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Health dynamics and reporting bias at retirement: An analysis using high-frequency data
Jiayi Wen1, Zixi Ye2, Xuan Zhang3
1Center for Macroeconomic Research, Xiamen University, Fujian, China; School of Economics, Xiamen University, Fujian, China; Wang Yanan Institute of Studies in Economics (WISE), Xiamen University, Fujian, China.
Journal of Health Economics
|June 10, 2026
Summary
This study introduces a new method to detect reporting bias in self-reported health (SRH) after retirement. Findings show no evidence of bias when analyzing short-term health changes, suggesting health is a stock, not flow.
Area of Science:
- Health Economics
- Biostatistics
- Sociology of Health
Background:
- Subjective health measures are crucial for policy but susceptible to reporting bias.
- Retirement is a significant life event that can trigger changes in self-reported health (SRH).
- Understanding whether SRH changes reflect true health or reporting bias is vital for accurate policy evaluation.
Purpose of the Study:
- To develop and apply a novel approach for identifying state-dependent reporting bias in SRH.
- To differentiate between stock and flow outcomes in health to better interpret SRH changes.
- To investigate the impact of retirement on SRH, distinguishing genuine health shifts from reporting biases.
Main Methods:
- Differentiating health as a stock (enduring) versus flow (transient) based on established health theory.
- Employing a regression discontinuity design-inspired strategy for robust identification.
- Utilizing a unique high-frequency dataset capturing monthly health and retirement information.
Main Results:
- Traditional long-term analyses suggest a decline in SRH post-retirement.
- This apparent decline diminishes significantly when using a narrow observation window.
- No evidence of state-dependent reporting bias was found when examining short-term health dynamics.
Conclusions:
- Health should be conceptualized as a stock, implying that abrupt SRH shifts post-retirement are unlikely to reflect true health changes.
- The distinction between stock and flow health outcomes is critical for accurate interpretation of SRH data in policy contexts.
- The study highlights the importance of high-frequency data and specific analytical strategies to avoid misinterpreting reporting bias as genuine health dynamics.
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