The impact of population aging on economic growth is a very important issue in the process of population structure change. This paper first proposes research hypotheses based on a systematic literature review and theoretical analysis of the negative and positive effects of population aging on economic growth. Then, based on the data of 30 provinces in the Chinese Mainland from 2000 to 2019, this paper empirically tests the impact of population aging on economic growth and its impact mechanism using a static panel data model, a dynamic panel data model and a mediating effect model. Our empirical analysis leads to the following conclusions. First, population aging has a significant inhibitory effect on economic growth. Second, the industrial structure upgrading plays a mediating role in the process of population aging affecting economic growth; that is, population aging inhibits economic growth by affecting the overall upgrading of the industrial structure as well as the industrial rationalization and optimization. Last, some policy implications are proposed based on the research conclusions.

Enterprise financial asset allocation depends on its motivation, which significantly influences its R&D innovation. In this study, we theoretically analyzed the motivation behind the crowding-out effect of enterprise financialization on R&D innovation and constructed a panel data model to identify enterprise financialization behavior. Furthermore, we analyzed the characteristics of enterprises with two types of effects on R&D innovation: Crowding-out and non-crowding-out. Using disclosed data from listed companies in China as the sample, the following conclusions were drawn. First, there are two types of motivation for enterprise financial assets: reservoir motivation and substitute motivation. This difference in motivation leads to whether there is a crowding-out effect of enterprise financialization on R&D innovation. Second, based on whether there is a crowding-out effect on enterprise R&D innovation, we found that the difference in reservoir motivation between the two types of samples is reflected in the intensity of inhibition, while the difference in substitute motivation is reflected in significance. Third, there are differences in the mechanism variables of financialization motivation among different samples. The moderating effect of equity concentration is reflected in its intensity, while the moderating effect of financing constraints is reflected in its significance.

The impact of financialization on R&D innovation in enterprises exhibits typical nonlinear dynamic characteristics. Based on this, in this study we constructed a threshold regression model to test the moderate range that can maintain the positive relationship between financialization and R&D innovation of enterprises, and also analyzed the nonlinear influencing mechanism by setting the interaction term. Considering the differences in factor intensity of various industries, in this research we also tested the heterogeneity in the moderate range among different industries. Using annual data of Chinese listed companies from 2010 to 2019, the following empirical findings were obtained: First, the impact of enterprise financialization on R&D innovation presents a nonlinear feature, i.e., a typical threshold feature. The level of enterprise financialization first positively affects the R&D innovation of enterprises, and after it reaches a certain turning point, the financialization behavior negatively affects the R&D innovation of enterprises. Second, the nonlinear feature of the mechanism by which enterprise financialization affects R&D innovation is reflected in the substitute effect, and there are disparities among different financialization levels. Third, industries with varying levels of factor intensity not only demonstrate differences in moderate intervals, but also exhibit significant disparities in the degree of impact exerted by financialization on innovation.