中国居民医疗保健消费：总量与不平等——基于收入来源视角的分析

江西财经大学学报 ›› 2026, Vol. 0 ›› Issue (2): 60-72.

中国居民医疗保健消费：总量与不平等——基于收入来源视角的分析

陶春海¹, 冷文杰², 吴施美²

1.江西财经大学统计与数据科学学院,江西南昌 330013;
2.湖南大学经济与贸易学院,湖南长沙 410079

收稿日期:2025-03-28 修回日期:2026-02-16 出版日期:2026-03-25 发布日期:2026-03-31
通讯作者: 冷文杰,湖南大学博士研究生,主要从事健康经济和能源经济研究,联系方式2654952806@qq.com;
作者简介:陶春海,江西财经大学教授,主要从事卫生统计研究;吴施美,湖南大学副教授,主要从事能源经济和不平等研究。
基金资助:
国家社会科学基金重大项目“‘双循环'新格局下我国大健康大产业升级的统计测度与进程监测研究”（21&ZD150）; 国家社会科学基金青年项目“健康中国战略背景下国民健康区域差异的测度、分解与对策研究”（21CTJ022）

Healthcare Consumption among Chinese Residents: Total Expenditure and Inequality — An Analysis from the Income Sources Perspective

Tao Chun-hai¹, Leng Wen-jie², Wu Shi-mei²

1. Jiangxi University of Finance and Economics, Nanchang 330013;
2. Hunan University,Changsha 410079, China

Received:2025-03-28 Revised:2026-02-16 Online:2026-03-25 Published:2026-03-31

摘要/Abstract

摘要： 从收入来源视角探讨居民医疗保健消费及其不平等与不同来源收入的关系,对“健康中国”战略的实现具有重要现实意义。基于心理账户理论,考察不同收入来源对中国居民医疗与保健消费及其不平等的影响。研究发现：居民转移性收入和财产性收入对医疗保健消费的影响大于经营性收入和工资性收入;居民工作类型和受教育程度对其医疗保健的边际消费倾向具有调节作用。此外,居民转移性收入中是否包含社会捐助,以及财产性收入中是否包含金融投资收益,对其医疗保健的边际消费倾向也存在异质性影响;医疗费用主要表现为健康资本折旧效应,而保健费用主要表现为收入财富效应;各类收入均有助于缩小医疗保健消费的不平等,但城镇居民内部的不平等程度仍高于农村居民。政府应依据收入来源异质性动态调整转移支付标准并引导健康金融资产配置,同时通过提升健康素养与优化医疗资源布局,精准缩小城乡区域差距以推进消费均等化。

关键词: 收入来源, 医疗保健消费, 基尼系数, ELES模型, RIF回归分解

Abstract: Exploring the relationship between residents'healthcare consumption, inequality, and income from different sources from the perspective of income sources is of great practical significance for the implementation of the“Healthy China”strategy. Based on the theory of psychological accounts, this study examines the impact of different income sources on healthcare consumption and inequality among Chinese residents. Research has found that the residents'transfer income and property income have a greater impact on healthcare consumption than their operating income and wage income. The type of work and education level of residents have a moderating effect on their marginal propensity to consume healthcare. In addition, whether the residents'transfer income includes social donations, and whether their property income includes financial investment returns, also have heterogeneous effects on their marginal propensity to consume healthcare. Medical expenses mainly manifest as the depreciation effect of health capital, while healthcare expenses mainly manifest as the income wealth effect. All types of income contribute to narrowing the inequality in healthcare consumption, but the level of inequality within urban residents is still higher than that among rural residents. The government should dynamically adjust the transfer payment standards based on the heterogeneity of income sources and guide the allocation of health financial assets. At the same time, by improving health literacy and optimizing the layout of medical resources, the gap between urban and rural areas should be precisely narrowed to promote consumption equality.

Key words: source of income, healthcare consumption, Gini coefficient, ELES model, RIF regression decomposition

中图分类号:

F063.2

陶春海, 冷文杰, 吴施美. 中国居民医疗保健消费：总量与不平等——基于收入来源视角的分析[J]. 江西财经大学学报, 2026, 0(2): 60-72.

