Services for Aging Persons in China: Spatial Variations in Supply and Demand (Global Perspectives on Health Geography) 3030980316, 9783030980313

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Global Perspectives on Health Geography

Xiaoping Shen · Shangyi Zhou Xiulan Zhang

Services for Aging Persons in China Spatial Variations in Supply and Demand

Global Perspectives on Health Geography Series Editor Valorie Crooks, Department of Geography Simon Fraser University Burnaby, BC, Canada

Global Perspectives on Health Geography showcases cutting-edge health geography research that addresses pressing, contemporary aspects of the health-place interface. The bi-directional influence between health and place has been acknowledged for centuries, and understanding traditional and contemporary aspects of this connection is at the core of the discipline of health geography. Health geographers, for example, have: shown the complex ways in which places influence and directly impact our health; documented how and why we seek specific spaces to improve our wellbeing; and revealed how policies and practices across multiple scales affect health care delivery and receipt. The series publishes a comprehensive portfolio of monographs and edited volumes that document the latest research in this important discipline. Proposals are accepted across a broad and ever-developing swath of topics as diverse as the discipline of health geography itself, including transnational health mobilities, experiential accounts of health and wellbeing, global-­ local health policies and practices, mHealth, environmental health (in)equity, theoretical approaches, and emerging spatial technologies as they relate to health and health services. Volumes in this series draw forth new methods, ways of thinking, and approaches to examining spatial and place-based aspects of health and health care across scales. They also weave together connections between health geography and other health and social science disciplines, and in doing so highlight the importance of spatial thinking. Dr. Valorie Crooks (Simon Fraser University, [email protected]) is the Series Editor of Global Perspectives on Health Geography. An author/editor questionnaire and book proposal form can be obtained from Publishing Editor Zachary Romano ([email protected]). More information about this series at https://link.springer.com/bookseries/15801

Xiaoping Shen • Shangyi Zhou • Xiulan Zhang

Services for Aging Persons in China Spatial Variations in Supply and Demand

Xiaoping Shen Department of Geography Central Connecticut State University New Britain, CT, USA

Shangyi Zhou Faculty of Geographical Science Beijing Normal University Beijing, China

Xiulan Zhang School of Social Development and Public Policy Beijing Normal University Beijing, China

ISSN 2522-8005     ISSN 2522-8013 (electronic) Global Perspectives on Health Geography ISBN 978-3-030-98031-3    ISBN 978-3-030-98032-0 (eBook) https://doi.org/10.1007/978-3-030-98032-0 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book is a unique approach to studying the spatial relationship between the supply and demand of services for aging persons. China, a developing country with the largest aging population in the world, faces the unprecedented challenge of aging but not affluence. However, similar to all events in the world, challenges are accompanied by opportunities. China’s large and rapidly growing aging population not only affects hundreds of millions of people but also has created a record-making demand for over 1-trillion-dollars-worth of a variety of building, manufacturing, and other services for aging persons, presenting significant challenges as well as opportunities for government agencies and businesses in China and around the world. Using the latest statistics, nationwide surveys of aging persons, population censuses, nursing home websites, government websites, surveys done by authors, and many other sources, this book maps the spatial patterns and changes of the aging population as well as services for aging persons in China. Based on geographers’ perspective and integrated explanation, this book provides a topical, comprehensive, and comparative analysis of the supply and demand of services for aging persons at the national, regional, and local dimensions. In addition, utilizing statistical and Geographic Information System (GIS) analyses, this book not only reveals the affordability and spatial accessibility between supply and demand but also identifies underserved areas for meal, living facilities, medical, and community services to provide suggestions for future planning and development of services for policy makers and businesses. The text is divided into three parts, with eight chapters that can be read together or as stand-­ alone materials. Chapter 1 introduces China’s aging population and services for aging persons as well as major datasets and regional maps used by the book, and future development concepts. Part I provides a historical review of China’s population aging, development of services for aging persons (Chap. 2), and a review of government policies (Chap. 3) in the past seven decades. Part II provides analyses of the spatial variations of supply and demand as well as the affordability and accessibility of specific services for aging persons, including meal (Chap. 4), housing (Chap. 5), and health (Chap. 6) services. Part III analyzes the regional and local dimensions of community services (Chap. 7) and Beijing-Tianjin-Hebei regional cooperation in housing facilities (Chap. 8). As one of the first comprehensive studies on the subject, this manuscript expands the empirical and theoretical understanding of the impact of the world’s largest and a fast-growing aging population on socioeconomic, cultural, and political systems in a developing country. The initial idea of this project began from a collaborative project between Shangyi Zhou and me on meal services for aging persons in 2010, which resulted in several conference presentations and a coauthored paper on “Spatial Accessibility and Supply–Demand Analysis of Elderly Dining Services in Beijing” published in Professional Geographer. We then moved to a project about housing facilities for aging persons and presented papers at the 33rd International Geographical Congress in Beijing in 2016 and at the Annual Meeting of American Association of Geographers (AAG) in 2017. At that time, I proposed the idea to write a coauthored book on China’s services for aging persons because I saw that the demand and supply of services for aging persons have grown rapidly and need more geographic studies. Also, I was planning to apply for a sabbatical leave at the time so this book would be a perfect project. Shangyi loved the idea, and we decided to invite Xiulan Zhang to the project for her expertise on policies and v

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Preface

community services. Xiulan happily joined the team. I contacted several publishers and received enthusiastic support from Springer. Our book proposal was accepted by Springer and I was awarded the sabbatical leave for the book project. Then, the research for the project began. You may wonder if we knew each other before the project. Yes, we all earned our Bachelor of Science degrees in geography from Beijing Normal University. Shangyi and I were undergraduate classmates, and Xiulan and I had the same thesis advisor, Prof. Wu Yiguang, for our Master of Science degrees in geography from Beijing Normal University. Although Shangyi received her master’s degree in economics from Peking University and Ph.D. in human geography from Beijing Normal University, Xiulan got her Ph.D. in social welfare from University of California at Berkley in the United States, and I earned my Ph.D. in economic geography from University of Ottawa in Canada—we have remained in touch for four decades, collaborated on various research projects, and published coauthored papers in journals such as Professional Geographer and Annals of AAG. More importantly, we are all interested in researching aging population and actively participate in gerontological associations or programs. For example, Shangyi is an executive member of the Gerontological Society of China, Xiulan is an advisory committee member of the National Aging Association of China, and I am a member of Gerontology Committee at Central Connecticut State University and was a board member of the Services for Elderly of Farmington in Connecticut, the United States. This book is intended to be used as a primary or supplementary text for university courses in geography, gerontology, socioeconomic, planning, and related fields, as well as used as a reference by policymakers, city planners, real estate developers, healthcare providers and a variety of businesses that provide products and services to aging persons. This book is timely given that China’s population aging is entering its fastest growing stage and the government’s 14th Five-Year Plan (2021–2025) has recently presented an ambitious blueprint of service development for aging persons with financial and policy supports. We hope this book provides readers with an improved understanding of spatial variation of supply and demand as well as contributes to the future development of services for the aging population in China and in other developing countries. The responsible authors for each chapter in the book are: Chap. 1—Xiaoping Shen, Chap. 2—Xiaoping Shen and Yurong Zhang, Chap. 3—Yurong Zhang and Xiulan Zhang, Chap. 4— Shangyi Zhou and Mengting Luo, Chap. 5—Shangyi Zhou, Yingting Li, and Mengting Luo, Chap. 6—Xiaoping Shen, Chap. 7—Xiulan Zhang and Yurong Zhang, Chap. 8—Shangyi Zhou and Mengting Luo. New Britain, CT, USA July 10, 2021

Xiaoping Shen

Acknowledgments

We are very grateful to Yurong Zhang (Assistant Professor of the School of Social Development and Public Policy at Beijing Normal University), Mengting Luo (Assistant Researcher of CCID Consulting), and Yingting Li (Teacher of Danzhou No. 1 Middle School in Hainan), who have made significant contribution to Chaps. 2, 3, and 7, Chaps. 4, 5, and 8, and Chap. 5, respectively. We would like to thank the anonymous reviewers of our book proposal for their constructive comments. We thank Mr. Zachary Romano, Senior Editor of Earth Sciences, Geography and Environment, along with the Internal Editorial Board of Springer US for their enthusiastic guidance, support, and approval of our proposal and manuscript. We are very grateful to Dr. Valorie Crooks, the Editor of Global Perspectives on Health Geography—a Springer book series—for accepting our book as part of the cutting-edge health geography research series, which addresses pressing, contemporary aspects of the health-place interface, and her invaluable and constructive comments to our manuscript. Since we signed the book contract with Springer in 2017, many unexpected events have taken place, among them the Covid-19 pandemic, that disrupted our work and delayed the completion of the book far past the date originally intended. We are grateful to Springer for their patience and forbearance with us. We would like to express our gratitude to Anastasia Dildin, a graduate student at Central Connecticut State University, for her careful editing of the entire book. We are also grateful to Xiang Li (Beijing Daxing No. 1 Middle School) who arranged for Shangyi Zhou and Xiaoping Shen to visit Jingrui Community Center in Daxing, Beijing. Xiaoping Shen is most grateful to Central Connecticut State University for the sabbatical leave granted to her during the 2017–2018 academic year so that she could focus on the book project. She is also grateful to have had the support of four CSU-AAUP (Connecticut State University-American Association of University Professors) Faculty Research Grants between 2014 and 2017 for her work related to the project. She has also received a grant from the M.K.  Chung/Hyundai Motor America Fund’s Faculty International Research-Travel Grant Program, which supported the presentation of her paper on services for aging persons at the 33rd International Geographical Congress in China. Xiaoping also wishes to thank Carrie Andreoletti—Professor and Assistant Chair of Psychological Science and Gerontology Program Coordinator at Central Connecticut State University—for her support on the project. Last but not least, the manuscript of this book has benefited considerably from the help of students at Beijing Normal University, who surveyed businesses for aging persons, as well as from the help of the professional surveyors of two major surveys—“The Fourth Survey on the Living Conditions of the Elderly in Urban and Rural Areas in China” and “The Aging Person Survey by Renmin University of China.” We are grateful to all aging respondents and the staff of nursing homes, assistant living facilities, community centers, restaurants, other service businesses, and government agencies in China for volunteering their time to be interviewed.

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Contents

1 Introduction�����������������������������������������������������������������������������������������������������������������   1 1.1 Understanding China’s Aging Population and Services for Aging Persons �������   1 1.2 Using the Book ���������������������������������������������������������������������������������������������������   9 1.2.1 Data ���������������������������������������������������������������������������������������������������������   9 1.2.2 Base Maps�����������������������������������������������������������������������������������������������  10 1.3 Chapters���������������������������������������������������������������������������������������������������������������  11 1.4 Future Development���������������������������������������������������������������������������������������������  13 References���������������������������������������������������������������������������������������������������������������������  14 Part I History and Context 2 China’s Population Aging and Regional Variation �������������������������������������������������  19 2.1 Population Aging in the Past Seven Decades�������������������������������������������������������  19 2.1.1 The Largest Aging Population in the World �������������������������������������������  19 2.1.2 Stages of Population Aging���������������������������������������������������������������������  19 2.2 Regional Variation and Changes �������������������������������������������������������������������������  23 2.2.1 Regional Variation at Provincial Level ���������������������������������������������������  24 2.2.2 Regional Variation at County Level���������������������������������������������������������  26 2.2.3 Urban and Rural Variation�����������������������������������������������������������������������  28 2.2.4 Difference of Ethnic Regions�������������������������������������������������������������������  29 2.3 Factors Affecting Aging Population Variation�����������������������������������������������������  30 2.3.1 Data and Method�������������������������������������������������������������������������������������  30 2.3.2 Analysis and Results �������������������������������������������������������������������������������  30 2.3.3 Discussion �����������������������������������������������������������������������������������������������  32 2.4 Summary �������������������������������������������������������������������������������������������������������������  33 References���������������������������������������������������������������������������������������������������������������������  34 3 Government Policies and Programs of Services for Aging Persons: A Review�����  37 3.1 Policies and Programs Between 1950 and 1978 �������������������������������������������������   37 3.2 Policies and Programs Since the Economic Reform Started in 1978 �����������������   38 3.3 Central Government Policies�������������������������������������������������������������������������������  39 3.3.1 Government Purchases of Aging Persons Care Services�������������������������  40 3.3.2 Open the Aging Persons Care Services Market and Encourage Private and Social Capitals to Enter�������������������������������  40 3.4 National Programs�����������������������������������������������������������������������������������������������  42 3.4.1 Aging Persons Care and Service Undertakings���������������������������������������  42 3.4.2 The Allowance System of the Advanced Aging Persons�������������������������  49 3.4.3 Long-Term Care���������������������������������������������������������������������������������������  51 3.5 Regional and Local Government Initiatives���������������������������������������������������������  53 3.5.1 Shanghai���������������������������������������������������������������������������������������������������  53 3.5.2 Beijing�����������������������������������������������������������������������������������������������������  54

