Neighbourhood Factors and Children


Date Source: 

National Longitudinal Survey of Children and Youth (NLSCY)


Dafna Kohen,Sander Post, Karla Nobrega, and Patricia Whitridge from Statistics Canada


The data for this study are taken from the synthetic file released for cycle three of the National Longitudinal Survey of Children and Youth (NLSCY). The data provided represent only a subset of the data available. The provided data represent children aged 4, 5 or 6, living in one of 24 major metropolitan areas. 1,016 records are provided. In addition to studying the relationship between child outcomes and determinants, you will learn about hierarchical methods and small area statistics.



Neighbourhood factors such as poverty and residential instability have been identified as being important in explaining neighbourhood problems such as delinquency and crime encountered in many poor urban neighbourhoods (Sampson, 1992; Sampson & Groves, 1989; Sampson & Morenoff, 1997). Neighbourhood conditions of poverty and instability impede the establishment of formal and informal institutions of neighbourhood organization which are believed to maintain and foster strong community relations as well as public order within a community. For example, neighbourhood safety and cohesion or a sense of trust and belonging are seen to strengthen the community and have positive effects on its members. Often these factors are spatially based so that poverty conditions co-occur in similar areas (Massey, 1990; 1996; Massey & Denton, 1993). The geographic or spatial associations may be due in part to housing policies, housing affordability, as well as to conditions of ethnic and economic segregation (Wilson, 1987). For example, public housing is often found in predominantly low socio-economic neighbourhoods leading to areas of isolated and concentrated poverty as well as other separate areas of concentrated affluence. These differences as well as the conditions of neighbourhoods children reside in may be important for child health and well-being. When discussing the associations of neighbourhood characteristics with child outcomes it is important to note that both risk and protective factors occur at multiple levels, individual, family, and neighbourhood and it is not just a single protective or risk factor but the accumulation of factors that result in negative or positive child and family outcomes.

The emerging literature on the effects of neighbourhood factors on children and youth has focused on structural characteristics of the neighbourhood such as income/socio-economic conditions and residential instability yet most of the literature is based on studies conducted in the United States. Most studies have focused on outcomes in early childhood or late adolescence (see Leventhal & Brooks-Gunn, 2002 for review). Some consistent findings have been reported. For example, neighbourhood effects for socio-economic factors are more common than effects of residential instability across all child outcomes, and neighbourhood effects are generally small (explaining 5-10% of the variability in outcomes). As would be expected, family level factors tend to be more strongly associated with individual child outcomes than neighbourhood level factors but neighbourhood effects are consistently reported even after controlling for family level factors, for outcomes of children, youth, and adolescents.

Data Description

National Longitudinal Survey of Children and Youth

The National Longitudinal Survey of Children and Youth (NLSCY) is a long-term survey designed to measure child development and well-being. The first cycle of the survey was conducted by Statistics Canada in 1994-1995 on behalf of Human Resources Development Canada. The requirement for the NLSCY design was to select a representative sample of children in Canada and to follow and monitor these children over time into adulthood. All of the information for the household collection was collected in a face-to-face or telephone interview using computer-assisted interviewing (CAI). Questions were asked to the respondent in the home or by telephone and directly entered into a computer by the interviewer.

Before the NLSCY was undertaken there were few statistical studies describing a broad range of characteristics of children in Canada. Measures of health, well-being and life opportunities are needed, however, if governments and researchers hope to learn more about the ongoing life conditions of Canadian children and youth, and their developmental experiences. Longitudinal data are central to discovering developmental changes occurring in children over time, and studying the impacts of the social environment of the child and various family-related factors.

The primary objective of the NLSCY is to develop a national database on the characteristics and life experiences of children and youth in Canada as they grow from infancy to adulthood. The more specific objectives of the NLSCY are:

  • To determine the prevalence of various biological, social and economic characteristics and risk factors of children and youth in Canada,
  • To monitor the impact of such risk factors, life events and protective factors on the development of these children,
  • To provide this information to policy and program officials for use in developing effective policies and strategies to help young people live healthy, active and rewarding lives.

