neighborhoods and selection 1 running head: …

NEIGHBORHOODS AND SELECTION 1

Running head: NEIGHBORHOODS AND SELECTION

Reciprocal Associations between Neighborhood Context and Parent Investments: Selection

Effects in Two Longitudinal Samples

September 2016

Thomas J. Schofield1 Melissa Merrick2

M. Brent Donnellan3 Chia-Feng Chen4

Fragile Families Working Paper WP16-08-FF

1 Department of Human Development and Family Studies, Iowa State University, 1360 Palmer, Iowa State

University, Ames, IA 50010. USA [email protected] phone:515-294-6914 fax:515-294-2502

2 Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control

and Prevention, Atlanta GA. USA [email protected]

3 Department of Psychology, Texas A&M University, College Station TX. USA [email protected]

4 Department of Human Development and Family Studies, Iowa State University. Ames, IA 50010. USA

[email protected]

https://owa.princeton.edu/owa/redir.aspx?C=yIwdOOu8IIndzA5IV66d-yqvdelVHG32PWsFWPt-5ZOI94aOrNzTCA..&URL=mailto%3atommy%40iastate.edu


Abstract

The present study addresses the degree to which neighborhood disadvantage and parenting

investments are reciprocally linked over time, and the relative degree to which both show

indirect effects on child externalizing through each other. Data come from two studies: the first

followed families from the child’s birth to age 11 (N = 1,364), the second followed children from

birth to age 9 (N = 4,898). In both studies, material and emotional parenting

investments/resources predicted selection into neighborhoods over time, and neighborhood

disadvantage frequently predicted relative change in parenting investments. The prediction to

change in child externalizing was larger for parenting investments than it was for neighborhood

characteristics in most of the models tested.

Keywords: social environments; communities; neighborhoods


Reciprocal Associations between Neighborhood Context and Parent Investments: Selection

Effects in Two Longitudinal Samples

The link between neighborhood attributes and child development has been replicated

across scores of samples, among families across a wide range of socioeconomic status (SES) and

ethnic backgrounds (Leventhal & Brooks Gunn, 2009). Based on this literature, policymakers

and other stakeholders frequently interpret these associations as causal. That is, neighborhood

context is now believed to create changes in child socioemotional outcomes. Even so, a

longstanding concern about neighborhood effects on child development is that families may

select nonrandomly into neighborhoods. Selection into neighborhoods would inflate associations

between neighborhood attributes and child outcomes, and call into question the underlying

directional assumption that correlations between neighborhoods and family processes reflect a causal

effect of neighborhoods. In the current study, we address this important issue, focusing on the outcome of

child externalizing behavior problems.

People select into environments (Jaffee & Price, 2007). Selection effects can refer to

constraints or preferences that are volitional (Scarr & McCartney, 1983) or nonconscious; they

result in people moving into environments that are in some way correlated with preexisting

personal characteristics (Cao, Mokhtarian, & Handy, 2009). In terms of the neighborhood

context, such selection effects are what urban sociologists such as Gans (1968) referred to when

he proposed that community life is primarily the aggregate of individual characteristics.

Selection is frequently studied in work on social contexts. For example, recent work on the

hypothesized influence of peers on adolescent development specifies both selection into peer

group, as well as peer influence (Dishion, 2013). Similar considerations are made when studying

the influence of one’s spouse/coparent on parenting behavior (Schofield et al., 2009; Schofield &


Weaver, 2016). Failure to model selection into environments (e.g., niche picking) can spuriously

inflate associations between the environment and later development (Liberson, 1986), and can

create other errors of inference. For instance, one possibility is that poorer people tend to live in

poorer neighborhoods because they cannot leave the resource-providing social networks in

which they are embedded (such as childcare provided by friends, neighbors or family members).

Another possibility is that poorer families may have the income to move into a better

neighborhood yet lack the credit history to qualify for a good interest rate on a mortgage.

Consequently, even in instances where residents could potentially afford to live in a different

neighborhood, it would nonetheless be impractical to leave their current neighborhood. If the

poorest or least capable residents were to remain in a particular neighborhood while the more

resourceful or capable residents were to move to other neighborhoods, this would lead to an

overestimation of neighborhood effects. If selection is occurring with regard to neighborhoods, it

will require a reevaluation of how neighborhood models are to be specified. There is reason to

expect that selection occurs at the neighborhood level, and that the selection is related to the

same traits and processes that influence family dynamics. However, the selection-based

interpretation is less common partially due to a lack of evidence of selection into neighborhoods.

There have been very few tests of selection into neighborhoods (Bagley & Mokhtarian,

2002; Hedman, & van Ham, 2010). Qualitative research suggests multiple drivers of

neighborhood choice (Batty & Flint, 2010; Hickman, 2010; Mee, 2007), yet the few quantitative

studies that have tested for such selection effects include only demographic variables (Diez-Roux

et al., 1997; Ecob & Macintyre, 2000; McKinnish & White, 2011; Sampson & Sharkey, 2008).

However, people who live near each other are more likely to be similar with regard to their

personality (Jokela et al., 2014), their rate of extreme marital conflict (Hetling & Zhang, 2010),


and child maltreatment experiences (Zhou, 2006). The typical interpretation of these correlations

is that the neighborhood context is exerting a causal influence on residents, making them friendly

or aggressive, responsible or impulsive. The selection-based interpretation is that residents with

similar personalities, levels of marital conflict, or child maltreatment are either attracted to or

pushed into particular neighborhoods. Therefore, an adequate accounting of preexisting

characteristics would include not only SES, but also parent attributes which are associated with

later child behavioral outcomes and have been shown to be distributed nonrandomly across

neighborhoods including the home environment (Bradley & Caldwell, 1977), personality

(McCabe, 2014), parenting behaviors (Cowan, Cowan, & Barry, 2011), support from a stable

nurturing romantic relationship (Erel & Burman, 1995), and so on. Exemplars of many of these

categories are distributed nonrandomly across neighborhoods, further implicating them in the

hypothesized selection process (Hetling & Zhang, 2010; Jokela et al., 2014; Zhou, 2006).

