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

• Date: 01 March 2018
• Author: Guangwen Shu,Shuang Li,Zewei Yang,Hongsheng Li
• DOI: http://doi.org/10.1002/for.2486
• Published date: 01 March 2018

RESEARCH ARTICLE

Projection of population structure in China using least

squares support vector machine in conjunction with a Leslie

matrix model

Shuang Li

1

| Zewei Yang

1

| Hongsheng Li

2

| Guangwen Shu

3

1

International School of Software, Wuhan

University, Wuhan, China

2

State Key Laboratory of Resources and

Environmental Information System,

Institute of Geographic Sciences and

Natural Resources Research, Chinese

Academy of Sciences, Beijing, China

3

School of Pharmaceutical Sciences,

South‐Central University for Nationalities,

Wuhan, China

Correspondence

Guangwen Shu, School of Pharmaceutical

Sciences, South‐Central University for

Nationalities, Wuhan, 430074, China

Email: shuguangwen@whu.edu.cn

Funding information

National Natural Science Foundation of

China, Grant/Award Number: 41421001

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 pro-

cess 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 per-

centage 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 pop-

ulation. 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.

KEYWORDS

fertility rate, Lesliematrix model, ls‐SVM, mortality rate,population structure

Received: 19 January 2017 Revised: 1 June 2017 Accepted: 19 June 2017

DOI: 10.1002/for.2486

Journal of Forecasting. 2018;37:225–234. Copyright © 2017 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/for 225