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3.6 NGOs for Aging Persons Care Services in China�����������������������������������������������  57 3.7 Recent Policy Development���������������������������������������������������������������������������������  60 3.8 Summary and Discussion�������������������������������������������������������������������������������������  62 References���������������������������������������������������������������������������������������������������������������������  62 Part II Spatial Analysis of Supply and Demand of Services for Aging Persons 4 Living Facilities�����������������������������������������������������������������������������������������������������������  69 4.1 The Status of Aging Person Living Facilities in China���������������������������������������  69 4.1.1 Supply and Demand Status of Aging Person Living Facilities���������������  70 4.1.2 The Spatial Distribution of Living Facilities for Aging Persons�������������  73 4.2 Spatial Pattern of Demand for Aging Person Living Facilities���������������������������  75 4.2.1 Research Status of Estimating Demand for Aging Person Living Facilities���������������������������������������������������������������������������  75 4.2.2 A Method of Estimating the Demand for Different Levels of Services �  78 4.2.3 Estimated Results of Demand for Different Types of Nursing Beds�������  79 4.2.4 Spatial Pattern of Aging Persons’ Demand for Living Facilities�������������  81 4.3 The Gap Between the Supply and Demand of Nursing Beds�����������������������������  81 4.4 Challenge and Future Development���������������������������������������������������������������������  82 4.4.1 Challenge�������������������������������������������������������������������������������������������������  82 4.4.2 Suggestion for Future Development �������������������������������������������������������  83 References���������������������������������������������������������������������������������������������������������������������  84 5 Meal Services���������������������������������������������������������������������������������������������������������������  87 5.1 The Demand of Meal Services for Aging Persons and the Related Policies�������  87 5.1.1 Spatial Pattern of Demand�����������������������������������������������������������������������  87 5.1.2 The Policies of Meal Services for Aging Persons�����������������������������������  89 5.2 The Implication of Meal Service for Aging Persons at Province Level �������������  90 5.2.1 The Kinds of Meal Service for Aging Persons ���������������������������������������  90 5.2.2 The Major Types of Meal Services for Aging Persons in China�������������  94 5.3 Relationship Between Supply and Demand: A Case Study in Beijing���������������  95 5.3.1 The Related Research of Meal Delivery for Aging Persons �������������������  96 5.3.2 Research Area and Data Source���������������������������������������������������������������  97 5.3.3 The Route Optimizing of Meal’s Delivery for Aging Persons�����������������  97 5.4 Challenges and Future Development�������������������������������������������������������������������  98 5.4.1 The Challenges of Meal Service for Aging Persons in China�����������������  98 5.4.2 The Major Tasks of Meal Service in China in Near Future���������������������  98 5.4.3 The Opportunities of Meal Service for Aging Person in China���������������  99 References���������������������������������������������������������������������������������������������������������������������  99 6 Health Services ����������������������������������������������������������������������������������������������������������� 101 6.1 Medical Service Supply��������������������������������������������������������������������������������������� 101 6.1.1 Medical Service System��������������������������������������������������������������������������� 101 6.1.2 Spatial Distribution of Medical Resources ��������������������������������������������� 103 6.2 Spatial Distribution of Demand��������������������������������������������������������������������������� 105 6.2.1 Distribution of Aging Persons����������������������������������������������������������������� 106 6.2.2 Spatial Distribution of Aging Persons with Chronic Diseases����������������� 106 6.3 Relationship Between Supply and Demand��������������������������������������������������������� 109 6.3.1 Distance Between Aging Persons and Their Doctors ����������������������������� 109 6.3.2 Accessibility of Aging Persons to Healthcare Services��������������������������� 110 6.4 Challenges and Future Development������������������������������������������������������������������� 114 References��������������������������������������������������������������������������������������������������������������������� 115

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Part III Regional and Local Case Studies 7 Community Services��������������������������������������������������������������������������������������������������� 119 7.1 Provision of Community Aging Person Care Services ��������������������������������������� 119 7.1.1 Community Institutional Aging Person Care Services ��������������������������� 119 7.1.2 Community Mutual Assistance Care for the Aging Persons������������������� 120 7.1.3 Community Aging Person Day Care Services����������������������������������������� 126 7.2 Senior Community Activities and Senior Associations��������������������������������������� 129 7.2.1 Provision and Participation in Community Senior Centers��������������������� 129 7.2.2 Establishment and Participations in Senior Associations ����������������������� 131 7.3 Discussion: Challenges and Future Development����������������������������������������������� 132 References��������������������������������������������������������������������������������������������������������������������� 137 8 Regional Cooperation for Nursing Home Facilities in Beijing-Tianjin-Hebei ����� 139 8.1 The Gap Between Demand and Supply of Nursing Home Facilities in Beijing ��������������������������������������������������������������������������������������������� 139 8.2 Development of a Cohesive Super-­Metropolitan Region in Jing-Jin-Ji��������������� 140 8.2.1 Research Object and Data Processing����������������������������������������������������� 141 8.2.2 Standard Price Distribution of Nursing Homes in Jing-Jin-Ji����������������� 142 8.3 Acceptable Distance and Travel Time Between Current Home and a Nursing Home ������������������������������������������������������������������������������������������� 143 8.3.1 Residents’ Choice About Distance from Home��������������������������������������� 145 8.3.2 Suitable Spatial Scope of Nursing Home Facilities��������������������������������� 145 8.4 Opportunities for Building New Nursing Home Facilities in Greater Beijing������������������������������������������������������������������������������������������������� 146 References��������������������������������������������������������������������������������������������������������������������� 148 Index������������������������������������������������������������������������������������������������������������������������������������� 149

List of Figures

Fig. 1.1 Population aged 65 and above by selected country and region, 1960–2019. (Source: The World Bank 2020)������������������������������������������������������������������������������ 2 Fig. 1.2 Population aged 65 and above in China, 1990–2020. (Source: National Bureau of Statistics of the People’s Republic of China 2020, 2021a, b)����������������� 3 Fig. 1.3 Comparison of China’s life expectancy at birth with income groups in the world, 1960–2018. (Source: The World Bank 2021)������������������������������������� 4 Fig. 1.4 Yanda Golden Age Health Nursing Center, Yanjiao District, Hebei province. (a) Main gate. (b) Apartment buildings for aging persons. (c) Living room. (d) Nursing bed. (e) Lift for moving patient. (f) Swimming pool. (Xiaoping Shen)������������������������������������������������������������������������������������������������������� 5 Fig. 1.5 Cuncao Chunhui Home for the Aged Hepingli Center, Dongcheng District, Beijing. (a) The front gate. (b) The dining room. (Xiaoping Shen)������������������������� 6 Fig. 1.6 The Fifth Social Welfare Institution of Beijing. (a) The building of the the Institution. (b) A standard room. (Xiaoping Shen)�������������������������������������������������� 6 Fig. 1.7 Minhang Xinzhuang Zhen Home for the Aged, Shanghai. (a) One of the buildings of the Home for the Aged. (b) Playground in the backyard. (c) Residents are eating lunch in the dining hall. (Xiaoping Shen)������������������������� 7 Fig. 1.8 Tianhe Home for the Aged, Yuhuazhai, Xi’an. (a) the building of the Home for the Aged. (b) Serving lunch to each floor for the convenience of disabled residents. (Xiaoping Shen)��������������������������������������������������������������������������������������� 7 Fig. 1.9 Jingrui Community Center, Daxing, Beijing. (s) The community center. (b) The daycare room for aging persons. (c) The dining room. (d) The lunch combo for about US $2.5. (Xiaoping Shen)������������������������������������������������������������ 8 Fig. 1.10 Provinces and regions of the People’s Republic of China������������������������������������� 11 Fig. 1.11 Population density of China, 2017. (Source: National Bureau of Statistics of the People’s Republic of China 2018a, b)��������������������������������������������������������� 12 Fig. 2.1 Proportion of aged population in China’s previous censuses. (Source: Population Census Office Under the State Council 1955–2012)��������������������������� 20 Fig. 2.2 (a) Population aged 65 and above in total population by selected countries, 1960–2019. (b) Proportion of population aged 65 and above grew from 7% to 13% by selected economies. (Source: United Nations 2019; Institute of Medicine and National Research Council Committee on an Aging Society 1988)���������������������������������������������������������������������������������������������� 20 Fig. 2.3 Birth rates, death rates, and natural increase rates in China, 1949–2019. (Source: National Bureau of Statistics of the People’s Republic of China 2009, 2020)������������������������������������������������������������������������������������������������������������ 22 Fig. 2.4 China’s population pyramid, 1982. (Source: National Bureau of Statistics of the People’s Republic of China 1984)��������������������������������������������������������������� 22

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Fig. 2.5 China’s population pyramid, 2019. (Source: National Bureau of Statistics of the People’s Republic of China 2020)��������������������������������������������������������������� 24 Fig. 2.6 Percent of population aged 65 and over by region, 1982–2019. (Source: National Bureau of Statistics of the People’s Republic of China 2020)���������������� 25 Fig. 2.7 Percentage of population aged 65 and over by county, 1982–2010. (Source: Population Census Office Under the State Council 1955–2012)��������������������������� 27 Fig. 2.8 Percentage share of people aged 65 and above in urban, town, or rural areas, 2018. (Source: National Bureau of Statistics of the People’s Republic of China 2019)���������������������������������������������������������������������������������������� 29 Fig. 2.9 Residual map of regression analysis between percentage of population aged 65 and above (Y) and multiple area factors by county, 2010. (Source: Population Census Office Under the State Council 2012)���������������������� 33 Fig. 2.10 Life expectancy at birth by region, 2010. (Source: National Bureau of Statistics of the People’s Republic of China 2019)������������������������������������������� 34 Fig. 3.1 Legal services received/10,000 people aged 60 and above, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018a)��������������������� 43 Fig. 3.2 Geriatric hospitals by region, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018a)����������������������������������������������������������������������� 47 Fig. 3.3 Geriatric hospital beds by region, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018a)������������������������������������������������������������� 48 Fig. 3.4 Self-payments of long-term care by region, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018a)������������������������������������ 55 Fig. 4.1 The percentage of the aging persons aged 65 and over in China (2000–2020). (Source: Ministry of Civil Affairs of the People’s Republic of China (2001–2021))������������������������������������������������������������������������������������������� 71 Fig. 4.2 The physical condition of the aging persons in 31 provincial units in China. (Source: Ministry of Civil Affairs of the People’s Republic of China (2018b)������������������������������������������������������������������������������������������������������������������� 72 Fig. 4.3 The occupancy rate of the beds in nursing houses in China (2017). (Source: Ministry of Civil Affairs of the People’s Republic of China (2018c))������������������� 74 Fig. 5.1 The number of policies about meal service for aging persons. (Source: Peking University Magical Law Information Database)��������������������������������������� 91 Fig. 6.1 Beds of medical institutions per 1000 persons by rural and urban areas, 2018. (Source: National Bureau of Statistics of the People’s Republic | of China 2019)����������������������������������������������������������������������������������������������������� 103 Fig. 6.2 Number of licensed medical doctors and registered nurses per 1000 persons, 2018. (Source: National Bureau of Statistics of the People’s Republic of China 2019)�������������������������������������������������������������������������������������� 104 Fig. 6.3 Medical beds by county, 2017. (Source: Rural Socioeconomic Investigation Department 2018; City Socioeconomic Investigation Department 2018)����������� 105 Fig. 6.4 Percentage of population aged 65 and above by region, 2000–2018. (Source: National Bureau of Statistics of the People’s Republic of China 2001, 2011, 2019)��������������������������������������������������������������������������������� 107 Fig. 6.5 Percentage of rural and urban population aged 65 and above went to their doctors in medical institutions, 2015. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)������������������������������������������������������������� 111 Fig. 6.6 Location Quotient of medical beds by county, 2017. (Source: Rural Socioeconomic Investigation Department 2018; Population Census Office under the State Council 2012; City Socioeconomic Investigation Department 2018)�������������������������������������������������������������������������������������������������������������������� 113

List of Figures

List of Figures

xv

Fig. 7.1 Rural community long-term care facilities, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)������������������������������������ 121 Fig. 7.2 Rural community long-term care beds, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)������������������������������������������������� 123 Fig. 7.3 Rural community mutual assistance care facilities, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)��������������������� 125 Fig. 7.4 Rural community mutual assistance care beds, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)������������������������������������ 127 Fig. 7.5 Day care beds in community long-term care facilities, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)��������������������� 130 Fig. 7.6 Day care beds in community mutual assistance care centers, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)��������������������� 132 Fig. 7.7 Satisfaction with senior associations, 2017. (Source: Ministry of Civil Affairs of the People’s Republic of China 2018)������������������������������������������������� 136 Fig. 8.1 Distribution of the nursing homes in this survey. (Source: Collected by authors from Yang Lao website (www.yanglao.com.cn), China Jing Lao website (www.jinglaowang.com), Ai Lao website (www.ailaoweb.com), aging persons paradise website (www.lao800.com), Aging persons First website (formerly known as China Aging person career Network www.jinglao.net), China Yang Lao website (www.older99.com), 7yly navigation (www.7yly.com), etc.)������������������������������������������������������������������������ 141 Fig. 8.2 Monthly standard price interpolation map of nursing homes in Jing-Jin-Ji. (Source: Collected by authors from Yang Lao website (www.yanglao.com.cn), China Jing Lao website (www.jinglaowang.com), Ai Lao website (www.ailaoweb.com), aging persons paradise website (www.lao800.com), Aging persons First website (formerly known as China Aging person career Network www.jinglao.net), China Yang Lao website (www.older99.com), 7yly navigation (www.7yly.com), etc.)���������������������������������������������������������������� 142 Fig. 8.3 Monthly standard prices of the nursing homes in Jing-­Jin-­Ji at county level. (Source: Collected by authors from Yang Lao website (www.yanglao.com.cn), China Jing Lao website (www.jinglaowang.com), Ai Lao website (www.ailaoweb.com), aging persons paradise website (www.lao800.com), Aging persons First website (formerly known as China Aging person career Network www.jinglao.net), China Yang Lao website (www.older99.com), 7yly navigation (www.7yly.com), etc.)���������������� 144 Fig. 8.4 The proportion of the acceptable longest transportation time to nursing home facilities. (Source: Survey data collected by authors)�������������������������������� 145 Fig. 8.5 The geographic representation of the longest acceptable transportation time to nursing home facilities. (Source: Nursing home data are collected by authors. Transportation network data come from Baidu Map)����������������������� 146 Fig. 8.6 The spatial range of the nursing home facilities for the aging persons in Beijing. (Source: Nursing home data are collected by authors)���������������������� 147