A second perspective that supports selection into neighborhoods comes from economic

work on the material investments parents make in their children (Mayer, 1997). That is, where

children live reflects in part an investment parents make in their child, similar to having reading

materials in the home or health insurance for the child. Higher quality neighborhoods afford

children greater opportunities for safety, outdoor recreation, and positive peer interactions that

may not be available in lower quality neighborhoods. Economic forces are indeed a driver of

selection, as only families that can afford to move to better neighborhoods have the option to do

so. Once economic constraints are taken into account, however, the choice of neighborhoods

becomes an indicator of material investment in the child’s well-being. Therefore, selection into

neighborhoods ought to be subject to the same drivers that predict other parental investments

(either material or emotional see Schofield et al., 2011).


In the current study, we hypothesize reciprocal associations between neighborhood SES

and parent investments (Figure 1). We expect neighborhood SES will predict relative change in

parent investments (Path A) and child externalizing problems (Path C). These paths are

consistent with the dominant perspective that neighborhoods causally influence family processes

and child development. However, we also expect that parent investments (and related resources)

will predict selection into or out of neighborhoods (Path B). This path is very rarely measured,

and has never been tested over time to our knowledge. Finally, we expect that parent investments

and related resources will predict relative changes in child externalizing problems (Path D). This

last path is consistent with the dominant perspective that parents causally influence child

development.

We focus on neighborhood SES because it is the canonical variable of interest for many

developmental studies of neighborhood effects (Leventhal & Brooks-Gunn, 2009), and we focus

on externalizing problems because most neighborhood effects relate to deviant or delinquent

behavior, of which externalizing behavior problems are the developmental precursors. We

include household income as a parenting investment in all models, because of the clear role

economic constraints would have on the ability to relocate into or out of a neighborhood. In

addition to these theoretical processes, the present study also addresses the issue of replication.

Concerns about the robustness of research results, and the capacity to replicate across

populations have long been a concern in the field of child development (Weisz, 1978). In order

to more comprehensively evaluate the current hypotheses, we test our theoretical model across

two longitudinal samples: families participating in the NICHD Study of Early Child Care and

Youth Development and families participating in the Fragile Families Study. Both samples

experience considerable variation in neighborhood SES, both include prospective longitudinal


data which are necessary to test for selection (Jokela et al. 2014), and both include multiple

measures of parental investments which are necessary to assess the support for these hypotheses.

Method

Primary Sample

Participants

Participants were the families in the National Institute of Child Health and Human

Development (NICHD) Study of Early Child Care and Youth Development (SECCYD). These

1,364 families were recruited in 1991, shortly after their child’s birth, from hospitals at 10 sites

across the United States. Specific recruitment procedures are detailed elsewhere (NICHD Early

Child Care Research Network, 2005). This study was conducted with approval from the

affiliated Institutional Review Boards. This sample included a substantial proportion of low-

education parents (30% had a high school degree or less), ethnic minority families (13% were

African American compared with the national proportion of 12%), and the mean income level

was the same as the U.S. average ($37,000).

Procedures and Variables

Detailed measures of family demographics, maternal behaviors, and children’s

characteristics and adjustment were obtained from multiple informants beginning when children

were 1 month of age and continued annually until they were 15 years old.

Neighborhood socioeconomic status (SES). Family addresses were geocoded and used

at seven assessments to identify corresponding neighborhood attributes from the 1990 U.S.

census (at the blockgroup level). In the current study, census measures of neighborhood SES

included percent of female-headed households, residents who are unemployed, in poverty, and

lack a high school degree. These data were available at the 1 month, 15 month, 36 month, 54

http://psycnet.apa.org/journals/fam/29/1/39.html#c40

http://psycnet.apa.org/journals/fam/29/1/39.html#c40


month, first grade, third grade, and fifth grade assessments (mean α = .75, range: .71-.78).

Descriptive statistics for neighborhood variables are presented in Table 1.

Income to needs ratio. Family income was reported by mothers at each assessment.

Support from romantic partner. Mothers completed the intimacy subscale of the Love

and Relationships Scale (created for this study) at the following assessments: 1 month, 36

months, 54 months, first grade, and third grade. Six items were answered on a range from

1(strongly disagree) to 5(strongly agree). Sample items included “My spouse/partner can really

understand my hurts and joys” and “My spouse/partner listens to me when I need someone to

talk to” (α = .87).

Parenting behavior. Mothers were recorded when interacting independently with their

children during play scenarios and problem-solving tasks. Observations were obtained eight

times between the child’s birth and grade 5, and sent to a single site for coding. In order to

maintain a developmentally appropriate measure of sensitivity, indicators changed somewhat

over time (NICHD Early Child Care Research Network, 1997, 1998). At 6, 15, and 36 months,

sensitivity was the sum of three 4-point ratings: sensitivity to the child’s non-distress signals

(e.g., acknowledging the child's affect, contingent vocalizations by the mother, facilitating the

manipulation of an object or child movement), positive regard (e.g., speaking in a warm tone of

voice, hugging or other expressions of physical affection, an expressive face), and reflected

intrusiveness (e.g., taking away objects or food while the child still appears interested, not

allowing the child to handle toys he/she reaches for, insisting that the child do something in

which he/she is not interested, not allowing the child to make choices).

At 54 months and in grades 1, 3 and 5, sensitivity was the sum of three 7-point ratings:

supportive presence (e.g., pay attention to the child when the child talks, be engaged in the


interaction, appear to enjoy interacting with the child), respect for autonomy (e.g., ask the child’s

opinion, negotiate rule with the child, acknowledge the child’s perspective), and reflected

hostility (e.g., point out child's weaknesses, put the child down, use a negative or sarcastic tone

of voice). Inter-coder reliability was established by having two coders assess approximately 20%

of the tapes, randomly drawn from each assessment period (rICC > .70). Confirmatory factor

analyses supported a single factor solution at each timepoint, with good fit and standardized

loadings above .40.

