The mental health of rural children is closely related to their household characteristics, with household income level as one of the important influencing factors. In general, improvement in household income level is deemed to play an important role in promoting children’s mental health. However, the impact and mechanism of household income status perception bias on children’s mental health due to changes in the structure of household expenditure are under studied. On the basis of the perspective of household income status perception bias, we constructed a representative behavior household model of income status perception bias and a three-wave panel. We adopted the data from Chinese household tracking surveys in 2012, 2014, and 2016 to empirically analyze the mechanism and channel of household income status perception bias on children’s mental health. Results reveal that: (1) A significant negative correlation exists between household income level and income status perception bias, and poor households are likely to have income status perception bias. (2) A significant positive correlation exists between income status perception bias of poor households and their gift-giving expenditure, whereas a negative correlation exists between income status perception bias and expenditure for children’s education. The more the poor households overestimate their income status, the more inclined they are to increase their gift spending and reduce expenditure for children’s education, thereby changing the structure of family expenditure. (3) A significant negative correlation exists between poor household income status perception bias and the mental health status of their children, whereas a positive correlation exists between household expenditure for children’s education and children’s mental health status. That is, the more that poor households overestimate their relative income status, the greater the mental pressure on children. Finally, the reduction of expenditure on children’s education by rural households is an effective channel through which income status perception bias among poor households affects children’s mental pressure.
Improving intergenerational mobility is crucial for enhancing the efficacy of human capital, ensuring social vitality, and supporting sustainable long-term economic growth. Based on the China Labor-force Dynamic Survey (CLDS) of 2014, this paper empirically examines the effect of adolescent household migration on intergenerational educational mobility by using a fixed-effect model. The study found that: (1) Household migration in the adolescent period significantly improves intergenerational educational mobility. (2) The quality and quantity of education of offspring are the channels through which household migration improves the intergenerational educational mobility of the household. (3) There are significant differences between urban and rural areas, gender, and household resource allocation in the effect of adolescent household migration on intergenerational educational mobility. As the majority of poor households are unable to improve intergenerational mobility through migration due to its costs and institutional barriers, this paper suggests that the government should concentrate on reducing regional disparities in educational resources, advancing rural education reform, and enhancing social security.
In order to explore the cumbrance effect of state-owned enterprises (SOE) on regional financial efficiency (FINC) and expand the research field of efficiency loss of SOE, this paper bases on the externality of efficiency loss, considering that the soft budget constraint caused by the policy burden of SOE would lead to the reduction of their own benefits and the financial crowding out effect on regional non-state-owned enterprises, and then reduces the efficiency of regional financial institutions. Using 31 provinces (municipalities and autonomous regions) panel data from 2006 to 2015, applying fixed effects-random effects model to verify the impact of the proportion of SOE on FINC, the results of which show that the higher the proportion of SOE is, the higher non-performing loan rate (NPLR) in the region will be. Finally, according to the above analysis, this paper puts forward corresponding policy suggestions.
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