Analysing Biases in Genealogies Using Demographic Microsimulation.

IF 2.6 2区 社会学 Q2 DEMOGRAPHY
Liliana P Calderón-Bernal, Diego Alburez-Gutierrez, Emilio Zagheni
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Abstract

An incomplete understanding of biases affecting the representativeness of genealogies has hindered their full exploitation. We report on a series of experiments on synthetic populations assessing how structural biases in ascendant genealogies affect the accuracy of demographic estimates. Using the SOCSIM microsimulation programme and Swedish fertility and mortality data (1751-2022), we analyse three biases: lineage survival, limited coverage of collateral kin, and selective omission. Comparing demographic measures from 'fully recorded' and 'bias-infused' synthetic populations, we find that across the period, including only direct ancestors can underestimate total fertility rate (TFR) ([Formula: see text]) and overestimate life expectancy at birth ([Formula: see text]) ([Formula: see text]), mainly due to missing infant, child, and some young adult deaths. Including direct ancestors' offspring shifts TFR to overestimation ([Formula: see text]) while improving mortality estimation across all ages, with [Formula: see text] overestimation reduced to [Formula: see text]. Our study shows that completeness of family trees is essential for obtaining accurate demographic estimates from genealogies.

Abstract Image

Abstract Image

Abstract Image

用人口统计学微观模拟分析宗谱偏差。
对影响家谱代表性的偏见的不完全理解阻碍了它们的充分利用。我们报告了一系列关于合成种群的实验,以评估上升谱系的结构偏差如何影响人口估计的准确性。使用SOCSIM微模拟程序和瑞典的生育和死亡率数据(1751-2022),我们分析了三种偏差:血统生存、有限的侧系亲属覆盖和选择性遗漏。通过比较“完全记录”和“带有偏见”的合成人口的人口统计指标,我们发现,在整个时期内,只包括直系祖先可能会低估总生育率(TFR)([公式:见文]),高估出生时预期寿命([公式:见文])([公式:见文]),主要原因是婴儿、儿童和一些年轻人的死亡。包括直系祖先的后代会使TFR高估([公式:见文]),同时提高所有年龄段的死亡率估计,使[公式:见文]高估降低为[公式:见文]。我们的研究表明,家谱的完整性对于从家谱中获得准确的人口统计估计是必不可少的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.20
自引率
8.00%
发文量
44
期刊介绍: European Journal of Population addresses a broad public of researchers, policy makers and others concerned with population processes and their consequences. Its aim is to improve understanding of population phenomena by giving priority to work that contributes to the development of theory and method, and that spans the boundaries between demography and such disciplines as sociology, anthropology, economics, geography, history, political science, epidemiology and other sciences contributing to public health. The Journal is open to authors from all over the world, and its articles cover European and non-European countries (specifically including developing countries) alike.
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