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Using online genealogical data for demographic research: An empirical examination of the FamiLinx database

Author

Listed:
  • Andrea Colasurdo

    (Max-Planck-Institut für Demografische Forschung)

  • Riccardo Omenti

    (Università di Bologna (UNIBO))

Abstract

Background: Online genealogies are promising data sources for demographic research, but their limitations are understudied. This paper takes a critical approach to evaluating the potential strengths and weaknesses of using online genealogical data for population studies. We focus on the FamiLinx dataset, which contains demographic information and kinship ties across multiple countries and centuries. Objective: We propose novel measures to assess the completeness and the quality of demographic variables in the FamiLinx data at both the individual and the familial level over the 1600–1900 period. Utilizing Sweden as a test country, we investigate how the age–sex distribution and the mortality levels of the digital population extracted from FamiLinx diverge from the registered population. Methods: We employ descriptive statistics, negative binomial regression modeling, and standard life table techniques for our measures of completeness and quality. Results: Missing values and accuracy in demographic information from FamiLinx are selective. When one demographic variable is available, researchers can effectively anticipate the availability of other demographic information. The completeness and quality of demographic variables within kinship networks are markedly higher for individuals with more complete and accurate demographic information. Populations from FamiLinx display lower mortality levels than the registered population and their representativeness improves towards the end of the 19th century. Contribution: This study sheds new light on the opportunities and challenges of harnessing online genealogies for demographic research. Although this data source offers much promise, its usability in population studies is dependent on the quality and completeness of its recorded demographic information and their selectivity.

Suggested Citation

  • Andrea Colasurdo & Riccardo Omenti, 2024. "Using online genealogical data for demographic research: An empirical examination of the FamiLinx database," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 51(41), pages 1299-1350.
    • Handle: RePEc:dem:demres:v:51:y:2024:i:41
    DOI: 10.4054/DemRes.2024.51.41
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    References listed on IDEAS

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    1. Cummins, Neil, 2017. "Lifespans of the European Elite, 800–1800," The Journal of Economic History, Cambridge University Press, vol. 77(2), pages 406-439, June.
    2. repec:cai:poeine:pope_704_0729 is not listed on IDEAS
    3. Chen-Hao Hsu & Oliver Posegga & Kai Fischbach & Henriette Engelhardt, 2021. "Examining the trade-offs between human fertility and longevity over three centuries using crowdsourced genealogy data," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
    4. Robert Mare, 2011. "A Multigenerational View of Inequality," Demography, Springer;Population Association of America (PAA), vol. 48(1), pages 1-23, February.
    5. Marco Cozzani & Saverio Minardi & Giulia Corti & Nicola Barban, 2023. "Birth month and adult lifespan: A within-family, cohort, and spatial examination using FamiLinx data in the United States (1700–1899)," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 49(9), pages 201-218.
    6. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    7. Camarda, Carlo G., 2012. "MortalitySmooth: An R Package for Smoothing Poisson Counts with P-Splines," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i01).
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    More about this item

    Keywords

    genealogies; kinship network; data quality; FamiLinx; completeness; digital data;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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