List of Tables

Table 1.1 Sample information of the Fourth Survey on the Living Conditions of the aging persons in Urban and Rural Areas in China, 2015�������������������������� 10 Table 1.2 Divisions of administrative areas in China (end of 2020)����������������������������������� 13 Table 2.1 Age composition and dependency ratio of population, 1990–2020�������������������� 23 Table 2.2 Percentage of population aged 65 and over by region, 1982–2019�������������������� 26 Table 2.3 Percentage share of population and aged population by urban, town, and rural areas, 1990–2018��������������������������������������������������������������������������������� 28 Table 2.4 Area factors��������������������������������������������������������������������������������������������������������� 31 Table 2.5 Stepwise analysis model summary���������������������������������������������������������������������� 31 Table 2.6 Coefficients of multiple regression analysis�������������������������������������������������������� 32 Table 3.1 Senior associations/10,000 people aged 60 and above, and % participation, by region, 2017������������������������������������������������������������������������������ 44 Table 3.2 Schools established for aging persons, by region, 2017�������������������������������������� 45 Table 3.3 Regional distribution of geriatric hospitals and beds, 2017�������������������������������� 46 Table 3.4 Regional distribution of hospices and beds, 2017����������������������������������������������� 49 Table 3.5 Aging stipends for people aged 80 and above, by region and t ype of stipends, 2017������������������������������������������������������������������������������������������� 52 Table 3.6 Long-term care provisions by region, 2017�������������������������������������������������������� 54 Table 3.7 The transportation policies for aging persons in Beijing, Guangzhou, and Tianjin�������������������������������������������������������������������������������������� 58 Table 4.1 The total number of Chinese aging person living facilities and beds by province, 2017��������������������������������������������������������������������������������� 73 Table 4.2 The categories of living facilities for aging persons������������������������������������������� 74 Table 4.3 Nine types of beds in living facilities for aging persons������������������������������������� 74 Table 4.4 Number of living facilities surveyed by provincial units, 2016�������������������������� 75 Table 4.5 The average price of different types of beds in nursing facilities by provinces�������������������������������������������������������������������������������������������������������� 76 Table 4.6 Estimate models for the demand of nursing facilities����������������������������������������� 78 Table 4.7 The personal questionnaire (short form) excerpts from the Fourth Survey on the Living Conditions of the Aging Persons in Urban and Rural Areas in China, 2015�������������������������������������������������������������������������� 78 Table 4.8 Aging persons with different living capacities and willing to be cared in nursing home�������������������������������������������������������������������������������� 79 Table 4.9 Affordability of aging persons who are willing to live in nursing facilities (unit: Yuan)�������������������������������������������������������������������������� 79

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List of Tables

Table 4.10 Aging persons who are willing and able to pay for a bed in a nursing facility���������������������������������������������������������������������������������������������� 80 Table 4.11 The potential demand for different types of nursing facility beds by province�������������������������������������������������������������������������������������������������� 80 Table 4.12 The gap between supply and demand of beds for aging person care in China�������������������������������������������������������������������������������������������������������� 82 Table 4.13 Percentage of survey respondents able to afford nursing facility beds within different price ranges����������������������������������������������������������������������� 83 Table 5.1 The demand for aging persons meal service in each surveyed province������������ 88 Table 5.2 The nine combinations of meal services for aging persons�������������������������������� 89 Table 5.3 The estimated number of aging persons for four levels of meal service in surveyed provinces������������������������������������������������������������������������������������������ 89 Table 5.4 The year of first policy on meal service for aging persons in every provincial unit���������������������������������������������������������������������������������������� 92 Table 5.5 Types of meal service for aging persons������������������������������������������������������������� 95 Table 6.1 Table 6.2 Table 6.3 Table 6.4

Healthcare institutions and medical beds, 1978–2018�������������������������������������� 102 People aged 65 and above with chronic conditions in China, 2015������������������ 108 Aging persons demand for medical services, 2015������������������������������������������� 109 Medical institutions where aging persons normally see their doctors, 2015������������������������������������������������������������������������������������������������������������������� 110 Table 6.5 Percentage of aging persons travel the distance to see their frequently visited doctors by region, 2015������������������������������������������������ 112 Table 6.6 Distribution of county Location Quotient for medical beds by region, 2017�������������������������������������������������������������������������������������������������� 114 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5

Table 7.11 Table 7.12

Community long-term care facilities by region, 2017�������������������������������������� 122 Community long-term care beds by region, 2017��������������������������������������������� 124 Community mutual assistance care facilities by region, 2017�������������������������� 126 Community mutual assistance care beds by region, 2017��������������������������������� 128 Community long-term care beds per 10,000 persons aged 65 and above by region, 2017��������������������������������������������������������������������������������� 129 Day care beds in community long-term care facilities by region, 2017������������ 131 Day Care beds in community mutual assistance care centers by region, 2017������������������������������������������������������������������������������������������������������� 133 Community day care beds per 10,000 persons aged 65 and above by region, 2017�������������������������������������������������������������������������������������������������� 133 Provision of community senior centers and participation rates by region, 2017�������������������������������������������������������������������������������������������������� 134 Establishment of senior associations and participation rates by region, 2017�������������������������������������������������������������������������������������������������� 134 Reasons for not being a member in senior associations by region, 2017���������� 135 Satisfaction with senior associations by region, 2017��������������������������������������� 137

Table 8.1 Table 8.2 Table 8.3 Table 8.4

Correlation of two independent variables��������������������������������������������������������� 143 Anova���������������������������������������������������������������������������������������������������������������� 144 Coefficient a������������������������������������������������������������������������������������������������������ 144 Main road types and maximum speed limits in Beijing-Tianjin-Hebei������������ 146

Table 7.6 Table 7.7 Table 7.8 Table 7.9 Table 7.10

1

Introduction

Longevity is one of the ultimate goals of individuals and societies: The human race is doing everything possible, from ensuring basic food supply, to encouraging the development of sophisticated and cutting-edge science, technology, and medical research, all in the pursuit of longer life. As such, life expectancy is the first of the three indexes used by the United Nations for calculating the United Nations Human Development Index (United Nations Development Programme 2020). However, while people admire and envy those who are 100 years and older, increased proportions of aging populations have challenged all developed countries in the world. China, the largest developing country whose life expectancy has long exceeded its income level, is joining the club of aging countries, and is facing the challenge of not only serving the largest aging population, but also serving them with its middle-income level. Although people refer to population aging as a silver tsunami, this tsunami is creating a sunrise silver industry that can bring new growth to the economy. How this silver industry is progressing in different regions is the focus of this book. Although studies on the location and preferences of services for aging persons in developed countries like Sweden, Canada, and the United States such as nursing facilities (Hellberg et  al. 2011; Coe and Boyle 2013; Wagner et  al. 2010; Hawes et  al. 2003), business location allocation (Weber 1929; Cooper 1963), supply and demand analyses (McConnell et al. 2009; Colander et al. 2008), food services for (Braysy et al. 2009; Yamanaka et al. 2003), and housing preferences of, the aging persons (Kramer and Pfaffenbach 2016; Meyer 1990; Gobillon and Wolff 2011) have been well documented, service development for aging persons in China is still in its early stages. Only a few studies have examined these issues, and fewer have studied the spatial variations of the demand and supply of services (Liu et al. 2014; Li and Shen 2013; He et al. 2008; Zhou et al. 2013, 2016). Using the latest statistical and survey data, this book uses comprehensive and in-depth spatial and GIS analyses to reveal the spatial relationship between supply and demand of food,

housing, healthcare, and community services for aging persons at national, regional, and local dimensions, as well as to identify the underserved areas for the large and fast growing aging population in China (China Center for Economic Research and Peking University 2009–2017; National Bureau of Statistics of the People’s Republic of China Various Years; Population Census Office Under the State Council 1953–2020; China Aging Science Research Center 2018; Ministry of Civil Affairs of the People’s Republic of China 2018). In addition, this book investigates key area factors that affect the spatial disparity of affordable and accessible services for aging persons (Yang 2005; Graham 2007; Gong et  al. 2016) and attempts to provide suggestions on future development and planning of services for aging persons for policy makers and businesses (Song 2010). This book can be used as a primary or supplementary text for advanced undergraduate and graduate courses in economic geography, business geography, health geography, gerontology, planning, China studies, and regional development studies. The book will also have practical appeal to policy makers, government officials, city planners, real estate developers, healthcare providers, and a variety of businesses that provide services and products for aging persons.

1.1

 nderstanding China’s Aging U Population and Services for Aging Persons

Since 2010, approximately 6 million people per year enter the 65 and over age group in China. This annual increase is getting higher each year, reaching 12.97 million in 2020 (National Bureau of Statistics of the People’s Republic of China 2021a, b). This trend is expected to continue over the next two to three decades because the baby boomer generation is entering the aging group. According to Seventh Census in 2020, the total number of people aged 65 and over has reached 190.64 million persons in China, accounting for

© Springer Nature Switzerland AG 2022 X. Shen et al., Services for Aging Persons in China, Global Perspectives on Health Geography, https://doi.org/10.1007/978-3-030-98032-0_1

1

2

1 Introduction

more than 25% of the world’s total aging population (The World Bank 2020; National Bureau of Statistics of the People’s Republic of China 2021a, b). Figure 1.1 shows that China’s aging population has increased faster than in the selected major economies in the world, particularly in the past decade. Figure 1.2 shows that the percentage of the aging persons in China’s total population has increased rapidly from 7% in 2000 to 12.6% in 2019, which is approximately the same percentage of aging population in the United States in 1990. However, it took the United States 50 years (1940–1990) for their percentage of 65+ population to increase from 6.8% to 12.5%, compared to 19 years in China (National Bureau of Statistics of the People’s Republic of China Various Years). Population aging in a society is mainly caused by an increase in the number of the aging persons due to high life expectancy and low birth rates (Zeng et al. 2014). Both high life expectancy and low birth rate are related to a number of factors including quality and stability of life, income and nutrition, medical services, education, and living environment. As such, most developed countries have a higher percentage of aging population than developing countries.

Chinese people have experienced improvement of living conditions in the past 70 years; hence, their population aging is on the same track as in developed countries. However, China’s life expectancy has increased much faster (from 30.95 years in 1930 to 76.34 years in 2015) than the growth of its income level. This is particularly noticeable from 1950 to 1970, when life expectancy in China surpassed middleand upper-middle-income groups while its per capita GDP (PPP) was one of the lowest in the world (Fig. 1.3). The rapid increase of life expectancy during 1950–1970 came after the end of decades long anti-Japanese war (WWII) and civil war. The increase was largely due the peace and stability that followed the end of the wars, as well as the improved food supply. In addition to the improvement of nutrition, other accomplishments of the early years of the People’s Republic of China in public health also played an important role. In a relatively short time, China eliminated or greatly reduced such dreaded diseases as typhoid, smallpox, cholera, scarlet fever, tuberculosis, trachoma, and venereal disease. Parasitic diseases such as schistosomiasis, hookworm, and malaria were brought under control and today pose no serious threat to the population. These

18,00,00,000 160348563

16,00,00,000 14,00,00,000 12,00,00,000

10,00,00,000

91576481

8,00,00,000

87149088

6,00,00,000

53206334

4,00,00,000

35356768

2,00,00,000

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

0

China

United States

Japan

India

European Union

Fig. 1.1  Population aged 65 and above by selected country and region, 1960–2019. (Source: The World Bank 2020)

1.1  Understanding China’s Aging Population and Services for Aging Persons

20000

14.0 Number of aging persons

Percent of aging persons

12.0

16000 10.0

14000 12000

8.0

10000 6.0

8000 6000

4.0

4000 2.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

2000 0

Percent of Persens aged 65 and over

Numer of persons aged 65 and over in 10,000

18000

3

0.0

Fig. 1.2  Population aged 65 and above in China, 1990–2020. (Source: National Bureau of Statistics of the People’s Republic of China 2020, 2021a, b)

a­ccomplishments were achieved through massive inoculation drives, pest eradication programs, environmental cleanup campaigns, and preventive medicine. These programs have significantly reduced mortality rate and increased life expectancy at birth, despite China’s low-income level at the time. China’s birth rate has dropped from about 40 per thousand in the 1960s to 10.5 per thousand in 2019. Besides the normal birth rate decrease following the demographic transition stages, China’s family planning policies from the 1970s to 2010s have played an important role in the rapid birth rate decrease. Although taking care of aging parents/grandparents has been a core value and long observed tradition in Chinese culture, family planning policies and rapid urbanization have altered the family structure. The percentage of three and more generation households out of total households has decreased in the past 30 years, especially in large cities. According to the census 2020 press release, “the average size of a family household was 2.62 persons, or 0.48 person less than the 3.10 persons in 2010. The family households continued to downsize because of increasing population mobility and the fact that after marriage, young people increasingly chose to live separately from their parents with improved housing conditions” (National Bureau of Statistics of the People’s Republic of China 2021a, b). This has resulted

in about half of all aging persons living in empty nests in Beijing and other major cities (Population Census Office Under the State Council 1953–2020). The fast growth of the aging population, particularly the aging empty nesters, is not only affecting hundreds of millions of people’s lives and changing economic, social, cultural, and political systems, but also creating unprecedented demand on a variety of goods and services for aging persons and presenting challenges as well as huge opportunities to governments and businesses in China and around the world. For the first time in history, a special chapter whose goal was to “Actively respond to an aging population” was added to China’s Thirteenth Five-Year Plan (2016–2020), indicating that the aging population has become an important issue in government policies (State Council of the People’s Republic of China 2016). In the Fourteenth Five-Year Plan (2021–2025), the chapter title was changed to “Implementation of the National Strategy to Actively Address the Ageing Population.” This not only upgraded the aging population issue to the level of “national strategy” but also required “implementation” (State Council of the People’s Republic of China 2021). Although studies in developed countries like Sweden, Canada, and the United States on the location and preferences of senior services such as nursing facilities (Hellberg