Attitudes about being a parent. Mothers reported on their beliefs about parenting three

times between the child’s birth and grade six using the Modernity scale (Schaefer & Edgerton,

1985). The 30 items on this scale provide an estimate of how progressive (democratic, child-

centered) versus traditional (authoritarian, strict, adult-centered) the parent’s attitudes are toward

child rearing and discipline. Sample items include “parents should teach their children that they

should be doing something useful at all times” (reflected), “parents should go along with the

game when their child is pretending something” and “children have a right to their own point of

view and should be allowed to express it.” Responses ranged from 1=strongly disagree to

5=strongly agree. Consistent with prior work using this scale (Dowsett, Huston, Imes, &

Gennetian, 2008), we combined the items into a single index, which had composite reliabilities

above .80 across all timepoints for both parents.

Mother personality. Mothers completed the 12-item scales for agreeableness (α = .74),

neuroticism (α = .84), and extraversion (α = .75) from the revised Neo Personality Inventory

(Costa & McCrae, 1992) when the child was 6 months old.


Maternal mental health. Mothers’ depressive symptomatology was assessed using

questions from the Center for Epidemiologic Studies Depression Scale (Radloff, 1977) when

children were 1, 6 and 15 months, and in fifth grade (all α > .85).

Home environment. The 55-item HOME Inventory (Bradley & Caldwell, 1984) was

used to measure the quality and quantity of stimulation and support available at home when

children were 15 months old and again when children were in third grade (α > .80).

Child externalizing behavior problems. Mothers completed age-appropriate versions of

the Child Behavior Checklist (CBCL; Achenbach 1991) when children were 24 months, 36

months, 54 months, in Kindergarten, and in grades 1, 3, and 4. The T score standardization was

used in the current analyses.

Analyses

As shown in Figure 1, all hypothesis tests were conducted longitudinally. The seven

assessments of neighborhood disadvantage were fit into an autoregressive model, as were the

seven assessments of family income, and the measures of other investments (which varied in the

number of times they were measured). Separate models were run for each investment resource,

though they all included household income as a covariate, and externalizing problems as an

outcome. We used Mplus Version 6 (Muthén & Muthén, 2012) to estimate the models using full

information maximum likelihood estimation. Study hypotheses were evaluated using structural

equation models with model fit assessed using the standard chi-square index of statistical fit that

is routinely provided under maximum likelihood estimation of parameters, as well as practical fit

indices (Browne & Cudeck, 1993; Tucker & Lewis, 1973; Hu & Bentler, 1999). Models were

simplified by equating equivalent paths across time (when it did not result in a significant loss of

fit).


Results

As shown in Table 1, the families in this study lived in neighborhoods reflecting a wide

range of income, employment, household structure, and education. Across the seven

assessments, 9.5% of the residents had an income-to-needs ratio lower than one, and that

estimate ranged across neighborhoods from 0% to 100%. Separate models were estimated for

each parent investment/investment resource. The fit indices from these six models are presented

on the left side of Table 2. For example, the model in which the parenting investment resource

was support from a romantic partner had an absolute fit of χ2(232) = 909.6, with acceptable

relative fit: RMSEA of .046, TLI = .957.

The standardized coefficients from these six analytic models appear in Table 3. For

example, the first model was the model in which the parenting investment resource was support

from a romantic partner. Information from this model appears in the first column of data. Path A

in Figure 1 reflects the hypothesis that neighborhood socioeconomic disadvantage will predict

parenting investment resources (both material and emotional). Consistent with this hypothesis, in

this first model the coefficients from neighborhood socioeconomic disadvantage to income were

all significant, and ranged in magnitude from -.12 to -.03. The coefficients from neighborhood

disadvantage to support from a romantic partner were not significant [range: .03 to .03]. Across

the remaining models neighborhood disadvantage frequently predicted lower household income,

and predicted emotional parenting investments two thirds of the time. The magnitudes of these

coefficients are small, because they represent change over a relatively brief period

(approximately 2 years). Over time, they would cumulate into a larger effect.

Path B in Figure 1 reflects the hypothesis that parenting investment resources (both

material and emotional) will predict neighborhood socioeconomic disadvantage. Consistent with


this hypothesis, household income predicted changes in neighborhood socioeconomic

disadvantage consistently across all six models (100% of paths tested). Emotional investment

resources also predicted changes in neighborhood socioeconomic disadvantage (92% of paths

tested). The neighborhood data across these assessments all came from the same census year,

meaning that observed changes in neighborhood socioeconomic disadvantage only represent

families who relocated, not changes over time in neighborhood socioeconomic condition.

Path C1 and C2 in Figure 1 reflect the hypothesis that neighborhood disadvantage will

predict changes over time in child externalizing behavior problems, after accounting for parents’

emotional and material investment resources. Consistent with this hypothesis, neighborhood

disadvantage consistently predicted relative changes in externalizing behavior across all six

models (100% of paths tested).

Paths D1 and D2 in Figure 1 reflect the expectation that parenting investment resources

will predict changes over time in child externalizing behavior problems, net of neighborhood

socioeconomic disadvantage. Consistent with this expectation, relative changes in externalizing

problems were predicted by family income (100% of paths tested) and emotional investment

resources (100% of paths tested). To compare the magnitude of these coefficients, a model

constraint was applied which constrained the path from neighborhood disadvantage to

externalizing to be equal in magnitude (in absolute value) to the path from emotional investments

to externalizing. This model constraint significantly worsened model fit for all models except

observed sensitivity. Similar model constraints comparing neighborhood disadvantage to

household income significantly worsened model fit for all models except home environment.

Both emotional investment and material investment resources were more strongly linked than

neighborhood disadvantage to changes in child externalizing problems.


Finally, inclusion of both income and emotional investments as longitudinal processes

allowed for an examination of reciprocal associations over time between those two constructs.

Household income was associated with increases in emotional investments (80% of paths tested),

and emotional investments predicted increases in household income (100% of paths tested).

