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A Framework for Reconsidering the Lake Wobegon Effect


  • M. Ryan Haley
  • Marianne F. Johnson
  • M. Kevin McGee


The Lake Wobegon Effect (LWE) describes the potential measurement-error bias introduced into survey-based analyses of education issues. Although this effect potentially applies to any student-report variable, the systematic overreporting of academic achievements such as grade point average is often of preeminent concern. This concern can be easily circumvented if official records data are available; however, many researchers can only access student-reported data. In this article, the authors examine whether using student-survey data in place of official records data meaningfully biases regression estimates. They motivate their contribution by noting a useful statistical feature of overreporting on bounded variables such as grade point average. Specifically, the misreports will be negatively correlated with the true grade point average, yielding a form of nonclassical measurement error that actually counteracts the bias. The authors connect this observation to reliability ratios used in labor economics, which are simple ways to adjust for attenuation bias, when needed. In two applications, we find that it is unnecessary to correct for the LWE bias because it is so small.

Suggested Citation

  • M. Ryan Haley & Marianne F. Johnson & M. Kevin McGee, 2010. "A Framework for Reconsidering the Lake Wobegon Effect," The Journal of Economic Education, Taylor & Francis Journals, vol. 41(2), pages 95-109, March.
  • Handle: RePEc:taf:jeduce:v:41:y:2010:i:2:p:95-109
    DOI: 10.1080/00220481003617228

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    Cited by:

    1. William Bosshardt & Peter E. Kennedy, 2011. "Data Resources and Econometric Techniques," Chapters,in: International Handbook on Teaching and Learning Economics, chapter 35 Edward Elgar Publishing.
    2. Wisdom Akpalu & Richard Vogel & Xu Zhang, 2012. "The Rate Of Time Preference, Seat Location Choice And Student Performance In The Classroom," New York Economic Review, New York State Economics Association (NYSEA), vol. 43(1), pages 33-45.

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