Functional data analysis approach in population studies: an application to the gender gap in life expectancy

Abstract

AbstractThis work analyses the contribution of ages and causes of death to gender gap in life expectancy in 20 European and non-European countries between 1959 and 2015, using Functional Data Analysis. Data were retrieved from the WHO Mortality Database and from the Human Mortality Database. We propose a Functional Principal Component Analysis of the age profiles of cause-specific contributions, to identify the main components of the distribution of the age-specific contributions according to causes of death, and to summarize them with few components. Our findings show that the narrowing gender gap in life expectancy was mainly driven by decreasing differences in cardiovascular diseases. Additionally, the study reveals that the age cause contributions act almost entirely on only two dimensions: level (extent of the cause-specific contribution to the overall mortality gender gap) and age pattern (location of the curves across ages). Notably, in the last period, it is not the "quantum" of the cause-specific contributions that matters, but the "timing", i.e. location across the age spectrum. Moreover, our results show that in the most recent period the gender gap in life expectancy is affected by composition of the causes of death more than it was in previous periods. We emphasise that Functional Data Analysis could prove useful to deepen our understanding of complex demographic phenomena.