Health dynamics and reporting bias at retirement: An analysis using high-frequency data
中文摘要
我们提出一种新方法来识别主观健康测量中的状态依赖性报告偏误。其核心思想是,健康作为一种存量运行,因此退休后自评健康(SRH)的突然变化更可能反映报告偏误而非真实变化。为捕捉此类变化,我们的分析整合了三个关键要素:(1)基于经典健康理论区分存量与流量结果;(2)利用受断点回归设计启发的识别策略;(3)挖掘一个关于月度健康与退休的独特高频数据集。传统估计发现,退休后较长时期内自评健康下降;然而,随着观察窗口缩短,这一下降逐渐减小,未发现状态依赖性报告偏误的证据。我们对短期健康动态的分析还强调,在政策评估中应区分存量与流量健康结果。
Abstract
We introduce a novel approach to identify state-dependent reporting bias in subjective health measures. The central idea is that health operates as a stock, making abrupt shifts in self-reported health (SRH) following retirement more likely to reflect reporting bias than actual changes. To capture such shifts, our analysis integrates three key elements: (1) differentiating stock and flow outcomes based on classical health theory; (2) leveraging an identification strategy inspired by regression discontinuity design; and (3) exploiting a unique high-frequency dataset on monthly health and retirement. Traditional estimates find a decline in SRH after retirement over longer periods; however, this decline steadily diminishes as the observation window narrows, showing no evidence of state-dependent reporting bias. Our analysis of short-term health dynamics also emphasizes distinguishing stock and flow health outcomes in policy evaluations.