In this study we apply the demographic methodology of multi-state population projection to the task. This method is based on a multi-dimensional expansion of the life table (increment-decrement tables) and of the cohort-component projection method, developed at IIASA during the 1970s.
The approach and results appeared in 2001 in Population and Development Review (Lutz and Goujon 2001). Population projections by level of education are a logical next step to improving population forecasts and to make them more relevant.
As discussed in Lutz et al. (1999), adding education to age and sex as an explicitly considered demographic dimension in population forecasting also effects the demographic output parameters themselves because a significant source of so far unobserved heterogeneity is observed and endogenized explicitly. Therefore, it may be considered an improvement even of the purely demographic output parameters of the projection. More importantly, however, the overriding substantive importance of education means that the future educational composition of the population is of interest in its own right.
The specific structure of the multi-state model chosen for this study can be seen in the figure. The educational projections presented here are based on the basic assumptions of the demographic projections developed by Lutz et al. (2001). The fertility, mortality, and migration assumptions follow the median paths of their uncertainty distributions. In addition, three alternative educational scenarios are defined on the basis of different sets of transition rates between educational groups.
The research subdivides the population into four distinct groups according to educational attainment.
Each subpopulation is further stratified by age (five-year age groups) and sex, and can be represented through a separate population pyramid.
The key parameters of the model are three sets of age- and sex-specific educational transition rates,namely, the age-specific intensities for young men or women to move, for example, from the category of primary educational attainment to that of secondary attainment. Another important feature that gives this model a dynamic element is the fact that it considers different fertility rates for different educational groups. Migration and mortality are only considered by age and sex in this application.
Last edited: 22 July 2013
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