Largey (2006), Campbell and Lee (1992), Freeman (2001), Glaeser and

(1)_{The Roper Center, University of Connecticut manages and disseminates both restricted and}

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Gottlieb (2006), Glaeser et al (2000), Guest and Wierzbicki (1999), Iyer et al (2005),
and Leyden (2003).

The restricted version of the Social Capital Benchmark Survey data includes
counties and census tracts where the respondent resided, which allows for the
deriva-tion of three variables to capture mean household income, the percentage of college
graduates, and racial diversity at the census-tract level. Freeman (2001) indicated that
the level of poverty in a neighborhood negatively affects the social capital of its
residents. Thus, the census-tract mean household income captures any effect of income
on social capital. In addition, since living in a neighborhood with higher education
Table 1. Definition and measures of social-capital variables.

Variable Definition

SOCTRUST Social trust: combines answers to questions of levels at which respondents
trust different groups in society (people in the neighborhood, work
colleagues, people at church or places of worship, people working in
stores, local police). The index is standardized with respect to the
national norms.

DIVRSITY Diversity of friendship: counts the number of different types of personal
friends the respondents has from the 11 possible types.

GRPINVLV Number of formal group involvements: counts the number of different
nonreligious groups the respondent has been a member of from 18
possible types.

FAITHBASED Faith-based social capital: combines answers to questions that ask
participants whether they are a member of a local church or other
religious community, how often they attend religious services, whether
they have taken part in any activity with other people at their church or
place of worship other than attending services, and whether they have
any affiliation with nonchurch religious organizations. Their levels of

contributing and volunteering were recorded to calculate the index. The
index was computed as the mean of the standardized variables obtained
from the answers.

SCHMOOZ Informal social interaction index: mean of standardized responses to the
question asking the respondent to supply the estimate of the number of
times he or she has undertaken certain social activities in the past 12
months. Those activities include times of playing cards with others,
visiting relatives or having them visit, of having friends over, socializing
with coworkers outside of work, and socializing with friend in public
places.

ORGINTER Organized group interactions: mean of the scores standardized against the
national normal of a 3-item question. It asks how many times in the
past 12 months the respondent has attended (1) any public meetings
in which there was a discussion of town or school affairs, (2) a club
meeting, and (3) a celebration, parade, or an event in his or her
community.

CHARITY Giving and volunteering: combines reversed polarity versions of
volunteering for different types of organizations: arts, health-related,
neighborhood, religious, youth groups, and those which help the poor
or elderly; the total number of times volunteered, and contributions to
secular charities and religious causes.

PROTEST Nonelectoral political participation: signing petitions, attending political
meetings or rallies, joining in any demonstrations, protests, boycotts,
or marches; also, involvement in local reform efforts, membership of
political groups, ethnic, national, or civil-rights groups, or labor unions.
ELECPOL Electoral politics: combines past voting, voter registration, interest in

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attainment might raise one's opportunities to interact and network, the percentage of
college graduates is another important census-tract variable.

Putnam (2000) suggested that racial diversity could negatively affect the bridging
social capital, which is needed to build solidarity among members of a community.
In their experimental analysis, Glaeser et al (2000) gave evidence of association
between lower levels of trustworthiness and high racial diversity. Kahn and Costa
(2002) documented empirical evidence from fifteen studies to show lower social capital
in more heterogeneous communities by calculating the Simpson's index of diversity
Table 2. Definition and descriptive statistics of independent and dependent variables.

Variable name Description Mean Min Max

Level 1 descriptive statistics (sample size: 22191)

WEIGHT Sampling weight

BLACK ˆ 1 for Black respondent
ASIAN ˆ 1 for Asian respondent
HISPN ˆ 1 for Hispanic respondent

AGE Age of respondent

GENDER ˆ 1 for male respondent

OWN ˆ 1 for homeowner

MARITAL ˆ 1 for married respondent

SMCOLL ˆ 1 for respondent with some college
COLGD ˆ 1 for respondent with college degree
LIVCOM < 1year ˆ 1 if lived in community < 1 year
LIVCOM1-5years ˆ 1 if lived in community 1 ± 5 years
LIVCOM6-10years ˆ 1 if lived in community 6 ± 10 years
EMP ˆ 1 if respondent employed

INCOME0 ˆ 1 for undisclosed income
IN30-50K ˆ 1 for annual household income

30 ± 50 K

IN50-75K ˆ 1 for annual household income
50 ± 75 K

INover75K ˆ 1 for annual household income over
75 K

KIDS0 ˆ 1 for undisclosed children situation
KIDS1 ˆ 1 for having children 17 years of age

or younger in household
SOCTRUST Social trust

DIVRSITY Diversity of friendship

GRPINVLV Number of group involvements
FAITHBASED Faith-based social capital
SCHMOOZ Informal social interaction
ORGINTER Organized group interaction

