Using 6 longitudinal data sets, the authors estimate links between three key elements of school readiness--school-entry academic, attention, and socioemotional skills--and later school reading and math achievement. In an effort to isolate the effects of these school-entry skills, the authors ensured that most of their regression models control for cognitive, attention, and socioemotional skills measured prior to school entry, as well as a host of family background measures. Across all 6 studies, the strongest predictors of later achievement are school-entry math, reading, and attention skills. A meta-analysis of the results shows that early math skills have the greatest predictive power, followed by reading and then attention skills. By contrast, measures of socioemotional behaviors, including internalizing and externalizing problems and social skills, were generally insignificant predictors of later academic performance, even among children with relatively high levels of problem behavior. Patterns of association were similar for boys and girls and for children from high and low socioeconomic backgrounds.

Identifying the types of mathematics content knowledge that are most predictive of students' long-term learning is essential for improving both theories of mathematical development and mathematics education. To identify these types of knowledge, we examined long-term predictors of high school students' knowledge of algebra and overall mathematics achievement. Analyses of large, nationally representative, longitudinal data sets from the United States and the United Kingdom revealed that elementary school students' knowledge of fractions and of division uniquely predicts those students' knowledge of algebra and overall mathematics achievement in high school, 5 or 6 years later, even after statistically controlling for other types of mathematical knowledge, general intellectual ability, working memory, and family income and education. Implications of these findings for understanding and improving mathematics learning are discussed.

Taking a longitudinal perspective, we tested a developmental-contextual model of entrepreneurship in a nationally representative sample. Following the lives of 6,116 young people in the 1970 British Birth Cohort from birth to age 34, we examined the role of socioeconomic background, parental role models, academic ability, social skills, and self-concepts as well as entrepreneurial intention expressed during adolescence as predictors of entrepreneurship by age 34. Entrepreneurship was defined by employment status (being self-employed and owning a business). For both men and women, becoming an entrepreneur was associated with social skills and entrepreneurial intentions expressed at age 16. In addition, we found gender-specific pathways. For men, becoming an entrepreneur was predicted by having a self-employed father; for women, it was predicted by their parents' socioeconomic resources. These findings point to conjoint influences of both social structure and individual agency in shaping occupational choice and implementation.

Drawing on nationally representative data collected for two age cohorts in the UK, this paper a) assesses the effect of multiple independent socioeconomic risk factors in shaping the transition from school to work; and b) identifies potential protective factors enabling young people to beat the odds. By comparing experiences and findings across two cohorts we assess the generalisability of findings across contexts, i.e. the 2008 and 1980s recessions. The results show that some young people exposed to even severe socioeconomic risks avoid being NEET (not in education, employment or training). Factors that appear to reduce the cumulative risk effect in both cohorts include prior attainment, educational aspirations and school engagement, as well as the social mix of the school environment.

This study examines whether self-concept of ability in math and reading predicts later math and reading attainment across different levels of achievement. Data from three large-scale longitudinal data sets, the Avon Longitudinal Study of Parents and Children, National Institute of Child Health and Human Development-Study of Early Child Care and Youth Development, and Panel Study of Income Dynamics-Child Development Supplement, were used to answer this question by employing quantile regression analyses. After controlling for demographic variables, child characteristics, and early ability, the findings indicate that self-concept of ability in math and reading predicts later achievement in each respective domain across all quantile levels of achievement. These results were replicated across the three data sets representing different populations and provide robust evidence for the role of self-concept of ability in understanding achievement from early childhood to adolescence across the spectrum of performance (low to high).