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. We model the structure of earnings measurement error using data from the Panel Study of Income Dynamics Validation Study (PSDIVS). We then use Rubin's (1987) multiple imputation techniques to estimate consistent earnings equations under non-classical earnings measurement error in the PSID. Our 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.
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