Abstract
This paper explores the relative importance of social factors and health measures in predicting educational achievement in early and late adolescence using population-based administrative data. The sample was made up of 41,943 children born in Manitoba, Canada between 1982 and 1989 and remaining in the province until age 18. Multilevel modeling nests each individual (level 1) within a family (level 2) residing within a neighborhood (level 3). Most important in predicting adolescent achievement were a broad socioeconomic status index (and a narrower measure of household income), being on social assistance, mother’s age at first birth, gender, residential mobility, the presence of ADHD/Conduct disorders, and measures of family functioning (child taken into care or offered protection services and family structure history). Family size, birth order, and newborn characteristics (birthweight, APGAR, gestational age) were statistically significant but of little importance in explaining the outcomes. Both examining regression coefficients and systematically omitting variables showed social factors (often emphasized by epidemiologists) to have markedly greater effects than the combination of health measures (often stressed by economists) in predicting achievement. However, mental health in childhood is identified as among the important predictors. Record linkage across population datasets from health, education, and family services ministries allowed: tracking health and educational attainment at different times in a child’s life, following a large number of cases across childhood, considerable sensitivity testing, controlling for unmeasured family and neighborhood effects, generating an extensive list of predictors, estimating effect sizes, and comparing Manitoba results with those of well-known American studies.
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Notes
The data and methods are described in more detail in the Extended Appendix to Currie et al. (2010). Material updated to reflect the additional years of information used in this paper is available on request from the corresponding author.
Linear modeling was carried out using PROC MIXED in Unix SAS Version 9. The REML (Restricted Maximum Likelihood) method was used to estimate variance–covariance parameters.
Other statistics on model fit produced very similar results. These data, as well as the relevant intercepts and intraclass correlation coefficients, are available from the corresponding author.
A Ramsey RESET (regression) test for misspecification (which deals with both omitted variables and incorrect functional form) was significant at the .01 level for the large Manitoba sample (Grade 12 index) but showed only minimal improvement in fit (from .325 to .329) (Ramsey, 1969). Such a finding of bias is quite common (Clarke 2005) and ours seems relatively small. Similar tests were not available for the American and UK studies.
“Systems’ conditions include “respiratory, alimentary, heart, haematological, urogenital, central nervous system, epilepsy.” “These condition are too rare in the NCDS cohort to analyze their impacts separately for each chronic illness.” Case et al. (2005), p. 368.
Manitoba analyses have eliminated intellectually disabled children; ‘mental retardation’ and ‘psychoses’ are among the codes used. As noted in the working paper (http://www.wws.prineton.edu/chw/research/papers.php), such children are included in the UK analysis. In addition, the UK children (in 1958) were likely to be less healthy than their more recent Canadian comparison group.
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Acknowledgments
We are grateful to many individuals for making this research possible: Heather Hunter, John Van Walleghem, Richard Perrault, Carol Crerar, Jean Britton, Ken Clark, Irene Huggins, Allyson Matczuk and Shirley McLellan from Manitoba Education, Louis Barre and Deborah Malazdrewicz from Manitoba Health, Jan Forster, Harvey Stevens (now retired), Linda Burnside, Charlene Paquin, Joy Cramer, Kathy Reid, Margaret Ferniuk, Elin Ibrahim, Tracy Lewis and Kim Hucko from Manitoba Family Services and Consumer Affairs. This research has been funded by the Social Sciences and Humanities Research Council (Manitoba Health Project Number 2007/2008—20C), the Canadian Institutes for Health Research, the Lupina Foundation, and the Canadian Institute for Advanced Research. The results and conclusions presented are those of the authors. No official endorsement by the ministries involved is intended or should be inferred. The authors thank Shannon Lussier and Theresa Daniuk for manuscript preparation.
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Appendices
Appendix 1: Intellectual Disability
We removed children who ever had a diagnosis of mental retardation from the sample. We looked for children in the Ministry of Education data with a “special needs” flag, identified by the variables ‘MH’ = ‘Multiple Handicaps’, ‘AUT’ = ‘Autism’, or ‘ASD’ = ‘Autism Spectrum Disorder’. Hospital and physician visit data for at least one of the following diagnoses at some time during the child’s childhood years (up to age 14) were also considered (ICD-9-CM codes): ‘317—Mild Mental Retardation (MR)’, ‘318—Other MR’ ‘319—Unspecified MR’, ‘299—Psychoses’, ‘758—Downs and related’, ‘759—Fragile X, Prader-W’, and ‘760—FAS’. With hospital coding moving to ICD-10 (in April 2004), additional codes (from the hospital data) were used to flag mental retardation: ‘F70—Mild mental retardation’, ‘F71—Moderate mental retardation’, ‘F72— Severe mental retardation’, ‘F73—Profound mental retardation’, ‘F74’, ‘F75’, ‘F76’,’F77’, ‘F78—Other mental retardation’, and ‘F79—Unspecified mental retardation’.
Appendix 2: Neighborhood of Residence
Our main analyses assigned neighborhood of residence to children in each family on the basis of the oldest sibling’s residence at age 17. Residential changes were bracketed by reassigning family neighborhood and income based on residence (1986 census enumeration area) at the time of the youngest sibling’s birth. Neighborhood environments were generally persistent, but 26.5% of the families did change neighborhood between the time of the oldest sibling’s turning 17 and the time of the youngest sibling’s birth. Household income for the relevant census area (rather than SEFI) had to be used for further comparison. SEFI was not available for 1986 while 1986 Winnipeg census enumeration areas (N = 757) were larger than the dissemination areas (N = 1,183) used subsequently by Statistics Canada. These size differences probably contributed to the differing level 3 variances in the null model: 0.17 in the ‘oldest child at age 17’ model versus 0.12 in the ‘youngest child at birth’ model. A considerable pattern of available variance was explained by mean neighborhood household income in the full model (87.7% ‘for oldest child’ versus 88.4% ‘for youngest child’).
Appendix 3: Index Validation
Validation was approached in two ways. First of all, the score on the index of Grade 9 achievement can be compared with the Grade 12 achievement scores (Winnipeg sample) (Table 6). Correlations among indices ranged from .694 to .746 (Winnipeg sample) (Table 7). The Grade 9 index scores correspond reasonably closely to the average scores on the Grade 12 measure. These achievement scores can be further assessed in terms of a student’s probability of high school graduation within 5 years (Roos et al. 2008).
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Roos, L.L., Hiebert, B., Manivong, P. et al. What is Most Important: Social Factors, Health Selection, and Adolescent Educational Achievement. Soc Indic Res 110, 385–414 (2013). https://doi.org/10.1007/s11205-011-9936-0
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DOI: https://doi.org/10.1007/s11205-011-9936-0