CHARITY Giving and volunteering

PROTEST Nonelectoral political participation
ELECPOL Electoral politics

Level 2 descriptive statistics (sample size: 6436)

INCOMETR Mean household income in census trust 34 325 0 150 001
COLGRAD Percentage of college graduates in

census tract 15.6 0 69.16

RACDIVER Racial-diversity index in census tract 0.2 0 0.81
Level 3 descriptive statistics (sample size: 259)

RURAL Percentage of county residents in rural

areas 36.63 0 100

LNCOMPACT Natural log of county-sprawl index 4.57 4.21 5.86

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(Keylock, 2005) for racial and birthplace diversity. Similarly, in this study the Simpson's
index of diversity was used to measure the heterogeneity in racial composition in a
census tract. For each census tract, the index is constructed as follows. Census-tract
racial diversity

D ˆ 1 ÿXS

i ˆ 1

_n

i

N
2

,

where n represents the number of individuals of race i in a census tract (i ˆ Black,
Asian, White, or Hispanic), S is the number of races, and N is the total population
sample size in the census tract. The index has value from 0 to 1 and represents the
probability that two individuals, randomly selected from a specific tract, are from
different racial groups. Therefore, as the value of D approaches one, diversity
increases. The demographic data came from the 1990 Population Census for White,
Black, Hispanic, and Asian ( />twps0056/twps0056.html).

At the county level, the county sprawl index that Ewing et al (2003) developed in
their study of urban sprawl impacts on health was used. The index is composed of
residential density and street accessibility. More compact urban form implies higher
gross and net population density and a higher percentage of people living in high
densities. Street accessibility is defined in terms of the length, and size of blocks (in
square miles). The length of each side of a block and its size in a more compact urban
neighborhood should be smaller than those in a less compact suburban area with less
connected cul-de-sacs and fewer alternative routes. The component was transformed to
a scale with a mean value of 100 and standard deviation of 25.(2)_{This study uses only a}
subset of the 259 counties and statistically equivalent entities such as independent cities.
The higher the value of the index, the less sprawled or more `compact' an urban county is.
At the county level, two or more variables including the percentage of county
population living in rural areas and the county population size are deployed to control

for possible effects of county sizes and of rural versus urban and suburban settings
on the social capital of county residents. Putnam (2000) suggested that urban and
suburban settings are less conducive to social-interaction activities. This possibility
will be tested in this study. Table 2 presents descriptive statistics for all variables.
Method

The hierarchical-modeling technique is adopted to examine the possible impacts of
urban form on social capital. Conceptually, the hierarchical model differs from
multi-variate regression models in several aspects, but most importantly, the former has
more than one error term in its equation to account for errors at different levels of
analysis.

Hierarchical modeling takes into account the dependence between observations.
In this study, Social Capital Community Benchmark survey participants are clustered
in census tracts in different counties. When using the ordinary least squares (OLS)
models, the researcher is likely to ignore the fact that people residing in the same
census tract or in the same county may be related, which violates OLS assumptions
that the observations are independent (Gelman and Hill, 2007; Raudenbush and Bryk,
2002; Snijders and Bosker, 1999), leading to standard errors that are too small. As a
result, the researcher is more likely to reject the null hypothesis than in the case
where hierarchical modeling is used. In addition, by using hierarchical modeling, the
researcher can test a realistic hypothesis that there exists some cross-level interaction

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between personal characteristics and place characteristics (Luke, 2004; Raudenbush
and Bryk, 2002).(3)_{The technique allows the researcher to disentangle and examine the}
complex effects of the environmental factors on an individual's outcome.

In this study, the hierarchical nature of the dataset and the original research
hypotheses have rendered the use of hierarchical modeling necessary. To test the
validity of 3-level hierarchical models, a fully unconditional model which is similar to

ANOVA is conducted. This fully unconditional model partitions total variance in the
dependent variables into three components of variance: variance among different
surveyed individuals within census tracts, variance among census tracts within counties,
and variance among different counties.

Using HLMß for Windows version 6 to estimate the model, it was possible to
show that variance among individuals living in the same census tracts account for
most variance in the social-capital data (table 3). Even though most variability is
within counties, statistically significant variance at census-tract and county levels
requires the inclusion of tract-level and county-level predictors in the analysis.

The model is specified as a random intercept model with nonrandomly varying
slopes. In level 2, the slopes have been assumed to be fixed and only the intercept has
the random-effect component. Variables of interest at level 2 (census tract) include
racial diversity in 1990 (RACDIVER), median household income in 1989 (INCOMETR),
and the percentage of college graduates in 1990 (COLGRAD). The data are from 1990
population census. To reduce complex computation due to the presence of cross-level
interaction terms, it is also assumed that variables at the tract level only affect the
intercepts of the set of regressions at the individual level. In level 3, county population
size (LNPOP), sprawl (LNCOMPACT), and the percentage of the county population
living in rural areas (RURAL) are assumed to affect the intercept of the model. This is
equivalent to saying that models to predict social capital have intercepts varying
across different counties depending on the county population size, the degree of
sprawl, and the degree of urban or suburban settings of the county. In addition, it
is also assumed that sprawl has differential impacts on the relationship between an
individual's race and income and his or her social capital because of his or her housing
preferences (Rong, 2006). While it is harder to predict the direction of the relationship of
race and urban sprawl with respect to social capital a priori, it is possible that
high-income people increase their social capital when living in more compact counties,
assuming that less sprawl means less social interaction. Similarly, individuals living

with children could gain more social capital by living in more compact counties, other
things being constant.

