1 Defining Neighborhoods, Collecting Data, and Studying Neighborhood Effects In Los Angeles Narayan...

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Defining Neighborhoods,Collecting Data, and

Studying Neighborhood EffectsIn Los Angeles

Narayan SastryRAND Corporation

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Overview

• L.A.FANS– Background and goals of L.A.FANS– Design and content of survey– Wave 1 fieldwork results– Plans for Wave 2 and beyond

• Neighborhood definitions and location of regular activities in Los Angeles

• Neighborhood effects on children’s achievement

• Results for Los Angeles compared to the US

• Neighborhood effects vs. school effects

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Background and Goals of L.A.FANS

• Effects of neighborhoods, schools, families, and peers on children’s well-being

• Understanding patterns of residential mobility, segregation, and urban change

• Status of children, immigrants, the poor, and racial/ethnic groups

• Passage of welfare reform: changes in program participation and neighborhood effects

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Methodological and Practical Challenges in Studying Contextual Effects

• Incomplete controls for family background and characteristics, due to data limitations

• Contextual exposures may be endogenous

• Separating the influence of multiple contexts: families, schools, peer groups, neighborhoods, etc.

• Measuring or obtaining contextual characteristics, especially social processes

• Defining neighborhoods and other contexts

• Determining “exposure” to contextual environments

– Individuals change neighborhoods

– Neighborhoods change as individuals move in and out

• Residential moves (or stability) may be important

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Overcoming Methodological and Practical Challenges

• Requires collection and assembly of better data– Longitudinal data on both contexts and individuals– Collect data on previously unmeasured factors that

influence both choice of context and outcomes– Collect richer contextual data– Experimental data

• Use of appropriate statistical models to address issues of:– Endogeneity of contextual characteristics

• Fixed effects, IV, simultaneous equations, etc.– Clustering of observations at multiple contextual levels

• Better substantive understanding of neighborhood mobility and neighborhood selection and of neighborhood change

• Better description of neighborhood exposure

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L.A.FANS Motivation and Justification

• Why needed?– Existing data have shortcomings

• Selection effects• Inadequate or incomplete measures• Cross sectional

• Why now?– Data shortcomings slowing research– Policy interest– Complements other studies

• Why Los Angeles?– Size and diversity

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L.A.FANS: Key Research Features

• Jointly study neighborhood choice, residential mobility, and effects of neighborhoods on child, adult, and family outcomes

• Study effects of wide variety of community characteristics

• Evaluate dynamic effects of neighborhood characteristics measured at regular intervals

• Multilevel, longitudinal design to control for unmeasured child, family, school, and community factors

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L.A.FANS Timeline

• Project began in 1999

• Wave 1 fieldwork: April 2000-January 2002

• Wave 1 data released in early 2002

• Wave 2 work began in May 2004

• Wave 2 fieldwork: July 2006-March 2008

• Wave 2 data to be released in Summer of 2008

• Wave 3 planned for 2012-2013

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L.A.FANS Study Design

• Stratified random sample of 65 neighborhoods– Defined neighborhoods as census tract– Three strata: Very poor, poor, non-poor– Oversampled very poor and poor strata

• Sampled households– 40-50 HH per neighborhood, 70% with children

• Sampled individuals– Adult (age 18+)– Child (under age 18)

• Respondents– Sampled individuals– HH head– Child’s primary caregiver and sibling

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Important L.A.FANS Questionnaire Features

• Use of standard measures from national surveys

• Integrated interactive calendar for collecting retrospective data

• Geographic information for individuals--“social space” versus geographically defined neighborhood boundary

• English and Spanish instruments

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Definition of Neighborhood

• “Neighborhood” is an amorphous concept

• Likely to vary among individuals livings in close proximity

• Individual definition can vary by context

• Neighborhood boundaries set by consensus and/or government often do not correspond to individuals’ definitions

• Most likely definition: area relatively close to home with fuzzy boundaries that can expand/shrink depending on context

