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Aggregate Versus Disaggregate Data in Measuring School Quality

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  • Francisca Richter

  • B. Brorsen

Abstract

This article develops a measure of efficiency to use with aggregated data. Unlike the most commonly used efficiency measures, our estimator adjusts for the heteroskedasticity created by aggregation. Our estimator is compared to estimators currently used to measure school efficiency. Theoretical results are supported by a Monte Carlo experiment. Results show that for samples containing small schools (sample average may be about 100 students per school but sample includes several schools with about 30 or less students), the proposed aggregate data estimator performs better than the commonly used OLS and only slightly worse than the multilevel estimator. Thus, when school officials are unable to gather multilevel or disaggregate data, the aggregate data estimator proposed here should be used. When disaggregate data are available, standardizing the value-added estimator should be used when ranking schools. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • Francisca Richter & B. Brorsen, 2006. "Aggregate Versus Disaggregate Data in Measuring School Quality," Journal of Productivity Analysis, Springer, vol. 25(3), pages 279-289, June.
    • Handle: RePEc:kap:jproda:v:25:y:2006:i:3:p:279-289
    DOI: 10.1007/s11123-006-7644-6
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    References listed on IDEAS

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    1. Hanushek, Eric A & Rivkin, Steven G & Taylor, Lori L, 1996. "Aggregation and the Estimated Effects of School Resources," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 611-627, November.
    2. Eric A. Hanushek & Lori L. Taylor, 1990. "Alternative Assessments of the Performance of Schools: Measurement of State Variations in Achievement," Journal of Human Resources, University of Wisconsin Press, vol. 25(2), pages 179-201.
    3. Geoffrey Woodhouse & Min Yang & Harvey Goldstein & Jon Rasbash, 1996. "Adjusting for Measurement Error in Multilevel Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 201-212, March.
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    Cited by:

    1. B. Brorsen & Taeyoon Kim, 2013. "Data aggregation in stochastic frontier models: the closed skew normal distribution," Journal of Productivity Analysis, Springer, vol. 39(1), pages 27-34, February.

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    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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