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Speeding, Tax Fraud, and Teaching to the Test

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  • Edward P. Lazear

Abstract

Educators worry that high-stakes testing will induce teachers and their students to focus only on the test and ignore other, untested aspects of knowledge. Some counter that although this may be true, knowing something is better than knowing nothing and many students would benefit even by learning the material that is to be tested. Using the metaphor of deterring drivers from speeding, it is shown that the optimal rules for high-stakes testing depend on the costs of learning and of monitoring. For high cost learners, and when monitoring technology is inefficient, it is better to announce what will be tested. For efficient learners, de-emphasizing the test itself is the right strategy. This is analogous to telling drivers where the police are posted when police are few. At least there will be no speeding on those roads. When police are abundant or when the fine is high relative to the benefit from speeding, it is better to keep police locations secret, which results in obeying the law everywhere. Children who are high cost learners are less likely to learn all the material and therefore learn more when they are told what is on the exam. The same logic also implies that tests should be clearly defined for younger children, but more amorphous for more advanced students.

Suggested Citation

  • Edward P. Lazear, 2004. "Speeding, Tax Fraud, and Teaching to the Test," NBER Working Papers 10932, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:10932
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    Cited by:

    1. Gregory DeAngelo & Gary Charness, 2012. "Deterrence, expected cost, uncertainty and voting: Experimental evidence," Journal of Risk and Uncertainty, Springer, vol. 44(1), pages 73-100, February.
    2. Hendrik Jürges & Wolfram F. Richter & Kerstin Schneider, 2005. "Teacher Quality and Incentives: Theoretical and Empirical Effects of Standards on Teacher Quality," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 61(3), pages 298-326, November.
    3. Jan Eeckhout & Nicola Persico & Petra E. Todd, 2010. "A Theory of Optimal Random Crackdowns," American Economic Review, American Economic Association, vol. 100(3), pages 1104-1135, June.

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