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(M.O. Caughy)

Health & Place 13 (2007) 788–798

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No neighborhood is an island: Incorporating distalneighborhood effects into multilevel studies of child

developmental competence

Margaret O’Brien Caughya,�, Karen L. Hayslett-McCallb, Patricia J. O’Campoc

aUniversity of Texas School of Public Health, 6011 Harry Hines Blvd, 8th Floor, Room 112, Dallas, TX 75390, USAbUniversity of Texas at Dallas, USA

cSt. Michael’s Hospital, University of Toronto, Canada

Received 17 April 2006; received in revised form 16 January 2007; accepted 30 January 2007

Abstract

The purpose of this study was to examine whether incorporating information regarding neighborhoods which were more

distal to the child’s neighborhood added any explanatory power to models of child cognitive competence. Participants

included a sample of young African-American children living in an urban setting in the northeast United States. Spatial

geographic methods were used to estimate the concentration of economic disadvantage, population instability, and crime

in the neighborhoods surrounding the child’s residence, and multilevel modeling methods were used to estimate the

contribution of these factors to between-neighborhood variance in child cognitive scores. Results indicated that the

conditions of distal neighborhoods were related to cognitive scores among the preschooler-age children in this sample.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Neighborhoods; Spatial analysis; Child development

Introduction

In the last 10–15 years, there have been numerousreports in the literature documenting that neighbor-hood characteristics contribute significantly tocognitive outcomes in children over and above thevariance explained by differences in family char-acteristics (Brooks-Gunn et al., 1993; Caughy andO’Campo, 2006; Chase-Lansdale and Gordon,

e front matter r 2007 Elsevier Ltd. All rights reserved

althplace.2007.01.006

ing author. Tel.: +1 214 648 1080;

1081.

dress: Margaret.Caughy@UTSouthwestern.edu

.

1996; Chase-Lansdale et al., 1997; Kohen et al.,2002; Leventhal and Brooks-Gunn, 2004; Shumowet al., 1998). A number of theorists have hypothe-sized about the processes by which neighborhoodsaffect the families and children living within themwith early published reports focused primarily oncommunity role models as the mechanism under-lying the association between neighborhood socio-economic characteristics and child cognitiveoutcomes. Brooks-Gunn et al. (1993) reported thathigh levels of affluent neighbors were associatedwith higher IQ scores among three-year olds andlower high school drop out rates among adoles-cents. These researchers theorized that the presence

.

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of affluent neighbors provided a positive role modelfor parents and children in the neighborhood.Sampson (1992) proposed that neighborhood struc-tural and population characteristics influence thefunctioning of families and children by alteringlevels of community cohesion and informal control.As a sociologist studying the roots of crime anddelinquency, Sampson built upon the long traditionof social disorganization theory which posits thatneighborhood poverty, ethnic diversity, and popu-lation instability contribute to the breakdown ofsocial cohesion in the neighborhood and, in turn,results in higher rates of juvenile delinquency.Sampson hypothesized that neighborhood collectiveefficacy affected child development by reducinglevels of nurturing and supportive parenting withinthe family (Sampson, 1992). There is an extensiveliterature documenting that sensitive and responsiveparenting combined with the provision of opportu-nities for age-appropriate cognitive stimulation andconsistent firm discipline is associated with moreoptimal child development outcomes (see, e.g.,Brooks-Gunn et al., 1999; Collins et al., 2000;Guo and Harris, 2000).

In this paper, we examine the contribution ofthree different dimensions of neighborhoods—con-centrated economic disadvantage, population in-stability, and crime—to the cognitive functioning ofyoung urban-dwelling African-American children.As described above, Sampson (1992) lays out atheory whereby concentrated economic disadvan-tage and population instability contribute to higherlevels of community social disorganization which inturn compromises nurturing and supportive parent-ing as well as family management processes such aschild monitoring. If population instability under-mines social organization in the community, itmight be more difficult for parents to accessresources in the community, such as after schoolprograms or extracurricular activities that mightfoster the development of cognitive skills in theirchildren. In a qualitative study of adolescentoutcomes in a diverse set of poor neighborhoodsin Philadelphia, Furstenberg (1993) reports that inneighborhoods with few social resources, parentshad to be exceptionally motivated ‘‘to cultivate thesparse opportunities within their community and tosearch out opportunities beyond the confines oftheir local area’’ (p. 243).

