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Identifying the Returns to Lying When the Truth is Unobserved

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Yingyao Hu
Arthur Lewbel

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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 returns to lying to be about 7% 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.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 540.

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Date of creation: Nov 2007
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Handle: RePEc:jhu:papers:540

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  1. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 1-21, June. [Downloadable!]
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  3. Xiaohong Chen & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments," CeMMAP working papers CWP17/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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  4. Thomas J. Kane & Cecilia Elena Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," NBER Working Papers 7235, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  5. Lewbel, Arthur, 2007. "A local generalized method of moments estimator," Economics Letters, Elsevier, vol. 94(1), pages 124-128, January. [Downloadable!] (restricted)
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  7. Chunrong Ai & Xiaohong Chen, 2003. "Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions," Econometrica, Econometric Society, vol. 71(6), pages 1795-1843, November. [Downloadable!] (restricted)
  8. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January. [Downloadable!] (restricted)
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  10. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, 03. [Downloadable!] (restricted)
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  11. Thomas J. Kane & Cecilia Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section.. [Downloadable!]
  12. Aprajit Mahajan, 2006. "Identification and Estimation of Regression Models with Misclassification," Econometrica, Econometric Society, vol. 74(3), pages 631-665, 05. [Downloadable!] (restricted)
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