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Research On The Infl-uence Of Population Fertility Policy On China's Economic Growth

Posted on:2020-10-17Degree:MasterType:Thesis
Country:ChinaCandidate:Z T LiFull Text:PDF
GTID:2427330572986878Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
Economic growth is influenced by multiple factors,not only including basic factors like labor,capital and land,but also many other factors,such as technological progress and institutions.Among them,the demographic factors have always been the key factors that researchers are concerned about.In China,since 1970s,the strict implementation of the population birth policy has significantly controlled the rapid growth of the population.The total fertility rate has been reduced from 6.4 to 1.6.From 1965 to the implementation of the separate second child(selective two-child policy)policy in 2013.At the total dependency ratio fell from 62.6%to 35.3%from 1982 to 2013,creating a demographic dividend that promotes China's economy.However,the follow-up effect of the population's birth policy making China's population aging an increasingly serious problem.The proportion of the population over 65 and up has increased from 7%to 8.9%from 2000 to 2010.The population over 60(and up)has reached 250 million in 2018.The percentage of the working-age labor force continues to decrease.Facing the fading of the demographic dividend and the risky situation of aging before wealthy,studying the impact of the birth policy on economic growth,measuring the optimal birth zone,and exploring the appropriate population birth policy to promote China's economic growth are of great practical significance.This paper links the population birth policy with the issue of economic growth.Firstly,summarizing the literature on the relationship between population and economic growth,this paper expounds the theory of population theory and economic growth,sorts out the evolution track of China's population birth policy,and the change of quantity,structure and economic growth of population caused by the implementation of the population birth policy is been analyzed.Secondly,constructing the three-generation generation model to explore the transmission mechanism of the birth policy's impact on economic growth through the theoretical deduction;that is,the birth policy affects the labor supply and family education through population age structure Investment and savings have an impact on economic growth.Thirdly,using the fixed effect model,base on the panel data of 29 provinces and cities in China from 1991 to 2017 to analyze the impact of different types of population birth policies on the economic growth of various regions in China.At the same time,the total fertility rate range that is most conducive to China's economic growth was measured by numerical simulation;finally,summarize the main conclusions and propose countermeasures.Conclusion:(1)The strict or loose implementation of the birth policy has a negative impact on economic growth,while the moderately loose implementation of the birth policy has a positive impact;(2)The robustness of the model is tested and the conclusion is robust when the population growth rate as an endogenous variable;(3)Numerical simulation results show that the optimal growth interval for promoting economic growth is[1.5,2.1].In combination with the adjustment of China's fertility policy,the status quo of the expected target has not been met,and suggestions are made:(1)further loose the birth policy and increase the supply of labor in the future;(2)continue to increase investment in education,accelerate the accumulation of human capital,improve the quality of labor,and promote China's economy;(3)Improve the retirement system and improve the utilization of the elderly's resources;(4)Further improve the social security system and encourage consumption;(5)Accelerate the implementation of family development support policies,reduce the burden of family support,and protect women's employment rights,etc.
Keywords/Search Tags:economic growth, population birth pol icy, Generational alternation model model
PDF Full Text Request
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