With the accelerating aging of the population and the worsening psychological conditions of older people, the traditional mode of family support for the elderly in China does not always meet the physical and psychological needs of the elderly, and more social support modes for the elderly are needed. Based on 3,513 valid questionnaires on the long-term care and protection needs of Chinese residents, this paper uses a logit regression model to analyze the factors influencing the willingness of the elderly to choose nursing care. The results show that intergenerational family support for the elderly is a significant psychological driver on the willingness of the elderly to choose nursing care. Compared with the elderly living with family, empty nesters or older people living alone are more inclined to select nursing care when they have difficulties taking care of themselves. The physical health of the elderly affects their willingness to choose nursing care, and elderly individuals with more hospitalizations are less likely to select nursing care. In addition, elderly females who are relatively young, have a high level of education, have a high income, have a nursing home near the residence, and are already covered by medical insurance are more willing to choose nursing care. The results of this study are of great importance for improving the medical services and aging care services for the elderly and providing theoretical support for alleviating the psychological and social pressure brought by population aging.

Prior research tends to propose and examine the negative relationship between market segmentation and energy efficiency. Does market segmentation necessarily impair energy efficiency? Considering the critical role that Chinaese government play in managing erergy efficiency, we propose a non-linear relationship between market segmentation and energy efficiency. Using data of 30 provinces in Mainland China during 2000 to 2017, we find an inverse U-shaped relationship between market segmentation and energy efficiency. Our findings remain robust after controlling endogeneity issues. Therefore, a moderate level of market segmentation is acceptable and beneficial for long-term improvement of energy efficiency in emerging economies.

The political environment has a significant impact on the sustainable development of enterprises. This manuscript aims to investigate the effect of policy uncertainty and official social capital on enterprises’ effective tax rate (ETR) due to the change of officials. Based on the panel data from the Chinese Industrial Enterprise Database from 1998 to 2009, it is shown that the policy uncertainty caused by the change of local government officials significantly increases the ETR of enterprises. Meanwhile, municipal officials who have social ties with provincial officials in their province also tend to raise the ETR of industrial enterprises, and this tendency is more evident when the officials take office. Further research shows that the effects vary in many aspects for policy uncertainty and social capital on the ETR of enterprises. The findings of this manuscript provide support for a deeper understanding of the change in local government fiscal policies and give suggestions to strengthen political environmental governance for the sustainable development of enterprises.

In the presence of non-normally distributed asset returns, an optimal portfolio selection should consider higher-order (co-)moments when no sampling errors exist. However, the curse of dimensionality has already been a serious concern in mean-variance analyses; higher-order (co-)moments also face the same dilemma. This study uses mixed-frequency (MF) data under the assumption that stock returns are generated by a mixed-data sampling (MIDAS) regression model, which shows to provide well-improved estimates for the covariance, co-skewness, and co-kurtosis matrices for higher dimension. We discover that the new, improved estimates used in higher-order (co-)moment portfolio selections can dominate the other existing structured estimates from an out-of-sample perspective in most instances.

The traditional estimation of higher order co-moments of non-normal random variables by the sample analog of the expectation faces a curse of dimensionality, as the number of parameters increases steeply when the dimension increases. Imposing a factor structure on the process solves this problem; however, it leads to the challenging task of selecting an appropriate factor model. This paper contributes by proposing a test that exploits the following feature: when the factor model is correctly specified, the higher order co-moments of the unexplained return variation are sparse. It recommends a general to specific approach for selecting the factor model by choosing the most parsimonious specification for which the sparsity assumption is satisfied. This approach uses a Wald or Gumbel test statistic for testing the joint statistical significance of the co-moments that are zero when the factor model is correctly specified. The asymptotic distribution of the test is derived. An extensive simulation study confirms the good finite sample properties of the approach. This paper illustrates the practical usefulness of factor selection on daily returns of random subsets of S&P 100 constituents.