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Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model
Li, Shuang1; Yang, Zewei1; Li, Hongsheng2; Shu, Guangwen3
2018-03-01
Source PublicationJOURNAL OF FORECASTING
ISSN0277-6693
Volume37Issue:2Pages:225-234
Corresponding AuthorShu, Guangwen(shuguangwen@whu.edu.cn)
AbstractChina is a populous country that is facing serious aging problems due to the single-child birth policy. Debate is ongoing whether the liberalization of the single-child policy to a two-child policy can mitigate China's aging problems without unacceptably increasing the population. The purpose of this paper is to apply machine learning theory to the demographic field and project China's population structure under different fertility policies. The population data employed derive from the fifth and sixth national census records obtained in 2000 and 2010 in addition to the annals published by the China National Bureau of Statistics. Firstly, the sex ratio at birth is estimated according to the total fertility rate based on least squares regression of time series data. Secondly, the age-specific fertility rates and age-specific male/female mortality rates are projected by a least squares support vector machine (LS-SVM) model, which then serve as the input to a Leslie matrix model. Finally, the male/female age-specific population data projected by the Leslie matrix in a given year serve as the input parameters of the Leslie matrix for the following year, and the process is iterated in this manner until reaching the target year. The experimental results reveal that the proposed LS-SVM-Leslie model improves the projection accuracy relative to the conventional Leslie matrix model in terms of the percentage error and mean algebraic percentage error. The results indicate that the total fertility ratio should be controlled to around 2.0 to balance concerns associated with a large population with concerns associated with an aging population. Therefore, the two-child birth policy should be fully instituted in China. However, the fertility desire of women tends to be low due to the high cost of living and the pressure associated with employment, particularly in the metropolitan areas. Thus additional policies should be implemented to encourage fertility.
Keywordfertility rate Leslie matrix model ls-SVM mortality rate population structure
DOI10.1002/for.2486
WOS KeywordLSSVM MODEL ; GROWTH-RATE ; FERTILITY ; FORECASTS ; MORTALITY ; RATES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41421001]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaBusiness & Economics
WOS SubjectEconomics ; Management
WOS IDWOS:000425088200006
PublisherWILEY
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57023
Collection中国科学院地理科学与资源研究所
Corresponding AuthorShu, Guangwen
Affiliation1.Wuhan Univ, Int Sch Software, Wuhan, Hubei, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.South Cent Univ Nationalities, Sch Pharmaceut Sci, Wuhan 430074, Peoples R China
Recommended Citation
GB/T 7714
Li, Shuang,Yang, Zewei,Li, Hongsheng,et al. Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model[J]. JOURNAL OF FORECASTING,2018,37(2):225-234.
APA Li, Shuang,Yang, Zewei,Li, Hongsheng,&Shu, Guangwen.(2018).Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model.JOURNAL OF FORECASTING,37(2),225-234.
MLA Li, Shuang,et al."Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model".JOURNAL OF FORECASTING 37.2(2018):225-234.
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