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dc.contributor.authorHu, Yingyaoen_US
dc.contributor.authorLewbel, Arthuren_US
dc.description.abstractConsider 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.en_US
dc.publisherJohns Hopkins Univ., Dep. of Economics Baltimore, Md.en_US
dc.relation.ispartofseriesWorking papers // the Johns Hopkins University, Department of Economics 540 [rev.]en_US
dc.subject.keywordBinary regressoren_US
dc.subject.keywordmeasurement erroren_US
dc.subject.keywordunobserved factoren_US
dc.subject.keyworddiscrete factoren_US
dc.subject.keywordprogram evaluationen_US
dc.subject.keywordtreatment effectsen_US
dc.subject.keywordreturns to schoolingen_US
dc.subject.keywordwage modelen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwStatistischer Fehleren_US
dc.titleIdentifying the returns to lying when the truth is unobserveden_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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