Underlying these objectives is the need to:

  • Fill an existing information gap regarding the characteristics and experiences of children in Canada, particularly in their early years,
  • Focus on all aspects of the child in a holistic manner (i.e., the child, his/her family, school, and community),
  • Provide national, and as far as possible, provincial-level data,
  • Explore subject areas that are amenable to policy intervention and which affect a significant segment of the population.

Background: Survey Weights

Suppose we have a finite population P, of size N=100 individuals. We are interested in estimating a total, mean or other variable of interest from this population. In a simple random sample s of size n=20 (in a simple random sample each individual has the same probability to be selected in the sample) we observe y1, y2, …, y20. How can we estimate the population total Y of y1 to y100? Since the population size is N=100, each individual in the sample represents 5 individuals in the population and is assigned a sampling weight of 5. If wi is the sampling weight of individual I in the sample, in this example, wi=5 for I=1,…,20, the estimator of the total Υ is: 

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In the previous example all the individuals had the same sampling weight. In surveys it is common to select a sample with unequal probability of selection and hence unequal weights. In the data set for this case study, each individual has an associated sampling weight, but they are not all equal, since the survey was not a simple random sample. Using these in the analysis will help the results reflect the survey population, not just the survey sample.

Background: Geo-Codes

Census Metropolitan Area (CMA)

A very large urban area, together with adjacent urban and rural areas that have a high degree of economic and social integration with that urban area. A CMA is comprised of one or more contiguous census subdivisions (CSD). CMA’s are defined by Statistics Canada.

A CMA is delineated around an urban area (called the urbanized core and having a population of at least 100,000, based on the previous census). Census subdivisions are included in the CMA on the basis of decennial place-of-work commuting data. Once an area becomes a CMA, it is retained in the program even if its population subsequently declines.

Census Metropolitan Area (CMA) codes are listed in the data documentation.

Background: Linking data files

If you choose to include the CMA level variables, you must first merge this data by CMA onto the NLSCY synthetic file. In addition to this file you are free to add on other macro level variables from other sources.

Macro Level Data: National Longitudinal Survey of Children and Youth (NLSCY)
Data file sheet for Download
: Excel

The data for this study were taken from published results and we would like to thank Nancy Ross of McGill University for allowing us to use the data in this case study. The file contains five variables, the combined province-CMA code, the median share of income, the Gini coeficient, the percentage of persons below the poverty line, and the median income for each of the CMAs.

Income inequality measures were calculated for households in 53 Canadian and 282 U.S. metropolitan areas with populations greater than 50,000 in 1991 (Canada). Income inequality measures for Canadian metropolitan areas were derived from a specially prepared micro data file of the 2B sample of the 1991 Census of Population. The 2B sample represents information gathered from 20% of Canadian households which includes detailed information regarding income sources and amounts. Income included income for all household members from wages and salaries, net self-employment income, government transfers and investment income. All of the measures were calculated with earned household income over 1,000 dollars.

Median Share:

A median share is a middle-sensitive measure of income inequality defined as the proportion of total or earned household income belonging to the less well-off 50 percent of households within a geographical area. In order to estimate the median share, the population has first to be ranked from low to high income, second, identify the income category containing the 50th percentile of the population, i.e., the median, and finally, calculate the proportion of total household income earned by the first half of the population.

The median income falls within the income category that contains the 50 th percentile of the population ranked from low to high. The median income value can be linearly extrapolated assuming that the distribution of income within the income category is linear.

Gini Coefficient:

The Gini coefficient is an overall measure quantifying the degree of income inequality of a particular income distribution and can be derived directly from the Lorenz curve. The Lorenz curve represents the cumulative distribution of households (horizontal axis) against the cumulative distribution of income (vertical axis) (Figure 1). In situations of perfect equality, the shares of population and income will be equal and a 45-degree line on the graph represents this perfect equality. For example, in a situation of perfect equality, 10% of population has 10% of income. In reality, the actual cumulative shares of income possessed by the cumulative shares of the population will fall below this line of perfect equality. It is this Lorenz Curve that allows the estimation of the Gini Coefficient, a global income inequality measure explained below.

