MODELING EARNINGS MEASUREMENT ERROR - A MULTIPLE IMPUTATION APPROACH

Recent survey validation studies suggest that measurement error in ear nings data is pervasive and violates classical measurement error assum ptions, and therefore may bias estimation of cross-section and longitu dinal earnings models. We model the structure of earnings measurement error using data from the Panel Study of Income Dynamics Validation St udy (PSIDVS). We then use Rubin's (1987) multiple imputation technique s to estimate consistent earnings equations under non-classical earnin gs measurement error in the PSID. Our technique is readily generalized , and the empirical results demonstrate the potential importance of co rrecting for measurement error in earnings and related data, particula rly during recessions.