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How does pre-school attendance affect school performance? An application of Gini-BMA methodology on PISA 2018 dataset

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  • Dimiski, Anastasia

Abstract

Evidence flourishes in the literature of a direct link between asymmetries in pre-primary education and achievement test scores in the primary school. However, children tend to reap the greatest benefits if school programs are of high quality. Premised on this commonality between pre-primary education and academic achievement in the primary school, and considering variations in quality indicators relative to students’,schools’ and families’ characteristics, this study mainly investigates the effect of universal pre-school education on the students’ academic results in 24 OECD and non-OECD countries. Data are obtained from the OECD Programme for International Student Assessment (PISA) 2018. Employing a Gini Regression Bayesian Model Averaging (BMA) approach to account for theory uncertainty we find that attendance of pre-primary institutions has a beneficial impact on PISA test scores for 15-year-old students in most of the countries. However, there are cases where early childhood coincides with a decline in students’ outcomes in both reading and science.

Suggested Citation

  • Dimiski, Anastasia, 2023. "How does pre-school attendance affect school performance? An application of Gini-BMA methodology on PISA 2018 dataset," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    • Handle: RePEc:eee:joecas:v:28:y:2023:i:c:s1703494923000336
    DOI: 10.1016/j.jeca.2023.e00321
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    Keywords

    Asymmetries in students’ performance; Pre-primary education; Gini regression coefficient; BMA methodology; PISA;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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