Sharma Sharan, Desai Sonalde, Barik Debasis, Sharma O P
Department of Sociology and Joint Program in Survey Methodology, University of Maryland, United States, and Non-resident Fellow, National Council of Applied Economic Research, New Delhi, India.
Department of Sociology, University of Maryland, United States, and Professor, National Council of Applied Economic Research, New Delhi, India.
Demogr Res. 2024 Jan-Jun;50:929-966. doi: 10.4054/demres.2024.50.32. Epub 2024 May 7.
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.
We focus on births missing in fertility histories. We estimate the prevalence of such omissions and study their associated factors.
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.
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.
Omissions in fertility histories are non-ignorable. They do not randomly occur but affect some population sub-groups and some interview contexts more than others.
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 the bias through improved survey design.
生育史容易出现测量误差，如出生日期错误、出生顺序错误、性别错误和遗漏。这些误差会使生育率和儿童死亡率等人口统计估计产生偏差。
我们关注生育史中遗漏的出生情况。我们估计此类遗漏的发生率，并研究其相关因素。
我们利用一项面板调查（印度人类发展调查），在相隔数年的两轮调查中对相同的女性进行访谈。我们比较两轮调查的数据，识别遗漏的出生情况。将第二轮调查中的遗漏情况建模为若干儿童、母亲、家庭和调查访谈员变量的函数。分别对第一轮调查中报告为存活或死亡的遗漏情况进行模型拟合。
我们保守估计遗漏发生率为4%。绝大多数遗漏的出生情况是死亡儿童，尤其是婴儿，贫困家庭的儿童被遗漏的风险更高。对于第一轮调查中存活的儿童，与男童相比，女童在第二轮调查中更有可能被遗漏。访谈员能够察觉与遗漏相关的受访者行为。
生育史中的遗漏情况不可忽视。它们并非随机发生，而是对某些人口亚群体和某些访谈情境的影响大于其他群体。
我们研究了生育史中被忽视但重要的出生遗漏现象。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将偏差降至最低的策略。
