Population Development Challenges in China: Family Planning Policy and Provincial Population Difference [1st ed.] 9789811580093, 9789811580109

Table of contents :
Front Matter ....Pages i-xix
Introduction (Pengkun Wu)....Pages 1-6
China’s Serious Population Challenges (Pengkun Wu)....Pages 7-21
Front Matter ....Pages 1-1
Population Size Challenge: Low Total Fertility Rate (Pengkun Wu)....Pages 25-65
Population Structure Challenge: Serious Population Ageing (Pengkun Wu)....Pages 67-109
Population Development Under Different Family Planning Policies (Pengkun Wu)....Pages 111-143
How to Adjust the Family Planning Policy in China? (Pengkun Wu)....Pages 145-175
Front Matter ....Pages 175-175
The Necessity of Family Planning Policy Adjustment Among China’s Provinces (Pengkun Wu)....Pages 179-203
The Degree of Correlation Between the Implementation Time and the Necessity of Family Planning Policy Adjustment (Pengkun Wu)....Pages 205-216
Spatial Aggregation and Spatial Econometric Analysis of the Elderly Dependency Ratio (Pengkun Wu)....Pages 217-232
Conclusions and Suggestions for Addressing China’s Population Challenges (Pengkun Wu)....Pages 233-238
Back Matter ....Pages 239-241

Citation preview

Pengkun Wu

Population Development Challenges in China Family Planning Policy and Provincial Population Difference

Population Development Challenges in China

Pengkun Wu

Population Development Challenges in China Family Planning Policy and Provincial Population Difference

123

Pengkun Wu Sichuan University Chengdu, China

ISBN 978-981-15-8009-3 ISBN 978-981-15-8010-9 https://doi.org/10.1007/978-981-15-8010-9

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface and Acknowledgements

On November 15, 2013, the Third Plenary Session of the 18th Central Committee of the Communist Party of China officially released the Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform, meaning that China was aware of the serious population challenges and was starting to adjust the family planning policy. This document aroused my interest in studying China’s population. In the last 30 years, China has strictly implemented the one-child policy with the aim of controlling the total population number. Currently, however, China does not need to worry about the total population size, but it should be aware of the low total fertility rate. Another population challenge in China is the serious population ageing phenomenon. Analysing the challenge posed by the population structure, this book also identifies a significant regional population structure challenge, e.g., provincial population differences. This book summarizes the academic findings and hopes to help the public understand China’s current population challenges. When analysing population problems, scholars usually adopt qualitative methods or use quantitative methods. This book adopts various mathematical methods, such as system dynamics, mathematical programming, and spatial econometric analysis, to analyse China’s population development challenges, making the results more scientific and reliable. First, this book briefly introduces China’s population development challenges in Chap. 2 and then analyses two major challenges from Chaps. 3 to 9. Specifically, this book explores how to adjust China’s family planning policy to respond to the current population size and population structure challenges in Part I (Chaps. 3–6), and it discusses how to reduce the serious provincial population difference in Part II (Chaps. 7–9). Effective suggestions for addressing China’s population challenges are proposed based on the results of these chapters. Finally, this book concludes by presenting the research findings in Chap. 10. The starting and ending chapters are suitable for all interested audiences. I believe these three chapters can provide many interesting results and suggestions for readers, since these contents are summarized based on our interesting

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Preface and Acknowledgements

quantitative results. Chapters 3–9 involve many mathematical models; thus, they are suitable for graduate students and researchers. Now that this book is finished, I want to express my deepest and sincerest appreciation to all those who helped me during the process of finishing this book. I am especially grateful to the editors for their earnest and responsible work in handling this book. My family has provided crucial support during the process. Many of my achievements, including this book, would not have happened without their support. In particular, I want to acknowledge my cute daughter, Xinyue. When I was working on this book, she came into the world as a very precious gift. This book is supported in part by the Humanities and Social Sciences Fund of Ministry of Education of China (20YJC630159), the Natural Science Foundation of Hunan Province (2016JJ2094 and 2019JJ50403), and the Fundamental Research Funds for the Central Universities (YJ202008 and SCU-BS-PY-202049). Chengdu, China

Pengkun Wu

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

China’s Serious Population Challenges . . . . . . . . . . . . . . . . . 2.1 The Challenge of Adjusting the Family Planning Policies 2.1.1 Population Size Challenge . . . . . . . . . . . . . . . . 2.1.2 Population Structure Challenge . . . . . . . . . . . . . 2.2 The Challenge of Reducing the Provincial Population Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Challenge One: Family Planning Policy

Population Size Challenge: Low Total Fertility Rate . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Research Designs and General Findings . . . . . . . . . . . . . 3.2.1 Research Designs . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Descriptive Quantitative Analysis . . . . . . . . . . . 3.3 Determinants of the Fertility Intention and Total Fertility Rate in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Demographic Determinants . . . . . . . . . . . . . . . . 3.3.2 Social Determinants . . . . . . . . . . . . . . . . . . . . . 3.3.3 Economic Determinants . . . . . . . . . . . . . . . . . . 3.4 Estimation of the Total Fertility Rate . . . . . . . . . . . . . . . 3.4.1 Estimated Methods and Data Sources . . . . . . . . 3.4.2 Estimated Total Fertility Rate in China . . . . . . . 3.5 Data Sources for Studying China’s Total Fertility Rate . .

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3.6

Suggestions for Improving the Total Fertility Rate 3.6.1 Economic Supporting . . . . . . . . . . . . . . . 3.6.2 Maternity Benefits . . . . . . . . . . . . . . . . . 3.6.3 Social Welfare . . . . . . . . . . . . . . . . . . . . 3.6.4 Population Policies . . . . . . . . . . . . . . . . . 3.7 Conclusions and Discussions . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Population Structure Challenge: Serious Population Ageing . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 China’s Population Ageing: A Brief Sketch . . . . . . . . . . 4.2.1 Historical Changes . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Current Circumstances . . . . . . . . . . . . . . . . . . . 4.2.3 Future Predictions . . . . . . . . . . . . . . . . . . . . . . 4.3 Consequences of Serious Population Ageing . . . . . . . . . 4.3.1 Labour Market . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Society and the Environment . . . . . . . . . . . . . . 4.3.3 Family Finances and Consumption . . . . . . . . . . 4.3.4 Exported Products . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Macroeconomy . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Data Sources for Studying China’s Population Ageing . . 4.5 Suggestions for Responding to Population Ageing . . . . . 4.5.1 Improving the Retirement Security System . . . . 4.5.2 Building Elderly-Friendly Communities . . . . . . . 4.5.3 Developing the Elderly Service Industry . . . . . . 4.5.4 Promoting the Healthy Physical and Mental Development of the Elderly . . . . . . . . . . . . . . . 4.5.5 Increasing Financial Supports . . . . . . . . . . . . . . 4.6 Conclusions and Discussions . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Population Development Under Different Family Planning Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 System Dynamics Model for Population Simulation . . . . . 5.2.1 Song Jian’s Population Development Equation . . . 5.2.2 Establishing a System Dynamics Model . . . . . . . . 5.2.3 Model Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Population Simulation in China Under the Two-Child Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Parameter Determination . . . . . . . . . . . . . . . . . . . 5.3.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Theoretical Population Simulation Results . . . . . . 5.3.4 Population Simulation Results When Considering Fertility Intentions . . . . . . . . . . . . . . . . . . . . . . .

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5.4

Family Planning Policy Comparison . . . . . . . . . . . . . . . . . 5.4.1 Policy Comparison Between the Two-Child Policy and the Selective Two-Child Policy . . . . . . . . . . . . 5.4.2 Policy Comparison Among the Two-Child Policy, the One-Child Policy, and Cancellation of Family Planning Policy . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Population Simulation in Jiangxi Province Under the Two-Child Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Parameter Determination . . . . . . . . . . . . . . . . . . . . 5.5.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Population Simulation Results . . . . . . . . . . . . . . . . 5.5.4 Family Planning Policy Comparison for Jiangxi Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

How to Adjust the Family Planning Policy in China? . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Prejudgement: When to Adjust the Family Planning Policy . 6.2.1 The Equation for Calculating the Time for Implementing the Selective Two-Child Policy . . . . 6.2.2 The Equation for Calculating the Time for Implementing the Two-Child Policy . . . . . . . . . . . 6.2.3 Calculation Results for the Implementation Time . . 6.2.4 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . 6.3 Non-Linear Integer Programming Model for Adjusting the Family Planning Policy . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Demographic Dividend Analysis . . . . . . . . . . . . . . 6.3.2 The Non-Linear Integer Programming Model . . . . . 6.3.3 Data Description for the Non-Linear Integer Programming Model . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 The Optimal Results for the Total Dependency Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Reform Path for China’s Family Planning Policy . . . . . . . . 6.4.1 Reform Intensity . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Reform Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Evaluations for the Proposed Reform Path . . . . . . . . . . . . . 6.5.1 Population Simulation Under the Proposed Reform Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Policy Comparison . . . . . . . . . . . . . . . . . . . . . . . .

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6.6 6.7

Sensitivity Analysis . . . . . . . . . . . . . . . . . Conclusions and Discussions . . . . . . . . . . 6.7.1 Conclusions and Suggestions . . . 6.7.2 Research Implications . . . . . . . . . 6.7.3 Limitations and Further Research References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part II 7

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Challenge Two: Provincial Population Difference

The Necessity of Family Planning Policy Adjustment Among China’s Provinces . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 The Evaluation Index System on the Necessity of Family Planning Policy Adjustment . . . . . . . . . . . . . 7.2.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Evaluation Indexes . . . . . . . . . . . . . . . . . . . . . 7.2.3 Evaluation Index System Construction . . . . . . . 7.3 Evaluation of the Necessity of Policy Adjustment . . . . . 7.3.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Evaluation Model . . . . . . . . . . . . . . . . . . . . . . 7.3.3 The Quantitative Values of the Indicators . . . . 7.3.4 Weight Definition . . . . . . . . . . . . . . . . . . . . . . 7.3.5 Evaluation Results . . . . . . . . . . . . . . . . . . . . . 7.4 Province Classification . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Province Classification Under Each Indicator . . 7.4.2 Province Classification Based on the Necessity of Policy Adjustment . . . . . . . . . . . . . . . . . . . 7.5 Spatial Analysis of Provinces . . . . . . . . . . . . . . . . . . . 7.5.1 Exploratory Spatial Data Analysis and Moran’s I Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Local Indicators of Spatial Association (LISA) Analysis of the Necessity of Policy Adjustment 7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Degree of Correlation Between the Implementation Time and the Necessity of Family Planning Policy Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 System 1: Quantification Scores of the Time of Implementation of the Selective Two-Child Policy .

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8.2.2

System 2: Quantification Scores of the Necessity of Family Planning Policy Adjustment . . . . . . . . . . . 8.2.3 Calculation of the Degrees of Correlation . . . . . . . . 8.3 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Calculation Results of the Degrees of Correlation for China’s Provinces . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Spatial Aggregation Analysis of the Degrees of Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Overall Evaluation of Implementing the Selective Two-Child Policy in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Spatial Aggregation and Spatial Econometric Analysis of the Elderly Dependency Ratio . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Elderly Dependency Rate . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 China’s Elderly Dependency Ratio . . . . . . . . . . . 9.2.2 Elderly Dependency Ratio in China’s Provinces . . 9.2.3 Coefficient of Variation Among the 31 Provinces . 9.3 Spatial Aggregation Analysis of the Elderly Dependency Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Moran’s I Index Analysis . . . . . . . . . . . . . . . . . . 9.3.2 LISA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Spatial Econometric Analysis . . . . . . . . . . . . . . . . . . . . . 9.4.1 Influence Factors Analysis . . . . . . . . . . . . . . . . . 9.4.2 Data for the Variables . . . . . . . . . . . . . . . . . . . . . 9.4.3 Spatial Econometric Model . . . . . . . . . . . . . . . . . 9.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Conclusions and Suggestions for Addressing China’s Population Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Tracking Fertility Intentions and Further Relaxing the Family Planning Policy in Due Time . . . . . . . 10.2.2 Publicizing the Population Conditions in China and All Provinces . . . . . . . . . . . . . . . . . . . . . . . . 10.2.3 Supervising the Population Development in China and All Provinces . . . . . . . . . . . . . . . . . . . . . . . .

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10.2.4 Expanding Research into Other Related Fields . . . . . . . 237 10.2.5 Paying Attention to the Spatial Aggregation of China’s Provinces . . . . . . . . . . . . . . . . . . . . . . . . . 237 10.3 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

About the Author

Pengkun Wu is currently an associate professor and MPhil supervisor at the Business School of Sichuan University. He holds two Ph.D.s from The Hong Kong Polytechnic University (2018) and Harbin Institute of Technology (2019). His research interests include China’s population development, fake online information (fake news on the Internet and fake reviews in e-commerce), decision support systems, and e-commerce. He has published over 10 papers in international journals, including Decision Support Systems, International Journal of Production Research, Applied Mathematical Modelling, Social Indicators Research, Journal of the Operational Research Society, and others.

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List of Figures

Fig. 1.1 Fig. 1.2 Fig. 2.1

Fig. 2.2 Fig. 3.1 Fig. Fig. Fig. Fig.

3.2 4.1 5.1 5.2

Fig. 5.3 Fig. 5.4 Fig. 5.5

Fig. 5.6

Fig. 5.7 Fig. 6.1 Fig. 6.2 Fig. 6.3

The historical development of China’s family planning policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The road map of this book . . . . . . . . . . . . . . . . . . . . . . . . . . . . The total population number and total fertility rate from 1970 to 2069. a Growth rate from 1980 to 2028. b Total fertility rate from 1970 to 2028 . . . . . . . . . . . . . . . . . . Dependency rates in China from 1990 to 2018 . . . . . . . . . . . . Annual number of identified journal articles about China’s total fertility rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The determinants affecting the total fertility rate . . . . . . . . . . . The effects of population ageing . . . . . . . . . . . . . . . . . . . . . . . Basic flow diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improved flow diagram for the population number of newborn babies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The system dynamics presented in Vensim . . . . . . . . . . . . . . . Simulation results of the comparison between the two-child policy and the selective two-child policy . . . . . . . . . . . . . . . . . Simulation results of the comparison among the two-child policy, the one-child policy, and cancellation of family planning policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation results of the comparison among the selective two-child policy, the one-child policy, and cancellation of family planning policy in Jiangxi Province . . . . . . . . . . . . . Sensitivity analysis of the willingness to have a second baby . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The difference in demographic dividends between the two-child policy and the one-child policy . . . . . . . . . . . . . . Reform intensity of the family planning policy during 2016–2100 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation results under the proposed reform path . . . . . . . . . .

.. ..

2 4

.. ..

11 17

. . . .

. 28 . 45 . 95 . 116

. . 117 . . 117 . . 129

. . 130

. . 136 . . 138 . . 156 . . 156 . . 167

xv

xvi

Fig. 6.4 Fig. 6.5 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 8.1 Fig. 8.2 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4

List of Figures

Simulation results under three different family planning policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reform intensities of family planning policies from 2016 to 2100 under different research intervals . . . . . . . . . . . . . . . . . Evaluation index system for the necessity of family planning policy adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution diagrams of each indicator for the 31 Chinese provinces . . . . . . . . . . . . . . . . . . . . . . . . . . The scatter diagram of Moran’s I index based on the necessity of family planning policy adjustment . . . . . . . The cluster diagram of the provinces in terms of the necessity of family planning policy adjustment . . . . . . . . . . . . . . . . . . . . The fishbone summarizing the factors influencing the necessity of family planning policy adjustment . . . . . . . . . . . . . . . . . . . . Spatial aggregation diagram of the degrees of correlation . . . . The historical evolution of China’s elderly dependency ratio from 2000 to 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The change trend of the coefficient of variation of the elderly dependency ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The change trend of the values of Moran’s I index in terms of the elderly dependency ratio . . . . . . . . . . . . . . . . . . . . . . . . Spatial aggregation diagrams for 2003, 2008, and 2013. a Spatial aggregation diagram for 2003. b Spatial aggregation diagram for 2008. c Spatial aggregation diagram for 2013 . . . .

. . 168 . . 170 . . 186 . . 194 . . 198 . . 199 . . 209 . . 213 . . 219 . . 223 . . 224

. . 225

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table Table Table Table

4.6 4.7 4.8 5.1

The data of three population size indicators . . . . . . . . . . . . . The main population indicators in China and five other countries with large populations between 1973 and 2019 . . The sex ratio of China from 1970 to 2018 . . . . . . . . . . . . . . The age composition in China from 1990 to 2018 . . . . . . . . Provincial population data in China in 2018 (Sampling fraction: 0.820‰) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The journal information . . . . . . . . . . . . . . . . . . . . . . . . . . . . The demographic determinants affecting the fertility intention and total fertility rate . . . . . . . . . . . . . . . . . . . . . . . The social determinants affecting fertility intentions and total fertility rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The economic determinants affecting fertility intentions and total fertility rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimations of China’s total fertility rate since 2000 . . . . . . Total fertility rate in China’s towns and villages from 2006 to 2016 (estimated from the 2017 fertility survey) . . . Data sources adopted to study the total fertility rate . . . . . . China’s population ageing indicators from 1990 to 2018 . . . The basic population characteristics in six countries with large populations in 2019 . . . . . . . . . . . . . . . . . . . . . . . Effect of population ageing on the labour market. . . . . . . . . Effect of population ageing on society and the environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of population ageing on family finances and consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of population ageing on exported products . . . . . . . . Effect of population ageing on the macroeconomy. . . . . . . . Data sources adopted to study population ageing . . . . . . . . . The population data at different ages in the 2000 population census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..

9

.. .. ..

12 14 16

.. ..

18 29

..

31

..

35

.. ..

40 46

.. .. ..

51 53 70

.. ..

71 73

..

78

. . . .

81 86 89 96

. . . .

. . 119 xvii

xviii

Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 8.1 Table 8.2

List of Tables

Model test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fertility factors among different ages before and after issuing the new family planning policy . . . . . . . . . . . . . . . . . . . . . . The population data under different ages in the 2010 population census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The theoretical population simulation results under the new family planning policy . . . . . . . . . . . . . . . . . . . . . . Population simulation results when considering fertility intentions under the new two-child policy . . . . . . . . . . . . . . Fertility factors among different ages in Jiangxi Province before and after issuing the new family planning policy . . . The population data of Jiangxi Province at different age intervals in the 2010 population census . . . . . . . . . . . . . . . . Population simulation results in Jiangxi Province under the new family planning policy . . . . . . . . . . . . . . . . . . . . . . Influence mechanisms of population indicators on family planning policy adjustment . . . . . . . . . . . . . . . . . . . . . . . . . Population indicator data in 1982, in 1987, and from 1990 to 2013 under the one-child policy . . . . . . . . . . . . . . . Predicted population indicator data from 2015 to 2050 under the selective two-child policy . . . . . . . . . . . . . . . . . . . Sensitivity analysis of the growth rate of per capita GDP . . The total population and children dependency ratio in 2019 under different growth rates of per capita GDP . . . . Definition of notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The upper and lower bounds of newborn babies during 2016–2100 (unit: million) . . . . . . . . . . . . . . . . . . . . . . . . . . The required data for the non-linear integer programming model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The rational numbers of newborn babies during 2016–2100 (unit: million) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population development under the proposed reform path . . . Required data of the nine indicators . . . . . . . . . . . . . . . . . . . The quantitative values of nine indicators after normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The evaluation scores for the necessity of family planning policy adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The province classification based on the necessity of family planning policy adjustment . . . . . . . . . . . . . . . . . . The implementation time and the quantification scores for China’s 31 provinces . . . . . . . . . . . . . . . . . . . . . . . . . . . The degrees of correlation of the 31 provinces . . . . . . . . . .

. . 120 . . 122 . . 123 . . 125 . . 126 . . 132 . . 133 . . 134 . . 149 . . 150 . . 153 . . 154 . . 154 . . 158 . . 159 . . 162 . . 164 . . 165 . . 187 . . 189 . . 192 . . 195 . . 208 . . 211

List of Tables

Table 8.3

Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5

xix

Weight of the 31 provinces for evaluating the overall degree of rationality of implementing the selective two-child policy in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The elderly dependency ratio of the 31 provinces from 2000 to 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The coefficient of variation of the elderly dependency ratio from 2002 to 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moran’s I index values from 2003 to 2013 . . . . . . . . . . . . . Descriptive statistics of the seven variables . . . . . . . . . . . . . Spatial regression analysis results of the influencing factors of the elderly dependency ratio . . . . . . . . . . . . . . . . . . . . . .

. . 214 . . 220 . . 222 . . 224 . . 227 . . 229

Chapter 1

Introduction

Abstract In this chapter, I attempt to describe the research background, theme, and structure of this book. For this purpose, I start by introducing the history of China’s family planning policies. Accordingly, I propose two serious population challenges in contemporary China. That is, how can China adjust its family planning policy to address the current population size and population structure challenges? Additionally, how can China respond to the serious provincial population differences? Finally, I present the road map of this book and describe the contents of all the chapters. Keywords Family planning policy · Population challenges · Provincial population differences · China In the early years of the foundation of the People’s Republic of China (China includes Mainland China, Hong Kong and Macao SARs, and Taiwan Province; this book analyses the population problems only in Mainland China and excludes Hong Kong and Macao SARs, and Taiwan Province; thus, in this book, China refers to Mainland China), China encouraged all families to have many children, causing an uncontrollable rise in China’s population number. Thus, the government and researchers started to pay attention to the population challenges in China. The first mention of population policy in China occurred in 1955. Then, China proposed guidance on actively advocating family planning in 1962 and soon established a government agency in 1964. China officially started to implement its family planning policy in 1973. During the first 10 years of the family planning policy implementation (i.e., 1974–1983), the policy was frequently adjusted to fit national conditions, from the two-child policy to the one-child policy, and then, some provinces allowed rural families to have two children. Since 1982, when the family planning policy was written into China’s Constitution, the one-child family planning policy has not been substantially adjusted. This was the case until November 15, 2013, when the Third Plenary Session of the 18th Central Committee of the Communist Party of China officially released the Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform and adjusted China’s family planning policy from the one-child policy to the selective two-child policy (The Third Plenary Session of the 18th Central Committee of the Communist Party of China 2013). Under the selective two-child policy, families © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_1

1

2

1 Introduction 1949

1955 The first mention of population policy in China occurred, but no specific policies were designed.

1962 China proposed the guidance on actively advocating family planning. 1964 China established the government agency called the Family Planning Commission of the State Council.

1949 - 1983 Exploration and germination of the family planning policy

1973 China initiated the "wan-xiao-shao" program, advocating late marriage, long spacing, and few children. 1978 China wrote the family planning policy into law, and officially implemented the one-child policy.

1982 The family planning policy was written into China's Constitution.

1983 - 2012 Stable development of the family planning policy

2002 China started to implement the Population and Family Planning Law of the People's Republic of China.

2013 - 2020 Maturity of the family planning policy

2013 China adjusted the family planning policy from the one-child policy to the selective two-child policy. 2016 China adjusted the family planning policy from the selective two-child policy to the two-child policy.

2020

Fig. 1.1 The historical development of China’s family planning policy

including at the least only one child can have two children and other families can still have only one child. Then, in 2016, China officially implemented the two-child policy. The nation’s Law on Population and Family Planning allows all families to have two children. The history of China’s population policies and the important milestones since the establishment of the People’s Republic of China are described in Fig. 1.1. China’s family planning policy adopted in the 1970s has resulted in remarkable achievements (Gietel-Basten et al. 2019; Guo 2016; Hesketh and Zhu 1997; Jiang et al. 2013; Peng 2011). China’s population growth rate declined from 2.583% in 1970 to 0.479% in 2010, and the total fertility rate declined from 5.81 in 1970 to 1.18 in 2010. Since the introduction of the family planning policy, the number of births averted has exceeded 458 million, and the total population size has been well controlled (Tao and Yang 2011).

1 Introduction

3

However, the family planning policy has also brought some negative consequences, such as gender inequality, only-child education, and serious population ageing (Cai 2012; Zhang and Chen 2020). In summary, the family planning policy has controlled the population size, but destroyed the population structure. A healthy population size and population structure are closely related to economics and society. Thus, how to adjust the family planning policy to improve the population size and population structure is very important in contemporary China. In addition to adjusting its family planning policies, China faces another population challenge: provincial population differences. In China, whose population exceeds 1.4 billion and accounts for approximately one-fifth of the total population worldwide, provincial differences exist in multiple fields (Biggeri et al. 2017). For instance, the distribution of China’s water resources is geographically uneven (Wu et al. 2016), with 81% of such resources being intensively distributed in the Yangtze River basin and southern regions (Chen and Xia 1999). Yang and Mukhopadhaya measured multidimensional poverty in China and identified that the eastern provinces are generally poorer than the central provinces (Yang and Mukhopadhaya 2017). Different levels of development in the provinces are fundamental for the disparities under the population situation. Similar to these indicators, China’s population also has significant regional differences among provinces (Yi et al. 2011). For instance, Jiangxi Province has a slight ageing phenomenon and greater pressure on the total population, while the opposite holds true for Beijing Province. Accurate research on the spatial pattern of the population is critical for policy-making and spatial planning in all related fields, including urbanization, land use development, ecological conservation, and environmental protection (Deng et al. 2015). In 2013, when implementing the selective two-child policy, China did not set a timetable for all provinces and allowed provincial governments to start implementing the policy based on their actual population conditions. The 31 provinces in Mainland China had different population sizes and structural conditions, and thus, they had different levels of urgency to adjust their family planning policies. Zhejiang Province implemented the selective two-child policy on January 17, 2014, making it the first province to do so, and Xinjiang Province was the last to implement it on November 17, 2014. Except for Xinjiang Province and Tibet Province, the other 29 provinces in Mainland China implemented the selective two-child policy within 5 months, i.e., from January 17, 2014, to June 3, 2014. Although there are serious provincial population differences, it appears that the provinces still cannot adjust their family planning policies based on their provincial population conditions. We should better explore the provincial population differences and address this challenge. Thus, this book identifies the two serious population challenges in contemporary China: how to adjust the family planning policy to improve the population size and population structure, and how to face the serious provincial population differences. To explore these two population challenges in contemporary China, this book contains ten chapters. The road map of this book is shown in Fig. 1.2. This chapter establishes the intention and the main idea behind the book, identifying the two serious population challenges in contemporary China. This book

4

1 Introduction

Chapter 1: Introduction

Chapter 2: The Serious China’s Population Challenges

Chapter 3: Population Size Challenge: Low Total Fertility Rate

Chapter 4: Population Structure Challenge: Serious Population Aging

Adjusting Family Planning Policy to Address Population Size and Structure Challenges

Part I: Family Planning Policy

Chapter 5: Population Development under Different Family Planning Policies

Chapter 6: How to Adjust the Family Planning Policy in China?

Chapter 7: Adjusting Necessary of the Family Planning Policy among Provinces

Part II: Provincial Population Difference

Chapter 8: Correlation Degree of Implementation Time and Adjusting Necessity of the Family Planning Policy

Chapter 9: Spatial Aggregation and Spatial Econometric Analysis about the Elderly Dependency Ratio

Chapter 10: Conclusions and Suggestions for Relieving China’s Population Challenges

Fig. 1.2 The road map of this book

contains ten chapters, with the aim of analysing the identified population challenges. The road map of this book and the contents of the ten chapters are introduced. Chapter 2 analyses China’s population data and proposes two population challenges in contemporary China: how to adjust the family planning policy to respond to the challenges posed by the population size and population structure; and how to reduce the serious provincial population differences.

References

5

Chapter 3 employs a meta-analysis to understand the low total fertility rate in China and explores the factors affecting the total fertility rate. Based on a detailed meta-analysis of the total fertility rate, I am aware of China’s low total fertility rate and propose some effective suggestions on how to increase China’s total fertility rate. Chapter 4 employs a meta-analysis to understand the serious population ageing in China, and it explores the effects of the population structure on social development and economic operations. Based on a detailed meta-analysis of the population structure, I am aware of China’s serious population ageing and vanishing population dividends and propose some effective suggestions on how to respond to the serious population ageing. Chapter 5 builds a system dynamics (SD) model based on Song Jian’s population development equation to predict future population development. Some key parameters in the SD model are affected by family planning policy implementation. Thus, the SD model can predict China’s population development under different family planning policies. Based on the predicted results, this book proposes some effective suggestions on how to manage population development under different family planning policies. Chapter 6 builds a mathematical programming model to explore how to adjust the family planning policy to respond to the challenges posed by the population size and population structure. By employing mathematical programming, this book proposes an optimal path for adjusting China’s family planning policies. Based on the optimal results, this book proposes some effective suggestions on how to adjust China’s family planning policies. Chapter 7 establishes an evaluation index system to quantify the necessity of adjusting China’s family planning policy. Based on the quantified values, this book compares the necessity of policy adjustment among the 31 provinces in China and proposes some effective suggestions for responding to the provincial differences in the extent to which the provinces need to adjust their family planning policy. Chapter 8 collects specific data on selective family planning policy implementation and analyses the degree of correlation between the implementation time and the necessity of policy adjustment calculated in Chap. 7. The degrees of correlation help in understanding whether the 31 provinces are suitably adjusting their family planning policy. Based on the results, this book proposes some effective suggestions for facing the provincial population differences. Chapter 9 conducts a spatial econometric analysis of regional population distributions and identifies the seriousness of the provincial population differences. Based on the results, this book proposes some effective suggestions for reducing the provincial population differences. Chapter 10 concludes the whole book and summarizes the effective suggestions for responding to China’s two population challenges.

6

1 Introduction

References As this book studies China’s population challenges, many Chinese references are cited. When citing Chinese references, this book provides the Chinese reference information under the translated English version of the reference and marks the Chinese reference information in italics. This note applies to the whole book. Biggeri, L., Ferrari, G., & Zhao, Y. (2017). Estimating cross province and municipal city price level differences in China: Some experiments and results. Social Indicators Research, 131(1), 169–187. Cai, Y. (2012). China’s demographic prospects: A UN perspective. International Economic Review (1), 73–81 (in Chinese). Chen, J., & Xia, J. (1999). Facing the challenge: Barriers to sustainable water resources development in China. Hydrological Sciences Journal, 44(4), 507–516. Deng, Y., Liu, S., Cai, J., Lu, X., & Nielsen, C. P. (2015). Spatial pattern and its evolution of Chinese provincial population: Methods and empirical study. Journal of Geographical Sciences, 25(12), 1507–1520 (in Chinese). 邓羽, 刘盛和, 蔡建明 and 鲁玺. 2015. "中国省际人口空间格局演化的分析方法与实证," 地理 学报 25 (12), pp. 1507–1520. Gietel-Basten, S., Han, X., & Cheng, Y. (2019). Assessing the impact of the “one-child policy” in China: A synthetic control approach. PLoS One, 14(11). Guo, Z. (2016). Understanding fertility trends in China. In C. Z. Guilmoto & G. W. Jones (Eds.), Contemporary demographic transformations in China, India and Indonesia (pp. 97–111). Switzerland: Springer International Publishing. Hesketh, T., & Zhu, W. X. (1997). Health in China: The one child family policy: The good, the bad, and the ugly. BMJ, 314(7095), 1685. Jiang, Q., Li, S., & Feldman, M. W. (2013). China’s population policy at the crossroads: Social impacts and prospects. Asian Journal of Social Science, 41(2), 193–218. Peng, X. (2011). China’s demographic history and future challenges. Science, 333(6042), 581–587. Tao, T., & Yang, F. (2011). The impact of China’s family planning policy on demographic transition. Population Research, 35(1), 103–112 (in Chinese). 陶涛 and 杨凡. 2011. "计划生育政策的人口效应," 人口研究 35 (1), pp. 103–112. The Third Plenary Session of the 18th Central Committee of the Communist Party of China. (2013). Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform (in Chinese). 中国共产党第十八届中央委员会第三次全体会议. 2013. "中共中央关于全面深化改革若干 重大问题的决定." Wu, P., Xia, C., Liu, F., Wu, Y., & He, Y. (2016). An integrated water strategy based on the current circumstances in China. Applied Mathematical Modelling, 40(17–18), 8108–8124. Yang, J., & Mukhopadhaya, P. (2017). Disparities in the level of poverty in China: Evidence from China family panel studies 2010. Social Indicators Research, 132(1), 411–450. Yi, B., Wu, L., Liu, H., Fang, W., Hu, Y., & Wang, Y. (2011). Rural-urban differences of neonatal mortality in a poorly developed province of China. BMC Public Health, 11(1), 477. Zhang, Y., & Chen, D. (2020). The population policy change and current population development during the 70 years in the new China. Macroeconomic Management (5), 62–69 (in Chinese). 张越 and 陈丹. 2020. "新中国70年的人口政策变迁与当代人口," 宏观经济管理 (5), pp. 62–69.

Chapter 2

China’s Serious Population Challenges

Abstract In this chapter, I analyse population development data from 1970 to 2019 to introduce the two serious population challenges in contemporary China. To show the challenge of adjusting the family planning policy, I point out that China’s current population size and population structure challenges are the low total fertility rate and serious population ageing. In addition, I present another population challenge in China, i.e., the serious provincial population differences. Keywords Population challenge · Population size · Population structure · Family planning policy · Total fertility rate · Population ageing As described in Chap. 1, China started to implement the one-child policy in 1973. The strict family planning policy helped control the total number of China’s population, but further destroyed the population structure (Ding and Hesketh 2006; Gietel-Basten et al. 2019; Guo 2016; Hesketh and Zhu 1997; Jiang et al. 2013; Peng 2011; Zhang and Chen 2020). China’s population growth rate declined from 2.583% in 1970 to 0.479% in 2010, and the total fertility rate declined from 5.81 in 1970 to 1.18 in 2010. Since the introduction of the family planning policy, the number of births averted has exceeded 458 million, and the total population size has been well controlled (Tao and Yang 2011). This book aims to address two serious population challenges in contemporary China: how to adjust the family planning policy to improve the population size and population structure, and how to face the serious provincial population differences. To introduce these two serious population challenges, this chapter first describes the challenges posed by the population size and population structure, and then analyses the provincial population differences.

2.1 The Challenge of Adjusting the Family Planning Policies In the 1970s, China started to implement its family planning policy due to the rapidly increasing population size and the accepted population structure. If China © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_2

7

8

2 China’s Serious Population Challenges

had continued to allow all families to have babies without restrictions, its population would have become unsustainable. Therefore, China implemented the family planning policy (Guo 2015). However, after roughly 40 years, China now faces other kinds of serious population challenges. China’s population number is well controlled, but China now has a low total fertility rate (Wu and Mu 1995). As a result, China is losing its demographic dividends and is facing a serious population ageing phenomenon.

2.1.1 Population Size Challenge From the statistical data (Kuaiyi Financial Network 2019; National Bureau of Statistics of China 2020), I obtain data about three main population size indicators and present them in Table 2.1. Although China’s population is still increasing, the strict one-child policy has controlled the growth rate of the total population number and made the population sustainable. Currently, China has a low total fertility rate and a serious population ageing phenomenon (Wu and Mu 1995). It is expected that China’s population will decline in the next decade. To predict future population development, I employ the grey model to predict the total population number and total fertility rate in future decades. The grey model is a popular and accurate method of forecasting and dealing with systems that are characterized by poor information or limited information. In this model, the relations and rules among historical data can be assessed to better forecast the trend of future data. The grey model can be applied when the data are non-negative, random, and effective (Hsu and Chen 2003). The most widely used grey model is GM (1,1), where there is only one variable and the relationship in the differential equation is of the first order. The efficacy of the grey model (GM (1,1)) has been demonstrated in predicting future consumption data (Yu and Lu 2012), health expenditure (Yu and Jia 2020), and even the population development (Xie et al. 2018). Similarly, this book uses GM (1,1) to forecast the future total population number and total fertility rate. When the original data are described as X 0 = (x0 (1), x0 (2), x0 (3), · · · , x0 (n)), where n is the number of data, the steps for building GM (1,1) are introduced as follows: (1) Accumulated generating operation: Accumulate the original data to weaken the volatility and randomness of the original data and obtain the new data series:

X 1 = (x1 (1), x1 (2), x1 (3), · · · , x1 (n)),

(2.1)

where x1 (t) is the accumulated value of the first t data in the original data series:

2.1 The Challenge of Adjusting the Family Planning Policies

9

Table 2.1 The data of three population size indicators Year

Total population number (million)

Growth rate

Total Year fertility rate

Total population number (million)

Growth Total rate (%) fertility rate

1970

829.92

N/A

5.72

1995 1211.21

10.55

1.66

1971

852.29

N/A

5.40

1996 1223.89

10.42

1.62

1972

871.77

N/A

5.04

1997 1236.26

10.06

1.60

1973

892.11

N/A

4.64

1998 1247.61

9.14

1.60

1974

908.59

N/A

4.24

1999 1257.86

8.18

1.59

1975

924.20

N/A

3.86

2000 1267.43

7.58

1.60

1976

937.17

N/A

3.51

2001 1276.27

6.95

1.60

1977

949.74

N/A

3.20

2002 1284.53

6.45

1.60

1978

962.59

12.00% 2.94

2003 1292.27

6.01

1.60

1979

975.42

N/A

2.75

2004 1299.88

5.87

1.61

1980

987.05

11.87

2.61

2005 1307.56

5.89

1.61

1981 1000.72

14.55

2.55

2006 1314.48

5.28

1.61

1982 1016.54

15.68

2.54

2007 1321.29

5.17

1.62

1983 1030.08

13.29

2.56

2008 1328.02

5.08

1.62

1984 1043.57

13.08

2.61

2009 1334.50

4.87

1.62

1985 1058.51

14.26

2.65

2010 1340.91

4.79

1.63

1986 1075.07

15.57

2.67

2011 1347.35

4.79

1.63

1987 1093.00

16.61

2.64

2012 1354.04

4.95

1.64

1988 1110.26

15.73

2.58

2013 1360.72

4.92

1.65

1989 1127.04

15.04

2.46

2014 1367.82

5.21

1.66

1990 1143.33

14.39

2.31

2015 1374.62

4.96

1.67

1991 1158.23

12.98

2.14

2016 1382.71

5.86

1.68

1992 1171.71

11.60

1.98

2017 1390.08

5.32

1.68

1993 1185.17

11.45

1.84

2018 1395.38

3.81

1.69

1994 1198.50

11.21

1.73

2019 1400.50

N/A

N/A

x1 (t) =

t 

x0 (k), t = 1, 2, · · · , n.

(2.2)

k=1

(2) Build the first-order differential equation for the new data series X 1 as follows:

d x1 + ax1 = u. dt

(2.3)

where a and u represent the development parameter and grey acting quantity, respectively. When a and u are calculated, the future data can be predicted.

10

2 China’s Serious Population Challenges

(3) Build the mean value of the accumulated data and the constant variables as follows: ⎡

− 21 [x1 (1) + x1 (2)] 1 ⎢ − 1 [x (2) + x (3)] 1 1 ⎢ 2 1 B = ⎢ ⎢ .. ⎣ . − 21 [x1 (n − 1) + x1 (n)] ⎡ ⎤ x0 (2) ⎢ x0 (3) ⎥ ⎢ ⎥ ⎥ Yn = ⎢ ⎢ .. ⎥. ⎣ . ⎦

⎤ ⎥ ⎥ ⎥ .. ⎥ .⎦ 1

(2.4)

(2.5)

x0 (n) (4) Use the least squares method to solve parameters a and u:

aˆ =



−1 T  a B Y n. = BT B u

(2.6)



a (5) Put parameters aˆ = in Eq. (2.3), and solve it to obtain the following: u xˆ1 (t + 1) =

x0 (1) −

u u  −at e + , a a

(2.7)

where xˆ1 (t) is the predicted value of x1 (t). (6) Handle the xˆ1 (t) value to obtain the predicted xˆ0 (t):

xˆ0 (t) = xˆ1 (t + 1) − xˆ1 (t).

(2.8)

(7) Test the GM (1,1) used by calculating the following:

e0 (t) = x0 (t) − xˆ0 (t)

q0 (t) =

e0 (t) . x0 (t)

(2.9)

(2.10)

2.1 The Challenge of Adjusting the Family Planning Policies

(a) Growth rate from 1980 to 2028

11

(b) Total fertility rate from 1970 to 2028

Fig. 2.1 The total population number and total fertility rate from 1970 to 2069. a Growth rate from 1980 to 2028. b Total fertility rate from 1970 to 2028

The birth rate and the death rate are involved in predicting the population number. It is impractical to directly predict the population number. When predicting the total population number, I should first predict the birth rate and the death rate and then use the current population number to add the birth number and to subtract the death number. In a mature society, the death rate is stable; thus, we need to predict only the birth rate. The most important factor affecting the birth rate is the total fertility rate. To judge future population development, I employ GM (1,1) to predict the growth rate and total fertility rate in the next 10 years, i.e., from 2019 to 2028, and depict them in Fig. 2.1. The growth rate from 1980 to 2018 and the total fertility rate from 1970 to 2018 are actual data. The growth rate and the total fertility rate from 2019 to 2028 are predicted data. From Fig. 2.1, I observe that the growth rate and the total fertility rate have obvious declining trends. Although China’s population number is still increasing, I can expect the number to quickly shift decisively downward. In addition to the low total fertility rate, China also faces a serious population ageing, meaning that the rate of decline will be striking. As we cannot change the population ageing phenomenon, we can only encourage residents to have children and improve the total fertility rate. The total fertility rate measures the average number of live births a hypothetical cohort of women would have at the end of their reproductive period. The childbearing age usually ranges 15 to 44 or 15 to 49. Generally, the total fertility rate is larger than 2.1, which is regarded as the standard level for promoting the sustainable population development (Goldstein et al. 2009; Guo 2008, 2010; Huang 2020; Zhang and Chen 2020). For developing countries, newborn children cannot offset women and their mates if the total fertility rate is smaller than 2.1. In many developing countries, the total fertility rate is higher than 5, meaning an extremely rapid increase. For most developed countries, the total fertility rate is smaller than 2, meaning their residents do not want to have many babies and that their population number is declining.

12

2 China’s Serious Population Challenges

China’s total fertility rate was higher than 5 before 1973 when the one-child policy was implemented and has been smaller than 2 since 1992. As of 2019, six countries have a population of more than 200 million: China, India, America, Indonesia, Brazil, and Pakistan. Based on United Nations data (United Nations 2019), I compare the population indicators of China with those of the other five countries between 1973 and 2019 and present these data in Table 2.2. From 1973 to 2019, China’s total population number increased by 61.1926%, which is only slightly higher than the rate of 52.9262% in America. The rates of increase in the other four countries are 243.2146% (Pakistan), 129.7389% (India), 117.8953% (Indonesia), and 106.3877% (Brazil). Although the rate of increase in China is slightly higher than that in America, the total fertility rate in China (1.69) is smaller than that in America (1.78). China does not need to worry about pressure from the total population number, but it should be concerned about the low fertility rate. China’s demographic dividends are continuously vanishing. Table 2.2 reveals that China had a high total fertility rate and did not face population ageing pressure; thus, it implemented the strict one-child policy in 1973. Currently, China has a low total fertility rate but faces serious population ageing pressure; thus, it needs to relax its strict family planning policy. To better understand the population ageing pressure, I analyse the population structure challenge. Table 2.2 The main population indicators in China and five other countries with large populations between 1973 and 2019 Countries

Total population (million)

Proportion of the people older than 65 (%)

Total fertility rate

Life expectancy

61.67

1973 China

889.485

4.1

4.85

India

594.770

3.5

5.41

49.37

America

215.179

10.7

2.03

71.42

Indonesia

124.200

3.4

5.30

54.02

Brazil

102.259

3.6

4.63

59.94

63.099

3.8

6.60

53.93

China

1433.784

12.0

1.69

76.62

India

Pakistan 2019

1366.418

6.6

2.24

69.27

America

329.065

16.6

1.78

78.81

Indonesia

270.626

6.3

2.32

71.41

Brazil

211.050

9.6

1.74

75.56

Pakistan

216.565

4.3

3.55

67.02

Note (i) For statistical purposes, the data for China do not include Hong Kong and Macao Special Administrative Regions (SAR) or Taiwan Province. (ii) The data in Table 2.1 are from the China Statistical Yearbook, and the data in Table 2.1 are from standard estimated data by the United Nations. Therefore, there are some minor differences between these two datasets

2.1 The Challenge of Adjusting the Family Planning Policies

13

2.1.2 Population Structure Challenge Table 2.2 shows that the proportion of people older than 65 in China increased by 192.6829% from 1973 to 2019. There are two reasons for the significant increase in the proportion of elderly people: the high life expectancy and the low total fertility rate. More people are becoming older, while fewer children are coming into the world. The proportion of elderly people is an important indicator for analysing the population structure challenge. I should also analyse some other indicators to gain a comprehensive understanding of the population structure challenge in China. Two important natural population structures include the sex ratio and the age composition. The sex ratio from 1970 to 2018 was obtained from the China Statistical Yearbook (National Bureau of Statistics of China 2020) and is presented in Table 2.3. Although some studies judge that the one-child policy destroyed the sex ratio, the data cannot support this statement. In 1970 before the implementation of the family planning policy, the sex ratio was 105.89. In 2018 after the implementation of the strict one-child policy, the sex ratio was 104.62. Therefore, I can judge that China’s sex ratio is within the normal range and that the strict family planning policy has not destroyed the sex ratio. The age composition is very important and is typically used to measure demographic dividends. Demographic dividends refer to the advantageous demographic structure that can result in social and economic benefits. In general, demographic dividends can be thought of as positive when the proportion of the labour-aged population (aged 15–64) is large. The labour-aged population should support children (aged =65). The age composition data from 1990 to 2018 were obtained from the China Statistical Yearbook (National Bureau of Statistics of China 2020) and are presented in Table 2.4. The number of children (aged =65) always increased. To analyse whether the age composition is suitable, I calculate the dependency rates. The children dependency rate measures the proportion of children (aged =65) in the labour-aged population (aged 15–64). The total dependency rate is the sum of the children dependency rate and the elderly dependency rate. I depict the development trends of the dependency rates in China from 1990 to 2018 in Fig. 2.2. Due to the strict family planning policy, China’s children dependency rate and total dependency rate significantly decreased from 1990 to 2011 but started to slightly increase in 2016 with the relaxation of the family planning policy. However, China’s elderly dependency rate always increased from 1990 to 2018, and the amplitude of this increase grew. China faces a serious population ageing phenomenon. If the

14

2 China’s Serious Population Challenges

Table 2.3 The sex ratio of China from 1970 to 2018 Year

Total population number (million)

Male population number (million) (Proportion %)

Female population number (million) (Proportion %)

1970

829.92

426.86 (51.43)

403.06 (48.57)

1971

852.29

438.19 (51.41)

414.10 (48.59)

1972

871.77

448.13 (51.40)

423.64 (48.60)

1973

892.11

458.76 (51.42)

433.35 (48.58)

1974

908.59

467.27 (51.43)

441.32 (48.57)

1975

924.20

475.64 (51.47)

448.56 (48.53)

1976

937.17

482.57 (51.49)

454.60 (48.51)

1977

949.74

489.08 (51.50)

460.66 (48.50)

1977

949.74

489.08 (51.50)

460.66 (48.50)

1978

962.59

495.67 (51.49)

466.92 (48.51)

1979

975.42

501.92 (51.46)

473.50 (48.54)

1980

987.05

507.85 (51.45)

479.20 (48.55)

1981

1000.72

515.19 (51.48)

485.53 (48.52)

1982

1016.54

523.52 (51.50)

493.02 (48.50)

1983

1030.08

531.52 (51.60)

498.56 (48.40)

1984

1043.57

538.48 (51.60)

505.09 (48.40)

1985

1058.51

547.25 (51.70)

511.26 (48.30)

1986

1075.07

555.81 (51.70)

519.26 (48.30)

1987

1093.00

562.90 (51.50)

530.10 (48.50)

1988

1110.26

572.01 (51.52)

538.25 (48.48)

1989

1127.04

580.99 (51.55)

546.05 (48.45)

1990

1143.33

589.04 (51.52)

554.29 (48.48)

1991

1158.23

594.66 (51.34)

563.57 (48.66)

1992

1171.71

598.11 (51.05)

573.60 (48.95)

1993

1185.17

604.72 (51.02)

580.45 (48.98)

1994

1198.50

612.46 (51.10)

586.04 (48.90)

1995

1211.21

618.08 (51.03)

593.13 (48.97)

1996

1223.89

622.00 (50.82)

601.89 (49.18)

1997

1236.26

631.31 (51.07)

604.95 (48.93)

1998

1247.61

639.40 (51.25)

608.21 (48.75)

1999

1257.86

646.92 (51.43)

610.94 (48.57)

2000

1267.43

654.37 (51.63)

613.06 (48.37)

2001

1276.27

656.72 (51.46)

619.55 (48.54)

2002

1284.53

661.15 (51.47)

623.38 (48.53)

2003

1292.27

665.56 (51.50)

626.71 (48.50) (continued)

2.1 The Challenge of Adjusting the Family Planning Policies

15

Table 2.3 (continued) Year

Total population number (million)

Male population number (million) (Proportion %)

Female population number (million) (Proportion %)

2004

1299.88

669.76 (51.52)

630.12 (48.48)

2005

1307.56

673.75 (51.53)

633.81 (48.47)

2005

1307.56

673.75 (51.53)

633.81 (48.47)

2006

1314.48

677.28 (51.52)

637.20 (48.48)

2007

1321.29

680.48 (51.50)

640.81 (48.50)

2008

1328.02

683.57 (51.47)

644.45 (48.53)

2009

1334.50

686.47 (51.44)

648.03 (48.56)

2010

1340.91

687.48 (51.27)

653.43 (48.73)

2011

1347.35

690.68 (51.26)

656.67 (48.74)

2012

1354.04

693.95 (51.25)

660.09 (48.75)

2013

1360.72

697.28 (51.24)

663.44 (48.76)

2014

1367.82

700.79 (51.23)

667.03 (48.77)

2015

1374.62

704.14 (51.22)

670.48 (48.78)

2016

1382.71

708.15 (51.21)

674.56 (48.79)

2017

1390.08

711.37 (51.17)

678.71 (48.83)

2018

1395.38

713.51 (51.13)

681.87 (48.87)

family planning policy is not relaxed now, it is expected that in 10 years, China will bear a very serious elderly dependency rate. I further explore the effects of adjusting the family planning policy on the dependency rates in Chaps. 5 and 6.