Replication Sample

Participants

The Fragile Families and Child Well-being Study (Fragile Families), is a longitudinal

birth cohort study of 4,898 children born between 1998 and 2000 in 20 U.S. cities with

populations of 200,000 or more (Reichman et al., 2001). This study deliberately oversampled

births to unmarried parents, yielding a sample where one-quarter of births were to married

parents and three-quarters were to unmarried parents. The baseline interviews were conducted

shortly after the child’s birth with follow-up interviews one, three, five, and nine years later. This

study was conducted with approval from the affiliated Institutional Review Boards. Eighty-seven

percent of eligible mothers responded to the first wave of the survey, and subsequent response

rates have been above 70%. The sample is ethnically diverse: 47% Black, 21% White, 27%

Latino, and 5% other. Just over 80% of the mothers had been born in the U.S. Mother’s average

age was 25 years, and two thirds had at least graduated from high school.

Measures

Neighborhood socioeconomic status (SES). Family addresses were geocoded and used

at each assessment to identify corresponding neighborhood attributes from the 2000 U.S. census

(at the tract level). In the current study, census measures of neighborhood SES included percent

of female-headed households, residents who are unemployed, in poverty, and lack a high school


degree (mean α = .88, range: .87-.89). Descriptive statistics for neighborhood variables are

presented in Table 1.

Income to needs ratio. Family income was reported by mothers at each assessment.

Support from romantic partner. Support from the mother’s romantic partner was

assessed at year one and year three using a 5-item scale created for this study. Items were

answered on a scale from 1(never) to 3(often). Sample items include “How often is your partner

fair/willing to compromise when you have a disagreement” and “How often does your current

partner encourage you or help with things that are important to you” (α = .60 at year 1, .60 at

year 3).

Parenting behavior. At years two, three, and five mothers reported on the degree to

which their child was spanked by the mother, father, or mother’s romantic partner. Two

questions were answered in reference to each caregiver: “Has [caregiver] spanked the child in the

past month” 0(no) and 1(yes) and “how often did [caregiver] spank the child in the past month”

1(once or twice), 2(a few times in the past month), 3(a few times each week) and 4(every day).

From these questions an overall scale was created for each parent ranging from 0(did not spank

child) to 5(spanked every day), then combined across caregivers (α = .66 at year 2, .68 at year 3,

.62 at year 5).

Attitudes about being a parent. At years two, three, and five mothers completed a four-

item scale created for this study which assessed the mother’s impression of being a parent. Items

were answered on a scale from 1(strongly disagree) to 4(strongly agree) and included items such

as “Taking care of children is more work than pleasure” and “would be doing better in life

without child” (mean α = .64, range: .61-.66).


Mother personality. At year three mothers completed six items assessing impulsivity (α

= .84) from the Impulsivity scale (Dickman, 1990).

Maternal mental health. Maternal depression was reported by the mother at years two,

three, and five. The Fragile Families draws on the Composite International Diagnostic Interview

- Short Form (CIDI-SF), Section A (Kessler et al. 1998) to assess depression. The CIDI is a

standardized instrument for assessment of mental disorders and follows the criteria of the

Diagnostic and Statistical Manual of Mental Disorders – Fourth Edition (DSM-IV; APA 1994).

The CIDI-SF takes a portion of the full set of CIDI questions and estimates the probability that a

respondent would be positively diagnosed with depression if given the full CIDI interview.

Specifically, women were asked if they had experienced feelings of depression or anhedonia in

the past year, lasting two weeks or more. If so, they were asked about seven additional

symptoms: (1) losing interest (2) feeling tired (3) weight changes (4) trouble with sleep (5)

trouble concentrating (6) feeling worthless or (7) thinking about death. Symptoms were summed

into a scale of depressive symptomatology.

Home environment. At year three and year five mothers completed a 29-item version of

the HOME Inventory (Bradley & Caldwell, 1984) with items measuring parental warmth, verbal

skills, lack of hostility, and physical interior of home (α = .82 at year 3, .84 at year 5).

Child externalizing behavior problems. At year 5 mothers completed a 14-item version

of the Child Behavior Checklist (CBCL; Achenbach 1991), and a 38-item version at year 9.

Results

As shown on the right side of Table 1, the families in this study lived in neighborhoods

reflecting a wide range of income, employment, household structure, and education. Across the

five assessments, 17.4% of the families in these neighborhoods were living below the poverty


line, and that estimate ranged across neighborhoods from 0% to 100%. Separate models were

estimated for each parent investment/investment resource. The fit indices from these six models

are presented on the right side of Table 2. For example, the model in which the parenting

investment resource was support from a romantic partner had an absolute fit of χ2(53) = 862.2,

with acceptable relative fit: RMSEA of .056, TLI = .907.

The standardized coefficients from these six analytic models appear in Table 4. Although

neighborhood disadvantage continued to predict relative changes in family income (83% of paths

tested), it was less frequently linked with changes in emotional investment resources across

models (43% of paths tested) than it was in the SECCYD. Income consistently predicted relative

changes in neighborhood disadvantage (96% of paths tested), as did emotional investment

resources (64% of paths tested). The neighborhood data across these assessments all came from

the same census year, meaning that observed changes in neighborhood socioeconomic

disadvantage only represent families who relocated, not changes over time in neighborhood

socioeconomic condition. Externalizing behavior problems was predicted by neighborhood

disadvantage (67% of paths tested), household income (83% of paths tested), and emotional

investment resources (100% of paths tested).

Similar to Study 1, model constraints were applied which constrained the path from

neighborhood disadvantage to externalizing to be equal in magnitude (in absolute value) to the

path from emotional investments to externalizing. This model constraint significantly worsened

model fit for all models except home environment. Similar model constraints comparing

neighborhood disadvantage to household income significantly worsened model fit for all models

except home environment. Both emotional investment and material investment resources (when


significant) were more strongly linked than neighborhood disadvantage to changes in child

externalizing problems.