(3)_{See Raudenbush and Bryk (2002) for a complete theoretical discussion of multilevel modeling.}

Table 3. Variance analysis of 3-level hierarchical data.
Variance (%)

level 1 level 2 level 3

Charity (giving) 98.6 0.5 0.9

Diversity of friendship 98.4 0.7 0.9

Electoral politics 95.2 1.4 3.4

Faith-based social capital 94.9 0.7 4.4
Informal social interaction 98.8 0.4 0.8
Number of group involvements 99.3 0.4 0.3
Organized-group interaction 99.1 0.6 0.3
Nonelectoral political participation 96.5 0.9 2.5

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The model specification is as follows.
Level 1 (person level):

Yijk ˆ p0 jk ‡ p1 jk…BLACK†ijk ‡ p2 jk…ASIAN†ijk‡ p3 jk…HISP†ijk‡ p4 jk…EMP†ijk

‡ p5 jk…IN30-50K†ijk‡ p6 jk…IN50-75K†ijk‡ p7 jk…INover75K†ijk‡ p8 jk…KIDS1†ijk

‡ p9 jk…AGE†ijk‡ p10 jk…GENDER†ijk ‡ p11 jk…OWN†ijk‡ p12 jk…MARITAL†ijk

‡ p13 jk…SMCOLL†ijk‡ p14 jk…COLGD†ijk ‡ p15 jk…LIVCOM < 1year†ijk

‡ p16 jk…LIVCOM1-5years†ijk ‡ p17 jk…LIVCOM6-10years†ijk

‡ p18 jk…INCOME0†ijk‡ p19 jk…KIDS0†ijk‡ eijk .

Level 2 (census-tract level):

p0 jk ˆ b00 k‡ b01 k…INCOMETR†jk‡ b02 k…COLGRAD†jk ‡ b03 k…RACDIVER†jk ‡ r0 jk,

p1 jk ˆ b10 k,

p19 jk ˆ b190 k .

Level 3 (county level):

b00 k ˆ g000‡ g001…LNCOMPACT†k‡ g002…LNPOP†k‡ g003…RURAL†k‡ u00 k,

b10 k ˆ g100‡ g101…LNCOMPACT†k,

b80 k ˆ g800‡ g801…LNCOMPACT†k,

b90 k ˆ g900,

b190 k ˆ g1900 ,

where

Yijk is the social-capital-factor measure of person i in census tract j in county k;

[g000‡ g001RURAL ‡ g002LNCOMPACT ‡ g003LNPOP ‡ g010INCOMETR

‡ g020COLGRAD ‡ g030RACDIVER] is county mean social capital;

eijk is the random effect varying across different individuals;

rjk is the random effect of the intercept across different census tracts in county k;

uk is the random component varying across different counties.

The social-capital survey's individual weight for each observation or surveyed
person at level 1 is applied for nine equations corresponding to nine factors of social
capital. There are no weights assigned to census tracts and counties. Because
LNCOMPACT is highly correlated with RURAL (r ˆ ÿ0:75), LNCOMPACT is centered
on its grand mean at level 3 to avoid multicollinearity. Tests of normality are performed
and the standard errors of coefficient estimates are robust standard errors.

Findings
Urban sprawl

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children seventeen years old or younger, and whose household income in 1999 was less
than $30 000. The degree of compactness is negatively related to informal social interaction
(ÿ0:3), to faith-based social capital (ÿ0:41), and to giving and volunteering (ÿ1:13).

Table 4. Hierarchical model results. (See table 2 for definitions of variables.)

Fixed effect Diversity of Informal social Organized-group

friendship interaction interaction

coefficient t-statistics coefficient t-statistics coefficient t-statistics
County mean social capital