4

1 Introduction

85 80

Life expectancy at birth

75 70 65 60 55 50 45 40

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

35

China

High income countries

Upper middle income countries

Middle income countries

Low income countries Fig. 1.3  Comparison of China’s life expectancy at birth with income groups in the world, 1960–2018. (Source: The World Bank 2021)

et al. 2011; Coe and Boyle 2013; Wagner et al. 2010; Hawes et  al. 2003), business location allocation (Weber 1929; Cooper 1963), supply and demand analyses (McConnell et al. 2009; Colander et al. 2008), aging people’s food services (Braysy et al. 2009; Yamanaka et al. 2003), and housing preferences (Kramer and Pfaffenbach 2016; Meyer 1990; Gobillon and Wolff 2011) have been well documented, service development for aging persons in China is still in its early stage and has had relatively few studies tackling it, with especially few focusing on the spatial variation of supply and demand (Liu et al. 2014; Li and Shen 2013; He et al. 2008; Zhou et  al. 2013, 2016). As such, it is very important to examine the spatial patterns of services for aging persons, including the variation in attributes such as affordability, accessibility, and ability to meet the demands of an aging population, especially those with low incomes. An understanding of issues, such as where the spatial concentrations of aging populations are located, why certain areas are underserved when compared to others, and which factors contribute the most to the observed geographic disparity, could be invaluable to the development of policies aimed at effectively reducing the disparity of services for aging persons. In addition, the development of affordable and acces-

sible services for aging persons in China has many of the ground-breaking changes in cultural, socioeconomic, and political systems of a rapidly aging developing country. During the years in which we researched services for aging persons, we have visited a variety of facilities and services in multiple cities and provinces in China. The following figures are some of the services for aging persons that we have visited in the past, included here to give you some ideas about the services for aging persons in China. The Yanda Center has over 10,000 beds and serves aging persons with different needs, from independent living to bed-­ bound nursing care. Figure 1.4a is the front gate of the center and Fig. 1.4b is inside of the center, showing apartment buildings in the background and a supermarket on the front right. Figure  1.4c is a well-furnished living room for independent living. Figure  1.4d, e shows a nursing care room that is equipped with a life support system and a lift with a track in the ceiling (Fig.  1.4e) that can move a patient from bed to bathroom and hallway. Figure 1.4f is the center’s indoor swimming pool. This center is a privately owned commercial facility with upper-scale hardware and services such as a full-scale hospital, gyms, a movie theatre, church, mosque, supermarket, and courses for aging persons. The price to live in this center

1.1  Understanding China’s Aging Population and Services for Aging Persons

5

Fig. 1.4  Yanda Golden Age Health Nursing Center, Yanjiao District, Hebei province. (a) Main gate. (b) Apartment buildings for aging persons. (c) Living room. (d) Nursing bed. (e) Lift for moving patient. (f) Swimming pool. (Xiaoping Shen)

ranges from about US $1000 per month per person in an independent living all-inclusive one-bedroom apartment to about US $3000 in a full nursing care apartment, which is also allinclusive. The center is located right outside of Beijing, so its target residents are retired professionals from Beijing (Yanda Golden Age Health Nursing Center 2015). Figure 1.5 depicts 1 of 11 state-owned Home for the Aged system in Beijing. These Home for the Aged facilities are located in large-scale mature communities in the city with the purpose of realizing the “location embeddedness” of elderly care institutions, and at the same time integrating surrounding professional service resources, so that the aged persons can live the life of “not leaving home nor the neighborhood.” The Hepingli Home for Aged facility was established in 2011 and has only 50 rooms to accommodate a maximum of 100 aging persons at an affordable price. In addition, since the facility is a part of the large Hepingli residential community, it provides various professional services, such as medical, psychological, homecare, and meal services, to more than 5000 aging persons in the community (Cuncao Chunhui Home 2021). Figure 1.5a is the front gate of the Home and Fig. 1.5b is the dining room.

Built in 1999, the Fifth Social Welfare Institution of Beijing (Fig.  1.6) is one of the earliest facilities that were invested in by Beijing’s municipal government. This 17-story facility has 450 beds, accepts persons aged 60 and over who can live independently, and is suitable for group living. Because this institution is owned by Beijing’s government, it is subsidized by the government to offer affordable prices (The Fifth Social Welfare Institution of Beijing 2019). However, due to its good location and services as well as affordable prices, there is an exceptionally long waiting list. When we visited it in 2015, we were told that the waiting time for a bed in a double bed room (see Fig. 1.6b) may be as long as 5 years. Minhang Xinzhuang Zhen Home for the Aged in Shanghai is a Xinzhuang Zhen (town) community-owned facility that was built in 2009. The facility has 400 beds in different sized rooms, from single bed rooms to four beds in one room. It accepts aging persons in various health conditions with different needs, from independent living to nursing care (365 Health Care Health Network 2020). Figure 1.7a shows one of the buildings and Fig. 1.7b shows the playground in the Home’s backyard. As Fig. 1.7c showed, residents were eat-

6

1 Introduction

Fig. 1.5  Cuncao Chunhui Home for the Aged Hepingli Center, Dongcheng District, Beijing. (a) The front gate. (b) The dining room. (Xiaoping Shen)

Fig. 1.6  The Fifth Social Welfare Institution of Beijing. (a) The building of the the Institution. (b) A standard room. (Xiaoping Shen)

ing lunch in the dining hall when we visited the facility. Similar to other state- or city-owned facilities, the price for this community-owned facility is subsidized so that it can provide affordable good services, but priority is given to the local residents in Xinzhuang Zhen, Shanghai. The Tianhe Home covers an area of 191 acres, a construction area of 10,000 sq. m, and has 500 beds. This commercial facility is built and managed by a company with government approval. It accepts aging persons in all health conditions. Because it is located in a village in suburban Xi’an, the price is not very high and the services are good. There is a big dining hall in the facility just like the one in Shanghai, but they also provide lunch to each floor for the convenience of dis-

abled residents. In northern parts of China such as Xi’an, steam bread (mantou) and soup are the staple food of daily meals as seen in Fig. 1.8b. In 2009, the Chinese government issued a policy on providing Home-based Care Services to the elderly. The policy set forth a “9064 model” that allocated home-based care for 90% of aging persons, government-funded community care for 6%, and facility care for 4%. The policy encouraged the government agencies in Beijing, Shanghai, and other major cities to select a number of qualified and sizable catering enterprises to establish affordable elderly and handicapped meal services in urban and rural communities (Beijing Municipal Bureau of Civil Affairs and Beijing Disabled

1.1  Understanding China’s Aging Population and Services for Aging Persons

7

Fig. 1.7  Minhang Xinzhuang Zhen Home for the Aged, Shanghai. (a) One of the buildings of the Home for the Aged. (b) Playground in the backyard. (c) Residents are eating lunch in the dining hall. (Xiaoping Shen)

Fig. 1.8  Tianhe Home for the Aged, Yuhuazhai, Xi’an. (a) the building of the Home for the Aged. (b) Serving lunch to each floor for the convenience of disabled residents. (Xiaoping Shen)

8

Persons’ Federation 2009; Li 2010). Since the promulgation of the policy, many restaurants have been designated as “Elderly Dining Service (laonian canzhuo 老年餐桌)” providers, and many community service centers also provide meal services for local aging persons. The Jingrui Community Center in Fig. 1.9 provides meal services for eat-in, takeout, and delivery. We had our lunch at the center during our visit. The combo meal (Fig. 1.9d) included four dishes of two vegetable and two meat dishes plus a box of white rice, two steam breads, and a small bowl of soup, and cost 15 Chinese Yuan (about US $2.5). In addition to the meal services for aging persons in the community, this service center also provides a daycare room (Fig.  1.9b) for aging persons, ping pong tables, and other gym machines, as well as a health alert button service that covers the community. Currently, many businesses are offering variety of services for aging persons in China. Take insurance companies as an example: It is reported that 10 large insurance companies in China, including China Life, Ping An, Taikang Life, and Xinhua Insurance, have deployed 47 retirement commu-

1 Introduction

nities with 84,000 beds in locations not limited to first-tier cities such as Beijing, Shanghai, and Guangzhou (Li and Guo 2021). Taikang Life has been a part of the medical care field since 2007 and obtained the industry’s first pilot project permit for a retirement community investment in 2009. At present, the Taikang Home Community for Aged has been deployed or under construction in 22 cities in the core areas of Beijing–Tianjin–Hebei, Chang Jiang River Delta, Guangdong–Hong Kong–Macao Greater Bay Area, and Southwest China. It will accommodate about 55,000 aging persons. Seven elderly care communities have been put into operation in which nearly 4500 aging persons live. In the future, pension insurance will be the main battlefield for the development of the life insurance industry (Li and Guo 2021). Insurance companies combine the rights and interests of customers in the retirement community with insurance policies to provide them with comprehensive retirement services that integrate finance, home care, and medical care, which is a better way to meet market demand.

Fig. 1.9  Jingrui Community Center, Daxing, Beijing. (a) The community center. (b) The daycare room for aging persons. (c) The dining room. (d) The lunch combo for about US $2.5. (Xiaoping Shen)

1.2  Using the Book

In addition, new technologies are used to provide better services by communities for aging persons. For example, the community center of Jiangsu Road, Changning District, Shanghai, has installed a smart water meter for aging persons living alone. When the reading is less than 0.01 cu. m within 12 h, the system will warn the staff of community center to contact the aging person and make sure everything is okay. This new initiative adds an intelligent digital guardian (Sun 2020).

1.2

Using the Book

1.2.1 Data This book uses the latest datasets including the nationwide Fourth Survey on the Living Conditions of the Aging Persons in Urban and Rural Areas in China conducted in 2015 by the Ministry of Civil Affairs and the National Bureau of Statistics (Ministry of Civil Affairs of the People’s Republic of China 2018), China Longitudinal Aging Social Survey conducted by Renmin University of China in 2014 (Renmin University of China 2016), seven population censuses including full census datasets from 1953 to 2010 and a few key figures in the first press release from the 2020 census (Population Census Office Under the State Council 1953–2020), large quantities of statistics from the past seven decades (National Bureau of Statistics of the People’s Republic of China Various Years), and documents of government policies at national and regional levels published in the past seven decades. Since a couple of major datasets are used by several chapters, the information about them is provided below. The most important dataset used in this book is the Fourth Survey on the Living Conditions of the Aging Persons in Urban and Rural Areas in China. The survey was conducted in September 2015, and the data became available in 2018 (China Aging Science Research Center 2018). According to the Ministry of Civil Affairs (2018), the time of the investigation was 0:00 on August 1, 2015. The subjects of the survey were Chinese citizens aged 60 and above who live in China (except Hong Kong, Macao, and Taiwan). The survey covers 31 provinces, autonomous regions, municipalities, and the Xinjiang Production and Construction Corps. The sample involved 466 county-level units (counties, cities, or districts). The design sample size of the survey was 223,680, and the sampling ratio was about 1 per thousand. About 40,000 surveyors and staff interviewed 222,700 aging persons and collected the sample results. The number of effective samples was 220,170, and the effective rate of the samples was 98.8% (Ministry of Civil Affairs of the People’s Republic of China 2018). The geographic distribution and the characteristics of the collected results are listed in Table 1.1. The sample distributions of sex and age groups, income levels, and propor-

9

tion of (subjects living in) urban areas are based on the distribution of population in the region. The survey has three questionnaires that include questions in personal, community (village/residential), and township/ street categories. The personal questionnaire includes 102 questions in eight sections including basic status, family status, health and medical status, nursing care service status, economic status, living environment status, social participation status, rights protection status, and spiritual and cultural life status. After each interview, the surveyor was required to fill out the interviewee’s living and health conditions based on the surveyor’s observation, as well as to record if anyone else answered the questions for the aged interviewee and why. In order to understand the rural and urban community services for aged persons, the relevant staff of the community are asked to answer 36 questions in the following five categories: geography and population, infrastructure and activity venues, aging service system construction, services for aged, and the urgent problem of aging person services. The completed questionnaire was required to be submitted to the town or township-level government. The township supervisor reviewed and signed the questionnaire and sent it to the county’s Office of the Working Committee on Aging, which submitted it to the China Research Center for Aging after it was reviewed and signed by the county-level supervisor (Ministry of Civil Affairs of the People’s Republic of China 2018). The third questionnaire is for the staff of rural township or town and urban street government to fill out. The 54 questions are in eight categories: basic condition, income guarantee for the elderly, health protection for the aged, construction of the service system, protection of the rights and interests of the aged, social participation of the aged, spiritual, and cultural, and work for aging persons. After being reviewed and signed by the township-level supervisors, the answers were sent to the office of the county (city or urban district at county level) Working Committee on Aging, and the county-level supervisors also reviewed and signed the answers before submitting to the China Research Center for Aging. The China Longitudinal Aging Social Survey conducted by Renmin University of China was conducted in 28 provinces (cities, autonomous regions) across the country from August to October 2014 (Renmin University of China 2016). The survey obtained 11,511 valid samples of individual questionnaires distributed in 134 counties and urban districts, and 462 villages, as well as 462 sample community questionnaires. The survey is based on a comprehensive understanding of the basic conditions, health, and economic conditions of the aging persons, and focuses on grasping the needs of the aging persons for services, the status of care resources, care concepts and planning, intergenerational relationships, and social adaptation (Renmin University of China 2016). This survey collected small samples but has its own value including its continuation from multiple year

10

1 Introduction

Table 1.1  Sample information of the Fourth Survey on the Living Conditions of the aging persons in Urban and Rural Areas in China, 2015

Region Shanghai Yunnan Inner Mongolia Beijing Jilin Sichuan Tianjin Ningxia Anhui Shandong Shanxi Guangdong Guangxi Xinjiang Jiangsu Jiangxi Hebei Henan Zhejiang Hainan Hubei Hunan Gansu Fujian Tibet Guizhou Liaoning Chongqing Shaanxi Qinghai Heilongjiang Total

Total 60+ persons surveyed

Percent of women (%)

4262 6633 3325 3342 4189 16,186 1917 939 11,246 17,611 5159 13,336 8127 2381 15,506 6181 10,718 14,678 9536 1417 9412 11,891 3303 5220 924 5715 8605 6185 5684 954 5587 220,169

Percent of urban (%)

52 54 53

Age group 60–74 75–84 (%) (%) 70 23 75 21 78 19

85+ (%) 8 5 3

87 36 46

Household annual income (Yuan) 100,000 (%) (%) (%) 6 72 20 48 48 3 49 48 2

54 52 51 54 50 50 54 52 53 52 50 52 53 53 53 51 52 52 52 51 52 58 52 53 50 53 53 49 52

64 79 74 72 76 72 73 77 67 68 71 72 75 78 76 72 65 72 70 77 73 76 74 75 75 68 69 78 73

7 4 5 4 3 5 5 3 8 7 6 6 5 4 5 6 9 5 5 2 5 4 4 5 5 6 5 3 5

84 51 50 81 47 41 48 36 61 41 65 63 58 43 35 57 35 57 50 24 72 26 44 62 55 68 56 55 52

9 46 45 12 36 55 60 68 31 44 36 34 36 62 67 20 56 39 56 58 22 39 54 37 44 34 21 45 44

29 18 21 23 21 22 22 20 25 25 23 22 21 18 19 23 26 23 24 21 22 20 22 21 20 27 27 19 22

62 52 52 76 63 42 37 30 58 53 59 55 61 34 30 62 41 56 41 40 64 54 37 60 54 63 76 49 49

28 2 3 12 1 3 2 1 9 2 5 9 3 1 1 17 1 4 2 1 14 5 7 3 2 3 3 4 5

Source: Ministry of Civil Affairs of the People’s Republic of China (2018)

s­urveys and its focus on the social and life issues facing aging persons, such as the needs of care services, the status of care resources, the concept and planning of care, intergenerational relationships, and social adaptation which supplements the existing surveys in these areas. Other datasets used by this book include China’s seven population censuses from 1954 to 2010 and historical and contemporary statistics from 1949 to 2020 published by National Bureau of Statistics and various ministries and provinces. In addition, some chapters have utilized data collected by the authors.