The Gini coefficient is calculated as follows:

alt text

where A is the area surrounded with the line of perfect equality and the Lorenz curve and B is the area below Lorenz curve (see Figure 1). It is clear from the Figure that the Gini coefficient is a middle-sensitive inequality measure since the measure is more sensitive to the middle range of the income distribution. As is true for any measure of proportions, the Gini coefficient lies between 0 and 1, where a Gini coefficient close to 0 indicates a more equal income distribution while a coefficient close to 1 indicates a more unequal income distribution. Using this measure in isolation, however, can be somewhat misleading given that, for example, two Gini coefficients can be equivalent with totally different underlying Lorenz curves. 

Figure 1: Lorenz Curve for the State of Alabama, 1990

alt text

In order to facilitate the calculation of the Gini coefficient, the above equation can be re-expressed as Gini=1-2B, since the area under the line of perfect equality (see Figure 1) is A+B=1⁄2, therefore A=1⁄2 -B and the Gini=(1⁄2-B)/1⁄2 . As such, only the area B needs to be calculated to estimate the Gini coefficient.

Proportion of persons below the poverty threshold of half the median income

This measure is defined as the proportion of persons below half of the median inco me. Persons living under this threshold are considered living in poverty for the purposes of this study.

Coefficient of Variation

The coefficient of variation (CV) is a summary measure of income dispersion (illustrating also the degree of inequality in the income distribution) and is considered to be a “top-sensitive” income inequality measure. High incomes will result in a greater increase in the CV compared to low or average incomes. The CV is the standard deviation of income divided by the average income and can be written as:

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is the overall average income, pi is the proportion of the population within income category i and yi is the average income within the income category i.

Urban, population 100,000 to 499,999

The proportion pi is identical to the rectangle width used for the Gini coefficient calculation. This measure gives more weight to larger deviations and expresses the standard deviation as a proportion to the average income. The larger the CV, the greater the income inequality and the skewness in the income distribution.

Median Income

The median income of the city is the median income where income included income for all household members from wages and salaries, net self-employment income, government transfers and investment income above 1,000.


Research Question: 

For this case study, a survey example will be used to:

a. Study hierarchies (Problem 1 Hierarchical Linear Models) in survey data - understand micro and macro levels.

  • Study the relationship between the child outcomes (Child chronic health problems (Count of the conditions a child has had; 3 categories), Child Injury (binary), or Cognitive Competence (continuous)) and micro and macro level dependent variables using a hierarchical linear model.
  • Compare the hierarchical model to traditional regression models.

b. Study the small area (Problem 2 Small Area Statistics) issues with this data set - understand issues.

  • Decide on a method to estimate outcomes in areas with sparse individual level data.
  • Compare results with methods that do not take into account the small area problem.


The data for this study are taken from the synthetic file released for cycle three of the National Longitudinal Survey of Children and Youth (NLSCY). All variables taken directly from NLSCY data have the original variable names. The questions were asked of the Person Most Knowledgeable (PMK) of the child. This was, in most cases, the mother. The data provided represent only a subset of the data available. The provided data represent children aged 4, 5 or 6, living in one of 24 major metropolitan areas. 1016 records are provided. The data descriptions below have generally been reduced from the actual documentation available with the synthetic file to remove values that do not occur in the case study file. Two original variables, CDMCD08 - Number of siblings and CSFHQ01 - Years of residency have been collapsed as values get very sparse. One additional variable was created for this case study, entitle “chronic”, it is a count of the number of chronic conditions the child has.

The following pages describe the layout of the flat file. In the header of each section is the name of the variable, the position of the first byte of data, and the length of the variable (in bytes).

Following that is a brief description of the item, often including the question asked of the respondent, as well as a set of the codes found in the data file and their associated meaning. 

