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Donna Chen, Brent Kreider, Elizabeth Merwin, and Steven Stern

Diagnosis Measurement Error and Corrected Instrumental Variables

Health diagnosis indicators used as explanatory variables in econometric models often suffer from
substantial measurement error. This measurement error can lead to seriously biased inferences
about the effects of health conditions on the outcome measure of interest, and the bias generally
spills over into inferences about the effects of policy/treatment variables. We generalize an existing
instrumental variables (IV) method to make it compatible with the types of instruments typically
available in large datasets containing health diagnoses. In particular, we relax the classical IV
assumption that the instruments must have uncorrelated measurement errors. We identify and
estimate the covariance matrix of the measurement errors and then use this information to derive
a correction term to mitigate or eliminate the bias associated with classical IV. Our Monte Carlo
simulations suggest that this corrected IV method can produce estimates far superior to those
produced by OLS or classical IV.