The formal demography of kinship VI
Demographic stochasticity and variance in the kinship network
Bibliographic Data
| ID | 7743972 |
|---|---|
| Authors | Hal Caswell (0000-0003-4394-6894, corresponding author) |
| Year | 2024 |
| Volume | 51 |
| Pages | 1201-1256 |
| Publication date | 2024-11-19 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Demographic Research (JOURNAL) |
| Journal identifiers | ISSN: 1435-9871 • E-ISSN: 2363-7064 |
| Publisher | Max Planck Institute for Demographic Research (PUBLISHER • DE) |
| DOI | 10.4054/demres.2024.51.39 |
| OpenAlex | W4404471521 |
| Language | EN |
| Citations received | 4 |
| References cited | 52 |
BACKGROUND: Although the matrix model for kinship networks includes many demographic processes, it is deterministic. It provides values of age-stage distributions of kin, but no information on (co)variances. Because kin populations are small, demographic stochasticity is expected to create appreciable inter-individual variation. OBJECTIVE: To develop a stochastic kinship model that includes demographic stochasticity and projects (co)variances of kin age distributions, and functions thereof. METHODS: Kin populations are described by multitype branching processes. Means and covariances are projected using matrices that are generalizations of the deterministic model. The analysis requires only an age-specific mortality and fertility schedule. Both linear and nonlinear transformations of the kin age distribution are treated as outputs accompanying the state equations. RESULTS: The stochastic model follows the same mathematical framework as the deterministic model, modified to treat initial conditions as mixture distributions. Variances in numbers of most kin are compatible with Poisson distributions. Variances for parents and ancestors are compatible with binomial distributions. Prediction intervals are provided, as are probabilities of having at least one or two kin of each type. Prevalences of conditions are treated either as fixed or random proportions. Dependency ratios and their variances are calculated for any desired group of kin types. An example compares Japan under 1947 rates (high mortality, high fertility) and 2019 rates (low mortality, low fertility). CONTRIBUTION: Previous presentations of the kinship model have acknowledged the limitation to expected values. That limitation is now removed; both means and variances are easily calculated with minimal modification of code
Covariance · Econometrics · Economics · Geography · Kinship · Sociology · Statistics · Demographic Trends and Gender Preferences · Income, Poverty, and Inequality · Insurance, Mortality, Demography, Risk Management · Mathematics
Microsimulation in Demographic Research
Tracking the reach of Covid-19 kin loss with a bereavement multiplier applied to the United States
Family formation and the frequency of various kinship relationships
Kinship resources for the elderly
Projections of white and black older adults without living kin in the United States, 2015 to 2060
Worldwide trends in underweight and obesity from 1990 to 2022
Quantifying Economic Dependency
The Role of Kinship in Racial Differences in Exposure to Unemployment
Correlations between frequencies of kin
The Eventual Frequencies of Kin in a Stable Population
Shared Lifetimes, Multigenerational Exposure, and Educational Mobility
Lifetime reproduction and the second demographic transition
Economic support ratios and the demographic dividend in Europe
The "Sandwich Generation" Revisited
Ancestry Matters
The Impact of the HIV/Aids Epidemic on Kinship Resources for Orphans in Zimbabwe
Long-Term Effects of the Demographic Transition on Family and Kinship Networks in Britain
Kinmatrix
The formal demography of kinship V
How does the demographic transition affect kinship networks
The formal demography of kinship III
The formal demography of kinship II
The formal demography of kinship
Tracing very long-term kinship networks using Socsim
| Unique citing works | 4 |
|---|---|
| Citations per year | 4 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |