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Brothers, sisters, and the legacy of sibship: Childhood coresiding siblings and late-life cognitive decline in the United States

Yiang Li

Demographic Research2026https://doi.org/10.4054/demres.2026.54.8article
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0.50

Abstract

BACKGROUNDSiblings are the most common household companions in childhood, yet demographers know little about how sibship configurations reverberate across the life course to shape population-level cognitive health at older ages. OBJECTIVEThe author assesses whether coresiding siblings in childhood are linked to late-life cognitive trajectories and asks how associations differ by sibship size, age spacing, and sex composition. METHODSThis study uses 12 biennial waves of the Health and Retirement Study (1998-2020; 42,530 person-wave observations) linked to respondents' records in the 1940 full-count US census (N = 6,187).The author estimates late-life cognitive decline trajectories by childhood coresiding sibship structure, adjusting for childhood socioeconomic, demographic, geographic, and household attributes alongside time-varying adulthood health covariates. RESULTSCompared with only children, individuals who coreside with more than one sibling experience faster annual declines in cognition, net of covariates.Decline steepens monotonically with each additional sibling and is faster when at least one sibling is closely spaced.Men raised with only brothers display the lowest baseline scores, whereas women, especially those with brothers, show the most rapid deterioration.Findings are robust to birth order controls and alternative model specifications. CONCLUSIONSResource dilution in large or closely spaced sibships erodes cognitive reserve over the life course, consistent with gendered sibling caregiving norms that magnify women's later-life risks.

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https://doi.org/https://doi.org/10.4054/demres.2026.54.8

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@article{yiang2026,
  title        = {{Brothers, sisters, and the legacy of sibship: Childhood coresiding siblings and late-life cognitive decline in the United States}},
  author       = {Yiang Li},
  journal      = {Demographic Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4054/demres.2026.54.8},
}

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R · text relevance †0.50 × 0.4 = 0.20

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