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Time to learn? The organizational structure of schools and student achievement

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  • Ozkan Eren
  • Daniel Millimet

Abstract

Utilizing parametric and nonparametric techniques, we asses the impact of a heretofore relatively unexplored ‘input’ in the educational process, time allocation, on the distribution of academic achievement. Our results indicate that school year length and the number and average duration of classes affect student achievement. However, the effects are not homogeneous — in terms of both direction and magnitude — across the distribution. We find that test scores in the upper tail of the distribution benefit from a shorter school year, while a longer school year increases test scores in the lower tail. Furthermore, test scores in the lower quantiles increase when students have at least eight classes lasting 46–50 min on average, while test scores in the upper quantiles increase when students have seven classes lasting 45 min or less or 51 min or more.
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  • Ozkan Eren & Daniel Millimet, 2007. "Time to learn? The organizational structure of schools and student achievement," Empirical Economics, Springer, vol. 32(2), pages 301-332, May.
    • Handle: RePEc:spr:empeco:v:32:y:2007:i:2:p:301-332
    DOI: 10.1007/s00181-006-0093-2
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    Cited by:

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    2. Cuffe, Harold E. & Waddell, Glen R. & Bignell, Wesley, 2014. "Too Busy for School? The Effect of Athletic Participation on Absenteeism," IZA Discussion Papers 8426, Institute of Labor Economics (IZA).
    3. Simon Søbstad Bensnes, 2016. "Preparation time, exam scores, and tertiary education," Working Paper Series 17216, Department of Economics, Norwegian University of Science and Technology.
    4. Simon Søbstad Bensnes, 2020. "Scheduled to Gain: Short‐ and Longer‐Run Educational Effects of Examination Scheduling," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(3), pages 879-910, July.
    5. Trevor Collier & Daniel Millimet, 2009. "Institutional arrangements in educational systems and student achievement: a cross-national analysis," Empirical Economics, Springer, vol. 37(2), pages 329-381, October.
    6. Philipp Mandel & Bernd Süssmuth, 2011. "Total Instructional Time Exposure and Student Achievement: An Extreme Bounds Analysis Based on German State-Level Variation," CESifo Working Paper Series 3580, CESifo.
    7. Michael Doersam & Verena Lauber, 2019. "The Effect of a Compressed High School Curriculum on University Performance," Working Paper Series of the Department of Economics, University of Konstanz 2019-03, Department of Economics, University of Konstanz.
    8. Dörsam, Michael & Lauber, Verena, 2015. "The Effect of a Compressed High School Curriculum on University Performance," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 140876, Verein für Socialpolitik / German Economic Association.
    9. Cotti, Chad & Gordanier, John & Ozturk, Orgul, 2018. "Class meeting frequency, start times, and academic performance," Economics of Education Review, Elsevier, vol. 62(C), pages 12-15.
    10. Dave E. Marcotte & Steven W. Hemelt, 2008. "Unscheduled School Closings and Student Performance," Education Finance and Policy, MIT Press, vol. 3(3), pages 316-338, July.
    11. Mauro Mediavilla Bordalejo, 2010. "Las becas y ayudas al estudio como elemento determinante de la continuidad escolar en el nivel secundario post-obligatorio. Un análisis de sensibilidad a partir de la aplicación del Propensity Score M," Investigaciones de Economía de la Educación volume 5, in: María Jesús Mancebón-Torrubia & Domingo P. Ximénez-de-Embún & José María Gómez-Sancho & Gregorio Gim (ed.), Investigaciones de Economía de la Educación 5, edition 1, volume 5, chapter 29, pages 561-582, Asociación de Economía de la Educación.

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    More about this item

    Keywords

    Student achievement; School quality; Stochastic dominance; Quantile treatment effects; Inverse propensity score weighting; C14; I21; I28;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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