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Measuring School Value Added with Administrative Data: The Problem of Missing Variables

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  • Lorraine Dearden
  • Alfonso Miranda
  • Sophia Rabe‐Hesketh

Abstract

The UK Department for Education (DfE) calculates contextualised value added (CVA) measures of school performance using administrative data that contain only a limited set of explanatory variables. Differences on schools’ intake regarding characteristics such as mother’s education are not accounted for due to the lack of background information in the data. In this paper we use linked survey and administrative data to assess the potential biases that missing control variables cause in the calculation of CVA measures of school performance. We find that ignoring the effect of mother’s education leads DfE to erroneously over-penalise low achieving schools that have a greater proportion of mothers with low qualifications and to over-reward high achieving schools that have a greater proportion of mothers with higher qualifications. This suggests that collecting a rich set of controls in administrative records is necessary for producing reliable CVA measures of school performance.

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1111/j.1475-5890.2011.00136.x
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Bibliographic Info

Article provided by Institute for Fiscal Studies in its journal Fiscal Studies.

Volume (Year): 32 (2011)
Issue (Month): 2 (06)
Pages: 263-278

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Handle: RePEc:ifs:fistud:v:32:y:2011:i::p:263-278

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Cited by:
  1. Sofia N. Andreou & Panos Pashardes, 2012. "Consumers’ Valuation of Level and Egalitarian Education Attainment of Schools in England," University of Cyprus Working Papers in Economics 10-2012, University of Cyprus Department of Economics.
  2. Sofia N. Andreou & Panos Pashardes, 2013. "Consumers’ Valuation of Academic and Equality-inducing Aspects of School Performance in England," University of Cyprus Working Papers in Economics 09-2013, University of Cyprus Department of Economics.
  3. Alfonso Miranda & Sophia Rabe-Hesketh & John W. McDonald, 2012. "Reducing bias due to missing values of the response variable by joint modeling with an auxiliary variable," DoQSS Working Papers 12-05, Department of Quantitative Social Science - Institute of Education, University of London.

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