The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China’s eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions’ impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.

The selected sample enterprises are classified according to the National Economic Industry Classification. The first two companies with code 18 are selected, namely "Textile, Clothing and Apparel Industry". 25 clothing listed companies are selected, with a total of 100 sets of sample data over 4 years. There are a total of 15 indicators: R&D expenses/yuan X1; The proportion of R&D expenses to operating income/% X2; Number of R&D personnel/person X3; The proportion of R&D personnel to the total number of employees/% X4; Number of authorized patents per year/X5; Intangible asset ratio/% X6; YoY increase or decrease in total assets/% X7; YoY increase or decrease in operating revenue/% X8; Operating profit margin/% X9; Government subsidy/yuan X10; Per capita GDP of each province and city is X11 yuan; The ratio of import and export volume to GDP of each province and city/% X12; Science and technology expenditure per province and city/yuan X13; Net intangible assets of digital technology divided by total assets/% X14; The proportion of executives with an information technology background in the executive team is 15%. Patent data comes from the China Research Data Service Platform, regional development level, regional openness, and regional scientific research environment indicator data come from provincial and municipal government websites, enterprise digitalization level related data comes from the annual reports of listed clothing companies, and the remaining indicators come from the Guotai An database. For missing values in scientific and technological expenditures and net intangible assets of digital technology in various provinces and cities, linear interpolation method is used to supplement them.