We introduce a new modelling framework to explain socio-economic differences in mortality in terms of an affluence index that combines information on individual wealth and income. The model is illustrated using data on older Danish males over the period 1985–2012 reported in the Statistics Denmark national register database. The model fits the historical mortality data well, captures their key features, generates smoothed death rates that allow us to work with a larger number of sub-groups than has previously been considered feasible, and has plausible projection properties.

This study analyzes the complexity of female longevity improvements. As socioeconomic status is found to influence health and mortality, we partition all individuals, at each age in every year, into five socioeconomic groups based on an affluence measure that combine an individual's income and wealth. We identify the particular socioeconomic groups that have been driving the standstill for Danish females at older ages. Within each socioeconomic group, we further analyze the cause of death patterns. The decline in life expectancy for Danish females is present for four out of five subgroups, however, with particular large decreases for the low-middle and middle-affluence groups. Cancers, smoking-related lung and bronchus causes, and other diseases particularly contribute to the stagnation. For four of the five socioeconomic groups only small cardiovascular improvement are experienced during the period of stagnating life expectancy compared to an equally long and subsequent period.

The prototypical Lee–Carter mortality model is characterized by a single common time factor that loads differently across age groups. In this paper, we propose a parametric factor model for the term structure of mortality where multiple factors are designed to influence the age groups differently via parametric loading functions. We identify four different factors: a factor common for all age groups, factors for infant and adult mortality, and a factor for the “accident hump” that primarily affects mortality of relatively young adults and late teenagers. Since the factors are identified via restrictions on the loading functions, the factors are not designed to be orthogonal but can be dependent and can possibly cointegrate when the factors have unit roots. We suggest two estimation procedures similar to the estimation of the dynamic Nelson–Siegel term structure model. First, a two-step nonlinear least squares procedure based on cross-section regressions together with a separate model to estimate the dynamics of the factors. Second, we suggest a fully specified model estimated by maximum likelihood via the Kalman filter recursions after the model is put on state space form. We demonstrate the methodology for US and French mortality data. We find that the model provides a good fit of the relevant factors and, in a forecast comparison with a range of benchmark models, it is found that, especially for longer horizons, variants of the parametric factor model have excellent forecast performance.

We introduce a new modelling framework to explain socioeconomic differences in mortality in terms of an affluence index that combines information on individual wealth and income. The model is illustrated using data on older Danish males over the period 1985-2012 reported in the Statistics Denmark national register database. The model fits the historical mortality data well, captures their key features, generates smoothed death rates that allow us to work with a larger number of subgroups than has previously been considered feasible, and has plausible projection properties.