The Effect of Big Data-Based Digital Payments on Household Healthcare Expenditure

Help or Hurt? The Impact of Digital Finance on the Physical Health of the Elderly in China

Under the backdrop of global aging, the escalating number of elderly individuals in poor health poses a growing social burden and impacts economic development and social stability. A fundamental question arises as to whether the advancements of digital finance (DF) can effectively enhance the physical health of the elderly. This study aims to investigate the impact of DF on the physical health of the elderly by utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2013, 2015, and 2018. The results reveal a significant positive impact of DF on enhancing the physical health of the elderly. Furthermore, the study demonstrates that this impact is particularly pronounced among the elderly with higher educational attainment, stronger intergenerational links, and those residing in central cities. A mechanism analysis further reveals that DF contributes to improving the physical health of the elderly by augmenting household disposable income, alleviating liquidity constraints, and enhancing the utilization of medical services. These findings offer valuable insights for the future development of DF and the implementation of policies promoting healthy aging and active aging.

Medical insurance, health risks, and household financial asset allocation: evidence from China household finance survey

There is a lack of micro evidence on whether medical insurance may optimize the household financial asset allocation by transferring health risk, despite the fact that health risk is a significant component driving families' precautionary savings. This article empirically examines the impact of health risk and social medical insurance on household risky financial asset allocation using a Probit model, based on data from the 2015-2019 China Household Finance Survey (CHFS). The findings indicate that social medical insurance, with its lower level of security, reduces the likelihood, but it can alter households' preferences for risk by lowering marginal effect of health risk. According to the findings of the heterogeneity analysis, people who live in rural and less developed areas are more likely to experience the risk-inhibiting effects of social medical insurance and health risk. The eroding and risk-suppressing impacts of social medical insurance are likewise less pronounced for households headed by women and older people, as is the health risk's suppressive influence on household involvement in risky financial markets. Compared with social medical insurance, commercial medical insurance with a higher level of coverage can dramatically increase household participation in riskier financial markets. This article provides micro-empirical evidence for the household asset allocation effect of medical insurance.

Digital village construction, human capital and the development of the rural older adult care service industry

Background

The advancement of digital villages in China is shaped by the degree of human capital within the rural labor force, which not only restricts the potential of digital village but also influences the impact of digital empowerment on the progression of the rural older adult care service industry.

Materials and methods

Using panel data from 30 Chinese provinces between 2011 and 2020, we created benchmark and threshold regression models to investigate the influence of digital village construction on the development of the rural older adult care service industry and to delineate the threshold effects of human capital on it. We further scrutinized the correlation between the two systems, along with the factors that affect it, through a coupling coordination model.

Results

Preliminary, the baseline regression outcomes show that the digital village construction is conducive to the progression of the rural older adult care service industry (p < 0.05). Moreover, we identified a significant nonlinear threshold relationship between the digital village, human capital, and the advancement of the rural older adult care service industry (HUM1ST, p < 0.05; HUM2DT, p < 0.01; HUM3DT, p < 0.01). These results indicate that the digital technology's effect on the development of the rural older adult care service industry is limited by the rural human capital level. Lastly, we found that higher levels of human capital enhance the coupling of the digital village with the rural older adult care service industry (p < 0.01), with the influence of per capita education level being the most pronounced (CoefHUM1 > CoefHUM2 > CoefHUM3).

Conclusion

The digital village substantially empowers the rural older adult care service industry, with human capital exhibiting a significant threshold effect on this empowerment. Furthermore, variances in the level of human capital have a considerable impact on the integration of the digital village and the rural older adult care service industry.

Socioeconomic disparities and inequality of mass sports participation: Analysis from Chinese General Social Survey 2010-2018

Background

With industrialization, urbanization, and modernization, mass sports have entered people's daily lives to maintain their health status. However, less attention has been paid to the heterogeneity and inequality of access to mass sports, especially in developing countries. This study aims to analyze the factors that affect mass sports participation in developing countries represented by China, and explain the changing trends and inequality in the class differentiation and mobility of public sports participation.

Methods

The study selected the Chinese General Social Survey (CGSS) data in 2010 and 2018 as the research samples, and used an ordered Probit model and sub-sample regression to analyze the factors and trends of Chinese residents' mass sports participation and the influencing factors. By stratified three-stage probability sampling, the study obtained 4,940 valid responses, including 1,014 in CGSS 2010 and 3926 in CGSS 2018.

Results

First, in terms of social factors, urban residents have a higher frequency of sports participation than rural residents. Second, regarding family factors, residents with higher social classes are more likely to participate in sports than those with lower social classes. Third, in terms of self-induced factors, the elderly are more motivated to exercise than the young. Residents with public-sector jobs, high incomes, and higher education levels are keener to participate in sports. Fourth, residents' mass sports participation rate has generally shown an upward trend over time. Fifth, with time changes, the sports participation rate varies between urban and rural areas, between ethnic minorities and Han ethnic, between old and young age groups, and between higher and lower education levels will continue to shrink, but differences between social classes will further increase over time.

Conclusions

Our analysis demonstrated that hidden inequality existed in accessing mass sports participation in developing countries, and the self-induced characteristics were significantly correlated with the quality of sports participation. Future public sports policies should address the inequity to ensure equal access to affordable qualified personal mass sports.