Background: Fertility histories are subject to measurement errors such as incorrect birth dates, incorrect birth orders, incorrect sex, and omissions. These errors can bias demographic estimates such as fertility rates and child mortality rates. Objective: We focus on births missing in fertility histories. We estimate the prevalence of such omissions and study their associated factors. Methods: We leverage a panel survey (the India Human Development Survey) where the same women were interviewed in two waves several years apart. We compare data across waves and identify omitted births. Omissions in the second wave are modeled as a function of several child, mother, household, and survey interviewer variables. Models are fit separately to omissions reported alive or dead in the first wave. Results: We conservatively estimate the prevalence of omissions at 4%. A large majority of omitted births are those of dead children, especially infants, with children in poorer households at greater risk of being omitted. For children alive in wave 1, female children are much more likely to be omitted in wave 2 compared to male children. Interviewers can detect respondent behaviors associated with omissions. Conclusions: Omissions in fertility histories are non-ignorable. They do not randomly occur, and they affect some population subgroups and some interview contexts more than others. Contribution: We investigate the understudied but important phenomenon of omitted births in fertility histories. We bring attention to possible biases in demographic estimates. We shed light on the survey process and propose strategies for minimizing bias through improved survey design.

中文翻译：

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消失并被遗忘？印度出生史遗漏的预测因素（作者：Sharan Sharma、Sonalde Desai、Debasis Barik、Om Prakash Sharma）

背景：生育史可能会出现测量误差，例如出生日期不正确、出生顺序不正确、性别不正确和遗漏。这些错误可能会使生育率和儿童死亡率等人口统计估计产生偏差。目标：我们关注生育史中缺失的出生情况。我们估计此类遗漏的发生率并研究其相关因素。方法：我们利用一项小组调查（印度人类发展调查），对相同的女性进行了两轮采访，时间间隔几年。我们比较不同批次的数据并确定遗漏的出生率。第二波中的遗漏被建模为几个儿童、母亲、家庭和调查访谈者变量的函数。模型分别与第一波中报告的存活或死亡的遗漏进行拟合。结果：我们保守估计遗漏发生率为 4%。绝大多数遗漏出生是死亡儿童，尤其是婴儿，贫困家庭的儿童遗漏出生的风险更大。对于第一波中存活的儿童来说，与男孩相比，女孩在第二波中被遗漏的可能性要大得多。访谈员可以发现与遗漏相关的受访者行为。结论：生育史中的遗漏是不可忽视的。它们不是随机发生的，它们对某些人口亚组和某些采访环境的影响比其他人更大。贡献：我们调查了生育史中遗漏出生这一尚未得到充分研究但很重要的现象。我们提请注意人口统计估计中可能存在的偏差。我们阐明了调查过程，并提出了通过改进调查设计来最大限度地减少偏见的策略。