Longitudinal analysis of Swiss Labour Force Survey data by multivariate logistic regression

In longitudinal surveys, simple estimates of change, such as differences of percentages may not always be efficient enough to detect changes of practical relevance, especially in sub-populations. The use of models, which can represent the dependence structure of the longitudinal survey, can help to solve this problem. One of the main characteristics observed by the Swiss Labour Force Survey (SLFS) is the employment status. As the survey is designed as a rotating panel, the data from the SLFS are multivariate categorical data, where a large proportion of the response profiles are missing by design. The multivariate logistic model, introduced by Glonek and McCullagh (1995) as a generalisation of logistic regression, is attractive in this context, since it allows for dependent repeated observations and incomplete response profiles. We show that, using multivariate logistic regression, we can represent the complex dependence structure of the SLFS by a small number of parameters, and obtain more efficient estimates of change.