Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating

Brian A. Jacob, Steven D. Levitt

NBER Working Paper No. 9413
Issued in January 2003
NBER Program(s):Children, Law and Economics, Public Economics, Economics of Education

We develop an algorithm for detecting teacher cheating that combines information on unexpected test score fluctuations and suspicious patterns of answers for students in a classroom. Using data from the Chicago Public Schools, we estimate that serious cases of teacher or administrator cheating on standardized tests occur in a minimum of 4-5 percent of elementary school classrooms annually. Moreover, the observed frequency of cheating appears to respond strongly to relatively minor changes in incentives. Our results highlight the fact that incentive systems, especially those with bright line rules, often induce behavioral distortions such as cheating. Statistical analysis, however, may provide a means of detecting illicit acts, despite the best attempts of perpetrators to keep them clandestine.

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Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.3386/w9413

Published: Jacob, Brian A. and Steven D. Levitt. "Rotten Apples: An Investigation Of The Prevalence And Predictors Of Teacher Cheating," Quarterly Journal of Economics, 2003, v118(3,Aug), 843-878. citation courtesy of

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