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The Reliability of Students' Earnings Expectations

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  • Diaz-Serrano, Luis

    () (Universitat Rovira i Virgili)

  • Nilsson, William

    () (University of the Balearic Islands)

Abstract

Eliciting expectation and introducing probabilistic questions into surveys have gained important interest. In this study, we focus on the reliability of students’ earnings expectations. To what extent is observed log earnings expectations affected by random measurement error (noise)? A test-retest method is applied and reliability is found to be fairly low; about 0.59 in 2015 and about 0.67 in 2016. Particularly homogeneous samples exaggerate problems of measurement error. The analysis show how these measures of reliability can be adjusted to become more suitable to other studies, where different degrees of homogeneity are present.

Suggested Citation

  • Diaz-Serrano, Luis & Nilsson, William, 2017. "The Reliability of Students' Earnings Expectations," IZA Discussion Papers 10700, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp10700
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    References listed on IDEAS

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    1. Schweri, Juerg & Hartog, Joop & Wolter, Stefan C., 2011. "Do students expect compensation for wage risk?," Economics of Education Review, Elsevier, vol. 30(2), pages 215-227, April.
    2. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    3. Adeline Delavande & Xavier Giné & David McKenzie, 2011. "Eliciting probabilistic expectations with visual aids in developing countries: how sensitive are answers to variations in elicitation design?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(3), pages 479-497, April.
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    More about this item

    Keywords

    earnings expectations; reliability; measurement error;

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education

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