Can Migrants save Greece from Ageing? A Computable General Equilibrium Approach using G-AMOS.

The population of Greece is projected to age in the course of the next three decades. This paper combines demographic projections with a multi-period economic Computable General Equilibrium (CGE) modelling framework to assess the macroeconomic impact of these future demographic trends. The simulation strategy adopted in Lisenkova et. al. (2008) is also employed here. The size and age composition of the population in the future depends on current and future values of demographic parameters such as the fertility, mortality rates and the level of annual net migration. We use FIV-FIV software in order to project population changes for 30 years. Total population and working age population changes are introduced to the GAMOS modelling framework calibrated for the Greek economy for the year 2004. Positive net migration is able to cancel the negative impacts of an ageing population that would otherwise occur as a result of the shrinking of the labour force. The policy implication is that a viable, long-lasting migration policy should be implemented, while the importance of policies that could increase fertility should also be considered.

Volatility of Housing

The aim of the project is to develop a theoretical framework where homelessness arises due to various economic and social factors that vary over time. The ultimate goal is i) to understand whether homelessness spells, entrances and exits could be predicted and if so what information is necessary; and ii) to design and evaluate a homelessness prevention programme in a changing and uncertain environment. Examples of the questions we want to answer are: Should it be made easier for people to borrow money so that they can get out of homelessness, or will such borrowing allow people to over-consume today and so fall into homelessness tomorrow? Should precautionary savings be encouraged so that people have cushions to withstand future shocks, or will savings just delay entry into homelessness? What interventions will affect the probability of becoming homeless and how will they affect behaviour? How will interventions affect incentives to save and to consume before homelessness prevention programmes kick in?