• No single definition of neighborhood is likely to be entirely satisfactory

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Neighborhood Data

• Systematic social observation of neighborhoods by trained interviewers

• Database of neighborhood characteristics and services from census and administrative data for all of L.A. County

• Neighborhood characteristics for sampled tracts based on summarizing respondent reports

• Neighborhood data integrated with a geographic information system (GIS)

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L.A.FANS-1 Fieldwork Results

Interviewed 3,090 households, 3,615 adults, 3,207 children

Response rates compare favorably to other surveys• 85% for adults• 87% for children• 89% for children’s primary caregivers

Tremendously diverse sample• 55% Hispanic, 26% white, 10% black, 7% Asian• 32% native-born, 55% first generation immigrant,

10% second generation immigrant

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Study Design: Follow-up Waves

• Three waves

• Will follow sampled individuals

• Will follow sampled neighborhoods by adding a sample of new entrants

Time 2

Residents in Neighborhood

Time 1(a) New Entrants

(c) Outmovers

(b) StayersResidents in

Neighborhood

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Plans for Wave 2 of L.A.FANS

Panel sample of Wave 1 respondents: Will track and interview, regardless of where they moved

Sample of New Entrants in 65 L.A.FANS neighborhoods New directions in Wave 2

• Biomarkers of stress, chronic disease and precursors, pulmonary function

• Dried blood spot samples• Spirometry• Anthropometry

• Additional questions on stress, health, work conditions, etc.

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Overview

• L.A.FANSL.A.FANS– Background and goals of L.A.FANSBackground and goals of L.A.FANS– Design and content of surveyDesign and content of survey– Wave 1 fieldwork resultsWave 1 fieldwork results– Plans for Wave 2 and beyondPlans for Wave 2 and beyond





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Neighborhood Definitions in L.A.

• What are residents’ perceptions of neighborhood size?

• How does perceived neighborhood size vary by individual characteristics and by area characteristics?– “When you are talking to someone about your

neighborhood, what do you mean? Is it…1. The block or street you live on?2. Several blocks or streets in each direction?3. The area within a 15-minute walk from home?4. An area larger than a 15 minute walk from

home?

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Spatial Dimensions of Daily Life in L.A.

• How salient are neighborhoods to the daily life of residents in a highly mobile, spread-out, multi-nucleated city like Los Angeles?

– Primary place of work– Grocery store– Place of worship– Health care provider

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Reported Neighborhood Size for Adults

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Location of Regular Activities in L.A.FANS

Grocery Store

Place of Worship

Place of Work

Health care provider

Mean distance 1.37 2.83 8.15 4.61

Within 15 min walk 34% 28% 22% 17%

Location by tract

Same tract 16% 12% 12% 2%

Next tract 47% 32% 10% 14%

2 tracts away 24% 24% 11% 22%

> 2 tracts away 14% 32% 67% 62%

N 1,367 547 838 1,044

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Location of Grocery Stores for Illustrative Tract

#

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Census Tract1st Order Contiguity Census Tracts2nd Order Contiguity Census Tracts3rd Order Contiguity Census Tracts

2 Mile Radius

Within 15 minutes walk# Grocery Stores

; Mean center of residences

0.5 0 0.5 1 Miles

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Location of Places of Worship for Illustrative Tract

;

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2 Mile Radius

Within 15 minutes walk


# Religious Organizations

2 0 2 4 Miles

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Location of Workplaces for Illustrative Tract

#

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;


Within 15 minutes walk2 Mile Radius

# Place of Work


2 0 2 4 Miles

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Location of Health Care Providers for Illustrative Tract

;

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Within 15 minutes walk

2 Mile Radius


# Adult Health Care Providers

2 0 2 4 Miles

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Neighborhood Definition Results

• Substantial systematic variation in neighborhood definition responses by respondent and tract characteristics

– Respondents: Education (+), immigrant status (-), relatives in area (+)

