Modeling Earnings Measurement Error: A Multiple Imputation Approach.

Recent survey validation studies suggest that measurement error in earnings data is pervasive and violates classical measurement error assumptions and, therefore, may bias estimation of cross-section and longitudinal earnings models. The authors model the structure of earnings measurement error using data from the Panel Study of Income Dynamics Validation Study (PSIDVS). They then use Donald B. Rubin's (1987) multiple imputation techniques to estimate consistent earnings equations under nonclassical earnings measurement error in the PSID. The authors' technique is readily generalized and the empirical results demonstrate the potential importance of correcting for measurement error in earnings and related data, particularly during recessions. Copyright 1996 by MIT Press.