2.2 The Challenge of Reducing the Provincial Population Differences The regional composition is also an important population structure. In China, whose population exceeds 1.4 billion and accounts for approximately one-fifth of the total population worldwide, provincial differences exist in multiple fields (Biggeri et al. 2017). For instance, the distribution of China’s water resources is geographically uneven (Wu et al. 2016), with 81% of such resources being intensively distributed in the Yangtze River basin and southern regions (Chen and Xia 1999). Yang and Mukhopadhaya measured multidimensional poverty in China and identified that the eastern provinces are generally poorer than the central provinces (Yang and Mukhopadhaya 2017). Different levels of development in the provinces are fundamental for the disparities under the population situation. Similar to these indicators, China’s population has significant regional differences among provinces (Yi et al. 2011). For instance, Jiangxi Province has a slight ageing phenomenon and greater pressure on the total population, while the opposite holds true for Beijing

16

2 China’s Serious Population Challenges

Table 2.4 The age composition in China from 1990 to 2018 Year

Total population number (million)

Number of people aged 0–14 (million) (Proportion %)

Number of people aged 15–64 (million) (Proportion %)

Number of people aged above 65 (million) (Proportion %)

1990

1143.33

316.59 (27.7)

763.06 (66.7)

63.68 (5.6)

1991

1158.23

320.95 (27.7)

767.91 (66.3)

69.38 (6.0)

1992

1171.71

323.39 (27.6)

776.14 (66.2)

72.18 (6.2)

1993

1185.17

321.77 (27.2)

790.51 (66.7)

72.89 (6.2)

1994

1198.50

323.60 (27.0)

798.68 (66.6)

76.22 (6.4)

1995

1211.21

322.18 (26.6)

813.93 (67.2)

75.10 (6.2)

1996

1223.89

323.11 (26.4)

822.45 (67.2)

78.33 (6.4)

1997

1236.26

320.93 (26.0)

834.48 (67.5)

80.85 (6.5)

1998

1247.61

320.64 (25.7)

843.38 (67.6)

83.59 (6.7)

1999

1257.86

319.50 (25.4)

851.57 (67.7)

86.79 (6.9)

2000

1267.43

290.12 (22.9)

889.10 (70.1)

88.21 (7.0)

2001

1276.27

287.16 (22.5)

898.49 (70.4)

90.62 (7.1)

2002

1284.53

287.74 (22.4)

903.02 (70.3)

93.77 (7.3)

2003

1292.27

285.59 (22.1)

909.76 (70.4)

96.92 (7.5)

2004

1299.88

279.47 (21.5)

921.84 (70.9)

98.57 (7.6)

2005

1307.56

265.04 (20.3)

941.97 (72.0)

100.55 (7.7)

2006

1314.48

259.61 (19.8)

950.68 (72.3)

104.19 (7.9)

2007

1321.29

256.60 (19.4)

958.33 (72.5)

106.36 (8.1)

2008

1328.02

251.66 (19.0)

966.80 (72.7)

109.56 (8.3)

2009

1334.50

246.59 (18.5)

974.84 (73.0)

113.07 (8.5)

2010

1340.91

222.59 (16.6)

999.38 (74.5)

118.94 (8.9)

2011

1347.35

221.64 (16.5)

1002.83 (74.4)

122.88 (9.1)

2012

1354.04

222.87 (16.5)

1004.03 (74.1)

127.14 (9.4)

2013

1360.72

223.29 (16.4)

1005.82 (73.9)

131.61 (9.7)

2014

1367.82

225.58 (16.5)

1004.69 (73.4)

137.55 (10.1)

2015

1374.62

227.15 (16.5)

1003.61 (73.0)

143.86 (10.5)

2016

1382.71

230.08 (16.7)

1002.60 (72.5)

150.03 (10.8)

2017

1390.08

233.48 (16.8)

998.29 (71.8)

158.31 (11.4)

2018

1395.38

235.23 (16.9)

993.57 (71.2)

166.58 (11.9)

Province. Accurate research on the spatial pattern of the population is critical for policy-making and spatial planning in all related fields, including urbanization, land use development, ecological conservation, and environmental protection (Deng et al. 2015). To analyse the provincial population difference, I compare the population size and population structure in the 31 provinces in China in 2018 in Table 2.5. The data

2.2 The Challenge of Reducing the Provincial Population Differences

17

Fig. 2.2 Dependency rates in China from 1990 to 2018

were obtained from the China Statistical Yearbook, which surveys the data with a sampling fraction of 0.820‰ (National Bureau of Statistics of China 2020). The statistical data in Table 2.5 clearly reveal that there are significant provincial population differences in China. The eastern provinces have a higher population than the western provinces. For example, the population in Guangdong and Shandong is significantly higher than that in Ningxia and Qinghai. Some provinces have higher children dependency rates but lower elderly dependency rates, such as Jiangxi Province, Henan Province, Guangxi Province, Hainan Province, Tibet Province, Xinjiang Province, Qinghai Province, and Ningxia Province. These provinces are usually central or western provinces and economically underdeveloped provinces. In these provinces, adjusting the family planning policy is a lower priority. Some other provinces have lower children dependency rates but higher elderly dependency rates, such as Beijing Province, Tianjin Province, Liaoning Province, Jilin Province, Heilongjiang Province, and Shanghai Province. These provinces are usually northern provinces or economically developed provinces. For these provinces, adjusting the family planning policy is a higher priority. By simply describing the provincial population in China, this book finds that for different provinces adjusting the family planning policy has different levels of priority (Gu et al. 2007; Tan and Zeng 2014). When adjusting the family planning policy, I must consider the serious provincial population differences. However, a few studies have paid attention to the serious population challenge in China. In Part II, this book analyses this population challenge and proposes some suggestions to reduce the provincial population differences.

18

2 China’s Serious Population Challenges

Table 2.5 Provincial population data in China in 2018 (Sampling fraction: 0.820‰)

China

Total population Number number (thousand) of people aged 0–14 (thousand)

Number of people aged 15–64 (thousand)

Number Children Elderly of people dependency dependency aged rate (%) rate (%) above 65 (thousand)

1144.648

192.962

815.039

136.645

23.68

16.77

Beijing

17.673

1.85

13.834

1.989

13.37

14.38

Tianjin

12.794

1.314

10.083

1.397

13.03

13.85

Hebei

61.907

42.612

7.854

26.85

18.43

Shanxi

30.481

4.753

22.580

3.147

21.05

13.94

Inner Mongolia

20.781

2.759

15.976

2.047

17.27

12.81

Liaoning

35.744

3.628

26.763

5.353

13.56

20.00

Jilin

22.173

2.725

16.705

2.743

16.31

16.42

Heilongjiang

30.946

3.267

23.899

3.780

13.67

15.82

Shanghai

19.877

1.955

14.950

2.972

13.08

19.88

Jiangsu

65.996

9.061

47.499

9.435

19.08

19.86

Zhejiang

47.034

6.441

34.485

6.108

18.68

17.71

Anhui

51.812

9.631

35.341

6.840

27.25

19.35

Fujian

32.309

5.402

23.842

3.065

22.66

12.86

11.44

Jiangxi

38.080

7.725

26.648

3.707

28.99

13.91

Shandong

82.408

14.842

55.071

12.495

26.85

22.69

Henan

78.679

16.761

53.220

8.698

31.49

16.34

Hubei

48.498

7.445

34.995

6.059

21.27

17.31

Hunan

56.516

11.013

38.446

7.057

28.65

18.36

Guangdong

93.024

15.729

69.608

7.687

22.60

11.04

Guangxi

40.353

8.821

27.486

4.046

32.09

14.72

Hainan

7.658

1.469

5.561

0.629

26.42

11.30

Chongqing

25.416

4.303

17.435

3.678

24.68

21.09

Sichuan

68.344

11.185

46.915

10.244

23.84

21.83

Guizhou

29.487

6.558

19.585

3.344

33.49

17.08

Yunnan

39.584

7.157

28.637

3.790

24.99

13.24

Tibet

2.817

0.663

1.994

0.160

33.23

8.04

Shaanxi

31.684

4.555

23.592

3.536

19.31

14.99

Gansu

21.614

3.802

15.366

2.446

24.74

15.92

4.945

0.967

3.603

0.375

26.83

10.42

Qinghai Ningxia

5.638

1.131

4.000

0.507

28.29

12.67

Xinjiang

20.375

4.610

14.307

1.458

32.22

10.19

2.3 Conclusions

19

2.3 Conclusions The population challenges mainly include the population size challenge and population structure challenge. In the 1970s, China’s population size challenge was the rapid increase in the total population number. However, in contemporary China, the country’s population size challenge is the low total fertility rate and the upcoming drop in the total population number. The main population structure challenge for contemporary China is the increasingly serious population ageing due to the low total fertility rate and the increase in life expectancy. Therefore, China has started to adjust its family planning policy to respond to the current population size and population structure challenges. When analysing the regional population structure, this book also finds obvious provincial population differences. The eastern provinces have a higher population than the western provinces. Some provinces have higher children dependency rates but lower elderly dependency rates, while some other provinces have lower children dependency rates but higher elderly dependency rates. For China’s 31 provinces, adjusting the family planning policy has different levels of priority. Therefore, this book not only explores the optimal adjustment of the family planning policy but also pays attention to the provincial population differences.

References Biggeri, L., Ferrari, G., & Zhao, Y. (2017). Estimating Cross province and municipal city price level differences in China: Some experiments and results. Social Indicators Research, 131(1), 169–187. Chen, J., & Xia, J. (1999). Facing the challenge: Barriers to sustainable water resources development in China. Hydrological Sciences Journal, 44(4), 507–516. Deng, Y., Liu, S., Cai, J., Lu, X., & Nielsen, C. P. (2015). Spatial pattern and its evolution of chinese provincial population: Methods and empirical study. Journal of Geographical Sciences, 25(12), 1507–1520. (in Chinese). 邓羽, 刘盛和, 蔡建明 and 鲁玺. 2015. “中国省际人口空间格局演化的分析方法与实证,” 地理 学报 25 (12), pp. 1507–1520. Ding, Q. J., & Hesketh, T. (2006). Family size, fertility preferences, and sex ratio in China in the Era of the one child family policy: Results from national family planning and reproductive health survey. British Medical Journal Publishing Group. Gietel-Basten, S., Han, X., & Cheng, Y. (2019). Assessing the impact of the “One-Child Policy” in China: A synthetic control approach. PLoS one, 14(11). Goldstein, J. R., Sobotka, T., & Jasilioniene, A. (2009). The end of “Lowest-Low” fertility? Population and Development Review, 35(4), 663–699. Gu, B., Wang, F., Guo, Z., & Zhang, E. (2007). China’s local and national fertility policies at the end of the twentieth century. Population and Development Review, 33(1), 129–148. Guo, Z. (2008). China’s low fertility and its determinants. Population Research, 32(4), 1–12. (in Chinese). 郭志刚. 2008. “中国的低生育水平及其影响因素,” 人口研究 32 (4), pp. 1–12. Guo, Z. (2010). China’s low fertility and its neglected demographic risk. International Economic Review, 6, 112–126. (in Chinese).

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郭志刚. 2010. “中国的低生育率与被忽略的人口风险,” 国际经济评论 (6), pp. 112–126. Guo, Z. (2015). Be aware of the risk of low fertility in China. International Economic Review, 2, 100–119. (in Chinese). 郭志刚. 2015. “清醒认识中国低生育率风险,” 国际经济评论 (2), pp. 100–119. Guo, Z. (2016). Understanding Fertility Trends in China. In C. Z. Guilmoto & G. W. Jones (Eds.), Contemporary demographic transformations in China, India and Indonesia (pp. 97–111). Switzerland: Springer International Publishing. Hesketh, T., & Zhu, W. X. (1997). Health in China: The one child family policy: The good, the bad, and the ugly. BMJ, 314(7095), 1685. Hsu, C.-C., & Chen, C.-Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44(14), 2241–2249. Huang, K. (2020). The structural characteristics of China’s low fertility process. Northwest Population, 41(3), 12–21. (in Chinese). 黄匡时. 2020. “中国低生育进程的结构性特征,” 西北人口 41 (3), pp. 12–21. Jiang, Q., Li, S., & Feldman, M. W. (2013). China’s population policy at the crossroads: Social impacts and prospects. Asian Journal of Social Science, 41(2), 193–218. Kuaiyi Financial Network. 2019. The data about China’ total fertility rate. https://www.kylc.com/ stats/global/yearly_per_country/g_population_fertility_perc/chn.html. (in Chinese). 快易理财网. 2019. “中国历年总和生育率统计,” https://www.kylc.com/stats/global/yearly_per_ country/g_population_fertility_perc/chn.html. National bureau of statistics of China. (2020). China statistical yearbook 2019. Beijing: China Statistics Press. (in Chinese). 国家统计局. 2020. “中国统计年鉴2019,” 北京: 中国统计出版社. Peng, X. (2011). China’s demographic history and future challenges. Science, 333(6042), 581–587. Tan, Y., & Zeng, Y. (2014). Spatial-temporal pattern analysis and spatial disparity research on provincial transition of mechanisms in stabilizing low fertility in China. Population Journal, 36(2), 5–18. (in Chinese). 谭远发 and 曾永明. 2014. “我国低生育水平稳定机制的时空演变及空间差异研究,” 人口学刊 36 (2), pp. 5–18. Tao, T., & Yang, F. (2011). The implact of China’s family planning policy on demographic transition. Population Research, 35(1), 103–112. (in Chinese). 陶涛 and 杨凡. 2011. “计划生育政策的人口效应,” 人口研究 35 (1), pp. 103–112. United Nations, D. o. E. a. S. A., Population Division. (2019). World Population Prospects 2019. https://population.un.org/wpp/Download/Standard/Population/. Wu, C., & Mu, G. (1995). Research on the low fertility rate: Supplement and development for the demographic transition. Social Sciences in China, 1, 83–98. (in Chinese). 邬沧萍 and 穆光宗. 1995. “低生育研究——人口转变论的补充和发展,” 中国社会科学 (1), pp. 83–98. Wu, P., Xia, C., Liu, F., Wu, Y., & He, Y. (2016). An integrated water strategy based on the current circumstances in China. Applied Mathematical Modelling, 40(17–18), 8108–8124. Xie, N., Wang, R., & Chen, N. (2018). Measurement of shock effect following change of one-child policy based on grey forecasting approach. Kybernetes. Yang, J., & Mukhopadhaya, P. (2017). Disparities in the level of poverty in China: Evidence from China family panel studies 2010. Social Indicators Research, 132(1), 411–450. Yi, B., Wu, L., Liu, H., Fang, W., Hu, Y., & Wang, Y. (2011). Rural-urban differences of neonatal mortality in a poorly developed province of China. BMC Public Health, 11(1), 477. Yu, X., & Jia, H. (2020). Analysis of influence factors and scale forecast of total health expenditure in Jilin province. Jilin University Journal Social Sciences Edition, 60(1), 130–140. (in Chinese). 于洗河 and 贾欢欢. 2020. “吉林省卫生总费用影响因素分析及规模预测——基于灰色系统理 论的研究,” 吉林大学社会科学学报 60 (1), pp. 130–140. Yu, X., & Lu, Z. (2012). Prediction of energy consumption based on grey model—Gm (1,1) (pp. 192– 199). Berlin, Heidelberg: Springer, Berlin Heidelberg.

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Part I

Challenge One: Family Planning Policy

Chapter 3

Population Size Challenge: Low Total Fertility Rate

Abstract The total fertility rate measures the average number of children who will be borne by a woman over her lifetime, and its accurate measurement can help better predict future population development. Despite the efforts of many parties to provide a measure, we still cannot systematically realize the determinants affecting the total fertility rate or accurately estimate it. This chapter conducts a meta-analysis to examine the determinants affecting the fertility intentions of China’s fertile women and the estimations of the total fertility rate in China. Thirty-one determinants affecting China’s total fertility rate are identified considering three aspects: demographic, social, and economic. The methods and data for estimating China’s total fertility rate are analysed, and the estimation results are compared. Notably, research on the total fertility rate is often limited by a lack of authoritative datasets. To alleviate this problem, this chapter compiles and summarizes the existing datasets used to report the total fertility rate. Keywords Total fertility rate · Population development · Meta-analysis · Fertility intention · Fertile women · China

3.1 Introduction The total fertility rate, sometimes also called the fertility rate, absolute/potential natality, period total fertility rate, or total period fertility rate, measures the average number of children that will be borne by a woman over her lifetime. Due to some limitations to the indicator definition, scholars have continuously attempted to refine the total fertility rate and accurately measure fertility levels (Bongaarts and Feeney 1998; Gao and Chen 2013). The accurate estimation of the total fertility rate is very important for predicting population development and designing a suitable population policy. Due to various obstacles, however, it is very difficult to accurately estimate the total fertility rate. Based on the 2010 population census data (Population Census Office under the State Council and Department of Republation and Employment Statistics National Bureau of Statistics 2012), China’s estimated total fertility rate in 2010 was only 1.18, while the actual total fertility rate in cities, towns, and villages was only 0.89, 1.16, and © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_3

25

26

3 Population Size Challenge: Low Total Fertility Rate

1.44, respectively. However, most scholars query the population census and think the total fertility rate has been significantly underestimated (Wang and Ge 2013). The famous demographer and president of the China Population Society, Prof. Zhai Zhenwu, argues that the total fertility rate has been significantly underestimated and that China’s total fertility rate should be approximately 1.6 (Guo 2015; Zhai et al. 2015). Based on data for the number of newborn infants from the China Statistics Bureau, the total fertility rate from 2000 to 2010 should fall into the range of 1.50 to 1.64 (Cui et al. 2013; Peng 2011). The significant differences between the above total fertility rates estimated for 2010 in China by different authoritative experts and institutions reflect the large difficulties in accurately estimating the total fertility rate. China has entered a period with a low total fertility rate since the 1990s (Guo 2008; Zhao and Guo 2010). From 1950 to 2015, the decrease in the total fertility rate in America, Japan, and India were 42.42% ((3.3 − 1.9)/3.3), 53.33% ((3 − 1.4)/3), and 59.32% ((5.9 − 2.4)/5.9), respectively, while that of China was 73.33% ((6 − 1.6)/6). China’s current total fertility rate is far smaller than the worldwide average (2.45) and even than the average for developed countries (1.67) (Ren et al. 2019). A rate of 2.1 is regarded as the standard level for promoting sustainable population development (Goldstein et al. 2009; Guo 2008, 2010; Huang 2020; Zhang and Chen 2020). Although under the new two-child policy, the fertility rates became slightly higher for the second and third babies and further, the fertility rate for the first birth saw a large reduction, which led to a decline in the overall fertility rate (Guo 2017). Data from the 66 countries and regions that entered the post-transitional stage before 1997 reveal that the risk of a low-fertility trap has spread from 22 countries and regions to other parts of the world (Wu 2019). China is facing a very high risk of falling into the low-fertility trap (Chen and Zhai 2016; Liu and Chen 2019); thus, China has prioritized improving the total fertility rate by designing policies to increase the fertility intentions of fertile women. Compared with controlling the total population number and encouraging fertility intentions, increasing the total fertility rate creates a greater challenge for governments. The low total fertility rate would have many negative consequences, such as the shrinking workforce, the increasing ageing population, a limited population size, the vanishing demographic dividend, and a serious leftover men phenomenon (Ren et al. 2019). However, policymakers still cannot clearly understand the determinants of the low total fertility rate (Wang 2010). Before encouraging fertility intentions, policymakers should first realize the determinants affecting the fertility intentions of fertile women and the total fertility rate. Existing studies reveal that the determinants include social determinants, economic determinants, and demographic determinants (Cai 2010; Chen et al. 2010; Guo 2000; Morgan et al. 2009; Zhenzhen et al. 2009). Although these existing studies indicate the determinants affecting the total fertility rate, they do not allow them to be integrated, understood, and compared. Therefore, this study conducts a meta-analysis to summarize and analyse the determinants affecting the total fertility rate and the estimations of the total fertility rates. The remainder of the chapter is structured as follows. Section 3.2 presents the research design and some general quantitative findings from the reviewed literature.

3.1 Introduction

27

Then, Sect. 3.3 conducts the meta-analysis to analyse the determinants affecting the fertility intentions of fertile women and the total fertility rate in China. Subsequently, Sect. 3.4 summarizes the existing authoritative estimations of the total fertility rate, and Sect. 3.5 provides the important datasets. Section 3.6 proposes effective suggestions to improve fertility intentions and the total fertility rate. Finally, conclusions and discussions are presented in Sect. 3.7.

3.2 Research Designs and General Findings 3.2.1 Research Designs To understand the fertility intentions of the fertility rate and the total fertility rate in China, this chapter conducts the meta-analysis to analyse the existing related literature from Web of Science Core Collection database and CNKI database. The Web of Science Core Collection database is one of the world’s largest abstract and citation database of peer-reviewed research literature. The CNKI database is one of China’s largest abstract and citation databases. To obtain full-scale results, this chapter conducts an extensive and systemic search using the combination of these two keywords (“total fertility rate” and “China”) from the Web of Science Core Collection database. All articles reviewed herein are obtained as of May 31, 2020. I also analyse the related references of the journal articles. In the CNKI database, this chapter conducts an extensive and systemic search using the keyword “总和生育率”, which is the Chinese keyword representing the “total fertility rate”. As all searched articles are written in Chinese for studying the Chinese population, it is not necessary to add the keyword “China”. The search for relevant papers in this process is by no means exhaustive, but the main influence factors affecting the total fertility rate can be found. Only full-length articles are used for the meta-analysis. I thus exclude conference papers, conference reviews, book chapters, editorials, short surveys, and letters from the analysis. Finally, 71 articles are analysed for understanding China’s total fertility rate. Among the 71 articles, 55 articles are published in Chinese, and the rest 16 articles are published in English.

3.2.2 Descriptive Quantitative Analysis 3.2.2.1

Overall Growth

Figure 3.1 illustrates the annual number of the identified journal articles on China’s total fertility rate.

28

3 Population Size Challenge: Low Total Fertility Rate

Fig. 3.1 Annual number of identified journal articles about China’s total fertility rate

In our identified journal articles, the easiest article was published in 2004, and I can identify significant increasing trends in the number of identified journal articles. Although China’s total fertility rate has been studied for more than 16 years, I can still find ever-increasing interesting articles published in high-quality journals. In 2019, the number of English journal articles was as high as six.

3.2.2.2

Publication Sources

The 71 journal articles are distributed across 38 different journals, including 14 English journals and 24 Chinese journals. The specific publication information are presented in Table 3.1. Top-tier journals have also published research on total fertility rate; these journals include Nature, PLOS One, Population and Development Review, World Development, Chinese Journal of Population Science (in Chinese), Economic Research Journal (in Chinese), Insurance Studies (in Chinese), Journal of Financial Research (in Chinese), Population Research (in Chinese), and Social Sciences in China (in Chinese). The journals that published three or more than three articles include Population Research (in Chinese) (18 articles), Chinese Journal of Population Science (in Chinese) (6), Population Journal (in Chinese) (3), and Population and Development (in Chinese) (3). Two English journals published two articles that include Demographic Research (2) and Population Research and Policy Review (2). The top two leading Chinese journals in the demographic field, i.e., Population Research (in Chinese) and Chinese Journal of Population Science (in Chinese), are very interested in the topic about the total fertility rate due to the below two reasons. First, the low total fertility rate is very important for the population development in

3.2 Research Designs and General Findings

29

Table 3.1 The journal information Journal

Article number Journal

Article number

English Journal Asian Population Studies

1

Demographic Research

2

Heliyon

1

International Journal of Environmental Research and Public Health

1

Journal of Biosocial Science

1

Journal of Population Research

1

Marriage & Family Review

1

Nature

1

PLOS One

1

Population and Development Review

1

Population Research and Policy Review

2

Reproductive Health

1

The Journal of Human Resources

1

World Development

1

Academic Bimestrie (in Chinese)

2

Chinese Journal of Population 6 Science (in Chinese)

Economic Perspectives (in Chinese)

1

Economic Research Journal (in Chinese)

1

Insurance Studies (in Chinese)

1

International Economic Review (in Chinese)

1

Journal of Catastrophology (in Chinese)

1

Journal of Dongbei University of Finance and Economics (in Chinese)

2

Journal of Financial Research (in Chinese)

2

Journal of Peking University (Philosophy and Social Sciences) (in Chinese)

1

Journal of Shanxi University of Finance and Economics (in Chinese)

1

Modern Finance and 1 Economics-Journal of Tianjin University of Finance and Economics (in Chinese)

North-west Population Journal (in Chinese)

3

Population and Development (in Chinese)

3

Population and Health (in Chinese)

1

Population Journal (in Chinese)

3

Chinese Journal

Population Research (in Chinese)

18

Social Sciences in China (in Chinese)

1

Sociological Studies (in Chinese)

1

South China Population (in Chinese)

1

Statistical Research (in Chinese)

1

Statistics & Decisions (in Chinese)

1

Theory Monthly (in Chinese)

1

Youth Studies (in Chinese)

1

30

3 Population Size Challenge: Low Total Fertility Rate

China. Second, the public still have little understanding about the total fertility rate, and need to know more about the determinants causing the low total fertility rate and the accurate estimation of the total fertility rate. Therefore, Sects. 3.3 and 3.4 would summarize the determinants causing the low total fertility rate and the existing methods and datasets for estimating the total fertility rate.

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China The determinants affecting the fertility behaviours are complex (Chen 2019; Xu 2010). The explanations for the low total fertility rate can be summarized considering three aspects: demographic determinants, social determinants, and economic determinants.

3.3.1 Demographic Determinants The demographic determinants include marriage age, length of the interval between marriage and childbearing, marriage delay, divorce rate, educational background, labour participation, participation in pension insurance, retirement age, infertility rate, and rural residence. The existing studies for analysing demographic determinants are summarized in Table 3.2. China’s marriage age is continuously increasing and causes a low total fertility rate (Guo and Tian 2017; Shi et al. 2019; Wang et al. 2019a; Wei et al. 2018), as delaying marriage and fertility negatively affect the fertility intentions (Guo and Tian 2017; Shi et al. 2019; Wang et al. 2019a). The length of the interval between the first marriage and the first childbearing directly affects the fertility level of the fertile women (Fu et al. 2013). In China, nearly 70% of women give birth to a child within one and a half years of marriage, and 90% of women of childbearing age give birth to a child within two and a half years of marriage (Li and Jiang 2009). A decrease in nuptiality or in the marital fertility rate would also reduce the total fertility rate (Yip et al. 2015). One study reveals that marriage deferral can explain approximately two-fifths of the Chinese fertility decline in the 1990s (Retherford et al. 2005, 2004). In China, the current divorce rate is relatively high and thus restrains fertility levels (Yang 2015). These marriage-related determinants are highly affected by the educational background of fertile women (Li et al. 2016). The total fertility rate is negatively affected by education level (Wang and Zhang 2020; Yang et al. 2007), as education level increases the interval between the first marriage and the first childbearing and thus reduces the total fertility rate (Liang 2013). Since education is assumed to present a cost at a young age and result in higher productivity during adulthood, the fertility

Women’s marriage age is significantly A multistage stratified cluster and negatively related to desired sampling survey with 2,516 women fertility respondents in rural Shaanxi The late marriage age decreases the total fertility rate

Marriage postponement caused a decline in the fertility level between 1989 and 2000 as well as between 2000 and 2010

Guo and Tian (2017)

Jiang et al. (2019)

Divorce rate

Yang (2015)

The divorce rate restrains the fertility levels

The decrease in nuptiality and marital fertility rate would reduce the total fertility rate

Yip et al. (2015)

N/A (continued)

The 1992, 2000, and 2010 population census data and the 1991, 2001, and 2011 Hong Kong population statistics

Marriage deferral can explain Chinese The 1990 and 2000 population census fertility decline in the 1990s with the data extant of about two-fifths

Retherford et al. (2004, 2005)

Marriage delay

Age interval between first marriage The 2000 and 2010 population census and first childbearing is increasing and data causes the low total fertility rate

Fu et al. (2013), Liang (2013)

Age interval between marriage and childbearing

The 1990, 2000, and 2010 population census data

The 1990, 2000, and 2010 population census data, the 1995, 2005, and 2015 small population census data with 1% sample, and the 1‰ population sample data

2017 fertility survey data investigated by the National Health Commission of China

Wei et al. (2018)

Data

The late marriage age decreases the total fertility rate

Marriage age

Influence mechanism

Studies

Shi et al. (2019), Wang et al. (2019a, b)

Variables

Table 3.2 The demographic determinants affecting the fertility intention and total fertility rate

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 31

Sasser (2005)

Chen and Zhang (2019)

Dong and Kang (2020)

Guo (2008)

Participating in pension insurance

Retirement time

Infertility rates

The current high infertility rates reduce the total fertility rate

Extending the retirement time would reduce the time of the elderly people for caring for the children, and thus reduce the total fertility rate

Participating in pension insurance reduces people’s total fertility desire by 14.1%, the desire to give birth to sons is reduced by 7.2%, and the desire to give birth to daughters is reduced by 5.4%

The labour participation rate of the fertile women reduces the total fertility rate

When education is assumed to present Thousands of alternative simulations a cost at a young age and results in for different fertility levels higher productivity during adult age, the fertility rate that in the long run keeps dependency at a minimum turns out to lie well below replacement fertility both in Europe and in China

Striessnig and Lutz (2014)

Labour participation

The education level increases the The 2000 and 2010 population census interval between first marriage and data first childbearing, and thus reduces the total fertility rate

N/A (continued)

The 2010 population census data and the 2005 people sample data

Chinese General Social Survey (CGSS) 2010, 2012, 2013, and 2015 data

The data with labour participation rate and income

The statistical data from 109 countries

Liang (2013)

Data

Woman literate rate negatively affects the total fertility rate

Education background

Influence mechanism

Studies

Yang et al. (2007)

Variables

Table 3.2 (continued)

32 3 Population Size Challenge: Low Total Fertility Rate

Studies

Wang and Chi (2017)

Variables

Rural residency

Table 3.2 (continued) Data

The significant positive effect of rural Geocoded 2010 county-level census Hukou is found only in places close to data Beijing, suggesting that there is some unique rural residency characteristic in this place that operates independently from economic and fertility policy factors

Influence mechanism

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 33

34

3 Population Size Challenge: Low Total Fertility Rate

rate, which in the long run keeps dependency at a minimum, lies well below replacement fertility both in Europe and China (Striessnig and Lutz 2014). There is no doubt that a high education level increases the labour participation rate of fertile women. As working women must invest more to have children and address the trade-off between career and family, the labour participation rate of fertile women reduces the total fertility rate (Sasser 2005). Participating in pension insurance also reduces people’s total fertility desire by 14.1%, with the desire to give birth to sons being reduced by 7.2%, and the desire to give birth to daughters by 5.4% (Chen and Zhang 2019). Extending the retirement age would reduce the time that elderly people can spend caring for children and reduce the total fertility rate (Dong and Kang 2020). One serious physical condition is the current high infertility rate, which is estimated to be approximately 15% (Guo 2008). The high infertility rate directly reduces the total fertility rate. In some specific regions, rural Hukou residency also has a significant positive effect on the total fertility rate (Wang and Chi 2017).

3.3.2 Social Determinants Social determinants include social stability, political freedom, retirement security system, urbanization rate, floating population, regional education quality, environmental pollution, gender ratio, women’s social status, social fertility culture, and the family planning policy. The existing studies analysing social determinants are summarized in Table 3.3. The fertility intentions of fertile women are affected by whether society is stable. During the period of war, women usually have very slow fertility intentions (Wang and Zhang 2020). Earthquake disasters have a significant impact on the fertility pattern of the population in the disaster areas and have played an inhibiting role in the fertility level in the short term (Jiao et al. 2019). Deteriorating political freedom in upper- to middle-income countries exerts downward pressure on fertility rates, while it contributes a positive effect to fertility rates in lower middle- and low-income countries (Wang and Sun 2016). Changes in retirement security rate have a negative incentive for the marriage structure and the total fertility rate (Wang and Liu 2015; Wei et al. 2018). Due to various demographic factors and the current sound social security system, many fertile women marry late (Wang and Liu 2015) and have low fertility intentions. Fertility intentions differ between cities, towns, and villages. With the accelerated growth in the urbanization rate, the total fertility rate is decreasing (Guo and Yu 2016). Urbanization was responsible for approximately 22% of the decrease in the total fertility rate from 1982 to 2008, and its effect was especially important from 2001 to 2008. In most provinces, urbanization is associated with a decline in provincial-level fertility (Guo et al. 2012). The floating population reduces the total fertility rate, as the fertility rate of the floating population is significantly smaller than that of the urban population and even smaller than that of the rural population (Guo 2008). The

Urbanization rate

Guo and Yu (2016)

Wei et al. (2018)

Retirement security system Wang and Liu (2015)

Wang and Sun (2016)

Urbanization rate would reduce the total fertility rate

Social security benefits are significantly and negatively related to desired fertility

Retirement security system negatively affects the marriage structure and total fertility rate

Worsening political freedom in upper-income countries exerts downward pressure on fertility rates, while it contributes a positive effect to fertility rates in lower-income countries

Earthquake disasters have a significant impact on the population fertility pattern in the disaster areas, and have played an inhibiting role in the fertility level in the short term

Jiao et al. (2019)

Political Freedom

The total fertility rate is increased by social stability

Social stability

Influence mechanism

Studies

Wang and Zhang (2020)

Variables

Table 3.3 The social determinants affecting fertility intentions and total fertility rate

(continued)

The two-period OLG model and numerical experiment

A stratified cluster sampling survey with 2,516 women respondents in rural Shaanxi

Related data of China and countries of ASEAN from 1989 to 2013

Samples covering 70 countries in four income categories from 1973 to 2011

Population data from Sichuan Statistics Yearbook 2009

N/A

Data

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 35

Wang et al. (2019a, b)

Guo et al. (2019)

Environmental pollution

PM2.5 concentration has a negative impact on the second baby intentions of the floating population. It exhibits marked regional heterogeneity: the desire for a second baby across migrant groups living in South China decreases if PM2.5 concentration goes up, while migrants coming from, and living in, North China show strong intentions to have a second baby despite an increase in PM2.5 concentration in northern cities

Regional education quality has a direct negative impact on the childbearing willingness for a second baby

The fertility level of the floating population is relatively small

Li and Guo (2014)

(continued)

Chinese Floating Population Dynamic Survey in 2014 administered by the National Health Commission, the National Prefecture-level City Matching Data administered by the National Bureau of Statistics of China, and the air pollution index PM2.5 collected by the Green Peace Organization

Chinese General Social Survey (CGSS) 2015 data

2012 floating population monitoring data

A large number of the migrant population The 1990 and 2000 population census causes the low total fertility rate data

Liang (2006)

N/A

Population floating reduces the total fertility rate

Guo (2008)

Urbanization was responsible for about The 1982 and 1990 population census, 22% of the decrease in the total fertility and the 2001 and 2008 population survey rate from 1982 to 2008, and its effect was with 1‰ sample especially important from 2001 to 2008. In most provinces, urbanization is associated with a decline in provincial fertility

Guo et al. (2012)

Data

Influence mechanism

Studies

Regional education quality

Population floating

Variables

Table 3.3 (continued)

36 3 Population Size Challenge: Low Total Fertility Rate

Traditional rural women’s cultural views towards fertility are significantly but positively related to their desired fertility

Wei et al. (2018)

Family planning policy

Young people abandon the traditional Interview data opinions on family and children, and have low fertility intentions

Wu et al. (2016)

Population policies, measured by the Samples covering 70 countries in four contraception prevalence rate, are income categories from 1973 to 2011 effective in reducing the total fertility rate

Wang and Sun (2016)

The population census data and the yearly population data with a 1% sample from 1982 to 2015

The new two-child policy has a limited effect in increasing the total fertility rate in the long term

Li et al. (2019a, b), Zhang et al. (2019)

Chinese General Social Survey (CGSS) 2012 data

The family planning policy is very effective to control the total fertility rate in China

Zhu (2020)

Ding et al. (2019), Shi et al. (2019), The family planning policy is effective to 2017 fertility survey data investigated by Wang et al. (2019a, b), Li et al. (2019a, control China’s total fertility rate. The the National Health Commission of b), Chen (2019) two-child policy increased China’s total China fertility rate in 2016

A multistage stratified cluster sampling survey with 2,516 women respondents in rural Shaanxi

N/A

Social fertility culture

Total fertility rate is reduced by women’s social status

Wang and Zhang (2020)

N/A

Women’s social status

Traditional “son preference” opinion causes the unbalanced gender ratio and thus reduces the total fertility rate

Guo (2008)

The optimal fertility level falls even lower Thousands of alternative simulations for when climate change is factored in as well different fertility levels

Striessnig and Lutz (2014)

Data

Influence mechanism

Studies

Gender ratio

Variables

Table 3.3 (continued)

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 37

38

3 Population Size Challenge: Low Total Fertility Rate

fertility level of the floating population is relatively small (Li and Guo 2014), and a large number of migrants cause a low total fertility rate (Liang 2006). The total fertility rates show significant differences between the 31 provinces. Regional education quality has a direct negative impact on the willingness to bear a second baby (Wang et al. 2019b). Environmental pollution also significantly reduces the total fertility rate. The PM2.5 concentration has a negative impact on the second baby intentions of the floating population, although this impact exhibits marked regional heterogeneity: the desire for a second baby across migrant groups living in South China decreases if the PM2.5 concentration increases, while migrants coming from and living in North China show strong intentions to have a second baby despite an increase in the PM2.5 concentration in northern cities (Guo et al. 2019). The optimal fertility level falls even lower when climate change is factored in as well (Striessnig and Lutz 2014). Family planning policy and social fertility culture are two important social constraints affecting Chinese families. The family planning policy is an external system that restricts people’s fertility behaviours, and the social fertility culture is the societal opinions that affect people’s fertility behaviours. Decades ago, the Chinese people strongly considered childbearing to represent continuing the family bloodline, and traditional fertility culture is significantly and positively related to the total fertility rate (Wei et al. 2018). However, the traditional “son preference” causes an unbalanced gender ratio, which reduced the total fertility rate (Guo 2008). Currently, women’s social status has significantly increased, and the former “men outside the home, women inside” lifestyle has shifted to become the “equality of men and women”. The improvement of women’s social status has led young people to abandon traditional opinions about family and children and to develop relatively low fertility intentions (Wu et al. 2016). Measured by the prevalence rate for contraception (Wang and Sun 2016), the family planning policy has been very effective in controlling the total fertility rate in China (Ding et al. 2019; Zhu 2020). The two-child policy increased China’s total fertility rate in 2016, particularly the willingness to bear a second baby (Li et al. 2019b; Shi et al. 2019; Wang et al. 2019a; Zhang et al. 2019). However, some scholars propose that not all women will have increased fertility intentions under the twochild policy because two children increase their opportunity costs by reducing the labour supply, wage income, and healthy human capital (Li et al. 2018; Zhu 2020). Implementation of the two-child policy will have short-term effects on China’s future birth rates (Zhang et al. 2019) but a limited ability to increase fertility levels and limited effects on the national population size and structure (Li et al. 2019a).

3.3.3 Economic Determinants Economic determinants can be divided into social-economic background, family economic structure, and fertility economics for childbearing. The specific economic determinants include globalization, regional human development index, house price,

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China

39

partible inheritance mechanism, income level, household consumption, fertility benefits, childbearing cost, opportunity cost of childbearing, and time pressure. The existing studies analysing economic determinants are summarized in Table 3.4. The socio-economic background includes globalization, regional human development index, house prices, and partible inheritance mechanism. The total fertility rate is negatively affected by globalization, as one of the characteristics of globalization is rising economic inequality and increasing pressure from the intermediate social class. As globalization increases, fertility intentions show a significant U curve correlating with economic resources (Wang 2010). Globalization increases the proportion of families at the middle-income level and thus reduces the total fertility rate. Some studies have found that the total fertility rate is negatively related to the regional human development index (Tao et al. 2017; Zhou 2015), while others find that the total fertility rate shows a U-shaped relationship with the regional human development index (Chen and Zhai 2016; Myrskylä et al. 2009). High house prices restrain the development of the total fertility rate (Guo and Jiang 2018). Under the traditional partible inheritance mechanism, fertility intentions were significantly negatively associated with birth order (Li and Zhen 2015). The family economic structure includes family income and household consumption. The fertility intentions of families with high or low incomes are significantly higher than those with middle incomes. The high fertility rates of people with low incomes create the low-quality labour population (Guo and Yu 2017). A rural family may reduce the number of births according to the trade-off between quality and quantity, which may be the main cause of the fertility decline in China (Song et al. 2012). Families with high incomes can provide superior conditions for their children and have a relatively high willingness to give birth (Zhu and Chen 2012), while families with middle incomes have high life requirements and are under strong social pressure. Therefore, fertility level and household consumption are shown as inverted U-shaped curves (Xue 2016). From the perspective of fertility economics, fertility decision-making is mainly determined by fertility benefits and costs. The families in the low or medium classes in terms of fertility benefits have a low desire for a second baby, while those in the higher class for fertility benefits have a high desire. Fertility benefits can be grouped into reasons related to one’s path in life, anticipated positive experiences, and feelings drawn towards children. To have a child makes one’s family complete; is the next stage of life; provides happiness, fun, and enjoyment; brings care and company in old age; and children are “lovely” and “cute” (Adams 2016). The significant benefit factors of having a second baby include parenting joy, health risks, mutual care among siblings, and the flourishing of the family (Chen et al. 2019). Fertility costs include the childbearing cost, the opportunity costs for childbearing, and time pressure. The fertility intentions of fertile women are reduced by high childbearing costs, which include family childbearing costs and the social childbearing costs (Wang and Liu 2017; Wang and Long 2018; Xia and Yang 2019). However, the unbalanced childbearing cost allocation mechanism causes China’s families to bear most of the childbearing costs (Xia and Yang 2019). The fertility intentions of some fertile women will not be increased by the two-child policy (Zhou and Chen 2013), since

Li and Zhen (2015)

The fertility intention shows a significant U-shaped curve correlation with the regional human development index

Myrskylä et al. (2009)

Partible inheritance mechanism

The fertility intention shows a significant U-shaped curve correlation with the regional human development index

Chen and Zhai (2016)

Guo and Jiang (2018)

There is a strong reverse relationship between the regional human development index and the total fertility rate among the 109 studied countries

Zhou (2015)

House price

The total fertility rate is negatively related to the provincial human development index

Tao et al. (2017)

Regional human development index

Under the traditional partible inheritance mechanism, fertility intention was significantly negatively associated with birth order

High house prices restrain the development of the total fertility rate

Globalization causes the low total fertility rate

Globalization

Influence mechanism

Studies

Wang (2010)

Variables

Table 3.4 The economic determinants affecting fertility intentions and total fertility rate

(continued)

Historical birth data of the Que family from 1670 to 1996

China Statistics Yearbook 2006–2017

N/A

The 1990 and 2000 data about the total fertility rate and human development index from the United Nations Development Programme

Data from 109 countries with over 5 million people in 2010

The 1982, 1990, 2000, and 2010 population census data

N/A

Data

40 3 Population Size Challenge: Low Total Fertility Rate

Xue (2016)

Chen et al. (2019)

Fertility benefit

China Statistics Yearbook 1978–2014

2010 Chinese family monitoring survey

(continued)

Participants in the lowest- and A total of 396 participants medium-benefit classes have low fertility desire for a second baby, while those in the high-benefit classes have a high desire. Fertility benefit factors include parenting joy, health risks, mutual care among siblings, and flourishing of family

The fertility level and household consumption are shown as an inverted U-shaped curve

Fertility rate of people with low incomes is high, causing the low quality of the labour population

Guo and Yu (2017)

Household consumption

The fertility decision-making of rural China Statistics Yearbook families is determined mainly by the 2005–2010 incomes and the childbearing costs with fixed land. Rural families reduce the number of births according to the trade-off between quality and quantity, which is maybe the main cause of the level of fertility decline in China

The income data of the 31 provinces from 1978 to 2013

Song et al. (2012)

Data

The fertility intention shows a significant U-shaped curve correlation with the family income

Income level

Influence mechanism

Studies

Li (2016)

Variables

Table 3.4 (continued)

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 41

Opportunity costs for childbearing

Wang and Liu (2017), Wang and Long The current high childbearing costs (2018) reduce the fertility intention of the fertile women

Childbearing cost

Zhu (2020)

The family planning policy is very effective to control the total fertility rate in China

Pecuniary costs of having children are significantly and negatively related to their desired fertility

Wei et al. (2018)

(continued)

Chinese General Social Survey (CGSS) 2012 data

A multistage stratified cluster sampling survey with 2,516 women respondents in rural Shaanxi

The fertility decision-making of rural China Statistics Yearbook families is mainly determined by the 2005–2010 incomes and the costs of raising children with fixed land. Therefore, rural families may reduce the number of births according to the trade-off between quality and quantity, which is maybe the main cause of the level of fertility decline in China

N/A

Twenty-five semi- structured, in-depth interviews

Data

Song et al. (2012)

To have a child makes one’s family complete; is the next stage of life; provides happiness, fun, and enjoyment; brings care and company in old age; and children are “lovely” and “cute”. These reasons can be grouped into life course reasons, anticipated positive experiences, and feelings that draw towards children

Fertility benefit

Influence mechanism

Studies

Adams (2016)

Variables

Table 3.4 (continued)

42 3 Population Size Challenge: Low Total Fertility Rate

Time pressure

Variables

Table 3.4 (continued)

The opportunity cost for childbearing is significantly and negatively related to their desired fertility

Wei et al. (2018)

Time pressure significantly affects the fertility desire for a second baby

Qualitative analysis

Ji and Zheng (2018)

Chen et al. (2019)

Influence mechanism

Studies

A total of 396 participants

A multistage stratified cluster sampling survey with 2,516 women respondents in rural Shaanxi

N/A

Data

3.3 Determinants of the Fertility Intention and Total Fertility Rate in China 43

44

3 Population Size Challenge: Low Total Fertility Rate

the two-child policy increases their opportunity costs by reducing the labour supply, wage income, and healthy human capital (Zhu 2020). The potential opportunity costs significantly reduce the total fertility rate (Ji and Zheng 2018; Wei et al. 2018). Time pressure is also a large barrier to fertility desire (Chen et al. 2019), and extending the retirement age would significantly increase time pressure (Dong and Kang 2020). Based on the above meta-analysis, this chapter draws Fig. 3.2 to analyse the factors influencing the total fertility rate.

3.4 Estimation of the Total Fertility Rate To date, China’s total fertility rate has not been well measured due to the lack of authoritative datasets. China has more than 1.4 billion people and 9.6 million square kilometres, so it is very difficult to accurately survey all fertile women. Many scholars are continuously searching for ways to survey fertile women and calculate the total fertility rate. To help realize the estimation of the total fertility rate, this section summarizes and compares the estimation results for China’s total fertility rate. The existing estimations of China’s total fertility rate since 2000 are summarized in Table 3.5.

