Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, ...Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric 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 av- erage 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 ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.展开更多
文摘Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric 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 av- erage 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 ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.
