Identifying the returns to lying when the truth is unobserved

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 540 [rev.]

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Estimation: General ; Single Equation Models; Single Variables: General
• Subject: Binary regressor ; misclassification ; measurement error ; unobserved factor ; discrete factor ; program evaluation ; treatment effects ; returns to schooling ; wage model ; Schätztheorie ; Nichtparametrisches Verfahren ; Statistischer Fehler ; Bildungsertrag ; Bildungsniveau

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hu, Yingyao ; Lewbel, Arthur
• Event: Veröffentlichung
• (who): The Johns Hopkins University, Department of Economics
• (where): Baltimore, MD
• (when): 2007

Further information

• Handle: http://hdl.handle.net/10419/49867
• Last update: 10.03.2025, 11:44 AM CET