3.4.1 Estimated Methods and Data Sources Various methods have been adopted to calculate China’s total fertility rate based on different data, and these studies reach different conclusions. The total fertility rate can be the summation of the fertility rate based on birth order(Lu 1989; Zhang 2006), calculated based on the fertility rate at different ages multiplied by the proportion of fertile women at those ages (Wang 2003, 2019; Xu 2010; Zhang 2006; Zhou and Chen 2013), or calculated by combining these two methods (Chen and Gao 2013). Some scholars obtain population data for selected decades and use the backward survival method to reverse calculate the number of newborn infants and the total fertility rate (Guo 2011; Li and Li 2012; Zhu 2012). However, some question whether population census data are scientific, and these data have serious omission problems (Goodkind 2004; Yi 1996; Zhang and Cui 2003). The estimated total fertility rate as calculated directly based on population census data is usually smaller than 1.5. To overcome the shortfalls in the population census, some scholars first check and amend the population census data and then use the data to estimate the total fertility rate (Chen 2016; Chen and Zhang 2015; Cui et al. 2013; Mi and Yang 2016; Wang 2003; Wang and Ge 2013; Wang et al. 2004; Zhao 2015). The estimated total fertility rate based on amended census population data usually falls between 1.5 and 1.6. Some scholars believe that data from educational institutions or household data are more scientific than data from the population census. They estimate the number of children and fertile women based on education data and residential data from the education

+

+

Wang and Zhang (2020)

Guo et al (2019), Striessnig and Lutz (2014)

-

Traditional Son Preference Opinion

Guo (2008)

+

Gender Ratio

Guo (2008)

-

-

Sasser (2005)

Yang et al (2007), Striessnig and Lutz (2014)

-

-

-

-

-

-

+

-

Chen and Zhang (2019)

Shi et al (2019), Wang et al (2019), Guo and Tian (2017), Robert et al (2004), Jiang et al (2019), Wei et al (2018)

Fu et al (2013), Liang (2013)

Yang (2015)

Retherford et al (2004), Retherford et al (2005), Yip et al (2015)

Guo (2008)

Wang and Chi (2017)

Environmental pollution

Education Background

+

Labor Participation

Participation in Pension Insurance +

Marriage Age

Age Interval between Marriage and Childbearing

Divorce Rate

Marriage Delay

Infertility Rate

Rural Residence

Women’s social status

+

-

Fig. 3.2 The determinants affecting the total fertility rate

Population Floating

Liang (2006), Guo (2008), Li ad Guo (2014)

Wang and Liu (2015), Wei et al (2018)

Retirement Security System

-

Liang (2013)

-

Wang and Liu (2015)

Physical Condition

Guo (2008)

Dong and Kang (2020)

Wei et al (2018)

-

Modern fertility culture

Wu et al (2016)

-

Total Fertility Rate

-

-

-

Globalization

Childbearing Cost

Time Pressure

Fertility Costs

Fertility benefits

Household Consumption

Family Income

House price

Partible Inheritance Mechanism

Regional Human Development Index

Regional Education Quality

+

Social Stability

Wang and Zhang (2020), Jiao et al (2019)

Opportunity Costs for Childbearing

Wang et al (2019)

Zhu (2020), Ji and Zheng (2018), Wei et al (2018)

Chen et al (2019) Wang and Liu (2017), Wang and Long (2018), Song et al (2012), Wei et al (2018)

Adams (2016), Chen et al (2019)

-

+

Xue (2016)

Li et al (2016)

Guo and Jiang (2018)

Li and Zhen (2015)

Zhou (2015), Tao et al (2017)

Song et al (2012), Guo and Yu (2017)

Urbanization Rate

Guo and Yu (2016)

-

Wang (2010) Chen et al (2016), Myrskylä et al (2009)

-

U

-

-

U -

U

U

Retirement Time

-

Zhu (2020)

Family Planning Policy

Ding et al (2019), Shi et al (2019), Wang et al (2019), Li et al (2019), Zhu (2020), Chen (2019), Wang and Sun (2016)

Political Freedom

Wang and Sun (2016)

-

For upper For low middle income income

+

3.4 Estimation of the Total Fertility Rate 45

46

3 Population Size Challenge: Low Total Fertility Rate

Table 3.5 Estimations of China’s total fertility rate since 2000 Estimated year Study

Data

Method

Findings

2000

Wang et al. (2004)

The 2000 population census data

Statistical Analysis

There are significant provincial differences in China’s 31 provinces in terms of the total fertility rate. The total fertility rate of the 31 provinces ranges 0.8–2.5

2006

Zhou and Pan (2010)

Agricultural and non-agricultural population data from the Household Sector of the Ministry of Public Security in 2006

Estimating the family structure in cities, towns, or villages of different provinces, and then estimating the total fertility rate

Under the strict one-child policy, the total fertility rate is estimated to be 1.452, and the ideal total fertility rate is 1.65 at most

2000–2009

Zhu (2012)

The 2000 and 2010 population census data

Using the population difference to reversely calculate the total fertility rate

The average total fertility rate was 1.48 during the years 2000–2009

1982–2010

Chen (2015)

The age Integrated distribution data in approach and the population variable-r method census

China’s overall total fertility rate in the 2000s is around 1.6

2000–2010

Guo (2011)

The 1990, 2000, and 2010 population census data

China’s total fertility rate is smaller than 1.5, and even smaller than 1.4 in some years

Using the population difference to reversely calculate the total fertility rate

(continued)

and household sectors and use that to calculate the total fertility rate (Chen 2014, 2016; Chen and Yang 2014; Yang and Zhao 2013; Zhou and Pan 2010). The estimated total fertility rate based on education or residential data is usually approximately 1.6 or even higher. The estimated total fertility rate based on the population census data is significantly smaller than that based on education and residential data. In addition to obtaining these official statistical data, some scholars estimate the total fertility rate by conducting surveys (Wei et al. 2018). In 2017, China conducted an official fertility survey, and the estimated that the total fertility rate based on

3.4 Estimation of the Total Fertility Rate

47

Table 3.5 (continued) Estimated year Study

Data

Method

Findings

2000–2010

Hao and Qiu (2011)

The population statistical data from China Statistics Bureau

Estimated based on the population statistical data

During the years 2000–2010, China’s overall total fertility rates fall into the range 1.22–1.47; the total fertility rates in China’s cities and villages fall into the range 0.94–1.22, and from 1.43 to 1.73, respectively

2000–2010

Li and Li (2012) The 2000 and 2010 population census data

Using the population difference to reversely calculate the total fertility rate

The average total fertility rate was 1.57 during the years 2000–2010

2000–2010

Chen (2014)

The 2000 and 2010 population census data, the education data from 2000 to 2010, and the household data from 2000 to 2010

Estimating the number of children by using the education data and estimating the number of fertile women by analysing the population census data

From 2000 to 2010, the total fertility rate is smaller than 1.5 in the early period and is approximately 1.7 in the late period

2000–2010

Zhao (2015)

The 2000 and Variable-r method 2010 population census data and the annual fertility pattern sampling surveys by National Bureau of Statistics of China

Chinese fertility between 2000 and 2010 is around 1.60

2008–2010

Zhai et al. (2015)

The resident data in 2015

The estimated total fertility rate from 2008 to 2010 is at least 1.66, 1.66, and 1.63

Using the resident data to obtain the number of newborn infants and fertile women, and calculate total fertility rate

(continued)

48

3 Population Size Challenge: Low Total Fertility Rate

Table 3.5 (continued) Estimated year Study

Data

Method

Findings

2010

Chen and Yang (2014)

The 2010 population census data

Estimating the number of children and the number of fertile women by analysing the population census data

The estimated total fertility rate in 2010 is 1.66

1982–2012

Mi and Yang (2016)

The 1982, 1990, Linear regression 2000, and 2010 analysis population census data, and the 1995, 2005, and 2015 small population census data with 1% sample

China’s total fertility rate is estimated to be 1.595 in 2010

2000–2012

Yang and Zhao (2013)

The 2000 and 2010 population census data, education statistical data, and the household resident data in 2011 and 2012

Estimating the number of children and the number of fertile women based on the education and resident data and the population census data

The average total fertility rate was at least approximately 1.6 during the years 2000–2012

2000–2012

Wang and Ge (2013)

The 1990, 2000 and 2010 population census data

Amend the population census data and then estimate the total fertility rate

The average total fertility rate was at least approximately 1.52 during the years 2000–2012

2000–2012

Gu (2015)

The 2000 and 2010 population census data, and the 2005 small population census data with 1% sample

Regression model based on the data of the general fertility rate

China’s total fertility rate first decreases and then increases. The total fertility rate in 2012 is high, at 1.43

2005–2013

Chen and Zhang The 1990 and (2015) 2010 population census data

Amend the population census data and then estimate the total fertility rate

From 2005 to 2013, the estimated total fertility rate is at least 1.5, and most probably would be around 1.6 (continued)

3.4 Estimation of the Total Fertility Rate

49

Table 3.5 (continued) Estimated year Study

Data

2000–2014

Chen (2016)

The 2000 and Generalized stable 2010 population population model census data and the resident data from 2008 to 2014

Method

Findings China’s total fertility rate is at least 1.5, and has an increasing trend

2006–2016

He et al. (2018)

The 2017 fertility Statistical analysis survey data investigated by the National Health Commission of China

The total fertility rate between 2006 and 2011 was between 1.60 and 1.70, and fluctuated between 2012 and 2016, peaking in the years 2012 and 2016

2003–2018

Wang (2019)

China Statistics Yearbook

Holt’s Exponential Population Smoothing model structure was ageing fast, fertility rates continued to decrease to a substantially low level, and three north-eastern provinces displayed notable socio-economic issues associated with low-fertility trap

this survey is highly consistent with the estimated results based on education and residential data (Chen and Duan 2019). Although some scholars argue that education and residential data are more scientific, others think that education or residential data may have false report problems due to potential population subsidies. As the indicators described in the above paragraph are hard to obtain, some scholars analyse the relationship between the total fertility rate and some easily obtained indicators and derive formulas for calculating the total fertility rate. For instance, Zhu and Qiao (2018) derived a formula for calculating the total fertility rate based on the birth rate (Qiao and Zhu 2018; Zhu and Qiao 2018). Some scholars adopt the variable-r method and use the growth rate in populations of different ages to estimate the fertility level (Chen 2015; Zhao 2015). To measure the total fertility rate, some scholars also use related indicators, such as the mean childbearing age at first birth (Wang and Zhong 2015) and the general fertility rate (Gu 2015).

50

3 Population Size Challenge: Low Total Fertility Rate

3.4.2 Estimated Total Fertility Rate in China When performing these estimations, most scholars are worried about the low total fertility rate in China. They believe that China’s total fertility rate is far smaller than the replacement level and thus suggest that the family planning policy should be further relaxed (Guo 2011; Li and Li 2012; Zhou and Pan 2010; Zhu 2012). New authoritative fertility data provided by the National Health Commission of the People’s Republic of China reveal that the total fertility rate between 2006 and 2011 was between 1.60 and 1.70 and fluctuated between 2012 and 2016, peaking between 2012 and 2016 (He et al. 2018). The total fertility rate reported from the 2017 China fertility survey is relatively higher. In the 2017 China fertility survey, 12,500 investigators and 3128 supervisors obtained 246,840 samples from 12,500 village sample points representing 6078 towns in 2737 counties of 31 provinces (Zhuang et al. 2018). Because the report is new and authoritative, it is very important for fertility rate tracking and policymaking. The relatively high estimated total fertility rate shows that China’s total fertility rate has been significantly underestimated and that the strict family planning policy still plays an important role in controlling the total population number (Chen 2014, 2016; Chen and Yang 2014; Chen and Zhang 2015; Yang 2015). There are significant differences between the cities, towns, and villages in different provinces in terms of the total fertility rate (Gu et al. 2019; He et al. 2018; Huang 2020; Jiang et al. 2019; Li et al. 2019b; Liang 2013; Shi et al. 2019; Wang et al. 2019a). The total fertility rate of the provinces with high populations of ethnic minorities has increased to a replacement level in the past 10 years and is higher than the national average (Yuan et al. 2019). The total fertility rates from 2006 to 2016 estimated from the 2017 fertility survey are presented in Table 3.6. The total fertility rate in villages is significantly higher than that in cities or towns, but there is a smaller difference in the total fertility rate between villages and cities or towns (Hao and Qiu 2011). There is significant global spatial dependency and a significant local clustering trend for the provincial total fertility rate (Dorius 2008; Gu 2015; Guo et al. 2012; Li et al. 2017; Tan and Zeng 2014; Wang and Chi 2017; Wang et al. 2004, 2015; Wang 2019; Zhang et al. 2012). The total fertility rates are low in Beijing, Shanghai, Liaoning, Heilongjiang, Jilin, and Tianjin, which are developed municipalities or are located in the north-eastern regions. The provinces with low total fertility rates are concentrated in the north, north-eastern, and south-eastern provinces (Tan and Zeng 2014). Three north-eastern provinces displayed notable socio-economic issues associated with the low-fertility trap (Wang 2019). However, the total fertility rates are relatively high in Guangxi, Guizhou, Xinjiang, and Hainan, which are located in the western and southern regions, and particularly in the south-western regions. The total fertility rates in the western provinces are usually higher than those in the central and eastern provinces (Gu 2015). Thus, we should not ignore the spatial effects of the total fertility rate, including interdependence and heterogeneity. More analyses of the spatial aggregation of the 31 provinces in terms of population indicators are conducted in Chaps. 7 and 9.

1.62

1.21

1.98

0.77

China

Town

Village

Difference between town and village

2006

0.71

2.02

1.31

1.69

2007

0.86

2.12

1.26

1.71

2008

0.79

2.06

1.27

1.68

2009

0.72

1.99

1.27

1.64

2010

0.73

1.97

1.24

1.61

2011

0.67

2.12

1.45

1.78

2012

0.71

1.92

1.21

1.55

2013

Table 3.6 Total fertility rate in China’s towns and villages from 2006 to 2016 (estimated from the 2017 fertility survey)

0.62

1.99

1.37

1.67

2014

0.53

1.69

1.16

1.41

2015

0.51

2.05

1.54

1.77

2016

3.4 Estimation of the Total Fertility Rate 51

52

3 Population Size Challenge: Low Total Fertility Rate

3.5 Data Sources for Studying China’s Total Fertility Rate As described in Sects. 3.3 and 3.4, I can summarize the data sources for studying China’s total fertility rate in Table 3.7. Population census data have been widely adopted to study the total fertility rate. The small population census data and the yearly population data, both based on a 1‰ sample, are also adopted by many studies. In addition, some studies adopt provincial and regional data to analyse the population development differences among the 31 provinces in China or among counties. The significant population development differences in China are further analysed in Part II. In addition to using statistical population data, some scholars use populationrelated statistical data, such as residential data and education data, to estimate China’s total fertility rate. To analyse various determinants affecting the total fertility rates, many studies use data from other fields, such as data on the labour participation rate, income, regional development index data, and even environmental data. The fertility intentions of fertile women vary with time and cannot be directly calculated through any indicators. Thus, ongoing surveys are very important for analysing and estimating the total fertility rate. As authentic data have been provided by the National Health Commission of China, the 2017 fertility survey data have been widely used by many scholars to estimate the total fertility rate. Another important related survey is the Chinese General Social Survey (CGSS). The CGSS 2010, 2012, 2013, and 2015 data are widely used by scholars to study the total fertility rate. In addition to these two widely recognized datasets, some individuals and institutes also conduct their own surveys, such as the 2010 Chinese family monitoring survey, the multistage stratified cluster sampling survey, and the annual fertility pattern sampling survey. Some studies design experiments and conduct face-to-face interviews to study the total fertility rate in China.

3.6 Suggestions for Improving the Total Fertility Rate After summarizing the influencing factors causing the low total fertility rate, this study offers some effective suggestions for improving the total fertility rate, specifically the fertility rate for the first birth. It is extremely important that the fertility level of the first birth be improved; otherwise, policy adjustment will only increase the number of second baby in the short term, and the overall fertility level will thereafter be reduced (Guo 2017). The suggestions include four main targets: economic support, maternity benefits, social welfare, and population policies.

3.6 Suggestions for Improving the Total Fertility Rate

53

Table 3.7 Data sources adopted to study the total fertility rate Data categories

Data information

Studies

Population statistical data

The 1982, 1990, 2000, and 2010 population census data

Retherford et al. (2004), Wang et al. (2004), Retherford et al. (2005), Liang (2006), Guo (2011), Guo et al. (2012), Li and Li (2012), Zhu (2012), Fu et al. (2013), Liang (2013), Wang and Ge (2013), Yang and Zhao (2013), Chen (2014), Chen and Yang (2014), Chen (2015), Chen and Zhang (2015), Gu (2015), Yip et al. (2015), Zhao (2015), Chen (2016), Chen and Zhai (2016), Mi and Yang (2016), Guo and Tian (2017), Tao et al. (2017), Jiang et al. (2019), Li et al. (2019a, b), Zhang et al. (2019), Dong and Kang (2020)

The 1995, 2005, and 2015 small population census data with 1% sample

Gu (2015), Mi and Yang (2016), Guo and Tian (2017), Dong and Kang (2020)

The yearly population data with 1‰ sample

Hao and Qiu (2011), Guo et al. (2012), Song et al. (2012), Xue (2016), Guo and Tian (2017), Guo and Jiang (2018), Li et al. (2019a, b), Wang (2019), Zhang et al. (2019)

Historical birth data of the Li and Zhen (2015) Que family from 1670 to 1996

Population statistical data

Population data from Sichuan Statistics Yearbook 2009

Jiao et al. (2019)

The 1991, 2001, and 2011 Hong Kong population statistics

Yip et al. (2015)

Chinese floating population monitoring and dynamic survey in 2012 and 2014

Li and Guo (2014), Guo et al. (2019)

The national prefecture-level city matching data

Guo et al. (2019)

Geocoded 2010 county-level census data

Wang and Chi (2017)

The population statistical data Yang et al. (2007) from 109 countries (continued)

54

3 Population Size Challenge: Low Total Fertility Rate

Table 3.7 (continued) Data categories

Data information

Studies

The population statistical data Wang and Sun (2016) from 70 countries in four income categories from 1973 to 2011

Population- related statistical data

Data of 109 countries with over 5 million people in 2010

Zhou (2015)

Related data of China and countries of ASEAN from 1989 to 2013

Wang and Liu (2015)

The household/residence data

Zhou and Pan (2010), Yang and Zhao (2013), Chen (2014), Zhai et al. (2015), Chen (2016)

The education data

Yang and Zhao (2013), Chen (2014)

The data with labour participation rate and income

Sasser (2005)

The human development Chen and Zhai (2016) index from the United Nations Development Programme

Survey data

The income data of the 31 provinces from 1978 to 2013

Li (2016)

Air pollution index PM2.5 collected by the Green Peace Organization

Guo et al. (2019)

2017 fertility survey data investigated by the National Health Commission of China

He et al. (2018), Ding et al. (2019), Li et al. (2019a, b), Chen (2019), Shi et al. (2019), Wang et al. (2019a, b)

2010 Chinese family monitoring survey

Guo and Yu (2017)

A multistage stratified cluster sampling survey with 2,516 women respondents in rural Shaanxi

Wei et al. (2018)

The annual fertility pattern sampling surveys by National Bureau of Statistics of China

Zhao (2015)

Chinese General Social Survey (CGSS) 2010, 2012, 2013, and 2015 data

Chen and Zhang (2019), Wang et al. (2019a, b), Zhu (2020)

Experiments with a total of 396 participants

Chen et al. (2019)

Interview data

Wu et al. (2016) (continued)

3.6 Suggestions for Improving the Total Fertility Rate

55

Table 3.7 (continued) Data categories

Data information

Studies

Twenty-five semi-structured, in-depth interviews

Adams (2016)

3.6.1 Economic Supporting The implementation of the two-child policy and the changing form of the future population require not only a scientific assessment of the cost of the second baby but also a reasonable mechanism for allocating reproductive cost. Reducing childbearing costs can increase fertility intentions and the total fertility rate. There are two main paths for reducing childbearing costs. First, the government can provide monetary support to help families raise their children. In many countries or regions, including France, Finland, Belgium, Italy, England, Australia, Singapore, and the Taiwan Province of China, governments offer a one-time birth subsidy for newborn babies (Yang 2016). The size of this one-time monetary reward ranges 150–1500 U.S. dollars. Such one-time monetary reward would be a powerful motivator for changing the fertility intentions of fertile women (Wang and Sun 2017). Second, ongoing child allowances are also very important for encouraging fertility willingness. Children’s allowances can be provided through various methods. First, children could be provided a regular monthly allowance. Some countries, such as France and Sweden, provide the monthly allowance until the children grow to a certain age. The second way for providing an allowance for children is the education allowance. Governments can not only reduce and even eliminate education fees but also provide allowances for children in school. The third way of providing an allowance is tax reduction and exemption. Families experience greater costs to raise children, so their taxes can be reduced and even exempted. Since 2019, China has already implemented a completely new tax mechanism, in which some costs of children can be deducted from the tax base. A tax benefit policy is difficult and complex to introduce and maintain and requires sophisticated targeted moderating (Wang and Sun 2017).

3.6.2 Maternity Benefits When having a child, family members, especially the mother, experience far greater workloads. If they still need to work and expend effort to balance work and family, they will have very low willingness to have a new baby and may even experience stress. To help families enjoy raising children and even to be willing to have a new child, governments should provide sufficient maternity benefits.

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First, maternity leave should be sufficient. As women’s bodies present new challenges with increasing age, some countries or regions provide maternity leave related to the number of births. The salaries of fertile women during maternity leave can be provided by both governments and employers. Second, yearly parental leave can encourage families to have more children. In some countries, family members can obtain parental leave to spend time with their children. Having sufficient time to provide companionship to their children can bring happiness to both the parents and the children, help children grow in a healthy manner, and help parents experience better moods to increase their willingness to have more children. Third, most maternity benefits are designed for the mothers, but fathers should also have leave to take care of their children and wives. Even though fathers can take holidays to spend with their wives and children, leave for fathers is currently limited. Increasing leave time for fathers can also increase the fertility intentions of families. Fourth, designing flexible working hours and workspaces can offer fathers and mothers sufficient time to spend with their children. During the period of the coronavirus disease 2019 (COVID-2019), most people had to work from home, but the efficiencies gained were found to be ideal for some kinds of work. Therefore, for families with new babies, employers could provide fathers and mothers with flexible work hours and workspaces. Advanced technologies can help promote the development of this mechanism.

3.6.3 Social Welfare Families would have higher fertility intentions if they could enjoy superior social welfare. Therefore, to encourage the fertility intentions of fertile women, we should continuously improve social welfare. First, governments need to provide sufficient education and facilities for developing children. If the children cannot obtain a good education from society, families will have a low willingness to have a new child. In addition to governments, employers can establish some facilities for children in their offices. Second, families with more children experience high housing pressure. As described in Sect. 3.3, high house prices restrain the development of the total fertility rate (Guo and Jiang 2018). In some countries or regions, families with more children are prioritized to receive the housing welfare provided by governments. If families do not need to worry about the housing conditions for their children, they will have higher willingness to have more children. Third, improving maternal health services is very important for fertile women. Many puerperae have postpartum health problems. If women cannot experience a pleasant recovery process after childbirth, they will have a relatively low willingness to have new children.

3.6 Suggestions for Improving the Total Fertility Rate

57

3.6.4 Population Policies Suitable population policies are very important for controlling the total fertility rate. Family planning policies should match the national situation, including economic levels, traditional cultures, social developments, and population. China’s family planning policies help China control the total population number but have destroyed the demographic structure. Currently, China faces a low total fertility rate and has prioritized relaxing its strict family planning policy. A more detailed analysis of the adjustment of China’s family planning policy is conducted in Chap. 6. When relaxing family planning policies, governments should also continuously improve public services, reasonably allocate educational and medical resources, and strengthen basic education and medical facilities.

3.7 Conclusions and Discussions A comprehensive understanding of the determinants of the low total fertility rate and an accurate estimation of China’s total fertility rate are very important for the scientific adjustment of population policies in China. Although the importance and significant effects of the total fertility rate have been widely recognized, existing studies are limited. In this study, I identified 71 relevant journal articles, comprising 55 Chinese journal articles and 16 English journal articles published as of May 31, 2020. We first conduct descriptive statistical analyses and obtain some general findings regarding overall growth and publication sources. A total of 31 determinants affecting the total fertility rates are identified, and these 31 determinants are divided into three groups: demographic determinants, social determinants, and economic determinants. Then, the estimations of the total fertility rate are summarized and evaluated. The methods and data adopted to estimate China’s total fertility rate are also summarized. By reviewing these 71 relevant journal articles, the data sources for studying China’s total fertility rate are examined. The main contribution of this study is its summary of the determinants of the total fertility rate in China, which can allow a better response to China’s total fertility rate. Moreover, this study helps us better evaluate China’s current total fertility rate and determine how to choose suitable data sources for studying the total fertility rate. This study also informs academicians and practitioners in this research field of the current research situation, the implications and limitations of existing studies, and the prospective research directions in this area.

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Chapter 4

Population Structure Challenge: Serious Population Ageing

Abstract Affected by the strict family planning policy, China’s population structure has become skewed towards the older generations. Population ageing significantly affects China’s social and economic development, and thus it is extremely important that China clearly realizes the consequences of population ageing to create suitable population policies. To this end, this chapter conducts a meta-analysis to examine the impacts of population ageing. This chapter also comprehensively compiles and summarizes the existing datasets for studying population ageing. Keywords Population ageing · Demographic dividends · Meta-analysis · Family planning policy · China

4.1 Introduction As analysed in Chap. 2, the strict one-child policy implemented in 1973 controlled the total number of China’s population but destroyed the population structure (Ding and Hesketh 2006; Gietel-Basten et al. 2019; Guo 2015; Guo 2016; Hesketh and Zhu 1997; Jiang et al. 2013; Peng 2011; Zhang and Chen 2020). When the total fertility rate is smaller, the level needed to promote sustainable population development (i.e., 2.1), the number of newborn infants is smaller than the population one generation ago, causing a decrease in the total population and an increase in the proportion of the older population (Goldstein et al. 2009; Guo 2008; Guo 2010; Huang 2020a; Zhang and Chen 2020). The combination of high life expectancy and a low total fertility rate increases the degree of population ageing, and society has changed from young to old in China and even worldwide (Zhai and Liu 2019). China became a population ageing society in approximately 2000, when the proportion of people older than 65 reached 7%, which is the standard for population ageing of the United Nations (Jiang 2018). Just after 20 years, i.e., in 2020, the proportion of people older than 65 has reached 11.9%. In China, population ageing is not a mere natural economic consequence of increased wealth leading to declined fertility. It is the result of a deliberate policy: the one-child policy (Hou 2019). As a result, China’s population ageing has two unique characteristics: “ageing before getting rich” (Johnston 2019; Wan et al. 2017) and © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_4

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4 Population Structure Challenge: Serious Population Ageing

the “silver wave” (Bai and Lei 2020; Han 2019). Compared to countries with similar population ageing, China has a low per capita GDP, and when compared to countries with similar per capita GDP, China’s population ageing phenomenon is very serious. The other characteristics of China’s population ageing include (i) accelerated ageing and (ii) regionally imbalanced ageing. As predicted in Chap. 5, the proportion of people older than 65 would reach as high as 42.98%, 35.02%, and 24.26% by 2050 under the original one-child policy, the current two-child policy, and no family planning policy, respectively. Our population simulation results are consistent with the predicted results in the existing studies (Cao 2020; Shi et al. 2018; Wan et al. 2017). China’s future population ageing will impose major challenges for China’s sustainable development on the supply and demand sides over the long term (Bai and Lei 2020). Many studies predict that the proportion of elderly people in China will exceed one-third (Subject Group of the PBC Jinan Brance 2020). This section proposes the population structure challenge by analysing the effects of serious population ageing and the vanishing population dividends and then proposes some suggestions for responding to the ageing society. To understand the regional diversity in population ageing (Chen et al. 2019; Guo et al. 2019; Jing 2019; Li et al. 2019b; Wang 2019a; Wu and Song 2020; Xu et al. 2020, 2019; Ying et al. 2020; Zhong 2019; Zhou et al. 2019b, 2019c), this book conducts a spatial aggregation analysis of China’s provinces in terms of the elderly dependency rate in Chap. 9. Serious population ageing is addressed in various fields (Hsu and Chen 2003; Huang 2020b; Jia 2020). The adverse effects of population ageing are reflected in some fields, such as labour force supply, capital accumulation, the retirement security system, and health care (Jiang 2018). Serious population ageing leads to a decrease in the demographic dividend, and as a result, the labour population decreases, and the labour cost significantly increases. Society bears a heavy burden in supporting older people. To cope with rapid population ageing, we must establish a scientific perspective on population development and propose useful policy suggestions on this basis. Although from these existing studies, we can scatteredly realize the effect of population ageing on a specific objective, but still cannot understand and compare the integrated effects of population ageing from a study. Therefore, this study conducts the meta-analysis to summarize and analyse the effects of population ageing, and summarize the data sources for studying population ageing. Although existing studies indicate the effect of population ageing on specific objectives, the effects of population ageing cannot be integrated, understood, and compared across studies. Therefore, this study conducts a meta-analysis to summarize and analyse the effects of population ageing and summarize the data sources for studying population ageing. To further understand the effects of population ageing in China, this chapter conducts a meta-analysis by analysing the existing related literature from the Web of Science Core Collection (SCI) database and the Chinese Social Sciences Citation Index (CSSCI) database. The SCI database is one of the world’s largest abstract and citation databases of peer-reviewed research literature, while the CSSCI database is one of the most authentic Chinese databases and covers almost all high-quality

4.1 Introduction

69

Chinese social science journals. To obtain full-scale results, an extensive and systemic search was conducted using the combination of two keywords (“population ageing” and “China”) for the SCI database and the keyword “人口老龄化”, which is the Chinese keyword representing “population ageing”, for the CSSCI database. As all searched articles written in Chinese already study the Chinese population, it was not necessary to add the keyword “China”. From the CSSCI database, I was able to obtain more than one thousand relevant papers about the effect of population ageing and found that most of the topics in these articles were repetitive. Thus, I only analysed articles published from January 1, 2019, to May 31, 2020. The search for relevant papers in this process was by no means exhaustive, but the main effects of population ageing in China can be clearly established. Only full-length articles were used for the meta-analysis. We thus exclude conference papers, conference reviews, book chapters, editorials, short surveys, and letters from the analysis. Ultimately, 52 articles were analysed to understand the effects of population ageing in China. Among the 52 articles, 43 articles were published in Chinese, and the remaining 9 articles were published in English. The remainder of the chapter is structured as follows. Section 4.2 analyses the development of China’s population ageing, including historical changes, current circumstance, and future prediction. Section 4.3 conducts the meta-analysis to analyse the effects of population ageing. Subsequently, Sect. 4.4 summarizes the important datasets for studying population ageing, Sect. 4.5 proposes effective approaches to respond to population ageing, and finally, conclusions and discussions are presented in Sect. 4.6.

4.2 China’s Population Ageing: A Brief Sketch 4.2.1 Historical Changes After 1949, when new China was established, China’s population experienced explosive growth because the government took measures to encourage fertility. However, China quickly realized that this high population growth rate would bring enormous challenges. To control the irrational population growth, China began to implement the one-child family planning policy in the 1970s, restricting every family to have only one child. Even under the control of this strict family planning policy, China’s population has experienced a drastic jump in recent decades. Specifically, the period between 1949 and 2014 witnessed a drastic jump in China’s population from 541.67 million to 1367.82 million (National Bureau of Statistics of China 2020). It is difficult to image China’s population if family planning policies had not been implemented in a timely manner. Despite the considerable contribution to controlling population size, the one-child policy also brought some problems, such as a large gender gap due to the inherent Chinese preference for sons (Jiang et al. 2016) and serious population ageing resulting

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4 Population Structure Challenge: Serious Population Ageing

Table 4.1 China’s population ageing indicators from 1990 to 2018 Year

Number of people older than 65 (million)

Proportion of people older than 65 (%)

Year

Number of people older than 65 (million)

Proportion of people older than 65 (%)

1990

63.68

5.6

2005

100.55

7.7

1991

69.38

6.0

2006

104.19

7.9

1992

72.18

6.2

2007

106.36

8.1

1993

72.89

6.2

2008

109.56

8.3

1994

76.22

6.4

2009

113.07

8.5

1995

75.10

6.2

2010

118.94

8.9

1996

78.33

6.4

2011

122.88

9.1

1997

80.85

6.5

2012

127.14

9.4

1998

83.59

6.7

2013

131.61

9.7

1999

86.79

6.9

2014

137.55

10.1

2000

88.21

7.0

2015

143.86

10.5

2001

90.62

7.1

2016

150.03

10.8

2002

93.77

7.3

2017

158.31

11.4

2003

96.92

7.5

2018

166.58

11.9

2004

98.57

7.6

from a skewed demographic structure (Cai 2012; Cheema 2013; Ince Yenilmez 2015; Lee and Mason 2010; Mai et al. 2013). China’s population ageing over time is presented in Table 4.1. Over 29 years, the proportion of people older than 65 significantly increased from 5.6% in 1990 to 11.9% in 2018.

4.2.2 Current Circumstances Currently, China’s total population exceeds 1.3 billion and accounts for approximately one-fifth of the world’s population. The world’s six most populous countries today are China, India, America, Indonesia, Brazil, and Pakistan. Based on the data from the United Nations (United Nations 2019), the basic population characteristics in these six countries for 2019 are presented in Table 4.2. A comparison gives us more understanding of China’s population circumstances. Except for India, other countries have far smaller populations than China. However, China’s total fertility rate and birth rate are the lowest among these five countries. Therefore, China’s total population is expected to be ideal in the future, although its population size currently ranks first in the world. Similar to America, China faces serious population ageing, since the proportion of people older than 65 in China is much higher than in the other four countries:

4.2 China’s Population Ageing: A Brief Sketch

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Table 4.2 The basic population characteristics in six countries with large populations in 2019 Countries

Total population (million)

Proportion of people older than 65 (%)

Total fertility rate

Life expectancy

China

1433.784

12.0

1.69

76.62

India

1366.418

6.6

2.24

69.27

America

329.065

16.6

1.78

78.81

Indonesia

270.626

6.3

2.32

71.41

Brazil

211.050

9.6

1.74

75.56

Pakistan

216.565

4.3

3.55

67.02

India, Indonesia, Brazil, and Pakistan. High population ageing is caused by a low total fertility rate and relatively high life expectancy. By recognizing the current population circumstances, we can predict that China’s population stress from its demographic structure will far exceed that from its population size. Thus, China should urgently adjust its family planning policy to control its population size and optimize its population structure (Zhou and Lin 2017).

4.2.3 Future Predictions The United Nations predicts future population developments for all countries, including China. Based on the official predicted data for China’s population development before 2100 (Department of Economic and Social Affairs of the United Nations 2015), China’s total population initially experienced precipitous growth and will increase to its peak at approximately 1.4 billion in 2020. If China’s population growth is high, the total population will reach a plateau at approximately 1.5 billion from 2020 to 2100; if growth is medium, the total population will drop to 1 billion in 2100; and if growth is low, the total population will drastically plummet to only 0.6 billion in 2100. The period between 1950 and 2100 will also witness an obvious change in China’s demographic structure. The population aged 0–14 increased to 350 billion in 1980 but is predicted to drop to approximately 130 million in 2100. There was a drastic increase in the population aged 15–64 from 320 million in 1950 to 1000 million in 2010, but this will be followed by an obvious drop to 520 million in 2100. The population older than 65 will rapidly increase to approximately 410 million in 2060 and then slowly decrease. Although China had a strong demographic dividend in the past, society in the future will undoubtedly be ageing, and population ageing will bring latent pressure upon society and the country. The current demographic dividend will quickly become demographic debt if China cannot establish suitable family planning policies in a timely and accurate manner.

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4 Population Structure Challenge: Serious Population Ageing

4.3 Consequences of Serious Population Ageing Population ageing directly reduces the demographic dividend and has many significant effects in various fields (Fu et al. 2020; Zhang et al. 2019).

4.3.1 Labour Market The effect of population ageing on the labour market can be reflected in these indicators: employment rate, labour supply, labour productivity, human capital, human capital investment, and the burden of elderly care. The existing studies analysing these effects are summarized in Table 4.3. An ageing population will lead to a decline in the working-age population, labour shortages (Jiang 2019), and an increased unemployment rate (Fang and Zhang 2019b). The falling projections for the working-age population in China have led to predictions of much slower economic growth. Population ageing has negative consequences on the labour supply in China (Liu 2019b). Three mechanisms are proposed to contribute to higher effective labour supply growth: further improvement in educational attainment due to cohort replacement and rising college enrolment; improvement in aggregate labour quality due to urbanization; and higher labour force participation due to later retirement (Cao et al. 2020). An ageing labour force drives firms to adopt automated technology and robots, which increases labour productivity but simultaneously decreases labour demand (Feng 2019). Population ageing forces companies to replace the labour force with capital and artificial intelligence and promotes improved human capital through greater experience in industry. At the same time, artificial intelligence compensates for the labour shortage caused by the ageing population, weakens the decline in labour productivity it causes, and promotes industrial structure upgrades. Furthermore, when artificial intelligence develops to a certain extent, the positive effect of population ageing on the upgrading of industrial structure becomes more obvious, and the effect of artificial intelligence in strengthening this effect is prominent (Wang et al. 2020). Population ageing promotes the upgrading of the manufacturing structure in general; specifically, ageing reduces the labour supply, which increases labour cost, and thereby forces the manufacturing industry to upgrade its structure. At the same time, ageing increases the return on family investment in education, actively improving the level of human capital accumulation, and therefore again promoting the upgrading of the manufacturing industry structure. On the other hand, ageing will also slow technological progress, which will have a significant negative impact on the upgrading of the manufacturing industry (Zhao and Liu 2019). Population ageing has not only significantly promoted the internal upgrading of the local industrial structure but also generated a significant positive spatial spillover effect on the

Ageing of labour forces firms to adopt automatic technology and robots, which increases labour productivity, but decreases labour demand at the same time

Feng (2019)

(continued)

Tabulation on the 2010 Population Census of the People’s Republic of China; China Statistics Yearbook

Due to the initial state, population ageing has China Statistics Yearbook, China Finance significantly a positive effect on labour Statistics Yearbook productivity, however, threshold study indicates that this positive effect have been weakened in the areas with high degree of ageing; this phenomenon of “progressive decline” should arouse a high degree of great concern

China Statistics Yearbook

Tabulation on the 2000 and the 2010 Population Census of the People’s Republic of China

N/A

Data

Feng et al. (2019a, b)

An ageing population will lead to a decline in working-age population and labour shortages

Jiang (2019)

Labour Supply

Labour productivity

Population ageing brings negative consequences on the labour supply in China

Liu (2019a, b, c)

Employment rate

Influence mechanism

Studies

Fang and Zhang (2019a, b) Population ageing increases the unemployment rate

Variables

Table 4.3 Effect of population ageing on the labour market

4.3 Consequences of Serious Population Ageing 73

The impact of population ageing on labour China Statistics Yearbook; Provincial Statistics productivity varies significantly in different Yearbooks periods. In the short term, the current level of population ageing and the one-period lag level of population ageing have significant different effects on labour productivity. However, the long-term effect of the elderly dependency ratio on labour productivity has been identified to be not significant. At the same time, the relationship between population ageing and labour productivity shows an “Inverted U” shape Change of age structure with the increase of China Statistics Yearbook; China Population total and child dependency ratios had a Statistics Yearbook; China Labour Statistics significant negative impact on provincial labour Yearbook productivity. Increasing the labour input and accelerating the upgrading of human capital were the main lines to deal with the challenges of ageing. From the perspective of demographic dividend and population urbanization, the impact of change of age structure on labour productivity showed interprovincial and regional development differences

Fu et al. (2020)

(continued)

China Statistics Yearbook; China Population and Employment Statistics Yearbook

Li (2019)

Data

Labour productivity shows an invert U-type relationship with population ageing

Labour productivity

Influence mechanism

Studies

Jiang and Huang (2019)

Variables

Table 4.3 (continued)

74 4 Population Structure Challenge: Serious Population Ageing

Studies

Data

Population ageing promotes the upgrading of manufacturing structure in general. The ageing reduces the supply of labour force, then increases the cost of labour force, and therefore forces the manufacturing industry to upgrade its structure. At the same time, the ageing increases the return of family education investment, so actively improves the level of human capital accumulation and promotes industry structure upgrading Population ageing not only has significantly promoted the integral upgrading of the local industrial structure, but also generated a significant positive spatial spillover effect on the integral upgrading of the industrial structure in the neighbouring regions

Liu and Zhao (2019)

(continued)

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Industrial Economy Statistics Yearbook; China Tertiary Industry Statistics Yearbook

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Industrial Economy Statistics Yearbook; China Science and Technology Statistics Yearbook

Artificial intelligence fills labour shortage China Statistics Yearbook caused by population ageing, and weakens the decline of labour productivity caused by it. When artificial intelligence develops to a certain extent, the positive effect of population ageing on industrial structure upgrading is more obvious, and artificial intelligence strengthens this effect

Influence mechanism

Zhao and Liu (2019)

Industry structure upgrade Wang et al. (2020)

Variables

Table 4.3 (continued)

4.3 Consequences of Serious Population Ageing 75

Data

China has already sunk into the demographic National Bureau of Statistics of China dilemma of population ageing with a baby bust, which means a declining fertility rate combined with a rapidly ageing population, resulting in a serious burden of elderly care

Bai and Lei (2020)

Burden of elderly care

China Statistics Yearbook

While the higher of the public pension to China Statistics Yearbook; China Population and proportion of gross domestic product, the lower Employment Statistics Yearbook of public spending on education to gross domestic product, per capita growth of human capital is a downward trend

Influence mechanism

The human capital investment has an invert U-type relationship with population ageing

Human capital

Human capital investment Cai (2020)

Studies

Wang (2019a, b, c)

Variables

Table 4.3 (continued)

76 4 Population Structure Challenge: Serious Population Ageing

4.3 Consequences of Serious Population Ageing

77

integral upgrading of the industrial structure in neighbouring regions (Liu and Zhao 2019). Increasing labour input and accelerating the upgrading of human capital have been the main approaches to addressing the challenges of ageing. From the perspective of the demographic dividend and population urbanization, the impact of changes in age structure on labour productivity shows obvious interprovincial and regional differences (Fu et al. 2020). Some scholars find that labour productivity shows an inverted U-shaped relationship with population ageing (Jiang and Huang 2019; Li 2019). The impact of population ageing on labour productivity varies significantly in different periods. In the short term, population ageing has a significantly positive effect on labour productivity; however, a threshold study indicates that this positive effect is weakened in areas with a high degree of ageing. This phenomenon of a “progressive decline” should raise a high degree of great concern (Feng et al. 2019a). Changes in age structure that increase the total dependency ratio and child dependency ratio have a significant negative impact on provincial labour productivity. Some studies find that human capital investment has an inverted U-shaped relationship with population ageing (Cai 2020), while some studies reveal that population ageing restricts the increase in human capital (Wang 2019c). China has already fallen into the demographic dilemma of population ageing with a baby bust, which means a declining fertility rate combined with a rapidly ageing population, resulting in a serious elder care burden (Bai and Lei 2020).

4.3.2 Society and the Environment The effect of population ageing on society and the environment can be reflected in two indicators: carbon emissions and technology innovation. The existing studies analysing these effects are summarized in Table 4.4. There is a significant negative correlation between the ageing of households and household carbon emissions. In fact, the characteristics of household ageing can help reduce the carbon emission levels of households. The families in which the elderly and young people live together are more energy-saving and environmentally friendly. Therefore, government-related social policies should encourage and guide the elderly to live with young people, as the lifestyle and habits of the elderly will affect the family’s consumption behaviour and consumption structure, shifting the family towards becoming more environmentally friendly (Tong and Zhou 2020). The structural transition of the population has a significant non-linear impact on per capita CO2 emissions as the population growth rate in China decelerates. Both demographic ageing and urban–rural migration have a stronger impact on per capita CO2 emissions than other factors. An increase in the number of households due to urbanization and family downsizing has resulted in a positive effect on per capita CO2 emissions, without a threshold turning point. An increased share of the service sector in employment can reduce per capita CO2 emissions only if the sector employs more than 31.56% of the total employed population. Overall, policymakers should pay

Wang et al. (2019a, b) Population structural transition has a significant China Statistics Yearbook; China Emission non-linear impact on per capita CO2 emissions, as Accounts and Datasets the population growth rate decelerates. Both demographic ageing and urban–rural migration have a stronger impact on per capita CO2 emissions than other factors. An increase in the number of households due to urbanization and family downsizing has resulted in a positive effect on per capita CO2 emissions, without a threshold turning point. An increased share of the service sector in employment can reduce per capita CO2 emissions only if the sector employs more than 31.56% of the total employed population. Overall, policymakers should pay attention to the prominence of the demographic structural transition for effective climate policy (continued)

China Household Finance Survey (CHFS) Data in 2013

Carbon emission

Data

Carbon emission

Influence mechanism

Studies

Tong and Zhou (2020) There is a significant negative correlation between the ageing characteristics of households and household carbon emissions. In fact, the characteristics of household ageing can help reduce the carbon emission levels of households. The main families where the elderly and young people live together are more energy-saving and environmentally friendly

Variables

Table 4.4 Effect of population ageing on society and the environment

78 4 Population Structure Challenge: Serious Population Ageing

Studies

Jin and Zhao (2019)

Data

The demographic structure change has a significant China Statistics Yearbook; China Population and impact on the promotion of technological innovation. Employment Statistics Yearbook; China Science and The increase in the proportion of the labour force Technology Statistics Yearbook has promoted the level of technological innovation. The increase in the proportion of children and the proportion of the elderly has significantly inhibited the level of technological innovation

China Statistics Yearbook; China Population and Employment Statistics Yearbook

In the R&D process, population ageing has a direct China Statistics Yearbook; China Population and negative impact on technological innovation, but by Employment Statistics Yearbook; China Labour improving the education level of human capital, it Statistics Yearbook has a positive impact on technological innovation. In the process of industrialization, the direct impact of population ageing on technological innovation is not significant, but it has a positive impact on technological innovation by improving experience level of human capital and changing factor endowment structure

Influence mechanism

Zhang and Yin (2019) The development speed of population ageing has a linear inhibitory effect on regional technological innovation

Technology Innovation He and Huang (2020)

Variables

Table 4.4 (continued)

4.3 Consequences of Serious Population Ageing 79

80

4 Population Structure Challenge: Serious Population Ageing

attention to the prominence of the demographic structural transition for an effective climate policy (Wang et al. 2019a). The effect of population ageing on technology innovation is not consistent. In the R&D process, population ageing has a direct negative impact on technology innovation, but by improving the education level of human capital, it has a positive impact on technology innovation. In the process of industrialization, the direct impact of population ageing on technology innovation is not significant, but it has a positive impact on technology innovation by improving the experience level of human capital and changing the factor endowment structure (He and Huang 2020). The speed with which the population ages has a linear inhibitory effect on regional technological innovation (Zhang and Yin 2019). Changes in the demographic structure have a significant impact on the promotion of technological innovation. The increase in the proportion of the labour force increases the level of technological innovation, while the increase in the proportion of children and the proportion of the elderly significantly inhibits the level of technological innovation (Jin and Zhao 2019).

4.3.3 Family Finances and Consumption The effect of population ageing on family finances and consumption can be reflected in these indicators: income inequality, household consumption, balance of medical insurance funds, health expenditures, household commercial insurance consumption, pension expenditure burden, public education expenditures, housing price, housing consumption, house usage rate, government debt risk, and resident debt risk. The existing studies analysing these effects are summarized in Table 4.5. Population ageing increases the urban household consumption level, in line with the life cycle hypothesis (Wang and Zhou 2019). At annual income growth rates of 3%, 4%, and 5%, China’s total household consumption in 2049 will be 71.0, 97.8, and 133.8 trillion CNY, respectively, which are 3.1–5.8 times the total household consumption in 2015. Even excluding the income growth effect, the future consumption increase driven by rapid urbanization is much larger than the consumption decrease through demographic change (Wang and Yu 2020). With an ageing population, the working population decreases, which induces increases in income inequality. Older families in Western China suffer the most severe income inequality, and gaps between groups in different regions are progressively increasing as the population ages (Zhang et al. 2020). Population ageing increases health expenditures (Yang et al. 2019) and is an important factor influencing the continuous increase in total health expenditures (Yu and Jia 2020). The dependency burden plays a key role in the commercial insurance market in China. It is found that the child dependency ratio has a positive effect on households’ demand for commercial insurance, whereas the elderly dependency ratio has a negative effect. The child dependency ratio for eastern and high-income households in China has a larger influence on commercial insurance purchases, while the elderly dependency ratio has less impact (Li et al. 2020).

Studies

Zhang et al. (2020)

Wang and Zhou (2019)

Qiu et al. (2020)

Yu and Jia (2020)

Li et al. (2020)

Variables

Income inequality

Household consumption

Balance of medical insurance funds

Health expenditure

Household commercial insurance consumption

The Survey Data in the Micro-Level Model Covers 39,520 Families and Includes 829,920 Equations and Relevant Internal Variables

Data

Jilin Statistics Yearbook; Study Report on Total Health Expenditure in China

China Statistical Yearbook; China Health and Family Planning Statistical Yearbook

(continued)

Dependency burdens play a key role in China Household Finance Survey commercial insurance market in China. It is (CHFS) Data in 2015 found that the child dependency ratio has a positive effect on the households’ demand for commercial insurance, whereas the old-age dependency ratio has a negative effect. Furthermore, child dependency ratio of the eastern and high-income households in China has a larger influence on commercial insurance purchase, while the old-age dependency ratio has less impact

Population ageing is an important factor influencing the continuous increase of total health expenditure

The proportion of revenue in the medical insurance fund is progressively declining, and population ageing may threaten the balance between revenue and expenditure

Population ageing increases urban household China Statistics Yearbook consumption level, in line with the life cycle hypothesis

With an ageing population, the working population decreases, and income inequality increases. Older families in Western China would suffer from the most severe income inequality

Influence mechanism

Table 4.5 Effect of population ageing on family finances and consumption

4.3 Consequences of Serious Population Ageing 81

Wang (2019a, b, c)

In the next 30 years, with the large number National Bureau of Statistics of China of people born in the baby booms approaching or reaching old age, the number of pension recipients will rise sharply. At the same time, the number of contributors will begin to decrease, which will in turn expand the potential funding gap over the long term and may result in huge implicit pension debt

Bai and Lei (2020)

Public expenditure

Population ageing increases the pension expenditure

Wang (2019a, b, c)

(continued)

While the higher of the public pension to China Statistics Yearbook; China proportion of gross domestic product, the Population and Employment Statistics lower of public spending on education to Yearbook gross domestic product, per capita growth of human capital is a downward trend

China Statistics Yearbook; China Population and Employment Statistics Yearbook

Population ageing and social security Provincial Statistics Yearbook; China expenditure have significant negative impacts Labour Statistics Yearbook; China on China’s economic growth rate, through Nationalities Statistics Yearbook restraining the growth rate of technological level and inhibiting the growth rate of deposit

Zhang and Qiu (2019)

Data

Population ageing increases the pension N/A expenditure burden, which is also affected by the life expectancy. Delaying retirement can reduce the pension expenditure burden

Pension expenditure burden

Influence mechanism

Studies

Cao et al. (2019)

Variables

Table 4.5 (continued)

82 4 Population Structure Challenge: Serious Population Ageing

Housing consumption

Housing price

Public education expenditure

Variables

Table 4.5 (continued)

Zhou et al. (2019a, b, c)

Population ageing has a significant driving effect on housing consumption in China

The impact of the proportion of the working-age population on housing price growth increases as the population growth rates rise

Choi et al. (2019)

(continued)

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Real Estate Statistics Yearbook

China Statistics Yearbook

Population ageing has a significant negative China Statistics Yearbook, Wind impact on housing prices in the short term, Database and thus increases the local government debt risk

Liu (2019a, b, c)

At annual income growth rates of 3, 4, and Household Level Survey Data 5%, China’s total household consumption in 2049 will be 3.1–5.8 times of the total household consumption in 2015. Excluding income growth effect, future consumption increased by rapid urbanization is much larger than the consumption depressed by the demographic change

Wang and Yu (2020)

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Financial Statistics Yearbook; China Nationality Statistics Yearbook; China Labour Statistics Yearbook

Data

An increase in longevity results in a decrease N/A of public education-to-GDP ratio

The impact of population ageing on the levels and relative proportions of public welfare expenditures depends on several variables. These estimations depend on what proportion of the elderly population benefits from the welfare expenditures, and on the level of income tax burden placed on young people

Gong et al. (2019)

Wang (2019a, b, c)

Influence mechanism

Studies

4.3 Consequences of Serious Population Ageing 83

The social security expenditure of the Chinese government grows faster than typical developed countries; the growth rate should be limited to 10%. Opening population policy would effectively restrain population ageing, but would be inefficient to slow down the government debt risk

Zhu and Yan (2019)

Resident debt risk

Population ageing has a significant negative China Statistics Yearbook, Wind impact on housing prices in the short term, Database and thus increases the local government debt risk

Liu (2019a, b, c)

Government debt risk

Data

In the long term, China’s population ageing N/A reduces the housing price and thus increases resident debt risk. In the short time, population ageing will have a positive impact on the resident debt risk, but it is not as significant as in the long term

China Statistics Yearbook

At the national level, population ageing and China statistics yearbook house prices have a positive impact on housing vacancy rate. At the regional level, population ageing in the central region has the greatest impact on house vacancy rate, and the impact of house prices on house vacancy rate is positive in all regions. The mediation effect indicates that house prices are indeed the middle bridge between population ageing and the house vacancy rate

House usage rate

Influence mechanism

Studies

Zhou et al. (2020)

Variables

Table 4.5 (continued)

84 4 Population Structure Challenge: Serious Population Ageing

4.3 Consequences of Serious Population Ageing

85

Population ageing increases the pension expenditure burden (Zhang and Qiu 2019), which is also affected by life expectancy. Delaying retirement can reduce the pension expenditure burden (Cao et al. 2019). A higher proportion of public pension to gross domestic product means a lower proportion of public spending on education to gross domestic product and therefore lower per capita growth in human capital (Wang 2019c). The impact of population ageing on the levels and relative proportions of public welfare expenditures depends on several variables. These estimations depend on the proportion of the elderly population benefitting from welfare expenditures and on the level of the income tax burden placed on young people (Gong et al. 2019). An increase in longevity results in a decrease in the public educationto-GDP ratio (Wang 2019b). The proportion of revenue in medical insurance funds is progressively declining, and population ageing may threaten the balance between revenue and expenditure (Qiu et al. 2020). Population ageing has a significant negative impact on housing prices in the short term and thus increases local government debt risk (Liu 2019c). The proportion of the working-age population has an increasing impact on housing price growth as the population growth rates rise (Choi et al. 2019). Population ageing has a significant driving effect on housing consumption in China (Zhou et al. 2019a). Population ageing and housing prices have a positive impact on the house vacancy rate. With the increase in population ageing, the house usage rate decreases (Zhou et al. 2020). At the regional level, population ageing in the central region has the greatest impact on the house vacancy rate, and the impact of house prices on the house vacancy rate is positive in all regions. The mediation effect indicates that house prices are indeed the bridge between population ageing and the house vacancy rate. An open population policy would effectively restrain population ageing but would be inefficient and slow the government debt risk (Zhu and Yan 2019). In the long term, China’s population ageing reduces housing prices and thus increases resident debt risk. In the short term, population ageing will have a positive impact on resident debt risk, but the short-term impact is not as significant as the long-term impact (Li and Gao 2019).