The impact of community crime and violence onchild development has been more extensivelystudied in relation to socioemotional outcomes than

cognitive outcomes. Garbarino et al. (1991) pro-vided a summary of effects of exposure to extremeviolence, such as when a child grows up in a warzone, as well as exposure to chronic violence such ascommunity crime. The empirical evidence is stronglinking high levels of crime in the neighborhoodwith adjustment problems in children (Aneshenseland Sucoff, 1996; Ceballo et al., 2001; Shumow etal., 1998; Martinez and Richters, 1993; Cicchettiand Lynch, 1993; Simons et al., 2002). Given thestrong link between child behavior problems andacademic outcomes, one would hypothesize thatneighborhood crime would also negatively affectcognitive development. Shumow et al. (1998)provide one of the few studies that has examinedthe relation between neighborhood crime andcognitive outcomes. Utilizing a composite index ofneighborhood risk which included rates of violentcrimes, Shumow et al. (1998) reported that neigh-borhood risk was associated with poorer academicachievement in fifth grade. Another way thatneighborhood crime could compromise cognitivedevelopment is by limiting the child’s access tooutdoor play opportunities. Qualitative data sug-gest that parents living in high risk neighborhoodsare less likely to allow their child to play outside(Jarrett, 1999; Burton and Price-Spratlen, 1999;Furstenberg, 1993; Furstenberg and Hughes, 1997),and these limitations might also function to limitopportunities for exploration that would foster thedevelopment of cognitive skills.

Two of the major limitations of the existingliterature on neighborhood effects on child compe-tence, however, are the narrow definition ofneighborhoods which is often utilized and thestatistical methods used to analyze multilevelneighborhood associations.

Most investigators utilize data from the census,and consequently, they have used a geographic unitbased on census boundaries to represent theneighborhood, such as census tract boundaries orcensus block group boundaries. Although there hasbeen considerable debate in the neighborhoodliterature regarding the best geographic unit torepresent neighborhoods (O’Campo, 2003; O’Cam-po and Caughy, 2006), it is likely that none of thesemunicipally defined neighborhoods perfectly ap-proximates the boundaries of neighborhoods asthey are perceived by residents.

Conceptually, people are influenced not only bytheir immediate surroundings, but by areas furtheraway from their homes. For example, shopping

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areas may be close but not in an immediateneighborhood. Similarly, schools and parks maybe nearby but not within a few blocks of a family’shome. Yet, to date, all of the studies of neighbor-hood effects on child well-being have modeledneighborhoods as if they exist independently ofone another and without respect to the conditions ofthe neighborhoods surrounding them. Traditionalmultilevel models ignore the spatial aspect ofneighborhoods in that they cannot address thespatial scale of variation (Chaix et al., 2005).Autocorrelation models are one approach towardcapturing the spatial distribution of individualoutcomes. Initial attempts have been made toexamine spatial models for mental health outcomes(Chaix et al., 2005), and we capitalize on auto-correlation models for modeling spatial effects onchild outcomes.

