Old and alone? Projecting the future family situation of German elderly using SOCSIM

Harald Wilkoszewski, Max Planck Institute for Demographic Research

As population ageing is putting more and more pressure on European social security systems, political decision-makers increasingly demand projections about future demographic developments in order to plan political reforms accordingly. Germany is one of the European countries, where the political effects of early population ageing have been particularly evident over the past decade due to budget constraints, high unemployment, and very low fertility rates. At the same time detailed population projections for Germany are practically not available. This paper aims at providing future demographic prospects for Germany using SOCSIM, a demographic micro simulation programme based on Monte Carlo projections of kinship structures. In contrast to macro simulations, the fundamental unit in micro simulations is the individual member of the population. Main advantages of this method are its capacities to handle a large state space with many covariates, to explicitly retain the relation of individuals, and to provide a rich output including the probabilistic distribution of outcomes. Vital rates for the micro simulation in this paper are taken from Destatis, Eurostat, the Human Mortality Database and own estimations. Special attention will be paid to the family situation of the elderly, here defined as people aged 55+. The main goal of the simulation is to show future changes in the relative size of this age group and to highlight what kind of overall composition of the German population may occur. The number of elderly people is expected to grow significantly over the next decades, in particular those being childless over their whole life course. However, it still remains an open question, how big the magnitude of these changes will be. This is of high political relevance as people aged 55+ may be the main target group of future social policy reforms.

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Presented in Poster Session 1

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