Identification of Temporal Patterns in Income and LivingConditions of Czech Households: Clustering Based onMixed Type Panel Data from the EU-SILC Database
Abstract
The EU-SILC database contains annually gathered rotating-panel data on a household level covering indicators of monetary poverty, severe material deprivation or low work household intensity. Data are obtained via questionnaires leading to outcome variables of diverse nature: numeric, binary, ordinal being gathered at each occasion in each household.
Only limited number of approaches exist in the literature to analyze such mixed-type panel data. We present a statistical model for such type of data which is built on a thresholding approach to linkbinary or ordinal variables to their latent numeric counterparts.
All, possibly latent, numeric outcomes are then jointly modelled using a multivariate version of the linear mixed-effects model. A mixture of such models is then used to model heterogeneity in temporal evolution of considered outcomes across households.
A Bayesian variant of the Model Based Clustering (MBC) methodology is finally exploited to classify households into groups with similar evolution of indicators of monetary poverty, material deprivation or low work household intensity. The method is applied to socially-economic focused dataset of Czech households gathered in a time span 2005-2016.