4.3.4 Exported Products The effect of population ageing on exported products can be reflected in these indicators: manufacturing exports, product export sophistication, export product quality, export product competitive advantage, and the optimization of the service trade export structure. The existing studies analysing these effects are summarized in Table 4.6. The relationship between the ageing of the population and the sophistication of agricultural product exports is not simply linear, and the influence of educational level should be considered in this kind of relationship. With an increase in the educational level, the adverse effects of population ageing on the sophistication of agricultural product exports have gradually weakened, even ultimately becoming a facilitating relationship (Li et al. 2019a). Population ageing significantly reduces the quality of

Studies

Zhang and Wu (2019)

Li et al. (2019a, b)

Variables

Manufacturing export

Product export sophistication

Table 4.6 Effect of population ageing on exported products

(continued)

China Population and Employment Statistics Yearbook; China Industrial Economy Statistics Yearbook

Data

The relationship between the ageing of World Banking Database population and the export sophistication of agricultural products is not just simply linear, and this kind of relationship should consider the influence of educational level. With the increase of educational level, the adverse effects of population ageing on the export sophistication of agricultural products has been gradually weakened, and even facilitated in the end

Population ageing significantly promotes the export of industries with age-appreciating skills, and erodes the export of industries with age-depreciating skills and physical abilities. Industries with low foreign participation are more adversely affected by population ageing. Compared with the eastern and central regions, the export of the manufacturing industry in the western region is more inhibited by population ageing. The manufacturing export is affected by the ageing of rural population. The interprovincial population movement reduces the impact of population ageing on the manufacturing export

Influence mechanism

86 4 Population Structure Challenge: Serious Population Ageing

Studies

Zhang and Yuan (2019)

Ao et al. (2019)

Wang and Liu (2020)

Variables

Export product competitive advantage

Export product quality

Optimization of Service Trade Export Structure

Table 4.6 (continued) Data

The ageing population will force the optimization of the export structure of service trade

United Nations Conference on Trade and Development (UNCTAD) database

Population ageing significantly reduces the China Industrial Enterprises Database, quality of export products, and the effects China Customs Trade Database vary among different regions, different industries, and different stock structures

Population ageing and the export Statistical Data from 81 Countries competitive advantage of technology-intensive products present a typical inverted U-shaped relationship. When the degree of population ageing is lower than the critical value, population ageing will enhance the export competitive advantage of technology-intensive products, whereas when the degree of population ageing is higher than the critical value, population ageing will inhibit the export competitive advantage of technology-intensive products

Influence mechanism

4.3 Consequences of Serious Population Ageing 87

88

4 Population Structure Challenge: Serious Population Ageing

exported products, and the effects vary between different regions, different industries, and different stock structures (Ao et al. 2019). Population ageing and the competitive advantage of technology-intensive product exports present a typical inverted U-shaped relationship. When the degree of population ageing is lower than the critical value, population ageing will enhance the competitive advantage of technologyintensive product exports, whereas when it is higher than the critical value, population ageing will inhibit this competitive advantage (Zhang and Yuan 2019). Population ageing significantly promotes exports among industries that intensively use experience-based skills and diminishes exports by industries that rely on skills affected by age, including physical abilities. An expanded analysis shows that industries with low foreign participation are more adversely affected by population ageing. Compared with the eastern and central regions, manufacturing industry exports in the western region are more significantly inhibited by population ageing. Manufacturing industry exports are mainly affected by the ageing of the rural population. Moreover, interprovincial population movement reduces the impact of population ageing on manufacturing industry exports (Zhang and Wu 2019). Population ageing will drive optimization of the service trade export structure (Wang and Liu 2020).

4.3.5 Macroeconomy The effect of population ageing on the macroeconomy can be reflected in these indicators: inclusive finance, capital gains, economic development, high-quality economic development, tax revenue, saving rate, inflation rate, economic output, international capital flow, and effectiveness of the monetary policy. The existing studies analysing these effects are summarized in Table 4.7. The impact of population ageing on the inclusive finance index has a threshold effect based on the level of population ageing and the degree of informatization. After the level of population ageing exceeds the threshold, its negative impact on the development of inclusive finance will be strengthened (Subject Group of the PBC Jinan Brance 2020). When informatization exceeds the threshold, the negative effect of population ageing on the development of inclusive finance will also increase (Subject Group of the PBC Jinan Brance 2020). Under the current family planning policy, China’s capital gains will enter a downward spiral before 2050. There is a risk of capital outflow, and the capital engine of the Chinese economy may lose steam. Capital gains may fall, which could weaken the role of the monetary policy in regulating macroeconomic conditions. An accumulation-based pension system reform will not serve all purposes, but personal pensions face the risk of failing to appreciate or even to maintain their value (Yang et al. 2020). Although a few studies find that population ageing has a significant positive impact on economic development (Li and Qin 2020), most studies find a negative effect (Bai and Lei 2020; Liu 2020; Lv and Lai 2019). Economic development can be reflected

4.3 Consequences of Serious Population Ageing

89

Table 4.7 Effect of population ageing on the macroeconomy Variables

Studies

Influence mechanism

Data

Inclusive finance

Subject Group of the PBC Jinan Branch (2020)

The impact of population ageing on inclusive finance index has a threshold effect based on the level of population ageing and the degree of informatization. After the level of population ageing exceeds the threshold, its negative impact on the inclusive finance will be strengthened

China Statistics Yearbook

Capital gains

Yang et al. (2020)

Under the current N/A family planning policy, China’s capital gains will enter a downward spiral before 2050. There is a risk of capital outflow. Capital gains may fall, and the role of the monetary policy in regulating the macroeconomic condition could be weakened

Economic development

Li and Qin (2020)

Population ageing has a China Statistics significant positive Yearbook impact on economic development, but the impact of population ageing on economic development has significant regional differences

Dai and Ma (2019)

Population ageing has a Shandong Statistics negative effect on the Yearbook per capita GDP increase rate. Specifically, population ageing promotes the primary industry, and restrains the development of the secondary industry and the tertiary industry (continued)

90

4 Population Structure Challenge: Serious Population Ageing

Table 4.7 (continued) Variables

Economic development

Studies

Influence mechanism

Data

Lv and Lai (2019)

Health investment (including government and individuals) has a significantly promoting effect on economic development, while population ageing has a significantly negative impact on China’s economic development

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Industrial Economy Statistics Yearbook; China Finance Statistics Yearbook

Zhang et al. (2020)

China’s economy changes from the high-speed increase to the high-quality development. Population ageing reduced the demographic dividend and increased the pension pressure, restricting the economic development

N/A

Lv et al. (2019)

Population ageing negatively affects the economic development

China Statistics Yearbook; China Population and Employment Statistics Yearbook; China Industrial Economy Statistics Yearbook; China Science and Technology Statistics Yearbook; China Labour Statistics Yearbook

Wang (2019a, b, c)

The greater longevity N/A results in a higher pension-to-GDP ratio. However, an increase in longevity produces an initial increase followed by a decrease in public education-to-GDP ratio. This results in a growth rate that shows an upside-down U pattern (continued)

4.3 Consequences of Serious Population Ageing

91

Table 4.7 (continued) Variables

Studies

Influence mechanism

Data

Liu (2020)

Under the constraining effect of ageing on economic growth, the implementation of a two-child policy alone could promote economic growth, albeit in a limited fashion: it would be more economically advantageous to combine a two-child policy with a policy that promotes human capital growth

N/A

Economic development

Bai and Lei (2020)

There is a strong association between higher level of population ageing and slower economic growth in China

National Bureau of Statistics of China

High-quality economic development

He and Liu (2020)

Population ageing has a China Statistics significant negative Yearbook effect on high-quality economic development, which makes the transformation and upgrade of manufacturing play a positive partial mediating effect through the anti-driving mechanism, alleviating the inhibition

Economic output

Fang and Zhang (2019a, b)

Population ageing will lead to a decline in the inflation rate and the level of potential economic output

N/A

International capital flow

Fang and Zhang (2019a, b)

Population ageing will increase the unemployment rate, trigger international capital flows and asset price fluctuations, and then breed financial risks

N/A

(continued)

92

4 Population Structure Challenge: Serious Population Ageing

Table 4.7 (continued) Variables

Studies

Influence mechanism

Data

Tax revenue

Han and Zhao (2019)

Economic development is the main factor causing the increase of tax revenue, which would be reduced by population ageing

China Population and Employment Statistics Yearbook; China Tax Statistics Yearbook

Inflation rate

Fang and Zhang (2019a, b)

Population ageing will lead to a decline in the inflation rate and the level of potential economic output

N/A

Saving rate

Miao and Zhou (2020)

As Chinese ageing N/A problem becomes more significant, the ratio of the liquid saving assets held by families to the total financial assets will rise, and ratio of the capital saving assets to the total financial assets will decline generally. The financial asset restructuring resulting from the ageing problem in China may cause the decrease of labour forces, consumptions and outputs, and in some cases even cause the decline of innovation investment in enterprises, or may further lead to the change of the model of economic growth represented by a higher investment rate

Effectiveness of the monetary policy

Fang and Zhang (2019a, b)

Population ageing will CEIC and FRED weaken effectiveness of Database the monetary policy

by the rate of increase in per capita GDP. Population ageing has a negative effect on the rate of increase in per capita GDP; specifically, population ageing promotes the primary industry and restrains the development of secondary and tertiary industries (Dai and Ma 2019). For instance, population ageing increases health investments (including government and individuals) (Lv and Lai 2019), but reduces economic

4.3 Consequences of Serious Population Ageing

93

development in various fields, such as educational expenditures and human capital investment (Wang 2019c). The effects of population ageing on economic development can be reflected along three pathways: technology innovation, human capital, and pension pressure. Technological innovation has an obvious “bump” effect on economic growth; the factor endowment structure produces two thresholds, and the higher the threshold is, the more significant the effect of the ageing population will be on economic growth; the more serious the phenomenon of population ageing, the more obvious the “stepwise” diminishing effect will be on economic growth. Second, human capital has a negative effect on economic growth that gradually weakens as the number of years of education increases. Third, population ageing reduces the demographic dividend and increases pension pressure, restricting economic development (Zhang et al. 2019). Population ageing and social security expenditure have significant negative impacts on China’s economic growth rate by restraining the growth rate at the technological level and inhibiting the growth rate of deposits (Zhang and Qiu 2019). Greater longevity results in a higher pension-to-GDP ratio. However, an increase in longevity produces an initial increase followed by a decrease in the public education-to-GDP ratio. This results in a growth rate that shows an upside-down U pattern (Wang 2019b). In the next 30 years, with the large number of people born during the baby boom approaching or reaching old age, the number of pension recipients will rise sharply. At the same time, the number of contributors to the pension system will begin to decrease, which will in turn expand the potential funding gap over the long term and may result in high implicit pension debt (Bai and Lei 2020). Economic development is the main factor causing the increase in tax revenue, and this would be reduced by population ageing (Han and Zhao 2019). China’s economy has changed from high-speed increases to high-quality development. Some studies argue that population ageing may have a positive impact on economic development, but it negatively affects high-quality economic development. The transformation and upgrading of manufacturing play a positive partial mediating effect on the mechanism driving this decline, alleviating it somewhat (He and Liu 2020). Population ageing has a significant effect on the macroeconomic development. As China’s ageing problem becomes more significant, the ratio of liquid assets held by families to total financial assets will rise, and the ratio of capital assets to total financial assets will generally decline. The financial asset restructuring resulting from the ageing problem in China may cause a decrease in the labour force, consumption, and outputs. In some cases, it may even cause a decline in investment in innovative enterprises or further lead to a change in the model of economic growth represented by a higher investment rate (Miao and Zhou 2020). Population ageing will lead to a decline in the inflation rate and in the level of potential economic output, increase the unemployment rate, trigger international capital flows and asset price fluctuations, and ultimately breed financial risk. By using quarterly data from 19 developed countries from 1960 to 2017, Fang and Zhang (2019a, b) adopt a time-varying parameter vector autoregressive model to show that population ageing will weaken the effectiveness of the monetary policy (Fang and Zhang 2019a).

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Based on the above meta-analysis, this chapter draws Fig. 4.1 to depict the effects of population ageing.

4.4 Data Sources for Studying China’s Population Ageing When exploring the effects of population ageing, many studies choose to study the proportion of elderly people, who are commonly defined as those above 60 years (Tian 2020; Wang et al. 2019a; Wang and Yu 2020; Wang 2019c) or above 65 years of age (Ao et al. 2019; Cai 2020; Choi et al. 2019; Dai and Ma 2019; Feng et al. 2019a; Gong et al. 2019; Han and Zhao 2019; Jin and Zhao 2019; Li 2019; Li and Qin 2020; Liu and Zhao 2019; Lv et al. 2019; Lv and Lai 2019; Wang and Liu 2020; Wang and Zhou 2019; Yu and Jia 2020; Zhang and Wu 2019; Zhang and Qiu 2019; Zhang and Yuan 2019; Zhao and Liu 2019). To measure the degree of population ageing, many scholars use the elderly dependency ratio (Fang and Zhang 2019a; He and Liu 2020; Jiang and Huang 2019; Li et al. 2019a, 2020; Li 2019; Li and Gao 2019; Liu 2019c; Wang et al. 2020; Zhang and Wu 2019; Zhang and Yuan 2019; Zhou et al. 2019a) or the total dependency ratio (Fu et al. 2020; Subject Group of the PBC Jinan Brance 2020). A few studies have built new indicators or index frameworks to measure the degree of population ageing (Jiang 2019). However, it is clear that most studies use the proportion of the people older than 65 or the elderly dependency ratio to measure the degree of population ageing. I summarize the data sources for studying China’s population ageing in Table 4.8. Various statistical yearbooks have been widely adopted to study population ageing, specifically the China Statistics Yearbook and the China Population and Employment Statistics Yearbook. In addition to using the statistics yearbooks, some scholars use other statistical datasets. Dome effects of population ageing cannot be directly quantified from the indicators in the statistics yearbooks or existing statistical databases. Thus, continuous surveys are also very important for studying population ageing.

4.5 Suggestions for Responding to Population Ageing China is experiencing a serious population ageing phenomenon and should adopt various measures in response. In addition to the overall population ageing in China, we should also pay attention to the significant provincial differences in population ageing. More analyses of these regional differences are presented in Chap. 9. The adjustment of the family planning policy will not only improve future capital gains but also change the characteristics of capital gains after 2035 (Yang et al. 2020). In addition to adjusting the family planning policy, five other suggestions at the national level are proposed for responding to population ageing.

Employment rate

Saving rate

+

(Feng et al 2019; Fu et al 2020)

(Miao and Zhao 2020)

(Fang and Zhang 2019)

+

+

Urbanization

Labor supply

Human capital investment

(Cai 2020)

Human capital

-

-

-

-

+

+

-

-

-

-

+

-

Technology innovation

Resident debt risk

Tax revence

Balance of medical insurance funds

Health expenditure

Pension expenditure burden

Public expenditure

Public welfare expenditure

Public education expenditure

Economic output

Government debt risk

-

House usage rate

Household consumption

Household commerical insurance consumption

House consumption

House price

Income inequality

High-quality economic development

Economic development

(He and Huang (Jin and Zhao 2019; 2020) Zhang and Yin 2019) -

-

(Wang and Yu 2020)

(Zhou et al 2020)

+

(Wang and Zhou 2019)

-

+

(Zhou et al 2019)

(Li et al 2020)

-

-

-

(Choi et al 2019; Liu 2019; Li and Gao 2019)

(Zhang et al 2020)

(He and Liu 2020)

(Dai and Ma 2019; Lv and Lai, 2019; Lv et al 2019; Bai and Lei 2020; Li and Qin 2020; Zhang et al 2020; Zhang and Qiu 2019)

(Wang 2019)

(Han and Zhao 2019)

(Qiu et al 2020)

(Yu and Jie 2020)

(Cao et al 2019; Wang 2019; Zhang and Qiu 2019; Bai and Lei 2020)

(Wang 2019)

(Gong et al 2019)

(Wang 2019)

Carbon emission

-

(Tong and Zhou 2020)

Population Aging

-

Demographic dividend

(Wang 2019)

(Li et al 2019) (Wang and Liu 2020)

-

(Ao et al 2019)

+

-

(Zhang and Yuan 2019)

(Zhang and Wu 2019)

(Liu and Zhao 2019; Wang et al 2020)

+

(Jiang 2019; Liu 2019) -

Optimization of Service Trade Export Structure

Product export sophistication

Export product quality

Export product competitive advantage

Manufacturing export

International capital flow

Labor productivity

(Fang and Zhang (Jiang and Huang 2019; Li 2019) 2019) -

Fig. 4.1 The effects of population ageing

(Li et al 2020)

-

Burden of elderly care

+

(Bai and Lei 2020)

Education level

U

(Wang et al 2020)

Industry structure upgrade

(Yang et al 2020)

U

+

(Fang and Zhang 2019)

(Fang and Zhang 2019)

(Subject Group of the PBC Jinan Brance 2020)

-

-

-

Capital gains

U

Inclusive finance

-

Inflation rate

(Fang and Zhang 2019)

+

(Liu 2019; Zhu and Yan 2019)

U

Effectiveness of monetary policy

+

(Li and Gao 2019)

+

+ (Wang and Yu 2020)

Hump (Lv et al 2019)

(Liu 2019)

(Li and Gao 2019)

(Lv et al 2019)

-

(Zhou et al 2020)

-

(Zhang and Qiu 2019)

-

-

4.5 Suggestions for Responding to Population Ageing 95

96

4 Population Structure Challenge: Serious Population Ageing

Table 4.8 Data sources adopted to study population ageing Data categories

Data information

Official statistical yearbook China Statistics Yearbook

Studies Feng (2019); Feng et al. (2019a, b); Gong et al. (2019); Jiang (2019); Jiang and Huang (2019); Jin and Zhao (2019); Li (2019); Liu (2019a, b, c); Liu and Zhao (2019); Lv and Lai (2019); Lv et al. (2019); Wang (2019a, b, c); Wang and Zhou (2019); Zhang and Yin (2019); Zhao and Liu (2019); Zhu and Yan (2019); Cai (2020); Fu et al. (2020); He and Huang (2020); He and Liu (2020); Li and Qin (2020); Qiu et al. (2020); Subject Group of the PBC Jinan Branch (2020); Wang et al. (2020)

China Population And Employment Statistics Yearbook

Gong et al. (2019); Han and Zhao (2019); Jiang and Huang (2019); Jin and Zhao (2019); Liu and Zhao (2019); Lv and Lai (2019); Lv et al. (2019); Wang (2019a, b, c); Zhang and Wu (2019); Zhang and Yin (2019); Zhao and Liu (2019); Fu et al. (2020); He and Huang (2020)

China Labour Statistics Yearbook

Gong et al. (2019); Lv et al. (2019); Zhang and Qiu (2019); Fu et al. (2020); He and Huang (2020)

China Industrial Economy Statistics Yearbook

Liu and Zhao (2019); Lv and Lai (2019); Lv et al. (2019); Zhang and Wu (2019); Zhao and Liu (2019)

China Science And Technology Statistics Yearbook

Lv et al. (2019); Jin and Zhao (2019); Zhao and Liu (2019)

China Tertiary Industry Statistics Yearbook

Liu and Zhao (2019)

China Finance Statistics Yearbook

Feng et al. (2019a, b); Gong et al. (2019); Lv and Lai (2019)

Official statistical yearbook China Health and Family Planning Statistical Yearbook

Qiu et al. (2020)

China Tax Statistics Yearbook

Han and Zhao (2019)

China Nationalities Statistics Yearbook

Gong et al. (2019); Zhang and Qiu (2019) (continued)

4.5 Suggestions for Responding to Population Ageing

97

Table 4.8 (continued) Data categories

Statistical Dataset

Survey Data

Data information

Studies

Provincial Statistics Yearbooks

Dai and Ma (2019); Li (2019); Yu and Jia (2020); Zhang and Qiu (2019);

National Bureau of Statistics of China

Bai and Lei (2020)

Tabulation on the 2000 and the 2010 Population Census of the People’s Republic of China

Feng (2019); Liu (2019a, b, c)

China Industrial Enterprises Database

Ao et al. (2019)

China Customs Trade Database

Ao et al. (2019)

China Emission Accounts and Datasets

Wang (2019a, b, c)

United Nations Conference on Trade and Development (UNCTAD) database

Wang and Liu (2020)

Wind Database

Liu (2019a, b, c)

World Banking Database

Li et al. (2019a, b)

CEIC and FRED Database

Fang and Zhang (2019a, b)

Statistical Data from 81 Countries

Zhang and Yuan (2019)

Study Report on Total Health Expenditure in China

Yu and Jia (2020)

China Household Finance Survey (CHFS) Data in 2013

Tong and Zhu (2020)

The Survey Data in the Zhang et al. (2020) Micro-Level Model Covers 39,520 Families and Includes 829,920 Equations and Relevant Internal Variables Household Level Survey Data

Wang and Yu (2020)

China Household Finance Survey (CHFS) in 2015

Li et al. (2020)

4.5.1 Improving the Retirement Security System As the proportion of elderly people continues to increase, the elderly dependency ratio and the total dependency ratio decrease. To slow the pressure on social and economic development, governments can extend the retirement age and invite older people to return to employment. In practice, older professionals can provide higher work performance in specific job positions. The re-employment of older people can give full play to their potential, remedy the economic efficiency caused by the

98

4 Population Structure Challenge: Serious Population Ageing

reduced demographic dividend, and promote the development and utilization of other resources. Under the current retirement security system, China’s pension insurance expenditures will face serious pressure (Cao et al. 2019), and the accumulated balance of the pension will be in deficit by 2050. If retirement is delayed, the accumulated pension balance will increase sharply, and China’s pension insurance will become sustainable (Yang 2020). To motivate the second round of demographic dividends, China should highly prioritize an adjustment to the retirement policy now (Ao et al. 2019; Zhang et al. 2019). When and how to adjust the official retirement age are currently major policy concerns (Feng et al. 2019b). In addition to guaranteeing the sustainable development of official pension insurance, China should develop an enterprise annuity system, which is considered to be the second pillar for guaranteeing quality of life for the large elderly population in China (Wang 2020).

4.5.2 Building Elderly-Friendly Communities When facing serious population ageing, all countries face major financial challenges in paying pension insurance and thus must continuously explore how to better meet the requirements of elderly people. Germany has developed a mode in which people of different generations live together, and the USA had developed the “village model” and time banking (Jin 2019). Due to an increasing elderly population and small-scale families, China is paying more attention to institutional elderly care. However, the belief that “sending parents to nursing homes is unfilial” is still influential. The traditional opinion “bring up sons to support parents in their old age” still plays a dominant role, but its influence is decreasing (Lu et al. 2019). However, an increasing number of elderly people do not want to live with their children, and some do not have any children (Ning and Shi 2019). Institutional elderly care has changed the understanding of filial piety among elderly residents and their adult children (Zhang 2019). Rapid population ageing generates high demand among elderly people for institutional care services. Although there is still a short supply of nursing homes, a structural imbalance in the utilization of nursing homes has also recently appeared in many cities in China. While elderly people must wait for a long time for a bed in a nursing home in many cities, suburban nursing homes are not being fully utilized (Song et al. 2020). It is essential that China propose the establishment of novel elderly residential and social communities to provide quality care for the elderly and children, with both cost control and a community orientation as the goal (Hou 2019). With time and cultural changes, China has been developing elderly-friendly communities. The mutual-aid social care system for the elderly has developed from the traditional informal mutualaid network, complementing home-based care for elderly people. The key point is to

4.5 Suggestions for Responding to Population Ageing

99

provide mutual-aid services at a low cost by attracting social investment and human resources (Liu 2019a). With the development of various advanced technologies, such as artificial intelligence technology, 5G applications, and the Internet of Things, we can have high expectations for future elderly-friendly communities (Huang et al. 2019; Liu et al. 2020; Wang et al. 2020). The possibilities for these communities need to be further explored in the context of digital city construction, practice, evaluation, and development trends (Zhao and Fu 2020). Houses are a particular issue for the elderly, particularly in China, where house prices are very high. Compared with young families, older people have unique requirements for their homes. These requirements include a small house size, specific healthcare equipment, and a convenient location to hospitals. However, houses in China have not considered the requirements of older people and mainly focus on the needs of the young (Zhou et al. 2020). With the increase in the proportion of older people, the housing market should pay more attention to the house requirements of old people.

4.5.3 Developing the Elderly Service Industry Compared with the speed of population ageing, academic research on the pension industry in China started late and has delivered scattered results and insufficient micro- and quantitative analyses (Yi et al. 2019). In the future, more studies need to be conducted that focus on developing the elderly service industry. China is inevitably facing pressure from serious population ageing and thus must prioritize developing the elderly service industry. Governments should invest more capital in the elderly service industry and encourage private individuals and institutions to do the same. The elderly service industry should at least provide the infrastructure necessary for the elderly. For example, governments can set a fitness equilibrium and establish universities for elderly people. In addition to providing entertainment facilities, governments can energetically cultivate and develop the product market for elderly people. Because there are significant differences among the 31 provinces in terms of population ageing, we should also appreciate that each province has different requirements for developing elderly service industries (Yin and Zhu 2019).

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4 Population Structure Challenge: Serious Population Ageing

4.5.4 Promoting the Healthy Physical and Mental Development of the Elderly It is necessary to guarantee the physical and mental health of the elderly as a foundation for improving their quality of life and happiness (Chen et al. 2020; Feng et al. 2020; Lu et al. 2020). Social support plays an important role in the physical and mental health of the elderly and constitutes an essential resource for healthy ageing. With the rapid economic and social development over the last 40 years in China, the acceleration of urbanization, and the disintegration of traditional extended families, older Chinese adults may be receiving less social support, leading to deterioration in their quality of life (Zhao et al.). To improve the healthy physical and mental development of the elderly, we should continuously offer them social support. Other important measures for promoting the healthy physical and mental development of the elderly include addressing issues related to fitness instructors and nursing services: expanding their numbers, speeding up the healthy development their services, and improving their service quality. Speeding up the “integration of sports and nursing care” can help China to meet the demand for physical health and nursing services for the elderly and effectively reduce the major burden of medical expenses (Hu and Li 2020; Wang et al. 2019b; Zhao et al. 2020).

4.5.5 Increasing Financial Supports Various financial institutions can provide inclusive, diverse, and extensive financial support for the development of the elderly service industry (Subject Group of the PBC Jinan Brance 2020). Under the current sharing economy, the government should fully respect the dominant role of marketing and increase the effective supply from the financial market; financial institutions need to know which market positions are suitable so that they can play an important role in the elderly service industry (Zhang and Lu 2019). Specifically, the insurance companies provide insurance protection, while new types of financial companies, such as those offering financing guarantees, microloans, and internet finance, need to complement service provision.

4.6 Conclusions and Discussions A comprehensive understanding of the effects of population ageing is very important for the scientific responding for population ageing and the adjustments of the population policies in China. This chapter first briefly introduces China’s population ageing by analysing the historical changes, current circumstance, and future prediction.

4.6 Conclusions and Discussions

101

In this study, I obtain 52 relevant journal articles consisting of 43 Chinese journal articles and 9 English journal articles published from January 1, 2019, to May 31, 2020. A total of 31 determinants affecting the total fertility rates are identified, and these 36 indicators are identified as the influence of population ageing from five groups: labour market, social environment, family finances and consumptions, export products, and macroeconomy. Then, through analysing these relevant journal articles, the data sources for studying China’s population ageing are analysed. The main contribution of this study is to systematically summarize the effects of population ageing in China, and thus to better respond to population ageing. Moreover, this study help us better evaluate China’s current population ageing and realize how to choose suitable data sources for studying population ageing. Furthermore, this study informs academicians and practitioners of this area regarding the current research situations, implications, and limitations of existing studies, as well as the prospective research directions in this area.

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Chapter 5

Population Development Under Different Family Planning Policies

Abstract China’s family planning policy has frequently been adjusted over the past few years by proposed innovative reforms in response to various population crises. In 2016, a two-child family planning policy was implemented with the aims of increasing the total fertility rate and relieving population ageing. Thus, there is an urgent need to test the effects of the new two-child policy. A system dynamics model is established on the basis of Song Jian’s population development equation, and simulation experiments are conducted to test the new family planning policy. The results show that the total population will peak at 1.448 billion in 2022 and will decrease to 0.961 billion by 2050 under the regulation of the new family planning policy. Essentially, however, population structure problems can only be moderately optimized; they cannot be fully resolved. According to the comparative analyses of three different possible family planning policies, the two-child policy is found to be reasonable for contemporary China. This chapter also predicts the population development in Jiangxi Province under the three different possible family planning policies and finds significantly different population challenges in Jiangxi Province and China as a whole. Finally, a sensitivity analysis of the willingness of fertile women to have a second baby is conducted. Some specific measures are proposed as important suggestions for family planning policy tracking. Keywords Two-child policy · Song jian’s population development equation · System dynamics model · Simulation experiment · Family planning policy

5.1 Introduction An excerpt from Article 53 of China’s new constitution, which went into effect on March 3, 1978, and made the People’s Republic of China one of the handful of countries to have written population issues into the highest law (Orleans 1979), reads as follows: “The state advocates and encourages family planning”. The onechild policy that was introduced in the 1970s made an important contribution to controlling China’s total population (Ding and Hesketh 2006; Gietel-Basten et al. 2019; Guo 2016; Jiang et al. 2013). However, this policy also led to many problems, such as a growing ageing population due to the skewed demographic structure (Cai © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_5

111

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2012; Cheema 2013; Ince Yenilmez 2015; Lee and Mason 2010; Mai et al. 2013; Peng 2011; Zhang and Chen 2020). Thus, there was an urgent need to adjust the old family planning policy based on China’s current population situation (Zhou and Lin 2017). Reforms were enacted in the past few years that led to the two-child policy in January 2016. The proposition that the new family planning policy can benefit China’s population development must urgently be verified. Many scholars have studied population development in China along with family planning policy adjustment. Their studies have covered a wide range of topics, including the fertility decline (Guilmoto 2016), the mortality rate (Zhao et al. 2016), population migration (Tan et al. 2017), and demographic characteristics (Chen and Yang 2016). They have kept a watchful eye on the question of population development; however, these studies have failed to track the new family planning policy, which requires immediate investigation. Thus, this chapter establishes a system dynamics model and conducts simulation experiments to verify the newly adopted family planning policy. Population prediction research can be traced back to 1696, when Professor Gregory King built a mathematical model that could be calculated manually (King 1802). Then, Malthus proposed the Malthus growth model based on the assumption that the population growth rate is stable (Thomas 1815; Thomas and Godwin 1978). In 1938, Verhulst proposed a logistic block population growth model based on the Malthus model that could better describe variation in population growth (Verhulst 1938, 1945). In 1945, Leslie derived the Leslie matrix model, a well-known comprehensive prediction model containing multiple factors (Bacaër 2011; Leslie 1945). The Leslie discrete dynamic model is still widely adopted to predict population development (Cao 2020; Cheng et al. 2019; Li and Li 2012; Li et al. 2018). Subsequently, Andrew and Meen developed an innovative optimal population model to accurately describe the dynamic changes that occur as people age (Andrew and Meen 2006). Abia and Angufo continued to perfect the method by improving prediction accuracy, and they proposed a non-linear population dynamic system (Ren et al. 2008). Three types of stochastic models are commonly used in population biology: discrete-time Markov chain (DTMC) models, continuous-time Markov chain (CTMC) models, and stochastic differential equation (SDE) models (Allen and Allen 2003). Currently, the dominant population projection methodology in demography is the cohort component method (Chen and Zhang 2015; Smith et al. 2013). The other commonly used methodologies used to predict population development include the age shift equation (Zhu 2012) and others. These various models in predicting population development can be summarized as two characteristics: discrete and dynamic. It is very important to choose a suitable model to predict China’s population development under different family planning policies. Although the above methods can well predict population development, they are limited in their abilities to conveniently predict the population in various situations. To compare the population development under different family planning policies, this chapter establishes a system dynamics model and employs Song Jian’s population development equation (Song et al. 1980). In the 1980s, Song Jian, Yu Jing-yuan, and Li Guang-yuan developed an acclaimed

5.1 Introduction

113

population development equation to predict the evolution of China’s population, and the model is called as Song Jian’s population development equation (Song et al. 1980). The model contains multidimensional indicators, including the birth rate, mortality rate, migration rate, and total fertility rate. By means of a relatively reasonable set of prediction variables, the model’s high precision promotes its widespread use. In China, whose population exceeds 1.4 billion and accounts for one-fifth of the total population on earth, there are significant provincial differences in multiple fields (Biggeri et al. 2017). For instance, the distribution of China’s water resources is geographically uneven (Wu et al. 2016), with 81% of such resources being intensively distributed in the Yangtze River basin and southern regions (Chen and Xia 1999). Yang and Mukhopadhaya measured multidimensional poverty in China and identified that the eastern provinces are generally poorer than the central provinces (Yang and Mukhopadhaya 2017). Different levels of development in the provinces are fundamental for the disparities under the population situation. Similar to these indicators, China’s population also has significant regional differences among provinces (Yi et al. 2011). For instance, Jiangxi Province has a slight ageing phenomenon and greater pressure on the total population, while the opposite holds true for Beijing Province. Accurate research on the spatial pattern of the population is critical for policy-making and spatial planning in all related fields, including urbanization, land use development, ecological conservation, and environmental protection (Deng et al. 2015). Therefore, to compare population development in different provinces or regions, this chapter also predicts the population development in Jiangxi Province and compares it with China’s overall population development. The remainder of the chapter is structured as follows. In Sect. 5.2, the system dynamics model for population simulation is established based on Song Jian’s population development equation. Then, in Sect. 5.3, simulation experiments are presented to predict the population development up to the year 2050 after analysis of the model’s parameters. Subsequently, in Sect. 5.4, additional studies are introduced to verify the new family planning policy by comparing the differences in population development under three different possible family planning policies. To compare the differences in population development among different provinces or regions, Sect. 5.5 predicts and analyses the population development in Jiangxi Province under the three different possible family planning policies. A sensitivity analysis of the willingness to have a second baby is provided in Sect. 5.6. Finally, conclusions and suggestions are presented in Sect. 5.7.

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5.2 System Dynamics Model for Population Simulation 5.2.1 Song Jian’s Population Development Equation In 1980, the famous demographer Song Jian proposed the following population development equation in his paper titled “Prediction on the process of population development” (Song et al. 1980). r2 ⎧ ψ(t) = β(t) K i (t)h i (t)L i (t) ⎪ ⎪ i=r1 ⎪ ⎪ ⎪ ⎪ ⎪ L 0 (t) = [1 − u 00 (t)]ψ(t) ⎪ ⎪ ⎪ ⎪ L (t + 1) = [1 − u (t)]L (t) + f (t) ⎪ 1 0 0 0 ⎪ ⎪ ⎨ L 2 (t + 1) = [1 − u 1 (t)]L 1 (t) + f 1 (t) ⎪ ⎪ ······ ⎪ ⎪   ⎪ ⎪ ⎪ L m (t + 1) = 1 − u m−1 (t) L m−1 (t) + f m−1 (t) ⎪ ⎪ ⎪ ⎪ ⎪ L i (0) = L 0i ⎪ ⎪ ⎩ i = 0, 1, 2, . . . , m

(5.1)

where m denotes the maximum age in the system, r1 and r2 denote the beginning and ending age of having babies, respectively, β(t) denotes the total fertility rate in year t, ψ(t) denotes the total number of newborn babies in year t, u 00 (t) denotes the infant mortality in year t, u i (t) denotes the average death rate of age i in year t, L i (t) denotes the population number of age i in year t, f i (t) denotes the population migration of age i in year t, K i (t) denotes the proportion of women of age i in year fertility factor of age i in year t, which should satisfy the t, and h i (t) denotes the r2 h i (t) = 1. normalization condition i=r 1

5.2.2 Establishing a System Dynamics Model According to Song Jian’s population development equation, L i (t) indicates that the population number of age i at year t can be taken as a stock, and the survival rate of age i − 1 in year t is the corresponding inflow variable. In addition, the numbers of the immigrant and emigrant populations are approximately equal every year; hence, f i (t) = 0 is roughly affirmed. Thus, the system dynamics equation can be obtained as follows:

5.2 System Dynamics Model for Population Simulation

⎧ r2 ⎪ ψ(t) = β(t) K i (t)h i (t)L i (t) ⎪ ⎪ i=r1 ⎪ ⎪ ⎪ ⎪ L i (t) = L i (t − t) + [Ri (t − t)]t ⎨ . L i (t)|t=t0 = L i (t0 ) ⎪ ⎪   ⎪ ⎪ ⎪ Ri (t) = 1 − u i−1 (t) L i−1 (t) − L i (t) ⎪ ⎪ ⎩ i = 0, 1, 2, . . . , m

115

(5.2)

In particular, ψ(t), which denotes the total number of newborn babies in year t, is a special inflow when the stock is L 0 (t). The system dynamics equation when i = 0 should be listed separately: ⎧ ⎪ ⎨ L 0 (t) = L 0 (t − t) + [R0 (t − t)]t L 0 (t)|t=t0 = L 0 (t0 ) . ⎪ ⎩ R0 (t) = [1 − u 00 (t)]ψ(t) − L 0 (t)

(5.3)

Mortality has a high impact on the population forecast according to the above system dynamics equations. The statistical data show that the mortality rates of children under 5 and elderly people are higher than those under other ages. Meanwhile, u i (t) will decline per yearly increase in the improvement of living standards and healthcare levels. Specifically, the death rates will be reduced to the existing minimum value all over the world over the coming years, at a rate of decline of 1‰ yearly for children under 5 and elderly people and 0.5‰ yearly for infants (Song et al. 1980). Therefore, u i (t) is defined as follows: ⎧ ⎨

u i (t0 )[1 − (t − t0 )ε], ε = 10−3 , i ≤ 4 or i ≥ 60 u i (t) = 4 < i < 60 . u i (t0 ),  ⎩   i =0 u i (t0 ) 1 − (t − t0 )ε , ε = 2 × 10−3 ,

(5.4)

Since many of the parameters used here will change over time, a new variable, G(t), is defined in Eq. (5.5) to represent these changes over time. G(t) = t − t0 .

(5.5)

Therefore, the systematic dynamic flow diagram can be described as follows as in Fig. 5.1. The main factor influencing population development is ψ(t), which indicates the total number of newborn babies. In Eq. (5.2), four variables determine ψ(t): β(t), K i (t), h i (t), and L i (t). Between K i (t) and L i (t), statistical data can be found directly. The other two variables, β(t) and h i (t), are affected by the family planning policy. In terms of the research by Song Jian, h i (t), which denotes the fertility factor of age i in year t, can be accurately expressed by the chi-square distribution as follows (Song et al. 1980):

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5 Population Development Under Different Family Planning Policies

Fig. 5.1 Basic flow diagram

h i (t) =

⎧ ⎨ ⎩

1  (r n(t)

2 2  n(t) 2

− r1 )

n(t) 2 −1

  1 , r ≥ r1 exp − r −r 2

.

(5.6)

r < r1

0

This function will be maximized when r0 = r1 + n − 2, where r0 denotes the age when the number of newborn babies reaches its peak and r1 denotes the starting age of having babies. n is controlled by the family planning policy. Many complex factors affect total fertility rate β(t), including the economic development level, average fertility rate, and urbanization rate. Combining these variables, β(t) can be calculated by the following equation (Wang 2013): β(t) = 6.9756 − 2.5075 F P(t) − 0.5996 In[Y (t)] +5.1343 C(t) + 41.5659 B R(t)

(5.7)

where F P(t), Y (t), C(t), B R(t) denote the family planning policy regulatory factor, per capita GDP, urbanization rate, and fertility rate in year t, respectively. The urbanization rate can be determined from Eq. (5.8) (Song et al. 1980). Based on the experiences of developed countries such as South Korea, the growth speed of per capita GDP stabilizes at approximately 5% after rapid development. Thus, the annual growth speed of per capita GDP in China can also be simply identified as 5%.  C(t) = 0.8 1−

1 1 + e−128.0673+0.063948t

 .

(5.8)

Then, the flow diagram for the population number of newborn babies in Fig. 5.1 can be expanded to Fig. 5.2. The whole system dynamics model is depicted in Vensim software and presented in Fig. 5.3.

5.2.3 Model Test The census data collected in 2010 are put into the model to predict the total population development from 2011 to 2050. With the aim of validating the feasibility of the

5.2 System Dynamics Model for Population Simulation

117 L0 (t)

R0 (t) G(t)

u00 (t) β(t)

BR(t)

C(t)

Y(t)

ψ(t) K(t)

FP(t)

h(t)

L(t)

n

Fig. 5.2 Improved flow diagram for the population number of newborn babies

Fig. 5.3 The system dynamics presented in Vensim

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5 Population Development Under Different Family Planning Policies

system dynamics model, the census data collected in 2000 are put into the model to predict the total population from 2001 to 2010. The population data in the 2000 population census (Population Census Office under the State Council and Department of Republation and Employment Statistics National Bureau of Statistics 2002) are presented in Table 5.1. This section compares the prediction values with the actual values in Table 5.2. The relative errors vary from 0.07 to 0.74%, and the average value is 0.199%. The system dynamics model shows very high prediction precision. Therefore, this model is applied to simulate population development in the following sections.

5.3 Population Simulation in China Under the Two-Child Policy Due to the country’s unreasonable population structure, China’s family planning policy has frequently been adjusted over the past few years by proposing innovative reforms. With the aims of increasing the total fertility rate and reducing population ageing, the two-child policy was implemented in 2016. Therefore, there is an urgent need for the effects of the new family planning policy to be tested. In this section, simulation experiments are conducted based on the system dynamics model to predict population development up to the year 2100.

5.3.1 Parameter Determination As mentioned above, family planning policies influence the total fertility rate β(t) and fertility factors h i (t) and thus determine the number of newborn babies. These two parameters must first be determined based on the implementation of a new family planning policy.

5.3.1.1

Policy Regulatory Factor

Four factors jointly determine the total fertility rate. Among them, three variables, B R(t), C(t), Y (t), have little relationship with family planning policy-making. Family planning policies mainly influence the regulatory policy factors that determine the total fertility rate. In Eq. (5.7), the policy regulatory factor is defined as 1 for the implementation of a strict one-child family planning policy. If a family planning policy is cancelled entirely, the policy regulatory factor is defined as 0. Clearly, the policy regulatory factor under a new family planning policy falls between 0 and 1. Then, I search the

5.3 Population Simulation in China Under the Two-Child Policy

119

Table 5.1 The population data at different ages in the 2000 population census Age

Total population

Women’s proportion (%)

Average death rate (‰)

Age

Total population

Women’s proportion (%)

Average death rate (‰)

0

13,793,799

45.92

26.9

51

13,913,927

47.76

4.87

1

11,495,247

44.91

2.49

52

12,083,027

48.22

5.51

2

14,010,711

45.03

1.60

53

11,737,540

47.98

5.88

3

14,454,335

45.36

1.17

54

10,858,446

48.42

6.68

4

15,224,282

45.76

0.87

55

10,045,173

48.39

7.23

5

16,933,559

45.92

0.71

56

9,708,711

47.89

7.56

6

16,470,140

46.17

0.59

57

8,821,540

48.22

8.63

7

17,914,756

46.47

0.54

58

8,869,780

48.11

9.34

8

18,752,106

46.60%

0.51

59

8,925,171

47.94

10.81

9

20,082,026

46.84

0.45

60

9,141,141

47.83

12.84

10

26,210,044

47.31

0.45

61

7,574,442

48.22

13.03

11

25,137,678

47.84

0.41

62

8,611,865

47.61

14.73

12

24,576,191

48.00

0.40

63

8,128,275

47.96

15.56

13

26,282,644

48.18

0.41

64

8,248,125

48.57

17.86

14

23,190,076

48.15

0.44

65

7,808,592

49.51

20.08

15

20,429,326

48.12

0.50

66

7,220,066

49.51

20.83

16

20,313,426

48.47

0.53

67

7,456,399

49.00

23.77

17

20,065,048

48.79

0.60

68

6,503,311

50.02

27.31

18

23,100,427

48.99

0.72

69

5,792,092

49.78

31.56

19

19,122,938

49.00

0.79

70

6,499,332

50.43

36.39

20

18,393,809

49.36

0.92

71

5,045,743

51.05

37.66

21

18,924,822

49.55

0.91

72

5,359,819

51.33

43.89

22

18,831,591

49.45

0.98

73

4,606,896

51.86

46.37

23

17,931,155

49.14

0.99

74

4,062,359

52.78

50.27

24

20,491,797

49.08

1.05

75

4,057,266

53.50

55.17

25

21,136,635

49.06

1.08

76

3,575,411

54.48

60.35

26

22,874,423

48.96

1.05

77

3,025,726

55.24

65.15

27

23,630,435

48.70

1.11

78

2,838,736

55.81

74.48

28

24,800,391

48.65

1.11

79

2,431,191

56.69

86.03

29

25,160,381

48.60

1.17

80

2,310,212

58.53

99.46

30

28,012,344

48.61

1.27

81

1,740,870

58.91

103.48

31

25,018,386

48.77

1.26

82

1,546,079

60.36

114.20

32

27,718,516

48.55

1.35

83

1,304,628

61.08

122.84

33

21,736,582

48.78

1.33

84

1,087,369

62.31

133.09

34

24,828,470

48.64

1.44

85

898,283

63.73

141.60 (continued)

120

5 Population Development Under Different Family Planning Policies

Table 5.1 (continued) Age

Total population

Women’s proportion (%)

35

24,799,129

48.41

36

24,144,848

48.75

37

27,866,189

38

20,923,112

39 40

Average death rate (‰)

Age

Total population

Women’s proportion (%)

Average death rate (‰)

1.58

86

772,037

64.37

150.97

1.56

87

583,159

65.68

165.52

48.44

1.71

88

441,245

66.68

180.98

48.39

1.79

89

335,974

67.60

196.43

11,414,017

49.14

1.94

90

285,875

69.17

220.74

14,684,726

48.18

2.19

91

181,689

70.58

236.48

41

13,059,787

47.78

2.17

92

140,092

71.12

255.08

42

17,135,981

47.56

2.42

93

100,237

72.26

269.57

43

18,998,424

47.95

2.47

94

75,701

73.70

270.45

44

17,364,027

48.52

2.67

95

58,097

71.40

277.30

45

18,612,172

48.15

3.00

96

42,063

70.18

281.17

46

18,513,434

48.55

3.15

97

31,168

67.70

274.98

47

16,745,695

48.79

3.47

98

23,294

67.38

295.98

48

17,236,621

48.66

3.73

99

15,134

69.96

282.02

49

14,413,123

49.08

4.18

100

17,877

74.07

363.98

50

14,711,260

48.53

4.88

Table 5.2 Model test results 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Predicted value (billion)

12.78

12.81

12.91

12.97

13.08

13.17

13.18

13.27

13.44

13.39

Actual value (billion)

12.76

12.84

12.92

12.99

13.07

13.14

13.21

13.28

13.34

13.40

Relative error (%)

0.15

0.23

0.07

0.15

0.07

0.22

0.22

0.07

0.74

0.07

Average error (%)

0.199

literature and conduct some analyses to quantify the policy regulatory factor under the new family planning policy. The lifetime fertility of fertile women is 1 under the one-child policy for which the policy regulatory factor is 1. Before implementing the family planning policy, when the policy regulatory factor is 0, the lifetime fertility of fertile women is 2.483 (Zhong and Wang 2015). Thus, when the lifetime fertility of fertile women under the new family planning policy is η, the corresponding policy regulatory factor can

5.3 Population Simulation in China Under the Two-Child Policy

121

be calculated through the following equation: FP = 1 −

η−1 . 2.483 − 1

(5.9)

Under the two-child policy, each fertile woman is encouraged to have two children. Thus, the lifetime fertility of fertile women can be theoretically regarded as 2, and the corresponding policy regulatory factor under the new family planning policy is 0.326. However, many couples still do not want to have a second baby despite the implementation of the two-child policy. Fertility intentions must be considered in the comprehensive testing of the new family planning policy. According to the authoritative investigation results, the overall intention to have a second baby in China stabilizes at 55% (Zhong and Wang 2015). Therefore, the lifetime fertility of fertile women is 1.55 after considering this intention, and the corresponding policy regulatory factor is 0.629.

5.3.1.2

Fertility Factors

The fertility factors among different ages h i (t) are influenced by the beginning age of having babies r1 and parameter n. According to the official data, the minimum fertility age is 15, and when the number of newborn babies reaches its peak, the age is 25, and parameter n is 12. The two-child policy has obvious effects on reducing the parameter n and the age when the number of newborn babies reaches its peak r0 (He et al. 2018; Huang 2020; Shi et al. 2019). According to further analysis of the statistical data, the fertility rates when the observed age is between 23 and 25 are all greater than 0.09. However, the rate rapidly decreases to 0.07133 and 0.05730 when the age falls to 22 and 21, respectively. Thus, the new family planning policy changes r0 to 23, and the corresponding parameter n is 10. The fertility factors among different ages h i (t) can be calculated through Eq. (5.6), and the concrete values before and after issuing the new family planning policy are shown in Table 5.3.

