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Genealogy biases impact demographic estimates. Incomplete family trees underestimate fertility and overestimate life expectancy, highlighting the need for comprehensive data in demographic research.

Keywords:
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Area of Science:

  • Demography
  • Historical Demography
  • Computational Social Science

Background:

  • Genealogies offer rich demographic data but are susceptible to biases.
  • Understanding these biases is crucial for accurate historical demographic analysis.

Purpose of the Study:

  • To assess the impact of structural biases in ascendant genealogies on demographic estimates.
  • To quantify how lineage survival, collateral kin coverage, and selective omission affect fertility and mortality measures.

Main Methods:

  • Utilized the SOCSIM microsimulation program with Swedish historical data (1751-2022).
  • Compared demographic estimates from 'fully recorded' synthetic populations against 'bias-infused' ones.
  • Analyzed biases including lineage survival, limited collateral kin, and selective omission.

Main Results:

  • Including only direct ancestors underestimated total fertility rate (TFR) and overestimated life expectancy at birth, primarily due to unrecorded infant/child deaths.
  • Including direct ancestors' offspring led to TFR overestimation but improved mortality estimates across all ages.
  • Bias-infused genealogies significantly altered demographic measures compared to fully recorded ones.

Conclusions:

  • Completeness of family trees is paramount for accurate demographic estimations from genealogical data.
  • Structural biases in genealogies can lead to significant distortions in historical demographic reconstructions.
  • Careful consideration of potential biases is essential for the reliable exploitation of genealogical resources.