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Solving endogeneity problems in multilevel estimation: an example using education production functions

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  • Saïd Hanchane
  • Tarek Mostafa

Abstract

This paper explores endogeneity problems in multilevel estimation of education production functions. The focus is on level 2 endogeneity which arises from correlations between student characteristics and omitted school variables. Theses correlations are mainly the result of student stratification between schools. From an econometric point of view, the correlations between student and school characteristics imply that the omission of some variables may generate endogeneity bias. Therefore, an estimation approach based on the Mundlak [20] technique is developed in order to tackle bias and to generate consistent estimates. Note that our analysis can be extended to any multilevel-structured data (students nested within schools, employees within firms, firms within regions, etc). The entire analysis is undertaken in a comparative context between three countries: Germany, Finland and the UK. Each one of them represents a particular system. For instance, Finland is known for its extreme comprehensiveness, Germany for early selection and the UK for its liberalism. These countries are used to illustrate the theory and to prove that the level of bias arising from omitted variables varies according to the characteristics of education systems.

Suggested Citation

  • Saïd Hanchane & Tarek Mostafa, 2012. "Solving endogeneity problems in multilevel estimation: an example using education production functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1101-1114, November.
  • Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1101-1114
    DOI: 10.1080/02664763.2011.638705
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    References listed on IDEAS

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    Cited by:

    1. Susana Faria & Maria Conceição Portela, 2016. "Student Performance in Mathematics using PISA-2009 data for Portugal," Working Papers de Gestão (Management Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    2. Lu, Ren & Ruan, Min & Reve, Torger, 2016. "Cluster and co-located cluster effects: An empirical study of six Chinese city regions," Research Policy, Elsevier, vol. 45(10), pages 1984-1995.
    3. Jorge Manzi & Ernesto San Martín & Sébastien Van Bellegem, 2014. "School System Evaluation by Value Added Analysis Under Endogeneity," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 130-153, January.

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