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

Yingyao Hu and Arthur Lewbel

Frontiers of Economics in China-Selected Publications from Chinese Universities, 2012, vol. 7, issue 2, 163-192

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.

Keywords: binary regressor; misclassification; measurement error; unobserved factor; discrete factor; program evaluation; treatment effects; returns to schooling; wage model (search for similar items in EconPapers)
JEL-codes: C13 C14 C20 I2 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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http://journal.hep.com.cn/fec/EN/10.3868/s060-001-012-0008-8 (application/pdf)

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Working Paper: Returns to Lying? Identifying the Effects of Misreporting When the Truth is Unobserved (2009)
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