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Catching Cheating Teachers: The Results of an Unusual Experiment in Implementing Theory

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  • Brian A. Jacob
  • Steven D. Levitt

Abstract

This paper reports on the results of a prospective implementation of methods for detecting teacher cheating. In Spring 2002, over 100 Chicago Public Schools elementary classrooms were selected for retesting based on the cheating detection algorithm. Classrooms prospectively identified as likely cheaters experienced large test score declines. In contrast, classes that had large test score gains on the original test, but were prospectively identified as being unlikely to have cheated, maintained their original gains. Randomly selected classrooms also maintained their gains. The cheating detection tools were thus demonstrated to be effective in distinguishing between classrooms that achieved large test-score gains as a consequence of cheating versus those whose gains were the result of outstanding teaching. In addition, the data generated by the implementation experiment highlight numerous ways in which the original cheating detection methods can be improved in the future.

Suggested Citation

  • Brian A. Jacob & Steven D. Levitt, 2003. "Catching Cheating Teachers: The Results of an Unusual Experiment in Implementing Theory," NBER Working Papers 9414, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:9414
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    References listed on IDEAS

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    1. Richards, Craig E. & Sheu, Tian Ming, 1992. "The South Carolina school incentive reward program: A policy analysis," Economics of Education Review, Elsevier, vol. 11(1), pages 71-86, March.
    2. Brian A. Jacob & Steven D. Levitt, 2003. "Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 843-877.
    3. Holmstrom, Bengt & Milgrom, Paul, 1991. "Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 7(0), pages 24-52, Special I.
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    Cited by:

    1. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Aug 2024.
    2. MacCoun Robert & Cook Philip J. & Muschkin Clara & Vigdor Jacob L, 2008. "Distinguishing Spurious and Real Peer Effects: Evidence from Artificial Societies, Small-Group Experiments, and Real Schoolyards," Review of Law & Economics, De Gruyter, vol. 4(3), pages 695-714, December.
    3. Sah, Raaj, 2007. "Corruption across countries and regions: Some consequences of local osmosis," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2573-2598, August.
    4. Kaoru Nabeshima, 2003. "Raising the quality of secondary education in East Asia," Policy Research Working Paper Series 3140, The World Bank.
    5. Wim J. van der Linden & Minjeong Jeon, 2012. "Modeling Answer Changes on Test Items," Journal of Educational and Behavioral Statistics, , vol. 37(1), pages 180-199, February.
    6. Supanika Leurcharusmee, 2012. "Is penalty for academic cheating an incredible threat?," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(2), pages 81-96, June.

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    More about this item

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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