5.3.2 Data Sources The required data were taken from the Tabulation on the 2010 Population Census of the People’s Republic of China (Population Census Office under the State Council and Department of Republation and Employment Statistics National Bureau of Statistics 2012) and China Statistical Yearbook 2019 (National Bureau of Statistics of China 2020). Specifically, total population, women’s proportion, and average death rate are directly acquired from the Tabulation on the 2010 Population Census of the People’s

122

5 Population Development Under Different Family Planning Policies

Table 5.3 Fertility factors among different ages before and after issuing the new family planning policy Age

15

16

17

18

19

20

21

22

23

Before

0

0.0001

0.0015

0.0071

0.0180

0.0334

0.0504

0.0661

0.0781

After

0

0.0008

0.0077

0.0235

0.0451

0.0668

0.0840

0.0944

0.0977

Age

24

25

26

27

28

29

30

31

32

Before

0.0854

0.0877

0.0857

0.0803

0.0727

0.0639

0.0547

0.0458

0.0376

After

0.0949

0.0877

0.0779

0.0669

0.0559

0.0456

0.0365

0.0286

0.0221

Age

33

34

35

36

37

38

39

40

41

Before

0.0304

0.0241

0.0189

0.0146

0.0112

0.0085

0.0064

0.0047

0.0035

After

0.0169

0.0127

0.0095

0.0070

0.0051

0.0037

0.0027

0.0019

0.0013

Age

42

43

44

45

46

47

48

49

Before

0.0026

0.0019

0.0013

0.0010

0.0007

0.0005

0.0003

0.0002

After

0.0009

0.0007

0.0005

0.0003

0.0002

0.0002

0.0001

0.0001

Republic of China; per capita GDP is obtained from China Statistical Yearbook 2019. The specific data are shown in Table 5.4.

5.3.3 Theoretical Population Simulation Results After determining the model parameters, the system dynamics model is utilized to verify the new definition of fertility. The population simulation experiment is implemented based on Vensim software. When considering only the theoretical influence, the total population and population structure under the new family planning policy from 2020 to 2050 can be predicted, as shown in Table 5.5. Under the new family planning policy, the theoretical total population will reach a peak of 1.517 billion in 2022 after experiencing a continuous increase over the next few years, followed by entry into a continuous phase of decline until 2050, when the final total population will be 1.147 billion. Based on the simulation results, China will face great population stress in the next few years. Especially from 2018 to 2026, the total population will exceed 1.5 billion, which is regarded as China’s population limit (Zhai 2013a, b). During the research period, the proportion of children (0–14) varied from 15.79 to 31.65%, and three stages were clearly identified: (i) the proportion increases rapidly from 2020 to 2024 due to the encouragement of the new family planning policy; (ii) the proportion drops sharply from 31.37 to 15.79% because of the decreased fertility intentions caused by economic development; and (iii) the proportion resumes the increasing trend with a higher annual growth rate of 3.86% from 2039 to 2050. Meanwhile, the proportion of labour-aged individuals (15–64) drops to 46.57%.

5.3 Population Simulation in China Under the Two-Child Policy

123

Table 5.4 The population data under different ages in the 2010 population census Age

Total population

Women’s proportion (%)

Average death rate (‰)

Age

Total population

Women’s proportion (%)

Average death rate (‰)

0

13,786,434

45.88

3.82

51

12,838,832

48.40

3.75

1

15,657,955

45.24

1.11

52

16,617,709

48.43

3.98

2

15,617,375

45.52

0.63

53

18,351,980

48.65

4.41

3

15,250,805

45.76

0.45

54

16,847,642

49.31

4.98

4

15,220,041

45.82

0.37

55

17,610,528

49.05

5.18

5

14,732,137

45.78

0.33

56

17,738,127

49.37

5.64

6

14,804,470

45.73

0.32

57

16,093,888

49.68

6.09

7

13,429,161

45.70

0.28

58

16,167,933

49.57

6.81

8

13,666,956

45.68

0.28

59

13,701,998

49.82

7.67

9

14,248,825

45.78

0.28

60

13,618,204

49.21

8.54

10

14,454,357

45.82

0.30

61

13,029,125

48.65

9.38

11

13,935,714

46.02

0.29

62

11,276,853

49.28

10.38

12

15,399,559

46.17

0.30

63

10,791,633

49.10

11.12

13

15,225,032

46.40

0.29

64

9,951,467

49.60

13.01

14

15,893,800

46.75

0.30

65

9,073,411

49.70

14.21

15

18,024,484

47.16

0.34

66

8,640,965

49.18

14.74

16

18,790,521

47.87

0.35

67

7,942,141

49.59

17.23

17

20,775,369

48.20

0.39

68

7,740,868

49.56

18.64

18

20,755,274

48.23

0.41

69

7,715,897

49.65

21.91

19

21,543,466

48.57

0.43

70

7,389,412

49.60

25.57

20

28,026,954

49.33

0.47

71

6,265,718

50.27

26.73

21

26,556,649

49.70

0.47

72

6,893,225

49.96

30.94

22

24,474,192

49.82

0.50

73

6,343,869

50.36

33.59

23

25,695,955

49.89

0.54

74

6,080,173

51.25

37.44

24

22,658,768

50.16

0.56

75

5,632,477

52.23

41.51

25

19,933,683

49.98

0.58

76

5,175,500

52.58

42.19

26

19,709,177

49.87

0.57

77

5,082,383

52.38

50.97

27

19,480,836

49.69

0.59

78

4,254,858

53.38

56.20

28

22,322,147

49.50

0.61

79

3,706,915

53.32

62.12

29

19,568,009

49.33

0.68

80

3,737,259

54.07

74.28

30

18,928,369

49.26

0.70

81

2,816,693

55.34

77.91

31

19,866,458

48.95

0.77

82

2,757,918

56.03

85.81

32

19,474,874

49.11

0.81

83

2,237,138

56.88

93.52

33

18,179,478

48.90

0.83

84

1,824,190

58.02

103.63

34

20,689,024

48.88

0.94

85

1,648,160

59.18

111.00 (continued)

124

5 Population Development Under Different Family Planning Policies

Table 5.4 (continued) Age

Total population

Women’s proportion (%)

Average death rate (‰)

Age

Total population

Women’s proportion (%)

Average death rate (‰)

35

21,186,516

48.94

1.03

86

1,344,215

60.52

118.88

36

22,906,980

48.96

1.06

87

1,065,276

61.61

130.07

37

23,990,208

48.80

1.14

88

858,879

62.24

144.18

38

24,730,460

48.80

1.21

89

715,398

63.23

156.87

39

25,211,795

48.69

1.34

90

553,805

64.97

176.52

40

27,397,219

48.93

1.51

91

371,079

65.91

185.24

41

24,956,297

49.02

1.55

92

287,676

67.27

202.11

42

27,032,542

49.01

1.82

93

209,291

68.12

207.35

43

21,355,748

49.16

1.89

94

156,456

68.34

208.91

44

24,012,158

48.97

2.07

95

117,522

69.14

220.01

45

23,962,574

48.87

2.31

96

90,889

68.46

220.99

46

23,355,778

49.19

2.36

97

68,648

67.89

204.84

47

26,972,157

48.82

2.54

98

54,689

66.44

198.13

48

20,075,084

49.07

3.11

99

38,231

67.61

257.65

49

11,228,960

49.88

3.28

100

35,934

75.37

454.35

50

14,097,008

48.89

3.64

Even worse, the proportion of elderly people (65+) will far exceed the population ageing standard of the United Nations, i.e., 7%, with a proportion of 29.34% in 2050. Theoretically, the total population will exceed China’s population limit under the new family planning policy. Meanwhile, the population structure can only be moderately optimized; essentially, it cannot be resolved. Thus, the family planning policy should be further relaxed in the future.

5.3.4 Population Simulation Results When Considering Fertility Intentions Simply regarding the lifetime fertility of fertile women as 2 under the two-child policy is unscientific. To make the population simulation more realistic, fertility intentions should be taken into account. On the basis of our analysis in Sect. 5.3.1, the lifetime fertility of fertile women after considering fertility intentions is 1.55, and the policy regulatory factor is 0.629. Another population simulation experiment is conducted based on Vensim. The total population and population structure when considering fertility intention under the new two-child policy from 2015 to 2050 are shown in Table 5.6.

5.3 Population Simulation in China Under the Two-Child Policy

125

Table 5.5 The theoretical population simulation results under the new family planning policy Year

Total Population (billion)

Proportion of children (0–14) (%)

Proportion of labour-aged population (15–64) (%)

Proportion of elderly people (65+) (%)

Dependency ratio (%)

2020

1.550

29.59

58.31

12.08

71.46

2021

1.562

30.48

57.05

12.46

75.27

2022

1.571

31.24

55.83

12.92

79.10

2023

1.571

31.63

55.03

13.32

81.68

2024

1.562

31.65

54.78

13.56

82.53

2025

1.545

31.37

54.69

13.93

82.82

2026

1.524

30.46

55.37

14.15

80.58

2027

1.498

29.23

55.82

14.93

79.12

2028

1.471

27.81

56.03

16.14

78.46

2029

1.443

26.26

56.56

17.16

76.78

2030

1.413

24.63

57.12

18.24

75.06

2031

1.384

22.97

57.69

19.33

73.33

2032

1.356

21.34

58.39

20.25

71.23

2033

1.329

19.83

58.64

21.52

70.51

2034

1.305

18.48

58.88

22.62

69.81

2035

1.283

17.37

58.77

23.85

70.14

2036

1.264

16.54

58.59

24.86

70.67

2037

1.248

16.01

58.20

25.77

71.79

2038

1.236

15.79

57.64

26.55

73.47

2039

1.226

15.87

56.93

27.18

75.62

2040

1.218

16.23

56.16

27.60

78.05

2041

1.212

16.81

55.26

27.92

80.94

2042

1.208

17.57

54.42

28.00

83.74

2043

1.204

18.46

53.40

28.12

87.25

2044

1.200

19.43

52.30

28.25

91.16

2045

1.196

20.42

51.27

28.30

95.03

2046

1.190

21.38

50.21

28.39

2047

1.183

22.26

49.01

28.71

104.0

2048

1.174

23.02

48.10

28.86

107.8

2049

1.162

23.64

47.29

29.06

111.4

2050

1.147

24.07

46.57

29.34

114.6

99.13

126

5 Population Development Under Different Family Planning Policies

Table 5.6 Population simulation results when considering fertility intentions under the new twochild policy Year

Total Population (billion)

Proportion of children (0–14) (%)

Proportion of labour-aged population (15–64) (%)

Proportion of elderly people (65+) (%)

Dependency ratio (%)

2020

1.443

24.36

62.65

12.98

59.61

2021

1.447

24.94

61.59

13.45

62.34

2022

1.448

25.42

60.55

14.01

65.13

2023

1.445

25.64

59.86

14.49

67.04

2024

1.435

25.58

59.64

14.76

67.65

2025

1.420

25.30

59.52

15.16

67.98

2026

1.401

24.60

60.00

15.39

66.65

2027

1.380

23.64

60.13

16.21

66.29

2028

1.357

22.53

59.95

17.51

66.78

2029

1.332

21.30

60.10

18.58

66.36

2030

1.307

19.99

60.28

19.72

65.88

2031

1.282

18.64

60.47

20.88

65.36

2032

1.256

17.31

60.83

21.85

64.37

2033

1.232

16.04

60.73

23.21

64.64

2034

1.209

14.89

60.68

24.41

64.77

2035

1.187

13.89

60.33

25.77

65.75

2036

1.167

13.09

59.98

26.92

66.72

2037

1.149

12.48

59.50

28.01

68.06

2038

1.132

12.09

58.91

28.98

69.73

2039

1.117

11.91

58.25

29.83

71.65

2040

1.103

11.90

57.59

30.49

73.62

2041

1.089

12.07

56.85

31.07

75.88

2042

1.076

12.36

56.22

31.40

77.86

2043

1.064

12.76

55.41

31.81

80.45

2044

1.052

13.23

54.52

32.23

83.40

2045

1.039

13.74

53.68

32.57

86.26

2046

1.025

14.25

52.78

32.95

89.43

2047

1.011

14.73

51.66

33.59

93.54

2048

0.996

15.16

50.82

34.01

96.76

2049

0.979

15.50

50.00

34.48

2050

0.961

15.75

49.21

35.02

99.98 103.1

5.3 Population Simulation in China Under the Two-Child Policy

127

The comparison between the predicted values and the actual values from 2015 to 2019 verifies the accuracy of the simulation results when considering fertility intentions. When considering fertility intentions, the predicted total population of China from 2015 to 2019 is 1.389, 1.403, 1.415, 1.427, and 1.436 billion. As described in the statistical data, the actual total population of China from 2015 to 2019 is 1.374, 1.383, 1.390, 1.395, and 1.401 billion. The calculated relative errors between the predicted values and the actual values are 1.09, 1.45, 1.80, 2.29, and 2.50%. The average error is 1.826%, meaning that the predicted results when considering fertility intentions are suitable. The results in Table 5.6 show that the total population will reach a peak of 1.448 billion in 2022 after experiencing a continuous but slow increase over the next few years, followed by entry into a sustained phase of decline until 2050 when the final total population will be 0.961 billion. The total population can be consistently controlled because the maximum value of 1.448 billion is below 1.5 billion, which is regarded as the upper limit of China’s population. During the research period, the proportion of children (0–14) varied from 11.90 to 25.64%, and three stages were clearly identified: (i) the proportion increases rapidly from 2020 to 2023 due to the encouragement of the new family planning policy; (ii) the proportion drops sharply from 25.58 to 11.90% because of the decreased fertility intentions caused by economic development and changes in social views; and (iii) the proportion resumes the increasing trend but with a slower annual growth rate of 2.70% from 2041 to 2050. Meanwhile, the proportion of labour-aged people (15–64) drops from 62.65 to 49.21% from 2020 to 2050. Even worse, the proportion of elderly people (65+ will far exceed the population ageing standard of the United Nations, i.e., 7%, with an extremely high proportion of 35.02% in 2050. In conclusion, the total population can be well controlled under the new family planning policy after considering fertility intentions. However, the population structure presents an inverted pyramid structure. The population simulation results are consistent with the predicted results in the existing studies (Cao 2020; Li et al. 2019; Shi et al. 2018; Wan et al. 2017). From the population simulation results, family planning policies should be relaxed in the future. Comparing these two experiments, I find that the population simulation considering fertility intentions is undoubtedly more reliable. Therefore, when further analysing the effects of the new family planning policy, I refer to the two-child policy after considering fertility intention in the following sections.

5.4 Family Planning Policy Comparison With the aim of further analysing the new family planning policy, this section compares it with three other possible family planning policies, including the onechild policy, the selective two-child policy, and cancellation of fertility planning policy.

128

5 Population Development Under Different Family Planning Policies

5.4.1 Policy Comparison Between the Two-Child Policy and the Selective Two-Child Policy On the basis of Vensim, the system dynamics model is used to simulate the population development for the two-child policy and the selective two-child policy. Six indicators are selected to compare the differences between these two fertility policies: the total population, total number of newborn babies, total fertility rate, proportion of the labour-aged population (15–64), proportion of the elderly population (65+), and total dependency ratio. Among them, the first three indicators reflect the population size, and the other three reflect the population structure. The simulation results under the two family planning policies are shown in Fig. 5.4. The population presents similar development trends in both population size and population structure between the two-child policy and the selective two-child policy, meaning that these two family planning policies have a similar influence. The two-child policy can be deemed the optimized measure of the selective twochild policy. If the two-child policy were adopted in 2013, the total population would approach 1.5 billion in the period from 2018 to 2026. Thus, it is reasonable that the selective two-child policy was established as a transitional arrangement. In fact, China implemented the selective two-child policy in 2013 to have the expected effect of population regulation. Then, the two-child policy was issued as a modified programme in 2016. The combination of these two family planning policies is effective.

5.4.2 Policy Comparison Among the Two-Child Policy, the One-Child Policy, and Cancellation of Family Planning Policy To further research the two-child policy, the system dynamics model is used to simulate the population development for the two-child policy, the one-child policy, and cancellation of fertility planning policy. The same indicators are adopted here to compare the differences among the three family planning policies. The simulation results under the three family planning policies are shown in Fig. 5.5. If the one-child policy continues, the total population will remain above 1.3 billion before 2021, followed by entry into a continuous decline until 2050, when the final total population will be only 0.7836 billion. During the research period, the total number of newborn babies will decrease from 13.78 to 2.745 million, and the total fertility rate will decrease slowly from 1.337 to 1.274. The proportion of children (0–14) will drop rapidly to only 6.37%, which is not a desirable number. The proportion of the labour-aged population (15–64) will drop from 73.40 to 50.64%. Even worse, the proportion of elderly people (65+) will far exceed the population ageing standard of the United Nations, i.e., 7%, with a proportion of 42.98% in 2050. The

5.4 Family Planning Policy Comparison

129

Fig. 5.4 Simulation results of the comparison between the two-child policy and the selective two-child policy

total dependency ratio will slowly rise from 0.3620 to 0.9746, and the demographic dividends will completely vanish. By cancelling the family planning policy, the total population will reach a terrible peak of 1.704 billion in 2023 after experiencing a continuous increase over the next few years. The maximum total population far exceeds 1.5 billion, which is regarded as China’s population limit. The total number of newborn babies will first increase to 57.08 million and then slowly decrease to 27.93 million. The proportion of children (0–14) will remain approximately 30%. The proportion of the labour-aged population (15–64) will decline from 73.40% to 43.23%, and the proportion of elderly people (65+) will be 24.26% by 2050. This population structure is superior to that under

130

5 Population Development Under Different Family Planning Policies

Fig. 5.5 Simulation results of the comparison among the two-child policy, the one-child policy, and cancellation of family planning policy

other possible fertility policies. However, the total dependency ratio will be 1.313, which is more serious. Therefore, different family planning policies will result in different population development trends. Under the one-child policy, the population structure will be severely worse although the total population can be well controlled. If the fertility restrictions are canceled, the total population will far exceed the load limit of China’s population. Thus, issuing the two-child policy with a regulated degree between the other two policies is best suited to China’s population conditions.

5.5 Population Simulation in Jiangxi Province Under the Two-Child Policy

131

5.5 Population Simulation in Jiangxi Province Under the Two-Child Policy In addition to predicting China’s population development, this section predicts the population development in Jiangxi Province. By analysing the differences in population development in different regions, this section can preliminarily judge the significant provincial differences, which will be further analysed in Part II of this book.

5.5.1 Parameter Determination The fertility factors among different ages h i (t) are influenced by the beginning age of having babies r1 and parameter n. According to the official data in Jiangxi Province (Statistic Bureau of Jiangxi Province and Jiangxi Survey Team of National Bureau of Statistics 2020), the minimum fertility age is 15, and when the number of newborn babies reaches its peak, the age is 23, and parameter n is 10. According to further analysis of the statistical data, the new family planning policy does not affect r1 but changes r0 to 21, and the corresponding parameter n is 8. The fertility factors among different ages h i (t) can be calculated through Eq. (5.6), and the concrete values before and after issuing the new family planning policy are shown in Table 5.7.

5.5.2 Data Sources The required data were taken from the Tabulation on the 2010 Population Census of Jiangxi Province in Jiangxi Economic and Social Development Statistical Database (National Bureau of Statistics of China 2010) and the Jiangxi Statistical Yearbook 2019 (Statistic Bureau of Jiangxi Province and Jiangxi Survey Team of National Bureau of Statistics 2020). The specific data are shown in Table 5.8.

5.5.3 Population Simulation Results After determining the model parameters, the system dynamics model predicts the population development in Jiangxi Province. The population simulation experiment is implemented based on Vensim. The total population and population structure under the new family planning policy from 2020 to 2050 can be predicted, as shown in Table 5.9. There are significant differences in population structure between China and Jiangxi Province, and the population ageing phenomenon in Jiangxi Province is

33

0.0169

0.0075

Age

Before

After

0.0003

0.0844

After

After

0.0949

Before

42

24

Age

0.0009

0

After

Before

0

Before

Age

15

Age

0.0002

0.0007

43

0.0053

0.0127

34

0.0702

0.0877

25

0.0063

0.0008

16

0.0001

0.0005

44

0.0038

0.0095

35

0.0567

0.0779

26

0.0307

0.0077

17

0.0001

0.0003

45

0.0027

0.0070

36

0.0446

0.0669

27

0.0628

0.0235

18

0.0001

0.0002

46

0.0019

0.0051

37

0.0344

0.0559

28

0.0902

0.0451

19

0.0000

0.0002

47

0.0013

0.0037

38

0.0261

0.0456

29

0.1069

0.0668

20

0.0000

0.0001

48

0.0009

0.0027

39

0.0194

0.0365

30

0.1120

0.0840

21

Table 5.7 Fertility factors among different ages in Jiangxi Province before and after issuing the new family planning policy

0.0000

0.0001

49

0.0006

0.0019

40

0.0143

0.0286

31

0.1079

0.0944

22

0.0004

0.0013

41

0.0104

0.0221

32

0.0977

0.0977

23

132 5 Population Development Under Different Family Planning Policies

5.5 Population Simulation in Jiangxi Province Under the Two-Child Policy

133

Table 5.8 The population data of Jiangxi Province at different age intervals in the 2010 population census Age

Average population

Women’s proportion (%)

Average death rate (‰)

Age

Average population

Women’s proportion (%)

Average death rate (‰)

0

578,832

44.85

3.820

1–4

720,986

43.15

0.64

5–9

646,411

43.22

0.298

10–14

613,480

43.99

0.296

15–19

685,198

46.46

0.384

20–24

805,143

50.51

0.508

25–29

606,410

51.65

0.606

30–34

718,591

50.48

0.810

35–39

805,946

49.57

1.156

40–44

756,149

49.40

1.768

45–49

609,978

49.47

2.720

50–54

494,383

49.15

4.152

55–59

459,462

49.19

6.278

60–64

342,191

48.67

10.486

65–69

228,754

49.09

17.346

70–74

198,073

50.46

30.854

75–79

133,921

53.03

50.598

80–84

74,153

55.95

87.030

85–89

32,383

61.16

132.2

90–94

8392

67.02

196.026

95–99

1863

66.70

220.324

100

599

72.95

454.350

Note The data in this table were obtained from the Tabulation on the 2010 Population Census of the Jiangxi Province in Jiangxi Economic and Social Development Statistical Database, and the website is: https://tongji.cnki.net/kns55/Dig/dig.aspx

slight. To better describe the population structure in Jiangxi Province, this section uses the three indicators: the proportion of children (0–20), the proportion of the labouraged population (21–60), and the proportion of the elderly people (61+). As the population structure are significantly different between China and Jiangxi Province, I use the different age intervals to distinguish children, labour-aged population, and the elderly population. The predicted results in Table 5.9 show that the population number in Jiangxi Province continuously increases and will reach up to 66.67 million in 2050, causing a huge population size structure. The number of newborn babies will increase until 2022, slightly decrease from 2023 to 2027, and then continuously increase after 2027. The number of newborn babies will reach 11.8 million in 2050. The underlying reason for the increase in newborn babies is the current population structure. Despite the strict family planning policy, Jiangxi Province will still face a huge challenge in controlling the total population number. The proportion of children (0–20) will stably increase and would reach 34.82% in 2050, but the proportion of the labour-aged population (21–60) will slightly decrease and drop to 45.04% in 2050. The proportion of the elderly people (61+) will slightly increase and reach 20.13% in 2050. Although Jiangxi Province also faces slight population ageing pressure, the population ageing phenomenon in Jiangxi Province is very slight compared with that in China. In 2050, China’s population structure will present an obvious inverted pyramid structure, but Jiangxi Province’s population structure will not. Therefore, I can preliminarily understand the different population size and population structure challenges for Jiangxi Province and China.

134

5 Population Development Under Different Family Planning Policies

Table 5.9 Population simulation results in Jiangxi Province under the new family planning policy Year

Total Population (million)

Newborn babies (thousand)

Proportion of children (0–20) (%)

Proportion of the labour-aged population (21–60) (%)

Proportion of the elderly people (61+) (%)

Total fertility rate

2015

46.49

937.8

31.05

57.17

11.77

2.610

2016

47.19

955.0

31.15

56.71

12.13

2.639

2017

47.88

960.2

31.25

56.26

12.47

2.656

2018

48.56

957.5

31.36

55.85

12.78

2.666

2019

49.22

948.2

31.60

55.26

13.12

2.669

2020

49.86

934.9

31.83

54.71

13.44

2.668

2021

50.47

920.0

32.03

54.21

13.74

2.666

2022

51.06

905.3

32.22

53.75

14.02

2.662

2023

51.62

892.4

32.39

53.31

14.28

2.660

2024

52.16

882.2

32.50

52.77

14.72

2.658

2025

52.69

875.1

32.60

52.26

15.13

2.658

2026

53.19

871.1

32.70

51.77

15.52

2.659

2027

53.69

870.9

32.80

51.30

15.88

2.662

2028

54.18

874.4

32.91

50.85

16.22

2.667

2029

54.67

882.0

32.90

50.30

16.78

2.673

2030

55.15

891.4

32.92

49.77

17.30

2.679

2031

55.62

901.4

32.95

49.25

17.79

2.684

2032

56.10

913.0

33.00

48.74

18.24

2.688

2033

56.59

927.6

33.33

47.98

18.67

2.693

2034

57.07

949.8

33.29

47.55

19.14

2.703

2035

57.58

980.5

33.14

47.28

19.56

2.716

2036

58.10

1018

32.98

47.06

19.95

2.733

2037

58.66

1061

32.85

46.84

20.29

2.752

2038

59.26

1105

32.77

46.62

20.60

2.769

2039

59.87

1148

32.75

46.52

20.72

2.784

2040

60.51

1187

32.80

46.38

20.81

2.793

2041

61.17

1218

32.90

46.21

20.87

2.796

2042

61.84

1241

33.06

46.02

20.90

2.792

2043

62.51

1254

33.26

45.80

20.92

2.780

2044

63.18

1259

33.49

45.74

20.75

2.762

2045

63.82

1255

33.74

45.68

20.57

2.738

2046

64.44

1245

33.98

45.62

20.38

2.710

2047

65.04

1231

34.22

45.58

20.19

2.678 (continued)

5.5 Population Simulation in Jiangxi Province Under the Two-Child Policy

135

Table 5.9 (continued) Year

Total Population (million)

Newborn babies (thousand)

Proportion of children (0–20) (%)

Proportion of the labour-aged population (21–60) (%)

Proportion of the elderly people (61+) (%)

Total fertility rate

2048

65.61

1214

34.44

45.55

20.00

2.645

2049

66.15

1197

34.64

45.27

20.07

2.611

2050

66.67

1180

34.82

45.04

20.13

2.577

5.5.4 Family Planning Policy Comparison for Jiangxi Province After predicting population development under the new family planning policies, this section compares the differences in population development under the three possible family planning policies: the selective two-child policy, the one-child policy and cancellation of family planning policy. The results are presented in Fig. 5.6. If the original strict one-child policy is still implemented, the total population number in Jiangxi Province will reach a peak of 50.27 million in 2039 and then slightly decrease. The number of newborn babies will continuously decrease and will be 45.21 in 2050. The total fertility rate will be stable at approximately 1.5. The proportion of the children (0–20) will continuously decrease and will be 22.56% in 2050, and the proportion of the labour-aged population (21–60) will slightly decrease and will be 50.05% in 2050. The proportion of the elderly people will increase to 27.37% in 2050. The changing trend of the population structure in Jiangxi Province is similar to that in China, but the population ageing phenomenon is relatively slight in Jiangxi Province. Keeping the strict one-child policy will increase the degree of population ageing, but the degree of increase is not serious. The proportion of the children (0–20) and the proportion of the labour-aged population (21–60) will still be optimistic under the strict one-child policy. More importantly, the total population number in Jiangxi Province will be acceptable under the strict one-child policy but will significantly exceed the upper limit of the population in Jiangxi Province under the selective two-child policy or two-child policy. Therefore, in Jiangxi Province, adjusting the family planning policy is a low priority, and it is better to continue implementing the strict one-child policy. If the family planning policy is cancelled now, the total population number in Jiangxi Province will rapidly increase and even exceed 100 million in 2042. The total population number in Jiangxi Province will far exceed the upper limit of the population in Jiangxi Province. The total fertility rate may increase to 5.756, and will still be 4.910 in 2050. The proportion of the children (0–20) will increase to 56.59% in 2050, which is very high and not optimal. The proportion of the labouraged population (21–60) will rapidly decrease to 32.60% in 2050, indicating a labour shortage in Jiangxi Province in the future. The proportion of the elderly people (61+)

136

5 Population Development Under Different Family Planning Policies

(a) Total population

(c) Total fertility rate

(e) Proportion of labour-aged population (21-60)

(b) Number of new-born babies

(d) Proportion of children (0-20)

(f) Proportion of elderly people (61+)

Fig. 5.6 Simulation results of the comparison among the selective two-child policy, the one-child policy, and cancellation of family planning policy in Jiangxi Province

will be just 10.30% in 2050, which is highly optimal. Therefore, it is not suitable for Jiangxi Province to relax the family planning policy.

5.6 Sensitivity Analysis

137

5.6 Sensitivity Analysis The willingness to have a second baby can directly determine the total number of newborn babies and thus affect population development. However, fertility intentions are affected by many factors that are not steady, although they can be predicted based on statistical data. As described above, the willingness to have a second baby under the new family planning policy is regarded as 55%, and the corresponding policy regulatory factor is 0.629. With the aim of detecting the impact caused by fertility intentions, the sensitivity analysis is presented here. The value is set in the interval of 0%–100% and changes by 20% per unit; thus, the corresponding policy regulatory factors are 1, 0.865, 0.730, 0.595, 0.461, and 0.326. The simulation results of the sensitivity analysis of the willingness to have a second baby are shown in Fig. 5.7. The total population and total dependency ratio are significantly influenced by fertility intentions. When the willingness to have a second baby reaches 80%, the total population will exceed the redline of 1.5 billion. Therefore, China should, on the one hand, encourage fertile women to have a second baby and, on the other hand, ensure that their willingness to do so does not exceed 80%. The total dependency ratio shows the same variation trend over the next 15 years regardless of how high the degree of willingness might be. The discrepancies under different fertility intentions will become obvious after 2030. Conversely, the total dependency ratio has apparent distinctions in the early periods. Therefore, fertility intentions have a significant influence on population development. With the aim of optimizing its population, China should try to maintain the willingness to have a second baby at 80%.

5.7 Conclusions China has frequently adjusted its family planning policy in recent years, and it issued the two-child policy in 2016 due to various population crises. To verify the new family planning policy, this chapter establishes a system dynamics model on the basis of the Song Jian’s population development equation to simulate population development. Under the new family planning policy, the total population will reach a peak of 1.448 billion in 2022 after experiencing a continuous but slow increase over the next few years, followed by entry into a sustained phase of decline until 2050, when the final total population will be 0.961 billion. The total population can be consistently controlled because the maximum value of 1.448 billion is below 1.5 billion, which is regarded as the upper limit of China’s population. However, the proportion of the elderly people (65+) will far exceed the population ageing standard of the United Nations, i.e., 7%, with an extremely high proportion of 35.02% in 2050. Therefore, the total population can be controlled; however, the population structure still presents

138

5 Population Development Under Different Family Planning Policies

Fig. 5.7 Sensitivity analysis of the willingness to have a second baby

an inverted pyramid structure. China’s family planning policy should still be further relaxed in the future. With the aim of further analysing the new family planning policy, three other possible family planning policies are compared. If the one-child policy continues, the population structure will be severely worse, although the total population can be well controlled. The proportion of the elderly people (65+) will far exceed the

5.7 Conclusions

139

population ageing standard of the United Nations, i.e., 7%, with a proportion of 42.98% in 2050. If the family planning policy is cancelled, the total population will reach a terrible peak of 1.704 billion in 2023 after experiencing a continuous increase over the next few years. The maximum total population far exceeds 1.5 billion, which is regarded as China’s population limit. The proportion of the elderly people (65+) will be only 24.26% by 2050. This population structure is superior to that under other possible family planning policies. Thus, different family planning policies will result in different population development trends. Issuing a two-child policy with a regulated degree between the other two policies is best suited to China’s population conditions. The population development in Jiangxi Province is also predicted under the different planning policies. Under the new family planning policy, the total population number in Jiangxi Province will stably increase in the next 35 years and will increase to 66.67 million in 2050. For Jiangxi Province, the population size challenge is very large, but the population ageing phenomenon is not serious. The proportion of the elderly people (61+) will be only 20.13% in 2050. If the family planning policies are cancelled now, the total population number in Jiangxi Province will rapidly increase; the children dependency ratio will be very high. Compared with the population results under these three possible family planning policies, in Jiangxi Province adjusting the family planning policies is a low priority, and it is better to continue implementing the strict one-child policy. Finally, a sensitivity analysis is carried out to detect the impact caused by fertility intentions. The total population and total dependency ratio are significantly influenced by fertility intentions. When the willingness to have a second baby reaches 80%, the total population will exceed the redline of 1.5 billion. Therefore, it was reasonable to implement the selective two-child policy in 2013 and to then issue the two-child policy in 2016. With the aim of guaranteeing the coordinated development of the population, China should encourage fertile women to have a second baby, on the one hand, and warn that their willingness to do so cannot exceed 80%, on the other hand. Acknowledgements Reprinted by permission from Springer Nature Customer Service Centre GmbH: Springer, Quality & Quantity, Simulating population development under new fertility policy in China based on system dynamics model by Pengkun Wu, Qing Wu, and Yudan Dou (January 1, 2016).

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Zhou, Y., & Lin, Y. (2017). Does China need to abolish family planning policy?-Analysis of family planning policy and two-child policy. In International Conference on Transformations and Innovations in Management (ICTIM 2017). Atlantis Press. Zhu, Q. (2012). Estimation on fertility level in China 2000–2010: preliminary study based on the 2010 population census. 中国人口科学 (4), 68–77 (in Chinese). “ ” ," . 2012. " 2000 2010

Chinese Journal of Population Science (4), pp. 68-77.

Chapter 6

How to Adjust the Family Planning Policy in China?

Abstract Recent years have witnessed a sharp drop in China’s demographic dividends; therefore, some reform measures have been adopted with regard to China’s family planning policy to optimize the population structure and to maintain demographic dividends. However, our simulation results reveal that the new two-child family planning policy cannot effectively manage the ageing population and that China’s family planning policies require further adjustment. Based on a combination of simulation results and formula derivation, the new two-child family planning policy will deteriorate the demographic dividends before 2050. With the aim of stabilizing the demographic dividends in an ideal range, this section builds a nonlinear integer programming model to propose an appropriate reform path for China’s family planning policy. Then, this section simulates and compares the demographic developments under the proposed reform path with those under three possible family planning policies, i.e., the one-child policy, the two-child policy, and cancellation of family planning policy, verifying that the proposed reforming path obtains better performance stabilizing the demographic dividends than these three family planning policies. Finally, a sensitivity analysis of the upper bound of the research interval is conducted to evaluate the effect of the upper bound on the proposed reform path. Based on these results, this section suggests that China should continue to implement its current strict family planning policy until 2032, gradually begin to relax it, especially from 2036 to 2041, and completely cancel its family planning policy after 2065. Keywords Demographic dividends · Family planning policy · Non-linear integer programming model · One-child policy · Two-child policy

6.1 Introduction China has frequently adjusted its family planning policies in the past few years. As described in Chap. 5, the current family planning policies still cannot satisfy China’s future population development. After comprehensively considering the current population situation, China should urgently adjust its family planning policy to optimize its population structure and to maintain its demographic dividends (Cai 2012; Zhang © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_6

145

146

6 How to Adjust the Family Planning Policy in China?

and Chen 2020; Zhou and Lin 2017). Designing a scientific reform path for adjusting the family planning policy is very important for China. The one-child policy, which was introduced in the 1970s, has made a tremendous contribution to controlling China’s population size (Gietel-Basten et al. 2019; Guilmoto and Jones 2015; Peng 2011). However, this policy has also led to many problems, such as a huge gender gap due to the inherent Chinese preference for sons (Jiang et al. 2016) and serious population ageing as a result of the nation’s skewed demographic structure (Cai 2012; Cheema 2013; Ince Yenilmez 2015; Lee and Mason 2010; Mai et al. 2013). The Third Plenary Session of the 18th CPC Central Committee adopted the “Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform”, adjusting its family planning policy from a one-child policy to a selective two-child policy on November 15, 2013 (The Third Plenary Session of the 18th Central Committee of the Communist Party of China 2013). Nonetheless, just 2 years later, the population and family planning law was revised again, and China began to implement a new two-child policy on January 1, 2016. To summarize, China has implemented three fertility policies, i.e., the one-child policy, the selective two-child policy, and the two-child policy, in the 1970s, 2013, and 2016, respectively. These recent, frequent reforms with regard to China’s family planning policies have mainly been caused by the fact that the one-child policy pushed the nation’s fertility rate below the replacement level and seriously worsened its demographic structure (Cai 2010). Another reason leading to the adjustment of family planning policies is that Chinese women’s attitudes towards fertility have been transformed due to the rapid socio-economic development (Cao et al. 2010). To ensure the sustainable development of China’s population, many experts have analysed and compared some of the currently debated reform directions with regard to China’s family planning policies and proposed a three-stage “soft landing” strategy for family planning policy adjustment (Jiang et al. 2016, 2012; Yi 2007). Although it is widely recognized that China’s family planning policy should be relaxed at some stage in the future, finding a suitable reform path for China’s family planning policy is still a complex project that must be explored in depth. Adjusting the family planning policy has many significant effects in many fields. Population policies affect the match between capital and labour, and thus affect economic development (Guo et al. 2014; Xu et al. 2012). From the perspective of the substitutive effects of the population quality and quantity, Qu built a model to analyse the effect of the population policy on economic growth, finding that China’s current population policy is beneficial for economic growth (Qu 2013). The variable parameter model quantifying the effects of population policy on carbon emissions reveals that the population policy is one of the essential strategies for responding to climate change (Wang 2013). In fact, the influence is mutual. The above fields are affected by population policy, and conversely, they will also affect population policy adjustment. The first order of importance for family planning policy adjustment is the optimization of the population size and population structure as a whole, especially to solve the problem of population ageing, which is directly connected to the demographic dividends (Ogawa and Chen 2013). Then, the development accounting framework is

6.1 Introduction

147

extended by incorporating the population structure and is applied to analyse a panel dataset of Chinese provinces, revealing that demographic dividends significantly affect economic growth (Zhang et al. 2015a). Reducing the demographic dividends directly affects economic indicators, such as real estate prices (Zhang et al. 2015b), and can even lead to a global economic downturn (Gomez and Lamb 2013). In addition to the economy, social stability and government policies are also closely related to the demographic dividends (Aguirre 2016). Unfortunately, recent years have shown that China’s demographic dividends are close to being cashed out. Data from the 6th National Population Census show that China’s total fertility rate was only 1.18, which is far below the replacement level of approximately 1.8 (National Population Development Strategy Research Group 2007). The extremely low fertility intentions undoubtedly lead to a worse population structure. The young population aged 0–15 is sharply reduced; however, the share of the population aged more than 65 years increases considerably. Although much of China’s demographic dividends now lie in the past, China will not necessarily enter a period of demographic taxation if the family planning policy can be adjusted in a timely manner (Golley and Tyers 2012). Furthermore, the demographic dividends can also be stabilized at an ideal level if the family planning policy is adjusted in an accurate and timely manner (Ssewamala 2015). Based on this background, in 2016, the new two-child policy was issued to improve China’s population. Most people think that the proactive two-child policy can benefit China’s population development and prolong the duration of its demographic dividends. As analysed in Chap. 5, however, the new two-child policy will deteriorate China’s demographic dividends before 2050. Many specialists directly state that the main purpose of adjusting the family planning policy is to extend the demographic dividends (Liu 2010; Peng 2013). A healthy demographic structure refers to demographic dividends that can be maintained for a long period, not simply for the present decade (Oosthuizen 2015). Extremely high demographic dividends are not ideal since the current demographic dividends will be converted into future demographic debt (Van Der Gaag and de Beer 2015). A reasonable family planning policy should consistently cause the demographic dividends to fall into appropriate and sustainable intervals. To meet this requirement for the demographic dividend, this section builds a non-linear integer programming model to explore an ideal reform path for China’s family planning policy. The remainder of this chapter is organized as follows. Section 6.2 builds a mathematical model to conduct the prejudgement and to analyse whether 2016 was suitable for implementing the two-child policy. Aiming to stabilize the demographic dividends, Sect. 6.3 builds a non-linear integer programming model to calculate the rational number of newborn infants from 2016 to 2100. Then Sect. 6.4 proposes the specific reform path for China’s fertility policies in terms of the optimized results. Next, Sect. 6.5 evaluates the proposed reform path by comparing population developments under it with those under three other possible family planning policies: the one-child policy, the two-child policy, and cancellation of family planning policy. In Sect. 6.6, to further validate the optimization model, a sensitivity analysis of the upper bound of the research interval is conducted. Finally, Sect. 6.7 concludes this chapter and analyses the contributions and limitations.

148

6 How to Adjust the Family Planning Policy in China?

6.2 Prejudgement: When to Adjust the Family Planning Policy Since 2013, when the selective two-child policy was implemented, some scholars have begun to analyse when the two-child policy should have been implemented. The adjustment of the family planning policy has been a hot topic in both academia and society. On July 23, 2015, the authoritative media said that the two-child policy would be implemented in 2015. Then, on July 24, 2015, the National Health and Family Planning Commission of the People’s Republic of China announced that the two-child policy would not be implemented in 2015. Subsequently, in October 2015, China announced that the two-child policy would be officially implemented on January 1, 2016. The frequent adjustments of the selective two-child policy and the two-child policy are not the end but the start of the adjustment of the family planning policy. Thus, this section builds a non-linear integer programming model to explore the reform path of the family planning policy. Before proposing the non-linear integer programming model, this section first builds a simple model for analysing when the two-child policy should be implemented and whether 2016 is suitable for implementing the two-child policy.

6.2.1 The Equation for Calculating the Time for Implementing the Selective Two-Child Policy Family planning policy adjustment should fully consider the actual situation of population development, including the population size and population structure. First, I consider the effect of the population size on family planning policy adjustment. The total population number in year k is L(k); the ideal total population number is N ; and the possible amplitude of change is α. Thus, the family planning policy should be relaxed in year k if L(k) < N (1 − α); the family planning policy should be maintained strictly in year k if L(k) > N (1 + α); and the family planning policy does not need to be adjusted in year k if N (1 − α) ≤ L(k) ≤ N (1 + α). Second, I consider the effect of the population structure on family planning policy adjustment. The children dependency ratio (the proportion of the children (0–14) divided by the proportion of the labour-aged population (15–64) in year k is y1 (k), and its lower limit and upper limit are z 1 and z 2 , respectively. The elderly dependency ratio (the proportion of the elderly people (65+ ) divided by the proportion of the labour-aged population (15–64) in year k is y2 (k), and its upper limit is m. Thus, the family planning policy should be relaxed in year k if y1 (k) < z 1 or y2 (k) > m; the family planning policy should be maintained strictly in year k if y1 (k) > z 2 ; and the family planning policy does not need to be adjusted in year k if z 1 ≤ y1 (k) ≤ z 2 and y2 (k) ≤ m.

6.2 Prejudgement: When to Adjust the Family Planning Policy

149

Table 6.1 Influence mechanisms of population indicators on family planning policy adjustment Population indicators

Adjustment of family planning policy

L(k) < N (1 − α) or y1 (k) < z 1 or y2 (k) > m

Relax the family planning policy

L(k) > N (1 + α) or y1 (k) > z 2

Implement a strict family planning policy

N (1 + α) ≤ L(k) ≤ N (1 + α) and z 1 ≤ y1 (k) ≤ z 2 Keep the current family planning policy and y2 (k) ≤ m

In conclusion, family planning policy adjustment is directly affected by the population size and population structure, and the specific influence mechanisms are summarized in Table 6.1. In contemporary society, China faces serious challenges posed by the low total fertility rate and population ageing, and the total population number is still in the tolerance interval. The main problem for contemporary China is choosing a suitable time to relax the family planning policy. If the family planning policy can be relaxed in year k, the requirements for the population indicators are presented in Eq. (6.1), in which t0 is the initial year for analysing the family planning policy adjustment. ⎧ L(k) < N (1 − α) or y1 (k) < z 1 or y2 (k) > m ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ N (1 − α) ≤ L(t) ≤ N (1 + α) z 1 ≤ y1 (t) ≤ z 2 ⎪ ⎪ ⎪ y2 (t) ≤ m ⎪ ⎪ ⎪ ⎩ t = t0 , t0 + 1, . . . , k − 1

.

(6.1)

China started to implement the strict one-child family planning policy in 1973, and the family planning policy was frequently adjusted in the first 10 years. Since 1984, the one-child policy has remained stable and was not adjusted again until 2013, when China started to implement the selective two-child policy. Therefore, when setting t0 = 1984 and k = 2013, the population indicators from 1984 to 2013 should meet as shown in Eq. (6.2): ⎧ L(2013) < N (1 − α) or y1 (2013) < z 1 or y2 (2013) > m ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ N (1 − α) ≤ L(t) ≤ N (1 + α) z 1 ≤ y1 (t) ≤ z 2 ⎪ ⎪ ⎪ ⎪ ⎪ y2 (t) ≤ m ⎪ ⎩ t = 1984, 1985, . . . 2012

.

(6.2)

The required population indicator data for 1982 and 1987 and from 1990 to 2013 can be obtained from the China Statistics Yearbook 2019 (National Bureau of Statistics of China 2020) and are presented in Table 6.2. Equation (6.2) is satisfied in 1982, in 1987, and from 1990 to 2013. It is obvious that the total population number and the elderly dependency ratio increased, and that the children dependency ratio decreased

Total population (billion)

1.017

1.093

1.143

1.158

1.172

1.185

1.199

1.211

1.224

1.236

1.248

1.258

1.267

Year

1982

1987

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

32.6

37.5

38.0

38.5

39.3

39.6

40.5

40.7

41.7

41.8

41.5

43.5

54.6

Children dependency ratio (%)

9.9

10.2

9.9

9.7

9.5

9.2

9.5

9.2

9.3

9.0

8.3

8.3

8.0

Elderly dependency ratio (%)

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

Year

1.361

1.354

1.347

1.341

1.335

1.328

1.321

1.314

1.308

1.300

1.292

1.285

1.276

Total population (billion)

Table 6.2 Population indicator data in 1982, in 1987, and from 1990 to 2013 under the one-child policy

22.2

22.2

22.1

22.3

25.3

26.0

26.8

27.3

28.1

30.3

31.4

31.9

32.0

Children dependency ratio (%)

13.1

12.7

12.3

11.9

11.6

11.3

11.1

11.0

10.7

10.7

10.7

10.4

10.1

Elderly dependency ratio (%)

150 6 How to Adjust the Family Planning Policy in China?

6.2 Prejudgement: When to Adjust the Family Planning Policy

151

from 1982 to 1990 in China. Thus, I can judge that Eq. (6.2) is satisfied from 1984 to 2013. The population indicator data in 2013 should satisfy L(2013) < N (1 − α), y1 (2013) < z 1 , or y2 (2013) > m. The children dependency ratio in 2012 and 2013 are the same; thus, I ensure that z 1 ≤ 0.222. Since L(1982) ≥ N (1 − α) and L(2013) ≥ L(1982), I have L(2013) ≥ N (1 − α). To ensure that y2 (2012) ≤ m and y2 (2013) > m are established, I calculate the value of m as m = 0.131. Based on the study of Prof. Zhai, a famous Chinese demographer, the upper limit of China’s total population number is 1.5 billion (Zhai 2013a,  2013b). Because N (1 − α) = 10.17 . L(1982) ≥ N (1−α), I set L(1982) = N (1−α) and thus have N (1 + α) = 15  N = 12.585 . Therefore, I have α = 0.1919 China’s children dependency ratio continuously decreases; thus, I have z 1 ≤ y < y1 (1984) < y1 (1982) ≤ z 2 . Therefore, this section sets (2012) 1  z 1 = y1 (2012) = 0.222 . z 2 = y1 (1982) = 0.546 In conclusion, by analysing the adjustment of the family planning policy from the one-child policy to the selective two-child policy, this section ensures the parameter values. China’s ideal total population number is set as N = 12.585, and the possible amplitude of change is α = 0.1919. The lower limit and upper limit of the children dependency ratio are z 1 = 0.222 and z 2 = 0.546, respectively. The upper limit of the elderly dependency ratio is m = 0.131.

6.2.2 The Equation for Calculating the Time for Implementing the Two-Child Policy The threshold value of the population size is stable since this value is decided by the complexity of society. In contemporary China, the population structure is continuously deteriorating, and the ideal value cannot be quickly optimized. Therefore, for different stages, China should set different optimization goals for the population structure and accordingly adjust the family planning policy. The upper limit of the population structure when analysing the time for implementing the two-child policy is the value when analysing the time for implementing the selective two-child policy multiplied by the degree of relaxation of the two-child policy relative to the selective two-child policy. As calculated in Chap. 5, the regulatory degrees of cancelling the family planning policy, implementing the strict onechild policy, and implementing the selective two-child policy are 0, 1, and 0.7158, respectively. The two-child policy has part of the constraining force of the strict onechild policy and has part of the loosening effect of cancelling the family planning policy; thus, this section sets the regulatory degree as 0.5. Therefore, the upper limit

152

6 How to Adjust the Family Planning Policy in China?

of the population structure when analysing the two-child policy is the value when analysing the time for implementing the selective two-child policy multiplied by 1.4316, which is obtained from 0.7158 ÷ 0.5. Therefore, the calculation equation for analysing the adjustment time for the selective two-child policy is changed to be the calculation equation for analysing the adjustment time for the two-child policy. When the time for implementing the two-child policy is set as k, the population indicator data should satisfy Eq. (6.3): ⎧ L(k) < 10.17 or y1 (k) < 0.222 or y2 (k) > 0.1875 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ 10.17 ≤ L(t) ≤ 15 0.222 ≤ y1 (t) ≤ 0.7817 . ⎪ ⎪ ⎪ y2 (t) ≤ 0.1875 ⎪ ⎪ ⎪ ⎩ t = 2015, 2016, . . . k − 1

(6.3)

6.2.3 Calculation Results for the Implementation Time Population development data are required for calculating the adjustment time of the family planning policy. When calculating the time for implementing the selective two-child policy, I can obtain the actual population development data under the onechild policy from the China Statistics Yearbook 2019. However, when calculating the time for implementing the two-child policy, I cannot obtain all population development data under the selective two-child policy. Therefore, this section first adopts the system dynamics model built in Chap. 5 to predict the population development data under the selective two-child policy and presents the data in Table 6.3. The data in Table 6.3 are put into Eq. (6.3) to calculate the adjustment time for implementing the two-child policy, and I obtain k = 2019. Therefore, this section suggests China should implement the two-child policy in 2019 for sustainable population development. China officially implemented the two-child policy in 2016. The slight difference between our result and the actual adjustment time may be due to the set errors of some parameters, such as the regulatory degree of the two-child policy, and the estimated total fertility rate, which is very hard to be accurately estimated (Ding et al. 2019).