Base 5.27* 43.0 0.73* 20.9 ÿ0.10* ÿ2.8

RURAL 4.0  10ÿ3 _1.3 _{ÿ3.0  10}ÿ4 _ÿ0.5 _{ÿ7.0  10}ÿ4 _ÿ1.3

LNCOMPACT ÿ0.22 ÿ0.5 ÿ0.30* ÿ5.0 0.04 0.7

LNPOP 0.06 1.4 4.0  10ÿ4 _0.0 _ÿ0.01 _ÿ1.8

INCOMETR ÿ8.0  10ÿ6_{* ÿ3.1} _{ÿ2.0  10}ÿ6_{* ÿ3.3} _{ÿ3.0  10}ÿ6_{* ÿ5.4}

COLGRAD 0.01* 4.2 3.0  10ÿ4 _0.4 _{2.0  10}ÿ3 _2.2

RACDIVER 0.68* 4.5 ÿ0.11** ÿ2.2 ÿ0.04 ÿ1.0

Racial differentiation for BLACK

Base ÿ0.28* ÿ3.4 ÿ0.14* ÿ7.2 ÿ0.01 ÿ0.2

LNCOMPACT 0.82** 2.2 ÿ0.02 ÿ0.4 ÿ0.27* ÿ2.7

Racial differentiation for ASIAN

Base ÿ1.11* ÿ5.1 ÿ0.21* ÿ3.5 ÿ0.20* ÿ3.4

LNCOMPACT ÿ0.79 ÿ1.6 ÿ4.0  10ÿ3 _0.0 _0.12 _0.6

Racial differentiation for HISPN

Base ÿ0.91* ÿ6.5 ÿ0.29* ÿ9.2 ÿ0.06 ÿ1.8

LNCOMPACT 0.57 1.6 0.09 0.8 ÿ0.15 ÿ1.5

Employment differentiation (EMP)

Base 0.39* 7.4 ÿ0.12* ÿ8.0 ÿ0.01 ÿ0.9

LNCOMPACT 0.24 1.2 0.12 1.8 0.07 1.7

Income differentiation for IN30-50K

Base 0.51* 7.6 0.06* 4.2 0.09* 4.9

LNCOMPACT 0.37 1.8 0.10** 2.0 ÿ0.07 ÿ1.2

Income differentiation for IN50-75K

Base 0.67* 7.9 0.08* 3.9 0.12* 5.4

LNCOMPACT 0.27 0.9 0.19* 2.9 ÿ0.07 ÿ0.9

Income differentiation for INover75K

Base 0.92* 12.8 0.14* 7.8 0.21* 7.1

LNCOMPACT 0.29 1.4 0.15* 2.6 ÿ0.18 ÿ1.4

Child differentiation (KIDS1)

Base 0.001 0.0 ÿ0.02 ÿ1.4 0.12* 8.7

LNCOMPACT 0.13 0.7 0.05 1.1 ÿ0.02 ÿ0.4

AGE ÿ0.01* ÿ4.0 ÿ0.01* ÿ28.6 ÿ3.0  10ÿ3_{* ÿ5.7}

GENDER ÿ0.03 ÿ0.6 ÿ0.05* ÿ4.4 ÿ0.02** ÿ1.9

OWN 0.09 1.2 ÿ0.04* ÿ2.3 0.02 1.8

MARITAL 0.10** 2.3 ÿ0.14* ÿ9.9 ÿ0.03* ÿ2.5

SMCOLL 0.96* 15.5 0.08* 4.6 0.15* 11.0

COLGD 1.08* 19.5 ÿ2.0  10ÿ3 _ÿ0.1 _0.29* _17.2

LIVCOM<1year ÿ0.24* ÿ3:0 ÿ0.09* ÿ4.1 ÿ0.12* ÿ5.4

LIVCOM1-5years ÿ0.31* ÿ5.3 ÿ0.09* ÿ5.6 ÿ0.11* ÿ8.1

LIVCOM6-10years ÿ0.13** ÿ2.3 ÿ0.04* ÿ2.4 ÿ0.03** ÿ2.1

INCOME0 0.33* 4.5 0.06* 2.9 0.07* 4.3

KIDS0 0.77 1.3 ÿ0.10 ÿ1.3 ÿ0.13 ÿ1.1

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However, it is positively related to electoral politics (0.38) and nonelectoral political
participation (0.79). These findings confirm recent findings of Glaeser and Gottlieb (2006)
concerning faith-based social capital and social interaction, and findings by Brueckner
Table 5. Hierarchical model results. (See table 2 for definitions of variabless.)

Fixed effect Number of Faith-based Social trust

group involvements social capital

coefficient t-statistics coefficient t-statistics coefficient t-statistics
County mean social capital

