Studying The Effect Of Population Aging On Economic Growth Based On Panel Data

The 21 st century is an age of population aging.As a basic variable,the population factor has had an important impact on China's socio-economic development.At the same time,according to the population development forecast,China will face a severe population aging challenge in the 21 st century.And how will this challenge affect China's economic growth? This article will analyze its impact on economic development from a number of related indicators of ageing,such as: the population ratio of the elderly,elderly dependency ratio,illiteracy rate,unemployment rate and the number of medical security institutions.In order to achieve comprehensive and sustainable economic development,it is necessary to explore and analyze the problem of population aging so that scientific and effective policies can be formulated to assist economic development.Aging population has an impact on economic growth in both time and space.The innovation of this article breaks the limits of the quantitative analysis,places the spatial weights into the econometric model,and uses the panel data of 31 provincial regions from 2007 to 2016 in 2007-2016 to develop the impact of population aging on China's economic growth.Research;combined with GeoDa's exploratory spatial econometric analysis,Moran's index in Moran's scatterplot is all positive,indicating that there is a significant spatial positive correlation between the effects of aging on the economic growth in China.Analyze the Moran scatterplot,LISA The aggregated graph and the LISA saliency map show that despite the strong global spatial autocorrelation of China's 31 regions per capita GDP,most regions in China still do not have significant local spatial autocorrelation.The level of economic development in our country is mainly manifested as "spatial staggering," and areas with relatively high levels of development are generally relatively backward areas,and similarly underdeveloped areas are also developed with relatively good ones.The empirical research part of this paper is combined with Eviews.At the beginning of the model establishment,in order to prevent the occurrence of pseudo-regression,the unit root test is used to perform first-order differential processing on the unstable sequences to ensure the stationarity of the sequences.The Hausman test determines that the panel data model is Fixed effects or random effects model,the original hypothesis: the individual effects and explanatory variables are not related;Firstly,a random effect model was established.The estimation results showed that the P value was greater than the significance level of 0.05,accepting the original hypothesis,so we chose the random effect model;after we established the random effect variable intercept model and the random effect variable coefficient model,we got correlations.The sum of squared residuals;the next step,combined with the analysis of covariance analysis: mainly determine whether the panel data model is a constant coefficient,a variable coefficient,or a variable intercept model.Combine the test results: select the random effect variable intercept model,The results show that: The explanatory variable of the elderly population had no significant negative effect on the per capita GDP of the explained variable.The number of people in the structure and the ratio of elderly dependents have a one-to-one increase in per capita GDP.With positive action.