By not capturing the spatial nature of neighbor-hoods, we neglect the very real possibility that theeffects of the immediate neighborhood environmentmight be further moderated by the effects of moredistal neighborhood environments. For example,one would hypothesize that living in a poorneighborhood surrounded by poor neighborhoodsmight have qualitatively different effects on childrenthan living in a poor neighborhood surrounded bynon-poor neighborhoods. Indeed, researchers suchas Wilson (1987) and Jargowsky (1997) havefocused on the deleterious effects of high-densityurban poverty in which poor families, primarilyethnic minorities, live in neighborhoods which areincreasingly more isolated. However, none of theextant research on neighborhood effects on childrenhas captured these more spatial characteristics ofurban poverty by attempting to incorporate char-acteristics of more distal neighborhoods in modelingthe effects of neighborhoods on children. Sampsonet al. (1999) approached this issue but examiningwhether there was spatial autocorrelation betweenmeasures of neighborhood social processes relevantto childrearing in the immediate neighborhood andsocial processes in surrounding neighborhoods.Although the results of their analyses did supportthe hypothesized spatial relations, these findings didnot extend to an examination of whether theserelations made any difference in the development ofchildren.

There are methods drawn from geography thatcan be used to incorporate effects of more distalneighborhoods when examining neighborhood ef-fects on child competence. Because our study

utilizes spatial data, Geospatial Information Sys-tems (GIS) is used in the context of address-matching, variable creation, and exploratory ana-lyses to examine the effects of spatial clustering,through the development of our multilevel regres-sion models. The geographic correlation of neigh-borhood events and social characteristics oftencause problems for traditional statistical techniquesbecause one of the primary assumptions thatresearchers must make in order to use traditionalmethods is the assumption of independence(McClendon, 1994; Anselin, 1995, 1996; Anselinand Kelejian, 1997). That being said, multilevelmodels allow researchers to control for some typesof clustering, such as the presence of multiplechildren in the same neighborhood (Bryk andRaudenbush, 1992). However, multilevel models,as they currently are developed, are unable tosimultaneously control for the effects of spatialautocorrelation (i.e., the relationship between in-dependent variables at the second level) (Anselin,1988, 1998; Anselin and Kelejian, 1997; Robinson,1998; Ding and Fotheringham, 1992).

Spatial autocorrelation can be checked for usingtests such as the Moran’s I statistic (Moran, 1948),which is a univariate statistic designed to test thenull hypothesis of the absence of spatial clustering(Cliff and Ord, 1981; Baller et al., 2001). In simpleterms, Moran’s I measures the deviation fromspatial randomness, or the concentration of anattribute over space. Moran’s I is similar to aPearson correlation coefficient and is scaled to beless than one in absolute value. If locations are closetogether and tend to be similar in attributes, this willbe reflected with a positive spatial autocorrelationscore (contagion, spillover, externalities) and under-estimated regression coefficients (Robinson, 1998).Conversely, if locations are proximate but insteadhave very dissimilar values, this is reflected as anegative spatial autocorrelation score (competition,revulsion) and overestimated regression coefficients(Robinson, 1998). Larger absolute values indicatehigher levels of spatial autocorrelation in the data.When values are independent of their location, thenzero autocorrelation is present. (Ding and Fother-ingham, 1992; Baller et al., 2001).

In this study, we draw upon spatial analyticmethods from geography to examine neighborhoodeffects on the developmental competence of chil-dren. Using data from a sample of young African-American children living in an urban setting, weaddress the question of whether or not incorporating

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information from neighborhoods more distal fromthe one in which the child lives adds explanatorypower for between neighborhood differences incognitive functioning. The extant literature regardingneighborhood effects on child cognitive developmentprovide little guidance regarding theories of spatialeffects. As previously stated, Wilson (1987), in hisseminal work documenting accelerated rates ofconcentrated economic disadvantage in the US,focused on the isolation of poor individuals in thenewly emerging urban ghettos of the 1980s. Inpositing how spatial effects of neighborhood struc-tural effects affect child cognitive outcomes, oneapproach would be to extend theories of how themore proximal neighborhood affects children. Forexample, if concentrated neighborhood economicdisadvantage in the immediately surrounding neigh-borhood limits the resources available to familieswith young children, then concurrent economicdisadvantage in more distal neighborhoods may actto exacerbate the limitation of resources for familiesand children in poor neighborhoods. If high crime ina neighborhood results in parents limiting theirchildren’s access to outdoor play, living in a highcrime neighborhood surrounded by more high crimeneighborhoods could further limit outdoor play byincreasing the distance parents would have to travelto reach safe play alternatives.