– Neighborhood: Area (+), density (+), geographic area (~), SES (+)

• Surprising number of regular activities take place close to home

• Neighborhoods appear to be salient for daily life

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Overview






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Family and Neighborhood Effects onChildren’s Reading & Math Scores

• Basic skills, such as reading and math, are essential to children’s success in life

• Skills gap between children of well-educated and poorly-educated parents has remained roughly constant since 1975

• American cities have long been characterized by high levels of residential segregation between the rich and poor

• High levels of residential segregation in the United States may play an important role in the persistent skills gap

• To what degree can neighborhood disadvantage account for inequality in children’s reading and math skills?

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L.A.FANS Assessment Data

• Reading and problem solving skills assessed using

Woodcock-Johnson Revised standardized tests

• Passage Comprehension (PCGs and ages 6-17)

• Letter-Word Identification (ages 3-17)

• Applied Problems (ages 3-17)

• Raw scores were converted to standardized scores

based on national norms (M=100, SD=15)

• Sample size of 2,350 children

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Methods

• To describe inequality in skills, we used Lorenz and concentration curves (and Gini coefficients and concentration indices)

• Allow us to assess skills inequality directly and comprehensively

• Examine proportion of skills inequality that is attributable to:• Inequality in family SES• Inequality in neighborhood income,

both before and after controlling for other child, family, and neighborhood characteristics

• Multilevel linear regression models with family and neighborhood random effects

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Lorenz/Concentration Curve for Child Achievement

00

1

1

Cumulative proportion of test scores

Cumulative proportion of children ranked by test score/SES

Perfect equality(Gini=0/CI=0)

Lorenz/Concentration curve(Gini<0/CI<0)

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Summary Statistics for Children’s Reading and Mathematics Achievement in L.A.FANS

Measure Mean Std. Dev. Obs. Gini (S.E.)

Reading

Standard score 102.6 18.3 2,350 0.0969 (0.0016)

Percentile rank 53.1 30.0 2,350

Mathematics

Standard score 102.0 17.4 2,336 0.0944 (0.0015)

Percentile rank 52.8 30.0 2,336

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Summary Statistics for Socioeconomic Status Measures in L.A.FANS

Measure Median Mean Std. Dev. Obs. Gini (S.E.)

Family income ($)

28,400 55,115 102,575 1,576 0.5786(0.0145)

Family assets ($)

6,066 142,551 578,904 1,576 0.8732(0.0064)

Mother’s schooling (years)

12.0 11.6 4.4 1,576 0.1975(0.0052)

Mother’s reading achievement

84.0 85.0 18.3 1,576 0.1160(0.0027)

Tract median family income ($)

35,683 44,859 27,563 65 0.3054(0.0225)

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Covariates in Models

• Child age, sex, birthweight, race/ethnicity

• Language of test

• Mother’s immigration status, reading score, height, years of schooling

• Family income and assets

• Tract median family income, immigrant concentration score, residential stability score, racial/ethnic diversity score

• Problem of neighborhood endogeneity may be mitigated by our comprehensive controls for family characteristics

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Summary Statistics for Characteristics of Children in the L.A.FANS Analysis

Variable Summary statistic

Child age (years) 9.7 (4.2)

Race/Ethnicity

Latino 63%

Black 9

White 19

Asian 7

Other 2

Language of test

English 82%

Spanish 18

Mother’s immigrant status

Native-born 37%

Pre-1990 immigrant 41

Post-1990 immigrant 22

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lor0

x

lor0x

lor0y lor1y lor2y lor0x

0 1

0

1

Decomposition of Inequality in Children’s Math Achievement by Tract Median Family Income