Base 0.70* 4.9 ÿ0.61* ÿ17.4 ÿ0.45* ÿ12.3

RURAL ÿ2.0  10ÿ3 _ÿ0.8 _{ÿ3.0  10}ÿ3_{** ÿ2.3} _{ÿ9.0  10}ÿ4 _ÿ1.2

LNCOMPACT 0.22 1.2 ÿ0.41* ÿ2.6 ÿ0.19 ÿ1.7

LNPOP ÿ0.01 ÿ0.5 ÿ0.03 ÿ1.6 ÿ0.02 ÿ2.0

INCOMETR ÿ7.0  10ÿ6_{* ÿ2.8} _{4.0  10}ÿ6_* _3.7 _{2.0  10}ÿ6_* _2.6

COLGRAD 9.0  10ÿ3_* _2.9 _{ÿ3.0  10}ÿ3_* _ÿ2.7 _{4.0  10}ÿ3_* _7.4

RACDIVER ÿ0.06 ÿ0.4 8.0  10ÿ4 _0.0 _ÿ0.19* _ÿ4.4

Racial differentiation for BLACK

Base 0.81* 8.2 0.23* 9.0 ÿ0.45* ÿ20.1

LNCOMPACT ÿ0.77 ÿ1.7 ÿ0.14 ÿ1.0 0.13 1.4

Racial differentiation for ASIAN

Base ÿ0.33 ÿ1.7 ÿ0.07 ÿ0.7 ÿ0.25* ÿ4.2

LNCOMPACT ÿ0.04 ÿ0.1 0.11 0.8 0.32 1.6

Racial differentiation for HISPN

Base ÿ0.17 ÿ1.6 0.02 0.6 ÿ0.40* ÿ13.4

LNCOMPACT ÿ0.27 ÿ1.1 0.23 1.8 0.26** 2.0

Employment differentiation (EMP)

Base 0.10** 2.1 ÿ0.06* ÿ4.2 0.02 2.0

LNCOMPACT ÿ0.01 ÿ0.1 0.01 0.2 0.03 1.1

Income differentiation for IN30-50K

Base 0.47* 7.3 0.11* 7.4 0.09* 4.2

LNCOMPACT ÿ0.15 ÿ0.6 ÿ0.16 ÿ1.5 0.01 0.1

Income differentiation for IN50-75K

Base 0.63* 8.0 0.12* 7.0 0.11* 5.8

LNCOMPACT ÿ0.38 ÿ1.0 ÿ0.16* ÿ2.6 ÿ0.06 ÿ1.0

Income differentiation for INover75K

Base 1.08* 12.2 0.16* 8.5 0.11* 4.7

LNCOMPACT ÿ0.61** ÿ2.0 ÿ0.25* ÿ3.4 ÿ0.04 ÿ0.9

Child differentiation (KIDS1)

Base 0.38* 8.4 0.10* 8.7 ÿ0.02** ÿ1.9

LNCOMPACT 0.14 0.6 0.17* 4.4 0.01 0.2

AGE 0.02* 7.5 0.01* 10.2 7.0  10ÿ3_* _13.6

GENDER 0.15* 3.1 ÿ0.11* ÿ9.7 ÿ0.07* ÿ6.3

OWN 0.19* 3.1 0.09* 6.1 0.08* 5.4

MARITAL ÿ0.06 ÿ1.2 0.14* 12.0 0.08* 6.6

SMCOLL 0.93* 17.4 0.18* 14.3 0.13* 9.8

COLGD 1.68* 22.1 0.28* 13.7 0.27* 22.1

LIVCOM<1year ÿ0.57* ÿ7.1 ÿ0.16* ÿ6.2 ÿ0.02 ÿ1.1

LIVCOM1-5years ÿ0.34* ÿ5.5 ÿ0.12* ÿ8.5 ÿ0.03* ÿ2.7

LIVCOM6-10years 0.00 0.0 ÿ0.06* ÿ3.3 ÿ0.03** ÿ2.1

INCOME0 0.29* 5.1 0.10* 5.1 0.04 1.7

KIDS0 0.11 0.2 0.08 0.5 0.07 0.5

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and Largey (2006) concerning social interaction that more compact residence leads to less
social capital. However, similar to Leyden's results (2003), the findings provide evidence
that compact urban form supports political participation as suggested by Putnam (2000).
Table 6. Hierarchical model results. (See table 2 for definitions of variables.)

Fixed effect Giving and volunteering Electoral politics Nonelectoral political
participation

coefficient t-statistics coefficient t-statistics

coefficient t-statistics
County mean social capital

Base 1.82* 9.5 1.00* 17.5 0.40* 5.6

RURAL ÿ0.01** ÿ2.2 ÿ3.0  10ÿ3 _ÿ2.0 _{3.0  10}ÿ3 _2.2

LNCOMPACT ÿ1.13** ÿ2.6 0.38** 2.0 0.79* 3.5

LNPOP ÿ0.06 ÿ1.0 ÿ0.09* ÿ3.2 1.0  10ÿ3 _0.0

INCOMETR 1.0  10ÿ6 _0.3 _{ÿ4.0  10}ÿ6_{* ÿ3.2} _{ÿ7.0  10}ÿ6_{* ÿ8.3}

COLGRAD 0.01** 2.5 0.01* 8.5 0.01* 5.1

RACDIVER 0.21 1.1 ÿ0.10 ÿ1.7 0.06 0.7

Racial differentiation for BLACK

Base 0.36* 2.7 0.02 0.6 0.20* 4.4

LNCOMPACT ÿ0.53 ÿ1.2 ÿ0.07 ÿ0.4 ÿ0.18 ÿ1.1

Racial differentiation for ASIAN

Base ÿ1.23* ÿ3.1 ÿ0.73* ÿ6.7 ÿ0.21 ÿ2.4

LNCOMPACT 1.02 0.8 0.15 0.6 ÿ0.77 ÿ4.0

Racial differentiation for HISPN

Base ÿ0.72* ÿ4.2 ÿ0.58* ÿ8.7 ÿ0.06 ÿ0.9

LNCOMPACT 0.32 0.6 ÿ0.28** ÿ2.1 ÿ0.29 ÿ1.4

Employment differentiation (EMP)