6.2.4 Sensitivity Analysis As described in Eq. (5.7), many factors, such as per capita GDP and the urbanization rate, affect the total fertility rate, and thus affect population development. In developed countries such as South Korea, the growth rate of per capita GDP is stable at approximately 5%. Currently, China proposes that its growth rate of

Total population (billion)

1.374

1.384

1.394

1.401

1.407

1.410

1.411

1.410

1.404

1.393

1.377

1.358

1.338

1.315

1.291

1.267

1.242

1.217

Year

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

24.60

26.75

28.85

30.91

32.87

34.47

36.03

37.47

37.97

38.12

37.59

36.50

35.30

33.84

32.21

30.43

28.57

26.77

Children dependency ratio (%)

36.28

34.81

32.93

31.07

29.30

27.01

25.67

25.46

24.75

24.21

23.14

21.84

20.72

19.49

18.23

17.15

16.04

15.20

Elderly dependency ratio (%)

2050

2049

2048

2047

2046

2045

2044

2043

2042

2041

2040

2039

2038

2037

2036

2035

2034

2033

Year

8889

9087

9276

9456

9628

9794

9954

1.011

1.026

1.042

1.058

1.074

1.091

1.109

1.129

1.149

1.171

1.194

Total population (billion)

Table 6.3 Predicted population indicator data from 2015 to 2050 under the selective two-child policy

22.38

21.90

21.29

20.59

19.74

18.97

18.25

17.59

17.06

16.73

16.56

16.62

16.93

17.51

18.40

19.59

21.01

22.74

Children dependency ratio (%)

74.67

72.14

69.81

67.66

64.80

62.84

61.12

59.24

57.53

56.18

54.33

52.44

50.29

48.02

45.71

43.40

40.79

38.68

Elderly dependency ratio (%)

6.2 Prejudgement: When to Adjust the Family Planning Policy 153

154

6 How to Adjust the Family Planning Policy in China?

per capita GDP should strive to achieve a level of 7%. Therefore, when simulating population development, Sect. 6.2 sets per capita GDP as 7%. However, it is very difficult to accurately predict the growth rate of per capita GDP and fluctuations in per capita GDP will affect future population development. With the aim of detecting the impact caused by per capita GDP, a sensitivity analysis is presented here. The value is set in the interval of 0–10% and changes by 1% per unit. The simulation results of the sensitivity analysis of the effect of per capita GDP on the adjustment year are shown in Table 6.4. Table 6.4 reveals that the adjustment time for implementing the two-child policy does not change when per capita GDP falls into the range of 0–10%. Therefore, I judge that the adjustment time for implementing the two-child policy is not affected by per capita GDP since the main factor affecting the implementation time is the population structure factor, not the population size factor. The primary goal of adjusting the family planning policy is to reduce the elderly dependency ratio, which is currently very high in China but cannot be directly affected by economic factors. Economic factors directly affect the number of newborn babies and the children dependency ratio, and cannot directly affect the population structure. In the sensitivity analyses, the total population number and the children dependency ratio are presented in Table 6.5. When the growth rate of per capita GDP changes from 0 to 10%, the total population number and the children dependency ratio do not significantly change, and these population indicators can now be accepted. The main factor causing the adjustment of the family planning policy is serious population ageing. Table 6.4 Sensitivity analysis of the growth rate of per capita GDP Growth rate of per 0 capita GDP (%) Adjustment year

1

2

3

4

5

6

7

8

9

10

2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019

Table 6.5 The total population and children dependency ratio in 2019 under different growth rates of per capita GDP Growth rate of per capita GDP (%)

0

1

2

3

4

Total population (billion)

1.427

1.424

1.421

1.418

1.415

Children dependency ratio (%)

36.04

35.71

35.40

35.08

34.77

Growth rate of per capita GDP (%)

6

7

8

9

10

Total population (billion)

1.410

1.407

1.404

1.402

1.399

Children dependency ratio (%)

34.15

33.84

33.54

33.24

32.95

5 1.413 34.46

6.3 Non-Linear Integer Programming Model for Adjusting the Family Planning Policy

155

6.3 Non-Linear Integer Programming Model for Adjusting the Family Planning Policy The first order of importance for the family planning policy adjustment is optimization of the population size and population structure as a whole, especially to solve the problem of population ageing which is directly connected to the demographic dividends. Therefore, this section first analyses the demographic dividends in China and then builds a non-linear integer programming model for family planning policy adjustment.

6.3.1 Demographic Dividend Analysis Demographic dividends refer to the advantageous demographic structure that can result in social and economic benefits. In general, demographic dividends can be thought to be positive when the proportion of the labour-aged population (aged 15–64) is large. The total dependency ratio is often used to measure demographic dividends. The total dependency ratio is measured as the number of people aged 100 ≈ 100 ≈

(6.4)

If the one-child family planning policy was maintained, the proportion of the labour-aged population (15–64) would be F/T . Under the new two-child policy, this proportion changes to (F + z 2 )/(T + z 1 + z 2 ). The difference between these two fractions is calculated from Eq. (6.5): F z2 − F + z2 F F(z 1 + z 2 ) − T z 2 T z1 + z2 − = = T + z1 + z2 T T + z1 + z2 T (T + z 1 + z 2 ) z1 + z2 ⎧ F ⎪ ⎪ ⎪ ⎪ T , ⎪ ⎪ 0 ≤ i ≤ 15 ⎪ ⎪ T +i ⎪ ⎪ ⎪ ⎪ i ⎪ ⎪ ⎪ i − 15 F ⎪ ⎪ − ⎪ ⎪ T i , 15 < i ≤ 64 ⎪ . ⎪ ⎪ ⎪ T +i ⎪ ⎨ i = 50 F ⎪ ⎪ − ⎪ ⎪ ⎪ T i , ⎪ 64 < i ≤ 100 ⎪ ⎪ T + i ⎪ ⎪ ⎪ ⎪ ⎪ i ⎪ ⎪ F ⎪ ⎪ − 0.5 ⎪ ⎪ ⎪ T ⎪ , i > 100 ⎪ ⎪ ⎪ ⎩ T + 100 100

(6.5)

In Eq. (6.5), the denominators are always greater than 0, and the sign (positive or negative) of the numerators must be further judged. Based on the value of F/T predicted by the United Nations (Department of Economic and Social Affairs of the United Nations 2015), the sign of these numerators can be inferred. When 0 ≤

158

6 How to Adjust the Family Planning Policy in China?

i ≤ 15, the numerator is always positive; when 15 < i ≤ 64, the value of the numerator decreases gradually, and its sign changes between 2055 and 2060; when 64 < i ≤ 100, the numerator is always less than 0; and when i > 100, the sign of the numerator depends on whether the value of F/T is larger than 0.5. Therefore, the new two-child policy can replenish the total labour-aged population in the medium and long terms. In the near term, however, the two-child policy will deteriorate the total dependency ratio. China’s demographic dividends can be considerably improved by implementing the new family planning policy after 2050. China’s demographic dividends cannot be completely stabilized in an ideal range by only implementing the new two-child policy. By analysing the impact of the new two-child policy on demographic dividends, this section finds that China must find a suitable reform path for its family planning policy to optimize the demographic dividends.

6.3.2 The Non-Linear Integer Programming Model Thus, this section builds a non-linear integer programming model with the aim of reducing the total dependency ratio. The definitions of all notations used in the non-linear integer programming model are presented in Table 6.6. A reasonable family planning policy should ensure that the demographic dividends fall into an appropriate and sustainable interval consistently over time. Therefore, the foremost objective of adjusting the family planning policy in China is to minimize the average total dependency ratio. According to the theoretical analyses in Sect. 6.3.1.2, the new two-child policy can improve the demographic dividends just after 2050. Thus, the research interval here is set from 2016 to 2100. Accordingly, the objective function can be described in Eq. (6.6): Table 6.6 Definition of notations

Notation

Definition

P(t)

Total population in year t

ψ(t)

The numbers of newborn infants in year t

ψu(t)

Upper bound of ψ(t)

ψl(t)

Lower bound of ψ(t)

L i (t)

The number of population aged i in year t

ui

Death rate of age i

α(t)

Proportion of labour-aged population (aged 15–64) in year t

o(t)

Proportion of aged population (aged 65+) in year t

d(t)

Total dependency ratio in year t

m

The maximum age in China, here m = 100

6.3 Non-Linear Integer Programming Model for Adjusting the Family Planning Policy

Min

2100  1 d(t). × 85 t=2016

159

(6.6)

The population size and population structure should be simultaneously controlled when family planning policy is adjusted. Based on the official planning report (Zhai 2013a, 2013b), China’s total population cannot exceed 1.5 billion, and the proportion of the elderly population (aged 65+) should not surpass 25%. Thus, two constraint conditions are determined: P(t) < 1,500,000,000 and o(t) ≤ 0.25. Furthermore, the calculated ideal numbers of newborn infants should be rational. For each year, the value should fall into a feasible interval ψ(t) whose upper bound ψu(t) and lower bound ψl(t) are the calculated numbers of newborn babies when cancelling the family planning policy and when still implementing the strict onechild policy, respectively. The specific interval values ψ(t) from 2016 to 2100 are presented in Table 6.7. Table 6.7 The upper and lower bounds of newborn babies during 2016–2100 (unit: million) 2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

ψu(t)

41.37

51.52

55.45

56.89

57.08

56.36

54.84

52.52

49.45

45.77

41.70

ψl(t)

14.39

15.19

15.99

16.69

17.23

17.54

17.56

17.26

16.61

15.65

14.46

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

ψu(t)

37.58

33.34

29.49

26.05

23.06

20.52

18.42

16.75

15.53

14.86

14.84

ψl(t)

13.12

11.75

10.43

9.222

8.158

7.253

6.504

5.897

5.410

5.023

4.721

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

ψu(t)

15.55

16.97

19.04

21.64

24.60

27.70

30.73

33.48

35.76

37.39

38.29

ψl(t)

4.490

4.320

4.201

4.125

4.085

4.069

4.068

4.069

4.062

4.036

3.982

2049

2050

2051

2052

2053

2054

2055

2056

2057

2058

2059

ψu(t)

38.42

37.82

36.58

34.83

32.70

30.36

27.93

25.51

23.20

21.09

19.21

ψl(t)

3.894

3.770

3.612

3.424

3.211

2.982

2.745

2.507

2.276

2.055

1.851

2060

2061

2062

2063

2064

2065

2066

2067

2068

2069

2070

ψu(t)

17.63

16.36

15.44

14.88

14.68

14.81

15.27

16.01

16.97

18.11

19.35

ψl(t)

1.664

1.496

1.348

1.219

1.107

1.012

0.931

0.862

0.803

0.752

0.708

2071

2072

2073

2074

2075

2076

2077

2078

2079

2080

2081

ψu(t)

20.61

21.79

22.82

23.62

24.13

24.30

24.13

23.63

22.84

21.81

20.61

ψl(t)

0.668

0.632

0.597

0.563

0.529

0.495

0.461

0.426

0.390

0.355

0.320

2082

2083

2084

2085

2086

2087

2088

2089

2090

2091

2092

ψu(t)

19.30

17.96

16.63

15.37

14.22

13.20

12.33

11.63

11.09

10.72

10.51

ψl(t)

0.286

0.254

0.223

0.195

0.169

0.145

0.123

0.104

0.087

0.072

0.060

2093

2094

2095

2096

2097

2098

2099

2100

ψu(t)

10.44

10.50

10.67

10.91

11.20

11.51

11.81

12.06

ψl(t)

0.048

0.039

0.031

0.023

0.017

0.012

0.007

0.003

160

6 How to Adjust the Family Planning Policy in China?

By comprehensively considering these relationships among the variables, this section expresses all constraint conditions as follows: ⎧ m  ⎪ ⎪ ⎪ P(t) = L i (t) + (1 − u 0 )ψ(t) ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ ⎪ ⎪ L 1 (t + 1) = [1 − u 0 ]ψ(t) ⎪ ⎪ ⎪ ⎪ ⎪ L i+1 (t + 1) = [1 − u i ]L i (t) ⎪ ⎪ ⎪ 64 ⎪ ⎪ ⎪ ⎪ ⎨ α(t) = i=15 L i (t) P(t) . m ⎪ ⎪ ⎪ i=65 L i (t) ⎪ ⎪ o(t) = ⎪ ⎪ P(t) ⎪ ⎪ ⎪ ⎪ 1 − α(t) ⎪ ⎪ ⎪ d(t) = ⎪ ⎪ α(t) ⎪ ⎪ ⎪ ⎪ ⎪ ψl(t) ≤ ψ(t) ≤ ψu(t) ⎪ ⎪ ⎩ i = 1, 2, . . . , m. m = 100

(6.7)

Based on the analyses above, the non-linear integer programming model can be described as follows: 2100  1 × d(t) Min 85 t=2016 ⎧ P(t) < 1,500,000,000 ⎪ ⎪ ⎪ ⎪ ⎪ o(t) ≤ 0.25 ⎪ ⎪ ⎪ ⎪ m ⎪  ⎪ ⎪ ⎪ P(t) = L i (t) + (1 − u 0 )ψ(t) ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ ⎪ ⎪ ⎪ L 1 (t + 1) = [1 − u 0 ]ψ(t) ⎪ ⎪ ⎪ ⎪ L (t + 1) = [1 − u ]L (t) ⎪ i+1 i i ⎪ ⎪ ⎪ 64 ⎪ ⎨ i=15 L i (t) s.t. α(t) = . P(t) ⎪ ⎪  ⎪ m ⎪ ⎪ ⎪ i=65 L i (t) ⎪ o(t) = ⎪ ⎪ ⎪ P(t) ⎪ ⎪ ⎪ ⎪ 1 − α(t) ⎪ ⎪ d(t) = ⎪ ⎪ ⎪ α(t) ⎪ ⎪ ⎪ ⎪ ψl(t) ≤ ψ(t) ≤ ψu(t) ⎪ ⎪ ⎪ ⎪ ⎪ ψ(t) ∈ integers ⎪ ⎪ ⎪ ⎩ i = 1, 2, . . . , m. m = 100

(6.8)

6.3 Non-Linear Integer Programming Model for Adjusting the Family Planning Policy

161

6.3.3 Data Description for the Non-Linear Integer Programming Model The population aged i in year t (L i (t)) can be acquired directly from the China Population and Employment Statistics Yearbook (Department of Population and Employment Statistics National Bureau of Statistics of China 2015). The death rate of age i (u i ) can be obtained from the Tabulation on the 2010 Population Census of the People’s Republic of China (Population Census Office under the State Council and Department of Republation and Employment Statistics National Bureau of Statistics 2012). The specific data are shown in Table 6.8.

6.3.4 The Optimal Results for the Total Dependency Ratio Based on the Lingo platform, this section calculates the rational numbers of newborn babies from 2016 to 2100 and presents them in Table 6.9. Under the reform path proposed by the programming model, the average total dependency ratio is as low as 0.542293.

6.4 Reform Path for China’s Family Planning Policy If the numbers of newborn babies are consistent with the optimal results shown in Table 6.9, China’s demographic dividends will have satisfactory performance. Thus, it is necessary for policymakers to propose a reasonable reform path for China’s family planning policy with the goal of making the actual numbers of newborn babies and the optimal values consistent.

6.4.1 Reform Intensity The reform intensity of the family planning policy in year t is defined as φ(t) and can be calculated from Eq. (6.9). The value of φ(t) ranges 0 to 1. Specifically, φ(t) = 1 means that China must cancel its family planning policy to encourage fertility, and φ(t) = 0 means that China must implement a strict family planning policy to control fertility. φ(t) =

ψ(t) − ψl(t) . ψu(t) − ψl(t)

(6.9)

162

6 How to Adjust the Family Planning Policy in China?

Table 6.8 The required data for the non-linear integer programming model Age

Total population (million)

Mortality rate (‰)

Age

Total population (million)

Mortality rate (‰)

0

14.85644769

3.82

51

26.70924574

3.75

1

14.07785888

1.11

52

20.45620438

3.98

2

16.13260341

0.63

53

10.99635036

4.41

3

15.56690998

0.45

54

14.13138686

4.98

4

17.21289538

0.37

55

12.74817518

5.18

5

16.20559611

0.33

56

16.33576642

5.64

6

16.05961071

0.32

57

17.8892944

6.09

7

15.013382

0.28

58

16.43187348

6.81

8

15.22506083

0.28

59

17.38442822

7.67

9

14.30048662

0.28

60

17.57542579

8.54

10

14.94160584

0.30

61

15.45985401

9.38

11

13.24939173

0.29

62

15.68004866

10.38

12

13.80656934

0.30

63

13.21411192

11.12

13

14.34306569

0.29

64

13.02068127

13.01

14

14.56690998

0.30

65

12.37226277

14.21

15

14.34671533

0.34

66

10.66058394

14.74

16

16.1350365

0.35

67

9.920924574

17.23

17

15.89659367

0.39

68

9.407542579

18.64

18

15.77128954

0.41

69

8.379562044

21.91

19

16.58272506

0.43

70

8.00243309

25.57

20

18.92822384

0.47

71

7.236009732

26.73

21

20.27372263

0.47

72

6.936739659

30.94

22

20.72384428

0.50

73

6.899026764

33.59

23

21.76520681

0.54

74

6.367396594

37.44

24

28.75425791

0.56

75

5.412408759

41.51

25

25.56812652

0.58

76

5.790754258

42.19

26

24.44038929

0.57

77

5.420924574

50.97

27

26.33941606

0.59

78

4.809002433

56.20

28

23.26277372

0.61

79

4.515815085

62.12

29

20.63868613

0.68

80

4.003649635

74.28

30

19.55961071

0.70

81

3.96836983

77.91

31

19.53527981

0.77

82

3.077858881

85.81

32

22.63381995

0.81

83

2.596107056

93.52

33

19.52554745

0.83

84

2.520681265

103.63

34

19.16788321

0.94

85

1.901459854

111.00 (continued)

6.4 Reform Path for China’s Family Planning Policy

163

Table 6.8 (continued) Age

Total population (million)

Mortality rate (‰)

Age

35 36

Total population (million)

Mortality rate (‰)

19.6459854

1.03

86

1.718978102

118.88

19.33698297

1.06

87

1.323600973

130.07

37

18.24087591

1.14

88

1.00729927

144.18

38

20.95620438

1.21

89

0.866180049

156.87

39

21.32360097

1.34

90

0.698296837

176.52

40

22.81508516

1.51

91

0.551094891

185.24

41

24.05109489

1.55

92

0.368613139

202.11

42

24.76034063

1.82

93

0.316301703

207.35

43

24.99148418

1.89

94

0.201946472

208.91

44

27.41970803

2.07

95

0.070356853

220.01

45

24.69221411

2.31

96

0.070356853

220.99

46

26.59367397

2.36

97

0.070356853

204.84

47

21.53406326

2.54

98

0.070356853

198.13

48

23.80170316

3.11

99

0.070356853

257.65

49

24.11922141

3.28

100

0.070356853

454.35

50

22.486618

3.64

Note The China Population and Employment Statistics Yearbook offers only the total population over 95 and lacks an accurate population for each age range 95–100. Due to the minimal impact of these ages on our results, the population for each age range 95–100 is handled simply by dividing the total population aged 95–100 by 6

6.4.2 Reform Path The reform intensity of the family planning policy from 2016 to 2100 is computed from Eq. (6.9) and shown in Fig. 6.2. In terms of the value of φ(t), the whole research interval can be divided into five stages: (1) Stage 1 (2016–2033): China should continue to implement its current strict family planning policy, and begin to relax it with a reform intensity of 0.117069 in 2033; (2) Stage 2 (2034–2048): China should cancel its family planning policy and encourage fertility; (3) Stage 3 (2049–2057): China should discourage fertility from 2049 to 2051 but then reverse its position to gradually encourage fertility from 2052 to 2057; (4) Stage 4 (2058–2063): China should make every effort to encourage fertility; and (5) Stage 5 (2064–2100): China should no longer encourage fertility, and it must take measures to restrict fertility, for example, by reducing and even canceling subsidies and welfare in the birth period.

164

6 How to Adjust the Family Planning Policy in China?

Table 6.9 The rational numbers of newborn babies during 2016–2100 (unit: million) ψ(t) ψ(t) ψ(t) ψ(t) ψ(t) ψ(t) ψ(t) ψ(t) ψ(t)

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

14.39

15.19

15.99

16.69

17.23

17.54

17.56

17.26

16.61

15.65

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

14.46

13.12

11.75

10.43

9.222

8.158

7.253

7.899

16.75

15.53

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

14.86

14.84

15.55

16.97

19.04

21.64

24.60

27.70

30.73

33.48

2046

2047

2048

2049

2050

2051

2052

2053

2054

2055

35.76

37.39

38.29

32.41

21.23

21.46

21.62

21.74

21.88

21.95

2056

2057

2058

2059

2060

2061

2062

2063

2064

2065

22.01

21.99

21.09

19.21

17.63

16.36

15.44

14.88

9.527

1.012

2066

2067

2068

2069

2070

2071

2072

2073

2074

2075

0.931

0.862

0.803

0.752

0.708

0.668

0.632

0.597

0.564

0.529

2076

2077

2078

2079

2080

2081

2082

2083

2084

2085

0.495

0.461

0.426

0.390

0.355

0.320

0.286

0.254

0.223

0.195

2086

2087

2088

2089

2090

2091

2092

2093

2094

2095

0.169

0.145

0.123

0.104

0.087

0.072

0.060

0.048

0.039

0.031

2096

2097

2098

2099

2100

0.023

0.017

0.012

0.007

0.003

6.5 Evaluations for the Proposed Reform Path To comprehensively evaluate the results of the proposed non-linear integer programming model and the reform path, this section compares the population developments under the proposed reform path with that under three possible family planning policies: the one-child policy, the two-child policy, and cancellation of the family planning policy.

6.5.1 Population Simulation Under the Proposed Reform Path Based on the Lingo platform, this section simulates the population development from 2016 to 2100 under the proposed reform path of adjusting China’s family planning policy and calculates four key indicators: the total population, total dependency ratio, proportion of the labour-aged population (aged 15–64), and proportion of the elderly people (aged 65+). The predicted population development under the proposed reform path is presented in Table 6.10. The values of these four indicators are presented in Fig. 6.3. As shown in Fig. 6.3, all four key indicators perform well during the whole research period. The total

6.5 Evaluations for the Proposed Reform Path

165

Table 6.10 Population development under the proposed reform path Year

Total Population (billion)

Total dependency ratio

Proportion of labour-aged population (15–64) (%)

Proportion of elderly people (65+) (%)

2016

1.374

0.368342

73.0812

10.5193

2017

1.380

0.376104

72.6689

10.9489

2018

1.386

0.388286

72.0313

11.5130

2019

1.393

0.401255

71.3646

12.0204

2020

1.401

0.414859

70.6784

12.6328

2021

1.408

0.429022

69.9779

13.1921

2022

1.414

0.440142

69.4376

13.6523

2023

1.421

0.452884

68.8286

14.1791

2024

1.426

0.452884

68.8286

14.5800

2025

1.429

0.462254

68.3876

14.7313

2026

1.431

0.462481

68.3770

14.9613

2027

1.432

0.459175

68.5319

14.9841

2028

1.431

0.466195

68.2038

15.6035

2029

1.427

0.483213

67.4212

16.6069

2030

1.423

0.491598

67.0422

17.3291

2031

1.417

0.501531

66.5987

18.1441

2032

1.409

0.508365

66.2969

18.9270

2033

1.402

0.510859

66.1875

19.5360

2034

1.403

0.528717

65.4143

20.3167

2035

1.402

0.541223

64.8835

20.9609

2036

1.401

0.556225

64.2581

21.7593

2037

1.399

0.567005

63.8160

22.3731

2038

1.397

0.578323

63.3584

22.9340

2039

1.396

0.590847

62.8596

23.3981

2040

1.397

0.605158

62.2992

23.7242

2041

1.400

0.621951

61.6541

23.8881

2042

1.406

0.643878

60.8318

23.9536

2043

1.414

0.667388

59.9741

23.7766

2044

1.425

0.699048

58.8565

23.5999

2045

1.439

0.737359

57.5586

23.3809

2046

1.454

0.780451

56.1655

23.0843

2047

1.471

0.829146

54.6703

22.7746

2048

1.488

0.885071

53.0484

22.6260

2049

1.500

0.906829

52.4431

22.3740 (continued)

166

6 How to Adjust the Family Planning Policy in China?

Table 6.10 (continued) Year

Total Population (billion)

Total dependency ratio

Proportion of labour-aged population (15–64) (%)

Proportion of elderly people (65+) (%)

2050

1.500

0.917232

52.1585

22.2802

2051

1.500

0.931721

51.7673

22.2364

2052

1.500

0.952209

51.2240

22.3324

2053

1.500

0.978138

50.5526

22.5954

2054

1.500

0.996336

50.0918

22.7321

2055

1.500

1.011747

49.7080

22.9248

2056

1.500

1.027625

49.3188

23.2914

2057

1.500

1.01859

49.5395

23.2455

2058

1.499

0.997645

50.0590

23.1509

2059

1.496

0.965726

50.8718

23.0580

2060

1.492

0.922969

52.0029

22.9134

2061

1.487

0.87012

53.4725

22.6553

2062

1.481

0.813551

55.1405

22.3661

2063

1.474

0.757775

56.8901

22.0994

2064

1.463

0.710706

58.4554

21.9236

2065

1.443

0.674133

59.7324

21.7710

2066

1.423

0.638227

61.0416

21.6429

2067

1.403

0.602351

62.4083

21.5072

2068

1.383

0.566189

63.8493

21.3463

2069

1.363

0.529735

65.3708

21.1570

2070

1.344

0.496379

66.8280

21.0790

2071

1.324

0.462718

68.3659

20.9694

2072

1.305

0.431015

69.8805

20.9271

2073

1.286

0.401056

71.3747

20.8834

2074

1.267

0.375808

72.6845

20.9235

2075

1.248

0.35342

73.8869

20.9892

2076

1.230

0.334378

74.9413

21.1419

2077

1.211

0.314808

76.0567

21.1979

2078

1.193

0.296925

77.1055

21.3127

2079

1.175

0.284218

77.8684

21.2983

2080

1.158

0.284507

77.8509

21.3596

2081

1.140

0.284611

77.8446

21.4073

2082

1.123

0.286138

77.7521

21.5394

2083

1.106

0.289124

77.5721

21.7581 (continued)

6.5 Evaluations for the Proposed Reform Path

167

Table 6.10 (continued) Year

Total Population (billion)

Total dependency ratio

Proportion of labour-aged population (15–64) (%)

Proportion of elderly people (65+) (%)

2084

1.090

0.293454

77.3124

22.0559

2085

1.073

0.299073

76.9779

22.4283

2086

1.057

0.305636

76.5910

22.8532

2087

1.041

0.312729

76.1772

23.3051

2088

1.025

0.319854

75.7659

23.7543

2089

1.009

0.326453

75.3890

24.1691

2090

0.9933

0.332055

75.0720

24.5235

2091

0.9776

0.336129

74.8431

24.7891

2092

0.9619

0.338319

74.7206

24.9471

2093

0.9463

0.338652

74.7020

25.0000

2094

0.9308

0.336992

74.7948

24.9399

2095

0.9153

0.33346

74.9929

24.7725

2096

0.8998

0.328268

75.2860

24.5082

2097

0.8843

0.321693

75.6606

24.1604

2098

0.8688

0.316338

75.9683

23.8772

2099

0.8532

0.326632

75.3789

24.4890

2100

0.8377

0.335323

74.8883

25.0000

20

1.5

15

1

10 0.5

5 0 2010

2030

2050

2070

2090

2110

0 2010

a.Total population (unit: million) 1 0.8 0.6 0.4 0.2 0 2010

2030

2050

2070

2090

2030

2050

2070

2090

2110

b. Total dependency ratio

2110

1 0.8 0.6 0.4 0.2 0 2010

c. Proportion of labor-age population

Fig. 6.3 Simulation results under the proposed reform path

2030

2050

2070

2090

d. Proportion of older population

2110

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6 How to Adjust the Family Planning Policy in China?

population will increase to a peak of 1.427 billion in 2027, and it will then drop to 0.8 billion in 2100. The total dependency ratio will inevitably experience a dramatic jump and reach the highest point of approximately 1 in 2055. Fortunately, however, the value will rapidly decrease and remain stable at the ideal level of 0.3 after 2075. Under the proposed reform path of the family planning policy adjustment, China’s demographic dividends can be well sustained except in a short period around 2060, when the proportion of the labour-aged population will be less than 50%. The proportion of the elderly population will stabilize at approximately 0.2 during the whole research period.

6.5.2 Policy Comparison With the help of the Vensim platform, this book builds a system dynamics model to simulate population development under different family planning policies in Chap. 5. To compare the possible population development under different family planning policies, including the one-child policy, the two-child policy, and cancellation of the family planning policy, this section compares the policy effects among these four family planning policies. The same four indicators are selected and shown in Fig. 6.4. The total population can be consistently well-controlled only under the two-child policy. If the family planning policy is cancelled now, China’s total population will

Fig. 6.4 Simulation results under three different family planning policies

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soon exceed 1.5 billion and reach a peak of 1.728 billion in 2029. If the one-child policy is maintained, the total population will plummet to 0.3 billion, which is also unsuitable. The total dependency ratio cannot obtain good performance under these three family planning policies. The two-child policy and cancellation of the family planning policy will maintain the total dependency ratio at approximately 1. If the strict one-child policy were kept, this indicator would exceed 2, causing a terrible demographic debt. The current good demographic dividends will quickly disappear under these three family planning policies. Similar to the total dependency ratio, the proportion of the labour-aged population (aged 15–64) will also obtain poor performance. Under these three family planning policies, this proportion will plummet to 0.5 after 2060. The proportion of the elderly population (aged 65+) can obtain relatively good performance only under the cancellation of the family planning policy. If the family planning policy is cancelled now, the maximum of this proportion will be 30.27% in 2067. If the one-child policy is maintained, this proportion will even exceed 60%, indicating a disaster in which elderly people will account for more than half of the total population. Under the current two-child policy, this proportion seems to be closer to 50% in the future. The results of the comparison between Figs. 6.3 and 6.4 show that the policy effects of the proposed reform path significantly precede those of these three fertility policies. Under the proposed reform path, the total population will not exceed 1.5 billion and it will stabilize at a reasonable level of approximately 0.84 billion in 2100. The average annual total dependency ratio will be only 0.542293, and the proportion of the labour-aged population (aged 15–64) will be larger than 70% after 2072. Furthermore, the proportion of the elderly population will consistently not exceed 25%. Based on these results, I can clearly state that implementing only one fixed family planning policy from this point forward is insufficient and that these different family planning policies should be grouped together to optimize the population size and population structure. The proposed reform path adopted by the non-linear integer programming model is a perfect package of different family planning policies, leading to positive outcomes for all these indicators.

6.6 Sensitivity Analysis Some uncertain factors, such as the upper bound of the research interval, may influence the reform path for adjusting China’s family planning policy. Although the proportion of the elderly population will not exceed 25% before 2100, this proportion will still be lower than 25% after 2100 when the research interval is from 2016 to 2100. A sensitivity analysis of the upper bound of the research interval is conducted to evaluate the effect of the upper bound on the proposed reform path. The value of the upper bound is set from 2100 to 2150, changing by 10 per unit. For these

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six research intervals, this section calculates the corresponding reform path with the help of Lingo and presents the results in Fig. 6.5. As shown in Fig. 6.5, the reform path will be smoother and more realistic with the enlargement of the research interval. After systematic and in-depth comparison analysis, the whole research interval can be divided into two stages: (i) in Stage 1 (before 2036), China should continue to implement its current strict family planning policy; and (ii) in Stage 2 (after 2036), China should relax and even cancel its family planning policy and encourage fertility intentions. However, if China’s family planning policy is reformed only in 2036, it will not be possible to effectively sustain the country’s demographic dividends through 2100. Therefore, based on combining the reform path proposed in Sect. 6.4.2 and the results of the analyses presented in the above paragraph, the reform path should be revised as

(a) Research interval: from 2016 to 2100

(b) Research interval: from 2016 to 2110

(c) Research interval: from 2016 to 2120

(d) Research interval: from 2016 to 2130

(e) Research interval: from 2016 to 2140

(f) Research interval: from 2016 to 2150

Fig. 6.5 Reform intensities of family planning policies from 2016 to 2100 under different research intervals

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follows: (i) in Stage 1 (2016–2032), China should continue to implement its current strict family planning policy; (ii) in Stage 2 (2033–2064), China should gradually begin to relax its family planning policy, especially during 2036–2041; and (iii) in Stage 3 (2065–2100), China should cancel its family planning policy and encourage fertility.

6.7 Conclusions and Discussions 6.7.1 Conclusions and Suggestions Recent years have witnessed a sharp drop in China’s demographic dividends; therefore, some measures to reform China’s family planning policy have been adopted to optimize the population structure and to maintain the demographic dividends. Most people think that the proactive two-child policy can benefit China’s population development and prolong the duration of its demographic dividends. However, our simulation results and formula derivation reveal that China’s demographic dividend will deteriorate based on the current two-child policy before 2050. Thus, China must find a suitable reform path for the family planning policy and further adjust its family planning policy to optimize the demographic dividends. To find a suitable reform path, this section builds a non-linear integer programming model to calculate the rational numbers of newborn babies from 2016 to 2100. The aim of the programming model is to minimize the average total dependency ratio, and the constraint conditions consistently control the population size and population structure in an ideal range. By solving this non-linear integer programming model, this section proposes a suitable reform path for China’s family planning policy. Then, this section compares the population developments under the proposed reform path with that under three different family planning policies, i.e., the onechild policy, the two-child policy, and cancellation of the family planning policy, to evaluate our programming model. The comparison results show that the policy effects of the proposed reform path significantly precede those of these three fertility policies. Under the proposed reform path, the total population will not exceed 1.5 billion and will stabilize at a reasonable level of approximately 0.84 billion in 2100. The average annual total dependency ratio will be only 0.542293, and the proportion of the labour-aged population (aged 15–64) will be larger than 70% after 2072. The proportion of the elderly population (aged 65+) will consistently not exceed 25%. Finally, a sensitivity analysis of the upper bounds of the research interval is conducted to evaluate the effect of the upper bounds on the proposed reform path. After sufficiently considering the results of the sensitivity analysis, I provide the following final reform path: (i) in Stage 1 (2016–2032), China should continue to implement its current strict family planning policy; (ii) in Stage 2 (2033–2064), China should gradually begin to relax its family planning policy, especially during 2036–2041; and (iii) in Stage 3 (2065–2100), China should cancel its family planning policy and encourage fertility.

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6.7.2 Research Implications In this section, I would like to assess the implications of this chapter in terms of three dimensions: methodology, perspective, and results. An original method is used to research population ageing. A non-linear integer programming model is developed to find a suitable reform path for China’s family planning policy. Through comparison analysis and sensitivity analysis, I verify that the new programming model is feasible, efficient, and consistent. Moreover, this innovative programming model can be applied to analyse the population problems of other countries. When analysing the impact of the family planning policy on the demographic dividends, prior scholars have generally provided only the results of the policy effects and some suggestions; however, they have not been able to tackle the burdens of population ageing. If we want to thoroughly resolve this issue, we must understand that China’s family planning policy must be replanned. This chapter proposes a suitable reform path for China’s family planning policy from a totally new perspective of stabilizing the demographic dividends. The results hold tremendous practical significance and reference value. The reform path, which is a package of different family planning policies, ensures that China’s demographic dividends can be stabilized at a perfect level by simultaneously controlling the population size and population structure.

6.7.3 Limitations and Further Research Although this new programming model proposes a feasible and efficient reforming path for China’s family planning policy, there are nevertheless several limitations. When building this model, this section makes some simplified assumptions. For instance, the mortality rate in China is assumed to be a constant in the future because current advances in medical technology have lowered the mortality rate such that it can decrease only within a limited range. Although a virtually unchanging mortality rate is not realistic, the assumption can be accepted because its influence on the results is very small. It is difficult to accurately meet expectations with regard to the planned numbers of newborn infants in a realistic society. Although it is impossible to ensure that the actual numbers of newborn infants are the same as the ideal numbers presented, the planned numbers can provide a reasonable standard for the government to achieve. If China makes every effort to control the actual numbers of newborn infants to close to be the standard, the target can be considered to have been achieved, and the demographic dividends can be significantly improved. The population issue is a complicated engineering system related to multidisciplinary fields such as economics, sociology, and organizational behaviour. This chapter provides a feasible and efficient reform path for China’s family planning

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policy to minimize the average annual total dependency ratio while simultaneously controlling the population size and population structure. Going forward, we should track Chinese women’s fertility intentions, constantly monitor population development, and pay closer attention to the actual numbers of newborn babies. Acknowledgements Reprinted by permission from Springer Nature Customer Service Centre GmbH: Springer, Social Indicators Research, and Reforming path of China’s fertility policy in stabilizing demographic dividends perspective by Pengkun Wu, Chong Wu, and Yuanyuan Wu (January 1, 2017).

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Part II

Challenge Two: Provincial Population Difference

Chapter 7

The Necessity of Family Planning Policy Adjustment Among China’s Provinces

Abstract The one-child family planning policy introduced in the 1970s to control China’s population changed the population structure over the decades that followed. Significant problematic effects, such as population ageing, are more serious today and have led to active reforms of the country’s family planning policy. Because of China’s regional diversity, every province has its own unique necessity of family planning policy adjustment. To understand the provincial differences with regard to the necessity of adjustment, this section builds original formulas to quantify the necessity of adjustment and then utilizes the k-means cluster method to divide China’s 31 provinces into four categories. The results show that serious regional disparities do indeed exist among the provinces. For some, such as Shanghai and Beijing, an urgent family planning policy reform is required, but for others, such as Guangxi, the opposite holds true. Meanwhile, neighbouring provinces always tend to be clustered in the same categories, clearly indicating spatial aggregation. In addition to the geographic element, factors such as the economy also lead to an aggregation effect. Furthermore, most provinces show similar classification results under two separate conditions, combining all various indicators and considering only the demographic indicators, presenting coordinated development among China’s population, economy, and society. However, many Chinese provinces cannot clearly identify the need for family planning policy adjustment through a straightforward diagnosis. Therefore, certain target measures are proposed as important suggestions for tracking China’s family planning policy. Keywords Necessity of adjustment · Family planning policy · Regional diversity · Provincial differences · Population ageing · Spatial aggregation

7.1 Introduction As described in Part I, China still faces serious challenges posed by the population size and population structure and should adjust the family planning policy in a timely manner. However, the necessity of family planning policy adjustment is significantly different among the 31 provinces.

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In China, whose population exceeds 1.4 billion and accounts for one-fifth of the earth’s population, provincial differences exist in multiple fields (Biggeri et al. 2017). Only by clearly understanding and responding well to the existence of these provincial differences can a specific industry sustainably develop. For instance, in the field of water resources, the distribution is geographically uneven (Wu et al. 2016); 81% of such resources are intensively distributed in the Yangtze River basin and southern regions (Chen and Xia 1999). In the field of wealth distribution, the eastern provinces are generally poorer than the central provinces (Yang and Mukhopadhaya 2017). In the field of housing property, population census data reveal that institutional change and wealth disparities cause different attitudes towards housing choices in different regions (Yi 2006). In the field of administration management, the differences in the population size, transfer payment ratio, and employment structure cause provincial differences in the number of civil servants (Sun and Li 2011). In the poster service field, GDP, the population size and the urbanization rate cause provincial differences in postal service revenues (Wu et al. 2008). In the field of the land use rate, urban land use and socio-economic statistical data reveal provincial differences in the intensive land use level in urban regions (Cao and Xu 2008). In the field of higher education, there are significant provincial differences in both the absolute size and relative size of the development of private higher education (Fang and Zhong 2011). Different provincial development levels are fundamental to the disparities under the population situation. Similar to these indicators, China’s population also has significant regional differences among the provinces (Yi et al. 2011). For instance, Jiangxi Province has a slight ageing phenomenon and greater pressure on the total population, whereas the opposite holds true for Beijing Province. Accurate research on the spatial pattern of the population is critical for policy-making and spatial planning in all related fields, including urbanization, land use development, ecological conservation, and environmental protection (Deng et al. 2015). Existing studies reveal that the children dependency ratio has a high spatial aggregation effect in the south-west and north-east regions (Liu et al. 2011). Studying regional differences using methods such as variance analysis and decomposition, Li et al. found that China’s population ageing has significant regional differences and that the elderly dependency ratio is serious, moderate, and slight in China’s eastern regions, central regions, and western regions, respectively (Li and Wang 2008). However, a few studies have analysed the significant provincial population differences in China. China’s family planning policy should be adjusted as soon as possible due to continuous pressures from many sides. However, there is a very large regional disparity in the necessity of family planning policy adjustment among the 31 provinces in China. Gaining a clear understanding of these differences will significantly contribute to guiding and tracking China’s population development. In this chapter, the necessity of family planning policy adjustment for each province is quantified, and the 31 provinces are classified into different categories. The remainder of the chapter is structured as follows. In Sect. 7.2, the evaluation index system addressing the necessity of family planning policy adjustment is constructed. Then, the indicators are quantified and the evaluation scores of the necessity of adjustment for the 31 provinces are calculated in Sect. 7.3. Subsequently

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in Sect. 7.4, the classifications of provinces under different conditions are analysed in depth to explore the provincial differences. Section 7.5 analyses the spatial aggregation of the 31 provinces in terms of the necessity of family planning policy adjustment. Finally, in Sect. 7.6, conclusions are drawn and suggestions are proposed.

7.2 The Evaluation Index System on the Necessity of Family Planning Policy Adjustment Many scholars have claimed that China’s family planning policy should be reformed as soon as possible due to continuous pressures from many sides, such as population ageing. To date, however, no one has systematically researched a complete framework for determining the necessity of family planning policy adjustment for China. An evaluation index system on the necessity of family planning policy adjustment is first constructed as preparatory work. Studies on group thinking and brainstorming are frequently used to build index systems (Yazdani and Tavakkoli-Moghaddam 2012). A rough framework can be proposed based on studies, and then, pivotal indicators can be efficiently found through brainstorming meetings. As a result, the complete index system for this question (i.e., the necessity of family planning policy adjustment for China) can be presented.

7.2.1 Literature Review As China’s family planning policy has often been adjusted, scholars have paid more attention to the country’s population development and have conducted in-depth studies on the various causes of the necessity of family planning policy adjustment. The primary population problems in China now include the huge population size, serious population ageing, and the obvious contradiction between the population and environmental resources (Li 2013). The pressure of China’s population is so great that the main target of family planning policy is to control the total population. China’s population increased rapidly in the 1950s and 1960s with national economic development and national encouragement, and it reached 830 million in 1970 (Li and Lin 2016; Rhode 2005). Subsequently, policymakers developed a stricter and controversial family planning policy (i.e., the one-child policy) and launched a stronger campaign to implement it. The reform created an extremely low total fertility rate that is currently far below the replacement level (Cai 2010; Guilmoto 2016; Yi 2007). According to statistical data, the total fertility rate has been lower than 1.6 since the mid-1990s (Zhao and Guo 2010), and the declining trend has become more serious (Zhao and Guo 2007).

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Hence, many demographers and researchers have become proactive in lobbying the government to adjust its family planning policy (Zhao 2015). As found in Chapter 5, however, the population structure has severely deteriorated under the original family planning policy, and today it presents the obvious shape of an inverted pyramid. A world ageing report finds that population ageing is taking place all over the world and that the proportion of the elderly people will increase from 9.7% in 1990 to 21.1% in 2050 (Wang et al. 2016). China’s population ageing problem is more serious than that of other countries due to the unique one-child policy adopted in the late 1970s (Li 2013; Zhao 2015). In China, the proportion of the elder people (65+) was 8.2% in 2010, and it is projected to be 11.8%, 16.2%, 22.7%, 24.7%, and 30.2% in 2020, 2030, 2040, 2050, and 2065, respectively (Li and Lin 2016). Thus, improving the population structure should be one of the vital factors that determine the necessity of family planning policy adjustment. Rapid population ageing leads to new problems and challenges and influences social and economic development (Liao 2013). In China, population ageing caused by the original family planning policy is already placing a burden on government finances (Braun and Joines 2015; Hondroyiannis and Papapetrou 2000). Population ageing also affects many aspects of society, including health, social security, education, sociocultural activities, family life, and the labour market (Bujard 2015; Ince Yenilmez 2015). Therefore, China must adjust its population policies over different periods to promote the healthy and sustainable social and economic development (Chen and Yang 2016; Li 2013). After a review of the literature, I obtained our initial grade assessment indexes, including demographic factors (i.e., population size factors and population structure factors) and socio-economic factors (i.e., economic factors and social factors).

7.2.2 Evaluation Indexes Brainstorming is an efficient method to collect the opinions of experts when they sit together to discuss a problem face to face. The participants in our brainstorm meeting were composed of a moderator, common participants, and recorders. Based on our meetings, the accepted indicators, which are regarded as secondary assessment indicators in the index system, are introduced as follows.

7.2.2.1

Population Size Factors

For the population size factors, two secondary indicators are proposed. (i) The first concerns controlling the total fertility rate. The total fertility rate reflects the number of newborn infants and directly affects the total population in the future. Data from the 6th National Population Census show that in 2010, China’s total fertility rate was only 1.18, which is far below the replacement level of approximately 1.8 (National Population Development Strategy Research Group 2007). (ii) The second concerns

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controlling the total population. The main target of the family planning policy is to control the total population, whose pressure in the future could depend on the proportion of women of childbearing age. The calculation formulas of these two indicators are shown in Eq. (7.1): ⎧ 1.8 − TFRi ⎪ i ⎪ ⎨ C11 = 1.8 − TFR . WCP ⎪ i ⎪ ⎩ C12 = WCPi

(7.1)

i denotes the necessity of family planning policy adjustIn the above indicators, C11 ment of the ith province when considering only controlling the total fertility rate; TFR denotes the overall total fertility rate in China; TFRi denotes the total fertility i denotes the necessity of family planning policy adjustrate of the ith province; C12 ment of the ith province when considering only controlling the total population; WCP denotes the overall proportion of women of childbearing age in China; and WCPi denotes the proportion of women of childbearing age in the ith province.

7.2.2.2

Population Structure Factors

For the population structure factors, three secondary indicators are proposed. (i) The first concerns addressing population ageing. Considering the serious stage of population ageing, China must urgently reform the family planning policy (Zhang and Chen 2020). The severity of population ageing in the future could depend on the average age, which can reflect whether the age distribution is rational. (ii) The second concerns improving the demographic dividends. China’s demographic dividends first became negative in 2014 because of the strict one-child family planning policy, which must now be reformed. In the future, the demographic dividends could depend on the proportion of children. (iii) The third concerns dealing with the gender inequality. The strict family planning policy has led to a great gender gap due to the preference for sons; thus, the policy must now be reformed. The calculation formulas for these three indicators are shown in Eq. (7.2): ⎧ AGi i ⎪ C21 = ⎪ ⎪ ⎪ AG ⎪  ⎪ ⎪ ⎪ AGi = NDi j × αi j ⎪ ⎪ ⎨ j

⎪ CP ⎪ i ⎪ C22 = ⎪ ⎪ ⎪ CP i ⎪ ⎪ ⎪ ⎪ ⎩ C i = MRi 23 MR

.

(7.2)

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i In the above indicators, C21 denotes the necessity of family planning policy adjustment of the ith province when considering only addressing population ageing; AG denotes the overall average age in China; AGi denotes the average age in the ith province; NDi j denotes the average age in the jth age interval in the ith province; αi j denotes the proportion of the population in the jth age interval in the ith province; i denotes the necessity of family planning policy adjustment of the ith province C22 when considering only improving the demographic dividends; CP denotes the overall proportion of children in China; CPi denotes the proportion of children in the ith i denotes the necessity of family planning policy adjustment of the ith province; C23 province when considering only dealing with the gender inequality; MR denotes the overall male–female ratio in China; and MRi denotes the male–female ratio in the ith province.

7.2.2.3

Economic Factors

For the economic factors, two secondary indicators are proposed. (i) The first concerns eliminating the adverse selection of population quality. The adverse selection of population quality means an abnormal phenomenon whereby the number of newborn infants in underdeveloped provinces is much greater than that in developed provinces. Fertility should certainly be encouraged more in provinces with a healthy economy. (ii) The second concerns maintaining economic growth. According to some studies, family planning policy can also directly affect regional economic development (Liu and Lu 2008; Zhang and Wang 2014). If the family planning policy is relaxed, then some barriers impeding economic development will be created. Thus, the provinces with a high economic growth speed should be protected by reducing the necessity of policy adjustment. The calculation formulas of these two indicators are shown in Eq. (7.3): ⎧ PGRi ⎪ i ⎪ ⎨ C31 = PGR . ⎪ i ⎪ C = GSGR ⎩ 32 GSGRi

(7.3)

i denotes the necessity of family planning policy adjustIn the above indicators, C31 ment of the ith province when considering only eliminating the adverse selection of population quality; PGR denotes the per capita GDP of China; PGRi denotes per i capita regional GDP of the ith province; C32 denotes the necessity of family planning policy adjustment of the ith province when considering only maintaining economic growth; GSGR denotes the growth speed of China’s GDP; and GSGRi denotes the growth speed of the regional GDP of the ith province.

7.2 The Evaluation Index System on the Necessity …

7.2.2.4

185

Social Factors

For the social factors, two secondary indicators are proposed. (i) The first concerns improving the family structure. In China, under the original one-child family planning policy, the family structure tends to have a “4-2-1” pattern, and it must urgently be improved. (ii) The second concerns considering fertility intentions. A new family planning policy should adequately consider fertility intentions, which can be measured by the ideal number of children. The calculation formulas of these two indicators are shown in Eq. (7.4): ⎧ HS ⎪ i ⎪ ⎨ C41 = HSi ⎪ INCi ⎪ i ⎩ C42 = INC

(7.4)

i In the above indicators, C41 denotes the necessity of family planning policy adjustment of the ith province when considering only improving the family structure; HS denotes the overall household size in China; HSi denotes the household size in the i denotes the necessity of family planning policy adjustment of the ith province; C42 ith province when considering only fertility intentions; INC denotes the overall ideal number of children in China; and INCi denotes the ideal number of children of the ith province.