Discussion

Overall, as hypothesized, neighborhood SES predicted change over time in parent

investment resources. Support for this hypothesis was more frequent for family income than for

emotional parent investments. It may be the case that neighborhood context more consistently

offers opportunities or resources for changing income than opportunities or resources for

changing emotional investments. Similar findings have been documented regarding parent SES

and parent investments. For instance, after accounting for selection effects, parent SES predicts

material investments but not emotional investments (Schofield et al., 2011). Of course, it may be

that different elements of the family environment not included in the current study would be

more affected by neighborhood socioeconomic disadvantage. A third possibility is that the

structural characteristics which influence these individual and family level attributes (Kling et

al., 2001) are not reflected in aggregate census data.

There was support for selection into neighborhoods based on both material and emotional

investment resources. In both samples, people moved into (or out of) neighborhoods based on the

six emotional investment resources, as well as the material investment resource (i.e., household

income). Although this is consistent with previous research on personal characteristics and

selection into neighborhoods (Jokela, 2009), it is important that our findings not be interpreted to

suggest that individuals choose neighborhoods based on individual characteristics alone. There

are many additional structural determinants that impact decision-making, and an extensive body

of literature in the field of social epidemiology (Berkman, Kawachi, & Glymour, 2014) around

the social construction of health and health inequities that goes beyond individual characteristics


as critical drivers of relocation (Arroyo, 2006). For example, while education is one potential

consideration for choosing to live in a particular neighborhood, high quality education is not

equitably available to all. High poverty neighborhoods receive less funding per student than low

poverty neighborhoods, which can contribute to high teacher turnover rates, poorer educational

facilities, and other impacts (Arroyo, 2006; Condron & Roscigno, 2003). One study showed that

barriers to education may be even greater for African American children because of harsher,

more negative disciplinary responses due, in part, to racial stereotypes by teachers and

administrators (Okonofua & Eberhardt, 2015). Policies and structural inequities that that inhibit

moving out of impoverished neighborhoods are certainly not limited to education; additional

challenges for people living in poverty include, among others, reduced access to affordable

housing, banking services, and employment opportunities (Metzler et al., in press). As such,

future efforts in the exploration between neighborhood attributes and health should continue to

integrate a health equity perspective in drafting research aims, generating hypotheses, and

drawing conclusions. Nevertheless, selection effects based on individual or family-level

characteristics are real and merit continued study.

This nonexperimental data cannot provide strong causal inference. Although both

samples showed variation in most neighborhood characteristics, it remains possible that other

samples could provide a different pattern of results. We would note, however, that the current

two datasets offered us rich measurement of family and personal characteristics, which are

necessary to adequately test for selection effects. Third, although these analyses covered the

years from birth to late childhood, it is possible that outcomes measured in later periods of

development would provide less support for our hypotheses. Some family environment measures

had comparatively modest reliability; we would expect larger effects with more reliable


measures. Finally, our focus was on socioeconomic disadvantage, and a different pattern of

results could emerge for indicators of neighborhood social organization, like collective efficacy.

The neighborhood environment likely affects resident behavior. At the same time,

residents are not randomly sorted into neighborhoods. Studies of neighborhood effects should

include longitudinal tests of both processes (i.e., selection and neighborhood effects) in order to

make justifiable claims about which process is operating. Inferring causal effects from cross-

sectional data on neighborhoods and family processes will likely conflate the causal effect of

neighborhood environment with previous selection into the neighborhood environment.


References

Arroyo, C. G. (2006). The funding gap. The Education Trust Web site.

http://1k9gl1yevnfp2lpq1dhrqe17.wpengine.netdna-cdn.com/wp-

content/uploads/2013/10/FundingGap2006.pdf Accessed July 12, 2015.

Bagley, M. N. & Mokhtarian, P. L. (2002). The impact of residential neighborhood type on

travel behavior: A structural equations modeling approach. Annals of Regional Science,

36, 279-297.

Batty, E. and Flint, J. (2010) Self esteem, comparative poverty and neighbourhoods: A

working paper. Produced as part of Living Through Change in Challenging

Neighbourhoods Study. Joseph Rowntree Foundation: York. Available at:

http://research.shu.ac.uk/cresr/living-through-change/reports.html

Berkman, L.F., Kawachi, I., & Glymour, M. (2014). Social Epidemiology. (Ed.). New York,

NY: Oxford University Press.

Berry, T.R., Spence, J.C., Blanchard, C., Cutumisu, N., Edwards, J., & Nykiforuk, C. (2010).

Changes in BMI over 6 years: the role of demographic and neighborhood characteristics.

International Journal of Obesity, 34, 1275-83. doi: 10.1038/ijo.2010.36.

Berry, T.R., Spence, J.C., Blanchard, C.M., Cutumisu, N., Edwards, J., & Selfridge, G.

(2010). A longitudinal and cross-sectional examination of the relationship between

reasons for choosing a neighbourhood, physical activity and body mass index.

International Journal of Behavior Nutrition and Physical Activity, 5, 57. doi:

10.1186/1479-5868-7-57.

Bradley, R. H,. & Caldwell, B. M. (1977). Home observation for measurement of the

http://1k9gl1yevnfp2lpq1dhrqe17.wpengine.netdna-cdn.com/wp-content/uploads/2013/10/FundingGap2006.pdf

http://1k9gl1yevnfp2lpq1dhrqe17.wpengine.netdna-cdn.com/wp-content/uploads/2013/10/FundingGap2006.pdf


environment: a validation study of screening efficiency. American Journal of Mental

Deficiency, 81, 417-420.

Bradley, R.H., & Caldwell, B.M. (1984). The relation of infants' home environments to

achievement test performance in first grade: a follow-up study. Child Development, 55,

803-809. PMID: 6734319

Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human

development. In R. M. Lerner & W. Damon (Eds.), Handbook of Child Psychology: Vol.

1. Theoretical models of human development (6th ed., pp. 793–828). Hoboken, NJ:

Wiley.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A.

Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury

Park, CA: Sage.

Cao, X., Mokhtarian, P. L., & Handy, S. L. (2009). Examining the impacts of residential self-

selection on travel behavior: A focus on empirical findings. Transport Reviews, 29, 359-

395.