Base 3.0  10ÿ3_* _0.0 _0.06* _3.4 _0.11* _4.5

LNCOMPACT 0.17 0.4 0.27* 4.6 0.15 1.6

Income differentiation for IN30-50K

Base 1.04* 11.4 0.21* 5.6 0.15* 4.7

LNCOMPACT ÿ0.95** ÿ2.0 0.16 1.5 0.25 1.6

Income differentiation for IN50-75K

Base 1.39* 14.1 0.34* 10.8 0.28* 6.0

LNCOMPACT ÿ0.60 ÿ1.4 ÿ0.24** ÿ1.9 0.32 1.2

Income differentiation for INover75K

Base 2.58* 19.3 0.44* 11.4 0.35* 7.2

LNCOMPACT ÿ1.49* ÿ3.0 ÿ0.32 ÿ1.6 0.12 0.9

Child differentiation (KIDS1)

Base 0.57* 9.1 ÿ0.06* ÿ2.9 0.04 1.3

LNCOMPACT 0.56** 2.4 ÿ0.07 ÿ0.7 ÿ0.18 ÿ1.4

AGE 0.02 5.4 0.03* 36.9 2.0  10ÿ4 _0.2

GENDER ÿ0.44* ÿ7.6 0.14* 6.7 0.10* 5.2

OWN 0.64* 7.3 0.26* 11.7 0.09* 3.5

MARITAL 0.42* 4.5 0.13* 5.5 ÿ0.06* ÿ2.7

SMCOLL 1.45* 17.6 0.53* 21.0 0.40* 12.4

COLGD 2.51* 25.7 0.88* 34.5 0.75* 18.4

LIVCOM<1year ÿ0.89* ÿ6.3 ÿ0.40* ÿ10.3 ÿ0.25* ÿ6.4

LIVCOM1-5years ÿ0.63* ÿ7.1 ÿ0.27* ÿ12.7 ÿ0.18* ÿ7.4

LIVCOM6-10years ÿ0.13 ÿ1.5 ÿ0.13* ÿ4.1 ÿ0.04 ÿ1.2

INCOME0 ÿ0.32* ÿ2.9 0.12* 3.5 0.05 1.6

KIDS0 ÿ1.04 ÿ1.4 0.30 1.1 ÿ0.12 ÿ0.6

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By summing the coefficient for LNCOMPACT present in the race, employment
status, income-brackets, and child-status components with that present in the county
mean social-capital component in each equation and testing for significance, one can
comment on the overall impacts of urban form on different social capital factors
for different household groups. Table 7 summarizes those findings with statistical
significance p < 0:01.

For most household types, urban compactness has negative impacts on social
interaction, faith-based social capital, and giving and volunteering; meanwhile, it has
positive impacts on electoral politics and nonelectoral political participation.

The following sections discuss in detail the impacts of sprawl on the relationship
between different household types and social capital, with respect to the county average.
The impacts of sprawl and race

Regarding the effects of race on social capital, the findings indicate that respondents
belonging to different racial groups have different degrees of social capital; and
urban sprawl can increase the social-capital gaps among different racial groups.
Compared to white Americans, African American participants take part in more
orga-nizations, have higher faith-based social capital, do more volunteering, join more
nonelectoral political activities such as protests, marches, and demonstrations, and
join more political groups. Nevertheless, they have less social trust and less informal
social interaction. The social-capital gap between white Americans and African
Americans, however, is only influenced by urban sprawl in the case of the diversity
of friendship [ÿ0:28(1) ‡ 0:82LNCOMPACT] and the participation in club meetings,
community meetings, and events (ÿ0:27LNCOMPACT). In the former case, any practical
levels of compactness will lead to higher numbers of friends from different backgrounds
for African Americans and they will increase their network at a faster rate as sprawl
decreases. In the latter, African Americans participate less in organized-group activities
compared with white Americans but more sprawl narrows the gap.

Compared with white respondents, Asian respondents have less diverse friendship
and social trust, participate less in informal social interaction, and also participate less
in volunteering or giving and politics. Hispanic respondents share patterns similar to
the Asian population. As far as the role of urban form is concerned, sprawl does not
have amplifying or attenuating effects on the relationship between being Asian and
Table 7. Overall effects of LNCOMPACT on social capital for different household types. (See table 2
for definitions of variables.)

