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Economic growth and socioeconomic changes have transformed nearly every aspect of childhood in China, and many are worried by the increasing prevalence of mental health issues among children, particularly depression. To provide insight into the distribution of depressive symptoms among children in China and identify vulnerable groups, we use data from the 2012 China Family Panel Survey (CFPS), a survey that collected data from a large, nationally representative sample of the Chinese population. Using the CFPS data, we construct a sample of 2679 children aged 10–15 years old from 25 provinces in China. According to our results, the incidence of depression varies by geographic area. Specifically, we find that rates of depressive symptoms are significantly lower in urban areas (14% of sample children) than in rural areas (23% of sample children). Our results also show that children from ethnic minorities, from poorer families, and whose parents are depressed are more likely to be depressed than other children. In contrast, we find that depressive symptoms do not vary by gender.

PurposeThe development of digital inclusive finance appears to be able to solve the difficulty of traditional finance, which cannot completely cover agriculture and farmers and provides better financial services and products to Chinese farmers. Thus, it improves the farmers' enthusiasm for agricultural production. The purpose of this paper is to clarify whether this goal is indeed being achieved.Design/methodology/approachThis paper theoretically analyzes the mechanism that influences the effect of digital inclusive finance on rural households' agricultural production decisions and conducts an empirical study based on a sample from the Chinese family database (CFD).FindingsFirst, the development of digital financial inclusion in general can encourage rural households to reduce agricultural production. Second, the negative effect of digital inclusive finance on households' agricultural output is realized by widening the gap between the efficiency of non-agricultural economic activities and the efficiency of agricultural production. The wider the gap is, the lower the enthusiasm of households for agricultural production. Third, the mediating effect of “digital financial inclusion – difference in efficiency – agricultural output” has a significant negative effect on households with low agricultural production efficiency, but not households with high agricultural production efficiency. Digital inclusive finance has no significant effect on the difference in efficiency between the two economic activities of high-efficiency households, but a greater difference in efficiency between the two economic activities corresponds to higher enthusiasm of households for agricultural production.Originality/valueTo the best of our knowledge, this paper is the first to analyze the impact of digital financial inclusion on Chinese farmers' agricultural production. The findings of this study can provide policy-related insights to help local governments promote the development of digital finance in China's agricultural economy.

China’s rapid urbanization in the past several decades have been accompanied by rural labor migration. An important question that has emerged is whether rural labor migration has a positive or negative impact on the depressive symptoms of children left behind in the countryside by their migrating parents. This paper uses a nationally representative panel dataset to investigate whether parental migration impacts the prevalence of depressive symptoms among left-behind children in China. Using DID and PSM-DID methods, our results show that parental migration significantly increases the depression scores of 10 and 11-year-old children by 2 points using the CES-D depression scale. Furthermore, we also find that the negative effect of decreased parental care is stronger than the positive effect of increased income in terms of determining the depressive symptoms status of children in rural China.

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Objective To determine whether or not chronic disease positively impacts medical costs among the rural elderly in China and to calculate medical expenditure induced by chronic disease between different groups of the rural elderly, as well as provide insight into the factors that affect medical losses induced by chronic disease among different household registration groups and different New Rural Pension Scheme statuses. Design To estimate the share of medical expenses induced by chronic disease, this paper uses a two-part model and a four-part model to analyze the causal effect of chronic disease on medical services, and then uses a counter-factual method to estimate the share of medical expenses. Setting The rapid development of China has changed nearly every aspect of life for the rural elderly. Many are concerned about the increasing prevalence of physical health issues, particularly chronic diseases, among the rural elderly. Nevertheless, there are no articles using nationally representative panel datasets that report differences in the cost of chronic disease between sub-populations. Participants The panel dataset used for this study comes from the China Family Panel Studies (CFPS) data. In this study, we use the CFPS data for the years 2012, 2014 and 2016 to create a panel dataset that includes 2730 rural elderly for three years. Interventions This article reports the representative estimate of medical expenditures attributable to chronic disease among rural elderly in China. On this basis, we estimate per capita medical spending among rural elderly under the condition of logarithmic normal distribution of different parameters between urban and rural or between groups that participate in the New Rural Pension Program and groups that do not. Main Outcome Measures This study indicates that physical health status has a significant impact on both the probability of undergoing medical care and the size of medical expenditures among the rural elderly, and the influence was significant for all patients, including outpatients and inpatients. Results Chronic illness has significant effects on individual medical expenses, and they aggregately contribute to 63.96% of total personal expected medical expenditure. Specifically, the medical spending caused by chronic disease was part of a non-uniform distribution, with the rural, male, older, married, and higher-educated groups spending more money on medical costs induced by chronic disease. Conclusions Examining trends in the prevalence of chronic diseases and evaluating medical spending on chronic diseases can prevent and control potential medical costs among rural elderly, especially for vulnerable groups, which helps to predict future health care needs.

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