Method

Participants

Data for the spatial analyses were drawn from astudy of African-American families living in Balti-more with preschool-age children between 3 and 41

2

years of age. Recruitment methods for both studieswere similar. Participants were recruited fromBaltimore City neighborhoods through door-to-door-canvassing, targeted mailing lists, and referralsfrom other participants. Neighborhoods were de-fined as census block groups and were stratified byaverage household wealth and racial composition toensure representativeness of the study neighbor-hoods. Residents who had lived in their currentneighborhood for less than six months, and childrenwith disabilities were excluded from both studies.Two home visits were conducted no more than twoweeks apart, each lasting two and a half hours onaverage. The first interview consisted of an interviewwith the primary caregiver, hereafter referred to asthe parent, and the second interview consisted of an

assessment of the child as well as additionalquestions for the parent. Interviews were completedwith 200 African-American families for the pre-schoolers study between January 1998 and August1999. Participants were drawn from a total of 57neighborhoods with an average of 3.51 respondentsper neighborhood (range 1–11).

Measures

Measures included family demographic charac-teristics, child cognitive competence, neighborhoodconcentrated disadvantage, neighborhood popula-tion instability, and neighborhood crime rate.Family demographic characteristics included parenteducation, family size, and family income. Familysize and income data were used to estimate a familyincome-to-needs ratio based on federal povertyguidelines.

Child cognitive competence was assessed using theshort form of the Kaufman Assessment Battery forChildren (K-ABC, Kaufman and Applegate, 1988).The K-ABC is comprised of three subscales:Achievement (factual knowledge), Sequential Pro-cessing (‘‘stepwise’’ processing), and SimultaneousProcessing (higher order problem solving). Inanother published analysis (author reference), wedemonstrated that between-neighborhood differ-ences were only significant for the KABC Simulta-neous Processing Scale. Therefore, we will only usethis subscale for the current analysis.

Neighborhood variables included concentratedeconomic disadvantage, population instability andcrime. The current research uses an economicdisadvantage scale to describe social structureacross neighborhoods, created from 2000 censusvariables (Morenoff and Sampson, 1997; Sampsonet al., 1997, 1999). Specifically, the concentrated

economic disadvantage scale describes the relativeconditions of poverty stricken neighborhoods. Thisscale was defined as percent of individuals below thepoverty line, percent receiving public assistance,percent unemployed, and percent of householdsthat are female-headed with children. Following theprecedent of Sampson et al. (1997, 1999), the abovevariables were processed using factor analysis. Thevariables were highly interrelated and each loadedon a single factor using either principal componentsor alpha-scoring factor analysis (Morenoff andSampson, 1997; Sampson et al., 1997, 1999).

Prior research also suggests that populationinstability within neighborhoods (i.e., tracts) was

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1Values were categorized as high or low through the use of the

Anselin’s ‘‘Dynamic Exploratory Spatial Data Analysis’’ tool in

ArcView 3.3. In this method, a scatterplot is created between the

variable and its ‘‘lagged’’ counterpart. Thus, values that would

fall in the quadrant that are high in the independent variable (i.e.,

concentrated economic disadvantage) and those that were in the

high quadrant for the ‘‘lagged’’ independent variable (i.e.,

‘‘lagged’’ concentrated economic disadvantage) are displayed in

the maps as black tracts.

M.O. Caughy et al. / Health & Place 13 (2007) 788–798792

also conceptually important. Thus, we combine twoCensus measures into an index of population

instability. This index was created by calculatingthe z-scores for the census measures of residentialmobility and percent renters, summing the z-scoresand then divided by two (Sampson et al., 1997, 1999).Residential instability is often associated with negativeneighborhood characteristics like declining neighbor-hood physical structures (Wilson and Kelling,1982; Stark, 1987), worse residential health and well-being (Ross, 1993, 2000), and more crime and socialdisorganization (Sampson et al., 1997, 1999).