For For For For For For For For

BLACK ASIAN HISPN employed IN30-50K IN50-75K INover75K KIDS1

Informal social ± ± ± ± ±

interaction

Organized group ±
interaction

Faith-based ± ± ± ±

social capital

Giving and ± ± ± ±

volunteering

Electoral politics ‡ ‡

Nonelectoral ‡ ‡ ‡ ‡ ‡ ‡

political
participation

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social capital. However, the Hispanic population has higher social trust in more
compact counties [ÿ0:40(1† ‡ 0:26LNCOMPACT]. Even in the most sprawled county
of Jasper (IN) in the dataset (LNCOMPACT ˆ 4:21), the Hispanic social-trust index
exceeds that of the white population by 0.69. However, compact living conditions make
the Hispanic population fall further behind the white population in electoral political
participation [ÿ0:58(1) ÿ 0:28LNCOMPACT].

The impacts of sprawl and employment status

Being employed is correlated positively with diversity of friendship and the number of
group involvements. An employed person is also more likely to participate in civic
activities such as giving and volunteering, and has high political participation,
compared with the group consisting of students, retirees, people who are between
jobs, and the unemployed; however, the faith-based social capital of an employed
person and his or her degree of informal social interaction are less. Urban
com-pactness only has amplifying effects that increase the gap between the employed
and nonemployed groups of people with respect to electoral-politics participation
[0:06(1) ‡ 0:27LNCOMPACT].

The impacts of sprawl and income

The relationship between income and social capital is positive for most social-capital
factors. The role of urban sprawl is only statistically significant for some factors of
social capital and can either reduce or widen the gap between income groups. In the
case of informal social interaction (playing cards with others, visiting relatives or
having them visit, having friends over, socializing with coworkers outside of work,
and socializing with friends in public places), a higher degree of compactness expands
the social-capital gap between different income groups [0:06(1) ‡ 0:1LNCOMPACT for
$30 ^ 50 K; 0:08(1† ‡ 0:19LNCOMPACT for $50 ^ 75 K; and 0:14(1) ‡ 0:15LNCOMPACT
for above $75 K]. Meanwhile, the negative signs of LNCOMPACT for different income
groups suggest that more compact development helps reduce the gap in income that
affects social-capital factors including the number of group involvements, faith-based
social capital, giving and volunteering, and electoral politics. The opposite signs of the
base and of LNCOMPACT also suggest that the magnitude of LNCOMPACT can reverse
the relationship between social capital and income. For example, after controlling for
other factors, the difference between the levels of participation in electoral politics of a
person whose household income of $50 ^ 75 K and of a person whose household
income is under $30 K is 0:34(1) ÿ 0:24LNCOMPACT. In any counties as sprawled
as Jasper (IN), the difference is ÿ0:67, meaning the person of the lower household

income participates more in electoral politics. This pattern applies to more income
groups with respect to faith-based social capital. The effect of living in counties that
have sprawl similar to that in Jasper for a person whose household income is below
$30 K is 0.55 units higher compared with a person whose income is $50 ^ 75 K, and
0.89 units higher compared with a person whose income is above $75 K (table 5).
Such effects of urban form can also be detected for household income over $75 K
and $30 ^ 50 K in the case of giving and volunteering activities, and for household
income over $75 K in the case of the number of group involvements.

The impacts of sprawl and child presence

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Similar to the relationship between income and informal social interaction, living in
less sprawled or more compact counties adds to the difference between those who live
with children of 17 years of age or younger and those who do not, when faith-based
social capital and giving and volunteering are concerned (tables 5 and 6). However, the
presence of children in the same household is negatively related to the respondent's
social trust and electoral-political participation.

Findings of other variables and controls

Table 8 summarizes the direction of relationships between individual social-capital
factors and other independent variables at the person and place levels. Among
indi-vidual characteristics, education attainment and length of residence in the community
have significantly positive association with all nine factors of social capital. As an
individual's levels of education and length of residence in the community increase, he
or she has more social interactions with friends and neighbors informally and in
group activities, more religious participation, and more civic and political
participa-tion. In particular, the relationship between residence tenure across all factors of
social capital may suggest that endogeneity is not the issue. If people with high levels
of social capital move to neighborhoods composed of people with similar levels of

social capital, there should be no statistically significant difference in social capital
between those who have lived in the neighborhood for under five years and those
who have lived there for longer, at least in social-capital factors that are mostly
determined outside the residential neighborhood, such as informal social interaction
and electoral-political participation.

Owning a house is related positively to the number of group involvements,
faith-based social capital, social trust, and community and political participation. However,
homeownership is related negatively to informal social interaction and not
signi-ficant for the remaining social-interaction variables: diversity of friendship and
organized-group interaction.

