Measuring Measurement Error

Although proxy variables are pervasive in empirical work, the quality of proxy variables---in terms of how closely they track underlying economic forces---is not known. We derive novel regression specifications to infer the severity of measurement error using a sample of 2,552 instrumental variables regressions from 323 papers published in top economics and finance journals. We estimate that over 30% of the variation in the average regressor is white noise. For some proxies, our estimates exceed 95%. Our findings suggest that measurement error is a severe, pervasive, and understated source of bias in economics and finance