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The effect of school spending on student achievement: addressing biases in value‐added models

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  • Cheti Nicoletti
  • Birgitta Rabe

Abstract

The estimation of education production models used to evaluate the effect of school inputs and past skills on test scores, often called value‐added models, can be biased by three main econometric issues: unobserved child characteristics, unobserved family and school characteristics and measurement error. We propose a two‐step estimation technique which exploits the availability of test scores across time, subjects, families and schools in a unique administrative data set for England to correct for these potential biases. Our empirical results suggest that omitting school characteristics biases the estimation of the effect of school expenditure, whereas omitting unobserved child endowment biases the estimation of the effect of past skills but not the effect of school expenditure.

Suggested Citation

  • Cheti Nicoletti & Birgitta Rabe, 2018. "The effect of school spending on student achievement: addressing biases in value‐added models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 487-515, February.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:2:p:487-515
    DOI: 10.1111/rssa.12304
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    Cited by:

    1. Gloria Moroni & Cheti Nicoletti & Emma Tominey, 2019. "Child Socio-Emotional Skills: The Role of Parental Inputs," Discussion Papers 19/08, Department of Economics, University of York.
    2. Stephen Gibbons & Vincenzo Scrutinio & Shqiponja Telhaj, 2018. "Teacher turnover: does it matter for pupil achievement?," CEP Discussion Papers dp1530, Centre for Economic Performance, LSE.
    3. Gibbons, Stephen & Scrutinio, Vincenzo & Telhaj, Shqiponja, 2021. "Teacher turnover: Effects, mechanisms and organisational responses," Labour Economics, Elsevier, vol. 73(C).
    4. Doris, Aedin & O'Neill, Donal & Sweetman, Olive, 2019. "Good Schools or Good Students? The Importance of Selectivity for School Rankings," IZA Discussion Papers 12459, Institute of Labor Economics (IZA).
    5. J. R. Lockwood & D. McCaffrey, 2020. "Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1333-1362, October.
    6. Chunhan Huang & Junyun Shi & Xiaodong Zeng, 2023. "Personality Traits, Student-Teacher Relationships and Boys’ Academic Crisis in China: Evidence From the Least Developed Regions," SAGE Open, , vol. 13(4), pages 21582440231, December.
    7. Nicole Black & Sonja C. de New, 2020. "Short, Heavy and Underrated? Teacher Assessment Biases by Children's Body Size," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(5), pages 961-987, October.

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