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Research Influence Of Population Aging On Industrial Structure Upgrading

Posted on:2021-01-15Degree:MasterType:Thesis
Country:ChinaCandidate:J LiFull Text:PDF
GTID:2427330626458882Subject:Regional Economics
Abstract/Summary:PDF Full Text Request
According to the data of the national bureau of statistics,in 2000,the proportion of the population aged 65 and above in China was 6.96%,and the proportion of the population aged 60 and above reached 10.2%.That is to say,China entered an aging society in 2000.With the continuous decline of the fertility rate and natural growth rate,the proportion of the elderly population in China rose sharply.In 2018,the proportion of the population aged 65 and over in the total population reached 11.94%,which is not far from the deep aging society.The report to the 19 th national congress of the communist party of China(CPC)pointed out that China's economy has changed from a stage of high growth to a stage of high-quality development,and is now in the crucial period of transforming the development mode,optimizing the economic structure and transforming the growth drivers.Under the circumstances of an ageing population increasing,in order to promote the transformation and upgrading of industrial structure in our country and realize the strategic target for high quality and economic development,it is particularly to research the effects of an ageing population to the transformation and upgrading of industrial structure.On the one hand,which can avoid the aging of population industry structure reasonably upgrade may bring adverse effect,and effective grasp the potential opportunities that exist in the process of economic development,even provide useful policy Suggestions related to government departments.On the other hand,which also can find the breach of the upgrading of industrial structure from various aspects,and promote the positive development in the economic stability.Therefore,it has a certain theoretical significance and practical valueFirstly,this paper summarizes the existing research literature on population aging and industrial structure,analyzes relevant theories and puts forward research hypotheses.Then,the impact of population aging on industrial structure upgrading is mainly verified from two perspectives: one is the overall upgrading of industrial structure;the other is the internal upgrading of industrial structure.The benchmark regression model is constructed by using the provincial panel data of 31 provinces from 2000 to 2017,and further divided into three regions,namely eastern,central and western,to illustrate the regional heterogeneity of population aging affecting industrial structure upgrading.Finally,the robustness of the empirical conclusion is tested.Based on the established sample interval and statistical inference level,this paper mainly draws the following research conclusions:(1)population aging not only promotes the overall upgrading of the industrial structure significantly,but also facilitates the rationalization of the industrial structure.(2)in terms of sub-regions,the aging population has the strongest promoting effect on the overall upgrading of the industrial structure in the western region,followed by the central region and the eastern region.(3)the aging population also plays an obvious positive role in promoting the rationalization of industrial structure in the eastern and central regions,and has a greater impact on the central region,which is more than five times that of the eastern region,but has no significant impact on the rationalization of industrial structure in the western region.The above research shows that there is no opposition between population aging and industrial structure upgrading.China should comply with the development trend of population aging,make top-level design of macroeconomic policies,and make full use of the induced effect of population aging on industrial structure upgrading to promote the overall upgrading and rational development of industrial structure.
Keywords/Search Tags:population aging, industrial structure upgrading, baseline regression model, regional difference
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