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Quantile regressions analysis of the Italian school system

  • Marilena Furno

    (University of Cassino)

The score on a reading literacy test of 15 years old Italian students is here analyzed. The data depict a fracture in the Italian school system. By means of quantile regressions and by repeatedly implementing a quantile regression based test for structural break, computed in different sub-samples and at various quantiles, one can pin down the determinants of the gap and rank them. We find that the difference in curricula is the main factor in explaining the gap in the students scores; that the regional difference is linked to structural and behavioral variables, like poor library facilities and students absenteeism, both mirroring the economic lag of the southern Italian regions. In terms of policy actions, curbing absenteeism in the south can reduce the regional gap. If instead the target is to enhance excellence, funds should be directed toward academic track, public schools, north-centre regions.

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Paper provided by Universita' di Cassino, Dipartimento di Scienze Economiche in its series Working Papers with number 2008-06.

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Length: 41 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:css:wpaper:2008-06
Contact details of provider: Postal: Dipartimento di Scienze Economiche Via S. Angelo Loc. Folcara 03043 Cassino (FR) - Italy
Phone: +3907762994734
Fax: +3907762994834
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  1. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, 03.
  2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  3. D. W. K. Andrews, 2003. "End-of-Sample Instability Tests," Econometrica, Econometric Society, vol. 71(6), pages 1661-1694, November.
  4. Bai, Jushan, 1995. "Least Absolute Deviation Estimation of a Shift," Econometric Theory, Cambridge University Press, vol. 11(03), pages 403-436, June.
  5. Leslie G. Godfrey & Chris D. Orme, 2000. "Controlling the significance levels of prediction error tests for linear regression models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 66-83.
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