Bevölkerungsdynamik und Nachhaltiges Wohlbefinden
Auf einen Blick
Schätzung und Prognose von Nachkommensterblichkeit über den Lebensverlauf für alle Länder der Welt
Diego Alburez-Gutierrez, Ugofilippo Basellini, Emilio Zagheni; in Zusammenarbeit mit Martin Kolk (Stockholm University, Demography Unit, Schweden)
The death of a child can be a life-changing event with long-term consequences for parents left behind. Studies in demography, sociology, and public health have shown that offspring mortality can affect the physical and mental health of bereaved parents. Child death and child survival also affect the availability of networks of support throughout the life course and the intergenerational transmission of resources (e.g., financial, social, emotional). Demographic research has shown a consistent decline in period mortality at young ages, particularly under-5 and under-1 mortality. However, these measures do not reflect the actual lived experience of mortality from the parental perspective. This is because a parent’s exposure to offspring mortality is determined by past and present demographic regimes, and over multiple generations, that are not captured by period measures of mortality. A new approach is thus needed to characterize the experience of child death from a parent’s perspective.
In this project, we develop a parent-centered measure of offspring mortality that is presently missing in the demographer’s toolbox. To this end, we introduce the “Kin-Cohort Method”, which builds on existing formal demographic approaches and uses simple demographic rates as input. This powerful analytical tool allows us to consider changes in the timing and variability of offspring mortality over time from a parental perspective. The approach aims at complementing population-level summaries of mortality (e.g., mortality rates) in order to understand the demographic mechanisms that influence the lived experience of child death in historical, contemporary, and future populations.
The research project combines methodological and empirical innovations to account for the way in which offspring mortality is affected by changes in mortality, fertility, and population structure around the world. A first component used the Kin-Cohort Method to estimate the prevalence and incidence of offspring mortality for every country in the world, using historical and projected fertility and mortality rates. Initial results have shown that child death will be more uncommon among women in the future and that it will increasingly shift to adult offspring rather than young offspring. On average, women born in 1950 can expect 88% of their children to outlive them. This compares to 93% for those born in the year 2000 . We also document persisting inequalities between the Global North and the Global South. A European woman currently aged 70 will have experienced, on average, 0.1 child deaths throughout her life. A same-aged woman born in sub-Saharan Africa will have lost 2.4 children.
A second component will focus on understanding the way in which offspring mortality is affected by factors unaccounted for by the Kin-Cohort Method, such as intergenerational correlations and mortality clustering in different populations. To do this, we will combine our analytical methods with empirical data from population-level register data and “big” online genealogical data sources. The latter are a rich source of demographic information containing millions of profiles, and these can provide insights into population behavior at an unprecedented temporal and geographic scale. This empirical analysis will initially focus on Sweden, where high-quality historical demographic and genealogical data are available for the same time period.
Alterung, Sterblichkeit und Langlebigkeit, Demografischer Wandel, Historische Demografie, intergenerationelle Beziehungen, Lebensverlauf
Coimbra Vieira, C.; Alburez-Gutierrez, D.; Nepomuceno, M. R.; Theile, T.:
In: WebSci '22: proceedings of the 14th ACM Web Science Conference, Barcelona, Spain, 26-29 June 2022, 185–190. New York: Association for Computing Machinery (ACM). (2022)
Alburez-Gutierrez, D.; Kolk, M.; Zagheni, E.:
Demography 58:5, 1715–1735. (2021)
Smith-Greenaway, E.; Alburez-Gutierrez, D.; Trinitapoli, J.; Zagheni, E.:
BMJ Global Health 6:4, e004837–e004837. (2021)
Williams, I.; Alburez-Gutierrez, D.:
MPIDR Working Paper WP-2021-001. (2021)
Alburez-Gutierrez, D.; Aref, S.; Gil-Clavel, B. S.; Grow, A.; Negraia, D. V.; Zagheni, E.:
In: Smart statistics for smart applications : book of short papers SIS2019, 23–30. Pearson. (2019)