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

Steven Levitt

Berkeley Olin Program in Law & Economics, Working Paper Series from Berkeley Olin Program in Law & Economics

Abstract: 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.

Date: 2002-12-01
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Related works:
Journal Article: Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating (2003)
Working Paper: Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating (2003)
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