BackgroundThe relationship between health and income is an essential part of human capital research. The majority of current analyses using classical regression models show that health has a significant impact on income after controlling for the endogeneity of health due to the measurement error and reverse causality. Currently, the Chinese government implements various policies including health related policies to fiercely fight for the domestic poverty issues, and thus only estimating the average effect of health on income could underestimate the impact for low income population and will make policy makers neglect or not pay enough attention to the significant role of health in poverty alleviation. To study the effect of health on income for workers at different income quantiles, we apply the quantile regression method to a panel data from a Chinese household survey. Furthermore, we test the heterogeneity of this health-income effect for different subgroups of workers characterized by sex, registered residence, and residential area. Lastly, we provide an explanation on the possible mechanism of the health-income effect.MethodsThis study uses data from four waves of the China Family Panel Studies (CPFS)- a biennial longitudinal study spanning from 2012 to 2018. The final data used in the regression analysis includes a balanced sample of 19,540 person-year observations aged between 18 to 70 years, with complete information of demographic and social economic status characteristics, job information, and health status of individuals. We use lagged self-reported health to control the potential endogeneity problem caused by reverse causality between health and income. Our identification on heterogenous treatment effects relies on panel quantile regressions, which generate more information than the commonly used mean regression method, and hopefully could reveal the effects of health on income for workers with income distributed at a wide range of quantiles. In addition, we compare the results derived from panel quantile regressions and mean regressions. Finally, we added interaction terms between health and other independent variables to recover the influence channel of health on income.Results The regression estimates show that the effects of health on income are more pronounced for workers distributed on the lower ends of income spectrum, and the health-income effect decreases monotonically with the increase of income. The treatment effect is robust to alternative measures of health and seems to be more pronounced for females than males, for rural workers than their urban counterparts. Finally, we find that health not only directly affects worker’s income but also has different effects on income for different occupation cohorts.ConclusionsThis study provides a different perspective on the impact of individual health status on income, uncovering the heterogeneous effects of health deterioration on income reduction for workers with different incomes by using panel data and rather advanced statistical techniques- panel quantile regressions. At present, the Chinese government is making every effort to solve the problem of poverty and our findings suggest public policies on health and income protections should emphasize different needs of workers with different incomes and special attention should be paid to low-income workers who are much more financially fragile to health deterioration than other income groups.

Background Analyses using classical regression models show that health has significant impact on

Background: The relationship between health and income is an essential part of human capital research. The majority of current analyses using classical regression models show that health has a significant impact on income after controlling for the endogeneity of health due to the measurement error and reverse causality. Currently, the Chinese government implements various policies including health related policies to fiercely fight for the domestic poverty issues, and thus only estimating the average effect of health on income could underestimate the impact for low income population and will make policy makers neglect or not pay enough attention to the significant role of health in poverty alleviation. To study the effect of health on income for workers at different income quantiles, we apply the quantile regression method to a panel data from a Chinese household survey. Furthermore, we test the heterogeneity of this health-income effect for different subgroups of workers characterized by sex, registered residence, and residential area. Lastly, we provide an explanation on the possible mechanism of the health-income effect. Methods: This study uses data from four waves of the China Family Panel Studies (CPFS)-a biennial longitudinal study spanning from 2012 to 2018. The final data used in the regression analysis includes a balanced sample of 19, 540 person-year observations aged between 18 to 70 years, with complete information of demographic and social economic status characteristics, job information, and health status of individuals. We use lagged self-reported health to control the potential endogeneity problem caused by reverse causality between health and income. Our identification on heterogenous treatment effects relies on panel quantile regressions, which generate more information than the commonly used mean regression method, and hopefully could reveal the effects of health on income for workers with income distributed at a wide range of quantiles. In addition, we compare the results derived from panel quantile regressions and mean regressions. Finally, we added interaction terms between health and other independent variables to recover the influence channel of health on income. Results: The regression estimates show that the effects of health on income are more pronounced for workers distributed on the lower ends of income spectrum, and the health-income effect decreases monotonically with the increase of income. The treatment effect is robust to alternative measures of health and seems to be more pronounced for females than males, for rural workers than their urban counterparts. Finally, we find that health not only directly affects worker's income but also has different effects on income for different occupation cohorts.

Background Mental illness is often a chronic disease involving long treatment durations. It is characterised by high relapse rates and a high degree of disability, resulting in high socioeconomic aggregate costs to families and the health insurance system. However, the evidence of policy effects on the cost of mental illness to patients is insufficient. In 2016, the Chinese Government integrated the Urban Resident Basic Medical Insurance (URBMI) with the Rural New Cooperative Medical Scheme (RNCMS) to form the Urban and Rural Resident Basic Medical Insurance Scheme (URRBMI). We aimed to evaluate the effect of this policy integration on the medical and out-of-pocket costs of accessing mental health services to patients with severe mental illness in a single province in China. Methods This study is based on random stratified sampling of the medical record data of patients with severe mental illness in 21 cities in a single province in China from Jan 1, 2014, to Dec 31, 2017. We divided the cities into four categories based on the year of implementation of the integration policy. We applied propensity score matching with a difference-indifference approach to analyse the effect of the integration policy on the medical costs to patients with severe mental illness. Findings Establishment of URRMI was associated with significantly reduced total medical and out-of-pocket costs to patients with severe mental illness (p<0•01 for both), and the effect increased over time. The reduction in total medical costs appeared to be caused by reduced diagnosis and medicine costs. The medical cost gap between patients in urban and rural settings decreased from 189•1% before policy integration to 59•2% after policy integration. Interpretation The results suggest that integrating the URBMI with the RNCMS effectively reduced medical costs to patients with severe mental illness. Although the URRMI scheme appeared to reduce the cost gap between rural and urban residents, more efforts should be spent on decreasing the out-of-pocket costs of severe mental illness in rural patients.