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A general algorithm for the decomposition of differences between two values of an aggregate demographic measure in respect to age and other dimensions is proposed. It assumes that the aggregate measure is computed from similar matrices of discrete demographic data for two populations under comparison. The algorithm estimates the effects of replacement for each elementary cell of one matrix by respective cell of another matrix. Application of the algorithm easily leads to the known formula for the age-decomposition of differences between two life expectancies. It also allows to develop new formulae for differences between healthy life expectancies. In the latter case, each age-component is split further into effects of mortality and effects of health. The application of the algorithm enables a numerical decomposition of the differences between total fertility rates and between parity progression ratios by age of the mother and parity. Empirical examples are based on mortality data from the USA, the UK, West Germany, and Poland and on fertility data from Russia.

This paper presents a toolkit for measuring and analysing inter-individual inequality in length of life by Gini coefficient. Gini coefficient is treated as an additional function of the life table. A new method for the estimation of Gini coefficient from life table data has been developed and tested on the basis of hundreds of life tables. The method provides precise estimates of Gini coefficient for abridged life tables even if the last age group is 85+. New formulae have been derived for the decomposition of differences in Gini coefficient by age and cause of death. A method for further decomposition of age-components into effects of mortality and population group has been developed. It permits the linking of inter-individual inequalities in length of life with inter-group inequalities. Empirical examples include the decomposition of secular decrease in Gini coefficient in the USA by age, decomposition of the difference in Gini coefficient between the UK and the USA by age and cause of death, temporal changes in the effects of elimination of causes of death on Gini coefficient, and decomposition of changes in Gini coefficient in Russia by age and educational group. Consideration of the variations in Gini coefficient during the last decades and across modern populations show that these variations are driven not only by historical shifts in the distribution of deaths by age, but also by peculiar health and social situations.

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This study confirms a stronger genetic influence in CD than in UC. The high preponderance in being affected of the first-born twin and the fact that concordance was only 35% for CD and 16% for UC monozygotic twins highlight the important role of environmental trigger factors.