Aggregate Versus Disaggregate Data in Measuring School Quality
AbstractThis 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
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Bibliographic InfoArticle provided by Springer in its journal Journal of Productivity Analysis.
Volume (Year): 25 (2006)
Issue (Month): 3 (06)
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Web page: http://www.springerlink.com/link.asp?id=100296
Data aggregation; Error components; School quality; C23; I21;
Find related papers by 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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- 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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