  I partially identify the marginal treatment effect (MTE) when the treatment
is misclassified. I explore two restrictions, allowing for dependence between
the instrument and the misclassification decision. If the signs of the
derivatives of the propensity scores are equal, I identify the MTE sign. If
those derivatives are similar, I bound the MTE. To illustrate, I analyze the
impact of alternative sentences (fines and community service v. no punishment)
on recidivism in Brazil, where Appeals processes generate misclassification.
The estimated misclassification bias may be as large as 10% of the largest
possible MTE, and the bounds contain the correctly estimated MTE.