Variable: PRCMA

Province CMA code
10001 St. Johns
12205 Halifax
13310 Saint John
24408 Chicoutimi
24421 Quebec
24442 Trois Rivers
24462 Montreal
35532 Oshawa
35535 Toronto
35537 Hamilton
35539 St. Catherines
35541 Kitchener
35555 London
35559 Windsor
35580 Sudbury
35595 Thunder Bay
36505 Ottawa-Hull
46602 Winnipeg
47705 Regina
47725 Saskatoon
48825 Calgary
48835 Edmonton
59933 Vancouver
59935 Victoria

Variable: MEDSHARE 
Median Share 
Continuous variable

Variable: GINI 
Gini Coefficient 
Continuous variable

Variable: POVPOP 
The proportion of persons below half the median income 
Continuous variable

Variable: MEDINC 
Median Income 
Continuous variable 

  1. Variable: CHILDID (Position: 1, Length: 6)
  2. Child Identification Number. 
    This is a six digit identifier number. There is no intrinsic meaning to the number. It is only used to identify a record. 
  3. Variable: CMMCQ01 (Position: 7, Length: 1)
  4. Age of Child. 
    Code Meaning
    4 4 YEARS
    5 5 YEARS
    6 6 YEARS


  5. Variable: CMMCQ02 (Position: 8, Length: 1)
  6. Gender of child. 
    Code Meaning
    M MALE


  7. Variable: SHXSECWT (Position: 9, Length: 10)
  8. Child’s cross sectional share weight (xxxxx.xxxx). 
  9. Variable: CDMCD08 (Position: 18, Length: 1)
  10. Total number of siblings (of the child) living in the household (including full, half, step, adopted and foster siblings and excluding the child him/herself). This includes siblings of all ages. 
    Code Meaning
    00 0 Siblings
    01 1 sibling
    02 2 or more siblings
    97 DON’T KNOW
    98 REFUSAL


  11. Variable: CGEHbD06 (Position: 19, Length: 3)
  12. Census Metropolitan Area (CMA) code. 
    Code Meaning
    001 St. Johns
    205 Halifax
    310 Saint John
    408 Chicoutimi
    421 Quebec
    442 Trois Rivers
    462 Montreal
    532 Oshawa
    535 Toronto
    537 Hamilton
    539 St. Catherines
    541 Kitchener
    555 London
    559 Windsor
    580 Sudbury
    595 Thunder Bay
    505 Ottawa-Hull
    602 Winnipeg
    705 Regina
    725 Saskatoon
    825 Calgary
    835 Edmonton
    933 Vancouver
    935 Victoria


  13. Variable: CGEHbD04 (Position: 22, Length: 1)
  14. Size of area of residence in which the child lives, according to 1996 Census counts. 
    Code Meaning
    4 Urban, population 100,000 to 499,999
    5 Urban, population 500,000 or over
    *Note: There are, of course, other levels, smaller cities and rural areas. However, as we only provided data for larger urban centers, no records on the file represent records from towns smaller than 100,000.


  15. Variable: CHLCQ37 (Position: 23, Length: 1)
  16. In the past 12 months was he/she injured? 
    Code Meaning
    1 YES
    2 NO
    *Note: Note: A subsequent question asked the nature of the injury, or most serious injury in the case of multiple injuries. Answers fell into the following categories: 
    • OTHER


  17. Variable: CINHD08 (Position: 24, Length: 6)
  18. Socio-economic status - Cross Sectional 
    This variable is derived from 5 other variables: Education of PMK, Education of spouse, Occupational prestige of PMK, Occupational prestige of Spouse, and Household Income. A full explanation of the derivation of this variable is given in Appendix A. In general, however, a higher value of this variable indicates a higher socio-economic status. 
    Code Meaning
    -4.000 : 02.000 -4.000 : 02.000
    99.997 DON’T KNOW
    99.998 REFUSAL
    99.999 NOT STATED


  19. Variable: CPPCS01 (Position: 30, Length: 3)
  20. Standard Score for PPVT-R This variable shows the childs score on the revised Peabody Picture Vocabulary Test. This is further described in Appendix B. The score is standardized to two month age cohorts - so a score of 100 for a 5 year old is equivalent to a score of 100 for a 6 year old. 
    Code Meaning
    040:160 040:160
    999 NOT STATED


