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Returns to Lying? Identifying the Effects of Misreporting When the Truth Is Unobserved

Author

Listed:
  • Yingyao Hu

    (Department of Economics, Johns Hopkins University, Baltimore, MD 21218, USA)

  • Arthur Lewbel

    (Department of Economics, Boston College, Boston, MA 02467, USA)

Abstract

Consider an observed binary regressor D and an unobserved binary variable D*, both of which affect some other variable Y . This paper considers nonparametric identification and estimation of the effect of D on Y , conditioning on D* = 0. For example, suppose Y is a person¡¯s wage, the unobserved D* indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in average wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D* is obtained either by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.

Suggested Citation

  • Yingyao Hu & Arthur Lewbel, 2012. "Returns to Lying? Identifying the Effects of Misreporting When the Truth Is Unobserved," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 7(2), pages 163-192, June.
    • Handle: RePEc:fec:journl:v:7:y:2012:i:2:p:163-192
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    File URL: http://journal.hep.com.cn/fec/EN/10.3868/s060-001-012-0008-8
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    Cited by:

    1. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "Identifying the effect of a mis-classified, binary, endogenous regressor," Papers 2011.07272, arXiv.org.
    2. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    3. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Local Average and Marginal Treatment Effects with a Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Sep 2024.
    4. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.

    More about this item

    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • I2 - Health, Education, and Welfare - - Education

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