The Baltimore, MD Police Department providedPart I crime events for the years 1998 and 1999.Events were aggregated to the census block group.The violent crime rate was computed by adding themurder, rape, robbery, and aggravated assaultincidents per block group and then transformingthat number into a rate per 10,000 persons.

The concentrated economic disadvantage, popula-

tion instability, and violent crime rates for surround-

ing neighborhoods were computed using Moran’s I.Moran’s I measures the deviation from spatialrandomness, or the concentration of an attributeover space, and is calculated as follows:

I ¼ SiSjwijðyi � mÞðyj � mÞ=Siðyi � mÞ2,

where wij are elements of a row-standardized spatialweights matrix, y is the robbery rate, and m is theaverage robbery rate in the sample (Cliff and Ord,1973, 1981; Ding and Fotheringham, 1992). Weemployed the SpaceStat (Anselin, 1988) program,and used the ‘‘queen’’ join count statistic (edge-to-edge and vertex-to-vertex) to calculate Moran’s I forour neighborhood level data (Robinson, 1998).Moran’s I values greater than .10 that are statisti-cally significant are evidence of the presence ofspatial autocorrelation (Anselin, 1988, 1998). In thisstudy, the spatial concentration of concentratedeconomic disadvantage was .36 (po.001) popula-tion instability was .40 (po.001), and violent crimewas .13 (po.01). Fig. 1 displays the Moran’s I forconcentrated economic disadvantage, populationinstability, and violent crime. Tracts that are shadedin black are those with high concentrations of eachindependent variable that are surrounded by othertracts with high concentrations. Conversely, whitetracts are those with low concentrations of eachindependent variable surrounded by other trackswith low concentrations. The two shades of graydepict tracks that are either high or low in

concentration of the independent variable that isthen surrounded by the opposite values.1

Analysis methods

All neighborhood variables were dichotomizedwith those in the highest quartile of the neighbor-hood variable coded 1 and all others coded 0. Forthe exploratory analyses, t-tests were used toexamine the association between child cognitivecompetence and neighborhood characteristics. Inseparate multilevel multivariate models, we examinethe independent contributions of neighborhoodconcentrated economic disadvantage, neighborhoodpopulation instability, and neighborhood crime.For the purpose of the multilevel analysis, level 1was defined as the individual, and level 2 wasdefined as the census block group or neighborhood.In a stepwise fashion, characteristics of the neigh-borhood of residence, characteristics of the sur-rounding neighborhoods, and interactions betweenthe immediate neighborhood and the surroundingneighborhood were added to the models to deter-mine if characteristics of neighborhoods more distalfrom the child’s residence influence child compe-tence. All models were adjusted for family income-to-needs ratio. Controlling for these family-levelsocioeconomic measures was necessary in that theeconomic situation of a family is a major determi-nant of the kind of neighborhood in which a familycan live. All multivariate analyses were conductedusing MLwiN (Rabash et al., 1999), a multilevelmodeling analysis software that adjusts for thecorrelations between observations that may beclustered, in this case, in the same neighborhood.

Results

The characteristics of the study sample aredisplayed in Table 1. The majority of the respon-dents were mothers of the target children. Almosthalf of the participating families lived below the

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Fig. 1. Moran’s I ranges of three independent variables in their census tract (concentrated economic disadvantage, residential instability,

and violent crime) in relation to the value of these variable values in the surrounding tracts.

M.O. Caughy et al. / Health & Place 13 (2007) 788–798 793

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Table 1

Characteristics of study sample (N ¼ 200)

N %

Primary caregiver

Mother 172 86.0

Father 4 2.0

Grandparent 18 9.0

Other relative 5 2.5

Other unrelated person 0 0

Missing 1 .5

Federal poverty level

o100% poverty 89 44.5

100–179% poverty 50 25.0

180%+ poverty 61 30.5

Missing

Educational attainment

Less than high school 47 23.5

High school 83 41.0

More than high school 71 35.5

Child gender

Boy 93 46.5

Girl 107 53.5

Child age (years)

Mean (SD) 3.74 (.46)

Range 2.90–4.83

M.O. Caughy et al. / Health & Place 13 (2007) 788–798794

federal poverty line, and approximately a quarterlived above 180% of the poverty level.