Cohen, D. A., Mason, K., Bedimo, A., Scribner, R., Basolo, V., & Farley, T. A. (2003).

Neighborhood physical conditions and health. American Journal of Public Health, 93,

467-471.

Conger, R. D., Patterson, G. R., & Ge, X. (1995). It takes two to replicate: A

mediational model for the impact of parents’ stress on adolescent adjustment. Child

Development, 66, 80-97.

Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) and


NEO Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL: Psychological

Assessment Resources.

Cowan, C. P., Cowan, P. A., & Barry, J. (2011). Couples' groups for parents of preschoolers:

Ten-year outcomes of a randomized trial. Journal of Family Psychology, 25, 240-250.

Derogatis, L.R. (1983). SCL–90–R: Administration, scoring, and procedures manual II.

Baltimore: Clinical Psychometric Research.

Dickman, S.J. (1990) Functional and Dysfunctional Impulsivity: Personality and Cognitive

Correlates. Journal of Personality and Social Psychology, 58, 95-102.

Digman, J.M. (1997). Higher-order factors of the Big Five. Journal of Personality and

Social Psychology, 73, 1246-1256. PMID: 9418278

Diez-Roux, A. V., Nieto, F. J., Muntaner, C., Tyroler, H. A., Comstock, G. W., Shahar, E.,

Cooper, L. S., Watson, R. L., & Szklo, M. (1997). Neighborhood environments and

coronary heart disease: A multilevel analysis. American Journal of Epidemiology, 146,

48-63.

Ecob, R., & Macintyre, S. (2000). Small area variations in health related behaviours; do

these depend on the behaviour itself, its measurement, or on personal characteristics?

Health Place, 6, 261-274. PMID: 11027952

Erel, O., & Burman, B. (1995). Interrelatedness of marital relations and parent-child relations: A

meta-analytic review. Psychological Bulletin, 118, 108-132. doi: 10.1037/0033-

2909.118.1.108

Frank, J. D., Saelens, B. E., Powell, K. E., & Chapman, J. E. (2007). Stepping towards causation:

Do built environments or neighborhood and travel preferences explain physical activity,

driving, and obesity? Social Science and Medicine, 65, 1898-1914.


Gans, H. J. (1968). People and Plans: Essays on Urban Problems and Solutions. New

York: Basic Books.

Garner, DM, & Garfinkel, PE. (1979). The Eating Attitudes Test: an index of the symptoms

of anorexia nervosa. Psychological medicine, 9, 273-9. PMID: 472072

Hedman, L. & van Ham, M. (2010). Understanding Neighbourhood Effects: Selection Bias

and Residential Mobility. In Neighbourhood Effects Research: New Perspectives (pp. 79-

99). Springer.

Hetling, A., & Zhang, H. (2010). Domestic violence, poverty, and social services: does location

matter? Social Science Quarterly, 91, 1144-1163.

Hickman, P. (2010). Understanding residential mobility and immobility in challenging

neighborhoods: Research paper number 8. Centre for Regional Economic and Social

Research. Sheffield Hallam University. Available at:

http://research.shu.ac.uk/cresr/living-through-

change/documents/RP8_ResidentialMobility.pdf

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:

Conventional criteria versus new alternatives. Structural equation modeling, 6, 1-55.

Janz, K.F., Witt, J. & Mahoney, L. T. (1995). The stability of children's physical activity as

measured by accelerometry and self report. Medical Science and Sports Exercise, 9,

1326-1332. PMID: 8531633

Jokela, M. (2009). Personality predicts migration within and between US states. Journal of

Research on Personality, 43, 79–83.

Jokela, M., Bleidorn, W., Lamb, M. E., Gosling, S. D., & Rentfrow, P. J. (2014). Geographically


varying associations between personality and life satisfaction in the London metropolitan

area. Proceedings of the National Academy of Sciences, 112, 725-730.

Keegan, T.H., Hurley, S., Goldberg, D., Nelson, D.O., Reynolds, P., Bernstein, L., Horn-

Ross, P.L., & Gomez, S.L. (2012). The association between neighborhood characteristics

and body size and physical activity in the California teachers study cohort. American

Journal of Public Health, 102, 689-697. doi: 10.2105/AJPH.2011.300150

Kiecolt-Glaser, J. K.,& Newton, T. L. (2001). Marriage and health: His and hers. Psychological

Bulletin, 127, 472-503.

King, W. C., Brach, J. S., Belle, S., Killingsworth, R., Fenton, M., et al. (2003). The relationship

between convenience of destinations and walking levels in older women. American

Journal of Health Promotion, 18, 74-82.

King, A. C., Toobert, D., Ahn, D., Resnicow, K., Coday, M., et al. (2006). Perceived

environments as physical activity correlates and moderators of intervention in five

studies. American Journal of Health Promotion, 21, 24-35.

Kling, J. R., Liebman, J. B., & Katz, L. F. (2001). Bullets don’t got no name: Consequences of

fear in the ghetto. Joint Center for Poverty Research, Working Paper 255. Available at

http://www.jcpr.org/wpfiles/kling_liebman_latz.PDF?CFID=1802590&CFTOKEN=5318

0781

Kobasa, S. C. (1979). Stressful life events, personality, and health: An inquiry into hardiness.

Journal of Personality and Social Psychology, 37, 1-11.

Landgraf, J.M., Abetz, L., & Ware, J.E. (1996). The CHQ Users’ Manual (1st edition).

Boston: The Health Institute, New England Medical Center.

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: The effects of


neighborhood residence on child and adolescent outcomes. Psychological Bulletin, 126,

309-337.

Leventhal, T., Fauth, R.C., & Brooks-Gunn, J. (2005). Neighborhood poverty and public

policy: a 5-year follow-up of children's educational outcomes in the New York City

moving to opportunity demonstration. Developmental Psychology, 41, 933-52.

Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., &

Sanbonmatsu, L. (2013). Long-term neighborhood effects on low-income families:

Evidence from Moving to Opportunity. American Economic Review Papers and

Proceedings, 103, 226-231.

Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: How can we

conceptualize, operationalize and measure them? Social Science and Medicine, 55, 125-

139.

McCabe, J. E. (2014). Maternal personality and psychopathology as determinants of parenting

behavior: A quantitative integration of two parenting literatures. Psychological Bulletin,

140, 722-750. doi: 10.1037/a0034835

Mee, K. (2007) “I ain’t been to heaven yet? Living here, this is heaven to me”: Public

housing and the making of home in inner Newcastle. Housing, Theory and Society, 24,

207-228.

McKinnish, T. & White, T. K. (2011). Who Moves to Mixed-Income Neighborhoods? Regional

Science and Urban Economics, 41, 187–195. doi: 10.1016/j.regsciurbeco.2011.01.007

Metzler, M., Merrick, M. T., Klevens, J. Ford, D., & Ports, K. A. (In press). Adverse Childhood

Experiences and Life Opportunities: Time to Shift the Narrative? Children and Youth

Services Review.


Murray, A. L. & Booth, T. (2015). Personality and physical health. Current opinion in

psychology, 5, 50-55.

NICHD Early Child Care Research Network. (2005). Child care and child development:

Results from the NICHD Study of Early Child Care and Youth Development. New York,

NY: Guilford Press.

Norman, G. J., Carlson, J. A., O’Mara, S., Sallis, J. F., Patrick, K., Frank, L. D., & Godbole, S.

V. (2013). Neighborhood preference, walkability, and walking in overweight/obese men.

American Journal of Health Behavior, 37, 277-282.

Østbye, T., Malhotra, R., Stroo, M., Lovelady, C., Brouwer, R., Zucker, N., & Fuemmeler, B.

(2013). The effect of the home environment on physical activity and dietary intake in

preschool children. International Journal of Obesity, 37, 1314-1321.

Pabayo, R., Belsky, J., Gauvin, L., & Curtis, S. (2011). Do area characteristics predict

change in moderate-to-vigorous physical activity from ages 11 to 15 years? Social

Science and Medicine, 72, 430-438. doi: 10.1016/j.socscimed.2010.09.039.

Petersen, A.C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of

pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence,

17, 117-133. doi: 10.1007/BF01537962.

Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the

general population. Applied Psychological Measurement, 1, 385-401.

Ross, C.E., & Mirowsky, J. (2008). Age and the balance of emotions. Social Science and

Medicine, 66, 2391-400. doi: 10.1016/j.socscimed.2008.01.048.

Sampson, R. J., & Sharkey, P. (2008). Neighborhood selection and the social reproduction

of concentrated racial inequality. Demography, 45, 1-29.


Sanbonmatsu, L., Kling, J. R., Duncan, G. J., & Brooks-Gunn J. (2006). Neighborhoods and

academic achievement: Results from the Moving to Opportunity experiment. Journal of

Human Resources, 41, 649-691.

Scarr, S. & McCartney, K. (1983). How people make their own environments: A theory of

genotype environment effects. Child Development, 54, 424-435. PMID: 6683622

Schaefer, E. S., & Edgerton, M. (1985). Parent and child correlates of parental modernity. In I. E.

Siegel (Ed.), Parental belief systems (pp. 287–318). Hillsdale, NJ: Erlbaum.

Sciandra, M., Sanbonmatsu, L., Duncan, G.J., Gennetian, L.A., Katz, L.F., Kessler, R.C.,

Kling, J.R., & Ludwig, J. (2013). Long-term effects of the Moving to Opportunity

residential mobility experiment on crime and delinquency. Journal of Experimental

Criminology, 9. doi: 10.1007/s11292-013-9189-9.

Smith, K. R., Zick, C. D., Kowaleski-Jones, L., Brown, B. B., Fan, J. X., & Yamada, I. (2011).

Effects of neighborhood walkability on healthy weight: Assessing selection and causal

influences. Social Science Research, 40, 1445-1455.

Stevens, R. B., & Brown, B. B. (2011). Walkable new urban LEED_neighborhood-development

(LEED_ND) community design and children’s physical activity: Selection,

environmental, or catalyst effects? Physical Activity, 8.

Thornberry, T., Huzinga, D., & Loeber, R. (1989). Measures developed for multisite study

on causes and correlates of delinquency. Unpublished manuscript.

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor

analysis. Psychometrika, 38, 1–10.

Veit, C.T., & Ware, J.E. (1983). The structure of psychological distress and well-being in

general populations. Journal of Consulting and Clinical Psychology, 51, 730-42.


Weisz, J. R. (1978). Transcontextual validity in developmental research. Child

Development, 49, 1-12.

Wells, N. M., Evans, G. W., & Yang, Y. (2010). Environments and health: Planning decisions as

public-health decisions. Journal of Architectural and Planning Research, 27, 124–143.

Yen, I.H., Michael, Y.L., & Perdue, L. (2009). Neighborhood environment in studies of

health of older adults: a systematic review. American Journal of Preventive Medicine, 37,

455-463. doi: 10.1016/j.amepre.2009.06.022.

Yu, C., & Zhu, X. (2013). Impacts of residential self-selection and built environments on

children’s walking to school behaviors. Environment and Behavior, 47, 268-287.

