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Regression Coefficient Identification Decay in the Presence of Infrequent Classification Errors

(forthcoming in Review of Economics and Statistics)

Brent Kreider, Iowa State University

Abstract.  Recent evidence from Bound et al. (2001) and Black et al. (2003) suggests that reporting
errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics
literature has not considered the consequences of fully arbitrary measurement error for identification of
regression coefficients. This paper highlights the severity of the identification problem given the presence
of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance
misclassification rates of less than 1.3 percent generate double-digit percentage point ranges of uncertainty
about the variable's true marginal effect on the use of health services.