Philippe Liégeois

Income growth in the 21st century: Forecasts with an overlapping generations model

We forecast income growth over the period 2000-2050 in the US, Canada, and France. To ground the forecasts on relationships that are as robust as possible to changes in the environment, we use a quantitative theoretical approach which consists in calibrating and simulating a general equilibrium model. Compared to existing studies, we allow for life uncertainty and migrations, use generational accounting studies to link taxes and public expenditures to demographic changes, and take into account the interaction between education and work experience. Forecasts show that growth will be weaker over the period 2010-2040. The gap between the US and the two other countries is increasing over time. France will catch-up and overtake Canada in 2020. Investigating alternative policy scenarios, we show that increasing the effective retirement age to 63 would be most profitable for France, reducing its gap with the US by one third. A decrease in social security benefits would slightly stimulate growth but would have no real impact on the gap between the countries.

Vieillissement et inégalités intergéné rationnelles en france

This paper investigates the impact of demographic shocks and budgetary policy on the welfare of all generations. For this purpose, we build a large scale OLG model that captures most crucial variables for the analysis of population shocks conse- quences in France. We introduce life uncertainty (implying precautionary saving) and a bequest motive (stimulating wealth accumulation at old age) so as to repro- duce realistic wealth profiles per age. The microeconomic foundations provide an intertemporal indicator of welfare for each generation. Five policy scenarios are distinguished (indexation of pension through transfer adjustments, imperfect index- ation of transfers on growth in order to keep the tax rate constant, the creation of an implicit fund, increase in the public debt, or increase in the retirement age).

Simulating Computable Overlapping Generations Models with Troll

In this paper, we examine the performance of the TROLL stacked-time algorithm in the simulation of large scale overlapping generations (OLG) models. At each period of time, the number of equations is proportional to the individual length of lifetime. The model size and the data requirements may thus be very large. Given the repetitive structure of the equations, we show how TROLL specific macrocommands can be used to explode a generic version (in which the lifetime is parameterized) into a complete model. A similar technique applies to explode the initial dataset on the whole simulation horizon. The stability properties of the model are derived and the stacked-time algorithm performances are checked for a large scale model with endogenous labour supply and uncertain lifetime. It turns out that TROLL performances are very attractive even for a system of 300000 simultaneous equations.