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Hierarchical multistate models from population data: an application to parity statuses
1Population Research Institute, Pennsylvania State University , University Park , PA , United States of America.
Peerj
|October 6, 2016
Summary
Hierarchical models can analyze population changes using state counts. New research reveals a shift in US women's childbearing patterns, with a growing number remaining childless.
Area of Science:
- Demography
- Population Studies
- Sociology
Background:
- Hierarchical models are defined by N states and N-1 transfer rates.
- Demographic measures can be derived from population counts at different time points.
- Analyzing population stocks aids in understanding population flows.
Purpose of the Study:
- To expand demographic analysis by using population stocks to determine flows in hierarchical models.
- To apply hierarchical models to childbearing patterns in US women.
- To construct a parity status life table for US women (2005-2010).
Main Methods:
- Utilizing hierarchical models to analyze demographic data.
- Calculating demographic measures directly from population counts over time.
- Constructing a parity status life table using US Census data on women's age and parity.
Main Results:
- Nearly a quarter of American women are projected to remain childless.
- A shift in childbearing patterns is observed, moving from a 2-4 child pattern to a 0-3 child pattern.
- The study provides a detailed analysis of childbearing trends based on parity status.
Conclusions:
- Hierarchical models offer a valuable tool for demographic analysis, particularly in understanding fertility trends.
- The findings indicate a significant change in US women's reproductive behavior.
- The constructed parity status life table provides insights into future childbearing trends.
Keywords:
ChildlessnessHierarchical modelsMultistate modelsParity status life tablesPolytreesSequential cross-sectionsMore Related Videos
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