Bias and inefficiency of an ordinary least squares estimator for logit regressions with continuous dependent variables measured with error

In a recent paper in this journal, Manning showed that the ordinary least squares (OLS) estimator of regression coefficients in a logit model with continuous and bounded dependent variable measured with error is unbiased but inefficient. This note shows that, contrary to the author's claim, the OLS estimator is biased in general. Manning's form of heteroscedasticity is also shown to be incorrect. It is shown that Manning's assumptions on the distribution of measurement error are inadequate for determining the form of heteroscedasticity.