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Title:Identifying the returns to lying when the truth is unobserved PDF Logo
Authors:Hu, Yingyao
Lewbel, Arthur
Issue Date:2007
Series/Report no.:Working papers // the Johns Hopkins University, Department of Economics 540 [rev.]
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.
Subjects:Binary regressor
misclassification
measurement error
unobserved factor
discrete factor
program evaluation
treatment effects
returns to schooling
wage model
JEL:C14
C13
C20
I2
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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