• Vienna Institute of Demography (Ed.) - Miguel Sánchez-Romero - Michaela Kreyenfeld - Iñaki Permanyer - Michaela Potančoková - Vanessa di Lego (Guest Eds.)

Vienna Yearbook of Population Research 2025

Population inequality matters

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Research Articles

Inequality, heterogeneity, and chance: Multiple factors and their interactions
Hal Caswell - Silke F. van Daalen

Life tables under current risk composition based on observed, fixed characteristics
Magdalena Muszyńska-Spielauer - Tim Riffe

Leadership skills and family formation among males. A study based on Swedish register data
Steffen Peters - Kieron Barclay

Life course heterogeneity and the future labour force – A dynamic microsimulation analysis for Austria
Thomas Horvath - Martin Spielauer - Philipp Warum

Data and Trends

The contributions of past immigration to current age-sex structures of immigrant populations in Australia
James Raymer - James O’Donnell - Qing Guan

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Vienna Yearbook of Population Research 2025
ISSN 1728-4414
Print Edition
ISSN 1728-5305
Online Edition
ISBN 978-3-7001-9681-5
Print Edition
ISBN 978-3-7001-9682-2
Online Edition



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Inequality, heterogeneity, and chance: Multiple factors and their interactions

    Hal Caswell, Silke F. van Daalen

Vienna Yearbook of Population Research 2025, pp. , 2025/03/10

Population inequality matters

doi: 10.1553/p-j2pg-fm66


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doi:10.1553/p-j2pg-fm66


Abstract

A heterogeneous population is a mixture of groups differing in vital rates. In such a population, some of the variance in demographic outcomes (e.g., longevity, lifetime reproduction) is due to heterogeneity and some is the result of stochastic demographic processes. Many studies have partitioned variance into its between-group and within-group components, but have focused on single factors. Especially for longevity, variance due to stochasticity is far greater than variance due to heterogeneity. Here we extend such analyses to multiple-factor studies, making it possible to calculate the contributions to variance of each factor and each of the interactions among factors. We treat the population as a mixture and use the marginal mixing distributions to compute variance components. Examples are presented: longevity as a function of sex, race and U.S. state of residence; and lifetime reproduction among a set of developed countries and as a result of resource availability and pesticide exposure.

Keywords: Heterogeneity; Stochasticity; Variance partitioning; Longevity; Lifetime reproductive output; Markov chains with rewards