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Estimating Teacher Effectiveness From Two-Year Changes in Students’ Test Scores

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  • Andrew Leigh

Abstract

Using a dataset covering over 10,000 Australian primary school teachers and over 90,000 pupils, I estimate how effective teachers are in raising students’ test scores from one exam to the next. Since the exams are conducted only every two years, it is necessary to take account of the teacher’s work in the intervening year. Even after adjusting for measurement error, the resulting teacher fixed effects are widely dispersed across teachers, and there is a strong positive correlation between a teacher’s gains in literacy and numeracy. Teacher fixed effects show a significant association with some, though not all, observable teacher characteristics. Experience has the strongest effect, with a large effect in the early years of a teacher’s career. Female teachers do better at teaching literacy. Teachers with a master’s degree or some other form of further qualification do not appear to achieve significantly larger test score gains. Overall, teacher characteristics found in the departmental payroll database can explain only a small fraction of the variance in teacher performance.

Suggested Citation

  • Andrew Leigh, 2009. "Estimating Teacher Effectiveness From Two-Year Changes in Students’ Test Scores," CEPR Discussion Papers 619, Centre for Economic Policy Research, Research School of Economics, Australian National University.
  • Handle: RePEc:auu:dpaper:619
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    File URL: https://www.cbe.anu.edu.au/researchpapers/cepr/DP619.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education, Elsevier.
    2. Yi Long & Chris Nyland & Russell Smyth, 2016. "Fiscal Decentralisation, the Knowledge Economy and School Teachers’ Wages in Urban China," Monash Economics Working Papers 13-16, Monash University, Department of Economics.
    3. Azam, Mehtabul & Kingdon, Geeta Gandhi, 2015. "Assessing teacher quality in India," Journal of Development Economics, Elsevier, vol. 117(C), pages 74-83.
    4. Son Nghiem & Ha Trong Nguyen & Luke B. Connelly, 2016. "The Efficiency of Australian Schools: A Nationwide Analysis Using Gains in Test Scores of Students as Outputs," Economic Papers, The Economic Society of Australia, vol. 35(3), pages 256-268, September.
    5. Mike Helal & Michael Coelli, 2016. "How Principals Affect Schools," Melbourne Institute Working Paper Series wp2016n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. Chris Ryan, 2017. "Secondary School Teacher Effects on Student Achievement in Australian Schools," Melbourne Institute Working Paper Series wp2017n11, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    7. Bernhard Enzi, 2017. "The Effect of Pre-Service Cognitive and Pedagogical Teacher Skills on Student Achievement Gains: Evidence from German Entry Screening Exams," ifo Working Paper Series 243, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. Andrew Leigh & Xiaodong Gong, 2009. "Estimating cognitive gaps between Indigenous and non-Indigenous Australians," Education Economics, Taylor & Francis Journals, vol. 17(2), pages 239-261.
    9. Lagerström, Jonas, 2011. "How important are caseworkers – and why? New evidence from Swedish employment offices," Working Paper Series 2011:10, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    10. Coelli, Michael & Green, David A., 2012. "Leadership effects: school principals and student outcomes," Economics of Education Review, Elsevier, vol. 31(1), pages 92-109.
    11. Nghiem, Son & Nguyen, Ha & Connelly, Luke, 2014. "The Efficiency of Australian Schools: Evidence from the NAPLAN Data 2009-2011," MPRA Paper 56231, University Library of Munich, Germany.

    More about this item

    Keywords

    educational economics; educational finance; efficiency; productivity;

    JEL classification:

    • 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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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