As one becomes older, a person increases his or her civic and political
participa-tion as indicated by a positive relaparticipa-tionship between AGE and the number of group
involvements, faith-based social capital, giving and volunteering, and electoral politics.
However, his or her intensity of social interaction, as indicated by informal social
interaction and organized-group interaction, declines. This result is consistent with
Putnam's observation of the difference between the number of organizations that a
person belongs to and the frequency at which that person participates in his or her
member activities (Putnam, 2000).

Males are more likely to belong to a variety of groups and to participate in
political activities; however, females are more likely to be involved in more
social-interaction activities and in volunteering activities. Females also have higher levels of
social trust and of faith-based social capital.

Marriage significantly reduces the intensity of informal and organized
social-interaction activities. This is probably because married couples tend to spend more
time together, leaving them less time to devote to visiting friends and clubbing. Also,
married respondents are less likely to participate in nonelectoral political activities

such as signing petitions, joining protests, and boycotting, and likely to be involved
in labor unions and ethnic, nationality, or civil rights groups.

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friendship social

interaction groupinteraction groupinvolvements socialcapital volunteering politics politicalparticipation

AGE ± ± ± ‡ ‡ ‡ ‡ ‡

Being male ± ± ‡ ± ± ± ‡ ‡

Owning house ± ‡ ‡ ‡ ‡ ‡ ‡

Being married ‡ ± ± ‡ ‡ ‡ ‡ ±

Education attainment ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡

Length of residence ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡

Being Black

(versus White) on sprawldepends ± on sprawldepends ‡ ‡ ± ‡ ‡

Being Asian

(versus White) ± ± ± ± ± ± ±

Being Hispanic

(versus White) ± ± on sprawldepends ± ±

Being employed ‡ ± ‡ ± ‡ ‡ ‡

Presence of children
17 years or younger
in household

‡ ‡ ‡ ± ‡ ±

Household income ‡ ‡ ‡ depends

on sprawl on sprawldepends ‡ on sprawldepends on sprawldepends ‡
Not disclosing income

(versus income
< 30 K)

‡ ‡ ‡ ‡ ‡ ± ‡

Not disclosing child
information
Tract mean household

income ± ± ± ± ‡ ‡ ± ±

Tract percentage of

college graduates ‡ ‡ ‡ ± ‡ ‡ ‡ ‡

Tract racial diversity ‡ ± ±

Percentage of rural

population in county ± ± ± ‡

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At the census-tract level, mean household income is negatively related to most
individual social-capital factors except for faith-based social capital and social trust,
which have a positive correlation with the mean household income in the census tract.

The relationships between giving and volunteering and mean household income,
how-ever, is statistically insignificant. Another census-tract variable, the percentage of
college graduates, is positively related to most individual social-capital factors except
for faith-based social capital, with which it has a negative relationship. This percentage
appears to be irrelevant with respect to informal social interaction. The remaining
census-tract variable, racial diversity, is positively related to diversity of friendship
but negatively related to informal social interaction and social trust. In other words,
if one lives in an area with high racial diversity, one tends to have more friends from a
variety of backgrounds, but to interact less with friends and have less trust in different
groups in society. This result supports the argument about diversity and social capital
by different scholars, most notably Glaeser et al (2000), Kahn and Costa (2002), and
Putnam (2000). At the county level, county population size is shown to have
statis-tically significant negative impacts on a person's participation in electoral politics.
Meanwhile, the percentage of rural population living in the county is negatively related
to faith-based social capital and giving and volunteering, but it is positively related to
nonelectoral political participation.

Conclusion

By controlling for place characteristics, the analysis provides important statistical
information on the association between different socioeconomic and demographic
characteristics and different aspects of social capital. Compact living, as characterized
by high population density and street accessibility at the county level, is found to be
unfavorable to social interaction, faith-based social capital, and giving and
volunteer-ing. However, the analysis shows that compact living is positively related to political
participation such as voting, involvement in political groups and local reforms, and
interest in national affairs.

It also sheds light on the role of urban form as a possible confounder of the
association between socioeconomic and demographic characteristics and social

cap-italöthere exist differential impacts of sprawl on the relationship between some factors
of social capital and race, income, employment status, and child status. Social
inter-action may not always take place in more compact areas as some authors have
contended, but compact living can amplify the positive effects of income on social
interaction. Compact living can compensate for or widen the social-capital gap
intrinsic to race and income. However, planners should be aware that most variation
in individual social capital is explained by personal characteristics and less at the
neighborhood or county level.

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Regardless of those limitations, this study has contributed to the debate on urban
sprawl and provided evidence to support conclusions different from earlier studies.
The findings strongly suggest that focusing on urban form, at least at the county level,
may not be an ideal approach to improving social capital, which may disappoint New
Urbanists and other compact-living enthusiasts. It is because social capital is not a
simple factor and social-capital determinants such as a person's education may be
more important.

Finally, the study raises new questions about land-use planning and social equality
such as whether more equal opportunities for certain groups in society can be achieved
via land-use-planning policies due to the complex interaction between urban form and
the relationships between individual characteristics and social capital.

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