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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Length: 35 pages Date of creation: 28 Nov 2007 Date of revision:
16 Jun 2009 Handle: RePEc:boc:bocoec:678
Note: previously titled "Identifying the Returns to Lying When the Truth is Unobserved" Contact details of provider: Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA Phone: 617-552-3670 Fax: +1-617-552-2308 Email: Web page: http://fmwww.bc.edu/EC/ More information through EDIRC
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Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General I2 - Health, Education, and Welfare - - Education
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