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.
More papers in Boston College Working Papers in Economics from Boston College Department of Economics Address: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA Contact information at EDIRC. Series data maintained by Christopher F Baum ().
This site is part of RePEc
and all the data displayed here is part of the RePEc data set.
Is your work missing from RePEc? Here is how to
contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .