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

69 Pages Posted: 3 Jan 2003 Last revised: 31 Oct 2010

See all articles by Brian Jacob

Brian Jacob

Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER)

Steven D. Levitt

University of Chicago; National Bureau of Economic Research (NBER); American Bar Foundation

Date Written: January 2003

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.

Suggested Citation

Jacob, Brian and Levitt, Steven D., Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating (January 2003). NBER Working Paper No. w9413. Available at SSRN: https://ssrn.com/abstract=366450

Brian Jacob (Contact Author)

Harvard University - Harvard Kennedy School (HKS) ( email )

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617-384-7968 (Phone)
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National Bureau of Economic Research (NBER)

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Steven D. Levitt

University of Chicago ( email )

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773-702-8490 (Fax)

National Bureau of Economic Research (NBER)

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American Bar Foundation

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Chicago, IL 60611
United States

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