Cumulative proportion of children ranked by outcome/tract median family income

Cumulative proportion of standard scores

Lorenz curve

Concentration curve

Adjusted Concentration curve

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Children’s Math Achievement by Tract Median Family Income: Deviations from Diagonal

lor0x

lor0yd lor1yd lor2yd

0 1

-.065619

.000037

Deviation of Lorenz/concentration curve from diagonal


Lorenz curve

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lor0x


0 1

-.065619

.000037



Lorenz curve

Concentration curve

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lor0x


0 1

-.065619

.000037



Lorenz curve

Concentration curve

Adjusted Concentration curve

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Inequality in Children’s Reading and Mathematics Achievement

Measure Reading Mathematics

Gross percent of total inequality explained through CI by: Family income 20% 34% Family non-housing assets 21 35 Tract median family income

19 33

Mother’s reading score 26 33 Mother’s years of school 23 33

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Decomposition of Inequality in Children’s Reading and Mathematics Achievement

Measure Reading Mathematics Net percent of total inequality explained through adjusted CI by: Family income 2% 0% Family non-housing assets 4 7 Tract median family income 11 16 Mother’s reading score 23 16 Mother’s years of school 9 8

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Decomposition of Inequality in Children’s Reading and Mathematics Achievement

Measure Reading Mathematics Net percent of total inequality explained through adjusted CI by: Family income 2% 0% Family non-housing assets 4 7 Tract median family income 11 16 Mother’s reading score 23 16 Mother’s years of school 9 8

PSID/CHS-1 (Children ages 3-12)

Family income 6% 5% Family assets 2 5 Tract median family income 15 10 Mother’s reading score 22 18 Mother’s years of school 11 10

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Multilevel Structure of Children’s Reading and Math Achievement

Reading Math

Raw sources of variation

Family 0.35 0.43

Neighborhood 0.09 0.19

Adjusted sources of variation

Family 0.26 0.26

Neighborhood 0.09 0.16

Individual 0.65 0.58

Observations

Children 2,350 2,293

Families 1,581 1,576

Neighborhoods 65 65

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Multilevel Linear Regression Model Results: Reading and Math

Variable Reading Math

Model Chi-squared (df) 499.23***(21) 418.54***(21)

Fraction of variance due to

Family 0.22*** 0.25***

Neighborhood 0.01* 0.02**

Observations



Neighborhoods 65 65

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Variable Reading MathChild age (years) -0.03 (0.09) -0.24*** (0.08)Female 2.71*** (0.68) -0.02 (0.63)

Child Race

Latino -3.09** (1.26) -2.81** (1.18) Black -2.66* (1.57) -4.13*** (1.51) White . . . . Asian 4.04** (1.76) 4.74*** (1.66) Other 1.00 (2.87) 0.59 (2.66) Tested in Spanish 7.89*** (1.10) -5.65*** (1.02Birthweight (kg) 0.58 (0.58) 1.16** (0.54)

Mother’s immigration status

Native-born . . . . Pre-1990 immigrant 3.63*** (1.13) 1.38 (1.05) Post-1990 immigrant 6.03*** (1.32) 2.62** (1.22) Mother’s reading score 0.23*** (0.03) 0.16*** (0.02) Mother’s education (years) 0.33*** (0.11) 0.31*** (0.10) Mother’s height (cm) 0.01 (0.05) 0.09* (0.05) Log family income 0.17 (0.20) 0.03 (0.19) Log family assets 0.15 (0.12) 0.30*** (0.11)Tract median family income ($10k) 0.87*** (0.29) 1.21*** (0.30)Tract immigrant concentration score 0.31 (0.89) 0.55 (0.90) Tract residential stability score -0.74 (0.57) -1.22** (0.58) Tract race/ethnic diversity score -3.65 (3.17) -1.07 (3.23) Constant 67.43*** (9.43) 63.14*** (8.82)


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Child Race





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Child Race

Latino -3.09** (1.26) -2.81** (1.18) Black -2.66* (1.57) -4.13*** (1.51) White . . . . Asian 4.04** (1.76) 4.74*** (1.66) Other 1.00 (2.87) 0.59 (2.66) Tested in Spanish 7.89*** (1.10) -5.65*** (1.02)Birthweight (kg) 0.58 (0.58) 1.16** (0.54)




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Child Race





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Child Race





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Child Race





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Child Race





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Overview






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Are Results for Los Angeles Anomalous?