Differences in child cognitive competence byneighborhood characteristics are displayed in Table2. High concentrated economic disadvantage andpopulation instability were associated with lowercognitive scores. High rates of violent crime werealso associated with significantly lower cognitivescores.

Multilevel regression results are displayed inTable 3. The unadjusted between-neighborhoodvariance in cognitive scores was .197, t ¼ 2.24,po.05. For concentrated economic disadvantage,both the conditions of the immediate neighborhoodas well as the conditions of the surroundingneighborhood were associated with differences inchild cognitive competence. High concentratedeconomic disadvantage in the child’s immediateneighborhood was associated with a half a standarddeviation lower K-ABC Simultaneous Processingscore, and a similar difference was associated withhigh concentrated economic disadvantage in thesurrounding neighborhood. However, there was asignificant interaction between high concentratedeconomic disadvantage in the immediate neighbor-

hood and high concentrated economic disadvantagein the surrounding neighborhood. The interaction isdisplayed in Fig. 2. As can be seen in Fig. 2,cognitive scores were lowest in poor neighborhoodssurrounded by non-poor neighborhoods and innon-poor neighborhoods surrounded by poorneighborhoods. Cognitive scores for children livingin poor neighborhoods surrounded by poor neigh-borhoods were intermediate between these childrenand those children living in non-poor neighbor-hoods surrounded by non-poor neighborhoods.

The impact of population instability on cognitivescores is displayed in the second panel of Table 3.High population instability in the immediate neigh-borhood was not associated with cognitive scores,but high population instability in the surroundingneighborhoods was associated with a half a standarddeviation lower cognitive score. The interactionbetween population instability in the immediateneighborhood and population instability in thesurrounding neighborhoods was not significant.

The impact of violent crime on cognitive scores isdisplayed in the third panel of Table 3. High rates ofviolent crime in the immediate neighborhood wereassociated with an almost fifteen point lowercognitive score. High violent crime rates in thesurrounding neighborhood were not associated withcognitive scores.

In the final model displayed in Table 4, weincluded all neighborhood variables that hadsignificant direct effects, significant distal effects,and/or interactions between direct and distal effects.All of the effects that had been significant in earliermodels retained significance with the exception ofpopulation instability in surrounding neighbor-hoods. After inclusion of all neighborhood vari-ables, the between-neighborhood variance was notsignificant. Although population instability in sur-rounding neighborhoods had been associated withlower cognitive score when examined separately,this effect was no longer significant when concen-trated economic disadvantage and violent crimewere included in the model as well.

Discussion

The purpose of this study was to examine thecontributions of distal neighborhood environmentsto the developmental competence of young children.Both the proximal neighborhood as well as themore distal neighborhood contributed unique var-iance to cognitive competence in this sample of

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Table 2

Average cognitive scores by neighborhood characteristics.

Low High t

M SD M SD

Concentrated economic disadvantage 122.81 21.06 109.73 20.90 3.76**

Population instability 121.66 21.41 112.72 21.56 2.48*

Property crime rate 121.22 20.26 114.5 25.02 1.89+

Violent Crime rate 124.66 19.65 106.56 21.45 5.62**

+po.1; *po.05; **po.01.

