Health inequality and health types
利用k均值聚类法从美国健康与退休研究中识别出五种健康类型，解释了84%的健康轨迹变异，并发现这些类型主要由过去的健康动态而非人口特征决定，对理解健康异质性和政策评估有重要意义。
Summary Although health affects many economic outcomes, its dynamics are still poorly understood. We use k-means clustering, a machine learning technique, and data from the Health and Retirement Study to identify health types during middle and old age. We identify five health types: the vigorous resilient, the fair-health resilient, the fair-health vulnerable, the frail resilient, and the frail vulnerable. They are characterized by different starting health and health and mortality trajectories. Our five health types account for 84% of the variation in health trajectories and are not explained by observable characteristics, such as age, marital status, education, gender, race, health-related behaviours, and health insurance status, but rather by one’s past health dynamics. We also show that health types are important drivers of health and mortality heterogeneity and dynamics. Our results underscore the importance of better understanding health type formation and of modelling it appropriately to properly evaluate the effects of health on people’s decisions and the implications of policy reforms.