Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools

Erich Battistin (), Michele De Nadai and Daniela Vuri ()

Journal of Econometrics, 2017, vol. 200, issue 2, 344-362

Abstract: We derive bounds on the distribution of math and language scores of elementary school students in Italy correcting for pervasive manipulation. A natural experiment that randomly assigns external monitors to schools is used to deal with endogeneity of manipulation, as well as its mismeasurement in the data. Bounds are obtained from properties of the statistical model used to detect classes with manipulated scores, and from restrictions on the relationship between manipulation and true scores. Our results show that regional rankings by academic performance are reversed once manipulation is taken into account.

Keywords: Measurement error; Non-parametric bounds; Partial identification; Score manipulation (search for similar items in EconPapers)
JEL-codes: C14 C31 C81 I21 J24 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030440761730101X
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools (2016)
Working Paper: Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools (2014)
Working Paper: Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools (2014)
Working Paper: Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:200:y:2017:i:2:p:344-362

DOI: 10.1016/j.jeconom.2017.06.015

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().