7.2.3 Evaluation Index System Construction The evaluation index system for quantifying the extent of the necessity of family planning policy adjustment is preliminarily constructed in this section, and it contains the target layer, and the primary and secondary index layers. The target layer is the necessity of family planning policy adjustment; the primary index layer contains the four macroscopic factors described in Sect. 7.2.1; and the secondary index contains the nine indicators described in Sect. 7.2.2. The evaluation index system for the necessity of family planning policy adjustment is shown in Fig. 7.1.

7.3 Evaluation of the Necessity of Policy Adjustment 7.3.1 Data Sources When adjusting the family planning policy in 2013, China understood the provincial population differences and allowed the 31 provinces to implement the selective twochild policy. Section 8 analyses the implementation time of the selective two-child

186

7 The Necessity of Family Planning Policy … The 1st index layer

Population size factors (C1)

Demographic factors

Population structure factors (C2) Adjusting necessity of the family planning policy

Economic factors (C3)

The 2nd index layer

Corresponding required data

Controlling total fertility rate (C11)

Total fertility rate (TFR)

Controlling total population (C12)

Proportion of women of childbearing age (WCP)

Improving population ageing (C21)

Average age (AG)

Improving demographic dividend (C22)

Proportion of children (CP)

Improving gender imbalance (C23)

Male-female ratio (MR)

Eliminating adverse selection of population quality (C31)

Per capita regional GDP (PRG)

Keeping economic growth (C32)

Growth speed of regional GDP (GSRG)

Improving family structure (C41)

Household size (HS)

Considering fertility intention (C42)

Ideal number of children (INC)

Economic-social factors Social factors (C4)

Fig. 7.1 Evaluation index system for the necessity of family planning policy adjustment

policy. Thus, this chapter quantifies the necessity of implementing the selective twochild policy for the 31 provinces. Since the selective two-child policy was officially implemented in 2013, this chapter quantifies the necessity of policy adjustment for the 31 provinces in 2012. The required data can be taken from the China Statistics Yearbook 2013, the Tabulation on the 2010 Population Census of the People’s Republic of China, and some research papers. Specifically, the male–female ratio (MR), per capita regional GDP (PRG), the growth speed of regional GDP (GSRG), and household size (HS) can be directly acquired from the China Statistics Yearbook 2013 (National Bureau of Statistics of China 2014). The total fertility rate (TFR), average age (AG), the proportion of women of childbearing age (WCP), and the proportion of children (CP) can be calculated using the provincial data from the Tabulation on the 2010 Population Census of the People’s Republic of China (Population Census Office under the State Council and Department of Republation and Employment Statistics National Bureau of Statistics 2012). The data on the ideal number of children can be obtained by combining the results of two papers published by leading Chinese journals (Wang et al. 2004; Zhuang et al. 2014). The specific data are shown in Table 7.1.

7.3.2 Evaluation Model The evaluation indexes include nine indicators (i.e., {C11 , C12 , C21 , C22 , C23 , C31 , C32 , C41 , C42 }), five of which (i.e., {C11 , C12 , C21 , C22 , C23 }) are demographic factors. The overall evaluation of

7.3 Evaluation of the Necessity of Policy Adjustment

187

Table 7.1 Required data of the nine indicators TFR

WCP (%)

AG

CP (%)

MR

PRG

GSRG (%)

China

1.1811

16.2258

Beijing

0.7067

21.2645

Tianjin

0.9128

19.8556

37.504

Hebei

1.3107

16.0316

Shanxi

1.0951

16.0108

Inner Mongolia

1.0708

17.1294

Liaoning

0.7409

15.6204

HS

INC

35.684

24.1003

105.12

38420

37.263

14.0652

105.18

87475

9.154

3.02

1.93

7.124

2.53

15.9933

98.61

1.76

93173

9.341

2.76

1.76

35.371

24.2061

34.715

26.5369

104.60

36584

7.698

3.26

1.91

104.46

33628

7.242

3.06

36.391

1.92

20.8099

104.44

63886

10.20

2.78

1.78

39.548

17.1662

100.53

56649

11.60

2.69

1.73

12.88

Jilin

0.7600

16.4469

38.052

18.1251

103.47

43415

2.88

1.69

Heilongjiang

0.7514

16.8419

38.058

17.8324

103.66

35711

8.812

2.77

1.61

Shanghai

0.7367

19.2282

39.049

13.4845

108.78

85373

3.407

2.35

1.65

Jiangsu

1.0539

16.2028

38.012

20.1831

98.90

68347

9.724

2.97

1.81

Zhejiang

1.0171

17.1086

37.298

19.8078

104.58

63374

6.96

2.68

1.85

Anhui

1.4816

14.4025

36.011

25.7483

108.78

28792

12.21

3.02

1.86

Fujian

1.1195

18.6220

34.827

23.0998

101.89

52763

11.37

2.82

2.04

Jiangxi

1.3851

15.8638

33.116

29.591

107.64

28800

10.13

3.43

2.09

Shandong

1.1663

15.9250

37.103

21.4376

102.14

51768

2.91

1.94

Henan

1.3011

15.3053

33.880

28.8910

102.26

31499

3.37

1.97

Hunan

1.4172

15.4115

36.068

23.9882

106.57

33480

Guangdong

1.0644

18.709

32.783

26.4508

111.94

54095

Guangxi

1.7898

15.3850

33.843

29.1591

107.72

27952

Hainan

1.5127

15.9796

33.270

28.8048

113.60

32377

Chongqing

1.1643

13.8126

37.687

24.7822

101.37

9.365 9.902 12.05

3.07

1.98

3.26

2.24

10.37

3.32

2.11

12.04

3.64

2.14

38914

12.79

2.67

1.87

6.472

Sichuan

1.0751

14.1111

37.086

24.6965

109.96

29608

13.30

2.85

1.88

Guizhou

1.7479

13.9730

32.948

33.4001

105.66

19710

20.09

3.10

2.18

Yunnan

1.4098

16.8728

33.122

28.7598

105.88

22195

15.21

3.26

2.02

Tibet

1.0496

19.0892

29.500

33.3879

98.72

22936

14.24

4.07

2.30

Shaanxi

1.0545

16.3063

35.734

23.6306

107.08

38564

15.24

3.07

1.93

Gansu

1.2779

15.1629

34.464

27.6819

106.01

21978

12.16

3.22

1.96

Qinghai

1.3701

16.9857

32.264

29.6121

106.35

33181

12.39

3.40

2.18

Ningxia

1.3627

16.9466

32.226

30.1549

104.52

36394

10.14

3.35

2.05

Xinjiang

1.5289

17.8427

32.308

28.4852

103.97

33796

12.33

3.16

2.18

188

7 The Necessity of Family Planning Policy …

ith province Pi can be calculated by multiplying weight α, which is generated by an entropy weight model. Meanwhile, when considering only the five demographic factors, evaluation score Q i can be calculated by multiplying weight β. Thus, the evaluation model is constructed as shown in Eq. (7.5): 

i + α Ci + α Ci + α Ci + α Ci + α Ci + α Ci + α Ci + α Ci Pi = α1 C11 2 12 3 21 4 22 6 31 7 32 8 41 9 42 5 23 i + β Ci + β Ci + β Ci + β Ci Q i = β1 C11 2 12 3 21 4 22 5 23

.

(7.5)

7.3.3 The Quantitative Values of the Indicators The nine indicators can be quantified from Eqs. (7.1) to (7.4). However, the scores of the nine indicators present significantly different distributions. For example, the highest score of indicator C32 is 2.6868; however, the highest score of indicator C23 is only 1.0807. To eliminate this difference, Eq. (7.6) is established. After normalization, all these scores have the same distribution interval between 0 and 1, and the closer the score is to 1, the more urgent the necessity of reform will be. y=

x − min(x) . max(x) − min(x)

(7.6)

Table 7.2 shows that some provinces perform differently in these nine indicators. For instance, Chongqing obtains two low scores of 0.2614 and 0.1166 for two economic factors; however, it obtains two high scores of 0.5775 and 1 for two population size factors. Furthermore, Beijing obtains a score of 1 for the indicator C11 ; however, it obtains a score of only 0 for the indicator C12 . Such significant differences also exist for other provinces.

7.3.4 Weight Definition After quantifying the scores of these nine indicators, this section determines the weights in Eq. (7.5). The methods for determining the weights can be divided into two categories: subjective weighting methods (including the analytic hierarchy process, binomial coefficient method, and fuzzy mathematics method) and objective weighting methods (including principal component analysis and the entropy weight, and multi-objective coefficient methods). Under the condition that all scores are known and precise, it is approximate to adopt the entropy weight method, which can eliminate subjective error. Entropy represents the amount of uncertainty in a system, and higher entropy means more uncertainty (Farashi 2016). The entropy weight method is built based on Shannon entropy, originally developed by Shannon and Weaver (Shannon and

0.9508

0.9587

0.9723

0.6794

0.7134

0.2846

0.6189

0.3736

0.5757

0.4512

0.4146

0.3440

0.6697

0

0.2558

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Guangdong

Guangxi

Hainan

0.9684

Liaoning

Jilin

0.6414

0.6638

0.4423

Hebei

Inner Mongolia

0.8097

Tianjin

Shanxi

1

Beijing

C11

0.6130

0.7084

0.2532

0.7040

0.6483

0.7217

0.6215

0.6310

0.2630

0.8831

0.4503

0.5790

0.1963

0.4867

0.5429

0.6697

0.4475

0.6082

0.6050

0.1315

0

C12

0.3752

0.4322

0.3267

0.6537

0.7194

0.4359

0.7567

0.3599

0.5302

0.6480

0.7761

0.8471

0.9503

0.8517

0.8511

1

0.6858

0.5190

0.5843

0.7966

0.7726

C21

0.1080

0.0985

0.1779

0.2657

0.3365

0.1057

0.3778

0.0871

0.3019

0.2012

0.4646

0.4434

1

0.5911

0.5706

0.6403

0.4096

0.1751

0.2572

0.7369

0.9308

C22

Table 7.2 The quantitative values of nine indicators after normalization

1

0.6077

0.8893

0.5310

0.3482

0.2435

0.2355

0.6024

0.2188

0.6785

0.3983

0.0193

0.6785

0.3369

0.3242

0.1281

0.3889

0.3903

0.3996

0

0.4383

C23

0.1724

0.1122

0.4681

0.1874

0.2568

0.1605

0.4364

0.1237

0.4499

0.1236

0.5944

0.6621

0.8938

0.2178

0.3227

0.5028

0.6013

0.1895

0.2297

1

0.9224

C31

0.1365

0.1914

0.4297

0.1363

0.1166

0.2101

0.2339

0.2008

0.1566

0.1318

0.3851

0.2177

1

0.2614

0.1143

0.1495

0.1980

0.3623

0.3287

0.2350

0.3717

C32

0.1614

0.3086

0.3395

0.4450

0.4690

0.2838

0.5446

0.2549

0.6056

0.4750

0.7086

0.5060

1

0.6412

0.5645

0.7009

0.6340

0.4510

0.3395

0.6485

0.8316

C41

0.7681

0.7246

0.9130

0.5362

0.3188

0.5217

0.4783

0.6957

0.6232

0.3623

0.3478

0.2899

0.0580

0

0.1159

0.1739

0.2464

0.4493

0.4348

0.2174

0.2174

C42 0.5084

0.3990

0.3537

0.4963

0.4226

0.4031

0.3482

0.4734

0.3699

0.4187

0.4209

0.5376

0.4716

0.7499

0.4828

0.4841

0.5482

0.4750

0.4207

0.4023

(continued)

Average value 0.6094

7.3 Evaluation of the Necessity of Policy Adjustment 189

0.5775

0.6599

0.0387

0.3508

0.6834

0.6789

0.4726

0.3875

0.3943

0.2409

Chongqing

Sichuan

Guizhou

Yunnan

Tibet

Shaanxi

Gansu

Qinghai

Ningxia

Xinjiang

C11

Table 7.2 (continued)

0.3555

0.4723

0.4669

0.7459

0.5636

0.2112

0.4825

0.9672

0.9396

1

C12

0.2795

0.2713

0.2751

0.4940

0.6204

0

0.3605

0.3432

0.7550

0.8148

C21

0.1168

0.0729

0.0866

0.1399

0.2799

0.0002

0.1092

0

0.2386

0.2355

C22

0.3576

0.3943

0.5163

0.4937

0.5650

0.0073

0.4850

0.4703

0.7572

0.1841

C23

0.1917

0.2271

0.1834

0.0309

0.2566

0.0439

0.0338

0

0.1347

0.2614

C31

0.1285

0.2004

0.1269

0.1332

0.0650

0.0839

0.0655

0

0.1043

0.1166

C32

0.3935

0.2936

0.2692

0.3607

0.4450

0

0.3395

0.4275

0.5849

0.7164

C41

0.8261

0.6377

0.8261

0.5072

0.4638

1

0.5942

0.8261

0.3913

0.3768

C42

Average value

0.3211

0.3293

0.3487

0.3753

0.4376

0.2256

0.3134

0.3414

0.5073

0.4759

190 7 The Necessity of Family Planning Policy …

7.3 Evaluation of the Necessity of Policy Adjustment

191

Weaver 1949). The concept of entropy is well suited to measuring the relative intensities of contrast criteria, and it can represent the average intrinsic information transmitted for decision-making (Inuiguchi and Sakawa 1995). Thus, the entropy weight method is an appropriate and convenient choice for this chapter. The entropy weight method is developed according to the following definitions. There are 31 provinces for evaluation and each has 9 evaluation criteria, forming  decision matrix X = xi j ; i = 1, 2, . . . , 31; j = 1, 2, . . . , 9 . Then, the steps of the entropy weight method can be expressed as follows (Delgado and Romero 2016; Ji et al. 2015):  Step 1: Decision matrix X = xi j ; i = 1, 2, . . . , 31; j = 1, 2, . . . , 9 is normalized for each criterion, and normalized values X i j are calculated from Eq. (7.7): X i j = xi j /

31 

xi j .

(7.7)

i=1

Step 2: The entropy e j of each criterion is calculated from Eq. (7.8):  1 

Xi j × I n Xi j . I n31 i=1 31

ej = −

(7.8)

Step 3: The degree of divergence d j of the intrinsic information in each criterion is calculated from Eq. (7.9): dj = 1 − ej.

(7.9)

Step 4: The entropy weight α j of each criterion is calculated from Eq. (7.10): αj = dj/

9 

dj.

(7.10)

j=1

The data in Table 7.2 are put into Eqs. (7.7–7.10), and then, the weight of these nine indicators can be determined as α j = (0.08, 0.07, 0.06, 0.19, 0.11, 0.19, 0.15, 0.06, 0.10). Meanwhile, the weights of the five demographic factors are calculated as β j = (0.1541, 0.1347, 0.1164, 0.3787, 0.2160).

7.3.5 Evaluation Results Combining the evaluation model established in Sect. 7.3.2, the quantitative values of the nine evaluation indicators described in Sect. 7.3.3, and the weights calculated in Sect. 7.3.4, this section obtains the evaluation scores for the necessity of family

192

7 The Necessity of Family Planning Policy …

planning policy adjustment for the 31 Chinese provinces and presents the evaluation results in Table 7.3. Most of the provinces show similar performance under the two conditions. For instance, Beijing obtains an overall evaluation score of 0.65021 and obtains a similar score of 0.69128 when considering only demographic factors. Nevertheless, a few provinces, especially the north-east provinces, still have a certain gap between these two scores. These three provinces, i.e., Liaoning, Jilin, and Heilongjiang, obtain scores of approximately 0.6 when considering only demographic factors but obtain scores of only approximately 0.4 after taking socio-economic factors into account. Table 7.3 The evaluation scores for the necessity of family planning policy adjustment Beijing

Tianjin

Hebei

Shanxi

Inner Mongolia

Liaoning

Overall 0.65021 Evaluation (P)

0.54344

0.35769

0.35862

0.44848

0.49307

Demographic scores (Q)

0.69128

0.51437

0.40143

0.39185

0.48158

0.62611

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

Overall 0.42940 Evaluation (P)

0.42771

0.79564

0.44484

0.51046

0.33985

Demographic scores (Q)

0.60492

0.60915

0.81223

0.45347

0.52298

0.46103

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Overall 0.38351 Evaluation (P)

0.31096

0.42581

0.28557

0.34620

0.35616

Demographic scores (Q)

0.35416

0.34759

0.45453

0.31016

0.43766

0.43929

Guangdong

Guangxi

Hainan

Chongqing

Sichuan

Guizhou

Overall 0.47983 Evaluation (P)

0.29662

0.34944

0.37286

0.40251

0.24538

Demographic scores (Q)

0.43481

0.31431

0.42257

0.44757

0.57011

0.27780

Yunnan

Tibet

Shaanxi

Gansu

Qinghai

Ningxia

Overall 0.24892 Evaluation (P)

0.18434

0.37255

0.29372

0.29972

0.28728

Demographic scores (Q)

0.13545

0.48084

0.39047

0.29896

0.26876

0.30715 Xinjiang

Overall 0.27888 Evaluation (P) Demographic scores (Q)

0.23904

7.3 Evaluation of the Necessity of Policy Adjustment

193

The main reason for this discrepancy is probably the severe economic recession in the north-east regions. As phenomenon that warrants our attention, the evaluation scores show a wide difference among the 31 Chinese provinces. This means that there is a very large disparity in the necessity of family planning policy adjustment among the provinces.

7.4 Province Classification After quantifying the necessity of family planning policy adjustment in China, this section conducts the province classification. A k-means cluster method is selected in this section to classify the 31 Chinese provinces. The k-means cluster method aims to divide a data point set Z = {Z 1 , Z 2 , . . . , Z n } into K clusters and to find a {L 1 , L 2 , . . . , L K }of the universal set Z by minimizing objective function subset

Lk = J = i=1 Z , where d Z d , L , L j is the Euclidean distance between i j j j i j j z j ∈l j data point Z j and cluster centre L j (Li et al. 2015).

7.4.1 Province Classification Under Each Indicator The data of each column in Table 7.2 are successfully put into a k-means cluster; then, the 31 provinces are divided into four categories. The classification sub-graphs for each indicator are displayed in Fig. 7.2. Based on the analyses of the nine sub-graphs, some meaningful correlations can be found. Neighbouring provinces are always clustered into the same category, indicating clear spatial aggregation. For instance, the three north-east provinces (i.e., Liaoning, Jilin and Heilongjiang) are always assigned to the same group. The spatial aggregation of the necessity of adjustment is analysed in Sect. 7.5, and more analyses of the spatial aggregation of China’s population are conducted in Chap. 9. In addition to the geographic element, other factors, including the economy, lead to a similar aggregation effect. Because of their similar advanced economic status, two non-adjacent provinces (i.e., Beijing and Shanghai) usually always become the only two provinces divided into the first group, reflecting that they require the family planning policy adjustment. Four of these nine indicators make greater contributions to the provincial differences than the other five, and these indicators control the total fertility rate, control the total population, address population ageing, and consider fertility intentions. This result further explains that there are remarkable provincial differences in the severity of the population crisis among the 31 Chinese provinces. Meanwhile, the intention to have babies also performs significantly differently among the 31 Chinese provinces due to cultural diversity. Furthermore, fertility intentions should receive more attention than the other indicators because the provinces whose fertility intentions are strong always face

194

7 The Necessity of Family Planning Policy …

Fig. 7.2 Spatial distribution diagrams of each indicator for the 31 Chinese provinces

immense population pressure. Most provinces should always focus on changes in this indicator to prevent explosive population growth, except two adjacent provinces (i.e., Xinjiang and Tibet) that have extremely low fertility intentions and that always fall into the fourth category.

7.4.2 Province Classification Based on the Necessity of Policy Adjustment Based on the data shown in Table 7.3, the k-means cluster method is used to divide the 31 Chinese provinces into four categories. The concrete classification results are presented in Table 7.4. The provinces in the first category, such as Shanghai and Beijing, should reform their family planning policy immediately. In contrast, the provinces in the fourth category, such as Guangxi, do not need to try hard to reform their family planning

7.4 Province Classification

195

Table 7.4 The province classification based on the necessity of family planning policy adjustment Province classification when considering all these nine indicators comprehensively The 1st category

Shanghai (0.79564), Beijing (0.65021), Tianjin (0.54344), Zhejiang (0.51046), Liaoning (0.49307), Guangdong (0.47983), Inner Mongolia (0.44848), Jiangsu (0.44484)

The 2nd category

Jilin (0.42940), Heilongjiang (0.42771), Shandong (0.42581)

The 3rd category

Sichuan (0.40251), Fujian (0.38351), Chongqing (0.37286), Shaanxi (0.37255), Shanxi (0.35862), Hebei (0.35769), Hunan (0.35616), Hainan (0.34944), Hebei (0.34620), Anhui (0.33985), Jiangxi (0.31096), Qinghai (0.29972)

The 4th category

Guangxi (0.29662), Gansu (0.29372), Ningxia (0.28728), Henan (0.29557), Xinjiang (0.27888), Yunnan (0.24892), Guizhou (0.24538), Tibet (0.18434)

Province classification when only considering the five demographic indicators The 1st category

Shanghai (0.81223), Beijing (0.69128), Liaoning (0.62611), Heilongjiang (0.60915), Jilin (0.60492), Sichuan (0.57011), Zhejiang (0.52298), Tianjin (0.51437), Inner Mongolia (0.48158), Shaanxi (0.48084), Anhui (0.46103), Shandong (0.45053)

The 2nd category

Jiangsu (0.45347), Chongqing (0.44757), Hunan (0.43929), Hubei (0.43766), Guangdong (0.43481), Hunan (0.42257), Hubei (0.40143), Shanxi (0.39185), Gansu (0.39047), Fujian (0.35416)

The 3rd category

Jiangxi (0.34759)

The 4th category

Guangxi (0.31431), Henan (0.31016), Yunnan (0.30715), Qinghai (0.29896), Guizhou (0.27780), Guizhou (0.26876), Xinjiang (0.23904), Tibet (0.13545)

policy. For the provinces in the second or third category, the necessity of policy adjustment falls between those of the two categories described above. When considering all nine indicators, eight provinces belong to the first category. Except for Inner Mongolia, all seven other provinces are developed regions. Three provinces in the second category have a bias towards the north-east region, especially Jilin and Heilongjiang. Twelve provinces belong to the third category, and they are mainly distributed in the central region. The provinces in the fourth category, including Guangxi, Yunnan, Tibet, and Xinjiang, are widely distributed in the western region. When considering only the five demographic indicators, in most provinces family planning policy reform is a higher priority. Twelve provinces in the first category either have a bias towards the north-east region or have a developed economy. Ten provinces in the second category are mainly distributed in the south-east region. Jiangxi is the sole province in the third category. The other eight provinces, which belong to the fourth category, are mainly located in the western region. In short, most provinces are divided into the same categories under two conditions, i.e., combining all nine indicators comprehensively and considering only the five demographic indicators, presenting coordinated development among China’s population, economy, and society. Nevertheless, a number of provinces still have sharply contrasting categories under these two conditions. For instance, Sichuan,

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7 The Necessity of Family Planning Policy …

Shaanxi, and Anhui Provinces have a strong necessity of policy adjustment when considering only the demographic factors; however, they have only a slight necessity of policy adjustment when all nine indicators are taken into account.

7.4.2.1

Diagnosis on the Provincial Behaviours

From the above analyses, the provincial differences in the necessity of family planning policy reform are significant. It is essential to determine whether the provinces have understood this regional disparity. I conduct a simple study to determine whether the time of implementation of the selective two-child policy is suitable. By comparing the implementation time of the selective two-child policy, this section selects ten provinces that implemented the selective two-child policy earlier as the research objects. These ten provinces are Zhejiang, Jiangxi, Anhui, Tianjin, Beijing, Shanghai, Guangxi, Shaanxi, Sichuan, and Chongqing. Among these ten provinces, only Zhejiang, Tianjin, Beijing, and Shanghai belong to the first category. That is, the other six provinces, especially Jiangxi and Guangxi, perhaps do not need to the one-child policy as quickly. Accordingly, six out of ten provinces do not have a clear understanding of the extent to which they need to adjust their family planning policy. Further analyses of whether the provinces have understood their population situations and suitably adjusted their family planning policy are conducted in Chapter 8.

7.5 Spatial Analysis of Provinces There is positive spatial aggregation when the performance in neighbouring provinces is similar, and there is negative spatial aggregation when the performance in neighbouring provinces is different. This section conducts spatial analysis of the 31 provinces in terms of the necessity of family planning policy adjustment.

7.5.1 Exploratory Spatial Data Analysis and Moran’s I Index Exploratory spatial data analysis is an approach to analysing datasets to summarize their main spatial characteristics, often with visual methods. The method mines in depth the relational structural pattern of spatial data, the spatial distribution of social and economic indicators, and the mechanism of spatial interaction. The dependency relationship of the geographic space in economic and societal development reflects the spatial dependency relationship and is regarded as spatial autocorrelation. The reasons for spatial autocorrelation include spatial aggregation and spatial overflow. This section calculates Moran’s I index to test spatial

7.5 Spatial Analysis of Provinces

197

aggregation. The equation for calculating Moran’s I index is presented in Eq. (7.11): n n

Moran I =

 wi j (xi − x) x j − x

i=1 j=1

S2 ⎧ (xi − x)2 ⎪ ⎪ ⎪ i 2 ⎪ ⎪ ⎨S = n n ⎪ ⎪ xi ⎪ ⎪ ⎪ ⎩ x = i=1 n

n n

wi j

i=1 j=1

(7.11)

where xi represents the elderly dependency ratio of the ith province and n is the total number of regions; here, n = 31. wi j is the binary adjacency weight matrix and defines the adjacency relationship of spatial objects. The wi j matrix is usually obtained using the binary adjacent standard or the distance standard. This section adopts the binary adjacent standard; thus, the value is 1 for an adjacent province and is 0 for a non-adjacent province. The value of Moran’s I index falls into the range of −1 to 1. Regarding the calculated value of Moran’s I index, standardized Z statistics are used to test the statistical significance of this value. The Z statistics are calculated from Eq. (7.12):

⎧ ⎪ ⎪ ⎨

I − E n (I ) Z=√ V A Rn (I )

1 n−1 n 2 w1 + nw2 + 3w02 ⎪ ⎪

 − E n2 (I ) ⎩ V A Rn (I ) = w02 n 2 − 1 where w0 =

E n (I ) = −

n n i=1 j=1

wi j , w1 =

(7.12)

n n n

2 wi j + w ji /2, and w2 = (wi. + w.i )2 . i=1 j=1

i=1

wi. and w.i represent the sum of rows i or columns i of the spatial weight matrix. When the value of Moran’s I index is positive and significant, there is significant positive spatial aggregation among provinces, and the provinces tend to have highhigh spatial aggregation or low-low spatial aggregation. When the value of Moran’s I index is negative and significant, there is significant negative spatial aggregation among provinces and the provinces tend to have high-low spatial aggregation. If the value of Moran’s I index is not significant, the spatial aggregation is not significant, and the spatial distribution is random. This section analyses the spatial aggregation of the 31 provinces in China in terms of the necessity of family planning policy adjustment. Using OpenGeoDa software, this section calculates the value of Moran’s I index and obtains 0.3559. The Z statistic

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7 The Necessity of Family Planning Policy …

Fig. 7.3 The scatter diagram of Moran’s I index based on the necessity of family planning policy adjustment

for the value of Moran’s I index is 3.4424, and its corresponding P value is smaller than 0.01. Therefore, there is significant positive spatial aggregation among the 31 provinces in China in terms of the necessity of family planning policy adjustment. The scatter diagram of Moran’s I index is shown in Fig. 7.3. Based on analysing spatial aggregation in terms of the necessity of family planning policy adjustment, the 31 provinces in China can be divided into four different quartiles. In the first quartile with high-high aggregation, the provinces with a high necessity of family planning policy adjustment are gathered in high-high patterns. In the second quartile with low-high aggregation, the provinces with a low necessity of family planning policy adjustment are surrounded by the provinces with a high necessity of policy adjustment. In the third quartile with low-low aggregation, the provinces with a low necessity of family planning policy adjustment are gathered in low-low patterns. In the fourth quartile with high-low aggregation, the provinces with a high necessity of family planning policy adjustment are surrounded by the provinces with a low necessity of policy adjustment. In the first and third quartiles, the provinces present positive spatial aggregation. In the second and fourth quartiles, the provinces present negative spatial aggregation. As shown in Fig. 7.3, the number of provinces with high-high or low-low aggregation is significantly more than those with high-low or low-high aggregation, thus reflecting the significant spatial aggregation of China’s provinces in terms of the necessity of family planning policy adjustment. Moreover, Fig. 7.3 reveals that the number of provinces with low-low aggregation is slightly more than that with high-high aggregation, reflecting that more provinces have a relatively low necessity of family planning policy adjustment.

7.5 Spatial Analysis of Provinces

199

Fig. 7.4 The cluster diagram of the provinces in terms of the necessity of family planning policy adjustment

7.5.2 Local Indicators of Spatial Association (LISA) Analysis of the Necessity of Policy Adjustment This section employs OpenGeoDa to analyse the LISA significance in terms of the necessity of family planning policy adjustment and draws the cluster diagram in Fig. 7.4. The cluster diagram clearly shows the province classification based on four quartiles: high-high aggregation, low-low aggregation, low-high aggregation, and high-low aggregation. Figure 7.4 shows that Jiangsu Province exhibits significant high-high aggregation. Xinjiang Province, Tibet Province, Qinghai Province, and Yunnan Province exhibit low-low aggregation. Sichuan Province exhibits high-low aggregation.

7.6 Conclusions To explore the provincial differences in the necessity of family planning policy adjustment in China, first, an evaluation index system for the necessity of policy adjustment is constructed. Then, the evaluation scores of the necessity of policy adjustment for China’s 31 provinces are calculated by combining the quantitative values of nine evaluation indicators and weights determined by the entropy weight method. Furthermore, the k-means cluster method is used to divide the 31 provinces into four categories based on the evaluation scores to determine the necessity of policy adjustment. A simple diagnosis of provincial behaviours is performed to determine whether the provinces understand the differences. Finally, spatial aggregation in terms of the necessity of policy adjustment is calculated to further analyse the provincial population differences. Accordingly, some meaningful suggestions for adjusting the family planning policy for China’s 31 provinces are proposed in this chapter.

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The evaluation scores have a wide distribution; thus, there is a very large regional disparity in the necessity of family planning policy adjustment among the 31 provinces in China. Although a number of provinces obtain high evaluation scores and must adjust their family planning policy, some provinces do not need to engage in any reform at present. Many provinces have begun to adopt the new loose family planning policy, probably because they simply need to follow the trend of national reform. Under a particular condition in which all provinces have implemented the new loose family planning policy, the provinces with a low necessity of policy adjustment should receive more attention. Furthermore, most provinces perform similarly under two conditions: when considering all nine indicators and when considering only the five demographic indicators. This positive similarity reflects the coordinated development among China’s population, economy, and society. However, provinces such as Sichuan, Shaanxi, and Anhui obtain significantly different scores under these two conditions, reflecting that these provinces have not obtained the optimum balance between population evolution and socio-economic development. In the future, policymakers should keep a watchful eye on this imbalance. To promote balanced population development, it is essential to ensure that the provinces recognize the provincial differences. Regrettably, a straightforward diagnosis indicates that many provinces in China have determined the extent to which they need to adjust their family planning policy. China understands the significant differences among the provincial populations and is paying closer attention to the provinces that do not have a clear understanding of the extent to which they need to adjust their family planning policy. Each province should promote the family planning policy reasonably according to the realities of its population situation. Finally, the spatial analysis of the 31 provinces is conducted in terms of the necessity of family planning policy adjustment. The calculated value of Moran’s I index is 0.3559, which is significant at 0.01 level, revealing the significant spatial aggregation among the 31 provinces in China in terms of the necessity of family planning policy adjustment. The number of provinces with low-low aggregation is slightly more than that with high-high aggregation, reflecting that more provinces have a relatively low necessity of family planning policy adjustment. Specifically, Jiangsu Province exhibits significant high-high aggregation, and Xinjiang Province, Tibet Province, Qinghai Province, and Yunnan Province exhibit low-low aggregation. Acknowledgments Reprinted by permission from Springer Nature Customer Service Centre GmbH: Springer, Social Indicators Research, and Research on fertility policy in China: the relative necessity for reform among the different provinces by Pengkun Wu, Yuanyuan Wu, and Chong Wu (January 1, 2016).

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Chapter 8

The Degree of Correlation Between the Implementation Time and the Necessity of Family Planning Policy Adjustment

Abstract After analysing the obvious provincial population differences, this chapter studies the degree of correlation between the implementation time of the selective two-child policy and the necessity of family planning policy adjustment, which is calculated in Chap. 7, and conducts spatial aggregation analysis. Then, this chapter uses the provincial population number as the weight to calculate the overall degree of rationality of implementing the selective two-child policy. The results show that Shanghai Province suitably implements the selective two-child policy with the highest degree of correlation and that Jiangxi Province improperly implements the family planning policy with the lowest degree of correlation. When considering all the influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.93709 and 0.31425, respectively. When considering only the population-related influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.95368 and 0.35088, respectively. In the spatial aggregation analysis, the north-east provinces have relatively high degrees of correlation. When adjusting their family planning policies, most provinces have considered their population factors but have overlooked their economic and social factors. When considering all the influencing factors or only the population-related influencing factors, the overall degrees of correlation for China as a whole are 0.64207 and 0.70159, respectively. The overall score is relatively high but still has high room for improvement. In the future, the entire public should continue to pay attention to the provincial population differences when adjusting the family planning policy. Keywords Provincial population difference · Degree of correlation · Implementation time · Family planning policy · Selective two-child policy · Spatial aggregation

8.1 Introduction In 1973, China started to implement the strict one-child policy, which was in force until 2013. The strict one-child policy introduced in the 1970s has made a tremendous contribution to controlling China’s population size (Guilmoto and Jones 2015). However, this policy has also led to many problems, such as a huge gender inequality © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_8

205

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due to the inherent Chinese preference for sons (Jiang et al. 2016) and serious population ageing as a result of the nation’s skewed demographic structure (Cheema 2013; Ince Yenilmez 2015; Lee and Mason 2010; Mai et al. 2013). The Third Plenary Session of the 18th CPC Central Committee adopted the “Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform”, adjusting its family planning policy from a one-child policy to a selective two-child policy on November 15, 2013 (The Third Plenary Session of the 18th Central Committee of the Communist Party of China 2013). However, due to the significant provincial population differences, China has not designed a fixed timetable for implementing the selective two-child policy for all provinces and allows the provinces to implement the selective two-child policy according to their unique population situation. Zhejiang Province was the first province to implement the selective two-child policy on January 17, 2014. Xinjiang Province, the last province to do so, implemented the selective two-child policy on November 17, 2014. Although Chap. 7 reveals the significant provincial population differences in terms of the necessity of family planning policy adjustment, all 31 provinces officially implemented the new family planning policy within 10 months. As revealed in Chap. 7, the 31 provinces have different levels of necessity of policy adjustment. Jiangxi Province has a large population size challenge, but its population ageing phenomenon is not serious. In Jiangxi Province, adjusting the family planning policy is a low priority, and it is better to continue to implement the strict one-child policy. However, Jiangxi Province officially implemented the selective two-child policy on November 18, 2014, becoming the second province to implement the reform. A simple diagnosis of whether the provinces can clearly understand their population situation and suitably implement the selective two-child policy is conducted in Sect. 7.4.3 finding that some provinces do not have a clear understanding of the extent to which they need to adjust their family planning policy. By extending the simple diagnosis, this section further analyses the degree of correlation between the time of implementation of the selective two-child policy and the necessity of family planning policy adjustment. The remainder of the chapter is structured as follows. In Sect. 8.2, the research model for calculating the degree of correlation between the implementation time and the necessity of family planning policy adjustment is established. Then, Sect. 8.3 calculates the degrees of correlation and analyses spatial aggregation and the influencing factors. Subsequently, in Sect. 8.4, the overall evaluation of the time of implementation of the selective two-child policy in China is conducted. Finally, in Sect. 8.5, conclusions are drawn, and suggestions are proposed.

8.2 Research Method

207

8.2 Research Method To measure the degree of correlation between the time of implementation of the selective two-child policy and the necessity of family planning policy adjustment, this section establishes the calculation equations. The time of implementation of the selective two-child policy and the necessity of policy adjustment to implement the selective two-child policy are regarded as two systems. After quantifying the scores of these two systems, this section establishes the equation to measure the coordination of these two systems.

8.2.1 System 1: Quantification Scores of the Time of Implementation of the Selective Two-Child Policy To measure the time of implementation of the selective two-child policy in the 31 provinces, this section calculates the quantification score of system 1 by establishing Eq. (8.1): U1i = 1 −

Q i − min(Q i ) . max(Q i ) − min(Q i )

(8.1)

When the time of implementation of the selective two-child policy in province i is Q i , the quantification score for the implementation time is calculated as U1i . The value of the quantification score is 1 for the province with the earliest time of implementation of the selective two-child policy, and the value of the quantification score is 0 for the province with the latest time of implementation of the selective twochild policy. For all 31 provinces, the values of the quantification score fall into the range of 0 to 1. The earlier the selective two-child policy is implemented, the higher the value of the quantification score will be. The specific time of implementation of the selective two-child policy and the corresponding quantification scores for the implementation time are presented in Table 8.1.

8.2.2 System 2: Quantification Scores of the Necessity of Family Planning Policy Adjustment As analysed in Chap. 7, there is a very large regional disparity in the necessity of family planning policy adjustment among China’s 31 provinces. Although a number of provinces obtain high evaluation scores and must adjust their family planning policy, some provinces do not need to engage in any reform at present. To measure the necessity of family planning policy adjustment, an evaluation containing the target layer and primary and secondary index layers is established in Sect. 7.2 and is

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8 The Degree of Correlation Between the Implementation …

Table 8.1 The implementation time and the quantification scores for China’s 31 provinces System 1

System 2

Implementation time

Quantification score

Necessity of policy adjustment considering all factors

Necessity of policy adjustment considering only population factors

Beijing

February 21, 2014

0.88487

0.65021

0.69128

Tianjin

February 14, 2014

0.90790

0.54344

0.51437

Hebei

May 30, 2014

0.56250

0.35769

0.40143

Shanxi

May 29, 2014

0.56579

0.35862

0.39185

Inner Mongolia

March 31, 2014

0.75987

0.44848

0.48158

Liaoning

March 27, 2014

0.77302

0.49307

0.62611

Jilin

March 28, 2014

0.76974

0.42940

0.60492

Heilongjiang

April 22, 2014

0.68750

0.42771

0.60915

Shanghai

March 1, 2014

0.85855

0.79564

0.81223

Jiangsu

March 28, 2014

0.76974

0.44484

0.45347

Zhejiang

January 17, 2014 1

0.51046

0.52298

Anhui

January 23, 2014 0.98026

0.33985

0.46103

Fujian

March 29, 2014

0.76645

0.38351

0.35416

Jiangxi

January 18, 2014 0.99671

0.31096

0.34759

Shandong

May 30, 2014

0.56250

0.42581

0.45453

Henan

May 29, 2014

0.56579

0.28557

0.31016

Hubei

March 27, 2014

0.77302

0.34620

0.43766

Hunan

March 28, 2014

0.76974

0.35616

0.43929

Guangdong

March 27, 2014

0.77302

0.47983

0.43481

Guangxi

March 1, 2014

0.85855

0.29662

0.31431

Hainan

June 1, 2014

0.55592

0.34944

0.42257

Chongqing

March 26, 2014

0.77632

0.37286

0.44757

Sichuan

March 20, 2014

0.79605

0.40251

0.57011

Guizhou

May 19, 2014

0.60197

0.24538

0.27780

Yunnan

March 28, 2014

0.76974

0.24892

0.30715

Tibet

November 6, 2014

0.03618

0.18434

0.13545

Shaanxi

March 1, 2014

0.85855

0.37255

0.48084

Gansu

March 26, 2014

0.77377

0.29372

0.39047 (continued)

8.2 Research Method

209

Table 8.1 (continued) System 1

System 2

Implementation time

Quantification score

Necessity of policy adjustment considering all factors

Necessity of policy adjustment considering only population factors

Qinghai

March 27, 2014

0.75000

0.29972

0.29896

Ningxia

May 28, 2014

0.56908

0.28728

0.26876

Xinjiang

November 17, 2014

0

0.27888

0.23904

Note The data on the time of implementation of the selective two-child policy for China’s 31 provinces

shown in Fig. 7.1 based on studying the existing literature (Liu and Lu 2008; National Population Development Strategy Research Group 2007; Yuan 2014; Zhai 2013; Zhai and Zhao 2014; Zhang and Wang 2014). The target layer is the necessity of family planning policy adjustment; the primary index layer contains the four macroscopic factors described in Sect. 7.2.1, and the secondary index contains the nine indicators described in Sect. 7.2.2. To better understand the evaluation index system that was built, this section combs the influencing factors through the fishbone diagram in Fig. 8.1. A fishbone diagram designed by Prof. Ishikawa from the University of Tokyo is an innovative approach for identifying reasons for a problem, and it can be widely used in the fields of technology and management (Khaloo and Khosravi 2013). The process for calculating the necessity of family planning policy adjustment of the i th province (U2i ) is introduced in Sect. 7.3. The necessity of policy adjustment for the 31 provinces is summarized together with the quantification scores in System 1 in Table 8.1. Economic factor Eliminating adverse selection of population quality

Maintaining economic growth

Improving family structure

Considering fertility intention

Social factor

Population size factor Controlling total fertility rate Controlling total population

Improving population aging Improving demographic dividend

Necessity of family planning policy adjustment

Improving gender inequality

Population structure factor

Fig. 8.1 The fishbone summarizing the factors influencing the necessity of family planning policy adjustment

210

8 The Degree of Correlation Between the Implementation …

8.2.3 Calculation of the Degrees of Correlation The quantification scores in system 1 and system 2 are in the same direction. The provinces with earlier implementation times have high quantification values in system 1, and the provinces with a high necessity of policy adjustment have high quantification values in system 2. The ranges for the quantification scores in these two systems are all from 0 to 1. Therefore, this section quantifies the degree of correlation by calculating the value of the difference between the two quantified values of these two systems, as shown in Eq. (8.2):   C j = 1 − U1 j − U2 j .

(8.2)

Since the values of U1 j and U2 j fall into the range of 0–1, the value of the degree of correlation (C j ) also falls into the range of 0–1. Based on existing studies, this section uses the median segmentation method to analyse the degree of correlation. If 0 ≤ C j ≤ 0.3, province j has a low degree of correlation, and its time of implementation of the selective two-child policy is relatively not suitable. If 0.3 < C j ≤ 0.5, province j has a relatively high degree of correlation, and its time of implementation of the selective two-child policy is relatively not suitable. If 0.5 < C j ≤ 0.8, province j has a relatively high degree of correlation, and its time of implementation of the selective two-child policy is relatively suitable. If 0.8 < C j ≤ 1, the province j has high correlation degree and its implementation time of the selective two-child policy is suitable.

8.3 Results Analysis 8.3.1 Calculation Results of the Degrees of Correlation for China’s Provinces By putting the quantification scores in the systems 1 and 2 into Eq. (8.2), this section calculates the degrees of correlation for the 31 provinces in China. As Chap. 7 calculates the necessity of family planning policy adjustment under two conditions, i.e., considering all influencing factors and considering only the population-related influencing factors, this section also calculates the degrees of correlation under these two conditions. The calculated degrees of correlation are presented in Table 8.2. The results of the degrees of correlation reveal that Shanghai, Shandong, and Tibet implement the selective two-child policy in a suitable time, while Jiangxi, Anhui, and Guangxi implement the selective two-child policy in an unsuitable time. The degrees of correlation of Shanghai when considering all influencing factors and considering only the population-related influencing factors are high, 0.939902 and 0.956492, respectively. However, the degrees of correlation of Jiangxi Province

8.3 Results Analysis

211

Table 8.2 The degrees of correlation of the 31 provinces Province

Degrees of correlation considering all factors

Degrees of correlation considering only population factors

Province

Degrees of correlation considering all factors

Degrees of correlation considering only population factors

Beijing

0.764964

0.806034

Hubei

0.575708

0.667168

Tianjin

0.635243

0.606173

Hunan

0.588947

0.672077

Hebei

0.797034

0.840774

Guangdong

0.709338

0.664318

Shanxi

0.794686

0.827916

Guangxi

0.440882

0.458572

Inner Mongolia

0.691103

0.724203

Hainan

0.795342

0.868472

Liaoning

0.722578

0.855618

Chongqing

0.59909

0.6738

Jilin

0.662187

0.837707

Sichuan

0.609067

0.776667

Heilongjiang

0.742464

0.923904

Guizhou

0.648659

0.681079

Shanghai

0.939902

0.956492

Yunnan

0.481707

0.539937

Jiangsu

0.677627

0.686257

Tibet

0.851726

0.900616

Zhejiang

0.51046

0.52298

Shaanxi

0.516812

0.625102

Anhui

0.359522

0.480702

Gansu

0.51995

0.6167

Fujian

0.619576

0.590226

Qinghai

0.529228

0.528468

Jiangxi

0.314239

0.350869

Ningxia

0.720067

0.701547

Shandong

0.865154

0.893874

Xinjiang

0.72112

0.76096

Henan

0.721636

0.746226

when considering all influencing factors and considering only the population-related influencing factors are only 0.314239 and 0.350869, respectively. When considering all the influencing factors, only three provinces suitably implement the selective two-child policy, and four provinces unsuitably implement the policy. The other 24 provinces implement the selective two-child policy at a relatively suitable time. The average value of the degree of correlation is 0.649226 when considering all the influencing factors. When considering only the population-related influencing factors, ten provinces suitably implement the selective two-child policy, and three provinces unsuitably implement the policy. The other 18 provinces implement the selective two-child policy at a relatively suitable time. The average value of the degree of correlation is 0.702756 when considering all the influencing factors. The value of the degree of correlation when considering only the population-related influencing factors is much better than that when considering all the influencing factors. The degrees of correlation of most provinces are higher than 0.5, indicating that most provinces can implement the selective two-child policy at a suitable time. Among the 31 provinces, 26 provinces have a higher degree of correlation when considering only the population-related influencing factors, and only 5 provinces

212

8 The Degree of Correlation Between the Implementation …

(i.e., Guangdong Province, Ningxia Province, Tianjin Province, Fujian Province, and Qinghai Province) have a higher degree of correlation when considering all the influencing factors. When implementing the selective two-child policy, most provinces have considered the population-related influencing factors but have ignored related economic and social influencing factors. In fact, population development is closely related to economic and social development. The provinces should pay more attention to the overall economic and social indicators when adjusting their family planning policy. Tables 8.1 and 8.2 also reveal a spatial aggregation phenomenon. Neighbouring provinces usually implement the selective two-child policy at a similar time and have similar degrees of correlation. Thus, spatial aggregation analysis of the degrees of correlation is conducted in the next section.

8.3.2 Spatial Aggregation Analysis of the Degrees of Correlation As described in Sect. 8.2.3, this chapter uses the median segmentation method to divide the 31 provinces into four groups, and accordingly, shows the spatial distribution diagram in Fig. 8.2. When considering only the population-related influencing factors, the north-east provinces, including Heilongjiang Province, Jilin Province, and Liaoning Province, have a high degree of correlation. However, when considering all the influencing factors, the degrees of correlation of the north-east provinces decrease from approximately 0.9 to approximately 0.7. The difference in the north-east provinces reveals that the population development in the north-east provinces is not matched with the economic and social development in these provinces. In addition to these provinces with high degrees of correlation having spatial aggregation effects, the provinces with low degrees of correlation, such as Jiangxi Province, Anhui Province, and Guangxi Province, also exhibit spatial aggregation.

8.4 Overall Evaluation of Implementing the Selective Two-Child Policy in China In Sect. 8.3, the average value of the degree of correlation is simply calculated as a numerical average value. However, the weight for the 31 provinces should be different when calculating the average value of the degree of correlation and evaluating the overall degree of rationality of implementing the selective two-child policy in China. For instance, the weight for Xinjiang Province, whose population number is only 3.08 million, should be different from that of Guangdong Province, whose population number is 105.94 million. Therefore, this section regards the population number in the 31 provinces as the weight and recalculates the weighted average value of the

8.4 Overall Evaluation of Implementing the Selective Two-Child Policy in China

213

(a) Spatial aggregation analysis when considering all the influencing factors

(b) Spatial aggregation analysis when considering only the population-related influencing factors Fig. 8.2 Spatial aggregation diagram of the degrees of correlation

214

8 The Degree of Correlation Between the Implementation …

degree of correlation. Since the data used in this section are the 2013 data, this section obtains the provincial population proportion in 2013 (National Bureau of Statistics of China 2014) as the weight. The weights of the 31 provinces are shown in Table 8.3. By multiplying the weights in Table 8.3 by the degrees of correlation in Table 8.2, this section obtains weighted average values of 0.64399 and 0.697791 when considering all the influencing factors and only the population-related influencing factors, respectively. The overall degree of rationality when considering all the influencing factors, 0.64399, is significantly lower than that when considering only the population-related influencing factors, 0.697791. Economic and social factors should receive more attention from policymakers. The numerical average values of the degree of correlation are slightly higher than the weighted average values of the degree of correlation, meaning that the degree of correlation of provinces with large populations is smaller than that of provinces with small populations. The provinces with large populations should improve their degree of correlation. These two degrees of rationality of implementing the selective two-child policy in China are 0.64399 and 0.697791. Both of these values fall into the range of 0.5–0.8, meaning that China’s provinces can implement the selective two-child policy at a relatively suitable time. Table 8.3 Weight of the 31 provinces for evaluating the overall degree of rationality of implementing the selective two-child policy in China Province

Total population number (million)

Weight

Province

Total population number (million)

Weight

Beijing

20.69

0.0153

Hubei

57.79

0.0429

Tianjin

14.13

0.0105

Hunan

66.93

0.0496

Hebei

72.88

0.0540

Guangdong

105.94

0.0786

Shanxi

36.11

0.0268

Guangxi

46.82

0.0347

Inner Mongolia

24.90

0.0185

Hainan

8.87

0.0066

Liaoning

43.89

0.0325

Chongqing

29.45

0.0218

Jilin

27.50

0.0204

Sichuan

80.76

0.0599

Heilongjiang

38.34

0.0284

Guizhou

34.84

0.0258

Shanghai

23.80

0.0177

Yunnan

46.59

0.0346

Jiangsu

79.20

0.0587

Tibet

Zhejiang

54.77

0.0406

Anhui

59.88

Fujian

37.48

Jiangxi

3.08

0.0023

Shaanxi

37.53

0.0278

0.0444

Gansu

25.78

0.0191

0.0278

Qinghai

5.73

0.0042

45.04

0.0334

Ningxia

6.47

0.0048

Shandong

96.85

0.0718

Xinjiang

22.33

0.0166

Henan

94.06

0.0698

8.5 Conclusions

215

8.5 Conclusions The 31 provinces in China successively implemented the selective two-child policy from January 17, 2014, to November 17, 2014. Most provinces can implement the selective two-child policy at a suitable time. The overall degrees of rationality of implementing the selective two-child policy in China are 0.64399 and 0.697791. China’s provinces can implement the selective two-child policy at a relatively suitable time. However, not all provinces suitably adjust their family planning policy. Among the 31 provinces, Shanghai well suitably implements the selective two-child policy, with degrees of correlation of 0.939902 and 0.956492; Jiangxi Province unsuitably implements the policy, with degrees of correlation of 0.314239 and 0.350869. The provinces with low degrees of correlation, such as Jiangxi Province and Anhui Province, should pay more attention to future population development under the new family planning policy. When considering all the influencing factors, only Shanghai Province, Shandong Province, and Tibet Province suitably implement the selective two-child policy, and Yunnan Province, Guangxi Province, Anhui Province, and Jiangxi Province unsuitably implement the policy. However, when considering only the population-related influencing factors, the number of provinces suitably implementing the selective twochild policy increases from 4 to 10, and only Anhui Province, Guangxi Province, and Jiangxi Province unsuitably implement the policy. When implementing the selective two-child policy, most provinces have considered the population-related influencing factors but ignored the economic and social influencing factors. In addition to the population-related influencing factors, the economic and social influencing factors should also receive more attention when adjusting the family planning policy. There is significant spatial aggregation among the 31 provinces in terms of both the implementation time and the degree of correlation. Neighbouring provinces usually implement the selective two-child policy at a similar time and have similar degrees of correlation. For instance, all three north-east provinces (Heilongjiang Province, Jilin Province, and Liaoning Province) have high degrees of correlation when considering only the population-related influencing factors and have relatively high degrees of correlation when considering all the influencing factors.

