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Returns to Lying? Identifying the Effects of Misreporting When the Truth Is Unobserved |
Yingyao Hu1(), Arthur Lewbel2() |
1. Department of Economics, Johns Hopkins University, Baltimore, MD 21218, USA; 2. Department of Economics, Boston College, Boston, MA 02467, USA |
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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 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.
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Keywords
binary regressor
misclassification
measurement error
unobserved factor
discrete factor
program evaluation
treatment effects
returns to schooling
wage model
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Corresponding Author(s):
Yingyao Hu,Email:yhu@jhu.edu; Arthur Lewbel,Email:lewbel@bc.edu
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Issue Date: 05 June 2012
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