Table 3

Multilevel regression of cognitive scores on proximal and distal neighborhood characteristics (N ¼ 200)

Model 1 Model 2 Model 3

Variable b se t b se t b se t

Concentrated disadvantage

Constant 116.01 2.95 39.34** 118.67 3.10 38.23** 120.41 2.94 41.03**

Family income-to-needs ratio .05 .02 3.07** .04 .02 2.67** .04 .01 2.86**

Concentrated economic

disadvantage (high)

�10.05 4.17 �2.41* �8.85 3.94 �2.25* �16.95 4.02 �4.22**

Conc econc Disadv (surround’g

neigh) (high)

— — — �8.73 3.82 �2.28* �16.43 4.01 �4.09**

Conc econ disadv� conc econ.

disadv. (surround’g neigh)

— — — — — — 24.01 6.96 3.45**

Population instability

Constant 114.73 3.01 38.09** 116.11 3.00 38.70* 116.55 3.00 38.86**

Family income-to-needs ratio .05 .02 3.27** .05 .02 3.07** .05 .02 3.00**

Population instability (high) �5.56 4.24 �1.31 .05 4.83 .01 �4.01 6.06 �.66

Population instability

(surround’g neigh) (high)

— — — �10.58 4.96 �2.13* �15.37 6.62 �2.32*

Pop instability�pop instability

(surround’g neigh)

— — — — — — 10.18 9.65 1.05

Crime

Constant 118.46 2.77 42.70** 117.61 2.82 41.77** 117.37 2.84 41.31**

Family income-to-needs ratio .04 .01 2.86** .04 .01 3.00** .04 .01 2.93**

Violent crime rate (high) �15.29 3.35 �4.57** �16.34 3.39 �4.82** �15.01 4.04 �3.71**

Violent crime rate (surround’g

neigh) (high)

— — — 3.85 3.47 1.11 5.63 4.55 1.24

Violent crime� violent crime

(surround’g neigh)

— — — — — — �4.20 7.01 �.60

*po.05; **po.01.

M.O. Caughy et al. / Health & Place 13 (2007) 788–798 795

African-American preschoolers living in an urbansetting. High levels of concentrated economicdisadvantage in the immediate neighborhood aswell as high levels of economic disadvantage in thesurrounding neighborhoods were associated withsignificantly lower problem solving skills. Popula-tion instability in more distal neighborhoods wasalso associated with lower cognitive scores. Theeffect size of the association of distal concentrated

economic disadvantage with child competence wasabout .50, which would be considered large. Theseresults support the importance of incorporatinginformation from more distal neighborhoods whenexploring the relation between neighborhood char-acteristics and measures of child competence.

Our results also indicated an interesting interac-tion between concentrated economic disadvantagein the immediate neighborhood and concentrated

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90

95

100

105

110

115

120

125

Cognitiv

e s

core

Non-poor/non-poor Poor/non-poor Non-poor/poor Poor/poor

Poverty of neighborhood/surrounding neighborhood

Fig. 2. Average cognitive scores for children living in neighborhoods of differing levels of economic disadvantage surrounded by

neighborhoods of differing levels of economic disadvantage.

Table 4

Final multilevel regression model for cognitive scores on

proximal and distal neighborhood characteristics (N ¼ 200)

Variable b se t

Constant 121.39 2.93 41.37**

Family income-to-needs

ratio

.04 .01 2.57*

Concentrated economic

disadvantage (high)

�11.73 5.07 �2.32*

Conc economic

disadvantage (surround’g

neigh) (high)

�11.74 4.67 �2.52*

Conc econ disadv� conc

econ disadv (surround’g

neigh)

19.39 7.27 2.67**

Population instability

(surround’g neigh) (high)

�.24 3.78 �.06

Violent crime rate (high) �8.84 4.05 2.19*

*po.05; **po.01.

M.O. Caughy et al. / Health & Place 13 (2007) 788–798796

economic disadvantage in the surrounding area.Cognitive scores for preschoolers living in poorneighborhoods surrounded by poor neighborhoodswere intermediate to scores for preschoolers livingin non-poor neighborhoods surrounded by non-poor neighborhoods and those preschoolers livingin poor neighborhoods surrounded by non-poorneighborhoods as well as those preschoolers livingin non-poor neighborhoods surrounded by poorneighborhoods. This is contrary to our theory ofpotential spatial effects outline in the introductionin which we posited that distal neighborhood riskmight act to exacerbate risk factors in the moreproximal neighborhood. Perhaps having a poorneighborhood surrounded by other poor neighbor-hoods gives rise to certain social processes which

buffer children against some of the negative effectsof urban poverty.

