Most population projections forecast the population using only demographic characteristics

(age and sex), but the inclusion of additional dimension such as education

(Lutz et al. 2014) and sociocultural variables (Bélanger et al. 2019) is an emerging

approach in the social sciences (Spielauer 2010). Indeed, in addition to providing

a richer set of outputs, including additional dimensions provides more flexibility

in the generation of policy-relevant alternative projection scenarios. Furthermore, it

improves the overall quality of the projection, as more sources of heterogeneity are

considered, which also allows for a more refined modeling of demographic events.

Traditional demographic projections using the cohort-component method can only

provide outcomes related to the age and sex structure of a population. When extended

to multistate and multiregional applications (Rogers 1980, 1995), more dimensions

can also be added (such as region or education). Microsimulation is a powerful

tool that can be used to create population projections when the number of dimensions

becomes large. Such a model is very flexible and characterised by the stochastic

simulation of individual life courses based on derived parameters and individual characteristics

(Van Imhoff and Post 1998). Until the late 90s, computer power was not

sufficient to use microsimulation for very complex population projection.