1.2.2 Base Maps China is a vast country with 34 provincial level regions and 2844 county-level administrative units in 2020 (National Bureau of Statistics of the People’s Republic of China 2021a,

b). Since this book focuses on the spatial and regional variations in demand and supply of services for aging persons, many maps at both province and county level are included in every chapter. Also, many discussions mention various regions in China such as eastern, northeastern, central, and western regions. To avoid overcrowding every map by regional names, the base maps for province- and county-­ level regions are provided here. Figure 1.10 shows the names and locations of the four large geographic regions: the eastern, northeastern, central, and western regions. The provincial-level administrative regions (hereafter referred to as provinces) include provinces, autonomous regions, provincial-level municipalities, and special administrative regions. Readers can use this map to find the location of large geographic regions and provincial-­ level regions discussed in the book.

1.3 Chapters

11

Fig. 1.10  Provinces and regions of the People’s Republic of China

Figure 1.11 shows 2017 population density at county level to provide a general understanding of China’s population distribution. The location and name of provincial capitals and special administrative cities are displayed in the map for your reference because some of those major cities are discussed in ensuing chapters. Given that China has over 2800 county-level units, it is not possible to show the names and boundaries of all these county-level units using a map. Based on the characteristics of topography and climate, the Hu Huanyong Line (Fig.  1.11), named after its discoverer, runs from Heihe of Heilongjiang province in the northeast to Tengchong of Yunnan province in the southwest, dividing China into eastern and western regions (Hu 1990). The eastern region has about 45% of land area but has about 95% of population. Table 1.2 shows information on the number and type of county-level units in 2019. The autonomous counties are similar to the provincial-level autonomous regions in that the administrative divisions are established with self-­ governance agencies to exercise autonomy in areas where ethnic minorities live.

1.3

Chapters

In addition to this introduction chapter, there are seven chapters in three parts. Part I, history and context, includes two chapters. Using China’s six population censuses from 1954 to 2010 and statistics, Chap. 2 offers a review of the historical changes of population structure and the impact of government policies on population aging. It maps the regional variations and changes of aging persons in the past half a century. Utilizing stepwise and multiple regression analyses, this chapter reveals the key factors that influence the regional variation of aging. China’s services for aging persons were almost nonexistent before 1949. After the establishment of People’s Republic of China, the government started social welfare housing for homeless people who were disabled with no family and no income. After the economic reforms in 1978, the government began to provide services to aged and disabled people. At the end of 1990s, the government issued policies to encourage local government, private businesses,

12

1 Introduction

Fig. 1.11  Population density of China, 2017. (Source: National Bureau of Statistics of the People’s Republic of China 2018a, b)

and nongovernment organizations (NGO) to provide services for aging persons (Zhao et al. 2016). Consulting a huge number of government documents published across the past seven decades, Chap. 3 offers a historical review of government policies at the national and regional level with special attention to the most recent changes in policies on services for aging persons (Jing and Gong 2012; Li and Lam 2017; Chen et al. 2015; Zhao et al. 2016; Yuan and Ngai 2012; Xu and Chow 2011). Part II offers analyses on the spatial variations of supply and demand of specific services for aging persons and includes Chaps. 4–6, which focus on  living  facilities, meal services, and health services, respectively. The Chinese government issued a home-based care policy in the beginning of the century that allocated home-based care for 90% of aging persons, government-funded community care for 6%, and facility care for only 4%. Since senior housing facilities are not included on the priority list, there results a serious shortage, particularly of the affordable and accessible ones. Using the Fourth Survey

on the Living Conditions of the Aging Persons in Urban and Rural Areas in China in 2015 and databases of senior housing facilities, Chap. 4 provides the spatial patterns of supply and demand, identifies locations that need new facilities at certain price ranges, and analyzes the effects of socioeconomic factors. Given the limited availability and the high cost of nursing homes or assisted living facilities, aging independently at home has been the main lifestyle choice of the aging Chinese. Therefore, healthy and affordable meals through accessible dining services for aging persons have become necessary, particularly for those who are not in good health but are living alone. Using the nationwide elderly survey data, Chap. 5 maps the spatial pattern of demand and the affordable price ranges of food services for aging persons. Since there are a variety of food providers, such as fast food and local restaurants, community centers, central kitchens, etc., a case study is included in this chapter to show the gap between demand and supply in food services for aging persons in Beijing.

1.4  Future Development

13

Table 1.2  Divisions of administrative areas in China (end of 2020) Provinces, autonomous regions, and municipalities National total Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang Hong Kong Macau Taiwan

Number of divisions at county level 2844 16 16 167 117 103 100 60 121 16 95 90 104 85 100 136 158 103 122 122 111 25 38 183 88 129 74 107 86 44 22 106

Districts under the jurisdiction of cities 973 16 16 49 26 23 59 21 54 16 55 37 45 29 27 58 53 39 36 65 41 10 26 55 16 17 8 30 17 7 9 13

Cities at county Autonomous level Counties counties 388 1312 117

21 11 11 16 20 21

91 80 17 17 16 45

21 20 9 12 12 26 22 26 18 20 9 5

19 32 50 44 61 52 83 35 61 34 49 4 8 106 51 66 66 71 57 25 11 61

18 9 17 6 5 5 2 26

6

8 3 1

1

2 7 3 12 6 4 4 11 29

7 7 6

Source: National Bureau of Statistics of the People’s Republic of China (2021a, b)

The demand of aging persons for medical services is significantly greater than the demand of younger people, especially when looking at the oldest-old people aged 85 and over. Using the Fourth Survey on the Living Conditions of the Aging Persons in Urban and Rural Areas in China and census data, Chap. 6 discusses China’s medical service system and distribution. A Location Quotient analysis is conducted to examine the accessibility of healthcare services for aging persons and identify the underserved areas at county level. Part III discusses regional and local case studies in two chapters. Chapter 7 discusses the provision and demand for community-based services for the aging persons in China. It includes aging care services, community participation, and organizations at the community level, as well as discussing reasons that have prohibited the aging persons from participating in community activities.

Using the Beijing–Tianjin–Hebei coordinated development strategy that created a super connection among the three adjacent regions as a case study, Chap. 8 reviews the cost variation of services in nursing homes in the three places and discusses the possibilities of and the obstacles to redirecting aging persons in Beijing to facilities for aging persons in Hebei or Tianjin, especially those persons with relatively small pensions.

1.4

Future Development

China’s 14th Five-Year Plan (2021–2025)—passed by the National People’s Congress in March 2021—states that the country will formulate a long-term population development strategy, optimize the birth policy, and improve the popula-

14

tion service system for aging persons, utilizing the concept of “one old and one young” (yilao yixiao 一老一小) to promote the long-term, balanced development of the population (State Council of the People’s Republic of China 2021). More detailed policies are emerging as discussed below. The first major policy of the Five-Year Plan is to increase the birth rate. In early 2021, the Chinese government issued a policy encouraging each family to have three children. As the result of the analysis in Chap. 2 shows, the proportion of aging population is negatively correlated with birth rate. This policy seeks to allay the process of population aging over time. The second major policy is to gradually delay the statutory retirement ages in the next 5 years (State Council of the People’s Republic of China 2021). China has some of the lowest retirement ages in the world: 50 for blue-collar female workers, 55 for white-collar female workers, and 60 for most men and a small number of women in leadership or professional positions, such as university professors and government officials. The retirement standards were set in the 1950s, when the life expectancy was only in the low 40s, but it had reached 76.7  in 2018, according to the World Bank (The World Bank 2021; Wang and Dong 2021). Although raising the retirement age has been one of the biggest challenges worldwide and China abandoned a previous attempt in 2015, the government has expressed a clear determination to do so in the new Five-Year Plan. Although deeply unpopular, this policy is expected to reduce or at least slow down the increasing number of people on pension, maintain the labor force, and improve the economy. Third, several detailed plans for providing better services for aging persons are included in the 14th Five-Year Plan, including the following: 1. Supporting 2 million households with aging persons (including those both with and without special difficulties and disabilities) by implementing age-appropriate renovations involving assistive devices, tracking devices, and other facilities. 2. Supporting 500 districts and counties to build a model community home care service network with chain operation and standardized management, providing disability care, day care, meals, bathing, cleaning, medical assistance, and other services. 3. Supporting the transformation of about 300 training and recuperation institutions into inclusive elder care institutions, increasing the number of nursing beds in 1000 public elder care institutions, and supporting cities to build integrated medical and elder care facilities relying on primary medical and health resources. These policies show the substantial financial support and commitment of the central government to improving services for aging persons, a project that will also provide considerable opportunities for businesses. At this point, figuring

1 Introduction

out how to deliver these resources to aging persons in logistically underserved regions becomes an immensely important and urgent task. This book has provided detailed spatial analyses of the relationship between supply and demand for all the services specified by the policies regarding aging persons. We hope that our work will be useful not only in socioeconomic, geographic, and market trend analysis, but also practically applicable in the resource allocation decisions made by government agencies as well as in the investment and building plans of private businesses.

References 365 Health Care Health Network (2020) Minhang Xinzhuang Zhen Home for the Aged. Available at: http://www.365kyh.com/organ/ detail/17157368000000000. Accessed 1 Jun 2021 Beijing Municipal Bureau of Civil Affairs and Beijing. Disabled Persons’ Federation (2009) Guanyu Beijing shi shimin jujia yanglao (zhucan) fuwu (jiuyang) banfa de tongzhi [Beijing old-age citizens (or handicapped) home care services]. Beijing: Beijing Municipal Bureau of Civil Affairs and Beijing Disabled Persons’ Federation. Braysy O, Nakari P, Dullaert W et al (2009) An optimization approach for communal home meal delivery service: a case study. J Comput Appl Math 232(2009):46–53 Chen B, Yang BL, Zou SS (2015) Network structure, resource exchange, and motivations of partnerships in a community-based elderly care network. Palgrave Macmillan, New York China Aging Science Research Center (2018) The blue book on aging: survey report on the living conditions of the elderly in urban and rural China (2018). Social Science Literature Press, Beijing China Center for Economic Research and Peking University (2009–2017) China health and retirement longitudinal study, CHARLS. Peking University, Beijing Coe NB, Boyle MA (2013) The asset and income profiles of residents in seniors housing and care communities: what can be learned from existing data sets. Res Aging 35(1):55–77 Colander DC, Schiller BR, Colander D (2008) Microeconomics. McGraw-Hill, Boston Cooper L (1963) Location-allocation problems. Oper Res 11(3):331–343 Cuncao Chunhui Home (2021) Cuncao Chunhui home for the aged. Available at: http://www.cuncaochunhui.cn/. Accessed 15 Jun 2021 Gobillon L, Wolff FC (2011) Housing and location choices of retiring households: evidence from France. Urban Stud 48(2):331–347 Gong CH, Kendig H, He X (2016) Factors predicting health services use among older people in China: an analysis of the china health and retirement longitudinal study 2013 (report). BMC Health Serv Res 16:63 Graham H (2007) Unequal lives health and socioeconomic inequalities. McGraw-Hill Education, Maidenhead Hawes C, Phillips CD, Rose M et al (2003) A national survey of assisted living facilities. Gerontologist 43(6):875–882 He SJ, Liu YT, Wu FL et al (2008) Poverty incidence and concentration in different social groups in urban China, a case study of Nanjing. Cities 25(3):121–132 Hellberg I, Augustsson V, Muhli UH (2011) Seniors’ experiences of living in special housing accommodation. Int J Qual Stud Health Well Being 6(1):5894 Hu H (1990) The distribution, regionalization and prospect of China’s population. Acta Geograph Sin 45(2):139–145 Jing Y, Gong T (2012) Managed social innovation: the case of government-­sponsored venture philanthropy in Shanghai. Aust J Public Adm 71(2):233–245

