Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India
从不可比调查计算可比统计量：以印度贫困为例AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究贫困与不平等指标的跨期可比性，并以印度贫困变化为应用，最直接对应微观分布效应方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
从不可比调查计算可比统计量：以印度贫困为例
- 研究问题
- 当问卷设计改变导致不同轮调查不可比时，如何恢复贫困或不平等指标的跨期可比性，并判断印度官方贫困下降有多少反映真实变化？
- 方法与思路
- 作者以逆概率加权恢复由矩条件隐式定义的参数可比性，要求辅助变量的报告不受调查设计影响，且其与目标变量的关系跨期稳定。
- 材料陈述的贡献
- 摘要分析分层整群抽样下的渐近性质，并用早期调查轮次支持识别假设的可信性；作者认为印度官方贫困降幅大部分真实，但并非全部。
- 阅读关注点
- 阅读时应核对辅助变量的选择及其跨期稳定性证据、权重构造和矩条件，检查分层整群抽样的方差处理，以及贫困估计对假设的敏感性。
- 阅读边界
- 可比性依赖辅助变量不受问卷变化影响及关系稳定，历史轮次证据不能保证这些条件始终成立；摘要未给出调整幅度或完整敏感性结果。
原文摘要
Applied economists are often interested in studying trends in economic indicators such as inequality or poverty; however, comparisons over time can be made impossible by changes in data collection methodology. We describe an easily implemented procedure, based on inverse probability weighting, that allows recovery of comparability of estimated parameters identified implicitly by a moment condition. The validity of the procedure requires the existence of a set of auxiliary variables whose reports are not affected by the different survey design and whose relationship with the main variable of interest is stable over time. We analyze the asymptotic properties of the estimator when data belong to a stratified and clustered survey. The main empirical motivation of the article is provided by a recent controversy regarding the extent of poverty reduction in India in the 1990s. Due to changes in the expenditure questionnaire adopted for data collection in the 1999–2000 round of the Indian National Sample Survey, poverty is likely to be understated relative to previous rounds. We use previous waves of the same survey to provide evidence supporting the plausibility of the identifying assumptions and conclude that most, but not all, of the very large reduction in poverty implied by the official figures appears to be real, not a statistical artifact.