Quantile regressions analysis of the Italian school system
AbstractThe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Universita' di Cassino, Dipartimento di Scienze Economiche in its series Working Papers with number 2008-06.
Length: 41 pages
Date of creation: Jun 2008
Date of revision:
Contact details of provider:
Postal: Dipartimento di Scienze Economiche Via S. Angelo Loc. Folcara 03043 Cassino (FR) - Italy
Web page: http://www.eco-giu.uniclam.it/Dipartimento/Info
More information through EDIRC
quantile regression; structural break; test;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004.
"Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure,"
NBER Working Papers
10428, National Bureau of Economic Research, Inc.
- 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.
- Donald W.K. Andrews, 2002.
"End-of-Sample Instability Tests,"
Cowles Foundation Discussion Papers
1369, Cowles Foundation for Research in Economics, Yale University.
- 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.
- Bai, Jushan, 1995. "Least Absolute Deviation Estimation of a Shift," Econometric Theory, Cambridge University Press, vol. 11(03), pages 403-436, June.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Marilena Furno, 2012. "Tests for structural break in quantile regressions," AStA Advances in Statistical Analysis, Springer, vol. 96(4), pages 493-515, October.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gennaro Zezza).
If references are entirely missing, you can add them using this form.