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School outcomes in New South Wales and Queensland: a regression discontinuity approach

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  • Paul W. Miller
  • Derby Voon

Abstract

This paper examines the differences in school (NAPLAN) outcomes between New South Wales and Queensland. It shows that there are pronounced differences in Year 3 NAPLAN results between these states, though these dissipate when later class years are considered. The reasons for these state effects in school outcomes are explored using an empirical framework grounded in the regression discontinuity literature. There is no systematic evidence of school-specific unobservables that are linearly related to the distance of schools from the New South Wales-Queensland border that account for this state effect. In other words, the lower outcomes of schools in Queensland relative to schools in New South Wales appear to be a pure institutional phenomenon. This conclusion carries over to the various sensitivity tests undertaken based on the partition of the sample by school sector and by distance from the border. Accordingly, the border effects are expected to be eroded by the recent reforms to the school starting age in Queensland and by the national curriculum currently proposed for implementation in Australia.

Suggested Citation

  • Paul W. Miller & Derby Voon, 2014. "School outcomes in New South Wales and Queensland: a regression discontinuity approach," Education Economics, Taylor & Francis Journals, vol. 22(5), pages 427-448, October.
    • Handle: RePEc:taf:edecon:v:22:y:2014:i:5:p:427-448
    DOI: 10.1080/09645292.2011.595571
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    1. Sandra E. Black & Paul J. Devereux & Kjell G. Salvanes, 2011. "Too Young to Leave the Nest? The Effects of School Starting Age," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 455-467, May.
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    Cited by:

    1. Alfred A. Haug & Vincent C. Blackburn, 2017. "Government secondary school finances in New South Wales: accounting for students’ prior achievements in a two-stage DEA at the school level," Journal of Productivity Analysis, Springer, vol. 48(1), pages 69-83, August.
    2. Sarah Cornell-Farrow & Robert Garrard, 2018. "Machine Learning Classifiers Do Not Improve the Prediction of Academic Risk: Evidence from Australia," Papers 1807.07215, arXiv.org, revised Jan 2020.

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