References Kramer C, Pfaffenbach C (2016) Should I stay or should I go? Housing preferences upon retirement in Germany. J Housing Built Environ 31(2):239–256 Li Y (2010) Beijing’s elderly care policy reflects the people’s livelihood and welfare—Interpretation of citizens old-age home (handicap) care services. Social Welfare 4:27–28 Li C, Guo Z (2021) Insurance companies are out to capture the market of pension community. Available at: http://www.xinhuanet.com/ fortune/2021-­05/27/c_1127495926.htm. Accessed 27 May 2021 Li W, Lam W-F (2017) Network structure, resource availability, and innovation: a study of the adoption of innovation in elderly services in Shanghai. Springer Singapore, Singapore Li M, Shen KR (2013) Population aging and housing consumption: a nonlinear relationship in China. Chin World Econ 21(5):60–77 Liu YF, Dijst M, Geertman S (2014) Residential segregation and well-­ being inequality between local and migrant elderly in Shanghai. Habitat Int 42(3):175–185 McConnell C, Brue S, Flynn S (2009) Economics, 18th edn. McGraw-­ Hill, Boston Meyer JW (1990) Research on services for the elderly. Urban Geogr 11(4):394–401 Ministry of Civil Affairs of the People’s Republic of China (2018) The fourth survey on the living conditions of the aging persons in urban and rural areas in China. China Ministry of Civil Affairs, Beijing National Bureau of Statistics of the People’s Republic of China (2018a) China city statistical yearbook, 2018. China Statistics Press, Beijing National Bureau of Statistics of the People’s Republic of China (2018b) China statistical yearbook (county-level), 2018. China Statistics Press, Beijing National Bureau of Statistics of the People’s Republic of China (2020) China statistical yearbook, 2020. China Statistics Press, Beijing National Bureau of Statistics of the People’s Republic of China (2021a) China statistical yearbook, 2021. China Statistics Press, Beijing National Bureau of Statistics of the People’s Republic of China (2021b) Communiqué of the seventh national population census (no. 8). Available at: http://www.stats.gov.cn/english/PressRelease/202105/ t20210510_1817193.html. Accessed 20 May 2021 National Bureau of Statistics of the People’s Republic of China (1981–­ 2021) Statistical yearbook of China 1981–2020. China Statistics Press, Beijing Population Census Office Under the State Council (1954–2021) Census population of China, 1953, 1964, 1982, 1990, 2000, 2010, 2020. National Bureau of Statistics of China, Beijing Renmin University of China (2016) Chinese aging persons social tracking survey. Available at: https://www.sinoss.net/2016/0308/69454. html. Accessed 6 Jul 2020 Song S (2010) China’s challenges of aging century. Economic Management Press, Beijing State Council of the People’s Republic of China (2016) The Thirteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China. State Council of the People’s Republic of China, Beijing

15 State Council of the People’s Republic of China (2021) The Fourteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China and outline of long-term goals for 2035. State Council of the People’s Republic of China, Beijing Sun X (2020) Smart water meters “guard” the elderly living alone, making technology warmer. Available at: www.sohu. com/a/437467493_120179484. Accessed 27 May 2021 The Fifth Social Welfare Institution of Beijing (2019) The Fifth Social Welfare Institution of Beijing, Beijing The World Bank (2020) Population ages 65 and above, total. Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO. Accessed 6 May 2021 The World Bank (2021) Life expectancy at birth, total (years)— China|Data. Available at: https://data.worldbank.org/indicator/ SP.DYN.LE00.IN?end=2018&locations=CN&start=1960&view=c hart. Accessed 6 Mar 2021 United Nations Development Programme (2020) Human development index (HDI)|human development reports. Available at: http://hdr. undp.org/en/content/human-­development-­index-­hdi. Accessed 10 May 2021 Wagner SL, Shubair MM, Michalos AC (2010) Surveying older adults’ opinions on housing: recommendations for policy. Soc Indic Res 99(3):405–412 Wang V, Dong J (2021) A graying China may have to put off retirement. Workers Aren’t Happy. The New York Times Weber A (1929) Theory of the location of industries [Translated by Carl J. Friedrich from Weber’s 1909 book]. The University of Chicago Press, Chicago Xu Q, Chow JC (2011) Exploring the community-based service delivery model: elderly care in China (Special issue: Older people). Int Soc Work 54(3):374–387 Yamanaka K, Almanza BA, Nelson DC et al (2003) Older Americans’ dining out preferences. J Foodserv Bus Res 6(1):87–103 Yanda Golden Age Health Nursing Center (2015) Yanda Golden Age Health Nursing Center. Hebei, China Yang Z (2005) China’s population aging and regional industrial restructuring. Social Science Literature Publishing House, Beijing Yuan R, Ngai SS-Y (2012) Social exclusion and neighborhood support: a case study of empty-nest elderly in urban Shanghai. J Gerontol Soc Work 55(7):587–608 Zeng Y, Land KC, Gu D et al (2014) Household and living arrangement projects: the extended cohort-component method and applications to the U.S. and China. Springer, Dordrecht Zhao R, Wu Z, Tao C (2016) Understanding service contracting and its impact on NGO development in China. Voluntas 27(5):2229–2251 Zhou S, Cheng Y, Xiao M et  al (2013) Assessing the location of public-and-community facilities for the elderly in Beijing, China. GeoJournal 78(3):539–551 Zhou S, Shen X, Jiang W (2016) Spatial accessibility and supply-­ demand analysis of elderly dining services in Beijing. Prof Geogr 68(4):674–685

Part I History and Context

2

China’s Population Aging and Regional Variation

2.1

 opulation Aging in the Past Seven P Decades

2.1.1 T  he Largest Aging Population in the World China’s population has been aging rapidly in the past three decades. The number of Chinese people aged 65 and over reached 190.6 million and accounted for 13.5% of the total population in 2020 (National Bureau of Statistics of the People’s Republic of China 2021). According to the estimates by the Population Division of the Department of Economic and Social Affairs of the United Nations, in 2019, the global population was 7.67 billion, with China’s population of 1.4 billion accounting for 18%. However, the world population aged  65 and over is estimated to be about 698 million, with China’s aging population accounting for 25% of the world’s total aging persons (i.e., one out of every four aging persons in the world is in China). China’s aging population is not only the largest amongst individual countries, but also surpassed the total population aged 65 and over in the European Union and the United States combined in 2017 (The World Bank 2020, 2021). In addition to the large size of its aging population, the process and speed of population aging is faster in China than in most countries in the world. Demographic data show that in the 19 years since 2000, when China became an elderly society (7% of population aged 65 and over), the aging population has increased by 87.8 million and doubled in number (National Bureau of Statistics of the People’s Republic of China 2020). Also, the aging process has further accelerated in recent years. From 2018 to 2019, a record breaking population of more than 10 million new aging persons was added to the aging group. Based on China’s six population censuses and statistics, Fig.  2.1 shows the changing percentages of aging population proportions in the past six decades (Population Census Office Under the State Council 1955–

2012). The higher growth rate of the population aged 60 and over than that of the population aged 65 and over indicates that more people will be entering the aging population every year for years to come. Compared with other countries, China’s percentage of aging population is still lower than most developed countries, but its aging rate is one of the fastest in the world (Fig.  2.2a, b). It only took 19  years (2000–2019) for the aging proportion of China’s population to grow from 7% to 12.6%. In contrast, it took more than 50 years (1940–1990) for the United States and Europe. Japan and South Korea also experienced a fast-aging process, but Japan’s aging process in the first two decades was still slower than that of China. Only South Korea’s aging process during the early stage was faster than China’s (United Nations 2019).

2.1.2 Stages of Population Aging Based on six population censuses, China’s population structure change in the past 70  years can be divided into four stages, from the youthful stage to the accelerated aging stage (Chen and Hao 2014). First was the youthful stage, from the 1950s to the 1960s. The crude birth rate was 3–4%, the crude death rate was between 1% and 2%, the natural increase rate was more than 2% in most years, and the 0–14 age group grew rapidly. Figure 2.3 shows the birth rate, death rate, and natural increase rate from 1949 to 2019. The three rates fluctuated very much during this period. Second, the adulthood stage lasted from the 1970s to the 1980s. Due to the declining fertility rate combined with the large number of people born between the 50s and early 70s entering adulthood, the range of young adults aged 15–24 became the largest age cohort at the end of the 1980s. Early during this time period, China issued family planning policies that encouraged families to have no more than two children. These policies changed to the one child one family policy in 1980. Although the policies were

© Springer Nature Switzerland AG 2022 X. Shen et al., Services for Aging Persons in China, Global Perspectives on Health Geography, https://doi.org/10.1007/978-3-030-98032-0_2

19

20

2  China’s Population Aging and Regional Variation

20

18.7

18 16 13.26

Percentage (%)

14 12

10.33

10 8 6

13.5

7.63

7.32 6.08

4.91

4.41

8.87

8.58 6.96 5.57

3.53

4 2 0

1953

1964

1982

Aged 60 and above

1990 Year

2000

2010

2020

Aged 65 and above

Fig. 2.1  Proportion of aged population in China’s previous censuses. (Source: Population Census Office Under the State Council 1955–2012) 30

25

Percentage (%)

20

15

10

5

1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

0

China

Italy

Japan

United States

Germany

South Korea

Fig. 2.2 (a) Population aged 65 and above in total population by selected countries, 1960–2019. (b) Proportion of population aged 65 and above grew from 7% to 13% by selected economies. (Source:

United Nations 2019; Institute of Medicine and National Research Council Committee on an Aging Society 1988)

implemented less strictly in rural areas and were mostly not enforced in minority areas, their implementation resulted in the drop of the birth rate to around 2% and the decline of the natural increase rate from 2.3% in 1970 to

1.5% in 1989 (National Bureau of Statistics of the People’s Republic of China 2020). Figure 2.4 shows the age structure in 1982. Although only a few years passed after the family planning policies were implemented, the effect of

2.1  Population Aging in the Past Seven Decades

21

14.0

Europe

US

Japan

South Korea

China 13.0

13.0

Percentage of population aged 65 and above

12.7

12.9

12.7

12.6

12.4 12.0

11.9 11.6

11.5

11.0 10.7

10.5

10.7 10.3

10.1

10.1

10.0 9.5

9.5 9.0

8.9

8.9

8.8

8.9

8.4

8.0

8.0

7.7

7.7 7.2

7.0

6.9

6.9

6.8

2019

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

1965

1960

1955

1950

1940

6.0

Fig. 2.2 (continued)

the policies is clearly visible in the youngest two age cohorts from age 0 to 9 years in 1982. Third was the beginning of aging stage from the 1990s to the 2000s. With the continued decline of fertility rates from 2.73 in 1985–1990 to 1.83 in 1991–1995, China entered the stage where the fertility rate is below the replacement level of 2.1. The general population continued to grow into adulthood and began aging. By the year 2000, China’s population aged 65 and over reached 7%, and China officially qualified as an elderly society according to the United Nations’ standard (Park 2019). Since then, the proportion of the population aged 65 and over has increased annually. Table  2.1 shows that the aging population grew at the speed about 0.1–0.2% annually in the 2000s. Fourth, China has been at the accelerating aging stage since 2010. The sixth census in 2010 showed that the population aged 65 years and over was 119 million, accounting for 8.87% of the total population (National Bureau of Statistics of the People’s Republic of China 2020). The average annual growth rate of the aging population was 0.2% between 2000 and 2010 but doubled to 0.4% from 2011 to 2019. From

2018 to 2019, the percentage of aging population increased by 0.7% in the total population (National Bureau of Statistics of the People’s Republic of China 2020). Figure 2.5 shows that adult people aged 45–54 were the largest age cohorts and accounted for 17.3% of total population in 2019. When this group of people starts to enter the aging group in 2030, China will see the fastest growth of its aging population from 2030 to 2040. It is estimated by the United Nations that the number of people aged 65 and over in China will reach 26% of the total population, or 366 million, by 2050, which will be larger than the current total US population (331 million) (Mather 2020). The general trend of aging in China in the future is manifested not only in the expansion and accelerated growth of the aging population, but also in the continuous deepening of aging. China is experiencing rapid aging within the internal age structure of the older population, that is, the older population is getting even older. With the advancement of medical technology, the average life expectancy will continue to increase, and healthier and longer life will further promote the aging trend into older ages. The analysis of population

22

2  China’s Population Aging and Regional Variation 50

40

Birth Rate

Death Rate

Natural Increase Rate

Per 1,000

30

20

0

1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

10

-10

Fig. 2.3  Birth rates, death rates, and natural increase rates in China, 1949–2019. (Source: National Bureau of Statistics of the People’s Republic of China 2009, 2020)

Male (%)

0.00

95+

Female (%)

0.00

0.01 90-94 0.02 0.03 85-89

0.07

0.13 80-84 0.23 0.35 75-79 0.51 0.64 70-74 1.01 1.37 1.74 2.15

4.67 3.77 6.35

60-64 55-59 50-54

1.11 1.36 1.63 1.92

2.50

45-49

2.22

2.57

40-44

2.25

2.85 3.78

65-69

0.79

35-39 30-34 25-29 20-24

2.56 3.49

4.46 3.63 6.13

15-19

6.76

6.37

10-14

5.68 4.88

5-9 0-4

5.35 4.56

Fig. 2.4  China’s population pyramid, 1982. (Source: National Bureau of Statistics of the People’s Republic of China 1984)

2.2  Regional Variation and Changes

23

Table 2.1  Age composition and dependency ratio of population, 1990–2020 Total population Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

10,000 persons 114,333 115,823 117,171 118,517 119,850 121,121 122,389 123,626 124,761 125,786 126,743 127,627 128,453 129,227 129,988 130,756 131,448 132,129 132,802 133,450 134,091 134,735 135,404 136,072 136,782 137,462 138,271 139,008 139,538 140,005 141,212

Aged 65 and above Population 10,000 persons 6368 6938 7218 7289 7622 7510 7833 8085 8359 8679 8821 9062 9377 9692 9857 10,055 10,419 10,636 10,956 11,307 11,894 12,288 12,714 13,161 13,755 14,386 15,003 15,831 16,658 17,603 19,064

Gross dependency ratio (%) Proportion % 5.6 6.0 6.2 6.2 6.4 6.2 6.4 6.5 6.7 6.9 7.0 7.1 7.3 7.5 7.6 7.7 7.9 8.1 8.3 8.5 8.9 9.1 9.4 9.7 10.1 10.5 10.8 11.4 11.9 12.6 13.5

49.8 50.8 51.0 49.9 50.1 48.8 48.8 48.1 47.9 47.7 42.6 42.0 42.2 42.0 41.0 38.8 38.3 37.9 37.4 36.9 34.2 34.4 34.9 35.3 36.2 37.0 37.9 39.2 40.4 41.5 45.9

Elderly dependency ratio (%) 8.3 9.0 9.3 9.2 9.5 9.2 9.5 9.7 9.9 10.2 9.9 10.1 10.4 10.7 10.7 10.7 11.0 11.1 11.3 11.6 11.9 12.3 12.7 13.1 13.7 14.3 15.0 15.9 16.8 17.8 19.7

Source: National Bureau of Statistics of the People’s Republic of China (2021)

data from 2000 and 2010 has revealed that, since the twenty-­ first century, China’s population aging has become increasingly prominent, and the population of older than 75 has grown faster than the general aging population (Zeng et al. 2017). In 2019, there were 30.7 million oldest old people (aged 80 and above) in China, which is 2.5-fold of the 12 million in 2000. By 2050, this number is expected to triple, reaching 90.4 million and making it the world’s largest group of oldest population aged 80 and over (WHO 2015). Compared with young aging people (65–75), the older aging group has higher risks of disease and disability, as well as more urgent needs for aging care services. The deepening of aging will bring greater challenges to China’s aging care service system. In the experience of other aged countries, their economies had reached either high-income level such as West European, Japan, and North American countries, or at least at upper-­middle-­income level such as South Korea before

their population aging. China’s aging process is ahead of its social and economic development. According to the World Factbook by the CIA, China’s per capita GDP (PPP) is still below the world average and only ranked 96 out of 228 countries in 2018 (Central Intelligence Agency 2020). China is currently the only developing country that has a higher than 12% aging population but only has a middleincome level.

