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