Zhou, Y. (2006). Spatial Analysis of Substantiated Child Maltreatment in Metro Atlanta,

Georgia. Thesis, Georgia State University.

http://scholarworks.gsu.edu/geosciences_theses/7

http://scholarworks.gsu.edu/geosciences_theses/7


Table 1

Neighborhood Attributes Across Both Studies

Early Child Care and

Youth Development

Fragile Families Project

Percent of: M SD Min Max M SD Min Max

Residents with income-to-needs ratio less than 1.00 9.5 10.2 0 100

- - - -

Families below poverty level - - - -

17.4 13.0 0 100

Residents who are unemployed 4.6 4.7 0 51

10.00 7.2 0 66

Households headed by single mothers with children under 18 9.1 9.2 0 76

20.2 13.0 0 100

Residents over 25 without a high school degree 17.6 13.6 0 91

71.3 15.1 28 100


Table 2

Fit Indices From Final Models Across Both Studies

SECCYD

Fragile Families Study

Emotional investment/resource χ2 df RMSEA TLI

χ2 df RMSEA TLI

Support from romantic partner 909.6 232 0.046 0.957

862.2 53 0.056 0.907

Parenting behavior 1069.8 250 0.049 0.949

509.6 57 0.04 0.949

Attitude about parenting 779.9 194 0.047 0.964

927.1 54 0.057 0.904

Parent personality 672.2 162 0.048 0.965

358.8 38 0.042 0.955

Parent depression 1060.6 256 0.048 0.952

796.7 69 0.046 0.93

Home environment 1023.3 230 0.05 0.952

404.1 56 0.037 0.961

Note. SECCYD= Study of Early Child Care and Youth Development, RMSEA= Root mean square error

of approximation, TLI = Tucker Lewis index


Table 3

Standardized Coefficients From Models of Investments, Neighborhoods, and Externalizing (Study of Early Child Care and Youth Development)

Support Observed Parenting Agreeable- Maternal Home

Path from RP sensitivity attitudes ness depression environment

A. Neighborhood→income [-.12,-.03]100 [-.12,-.02]100 [-.04,-.01]17 [-.12,-.02]100 [-.12,-.02]100 [-.02,-.03]100

A. Neighborhood→emotional investment [.00,.00]0 [.00,-.20]80 [.02,-.09]50 - [.04,.05]100 [-.16,-.12]100

B. Income→neighborhood [-.08,-.05]100 [-.07,-.05]100 [-.06,-.04]100 [-.05,-.08]100 [-.05,-.08]100 [-.07,-.04]100

B. Emotional investment→neighborhood [-.02,.01]67 [-.07,.02]83 [-.13,-.04]100 [-.03,-.03]100 [-.04,-.05]100 [-.06,-.03]100

C. Neighborhood→externalizing [.03,.04]100 [.03,.04]100 [.02,.03]100 [.02,.03]100 [.02,.03]100 [.02,.03]100

D. Income→externalizing [-.11,-.03]100 [-.14,-.03]100 [-.09,-.03]100 [-.13,-.03]100 [-.10,-.02]100 [-.03,-.03]100

D. Emotional investments→externalizing [-.07,-.05]100 [-.03,-.02]100 [-.14,-.03]100 [-.10,-.09]100 [.08,.23]100 [-.07,-.05]100

Stability in neighborhood [.72,.87]100 [.73,.86]100 [.72,.87]100 [.72,.86]100 [.73,.87]100 [.72,.86]100

Stability in income [.64,.94]100 [.63,.92]100 [.64,.93]100 [.64,.93]100 [.63,.94]100 [.63,.93]100

Stability in emotional investment [.53,.63]100 [.30,.48]100 [.72,.81]100 - [.41,.51]100 [.46,.59]100

Income→emotional investment [.02,.02]0 [.08,.23]100 [.04,.04]100 - [-.04,-.05]100 [.14,.18]100

Emotional investment→income [.02,.03]100 [.03,.04]100 [.03,.04]100 [.02,.02]100 [-.02,-.02]100 [.03,.04]100

Note. [min, max]# = percent of paths over time measuring this relation that were statistically significant. RP=romantic partner. Boldface = p < .05


Table 4

Standardized Coefficients From Models of Investments, Neighborhoods, and Externalizing (Fragile Families Study)

Support Parent Parenting

Maternal Home

Path from RP harshness attitudes Impulsivity depression environment

A. Neighborhood→income [-.26,-.01]75 [-.21,-.01]75 [-.11,-.01]75 [-.23,-.04]100 [-.12,-.02]100 [-.22,-.00]75

A. Neighborhood→emotional investment [.03,.03]0 [-.01,.03]67 [.02,-.09]50 - [.01,.01]0 [-.07,-.07]100

B. Income→neighborhood [-.27,-.04]100 [-.24,-.04]100 [-.23,-.03]100 [-.23,-.04]100 [-.05,-.08]100 [-.20,-.00]75

B. Emotional investment→neighborhood [-.04,.00]50 [-.02,-.01]0 [-.13,-.04]100 [.02,.02]100 [.01,.04]33 [-.16,-.16]100

C. Neighborhood→externalizing [.03,.03]100 [.03,.03]100 [.02,.02]00 [.00,.00]00 [.04,.04]100 [.04,.04]100

D. Income→externalizing [-.10,-.03]67 [-.07,-.02]67 [-.08,-.02]100 [-.08,.01]67 [-.07,-.07]100 [-.06,-.06]100

D. Emotional investments→externalizing [-.07,-.05]100 [.14,.13]100 [-.20,-.12]100 [.25,.17]100 [.09,.09]100 [-.05,-.05]100

Stability in neighborhood [.01,.80]75 [.72- .90] [.72- .90] [.72- .90] [.72- .90] [.72- .90]

Stability in income [.31,.64]100 [.80-1.13] [.81-1.13] [.81-1.14] [.81-1.13] [.81-1.13]

Stability in emotional investment [.17,.17]100 [.38,.48]100 [.51- .58]100 - [.33- .38]100 [.32- .32]100

Income→emotional investment [-.01,.00]0 [-.08,.02]67 [.04,.04]100 - [-.07,-.07]100 [.21,.21]100

Emotional investment→income [.03,.07]100 [.02,.02]100 [-.02,.05]33 [.03,.03]100 [-.03,-.03]100 [.06,.06]100

Note. [min, max]# = percent of paths over time measuring this relation that were statistically significant. RP=romantic partner. Boldface = p < .05


Neighborhood

SES

Neighborhood

SES

Parent

investments

Parent

investments

βB

βA

Child

externalizing

Child

externalizing

βC1 βC2

βD2βD1

Figure 1. Theoretical Model of Reciprocal Associations between Neighborhood Context and Parent Investment

neighborhoods and selection 1 running head: …

Documents