2.2

Regional Variation and Changes

Due to the dynamics of economic and social development between regions and the differences between urban and rural areas, the aging population in China is unevenly distributed (Liu et al. 2014). The differences in distribution of the aging populations across China are mainly manifested as differences among provinces and county level regions, as well as

24

2  China’s Population Aging and Regional Variation

Male (%)

0.01

95+

0.03

0.07

90-94

0.13

85-89

0.38

0.27 0.57 0.97 1.58 2.47 2.79 3.41 4.31 4.46

80-84 75-79 70-74 65-69 60-64

Female (%)

0.74 1.10 1.68 2.58 2.77 3.38

55-59 50-54

4.22

45-49

4.30

3.59

40-44

3.46

3.60

35-39

3.52

4.33

4.28

30-34

3.86

3.62

25-29

3.01 2.77

20-24 15-19

2.63 2.34

2.98

10-14

2.50

3.00

5-9

2.56

3.06

0-4

2.69

Fig. 2.5  China’s population pyramid, 2019. (Source: National Bureau of Statistics of the People’s Republic of China 2020)

in the uneven distribution between urban and rural areas (Wang et al. 2013).

2.2.1 Regional Variation at Provincial Level Interregional difference refers to the differentiated spatial distribution between provinces and counties that is affected by different levels of social, economic, and historical development. In general, aging in coastal regions has been ahead of central and western regions. This is correlated to their economic development level because more economically advanced regions usually have a higher standard of living, superior sanitation, and better medical services, all of which result in longevity (Du et al. 2017). This advanced aging is also due to the fact that family planning policy was implemented more in these advanced regions than in the rural and west regions. Among all provincial level regions, Shanghai, the most economically advanced city in China, became an

elderly society first (7% of people aged 65 and over) in 1979, 20 years before the entire country reached that level, and is still the most aged provincial-level megacity in China. In 1982, some other major cities and coastal regions also had a higher percentage of aging population, but the situation changed after the initial aging period due to large numbers of young people migrating from rural to urban and from interior to coastal provinces for manufacturing jobs after the middle of the 1990s. Based on four population censuses from 1982 to 2010 and statistics from 2019, Fig. 2.6 shows China’s population aging at provincial-level regions. The shade of green of each region shows the percentage of population aged 65 and over in 2019, and the bar chart shows the percentage share of aging population in the four censuses and 2019. By the end of 2019, all provincial level regions, except Tibet, had more than 7% of their populations aged 65 and over, while six provinces even exceeded 15%. The aging population shows a spatial clustering phenomenon. The provinces with a high

2.2  Regional Variation and Changes

25

Fig. 2.6  Percent of population aged 65 and over by region, 1982–2019. (Source: National Bureau of Statistics of the People’s Republic of China 2020)

percentage of aging population are concentrated in the northeastern and eastern provinces, from Heilongjiang to Zhejiang, and the provinces along Chang Jiang River (Yangtze River), from Shanghai to Sichuan. The lowest-degree aging areas are in the west, with Tibet and Xinjiang as the center. However, Guangdong province, the most economically developed province, has aged slowly; its percentage of aging population is at the same level as Qinghai and Xinjiang in 2019 because it has attracted large numbers of migrant workers from rural and interior provinces (National Bureau of Statistics of the People’s Republic of China 2020). The bar charts in Fig. 2.6 show that the aging process was relatively slow before 2000 in most provinces, but really accelerated after 2010 for almost every province, except Tibet, Guangdong, Xinjiang, and Hainan. Table 2.2 provides detailed information on aging population percentages reported by the past four population censuses and China Statistical Yearbook (2020) as well as the percentage growth of aging population in 2019 compared with 1982.

The spatial scope of China’s population aging has grown from Shanghai, the only city considered an elderly society in the 1980s and 1990s. The number of provinces at this level reached 12 provinces in the 2000s, 27 provinces in the 2010s, and all provinces except Tibet in 2019. By the end of 2019, 13 provinces had populations that were over 13% people aged 65 and over, and 6 provinces had over 15% of their population aged 65 and over. The growth rate distribution of aging population from 1982 to 2019 is different from the distribution of aging population by region. The national average growth is 156%, but Heilongjiang had 303% growth, the highest in the country and almost double of the national average. Spatially, fast-­ aging areas are concentrated in northeast and greater Sichuan (including Chongqing) regions. Other provinces with high levels of growth are Anhui and Gansu. All the regions with high growth rates are either old industrial regions, such as those in the northeast, or less developed provinces that young people moved out of for jobs, such as Anhui and Gansu. Although Shanghai has had the highest percentage of aging

26

2  China’s Population Aging and Regional Variation

Table 2.2  Percentage of population aged 65 and over by region, 1982–2019 Region Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang National Total

1982 5.64 5.54 5.66 4.99 3.61 4.80 3.97 3.42 7.37 5.54 5.77 4.08 4.34 4.51 5.63 5.23 4.99 4.97 5.44 5.11 5.44 4.67 4.67 4.68 4.51 4.64 4.58 3.48 2.71 3.25 3.73 4.91

1990 6.3 6.5 5.1 5.4 4.0 5.7 4.5 3.8 9.4 6.8 6.8 5.4 5.1 5.1 6.2 5.8 5.5 5.6 5.9 5.4 5.4 5.7 5.7 4.6 4.9 4.6 5.2 4.1 3.1 3.5 3.9 5.6

2000 8.36 8.33 6.86 6.20 5.35 7.83 5.85 5.42 11.53 8.76 8.84 7.45 6.54 6.11 8.03 6.96 6.31 7.29 6.05 7.12 6.58 7.90 7.45 5.79 6.00 4.50 5.93 5.00 4.33 4.47 4.53 6.96

2010 8.71 8.52 8.24 7.58 7.56 10.31 8.38 8.32 10.12 10.89 9.34 10.18 7.89 7.60 9.84 8.36 9.09 9.78 6.75 9.24 7.80 11.56 10.95 8.57 7.63 5.09 8.53 8.23 6.30 6.41 6.19 8.87

2019 11.5 12.1 13.1 11.0 10.2 15.9 13.3 13.8 16.3 15.1 14.0 14.0 10.0 10.2 15.8 11.6 13.1 13.1 8.6 10.2 9.3 15.3 15.7 11.6 9.9 6.0 12.0 11.5 8.5 9.5 8.1 12.6

Increase ratea 1982–2019 (%) 103 118 132 120 182 232 235 303 121 172 143 242 130 125 181 122 162 164 58 99 71 229 237 147 119 30 163 231 215 192 116 156

Note: The number is underlined if a region’s percentage share of population aged 65 and above is 7% or higher Source: National Bureau of Statistics of the People’s Republic of China (2020) a The increase rate is calculated as: percentage of aged population in 2019 minus the percentage in 1982, then divided the difference by the percentage of aged population in 1982

level units in China, and the difference between the lowest and the highest percent of aging population is much greater than at the provincial level. In 2010, the highest percentage of aging population was 19% in Rudong of Jiangsu province, and the lowest was only 0.24% in Mangya Xingwei of Qinghai province. In 1982, only 61 (out of 2283) county-level units had a concentration higher than 7% of people aged 65 and over. Shanghai had a high percentage of aging population (8.1%), but the county with highest percentage (9.2%) was Ye Xian in Shandong province. Among the 61 counties, 51 were in 2.2.2 Regional Variation at County Level coastal provinces from Hebei to Guangdong, including three large cities: Beijing, Tianjin, and Shanghai. Other county-­ Figure 2.7 displays the distribution of the aging population at level aging units can be found in Shanxi, Shaanxi, and the county level according to four population censuses from Xinjiang (Population Census Office Under the State Council 1982, 1990, 2000, and 2010. There are over 2200 county-­ 1955–2012). population, its aging process is slower than the national average and its percentage of aging population even decreased from 11.53% in 2000 to 10.12% in 2010 (National Bureau of Statistics of the People’s Republic of China 2018). The regions with low increase rates are Tibet, Guangdong, Hainan, and Beijing. Except for Tibet, which has the highest birth rate in the country, other slow aging regions have rapid economic growth and have attracted many young migrant workers in the past three decades.

2.2  Regional Variation and Changes

27

Fig. 2.7  Percentage of population aged 65 and over by county, 1982–2010. (Source: Population Census Office Under the State Council 1955–2012)

In 1990, 158 county-level units had aging population percentages higher than 7%. There are 122 (or 77%) of these aging counties in the coastal region from Liaoning to Hainan provinces. The area with the highest concentration of aging populations is in Chang Jiang Delta and surrounding areas. Other aging areas were large cities such as Guangzhou, Qingdao, Yantai, Chengdu, and surrounding areas (Population Census Office Under the State Council 1955–2012). In 2000, the number of county-level units with 7% or higher aging population jumped to 964, but still concentrated in coastal areas, as well as in Sichuan and Chongqing and surrounding areas. By 2010, the number of aging counties was doubled from 2000 and reached 1819. Most of the 464 counties that had a lower than 7% percentage aging population in 2010 were in the west region (Wang et  al. 2016). However, the percentage in the core area of Guangzhou and

Pearl River Delta either grew slowly or reduced to below 7% in 2010. Some cities had very low levels of aging people in 2010, including those within counties under the jurisdiction of the Pearl River Delta such as Shenzhen (1.79%), Dongguan (2.29%), Zhongshan (4.43%), Zhuhai (5.01%), and Foshan (5.25%). Guangzhou and the counties under its jurisdiction had an average aging population of 6.8% in 2010 that experienced only a 0.8% increase from 6% in 1990 (Population Census Office Under the State Council 1955–2012). Wang et al. (2016) studied the speed of aging at county level between 2000 and 2010 and reported that the fast-aging regions are in the Chengdu and Chongqing areas, along the Lanzhou and Xinjiang railway, on the northern slope of Tianshan Mountain, and in the northeast region. The fastest aging city is Genhe in Inner Mongolia, whose aging population increased almost three-fold from 4.29% in 2000 to 12.91% in 2010. On the other side, the percent of aging

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2  China’s Population Aging and Regional Variation

p­ eople in 98 counties decreased during the decade. Some urban districts in large cities such as Shanghai, Tianjin, Jiangsu, Zhejiang, Guangdong, and Fujian, as well as 34 counties in Tibet and Xinjiang, experienced a decrease in their aging population percentage (Wang et  al. 2016). The place with the greatest decrease in their aging population was Kunshan city in Jiangsu province, whose aging population was reduced from 8.45% in 2000 to 5.53% in 2010 due to large number of young migrant workers (Shen and Ma 2005).

2.2.3 Urban and Rural Variation China’s urban areas, particularly large cities, are more developed economically and have a higher level of standard of living, better social insurances, and better medical services than in rural areas. In addition, the family planning policies were implemented much more strictly in major cities, such as Beijing and Shanghai, than in small cities and rural areas. As such, it is expected that urban areas in China would lead the aging process and have a higher percentage of aging population than that in rural areas. Indeed, the provincial-level data show that the mega cities aged earlier than other provincial level regions (Table 2.2). Shanghai entered elderly society in 1982, and all four provincial level cities (Shanghai, Beijing, Tianjin, and Chongqing) entered the aging society in 2000, compared to only 8 out of 27 provinces that entered elderly society in 2000 (Population Census Office Under the State Council 1955–2012). However, the population of age cohorts by urban, town, and rural areas tells a different story. Table 2.3 shows that the percentage share of overall urban population has increased from 18.7% in 1990 to 35.2% in 2018, while the share of population aged 65 and over in urban areas has been below the national average during the entire time period. Rural areas have always had the highest percentages of aging population. The aging population share in towns is always below the national average and was the lowest in 1990 and 2000 but exceeded the urban percentage of aging population after 2010. As such, the aging process in urban areas is slower than that in towns and rural areas in the past three decades,

even though the entire nation is aging rapidly (National Bureau of Statistics of the People’s Republic of China 2018). Since urban areas are the engine of industrialization and economic development, China has experienced the largest internal human migration in world history during the past three decades. In the peak migration year of 2014, over 250 million migrant workers (floating population) moved temporarily from rural to urban areas. The number of migrant workers has been decreasing after the peak but was still 236 million in 2019 (National Bureau of Statistics of the People’s Republic of China 2020). When such a large number of people, mostly young, moved from rural to urban areas to attain better paid jobs and open new businesses, their aging parents and grandparents were left in rural homes, which resulted in an accelerated aging process in rural areas. Figure 2.8 depicts the percentage share of rural and urban aging populations by provinces in 2018. More urbanized coastal provinces from Shandong to Guangdong have significantly lower percentages of aging population in city areas than in rural areas because their urban areas receive a large number of young migrant workers. The less urbanized provinces in the west have either a similar percentage share in city and rural areas, such as Gansu, or the percentage share is slightly higher in cities than in rural areas, such as Qinghai, Ningxia, Xinjiang, and Yunnan. The area with the highest percentage of aging population is 20.9% in the rural area of Chongqing. The three provinces in the northeast and in the Jing-jin-ji region (Beijing, Tianjin, and Hebei) all have similar levels of aging populations in rural and urban areas. Central regions from Shanxi to Guizhou (except Jiangxi) all have higher percentages of aging populations in rural areas than in urban areas, mostly because many young people in these regions have moved to coastal and urban areas. The “urban-rural inversion” of the aging population that has appeared has a widening trend. According to the four censuses from 1982 to 2010 and statistics in 2018, the aging rates of urban (including towns) and rural areas in China were respectively 4.56% and 5.00% with a gap of 0.44% in 1982. By 2018, the aging gap had widened to 3.48 percentage points, which is about eight-fold the increase in the gap between 1982 and 2018 (National Bureau of Statistics of the People’s Republic of China 2018).

