Periodic Reporting for period 4 - FORMKIN (The formal demography of kinship and family)
Reporting period: 2022-12-01 to 2024-05-31
Any individual is surrounded by a network of kin (parents and children, siblings, aunts, nieces, cousins,...). This network is shaped by the schedules of mortality and fertility in the population. The goal of this project was to develop and apply a formal demographic theory for kinship networks based on demographic information.
Kinship and family structures are critical and rapidly changing components of society. Relatives provide and receive services and support from each other; think of grandparents helping to care for grandchildren, or of inter-generational bequests, or of the need for families to care for their elderly parents, grandparents, and other relatives. These types of family structures have changed dramatically in recent history. Yet, until this project there have been only partial solutions to the problem of how kinship structures are determined.
The overall objectives of the project were (1) to develop the necessary theory for kinship dynamics, using new classes of population models, (2) to extend those models to include aspects of kinship not previously calculated, and (3) to apply the new models to data that permit comparisons among countries, historical periods, ethnic groups, and economic conditions.
Conclusions: The theory for kinship dynamics has been developed in a series of six (to date) major papers. The first developed the model for time-invariant, one-sex, deterministic, age-classified populations. This was then generalized to time-varying, two-sex, stochastic, and multistate-classifications, and to include both living and dead kin. The model has been applied to project kinship dynamics for all countries in the world, to compare kinship structures across racial groups, to explore changes during the demographic transition, and to project the kin available to help care for relatives suffering from dementia. An R package has been developed that implements the model. The model has been adopted by research groups in other institutions.
Kinship and family structures are critical and rapidly changing components of society. Relatives provide and receive services and support from each other; think of grandparents helping to care for grandchildren, or of inter-generational bequests, or of the need for families to care for their elderly parents, grandparents, and other relatives. These types of family structures have changed dramatically in recent history. Yet, until this project there have been only partial solutions to the problem of how kinship structures are determined.
The overall objectives of the project were (1) to develop the necessary theory for kinship dynamics, using new classes of population models, (2) to extend those models to include aspects of kinship not previously calculated, and (3) to apply the new models to data that permit comparisons among countries, historical periods, ethnic groups, and economic conditions.
Conclusions: The theory for kinship dynamics has been developed in a series of six (to date) major papers. The first developed the model for time-invariant, one-sex, deterministic, age-classified populations. This was then generalized to time-varying, two-sex, stochastic, and multistate-classifications, and to include both living and dead kin. The model has been applied to project kinship dynamics for all countries in the world, to compare kinship structures across racial groups, to explore changes during the demographic transition, and to project the kin available to help care for relatives suffering from dementia. An R package has been developed that implements the model. The model has been adopted by research groups in other institutions.
1. Theoretical development. The state of the art in the formal demography of kinship, at the beginning of this project, was limited to deterministic, time-invariant, one-sex, age-classified models for living kin. The project has removed those limitations by developing a single model framework that can include stochasticity, time-variation, two sexes, multistate classification, and account for living and dead kin. Extensions to theory for kin overlap and shared lives and to the sensitivity analysis to changes in the demographic rates are in the final stages of development.
2. Applications of the kinship model. The model has been applied to several important developments in human demography. It has been used to study racial inequality in unemployment among relatives, to study changes in kinship over the demographic transition, and in a global projection of kinship structures in every country in the world. Other researchers have used the model to study kin deaths due to violence.
3. Health demography. The health of kin is an important factor in anyone's life. Two major studies have been completed using the kinship model to study the burden of dementia among relatives. In one case the application has been to racial groups in the United States. In the other case, the analysis has focused on future changes in the kinship network in China, with global comparisons. Other studies of health, including length of life lost due to Covid and a general method for analyzing incidence-based health models, provide tools that will be applied to the kinship network.
4. Variance within and among groups. Kinship networks vary among individuals; some individuals have many relatives, others few, even when subject to the same mortality and fertility conditions. This has led to a detailed analysis of the variance (sometimes called inequality) in demographic outcomes, and to partitioning it into components due to heterogeneity and to stochastic processes. This development has proceeded on three fronts: explicit application to the kinship network, statistical methods for analyzing mixture distributions, and application to both human and animal demography. The results raise (and sometimes answer) fundamental questions about inequality (of opportunity and of outcome), heterogeneity, and chance.
2. Applications of the kinship model. The model has been applied to several important developments in human demography. It has been used to study racial inequality in unemployment among relatives, to study changes in kinship over the demographic transition, and in a global projection of kinship structures in every country in the world. Other researchers have used the model to study kin deaths due to violence.
3. Health demography. The health of kin is an important factor in anyone's life. Two major studies have been completed using the kinship model to study the burden of dementia among relatives. In one case the application has been to racial groups in the United States. In the other case, the analysis has focused on future changes in the kinship network in China, with global comparisons. Other studies of health, including length of life lost due to Covid and a general method for analyzing incidence-based health models, provide tools that will be applied to the kinship network.
4. Variance within and among groups. Kinship networks vary among individuals; some individuals have many relatives, others few, even when subject to the same mortality and fertility conditions. This has led to a detailed analysis of the variance (sometimes called inequality) in demographic outcomes, and to partitioning it into components due to heterogeneity and to stochastic processes. This development has proceeded on three fronts: explicit application to the kinship network, statistical methods for analyzing mixture distributions, and application to both human and animal demography. The results raise (and sometimes answer) fundamental questions about inequality (of opportunity and of outcome), heterogeneity, and chance.
The model structure describing kinship dynamics is a totally new development of matrix population models; it expresses the age structure of the kinship network as a coupled set of matrix difference equations for the kin at every age of a focal individual. The multistate version of the model combines age structure with other criteria, such as sex, parity, or health status. The time-varying version of the model allows the demographic rates to change over time, both in the historic past and in projections of future conditions. The two-sex version of the model permits the mortality and/or fertility rates to differ between males and females. Because the experience of the death of relatives has such important psychological, social, and medical effects, the version of the model incorporating bereavement includes deaths classified by age and cause of death. The stochastic version of the model provides, for the first time, not only the mean kinship network but the variances and covariances of kin numbers due to demographic stochasticity.