How Well are Earnings Measured in the Current Population Survey? Bias from Nonresponse and Proxy respondents

Earnings nonresponse is currently about 30 % in the CPS-ORG and 20 % in the March CPS. Census imputes missing earnings by assigning nonrespondents the reported earnings of matched donors. Even if nonresponse is random, in a wage equation there exists severe “match bias ” on coefficients attached to non-match imputation attributes (union status, industry, foreign-born, etc.) and imperfectly matched criteria (schooling, age, etc.). Bollinger-Hirsch (JOLE, July 2006) show that if nonresponse is conditional missing at random (i.e., ignorable nonresponse) then unbiased wage equation estimation can be achieved in several ways, most easily by deleting imputed earners from the sample. The focus of this paper is twofold. First, we examine whether or not nonresponse is ignorable in the CPS-ORG and March CPS. Second, we assess the effect of “proxy ” respondents on earnings, since roughly half of CPS records are based on self-responses and half on responses from another household member. Earnings nonresponse varies little with respect to most earnings attributes, but is noticeably highest among those in the top percentiles of the predicted earnings distribution. Wage effects due to nonresponse and proxy reports are estimated using selection models and longitudinal analysis. Based on reasonable instruments to identify selection, we conclude that there is negative selection into response among men, but relatively little selection among women. Wage equation slope coefficients are affected little by selection (as compared t