References Cheema, A. N. (2013). Upcoming picture of ageing population: Pakistan. Quality & Quantity, 47(5), 2399–2413. Guilmoto, C. Z., & Jones, G. W. (2015). Contemporary demographic transformations in China. India and Indonesia: Springer. Ince Yenilmez, M. (2015). Economic and social consequences of population ageing the dilemmas and opportunities in the twenty-first century. Applied Research in Quality of Life, 10(4), 735–752.

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Jiang, Q., Li, Y., & Sánchez-Barricarte, J. J. (2016). Fertility intention, son preference, and second childbirth: Survey findings from Shaanxi Province of China. Social Indicators Research, 125(3), 935–953. Khaloo, A. R., & Khosravi, H. (2013). Modified fish-bone model: A simplified mdof model for simulation of seismic responses of moment resisting frames. Soil Dynamics and Earthquake Engineering, 55, 195–210. Lee, R., & Mason, A. (2010). Some macroeconomic aspects of global population ageing. Demography, 47(1), S151–S172. Liu, Y., & Lu, M. (2008). How could loosening the one-child policy affect China’s economic growth? A theoretical analysis based on within household old age support. China Economic Quarterly, 9(2), 1271–1300. (in Chinese). 刘永平 and 陆铭. 2008. “放松计划生育政策将如何影响经济增长——基于家庭养老视角的理 论分析,” 经济学(季刊) 9 (2), pp. 1271–1300. Mai, Y., Peng, X., & Chen, W. (2013). How fast is the population ageing in China? Asian Population Studies, 9(2), 216–239. National Bureau of Statistics of China. (2014). China statistical yearbook 2013. Beijing: China Statistics Press. (in Chinese). 国家统计局. 2014. “中国统计年鉴2013,” 北京:中国统计出版社. National Population Development Strategy Research Group. (2007). The national population development strategy study. Population Research, 31, 1–10. (in Chinese). 国家人口发展战略研究课题组. 2007. “国家人口发展战略研究报告,” 人口研究 31, pp. 1–10. The Third Plenary Session of the 18th Central Committee of the Communist Party of China. (2013). Decision of the Central Committee of the Communist Party of China on Some Major Issues Concerning Comprehensively Deepening the Reform. (in Chinese). 中国共产党第十八届中央委员会第三次全体会议. 2013. “中共中央关于全面深化改革若干 重大问题的决定.” Yuan, X. (2014). The trend of the family planning policy after “population transition”. Exploration and Free Views, 4, 45–49. (in Chinese). 原新. 2014. ““人口转型”后的计划生育政策走向,” 探索与争鸣 (4), pp. 45–49. Zhai, Z. (2013). China’s population changing and the policy effect of the selective two-child policy. Social Science Newspaper. (in Chinese). 翟振武. 2013. “中国人口变化与“单独二孩”政策影响,” 社会科学报.. Zhai, Z., & Zhao, M. (2014). The antecedents and consequences of the selective two-child policy. Population and Family Planning, 3, 10–12. (in Chinese). 翟振武 and 赵梦晗. 2014. ““单独二孩”政策的前因与后果,” 人口与计划生育 (3), pp. 10–12. Zhang, G., & Wang, X. (2014). Does family planning policy boost China’s economic growth? Based on educational human capital theory. Journal of Zhongnan University of Economis and Law, 3, 3–11. (in Chinese). 张国旺 and 王孝松. 2014. “计划生育政策是否促进了中国经济增长?——基于教育人力资本 视角的理论和经验研究,” 中南财经政法大学学报 (3), pp. 3–11.

Chapter 9

Spatial Aggregation and Spatial Econometric Analysis of the Elderly Dependency Ratio

Abstract To analyse the provincial differences in terms of China’s population, this chapter analyses the elderly dependency ratio from 2002 to 2018. Then, the values of Moran’s I index are calculated, and LISA analysis is conducted to analyse the spatial aggregation of China’s provinces. Furthermore, this chapter uses a spatial econometric model to analyse the influencing factors affecting the elderly dependency ratio. The results show that China’s elderly dependency ratio is becoming serious. Over time, the provincial differences in terms of the elderly dependency ratio slightly increase, but the spatial aggregation effect of China’s provinces decreases. During the research period, the provinces with a high-high pattern shift from the eastern provinces to the central provinces, and the provinces with a low-low pattern continuously compress into the north-west provinces. The results of the spatial econometric model reveal that the increases in the urbanization rate and the population size increase the elderly dependency ratio and that household consumption expenditure reduces the elderly dependency ratio. Keywords Provincial population difference · Elderly dependency ratio · Moran’s I index · Spatial aggregation · Spatial econometric model

9.1 Introduction Chapter 7 reveals the significant provincial differences in terms of the necessity of family planning policy adjustment. Due to the provincial population differences, in China’s 31 provinces, adjusting the family planning policy has different levels of priority (Gu et al. 2007; Tan and Zeng 2014). Then, Chapter 8 analyses the degree of correlation between the implementation time and the necessity of policy adjustment. Both Chaps. 7 and 8 reveal the spatial aggregation of China’s provinces in terms of the necessity of policy adjustment, the implementation time, and the degree of correlation between them. By extending the analysis of the above two chapters, this chapter employs the spatial econometric method to analyse the spatial aggregation of the provincial population structure. In terms of spatial distribution, there are significant differences in the distribution of many population indicators, such as the demographic dividends (Hu et al. 2020), © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_9

217

218

9 Spatial Aggregation and Spatial Econometric …

urbanization rate (Guo et al. 2012), housing price and housing usage rate (Zhou et al. 2020), industrial structure (Zhao et al. 2020), consumption structure (Tian 2020), product quality (Ao et al. 2019), and total fertility rate (Dorius 2008; Tan and Zeng 2014; Wang and Chi 2017; Wang et al. 2004; Zhang et al. 2012). The degrees of spatial autocorrelations for some of the above-mentioned population indicators are even enhanced by serious population ageing (Zhao et al. 2020). The indicators measuring the population structure include the proportion of the population in different age intervals, the dependency ratio, and others. As revealed in Part I, China now faces a serious population ageing challenge (Cai 2012; Huang 2020; Peng 2011; Zhang and Chen 2020). Actually, there is also a significant spatial aggregation of the 31 provinces in terms of population ageing in China (Chen et al. 2019; Guo et al. 2019; Jing 2019; Li et al. 2019; Wang 2019; Wang et al. 2019; Wu and Song 2020; Xu et al. 2020, 2019; Ying et al. 2020; Zhong 2019; Zhou et al. 2019a, b). This chapter chooses and analyses the elderly dependency ratio to analyse the spatial aggregation of the 31 provinces. Besides identifying the spatial aggregation of the 31 provinces in terms of population ageing, this study also explores the reasons affecting the elderly dependency ratio. Before 2003, the China Statistical Yearbook has not summarized the elderly dependency ratio for the 31 provinces, so this chapter chooses and analyses the elderly dependency ratio from 2002 to 2018. The variable coefficients and the spatial aggregation of the elderly dependency ratio in 11 years are analysed to help propose an effective suggestion to optimize China’s population structure and respond to the significant challenge posed by the provincial population structure. The remainder of the chapter is structured as follows. In Sect. 9.2, the historical development of the elderly dependency ratio in China from 2000 to 2018 is analysed. Then, Sect. 9.3 analyses the spatial aggregation effects of China’s 31 provinces in terms of the elderly dependency ratio. Subsequently, in Sect. 9.4, a spatial econometric analysis is conducted to analyse the influencing factors causing spatial aggregation. Finally, in Sect. 9.5, conclusions are drawn, and suggestions are proposed.

9.2 Elderly Dependency Rate 9.2.1 China’s Elderly Dependency Ratio As provided by the China Statistics Yearbook (National Bureau of Statistics of China 2020), China’s elderly dependency ratios from 2002 to 2018 were 11.57, 11.96, 11.87, 12.71, 12.72, 12.86, 13.04, 13.24, 11.90, 12.27, 12.68, 13.10, 13.70, 14.33, 14.96, 15.86, and 16.77%. The historical development of the elderly dependency ratio in China is shown in Fig. 9.1. Figure 9.1 reveals a continuous increase in the elderly dependency ratio in China, and the rate of increase constantly accelerates. It takes 12 years for China to increase

9.2 Elderly Dependency Rate

219

Fig. 9.1 The historical evolution of China’s elderly dependency ratio from 2000 to 2018

the elderly dependency ratio from 11.57% in 2002 to 13.70% in 2014. However, China takes only 4 years to increase the elderly dependency ratio from 13.70% in 2014 to 16.77% in 2018. This drastic increase in the elderly dependency ratio portends future serious population ageing in China.

9.2.2 Elderly Dependency Ratio in China’s Provinces Then, from the China Statistics Yearbooks (2003–2019) (National Bureau of Statistics of China 2020), this section obtains the elderly dependency ratio of the 31 provinces in China from 2002 to 2018 and presents the data in Table 9.1. Among the 31 provinces, only Tianjin, Guangdong, Hainan, and Tibet had a decrease in the elderly dependency ratio from 2002 to 2018. The greatest decrease in the elderly dependency ratio happened in Tibet, whose elderly dependency ratio decreased from 9.36% in 2002 to 8.04% in 2018. The other 27 provinces had an increasing elderly dependency ratio from 2002 to 2018. In some provinces, such as Liaoning, Jilin, Heilongjiang, Shandong, and Ningxia, the elderly dependency ratio even increased drastically by approximately two times. In 2018, the elderly dependency ratio in 12 provinces was higher than China’s dependency ratio (16.77%). These 12 provinces include Shandong (22.69%), Sichuan (21.83%), Chongqing (21.09%), Liaoning (20.00%), Shanghai (19.88%), Jiangsu (19.86%), Anhui (19.35%), Hebei (18.43%), Hunan (18.36%), Zhejiang (17.71%), Hubei (17.31%), and Guizhou (17.08%). Shandong, Sichuan, Chongqing, and Liaoning which have a very serious population ageing phenomenon.

2016 (%)

2017 (%)

2018 (%)

Increase rate (%)

17.70 21.80 20.30 15.10 18.60 18.30 16.50 17.90 12.40

13.80 15.80 14.70 14.70 14.90 14.90 15.70 16.20 14.30 14.20 15.20 16.40 16.20 17.20 18.50 19.10 19.80 43.20

15.40 15.80 13.10 14.30 13.30 14.20 14.10 14.70 12.00 10.80 11.00 11.60 12.20 14.80 15.40 16.50 17.70 15.00

Zhejiang

9.60 12.20 13.10 14.10 14.10 16.10 16.40 89.10

10.80

11.70 12.40 12.50 13.40 12.70 13.00 13.00 13.00 13.20 14.50 14.30 14.90 15.70 16.20 16.30 18.60 22.60 93.90

10.70 11.60 11.50 11.60 11.30 10.50 12.80 12.30 11.80 12.40 12.40 12.70 12.40 14.20 14.50 15.80 16.30 52.10

12.90 11.10 11.20 12.70 13.30 13.20 13.40 13.50 11.80 13.30 14.30 13.10 13.90 15.20 15.80 17.00 17.30 33.70

12.00 12.50 12.00 14.20 14.70 14.10 14.40 15.60 13.40 14.60 15.80 14.80 15.30 15.90 17.00 17.50 18.30 52.10

11.50 12.30 11.80 10.30

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Guangdong

9.70 10.00 10.20

9.90

8.80

8.60

9.10

9.50 10.90

(continued)

9.60 10.10 10.20 11.00 -4.30

9.90 11.40 12.60 12.60 13.00 12.10 11.50 10.70 10.70 11.40 12.60 13.20 13.00 13.80 14.20 13.90 28.50

12.10 11.70 12.20 15.10 14.90 15.30 15.50 14.30 14.10 14.60 14.40 14.80 14.50 15.70 16.10 19.10 19.30 58.70

10.60 11.20 11.70 12.00 12.80 13.90 13.80 13.70 10.20 10.00 11.50 10.80 10.10 12.20 13.80 13.20 12.80 20.60

Anhui

9.30 10.90 13.30 12.00 16.40 16.70 18.80 19.80 12.10

9.90 11.10 11.30 11.80 13.70 15.30 15.50 15.80 90.60

9.80 10.50 11.20 11.60 11.20 10.50 11.00

9.80 10.30 11.40 11.60 10.90 10.40

Jiangsu

9.40

8.60

Shanghai

8.50

8.60

8.30

8.60 10.00 11.00 12.10 12.30 12.10 14.30 12.80 31.70

Heilongjiang

9.60

9.90 10.60 10.80 10.00 10.20 10.40 10.40 11.10 12.10 11.40 11.90 13.90 39.60

Jilin

9.00

9.50

9.90 10.50 10.20 10.70 10.50 10.90

9.80 10.80

10.60 12.80 12.30 12.80 13.70 13.80 14.80 14.80 13.10 13.80 12.40 12.80 15.60 16.80 17.30 18.50 20.00 88.10

2015 (%)

Liaoning

2014 (%)

9.70 10.30

2013 (%)

Inner Mongolia

2012 (%)

9.90 10.10

2011 (%)

10.70 10.30 10.70 11.00 11.30 11.90 11.60 11.80 11.00 11.00 12.40 12.50 12.90 14.20 15.40 16.80 18.40 71.90

2010 (%)

Shanxi

2009 (%)

14.30 14.50 14.10 12.40 13.60 13.90 15.90 13.90 10.40 12.20 13.40 14.80 15.00 12.90 14.60 14.50 13.80 -3.60

2008 (%)

Hebei

2007 (%)

Tianjin

2006 (%)

11.50 11.90 11.80 12.70 12.70 12.80 13.00 13.20 11.90 12.20 12.60 13.10 13.70 14.30 14.90 15.80 16.70 44.90

2005 (%)

13.80 14.30 14.10 13.70 14.20 12.70 12.80 12.60 10.50 10.70 10.40 10.50 10.50 13.40 15.10 16.30 14.30 3.70

2004 (%)

Beijing

2003 (%)

China

2002 (%)

Table 9.1 The elderly dependency ratio of the 31 provinces from 2000 to 2018

220 9 Spatial Aggregation and Spatial Econometric …

2014 (%)

2015 (%)

2016 (%)

2017 (%)

2018 (%)

Increase rate (%)

7.70

7.50

7.20

7.80

8.00

7.00

8.20

8.00 -14

8.80

6.70

Xinjiang

8.30

7.20

6.90

9.50

8.70

8.20

8.30

9.20

8.80

8.60 9.30

8.50

9.80 9.10

9.50

9.40 10.00

8.80

9.10 9.20

9.40

9.70 8.40

8.80

8.60 9.00

7.40

8.00 9.30

9.20

9.60 8.70

9.70

9.80

9.70

9.80 10.90 10.40 31.20 9.50 10.00 10.30 10.40 10.10 15.10

9.20 10.10 10.60 11.50 12.60 82.30

9.50

9.20 10.40 10.40 10.90 11.40 11.40 11.10 12.00 12.40 11.90 11.90 12.70 13.60 14.30 15.90 75.70

9.30

Ningxia

8.40

9.40

9.20

7.90

9.20

9.00

9.20

Qinghai

9.30

Gansu

9.00

9.30

11.50 10.80 10.50 12.00 12.30 13.20 12.90 13.30 11.10 11.10 12.10 13.00 14.20 13.80 14.40 15.10 14.90 30.20

Shaanxi

9.80 11.10 10.40 11.70 11.40 11.30 11.30 -0.60

Tibet

9.40

10.20 10.20 11.00 11.00 10.70 10.60 11.20 12.30 10.60 10.50 10.60 11.10 12.00 11.60 11.60 11.50 13.20 28.90

2013 (%)

Yunnan

2012 (%)

10.30 11.40 11.20 12.90 12.40 12.80 12.30 12.30 12.90 13.60 13.40 13.50 13.40 13.90 14.10 14.40 17.00 64.30

2011 (%)

12.20 12.10 12.20 16.20 16.40 15.70 16.00 17.20 15.10 16.70 16.40 18.00 20.00 18.10 19.40 19.80 21.80 78.30

2010 (%)

Guizhou

2009 (%)

12.80 12.80 16.90 16.00 16.50 16.80 17.30 16.50 16.10 17.30 18.20 18.60 20.00 18.60 19.70 20.60 21.00 64.20

2008 (%)

Sichuan

2007 (%)

Chongqing

2006 (%)

11.30 11.40 10.90 12.60 12.50 12.70 12.80 12.30 10.80

2005 (%)

12.40 13.10 11.90 14.30 13.00 13.30 13.60 13.40 13.30 13.90 13.70 13.40 13.90 14.40 14.00 14.30 14.70 18.10

2004 (%)

Hainan

2003 (%)

Guangxi

2002 (%)

Table 9.1 (continued)

9.2 Elderly Dependency Rate 221

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9 Spatial Aggregation and Spatial Econometric …

The elderly dependency ratio in the other 19 provinces has a relatively minor elderly dependency ratio. The elderly dependency ratio in Tibet is only 8.04%. The provinces whose elderly dependency ratio is smaller than 12% include Hainan (11.30%), Guangdong (11.04%), Qinghai (10.42%), Xinjiang (10.19%), and Tibet (8.04%). In some economically developed provinces, such as Guangdong, the large labour-aged population reduces the elderly dependency ratio. Except for Guangdong Province, the other four provinces have a relatively backward economy.

9.2.3 Coefficient of Variation Among the 31 Provinces Then, to evaluate the provincial difference in terms of the elderly dependency ratio, this section calculates the coefficient of variation of the elderly dependency ratio from 2002 to 2018. The calculation equation for the coefficient of variation is shown below:   n 1  2 xi j − x j /x j (9.1) V j = n i=1 where V j represents the coefficient of variation in year j, xi j represents the elderly dependency ratio of province i in year j, and x j represents the average value of the elderly dependency ratio in the 31 provinces in year j. Here, n = 31, which represents the 31 Chinese provinces. The calculated values of the coefficient of variation from 2002 to 2018 are presented in Table 9.2. During the study period, the smallest value (0.1690) of the coefficient of variation is higher than 0.15, indicating the significant provincial population differences. Then the coefficient of variation of the elderly dependency ratio is shown in Fig. 9.2 to observe its development trend. A slight increasing trend is clearly displayed in Fig. 9.2. The provincial differences in the population ageing phenomenon should receive sufficient attention. Table 9.2 The coefficient of variation of the elderly dependency ratio from 2002 to 2018 coefficient of variation coefficient of variation coefficient of variation

2002

2003

2004

2005

2006

2007

0.1973

0.2332

0.2115

0.1710

0.1891

0.1796

2008

2009

2010

2011

2012

2013

0.1690

0.1767

0.1764

0.2232

0.1970

0.1972

2014

2015

2016

2017

2018

0.2106

0.1836

0.2004

0.2054

0.2276

9.3 Spatial Aggregation Analysis of the Elderly Dependency Rate

223

Fig. 9.2 The change trend of the coefficient of variation of the elderly dependency ratio

9.3 Spatial Aggregation Analysis of the Elderly Dependency Rate After finding a significant provincial difference in terms of the elderly dependency ratio, this section explores the spatial aggregation of China’s provinces.

9.3.1 Moran’s I Index Analysis Similar to Chaps. 7 and 8, this section analyses the spatial aggregation of the elderly dependency ratio from 2003 to 2013. Using OpenGeoDa, this section calculates the values of Moran’s I index and the corresponding Z statistics and thus judges the spatial aggregation of the 31 provinces in terms of the elderly dependency ratio. The values of Moran’s I index are presented in Table 9.3. The elderly dependency ratio has a positive spatial aggregation effect, meaning that the provinces with high elderly dependency ratios are gathered in the high-high patterns and that the provinces with low elderly dependency ratios are gathered in the low-low patterns. Although Fig. 9.2 reveals a slightly increasing trend of the provincial differences in terms of the elderly dependency ratio, Fig. 9.3 reveals a decreasing trend of the spatial aggregation effect of China’s provinces.

224

9 Spatial Aggregation and Spatial Econometric …

Table 9.3 Moran’s I index values from 2003 to 2013 Year

Moran’s I

Z

P

Year

Moran’s I

Z

P

2003

0.4719

4.470796

0.01***

2004

0.3917

3.821942

0.01***

2005

0.3886

3.835455

0.01***

2006

0.3162

3.367052

0.01***

2007

0.3292

3.325688

0.01***

2008

0.2627

2.542955

0.01***

2009

0.2925

2.914132

0.01***

2010

0.3126

3.116216

0.01***

2011

0.2441

2.302075

0.02**

2012

0.2585

2.336269

0.02**

2013

0.2312

2.232068

0.02**

Note ***, **, and *represent significance at the 0.01, 0.05, and 0.1 levels, respectively

Fig. 9.3 The change trend of the values of Moran’s I index in terms of the elderly dependency ratio

9.3.2 LISA Analysis The specific spatial aggregation of China’s provinces from 2003 to 2013 is analysed using OpenGeoDa software to conduct LISA analysis. The spatial aggregation diagram of China’s provinces in terms of the elderly dependency ratio in 2003, 2008, and 2013 is shown in Fig. 9.4. In 2003, eight provinces exhibited positive spatial aggregation. Three provinces fall into the high-high pattern: Jiangsu, Shanghai, and Zhejiang. These three provinces are all developed eastern provinces. Five provinces fall into the low-low pattern: Xinjiang, Tibet, Qinghai, Gansu, and Inner Mongolia. These five provinces are all undeveloped north-west provinces. The five provinces have a low population density. Figure 9.4a highly coincides with the classic Ai-hui Line. The population sizes are closely related with the population structure.

9.3 Spatial Aggregation Analysis of the Elderly Dependency Rate Fig. 9.4 Spatial aggregation diagrams for 2003, 2008, and 2013. a Spatial aggregation diagram for 2003. b Spatial aggregation diagram for 2008. c Spatial aggregation diagram for 2013

225

a

b

c

In 2008, only four provinces exhibited positive spatial aggregation. Two provinces fall into the high-high pattern: Jiangsu and Zhejiang. The elderly dependency ratio of Shanghai significantly decreased from 2003 to 2008. Two provinces fall into the low-low pattern: Xinjiang and Gansu. One south-west province, Guizhou, falls into the low–high pattern. The elderly dependency ratio of the neighbouring provinces of Guizhou significantly increased from 2003 to 2008. In 2013, only one north-west province, Xinjiang, fell into the low-low pattern. Four provinces fall into the high-high pattern: Guizhou, Chongqing, and Hubei. The provinces with a high-high pattern shift from the eastern provinces to the central

226

9 Spatial Aggregation and Spatial Econometric …

provinces. The population ageing pressure in the central provinces, such as Sichuan and Chongqing, is currently relatively large. In summary, the elderly dependency ratios from 2003 to 2013 exhibit significant positive spatial aggregation. The provinces with a high-high pattern shift from the eastern provinces to the central provinces, and the provinces with a low-low pattern continuously compress into the north-west provinces.

9.4 Spatial Econometric Analysis 9.4.1 Influence Factors Analysis Chapter 2 performs a meta-analysis to analyse the influencing factors affecting the elderly dependency ratio. Similar to Chap. 7, these influencing factors include social factors, economic factors, and population size factors. The social factors affecting the elderly dependency ratio include the educational level, the retirement security system, and the urbanization rate. A higher educational level improves the culture of the population and affects views on fertility, causing a low fertility rate and a low mortality rate. The variable for measuring the educational level of fertile women is the illiteracy rate of fertile women (F V W M L). The retirement security system helps residents meet their material and cultural needs, thus affecting the mortality rate and the migration of the elderly population. The variable for measuring the retirement security system for the elderly population is the proportion of people without medical insurance (Y B B L). The urbanization rate (C Z H L), reflecting the proportion of people living in cities, affects the fertility rate and the elderly dependency ratio. The economic factors affecting the elderly dependency ratio include the per capita gross regional product (R J SC Z Z ) and household consumption expenditure (J M X F ZC). These two factors directly affect the total fertility rate and life expectancy, thus affecting the elderly dependency ratio. The population size factors affect and are affected by the population structure factors. The population size factors include the population size (R K Z S) and urban population density (C S R K M D).

9.4.2 Data for the Variables As described in Sect. 9.4.3, seven variables are chosen to analyse how social factors, economic factors, and population size factors affect the elderly dependency ratio. The data for these variables were obtained from the China Statistics Yearbook (National Bureau of Statistics of China 2020). The descriptive statistics for these variables are presented in Table 9.4.

9.4 Spatial Econometric Analysis

227

Table 9.4 Descriptive statistics of the seven variables Variables

Maximum value

Minimum value

Average value

Standard deviation

Dependent variables

Elderly dependency ratio

18.62%

7.23%

12.49%

2.50

Social factors

FV W ML

45.26%

2.34%

8.20%

7.66

Y BBL

108.92%

17.56%

43.70%

22.50

CZHL

89.60%

23.71%

54.45%

13.71

Economic factors

R J SC Z Z

99,607

22,921.67

47,046.55

20,438.87

J M X F ZC

39,223

6275.48

15,889.07

7131.88

Population size factors

R K Z S(million)

106.44

3.12

43.72

2740.42

C S R K M D(people per square kilometres)

5541

1059

2817.61

1173.60

9.4.3 Spatial Econometric Model To mitigate the problem of heteroscedasticity, this section takes the logarithm of the variables and builds the model in Eq. (9.2): I n L N FY Bi = αi0 + αi1 I n F V W M L i + αi2 I n Y B B L i + αi3 I n C Z H L i + αi4 I n R J SC Z Z i + αi5 I n J M X F ZCi + αi6 I n R K Z Si + αi7 I n C S R K M Di + ξi

(9.2)

where αi represents the regression coefficient and ξi represents the random error term. Here, i = 1, 2, 3, ν, 31 which represents the 31 provinces in China. Out of the many kinds of spatial econometric models that exist, this section chooses regression models with a space constant coefficient, including the spatial lag model and the spatial error model. The spatial lag model mainly explores whether a variable has the diffusion phenomenon or an overflow effect in a specific region, and it is shown below: y = ρW y + Xβ + ξ

(9.3)

where y represents the dependent variable; ρ represents the space lag coefficient; W represents the spatial weight adjacency matrix; W y represents the spatial lag dependent variable; X represents the n × k exogenous explanatory variable matrix; β reflects the effects of independent variable X on dependent variable y; and ξ represents the random errors. The spatial error model is expressed as follows:

228

9 Spatial Aggregation and Spatial Econometric …



y = βX + ξ ξ = λWξ + u

(9.4)

where y represents the dependent variable; X represents the n×k exogenous explanatory variable matrix; β reflects the effects of independent variable X on dependent variable y; ξ represents the random errors; λ represents the n × 1 space error coefficient and measures the spatial dependent effects; Wξ represents the spatial error dependent variable; and u represents the random error variables of the normal distribution.

9.4.4 Results The values of Moran’s I index of F V W M L, Y B B L, C Z H L, R J SC Z Z , J M X F ZC, R K Z S, and C S R K M D are 0.3832, 0.0622, 0.3802, 0.4173, 0.3743, 0.2570, and -0.1130, respectively. Five of the seven variables (i.e., F V W M L, C Z H L, R J SC Z Z , J M X F ZC, and R K Z S) have a significant spatial aggregation effect. Thus, Eq. (9.2) is revised to the new Eq. (9.5): I n L N FY Bi = αi0 + αi1 I n F V W M L i + αi2 I n C Z H L i + αi3 I n R J SC Z Z i +αi4 I n J M X F ZCi + αi5 I n R K Z Si + ξi . (9.5) Using OpenGeoDa software to run the regression analysis, this section obtains the results in Table 9.5. There is a significant spatial aggregation effect of the elderly dependency ratio. Compared with the spatial lag model (SLM), the spatial error model (SEM) improves the R2 value. The results show that the spatial aggregation effect of China’s provinces in 2013 is smaller than that in 2003. The results reveal that the elderly dependency ratio is significantly related to the urbanization rate, household consumption expenditure, and the population size. The urbanization rate increases the proportion of people living in cities. A high urbanization rate causes a low total fertility rate and a low mortality rate, thus causing a high elderly dependency ratio. Household consumption expenditure is negatively related to the elderly dependency ratio. Household consumption expenditure represents people’s views on consumption. Households with strong views on consumption usually have a high willingness to have more children, thus reducing the elderly dependency ratio.

9.5 Conclusions

229

Table 9.5 Spatial regression analysis results of the influencing factors of the elderly dependency ratio OLS

SLM

SEM

C O N ST AN T

1.2958 (1.0765)

1.3819 (1.2517)

1.6572 (1.4733)

FV W ML

0.1233 (1.4115)

0.1249 (1.5888)

0.0891 (1.1327)

CZHL

0.8610* (1.7592)

0.9075** (1.9828)

0.8612** (2.0456)

R J SC Z Z

−0.0138(−0.0602)

−0.0171 (−0.0829) 0.1086 (0.5436)

J M X F ZC

−0.3694 (−1.0177) −0.3966 (−1.1978) −0.5282* (−1.7244)

RK Z S

0.1547*** (3.4277) 0.1503*** (3.5529) 0.1442*** (3.4080)

Spatial lag (W _Y B L)

0.0238 (0.3504)

Spatial error (L AM B D A)

0.3729* (1.7493)

Model tests R2

0.4089

0.4113

0.4466

Log likelihood

13.8494

13.9086

14.3461

Akaike information criterion −15.6988

−13.8173

−16.6922

−7.09491

−3.77937

−8.08828

Schwarz criterion

Note ***, **, and *represent significance at the 0.01, 0.05, and 0.1 levels, respectively

9.5 Conclusions China’s elderly dependency ratio continuously increased from 2003 to 2018, reflecting the serious population ageing challenge in China. Among China’s provinces, only Tianjin, Guangdong, Hainan, and Tibet had a decrease in their elderly dependency ratios from 2002 to 2018. The other 27 provinces had an increase in their elderly dependency ratios from 2002 to 2018. In some provinces, such as Liaoning, Jilin, Heilongjiang, Shandong, and Ningxia, the elderly dependency ratio even increased drastically by approximately two times. In 2018, the elderly dependency ratio in 12 provinces was higher than China’s dependency ratio (16.77%). Some provinces, such as Shandong, Sichuan, Chongqing, and Liaoning, have a very serious population ageing phenomenon. These provinces with a large population ageing challenge should pay more attention to the retirement security system and mechanisms for improving social welfare for the elderly population, and they should effectively develop industries for the elderly population. The elderly dependency ratio has a positive spatial aggregation effect. There was a decreasing trend in the spatial aggregation effect of China’s provinces from 2003 to 2013. During the research period, the provinces with a high-high pattern shift from the eastern provinces to the central provinces, and the provinces with a low-low pattern continuously compress into the north-west provinces. The spatial econometric model reveals that the elderly dependency ratio is significantly positively related to the urbanization rate and the population size. It is expected that China’s urbanization rate will still rapidly increase in the coming decades and that China will still face a serious population ageing challenge in the future.

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9 Spatial Aggregation and Spatial Econometric …

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Chapter 10

Conclusions and Suggestions for Addressing China’s Population Challenges

Abstract I first summarize all the contents introduced in the above nine chapters. Then, I provide some effective suggestions for addressing the two serious population challenges in contemporary China. Finally, some future research directions for exploring and addressing China’s population challenges are identified. Keyword Population challenge · Family planning policy · Provincial population difference

10.1 Conclusions A healthy population size and population structure directly affect social development and economic operations. To guarantee a stable population size and population structure, China implemented a family planning policy in the 1970s. The strict one-child policy well controlled the total population number but deteriorated the population structure. China now faces serious population ageing pressure, and the proportion of the elderly dependency ratio is markedly increasing. Moreover, China now faces another kind of population size challenge, i.e., the low total fertility rate. To better respond to the current challenges posed by the population size and population structure, this book uses the meta-analysis method to understand the influencing factors affecting the low total fertility rate and the effects of serious population ageing and vanishing population dividends. To address the current challenges posed by the population size and population structure analysed in Chaps. 3 and 4, China implemented the selective two-child policy in 2013. Only 2 years later, China announced that the two-child policy would be officially implemented on January 1, 2016. To test the new family planning policy, this book develops a system dynamics model based on Song Jian’s population development equation and runs simulation experiments to test the new two-child policy. The results show that the total population will peak at 1.448 billion in 2022 and will decrease to 0.961 billion by 2050 under the regulation of the new two-child policy. Essentially, however, population structure problems can only be moderately optimized; they cannot be fully resolved. According to comparative analyses of three different possible family planning policies, the two-child policy is found to © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9_10

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be reasonable for China. This chapter also predicts the population development in Jiangxi Province under the three different possible family planning policies and finds the significantly different population challenges in Jiangxi Province and China as a whole. Finally, a sensitivity analysis of the willingness of fertile women to have a second baby is conducted. Some specific measures are therefore proposed as important suggestions for family planning policy tracking. Although the new two-child policy is verified as effective for contemporary China, the new two-child policy cannot effectively manage future population ageing. As predicted in Chap. 5, the new two-child family planning policy will deteriorate the demographic dividends before 2050. The family planning policy still needs to be adjusted to address the challenges posed by the population size and population structure. Aiming to stabilize the demographic dividends at an ideal range, this book builds a non-linear integer programming model to propose an appropriate reform path for China’s family planning policy. Then, this book simulates and compares the demographic developments under the proposed reform path with those under three possible family planning policies, i.e., the one-child policy, the two-child policy, and cancellation of fertility restrictions, verifying that the proposed reform path obtains better performance by stabilizing the demographic dividends than these three family planning policies. Finally, a sensitivity analysis of the upper bound of the research interval is conducted to evaluate the effect of the upper bound on the proposed reform path. Based on these results, China should continue to implement its current strict family planning policy until 2032, gradually begin to relax it, especially from 2036 to 2041, and completely cancel its family planning policy after 2065. In addition to adjusting the family planning policy to address the challenges posed by the population size and population structure analysed in Part I (i.e., Chaps. 3–6), China faces another kind of population challenge: provincial population differences. Because of China’s regional diversity, every province has its own unique necessity of family planning policy adjustment. To understand the provincial differences with regard to the necessity of policy adjustment, this book builds original formulas to quantify the necessity of policy adjustment and then utilizes the k-means cluster method to divide China’s 31 provinces into four categories. The results show that serious regional disparities do indeed exist among the provinces. Some provinces, such as Shanghai and Beijing, urgently require family planning policy reform, but for other provinces, such as Guangxi, the opposite holds true. Meanwhile, neighbouring provinces always tend to be clustered in the same categories, clearly indicating spatial aggregation. In addition to the geographic element, factors such as the economy also lead to the aggregation effect. Furthermore, most provinces show similar classification results under two separate conditions, i.e., combining all various indicators and considering only demographic indicators, presenting coordinated development among China’s population, economy, and society. However, many Chinese provinces cannot clearly identify the extent to which they need to adjust their family planning policy through a straightforward diagnosis. After analysing the obvious provincial population differences in terms of the necessity of family planning policy adjustment in Chap. 7, this book then studies

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the degree of correlation between the time of implementation of the selective twochild policy and the necessity of family planning policy adjustment and conducts spatial aggregation analysis. The provincial population number is regarded as the weight for calculating the overall degree of rationality of implementing the selective two-child policy. The results show that Shanghai Province suitably implements the selective two-child policy with the highest degrees of correlation and that Jiangxi Province improperly implements the family planning policy with the lowest degrees of correlation. When considering all the influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.93709 and 0.31425, respectively. When considering only the population-related influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.95368 and 0.35088, respectively. In the spatial aggregation analysis, the north-east provinces have relatively high degrees of correlation. When adjusting their family planning policies, most provinces have considered their population factors but have overlooked their economic and social factors. When considering all the influencing factors and only the population-related influencing factors, the overall degrees of correlation in China are 0.64207 and 0.70159, respectively. The overall score is relatively high but still has high room for improvement. To analyse the provincial differences in terms of the elderly dependency ratio, this book then calculates the values of Moran’s I index and conducts LISA analysis to explore the spatial aggregation of China’s provinces. The results show that China’s elderly dependency ratio is becoming serious. Over time, the provincial differences in terms of the elderly dependency ratio slightly increase, but the spatial aggregation effect of China’s provinces decreases. During the research period, the provinces with a high-high pattern shift from the eastern provinces to the central provinces, and the provinces with a low-low pattern continuously compress into the north-west provinces. Then, a spatial econometric model is used to analyse the influencing factors affecting the elderly dependency ratio. The increases in the urbanization rate and the population size increase the elderly dependency ratio, and household consumption expenditure reduces the elderly dependency ratio.

10.2 Suggestions Conducting the research above, this book proposes five suggestions for responding to China’s current population challenges. The five suggestions include tracking fertility intentions and further relaxing the family planning policy at a suitable time; publicizing the population conditions in China and all provinces and ensuring stable population development; supervising population development in China and all provinces, particularly the provinces with a low necessity of policy adjustment; expanding research into other related fields and paying attention to the effects of population development on social development and economic operations; and paying attention to the spatial aggregation of China’s provinces.

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10.2.1 Tracking Fertility Intentions and Further Relaxing the Family Planning Policy in Due Time Testing the effects of the new two-child policy in Chap. 5, this book finds that the implementation of the two-child policy is rational and necessary. However, the twochild policy can only relieve the pressure of population ageing and cannot completely solve the future population ageing problem. As predicted in Chap. 5, China’s population size will always be within the ideal range, but the population ageing pressure will far exceed our acceptable range. Therefore, we should continue to evaluate China’s population development and further relax the family planning policy in due time. China’s mortality rate will undoubtedly stabilize at a relatively low level, and life expectancy will further increase. To continuously evaluate population development, China should track the fertility intentions of fertile women and understand the total fertility rate. As analysed in Chap. 6, this book suggests that China should continue to implement its current strict family planning policy until 2032, gradually begin to relax it, especially from 2036 to 2041, and completely cancel its family planning policy after 2065. This suggestion aims to optimize the demographic dividends but does not consider other influencing factors. Thus, we should analyse more related influencing factors and explore the suitable time for adjusting the family planning policy.

10.2.2 Publicizing the Population Conditions in China and All Provinces Testing the effects of the two-child policy in Jiangxi Province in Chap. 5, this book finds that the population size and population structure in Jiangxi Province are totally different from those in China. China does not need to worry about the total population number but faces serious population ageing. Jiangxi faces a serious population size challenge, but its population ageing is not serious. Thus, in China, relaxing the family planning policy is a high priority, but in Jiangxi Province, doing so is a low priority. Then, Chap. 7 quantifies the necessity of family planning policy adjustment and finds that there are significant provincial differences in terms of the necessity of policy adjustment. China now implements the same family planning policy for all 31 provinces. Some provinces with a low necessity of policy adjustment will face large population challenges. Thus, the 31 provinces, specifically those with a low necessity of policy adjustment, should seize all opportunities to publicize their population conditions. The coming 2020 population census is a good opportunity for all provinces to publicize their population conditions.

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10.2.3 Supervising the Population Development in China and All Provinces As analysed in Chap. 7, the 31 provinces have different levels of necessity of family planning policy adjustment. Thus, China has not designed a fixed timetable for the 31 provinces to adjust their family planning policy from the one-child policy to the selective two-child policy. However, not all provinces have suitably adjusted their family planning policy. For instance, Jiangxi Province has a low necessity of policy adjustment but was the second province to implement the selective two-child policy. As calculated in Chap. 8, thedegree of correlation between the implementation time and the necessity of policy adjustment is very low. Therefore, we should pay more attention to population development in the provinces with a low degree of correlation, as these provinces that have not clearly understood their population situations.

10.2.4 Expanding Research into Other Related Fields As analysed in Chap. 8, some provinces have mainly considered their population situations but have not considered the related social and economic factors when adjusting their family planning policy. Population development significantly affects social development and economic operations, and vice versa. Thus, when analysing population development, we should also study more population-related fields. For instance, as population ageing is currently very serious, China should make great efforts to develop industries for the elderly people.

10.2.5 Paying Attention to the Spatial Aggregation of China’s Provinces As analysed in Chaps. 7–9, there is significant spatial aggregation of China’s provinces in terms of the necessity of family planning policy adjustment and the elderly population ratio. Even when adjusting the family planning policy, the influence of neighbouring provinces is very significant. It is very important for policymakers to realize and utilize spatial aggregation. The provinces with significant spatial aggregation effects should not only fully consider their conditions but also consider the situations of their neighbouring provinces.

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10.3 Future Research Directions (1) Developing a model to better predict population development. Chap. 5 builds a system dynamics model based on Song Jian’s population development equation to present China’s population development. In this model, some parameter values are assumed based on existing data or the existing literature. To better predict population development, we should track population development and set scientific parameter values. Moreover, the dominant population projection methodology in demography is the cohort component method. Thus, we can develop a model such as the cohort component method to predict population development and compare the predicted values under different prediction models. (2) Quantifying the effects of population indicators on social development and economic operations. As analysed in Chap. 7, social factors and economic factors affect population development and the necessity of family planning policy adjustment. Moreover, Chap. 9 finds that the urbanization rate and household consumption expenditure significantly affect the elderly dependency ratio. In the future, we can conduct studies to quantify the influencing mechanisms between these social and economic factors and population development. Understanding the influencing mechanisms can help better address China’s population challenges. (3) Conducting surveys to understand the fertility intentions of fertile women. The total fertility rate directly affects population development. As analysed in Chap. 3, various factors affect the fertility intentions of fertile women and the total fertility rate. It is very difficult to accurately determine the real-time fertility intentions of fertile women and the total fertility rate. Only after accurately understanding the total fertility rate can we effectively predict population development and design suitable policies. After understanding the factors affecting fertility intentions and the total fertility rate, we can better design a questionnaire to survey fertility intentions. The surveyed total fertility rate can be put into the prediction model to better predict population development.

Index

A Adjusting the family planning policy, 7, 15, 17, 19, 94, 135, 139, 145–147, 154, 155, 158, 185, 200, 205, 206, 215, 217, 234, 236, 237 Ageing before getting rich, 67

C Cancellation of family planning policy, 128, 130, 135, 136, 145 Children dependency ratio, 139, 148–151, 153, 154, 180 Coefficient of variation, 222, 223

D Debt risk, 80, 83–85 Degree of correlation, 5, 205–207, 210–212, 214, 215, 217, 235, 237 Demographic dividends, 8, 12, 13, 26, 68, 71, 72, 74, 77, 90, 93, 98, 129, 145– 147, 155, 156, 158, 161, 168–172, 183, 184, 217, 234, 236 Demographic structure, 13, 57, 70, 71, 79, 80, 111, 146, 147, 155, 206

E Elderly dependency ratio, 74, 80, 94, 97, 148–151, 153, 154, 180, 197, 217– 220, 222–229, 233, 235, 238 Elderly people, 13, 32, 34, 68, 94, 97–99, 115, 124–129, 133–135, 137–139, 148, 164–167, 169, 182, 237 Entropy weight, 188, 191, 199 Environment pollution, 34, 36, 38 © Springer Nature Singapore Pte Ltd. 2020 P. Wu, Population Development Challenges in China, https://doi.org/10.1007/978-981-15-8010-9

Evaluation index system, 5, 180, 181, 185, 186, 199, 209 Exported product, 85, 86, 88

F Family finance, 80, 81, 101 Family planning policy, 1–5, 7, 8, 12, 13, 15, 19, 25, 34, 37, 38, 42, 50, 57, 67–69, 71, 88, 89, 94, 111–113, 115, 116, 118, 120–122, 124, 125, 127–139, 145–149, 151, 152, 154–159, 161, 163, 164, 168–173, 179–186, 192– 200, 205–207, 209, 210, 212, 215, 217, 233–238 Fertile women, 32, 42, 47, 48 Fertility intention, 13, 25–27, 30, 31, 34, 35, 37–42, 52, 55, 56, 121, 122, 124, 126, 127, 137, 139, 147, 170, 173, 185, 193, 194, 235, 236, 238

G Gender inequality, 3, 183, 184, 205 Grey model, 8

H Human capital, 38, 44, 72, 74–77, 79, 80, 82, 85, 91, 93

J Jiangxi province, 3, 15, 17, 111, 113, 131– 136, 139, 180, 205, 206, 210, 212, 215, 234–237 239

240 K k-means cluster method, 179, 193, 194, 199, 234

L Labour-aged population, 13, 133, 155–158, 164, 168, 169, 171 Labour market, 72, 73, 101, 182 Local Indicators of Spatial Association (LISA), 199, 217, 224, 235

M Mathematical programming, 5 Meta-analysis, 5, 25–27, 44, 67–69, 94, 226, 233 Moran’s I index, 196–198, 200, 217, 223, 224, 228, 235

N Necessity of family planning policy adjustment, 179–181, 198, 205, 207, 236 Newborn babies, 55, 114–118, 121, 129, 133, 135, 137, 159, 161, 164, 171, 173 Non-linear integer programming model, 145, 147, 148, 155, 158, 160–162, 164, 169, 171, 172, 234 North-east, 180, 192, 193, 195, 205, 212, 215, 235

O One-child policy, 1, 7, 8, 12, 13, 46, 67–69, 111, 120, 127, 128, 130, 135, 138, 139, 145–147, 149–152, 155, 156, 159, 164, 168, 169, 171, 181, 182, 196, 205, 206, 233, 234, 237 OpenGeoDa, 197, 199, 223, 224, 228

P Per capita GDP, 68, 92, 116, 122, 152, 154, 184 Per capita regional GDP, 184, 186 Population ageing, 3, 5, 7, 8, 11–13, 19, 67– 96, 98–101, 111, 118, 124, 127, 128, 131, 133, 135, 137, 139, 146, 149, 154, 155, 172, 179–184, 193, 206, 218, 219, 222, 226, 229, 233, 234, 236, 237

Index Population census, 25, 26, 31, 32, 36, 37, 40, 44, 46–49, 52, 53, 73, 97, 118, 119, 121, 123, 131, 133, 147, 161, 180, 182, 186, 236 Population challenge, 1, 3–5, 7, 8, 17, 19, 111, 233–236, 238 Population floating, 36 Population size, 1–5, 7–9, 16, 19, 25, 26, 38, 69–71, 128, 133, 139, 146, 148, 149, 151, 154, 155, 159, 169, 171– 173, 179–182, 188, 205, 206, 217, 224, 226–229, 233–236 Population structure, 1, 3–5, 7, 12, 13, 15, 16, 19, 49, 67, 68, 71, 111, 118, 122, 124, 127–131, 133, 135, 137–139, 145– 149, 151, 152, 154, 155, 159, 169, 171–173, 179, 182, 183, 217, 218, 224, 226, 233, 234, 236 Provincial population difference, 1, 3–5, 7, 15–17, 19, 180, 185, 200, 205, 206, 217, 222, 234

R Reform path, 145–148, 158, 161, 163–165, 167–172, 234 Regional diversity, 68, 179, 234 Retirement security system, 34, 35, 68, 97, 98, 226, 229

S Selective one-child policy, 1, 135, 146, 151, 206, 237 Sex ratio, 13, 14 Silver wave, 68 Song Jian’s population development equation, 5, 111–114, 137, 233, 238 Spatial aggregation, 50, 68, 179–181, 193, 196–200, 205, 206, 212, 213, 215, 217, 218, 223–226, 228, 229, 234, 235, 237 Spatial econometric, 5, 217, 218, 226, 227, 229, 235 System dynamics, 5, 111–118, 122, 128, 131, 137, 152, 155, 168, 233, 238

T Technology innovation, 77, 79, 80, 93 Tibet province, 3, 17, 199, 200, 215 Total dependency ratio, 77, 94, 97, 128–130, 137, 139, 155, 158, 161, 164–169, 171, 173

Index Total fertility rate, 2, 5, 7–9, 11–13, 19, 25– 28, 30–32, 34–40, 42, 44–53, 55–57, 67, 70, 71, 101, 111, 113, 114, 116, 118, 128, 134, 135, 147, 149, 152, 181–183, 186, 193, 218, 226, 228, 233, 236, 238 Total population, 2, 3, 7–9, 11, 12, 14–16, 18, 19, 26, 50, 57, 67, 70, 71, 111, 113, 116, 118–131, 133–135, 137– 139, 148–151, 153, 154, 156–159, 162–169, 171, 180–183, 193, 214, 233, 236 Two-child policy, 1–3, 13, 26, 37–39, 44, 55, 68, 91, 111, 112, 118, 121, 124, 126– 131, 135–137, 139, 145–149, 151– 158, 164, 168, 169, 171, 185, 186,

241 196, 205–207, 209–212, 214, 215, 233–237

U Urbanization, 3, 16, 34–36, 72, 74, 77, 78, 80, 83, 100, 113, 116, 152, 180, 217, 218, 226, 228, 229, 235, 238

V Vensim, 116, 117, 122, 124, 128, 131, 168