Table 2.3  Percentage share of population and aged population by urban, town, and rural areas, 1990–2018 Year 1990 2000 2010 2018

Percent of population in national total National Urban Town 100 18.7 7.51 100 23.5 13.4 100 30.3 20 100 35.2 24.4

Rural 73.8 63.1 49.7 40.4

Percent of population aged 65 and over National Urban Town 5.57 5.38 4.42 7.10 6.67 5.99 8.92 7.68 7.97 11.94 10.36 11.07

Rural 5.74 7.50 10.06 13.84

Source: Population Census Office Under the State Council (1955–2012) and National Bureau of Statistics of the People’s Republic of China (2019)

2.2  Regional Variation and Changes

29

Fig. 2.8  Percentage share of people aged 65 and above in urban, town, or rural areas, 2018. (Source: National Bureau of Statistics of the People’s Republic of China 2019)

It should be noted that existing studies have found that the “urban-rural inversion” pattern of aging is not unique to China and has happened in other countries. It is only a stage in the process of population aging in China. Along with socioeconomic development, the completion of large-scale rural to urban migration and high-level urbanization, the “urban-rural inversion” state of the aging population will change, that is, the urban aging population will exceed the rural aging population (Li 2006a, b; Du and Wang 2010). With the continuous acceleration of urbanization, the aging population in urban areas will also continue to increase. In 2018, the city and town aging population accounted for 53% (88.6 million) of the total aging population and the rural aging population accounted for 47% (78 million), while in 2000, the urban and rural aging population accounted for 34.2% and 65.8% of the national aging population, respectively. During those 18  years, the urbanization level of the aging population in China has increased by 18.8 percentage

points (China National Commission on Aging 2016; National Bureau of Statistics of the People’s Republic of China 2018).

2.2.4 Difference of Ethnic Regions In addition, studies have found that the distribution of aging populations in different ethnic regions is also different (Li et al. 1999). China’s 56 ethnic groups and the 55 non-Han ethnic minorities accounted about 11% of the total population in 2010. There are five provincial-level minority autonomous regions and 117 minority autonomous counties spread across most provinces. The percentage of minority populations 65  years old and over is about 8%: that is about 1% lower than Han population in 2010 (Population Census Office Under the State Council 1955–2012). Wang et  al. (2016) compared the percentage share of aging population between minority autonomous counties and nonautonomous

30

2  China’s Population Aging and Regional Variation

counties in both 2000 and 2010 and found that the minority regions have consistently lower percentages of aging populations and a slower aging process than nonminority regions. The main reason for this trend is the stringent implementation of the family planning policy on the Han population, which has effectively decreased the fertility rate and natural growth rate of the population, thereby increasing the coefficient of the aging population significantly. In contrast, in minority areas, the family planning policies’ implementation was either flexible or completely voluntary, so the birth rate among minority populations has been higher than that of Han populations in the past decades. As a result, the non-Han population share in China’s total population has increased from 6.7% in 1982 to 8.9% in 2020 (National Bureau of Statistics of the People’s Republic of China 2021). In addition, the higher birth rate has slowed down the aging process in the minority regions when compared to nonminority regions.

Previous studies indicated that regional population changes and aging are influenced mainly by demographic and socioeconomic factors, although natural environment plays an important role in human life. The major factors include factors in demographic category, such as birth rate, death rate, life expectancy, and population structure, as well as factors in socioeconomic category, such as income, medical services, and education factors (Yu 2013; Du 1994; Wang et al. 2016). It is evidenced that more socioeconomically developed countries are usually at an advanced stage of population aging, such as Japan, Germany, the United States, and France. Although China’s aging and regional variation of aging populations have followed the general trend, China also has its own unique characteristics that have impacted aging, including the effects of population policy and the large-scale rural to urban migration in recent decades. An understanding of which factors most influence the observed geographic variation of aging could be invaluable to the development of intervention policies to slow down the aging process and provide better services to the aging population. Using county-level demographic and socioeconomic data and statistical analysis, this study attempts to answer the question of factor significance.

The dependent variable is the percentage of the population aged 65 and above (Pct65up) at county level in 2010. It would be better if more recent analysis could be done, but aging population data at the county level are only reported via population censuses, and the 2010 census is the most recent one (2020 census data at county level are not available yet). Based on previous discussion, nine factors in three categories (demographic, socioeconomic, and migration) are included in the analysis as independent variables. Table 2.4 shows the description and unit of the factors. The demographic category is represented by birth rate and death rate because the preliminary analysis found that natural increase rate is highly correlated with the birth rate. The socioeconomic status category is represented by income (GDP per capita), education (average years of education and illiteracy rate), and medical resources (hospital beds per 1000 persons). The third category is percentage of immigrants from three different sources (the same county or city, the same province, and other provinces) that are represented by three factors. Data for all factors are at county level. Population migration is an important category affecting the regional distribution of population aging. According to the 2010 census, migrants or temporary residents are persons who do not have local resident registration cards (hukou 尸 口) but have lived in the area for six months or longer. The sources of migrants are based on the address of the migrant’s resident registration card. Three sources of migrants are reported by the census: within the city, within the province, and outside of the province. The percentage of migrants used in this analysis is calculated as: the number of migrants divided by the total population of the county level unit and then multiplied by 100. The China Floating Population Development Report 2016 shows that the size of China’s floating population in 2016 was 245 million and was mainly made up of migrant workers who went to cities to work and start businesses (National Health Commission of the People’s Republic of China 2017). The large-scale floating population from rural to urban areas has two major impacts on the distribution of the aging population between regions. On the one hand, the large-scale floating population slows down population aging in the main inflow areas. On the other hand, the large-scale outflow of rural population of working age has accelerated the speed and scale of the aging process in the rural outflow areas. Consequently, the “urban-rural inversion“pattern of the aging population has formed.

2.3.1 Data and Method

2.3.2 Analysis and Results

Using 2010 census data and 2017 statistics at the county level of 2851  units, this study has conducted a multiple regression analysis to find the main factors that affect aging.

A preliminary analysis tested the relationship between the aging population and the area factors and found a closer relationship between birth rate and natural increase rate, so the

2.3

 actors Affecting Aging Population F Variation

2.3  Factors Affecting Aging Population Variation

31

Table 2.4  Area factors Category Demographic Socioeconomic status

Factor BirthRate DeathRate GDPpc Bedp1kpop AvgYEdu PctIlliterate

Migration

MigrtinCnty%

MigrtinProv% MigrtOtherProv% a b

Description Birth per 1000 personsa Death per 1000 personsa GDP per capitab Number of hospital beds per 1000 personsb Average year of education in population aged 25 and overa Percent of illiterates in population aged 15 and overa Percent of immigrants from the same county or city area in total populationa Percent of immigrants from the same province in total populationa Percent of immigrants from other provinces in total populationa

Unit Births per 1000 Death per 1000 Yuan Beds per 1000 Years % %

% %

Indicators for 2010 at county level (Population Census Office Under the State Council 2012) Indicators for 2017 at county level (National Bureau of Statistics of the People’s Republic of China 2018)

Table 2.5  Stepwise analysis model summary Model 1 2 3 4 5 6 7

R 0.480a 0.632b 0.650c 0.674d 0.685e 0.686f 0.687g

R2 0.230 0.399 0.423 0.454 0.469 0.471 0.472

Adjusted R2 0.230 0.399 0.422 0.453 0.468 0.470 0.470

Std. error of the estimate 2.004421341438453 1.770530191186099 1.736207568601413 1.688800307740405 1.665067204346754 1.663005023646148 1.662087878532805

Dependent variable: pct65up Predictors: (Constant), DeathRate b Predictors: (Constant), DeathRate, BirthRate c Predictors: (Constant), DeathRate, BirthRate, AvgYEdu d Predictors: (Constant), DeathRate, BirthRate, AvgYEdu, MigrtinProv% e Predictors: (Constant), DeathRate, BirthRate, AvgYEdu, MigrtinProv%, MigrtOtherProv% f Predictors: (Constant), DeathRate, BirthRate, AvgYEdu, MigrtinProv%, MigrtOtherProv%, PctIliterate g Predictors: (Constant), DeathRate, BirthRate, AvgYEdu, MigrtinProv%, MigrtOtherProv%, PctIliterate, Bedp1kpop a

natural increase rate is excluded from further analysis. Using IBM SPSS 27, a stepwise analysis is conducted. It selects the area factors that are most highly correlated with percentage of aging population and gives each model’s R2. Using the Criteria that Probability-of-F-to-enter ≤0.050 and Probability-of-F-to-remove ≥0.100, the stepwise analysis creates seven models that have one, two, three, and up to seven factors in each model. Table 2.5 shows the predictors (factors) and the R2 of each of the seven models. The factors in order of most to least contribution to a model’s R2 are (1) death rate, (2) birth rate, (3) average years of education, (4) migrants within province, (5) migrants from other province, (6) percentage of illiterates, and (7) hospital beds per 1000 persons. The R2 of the model with seven factors is 0.472, and adjusted R2 is 0.470. Given that there are 2851 observations,

and the p values are less than 0.01, the result is significant. The other two factors (GDP per capita, migrants within city) are removed by the stepwise analysis. However, further analysis found that the last two area factors, percentage of illiterates and hospital beds per 1000 persons, have p values higher than 0.01 and their contribution to R2 are only 0.002 and 0.001 respectively, so these two factors are excluded from the final multiple regression analysis. Table 2.6 reports coefficients of the final multiple regression analysis between the dependent variable, percentage of population aged 65 and over, and the first five area factors selected by the stepwise analysis. The coefficients of individual factors show that birth rate, migrants within the province, and migrants from other provinces have negative coefficients with the percentage of aging population. That indicates that higher birth rates or migration rates will reduce the aging level of the region. In addition, death rates and average years of education are positively correlated with percentage of aging population in a region. The positive coefficients for these factors mean that higher death rates and education levels are associated with higher levels of aging. Among all factors, the death rate has the highest positive coefficient and contributes the most to the R2, the value that indicates the strongest impact on the level of aging population in a region. While the effect of death rate on the age structure can go in two ways, its direction of action depends on the net effect of changes in the mortality of children or aging persons (Liu 1997). Studies have found that, in the 40  years from 1950 to 1990, the infant mortality rate has decreased continuously from 135 deaths per 1000 live births within one year in 1960, to 6.7  in 2017 (United Nations 2019; National Bureau of Statistics of the People’s Republic of China 2019). That’s why the death rate is highly associated with percentage of aging population today.

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2  China’s Population Aging and Regional Variation

Table 2.6  Coefficients of multiple regression analysis Model (Constant) DeathRate BirthRate AvgYEdu MigrtinProv% MigrtOtherProv%

Unstandardized coefficients B Std. error 2.985 0.387 0.755 0.023 0.01 −0.209 0.482 0.031 0.005 −0.058 0.005 −0.043

Standardized coefficients Beta 0.553 −0.357 0.309 −0.201 −0.142

t

Sig.

7.714 32.851 −20.935 15.375 −10.634 −9.117

0.000 0.000 0.000 0.000 0.000 0.000

99.0% Confidence interval for B Lower bound Upper bound 1.987 3.982 0.696 0.814 −0.235 −0.183 0.401 0.563 −0.072 −0.044 −0.055 −0.031

Dependent variable: Pct65up

Birth rate has affected the age structure of China’s population significantly. The decline in birth rate over the past decades has led to the emergence of the aging population structure (Du 1994). The results show that the birth rate is the most significant factor that negatively affects population aging. Hence, the most effective way to slow down population aging is to increase birth rate.

2.3.3 Discussion Figure 2.9 shows the distribution of residuals of the multiple regression analysis. Since the residual value is calculated as the actual percentage of aging population in a county minus the percent of aging population predicted by the multiple regression model, the counties that show residual values around 0 (colored in yellow in the map) are the ones that behave normally with respect to the percentage of aging population that they have versus the value predicted by the five area factors. That is, they have either a high aging population and high values of the positive correlated factors, such as death rate and level of education, as well as low values of negative correlated factors, such as birth rate (e.g., Shanghai and Beijing), or they have low percent of aging population and low values of the positive correlated factors, such as death rate, as well as high values of negative correlated factors, such as birth rate (e.g., yellow-colored areas in Tibet and Xinjiang). Some counties with high positive or negative residual values deviate significantly from the norm. The positive residual values indicate that the model under predicted the aging population in the region, and the negative residual values indicate that the model over predicted the aging population. To illustrate, southern Jiangsu and the greater Sichuan area have some of the highest percentages of aging population in the nation, but the highest positive residual values indicate that the model under predicted their aging population. That means, other factor(s) in addition to the five factors in the model may affect the aging population. On the other hand, some counties in Tibet, Xinjiang, and Qinghai have relatively low percentages of aging population, but the negative residual shows that the model over predicted their aging

population. Again, other factor(s) not included in the model may cause the over prediction. The overall R2 is 0.469, and the adjusted R2 is 0.468 with p value