Tao Chun-hai, Leng Wen-jie, Wu Shi-mei. Healthcare Consumption among Chinese Residents: Total Expenditure and Inequality — An Analysis from the Income Sources Perspective[J]. Journal of Jiangxi University of Finance and Economics, 2026, 0(2): 60-72.

参考文献

[1] 顾海. 统筹城乡医保制度、与收入相关的医疗服务利用和健康不平等[J]. 社会科学辑刊, 2019, (2): 88-97.
[2] 牛文浩, 申淑虹, 罗岚, 等. 地形差异视角下耕地流转对农户收入差距的影响及其分解——以黄河流域中上游1879份农户数据为例[J]. 自然资源学报, 2023, (5): 1262-1282.
[3] 王湘红, 文秀泽, 孙文凯. 收入结构对家庭消费倾向和消费结构的影响——基于心理账户视角的研究[J]. 经济理论与经济管理, 2022, (9): 68-81.
[4] 张柯, 方时姣, 张振华. 心理账户视角下家庭收入对家庭能源消费与能源贫困的影响[J]. 资源科学, 2024, (5): 895-909.
[5] 张学敏, 沈丽媛. 受教育程度如何影响消费水平?——基于我国家庭结构、消费类别和居住区域的实证研究[J]. 西南大学学报 (社会科学版), 2018, (4): 100-111+194-195.
[6] 赵广川, 马超. 中国农村居民收入对医疗保健消费不平等的影响[J]. 消费经济, 2019, (5): 14-21.
[7] 赵广川, 马超, 郭俊峰. 中国农村居民医疗消费支出不平等及其演变[J]. 统计研究, 2015, (10): 65-73.
[8] Bello L O, Baiyegunhi L J S, Danso-Abbeam G, et al. Gender decomposition in smallholder agricultural performance in rural Nigeria[J]. Scientific African, 2021, 13: e00875.
[9] Case A, Deaton A.Mortality and morbidity in the 21st century[J]. Brookings Papers on Economic Activity, 2017, 2017: 397-476.
[10] Firpo S, Fortin N M, Lemieux T.Unconditional quantile regressions[J]. Econometrica, 2009, 77(3): 953-973.
[11] Grossman M.On the concept of health capital and the demand for health[J]. Journal of Political Economy, 1972, 80(2): 223-255.
[12] Heath C, O'curry S.Mental Accounting and Consumer Spending[J]. Advances in Consumer Research, 1994, 21(1): 1-9.
[13] Hossain M J, Al-Amin A K M A. Non-farm income and consumption expenditures in rural Bangladesh: empirical evidence from multilevel regression modelling[J]. Journal of Quantitative Economics, 2019, 17(2): 377-396.
[14] Kivetz R.Advances in research on mental accounting and reason-based choice[J]. Marketing Letters, 1999, 10(3): 249-266.
[15] Lluch C.The extended linear expenditure system[J]. European Economic Review, 1973, 4(1): 21-32.
[16] Rios-Avila F.Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition[J]. The Stata Journal, 2020, 20(1): 51-94.
[17] Sand R.The propensity to consume income from different sources and implications for saving: an application to Norwegian farm households[R]. International Agricultural Policy Reform and Adjustment Project (IAPRAP), 2002.
[18] Thaler R.Mental accounting and consumer choice[J]. Marketing Science, 1985, 4(3): 199-214.
[19] Thaler R H.Anomalies: Saving, fungibility, and mental accounts[J]. Journal of Economic Perspectives, 1990, 4(1): 193-205.
[20] Thaler R H.Mental accounting matters[J]. Journal of Behavioral Decision Making, 1999, 12(3): 183-206.
[21] Tur-Sinai A, Magnezi R, Grinvald-Fogel H.Assessing the determinants of healthcare expenditures in single-person households[J]. Israel Journal of Health Policy Research, 2018, 7(1): 1-9.
[22] Yacour S, Soumbara S A, El Ghini A.Environmental quality, economic growth, and healthcare expenditure nexus for North Africa: A panel cointegration analysis[J]. Environmental Modeling & Assessment, 2024, 29(2): 307-321.