首页|我国中老年人健康的机会不平等——来自机器学习的新证据

我国中老年人健康的机会不平等——来自机器学习的新证据

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伴随着我国老龄化进程的不断加快,老年人健康不平等问题变得越发突出.基于2011-2018 年中国健康与养老追踪调查数据,使用线性回归、条件推断树和条件森林三种估计方法,对我国45 岁及以上中老年居民健康(适应负荷和自评健康)的机会不平等进行测度,并对各变量在健康机会不平等的相对贡献大小进行度量.结果表明,适应负荷与自评健康的机会不平等相对值分别介于 3.21%—7.76%与 5.15%—10.44%之间,人口学特征(年龄和性别)和儿时社会经济条件均为造成适应负荷和自评健康机会不平等的主要因素.与线性回归结果中出生地区/省份是最重要的影响因素不同,两种机器学习的估计结果表明,人口学特征与儿时社会经济条件是造成适应负荷机会不平等的两大诱因.研究证实基于条件森林的健康机会不平等测度效果优于传统的线性回归,这一研究结果对于使用单个客观健康指标、调整可观测的环境变量之后依然是稳健的.健康的机会不平等是隐藏于健康不平等背后的深层原因,而针对我国中老年人健康机会不平等的综合评估对于帮助老年人有效减少健康不平等的公共政策的出台具有重要的现实意义.
Inequality of Opportunity in Health among the Middle-aged and Elderly People in China:New Evidence from Machine Learning Methods
With the continuous acceleration of China's ageing process,the issue of health inequality among the elderly has become increasingly prominent and important.Drawing on data from 2011-2018 China Health and Retirement Longitudinal Survey,this study employs three estimation methods,namely,linear regression,conditional inference tree and conditional forest,to measure the inequality of opportunity in health adaptive load and self-rated helth among middle-aged and elderly adults aged 45 and above in China.It also measures the relative contribution of each circumstance variable to the inequality of opportunity in health.The results show that the relative values of inequality of opportunity for allostatic load(self-reported health)ranges from 3.21%(5.15%)to 7.76%(10.44%)respectively.The decomposition results further indicate that demographic characteristics(age and gender)and childhood socioeconomic status are the key contributors for inequality of opportunity in both allostatic load and self-reported health.Unlike linear regression results where region/province of birth is the most important factor,the two machine learning estimates show that demographic characteristics(age and gender)and childhood socioeconomic conditions are the two main factors accounting for the opportunity inequality of allostatic load.This paper proves that the health opportunity inequality measurement based on conditional forest is better than the traditional linear regression.This result remains robust for individual objective health indicators and the adjustment for observable environmental variables.Health opportunity inequality is the underlying reasons behind health inequality,and a comprehensive evaluation of health inequality for middle-aged and elderly people in China is of great practical significance for the introduction of effective public policies to reduce health inequalities for the elderly.

inequality of opportunity in healthShapley-value decompositionconditional inference treeconditional forest

聂鹏、徐泊阳、周博、赵方

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西安交通大学 经济与金融学院,陕西 西安 710061

健康机会不平等 Shapley值分解 条件推断树 条件森林

国家自然科学基金

72074178

2024

人口与经济
首都经济贸易大学

人口与经济

CSTPCDCSSCICHSSCD北大核心
影响因子:2.196
ISSN:1000-4149
年,卷(期):2024.(3)
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