Estimating the Variance of Measurement Errors in Running Variables of Sharp Regression Discontinuity Designs

Abstract

Estimation of a treatment effect by a regression discontinuity design faces a severe challenge when the running variable contains measurement errors since the errors smoothen the discontinuity on which the identification depends. The existing studies show that the variance of the measurement errors plays a vital role in both bias correction and identification under such situations. However, the methodologies to estimate the variance from data are relatively undeveloped. This paper proposes two estimators for the variance of measurement errors of running variables of sharp regression continuity designs. The proposed estimators can be constructed merely from data of observed running variable and treatment assignment, and do not require any other external source of information.