• Compare results from L.A.FANS and PSID/CDS

• PSID/CDS– Wave 1, 1997– Nationally representative– 1,295 children 3-12 years of age with complete

information– Used same Woodcock-Johnson reading and math

assessments as L.A.FANS

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Are Results for Los Angeles Anomalous?

• Compare results from L.A.FANS and PSID/CDS

• PSID/CDS– Wave 1, 1997– Nationally representative– 1,295 children 3-12 years of age with complete

information– Used same Woodcock-Johnson reading and math

assessments as L.A.FANS

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Observed Inequality in Children Ages 3-12 Reading Achievement by SES

Measure L.A.FANS PSID/CDS

Percent of total inequality

explained by CI

Family income 21% 36%

Family assets 20 34

Mother’s schooling 22 35

Mother’s reading score 25 41

Tract median family income 19 32

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Observed Inequality in Children Ages 3-12 Reading Achievement by SES

Measure L.A.FANS PSID/CDS

Percent of total inequality

explained by CI

Family income 21% 36%

Family assets 20 34

Mother’s schooling 22 35

Mother’s reading score 25 41

Tract median family income 19 32

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Overview






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Do Neighborhood Effects Actually Reflectthe Influence of Local Schools?

• L.A.FANS data are nested by neighborhood and family, but are cross-classified by

– Neighborhood and school

– Family and school

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Cross-Classified Structure of L.A.FANS: Data and Models

• On average, children in a neighborhood attend 12 different schools (range: 6-24)

• Schools have students from 1-5 neighborhoods (mean: 1.2)

• 556 families have two children in the sample. 646 children from these families attend different schools, while 466 attend the same school

• Consider only children who are enrolled in school• Estimate multilevel cross-classified random effects

models to examine correlation by neighborhood and school

• Estimate models using Markov-chain Monte Carlo approach

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Intra-Class Correlations From Cross-Classified Models Without Covariates: L.A.FANS

Variable Reading Math

Fraction of variance due to

Family 0.27*** 0.26***

School 0.00 0.02

Neighborhood 0.09*** 0.18***

Observations



Schools 647 647

Neighborhoods 65 65

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MCMC Diagnostics for Neighborhood Random Effects

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MCMC Diagnostics for School Random Effects

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Conclusions

• Neighborhood disadvantage has a strong effect on children’s reading and math skills, beyond that of growing up in a poor family

– Worse schools, child care, and children’s services– Few well-educated and successful adult role models for

children who may thus see little value in doing well in school

– More stressful and less supportive social environments for families and children may impede learning and lead to behavior problems

– Stressful neighborhood environments may also cause parents to employ parenting behaviors which adversely affect children’s behavior and learning

– Residential selection• Previous research has not generally considered the effects of median

neighborhood income levels but has focused instead on the effects of the extremes of the income distribution

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Next Steps

• School effects

• Alternative neighborhood definitions

• Spatially lagged effects

• Age-stratified models

• Neighborhood social processes

• Family processes

• Panel data models

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Public Use and Restricted Data

• Want L.A.FANS data to be used by broadest possible group of researchers

• Want to make as much data available as possible

• Strongly committed to protecting privacy of L.A.FANS respondents and confidentiality of L.A.FANS data

• Indirect identification of L.A.FANS respondents a major concern

• Devised a tiered system that ties user restrictions and requirements to risk and consequences of disclosure

• Produced extensive documentation available on web

• Data released within four months of fieldwork end

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To Learn More…

• Project web site:

• www.lasurvey.rand.org

• Download data and view questionnaires

• Project documentation

• Seven volumes with codebooks, questionnaires, procedures and reports

• Working papers and forthcoming publications

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