  21. Variable: CSFHQ01 (Position: 33, Length: 2)
  22. This section asks questions about your neighbourhood. How many years have you lived at this address? (ENTER 0 IF LESS THAN 1 YEAR.) 
    Code Meaning
    00 : 12 0 to 12 years
    13 13 or more years
    97 DON’T KNOW
    98 REFUSAL


  23. Variable: CSFHS6 (Position: 35, Length: 2)
  24. Neighbours Score. This is a derived variable that measures neighbourhood cohesiveness.This factor was derived using the following weighted items: CSFHQ06A, CSFHQ06B, CSFHQ06C, CSFHQ06D and CSFHQ06E. The values were reversed to create this scale. No imputation was done for this score. The score varies between 0 and 15, a high score indicating a high degree neighbour cohesiveness. The component questions are described in appendix C. 
    Code Meaning
    01 00
    01 01
    02 02
    03 DON’T KNOW
    03 REFUSAL
    04 04
    05 05
    06 06
    07 07
    08 08
    09 09
    10 10
    11 11
    12 12
    13 13
    14 14
    15 15
    97 DON’T KNOW
    98 REFUSAL
    989 NOT STATED


  25. Variable: Chronic (Position: 37, Length: 1)
  26. This variable derived for the case study. It determines how many chronic conditions a child has, from the following set of conditions. Only records with an answer of Yes were counted as having a condition - no imputation was done for missing data: 
    Code Meaning
    0 No Chonic Conditions on the list above.
    1 1 Chronic condition from the list above.
    2 2 or more conditions from the list above.
    • Asthma
    • Allergies
    • Bronchitis
    • Heart Condition
    • Epilepsy
    • Cerebral Palsy
    • Kidney Problems/Disease
    • Mental Handicap
    • Learning Disability
    • Emotional/Psychological/Nervous
Code Meaning
0 No Chonic Conditions on the list above.
1 1 Chronic condition from the list above.
2 2 or more conditions from the list above.

Macro Level Data: Individual data from the synthetic NLSCY Files available for download: Text Excel SAS



  • Kreft, Ita and De Leeuw, Jan. Introducing Multilevel Modelling, London, Sage publications Ltd. 1998
  • Goldstien H. Multilevel Statistical Models, New York, John-Wiley, 1995
  • Rao, J.N.K. Small Area Estimation, New York, John-Wiley, 2003
  • Neter, Kutner, Nachtshen, Wasserman. Applied Linear Statistical Models, McGraw-Hill Inc, 1996
  • Dunn, L. M. & Dunn, L. M. (1981). Peabody Picture Vocabulary Test-Revised. Circle Pines, MN: American Guidance Service.
  • Leventhal, T. & Brooks-Gunn, J. (2000). The neighbourhoods they live in: The effects of neighbourhood residence upon child and adolescent outcomes. Psychological Bulletin, 126, 309-337.
  • Massey, D. S. (1996). The age of extremes: Concentrated affluence and poverty in the twenty-first century. Demography, 33, 395-412.
  • Massey, D. S. & Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press.
  • Naglieri, J. & Pfieffer, S. (1983). Stability, concurrent and predictive validity of the PPVT-R. Journal of Clinical Psychology, 39, 965-967.
  • Sampson, R. & Groves, W. (1989). Community structure and crime: Testing social disorganization theory. American Journal of Sociology, 94, 774-802.
  • Sampson, R. J. (1992). Family management and child development: Insights from social disorganization theory. In J.McCord (Ed.), Facts, Frameworks, and Forecasts (pp. 63-93). New Brunswick, U.S.A.: Transaction.
  • Sampson, R. J. & Morenoff, J. (1997). Ecological perspectives on the neighborhood context of urban poverty: Past and present. In J.Brooks-Gunn, G. J. Duncan, & J. L. Aber (Eds.), Neighborhood Poverty: Policy Implications in Studying Neighborhoods (pp. 1-22). New York: Russell Sage Foundation Press.
  • Wilson, W. J. (1987). The truly disadvantaged. Chicago, IL: University of Chicago Press.