UC Berkeley

Although the study of mortality is central to demography, comparatively little is known about its lived experience: how population-level mortality translates to individual experiences of loss within families and communities. Given the significance of bereavement for the health and socioeconomic outcomes of survivors, better understanding the impacts of mortality crises on family networks is crucial for predicting their longer-term consequences. The data required to study kin loss, however, is seldom available, leading many demographers to rely on computational approaches such as microsimulation to estimate the impact of these events.

This dissertation presents microsimulation-based approaches to examining the impact of a mortality crisis, in this case COVID-19, on kin networks in the short and long term. The first study presents estimates of monthly excess kin loss by age and type of kin relation in 31 countries during the period of March 2020 to June 2021. These estimates demonstrate a generational pattern of kin loss reflecting COVID-19 age-specific excess mortality risk, and highlight the significant effect of excess mortality on family bereavement. The second study extends this estimation approach to 120 countries over the 2020-2021 period, documenting high rates of excess kin loss in many low-and-middle-income countries higher than or comparable to those observed in high-income countries. It also considers the extent to which differences in country estimates were shaped by both excess mortality and pre-existing kinship structure. The third study projects how "kinship memory'', the estimated share of national populations related to victims of COVID-19 excess mortality, may change over the next century in 120 countries around the world, and considers what this may mean for how this crisis is remembered in the future. These three studies combined highlight the significant burden of COVID-19 excess mortality in terms of bereavement experienced by surviving family members, and demonstrate the importance of computational approaches in helping better understand the experiences of populations for which limited data exists.