Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model

Abstract

China 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.

Иргэдийн Шилжилт Хөдөлгөөн, Сонгогчдын Тооны Шинжилгээ

“…Насны ангилал, шилжилт хөдөлгөөний задаргаатай динамик загварын тэгшитгэл нь эдийн засгийн өсөлт, бүс нутгийн хөгжил болон төрөл бүрийн урт хугацааны төлөвлөлтийн загваруудад нэгэн бүрэлдэхүүн болж ордог. A.Koslucher-ийн диссертац [6], Shuang Li нарын өгүүлэлд [7] насны ангилалтай динамик загварыг хүн ам зүйн судалгаанд ашиглаж буй гадаадын судлаачдын сүүлийн үеийн үр дүнгийн талаарх илүү дэлгэрэнгүй мэдээллийг дурджээ.…”

Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data

“…There are also certain limitations of the grey model: (1) when the dispersion degree of population data is large, the prediction accuracy will be reduced; (2) considerable deviation is shown in long-term population prediction. In addition, with the advancement of machine learning, experts and scholars have constructed Least Squares Support Vector Machine (LS-SVM) to predict age-specific fertility rate and age-specific male/female mortality rate [67].…”

Application of the Leslie Matrix to Predict the Number and Growth Rate of Women in West Java 2021

“…Population growth can provide information on whether changes in population numbers for the following year always increase, decrease or remain. Therefore, the Leslie matrix is used as a population growth model to determine the number prediction and growth rate prediction of a population for the following year (Chandler et al, 2004;Rand et al, 2020;Li et al, 2018).…”