Crime rates in the neighborhood were associatedwith lower cognitive scores for the preschoolers inthis study. Specifically, living in a neighborhoodwith a high rate of violent crime was associated withmore than a 14-point lower cognitive score. Crimerates in the surrounding neighborhoods were notassociated with cognitive scores, however. In theintroduction, we hypothesized that crime wouldaffect child cognitive development by decreasing thelikelihood that parents would allow children to playoutside. However, data available regarding fear ofcrime did not indicate any differences in fear levelsbetween high crime neighborhoods surrounded byhigh crime neighborhoods and high crime neighbor-hoods surrounded by low crime neighborhoods(data not shown). More research to explore theprocesses at both the community and family levelthat might mediate the interrelations of communitycrime, perceptions of crime, parenting behavior andchild development.

There are limitations of this research that shouldbe kept in mind when interpreting the results.Specifically, the temporal relation between theneighborhood measures and the child measuresdiffers by neighborhood measure. Specifically,although 2000 census data were used to create theindices of concentrated economic disadvantage andpopulation instability, and estimates of crime rateswere based on crime data from 1998 and 1999. Thedevelopmental outcomes for the children in thestudy were collected in 1998. Therefore, crime datawere more temporally proximal to the data collec-tion period for the child outcomes than were the

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Census data. Although dramatic changes in neigh-borhood characteristics over this period of time areunlikely, it should be kept in mind that thecharacteristics of the neighborhoods at the timewhen children were interviewed may have beenslightly different.

Another limitation of this analysis is that we didnot include a wide range of family-level measures,such as measures of parenting that might explain theassociation between proximal and distal neighbor-hood structural features and child cognitive compe-tence. Parenting as a mediator is one of thehypothesized paths by which neighborhoods affectchild outcomes. The decision to not include parent-ing measures in this analysis was based in part onfindings that these factors did not explain differ-ences in child cognitive outcomes between neighbor-hoods (author reference). This should not be takenas conclusive evidence that parenting processes arenot mediating the associations observed. It may bethat the parenting measures utilized in this investi-gation did not tap the most relevant dimensions.Another potential limitation is that the smallnumber of participants in each neighborhood mayhave limited our ability to detect how variability inparenting in the neighborhood may have mediatedneighborhood effects on children.

Despite these limitations, this study demonstratesthat much can be gained by facilitating interdisci-plinary research with individuals such as geographerswho have significant experience with defining neigh-borhoods and assessing spatial effects on individuals.As the interest in studying neighborhoods amongchild development researchers has increased, theability to move the field forward in a meaningfulway is dependent on theories that are developed toexplain neighborhood effects as well as the accuracywith which we model those theories. This also extendsto other areas of neighborhood research. There isgrowing interest in examining how neighborhoodcharacteristics contribute to a wide range of outcomesincluding mental health and physical health. Futureresearch should capitalize on innovative designs andstatistical methodologies to more accurately capturethe complexities of the contexts in which people liveand how these complexities may affect the health andwell-being of individuals.

Acknowledgments

This research was supported by Grant #MCJ-240731-01-1 from the Maternal and Child Health

Bureau and Grant #RO1HD4041901A1 from theNational Institute of Child Health and Develop-ment. The authors would like to thank DeborahBrothers and Bennette Drummond-Fitzgerald forconducting interviews, and Kimberly Lohrfink forproviding project management. Data managementand analysis support was expertly provided byYiHua Chen, Crystal Evans, Patricia Gwayi-Chore,and LiChing Lee. Finally, we would like to thankthe families who so graciously welcomed us intotheir homes.

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