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The effect of school resources on pupil attainment: a multilevel simultaneous equation modelling approach

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  • Steele, Fiona
  • Vignoles, Anna
  • Jenkins, Andrew

Abstract

Improving educational achievement in UK schools is a priority, and of particular concern is the low achievement of specific groups, such as those from lower socio-economic backgrounds. An obvious question is whether we should be improving the outcomes of these pupils by spending more on their education. The literature on the effect of educational spending on the achievement of pupils has some methodological difficulties, in particular the endogeneity of school resource levels, and the intraschool correlations in pupils' responses. We adopt a multi-level simultaneous equation modelling approach to assess the effect of school resources on pupil attainment at age 14 years. The paper is the first to apply a simultaneous equation model to estimate the effect of school resources on pupils' achievement, using the newly available national pupil database and pupil level annual school census.

Suggested Citation

  • Steele, Fiona & Vignoles, Anna & Jenkins, Andrew, 2007. "The effect of school resources on pupil attainment: a multilevel simultaneous equation modelling approach," LSE Research Online Documents on Economics 26481, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:26481
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    Cited by:

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    3. Damien Rousselière & Samira Rousselière, 2010. "On the impact of trust on consumer willingness to purchase GM food:Evidence from a European survey," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 91(1), pages 5-26.
    4. Nattavudh Powdthavee, 2009. "How important is rank to individual perception of economic standing? A within-community analysis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(3), pages 225-248, September.
    5. Manzi, Jorge & San Martin, Ernesto & Van Bellegem, Sébastien, 2010. "School System Evaluation By Value-Added Analysis under Endogeneity," IDEI Working Papers 631, Institut d'Économie Industrielle (IDEI), Toulouse.
    6. Jacobo Rozo Alzate, 2017. "La educación secundaria y sus dos dimensiones. Efectos del barrio y del colegio sobre los resultados saber 11," Revista de Economía del Rosario, Universidad del Rosario, vol. 20(1), pages 33-69, June.
    7. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    8. Yamauchi, Futoshi, 2011. "School quality, clustering and government subsidy in post-apartheid South Africa," Economics of Education Review, Elsevier, vol. 30(1), pages 146-156, February.
    9. Mauricio Salgado & Elio Marchione & Nigel Gilbert, 2014. "Analysing Differential School Effectiveness Through Multilevel and Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-3.
    10. Alejandro Carrasco & Ernesto San Mart’n, 2012. "Voucher system and school effectiveness: Reassessing school performance difference and parental choice decision-making," Estudios de Economia, University of Chile, Department of Economics, vol. 39(2 Year 20), pages 123-141, December.
    11. Kerris Cooper & Kitty Stewart, 2017. "Does Money Affect Children's Outcomes? An update," CASE Papers /203, Centre for Analysis of Social Exclusion, LSE.
    12. Pal, Sarmistha & Saha, Bibhas, 2014. "In 'Trusts' We Trust: Socially Motivated Private Schools in Nepal," IZA Discussion Papers 8270, Institute of Labor Economics (IZA).
    13. Steven Bradley & Robert Crouchley, 2017. "The effects of test scores and truancy on youth unemployment and inactivity: A simultaneous equations approach," Working Papers 189398493, Lancaster University Management School, Economics Department.
    14. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
    15. Rocío Hernández-Sanjaime & Martín González & Jose J. López-Espín, 2020. "Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms," Mathematics, MDPI, vol. 8(12), pages 1-12, November.
    16. Hend Gabr & Fiona Carmichael & Hui Li, 2019. "A Multilevel Simultaneous Equations Modelling Approach to Investigate the Relationship between Poverty and Labour-Force Participation among the Elderly in Egypt," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 22(1), pages 01-12, October.
    17. Glen Bramley & David Watkins & Noah Kofi Karley, 2011. "An Outcome-Based Resource Allocation Model for Local Education Services in Wales," Environment and Planning C, , vol. 29(5), pages 848-871, October.
    18. Pal, Abhipsa & Herath, Tejaswini & De', Rahul & Raghav Rao, H., 2021. "Why do people use mobile payment technologies and why would they continue? An examination and implications from India," Research Policy, Elsevier, vol. 50(6).
    19. Amini, Chiara & Nivorozhkin, Eugene, 2015. "The urban–rural divide in educational outcomes: Evidence from Russia," International Journal of Educational Development, Elsevier, vol. 44(C), pages 118-133.
    20. Bayrakdar, Sait & Guveli, Ayse, 2020. "Inequalities in home learning and schools’ provision of distance teaching during school closure of COVID-19 lockdown in the UK," ISER Working Paper Series 2020-09, Institute for Social and Economic Research.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • J1 - Labor and Demographic Economics - - Demographic Economics

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