Periodic Reporting for period 2 - Unequal Lifespans (Inequalities in Lifespans before and after Retirement: Trailblazing Demographic Theory and Analysis)
Periodo di rendicontazione: 2022-07-01 al 2023-06-30
How unequal are lifespans at older ages? How unequal will they be in the future?
Given the relevance of these questions—for assessing pension reforms and other social and health policies—it is remarkable how little demographic modeling has been devoted to inequalities at older ages in individual lifespans.
The research project is organized into three ambitions that aim to:
- broaden discourse and conceptually shift thinking about retirement to include individual lifespan inequalities,
- establish a demographic theory of old-age mortality and test the hypothesis that progress is being made in cutting death rates after age 90, and
- develop a forecasting method to predict lifespan distributions (and inequalities) based on strong regularities of mortality trajectories at older ages, and to quantify the uncertainties in these predictions.
Current forecasting methods appear inadequate for capturing likely reductions in death rates at ages when most people die. Preliminary findings suggest that this project will reveal new perspectives on lifespans at older ages as well as provide novel input for discussions of the challenges of raising retirement ages.
Given the relevance of these questions—for assessing pension reforms and other social and health policies—it is remarkable how little demographic modeling has been devoted to inequalities at older ages in individual lifespans.
The research project is organized into three ambitions that aim to:
- broaden discourse and conceptually shift thinking about retirement to include individual lifespan inequalities,
- establish a demographic theory of old-age mortality and test the hypothesis that progress is being made in cutting death rates after age 90, and
- develop a forecasting method to predict lifespan distributions (and inequalities) based on strong regularities of mortality trajectories at older ages, and to quantify the uncertainties in these predictions.
Current forecasting methods appear inadequate for capturing likely reductions in death rates at ages when most people die. Preliminary findings suggest that this project will reveal new perspectives on lifespans at older ages as well as provide novel input for discussions of the challenges of raising retirement ages.
Studying and developing meaningful lifespan inequality measures was the first major aim. The cross-sectional average inequality in lifespan (Nepomuceno et al., 2022) is a novel measure that relates the variation of lifespans in a given year to the experience of the cohorts present in the study population. The dynamics in Drewnowski's index (Aburto et al., 2022) and outsurvival (Bergeron-Boucher et al., 2022) uncover useful relationships with life expectancy and lifespan inequality, while mortality crises (Vigezzi et al., 2022) and violence (Aburto et al., 2023) appear to be major determinants of high dispersion in lifetime uncertainty. In addition, a major breakthrough is developing a decomposition method for lifespan inequality (Permanyer et al., 2023) that accounts not only for inter- and intra-group differences, but also for the average inter-individual difference.
Quantifying the progress in old-age mortality, as well as developing relevant mathematical and forecasting models inspires the second major set of findings. Missov et al. (forthcoming) propose a novel method to accurately estimate (with uncertainty bounds) the rates of mortality improvement at the oldest ages: while age-specific death rates until age 100 in ten European countries decrease at an average pace from 0.5% to 2% per year, the mortality progress for centenarians is negligibly small. In two forthcoming papers, Patricio and Missov develop a mathematical framework of old-age mortality based on competing risks that allows separating old-age from premature deaths: differences in mortality between populations are mostly due to differences in premature deaths. Vazquez-Castillo et al. (forthcoming) develop a model for accurately estimating the modal age at death. The latter is used by Bergeron-Boucher et al. (forthcoming) to develop a model to forecast the age-at-death distribution that directly forecasts the modal age at death and lifespan variation while accounting for dependence between ages. As it takes advantage of the almost linear increase in the mode, the introduced model increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.
Quantifying the progress in old-age mortality, as well as developing relevant mathematical and forecasting models inspires the second major set of findings. Missov et al. (forthcoming) propose a novel method to accurately estimate (with uncertainty bounds) the rates of mortality improvement at the oldest ages: while age-specific death rates until age 100 in ten European countries decrease at an average pace from 0.5% to 2% per year, the mortality progress for centenarians is negligibly small. In two forthcoming papers, Patricio and Missov develop a mathematical framework of old-age mortality based on competing risks that allows separating old-age from premature deaths: differences in mortality between populations are mostly due to differences in premature deaths. Vazquez-Castillo et al. (forthcoming) develop a model for accurately estimating the modal age at death. The latter is used by Bergeron-Boucher et al. (forthcoming) to develop a model to forecast the age-at-death distribution that directly forecasts the modal age at death and lifespan variation while accounting for dependence between ages. As it takes advantage of the almost linear increase in the mode, the introduced model increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.
Permanyer et al. (2023) make a major breakthrough in decomposition methods by setting up a procedure that quantifies the group and individual differences in lifetime inequality measures. In the absence of violent deahts, Aburto et al. (2023) find an astonishing similarity in lifespan inequality across all continents.
Missov et al. (forthcoming) develop a new statistical method, the segmented quantile regression, to come up with estimates and uncertainty intervals for the current rates of mortality improvement (CRMI) at the oldest ages. These estimates can be used as input of mortality forecasts and increase the accuracy of the latter. In addition, Patricio and Missov (in two forthcoming articles) come up with a competing-risk model for senescent and non-senescent (premature) deaths, in which the two components can be estimated separately. While senescent patterns of human populations are astonishingly similar, it is mostly the non-senescent patterns that account for inter-population differences in mortality.
Bergeron-Boucher et al. (forthcoming) take advantage of a regularity, the almost linear increase of the modal age at death, to come up with a novel, high-accuracy, but easy to implement forecasting method for the age-at-death distribution. This is reassuring that incorporating steady mortality regularities increase the accuracy of mortality forecasts.
Missov et al. (forthcoming) develop a new statistical method, the segmented quantile regression, to come up with estimates and uncertainty intervals for the current rates of mortality improvement (CRMI) at the oldest ages. These estimates can be used as input of mortality forecasts and increase the accuracy of the latter. In addition, Patricio and Missov (in two forthcoming articles) come up with a competing-risk model for senescent and non-senescent (premature) deaths, in which the two components can be estimated separately. While senescent patterns of human populations are astonishingly similar, it is mostly the non-senescent patterns that account for inter-population differences in mortality.
Bergeron-Boucher et al. (forthcoming) take advantage of a regularity, the almost linear increase of the modal age at death, to come up with a novel, high-accuracy, but easy to implement forecasting method for the age-at-death distribution. This is reassuring that incorporating steady mortality regularities increase the accuracy of mortality forecasts.