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Gray Relational Analysis And Prediction Of Population Aging In Guizhou Province

Posted on:2015-01-02Degree:MasterType:Thesis
Country:ChinaCandidate:C R XuFull Text:PDF
GTID:2207330467452210Subject:Applied Statistics
Abstract/Summary:PDF Full Text Request
In this paper, related to the statistics of the during from theyear of2004to the year of2011, under the use of gray correlationanalysis, principal component analysis method and gray predictionmethod,to analyze the population aging factors in Guizhou Province, andalso to predict the situation within the next five years in Guizhou. Inthe year of2000, our country entered the aging population progress,aging ahead of the social and economic development of China, and agingpopulation is an important feature of China, also, the country ’s agingpopulation base is very large, heavy, and aging quickly, unlike otherdeveloped countries, their features are the same, rich before gettingold, while, our feature is old before getting rich. Coupled with Chinais entering the aging society at a lower level of economic circumstances,so for our country, the problem of aging will bring many challenges andproblems, not only is the population aging on economic, social andcultural structure, will also have a significant impact on consumptionstructure, etc. Guizhou Province step into the aging process began inthe year of2003, but compared to most of the provinces in China, Guizhouprovince is entering the aging society in the case of more economicallyunderdeveloped conflict situation, and the relationship between theaging of the population and the economy, will become more severe andit will be a long-term problem. The aging process of Guizhou Province isalready a lot of pressure and it is imperative. Therefore, as for GuizhouProvince, to study population aging and do relative researches are verymeaningful. The basic idea of this paper is that by drawing on relevantliterature and data and can be collected easily, at this point of view,taking into account the two factors that most directly affect the agingof the population: the birth and death rates, then try to establish amore objective and comprehensive index system, including: health carespending, junior high gross enrollment rate, per capita consumptionexpenditure of urban and rural per capita consumption expenditure, sexratio, the registered urban unemployment rate, the number participatingin the basic old-age insurance, the rate of urbanization, educationspending, the total wastewater, waste gas the main pollutant emissions,health care institutions number of beds, the illiteracy rate, the ratioof per capita GDP,0-14-year-old population, population density, theuse of the main factors by gray relational analysis and principal component analysis to study the impact of an aging population in GuizhouProvince and the main ingredients, through the gray prediction methodto predict the proportion of the population aged65and above in GuizhouProvince within the next five years, that is to say, the development trendof population aging within the next five years in Guizhou Province, andto analyze the empirical results, make relevant policy recommendations.The empirical analysis in this article showed that the rate ofurbanization and other demographic indicators are the most importantfactors in Guizhou Province population aging process, social development,and government policy are important impact of population aging ingredient,and according to forecasts, within the next five years will show asignificant increase in the aging population trend in Guizhou Province.According to the results of empirical analysis, this paper intends topropose policy recommendations include: ideologically face aging,promote the development of community care, improve old age socialsecurity system, strengthen the construction of grass-roots work onaging, aging industrial development, the elderly shouldself-improvement. Alleviate the problem of an aging population in GuizhouProvince mainly requires the joint efforts of government and society.
Keywords/Search Tags:Guizhou Province, population aging, gray correlation analysis, principal component analysis, gray prediction, the rate of urbanization
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