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Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children


  • Marie Connolly

    (Department of Economics, University of Quebec in Montreal)

  • Catherine Haeck

    () (Department of Economics, University of Quebec in Montreal)


We estimate the nonlinear impact of class size on student achievement by exploiting regulations that cap class size at 20 students per class in kindergarten. Based on student-level information from a previously unexploited and unique large-scale census survey of kindergarten students, this study provides clear evidence of the nonlinearity of class size effects on development measures. While the effects are largest on cognitive development, class size reductions also improve noncognitive skills for children living in disadvantaged areas. These findings suggest that sizeable class size reductions targeted at disadvantaged areas would achieve better results than a marginal reduction across the board.

Suggested Citation

  • Marie Connolly & Catherine Haeck, 2018. "Nonlinear Class Size Effects on Cognitive and Noncognitive Development of Young Children," Working Papers 18-01, Research Group on Human Capital, University of Quebec in Montreal's School of Management, revised Dec 2020.
  • Handle: RePEc:grc:wpaper:18-01

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    Cited by:

    1. Marie Connolly & Catherine Haeck, 2018. "Le lien entre la taille des classes et les comp├ętences cognitives et non cognitives," CIRANO Project Reports 2018rp-18, CIRANO.

    More about this item


    class size; cognitive development; noncognitive development; kindergarten; nonlinear effects;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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