The German Microcensus (MC) is a large scale rotating panel survey over three years. The MC is attractive for longitudinal analysis over the entire participation duration because of the mandatory participation and the very high case numbers (about 200 thousand respondents). However, as a consequence of the area sampling that is used for the MC , residential mobility is not covered and consequently statistical information at the new residence is lacking in theMCsample. This raises the question whether longitudinal analyses, like transitions between labour market states, are biased and how different methods perform that promise to reduce such a bias. Based on data of the German Socio-Economic Panel (SOEP), which covers residential mobility, we analysed the effects of missing data of residential movers by the estimation of labour force flows. By comparing the results from the complete SOEP sample and the results from the SOEP, restricted to the non-movers, we concluded that the non-coverage of the residential movers can not be ignored in Rubin’s sense. With respect to correction methods we analysed weighting by inverse mobility scores and loglinear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60 percent whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80 percent. The estimation of loglinear models for nonignorable nonresponse leads to very unstable results.